Research On Energy-saving And Emission Reducting Generating Dispatch Based On Multi-Objective Intelligence Algorithm

In the context of energy-saving and emission-reduction, based on an overall consideration of minimizing the coal consumption and pollutant emission of generating units, this essay is discussing a multi-objective load dispatch optimal formulation based on an improved Multi-Objective Particle Swarm Optimization(MOPSO) algorithm, and a multi-objective units commitment optimal formulation based on Multi-Objective Evolutionary Algorithms(MOEA) and MOPSO algorithm.The design idea of a multi-objective load dispatch optimization formulation based on an improved MOPSO algorithm is introduced as follow. The improved MOPSO algorithm introduced the conception of semi-feasible region to treat constrained conditions, avoiding the complicated process of finding appropriate penalty parameters.Elite filing technology, which to build the external file and the individual non-dominated solution set, were used to enhance the speed of convergence and the quality of solutions. Adaptive grid method was adopted to renew and maintain the external file set in order to gain the Pareto front distributed uniformly. The rules of individual best selection and global best selection were set up based on the concept of semi-feasible region. Using the formulation into a real power plant with six generating units for multi-objective load dispatch purpose, well-distribution Pareto-optimal solutions were obtained. Fuel cost and emission of pollutionare reduced effectively, and the result confirms the feasibility and validity of the formulation.The multi-objective unit commitment optimal formulation based on MOEA and MOPSO algorithm changed the UC problem into two-level sub-problems, which were 0-1 integer programming for the condition of unit performance and continuous variables programming for multi-objective load dispatch optimal problem. MOEA and the improved MOPSO algorithm were separately applied to solve the two-level sub-problems.The main design ideas of the formulation are introduced as follows. A heuristic approach was designed to create the initial population in order to enhance the convergence rate. External file set was non-dominated sorted, and based on the non-dominated sort, a roulette approach was applied to select parents in order to reserve the excellent individuals. The line crossover and the row crossover were used as the operational methods, which improve the exploratory ability of the algorithm. During the variation-operation, many thermal power plants were operating at the same time, by shutting down the small ones could improve the convergence rate effectively. Adaptive grid method was adopted to renew and maintain the external file set.The improved MOPSO algorithm introduced above was applied to solve the multi-objective load dispatch problem which was the second level sub-problem, and parallel computing was introduced to enhance the computing speed significantly. At last, taking IEEE 30-bus 9-unit system as an example, the essay demonstrated the feasibility and efficiency of the multi-objective units commitment optimal formulation provided above.