Renewable energy-based Distributed Generation (DG) is the most imperative part of modern power system and offers many potential benefits. To attain maximum benefits offered by DG integration, it is important to model the time varying characteristics of both load and generation. Therefore, this paper presents a new Weibull distribution-based time-coupled Probabilistic Generation model for optimal placement and sizing of wind DG with time varying voltage dependent (TVVD) loads. At first, Probabilistic model is proposed for wind speed uncertainty modeling to calculate the hourly output power from wind DG. Afterwards, the values of output power are considered for determining optimal allocation and penetration of wind DG in distribution network to minimize the Average Multi-Objective Index (AIMO) using Particle Swarm Optimization (PSO). The strength of the proposed methodology is validated on IEEE 33 and 69-bus systems. Results depict that, proposed methodology is appropriate for wind speed modeling and is suitable for implementing in power system planning.

基于可再生能源的分布式发电（DG）是现代电力系统中最重要的部分，具有许多潜在的好处。为了获得DG集成所提供的最大利益，对负荷和发电的时变特性进行建模非常重要。因此，本文提出了一种新的基于Weibull分布的时间耦合概率生成模型，用于具有随时间变化的电压相关（TVVD）负载的风DG的最佳布置和尺寸。首先，提出了概率模型用于风速不确定性建模，以计算风电DG的每小时输出功率。之后，考虑使用输出功率的值来确定风电DG在配电网中的最佳分配和渗透，以使用粒子群优化（PSO）最小化平均多目标指数（AIMO）。所提出方法的强度已在IEEE 33和69总线系统上得到验证。结果表明，所提出的方法适用于风速建模，并且适合在电力系统规划中实施。