A Unit Commitment Method Based On A Meta-heuristic Algorithm

It is well-known that generators in power plants consume a large amount of fossil energy each year,with coal,oil and natural gas as the main source.However,with the decline of coal quality and reserves in China in recent years,the coal consumption of thermal power plants in China has been increasing year by year.Therefore,how to reduce the consumption of coal for power generation is a very difficult and important scientific topic today.In power plants,the unit commitment problem(UCP)is an important part that can significantly optimize fuel consumption.Choosing the optimal units and rationally ar-ranging the units sequence can significantly reduce the fuel consumption level.To this end,in recent years,many algorithms to solve the unit commitment problem have been studied.However,the current optimization algorithms have the problem of easily falling into the local optimum when solving the unit commitment problem,at the same time,due to the structural limitations of the algorithms themselves,they often consume a lot of computing resources,and the final calculation results are not satisfactory.In addition,no separate analysis was performed on the corresponding economic load dispatch problem(ELD),which also caused the calculation efficiency of the algorithm to decrease and the calculation time cost to increase significantly,and the final fuel consumption remains remains very high.In order to solve the above problems,This paper studies the unit commitment prob-lem and the meta-heuristic algorithm and proposes a new optimization algorithm.This algorithm splits the unit combination problem into two layers of problems,and analyzes and optimizes each layer separately.In the lower problem,the algorithm uses a simple convex optimization method for the low-dimensional ELD problem,which significantly reduces the complexity and time cost of the algorithm,and provides the each result's fit-ness for the upper problem.In the upper-layer problem,the algorithm focuses on solving the unit commitment problem,using an improved particle swarm algorithm based on the elite strategy and a simulated annealing algorithm to search the solution space of the gen-erator schedule.Thanks to the algorithm's excellent performance of jumping out of the local optimum,the diversity of the results of the UCP is significantly higher than other algorithms,so it can get a more excellent and efficient solution,and can ultimately re-duce the fuel consumption significantly.In addition,in order to make the results of the proposed algorithm available and to meet the various constraints of the unit combination problem,the algorithm also designed a variety of mechanisms to meet the constraints.These mechanisms significantly improve quality of results.We also designed corresponding experiments are also designed to verify the final results of the proposed algorithm.The experimental results show that compared with other typical algorithms for solving unit commitment problem,the proposed algorithm has a stronger search ability,and the final results significantly better than other algorithms.At the same time,in order to verify how each of the improved modules we designed has an impact on performance,we also performed corresponding ablation experiments and conducted a detailed analysis,which provided new ideas for the subsequent research on the unit commitment problem.