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New Trends in Power Networks' Transition towards Renewable Energy

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A special issue of Energies (ISSN 1996-1073). This special issue belongs to the section "F: Electrical Engineering".

Deadline for manuscript submissions: closed (26 July 2023) | Viewed by 18560

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Special Issue Editors

Prof. Dr. Farrukh Aslam Khan

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Guest Editor

Center of Excellence in Information Assurance, King Saud University, Riyadh 11653, Saudi Arabia
Interests: cyber security; smart grid; energy consumption forecasting; ad hoc and sensor networks; internet of things; e-health; computational intelligence; evolutionary computation
Special Issues, Collections and Topics in MDPI journals

Dr. Ghulam Hafeez

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Guest Editor

Department of Electrical Engineering, University of Engineering and Technology, Mardan, Pakistan
Interests: computational intelligence; forecast process; electricity demand and time-series forecasting; energy management; demand side management; building management systems; decision making; power engineering computing; power and energy systems; operation of electricity market; electric vehicles in smart power grids; Smart Gird

Special Issue Information

Dear Colleagues,

Fossil fuel problems, environmental concerns, and energy security issues obligate the energy sector around the globe to increase power generation from renewable energy sources. This new trend of generation from renewable energy sources has changed the power network paradigm from a centralized to a distributed generation framework. This transition has laid the foundation of the modern power network, namely the smart power network, which is an evolution of the traditional power network with two-way interaction between energy, control, and communication infrastructure between consumers and electric utility. Smart power networks enable the transition towards renewable energy, energy storage devices, and active load management to enhance sustainable development, minimize carbon emissions, and improve power system reliability. This transition creates new challenges and opportunities. To cope with the new challenges and avail these opportunities, advanced automation, control, and ICT concepts, as well as new architectures, algorithms, and procedures are needed. In smart power networks, stability, reliability, and security should be addressed and analyzed. Additionally, protection schemes are essential to handle contingencies and unexpected operation problems in the power network and maintain the operation in a stable and secure manner.

This Special Issue aims to address the ongoing transition towards renewable energy and new trends and innovations in power and energy networks. We invite the submission of original research articles and comprehensive reviews as well as surveys related to new trends in power network transition towards renewable energy.

Prof. Dr. Farrukh Aslam Khan
Dr. Ghulam Hafeez
Guest Editors

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Keywords

Design, modelling, planning, operation, and management of renewable energy sources
Modeling, forecasting, and management of uncertainty in renewable energy sources
Electricity market, electrical power, and energy systems
Modern power networks and renewable energy resources
Technologies for optimal power network operation with a high penetration of renewable energy sources
Renewable-energy-integrated smart grids and microgrids
Power network flexibility, reliability, sustainability, and resiliency
Power network dynamics, protection, stability, and security
Methodologies and applications of intelligent methods for the operation and control of power networks
Demand-side management and demand response
Application of artificial intelligence, IoT, and big data analytics in power networks

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Published Papers (7 papers)

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Research

16 pages, 659 KiB

Open AccessArticle

Ensemble-Learning-Based Decision Support System for Energy-Theft Detection in Smart-Grid Environment

by Farah Mohammad, Kashif Saleem and Jalal Al-Muhtadi

Energies 2023, 16(4), 1907; https://doi.org/10.3390/en16041907 - 14 Feb 2023

Cited by 5 | Viewed by 2382

Abstract

Theft of electricity poses a significant risk to the public and is the most costly non-technical loss for an electrical supplier. In addition to affecting the quality of the energy supply and the strain on the power grid, fraudulent electricity use drives up prices for honest customers and creates a ripple effect on the economy. Using data-analysis tools, smart grids may drastically reduce this waste. Smart-grid technology produces much information, including consumers’ unique electricity-use patterns. By analyzing this information, machine-learning and deep-learning methods may successfully pinpoint those who engage in energy theft. This study presents an ensemble-learning-based system for detecting energy theft using a hybrid approach. The proposed approach uses a machine-learning-based ensemble model based on a majority voting strategy. This work aims to develop a smart-grid information-security decision support system. This study employed a theft-detection dataset to facilitate automatic theft recognition in a smart-grid environment (TDD2022). The dataset consists of six separate electricity thefts. The experiments are performed in four different scenarios. The proposed machine-learning-based ensemble model obtained significant results in all scenarios. The proposed ensemble model obtained the highest accuracy of 88%, 87.24%, 94.75%, and 94.70% with seven classes including the consumer type, seven classes excluding the consumer type, six classes including the consumer type, and six classes excluding the consumer type. The suggested ensemble model outperforms the existing techniques in terms of accuracy when the proposed methodology is compared to state-of-the-art approaches. Full article

(This article belongs to the Special Issue New Trends in Power Networks' Transition towards Renewable Energy)

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31 pages, 2139 KiB

Open AccessArticle

Optimal Energy Consumption Scheduler Considering Real-Time Pricing Scheme for Energy Optimization in Smart Microgrid

by Fahad R. Albogamy

Energies 2022, 15(21), 8015; https://doi.org/10.3390/en15218015 - 28 Oct 2022

Cited by 4 | Viewed by 2399

Abstract

Energy consumption schedulers have been widely adopted for energy management in smart microgrids. Energy management aims to alleviate energy expenses and peak-to-average ratio (PAR) without compromising user comfort. This work proposes an energy consumption scheduler using heuristic optimization algorithms: Binary Particle Swarm Optimization (BPSO), Wind Driven Optimization (WDO), Genetic Algorithm (GA), Differential Evolution (DE), and Enhanced DE (EDE). The energy consumption scheduler based on these algorithms under a price-based demand response program creates a schedule of home appliances. Based on the energy consumption behavior, appliances within the home are classified as interruptible, noninterruptible, and hybrid loads, considered as scenario-I, scenario-II, and scenario-III, respectively. The developed model based on optimization algorithms is the more appropriate solution to achieve the desired objectives. Simulation results show that the expense and PAR of schedule power usage in each scenario are less compared to the without-scheduling case. Full article

(This article belongs to the Special Issue New Trends in Power Networks' Transition towards Renewable Energy)

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14 pages, 413 KiB

Open AccessArticle

Demand Side Management Strategy for Multi-Objective Day-Ahead Scheduling Considering Wind Energy in Smart Grid

by Kalim Ullah, Taimoor Ahmad Khan, Ghulam Hafeez, Imran Khan, Sadia Murawwat, Basem Alamri, Faheem Ali, Sajjad Ali and Sheraz Khan

Energies 2022, 15(19), 6900; https://doi.org/10.3390/en15196900 - 21 Sep 2022

Cited by 24 | Viewed by 2063

Abstract

Distributed energy resources (DERs) and demand side management (DSM) strategy implementation in smart grids (SGs) lead to environmental and economic benefits. In this paper, a new DSM strategy is proposed for the day-ahead scheduling problem in SGs with a high penetration of wind energy to optimize the tri-objective problem in SGs: operating cost and pollution emission minimization, the minimization of the cost associated with load curtailment, and the minimization of the deviation between wind turbine (WT) output power and demand. Due to climatic conditions, the nature of the wind energy source is uncertain, and its prediction for day-ahead scheduling is challenging. Monte Carlo simulation (MCS) was used to predict wind energy before integrating with the SG. The DSM strategy used in this study consists of real-time pricing and incentives, which is a hybrid demand response program (H-DRP). To solve the proposed tri-objective SG scheduling problem, an optimization technique, the multi-objective genetic algorithm (MOGA), is proposed, which results in non-dominated solutions in the feasible search area. Besides, the decision-making mechanism (DMM) was applied to find the optimal solution amongst the non-dominated solutions in the feasible search area. The proposed scheduling model successfully optimizes the objective functions. For the simulation, MATLAB 2021a was used. For the validation of this model, it was tested on the SG using multiple balancing constraints for power balance at the consumer end. Full article

(This article belongs to the Special Issue New Trends in Power Networks' Transition towards Renewable Energy)

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17 pages, 3523 KiB

Open AccessArticle

Certainty-Equivalence-Based Sensorless Robust Sliding Mode Control for Maximum Power Extraction of an Uncertain Photovoltaic System

by Zaheer Alam, Qudrat Khan, Laiq Khan, Safeer Ullah, Syed Abdul Mannan Kirmani and Abdullah A. Algethami

Energies 2022, 15(6), 2029; https://doi.org/10.3390/en15062029 - 10 Mar 2022

Cited by 5 | Viewed by 1970

Abstract

Photovoltaic (PV) arrays and their electronic converters are subject to various environmental disturbances and component-related faults that affect their normal operations and result in a considerable energy loss. Therefore, it is ever demanding to design such closed-loop operating algorithms that tolerate faults, present acceptable performance, and avoid wear and tear in the systems. In this work, the core objective is to extract maximum power from a PV array subject to environmental disturbances and plant uncertainties. The system is considered under input channel uncertainties (i.e., faults) along with variable resistive load and charging stations. A neuro-fuzzy network (NFN)-based reference voltage is generated to extract maximum power while considering variable temperature and irradiance as inputs. Furthermore, the estimated reference is tracked by the actual PV voltage under two types of controllers: certainty-equivalence-based robust sliding mode (CERSMC) and certainty-equivalence-based robust integral sliding mode (CERISMC). These strategies benefit from improving the robustness against faults (disturbances). The proposed methods use the inductor current, which is recovered via the velocity observer and the flatness property of nonlinear systems. The system’s stability is proven in the form of very appealing theorems. These claims are validated by the simulation results, which are carried out in a MATLAB environment. Full article

(This article belongs to the Special Issue New Trends in Power Networks' Transition towards Renewable Energy)

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24 pages, 6385 KiB

Open AccessArticle

Automatic Generation Control in Modern Power Systems with Wind Power and Electric Vehicles

by Kaleem Ullah, Abdul Basit, Zahid Ullah, Fahad R. Albogamy and Ghulam Hafeez

Energies 2022, 15(5), 1771; https://doi.org/10.3390/en15051771 - 27 Feb 2022

Cited by 18 | Viewed by 3806

Abstract

The modern power system is characterized by the massive integration of renewables, especially wind power. The intermittent nature of wind poses serious concerns for the system operator owing to the inaccuracies in wind power forecasting. Forecasting errors require more balancing power for maintaining frequency within the nominal range. These services are now offered through conventional power plants that not only increase the operational cost but also adversely affect the environment. The modern power system emphasizes the massive penetration of wind power that will replace conventional power plants and thereby impact the provision of system services from conventional power plants. Therefore, there is an emergent need to find new control and balancing solutions, such as regulation reserves from wind power plants and electric vehicles, without trading off their natural behaviors. This work proposes real-time optimized dispatch strategies for automatic generation control (AGC) to utilize wind power and the storage capacity of electric vehicles for the active power balancing services of the grid. The proposed dispatch strategies enable the AGC to appropriately allocate the regulating reserves from wind power plants and electric vehicles, considering their operational constraints. Simulations are performed in DIgSILENT software by developing a power system AGC model integrating the generating units and an EVA model. The inputs for generating units are considered by selecting a particular day of the year 2020, when wind power plants are generating high power. Different coordinated dispatch strategies are proposed for the AGC model to incorporate the reserve power from wind power plants and EVs. The performance of the proposed dispatch strategies is accessed and discussed by obtaining responses of the generating units and EVs during the AGC operation to counter the initial power imbalances in the network. The results reveal that integration of wind power and electric vehicles alongside thermal power plants can effectively reduce real-time power imbalances acquainted in power systems due to massive penetration of wind power that subsequently improves the power system security. Moreover, the proposed dispatch strategy reduces the operational cost of the system by allowing the conventional power plant to operate at their lower limits and therefore utilizes minimum reserves for the active power balancing services. Full article

(This article belongs to the Special Issue New Trends in Power Networks' Transition towards Renewable Energy)

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29 pages, 6126 KiB

Open AccessArticle

Analysis of Linear Hybrid Excited Flux Switching Machines with Low-Cost Ferrite Magnets

by Muhammad Qasim, Faisal Khan, Basharat Ullah, Himayat Ullah Jan and Hend I. Alkhammash

Energies 2022, 15(4), 1346; https://doi.org/10.3390/en15041346 - 13 Feb 2022

Cited by 1 | Viewed by 1703

Abstract

Linear hybrid excited flux switching machines (LHEFSM) combine the features of permanent magnet flux switching machines (PMFSM) and field excited flux switching machines (FEFSM). Because of the widespread usage of rare-earth PM materials, their costs are steadily rising. This study proposes an LHEFSM, a dual stator LHEFSM (DSLHEFSM), and a dual mover LHEFSM (DMLHEFSM) to solve this issue. The employment of ferrite magnets rather than rare-earth PM in these suggested designs is significant. Compared to traditional designs, the proposed designs feature greater thrust force, power density, reduced normal force, and a 25% decrease in PM volume. A yokeless primary structure was used in a DSLHEFSM to minimize the volume of the mover, increasing the thrust force density. In DMLHELFSM, on the other hand, a yokeless secondary structure was used to lower the secondary volume and the machine’s total cost. Single variable optimization was used to optimize all of the proposed designs. By completing a 3D study, the electromagnetic performances acquired from the 2D analysis were confirmed. Compared to conventional designs, the average thrust force in LHEFSM, DSLHEFSM, and DMLHEFSM was enhanced by 15%, 16.8%, and 15.6%, respectively. Overall, the presented machines had a high thrust force density, a high-power density, a high no-load electromotive force, and a low normal force, allowing them to be used in long-stroke applications. Full article

(This article belongs to the Special Issue New Trends in Power Networks' Transition towards Renewable Energy)

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17 pages, 918 KiB

Open AccessArticle

Towards Big Data Electricity Theft Detection Based on Improved RUSBoost Classifiers in Smart Grid

by Rehan Akram, Nasir Ayub, Imran Khan, Fahad R. Albogamy, Gul Rukh, Sheraz Khan, Muhammad Shiraz and Kashif Rizwan

Energies 2021, 14(23), 8029; https://doi.org/10.3390/en14238029 - 1 Dec 2021

Cited by 9 | Viewed by 2386 | Correction

Abstract

The advent of the new millennium, with the promises of the digital age and space technology, favors humankind in every perspective. The technology provides us with electric power and has infinite use in multiple electronic accessories. The electric power produced by different sources is distributed to consumers by the transmission line and grid stations. During the electric transmission from primary sources, there are various methods by which to commit energy theft. Energy theft is a universal electric problem in many countries, with a possible loss of billions of dollars for electric companies. This energy contention is deep rooted, having so many root causes and rugged solutions of a technical nature. Advanced Metering Infrastructure (AMI) is introduced with no adequate results to control and minimize electric theft. Until now, so many techniques have been applied to overcome this grave problem of electric power theft. Many researchers nowadays use machine learning algorithms, trying to combat this problem, giving better results than previous approaches. Random Forest (RF) classifier gave overwhelmingly good results with high accuracy. In our proposed solution, we use a novel Convolution Neural Network (CNN) with RUSBoost Manta Ray Foraging Optimization (rus-MRFO) and RUSBoost Bird Swarm Algorithm (rus-BSA) models, which proves to be very innovative. The accuracy of our proposed approaches, rus-MRFO and rus-BSA, are 91.5% and a 93.5%, respectively. The proposed techniques have shown promising results and have strong potential to be applied in future. Full article

(This article belongs to the Special Issue New Trends in Power Networks' Transition towards Renewable Energy)

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