Optimal Scheduling of Distribution System with Edge Computing and Data-Driven Modeling of Demand Response

Abstract

High proportions of renewable energy enlarge the peak-valley difference of the net load of the distribution system, which puts forward higher requirements for the operation scheduling of the distribution system. From leveraging the demand-side adjustment capabilities perspective, an optimal scheduling method of distribution system with data-driven modeling of price-based demand response is proposed in this paper. By introducing the edge computing paradigm, a collaborative interaction framework between the control center and the edge nodes is designed for optimization of distribution system. At the edge nodes, an XGBoost-based classification modeling method of price-based demand response is proposed for large-scale differentiated users; at the control center, a two-stage optimization method integrating pre-scheduling and re-scheduling is proposed based on demand response results from all edge nodes. Through the information interaction between the control center and edge nodes, the optimized scheduling of the distribution system with large scale user participation is realized. Finally, a case study is implemented on the modified IEEE 33 node system, which verifies that the proposed XGBoost-based classification modeling method for price-based demand response has lower errors, and it is beneficial to improve the economics of the system operation. Moreover, the simulation results show that the application of edge computing can significantly reduce the calculation time of the optimal scheduling problem with price-based demand response modeling of large-scale users.