Optimal Allocation of Biomass Distributed Generators Using Modified Hunger Games Search to Reduce CO₂ Emissions

Biomass is a renewable energy source because it is contained in organic material such as plants. This paper introduces a modified hunger games search for solving global optimization and biomass distributed generator problems. The hunger search algorithm is a very recent optimization algorithm. Despite its merits, it still needs some modifications. The proposed approach includes a new binary τ-based crossover strategy with satisfaction fulfillment step mechanisms. This new algorithm is designed to improve the original hunger games search algorithm by addressing some of its shortcomings, specifically, in solving problems related to global optimization such as finding the best possible solutions for biomass distributed generators. To assess the power of the new approach, its performance was evaluated on the IEEE CEC’2020 test suite against five recent and competitive algorithms. This comparison process included applying the Wilcoxon sign rank and Friedman statistical tests. Reducing the system losses and enhancing the network’s voltage profile are two main issues in the stability of radial distribution networks. Optimal allocation of biomass distributed generators in radial distribution networks can not only improve their stability but also guarantee good service to the customers. Consequently, this research work suggests an effective strategy based on the proposed approach to produce the optimal positions, sizes, and power factors of the biomass distributed generators in the network. Accordingly, the target is to mitigate the network’s active power loss such that the power flow and the bus voltage have to be maintained at their standard limits. Three distribution networks were considered for validating the superiority of the new proposed algorithm. These networks are the IEEE 33-bus, IEEE 69-bus, and IEEE 119-bus. The obtained results were compared with the gravitational search algorithm, whale optimization algorithm, grey wolf optimizer, Runge Kutta method, and the original hunger search algorithm. The new approach outperformed the other considered approaches in obtaining the optimal parameters, which mitigated the power loss to 11.6300, 5.2291, and 145.489 kW, with loss reduction of 94.49%, 97.68%, and 88.79% for the three networks, respectively.