Research On Multi-objective Optimal Scheduling Model And Algorithm For Micro Grid

The problems of increasingly severe energy shortage and environmental pollution guide the development of electric power into the smart grid road. As a bridge connecting the traditional power system and smart grid, it is important significance to research Micro Grid. Due to the micro grid in the presence of multiple distributed power, how to adopt the reasonable scheduling strategy, making micro power grid under the premise of the normal operation, reduce the operation cost and pollution gas emissions to ensure the economic and environmental protection operation, the micro grid optimal dispatch problem has become a hot topic in microgrid..In fact, the micro grid scheduling is a complex multi-objective optimization problem with obvious characteristics of multi-constrait and nonlinear, if consider the operation cost or environmental benefits as a target only, the optimization result is one-sided and can’t play the actual role of micro grid. Meanwhile, to solve such problems using intelligent optimization algorithms is easy to convergence into local optimal solution. Therefore, this paper will explore the new methods to solve the multi-objective optimization problem of micro grid.This paper establish a model of energy consumption and output of various distributed power, combine with the constraint conditions of micro grid operation, a multi-objective optimization model is built with the economic costs and environmental pollution as objective functions. At the same time, put forward two kinds of storage battery operation strategy: peak shaving strategy and dynamic programming operation strategy based on the residual capacity.Then, in view of the phenomenon that the conventional intelligent algorithm can not effectively deal with the multi-objective optimization model, a new biological heuristic algorithm named Bird Swarm Algorithm(BSA) is introduced. The algorithm imitates birds foraging, vigilance, migration habits, to generate the corresponding update strategy and overcome the defects of precocious lead by lack of population diversity, then through respectively on several typical single objective and multi-objective function optimization results verify the effectiveness of the algorithm. At the same time, this paper introduces a method to select the best compromise solution from the Pareto solution: the shortest distance method.Finally, as an example of the micro grid model proposed in this paper for a test, is solved by BSA, and compare with particle swarm algorithm(PSO) and differential evolution algorithm(DE) which current commonly used intelligent optimization algorithm. The numerical results show that BSA has better convergence accuracy and convergence quality than both of them without affecting the computation time. In addition, the comparative analysis of the two operational strategies of the storage battery shows that the dynamic programming strategy has better economic and environmental value than the peak shaving strategy.