Load Distribution Based On Immune Mind Evolutionary Algorithm

Load distribution is to seek the optimal load distribution among units so that energy consumption is the least in a power plant even in a grid. With China's reform of the electricity market is continually deepening, and the power plant are working to improve operational level, the whole plant to reduce coal consumption. Therefore optimal load distribution plant research has important significance of the works.Immune Mind Evolutionary Algorithms (IMEA), as in the search, optimization and machine learning methods to provide the optimal solution, is a intelligent optimization algorithm based on clonal selection and Mind Evolutionary Algorithm, which does not require continuous and objective function may be minimal, nor Genetic Algorithm crossover and mutation operation, but through clone, mutation, selection, reorganization to achieve the objective function of optimization, has now become a new field of intelligent computing research, the research is still at the fledgling stage. This paper use Immune Mind Evolutionary Algorithm to solve the thermal power plant unit optimal load distribution problem.Identify the unit coal consumption curve of the optimal load distribution is the basis, the paper first discusses the measurement of the economic power of various economic indicators, and ultimately determine the power supply coal consumption as the goal of optimal load distribution function, the unit operation After the original data physical methods, mathematical methods reasonable screening, thermodynamic calculation of the data obtained with the corresponding to the power supply coal consumption rate, and then using polynomial fitting way to strike a unit coal consumption characteristic curve.This paper introduces thermal power plant unit optimal load distribution modeling process. Model for the establishment of objective function and constraints confirmation, in accordance with specific units, respectively, the corresponding optimal load distribution model, and the establishment of model based on the rules of choice, with units operating economy and safety considerations a number of restrictive conditions. Finally be used for optimization model. The paper considered optimal load distribution unit start-up and shutdown conditions, the crew on the optimization process start-up and shutdown and start-up and shutdown of the energy loss and relevant information on the unit commitment in the process of the energy loss and the calculation method Unit peak shaving way to determine when the unit should be considered commitment energy loss and life loss.In the above inspection on the basis of the relevant literature, and drawing on previous work experience, proposed use of immune thinking evolutionary algorithm to solve the multi-unit optimal load distribution, the algorithm Cloning, variation, selection, the reorganization has been designed Operator , encoding using real-coded, constraints on the treatment by adding some of the ideological constraints in the preparation of the environmental MATLAB7.0 immune thinking evolutionary algorithm procedures, and examples were tested by immunohistochemistry thinking evolutionary algorithms and the accuracy of superiority This shows that the algorithm has good engineering practice.