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Planning, Operation, and Control of New Power Systems
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Planning, Operation, and Control of New Power Systems

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

Deadline for manuscript submissions: 20 May 2025 | Viewed by 4191

Special Issue Editors

Dr. Hui Hou

E-Mail Website
Guest Editor
School of Automation, Wuhan University of Technology, Wuhan 430070, China
Interests: power system security risk assessment; optimal planning and operation of energy internet; interaction between electric vehicles and power grid
Dr. Siyang Liao

E-Mail Website
Guest Editor
School of Electrical Engineering, Wuhan University, Wuhan 430072, China
Interests: power system operation and control with a high proportion of new energy
Dr. Yunqi Wang

E-Mail Website
Guest Editor
School of Engineering, RMIT University, Melbourne, VIC 3000, Australia
Interests: integration of distributed energy resources (DERs) and the development of advanced methodologies for enhancing grid resilience, efficiency, and flexibility; decentralized energy systems; demand-side management; the application of artificial intelligence and machine learning in optimizing power systems
Dr. Ying Du

E-Mail Website
Guest Editor
BEEE, The Hong Kong Polytechnic University, Hong Kong, China
Interests: reliability and resiliency of distribution systems under increasingly frequent and serious natural disasters; high penetration of renewable energy
Special Issues, Collections and Topics in MDPI journals

Special Issue Information

Dear Colleagues,

The rapid development and wide application of communication technology, multi-energy complementary technology, and distributed power generation technology have made the popularity of new power systems increase rapidly across the world, supporting and promoting the adjustment and upgrading of the world’s energy structures. With the proposal of a carbon neutrality goal and the continuous improvement of the penetration rate of renewable energy, traditional optimization methods are difficult to adapt to the increasingly complex energy environment and the requirements of new power systems’ intelligent decision making. Meanwhile, the continuous refinement of electricity and carbon market mechanisms necessitates stricter requirements for the planning and operation of new power systems.

To enhance self-balancing capability and energy efficiency, the guest Editorial Team is seeking original research and review papers related to the optimal planning, operation, and control of new power systems.

Dr. Hui Hou
Dr. Siyang Liao
Dr. Yunqi Wang
Dr. Ying Du
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

  • new power systems
  • carbon neutral carbon peak
  • optimal planning, operation and control

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Published Papers (7 papers)

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Research

19 pages, 4590 KiB  
Article
Restoration Strategy for Urban Power Distribution Systems Considering Coupling with Transportation Networks Under Heavy Rainstorm Disasters
by Dongli Jia, Zhao Li, Yongle Dong, Xiaojun Wang, Mingcong Lin, Kaiyuan He, Xiaoyu Yang and Jiajing Liu
Energies 2025, 18(2), 422; https://doi.org/10.3390/en18020422 - 19 Jan 2025
Viewed by 375
Abstract
With the increasing frequency of extreme weather events such as heavy rainstorm disasters, the stable operation of power systems is facing significant challenges. This paper proposes a two-stage restoration strategy for the distribution networks (DNs). First, a grid-based modeling approach is developed for [...] Read more.
With the increasing frequency of extreme weather events such as heavy rainstorm disasters, the stable operation of power systems is facing significant challenges. This paper proposes a two-stage restoration strategy for the distribution networks (DNs). First, a grid-based modeling approach is developed for urban DNs and transportation networks (TNs), capturing the dynamic evolution of heavy rainstorm disasters and more accurately modeling the impact on TNs and DNs. Then, a two-stage restoration strategy is designed for the DN by coordinating soft open points (SOPs) and mobile energy storage systems (MESSs). In the disaster progression stage, SOPs are utilized to enable the flexible reconfiguration and islanding of the DN, minimizing load loss. In the post-disaster recovery stage, the MESS and repair crew are optimally dispatched, taking into account the state of the TN to expedite power restoration. Finally, the experimental results demonstrate that the proposed method reduces load loss during restoration by 8.09% compared to approaches without precise TN and DN modeling. Full article
(This article belongs to the Special Issue Planning, Operation, and Control of New Power Systems)
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16 pages, 4967 KiB  
Article
Bi-Level Game Strategy for Virtual Power Plants Based on an Improved Reinforcement Learning Algorithm
by Zhu Liu, Guowei Guo, Dehuang Gong, Lingfeng Xuan, Feiwu He, Xinglin Wan and Dongguo Zhou
Energies 2025, 18(2), 374; https://doi.org/10.3390/en18020374 - 16 Jan 2025
Viewed by 495
Abstract
To address the issue of economic dispatch imbalance in virtual power plant (VPP) systems caused by the influence of operators and distribution networks, this study introduces an optimized economic dispatch method based on bi-level game theory. Firstly, a bi-level game model is formulated, [...] Read more.
To address the issue of economic dispatch imbalance in virtual power plant (VPP) systems caused by the influence of operators and distribution networks, this study introduces an optimized economic dispatch method based on bi-level game theory. Firstly, a bi-level game model is formulated, which integrates the operational and environmental expenses of VPPs with the revenues of system operators. To avoid local optima during the search process, an enhanced reinforcement learning algorithm is developed to achieve rapid convergence and obtain the optimal solution. Finally, case analyses illustrate that the proposed method effectively accomplishes multi-objective optimization for various decision-making stakeholders, including VPP and system operators, while significantly reducing curtailment costs associated with the extensive integration of distributed renewable energy. Furthermore, the proposed algorithm achieves fast iteration and yields superior dispatch outcomes under the same modeling conditions. Full article
(This article belongs to the Special Issue Planning, Operation, and Control of New Power Systems)
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16 pages, 2276 KiB  
Article
Adaptive Control of VSG Inertia Damping Based on MADDPG
by Demu Zhang, Jing Zhang, Yu He, Tao Shen and Xingyan Liu
Energies 2024, 17(24), 6421; https://doi.org/10.3390/en17246421 - 20 Dec 2024
Viewed by 452
Abstract
As renewable energy sources become more integrated into the power grid, traditional virtual synchronous generator (VSG) control strategies have become inadequate for the current low-damping, low-inertia power systems. Therefore, this paper proposes a VSG inertia and damping adaptive control method based on multi-agent [...] Read more.
As renewable energy sources become more integrated into the power grid, traditional virtual synchronous generator (VSG) control strategies have become inadequate for the current low-damping, low-inertia power systems. Therefore, this paper proposes a VSG inertia and damping adaptive control method based on multi-agent deep deterministic policy gradient (MADDPG). The paper first introduces the working principles of virtual synchronous generators and establishes a corresponding VSG model. Based on this model, the influence of variations in virtual inertia (J) and damping (D) coefficients on fluctuations in active power output is examined, defining the action space for J and D. The proposed method is mainly divided into two phases: “centralized training and decentralized execution”. In the centralized training phase, each agent’s critic network shares global observation and action information to guide the actor network in policy optimization. In the decentralized execution phase, agents observe frequency deviations and the rate at which angular frequency changes, using reinforcement learning algorithms to adjust the virtual inertia J and damping coefficient D in real time. Finally, the effectiveness of the proposed MADDPG control strategy is validated through comparison with adaptive control and DDPG control methods. Full article
(This article belongs to the Special Issue Planning, Operation, and Control of New Power Systems)
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17 pages, 5297 KiB  
Article
Multi-Objective Coordinated Optimization Method of Active and Reactive Power Considering Power Characteristics of Renewable Energy Converters
by Xuebin Wang, Guobin Fu, Yanbo Chen, Rui Song, Haibin Sun, Jiahao Ma and Tao Huang
Energies 2024, 17(24), 6370; https://doi.org/10.3390/en17246370 - 18 Dec 2024
Viewed by 441
Abstract
In new power systems with a high proportion of renewable energy, optimization criteria based solely on economic efficiency or system stability may lead to a reduction in the static stability domain of the system or lead to long-term deviations from economic operation, thus [...] Read more.
In new power systems with a high proportion of renewable energy, optimization criteria based solely on economic efficiency or system stability may lead to a reduction in the static stability domain of the system or lead to long-term deviations from economic operation, thus reducing the overall applicability of such methods. This paper proposes a multi-objective active–reactive power coordinated optimization model that considers both economic efficiency and static stability indicators. The goal of the model is to minimize operating costs while optimizing static stability margins. It combines the reactive power support capabilities of converters and other reactive power compensation equipment to ensure safe and economical dispatch of the system. The proposed method is verified through a case study, which shows that this method can make full use of the potential reactive power regulation capability of the converter. At the same time, the economics and stability of the system are significantly improved by using this method. The overall improvement is about is 12.3%. Full article
(This article belongs to the Special Issue Planning, Operation, and Control of New Power Systems)
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16 pages, 1448 KiB  
Article
Battery Control for Node Capacity Increase for Electric Vehicle Charging Support
by Md Wakil Ahmad, Alexandre Lucas and Salvador Moreira Paes Carvalhosa
Energies 2024, 17(22), 5554; https://doi.org/10.3390/en17225554 - 7 Nov 2024
Viewed by 748
Abstract
The integration of electric vehicles (EVs) into the power grid poses significant challenges and opportunities for energy management systems. This is especially concerning for parking lots or private building condominiums in which refurbishing is not possible or is costly. This paper presents a [...] Read more.
The integration of electric vehicles (EVs) into the power grid poses significant challenges and opportunities for energy management systems. This is especially concerning for parking lots or private building condominiums in which refurbishing is not possible or is costly. This paper presents a real-time monitoring approach to EV charging dynamics with battery storage support over a 24 h period. By simulating EV demand, state of charge (SOC), and charging and discharging events, we provide insights into the operational strategies for energy storage systems to ensure maximum charging simultaneity factor through internal power enhancement. The study uses a time-series analysis of EV demand, contrasting it with the battery’s SOC, to dynamically adjust charging and discharging actions within the constraints of the upstream infrastructure capacity. The model incorporates parameters such as maximum power capacity, energy storage capacity, and charging efficiencies, to reflect realistic conditions. Results indicate that real-time SOC monitoring, coupled with adaptive charging strategies, can mitigate peak demands and enhance the system’s responsiveness to fluctuating loads. This paper emphasizes the critical role of real-time data analysis in the effective management of energy resources in existing parking lots and lays the groundwork for developing intelligent grid-supportive frameworks in the context of growing EV adoption. Full article
(This article belongs to the Special Issue Planning, Operation, and Control of New Power Systems)
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20 pages, 603 KiB  
Article
A Day-Ahead Economic Dispatch Method for Renewable Energy Systems Considering Flexibility Supply and Demand Balancing Capabilities
by Zheng Yang, Wei Xiong, Pengyu Wang, Nuoqing Shen and Siyang Liao
Energies 2024, 17(21), 5427; https://doi.org/10.3390/en17215427 - 30 Oct 2024
Viewed by 661
Abstract
The increase in new energy grid connections has reduced the supply-side regulation capability of the power system. Traditional economic dispatch methods are insufficient for addressing the flexibility limitations in the system’s balancing capabilities. Consequently, this study presents a day-ahead scheduling method for renewable [...] Read more.
The increase in new energy grid connections has reduced the supply-side regulation capability of the power system. Traditional economic dispatch methods are insufficient for addressing the flexibility limitations in the system’s balancing capabilities. Consequently, this study presents a day-ahead scheduling method for renewable energy systems that balances flexibility and economy. This approach establishes a dual-layer optimized scheduling model. The upper-layer model focuses on the economic efficiency of unit start-up and shut-down, utilizing a particle swarm algorithm to identify unit combinations that comply with minimum start-up and shut-down time constraints. In contrast, the lower-layer model addresses the dual uncertainties of generation and load. It employs the Generalized Polynomial Chaos approximation to create an opportunity-constrained model aimed at minimizing unit generation and curtailment costs while maximizing flexibility supply capability. Additionally, it calculates the probability of flexibility supply-demand insufficiency due to uncertainties in demand response resource supply and system operating costs, providing feedback to the upper-layer model. Ultimately, through iterative solutions of the upper and lower models, a day-ahead scheduling plan that effectively balances flexibility and economy is derived. The proposed method is validated using a simulation of the IEEE 30-bus system case study, demonstrating its capability to balance system flexibility and economy while effectively reducing the risk of insufficient supply-demand balance. Full article
(This article belongs to the Special Issue Planning, Operation, and Control of New Power Systems)
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26 pages, 3842 KiB  
Article
Multi-Objective Optimization Operation of Multi-Agent Active Distribution Network Based on Analytical Target Cascading Method
by Yiran Zhao, Yong Xue, Ruixin Zhang, Jiahao Yin, Yang Yang and Yanbo Chen
Energies 2024, 17(20), 5022; https://doi.org/10.3390/en17205022 - 10 Oct 2024
Viewed by 653
Abstract
In the context of the green energy transition, the rapid expansion of flexible resources such as distributed renewable energy, electric vehicles (EVs), and energy storage has significantly impacted the operation of distribution networks. This paper proposes a multi-objective optimization approach for active distribution [...] Read more.
In the context of the green energy transition, the rapid expansion of flexible resources such as distributed renewable energy, electric vehicles (EVs), and energy storage has significantly impacted the operation of distribution networks. This paper proposes a multi-objective optimization approach for active distribution networks (ADNs) based on analytical target cascading (ATC). Firstly, a dynamic optimal power flow (DOPF) calculation method is developed using second-order conic relaxation (SOCR) to address power flow and voltage issues in the distribution network, incorporating active management (AM) elements. Secondly, this study focuses on aggregating the power of flexible resources within station areas connected to distribution network nodes and incorporating these resources into demand response (DR) programs. Finally, a two-layer model for collaborative multi-objective scheduling between station areas and the active distribution network is implemented using the ATC method. Case studies demonstrate the model’s effectiveness and validity, showing its potential for enhancing the operation of distribution networks amidst the increasing integration of flexible resources. Full article
(This article belongs to the Special Issue Planning, Operation, and Control of New Power Systems)
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