Clearing Model and Pricing Method for Capacity Market Considering Flexible Regulation Requirement

被引:0
|
作者
Qu Y. [1 ]
Xiao Y. [1 ]
Zhang C. [1 ]
Wang X. [1 ]
机构
[1] School of Electrical Engineering, Xi’an Jiaotong University, Xi’an
来源
Dianli Xitong Zidonghua/Automation of Electric Power Systems | 2024年 / 48卷 / 11期
关键词
capacity market; incentive compatibility; power system flexibility; pricing mechanism; robust optimization;
D O I
10.7500/AEPS20230206005
中图分类号
学科分类号
摘要
The increase in the proportion of renewable energy has led to a significant increase in the requirement for flexible regulation resources in the power system. However, the traditional capacity market only aims to guarantee the system adequacy during the peak load period. There may still be a shortage of power supply due to insufficient flexible regulation capacity. Therefore, this paper proposes a robust optimal clearing model and a pricing method for the capacity market that considers the flexible regulation requirement. The settlement rules for different resource types are provided. And it is verified that the proposed capacity market pricing mechanism can satisfy the properties of social efficiency, balance of payments, individual rationality, and incentive compatibility. Finally, the IEEE 118-bus system is used for the case study. The results show that the proposed capacity market mechanism can simultaneously guarantee the adequacy of the system to cope with peak load and flexible regulation requirement, reasonably describe the capacity value of flexible regulation resources, and effectively distinguish between the effective capacity contribution of different types of resources and the responsibility of causing flexible regulation requirement, which helps to guide renewable energy units to suppress their output uncertainty fluctuations and incentivize flexible resources to provide capacity to meet system flexible regulation requirement. © 2024 Automation of Electric Power Systems Press. All rights reserved.
引用
收藏
页码:64 / 76
页数:12
相关论文
共 37 条
  • [31] LIANG Junwen, LIN Shunjiang, LIU Mingbo, Et al., Distributed robust optimal dispatch in active distribution networks[J], Power System Technology, 43, 4, pp. 1336-1344, (2019)
  • [32] BATLLE C., De-rating of wind and solar resources in capacity mechanisms:a review of international experiences [J], Renewable and Sustainable Energy Reviews, 112, pp. 253-262, (2019)
  • [33] WANG J H., Electrified transportation network, Modeling and Optimization of Interdependent Energy Infrastructures, pp. 343-454, (2020)
  • [34] WANG J X, ZHONG H W,, TANG W Y,, Et al., Tri-level expansion planning for transmission networks and distributed energy resources considering transmission cost allocation[J], IEEE Transactions on Sustainable Energy, 9, 4, pp. 1844-1856, (2018)
  • [35] JAIN R., Market mechanisms for buying random wind[J], IEEE Transactions on Sustainable Energy, 6, 4, pp. 1615-1623, (2015)
  • [36] MARTINEZ-ANIDO C B,, HODGE B M., An extended IEEE 118-bus test system with high renewable penetration[J], IEEE Transactions on Power Systems, 33, 1, pp. 281-289, (2018)
  • [37] ZHENG K D,, Et al., Locational pricing of uncertainty based on robust optimization[J], CSEE Journal of Power and Energy Systems, 7, 6, pp. 1345-1356, (2020)