Demand response (DR) management systems are a potentially growing market due to their ability to maximize energy savings by allowing customers to manage their energy consumption at times of peak demand in response to financial incentives from the electricity supplier. Successful execution of a demand response program requires an effective management system where the home energy management system (HEMS) is a promising solution nowadays. HEMS is developed to manage energy use in households and to conduct the management of energy supply, either from the grid or the alternative energy sources like solar or wind power plants. With the increase of vehicle electrification, in order to achieve a more reliable and efficient smart grid (SG), cooperation between electric vehicles (EVs) and residential systems is required. This cooperation could involve not only vehicle to grid (V2G) operation but a vehicle to home (V2H) too. V2H operation is used to transfer the power and relevant data between EVs and residential systems. This paper provides an efficient HEMS enhanced by smart scheduling and an optimally designed charging and discharging strategy for plugged-in electric vehicles (PEVs). The proposed design uses a fuzzy logic controller (FLC) for smart scheduling and to take the charging (from the grid)/discharging (supply the household appliances) decision without compromising the driving needs. Simulations are presented to demonstrate how the proposed strategies can help to reduce electricity costs by 19.28% and 14.27% with 30% and 80% state of charge (SOC) of the PEV respectively compared to the case where G2V operation only used along with the photovoltaic (PV) production, improve energy utilization by smoothing the energy consumption profile and satisfy the user’s needs by ensuring enough EV battery SOC for each planned trip.
Demand response (DR) management systems are a potentially growing market due to their ability to maximize energy savings by allowing customers to manage their energy consumption at times of peak demand in response to financial incentives from the electricity supplier. Successful execution of a demand response program requires an effective management system where the home energy management system (HEMS) is a promising solution nowadays. HEMS is developed to manage energy use in households and to conduct the management of energy supply, either from the grid or the alternative energy sources like solar or wind power plants. With the increase of vehicle electrification, in order to achieve a more reliable and efficient smart grid (SG), cooperation between electric vehicles (EVs) and residential systems is required. This cooperation could involve not only vehicle to grid (V2G) operation but a vehicle to home (V2H) too. V2H operation is used to transfer the power and relevant data between EVs and residential systems. This paper provides an efficient HEMS enhanced by smart scheduling and an optimally designed charging and discharging strategy for plugged-in electric vehicles (PEVs). The proposed design uses a fuzzy logic controller (FLC) for smart scheduling and to take the charging (from the grid)/discharging (supply the household appliances) decision without compromising the driving needs. Simulations are presented to demonstrate how the proposed strategies can help to reduce electricity costs by 19.28% and 14.27% with 30% and 80% state of charge (SOC) of the PEV respectively compared to the case where G2V operation only used along with the photovoltaic (PV) production, improve energy utilization by smoothing the energy consumption profile and satisfy the user’s needs by ensuring enough EV battery SOC for each planned trip.
Until now, computational burden alleviation and stability issues for the three-phase four-leg converter has not yet been thoroughly investigated. However, compared to the conventional controllers, the implementation of predictive current control approach for 3-Ø, 4-L inverter suffers a large computational burden due to its additional fourth-leg. Motivated by this fact, this article provides an alternative predictive current control implementation for 3-Ø, 4-L inverter which offers reduced computational effort to achieve similar performance as the conventional FCS-MPC and ensures the global stability of the closed-loop system. To further understand the consequences of the developed control law, theoretical stability analysis has been carried out that links Lyapunov's direct method with the closed-loop system behavior. The outcome of the theoretical stability analysis demonstrates the global stability of the overall system which is later supported by the experimental results. With the proposed method, the number of possible voltage vectors required to obtain the optimal voltage vector in each sampling interval reduces from sixteen to five and thereby simplifies the prediction process. It is also derived that the Lyapunov function-based approach actually yields to the dead-beat control, which has not been previously highlighted in the previous papers. The current work also provides experimental results for different loading conditions (balanced and unbalanced) which further demonstrates the efficacy of the proposed method.
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