Comparative assessment of multi-objective optimization of hybrid energy storage system considering grid balancing

Date

2023-08-01

Author

Rasool, Muhammad Haseeb
Taylan, Onur
Perwez, Usama
Batunlu, Canras

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With the accelerated transition towards affordable and clean energy sources, the energy sector is undergoing a structural transformation that has resulted in a further increase in the complexity of energy system planning with rapid changes in techno-economic, environmental, reliability and social constraints. This signifies the consideration of purpose-driven multi-objective functions depending upon the functionality and applicability of the model. However, most of the studies adopt conventional bi-objective optimization either involving techno-economic, reliability and grid balancing parameters but there is a lack of comparative assessment of multi-objective optimization sizing for grid-interactive hybrid renewable energy system (HRES) consisting of short and long-term, battery and pumped hydro storage (PHS), energy storage systems (ESS). This study presents a comparative multi-objective framework to assess bi- and tri-objective function sizing techniques under grid balancing and non-balancing modes, to understand the scope and adaptivity of the modeling process for large-scale grid-interactive HRES. The analysis of results shows that the non-balancing mode underestimates the cost of energy (COE) by 18–30% compared to the grid balancing mode due to smaller decision variable space while long-term ESS dominance is vital for the reduction of grid burden compared to short-term ESS. In terms of configuration, a hybrid ESS system, 0.22MWh battery, 18.1MWh PHS, and 5.4MWPV capacity, is the best optimal configuration in grid balancing mode with the COE, EEI and EII equal to 0.09 $/kWh, 7.5% and 10.5% respectively, whereas higher grid energy mismatch is induced by non-balancing mode with the overestimation of EEI and EII indexes up to 30% and 33% respectively. The environmental analysis shows that the carbon emissions avoided (CEA) are underestimated by 59.1% with the non-consideration of grid balancing. This signifies that the adaptive optimization model improves the design and planning process of grid-interactive HRES by capturing larger uncertainties related to COE, grid balancing, and CEA with changes in the system and ESS sizing. Overall, this analysis provides a purpose-driven perspective to energy modelers and policymakers for the energy system modeling process of grid-interactive HRES.

URI

https://doi.org/10.1016/j.renene.2023.119107
https://hdl.handle.net/11511/104978

Collections

Department of Mechanical Engineering, Article