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

Deadline for manuscript submissions: closed (24 April 2026) | Viewed by 10808

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Special Issue Editor

Dr. Peng Zhang

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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 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. 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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Published Papers (5 papers)

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Research

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16 pages, 928 KB

Open AccessArticle

Optimizing the Configuration of MOGWO’s Distributed Energy Storage for Low-Carbon Enhancements

by Haizhu Yang, Qilong Ma, Peng Zhang, Zhongwen Li, Zhiping Cheng and Lulu Wang

Energies 2026, 19(6), 1393; https://doi.org/10.3390/en19061393 - 10 Mar 2026

Viewed by 436

Abstract

With the deepening implementation of the dual-carbon strategy, the penetration rates of distributed power sources and flexible loads in new distribution grids continue to rise, posing significant challenges to system security and stability due to output fluctuations and randomness. To enhance voltage quality and achieve low-carbon economic operation in distribution grids, this paper proposes a multi-objective optimization model for Distributed Energy Storage System allocation. The model integrates power quality, economic benefits, and net carbon emissions. To efficiently solve this high-dimensional nonlinear problem, an improved Multi-Objective Gray Wolf Optimization algorithm is proposed. It employs a chaotic map to initialize the population, enhancing global distribution uniformity. A nonlinear convergence factor is introduced to dynamically balance global exploration and local exploitation. A dynamic grouping collaboration strategy is designed, combining Lévy flight and the elite crossover strategy to enhance search capability and convergence accuracy. Simulations on an IEEE 33-node system show that the improved MOGWO-optimized energy storage scheme reduces average voltage deviation by 37.0%, total operating costs by 7.0%, and net carbon emissions by 4.1%, compared to a no-storage scenario. Compared to the standard MOGWO algorithm, the proposed method achieves further optimization across all objectives, validating its effectiveness and superiority in realizing coordinated energy storage planning that balances safety, economy, and low-carbon goals. Full article

(This article belongs to the Special Issue Advancements in the Integrated Energy System and Its Policy)

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23 pages, 2660 KB

Open AccessArticle

Carbon Trading-Driven Optimal Collaborative Scheduling of Integrated Energy Systems with Multiple Flexible Loads

by Zhenxing Wen, Tao Wu, Dingming Zhuo, Yutao Zhou, Lei Wang and Dongguo Zhou

Energies 2026, 19(3), 746; https://doi.org/10.3390/en19030746 - 30 Jan 2026

Viewed by 465

Abstract

To address the challenges associated with energy decarbonization and economic operation in integrated energy systems (IESs), this paper proposes a collaborative optimal dispatch strategy for IES that considers multiple flexible loads under a carbon trading mechanism. First, a mathematical model of user-side loads is constructed according to the characteristics of flexible loads. Second, a comprehensive optimization framework is constructed by embedding the carbon trading mechanism into the IES operational model. The objective function minimizes the total operating costs, including energy purchase costs, fuel costs, carbon trading costs, operation and maintenance costs, compensation costs, and green certificate revenues. The CPLEX solver is then employed to solve the model. Finally, a case study is conducted to validate the proposed method. Simulation results demonstrate that the carbon trading mechanism effectively leverages the demand response capabilities and coordinates multiple resources, including electricity, heat, and storage, thereby achieving low-carbon economic operation of the system. Full article

(This article belongs to the Special Issue Advancements in the Integrated Energy System and Its Policy)

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16 pages, 4482 KB

Open AccessArticle

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 3 | Viewed by 2130

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 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 KB

Open AccessArticle

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 2 | Viewed by 2445

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 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 KB

Open AccessReview

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

Cited by 13 | Viewed by 4364

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 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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