A Multi-Time-Scale Source-Load-Storage Collaborative Optimization Scheduling Model with Source-Load Matching Degree

Abstract

In the context of a high proportion of new energy sources accessing distribution networks, the uncertainty surrounding new energy sources and loads puts pressure on distribution networks to reduce peak demand. For medium and low voltage distribution networks with a high proportion of new energy sources, this paper proposes a multi-time-scale active distribution network scheduling model that considers flexible load scheduling. Firstly, based on information entropy theory, a source-load matching index is proposed to measure the degree to which the load matches the new energy output. A regulation plan is then formulated for the day-ahead energy storage system and flexible load to optimise the day-ahead load of the distribution network. Secondly, during the intraday phase, the output of gas turbine units, flexible loads and energy storage is optimised to minimise dispatching costs and achieve an optimal operating scenario for fully absorbing new energy. Finally, the economic cost optimisation of the multi-time scale source-load-storage collaborative optimal scheduling model is verified using the IEEE-33 node, and the model is solved using the CPLEX and particle swarm optimisation algorithms. The effectiveness of the proposed model was verified.