Two-stage Dispatch of Microgrid Based on CVaR Theory Under Electricity Spot Market

被引：0
|
作者：
Guo H. [1 ,2 ]
Gao R. [1 ,2 ]
Yang P. [2 ,3 ]
机构：
[1] School of Electric Power, South China University of Technology, Guangzhou, 510640, Guangdong Province
[2] Guangdong Key Laboratory of Clean Energy Technology, South China University of Technology, Guangzhou, 510640, Guangdong Province
[3] National-Local Joint Engineering Laboratory for Wind Power Control and Integration Technology, South China University of Technology, Guangzhou, 510640, Guangdong Province
来源：
Dianwang Jishu/Power System Technology | 2019年 / 43卷 / 08期
基金：
中国国家自然科学基金;
关键词：
CVaR; Microgrid; Spot market;
D O I：
10.13335/j.1000-3673.pst.2019.0252
中图分类号：
学科分类号：
摘要：
Playing the role of electricity retailer in electricity market, grid-connected microgrid needs to face the risk of price fluctuations under spot market environment, and its operation dispatch thus becomes more complicated. In this paper, based on the trading mechanism of spot market in Guangdong Province, as a risk measurement tool, conditional value at risk (CVaR) is used to construct a two-stage risk dispatch model of microgrid, as it can be an electricity retailer to participate in day-ahead and real-time market. On this basis, the influence of day-ahead and real-time risk preference coefficients on dispatch results is discussed. The influence of deviation spread earnings transfer settlement mechanism on microgrid revenue, and the influence of energy storage participation on the results are analyzed. Price uncertainty is reflected by simulating the data of the day-ahead and real-time electricity price scenarios. Case studies confirm correctness of the proposed model, providing theoretical basis for microgrid electricity retailers to schedule internal resources in spot market. © 2019, Power System Technology Press. All right reserved.
引用
收藏
页码：2665 / 2673
页数：8
相关论文
共 22 条
  • [1] Yang X., Su J., Lu Z., Et al., Overview on micro-grid technology, Proceedings of the CSEE, 34, 1, pp. 57-70, (2014)
  • [2] Bai K., Gu J., Peng H., Et al., Optimal allocation for multi-energy complementary microgrid based on scenario generation of wind power and photovoltaic output, Automation of Electric Power Systems, 42, 15, (2018)
  • [3] Yang L., Li H., Yu X., Et al., Multi-objective day-ahead optimal scheduling of isolated microgrid considering flexibility, Power System Technology, 42, 5, pp. 1432-1440, (2018)
  • [4] Wang S., Wu Z., Zhuang J., Optimal dispatching model of CCHP type regional multi-microgrids considering interactive power exchange among microgrids and output coordination among micro-sources, Proceedings of the CSEE, 37, 24, (2017)
  • [5] Zhang Z., Wang J., An active and reactive power joint real-time dispatch approach for microgrid using model predictive control, Proceedings of the CSEE, 36, 24, (2016)
  • [6] Ren J., Qu W., Dynamic economic dispatch based on chance-constrained programming for islanded microgrid, Electric Power Automation Equipment, 36, 3, pp. 73-78, (2016)
  • [7] Wang L., Li Q., Ding R., Et al., Robust multi-objective optimization scheduling of micro-grids with renewable energy, Transactions of China Electrotechnical Society, 32, 5, pp. 184-192, (2017)
  • [8] Lu Z., Tang Z., Microgrid robust dispatch with uncertainty budget adjustment strategy based on grey entropy relation optimization, Electric Power Automation Equipment, 37, 9, pp. 38-45, (2017)
  • [9] Pourmousavi S.A., Nehrir M.H., Sharma R.K., Multi-timescale power management for islanded microgrids including storage and demand response, IEEE Transactions on Smart Grid, 6, 3, pp. 1185-1195, (2015)
  • [10] Dou X., Xu M., Dong J., Et al., Multi-time scale based improved energy management model for microgrid, Automation of Electric Power Systems, 40, 9, pp. 48-55, (2016)
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