考虑可再生能源配额的区域电力市场竞价行为推演

doi:10.11930/j.issn.1004-9649.202505071

摘要/Abstract

摘要：

随着可再生能源渗透率持续提升及可调机组容量增加，其参与电力市场竞价交易正在逐步成为重要趋势。为深入分析区域电力市场中新能源发电商（renewable energy generators，REG）与传统发电商（conventional power generators，CPG）之间的竞争博弈行为，提出了一种多领导者-单跟随者（multiple leaders-one follower，MLOF）纳什-斯塔克尔伯格（nash-stackelberg，NS）博弈模型。上层基于绿证交易机制构建了新能源与传统发电商联合参与电能量及辅助服务市场的投标报价策略模型，下层为电力-备用-平衡联合市场出清模型，确定市场出清结果及价格信号。在此基础上，基于高效分布式算法对复杂博弈模型进行求解，以提升计算效率并保护各方数据隐私。仿真分析表明，所提模型能够有效提升新能源发电商的市场收益，增强其在多主体竞争中的博弈能力。

关键词: 新能源发电商, 联合市场, 分布式求解

Abstract:

With the continuous increase of the renewable energy penetration and the increment of the adjustable units capacity, it has been an important trend for them to participate the electricity market auction transaction. In order to deeply analyze the competitive game behavior between renewable energy generators (REG) and conventional power generators (CPG) in the regional electricity market, multiple leaders-one follower (MLOF) Nash-Stackelberg (NS) game model is proposed. The upper layer constructs a bidding strategy model for the joint participation of renewable and conventional power generators in the electricity energy and ancillary service market based on the green certificate trading mechanism. The lower layer is a joint market clearing model of electricity-reserve-, which determines the market clearing results and price signals. On this basis, an efficient distributed algorithm is carried out to solve the complex game model. So as to improve the computational and protect the data privacy of all parties. Simulation analysis shows that the proposed model can effectively improve the market returns of renewable energy generators, and enhance their game ability in multi-subject.

Key words: new energy power generation, joint market, distributed solution

许喆, 陈晓东, 贾旭东, 陈紫颖. 考虑可再生能源配额的区域电力市场竞价行为推演[J]. 中国电力, 2025, 58(10): 225-234.

XU Zhe, CHEN Xiaodong, JIA Xudong, CHEN Ziying. Bidding Behavior Evolution in Regional Electricity Markets Considering Renewable Energy Quotas[J]. Electric Power, 2025, 58(10): 225-234.

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链接本文: https://www.electricpower.com.cn/CN/10.11930/j.issn.1004-9649.202505071

https://www.electricpower.com.cn/CN/Y2025/V58/I10/225

图/表 11

图 1 GCT机制下现货市场中发电企业的交易框架

Fig.1 Trading framework for generation companies in spot markets under GCT mechanism

图 2 改进的IEEE 39系统拓扑

Fig.2 Improved IEEE 39-bus system topology diagram

表 1 发电机组参数

Table 1 Generator parameters

发电公司	编号	节点	最大出力/ MW	最大备用/ MW	发电成本/ (元·(MW·h)^–1)	备用成本/ (元·(MW·h)^–1)
C1	G1	30	500	50	35	24
	G2	37	600	60	40	30
	G3	39	400	40	38	28
C2	G4	33	300	30	45	32
	G5	34	350	35	30	20
	G6	35	450	45	50	26
C3	W1	31	300	—	18	18
C3	W2	32	300	—	15	—
C4	S1	36	200	—	16	16
C4	S2	38	200	—	12	—

图 3 风电、光伏和负荷功率

Fig.3 Wind power, PV power, and load power

图 4 4种方案的电力市场出清价格

Fig.4 Electricity market clearing prices of four schemes

图 5 电力市场的供需情况

Fig.5 Supply-demand situation in electricity market

图 6 备用市场的供需情况

Fig.6 Supply-demand balance in reserve market

表 2 C3在4种方案下的收益

Table 2 C3 revenues under four schemes 单位：元

方案	电力市场收益	备用市场收益	平衡市场收益	绿证收益	总收益
1	384274	145203	19546	80308	629331
2	364197	145203	16485	0	525885
3	196726	0	0	84985	281711
4	395026	0	0	84985	480011

图 7 新能源储能配置比例对C3公司收益的影响

Fig.7 The impact of renewable energy storage configuration ratio on the revenue of Company C3

图 8 两种算法的迭代求解

Fig.8 Iterative solution of two algorithms

表 3 风电公司在4种方案下的收益

Table 3 Wind power company revenues under four schemes 单位：元

方案	电力市场收益	备用市场收益	平衡市场收益	绿证收益	总收益
1	2456151	1054684	145487	535391	4191713
2	2437980	1015462	156485	0	3609927
3	1311506	0	0	566568	1878074
4	2556120	0	0	566568	3122688

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