Electric vehicles (EVs) are one of a prominent solution for the sustainability issues needing dire attention like global warming, depleting fossil fuel reserves, and greenhouse gas (GHG) emissions. Conversely, EVs are shown to emit higher emissions (measured from source to tailpipe) for the fossil fuel-based countries, which necessitates renewable energy sources (RES) for maximizing EV benefits. EVs can also act as a storage system, to mitigate the challenges associated with RES and to provide the grid with ancillary services, such as voltage regulation, frequency regulation, spinning reserve, etc. For extracting maximum benefits from EVs and minimizing the associated impact on the distribution network, modelling optimal integration of EVs in the network is required. This paper focuses on reviewing the state-of-the-art literature on the modelling of grid-connected EV-PV (photovoltaics) system. Further, the paper evaluates the uncertainty modelling methods associated with various parameters related to the grid-connected EV-PV system. Finally, the review is concluded with a summary of potential research directions in this area. The paper presents an evaluation of different modelling components of grid-connected EV-PV system to facilitate readers in modelling such system for researching EV-PV integration in the distribution network.

Microgrids with renewable distributed generation and energy storage offer sustainable energy solutions. To maintain the availability of energy to the connected loads, considering priority and to interrupt the smallest portion of the microgrid under any abnormal conditions, reconfiguration is critical to restore service to a section or to meet some operational requirements of dropping minimum loads. Reconfiguration is the process of modifying the microgrid's topological structure by changing the status (open/close) of the circuit breakers or switches. In this work, constraints are the power balance equation and power generation limits, and we assumed that the system is designed with the entire planning and operational control criterion to meet the voltage violation and line overloading constraints. This paper offers novel real-time implementation of intelligent algorithm for microgrid reconfiguration. Intelligent algorithm is based on the genetic algorithms and has been tested on two test systems including shipboard power system and modified Consortium for Electric Reliability Technology Solutions (CERTS) microgrid. Real-time test bed utilizes real-time digital simulator and commercial real-time controllers from Schweitzer Engineering Lab. Reconfiguration algorithm has been implemented in the real time using real-time test bed, e.g., microgrid system, and satisfactory results were obtained.

Networked microgrid can operate in different possible configurations including: islanded microgrid, a grid-connected microgrid without a tie-line converter, a grid-connected microgrid with a tie-line converter, and networked microgrids. These possible alternative configurations and intermittent renewable energy offer challenges in designing control and management algorithms for voltage, frequency, and power in all possible operating scenarios. In this paper, a novel multi-layer architecture for control algorithm is designed based on large-signal model that enables microgrid to operate in wide range of operating points. Goals of the designed controls are to regulate voltage magnitude and frequency, as well as output power of the distributed generations. Local controls also integrate with a microgrid level energy management system or microgrid central controller (MGCC) for power and energy balance for the microgrid in islanded, grid-connected, or networked microgrid mode. The MGCC coordinates the lower level controls in centralized manner. In this paper, with the communication network failure, local lower level droop control will be activated. Designed control algorithm works with high R/X ratio and simulation results indicate satisfactory performance.