Kalpesh Chaudhari

Hybrid Optimization for Economic Deployment of ESS in PV-Integrated EV Charging Stations

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.

Validation of Faster Joint Control Strategy for Battery- and Supercapacitor-Based Energy Storage System

Energy storage system (ESS) is generally used to manage the intermittency of the renewable energy sources (RESs). The proper control strategy is needed to effectively maintain the power balance between the RESs, load demand, and ESS. The conventional control strategy for the hybrid energy storage system (HESS) uses the high-/low-pass filter method for system net power decomposition and the ESS power dispatch. In this paper, a new joint control strategy is proposed for photovoltaic-based dc grid system, with battery and supercapacitor (SC) as a HESS. The new joint control strategy utilizes the uncompensated power from the battery system to increase the performance of the overall HESS. The advantages of the proposed control strategy over the conventional control strategy are faster dc-link voltage restoration and effective power sharing between the battery and the SC. The detailed stability analysis of the proposed control strategy is also presented. The effectiveness of the proposed control strategy over the conventional control strategy is validated with the short-term and long-term experimental studies.

TOU pricing based energy management of public EV charging stations using energy storage system

Adoption of EVs on large scale is greatly dependent on electricity price. Energy storage system (ESS) can bring down cost of EV charging using time of utilization (TOU) pricing of wholesale electricity market. In this study, analysis of energy management using energy storage deployed at public EV charging stations is done to find benefits of TOU pricing of wholesale electricity using uncontrolled EV charging model. The objective of this analysis is to minimize the cost of EV charging. Heuristic algorithm proposed in this study performs real time energy management by analysing wholesale electricity price (WEP) trend over past 30 days. It minimises the power imported from grid during high price hours and charges the battery at low price hours while continuously feeding the dynamic EV charging load demand. Results obtained show the significant reduction in cost of EV charging due to ESS in utilization of TOU pricing of WEP.

Energy storage management for EV charging stations: Comparison between uncoordinated and statistical charging loads

Electricity price is essential factor in the deployment of electric vehicles (EVs) on large scale. In wholesale electricity market, EV charging stations(ECS) connected with suitably sized energy storage system (ESS) can save substantial amount of money by managing their time of utilisation (TOU). In this study, a real-time EV charging model at ECS along with ESS degradation model is considered to analyse effect of the ESS for TOU pricing benefits. The objective is to minimize the EVs charging cost. The proposed algorithm focuses on real-time energy management using combination of heuristic approach and deterministic approach. The algorithm analyses electricity pricing trend using historical and forecasted statistical data and controls the power imported from the grid, while feeding dynamic EV charging load. Significant amount of cost saving is seen in results due to deployment of ESS in utilizing TOU pricing benefits of wholesale electricity price.

Application of two stage rate limit control for different operating modes of battery

The present work deals with the application of two stage rate limit control for battery energy storage system under different operating modes. Energy storage systems are playing very important role in renewable power systems and microgrids. Depending on the nature of power requirement, utilization of multiple energy storage units with distinguised characteristics in terms of power and energy density is quite common in microgrids. Batteries are one of the most commonly used energy storage systems in microgrids, to support slow transients or steady state load demands. The power charge/discharge rates affects the stress levels in the battery, which in turn affects the battery life span; and its power pattern affects stored/discharge energy from the battery, which in turn affects the state of charge (SOC) of the battery. Therefore, to optimize the charge/discharge rates a two stage rate limit control scheme is proposed, and its application for different operating modes is discussed in this paper. This control scheme is verified in OPAL-RT by creating real-time control hardware in loop (CHIL) with dSPACE.

Modeling of charging profiles for stationary battery systems using curve fitting approach

Stationary Battery Systems (SBS) are becoming a critical component in power distribution network across the world. Penetration of renewable energy sources which are intermittent in nature is a huge influence on the requirement of SBS. Furthermore, SBS are used in other applications such as peak load management, load-shifting, voltage regulation and power quality improvement. With increase in penetration on SBS, the requirement for modeling charging characteristics considering capacity loss is also increasing drastically. Minimal resource requirement and capability to leverage on smart meter data are the important parameters that are to be focused while developing any model for such applications. In this paper, an analysis on different curve fitting approaches that can be used for predicting the charging profiles of SBS based on lithium iron phosphate batteries is presented.

Agent-based modelling of EV energy storage systems considering human crowd behavior

Large scale adoption of electric vehicles (EVs) would significantly increase the overall electricity demand of the 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 energy storage systems (ESS) used in EVs is an essential part of the infrastructure planning. Charging demand of ESS used in EVs is affected by several factors such as driver behavior, location of charging stations and electricity pricing. In order to implement the optimal charging infrastructure, it is important to consider all the crucial factors that affect the charging demand of ESS in EVs. Several studies have modelled and simulated the charging demand of individual as well as group of EVs. However, in many cases the models did not include factors that deal with the social characteristics of EV drivers, while the others did not emphasise on the economic elements. This paper aims to evaluate the effects of above factors on the EV charging demand using a simulation model. Agent-based approach using NetLogo is employed in this study to closely mimic the human crowd behaviour and its influence on the load demand due to charging of ESS used in EVs.

Energy Management and Control for Grid Connected Hybrid Energy Storage System under Different Operating Modes

DC-coupled microgrids are simple as they do not require any synchronization when integrating different distributed energy generations. However, the control and energy management strategy between the renewable energy sources and the energy storages under different operating modes is a challenging task. In this paper, a new energy management scheme is proposed for the grid connected hybrid energy storage with the battery and the supercapacitor under different operating modes. The main advantages of the proposed energy management scheme are effective power sharing between the different energy storage systems, faster dc link voltage regulation to generation and load disturbances, dynamic power sharing between the battery and the grid based on the battery state of charge, reduced rate of charge/discharge of battery current during steady state and transient power fluctuations, improved power quality features in ac grid and seamless mode transitions. The effectiveness of the proposed method is validated by both simulation and experimental studies.