Abstract:
The future world trend is towards decarbonization, considering that this research focuses on increasing renewable energy generation while minimizing thermal energy generation in power systems. When maximizing solar energy generation, energy storage can be used as a controllable resource to minimize solar energy curtailments. A Hybrid Energy Storage System (HESS) combines Pumped Hydro Storage (PHS) and Battery Storage (BS) to maximize the overall performance. Here, a techno-economic multi-objective optimization algorithm is introduced to size and cost solar PV and BS, while considering the optimal time to integrating PHS into the system. The proposed algorithm aims to minimize the total investment cost, the operation and maintenance cost, and the penalty cost incurred due to solar surplus and thermal (oil) generation. The genetic algorithm is used to achieve multi-objective optimization, implemented using the Pymoo open-source framework in Python. An energy management system is utilized to operating hours, ensuring proper regulation. Results are presented in a Pareto optimal chart for better analysis, and economic analyses were conducted for various PHS installation times. Finally, the optimal duration for integrating PHS, considering both economical and environment facts, was determined, with solar and BS capacities for each two-year interval throughout the planning horizon.
