Document Type : Research Paper

Authors

1 Professor, Economics, STM College, University of Saskatchewan, Saskatchewan, Canada

2 Assistant Professor, Economics, Allameh Tabataba’i University, Tehran, Iran

3 Research Associate, Electrical Engineering, Power Research Institute, Tehran, Iran.

4 Associate Professor, Math-Stat-Computer Science, Allameh Tabataba’i University, Tehran, Iran.

5 Assistant Professor, Electrical Engineering, Lorestan University, Lorestan, Iran.

Abstract

We develop an agent-based model to study the effects of the electricity market reform on the electricity prices, the power plants’ technology mix as well as their capacity utilization and profits. The reform’s main objective is to open the electricity market to more competition and increase the efficiency. The new wholesale electricity market will operate based on the one-day-ahead auctions organized by the independent system operator. We set up two scenarios in which the market clearing mechanism changes from the pay-as-bid to market clearing price (MCP) and the fuel subsidy to power plants will be removed. The effects of the two scenarios will be analyzed on the market prices, plant revenues, market shares, and power generating units’ capacities. The simulation results show that the electricity prices are higher during the peak load mainly due to the entry of the higher cost plants during those periods. Electricity prices are lower under the MCP scenario and higher under the fuel subsidy removal scenario, leading to lower and higher revenues for the power plants, respectively. The results also indicate that the market shares and the capacity utilization of the more efficient plants will increase under both scenarios.

Keywords

اصغری اسکویی، محمدرضا، فلاحی، فرهاد، دوستی‌زاده، میثم و مشیری، سعید (۱۳۹۷). کاربرد یادگیری تقویتی در یک مدل سازی عامل محور برای بازار عمده‌فروشی برق ایران. فصلنامه پژوهش‌‌های اقتصاد انرژی ایران. شماره 25. صفحه 40-1.
ترازنامه انرژی (سال‌‌های مختلف). وزارت نیرو.
فلاحی، فرهاد (1392). بهینه‌سازی برنامه‌ریزی آرایش تولید واحدها براساس اولویت قیمت‌‌های پذیرفته شده بازار با لحاظ محدودیت‌‌های بارگذاری شبکه انتقال با روش‌‌های جدید. گروه پژوهشی اقتصاد و مدیریت برق. پژوهشگاه نیرو.
مروت،‌ حبیب (۱۳۹۶). مروری بر اقتصاد محاسباتی مبتنی بر عامل.‌ سیاست‌گذاری پیشرفت اقتصادی.‌ شماره ۱۵.‌صفحه ۸۱- ۱۲۵.
مزدآور، سیدعلیرضا، قرا‌گوزلو، حبیب و اکبری فرود، اصغر (۱۳۹۳). تاثیر مدلسازی پرداخت هزینه روشن و خاموش شدن واحدهای نیروگاهی در استراتژی قیمت دهی آنها در بازار برق ایران. کنفرانس بین المللی برق. پژوهشگاه نیرو.
مشیری، سعید، مروت، حبیب و نصیری،‌ عباس (1397). بررسی تاثیر افزایش قیمت سوخت بر قیمت برق با استفاده از مدل‌سازی عامل بنیان بازار برق. فصلنامه مطالعات اقتصاد انرژی. سال چهاردهم. شماره 56. 1-34.
Bower, J., & Bunn, D. (2001). Experimental analysis of the efficiency of uniform-price versus discriminatory auctions in the England and Wales electricity market. Journal Of Economic Dynamics And Control, 25
(3-4), 561-592.
Bower, J., Bunn, D. W., & Wattendrup, C. (2001). A model-based analysis of strategic consolidation in the German electricity industry. Energy Policy, 29 (12), 987-1005.
Browne, O., Poletti, S., & Young, D. (2015). How does market power affect the impact of large scale wind investment in'energy only'wholesale electricity markets?. Energy Policy, 87, 17-27.
Bublitz, A., Genoese, M., & Fichtner, W. (2014, May). An agent-based model of the German electricity market with short-time uncertainty factors. In 11th International Conference on the European Energy Market (EEM14) (pp. 1-5). IEEE.
Cau, T. D. H., & Anderson, E. J. (2002, July). A co-evolutionary approach to modelling the behaviour of participants in competitive electricity markets. In IEEE Power Engineering Society Summer Meeting, (Vol. 3, pp. 1534-1540). IEEE.
Epstein, J. M. (1999). Agent‐based computational models and generative social science. Complexity4(5), 41-60.
Erev, I., & Roth, A. E. (1998). Predicting how people play games: Reinforcement learning in experimental games with unique, mixed strategy equilibria. American Economic Review, 848-881.
Gallo, G. (2016, January). An integrated agent-based and production cost modeling framework for renewable energy studies. In 2016 49th Hawaii International Conference on System Sciences(HICSS) (pp. 2390-2399). IEEE.
Grigsby, L. L. (Ed.). (2001). The electric power engineering handbook (pp. 89-97). Boca Raton: CRC Press.
Kremers, E. A. (2013). Modelling and simulation of electrical energy systems through a complex systems approach using agent-based models. KIT scientific publishing.
Li, H., & Tesfatsion, L. (2009, July). The AMES wholesale power market test bed: A computational laboratory for research, teaching, and training. In 2009 IEEE Power & Energy Society General Meeting (pp. 1-8). IEEE.
Pisica, I., Axon, C. J., Hobson, P. R., Taylor, G. A., & Wallom, D. C. (2014, October). A multi-agent model for assessing electricity tariffs. In IEEE PES Innovative Smart Grid Technologies, Europe (pp. 1-6). IEEE.
Shahidehpour, M., Yamin, H., & Li, Z. (2003). Market operations in electric power systems: forecasting, scheduling, and risk management. John Wiley & Sons.
Tesfatsion, L. (2006). Agent-based computational economics: A constructive approach to economic theory. Handbook of computational economics2, 831-880.
Wallace, S. W., & Fleten, S. E. (2003). Stochastic programming models in energy. Handbooks in operations research and management science, 10, 637-677.
Weidlich, A., & Veit, D. (2008). A critical survey of agent-based wholesale electricity market models. Energy Economics, 30(4), 1728-1759.