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The Development and Modeling of Energy Storage Systems for Renewable-Based Electric Systems

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A special issue of Energies (ISSN 1996-1073). This special issue belongs to the section "D: Energy Storage and Application".

Deadline for manuscript submissions: 20 September 2024 | Viewed by 3070

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Special Issue Editors

Dr. Dario Pelosi

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Guest Editor

Department of Engineering, University of Perugia | UNIPG, Perugia, Italy
Interests: micro-grids; battery aging; energy storage; renewables; dynamic modeling; power systems; electric architectures; fast-charging infrastructure; electric mobility

Dr. Linda Barelli

E-Mail Website
Guest Editor

Department of Engineering, University of Perugia, Via Duranti 93, 06125 Perugia, Italy
Interests: batteries; electrolyzers; hybrid energy storage systems; hybrid propulsion systems; integration of energy storage into renewable-based micro grids; power micro-grids
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Special Issue Information

Dear Colleagues,

Environmental concerns are driving a huge change in the production and transmission of energy. A crucial aspect is represented by the transition from a centralized power production, mainly based on fossil fuels, to a decentralized one, consisting of renewable sources. As a matter of fact, renewables are considered as non-programmable power sources since they present a strong intermittent and fluctuating behavior that can lead to safety and reliability issues on the national power systems. To accelerate this transition and meet the user demands, non-programmable energy produced by renewables needs to be stored and used when necessary. Therefore, energy storage is essential to store the produced energy, while allowing its postponed use. The development and modeling of new energy storage systems and the technological improvement of the existing ones could be a milestone for a massive penetration of renewables.

Therefore, this Special Issue aims to disseminate the most recent advances related to the development, modeling, application, electrical architecture topologies, and control of all types of energy storage systems coupled with renewable-based electric systems.

Topics of interest for publication include (but not limited to):

Energy storage support to renewable-based micro-grids;
Novel applications of energy storage systems;
Advanced modeling approaches;
Transient stability analysis;
Energy storage control and management strategies;
Experimental testing of energy storage systems in renewable-based systems;
Power smoothing;
Power quality;
Cycle and calendar aging effects;
Techno-economic analysis;
Grid support.

Dr. Dario Pelosi
Dr. Linda Barelli
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. 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

energy storage
renewables
electric systems
power quality
aging
advanced modeling
micro-grids
grid support

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Published Papers (2 papers)

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Research

23 pages, 2979 KiB

Open AccessArticle

Hybrid Intelligent Control System for Adaptive Microgrid Optimization: Integration of Rule-Based Control and Deep Learning Techniques

by Osman Akbulut, Muhammed Cavus, Mehmet Cengiz, Adib Allahham, Damian Giaouris and Matthew Forshaw

Energies 2024, 17(10), 2260; https://doi.org/10.3390/en17102260 - 8 May 2024

Viewed by 952

Abstract

Microgrids (MGs) have evolved as critical components of modern energy distribution networks, providing increased dependability, efficiency, and sustainability. Effective control strategies are essential for optimizing MG operation and maintaining stability in the face of changing environmental and load conditions. Traditional rule-based control systems are extensively used due to their interpretability and simplicity. However, these strategies frequently lack the flexibility for complex and changing system dynamics. This paper provides a novel method called hybrid intelligent control for adaptive MG that integrates basic rule-based control and deep learning techniques, including gated recurrent units (GRUs), basic recurrent neural networks (RNNs), and long short-term memory (LSTM). The main target of this hybrid approach is to improve MG management performance by combining the strengths of basic rule-based systems and deep learning techniques. These deep learning techniques readily enhance and adapt control decisions based on historical data and domain-specific rules, leading to increasing system efficiency, stability, and resilience in adaptive MG. Our results show that the proposed method optimizes MG operation, especially under demanding conditions such as variable renewable energy supply and unanticipated load fluctuations. This study investigates special RNN architectures and hyperparameter optimization techniques with the aim of predicting power consumption and generation within the adaptive MG system. Our promising results show the highest-performing models indicating high accuracy and efficiency in power prediction. The finest-performing model accomplishes an

R^{2}

value close to 1, representing a strong correlation between predicted and actual power values. Specifically, the best model achieved an

R^{2}

value of 0.999809, an MSE of 0.000002, and an MAE of 0.000831. Full article

(This article belongs to the Special Issue The Development and Modeling of Energy Storage Systems for Renewable-Based Electric Systems)

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14 pages, 2863 KiB

Open AccessArticle

Experimental Investigation of Fast−Charging Effect on Aging of Electric Vehicle Li−Ion Batteries

by Dario Pelosi, Michela Longo, Dario Zaninelli and Linda Barelli

Energies 2023, 16(18), 6673; https://doi.org/10.3390/en16186673 - 18 Sep 2023

Cited by 3 | Viewed by 1524

Abstract

A huge increase in fast−charging stations will be necessary for the transition to EVs. Nevertheless, charging a battery pack at a higher C−rate impacts its state of health, accelerating its degradation. The present paper proposes a different and innovative approach that considers the daily routine of an EV Li−ion battery based on a standard driving cycle, including charging phases when the depth of discharge is 90%. Through dynamic modeling of the EV battery system, the state of charge evolution is determined for different charging C−rates, considering both real discharging and charging current profiles. Finally, by applying a suitable post−processing procedure, aging test features are defined, each being related to a specific EV battery working mode, including charging at a particular C−rate, considering the global battery operation during its lifespan. It is demonstrated that, according to the implemented procedure, fast−charging cycles at 50 kW reduce battery lifespan by about 17% with respect to charge in a 22 kW three−phase AC column, in parity with the discharge rate. Thus, this work can provide a deep insight into the expected massive penetration of electric vehicles, providing an estimate of battery useful life based on charging conditions. Full article

(This article belongs to the Special Issue The Development and Modeling of Energy Storage Systems for Renewable-Based Electric Systems)

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Planned Papers

The below list represents only planned manuscripts. Some of these manuscripts have not been received by the Editorial Office yet. Papers submitted to MDPI journals are subject to peer-review.

Title: Energy storage system integration to renewable generation for power smoothing
Authors: Linda Barelli
Affiliation: Linda Barelli

Title: Enhancing Microgrid Management with Learned Optimal Epsilon Variable and Deep Learning-based Visualization
Authors: 1. Osman Akbuluta,b, 2. Muhammed Cavusc,d, 3. Mehmet Cengiza, 4. Adib Allahhame, 5. Damian Giaourisc, 6. Matthew Forshawa
Affiliation: a - School of Computing, Newcastle University, 1 Science Square, Newcastle upon Tyne, NE4 5TG, United Kingdom b - Department of Computer Engineering, Faculty of Engineering, Duzce University, Duzce, Turkey c - School of Engineering, Newcastle University, Newcastle upon Tyne NE1 7RU, UK d- School of Engineering, Iskenderun Technical University, İskenderun 31200, Turkey e - Faculty of Engineering and Environment, Northumbria University, Newcastle NE1 8ST, UK
Abstract: Microgrids play a crucial role in modern energy systems, yet their efficient management remains challenging due to the dynamic nature of renewable energy sources and fluctuating demand. In this study, we propose an integrated approach leveraging a learned optimal epsilon variable, visualization techniques, and deep learning methods for enhanced microgrid control. The learned optimal epsilon variable dynamically adjusts control parameters, optimizing microgrid operations to minimize costs and meet operational constraints. Visualization tools offer intuitive insights into system performance, aiding decision-making and facilitating stakeholder understanding. Deep learning techniques are employed to enhance predictive capabilities, improving the accuracy of renewable energy and demand forecasts. Through extensive simulations and case studies, we demonstrate the efficacy of our integrated approach in achieving optimal microgrid performance, ensuring grid stability, and maximizing renewable energy utilization. This research contributes to advancing microgrid management practices, paving the way for more sustainable and efficient energy systems.

Title: Improved functionality of single phase storage system for domestic solution
Authors: Krzysztof Bodzek (CA), Michalak Jarosław, Arkadiusz Domoracki, Grzegorz Jarek, Michał Jeleń, Marcin Zygmanowski.
Affiliation: This study explores an innovative single-phase DC power system with a nominal voltage of around 370 V, focusing on an energy storage configuration for optimal performance. It examines the system's operational modes with an emphasis on an integrating converter's role in enhancing energy quality and management at the connection point. This includes leveraging feedback from load currents to improve the converter's island mode operation and introducing strategies for active power shaping to protect the network from excessive voltages. The paper discusses energy management strategies aimed at optimizing power flow, using simulations to evaluate efficiency improvements related to the Dual Active Bridge (DAB) with a Self-Powered Switch (SPS). Additionally, it touches on the potential efficiency gains through duty cycle adjustments for low power levels, supported by lab-based implementations. The novelty of the research lies in its approach to solving reactive power and energy quality issues through standard power control mechanisms at the connection point, thereby improving efficiency without complexity. The findings offer fresh perspectives on power control strategies, indicating significant potential for energy system efficiency and reliability enhancements.