Research On Optimal Scheduling Of Hydro-thermal-renewable Power System

In order to ensure energy security,strengthen environmental protection and cope with climate change,many countries of the world are developing renewable energy to replace traditional fossil energy.As the largest developing country in the world,China's energy structure and development are facing great challenges.The problem of energy security is threatening the smooth implementation of the national energy strategy.With the development of society and economy,China's energy demand is gradually increasing,and the supply of energy is not optimistic.At the same time,the problem of environmental pollution is also increasing.Speeding up the development and utilization of renewable energy has become the only way for China to deal with the energy crisis.As an important part of renewable energy,wind and solar energy are gradually infiltrating into the power system of our country.However,due to its great randomness,it will bring great challenges and a series of requirements to the traditional power system.China is rich in water energy resources.Hydropower energy is the first large renewable energy for large-scale development.The development of hydropower can greatly reduce the consumption of traditional fossil energy.At the same time,some negative effects of new energy generation will be eliminated through controllable adjustment.Therefore,it is very important for China's energy strategy to strategize the optimal strategy of complementary scheduling power system.This article has carried out a series of discussions around this issue.The main work has been done is as follows(1)The cause of the uncertainty of renewable energy generation is analyzed,the probability distribution model of prediction error is studied,and the scene of renewable energy generation is discussed with the method of scene analysis.Using Latin hypercube sampling method,a large number of scenarios are generated to simulate the possible power generation scenarios of the renewable energy system.At the same time,the K-means clustering method is used to divide the scenerios with similar features into the same category,and the representative scene is selected to reduce the computational complexity of the model.Finally,the example analysis results show that the proposed method can well represent the uncertainty of renewable energy generation.(2)The optimization scheduling models of traditional unit commitment,the unit commitment considering renewable energy system and the multi energy complementary power system are calculated,and the optimal scheduling strategy is given by example analysis.From the shallower to the deeper,the popular problems in the current power system are analyzed.In order to analyze the scenerio more simply,the parameters representing the fluctuation and economy of the scenerio are presented,and the levels of different prediction errors are quantitatively analyzed.Three scenarios of prediction error level are generated.Through the calculation of the scenerio evaluation parameters of the corresponding scenerio,the influence of the renewable energy generation uncertainty to the power system is displayed.At the same time,the hydropower system model and the unit combination model are coupled to establish a hydro-thermal-renewable complementarity system.A multi-objective optimization scheme is proposed,which including economic and environmental objective function,and provides a theoretical basis for the high efficient operation of the multi energy complementary power system.(3)Complex models need efficient algorithms to solve them.Therefore,a new intelligent optimization algorithm is proposed in this paper.The optimal scheduling results are compared with other algorithms.The results show that the algorithm can solve the unit combination problem well,and the algorithm has faster convergence speed and better optimization effect.In order to solve the requirement of multi energy complementary power system model,the continuous variable and discrete variable are coupled,and the multi-objective mechanism optimization mechanism is added,which greatly expands the application scope of the algorithm.