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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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    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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