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.

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The Internet of Things (IoT) technology has immense potential for application in improvement and development of Smart Grid. The rising number of distributed generation, aging of present grid infrastructure and appeal for the transformation of networks have sparked the interest in smart grid. The need for energy storage system primarily the electrical energy storage systems is growing as the prospects for their usage is becoming more compelling. Dynamic electrical energy storage system viz., Electric Vehicles (EVs) are relatively standard due to their excellent electrical properties and flexibility but the possibility of damage to their batteries is there in case of overcharging or deep discharging and their mass penetration profoundly impacts the grid. To circumvent the possibility of damage, EVs' batteries need a precise state of charge estimation to increase their lifespan and to protect the equipment they power. Based on ease of implementation and less overall complexity, this paper proposes a real-time Battery Monitoring System (BMS) using coulomb counting method for SoC estimation and messaging based MQTT as the communication protocol. The proposed BMS is implemented on hardware platform using appropriate sensing technology, central processor, interfacing devices and the Node-RED environment. An optimization model aimed at maximizing the trade revenue for EVs' aggregator is presented aimed at enabling the smart charging.

Contemporary Smart Grid (SG) systems are enticed by smart devices and entities due to unfolded developments in intelligent transportation technologies (ITT). The SG ecosystem, when introduced to Internet of Things (IoT) makes every object active and brings them online. However, the traditional cloud deployments look puerile to meet the analytics and computational exigencies for such dynamic subsystems. Starting with highlighting the mission critical requirements of an idealized SG infrastructure, this work proposes an edge centered FOG (From cOre to edGe) computing model primarily focused to realize the processing and computational objectives of SG. The motive of the work is to comprehend the applicability of fog computing algorithms to interplay with the core centered cloud computing support, thus enabling to come up with a new breed of real-time and latency free utilities. Further, for demonstrating the feasibility of the proposed framework, a comparative optimization framework is proposed that captures the monetary expenses due to the power consumption, latency and emission issues in both cloud based as well as fog commuting frameworks. Finally, the suitability and viability of fog computing approaches are demonstrated through its comparative results of the metrics with that of traditional data center or cloud computing approach. Results clearly demonstrate the superiority of FOG computing over its cloud counterpart.

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