Multi-Fuel Allocation for Power Generation Using Genetic Algorithms
DOI:
https://doi.org/10.6000/1929-7092.2017.06.25Keywords:
Multi-fuel Allocation, Power Generation, Genetic Algorithms, Power EnergyAbstract
The ever increasing growth of energy consumption has stimulated an energy crisis, not only in terms of energy demand, but also the impact of climate change from greenhouse gas (GHG) emissions. Renewable energy sources (RES) have high potential toward sustainable development, with a wide variety of socioeconomic benefits, including diversification of energy supply and creation of domestic industry. This paper presents a solution to optimal multi-fuel allocation for the electric power generation planning problem via genetic algorithms (GA). The objective is to maximize the electric power energy output and minimize generation cost. This is a difficult problem because of its data variation and volatility. GA can provide an appropriate heuristic search method and return an actual or near optimal solution. This paper uses some heuristics during crossover and mutation for tuning the system to obtain a better candidate solution. An experimental result showed significantly improved results compared with other techniques. The results in this paper should be useful for connecting power generation with economic growth.
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https://doi.org/10.1109/TPWRS.2014.2316529
Aqeel A. and Gholamreza Z. (2011), Sustainable Energy Systems: Role of Optimization Modeling Techniques in Power Generation and Supply – A review, ELSEVIER, Renewable and Sustainable Energy Reviews, 3480-3500
Christian W., Sean M., and Michael B. (2008), Biogas plant optimization using Genetic Algorithms and Particle Swarm Optimization, Signals and Systems Conference, 208. (ISSC), Galway, IET Irish, pp 244-249.
Fakhimghanbarzadeh B., Hosein M. and Hossein A. (2010), Optimization of Biomass Waste Gasification Combined Heat and Power System, Electric Power and Energy Conference (EPEC), IEEE.
https://doi.org/10.1109/epec.2010.5697239
Japan Institute of Energy, (2014), Asian Biomass Handbook.
Jorg Z., Thomas B-B., Oliver F., Wolfgang K. and Matin Z. (2010), Optimization of Biogas Production with Computational Intelligence A Comparative Study, Evolutionary Computation (CEC), IEEE Congress on.
Lijun W., Curtis L. W., David D. J., and Milford A. H. (2008), Contemporary issues in thermal gasification of biomass and its application to electricity and fuel production, Biomass and Bioenergy 32, pp 573-581.
https://doi.org/10.1016/j.biombioe.2007.12.007
Market Report, Canada, (2016), Independent Electricity System Operator (IESO).
Michalewicz Z. (1995), A survey of constraint handling techniques in evolutionary computation methods, Proceedings of the 4th Annual Conference on Evolutionary Programming, pp. 135-155.
OVO Energy Ltd.,(2017), https://www.ovoenergy.com
Sedighizadeh M., Rafiei M., and Hakimi A. (2013), Optimizing A TypicalBiomass Fueled Power Plant Using Genetic Algorithm and Binary Particle Swarm Optimization, International Journal on Technical and Physical Problems of Engineering, (IJTPE), June, No. 15, Vol. 5, 15-21.
Senjyu T., Hayashi D., Urasaki N. and Funabashi T. (2016), Optimum Configuration for Renewable Generating Systems in Residence Using Genetic Algorithm, IEEE Transactions on Energy Conversion, Vol. 21, No.2, June, 459-466.
https://doi.org/10.1109/TEC.2006.874250
Twarath S. (2012), Alternative Energy Development Plan: AEDP 2012-2021, International Journal of Renewable Energy, Vol. 7, No.1.
Warsono, D. J. King, Özveren C. S. and Bradley D.A. (2007), Economic Load Dispatch Optimization of Renewable Energy in Power System Using Genetic Algorithm, PowerTech, IEEE Lausanne, 2174-2179.
Xiang W, Bayan S. S., and Oliver R. H. (2005), An Improved Resource Allocation Scheme for Plane Cove Multiple Access Using Genetic Algorithm, IEEE Transactions on Evolutionary Computation, Vol.9, No.1, February, 74-81.
https://doi.org/10.1109/TEVC.2004.838662
Yang M.-D., Chen Y.-P., Lin Y.-H., Ho Y.-F., and Lin J.-Y. (2016), Multiobjective optimization using nondominated sorting genetic algorithm-II for allocation of energy conservation and renewable energy facilities in a campus, Energy and Buildings 122, 120–130.
https://doi.org/10.1016/j.enbuild.2016.04.027
Ying-Yi H. and Ruo-Chen L. (2012), Optimal Sizing of HybridWind/PV/Diesel Generation in a Stand-Alone Power System Using Markov-Based Genetic Algorithm, IEEE Transactions on Power Delivery, Vol. 27, No. 2, April, 640-647.
https://doi.org/10.1109/TPWRD.2011.2177102
Younes M., Rahli M. and Abdelhakem-Koridak L. (2007), Optimal Power Flow Based on Hybrid Genetic Algorithm, Journal of Information Science and Engineering 23, 1801-1816.
Zhang J., Chung H. S.-H., and Lo W.-L. (2007), Clustering-based adaptive crossover and mutation probabilities for genetic algorithms, IEEE Trans. Evolutionary Computation, Vol. 11, No. 3, Jun., 326-335.
https://doi.org/10.1109/TEVC.2006.880727
Zhao M., Chen Z., and Blaabjerg F. (2009), Optimization of electrical system for offshore wind farms via genetic algorithm, IET Renewable Power Generation, Vol. 3, Issue. 2, June 2009, 205-216.
https://doi.org/10.1049/iet-rpg:20070112
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Published
2017-06-09
How to Cite
Choeichum, A., Keeratipranon, N., & Khemapatapan, C. (2017). Multi-Fuel Allocation for Power Generation Using Genetic Algorithms. Journal of Reviews on Global Economics, 6, 258–268. https://doi.org/10.6000/1929-7092.2017.06.25
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Special Issue - Recent Topical Research on Global, Energy, Health & Medical, and Tourism Economics, and Global Software
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