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Economic Analysis of Profitability of Using Energy Storage with Photovoltaic Installation in Conditions of Northeast Poland

Author

Listed:
  • Maciej Neugebauer

    (Faculty of Technical Science, University of Warmia and Mazury in Olsztyn, Oczapowskiego Str. 2, 10-719 Olsztyn, Poland)

  • Jakub d’Obyrn

    (Faculty of Economic Sciences, University of Warmia and Mazury in Olsztyn, Oczapowskiego Str. 2, 10-719 Olsztyn, Poland)

  • Piotr Sołowiej

    (Faculty of Technical Science, University of Warmia and Mazury in Olsztyn, Oczapowskiego Str. 2, 10-719 Olsztyn, Poland)

Abstract

This work presents an economic analysis of the use of electricity storage in PV installations, based on previously adopted assumptions, i.e., the type and location of the tested facility and comparative variants, divided into the share of the storage in the installation, and the billing system. The work takes into account the share of the energy shield and assumes a consumption limit of 2000 kWh. The cost of building the installation is based on July 2023 prices. The work assumes potential directions of changes in electricity prices, based on which the degree of investment profitability for a given price situation is determined. Depending on the adopted change in the direction of electricity prices, with a low price increase rate, for installations in the new billing system (net-billing), the optimal choice is an installation without energy storage with a power exceeding the actual energy demand. Assuming a high increase in electricity prices, the optimal choice is an installation with energy storage with an installation capacity exceeding the actual demand. For installations billed using the net-metering system, the optimal choice is an installation without storage with an appropriately selected installation power. This article shows how much you can gain after installing a PV installation and not only what costs must be incurred to complete the investment. Profit analysis will enable a more complete assessment of the profitability of investing in PV panels (with or without energy storage). It describes the verification of the profitability of a PV installation for a standard user depending on various types of settlements with the electricity supplier and the lack or installation of an energy-storage facility.

Suggested Citation

  • Maciej Neugebauer & Jakub d’Obyrn & Piotr Sołowiej, 2024. "Economic Analysis of Profitability of Using Energy Storage with Photovoltaic Installation in Conditions of Northeast Poland," Energies, MDPI, vol. 17(13), pages 1-13, June.
    • Handle: RePEc:gam:jeners:v:17:y:2024:i:13:p:3075-:d:1419798
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    References listed on IDEAS

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    1. Ramli, Mohd Zulkifli & Salam, Zainal, 2019. "Performance evaluation of dc power optimizer (DCPO) for photovoltaic (PV) system during partial shading," Renewable Energy, Elsevier, vol. 139(C), pages 1336-1354.
    2. Mulleriyawage, U.G.K. & Shen, W.X., 2020. "Optimally sizing of battery energy storage capacity by operational optimization of residential PV-Battery systems: An Australian household case study," Renewable Energy, Elsevier, vol. 160(C), pages 852-864.
    3. Chen, Qi & Kuang, Zhonghong & Liu, Xiaohua & Zhang, Tao, 2024. "Optimal sizing and techno-economic analysis of the hybrid PV-battery-cooling storage system for commercial buildings in China," Applied Energy, Elsevier, vol. 355(C).
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    Cited by:

    1. Szymon Firląg & Abdullah Sikander Baig & Dariusz Koc, 2025. "Historical Analysis of Real Energy Consumption and Indoor Conditions in Single-Family Passive Building," Sustainability, MDPI, vol. 17(2), pages 1-35, January.
    2. Fernando R. Mazarrón & José Álvarez & Jorge Cervera-Gascó & Jesús López-Santiago, 2025. "Impact of Electricity Price Reductions on the Profitability and Sizing of Photovoltaic Systems in Agri-Food Industries," Agriculture, MDPI, vol. 15(6), pages 1-12, March.
    3. Paweł Kut & Katarzyna Pietrucha-Urbanik, 2025. "Forecasting Short-Term Photovoltaic Energy Production to Optimize Self-Consumption in Home Systems Based on Real-World Meteorological Data and Machine Learning," Energies, MDPI, vol. 18(16), pages 1-31, August.

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