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The Methods of Assessing the Efficiency of a Virtual Power Plant—Case Study

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  • Edyta Ropuszyńska-Surma

    (Faculty of Management, Wrocław University of Science and Technology, 50-370 Wrocław, Poland)

  • Magdalena Węglarz

    (Faculty of Management, Wrocław University of Science and Technology, 50-370 Wrocław, Poland)

Abstract

In the case of new investment in RES technology, there are two issues related to efficiency assessment. The first one is how to join technical, financial and economic efficiency assessments in one. An investment feasibility study is usually conducted through a financial analysis to obtain the internal rate of return and the net present value. However, a new RES investment is typically financially unprofitable unless the environmental and social aspects are included. The second one is a lack of required financial data. The new RES investment is often innovative and neither the owner nor other entities have financial data on the operating costs and expenditure for the last periods. Therefore, in this paper, we proposed two methods of efficiency assessment. The first one is based on the avoided costs theory. Furthermore, the second one belongs to heuristic methods and is based on the experts’ assessment of different kinds of parameters. The purpose of this study is to assess the efficiency of the pilot project of VPPs using two recommended methods. This paper emphasizes the advantages and disadvantages of each method. The actual technical and financial data for the period of six months from the pilot study were calculated.

Suggested Citation

  • Edyta Ropuszyńska-Surma & Magdalena Węglarz, 2023. "The Methods of Assessing the Efficiency of a Virtual Power Plant—Case Study," Energies, MDPI, vol. 17(1), pages 1-28, December.
    • Handle: RePEc:gam:jeners:v:17:y:2023:i:1:p:174-:d:1309380
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    References listed on IDEAS

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    1. Renar, Jože & Tomšič, Miha, 1992. "Optimal electric power conservation investments using utility avoided-costs," Energy, Elsevier, vol. 17(5), pages 499-508.
    2. Nowotarski, Jakub & Weron, Rafał, 2018. "Recent advances in electricity price forecasting: A review of probabilistic forecasting," Renewable and Sustainable Energy Reviews, Elsevier, vol. 81(P1), pages 1548-1568.
    3. Danielsen, Albert L. & Gupta, Nainish K. & Klein, Peter G., 1999. "Contracts and the Institutional Environment for Electricity Reform," The Electricity Journal, Elsevier, vol. 12(10), pages 51-60, December.
    4. Zhu, Xingyi & Zhan, Xiangyan & Liang, Hao & Zheng, Xuyue & Qiu, Yuwei & Lin, Jian & Chen, Jincan & Meng, Chao & Zhao, Yingru, 2020. "The optimal design and operation strategy of renewable energy-CCHP coupled system applied in five building objects," Renewable Energy, Elsevier, vol. 146(C), pages 2700-2715.
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