面向系统灵活性提升的大用户激励型需求响应优化策略

doi:10.19595/j.cnki.1000-6753.tces.240822

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摘要

针对大规模可再生能源接入电力系统所导致的灵活爬坡产品（FRP）需求激增问题,提出一种大用户激励型需求响应（DR）策略,为独立系统运营商（ISO）获取需求侧灵活性资源提供有效途径。采用用户需求管理行为模型（CDMBM）,估计能够反映DR行为差异化的用户响应成本函数;构建ISO与多个大用户之间的激励型DR主从博弈模型,以ISO为决策主体的电能与FRP联合出清优化模型作为上层模型,以大用户参与联合市场DR决策的优化模型作为下层模型,通过将主从博弈问题转换为带均衡约束的数学优化问题,求解博弈均衡解并进而得到具有差异化的用户DR单位补偿费用。通过算例分析,验证了所提策略的有效性。

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	黄大为
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	于娜
	孔令国

关键词 ：需求响应, FRP市场, 主从博弈, 均衡约束, 单位激励补偿费用

Abstract：

With the increasing scale of renewable energy such as wind and solar energy into the power system, its generation power volatility and uncertainty make the system net load curve steeper. Independent system operators (ISO) needs to introduce more flexible resources to cope with the surge in flexibility ramping products (FRP) demand. Implementing demand response (DR) for large-scale users has become an important way to improve system flexibility. How to design reasonable incentive compensation measures to guide large-scale users to provide DR services and participate in the FRP market is of great significance to improve the flexibility of the system. Existing studies have proposed reasonable incentive compensation mechanisms and DR strategies under the market environment, but have not considered the relationship between the difference of large-scale users' response willingness and response cost. Therefore, this paper proposed a DR optimization strategy that adapts to the joint clearing of electric energy and FRP, and realized differentiated compensation according to the response willingness of large-scale users.
First, the customer demand management behavior model (CDMBM) was used to estimate the response cost function of users by using the historical DR participation data of large- scale users. Characterizing the differences in user response costs by introducing a response willingness parameter in the cost function. The higher the willingness of large-scale users to respond, the lower the cost of response. CDMBM provided a basis for the accurate modeling of large-scale users' decision behaviors of reduction-type DR and absorption-type DR.
Secondly, an incentive DR master-slave game model was constructed between ISO and multiple large-scale users. The upper level model was the optimization model of joint clearing of electric energy and FRP with ISO as the decision-making body, and the lower level model was the optimization model of DR decision-making with large-scale users participating in the joint market. ISO aimed to minimize incentive compensation costs with the goal of meeting the flexibility requirements, while large-scale users aimed to maximize the benefits of providing DR services. Both parties are interdependent.
Finally, by taking the KKT condition of the lower model as the constraint condition of the upper model, the master-slave game problem was transformed into a mathematical optimization problem with linear equilibrium constraint for solving. The game behavior between ISO and large-scale users was described by the master-slave game equilibrium. The DR unit incentive compensation cost considering user differentiation was obtained according to the equilibrium result of the game between both parties.
The results show that differentiated compensation is carried out according to the response willingness of large-scale users. Large-scale users are effectively encouraged to participate in the joint clearing market of electric energy and FRP market. The unit incentive compensation cost not only meets the needs of large-scale users and ISO, but also effectively alleviates the sharp increase in incentive compensation costs when there is a lack of flexible ramping capability. Large-scale users reserve flexible ramping capacity in the FRP market to provide flexible climbing capacity, which reduce the pressure of thermal power units. The cost of FRP accounts for only 0.31% of the total system cost. The proposed DR optimization strategy enhances the system's flexibility while considering economic efficiency.

Key words： Demand response Flexible ramping products(FRP) market master-slave game equilibrium constraints unit incentive compensation cost

收稿日期: 2024-05-21

PACS:

TM73

基金资助:

国家重点研发计划项目（2018YFB1503101）和国家自然科学基金项目（51307019）资助

通讯作者: 于娜女,1977年生,副教授,硕士生导师,研究方向为电力市场需求侧研究等。E-mail：yuna0616@163.com

作者简介: 黄大为男,1976年生,副教授,硕士生导师,综合能源系统运行与规划,电力系统运行与控制等。E-mail：hdw76@163.com

引用本文:

黄大为, 郭念康, 于娜, 孔令国. 面向系统灵活性提升的大用户激励型需求响应优化策略[J]. 电工技术学报, 0, (): 240822-240822. Huang Dawei, Guo Niankang, Yu Na, Kong Lingguo. Optimization Strategy for Enhancing System Flexibility Through Incentive Demand Response of Large-Scale Users. Transactions of China Electrotechnical Society, 0, (): 240822-240822.

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