Peer-Reviewed Journal Details
Mandatory Fields
Kelly J.J.;Leahy P.G.
2020
April
Journal Of Energy Storage
Optimal investment timing and sizing for battery energy storage systems
Validated
WOS: 4 ()
Optional Fields
Battery degradation Battery energy storage systems Deterministic Policy Gradient Algorithm Net Present Value Real Option Analysis Reinforcement learning Stochastic optimization
28
© 2020 Elsevier Ltd Due to electricity market deregulation over the past two decades, the responsibility for new generation is with private investors who seek profit maximisation. Battery Energy Storage Systems (BESS), which are one solution to combat the intermittent nature of renewable energy sources, also require private investment for widespread deployment. This paper develops a methodology for applying Real Options Analysis to a BESS project from the perspective of private investors to determine the optimal investment time and BESS capacity size (MWh). Two models with different timescales are utilized: the operational model which is hourly, and the planning model which is yearly. The operational model is solved using a reinforcement learning algorithm called Deterministic Policy Gradient, while the planning model is solved using a MATLAB inbuilt nonlinear global optimiser called patternsearch. The methodology is demonstrated for a 100 MW BESS connected to the Irish grid and trading exclusively in the day-ahead market. Three different BESS CAPEX future realisations are analysed along with three different BESS manufacturers’ degradation warranties for C-Rates under 0.37C. The results show that BESS CAPEX has minimal influence on investment timing but has a significant effect on BESS size. Furthermore, extrapolating degradation warranty for C-Rates greater than 0.37C does not influence optimal investment timing or sizing, while a change in BESS energy retention limit at year 10 can have a significant influence on the viability of a BESS project.
2352-152X
10.1016/j.est.2020.101272
Grant Details