This paper presents optimal strategies in home energy management system (HEMS) integrating solar power, energy storage, and vehicle-to-grid (V2G) capability for predetermined scenarios. The proposed system aims to address the demand response (DR) schemes, both real-time pricing and emergency load curtailment, V2G mode of operation. In the interruption event, the HEMS operates in the vehicle-to-home (V2H) mode to ensure supply continuity. The control algorithms are experimentally implemented on the created hardware platform of EV and home batteries, solar power, and household loads with preset priorities. Both AC and DC charging technologies for EVs are considered. The HEMS simultaneously optimizes the scheduling of charging and discharging of EVs and home battery. Results on operation under predetermined scenarios have confirmed the validity of the proposed HEMS.

The grid-connected photovoltaic (PV) power generation system is expected to be more widespread in distribution systems due to increasing fossil-fuel cost. Recent advances in PV technology and great demand has led to cost reductions. This paper presents the effects of PV system installation location on the power quality of the distribution system. A measurement setup is prepared for a PV grid-connected system. The system consists of a PV array and a grid-connected inverter. The measured data are used to investigate the effects of a PV system. A feeder line with PV system is simulated in Matlab. This study focuses on both radial and loop operated distribution system. The distribution system's power quality, in terms of voltage, active power, total losses, and power factor of the network are presented. The simulation results show that implementation of the PV grid-connected system could improve the power quality of a distribution feeder

To achieve a free-positioning wireless power transfer (WPT) system, the output power must be regulated throughout the operation. This paper presents a novel output power control of a WPT system, based on the model predictive control (MPC). The output power is predicted by utilizing the system’s mathematical model. The optimal duty cycle for a desired output power is obtained through the minimization of the objective function, which is simple and easy to implement, with no need for gain tuning. The proposed controller is implemented on the primary side, without any measurement or communication devices on the secondary side. This reduces the cost, size, and complexity of the WPT system. The load resistance and mutual inductance identification method is also introduced. It is based on the reflected impedance knowledge, where only the information of primary current is required. Experimental results of the output power step response show better performance compared with conventional Proportional-Integral (PI) control. The proposed controller is experimentally validated on a 200 W kettle. The output power can be kept constant at 200 W while the kettle is laterally moved. With the proposed controller, the kettle can be placed freely up to 7 cm from the align position, which is 63.64% of the primary coil’s outer radius.

This research presents a new method based on a combined temporal convolutional neural network and long-short term memory neural network for the real-time assessment of short-term voltage stability to keep the electric grid in a secure state. The assessment includes both the voltage instability (stable state or unstable state) and the fault-induced delayed voltage recovery phenomenon subjected to disturbance. The trained model uses the time series post-disturbance bus voltage trajectories as the input in order to predict the stability state of the power system in a computationally efficient manner. The proposed method also utilizes a transfer learning approach that acclimates to the pre-trained model using only a few labeled samples, which assesses voltage instability under unseen network topology change conditions. Finally, the performance evaluated on the IEEE 9 Bus and New England 39 Bus test systems shows that the proposed method gives superior accuracy with higher efficacy and thus is suitable for online application.

In inductive power transfer (IPT) system, the magnetic coupling has significant effects on power and system efficiency. The voltage and current on the secondary side are coupling dependent. Load measurements are not possible through sensors on the primary side. This paper proposes the secondary-side voltage controller based on the primary-side control by means of bifurcation approach. The main advantage of operation under bifurcation is that the coupling and load on the secondary have no effect on the voltage gain. This enables the secondary voltage control through the primary voltage. With the proposed sensing coil on the primary side, the bifurcation detection is possible. The proposed control can utilize the frequency split phenomenon and track the bifurcation frequency to ensure maximum efficiency in various coupling conditions. In addition, the design guidelines of the parallel-parallel compensation configuration are introduced for a desired load profile and transfer distance. The constant voltage gain with respect to conditions of magnetic coupling variation is verified by using finite element analysis and experimental study. A 1-kW IPT prototype is constructed to validate the proposed bifurcation detection and voltage control. Experimental results show that the proposed IPT can achieve up to 90% of maximum efficiency while maintaining constant secondary voltage.

This paper proposes a computationally efficient method based on deep neural network and a meta-heuristic optimization algorithm known as bat algorithm to coordinate power oscillation damping controllers incorporated in renewable energy stations to enhance system small signal stability considering uncertainties. The proposed method consists of three main stages: database generation, supervised learning, and optimization using the created model. A database is first created of Probabilistic small-signal stability margin calculated using the combined cumulant and Gram-Charlier expansion and the parameters of damping controllers, which is then given to different supervised machine learning algorithms such as linear regression, support vector machine, random forest, and chiefly deep neural network to create a surrogate model. The surrogate model provides an approximate relationship between the probabilistic small-signal instability margin and damping controller parameters. An optimization problem is then formulated to minimize the surrogate probabilistic instability margin with damping controller parameters acting as constraints. Finally, this optimization problem is solved using the bat algorithm to obtain the optimized parameters for power oscillation damping controllers. Our study results tested on a large IEEE 16 machines, 68 bus system show that the power oscillation damping controllers optimized using the proposed method can largely improve the system low frequency oscillatory stability margin in a very low computational time (around 19 times faster than the conventional method). This study can be used by the power system operators to tune the parameters of damping controller in a fast manner.