Integration of renewable energy resources introduces several uncertainties for planning of distribution networks, requiring the consideration of random variables. This paper proposes a chance-constrained distribution network planning to deal with the long-term uncertainties related to load power, wind power, and solar power using probability density functions under a pseudo-dynamic approach. The model is constructed through linearized load flow equations which are combined with probability density functions using convolution. The optimization problem is then solved by integer genetic algorithm; minimizing the installation and maintenance costs of substations, feeders, and renewable generators and the expected cost for purchased energy from the upper grid. The chance constraints are formulated for voltage limits, feeder currents’ limits, and substation limits in order to control the satisfaction level of power system parameters. The proposed method, which is computationally efficient, is applied to the 24-nodes and 34-nodes test networks to compare the obtained results with Monte Carlo Simulation along with the full AC load flow and the results show the importance of considering chance constraints and penetration level of renewable energy in terms of investment variables through case studies.

可再生能源资源的整合给配电网络的规划带来了一些不确定性，需要考虑随机变量。本文提出了一种机会受限的配电网规划，以伪动态方法利用概率密度函数来处理与负荷电力，风能和太阳能有关的长期不确定性。该模型是通过线性潮流流程方程构建的，该方程使用卷积与概率密度函数组合。然后用整数遗传算法解决优化问题。最小化变电站，馈线和可再生发电机的安装和维护成本，以及从上层电网购买能源的预期成本。机会限制是针对电压限制，馈线电流限制，和变电站限值，以控制电力系统参数的满意度。该算法计算效率高，被应用于24节点和34节点测试网络，以将获得的结果与蒙特卡洛模拟以及完整的交流潮流进行比较，结果表明考虑机会约束和渗透的重要性通过案例研究，根据投资变量确定可再生能源的水平。