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An Integration Mechanism between Demand and Supply Side Management of Electricity Markets

Author

Listed:
  • Zixu Liu

    (School of Computer Science, University of Manchester, Manchester M13 9PL, UK)

  • Xiaojun Zeng

    (School of Computer Science, University of Manchester, Manchester M13 9PL, UK)

  • Fanlin Meng

    (BRE Trust Center for Sustainable Engineering, Cardiff University, Cardiff CF24 3AA, UK)

Abstract

One of the main challenges in the emerging smart grid is to jointly consider the demand and supply, which is also reflected in the wholesale market (supply side) and the retail market (demand side). When integrating the demand and supply side into one framework, the mechanism for determining the market clearing price has been changed. This is due to the demand variations in the demand side in response to the market clearing price and the change of generation costs in the supply side from the demand variation. In order to find the best balance between the supply and demand under the demand response management scheme, this paper proposes a new integrated supply and demand coordination mechanism for the electricity market and smart pricing methods for generator and retailers. Another important contribution of this paper is to develop an efficient algorithm to find the match equilibrium between the demand and supply sides in the new proposed mechanism. Experimental results demonstrate that the new mechanism can effectively handle unpredictable demand under dynamic retail pricing and support the ISO to dispatch the generation economically. It can also help in achieving the goals of dynamic pricing such as maximizing the profits for retailers.

Suggested Citation

  • Zixu Liu & Xiaojun Zeng & Fanlin Meng, 2018. "An Integration Mechanism between Demand and Supply Side Management of Electricity Markets," Energies, MDPI, vol. 11(12), pages 1-23, November.
    • Handle: RePEc:gam:jeners:v:11:y:2018:i:12:p:3314-:d:185974
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    References listed on IDEAS

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    1. Yu, Mengmeng & Hong, Seung Ho, 2017. "Incentive-based demand response considering hierarchical electricity market: A Stackelberg game approach," Applied Energy, Elsevier, vol. 203(C), pages 267-279.
    2. Zheng Ma & Joy Dalmacio Billanes & Bo Nørregaard Jørgensen, 2017. "Aggregation Potentials for Buildings—Business Models of Demand Response and Virtual Power Plants," Energies, MDPI, vol. 10(10), pages 1-19, October.
    3. Blumsack, Seth & Fernandez, Alisha, 2012. "Ready or not, here comes the smart grid!," Energy, Elsevier, vol. 37(1), pages 61-68.
    4. Leehter Yao & Wei Hong Lim & Sew Sun Tiang & Teng Hwang Tan & Chin Hong Wong & Jia Yew Pang, 2018. "Demand Bidding Optimization for an Aggregator with a Genetic Algorithm," Energies, MDPI, vol. 11(10), pages 1-22, September.
    5. Walawalkar, Rahul & Fernands, Stephen & Thakur, Netra & Chevva, Konda Reddy, 2010. "Evolution and current status of demand response (DR) in electricity markets: Insights from PJM and NYISO," Energy, Elsevier, vol. 35(4), pages 1553-1560.
    6. Faruqui, A. & Hajos, A. & Hledik, R.M. & Newell, S.A., 2010. "Fostering economic demand response in the Midwest ISO," Energy, Elsevier, vol. 35(4), pages 1544-1552.
    7. Faria, P. & Vale, Z., 2011. "Demand response in electrical energy supply: An optimal real time pricing approach," Energy, Elsevier, vol. 36(8), pages 5374-5384.
    8. Aalami, H.A. & Moghaddam, M. Parsa & Yousefi, G.R., 2010. "Demand response modeling considering Interruptible/Curtailable loads and capacity market programs," Applied Energy, Elsevier, vol. 87(1), pages 243-250, January.
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    Cited by:

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