A Transparent BESS Sizing Framework for Ramp-Rate Control of a 100 MW Solar PV Plant Using SoDa Synthetic Power Profiles

Muhammad Haikal  Erniza Putra; Faiz Husnayain

doi:10.59261/jbt.v7i3.717

Authors

Muhammad Haikal Erniza Putra Universitas Indonesia
Faiz Husnayain Universitas Indonesia https://orcid.org/0000-0003-2619-896X

DOI:

https://doi.org/10.59261/jbt.v7i3.717

Keywords:

Battery Energy Storage System, Solar Photovoltaic, Ramp-Rate, SoDa, Deterministic Grid Search

Abstract

Background: The integration of utility-scale solar photovoltaic power plants requires attention to short-term power fluctuations because photovoltaic output can change rapidly due to variations in irradiance, temperature, atmospheric conditions, and cloud movement.

Objective: This study aims to evaluate a SoDa-based synthetic photovoltaic power profile and optimize Battery Energy Storage System capacity for ramp-rate control of a 100 MW solar photovoltaic power plant.

Methods: A quantitative simulation and optimization approach was applied. A one-minute synthetic photovoltaic power profile was generated using SoDa, evaluated for monthly consistency against NASA POWER, and analyzed under multiple ramp-rate limit scenarios. The optimum BESS power and energy capacities were determined using deterministic grid search and benchmarked against Particle Swarm Optimization (PSO).

Results: The synthetic profile demonstrated adequate monthly consistency with NASA POWER (Pearson r = 0.860, rRMSE = 5.99%), confirming its suitability for pre-feasibility ramp-rate analysis. Stricter ramp-rate limits produced markedly more violations and required higher BESS capacities, ranging from 10 MW/10 MWh for moderate limits up to 20.5 MW/20.5 MWh for the most stringent scenario, with 100% compliance achieved in all cases. This framework demonstrates the practical value of synthetic data-driven BESS sizing for early-stage solar project planning in data-scarce environments.

Conclusion: Stricter ramp-rate limits increase BESS capacity requirements once the minimum capacity constraint is no longer sufficient. This study contributes an auditable, transparent pre-feasibility framework that integrates synthetic data generation, and advancing the literature on data-driven energy storage sizing for utility-scale solar PV projects.

References

Amorim, W. C. S., Cupertino, A. F., Pereira, H. A., & Mendes, V. F. (2024). On Sizing Of Battery Energy Storage Systems For Pv Plants Power Smoothing. Electric Power Systems Research, 229, 110114. https://doi.org/10.1016/J.Epsr.2024.110114

Bergstra, J., & Bengio, Y. (2012). Random Search For Hyper-Parameter Optimization. Journal Of Machine Learning Research, 13(10), 281–305.

Bright, J. M., Smith, C. J., Taylor, P. G., & Crook, R. (2015). Stochastic Generation Of Synthetic Minutely Irradiance Time Series Derived From Mean Hourly Weather Observation Data. Solar Energy, 115, 229–242. https://doi.org/10.1016/J.Solener.2015.02.032

Bunn, P. T. W., Boeman, L. J., Lorenzo, A. T., & Raub, J. (2024). The Expected Solar Performance And Ramp Rate Tool: A Decision-Making Tool For Planning Prospective Photovoltaic Systems. Frontiers In Energy Research, 12. https://doi.org/10.3389/Fenrg.2024.1434019

Carreño, I. L., Ramakrishna, R., Scaglione, A., Arnold, D., Roberts, C., Ngo, S.-T., Peisert, S., & Pinney, D. (2020). Soda: An Irradiance-Based Synthetic Solar Data Generation Tool. 2020 Ieee International Conference On Communications, Control, And Computing Technologies For Smart Grids (Smartgridcomm), 1–6. https://doi.org/10.1109/Smartgridcomm47815.2020.9302941

Cole, W., & Karmakar, A. (2023). Cost Projections For Utility-Scale Battery Storage: 2023 Update. In National Renewable Energy Laboratory. https://doi.org/10.2172/1972553

De La Parra, I., Marcos, J., García, M., & Marroyo, L. (2015). Storage Requirements For Pv Power Ramp-Rate Control In A Pv Fleet. Solar Energy, 118, 426–440. https://doi.org/10.1016/J.Solener.2015.05.046

Fregosi, D., Pilot, N., Bolen, M., & Hobbs, W. B. (2023). An Analysis Of Storage Requirements And Benefits Of Short-Term Forecasting For Pv Ramp Rate Mitigation. Ieee Journal Of Photovoltaics, 13(2), 315–324. https://doi.org/10.1109/Jphotov.2022.3231713

Freitas, D., Lopes, L. G., & Morgado-Dias, F. (2020). Particle Swarm Optimisation: A Historical Review Up To The Current Developments. Entropy, 22(3), 362. https://doi.org/10.3390/E22030362

Garniwa, P. M., Azzahra, R. O., & Dimyati, M. (2024). Global Horizontal Irradiance Estimation In Tropical Terrain Using Semi-Empirical Approach: A Seasonal Assessment In West Java, Indonesia. International Journal Of Remote Sensing And Earth Sciences, 21(2). https://doi.org/10.30536/Ijreses.V21i2.11405

Harsarapama, A. P., Aryani, D. R., & Rachmansyah, D. (2020). Open-Source Satellite-Derived Solar Resource Databases Comparison And Validation For Indonesia. Journal Of Renewable Energy, 2020(1), 2134271. https://doi.org/10.1155/2020/2134271

Kandpal, V., Jaswal, A., Santibanez Gonzalez, E. D. R., & Agarwal, N. (2024). Energy Efficiency And Renewable Energy Technologies. In Sustainable Energy Transition: Circular Economy And Sustainable Financing For Environmental, Social And Governance (ESG) Practices (Pp. 89–123). Springer.

Kennedy, J. (2010). Particle Swarm Optimization. In C. Sammut & G. I. Webb (Eds.), Encyclopedia Of Machine Learning (Pp. 760–766). Springer Us. https://doi.org/10.1007/978-0-387-30164-8_630

Laboratory, N. R. E. (2024). 2024 Annual Technology Baseline: Battery Storage. In Nrel. https://Atb.Nrel.Gov/Electricity/2024/Utility-Scale_Battery_Storage

Lajić, R., Divnić, D., Risojević, V., & Mirjanić, D. (2024). Generative Neural Network Models For Synthetic Solar Irradiance Sequences. Journal Of Renewable And Sustainable Energy, 16(5).

Lave, M., & Kleissl, J. (2011). Optimum Fixed Orientations And Benefits Of Tracking For Capturing Solar Radiation In The Continental United States. Renewable Energy, 36(3), 1145–1152. https://doi.org/10.1016/J.Renene.2010.07.032

Marcos, J., Storkël, O., Marroyo, L., Garcia, M., & Lorenzo, E. (2014). Storage Requirements For Pv Power Ramp-Rate Control. Solar Energy, 99, 28–35. https://doi.org/10.1016/J.Solener.2013.10.037

Mavromatidis, G., Orehounig, K., & Carmeliet, J. (2018). A Review Of Uncertainty Characterisation Approaches For The Optimal Design Of Distributed Energy Systems. Renewable And Sustainable Energy Reviews, 88, 258–277. https://doi.org/10.1016/J.Rser.2018.02.021

Montalà Palau, M., Cheah Mañé, M., & Gomis-Bellmunt, O. (2025). Techno-Economic Optimization For Bess Sizing And Operation Considering Degradation And Ramp Rate Limit Requirement. Journal Of Energy Storage, 105, 114631. https://doi.org/10.1016/J.Est.2024.114631

Pfenninger, S., Hirth, L., Schlecht, I., Schmid, E., Wiese, F., Brown, T., Davis, C., Gidden, M., Heinrichs, H., Heuberger, C., Hilpert, S., Krien, U., Matke, C., Nebel, A., Morrison, R., Müller, B., Pleßmann, G., Reeg, M., Richstein, J. C., … Wingenbach, C. (2018). Opening The Black Box Of Energy Modelling: Strategies And Lessons Learned. Energy Strategy Reviews, 19, 63–71. https://doi.org/10.1016/J.Esr.2017.12.002

Poli, R., Kennedy, J., & Blackwell, T. (2007). Particle Swarm Optimization. Swarm Intelligence, 1(1), 33–57. https://doi.org/10.1007/S11721-007-0002-0

Priyadarshi, R., & Kumar, R. R. (2025). Evolution Of Swarm Intelligence: A Systematic Review Of Particle Swarm And Ant Colony Optimization Approaches In Modern Research. Archives Of Computational Methods In Engineering, 32(6), 3609–3650.

Rimal, Y., Sharma, N., & Alsadoon, A. (2024). The Accuracy Of Machine Learning Models Relies On Hyperparameter Tuning: Student Result Classification Using Random Forest, Randomized Search, Grid Search, Bayesian, Genetic, And Optuna Algorithms. Multimedia Tools And Applications, 83(30), 74349–74364.

Salazar-Peña, N., Tabares, A., & González-Mancera, A. (2023). Sequential Stochastic And Bootstrap Methods To Generate Synthetic Solar Irradiance Time Series Of High Temporal Resolution Based On Historical Observations. Solar Energy, 264, 112030. https://doi.org/10.1016/J.Solener.2023.112030

Sengupta, M., Xie, Y., Lopez, A., Habte, A., Maclaurin, G., & Shelby, J. (2018). The National Solar Radiation Data Base (NSRDB). Renewable And Sustainable Energy Reviews, 89, 51–60. https://doi.org/10.1016/J.Rser.2018.03.003

Shahcheraghian, A., Madani, H., & Ilinca, A. (2024). From White To Black-Box Models: A Review Of Simulation Tools For Building Energy Management And Their Application In Consulting Practices. Energies, 17(2), 376.

Short, W., Packey, D. J., & Holt, T. (1995). A Manual For The Economic Evaluation Of Energy Efficiency And Renewable Energy Technologies. National Renewable Energy Laboratory (NREL). https://doi.org/10.2172/43314

Talvi, M., Roinila, T., & Lappalainen, K. (2023). Effects Of Ramp Rate Limit On Sizing Of Energy Storage Systems For Pv, Wind And Pv–Wind Power Plants. Energies, 16(11), 4313. https://doi.org/10.3390/En16114313

Vega–Garita, V., Alpizar–Gutierrez, V., Calderon–Obaldia, F., Núñez–Mata, O., Arguello, A., & Immonen, E. (2024). Iterative Sizing Methodology For Photovoltaic Plants Coupled With Battery Energy Storage Systems To Ensure Smooth Power Output And Power Availability. Energy Conversion And Management: X, 24, 100716. https://doi.org/10.1016/J.Ecmx.2024.100716

Peraturan Menteri Energi Dan Sumber Daya Mineral Nomor 20 Tahun 2020 Tentang Aturan Jaringan Sistem Tenaga Listrik, Nomor 20 Tahun 2020 Permen Esdm 1019 (2020).

(2025a). Renewables 2025: Analysis And Forecast To 2030. Iea (International Energy Agency).

(2025b). Power Data Methodology. Nasa Power Docs; National Aeronautics And Space Administration.