1.三峡大学 梯级水电站运行与控制湖北省重点实验室,湖北 宜昌 443002
2.三峡大学 电气与新能源学院,湖北 宜昌 443002
3.国网湖南省电力有限公司娄底供电公司,湖南 娄底 417000
钟 浩(1983 —),男,副教授,博士,主要研究方向为电力系统运行分析与控制、梯级水电站运行与控制(E-mail:zhonghao022@163.com);
杜 涛(2001 —),男,硕士研究生,通信作者,主要研究方向为电力系统运行与控制(E-mail:1601426509@qq.com)。
收稿:2025-02-19,
修回:2025-10-11,
网络首发:2025-11-20,
纸质出版:2026-05-10
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钟浩,杜涛,邹贤求等.电力市场环境下多流域型虚拟电厂抽水蓄能机组容量优化配置[J].电力自动化设备,2026,46(05):44-52.
Zhong Hao,Du Tao,Zou Xianqiu,et al.Optimal capacity allocation of pumped storage units in multiple river basin virtual power plant under electricity market environment[J].Electric Power Automation Equipment,2026,46(05):44-52.
钟浩,杜涛,邹贤求等.电力市场环境下多流域型虚拟电厂抽水蓄能机组容量优化配置[J].电力自动化设备,2026,46(05):44-52. DOI: 10.16081/j.epae.202511016.
Zhong Hao,Du Tao,Zou Xianqiu,et al.Optimal capacity allocation of pumped storage units in multiple river basin virtual power plant under electricity market environment[J].Electric Power Automation Equipment,2026,46(05):44-52. DOI: 10.16081/j.epae.202511016.
针对偏远地区小水电富集流域灵活性资源不足以及风、光、水电站因容量小而难以参与电力市场的问题,提出一种电力市场环境下多流域型虚拟电厂(RBVPP)抽水蓄能机组容量优化配置方法。通过改造流域库容式小水电,构建混合式抽水蓄能电站,联合风电、光伏及小水电组建RBVPP。建立基于合作博弈的双层优化配置模型。上层以各RBVPP年净利润加权和最大为目标,优化抽水蓄能装机容量。下层模拟RBVPP联盟参与日前电力市场:在第一阶段,考虑可再生能源不确定性,建立以联盟收益最大和电网购电成本最小为目标的竞标与出清模型;在第二阶段,结合流域水力耦合特性,以水量贡献度为权重,利用改进的Shapley值法进行收益分配,并将结果反馈至上层进一步优化容量配置。采用粒子群优化算法嵌套CPLEX对模型进行求解。算例结果表明,所提方法有效提升了RBVPP的市场灵活性与经济性,实现了收益均衡与资源优化配置。
Aiming at the problems that the flexible resource of small hydropower rich basin in remote area is insufficient and the wind,solar and hydropower stations are difficult to participate in electricity market because of small capacity,an optimal capacity allocation method of pumped storage units in multiple river basin virtual power plant(RBVPP) under electricity market environment is proposed. A hybrid pumped sto-rage power station is built through the transformation of small hydropower with storage capacity in the river basin,and RBVPP is formed by combining wind power,photovoltaic and small hydropower. A two-layer optimal allocation model based on cooperative game is established. The upper layer optimizes the installed capacity of pumped storage with the goal of maximizing the weighted sum of annual net profits of each RBVPP. The lower layer simulates RBVPP alliance participating in the day-ahead electricity market. In the first stage,the uncertainty of renewable energy is considered,and a bidding and clearing model is established with the goal of maximizing the alliance income and minimizing the power purchase cost of power grid. In the second stage,combined with the hydraulic coupling characteristic of river basin,the improved Shapley value method is used to allocate the income with the water contribution as the weight,and the results are fed back to the upper layer to further optimize the capacity allocation. The particle swarm optimization algorithm nested with CPLEX is used to solve the model. The example results show that the proposed method effectively improves the market flexibility and economy of RBVPP,and realizes the income balance and opti-mal resource allocation.
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