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Energy Management System and Sustainability
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Energy Management System and Sustainability

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

Deadline for manuscript submissions: closed (31 December 2024) | Viewed by 9320

Special Issue Editors

Dr. Shuli Wen

E-Mail Website
Guest Editor
School of Electronic Information and Electrical Engineering, Shanghai Jiao Tong University, Shanghai 200240, China
Interests: maritime transportation electrification and energy management system; energy storage technology; machine learning
Dr. Rui Zhang

E-Mail Website
Guest Editor
School of Electrical Engineering and Telecommunications, University of New South Wales, Sydney 2052, Australia
Interests: power system operation, control, and stability; data analytics and machine learning applications in power engineering
Dr. Yuchen Zhang

E-Mail Website
Guest Editor
School of Electrical Engineering and Telecommunications, University of New South Wales, Kensington, NSW 2033, Australia
Interests: power system stability; renewable energy integration; wind farm condition monitoring; wind farm planning; smart campus; energy storage modeling and control; machine learning; data analytics; data-driven applications in power engineering
Special Issues, Collections and Topics in MDPI journals

Special Issue Information

Dear Colleagues,

Energy management systems (EMSs) are responsible for delivering an optimal energy scheduling strategy in day-ahead and real-time power markets. An energy management system supports a reliable power grid, maximizes the penetration of renewable energy, and optimizes the cost and economic efficiency associated with the electricity market. To achieve both economic and environmental benefits, EMSs are used to solve dispatch optimization problems for the available production and storage capacity given the market data, real-time state, and operational constraints of the power grid, production, and consumption forecasting information. However, the intermittency and uncertainties caused by the high penetration of renewables and flexible loads pose critical challenge to EMSs.

The aim of this Special Issue is to present the emerging techniques and latest results of EMSs in modern power systems, laying the foundation for future hybrid electrified systems with renewable energies. Presentations and research papers are welcome to be submitted, and the topics of interest for this Special Issue include, but are not limited to, the following:

  • Optimal operation and control strategies for enhancing power system flexibility;
  • Renewable generation and integration techniques in distributed microgrids;
  • Energy storage techniques in electrified power systems;
  • Energy management and economic operation of distributed microgrids;
  • Resilience and security techniques for multi-energy systems;
  • Coordinated operation and communication for transportation electrification systems;
  • Energy routers and power converter topology and design;
  • Advanced controller design for electrified transportation systems;
  • Intelligent algorithms for multi-energy systems;
  • AI techniques and application in EMS;
  • Digital control techniques in power systems;
  • Real-time simulation testing for electrified transportation systems.

We look forward to receiving your contributions. 

Dr. Shuli Wen
Dr. Rui Zhang
Dr. Yuchen Zhang
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

  • energy management
  • artificial intelligence theory and application virtual power plant
  • hybrid energy storage system
  • renewable energy
  • integrated energy systems
  • intelligent method

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Published Papers (4 papers)

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Research

18 pages, 7262 KiB  
Article
Multi-Energy Coupling Load Forecasting in Integrated Energy System with Improved Variational Mode Decomposition-Temporal Convolutional Network-Bidirectional Long Short-Term Memory Model
by Xinfu Liu, Wei Liu, Wei Zhou, Yanfeng Cao, Mengxiao Wang, Wenhao Hu, Chunhua Liu, Peng Liu and Guoliang Liu
Sustainability 2024, 16(22), 10082; https://doi.org/10.3390/su162210082 - 19 Nov 2024
Viewed by 1132
Abstract
Accurate load forecasting is crucial to the stable operation of integrated energy systems (IES), which plays a significant role in advancing sustainable development. Addressing the challenge of insufficient prediction accuracy caused by the inherent uncertainty and volatility of load data, this study proposes [...] Read more.
Accurate load forecasting is crucial to the stable operation of integrated energy systems (IES), which plays a significant role in advancing sustainable development. Addressing the challenge of insufficient prediction accuracy caused by the inherent uncertainty and volatility of load data, this study proposes a multi-energy load forecasting method for IES using an improved VMD-TCN-BiLSTM model. The proposed model consists of optimizing the Variational Mode Decomposition (VMD) parameters through a mathematical model based on minimizing the average permutation entropy (PE). Moreover, load sequences are decomposed into different Intrinsic Mode Functions (IMFs) using VMD, with the optimal number of models determined by the average PE to reduce the non-stationarity of the original sequences. Considering the coupling relationship among electrical, thermal, and cooling loads, the input features of the forecasting model are constructed by combining the IMF set of multi-energy loads with meteorological data and related load information. As a result, a hybrid neural network structure, integrating a Temporal Convolutional Network (TCN) with a Bidirectional Long Short-Term Memory (BiLSTM) network for load prediction is developed. The Sand Cat Swarm Optimization (SCSO) algorithm is employed to obtain the optimal hyper-parameters of the TCN-BiLSTM model. A case analysis is performed using the Arizona State University Tempe campus dataset. The findings demonstrate that the proposed method can outperform six other existing models in terms of Mean Absolute Percentage Error (MAPE) and Coefficient of Determination (R2), verifying its effectiveness and superiority in load forecasting. Full article
(This article belongs to the Special Issue Energy Management System and Sustainability)
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13 pages, 2316 KiB  
Article
Optimization and Evaluation of a Stand-Alone Hybrid System Consisting of Solar Panels, Biomass, Diesel Generator, and Battery Bank for Rural Communities
by Juan Lata-García, Néstor Zamora Cedeño, Gary Ampuño, Francisco Jurado, M. Lakshmi Swarupa and Wellington Maliza
Sustainability 2024, 16(20), 9012; https://doi.org/10.3390/su16209012 - 17 Oct 2024
Cited by 5 | Viewed by 2784
Abstract
In a modern and globalized world, the advances in technology are rapid, especially in terms of energy generation through renewable sources, which is intended to mitigate global warming and reduce all the ravages that are currently occurring around the world. Photovoltaic and biomass [...] Read more.
In a modern and globalized world, the advances in technology are rapid, especially in terms of energy generation through renewable sources, which is intended to mitigate global warming and reduce all the ravages that are currently occurring around the world. Photovoltaic and biomass generation sources are attractive for implementation due to the abundant energy resources they offer; however, the intermittency of these sources is a disadvantage when it comes to the needs of the load, decreasing the reliability of the system. Therefore, it is essential to use a backup and storage system such as a diesel generator and a battery bank to continuously supply the load demand. This work presents a case study to meet the energy needs of a community made up of 17 low-income homes on an island in the Gulf of Guayaquil in Ecuador. The optimization and economic evaluation of the hybrid system is achieved using specialized software, resulting in the optimized architecture of the renewable energy system based on the available resources of the locality. The architecture is made up of a 22 kW photovoltaic generator and a 1.5 kW biomass generator, while the diesel generator is 12 kW, the battery bank is made up of 58 units of 111 Ah, and the dispatch strategy is load tracking. The results of the economic evaluation indicate that the total cost of the system (TNPC) is USD 96,033, the initial cost for the implementation of the system is USD 36,944, and the levelized cost of energy is USD 0.276, which makes it attractive for implementation. The importance of this research lies in its practical approach to solving electrification challenges in isolated and low-income communities through a hybrid renewable energy system. By demonstrating how intermittent sources like solar and biomass can be effectively combined with backup and storage systems, the study provides a reliable, economically viable, and implementable solution, addressing both the global need to mitigate climate change and the local need for accessible energy in vulnerable regions. Full article
(This article belongs to the Special Issue Energy Management System and Sustainability)
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27 pages, 5063 KiB  
Article
Predicting Energy Consumption for Hybrid Energy Systems toward Sustainable Manufacturing: A Physics-Informed Approach Using Pi-MMoE
by Mukun Yuan, Jian Liu, Zheyuan Chen, Qingda Guo, Mingzhe Yuan, Jian Li and Guangping Yu
Sustainability 2024, 16(17), 7259; https://doi.org/10.3390/su16177259 - 23 Aug 2024
Viewed by 2316
Abstract
Hybrid energy supply systems are widely utilized in modern manufacturing processes, where accurately predicting energy consumption is essential not only for managing productivity but also for driving sustainable development. Effective energy management is a cornerstone of sustainable manufacturing, reducing waste and enhancing efficiency. [...] Read more.
Hybrid energy supply systems are widely utilized in modern manufacturing processes, where accurately predicting energy consumption is essential not only for managing productivity but also for driving sustainable development. Effective energy management is a cornerstone of sustainable manufacturing, reducing waste and enhancing efficiency. However, conventional studies often focus solely on predicting single types of energy consumption and overlook the integration of physical laws and information, which are essential for a comprehensive understanding of energy dynamics. In this context, this paper introduces a multi-task physics-informed multi-gate mixture-of-experts (pi-MMoE) model that not only considers multiple forms of energy consumption but also incorporates physical principles through the integration of physical information and multi-task modeling. Specifically, a detailed analysis of manufacturing processes and energy patterns is first conducted to study various energy types and extract relevant physical laws. Next, using industry insights and thermodynamic principles, key equations for energy balance and conversion are derived to create a physics-based loss function for model training. Finally, the pi-MMoE model framework is constructed, featuring multi-expert networks and gating mechanisms to balance cross-task knowledge sharing and expert learning. In a case study of a textile factory, the pi-MMoE model reduced electricity and steam prediction errors by 14.28% and 27.27%, respectively, outperforming traditional deep learning methods. This demonstrates that the model can improve prediction performance, providing a novel approach to intelligent energy management and promoting sustainable development in manufacturing. Full article
(This article belongs to the Special Issue Energy Management System and Sustainability)
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16 pages, 700 KiB  
Article
Long-Term Forecast of Energy Demand towards a Sustainable Future in Renewable Energies Focused on Geothermal Energy in Peru (2020–2050): A LEAP Model Application
by Diego G. De la Cruz Torres, Luis F. Mazadiego, David Bolonio and Ramón Rodríguez Pons-Esparver
Sustainability 2024, 16(12), 4964; https://doi.org/10.3390/su16124964 - 11 Jun 2024
Cited by 1 | Viewed by 2459
Abstract
The present study aims to describe the potential sources of energy in Peru with the purpose of implementing them to achieve a sustainable system, taking advantage of the natural resources in the Peruvian land. To achieve this, three alternative scenarios have been defined [...] Read more.
The present study aims to describe the potential sources of energy in Peru with the purpose of implementing them to achieve a sustainable system, taking advantage of the natural resources in the Peruvian land. To achieve this, three alternative scenarios have been defined and analyzed using the LEAP (Long-range Energy Alternatives Planning) software [Software Version: 2020.1.112]. The scenarios are as follows: the first one, the Business-as-Usual scenario, is based on normal trends according to historical data and referencing projections made by Peruvian state entities; the second one is focused on Energy Efficiency, the highlighted characteristic is taking into consideration the efficient conditions in transmission and distribution of electric energy; and the third one, centered on Geothermal Energy, focused on the development of this type of energy source and prioritizing it. The primary purpose of this analysis is to identify the advantages and disadvantages inherent in each scenario in order to obtain the best out of each one. In this way, the intention is to propose solutions based on Peru’s national reality or possible uses of the country’s energy potential to supply its energy demand. Currently, Peru’s energy demand relies on fossil fuels, hydraulic, and thermal energy. However, there is the possibility of transforming this system into a sustainable one by strengthening existing and growing energy sources such as solar and wind energy and new technologies for hydraulic and thermal energy, in addition to considering geothermal energy as the main energy source in the third scenario. The new system mentioned satisfactorily indicates that the CO2 equivalent emissions decrease significantly in the third scenario, with a 15.8% reduction compared to the first scenario and a 9.7% reduction in comparison to the second. On the other hand, the second scenario shows a 5.6% decrease in CO2 emissions compared to the first, resulting from improvements in technology and energy efficiency without requiring significant modifications or considerable investments, as in the third scenario. Full article
(This article belongs to the Special Issue Energy Management System and Sustainability)
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