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The chaos particle swarm optimization algorithm was presented to solving optimal power flow. The proposed OPF considers the total cost of generators as the objective functions. To enhance the performance of algorithm, a premature convergence strategy was proposed. The strategy can be divided into two parts. In the first part, a method is introduced to judge premature convergence, while another part provides an advance method to improve the performance of algorithm with searching the solution in total feasible region. The control strategy used to prevent premature convergence will obtain starting values for initial particle before program iterating, so it can provide bitter probability of detecting global optimum solution. The simulation results on standard IEEE 30-bus system minimizing fuel cost of generator show the effectiveness of the chaos particle swarm optimization algorithm, and can obtain a good solution.

With the rapid development of DG, especially the access of large-scale renewable energy, traditional simple distribution network with unique source turns into ADN with multiple sources, making the distribution network more complicated. In this paper, The power source and grid planning of traditional and intelligent distribution network are discussed, based on which the problems ADN faces and the research difficulty are focused on. The key technology of ADN planning is analyzed, including the uncertainty of load forecasting, the ADN absorption capacity for DG and the cost-effectiveness of ADN planning. Some suggestions for the research direction of ADN in the future are made at the end, providing reference for the ADN planning with large-scale renewable energy access.