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Smart Electricity Grid and Sustainable Power Systems

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

Deadline for manuscript submissions: closed (15 March 2026) | Viewed by 1721

Special Issue Editor


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Guest Editor
Key Laboratory of Intelligent Power Grid Ministry of Education, Tianjin University, Tianjin 300072, China
Interests: power quality of power system; distributed power generation and micro-grid; intelligent power consumption and energy management
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Special Issue Information

Dear Colleagues,

Power grids are evolving into smart grids with complicated integrations of sustainable energy sources, networks, loads, and storage towards a decarbonized future of human beings. A smart grid refers to a power-supplying system composed of interconnected devices for supplying, transmitting, storing, or converting energy in or between different forms. Additionally, to realize the goal of ‘double carbon’, the mass access of sustainable energy becomes another significant feature of smart grids. A smart grid with a high penetration of sustainable energy is highly complex with multiple dynamic modes, which brings technical challenges to the operation of such a system. The platform technology, including the fundamental hardware and application software for system monitoring and control, underlies the smart grid to meet the energy demand of end-users in an economic, secure, and reliable manner, environmentally and friendly. The development and construction of smart grids that integrate sustainable energy urgently require further theoretical innovation, key technological breakthroughs, and engineering practice in planning, operation, and security technology.

To promote theoretical and practical study in the sustainable management of smart grids, the editorial board of Sustainability invites potential authors to submit articles for review and publication in this Special Issue of Sustainability on Smart Electricity Grid and Sustainable Power Systems.

In this Special Issue, original research articles and reviews are welcome. We look forward to receiving your contributions.

Prof. Dr. Jidong Wang
Guest Editor

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. 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

  • smart grid
  • sustainable energy
  • low carbon operation
  • energy storage
  • sustainable management
  • distributed generation
  • demand response

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Published Papers (1 paper)

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Research

24 pages, 6915 KB  
Article
A Framework for Sustainable Power Demand Response: Optimization Scheduling with Dynamic Carbon Emission Factors and Dual DPMM-LSTM
by Qian Zhang, Xunting Wang, Jinjin Ding, Haiwei Wang, Fulin Zhao, Xingxing Ju and Meijie Zhang
Sustainability 2025, 17(20), 9123; https://doi.org/10.3390/su17209123 - 15 Oct 2025
Viewed by 691
Abstract
In the context of achieving sustainable development goals and promoting a sustainable, low-carbon global energy transition, accurately quantifying and proactively managing the carbon intensity of power systems is a core challenge in monitoring the sustainability of the power sector. However, existing demand response [...] Read more.
In the context of achieving sustainable development goals and promoting a sustainable, low-carbon global energy transition, accurately quantifying and proactively managing the carbon intensity of power systems is a core challenge in monitoring the sustainability of the power sector. However, existing demand response methods often overlook the dynamic characteristics of power system carbon emissions and fail to accurately characterize the complex relationship between power consumption and carbon emissions, which results in suboptimal emission reduction results. To address this challenge, this paper proposes and validates an innovative low-carbon demand response optimization scheduling method as a sustainable tool. The core of this method is the development of a dynamic carbon emission factor (DCEF) assessment model. By innovatively integrating marginal and average carbon emission factors, it becomes a dynamic sustainability indicator that can measure the environmental performance of the power grid in real time. To characterize the relationship between power consumption behavior and carbon emissions, we employ an adaptive Dirichlet process mixture model (DPMM). This model does not require a preset number of clusters and can automatically discover patterns in the data, such as grouping holidays and working days with similar power consumption characteristics. Based on the clustering results and historical data, a dual long short-term memory (LSTM) deep learning network architecture is designed to achieve a coordinated prediction of power consumption and DCEFs for the next 24 h. On this basis, a method is established with the goal of maximizing carbon emission reduction while considering constraints such as fixed daily power consumption, user comfort, and equipment safety. Simulation results demonstrate that this approach can effectively reduce regional carbon emissions through accurate prediction and optimized scheduling. This provides not only a quantifiable technical path for improving the environmental sustainability of the power system but also decision-making support for the formulation of energy policies and incentive mechanisms that align with sustainable development goals. Full article
(This article belongs to the Special Issue Smart Electricity Grid and Sustainable Power Systems)
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