Generation Expansion Planning Framework Integrating Battery Energy Storage Systems into the Screening Curve Method

Abstract

Generation expansion planning is crucial for ensuring a long-term, least-cost electricity supply. The screening curve method is a widely used tool for this purpose and is valued for its straightforward approach. Simultaneously, battery energy storage systems have become increasingly important for peak shaving, supporting the integration of renewable energy and reducing the dependence on thermal units. Despite these advantages, current formulations of the screening curve method do not account for the role of energy storage systems, limiting their utility as decision-support tools for modern low-carbon systems. This study proposes a new approach that incorporates battery operation into the screening curve framework to identify the least-cost generation mix. The method employs a root-finding peak shaving strategy and utilizes a genetic algorithm for optimal battery sizing while maintaining analytical simplicity. Two case studies of the Brazilian system, with and without renewable intermittent sources, were used to evaluate the approach, with the total cost as the performance criterion. The results indicate that battery integration consistently reduces the total system costs by up to 0.68% annually, primarily driven by an average 20% reduction in thermal start-up events. Furthermore, mathematical validation against mixed-integer linear programming benchmarks demonstrated that the proposed heuristic achieved an optimality gap within 0.37%. These findings validate the proposed approach as an effective decision-support framework, offering policymakers and system operators a transparent means of assessing the economic synergy between storage and generation.