基于分布式近似动态规划的电动汽车实时充电调度策略OA

To address the challenges about high computational burden,poor privacy and reliability in centralized scheduling of large-scale electric vehicles(EVs),this paper proposes a real-time EV charging scheduling strategy based on the distributed approximate dynamic programming(ADP).First,a polytope-based EV charging model is constructed,and a corresponding EV aggregation and disaggregation method based on polytope approximation is proposed,which can reduce computational complexity while ensuring sufficient optimization accuracy.Furthermore,a centralized scheduling model for aggregated EVs is established,where the model is reformulated under the Markov decision process so as to align with the practical real-time scheduling process.Next,this study proposes a distributed ADP algorithm to solve the scheduling model under stochastic environments.The value function is approximated using piecewise linear functions,and a consensus theory incorporating power limitation factors is applied for distributed optimization.Moreover,by integrating the ADP algorithm with consensus theory,a distributed piecewise linear function slope update method is proposed,which can embed empirical information about the system's stochastic factors into the value function,assisting decision-making by only utilizing local information and communication between neighboring elements.As a result,the approximate global optimal real-time EV scheduling strategy is obtained in a distributed manner.Finally,case studies on the modified IEEE 6-bus system and IEEE 118-bus system validate the effectiveness of the proposed polytope-based aggregation method,consensus-based distributed optimization method,and distributed ADP stochastic solving method.