This paper proposes an adaptive power management strategy for Photovoltaic (PV) generation units in a standalone microgrid. In existing microgrid configurations, the PV units are controlled using droop control technique which ensures load sharing among the DG inverters. However, the intermittency in power generation of PV based Distributed Generation (DG) pose many challenges to the standalone operation of a microgrid, the ineffective VA utilization being one among them. This paper proposes an adaptive droop control which enables the maximum utilization of the VA rating of the interfacing inverter in a PV/battery hybrid system. With the proposed technique, as the generation goes down, the DG inverter is controlled to support the reactive power demand of the load, thereby utilizing the rating of the inverter switches effectively. The applicability of the proposed adaptive control method is analyzed using detailed simulation and real-time hardware in loop studies.

The presence of unbalance in the load, particularly in islanded microgrids, has recently gained attention as it leads to unbalance in load voltage and current. Existing works in this area mainly focus on reducing the unbalance in load voltage or sharing the negative sequence current equally. Most of these methods rely on the knowledge of load voltages, which requires a low-bandwidth communication infrastructure to send the load information to the local DG controllers. However, low bandwidth communication increases the overall cost and reduces the reliability of the system. In this paper, a decentralized control approach for islanded microgrids is proposed to share the load current unbalance factors (CUFs) equally among the distributed generation (DG) units. It is shown that in an islanded microgrid, the presence of unbalance in the load current affects the output voltages of DG causing them to become unbalanced as well. A droop-based control approach is developed to compensate for the unbalance in DG output voltages to achieve equal sharing of the CUF. A detailed MATLAB/Simulink based as well as OPAL-RT based real-time hardware-in-the-loop experimental studies are presented to validate the performance of the proposed decentralized approach.

This study deals with fault ride-through (FRT) capability and output power fluctuation suppression of wind turbine systems (WTS) having PMSG (permanent-magnet synchronous generator) for mitigating grid frequency variation and voltage flicker in the distribution system. The coordinated control of a cost-effective scheme based on energy storage supercapacitors (ESSs) and braking resistors (BR) is introduced to perform the multiple tasks of the WTS. In this hybrid scheme, the ESSs are initially used to absorb the fluctuated power component with the constraints of their ratings during the grid faults and wind speed variation conditions prior to the activation of the BRs when the ESSs cannot fully consume the mismatched power between the PMSG and grid during severe grid faults. With the additional BRs, the capacity of the costly ESSs is remarkably reduced, while the performance of the fault ride-through capability and power smoothening for the WTS are still kept satisfactory and in compliance with the requirements of advanced grid codes. Detailed experimental implementation and its results for a down-scaled prototype in a laboratory are shown to verify the effectiveness of the introduced scheme along with the simulation results with the high-power rating WTS.

This paper proposes an adaptive power management strategy for standalone microgrid with PV and diesel generation. System security in a microgrid becomes challenging due to the intermittent power generation of PV units. Battery storage and diesel generation have been incorporated in the microgrid to allow continuous supply of power to the customers. The existing power management strategies mainly focus on PV/battery hybrid system. In this paper, a novel adaptive power management control algorithm is developed to achieve smooth transition between different generation units. The proposed adaptive control strategy is analyzed by Matlab/Simulink based simulation studies.

Electric Vehicles (EVs) are being increasingly used in the transportation domain due to recent advancements in the field of energy systems. As such, there is an increased need for intelligent and sustainable EV charging infrastructure for large-scale EV integration to minimize the impact on the grid. Renewable-powered Community EV Charging Stations (CECSs) have emerged as a possible option to solve this problem, but their usage is hindered by the challenges associated with renewable power sources, stochasticity in the demand for EV charging services, and the possibility of charging congestion. In order to overcome these challenges, this paper proposes an energy management hierarchy that utilizes fuzzy Q-learning reinforcement learning (FQ-RL) coupled with Stackelberg game-theoretic optimization for efficient and personalized EV charging management. In the upper layer of decision-making, the FQ-RL decides on the charging tariff based on information from the real-time operation of the system, such as SPV power generation, state of charge (SoC) of the battery, and occupancy in the EV queue. In the lower decision layer, the Stackelberg game approach is used to model the interaction between the charging station provider and the EV drivers to make coordinated charging decisions via dynamic pricing considering their preferences and flexibility. The framework improves the interaction between the CECS and the utility grid through higher usage of renewables, reduced grid reliance, charging station congestion avoidance, and profitability of charging stations. Compared with the flat-rate tariff baseline, the proposed FQ–RL–Stackelberg framework using the AHP-selected balanced configuration achieved a 30.12% increase in delivered charging energy, a 13.57% reduction in peak grid demand, and an 83.33% reduction in unserved EVs. These results demonstrate the potential of intelligent charging strategies for developing sustainable, user-centric, and renewable-integrated EV charging infrastructure that supports future smart grids and intelligent transportation systems.