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Electronics
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Energy Sources Integrated with Power Distribution Systems Using Machine Learning...
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Energy Sources Integrated with Power Distribution Systems Using Machine Learning Approach

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

Deadline for manuscript submissions: 15 April 2025 | Viewed by 5512

Special Issue Editors

Dr. Haichuan Yang

E-Mail Website
Guest Editor
Graduate School of Technology, Industrial and Social Sciences, Tokushima University, Toyama-shi 930-8555, Japan
Interests: metaheuristics; evolutionary computation
Special Issues, Collections and Topics in MDPI journals
Prof. Dr. Shangce Gao

E-Mail Website
Guest Editor
Faculty of Engineering, University of Toyama, Toyama-shi 930-8555, Japan
Interests: artificial intelligence; computational intelligence; neural networks
Special Issues, Collections and Topics in MDPI journals
Dr. Qing Yu

E-Mail Website
Guest Editor
School of Urban Planning & Design, Peking University Shenzhen Graduate School, Shenzhen 518055, China
Interests: smart city; electric vehicle; vehicle–grid integration; urban energy system

Special Issue Information

Dear Colleagues,

The rise of distributed energy sources (DES) integrated into urban power distribution systems and energy management is not just an emerging field of research, but a critical step towards revolutionizing our approach to energy distribution and consumption. As we embark on a new era in novel distributed and grid-interactive technologies to achieve system energy efficiency, reliability and reduce energy costs, the potential of machine learning (ML) to transform these sectors is immense. To address these challenges, interdisciplinary research is paramount. An integrated perspective that considers the inter-relationships between power distribution systems, distribution energy sources, transportation systems, building technologies, etc., is necessary to fully harness the digitalization of next-generation urban power distribution and energy systems. Collaborative approaches are key to bridge the gap between theoretical research and practical implementation, ensuring that the benefits of ML are realized in a way that enhances the efficiency and sustainability of transportation and energy systems.

The focus of this Special Issue is on understanding the evolving interplay between power distribution and energy systems, distribution energy sources, transportation systems, building technologies with the integration of ML approaches. This is about creating a cohesive, intelligent energy ecosystem that can adapt to the changing demands of DES-integrated urban power distribution systems and contribute to a more sustainable and resilient future. In this Special Issue, original research articles and reviews are welcome. The research areas may include (but are not limited to) the following:

  1. Novel distributed and grid-interactive technologies;
  2. Distributed energy sources integrated urban power system;
  3. Electric–vehicle grid integration;
  4. Smart energy and electric power systems;
  5. Energy-efficient transportation systems;
  6. Power distribution systems optimization;
  7. Smart grid technologies;
  8. Power system applications: forecasting, fault diagnosis, energy management, and power quality disturbances detection;
  9. Stability assessment and control;
  10. Sustainable power and energy systems.

We look forward to receiving your contributions.

Dr. Haichuan Yang
Prof. Dr. Shangce Gao
Dr. Qing Yu
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 100 words) can be sent to the Editorial Office for announcement on this website.

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

  • power and energy system
  • distributed energy sources
  • power distribution systems
  • evolutionary computation
  • reinforcement learning
  • neural networks and learning systems
  • complex systems and networks

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Published Papers (4 papers)

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Research

30 pages, 5917 KiB  
Article
Boston Consulting Group Matrix-Based Equilibrium Optimizer for Numerical Optimization and Dynamic Economic Dispatch
by Lin Yang, Zhe Xu, Fenggang Yuan, Yanting Liu and Guozhong Tian
Electronics 2025, 14(3), 456; https://doi.org/10.3390/electronics14030456 - 23 Jan 2025
Viewed by 568
Abstract
Numerous optimization problems exist in the design and operation of power systems, critical for efficient energy use, cost minimization, and system stability. With increasing energy demand and diversifying energy structures, these problems grow increasingly complex. Metaheuristic algorithms have been highlighted for their flexibility [...] Read more.
Numerous optimization problems exist in the design and operation of power systems, critical for efficient energy use, cost minimization, and system stability. With increasing energy demand and diversifying energy structures, these problems grow increasingly complex. Metaheuristic algorithms have been highlighted for their flexibility and effectiveness in addressing such complex problems. To further explore the theoretical support of metaheuristic algorithms for optimization problems in power systems, this paper proposes a novel algorithm, the Boston Consulting Group Matrix-based Equilibrium Optimizer (BCGEO), which integrates the Equilibrium Optimizer (EO) with the classic economic decision-making model, the Boston Consulting Group Matrix. This matrix is utilized to construct a model for evaluating the potential of individuals, aiding in the rational allocation of computational resources, thereby achieving a better balance between exploration and exploitation. In comparative experiments across various dimensions on CEC2017, the BCGEO demonstrated superior search performance over its peers. Furthermore, in dynamic economic dispatch, the BCGEO has shown strong optimization capabilities and potential in power system optimization problems. Additionally, the experimental results in the spacecraft trajectory optimization problem suggest its potential for broader application across various fields. Full article
(This article belongs to the Special Issue Energy Sources Integrated with Power Distribution Systems Using Machine Learning Approach)
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26 pages, 10477 KiB  
Article
Interval Constrained Multi-Objective Optimization Scheduling Method for Island-Integrated Energy Systems Based on Meta-Learning and Enhanced Proximal Policy Optimization
by Dongbao Jia, Ming Cao, Jing Sun, Feimeng Wang, Wei Xu and Yichen Wang
Electronics 2024, 13(17), 3579; https://doi.org/10.3390/electronics13173579 - 9 Sep 2024
Cited by 1 | Viewed by 975
Abstract
Multiple uncertainties from source–load and energy conversion significantly impact the real-time dispatch of an island integrated energy system (IIES). This paper addresses the day-ahead scheduling problems of IIES under these conditions, aiming to minimize daily economic costs and maximize the output of renewable [...] Read more.
Multiple uncertainties from source–load and energy conversion significantly impact the real-time dispatch of an island integrated energy system (IIES). This paper addresses the day-ahead scheduling problems of IIES under these conditions, aiming to minimize daily economic costs and maximize the output of renewable energies. We introduce an innovative algorithm for Interval Constrained Multi-objective Optimization Problems (ICMOPs), which incorporates meta-learning and an improved Proximal Policy Optimization with Clipped Objective (PPO-CLIP) approach. This algorithm fills a notable gap in the application of DRL to complex ICMOPs within the field. Initially, the multi-objective problem is decomposed into several single-objective problems using a uniform weight decomposition method. A meta-model trained via meta-learning enables fine-tuning to adapt solutions for subsidiary problems once the initial training is complete. Additionally, we enhance the PPO-CLIP framework with a novel strategy that integrates probability shifts and Generalized Advantage Estimation (GAE). In the final stage of scheduling plan selection, a technique for identifying interval turning points is employed to choose the optimal plan from the Pareto solution set. The results demonstrate that the method not only secures excellent scheduling solutions in complex environments through its robust generalization capabilities but also shows significant improvements over interval-constrained multi-objective evolutionary algorithms, such as IP-MOEA, ICMOABC, and IMOMA-II, across multiple multi-objective evaluation metrics including hypervolume (HV), runtime, and uncertainty. Full article
(This article belongs to the Special Issue Energy Sources Integrated with Power Distribution Systems Using Machine Learning Approach)
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20 pages, 4089 KiB  
Article
A Green Wave Ecological Global Speed Planning under the Framework of Vehicle–Road–Cloud Integration
by Zhe Li, Xiaolei Ji, Shuai Yuan, Zengli Fang, Zhennan Liu and Jianping Gao
Electronics 2024, 13(17), 3516; https://doi.org/10.3390/electronics13173516 - 4 Sep 2024
Viewed by 1107
Abstract
In response to energy consumption and traffic efficiency reduction caused by intersection congestion, a global speed planning that considered both ecological speed and green wave speed was conducted under the vehicle–road–cloud integration framework. After establishing an instantaneous energy consumption model for pure electric [...] Read more.
In response to energy consumption and traffic efficiency reduction caused by intersection congestion, a global speed planning that considered both ecological speed and green wave speed was conducted under the vehicle–road–cloud integration framework. After establishing an instantaneous energy consumption model for pure electric vehicles, a radial basis neural network model was used to estimate the queue length of traffic flow, and an isolated-intersection-based eco-approach and departure (I-EAD) plan was proposed based on a valid traffic signal light model. A two-stage optimization multi-intersections-based eco-approach and departure (M-EAD) strategy with multiple objectives and constraints was proposed to solve the optimal green light window and the optimal speed trajectory. The results of the SUMO/Matlab/Simulink/Python joint simulation platform show that the M-EAD strategy reduces the average travel energy consumption by 16.65% and 8.31%, and the average travel time by 26.33% and 12.53%, respectively, compared to the intelligent driver model (IDM) and I-EAD strategy. The simulation results of the typical traffic scenarios and random traffic scenarios indicate that the speed optimization strategies in this study have good optimization effects on energy conservation and traffic efficiency. Full article
(This article belongs to the Special Issue Energy Sources Integrated with Power Distribution Systems Using Machine Learning Approach)
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16 pages, 761 KiB  
Article
A Multi-Local Search-Based SHADE for Wind Farm Layout Optimization
by Yifei Yang, Sichen Tao, Haotian Li, Haichuan Yang and Zheng Tang
Electronics 2024, 13(16), 3196; https://doi.org/10.3390/electronics13163196 - 13 Aug 2024
Viewed by 990
Abstract
Wind farm layout optimization (WFLO) is focused on utilizing algorithms to devise a more rational turbine layout, ultimately maximizing power generation efficiency. Traditionally, genetic algorithms have been frequently employed in WFLO due to the inherently discrete nature of the problem. However, in recent [...] Read more.
Wind farm layout optimization (WFLO) is focused on utilizing algorithms to devise a more rational turbine layout, ultimately maximizing power generation efficiency. Traditionally, genetic algorithms have been frequently employed in WFLO due to the inherently discrete nature of the problem. However, in recent years, researchers have shifted towards enhancing continuous optimization algorithms and incorporating constraints to address WFLO challenges. This approach has shown remarkable promise, outperforming traditional genetic algorithms and gaining traction among researchers. To further elevate the performance of continuous optimization algorithms in the context of WFLO, we introduce a multi-local search-based SHADE, termed MS-SHADE. MS-SHADE is designed to fine-tune the trade-off between convergence speed and algorithmic diversity, reducing the likelihood of convergence stagnation in WFLO scenarios. To assess the effectiveness of MS-SHADE, we employed a more extensive and intricate wind condition model in our experiments. In a set of 16 problems, MS-SHADE’s average utilization efficiency improved by 0.14% compared to the best algorithm, while the optimal utilization efficiency increased by 0.3%. The results unequivocally demonstrate that MS-SHADE surpasses state-of-the-art WFLO algorithms by a significant margin. Full article
(This article belongs to the Special Issue Energy Sources Integrated with Power Distribution Systems Using Machine Learning Approach)
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