The optimal hourly electricity price considering wind electricity uncertainty based on conditional value at risk

References

• Abapour, S., B. Mohammadi-Ivatloo, and M. T. Hagh. 2020. Robust bidding strategy for demand response aggregators in electricity market based on game theory. Journal of Cleaner Production 243:118393. doi:10.1016/j.jclepro.2019.118393.
   Web of Science ®Google Scholar
• Acar, B., O. Selcuk, and S. A. Dastan. 2019. The merit order effect of wind and river type hydroelectricity generation on Turkish electricity prices. Energy Policy 132:1298–319. doi:10.1016/j.enpol.2019.07.006.
   Web of Science ®Google Scholar
• Andruszkiewicz, J., J. Lorenc, and A. Weychan. 2020. Seasonal variability of price elasticity of demand of households using zonal tariffs and its impact on hourly load of the power system. Energy 196:117175. doi:10.1016/j.energy.2020.117175.
   Web of Science ®Google Scholar
• Avriel, M. 2003. Nonlinear programming: Analysis and methods. Courier Corporation.
   Google Scholar
• Baldursson, F. M., and N. H. M. Fehr. 2007. Vertical integration and long-term contracts in risky markets. No. 2007, 01. Memorandum.
   Google Scholar
• Bazmohammadi, S., A. A. Foroud, and N. Bazmohammadi. 2018. Portfolio optimization in electricity market using a novel risk based decision making approach. Scientia Iranica. 25 (6):3569–83.
   Web of Science ®Google Scholar
• Benhmad, F., and J. Percebois. 2018. Photovoltaic and wind power feed-in impact on electricity prices: The case of Germany. Energy Policy 119:317–26. doi:10.1016/j.enpol.2018.04.042.
   Web of Science ®Google Scholar
• Borenstein, S., and J. Bushnell. 1999. An empirical analysis of the potential for market power in California’s electricity industry. The Journal of Industrial Economics 47 (3):285–323. doi:10.1111/1467-6451.00102.
   Web of Science ®Google Scholar
• Burke, P. J., and A. Abayasekara. 2018. The price elasticity of electricity demand in the United States: A three-dimensional analysis. The Energy Journal 39 (2). doi:10.5547/01956574.39.2.pbur.
   Web of Science ®Google Scholar
• Case, K. E., & Fair, R. C. (2007). Principles of microeconomics. Pearson Education.
   Google Scholar
• Chattopadhyay, D. 2004. Multicommodity spatial Cournot model for generator bidding analysis. IEEE Transactions on Power Systems 19 (1):267–75. doi:10.1109/TPWRS.2003.821436.
   Web of Science ®Google Scholar
• Chattopadhyay, D., and T. Alpcan. 2014. A game-theoretic analysis of wind generation variability on electricity markets. IEEE Transactions on Power Systems 29 (5):2069–77. doi:10.1109/TPWRS.2014.2306192.
   Web of Science ®Google Scholar
• Chen, C., S. Kishore, and L. V. Snyder (2011, May). An innovative RTP-based residential power scheduling scheme for smart grids. In 2011 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP), 5956–59. IEEE.
   Google Scholar
• Chindarkar, N., and N. Goyal. 2019. One price doesn’t fit all: An examination of heterogeneity in price elasticity of residential electricity in India. Energy Economics 81:765–78. doi:10.1016/j.eneco.2019.05.021.
   Web of Science ®Google Scholar
• Choi, D. G., M. K. Lim, K. Murali, and V. M. Thomas. 2020. Why have voluntary time‐of‐use tariffs fallen short in the residential sector? Production and Operations Management 29 (3):617–42. doi:10.1111/poms.13126.
   Web of Science ®Google Scholar
• Chronopoulos, M., B. De Reyck, and A. Siddiqui. 2014. Duopolistic competition under risk aversion and uncertainty. European Journal of Operational Research 236 (2):643–56. doi:10.1016/j.ejor.2014.01.018.
   Web of Science ®Google Scholar
• Cory-Wright, R., and G. Zakeri. 2020. On stochastic auctions in risk-averse electricity markets with uncertain supply. Operations Research Letters. doi:10.1016/j.orl.2020.04.009.
   Web of Science ®Google Scholar
• Csereklyei, Z., S. Qu, and T. Ancev. 2019. The effect of wind and solar power generation on wholesale electricity prices in Australia. Energy Policy 131:358–69. doi:10.1016/j.enpol.2019.04.007.
   Web of Science ®Google Scholar
• Cui, W., and L. Li. 2018. A game-theoretic approach to optimize the time-of-use pricing considering customer behaviors. International Journal of Production Economics 201:75–88. doi:10.1016/j.ijpe.2018.04.022.
   Web of Science ®Google Scholar
• Cunningham, L. B., R. Baldick, and M. L. Baughman. 2002. An empirical study of applied game theory: Transmission constrained Cournot behavior. IEEE Transactions on Power Systems 17 (1):166–72. doi:10.1109/59.982209.
   Web of Science ®Google Scholar
• Dahlgren, R., C. C. Liu, and J. Lawarree. 2003. Risk assessment in energy trading. IEEE Transactions on Power Systems 18 (2):503–11. doi:10.1109/TPWRS.2003.810685.
   Web of Science ®Google Scholar
• Dahlke, S. 2018. Effects of wholesale electricity markets on wind generation in the midwestern United States. Energy Policy 122:358–68. doi:10.1016/j.enpol.2018.07.026.
   Web of Science ®Google Scholar
• Dai, T., and W. Qiao. 2013. Trading wind power in a competitive electricity market using stochastic programing and game theory. IEEE Transactions on Sustainable Energy 4 (3):805–15. doi:10.1109/TSTE.2013.2251917.
   Web of Science ®Google Scholar
• de Miera, G. S., P. Del Río González, and I. Vizcaíno. 2008. Analysing the impact of renewable electricity support schemes on power prices: The case of wind electricity in Spain. Energy Policy 36 (9):3345–59. doi:10.1016/j.enpol.2008.04.022.
   Web of Science ®Google Scholar
• Deng, T., W. Yan, S. Nojavan, and K. Jermsittiparsert. 2020. Risk evaluation and retail electricity pricing using downside risk constraints method. Energy 192:116672. doi:10.1016/j.energy.2019.116672.
   Web of Science ®Google Scholar
• Doege, J., P. Schiltknecht, and H. J. Lüthi. 2006. Risk management of power portfolios and valuation of flexibility. Or Spectrum 28 (2):267–87. doi:10.1007/s00291-005-0005-4.
   Web of Science ®Google Scholar
• Dong, C., C. T. Ng, and T. C. E. Cheng. 2017. Electricity time‐of‐use tariff with stochastic demand. Production and Operations Management 26 (1):64–79. doi:10.1111/poms.12610.
   Web of Science ®Google Scholar
• Downward, A., D. Young, and G. Zakeri. 2016. Electricity retail contracting under risk-aversion. European Journal of Operational Research 251 (3):846–59. doi:10.1016/j.ejor.2015.11.040.
   Web of Science ®Google Scholar
• Ersoz, S., T. C. Akinci, H. S. Nogay, and G. Dogan. 2013. Determination of wind energy potential in Kirklareli-Turkey. International Journal of Green Energy 10 (1):103–16. doi:10.1080/15435075.2011.641702.
   Web of Science ®Google Scholar
• Feehan, J. P. 2018. The long-run price elasticity of residential demand for electricity: Results from a natural experiment. Utilities Policy 51:12–17. doi:10.1016/j.jup.2018.02.003.
   Web of Science ®Google Scholar
• Gao, R., H. Guo, R. Zhang, T. Mao, Q. Xu, B. Zhou, and P. Yang. 2019. A two-stage dispatch mechanism for virtual power plant utilizing the CVaR theory in the electricity spot market. Energies 12 (17):3402. doi:10.3390/en12173402.
   Web of Science ®Google Scholar
• Groeneveld, R. A. 2020. An introduction to probability and statistics using BASIC. CRC Press.
   Google Scholar
• Guo, H., Q. Chen, Q. Xia, and C. Kang. 2019. Electricity wholesale market equilibrium analysis integrating individual risk-averse features of generation companies. Applied Energy 252:113443. doi:10.1016/j.apenergy.2019.113443.
   Web of Science ®Google Scholar
• Hatami, A., H. Seifi, and M. K. Sheikh-El-Eslami. 2011. A stochastic-based decision-making framework for an electricity retailer: Time-of-use pricing and electricity portfolio optimization. IEEE Transactions on Power Systems 26 (4):1808–16. doi:10.1109/TPWRS.2010.2095431.
   Web of Science ®Google Scholar
• Hobbs, B. E. 2001. Linear complementarity models of Nash-Cournot competition in bilateral and POOLCO power markets. IEEE Transactions on Power Systems 16 (2):194–202. doi:10.1109/59.918286.
   Web of Science ®Google Scholar
• Hogan, W. W. 1997. A market power model with strategic interaction in electricity networks. The Energy Journal, 18 (4), 107–41.
   Web of Science ®Google Scholar
• Huang, X., X. Wang, L. Chen, and C. Ye. 2020. Optimal portfolio strategies of purchasing electricity for electricity company based on distributional robust CVaR. E&ES 467 (1):012200.
   Google Scholar
• Jabr, R. A. 2005. Robust self-scheduling under price uncertainty using conditional value-at-risk. IEEE Transactions on Power Systems 20 (4):1852–58. doi:10.1109/TPWRS.2005.856952.
   Web of Science ®Google Scholar
• Jiang, Y., K. Zhou, X. Lu, and S. Yang. 2020. Electricity trading pricing among prosumers with game theory-based model in energy blockchain environment. Applied Energy 271:115239. doi:10.1016/j.apenergy.2020.115239.
   Web of Science ®Google Scholar
• Jun, D., W. Pei, P. Ainiwaer, and N. Shilin. 2019. Research on time-of-use electricity price model based on hierarchical clustering-price elasticity theory. American Journal of Electrical Power and Energy Systems 8 (4):95–103. doi:10.11648/j.epes.20190804.12.
   Google Scholar
• Keivanpour, S., A. Ramudhin, D. Ait Kadi, and S. Kimiagari. 2019. Global offshore wind energy deployment: A geo-clustering approach. International Journal of Green Energy 16 (13):1054–60. doi:10.1080/15435075.2019.1653876.
   Web of Science ®Google Scholar
• Khalid, A., N. Javaid, A. Mateen, M. Ilahi, T. Saba, and A. Rehman. 2019. Enhanced time-of-use electricity price rate using game theory. Electronics 8 (1):48. doi:10.3390/electronics8010048.
   Web of Science ®Google Scholar
• Kong, L., Z. Li, L. Liang, and J. Xie. 2017. RES-E capacity investment under uncertain renewable energy supply and volatile electricity spot price. Industrial Management & Data Systems 117 (6):1145–65. doi:10.1108/IMDS-06-2016-0213.
   Web of Science ®Google Scholar
• Krishnamurthy, C. K. B., and B. Kriström. 2015. A cross-country analysis of residential electricity demand in 11 OECD-countries. Resource and Energy Economics 39:68–88. doi:10.1016/j.reseneeco.2014.12.002.
   Web of Science ®Google Scholar
• Labandeira, X., J. M. Labeaga, and X. López-Otero. 2017. A meta-analysis on the price elasticity of energy demand. Energy Policy 102:549–68. doi:10.1016/j.enpol.2017.01.002.
   Web of Science ®Google Scholar
• Liang, L., J. Xie, L. Liu, and Y. Xia. 2017. Revenue sharing contract coordination of wind turbine order policy and aftermarket service based on joint effort. Industrial Management & Data Systems 117 (2):320–45. doi:10.1108/IMDS-03-2016-0088.
   Web of Science ®Google Scholar
• Lin, Y. H., and M. S. Tsai. 2015. An advanced home energy management system facilitated by nonintrusive load monitoring with automated multiobjective power scheduling. IEEE Transactions on Smart Grid 6 (4):1839–51. doi:10.1109/TSG.2015.2388492.
   Web of Science ®Google Scholar
• Liu, H., Z. Zhang, Z. M. Chen, and D. Dou. 2019. The impact of China’s electricity price deregulation on coal and power industries: Two-stage game modeling. Energy Policy 134:110957. doi:10.1016/j.enpol.2019.110957.
   Web of Science ®Google Scholar
• Maddouri, M., H. Elkhorchani, and K. Grayaa. 2020. Game theory and hybrid genetic algorithm for energy management and real-time pricing in smart grid: The Tunisian case. International Journal of Green Energy, 17 (12):816–826.
   Web of Science ®Google Scholar
• Market Report. 2018. https://www.misoenergy.org/markets-and-operations/market-reports/#nt=%2FMarketReportType%3ASummary&t=10&p=0&s=MarketReportPublished⋅desc.
   Google Scholar
• Matar, W. 2018. Households’ response to changes in electricity pricing schemes: Bridging microeconomic and engineering principles. Energy Economics 75:300–08. doi:10.1016/j.eneco.2018.08.028.
   Web of Science ®Google Scholar
• Mendenhall, W., R. J. Beaver, and B. M. Beaver. 2012. Introduction to probability and statistics. Cengage Learning.
   Google Scholar
• Miller, M., and A. Alberini. 2016. Sensitivity of price elasticity of demand to aggregation, unobserved heterogeneity, price trends, and price endogeneity: Evidence from US data. Energy Policy 97:235–49. doi:10.1016/j.enpol.2016.07.031.
   Web of Science ®Google Scholar
• Mohsenian-Rad, A. H., and A. Leon-Garcia. 2010. Optimal residential load control with price prediction in real-time electricity pricing environments. IEEE Transactions on Smart Grid 1 (2):120–33. doi:10.1109/TSG.2010.2055903.
   Web of Science ®Google Scholar
• Nekouei, E., T. Alpcan, and D. Chattopadhyay. 2014. Game-theoretic frameworks for demand response in electricity markets. IEEE Transactions on Smart Grid 6 (2):748–58. doi:10.1109/TSG.2014.2367494.
   Web of Science ®Google Scholar
• Noori, M., M. Kucukvar, and O. Tatari. 2015. Economic input–output based sustainability analysis of onshore and offshore wind energy systems. International Journal of Green Energy 12 (9):939–48. doi:10.1080/15435075.2014.890103.
   Web of Science ®Google Scholar
• Ohler, A., H. Mohammadi, and D. G. Loomis. 2020. Electricity restructuring and the relationship between fuel costs and electricity prices for industrial and residential customers. Energy Policy 142:111559. doi:10.1016/j.enpol.2020.111559.
   Web of Science ®Google Scholar
• Onea, F., and E. Rusu. 2014. An evaluation of the wind energy in the North-West of the Black Sea. International Journal of Green Energy 11 (5):465–87. doi:10.1080/15435075.2013.773513.
   Web of Science ®Google Scholar
• Philpott, A., M. Ferris, and R. Wets. 2016. Equilibrium, uncertainty and risk in hydro-thermal electricity systems. Mathematical Programming 157 (2):483–513. doi:10.1007/s10107-015-0972-4.
   Web of Science ®Google Scholar
• Pishro-Nik, H. 2016. Introduction to probability, statistics, and random processes.
   Google Scholar
• Qiu, Y., G. Colson, and M. E. Wetzstein. 2017. Risk preference and adverse selection for participation in time-of-use electricity pricing programs. Resource and Energy Economics 47:126–42. doi:10.1016/j.reseneeco.2016.12.003.
   Web of Science ®Google Scholar
• Ralph, D., and Y. Smeers. 2011. Pricing risk under risk measures: An introduction to stochastic-endogenous equilibria. Available at SSRN 1903897.
   Google Scholar
• Ren, H., A. Zhang, K. Li, F. Wang, Y. Li, M. Shafie-khah, and J. P. Catalão (2018, September). Purchase’Portfolio optimization of power supply company with distributed PV considering EVs.In 2018 IEEE Industry Applications Society Annual Meeting (IAS), 1–9. IEEE.
   Google Scholar
• Rockafellar, R. T., and S. Uryasev. 2002. Conditional value-at-risk for general loss distributions. Journal of Banking & Finance 26 (7):1443–71. doi:10.1016/S0378-4266(02)00271-6.
   Web of Science ®Google Scholar
• Romero-Jordán, D., P. Del Río, and C. Peñasco. 2016. Erratum to “An analysis of the welfare and distributive implications of factors influencing household electricity consumption”[Energy Policy 88 (2016) 361–370]. Energy Policy 100 (95):61. doi:10.1016/j.enpol.2016.04.021.
   Google Scholar
• Ros, A. J. 2017. An econometric assessment of electricity demand in the United States using utility-specific panel data and the impact of retail competition on prices. The Energy Journal 38 (4). doi:10.5547/01956574.38.4.aros.
   Web of Science ®Google Scholar
• Schreiber, M., M. E. Wainstein, P. Hochloff, and R. Dargaville. 2015. Flexible electricity tariffs: Power and energy price signals designed for a smarter grid. Energy 93:2568–81. doi:10.1016/j.energy.2015.10.067.
   Web of Science ®Google Scholar
• Shapiro, A., D. Dentcheva, and A. Ruszczyński. 2014. Lectures on stochastic programming: Modeling and theory. Society for Industrial and Applied Mathematics.
   PubMedGoogle Scholar
• Siriruk, P., and J. Valenzuela. 2010. Cournot equilibrium considering unit outages and fuel cost uncertainty. IEEE Transactions on Power Systems 26 (2):747–54. doi:10.1109/TPWRS.2010.2058818.
   Web of Science ®Google Scholar
• Smeers, Y., and J. Y. Wei 1997. Spatially oligopolistic model with opportunity cost pricing for transmission capacity reservations: A variational inequality approach. Université Catholique de Louvain. Center for Operations Research and Econometrics [CORE].
   Google Scholar
• Srinivasan, D., S. Rajgarhia, B. M. Radhakrishnan, A. Sharma, and H. P. Khincha. 2017. Game-Theory based dynamic pricing strategies for demand side management in smart grids. Energy 126:132–43. doi:10.1016/j.energy.2016.11.142.
   Web of Science ®Google Scholar
• Sun, Y., and Y. Yu. 2017. Revisiting the residential electricity demand in the United States: A dynamic partial adjustment modelling approach. The Social Science Journal 54 (3):295–304. doi:10.1016/j.soscij.2017.02.004.
   Web of Science ®Google Scholar
• Tang, R., S. Wang, and H. Li. 2019. Game theory based interactive demand side management responding to dynamic pricing in price-based demand response of smart grids. Applied Energy 250:118–30. doi:10.1016/j.apenergy.2019.04.177.
   Web of Science ®Google Scholar
• Tiwari, A. K., and A. N. Menegaki. 2019. A time varying approach on the price elasticity of electricity in India during 1975–2013. Energy 183:385–97. doi:10.1016/j.energy.2019.06.049.
   Web of Science ®Google Scholar
• Wiser, R., and M. Bolinger. 2015 Wind technologies market report. Tech. rep., U.S. Department of Energy. Accessed May 2018. https://energy.gov/sites/prod/files/2016/08/f33/2015-Wind-Technologies-Market-Report-08162016.pdf.
   Google Scholar
• Xiao, P., W. Hu, X. Xu, W. Liu, Q. Huang, and Z. Chen. 2020. Optimal operation of a wind-electrolytic hydrogen storage system in the electricity/hydrogen markets. International Journal of Hydrogen Energy. 45 (46): 24412–23. World Wind Energy Association. http://wwindea.org/blog/2018/02/12/2017-statistics/.
   Web of Science ®Google Scholar
• Xie, J., Y. Xia, L. Liang, W. Zhang, and M. Shi. 2018. Pricing strategy for renewable energy source electricity in the competitive hybrid electricity market. Industrial Management & Data Systems 118 (5):1071–93. doi:10.1108/IMDS-08-2017-0341.
   Web of Science ®Google Scholar
• Xu, Z., Z. Hu, Y. Song, and J. Wang. 2015. Risk-averse optimal bidding strategy for demand-side resource aggregators in day-ahead electricity markets under uncertainty. IEEE Transactions on Smart Grid 8 (1):96–105. doi:10.1109/TSG.2015.2477101.
   Web of Science ®Google Scholar
• Yang, L., C. Dong, C. J. Wan, and C. T. Ng. 2013. Electricity time-of-use tariff with consumer behavior consideration. International Journal of Production Economics 146 (2):402–10. doi:10.1016/j.ijpe.2013.03.006.
   Web of Science ®Google Scholar
• Yang, P., G. Tang, and A. Nehorai. 2012. A game-theoretic approach for optimal time-of-use electricity pricing. IEEE Transactions on Power Systems 28 (2):884–92. doi:10.1109/TPWRS.2012.2207134.
   Web of Science ®Google Scholar
• Yau, S., R. H. Kwon, J. S. Rogers, and D. Wu. 2011. Financial and operational decisions in the electricity sector: Contract portfolio optimization with the conditional value-at-risk criterion. International Journal of Production Economics 134 (1):67–77. doi:10.1016/j.ijpe.2010.10.007.
   Web of Science ®Google Scholar
• Yousef, A. M., and A. M. Kassem. 2012. Stand alone wind energy conversion system based on functional predictive control. International Journal of Green Energy. (just-accepted).
   Google Scholar
• Zhou, Y., A. Scheller‐Wolf, N. Secomandi, and S. Smith. 2019. Managing wind‐based electricity generation in the presence of storage and transmission capacity. Production and Operations Management 28 (4):970–89. doi:10.1111/poms.12946.
   Web of Science ®Google Scholar
• Zhu, X., L. Li, K. Zhou, X. Zhang, and S. Yang. 2018. A meta-analysis on the price elasticity and income elasticity of residential electricity demand. Journal of Cleaner Production 201:169–77. doi:10.1016/j.jclepro.2018.08.027.
   Web of Science ®Google Scholar
• Zhu, Z., L. Kong, J. Xie, J. Li, and B. Cao. 2019. Contract coordination optimization of a multi-power supplier-single dominant grid supply chain in hybrid electricity market. Industrial Management & Data Systems. doi:10.1108/IMDS-08-2018-0340.
   Web of Science ®Google Scholar
• Zugno, M., J. M. Morales, P. Pinson, and H. Madsen. 2013. A bilevel model for electricity retailers’ participation in a demand response market environment. Energy Economics 36:182–97. doi:10.1016/j.eneco.2012.12.010.
   Web of Science ®Google Scholar