Multi-energy complementary optimal scheduling based on hydrogen gas turbine considering the flexibility of electrolyser

Abstract

With the transition towards a low-carbon energy system, renewable energy resources have been extensively developed. However, the limited ability of the power system to absorb renewable energy sources with high volatility, such as wind and solar power, has led to significant curtailment. Redundant electric energy can be converted into storable hydrogen energy through electrolysis and utilized for heating purposes. By leveraging the complementarity of diverse energy sources, optimal allocation of renewable energy can be achieved across a broader scope. However, in the current scheduling of multi-energy systems, the efficiency of electrolyser is crudely assumed to be a constant, which results in scheduling solutions that deviate from the Pareto optimum. Therefore, a polymer electrolyte membrane electrolyser's model with non-linear relationship between the load rate and conversion efficiency is proposed in this paper. To tackle the non-convex optimal scheduling challenge, an adaptive chaos-augmented particle swarm optimization algorithm is introduced, which effectively enhances computational efficiency while preventing entrapment in local optima. Case studies based on IEEE 14-node system verified the effectiveness of the proposed method.