Generation expansion planning with high shares of variable renewable energies

Mohamed M. Abdelzaher; Almoataz Y. Abdelaziz; Hassan M. Mahmoud; Said F. Mekhamer; Samia G. Ali; Hassan H. Alhelou; Mohamed M. Abdelzaher; Almoataz Y. Abdelaziz; Hassan M. Mahmoud; Said F. Mekhamer; Samia G. Ali; Hassan H. Alhelou

doi:10.3934/energy.2020.2.272

AIMS Energy

2020, Volume 8, Issue 2: 272-298. doi: 10.3934/energy.2020.2.272

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Research article Special Issues

Generation expansion planning with high shares of variable renewable energies

1.
Department of Strategic Planning, Egyptian Electricity Holding Company, Cairo, Egypt
2.
Faculty of Engineering & Technology, Future University in Egypt, Cairo, Egypt
3.
Electrical Power and Machines Department, Faculty of Engineering, Kafr El-sheikh University, Egypt
4.
Department of Electrical Power Engineering, Faculty of Mechanical and Electrical Engineering, Tishreen University, Lattakia 2230, Syria
5.
Department of Electrical and Computer Engineering, Isfahan University of Technology, Isfahan

Received: 20 November 2019 Accepted: 10 March 2020 Published: 25 March 2020

Worldwide, the utilization of Renewable Energies (REs) for electricity generation is growing rapidly driven by the increasing fears of fossil fuels depletion, the price volatility of these fuels and the necessity of reducing the Green House Gas (GHG) emissions to preserve the environment. On the other hand, REs especially the Variable Renewable Energies (VREs) like wind and solar power suffer from intermittency in its output generation. This intermittency can introduce severe technical and economic problems for the power systems with high penetration from these energies. This intermittency should be mitigated not only during the system operation phase but also during power system planning phase. For this purpose, the classical power system planning methodologies and models should be upgraded to account for this intermittency in a way to find the optimum solutions to mitigate it. In this regard, this paper will focus on developing a new Generation Expansion Planning (GEP) model to find the optimum mix of dispatchable generation technologies that can allow the integration of VREs into the power system while mitigating the technical and economic impacts of its intermittency. In addition, a number of new concepts related to generation mix flexibility, VREs capacity credit and role of system operating reserve in integrating VREs will be revisited. Then, the developed GEP model will be applied to a case study handling the future expansion scenarios of VREs in the Egyptian grid. Results obtained show that, increasing the share of VREs in the grid will shift the mix of new generation capacities from the least cost and low flexibility options into more expensive and flexible generation options.
Citation: Mohamed M. Abdelzaher, Almoataz Y. Abdelaziz, Hassan M. Mahmoud, Said F. Mekhamer, Samia G. Ali, Hassan H. Alhelou. Generation expansion planning with high shares of variable renewable energies[J]. AIMS Energy, 2020, 8(2): 272-298. doi: 10.3934/energy.2020.2.272

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Abstract

Worldwide, the utilization of Renewable Energies (REs) for electricity generation is growing rapidly driven by the increasing fears of fossil fuels depletion, the price volatility of these fuels and the necessity of reducing the Green House Gas (GHG) emissions to preserve the environment. On the other hand, REs especially the Variable Renewable Energies (VREs) like wind and solar power suffer from intermittency in its output generation. This intermittency can introduce severe technical and economic problems for the power systems with high penetration from these energies. This intermittency should be mitigated not only during the system operation phase but also during power system planning phase. For this purpose, the classical power system planning methodologies and models should be upgraded to account for this intermittency in a way to find the optimum solutions to mitigate it. In this regard, this paper will focus on developing a new Generation Expansion Planning (GEP) model to find the optimum mix of dispatchable generation technologies that can allow the integration of VREs into the power system while mitigating the technical and economic impacts of its intermittency. In addition, a number of new concepts related to generation mix flexibility, VREs capacity credit and role of system operating reserve in integrating VREs will be revisited. Then, the developed GEP model will be applied to a case study handling the future expansion scenarios of VREs in the Egyptian grid. Results obtained show that, increasing the share of VREs in the grid will shift the mix of new generation capacities from the least cost and low flexibility options into more expensive and flexible generation options.

References

[1]	IEA Renewables 2018 market analysis (2018), Available from: https://www.iea.org/renewables2018/power.
[2]	Madrigal M, Porter K (2013) Operating and planning electricity grids with variable renewable generation: Review of emerging lessons from selected operational experiences and desktop studies. World Bank.
[3]	Bird L, Milligan M, Lew D (2013) Integrating variable renewable energy: Challenges and solutions. National Renewable Energy Laboratory.
[4]	Bergh KV, Delarue E (2015) Cycling of conventional power plants: technical limits and actual costs. Energy Convers Manage 97: 70-77. doi: 10.1016/j.enconman.2015.03.026
[5]	Kumar N, Besuner P, Lefton S (2012) Power Plant Cycling Costs, NREL.
[6]	Hart EK, Stoutenburg ED, Jacobson MZ (2012) The potential of intermittent renewables to meet electric power demand: Current methods and emerging analytical techniques. Proceedings of the IEEE 100: 322-334. doi: 10.1109/JPROC.2011.2144951
[7]	Ma J, Silva V, Belhomme R (2013) Evaluating and planning flexibility in sustainable power systems, power and energy society general meeting (PES). IEEE Trans Sustainable Energy 4: 200-209. doi: 10.1109/TSTE.2012.2212471
[8]	Holttinen H, Kiviluoma J, Estanqueiro A, et al. (2010) Variability of load and net load in case of large scale distributed wind power. 10th international workshop on large-scale integration of wind power into power systems as well as on transmission networks for offshore wind farms proceedings, Aarhus, Denmark.
[9]	Lannoye E, Milligan M (2010) Integration of variable generation: Capacity value and evaluation of flexibility. Power and Energy Society General Meeting IEEE: 1-6.
[10]	Lannoye E, Flynn D (2011) The role of power system flexibility in generation planning. Power and Energy Society General Meeting IEEE: 1-6.
[11]	Lannoye E, Flynn D (2012) Evaluation of power system flexibility. IEEE Trans Power Syst 27: 922-931. doi: 10.1109/TPWRS.2011.2177280
[12]	Cochran J, Milligan M, Muller S, et al. (2014) Flexibility in 21st Century power systems. NREL.
[13]	Muller S (2013) Evaluation of power system flexibility adequacy-The flexibility assessment tool (FAST2). OECD/IEA.
[14]	Ela E, Milligan M, Kirby B (2011) Operating reserves and variable generation. NREL.
[15]	Milligan M, Donohoo P, Lew D (2010) Operating reserves and wind power integration: An international comparison. NREL.
[16]	Zhou Z, Levin T, Conzelmann G (2016) Survey of U.S. Ancillary Services Markets report. Argonne labs.
[17]	Keane A, Milligan M, Dent C (2010) Capacity value of wind power. IEEE 26: 564-572.
[18]	Wilton E, Delarue E, D'haeseleer W, et al. (2014) Reconsidering the capacity credit of wind power: Application of cumulative prospect theory. Renewable Energy 68: 752-760. doi: 10.1016/j.renene.2014.02.051
[19]	Abdelzaher MM, Mahmoud HM, Abdelaziz AY, et al (2015) Capacity credit evaluation of zafarana wind farm using approximate and reliability based methods. 17^th International Middle-East Power System Conference (MEPCON'2015), Mansoura University, Egypt.
[20]	Guidebook A (1984) Expansion Planning for Electrical Generating Systems. Int. At. Energy Agency, EUA.
[21]	Electric Power Research Institute (1999) EGEAS: electric generation expansion analysis system, version 9.02B.
[22]	IAEA (2001) WASP: Wien Automatic System Planning Package, version IV.
[23]	Hossein S, Sepasian MS (2011) Electric Power System Planning: Issues, Algorithms and Solutions, Springer Science & Business Media.
[24]	Abdelzaher MM, Mahmoud HM, Abdelaziz AY, et al (2018) Impact of generation mix flexibility on the integration of variable renewable energies. Int J Eng, Sci Technol 10: 1-16.
[25]	EEHC Annual Report (2016). Available from: www.moe.gov.eg.
[26]	IAEA (2011) MESSAGE: Model for Energy Supply Strategy Alternatives and their General Environmental Impacts. version V.
[27]	Loulou R, Goldstein G, Noble K (2004) Documentation for the MARKAL Family of Models. IEA-ETSAP.
[28]	Loulou R, Goldstein G, Kanudia A, et al. (2016) Documentation for the TIMES Model-Part I. IEA-ETSAP.
[29]	Zhu J, Chow MY (1997) A review of emerging techniques on generation expansion planning. IEEE Trans Power Syst 12: 1722-1728. doi: 10.1109/59.627882
[30]	Zhang T, Baldick R, Deetjen T (2015) Optimized generation capacity expansion using a further improved screening curve method. Electr Power Syst Res 124: 47-54. doi: 10.1016/j.epsr.2015.02.017
[31]	Tanabe R, Yasuda K, Yokoyama R (1992) Practical method for generation expansion planning based on the dynamic programming. IEEJ Trans Electr, Inf Syst 112: 285-294.
[32]	Mo B, Hegge J, Wangensteen I (1991) Stochastic generation expansion planning by means of stochastic dynamic programming. IEEE Trans Power Syst 6: 662-668. doi: 10.1109/59.76710
[33]	David AK, Zhao RD (1989) Integrating expert systems with dynamic programming in generation expansion planning. IEEE Trans Power Syst 4: 1095-1101. doi: 10.1109/59.32604
[34]	Wu F, Yen Z, Hou Y, et al. (2004) Applications of AI techniques to generation planning and investment. In IEEE Power Engineering Society General Meeting: 936-940.
[35]	Gorenstin BG, Campodonico NM, Costa JP, et al. (1993) Power system expansion planning under uncertainty. IEEE Trans Power Syst 8: 129-136. doi: 10.1109/59.221258
[36]	Park JB, Park YM, Won JR, et al. (2000) An improved genetic algorithm for generation expansion planning. IEEE Trans Power Syst 15: 916-922.
[37]	Sirikum J, Techanitisawad A, Kachitvichyanukul V (2007) A new efficient GA-benders' decomposition method: For power generation expansion planning with emission controls. IEEE Trans Power Syst 22: 1092-1100. doi: 10.1109/TPWRS.2007.901092
[38]	Yildirim M, Erkan K, Ozturk S (2006) Power generation expansion planning with adaptive simulated annealing genetic algorithm. Int J Energy Res 30: 1188-1199. doi: 10.1002/er.1214
[39]	IEA World Energy Outlook (2016). Available from: www.iea.org.
[40]	Intergovernmental Panel on Climate Change. Available from: www.ipcc.ch.
[41]	EIA Annual Energy Outlook 2018 (2018) Cost and Performance Characteristics of New Generating Technologies.
[42]	OECD, IEA. NEA (2015) Projected Costs of Generating Electricity Report 2015.
[43]	Energiewende A (2017) Flexibility in thermal power plants Report.

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