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Smart Energy Systems: Dynamics, Control and Optimal Planning for Low-Carbon Energy Systems

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A special issue of Energies (ISSN 1996-1073). This special issue belongs to the section "F5: Artificial Intelligence and Smart Energy".

Deadline for manuscript submissions: closed (20 May 2026) | Viewed by 5383

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Special Issue Editors

Prof. Dr. Li Sun

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School of Energy and Environment, Southeast University, Nanjing, China
Interests: smart energy; application of machine learning technology in energy systems; multi-storage collaborative technology; carbon neutrality cutting-edge technology; thermal management technology for new energy systems; fuel cell optimization control (UAV & cogeneration Systems)
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Dr. Ali Khosravi

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Department of Mechanical and Electrical Engineering, University of Southern Denmark, 6400 Sønderborg, Denmark
Interests: power-to-methanol technology; solid oxide fuel cell (SOFC) systems; energy efficiency in data centers; hydrogen production and utilization; renewable energy application (solar energy, hydropower, etc.); thermal management technologies; cryptocurrency energy consumption and environmental impact
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Dr. Chunrong Zhao

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Guest Editor

Faculty of Engineering, The University of Sydney, Sydney 2006, Australia
Interests: thermal energy storage; melting and solidification characteristics; heat transfer enhancement; phase change material; thermochemical energy storage; lithium-ion battery thermal management
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Dr. Pengfei Lv

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School of Energy and Power Engineering, North University of China, Taiyuan 030051, China
Interests: energy storage technology; carbon capture and storage; thermal management and storage; geothermal energy exploitation; hydrate extraction and application
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Prof. Dr. Kwang Y. Lee

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Guest Editor

School of Engineering and Computer Science, Baylor University, Waco, TX 76706, USA
Interests: power systems control; intelligent control systems; optimal power system operation and planning; integration of modern control theory and AI in power systems
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Special Issue Information

Dear Colleagues,

The global transition toward sustainable, low-carbon energy systems has become imperative to address climate change, enhance energy security, and meet rising energy demands. As renewable energy sources (e.g., solar, wind, hydro) gain prominence, coupled with advancements in digitalization, electrification, and decentralized energy generation, traditional energy systems—characterized by rigidity, inefficiency, and reliance on fossil fuels—are increasingly inadequate. Smart Energy Systems (SES) emerge as a transformative solution: they integrate cutting-edge technologies, multi-energy carriers (electricity, heat, gas, and mobility), and data-driven optimization to enable flexible, resilient, and carbon-neutral energy flows across scales (from microgrids to transnational networks), with a core focus on understanding their dynamics, refining control mechanisms, and facilitating optimal planning.

Against this backdrop, this Special Issue aims to map the state of the art and chart the future trajectory of Smart Energy Systems. We invite rigorous, cross-disciplinary contributions that advance theory, technology, or socio-economic understanding of the dynamics inherent in renewable integration, the design of robust control strategies for managing energy flows, the optimal planning of storage and infrastructure, and demand-response efficiency and the alignment of innovation with equitable, sustainable development—all in service of advancing low-carbon energy systems.

Topics of interest for publication include, but are not limited to,

Integration of variable renewable energy (VRE) into smart grids (e.g., forecasting, grid stability).
Smart microgrids and nanogrids: design, control, and resilience for off-grid and urban applications.
Advanced energy storage technologies (e.g., batteries, hydrogen, thermal storage) and their smart management.
Multi-energy systems: synergies between electricity, heating, cooling, gas, and transport (e.g., EV integration, power-to-X).
SES in urban and rural contexts: smart architectures and infrastructure, sustainable city planning, rural electrification, and community-led initiatives.
Demand response and user engagement: smart metering, behavioral analytics, and incentives for flexible energy use.
Resilience and security of SES: mitigating cyber threats, extreme weather impacts, and supply chain disruptions.
Digitalization and AI in SES: machine learning, IoT, digital twins, and big data for optimization and monitoring.
Emerging technologies: blockchain for peer-to-peer energy trading, quantum computing for complex system optimization.

Prof. Dr. Li Sun
Dr. Ali Khosravi
Dr. Chunrong Zhao
Dr. Pengfei Lv
Prof. Dr. Kwang Y. Lee
Guest Editors

Manuscript Submission Information

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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

smart grid
microgrids
grid automation
VRE integration
energy management systems
multi-energy systems
advanced energy storage
demand response

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Published Papers (7 papers)

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Research

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25 pages, 6214 KB

Open AccessArticle

Comparative Analysis of View Factor and Ray Tracing Methods for Energy Yield Prediction in Bifacial Photovoltaic Systems Under Various Installation Configurations

by Seokhun Yoo and Kyungsoo Lee

Energies 2026, 19(8), 1905; https://doi.org/10.3390/en19081905 - 14 Apr 2026

Viewed by 348

Abstract

This study implemented and validated View Factor (VF) and Ray Tracing (RT) simulation models against four-season field-measured data to evaluate the accuracy of energy yield prediction in bifacial PV systems under three installation configurations: (1) Single-row tilted, (2) Multi-row tilted, and (3) Vertical East–West facing. Front-side and rear-side irradiance and electrical energy yield were evaluated using nRMSE and nMBE metrics, and the relationship between irradiance component ratios (direct, diffuse, reflected) derived from RT results and error trends was analyzed. For front-side irradiance prediction, VF and RT methods showed similar performance in Single-row tilted and Multi-row tilted systems (nRMSE difference within 1 percentage point), while the RT method generally showed lower error than the VF method for rear-side irradiance prediction across the evaluated systems. Notably, in the Multi-row tilted system with high structural complexity, RT achieved nRMSE of 13.3%, which was 14.4 percentage points lower than VF (27.7%). Critically, the performance difference between the two methods was maximized under diffuse-dominant conditions. During shaded periods of the Vertical East–West facing system (diffuse ratio 76–81%), VF method’s nRMSE increased to 14.1–33.5%, while RT method maintained a stable level of 8.8–14.3%. This difference is likely related to differences in diffuse-radiation treatment between the two approaches, including the anisotropic sky representation used in the RT workflow and simplified assumptions in the VF-based rear-side model. In the temporal resolution analysis, the 1 h interval showed the lowest nRMSE for cumulative energy prediction, while the 5 min interval more accurately reproduced peak timing and rapid power fluctuations. This study suggests that the RT method can improve rear-side irradiance prediction accuracy, particularly under conditions of increased structural complexity and higher diffuse radiation ratios. It also indicates that simulation temporal resolution should be selected according to research objectives, such as long-term energy yield estimation or short-term power fluctuation analysis. Full article

(This article belongs to the Special Issue Smart Energy Systems: Dynamics, Control and Optimal Planning for Low-Carbon Energy Systems)

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29 pages, 10526 KB

Open AccessArticle

A Distributed Stochastic Optimization Scheduling Method Using Diffusion-TS Generated Scenario for Integrated Energy System

by Panpan Xia, Chen Chen, Li Sun and Lei Pan

Energies 2026, 19(7), 1763; https://doi.org/10.3390/en19071763 - 3 Apr 2026

Viewed by 453

Abstract

The optimal dispatch of integrated energy systems (IESs) is strongly affected by uncertainties on both the supply and demand sides. To model wind power uncertainty and embed it into dispatch decision-making, this paper develops a distributed stochastic scheduling method driven by Diffusion-TS-based scenario generation. First, a conditional Diffusion-TS model is developed to generate high-fidelity wind power scenarios from day-ahead forecasts, and a temperature parameter is introduced to balance scenario diversity and fidelity. Second, a distributed stochastic scheduling framework with chance constraints is established, in which the probabilistic constraints are reformulated into a mixed-integer linear programming problem to address source-load fluctuations while preserving subsystem privacy. Third, the block coordinate descent method is used to decompose the system into cooling, heating, and electricity subproblems for iterative solution. Case study results show that the average CRPS of the generated scenarios is 162.16 MW, which is 34% lower than that of the deterministic forecast benchmark. The total cost of distributed deterministic dispatch is 2.8% higher than that of centralized deterministic dispatch, while the total cost of distributed stochastic dispatch is 53.1% higher than that of distributed deterministic dispatch, reflecting the additional economic cost of uncertainty-aware scheduling. Compared with the traditional LHS-Kmeans method, the scenarios generated by Diffusion-TS are closer to the actual wind power output. Although the resulting dispatch cost is higher, the obtained scheduling results are more consistent with realistic wind power conditions. Overall, the proposed method provides a practical technical route for the secure and economical operation of IESs under uncertainty. Full article

(This article belongs to the Special Issue Smart Energy Systems: Dynamics, Control and Optimal Planning for Low-Carbon Energy Systems)

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24 pages, 5291 KB

Open AccessArticle

Solar Power in Italy: Evaluating the Potential of Concentrated Solar Power and Photovoltaic Technologies

by Giampaolo Caputo, Irena Balog and Giuseppe Canneto

Energies 2026, 19(6), 1446; https://doi.org/10.3390/en19061446 - 13 Mar 2026

Viewed by 786

Abstract

Italy’s abundant solar resources and its strategic Mediterranean location offer strong opportunities to accelerate the transition to a low-carbon energy system. This study presents a comparative techno-economic assessment of concentrating solar power (CSP) plants with 8 h of thermal energy storage (TES) and a 1 MW photovoltaic (PV) plant to evaluate their roles in exploiting Italy’s solar potential. The analysis covers four representative locations (Montalto, Val Basento, Ferrara, and Priolo) and examines solar availability, seasonal performance, capacity factor, electricity generation, land use, and levelized cost of electricity (LCOE). Both technologies show marked seasonal variability, with lower winter performance and summer peaks. Southern sites outperform the northern ones, with Priolo achieving the highest generation and Ferrara the lowest. CSP benefits from dispatchable operation enabled by TES, providing nearly constant rated output and summer capacity factors up to 78%, with annual production exceeding 4 GWh at the best site. In contrast, PV operates non-dispatchably, with capacity factors below 31% and annual generation between 1.47 and 1.72 GWh. The North–South performance gradient is stronger for CSP due to its dependence on direct normal irradiance. PV technology offers higher land use efficiency, producing over twice the energy per unit area compared to CSP technology, while CSP technology requires larger areas but ensures greater operational flexibility. Economically, PV technology achieves a lower LCOE, whereas CSP technology entails higher costs but adds value through dispatchability and improved grid integration. Overall, combining CSP and PV systems can enhance grid stability, reduce emissions, and strengthen Italy’s energy security, highlighting the importance of coordinated planning and investment in complementary solar technologies for decarbonization and for regions with similar climatic conditions. Full article

(This article belongs to the Special Issue Smart Energy Systems: Dynamics, Control and Optimal Planning for Low-Carbon Energy Systems)

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24 pages, 2107 KB

Open AccessArticle

Decentralized Dynamic Parameter Identification of Modern Power Systems Using Ambient Measurements

by Wen Hua, Wei Dong, Lebing Zhao and Ying Yang

Energies 2026, 19(3), 823; https://doi.org/10.3390/en19030823 - 4 Feb 2026

Viewed by 443

Abstract

With the integration of high-penetration power electronics, the dynamic characteristics of modern power systems are jointly dominated by synchronous generators (SGs) and virtual synchronous machines (VSMs). However, the accuracy of these system parameters cannot always be guaranteed in real-world scenarios. To tackle this issue, we propose a robust parameter identification and correction framework based on trajectory sensitivity analysis and Pareto smoothed importance sampling (PSIS). First, to identify the sources of dynamic anomalies, we employ trajectory sensitivity analysis to quantify the impact of parameter variations and screen out key parameters for targeted identification. Subsequently, to utilize the readily available ambient measurements, we incorporate successive variational mode decomposition (SVMD). This method adaptively extracts the dominant variation modes, thereby recovering high-quality data for the identification process. Finally, to circumvent the weight degradation problem inherent in traditional particle filters, we propose a cost-effective PSIS algorithm to obtain the robust posterior distribution of modern system parameters. Simulation results demonstrate the excellent performance of the proposed method. It can not only precisely estimate the key parameters of both SGs and VSMs but also realize the automatic correction of dynamic models under complex operating scenarios. Full article

(This article belongs to the Special Issue Smart Energy Systems: Dynamics, Control and Optimal Planning for Low-Carbon Energy Systems)

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26 pages, 5028 KB

Open AccessArticle

Optimal Dispatch of Energy Storage Systems in Flexible Distribution Networks Considering Demand Response

by Yuan Xu, Zhenhua You, Yan Shi, Gang Wang, Yujue Wang and Bo Yang

Energies 2026, 19(2), 407; https://doi.org/10.3390/en19020407 - 14 Jan 2026

Viewed by 539

Abstract

With the advancement of the “dual carbon” goal, the power system is accelerating its transition towards a clean and low-carbon structure, with a continuous increase in the penetration rate of renewable energy generation (REG). However, the volatility and uncertainty of REG output pose severe challenges to power grid operation. Traditional distribution networks face immense pressure in terms of scheduling flexibility and power supply reliability. Active distribution networks (ADNs), by integrating energy storage systems (ESSs), soft open points (SOPs), and demand response (DR), have become key to enhancing the system’s adaptability to high-penetration renewable energy. This work proposes a DR-aware scheduling strategy for ESS-integrated flexible distribution networks, constructing a bi-level optimization model: the upper-level introduces a price-based DR mechanism, comprehensively considering net load fluctuation, user satisfaction with electricity purchase cost, and power consumption comfort; the lower-level coordinates SOP and ESS scheduling to achieve the dual goals of grid stability and economic efficiency. The non-dominated sorting genetic algorithm III (NSGA-III) is adopted to solve the model, and case verification is conducted on the standard 33-node system. The results show that the proposed method not only improves the economic efficiency of grid operation but also effectively reduces net load fluctuation (peak–valley difference decreases from 2.020 MW to 1.377 MW, a reduction of 31.8%) and enhances voltage stability (voltage deviation drops from 0.254 p.u. to 0.082 p.u., a reduction of 67.7%). This demonstrates the effectiveness of the scheduling strategy in scenarios with renewable energy integration, providing a theoretical basis for the optimal operation of ADNs. Full article

(This article belongs to the Special Issue Smart Energy Systems: Dynamics, Control and Optimal Planning for Low-Carbon Energy Systems)

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21 pages, 3175 KB

Open AccessArticle

Improved Coordinated Control Strategy for Auxiliary Frequency Regulation of Gas-Steam Combined Cycle Units

by Zunmin Hu, Yilin Zhang, Tianhai Zhang, Xinyu Xiao, Li Sun and Lei Pan

Energies 2025, 18(22), 5997; https://doi.org/10.3390/en18225997 - 15 Nov 2025

Viewed by 850

Abstract

With the increasing penetration of renewable energy, the frequency regulation burden on thermal power units is growing significantly. Among them, combined cycle gas turbine (CCGT) units are playing an increasingly important role in grid ancillary services due to their high efficiency and low emissions. This paper investigates coordinated control strategies to improve the auxiliary frequency regulation capability of CCGTs, addressing the limitations of traditional control approaches where gas turbines dominate while steam turbines respond passively. A decentralized model predictive control (MPC) strategy based on rate-limited signal decomposition is proposed to improve auxiliary frequency regulation. First, a dynamic model of the F-class CCGT systems oriented towards control is established. Then, predictive controllers are designed separately for the top and bottom cycles, with control accuracy improved through a fuzzy prediction model, Kalman filtering and state augmentation. Furthermore, a multi-scale decomposition method for AGC (Automatic Generation Control) signals is developed, separating the signals into load-following and high-frequency components, which are allocated to the gas and steam turbines respectively for coordinated response. Comparative simulations with a conventional MPC strategy demonstrate that the proposed method significantly improves power tracking speed, stability, and overshoot control, with the IAE (Integral of Absolute Error) index reduced by 83.7%, showing strong potential for practical engineering applications. Full article

(This article belongs to the Special Issue Smart Energy Systems: Dynamics, Control and Optimal Planning for Low-Carbon Energy Systems)

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Review

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28 pages, 3071 KB

Open AccessReview

A Critical Review of State-of-the-Art Stability Control of PV Systems: Methodologies, Challenges, and Perspectives

by Runzhi Mu, Yuming Zhang, Yangyang Wu, Xiongbiao Wan, Xiaolong Song, Deng Wang, Liming Sun and Bo Yang

Energies 2026, 19(2), 507; https://doi.org/10.3390/en19020507 - 20 Jan 2026

Cited by 5 | Viewed by 1252

Abstract

With the continuous and rapid growth of global photovoltaic (PV) installed capacity, the fluctuation, intermittence, and randomness of its output aggravate the inertia loss of traditional power systems, which poses severe challenges to grid voltage stability, frequency regulation, and safe operation of equipment. Stability control of PV power stations has become a necessary aspect of technical support for the construction of new power systems (NPSs). In this paper, a technical analysis framework of stability control of photovoltaic power stations is systematically constructed. First, the core stability problems of photovoltaic systems are sorted out. Then, a technical review of the three control levels, namely the equipment, system, and grid, is carried out. At the same time, the application potential of emerging technologies such as data-driven and AI control, digital twin predictive control, and advanced grid-forming (GFM) inverters is described. Based on existing reviews, this paper proposes an equipment–system–grid hierarchical analysis framework and explicitly integrates emerging technologies with classical methods. This framework provides references for the selection, engineering deployment, and future research directions of stability control technologies for photovoltaic power plants, while also offering technical support for the safe and efficient operation of high-penetration renewable energy power grids. Full article

(This article belongs to the Special Issue Smart Energy Systems: Dynamics, Control and Optimal Planning for Low-Carbon Energy Systems)

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