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E-Vehicles are used for transportation and, with a vehicle-to-grid optimization approach, they may be used for supplying a backup source of energy for renewable energy sources. Renewable energy sources are integrated to maintain the demand of consumers, mitigate the active and reactive power losses, and maintain the voltage profile. Renewable energy sources are not supplied all day and, to meet the peak demand, extra electricity may be supplied through e-Vehicles. E-Vehicles with random integration may cause system unbalancing problems and need a solution. The objective of this paper is to integrate e-Vehicles with the grid as a backup source of energy through the grid-to-vehicle optimization approach by reducing active and reactive power losses and maintaining voltage profile. In this paper, three case studies are discussed: (i) integration of renewable energy sources alone; (ii) integration of e-Vehicles alone; (iii) integration of renewable energy sources and e-Vehicles in hybrid mode. The simulation results show the effectiveness of the integration and the active and reactive power losses are minimum when we used the third case.

E-Vehicles are used for transportation and, with a vehicle-to-grid optimization approach, they may be used for supplying a backup source of energy for renewable energy sources. Renewable energy sources are integrated to maintain the demand of consumers, mitigate the active and reactive power losses, and maintain the voltage profile. Renewable energy sources are not supplied all day and, to meet the peak demand, extra electricity may be supplied through e-Vehicles. E-Vehicles with random integration may cause system unbalancing problems and need a solution. The objective of this paper is to integrate e-Vehicles with the grid as a backup source of energy through the grid-to-vehicle optimization approach by reducing active and reactive power losses and maintaining voltage profile. In this paper, three case studies are discussed: (i) integration of renewable energy sources alone; (ii) integration of e-Vehicles alone; (iii) integration of renewable energy sources and e-Vehicles in hybrid mode. The simulation results show the effectiveness of the integration and the active and reactive power losses are minimum when we used the third case.

The precise prediction of power estimates of wind–solar renewable energy sources becomes challenging due to their intermittent nature and difference in intensity between day and night. Machine-learning algorithms are non-linear mapping functions to approximate any given function from known input–output pairs and can be used for this purpose. This paper presents an artificial neural network (ANN)-based method to predict hybrid wind–solar resources and estimate power generation by correlating wind speed and solar radiation for real-time data. The proposed ANN allows optimization of the hybrid system’s operation by efficient wind and solar energy production estimation for a given set of weather conditions. The proposed model uses temperature, humidity, air pressure, solar radiation, optimum angle, and target values of known wind speeds, solar radiation, and optimum angle. A normalization function to narrow the error distribution and an iterative method with the Levenberg–Marquardt training function is used to reduce error. The experimental results show the effectiveness of the proposed approach against the existing wind, solar, or wind–solar estimation methods. It is envisaged that such an intelligent yet simplified method for predicting wind speed, solar radiation, and optimum angle, and designing wind–solar hybrid systems can improve the accuracy and efficiency of renewable energy generation.

The precise prediction of power estimates of wind–solar renewable energy sources becomes challenging due to their intermittent nature and difference in intensity between day and night. Machine-learning algorithms are non-linear mapping functions to approximate any given function from known input–output pairs and can be used for this purpose. This paper presents an artificial neural network (ANN)-based method to predict hybrid wind–solar resources and estimate power generation by correlating wind speed and solar radiation for real-time data. The proposed ANN allows optimization of the hybrid system’s operation by efficient wind and solar energy production estimation for a given set of weather conditions. The proposed model uses temperature, humidity, air pressure, solar radiation, optimum angle, and target values of known wind speeds, solar radiation, and optimum angle. A normalization function to narrow the error distribution and an iterative method with the Levenberg–Marquardt training function is used to reduce error. The experimental results show the effectiveness of the proposed approach against the existing wind, solar, or wind–solar estimation methods. It is envisaged that such an intelligent yet simplified method for predicting wind speed, solar radiation, and optimum angle, and designing wind–solar hybrid systems can improve the accuracy and efficiency of renewable energy generation.

Conventional outage management practices in distribution systems are tedious and complex due to the long time taken to locate the fault. Emerging smart technologies and various cloud services offered could be utilized and integrated into the power industry to enhance the overall process, especially in the fault monitoring and normalizing fields in distribution systems. This paper introduces smart fault monitoring and normalizing technologies in distribution systems by using one of the most popular cloud service platforms, the Microsoft Azure Internet of Things (IoT) Hub, together with some of the related services. A hardware prototype was constructed based on part of a real underground distribution system network, and the fault monitoring and normalizing techniques were integrated to form a system. Such a system with IoT integration effectively reduces the power outage experienced by customers in the healthy section of the faulted feeder from approximately 1 h to less than 5 min and is able to improve the System Average Interruption Duration Index (SAIDI) and System Average Interruption Frequency Index (SAIFI) in electric utility companies significantly.

Conventional outage management practices in distribution systems are tedious and complex due to the long time taken to locate the fault. Emerging smart technologies and various cloud services offered could be utilized and integrated into the power industry to enhance the overall process, especially in the fault monitoring and normalizing fields in distribution systems. This paper introduces smart fault monitoring and normalizing technologies in distribution systems by using one of the most popular cloud service platforms, the Microsoft Azure Internet of Things (IoT) Hub, together with some of the related services. A hardware prototype was constructed based on part of a real underground distribution system network, and the fault monitoring and normalizing techniques were integrated to form a system. Such a system with IoT integration effectively reduces the power outage experienced by customers in the healthy section of the faulted feeder from approximately 1 h to less than 5 min and is able to improve the System Average Interruption Duration Index (SAIDI) and System Average Interruption Frequency Index (SAIFI) in electric utility companies significantly.

As part of the design process of a chiller plant, one of the final stages is the energy testing of the system in relation to future operating conditions. Recent studies have suggested establishing robust solutions, but a conservative approach still prevails at this stage. However, the results of some recent studies suggest the application of a new co-design (control–design) approach. The present research involves a comparative analysis between the use of conventional staging and the co-design approach in the design phase of a chiller plant. This paper analyzes the energy consumption estimations of six different chiller plant combinations for a Cuban hotel. For the conservative approach using on/off traditional staging, the results suggest that the best option would be the adoption of a chiller plant featuring a symmetrical configuration. However, the outcomes related to the co-design approach suggest that the best option would be an asymmetrical configuration. The energy savings results were equal to 24.8% and the resulting coefficient of performance (COP) was 59.7% greater than that of the symmetrical configuration. This research lays firm foundations for the correct choice and design of a suitable chiller plant configuration for a selected hotel, allowing for significant energy savings in the tourism sector.

As part of the design process of a chiller plant, one of the final stages is the energy testing of the system in relation to future operating conditions. Recent studies have suggested establishing robust solutions, but a conservative approach still prevails at this stage. However, the results of some recent studies suggest the application of a new co-design (control–design) approach. The present research involves a comparative analysis between the use of conventional staging and the co-design approach in the design phase of a chiller plant. This paper analyzes the energy consumption estimations of six different chiller plant combinations for a Cuban hotel. For the conservative approach using on/off traditional staging, the results suggest that the best option would be the adoption of a chiller plant featuring a symmetrical configuration. However, the outcomes related to the co-design approach suggest that the best option would be an asymmetrical configuration. The energy savings results were equal to 24.8% and the resulting coefficient of performance (COP) was 59.7% greater than that of the symmetrical configuration. This research lays firm foundations for the correct choice and design of a suitable chiller plant configuration for a selected hotel, allowing for significant energy savings in the tourism sector.