Abstract: Network reconstruction of the power grid is a nonlinear combination optimization problem. With the rapid development of smart grid and the sharp expansion of grid scale, computational complexity of the network reconstruction algorithm increases significantly. Ant colony algorithm has been widely used in solving the combinatorial optimization problem because of its robustness, parallelism and positive feedback mechanism. However, the existing work suffers from the slow computation speed and easily falls into the local optimum. In order to solve the above mentioned problems, this paper proposes a reduction-accumulation dual strategy based ant colony algorithm for network reconfiguration of the power system. On one hand, by defining a reduction factor, number of the ants can decrease continuously given the convergence stability in the iterative process. It implements a dynamically adaptive mechanism in selecting ant number and speeds up the computation. On the other hand, the algorithm defines an accumulation factor and increases a stage of pheromone accumulation. This can guide the algorithm to jump out of the local optimal and improve the probability of finding the optimal topology. With the same numbers of updated pheromone and initial ants, the experimental results show that the proposed algorithm can improve the computation speed by about 25% and reduce the minimum net loss by about 9%, compared with the existing work.

Key words: ant colony algorithm, reduction-accumulation dual strategy, network reconfiguration, dynamic adaptation, pheromone

摘要： 电网的网络重构本质上属于非线性组合优化问题。随着智能电网的快速发展和电网规模的急剧扩张，网络重构算法的计算复杂度也大幅增加。蚁群算法具有鲁棒性、可并行性和正反馈机制等优点，因而被广泛应用于组合优化问题的求解之中。然而，现有的蚁群算法仍存在计算速度慢，易于陷入局部最优等缺点。为解决上述问题，提出了一种削减-累加双策略的蚁群算法并将其应用于电力系统的网络重构计算中。一方面，定义削减因子，使迭代过程中的蚂蚁数量随算法收敛的稳定程度而不断减少，实现动态自适应的蚂蚁数量选择机制以加快计算速度；另一方面，定义积累因子，增加了信息素的积累阶段，引导算法跳出局部最优，提高找到最优拓扑结构的概率。实验结果表明，在信息素更新次数和初始蚂蚁数量都相同的情况下，与已有工作相比，提出的算法能够将计算速度提升约25%；同时，将最小网损降低约9%。

关键词: 蚁群算法, 削减-累加双策略, 网络重构, 动态自适应, 信息素