Analyzing market plans for enhanced energy hub efficiency: strategies for integrating multiple energy sources and collaboration

In this study, we introduce the Archimedes optimization algorithm as a key component of our methodology. The Archimedes algorithm, renowned for its robustness and efficiency, is specifically tailored to handle complex optimization problems encountered in energy management systems. By leveraging its unique capabilities, we ensure precise solutions and rapid convergence even under challenging solution conditions characterized by uncertain energy demand forecasts. The proposed algorithm operates by iteratively refining solutions based on probabilistic load predictions and system constraints. It employs sophisticated optimization techniques to navigate the multi-dimensional solution space efficiently, thereby identifying optimal strategies for energy resource allocation within EHs. By incorporating the Archimedes algorithm into our framework, we can effectively capture the dynamic interactions between various energy carriers and adapt to changing market conditions in real-time. Furthermore, the Archimedes algorithm's ability to provide near-optimal solutions without the need for exhaustive computational resources makes it particularly well-suited for practical implementation in energy management systems. Its efficiency ensures that decision-makers can obtain actionable insights and make informed decisions quickly, thereby enhancing the operational efficiency and economic viability of EHs. The proposed optimization algorithm outperforms alternatives like Archimedes optimization algorithm (APO) and genetic algorithm (GA). On average, it requires 25% fewer iterations for convergence. Moreover, it achieves a 90% convergence rate within just 50 iterations, compared to 70 iterations for APO and 60 iterations for GA, based on 30 distinct runs.