The Experts below are selected from a list of 21 Experts worldwide ranked by ideXlab platform
Ding Maosheng - One of the best experts on this subject based on the ideXlab platform.
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a generation and load integrated scheduling on interaction mode on customer side
Automation of electric power systems, 2012Co-Authors: Ding MaoshengAbstract:In view of the challenge by the system peak-valley difference due to the large-scale wind power anti-peak-regulation characteristic and air-conditioning load to both the economy and security of power system operation,a new interactive mode of voluntarily declaring power consumption willingness and peak-load shifting cost is designed.The interactive mode is based on an analysis of load shifting potential of commercial and industrial customers.Based on this mode,a load model of interactive customer fully reflecting customer willingness on the customer side is developed.A generation and load integrated scheduling model is built by embedding the load model of interactive customer into the traditional security constrained unit combined model.The load model of interactive customer can participate in power network operation as scheduling resources,reducing the cost of Expensive Generator start and stop as well as the operation cost of the power system.New England 39-bus system validates the effectiveness of the proposed mode and model.
Joao P. S. Catalao - One of the best experts on this subject based on the ideXlab platform.
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A Hybrid Probabilistic Algorithm for Computationally Efficient Estimation of Power Generation in AC Optimal Power Flow
2020 IEEE 14th International Conference on Compatibility Power Electronics and Power Engineering (CPE-POWERENG), 2020Co-Authors: Mohamed Lotfi, Shaden Fikry, Gerardo J. Osorio, Mohammad Sadegh Javadi, Sergio F. Santos, Joao P. S. CatalaoAbstract:Decentralization of power systems is creating a need for tools which can provide fast and accurate optimal power flow (OPF) solutions, without being dependent on the availability of all system information and/or uncertain variables. In this study, a hybrid probabilistic algorithm is proposed to accurately and efficiently predict ideal generation levels of individual Generators to minimize the total system cost (as per AC-OPF), while having no information on the grid structure and with limited information on system variables. The proposed hybrid algorithm combines the use of correlation analysis, k-means clusters, and kernel density estimation (KDE), to predict ideal generation levels of each Generator based only on historical datasets of local information (i.e. adjacent load centers). By simulating the AC-OPF problem on the IEEE 9-bus test system, a historical dataset of 1000 samples is synthetically generated and randomized local information is given as input for each agent. Quasi-deterministic Monte-Carlo simulations with 100000 samples were used for validation. In the most uncertain operating conditions, the proposed algorithm was capable of predicting the ideal generation level of the most Expensive Generator with a 1.65% error, while being three times faster than a Neural Network (NN), taking only 0.39 seconds to run on a standard laptop computer.
Muhamad Azman Miskam - One of the best experts on this subject based on the ideXlab platform.
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Design and simulation of SOI-MEMS electrostatic vibration energy harvester for micro power generation
International Conference on Electrical Control and Computer Engineering 2011 (InECCE), 2011Co-Authors: Othman Sidek, Muhammad Afif Khalid, Mohammad Zulfikar Ishak, Muhamad Azman MiskamAbstract:The micro power generation using vibration energy sources appears to be attractive due to its applicability on many situations and environmental conditions. The electrostatic energy harvester has various advantageous over piezoelectric and electromagnetic systems such as reduction in fabrication process complexity and cost, feasibility on MEMS and IC integration, improves the possibility level of integration with silicon based microelectronics and produce voltage within the usable range which can obviating the need of large and Expensive Generator. This paper presents the design and simulation of SOI-MEMS electrostatic vibration energy harvester using the architect module in CoventorWare2010. Simulation results shows that the newly proposed electrostatic energy harvesting system can produce maximum output power of 5.891µW when the excitation frequency is 2 kHz.
Mohamed Lotfi - One of the best experts on this subject based on the ideXlab platform.
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A Hybrid Probabilistic Algorithm for Computationally Efficient Estimation of Power Generation in AC Optimal Power Flow
2020 IEEE 14th International Conference on Compatibility Power Electronics and Power Engineering (CPE-POWERENG), 2020Co-Authors: Mohamed Lotfi, Shaden Fikry, Gerardo J. Osorio, Mohammad Sadegh Javadi, Sergio F. Santos, Joao P. S. CatalaoAbstract:Decentralization of power systems is creating a need for tools which can provide fast and accurate optimal power flow (OPF) solutions, without being dependent on the availability of all system information and/or uncertain variables. In this study, a hybrid probabilistic algorithm is proposed to accurately and efficiently predict ideal generation levels of individual Generators to minimize the total system cost (as per AC-OPF), while having no information on the grid structure and with limited information on system variables. The proposed hybrid algorithm combines the use of correlation analysis, k-means clusters, and kernel density estimation (KDE), to predict ideal generation levels of each Generator based only on historical datasets of local information (i.e. adjacent load centers). By simulating the AC-OPF problem on the IEEE 9-bus test system, a historical dataset of 1000 samples is synthetically generated and randomized local information is given as input for each agent. Quasi-deterministic Monte-Carlo simulations with 100000 samples were used for validation. In the most uncertain operating conditions, the proposed algorithm was capable of predicting the ideal generation level of the most Expensive Generator with a 1.65% error, while being three times faster than a Neural Network (NN), taking only 0.39 seconds to run on a standard laptop computer.
Othman Sidek - One of the best experts on this subject based on the ideXlab platform.
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Design and simulation of SOI-MEMS electrostatic vibration energy harvester for micro power generation
International Conference on Electrical Control and Computer Engineering 2011 (InECCE), 2011Co-Authors: Othman Sidek, Muhammad Afif Khalid, Mohammad Zulfikar Ishak, Muhamad Azman MiskamAbstract:The micro power generation using vibration energy sources appears to be attractive due to its applicability on many situations and environmental conditions. The electrostatic energy harvester has various advantageous over piezoelectric and electromagnetic systems such as reduction in fabrication process complexity and cost, feasibility on MEMS and IC integration, improves the possibility level of integration with silicon based microelectronics and produce voltage within the usable range which can obviating the need of large and Expensive Generator. This paper presents the design and simulation of SOI-MEMS electrostatic vibration energy harvester using the architect module in CoventorWare2010. Simulation results shows that the newly proposed electrostatic energy harvesting system can produce maximum output power of 5.891µW when the excitation frequency is 2 kHz.
