Optimal scheduling of power systems considering carbon markets: Based on blockchain theory and multi-objective particle swarm optimization algorithm

Abstract

In the context of double carbon, it is an inevitable requirement for the low-carbon power industry to take economic efficiency and low carbon into consideration. This article introduces the carbon emission constraint into the economic dispatching of the power system. Then, combined with the blockchain theories, the methods of particle swarm optimization and multi-objective particle swarm optimization (MOPSO) are employed to simulate the economic and environmental scheduling of a power generation system based on six thermal power units. Research shows that the constraint processing approach is practical and effective, and it can firmly adhere to equality requirements, which is superior to other algorithms’ constraint processing methods; the algorithm is stable, and the global optimal solution can be determined under different initial solutions. In the process of multi-objective optimization, the solutions of POF obtained by using the slope method are evenly distributed.