Optimal Sizing and Techno-Economic Evaluation of a Utility-Scale Wind–Solar–Battery Hybrid Plant Considering Weather Uncertainties, as Well as Policy and Economic Incentives, Using Multi-Objective Optimization

Shree Om Bade (), Olusegun Stanley Tomomewo (), Michael Maan, Johannes Van der Watt and Hossein Salehfar ()
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Shree Om Bade: Department of Energy and Petroleum Engineering, University of North Dakota, Grand Forks, ND 58202, USA
Olusegun Stanley Tomomewo: Department of Energy and Petroleum Engineering, University of North Dakota, Grand Forks, ND 58202, USA
Michael Maan: Institute for Energy Studies, University of North Dakota, Grand Forks, ND 58202, USA
Johannes Van der Watt: College of Engineering & Mines Research Institute, University of North Dakota, Grand Forks, ND 58202, USA
Hossein Salehfar: School of Electrical Engineering and Computer Science, University of North Dakota, Grand Forks, ND 58202, USA

Energies, 2025, vol. 18, issue 13, 1-39

Abstract: This study presents an optimization framework for a utility-scale hybrid power plant (HPP) that integrates wind power plants (WPPs), solar power plants (SPPs), and battery energy storage systems (BESS) using historical and probabilistic weather modeling, regulatory incentives, and multi-objective trade-offs. By employing multi-objective particle swarm optimization (MOPSO), the study simultaneously optimizes three key objectives: economic performance (maximizing net present value, NPV), system reliability (minimizing loss of power supply probability, LPSP), and operational efficiency (reducing curtailment). The optimized HPP (283 MW wind, 20 MW solar, and 500 MWh BESS) yields an NPV of $165.2 million, a levelized cost of energy (LCOE) of $0.065/kWh, an internal rate of return (IRR) of 10.24%, and a 9.24-year payback, demonstrating financial viability. Operational efficiency is maintained with <4% curtailment and 8.26% LPSP. Key findings show that grid imports improve reliability (LPSP drops to 1.89%) but reduce economic returns; higher wind speeds (11.6 m/s) allow 27% smaller designs with 54.6% capacity factors; and tax credits (30%) are crucial for viability at low PPA rates (≤$0.07/kWh). Validation via Multi-Objective Genetic Algorithm (MOGA) confirms robustness. The study improves hybrid power plant design by combining weather predictions, policy changes, and optimizing three goals, providing a flexible renewable energy option for reducing carbon emissions.

Keywords: hybrid power plant; multi-objective optimization; wind–solar–battery storage; techno-economic analysis; policy incentives (search for similar items in EconPapers)
JEL-codes: Q Q0 Q4 Q40 Q41 Q42 Q43 Q47 Q48 Q49 (search for similar items in EconPapers)
Date: 2025
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