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Improved particle swarm optimization algorithm to multi-objective optimization energy hub model with P2G and energy storage

In this paper, an Energy hub mathematical model with power-to-gas(P2G), energy storage, CCHP and electric energy feedback is constructed. Considering the factors such as the rate structure and uncertainty of wind power output, the energy hub model takes the minimum fuel cost of energy hub operation and the minimum interactive energy costs with power grid as the optimization objective to optimize. In order to solve the high dimension complex problem, an improved multi-objective particle swarm optimization algorithm is proposed to solve the optimization problem, with the consideration of the bad experience during the speed update, the repeated search to the worst position is avoided., the search efficiency of the algorithm is improved and the proof is carried out; By adopting the global optimal solution selection strategy based on the interval and the optimal selection strategy, it can obtain the approximate Pareto front end which is better than the convergence and diversity of the basic MOPSO algorithm. The feasibility of the model and the effectiveness of the improved algorithm are verified by an example.