Electric vehicle (EV) charging stations will play an important role in the smart city. Uncoordinated and statistical EV charging loads would further stress the distribution system. Photovoltaic (PV) systems, which can reduce this stress, also show variation due to weather conditions. In this paper, a hybrid optimization algorithm for energy storage management is proposed, which shifts its mode of operation between the deterministic and rule-based approaches depending on the electricity price band allocation. The cost degradation model of the energy storage system (ESS) along with the levelized cost of PV power is used in the case of EV charging stations. The algorithm comprises of three parts: categorization of real-time electricity price in different price bands, real-time calculation of PV power from solar irradiation data, and optimization for minimizing the operating cost of EV charging station integrated with PV and ESS. An extensive simulation study is carried out with an uncoordinated and statistical EV charging model in the context of Singapore to check effectiveness of this algorithm. Furthermore, detailed analysis of subsidy and incentive to be given by the government agencies for higher penetration of renewable energy is also presented. This work would aid in planning of adoption of PV-integrated EV charging stations, which would expectedly replace traditional gas stations in future.

Widespread adoption of electric vehicles (EVs) would significantly increase the overall electrical load demand in power distribution networks. Hence, there is a need for comprehensive planning of charging infrastructure in order to prevent power failures or scenarios where there is a considerable demand-supply mismatch. Accurately predicting the realistic charging demand of EVs is an essential part of the infrastructure planning. Charging demand of EVs is influenced by several factors such as driver behavior, location of charging stations, electricity pricing etc. In order to implement an optimal charging infrastructure, it is important to consider all the relevant factors which influence the charging demand of EVs. Several studies have modelled and simulated the charging demands of individual and groups of EVs. However, in many cases, the models do not consider factors related to the social characteristics of EV drivers. Other studies do not emphasize on economic elements. This paper aims at evaluating the effects of the above factors on EV charging demand using a simulation model. An agent-based approach using NetLogo is employed in this paper to closely mimic the human aggregate behaviour and its influence on the load demand due to charging of EVs.

A new zero voltage switching (ZVS) full-bridge DC-DC converter for battery charging is proposed in this paper. The proposed isolated DC-DC converter is used for the DC-DC conversion stage of electric vehicle charger. The primary switches in DC-DC converter turn-on at zero voltage over the battery charging range with the help of passive auxiliary circuit. The diode clamping circuit on the primary side minimizes the severity of voltage spikes across the secondary rectifier diodes which are commonly present in conventional full-bridge DC-DC converters. The main switches are controlled with asymmetrical pulse width modulation (APWM) technique resulting in higher efficiency. APWM reduces the current stress of the main switches and the circulating losses compared to the conventional phase-shift modulation (PSM) method by controlling the auxiliary inductor current over the entire operating range of the proposed converter. The steady-state analysis of auxiliary circuit and its design considerations are discussed in detail. A 100-kHz, 1.2-kW full-bridge DC-DC converter prototype is developed. The experimental results are presented to validate the analysis and efficiency of the proposed converter.

An aggregated number of electric vehicles (EVs) provides a platform for smart energy storage (SES) in buildings which can be used during periods of maximum demand or high electricity price as well as for leveling the intermittent outputs of renewable energy sources (RESs). However, the vehicle to grid (V2G) capacity varies over time based on the availability of the EVs and their individual state of charge (SOC). Hence a real-time V2G capacity estimation is important for utilizing EVs as SES efficiently. In this paper, an algorithm for half-hourly V2G capacity estimation using real-time EV scheduling is proposed. The algorithm is implemented as part of the Building Energy Management System (BEMS). The BEMS uses forecasted building load demand without EVs and predicted charging profiles of the EVs connected to the building for estimating the V2G capacity. The estimated V2G capacity and the availability of RESs are considered by the BEMS to schedule the EV charging/discharging. The proposed algorithm is applied to study three case scenarios using BEMS on residential, office, and commercial buildings in Singapore. The results obtained clearly show that a group of EVs connected to any high-rise building can be effectively used as a distributed storage system.

Conventionally, base stations of off-grid telecommunication (telecom) towers are powered by diesel generator, which results in high operating cost and carbon emission. Hence, the photovoltaic (PV) wind battery based hybrid renewable energy system (HRES) is proposed for off-grid telecom towers in rural areas. However, owing to high capital cost and intermittent nature of renewable energy resources, optimal sizing of HRES is inevitable to have economical and reliable power supply. An index named excess energy generation (EEG) is developed to reduce the surplus renewable energy, which cannot be sold to grid in off-grid towers. Therefore, three objective functions are formulated viz., loss of power-supply probability, cost of electricity, and EEG, which are incommensurate and contradictory in nature. Thus, a discrete multiobjective grey wolf optimization (DMGWO) algorithm is developed for the optimal sizing of HRES. DMGWO utilizes the concept of internal archive to store nondominated solutions and enables fast convergence rate without premature termination as a result of inherent leader selection mechanism. A practical approach is adopted in optimal sizing by considering wind capacity as discrete variable, which is handled by the corrective algorithm embedded in DMGWO. The intermittency in source and variability in telecom load is incorporated using probability distribution functions. The Euclidean distance approach is adopted to find the solution (configuration of PV panels, wind turbines, and battery) from the optimal Pareto front obtained from DMGWO. Also, sensitivity analysis is carried out to observe the effect of the design variables on the objective functions.