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Electronics
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Design and Control of Renewable Energy Systems in Smart Cities
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Design and Control of Renewable Energy Systems in Smart Cities

A special issue of Electronics (ISSN 2079-9292). This special issue belongs to the section "Power Electronics".

Deadline for manuscript submissions: 15 July 2026 | Viewed by 2186

Special Issue Editors

Prof. Dr. Mao Yang

E-Mail Website
Guest Editor
Key Laboratory of Modern Power System Simulation and Control & Renewable Energy Technology, Ministry of Education, Northeast Electric Power University, Jilin 132012, China
Interests: wind power prediction; nonlinear control
Prof. Dr. Teng Yun

E-Mail Website
Guest Editor
School of Electrical Engineering, Shenyang University of Technology, Shenyang 110000, China
Interests: large-scale renewable energy power generation and its grid-connected technology; multi-energy microgrid and multi-energy system optimal operation and control technology
Dr. Peng Sun

E-Mail Website
Guest Editor
Key Laboratory of Modern Power System Simulation and Control & Renewable Energy Technology, Ministry of Education, Northeast Electric Power University, Jilin 132012, China
Interests: integrated energy system optimal operation and control technology; configuration and planning of urban integrated energy system; low-carbon trading of new power system; inertia evaluation and optimization of multi-energy system

Special Issue Information

Dear Colleagues,

The global urgency to combat climate change, coupled with rapid urbanization, has positioned smart cities at the forefront of sustainable development. A cornerstone of this transformation is the transition from fossil fuel-based energy systems to those powered by renewable sources, such as solar, wind, geothermal, and biomass. However, the inherent intermittency and distributed nature of these resources present significant challenges to the stability, efficiency, and reliability of urban energy grids.

This Special Issue, entitled “Design and Control of Renewable Energy Systems in Smart Cities,” will compile high-quality research that addresses the critical challenges and opportunities at the intersection of renewable energy and smart urban environments. We will showcase the latest theoretical advancements, cutting-edge technological solutions, and compelling case studies that demonstrate how renewable energy systems can be effectively designed and intelligently controlled to power the cities of the future.

We invite the submission of original research articles and comprehensive review papers that explore, but are not limited to, the following topics:

(1) Large-scale renewable energy prediction and scheduling:

  • Multi-time scale rolling prediction for a high proportion of renewable energy;
  • Robust optimization and stochastic programming considering uncertainty;
  • Data-driven and physical model fusion prediction combined with numerical weather prediction;
  • Distributed and decentralized collaborative scheduling architecture.

(2) Advanced System Design and Integration:

  • Optimal sizing and planning of hybrid renewable energy systems for urban districts;
  • Design of building-integrated photovoltaics (BIPV) and other urban-friendly renewable technologies;
  • Architectures for multi-energy carrier systems (e.g., electricity, heat, hydrogen) in smart cities;
  • Life-cycle assessment and techno-economic analysis of renewable energy deployments.

(3) Intelligent Control and Optimization:

  • Advanced energy management systems (EMS) for smart buildings and microgrids;
  • AI, machine learning, and deep reinforcement learning for forecasting and control of renewable generation and load;
  • Model predictive control (MPC) and other optimization techniques for grid-interactive efficient buildings;
  • Distributed control strategies for coordinated operation of distributed energy resources (DERs).

(4) Grid Integration and Stability:

  • Strategies for mitigating the impacts of renewable intermittency on the urban grid;
  • Vehicle-to-Grid (V2G) and Grid-to-Vehicle (G2V) technologies for leveraging electric vehicles as mobile storage units;
  • Demand-side response (DSR) and flexibility aggregation to enhance grid reliability;
  • Power quality management and stability analysis in renewable-rich urban grids.

(5) Data-Driven and Cross-Disciplinary Approaches:

  • The role of big data, IoT sensors, and digital twins in urban energy system modeling and simulation;
  • Blockchain and peer-to-peer (P2P) energy trading platforms in local energy markets;
  • Socio-technical models and policy frameworks for encouraging renewable energy adoption in cities;
  • Resilience and reliability analysis of renewable-based energy systems against climate and cyber-physical threats.

(6) Research direction of environmental correlation analysis of urban renewable energy system;

  • Collaborative analysis of urban morphology and renewable energy potential;
  • Life-cycle environmental benefits and ecosystem services assessment;
  • Coupling analysis of urban energy-environment-health based on multi-source big data.

Prof. Dr. Mao Yang
Prof. Dr. Teng Yun
Dr. Peng Sun
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. Electronics 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

  • renewable energy integration
  • smart city
  • optimal control and design
  • artificial intelligence in energy systems
  • microgrid and distributed energy resources
  • forecasting and scheduling

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Further information on MDPI's Special Issue policies can be found here.

Published Papers (5 papers)

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Research

31 pages, 3428 KB  
Article
Optimal Scheduling Model for Renewable Energy Electrothermal Coupling System Considering Market Clearing Mechanism of Thermal Storage Power Plant
by Siyu Zheng, Hongyang Jin, Dong Zhang, Peng Sun and Dongyang Li
Electronics 2026, 15(11), 2371; https://doi.org/10.3390/electronics15112371 - 31 May 2026
Abstract
In the context of spot electricity markets, the fluctuation characteristics of node electricity prices play a crucial role in guiding the operational strategies of thermal power plants. However, constrained by the inelastic demand for heat, the strong coupling between electricity and heat in [...] Read more.
In the context of spot electricity markets, the fluctuation characteristics of node electricity prices play a crucial role in guiding the operational strategies of thermal power plants. However, constrained by the inelastic demand for heat, the strong coupling between electricity and heat in combined heat and power (CHP) units limits their ability to regulate electricity generation. These conditions present considerable difficulties for the economic feasibility and carbon reduction performance of these units, especially with high levels of renewable energy integration and during intensive peak-load shaving operations. In response to these challenges, this paper introduces an optimized dispatch method for renewable energy–electricity–heat coupled systems in thermal power plants with thermal storage, which incorporates the coordinated clearing of nodal electricity prices. First, a spot market clearing mechanism is established based on a DC optimal power flow model, and node electricity price signals reflecting network congestion characteristics are endogenously generated through the Lagrange multiplier of the node power balance constraint. Next, by introducing node injection power as a coupling variable between the grid clearing model and the CHP plant scheduling model, a co-optimization framework with bidirectional feedback between electricity prices and unit output is constructed. In conclusion, the integration of node electricity prices, deep peak-shaving costs, and carbon emission costs into a unified optimization objective leads to the development of a scheduling model for the renewable energy–electricity–heat coupled system, which includes CHP units, thermal storage, and grid interactions. The simulation results show that the proposed method can effectively improve the performance of the electric–thermal coupling system under the condition of a high proportion of renewable energy access. Under the typical daily load and new energy output conditions, the total cost of the system is reduced by about 9.7%, the carbon emission is reduced by about 18.3%, and the peak shaving capacity is increased from 25 MW to 58 MW, thus enhancing the flexible scheduling ability and market adaptability of the heat storage thermal power plant. Full article
(This article belongs to the Special Issue Design and Control of Renewable Energy Systems in Smart Cities)
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20 pages, 4630 KB  
Article
Deep Neural Network-Based Optimal Transmission Switching Method for Enhancing Power System Flexibility
by Dawei Huang, Yang Wang, Na Yu, Lingguo Kong and Miao Guo
Electronics 2026, 15(10), 2131; https://doi.org/10.3390/electronics15102131 - 15 May 2026
Viewed by 277
Abstract
With the large-scale grid integration of renewable energy sources such as wind power and photovoltaics, power system net load fluctuations have become significantly more severe, imposing higher demands on system flexibility. Traditional optimal transmission switching (OTS) models require the simultaneous optimization of continuous [...] Read more.
With the large-scale grid integration of renewable energy sources such as wind power and photovoltaics, power system net load fluctuations have become significantly more severe, imposing higher demands on system flexibility. Traditional optimal transmission switching (OTS) models require the simultaneous optimization of continuous and discrete variables, resulting in high computational complexity that renders them unsuitable for daily real-time scheduling in large-scale power systems. This paper develops a flexible real-time rolling optimization scheduling model that incorporates OTS and proposes a two-stage fast solution framework based on deep neural networks (DNN). In the offline training phase, a multilayer perceptron-based DNN is trained using load and renewable generation data to rapidly and accurately predict the optimal line switching scheme. In the online application phase, the network topology predicted by the DNN transforms the original mixed-integer linear programming problem into a standard linear programming problem, substantially reducing computational complexity and solution time. Case studies on the modified IEEE 118-bus and IEEE 300-bus systems show that the proposed method achieves high prediction accuracy, reduces solution time by up to 117 times, and maintains nearly identical system operating costs to the physics-driven approach in the majority of cases. The results demonstrate that the proposed approach effectively balances computational efficiency and economic performance, verifying the practical value of optimal transmission switching in enhancing large-scale renewable energy accommodation and overall power system flexibility. Full article
(This article belongs to the Special Issue Design and Control of Renewable Energy Systems in Smart Cities)
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19 pages, 961 KB  
Article
A Physics-Guided Residual Correction Framework for Four-Hour-Ahead Photovoltaic Power Forecasting
by Yihang Ou Yang, Yufeng Guo, Dazhi Yang, Junci Tang, Qun Yang, Yuxin Jiang, Lichaozheng Qin and Lai Jiang
Electronics 2026, 15(9), 1842; https://doi.org/10.3390/electronics15091842 - 27 Apr 2026
Viewed by 375
Abstract
Accurate ultra-short-term photovoltaic (PV) power forecasting is essential for secure grid dispatch and renewable-rich system operation, yet it remains difficult because of rapid weather fluctuations and error accumulation in multi-step prediction. This paper proposes a decoupled physics-guided residual-correction framework, built on an attention-based [...] Read more.
Accurate ultra-short-term photovoltaic (PV) power forecasting is essential for secure grid dispatch and renewable-rich system operation, yet it remains difficult because of rapid weather fluctuations and error accumulation in multi-step prediction. This paper proposes a decoupled physics-guided residual-correction framework, built on an attention-based sequence-to-sequence (Seq2Seq) architecture, for deterministic 4 h ahead rolling PV forecasting at 15 min resolution. In the first stage, a physical model maps numerical weather prediction (NWP) inputs to a deterministic baseline trajectory while preserving physical bounds. In the second stage, an Attention-Seq2Seq network learns the structured residual trajectory from historical sequences. The global attention mechanism allows the decoder to focus on the most informative historical states, helping reduce information loss and error accumulation over extended horizons. Experiments on a 22-month real-world PV dataset show that the proposed framework outperforms conventional linear and nonlinear benchmarks, reducing root mean square error (RMSE) and mean absolute error (MAE) by 23.79% and 39.17%, respectively, relative to the physical baseline. The framework also maintains robust instantaneous tracking under rapidly changing cloud conditions and preserves a 30–40% error reduction rate at Steps 12–16, supporting reliable intraday scheduling. Full article
(This article belongs to the Special Issue Design and Control of Renewable Energy Systems in Smart Cities)
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21 pages, 8311 KB  
Article
Distributed Voltage Control Strategy for Medium-Voltage Distribution Networks with High Penetration of Photovoltaics
by Dawei Huang, Feiyi Li, Pengyu Zhang, Lei Sun, Na Yu and Lingguo Kong
Electronics 2026, 15(8), 1612; https://doi.org/10.3390/electronics15081612 - 13 Apr 2026
Viewed by 279
Abstract
The integration of high-penetration distributed photovoltaics (PV) into distribution networks triggers frequent voltage limit violations, fluctuations, and increased network losses. To address the limited communication infrastructure inherent in medium-voltage distribution networks, this paper employs PV inverters as fast-response voltage regulation devices and proposes [...] Read more.
The integration of high-penetration distributed photovoltaics (PV) into distribution networks triggers frequent voltage limit violations, fluctuations, and increased network losses. To address the limited communication infrastructure inherent in medium-voltage distribution networks, this paper employs PV inverters as fast-response voltage regulation devices and proposes a real-time distributed voltage control strategy specifically for such networks. Firstly, a distribution network communication topology and voltage regulation architecture based on adjacent asynchronous communication are established. A reactive power-voltage tracking regulation method at PV grid connection points is introduced, utilizing the division and equivalence of voltage regulation feeder segments. By partitioning the distribution network into feeder segments centered around individual PV units, rapid reactive power-voltage tracking regulation based on local and neighboring information is achieved. Secondly, a three-stage cascaded real-time distributed voltage control strategy integrating both reactive power regulation and active power curtailment is designed. Within each regulation stage of this strategy, a voltage estimation process is embedded, enabling dynamic evaluation of the regulation effectiveness and adaptive determination for transitioning between stages. Finally, the proposed strategy is applied to modified IEEE 33-node and IEEE 69-node test systems. Simulation results verify the effectiveness and superiority of the proposed method in improving voltage quality and reducing network losses. Full article
(This article belongs to the Special Issue Design and Control of Renewable Energy Systems in Smart Cities)
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25 pages, 5169 KB  
Article
Distributed Integrated Energy System Optimization Method Based on Stackelberg Game
by Mao Yang, Weining Tang, Jianbin Li and Peng Sun
Electronics 2026, 15(4), 721; https://doi.org/10.3390/electronics15040721 - 7 Feb 2026
Viewed by 527
Abstract
As the composition of energy markets becomes increasingly diverse and distributed in character, it is difficult for traditional vertically integrated energy system (IES) structures and centralized optimization methods to stimulate coupled interactions and interactive synergies among multiple subjects. Consequently, a collaborative low-carbon scheduling [...] Read more.
As the composition of energy markets becomes increasingly diverse and distributed in character, it is difficult for traditional vertically integrated energy system (IES) structures and centralized optimization methods to stimulate coupled interactions and interactive synergies among multiple subjects. Consequently, a collaborative low-carbon scheduling strategy utilizing a leader–follower game framework is introduced for the distributed IES. Making the integrated energy system operator (IESO) a leader, distributed integrated energy supply system (DIESS) and smart user terminal (SUT) as followers, the optimal interaction operation strategy of each subject in the game process can be solved. Firstly, the overall energy interaction process of the system and the game objectives of each participant are introduced to construct a distributed collaborative optimization model with one leader and multiple followers. Secondly, the integrated demand response (IDR) and the ladder-type carbon trading scheme are considered, the two-stage operation process of the electrical gas technology (P2G) equipment is analyzed in detail, and the genetic algorithm nested CPLEX solver is used to solve the model. Finally, the results show that this paper can provide guarantee and theoretical support for the optimal operation of the integrated energy market in terms of trading model and algorithm. Full article
(This article belongs to the Special Issue Design and Control of Renewable Energy Systems in Smart Cities)
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