Optimal allocation of energy storage participating in peak shaving based on improved hybrid particle swarm optimization

With the increasing number of photovoltaic grid-connected in recent years, severe challenges are faced in the peak-shaving process of the power grid. Consequently, a rational optimization for allocating energy storage resources in the power grid has become a key and urgent issue to be studied. The economy and safety of energy storage involving in peak regulation is fully considered by this paper. Firstly, the objective function is obtained from the net income and average peak shaving difference function of the participating energy storages during peak shaving. The objective function is further normalized by combining the idea of matrix standardization using the Technique for Order Preference by Similarity to an Ideal Solution (TOPSIS) with the linear weighting method. Secondly, the objective function is functioned with the constraints under three aspects: power constraint, the capacity of storaging the energy, maximum and minimum power constraints and energy storage SOC constraint. Thirdly, using the genetic algorithm augments the particle swarm optimization algorithm to improve its optimization speed and convergence. Finally, the simulation is implemented in MATLAB software, which verifies the proposed improved hybrid particle swarm optimization algorithm. The proposed method benefits in fast convergence speed and strong global optimization ability.