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A special issue of Energies (ISSN 1996-1073).

Deadline for manuscript submissions: closed (16 April 2018) | Viewed by 19794

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Prof. Dr. King Jet Tseng

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Guest Editor

Department of Electrical Engineering, Singapore Institute of Technology, 10 Dover Drive, Singapore 138682, Singapore
Interests: battery; engineering; electrical and electronics; energy storage; energy conversion; energy
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Dr. Zhongbao Wei

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Guest Editor

Energy Research Institute @ NTU (ERIAN), Nanyang Technological University, Singapore 637141, Singapore
Interests: lithium-ion batteries; all-vanadium redox flow battery; battery management; system identification; condition monitoring; battery charge control
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Dr. Jianwei Li

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Guest Editor

Electrical Engineering & Computer Science department, B28 - Institut Montefiore, Grande Traverse 10, University of Liege, 4000 Liege Belgium
Interests: energy storage; power electronics; electric vehicles; battery; applied superconductivity; HVDC; renewable energy; smart grid

Dr. Hao Mu

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Guest Editor

Department of Vehicle Engineering, School of Mechanical Engineering, Beijing Institute of Technology, No.5 Zhongguancun Street, Beijing 100081, China
Interests: lithium-ion battery modelling; multi-model fusion estimation; joint estimation of multiple states for nonlinear systems

Prof. Dr. Rui Xiong

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Guest Editor

Department of Vehicle Engineering, Beijing Institute of Technology, Beijing 100081, China
Interests: intelligent electrified vehicle; batteries; energy storage; machine learning; AI; big-data; control; optimization and management.
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Special Issue Information

Dear Colleagues,

We are inviting your contributions to a Special Issue of Energies with the theme of “Intelligent Management and Control of Energy Storage Systems”.

The penetration of renewable energy sources like solar and wind power has been promoted to relieve the dependence on fossil fuels and the environmental problems that they cause. Unfortunately, the power generated by renewables is heavily intermittent, depending on weather conditions. In this regard, energy storage is the key technology to achieve stable and consistent power delivery, and to address the challenges associated with modernizing the power grid. In the meantime, energy storage systems (ESSs) have also been playing a key role in end-user electrification. This is evident from the proactive penetration of battery-powered electrical vehicles (EVs) in pursuit of an efficient and low-carbon society.

This vision has driven intensive studies on the development of advanced ESSs that combine performance and cost merits, while suitable management and control strategies are pivotal to enhance the overall safety, reliability, and cost efficiency[DM1] . The Special Issue, therefore, seeks to contribute to the energy storage agenda through enhanced scientific knowledge related to intelligent management, control, power electronics, and novel ESSs with application in a wide range of fields like EVs, power grids, distributed generation, etc. We cordially invite papers on technical developments, as well as reviews, communications, and case studies that provide critical overview and reflect the cutting-edge progress in this field. With this Special Issue, we aim to present a platform to communicate and synergize knowledge from both specialized and interdisciplinary studies.

Topics of interest of this Special Issue include, but are not limited to:

Advanced energy storage technologies;
Application of ESSs in EVs, power grid, and distributed generation;
Modelling, simulation, and optimization of ESSs;
Battery management and online condition monitoring methods for SOC, SOH, SOF, RUL, etc.;
Optimal charging techniques;
Hybrid Supply systems;
Power electronics topology and control;

Prof. Dr. King Jet Tseng
Dr. Zhongbao Wei
Dr. Jianwei Li
Dr. Hao Mu
Assoc. Prof. Dr. Rui Xiong
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 system
Battery management system
Modelling and optimization
State estimation
Hybrid supply system
Power electronics
Charge control
Electrical vehicles
Power grid
♦Distributed generation

Published Papers (4 papers)

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Research

20 pages, 6579 KiB

Open AccessArticle

A Probabilistic Approach for the Optimal Sizing of Storage Devices to Increase the Penetration of Plug-in Electric Vehicles in Direct Current Networks

by Elio Chiodo, Maurizio Fantauzzi, Davide Lauria and Fabio Mottola

Energies 2018, 11(5), 1238; https://doi.org/10.3390/en11051238 - 13 May 2018

Cited by 5 | Viewed by 3262

Abstract

The growing diffusion of electric vehicles connected to distribution networks for charging purposes is an ongoing problem that utilities must deal with. Direct current networks and storage devices have emerged as a feasible means of satisfying the expected increases in the numbers of vehicles while preserving the effective operation of the network. In this paper, an innovative probabilistic methodology is proposed for the optimal sizing of electrical storage devices with the aim of maximizing the penetration of plug-in electric vehicles while preserving efficient and effective operation of the network. The proposed methodology is based on an analytical solution of the problem concerning the power losses minimization in distribution networks equipped with storage devices. The closed-form expression that was obtained is included in a Monte Carlo simulation procedure aimed at handling the uncertainties in loads and renewable generation units. The results of several numerical applications are reported and discussed to demonstrate the validity of the proposed solution. Also, different penetration levels of generation units were analyzed in order to focus on the importance of renewable generation. Full article

(This article belongs to the Special Issue Intelligent Management and Control of Energy Storage Systems)

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15 pages, 1637 KiB

Open AccessFeature PaperArticle

Optimizing Energy Storage Capacity in Islanded Microgrids Using Immunity-Based Multiobjective Planning

by Ying-Yi Hong, Yong-Zhen Lai, Yung-Ruei Chang, Yih-Der Lee and Chia-Hui Lin

Energies 2018, 11(3), 585; https://doi.org/10.3390/en11030585 - 07 Mar 2018

Cited by 12 | Viewed by 3010

Abstract

Microgrid operation is challenging because the amount of electricity that is produced from renewables is uncertain and the inertia of distributed generation resources is very small. Energy storage systems can regulate energy, improve the reliability of the power system and enhance the transient stability. This paper determines the optimal capacities of energy storage systems in an islanded microgrid that is composed of wind-turbine generators, photovoltaic arrays, and micro-turbine generators. The energy storage system can enhance the reliability of the microgrid and eliminate the unnecessary load shedding when a severe transient (such as a generator outage) occurs in the islanded microgrid. The studied problem is expressed as a multi-objective programming formulation, which is solved using an immunity-based algorithm. Four objective functions are optimized: minimum of energy storage capacity, minimum of load shedding, maximum of the lowest swing frequency, and minimum of the Customer Average Interruption Duration Index (CAIDI). These four objective functions are subject to both steady-state constraints and the transient-state equality constraint. The steady-state constraints include the total shed load limit, the feasible range of energy storage capacities while the transient-state equality constraint is expressed by the dynamic equation. The Pareto optimums are explored and optimality of the problem is investigated. The simulation results based on an islanded 15-bus microgrid show the applicability of the proposed method. Full article

(This article belongs to the Special Issue Intelligent Management and Control of Energy Storage Systems)

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2881 KiB

Open AccessArticle

Parameters Identification and Sensitive Characteristics Analysis for Lithium-Ion Batteries of Electric Vehicles

by Yun Zhang, Yunlong Shang, Naxin Cui and Chenghui Zhang

Energies 2018, 11(1), 19; https://doi.org/10.3390/en11010019 - 22 Dec 2017

Cited by 18 | Viewed by 4615

Abstract

This paper mainly investigates the sensitive characteristics of lithium-ion batteries so as to provide scientific basises for simplifying the design of the state estimator that adapt to various environments. Three lithium-ion batteries are chosen as the experimental samples. The samples were tested at [...] Read more.

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C) and various current rates (0.5C, 1C, 1.5C) using a battery test bench. A physical equivalent circuit model is developed to capture the dynamic characteristics of the batteries. The experimental results show that all battery parameters are time-varying and have different sensitivity to temperature, current rate and state of charge (SOC). The sensitivity of battery to temperature, current rate and SOC increases the difficulty in battery modeling because of the change of parameters. The further simulation experiments show that the model output has a higher sensitivity to the change of ohmic resistance than that of other parameters. Based on the experimental and simulation results obtained here, it is expected that the adaptive parameter state estimator design could be simplified in the near future. Full article

(This article belongs to the Special Issue Intelligent Management and Control of Energy Storage Systems)

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1024 KiB

Open AccessEditor’s ChoiceArticle

Towards Efficient Energy Management of Smart Buildings Exploiting Heuristic Optimization with Real Time and Critical Peak Pricing Schemes

by Sheraz Aslam, Zafar Iqbal, Nadeem Javaid, Zahoor Ali Khan, Khursheed Aurangzeb and Syed Irtaza Haider

Energies 2017, 10(12), 2065; https://doi.org/10.3390/en10122065 - 05 Dec 2017

Cited by 106 | Viewed by 7786

Abstract

The smart grid plays a vital role in decreasing electricity cost through Demand Side Management (DSM). Smart homes, a part of the smart grid, contribute greatly to minimizing electricity consumption cost via scheduling home appliances. However, user waiting time increases due to the scheduling of home appliances. This scheduling problem is the motivation to find an optimal solution that could minimize the electricity cost and Peak to Average Ratio (PAR) with minimum user waiting time. There are many studies on Home Energy Management (HEM) for cost minimization and peak load reduction. However, none of the systems gave sufficient attention to tackle multiple parameters (i.e., electricity cost and peak load reduction) at the same time as user waiting time was minimum for residential consumers with multiple homes. Hence, in this work, we propose an efficient HEM scheme using the well-known meta-heuristic Genetic Algorithm (GA), the recently developed Cuckoo Search Optimization Algorithm (CSOA) and the Crow Search Algorithm (CSA), which can be used for electricity cost and peak load alleviation with minimum user waiting time. The integration of a smart Electricity Storage System (ESS) is also taken into account for more efficient operation of the Home Energy Management System (HEMS). Furthermore, we took the real-time electricity consumption pattern for every residence, i.e., every home has its own living pattern. The proposed scheme is implemented in a smart building; comprised of thirty smart homes (apartments), Real-Time Pricing (RTP) and Critical Peak Pricing (CPP) signals are examined in terms of electricity cost estimation for both a single smart home and a smart building. In addition, feasible regions are presented for single and multiple smart homes, which show the relationship among the electricity cost, electricity consumption and user waiting time. Experimental results demonstrate the effectiveness of our proposed scheme for single and multiple smart homes in terms of electricity cost and PAR minimization. Moreover, there exists a tradeoff between electricity cost and user waiting. Full article

(This article belongs to the Special Issue Intelligent Management and Control of Energy Storage Systems)

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