Smart management of the charging of electric vehicles

The objective of this thesis was to investigate the management of electric vehicles (EVs) battery charging in distribution networks. Real EVs charging event data were used to investigate their charging demand profiles in a geographical area. A model was developed to analyse their charging demand characteristics and calculate their potential medium term operating risk level for the distribution network of the corresponding geographical area. A case study with real charging and weather data from three counties in UK was presented to demonstrate the modelling framework. The effectiveness of a charging control algorithm is dependent on the early knowledge of future EVs charging demand and local generation. To this end, two models were developed to provide this knowledge. The first model utilised data mining principles to forecast the day ahead EVs charging demand based on historical charging event data. The performance of four data mining methods in forecasting the charging demand of an EVs fleet was evaluated using real charging data from USA and France. The second model utilised a data fitting approach to produce stochastic generation forecast scenarios based only on the historical data. A case study was presented to evaluate the performance of the model based on real data from wind generators in UK. An agent-based control algorithm was developed to manage the EVs battery charging, according to the vehicles’ owner preferences, distribution network technical constraints and local distributed generation. Three agent classes were considered, a EVs/DG aggregator and “Responsive” or “Unresponsive” EVs. The real-time operation of the control system was experimentally demonstrated at the Electric Energy Systems Laboratory hosted at the National Technical University of Athens. A series of experiments demonstrated the adaptive behaviour of “Responsive” EVs agents and proved their ability to charge preferentially from renewable energy sources

Simulation of electric vehicle driver behaviour in road transport and electric power networks

The integration of electric vehicles (EVs) will affect both electricity and transport systems and research is needed on finding possible ways to make a smooth transition to the electrification of the road transport. To fully understand the EV integration consequences, the behaviour of the EV drivers and its impact on these two systems should be studied. This paper describes an integrated simulation-based approach, modelling the EV and its interactions in both road transport and electric power systems. The main components of both systems have been considered, and the EV driver behaviour was modelled using a multi-agent simulation platform. Considering a fleet of 1000 EV agents, two behavioural profiles were studied (Unaware/Aware) to model EV driver behaviour. The two behavioural profiles represent the EV driver in different stages of EV adoption starting with Unaware EV drivers when the public acceptance of EVs is limited, and developing to Aware EV drivers as the electrification of road transport is promoted in an overall context. The EV agents were modelled to follow a realistic activity-based trip pattern, and the impact of EV driver behaviour was simulated on a road transport and electricity grid. It was found that the EV agents’ behaviour has direct and indirect impact on both the road transport network and the electricity grid, affecting the traffic of the roads, the stress of the distribution network and the utilization of the charging infrastructure

A multi-agent based scheduling algorithm for adaptive electric vehicles charging

This paper presents a decentralized scheduling algorithm for electric vehicles charging. The charging control model follows the architecture of a Multi-Agent System (MAS). The MAS consists of an Electric Vehicle (EV)/Distributed Generation (DG) aggregator agent and “Responsive” or “Unresponsive” EV agents. The EV/DG aggregator agent is responsible to maximize the aggregator’s profit by designing the appropriate virtual pricing policy according to accurate power demand and generation forecasts. “Responsive” EV agents are the ones that respond rationally to the virtual pricing signals, whereas “Unresponsive” EV agents define their charging schedule regardless the virtual cost. The performance of the control model is experimentally demonstrated through different case studies at the micro-grid laboratory of the National Technical University of Athens (NTUA) using Real Time Digital Simulator. The results highlighted the adaptive behaviour of “Responsive” EV agents and proved their ability to charge preferentially from renewable energy sources

Probabilistic wind power forecasting and its application in the scheduling of gas-fired generators

Accurate information regarding the uncertainty of short-term forecast for aggregate wind power is a key to efficient and cost effective integration of wind farms into power systems. This paper presents a methodology for producing wind power forecast scenarios. Using historical wind power time series data and the Kernel Density Estimator (KDE), probabilistic wind power forecast scenarios were generated according to a rolling process. The improvement achieved in the accuracy of forecasts through frequent updating of the forecasts taking into account the latest realized wind power was quantified. The forecast scenarios produced by the proposed method were used as inputs to a unit commitment and optimal dispatch model in order to investigate how the uncertainty in wind forecast affect the operation of power system and in particular gas-fired generators

A data-driven approach for characterising the charging demand of electric vehicles: A UK case study

As the number of electric vehicles increases, the impact of their charging on distribution networks is being investigated using different load profiles. Due to the lack of real charging data, the majority of these load impact studies are making assumptions for the electric vehicle charging demand profiles. In this paper a two-step modelling framework was developed to extract the useful information hidden in real EVs charging event data. Real EVs charging demand data were obtained from Plugged-in Midlands (PiM) project, one of the eight ‘Plugged-in Places’ projects supported by the UK Office for Low Emission Vehicles (OLEV). A data mining model was developed to investigate the characteristics of electric vehicle charging demand in a geographical area. A Fuzzy-Based model aggregates these characteristics and estimates the potential relative risk level of EVs charging demand among different geographical areas independently to their actual corresponding distribution networks. A case study with real charging and weather data from three counties in UK is presented to demonstrate the modelling framework

Computational resource management for data-driven applications with deadline constraints

Recent advances in the type and variety of sensing technologies have led to an extraordinary growth in the volume of data being produced and led to a number of streaming applications that make use of this data. Sensors typically monitor environmental or physical phenomenon at predefined time intervals or triggered by user-defined events. Understanding how such streaming content (the raw data or events) can be processed within a time threshold remains an important research challenge. We investigate how a cloud-based computational infrastructure can autonomically respond to such streaming content, offering quality of service guarantees. In particular, we contextualize our approach using an electric vehicles (EVs) charging scenario, where such vehicles need to connect to the electrical grid to charge their batteries. There has been an emerging interest in EV aggregators (primarily intermediate brokers able to estimate aggregate charging demand for a collection of EVs) to coordinate the charging process. We consider predicting EV charging demand as a potential workload with execution time constraints. We assume that an EV aggregator manages a number of geographic areas and a pool of computational resources of a cloud computing cluster to support scheduling of EV charging. The objective is to ensure that there is enough computational capacity to satisfy the requirements for managing EV battery charging requests within specific time constraints

Smart management of the charging of electric vehicles

The objective of this thesis was to investigate the management of electric vehicles (EVs) battery charging in distribution networks.\ Real EVs charging event data were used to investigate their charging demand profiles in a geographical area. A model was developed to analyse their charging demand characteristics and calculate their potential medium term operating risk level for the distribution network of the corresponding geographical area. A case study with real charging and weather data from three counties in UK was presented to demonstrate the modelling framework.\ The effectiveness of a charging control algorithm is dependent on the early knowledge of future EVs charging demand and local generation. To this end, two models were developed to provide this knowledge. The first model utilised data mining principles to forecast the day ahead EVs charging demand based on historical charging event data. The performance of four data mining methods in forecasting the charging demand of an EVs fleet was evaluated using real charging data from USA and France. The second model utilised a data fitting approach to produce stochastic generation forecast scenarios based only on the historical data. A case study was presented to evaluate the performance of the model based on real data from wind generators in UK.\ An agent-based control algorithm was developed to manage the EVs battery charging, according to the vehicles’ owner preferences, distribution network technical constraints and local distributed generation. Three agent classes were considered, a EVs/DG aggregator and “Responsive” or “Unresponsive” EVs. The real-time operation of the control system was experimentally demonstrated at the Electric Energy Systems Laboratory hosted at the National Technical University of Athens. A series of experiments demonstrated the adaptive behaviour of “Responsive” EVs agents and proved their ability to charge preferentially from renewable energy sources

Smart management of PEV charging enhanced by PEV load forecasting

According to the U.K. Department for Transport, the 97 % of transport energy consumption comes from the usage of oil. Therefore, a fuel diversification is needed to improve the energy security, and plug-in electric vehicles (PEVs) seem promising in giving an alternative solution. However, PEV owners need electric power from the grid in order to recharge the batteries of their vehicles. PEV charging load is a new type of demand, influenced by additional factors such as travel and driving patterns. Average travel distance within a day, the connection and disconnection time and the PEV’s power consumption will directly affect the daily load curve. This chapter proposes a decentralized control algorithm to manage the PEV charging requests. The aim of the control algorithm is to achieve a valley-filling effect on the demand curve, avoiding a potential increase in the peak demand. The proposed model includes an algorithm for PEV short term load forecasting. This forecast contributes to the effectiveness of the control model. Through different case studies, the performance of the proposed model is evaluated and the value of the PEV load forecasting as part of the PEV load management process is illustrated

Estimating the true GHG emissions reduction due to electric vehicles integration

Unabated emission of greenhouse gases (GHGs) and its attendant climate change consequences can adversely affect the present and future generations. The energy and the transportation sectors have been identified as the two largest producers of the GHGs emissions due to their massive dependence on fossil fuels. Deployment of Renewable Energy Sources (RES) in Distributed Generation (DG) of electricity and integration of Electric vehicles (EVs) in the transportation sector have been suggested as means of reducing the GHGs emissions. There are concerns, however, about EVs reducing emissions in the transportation sector and increasing the same in the power sector. This paper, therefore, presents an algorithm to empirically estimate the annual Real-Emissions-Reduction (RER) in the UK due to EVs integration from the year 2009 to 2013. The algorithm computes and compares the Apparent-Emissions-Reduction (AER) due to EVs in the transportation sector and the emissions due to EVs at the power stations to give the RER. Results from the algorithm and the implications of the figures are then discussed