Maximizing Battery Storage Efficiency and ROI in Grid-Connected Hybrid Solar Systems Using Real-Time Weather and Load Forecasting

Dhruv Shah

doi:10.18535/ijsrm/v13i07.ec01

Abstract

This paper comprehensively analyzes maximizing battery storage efficiency and return on investment (ROI) in grid-connected hybrid solar systems. The proposed framework optimizes battery charging/discharging cycles by incorporating real-time weather and load forecasting while ensuring demand fulfillment and extending battery life. Experimental results show that integrating machine learning-based forecasting techniques can improve overall system efficiency by 17.3% and increase ROI by 22.5% compared to conventional systems. The study evaluates multiple energy management strategies across diverse geographical locations and load profiles, providing valuable insights for system designers and energy managers seeking to enhance the economic viability of renewable energy storage solutions.

Keywords

Battery storageHybrid solar systemsEnergy forecastingReturn on investmentMachine learningEnergy management systemsGrid integration

References

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[refR-2] B. Zakeri and S. Syri, "Electrical energy storage systems: A comparative life cycle cost analysis," Renew. Sustain. Energy Rev., vol. 42, pp. 569–596, 2015.Google Scholar ↗

[refR-3] I. Staffell and M. Rustomji, "Maximising the value of electricity storage," J. Energy Storage, vol. 8, pp. 212–225, 2016.Google Scholar ↗

[refR-4] Y. Zhang, A. Lundblad, P. E. Campana, F. Benavente, and J. Yan, "Battery sizing and rule-based operation of grid-connected photovoltaic-battery system: A case study in Sweden," Energy Convers. Manag., vol. 133, pp. 249–263, 2017.Google Scholar ↗

[refR-5] P. Denholm, J. Jorgenson, M. Hummon, T. Jenkin, D. Palchak, B. Kirby, O. Ma, and M. O'Malley, "Value of energy storage for grid applications," National Renewable Energy Laboratory (NREL), Golden, CO, Tech. Rep. NREL/TP-6A20-58465, 2013.Google Scholar ↗

[refR-6] X. Zhang, J. Bao, R. Wang, C. Zheng, and M. Skyllas-Kazacos, "Dissipativity based distributed economic model predictive control for residential microgrids with renewable energy generation and battery energy storage," Renew. Energy, vol. 100, pp. 18–34, 2017.Google Scholar ↗

[refR-7] A. Nottrott, J. Kleissl, and B. Washom, "Energy dispatch schedule optimization and cost benefit analysis for grid-connected, photovoltaic-battery storage systems," Renew. Energy, vol. 55, pp. 230–240, 2013.Google Scholar ↗

[refR-8] F. Golestaneh, P. Pinson, and H. B. Gooi, "Very short-term nonparametric probabilistic forecasting of renewable energy generation—With application to solar energy," IEEE Trans. Power Syst., vol. 31, no. 5, pp. 3850–3863, 2016.Google Scholar ↗

[refR-9] W. Kong, Z. Y. Dong, Y. Jia, D. J. Hill, Y. Xu, and Y. Zhang, "Short-term residential load forecasting based on LSTM recurrent neural network," IEEE Trans. Smart Grid, vol. 10, no. 1, pp. 841–851, 2019.Google Scholar ↗

[refR-10] D. Yang, J. Kleissl, C. A. Gueymard, H. T. Pedro, and C. F. Coimbra, "History and trends in solar irradiance and PV power forecasting: A preliminary assessment and review using text mining," Sol. Energy, vol. 168, pp. 60–101, 2018.Google Scholar ↗

[refR-11] G. He, Q. Chen, C. Kang, P. Pinson, and Q. Xia, "Optimal bidding strategy of battery storage in power markets considering performance-based regulation and battery cycle life," IEEE Trans. Smart Grid, vol. 7, no. 5, pp. 2359–2367, 2016.Google Scholar ↗

[refR-12] Lazard, "Lazard's levelized cost of storage analysis—Version 6.0," 2020.Google Scholar ↗

[refR-13] S. Comello and S. Reichelstein, "The emergence of cost-effective battery storage," Nat. Commun., vol. 10, no. 1, pp. 1–9, 2019.Google Scholar ↗