In this study, we investigate the optimal location of access points （APs） to connect end nodes with a service provider through power-line communication in smartgrid communication networks. APs are the gateways of po...In this study, we investigate the optimal location of access points （APs） to connect end nodes with a service provider through power-line communication in smartgrid communication networks. APs are the gateways of power-distribution communication networks, connecting users to control centers. Hence, they are vital for the reliable, safe, and economical operation of a power system. This paper proposes a planning method for AP allocation that takes into consideration economics, reliability, network delay, and （n-l） resilience. First, an optimization model for the AP location is established, which minimizes the cost of installing APs, while satisfying the reliability, network delay, and （n-1） resilience constraints. Then, an improved genetic algorithm is proposed to solve the optimization problem. The simulation results indicate that the proposed planning method can deal with diverse network conditions satisfactorily. Furthermore, it can be applied effectively with high flexibility and scalability.展开更多
The availability of non-renewable energy sources such as crude oil, natural gas, coal etc., is fast diminishing. So the renewable energy sources such as solar, hydropower, geothermal, wind, tidal energy, are gaining m...The availability of non-renewable energy sources such as crude oil, natural gas, coal etc., is fast diminishing. So the renewable energy sources such as solar, hydropower, geothermal, wind, tidal energy, are gaining more and more importance. Many new developments to convert these renewable energy sources into usable forms are taking place. Most renewable energy sources are used to produce electricity. In this paper, a performance and efficiency simulation study of a smart-grid connected photovoltaic system using Chroma DC programmable power supply, AC programmable source and an Aurora Inverter is proposed. The simulation is performed in MATLAB environment where the Current-Voltage (I-V) and Power-Voltage (P-V) curves from the solar array simulator are generated and plotted. The proposed topology has been verified with satisfactory results. In addition, temperature and irradiance effects on I-V and P-V characteristic curves are verified. Also, the efficiency curves of the photovoltaic grid interface inverter are generated in the study. The MATLAB code developed in this paper is a valuable tool for design engineers comparing different inverters, calculating the optimum efficiency of a given inverter type.展开更多
In recent times, renewable energy production from renewable energy sources is an alternative way to fulfill the increased energy demands. However, the increasing energy demand rate places more pressure, leading to the...In recent times, renewable energy production from renewable energy sources is an alternative way to fulfill the increased energy demands. However, the increasing energy demand rate places more pressure, leading to the termination of conventional energy resources. However, the cost of power generation from coal-fired plants is higher than the power generation’s price from renewable energy sources. This experiment is focused on cost optimization during power generation through pumped storage power plant and wind power plant. The entire modeling of cost optimization has been conducted in two parts. The mathematical modeling was done using MATLAB simulation while the hydro and wind power plant’s emulation was performed using SCADA (Supervisory control and data acquisition) designer implementation. The experiment was conducted using ranges of generated power from both power sources. The optimum combination of output power and cost from both generators is determined via MATLAB simulation within the assumed generated output power range. Secondly, the hydro-generator and wind generator’s emulation were executed individually through synchronizing the grid to determine each generator’s specification using SCADA designer, which provided the optimum power generation from both generators with the specific speed, aligning with results generated through MATLAB. Finally, the operational power cost (with no losses consideration) from MATLAB was compared with the local energy provider to determine the cost-efficiency. This experiment has provided the operational cost optimization of the hydro-wind combined power system with stable wind power generation using SCADA, which will ultimately assist in operations of large-scale power systems, remotely minimizing multi-area dynamic issues while maximizing the system efficiency.展开更多
The amount of non-technical losses in Brazil is quite elevated, accounting for nearly 5.5% of the country＇s total generated power. Such losses are asymmetrically distributed within the various regions of the country....The amount of non-technical losses in Brazil is quite elevated, accounting for nearly 5.5% of the country＇s total generated power. Such losses are asymmetrically distributed within the various regions of the country. Meter tampering （fraud）, meter bypassing by regular consumers （theft） and irregular hookups to the network by unlawful consumers are the most predominant forms of irregularities. Part of it which is caused by non-technical losses is being passed on to the consumers through the tariffs they pay. This paper presents an overview of the current situation related to non-technical losses in Brazil involving： quantification, regional asymmetry, nature and stratification, tariff management, and strategies employed to its reduction. Advanced measurement techniques provided by smart-grids can significantly reduce them. It is suggested a potential reduction of 60%. An innovative way of using these indicators in order to identify irregularities is briefly presented in this work.展开更多
With the transition to electric vehicle technologies, large scale support infrastructure is being deployed. The vehicleto-grid (V2G) concept is an opportunity to take advantage from both infrastructure and electric ve...With the transition to electric vehicle technologies, large scale support infrastructure is being deployed. The vehicleto-grid (V2G) concept is an opportunity to take advantage from both infrastructure and electric vehicle drive. However, coordinating large?number of agents in a reasonable speed and lack of homogenous distribution of the service provided by vehicle users to the grid have been left unattended. We apply consensus theory to the V2G concept presenting a decentralized control solution to assure that all vehicles within a region, regardless of their technology, positioning or state of charge, can communicate with their neighbors and agree on how much energy each should individually exchange with the grid. Applying constraints to the system, we considered a 25,000 vehicle fleet connected to a?grid during peak hours. Simulating power changes and vehicles entering and leaving the system, two groups of 5 vehicles were studied: the first group remained in the system during all peak hours, while the second group only an hour. Results showed that the two groups of vehicles despite connecting to the system at different times were able to reach consensus in t = 15 s, and reported a maximum error of ε < 0.01% if left in the system during all peak hours.展开更多
Efficient energy management and grid stability strongly rely on accurate Short-Term Load Forecasting(STLF).Existing forecasting models,unfortunately,are often inaccurate and computationally demanding.To overcome these...Efficient energy management and grid stability strongly rely on accurate Short-Term Load Forecasting(STLF).Existing forecasting models,unfortunately,are often inaccurate and computationally demanding.To overcome these challenges,a novel hybrid model,combining both linear regression and machine learning techniques,is proposed in this study.The hybrid model,MLR-LSTM-FFNN,captures both temporal and non-linear de-pendencies in load data by integrating multi-linear regression(MLR)with long short-term memory(LSTM)networks and feed-forward neural networks(FFNN).Using datasets from Qatar,with 5 min,15 min,30 min,and 1 h time intervals and from Panama City with a 1 h interval,experiments were conducted to thoroughly test the robustness of the model.The results showed that the MLR-LSTM-FFNN hybrid model outperformed the baseline and state-of-the-art hybrid models for each of the datasets,in terms of lower RMSE,MAE,and MAPE values along with a faster training time.This superior performance across different datasets underscores the model’s scal-ability and reliability as an STLF approach,providing a practical solution to energy demand prediction tasks.The improvement in short-term forecasting accuracy provides utilities with a practical tool to optimize demand-side management,reduce operational costs,and enhance grid reliability.展开更多
基金supported by the National High Technology Research and Development Program of China(2012AA050801)
文摘In this study, we investigate the optimal location of access points （APs） to connect end nodes with a service provider through power-line communication in smartgrid communication networks. APs are the gateways of power-distribution communication networks, connecting users to control centers. Hence, they are vital for the reliable, safe, and economical operation of a power system. This paper proposes a planning method for AP allocation that takes into consideration economics, reliability, network delay, and （n-l） resilience. First, an optimization model for the AP location is established, which minimizes the cost of installing APs, while satisfying the reliability, network delay, and （n-1） resilience constraints. Then, an improved genetic algorithm is proposed to solve the optimization problem. The simulation results indicate that the proposed planning method can deal with diverse network conditions satisfactorily. Furthermore, it can be applied effectively with high flexibility and scalability.
文摘The availability of non-renewable energy sources such as crude oil, natural gas, coal etc., is fast diminishing. So the renewable energy sources such as solar, hydropower, geothermal, wind, tidal energy, are gaining more and more importance. Many new developments to convert these renewable energy sources into usable forms are taking place. Most renewable energy sources are used to produce electricity. In this paper, a performance and efficiency simulation study of a smart-grid connected photovoltaic system using Chroma DC programmable power supply, AC programmable source and an Aurora Inverter is proposed. The simulation is performed in MATLAB environment where the Current-Voltage (I-V) and Power-Voltage (P-V) curves from the solar array simulator are generated and plotted. The proposed topology has been verified with satisfactory results. In addition, temperature and irradiance effects on I-V and P-V characteristic curves are verified. Also, the efficiency curves of the photovoltaic grid interface inverter are generated in the study. The MATLAB code developed in this paper is a valuable tool for design engineers comparing different inverters, calculating the optimum efficiency of a given inverter type.
文摘In recent times, renewable energy production from renewable energy sources is an alternative way to fulfill the increased energy demands. However, the increasing energy demand rate places more pressure, leading to the termination of conventional energy resources. However, the cost of power generation from coal-fired plants is higher than the power generation’s price from renewable energy sources. This experiment is focused on cost optimization during power generation through pumped storage power plant and wind power plant. The entire modeling of cost optimization has been conducted in two parts. The mathematical modeling was done using MATLAB simulation while the hydro and wind power plant’s emulation was performed using SCADA (Supervisory control and data acquisition) designer implementation. The experiment was conducted using ranges of generated power from both power sources. The optimum combination of output power and cost from both generators is determined via MATLAB simulation within the assumed generated output power range. Secondly, the hydro-generator and wind generator’s emulation were executed individually through synchronizing the grid to determine each generator’s specification using SCADA designer, which provided the optimum power generation from both generators with the specific speed, aligning with results generated through MATLAB. Finally, the operational power cost (with no losses consideration) from MATLAB was compared with the local energy provider to determine the cost-efficiency. This experiment has provided the operational cost optimization of the hydro-wind combined power system with stable wind power generation using SCADA, which will ultimately assist in operations of large-scale power systems, remotely minimizing multi-area dynamic issues while maximizing the system efficiency.
文摘The amount of non-technical losses in Brazil is quite elevated, accounting for nearly 5.5% of the country＇s total generated power. Such losses are asymmetrically distributed within the various regions of the country. Meter tampering （fraud）, meter bypassing by regular consumers （theft） and irregular hookups to the network by unlawful consumers are the most predominant forms of irregularities. Part of it which is caused by non-technical losses is being passed on to the consumers through the tariffs they pay. This paper presents an overview of the current situation related to non-technical losses in Brazil involving： quantification, regional asymmetry, nature and stratification, tariff management, and strategies employed to its reduction. Advanced measurement techniques provided by smart-grids can significantly reduce them. It is suggested a potential reduction of 60%. An innovative way of using these indicators in order to identify irregularities is briefly presented in this work.
文摘With the transition to electric vehicle technologies, large scale support infrastructure is being deployed. The vehicleto-grid (V2G) concept is an opportunity to take advantage from both infrastructure and electric vehicle drive. However, coordinating large?number of agents in a reasonable speed and lack of homogenous distribution of the service provided by vehicle users to the grid have been left unattended. We apply consensus theory to the V2G concept presenting a decentralized control solution to assure that all vehicles within a region, regardless of their technology, positioning or state of charge, can communicate with their neighbors and agree on how much energy each should individually exchange with the grid. Applying constraints to the system, we considered a 25,000 vehicle fleet connected to a?grid during peak hours. Simulating power changes and vehicles entering and leaving the system, two groups of 5 vehicles were studied: the first group remained in the system during all peak hours, while the second group only an hour. Results showed that the two groups of vehicles despite connecting to the system at different times were able to reach consensus in t = 15 s, and reported a maximum error of ε < 0.01% if left in the system during all peak hours.
基金support from the Qatar National Research Fund through grant AICC05-0508-230001(Solar Trade(ST):An Equitable and Efficient Blockchain-Enabled Renewable Energy Ecosystem-“Oppor-tunities for Fintech to Scale up Green Finance for Clean Energy”)and from Qatar Environment and Energy Research Institute is gratefully acknowledged.
文摘Efficient energy management and grid stability strongly rely on accurate Short-Term Load Forecasting(STLF).Existing forecasting models,unfortunately,are often inaccurate and computationally demanding.To overcome these challenges,a novel hybrid model,combining both linear regression and machine learning techniques,is proposed in this study.The hybrid model,MLR-LSTM-FFNN,captures both temporal and non-linear de-pendencies in load data by integrating multi-linear regression(MLR)with long short-term memory(LSTM)networks and feed-forward neural networks(FFNN).Using datasets from Qatar,with 5 min,15 min,30 min,and 1 h time intervals and from Panama City with a 1 h interval,experiments were conducted to thoroughly test the robustness of the model.The results showed that the MLR-LSTM-FFNN hybrid model outperformed the baseline and state-of-the-art hybrid models for each of the datasets,in terms of lower RMSE,MAE,and MAPE values along with a faster training time.This superior performance across different datasets underscores the model’s scal-ability and reliability as an STLF approach,providing a practical solution to energy demand prediction tasks.The improvement in short-term forecasting accuracy provides utilities with a practical tool to optimize demand-side management,reduce operational costs,and enhance grid reliability.
