The development of energy regeneration capability in electric vehicles can extend their driving range making them a competent alternative for conventional internal combustion engine vehicles. In this study, a novel energy regeneration technique, called a two-boost method, for electric vehicles driven by a brushless DC (BLDC) motor, a widely used motor in vehicular technology, is proposed. Based on this technique, the BLDC motor driver, which is selected to be a three-phase inverter, is converted into two simultaneous boost converters during energy regeneration periods in order to transfer energy from the BLDC motor into the battery and provide the braking force. Also, this method is compared with the single-boost method. The simulation and experimental results show that the amount of regenerated energy in this method is more than twice of that in the single-boost method. Moreover, it is observed that in the two-boost method, the motor speed changes linearly without any control of the electrical torque. In addition, since the amount of regenerated energy in this method as compared to the single-boost method is higher, the energy regeneration efficiency in this method is increased significantly.

No abstract

No abstract

No abstract

Power utilities issue demand response (DR) during the hours of peak load in order to reduce the demand on the network and provide congestion relief to overloaded circuits. While traditional residential DR programs are mainly one-way in the form of remote on/off control of air conditioning (A/C) units, residential customers can adopt a more proactive role through utilizing the capabilities of smart meters and home energy management systems (HEMS). HEMS can monitor energy rates and DR incentives, and accordingly change the temperature setpoint of the A/C unit and/or shift appliance loads from peak to off-peak hours in order to maximize financial benefits. All this can be achieved in an automated human-out-of-the-loop fashion. From the HEMS’ standpoint, the task can be viewed as solving an optimization problem with the goal of reducing power consumption while maximizing financial gains. However, another equally important goal would be to ensure that the comfort level of residents, if present in the building, is not compromised. This is especially crucial during periods of extreme temperatures where maintaining an acceptable indoor temperature has a direct impact on the residents’ health, especially children and the elderly. What makes this multi-objective optimization problem more challenging is the uncertain nature of some model parameters, e.g., electricity rates, building occupancy levels, and demand. This paper presents a novel solution for energy management of a smart home using DR by considering the above factors. To ensure that the solution found is feasible against all possible uncertainties, a robust model is developed and solved for a given time horizon. As shown through simulation results, considering uncertainties are necessary, since they can change the solution in a nonnegligible way.