article · International Transactions on Electrical Energy Systems
Managing power networks efficiently requires optimising multiple factors, such as reducing fuel costs, emissions, and line losses, while maintaining voltage stability. A nature-inspired optimisation method known as the gorilla troops algorithm has been applied to address this optimal power flow challenge, both with and without the inclusion of thyristor-controlled series capacitor devices. The approach models the social behaviours of gorillas to navigate complex network variables. Testing was conducted on the standard IEEE 57-bus and 118-bus networks, as well as on a practical electric power network from the Egyptian West Delta. Across these tests, the method demonstrated reductions in power losses, fuel expenses, and environmental emissions, while simultaneously enhancing voltage stability. Comparative evaluations against several alternative contemporary algorithms showed superior performance, confirming the algorithm's effectiveness in balancing operational and environmental priorities within complex electrical grid infrastructures.
Operating modern electrical grids requires balancing environmental impact, economic costs, and system reliability. By optimising power flow and integrating advanced grid controllers, network managers can significantly cut transmission losses and carbon emissions without sacrificing stability. This computational strategy demonstrates that advanced nature-inspired algorithms can help utilities run cleaner, more cost-effective power networks on both standardised benchmarks and actual regional power grids.
This algorithm is directed at electrical utilities, grid operators, and power system software vendors seeking to optimise network dispatch and control flexible alternating current transmission systems. Because the tool was evaluated on practical grid data from the Egyptian West Delta network alongside standard simulation models, it sits at an applied research stage. Commercial integration would require embedding the algorithm into existing commercial energy management software and validating it in live dispatch environments.
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The optimal power flow issue (OPFI) can be solved in this work using the recently developed algorithm, gorilla troops algorithm (GTA). The goal of OPFI is to reduce numerous functions such as minimizing fuel costs, emissions, and power losses and improving the voltage stability related to electric power networks (EPNs). The GTA is inspired by gorillas’ social habits, which include migration to a strange region, migration toward a specified spot, traveling to other gorillas, competing for adult females, and escorting the silverback. The developed GTA is tested with and without the inclusion of the Thyristor-Controlled Series Capacitor (TCSC) devices in the system. The proposed GTA is applied on a practical Egyptian West Delta-EPN (WD-EPN) and the standard IEEE 57-bus EPN and with and without the inclusion of the TCSC devices to appraise the GTA algorithm’s performance in the OPFI. In addition, the proposed GTA is applied on a large-scale IEEE 118 bus system with higher outperformance compared to particle swarm optimization. The results illustrate that the fuel costs, emissions, voltage stability, and power losses are reduced for the standard IEEE 57-bus EPN with and without TCSC devices by a percentage of (18.847% and 18.818%), (59% and 58.97%), (13.405% and 11.507%), and (64.337% and 65.178%), respectively, while fuel costs, emissions, voltage stability, and power losses are reduced for WD-EPN with and without TCSC devices by a percentage of (8.547%, 8.565%), (13.641%, 13.6%), and (61.949%, 61.954%), respectively. A comparison study is conducted to demonstrate the GTA’s effectiveness when compared with other recently developed algorithms such as improved Salp Swarm Algorithm, quasi-reflection jellyfish search, Salp Swarm Algorithm, improved heap-based algorithm, bat search algorithm, social network search algorithm, electromagnetic field optimization, and other well-known algorithms as well. According to the comparison with these algorithms, the GTA demonstrates the best results among the attained results.
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DOI: 10.1155/2022/9448199
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