EVALUATING INTEGRATED URBAN BIOMASS STRATEGIES FOR A UK ECO-TOWN James E. Keirstead1, Nouri J. Samsatli1, A. Marco Pantaleo2, Nilay Shah1,* BP Urban Energy Systems Project, Dept of Chemical Engineering, Imperial College London 2 Centre for Process Systems Engineering, Dept. of Chemical Engineering, Imperial College London * Corresponding author: Prof. Nilay Shah, C502 Roderic Hill Building, Dept of Chemical Engineering Imperial College, London UK, SW7 2AZ,
[email protected], tel: +44 (0)20 7594 6621. 1
ABSTRACT: Improving the efficiency of urban energy systems is an increasingly important issue, as the energy consumption of cities has a significant effect on energy and climate policy priorities. One promising strategy for delivering these results is to increase the integration of energy services, for example through the use of combined heat and power systems. This paper introduces a tool for analyzing such integrated energy systems and demonstrates it in the case of a UK eco-town supplied with heat and power from biomass sources. We conclude that the methodology is a powerful tool for assessing potentially convoluted resource chains, a common feature of biomass energy strategies. While biomass offers a promising low-carbon solution for eco-towns such as the one studied here, we note that significant uncertainties remain in cost, carbon performance and resource availability. Keywords: urban area; modelling; combined heat and power generation (CHP) 1
INTRODUCTION
Secure, affordable and clean energy supplies are vital to the success of the world’s cities. Without these inputs, it can be difficult to sustain economic and social activity while mitigating local pollution and related health issues (1). The energy consumption of cities also has global significance: approximately 2/3 of the world’s primary energy is consumed in urban areas, creating 71% of global fossil-fuel related and direct greenhouse gas emissions (2). From a policy perspective, much of the activity to date has focused at this larger scale through United Nations activities such as the IPCC and UNFCCC. The Kyoto Protocol for example established a template for international action but its effectiveness in reducing carbon emissions is less clear and climate scientists have been warning that the new post-Kyoto regime will need to adopt more aggressive measures if dangerous anthropogenic climate change is to be avoided. Indeed in March 2009, the International Scientific Congress on Climate Change in Copenhagen observed that “given high rates of observed emissions, the worst-case IPCC scenario trajectories (or even worse) are being realised”, therefore increasing the likelihood of “abrupt or irreversible climatic shifts” and associated social and economic disruptions (3). These international activities have had a notable effect on regional policy in Europe. In particular, the European Union has been working to promote renewable energy as part of its climate change strategy (4). This directive sets out an EU-wide target of providing 20% of final energy consumption from renewable sources such as wind, solar and biomass by 2020. The target is then broken down by member state: the UK, for example, has agreed to increase its renewable energy mix from 1.3% in 2005 to 15% by 2020. The directive also highlights the role of urban and built environments in contributing to these targets. Articles 12.3 and 12.4 oblige member states to “consider” the use of renewables “when planning, designing, building and refurbishing industrial or residential areas” and to “require the use of minimum levels of energy from renewable sources in new or refurbished buildings.” A practical example of such a policy can already be seen in the UK, where since 2003 many local authorities have adopted the so-called Merton Rule, a local planning regulation which requires new developments (typically
over 1000 m2) to generate at least 10% of their energy needs from on-site renewable energy technologies (5). These policy targets are ambitious by themselves but the global economic downturn has introduced further challenges with restricted credit for new innovations, a more risk-averse investment climate and strained public sector finances. Yet the combination of scarce fiscal resources and the need to achieve greater carbon savings does provide a strong impetus for developing integrated urban energy systems that deliver maximum economic efficiency and multiple benefits. This paper presents a software tool designed to facilitate such integrated assessments of urban energy systems. As will be shown, we interpret “integrated” as referring to both the overall design of an urban energy system (from master plan to service network specification) and more specifically as the simultaneous consideration of multiple processes within an urban energy supply chain. After first presenting an overview of the software tool, we then demonstrate its use in a biomass energy assessment for a UK eco-town. In the concluding discussion, we consider the implications of the results for the specific eco-town case as well as evaluating the software tool more generally. 2
THE SYNTHETIC CITY TOOL KIT
The SynCity tool kit is the main activity of the BP Urban Energy Systems project at Imperial College London. The project began in 2005 and it adopts an interdisciplinary perspective with research expertise in process systems optimisation, urban and industrial ecology, transport and land-use modelling, energy systems modelling, energy policy and business strategy. The goal of the project is to “identify the benefits of a systematic, integrated approach to the design and operation of urban energy systems, with a view to at least halving the energy intensity of cities” (6). 2.1 Urban modelling technologies and SynCity Urban models are most commonly used for economic and spatial planning problems (7). However some groups have adapted these technologies to perform climate risk or environmental modelling; a notable example is the Tyndall Centre’s integrated assessment model which is currently being applied to examine the climate risks of London’s spatial plan (8).
There have also been efforts to develop tools for urban energy systems modelling (9-12). These applications are quite diverse, ranging from buildingscale to city-wide applications, but they share two common features. First, such models must include a representation of the spatial and temporal variations in urban energy demand, a characteristic that often leads to onerous input data requirements. Second, these models seek to explore both the supply and demand sides of urban energy use, for example by optimising provision strategies. However these examples also show that current practice consists largely of detailed models built for the assessment of a single aspect of existing systems (e.g. domestic sector demands in UK households, heat demand in Geneva). This means that these tools have limited applicability beyond the original problem case and they are unable to offer a holistic perspective on the entire urban energy design process. The SynCity tool kit represents an attempt to overcome these obstacles. The goal of this work is to create a city in silico where alternative energy systems and design strategies can be assessed quickly and easily. The system is integrated in the sense that it covers all major components of the urban energy equation within one modelling framework, using four component models: a layout model that optimises the location of housing, facilities and major transport networks on a site (green-field or brown-field); an agent-activity model that simulates the activities of individual citizens within the city and calculates associated resource demands; a resource-technology-network (RTN) model that determines the optimal location and mix of technologies and distribution networks to meet these resource demands; and a service network model that generates detailed engineering specifications for resource networks using the results of the RTN model (currently in development). The system can run all models sequentially or standalone model runs can be performed. The present paper focuses primarily on the RTN model. 2.2 The RTN Model The resource-technology-network (RTN) model is based on the premise that any urban energy system can be represented as a set of resources and a set of technologies that interconvert those resources. The resources are completely general, in that they can represent energy carriers (such as gas and electricity), non-energy resources (such as potable water) and any other material or energy stream involved in the provision of urban energy (such as waste heat, carbon dioxide, waste water, municipal solid waste or even money and people). The technologies represent any process that can convert a set of input resources to a set of output resources. For example, a CHP unit might convert a certain amount of natural gas into electricity, high-quality heat, waste heat, carbon dioxide and other atmospheric pollutants. The high-quality heat may then be converted to space and water heat in buildings by using a heat exchanger connected to a district heating network. The city is divided into a number of zones (of any shape and size), each of which has time-varying demands for certain resources (determined by the agent-activity model). The RTN model will determine how best to satisfy these demands through the provision of
technologies in various zones and networks to transport resources between zones. Depending on the problem, this might result in distributed provision of resources, with small-scale technologies in each zone; or a largescale technology in a single zone with a network to transport the resource to the rest of the city; or some combination of these two strategies. Storage technologies are also modelled, so that a resource surplus can be accumulated during periods of low demand and then used in peak periods, thus smoothing out production dynamics. The operation of transport and storage processes may also involve other resources, e.g. transporting a liquid fuel by road would require a certain amount of diesel and result in the generation of waste heat, carbon dioxide and other pollutants. Finally, unless the city is entirely self-sufficient, it will need to import some resources from other cities and surrounding hinterlands. The model can choose to import any resource into any zone, subject to a number of constraints: e.g. bounds on the rate of import in each zone and import may be restricted to a certain subset of the zones. Similarly any excess production of resources may be exported, subject to there being demand for them (this includes export of wastes, at a cost). The main constraint in the RTN model is the resource balance, which is shown in simplified form below. Prit + Qrit + Irit + Srit – Erit – Drit = 0
∀rit
where Prit is the net production rate of resource r in zone i at time t, Qrit is the net inflow of resource from all of the other zones (transportation), Irit is the rate of import, Srit is the net use of stored resource, Erit the rate of export and Drit is the demand of resource r in zone i at time t. Whereas Irit and Erit are degrees of freedom, and Drit is a parameter (determined by the agent-activity model), the other terms are variables that depend on which technologies are selected and their rates of utilization (e.g. if a CHP is chosen for zone i, then its rate at time t contributes to the values of Prit for all r). Prit may be positive or negative, representing production or consumption of the resource. Similarly, negative Qrit represents a net outflow of resource to other zones and negative Srit represents storage of surplus resource; Qrit depends on the rates of all transport technologies connected to zone i and Srit similarly depends on the rates of all storage processes in zone i. The relationships are defined by technology-specific parameters such as maximum and minimum operating rates, coefficients of performance and so on. One can see that the resource balance allows the demands in each zone to be met in a variety of ways: import, local production, receiving resource from another zone or by consuming stored resource. Similarly, if there is excess production or import of a resource, this can be sent to other zones, exported or stockpiled for later use. The RTN framework therefore allows complex resource chains to be modelled, facilitating simultaneous comparisons of diverse energy provision strategies operating at multiple scales. As there are binary variables representing the locations of technologies and network links, continuous variables representing the rates of processes and all of the constraints are linear, the RTN is therefore a mixedinteger linear programming model. The objective function is to minimize the total annualized cost (capital,
operating, resource imports etc.) of satisfying the resource demands. 3
THE CASE STUDY
To demonstrate the RTN model, we consider the case of a UK eco-town. Given rising demand for housing as well as substantial questions about how the building sector might contribute to national climate change and energy policy goals, the UK government has promoted “eco-towns” as an opportunity to drive innovation and to demonstrate how these policy goals might be jointly achieved. While the formal requirements for eco-town certification have not yet been announced, it has been suggested that the headline targets for these developments should be an 80% reduction in CO2 emissions (versus 1990 levels) and an ecological footprint two-thirds of the national average. To achieve these goals, the report suggests that the eco-towns will have to run on nearly 100% renewable energy for heat, cooling and electrical demand and that at least 50% onsite renewables “should be possible” (13). Twelve eco-town developments have been put forward for consideration and this paper considers one of these proposals. (The fate of this and other eco-town proposals is uncertain given the current economic climate and the falling UK housing market.) 3.1 Eco-town background The site is located in central England and our analysis has focused on one of the design phases, an area of 87 hectares intended to house 6500 people. An initial assessment of the proposal by government-commissioned consultants found that the site “might be a suitable location subject to meeting specific planning and design objectives” but more information was required particularly on the energy strategy for the site (14). Since then, the developers have commissioned a study of alternative energy systems to address some of these concerns. As noted above, it is anticipated that eco-towns should meet about 50% of their energy demand from onsite renewable sources. The developer’s energy strategy considered a range of renewable supply scenarios including large-scale wind, microgeneration technologies for heat and electricity (micro-wind, solar PV, solar thermal, heat pumps) and biomass (both individual boilers and district heat and power schemes). The report also noted the potential interaction between supply systems, e.g. increased use of heat pumps may provide low carbon heat but requires additional supplies of low carbon electricity; such trade-offs can be evaluated by the RTN model. The consultants outlined two feasible strategies, both of which require biomass district combined heat and power (CHP) systems. This raises questions about the choice of conversion technologies, the structure of the district heating network and the availability of the biomass material (both imports from surrounding regions as well as local supplies), hence providing a focus of the present study. The energy strategy also discusses the potential to generate biofuels for transport but this is not considered here.
3.2 Urban biomass technologies There are several options to produce heat and power from biomass and these can be generally classified according to criteria such as biomass type, technology type and size, and the degree of decoupling between biomass treatment and conversion processes (15). When integrating bioenergy into urban areas, the specific concerns are the availability of space for biomass storage and pre-treatment, the emission levels of biomass processes, and biomass transport issues including logistics and the costs of biofuel supply. These barriers are mainly caused by the low energy density of biofuels, which requires additional conditioning processes and consequently results in energy conversion efficiencies lower than what could be achieved via fossil fuel routes; scarcity and competing alternative uses of biomass feedstocks are also a concern. From a demand-side perspective however, the aggregation of demand and high energy costs typical of urban areas facilitate on-site CHP generation (combined heat and power generation near to the load), offering potentially high overall energetic, economic and environmental performance of bioenergy routes. Unfortunately the proximity of the plant to the load leads to an important disadvantage: emissions are close to people. This aspect, which is not easy to quantify, is also named “urban air pollution”. Since power plants are often far from urban centres, new local plants can have a major impact on local air quality (16-18). The effects of converting heating systems from electricity or gas-fired boilers to pellet heating systems have also been investigated (19). As a result of these issues, urban bioenergy solutions require a trade off between large plants – with high conversion efficiencies, low emission levels and low specific investment and operational costs – and the low space requirements, simplified logistics and transport issues, and ease of plant location typical of small plants. For this reason, several studies have aimed to optimise the location and size of biomass CHP plants on the basis of technical and economic factors and the geographic dispersion of biomass feedstocks (20-23). For example, a multi-criteria decision analysis methodology was applied to the Metropolitan Borough of Kirklees in Yorkshire, UK, to compare small-scale renewable energy schemes with large-scale alternatives. The results indicated that small-scale schemes were the most sustainable, despite large-scale schemes being more financially viable (24). The political, economic, social, and technological dimensions associated with regional energy systems have also been assessed concluding that a distributed energy system is a good option with respect to sustainable development (25). In general high density biofuels, clean conversion technologies and CHP systems are the most promising urban bioenergy routes. 3.3 Study scenarios In light of this review, we have chosen to examine three different scenarios for meeting the eco-town’s heat and power requirements. The first scenario, S0, is the baseline case. It assumes that renewable energy is not used on-site and that therefore all energy demands are met by imported resources: namely grid electricity for power and mains gas for heat. As shown in Figure 1, this scenario requires the definition of a “boiler” technology to convert
imported gas into the required heat; it creates waste heat as a by-product. Table I lists the primary input parameters for this scenario.
elec
elec
to 0.66, 1.4 and 2.9 MWe), each having a fixed heat/electricity ratio (no reserve boilers were integrated to CHP plants to cover the heat demand during winter). The net electricity conversion efficiency has been assumed to be 0.22, 0.23 and 0.24 respectively. The range of operating hours for these plants is 5,000–7,500 h/yr. elec
boiler
gas
elec
heat
waste heat
Forestry residues
Chip production er
chips
Figure 1. Resource chain for scenario S0. In this scenario, demands are met by imported grid resources. CHP
Table I. Major input parameters for baseline scenario S0. Resource cost data from (26), technology cost data from (27), network costs from authors’ estimates. Value Resources Electricity import cost Gas import cost
waste heat dist. heat
7.86 p/kWh 2.33 p/kWh Heat exchanger
Networks Electricity network cost Gas network cost
Boiler
Max capacity Unit cost Annual operating cost
waste heat
£50 000/km £100 000/km
Technologies 25.2 kW £600 £50
The next scenario, S1, investigates the potential for using imported biomass resources to meet the city’s energy requirements. Drawing from the energy consultant’s report discussed above, we hypothesise that the most likely biomass supply is one based on forestry residues and wood chips. Table II summarises the properties of these materials. Table II. Technical data of selected bioenergy resources. Type Moisture LHV Energy (% d.m.) (MJ/kg) density (MJ/m3) Forestry residues 35 11.4 3.75 Wood chips 20 14.6 7.29 Figure 2 shows the corresponding resource chain. Forestry residues are imported from nearby areas and converted locally into wood chips; these are then burned in a CHP plant and the resulting high-grade district heat distributed to smaller heat exchangers throughout the city to meet final heat demand. Chip-fired Organic Rankine Cycle (ORC) CHP plants are one of the most common solutions for CHP production via solid biomass. Although this technology has lower overall electrical efficiency compared to other options, its reliability and the possibility to generate large amounts of thermal energy for district heating make it attractive for this case study (28, 29). Another key question is what size of CHP unit to use. We have given the model the choice of three sizes (1.5, 3.0 and 6.0 MWt, corresponding respectively
heat
Figure 2. Resource chain for scenarios S1 and S2. Imported forestry residues are first converted to wood chips before being burned in the CHP unit. In S2, imported forestry residues are supplemented by local residues from urban parkland. Table III summarizes the inputs for this scenario. The costs are calculated on an annualized turn key basis, assuming a lifetime of 15 years and a discount rate of 6% for the boilers and 6 years at 6% for the chip production plant. Table III. Major input parameters for the imported biomass scenario S1 and S2. Resource cost data from (26, 30), technology cost data from (30, 31) and author’s estimate on the basis of data from manufacturers (32), network costs from authors’ estimates. Value Resources Electricity import cost Forest residues import cost
7.86 p/kWh £50/t
Networks Electricity network cost District heat network cost
£50 000/km £200 000/km
Technologies Chip production
Max capacity Unit Cost Ann. op. cost
5 t/h £250 000 £37 500
Biomass CHP (small)
Max capacity Unit Cost Ann. op. cost
1.5 MWt £1 450 000 £120 000
Biomass CHP (medium)
Max capacity Unit Cost Ann. op. cost
3 MWt £2 760 000 £220 000
Biomass CHP (large)
Max capacity Unit cost Ann. op. cost
6 MWt £5 180 000 £415 000
Heat exchanger
Max capacity Unit cost Ann. op. cost
30 kW £300 £50
Electric heater
Max capacity Unit cost Ann. op. cost
2.6 kW £400 £10
To complete the resource chain, technologies must also be introduced to store and transport the biomass resources throughout the city. Storage is provided for wood chips only, assuming a closed system with a capacity of 20 kt and losses of 2%; assuming a 20 year lifecycle, we estimate the annualised capital cost to be £210000 and the annual operating costs to be £48000. For transport, a road network is assumed using trucks with a capacity of 20 m3 and a distance between biomass storage and energy conversion plants ranging between 1 and 10 km; this data is summarised in Table IV. Table IV. Biomass transport parameters (assuming shortdistance transport via road) Resource Cost Fuel requirements (£/t∙km) (MJ/t∙km) Forestry residues 0.36 1.7 Wood chips 0.24 1.1 Scenario S2 uses the same data as S1 but assumes that parkland within the city produces forestry residues at a rate of 10 t/ha∙yr (as well as having the option of importing additional supplies) (33). It should also be noted that, in addition to the resources and technologies discussed so far, scenarios S1 and S2 also allow small electric resistance heaters to be used. This technology has been introduced to provide for a more realistic biomass energy strategy, one that does not rely entirely on imported biomass for heat demands. To illustrate the effect of this technology, each biomass scenario was run without (S1a and S2a) and with (S1b and S2b) electric heaters. 4
RESULTS
To explore the potential use of biomass in the ecotown, the SynCity system was first run through its initial layout and agent-activity model stages. This provides a map of the major land uses (see Figure 3) and calculates demands for energy resources, such as heat and electricity, distributed in both time and space (see Table V). (There is a mix of housing densities in the model; all are built to the PassivHaus standard as noted in the energy consultant’s report). Time is discretised into 16 periods: 4 intervals per day, 2 day types per week, and 2 seasons per year.
Figure 3. The land use plan for the eco-town. Cells are coloured according to their function (blue for housing, green for parkland, yellow for schools, purple for mixed use, grey for unused) and the links represent the road network and estimated traffic flows (not all links appear due to image resolution) . Table V. Comparison of SynCity agent-activity model results to eco-town reference data (provided by the developer). SynCity Eco-town reference Annual heat demand 5100 5200 (kWh per capita) Annual electricity demand 2500 2080 (kWh per capita) Daily motorised trips 0.48 0.45 (per household) The RTN model was then run for the three scenarios described above: a baseline case using grid-supplied electricity and gas resources (S0), a wood chip-fired district heating case using imported forestry residues (S1a and S1b, without and with electric heating), and a wood chip-fired district heating case using both imported and locally available forestry residues (S2a and S2b, without and with electric heating). In each case, the optimiser tried to determine the configuration of technologies and resource networks at a minimum annual cost. It was assumed that utility corridors, e.g. for gas or heat distribution networks, would follow the existing road structure.
4.1 Baseline (S0) The baseline scenario models a simple “business-asusual” resource provision strategy. To meet the ecotown’s demands for heat and electricity, grid imports of gas and electricity are used. Heat demands are satisfied by burning the imported gas in domestic condensing boilers. The optimiser therefore needs only to determine the structure of the distribution network, the quantity of imported resources and the number of boilers installed in each cell. The solution shown in Figure 4 demonstrates how gas and electricity are imported into the bottom left corner of the eco-town and then distributed throughout. The networks mirror each other as the model assumes that road corridors must be followed where available, although some additional connections are allowed as well. In the gas figure, a number of boiler technologies can be seen in cells where gas is converted to meet local heat demands. A summary of this scenario is given in Table VI; in this and following tables, “solution quality” represents 100% minus the optimisation integrality gap. (The integrality gap of an MILP is a measure of the quality of the solution and is the difference between the final solution and the fully relaxed solution, where all integer variables are treated as continuous.)
Table VI. Results of baseline scenario S0. Value Resources (Net consumption) Electricity Gas Transport fuel
65.6 TJ 168.7 TJ 1.4 TJ
Networks Electricity network length Gas network length
16.3 km 16.3 km
Technologies Boiler
Number installed Annual average output (% of max capacity)
395 44.8%
Optimisation Objective function (Total annual costs, £ mil) Solution quality
6.73 100%
4.2 Imported biomass with district heating (S1) In the second scenario, imported biomass is used to meet electricity and heat demands. We assume that forestry residues are imported into the eco-town and then converted into wood chips; these chips are used to fire CHP units which provide both electricity and district heat. Direct imports of wood chips were not considered as the goal was to explore the location of biomass storage and chipping facilities. As noted above, two versions of this scenario were run: S1a where all heat demand must be met by the CHP systems and S1b where electric resistance heaters can be used for part of the load. However since electric heaters are the cheapest available source of heat, the model was further constrained in S1b and S2b to require at least one chip production facility and one CHP unit. Figure 5 shows the resource distribution networks for the case without electric heating. In the top figure, forest residues are imported to the bottom left corner of the city and distributed primarily to a central zone, with some flow continuing to the top right corner of the city. At each of these locations, chip production facilities create wood chips which are then consumed locally by CHP units of both small and medium size. The district heating network and electricity networks then show how the end products are distributed. Note how the electricity network contains one power “island” where local electricity demand is completely satisfied by CHP units.
Figure 4. Distribution networks and technology locations for scenario S0 (gas top, electricity bottom). Lines represent resource flows of varying magnitudes. Resources are imported to the bottom left corner of the city.
Figure 5. Resource distribution networks for scenario S1a using only chip-fired CHP units for heat provision (from top to bottom: forestry resources, wood chips, district heat, electricity)
Figure 6. Resource distribution networks for scenario S1b using chip-fired CHP units and electric heating (from top to bottom: forestry resources, wood chips, district heat, electricity)
In contrast, Figure 6 shows the distribution networks in the presence of electric heating. In this case, there is less reliance on imported forest residues; note how these resources are imported into only one central cell for conversion into wood chips. The chips are then burned in one of three CHP units, coupled to a reduced district heat network. To meet the remaining head load, the electricity network imports additional power from the grid to be consumed in numerous electric heaters (“eheater”). Table VII presents a summary of these results. The difference in the length of the resource distribution networks, particularly for district heat, can clearly be seen. Note also that the biomass only scenario requires additional amounts of transport fuel; this is necessary to move the forest residues and resulting chips to their respective locations within the city. There are also significant exports of electricity in both cases and in the biomass only case, this is large enough to represent a net export of electricity from the city. Table VII. Results of imported biomass scenario S1. N = number of installed technologies, CF = capacity factor (annual average output as a percent of maximum capacity) Biomass With electric only heat (S1a) (S1b) Resources (Net consumption) Electricity Forest residues Transport fuel
-6.3 TJ 331 TJ 1.6 TJ
9.3 TJ 302 TJ 1.5 TJ
15.2 km 7.2 km
12.6 km 5.0 km
2 25.7% 5 28.3 % 2 50.3 % 0 309 44.5%
1 46.9 % 0 3 52.2% 999 15.4 % 226 55.6%
Networks Electricity network length Heat network length
4.3 Imported and local biomass with district heating (S2) In the second biomass scenario, the chip-fired CHP units are retained for the provision of electricity and district heat (with and without electric heaters). However rather than assuming that all biomass is imported in the form of forestry residues, we consider that the locally available parkland also produces these residues at a yield of 10 tonnes per hectare per year (see the green zones in Figure 3, which comprise a total of 11.7 hectares). Figure 7 and Figure 8 demonstrate that the basic network designs are similar to those in scenario S1. Without electric heating, the model has to build a much larger district-heat network to meet demand, which in turn must be supplied by extensive biomass supply chains. A key feature to note in these plots is the collection of forestry residues from the urban parkland areas, which results in notably different locations for the chip production facilities (i.e. compare Figure 5 and Figure 7). Table VIII summarizes the data from this scenario. Again the length of the district heat network and the required amount of transport fuel are notably larger in the case without electric heat; the production and export of surplus electricity in the biomass only scenario also remains. Table VIII. Results of imported and local biomass scenario S2. N = number of installed technologies, CF = capacity factor (annual average output as a percent of maximum capacity) Biomass With electric only heat (S2a) (S2b) Resources (Net consumption) Electricity Forest residues Transport fuel
-9.6 TJ 332 TJ 1.6 TJ
1.6 TJ 314 TJ 1.5 TJ
16.3 km 7.2 km
12.8 km 4.1 km
2 25.8% 3 29.2% 3 42.7% 0 311 44.4%
1 48.8% 0 4 40.6% 360 27.8% 279 46.8%
7.51
7.22
96.6%
97.6%
Technologies Networks Chip production Small CHP Medium CHP Electric heaters Heat exchangers
N CF N CF N CF N CF N CF
Electricity network length Heat network length Technologies Chip production Small CHP Medium CHP
Optimisation Electric heaters Objective function (Total annual costs, £ mil) Solution quality
7.68
7.17
96.0%
98.3%
Heat exchangers
N CF N CF N CF N CF N CF
Optimisation Objective function (Total annual costs, £ mil) Solution quality
Figure 7. Resource distribution networks for scenario S2a using imported and locally available biomass scenario without electric heating (from top to bottom: forestry residues, chips, district heat, electricity)
Figure 8. Resource distribution networks for scenario S2b using imported and locally-available biomass scenario with electric heat (from top to bottom: forestry residues, chips, district heat, electricity)
400
Gas
Wood
Transport
200 0
100
Net annual consumption (TJ)
300
Elec
S0
S1 a
S1 b
S2 a
S2 b
Scenario
Figure 9. Total net energy consumption of each scenario. Figure 10 considers the carbon implications of these results assuming the carbon footprints shown in Table IX. Scenario S1a and S2a are the lowest carbon scenarios, emitting only 0.2 tCO2 per capita on average (accounting for the carbon content of the exported electricity). Yet even the baseline case, at 2.9 tCO2 per capita, is significantly lower than UK average emissions of 9.8 tCO2. This can be explained by high thermal performance of the modelled dwellings (PassivHaus standard) and the lack of heavy industry in the city. Nonetheless only the biomass scenarios meet the proposed 80% CO2 reduction target for eco-towns. Table IX. Carbon emission factors for selected energy resources (34). Resource Emissions factors (kg CO2 per kWh) Electricity 0.537 Natural gas 0.185 Forestry residues 0.025 Transport fuel 0.245
3.0 2.0 1.5 1.0 0.5
Annual carbon emissions (tCO2 per capita)
2.5
Transport Wood Gas Elec
0.0
4.4 Comparative results These scenarios illustrate the dilemmas faced by an urban designer who wishes to use biomass for energy provision. First, Figure 9 compares the net energy consumption of each scenario. This represents the total imported energy flows plus any locally available resources, minus exported flows. The baseline scenario S0 consumes the least total energy and the four biomass cases require between 33% and 38% more (with and without electric heating respectively). These differences in demand are partly explained by the need for additional transport fuel in the biomass scenarios. However the main driver is that, for this small site, a biomass strategy offers efficiencies of scale and a lower specific resource costs for heat production (relative to electricity). Therefore in both the cases with and without electric heat, CHP units are sized to meet large amounts of the total heat demand; this results in the excess production of electricity as seen by the exports in S1a and S2a (despite net imports overall, exports also occur in S1b and S2b). Both biomass scenarios easily meet the 50% on-site renewables target envisaged for eco-towns.
S0
S1 a
S1 b
S2 a
S2 b
Scenario
Figure 10. Per capita carbon emissions of each scenario. Table X summarises the relative costs of each scenario. This shows that the biomass-only scenarios incur a cost penalty of 12-14% as a result of over-sizing the CHP units to meet heat demand; after introducing the option of electric heating, the costs are only 6-7% over the baseline scenario. These increased costs arise primarily from the additional capital requirements (e.g. the chip production facility and district heat network) and process costs (transportation of chips, forestry residues and district heat). Table X. Summary of total costs for each scenario. Scenario Total cost (£ mil) Relative to S0 So 6.73 1.00 S1a 7.68 1.14 S1b 7.17 1.06 S2a 7.51 1.12 S2b 7.22 1.07 From the perspective of the eco-town developer, the best overall scenario is likely to provide some balance between cost and carbon criteria. We explore this tradeoff by constructing a simple performance indicator for each scenario. First, for each scenario s ∈ {S0, S1a, S1b, S2a, S2b} and attribute a ∈ {cost, carbon}, a performance metric ps,a was calculated by normalizing the respective attribute value against the largest value in the scenario set. These scores were then weighted, such that the sum of the weights equals 1, to give an overall score, Ps, for scenario s. The range of Ps is 0 (worst) to 1 (best).
Ps 1 wa p s ,a a
wcos t 1 wcarbon Figure 11 plots each scenario with a range of cost weights between 0 and 1. The figure shows that where wcost is less than approximately 0.7, the best option is to harvest locally available biomass and forego the use of high-carbon electric heating. Where wcost is between 0.7 and 0.95, the carbon criterion is less important and therefore the preferred choice is a mix of electric heating and biomass. The business-as-usual gas import case
1.0
scores best only in those cases where cost is effectively the sole priority (wcost > 0.95). A more detailed analysis, perhaps using a willingness-to-pay framework, would be valuable to determine how this threshold translates into a policy-relevant carbon mitigation price. S1 a
S1 b
S2 a
S2 b
0.6 0.4 0.0
0.2
Performance index (1 = best)
0.8
S0
0.0
0.2
0.4
0.6
0.8
1.0
Cost weight
Figure 11. Comparison of scenarios with different cost weights. 5
size based on heat load is a key design decision. The biomass scenarios used a mix of CHP unit sizes to meet the heat load effectively, although there was some evidence of a preference for smaller plant sizes. This development is constructed at a relatively low density (individual lots are approximately 50 dwellings per hectare, but there are many empty lots within the site) and a developer who chooses to build at a higher density may therefore be able to take advantage of the efficiencies of larger plant. Moreover, in the proposed application a fixed heat/electricity ratio was assumed, without reserve boilers integrated to the CHP plants to meet the winter peak heat demand. More accurate modelling of this feature would be valuable for ensuring that the correct plant size was selected. Finally the local biomass resource in this case was only sufficient to meet approximately 0.6% of the total heat and power energy demand. This resource could be increased by converting currently unallocated land within the development to biomass production, but local biomass is still likely to only meet a fraction of total requirements. As mentioned above, this means that a full analysis of the carbon impacts of urban biomass solutions will need to account for emissions throughout supply chains that likely extend far beyond the city limits. Assessing the potential role of other renewable energy technologies, such as medium-scale wind, is therefore valuable for determining the most cost-effective low carbon strategy overall.
DISCUSSION
We conclude the paper by reviewing the implications of the results for two key audiences: eco-town developers and those with a more specific interest in the techniques discussed here. 5.1 Lessons for eco-towns The eco-town studied here illustrates many of the difficult decisions that planners and developers must make in designing low carbon cities, particularly those using biomass solutions. Specifically our results suggest that, for small ecotowns, biomass is a viable alternative except under the most rigid cost-minimization criteria. Yet from a project management perspective, there may still be significant implementation challenges not captured by our model. One of the obstacles in this particular case is that the proposed biomass supply chain introduced two innovations at once: a district heat system (requiring boilers, pipe networks and heat exchanges) as well a biomass fuel chain (requiring resource transportation, chip production and storage). In developments where a district heat system already exists, the switch to biomass fuel may be fairly straight-forward. Alternatively biomass processing can be omitted from the eco-town entirely by directly importing high-quality bio-energy resources. For example, in the case studied here, finished wood chips could be imported instead of raw forestry resources. This can also be achieved by using technologies other than the ORC CHP units considered here. Bio-oil or pellet-fired systems may rely to a greater extent on imported biofuels which can be produced and transported from larger facilities more cost-effectively (particularly bio-oil with its higher energy density). However the use of imported bio-energy resources suggests that more rigorous lifecycle carbon accounting techniques should be used. The case has also shown that selecting the right plant
5.2 Lessons for integrated urban energy modelling This paper introduced SynCity, a framework for integrated urban energy systems modelling. The software is currently in a prototype stage and therefore this analysis has been helpful in identifying potential improvements in the software and areas of further work. Generally we found that the specification of the RTN model, which relies on generic concepts of resources and processes, is a useful framework for modelling potentially convoluted resource chains. However the current version of the model does have some shortcomings. For example in this paper, we limited ourselves to the assessment of relatively simple biomass supply chains. In particular, processes which could be fired with substitutable inputs were ignored. One example of such a process is a bio-oil boiler that may run on either imported bio-oil or locally available cooking oil wastes. Modelling these processes adds extra complexity, so the present study was performed using an RTN model that only considers processes with fixed inputs and outputs. The RTN model has been extended to accommodate processes that may change their inputs and output continuously between two specified modes of operation, without much difficulty. It is also possible to model discrete modes of operation, but this adds significant extra complexity. However, neither of these versions has been thoroughly tested. We also noted that cost assumptions are extremely important to the performance of the model, but that robust cost data are not readily available and the turn key costs of CHP plants and related facilities are highly sitespecific. In the case of district heat networks for example, there is very little UK experience in building such systems and so it is difficult to find reliable estimates for building such a system in this proposed eco-town. Development on SynCity as a whole, and the RTN model in particular will continue, with a view to
addressing these concerns. Next steps for this specific case study include exploring the use of other biomass resources and in other sectors (e.g. bio-oils for both heating and transportation).
6
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ACKNOWLEDGEMENTS
The authors would like to acknowledge the financial support of BP via the BP Urban Energy Systems at Imperial College London.
