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Digital Modeling, Operation and Control of Sustainable Energy Systems

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A special issue of Energies (ISSN 1996-1073). This special issue belongs to the section "A: Sustainable Energy".

Deadline for manuscript submissions: closed (31 March 2026) | Viewed by 9079

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Special Issue Editors

Dr. Yi Yu

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Guest Editor

Department of Electrical and Electronic Engineering, The Hong Kong Polytechnic University, Kowloon, Hong Kong, China
Interests: microgrid modeling and control; cyber physical microgrid system; voltage regula-tion; predictive control; blockchain networked control; distributed control; AI-driven control; digital twin-based control system

Dr. Rongni Yang

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Guest Editor

School of Control Science and Engineering, Shandong University, Jinan 250000, China
Interests: renewable energy integration; smart grid; networked control systems; sliding mode control; 2-d systems

Dr. Yingping Cao

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Guest Editor

Department of Electrical and Electronic Engineering, The Hong Kong Polytechnic University, Hong Kong SAR, China
Interests: smart grid planning and operation; AI-driven risk assessment; multi-energy network modelling; energy system resilience; cyber-physical system modeling; renewable energy generation

Dr. Kuan Zhang

E-Mail Website
Guest Editor

State Key Laboratory of Alternate Electrical Power System with Renewable Energy Sources, North China Electric Power University, Beijing 102206, China
Interests: optimal operation of electricity-hydrogen integrated energy system; optimal scheduling and energy management of virtual power plant
Special Issues, Collections and Topics in MDPI journals

Dr. Hong Tan

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Guest Editor

College of Electrical Engineering and New Energy, China Three Gorges University, Yichang 443000, China
Interests: optimization and planning of integrated energy system; low-carbon dispatch of power system; electricity market

Special Issue Information

Dear Colleagues,

The ongoing energy revolution is driving a comprehensive evolution in power system architectures, with the large-scale integration of renewable energy sources (RESs) accelerating the development of sustainable integrated energy systems. While this transformation enhances the overall cleanliness of energy systems, it also introduces multidimensional technical challenges: system-level modeling complexities, stability boundaries reshaped by reduced inertia, and exponentially increasing coordination difficulties in optimizing source–grid–load–storage systems due to the integration of massive heterogeneous distributed resources. As a critical enabler, energy storage has demonstrated unique value in mitigating the volatility of RESs, enhancing grid flexibility, and promoting renewable energy utilization. However, for these highly nonlinear and self-coupled systems, developing a systematic framework that spans "modeling–optimization–control–planning" remains a challenge requiring urgent attention.

Notably, paradigm shifts in digital technologies are opening new pathways to address these challenges. In the realm of modeling, emerging digital modeling techniques enable cross-scale mapping from device-level parameter identification to system-level dynamic forecasting. On the control side, artificial intelligence (AI)-driven methodological innovations are reshaping traditional paradigms, with real-time autonomous decision-making systems based on deep reinforcement learning significantly improving the dispatch responsiveness of wind–solar–storage hybrid systems. At the implementation level, cloud–edge–end collaborative computing platforms provide the computational foundation for plug-and-play control of millions of distributed resources. Recent breakthroughs in data-driven predictive optimization algorithms, distributed autonomous control frameworks, and hybrid enhanced decision models in renewable energy and integrated energy systems mark the transition of this field into a new era of deep “physical-information” integration.

To advance the field of sustainable energy systems under the dual contexts of energy and digital transformation, we invite you to contribute high-quality innovative research to this Special Issue, focusing on the “modeling–control–operation” technology chain for renewable and integrated energy systems enabled by next-generation AI technologies. Key topics of interest include, but are not limited to, the following: the multi-scale hybrid modeling of sustainable energy systems; outage monitoring; event detection and the resilient operation of power systems; smart optimization and scheduling for low-carbon energy systems; multi-energy complementary technologies for urban, campus, and rural integrated energy systems; intelligent planning methods for modern power systems in the context of low-carbon transitions; applications of AI in forecasting and analysis of low-carbon energy systems; autonomous restoration control under extreme events; stability analysis and control of electronic power-dominated systems; and autonomous energy management algorithms.

By fostering dialogue across multiple disciplines, this Special Issue aims to promote the organic integration of methodological innovation and practical application, ultimately enhancing the operational efficiency and global reliability of sustainable energy systems.

Dr. Yi Yu
Dr. Rongni Yang
Dr. Yingping Cao
Dr. Kuan Zhang
Dr. Hong Tan
Guest Editors

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Keywords

sustainable energy systems
microgrids
renewable energy
stability analysis
control
digital modelling
energy management

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Published Papers (10 papers)

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Research

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22 pages, 2177 KB

Open AccessArticle

A Stackelberg Game-Based Model of the Distribution Network Planning in Local Energy Communities

by Javid Maleki Delarestaghi, Ali Arefi, Gerard Ledwich, Alberto Borghetti and Christopher Lund

Energies 2026, 19(7), 1662; https://doi.org/10.3390/en19071662 - 27 Mar 2026

Viewed by 609

Abstract

The electrical characteristics of distribution networks (DNs) are drastically changing, which is mainly due to widespread adoption of small-scale distributed energy resources (DERs) by end-users. In these cases, conventional planning models may lead to overinvestment choices. This paper presents a planning model for utility companies that explicitly incorporates a model of end-users’ energy-related decisions, considering a neighborhood energy trading scheme (NETS). The model is formulated based on the Stackelberg game (SG) approach, which guarantees the optimality of the final solution for each user and the utility. The proposed mixed-integer second-order cone programming (MISOCP) problem finds the optimal investment plan for transformers, lines, distributed generators (DGs), and energy storage systems (ESSs) for the utility, considering the scenarios of end-users’ investments in rooftop photovoltaic (PV) and battery systems that maximize their benefits. Additionally, a dynamic network charge (NC) scheme is designed to rationalize the network use. Also, Benders decomposition (BD) is used to improve the convergence of the solution algorithm. The numerical studies on a real 23-bus low voltage (LV) network in Perth, Australia, using real-world data reveals that the proposed planning model offers the lowest total cost and the highest penetration of DERs in comparison with conventional models. Full article

(This article belongs to the Special Issue Digital Modeling, Operation and Control of Sustainable Energy Systems)

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20 pages, 1181 KB

Open AccessArticle

Multidimensional Impact Assessment of Social Welfare Incorporating Dynamic Cross Subsidy and Tiered Carbon Trading

by Ya-Juan Cao, Bin-Yang Qiu, Qiu-Jie Wang, Yi-Hui Luo and Yun-Xiang Zhang

Energies 2026, 19(5), 1225; https://doi.org/10.3390/en19051225 - 28 Feb 2026

Viewed by 311

Abstract

In the context of advancing two pivotal national commitments, namely the “Dual Carbon” goals and the common prosperity strategy, energy policy formulation must move beyond purely economic or environmental considerations and adopt integrated social welfare assessments. This study develops an optimal dispatch model for a multi-microgrid system that incorporates dynamic cross subsidy and tiered carbon trading. From the perspective of welfare economics, the socioeconomic impacts of the proposed model are then systematically evaluated. First, a unified operational framework is established, combining dynamic electricity tariff cross subsidy with a tiered carbon trading mechanism. Next, a quantitative model for electricity tariff cross subsidy is proposed, and a dynamic subsidy rate linked to renewable energy output is designed to guide electricity consumption behavior. Finally, a comparative simulation is conducted across three scenarios: no subsidy, traditional cross subsidy, and the proposed dynamic cross subsidy. The results demonstrate that the proposed dynamic mechanism reduces system carbon emissions by 17.05% compared to the non-subsidy baseline while significantly optimizing total costs. Full article

(This article belongs to the Special Issue Digital Modeling, Operation and Control of Sustainable Energy Systems)

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16 pages, 3274 KB

Open AccessArticle

An Adaptive Inertia and Damping Control Strategy for Virtual Synchronous Generators to Enhance Transient Performance

by Wenzuo Tang, Bo Li, Xianqi Shao, Yun Ye, Yue Yu and Jiawei Chen

Energies 2026, 19(1), 204; https://doi.org/10.3390/en19010204 - 30 Dec 2025

Viewed by 849

Abstract

Virtual synchronous generator (VSG) technology introduces synthetic rotational inertia and damping into inverter-based systems, thereby enhancing regulation performance under grid-connected operation. However, the output characteristics of VSGs are strongly influenced by virtual inertia and damping. This paper develops a self-tuning inertia–damping coordination mechanism for VSGs. The coupling between virtual inertia and damping with respect to grid power quality is systematically investigated, and a power-angle dynamic response model for synchronous generators (SGs) under extreme operating conditions is established. Building on these results, an improved adaptive control strategy for the VSG’s virtual inertia and damping is proposed. The proposed strategy detects changes in frequency and load power, enabling adaptive tuning of virtual inertia and damping in response to system variations, thereby reducing frequency overshoot while accelerating the dynamic response. The effectiveness of the proposed strategy is validated by hardware-in-the-loop real-time simulations. Full article

(This article belongs to the Special Issue Digital Modeling, Operation and Control of Sustainable Energy Systems)

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22 pages, 4698 KB

Open AccessArticle

Energy-Aware Validation of the PIDA Control in the Hardware-in-the-Loop Environment

by Marcin Jabłoński and Paweł D. Domański

Energies 2025, 18(24), 6582; https://doi.org/10.3390/en18246582 - 17 Dec 2025

Viewed by 482

Abstract

The goal of this work is to compare the effectiveness of the classical PID (Proportional Integral Derivative) controller and its extended PIDA (Proportional Integral Derivative Acceleration) version in the energy-aware context. A control system is applied to the high-order integrating system of three cascaded interconnected tanks. A complete process model of a real plant is developed in the MATLAB/Simulink environment, and system identification is carried out using PRBS signals. Hardware-in-the-Loop validation experiments use a real industrial PLC controller. The analysis addresses process variable filtering, the Smith predictor, and compensation for valve nonlinearities. The research focuses not only on control performance but also on the usage of actuators, aiming at energy-aware control. The paper proves that a properly tuned PIDA controller, particularly with a correctly configured acceleration term with appropriate filtering, provides a significant improvement in control quality and disturbance rejection. Such a system allows for the introduction and highlighting of the energy-aware context in industrial control engineering. Energy-aware control allows one not only to use less energy in control but also to lower the actuator’s operating hours, reducing its maintenance costs. Full article

(This article belongs to the Special Issue Digital Modeling, Operation and Control of Sustainable Energy Systems)

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21 pages, 4006 KB

Open AccessArticle

Probabilistic Power Flow Analysis of Wind-Integrated Power Systems Considering Frequency Risk Under Typhoon Disasters

by Aonan Hu and Libao Shi

Energies 2025, 18(24), 6430; https://doi.org/10.3390/en18246430 - 9 Dec 2025

Viewed by 596

Abstract

Extreme disasters such as typhoons pose severe frequency stability challenges to modern power systems with a high penetration of new energy sources. Traditional probabilistic power flow (PPF) methods, which assume constant frequency, are insufficient for accurately capturing these risks. This paper proposes a PPF assessment method for wind-integrated power systems that considers system frequency characteristics under typhoon disasters. First, a probability model of wind power output uncertainty under typhoon disasters is constructed based on the hybrid adaptive kernel density estimation (HAKDE) method. Next, the frequency response characteristics are explicitly introduced, with the steady-state frequency deviation

Δ f

utilized as the state variable for the PPF solution, and an extended cumulant method PPF model is thus established. This model can concurrently determine the probability distributions and statistical characteristics of nodal voltages, branch power flows, and the steady-state frequency of the system. Case studies on a modified IEEE 39-bus system demonstrate that the proposed method effectively quantifies frequency violation probabilities that are overlooked by traditional models. Full article

(This article belongs to the Special Issue Digital Modeling, Operation and Control of Sustainable Energy Systems)

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14 pages, 1050 KB

Open AccessArticle

Construction and Application of Knowledge Graph for Power Grid New Equipment Start-Up

by Wei Tang, Yue Zhang, Xun Mao, Hetong Jia, Kai Lv, Lianfei Shan, Yongtian Qiao and Tao Jiang

Energies 2025, 18(20), 5471; https://doi.org/10.3390/en18205471 - 17 Oct 2025

Cited by 1 | Viewed by 1290

Abstract

To address the lack of effective risk-identification methods during the commissioning of new power grid equipment, we propose a knowledge graph construction approach for both scheme generation and risk identification. First, a gated attention mechanism fuses textual semantics with knowledge embeddings to enhance feature representation. Then, by introducing a global memory matrix with a decay-factor update mechanism, long-range dependencies across paragraphs are captured, yielding a domain-knowledge-augmentation universal information-extraction framework (DKA-UIE). Using the DKA-UIE, we learn high-dimensional mappings of commissioning-scheme entities and their labels, linking them according to equipment topology and risk-identification logic to build a commissioning knowledge graph for new equipment. Finally, we present an application that utilizes this knowledge graph for the automated generation of commissioning plans and risk identification. Experimental results show that our model achieves an average precision of 99.19%, recall of 99.47%, and an F₁-score of 99.33%, outperforming existing methods. The resulting knowledge graph effectively supports both commissioning-plan generation and risk identification for new grid equipment. Full article

(This article belongs to the Special Issue Digital Modeling, Operation and Control of Sustainable Energy Systems)

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15 pages, 1701 KB

Open AccessArticle

Enhanced Named Entity Recognition and Event Extraction for Power Grid Outage Scheduling Using a Universal Information Extraction Framework

by Wei Tang, Yue Zhang, Xun Mao, Mingqi Shan, Kai Lv, Xun Sun and Zhenhuan Ding

Energies 2025, 18(14), 3617; https://doi.org/10.3390/en18143617 - 9 Jul 2025

Cited by 1 | Viewed by 1074

Abstract

To enhance online dispatch decision support capabilities for power grid outage planning, this study proposes a Universal Information Extraction (UIE)-based method for enhanced named entity recognition and event extraction from outage documents. First, a Structured Extraction Language (SEL) framework is developed that unifies the semantic modeling of outage information to generate standardized representations for dual-task parsing of events and entities. Subsequently, a trigger-centric event extraction model is developed through feature learning of outage plan triggers and syntactic pattern entities. Finally, the event extraction model is employed to identify operational objects and action triggers, while the entity recognition model detects seven critical equipment entities within these operational objects. Validated on real-world outage plans from a provincial-level power dispatch center, the methodology demonstrates reliable detection capabilities for both named entity recognition and event extraction. Relative to conventional techniques, the F₁ score increases by 1.08% for event extraction and 2.48% for named entity recognition. Full article

(This article belongs to the Special Issue Digital Modeling, Operation and Control of Sustainable Energy Systems)

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15 pages, 1673 KB

Open AccessArticle

Smart Grid Self-Healing Enhancement E-SOP-Based Recovery Strategy for Flexible Interconnected Distribution Networks

by Wanjun Li, Zhenzhen Xu, Meifeng Chen and Qingfeng Wu

Energies 2025, 18(13), 3358; https://doi.org/10.3390/en18133358 - 26 Jun 2025

Viewed by 1012

Abstract

With the development of modern power systems, AC distribution networks face increasing demands for supply flexibility and reliability. Energy storage-based soft open points (E-SOPs), which integrate energy storage systems into the DC side of traditional SOP connecting AC distribution networks, not only maintain power flow control capabilities but also enhance system supply performance, providing a novel approach to AC distribution network fault recovery. To fully leverage the advantages of E-SOPs in handling faults in flexible interconnected AC distribution networks (FIDNs), this paper proposes an E-SOP-based FIDN islanding recovery method. First, the basic structure and control modes of SOPs for AC distribution networks are elaborated, and the E-SOP-based AC distribution network structure is analyzed. Second, with maximizing total load recovery as the objective function, the constraints of E-SOPs are comprehensively considered, and recovery priorities are established based on load importance classification. Then, a multi-dimensional improvement of the dung beetle optimizer (DBO) algorithm is implemented through Logistic chaotic mapping, adaptive parameter adjustment, elite learning mechanisms, and local search strategies, resulting in an efficient solution for AC distribution network power supply restoration. Finally, the proposed FIDN islanding partitioning and fault recovery methods are validated on a double-ended AC distribution network structure. Simulation results demonstrate that the improved DBO (IDBO) algorithm exhibits a superior optimization performance and the proposed method effectively enhances the load recovery capability of AC distribution networks, significantly improving the self-healing ability and operational reliability of AC distribution systems. Full article

(This article belongs to the Special Issue Digital Modeling, Operation and Control of Sustainable Energy Systems)

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22 pages, 4567 KB

Open AccessArticle

Thermodynamic-Based Perceived Predictive Power Control for Renewable Energy Penetrated Resident Microgrids

by Wenhui Shi, Lifei Ma, Wenxin Li, Yankai Zhu, Dongliang Nan and Yinzhang Peng

Energies 2025, 18(12), 3027; https://doi.org/10.3390/en18123027 - 6 Jun 2025

Viewed by 997

Abstract

Heating, ventilation, and air conditioning (HVAC) systems and microgrids have garnered significant attention in recent research, with temperature control and renewable energy integration emerging as key focus areas in urban distribution power systems. This paper proposes a robust predictive temperature control (RPTC) method and a microgrid control strategy incorporating asymmetrical challenges, including uneven power load distribution and uncertainties in renewable outputs. The proposed method leverages a thermodynamics-based R-C model to achieve precise indoor temperature regulation under external disturbances, while a multisource disturbance compensation mechanism enhances system robustness. Additionally, an HVAC load control model is developed to enable real-time dynamic regulation of airflow, facilitating second-level load response and improved renewable energy accommodation. A symmetrical power tracking and voltage support secondary controller is also designed to accurately capture and manage the fluctuating power demands of HVAC systems for supporting operations of distribution power systems. The effectiveness of the proposed method is validated through power electronics simulations in the Matlab/Simulink/SimPowerSystems environment, demonstrating its practical applicability and superior performance. Full article

(This article belongs to the Special Issue Digital Modeling, Operation and Control of Sustainable Energy Systems)

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Review

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13 pages, 2355 KB

Open AccessFeature PaperReview

Comparison Study of Converter-Based I–V Tracers in Photovoltaic Power Systems for Outdoor Detection

by Weidong Xiao

Energies 2025, 18(14), 3818; https://doi.org/10.3390/en18143818 - 17 Jul 2025

Viewed by 986

Abstract

Current–voltage (I–V) characteristics are an important measure of photovoltaic (PV) generators, corresponding to environmental conditions regarding solar irradiance and temperature. The I–V curve tracer is a widely used instrument in power engineering to evaluate system performance and detect fault conditions in PV power systems. Several technologies have been applied to develop the device and trace I–V characteristics, improving accuracy, speed, and portability. Focusing on the outdoor environment, this paper presents an in-depth analysis and comparison of the system design and dynamics to identify the I–V tracing performance based on different power conversion topologies and data acquisition methods. This is a valuable reference for industry and academia to further the technology and promote sustainable power generation. Full article

(This article belongs to the Special Issue Digital Modeling, Operation and Control of Sustainable Energy Systems)

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