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Applied Sciences
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Emerging Trends in Energy Management: Techniques, Applications and Future Directions
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Emerging Trends in Energy Management: Techniques, Applications and Future Directions

A special issue of Applied Sciences (ISSN 2076-3417). This special issue belongs to the section "Electrical, Electronics and Communications Engineering".

Deadline for manuscript submissions: 20 June 2026 | Viewed by 6994

Special Issue Editors

Dr. Roberto Romano

E-Mail Website
Guest Editor
1. Italian National Agency for New Technologies, Energy and Sustainable Economic Development (ENEA), 00044 Frascati, Italy
2. DTT S. c. a r. l., 00044 Frascati, Italy
Interests: power distribution system operation and planning; smart grids; demand response; renewable energy; energy storage systems; smart house; energy management systems
Dr. Alessandro Lampasi

E-Mail Website
Guest Editor
1. Energy and Sustainable Economic Development (ENEA), Italian National Agency for New Technologies, 00044 Frascati, Italy
2. DTT S. c. a r. l., 00044 Frascati, Italy
Interests: supercapacitors; power supplies and electrical systems; characterization, modeling and simulation of supercapacitors; hybrid energy storage
Special Issues, Collections and Topics in MDPI journals
Dr. Sabino Pipolo

E-Mail Website
Guest Editor
1. Italian National Agency for New Technologies, Energy and Sustainable Economic Development (ENEA), 00044 Frascati, Italy
2. DTT S. c. a r. l., 00044 Frascati, Italy
Interests: electrical and electronic systems for nuclear fusion; advanced power electronic systems for smart grid applications; energy storage; solid-state transformers, design and control of power electronics converters; design and control of matrix converters

Special Issue Information

Dear Colleagues,

The interest in efficient and sustainable energy management has gained unprecedented urgency due to rising global energy demands and environmental concerns. The field is undergoing a significant transformation driven by emerging technologies. Advances in smart home technologies, such as sensors, automation, and real-time data analytics, are enabling homes to optimize energy use, enhance comfort, and integrate renewable resources like solar panels more effectively. Simultaneously, smart grids are enhancing communication between energy providers and consumers, improving grid stability, and facilitating the integration of renewable energy. Technologies like artificial intelligence (AI) and machine learning are providing advanced tools for predictive analytics and dynamic energy management. Additionally, advancements in energy storage are boosting the reliability and scalability of renewable sources, while digital innovations are enhancing the security and efficiency of energy transactions.

This Special Issue invites researchers to submit contributions that explore these advancements, focusing on techniques, applications, and future directions in energy management, including but not limited to these aspects. The aim is to gather high-quality scientific papers that offer insights into how these technologies are transforming the field and to identify opportunities for further research and innovation.

Dr. Roberto Romano
Dr. Alessandro Lampasi
Dr. Sabino Pipolo
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. Applied Sciences 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
  • smart home technologies
  • artificial intelligence
  • renewable energy sources
  • energy storage

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Published Papers (5 papers)

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Research

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30 pages, 2525 KB  
Article
Short-Term Wind Power Forecasting Based on Adaptive LSTM and BP Neural Network
by Yizhuo Liu, Kai Song, Fulin Fan, Yuxuan Wang, Mingming Ge and Chuanyu Sun
Appl. Sci. 2025, 15(20), 11244; https://doi.org/10.3390/app152011244 - 20 Oct 2025
Viewed by 180
Abstract
To enhance power dispatching and mitigate grid connection fluctuations, this paper proposes a wind power prediction model based on Long Short-Term Memory-Back Propagation Neural Network (LSTM-BP) optimized by an adaptive Particle Swarm Optimization algorithm (aPSO). Initially, anomalies and missing values in raw wind [...] Read more.
To enhance power dispatching and mitigate grid connection fluctuations, this paper proposes a wind power prediction model based on Long Short-Term Memory-Back Propagation Neural Network (LSTM-BP) optimized by an adaptive Particle Swarm Optimization algorithm (aPSO). Initially, anomalies and missing values in raw wind farm data are addressed using the quartile method and filled via cubic spline interpolation. The data is then denoised using the Autoregressive Integrated Moving Average (ARIMA) model. Statistical and combined features are extracted, and Bayesian optimization is applied for optimal feature selection. To overcome the limitations of single models, a hybrid approach is adopted where a BP neural network is used in conjunction with LSTM. The optimal features are first input into the BP neural network to learn the current relationship between features and wind power. Then, historical data of both the features and wind power are fed into the LSTM to generate preliminary predictions. These LSTM outputs are subsequently passed into the trained BP neural network, and the final wind power prediction result is obtained through network integration. This combined model leverages the temporal learning capabilities of LSTM and the fitting strengths of BP, while aPSO ensures optimal parameter tuning, ultimately enhancing prediction accuracy and robustness in wind power forecasting. The experimental results show that the proposed model achieves a MAE of 0.54 MW and a MAPE of 10.5% in one-step prediction, reducing the error by over 35% compared to benchmark models such as ARIMA-LSTM and LSTM-BP. Multi-step prediction validation on 2000 sets of real wind farm data demonstrates the robustness and generalization capabilities of the proposed model. Full article
(This article belongs to the Special Issue Emerging Trends in Energy Management: Techniques, Applications and Future Directions)
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29 pages, 15120 KB  
Article
Optimal Clearing Strategy for Day-Ahead Energy Markets in Distribution Networks with Multiple Virtual Power Plant Participation
by Pei Wang, Sen Tian, Qian Xiao, Tianxiang Li, Zibo Wang, Ji Qiao, Hong Zhu and Wenlu Ji
Appl. Sci. 2025, 15(20), 11197; https://doi.org/10.3390/app152011197 - 19 Oct 2025
Viewed by 316
Abstract
Constrained by current market mechanisms, small-scale virtual power plants (SS-VPPs) on the distribution network side struggle to exert their market characteristics. To address this, this paper proposes a trading framework and operational strategy for distribution-side SS-VPPs to participate in the day-ahead energy market. [...] Read more.
Constrained by current market mechanisms, small-scale virtual power plants (SS-VPPs) on the distribution network side struggle to exert their market characteristics. To address this, this paper proposes a trading framework and operational strategy for distribution-side SS-VPPs to participate in the day-ahead energy market. First, an operation and trading framework for distribution networks involving SS-VPPs is proposed. This framework comprehensively considers the clearing process of the electricity energy market, the operation mechanism of the distribution network, and the cost structures of various stakeholders, while clarifying the day-ahead market clearing mechanism at the distribution network level. Next, accounting for energy balance constraints and distribution network congestion constraints, this paper establishes a collaborative optimization model between SS-VPPs and active distribution networks. After obtaining the energy optimization results for all stakeholders, distribution locational marginal pricing (DLMP) is determined based on the dual problem solution to achieve multi-stakeholder market clearing. Finally, simulations using a modified IEEE 33-node test system demonstrate the rationality and feasibility of the proposed strategy. The framework fully exploits the market characteristics and dispatch potential of SS-VPPs, significantly reduces overall system operating costs, and ensures the economic benefits of all participants. Full article
(This article belongs to the Special Issue Emerging Trends in Energy Management: Techniques, Applications and Future Directions)
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19 pages, 2733 KB  
Article
A Two-Layer User Energy Management Strategy for Virtual Power Plants Based on HG-Multi-Agent Reinforcement Learning
by Sen Tian, Qian Xiao, Tianxiang Li, Zibo Wang, Ji Qiao, Hong Zhu and Wenlu Ji
Appl. Sci. 2025, 15(12), 6713; https://doi.org/10.3390/app15126713 - 15 Jun 2025
Cited by 1 | Viewed by 760
Abstract
Household loads are becoming dominant in virtual power plants (VPP). However, their dispatch potential has not yet been explored due to the lack of detailed user power management. To solve this issue, a novel two-layer user energy management strategy based on HG-multi-agent reinforcement [...] Read more.
Household loads are becoming dominant in virtual power plants (VPP). However, their dispatch potential has not yet been explored due to the lack of detailed user power management. To solve this issue, a novel two-layer user energy management strategy based on HG-multi-agent reinforcement learning has been proposed in this paper. Firstly, a novel two-layer optimization framework is established, where the upper layer is applied to coordinate the scheduling and benefit allocation among various stakeholders and the lower layer is applied to execute intelligent decision-making for users. Secondly, the mathematical model for the framework is established, where a detailed household power management model is proposed in the lower layer, and the generated predicted power demands are used to replace the conventional aggregate model in the upper layer. As a result, the energy consumption behaviors of household users can be precisely described in the scheduling scheme. Furthermore, an HG-multi-agent reinforcement-based method is applied to accelerate the game-solving process. Case study results indicate that the proposed method leads to a reduction in user costs and an increase in VPP profit. Full article
(This article belongs to the Special Issue Emerging Trends in Energy Management: Techniques, Applications and Future Directions)
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18 pages, 3717 KB  
Article
Impact of Environmental Conditions on Renewable Energy Prediction: An Investigation Through Tree-Based Community Learning
by Ferdi Doğan, Saadin Oyucu, Derya Betul Unsal, Ahmet Aksöz and Majid Vafaeipour
Appl. Sci. 2025, 15(1), 336; https://doi.org/10.3390/app15010336 - 1 Jan 2025
Viewed by 1450
Abstract
The real-time prediction of energy production is essential for effective energy management and planning. Forecasts are essential in various areas, including the efficient utilization of energy resources, the provision of energy flexibility services, decision-making amidst uncertainty, the balancing of supply and demand, and [...] Read more.
The real-time prediction of energy production is essential for effective energy management and planning. Forecasts are essential in various areas, including the efficient utilization of energy resources, the provision of energy flexibility services, decision-making amidst uncertainty, the balancing of supply and demand, and the optimization of online energy systems. This study examines the use of tree-based ensemble learning models for renewable energy production prediction, focusing on environmental factors such as temperature, pressure, and humidity. The study’s primary contribution lies in demonstrating the effectiveness of the bagged trees model in reducing overfitting and achieving higher accuracy compared to other models, while maintaining computational efficiency. The results indicate that less sophisticated models are inadequate for accurately representing complex datasets. The results evaluate the effectiveness of machine learning methods in delivering valuable insights for energy sectors managing environmental conditions and predicting renewable energy sources Full article
(This article belongs to the Special Issue Emerging Trends in Energy Management: Techniques, Applications and Future Directions)
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Other

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42 pages, 1491 KB  
Systematic Review
Systematic Review of Hierarchical and Multi-Agent Optimization Strategies for P2P Energy Management and Electric Machines in Microgrids
by Paul Arévalo, Danny Ochoa-Correa, Edisson Villa-Ávila, Vinicio Iñiguez-Morán and Patricio Astudillo-Salinas
Appl. Sci. 2025, 15(9), 4817; https://doi.org/10.3390/app15094817 - 26 Apr 2025
Cited by 2 | Viewed by 3427
Abstract
The growing complexity of distributed energy systems and the rise of peer-to-peer energy markets demand innovative solutions for efficient, resilient, and sustainable energy management. However, existing research often remains fragmented, with limited integration between control strategies, optimization frameworks, and practical implementation. This paper [...] Read more.
The growing complexity of distributed energy systems and the rise of peer-to-peer energy markets demand innovative solutions for efficient, resilient, and sustainable energy management. However, existing research often remains fragmented, with limited integration between control strategies, optimization frameworks, and practical implementation. This paper presents a comprehensive systematic review, following the PRISMA methodology, that synthesizes findings from 94 high-quality studies and addresses the lack of consolidated insights across technical, operational, and architectural layers. This review highlights advancements in six key areas: optimization and modeling, multi-agent systems, simulations, blockchain and smart contracts, robust frameworks, and electric machines. Despite progress, several studies reveal challenges related to scalability, data privacy, computational complexity, and system adaptability, particularly in dynamic and decentralized environments. Stochastic–robust optimization and multi-agent systems improve decentralized coordination, while blockchain enhances security and automation in peer-to-peer trading. Simulations validate energy strategies, bridging theory and practice, and electric machines support renewable integration and grid flexibility. The synthesis underscores the need for unified frameworks that combine artificial intelligence, predictive control, and secure communication protocols. This review aims to provide a roadmap for advancing distributed energy systems toward scalable, resilient, and sustainable energy solutions. Full article
(This article belongs to the Special Issue Emerging Trends in Energy Management: Techniques, Applications and Future Directions)
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