Impact of demand response management on chargeability of electric vehicles

AbstractLarge-scale penetration of electric vehicles (EVs) would significantly increase the load requirements of buildings in highly urbanized cities. EVs exhibit higher degree of charging flexibility when compared to other interruptible loads in buildings. Hence, EVs can be assigned lower priority and interrupted before interrupting any other loads. Any temporary interruption will have minimum impact on EV owner's satisfaction/comfort. However, it should be ensured that the EVs could be charged to the owner's required state of charge (SOC) by the time of departure. The scheduling algorithms that are used to manage the EV charging process ensure that the charging requirements are fulfilled even when there are temporary interruptions. The capability of the scheduling algorithms to manage mismatches decreases with the decrease in time available for charging. In this paper, the impact of demand response management (DRM) on the chargeability of the EVs while using different priority criteria is examined. Subsequently, the proportion of interruption for each EV with different priority criteria and the need for determining the chargeability of EVs before shedding them are studied. A scheduling driven algorithm is proposed which can be used for determining the chargeability of EVs and can be used in combination with DRM

A comprehensive dataset from a smart grid testbed for machine learning based CPS security research

Data-sets play a crucial role in advancing the research. However, getting access to real-world data becomes difficult when it comes to critical infrastructures and more so if that data is being acquired for security research. In this work, a comprehensive dataset from a real-world smart electric grid testbed is collected and shared with the research community. A few of the unique features of the dataset and testbed are highlighted

A Novel System-Theoretic Matrix-Based Approach to Analysing Safety and Security of Cyber-Physical Systems

Cyber-Physical Systems (CPSs) are getting increasingly complex and interconnected. Consequently, their inherent safety risks and security risks are so intertwined that the conventional analysis approaches which address them separately may be rendered inadequate. STPA (Systems-Theoretic Process Analysis) is a top-down hazard analysis technique that has been incorporated into several recently proposed integrated Safety and Security (S&S) analysis methods. This paper presents a novel methodology that leverages not only STPA, but also custom matrices to ensure a more comprehensive S&S analysis. The proposed methodology is demonstrated using a case study of particular commercial cloud-based monitoring and control system for residential energy storage systems

Integration of electric vehicles into power grid

Electric Vehicles (EVs) are becoming a promising solution to the environmental problems caused by the gasoline powered internal combustion engine automobiles. The major challenges to the deployment of EVs are high initial cost, limited range, increased peak demand, etc. But with good charging infrastructure which is integrated into smart grid, most of the disadvantages can be eliminated. Moreover, the EV batteries can be considered as distributed storage and used for various smart grid applications. For integration of EVs into smart grid, the requirements and feasibility of different charging methodologies for EVs are to be investigated. Based on the investigation, economic and efficient models for EV charging along with the associated charging strategies are to be derived for efficient integration. The impacts of different charging methods on battery life and distribution system peak demand are the two most important factors to be considered while deriving the models. When the EVs are plugged into the grid for charging, all the operations including charging of their batteries and auxiliary functions such as grid support using Vehicle to Grid (V2G) and demand response management (DRM) are to be coordinated. This thesis presents a dynamic scheduling algorithm using novel priority criteria for integrating EVs into smart grid. The proposed dynamic scheduling algorithm is used for various other applications such as V2G capacity estimation and DRM. The scheduling algorithm is also used for verifying the methodology for estimating the number of EVs that can be supported by typical smart grid clusters with distributed energy resources. A novel data driven load model is used for determining the charging profiles of the EVs. Matlab based simulation models are developed to implement the proposed methods and illustrative case studies are used to evaluate the performance of the proposed system under a given set of conditions. The proposed dynamic scheduling algorithm uses model based scheduling approach to overcome the disadvantages of existing scheduling methods. The load models used are stochastic building load demand (without EVs) and predicted EV charging profiles. Predicting the charging profiles of EVs connected to a building incorporated with a Building Energy Management System (BEMS) will improve the energy efficiency of the building. The predicted charging profiles along with the stochastic load data can be used for calculating V2G capacity and for performing load/source scheduling. Data driven modeling has significant advantages in predicting the charging profiles of EVs, hence an Artificial Neural Network (ANN) based model is proposed for predicting the charging profiles of EVs connected to a building. The ANN model considers the previous charging profiles, initial State of Charge (SOC) and final SOC for predicting the charging profiles of EVs. Appropriate dynamic priority criteria required specifically for EV scheduling is also proposed. The algorithm is applied for both time coordinated EV charging and power coordinated EV charging. The objective function of the algorithm is to minimize the variance in priority values of the connected EVs. Minimizing the variance in priority values of the EVs will reduce the variations in fairness given to the EVs. The impact of different priority criteria and different combination of priority criteria on the fairness and chargeability of the EVs are also studied. Furthermore, the need for using weighted priority criteria is demonstrated. The proposed dynamic scheduling algorithm is also applied to the real-time V2G capacity estimation for a group of vehicles. Using scheduling for V2G capacity estimation is a novel approach and has significant advantages. The V2G capacity that a group of vehicles can provide is estimated by evaluating the chargeability of the EVs to desired final SOC. Using scheduling for determining the V2G capacity will have a greater significance as the accuracy of the estimation is not affected by the time at which the estimation is carried out. A comparison of the proposed method with other methods which do not employ scheduling for the V2G capacity estimation is presented to demonstrate the advantages. The proposed dynamic scheduling algorithm is also applied for novel pre-emptive DRM of EVs in a building. The load shedding of the EVs is carried based on the priority values given by the proposed dynamic priority criteria. The chargeability of the EVs is ensured before deploying the EVs for DRM. The dynamic priority based pre-emptive DRM of EVs is implemented as part of intelligent pre-emptive DRM in a building which ensures that contracted capacity or demand limit (CC/DL) is not exceeded and at the same time reduction of energy consumption in the building is achieved. A methodology which uses stochastic calculations (Monte-Carlo simulations) and linear programming for the estimating the optimal number of EVs that can be deployed under a high-rise building in Singapore is presented in this thesis. The proposed dynamic scheduling algorithm is used to validate the accuracy of the estimation. Load demand data for five years and load model of EVs are used for the validation. Although there are various obstacles for the deployment of EVs, unavailability of charging infrastructure is the biggest obstacle. Hence, the cost-benefit analysis for installation of charging stations in buildings is also discussed by varying different parameters. It is important to ensure that the number of EVs integrated into the smart grid does not bring any adverse effect. A novel method for determining the limits based on the hot-spot temperature of distribution transformer is also proposed.DOCTOR OF PHILOSOPHY (EEE

Virtual storage capacity using demand response management to overcome intermittency of solar PV generation

The integration of solar photovoltaic (PV) systems into the distribution network creates various stability and reliability issues associated with the intermittency of solar PV power generation. Energy storage is a vital component required for overcoming the intermittency of solar PV. This study presents a priority-based demand response management (DRM) for loads with large time constants to create virtual energy storage. The virtual energy storage thus created can be used for partial levelling of intermittent output from solar PVs. The proposed DRM algorithm involves controlling loads with large time constants such as air conditioning systems and refrigerators based on the forecasted solar PV generation. The proposed method is evaluated using data-driven simulations, weather data and mathematical models. The proposed algorithm is highly suitable for megacities that have high number of multi-storey residential buildings. Utilising the virtual storage capacity available from the appliances will reduce the investment as well as the operation cost of renewable energy such as solar PV. Analyses on impact on temperature, percentage of interruptions, cost savings and impact on energy storage sizing are also presented for evaluating the performance of the proposed algorithm.NRF (Natl Research Foundation, S’pore)Published versio

Edge security in smart inverters: Physical invariants based approach

The endeavour towards making power distribution systems (PDSs) smarter has made the interdependence on communication network indispensable. Further, prospective high penetration of intermittent renewable energy sources in the form of distributed energy resources (DERs) has resulted in the necessity for smart controllers on such DERs. Inverters are employed for the purpose of DC to AC power conversion in the distribution network where the present standards require these inverters to be smart. In general, distributed energy resource management systems (DERMS) calculate and send set points/operating points to these smart inverters using protocols such as smart energy profile (SEP) 2.0. Given the nature of sites at which such DERs are installed i.e., home area networks with a pool of IoT(Internet-of-Things) devices, the opportunity for a malicious actor to sabotage the operation is typically higher than that for a transmission system. National Electric Sector Cyber-security Organization Resource (NESCOR) has described several failure scenarios and impact analyses in case of cyber attacks on DERs. One such failure scenario concerns attacks on real/reactive power control commands. In this paper, it is demonstrated that physical invariant based security on the edge devices, i.e. smart controllers deployed in DER inverters, is an effective approach to minimize the impact of cyber attacks targeting reactive power control in DER inverters. The proposed defense is generic and can also be extended to attacks on real-power control. The proposed defense is validated on a co-simulation platform (OpenDSS and MATLAB/SIMULINK).Green Open Access added to TU Delft Institutional Repository ‘You share, we take care!’ – Taverne project https://www.openaccess.nl/en/you-share-we-take-care Otherwise as indicated in the copyright section: the publisher is the copyright holder of this work and the author uses the Dutch legislation to make this work public.Network Architectures and Service

A new ZVS full-bridge DC-DC converter for battery charging with reduced losses over full-load range

A new zero-voltage switching 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 the 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 an asymmetrical pulse-width modulation (APWM) technique resulting in higher efficiency. APWM reduces the current stress of the main switches and the circulating losses compared with the conventional phase-shift modulation 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

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.NRF (Natl Research Foundation, S’pore)EDB (Economic Devt. Board, S’pore

Smart lighting system using ANN-IMC for personalized lighting control and daylight harvesting

Lighting contributes a significant portion to the overall energy consumption in an office building. It is thus important to reduce the energy consumption of lighting systems especially for Net Zero Energy Buildings (NZEB). Maximizing daylight harvesting can significantly increase the energy savings. With increase in demand for satisfying occupant preferences in visual comfort, the need for personalized lighting in the office space is also rising. In this paper, a novel lighting control system for Net Zero Energy Buildings (NZEB) is proposed which models the lighting system using Artificial Neural Network (ANN) and utilizes this model with the Internal Model Control (IMC) principle for controller design. Modeling the lighting system using ANN reduces the challenge of modeling a large and complex system with inherent process variability without the need to analyze extensive data-sets. The proposed ANN-IMC controller uses feedback from sensors on the task table to maintain desired illuminance, is easy to tune with just one parameter and is robust to process variability. The proposed control design is applicable to square systems where the number of lights and number of sensors are equal. However, the proposed architecture can also be extended for controlling other lighting accessories such as roller blinds. The performance of the proposed lighting control system to harvest the daylight effectively is demonstrated using both simulation results and an experimental setup in test-bed environment. The versatility of the proposed system will allow an operator to deploy personalized lighting in an office space.NRF (Natl Research Foundation, S’pore)Accepted versio

Agent based aggregated behavior modelling for electric vehicle charging load

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.NRF (Natl Research Foundation, S’pore)Accepted versio