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The implementation of real-time price-based demand response program and integration of renewable energy resources (RESs) improves efficiency and ensure stability of electric grid. This paper proposes a novel intelligent optimization based demand-side management (DSM) framework for smart grid integrated with RESs. In the intelligent DSM framework the artificial neural network (ANN) forecasts energy usage behavior of consumers and real-time price-based demand response program (RTPDRP) of electric utility company (EUC). The smart energy management controller of the proposed intelligent DSM framework adapts forecasted energy usage behavior of consumers using forecasted RTPDRP to create operation schedule. The consumers implement the created schedule to minimize energy cost, peak load, carbon emission subjected to improving user comfort and avoiding rebound peaks. Simulations are conducted using our proposed hybrid genetic ant colony (HGAC) optimization algorithm to create schedule for three cases: EUC without RESs, EUC with RESs, and EUC with both RESs and storage technologies. To endorse the applicability and productivity of the proposed DSM framework based on HGAC optimization algorithm with five existing algorithms based frameworks. Simulation results show that the proposed DSM framework is superior compared with the existing frameworks in terms of energy cost minimization, peak load mitigation, carbon emission alleviation, and user discomfort minimization. The proposed HGAC optimization algorithm reduced electricity cost, carbon emission, and peak load by 12.16%, 4.00%, and 19.44% in case I; by 26.8%, 20.71%, and 33.3% in case II; and by 24.4%, 16.44%, and 37.08% in case III, respectively, compared to without scheduling.
Hassan Wasim Khan; Muhammad Usman; Ghulam Hafeez; Fahad R. Albogamy; Imran Khan; Zeeshan Shafiq; Mohammad Usman Ali Khan; Hend I. Alkhammash. Intelligent optimization framework for efficient demand-side management in renewable energy integrated smart grid. IEEE Access 2021, PP, 1 -1.
AMA StyleHassan Wasim Khan, Muhammad Usman, Ghulam Hafeez, Fahad R. Albogamy, Imran Khan, Zeeshan Shafiq, Mohammad Usman Ali Khan, Hend I. Alkhammash. Intelligent optimization framework for efficient demand-side management in renewable energy integrated smart grid. IEEE Access. 2021; PP (99):1-1.
Chicago/Turabian StyleHassan Wasim Khan; Muhammad Usman; Ghulam Hafeez; Fahad R. Albogamy; Imran Khan; Zeeshan Shafiq; Mohammad Usman Ali Khan; Hend I. Alkhammash. 2021. "Intelligent optimization framework for efficient demand-side management in renewable energy integrated smart grid." IEEE Access PP, no. 99: 1-1.
This paper proposes a hybrid control scheme for a newly devised hybrid multilevel inverter (HMLI) topology. The circuit configuration of HMLI is comprised of a cascaded converter module (CCM), connected in series with an H-bridge converter. Initially, a finite set model predictive control (FS-MPC) is adopted as a control scheme, and theoretical analysis is carried out in MATLAB/Simulink. Later, in the real-time implementation of the HMLI topology, a hybrid control scheme which is a variant of the FS-MPC method has been proposed. The proposed control method is computationally efficient and therefore has been employed to the HMLI topology to mitigate the high-frequency switching limitation of the conventional MPC. Moreover, a comparative analysis is carried to illustrate the advantages of the proposed work that includes low switching losses, higher efficiency, and improved total harmonic distortion (THD) in output current. The inverter topology and stability of the proposed control method have been validated through simulation results in MATLAB/Simulink environment. Experimental results via low-voltage laboratory prototype have been added and compared to realize the study in practice.
Muhammad Ali; Ghulam Hafeez; Ajmal Farooq; Zeeshan Shafiq; Faheem Ali; Muhammad Usman; Lucian Mihet-Popa. A Novel Control Approach to Hybrid Multilevel Inverter for High-Power Applications. Energies 2021, 14, 4563 .
AMA StyleMuhammad Ali, Ghulam Hafeez, Ajmal Farooq, Zeeshan Shafiq, Faheem Ali, Muhammad Usman, Lucian Mihet-Popa. A Novel Control Approach to Hybrid Multilevel Inverter for High-Power Applications. Energies. 2021; 14 (15):4563.
Chicago/Turabian StyleMuhammad Ali; Ghulam Hafeez; Ajmal Farooq; Zeeshan Shafiq; Faheem Ali; Muhammad Usman; Lucian Mihet-Popa. 2021. "A Novel Control Approach to Hybrid Multilevel Inverter for High-Power Applications." Energies 14, no. 15: 4563.
Energy optimization plays a vital role in energy management, economic savings, effective planning, reliable and secure power grid operation. However, energy optimization is challenging due to the uncertain and intermittent nature of renewable energy sources (RES) and consumer’s behavior. A rigid energy optimization model with assertive intermittent, stochastic, and non-linear behavior capturing abilities is needed in this context. Thus, a novel energy optimization model is developed to optimize the smart microgrid’s performance by reducing the operating cost, pollution emission and maximizing availability using RES. To predict the behavior of RES like solar and wind probability density function (PDF) and cumulative density function (CDF) are proposed. Contrarily, to resolve uncertainty and non-linearity of RES, a hybrid scheme of demand response programs (DRPS) and incline block tariff (IBT) with the participation of industrial, commercial, and residential consumers is introduced. For the developed model, an energy optimization strategy based on multi-objective wind-driven optimization (MOWDO) algorithm and multi-objective genetic algorithm (MOGA) is utilized to optimize the operation cost, pollution emission, and availability with/without the involvement in hybrid DRPS and IBT. Simulation results are considered in two different cases: operating cost and pollution emission, and operating cost and availability with/without participating in the hybrid scheme of DRPS and IBT. Simulation results illustrate that the proposed energy optimization model optimizes the performance of smart microgrid in aspects of operation cost, pollution emission, and availability compared to the existing models with/without involvement in hybrid scheme of DRPS and IBT. Thus, results validate that the proposed energy optimization model’s performance is outstanding compared to the existing models.
Kalim Ullah; Ghulam Hafeez; Imran Khan; Sadaqat Jan; Nadeem Javaid. A multi-objective energy optimization in smart grid with high penetration of renewable energy sources. Applied Energy 2021, 299, 117104 .
AMA StyleKalim Ullah, Ghulam Hafeez, Imran Khan, Sadaqat Jan, Nadeem Javaid. A multi-objective energy optimization in smart grid with high penetration of renewable energy sources. Applied Energy. 2021; 299 ():117104.
Chicago/Turabian StyleKalim Ullah; Ghulam Hafeez; Imran Khan; Sadaqat Jan; Nadeem Javaid. 2021. "A multi-objective energy optimization in smart grid with high penetration of renewable energy sources." Applied Energy 299, no. : 117104.
Real-time, accurate, and stable forecasting plays a vital role in making strategic decisions in the smart grid (SG). This ensures economic savings, effective planning, and reliable and secure power system operation. However, accurate and stable forecasting is challenging due to the uncertain and intermittent electric load behavior. In this context, a rigid forecasting model with assertive stochastic and non-linear behavior capturing abilities is needed. Thus, a support vector regression (SVR) model emerged to cater the non-linear time-series predictions. However, it suffers from computational complexity and hard-to-tune appropriate parameters problem. Due to these problems, forecasting results of SVR are not as accurate as required. To solve such problems, a novel hybrid approach is developed by integrating feature engineering (FE) and modified fire-fly optimization (mFFO) algorithm with SVR, namely FE-SVR-mFFO forecasting framework. FE eliminates redundant and irrelevant features to ensure high computational efficiency. The mFFO algorithm obtains and tunes the SVR model’s appropriate parameters to effectively avoid trapping into local optimum and returns accurate forecasting results. Besides, most literature studies are focused on forecast accuracy improvement. However, the forecasting model’s effectiveness and productiveness are determined equally by its stability and convergence rate. Considering only one objective (accuracy or stability or convergence rate) is inadequate; thus, the proposed FE-SVR-mFFO forecasting framework achieves these three relatively independent objectives simultaneously. To evaluate the effectiveness and applicability of the proposed framework, real half-hourly load data of five states of Australia (New South Wales (NSW), Queensland (QLD), South Australia (SA), Tasmania (TAS), and Victoria (VIC)) are employed as a case study. Experimental results show that the proposed framework outperforms benchmark frameworks like EMD-SVR-PSO, FS-TSFE-CBSSO, VMD-FFT-IOSVR, and DCP-SVM-WO in terms of accuracy, stability, and convergence rate.
Ghulam Hafeez; Imran Khan; Sadaqat Jan; Ibrar Ali Shah; Farrukh Aslam Khan; Abdelouahid Derhab. A novel hybrid load forecasting framework with intelligent feature engineering and optimization algorithm in smart grid. Applied Energy 2021, 299, 117178 .
AMA StyleGhulam Hafeez, Imran Khan, Sadaqat Jan, Ibrar Ali Shah, Farrukh Aslam Khan, Abdelouahid Derhab. A novel hybrid load forecasting framework with intelligent feature engineering and optimization algorithm in smart grid. Applied Energy. 2021; 299 ():117178.
Chicago/Turabian StyleGhulam Hafeez; Imran Khan; Sadaqat Jan; Ibrar Ali Shah; Farrukh Aslam Khan; Abdelouahid Derhab. 2021. "A novel hybrid load forecasting framework with intelligent feature engineering and optimization algorithm in smart grid." Applied Energy 299, no. : 117178.
Due to rapid population growth, technology, and economic development, electricity demand is rising, causing a gap between energy production and demand. With the emergence of the smart grid, residents can schedule their energy usage in response to the Demand Response (DR) program offered by a utility company to cope with the gap between demand and supply. This work first proposes a novel optimization-based energy management framework that adapts consumer power usage patterns using real-time pricing signals and generation from utility and photovoltaic-battery systems to minimize electricity cost, to reduce carbon emission, and to mitigate peak power consumption subjected to alleviating rebound peak generation. Secondly, a Hybrid Genetic Ant Colony Optimization (HGACO) algorithm is proposed to solve the complete scheduling model for three scenarios: without photovoltaic-battery systems, with photovoltaic systems, and with photovoltaic-battery systems. Thirdly, rebound peak generation is restricted by using Multiple Knapsack Problem (MKP) in the proposed algorithm. The presented model reduces the cost of using electricity, alleviates the peak load and peak-valley, mitigates carbon emission, and avoids rebound peaks without posing high discomfort to the consumers. To evaluate the applicability of the proposed framework comparatively with existing frameworks, simulations are conducted. The results show that the proposed HGACO algorithm reduced electricity cost, carbon emission, and peak load by 49.51%, 48.01%, and 25.72% in scenario I; by 55.85%, 54.22%, and 21.69% in scenario II, and by 59.06%, 57.42%, and 17.40% in scenario III, respectively, compared to without scheduling. Thus, the proposed HGACO algorithm-based energy management framework outperforms existing frameworks based on Ant Colony Optimization (ACO) algorithm, Particle Swarm Optimization (PSO) algorithm, Genetic Algorithm (GA), Hybrid Genetic Particle swarm Optimization (HGPO) algorithm.
Sajjad Ali; Imran Khan; Sadaqat Jan; Ghulam Hafeez. An Optimization Based Power Usage Scheduling Strategy Using Photovoltaic-Battery System for Demand-Side Management in Smart Grid. Energies 2021, 14, 2201 .
AMA StyleSajjad Ali, Imran Khan, Sadaqat Jan, Ghulam Hafeez. An Optimization Based Power Usage Scheduling Strategy Using Photovoltaic-Battery System for Demand-Side Management in Smart Grid. Energies. 2021; 14 (8):2201.
Chicago/Turabian StyleSajjad Ali; Imran Khan; Sadaqat Jan; Ghulam Hafeez. 2021. "An Optimization Based Power Usage Scheduling Strategy Using Photovoltaic-Battery System for Demand-Side Management in Smart Grid." Energies 14, no. 8: 2201.
An energy optimization strategy is proposed to minimize operation cost and carbon emission with and without demand response programs (DRPs) in the smart grid (SG) integrated with renewable energy sources (RESs). To achieve optimized results, probability density function (PDF) is proposed to predict the behavior of wind and solar energy sources. To overcome uncertainty in power produced by wind and solar RESs, DRPs are proposed with the involvement of residential, commercial, and industrial consumers. In this model, to execute DRPs, we introduced incentive-based payment as price offered packages. Simulations are divided into three steps for optimization of operation cost and carbon emission: (i) solving optimization problem using multi-objective genetic algorithm (MOGA), (ii) optimization of operating cost and carbon emission without DRPs, and (iii) optimization of operating cost and carbon emission with DRPs. To endorse the applicability of the proposed optimization model based on MOGA, a smart sample grid is employed serving residential, commercial, and industrial consumers. In addition, the proposed optimization model based on MOGA is compared to the existing model based on multi-objective particle swarm optimization (MOPSO) algorithm in terms of operation cost and carbon emission. The proposed optimization model based on MOGA outperforms the existing model based on the MOPSO algorithm in terms of operation cost and carbon emission. Experimental results show that the operation cost and carbon emission are reduced by 24% and 28% through MOGA with and without the participation of DRPs, respectively.
Kalim Ullah; Sajjad Ali; Taimoor Khan; Imran Khan; Sadaqat Jan; Ibrar Shah; Ghulam Hafeez. An Optimal Energy Optimization Strategy for Smart Grid Integrated with Renewable Energy Sources and Demand Response Programs. Energies 2020, 13, 5718 .
AMA StyleKalim Ullah, Sajjad Ali, Taimoor Khan, Imran Khan, Sadaqat Jan, Ibrar Shah, Ghulam Hafeez. An Optimal Energy Optimization Strategy for Smart Grid Integrated with Renewable Energy Sources and Demand Response Programs. Energies. 2020; 13 (21):5718.
Chicago/Turabian StyleKalim Ullah; Sajjad Ali; Taimoor Khan; Imran Khan; Sadaqat Jan; Ibrar Shah; Ghulam Hafeez. 2020. "An Optimal Energy Optimization Strategy for Smart Grid Integrated with Renewable Energy Sources and Demand Response Programs." Energies 13, no. 21: 5718.
The development of advanced metering infrastructure (AMI) in smart grid (SG) had enabled consumers to participate in demand-side management (DSM) using the price-based demand response (DR) programs offered by the distribution companies (DISCO). This way, not only the consumers minimize their electricity bills and discomfort, but also the DISCOs can handle peak power demand and reduce the carbon (CO2) emissions in a controlled manner. Building an optimization framework that will minimize cost, peak demand, waiting time, and CO2 emission is not only a challenging task but also a concern of DSM. Most analyses are based on cost and peak-to-average ratio (PAR) minimization, but the effectiveness of the DSM framework is equally determined by user comfort and CO2 emission. Considering only one objective (cost) or two objectives (cost and PAR) is not sufficient. Thus, for DSM framework to achieve these four relatively independent objectives at the same time, minimized cost, PAR, CO2 emission, and user discomfort, an energy management controller (EMC) based on our proposed algorithm hybrid bacterial foraging and particle swarm optimization (HBFPSO) is employed that return optimal power usage schedule for consumers. A novel DSM framework consists of four units: (i) DISCO, (ii) multi-layer perceptron (MLP) based forecast engine, (iii) AMI, and (iv) demand-side energy management modules is successfully developed in this work. To validate the proposed model, extensive simulations are conducted and results are compared with the benchmark models like genetic algorithm (GA), bacterial foraging optimization algorithm (BFOA), binary particle swarm optimization (BPSO), and a hybrid combination of genetic and binary particle swarm optimization (GBPSO) in terms of electricity cost, PAR, user comfort, and CO2 emissions. The simulation results demonstrate effectiveness of our proposed model to outperform all the benchmark models in optimizing the consumer and DISCO objectives. The proposed scheme has reduced electricity cost, user discomfort, PAR, and CO2 emission for the residential sector by 15.14%, 4.6%, 61.6%, and 52.86% in scenario 1, 62.60%, 4.56%, 60.77%, and 27.77% in scenario 2, and 26.03%, 4.54%, 63.78%, and 23.02% in scenario 3, as compared to without an EMC. Similarly, for commercial sector the proposed HBFPSO algorithm reduces electricity cost, user discomfort, PAR, and CO2 emission by 11.31%, 5.5%, 60.9%, and 38.18% in scenario 1, 64.9%, 5.56%, 44.08%, and 58.8% in scenario 2, 15.31%, 5.26%, 78.22%, and 15.58% in scenario 3. Likewise, the proposed algorithm also has superior performance for the industrial sector for all the three scenarios.
Abdullah Nawaz; Ghulam Hafeez; Imran Khan; Khadim Ullah Jan; Hui Li; Sheraz Ali Khan; Zahid Wadud. An Intelligent Integrated Approach for Efficient Demand Side Management With Forecaster and Advanced Metering Infrastructure Frameworks in Smart Grid. IEEE Access 2020, 8, 132551 -132581.
AMA StyleAbdullah Nawaz, Ghulam Hafeez, Imran Khan, Khadim Ullah Jan, Hui Li, Sheraz Ali Khan, Zahid Wadud. An Intelligent Integrated Approach for Efficient Demand Side Management With Forecaster and Advanced Metering Infrastructure Frameworks in Smart Grid. IEEE Access. 2020; 8 (99):132551-132581.
Chicago/Turabian StyleAbdullah Nawaz; Ghulam Hafeez; Imran Khan; Khadim Ullah Jan; Hui Li; Sheraz Ali Khan; Zahid Wadud. 2020. "An Intelligent Integrated Approach for Efficient Demand Side Management With Forecaster and Advanced Metering Infrastructure Frameworks in Smart Grid." IEEE Access 8, no. 99: 132551-132581.
Electricity demand is rising due to industrialisation, population growth and economic development. To meet this rising electricity demand, towns are renovated by smart cities, where the internet of things enabled devices, communication technologies, dynamic pricing servers and renewable energy sources are integrated. Internet of things (IoT) refers to scenarios where network connectivity and computing capability is extended to objects, sensors and other items not normally considered computers. IoT allows these devices to generate, exchange and consume data without or with minimum human intervention. This integrated environment of smart cities maintains a balance between demand and supply. In this work, we proposed a closed-loop super twisting sliding mode controller (STSMC) to handle the uncertain and fluctuating load to maintain the balance between demand and supply persistently. Demand-side load management (DSLM) consists of agents-based demand response (DR) programs that are designed to control, change and shift the load usage pattern according to the price of the energy of a smart grid community. In smart grids, evolved DR programs are implemented which facilitate controlling of consumer demand by effective regulation services. The DSLM under price-based DR programs perform load shifting, peak clipping and valley filling to maintain the balance between demand and supply. We demonstrate a theoretical control approach for persistent demand control by dynamic price-based closed-loop STSMC. A renewable energy integrated microgrid scenario is discussed numerically to show that the demand of consumers can be controlled through STSMC, which regulates the electricity price to the DSLM agents of the smart grid community. The overall demand elasticity of the current study is represented by a first-order dynamic price generation model having a piece-wise linear price-based DR program. The simulation environment for this whole scenario is developed in MATLAB/Simulink. The simulations validate that the closed-loop price-based elastic demand control technique can trace down the generation of a renewable energy integrated microgrid.
Taimoor Ahmad Khan; Kalim Ullah; Ghulam Hafeez; Imran Khan; Azfar Khalid; Zeeshan Shafiq; Muhammad Usman; Abdul Baseer Qazi. Closed-Loop Elastic Demand Control under Dynamic Pricing Program in Smart Microgrid Using Super Twisting Sliding Mode Controller. Sensors 2020, 20, 4376 .
AMA StyleTaimoor Ahmad Khan, Kalim Ullah, Ghulam Hafeez, Imran Khan, Azfar Khalid, Zeeshan Shafiq, Muhammad Usman, Abdul Baseer Qazi. Closed-Loop Elastic Demand Control under Dynamic Pricing Program in Smart Microgrid Using Super Twisting Sliding Mode Controller. Sensors. 2020; 20 (16):4376.
Chicago/Turabian StyleTaimoor Ahmad Khan; Kalim Ullah; Ghulam Hafeez; Imran Khan; Azfar Khalid; Zeeshan Shafiq; Muhammad Usman; Abdul Baseer Qazi. 2020. "Closed-Loop Elastic Demand Control under Dynamic Pricing Program in Smart Microgrid Using Super Twisting Sliding Mode Controller." Sensors 20, no. 16: 4376.
An operative and versatile household energy management system is proposed to develop and implement demand response (DR) projects. These are under the hybrid generation of the energy storage system (ESS), photovoltaic (PV), and electric vehicles (EVs) in the smart grid (SG). Existing household energy management systems cannot offer its users a choice to ensure user comfort (UC) and not provide a sustainable solution in terms of reduced carbon emission. To tackle these problems, this research work proposes a heuristic-based programmable energy management controller (HPEMC) to manage the energy consumption in residential buildings to minimize electricity bills, reduce carbon emissions, maximize UC and reduce the peak-to-average ratio (PAR). We used our proposed hybrid genetic particle swarm optimization (HGPO) algorithm and existing algorithms like a genetic algorithm (GA), binary particle swarm optimization algorithm (BPSO), ant colony optimization (ACO), wind-driven optimization algorithm (WDO), bacterial foraging algorithm (BFA) to schedule smart appliances optimally to attain our desired objectives. In the proposed model, consumers use solar panels to produce their energy from microgrids. We also perform MATLAB simulations to validate our proposed HGPO-HPEMC (HHPEMC), and results confirm the efficiency and productivity of our proposed HPEMC based strategy. The proposed algorithm reduced the electricity cost by 25.55%, PAR by 36.98%, and carbon emission by 24.02% as compared to the case of without scheduling.
Adil Imran; Ghulam Hafeez; Imran Khan; Muhammad Usman; Zeeshan Shafiq; Abdul Baseer Qazi; Azfar Khalid; Klaus-Dieter Thoben. Heuristic-Based Programable Controller for Efficient Energy Management Under Renewable Energy Sources and Energy Storage System in Smart Grid. IEEE Access 2020, 8, 139587 -139608.
AMA StyleAdil Imran, Ghulam Hafeez, Imran Khan, Muhammad Usman, Zeeshan Shafiq, Abdul Baseer Qazi, Azfar Khalid, Klaus-Dieter Thoben. Heuristic-Based Programable Controller for Efficient Energy Management Under Renewable Energy Sources and Energy Storage System in Smart Grid. IEEE Access. 2020; 8 (99):139587-139608.
Chicago/Turabian StyleAdil Imran; Ghulam Hafeez; Imran Khan; Muhammad Usman; Zeeshan Shafiq; Abdul Baseer Qazi; Azfar Khalid; Klaus-Dieter Thoben. 2020. "Heuristic-Based Programable Controller for Efficient Energy Management Under Renewable Energy Sources and Energy Storage System in Smart Grid." IEEE Access 8, no. 99: 139587-139608.
There will be a dearth of electrical energy in the prospective world due to exponential increase in electrical energy demand of rapidly growing world population. With the development of internet-of-things (IoT), more smart devices will be integrated into residential buildings in smart cities that actively participate in electricity market via demand response (DR) programs to efficiently manage energy in order to meet this increasing energy demand. Thus, with this incitement, an energy management strategy using price-based DR program is developed for IoT-enabled residential buildings. We propose a wind-driven bacterial foraging algorithm (WBFA), which is a hybrid of wind-driven optimization (WDO) and bacterial foraging optimization (BFO) algorithms. Subsequently, we devised a strategy based on our proposed WBFA to systematically manage the power usage of IoT-enabled residential building smart appliances by scheduling to alleviate peak-to-average ratio (PAR), minimize cost of electricity, and maximize user comfort (UC). This increases effective energy utilization, which in turn increases the sustainability of IoT-enabled residential buildings in smart cities. The WBFA-based strategy automatically responds to price-based DR programs to combat the major problem of the DR programs, which is the limitation of consumer’s knowledge to respond upon receiving DR signals. To endorse productiveness and effectiveness of the proposed WBFA-based strategy, substantial simulations are carried out. Furthermore, the proposed WBFA-based strategy is compared with benchmark strategies including binary particle swarm optimization (BPSO) algorithm, genetic algorithm (GA), genetic wind driven optimization (GWDO) algorithm, and genetic binary particle swarm optimization (GBPSO) algorithm in terms of energy consumption, cost of electricity, PAR, and UC. Simulation results show that the proposed WBFA-based strategy outperforms the benchmark strategies in terms of performance metrics.
Ghulam Hafeez; Zahid Wadud; Imran Ullah Khan; Imran Khan; Zeeshan Shafiq; Muhammad Usman; Mohammad Usman Ali Khan; Imran Khan. Efficient Energy Management of IoT-Enabled Smart Homes Under Price-Based Demand Response Program in Smart Grid. Sensors 2020, 20, 1 .
AMA StyleGhulam Hafeez, Zahid Wadud, Imran Ullah Khan, Imran Khan, Zeeshan Shafiq, Muhammad Usman, Mohammad Usman Ali Khan, Imran Khan. Efficient Energy Management of IoT-Enabled Smart Homes Under Price-Based Demand Response Program in Smart Grid. Sensors. 2020; 20 (11):1.
Chicago/Turabian StyleGhulam Hafeez; Zahid Wadud; Imran Ullah Khan; Imran Khan; Zeeshan Shafiq; Muhammad Usman; Mohammad Usman Ali Khan; Imran Khan. 2020. "Efficient Energy Management of IoT-Enabled Smart Homes Under Price-Based Demand Response Program in Smart Grid." Sensors 20, no. 11: 1.
Accurate electric load forecasting is important due to its application in the decision making and operation of the power grid. However, the electric load profile is a complex signal due to the non-linear and stochastic behavior of consumers. Despite much research conducted in this area; still, accurate forecasting models are needed. In this article, a novel hybrid short-term electric load forecasting model is proposed. The proposed model is an integrated framework of data pre-processing and feature selection module, training and forecasting module, and an optimization module. The data pre-processing and feature selection module is based on modified mutual information (MMI) technique, which is an improved version of the mutual information technique, used to select abstractive features from historical data. The training and forecasting module is based on factored conditional restricted Boltzmann machine (FCRBM), which is a deep learning model, empowered via learning to forecast the future electric load. The optimization module is based on our proposed genetic wind-driven (GWDO) optimization algorithm, which is used to fine-tune the adjustable parameters of the model. The accuracy of the proposed framework is evaluated through historical hourly load data of three USA power grids, taken from publicly available PJM electricity market. The proposed model is validated by comparing it with four recent forecasting models like Bi-level, mutual information-based artificial neural network (MI-ANN), ANN-based accurate and fast converging (AFC-ANN), and long short-term memory (LSTM) in terms of accuracy and convergence rate.
Ghulam Hafeez; Khurram Saleem Alimgeer; Imran Khan. Electric load forecasting based on deep learning and optimized by heuristic algorithm in smart grid. Applied Energy 2020, 269, 114915 .
AMA StyleGhulam Hafeez, Khurram Saleem Alimgeer, Imran Khan. Electric load forecasting based on deep learning and optimized by heuristic algorithm in smart grid. Applied Energy. 2020; 269 ():114915.
Chicago/Turabian StyleGhulam Hafeez; Khurram Saleem Alimgeer; Imran Khan. 2020. "Electric load forecasting based on deep learning and optimized by heuristic algorithm in smart grid." Applied Energy 269, no. : 114915.
Energy consumption forecasting is of prime importance for the restructured environment of energy management in the electricity market. Accurate energy consumption forecasting is essential for efficient energy management in the smart grid (SG); however, the energy consumption pattern is non-linear with a high level of uncertainty and volatility. Forecasting such complex patterns requires accurate and fast forecasting models. In this paper, a novel hybrid electrical energy consumption forecasting model is proposed based on a deep learning model known as factored conditional restricted Boltzmann machine (FCRBM). The deep learning-based FCRBM model uses a rectified linear unit (ReLU) activation function and a multivariate autoregressive technique for the network training. The proposed model predicts future electrical energy consumption for efficient energy management in the SG. The proposed model is a novel hybrid model comprising four modules: (i) data processing and features selection module, (ii) deep learning-based FCRBM forecasting module, (iii) genetic wind driven optimization (GWDO) algorithm-based optimization module, and (iv) utilization module. The proposed hybrid model, called FS-FCRBM-GWDO, is tested and evaluated on real power grid data of USA in terms of four performance metrics: mean absolute percentage deviation (MAPD), variance, correlation coefficient, and convergence rate. Simulation results validate that the proposed hybrid FS-FCRBM-GWDO model has superior performance than existing models such as accurate fast converging short-term load forecasting (AFC-STLF) model, mutual information-modified enhanced differential evolution algorithm-artificial neural network (MI-mEDE-ANN)-based model, features selection-ANN (FS-ANN)-based model, and Bi-level model, in terms of forecast accuracy and convergence rate.
Ghulam Hafeez; Khurram Saleem Alimgeer; Zahid Wadud; Zeeshan Shafiq; Mohammad Usman Ali Khan; Imran Khan; Farrukh Aslam Khan; Abdelouahid Derhab. A Novel Accurate and Fast Converging Deep Learning-Based Model for Electrical Energy Consumption Forecasting in a Smart Grid. Energies 2020, 13, 2244 .
AMA StyleGhulam Hafeez, Khurram Saleem Alimgeer, Zahid Wadud, Zeeshan Shafiq, Mohammad Usman Ali Khan, Imran Khan, Farrukh Aslam Khan, Abdelouahid Derhab. A Novel Accurate and Fast Converging Deep Learning-Based Model for Electrical Energy Consumption Forecasting in a Smart Grid. Energies. 2020; 13 (9):2244.
Chicago/Turabian StyleGhulam Hafeez; Khurram Saleem Alimgeer; Zahid Wadud; Zeeshan Shafiq; Mohammad Usman Ali Khan; Imran Khan; Farrukh Aslam Khan; Abdelouahid Derhab. 2020. "A Novel Accurate and Fast Converging Deep Learning-Based Model for Electrical Energy Consumption Forecasting in a Smart Grid." Energies 13, no. 9: 2244.
In this study, a novel framework is proposed for efficient energy management of residential buildings to reduce the electricity bill, alleviate peak-to-average ratio (PAR), and acquire the desired trade-off between electricity bill and user-discomfort in smart grid. The proposed framework is an integrated framework of artificial neural network (ANN) based forecast engine and our proposed day-ahead grey wolf modified enhanced differential evolution algorithm (DA-GmEDE) based home energy management controller (HEMC). The forecast engine forecasts price-based demand response (DR) signal and energy consumption patterns and HEMC schedules smart home appliances under the forecasted pricing signal and energy consumption pattern for efficient energy management. The proposed DA-GmEDE based strategy is compared with two benchmark strategies: day-ahead genetic algorithm (DA-GA) based strategy, and day-ahead game-theory (DA-game-theoretic) based strategy for performance validation. Moreover, extensive simulations are conducted to test the effectiveness and productiveness of the proposed DA-GmEDE based strategy for efficient energy management. The results and discussion illustrate that the proposed DA-GmEDE strategy outperforms the benchmark strategies by 33.3% in terms of efficient energy management.
Ghulam Hafeez; Khurram Saleem Alimgeer; Zahid Wadud; Imran Khan; Muhammad Usman; Abdul Baseer Qazi; Farrukh Aslam Khan. An Innovative Optimization Strategy for Efficient Energy Management With Day-Ahead Demand Response Signal and Energy Consumption Forecasting in Smart Grid Using Artificial Neural Network. IEEE Access 2020, 8, 84415 -84433.
AMA StyleGhulam Hafeez, Khurram Saleem Alimgeer, Zahid Wadud, Imran Khan, Muhammad Usman, Abdul Baseer Qazi, Farrukh Aslam Khan. An Innovative Optimization Strategy for Efficient Energy Management With Day-Ahead Demand Response Signal and Energy Consumption Forecasting in Smart Grid Using Artificial Neural Network. IEEE Access. 2020; 8 (99):84415-84433.
Chicago/Turabian StyleGhulam Hafeez; Khurram Saleem Alimgeer; Zahid Wadud; Imran Khan; Muhammad Usman; Abdul Baseer Qazi; Farrukh Aslam Khan. 2020. "An Innovative Optimization Strategy for Efficient Energy Management With Day-Ahead Demand Response Signal and Energy Consumption Forecasting in Smart Grid Using Artificial Neural Network." IEEE Access 8, no. 99: 84415-84433.
Electric energy consumption forecasting enables distribution system operators to perform efficient energy management by flexibly engaging energy consumers under the intelligent demand-response program in smart grids (SG). With this motivation, in this paper, a fast and accurate hybrid electrical energy forecasting (FA-HELF) framework is developed. The proposed framework integrates two modules with support vector machine (SVM) based forecaster. These modules are data pre-processing and feature engineering, and modified enhanced differential evolution (mEDE) based optimizer. First, feature selection algorithms like random forests and relief-F are combined to devise a hybrid feature selection algorithm to alleviate redundancy. Secondly, for feature extraction, a radial basis Kernel-based principal component analysis algorithm is employed to eliminate the dimensionality reduction problem. Finally, to conduct accurate and fast electrical energy consumption forecasting, the mEDE based optimizer is integrated with the SVM based forecaster. The resulting FA-HELF framework is tested on publicly available independent system operator New England (ISO-NE) control area hourly load data. The results demonstrate that the FA-HELF framework is robust and shows significant improvements when compared to other benchmark frameworks in terms of accuracy and convergence speed.
Ghulam Hafeez; Khurram Saleem Alimgeer; Abdul Baseer Qazi; Imran Khan; Muhammad Usman; Farrukh Aslam Khan; Zahid Wadud. A Hybrid Approach for Energy Consumption Forecasting With a New Feature Engineering and Optimization Framework in Smart Grid. IEEE Access 2020, 8, 96210 -96226.
AMA StyleGhulam Hafeez, Khurram Saleem Alimgeer, Abdul Baseer Qazi, Imran Khan, Muhammad Usman, Farrukh Aslam Khan, Zahid Wadud. A Hybrid Approach for Energy Consumption Forecasting With a New Feature Engineering and Optimization Framework in Smart Grid. IEEE Access. 2020; 8 (99):96210-96226.
Chicago/Turabian StyleGhulam Hafeez; Khurram Saleem Alimgeer; Abdul Baseer Qazi; Imran Khan; Muhammad Usman; Farrukh Aslam Khan; Zahid Wadud. 2020. "A Hybrid Approach for Energy Consumption Forecasting With a New Feature Engineering and Optimization Framework in Smart Grid." IEEE Access 8, no. 99: 96210-96226.
With the emergence of the smart grid (SG), real-time interaction is favorable for both residents and power companies in optimal load scheduling to alleviate electricity cost and peaks in demand. In this paper, a modular framework is introduced for efficient load scheduling. The proposed framework is comprised of four modules: power company module, forecaster module, home energy management controller (HEMC) module, and resident module. The forecaster module receives a demand response (DR), information (real-time pricing scheme (RTPS) and critical peak pricing scheme (CPPS)), and load from the power company module to forecast pricing signals and load. The HEMC module is based on our proposed hybrid gray wolf-modified enhanced differential evolutionary (HGWmEDE) algorithm using the output of the forecaster module to schedule the household load. Each appliance of the resident module receives the schedule from the HEMC module. In a smart home, all the appliances operate according to the schedule to reduce electricity cost and peaks in demand with the affordable waiting time. The simulation results validated that the proposed framework handled the uncertainties in load and supply and provided optimal load scheduling, which facilitates both residents and power companies.
Ghulam Hafeez; Noor Islam; Ammar Ali; Salman Ahmad; Muhammad Usman And Khurram Saleem Alimgeer. A Modular Framework for Optimal Load Scheduling under Price-Based Demand Response Scheme in Smart Grid. Processes 2019, 7, 499 .
AMA StyleGhulam Hafeez, Noor Islam, Ammar Ali, Salman Ahmad, Muhammad Usman And Khurram Saleem Alimgeer. A Modular Framework for Optimal Load Scheduling under Price-Based Demand Response Scheme in Smart Grid. Processes. 2019; 7 (8):499.
Chicago/Turabian StyleGhulam Hafeez; Noor Islam; Ammar Ali; Salman Ahmad; Muhammad Usman And Khurram Saleem Alimgeer. 2019. "A Modular Framework for Optimal Load Scheduling under Price-Based Demand Response Scheme in Smart Grid." Processes 7, no. 8: 499.
With the rapid advancement in technology, electrical energy consumption is increasing rapidly. Especially, in the residential sector, more than 80% of electrical energy is being consumed because of consumer negligence. This brings the challenging task of maintaining the balance between the demand and supply of electric power. In this paper, we focus on the problem of load balancing via load scheduling under utility and rooftop photovoltaic (PV) units to reduce electricity cost and peak to average ratio (PAR) in demand-side management. For this purpose, we adopted genetic algorithm (GA), binary particle swarm optimization (BPSO), wind-driven optimization (WDO), and our proposed genetic WDO (GWDO) algorithm, which is a hybrid of GA and WDO, to schedule the household load. For energy cost estimation, combined real-time pricing (RTP) and inclined block rate (IBR) were used. The proposed algorithm shifts load from peak consumption hours to off-peak hours based on combined pricing scheme and generation from rooftop PV units. Simulation results validate our proposed GWDO algorithm in terms of electricity cost and PAR reduction while considering all three scenarios which we have considered in this work: (1) load scheduling without renewable energy sources (RESs) and energy storage system (ESS), (2) load scheduling with RESs, and (3) load scheduling with RESs and ESS. Furthermore, our proposed scheme reduced electricity cost and PAR by 22.5% and 29.1% in scenario 1, 47.7% and 30% in scenario 2, and 49.2% and 35.4% in scenario 3, respectively, as compared to unscheduled electricity consumption.
Ghulam Hafeez; Nadeem Javaid; Sohail Iqbal; Farman Ali Khan. Optimal Residential Load Scheduling Under Utility and Rooftop Photovoltaic Units. Energies 2018, 11, 611 .
AMA StyleGhulam Hafeez, Nadeem Javaid, Sohail Iqbal, Farman Ali Khan. Optimal Residential Load Scheduling Under Utility and Rooftop Photovoltaic Units. Energies. 2018; 11 (3):611.
Chicago/Turabian StyleGhulam Hafeez; Nadeem Javaid; Sohail Iqbal; Farman Ali Khan. 2018. "Optimal Residential Load Scheduling Under Utility and Rooftop Photovoltaic Units." Energies 11, no. 3: 611.