Dual-layer GWO-ALO optimization with EGT for solar and wind hybrid energy system planning aligned with SDG-7

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Chinese explanation / 中文解读

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Original abstract

Ensuring an affordable, reliable and sustainable electricity supply remains a major challenge in semi-urban regions, where seasonal variability in renewable resources limits the performance of hybrid renewable energy systems (HRES). This study proposes a reliability-oriented techno-economic planning framework for a solar–wind HRES in Mahad, Maharashtra, India, using six years (2020–2025) of validated meteorological data. The framework integrates three methodological innovations within a unified optimization architecture. First, a novel Reliability–Cost Coupling (RCC) index, formulated as RCC = NPC/(1-LPSP) and serving as a convex barrier function, is introduced to simultaneously evaluate lifecycle cost and system reliability using a single optimization objective, thereby enabling automatic rejection of economically attractive but reliability-deficient configurations. Second, a dual-layer Grey Wolf Optimizer-Ant Lion Optimizer (GWO–ALO) algorithm is developed in which the Ant Lion Optimizer is initialized using high-quality feasible solutions identified by the Grey Wolf Optimizer, improving convergence performance and solution quality; statistical superiority over benchmark algorithms is confirmed through 30 independent optimization runs using the Wilcoxon signed-rank test (p < 0.05). Third, a formally integrated computational pipeline links HOMER Pro techno-economic simulation, RCC-guided metaheuristic optimization, Monte Carlo robustness assessment and Shapley-value-based game-theoretic tariff allocation into a coherent end-to-end planning framework. Baseline HOMER Pro simulations produced an average daily energy generation of 185.6 kWh/day, a renewable fraction of 73.8%, a Net Present Cost (NPC) of ₹1.25 crore (approx. USD 139,000) and a Levelized Cost of Energy (LCOE) of ₹7.20/kWh (approx. USD 0.080/kWh). Following optimization, the proposed framework reduced NPC to ₹1.17 crore (approx. USD 130,000), decreased LCOE to ₹6.75/kWh (approx. USD 0.075/kWh), increased the renewable fraction to 88%, achieved a Loss of Power Supply Probability (LPSP) of 0.005 and delivered a carbon payback period of approximately two years. Robustness was validated through Pareto-front analysis and 10,000 Monte Carlo simulations. The game-theoretic tariff allocation mechanism distributes lifecycle costs based on consumer reliability requirements, thereby promoting equitable pricing and demand-response stability. The proposed framework provides a scalable and transferable methodology for reliability-oriented HRES planning. It supports climate-resilient, low-carbon energy transitions in developing regions while advancing Sustainable Development Goals (SDGs) 7, 9, 11, 12, 13 and 17.

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