The Experts below are selected from a list of 5835 Experts worldwide ranked by ideXlab platform
B Saravanan - One of the best experts on this subject based on the ideXlab platform.
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a novel energy management algorithm for reduction of Main Grid dependence in future smart Grids using electric springs
Sustainable Energy Technologies and Assessments, 2017Co-Authors: Hari Charan S. Cherukuri, B SaravananAbstract:Abstract This work presents a new energy management algorithm suitable for micro Grids which are dependent on Main Grids during generation uncertainties in the micro sources. The proposed control algorithm tries to reduce the dependence of micro Grids on Main Grid by adjusting the power supplied to the non-critical loads. The presented methodology is implemented on a micro Grid system which consists of renewable power sources and loads, which are broadly classified as critical and non-critical loads. The micro Grid system considered for the study is dependent on Main Grid during generation deficits in the micro sources. In order to implement the proposed technique the non-critical loads present in the system are connected in series with A.C. electric springs. Application of A.C. electric springs in the micro Grid system to reduce the dependence on Main Grid is a different way of looking at the problem of reducing the Main Grid dependence and is probably the first attempt. The micro Grid system considered for the study is designed in MATLAB and simulations are carried out to implement the proposed energy management algorithm.
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A new control algorithm for energy conservation from Main Grid during generation intermittence in the micro Grids using A.C electric springs
2016 21st Century Energy Needs - Materials Systems and Applications (ICTFCEN), 2016Co-Authors: Hari Charan S. Cherukuri, B Saravanan, K S SwarupAbstract:The work presented in this paper addresses the problem of Main Grid dependability in micro Grids using Electric springs. The micro Grids which have more penetration of renewable sources are bound to depend on Main Grids during generation intermittence of the micro sources. In order to reduce the dependability of micro Grids on Main Grid an efficient energy management algorithm has been presented. The proposed algorithm schedules the non-critical loads present in the micro Grid using A.C Electric springs and the simulation studies are performed in MATLAB. The reduction in energy consumption from the Main Grid is achieved by making the non-critical loads consume lesser power during generation intermittence in the micro Grid.
Hari Charan S. Cherukuri - One of the best experts on this subject based on the ideXlab platform.
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Reduction of Main-Grid Dependence in Future DC Micro-Grids Using Electric Springs
2019 International Conference on Electrical Drives & Power Electronics (EDPE), 2019Co-Authors: Hari Charan S. Cherukuri, Saravanan Balasubramanian, Sanjecvikumar Padmanaban, Mahajan Sagar Bhaskar, Viliam Fedák, G. ArunkumarAbstract:The work presented in this article tries to reduce the Main Grid dependability of the micro-Grids using DC Electric springs and developing DC micro-Grid systems which are less dependent on Main-Grids during generation un-certainties. To make the micro-Grid structures less dependent on Main-Grids techniques like introducing storage, demand-side management, etc. have been used in the literature. This question of meeting the demand-supply and reduced Main-Grid dependability is more predominant in the micro-Grids if most of the power supplied by renewable energy sources. The DC Electric springs in order to reduce the power drawn from the Main-Grid during generation deficits make the non-critical loads consumed less power during generation intermittence. A DC micro-Grid system consisting of a renewable generator that is capable to supply local loads considered for the study. The micro-Grid system considered here depends on Main-Grid during insufficiency in the power generated by the renewable generator and the specific aim of this work is to reduce the power drawn from the Main Grid. The system models designed in the MATLAB and the energy consumption from the Main Grid in the presence and absence of DC Electric springs are studied.
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a novel energy management algorithm for reduction of Main Grid dependence in future smart Grids using electric springs
Sustainable Energy Technologies and Assessments, 2017Co-Authors: Hari Charan S. Cherukuri, B SaravananAbstract:Abstract This work presents a new energy management algorithm suitable for micro Grids which are dependent on Main Grids during generation uncertainties in the micro sources. The proposed control algorithm tries to reduce the dependence of micro Grids on Main Grid by adjusting the power supplied to the non-critical loads. The presented methodology is implemented on a micro Grid system which consists of renewable power sources and loads, which are broadly classified as critical and non-critical loads. The micro Grid system considered for the study is dependent on Main Grid during generation deficits in the micro sources. In order to implement the proposed technique the non-critical loads present in the system are connected in series with A.C. electric springs. Application of A.C. electric springs in the micro Grid system to reduce the dependence on Main Grid is a different way of looking at the problem of reducing the Main Grid dependence and is probably the first attempt. The micro Grid system considered for the study is designed in MATLAB and simulations are carried out to implement the proposed energy management algorithm.
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A new control algorithm for energy conservation from Main Grid during generation intermittence in the micro Grids using A.C electric springs
2016 21st Century Energy Needs - Materials Systems and Applications (ICTFCEN), 2016Co-Authors: Hari Charan S. Cherukuri, B Saravanan, K S SwarupAbstract:The work presented in this paper addresses the problem of Main Grid dependability in micro Grids using Electric springs. The micro Grids which have more penetration of renewable sources are bound to depend on Main Grids during generation intermittence of the micro sources. In order to reduce the dependability of micro Grids on Main Grid an efficient energy management algorithm has been presented. The proposed algorithm schedules the non-critical loads present in the micro Grid using A.C Electric springs and the simulation studies are performed in MATLAB. The reduction in energy consumption from the Main Grid is achieved by making the non-critical loads consume lesser power during generation intermittence in the micro Grid.
Huy Truong Dinh - One of the best experts on this subject based on the ideXlab platform.
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A Home Energy Management System With Renewable Energy and Energy Storage Utilizing Main Grid and Electricity Selling
IEEE Access, 2020Co-Authors: Huy Truong DinhAbstract:With the development of new technologies in the field of renewable energy and batteries, increasing number of houses have been equipped with renewable energy sources (RES) and energy storage systems (ESS) to reduce home energy cost. These houses usually have home energy management systems (HEMS) to control and schedule every electrical device. Various studies have been conducted on HEMS and optimization algorithms for energy cost and peak-to-average ratio (PAR) reduction. However, none of papers give a sufficient study on the utilization of Main Grid's electricity and selling electricity. In this paper, firstly, we propose a new HEMS architecture with RES and ESS where we take utilization of the electricity of the Main Grid and electricity selling into account. With the proposed HEMS, we build general mathematical formulas for energy cost and PAR during a day. We then optimize these formulas using both the particle swarm optimization (PSO) and the binary particle swarm optimization (BPSO). Results clearly show that, with our HEMS system, RES and ESS can help to drop home energy cost significantly to 19.7%, compared with the results of previous works. By increasing charge/discharge rate of ESS, energy cost can be decreased by 4.3% for 0.6 kW and 8.5% for 0.9 kW. Moreover, by using multi-objective optimization, our system can achieve better PAR with an acceptable energy cost.
Yashar Sahraei Manjili - One of the best experts on this subject based on the ideXlab platform.
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Intelligent decision making for energy management in microGrids with air pollution reduction policy
2012 7th International Conference on System of Systems Engineering (SoSE), 2012Co-Authors: Yashar Sahraei Manjili, Amir Rajaee, M. Jamshidi, Brian T. KelleyAbstract:Fuzzy Logic-based decision-making framework is implemented for energy management in microGrid systems in order to meet targets such as providing local consumers with required energy demand and making good revenue for the microGrid owner under a time-varying electricity cost policy while helping reduce negative environmental effects due to air polluting sources of electrical energy such as coal fire plants which operate in the Main Grid in order to provide local microGrid loads. Typically, a microGrid system has two modes of operation. It either works synchronously with the Main Grid or operates independently from the utility Grid in an isolated mode. Distributed renewable energy generators including solar, wind in association with batteries and Main Grid supply power to the consumer in the microGrid network. One day period is divided to a finite number of time slots. The Fuzzy intelligent approach implemented in this article determines the rate at which power has to be delivered to/taken from the storage unit during the next time slot depending on the electricity price per kWh of energy, local load demand, electricity generation rate through renewable resources, and air pollution factor which are sampled at predetermined rates. Cost function is defined as the sum of balance/revenue due to electricity trade between microGrid and the Main Grid, which includes the power provided to local load and distribution losses. Five different scenarios are considered for local load and microGrid assembly operation. Measures of balance/revenue will be extracted to represent benefits of using Fuzzy logic for energy management in microGrids with air pollution reduction policy.
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SoSE - Intelligent decision making for energy management in microGrids with air pollution reduction policy
2012 7th International Conference on System of Systems Engineering (SoSE), 2012Co-Authors: Yashar Sahraei Manjili, Amir Rajaee, M. Jamshidi, Brian KelleyAbstract:Fuzzy Logic-based decision-making framework is implemented for energy management in microGrid systems in order to meet targets such as providing local consumers with required energy demand and making good revenue for the microGrid owner under a time-varying electricity cost policy while helping reduce negative environmental effects due to air polluting sources of electrical energy such as coal fire plants which operate in the Main Grid in order to provide local microGrid loads. Typically, a microGrid system has two modes of operation. It either works synchronously with the Main Grid or operates independently from the utility Grid in an isolated mode. Distributed renewable energy generators including solar, wind in association with batteries and Main Grid supply power to the consumer in the microGrid network. One day period is divided to a finite number of time slots. The Fuzzy intelligent approach implemented in this article determines the rate at which power has to be delivered to/taken from the storage unit during the next time slot depending on the electricity price per kWh of energy, local load demand, electricity generation rate through renewable resources, and air pollution factor which are sampled at predetermined rates. Cost function is defined as the sum of balance/revenue due to electricity trade between microGrid and the Main Grid, which includes the power provided to local load and distribution losses. Five different scenarios are considered for local load and microGrid assembly operation. Measures of balance/revenue will be extracted to represent benefits of using Fuzzy logic for energy management in microGrids with air pollution reduction policy.
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Fuzzy control of electricity storage unit for Energy Management of Micro-Grids
2012Co-Authors: Yashar Sahraei Manjili, Amir Rajaee, M. Jamshidi, Brian KelleyAbstract:A Fuzzy Logic-based framework is proposed for control of Battery Storage Unit in Micro-Grid Systems to achieve Efficient Energy Management. Typically, a Micro-Grid system operates synchronously with the Main Grid and also has the ability to operate independently from the Main power Grid in an islanded mode. Distributed renewable energy generators including solar, wind in association with batteries and Main Grid supply power to the consumer in the Micro-Grid network. The goal here is to control the amount of power delivered to/taken from the storage unit in order to improve a cost function, defined based on summation of payment required for purchasing power from Main Grid or profit obtained by selling power to the Main Grid and distribution power loss, through reasonable decision making using predetermined human reasoning-based fuzzy rules. Profiles of system variables such as Consumer's Load Demand, Electricity Price Rate, and Renewable Electricity Generation Rate are assumed arbitrarily for obtaining general results. Measures of payment/profit will be extracted to compute amounts of cost and balance for the network which represent benefits of using Fuzzy logic for Storage Unit control with and without considering storage unit capacity limits. Simulation results are presented and discussed.
Brian Kelley - One of the best experts on this subject based on the ideXlab platform.
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SoSE - Intelligent decision making for energy management in microGrids with air pollution reduction policy
2012 7th International Conference on System of Systems Engineering (SoSE), 2012Co-Authors: Yashar Sahraei Manjili, Amir Rajaee, M. Jamshidi, Brian KelleyAbstract:Fuzzy Logic-based decision-making framework is implemented for energy management in microGrid systems in order to meet targets such as providing local consumers with required energy demand and making good revenue for the microGrid owner under a time-varying electricity cost policy while helping reduce negative environmental effects due to air polluting sources of electrical energy such as coal fire plants which operate in the Main Grid in order to provide local microGrid loads. Typically, a microGrid system has two modes of operation. It either works synchronously with the Main Grid or operates independently from the utility Grid in an isolated mode. Distributed renewable energy generators including solar, wind in association with batteries and Main Grid supply power to the consumer in the microGrid network. One day period is divided to a finite number of time slots. The Fuzzy intelligent approach implemented in this article determines the rate at which power has to be delivered to/taken from the storage unit during the next time slot depending on the electricity price per kWh of energy, local load demand, electricity generation rate through renewable resources, and air pollution factor which are sampled at predetermined rates. Cost function is defined as the sum of balance/revenue due to electricity trade between microGrid and the Main Grid, which includes the power provided to local load and distribution losses. Five different scenarios are considered for local load and microGrid assembly operation. Measures of balance/revenue will be extracted to represent benefits of using Fuzzy logic for energy management in microGrids with air pollution reduction policy.
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Fuzzy control of electricity storage unit for Energy Management of Micro-Grids
2012Co-Authors: Yashar Sahraei Manjili, Amir Rajaee, M. Jamshidi, Brian KelleyAbstract:A Fuzzy Logic-based framework is proposed for control of Battery Storage Unit in Micro-Grid Systems to achieve Efficient Energy Management. Typically, a Micro-Grid system operates synchronously with the Main Grid and also has the ability to operate independently from the Main power Grid in an islanded mode. Distributed renewable energy generators including solar, wind in association with batteries and Main Grid supply power to the consumer in the Micro-Grid network. The goal here is to control the amount of power delivered to/taken from the storage unit in order to improve a cost function, defined based on summation of payment required for purchasing power from Main Grid or profit obtained by selling power to the Main Grid and distribution power loss, through reasonable decision making using predetermined human reasoning-based fuzzy rules. Profiles of system variables such as Consumer's Load Demand, Electricity Price Rate, and Renewable Electricity Generation Rate are assumed arbitrarily for obtaining general results. Measures of payment/profit will be extracted to compute amounts of cost and balance for the network which represent benefits of using Fuzzy logic for Storage Unit control with and without considering storage unit capacity limits. Simulation results are presented and discussed.
