Research On Short-term Hydrothermal Scheduling Based On Improved Grey Wolf Optimization Algorithm

The essence of short-term hydrothermal schduling(STHS)is a complex mathematical optimization problem with multiple constraints and high dimensions,which is a great challenge to the applicability of the optimization method and the efficiency of solving the problem.There are a large number of constraints in this problem,which may make the scheduling scheme violate the constraints.Therefore,an efficient constraint processing method is very important for solving the STHS problem.In order to cope with these challenges,an optimization method is improved and an efficient constraint processing method is proposed in this thesis,the fuel costs of thermal power station and the constraint violations of the scheduling scheme are used as the criteria for selecting the optimal scheduling scheme,so as to obtain the scheduling scheme with higher economy and less constraint violations.In order to deal with the constraints of the reservoir’s volume,a two-level handling method where the rough adjustment is followed by the slight adjustment is adopted.This method can satisfy the volume constraint condition of the reservoir in each period of the dispatching.When dealing with the load balance constraints of power system,a method of load balance constraint processing based on valve point effect of thermal power plant is proposed.By analyzing the fuel cost curve of thermal power plants,it can be seen that the power generation of the valve point of thermal power plants has the highest cost performance.Therefore,when adjusting the power generation,the existing power generation of thermal power plants is adjusted to its nearest valve point,which meets the load balance constraint and makes the obtained scheduling scheme more economical.In terms of optimization method,the grey wolf optimizer(GWO)with great search performance is used to optimize the STHS problem.In the research process,it is found that the GWO algorithm has strong local search ability and convergences fast,but it is easy to fall into the local optimal solution in the process of searching,resulting in a low quality of scheduling scheme.In order to balance the search performance of GWO,the search mechanism of dragonfly algorithm(DA)is added in the GWO.DA has characteristics of being attracted by food source and dispersing the external enemie’s attention,so it has great global search ability.After a large number of experiments,an algorithm which combines grey wolf and dragonfly’s search mechanisms(GWO＿DA)is proposed,In each iteration,the grey wolf individual selects one of the search mechanisms to optimize according to the update status of the local optimal individual in the optimization process,so as to better balance the global search and local search ability of algorithm.In order to further improve the quality of global optimal solution,the chaotic search technology is introduced as a supplement,which can generate chaotic variables with the progress of iteration.Using the chaotic search technology to update the position of grey wolf can greatly increase the probability of updating the global optimal solution.The improved grey wolf algorithm is obtained: chaos search-grey wolf algorithm which combines grey wolf and dragonfly search mechanism(GWO＿DA＿CHAOS).In order to verify the feasibility and superiority of the proposed method,the proposed GWO＿DA＿CHAOS and constraint processing method are used to carry out simulation experiments in a total of four test cases in three test systems which include four-hydro one-thermal,four-hydro three-thermal and four-hydro ten-thermal.Experimental results show that the proposed method can optimize the STHS problem effectively,the scheduling scheme can meet all the constraints.The optimization results of GWO＿DA＿CHAOS are compared with the ones of other literatures in same test cases,the comparison results show that the proposed method has great competitive advantage and obtains scheduling scheme of less fuel costs.It also shows that the method proposed in this thesis is better than the ones of other literatures.