Optimal scheduling of residential houses with optimal photovoltaic energy utilization strategy using improved multi-objective equilibrium optimizer algorithm

In the background of strongly practicing the double carbon strategy, the use of solar photovoltaic (PV) energy in residential houses is an effective way to achieve energy saving and emission reduction. In this paper, a multi-objective optimal scheduling model for residential houses is established, and it takes electricity cost, demand response (DR) curtailment value and carbon emission as the optimization objectives. Then, a novel operation strategy, which is called optimal PV energy utilization strategy, is proposed to further reduce electricity cost and carbon emission, by properly managing the power flow. For the multi-objective optimal scheduling problem with multi-decision variables and nonlinearity, an improved multi-objective equilibrium optimizer (IMOEO) algorithm is proposed to obtain the Pareto front solutions. The algorithm uses constrained non-dominant mechanism to deal with the constraint problems of the model, as well as the hybrid opposite learning strategy and the spiral operator are used to improve the overall performance of the algorithm. Finally, the optimal compromise solution is obtained by using the technique for order preference by similarity to ideal solution (TOPSIS). The experimental results indicate that IMOEO algorithm has better performance in terms of convergence and diversity. What's more, compared with the original strategy, the optimal compromise solution obtained by the proposed strategy reduces 28.76%, 9.78% and 15.36% for these optimization objectives, respectively. Therefore, the proposed method is beneficial to achieve low-carbon electricity consumption in residential houses.

Automated summary

In this paper, a multi-objective optimal scheduling model and operation strategy for utilizing solar photovoltaic energy in residential houses are proposed. The improved IMOEO algorithm is used to obtain the optimal compromise solution, and the experimental results show better performance in terms of convergence and diversity, reducing electricity cost and carbon emission.