实验技术与管理2025，Vol.42Issue(5)：105-114,10.DOI:10.16791/j.cnki.sjg.2025.05.013
基于风-光-储多能协同的主动配电网动态优化实验平台设计
Experimental platform for active distribution network dynamic optimization with wind-solar-storage multienergy coordination
摘要
Abstract
[Objective]The advancement of dual carbon goals has driven rapid expansion in renewable energy installations,particularly wind and photovoltaic systems.However,the inherent intermittency and stochasticity of these renewable sources substantially challenge power system stability when integrated at scale into distribution networks.While dynamic optimization strategies leveraging wind-solar-storage synergy demonstrate considerable operational advantages,their implementation presents notable pedagogical complexities due to the interdisciplinary integration of stochastic optimization theory and deep reinforcement learning frameworks.[Methods]To address the limitations of conventional pedagogical experiments and cultivate students'innovative thinking in interdisciplinary applications,this paper develops an experimental platform for active distribution network(ADN)dynamic optimization with wind-solar-storage multienergy coordination.The formulated ADN dynamic optimization model,characterized by extensive binary variables and multidimensional uncertainties,inherently constitutes a high-dimensional,nonlinear stochastic optimization problem.Employing a divide-and-conquer methodology,we design a bilevel stochastic optimization framework for ADNs with distributed energy resources.To optimize the distribution network topology,the upper-level model is designed as an ADN dynamic reconfiguration model with binary variables.The lower-level model is formulated as an ADN operational optimization model incorporating wind,solar,and storage systems,ensuring economic efficiency and operational security of the distribution network.An innovative embedded mathematical model-double deep Q-network(EMM-DDQN)algorithm is proposed to efficiently solve this stochastic system.The upper-level dynamic reconfiguration model for ADNs,challenged by substantial uncertainties and integer variables,is addressed through a DDQN algorithm specifically designed for computationally efficient and precise solutions.When the grid topology is finalized through this process,the lower-level model executes operational optimization for renewable energy and energy storage-integrated ADNs,effectively reducing power losses,mitigating voltage deviations,and minimizing renewable curtailment rates.[Results]The experimental results of the dynamic optimization for ADNs based on wind-solar-storage multienergy coordination are as follows:1)The proposed EMM-DDQN algorithm can rapidly learn by dynamically interacting with the ADN environment.It converges at approximately 650 episodes and yields high-quality solutions.2)The ADN topology can be dynamically adjusted on the basis of load curves and renewable energy output.3)The dynamic operation strategy of the ADN,under the coordination of wind,solar,and storage,effectively mitigates voltage deviations,reduces network losses,and enhances the accommodation of renewable energy.4)In Scenario 1(without network reconfiguration),network losses and voltage deviations increase by 12.40%and 7.52%,respectively,compared to the proposed model.In Scenario 2(without energy storage),the renewable energy utilization rate decreases to 88.27%.In Scenario 3(without network reconfiguration and energy storage),the performance is the worst,with the average reward function value decreasing by 6.93%compared to the proposed model.In Scenario 4,the renewable energy utilization rate approaches its theoretical maximum,while network losses and voltage deviations reach optimal levels,ultimately yielding the highest average reward value.[Conclusions]This paper establishes a dynamic optimization experimental platform for ADNs based on wind-solar-storage multienergy coordination.A three-stage teaching framework—"key equipment-model formulation-system optimization"—is designed to effectively deepen students'understanding of ADN dynamic optimization methods.Using the simulation platform,students must integrate multidisciplinary knowledge,including power system analysis,optimization computation,and machine learning,to complete the full process from theoretical modeling to optimization solving.This hands-on practice enhances their comprehension of ADN dynamic optimization methods,considerably strengthens their ability to integrate interdisciplinary knowledge,and improves their capability to solve complex engineering problems.Ultimately,the platform serves as an effective educational tool for cultivating high-level talent under the framework of emerging engineering education.关键词
可再生能源/风-光-储多能协同/主动配电网/实验平台Key words
renewable energy/wind-solar-storage multienergy coordination/active distribution network/experimental platform分类
信息技术与安全科学引用本文复制引用
江昌旭,庄鹏威,林俊杰,郑文迪,邵振国..基于风-光-储多能协同的主动配电网动态优化实验平台设计[J].实验技术与管理,2025,42(5):105-114,10.基金项目
福州大学研究生教育教学改革项目(00489449) （00489449）
福州大学研究生教育教学改革重点项目(FYJG2023001) （FYJG2023001）
国家自然科学基金(72401069) （72401069）
