电力系统经济调度的量子粒子群改进算法

电力系统经济调度(economic dispatch，ED)通过合理配置电力资源，在满足实际运行约束的前提下，使发电成本率最小化。针对ED问题，将量子粒子群优化(quantum-behaved particle swarm optimization，QPSO)算法与随机扰动策略相结合，提出了一种改进的随机扰动量子粒子群优化(QPSO with random perturbation，RPQPSO)算法。扰动策略采用2种方式，在进化后期根据随机概率对群中的每个粒子进行扰动，避免算法陷入局部最优。为了验证其有效性，利用一组标准测试函数对RPQPSO算法进行了性能测试，测试表明，该算法有助于增加种群多样性，提高算法的全局搜索能力。通过将该算法应用到典型电力系统中进行测试，该算法相较于传统遗传算法、粒子群算法和QPSO算法，在解的质量、鲁棒性和收敛性等方面都取得了较大的提高，表现出优异的求解性能。

Enhanced quantum-behaved particle swarm optimization algorithm for power system dispatch problem

The economic dispatch (ED) of power system is an important issue in allocation of power resources, whose objective is to simultaneously minimize the generation cost rate while satisfying various equality and inequality constraints in power system operation. In this paper, a modified quantum-behaved particle swarm optimization (QPSO) method is proposed to solve the problem. The proposed method, denoted as RPQPSO, combines the QPSO algorithm with random perturbation operation to enhance the global search ability of the algorithm. The perturbation strategy adopts two methods to perturb each particle in the group according to the random probability at the late stage of evolution, so that the algorithm avoids falling into the local optimum. In order to verify its effectiveness, a set of standard test functions are used to test the performance of the RPQPSO algorithm. The test results show that the algorithm is helpful to increase the population diversity and improve the global search capability of the algorithm. The feasibility of the RPQPSO method is demonstrated by a typical power system. Compared with the traditional genetic algorithm (GA), the particle swarm optimization (PSO) and the QPSO, this algorithm has greatly improved the quality, robustness, and convergence of the solution. The simulation results show that the proposed RPQPSO method is able to obtain higher quality solutions in the ED problem than any other tested optimization algorithm.