Novel Design of Slim Mould Optimizer for the Solution of Optimal Power Flow Problems Incorporating Intermittent Sources: A Case Study of Algerian Electricity Grid

Nowadays, electrical power grids are facing increased penetration of renewable energy sources (RES), which result in increasing level of randomness and uncertainties for its operational quality. In addition, emerging need for efficient solutions to stochastic optimal power flow (OPF) problem has attracted considerable attention to ensure optimal and reliable grid operations in the presence of generation uncertainty and increasing demand. Therefore, this paper proposes an efficient Slime Mould-inspired Algorithm (SMA) that aims to minimize overall operating cost of main grid by managing the power flow among different generating resources. The problem is formulated as large-scale constrained optimization problem with non-linear characteristics. Its degree of complexity increases with incorporation of intermittent energy sources, making it harder to be solved using conventional optimization techniques. However, could be efficiently resolved by nature-inspired optimization techniques without any modification or approximation into the original-formulation. The objective function is the overall cost of system, including reserve cost for over-estimation and penalty cost for under-estimation of both PV-solar and wind energy. The SMA performance is evaluated on the IEEE 30-bus test system and Algerian power system, DZA 114-bus. The SMA is compared with four optimization algorithms: i) The well-studied meta-heuristics, i.e., Gorilla troops optimizer (GTO), and Orca predation algorithm (OPA), ii) Recently developed meta-heuristics, i.e., Artificial ecosystem optimizer (AEO), Hunger games search (HGS), and Jellyfish search (JS) optimizer, iii) ad high-performance meta-heuristics, Success-History based parameter adaptation for differential evolution method. The overall simulation results reveal that the SMA ranked first among the compared algorithms, and so, over and so, over different function landscapes.