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New Trends in Power System Operation, Control, and Trading Considering Flexible Resources

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

Deadline for manuscript submissions: 10 September 2026 | Viewed by 2146

Special Issue Editors

Dr. Ying Wang

E-Mail Website
Guest Editor
Key Laboratory of Measurement and Control of Complex Systems of Engineering, Ministry of Education, Southeast University, Nanjing 210096, China
Interests: power systems and integrated energy; electricity market
Dr. Shuai Fan

E-Mail Website
Guest Editor
School of Electrical Engineering, Shanghai Jiao Tong University, Shanghai 200240, China
Interests: virtual power plant; demand response; electricity market; distributed energy resources
Dr. Tao Wu

E-Mail Website
Guest Editor
School of Electrical Engineering, Chongqing University, Chongqing 400044, China
Interests: hydrogen integration; AI applications to power systems
Dr. Xuemei Dai

E-Mail Website
Guest Editor
School of Automation Engineering, Shanghai University of Electric Power, Shanghai 201306, China
Interests: power market; optimal dispatch; operation optimization
Dr. Chunyu Chen

E-Mail Website
Guest Editor
School of Electrical Engineering, China University of Mining and Technology, Xuzhou 221116, China
Interests: power system operation and contol
Special Issues, Collections and Topics in MDPI journals

Special Issue Information

Dear Colleagues,

The large-scale integration of flexible resources, such as high-penetration renewables, advanced storage technologies, and demand-side flexibility, is rapidly reshaping the architecture and operation of traditional power grids. This transformation brings not only technical and economic opportunities but also urgent challenges in system coordination, uncertainty management, and market design. In particular, breakthroughs in AI-driven trading mechanisms, real-time flexibility coordination, and sector-coupled synergies (e.g., hydrogen–storage integration) are emerging as crucial pathways toward resilient, efficient, and sustainable energy systems.

This Special Issue (SI), “New Trends in Power System Operation, Control, and Trading Considering Flexible Resources”, distinguishes itself by emphasizing novel approaches and transformative solutions that extend beyond incremental improvements. We aim to showcase innovative methods, analytical frameworks, experimental validations, and case studies that redefine how flexible resources are integrated into system operation, control, and trading at scale.

Topics for this Special Issue include, but are not limited to:

  • Energy storage and hydrogen technology
  • Power systems and integrated energy
  • Virtual power plant and demand flexibility
  • Applications of artificial intelligence in smart grids
  • Renewable energy-based power system dispatch
  • Low carbon power system operation
  • Electricity market design and flexibility
  • Evaluation and analysis of operational flexibility of power systems
  • Cyber security of flexible resources and smart grids
  • Coordinated bidding and offering of flexible resources
  • Power system flexibility and resilience
  • Local energy and flexibility market
  • Applications of artificial intelligence in flexible resources

Dr. Ying Wang
Dr. Shuai Fan
Dr. Tao Wu
Dr. Xuemei Dai
Dr. Chunyu Chen
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 250 words) can be sent to the Editorial Office for assessment.

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

  • power system operation and control
  • flexible resources
  • electricity market
  • artificial intelligence in power systems

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Published Papers (4 papers)

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Research

37 pages, 8631 KB  
Article
Unlocking Hydrogen Load Flexibility via Data-Driven Modeling for Enhanced Integrated Energy System Operation
by Rongwei He, Hongyang Jin and Dong Zhang
Energies 2026, 19(10), 2406; https://doi.org/10.3390/en19102406 - 17 May 2026
Viewed by 105
Abstract
Hydrogen energy, owing to its advantages of low-carbon cleanliness, long-term storage capacity, and multi-energy coupling potential, has emerged as a crucial medium for enhancing renewable energy accommodation within integrated energy systems. However, the pronounced heterogeneity in hydrogen load behaviors, temporal characteristics, and regulation [...] Read more.
Hydrogen energy, owing to its advantages of low-carbon cleanliness, long-term storage capacity, and multi-energy coupling potential, has emerged as a crucial medium for enhancing renewable energy accommodation within integrated energy systems. However, the pronounced heterogeneity in hydrogen load behaviors, temporal characteristics, and regulation capabilities poses significant challenges for unified modeling approaches, which struggle to accurately capture the multi-modal regulation potential of hydrogen demand, thereby limiting the precision of system operation optimization. To address this issue, this paper proposes a data-driven hydrogen load flexibility modeling method for integrated energy system (IES) operation optimization. A hybrid LSTM-ISODATA framework is designed to extract deep temporal dependencies and identify six representative hydrogen consumption patterns from typical load sequences. Each hydrogen load category is decomposed into shiftable, transferable, and reducible flexible forms, and a category-specific time-varying flexibility constraint matrix is established to characterize differentiated regulation capabilities. An electricity–heat–hydrogen integrated energy system operation optimization model embedded with classified flexible hydrogen loads is developed and solved via mathematical programming. Simulation results show that the proposed method reduces system operating costs by 10.3% compared with conventional unified modeling, while significantly promoting renewable energy utilization and system operational flexibility. The effectiveness and engineering applicability of the proposed model in IES optimal scheduling are fully validated. Full article
(This article belongs to the Special Issue New Trends in Power System Operation, Control, and Trading Considering Flexible Resources)
38 pages, 4882 KB  
Article
Market Operation Strategy for Wind–Hydro-Storage in Spot and Ramping Service Markets Under the Ramping Cost Responsibility Allocation Mechanism
by Yuanhang Zhang, Xianshan Li and Guodong Song
Energies 2026, 19(7), 1799; https://doi.org/10.3390/en19071799 - 7 Apr 2026
Viewed by 351
Abstract
The ramping requirement in new power systems primarily stems from net load variations and forecast errors of renewable energy and load. Designing an equitable cost allocation mechanism for ramping services based on these factors facilitates incentives for generation and load to actively reduce [...] Read more.
The ramping requirement in new power systems primarily stems from net load variations and forecast errors of renewable energy and load. Designing an equitable cost allocation mechanism for ramping services based on these factors facilitates incentives for generation and load to actively reduce ramping demands, thereby alleviating system ramping pressure. Accordingly, this paper proposes a fair ramping cost allocation mechanism based on the ramping responsibility coefficients of market participants. Under this mechanism, a market-oriented operation model for wind–hydro-storage joint operation is established to verify its effectiveness in market applications. First, a ramping cost allocation mechanism is constructed based on ramping responsibility coefficients. According to the responsibility coefficients of market participants for deterministic and uncertain ramping requirements, ramping costs are allocated to the corresponding contributors in proportion to the ramping demands caused by net load variations, load forecast deviations, and renewable energy forecast deviations. Specifically, for costs arising from renewable energy forecast errors, an allocation mechanism is designed based on the difference between the declared error range and the actual error. Second, within this allocation framework, hydropower and storage (including cascade hydropower and hybrid pumped storage) are utilized as flexible resources to mitigate wind power uncertainty and reduce its ramping costs. A two-stage day-ahead and real-time bi-level game model for wind–hydro-storage cooperative decision-making is developed. The upper level optimizes bilateral trading and market bidding strategies for wind–hydro-storage, while the lower level simulates the market clearing process. Through Stackelberg game modeling, joint optimal operation of wind–hydro-storage is achieved, ensuring mutual benefits. Finally, simulation results validate that the proposed ramping cost allocation mechanism can guide renewable energy to improve output controllability through economic signals. Furthermore, the bilateral trading and coordinated market participation of wind–hydro-storage realize win–win outcomes, reduce the ramping cost allocation for wind power by 23.10%, effectively narrow peak-valley price differences, and enhance market operational efficiency. Full article
(This article belongs to the Special Issue New Trends in Power System Operation, Control, and Trading Considering Flexible Resources)
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18 pages, 2776 KB  
Article
Application of Self-Disturbance-Robust Generalized Predictive Control in Feedwater Temperature Control
by Jianfu Fan, Jianwei Bi, Tingting Yang and Hong Qian
Energies 2026, 19(3), 831; https://doi.org/10.3390/en19030831 - 4 Feb 2026
Viewed by 447
Abstract
To address the issue of energy conservation of high-pressure heater systems in feedwater temperature elevating, this paper proposes an advanced control strategy based on a self-disturbance-compensating generalized predictive control (GPC) algorithm. Combined with the control of high-pressure heater water level, the feedwater temperature [...] Read more.
To address the issue of energy conservation of high-pressure heater systems in feedwater temperature elevating, this paper proposes an advanced control strategy based on a self-disturbance-compensating generalized predictive control (GPC) algorithm. Combined with the control of high-pressure heater water level, the feedwater temperature is controlled. Aiming at the high inertia and significant delay in high-pressure heater systems, a GPC algorithm is introduced to effectively compensate for system dynamic lag. Concurrently, to tackle multi-source and unmeasurable disturbances during high-pressure heater operation, an extended state observer is presented for their real-time observation and compensation. This significantly enhances the control system’s disturbance rejection capability, while maximizing the heat transfer efficiency of the high-pressure heater and reducing irreversible losses in the thermal system. Simulation experiment results demonstrate that the proposed method achieves superior stability and control performance compared to relevant control methods for feedwater temperature regulation, offering a solution to enhance the thermal economy of the power plant. Full article
(This article belongs to the Special Issue New Trends in Power System Operation, Control, and Trading Considering Flexible Resources)
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22 pages, 2589 KB  
Article
Optimal Bidding Strategy of Virtual Power Plant Incorporating Vehicle-to-Grid Electric Vehicles
by Honghui Zhang, Dejie Zhao, Hao Pan and Limin Jia
Energies 2026, 19(2), 465; https://doi.org/10.3390/en19020465 - 17 Jan 2026
Viewed by 617
Abstract
With the increasing penetration of renewable energy and electric vehicles (EVs), virtual power plants (VPPs) have become a key mechanism for coordinating distributed energy resources and flexible loads to participate in electricity markets. However, the uncertainties of renewable generation and EV user behavior [...] Read more.
With the increasing penetration of renewable energy and electric vehicles (EVs), virtual power plants (VPPs) have become a key mechanism for coordinating distributed energy resources and flexible loads to participate in electricity markets. However, the uncertainties of renewable generation and EV user behavior pose significant challenges to bidding strategies and real-time execution. This study proposes a two-stage optimal bidding strategy for VPPs by integrating vehicle-to-grid (V2G) technology. An aggregated EV schedulable-capacity model is established to characterize the time-varying charging and discharging capability boundaries of the EV fleet. A unified day-ahead and real-time optimization framework is further developed to ensure coordinated bidding and scheduling. Case studies on a modified IEEE-33 bus system demonstrate that the proposed strategy significantly enhances renewable energy utilization and market revenues, validating the effectiveness of coordinated V2G operation and multi-type flexible load control. Full article
(This article belongs to the Special Issue New Trends in Power System Operation, Control, and Trading Considering Flexible Resources)
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