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Control, Management and Optimization of Renewable Energy and Storage System

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A special issue of Sustainability (ISSN 2071-1050). This special issue belongs to the section "Energy Sustainability".

Deadline for manuscript submissions: closed (31 October 2023) | Viewed by 12369

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Special Issue Editor

Dr. Hegazy Rezk

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Guest Editor

Electrical Engineering, College of Engineering - Wadi Aldwaser, Prince Sattam bin Abdulaziz University, Wadi Aldwaser, Saudi Arabia
Interests: renewable energy; energy storage devices; energy management; advanced control; optimization
Special Issues, Collections and Topics in MDPI journals

Special Issue Information

Dear Colleagues,

A new vision for the next generation of electrical power systems is the smart grid. This new grid type is not only smart in improving the current electricity infrastructure but also has a long list of benefits. These benefits include improvements in the reliability and sustainability of power systems, smooth integration between renewable and alternative energy resources, gas emission control, and the ability to confront the global temperature increase. Renewable energy systems (RES), including wind energy and photovoltic systems, have become an inevitable part of microgrids. However, unlike conventional sources of power generation, the output of RES is intermittent. To mitigate this issue, intergation with energy storage devices is mandatory, and therefore, advanced control and energy management are required for handling the changing dynamics, nonlinearities, and uncertainties of the system. Efficient control and optimization strategies would improve performance and decrease the cost of energy.

We cordially invite you to submit your original contributions to this Special Issue, entitled “Control, Management, and Optimization of Renewable Energy and Storage System”. This is a Special Issue of Sustainability, an international peer-reviewed open access journal covered by various databases such as WOS and SCOPUS. The present Special Issue aims to collect innovative solutions and experimental research supported by appropriate modeling and design as well as state-of-the-art studies, in the following topics:

Distributed renewable energy systems;
Advanced controllers for renewable energy systems;
Maximum power point tracking;
Recent energy management strategies;
Energy storage devices;
Innovative technologies for energy storage;
Hydrogen and fuel cell.

Dr. Hegazy Rezk
Guest Editor

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. Sustainability 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 2400 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
energy storage devices
energy management
advanced control
optimization

Published Papers (9 papers)

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Research

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22 pages, 14913 KiB

Open AccessArticle

Experimentation of Multi-Input Single-Output Z-Source Isolated DC–DC Converter-Fed Grid-Connected Inverter with Sliding Mode Controller

by Kanagaraj N., Ramasamy M., Vijayakumar M. and Obaid Aldosari

Sustainability 2023, 15(24), 16875; https://doi.org/10.3390/su152416875 - 15 Dec 2023

Viewed by 657

Abstract

Converting devices are quickly becoming the most important part of renewable energy-producing systems that are linked to the grid. Applications that are linked to the grid are the most common place to find usage for two-port power converters that are built using single-input and single-output (SISO) ports. The incorporation of SISO power converters into the grid-connected hybrid system results in an increase in both its size and its cost. Multiple power sources may be connected to a single DC bus by means of hybrid power systems, which make use of multi-input power converters. To combine the hybrid wind and PV system with a common DC bus, this study suggests an isolated multi-input single-output (IMISO) Z-Source converter. It has been determined that the suggested system performs well in spite of dynamic load fluctuations and shifting input voltage circumstances. The sliding mode controller (SMC) has also been used to control a single-phase five-level (SPFL) inverter. The purpose of developing the laboratory prototype model was to verify the proposed IMISO Z-source converter-fed single-phase five-level (SPFL) inverter in the context of the circumstance that is being investigated. Full article

(This article belongs to the Special Issue Control, Management and Optimization of Renewable Energy and Storage System)

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17 pages, 3850 KiB

Open AccessArticle

An Intelligent Controller Based on Extension Theory for Batteries Charging and Discharging Control

by Kuei-Hsiang Chao and Jia-Yan Li

Sustainability 2023, 15(21), 15664; https://doi.org/10.3390/su152115664 - 6 Nov 2023

Viewed by 632

Abstract

The main purpose of this paper is to develop an intelligent controller for the DC-link voltage of bidirectional soft-switching converters used in the batteries with equalizing charge and discharge control. To accelerate the equalizing charge and discharge speed of batteries, the DC-link voltage controller of the bidirectional converters is designed based on extension theory. Firstly, the photovoltaic module arrays (PVMAs) are used with the intelligent maximum power point tracker (MPPT) for supplying the power to the load side. Through the bidirectional soft-switching converters, the PVMAs will be allowed to carry out the uniform charging and discharging for the storage battery in order to achieve the intended energy storage and auxiliary power supply functions. In terms of the controller design, the quantitative design techniques are utilized, by which the P-I controller parameters will be designed for the converter when attempting to achieve the same control performance at different working points. As a next step, the aforesaid parameters are used together with the extenics theory. Based on the variation in the output power of the bidirectional converter and that in the voltage of the storage battery, it allows the system to find out the intended P-I controller parameters that will be approximate to the prescribed control performance when operating under different working conditions. As a result, the P-I controller will be provided with more efficient control flexibility and control performances. Finally, actual test results demonstrated that the response time of the proposed intelligent extension controller is shortened by 3% compared to the quantitative design of the proportional–integral (P-I) controller. Based on the proposed quantitative design of an intelligent controller for uniform charging and discharging management of batteries, the sustainable utilization of renewable sources of energy can be improved. At the same time, the better economic benefit of the energy preservation system is obtained. In addition, it also prolongs the life cycle of batteries, and then enhances the reliability of the batteries. Full article

(This article belongs to the Special Issue Control, Management and Optimization of Renewable Energy and Storage System)

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15 pages, 2947 KiB

Open AccessArticle

Increasing Output Power of a Microfluidic Fuel Cell Using Fuzzy Modeling and Jellyfish Search Optimization

by Hesham Alhumade, Iqbal Ahmed Moujdin and Saad Al-Shahrani

Sustainability 2023, 15(14), 11279; https://doi.org/10.3390/su151411279 - 20 Jul 2023

Viewed by 739

Abstract

An efficient electrochemical energy conversion system with little to no environmental impact is the fuel cell (FC). FCs have demonstrated encouraging results in various applications and can even run on biofuel, such as bio-glycerol, a by-product of biodiesel. The most effective ways to operate FCs can significantly enhance their effectiveness. Incorporating fuzzy modeling and metaheuristic methods, this work used artificial intelligence to determine the ideal operating parameters for a microfluidic fuel cell (MFC). The concentrations of the following four variables were considered: bio-glycerol concentration, anode electrocatalyst loading, anode electrolyte concentration, and cathode electrolyte concentration. The output power density of the MFC was used to assess its performance. The output power density of the MFC was modeled using fuzzy logic, taking into account the aforementioned operational parameters. A jellyfish search optimizer (JSO) was then used to find the ideal operating conditions. The results were contrasted with response surface methodology (RSM) and experimental datasets to demonstrate the superiority of the proposed integration between fuzzy modeling and the JSO. In comparison with the measured and RSM approaches, the suggested strategy boosted the power density of the MFC by 9.38% and 8.6%, respectively. Full article

(This article belongs to the Special Issue Control, Management and Optimization of Renewable Energy and Storage System)

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17 pages, 4384 KiB

Open AccessArticle

Wavelet Packet-Fuzzy Optimization Control Strategy of Hybrid Energy Storage Considering Charge–Discharge Time Sequence

by Xinyu Zhao, Yunxiao Zhang, Xueying Cui, Le Wan, Jinlong Qiu, Erfa Shang, Yongchang Zhang and Haisen Zhao

Sustainability 2023, 15(13), 10412; https://doi.org/10.3390/su151310412 - 1 Jul 2023

Cited by 1 | Viewed by 947

Abstract

A hybrid energy storage system (HESS) can effectively suppress the high and low-frequency power fluctuations generated by wind farms under the intermittency and randomness of wind. However, for the existing power distribution strategies of HESS, power-type and energy-type energy storage have the problem of inconsistent charge–discharge states in the same time sequence, which makes it difficult to achieve optimal operation in terms of charge–discharge coordination and energy flow. To solve this problem, this study firstly adopts adaptive wavelet packet decomposition (WPD) to decompose the original wind power to acquire grid-connected power and HESS initial distribution power, to ensure that the supercapacitor and battery undertake the corresponding high and low-frequency power fluctuations, respectively; Then, for the inconsistent charge–discharge states, a charge–discharge time sequence optimization strategy based on the consistency index is proposed to correct the initial power distribution of HESS for the first time; Finally, aiming at the stage of charge (SOC) over-limit problem, the fuzzy optimization method is adopted to correct the HESS output power for the second time, which can reduce the unnecessary charge–discharge energy effectively. With typical daily output data of a 100 MW wind farm, the proposed control strategy is verified. The results show that it can make different energy storage technologies synchronously suppress wind power fluctuation in the same time sequence; compared with not considering charge–discharge time sequence optimization, the charge–discharge conversion times of the battery obtained by the proposed method are reduced from 71 to 14 times, and the charge–discharge conversion times of supercapacitor are reduced from 390 to 61 times; The cumulative reduction of unnecessary charge–discharge energy by HESS is 12.12 MWh. Besides, the SOC curves of HESS are controlled at a normal level, thus improving the economy and service life of HESS. Full article

(This article belongs to the Special Issue Control, Management and Optimization of Renewable Energy and Storage System)

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27 pages, 7330 KiB

Open AccessArticle

Accurate and Efficient Energy Management System of Fuel Cell/Battery/Supercapacitor/AC and DC Generators Hybrid Electric Vehicles

by Aissa Benhammou, Hamza Tedjini, Mohammed Amine Hartani, Rania M. Ghoniem and Ali Alahmer

Sustainability 2023, 15(13), 10102; https://doi.org/10.3390/su151310102 - 26 Jun 2023

Cited by 8 | Viewed by 1987

Abstract

The development of hybrid electric vehicles (HEVs) is rapidly gaining traction as a viable solution for reducing carbon emissions and improving fuel efficiency. One type of HEV that is gaining significant interest is the fuel cell/battery/supercapacitor HEV (FC/Bat/SC HEV), which combines fuel cell, battery, supercapacitor, AC, and DC generators. These FC/B/SC HEVs are particularly appealing because they excel at efficiently managing energy and cater to a wide range of driving requirements. This study presents a novel approach for exploiting the kinetic energy of a sensorless HEV. The vehicle has a primary fuel cell resource, a supercapacitor, and lithium-ion battery energy storage banks, where each source is connected to a special converter. The obtained hybrid system allows the vehicle to enhance autonomy, support the fuel cell during low production moments, and improve transient and steady-state load requirements. The exploitation of kinetic energy is performed by the DC and AC generators that are linked to the electric vehicle front wheels to transfer the HEV’s wheel rotation into power, contributing to the overall power balance of the vehicle. The energy management system for electric vehicles determines the FC setpoint power through the classical state machine method. At the same time, a robust speed controller-based artificial intelligence algorithm reduces power losses and enhances the supply efficiency for the vehicle. Furthermore, we evaluate the performance of a robust controller with a speed estimator, specifically using the adaptive neuro-fuzzy inference system (ANFIS) and the model reference adaptive system (MRAS) estimator in conjunction with the direct torque control-support vector machine (DTC-SVM), to enhance the torque and speed performance of HEVs. The results demonstrate the feasibility and reliability of the vehicle while utilizing the additional DC and AC generators to extract free kinetic energy, both of which contributed to 28% and 24% of the total power for the vehicle, respectively. This approach leads to a vehicle supply efficiency exceeding 96%, reducing the burden on fuel cells and batteries and resulting in a significant reduction in fuel consumption, which is estimated to range from 25% to 35%. Full article

(This article belongs to the Special Issue Control, Management and Optimization of Renewable Energy and Storage System)

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23 pages, 8820 KiB

Open AccessArticle

Two-Stage Robust Optimization for Prosumers Considering Uncertainties from Sustainable Energy of Wind Power Generation and Load Demand Based on Nested C&CG Algorithm

by Qiang Zhou, Jianmei Zhang, Pengfei Gao, Ruixiao Zhang, Lijuan Liu, Sheng Wang, Lin Cheng, Wei Wang and Shiyou Yang

Sustainability 2023, 15(12), 9769; https://doi.org/10.3390/su15129769 - 19 Jun 2023

Cited by 3 | Viewed by 977

Abstract

This paper develops a two-stage robust optimization (TSRO) model for prosumers considering multiple uncertainties from the sustainable energy of wind power generation and load demand and extends the existing nested column-and-constraint generation (C&CG) algorithm to solve the corresponding optimization problem. First, considering the impact of these uncertainties on market trading strategies of prosumers, a box uncertainty set is introduced to characterize the multiple uncertainties; a TSRO model for prosumers considering multiple uncertainties is then constructed. Second, the existing nested C&CG algorithm is extended to solve the corresponding optimization problem of which the second-stage optimization is a bi-level one and the inner level is a non-convex optimization problem containing 0–1 decision variables. Finally, a case study is solved. The optimized final overall operating cost of prosumers under the proposed model is CNY 3201.03; the extended algorithm requires only four iterations to converge to the final solution. If a convergence accuracy of 10⁻⁶ is used, the final solution time of the extended algorithm is only 9.75 s. The case study result shows that prosumers dispatch the ESS to store surplus wind power generated during the nighttime period and release the stored electricity when the wind power generation is insufficient during the daytime period. It can contribute to promoting the local accommodation of renewable energy and improving the efficiency of renewable energy utilization. The market trading strategy and scheduling results of the energy storage system (ESS) are affected by multiple uncertainties. Moreover, the extended nested C&CG algorithm has a high convergence accuracy and a fast convergence speed. Full article

(This article belongs to the Special Issue Control, Management and Optimization of Renewable Energy and Storage System)

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16 pages, 3595 KiB

Open AccessArticle

Optimized Artificial Intelligent Model to Boost the Efficiency of Saline Wastewater Treatment Based on Hunger Games Search Algorithm and ANFIS

by Hegazy Rezk, Abdul Ghani Olabi, Enas Taha Sayed, Samah Ibrahim Alshathri and Mohammad Ali Abdelkareem

Sustainability 2023, 15(5), 4413; https://doi.org/10.3390/su15054413 - 1 Mar 2023

Cited by 1 | Viewed by 1190

Abstract

Chemical oxygen demand (COD) and total organic carbon (TOC) removal efficiencies of saline wastewater treatment indicate the efficiency of the electrochemical oxidation process. Therefore, the main target of this paper is to simultaneously increase COD and TOC removal efficiencies using artificial intelligence and modern optimization. Firstly, an accurate model based on ANFIS was established to simulate the electrochemical oxidation process in terms of reaction time, pH, salt concentration, and DC applied voltage. Compared with ANOVA, thanks to ANFIS modelling, the RMSE values are decreased by 84% and 86%, respectively, for COD and TOC models. Additionally, the coefficient of determination values increased by 3.26% and 7.87% for COD and TOC models, respectively. Secondly, the optimal reaction time values, pH, salt concentration, and applied voltage were determined using the hunger games search algorithm (HGSA). To prove the effectiveness of the HGSA, a comparison with a slime mold algorithm, sine cosine algorithm, and Harris’s hawks optimization was conducted. The optimal values were found at a pH of 8, a reaction time of 36.6 min, a salt concentration of 29.7 g/L, and a DC applied voltage of 9 V. Under this condition, the maximum COD and TOC removal values were 97.6% and 69.4%, respectively. The overall efficiency increased from 76.75% to 83.5% (increased by 6.75%). Full article

(This article belongs to the Special Issue Control, Management and Optimization of Renewable Energy and Storage System)

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19 pages, 6616 KiB

Open AccessArticle

Optimal Energy Management for Hydrogen Economy in a Hybrid Electric Vehicle

by Seydali Ferahtia, Hegazy Rezk, Rania M. Ghoniem, Ahmed Fathy, Reem Alkanhel and Mohamed M. Ghonem

Sustainability 2023, 15(4), 3267; https://doi.org/10.3390/su15043267 - 10 Feb 2023

Cited by 5 | Viewed by 1900

Abstract

Fuel cell hybrid electric vehicles (FCEVs) are mainly electrified by the fuel cell (FC) system. As a supplementary power source, a battery or supercapacitor (SC) is employed (besides the FC) to enhance the power response due to the slow dynamics of the FC. Indeed, the performance of the hybrid power system mainly depends on the required power distribution manner among the sources, which is managed by the energy management strategy (EMS). This paper considers an FCEV based on the proton exchange membrane FC (PEMFC)/battery/SC. The energy management strategy is designed to ensure optimum power distribution between the sources considering hydrogen consumption. Its main objective is to meet the electric motor’s required power with economic hydrogen consumption and better electrical efficiency. The proposed EMS combines the external energy maximization strategy (EEMS) and the bald eagle search algorithm (BES). Simulation tests for the Extra-Urban Driving Cycle (EUDC) and New European Driving Cycle (NEDC) profiles were performed. The test is supposed to be performed in typical conditions t = 25 °C on a flat road without no wind effect. In addition, this strategy was compared with the state machine control strategy, classic PI, and equivalent consumption minimization strategy. In terms of optimization, the proposed approach was compared with the original EEMS, particle swarm optimization (PSO)-based EEMS, and equilibrium optimizer (EO)-based EEMS. The results confirm the ability of the proposed strategy to reduce fuel consumption and enhance system efficiency. This strategy provides 26.36% for NEDC and 11.35% for EUDC fuel-saving and efficiency enhancement by 6.74% for NEDC and 36.19% for EUDC. Full article

(This article belongs to the Special Issue Control, Management and Optimization of Renewable Energy and Storage System)

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Review

Jump to: Research

27 pages, 4802 KiB

Open AccessReview

Role of Metaheuristics in Optimizing Microgrids Operating and Management Issues: A Comprehensive Review

by Hegazy Rezk, A. G. Olabi, Enas Taha Sayed and Tabbi Wilberforce

Sustainability 2023, 15(6), 4982; https://doi.org/10.3390/su15064982 - 10 Mar 2023

Cited by 8 | Viewed by 1671

Abstract

The increased interest in renewable-based microgrids imposes several challenges, such as source integration, power quality, and operating cost. Dealing with these problems requires solving nonlinear optimization problems that include multiple linear or nonlinear constraints and continuous variables or discrete ones that require large dimensionality search space to find the optimal or sub-optimal solution. These problems may include the optimal power flow in the microgrid, the best possible configurations, and the accuracy of the models within the microgrid. Metaheuristic optimization algorithms are getting more suggested in the literature contributions for microgrid applications to solve these optimization problems. This paper intends to thoroughly review some significant issues surrounding microgrid operation and solve them using metaheuristic optimization algorithms. This study provides a collection of fundamental principles and concepts that describe metaheuristic optimization algorithms. Then, the most significant metaheuristic optimization algorithms that have been published in the last years in the context of microgrid applications are investigated and analyzed. Finally, the employment of metaheuristic optimization algorithms to specific microgrid issue applications is reviewed, including examples of some used algorithms. These issues include unit commitment, economic dispatch, optimal power flow, distribution system reconfiguration, transmission network expansion and distribution system planning, load and generation forecasting, maintenance schedules, and renewable sources max power tracking. Full article

(This article belongs to the Special Issue Control, Management and Optimization of Renewable Energy and Storage System)

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