Volume 7, Issue 1, March 2019, Page: 15-27
An Optimal CO2 Saving Dispatch Model for Wholesale Electricity Market Concerning Emissions Trade
Shijun Fu, Department of Logistic Engineering, Chongqing University of Arts and Sciences, Chongqing, China
Received: Apr. 21, 2019;       Accepted: May 29, 2019;       Published: Jun. 12, 2019
DOI: 10.11648/j.ajee.20190701.13      View  52      Downloads  114
Abstract
Deep CO2 mitigation provides a challenge to fossil fuel-fired power industry in liberalized electricity market process. To motivate generator to carry out mitigation action, this article proposed a novel dispatch model for wholesale electricity market under consideration of CO2 emission trade. It couples carbon market with electricity market and utilizes a price-quantity uncorrelated auction way to operate both CO2 allowances and power energy trade. Specifically, this CO2 saving dispatch model works as a dynamic process of, (i) electricity and environment regulators coordinately issue regulatory information; (ii) initial CO2 allowances allocation through carbon market auction; (iii) load demands allocation through wholesale market auction; and (iv) CO2 allowances submarket transaction. This article builds two stochastic mathematical programmings to explore generator’s auction decision in both carbon market and wholesale market, which provides its optimal price-quantity bid curve for CO2 allowances and power energy in each market. Through piece-wise adding up individual demand curve (supply curve) and matching with total supplied allowances (load demanded), market equilibrium is reached. Under this dispatch model, price upper-bound of bid allowances of generators is upward ordered and price lower-bound of bid electricity is downward ordered, according to their operational advantage from weak to strong. Meanwhile their bid electricity upper-bound gets respective capacity constraint or market share regulation. These features imply that the proposed model can prompt economic dispatch, improve resources allocation efficiency and bring about CO2 mitigation effect. Numerical simulations also verified the validity of this CO2 saving dispatch model.
Keywords
Wholesale Electricity Market, CO2 Emissions Trade, CO2 Saving Dispatch, Economic Dispatch, Combinatorial Auction
To cite this article
Shijun Fu, An Optimal CO2 Saving Dispatch Model for Wholesale Electricity Market Concerning Emissions Trade, American Journal of Energy Engineering. Vol. 7, No. 1, 2019, pp. 15-27. doi: 10.11648/j.ajee.20190701.13
Copyright
Copyright © 2019 Authors retain the copyright of this article.
This article is an open access article distributed under the Creative Commons Attribution License (http://creativecommons.org/licenses/by/4.0/) which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.
Reference
[1]
Hunt, Sally. (1996). Competition and choice in electricity. John Wiley and Sons Ltd, England.
[2]
State Electricity Regulatory Commission (SERC), Ministry of Finance of the People’s Republic of China, The World Bank. (2007). Report on China electricity regulatory bureau capacity improvement. China WaterPower Press, Beijing: China.
[3]
David Newbery, Michael G. Pollitt, Robert A. Ritz, Wadim Strielkowski. (2018). Market design for a high-renewables European electricity system. Renewable and Sustainable Energy Reviews 91: pp 695-707.
[4]
Iain MacGill. (2010). Electricity market design for facilitating the integration of wind energy: experience and prospects with the australian national electricity market. Energy Policy 38: pp 3180-3191.
[5]
E. Ela, M. Milligan, A. Bloom, A. Botterud, A. Townsend, T. Levin, B. A. Frew. (2016). Wholesale electricity market design with increasing levels of renewable generation: incentivizing flexibility in system operations. The Electricity Journal 29: pp 51-60.
[6]
Michael Milligan, Bethany A. Frew, Aaron Bloom, Erik Ela, Audun, Botterud, Aaron Townsend, Todd Levin. (2016). Wholesale electricity market desing with increasing levels of renewable generation: revenue sufficiency and long-term reliability. The Electricity Journal 29: pp 26-38.
[7]
Dogan Keles, Andreas Bublitz, Florian Zimmermann, Massimo Genoese, Wolf Fichtner. (2016). Analysis of design options for the electricity market: the German case. Applied Energy 183: pp 884-901.
[8]
Andreas Bublitz, Dogan Keles, Florian Zimmermann, Christoph Fraunholz, Wolf Fichtner. (2019). A survey on electricity market design: insights from theory and real-world implementations of capacity remuneration mechanisms. Energy Economics DOI: https://doi.org/10.1016/j.eneco.2019.01.030. (in press).
[9]
Donna Peng, Rahmatallah Poudineh. (2017). Electricity market design for a decarbonized future: an integrated approach. Oxford Institute for Energy Studies, Oxford, UK. ISBN 978-1-78467-094-8.
[10]
William W. Hogan. (2014). Electricity market design and efficient pricing: applications for New England and beyond. The Electricity Journal 27 (7): pp 23-49.
[11]
Florian Englmaier, Pablo Guillen, Loreto Llorente, Sander Onderstal, Rupert Sausgruber. (2009). The chopstick auction: a study of the exposure problem in multi-unit auctions. International Journal of Industrial Organization, 27 (2): pp 286-291.
[12]
Bizzat Hussain Zaidi, Dost Muhammad Saqib Bhatti, Ihsan Ullah. (2018). Combinatorial auctions for energy storage sharing amongst the households. Journal of Energy Storage 19: pp 291-301.
[13]
Bizzat Hussain Zaidi, Seung Ho Hong. (2018). Combinatorial double auctions for multiple microgrid trading. Electrical Engineering 100 (2): pp 1069-1083.
[14]
Paul Klemperer. (2002). What really matters in auction design. Journal of Economic Perspectives 16 (1): pp 169-189.
[15]
Hossein Haghighat, Hossein Seifi, Ashkan Rahimi Kian. (2008). The role of market pricing mechanism under imperfect competition. Decision Support Systems 2 (45): pp 267-277.
[16]
Oren S. (2004). When is a pay-as-bid preferable to uniform price in electricity markets. Proceeding of Power System Conference and Exposition. New York.
[17]
Fabra N. (2003). Tacit collusion in repeated auctions: uniform versus discriminatory. Journal of Industrial Economics 51 (3): pp 271-293.
[18]
Cramton P. (2004). Alternative pricing rules. Proceeding of Power System Conference and Exposition. New York, USA.
[19]
Wolfram C. (1999). Electricity markets: should the rest of the world adopt the UK reforms? Regulation 1 (22): pp 48-83.
[20]
Patrizia Beraldi,Domenico Conforti,Chefi Triki,Antonio Violi. (2004). Constrained auction clearing in the Italian electricity market. Quarterly Journal of the Belgian, French and Italian Operations Research Societies 2: pp 35-51.
[21]
Javier Contreras, Oscar Candiles, Jose Ignacio de la Fuente, Tomas Gomez. (2001). Auction design in day-ahead electricity markets. IEEE Transactions on Power Systems 16 (1): pp 409-417.
[22]
Fu, S. J. (2017). Combinatorial mitigation actions, a case study on European Union’s electricity sector. International Journal of Economy, Energy and Environment 2: pp 77–86.
[23]
Elmaghraby, Y. J. (2005). Multi-unit auctions with complementarities: Issues of efficiency in electricity auctions. European Journal of Operational Research 2: pp 430–448.
[24]
Yazhi Song, Tiansen Liu, Yin Li, Dapeng Liang. (2017). Region division of China’s carbon market based on the provincial/municipal carbon intensity. Journal of Cleaner Production 164: pp 1312–1323.
[25]
Verhaegen, K., Meeus, L., & Belmans, R. (2009). Towards an international tradable green certificate system-The challenging example of Belgium. Renewable & Sustainable Energy Reviews 13: pp 208–215.
[26]
Toczylowski, E., Zoltowska, I. (2009). A new pricing scheme for a multi-period pool-based electricity auction. European Journal of Operational Research 3: pp 1051–1062.
[27]
Parnia Samimi, Youness Teimouri, Muriati Mukhtar. (2016). A combinatorial double auction resource allocation model in cloud computing. Information Sciences 257: pp 201–216.
[28]
Gillenwater, M., Breidennich, C. (2009). Internalizing carbon costs in electricity markets: using certificates in a load-based emissions trading scheme. Energy policy 37: pp 290–299.
[29]
Dirk Briskorn, Kurt Jornsten, Philipp Zeise. (2016). A pricing scheme for combinatorial auctions based on bundle sizes. Computers & Operations Research 70: pp 9–17.
[30]
Meeus, L., Verhaegen, K., & Belmans, R. (2009). Block order restrictions in combinatorial electric energy auctions. European Journal of Operational Research 3: pp 1202–1206.
[31]
Seyedeh Aso Tafsiri, Saleh Yousefi. (2018). Combinatorial double auction–based resource allocation mechanism in cloud computing market. The Journal of Systems and Software 137: pp 322–334.
[32]
Kockar, I., Antonio, J. C., & James, R. M. (2009). Influence of the emissions trading scheme on generation scheduling. Electrical Power Energy Systems 31: pp 465–473.
[33]
Nasr Azadani, Josseinian S J, Moradzadeh B. (2010). Generation and reserve dispatch in a competitive market using constrained particle swarm optimization. International Journal of Electrical Power & Energy Systems 1 (32): pp 79-86.
Browse journals by subject