Research On Coal Inventory Optimization Of Power Plant Based On Particle Swarm Optimization

The development of science and technology has made intelligent electrical appliances enter thousands of households,people’s lives have been greatly improved,happiness index is constantly improving,and residents’ demand for electricity is also growing.In the process of power production and transmission,power plants must ensure that the power can be stably and continuously delivered to each family.Although the country has vigorously implemented energy conservation and emission reduction and clean energy generation in recent years,considering the cost and efficiency of power generation,coal-fired is still the main support of energy and power in China in a short time.If the supply and demand of coal is unbalanced,it can not guarantee the stable,normal and continuous supply of power,which will seriously affect the normal life of residents and industrial parks.However,the contradiction between the characteristics of the power energy storage and the phased nature of coal supply,the contradiction between the coal supply and storage in the location space and time space,and the uncertainty factors in the transportation process are too many,the power plant must find out the problems in the inventory management and adjust it to reduce the cost.In order to resist the problems in the production process of power plants,the power plant must ensure sufficient inventory to avoid the impact of coal supply on normal production;But keeping too much coal-fired inventory will not only cause the inventory cost to rise,but also increase the risk of enterprise operation.In view of this,this paper first combs the coal-fired inventory management and optimization algorithm of power plants,expounds the basic theory and strategy of inventory management,and finds the main problems in the current coal-fired inventory management of power plants.Considering the actual research process,it is impossible to clearly determine all factors affecting the coal-fired inventory management,nor can we clearly understand how various indicators affect the inventory management and how to affect the level of coal-fired inventory management.This paper studies the influencing factors of coal burning inventory in power plants by using the principal component analysis method to determine the low average demand satisfaction rate of coal The main problem is the high cost of inventory.Secondly,the paper takes the coal inventory management characteristics of power plants as constraints,and takes the average demand satisfaction rate and total cost of coal as the objective function to establish the coal-fired inventory control model,and then further optimizes the model by particle swarm optimization.Particle swarm optimization algorithm can not only provide a way of thinking for the research of coal-fired inventory optimization,but also can effectively reduce the inventory cost of coal-fired power plants.Finally,the paper proves the feasibility of the optimization model established in this paper by the example data of m power plant.At the same time,it improves the operation ability and profitability of the power plant while improving the level of coal-fired inventory management.This study provides a new method for the optimization decision of coal-fired inventory management in power plants,provides a new way of thinking for the determination of objective function,and improves the scientific nature of inventory optimization theory.In practice,the theoretical results can be used to guide the management of coal-fired inventory in power plants,and help to improve the comprehensive improvement of inventory cost and demand satisfaction rate of power plants,thus improving their competitive advantage.