Microgrid has emerged as an answer to growing demand for distributed generation(DG) in power systems. It contains several DG units including microalternator, photovoltaic system and wind generation. It turns out that ...Microgrid has emerged as an answer to growing demand for distributed generation(DG) in power systems. It contains several DG units including microalternator, photovoltaic system and wind generation. It turns out that sustained operation relies on the stability of these constituent systems. In this paper, a microgrid consisting of microalternator and photovoltaic system is modeled as a networked control system of systems(So S)subjected to packet dropouts and delays. Next, an observerbased controller is designed to stabilize the system in presence of the aforementioned communication constraints and simulation results are provided to support the control design methodology.展开更多
This article presents the simulation results and analysis related to the response of the generators within a microgrid towards an accidental overload condition that will require some load shedding action. A microgrid ...This article presents the simulation results and analysis related to the response of the generators within a microgrid towards an accidental overload condition that will require some load shedding action. A microgrid overload can occur due to various reasons ranging from poor load schedule, inadequate switching of circuits within the microgrid, outage of one or more generators inside the microgrid, illegal load connections by some low voltage consumers, etc. It was observed that among the main factors that determine the survival of the microgrid during its transition from the grid connected mode to the islanded mode of operation are the size and type of the load connected (passive or dynamic load) as well as the length of time during which the unexpected load is connected. Models of a speed and voltage regulators of a diesel generator, and important for coping with the overload conditions are provided in the paper. The novelty of the work lies in the load shedding simulation and analysis of the specific generators studied herein, regarding that in many countries the microgrid technology is seen as an important alternative towards the ever increasing load demand and also to assist the system during periods of blackout.展开更多
Energy forecasting using Renewable energy sources(RESs)is gradually gaining weight in the research field due to the benefits it presents to the modern-day environment.Not only does energy forecasting using renewable e...Energy forecasting using Renewable energy sources(RESs)is gradually gaining weight in the research field due to the benefits it presents to the modern-day environment.Not only does energy forecasting using renewable energy sources help mitigate the greenhouse effect,it also helps to conserve energy for future use.Over the years,several methods for energy forecasting have been proposed,all of which were more concerned with the accuracy of the prediction models with little or no considerations to the operating environment.This research,however,proposes the uses of Deep Neural Network(DNN)for energy forecasting on mobile devices at the edge of the network.This ensures low latency and communication overhead for all energy forecasting operations since they are carried out at the network periphery.Nevertheless,the cloud would be used as a support for the mobile devices by providing permanent storage for the locally generated data and a platform for offloading resource-intensive computations that exceed the capabilities of the local mobile devices as well as security for them.Electrical network topology was proposed which allows seamless incorporation of multiple RESs into the distributed renewable energy source(DRES)network.Moreover,a novel grid control algorithm that uses the forecasting model to administer a wellcoordinated and effective control for renewable energy sources(RESs)in the electrical network is designed.The electrical network was simulated with two RESs and a DNN model was used to create a forecasting model for the simulated network.The model was trained using a dataset from a solar power generation company in Belgium(elis)and was experimented with a different number of layers to determine the optimum architecture for performing the forecasting operations.The performance of each architecture was evaluated using the mean square error(MSE)and the r-square.展开更多
Energy forecasting using Renewable energy sources(RESs)is gradually gaining weight in the research field due to the benefits it presents to the modern-day environment.Not only does energy forecasting using renewable e...Energy forecasting using Renewable energy sources(RESs)is gradually gaining weight in the research field due to the benefits it presents to the modern-day environment.Not only does energy forecasting using renewable energy sources help mitigate the greenhouse effect,it also helps to conserve energy for future use.Over the years,several methods for energy forecasting have been proposed,all of which were more concerned with the accuracy of the prediction models with little or no considerations to the operating environment.This research,however,proposes the uses of Deep Neural Network(DNN)for energy forecasting on mobile devices at the edge of the network.This ensures low latency and communication overhead for all energy forecasting operations since they are carried out at the network periphery.Nevertheless,the cloud would be used as a support for the mobile devices by providing permanent storage for the locally generated data and a platform for offloading resource-intensive computations that exceed the capabilities of the local mobile devices as well as security for them.Electrical network topology was proposed which allows seamless incorporation of multiple RESs into the distributed renewable energy source(DRES)network.Moreover,a novel grid control algorithm that uses the forecasting model to administer a wellcoordinated and effective control for renewable energy sources(RESs)in the electrical network is designed.The electrical network was simulated with two RESs and a DNN model was used to create a forecasting model for the simulated network.The model was trained using a dataset from a solar power generation company in Belgium(elis)and was experimented with a different number of layers to determine the optimum architecture for performing the forecasting operations.The performance of each architecture was evaluated using the mean square error(MSE)and the r-square.展开更多
基金supported by the Deanship for Scientific Research(DSR)at KFUPM through Distinguished Professorship Research Project(IN-141003)
文摘Microgrid has emerged as an answer to growing demand for distributed generation(DG) in power systems. It contains several DG units including microalternator, photovoltaic system and wind generation. It turns out that sustained operation relies on the stability of these constituent systems. In this paper, a microgrid consisting of microalternator and photovoltaic system is modeled as a networked control system of systems(So S)subjected to packet dropouts and delays. Next, an observerbased controller is designed to stabilize the system in presence of the aforementioned communication constraints and simulation results are provided to support the control design methodology.
文摘This article presents the simulation results and analysis related to the response of the generators within a microgrid towards an accidental overload condition that will require some load shedding action. A microgrid overload can occur due to various reasons ranging from poor load schedule, inadequate switching of circuits within the microgrid, outage of one or more generators inside the microgrid, illegal load connections by some low voltage consumers, etc. It was observed that among the main factors that determine the survival of the microgrid during its transition from the grid connected mode to the islanded mode of operation are the size and type of the load connected (passive or dynamic load) as well as the length of time during which the unexpected load is connected. Models of a speed and voltage regulators of a diesel generator, and important for coping with the overload conditions are provided in the paper. The novelty of the work lies in the load shedding simulation and analysis of the specific generators studied herein, regarding that in many countries the microgrid technology is seen as an important alternative towards the ever increasing load demand and also to assist the system during periods of blackout.
基金the National Natural Science Foundation of China(No.41911530242,41975142)5150 Spring Specialists(05492018012,05762018039)+3 种基金Major Program of the National Social Science Fund of China(Grant No.17ZDA092)333 High-Level Talent Cultivation Project of Jiangsu Province(BRA2018332)Royal Society of Edinburgh,UK and China Natural Science Foundation Council(RSE Reference:62967_Liu_2018_2)under their Joint International Projects funding scheme and basic Research Programs(Natural Science Foundation)of Jiangsu Province(BK20191398).
文摘Energy forecasting using Renewable energy sources(RESs)is gradually gaining weight in the research field due to the benefits it presents to the modern-day environment.Not only does energy forecasting using renewable energy sources help mitigate the greenhouse effect,it also helps to conserve energy for future use.Over the years,several methods for energy forecasting have been proposed,all of which were more concerned with the accuracy of the prediction models with little or no considerations to the operating environment.This research,however,proposes the uses of Deep Neural Network(DNN)for energy forecasting on mobile devices at the edge of the network.This ensures low latency and communication overhead for all energy forecasting operations since they are carried out at the network periphery.Nevertheless,the cloud would be used as a support for the mobile devices by providing permanent storage for the locally generated data and a platform for offloading resource-intensive computations that exceed the capabilities of the local mobile devices as well as security for them.Electrical network topology was proposed which allows seamless incorporation of multiple RESs into the distributed renewable energy source(DRES)network.Moreover,a novel grid control algorithm that uses the forecasting model to administer a wellcoordinated and effective control for renewable energy sources(RESs)in the electrical network is designed.The electrical network was simulated with two RESs and a DNN model was used to create a forecasting model for the simulated network.The model was trained using a dataset from a solar power generation company in Belgium(elis)and was experimented with a different number of layers to determine the optimum architecture for performing the forecasting operations.The performance of each architecture was evaluated using the mean square error(MSE)and the r-square.
基金received funding from the National Natural Science Foundation of China(No.41911530242,41975142)5150 Spring Specialists(05492018012,05762018039)+3 种基金Major Program of the National Social Science Fund of China(Grant No.17ZDA092)333 High-Level Talent Cultivation Project of Jiangsu Province(BRA2018332)Royal Society of Edinburgh,UK and China Natural Science Foundation Council(RSE Reference:62967_Liu_2018_2)under their Joint International Projects funding schemebasic Research Programs(Natural Science Foundation)of Jiangsu Province(BK20191398).
文摘Energy forecasting using Renewable energy sources(RESs)is gradually gaining weight in the research field due to the benefits it presents to the modern-day environment.Not only does energy forecasting using renewable energy sources help mitigate the greenhouse effect,it also helps to conserve energy for future use.Over the years,several methods for energy forecasting have been proposed,all of which were more concerned with the accuracy of the prediction models with little or no considerations to the operating environment.This research,however,proposes the uses of Deep Neural Network(DNN)for energy forecasting on mobile devices at the edge of the network.This ensures low latency and communication overhead for all energy forecasting operations since they are carried out at the network periphery.Nevertheless,the cloud would be used as a support for the mobile devices by providing permanent storage for the locally generated data and a platform for offloading resource-intensive computations that exceed the capabilities of the local mobile devices as well as security for them.Electrical network topology was proposed which allows seamless incorporation of multiple RESs into the distributed renewable energy source(DRES)network.Moreover,a novel grid control algorithm that uses the forecasting model to administer a wellcoordinated and effective control for renewable energy sources(RESs)in the electrical network is designed.The electrical network was simulated with two RESs and a DNN model was used to create a forecasting model for the simulated network.The model was trained using a dataset from a solar power generation company in Belgium(elis)and was experimented with a different number of layers to determine the optimum architecture for performing the forecasting operations.The performance of each architecture was evaluated using the mean square error(MSE)and the r-square.
