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Optimal battery storage operation for PV systems with tariff incentives
Open accessAbstract
Many efforts are recently being dedicated to developing models that seek to provide insights into the techno-economic benefits of battery storage coupled to photovoltaic (PV) generation system. However, not all models consider the operation of the PV – battery storage system with a feed-in tariff (FiT) incentive, different electricity rates and battery storage unit cost. An electricity customer whose electricity demand is supplied by a grid connected PV generation system benefiting from a FiT incentive is simulated in this paper. The system is simulated with the PV modelled as an existing system and the PV modelled as a new system. For a better understanding of the existing PV system with battery storage operation, an optimisation problem was formulated which resulted in a mixed integer linear programming (MILP) problem. The optimisation model was developed to solve the MILP problem and to analyse the benefits considering different electricity tariffs and battery storage in maximising FiT revenue streams for the existing PV generating system. Real data from a typical residential solar PV owner is used to study the benefit of the battery storage system using half-hourly dataset for a complete year. A sensitivity analysis of the MILP optimisation model was simulated to evaluate the impact of battery storage capacity (kWh) on the objective function. In the second case study, the electricity demand data, solar irradiance, tariff and battery unit cost were used to analyse the effect of battery storage unit cost on the adoption of electricity storage in maximising FiT revenue. In this case, the PV is simulated as a new system using Distributed Energy Resources Customer Adoption Model (DER-CAM) software tool while modifying the optimisation formulation to include the PV onsite generation and export tariff incentive. The results provide insights on the benefit of battery storage for existing and new PV system benefiting from FiT incentives and under time-varying electricity tariffs.
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Cited by
Hesse, H., Schimpe, M., Kucevic, D., & Jossen, A. (2017) · Energies · doi:10.3390/en10122107 · Cited by 770
Yang, Y., Bremner, S., Menictas, C., & Kay, M. (2022) · Renewable and Sustainable Energy Reviews · doi:10.1016/j.rser.2022.112671 · Cited by 325
Pena-Bello, A., Barbour, E., Gonzalez, M. C., Patel, M. K., & Parra, D. (2019) · Renewable and Sustainable Energy Reviews · doi:10.1016/j.rser.2019.06.003 · Cited by 79
The nature of combining energy storage applications for residential battery technology
Parra, D., & Patel, M. K. (2019) · Applied Energy · doi:10.1016/j.apenergy.2019.01.218 · Cited by 63
Metadata
- Authors
- Sani Hassan, A., Cipcigan, L., & Jenkins, N.
- Venue
- Applied Energy
- Publisher
- Elsevier BV
- Published
- 2017-10
- Volume
- 203
- Pages
- 422-441
- ISSN
- 0306-2619 (ISSN-L)
- OpenAlex
- W2686511370
- Cited by
- 271
- References
- 0
- FWCI
- 13.20
- Type
- journal article
- Language
- en
- Open access
- hybrid
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- Sani Hassan, A., Cipcigan, L., & Jenkins, N. (2017). Optimal battery storage operation for PV systems with tariff incentives. Applied Energy. https://doi.org/10.1016/j.apenergy.2017.06.043
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Contains information from the Semantic Scholar Academic Graph Datasets, made available under the ODC Attribution License (ODC-BY 1.0).
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