Hierarchical GA–LP Framework with Explainable AI and Clustering for Generating and Interpreting Diverse Feasible Solutions in Net-Zero Energy Systems: An Illustrative Case Study

The transition to net-zero energy systems involves substantial uncertainty in exogenous conditions such as policy, fuel prices, and technology deployment. Conventional energy system optimization models, formulated as forward problems, excel at identifying a single least-cost solution but provide limited insight into the diverse configurations feasible within an acceptable cost range. This study proposes a hierarchical inverse-analysis framework integrating a genetic algorithm (GA) and linear programming (LP). The upper-level GA explores a broad space of exogenous conditions, including selected fuel-price assumptions, technology-cost conditions, equipment capacities, end-use electrification rates, CO2-capture installation rates, and CO2-storage limits, while the lower-level LP rigorously optimizes operations for each candidate. The framework applies explainable AI (SHAP) to identify dominant cost-determining factors and their interactions, and employs k-means clustering to compress the high-dimensional feasible solution space into illustrative archetypes. As an illustrative demonstration, the framework is applied to a hypothetical 2050 net-zero case for the Kanto region. The framework, under the assumed conditions, generates diverse feasible solutions, identifies influential cost-related conditions and their interactions, and organizes the generated solution set into five illustrative archetypes. The proposed framework extends energy system modeling beyond single-optimum solutions toward interpretable decision-support analytics for long-term net-zero planning under deep uncertainty.