Load Optimal Dispatch Of Thermal Power Plant Based On Multi-objective Optimization

With the development of national economy and social foundation career, the industry of electricity has rapid evolution too.In our country, the majority of industry mode is transforming coal and other primary energy source into electric energy. Therefore, how to improve the development of coal economy as improving the generating efficiency, has become the main urgent task to be accomplished. "Put the construction of resource-conserving and environmental-friendly society at the prominent position of industrialization and modernization development strategy" is the trend of industrial construction. Therefore, the main target of electric industry is not only increasing the numbers of heavy load power generator set, but also taking action to intensify energy conservation and emission reduction. Thus it can be seen, how to distribute the load of thermal power generating unit to increase the economic benefit of power plant operation, has become a critical problem to be solved. With the fierce market-based competition of electricity, plant-level load distribution for satisfying the requirement of lowest generating cost is far from enough, we need also take consideration of reaction time of control center for load adjustment as well as the unit abrasion produced by frequent load adjustment. So multi-objective optimization control method is meaningful to thermal power enterprise.In this paper, considering the economical efficiency of generating cost of thermal power plant, time of adjustment of load of thermal power generating unit and stability of unit operation, a mathematical optimized model is built. A recursive multi-objective co-evolution algorithm is developed to overcome the disadvantage of instability which caused by transforming the multi-objective problem into single-objective problem by distributing a positive coefficient to every object using the traditional multi-objective algorithm, and the defect of computing complexity of adaptive value in evolution process using intelligent optimized algorithm. This algorithm implements recursive method and gradually importing target at equivalent stage to solve the load distribution problem. Compare with the traditional multi-objective algorithm, this method reduce the deviation of targets, and the evolution process is relatively simple. It has strong flexibility and practicability. And the result of simulation experiment has proved the correctness and validity.