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Price based demand response is an important strategy to facilitate energy retailers and end-users to maintain a balance between demand and supply while providing the opportunity to end users to get monetary incentives. In this work, we consider real-time electricity pricing policy to further calculate the incentives in terms of reduced electricity price and cost. Initially, a mathematical model based on the backtracking technique is developed to calculate the load shifted and consumed in any time slot. Then, based on this, the electricity price is calculated for all types of users to estimate the incentives through load shifting profiles. To keep the load under the upper limit, the load is shifted in other time slots in such a way to facilitate end-users regarding social welfare. The user who is not interested in participating load shifting program will not get any benefit. Then the well behaved functional form optimization problem is solved by using a heuristic-based genetic algorithm (GA), wwhich converged within an insignificant amount of time with the best optimal results. Simulation results reflect that the users can obtain some real incentives by participating in the load scheduling process.
Thamer Alquthami; Ahmad Milyani; Muhammad Awais; Muhammad Rasheed. An Incentive Based Dynamic Pricing in Smart Grid: A Customer’s Perspective. Sustainability 2021, 13, 6066 .
AMA StyleThamer Alquthami, Ahmad Milyani, Muhammad Awais, Muhammad Rasheed. An Incentive Based Dynamic Pricing in Smart Grid: A Customer’s Perspective. Sustainability. 2021; 13 (11):6066.
Chicago/Turabian StyleThamer Alquthami; Ahmad Milyani; Muhammad Awais; Muhammad Rasheed. 2021. "An Incentive Based Dynamic Pricing in Smart Grid: A Customer’s Perspective." Sustainability 13, no. 11: 6066.
In this paper, a two-stage approach is proposed on a joint dispatch of thermal power generation and variable resources including a storage system. Although, the dispatch of alternate energy along with conventional resources has become increasingly important in the new utility environment. However, recent studies based on the uncertainty and worst-case scenario-oriented robust optimization methodology reveal the perplexities associated with renewable energy sources (RES). First, the load demand is predicted through a convolutional neural network (CNN) by taking the ISO-NECA hourly real-time data. Then, the joint dispatch of energy and spinning reserve capacity is performed with the integration of RES and battery storage system (BSS) to satisfy the predicted load demand. In addition, the generation system is penalized with a cost factor against load not served for the amount of energy demand which is not fulfilled due to generation constraints. Meanwhile, due to ramping of thermal units, the available surplus power will be stored in the backup energy storage system considering the state of charge of the storage system. The proposed method is applied on the IEEE-standard 6-Bus system and particle swarm optimization (PSO) algorithm is used to solve the cost minimization objective function. Finally, the proposed system performance has been verified along with the reliability during two worst-case scenarios, i.e., sudden drop in power demand and a short-fall at the generation end.
M. Wajahat Hassan; Thamer Alquthami; Ahmad H. Milyani; Ashfaq Ahmad; Muhammad Babar Rasheed. A Joint Optimization Model for Energy and Reserve Capacity Scheduling With the Integration of Variable Energy Resources. IEEE Access 2021, 9, 75252 -75264.
AMA StyleM. Wajahat Hassan, Thamer Alquthami, Ahmad H. Milyani, Ashfaq Ahmad, Muhammad Babar Rasheed. A Joint Optimization Model for Energy and Reserve Capacity Scheduling With the Integration of Variable Energy Resources. IEEE Access. 2021; 9 ():75252-75264.
Chicago/Turabian StyleM. Wajahat Hassan; Thamer Alquthami; Ahmad H. Milyani; Ashfaq Ahmad; Muhammad Babar Rasheed. 2021. "A Joint Optimization Model for Energy and Reserve Capacity Scheduling With the Integration of Variable Energy Resources." IEEE Access 9, no. : 75252-75264.
Commercial and residential buildings consume 20% of the world's total energy usage and forecasts indicate a growing trend of 1.5% per year by 2040. Share of energy consumption in the heating ventilation and air conditioning (HVAC) reached to 40–70% of the building energy usage. Reduction in the heat exchange between the interior and exterior spaces is an energy-efficient technique for lowering HVAC energy demand. In this study, the thermal performance of a PCM-based wall was numerically examined in the winter. The main purpose of this research was to investigate the efficacy of installing PCM inside the envelope on reducing heat loss from the conditioned space and at the same time its effect on air handling unit (AHU) energy usage. For this, seven different PCM layers at melting temperatures of 276.15–293.15 K with a thickness of 1 cm were loaded into the envelope. Based on the results, among the PCMs thermophysical properties, thermal conductivity has more manifest efficacy. Under the best conditions, owing to installing PCM of A3 inside the envelope, the energy-saving in December, January and February diminished by 11.25%, 11.23% and 10.35%, respectively. Also, thermodynamic calculations affirmed that the maximum reduction in AHU power usage owing to PCM loading was 11.73%. Using payback time technique, the economic analysis was performed and it was found that under the best conditions, the payback time is 18.6 years.
Nidal H. Abu-Hamdeh; Ammar A. Melaibari; Thamer S. Alquthami; Ahmed Khoshaim; Hakan F. Oztop; Aliakbar Karimipour. Efficacy of incorporating PCM into the building envelope on the energy saving and AHU power usage in winter. Sustainable Energy Technologies and Assessments 2021, 43, 100969 .
AMA StyleNidal H. Abu-Hamdeh, Ammar A. Melaibari, Thamer S. Alquthami, Ahmed Khoshaim, Hakan F. Oztop, Aliakbar Karimipour. Efficacy of incorporating PCM into the building envelope on the energy saving and AHU power usage in winter. Sustainable Energy Technologies and Assessments. 2021; 43 ():100969.
Chicago/Turabian StyleNidal H. Abu-Hamdeh; Ammar A. Melaibari; Thamer S. Alquthami; Ahmed Khoshaim; Hakan F. Oztop; Aliakbar Karimipour. 2021. "Efficacy of incorporating PCM into the building envelope on the energy saving and AHU power usage in winter." Sustainable Energy Technologies and Assessments 43, no. : 100969.
In this study, the effectiveness of the heat sink with an insulated wall equipped with nano-PCM and air-cooled one was compared. Heat sink effectiveness was defined as the ability of the heat sink to keep lower the base temperature. It was found that for each heat sink, a critical heat transfer coefficient can be supposed. If the convective heat transfer coefficient is less than the critical value, the effectiveness of the nano-PCM filled heat sink is superior to that of the air-cooled one up to the end of the melting time. Otherwise, it is recommended to use the air-cooled heat sink. A novel correlation was proposed to estimate the critical value, and it was shown that the closer the melting temperature to the ambient, the higher the critical value which means the greater the ability of the PCM-HS to compete with air-cooled one.
Nidal H. Abu-Hamdeh; Ammar A. Melaibari; Thamer S. Alquthami; Ahmed Khoshaim; Hakan F. Oztop; Ali Golmohammadzadeh. The role of convective heat transfer coefficient in CuO nanoparticles-PCM cooling ability in heat sinks with insulated side walls: comparison with the air cooled one. Journal of Thermal Analysis and Calorimetry 2021, 1 -11.
AMA StyleNidal H. Abu-Hamdeh, Ammar A. Melaibari, Thamer S. Alquthami, Ahmed Khoshaim, Hakan F. Oztop, Ali Golmohammadzadeh. The role of convective heat transfer coefficient in CuO nanoparticles-PCM cooling ability in heat sinks with insulated side walls: comparison with the air cooled one. Journal of Thermal Analysis and Calorimetry. 2021; ():1-11.
Chicago/Turabian StyleNidal H. Abu-Hamdeh; Ammar A. Melaibari; Thamer S. Alquthami; Ahmed Khoshaim; Hakan F. Oztop; Ali Golmohammadzadeh. 2021. "The role of convective heat transfer coefficient in CuO nanoparticles-PCM cooling ability in heat sinks with insulated side walls: comparison with the air cooled one." Journal of Thermal Analysis and Calorimetry , no. : 1-11.
In recent past, to meet the growing energy demand of electricity, integration of renewable energy resources (RESs) in an electrical network is a center of attention. Furthermore, optimal integration of these RESs make this task more challenging because of their intermittent nature. Therefore, in the present study power flow problem is treated as a multi-constraint, multi-objective optimal power flow (MOOPF) problem along with optimal integration of RESs. Whereas, the objectives of MOOPF are threefold: overall generation cost, real power loss of system and carbon emission reduction of thermal sources. In this work, a computationally efficient technique is presented to find the most feasible values of different control variables of the power system having distributed RESs. Whereas, the constraint satisfaction is achieved by using penalty function approach (PFA) and to further develop true Pareto front (PF), Pareto dominance method is used to categorize Pareto dominate solution. Moreover, to deal with intermittent nature of RES, probability density function (PDF) and stochastic power models of RES are used to calculate available power from RESs. Since, objectives of the MOOPF problem are conflicting in nature, after having the set of non-dominating solutions fuzzy membership function (FMF) approach has been used to extract the best compromise solution (BCS). To test the validity of developed technique, the IEEE-30 bus system has been modified with integration of RESs and final optimization problem is solved by using particle swarm optimization (PSO) algorithm. Simulation results show the achievement of proposed technique managing fuel cost value long with the optimal values of other objectives.
Muhammad Arsalan Ilyas; Ghulam Abbas; Thamer Alquthami; Muhammad Awais; Muhammad Babar Rasheed. Multi-Objective Optimal Power Flow With Integration of Renewable Energy Sources Using Fuzzy Membership Function. IEEE Access 2020, 8, 143185 -143200.
AMA StyleMuhammad Arsalan Ilyas, Ghulam Abbas, Thamer Alquthami, Muhammad Awais, Muhammad Babar Rasheed. Multi-Objective Optimal Power Flow With Integration of Renewable Energy Sources Using Fuzzy Membership Function. IEEE Access. 2020; 8 (99):143185-143200.
Chicago/Turabian StyleMuhammad Arsalan Ilyas; Ghulam Abbas; Thamer Alquthami; Muhammad Awais; Muhammad Babar Rasheed. 2020. "Multi-Objective Optimal Power Flow With Integration of Renewable Energy Sources Using Fuzzy Membership Function." IEEE Access 8, no. 99: 143185-143200.
The demand of devices for safe mobility of blind people is increasing with advancement in wireless communication. Artificial intelligent devices with multiple input and output methods are used for reliable data estimation based on maximum probability. A model of a smart home for safe and robust mobility of blind people has been proposed. Fuzzy logic has been used for simulation. Outputs from the internet of things (IoT) devices comprising sensors and bluetooth are taken as input of the fuzzy controller. Rules have been developed based on the conditions and requirements of the blind person to generate decisions as output. These outputs are communicated through IoT devices to assist the blind person or user for safe movement. The proposed system provides the user with easy navigation and obstacle avoidance.
Shahzadi Tayyaba; Muhammad Waseem Ashraf; Thamer Alquthami; Zubair Ahmad; Saher Manzoor. Fuzzy-Based Approach Using IoT Devices for Smart Home to Assist Blind People for Navigation. Sensors 2020, 20, 3674 .
AMA StyleShahzadi Tayyaba, Muhammad Waseem Ashraf, Thamer Alquthami, Zubair Ahmad, Saher Manzoor. Fuzzy-Based Approach Using IoT Devices for Smart Home to Assist Blind People for Navigation. Sensors. 2020; 20 (13):3674.
Chicago/Turabian StyleShahzadi Tayyaba; Muhammad Waseem Ashraf; Thamer Alquthami; Zubair Ahmad; Saher Manzoor. 2020. "Fuzzy-Based Approach Using IoT Devices for Smart Home to Assist Blind People for Navigation." Sensors 20, no. 13: 3674.
This research paper reports the implementation of short-term scheduling of hydro-thermal power plants by using an artificial bee colony algorithm. Short-term hydro-thermal scheduling is a type of economic dispatch problem in which thermal power plants are dispatched at the optimized operating point to reduce the fuel cost and to achieve in parallel the maximum cost-benefit from hydel power plants. The power system, considered in this study, is assumed to run optimally and the transmission losses have also been taken into account. The artificial bee colony algorithm proves itself to be most suited for this particular problem as it results in the minimum cost in the shortest time compared with those obtained from previously applied techniques.
Tehzeeb- Ul- Hassan; Thamer Alquthami; Saad Ehsan Butt; Muhammad Faizan Tahir; Kashif Mehmood. Short-term optimal scheduling of hydro-thermal power plants using artificial bee colony algorithm. Energy Reports 2020, 6, 984 -992.
AMA StyleTehzeeb- Ul- Hassan, Thamer Alquthami, Saad Ehsan Butt, Muhammad Faizan Tahir, Kashif Mehmood. Short-term optimal scheduling of hydro-thermal power plants using artificial bee colony algorithm. Energy Reports. 2020; 6 ():984-992.
Chicago/Turabian StyleTehzeeb- Ul- Hassan; Thamer Alquthami; Saad Ehsan Butt; Muhammad Faizan Tahir; Kashif Mehmood. 2020. "Short-term optimal scheduling of hydro-thermal power plants using artificial bee colony algorithm." Energy Reports 6, no. : 984-992.
In most demand response (DR) based residential load management systems, shifting a considerable amount of load in low price intervals reduces end user cost, however, it may create rebound peaks and user dissatisfaction. To overcome these problems, this work presents a novel approach to optimizing load demand and storage management in response to dynamic pricing using machine learning and optimization algorithms. Unlike traditional load scheduling mechanisms, the proposed algorithm is based on finding suggested low tariff area using artificial neural network (ANN). Where the historical load demand individualized power consumption profiles of all users and real time pricing (RTP) signal are used as input parameters for a forecasting module for training and validating the network. In a response, the ANN module provides a suggested low tariff area to all users such that the electricity tariff below the low tariff area is market based. While the users are charged high prices on the basis of a proposed load based pricing policy (LBPP) if they violate low tariff area, which is based on RTP and inclining block rate (IBR). However, we first developed the mathematical models of load, pricing and energy storage systems (ESS), which are an integral part of the optimization problem. Then, based on suggested low tariff area, the problem is formulated as a linear programming (LP) optimization problem and is solved by using both deterministic and heuristic algorithms. The proposed mechanism is validated via extensive simulations and results show the effectiveness in terms of minimizing the electricity bill as well as intercepting the creation of minimal-price peaks. Therefore, the proposed energy management scheme is beneficial to both end user and utility company.
Zubair Khalid; Ghulam Abbas; Muhammad Awais; Thamer Alquthami; Muhammad Babar Rasheed. A Novel Load Scheduling Mechanism Using Artificial Neural Network Based Customer Profiles in Smart Grid. Energies 2020, 13, 1062 .
AMA StyleZubair Khalid, Ghulam Abbas, Muhammad Awais, Thamer Alquthami, Muhammad Babar Rasheed. A Novel Load Scheduling Mechanism Using Artificial Neural Network Based Customer Profiles in Smart Grid. Energies. 2020; 13 (5):1062.
Chicago/Turabian StyleZubair Khalid; Ghulam Abbas; Muhammad Awais; Thamer Alquthami; Muhammad Babar Rasheed. 2020. "A Novel Load Scheduling Mechanism Using Artificial Neural Network Based Customer Profiles in Smart Grid." Energies 13, no. 5: 1062.
Despite the universal importance of price based demand response (DR) for managing electric vehicle (EV) charging load, the academic literature has explored various mechanisms to its implementation. The prequel to this work has demonstrated that implementation of load management schemes on the basis of price based DR programs leads to costlier scheduling for low or constant energy consumers. In this regard, the proposed work has considered and expanded the same idea from analytical as well as implementation point of view to multiple EV charging regions and respective loads. We present a novel mechanism to calculate EV charging prices using individualized energy consumption patterns of EVs in each region. In this regard, all EV regions/stations receive a dynamic price signal which is non-discriminatory in nature. The dynamic price signals are specifically designed to mitigate the impact of discriminatory prices on end user’s cost. Furthermore, the other objectives of these non-discriminatory prices are to lower energy cost and rebound peaks without affecting utility objective (i.e., net revenue). Initially, a new mathematical model is presented to calculate charging prices based on real time load demand and market dynamics. Then relatively a well behaved functional form of the optimization problem is formulated and the cost minimization objective function is solved by using genetic algorithm (GA). The optimization program successfully converges to give global optimum solution validating the effectiveness of proposed mechanism. Finally, the analytical and simulation results are conducted to show the achievements of our proposed work in terms of fair cost distribution with high user satisfaction. It is also proved that in both mechanisms, the utility’s revenue remains unaffected.
Muhammad Babar Rasheed; Muhammad Awais; Thamer Alquthami; Irfan Khan. An Optimal Scheduling and Distributed Pricing Mechanism for Multi-Region Electric Vehicle Charging in Smart Grid. IEEE Access 2020, 8, 40298 -40312.
AMA StyleMuhammad Babar Rasheed, Muhammad Awais, Thamer Alquthami, Irfan Khan. An Optimal Scheduling and Distributed Pricing Mechanism for Multi-Region Electric Vehicle Charging in Smart Grid. IEEE Access. 2020; 8 (99):40298-40312.
Chicago/Turabian StyleMuhammad Babar Rasheed; Muhammad Awais; Thamer Alquthami; Irfan Khan. 2020. "An Optimal Scheduling and Distributed Pricing Mechanism for Multi-Region Electric Vehicle Charging in Smart Grid." IEEE Access 8, no. 99: 40298-40312.
Solving the Power-Flow in realistic large-scale ill-conditioned systems supposes a challenging task for most of available solution methodologies. This paper tackles this issue by developing a novel efficient and robust Power-Flow method. It is mainly based on a Semi-Implicit approach but incorporates other numerical arrangements for enhancing its features. The resulting three-stage algorithm is validated using several realistic ill-conditioned systems ranging from 3012 to 70000-buses. Results show that the developed methodology constitutes an efficient and robust Power-Flow solution technique, outperforming the results obtained with other available approaches.
Marcos Tostado-Veliz; Salah Kamel; Thamer Alquthami; Francisco Jurado. A Three-Stage Algorithm Based on a Semi-Implicit Approach for Solving the Power-Flow in Realistic Large-Scale ill-Conditioned Systems. IEEE Access 2020, 8, 35299 -35307.
AMA StyleMarcos Tostado-Veliz, Salah Kamel, Thamer Alquthami, Francisco Jurado. A Three-Stage Algorithm Based on a Semi-Implicit Approach for Solving the Power-Flow in Realistic Large-Scale ill-Conditioned Systems. IEEE Access. 2020; 8 (99):35299-35307.
Chicago/Turabian StyleMarcos Tostado-Veliz; Salah Kamel; Thamer Alquthami; Francisco Jurado. 2020. "A Three-Stage Algorithm Based on a Semi-Implicit Approach for Solving the Power-Flow in Realistic Large-Scale ill-Conditioned Systems." IEEE Access 8, no. 99: 35299-35307.
Utilities around the world have realized the importance of wide installation smart meters (SMs) as they are considered to be a corner stone of any step toward grid modernization. These meters are expected to rely on to improve gird reliability, efficiency and enhance grid economic operation. With large rate of SMs integration, flood of smart meter data is being gathered on hourly basis. This paper presents an integrated data framework that incorporates tools and data preprocessing techniques for SM data analytics. This framework uses real data of smart meters installed by the Saudi Electricity Company (SEC) and is for different load profiles, such as residential, governmental, commercial, agriculture and industrial. The developed framework receives raw data from SM, preprocess it and then performs the required analysis using the applications layer. Benefits of such a framework are many: standardized data streamlining, unified different data spectrum and at the end create a trustworthy and validated real-based data that can be used to execute many of smart grid applications. This paper describes the structure of the framework, the function of each component and then presents results of several applications to test the validity and performance of the framework.
Thamer Alquthami; Ahmed AlAmoudi; Abdullah M. Alsubaie; Abdulrahman Bin Jaber; Nassir Alshlwan; Murad Anwar; Shafi Al Husaien. Analytics framework for optimal smart meters data processing. Electrical Engineering 2020, 102, 1241 -1251.
AMA StyleThamer Alquthami, Ahmed AlAmoudi, Abdullah M. Alsubaie, Abdulrahman Bin Jaber, Nassir Alshlwan, Murad Anwar, Shafi Al Husaien. Analytics framework for optimal smart meters data processing. Electrical Engineering. 2020; 102 (3):1241-1251.
Chicago/Turabian StyleThamer Alquthami; Ahmed AlAmoudi; Abdullah M. Alsubaie; Abdulrahman Bin Jaber; Nassir Alshlwan; Murad Anwar; Shafi Al Husaien. 2020. "Analytics framework for optimal smart meters data processing." Electrical Engineering 102, no. 3: 1241-1251.
Transient stability is important in power systems. Disturbances like faults need to be segregated to restore transient stability. A comprehensive review of fault diagnosing methods in the power transmission system is presented in this paper. Typically, voltage and current samples are deployed for analysis. Three tasks/topics; fault detection, classification, and location are presented separately to convey a more logical and comprehensive understanding of the concepts. Feature extractions, transformations with dimensionality reduction methods are discussed. Fault classification and location techniques largely use artificial intelligence (AI) and signal processing methods. After the discussion of overall methods and concepts, advancements and future aspects are discussed. Generalized strengths and weaknesses of different AI and machine learning-based algorithms are assessed. A comparison of different fault detection, classification, and location methods is also presented considering features, inputs, complexity, system used and results. This paper may serve as a guideline for the researchers to understand different methods and techniques in this field.
Ali Raza; Abdeldjabar Benrabah; Thamer Alquthami; Muhammad Akmal. A Review of Fault Diagnosing Methods in Power Transmission Systems. Applied Sciences 2020, 10, 1312 .
AMA StyleAli Raza, Abdeldjabar Benrabah, Thamer Alquthami, Muhammad Akmal. A Review of Fault Diagnosing Methods in Power Transmission Systems. Applied Sciences. 2020; 10 (4):1312.
Chicago/Turabian StyleAli Raza; Abdeldjabar Benrabah; Thamer Alquthami; Muhammad Akmal. 2020. "A Review of Fault Diagnosing Methods in Power Transmission Systems." Applied Sciences 10, no. 4: 1312.
Day-ahead electricity pricing is an important strategy for electricity providers to improve grid stability through load scheduling. In this paper, we investigate a general framework for modelling electricity retail pricing based on load demand and market price information. Without any a priori knowledge, we have considered a finite time approach with dynamic system inputs. Our objective is to minimize the average system cost and rebound peaks through energy procurement price, load scheduling and renewable energy source (RES) integration. Initially, the energy consumption cost is calculated based on market clearing price and scheduled load. Then, through reformulation and subsequent modification of optimization problem, we utilize a day-ahead price information to construct individualized price profiles for each user, respectively. To analyse the applicability of proposed pricing policy, analytical solution is obtained which is further validated through comparison with solution obtained from genetic algorithm (GA). From results, it is observed that proposed price policy is non-discriminatory in nature and each user obtained a fair electricity tariff rather than a day-ahead price, which is based on load demand and consumption variation of other users. We also show that optimization problem is sequentially solved with bounded performance guarantee and asymptotic optimality. Finally, simulations are carried in different scenarios; aggregated load and market price, and aggregated load, individualized load, market price and proposed price. Results reveal that our proposed mechanism can charge the price to each user with 23.77% decrease or 5.12% increase based on system requirements.
Muhammad Babar Rasheed; Muhammad Awais Qureshi; Nadeem Javaid; Thamer Alquthami. Dynamic Pricing Mechanism With the Integration of Renewable Energy Source in Smart Grid. IEEE Access 2020, 8, 16876 -16892.
AMA StyleMuhammad Babar Rasheed, Muhammad Awais Qureshi, Nadeem Javaid, Thamer Alquthami. Dynamic Pricing Mechanism With the Integration of Renewable Energy Source in Smart Grid. IEEE Access. 2020; 8 (99):16876-16892.
Chicago/Turabian StyleMuhammad Babar Rasheed; Muhammad Awais Qureshi; Nadeem Javaid; Thamer Alquthami. 2020. "Dynamic Pricing Mechanism With the Integration of Renewable Energy Source in Smart Grid." IEEE Access 8, no. 99: 16876-16892.
This paper presents the details of exhaustive investigations on a typical Saudi distribution network showing the effectiveness of various control techniques to mitigate the voltage rise issues due to high penetration of solar photovoltaic (PV) systems. One of the major goals of Saudi Arabia future economic plans is to expand on renewable energy installations throughout the Kingdom. It is planned to generate 9.5 GW of electric power from renewable energy sources by 2023. Further, energy regulators in the Kingdom have announced policies and regulations govern the integration of small- and large-scale solar PV. However, distributed solar PV systems come with technical challenges, especially during high level of integration including grid stability, voltage rise, flickering, frequency fluctuation, and protection and coordination schemes. One issue is that the voltage profile is expected to change and rise to unacceptable level, especially with high solar PV penetration. Various voltage rise mitigation techniques investigated include active power curtailment, reactive power injection, and also a hybrid combination of these two methods. The paper reports the results of both steady-state and dynamic analyses of the Saudi distribution network at various load levels. The investigations reveal that the hybrid approach is more effective in the voltage rise mitigation.
Thamer Alquthami; R. Sreerama Kumar; Abdullah Al Shaikh. Mitigation of voltage rise due to high solar PV penetration in Saudi distribution network. Electrical Engineering 2020, 102, 881 -890.
AMA StyleThamer Alquthami, R. Sreerama Kumar, Abdullah Al Shaikh. Mitigation of voltage rise due to high solar PV penetration in Saudi distribution network. Electrical Engineering. 2020; 102 (2):881-890.
Chicago/Turabian StyleThamer Alquthami; R. Sreerama Kumar; Abdullah Al Shaikh. 2020. "Mitigation of voltage rise due to high solar PV penetration in Saudi distribution network." Electrical Engineering 102, no. 2: 881-890.
In this paper, an efficient optimization technique, called improved moth-flame optimization (IMFO) is proposed to improve the performance of conventional Moth-flame optimization (MFO). Then, both of MFO and IMFO are applied to solve the coordination problem of standard and non-standard directional overcurrent relays (DOCRs). In the proposed IMFO, the leadership hierarchy of grey wolf optimizer is used to improve the performance of conventional MFO with the aim of finding the best optimum solution. The major goal for optimal coordination of DOCRs is to minimize the total operation time for all primary relays as well as satisfy the selectivity criteria between relay pairs without any violation in the operating constraints. The performance and feasibility of proposed IMFO are investigated using three different networks (8-bus network, 9-bus network, and 15-bus). The proposed IMFO is compared with conventional MFO and other well-known optimization techniques. The results show the effectiveness of the proposed IMFO in solving both standard and non-standard DOCRs coordination problems without any violation between primary and backup relays. In addition, the results show the power of proposed IMFO in finding the best optimal relay settings and minimizing the total operating time of relays which its reduction ratio reaches more than 28% with respect to the conventional MFO. Furthermore, the reduction in the total operating time of primary relays reaches more than 50 % with the usage of the non-standard relay curve.
Ahmed Korashy; Salah Kamel; Thamer Alquthami; Francisco Jurado. Optimal Coordination of Standard and Non-Standard Direction Overcurrent Relays Using an Improved Moth-Flame Optimization. IEEE Access 2020, 8, 87378 -87392.
AMA StyleAhmed Korashy, Salah Kamel, Thamer Alquthami, Francisco Jurado. Optimal Coordination of Standard and Non-Standard Direction Overcurrent Relays Using an Improved Moth-Flame Optimization. IEEE Access. 2020; 8 (99):87378-87392.
Chicago/Turabian StyleAhmed Korashy; Salah Kamel; Thamer Alquthami; Francisco Jurado. 2020. "Optimal Coordination of Standard and Non-Standard Direction Overcurrent Relays Using an Improved Moth-Flame Optimization." IEEE Access 8, no. 99: 87378-87392.
The smart grid (SG) has emerged as a key enabling technology facilitating the integration of variable energy resources with the objective of load management and reduced carbon-dioxide (CO 2 ) emissions. However, dynamic load consumption trends and inherent intermittent nature of renewable generations may cause uncertainty in active resource management. Eventually, these uncertainties pose serious challenges to the energy management system. To address these challenges, this work establishes an efficient load scheduling scheme by jointly considering an on-site photo-voltaic (PV) system and an energy storage system (ESS). An optimum PV-site matching technique was used to optimally select the highest capacity and lowest cost PV module. Furthermore, the best-fit of PV array in regard with load is anticipated using least square method (LSM). Initially, the mathematical models of PV energy generation, consumption and ESS are presented along with load categorization through Zero and Finite shift methods. Then, the final problem is formulated as a multiobjective optimization problem which is solved by using the proposed Dijkstra algorithm (DA). The proposed algorithm quantifies day-ahead electricity market consumption cost, used energy mixes, curtailed load, and grid imbalances. However, to further analyse and compare the performance of proposed model, the results of the proposed algorithm are compared with the genetic algorithm (GA), binary particle swarm optimization (BPSO), and optimal pattern recognition algorithm (OPRA), respectively. Simulation results show that DA achieved 51.72% cost reduction when grid and renewable sources are used. Similarly, DA outperforms other algorithms in terms of maximum peak to average ratio (PAR) reduction, which is 10.22%.
Ihsan Ullah; Muhammad Babar Rasheed; Thamer Alquthami; Shahzadi Tayyaba. A Residential Load Scheduling with the Integration of On-Site PV and Energy Storage Systems in Micro-Grid. Sustainability 2019, 12, 184 .
AMA StyleIhsan Ullah, Muhammad Babar Rasheed, Thamer Alquthami, Shahzadi Tayyaba. A Residential Load Scheduling with the Integration of On-Site PV and Energy Storage Systems in Micro-Grid. Sustainability. 2019; 12 (1):184.
Chicago/Turabian StyleIhsan Ullah; Muhammad Babar Rasheed; Thamer Alquthami; Shahzadi Tayyaba. 2019. "A Residential Load Scheduling with the Integration of On-Site PV and Energy Storage Systems in Micro-Grid." Sustainability 12, no. 1: 184.
This article evaluates the impact of dust accumulation on the performance of photovoltaic (PV) modules in two different locations inside Egypt, Cairo and Beni-Suef. Two identical PV modules were used for that purpose, where each module was exposed to the outdoor environment in order to collect dust naturally for a period of three weeks, each in its corresponding location. The approximate dust density on each of the two PV modules was estimated. Moreover, the electrical performance was evaluated and compared under the same indoor testing conditions. The results show a better electrical performance and less dust density for the PV module located in Cairo compared to that located in Beni-Suef. The results further provide an indication for the impact of soling in different locations within the same country through a clear and simple procedure. In addition, it paves the way for establishing a Photovoltaic Soiling Index (PVSI) in terms of a Photovoltaic Dust Coefficient, as well as a Photovoltaic Dust Interactive Map. The product of such concepts could be used by the Photovoltaic systems designers everywhere in order to estimate the impact of dust on the future performance of PV modules in small and large installations in different regions around the globe, and during different times of the year as well.
Thamer Alquthami; Karim Menoufi. Soiling of Photovoltaic Modules: Comparing between Two Distinct Locations within the Framework of Developing the Photovoltaic Soiling Index (PVSI). Sustainability 2019, 11, 4697 .
AMA StyleThamer Alquthami, Karim Menoufi. Soiling of Photovoltaic Modules: Comparing between Two Distinct Locations within the Framework of Developing the Photovoltaic Soiling Index (PVSI). Sustainability. 2019; 11 (17):4697.
Chicago/Turabian StyleThamer Alquthami; Karim Menoufi. 2019. "Soiling of Photovoltaic Modules: Comparing between Two Distinct Locations within the Framework of Developing the Photovoltaic Soiling Index (PVSI)." Sustainability 11, no. 17: 4697.
Recent work has shown that the impact of distributed energy resources (DERs), electric vehicles/plug-in hybrid electric vehicles (EVs/PHEVs), and smart appliances is favorable on the environment, economy, and reliability of the power grid. The benefits can be maximized by implementing coordinated smart controls. In the absence of coordinated controls, some negative effects may take place, such as reduced lifetime service of power distribution components and in particular distribution transformers. This paper presents a new smart house energy management system that can provide coordinated control of a residential house resources without customer inconvenience while minimizing overloading/overheating the distribution infrastructure.
Thamer Alquthami; A. P. Sakis Meliopoulos. Smart House Management and Control Without Customer Inconvenience. IEEE Transactions on Smart Grid 2016, 9, 2553 -2562.
AMA StyleThamer Alquthami, A. P. Sakis Meliopoulos. Smart House Management and Control Without Customer Inconvenience. IEEE Transactions on Smart Grid. 2016; 9 (4):2553-2562.
Chicago/Turabian StyleThamer Alquthami; A. P. Sakis Meliopoulos. 2016. "Smart House Management and Control Without Customer Inconvenience." IEEE Transactions on Smart Grid 9, no. 4: 2553-2562.