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Processes
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Advances in Optimal Operation of Modern Power Systems for Flexibility...
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Advances in Optimal Operation of Modern Power Systems for Flexibility Enhancement

A special issue of Processes (ISSN 2227-9717). This special issue belongs to the section "Energy Systems".

Deadline for manuscript submissions: 20 May 2026 | Viewed by 1384

Special Issue Editors

Dr. Wennan Zhuang

E-Mail Website
Guest Editor
School of Electrical and Automation Engineering, Nanjing Normal University, Nanjing 210023, China
Interests: flexibility enhancement; energy storage system; electricity–hydrogen–ammonia coupled system; modeling, planning, and regulation of modern power system
Dr. Xia Shen

E-Mail Website
Guest Editor
School of Renewable Energy, Hohai University, Nanjing 211100, China
Interests: microgrid transient-steady state coordinated control; control–protection integration analysis; power-to-hydrogen coupling system optimal control
Dr. Jiayong Li

E-Mail Website
Guest Editor
College of Electrical and Information Engineering, Hunan University, Changsha 410082, China
Interests: renewable energy-integrated distribution system optimal operation and control; modern distribution system digital simulation; distributed energy trading; application of big data and artificial intelligence in smart grid
Special Issues, Collections and Topics in MDPI journals
Dr. Guangsheng Pan

E-Mail Website
Guest Editor
School of Electrical Engineering, Southeast University, Nanjing 210096, China
Interests: modern power system; electric-hydrogen collaborative planning and operation optimization; low-carbon economy and energy policy

Special Issue Information

Dear Colleagues,

The increasing penetration of intermittent renewable energy sources and uncertainties in multiple loads have intensified flexibility requirements in the modern power system, characterized by integrated electricity, gas, heating, cooling, and hydrogen networks. Flexibility, defined as the system’s capability to maintain a supply–demand equilibrium and adapt to variabilities across multiple energy carriers and temporal scales, is essential for accommodating renewable intermittency and ensuring operational reliability. A wide array of flexibility resources is currently available, including multi-energy storage systems, demand response, and energy conversion technologies. However, developing refined modeling approaches for these flexible resources and effective collaborative scheduling and control strategies still poses challenges. Consequently, fully exploiting multi-energy flexibility to optimize both system operational efficiency and reliability remains a key research focus that demands global attention.

This Special Issue aims to cover innovative operation strategies in flexibility enhancement through advanced scheduling and adaptive control of multi-energy infrastructures. Suitable topics for this Special Issue include, but are not limited to, the following:

  • Multi-scale forecasting model of renewable energy and multi-energy load.
  • Modeling method of flexible resources in the modern power system.
  • Flexibility quantification and assessment methodology.
  • Data-driven scheduling strategy for flexibility enhancement.
  • Intelligent control method of distributed flexible resources.
  • Market mechanism for flexible service.

Dr. Wennan Zhuang
Dr. Xia Shen
Dr. Jiayong Li
Dr. Guangsheng Pan
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. Processes 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 2400 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

  • modern power system
  • flexibility enhancement
  • renewable energy integration
  • multi-energy coupling
  • data-driven optimization
  • distributed control
  • energy storage system
  • flexibility market

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Published Papers (4 papers)

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Research

25 pages, 3345 KB  
Article
Edge-Side Electricity-Carbon Coordinated Hybrid Trading Mechanism for Microgrid Cluster Flexibility
by Hualei Zou, Qiang Xing, Bitao Xiao, Xilong Xing, Andrew Yang Wu and Jiaqi Liu
Processes 2026, 14(1), 83; https://doi.org/10.3390/pr14010083 - 25 Dec 2025
Viewed by 253
Abstract
High penetration of renewable energy sources (RES) in power systems introduces substantial source-load uncertainty and flexibility challenges, leading to misalignments between economic optimization and environmental sustainability. An edge-side electricity-carbon coordinated hybrid trading mechanism was proposed to enhance flexibility in microgrid clusters. A three-layer [...] Read more.
High penetration of renewable energy sources (RES) in power systems introduces substantial source-load uncertainty and flexibility challenges, leading to misalignments between economic optimization and environmental sustainability. An edge-side electricity-carbon coordinated hybrid trading mechanism was proposed to enhance flexibility in microgrid clusters. A three-layer time-varying carbon emission factor (CEF) model is developed to quantify negative emissions as tradable Chinese Certified Emission Reductions (CCERs). An endogenous economic equilibrium point enables dynamic switching between Incentive-Based Demand Response during high-carbon periods and Price-Based Demand Response during low-carbon periods, based on marginal profit comparisons. A Wasserstein distance-based distributionally robust CVaR (WDR-CVaR) strategy constructs a data-driven ambiguity set to optimize decisions under worst-case distributional shifts in edge-side data. Simulations on a modified IEEE 33-bus system show that the mechanism increases the Multi-Energy Aggregator’s (MEA) expected profit by 12.3%, reduces carbon emissions by 17.6%, with WDR-CVaR demonstrating superior out-of-sample performance compared to sample average approximation methods. The approach internalizes environmental values through carbon-electricity coupling and edge intelligence, providing a resilient framework for low-carbon distribution network operations. Full article
(This article belongs to the Special Issue Advances in Optimal Operation of Modern Power Systems for Flexibility Enhancement)
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26 pages, 1419 KB  
Article
Hybrid AC/DC Transmission Grid Planning Based on Improved Multi-Step Backtracking Reinforcement Learning
by Zhe Wang, Yuxin Dai, Wenxin Yang, Yunzhang Yang, Zhiqi Zhang, Yahan Hu, Jianquan Liao and Tianchi Wu
Processes 2026, 14(1), 11; https://doi.org/10.3390/pr14010011 - 19 Dec 2025
Viewed by 222
Abstract
Hybrid AC/DC transmission expansion planning must balance investment cost, supply reliability and AC/DC stability, which challenges conventional mathematical programming and heuristic methods. This paper proposes a multi-objective planning framework based on an improved multi-step backtracking α-Q(λ) reinforcement learning algorithm with eligibility traces and [...] Read more.
Hybrid AC/DC transmission expansion planning must balance investment cost, supply reliability and AC/DC stability, which challenges conventional mathematical programming and heuristic methods. This paper proposes a multi-objective planning framework based on an improved multi-step backtracking α-Q(λ) reinforcement learning algorithm with eligibility traces and an adaptive learning factor. A tri-objective model minimises annual economic cost, expected power shortage and a comprehensive electrical index that combines electrical betweenness, commutation-failure margin and effective short-circuit ratio. The mixed-integer planning problem is reformulated as an interactive learning process, where the state encodes candidate line construction decisions, the action builds or cancels lines, and the eligibility-trace matrix is used to quantify line importance. Case studies on the Garver-6 system, the IEEE 24-bus reliability test system and a 500 kV regional hybrid AC/DC grid show that, compared with classical Q-learning, the proposed method yields lower annual cost, reduced expected power shortage and improved AC/DC stability; in the 500 kV system, the expected annual power shortage is reduced from 70,810 MWh to 28,320 MWh. Full article
(This article belongs to the Special Issue Advances in Optimal Operation of Modern Power Systems for Flexibility Enhancement)
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18 pages, 578 KB  
Article
Physics-Constrained Graph Attention Networks for Distribution System State Estimation Under Sparse and Noisy Measurements
by Zijian Hu, Zeyu Zhang, Honghua Xu, Ye Ji and Suyang Zhou
Processes 2025, 13(12), 4055; https://doi.org/10.3390/pr13124055 - 15 Dec 2025
Viewed by 332
Abstract
Accurate state estimation is essential for the real-time operation and control of modern distribution systems characterized by high renewable energy penetration, bidirectional power flows, and volatile loads. Conventional model-driven approaches such as the Weighted Least Squares (WLS) exhibit limited robustness under noisy and [...] Read more.
Accurate state estimation is essential for the real-time operation and control of modern distribution systems characterized by high renewable energy penetration, bidirectional power flows, and volatile loads. Conventional model-driven approaches such as the Weighted Least Squares (WLS) exhibit limited robustness under noisy and sparse measurements, while existing data-driven methods often neglect critical physical constraints inherent to power systems. To address these limitations, this paper proposes a physics-constrained Graph Attention Network (GAT) framework for distribution system state estimation (DSSE) that synergistically integrates data-driven learning with physical domain knowledge. The proposed method comprises three key components: (1) a Gaussian Mixture Model (GMM)-based data augmentation strategy that captures the stochastic characteristics of loads and distributed generation to generate synthetic samples consistent with actual operating distributions; (2) a GAT-based feature extractor with topology-aware admittance matrix embedding that effectively learns spatial dependencies and structural relationships among network nodes; and (3) a physics-constrained loss function that incorporates nodal power and voltage limit penalties to enforce operational feasibility. Comprehensive evaluations on the real-world 141-bus test system demonstrate that the proposed method achieves mean absolute error (MAE) reductions of 52.4% and 45.5% for voltage magnitude and angle estimation, respectively, compared to conventional Graph Convolutional Network (GCN)-based approaches. These results validate the superior accuracy, robustness, and adaptability of the proposed framework under challenging measurement conditions. Full article
(This article belongs to the Special Issue Advances in Optimal Operation of Modern Power Systems for Flexibility Enhancement)
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25 pages, 2590 KB  
Article
Enhancing Distribution Network Flexibility via Adjustable Carbon Emission Factors and Negative-Carbon Incentive Mechanism
by Hualei Zou, Qiang Xing, Hao Fu, Tengfei Zhang, Yu Chen and Jian Zhu
Processes 2025, 13(12), 4023; https://doi.org/10.3390/pr13124023 - 12 Dec 2025
Viewed by 292
Abstract
With increasing penetration of distributed renewable energy sources (RES) in distribution networks, spatiotemporal mismatches arise between static time-of-use (TOU) pricing and real-time carbon emission factors. This misalignment hinders demand-side flexibility deployment, potentially increasing high-carbon-period consumption and impeding low-carbon operations. To address this, the [...] Read more.
With increasing penetration of distributed renewable energy sources (RES) in distribution networks, spatiotemporal mismatches arise between static time-of-use (TOU) pricing and real-time carbon emission factors. This misalignment hinders demand-side flexibility deployment, potentially increasing high-carbon-period consumption and impeding low-carbon operations. To address this, the paper proposes an adjustable carbon emission factor (ADCEF) which decouples electricity from carbon liability using storage. The strategy leverages energy storage for carbon responsibility time-shifting to build a dynamic ADCEF model, introducing a negative-carbon incentive mechanism which quantifies the value of surplus renewables. A revenue feedback mechanism couples ADCEF with electricity prices, forming dynamic price troughs during high-RES periods to guide flexible resources toward coordinated peak shaving, valley filling, and low-carbon responses. Validated on a modified IEEE 33-bus system across multiple scenarios, the strategy shifts resources to carbon-negative periods, achieving 100% on-site excess RES utilization in high-penetration scenarios and, compared to traditional TOU approaches, a 27.9% emission reduction and 8.3% revenue increase. Full article
(This article belongs to the Special Issue Advances in Optimal Operation of Modern Power Systems for Flexibility Enhancement)
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