Multi-objective and multi-level models to optimize integration of hybrid renewable energy systems

In contemporary practical scenarios, the integration of diverse renewable energy sources, such as solar, wind, hydro, biomass, geothermal, and energy storage solutions like batteries, presents complex challenges. These challenges demand simultaneous optimization of energy production, system reliability enhancement, and cost minimization including those related to fossil fuels and greenhouse gas emissions. Hence, it is imperative to develop comprehensive models that address all these objectives. This paper proposes novel multi-objective and multi-level mathematical models tailored for Hybrid Renewable Energy Systems (HRESs), facilitating the simultaneous consideration of diverse objectives and decision-making levels within renewable energy integration frameworks. To effectively tackle the complexity of these models, two efficient hybrid algorithms are introduced. The first algorithm employs a combined smoothing approach to address multi-level problems, leveraging Karush–Kuhn–Tucker (KKT) conditions, mathematical principles, and heuristic functions to smooth the multi-level model. Additionally, Taylor approximation is employed to further refine the smoothed problem. The second algorithm, tailored for multi-objective models, operates in two phases: initially, a heuristic algorithm simplifies objective functions through interpolation; subsequently, the population is optimized using the Laying Chicken Algorithm (LCA), with a neural network refining the best LCA generation to identify the Pareto front in multi-objective problems. The proposed algorithms significantly improve system efficiency by optimizing the integration of diverse renewable energy sources and energy storage, leading to reduced operational costs and enhanced sustainability outcomes. These advancements offer promising real-world applications in optimizing energy systems, supporting the transition to cleaner, more sustainable energy infrastructure globally. Experimental results show that the proposed algorithm outperforms state-of-the-art methods, achieving Avg HV improvements of 1.50% for DTLZ1, 1.30% for DTLZ2, 3.28% for DTLZ3, 0.57% for DTLZ4, and 1.05% for DTLZ5. It also achieves significant reductions in Std Dev, with improvements of 98.37% for DTLZ1, 48.46% for DTLZ2, 20.41% for DTLZ3, 26.61% for DTLZ4, and 6.87% for DTLZ5, demonstrating its robustness and efficiency for complex multi-objective optimization problems.