智能优化算法及其在电力系统无功优化中的应用研究
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摘要
电力系统无功优化对确保电力系统优化运行具有重要作用,它直接关系到电力系统运行的安全性与经济性。随着智能优化算法的发展,寻求收敛性好、鲁棒性强且具有智能特征的优化算法,以求解电力系统无功优化问题已成为重要的研究方向,且具有重要的理论意义与工程实际意义。本文在已有研究成果的基础上,提出了单目标／多目标的导向搜索算法与单目标／多目标的动态多群体自适应差分进化算法,对电力系统静态单目标无功优化、静态多目标无功优化、动态单目标无功优化、动态多目标无功优化、典型函数优化等问题进行了深入的研究和探讨,主要内容包括以下五方面：
(1)提出了一种单目标导向搜索算法(Oriented Search Algorithm, OSA)。该算法将搜索个体模拟为人的搜索行为,搜索对象(目标函数最优解)模拟为可向搜索个体传送导向信息的智能体,以使搜索个体与搜索对象间相互通讯。通过对典型函数优化问题以及求解以系统网损最小为目标的电力系统静态单目标无功优化问题进行测试,结果验证了OSA的有效性,OSA是求解电力系统静态单目标无功优化问题的一种有效工具。
(2)提出了一种多目标导向搜索算法(Multi-objective Oriented Search Algorithm, MOOSA).该算法基于OSA的寻优思想,引入多目标优化算法的评价策略与选择策略,寻找分布均匀且收敛性好的Pareto最优前沿。通过对考虑静态电压稳定性的电力系统静态多目标无功优化问题进行测试,结果验证了该算法能够折衷不同目标函数值,找出使各目标函数尽量优的分布均匀的Pareto前沿,是一种有效的求解考虑静态电压稳定性的电力系统静态多目标无功优化工具。
(3)提出了一种单目标动态多群体自适应差分进化算法(Dynamic Multi-group Self-adaptive Differential Evolution Algorithm,DMSDE)。该算法为基于自适应控制参数改进差分进化算法(Self-adapting Control Parameters Modified Differential Evolution, SACPMDE)的一种改进算法。通过对电力系统静态单目标、多目标无功优化问题进行测试,结果验证了该算法是求解电力系统静态单目标、多目标无功优化问题的一种有效工具。为进一步提高算法性能,引入带局部搜索方法,即带局部搜索的动态多群体自适应差分进化算法(DMSDE with Local Search Algorithm, DMSDELS)。通过对典型函数优化问题进行测试,结果验证了DMSDELS具有较高的搜索精度和收敛性,且具有较强的跳出局部最优解能力。
(4)采用提出的单目标动态多群体自适应差分进化算法(Dynamic Multi-group Self-adaptive Differential Evolution Algorithm,DMSDE)求解动态单目标无功优化问题。该动态单目标兀功优化问题是针对在日负荷动态变化情况下,考虑控制设备如有载调压变压器抽头、无功补偿电容器组档位的日允许最大动作次数约束,使系统在合理电压下的日有功网损最小。通过仿真测试,结果验证了DMSDE是求解电力系统动态单目标无功优化问题的一种有效工具。
(5)提出了一种多目标动态多群体自适应差分进化算法(Multi-objective Dynamic Multi-group Self-adaptive Differential Evolution Algorithm,MODMSDE)。该算法基于DMSDE的寻优思想,引入多目标优化算法的非支配排序与拥挤度法以评价和选择新的种群。考虑到控制设备日允许最大动作次数的限制使不同时段的优化问题具有时间耦合性,提出了基于动态多目标优化的动态无功优化的执行策略。通过对动态多目标无功优化以及函数优化问题进行测试,结果验证了该算法的有效性,同时也验证了基于动态多目标优化的动态无功优化的执行策略是有效的。
Reactive power optimization plays an important role in optimal operation of power systems, which directly effects on the security and effectiveness of power system operation. With the development of intelligent optimization algorithms, exploring them with better convergence and robustness for solving reactive power optimization becomes an attractive research area and is of theoretical and engineering practical significance. On the basis of previous research achievements, the paper investigates single/multiple objective oriented search algorithms and single/multiple objective dynamic multi-group self-adaptive diferential evolution algorithms to deal with static single/multiple objective reactive power optimization, dynamic single/multiple objective reactive power optimization, and benchmark function optimization. The contents mainly include five parts listed as follows:
(1) A single-objective oriented search algorithm (OSA) is proposed. In OSA, the search individual simulates human behavior, and the search-object (the optimal solution of the objective function) works like an intelligent agent that can transmit oriented information to search individuals, so that search-individuals and the search-object can communicate with each other. Through testing on typical benchmark function optimization and static single-objective reactive power optimization based on OSA, the efficiency of the proposed algorithm is verified and OSA is proved to be an efficient tool for dealing with static single-objective reactive power optimization in power system.
(2) A multi-objective oriented search algorithm (MOOSA) is proposed. MOOSA is based on OSA with the evaluation and selection strategies of multi-objective optimization to search well distributed and converged at Pareto-optimal front. Through testing on static multi-objective reactive power optimization based on MOOSA, the simulations prove that the proposed algorithm has the capability of trading off different objective function values and searching a well distributed set of solutions as closed to the true Pareto-optimal front as possible. As a result, MOOSA is proved to be an efficient tool for dealing with multi-objective reactive power optimization considering static voltage stability in power system.
(3) A single-objective dynamic multi-group self-adaptive differential evolution algorithm (DMSDE) is proposed. DMSDE is an improved algorithm based on self-adapting control parameters modified differential evolution (SACPMDE). Through testing on static single/multiple objective reactive power optimization based on DMSDE, the results demonstrate that DMSDE is an efficient method to dispatch static single/multiple objective reactive power flow. In order to further improve the performance of the proposed algorithm, local search method is employed, namely DMSDE with local search algorithm (DMSDELS). The results show that DMSDELS is better in the search precision, convergence property and has strong ability to escape from the local sub-optima through testing on typical benchmark function optimization.
(4) DMSDE is employed to solve dynamic single-objective reactive power optimization. Subjected to the maximum daily allowable number of switching operations for control devices which includes transformer taps and capacitor banks, the objective of the dynamic single-objective reactive power optimization under the varying daily load of power system is to minimize active power losses while maintaining acceptable daily voltage profiles. The simulations on dynamic single-objective reactive power optimization illustrate that DMSDE is an efficient method.
(5) A multi-objective dynamic multi-group self-adaptive differential evolution algorithm (MODMSDE) is proposed. MODMSDE is based on DMSDE with the non-dominated sorting method and crowding-distance method of multi-objective optimization to evaluate and select new population. Considering that the limitation to the maximum daily allowable number of switching operations for control devices couples different time-period optimization together, an implementation based on dynamic multi-objective optimization is employed to solve dynamic multi-objective reactive power optimization. Through testing on dynamic-based reactive power optimization and benchmark function optimization based on MODMSDE, the efficiency of the proposed algorithm is verified and the implementation based on dynamic multi-objective optimization for the dynamic reactive power model is effective.
(1)提出了一种单目标导向搜索算法(Oriented Search Algorithm, OSA)。该算法将搜索个体模拟为人的搜索行为,搜索对象(目标函数最优解)模拟为可向搜索个体传送导向信息的智能体,以使搜索个体与搜索对象间相互通讯。通过对典型函数优化问题以及求解以系统网损最小为目标的电力系统静态单目标无功优化问题进行测试,结果验证了OSA的有效性,OSA是求解电力系统静态单目标无功优化问题的一种有效工具。
(2)提出了一种多目标导向搜索算法(Multi-objective Oriented Search Algorithm, MOOSA).该算法基于OSA的寻优思想,引入多目标优化算法的评价策略与选择策略,寻找分布均匀且收敛性好的Pareto最优前沿。通过对考虑静态电压稳定性的电力系统静态多目标无功优化问题进行测试,结果验证了该算法能够折衷不同目标函数值,找出使各目标函数尽量优的分布均匀的Pareto前沿,是一种有效的求解考虑静态电压稳定性的电力系统静态多目标无功优化工具。
(3)提出了一种单目标动态多群体自适应差分进化算法(Dynamic Multi-group Self-adaptive Differential Evolution Algorithm,DMSDE)。该算法为基于自适应控制参数改进差分进化算法(Self-adapting Control Parameters Modified Differential Evolution, SACPMDE)的一种改进算法。通过对电力系统静态单目标、多目标无功优化问题进行测试,结果验证了该算法是求解电力系统静态单目标、多目标无功优化问题的一种有效工具。为进一步提高算法性能,引入带局部搜索方法,即带局部搜索的动态多群体自适应差分进化算法(DMSDE with Local Search Algorithm, DMSDELS)。通过对典型函数优化问题进行测试,结果验证了DMSDELS具有较高的搜索精度和收敛性,且具有较强的跳出局部最优解能力。
(4)采用提出的单目标动态多群体自适应差分进化算法(Dynamic Multi-group Self-adaptive Differential Evolution Algorithm,DMSDE)求解动态单目标无功优化问题。该动态单目标兀功优化问题是针对在日负荷动态变化情况下,考虑控制设备如有载调压变压器抽头、无功补偿电容器组档位的日允许最大动作次数约束,使系统在合理电压下的日有功网损最小。通过仿真测试,结果验证了DMSDE是求解电力系统动态单目标无功优化问题的一种有效工具。
(5)提出了一种多目标动态多群体自适应差分进化算法(Multi-objective Dynamic Multi-group Self-adaptive Differential Evolution Algorithm,MODMSDE)。该算法基于DMSDE的寻优思想,引入多目标优化算法的非支配排序与拥挤度法以评价和选择新的种群。考虑到控制设备日允许最大动作次数的限制使不同时段的优化问题具有时间耦合性,提出了基于动态多目标优化的动态无功优化的执行策略。通过对动态多目标无功优化以及函数优化问题进行测试,结果验证了该算法的有效性,同时也验证了基于动态多目标优化的动态无功优化的执行策略是有效的。
Reactive power optimization plays an important role in optimal operation of power systems, which directly effects on the security and effectiveness of power system operation. With the development of intelligent optimization algorithms, exploring them with better convergence and robustness for solving reactive power optimization becomes an attractive research area and is of theoretical and engineering practical significance. On the basis of previous research achievements, the paper investigates single/multiple objective oriented search algorithms and single/multiple objective dynamic multi-group self-adaptive diferential evolution algorithms to deal with static single/multiple objective reactive power optimization, dynamic single/multiple objective reactive power optimization, and benchmark function optimization. The contents mainly include five parts listed as follows:
(1) A single-objective oriented search algorithm (OSA) is proposed. In OSA, the search individual simulates human behavior, and the search-object (the optimal solution of the objective function) works like an intelligent agent that can transmit oriented information to search individuals, so that search-individuals and the search-object can communicate with each other. Through testing on typical benchmark function optimization and static single-objective reactive power optimization based on OSA, the efficiency of the proposed algorithm is verified and OSA is proved to be an efficient tool for dealing with static single-objective reactive power optimization in power system.
(2) A multi-objective oriented search algorithm (MOOSA) is proposed. MOOSA is based on OSA with the evaluation and selection strategies of multi-objective optimization to search well distributed and converged at Pareto-optimal front. Through testing on static multi-objective reactive power optimization based on MOOSA, the simulations prove that the proposed algorithm has the capability of trading off different objective function values and searching a well distributed set of solutions as closed to the true Pareto-optimal front as possible. As a result, MOOSA is proved to be an efficient tool for dealing with multi-objective reactive power optimization considering static voltage stability in power system.
(3) A single-objective dynamic multi-group self-adaptive differential evolution algorithm (DMSDE) is proposed. DMSDE is an improved algorithm based on self-adapting control parameters modified differential evolution (SACPMDE). Through testing on static single/multiple objective reactive power optimization based on DMSDE, the results demonstrate that DMSDE is an efficient method to dispatch static single/multiple objective reactive power flow. In order to further improve the performance of the proposed algorithm, local search method is employed, namely DMSDE with local search algorithm (DMSDELS). The results show that DMSDELS is better in the search precision, convergence property and has strong ability to escape from the local sub-optima through testing on typical benchmark function optimization.
(4) DMSDE is employed to solve dynamic single-objective reactive power optimization. Subjected to the maximum daily allowable number of switching operations for control devices which includes transformer taps and capacitor banks, the objective of the dynamic single-objective reactive power optimization under the varying daily load of power system is to minimize active power losses while maintaining acceptable daily voltage profiles. The simulations on dynamic single-objective reactive power optimization illustrate that DMSDE is an efficient method.
(5) A multi-objective dynamic multi-group self-adaptive differential evolution algorithm (MODMSDE) is proposed. MODMSDE is based on DMSDE with the non-dominated sorting method and crowding-distance method of multi-objective optimization to evaluate and select new population. Considering that the limitation to the maximum daily allowable number of switching operations for control devices couples different time-period optimization together, an implementation based on dynamic multi-objective optimization is employed to solve dynamic multi-objective reactive power optimization. Through testing on dynamic-based reactive power optimization and benchmark function optimization based on MODMSDE, the efficiency of the proposed algorithm is verified and the implementation based on dynamic multi-objective optimization for the dynamic reactive power model is effective.
引文
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