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Residential battery sizing model using net meter energy data clustering

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  • Tang, Rui
  • Yildiz, Baran
  • Leong, Philip H.W.
  • Vassallo, Anthony
  • Dore, Jonathon

Abstract

The high upfront costs of batteries have limited the investment in retrofit residential energy storage systems for solar customers. Battery size is one of the most important factors that impact the financial return since it determines the major operational capabilities of the solar-coupled storage system. To select the optimal battery size for a photovoltaic solar customer, it is important to perform an analysis taking account of the customer’s on-site generation and consumption characteristics. However, in most cases there are insufficient pre-existing data of the required quality making it difficult to perform such analysis. In this paper, we propose a model that can achieve satisfactory battery sizing results with a limited amount of net meter electricity data. The model uses K-means clustering on customer net meter electricity data to discover important information to extrapolate limited input net/gross meter energy data and uses this in a techno-economic simulation model to determine the optimal battery size. The approach is validated using a set of 262 solar households, two tariff structures (flat and Time-of-Use) and a naive forecasting method as a comparison to the proposed model. The results indicate that the proposed model outperforms the alternative baseline model and can work with as little as one month of net meter energy data for both of the evaluated tariff structures. On average, the model results in 0.10 normalised root mean squared error in yearly battery savings and net present values, 0.07 normalised root mean squared error in annual electricity costs and a r-squared value of 0.717 in finding the optimal size of batteries. Moreover, this study reveals a linear correlation between the used clustering validity index (Davies-Bouldin Index), and errors in estimated annual battery savings which indicates that this index can be used as a metric for the developed battery sizing approach. With the ongoing rollouts of net meters, the proposed model can address the data shortage issue for both gross and net meter households and assist end users, installers and utilities with their battery sizing analysis.

Suggested Citation

  • Tang, Rui & Yildiz, Baran & Leong, Philip H.W. & Vassallo, Anthony & Dore, Jonathon, 2019. "Residential battery sizing model using net meter energy data clustering," Applied Energy, Elsevier, vol. 251(C), pages 1-1.
    • Handle: RePEc:eee:appene:v:251:y:2019:i:c:21
    DOI: 10.1016/j.apenergy.2019.113324
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    1. Talavera, D.L. & Muñoz-Rodriguez, F.J. & Jimenez-Castillo, G. & Rus-Casas, C., 2019. "A new approach to sizing the photovoltaic generator in self-consumption systems based on cost–competitiveness, maximizing direct self-consumption," Renewable Energy, Elsevier, vol. 130(C), pages 1021-1035.
    2. Viegas, Joaquim L. & Vieira, Susana M. & Melício, R. & Mendes, V.M.F. & Sousa, João M.C., 2016. "Classification of new electricity customers based on surveys and smart metering data," Energy, Elsevier, vol. 107(C), pages 804-817.
    3. Yang, Yuqing & Bremner, Stephen & Menictas, Chris & Kay, Merlinde, 2018. "Battery energy storage system size determination in renewable energy systems: A review," Renewable and Sustainable Energy Reviews, Elsevier, vol. 91(C), pages 109-125.
    4. Pflaum, Peter & Alamir, M. & Lamoudi, M.Y., 2017. "Battery sizing for PV power plants under regulations using randomized algorithms," Renewable Energy, Elsevier, vol. 113(C), pages 596-607.
    5. Ramírez, F. Javier & Honrubia-Escribano, A. & Gómez-Lázaro, E. & Pham, Duc T., 2017. "Combining feed-in tariffs and net-metering schemes to balance development in adoption of photovoltaic energy: Comparative economic assessment and policy implications for European countries," Energy Policy, Elsevier, vol. 102(C), pages 440-452.
    6. McLoughlin, Fintan & Duffy, Aidan & Conlon, Michael, 2015. "A clustering approach to domestic electricity load profile characterisation using smart metering data," Applied Energy, Elsevier, vol. 141(C), pages 190-199.
    7. Schopfer, S. & Tiefenbeck, V. & Staake, T., 2018. "Economic assessment of photovoltaic battery systems based on household load profiles," Applied Energy, Elsevier, vol. 223(C), pages 229-248.
    8. Khalilpour, Kaveh Rajab & Vassallo, Anthony, 2016. "Technoeconomic parametric analysis of PV-battery systems," Renewable Energy, Elsevier, vol. 97(C), pages 757-768.
    9. Jannesar, Mohammad Rasol & Sedighi, Alireza & Savaghebi, Mehdi & Guerrero, Josep M., 2018. "Optimal placement, sizing, and daily charge/discharge of battery energy storage in low voltage distribution network with high photovoltaic penetration," Applied Energy, Elsevier, vol. 226(C), pages 957-966.
    10. Rhodes, Joshua D. & Cole, Wesley J. & Upshaw, Charles R. & Edgar, Thomas F. & Webber, Michael E., 2014. "Clustering analysis of residential electricity demand profiles," Applied Energy, Elsevier, vol. 135(C), pages 461-471.
    11. Talent, Orlando & Du, Haiping, 2018. "Optimal sizing and energy scheduling of photovoltaic-battery systems under different tariff structures," Renewable Energy, Elsevier, vol. 129(PA), pages 513-526.
    12. Kursa, Miron B. & Rudnicki, Witold R., 2010. "Feature Selection with the Boruta Package," Journal of Statistical Software, Foundation for Open Access Statistics, vol. 36(i11).
    13. Schram, Wouter L. & Lampropoulos, Ioannis & van Sark, Wilfried G.J.H.M., 2018. "Photovoltaic systems coupled with batteries that are optimally sized for household self-consumption: Assessment of peak shaving potential," Applied Energy, Elsevier, vol. 223(C), pages 69-81.
    14. Motlagh, Omid & Paevere, Phillip & Hong, Tang Sai & Grozev, George, 2015. "Analysis of household electricity consumption behaviours: Impact of domestic electricity generation," Applied Mathematics and Computation, Elsevier, vol. 270(C), pages 165-178.
    15. Rodríguez-Gallegos, Carlos D. & Yang, Dazhi & Gandhi, Oktoviano & Bieri, Monika & Reindl, Thomas & Panda, S.K., 2018. "A multi-objective and robust optimization approach for sizing and placement of PV and batteries in off-grid systems fully operated by diesel generators: An Indonesian case study," Energy, Elsevier, vol. 160(C), pages 410-429.
    16. Chicco, Gianfranco, 2012. "Overview and performance assessment of the clustering methods for electrical load pattern grouping," Energy, Elsevier, vol. 42(1), pages 68-80.
    17. Khalilpour, Rajab & Vassallo, Anthony, 2016. "Planning and operation scheduling of PV-battery systems: A novel methodology," Renewable and Sustainable Energy Reviews, Elsevier, vol. 53(C), pages 194-208.
    18. Linssen, Jochen & Stenzel, Peter & Fleer, Johannes, 2017. "Techno-economic analysis of photovoltaic battery systems and the influence of different consumer load profiles," Applied Energy, Elsevier, vol. 185(P2), pages 2019-2025.
    19. Berrueta, Alberto & Heck, Michael & Jantsch, Martin & Ursúa, Alfredo & Sanchis, Pablo, 2018. "Combined dynamic programming and region-elimination technique algorithm for optimal sizing and management of lithium-ion batteries for photovoltaic plants," Applied Energy, Elsevier, vol. 228(C), pages 1-11.
    20. Quoilin, Sylvain & Kavvadias, Konstantinos & Mercier, Arnaud & Pappone, Irene & Zucker, Andreas, 2016. "Quantifying self-consumption linked to solar home battery systems: Statistical analysis and economic assessment," Applied Energy, Elsevier, vol. 182(C), pages 58-67.
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    2. Pan, Keda & Chen, Zhaohua & Lai, Chun Sing & Xie, Changhong & Wang, Dongxiao & Li, Xuecong & Zhao, Zhuoli & Tong, Ning & Lai, Loi Lei, 2022. "An unsupervised data-driven approach for behind-the-meter photovoltaic power generation disaggregation," Applied Energy, Elsevier, vol. 309(C).
    3. Yildiz, Baran & Bilbao, Jose I. & Roberts, Mike & Heslop, Simon & Dore, Jonathon & Bruce, Anna & MacGill, Iain & Egan, Renate J. & Sproul, Alistair B., 2021. "Analysis of electricity consumption and thermal storage of domestic electric water heating systems to utilize excess PV generation," Energy, Elsevier, vol. 235(C).
    4. Andrea Vasconcelos & Amanda Monteiro & Tatiane Costa & Ana Clara Rode & Manoel H. N. Marinho & Roberto Dias Filho & Alexandre M. A. Maciel, 2023. "Sizing with Technical Indicators of Microgrids with Battery Energy Storage Systems: A Systematic Review," Energies, MDPI, vol. 16(24), pages 1-26, December.
    5. Yildiz, Baran & Roberts, Mike & Bilbao, Jose I. & Heslop, Simon & Bruce, Anna & Dore, Jonathon & MacGill, Iain & Egan, Renate J. & Sproul, Alistair B., 2021. "Assessment of control tools for utilizing excess distributed photovoltaic generation in domestic electric water heating systems," Applied Energy, Elsevier, vol. 300(C).
    6. Tang, Rui & Dore, Jonathon & Ma, Jin & Leong, Philip H.W., 2021. "Interpolating high granularity solar generation and load consumption data using super resolution generative adversarial network," Applied Energy, Elsevier, vol. 299(C).
    7. Hajra Khan & Imran Fareed Nizami & Saeed Mian Qaisar & Asad Waqar & Moez Krichen & Abdulaziz Turki Almaktoom, 2022. "Analyzing Optimal Battery Sizing in Microgrids Based on the Feature Selection and Machine Learning Approaches," Energies, MDPI, vol. 15(21), pages 1-22, October.
    8. Mulleriyawage, U.G.K. & Shen, W.X., 2021. "Impact of demand side management on optimal sizing of residential battery energy storage system," Renewable Energy, Elsevier, vol. 172(C), pages 1250-1266.

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