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Renewable and Sustainable Energy Reviews 15 (2011) 3480–3500

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Renewable and Sustainable Energy Reviews journal homepage: www.elsevier.com/locate/rser

Sustainable energy systems: Role of optimization modeling techniques in power generation and supply—A review Aqeel Ahmed Bazmi a,b , Gholamreza Zahedi a,∗ a

Process Systems Engineering Centre (PROSPECT), Chemical Engineering Department, Faculty of Chemical Engineering, Universiti Teknologi Malaysia, 81310 Skudai, Johor Bahru, Malaysia b Biomass Conversion Research center (BCRC), Department of Chemical Engineering, COMSATS Institute of Information Technology, Lahore, Pakistan

a r t i c l e

i n f o

Article history: Received 10 December 2010 Accepted 30 May 2011 Available online 18 July 2011 Keywords: Power generation and supply, Optimization and modeling, Electricity generation technologies, Sustainable energy systems

a b s t r a c t Electricity is conceivably the most multipurpose energy carrier in modern global economy, and therefore primarily linked to human and economic development. Energy sector reform is critical to sustainable energy development and includes reviewing and reforming subsidies, establishing credible regulatory frameworks, developing policy environments through regulatory interventions, and creating marketbased approaches. Energy security has recently become an important policy driver and privatization of the electricity sector has secured energy supply and provided cheaper energy services in some countries in the short term, but has led to contrary effects elsewhere due to increasing competition, resulting in deferred investments in plant and infrastructure due to longer-term uncertainties. On the other hand global dependence on fossil fuels has led to the release of over 1100 GtCO2 into the atmosphere since the mid-19th century. Currently, energy-related GHG emissions, mainly from fossil fuel combustion for heat supply, electricity generation and transport, account for around 70% of total emissions including carbon dioxide, methane and some traces of nitrous oxide. This multitude of aspects play a role in societal debate in comparing electricity generating and supply options, such as cost, GHG emissions, radiological and toxicological exposure, occupational health and safety, employment, domestic energy security, and social impressions. Energy systems engineering provides a methodological scientific framework to arrive at realistic integrated solutions to complex energy problems, by adopting a holistic, systems-based approach, especially at decision making and planning stage. Modeling and optimization found widespread applications in the study of physical and chemical systems, production planning and scheduling systems, location and transportation problems, resource allocation in financial systems, and engineering design. This article reviews the literature on power and supply sector developments and analyzes the role of modeling and optimization in this sector as well as the future prospective of optimization modeling as a tool for sustainable energy systems. © 2011 Elsevier Ltd. All rights reserved.

Contents 1. 2.

Introduction and background . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . Discussion . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 2.1. Current state of power generation technologies. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 2.2. Decentralized systems . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 2.3. Optimization modeling studies related to power generation and supply techniques . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 2.3.1. Power supply and distribution . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 2.3.2. Power plant operation . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 2.3.3. Building energy consumption . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 2.3.4. Industrial energy consumption . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 2.3.5. Power plants and carbon dioxide capture and storage (CCS) . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 2.3.6. Renewable energy mix . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .

∗ Corresponding author. Tel.: +607 553583; fax: +607 5566177. E-mail address: [email protected] (G. Zahedi). 1364-0321/$ – see front matter © 2011 Elsevier Ltd. All rights reserved. doi:10.1016/j.rser.2011.05.003

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2.4. Impact of optimization modeling in power sector development . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 2.5. Future prospective . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . Conclusion and outlook . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . Acknowledgement . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . References . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .

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1. Introduction and background There has been an enormous increase in the demand for energy since the middle of the last century as a result of industrial development and population growth. Consequently, the development of new and renewable sources of energy has become a matter of priority in many countries all over the world. Electricity is conceivably the most multipurpose energy carrier in our modern global economy, and it is therefore primarily linked to human and economic development. Electricity growth has overtaken that of any other fuel, leading to ever-increasing shares in the overall mix. This trend is expected to continue throughout the following decades, with large parts of the world population in developing countries appealing connected to power grids. Electricity deserves precise attention with regard to its contribution to global greenhouse gas emissions, which is reflected in the continuing development of low-carbon technologies for power generation. A multitude of features play a role in societal debate in comparing electricity generating options, such as cost, gas emissions, radiological and toxicological exposure, greenhouse, occupational health and safety, employment, domestic energy security, and social impressions. Decision-makers will in general weight these aspects differently, and similarly the literature deals with these issues in inconsistent ways. Attempts to quantify the varied concerns of electricity generation in one end-point indicator in order to aid decision-making are anxious with problems, among which uncertainty and the discounting are perhaps the two most extremely challenging [1]. The formation of public perception is further complicated by the fact that media and political campaigns often comment more rapidly and decisively on contentious issues, thus reaching the public more effectively than sources of less biased factual information. For example nuclear energy is often portrayed and hence perceived as an invisible danger under the control of a few, and associated with military use, suppression of information, and high accident risk [2,3]. On the other hand of the spectrum, large hydroelectric dams are associated with the forceful resettlement of large numbers of people, and the destruction of archaeological heritage and biodiversity [4]. The concept of sustainable development is evolved for a liveable future where human needs are met while keeping the balance with nature. Driving the global energy system into a sustainable path has arisen as a major concern and policy objective. It is becoming gradually accepted that current energy systems, networks encompassing everything from primary energy sources to final energy services, are becoming unsustainable. Driven primarily by concerns over urban air quality, global warming caused by greenhouse gas (GHG) emissions and dependence on depleting fossil fuel reserves, a transition to alternative energy systems is receiving serious attention. Such a tradition will certainly involve meeting the growing energy demand of the future with greater efficiency as well as using more renewable energy sources (such as wind, solar, biomass, etc.). While many technical options exist for developing a future sustainable and less environmentally damaging energy supply, they are often treated separately driven by their own technical communities and political groups. Energy systems engineering provides a methodological scientific framework to arrive at realistic integrated solutions to complex energy problems, by adopting a holistic, systems-based approach. Superstructure based modeling strategy, along with MILP and MINLP solution algorithms

are efficient and effective in solving energy systems engineering problems, especially at decision making and planning stage. Based on this, multi-objective optimization and optimization under uncertainty produces further in-depth analyses and allows a decision maker to make the final decision from many aspects of view. The aim of this study is to update existing status of optimization modeling role in world energy assessments with information published during the past decade, focusing on electricity-generating technologies and the distribution or supply systems and to envisage the importance of optimization techniques for future developments in power sector. 2. Discussion 2.1. Current state of power generation technologies A mix of options to lower the energy per unit of GDP and carbon intensity of energy systems will be needed to achieve a truly sustainable energy future in a decarbonized world. Energy related GHG emissions are a by-product of the conversion and delivery sector which includes extraction/refining, electricity generation and direct transport of energy carriers in pipelines, wires, ships, etc., as well as the energy end-use sectors i.e. transport, buildings, industry, agriculture, forestry and waste. Fig. 1 elaborates complex interactions between primary energy sources and energy carriers to meet societal needs for energy services as used by the transport, buildings, industry and primary industry sectors. Electricity is one of the driving forces of the economic development of societies. At the start of the 21st century, world faces significant energy challenges. The concept of sustainable development is evolved for a liveable future where human needs are met while keeping the balance with nature. Initially, DC power systems were popular in the 1870s and 1880s. Small systems were sold to factories around the world, both in urban areas, and remote undeveloped areas for industrial/mining use. Thomas Edison, and Werner von Siemens lead the largest efforts to electrify the world. DC systems powered factories and small downtown areas, but did not reach 95% of residents. It became clear that to make real the dream of to supplying whole cities with electric power you would need to generate the power in one place (like a large river with great hydro-power potential) and transmit it to the city. This was done by several major advancements [6]: Alternating current: Developed first in Italy and Germany, it quickly proved to be the best method for harnessing electric power. American engineers like Elihu Thomson at GE and others at Westinghouse developed more advanced AC generators as they engaged in fierce competition. Three phase power: Three phase AC power was first developed in Germany by August Haselwander in 1887 and made its major world debut in 1891 at the Lauffen-Frankfurt demonstration [International Electro-Technical Exhibition] (built by Dolivo-Dobrowolsky and Oskar von Miller). Mill Creek 1 in California proved to be the first commercial use of three phase power [2]. Transformers: Transformers control voltage and are a very important part of the system. Rudimentary transformers were first developed in Austro-Hungary and England, with the first fully developed design coming from William Stanley in Massachusetts.

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Fig. 1. Complex interactions between primary energy sources and energy carriers to meet societal needs for energy services [5].

Electricity was originally generated at remote hydroelectric dams or by burning fossil fuels in the city centers, delivering electricity to nearby buildings and recycling the waste heat to make steam to heat the same buildings, while rural houses had no access to power. Over time, coal plants grew in size, facing pressure to locate far from population because of their pollution. Transmission wires carried the electricity many miles to users with a 10–15% loss [7]. Because it is not practical to transmit waste heat over long distances, the heat was vented. There was no good technology available for clean, local generation, so the wasted heat was a trade-off for cleaner air in the cities. Eventually a huge grid was developed and the power industry built all new generation in remote areas, far from users. All plants were specially designed and built on site, creating economies of scale. It cost less per unit of generation to build large plants than to build smaller plants. These conditions prevailed from 1910 through 1960, and everyone in the power industry and government came to assume that remote, central generation was optimal, that it would deliver power at the lowest cost versus other alternatives. Lenzen [8] reviewed eight power sector related technologies as described in subsequent text. Seven of these are generating technologies: hydro-, nuclear, wind, photovoltaic, concentrating solar, geothermal and biomass power. The remaining technology is car-

bon capture and storage. This selection is fairly representative for technologies that are important in terms of their potential capacity to contribute to a low-carbon world economy. Currently, only nuclear and hydropower generate significant low-carbon portions of global electricity. Table 1 shows a comparison among these technologies in terms of annual generation, CO2 emission, generation cost and major barriers in deployment. Carbon capture and storage is seen as a potentially significant CO2 mitigation route because it would allow retaining major parts of current electricity generation infrastructure and build on existing knowledge and practices. Capture technologies are well understood but remain to be demonstrated at a large commercial scale, which is not expected before 2020 [8]. Nuclear power is seen as a mature technology, with many reactoryears of experience, and modern reactors exhibiting a high degree of safety. Nuclear power currently contributes 14% of global electricity generation. The majority of nuclear reactors are thermal reactors, and this is expected to remain the case in the mid to long term. Current average capacity factors of 86% are among the highest of all technologies and levelised costs are competitive between 4 and 7 US¢/kWh. Future Generation-IV reactor designs such as fast reactors and compact liquid metal or salt reactors, as well as

Table 1 Current state of development of electricity-generating technologies, adopted from [8]. Technology

Annual generation (TWhel/y)

Capacity factor l (%)

Mitigation Potential (GtCO2 )

Energy requirements (kWhth/kWhel)

CO2 emissions (g/kWhel)

Generating cost (US¢/kWh)

Barriers

Coal Oil Gas Carbon capture and storage

7755 1096 3807 –

70–90 60–90 ≈60 n.a.

– – – 150–250

2.6–3.5 2.6–3.5 2–3 2–2.5 + 0.3–1

900 700 450 170–280

3–6 3–6 4–6 3–6 + 0–4

Nuclear fission

2793

86k

>180

0.12p

65

3–7

Large hydro

3121

41

200–300

0.1

45–200

4–10

Small hydro Wind Solar-photovoltaic Concentrating Solar Geothermal Biomass

≈250 260 12 ≈1 60 240

≈50 24.5 15 20–40 70–90 60

≈100 ≈450–500 25–200 25–200 25–500 ≈100

n.a. 0.05 0.4/1–0.8/1 0.3 n.a. 2.3–4.2

45 ≈65 40/150 – 100/200 50–90 20–140 35–85

4–20 3–7 10–20 15–25 6–8 3–9

Greenhouse gas emissions Resource constraints Fuel price Energy penalty, large-scale storage, late deployment Waste disposal, proliferation, public acceptance Resource potential, social and environmental impact Resource potential Variability and grid integration Generating cost Generating cost Uncertain field capacity Efficiency, feedstock availability, cost

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advanced fuel cycles promise advances in reactor fuel utilization, enhanced proliferation resistance, reduction of nuclear waste volumes, and passive safety, however no design satisfies all criteria, and deployment is not expected to start before 2030 [8]. Hydropower deploys 870 GW and contributes more than 3 PWh annually, or 17% of global electricity generation, and it therefore dominates the renewable technology suite. 90% of this electricity is generated by large hydro dams, with the remainder generated by small, mostly run-of-river plants. The long-term resource of large hydro is limited because most large rivers have already been dammed [8]. Wind power is the second strongest growing of all technologies examined in this report, with recent annual growth rates of about 34%. The technology is mature and simple, and decades of experience exist in a few countries. Due to strong economies of scale, wind turbines have grown to several megawatts per device, and wind farms have now been deployed off-shore. The wind energy industry is still small but competitive: 120 GW of installed wind power contributes only about 1.5% or 260 TWh to global electricity generation at average capacity factors of around 25%, and levelised costs between 3 and 7 US¢/kWh, including variability cost [8]. Photovoltaic power is the strongest growing of all technologies examined so far, with recent annual growth rates of around 40%. One of the largest markets was remote power supplies, in particular for developing-country communities that are not connected to electricity grids, but this has changed during recent years as developed countries have embarked on rebated residential-roof deployment programs. Photovoltaic modules are deployed dispersed at small scale, which makes it difficult to ascertain globally installed capacity, which is estimated at about 9 GW. Assuming an average capacity factor of 15%, global generation is 12 TWh [8]. Concentrating solar power sometimes also referred to as solar–thermal power, was strongly pursued in the 1980s and 1990s, but renewed interest has emerged recently. At present only 0.4 GW are operating at large-scale plant levels, generating some 1 TWh annually, using mostly parabolic troughs, but also tower, dish and Fresnel designs. Concentrating solar plants integrate well with conventional thermal plants, for example as fuel savers. The average capacity factor is at least 20%, but can reach beyond 40% when heat transfer fluids with high thermal capacity are used for hourly storage. Combined with storage, the capacity credit of concentrating solar power is higher than that of photovoltaic power, with sunny locations and high summer peak loads achieving credits of more than 80% [8]. Geothermal power has been utilized for power generation since 1920. Globally it only accounts for 10 GW deployed, but some countries derive a major proportion of their electricity from geothermal reservoirs. Geothermal plant efficiency depends on the quality of the resource. Low-temperature resources require one or two flashing processes in order to utilize steam turbines. Electricity generation has been growing slowly at about 4% annually, and is currently about 60 TWh at 70% average capacity factor, but capacity factors up to 90% are considered possible [8]. Geothermal boasts the largest technical potential of all technologies, however resource development can be slow due to a combination of uncertain field capacity and high drilling cost, requiring a step-wise development process, with results obtained from a small number of wells before the field is further expanded. Biomass power is secondary to uses of biomass for liquid transportation fuels, but it is currently used economically in dedicated applications such as pulp and sugar industries. The search for alternative sources of energy was largely dormant until the energy crises of the 1970s and early 1980s sparked renewed interest in the issue. Among the alternative energy sources, vegetable oil-based fuels were reconsidered, with biodiesel in form of esters of sun-

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flower oil to be reported in 1980 [9]. Biomass power is the area among these technologies which gained most encouraging attention of researchers these days. A lot of research work has been done in last three decades on biomass utilization to yield transportation fuels. Balat et al. [10] reviewed the biological and thermochemical methods that could be used to produce bioethanol and carried out an analysis of its global production trends. Demirbas [11] briefly reviewed the modern biomass-based transportation fuels such as fuels from Fischer–Tropsch synthesis, bioethanol, fatty acid (m)ethylester, biomethanol, and biohydrogen. Inayat et al. [12–14] developed mathematical models of hydrogen production process via biomass steam gasification using framework consisting of kinetics models for char gasification, methanation, Boudouard, methane reforming, water gas shift and carbonation reactions to represent the gasification and CO2 adsorption in the gasifier implemented in MATLAB to predict the producer gas composition, Bio-hydrogen yield and thermodynamic efficiency of process, additionally, developed a model for flowsheet of hydrogen production from empty fruit bunch from oil palm via steam gasification with in situ carbon dioxide capture, that incorporates the chemical reaction kinetics, mass and energy balances calculations with parameter analysis on the influence of the temperature, steam/biomass and sorbent/biomass ratios. On the other hand, due to the overwhelming scientific evidence is that the unfettered use of fossil fuels is causing the world’s climate to change; biomass power is gaining an increasing interest. Global deployment in biomass power is only around 50 GW generating 1.5%, or some 240 TWh [8] of electricity. Currently, biomass plants combust agricultural and forestry residues, and waste. The long-term potential of these types of feedstock is lower than that of dedicated energy crops, but the latter have preferential usage for biofuels. Dedicated biomass plants are small in size because of locally limited feedstock availability and transportation requirements, and hence suffer from dis-economies of scale. Further technical challenges are in developing gasifier, boiler and turbine designs that can handle variable- and low-quality biomass and deal with the resultant pollutant deposits and corrosion. Co-firing is regarded as the preferred option, but at biomass shares above 10% it leads to efficiency losses and requires structural changes to plant components such as feeders. Levelised costs are competitive at between 3 and 5 US¢/kWh. Capacity factors are lower than those for coal-fired power plants, at around 60% [8]. Currently world’s energy requirements are mostly fulfilled by fossil fuels. However, the overwhelming scientific evidence is that the unfettered use of fossil fuels is causing the world’s climate to change, with potential catastrophic effect. Until 1960s everyone in the power industry and government came to assume that remote, central generation was optimal, that it would deliver power at the lowest cost versus other alternatives, and there was an assumption that remote, central generation was optimal, that it would deliver power at the lowest cost versus other alternatives. Because of their high level of integration, are susceptible to disturbances in the supply chain. In the case of electricity especially, this supply paradigm is losing some of its appeal. Apart from vulnerability, centralized energy supply systems are losing its attractiveness due to a number of further annoying factors including the depletion of fossil fuels and their climate change impact, the insecurities affecting energy transportation infrastructure, and the desire of investors to minimize risks through the deployment of smaller-scale, modular generation and transmission systems. 2.2. Decentralized systems Small-scale decentralized systems are emerging as a viable alternative as being less dependent upon centralized energy sup-

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Table 2 Comparative description of different decentralized technologies [16,17]. Technology

Features

Suitable Mode

Co-generation

The average efficiency of co-generation systems is estimated to be 85%. The important co-generation technologies are bagasse co-generation, steam turbine combined heat, gas turbine combined heat Producer gas is the consequence of modern use of biomass and its conversion to higher forms of gaseous fuel through the process of gasification. For small-scale applications, biomass requirement range from about 5 kg/h up to about 500 kg/h The small and mini-hydro power generation systems are environmentally benign as it is run of the river technology where the river flow is not impeded; as a result the river flooding problem is eliminated. The system is classified as small-hydro if the system size varies between 2.5 and 25 MW, mini-hydro typically falls below 2 MW, micro-hydro schemes fall below 500 kW and pico-hydro below 10 kW capacity Efficiency of commercially available solar PV varies between 7 and 17%. Because of its high initial investment, cost of generation per kWh becomes high making it unaffordable The gas that is produced through anaerobic digestion of biomass and other wastes like vegetable residues, animal dung, etc. is called biogas. Biogas generally is 60% methane and 40% carbon dioxide Similar to PV systems wind energy systems are also site and season specific. Wind energy systems mostly operate in grid-connected mode, but only in a few villages isolated systems are operated to provide electricity for water pumping

Both GC and SA

Biomass power

Small and mini-hydro power

Solar PV power Biogas Wind power

ply, and can sometimes use multiple energy sources. On the basis of type of energy resources used, decentralized power is also classified as non-renewable and renewable. These classifications along with an overabundance of technological alternatives have made the prioritization process of decentralized power quite complicated for decision making. Establishing local generation and a local network may be cheaper, easier and faster than extending the central-station network to remote areas of modest load. The rural areas of many developing and emerging countries are unlikely ever to see the arrival of classical synchronized AC transmission lines. Decentralized local systems, including those using local resources of renewable energy such as wind, solar and biomass, appear much more feasible [15].There is abundant literature, which has discussed various approaches that have been used to support decision making under such complex situations. The implementation of decentralized energy systems depends upon the extent of decentralization. The extent of decentralization also determines the condition for the system to be operated in either grid-connected (GC) or stand-alone (SA) mode. A number of articles have been presented for both success and failure narratives of implementation of SA as well as GC systems. But most of the articles were applied to isolated cases. A generalized approach to assess suitability of SA and GC systems at a given location, based on techno-economic financial-environmental feasibility does not find adequate coverage. Table 2 elaborates the important available technologies for decentralized power generation applicable in mode(s) and their features. Only biomass based technologies (cogeneration and gasification) are found to be more versatile towards both GC and SA modes and both can serve as combined heat and power (CHP) system. High fossil fuel prices recorded between 2003 and 2008, combined with concerns about the environmental consequences of GHG emissions, have renewed interest in the development of alternatives to fossil fuels—specifically, nuclear power and renewable energy sources. A lot of studies have been made in last two decades to assess and implement decentralized power systems. Recent important and valued researches on different aspects of decentralized power system are tabulated as Table 3. High fossil fuel prices recorded between 2003 and 2008, combined with concerns about the environmental consequences of greenhouse gas emissions, have renewed interest in the development of alternatives to fossil fuels—specifically, nuclear power and renewable energy sources. In the mainstream media, these systems are increasingly associated with the benefits from virtually free, low-carbon and locally available renewable energy resources such as wind and solar power. But in the specific context of the built environment, the emphasis is on decentralized electricity generation associated

Both GC and SA

SA

SA SA GC

with heat production. It is therefore important to realize the potential of biomass based technologies in GHG emission reduction in developed countries and their role in promoting sustainable rural development in developing countries. World net electricity generation increases by 87% in the Reference case, from 18.8 trillion kWh in 2007 to 25.0 trillion kWh in 2020 and 35.2 trillion kWh in 2035 [100]. Renewable energy is the fastest-growing source of electricity generation in the International Energy Outlook 2010 (IEO2010) Reference case. Table 4 shows the world net renewable electricity generation by energy source, 2007–2035.The mix of primary fuels used to generate electricity has changed a great deal over the past four decades on a worldwide basis. Coal continues to be the fuel most widely used for electricity generation, although generation from nuclear power increased rapidly from the 1970s through the 1980s, and natural-gas-fired generation grew rapidly in the 1980s and 1990s. The use of oil for electricity generation has been declining since themid-1970s, when oil prices rose sharply. Total generations from renewable resources increases by 3.0% annually, and the renewable share of world electricity generation grew from 18% in 2007 to 23% in 2035. Almost 80% of the increase is in hydroelectric power and wind power. The contribution of wind energy, in particular, has grown swiftly over the past decade, from 18 GW of net installed capacity at the end of 2000 to 159 GW at the end of 2009—a trend that continues into the future. Of the 4.5 trillion kWh of new renewable generation added over the projection period, 2.4 trillion kWh (54%) is attributed to hydroelectric power and 1.2 trillion kWh (26%) to wind. Electricity generation from nuclear power increases from about 2.6 trillion kWh in 2007 to 4.5 trillion kWh in 2035 [101]. Wind and solar are intermittent technologies that can be used only when resources are available. Once built, the cost of operating wind or solar technologies, when the resource is available, is generally much less than the cost of operating conventional renewable generation. Solar power, for instance, is currently a “niche” source of renewable energy but can be economical where electricity prices are especially high, where peak load pricing occurs, or where government incentives are available. Abundant literature is available on issues, problems and progress in the power sector. Most of the existing literature is concerned with implications of climate change mitigation policies on energy technologies, prices, and emissions. For instance, the world moves towards concerted action to stabilize concentrations of greenhouse gases (GHG) in the earth’s atmosphere, the profile of energy resources and technologies being used. Table 5 elaborates the most recent potential researches (among this abundant literature) in energy and power sector (during last decade).

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Table 3 Recent important and valued researches of decentralized electricity systems; extracted from [17]. Author(s)

Year

Study Domain/Emphasis

Reference

M. A. Sheikh

2010

[18]

B. Iglinski, W. Kujawski, et al. F. Chen et al.

2010

A. Kumar et al.

2010

M.A. Eltawil and Z. Zhao

2010

Salas and Olías

2009

Carlos and Khang

2009

Doukas and Karakosta

2009

M. Asif

2009

B. Ghobadian et al.

2009

L. Chen et al. Y. Himri et al. J. Paska et al.

2009 2009 2009

N.T. Nguyen and M. Ha-Duong

2009

C. Gokcol et al. A. Yilanci, I. Dincer, and H.K. Ozturk

2009 2009

Walker Purohit Adhikari et al.

2008 2008 2008

Lybaek U.K. Mirza et al. M.R. Nouni et al.

2008 2008 2008

S. Bilgen et al. I. Rofiqul et al.

2008 2008

S. Sumathi et al.

2008

Zoulias and Lymberopoulos

2007

Kasseris et al.

2007

Hiremath et al. Purohit and Michaelowa A.M. Omer X. Zeng et al.

2007 2007 2007 2007

A.K. Hossain and O. Badr Holland et al. Gulli

2007 2006 2006

I.M. Bugaje

2006

Mahmoud and Ibrik

2006

Hiremath et al. Jebaraj and Iniyan Ravindranath et al.

2006 2006 2006

Bernal-Agustin and Dufo-Lopez

2006

Review of RE supply options; solar energy, wind energy, microhydel power, biogas and geothermal energy in Pakistan. Current status and future objectives of wind power, solar power and biomass power in the Kujawsko-Pomorskie Voivodeship (Poland). Potential to develop various renewable energies, such as solar energy, biomass energy, wind power, geothermal energy, hydropower in Taiwan and the review of the achievements, polices and future plans in this area. Review of the availability, current status, major achievements and future potentials of renewable energy options including biomass, hydropower, wind energy, solar energy and geothermal energy .in India. A review study to investigate and emphasize the importance of the grid-connected PV system regarding the intermittent nature of renewable generation, and the characterization of PV generation with regard to grid code compliance with a critically review on expected potential problems associated with high penetration levels and islanding prevention methods of grid tied PV. Extensive analysis of all the electrical parameters of grid-connected solar inverters for applications below 10 kW. A generalized framework to assess the factors affecting the successful completion of grid-connected biomass energy projects validated with real world data of power plants (Thailand). The economic, environmental and sustainable benefits as well as removal of barriers for satisfactory dissemination of important RES technologies. Renewable energy-based electricity supply options such as macro/micro hydro, Biomass in the form of crop residues and animal waste and municipal solid waste, small wind electric generators and photovoltaics in Pakistan. Potential and feasibility to develop various renewable energies, such as solar energy, biomass and biogas energy, wind power and geothermal energy in Iran. Feasibility of densified solid biofuels technology for utilizing agro-residues in China. A review of the use of renewable energy situation and future objectives in Algeria. An overview on the present state and perspectives of using renewable energy sources including hydropower, solar energy, wind energy biomass and biogas in Poland. An overview about possibilities of generating electricity and reducing carbon emissions in Vietnam, the potential of all renewable energy sources together for electricity generation, development of Vietnam’s power sector from 1995 to 2005 and official projections out to 2030 also to analyze the optimized integration of a large array of grid connected renewable energy technologies, i.e. hydro, geothermal, biomass, wind, solar etc. in the power electric generation system. using the IRP model, to meet the challenges of soaring electricity demand, growing environmental concerns, energy pricing climax, and energy security over the period 2010–2030. An overview on the importance and potential of biomass and its utilization for biomass energy in Turkey. An overview of solar hydrogen production methods, their current status up to the present 2009, preliminary energy and exergy efficiency analyses for Solar-hydrogen/fuel cell hybrid energy systems for stationary (case study, Denizli, Turkey). Assessment of the linkage between stand-alone systems and fuel poverty (case study, UK). A detailed estimation of small hydro power (SHP) potential in India under CDM. An overview of CDM portfolio in Thailand by cataloguing potential, opportunities and barriers for executing decentralized sustainable renewable energy projects in the context of CDM. Assessment of market opportunities in Asian countries for SA biomass CHP (case study, Thailand). Potential of biomass for energy generation in Pakistan. Renewable energy-based decentralized electricity supply options such as micro hydro, dual fuel biomass gasifier systems, small wind electric generators and photovoltaics in India. Renewable energy potential and utilization in Turkey and Global warming issues. Review of RE supply options; solar energy, wind energy, hydro power, biogas and tidal energy in Bangladesh with concluding remarks “There is no way other than taking bio and solar energy for reducing environmental degradation.” Potential of oil palm as bio-diesel crop and waste stream as a source to produce vast amounts of bio-gas and other values added products. Simulation and optimization of replacement option of conventional technologies with hydrogen technologies, fuel cells in an existing PV-diesel operated in stand-alone mode by using HOMER) tool. Optimization model of the wind-fuel cell hybrid system for larger output under strict and lenient grid network restrictions. Total potential, installed capacities of decentralized energy systems (case study, India). Feasibility of bagasse cogeneration projects under CDM with a total CER potential up to 26 million. Present status of rural energy recourses including solar energy biomass and biogas energy in Sudan. An overview on the technology status, potential and the future research and development of straw in the biomass energy portfolio in China. Biomass energy potential for the planning small- to medium-scale biomass-to-electricity plants in Bangladesh. Assessment of the critical factors for successful diffusion of standalone systems in rural regions. Social-cost benefit analysis of stand-alone combined heat and power (CHP) systems based on both internal and external system costs. Review of RE scenario in Africa using South Africa, Egypt, Nigeria and Mali as case studies with solar energy and wood biomass as major recourses. Computer-based dynamic economic evaluation model with key economic efficiency indicators to assess three supply options namely solar PV, diesel generators in SA system and grid extension. Review on decentralized energy planning models. Reviews on decentralized energy models. Assessment of carbon abatement potential of bioenergy technologies (BETs) by comparison with fossil fuel alternatives. Economic analysis on the grid-connected Solar PV system (case study, Spain).

2010

[19] [20]

[21] [22]

[23] [24] [25] [26]

[27] [28] [29] [30] [31]

[32] [33]

[34] [35] [36] [37] [38] [39] [40] [41]

[42] [43] [44] [45] [46] [47] [48] [49] [50] [51] [52] [53] [54] [55] [56] [57]

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Table 3 (Continued) Author(s)

Year

Study Domain/Emphasis

Reference

Faulin et al. Fernandez-Infantes et al.

2006 2006

[58] [59]

Dosiek and Pillay Rabah Nakata et al.

2005 2005 2005

Khan and Iqbal

2005

Pelet et al.

2005

Santarelli and Pellegrino

2005

Kamel and Dahl

2005

Jeong et al.

2005

Silveira

2005

Santarelli et al.

2004

Hoogwijk et al. Lindenberger et al.

2004 2004

Kishore et al.

2004

Beck and Martinot

2004

Bakos and Tsagas

2003

J. Chang, D.Y.C. Leung et al.

2003

Kumar et al.

2003

Kaldellis Atikol and Guven

2003 2003

Dasappa et al.

2003

Ro and Rahman

2003

Kolhe et al. Chakrabarti and Chakrabarti Martinot

2002 2002

Manolakos et al.

2001

Gupta Stone et al. Bates and Wilshaw

2000 2000 1999

Ackermann et al.

1999

Meurer et al.

1999

Vosen and Keller

1999

Rana et al. Sidrach-de-Cardona and Lopez Gabler and Luther

1998 1998

Ravindranath and Hall Ravindranath Ramakumar et al. Joshi et al.

1995 1993 1992 1992

Siyambalapitiya et al.

1991

Reddy et al.

1990

Potential of RETs in generating local employment (case study, Spain). A computer-based decision support system to design the GC PV system based on electrical, environmental and economic considerations. Design of a horizontal axis wind SA systems by simulation using MATLAB/SIMULINK. Practical implementation of a stand-alone solar PV to improve the quality of life of poor (case study, Kenya). System configuration and operation of hybrid systems for the supply of heat and power based on a non-linear programming optimization model and METANet economic modeling system (Japan). SA systems hybrid with other both renewable and nonrenewable sources of energy carriers as a potential solution to the problems of SA systems like low capacity factors, excess battery costs and limited capacity to store extra energy.(using HOMER software to optimize and arrive at the right combination of energy systems). Multi-objective evolutionary programming technique to rationalize the design of energy systems for remote locations. Mathematical optimization model to minimize the total investment cost of hydrogen based stand-alone system to supply electricity to residential users, integrated with renewable energy systems like solar PV and micro-hydro. Economic assessment of hybrid solar–wind systems against the diesel using NREL’s renewable energy simulation tool called HOMER (hybrid optimization model for electric renewables). A fuzzy logic algorithm as a strategy for effective load management resulting an improved resilience and system operation efficiency of a hybrid fuel-cell and battery stand-alone system. The potential of CDM in promoting bio-energy technologies to promote sustainable development in developing countries. Design methodology of a stand-alone system, by integrating renewable energy systems, based on energy analysis, electricity management and hydrogen management (case study, Italy). Some of the facts about geographical, technical and economic potential of wind across the globe. Analyses of modernization options for a local energy system, based on both demand reduction and supply-related measures as an extension of the optimization model called deco (dynamic energy, emission, and cost optimization). The potential role of biomass in global climate change mitigation and the extent of commercialization and mainstreaming of biomass energy technologies within the framework of clean development mechanism (CDM). A case study Policies and key barriers for diffusion of SA systems and GC systems like unfavorable pricing rules, private ownership, and lack of locational pricing leading to undervaluation of GC systems. Techno-economic assessment for technical feasibility and economic viability of a hybrid solar/wind installation for residential electrification and heat (case study, Greece). An overview on the research and development of renewable energy, such as solar, biomass, geothermal, ocean and wind energy in China. Power costs and optimum size of a stand-alone biomass energy plant based on agricultural residues, whole forest residues, and residues of lumber activities (case study, Canada). Financial analysis of grid-connected wind energy systems (of the entire Greek state). Sizing of the grid-connected cogeneration systems based on electrical load and thermal load in textile industries (case study, Turkey). Isolated biomass gasifiers being used to provide low temperature and high temperature thermal requirements of industries. A computer model tested controller system to improve the system stability of fuel cell GC systems in power distribution network. Economic viability of a stand-alone solar PV system along with a diesel-powered system. Feasibility study for solar energy based SPV stand-alone system based on socio-economic and environmental aspects (case study, India). An extensive discussion on the policies, strategies and lessons learnt from the GEF (Global environmental Facility) project on the status of grid-based renewable energy systems in developing countries. Simulation based software tool for optimizing the design of a hybrid energy system consisting of wind and PV to supply electricity and water for a remote island village. Policy approach in India for grid based RETs. Investment, operational costs and impact of rural electrification project initiatives (case study, India). Status of solar PV power systems, governmental policies towards renewable and key market barriers for the successful and quick diffusion of solar PV power systems. Simulation based validated economic optimization tool to evaluate different options for distributed generation, and improve power quality of an embedded wind generation system in weak grid conditions. Generation of measurement performance data of an autonomous SA hybrid renewable energy system (RES) to optimize the energy output and operational reliability with the aid of simulation programs. Optimization and simulation model for a SA solar powered battery-hydrogen hybrid system for fluctuating demand and supply scenarios using two storage algorithms for with or without prior knowledge about the future demand. Optimal RE mix for specific energy demand. Generalized model to evaluate energy losses and the performance of (a 2 kW) grid-connected photovoltaic system at different regions, climate conditions and irradiation (case study, Spain). Development and validation of simulation and optimization model for a wind–solar hybrid SA system to optimize the design of converters and storage devices so as to minimize the energy payback time. System configuration, operational details, and costing of a biogas unit (case study, India). Biomass Gasification as environmentally sound technology for decentralizes electricity. A knowledge based approach for the design of integrated renewable energy systems (IRES). Development of a linear mathematical model to optimize the energy mix of different energy source-end-use conversion devices to supply energy to villages (case study, India). Importance of the pre-evaluation of techno-economic-social parameters of the grid-connected rural electrification systems. Choice of technology for quality energy services (cost comparison).

2002

1998

[60] [61] [62] [63]

[64] [65]

[66] [67] [68] [69] [70] [71]

[72]

[73] [74] [75] [76] [77] [78] [79] [80] [81] [82] [83] [84] [85] [86] [87] [88] [89] [90]

[91] [92] [93] [94] [95] [96] [97] [98] [99]

A.A. Bazmi, G. Zahedi / Renewable and Sustainable Energy Reviews 15 (2011) 3480–3500

3487

Table 4 World net renewable electricity generation by energy source, 2007-2035 (Billion kWh) [100]. Region

2007

2015

2020

2025

2030

2035

Average annual percent change, 2007–2035

Hydropower Wind Geothermal Solar Other

2999 165 57 6 235

3689 682 98 95 394

4166 902 108 126 515

4591 1115 119 140 653

5034 1234 142 153 773

5418 1355 160 165 874

2.1 7.8 3.7 12.7 4.8

Total

3462

4958

5817

6618

7336

7972

3.0

2.3. Optimization modeling studies related to power generation and supply techniques Over the second half of the 20th century, optimization found widespread applications in the study of physical and chemical systems, production planning and scheduling systems, location and transportation problems, resource allocation in financial systems, and engineering design. A large number of problems in production planning and scheduling, location, transportation, finance, and engineering design require that decisions be made in the presence of uncertainty. The optimization under uncertainty includes the classical recourse-based stochastic programming, robust stochastic programming, probabilistic (chance-constraint) programming, fuzzy programming, and stochastic dynamic programming. These optimization techniques are briefly reviewed by Sahinidis [145]. During the course of 21st century, energy systems will be required to meet several important goals, including conformance with the environmental, economic, and social goals of sustainable development. The existence of multiple goals, multiple stockholders, and numerous available technologies lend itself to the use of a system approach to solving energy system problems. Energy systems engineering provides a methodological scientific framework to arrive at realistic integrated solutions to complex energy problems, by adopting a holistic, system-based approach. Such an integrated approach features: A superstructure representation where alternatives in terms of energy technologies, raw materials and possible routes towards electricity and hydrogen, among others, are captured. A mixed-integer optimization model which allows for the development of a single mathematical model to represent all possible energy system alternatives within the superstructure, along with appropriate solution algorithms (MILP, MINLP, etc.). A multi-objective optimization approach to simultaneously address and quantify the trade-offs among competing objectives, such as profitability, environmental impacts, energy consumption, and system operability. An optimization under uncertainty strategy to analyze the impact of technological uncertainties over a long-term horizon on the profit/energy consumption/environmental impacts of an energy system. Artificial intelligence (AI) techniques are applied for modeling, identification, optimization, prediction, forecasting and control of complex systems like Adaptive Control, Robust Pattern Detection, Optimization, Scheduling and Complex Mapping. AI is commonly defined as the science and engineering of making intelligent machines, especially intelligent computer programs. AI-based systems are being developed and deployed worldwide in a wide variety of applications, mainly because of their symbolic reasoning, flexibility and explanation capabilities. AI has been used in different sectors, such as engineering, economics, medicine, military, marine, etc. Mellita and Kalogirou [146] used AI techniques to solve problems in photovoltaic systems application including fore-

casting and modeling of meteorological data-, sizing of photovoltaic systems and modeling-, simulation, and control of photovoltaic systems and highlighted the potential of AI as design tool in photovoltaic systems. Nowicka-Zagrajeka et al. [147] addressed the issue of modeling and forecasting electricity loads applying a twostep procedure to a series of system-wide loads from the California power market using ANN approach. Chaudry et al. [148] developed a multi-time period combined gas and electricity network optimization model which takes into account the varying nature of gas flows, network support facilities such as gas storage and the power ramping characteristics of electricity generation units.

2.3.1. Power supply and distribution During the last decade several new concepts of energy planning and management such as decentralized planning, energy conservation through improved technologies, waste recycling, integrated energy planning, introduction of renewable energy sources and energy forecasting have emerged. Recent trends in electric utility restructuring have included increasing competition in an open electricity supply marketplace, which has sharpened attention to keeping operation and maintenance costs for infrastructure as low as possible. Some research literature suggests that one side-effect of restructuring has been a reduced willingness on the part of some utilities to invest in environmental protection beyond what is absolutely required by law and regulation [149]. Within the electricity sector, network planning is closely related to generation planning. In recent context, where centralized energy supply systems are losing its attractiveness due to a number of further annoying factors including the depletion of fossil fuels and their climate change impact, the actual operation of the generating units no longer depends on state-or utility-based centralized procedures, but rather on decentralized decisions of generation firms whose goals are to maximize their own profits. All firms compete to provide generation services at a price set by the market, as a result of the interaction of all of them and the demand. As a result, electricity firms are exposed to significantly higher risks and their need for suitable decision-support models has greatly increased. Hence, a new area of highly interesting research for the electrical industry has opened up. Numerous publications give evidence of extensive effort by the research community to develop electricity market models adapted to the new competitive context. Ventosa [150] reviewed the electricity generation market modeling focusing on a survey of the most relevant publications regarding electricity market modeling, identifying three major trends: optimization models, equilibrium models and simulation models and concluded That “the impressive advances registered in this research field underscore how much interest this matter has drawn during the last decade”. Jebaraj and Iniyan [151] presented a review on different types of models such as energy planning models, energy supply–demand models, forecasting models, renewable energy models, emission reduction models, optimization models and models based on neural network and fuzzy and suggested that the neural networks can be used in the energy forecasting and the

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Table 5 Potential researches in energy and power sector in last decade. Author(s)

Year

Study Domain/Emphasis

Reference

N. Boccard

2010

[102]

J. Clifton and B.J. Boruff

2010

Cansino, J.M., et al.

2010

I. Purohit and P. Purohit

2010

J. Badcock and M. Lenzen

2010

L. Kosnik

2010

U. Arena et al.

2010

Gomis-Bellmunt, O., et al.

2010

M. Thirugnanasambandam et al.

2010

M.M. Abu-Khader I. Altmana and T. Johnson

2009 2009

M. Bolinger and R. Wiser

2009

N. Caldés et al.

2009

C. Chen and E.S. Rubin

2009

Othman, M.R., et al.

2009

V. Fthenakis et al.

2009

J. Hansson et al.

2009

An overview of the ability of wind power output to serve electricity demand all around the year, hour by hour, focusing on “capacity credit”, methodology to assess the “social cost” of wind power and contribution (or lack there of) of wind power generation (WPG) to adequacy, with special analysis of the cost estimates for the six European countries (Germany, Denmark, Spain, France, Portugal and Ireland) on the basis of load and WPG output data. A review of policies designed to stimulate the contribution of renewable sources highlights the continued reliance upon fossil fuels to supply current and future electricity needs in Australia. Potential CSP sites are defined in the Wheat belt region of Western Australia through overlaying environmental variables and electricity infrastructure on a high resolution grid using widely available datasets and standard geographical information system (GIS) software. A comprehensive overview of the main tax incentives used in the EU-27 member States to promote green electricity focusing on the European regulation of tax incentives for green electricity, the actual share of renewable energy sources in gross electricity consumption, main tax incentives considered in direct taxes, and pigouvian and other taxes. A technical and economic assessment of concentrating solar power (CSP) technologies in India taking two projects namely PS-10 (based on power tower technology) and ANDASOL-1 (based on parabolic trough collector technology) as reference cases. The estimation of the extent of subsidization globally, via selected mechanisms, for a number of different electricity-generating technologies covering coal-fired, nuclear, wind, solar PV, concentrating solar, geothermal, biomass and hydroelectric power. An overview on cost-benefit perspective, topographical features for small scale hydropower sites in the US and to determine the cost-effectiveness of developing these sites. Concluding that while the average cost of developing small scale hydropower is relatively high, there still remain hundreds of sites on the low end of the cost scale that are cost-effective to develop right now. A comparison between the most promising design configurations for the industrial application of gasification based, biomass-to-energy co-generators in the 100–600 kWe range and the techno-economic performances of two energy generation devices, a gas engine and an externally fired gas turbine, have been estimated on the basis of the manufacturer’s specifications drawing conclusion that the internal combustion engine layout is the solution that currently offers the higher reliability and provides the higher internal rate of return for the investigated range of electrical energy production. The evaluation of power generated by variable and constant frequency offshore wind farms connected to a single large power converter, the evaluation of the power capture increase when employing a variable frequency wind farm connected to a HVDC grid by means of large power converter proving the grid frequency and voltage for the wind farm, focusing on the energy capture analysis, other extremely important issues related to variable frequency wind farm engineering. Review on the current status of the solar thermal technologies, performance analyses of existing designs (study), mathematical simulation (design) and fabrication of innovative designs with suggested improvements and development. A comprehensive review on recent advances in nuclear power sector. A review of organizational issues, the broad industrial structure of the current bio-power industry and current organizational mechanisms based on data from the U.S. Energy Information Administration. An overview of wind power sector growth both globally and specifically in the US demonstrating recent increases in wind turbine pricing, installed project costs, and wind power prices and the factors to mitigate the impact of rising costs on wind power prices in the United States in recent years. The socio-economic impacts of increasing the installed solar thermal energy power capacity in Spain, using an input–output analysis under two different scenarios: (i) based on two solar thermal power plants currently in operation (with 50 and 17 MW of installed capacity); (ii) the compliance to the Spanish Renewable Energy Plan (PER) 2005–2010 reaching 500 MW by 2010. The comprehensive overview the plant configurations of IGCC systems with and without CO2 capture, analysis of several factors influencing the performance and cost of IGCC systems with and without CO2 capture, including coal quality and CO2 removal efficiency, additionally factors in a probabilistic uncertainty analysis and the potential effects of two advanced technologies—an ion transport membrane (ITM) system for oxygen production and an H-frame gas turbine (GT) system for power generation—on the performance and cost of IGCC systems with CCS. A review summarizing the clean development mechanism (CDM) and adoption of CMD for Malaysia and Indonesia, a comparison of energy policies of both countries with advanced industrialized countries, current status of carbon capture and storage (CCS) technologies, and choice of coal fired power plants for Malaysia and Indonesia. A study to forecast future energy demand levels in three distinct stages (Present to 2020, 2020–2050, and 2050–2100) in realizing the development of the SW solar power plant for the US, and its extrapolation for the deployment level of existing solar technologies, supplemented by other renewable energy sources, to prove the feasibility for solar energy to supply that energy including (1) PV, (2) PV combined with compressed air energy storage (CAES) power plants, and (3) CSP plants with thermal storage systems with concluding remarks that the it is clearly feasible to replace the present fossil fuel energy infrastructure in the US with solar power and other renewables, and reduce CO2 emissions to a level commensurate with the most aggressive climate-change goals. A review on The European coal-fired power plant infrastructure, technical biomass co-firing potential and factors influencing the prospects for co-firing.

[103]

[104]

[105]

[106]

[107]

[108]

[109]

[110]

[111] [112]

[113]

[114]

[115]

[116]

[117]

[118]

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Table 5 (Continued) Author(s)

Year

Study Domain/Emphasis

Reference

D.L. Gallup

2009

[119]

M.I. Sohel et al.

2009

A. Yilanci et al.

2009

Neij, L.

2008

L. Kosnik

2008

D. Driver

2008

T. Oliver

2008

C. Yin et al.

2008

M. Mueller and R. Wallace

2008

S. Shanthakumar et al.

2008

C. Di Blasi

2008

Som, S. and A. Datta

2008

E.S. Rubin et al.

2007

K. Damen et al.

2007

J. Koornneef, M. Junginger, and A. Faaij

2007

J. Beer

2007

J. Decarolis and D. Keith

2006

R.B. Duffey

2005

A review study to highlight some production engineering advances in geothermal technology that have been made over about the past two decades. A theoretical analysis including modeling and simulation of a typical plant using New Zealand’s local weather data taking the Rotokawa binary cycle geothermal plant is as a test case and compared against other base load options, comparison of improved summer hot-day performance to other peak load options as well as policy implications. A review on solar-hydrogen/fuel cell hybrid energy systems describing solar hydrogen production methods, and their current status, and preliminary energy and exergy efficiency analyses for a photovoltaic-hydrogen/fuel cell hybrid energy system in Denizli, Turkey with three different energy demand paths – from photovoltaic panels to the consumer. Minimum and maximum overall energy and exergy efficiencies of the system are calculated based on these paths. An analytical framework for the analysis of future cost development of new energy technologies for electricity generation; based on an assessment of available experience curves, complemented with bottom-up analysis of sources of cost reductions and, for some technologies, judgmental expert assessments of long-term development paths. A study of the potential for water power development as one method to reduce US greenhouse gas emissions from new small/micro hydropower dams, uprating facilities at existing large hydropower dams, new generating facilities at existing non-hydropower dams, and hydrokinetics as well as the cost-effectiveness of developing these sources of water-based energy, concluding that while water power will never be the complete answer to emissions-free energy production, a strong case can be made that it can be a useful part of the answer. A review on materials priorities for energy and power sector and current status including materials for energy conservation, turbine technology, Water power, fuel cell technology, nuclear fission and fusion materials, high-temperature power generation materials, solar energy—photovoltaics (PVs), wind power and functional materials for energy generation and conservation. A study discussing the current status of the science and technologies for fossil-fuelled power generation and outlines likely future technologies, development targets and timescales followed by a description of the scientific and technological developments that are needed to meet these challenges. A review on the state-of-the-art knowledge on grate-fired boilers burning biomass: the key elements in the firing system and the development, the important combustion mechanism, the recent breakthrough in the technology, the most pressing issues, the current research and development activities, and the critical future problems to be resolved. A comprehensive overview on some of the key challenges to be met in the development of marine renewable energy technology. A critical review of various flue gas conditioning techniques employed for controlling the suspended particulate matter (SPM) level in thermal power stations including the in-depth analysis of data obtained from different thermal power stations of the world. A review on chemical kinetics of biomass/char combustion and gasification, critically analyzing the state of the art of rate laws and kinetic constants for the gasification, with carbon dioxide and steam, and the combustion of chars produced from lignocellulosic fuels, including a brief outline about yields and composition of pyrolysis products, and the role played by various factors, such as heating rate, temperature and pressure of the pyrolysis stage, feedstock and content/composition of the inorganic matter, on char reactivity. A comprehensive review pertaining to fundamental studies on thermodynamic irreversibility and exergy analysis in the processes of combustion of gaseous, liquid and solid fuels, concluding that the important consideration of fuel economy for a combustor of a power-producing unit pertains to the trade-off between the efficient conversion of energy quantity and minimum destruction of energy quality (exergy). A Study summarizing and comparing the results of recent studies of the current cost of fossil fuel power systems with and without CO2 capture, including pulverized coal (PC) combustion plants, coal-based integrated gasification combined cycle (IGCC) plants, and natural gas combined cycle (NGCC) plants; a broader range of key assumptions that influence these cost comparisons; and quantify the implications of CCS energy requirements on plant-level resource requirements and multi-media emissions. A generalized modeling tool is used to estimate and compare the emissions, efficiency, resource requirements and current costs of fossil fuel power plants with CCS on a systematic basis. A comparative study analyzing the promising electricity and hydrogen production chains with CO2 capture, transport and storage and energy carrier transmission, distribution and end-use to assess (avoided) CO2 emissions, energy production costs and CO2 mitigation costs. An overview analyzing the development and economical performance of fluidized bed combustion (FBC) and its derivatives circulating fluidized bed (CFB) and bubbling fluidized bed (BFB) with a descriptive overview given of the technology and the market penetration base on a database comprises technological and economical data on 491 FBC projects. A review of electric power generation system development with special attention to plant efficiency. An economic characterization of a wind system in which long-distance electricity transmission, storage, and gas turbines are used to supplement variable wind power output to meet a time-varying load. Role for nuclear power in the future hydrogen economy and synergy of nuclear with wind power for hydrogen generation.

[120]

[121]

[122]

[123]

[124]

[125]

[126]

[127] [128]

[129]

[130]

[131]

[132]

[133]

[134] [135]

[136]

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Table 5 (Continued) Author(s)

Year

Study Domain/Emphasis

Reference

B. Buhre et al.

2005

[137]

A. Khaliq and R. Kumar

2005

T. Nakata

2004

A. Sahin

2004

Z. En

2004

T. Tsoutsos et al.

2003

D. Egre and J.C. Milewski

2002

J. Werther et al.

2000

A comprehensive review of researches undertaken on Oxy-fuel combustion technology for coal-fired power generation, the status of the technology development and assessments providing comparisons with other power generation options, and identification of research needs in the area. The application of finite-time heat-transfer theory to optimize ecologically the power output of an endo-reversible and regenerative gas-turbine power-cycle for infinite thermal-capacitance rates to and from the reservoirs coupled to the constant temperature heat-reservoirs using finite time heat-transfer theory to determine the optimum values of power output, thermal efficiency and exergetic efficiency under a state of maximum ecological-function. A review on the various issues associated with the energy-economic model and its application to national energy policies, renewable energy systems, and the global environment. An overview of wind energy history, wind-power meteorology, the energy–climate relations, wind-turbine technology, wind economy, wind–hybrid applications and the 2004status of installed wind energy capacity all over the world. A review to provide a comprehensive account of solar energy sources and conversion methods with explanatory background material both from application and research points of view, applications of solar energy in terms of low and high temperature collectors, photovoltaic devices future electric energy generations based on solar power site-exploitation and transmission by different means over long distances such as fiber-optic cables and future perspective use of solar energy in combination with water and as a consequent electrolysis analysis generation of hydrogen gas. Feasibility on a solar power system based on the stirling dish (SD) technology reviews and comparison of the available stirling engines in the perspective of a solar stirling system. A review illustrating the necessity to evaluate each hydroelectric project in relation to the services it provides and to compare electricity supply projects on the basis of equivalent services provided to society. An overview of various issues related to the combustion of agricultural residues such as the problems associated with the properties of the residues such as low bulk density, low ash melting points, high volatile matter contents and the presence of nitrogen, sulfur, chlorine and sometimes high moisture contents and design considerations of facilities for the combustion of agricultural residues.

fuzzy logic for energy allocation. More researches in the area are reviewed in Table 5. 2.3.2. Power plant operation Since the early 1990s, multi-variable and multi-objective power plant optimization has been used extensively by US electric utilities, as neural-network-based software reached maturity and became available as commercial products. Reportedly, more than 400 units (coal-, oil- and gas-fired) have used such optimization software, of which approximately 280–300 are coal-fired boilers. However, it also is clear that while many of these units have used optimization software for some time (after they have been installed and calibrated successfully), they are not using them anymore. This is the result of many factors, including the fact that a number of optimization products are not available in the market because they have been acquired by competitors. Nowadays, power systems are being operated more and more close to their stability limits due to the economic and environmental constraints. Static voltage stability has become one of the major factors that are threatening the operation security of electric power system. In order to improve the Loading Margin of the system, the current available methods mainly include reactive power management [152–154], generation rescheduling [155–158] and load curtailment [159,160]. System operators (SO) generally use the reactive power management as the first option because of its low cost, and the load curtailment as the last option because of its high expense. However, the voltage stability problem can not always be fully settled by the first option. Furthermore if too many reactive power facilities were deployed, then a large amount of sunk costs would appear. Therefore, GR, a convenient and effective tool at hand for SO, is paying more and more attention. 2.3.3. Building energy consumption In many counties, building energy consumption is very often responsible for about 40% of the total final energy demand. In US,

[138]

[139] [140]

[141]

[142] [143]

[144]

buildings account for nearly 40% of energy use. In EU, about 57% of the total final energy consumption is used for space heating, 25% for domestic hot water and 11% for electricity [161]. In China, building energy consumption has been increasing at more than 10% a yearduring the past 20 years [162]. The increasing of energy consumptionleads to more environmental issues. Nowadays, many national governments have established some regulations and adopted some technologies to improve energy utilization efficiency and mitigate environmental impact such as an energy-efficient technology, combined cooling heating and power (CCHP) system is broadly identified as an alternativefor the world to meet and solve energy-related problems, such asincreasing energy demands, increasing energy cost, energy supplysecurity and environmental concerns [163–168]. When CCHP system isused for a building, it is called building cooling heating and power (BCHP) system [169–171]. The complexity of BCHP system structure and operation modeplus the variation building loads complicates the design of BCHP system. Many researchers haveoptimized different BCHP systems in consideration of differentoptimization objectives like The cost optimization method to determine the capacity of BCHP system so as to minimizethe capital and operation cost [172–179], even including CO2 emissionscosts [180–182], thermo-economic methodologies [183,184] and energy, economy and environment has also been optimized [185–189].Wu and Wang [163] reviewed CCHP systems, including various technologies, provide an alternativefor the world to meet and solve energy-related problems, such as energy shortages, energy supply security,emission control, the economy and conservation of energyand presented diverseCCHP configurations of existing technologies, particularly four typical systems of various size ranges.The worldwide status quo of CCHP development is briefly introduced by dividing the world into four mainsections: the US, Europe, Asia and the Pacific and rest of the world. It is concluded that, within decades, promising CCHPtechnologies can flourish with the cooperative efforts of governments, energy-related enterprises and professionalassociations.

A.A. Bazmi, G. Zahedi / Renewable and Sustainable Energy Reviews 15 (2011) 3480–3500

The optimization problems of BCHP systems have been statedas linear programming problem [173,181], non-linear programmingproblem [190,191], mixed integer programming problem [174,177,192] and multi-objective programming problem [187]. The classical solution methods to these optimization problems include simplex method [181,193], dynamic programming [194], Lagrangian relaxation [195,196], sequential quadratic programming [179,197], Newton’s method [198,199] and reduced gradient method [200,201]. Since 1998, an important quantity of researches uses artificial intelligence methods to optimize BCHP system such as branch-and-bound algorithm [177,192], genetic algorithm (GA) [180,189], evolutionary programming [187,193,202], and particle swarm optimization algorithm [8,203–205]. 2.3.4. Industrial energy consumption In industry, open-cycle gas-turbine power-plants are widely applied. Radcenco et al. [206] developed the optimization model for the performance of an open-cycle simple gas-turbine power-plant by incorporating into the power-plant model, and its optimization, the irreversibility due to the various pressure-drops distributed along the flow path. The analogy between irreversibility of thermal resistance and the irreversibility fluid-flow resistance was exploited by Bejan [207] and Radcenco [208], and was further studied by Bejan [209–213]. For the open Brayton cycles, the principle of optimally tuning the fuel-flow rate and subsequent distribution of pressure-drops was used [206]. Uran [214] developed an optimization system for combined heat and electricity production in the wood-processing industry constructing thermo-economic optimization model to minimize energy costs, using a mathematical formula that computes the lowest heat capacity as the cogeneration system starts to be more payable than a non-cogeneration system. A thermo-economic analysis of systems, cogeneration and non-cogeneration, including the payback period of the capital invested in the cogeneration system and harmonization of heat production and disposable wood waste as fuel found cogeneration or non-cogeneration optimal. 2.3.5. Power plants and carbon dioxide capture and storage (CCS) As generation of carbon dioxide (CO2 ) greenhouse gas is inherent in the combustion of fossil fuels, effective capture of CO2 from industrial and commercial operations is viewed as an important strategy which has the potential to achieve a significant reduction in atmospheric CO2 levels. Carbon dioxide capture and storage (CCS) is a CO2 diminutionoption that can contribute substantially to achieve ambitious CO2 reduction targets. Thiruvenkatachari et al. [215] briefly reviewed CO2 capture methods, classified existing and emerging post combustion CO2 capture technologies, compared their features, then addresses possible future system, and concluded that further work is required to be fully evaluated for their potential for large scale CO2 capture from fossil fuel-fired power stations. The electricity sector especially, with large point sources of CO2 , offers opportunities to apply CCS at a large scale [216]. Results of techno-economic energy models show that power plants combined with CCS can indeed compete from a mitigation perspective with other non- or low-emitting CO2 technologies such as nuclear energy or renewable energy. Necessary pre-conditions are strict climate policies, a decrease in the cost of CCS, and more specifically, an improvement in the performance of capture technologies by technological learning [217–219]. Van-den-Broek et al. [220] has extended the concept of technology learning curves to simultaneously consider improvements in key system performance parameters as well as cost variables in assessing the future development of power plants with CO2 capture. While early publications of learning curves were actually based on physical measures such as labor efficiency [221], they are nowadays mostly applied to identify cost trends. However, Yeh and

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Rubin [222] also used them to assess the efficiency improvements of pulverized coal-fired power plants. Van-den-Broek et al. [220], in their investigation, found that “When technology spill over is taken into account, the new power plants without CO2 capture also stimulate the improvement in power plants with CO2 capture as they consist largely of similar technologies. For example, the SPC boiler gets an additional experience of 1300 GW between 2001 and 2050 in both scenarios. Whereas in reference scenario in the study (REF), this is mainly a result from the capacity growth of supercritical pulverized coal-fired power plants (SPCs) without capture, in carbon constraint scenario in the study (CCC) it is half from the growth of SPCs without capture and half with capture. In both the REF and CCC scenarios, the additional experience (including replacement capacity) of the gas turbine combined cycle (GTCC) power block is around 3100 GW in 2050, but in each scenario this total reflects different additional capacities of natural gas combined cycle power plant (NGCC), natural gas combined cycle power plant with post combustion carbon dioxide capture (NGCC-CC), and integrated gasification combined cycle power plant on coal and biomass (IGCC), integrated gasification combined cycle power plant on coal and biomass with pre-combustion carbon dioxide capture (IGCC-CC) power plants”. 2.3.6. Renewable energy mix Renewable energy technologies produce marketable energy by converting natural phenomena into useful forms of energy These technologies use the sun’s energy and its direct and indirect effects on the earth (solar radiation, wind, falling water and various plants, i.e. biomass), gravitational forces (tides), and the heat of the earth’s core (geothermal) as the resources from which energy is produced. These resources have massive energy potential, however, they are generally diffused and not fully accessible, most of them are intermittent, and have distinct regional variability [223]. Nowadays, significant progress is made by improving the collection and conversion efficiencies, lowering the initial and maintenance costs, and increasing the reliability and applicability. A worldwide research and development using modeling, optimization and simulation tools in the field of renewable energy resources and systems is carried out during the last two decades. Some diverse and most promising researches of optimization and modeling in energy and power sector are highlighted in Table 6. 2.4. Impact of optimization modeling in power sector development Economies and societies have been changing more rapidly than ever throughout the past decades. The accelerating emergence of new technologies, new knowledge about climate dynamics, changing political and economic constellations, and increasing academic publishing activity mean that assessments of the state of global energy systems are becoming outdated more quickly than before. The beginning of the industrial revolution marked a profound change from gradual refinement of low-power systems to rapid-intensive systems of all sorts. Along with this acceleration of evolution came a rapid expansion of the ability of human beings to multiply their maximum power output through the application of technology. Energy systems are complex, often involving combinations of thermal, mechanical and electrical energy, which are used to achieve one or more of several possible goals, such as electricity generation, climate control of enclosed spaces, propulsion of transportation vehicles, and so on. Energy systems therefore lend themselves to the use of a system approach to problem solving. Decision makers of energy systems, in many instances, must take into account a number of goals (physical, financial or environmental) may be local, regional or global in nature. One if the best known concepts at the present time that considers multiple goals for the various economic activities that underpin human society, among

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Table 6 Modeling, optimization and simulation researches in energy and power sector. Author(s)

Year

Study Domain/Emphasis

Reference

L.d.S. Coelho and A.A.P. Santos

2011

[224]

A.M. Foley et al.

2010

A. Gómez-Barea and B. Leckner

2010

R.-H. Liang, Y.-K. Chen and Y.-T. Chen

2010

P. Bhatt, R. Roy and S.P. Ghoshal

2010

E. Cayer et al.

2010

Ren, H., & Gao, W.

2010

L. Jing, P. Gang and J. Jie

2010

Azadeh, A., et al.

2010

J.M. Yusta et al.

2010

Möst, D., & Keles, D.

2010

M.H. Amjadi et al.

2010

S. Porkar et al.

2010

D. Niu et al.

2010

T. Niknam et al.

2010

A nonlinear forecasting model based on radial basis function neural networks (RBF-NNs) with Gaussian activation functions and robust clustering algorithms to model the conditional mean and a parametric generalized autoregressive conditional heteroskedasticity (GARCH) specification to model the conditional volatility and to provide multi-step-ahead point and direction-of-change forecasts of the Spanish electricity pool prices. A reviews on the changing role of electricity systems modeling in a strategic manner, focusing on the modeling response to key developments, the move away from monopoly towards liberalized market regimes and the increasing complexity brought about by policy targets for renewable energy and emissions, providing an overview of electricity systems modeling techniques discusses a number of key proprietary electricity systems models used in the USA and Europe. A review on modeling of biomass gasification in bubbling and circulating fluidized bed (FB) gasifiers focusing on Mixing and reactions, Kinetic models, reactor modeling, fluidization models, identifying the need for further investigation and concluding that most of the FB biomass gasification models fit reasonably well experiments selected for validation, despite the various formulations and input data. A fuzzy optimization approach for solving the Volt/Var control problem in a distribution system with uncertainties with emphasis on wind to find an optimum combination of tap position for the main transformer under load tap changer (ULTC) and on/off status for switched capacitors in a day to minimize the voltage deviation on the secondary bus of the main transformer, reactive power flow through the main transformer and real power loss on feeders. A GA/particle swarm intelligence based optimization describing comparative performance analysis of the two specific varieties of controller devices for optimal transient performance of automatic generation control of an interconnected two-area power system, having multiple thermal–hydro–diesels mixed generating units. A parametric study and optimization performed on a transcritical power cycle using six performance indicators: thermal efficiency, specific net output, exergetic efficiency, total UA and surface of the heat exchangers as well as the relative cost of the system. The independent parameters are the maximum temperature and pressure of the cycle as well as the net power output. A mixed-integer linear programming (MILP) model developed for the integrated plan and evaluation of distributed energy resources (DER) systems. Given the site’s energy loads, local climate data, utility tariff structure, and information (both technical and financial) on candidate DER technologies, the model minimizes overall energy cost for a test year by selecting the units to install and determining their operating schedules. Furthermore, the economic, energetic and environmental effects of the DER system are evaluated. A novel design which combines the Organic Rankine Cycle (ORC) with the Compound Parabolic Concentrators (CPC) collectors based on simulation model of the low temperature solar thermal electric generation in areas of Canberra, Singapore, Bombay, Lhasa, Sacramento and Berlin with HCFC-123 as the working fluid, investigating and optimizing the influences of the CPC collector tilt angle adjustment, the connection between the heat exchangers and the collectors, and the ORC evaporation temperature. An innovative model of agent based simulation, based on Ant Colony Optimization (ACO) algorithm is proposed in order to compare three available strategies of clearing wholesale electricity markets, uniform, pay-as-bid, and generalized Vickrey rules. The supply side actors of the power market are modeled as adaptive agents to learn how to bid strategically to optimize their profit through indirect interaction with other actors of the market. A mathematical optimization model development to simulate costs and the electricity demand of a machining process using the generalized reduced gradient approach, to find the optimum production schedule that maximizes the industry profit considering the hourly variations of the price of electricity in the spot market, describing different price scenarios to analyze the impact of the spot market prices for electricity on the optimal scheduling of the machining process and on the industry profit. An overview and classification of stochastic models dealing with price risks in electricity markets, focusing on various stochastic methods developed in operation research with practical relevance and applicability, including the concepts of (1) stochastic processes for commodity prices (especially for electricity), (2) scenario generation and reduction, which is important due to the need for a structured handling of large data amounts; as well as (3) stochastic optimizing models for investment decisions, short- and mid-term power production planning and long-term system optimization. A study dealing with estimation of electricity demand of Iran based on economic indicators using Particle Swarm Optimization (PSO) Algorithm based on Gross Domestic Product (GDP), population, number of customers and average price electricity by developing two different estimation models: a linear model and a non-linear model. A study introducing a new framework included mathematical optimization model by a new software package interfacing two powerful softwares (MATLAB and GAMS) for obtaining the optimal distributed generation (DG) capacity sizing and sitting investments with capability to simulate large distribution system planning, minimizing total system planning costs for DG investment, DG operation and maintenance, purchase of power by the distribution companies (DISCOs) from transmission companies and system power losses. A novel technique to forecast day-ahead electricity prices based on Self-Organizing Map neural network (SOM) and Support Vector Machine (SVM) models. A price-based novel approach for daily Volt/Var control in distribution systems using Distributed Generation (DG) units adopted to determine the optimum active and reactive power dispatch for the DG units, the reactive power contribution of the capacitor banks, and the tap settings of the transformers in a day in advance, using fuzzy adaptive particle swarm optimization (FAPSO) method to solve the daily Volt/Var control which is a non-linear mixed-integer problem.

[225]

[226]

[227]

[228]

[229]

[230]

[231]

[232]

[233]

[234]

[235]

[236]

[237] [238]

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Table 6 (Continued) Author(s)

Year

Study Domain/Emphasis

Reference

J. Wang et al.

2010

[239]

J. Sadhukhan et al.

2009

F. Frombo et al.

2009

P.A. Østergaard

2009

A. Ehsani et al.

2009

A. Hatami et al.

2009

A. Yucekaya et al.

2009

M. Toksari

2009

Bunn, D., & Day, C.

2009

H. Siahkali and M. Vakilian

2009

Chicco, G., & Mancarella, P.

2009

P. Malo

2009

A.A. Rentizelas et al.

2009

Louit, D., et al.

2009

J.D. Mondol et al.

2009

H. Yang et al.

2009

F.E. Benth and S. Koekebakker

2008

N. Ayoub et al.

2008

C. Diblasi

2008

Parametric analysis and exergy analysis to examine the effects of thermodynamic parameters on the cycle performance and exergy destruction in each component and optimization of the thermodynamic parameters of the supercritical CO2 power cycle with exergy efficiency as an objective function by means of genetic algorithm (GA) under the given waste heat condition using an artificial neural network (ANN) with the multi-layer feed-forward network type and back-propagation training to achieve parametric optimization design rapidly. Process simulation and modeling of a cost-effective and cleaner combined heat and power (CHP) generation gasification plant from low-cost, fourth-generation biomass waste feedstocks using the Aspen simulator. A GIS-based Environmental Decision Support System (EDSS) to define planning and management strategies for the optimal logistics for energy production from woody biomass to solve the optimization problem relevant to the strategic decision level. A study reviewing a number of possible optimization criteria for the design of energy systems with large shares of fluctuating renewable energy sources (A case study of Western Denmark). Development of a procedure (General Algebraic Modeling System-GAMS Rev. 140) for compulsory provision of spinning reserve using a risk-constrained cost-based mechanism, focusing on electrical energy and spinning reserve simultaneously, generators are paid the opportunity cost associated with their reduced energy because compulsion is financially unattractive among them and the transmission system reliability is considered in a simplified manner when computing composite system risk. A mathematical method based on mixed-integer stochastic programming to determine the optimal sale price of electricity to customers and the electricity procurement policy of a retailer for a specified period. A study presenting two particle swarm optimization (PSO) algorithms to determine bid prices and quantities under the rules of a competitive power market, the first method uses a conventional PSO technique to find solutions and the second method uses a decomposition technique in conjunction with the PSO approach. A study presenting Turkey’s net electricity energy generation and demand based on economic indicators, Forecasting model for electricity energy generation and demand “Ant colony optimization electricity energy estimation (ACOEEE) model” is developed by the ant colony optimization (ACO) approach using population, gross domestic product (GDP), import and export.is first proposed. Detailed computational models of price formation in the England and Wales electricity pool, based on the data from 1990 to 2001 that provides a benchmark against which to assess generator conduct, and thereby help to diagnose the separate causes of market structure and market conduct when actual prices appear to be higher than marginal cost. Development of a new approach for solving the generation scheduling (GS) problem considering the reserve requirement, load balance and wind power availability constraints using particle swarm optimization (PSO) method applied to a 12-unit test system (including 10 conventional thermal generating units and 2 wind farms) to determine the acceleration constants of proposed PSO and the global variant-based passive congregation PSO. A comprehensive input–output matrix approach aimed at modeling small-scale trigeneration equipment taking into account the interactions among plant components and external energy networks that maintains the separation among the individual energy vectors, each of which can be associated to its time-dependent price, providing the basic framework for formulating optimization problems concerning management of trigeneration systems within an energy market context. A flexible Copula-MSM (Markov Switching Multifractal) approach for modeling spot and weekly futures price dynamics to separately model the dependence structure, while enabling use of multifractal stochastic volatility models to characterize fluctuations in marginal returns. An optimization model for multi-biomass tri-generation energy supply developed employing GIS to calculate the transportation cost from all potential biomass collection points to all potential CHP plant locations. Then, optimization is performed regarding the optimal sizing of the power plant (defining which kind of energy to produce for the specific area), and biomass collection and harvesting scheduling. A simple model to determine the optimal major (preventive) maintenance actions (MMA) interval based on a relative time scale (i.e. time since last major maintenance event) and the combination of data from different sections of a grid, under a normalization scheme, additionally, extended maintenance times and sequential execution of the MMAs resulting in the loss of important information for the characterization of the failure process, with a case study to illustrate the optimal tree trimming interval around an electricity distribution network. Optimizing the economic viability of grid-connected photovoltaic systems investigating effect of load matching between PV supply and load demand, sizing ratio, electricity buying and selling prices, utility rate schedule, PV inclination and PV capacity on PV contribution to building load and PV electricity cost. An optimal design model for designing hybrid solar–wind systems employing battery banks for calculating the system optimum configurations and ensuring that the annualized cost of the systems is minimized while satisfying the custom required loss of power supply probability. Stochastic modeling of financial electricity contracts traded in many deregulated power markets that deliver (either physically or financially) electricity over a specified time period, and is frequently referred to as swaps since they in effect represent an exchange of fixed for floating electricity price, using the Heath–Jarrow–Morton approach to model swap prices. An optimization model for designing and evaluating Biomass Utilization Networks (BUN) superstructure, in local areas using Generalized Algorithm and application of the proposed methodology as a case study to a local Japanese area. A review study on modeling chemical and physical processes of wood and biomass pyrolysis emphasizing on chemical kinetics in relation to primary reactions, described by both one- and multi-component mechanisms, and secondary reactions of tar cracking and polymerization.

[240]

[241]

[242] [243]

[244]

[245]

[246]

[247]

[248]

[179]

[249]

[250]

[251]

[252]

[253]

[254]

[255]

[256]

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Table 6 (Continued) Author(s)

Year

Study Domain/Emphasis

Reference

G. Baskar and M. Mohan

2008

[257]

S. Mariano et al.

2008

W. Zhang and Y. Liu

2008

A. Shunmugalatha and S. Slochanal

2008

E Smeets et al.

2007

Botterud, A., & Korpas, M.

2007

E. Erdogdu

2007

A. Rong and R. Lahdelma

2007

D. Henning et al.

2006

P. Chan et al.

2006

Olsina, F., et al.

2006

A.C. Caputo et al.

2005

Y. Brar, J. Dhillon and D. Kothari

2005

A. Rong and R. Lahdelma

2005

P. Ostergaard

2005

S.-J. Deng and W. Jiang

2005

E. Thorin et al.

2005

E. Castronuovo and J. Lopes

2004

J.L. Silveira and C.E. Tuna

2003

A study to explore the application of a proposed improved particle optimization to the security constrained economic load dispatch problem with a view to minimize the total fuel cost of thermal units. A novel method, based on nonlinear programming (NLP), for optimizing power generation efficiency and short-term hydro scheduling (STHS), particularly concerning head-sensitive reservoirs under competitive environment. A new formulation of multi-objective reactive power and voltage control for power system using Multi-objective Particle Swarm Optimization with active power loss, voltage deviation and the voltage stability index of the system as objectives. A study describing the hybrid particle swarm optimization, which incorporates the breeding and subpopulation process in genetic algorithm into particle swarm optimization to determine the optimum cost of generation for maximum loadability limit of power system. A review of existing databases and outlook studies, in order to develop a bottom-up model, called the Quickscan model, to estimate the technical potential of bioenergy crop production in the year 2050, based on an evaluation of data and studies on relevant factors such as population growth, per capita food consumption and the efficiency of food production including three types of biomass energy sources: dedicated bioenergy crops, agricultural and forestry residues and waste, and forest growth. Dynamic model to formulate the power generation investment problem for a decentralized and profit-maximizing investor operating in a restructured and competitive power system, investigating how uncertainty influences the optimal timing of investments in new power generation capacity, using A real options approach to take long-term uncertainty in load growth, and its influence on future electricity prices, into account in the investment optimization. An ARIMA modeling using co-integration analysis and autoregressive integrated moving average, focusing on “energy crisis in Turkey” by both providing an electricity demand estimation and forecast, and comparing the results with official projections. A heuristic modeling to investigate the impact of power-ramp constraints on CHP production planning and develop a robust heuristic for dealing with the power-ramp constraints based on the solution to the problem with relaxed ramp-constraints. A study to describe an energy system optimization model framework, its application to a local energy utility and analyses of issues that influence the production of district heating, electricity and steam, such as cogeneration, policy instruments and marginal costs with an overview of energy systems analysis and district heating in Sweden. A study on selecting electricity contracts for a large-scale chemical production plant, which requires electricity importation, under demand uncertainty, focusing on two common types of electricity contracts, time zone contract and loading curve contract. A multi-period linear probabilistic programming model is adopted for the contract selection and optimization, and by using the probabilistic programming, a solution procedure is proposed that allow users to determine the best electricity contract according to their desired confident level of the uncertainties. A simulation model based on system dynamics with extensive discussion on the underlying mathematical formulations and focusing on replicating the system structure of power markets and the logic of relationships among system components in order to derive its dynamical response, while the simulations suggest that there might be serious problems to adjust early enough the generation capacity necessary to maintain stable reserve margins, and consequently, stable long-term price levels. Thermal utilization processes plant cost optimization modeling of biomass utilization for direct production of electric energy by means of combustion and gasification-conversion processes over a capacity range from 5 to 50 MW taking into account total capital investments, revenues from energy sale and total operating costs including logistic costs, economic profitability of bio-energy plants in terms of net present value (NPV), and a mapping of logistic constraints on plant profitability in the specified capacity range. A multi-objective thermal power generation scheduling problem having four objectives including the economic index, an environmental index and security index to be minimized simultaneously while inequality constraints imposed to meet the real and reactive power flow limits on each line are incorporated as objectives to be minimized. A linear programming (LP) model formulating the hourly trigeneration problem with a joint characteristic for three energy components to minimize simultaneously the production and purchase costs of three energy components, as well as CO2 emissions costs, in addition exploring the structure of the problem to propose the specialized Tri-Commodity Simplex (TCS) algorithm. Modeling grid losses and the geographic distribution of electricity generation analyzing the different impacts on the transmission grid in two cases both using scattered load balancing (1) where load balance is sought kept locally in each 150 kV node throughout the transmission system and (2) where only the overall load balance of the entire system is kept. A class of stochastic mean-reverting models for electricity prices with Levy process-driven Ornstein–Uhlenbeck (OU) processes being the building blocks. A tool developed for long-term optimization of cogeneration systems in a competitive market environment based on mixed integer linear-programming and Lagrangian relaxation using a general approach without heuristics to solve the optimization problem of the unit commitment problem and load dispatch. A study exploiting the concept of the combined use of wind power production and hydro storage/production, through the development of an operational optimization approach applied to a wind generator park with little water storage ability, with the operational strategy to be followed for the hours ahead by a pump station and an hydraulic generator embedded in a wind/hydro pumping facility, using the Portuguese energy remuneration rules. A study focusing on thermo-economic analysis of cogeneration plants to produce electric power and saturated steam presenting a new methodology “the minimum Exergetic Production Cost (EPC)”, based on the Second Law of Thermodynamics.

[258]

[259]

[260]

[261]

[262]

[263]

[264]

[265]

[266]

[267]

[268]

[269]

[270]

[271]

[272] [273]

[274]

[275]

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3495

Table 6 (Continued) Author(s)

Year

Study Domain/Emphasis

Reference

J. Nowicka-Zagrajeka and R. Weron

2002

[276]

A. Williams, M. Pourkashanian and J.M. Jones

2001

Modeling and forecasting electricity loads with ARMA processes applying a two-step procedure to a series of system-wide loads from the California power market. A review on computer modeling of combustion of pulverized coal and biomass, 2001 status of available sub-models and understanding of combustion of pulverized coal and biomass from the viewpoint of comport modeling.

[277]

Fig. 2. Summary of citation of researches on power and supply during last decade in this article.

the management of energy systems, is “sustainable development”. Driving the global energy system into a sustainable path has been emerged as a major concern and policy objective. Deregulation in electricity markets requires fast and robust optimization tools for a secure and efficient operation of the electric power system. In addition, there is the need of integrating and coordinating operational decisions taken by different utilities acting in the same market. Superstructure based modeling strategy, along with MILP and MINLP solution algorithms are efficient and effective in solving energy systems engineering problems, especially at decision making and planning stage. Based on this, multi-objective optimization and optimization under uncertainty produces further in-depth analyses and allows a decision maker to make the final decision from many aspects of view. 2.5. Future prospective Energy policy is of great significance to energy systems, especially to the development of renewable or sustainable energy. Policymakers usually need to establish policies based on detailed assessment of competing technologies and huge amounts of scenario analyses. Likewise the power supply and distribution also need intellectual decision making. However, this procedure could be greatly facilitated by superstructure based modeling and optimization. The recent advancements in modeling, optimization and simulation tools open new horizon for researchers to utilize and implement these techniques and tools to power supply networks and energy planning and management. Based on this review study, this can be envisaged that there is now more room for research and development activities in power generation and supply sector.

increasing competition, resulting in deferred investments in plant and infrastructure due to longer-term uncertainties. Until 1960s, everyone in the power industry and government came to assume that remote, central generation was optimal, that it would deliver power at the lowest cost versus other alternatives. Because of their high level of integration, are susceptible to disturbances in the supply chain. In the case of electricity especially, this supply paradigm is losing some of its appeal. Small-scale decentralized systems are emerging as a viable alternative as being less dependent upon centralized energy supply, and can sometimes use more than one energy source. Researchers envisaged an increasing decentralization of power supply, expected to make a particular contribution to climate protection. Over abundances of technological alternatives have made the prioritization process of decentralized power quite complicated for decision making. During last two decades, a lot of research work has been carried out to cater these challenges. Fig. 2 shows a summary of citation summary of potential researches related to power and supply, clearly indicating the increasing trend in recent years. Recent advancement in optimization modeling provided an ease to researchers to find optimal and sustainable solutions of the complex problems associated with power generation and supply scenarios. This review study can be concluded that the modeling and optimization are proved as effective and useful tools for problem solving in power and supply sector and especially for policymakers to establish policies based on detailed assessment of competing technologies and huge amounts of scenario analyses. Acknowledgement Authors would like to acknowledge Universiti Teknologi Malaysia for financial support under grant No. PY/2011/00557.

3. Conclusion and outlook References Energy security has recently become an important policy driver and privatization of the electricity sector has secured energy supply and provided cheaper energy services in some countries in the short term, but has led to contrary effects elsewhere due to

[1] Krewitt W. External cost of energy—do the answers match the questions? Looking back at 10 years of ExternE. Energy Policy 2002;30:839–48. [2] Falk J, Green J, Mudd G. Australia, uranium and nuclear power. International Journal of Environmental Studies 2006;63:845–57.

3496

A.A. Bazmi, G. Zahedi / Renewable and Sustainable Energy Reviews 15 (2011) 3480–3500

[3] Högselius P. Spent nuclear fuel policies in historical perspective: An international comparison. Energy Policy 2009;37:254–63. [4] Égré D, Senécal P. Social impact assessment of large dams throughout the world: lessons learned over two decades. Impact Assessment and Project Appraisal 2003;21:215–24. [5] Sims REH, Schock RN, Adegbululgbe A, Fenhann J, Konstantinaviciute I, Moomaw W, et al. Energy supply. In: Metz B, Davidson OR, Bosch PR, Dave R, Meyer LA, editors. Climate Change 2007: mitigation Contribution of Working Group III to the Fourth Assessment Report of the Intergovernmental Panel on Climate Change. Cambridge, United Kingdom and New York, NY, USA: Cambridge University Press; 2007. [6] History of electric power generation & transmission; 2010 [cited 2010 September]. Available from: http://www.edisontechcenter.org/ HistElectPowTrans.html. [7] Casten TR, Downes B. Critical thinking about energy: the case for decentralized generation of electricity. The Skeptical Inquirer 2005;29(1):25–33. Research Library (Document ID: 771440801). [8] Lenzen M. Current state of development of electricity-generating technologies—a literature review. In: Integrated sustainability analysis. The University of Sydney; 2009. [9] Knothe G. Biodiesel and renewable diesel: a comparison. Progress in Energy and Combustion Science 2010;36:364–73. [10] Balat M, Balat H, Öz C. Progress in bioethanol processing. Progress in Energy and Combustion Science 2008;34:551–73. [11] Demirbas A. Progress and recent trends in biofuels. Progress in Energy and Combustion Science 2007;33:1–18. [12] Inayat A, Ahmad MM, Yusup S, Mutalib MIA. Biomass steam gasification with in-situ CO2 capture for enriched hydrogen gas production: a reaction kinetics modelling approach. Energies 2010;3:1472–84. [13] Inayat A, Ahmad MM, Mutalib MIA, Yusup S. Flowsheet development and modelling of hydrogen production from empty fruit bunch via steam gasification. Chemical Engineering Transactions 2010;21:427–32. [14] Inayat A, Ahmad MM, Mutalib MIA, Yusup S. Effect of process parameters on hydrogen production and efficiency in biomass gasification using modelling approach. Journal of Applied Sciences 2010;10(24):3183–90. [15] Walt P. Electricity: decentralized futures, electric futures: pointers and possibilities-transforming electricity: Working Paper 3; 1997 [cited 2010 July]. Available from: http://www.chathamhouse.org.uk/research/ eedp/papers/view/-/id/83/. [16] Francois B, Daniel SK. Centralised and distributed electricity systems. Energy Policy 2008;36:4504–8. [17] Bazmi AA, Zahedi G, Hashim H. Progress and challenges in utilization of palm oil biomass as fuel for decentralized electricity generation. Renewable and Sustainable Energy Reviews 2011;15:574–83. [18] Sheikh MA. Energy and renewable energy scenario of Pakistan. Renewable and Sustainable Energy Reviews 2010;14(1):354–63. [19] Iglinski B, Kujawski W, Buczkowski R, Cichosz M. Renewable energy in the Kujawsko-Pomorskie Voivodeship (Poland). Renewable and Sustainable Energy Reviews 2010;14(4):1336–41. [20] Chen F, Lu SM, Wang E, Tseng KT. Renewable energy in Taiwan. Renewable and Sustainable Energy Reviews 2010;14(7):2029–38. [21] Kumar A, Kumar K, Kaushik N, Sharma S, Mishra S. Renewable energy in India: current status and future potentials. Renewable and Sustainable Energy Reviews 2010;14(8):2434–42. [22] Eltawil MA, Zhao Z. Grid-connected photovoltaic power systems: technical and potential problems—A review. Renewable and Sustainable Energy Reviews 2010;14:112–29. [23] Salas V, Olías E. Overview of the state of technique for PV inverters used in low voltage grid-connected PV systems: inverters below 10 kW. Renewable and Sustainable Energy Reviews 2009, doi:10.1016/j.rser.2008.10.003. [24] Carlos RM, Khang DB. A lifecycle-based success framework for gridconnected biomass energy projects. Renewable Energy 2009;34(5): 1195–203. [25] Doukas H, Karakosta C, Psarras J. RES technology transfer within the new climate regime: a “helicopter” view under the CDM. Renewable and Sustainable Energy Reviews 2009;13(5):1138–43. [26] Asif M. Sustainable energy options for Pakistan. Renewable and Sustainable Energy Reviews 2009;13:903–9. [27] Ghobadian B, Najafi G, Rahimi H, Yusaf TF. Future of renewable energies in Iran. Renewable and Sustainable Energy Reviews 2009;13(3):689–95. [28] Chen L, Xing L, Han L. Renewable energy from agro-residues in China: solid biofuels and biomass briquetting technology. Renewable and Sustainable Energy Reviews 2009;13(9):2689–95. [29] Himri Y, Malik AS, Boudghene Stambouli A, Himri S, Draoui B. Review and use of the Algerian renewable energy for sustainable development. Renewable and Sustainable Energy Reviews 2009;13(6–7):1584–91. [30] Paska J, Salek M, Surma T. Current status and perspectives of renewable energy sources in Poland. Renewable and Sustainable Energy Reviews 2009;13(1):142–54. [31] Nguyen NT, Ha-Duong M. Economic potential of renewable energy in Vietnam’s power sector. Energy Policy 2009;37:1601–13. [32] Gokcol C, Dursun B, Alboyaci B, Sunan E. Importance of biomass energy as alternative to other sources in Turkey. Energy Policy 2009;37:424–31. [33] Yilanci A, Dincer I, Ozturk HK. A review on solar-hydrogen/fuel cell hybrid energy systems for stationary applications. Progress in Energy and Combustion Science 2009;35:231–44.

[34] Walker G. Decentralised systems and fuel poverty: are there any links or risks? Energy Policy 2008;36(12):4514–7. [35] Purohit P. Small hydro power projects under clean development mechanism in India: a preliminary assessment. Energy Policy 2008;36(6):2000–15. [36] Adhikari S, Mithulananthan N, Dutta A, Mathias A. Potential of sustainable energy technologies under CDM in Thailand: opportunities and barriers. Renewable Energy 2008;33(9):2122–33. [37] Lybaek R. Discovering market opportunities for future CDM projects in Asia based on biomass combined heat and power production and supply of district heating. Energy for Sustainable Development 2008;12(2):34–48. [38] Mirza UK, Ahmad N, Majeed T. An overview of biomass energy utilization in Pakistan. Renewable and Sustainable Energy Reviews 2008;12: 1988–96. [39] Nouni MR, Mullick SC, Kandpal TC. Providing electricity access to remote areas in India: an approach towards identifying potential areas for decentralized electricity supply. Renewable and Sustainable Energy Reviews 2008;12(5):1187–220. [40] Bilgen S, Keles S, Kaygusuz A, SarI A, Kaygusuz K. Global warming and renewable energy sources for sustainable development: a case study in Turkey. Renewable and Sustainable Energy Reviews 2008;12(2):372–96. [41] Rofiqul Islam M, Rabiul Islam M, Rafiqul Alam Beg M. Renewable energy resources and technologies practice in Bangladesh. Renewable and Sustainable Energy Reviews 2008;12(2):299–343. [42] Sumathi S, Chai SP, Mohamed AR. Utilization of oil palm as a source of renewable energy in Malaysia. Renewable and Sustainable Energy Reviews 2008;12(9):2404–21. [43] Zoulias E, Lymberopoulos N. Techno-economic analysis of the integration of hydrogen energy technologies in renewable energy-based stand-alone power systems. Renewable Energy 2007;32(4):680–96. [44] Kasseris E, Samaras Z, Zafeiris D. Optimization of a wind-power fuel-cell hybrid system in an autonomous electrical network environment. Renewable Energy 2007;32(1):57–79. [45] Hiremath R, Ravindranath NH, Somashekhar H. Status of decentralized energy planning—case studies report. Bangalore: Center for Sustainable Technologies (CST), IISc; 2007. [46] Purohit P, Michaelowa A. CDM potential of bagasse cogeneration in India. Energy Policy 2007;35(10):4779–98. [47] Omer AM. Renewable energy resources for electricity generation in Sudan. Renewable and Sustainable Energy Reviews 2007;11(7):1481–97. [48] Zeng X, Ma Y, Ma L. Utilization of straw in biomass energy in China. Renewable and Sustainable Energy Reviews 2007;11(5):976–87. [49] Hossain AK, Badr O. Prospects of renewable energy utilisation for electricity generation in Bangladesh. Renewable and Sustainable Energy Reviews 2007;11(8):1617–49. [50] Holland R, Perera L, Sanchez T, Wilkinson R. Decentralised rural electrification: the critical success factors. In: Experience of ITDG (Intermediate Technology Developmental Group). USA: MIT; 2006. [51] Gulli F. Small distributed generation versus centralised supply: a social costbenefit analysis in the residential and service sectors. Energy Policy 2006;34(7):804–32. [52] Bugaje IM. Renewable energy for sustainable development in Africa: a review. Renewable and Sustainable Energy Reviews 2006;10(6):603–12. [53] Mahmoud MM, Ibrik IH. Techno-economic feasibility of energy supply of remote villages in Palestine by PV-systems, diesel generators and electric grid. Renewable and Sustainable Energy Reviews 2006;10(2): 128–38. [54] Hiremath R, Shikha S, Ravindranath N. Decentralized energy planning; modeling and application—a review. Renewable and Sustainable Energy Reviews 2007;11(5):729–52. [55] Jebaraj S, Iniyan S. A review of energy models. Renewable and Sustainable Energy Reviews 2006;10(4):281–311. [56] Ravindranath NH, Balachandra P, Dasappa S, Usha RK. Bioenergy technologies for carbon abatement. Biomass and Bioenergy 2006;30(10):826–37. [57] Bernal-Agustin JL, Dufo-Lopez R. Economical and environmental analysis of grid connected photovoltaic systems in Spain. Renewable Energy 2006;31(8):1107–28. [58] TERI, Survey of renewable energy in India (TERI Project Report No. 2000RT45). Technical report. Tata Energy Research Institute, New Delhi, 2001. [59] Fernandez-Infantes A, Contreras J, Bernal-Agustin JL. Design of grid connected PV systems considering electrical, economical and environmental aspects: a practical case. Renewable Energy 2006;31(13):2042–62. [60] Dosiek L, Pillay P. Modeling of a stand alone horizontal axis wind turbine. Unpublished report; 2005 [cited 2010 June]. Available from: www.clarkson.edu/honors/research/summer papers/Dosiek-Luke.doc. [61] Rabah KVO. Integrated solar energy systems for rural electrification in Kenya. Renewable Energy 2005;30(1):23–42. [62] Nakata T, Kubo K, Lamont A. Design for renewable energy systems with application to rural areas in Japan. Energy Policy 2005;33(2):209–19. [63] Khan M, Iqbal M. Pre-feasibility study of stand-alone hybrid energy systems for applications in Newfoundland. Renewable Energy 2005;30(6): 835–54. [64] Pelet X, Favrat D, Leyland G. Multiobjective optimisation of integrated energy systems for remote communities considering economics and CO2 emissions. International Journal of Thermal Sciences 2005;44(12):1180–9. [65] Santarelli M, Pellegrino D. Mathematical optimization of a RES-H2 plant using a black box algorithm. Renewable Energy 2005;30(4):493–510.

A.A. Bazmi, G. Zahedi / Renewable and Sustainable Energy Reviews 15 (2011) 3480–3500 [66] Kamel S, Dahl C. The economics of hybrid power systems for sustainable desert agriculture in Egypt. Energy 2005;30(8):1271–81. [67] Jeong K, Lee W, Kim C. Energy management strategies of a fuel cell/battery hybrid system using fuzzy logics. Journal of Power Sources 2005;145(2):319–26. [68] Silveira S. Promoting bioenergy through the clean development mechanism. Biomass and Bioenergy 2005;28(2):107–17. [69] Santarelli M, Cali M, Macagno S. Design and analysis of stand-alone hydrogen energy systems with different renewable sources. International Journal of Hydrogen Energy 2004;29(15):1571–86. [70] Hoogwijk M, Vries Bd, Turkenburg W. Assessment of the global and regional geographical, technical and economic potential of onshore wind energy. Energy Economics 2004;26(5):889–919. [71] Lindenberger D, Bruckner T, Morrison R, Groscurth H, Kummel R. Modernization of local energy systems. Energy 2004;29(2):245–56. [72] Kishore VVN, Bhandari PM, Gupta P. Biomass energy technologies for rural infrastructure and village power-opportunities and challenges in the context of global climate change concerns. Energy Policy 2004;32(6): 801–10. [73] Beck F, Martinot E. Renewable energy policies and barriers. In: Cleveland CJ, editor. Technical report. Encyclopedia of Energy; 2004. [74] Bakos GC, Tsagas NF. Technoeconomic assessment of a hybrid solar/wind installation for electrical energy saving. Energy and Buildings 2003;35(2):139–45. [75] Chang J, Leung DYC, Wu CZ, Yuan ZH. A review on the energy production, consumption, and prospect of renewable energy in China. Renewable and Sustainable Energy Reviews 2003;7(5):453–68. [76] Kumar A, Cameron JB, Flynn PC. Biomass power cost and optimum plant size in western Canada. Biomass and Bioenergy 2003;24(6):445–64. [77] Kaldellis JK. Feasibility evaluation of Greek State 1990-2001 wind energy program. Energy 2003;28(14):1375–94. [78] Atikol U, Guven H. Impact of cogeneration on integrated resource planning of Turkey. Energy 2003;28(12):1259–77. [79] Dasappa S, Sridhar HV, Sridhar G, Paul PJ, Mukunda HS. Biomass gasification—a substitute to fossil fuel for heat application. Biomass and Bioenergy 2003;25(6):637–49. [80] Ro K, Rahman S. Control of grid-connected fuel cell plants for enhancement of power system stability. Renewable Energy 2003;28(3):397–407. [81] Kolhe M, Kolhe S, Joshi JC. Economic viability of stand-alone solar photovoltaic system in comparison with diesel-powered system for India. Energy Economics 2002;24(2):155–65. [82] Chakrabarti S, Chakrabarti S. Rural electrification programme with solar energy in remote region—a case study in an island. Energy Policy 2002;30(1):33–42. [83] Martinot E, Grid-based renewable energy in developing countries: policies, strategies, and lessons from the Global Environment Facility (GEF), Washington, DC, Technical report. World Renewable Energy Policy and Strategy Forum, Berlin, Germany; 2002. [84] Manolakos D, Papadakis G, Papantonis D, Kyritsis S. A simulation–optimisation programme for designing hybrid energy systems for supplying electricity and fresh water through desalination to remote areas: case study. The Merssini village, Donoussa island, Aegean Sea, Greece. Energy 2001;26(7):679–704. [85] Gupta AK. Policies for Accelerating Renewable Energy Policy Approaches: The Indian Experience. In: International conference on accelerating gridbased renewable energy power generation for a clean environment. Lewis Preston Auditorium, 1818 H Street, NW, Washington, DC: The World Bank; 2000. [86] Stone J, Ullal H, Chaurey A, Bhatia P, Ramakrishna. Mission initiative impact study—a rural electrification project in West Bengal. India. In: Photovoltaic specialists conference (2000), conference record of the twenty-eighth IEEE. 2000. p. 1571–4. [87] Bates J, Wilshaw A, Stand-alone PV systems in developing countries. Technical report. The International Energy Agency (IEA). Photovoltaic Power Systems (PVPS) programme; 1999. [88] Ackermann T, Garner K, Gardiner A. Embedded wind generation in weak grids-economic optimisation and power quality simulation. Renewable Energy 1999;18(2):205–21. [89] Meurer C, Barthels H, Brocke WA, Emonts B, Groehn HG. PHOEBUS—an autonomous supply system with renewable energy: six years of operational experience and advanced concepts. Solar Energy 1999;67(1–3): 131–8. [90] Vosen SR, Keller JO. Hybrid energy storage systems for stand-alone electric power systems: optimization of system performance and cost through control strategies. International Journal of Hydrogen Energy 1999;24(12): 1139–56. [91] Rana S, Chandra R, Singh SP, Sodha MS. Optimal mix of renewable energy resources to meet the electrical energy demand in villages of Madhya Pradesh. Energy Conversion and Management 1998;39(3–4):203–16. [92] Sidrach-de-Cardona M, Lopez LM. Evaluation of a grid-connected photovoltaic system in southern Spain. Renewable Energy 1998;1–4:527–30. [93] Gabler H, Luther J. Wind–solar hybrid electrical supply systems. Results from a simulation model and optimization with respect to energy pay back time. Solar and Wind Technology 1988;5(3):239–47. [94] Ravindranath NH, Hall DO. Biomass, energy and environment—a developing country perspective from India. Oxford University Press; 1995.

3497

[95] Ravindranath NH. Biomass gasification: environmentally sound technology for decentralized power generation, a case study from India. Biomass and Bioenergy 1993;4(1):49–60. [96] Ramakumar R, Abouzah I, Ashenayi K. A knowledge-based approach to the design of integrated renewable energy systems. IEEE Transactions on Energy Conversion 1992;7(4):648–59. [97] Joshi B, Bhatti TS, Bansal NK. Decentralized energy planning model for a typical village in India. Energy 1992;17(9):869–76. [98] Siyambalapitiya D, Rajapakse S, Mel Sd, Fernando S, Perera B. Evaluation of grid connected rural electrification projects in developing countries. IEEE Transactions on Power Systems 1991;6(1):332–8. [99] Reddy AKN, Sumithra GD, Balachandra P, D’sa A. Comparative costs of electricity conservation, centralized and decentralized electricity generation. Economic and Political Weekly 1990;15(2):1201–16. [100] EIA, International Energy Outlook-Highlights. U.S. Energy Information Administration, Office of Integrated Analysis and Forecasting; 2010. Washington, DC 20585: U.S. Department of Energy. [101] EIA, International Energy Outlook-Electricity. U.S. Energy Information Administration, Office of Integrated Analysis and Forecasting; 2010. Washington, DC 20585: U.S. Department of Energy. [102] Boccard N. Economic properties of wind power—A European assessment. Energy Policy 2010;38:3232–44. [103] Clifton J, Boruff BJ. Assessing thepotentialforconcentratedsolarpowerdevelopment in ruralAustralia. Energy Policy 2010;38:5272–80. ˜ [104] Cansino JM, Pablo-Romero MP, Román R, Yniguez R. Tax incentives to promote green electricity: an overview of EU-27 countries. Energy Policy 2010;38:6000–8. [105] Purohit I, Purohit P. Techno-economic evaluation of concentrating solar power generation in India. Energy Policy 2010;38:3015–29. [106] Badcock J, Lenzen M. Subsidies forelectricity-generatingtechnologies:a review. Energy Policy 2010;38:5038–47. [107] Kosnik L. The potential for small scale hydropower development in the US. Energy Policy 2010;38:5512–9. [108] Arena U, Di Gregorio F, Santonastasi M. A techno-economic comparison between two design configurations for a small scale, biomass-to-energy gasification based system. Chemical Engineering Journal 2010;162(2): 580–90. [109] Gomis-Bellmunt O, Junyent-Ferré A, Sumper A, Galceran-Arellano S. Maximum generation power evaluation of variable frequency offshore wind farms when connected to a single power converter. Applied Energy 2010;87(10):3103–9. [110] Thirugnanasambandam M, Iniyan S, Goic R. A review of solar thermal technologies. Renewable and Sustainable Energy Reviews 2010;14(1): 312–22. [111] Abu-Khader MM. Recent advances in nuclear power: a review. Progress in Nuclear Energy 2009;51:225–35. [112] Altmana I, Johnson T. Organization of the current U.S. biopower industry: a template for future bioenergy industries. Biomass and Bioenergy 2009;33:779–84. [113] Bolinger M, Wiser R. Wind power price trends in the United States: struggling to remain competitive in the face of strong growth. Energy Policy 2009;37:1061–71. [114] Caldés N, Varela M, Santamaría M, Sáez R. Economic impact of solar thermal electricity deployment in Spain. Energy Policy 2009;37:1628–36. [115] Chen C, Rubin ES. CO2 control technology effects on IGCC plant performance and cost. Energy Policy 2009;37:915–24. [116] Othman MR, Martunus, Zakaria R, Fernando WJN. Strategic planning on carbon capture from coal fired plants in Malaysia and Indonesia: a review. Energy Policy 2009;37:1718–35. [117] Fthenakis V, Mason JE, Zweibel K. The technical, geographical, and economic feasibility for solar energy to supply the energy needs of the US. Energy Policy 2009;37:387–99. [118] Hansson J, Berndes G, Johnsson F, Kjärstad J. Co-firing biomass with coal for electricity generation—An assessment of the potential in EU27. Energy Policy 2009;37:1444–55. [119] Gallup DL. Production engineering in geothermal technology: a review. Geothermics 2009;38(3):326–34. [120] Sohel MI, Sellier M, Brackney LJ, Krumdieck S. Efficiency improvementforgeothermalpowergenerationtomeetsummer peak demand. Energy Policy 2009;37:3370–6. [121] Yilanci A, Dincer I, Ozturk HK. A review on solar-hydrogen/fuel cell hybrid energy systems for stationary applications. Progress in Energy and Combustion Science 2009;35(3):231–44. [122] Neij L. Cost development of future technologies for power generation—A study based on experience curves and complementary bottom-up assessments. Energy Policy 2008;36:2200–11. [123] Kosnik L. The potential of water power in the fight against global warming in the US. Energy Policy 2008;36:3252–65. [124] Driver D. Making a material difference in energy. Energy Policy 2008;36:4302–9. [125] Oliver T. Clean fossil-fuelled power generation夽. Energy Policy 2008;36(12):4310–6. [126] Yin C, Rosendahl LA, Kær SK. Grate-firing of biomass for heat and power production. Progress in Energy and Combustion Science 2008;34:725–54. [127] Mueller M, Wallace R. Enabling scienceandtechnologyformarinerenewableenergy. Energy Policy 2008;36:4376–82.

3498

A.A. Bazmi, G. Zahedi / Renewable and Sustainable Energy Reviews 15 (2011) 3480–3500

[128] Shanthakumar S, Singh DN, Phadke RC. Flue gas conditioning for reducing suspended particulate matter from thermal power stations. Progress in Energy and Combustion Science 2008;34:685–95. [129] Di Blasi C. Combustion and gasification rates of lignocellulosic chars. Progress in Energy and Combustion Science 2009;35(2):121–40. [130] Som S, Datta A. Thermodynamic irreversibilities and exergy balance in combustion processes. Progress in Energy and Combustion Science 2008;34(3):351–76. [131] Rubin ES, Chen C, Rao AB. Cost and performance of fossil fuel power plants with CO2 capture and storage. Energy Policy 2007;35:4444–54. [132] Damen K, Vantroost M, Faaij A, Turkenburg W. A comparison of electricity and hydrogen production systems with CO2 capture and storage—Part B: chain analysis of promising CCS options. Progress in Energy and Combustion Science 2007;33(6):580–609. [133] Koornneef J, Junginger M, Faaij A. Development of fluidized bed combustion—An overview of trends, performance and cost. Progress in Energy and Combustion Science 2007;33(1):19–55. [134] Beer J. High efficiency electric power generation: the environmental role. Progress in Energy and Combustion Science 2007;33(2):107–34. [135] Decarolis J, Keith D. The economics of large-scale wind power in a carbon constrained world. Energy Policy 2006;34(4):395–410. [136] Duffey RB. Sustainable futures using nuclear energy. Progress in Nuclear Energy 2005;47(1–4):535–43. [137] Buhre B, Elliott L, Sheng C, Gupta R, Wall T. Oxy-fuel combustion technology for coal-fired power generation. Progress in Energy and Combustion Science 2005;31(4):283–307. [138] Khaliq A, Kumar R. Finite-time heat-transfer analysis and ecological optimization of an endoreversible and regenerative gas-turbine power-cycle. Applied Energy 2005;81(1):73–84. [139] Nakata T. Energy-economic models and the environment. Progress in Energy and Combustion Science 2004;30(4):417–75. [140] Sahin A. Progress and recent trends in wind energy. Progress in Energy and Combustion Science 2004;30(5):501–43. [141] En Z. Solar energy in progress and future research trends. Progress in Energy and Combustion Science 2004;30(4):367–416. [142] Tsoutsos T, Gekas V, Marketaki K. Technical and economical evaluation of solar thermal power generation. Renewable Energy 2003;28:873–86. [143] Egre D, Milewski JC. The diversity of hydropower projects. Energy Policy 2002;30:1225–30. [144] Werther J, Saenger M, Hartge E-U, Ogada T, Siagi Z. Combustion of agricultural residues. Progress in Energy and Combustion Science 2000;26:1–27. [145] Sahinidis NV. Optimization under uncertainty: state-of-the-art and opportunities. Computers and Chemical Engineering 2004;28:971–83. [146] Mellita A, Kalogirou SA. Artificial intelligence techniques for photovoltaic applications: a review. Progress in Energy and Combustion Science 2008;34:574–632. [147] Nowicka-Zagrajeka J, Nowicka-Zagrajeka RW, Weron R. Modeling electricityloads in California: ARMA models with hyperbolic noise. Signal Processing 2002;82:1903–15. [148] Chaudry M, Jenkins N, Strbac G. Multi-time period combined gas and electricity network optimisation. Electric Power Systems Research 2008;78:1265–79. [149] Parker EN. Sunny side of global warming. Nature 1999;399:416–7. [150] Ventosa M, Baíllo A, Ramos A, Rivier M. Electricity market modeling trends. Energy Policy 2005;33:897–913. [151] Jebaraj S, Iniyan S. A review of energy models. Renewable and Sustainable Energy Reviews 2006;10:281–311. [152] Minguez R, Milano F, Zarate-Miano R, Conejo AJ. Optimal network placement of SVC devices. IEEE Transactions on Power Systems 2007;22(4): 1851–60. [153] Dong F, Chowdhury BH, Crow ML. Improving voltage stability by reactive power reserve management. IEEE Transactions on Power Systems 2005;20(1):338–45. [154] Venkatesh B, Sandasivam G, Khan MA. A new optimal reactive power scheduling method for loss minimization and voltage stability margin maximization using successive multi-objective fuzzy LP technique. IEEE Transactions on Power Systems 2000;15(2):844–51. [155] Sode-Yome A, Mithulananthan N, Lee KY. A maximum loading margin method for static voltage stability in power systems. IEEE Transactions on Power Systems 2006;21(2):496–501. [156] Rosehart W, Canizares CA, Quintana VH. Multi-objective optimal power flows to evaluate voltage security costs in power networks. IEEE Transactions on Power Systems 2003;18(2):578–87. [157] Wang R, Lasseter RH. Re-dispatching generation to increase power system security margin and support low voltage bus. IEEE Transactions on Power Systems 2000;15(2):496–501. [158] Wiszniewski A. New criteria of voltage stability margin for the purpose of load shedding. IEEE Transactions on Power Delivery 2007;22(3):1367–71. [159] Nikolaidis VC, Vournas CD. Design strategies for load-shedding schemes against voltage collapse in the Hellenic system. IEEE Transactions on Power Systems 2008;23(2):582–91. [160] Milan F, Canizares CA, Invernizzi M. Multi-objective optimization for pricing system security in electricity markets. IEEE Transactions on Power Systems 2003;18(2):596–604. [161] Chwieduk D. Towards sustainable-energy buildings. Applied Energy 2003;76:211–7.

[162] Cai WG, Wu Y, Zhong Y, Ren H. China building energy consumption: situation, challenges and corresponding measures. Energy Policy 2009;37:2054–9. [163] Wu DW, Wang RZ. Combined cooling, heating and power: a review. Progress in Energy and Combustion Science 2006;32:459–95. [164] Chicco G, Mancarella P. Distributed multi-generation: a comprehensive view. Renewable and Sustainable Energy Reviews 2009;13:535–51. [165] Joel HS, Augusto SC. Trigeneration: an alternative for energy savings. Applied Energy 2003;76:219–27. [166] Wang JJ, Jing YY, Zhang CF, Zhang XT, Shi GH. Integrated evaluation of distributed triple-generation systems using improved grey incidence approach. Energy 2008;33:1427–37. [167] Wang JJ, Jing YY, Zhang CF, Shi GH, Zhang XT. A fuzzy multi-criteria decisionmaking model for trigeneration system. Energy Policy 2008;36:3823–32. [168] Medrano M, Brouwer J, McDonell V, Mauzey J, Samuelsen S. Integration of distributed generation systems into generic types of commercial buildings in California. Energy and Buildings 2008;40:537–48. [169] Wang JJ, Jing YY, Zhang CF, Zhang B. Distributed combined cooling heating and power system and its development situation in China. In: ASME 2nd international conference on energy sustainability. 2008. [170] Ge YT, Tassou SA, Chaer I, Suguartha N. Performance evaluation of a trigeneration system with simulation and experiment. Applied Energy 2009;86:2317–26. [171] Cao J. Evaluation of retrofitting gas-fired cooling and heating systems into BCHP using design optimization. Energy Policy 2009;37:2368–74. [172] Ren H, Gao W, Ruan Y. Optimal sizing for residential CHP system. Applied Thermal Engineering 2008;28:514–23. [173] Cho H, Mago PJ, Luck R, Chamra LM. Evaluation of CCHP systems performance based on operational cost, primary energy consumption, and carbon dioxide emission by utilizing an optimal operation scheme. Applied Energy 2009;86:2540–9. [174] Zhang B, Long W. An optimal sizing method for cogeneration plants. Energy and Buildings 2006;38:189–95. [175] Ziher D, Poredos A. Economics of a trigeneration system in a hospital. Applied Thermal Engineering 2006;26:680–7. [176] Kong XQ, Wang RZ, Li Y, Huang XH. Optimal operation of a micro-combined cooling, heating and power system driven by a gas engine. Energy Conversion and Management 2009;50:530–8. [177] Arcuri P, Florio G, Fragiacomo P. A mixed integer programming model for optimal design of trigeneration in a hospital complex. Energy 2007;32: 1430–47. [178] Kong XQ, Wang RZ, Huang XH. Energy optimization model for a CCHP system with available gas turbines. Applied Thermal Engineering 2005;25:377–91. [179] Chicco G, Mancarella P. Matrix modelling of small-scale trigeneration systems and application to operational optimization. Energy 2009;34:261–73. [180] Ooka R, Komamura K. Optimal design method for building energy systems using genetic algorithms. Building and Environment 2009;44:1538–44. [181] Rong A, Lahdelma R. An efficient linear programming model and optimization algorithm for trigeneration. Applied Energy 2005;82:40–63. [182] Piacentino A, Cardona F. EABOT—energetic analysis as a basis for robust optimization of trigeneration systems by linear programming. Energy Conversion and Management 2008;49:3006–16. [183] Cao JC, Liu FQ. Simulation and optimization of the performance in the airconditioning season of a BCHP system in China. Energy Building 2008;40: 185–92. [184] Cardona E, Piacentino A. Optimal design of CHCP plants in the civil sector by thermoeconomics. Applied Energy 2007;84:729–48. [185] Cardona E, Piacentino A, Cardona F. Matching economical, energetic and environmental benefits: an analysis for hybrid CHCP-heat pump systems. Energy Conversion and Management 2006;47:3530–42. [186] Mago PJ, Chamra LM. Analysis and optimization of CCHP systems based on energy, economical, and environmental considerations. Energy Building 2009;41:1099–106. [187] Sayyaadi H. Multi-objective approach in thermoenvironomic optimization of a benchmark cogeneration system. Applied Energy 2009;86:867–79. [188] Groscurth HM, Bruckner T, Kümmel R. Energy, cost, and carbon dioxide optimization of disaggregated, regional energy-supply systems. Energy 1993;18:1187–205. [189] Wang JJ, Jing YY, Zhang CF. Optimization of capacity and operation for CCHP system by genetic algorithm. Applied Energy 2010;87:1325–35. [190] Yokoyama R, Ito K, Matsumoto Y. Optimal multistage expansion planning of a gas turbine cogeneration plant. Journal of Engineering for Gas Turbines and Power 1996;118:803–9. [191] Ito K, Gamou S, Yokoyama R. Optimal unit sizing of fuel cell cogeneration systems in consideration of performance degradation. International Journal of Energy Research 1998;22:1075–89. [192] Li CZ, Gu JM, Huang XH. Influence of energy demands ratio on the optimal facility scheme and feasibility of BCHP system. Energy Building 2008;40:1876–82. [193] Weber C, Maréchal F, Favrat D, Kraines S. Optimization of an SOFC-based decentralized polygeneration system for providing energy services in an office-building in Tokyo. Applied Thermal Engineering 2006;26:1409–19. [194] Ito K, Yokoyama R, Shiba T. Optimal operation of a diesel engine cogeneration plant including a heat storage tank. Journal of Engineering for Gas Turbines and Power 1992;114:687–94. [195] Thorin E, Brand H, Weber C. Long-term optimization of cogeneration systems in a competitive market environment. Applied Energy 2005;81:152–69.

A.A. Bazmi, G. Zahedi / Renewable and Sustainable Energy Reviews 15 (2011) 3480–3500 [196] Rong A, Lahdelma R, Luh PB. Lagrangian relaxation based algorithm for trigeneration planning with storages. European Journal of Operational Research 2008;188:240–57. [197] Rentizelas AA, Tatsiopoulos IP, Tolis A. An optimization model for multibiomass tri-generation energy supply. Biomass and Bioenergy 2009;33:223–33. [198] Ashok S, Banerjee R. Optimal operation of industrial cogeneration for load management. IEEE Transactions on Power Systems 2003;18:931–7. [199] Chen BK, Hong CC. Optimum operation for a back-pressure cogeneration system under time-of-use rates. IEEE Transactions on Power Systems 1996;11:1074–82. [200] Khan JR. Modeling and optimization of a novel pressurized CHP system with water extraction and refrigeration. International Journal of Energy Research 2008;32:735–51. [201] Zhao H, Holst J, Arvastson L. Optimal operation of coproduction with storage. Energy 1998;23:859–66. [202] Sahoo PK. Exergoeconomic analysis and optimization of a cogeneration system using evolutionary programming. Applied Thermal Engineering 2008;28:1580–8. [203] Fukuyama Y, Nishida H, Todaka Y. Particle swarm optimization for optimal operational planning of energy plants. In: Innovation in swarm intelligence. Heidelberg: Springer; 2009. [204] Miyazaki T, Akisawa A, Kashiwagi T. The optimization of a cogeneration system for commercial buildings by the particle swarm optimization. Japan Society of Refrigeration and Air Conditioning Engineers 2006;23:145–56. [205] Wang J, Zhai ZJ, Jing Y, Zhang C. Particle swarm optimization for redundant building cooling heating and power system. Applied Energy 2010;87:3668–79. [206] Radcenco V, Vergas JVC, Bejan A. Thermodynamic optimization of a gasturbine power plant with pressure-drop irreversibilities. Transaction of ASME Journal of Energy Resource Technology 1998;120(3):233–40. [207] Bejan A. Entropy generation through heat and fluid flow. New York: Wiley; 1982. [208] Radcenco V. Generalized thermodynamics. Bucharest: Editura Technica; 1994 [in English]. [209] Bejan A. Maximum power from fluidflow. International Journal of Heat & Mass Transfer 1996;39(6):1175–81. [210] Bejan A. Entropy-generation minimization. Boca Raton (FL): CRC Press; 1996. [211] Bejan A. Advanced engineering thermodynamics. 2nd ed. Wiley: New York; 1997. [212] Chen L, Wu C, Sun, Yu J. Performance characteristic of fluid-flow converters. Journal of Institute of Energy 1998;71(489):209–15. [213] Chen L, Bi Y, Wu C. Influence of non-linear flow resistance relation on the power andefficiency from fluidflow. Journal of Physics D: Applied Physics 1999;32(12):1346–9. [214] Uran V. Optimization system for combined heat and electricity production in the wood-processing industry. Energy 2006;31:2996–3016. [215] Thiruvenkatachari R, Su S, An H, Yu XX. Post combustion CO2 capture by carbon fibre monolithic adsorbents. Progress in Energy and Combustion Science 2009;35:438–55. [216] IPCC, IPCC Special report on carbon dioxide capture and storage; 2005. New York: Cambridge University Press. [217] EC, World Energy Technology Outlook 2050–WETO H2; 2006. Brussels, Belgium: European Commission, Directorate-General for Research. [218] IEA, Energy technology perspectives—scenarios & strategies to 2050; 2006. Paris, France: International Energy Agencies. [219] MIT, The future of coal–options for a carbon constraint world; 2007. Cambridge, US: Massachusetts Institute of Technology. [220] van-den-Broek M, Hoefnagels R, Rubin E, Turkenburg W, Faaij A. Effects of technological learning on future cost and performance of power plants with CO2 capture. Progress in Energy and Combustion Science 2009;35: 457–80. [221] Sharp JA, Price DHR. Experience curve models in the electricity supply industry. International Journal of Forecasting 1990;6(4):p531. [222] Yeh S, Rubin ES. A centurial history of technological change and learning curves for pulverized coal-fired utility boilers. Energy 2007;32(10):p1996. [223] Arivalagan A, Raghavendra BG. Integrated energy optimization model for a cogeneration based energy supply system in the process industry. Electrical Power & Energy Systems 1995;17(4):227–33. [224] Coelho LdS, Santos AAP. A RBF neural network model with GARCH errors: application to electricity price forecasting. Electric Power Systems Research 2011;81(1):74–83. [225] Foley AM, Ó-Gallachóir BP, Hur J, Baldick R, McKeogh EJ. A strategic review of electricity systems models. Energy 2010;35:4522–30. [226] Gómez-Barea A, Leckner B. Modeling of biomass gasification in fluidized bed. Progress in Energy and Combustion Science 2010;36:444–509. [227] Ruey-Hsun L, Yu-Kai C, Yie-Tone C. Volt/Var control in a distribution system by a fuzzy optimization approach. International Journal of Electrical Power & Energy Systems 2011;33:278–87. [228] Bhatt P, Roy R, Ghoshal SP. GA/particle swarm intelligence based optimization of two specific varieties of controller devices applied to two-area multiunits automatic generation control. International Journal of Electrical Power & Energy Systems 2010;32(4):299–310. [229] Cayer E, Galanis N, Nesreddine H. Parametric study and optimization of a transcritical power cycle using a low temperature source. Applied Energy 2010;87(4):1349–57.

3499

[230] Ren H, Gao W. A MILP model for integrated plan and evaluation of distributed energy systems. Applied Energy 2010;87(3):1001–14. [231] Jing L, Gang P, Jie J. Optimization of low temperature solar thermal electric generation with Organic Rankine Cycle in different areas. Applied Energy 2010;87(11):3355–65. [232] Azadeh A, Skandari MR, Maleki-Shoja B. An integrated ant colony optimization approach to compare strategies of clearing market in electricity markets: agent-based simulation. Energy Policy 2010;38(10): 6307–19. [233] Yusta JM, Torres F, Khodr HM. Optimal methodology for a machining process scheduling in spot electricity markets. Energy Conversion and Management 2010;51(12):2647–54. [234] Möst D, Keles D. A survey of stochastic modelling approaches for liberalised electricity markets. European Journal of Operational Research 2010;207(2):543–56. [235] Amjadi MH, Nezamabadi-pour H, Farsangi MM. Estimation of electricity demand of Iran using two heuristic algorithms. Energy Conversion and Management 2010;51(3):493–7. [236] Porkar S, Poure P, Abbaspour-Tehrani-fard A, Saadate S. A novel optimal distribution system planning framework implementing distributed generation in a deregulated electricity market. Electric Power Systems Research 2010;80(7):828–37. [237] Niu D, Liu D, Wu DD. A soft computing system for day-ahead electricity price forecasting. Applied Soft Computing 2010;10(3):868–75. [238] Niknam T, Firouzi BB, Ostadi A. A new fuzzy adaptive particle swarm optimization for daily Volt/Var control in distribution networks considering distributed generators. Applied Energy 2010;87(6):1919–28. [239] Wang J, Sun Z, Dai Y, Ma S. Parametric optimization design for supercritical CO2 power cycle using genetic algorithm and artificial neural network. Applied Energy 2010;87(4):1317–24. [240] Sadhukhan J, Ng KS, Shah N, Simons HJ. Heat Integration Strategy for Economic Production of Combined Heat and Power from Biomass Waste. Energy & Fuels 2009;23:5106–20. [241] Frombo F, Minciardi R, Robba M, Rosso F, Sacile R. Planning woody biomass logistics for energy production: a strategic decision model. Biomass and Bioenergy 2009;33:372–83. [242] Østergaard PA. Reviewing optimisation criteria for energy systems analyses of renewable energy integration. Energy 2009;34(9):1236–45. [243] Ehsani A, Ranjbar A, Fotuhifiruzabad M. A proposed model for co-optimization of energy and reserve in competitive electricity markets. Applied Mathematical Modelling 2009;33(1):92–109. [244] Hatami A, Seifi H, Sheikheleslami M. Optimal selling price and energy procurement strategies for a retailer in an electricity market. Electric Power Systems Research 2009;79(1):246–54. [245] Yucekaya A, Valenzuela J, Dozier G. Strategic bidding in electricity markets using particle swarm optimization. Electric Power Systems Research 2009;79(2):335–45. [246] Toksari M. Estimating the net electricity energy generation and demand using the ant colony optimization approach: case of Turkey. Energy Policy 2009. [247] Bunn D, Day C. Computational modelling of price formation in the electricity pool of England and Wales. Journal of Economic Dynamics and Control 2009;33(2):363–76. [248] Siahkali H, Vakilian M. Electricity generation scheduling with large-scale wind farms using particle swarm optimization. Electric Power Systems Research 2009;79(5):826–36. [249] Malo P. Modeling electricity spot and futures price dependence: a multifrequency approach. Physica A: Statistical Mechanics and its Applications 2009;388(22):4763–79. [250] Rentizelas AA, Tatsiopoulos IP, Tolis A. An optimization model for multi-biomass tri-generation energy supply. Biomass and Bioenergy 2009;33(2):223–33. [251] Louit D, Pascual R, Banjevic D. Optimal interval for major maintenance actions in electricity distribution networks. International Journal of Electrical Power & Energy Systems 2009;31(7–8):396–401. [252] Mondol JD, Yohanis YG, Norton B. Optimising the economic viability of gridconnected photovoltaic systems. Applied Energy 2009;86:985–99. [253] Yang H, Wei Z, Chengzhi L. Optimal design and techno-economic analysis of a hybrid solar–wind power generation system. Applied Energy 2009;86(2):163–9. [254] Benth FE, Koekebakker S. Stochastic modeling of financial electricity contracts. Energy Economics 2008;30(3):1116–57. [255] Ayoub N, Seki H, Naka Y. A methodology for designing and evaluating biomass utilization networks. In: 18th European symposium on computer aided process engineering—ESCAPE 18. 2008. [256] Diblasi C. Modeling chemical and physical processes of wood and biomass pyrolysis. Progress in Energy and Combustion Science 2008;34(1):47–90. [257] Baskar G, Mohan M. Security constrained economic load dispatch using improved particle swarm optimization suitable for utility system. International Journal of Electrical Power & Energy Systems 2008;30(10):609–13. [258] Mariano S, Catalao J, Mendes V, Ferreira L. Optimising power generation efficiency for head-sensitive cascaded reservoirs in a competitive electricity market. International Journal of Electrical Power & Energy Systems 2008;30(2):125–33. [259] Zhang W, Liu Y. Multi-objective reactive power and voltage control based on fuzzy optimization strategy and fuzzy adaptive particle swarm. International Journal of Electrical Power & Energy Systems 2008;30(9):525–32.

3500

A.A. Bazmi, G. Zahedi / Renewable and Sustainable Energy Reviews 15 (2011) 3480–3500

[260] Shunmugalatha A, Slochanal S. Optimum cost of generation for maximum loadability limit of power system using hybrid particle swarm optimization. International Journal of Electrical Power & Energy Systems 2008;30(8):486–90. [261] Smeets E, Faaij A, Lewandowski I, Turkenburg W. A bottom-up assessment and review of global bio-energy potentials to 2050. Progress in Energy and Combustion Science 2007;33(1):56–106. [262] Botterud A, Korpas M. A stochastic dynamic model for optimal timing of investments in new generation capacity in restructured power systems. International Journal of Electrical Power & Energy Systems 2007;29(2):163–74. [263] Erdogdu E. Electricity demand analysis using cointegration and ARIMA modelling: a case study of Turkey. Energy Policy 2007;35(2):1129–46. [264] Rong A, Lahdelma R. An effective heuristic for combined heat-andpower production planning with power ramp constraints. Applied Energy 2007;84(3):307–25. [265] Henning D, Amiri S, Holmgren K. Modelling and optimisation of electricity, steam and district heating production for a local Swedish utility. European Journal of Operational Research 2006;175(2):1224–47. [266] Chan P, Hui C, Li W, Sakamoto H, Hirata K, Li P. Long-term electricity contract optimization with demand uncertainties. Energy 2006;31(13):2469–85. [267] Olsina F, Garces F, Haubrich H. Modeling long-term dynamics of electricity markets. Energy Policy 2006;34(12):1411–33. [268] Caputo AC, Palumbo M, Pelagagge PM, Scacchia F. Economics of biomass energy utilization in combustion and gasification plants: effects of logistic variables. Biomass and Bioenergy 2005;28:35–51.

[269] Brar Y, Dhillon J, Kothari D. Fuzzy satisfying multi-objective generation scheduling based on simplex weightage pattern search. International Journal of Electrical Power & Energy Systems 2005;27(7):518–27. [270] Rong A, Lahdelma R. An efficient linear programming model and optimization algorithm for trigeneration. Applied Energy 2005;82(1):40–63. [271] Ostergaard P. Modelling grid losses and the geographic distribution of electricity generation. Renewable Energy 2005;30(7):977–87. [272] Deng S-J, Jiang W. Levy process-driven mean-reverting electricity price model: the marginal distribution analysis. Decision Support Systems 2005;40(3–4):483–94. [273] Thorin E, Brand H, Weber C. Long-term optimization of cogeneration systems in a competitive market environment. Applied Energy 2005;81(2): 152–69. [274] Castronuovo E, Lopes J. Optimal operation and hydro storage sizing of a wind?hydro power plant. International Journal of Electrical Power & Energy Systems 2004;26(10):771–8. [275] Silveira JL, Tuna CE. Thermoeconomic analysis method for optimization of combined heat and power systems. Part I. Progress in Energy and Combustion Science 2003;29:479–85. [276] Nowicka-Zagrajeka J, Weron R. Modeling electricityloads in California: ARMA models with hyperbolic noise. Signal Processing 2002;82:1903–15. [277] Williams A, Pourkashanian M, Jones JM. Combustion of pulverized coal and biomass. Progress in Energy and Combustion Science 2001;27:587–610.