基于分布鲁棒优化的多虚拟电厂主从博弈动态定价模型及求解方法

陈渠; 杨苹; 陈年昊; 刘根饶; 颜璐

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基于分布鲁棒优化的多虚拟电厂主从博弈动态定价模型及求解方法

陈渠, 杨苹, 陈年昊, 刘根饶, 颜璐

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Distributionally Robust Optimization-Based Dynamic Pricing Model and Solution Method for Multiple Virtual Power Plants Stackelberg Game

CHEN Qu, YANG Ping, CHEN Nianhao, LIU Genrao, YAN Lu

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摘要

【目的】针对多虚拟电厂与配电网运营商的主从博弈格局,现有动态定价模型未充分考虑新能源出力的不确定性对博弈策略影响的问题,且传统求解方法在高维决策空间时求解质量差,为此提出一种基于分布鲁棒优化的主从博弈动态定价模型及求解方法。【方法】建立以配电网运营商为领导者、多虚拟电厂为跟随者的主从博弈动态定价模型;在虚拟电厂优化模型中引入分布鲁棒机会约束描述风电出力不确定性,仅需对已知风电功率的一阶矩和二阶矩信息构造模糊集,通过Cantelli不等式将机会约束转化为确定性等价形式;提出基于神经网络增强的区域粒子群优化算法(neural network-enhanced regional particle swarm optimization, NN-RPSO),通过构建虚拟电厂行为预测模型替代频繁调用下层优化,结合区域划分策略实现高效搜索。【结果】决策者可通过调节风险预算参数在运行风险与成本间权衡;所提算法相较于区域粒子群优化算法在提升配电网运营商收益47.2%的同时,减少下层模型调用次数约67.4%。【结论】所提模型为决策者提供了灵活应对不确定性的建模手段,所提求解算法显著提升了求解质量,为多主体主从博弈问题提供了有效解决方案。

Abstract

[Objective] In response to the Stackelberg game framework involving multiple virtual power plants (VPPs) and a distribution system operator (DSO), where existing dynamic pricing models inadequately capture the impact of renewable energy generation uncertainty on game strategies, and conventional solution methods suffer from poor solution quality in high-dimensional decision spaces, this paper proposes a dynamic pricing model and solution approach based on distributionally robust optimization (DRO) within a Stackelberg game framework. [Methods] A Stackelberg game dynamic pricing model is established, with the DSO as the leader and multiple VPPs as followers. In the VPP optimization model, a distributionally robust chance constraint is introduced to characterize wind power output uncertainty. By constructing an ambiguity set using only the first and second moments of wind power, the chance constraint is transformed into a deterministic equivalent form via the Cantelli inequality. Furthermore, a neural network-enhanced regional particle swarm optimization (NN-RPSO) algorithm is proposed, which builds a predictive model for VPP behavior to replace frequent calls to the lower-level optimization and integrates a regional partitioning strategy to achieve efficient search. [Results] Decision-makers can balance operational risk and cost by adjusting the risk budget parameters. Compared with the regional particle swarm optimization (RPSO) algorithm, the proposed algorithm increases the DSO's revenue by 47.2% while reducing the number of lower-level model calls by approximately 67.4%. [Conclusions] The proposed DRO model provides decision-makers with a flexible approach to handle uncertainty, and the proposed solution algorithm significantly improves solution quality, offering an effective solution for the multi-agent Stackelberg game problem.

导出引用

陈渠, 杨苹, 陈年昊, 刘根饶, 颜璐. 基于分布鲁棒优化的多虚拟电厂主从博弈动态定价模型及求解方法[J]. 电力建设. 0

CHEN Qu, YANG Ping, CHEN Nianhao, LIU Genrao, YAN Lu. Distributionally Robust Optimization-Based Dynamic Pricing Model and Solution Method for Multiple Virtual Power Plants Stackelberg Game[J]. Electric Power Construction. 0

中图分类号： TM73

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