Research On Reactive Power Optimization Of Distribution Network Including Small Hydropower

With the continuous development of smart grids,and the aim to meet people’s growing social needs,environmental problems caused by energy production are becoming increasingly tense.As a green and clean power source,small hydropower plays a vital role in the safe and continuous supply of electricity in some areas.However,due to the long power supply lines of the distribution network connected to the small hydropower,the complex grid structure and the small hydropower itself affected by seasonal factors,the excessively long electrical distance during the dry season in winter causes the terminal voltage of the distribution network to be seriously low.During the wet season in summer,the electricity cannot be absorbed locally,and there is a serious reverse power phenomenon,which results in the line voltage to often exceed the upper limit to form a steady-state over-voltage,Finally,this phenomenon seriously threatens the safe operation of the power grid,and leads to the most serious problem of the voltage and reactive power management of the distribution network in some areas of my country in recent years.Considering relationship between reactive power and voltage,the reactive power optimization research is now carried out on the problem of voltage over-limit caused by the access of small hydropower to the distribution network.Traditional reactive power optimization methods are mostly single-objective optimization,which only considers economic benefits to minimize active power loss,and ignores factors such as voltage quality and stability,so they have greater limitations.Aiming at the shortcomings of traditional reactive power optimization methods,this paper based on improved hierarchical forward-backward power flow calculations analyzes the characteristics of the small hydropower grid integration and unit models,and establishes the reactive power optimization model of the distribution network,whose objective function are the minimum grid active power loss and the minimum sum of the node voltage offset,Using the phase modulation operation of small hydroelectric generators,transformer ratio voltage regulation and reactive power compensation devices as control variables.At present,most optimization methods often turn multi-objective optimization problems into single-objective problems in a weighted manner when solving such problems.Because the selection of weighting factors directly affects the simplification goal by means of empirical constants,the results often have a certain degree of subjectivity factor.In this paper,Pareto solution is introduced to find the Pareto optimal solution set of the multi-objective optimization problem,so that the obtained Pareto optimal solution set takes into account the optimization of multiple objectives at the same time.In the process of solving the model,combined with the artificial intelligence algorithm,the bat algorithm and its application in the power system have been deeply studied,and the algorithm has been improved to avoid the algorithm from falling into the local optimum.The probabilistic elite pool disturbance strategy is introduced,setting the external memory to use the phasing out mechanism to sort the calculated target values by crowding distance,finally increasing the diversity of control variable values,and fully overcoming the problems of the algorithm being easy to fall into the local optimal value and the slow convergence speed in the later iteration to obtain the Pareto optimal solution set of the model.In this paper,the reactive power optimization model of the distribution network with small hydropower is established based on Matlab software,focusing on simulation and verification under the two extreme conditions of small load during high water period and high load during low water period.The simulation based on the improved bat algorithm under the over-voltage and low voltage conditions because of the two conditions mentioned above can both obtain a set of Pareto optimal solutions,which proves that the improved bat algorithm proposed in this paper can obtain good reactive power optimization results for decision-makers to choose.