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Smart Grid Analytics for Sustainability and Urbanization in Big Data

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A special issue of Sustainability (ISSN 2071-1050). This special issue belongs to the section "Sustainable Urban and Rural Development".

Deadline for manuscript submissions: closed (28 February 2023) | Viewed by 35636

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Special Issue Editors

Dr. Sheraz Aslam

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Guest Editor

Department of Electrical Engineering, Computer Engineering, and Informatics, Cyprus University of Technology, Limassol 3036, Cyprus
Interests: smart grids; data analytics; sustainable energy generations; intelligent transportation systems; sea transportation
Special Issues, Collections and Topics in MDPI journals

Dr. Herodotos Herodotou

E-Mail Website
Guest Editor

Department of Electrical Engineering, Computer Engineering, and Informatics, Cyprus University of Technology, Limassol 3036, Cyprus
Interests: smart grids; data analytics; data-intensive computing; data processing systems
Special Issues, Collections and Topics in MDPI journals

Dr. Nouman Ashraf

E-Mail Website
Guest Editor

Telecommunications Software and Systems Group (TSSG), Waterford Institute of Technology, Waterford X91 K0EK, Ireland
Interests: smart grid; data analytics; internet of agriculture

Special Issue Information

Dear Colleagues,

IoT devices are found in various parts of the smart grid, such as smart appliances, smart meters, and substations. These IoT devices generate petabytes of data, which are known to be one of the most scalable properties of a smart grid. Without smart grid analytics, it is difficult to make efficient use of data and to make sustainable decisions related to smart grid operations. With the energy system of the developing world heading towards smart grids, there needs to be a forum for analytics that can collect and interpret data from multiple endpoints. Data analytics platforms can analyze data to produce invaluable results that lead to many advantages, such as operational efficiency and cost savings. However, the state-of-the-art approaches developed to achieve the above-mentioned advantages, sustainable operations of the smart grid, and the urbanization of big data are still immature. Most of these approaches have a high computational cost, as they employ conventional tools for data analytics. To overcome this challenge, novel and elegant approaches are required to cope with the big data produced from smart devices in the smart grid environment. In this context, this Special Issue aims to publish novel research work and visionary reviews on advanced smart grid analytics technologies, algorithms, case studies, and their associated applications in the smart grid, power grid modernization, and smart energy trading systems.

Topics of interest include, but are not limited to, the following:

Smart grid analytics for urbanization in big data
Smart grid analytics for energy system planning
Smart grid analytics for sustainable operations and control
Smart grid analytics for emerging applications such as Vehicle to Grid (V2G)
Smart grid analytics for renewable energy power prediction and integration
Smart grid analytics for demand side management, load forecasting, and customer behavior analytics
Smart grid analytics for electricity theft detection
Smart grid analytics for power system resilience
Real-time data visualization in sustainable smart grids
Data collection, fusion, and management for smart metering infrastructure
Data processing, storage, and information management for sustainable smart grid operations
Data privacy and security for smart grid data and relevant applications
Data-driven-based modeling and solutions for smart grid and smart energy systems
Data-driven-based pricing and incentive schemes and protocols
Data analysis for integration of electric vehicles, their charging stations, and Intelligent Transportation Systems with power grids
Cloud computing, edge mining, and blockchain for sustainable smart grid and smart cities

This Special Issue solicits original work on “Big Data Analytics for Smart Grid and Smart Energy Systems”. All submitted articles must be written in excellent English, and must contain only original works that have not been published and are not currently being reviewed by any other journals or conferences.

Dr. Sheraz Aslam
Assist. Pr Herodotos Herodotou
Dr. Nouman Ashraf
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. 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

smart grids
smart grid analytics
smart cities
sustainable grid operation
urbanization of big data

Published Papers (14 papers)

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Research

26 pages, 4673 KiB

Open AccessArticle

Smart ‘Tourist Cities’ Revisited: Culture-Led Urban Sustainability and the Global Real Estate Market

by Ioannis Vardopoulos, Maria Papoui-Evangelou, Bogdana Nosova and Luca Salvati

Sustainability 2023, 15(5), 4313; https://0-doi-org.brum.beds.ac.uk/10.3390/su15054313 - 28 Feb 2023

Cited by 8 | Viewed by 3150

Abstract

Smart tourism destinations have received increasing attention during the last few years. Digital technologies have reshaped the smart city paradigm in terms of both resilience and sustainability, capitalizing cities’ cultural and historical components while providing unique potential for growth in the real estate industry. Real estate, in particular, is considered a main asset to the tourist experience, whether it is in the form of hospitality accommodation facilities, urban landscapes, or cultural heritage hotspots. In addition, the effect of cultural sites and overall destination attractiveness on real estate dynamics (land/housing prices and building activity) is well established. Thus, uncovering how enhanced technological throughputs and synergies, culture-led urban sustainability initiatives and the real estate dimension are directly (or indirectly) associated could support cities to better delineate policies for their promotion as international, sustainable, and resilient tourist destinations. With this perspective, the present study focused on four particular cities’ successful smart initiatives, namely Amsterdam, Barcelona, Seoul, and Stockholm, in an attempt to identify how developers and local authorities will need to transform in order to offer better services to residents and visitors. This work reveals that smart projects alone cannot secure the transition of existing (European) cities into smart and sustainable tourism destinations. In addition, this study also contributes to public policy by demonstrating how challenging it is to be smart without the support and involvement of the local community, highlighting the significance of public awareness. The empirical findings suggest that local authorities are of critical importance when shaping a well-structured and practically effective strategy for the integration of sustainable and technologically advanced smart features. Results are promising, and final reflections provide insights for tourism destinations policymakers, city authorities, and real estate professionals. Full article

(This article belongs to the Special Issue Smart Grid Analytics for Sustainability and Urbanization in Big Data)

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13 pages, 3282 KiB

Open AccessArticle

Optimal DG Location and Sizing to Minimize Losses and Improve Voltage Profile Using Garra Rufa Optimization

by Riyadh Kamil Chillab, Aqeel S. Jaber, Mouna Ben Smida and Anis Sakly

Sustainability 2023, 15(2), 1156; https://0-doi-org.brum.beds.ac.uk/10.3390/su15021156 - 07 Jan 2023

Cited by 6 | Viewed by 1567

Abstract

Distributed generation (DG) refers to small generating plants that usually develop green energy and are located close to the load buses. Thus, reducing active as well as reactive power losses, enhancing stability and reliability, and many other benefits arise in the case of a suitable selection in terms of the location and the size of the DGs, especially in smart cities. In this work, a new nature-inspired algorithm called Garra Rufa optimization is selected to determine the optimal DG allocation. The new metaheuristic algorithm stimulates the massage fish activity during finding food using MATLAB software. In addition, three indexes which are apparently powered loss compounds and voltage profile, are considered to estimate the effectiveness of the proposed method. To validate the proposed algorithm, the IEEE 30 and 14 bus standard test systems were employed. Moreover, five cases of DGs number are tested for both standards to provide a set of complex cases. The results significantly show the high performance of the proposed method especially in highly complex cases compared to particle swarm optimization (PSO) algorithm and genetic algorithm (GA). The DG allocation, using the proposed method, reduces the active power losses of the IEEE-14 bus system up to 236.7873%, by assuming 5DGs compared to the active power losses without DG. Furthermore, the GRO increases the maximum voltage stability index of the IEEE-30 bus system by 857% in case of the 4DGs, whereas GA rises the reactive power of 5DGs to benefit the IEEE-14 bus system by 195.1%. Full article

(This article belongs to the Special Issue Smart Grid Analytics for Sustainability and Urbanization in Big Data)

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20 pages, 1142 KiB

Open AccessArticle

Solar and Wind Energy Forecasting for Green and Intelligent Migration of Traditional Energy Sources

by Syed Muhammad Mohsin, Tahir Maqsood and Sajjad Ahmed Madani

Sustainability 2022, 14(23), 16317; https://0-doi-org.brum.beds.ac.uk/10.3390/su142316317 - 06 Dec 2022

Cited by 5 | Viewed by 1975

Abstract

Fossil-fuel-based power generation leads to higher energy costs and environmental impacts. Solar and wind energy are abundant important renewable energy sources (RES) that make the largest contribution to replacing fossil-fuel-based energy consumption. However, the uncertain solar radiation and highly fluctuating weather parameters of solar and wind energy require an accurate and reliable forecasting mechanism for effective and efficient load management, cost reduction, green environment, and grid stability. From the existing literature, artificial neural networks (ANN) are a better means for prediction, but the ANN-based renewable energy forecasting techniques lose prediction accuracy due to the high uncertainty of input data and random determination of initial weights among different layers of ANN. Therefore, the objective of this study is to develop a harmony search algorithm (HSA)-optimized ANN model for reliable and accurate prediction of solar and wind energy. In this study, we combined ANN with HSA and provided ANN feedback for its weights adjustment to HSA, instead of ANN. Then, the HSA optimized weights were assigned to the edges of ANN instead of random weights, and this completes the training of ANN. Extensive simulations were carried out and our proposed HSA-optimized ANN model for solar irradiation forecast achieved the values of MSE = 0.04754, MAE = 0.18546, MAPE = 0.32430%, and RMSE = 0.21805, whereas our proposed HSA-optimized ANN model for wind speed prediction achieved the values of MSE = 0.30944, MAE = 0.47172, MAPE = 0.12896%, and RMSE = 0.55627. Simulation results prove the supremacy of our proposed HSA-optimized ANN models compared to state-of-the-art solar and wind energy forecasting techniques. Full article

(This article belongs to the Special Issue Smart Grid Analytics for Sustainability and Urbanization in Big Data)

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18 pages, 4336 KiB

Open AccessArticle

Study of an Optimized Micro-Grid’s Operation with Electrical Vehicle-Based Hybridized Sustainable Algorithm

by Muhammad Shahzad Nazir, Zhang Chu, Ahmad N. Abdalla, Hong Ki An, Sayed M. Eldin, Ahmed Sayed M. Metwally, Patrizia Bocchetta and Muhammad Sufyan Javed

Sustainability 2022, 14(23), 16172; https://0-doi-org.brum.beds.ac.uk/10.3390/su142316172 - 03 Dec 2022

Cited by 2 | Viewed by 1212

Abstract

Recently, the expansion of energy communities has been aided by the lowering cost of storage technologies and the appearance of mechanisms for exchanging energy that is driven by economics. An amalgamation of different renewable energy sources, including solar, wind, geothermal, tidal, etc., is necessary to offer sustainable energy for smart cities. Furthermore, considering the induction of large-scale electric vehicles connected to the regional micro-grid, and causes of increase in the randomness and uncertainty of the load in a certain area, a solution that meets the community demands for electricity, heating, cooling, and transportation while using renewable energy is needed. This paper aims to define the impact of large-scale electric vehicles on the operation and management of the microgrid using a hybridized algorithm. First, with the use of the natural attributes of electric vehicles such as flexible loads, a large-scale electric vehicle response dispatch model is constructed. Second, three factors of micro-grid operation, management, and environmental pollution control costs with load fluctuation variance are discussed. Third, a hybrid gravitational search algorithm and random forest regression (GSA-RFR) approach is proposed to confirm the method’s authenticity and reliability. The constructed large-scale electric vehicle response dispatch model significantly improves the load smoothness of the micro-grid after the large-scale electric vehicles are connected and reduces the impact of the entire grid. The proposed hybridized optimization method was solved within 296.7 s, the time taken for electric vehicle users to charge from and discharge to the regional micro-grid, which improves the economy of the micro-grid, and realizes the effective management of the regional load. The weight coefficients λ₁ and λ₂ were found at 0.589 and 0.421, respectively. This study provides key findings and suggestions that can be useful to scholars and decisionmakers. Full article

(This article belongs to the Special Issue Smart Grid Analytics for Sustainability and Urbanization in Big Data)

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19 pages, 2044 KiB

Open AccessArticle

Detecting Nontechnical Losses in Smart Meters Using a MLP-GRU Deep Model and Augmenting Data via Theft Attacks

by Benish Kabir, Umar Qasim, Nadeem Javaid, Abdulaziz Aldegheishem, Nabil Alrajeh and Emad A. Mohammed

Sustainability 2022, 14(22), 15001; https://0-doi-org.brum.beds.ac.uk/10.3390/su142215001 - 13 Nov 2022

Cited by 8 | Viewed by 2309

Abstract

The current study uses a data-driven method for Nontechnical Loss (NTL) detection using smart meter data. Data augmentation is performed using six distinct theft attacks on benign users’ samples to balance the data from honest and theft samples. The theft attacks help to generate synthetic patterns that mimic real-world electricity theft patterns. Moreover, we propose a hybrid model including the Multi-Layer Perceptron and Gated Recurrent Unit (MLP-GRU) networks for detecting electricity theft. In the model, the MLP network examines the auxiliary data to analyze nonmalicious factors in daily consumption data, whereas the GRU network uses smart meter data acquired from the Pakistan Residential Electricity Consumption (PRECON) dataset as the input. Additionally, a random search algorithm is used for tuning the hyperparameters of the proposed deep learning model. In the simulations, the proposed model is compared with the MLP-Long Term Short Memory (LSTM) scheme and other traditional schemes. The results show that the proposed model has scores of 0.93 and 0.96 for the area under the precision–recall curve and the area under the receiver operating characteristic curve, respectively. The precision–recall curve and the area under the receiver operating characteristic curve scores for the MLP-LSTM are 0.93 and 0.89, respectively. Full article

(This article belongs to the Special Issue Smart Grid Analytics for Sustainability and Urbanization in Big Data)

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20 pages, 1253 KiB

Open AccessArticle

A Hybrid Deep Learning-Based Model for Detection of Electricity Losses Using Big Data in Power Systems

by Adnan Khattak, Rasool Bukhsh, Sheraz Aslam, Ayman Yafoz, Omar Alghushairy and Raed Alsini

Sustainability 2022, 14(20), 13627; https://0-doi-org.brum.beds.ac.uk/10.3390/su142013627 - 21 Oct 2022

Cited by 10 | Viewed by 1981

Abstract

Electricity theft harms smart grids and results in huge revenue losses for electric companies. Deep learning (DL), machine learning (ML), and statistical methods have been used in recent research studies to detect anomalies and illegal patterns in electricity consumption (EC) data collected by smart meters. In this paper, we propose a hybrid DL model for detecting theft activity in EC data. The model combines both a gated recurrent unit (GRU) and a convolutional neural network (CNN). The model distinguishes between legitimate and malicious EC patterns. GRU layers are used to extract temporal patterns, while the CNN is used to retrieve optimal abstract or latent patterns from EC data. Moreover, imbalance of data classes negatively affects the consistency of ML and DL. In this paper, an adaptive synthetic (ADASYN) method and TomekLinks are used to deal with the imbalance of data classes. In addition, the performance of the hybrid model is evaluated using a real-time EC dataset from the State Grid Corporation of China (SGCC). The proposed algorithm is computationally expensive, but on the other hand, it provides higher accuracy than the other algorithms used for comparison. With more and more computational resources available nowadays, researchers are focusing on algorithms that provide better efficiency in the face of widespread data. Various performance metrics such as F1-score, precision, recall, accuracy, and false positive rate are used to investigate the effectiveness of the hybrid DL model. The proposed model outperforms its counterparts with 0.985 Precision–Recall Area Under Curve (PR-AUC) and 0.987 Receiver Operating Characteristic Area Under Curve (ROC-AUC) for the data of EC. Full article

(This article belongs to the Special Issue Smart Grid Analytics for Sustainability and Urbanization in Big Data)

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30 pages, 6900 KiB

Open AccessArticle

Load Frequency Control and Automatic Voltage Regulation in a Multi-Area Interconnected Power System Using Nature-Inspired Computation-Based Control Methodology

by Tayyab Ali, Suheel Abdullah Malik, Ibrahim A. Hameed, Amil Daraz, Hana Mujlid and Ahmad Taher Azar

Sustainability 2022, 14(19), 12162; https://0-doi-org.brum.beds.ac.uk/10.3390/su141912162 - 26 Sep 2022

Cited by 22 | Viewed by 2914

Abstract

The stability control of nominal frequency and terminal voltage in an interconnected power system (IPS) is always a challenging task for researchers. The load variation or any disturbance affects the active and reactive power demands, which badly influence the normal working of IPS. In order to maintain frequency and terminal voltage at rated values, controllers are installed at generating stations to keep these parameters within the prescribed limits by varying the active and reactive power demands. This is accomplished by load frequency control (LFC) and automatic voltage regulator (AVR) loops, which are coupled to each other. Due to the complexity of the combined AVR-LFC model, the simultaneous control of frequency and terminal voltage in an IPS requires an intelligent control strategy. The performance of IPS solely depends upon the working of the controllers. This work presents the exploration of control methodology based on a proportional integral–proportional derivative (PI-PD) controller with combined LFC-AVR in a multi-area IPS. The PI-PD controller was tuned with recently developed nature-inspired computation algorithms including the Archimedes optimization algorithm (AOA), learner performance-based behavior optimization (LPBO), and modified particle swarm optimization (MPSO). In the earlier part of this work, the proposed methodology was applied to a two-area IPS, and the output responses of LPBO-PI-PD, AOA-PI-PD, and MPSO-PI-PD control schemes were compared with an existing nonlinear threshold-accepting algorithm-based PID (NLTA-PID) controller. After achieving satisfactory results in the two-area IPS, the proposed scheme was examined in a three-area IPS with combined AVR and LFC. Finally, the reliability and efficacy of the proposed methodology was investigated on a three-area IPS with LFC-AVR with variations in the system parameters over a range of Â ± 50%. The simulation results and a comprehensive comparison between the controllers clearly demonstrates that the proposed control schemes including LPBO-PI-PD, AOA-PI-PD, and MPSO-PI-PD are very reliable, and they can effectively stabilize the frequency and terminal voltage in a multi-area IPS with combined LFC and AVR. Full article

(This article belongs to the Special Issue Smart Grid Analytics for Sustainability and Urbanization in Big Data)

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19 pages, 5740 KiB

Open AccessArticle

Line Overload Alleviations in Wind Energy Integrated Power Systems Using Automatic Generation Control

by Kaleem Ullah, Abdul Basit, Zahid Ullah, Rafiq Asghar, Sheraz Aslam and Ayman Yafoz

Sustainability 2022, 14(19), 11810; https://0-doi-org.brum.beds.ac.uk/10.3390/su141911810 - 20 Sep 2022

Cited by 8 | Viewed by 1463

Abstract

Modern power systems are largely based on renewable energy sources, especially wind power. However, wind power, due to its intermittent nature and associated forecasting errors, requires an additional amount of balancing power provided through the automatic generation control (AGC) system. In normal operation, AGC dispatch is based on the fixed participation factor taking into account only the economic operation of generating units. However, large-scale injection of additional reserves results in large fluctuations of line power flows, which may overload the line and subsequently reduce the system security if AGC follows the fixed participation factor’s criteria. Therefore, to prevent the transmission line overloading, a dynamic dispatch strategy is required for the AGC system considering the capacities of the transmission lines along with the economic operation of generating units. This paper proposes a real-time dynamic AGC dispatch strategy, which protects the transmission line from overloading during the power dispatch process in an active power balancing operation. The proposed method optimizes the control of the AGC dispatch order to prevent power overflows in the transmission lines, which is achieved by considering how the output change of each generating unit affects the power flow in the associated bus system. Simulations are performed in Dig SILENT software by developing a 5 machine 8 bus Pakistan’s power system model integrating thermal power plant units, gas turbines, and wind power plant systems. Results show that the proposed AGC design efficiently avoids the transmission line congestions in highly wind-integrated power along with the economic operation of generating units. Full article

(This article belongs to the Special Issue Smart Grid Analytics for Sustainability and Urbanization in Big Data)

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18 pages, 2736 KiB

Open AccessArticle

A Comparison of Recent Requirements Gathering and Management Tools in Requirements Engineering for IoT-Enabled Sustainable Cities

by Muhammad Asgher Nadeem, Scott Uk-Jin Lee and Muhammad Usman Younus

Sustainability 2022, 14(4), 2427; https://0-doi-org.brum.beds.ac.uk/10.3390/su14042427 - 20 Feb 2022

Cited by 3 | Viewed by 3922

Abstract

The Internet of Things (IoT) is a paradigm that facilitates the proliferation of different devices such as sensors and Radio Frequency Identification (RFIDs) for real-time applications such as healthcare and sustainable cities. The growing popularity of IoT opens up new possibilities, and one of the most notable applications is related to the evolving sustainable city paradigm. A sustainable city is normally designed in such a way to consider the environmental impact and a social, economic, and resilient habitat for existing populations without compromising the ability of future generations to experience the same, while the process of managing project requirements is known as requirements management. To design a high-quality project, effective requirements management is imperative. A number of techniques are already available to perform the requirement gathering process, and software developers apply them to collect the requirements. Nevertheless, they are facing many issues in gathering requirements due to a lack of literature on the selection of appropriate methods, which affects the quality of the software. The software design quality can be improved by using requirements capture and management techniques. Some tools are used to comprehend the system accurately. In this paper, a qualitative comparison of requirements-gathering tools using Artificial Intelligence (AI) and requirements-management tools is presented for sustainable cities. With all the tools and techniques available for capturing and managing requirements, it has been proven that software developers have a wide range of alternatives for selecting the best tool that fits their needs, such as chosen by the AI agent. This effort will aid in the development of requirements for IoT-enabled sustainable cities. Full article

(This article belongs to the Special Issue Smart Grid Analytics for Sustainability and Urbanization in Big Data)

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28 pages, 2513 KiB

Open AccessArticle

Towards Electric Price and Load Forecasting Using CNN-Based Ensembler in Smart Grid

by Shahzad Aslam, Nasir Ayub, Umer Farooq, Muhammad Junaid Alvi, Fahad R. Albogamy, Gul Rukh, Syed Irtaza Haider, Ahmad Taher Azar and Rasool Bukhsh

Sustainability 2021, 13(22), 12653; https://0-doi-org.brum.beds.ac.uk/10.3390/su132212653 - 16 Nov 2021

Cited by 32 | Viewed by 2655

Abstract

Medium-term electricity consumption and load forecasting in smart grids is an attractive topic of study, especially using innovative data analysis approaches for future energy consumption trends. Loss of electricity during generation and use is also a problem to be addressed. Both consumers and utilities can benefit from a predictive study of electricity demand and pricing. In this study, we used a new machine learning approach called AdaBoost to identify key features from an ISO-NE dataset that includes daily consumption data over eight years. Moreover, the DT classifier and RF are widely used to extract the best features from the dataset. Moreover, we predicted the electricity load and price using machine learning techniques including support vector machine (SVM) and deep learning techniques such as a convolutional neural network (CNN). Coronavirus herd immunity optimization (CHIO), a novel optimization approach, was used to modify the hyperparameters to increase efficiency, and it used classifiers to improve the performance of our classifier. By adding additional layers to the CNN and fine-tuning its parameters, the probability of overfitting the classifier was reduced. For method validation, we compared our proposed models with several benchmarks. MAE, MAPE, MSE, RMSE, the f1 score, recall, precision, and accuracy were the measures used for performance evaluation. Moreover, seven different forms of statistical analysis were given to show why our proposed approaches are preferable. The proposed CNN-CHIO and SVM techniques had the lowest MAPE error rates of 6% and 8%, respectively, and the highest accuracy rates of 95% and 92%, respectively. Full article

(This article belongs to the Special Issue Smart Grid Analytics for Sustainability and Urbanization in Big Data)

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29 pages, 2236 KiB

Open AccessArticle

Efficient Energy Optimization Day-Ahead Energy Forecasting in Smart Grid Considering Demand Response and Microgrids

by Fahad R. Albogamy, Ghulam Hafeez, Imran Khan, Sheraz Khan, Hend I. Alkhammash, Faheem Ali and Gul Rukh

Sustainability 2021, 13(20), 11429; https://0-doi-org.brum.beds.ac.uk/10.3390/su132011429 - 16 Oct 2021

Cited by 11 | Viewed by 2571

Abstract

In smart grid, energy management is an indispensable for reducing energy cost of consumers while maximizing user comfort and alleviating the peak to average ratio and carbon emission under real time pricing approach. In contrast, the emergence of bidirectional communication and power transfer technology enables electric vehicles (EVs) charging/discharging scheduling, load shifting/scheduling, and optimal energy sharing, making the power grid smart. With this motivation, efficient energy management model for a microgrid with ant colony optimization algorithm to systematically schedule load and EVs charging/discharging of is introduced. The smart microgrid is equipped with controllable appliances, photovoltaic panels, wind turbines, electrolyzer, hydrogen tank, and energy storage system. Peak load, peak to average ratio, cost, energy cost, and carbon emission operation of appliances are reduced by the charging/discharging of electric vehicles, and energy storage systems are scheduled using real time pricing tariffs. This work also predicts wind speed and solar irradiation to ensure efficient energy optimization. Simulations are carried out to validate our developed ant colony optimization algorithm-based energy management scheme. The obtained results demonstrate that the developed efficient energy management model can reduce energy cost, alleviate peak to average ratio, and carbon emission. Full article

(This article belongs to the Special Issue Smart Grid Analytics for Sustainability and Urbanization in Big Data)

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27 pages, 10295 KiB

Open AccessArticle

A Dragonfly Optimization Algorithm for Extracting Maximum Power of Grid-Interfaced PV Systems

by Ehtisham Lodhi, Fei-Yue Wang, Gang Xiong, Ghulam Ali Mallah, Muhammad Yaqoob Javed, Tariku Sinshaw Tamir and David Wenzhong Gao

Sustainability 2021, 13(19), 10778; https://0-doi-org.brum.beds.ac.uk/10.3390/su131910778 - 28 Sep 2021

Cited by 27 | Viewed by 2601

Abstract

Currently, grid-connected Photovoltaic (PV) systems are widely encouraged to meet increasing energy demands. However, there are many urgent issues to tackle that are associated with PV systems. Among them, partial shading is the most severe issue as it reduces efficiency. To achieve maximum power, PV system utilizes the maximum power point-tracking (MPPT) algorithms. This paper proposed a two-level converter system for optimizing the PV power and injecting that power into the grid network. The boost converter is used to regulate the MPPT algorithm. To make the grid-tied PV system operate under non-uniform weather conditions, dragonfly optimization algorithm (DOA)-based MPPT was put forward and applied due to its ability to trace the global peak and its higher efficiency and shorter response time. Furthermore, in order to validate the overall performance of the proposed technique, comparative analysis of DOA with adaptive cuckoo search optimization (ACSO) algorithm, fruit fly optimization algorithm combined with general regression neural network (FFO-GRNN), improved particle swarm optimization (IPSO), and PSO and Perturb and Observe (P&O) algorithm were presented by using Matlab/Simulink. Subsequently, a voltage source inverter (VSI) was utilized to regulate the active and reactive power injected into the grid with high efficiency and minimum total harmonic distortion (THD). The instantaneous reactive power was adjusted to zero for maintaining the unity power factor. The results obtained through Matlab/Simulink demonstrated that power injected into the grid is approximately constant when using the DOA MPPT algorithm. Hence, the grid-tied PV system’s overall performance under partial shading was found to be highly satisfactory and acceptable. Full article

(This article belongs to the Special Issue Smart Grid Analytics for Sustainability and Urbanization in Big Data)

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27 pages, 2025 KiB

Open AccessArticle

Optimized Economic Load Dispatch with Multiple Fuels and Valve-Point Effects Using Hybrid Genetic–Artificial Fish Swarm Algorithm

by Abdulrashid Muhammad Kabir, Mohsin Kamal, Fiaz Ahmad, Zahid Ullah, Fahad R. Albogamy, Ghulam Hafeez and Faizan Mehmood

Sustainability 2021, 13(19), 10609; https://0-doi-org.brum.beds.ac.uk/10.3390/su131910609 - 24 Sep 2021

Cited by 6 | Viewed by 1567

Abstract

Economic Load Dispatch (ELD) plays a pivotal role in sustainable operation planning in a smart power system by reducing the fuel cost and by fulfilling the load demand in an efficient manner. In this work, the ELD problem is solved by using hybridized robust techniques that combine the Genetic Algorithm and Artificial Fish Swarm Algorithm, termed the Hybrid Genetic–Artificial Fish Swarm Algorithm (HGAFSA). The objective of this paper is threefold. First, the multi-objective ELD problem incorporating the effects of multiple fuels and valve-point loading and involving higher-order cost functions is optimally solved by HGAFSA. Secondly, the efficacy of HGAFSA is demonstrated using five standard generating unit test systems (13, 40, 110, 140, and 160). Finally, an extra-large system is formed by combining the five test systems, which result in a 463 generating unit system. The performance of the developed HGAFSA-based ELD algorithm is then tested on the six systems including the 463-unit system. Annual savings in fuel costs of $3.254 m, $0.38235 m, $2135.7, $9.5563 m, and $1.1588 m are achieved for the 13, 40, 110, 140, and 160 standard generating units, respectively, compared to costs mentioned in the available literature. The HGAFSA-based ELD optimization curves obtained during the optimization process are also presented. Full article

(This article belongs to the Special Issue Smart Grid Analytics for Sustainability and Urbanization in Big Data)

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17 pages, 326 KiB

Open AccessArticle

An Incentive Based Dynamic Pricing in Smart Grid: A Customer’s Perspective

by Thamer Alquthami, Ahmad H. Milyani, Muhammad Awais and Muhammad B. Rasheed

Sustainability 2021, 13(11), 6066; https://0-doi-org.brum.beds.ac.uk/10.3390/su13116066 - 27 May 2021

Cited by 6 | Viewed by 2566

Abstract

Price based demand response is an important strategy to facilitate energy retailers and end-users to maintain a balance between demand and supply while providing the opportunity to end users to get monetary incentives. In this work, we consider real-time electricity pricing policy to further calculate the incentives in terms of reduced electricity price and cost. Initially, a mathematical model based on the backtracking technique is developed to calculate the load shifted and consumed in any time slot. Then, based on this, the electricity price is calculated for all types of users to estimate the incentives through load shifting profiles. To keep the load under the upper limit, the load is shifted in other time slots in such a way to facilitate end-users regarding social welfare. The user who is not interested in participating load shifting program will not get any benefit. Then the well behaved functional form optimization problem is solved by using a heuristic-based genetic algorithm (GA), wwhich converged within an insignificant amount of time with the best optimal results. Simulation results reflect that the users can obtain some real incentives by participating in the load scheduling process. Full article

(This article belongs to the Special Issue Smart Grid Analytics for Sustainability and Urbanization in Big Data)

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