The increase in energy consumption and energy bills in Jordan have been escalating rapidly, which requires a special concern as a large percent of the energy is imported. The need for the reducing peak demand of the distribution network is essential to decrease the overall electricity generation cost. This study was aimed at presenting a model for a home that manages its energy consumption, and the maximum savings possible if the load shifting to off-peak times was applied. It also introduced a tariff that is more concerned in time of use rather than consumption only. The power consumption profile is collected for a sample house. The profile for a week was registered and graphed. The pricing suggested was calculated per day. Moreover, some samples applied worldwide were discussed to find a suitable model. It was found that a saving rate of 16% is achievable if the time of use charge is applied. Additionally, a peak load reduction of 3.5 kWH average per day (in the peak hours) is possible.
Abstract- Internet of Things (IoT) is increasingly becoming the vehicle to automate, optimize and enhance the performance of systems in the energy, environment, and health sectors. In this paper, we use Wi-Fi wrapped sensors to provide online and in realtime the current energy consumptions at a device level, in a manner to allow for automatic control of peak energy consumption at a household, factory level, and eventually at a region level, where a region can be defined as an area supported by a distinct energy source. This allows to decrease the bill by avoiding heavily and controllable loads during high tariff slice and/or peak period per household and to optimize the energy production and distribution in a given region. The proposed model relies on adaptive learning techniques to help adjust the current load, while taking into consideration the actual and real need of the consumer. The experiments used in this study makes use of current and voltage sensors, Arduino platform, and simulation system. The main performance indexes used are the control of a peak consumption level, and the minimum time needed to adjust the distribution of load in the system. The system was able to keep the maximum load at a maximum of 10 kW in less than 10 seconds of response time. The level and response time are controllable parameters.
The conversion process in the wind turbine, from mechanical (kinetic) energy to electrical energy, is affected by many factors that increase or decrease the useful output of the wind energy convertor. In this paper, three factors were studied experimentally on a Horizontal Axis Wind Energy Unit "EEEC" in laboratory-scale. The aim of this experiment was to study the influence of the number of blades, the angle of attack and the incident angle on the wind energy unit parameters to optimize its efficiency. For this purpose, the effect of the number of blades was studied firstly, in order to select the number of blades where the maximum inputs obtained at lab ambient temperature 25 °C and atmospheric pressure. Then, different readings of the incident angle and angle of attacks were taken. The data was analyzed using Microsoft Excel software. The results show that the maximum parameters of wind unite energy that produce the maximum efficiency, namely: voltage (volt), current (ampere) and rotational speed (rpm) are obtained when the number of blades is 4, the incident angle is 0° (when the rotor direction is with wind direction) and the angle of attack is 75°. Finally, these results were implemented in a simulation program (HOMER software) that uses this turbine in a resident along with storage to cover the needs of a selected house.
Abstract- Internet of Things (IoT) is increasingly becoming the vehicle to automate, optimize and enhance the performance of systems in the energy, environment, and health sectors. In this paper, we use Wi-Fi wrapped sensors to provide online and in realtime the current energy consumptions at a device level, in a manner to allow for automatic control of peak energy consumption at a household, factory level, and eventually at a region level, where a region can be defined as an area supported by a distinct energy source. This allows to decrease the bill by avoiding heavily and controllable loads during high tariff slice and/or peak period per household and to optimize the energy production and distribution in a given region. The proposed model relies on adaptive learning techniques to help adjust the current load, while taking into consideration the actual and real need of the consumer. The experiments used in this study makes use of current and voltage sensors, Arduino platform, and simulation system. The main performance indexes used are the control of a peak consumption level, and the minimum time needed to adjust the distribution of load in the system. The system was able to keep the maximum load at a maximum of 10 kW in less than 10 seconds of response time. The level and response time are controllable parameters.
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