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Cited by 28Electric Power System OptimizationSmart Grid Energy ManagementIntegrated Energy Systems Optimization

Double Layer Dynamic Game Bidding Mechanism Based on Multi-Agent Technology for Virtual Power Plant and Internal Distributed Energy Resource

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Gao, Yajing, Zhou, Xiaojie, Ren, Jiafeng, Wang, Xiuna, Li, Dongwei·2018·Energies
DOI 10.3390/en11113072
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Abstract

As renewable energies become the main direction of global energy development in the future, Virtual Power Plant (VPP) becomes a regional multi-energy aggregation model for large-scale integration of distributed generation into the power grid. It also provides an important way for distributed energy resources (DER) to participate in electricity market transactions. Firstly, the basic concept of VPP is outlined, and various uncertainties within VPP are modeled. Secondly, using multi-agent technology and Stackelberg dynamic game theory, a double-layer nested dynamic game bidding model including VPP and its internal DERs is designed. The lower layer is a bidding game for VPP internal market including DER. VPP is the leader and each DER is a subagent that acts as a follower to maximize its profit. Each subagent uses the particle swarm algorithm (PSA) to determine the optimal offer coefficient, and VPP carries out internal market clearing with the minimum variance of unit profit according to the quoting results. Then, the subagents renew the game to update the bidding strategy based on the outcomes of the external and internal markets. The upper layer is the external market bidding game. The trading center (TC) is the leader and VPP is the agent and the follower. The game is played with the goal of maximum self-interest. The agent uses genetic algorithms to determine the optimal bid strategy, and the TC carries out market clearance with the goal of maximizing social benefits according to the quotation results. Each agent renews the game to update the bidding strategy based on the clearing result and the reporting of the subagents. The dynamic game is repeated until the optimal equilibrium solution is obtained. Finally, the effectiveness of the model is verified by taking the IEEE30-bus system as an example.

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  • Most aggregator studies focus on large-scale or regional grid services, with less attention paid to localized overloads at individual transformers.

    “First, most aggregator studies focus on large-scale or regional grid services, with less attention paid to localized overloads at individual transformers [9][10][11].”

    From A Multi-Agent, Laxity-Based Aggregation Strategy for Cost-Effective Electric Vehicle Charging and Local Transformer Overload Prevention (2025) · cites this paper as [11]

    The Discussion (page unavailable) says VPP research mainly focuses on overall external operation, supporting the claim’s broad contrast between external and more localized concerns. But the gap it identifies is internal distributed-energy operation and benefits, not localized overloads at individual transformers. (LLM, conf=0.83)

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References 16 · Cited by 28

Research works printed but not linked: 36

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Authors
Gao, Y., Zhou, X., Ren, J., Wang, X., & Li, D.
Affiliations
  • China Electric Power Enterprise Association Power Construction Technology and Economic Consultation Center, Beijing 100000, China
  • North China Electric Power University (CN)
Corresponding author
  • Gao, Yajing
  • Zhou, Xiaojie
ORCID
Venue
Energies
Publisher
MDPI AG
Published
2018-11-07
Volume
11
Issue
11
Pages
3072
ISSN
1996-1073 (ISSN-L)
DOI
10.3390/en11113072
OpenAlex
W2900102355
Cited by
28
References
16
Citations by year
2026: 1 · 2025: 5 · 2024: 7 · 2023: 3 · 2022: 7 · 2021: 2 · 2019: 3
FWCI
0.48
Type
journal article
Language
en
Open access
Licence
Locations
  • Energies (publishedVersion, cc-by)
  • RePEc: Research Papers in Economics (submittedVersion)
  • DOAJ (DOAJ: Directory of Open Access Journals) (submittedVersion, cc-by-sa)
Funders
  • Nature Science Foundation of China (51607068)
  • Fundamental Research Funds for the Central Universities (2018MS082, 2017MS090)
Topics
  • Physical Sciences › Engineering › Electrical and Electronic Engineering › Electric Power System Optimization
  • Physical Sciences › Engineering › Electrical and Electronic Engineering › Smart Grid Energy Management
  • Physical Sciences › Engineering › Electrical and Electronic Engineering › Integrated Energy Systems Optimization
Keywords
virtual power plants, distributed energy resources, electricity market bidding, Stackelberg game, multi-agent systems, trading centers, renewable energy integration, bidding strategy optimization, particle swarm optimization, genetic algorithm

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