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An Event-Based Resource Management Framework for Distributed Decision-Making in Decentralized Virtual Power Plants

Author

Listed:
  • Jianchao Zhang

    (Department of Electrical and Electronic Engineering, Auckland University of Technology, Auckland 1010, New Zealand)

  • Boon-Chong Seet

    (Department of Electrical and Electronic Engineering, Auckland University of Technology, Auckland 1010, New Zealand)

  • Tek Tjing Lie

    (Department of Electrical and Electronic Engineering, Auckland University of Technology, Auckland 1010, New Zealand)

Abstract

The Smart Grid incorporates advanced information and communication technologies (ICTs) in power systems, and is characterized by high penetration of distributed energy resources (DERs). Whether it is the nation-wide power grid or a single residential building, the energy management involves different types of resources that often depend on and influence each other. The concept of virtual power plant (VPP) has been proposed to represent the aggregation of energy resources in the electricity market, and distributed decision-making (DDM) plays a vital role in VPP due to its complex nature. This paper proposes a framework for managing different resource types of relevance to energy management for decentralized VPP. The framework views VPP as a hierarchical structure and abstracts energy consumption/generation as contractual resources, i.e., contractual offerings to curtail load/supply energy, from third party VPP participants for DDM. The proposed resource models, event-based approach to decision making, multi-agent system and ontology implementation of the framework are presented in detail. The effectiveness of the proposed framework is then demonstrated through an application to a simulated campus VPP with real building energy data.

Suggested Citation

  • Jianchao Zhang & Boon-Chong Seet & Tek Tjing Lie, 2016. "An Event-Based Resource Management Framework for Distributed Decision-Making in Decentralized Virtual Power Plants," Energies, MDPI, vol. 9(8), pages 1-19, July.
    • Handle: RePEc:gam:jeners:v:9:y:2016:i:8:p:595-:d:74876
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    References listed on IDEAS

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    1. Su, Wencong & Huang, Alex Q., 2014. "A game theoretic framework for a next-generation retail electricity market with high penetration of distributed residential electricity suppliers," Applied Energy, Elsevier, vol. 119(C), pages 341-350.
    2. Hao Bai & Shihong Miao & Xiaohong Ran & Chang Ye, 2015. "Optimal Dispatch Strategy of a Virtual Power Plant Containing Battery Switch Stations in a Unified Electricity Market," Energies, MDPI, vol. 8(3), pages 1-22, March.
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    1. 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.
    2. Abouzar Estebsari & Luca Barbierato & Alireza Bahmanyar & Lorenzo Bottaccioli & Enrico Macii & Edoardo Patti, 2019. "A SGAM-Based Test Platform to Develop a Scheme for Wide Area Measurement-Free Monitoring of Smart Grids under High PV Penetration," Energies, MDPI, vol. 12(8), pages 1-27, April.
    3. Liwei Ju & Peng Li & Qinliang Tan & Zhongfu Tan & GejiriFu De, 2018. "A CVaR-Robust Risk Aversion Scheduling Model for Virtual Power Plants Connected with Wind-Photovoltaic-Hydropower-Energy Storage Systems, Conventional Gas Turbines and Incentive-Based Demand Responses," Energies, MDPI, vol. 11(11), pages 1-28, October.

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