Optimal planning for distribution networks considering system uncertainties using pseudo-inspired gravitational search algorithm

This paper addresses the integration challenges posed by renewable energy resources (RERs), specifically solar photovoltaic (PV) and wind generation, within modern power systems considering their intermittent behaviour. The proposed method employs relevant probability distribution functions to evaluate uncertainties associated with the stochastic nature of PV and wind turbine output powers. Additionally, uncertainties related to electricity prices and load variations are considered to enhance system performance and achieve overall objective function improvements. To manage uncertainty, scenario generation and reduction techniques are employed. The optimization of RER allocation and sizing is crucial for effective network operation. The paper introduces the pseudo-inspired gravitational search algorithm (PI-GSA) to identify the optimal configuration for the distribution networks. This algorithm considers a multi-objective function that minimizes the cost of power purchase from the grid, system power loss and voltage deviation, while simultaneously maximizing annual energy savings. The effectiveness and feasibility of the proposed PI-GSA algorithm are tested on IEEE 15-bus, IEEE 33-bus and IEEE 69-bus radial distribution networks. Simulation results indicate that the PI-GSA algorithm outperforms other algorithms, including PSO, modified PSO and Jaya algorithm, demonstrating its superiority in achieving optimized configurations for RER integration in distribution system.