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Energies
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Advancements in the Integrated Energy System and Its Policy
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Advancements in the Integrated Energy System and Its Policy

A special issue of Energies (ISSN 1996-1073). This special issue belongs to the section "A1: Smart Grids and Microgrids".

Deadline for manuscript submissions: 15 October 2025 | Viewed by 3184

Special Issue Editor

Dr. Peng Zhang

E-Mail Website
Guest Editor
School of Electrical and Information Engineering, Tianjin University, Tianjin 300354, China
Interests: integrated energy system; micro-grid; distributed grid

Special Issue Information

Dear Colleagues,

The integrated energy system (IES) facilitates the full consumption of renewable energy and provides an important means of improving the overall energy utilization efficiency of user-side energy systems. However, it still faces numerous unresolved challenges in relation to the theoretical modeling, system optimization, engineering construction, and policy support of IESs. On the one hand, the coupling between heterogeneous energy systems poses new challenges for the planning, operation, and modeling of multiple energy systems, increasing the dimension of system optimization. On the other hand, the feasibility of the collaborative operation of multiple energy systems not only involves technical challenges, but also other factors (such as economic entities and environmental benefits).

This Special Issue aims to present and disseminate advanced technologies and pilot studies based on IESs. It is also significant to suggest energy policies and evaluate the economic and environmental effect of IESs. We invite original high-quality submissions of research papers, case studies, and reviews across various topics related to energy engineering and its policies, including (but not limited to) the following:

  • Renewable energy systems and technologies;
  • Modeling, simulation, and optimization of energy systems;
  • Planning and dispatching of micro-grids;
  • Smart grids and energy management;
  • Energy Storage and conversion systems;
  • Energy efficiency and conservation;
  • Energy policy, economics, and planning;
  • Energy and environmental sustainability;
  • Strategy and evaluation of electrification.

Dr. Peng Zhang
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. Energies 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 2600 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

  • integrated energy systems
  • energy policies
  • micro-grids
  • smart grids

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Further information on MDPI's Special Issue policies can be found here.

Published Papers (3 papers)

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Research

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16 pages, 4482 KiB  
Article
Multiple Load Forecasting of Integrated Renewable Energy System Based on TCN-FECAM-Informer
by Mingxiang Li, Tianyi Zhang, Haizhu Yang and Kun Liu
Energies 2024, 17(20), 5181; https://doi.org/10.3390/en17205181 - 17 Oct 2024
Cited by 1 | Viewed by 970
Abstract
In order to solve the problem of complex coupling characteristics between multivariate load sequences and the difficulty in accurate multiple load forecasting for integrated renewable energy systems (IRESs), which include low-carbon emission renewable energy sources, in this paper, the TCN-FECAM-Informer multivariate load forecasting [...] Read more.
In order to solve the problem of complex coupling characteristics between multivariate load sequences and the difficulty in accurate multiple load forecasting for integrated renewable energy systems (IRESs), which include low-carbon emission renewable energy sources, in this paper, the TCN-FECAM-Informer multivariate load forecasting model is proposed. First, the maximum information coefficient (MIC) is used to correlate the multivariate loads with the weather factors to filter the appropriate features. Then, effective information of the screened features is extracted and the frequency sequence is constructed using the frequency-enhanced channel attention mechanism (FECAM)-improved temporal convolutional network (TCN). Finally, the processed feature sequences are sent to the Informer network for multivariate load forecasting. Experiments are conducted with measured load data from the IRES of Arizona State University, and the experimental results show that the TCN and FECAM can greatly improve the multivariate load prediction accuracy and, at the same time, demonstrate the superiority of the Informer network, which is dominated by the attentional mechanism, compared with recurrent neural networks in multivariate load prediction. Full article
(This article belongs to the Special Issue Advancements in the Integrated Energy System and Its Policy)
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19 pages, 2383 KiB  
Article
Comparative PSO Optimisation of Microgrid Management Models in Island Operation to Minimise Cost
by Dubravko Žigman, Stjepan Tvorić and Manuel Lonić
Energies 2024, 17(16), 3901; https://doi.org/10.3390/en17163901 - 7 Aug 2024
Cited by 1 | Viewed by 1462
Abstract
The rapid progress in renewable energy sources and the increasing complexity of energy distribution networks have highlighted the need for efficient and intelligent energy management systems. This paper presents a comparative analysis of two optimisation algorithms, P and M70, used for the optimal [...] Read more.
The rapid progress in renewable energy sources and the increasing complexity of energy distribution networks have highlighted the need for efficient and intelligent energy management systems. This paper presents a comparative analysis of two optimisation algorithms, P and M70, used for the optimal control of the operation of microgrids in islanded mode. The main objective is to minimise production costs while ensuring a reliable energy supply. Algorithm P prioritises the use of photovoltaic (PV) and battery storage and operates the diesel generator at minimum capacity to reduce fuel consumption and maximise the use of renewable energy sources. Algorithm M70, on the other hand, uses a heuristic approach to adaptively manage energy resources in real time. In this study, the performance of both algorithms is evaluated through simulation in different operating scenarios. The results show that both algorithms significantly improve the efficiency of the microgrid, with the M70 algorithm showing better adaptability and cost efficiency in dynamic environments. This research contributes to ongoing efforts to develop robust and scalable energy management systems for future smart grids. Full article
(This article belongs to the Special Issue Advancements in the Integrated Energy System and Its Policy)
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Review

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47 pages, 4575 KiB  
Review
A Review of Wind Power Prediction Methods Based on Multi-Time Scales
by Fan Li, Hongzhen Wang, Dan Wang, Dong Liu and Ke Sun
Energies 2025, 18(7), 1713; https://doi.org/10.3390/en18071713 - 29 Mar 2025
Viewed by 364
Abstract
In response to the ‘zero carbon’ goal, the development of renewable energy has become a global consensus. Among the array of renewable energy sources, wind energy is distinguished by its considerable installed capacity on a global scale. Accurate wind power prediction provides a [...] Read more.
In response to the ‘zero carbon’ goal, the development of renewable energy has become a global consensus. Among the array of renewable energy sources, wind energy is distinguished by its considerable installed capacity on a global scale. Accurate wind power prediction provides a fundamental basis for power grid dispatching, unit combination operation, and wind farm operation and maintenance. This study establishes a framework to bridge theoretical innovations with practical implementation challenges in wind power prediction. This work uses a narrative method to synthesize and discuss wind power prediction methods. Common classification angles of wind power prediction methods are outlined. By synthesizing existing approaches through multi-time scales, from the ultra-short term and short term to mid-long term, the review further deconstructs methods by model characteristics, input data types, spatial scales, and evaluation metrics. The analysis reveals that the data-driven prediction model dominates ultra-short-term predictions through rapid response to volatility, while the hybrid method enhances short-term precision. Mid-term predictions increasingly integrate climate dynamics to address seasonal variability. A key contribution lies in unifying fragmented methodologies into a decision support framework that prioritizes the time scale, model adaptability, and spatial constraints. This work enables practitioners to systematically select optimal strategies and advance the development of forecasting systems that are critical for highly renewable energy systems. Full article
(This article belongs to the Special Issue Advancements in the Integrated Energy System and Its Policy)
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