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

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

Deadline for manuscript submissions: closed (20 September 2024) | Viewed by 5337

Special Issue Editors

Dr. Dario Pelosi

E-Mail Website
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
Special Issues, Collections and Topics in MDPI journals

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 (3 papers)

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Research

19 pages, 2387 KiB  
Article
The Sharing Energy Storage Mechanism for Demand Side Energy Communities
by Uda Bala, Wei Li, Wenguo Wang, Yuying Gong, Yaheng Su, Yingshu Liu, Yi Zhang and Wei Wang
Energies 2024, 17(21), 5468; https://doi.org/10.3390/en17215468 - 31 Oct 2024
Viewed by 719
Abstract
Energy storage (ES) units are vital for the reliable and economical operation of the power system with a high penetration of renewable distributed generators (DGs). Due to ES’s high investment costs and long payback period, energy management with shared ESs becomes a suitable [...] Read more.
Energy storage (ES) units are vital for the reliable and economical operation of the power system with a high penetration of renewable distributed generators (DGs). Due to ES’s high investment costs and long payback period, energy management with shared ESs becomes a suitable choice for the demand side. This work investigates the sharing mechanism of ES units for low-voltage (LV) energy prosumer (EP) communities, in which energy interactions of multiple styles among the EPs are enabled, and the aggregated ES dispatch center (AESDC) is established as a special energy service provider to facilitate the scheduling and marketing mechanism. A shared ES operation framework considering multiple EP communities is established, in which both the energy scheduling and cost allocation methods are studied. Then a shared ES model and energy marketing scheme for multiple communities based on the leader–follower game is proposed. The Karush–Kuhn–Tucker (KKT) condition is used to transform the double-layer model into a single-layer model, and then the large M method and PSO-HS algorithm are used to solve it, which improves convergence features in both speed and performance. On this basis, a cost allocation strategy based on the Owen value method is proposed to resolve the issues of benefit distribution fairness and user privacy under current situations. A case study simulation is carried out, and the results show that, with the ES scheduling strategy shared by multiple renewable communities in the leader–follower game, the energy cost is reduced significantly, and all communities acquire benefits from shared ES operators and aggregated ES dispatch centers, which verifies the advantageous and economical features of the proposed framework and strategy. With the cost allocation strategy based on the Owen value method, the distribution results are rational and equitable both for the groups and individuals among the multiple EP communities. Comparing it with other algorithms, the presented PSO-HS algorithm demonstrates better features in computing speed and convergence. Therefore, the proposed mechanism can be implemented in multiple scenarios on the demand side. 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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23 pages, 2979 KiB  
Article
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
Cited by 7 | Viewed by 1913
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 [...] Read more.
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 R2 value close to 1, representing a strong correlation between predicted and actual power values. Specifically, the best model achieved an R2 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  
Article
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 1987
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 [...] Read more.
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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