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Application of Swarm Intelligence for Multi-Energy Virtual Power Plants
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Application of Swarm Intelligence for Multi-Energy Virtual Power Plants

A special issue of Energies (ISSN 1996-1073). This special issue belongs to the section "A1: Smart Grids and Microgrids".

Deadline for manuscript submissions: 25 August 2025 | Viewed by 568

Special Issue Editors

Dr. Yang Gao

E-Mail Website
Guest Editor
Key Laboratory of Control of Power Transmission and Conversion, Ministry of Education, Shanghai Jiao Tong University, Shanghai 200240, China
Interests: virtual power plant; microgrid; integrated energy system; digital twin
Dr. Xiao Hu

E-Mail Website
Guest Editor
School of Electrical Engineering, Northeast Electric Power University, Jilin 132012, China
Interests: microgrid; whole energy system; virtual power plant
Dr. Sheng Chen

E-Mail Website
Guest Editor
College of Energy and Electrical Engineering, Hohai University, Nanjing 211100, China
Interests: microgrid; whole energy system; virtual power plant
Special Issues, Collections and Topics in MDPI journals

Special Issue Information

Dear Colleagues,

With the increasing number of couplings between electricity, gas, thermal, and other energy vectors, traditional independent energy systems are evolving into a comprehensive energy system. From this aspect, the traditional single-energy virtual power plant is evolving into a multi-energy synergistic virtual power plant, which is more dispersed in space and time dimensions. The complex interactions between various subjects and the autonomous behaviors of users bring significant challenges to the virtual power plant control. The scale, volume, and categories of operation data also increase significantly.

Swarm intelligence originates from the observation and research of social creatures and human social behavior. Because of its advantages of flexibility and robustness, it is one of the intelligent forms that the new generation of artificial intelligence focuses on. The control concept of swarm intelligence adopts the idea of "weak centralization", which has the advantages of self-organization, efficient collaboration, and self-learning. There are many distributed devices with strong nonlinearity and multiple coupling variables in the system, which have natural adaptability and compatibility with swarm intelligence. Multi-agent technology is a collection of multiple agents whose goal is to divide a large and complex system into various small systems that can communicate and coordinate with each other. It is very suitable for the application of swarm intelligence.

Dr. Yang Gao
Dr. Xiao Hu
Dr. Sheng Chen
Guest Editors

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Please visit the Instructions for Authors page before submitting a manuscript. The Article Processing Charge (APC) for publication in this open access journal is 2600 CHF (Swiss Francs). Submitted papers should be well formatted and use good English. Authors may use MDPI's English editing service prior to publication or during author revisions.

Keywords

  • virtual power plant
  • swarm intelligence
  • energy management
  • operation control
  • digital twin

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Published Papers (3 papers)

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Research

26 pages, 1058 KiB  
Article
A Multi-Time Scale Dispatch Strategy Integrating Carbon Trading for Mitigating Renewable Energy Fluctuations in Virtual Power Plants
by Wanling Zhuang, Junwei Liu, Jun Zhan, Honghao Liang, Cong Shen, Qian Ai and Minyu Chen
Energies 2025, 18(10), 2624; https://doi.org/10.3390/en18102624 - 19 May 2025
Abstract
Under the “dual-carbon” strategic framework, the installed capacity of renewable energy sources has continuously increased, while that of conventional generation units has progressively decreased. This structural shift significantly diminishes the operational flexibility of power generation systems and intensifies grid imbalances caused by renewable [...] Read more.
Under the “dual-carbon” strategic framework, the installed capacity of renewable energy sources has continuously increased, while that of conventional generation units has progressively decreased. This structural shift significantly diminishes the operational flexibility of power generation systems and intensifies grid imbalances caused by renewable energy volatility. To address these challenges, this study proposes a carbon-aware multi-timescale virtual power plant (VPP) scheduling framework with coordinated multi-energy integration, which operates through two sequential phases: day-ahead scheduling and intraday rolling optimization. In the day-ahead phase, demand response mechanisms are implemented to activate load-side regulation capabilities, coupled with information gap decision theory (IGDT) to quantify renewable energy uncertainties, thereby establishing optimal baseline schedules. During the intraday phase, rolling horizon optimization is executed based on updated short-term forecasts of renewable energy generation and load demand to determine final dispatch decisions. Numerical simulations demonstrate that the proposed framework achieves a 3.76% reduction in photovoltaic output fluctuations and 3.91% mitigation of wind power variability while maintaining economically viable scheduling costs. Specifically, the intraday optimization phase yields a 1.70% carbon emission reduction and a 7.72% decrease in power exchange costs, albeit with a 3.09% increase in operational costs attributable to power deviation penalties. Full article
(This article belongs to the Special Issue Application of Swarm Intelligence for Multi-Energy Virtual Power Plants)
19 pages, 2710 KiB  
Article
A Fast-Converging Virtual Power Plant Game Trading Model Based on Reference Ancillary Service Pricing
by Jiangfan Yuan, Min Zhang, Hongxun Tian, Xiangyu Guo, Xiao Chang, Tengxin Wang and Yingjun Wu
Energies 2025, 18(10), 2567; https://doi.org/10.3390/en18102567 - 15 May 2025
Viewed by 89
Abstract
In order to improve the trading efficiency of virtual power plants (VPPs) participating in the market of multi-type auxiliary services under the gaming environment, an initial trading price setting method based on the information of VPPs’ response characteristics and real-time supply and demand [...] Read more.
In order to improve the trading efficiency of virtual power plants (VPPs) participating in the market of multi-type auxiliary services under the gaming environment, an initial trading price setting method based on the information of VPPs’ response characteristics and real-time supply and demand changes is proposed to accelerate the convergence speed of the game. Firstly, a master–slave game trading model is established based on the reference auxiliary service pricing, which consists of a tariff coefficient and a basic tariff. Secondly, the tariff coefficient model is constructed based on response information, including response rate, quality, and reliability. Again, the basic tariff model is constructed based on the real-time supply and demand situation and the real-time grid tariff. Finally, the effectiveness of the proposed method in accelerating the convergence speed of the game is verified by analyzing 12 VPPs under the three auxiliary service scenarios of peaking, frequency regulation, and reserve. Full article
(This article belongs to the Special Issue Application of Swarm Intelligence for Multi-Energy Virtual Power Plants)
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24 pages, 3105 KiB  
Article
Aggregation Method and Bidding Strategy for Virtual Power Plants in Energy and Frequency Regulation Markets Using Zonotopes
by Jun Zhan, Mei Huang, Xiaojia Sun, Zuowei Chen, Yubo Zhang, Xuejing Xie, Yilin Chen, Yining Qiao and Qian Ai
Energies 2025, 18(10), 2458; https://doi.org/10.3390/en18102458 - 10 May 2025
Viewed by 213
Abstract
Aggregating and scheduling flexible resources through virtual power plants (VPPs) is a key measure used to improve the flexibility of new power systems. To maximize the regulation potential of flexible resources and achieve the efficient, unified scheduling of flexible resource clusters by VPPs, [...] Read more.
Aggregating and scheduling flexible resources through virtual power plants (VPPs) is a key measure used to improve the flexibility of new power systems. To maximize the regulation potential of flexible resources and achieve the efficient, unified scheduling of flexible resource clusters by VPPs, this study proposed a flexible resource aggregation method for VPPs and a bidding strategy for participation in the electricity and frequency regulation markets. First, considering the differences in the grid frequency regulation demand across periods, an improved zonotope approximation method was adopted to internally approximate the feasible region of flexible resources, thereby achieving the efficient aggregation of feasible regions. On this basis, the aggregation model was applied to the optimization model for VPPs, and a day-ahead double-layer bidding model of VPPs participating in the electricity and frequency regulation markets was proposed. The upper layer optimizes the bidding strategies to maximize the VPP revenue, while the lower layer achieves joint market clearing with the goal of maximizing social welfare. Finally, case studies were undertaken to validate the effectiveness of the proposed method. Full article
(This article belongs to the Special Issue Application of Swarm Intelligence for Multi-Energy Virtual Power Plants)
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