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Dr. Sheraz Aslam
Cyprus University of Technology

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Research Keywords & Expertise

0 Smart Grid
0 Renewable and Sustainable Energy
0 Demand side management
0 home energy management system (HEMS)
0 heuristic and metaheuristic algorithms

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Smart Grid
Demand side management
home energy management system (HEMS)

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Short Biography

Sheraz Aslam (Member IEEE) received a B.S. degree in Computer Science from the Bahauddin Zakariya University (BZU), Multan, Pakistan, in 2015, and a M.S. degree in Computer Science with a specialization in energy optimization in the smart grid from the COMSATS University Islamabad (CUI), Islamabad, Pakistan, in 2018. He also worked as a research associate with Dr. Nadeem Javaid during his M.S. period at the same university. Currently, Sheraz is working as a researcher under the supervision of Dr. Herodotos Herodotou, at DICL Research Lab, Cyprus University of Technology (CUT), Limassol, Cyprus, where he is also a part of a European Union funded research project named STEAM. He has authored more than 40 research publications in ISI-indexed international journals and conferences, including the IEEE Internet of Things Journal, Renewable & Sustainable Energy Reviews, and Electric Power System Research. Mr. Sheraz also served as a TPC member and has been invited as a reviewer of international journals and conferences. His research interests include data analytics, generative adversarial networks, wireless networks, smart grid, and cloud computing. He is constantly looking for collaboration opportunities with professors and students from different universities around the globe.

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Journal article
Published: 29 July 2021 in Energies
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The optimal planning of grid-connected microgrids (MGs) has been extensively studied in recent years. While most of the previous studies have used fixed or time-of-use (TOU) prices for the optimal sizing of MGs, this work introduces real-time pricing (RTP) for implementing a demand response (DR) program according to the national grid prices of Iran. In addition to the long-term planning of MG, the day-ahead operation of MG is also analyzed to get a better understanding of the DR program for daily electricity dispatch. For this purpose, four different days corresponding to the four seasons are selected for further analysis. In addition, various impacts of the proposed DR program on the MG planning results, including sizing and best configuration, net present cost (NPC) and cost of energy (COE), and emission generation by the utility grid, are investigated. The optimization results show that the implementation of the DR program has a positive impact on the technical, economic, and environmental aspects of MG. The NPC and COE are reduced by about USD 3700 and USD 0.0025/kWh, respectively. The component size is also reduced, resulting in a reduction in the initial cost. Carbon emissions are also reduced by 185 kg/year.

ACS Style

Zi-Xuan Yu; Meng-Shi Li; Yi-Peng Xu; Sheraz Aslam; Yuan-Kang Li. Techno-Economic Planning and Operation of the Microgrid Considering Real-Time Pricing Demand Response Program. Energies 2021, 14, 4597 .

AMA Style

Zi-Xuan Yu, Meng-Shi Li, Yi-Peng Xu, Sheraz Aslam, Yuan-Kang Li. Techno-Economic Planning and Operation of the Microgrid Considering Real-Time Pricing Demand Response Program. Energies. 2021; 14 (15):4597.

Chicago/Turabian Style

Zi-Xuan Yu; Meng-Shi Li; Yi-Peng Xu; Sheraz Aslam; Yuan-Kang Li. 2021. "Techno-Economic Planning and Operation of the Microgrid Considering Real-Time Pricing Demand Response Program." Energies 14, no. 15: 4597.

Journal article
Published: 22 June 2021 in Forecasting
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Day-ahead electricity price forecasting plays a critical role in balancing energy consumption and generation, optimizing the decisions of electricity market participants, formulating energy trading strategies, and dispatching independent system operators. Despite the fact that much research on price forecasting has been published in recent years, it remains a difficult task because of the challenging nature of electricity prices that includes seasonality, sharp fluctuations in price, and high volatility. This study presents a three-stage short-term electricity price forecasting model by employing ensemble empirical mode decomposition (EEMD) and extreme learning machine (ELM). In the proposed model, the EEMD is employed to decompose the actual price signals to overcome the non-linear and non-stationary components in the electricity price data. Then, a day-ahead forecasting is performed using the ELM model. We conduct several experiments on real-time data obtained from three different states of the electricity market in Australia, i.e., Queensland, New South Wales, and Victoria. We also implement various deep learning approaches as benchmark methods, i.e., recurrent neural network, multi-layer perception, support vector machine, and ELM. In order to affirm the performance of our proposed and benchmark approaches, this study performs several performance evaluation metric, including the Diebold–Mariano (DM) test. The results from the experiments show the productiveness of our developed model (in terms of higher accuracy) over its counterparts.

ACS Style

Sajjad Khan; Shahzad Aslam; Iqra Mustafa; Sheraz Aslam. Short-Term Electricity Price Forecasting by Employing Ensemble Empirical Mode Decomposition and Extreme Learning Machine. Forecasting 2021, 3, 460 -477.

AMA Style

Sajjad Khan, Shahzad Aslam, Iqra Mustafa, Sheraz Aslam. Short-Term Electricity Price Forecasting by Employing Ensemble Empirical Mode Decomposition and Extreme Learning Machine. Forecasting. 2021; 3 (3):460-477.

Chicago/Turabian Style

Sajjad Khan; Shahzad Aslam; Iqra Mustafa; Sheraz Aslam. 2021. "Short-Term Electricity Price Forecasting by Employing Ensemble Empirical Mode Decomposition and Extreme Learning Machine." Forecasting 3, no. 3: 460-477.

Review
Published: 22 April 2021 in Energies
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Automatic generation control (AGC) is primarily responsible for ensuring the smooth and efficient operation of an electric power system. The main goal of AGC is to keep the operating frequency under prescribed limits and maintain the interchange power at the intended level. Therefore, an AGC system must be supplemented with modern and intelligent control techniques to provide adequate power supply. This paper provides a comprehensive overview of various AGC models in diverse configurations of the power system. Initially, the history of power system AGC models is explored and the basic operation of AGC in a multi-area interconnected power system is presented. An in-depth analysis of various control methods used to mitigate the AGC issues is provided. Application of fast-acting energy storage devices, high voltage direct current (HVDC) interconnections, and flexible AC transmission systems (FACTS) devices in the AGC systems are investigated. Furthermore, AGC systems employed in different renewable energy generation systems are overviewed and are summarized in tabulated form. AGC techniques in different configurations of microgrid and smart grid are also presented in detail. A thorough overview of various AGC issues in a deregulated power system is provided by considering the different contract scenarios. Moreover, AGC systems with an additional objective of economic dispatch is investigated and an overview of worldwide AGC practices is provided. Finally, the paper concludes with an emphasis on the prospective study in the field of AGC.

ACS Style

Kaleem Ullah; Abdul Basit; Zahid Ullah; Sheraz Aslam; Herodotos Herodotou. Automatic Generation Control Strategies in Conventional and Modern Power Systems: A Comprehensive Overview. Energies 2021, 14, 2376 .

AMA Style

Kaleem Ullah, Abdul Basit, Zahid Ullah, Sheraz Aslam, Herodotos Herodotou. Automatic Generation Control Strategies in Conventional and Modern Power Systems: A Comprehensive Overview. Energies. 2021; 14 (9):2376.

Chicago/Turabian Style

Kaleem Ullah; Abdul Basit; Zahid Ullah; Sheraz Aslam; Herodotos Herodotou. 2021. "Automatic Generation Control Strategies in Conventional and Modern Power Systems: A Comprehensive Overview." Energies 14, no. 9: 2376.

Review article
Published: 03 April 2021 in Renewable and Sustainable Energy Reviews
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Microgrids have recently emerged as a building block for smart grids combining distributed renewable energy sources (RESs), energy storage devices, and load management methodologies. The intermittent nature of RESs brings several challenges to the smart microgrids, such as reliability, power quality, and balance between supply and demand. Thus, forecasting power generation from RESs, such as wind turbines and solar panels, is becoming essential for the efficient and perpetual operations of the power grid and it also helps in attaining optimal utilization of RESs. Energy demand forecasting is also an integral part of smart microgrids that helps in planning the power generation and energy trading with commercial grid. Machine learning (ML) and deep learning (DL) based models are promising solutions for predicting consumers’ demands and energy generations from RESs. In this context, this manuscript provides a comprehensive survey of the existing DL-based approaches, which are developed for power forecasting of wind turbines and solar panels as well as electric power load forecasting. It also discusses the datasets used to train and test the different DL-based prediction models, enabling future researchers to identify appropriate datasets to use in their work. Even though there are a few related surveys regarding energy management in smart grid applications, they are focused on a specific production application such as either solar or wind. Moreover, none of the surveys review the forecasting schemes for production and load side simultaneously. Finally, previous surveys do not consider the datasets used for forecasting despite their significance in DL-based forecasting approaches. Hence, our survey work is intrinsically different due to its data-centered view, along with presenting DL-based applications for load and energy generation forecasting in both residential and commercial sectors. The comparison of different DL approaches discussed in this manuscript reveals that the efficiency of such forecasting methods is highly dependent on the amount of the historical data and thus a large number of data storage devices and high processing power devices are required to deal with big data. Finally, this study raises several open research problems and opportunities in the area of renewable energy forecasting for smart microgrids.

ACS Style

Sheraz Aslam; Herodotos Herodotou; Syed Muhammad Mohsin; Nadeem Javaid; Nouman Ashraf; Shahzad Aslam. A survey on deep learning methods for power load and renewable energy forecasting in smart microgrids. Renewable and Sustainable Energy Reviews 2021, 144, 110992 .

AMA Style

Sheraz Aslam, Herodotos Herodotou, Syed Muhammad Mohsin, Nadeem Javaid, Nouman Ashraf, Shahzad Aslam. A survey on deep learning methods for power load and renewable energy forecasting in smart microgrids. Renewable and Sustainable Energy Reviews. 2021; 144 ():110992.

Chicago/Turabian Style

Sheraz Aslam; Herodotos Herodotou; Syed Muhammad Mohsin; Nadeem Javaid; Nouman Ashraf; Shahzad Aslam. 2021. "A survey on deep learning methods for power load and renewable energy forecasting in smart microgrids." Renewable and Sustainable Energy Reviews 144, no. : 110992.

Journal article
Published: 01 February 2021 in IEEE Access
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Based on energy demand, consumers can be broadly categorized into low energy consumers (LECs) and high energy consumers (HECs). HECs use heavy load appliances, e.g., electric heaters and air conditioners, and LECs do not use heavy load appliances. Thus, HECs demand more energy compared to LECs. The usage of high energy consumption appliances by HECs leads to peak formation in various time intervals. Different pricing schemes, i.e., time of use (ToU), real time pricing (RTP), inclined block rate (IBR), and critical peak pricing (CPP), have been proposed previously. In ToU, an energy tariff is divided into three blocks, i.e., on-peak (high rates), off-peak (low rates), and mid-peak (between on-peak and off-peak rates) hours, and these rates are applied to all electricity users without distinction. The high energy demand by HECs causes the high peak formation; thus, higher rates should be applied to only HECs rather than all consumers, which is not the case in existing billing mechanisms. LECs are also charged higher rates in on-peak intervals and this billing mechanisms are unjustified. Thus, in this paper, a fair pricing scheme (FPS) based on power demand forecasting is developed to reduce extra bills of LECs. First, we developed a machine learning-based electricity load forecasting method, i.e., an extreme learning machine (ELM), in order to differentiate LECs and HECs. With the proposed FPS, electricity cost calculations for LECs and HECs are based on the actual energy consumption; thus, LECs do not subsidize HECs. Simulations were conducted for performance evaluation of our proposed FPS mechanism, and the results demonstrate LECs can reduce electricity cost up to 11.0075%, and HECs are charged relatively higher than previous pricing schemes as a penalty for their contribution to the on-peak formation. As a result, a fairer system is realized, and the total revenue of the utility company is assured.

ACS Style

Khursheed Aurangzeb; Sheraz Aslam; Syed Muhammad Mohsin; Musaed Alhussein. A Fair Pricing Mechanism in Smart Grids for Low Energy Consumption Users. IEEE Access 2021, 9, 22035 -22044.

AMA Style

Khursheed Aurangzeb, Sheraz Aslam, Syed Muhammad Mohsin, Musaed Alhussein. A Fair Pricing Mechanism in Smart Grids for Low Energy Consumption Users. IEEE Access. 2021; 9 ():22035-22044.

Chicago/Turabian Style

Khursheed Aurangzeb; Sheraz Aslam; Syed Muhammad Mohsin; Musaed Alhussein. 2021. "A Fair Pricing Mechanism in Smart Grids for Low Energy Consumption Users." IEEE Access 9, no. : 22035-22044.

Chapter
Published: 15 November 2020 in Progress in IS
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The maritime domain encompasses a diverse set of heterogeneous large-scale data about ships, routes and trajectories, port operations, fishing and maritime biodiversity, oceans, and environmental conditions. Performing timely and cost-effective analytical processing of this data is a key priority for maritime stakeholders in order to extract deep insights and automate various decision-making processes that will lead to optimising marine transport, improving fuel efficiency, and optimising port operational efficiency among others. The maritime data value chain defines the series of activities needed to appropriately manage data during the entire life-cycle of data as well as to extract value and useful insights from maritime data. The four key activities identified are: (1) data acquisition for collecting the data across different and geographically-dispersed data sources; (2) data pre-processing for transforming, integrating, and assessing the quality of the data; (3) data storage for storing data in a persistent and scalable way; and (4) data usage for processing the data and extracting value. This chapter provides an extensive overview of the maritime data value chain and discusses state-of-the-art technological solutions for managing and processing maritime data in efficient and effective ways.

ACS Style

Herodotos Herodotou; Sheraz Aslam; Henrik Holm; Socrates Theodossiou. Big Maritime Data Management. Progress in IS 2020, 313 -334.

AMA Style

Herodotos Herodotou, Sheraz Aslam, Henrik Holm, Socrates Theodossiou. Big Maritime Data Management. Progress in IS. 2020; ():313-334.

Chicago/Turabian Style

Herodotos Herodotou; Sheraz Aslam; Henrik Holm; Socrates Theodossiou. 2020. "Big Maritime Data Management." Progress in IS , no. : 313-334.

Journal article
Published: 31 October 2020 in Energies
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Cloud computing is the de facto platform for deploying resource- and data-intensive real-time applications due to the collaboration of large scale resources operating in cross-administrative domains. For example, real-time systems are generated by smart devices (e.g., sensors in smart homes that monitor surroundings in real-time, security cameras that produce video streams in real-time, cloud gaming, social media streams, etc.). Such low-end devices form a microgrid which has low computational and storage capacity and hence offload data unto the cloud for processing. Cloud computing still lacks mature time-oriented scheduling and resource allocation strategies which thoroughly deliberate stringent QoS. Traditional approaches are sufficient only when applications have real-time and data constraints, and cloud storage resources are located with computational resources where the data are locally available for task execution. Such approaches mainly focus on resource provision and latency, and are prone to missing deadlines during tasks execution due to the urgency of the tasks and limited user budget constraints. The timing and data requirements exacerbate the efficient task scheduling and resource allocation problems. To cope with the aforementioned gaps, we propose a time- and cost-efficient resource allocation strategy for smart systems that periodically offload computational and data-intensive load to the cloud. The proposed strategy minimizes the data files transfer overhead to computing resources by selecting appropriate pairs of computing and storage resources. The celebrated results show the effectiveness of the proposed technique in terms of resource selection and tasks processing within time and budget constraints when compared with the other counterparts.

ACS Style

Muhammad Qureshi; Muhammad Qureshi; Muhammad Fayaz; Muhammad Zakarya; Sheraz Aslam; Asadullah Shah. Time and Cost Efficient Cloud Resource Aallocation for Real-Time Data-Intensive Smart Systems. Energies 2020, 13, 5706 .

AMA Style

Muhammad Qureshi, Muhammad Qureshi, Muhammad Fayaz, Muhammad Zakarya, Sheraz Aslam, Asadullah Shah. Time and Cost Efficient Cloud Resource Aallocation for Real-Time Data-Intensive Smart Systems. Energies. 2020; 13 (21):5706.

Chicago/Turabian Style

Muhammad Qureshi; Muhammad Qureshi; Muhammad Fayaz; Muhammad Zakarya; Sheraz Aslam; Asadullah Shah. 2020. "Time and Cost Efficient Cloud Resource Aallocation for Real-Time Data-Intensive Smart Systems." Energies 13, no. 21: 5706.

Journal article
Published: 07 July 2020 in IEEE Access
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Among several approaches to privacy-preserving cryptographic schemes, we have concentrated on noise-free homomorphic encryption. It is a symmetric key encryption that supports homomorphic operations on encrypted data. We present a fully homomorphic encryption (FHE) scheme based on sedenion algebra over finite Zn rings. The innovation of the scheme is the compression of a 16-dimensional vector for the application of Frobenius automorphism. For sedenion, we have p16 different possibilities that create a significant bijective mapping over the chosen 16-dimensional vector that adds permutation to our scheme. The security of this scheme is based on the assumption of the hardness of solving a multivariate quadratic equation system over finite Zn rings. The scheme results in 256n multivariate polynomial equations with 256 + 16n unknown variables for n messages. For this reason, the proposed scheme serves as a security basis for potentially post-quantum cryptosystems. Moreover, after sedenion, no newly constructed algebra loses its properties. This scheme would therefore apply as a whole to the following algebras, such as 32-dimensional trigintadunion.

ACS Style

Iqra Mustafa; Hasnain Mustafa; Ahmad Taher Azar; Sheraz Aslam; Syed Muhammad Mohsin; Muhammad Bilal Qureshi; Nouman Ashraf. Noise Free Fully Homomorphic Encryption Scheme Over Non-Associative Algebra. IEEE Access 2020, 8, 136524 -136536.

AMA Style

Iqra Mustafa, Hasnain Mustafa, Ahmad Taher Azar, Sheraz Aslam, Syed Muhammad Mohsin, Muhammad Bilal Qureshi, Nouman Ashraf. Noise Free Fully Homomorphic Encryption Scheme Over Non-Associative Algebra. IEEE Access. 2020; 8 (99):136524-136536.

Chicago/Turabian Style

Iqra Mustafa; Hasnain Mustafa; Ahmad Taher Azar; Sheraz Aslam; Syed Muhammad Mohsin; Muhammad Bilal Qureshi; Nouman Ashraf. 2020. "Noise Free Fully Homomorphic Encryption Scheme Over Non-Associative Algebra." IEEE Access 8, no. 99: 136524-136536.

Journal article
Published: 19 May 2020 in IEEE Access
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Conventional RSA algorithm, being a basis for several proposed cryptosystems, has remarkable security laps with respect to confidentiality and integrity over the internet which can be compromised by state-of-the-art attacks, especially, for different types of data generation, transmission, and analysis by IoT applications. This security threat hindrance is considered to be a hard problem to solve on classical computers. However, bringing quantum mechanics into account, the concept no longer holds true. So, this calls out for the modification of the conventional pre-quantum RSA algorithm into a secure post-quantum cryptographic-based RSA technique. In this research, we propose a post-quantum lattice-based RSA (LBRSA) for IoT applications in order to secure the shared data and information. The proposed work is validated by implementing it in 60-dimensions. The key size is about 1.152 × 105-bits and generation time is 0.8 hours. Furthermore, it has been tested with AVISPA, which confirms security in the presence of an intruder. Moreover, the proposed LB-RSA technique is compared with the existing state-of-the-art techniques. The empirical results advocate that the proposed lattice-based variant is not only safe but beats counterparts in terms of secured data sharing.

ACS Style

Iqra Mustafa; Imran Ullah Khan; Sheraz Aslam; Ahthasham Sajid; Syed Muhammad Mohsin; Muhammad Awais; Muhammad Bilal Qureshi. A Lightweight Post-Quantum Lattice-Based RSA for Secure Communications. IEEE Access 2020, 8, 99273 -99285.

AMA Style

Iqra Mustafa, Imran Ullah Khan, Sheraz Aslam, Ahthasham Sajid, Syed Muhammad Mohsin, Muhammad Awais, Muhammad Bilal Qureshi. A Lightweight Post-Quantum Lattice-Based RSA for Secure Communications. IEEE Access. 2020; 8 (99):99273-99285.

Chicago/Turabian Style

Iqra Mustafa; Imran Ullah Khan; Sheraz Aslam; Ahthasham Sajid; Syed Muhammad Mohsin; Muhammad Awais; Muhammad Bilal Qureshi. 2020. "A Lightweight Post-Quantum Lattice-Based RSA for Secure Communications." IEEE Access 8, no. 99: 99273-99285.

Journal article
Published: 08 May 2020 in IEEE Internet of Things Journal
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The recent emergence of Internet of Things (IoT) technologies in mission-critical applications in the maritime industry has led to the introduction of the Internet of Ships (IoS) paradigm. IoS is a novel application domain of IoT that refers to the network of smart interconnected maritime objects, which can be any physical device or infrastructure associated with a ship, a port, or the transportation itself, with the goal of significantly boosting the shipping industry towards improved safety, efficiency, and environmental sustainability. In this manuscript, we provide a comprehensive survey of the IoS paradigm, its architecture, its key elements, and its main characteristics. Furthermore, we review the state of the art for its emerging applications, including safety enhancements, route planning and optimization, collaborative decision making, automatic fault detection and preemptive maintenance, cargo tracking, environmental monitoring, energy-efficient operations, and automatic berthing. Finally, the presented open challenges and future opportunities for research in the areas of satellite communications, security, privacy, maritime data collection, data management, and analytics, provide a road-map towards optimized maritime operations and autonomous shipping.

ACS Style

Sheraz Aslam; Michalis P. Michaelides; Herodotos Herodotou. Internet of Ships: A Survey on Architectures, Emerging Applications, and Challenges. IEEE Internet of Things Journal 2020, 7, 9714 -9727.

AMA Style

Sheraz Aslam, Michalis P. Michaelides, Herodotos Herodotou. Internet of Ships: A Survey on Architectures, Emerging Applications, and Challenges. IEEE Internet of Things Journal. 2020; 7 (10):9714-9727.

Chicago/Turabian Style

Sheraz Aslam; Michalis P. Michaelides; Herodotos Herodotou. 2020. "Internet of Ships: A Survey on Architectures, Emerging Applications, and Challenges." IEEE Internet of Things Journal 7, no. 10: 9714-9727.

Journal article
Published: 22 January 2020 in Electric Power Systems Research
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This study proposes an efficient energy management method to systematically manage the energy consumption in the residential area to alleviate the peak to average ratio and mitigate electricity cost along with user comfort maximization. We developed an efficient energy management scheme using mixed integer linear programming (MILP), which schedules smart appliances and charging/discharging of electric vehicles (EVs) optimally in order to mitigate energy costs. In the proposed model, consumer is able to generate its own energy from microgrid consisting of solar panels and wind turbines. We also consider an energy storage system (ESS) for efficient energy utilization. This work also performs energy forecasting using wind speed and solar radiation prediction for efficient energy management. Moreover, we perform extensive simulations to validate our developed MILP based scheme and results affirm the effectiveness and productiveness of our proposed energy efficient technique.

ACS Style

Sheraz Aslam; Adia Khalid; Nadeem Javaid. Towards efficient energy management in smart grids considering microgrids with day-ahead energy forecasting. Electric Power Systems Research 2020, 182, 106232 .

AMA Style

Sheraz Aslam, Adia Khalid, Nadeem Javaid. Towards efficient energy management in smart grids considering microgrids with day-ahead energy forecasting. Electric Power Systems Research. 2020; 182 ():106232.

Chicago/Turabian Style

Sheraz Aslam; Adia Khalid; Nadeem Javaid. 2020. "Towards efficient energy management in smart grids considering microgrids with day-ahead energy forecasting." Electric Power Systems Research 182, no. : 106232.

Special issue article
Published: 04 December 2019 in Transactions on Emerging Telecommunications Technologies
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Renewable energy resources (RERs) motivate electricity users to reduce their energy bills by taking benefit of self‐generated green energy. Different studies have already pointed out that, because of the absence of proper technical support and awareness, the energy users were not able to sufficiently take paybacks from the RERs. However, with the commencement of smart grids, the potential benefits of RERs and dynamic pricing schemes can be exploited. Nonetheless, the big issue is the accurate prediction of energy produced by intermittent RERs. In this work, we have proposed an efficient framework by integrating energy storage system (ESS) and RERs with smart homes. This framework has shown significant results, which make it helpful and suitable for energy management at a community level. We applied a multiheaded convolutional neural network model for precise and accurate prediction of produced energy by RERs. Moreover, we have considered a smart community consisting of 80 homes. Simulation results prove that the proposed framework helps to decrease the energy bill of consumers by 58.32% and 63.02% through integration of RERs without and with ESS, respectively.

ACS Style

Khursheed Aurangzeb; Sheraz Aslam; Syed Irtaza Haider; Syed Muhammad Mohsin; Saif Ul Islam; Hasan Ali Khattak; Sajid Shah. Energy forecasting using multiheaded convolutional neural networks in efficient renewable energy resources equipped with energy storage system. Transactions on Emerging Telecommunications Technologies 2019, 1 .

AMA Style

Khursheed Aurangzeb, Sheraz Aslam, Syed Irtaza Haider, Syed Muhammad Mohsin, Saif Ul Islam, Hasan Ali Khattak, Sajid Shah. Energy forecasting using multiheaded convolutional neural networks in efficient renewable energy resources equipped with energy storage system. Transactions on Emerging Telecommunications Technologies. 2019; ():1.

Chicago/Turabian Style

Khursheed Aurangzeb; Sheraz Aslam; Syed Irtaza Haider; Syed Muhammad Mohsin; Saif Ul Islam; Hasan Ali Khattak; Sajid Shah. 2019. "Energy forecasting using multiheaded convolutional neural networks in efficient renewable energy resources equipped with energy storage system." Transactions on Emerging Telecommunications Technologies , no. : 1.

Journal article
Published: 14 December 2018 in Energies
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An unprecedented opportunity is presented by smart grid technologies to shift the energy industry into the new era of availability, reliability and efficiency that will contribute to our economic and environmental health. Renewable energy sources play a significant role in making environments greener and generating electricity at a cheaper cost. The cloud/fog computing also contributes to tackling the computationally intensive tasks in a smart grid. This work proposes an energy efficient approach to solve the energy management problem in the fog based environment. We consider a small community that consists of multiple smart homes. A microgrid is installed at each residence for electricity generation. Moreover, it is connected with the fog server to share and store information. Smart energy consumers are able to share the details of excess energy with each other through the fog server. The proposed approach is validated through simulations in terms of cost and imported electricity alleviation.

ACS Style

Adia Khalid; Sheraz Aslam; Khursheed Aurangzeb; Syed Irtaza Haider; Mahmood Ashraf; Nadeem Javaid. An Efficient Energy Management Approach Using Fog-as-a-Service for Sharing Economy in a Smart Grid. Energies 2018, 11, 3500 .

AMA Style

Adia Khalid, Sheraz Aslam, Khursheed Aurangzeb, Syed Irtaza Haider, Mahmood Ashraf, Nadeem Javaid. An Efficient Energy Management Approach Using Fog-as-a-Service for Sharing Economy in a Smart Grid. Energies. 2018; 11 (12):3500.

Chicago/Turabian Style

Adia Khalid; Sheraz Aslam; Khursheed Aurangzeb; Syed Irtaza Haider; Mahmood Ashraf; Nadeem Javaid. 2018. "An Efficient Energy Management Approach Using Fog-as-a-Service for Sharing Economy in a Smart Grid." Energies 11, no. 12: 3500.

Journal article
Published: 24 November 2018 in Sustainable Computing: Informatics and Systems
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The integration of information and communication technologies in traditional grid brings about a smart grid. Energy management plays a vital role in maintaining the sustainability and reliability of a smart grid which in turn helps to prevent blackouts. Energy management at consumer's side is a complex task, it requires efficient scheduling of appliances with minimum delay to reduce peak-to-average ratio (PAR) and energy consumption cost. In this paper, the classification of appliances is introduced based on their energy consumption pattern. An energy management controller is developed for demand side management. We have used fuzzy logic and heuristic optimization techniques for cost, energy consumption and PAR reduction. Fuzzy logic is used to control the throttleable and interruptible appliances. On the other hand, the heuristic optimization algorithms, BAT inspired and flower pollination, are employed for scheduling of shiftable appliances. We have also proposed a hybrid optimization algorithm for the scheduling of home appliances, named as hybrid BAT pollination optimization algorithm. Simulation results show a significant reduction in energy consumption, cost and PAR.

ACS Style

Rabiya Khalid; Nadeem Javaid; Muhammad Hassan Rahim; Sheraz Aslam; Arshad Sher. Fuzzy energy management controller and scheduler for smart homes. Sustainable Computing: Informatics and Systems 2018, 21, 103 -118.

AMA Style

Rabiya Khalid, Nadeem Javaid, Muhammad Hassan Rahim, Sheraz Aslam, Arshad Sher. Fuzzy energy management controller and scheduler for smart homes. Sustainable Computing: Informatics and Systems. 2018; 21 ():103-118.

Chicago/Turabian Style

Rabiya Khalid; Nadeem Javaid; Muhammad Hassan Rahim; Sheraz Aslam; Arshad Sher. 2018. "Fuzzy energy management controller and scheduler for smart homes." Sustainable Computing: Informatics and Systems 21, no. : 103-118.

Conference paper
Published: 08 June 2018 in Advances in Intelligent Systems and Computing
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In this work, we propose a DSM scheme for electricity expenses and peak to average ratio (PAR) reduction using two well-known heuristic approaches: the cuckoo search algorithm (CSA) and strawberry algorithm (SA). In our proposed scheme, a smart home decides to buy or sell electricity from/to the commercial grid for minimizing electricity costs and PAR with earning maximization. It makes a decision on the basis of electricity prices, demand and generation from its own microgrid. The microgrid consists of a wind turbine and solar panel. Electricity generation from the solar panel and wind turbine is intermittent in nature. Therefore, an energy storage system (ESS) is also considered for stable and reliable power system operation. We test our proposed scheme on a set of different case studies. The simulation results affirm our proposed scheme in terms of electricity cost and PAR reduction with profit maximization.

ACS Style

Sheraz Aslam; Sakeena Javaid; Nadeem Javaid; Syed Muhammad Mohsin; Saad Sulman Khan; Mariam Akbar. An Efficient Home Energy Management and Power Trading in Smart Grid. Advances in Intelligent Systems and Computing 2018, 231 -241.

AMA Style

Sheraz Aslam, Sakeena Javaid, Nadeem Javaid, Syed Muhammad Mohsin, Saad Sulman Khan, Mariam Akbar. An Efficient Home Energy Management and Power Trading in Smart Grid. Advances in Intelligent Systems and Computing. 2018; ():231-241.

Chicago/Turabian Style

Sheraz Aslam; Sakeena Javaid; Nadeem Javaid; Syed Muhammad Mohsin; Saad Sulman Khan; Mariam Akbar. 2018. "An Efficient Home Energy Management and Power Trading in Smart Grid." Advances in Intelligent Systems and Computing , no. : 231-241.

Conference paper
Published: 01 June 2018 in 2018 14th International Wireless Communications & Mobile Computing Conference (IWCMC)
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In this paper, we propose a home energy management (HEM) scheme in the residential area for electricity cost and peak to average ratio (PAR) reduction. Furthermore, reduction in imported electricity from the external grid is also the objective of this study. Our proposed scheme schedules smart appliances as well as electrical vehicles (EVs) charging\discharging optimally according to the consumer preferences. Each consumer has its own grid-connected microgrid for electricity generation; which consists of wind turbine, solar panel, micro gas turbine (MGT) and energy storage system (ESS). Furthermore, the scheduling problem is mathematically formulated and solved by mixed integer linear programming (MILP). We also provide the comparison of the optimal solutions, while considering EVs with and without discharging capabilities. Findings from simulations affirm our proposed scheme in terms of above-mentioned objectives.

ACS Style

Sheraz Aslam; Nadeem Javaid; Muhammad Asif; Umar Iqbal; Zafar Iqbal; Mian Ahmer Sarwar. A mixed integer linear programming based optimal home energy management scheme considering grid-connected microgrids. 2018 14th International Wireless Communications & Mobile Computing Conference (IWCMC) 2018, 993 -998.

AMA Style

Sheraz Aslam, Nadeem Javaid, Muhammad Asif, Umar Iqbal, Zafar Iqbal, Mian Ahmer Sarwar. A mixed integer linear programming based optimal home energy management scheme considering grid-connected microgrids. 2018 14th International Wireless Communications & Mobile Computing Conference (IWCMC). 2018; ():993-998.

Chicago/Turabian Style

Sheraz Aslam; Nadeem Javaid; Muhammad Asif; Umar Iqbal; Zafar Iqbal; Mian Ahmer Sarwar. 2018. "A mixed integer linear programming based optimal home energy management scheme considering grid-connected microgrids." 2018 14th International Wireless Communications & Mobile Computing Conference (IWCMC) , no. : 993-998.

Journal article
Published: 20 April 2018 in Energies
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Microgrid is a community-based power generation and distribution system that interconnects smart homes with renewable energy sources (RESs). Microgrid efficiently and economically generates power for electricity consumers and operates in both islanded and grid-connected modes. In this study, we proposed optimization schemes for reducing electricity cost and minimizing peak to average ratio (PAR) with maximum user comfort (UC) in a smart home. We considered a grid-connected microgrid for electricity generation which consists of wind turbine and photovoltaic (PV) panel. First, the problem was mathematically formulated through multiple knapsack problem (MKP) then solved by existing heuristic techniques: grey wolf optimization (GWO), binary particle swarm optimization (BPSO), genetic algorithm (GA) and wind-driven optimization (WDO). Furthermore, we also proposed three hybrid schemes for electric cost and PAR reduction: (1) hybrid of GA and WDO named WDGA; (2) hybrid of WDO and GWO named WDGWO; and (3) WBPSO, which is the hybrid of BPSO and WDO. In addition, a battery bank system (BBS) was also integrated to make our proposed schemes more cost-efficient and reliable, and to ensure stable grid operation. Finally, simulations were performed to verify our proposed schemes. Results show that our proposed scheme efficiently minimizes the electricity cost and PAR. Moreover, our proposed techniques, WDGA, WDGWO and WBPSO, outperform the existing heuristic techniques.

ACS Style

Zafar Iqbal; Nadeem Javaid; Saleem Iqbal; Sheraz Aslam; Zahoor Ali Khan; Wadood Abdul; Ahmad Almogren; Atif Alamri. A Domestic Microgrid with Optimized Home Energy Management System. Energies 2018, 11, 1002 .

AMA Style

Zafar Iqbal, Nadeem Javaid, Saleem Iqbal, Sheraz Aslam, Zahoor Ali Khan, Wadood Abdul, Ahmad Almogren, Atif Alamri. A Domestic Microgrid with Optimized Home Energy Management System. Energies. 2018; 11 (4):1002.

Chicago/Turabian Style

Zafar Iqbal; Nadeem Javaid; Saleem Iqbal; Sheraz Aslam; Zahoor Ali Khan; Wadood Abdul; Ahmad Almogren; Atif Alamri. 2018. "A Domestic Microgrid with Optimized Home Energy Management System." Energies 11, no. 4: 1002.

Journal article
Published: 18 April 2018 in Sustainability
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Demand side management (DSM) is one of the most challenging areas in smart grids, which provides multiple opportunities for residents to minimize electricity cost. In this work, we propose a DSM scheme for electricity expenses and peak to average ratio (PAR) reduction using two well-known heuristic approaches: the cuckoo search algorithm (CSA) and strawberry algorithm (SA). In our proposed scheme, a smart home decides to buy or sell electricity from/to the commercial grid for minimizing electricity costs and PAR with earning maximization. It makes a decision on the basis of electricity prices, demand and generation from its own microgrid. The microgrid consists of a wind turbine and solar panel. Electricity generation from the solar panel and wind turbine is intermittent in nature. Therefore, an energy storage system (ESS) is also considered for stable and reliable power system operation. We test our proposed scheme on a set of different case studies. The simulation results affirm our proposed scheme in terms of electricity cost and PAR reduction with profit maximization. Furthermore, a comparative analysis is also performed to show the legitimacy and productiveness of CSA and SA.

ACS Style

Sheraz Aslam; Nadeem Javaid; Farman Ali Khan; Atif Alamri; Ahmad Almogren; Wadood Abdul. Towards Efficient Energy Management and Power Trading in a Residential Area via Integrating a Grid-Connected Microgrid. Sustainability 2018, 10, 1245 .

AMA Style

Sheraz Aslam, Nadeem Javaid, Farman Ali Khan, Atif Alamri, Ahmad Almogren, Wadood Abdul. Towards Efficient Energy Management and Power Trading in a Residential Area via Integrating a Grid-Connected Microgrid. Sustainability. 2018; 10 (4):1245.

Chicago/Turabian Style

Sheraz Aslam; Nadeem Javaid; Farman Ali Khan; Atif Alamri; Ahmad Almogren; Wadood Abdul. 2018. "Towards Efficient Energy Management and Power Trading in a Residential Area via Integrating a Grid-Connected Microgrid." Sustainability 10, no. 4: 1245.

Conference paper
Published: 01 April 2018 in 2018 5th International Multi-Topic ICT Conference (IMTIC)
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For the optimization of home energy consumption, we proposed a system model based on scheduling techniques and also, incorporated real-time coordination among household appliances by using game theory (GT). Main objective of scheduling techniques is to decrease the electricity cost of consumers by efficiently managing the home energy consumption on the basis of real-time pricing (RTP) signal whereas, coordination is implemented among household appliances in order to increase the user comfort by decreasing the appliances delay. Scheduling techniques: cuckoo search algorithm (CSA), earthworm algorithm (EWA), bat algorithm (BA) and a proposed hybrid scheme (HCEO), originated from CSA and EWA, are implemented and results demonstrate that proposed hybrid scheme has reduced the total electricity cost by 49.09% as compared to unscheduled case and it outperformed other scheduling techniques in terms of cost reduction. Performance of scheduling techniques before and after coordination is evaluated and a comparison is performed with each other. Results indicate that a trade-off exists between electricity cost and appliances delay.

ACS Style

Aqib Jamil; Nadeem Javaid; Sheraz Aslam. An Efficient Home Energy Optimization by Using Meta-heuristic Techniques While Incorporating Game-theoretic Approach for Real-time Coordination Among Home Appliances. 2018 5th International Multi-Topic ICT Conference (IMTIC) 2018, 1 -6.

AMA Style

Aqib Jamil, Nadeem Javaid, Sheraz Aslam. An Efficient Home Energy Optimization by Using Meta-heuristic Techniques While Incorporating Game-theoretic Approach for Real-time Coordination Among Home Appliances. 2018 5th International Multi-Topic ICT Conference (IMTIC). 2018; ():1-6.

Chicago/Turabian Style

Aqib Jamil; Nadeem Javaid; Sheraz Aslam. 2018. "An Efficient Home Energy Optimization by Using Meta-heuristic Techniques While Incorporating Game-theoretic Approach for Real-time Coordination Among Home Appliances." 2018 5th International Multi-Topic ICT Conference (IMTIC) , no. : 1-6.

Conference paper
Published: 01 April 2018 in 2018 5th International Multi-Topic ICT Conference (IMTIC)
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One of the emerging problems in the power systems is economic operation of electrical energy. Also, generation from multiple sources is required to be scheduled in a way to provide the optimal power and efficient operation of the electric network. In this study, an optimal power flow (OPF) problem is formulated and solved using an optimization technique named as bird swarm algorithm (BSA) with an aim of cost reduction and minimum carbon emissions. The proposed solution is tested on IEEE 30-bus testing system to obtain the optimal settings while keeping in consideration the stated constraints for active and reactive power, voltage stability and line capacity. Obtained results are then compared with the previous study, which as a result proved the effectiveness of our proposed algorithm.

ACS Style

Sundas Shafiq; Nadeem Javaid; Sheraz Aslam. Optimal power flow control in a smart micro-grid using bird swarm algorithm. 2018 5th International Multi-Topic ICT Conference (IMTIC) 2018, 1 -7.

AMA Style

Sundas Shafiq, Nadeem Javaid, Sheraz Aslam. Optimal power flow control in a smart micro-grid using bird swarm algorithm. 2018 5th International Multi-Topic ICT Conference (IMTIC). 2018; ():1-7.

Chicago/Turabian Style

Sundas Shafiq; Nadeem Javaid; Sheraz Aslam. 2018. "Optimal power flow control in a smart micro-grid using bird swarm algorithm." 2018 5th International Multi-Topic ICT Conference (IMTIC) , no. : 1-7.