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Sustainable Renewable Energy: Smart Grid and Electric Power System
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Sustainable Renewable Energy: Smart Grid and Electric Power System

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

Deadline for manuscript submissions: 23 June 2025 | Viewed by 910

Special Issue Editor

Prof. Dr. Ting Yang

E-Mail Website
Guest Editor
School of Electrical and Information Engineering, Tianjin University, Tianjin 300072, China
Interests: smart grids and energy internet; power system control and optimization; cyber physical power system; artificial intelligence and Internet of Things
Special Issues, Collections and Topics in MDPI journals

Special Issue Information

Dear Colleagues,

Currently, with the escalating issues of energy crisis and environmental pollution, energy and electric power sustainability has become an urgent global challenge. On the power production side, the rapid development and technological breakthroughs in renewable energy sources, represented by photovoltaic power generation, wind power generation, and biomass power generation, hold the promise of effectively addressing the sustainable development. On the electric power transmission, distribution, and utilization sides, the new type of electric power systems, as known as smart grids, have emerged as critical enablers for efficient energy operation and management. Through optimized scheduling of transmission, transformation, and distribution, comprehensive energy demand response, energy storage and regulation, interactions between electric vehicles and the grid, and electricity markets, the green, sustainable, safe, and efficient utilization of electric power resources can be achieved.

The aim of this special issue is to collect high-quality research papers on sustainable renewable energy with smart grid and electric power system. Meanwhile, to achieve energy sustainability, the collaboration and interdisciplinary research are indispensable. This special issue aims to provide a platform for researchers from different disciplines such as electrical engineering, economics, and policy studies to share their perspectives and insights. It seeks to foster collaboration, innovation, and knowledge exchange among researchers, practitioners, and policymakers, with the goal of advancing the field and accelerating the transition towards a sustainable renewable energy future. We invite researchers to submit their original contributions and share their expertise and latest achievements in the important research field of sustainable renewable energy development and the field of smart grid and electric power system.

Prof. Dr. Ting Yang
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 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

  • smart grids and sustainable renewable energy integration
  • power system control and optimization
  • energy storage and saving technologies
  • wind power and solar related technologies
  • power system wide area perception and advanced metering infrastructure
  • cyber physical power system
  • carbon reduction technologies
  • electric vehicles and smart grids
  • artificial intelligence and electric power system

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Published Papers (1 paper)

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Research

16 pages, 3478 KiB  
Article
Residential Load Forecasting Based on Long Short-Term Memory, Considering Temporal Local Attention
by Wenzhi Cao, Houdun Liu, Xiangzhi Zhang and Yangyan Zeng
Sustainability 2024, 16(24), 11252; https://doi.org/10.3390/su162411252 - 22 Dec 2024
Viewed by 668
Abstract
Accurate residential load forecasting is crucial for the stable operation of the energy internet, which plays a significant role in advancing sustainable development. As the construction of the energy internet progresses, the proportion of residential electricity consumption in end-use energy consumption is increasing, [...] Read more.
Accurate residential load forecasting is crucial for the stable operation of the energy internet, which plays a significant role in advancing sustainable development. As the construction of the energy internet progresses, the proportion of residential electricity consumption in end-use energy consumption is increasing, the peak load on the grid is growing year on year, and seasonal and regional peak power supply tensions, mainly for household electricity consumption, grow into common problems across countries. Residential load forecasting can assist utility companies in determining effective electricity pricing structures and demand response operations, thereby improving renewable energy utilization efficiency and reducing the share of thermal power generation. However, due to the randomness and uncertainty of user load data, forecasting residential load remains challenging. According to prior research, the accuracy of residential load forecasting using machine learning and deep learning methods still has room for improvement. This paper proposes an improved load-forecasting model based on a time-localized attention (TLA) mechanism integrated with LSTM, named TLA-LSTM. The model is composed of a full-text regression network, a date-attention network, and a time-point attention network. The full-text regression network consists of a traditional LSTM, while the date-attention and time-point attention networks are based on a local attention model constructed with CNN and LSTM. Experimental results on real-world datasets show that compared to standard LSTM models, the proposed method improves R2 by 14.2%, reduces MSE by 15.2%, and decreases RMSE by 8.5%. These enhancements demonstrate the robustness and efficiency of the TLA-LSTM model in load forecasting tasks, effectively addressing the limitations of traditional LSTM models in focusing on specific dates and time-points in user load data. Full article
(This article belongs to the Special Issue Sustainable Renewable Energy: Smart Grid and Electric Power System)
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