Solar-Wind Hybrid Renewable Energy Systems

Solar-Wind Hybrid Renewable Energy Systems: Evolutionary Technique (Full text in English)

Ahmed SAIDI1 1Department

of Electrical Engineering, Faculty of Technology, Tahri Mohammed University, BP 417 Route de Kenadsa, 08000 Béchar, Algeria

Abstract The demand for electricity is increasing exponentially because of the industrially revolution, which cannot be fulfilled by nonrenewable energy sources alone. Renewable energy sources such as solar and wind are omnipresent and environmental friendly. The renewable energy sources are emerging options to fulfil the energy demand, but unreliable due to the stochastic nature of their occurrence. Hybrid renewable energy system (HRES) combines two or more renewable energy sources like wind turbine and solar system. The objective of this paper is to present a comprehensive of various aspects of HRES. This paper discusses pre-feasibility analysis and reliability issues. The application of evolutionary technique such as genetic algorithm, fuzzy logic and neural network and future scope in hybrid renewable energy is also presented in this paper. Keywords: PV array, renewable energy, solar, wind, HRES, GA, Fuzzy, neural. Received: [August, 29, 2016] To cite this article: SAIDI A., “Solar-Wind Hybrid Renewable Energy Systems: Evolutionary Technique”, in Electrotehnica, Electronica, Automatica (EEA), 2016, vol. 64, no. 4, pp. 24-27, ISSN 1582-5175.

1. Introduction Renewable energy sources (solar, wind, etc.) are attracting more attention as alternative energy sources to conventional fossil fuel energy sources. This is not only due to the diminishing fuel sources, but also due to environmental pollution and global warming problems [1]. A wind-solar hybrid system was usually comprised of wind turbine, photovoltaic (PV) modules, controller, inverter and batteries. The major advantage of the hybrid system is that its reliability is enhanced compared with the simple wind energy system or solar energy system [23]. The research on wind-solar hybrid system mainly focuses on the modelling for system configuration, optimal matching between wind turbine and PV modules [4] Solar and wind energy system works normally in standalone or grid connected mode, but the efficiency of these sources is less due to the stochastic nature of solar and wind resources. The hybrid renewable energy sources with grid integration overcome this drawback of being unpredictable in nature. Hybrid renewable energy system (HRES) is a combination of renewable and conventional energy source; it may also combine two or more renewable energy sources. The HRES that combines solar and wind energy key resources, operates in two modes: simultaneous and sequential. In simultaneous mode, the solar and wind energy system produces energy

Figure 2. Global solar radiation potential

concurrently while in sequential mode they produce electricity alternatively [5-6].

Figure 1. Basic component of solar–wind hybrid renewable energy system

2. Pre-feasibility assessment of HRes Prior to installation and operation, the prefeasibility study of hybrid energy system is customarily carried out. The prefeasibility analyse is includes the study of climatic condition of the proposed site, availability of renewable energy sources and assessment of its potential load and load demand of application site. The prefeasibility study assists in finding out the best location to develop a solar wind hybrid renewable energy system. Few significant contributions of various researchers are discussed here. Rahman et al. [7] gave the feasibility study of Photovoltaic (PV) Fuel cell hybrid energy system considering difficulty in the use of PV and provide new avenues for the fuel cell technology.

Figure 3. Global wind energy potential

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A Chong et al. [11] presents prefeasibility analysis of a wind–solar HRES with rain water collection features for urban high rise application. Economic analysis includes manufacturing and preservation costs of the system over the given life span. The system is analysed for security, visual impact and noise pollution. Sinha et al. [12] presents prefeasibility analysis of solar–wind hybrid systems for a complex hilly terrain. The study is carried out to assess the potential for a solar-wind hybrid system for Hamirpur town located in Northern Province of India. The prefeasibility study indicates the quality of potential for utilizing solar-micro wind hybrid system to supplement the energy needs in hilly regions. Aydin et al. [13] explore geological information system (GIS) based site allocation for solar-wind HRES at western turkey. In this paper Fuzzy logic and geographic information system tool are used to search best and alternative locations of the target area that benefits financial and ecological criteria. Tao Ma et al. [14] presented a comprehensive feasibility study and techno-economic assessment of a remote solar-wind hybrid energy system with battery energy storage for a isolated island. Climatic condition is the major input to carried out pre-feasibility analysis. Figs. 2 and 3 shows global map solar energy and wind energy potential in all over world. Feasibility of solar-wind hybrid renewable energy system mainly depends on solar radiation and wind energy potential available at the specific location. Designing a hybrid renewable energy system requires appropriate weather data. 3. Evolutionary technique in HRES Evolutionary computation is a subfield of artificial intelligence that involves combinatorial optimization difficulty. The use of Darwinian principles for automated problem solving originates in the 1950s. 3.1. Genetic algorithm Genetic algorithm is a randomized hunt and optimization system guided by the morality of the natural genetic system. GA is an adaptive heuristic search algorithms based on the evolutionary ideas of naturals election and genetics. Daming et al. [15] explained an elitist strategy of optimal sizing of standalone hybrid PVwind power systems using genetic algorithm with loss of power supply probability as a constraint and minimizes the total capital of the entire system. Zhao et al. [16] used a genetic algorithm with PSO to observe the best probable capacity model of a solar wind hybrid renewable energy system with rapid global convergence. Gupta et al. [17] used GA to design and supply varying load located in the area of Jaipur (India). Result shows that the system can deliver energy in a standalone installation with an acceptable price. Ben et al. [18] developed optimum sizing of hybrid PV/Wind battery system using Fuzzy adaptive GA which decides the optimal number of PV panels, WT and storage units, Further GA is used to conclude optimal power arrangement of an off-grid hybrid renewable energy system. Tutkun et al. [19] develop authentic power scheduling of an off grid HRES used for heating and lighting in an archetypal residential house. In this paper, binary coded GA is used instead of assorted integer linear programming to minimize the operational unit cost of HRES.

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3.2. Fuzzy Chakraboty et al. [20] presents an optimal economic operation of smart grid by fuzzy advanced quantum evolutionary method. Adhikari et al. [21] explained analysis, design and control of a standalone integrated non-conventional energy conversion system based on the fuzzy logic control method by sensing the DC voltage and current output of solar and the rectified output voltage of permanent magnet brush less direct current (PMBLDC) generator driven by a wind turbine. Chakarborty et al. [22] developed intelligence economic operation of smart grid using fuzzy advanced quantum evolutionary method. 3.3. Neural Neural network is an interconnected group of artificial neurons that uses a mathematical model or computational model for information processing based on a connectionist approach to computation. Fidalgo et al. [23] applied artificial neural network (ANN) based approach for applying preventive control strategies for a large hybrid renewable energy system. ANNs are an essential part which is better than customary statistical methods in the dynamic security pattern class and also evaluates the degree of security. Jifang et al. [24] proposed a neural network control strategy for multi-energy common DC bus hybrid power supply by analysing the distinctiveness of solar energy, wind energy. Levenberg–Marquaret algorithm linked to neural network is used and momentum factor is introduced in the training. Duang et al. [25] develop a hybrid model for an hourly forecast of PV- wind renewable energy system and used computational intelligence of PSO for computing different definitions of the forecast error. 4. Reliability in HRES Reliability is defined as the probability of a device or system performing its purpose adequately for the intended operating period of time. It is also defined as the ability of electrical power system to supply the system load having reliable continuity and quality of supply. Fig. 6 presents reliability analysis indices. Billionton and Karki [26] addressed reliability analysis of small isolated renewable energy system by deterministic and probabilistic technique. Zhao et al. [27] deals with the comprehensive objective functions which not only include the investment cost, but also the reliability and optimal operation of the system. The objective function consists of the investment of wind turbine, PV and cost of loss of power energy in the system which can be calculated by reliability. Ardakani and Riahy [28] developed a design of an optimum HRES considering reliability indices subjected to financial and scientific constraints. The technical constraints related to system reliability are articulated by the equivalent loss factor. The reliability index is calculated from component stoppage that includes WT, PV array, battery and inverter failure. Zhao and Wang [29] presented impacts of renewable energy penetration on nodal price and nodal reliability in deregulated power system. In this paper the reliability of wind and solar power is investigated in a pool co-market operation. The method captures the chronological performance of enumeration reliability analysis. Kishore and Farnandez

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[30] presents a reliability assessment of PV-wind hybrid system using Monte-Carlo simulation. The paper discusses the various components of HES involving PV and wind energy conversion system (WECS) and their modelling for reliability studies. Several methods for reliability evaluation have been reported in this paper.

Figure 4. Reliability analysis indices

5. Future scope The utilization of solar–wind hybrid renewable energy system is increasing day by day and has shown tremens do us grow thin last few decades for electricity production all over the world. With the development of new technologies in the field of solar wind hybrid renewable energy system, a new problem arises, which become much more fascinating to be solved. These problems will be compensated by some future research in the respective field. The following lists give idea of future research this field [31]: − Some problems are reported to find out the exact location and climate condition, site to site data is needed, which is difficult to obtain for remote location. Hence it is necessary to develop an exact optimization technique and geographical software to find out the potential of solar radiation and wind velocity. − There are different types of sizing methods being used such as iterative method, artificial intelligence method, but these methods do not represent accurate dynamic performance of solar and wind energy system. Hence it is necessary to develop a unit sizing method which a void complexity in designing of the system and explain perfectly frequency response of the system in dynamic performance criteria. − It is necessary to develop centralized and multi-level controlling technique which a void the potential complexity of communication system and large computation burden which is subjected to single point failure. 6. References [1] [2]

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Acknowledgement This work was financially supported by the ENERGARID and SGRE laboratory and Tahri Mohammed University Bechar 08000 ALGERIA, under the scientific programme Electroenergetic Industrial Option Renewable Energy, 2015 /2016.

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Biography Ahmed SAIDI was born in IGLI Béchar (ALGERIA) on May 12, 1991. He received the licence degree in Electric Engineering in 2013 from the University of Béchar (Algeria). He received master in electric engineering in 2015 from university of Béchar (Algeria). He was preparing a Doctorate of improvement of the MPPT in a solar wind systems based on intelligent control. He is currently a Research member in the Smart Grid and Renewable Energy Laboratory since 2015. His research area interests are: power electronics, power Quality issues, renewable energy, artificial intelligence and power systems. e-mail: [email protected]