Multi-Objective Generation Expansion Planning with Renewable Integration and Uncertainty Modelling

Abstract

The global energy landscape confronts unprecedented challenges in generation expansion planning, characterized by increasing renewable energy integration, complex technological uncertainties, and the critical need to simultaneously optimize economic, environmental, and reliability objectives. Traditional deterministic planning approaches have proven inadequate for capturing the dynamic and stochastic nature of modern power systems. This research aims to develop a comprehensive multi-objective generation expansion planning framework capable of effectively modelling renewable energy integration, quantifying deep uncertainties, and providing robust decision support mechanisms for strategic infrastructure planning. The study employs a hybrid stochastic-robust optimization approach, integrating advanced techniques such as Monte Carlo scenario generation, econometric load forecasting models, spatial-temporal correlation modelling, multi-criteria decision analysis, and sophisticated mathematical optimization algorithms. The proposed framework generated 147 non-dominated Pareto solutions, validated through extensive simulation and benchmarking on the IEEE 118-bus test system. Key achievements include revealing non-linear cost-emission trade-offs and identified critical renewable integration thresholds. Performance was measured using the hyper volume indicator (a metric that quantifies the extent of the objective space covered by Pareto-optimal solutions), achieving a value of 0.847, which demonstrates superior coverage compared to traditional models. Ultimately, the research presents a transformative approach to generation expansion planning, offering a sophisticated methodology that bridges technological complexity, uncertainty management, and strategic decision-making in the evolving global energy landscape.