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An energy storage algorithm for ramp rate control of utility scale PV (photovoltaics) plants

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  • van Haaren, Rob
  • Morjaria, Mahesh
  • Fthenakis, Vasilis

Abstract

Balancing authorities are currently exploring options for preventing potential increases in ramping costs of conventional generators in the grid by setting ramping limits on variable energy resources. In this paper, we present the methodology and results of simulations on the smoothing performance of battery, flywheel and ultra-capacitor energy storage technologies connected to single large-scale PV (photovoltaics) plants subject to a 10%/minute ramping limit. The simulations were run using second-to-second output data of four large-scale PV plants of which two are in the Southeast of Canada (5 MW and 80 MW) and two in the Southwest of the US (21 and 30.24 MW). Energy storage units are sized for each plant on a baseline of 99% violation reductions and their performances are compared. We also present two dispatch strategies tailored to low and high cycle-life storage technologies which are modeled without forecasting measures and assuming perfect short-term forecast for the remainder of the averaging period.

Suggested Citation

  • van Haaren, Rob & Morjaria, Mahesh & Fthenakis, Vasilis, 2015. "An energy storage algorithm for ramp rate control of utility scale PV (photovoltaics) plants," Energy, Elsevier, vol. 91(C), pages 894-902.
    • Handle: RePEc:eee:energy:v:91:y:2015:i:c:p:894-902
    DOI: 10.1016/j.energy.2015.08.081
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    1. Greening, Benjamin & Azapagic, Adisa, 2013. "Environmental impacts of micro-wind turbines and their potential to contribute to UK climate change targets," Energy, Elsevier, vol. 59(C), pages 454-466.
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    2. Luana Pontes & Tatiane Costa & Amanda Souza & Nicolau Dantas & Andrea Vasconcelos & Guilherme Rissi & Roberto Dias & Mohamed A. Mohamed & Pierluigi Siano & Manoel Marinho, 2023. "Operational Data Analysis of a Battery Energy Storage System to Support Wind Energy Generation," Energies, MDPI, vol. 16(3), pages 1-20, February.
    3. Yang, Yuqing & Bremner, Stephen & Menictas, Chris & Kay, Merlinde, 2022. "Forecasting error processing techniques and frequency domain decomposition for forecasting error compensation and renewable energy firming in hybrid systems," Applied Energy, Elsevier, vol. 313(C).
    4. Ana Cabrera-Tobar & Eduard Bullich-Massagué & Mònica Aragüés-Peñalba & Oriol Gomis-Bellmunt, 2019. "Active and Reactive Power Control of a PV Generator for Grid Code Compliance," Energies, MDPI, vol. 12(20), pages 1-25, October.
    5. Valentin Silvera Diaz & Daniel Augusto Cantane & André Quites Ordovás Santos & Oswaldo Hideo Ando Junior, 2021. "Comparative Analysis of Degradation Assessment of Battery Energy Storage Systems in PV Smoothing Application," Energies, MDPI, vol. 14(12), pages 1-16, June.
    6. Abebe Tilahun Tadie & Zhizhong Guo & Ying Xu, 2022. "Hybrid Model-Based BESS Sizing and Control for Wind Energy Ramp Rate Control," Energies, MDPI, vol. 15(23), pages 1-17, December.
    7. Jiang, Zhimin & Cai, Jie & Moses, Paul S., 2020. "Smoothing control of solar photovoltaic generation using building thermal loads," Applied Energy, Elsevier, vol. 277(C).
    8. de Oliveira-Assis, Lais & Soares-Ramos, Emanuel P.P. & Sarrias-Mena, Raúl & García-Triviño, Pablo & González-Rivera, Enrique & Sánchez-Sainz, Higinio & Llorens-Iborra, Francisco & Fernández-Ramírez, L, 2022. "Simplified model of battery energy-stored quasi-Z-source inverter-based photovoltaic power plant with Twofold energy management system," Energy, Elsevier, vol. 244(PA).
    9. Ying Wang & Bo Feng & Qing-Song Hua & Li Sun, 2021. "Short-Term Solar Power Forecasting: A Combined Long Short-Term Memory and Gaussian Process Regression Method," Sustainability, MDPI, vol. 13(7), pages 1-16, March.
    10. Cirés, E. & Marcos, J. & de la Parra, I. & García, M. & Marroyo, L., 2019. "The potential of forecasting in reducing the LCOE in PV plants under ramp-rate restrictions," Energy, Elsevier, vol. 188(C).
    11. Raugei, Marco & Sgouridis, Sgouris & Murphy, David & Fthenakis, Vasilis & Frischknecht, Rolf & Breyer, Christian & Bardi, Ugo & Barnhart, Charles & Buckley, Alastair & Carbajales-Dale, Michael & Csala, 2017. "Energy Return on Energy Invested (ERoEI) for photovoltaic solar systems in regions of moderate insolation: A comprehensive response," Energy Policy, Elsevier, vol. 102(C), pages 377-384.
    12. Shivashankar, S. & Mekhilef, Saad & Mokhlis, Hazlie & Karimi, M., 2016. "Mitigating methods of power fluctuation of photovoltaic (PV) sources – A review," Renewable and Sustainable Energy Reviews, Elsevier, vol. 59(C), pages 1170-1184.
    13. Babak Arbab-Zavar & Emilio J. Palacios-Garcia & Juan C. Vasquez & Josep M. Guerrero, 2019. "Smart Inverters for Microgrid Applications: A Review," Energies, MDPI, vol. 12(5), pages 1-22, March.
    14. Chen, Xiaoyang & Du, Yang & Lim, Enggee & Fang, Lurui & Yan, Ke, 2022. "Towards the applicability of solar nowcasting: A practice on predictive PV power ramp-rate control," Renewable Energy, Elsevier, vol. 195(C), pages 147-166.
    15. Ballestrín, Jesús & Polo, Jesús & Martín-Chivelet, Nuria & Barbero, Javier & Carra, Elena & Alonso-Montesinos, Joaquín & Marzo, Aitor, 2022. "Soiling forecasting of solar plants: A combined heuristic approach and autoregressive model," Energy, Elsevier, vol. 239(PE).
    16. Hemmati, Reza & Saboori, Hedayat & Saboori, Saeid, 2016. "Stochastic risk-averse coordinated scheduling of grid integrated energy storage units in transmission constrained wind-thermal systems within a conditional value-at-risk framework," Energy, Elsevier, vol. 113(C), pages 762-775.

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