Constraint relaxation-based day-ahead market mechanism design to promote the renewable energy accommodation

被引:9
作者
Ma, Ziming [1 ,2 ]
Zhong, Haiwang [1 ,2 ]
Xia, Qing [1 ,2 ]
Kang, Chongqing [1 ,2 ]
Jin, Liming [3 ]
机构
[1] Tsinghua Univ, Dept Elect Engn, Beijing 100084, Peoples R China
[2] State Key Lab Control & Simulat Power Syst & Gene, Beijing 100084, Peoples R China
[3] State Grid Chongqing Elect Power Co, Chongqing 400015, Peoples R China
基金
中国国家自然科学基金;
关键词
Constraint relaxation; Day-ahead market; Renewable energy curtailment; Deep ramp; POWER-SYSTEM; ELECTRICITY; FLEXIBILITY; WIND;
D O I
10.1016/j.energy.2020.117204
中图分类号
O414.1 [热力学];
学科分类号
摘要
With the high penetration of renewable energy, such as hydro, wind and solar, curtailment is a serious issue in some regions, such as China. The key issue is lacking of flexibility, primarily due to the limited dispatchable range of thermal generators. There is an urgent need to expand the dispatchable range to promote the renewable energy accommodation. To this end, a new constraint relaxation-based day-ahead market mechanism is proposed. The trade-off between thermal units and renewable energy is achieved. Deep ramp offers and curtailment relief bids are proposed to express market participants' willingness for deep ramp and relieving curtailment. Thermal units submit their deep ramp offers to get compensation while renewable energy submits its curtailment relief bid to cut the energy price. Renewable energy is curtailed in the order of its curtailment relief bid. The model of constraint relaxation-based day-ahead market clearing procedure and model of equal curtailment without constraint relaxation are formulated, respectively. An accelerating technique combining Relaxation-based Neighborhood Search (RBNS) and Improved Relaxation Inducement (IRI) is introduced. A case study based on IEEE 118-bus system validates the effectiveness of the proposed mechanism and models and shows that the introduced accelerating technique can significantly reduce the computation time. (C) 2020 Elsevier Ltd. All rights reserved.
引用
收藏
页数:13
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