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Demand Response Programs Design and Use Considering Intensive Penetration of Distributed Generation

Author

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  • Pedro Faria

    (GECAD - Knowledge Engineering and Decision Support Research Center, IPP - Polytechnic Institute of Porto, Rua DR. Antonio Bernardino de Almeida, 431, 4200-072 Porto, Portugal)

  • Zita Vale

    (GECAD - Knowledge Engineering and Decision Support Research Center, IPP - Polytechnic Institute of Porto, Rua DR. Antonio Bernardino de Almeida, 431, 4200-072 Porto, Portugal)

  • José Baptista

    (INESC Technology and Science - INESC-TEC - Associate Laboratory, UTAD University, 5001-801Vila Real, Portugal)

Abstract

Further improvements in demand response programs implementation are needed in order to take full advantage of this resource, namely for the participation in energy and reserve market products, requiring adequate aggregation and remuneration of small size resources. The present paper focuses on SPIDER, a demand response simulation that has been improved in order to simulate demand response, including realistic power system simulation. For illustration of the simulator’s capabilities, the present paper is proposes a methodology focusing on the aggregation of consumers and generators, providing adequate tolls for the demand response program’s adoption by evolved players. The methodology proposed in the present paper focuses on a Virtual Power Player that manages and aggregates the available demand response and distributed generation resources in order to satisfy the required electrical energy demand and reserve. The aggregation of resources is addressed by the use of clustering algorithms, and operation costs for the VPP are minimized. The presented case study is based on a set of 32 consumers and 66 distributed generation units, running on 180 distinct operation scenarios.

Suggested Citation

  • Pedro Faria & Zita Vale & José Baptista, 2015. "Demand Response Programs Design and Use Considering Intensive Penetration of Distributed Generation," Energies, MDPI, vol. 8(6), pages 1-17, June.
    • Handle: RePEc:gam:jeners:v:8:y:2015:i:6:p:6230-6246:d:51542
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    References listed on IDEAS

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    1. 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. Qingwu Gong & Jiazhi Lei & Jun Ye, 2016. "Optimal Siting and Sizing of Distributed Generators in Distribution Systems Considering Cost of Operation Risk," Energies, MDPI, vol. 9(1), pages 1-18, January.
    2. Radu Godina & Eduardo M. G. Rodrigues & João C. O. Matias & João P. S. Catalão, 2015. "Effect of Loads and Other Key Factors on Oil-Transformer Ageing: Sustainability Benefits and Challenges," Energies, MDPI, vol. 8(10), pages 1-40, October.
    3. João Soares & Nuno Borges & Zita Vale & P.B. De Moura Oliveira, 2016. "Enhanced Multi-Objective Energy Optimization by a Signaling Method," Energies, MDPI, vol. 9(10), pages 1-23, October.
    4. Rongxiang Zhang & Xiaodong Chu & Wen Zhang & Yutian Liu, 2015. "Active Participation of Air Conditioners in Power System Frequency Control Considering Users’ Thermal Comfort," Energies, MDPI, vol. 8(10), pages 1-24, September.
    5. Ying Yu & Tongdan Jin & Chunjie Zhong, 2015. "Designing an Incentive Contract Menu for Sustaining the Electricity Market," Energies, MDPI, vol. 8(12), pages 1-22, December.
    6. Mingrui Zhang & Ming Gan & Luyao Li, 2019. "Sizing and Siting of Distributed Generators and Energy Storage in a Microgrid Considering Plug-in Electric Vehicles," Energies, MDPI, vol. 12(12), pages 1-17, June.
    7. Xiaoqing Hu & Beibei Wang & Shengchun Yang & Taylor Short & Lei Zhou, 2015. "A Closed-Loop Control Strategy for Air Conditioning Loads to Participate in Demand Response," Energies, MDPI, vol. 8(8), pages 1-32, August.
    8. Yajing Gao & Yanping Sun & Xiaodan Wang & Feifan Chen & Ali Ehsan & Hongmei Li & Hong Li, 2017. "Multi-Objective Optimized Aggregation of Demand Side Resources Based on a Self-organizing Map Clustering Algorithm Considering a Multi-Scenario Technique," Energies, MDPI, vol. 10(12), pages 1-20, December.
    9. 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.
    10. Lei Zhou & Yang Li & Beibei Wang & Zhe Wang & Xiaoqing Hu, 2015. "Provision of Supplementary Load Frequency Control via Aggregation of Air Conditioning Loads," Energies, MDPI, vol. 8(12), pages 1-20, December.
    11. Omid Abrishambaf & Pedro Faria & Luis Gomes & João Spínola & Zita Vale & Juan M. Corchado, 2017. "Implementation of a Real-Time Microgrid Simulation Platform Based on Centralized and Distributed Management," Energies, MDPI, vol. 10(6), pages 1-14, June.

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