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Sustainability
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Optimization and Application of Sustainable Distributed Power Generation
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Optimization and Application of Sustainable Distributed Power Generation

A special issue of Sustainability (ISSN 2071-1050). This special issue belongs to the section "Energy Sustainability".

Deadline for manuscript submissions: 31 January 2026 | Viewed by 1928

Special Issue Editors

Dr. Jian Zhang

E-Mail Website
Guest Editor
Resch School of Engineering, University of Wisconsin Green Bay, Green Bay, WI, USA
Interests: renewable energy; thermodynamics; distributed energy system;energy system modelling and optimization; energy incentive analysisg and optimization; energy incentive analysis
Special Issues, Collections and Topics in MDPI journals
Dr. Yonghong Xu

E-Mail Website
Guest Editor
Mechanical Electrical Engineering School, Beijing Information Science and Technology University, Beijing 100192, China
Interests: heat transfer
Special Issues, Collections and Topics in MDPI journals

Special Issue Information

Dear Colleagues,

The rise in global energy consumption, coupled with increasing environmental pollution, has accelerated the development of sustainable distributed power generation. Unlike traditional centralized power plants, which rely on fossil fuels and are often located far from the point of consumption, sustainable distributed power generation systems generate power from renewable energy sources close to where it is used, thereby reducing transmission losses and improving energy efficiency. Researchers have been conducting experimental and theoretical studies on various technologies, such as solar thermal collectors, photovoltaics, wind turbines, energy storage, and more.

The intention of this Special Issue is to explore sustainable distributed power generation systems from multiple perspectives, including new concepts and designs, design and performance optimizations, and practical applications. This Special Issue aims to promote the advancement of distributed power generation systems. In this Special Issue, original research articles and reviews are welcome. Research areas may include (but are not limited to) the following:

  • New concepts and design of sustainable distributed power generation systems;
  • Innovative application of different renewable energy sources including energy storage;
  • Optimization of sustainable distributed power generation;
  • Techno-economic analysis of sustainable distributed power generation systems;
  • Sustainable distributed power generation for smart building and smart city applications.

We look forward to receiving your contributions.

Dr. Jian Zhang
Dr. Yonghong Xu
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. Sustainability 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

  • sustainable distributed power generation
  • renewable energy sources
  • design and performance optimization
  • energy storage
  • techno-economic analysis
  • smart building and smart city

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Published Papers (2 papers)

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Research

18 pages, 4203 KiB  
Article
Enhancing Lithium-Ion Battery State-of-Health Estimation via an IPSO-SVR Model: Advancing Accuracy, Robustness, and Sustainable Battery Management
by Siyuan Shang, Yonghong Xu, Hongguang Zhang, Hao Zheng, Fubin Yang, Yujie Zhang, Shuo Wang, Yinlian Yan and Jiabao Cheng
Sustainability 2025, 17(13), 6171; https://doi.org/10.3390/su17136171 - 4 Jul 2025
Viewed by 343
Abstract
Precise forecasting of lithium-ion battery health status is crucial for safe, efficient, and sustainable operation throughout the battery life cycle, especially in applications like electric vehicles (EVs) and renewable energy storage systems. In this study, an improved particle swarm optimization–support vector regression (IPSO-SVR) [...] Read more.
Precise forecasting of lithium-ion battery health status is crucial for safe, efficient, and sustainable operation throughout the battery life cycle, especially in applications like electric vehicles (EVs) and renewable energy storage systems. In this study, an improved particle swarm optimization–support vector regression (IPSO-SVR) model is proposed for dynamic hyper-parameter tuning, integrating multiple intelligent optimization algorithms (including PSO, genetic algorithm, whale optimization, and simulated annealing) to enhance the accuracy and generalization of battery state-of-health (SOH) estimation. The model dynamically adjusts SVR hyperparameters to better capture the nonlinear aging characteristics of batteries. We validate the approach using a publicly available NASA lithium-ion battery degradation dataset (cells B0005, B0006, B0007). Key health features are extracted from voltage–capacity curves (via incremental capacity analysis), and correlation analysis confirms their strong relationship with battery capacity. Experimental results show that the proposed IPSO-SVR model outperforms a conventional PSO-SVR benchmark across all three datasets, achieving higher prediction accuracy: a mean MAE of 0.611%, a mean RMSE of 0.794%, a mean MSE of 0.007%, and robustness a mean R2 of 0.933. These improvements in SOH prediction not only ensure more reliable battery management but also support sustainable energy practices by enabling longer battery life spans and more efficient resource utilization. Full article
(This article belongs to the Special Issue Optimization and Application of Sustainable Distributed Power Generation)
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26 pages, 13678 KiB  
Article
Optimization Algorithms for Sustainable Operation of Multi-Unit Hydropower Plants
by Mariusz Lewandowski, Adam Góralczyk and Waldemar Janicki
Sustainability 2024, 16(24), 11093; https://doi.org/10.3390/su162411093 - 18 Dec 2024
Viewed by 894
Abstract
The work presented in this article concerns numerical studies on optimization methods used for the sustainable utilization of the energy potential of water, converting it into electricity in a hydropower plant equipped with more than one unit. These methods allow for maximization of [...] Read more.
The work presented in this article concerns numerical studies on optimization methods used for the sustainable utilization of the energy potential of water, converting it into electricity in a hydropower plant equipped with more than one unit. These methods allow for maximization of production in given hydrological conditions, leading to the balanced, lossless, and environmentally friendly use of the renewable energy source that is water. Methods are selected from three groups, i.e., analytical, enumeration, and randomized. The results of calculations of optimal points of selected test functions carried out using the Broyden–Fletcher–Goldfarb–Shanno Limited-Memory Version (L-BFGS-B), Explicit Complete Enumeration (ECE), and Genetic Algorithm (GA) methods provided basic information on the features of these methods. Based on these tests, the GA method was selected to solve the problem of the optimal load distribution in a hydropower plant equipped with three identical hydro units. The defined optimization problem consisted of finding a configuration of hydro units in operation that would guarantee the maximum efficiency of the power plant under the imposed hydrological conditions. During the numerical studies, a number of calculations were performed to identify the impact of procedures and parameters characteristic of the optimization methods on the obtained results. Particular attention was paid to the GA method and penalty functions, enabling the elimination of results from the area of prohibited solutions. Full article
(This article belongs to the Special Issue Optimization and Application of Sustainable Distributed Power Generation)
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