The Experts below are selected from a list of 7035 Experts worldwide ranked by ideXlab platform

Nadeem Javaid - One of the best experts on this subject based on the ideXlab platform.

  • efficient power scheduling in smart homes using hybrid grey wolf differential evolution optimization technique with real time and critical peak pricing schemes
    Energies, 2018
    Co-Authors: Muqaddas Naz, Zahoor Ali Kha, Wadood Abdul, Ahmad Almogre, Nadeem Javaid, Zafa Iqbal, Atif Alamri
    Abstract:

    With the emergence of automated environments, energy demand by consumers is increasing rapidly. More than 80% of total Electricity is being consumed in the residential sector. This brings a challenging task of maintaining the balance between demand and generation of electric power. In order to meet such challenges, a traditional grid is renovated by integrating two-way communication between the consumer and generation unit. To reduce Electricity cost and peak load demand, demand side management (DSM) is modeled as an optimization problem, and the solution is obtained by applying meta-heuristic techniques with different pricing schemes. In this paper, an optimization technique, the hybrid gray wolf differential evolution (HGWDE), is proposed by merging enhanced differential evolution (EDE) and gray wolf optimization (GWO) scheme using real-time pricing (RTP) and critical peak pricing (CPP). Load shifting is performed from on-peak hours to off-peak hours depending on the Electricity cost defined by the utility. However, there is a trade-off between user comfort and cost. To validate the performance of the proposed algorithm, simulations have been carried out in MATLAB. Results illustrate that using RTP, the peak to average ratio (PAR) is reduced to 53.02%, 29.02% and 26.55%, while the Electricity Bill is reduced to 12.81%, 12.012% and 12.95%, respectively, for the 15-, 30- and 60-min operational time interval (OTI). On the other hand, the PAR and Electricity Bill are reduced to 47.27%, 22.91%, 22% and 13.04%, 12%, 11.11% using the CPP tariff.

  • A New Meta-heuristic Optimization Algorithm Inspired from Strawberry Plant for Demand Side Management in Smart Grid
    Advances in Intelligent Networking and Collaborative Systems, 2018
    Co-Authors: Muhammad Sufyan Khan, C. H. Anwar Ul Hassan, Hazrat Abubakar Sadiq, Asad Rauf, Ishtiaq Ali, Nadeem Javaid
    Abstract:

    In recent years, different Demand Side Management (DSM) techniques have been proposed to involve users in decision making process of Smart Grid (SG). Power consumption pattern of shiftable home appliances is schedule to achieve desired benefits of high User Comfort (UC) and low energy consumption. In this paper, an Energy Management Controller (EMC) is designed by using two meta-heuristic algorithms: Strawberry Algorithm (SBA) and Enhanced Differential Evolution (EDE). The main objectives are Electricity Bill minimization, reduction in Peak to Average Ratio (PAR) and maximization of UC. However, there always exist a trade-off between cost minimization and UC maximization. Simulation results verify that, SBA perform better then EDE in terms of cost reduction while EDE perform far better than SBA in terms of UC maximization.

  • demand side optimization in smart grid using harmony search algorithm and social spider algorithm
    International Conference on P2P Parallel Grid Cloud and Internet Computing, 2017
    Co-Authors: Muhammad Junaid, Muhammad Hassan Rahim, Anwar Ur Rehman, Waqar Ali, Muhammad Awais, Tamour Bilal, Nadeem Javaid
    Abstract:

    Electricity is a valuable resource. With the increase of population, this valuable resource is being used inefficiently. To overcome this problem, Electricity providers use various techniques like introducing different pricing schemes. In peak hours, when the usage of Electricity is high, the utility increases the per unit cost. Therefore, usage of Electricity in peak hours result in high Electricity Bills. The Electricity Bills can be reduced by efficiently scheduling the home appliances so that few appliances are operated during peak hours. For this purpose many techniques have been proposed. In this paper, we propose a Social Spider Algorithm (SSA) for Demand Side Management (DSM). Harmony Search Algorithm (HSA) has been adapted to evaluate the results of SSA. These algorithms schedules the appliances in such a way that the usage of Electricity in peak hours is reduced. This results in reduction of Electricity Bill and Peak to Average Ratio (PAR).

  • load scheduling optimization using heuristic techniques and combined price signal
    Network-Based Information Systems, 2017
    Co-Authors: Iqra Fatima, Sikandar Asif, Sundas Shafiq, Ch Anwar Ul Hassan, Sajeeha Ansar, Nadeem Javaid
    Abstract:

    In this paper, a comparative analysis of two heuristic algorithms, i.e., enhanced differential evolution (EDE) and tabu search (TS) with unschedule load approach for its optimality is proposed. This paper aims to achieve minimum Electricity Bill and maximum peak to average ratio (PAR) reduction while considering the factor of user satisfaction. In order to achieve our aim, an objective function of Electricity cost reduction is made based upon the scheduling strategies. A combined model of pricing schemes, i.e., time of use (ToU) and critical peak pricing (CPP) is used to calculate Electricity Bill and to tackle the instability. We implemented a state of art user-defined taxonomy of appliances in our paper to deal with the user comfort appropriately in a residential area. Simulation results shows that our proposed strategy works better to encourage the users for intelligent power consumption.

  • an intelligent hybrid heuristic scheme for smart metering based demand side management in smart homes
    Energies, 2017
    Co-Authors: Awais Manzoor, Wadood Abdul, Nadeem Javaid, Ibrar Ullah, Ahmad Almogren, Atif Alamri
    Abstract:

    Smart grid is an emerging technology which is considered to be an ultimate solution to meet the increasing power demand challenges. Modern communication technologies have enabled the successful implementation of smart grid (SG), which aims at provision of demand side management mechanisms (DSM), such as demand response (DR). In this paper, we propose a hybrid technique named as teacher learning genetic optimization (TLGO) by combining genetic algorithm (GA) with teacher learning based optimization (TLBO) algorithm for residential load scheduling, assuming that electric prices are announced on a day-ahead basis. User discomfort is one of the key aspects which must be addressed along with cost minimization. The major focus of this work is to minimize consumer Electricity Bill at minimum user discomfort. Load scheduling is formulated as an optimization problem and an optimal schedule is achieved by solving the minimization problem. We also investigated the effect of power-flexible appliances on consumers’ Bill. Furthermore, a relationship among power consumption, cost and user discomfort is also demonstrated by feasible region. Simulation results validate that our proposed technique performs better in terms of cost reduction and user discomfort minimization, and is able to obtain the desired trade-off between consumer Electricity Bill and user discomfort.

Atif Alamri - One of the best experts on this subject based on the ideXlab platform.

  • efficient power scheduling in smart homes using hybrid grey wolf differential evolution optimization technique with real time and critical peak pricing schemes
    Energies, 2018
    Co-Authors: Muqaddas Naz, Zahoor Ali Kha, Wadood Abdul, Ahmad Almogre, Nadeem Javaid, Zafa Iqbal, Atif Alamri
    Abstract:

    With the emergence of automated environments, energy demand by consumers is increasing rapidly. More than 80% of total Electricity is being consumed in the residential sector. This brings a challenging task of maintaining the balance between demand and generation of electric power. In order to meet such challenges, a traditional grid is renovated by integrating two-way communication between the consumer and generation unit. To reduce Electricity cost and peak load demand, demand side management (DSM) is modeled as an optimization problem, and the solution is obtained by applying meta-heuristic techniques with different pricing schemes. In this paper, an optimization technique, the hybrid gray wolf differential evolution (HGWDE), is proposed by merging enhanced differential evolution (EDE) and gray wolf optimization (GWO) scheme using real-time pricing (RTP) and critical peak pricing (CPP). Load shifting is performed from on-peak hours to off-peak hours depending on the Electricity cost defined by the utility. However, there is a trade-off between user comfort and cost. To validate the performance of the proposed algorithm, simulations have been carried out in MATLAB. Results illustrate that using RTP, the peak to average ratio (PAR) is reduced to 53.02%, 29.02% and 26.55%, while the Electricity Bill is reduced to 12.81%, 12.012% and 12.95%, respectively, for the 15-, 30- and 60-min operational time interval (OTI). On the other hand, the PAR and Electricity Bill are reduced to 47.27%, 22.91%, 22% and 13.04%, 12%, 11.11% using the CPP tariff.

  • an intelligent hybrid heuristic scheme for smart metering based demand side management in smart homes
    Energies, 2017
    Co-Authors: Awais Manzoor, Wadood Abdul, Nadeem Javaid, Ibrar Ullah, Ahmad Almogren, Atif Alamri
    Abstract:

    Smart grid is an emerging technology which is considered to be an ultimate solution to meet the increasing power demand challenges. Modern communication technologies have enabled the successful implementation of smart grid (SG), which aims at provision of demand side management mechanisms (DSM), such as demand response (DR). In this paper, we propose a hybrid technique named as teacher learning genetic optimization (TLGO) by combining genetic algorithm (GA) with teacher learning based optimization (TLBO) algorithm for residential load scheduling, assuming that electric prices are announced on a day-ahead basis. User discomfort is one of the key aspects which must be addressed along with cost minimization. The major focus of this work is to minimize consumer Electricity Bill at minimum user discomfort. Load scheduling is formulated as an optimization problem and an optimal schedule is achieved by solving the minimization problem. We also investigated the effect of power-flexible appliances on consumers’ Bill. Furthermore, a relationship among power consumption, cost and user discomfort is also demonstrated by feasible region. Simulation results validate that our proposed technique performs better in terms of cost reduction and user discomfort minimization, and is able to obtain the desired trade-off between consumer Electricity Bill and user discomfort.

  • an optimized home energy management system with integrated renewable energy and storage resources
    Energies, 2017
    Co-Authors: Adnan Ahmad, Wadood Abdul, Nadeem Javaid, Atif Alamri, Ahmad Almogren, Asif Khan, Hafiz Majid Hussain, Iftikhar Azim Niaz
    Abstract:

    Traditional power grid and its demand-side management (DSM) techniques are centralized and mainly focus on industrial consumers. The ignorance of residential and commercial sectors in DSM activities degrades the overall performance of a conventional grid. Therefore, the concept of DSM and demand response (DR) via residential sector makes the smart grid (SG) superior over the traditional grid. In this context, this paper proposes an optimized home energy management system (OHEMS) that not only facilitates the integration of renewable energy source (RES) and energy storage system (ESS) but also incorporates the residential sector into DSM activities. The proposed OHEMS minimizes the Electricity Bill by scheduling the household appliances and ESS in response to the dynamic pricing of Electricity market. First, the constrained optimization problem is mathematically formulated by using multiple knapsack problems, and then solved by using the heuristic algorithms; genetic algorithm (GA), binary particle swarm optimization (BPSO), wind driven optimization (WDO), bacterial foraging optimization (BFO) and hybrid GA-PSO (HGPO) algorithms. The performance of the proposed scheme and heuristic algorithms is evaluated via MATLAB simulations. Results illustrate that the integration of RES and ESS reduces the Electricity Bill and peak-to-average ratio (PAR) by 19.94% and 21.55% respectively. Moreover, the HGPO algorithm based home energy management system outperforms the other heuristic algorithms, and further reduces the Bill by 25.12% and PAR by 24.88%.

Mostafa Parniani - One of the best experts on this subject based on the ideXlab platform.

  • Game theoretic based charging strategy for plug-in hybrid electric vehicles
    IEEE Transactions on Smart Grid, 2014
    Co-Authors: Sina Bahrami, Mostafa Parniani
    Abstract:

    In the future smart grids, Plug-in Hybrid Electric Vehicles (PHEVs) are seen as an important means of transportation to reduce greenhouse gas emissions. One of the main issues regarding to this sort of vehicles is managing their charging time to prevent high peak loads over time. Deploying advanced metering and automatic chargers can be a practical way not only for the vehicle owners to manage their energy consumption, but also for the utilities to manage the Electricity load during the day by shifting the charging loads to the off-peak periods. Additionally, an efficient charging schedule can reduce the users' Electricity Bill cost. In this paper we propose a new practical demand response (DR) program for PHEVs charging scheduling based on game theoretic approach, aiming at optimizing customers charging cost. In the proposed method, a stochastic model is given for starting time of charging, which makes the method a practical tool for simulating the vehicle owners charging behavior effectively.

  • a modified approach for residential load scheduling using smart meters
    IEEE PES Innovative Smart Grid Technologies Europe, 2012
    Co-Authors: Shahab Ahrami, Mostafa Parniani, A Vafaeimeh
    Abstract:

    Implementation of various incentive-based demand response strategies has great potential to decrease peak load growth and customer Electricity Bill cost. Using advanced metering and automatic demand management makes it possible to optimize energy consumption, to reduce grid loss, and to release generation capacities for the sake of providing sustainable Electricity supply. Executing an incentive-based program is a simple way for customers to monitor and manage their energy consumption, and therefore, to reduce their Electricity Bill. With these objectives, this paper examines the previously suggested load scheduling programs and proposes a new practical one for residential energy management. The method is aimed at optimizing customers' Bill cost and satisfaction by taking into consideration the generation capacity limitation and dynamic Electricity price in different time slots of a day. Moreover, the proposed optimization algorithm is compared with Particle Swarm Optimization (PSO) algorithm to illustrate high efficiency of the proposed algorithm as a practical industrial tool for peak load shaving.

Wadood Abdul - One of the best experts on this subject based on the ideXlab platform.

  • efficient power scheduling in smart homes using hybrid grey wolf differential evolution optimization technique with real time and critical peak pricing schemes
    Energies, 2018
    Co-Authors: Muqaddas Naz, Zahoor Ali Kha, Wadood Abdul, Ahmad Almogre, Nadeem Javaid, Zafa Iqbal, Atif Alamri
    Abstract:

    With the emergence of automated environments, energy demand by consumers is increasing rapidly. More than 80% of total Electricity is being consumed in the residential sector. This brings a challenging task of maintaining the balance between demand and generation of electric power. In order to meet such challenges, a traditional grid is renovated by integrating two-way communication between the consumer and generation unit. To reduce Electricity cost and peak load demand, demand side management (DSM) is modeled as an optimization problem, and the solution is obtained by applying meta-heuristic techniques with different pricing schemes. In this paper, an optimization technique, the hybrid gray wolf differential evolution (HGWDE), is proposed by merging enhanced differential evolution (EDE) and gray wolf optimization (GWO) scheme using real-time pricing (RTP) and critical peak pricing (CPP). Load shifting is performed from on-peak hours to off-peak hours depending on the Electricity cost defined by the utility. However, there is a trade-off between user comfort and cost. To validate the performance of the proposed algorithm, simulations have been carried out in MATLAB. Results illustrate that using RTP, the peak to average ratio (PAR) is reduced to 53.02%, 29.02% and 26.55%, while the Electricity Bill is reduced to 12.81%, 12.012% and 12.95%, respectively, for the 15-, 30- and 60-min operational time interval (OTI). On the other hand, the PAR and Electricity Bill are reduced to 47.27%, 22.91%, 22% and 13.04%, 12%, 11.11% using the CPP tariff.

  • an intelligent hybrid heuristic scheme for smart metering based demand side management in smart homes
    Energies, 2017
    Co-Authors: Awais Manzoor, Wadood Abdul, Nadeem Javaid, Ibrar Ullah, Ahmad Almogren, Atif Alamri
    Abstract:

    Smart grid is an emerging technology which is considered to be an ultimate solution to meet the increasing power demand challenges. Modern communication technologies have enabled the successful implementation of smart grid (SG), which aims at provision of demand side management mechanisms (DSM), such as demand response (DR). In this paper, we propose a hybrid technique named as teacher learning genetic optimization (TLGO) by combining genetic algorithm (GA) with teacher learning based optimization (TLBO) algorithm for residential load scheduling, assuming that electric prices are announced on a day-ahead basis. User discomfort is one of the key aspects which must be addressed along with cost minimization. The major focus of this work is to minimize consumer Electricity Bill at minimum user discomfort. Load scheduling is formulated as an optimization problem and an optimal schedule is achieved by solving the minimization problem. We also investigated the effect of power-flexible appliances on consumers’ Bill. Furthermore, a relationship among power consumption, cost and user discomfort is also demonstrated by feasible region. Simulation results validate that our proposed technique performs better in terms of cost reduction and user discomfort minimization, and is able to obtain the desired trade-off between consumer Electricity Bill and user discomfort.

  • an optimized home energy management system with integrated renewable energy and storage resources
    Energies, 2017
    Co-Authors: Adnan Ahmad, Wadood Abdul, Nadeem Javaid, Atif Alamri, Ahmad Almogren, Asif Khan, Hafiz Majid Hussain, Iftikhar Azim Niaz
    Abstract:

    Traditional power grid and its demand-side management (DSM) techniques are centralized and mainly focus on industrial consumers. The ignorance of residential and commercial sectors in DSM activities degrades the overall performance of a conventional grid. Therefore, the concept of DSM and demand response (DR) via residential sector makes the smart grid (SG) superior over the traditional grid. In this context, this paper proposes an optimized home energy management system (OHEMS) that not only facilitates the integration of renewable energy source (RES) and energy storage system (ESS) but also incorporates the residential sector into DSM activities. The proposed OHEMS minimizes the Electricity Bill by scheduling the household appliances and ESS in response to the dynamic pricing of Electricity market. First, the constrained optimization problem is mathematically formulated by using multiple knapsack problems, and then solved by using the heuristic algorithms; genetic algorithm (GA), binary particle swarm optimization (BPSO), wind driven optimization (WDO), bacterial foraging optimization (BFO) and hybrid GA-PSO (HGPO) algorithms. The performance of the proposed scheme and heuristic algorithms is evaluated via MATLAB simulations. Results illustrate that the integration of RES and ESS reduces the Electricity Bill and peak-to-average ratio (PAR) by 19.94% and 21.55% respectively. Moreover, the HGPO algorithm based home energy management system outperforms the other heuristic algorithms, and further reduces the Bill by 25.12% and PAR by 24.88%.

Antonio Fernandezcardador - One of the best experts on this subject based on the ideXlab platform.

  • analysis of the demand charge in dc railway systems and reduction of its economic impact with energy storage systems
    International Journal of Electrical Power & Energy Systems, 2017
    Co-Authors: David Rochdupre, Alvaro J Lopezlopez, Ramon Rodriguez Pecharroman, Asuncion P Cucala, Antonio Fernandezcardador
    Abstract:

    Abstract In addition to energy consumption, DC railway operators must also pay for the demand charge. This term of the Electricity Bill has not been studied in detail in the literature and penalizes power peaks. The big fluctuations on the power demand which characterize railway systems make the demand charge important for railway operators. This paper studies the impact of the demand charge on DC railway systems and proposes a solution based on Energy Storage Systems (ESSs) to reduce it. An analysis of the main parameters of the ESS regarding the reduction of the demand charge is provided, as well as an explanation of the effects of different control strategies on the system performance. Most of the savings obtained with the installation of ESSs come from the reduction in the energy consumption; nevertheless, the savings coming from the reduction in the demand charge are significant and contribute to the economic viability of the investment.