| In recent years,due to the scarcity of fossil energy sources and the increasingly serious environmental pollution problems,the penetration rate of new energy represented by wind and solar energy has continued to increase.In order to effectively respond to the problems caused by the large-scale integration of renewable energy in a longer time span and optimize long-term resources,it is really necessary to carry out a research on the monthly unit commitment of wind power and photovoltaic power generation.This paper summarizes the advantages and disadvantages of the existing methods for establishing and solving unit commitment models,combines the characteristics of large-scale new energy grid-connected power generation,and establishes a monthly unit commitment model that takes the randomness of new energy generation into account and proposes corresponding solutions.The research includes the following aspects:Firstly,the interval prediction information of wind power and photovoltaic power output obtained from the characteristics of medium and long-term weather information is incorporated into the monthly unit commitment model.Considering the varying value of the confidence interval will affect the reference value of the forecasting results of new energy power generation,the risk costs of wind abandonment and load shedding caused by the uncertainty of new energy generation under different confidence intervals(from small to large: 70%,80,90%,98%)are calculated,which are included into the total cost of power generation.Secondly,a multi-objective monthly unit commitment model that takes the random generation of new energy into account is established.In order to maintain the economic efficiency,reliability and environmental protection of the operating power system,this paper constructs a multi-objective function.The total power generation cost consisting of operating costs of thermal power units,risk costs of wind abandonment and load shedding is used as a criterion for measuring the economic operation of power grid.At the same time,the system reliability rate is introduced as an index to measure the system reliability,and the environmental impacts brought by the emission of polluting gas is taken into account.In addition,in order to appropriately adjust the weights according to the needs of different operating scenarios,and obtain a solution which is more in line with the actual situation,the evaluation function based on the geometric weighting method converts the the multi-objective programming function into a single objective function.Regarding constraints,apart from spinning reserve,unit start-off time,upper and lower limits of wind-light-fire output,transmission capacity is used to replace network constraints to deal with the low accuracy of power flow calculation when monthly unit commitment are treated as medium and long-term resource optimization.Then,the memetic algorithm based on a combination of global and local search is proposed to ameliorate the overall operating efficiency of the multi-objective optimization model and ensure the accuracy of obtaining optimal solution.This algorithm introduces a tabu search strategy into the global search process of the monthly unit commitment model that takes into account new energy grid-connected power generation by using an improved adaptive genetic algorithm with adaptive mutation and crossover operators to complete specific genetic search on the local optimal subspace and avoid falling into a local optimum.Finally,tests are respectively performed on 10～100 unit systems,IEEE 39 and IEEE118 systems to verify the practicability of the proposed model and the effectiveness of the solution method. |
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