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Distributed Energy Resources Scheduling and Aggregation in the Context of Demand Response Programs

Author

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  • Pedro Faria

    (GECAD—Research Group on Intelligent Engineering and Computing for Advanced Innovation and Development, 4200-072 Porto, Portugal
    IPP—Polytechnic Institute of Porto, Rua DR. Antonio Bernardino de Almeida, 431, 4200-072 Porto, Portugal)

  • João Spínola

    (GECAD—Research Group on Intelligent Engineering and Computing for Advanced Innovation and Development, 4200-072 Porto, Portugal
    IPP—Polytechnic Institute of Porto, Rua DR. Antonio Bernardino de Almeida, 431, 4200-072 Porto, Portugal)

  • Zita Vale

    (IPP—Polytechnic Institute of Porto, Rua DR. Antonio Bernardino de Almeida, 431, 4200-072 Porto, Portugal)

Abstract

Distributed energy resources can contribute to an improved operation of power systems, improving economic and technical efficiency. However, aggregation of resources is needed to make these resources profitable. The present paper proposes a methodology for distributed resources management by a Virtual Power Player (VPP), addressing the resources scheduling, aggregation and remuneration based on the aggregation made. The aggregation is made using K -means algorithm. The innovative aspect motivating the present paper relies on the remuneration definition considering multiple scenarios of operation, by performing a multi-observation clustering. Resources aggregation and remuneration profiles are obtained for 2592 operation scenarios, considering 548 distributed generators, 20,310 consumers, and 10 suppliers.

Suggested Citation

  • Pedro Faria & João Spínola & Zita Vale, 2018. "Distributed Energy Resources Scheduling and Aggregation in the Context of Demand Response Programs," Energies, MDPI, vol. 11(8), pages 1-17, July.
    • Handle: RePEc:gam:jeners:v:11:y:2018:i:8:p:1987-:d:160965
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    References listed on IDEAS

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    1. Gonzalez-Salazar, Miguel Angel & Venturini, Mauro & Poganietz, Witold-Roger & Finkenrath, Matthias & Kirsten, Trevor & Acevedo, Helmer & Spina, Pier Ruggero, 2016. "Development of a technology roadmap for bioenergy exploitation including biofuels, waste-to-energy and power generation & CHP," Applied Energy, Elsevier, vol. 180(C), pages 338-352.
    2. Herrero, Ignacio & Rodilla, Pablo & Batlle, Carlos, 2015. "Electricity market-clearing prices and investment incentives: The role of pricing rules," Energy Economics, Elsevier, vol. 47(C), pages 42-51.
    3. Behboodi, Sahand & Chassin, David P. & Crawford, Curran & Djilali, Ned, 2016. "Renewable resources portfolio optimization in the presence of demand response," Applied Energy, Elsevier, vol. 162(C), pages 139-148.
    4. Balkhair, Khaled S. & Rahman, Khalil Ur, 2017. "Sustainable and economical small-scale and low-head hydropower generation: A promising alternative potential solution for energy generation at local and regional scale," Applied Energy, Elsevier, vol. 188(C), pages 378-391.
    5. Siano, Pierluigi & Sarno, Debora, 2016. "Assessing the benefits of residential demand response in a real time distribution energy market," Applied Energy, Elsevier, vol. 161(C), pages 533-551.
    6. Chassin, David P. & Rondeau, Daniel, 2016. "Aggregate modeling of fast-acting demand response and control under real-time pricing," Applied Energy, Elsevier, vol. 181(C), pages 288-298.
    7. Wang, Ge & Zhang, Qi & Li, Hailong & McLellan, Benjamin C. & Chen, Siyuan & Li, Yan & Tian, Yulu, 2017. "Study on the promotion impact of demand response on distributed PV penetration by using non-cooperative game theoretical analysis," Applied Energy, Elsevier, vol. 185(P2), pages 1869-1878.
    8. Faria, P. & Vale, Z., 2011. "Demand response in electrical energy supply: An optimal real time pricing approach," Energy, Elsevier, vol. 36(8), pages 5374-5384.
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    Cited by:

    1. Tomasz Sikorski & Michal Jasiński & Edyta Ropuszyńska-Surma & Magdalena Węglarz & Dominika Kaczorowska & Paweł Kostyla & Zbigniew Leonowicz & Robert Lis & Jacek Rezmer & Wilhelm Rojewski & Marian Sobi, 2020. "A Case Study on Distributed Energy Resources and Energy-Storage Systems in a Virtual Power Plant Concept: Technical Aspects," Energies, MDPI, vol. 13(12), pages 1-30, June.
    2. Libor Dražan & René Križan & Miroslav Popela, 2021. "Design and Testing of a Low-Tech DEW Generator for Determining Electromagnetic Immunity of Standard Electronic Circuits," Energies, MDPI, vol. 14(11), pages 1-15, May.
    3. Saeid Esmaeili & Amjad Anvari-Moghaddam & Shahram Jadid, 2019. "Optimal Operational Scheduling of Reconfigurable Multi-Microgrids Considering Energy Storage Systems," Energies, MDPI, vol. 12(9), pages 1-23, May.
    4. Cátia Silva & Pedro Faria & Zita Vale, 2019. "Multi-Period Observation Clustering for Tariff Definition in a Weekly Basis Remuneration of Demand Response," Energies, MDPI, vol. 12(7), pages 1-24, April.
    5. Hongwei Li & Qing Xu & Shitao Wang & Huihui Song, 2022. "Peak Shaving Methods of Distributed Generation Clusters Using Dynamic Evaluation and Self-Renewal Mechanism," Energies, MDPI, vol. 15(19), pages 1-17, September.
    6. Michał Jasiński & Tomasz Sikorski & Dominika Kaczorowska & Jacek Rezmer & Vishnu Suresh & Zbigniew Leonowicz & Paweł Kostyła & Jarosław Szymańda & Przemysław Janik & Jacek Bieńkowski & Przemysław Prus, 2021. "A Case Study on a Hierarchical Clustering Application in a Virtual Power Plant: Detection of Specific Working Conditions from Power Quality Data," Energies, MDPI, vol. 14(4), pages 1-13, February.
    7. Konstantinos Kotsalos & Ismael Miranda & Nuno Silva & Helder Leite, 2019. "A Horizon Optimization Control Framework for the Coordinated Operation of Multiple Distributed Energy Resources in Low Voltage Distribution Networks," Energies, MDPI, vol. 12(6), pages 1-27, March.
    8. Pedro Faria & Zita Vale, 2019. "Distributed Energy Resources Management 2018," Energies, MDPI, vol. 13(1), pages 1-4, December.
    9. Abdul Rehman Yasin & Muhammad Ashraf & Aamer Iqbal Bhatti, 2018. "Fixed Frequency Sliding Mode Control of Power Converters for Improved Dynamic Response in DC Micro-Grids," Energies, MDPI, vol. 11(10), pages 1-18, October.
    10. Filipe Marangoni & Leandro Magatão & Lúcia Valéria Ramos de Arruda, 2020. "Demand Response Optimization Model to Energy and Power Expenses Analysis and Contract Revision," Energies, MDPI, vol. 13(11), pages 1-23, June.

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