Intelligent and Flexible Energy Management Strategies (EMSs) and Technologies

Abstract submission deadline

30 April 2025

Manuscript submission deadline

30 June 2025

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Topic Information

Dear Colleagues,

Intelligent Energy and Flexible management strategies (IEFMS) incorporate advanced technologies to achieve an efficient and flexible form of power management that ensures delivery at a time-scale ranging from seconds to years. The efficient implementation of energy storage systems (ESSs) must cover the power variability of distributed generation in the short-term, compensate for the intermittent nature of renewable generation and serve as a means of improving power quality and reliability.

Intelligent and flexible energy management strategies (EMSs) tackle the minimization of operational costs and cost for end-users, and minimization of emissions and peak loads, as well as satisfying the technical constraints for dynamic heterogeneous complex systems including renewables and non-renewable sources, ESS, demand-side management (DSM) and hybrid systems.

The topics of interest include:

• The state-of-the-art in intelligent control and smart energy management methods;
• Planning and flexible energy management in smart distribution networks in the presence of electric vehicles;
• Energy storage technologies and energy carriers (batteries, chemical, thermochemical storage, H2), maintenance, operability and aging of ESSs;
• Modelling and optimization methods (model predictive EMS; artificial intelligence; digital twins);
• Siting, sizing, and selection of ESSs, incorporating market prices and operating parameters and model predictive EMSs;
• Upstream network energy cost and flexibility benefits;
• Distributed power generation of micro/smart grids;
• Case studies for different applications in transportation, Home Energy Management Systems and renewable resources;
• Sustainability and EMS;
• Future directions and research perspectives in IEFMS and smart energy.

Prof. Dr. Simira Papadopoulou
Dr. Spyros Voutetakis
Topic Editors

Keywords

Participating Journals

Journal Name	Impact Factor	CiteScore	Launched Year	First Decision (median)	APC
Clean Technologies cleantechnol	4.0	6.1	2019	30 Days	CHF 1600	Submit
Electricity electricity	-	4.8	2020	27.2 Days	CHF 1000	Submit
Energies energies	3.0	6.2	2008	17.5 Days	CHF 2600	Submit
Mathematics mathematics	2.3	4.0	2013	17.1 Days	CHF 2600	Submit
Sustainability sustainability	3.3	6.8	2009	20 Days	CHF 2400	Submit

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Published Papers (6 papers)

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All Clean Technol. Electricity Energies Mathematics Sustainability

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20 pages, 1372 KiB  

Open AccessArticle

A Hydrogen-Integrated Aggregator Model for Managing the Point of Common Coupling Congestion in Green Multi-Microgrids

by Farshad Khavari and Jay Liu

Energies 2024, 17(16), 4018; https://doi.org/10.3390/en17164018 - 13 Aug 2024

Viewed by 485

Abstract

The rapid expansion of energy storage integration has not provided sufficient time to strengthen and expand the transmission and distribution network. This issue can lead to PCC congestion in green multi-microgrid (MMG) systems. In these systems, microgrids operate independently and connect to the [...] Read more.

The rapid expansion of energy storage integration has not provided sufficient time to strengthen and expand the transmission and distribution network. This issue can lead to PCC congestion in green multi-microgrid (MMG) systems. In these systems, microgrids operate independently and connect to the grid at a point of common coupling (PCC) without sharing operational data with neighboring microgrids. To address this issue, this paper proposes a bi-level optimization model designed to reschedule hydrogen storage systems. The first level allows each microgrid to optimize its energy transactions with the grid and communicates any unbalanced energy to the second level, where a hydrogen management system (HMS) is introduced. The HMS optimizes virtual hydrogen prices to address the PCC congestion and maximize the MMG’s profit. These virtual prices are then sent to the first level, allowing the microgrids to reschedule the hydrogen storage systems based on these virtual prices. Finally, the MMG’s profit is fairly allocated among the microgrids using the Shapley value method. The proposed method’s effectiveness is demonstrated using simulations, which show a six percent increase in MMG profit compared to scenarios that only share PCC capacity while maintaining the data privacy of all the involved microgrids. Full article

(This article belongs to the Topic Intelligent and Flexible Energy Management Strategies (EMSs) and Technologies)

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19 pages, 3218 KiB  

Open AccessArticle

Optimized Dual-Layer Distributed Energy Storage Configuration for Voltage Over-Limit Zoning Governance in Distribution Networks

by Meimei Hao, Jinchen Lan, Lianhui Wang, Yan Lin, Jiang Wang and Liang Qin

Energies 2024, 17(8), 1847; https://doi.org/10.3390/en17081847 - 12 Apr 2024

Cited by 1 | Viewed by 627

Abstract

In this study, an optimized dual-layer configuration model is proposed to address voltages that exceed their limits following substantial integration of photovoltaic systems into distribution networks. Initially, the model involved segmenting the distribution network’s voltage zones based on distributed photovoltaic governance resources, thereby [...] Read more.

In this study, an optimized dual-layer configuration model is proposed to address voltages that exceed their limits following substantial integration of photovoltaic systems into distribution networks. Initially, the model involved segmenting the distribution network’s voltage zones based on distributed photovoltaic governance resources, thereby elucidating the characteristics and governance requisites for voltages across distinct regions. Subsequently, a governance model for voltage limit exceedances, grounded in optimizing energy storage configurations, was formulated to mitigate photovoltaic power fluctuations by deploying energy storage systems. This model coordinates the reactive power output of photovoltaic installations with the active power consumption of energy storage systems, thereby augmenting voltage autonomy in the power grid. This study leveraged Karush–Kuhn–Tucker (KKT) conditions and the Big-M method to transform the dual-layer model into a single-layer linear model, thereby enhancing solution efficiency and precision. Finally, a simulation was carried out to demonstrate that the strategy proposed from this research not only achieves commendable economic efficiency, but also significantly improves the regional voltage effect by 28.7% compared to the optical storage capacity optimization model. Full article

(This article belongs to the Topic Intelligent and Flexible Energy Management Strategies (EMSs) and Technologies)

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19 pages, 3224 KiB  

Open AccessArticle

Low-Voltage Distribution Network Loss-Reduction Method Based on Load-Timing Characteristics and Adjustment Capabilities

by Cheng Huangfu, Erwei Wang, Ting Yi and Liang Qin

Energies 2024, 17(5), 1115; https://doi.org/10.3390/en17051115 - 26 Feb 2024

Viewed by 697

Abstract

The primary contributors to elevated line losses in low-voltage distribution networks are three-phase load imbalances and variations in load peak–valley differentials. The conventional manual phase sequence adjustment fails to capitalize on the temporal characteristics of the load, and the proliferation of smart homes [...] Read more.

The primary contributors to elevated line losses in low-voltage distribution networks are three-phase load imbalances and variations in load peak–valley differentials. The conventional manual phase sequence adjustment fails to capitalize on the temporal characteristics of the load, and the proliferation of smart homes has opened up new scheduling possibilities for managing the load. Consequently, this paper introduces a loss-reduction method for low-voltage distribution networks that leverages load-timing characteristics and adjustment capabilities. This method combines dynamic and static methods to reduce energy consumption from different time scales. To commence, this paper introduced a hierarchical fuzzy C-means algorithm (H-FCM), taking into account the distance and similarity of load curves. Subsequently, a phase sequence adjustment method, grounded in load-timing characteristics, was developed. The typical user load curve, derived from the classification of user loads, serves as the foundation for constructing a long-term commutation model, therefore mitigating the impact of load fluctuations on artificial commutation. Following this, this paper addressed the interruptible and transferable characteristics of various smart homes. This paper proposed a multi-objective transferable load (TL) optimal timing task adjustment model and a peak-shaving control strategy specifically designed for maximum sustainable power reduction of temperature-controlled loads (TCL). These strategies aim to achieve real-time load adjustment, correct static commutation errors, and reduce peak-to-valley differences. Finally, a simulation verification model was established in MATLAB (R2022a). The results show that the proposed method mainly solves the problems of three-phase imbalance and large load peak–valley difference in low-voltage distribution networks and reduces the line loss of low-voltage distribution networks through manual commutation and load adjustment. Full article

(This article belongs to the Topic Intelligent and Flexible Energy Management Strategies (EMSs) and Technologies)

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20 pages, 999 KiB  

Open AccessArticle

Energy Management for Hybrid Electric Vehicles Using Safe Hybrid-Action Reinforcement Learning

by Jinming Xu and Yuan Lin

Mathematics 2024, 12(5), 663; https://doi.org/10.3390/math12050663 - 24 Feb 2024

Cited by 1 | Viewed by 884

Abstract

Reinforcement learning has shown success in solving complex control problems, yet safety remains paramount in engineering applications like energy management systems (EMS), particularly in hybrid electric vehicles (HEVs). An effective EMS is crucial for coordinating power flow while ensuring safety, such as maintaining [...] Read more.

Reinforcement learning has shown success in solving complex control problems, yet safety remains paramount in engineering applications like energy management systems (EMS), particularly in hybrid electric vehicles (HEVs). An effective EMS is crucial for coordinating power flow while ensuring safety, such as maintaining the battery state of charge within safe limits, which presents a challenging task. Traditional reinforcement learning struggles with safety constraints, and the penalty method often leads to suboptimal performance. This study introduces Lagrangian-based parameterized soft actor–critic (PASACLag), a novel safe hybrid-action reinforcement learning algorithm for HEV energy management. PASACLag utilizes a unique composite action representation to handle continuous actions (e.g., engine torque) and discrete actions (e.g., gear shift and clutch engagement) concurrently. It integrates a Lagrangian method to separately address control objectives and constraints, simplifying the reward function and enhancing safety. We evaluate PASACLag’s performance using the World Harmonized Vehicle Cycle (901 s), with a generalization analysis of four different cycles. The results indicate that PASACLag achieves a less than 10% increase in fuel consumption compared to dynamic programming. Moreover, PASACLag surpasses PASAC, an unsafe counterpart using penalty methods, in fuel economy and constraint satisfaction metrics during generalization. These findings highlight PASACLag’s effectiveness in acquiring complex EMS for control within a hybrid action space while prioritizing safety. Full article

(This article belongs to the Topic Intelligent and Flexible Energy Management Strategies (EMSs) and Technologies)

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24 pages, 1015 KiB  

Open AccessReview

Recent Trends and Issues of Energy Management Systems Using Machine Learning

by Seongwoo Lee, Joonho Seon, Byungsun Hwang, Soohyun Kim, Youngghyu Sun and Jinyoung Kim

Energies 2024, 17(3), 624; https://doi.org/10.3390/en17030624 - 27 Jan 2024

Cited by 2 | Viewed by 2828

Abstract

Energy management systems (EMSs) are regarded as essential components within smart grids. In pursuit of efficiency, reliability, stability, and sustainability, an integrated EMS empowered by machine learning (ML) has been addressed as a promising solution. A comprehensive review of current literature and trends [...] Read more.

Energy management systems (EMSs) are regarded as essential components within smart grids. In pursuit of efficiency, reliability, stability, and sustainability, an integrated EMS empowered by machine learning (ML) has been addressed as a promising solution. A comprehensive review of current literature and trends has been conducted with a focus on key areas, such as distributed energy resources, energy management information systems, energy storage systems, energy trading risk management systems, demand-side management systems, grid automation, and self-healing systems. The application of ML in EMS is discussed, highlighting enhancements in data analytics, improvements in system stability, facilitation of efficient energy distribution and optimization of energy flow. Moreover, architectural frameworks, operational constraints, and challenging issues in ML-based EMS are explored by focusing on its effectiveness, efficiency, and suitability. This paper is intended to provide valuable insights into the future of EMS. Full article

(This article belongs to the Topic Intelligent and Flexible Energy Management Strategies (EMSs) and Technologies)

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0 pages, 3867 KiB  

Open AccessArticle

Analysis of Flexibility Potential of a Cold Warehouse with Different Refrigeration Compressors

by Ehsan Khorsandnejad, Robert Malzahn, Ann-Katrin Oldenburg, Annedore Mittreiter and Christian Doetsch

Energies 2024, 17(1), 85; https://doi.org/10.3390/en17010085 - 22 Dec 2023

Viewed by 877

Abstract

The research into new approaches to shift from fossil fuels to renewable energy sources (RES) has surged as environmental issues are on the rise, and fossil fuel sources are becoming scarce. The flexibility potential of cold supply systems has been discussed widely in [...] Read more.

The research into new approaches to shift from fossil fuels to renewable energy sources (RES) has surged as environmental issues are on the rise, and fossil fuel sources are becoming scarce. The flexibility potential of cold supply systems has been discussed widely in the literature, firstly due to their high share of electricity consumption worldwide and secondly because of their potential to store thermal energy in the form of cold energy. However, finding a clear definition of flexibility and a concise approach for its quantification is still under progress. In this work, a comprehensive definition of the flexibility of energy systems and a novel methodology for its quantification are introduced. The methodology was applied on a cold warehouse with real data regarding its cold energy demand. The cold warehouse was first modeled via oemof, which is a modular open source framework developed in Python 3.8 using a mixed integer linear programming (MILP) optimization approach. The operation optimization of the cold warehouse was conducted for three goals, namely “minimization of electricity costs”, “minimization of CO₂ emissions”, and “minimization of maximum used electric power (peak load minimization)”. Additionally, the effect of using different types of refrigeration compressors on the optimized operation of the cold warehouse was investigated. The results suggest that a cold warehouse possesses a high level of flexibility potential, which can be taken advantage of to reduce the electricity cost by up to 50%, the CO₂ emissions between 25% to 30%, and the maximum used electric power by 50%. Different compressor types produced very similar results, although their flexibility level may vary. Full article

(This article belongs to the Topic Intelligent and Flexible Energy Management Strategies (EMSs) and Technologies)

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