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Economic assessment of virtual power plants in the German energy market — A scenario-based and model-supported analysis

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  • Loßner, Martin
  • Böttger, Diana
  • Bruckner, Thomas

Abstract

The energy transition (“Energiewende”) in Germany will result in a substantial transformation of the energy supply system. Virtual power plants are expected to be important components of the new intelligent energy infrastructure. They aggregate beside different types of distributed generation units also active consumers and storage technologies in order to integrate these in a profit-maximising, system-stabilising, and sustainable way. The assessment of the economic performance of virtual power plants requires a scenario-based and model-supported analysis. In this relation, future energy market conditions are simulated using the scenario methodology. Starting from the year 2015, three scenarios have been identified that illustrate alternative energy developments in Germany by 2030. Based on these scenarios, the additional revenues potential of the modeled virtual power plant is identified when compared to an independent and non-market-oriented operation mode of distributed energy resources. According to the model results, revenues of the VPP can increase by 11% up to 30% in the analyzed scenarios in 2030 due to the market-oriented operation mode. Nevertheless, the amount and composition vary depending on technology-specific subsidies, temporary nature of power demand and price structures in the energy market. Fluctuating renewable energies are expected to benefit from the market-oriented operation mode in the virtual power plant, especially through the EEG direct marketing. The selective and regulated shutdown of renewable energies in times of negative electricity prices may lead to further cost savings. The utilization of temporary price fluctuations in the spot market and the demand-oriented provision of control power offer high additional revenue potential for flexible controllable technologies such as battery storage, biomethane as well as combined heat and power units. Finally, the determination of the long-term profitability of a virtual power plant still requires a full-scale cost–benefit analysis. For this holistic approach, the model results provide a reliable scientific basis.

Suggested Citation

  • Loßner, Martin & Böttger, Diana & Bruckner, Thomas, 2017. "Economic assessment of virtual power plants in the German energy market — A scenario-based and model-supported analysis," Energy Economics, Elsevier, vol. 62(C), pages 125-138.
    • Handle: RePEc:eee:eneeco:v:62:y:2017:i:c:p:125-138
    DOI: 10.1016/j.eneco.2016.12.008
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    References listed on IDEAS

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    Cited by:

    1. Behnaz Behi & Ali Baniasadi & Ali Arefi & Arian Gorjy & Philip Jennings & Almantas Pivrikas, 2020. "Cost–Benefit Analysis of a Virtual Power Plant Including Solar PV, Flow Battery, Heat Pump, and Demand Management: A Western Australian Case Study," Energies, MDPI, vol. 13(10), pages 1-24, May.
    2. Natalia Naval & Jose M. Yusta, 2020. "Water-Energy Management for Demand Charges and Energy Cost Optimization of a Pumping Stations System under a Renewable Virtual Power Plant Model," Energies, MDPI, vol. 13(11), pages 1-21, June.
    3. Behnaz Behi & Ali Arefi & Philip Jennings & Arian Gorjy & Almantas Pivrikas, 2021. "Advanced Monitoring and Control System for Virtual Power Plants for Enabling Customer Engagement and Market Participation," Energies, MDPI, vol. 14(4), pages 1-19, February.
    4. Pavel Atănăsoae, 2018. "The Operating Strategies of Small-Scale Combined Heat and Power Plants in Liberalized Power Markets," Energies, MDPI, vol. 11(11), pages 1-16, November.
    5. Tser-Yieth Chen & Chi-Jui Huang, 2019. "A Two-Tier Scenario Planning Model of Environmental Sustainability Policy in Taiwan," Sustainability, MDPI, vol. 11(8), pages 1-21, April.
    6. Naval, Natalia & Sánchez, Raul & Yusta, Jose M., 2020. "A virtual power plant optimal dispatch model with large and small-scale distributed renewable generation," Renewable Energy, Elsevier, vol. 151(C), pages 57-69.
    7. Khalil Gholami & Behnaz Behi & Ali Arefi & Philip Jennings, 2022. "Grid-Forming Virtual Power Plants: Concepts, Technologies and Advantages," Energies, MDPI, vol. 15(23), pages 1-26, November.
    8. Lerch, Philipp & Scheller, Fabian & Reichelt, David G. & Menzel, Katharina & Bruckner, Thomas, 2024. "Electricity cost and CO2 savings potential for chlor-alkali electrolysis plants: Benefits of electricity price dependent demand response," Applied Energy, Elsevier, vol. 355(C).
    9. Sara Bellocchi & Michele Manno & Michel Noussan & Michela Vellini, 2019. "Impact of Grid-Scale Electricity Storage and Electric Vehicles on Renewable Energy Penetration: A Case Study for Italy," Energies, MDPI, vol. 12(7), pages 1-32, April.
    10. Tomasz Sikorski & Michał Jasiński & Edyta Ropuszyńska-Surma & Magdalena Węglarz & Dominika Kaczorowska & Paweł Kostyła & Zbigniew Leonowicz & Robert Lis & Jacek Rezmer & Wilhelm Rojewski & Marian Sobi, 2019. "A Case Study on Distributed Energy Resources and Energy-Storage Systems in a Virtual Power Plant Concept: Economic Aspects," Energies, MDPI, vol. 12(23), pages 1-21, November.
    11. Marta Bottero & Federico Dell’Anna & Vito Morgese, 2021. "Evaluating the Transition Towards Post-Carbon Cities: A Literature Review," Sustainability, MDPI, vol. 13(2), pages 1-28, January.
    12. Ecker, Franz & Spada, Hans & Hahnel, Ulf J.J., 2018. "Independence without control: Autarky outperforms autonomy benefits in the adoption of private energy storage systems," Energy Policy, Elsevier, vol. 122(C), pages 214-228.
    13. Amit Kumer Podder & Sayemul Islam & Nallapaneni Manoj Kumar & Aneesh A. Chand & Pulivarthi Nageswara Rao & Kushal A. Prasad & T. Logeswaran & Kabir A. Mamun, 2020. "Systematic Categorization of Optimization Strategies for Virtual Power Plants," Energies, MDPI, vol. 13(23), pages 1-46, November.
    14. Schwidtal, Jan Marc & Agostini, Marco & Coppo, Massimiliano & Bignucolo, Fabio & Lorenzoni, Arturo, 2023. "Optimized operation of distributed energy resources: The opportunities of value stacking for Power-to-Gas aggregated with PV," Applied Energy, Elsevier, vol. 334(C).
    15. Dodiek Ika Candra & Kilian Hartmann & Michael Nelles, 2018. "Economic Optimal Implementation of Virtual Power Plants in the German Power Market," Energies, MDPI, vol. 11(9), pages 1-24, September.
    16. Yu, Songyuan & Fang, Fang & Liu, Yajuan & Liu, Jizhen, 2019. "Uncertainties of virtual power plant: Problems and countermeasures," Applied Energy, Elsevier, vol. 239(C), pages 454-470.
    17. Yetuo Tan & Yongming Zhi & Zhengbin Luo & Honggang Fan & Jun Wan & Tao Zhang, 2023. "Optimal Scheduling of Virtual Power Plant with Flexibility Margin Considering Demand Response and Uncertainties," Energies, MDPI, vol. 16(15), pages 1-14, August.
    18. Wang, Han & Riaz, Shariq & Mancarella, Pierluigi, 2020. "Integrated techno-economic modeling, flexibility analysis, and business case assessment of an urban virtual power plant with multi-market co-optimization," Applied Energy, Elsevier, vol. 259(C).
    19. Srđan Skok & Ahmed Mutapčić & Renata Rubesa & Mario Bazina, 2020. "Transmission Power System Modeling by Using Aggregated Distributed Generation Model Based on a TSO—DSO Data Exchange Scheme," Energies, MDPI, vol. 13(15), pages 1-15, August.
    20. Pavel Atănăsoae, 2022. "Allocation of Joint Costs and Price Setting for Electricity and Heat Generated in Cogeneration," Energies, MDPI, vol. 16(1), pages 1-20, December.
    21. Michal Jasiński & Tomasz Sikorski & Dominika Kaczorowska & Jacek Rezmer & Vishnu Suresh & Zbigniew Leonowicz & Paweł Kostyla & Jarosław Szymańda & Przemysław Janik, 2020. "A Case Study on Power Quality in a Virtual Power Plant: Long Term Assessment and Global Index Application," Energies, MDPI, vol. 13(24), pages 1-20, December.

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    More about this item

    Keywords

    Virtual power plant; Economic analysis; Scenario analysis; Electricity market; Renewable energy systems; Combined heat and power;
    All these keywords.

    JEL classification:

    • C61 - Mathematical and Quantitative Methods - - Mathematical Methods; Programming Models; Mathematical and Simulation Modeling - - - Optimization Techniques; Programming Models; Dynamic Analysis
    • Q41 - Agricultural and Natural Resource Economics; Environmental and Ecological Economics - - Energy - - - Demand and Supply; Prices
    • Q47 - Agricultural and Natural Resource Economics; Environmental and Ecological Economics - - Energy - - - Energy Forecasting
    • Q48 - Agricultural and Natural Resource Economics; Environmental and Ecological Economics - - Energy - - - Government Policy
    • Q55 - Agricultural and Natural Resource Economics; Environmental and Ecological Economics - - Environmental Economics - - - Environmental Economics: Technological Innovation

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