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Planning, Operation and Control of Renewable Energy Sources and Energy Storage Devices Assisted Hybrid Power System

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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: closed (20 April 2023) | Viewed by 18362

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Special Issue Editors

Prof. Rajvikram Madurai Elavarasan

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Guest Editor

Department of Electrical and Electronics Engineering, Thiagarajar College of Engineering, Madurai 625015, Tamil Nadu, India
Interests: sustainable development goals; energy storage; thermal energy storage; demand-side management; techno-economic analysis of RE systems
Special Issues, Collections and Topics in MDPI journals

Prof. Dr. Eklas Hossain

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Guest Editor

Electrical and Computer Engineering, Boise State University, Boise, ID 83725, USA
Interests: energy systems; smart grids; machine learning; big data analytics; power electronics; advanced control system
Special Issues, Collections and Topics in MDPI journals

Prof. Dr. Gauri Shankar

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Guest Editor

Department of Electrical Engineering, Indian Institute of Technology (Indian School of Mines) Dhanbad, Jharkhand, India
Interests: power system operation and control; soft computing techniques; power system planning; smart grid

Special Issue Information

Dear Colleagues,

The world is witnessing a paradigm shift from the use of only conventional fossil fuel-fired power generation systems to mixed power generation systems that include renewable source (RES)-based generating units and energy storage devices (ESDs) alongside conventional units due to global warming, declining fossil fuel reserves, and exponential increases in electrical energy demand. However, the growing use of intermittent RES-based units has brought new issues in terms of power system operation, control, and stability. As a result, the importance of ESDs in achieving the power system’s required performance has become critical. Hence, a thorough investigation into the design and development of optimal coordinated operation and controls for these units has become a pressing need. Apart from the aforementioned, other areas of interest include cybersecurity, reliability, the role of IoT, the application of soft computing techniques in optimal planning, design, operation, and control, power quality issues, RES power forecasting techniques, the application of data mining and machine learning techniques, virtual inertia emulation from ESDs, and so on.

In light of the above, this Special Issue of the journal seeks original high-quality contributions from researchers/academicians/industry professionals from all over the world in the fields listed below, but not limited to:

Design, development, and control of power electronics converters interfaced RES units and ESDs;
Coordinated operation and control of RES and ESD assisted hybrid power systems;
Energy management systems;
Role of the IoT in smart grid;
Application of soft computing, machine learning, and data mining techniques;
Virtual inertia emulation using ESDs;
Electric vehicles;
Power forecasting from RESs;
Power quality issues in smart grid;
Cyberphysical power system and security;
Cyberphysical power system testbed.

Prof. Rajvikram Madurai Elavarasan
Prof. Dr. Eklas Hossain
Prof. Dr. Gauri Shankar
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

Renewable energy sources
Energy storage
Hybrid power system
Soft computing technique
Optimal control
Energy management

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Published Papers (6 papers)

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Research

Jump to: Review

18 pages, 6577 KiB

Open AccessArticle

Frequency Fluctuation Mitigation in a Single-Area Power System Using LQR-Based Proportional Damping Compensator

by Pranta Das, Shuvra Prokash Biswas, Sudipto Mondal and Md Rabiul Islam

Energies 2023, 16(12), 4804; https://doi.org/10.3390/en16124804 - 19 Jun 2023

Cited by 3 | Viewed by 1892

Abstract

To maintain the stability of the power system, frequency fluctuations must be reduced in the shortest possible timeframe. Load frequency control (LFC) plays a critical role in achieving this objective by regulating the system frequency and the desired demand or output power in an interconnected network, thereby enabling the system to adapt the load disturbances. In order to effectively mitigate the frequency fluctuation caused by load variation in a single-area power system, a new control strategy integrating a linear quadratic regulator (LQR), a proportional controller, and a damping compensator is proposed in this paper. The proposed controller is named as the LQR-based proportional damping compensator which mitigates the frequency fluctuation of a single-area power system. MATLAB/Simulink simulation is conducted on a single-area power system to demonstrate the efficacy of the proposed control technique. The simulation results demonstrate that the proposed method successfully reduces frequency variations, maintains system frequency within reasonable limits, and substantially reduces the settling time as compared to other existing control techniques. Apart from the simulation analysis, to experimentally validate the performance of the proposed controller, a hybrid multiprocessor-based processor-in-loop (PIL) technique is also introduced in the paper. Both the simulation and experimental results prove the promising performance of the proposed controller for mitigating the frequency fluctuation of a single-area power system. Full article

(This article belongs to the Special Issue Planning, Operation and Control of Renewable Energy Sources and Energy Storage Devices Assisted Hybrid Power System)

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21 pages, 5399 KiB

Open AccessArticle

Age Estimation of a Hybrid Energy Storage System for Vehicular Start–Stop

by Emad Manla and Adel Nasiri

Energies 2023, 16(2), 623; https://doi.org/10.3390/en16020623 - 4 Jan 2023

Cited by 1 | Viewed by 1518

Abstract

Ultracapacitors are energy storage devices that have shown outstanding capability in a vast spectrum of applications, mainly in energy storage systems required to deliver short bursts of electrical energy. Ultracapacitors possess high power density while batteries possess high energy density. In this paper, a hybrid energy storage device comprising a lithium-ion ultracapacitor module and a lead acid battery was modeled, built, and tested for vehicular start–stop application, which requires a much larger number of engine cranking events than conventional vehicles. The combination of a lead acid battery with Li-ion ultracapacitors was chosen due to the fact that the vast majority of vehicles utilize lead acid batteries to crank the internal combustion engine. This allows retrofitting this hybrid setup in conventional vehicles along with the start–stop feature without inflicting damage to the already installed lead acid battery. The start–stop feature puts high stress on the lead acid battery, contributing to its faster aging. This feature is commonly found in hybrid vehicles to save the unnecessarily burned fuel during idling. This paper discusses aging of the lead acid battery as a result of being used in hybrid vehicles equipped with start–stop when used alone versus when used in the hybrid setup. The paper shows cranking tests performed on a number of cars to obtain voltage, current, power, and energy requirements for combustion engine cranking. Mathematical derivation, analysis, and an energy storage age estimation method are also presented. A set of cranking events followed by capacity checks performed on two automobile energy storage systems, one being a lead acid battery alone and the other being the proposed hybrid module, show the advantage of integrating the ultracapacitor module with the lead acid battery to extend its life span almost fivefold in a hybrid automobile. Full article

(This article belongs to the Special Issue Planning, Operation and Control of Renewable Energy Sources and Energy Storage Devices Assisted Hybrid Power System)

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19 pages, 4214 KiB

Open AccessFeature PaperArticle

Energy Management for an Air Conditioning System Using a Storage Device to Reduce the On-Peak Power Consumption

by Wunvisa Tipasri, Amnart Suksri, Karthikeyan Velmurugan and Tanakorn Wongwuttanasatian

Energies 2022, 15(23), 8940; https://doi.org/10.3390/en15238940 - 25 Nov 2022

Cited by 3 | Viewed by 2479

Abstract

To reduce the on-peak electrical power consumption, storage devices are widely performed with the help of an energy management system. According to IEA, residential air conditioning consumes 70% of the electricity, increasing by 4% every year. To minimize peak power consumption, thermal energy storage (TES) can be used to store cooled water for the air conditioning system. An efficient chilled water tank was designed and computationally investigated. Three-dimensional cylindrical tanks were simulated with seven different heights to diameter (H:D) ratios. At first, the temperature changes in a chilled water tank during discharging and charging periods were studied. An 11-h charging period was carried out during the off-peak time at night, while the discharging period was 13 h during the daytime. Under time constraints regarding peak and off-peak periods, a tank with an H:D = 2.0 can only be used for 13-h discharging. Then the chilled water was simulated with a set temperature of 4 °C during the charging. This resulted in the system being usable for six days, after which it had to be stopped for longer charging. A storage tank with an H:D ratio of 2.0 was found to be suitable for an air conditioning system. If six days of operations (one day off) were used, it could save 15.38% of electrical energy consumption and 51.65% of electricity cost. This saving leads to a 5.55-year payback period. Full article

(This article belongs to the Special Issue Planning, Operation and Control of Renewable Energy Sources and Energy Storage Devices Assisted Hybrid Power System)

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19 pages, 3589 KiB

Open AccessArticle

Parameters Identification of Proton Exchange Membrane Fuel Cell Model Based on the Lightning Search Algorithm

by Banaja Mohanty, Rajvikram Madurai Elavarasan, Hany M. Hasanien, Elangovan Devaraj, Rania A. Turky and Rishi Pugazhendhi

Energies 2022, 15(21), 7893; https://doi.org/10.3390/en15217893 - 24 Oct 2022

Cited by 12 | Viewed by 2342

Abstract

The fuel cell is vital in electrical distribution networks as a distributed generation in today’s world. A precise model of a fuel cell is extensively required as it rigorously affects the simulation studies’ transient and dynamic analyses of the fuel cell. This appears in several microgrids and smart grid systems. This paper introduces a novel attempt to optimally determine all unknown factors of the polymer exchange membrane (PEM) fuel cell model using a meta-heuristic algorithm termed the Lightning search algorithm (LSA). In this model, the current–voltage relationship is heavily nonlinear, including several unknown factors because of the shortage of fuel cell data from the manufacturer’s side. This issue can be treated as an optimization problem, and LSA is applied to detect its ability to solve this problem accurately. The objective function is the sum of the squared error between the estimated output voltage and the measured output voltage of the fuel cell. The constraints of the optimization problem involve the factors range (lower and upper limit). The LSA is utilized in minimizing the objective function. The effectiveness of the LSA-PEM fuel cell model is extensively verified using the simulation results performed under different operating conditions. The simulation results of the proposed model are compared with the measured results of three commercial fuel cells, such as Ballard Mark V 5 kW, BCS 500 W and Nedstack PS6 6 kW, to obtain a realistic study. The results of the proposed algorithm are also compared with different optimized models to validate the model and, further, to determine where LSA stands in terms of precision. In this regard, the proposed model can yield a lower SSE by more than 5% in some cases and high performance of the LSA-PEMFC model. With the results obtained, it can be concluded that LSA prevails as a potential optimization algorithm to develop a precise PEM fuel cell model. Full article

(This article belongs to the Special Issue Planning, Operation and Control of Renewable Energy Sources and Energy Storage Devices Assisted Hybrid Power System)

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16 pages, 2067 KiB

Open AccessArticle

A Novel Hybrid Feature Selection Method for Day-Ahead Electricity Price Forecasting

by Ankit Kumar Srivastava, Ajay Shekhar Pandey, Rajvikram Madurai Elavarasan, Umashankar Subramaniam, Saad Mekhilef and Lucian Mihet-Popa

Energies 2021, 14(24), 8455; https://doi.org/10.3390/en14248455 - 15 Dec 2021

Cited by 7 | Viewed by 2383

Abstract

The paper proposes a novel hybrid feature selection (FS) method for day-ahead electricity price forecasting. The work presents a novel hybrid FS algorithm for obtaining optimal feature set to gain optimal forecast accuracy. The performance of the proposed forecaster is compared with forecasters based on classification tree and regression tree. A hybrid FS method based on the elitist genetic algorithm (GA) and a tree-based method is applied for FS. Making use of selected features, aperformance test of the forecaster was carried out to establish the usefulness of the proposed approach. By way of analyzing and forecasts for day-ahead electricity prices in the Australian electricity markets, the proposed approach is evaluated and it has been established that, with the selected feature, the proposed forecaster consistently outperforms the forecaster with a larger feature set. The proposed method is simulated in MATLAB and WEKA software. Full article

(This article belongs to the Special Issue Planning, Operation and Control of Renewable Energy Sources and Energy Storage Devices Assisted Hybrid Power System)

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Review

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31 pages, 13576 KiB

Open AccessFeature PaperEditor’s ChoiceReview

Intelligent SOX Estimation for Automotive Battery Management Systems: State-of-the-Art Deep Learning Approaches, Open Issues, and Future Research Opportunities

by Molla Shahadat Hossain Lipu, Tahia F. Karim, Shaheer Ansari, Md. Sazal Miah, Md. Siddikur Rahman, Sheikh T. Meraj, Rajvikram Madurai Elavarasan and Raghavendra Rajan Vijayaraghavan

Energies 2023, 16(1), 23; https://doi.org/10.3390/en16010023 - 20 Dec 2022

Cited by 15 | Viewed by 6399

Abstract

Real-time battery SOX estimation including the state of charge (SOC), state of energy (SOE), and state of health (SOH) is the crucial evaluation indicator to assess the performance of automotive battery management systems (BMSs). Recently, intelligent models in terms of deep learning (DL) have received massive attention in electric vehicle (EV) BMS applications due to their improved generalization performance and strong computation capability to work under different conditions. However, estimation of accurate and robust SOC, SOH, and SOE in real-time is challenging since they are internal battery parameters and depend on the battery’s materials, chemical reactions, and aging as well as environmental temperature settings. Therefore, the goal of this review is to present a comprehensive explanation of various DL approaches for battery SOX estimation, highlighting features, configurations, datasets, battery chemistries, targets, results, and contributions. Various DL methods are critically discussed, outlining advantages, disadvantages, and research gaps. In addition, various open challenges, issues, and concerns are investigated to identify existing concerns, limitations, and challenges. Finally, future suggestions and guidelines are delivered toward accurate and robust SOX estimation for sustainable operation and management in EV operation. Full article

(This article belongs to the Special Issue Planning, Operation and Control of Renewable Energy Sources and Energy Storage Devices Assisted Hybrid Power System)

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