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Control algorithm for a residential photovoltaic system with storage

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  • Riesen, Yannick
  • Ballif, Christophe
  • Wyrsch, Nicolas

Abstract

High penetration of photovoltaic (PV) electricity could affect the stability of the low-voltage grid due to over-voltage and transformer overloading at times of peak production. Residential battery storage can smooth out those peaks and hence contribute to grid stability. A feed-in limit allows for the easy setting of a maximum power injection cap and motivates PV owners to increase their self-consumption. A simple control strategy for a residential battery system coupled with a PV system that maximizes self-consumption and minimizes curtailment losses due to a feed-in limit is presented. The algorithm used in this strategy does not require a forecast of insulation conditions. The performance of this algorithm is compared to a second algorithm—a control strategy based on linear optimization using a forecast. Assuming an exact forecast, this second algorithm is very close to the maximum self-consumption and minimum curtailment losses achievable and can be used to benchmark the simple strategy. The results show that the simple strategy performs as well as the second algorithm with exact forecasts and performs significantly better than the second algorithm using real forecasts. Moreover, it is shown that this result is valid for a large range of storage capacities and PV sizes. Furthermore, it is shown that with a time resolution of 15min for the input data (the resolution of most PV production and load data) self-consumption is overestimated by about 3% and curtailment losses are underestimated by the same amount. Load sensitivity simulations show that different load curve shapes do not fundamentally change the results. Finally, to assess the effect of load aggregation, the case where the strategy is applied separately to 44 households with storage is compared to the case where it is applied to a centralized storage system of the same size as the total storage of the 44 households. The reduction of the curtailment losses with the number of aggregated houses is showed.

Suggested Citation

  • Riesen, Yannick & Ballif, Christophe & Wyrsch, Nicolas, 2017. "Control algorithm for a residential photovoltaic system with storage," Applied Energy, Elsevier, vol. 202(C), pages 78-87.
    • Handle: RePEc:eee:appene:v:202:y:2017:i:c:p:78-87
    DOI: 10.1016/j.apenergy.2017.05.016
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    2. Cristian Sánchez & Lionel Bloch & Jordan Holweger & Christophe Ballif & Nicolas Wyrsch, 2019. "Optimised Heat Pump Management for Increasing Photovoltaic Penetration into the Electricity Grid," Energies, MDPI, vol. 12(8), pages 1-22, April.
    3. Muhammed Sait Aydin & Sahban W. Alnaser & Sereen Z. Althaher, 2022. "Using OLTC-Fitted Distribution Transformer to Increase Residential PV Hosting Capacity: Decentralized Voltage Management Approach," Energies, MDPI, vol. 15(13), pages 1-19, July.
    4. Muñoz-Rodríguez, Francisco José & Jiménez-Castillo, Gabino & de la Casa Hernández, Jesús & Aguilar Peña, Juan Domingo, 2021. "A new tool to analysing photovoltaic self-consumption systems with batteries," Renewable Energy, Elsevier, vol. 168(C), pages 1327-1343.
    5. Mahmoud Elkazaz & Mark Sumner & Seksak Pholboon & Richard Davies & David Thomas, 2020. "Performance Assessment of an Energy Management System for a Home Microgrid with PV Generation," Energies, MDPI, vol. 13(13), pages 1-23, July.
    6. Litjens, G.B.M.A. & Worrell, E. & van Sark, W.G.J.H.M., 2018. "Assessment of forecasting methods on performance of photovoltaic-battery systems," Applied Energy, Elsevier, vol. 221(C), pages 358-373.
    7. Fabio Bignucolo & Massimiliano Coppo & Giorgio Crugnola & Andrea Savio, 2017. "Application of a Simplified Thermal-Electric Model of a Sodium-Nickel Chloride Battery Energy Storage System to a Real Case Residential Prosumer," Energies, MDPI, vol. 10(10), pages 1-29, September.
    8. Nousdilis, Angelos I. & Christoforidis, Georgios C. & Papagiannis, Grigoris K., 2018. "Active power management in low voltage networks with high photovoltaics penetration based on prosumers’ self-consumption," Applied Energy, Elsevier, vol. 229(C), pages 614-624.
    9. Luthander, Rasmus & Nilsson, Annica M. & Widén, Joakim & Åberg, Magnus, 2019. "Graphical analysis of photovoltaic generation and load matching in buildings: A novel way of studying self-consumption and self-sufficiency," Applied Energy, Elsevier, vol. 250(C), pages 748-759.
    10. Zheng, Menglian & Wang, Xinhao & Meinrenken, Christoph J. & Ding, Yi, 2018. "Economic and environmental benefits of coordinating dispatch among distributed electricity storage," Applied Energy, Elsevier, vol. 210(C), pages 842-855.
    11. Haben, Stephen & Arora, Siddharth & Giasemidis, Georgios & Voss, Marcus & Vukadinović Greetham, Danica, 2021. "Review of low voltage load forecasting: Methods, applications, and recommendations," Applied Energy, Elsevier, vol. 304(C).

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