Topic Editors

Department of Industrial Engineering and Management, International Hellenic University, 57001 Thessaloniki, Greece
Chemical Process and Energy Resources Institute, Centre for Research and Technology Hellas, 57001 Thessaloniki, Greece

Intelligent and Flexible Energy Management Strategies (EMSs) and Technologies

Abstract submission deadline
closed (31 January 2024)
Manuscript submission deadline
31 March 2024
Viewed by
1844

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

  • energy-management system
  • energy storage
  • microgrid
  • demand-side management
  • distributed generation
  • renewables
  • electric vehicles

Participating Journals

Journal Name Impact Factor CiteScore Launched Year First Decision (median) APC
Clean Technologies
cleantechnol
3.8 4.5 2019 26.6 Days CHF 1600 Submit
Electricity
electricity
- - 2020 20.3 Days CHF 1000 Submit
Energies
energies
3.2 5.5 2008 16.1 Days CHF 2600 Submit
Mathematics
mathematics
2.4 3.5 2013 16.9 Days CHF 2600 Submit
Sustainability
sustainability
3.9 5.8 2009 18.8 Days CHF 2400 Submit

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Published Papers (2 papers)

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24 pages, 1015 KiB  
Review
Recent Trends and Issues of Energy Management Systems Using Machine Learning
Energies 2024, 17(3), 624; https://doi.org/10.3390/en17030624 - 27 Jan 2024
Viewed by 745
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
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22 pages, 3867 KiB  
Article
Analysis of Flexibility Potential of a Cold Warehouse with Different Refrigeration Compressors
Energies 2024, 17(1), 85; https://doi.org/10.3390/en17010085 - 22 Dec 2023
Viewed by 389
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
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