Karar Mahmoud received the B.S. and M.Sc. degrees in electrical engineering from Aswan University, Aswan, Egypt, in 2008 and 2012, respectively. In 2016, he received the Ph.D. degree from the Electric Power and Energy System Laboratory (EPESL), Graduate School of Engineering, Hiroshima University, Hiroshima, Japan. Currently, he is working as an assistant professor in Aswan university. His research interests include modeling, analysis, control, and optimization of distributed systems with distributed generations.
The old economic and social growth model, characterized by centralized fossil energy consumption,... more The old economic and social growth model, characterized by centralized fossil energy consumption, is progressively shifting, and the third industrial revolution, represented by the new energy and Internet technology is gaining traction. The energy Internet, as a core technology of the third industrial revolution, aims to combine the renewable energy and Internet technology to promote large-scale use and sharing of the distributed renewable energy, as well as the integration of multiple complex network systems such as electricity, transportation, and natural gas. Energy Internet is a new technology that enables the power networks to save energy. Multi-energy synchronization optimization, on the other hand, is a significant problem. This paper proposes an optimized approach based on the concept of layered control-collaborate optimization to solve this problem. The proposed method allows the distributed device to plan the heat, cold, gas, and electricity in the regional system in the most efficient way possible. The proposed optimization model is simulated using a real-number genetic algorithm. It improved the optimal scheduling between different regions and the independence of distributed equipment with minimal cost
Nowadays, the load monitoring system is an important element in smart buildings to reduce energy ... more Nowadays, the load monitoring system is an important element in smart buildings to reduce energy consumption. Nonintrusive load monitoring (NILM) is utilized to determine the power consumption of each appliance in smart homes. The main problem of NILM is how to separate each appliance's power from the signal of aggregated consumption. In this regard, this paper presents a combination between particle swarm optimization (PSO) and artificial neural networks (ANNs) to identify electrical appliances for demand‐side management. ANN is applied in NILM as a load identification task, and PSO is used to train the ANN algorithm. This combination enhances the NILM technique's accuracy, which is further verified by experiments on different datasets like Reference Energy Disaggregation Dataset, UK Domestic Appliance‐Level ElectricityUK‐DALE, and Indian data for Ambient Water and electricity Sensing. The high accuracy of the proposed algorithm is verified by comparisons with state of the art methods. Compared with other approaches, the total mean absolute error has decreased from 39.3566 to 18.607. Also, the normalized root mean square error (NRMSE) method has been used to compare the measured and predicted results. The NRMSE is in the range of 1.719%–16.514%, which means perfect consistency. This demonstrates the effectiveness of the proposed approach for home energy management. Furthermore, customer behavior has been studied, considering energy costs during day hours.
As the load on distribution networks grows, system operators and planners are constantly challeng... more As the load on distribution networks grows, system operators and planners are constantly challenged with the issue of voltage regulation or enhancing the quality of supply to customers at the load end of lengthy distribution lines. This paper presents the optimum determination of series capacitor units in a distribution system to maximize energy‐saving and enhance voltage levels. Interestingly, series capacitors can enhance the capability of transmission lines, reduce line losses, enhance the performance of buses with large induction motor loads and reduce voltage flicker. At the same time, the limitations of series compensation are taken into consideration while calculating its optimum values. To achieve the planning objective and optimal load flow objective, two strategies: The Improved Grey Wolf Optimization method (I‐GWO) and Tabu Search (TS), are hybridized to get the benefit of their advantages. The I‐GWO has a movement strategy called dimension learning‐based hunting for enha...
The old economic and social growth model, characterized by centralized fossil energy consumption,... more The old economic and social growth model, characterized by centralized fossil energy consumption, is progressively shifting, and the third industrial revolution, represented by the new energy and Internet technology is gaining traction. The energy Internet, as a core technology of the third industrial revolution, aims to combine the renewable energy and Internet technology to promote large-scale use and sharing of the distributed renewable energy, as well as the integration of multiple complex network systems such as electricity, transportation, and natural gas. Energy Internet is a new technology that enables the power networks to save energy. Multi-energy synchronization optimization, on the other hand, is a significant problem. This paper proposes an optimized approach based on the concept of layered control-collaborate optimization to solve this problem. The proposed method allows the distributed device to plan the heat, cold, gas, and electricity in the regional system in the most efficient way possible. The proposed optimization model is simulated using a real-number genetic algorithm. It improved the optimal scheduling between different regions and the independence of distributed equipment with minimal cost
Nowadays, the load monitoring system is an important element in smart buildings to reduce energy ... more Nowadays, the load monitoring system is an important element in smart buildings to reduce energy consumption. Nonintrusive load monitoring (NILM) is utilized to determine the power consumption of each appliance in smart homes. The main problem of NILM is how to separate each appliance's power from the signal of aggregated consumption. In this regard, this paper presents a combination between particle swarm optimization (PSO) and artificial neural networks (ANNs) to identify electrical appliances for demand‐side management. ANN is applied in NILM as a load identification task, and PSO is used to train the ANN algorithm. This combination enhances the NILM technique's accuracy, which is further verified by experiments on different datasets like Reference Energy Disaggregation Dataset, UK Domestic Appliance‐Level ElectricityUK‐DALE, and Indian data for Ambient Water and electricity Sensing. The high accuracy of the proposed algorithm is verified by comparisons with state of the art methods. Compared with other approaches, the total mean absolute error has decreased from 39.3566 to 18.607. Also, the normalized root mean square error (NRMSE) method has been used to compare the measured and predicted results. The NRMSE is in the range of 1.719%–16.514%, which means perfect consistency. This demonstrates the effectiveness of the proposed approach for home energy management. Furthermore, customer behavior has been studied, considering energy costs during day hours.
As the load on distribution networks grows, system operators and planners are constantly challeng... more As the load on distribution networks grows, system operators and planners are constantly challenged with the issue of voltage regulation or enhancing the quality of supply to customers at the load end of lengthy distribution lines. This paper presents the optimum determination of series capacitor units in a distribution system to maximize energy‐saving and enhance voltage levels. Interestingly, series capacitors can enhance the capability of transmission lines, reduce line losses, enhance the performance of buses with large induction motor loads and reduce voltage flicker. At the same time, the limitations of series compensation are taken into consideration while calculating its optimum values. To achieve the planning objective and optimal load flow objective, two strategies: The Improved Grey Wolf Optimization method (I‐GWO) and Tabu Search (TS), are hybridized to get the benefit of their advantages. The I‐GWO has a movement strategy called dimension learning‐based hunting for enha...
Recently, the penetration of renewable distributed generation (DG) technologies has dramatically ... more Recently, the penetration of renewable distributed generation (DG) technologies has dramatically increased in distribution systems. The most notable DG types are wind power, photovoltaic, and solar systems. These DG units are often distributed according to load centers in distribution systems. Renewable DG technologies are described as intermittent sources, for the reason that their output power varies depending on environmental conditions. Consequently, the performances distribution systems are greatly affected by these DG units. These resources may have positive or negative technical impacts on the grid, according to their selected sizes, locations, and types. The main objective of this work is to perform comprehensive modeling, analysis of distribution systems and optimally install multiple DG technologies. The methodology of DG allocation must be generic, where different DG technologies are incorporates to the optimization process. In addition, the performance of the developed method must be efficient in terms of CPU time and accuracy. To represent the allocation problem from a practical view, distribution system constraints, such as voltage limits, line flow limits, and maximum DG penetration are required to be completely considered. For this purpose, firstly, distribution system component models are developed using state of the art phase and sequence components frame of references. An efficient power flow method for analyzing distribution systems is presented. The proposed method utilizes efficient quadratic-based (QB) models for various components of distribution systems. The power flow problem is formulated and solved by a backward/forward sweep (BFS) algorithm. The proposed QBBFS method accommodates multi-phase laterals, different load types, capacitors, distribution transformers, and distributed generation (DG). The advantageous feature of the proposed method is robust convergence characteristics against ill conditions, guaranteeing lower iteration numbers than the existing BFS methods. The proposed method is tested and validated on several distribution test systems. The accuracy is verified using OpenDSS. Comparisons are made with other commonly used BFS methods. The results confirm the effectiveness and robustness of the proposed QBBFS with different loading conditions, high R/X ratio, and/or excessive DG penetration. Secondly, an efficient analytical (EA) method is proposed for optimally installing multiple distributed generation (DG) technologies to minimize power loss in distribution systems. Different DG types are considered, and their power factors are optimally calculated. The proposed EA method is also applied to the problem of allocating an optimal mix of different DG types with various generation capabilities. Furthermore, the EA method is integrated with the optimal power flow (OPF) algorithm to develop a new method, EA-OPF that effectively addresses overall system constraints. The proposed methods are tested using 33-bus and 69-bus distribution test systems. The calculated results are validated using the simulation results of the exact optimal solution obtained by an exhaustive OPF algorithm for both distribution test systems. The results show that the performances of the proposed methods are superior to existing methods in terms of computational speed and accuracy.
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Papers by Karar Mahmoud