Energies, Vol. 19, Pages 1546: Carbon-Cap-Feasible Robust Capacity Planning of Wind–PV–Thermal–Storage Systems with Fixed Energy-to-Power Ratios
Energies doi: 10.3390/en19061546
Authors:
Yuyang Yan
Husam I. Shaheen
Bo Yang
Gevork B. Gharehpetian
Yi Zuo
Ghamgeen I. Rashed
Planning capacity for wind–photovoltaic (PV)–thermal–storage systems with high renewable penetration requires models that address investment costs, operational feasibility, and strict carbon limits under uncertainty. This paper presents a two-stage robust optimization model for integrated wind–PV–thermal–storage capacity expansion that guarantees carbon compliance under worst-case renewable realizations. Unlike conventional approaches that relax carbon constraints through price penalties, we enforce the annual carbon emission cap as a hard operational constraint, ensuring candidate portfolios remain feasible even under adverse renewable conditions. To reflect practical storage design, a fixed energy-to-power (E/P) ratio couples storage energy capacity with power converter ratings, preventing unrealistic storage expansions. Renewable uncertainty is captured through a Bertsimas–Sim budgeted polyhedral set defined over representative days, balancing robustness with computational tractability. A tailored decomposition framework integrates economic dispatch and carbon-compliance verification within an outer column-and-constraint generation (C&CG) loop, simultaneously certifying worst-case operating cost and minimum achievable emissions. By exploiting strong duality, we generate two families of valid inequalities iteratively: economic cuts from the Economic subproblem (Economic-SP) and carbon-feasibility cuts from the Carbon subproblem (Carbon-SP). This dual-certification approach ensures capacity plans remain both economically optimal and carbon-compliant across all uncertainty realizations. Case studies on a realistic wind–PV–thermal–storage system demonstrate that the method produces carbon-compliant, robust capacity plans with manageable computational effort, converging in 10–15 iterations. The model explicitly captures operational coupling among renewables, thermal generation, and storage, providing a decision-support tool for low-carbon power systems under deep decarbonization targets.