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Energies
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Operation and Control of Distributed Power Resources Under Market Environment
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Operation and Control of Distributed Power Resources Under Market Environment

A special issue of Energies (ISSN 1996-1073). This special issue belongs to the section "F: Electrical Engineering".

Deadline for manuscript submissions: 25 April 2025 | Viewed by 2447

Special Issue Editors

Prof. Dr. Ciwei Gao

E-Mail Website
Guest Editor
School of Electrical Engineering, Southeast University, Nanjing 210096, China
Interests: electricity market; demand response and demand side management; integrated energy system
Special Issues, Collections and Topics in MDPI journals
Dr. Xingyu Yan

E-Mail Website
Guest Editor
School of Electrical Engineering, Southeast University, Nanjing 210096, China
Interests: virtual power plant; micro-grid; demand response; distributed trading
Dr. Yunting Yao

E-Mail Website
Guest Editor
School of Electrical Engineering and Automation, Nanjing Normal University, Nanjing 210098, China
Interests: electricity market; distributed trading; demand response

Special Issue Information

Dear Colleagues,

The research on distributed power resources’ operation and control in the electricity market is driven by the increasing integration of renewable energy sources, energy storage systems, and demand-side management technologies into the grid. These distributed resources offer the potential to enhance grid reliability, efficiency, and sustainability, but their variability and unpredictability present challenges for grid operators and market participants. To address these challenges, innovative solutions such as demand response, distributed trading, virtual power plants (VPPs), and load aggregators can be explored.

This Special Issue will deal with novel solutions for distributed power resources’ operation and control. Topics of interest for publication include, but are not limited to, the following:

  • Distributed resources analysis;
  • Distributed trading platform design and development;
  • Strategy of pro-sumers in distributed trading;
  • Mechanism design for distributed trading;
  • Distributed resources optimal control;
  • Demand response program design;
  • Demand response implementation;
  • Demand response strategy;
  • Virtual power plant planning, operation and control;
  • Distributed resources’ aggregation technology;
  • Distributed resources’ allocation and operation for micro-grid;
  • Efficiency and reliability of distributed resources’ exploitation;
  • Trading strategy of load aggregator in electricity market;
  • AI-based technologies for distributed resources’ operation and control.

Prof. Dr. Ciwei Gao
Dr. Xingyu Yan
Dr. Yunting Yao
Guest Editors

Manuscript Submission Information

Manuscripts should be submitted online at www.mdpi.com by registering and logging in to this website. Once you are registered, click here to go to the submission form. Manuscripts can be submitted until the deadline. All submissions that pass pre-check are peer-reviewed. Accepted papers will be published continuously in the journal (as soon as accepted) and will be listed together on the special issue website. Research articles, review articles as well as short communications are invited. For planned papers, a title and short abstract (about 100 words) can be sent to the Editorial Office for announcement on this website.

Submitted manuscripts should not have been published previously, nor be under consideration for publication elsewhere (except conference proceedings papers). All manuscripts are thoroughly refereed through a single-blind peer-review process. A guide for authors and other relevant information for submission of manuscripts is available on the Instructions for Authors page. Energies is an international peer-reviewed open access semimonthly journal published by MDPI.

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

  • distributed resources
  • virtual power plant
  • distributed trading
  • demand response
  • load aggregator
  • regulating potential evaluation
  • micro-grid operation

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Published Papers (3 papers)

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Research

15 pages, 3041 KiB  
Article
Research on Electric Hydrogen Hybrid Storage Operation Strategy for Wind Power Fluctuation Suppression
by Dongsen Li, Kang Qian, Ciwei Gao, Yiyue Xu, Qiang Xing and Zhangfan Wang
Energies 2024, 17(20), 5019; https://doi.org/10.3390/en17205019 - 10 Oct 2024
Viewed by 479
Abstract
Due to real-time fluctuations in wind farm output, large-scale renewable energy (RE) generation poses significant challenges to power system stability. To address this issue, this paper proposes a deep reinforcement learning (DRL)-based electric hydrogen hybrid storage (EHHS) strategy to mitigate wind power fluctuations [...] Read more.
Due to real-time fluctuations in wind farm output, large-scale renewable energy (RE) generation poses significant challenges to power system stability. To address this issue, this paper proposes a deep reinforcement learning (DRL)-based electric hydrogen hybrid storage (EHHS) strategy to mitigate wind power fluctuations (WPFs). First, a wavelet packet power decomposition algorithm based on variable frequency entropy improvement is proposed. This algorithm characterizes the energy characteristics of the original wind power in different frequency bands. Second, to minimize WPF and the comprehensive operating cost of EHHS, an optimization model for suppressing wind power in the integrated power and hydrogen system (IPHS) is constructed. Next, considering the real-time and stochastic characteristics of wind power, the wind power smoothing model is transformed into a Markov decision process. A modified proximal policy optimization (MPPO) based on wind power deviation is proposed for training and solving. Based on the DRL agent’s real-time perception of wind power energy characteristics and the IPHS operation status, a WPF smoothing strategy is formulated. Finally, a numerical analysis based on a specific wind farm is conducted. The simulation results based on MATLAB R2021b show that the proposed strategy effectively suppresses WPF and demonstrates excellent convergence stability. The comprehensive performance of the MPPO is improved by 21.25% compared with the proximal policy optimization (PPO) and 42.52% compared with MPPO. Full article
(This article belongs to the Special Issue Operation and Control of Distributed Power Resources Under Market Environment)
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17 pages, 3255 KiB  
Article
Bidding Strategy for the Alliance of Prosumer Aggregators in the Distribution Market
by Chunyi Wang, Jiawei Xing, Yuejiao Wang, Jing Xu, Zhixin Fu, Benjie Xu and Haoming Liu
Energies 2024, 17(19), 5006; https://doi.org/10.3390/en17195006 - 8 Oct 2024
Viewed by 570
Abstract
Photovoltaic energy storage system (PV-ESS) prosumer aggregators are characterized by a large number but small scale in the distribution system and are not competitive enough to participate in market transactions. For this reason, a prosumer aggregator alliance is proposed to participate in the [...] Read more.
Photovoltaic energy storage system (PV-ESS) prosumer aggregators are characterized by a large number but small scale in the distribution system and are not competitive enough to participate in market transactions. For this reason, a prosumer aggregator alliance is proposed to participate in the distribution market bidding strategy. Firstly, based on the framework for prosumer aggregator alliances participating in distribution market trading, a bilevel bidding model is constructed. The upper level represents the optimal decision-making model for the prosumer aggregators, while the lower level constitutes the distribution market-clearing model. Secondly, the additional benefits obtained by the alliance are distributed more fairly using the improved Shapley value based on the PV self-consumption rate. Given the problem that the traditional diagonalization algorithm (DA) has an excessive number of iterations when solving the game equilibrium problem of multiple subjects, the DA is improved by optimizing the initial value of the inputs. Finally, case studies are conducted based on the improved IEEE-33 bus distribution system to validate the feasibility and economic viability of the proposed strategy. The case study results show that forming cooperative alliances to participate in market bidding can significantly increase overall profits. The improved DA reduces the number of bids and computation time by 75% and 80%, respectively. Additionally, the improved Shapley value facilitates compensation for some of the aggregators. Full article
(This article belongs to the Special Issue Operation and Control of Distributed Power Resources Under Market Environment)
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18 pages, 2217 KiB  
Article
Research on Optimal Scheduling Strategy of Differentiated Resource Microgrid with Carbon Trading Mechanism Considering Uncertainty of Wind Power and Photovoltaic
by Bin Li, Zhaofan Zhou, Junhao Hu and Chenle Yi
Energies 2024, 17(18), 4633; https://doi.org/10.3390/en17184633 - 16 Sep 2024
Viewed by 733
Abstract
Accelerating the green transformation of the power system is the inevitable path of the energy revolution; the increasing installed capacity of new energy and the penetration rate of electricity, uncertainty regarding new energy output, and the rising proportion of distributed power supply access [...] Read more.
Accelerating the green transformation of the power system is the inevitable path of the energy revolution; the increasing installed capacity of new energy and the penetration rate of electricity, uncertainty regarding new energy output, and the rising proportion of distributed power supply access have led to the threat against the safe and stable operation of the current power system. With the increasing uncertainty on both sides of power supply and demand, the microgrid (MG) is needed to effectually aggregate, coordinate, and optimize resources, such as adjustable resources, distributed power supply, and distributed energy storage in a certain area on the demand side. Therefore, in this paper, the uncertainty of wind power and PV is first dealt with by Latin hypercube sampling (LHS). Secondly, differentiated resources in the MG region can be divided into adjustable resources, distributed power supply, and energy storage. Adjustable resources are classified according to demand response characteristics. At the same time, the MG operating cost and carbon trading mechanism (CTM) are comprehensively considered. Finally, a low-carbon economy optimal scheduling strategy with the lowest total cost as the optimization goal is formed. Then, in order to verify the effectiveness of the proposed algorithm, three different scenarios are established for comparison. The total operating cost of the proposed algorithm is reduced by about 30%, and the total amount of carbon trading in 24 h can reach nearly 600 kg, bringing economic and social benefits to the MG. Full article
(This article belongs to the Special Issue Operation and Control of Distributed Power Resources Under Market Environment)
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