Abstract: In previous researches, most electricity pricing strategies were time-of-use price, and the traditional non-dominated sorted genetic algorithm-II is mostly used to solve the multi-objective problem. To solve the problem of the fluctuation of distributed photovoltaic grid connection, a multi-objective time-of-use pricing optimization strategy is proposed for the grid with distributed photovoltaic power generation. Firstly, the response model of electricity consumption and electricity price is established, and the time period is divided based on the equivalent load. The multi-objective nonlinear distributed photovoltaic time-of-use pricing model is established with the objective of minimum load variance, minimum peak-valley difference of equivalent load, and maximum user satisfaction index. A multi-objective genetic algorithm combined with a neighborhood search algorithm is proposed to solve the complex problem and obtain the optimal pricing strategy. As shown in the numerical experiment, the pricing strategy proposed improves the power supply stability by 37.77%, and improves the utilization rate of distributed photovoltaic power generation by 4.51%, with user satisfaction improved to 74.3%. In addition, the proposed algorithm outperforms the widely used non-dominated sorted genetic algorithm-II. These results show that the proposed pricing strategy is effective.

Key words: distributed photovoltaic, time-of-use pricing, multi-objective optimization, user satisfaction, demand response