Multi-objective Operation Management Of A Microgrid With Multi-type Distributed Generations Based On Improved PSO

The evolving social have higher requirements at reliable, safe, economical and efficient environment-friendly operation of grid, this situation makes the proposed and the rapid development of the smart grid has become inevitable. A major feature of the smart grid is to promote access new energy, and use advance control methods rational management of new energy generation, which has great significance for solving the energy shortage and ecological pollution problems.We take micro-grid with multi-hybrid energy source as an example for analyzing the models and features of two clean energy generation systems (i.e. photovoltaic and wind), we also introduce the works and models of two new types of micro-power (i.e. fuel cell and micro-turbine). Hybrid energies work with storage batteries, and the micro-grid connects to the grid with real-time changed price and run with it. On this basis, the multi-objective optimization problem is proposed to optimize micro-grid operating costs and emissions.For the optimization of established model, regular algorithm like standard PSO has low stability. Therefore, we proposed an improved PSO algorithm to optimize the model from the perspective of single objective, by using simplex to modify the updating rules of particles and using piecewise function as inertia weight. Simulation results show that our algorithm is more stable and has higher accuracy.On the basis of our proposed single-objective PSO algorithm, we further present an improved multi-object PSO algorithm, by introducing the theory of Pareto optimality, fuzzy clustering and "roulette wheel". Results show that the algorithm obtains an expected group of Pareto optimal points.