Contribution to the optimization of distribution grid operation in the presence of distributed generation

Abstract

owadays, the integration of renewable energy source-based distributed generation (RES-based DG) into radial distribution grids has increased significantly, caused by technological advancements, technical requirements, financial incentives, and ecological concerns. RES deployment remains one of the most effective and viable strategies for meeting the growing demand for electricity and reinforcing the distribution systems performance. This integration hinges on identifying optimal locations and determining the appropriate power outputs of RES units when injected into radial distribution grids. However, this task is complicated due to the inherent stochastic nature of RES power generation, particularly from wind turbines (WTs) and solar photovoltaic (SPV) arrays, along with the fluctuations in load demand, necessitating the adoption of robust and advanced planning strategies. To this end, this thesis proposes to develop an appropriate time-varying probability load-generation model based on Weibull and Beta probability density functions (PDFs) to estimate the stochastic power output from WTs and SPV arrays, respectively. This model is based on hourly seasonal data, including wind speed, solar irradiance, and ambient temperature, collected over a specified time frame and location. An improved Frilled Lizard Optimization (IFLO) algorithm is developed and proposed for strategic RES planning, aiming to minimize total power losses, improve voltage profiles, and enhance voltage stability while adhering to operational constraints. The IFLO algorithm incorporates three advanced strategies: fitness distance balance, quasi-opposite-based learning, and Cauchy mutation, to strengthen its search capabilities and prevent to local optima traps. The proposed method effectively determines the optimal locations, rated capacities of SPV strings and WTs, and the power factor of WTs. Its performance is validated through simulations on the IEEE 69-bus medium-scale and 85-bus large-scale distribution grids. Simulation results decisively demonstrate that optimal RES allocation significantly improves system performance. Furthermore, the suggested technique outperforms other recent and effective optimization algorithms, including the grey wolf optimizer (GWO), jellyfish search optimizer (JSO), black-winged kite algorithm (BKA), and the original frilled lizard optimization (FLO), in solving the optimal planning problem of RES integration under both deterministic and probabilistic scenarios.