奖励机制与用户意愿结合的高峰期负荷博弈调度策略

doi:10.19595/j.cnki.1000-6753.tces.230874

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摘要

在高峰时段,居民冷/热设备占尖峰负荷的比重不断攀升,影响了低压配电网的优化运行,为补充供给侧调节能力,提升调控灵活性,亟需引导用户侧可调资源参与电网供需互动。本文提出了一种奖励机制与用户意愿结合的高峰期负荷博弈调度策略。依据用户用能意愿对高峰时段用电负荷进行动态划分,制定基于负载率-奖励函数的差异化补贴机制。将高峰时段需要提升功率的负荷群视为领导者,将具有灵活削减能力的负荷群视为追随者,建立Stackelberg博弈模型,证明博弈均衡的唯一性。进而本文提出了Stackelberg博弈下的用电高峰期负荷日内优化调度方法,优化博弈双方在追求效益最大时的策略。本文构建多通路混合专家网络求解设备动作意愿,提出基于用户意愿的单功率-多功率级负荷联合控制策略,实现负荷的实时精细化调控。最后,算例表明本文策略能够在实现聚合商与用户侧双赢、遵从用户调控意愿的同时,有效平抑用电高峰期负荷波动,减小峰谷差。

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关键词 ：主从博弈, 高峰期负荷, 用户意愿, 负载率-奖励函数, 混合专家网络

Abstract：

With the substantial growth in the number of cooling/heating loads, load spikes are easily to occure during the concentrated residential power consumption hours, especially in old neighborhoods with high load rate, which affects the safe and stable operation of low-voltage distribution networks. To supplement the supply-side regulation capability and enhance the flexibility of regulation, there is an urgent need to guide the user-side adjustable resources to participate in the interaction between grid supply and demand. This paper proposes a peak load game scheduling strategy, which combines the reward mechanism with the users' willingness to ensure the benefits of users and load aggregators, and complies with the users' regulatory willingness to achieve the ideal load fluctuation smoothing and peak shaving effect at the same time.
Firstly, based on the users' willingness of equipment regulation, the peak-hour loads are divided dynamically, and a differentiated subsidy mechanism based on the transformer load rate - reward function is formulated. The proposed mechanism fully stimulates the users' response motivation and stabilizes the load rate within a reasonable interval. Secondly, the Stackelberg game theory is introduced to solve the energy-use decision-making problem between load groups, where the load group that needs to boost power during peak hours is regarded as the leader, and the load group with flexible cutting ability is regarded as the follower. The Stackelberg game model is established along with the uniqueness of the game equilibrium proved. Then, the intraday optimal scheduling method of peak loads under the Stackelberg game is proposed, which searches for the optimal energy-use strategies for the main subjects of the game while guaranteeing their benefits. Finally, the multi-channel mixture-of-experts (MCME) network is constructed to solve the regulatory willingness at the equipment level, and a joint control strategy of single/multi-power-stage loads based on the users' willingness is proposed.
Simulation results show that by using the proposed load intraday optimization method, the standard deviation, peak-to-valley difference, and load peak all reach their minimum values, which are 33.13%, 35.31% and 8.25% lower than the pre-optimization curves. Both the user-side benefit and the net gain of the load aggregator reach their highest values, which verifies the advantages of the proposed strategy in peak shaving, load fluctuation smoothing and participants' gain guaranteeing. When applying the MCME model to solve the equipment regulatory willingness, both the mean absolute error and root mean square error reach their minimum values, indicating that the MCME model has a high solving accuracy. The optimal power change obtained by the intraday optimization method is very close to that obtained by the real-time control strategy, which verifies the effectiveness of the joint control strategy of single/multi-power-stage loads.
The simulation analysis draws the following conclusions: (1) The load intraday optimization method based on the reward mechanism can achieve a win-win situation for load aggregators and users, and achieve significant peak shaving and load fluctuation smoothing effects. (2) The MCME model can effectively learn the common and characteristic laws from operation data, and accurately solve the users' willingness to regulate the equipment. (3) The joint control strategy of single/multi-power-stage loads, which considers the users' willingness, can realize fine load regulation.

Key words： Stackelberg game peak load user willingness load rate-reward function Mixture-of-Experts network

收稿日期: 2023-06-08

PACS:

TM73

基金资助:

国家电网有限公司科技项目资助（5108-202218280A-2-379-XG）

通讯作者: 祁琪,女,1992年生,博士,硕士生导师,研究方向为电力系统分析与控制、主动配电网规划与优化运行、智能用电等E-mail：qigx0521@163.com

作者简介: 杨雪莹,女,1998年生,博士研究生,研究方向为用户侧灵活性资源调控、需求响应、人工智能技术。E-mail：18801336632@163.com

引用本文:

杨雪莹, 祁琪, 李启明, 杨春萍, 祁兵. 奖励机制与用户意愿结合的高峰期负荷博弈调度策略[J]. 电工技术学报, 2024, 0(0): 1-. Yang Xueying, Qi Qi, Li Qiming, Yang Chunping, Qi Bing. Peak Load Game Scheduling Strategy Combining Reward Mechanism and User Willingness. Transactions of China Electrotechnical Society, 2024, 0(0): 1-.

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