Business model evaluation of a hybrid wind-battery virtual power plant dynamically updating the remaining battery capacity.
03 HYB24-94
Presented by: Daniel Fernández-Muñoz
Objectives and context
The objective of this work is to analyse the impact on the business model evaluation of the battery capacity loss in a hybrid wind-battery virtual power plant participating in the day-ahead energy and secondary regulation reserve markets of the Spanish power system.
Methods/approach
A deterministic day-ahead optimisation model based on [1] will be used to simulate the self-scheduling of a hybrid wind-battery virtual power plant participating in the day-ahead energy and secondary regulation reserve markets of the Spanish power system for a whole year. This model considers the battery degradation costs as a function of the depth of discharge using the Lithium-ion battery lifetime characteristic proposed in [2]. Then, the battery capacity loss for the whole year will be quantified using the well-known Rainflow Counting Method [3], applied over the number of charge and discharge cycles of different depths. The remaining battery capacity will be assessed by properly weighting the ratios between the total number of cycles of each depth the battery can perform throughout its lifetime and the number of cycles of each depth performed in the simulation, using the same battery lifetime characteristic curve. This methodology will be hereinafter referred so as S1.
In addition, the same deterministic day-ahead optimisation model will be used to obtain the self-scheduling of the hybrid wind-battery virtual power plant but, in this case, the remaining battery capacity will be evaluated on a weekly basis, i.e., the battery capacity loss with respect the initial one will be assessed every week, and the remaining capacity obtained will be updated in the model accordingly. This procedure will be executed over the same cases than S1 until the remaining battery capacity reaches 80% of the initial one. This methodology will be hereinafter referred so as S2.
Finally, a business model evaluation will be done using as input the operation of the hybrid wind-battery virtual power plant obtained in both methodology S1 and S2 as a function of the Net Present Value (NPV) and Internal Rate of Return (IRR).
Outcomes/Conclusions
The generation schedules obtained in methodology S2 will have a lower income than the ones obtained in S1 since the battery capacity decreases as the number of cycles of the battery increase. Therefore, a less profitable but more realistic business model evaluation of the hybrid wind-battery virtual power plant will be obtained in methodology S2 rather than S1.
[1] Fernández-Muñoz, D., & Pérez-Díaz, J. I. (2023). Optimisation models for the day-ahead energy and reserve self-scheduling of a hybrid wind–battery virtual power plant. Journal of Energy Storage, 57, 106296.
[2] Stroe, D. I., Knap, V., Swierczynski, M., Stroe, A. I., & Teodorescu, R. (2017). Operation of a grid-connected lithium-ion battery energy storage system for primary frequency regulation: A battery lifetime perspective. IEEE Transactions on Industry Applications, 53(1), 430–438.
[3] ASTM E1049-85(2017), "Standard Practices for Cycle Counting in Fatigue Analysis." West Conshohocken, PA: ASTM International, 2017.
The objective of this work is to analyse the impact on the business model evaluation of the battery capacity loss in a hybrid wind-battery virtual power plant participating in the day-ahead energy and secondary regulation reserve markets of the Spanish power system.
Methods/approach
A deterministic day-ahead optimisation model based on [1] will be used to simulate the self-scheduling of a hybrid wind-battery virtual power plant participating in the day-ahead energy and secondary regulation reserve markets of the Spanish power system for a whole year. This model considers the battery degradation costs as a function of the depth of discharge using the Lithium-ion battery lifetime characteristic proposed in [2]. Then, the battery capacity loss for the whole year will be quantified using the well-known Rainflow Counting Method [3], applied over the number of charge and discharge cycles of different depths. The remaining battery capacity will be assessed by properly weighting the ratios between the total number of cycles of each depth the battery can perform throughout its lifetime and the number of cycles of each depth performed in the simulation, using the same battery lifetime characteristic curve. This methodology will be hereinafter referred so as S1.
In addition, the same deterministic day-ahead optimisation model will be used to obtain the self-scheduling of the hybrid wind-battery virtual power plant but, in this case, the remaining battery capacity will be evaluated on a weekly basis, i.e., the battery capacity loss with respect the initial one will be assessed every week, and the remaining capacity obtained will be updated in the model accordingly. This procedure will be executed over the same cases than S1 until the remaining battery capacity reaches 80% of the initial one. This methodology will be hereinafter referred so as S2.
Finally, a business model evaluation will be done using as input the operation of the hybrid wind-battery virtual power plant obtained in both methodology S1 and S2 as a function of the Net Present Value (NPV) and Internal Rate of Return (IRR).
Outcomes/Conclusions
The generation schedules obtained in methodology S2 will have a lower income than the ones obtained in S1 since the battery capacity decreases as the number of cycles of the battery increase. Therefore, a less profitable but more realistic business model evaluation of the hybrid wind-battery virtual power plant will be obtained in methodology S2 rather than S1.
[1] Fernández-Muñoz, D., & Pérez-Díaz, J. I. (2023). Optimisation models for the day-ahead energy and reserve self-scheduling of a hybrid wind–battery virtual power plant. Journal of Energy Storage, 57, 106296.
[2] Stroe, D. I., Knap, V., Swierczynski, M., Stroe, A. I., & Teodorescu, R. (2017). Operation of a grid-connected lithium-ion battery energy storage system for primary frequency regulation: A battery lifetime perspective. IEEE Transactions on Industry Applications, 53(1), 430–438.
[3] ASTM E1049-85(2017), "Standard Practices for Cycle Counting in Fatigue Analysis." West Conshohocken, PA: ASTM International, 2017.