Rising emissions in marine transportation sector leads to increased adoption of electricity-based cold-ironing (CI) facilities at seaports. Direct dependency on utility grid for CI facility can be avoided by incorporating energy storage systems, and renewable energy sources at seaports. A more viable option is the formation of a seaport microgrid by integrating multiple ship-board microgrids (SMGs) through port-based charging infrastructure. This paper proposes a grid connected ship-based seaport microgrid for efficient power sharing in a CI facility as a solution. Traditional power sharing relies on physical communication-based control in multiple microgrids which can be complex and costly. This paper utilizes a communication-less approach depending on multi-mode decentralized fuzzy based droop control, enabling efficient power sharing between SMGs and ships. This approach is particularly beneficial for autonomous ships and islands where traditional port electrification may not be technically feasible. The proposed power management scheme is validated on MATLAB® Simulink platform to obtain results after exhaustive numerical simulations.

The integration of an electric vehicle charging station (EVCS) with a low-voltage distribution network is enabled by bidirectional power converters (BPCs), which may function in EV-to-grid and grid-to-EV modes. The use of the least mean square (LMS) control approach is examined in this article for enhancement of power quality with nonlinear loads while facilitating bidirectional power flow. In case of AC-DC conversion, the DC bus voltage is controlled; conversely, in case of DC-DC conversion, the battery's charging/discharging current. The introduced LMS algorithm uses instantaneous load current, EVCS charging/discharging current, and DC bus voltage to generate control signals for the grid-tied BPC. The system is tested under multiple scenarios of nonlinear and unbalanced loads, and charging/discharging currents during both transitional and stable circumstances. The system's functionality is verified with extensive numerical simulations with MATLAB Simulink ® SimPowerSystems library.

This paper presents optimal scheduling for electric vehicle (EV) charging in a community supported by solar photovoltaic (SPV) and battery energy storage system (BESS)-based parking lot. Each incoming EVs to the charging station gets connected to separate charge points and becomes eligible to participate in vehicle-to-vehicle (V2V) power transfer to decrease the charging cost and enhance the self-utilization of SPV. The problem is formulated as an offline mixed integer linear programming (MILP) model and solved by considering full availability of information of the EV demand, grid prices, and SPV generation. Later, three different cases, i.e., dumb charging, smart charging with V2V and BESS, and smart charging with V2V, BESS and SPV are considered for analysis of the proposed scheduling approach. When supported by V2V power transfer, demand profile flattens and total cost incurred by the parking lot operator is also reduced for a single day. The proposed model is validated on MATLAB® platform by performance evaluation of simulation results.

This article proposes a super twisting sliding mode controller (ST-SMC) for its novel application in the domain of PV grid-connected water pumping system. The ST-SMC is intended to inject both active and reactive power with sinusoidal current of low total harmonic distortion (THD) to the non-linear load such as water pumping system. Excess active power generated by the PV array is fed to the grid and reactive power requirements of the load are fulfilled by the ST-SMC controlled inverter. The proposed control methodology is tested on the DC link voltage, maximum active power extracted from PV array, reactive power supplied by the inverter, and the THD of inverter current. The ST-SMC reduces chattering, provides easy real-time implementation, and offers insensitivity to parameter variations and model uncertainties. Thus, the proposed control strategy is able to maximize the extracted energy from PV while regulating DC–link voltages and achieving a power factor compensation and reduces the harmonics from inverter current. The proposed control methodology is implemented in MATLAB/Simulink and its performance is compared with conventional PI-based control method.

This paper reports an adaptive neural fuzzy inference system (ANFIS) based controller for a grid-connected tidal turbine (GCTT) system based on permanent magnet synchronous generator (PMSG). An energy storage system (ESS) provides the balance of power at the DC link to improve system stability under low-voltage ride-through (LVRT) conditions and tidal power fluctuations. The grid code suggests the required reactive power demanded by the grid during a fault. An ANFIS-based controller coordinates with the TT rotor speed and state-of-charge (SOC) to fulfill grid code requirements. During the occurrence of a fault, the operating conditions of the GCTT and the SOC of ESS may differ. So, it becomes necessary to establish coordination between both the TT rotor speed and the SOC of ESS. It results in different operating conditions of the GCTT and the ESS. The proposed control method generates the reference power for the machine-side converter of GCTT based on the current rotor speed and SOC of the ESS. Extensive numerical simulations have been performed on the MATLAB® Simulink platform to prove effectiveness of the proposed control method.

This paper proposes an intelligent control scheme for a two-stage integrated onboard electric vehicle (EV) battery charger connected to a single-phase household outlet which offers a close to ideal battery charging profile with power factor correction feature. Generally, the front-end AC-DC​ conversion stage is controlled by dual loop proportional-integral (PI) controllers, and tuning their gain constants is a difficult task. Furthermore, to achieve a close to ideal charging profile for an EV battery, the DC-DC conversion stage switches from constant current (CC) and constant voltage (CV) mode after a certain state of charge (SOC) which may lead to discontinuity in the charging current and voltage. This paper attempts to solve these issues by proposing an intelligent control scheme that includes the dynamic estimation of PI controller gain constants as well as provides a seamless mode transfer feature for battery charging. It is achieved by using fuzzy-PI-based control in the AC-DC conversion stage and Bayesian Regularization (BR) algorithm trained artificial neural network (ANN)-based control in the DC-DC conversion stage. The performance of the proposed control scheme is assessed both in steady-state and transient conditions in MATLAB® Simulink environment by comparing it against similar control schemes. The proposed intelligent control approach improves the dynamic response of DC link voltage, offers unity power factor operation and maintains the line current harmonics within IEEE 519 standards even during the switchover from CC to CV charging mode. Also, there is a decrease of 85% in the third harmonic component of the source current, 23.2% improvement in DC link voltage undershoot and 6.5% reduction in DC link voltage overshoot with reduced settling times using the proposed unified control scheme.

Chapter Contents: 1.1 Introduction 1.2 Conductive charging of EVs 1.2.1 EV charging infrastructure 1.2.2 Integration of EV with power grid 1.2.3 International standards and regulations 1.3 Inductive charging of EVs 1.3.1 Need for inductive charging of EV 1.3.2 Modes of IPT 1.3.3 Operating principle of IPT 1.3.4 Static inductive charging 1.3.5 Dynamic inductive charging 1.3.6 Bidirectional power flow 1.3.7 International standards and regulations 1.4 Conclusion References

This article presents a grid integrated single-stage solar photovoltaic system supported with a battery energy storage to charge the battery of an electric vehicle. An efficient energy management system based on the state of charge, generated SPV power, and total load demand is designed for battery energy storage. Each connected EV can choose to operate either in grid-to-vehicle or vehicle-to-grid mode without affecting the DC link voltage. Deficient power is taken from the grid and surplus SPV power is fed into the grid at unity power factor, where the grid-connected converter control plays a significant role. Sliding mode control (SMC) with an improved reaching law is implemented for DC link voltage and grid-connected converter current control. Model uncertainties and mismatched disturbances are estimated using a nonlinear disturbance observer (NDO). The NDO based SMC is compared with conventional SMC and linear PI control under various transient scenarios. The Lyapunov candidate function determines the stability of the proposed control system. The simulation results prove the robustness of NDO-SMC based control of grid-connected converter under varying irradiance and load requirements.