Charging of electric vehicles at commercial buildings

The objective of this thesis was to investigate the feasibility of EV charging management for reducing the electricity cost of commercial buildings. A predictive model was developed to assist the commercial building manager reduce its energy bills by predicting the “triad” peak dates and the building’s energy demand. Real weather data were analysed and considered to increase the accuracy of the forecast. The model was evaluated using real “triad” peak, weather and energy consumption data from a commercial building facility in Manchester. To enable the building manager reduce the EV charging costs, a charging control algorithm was developed and its impact on the demand profile and daily electricity cost of a commercial building facility were studied. The predictive model and the charging control algorithm were integrated into a cloud-based Local Energy Management System (LEMS) for the aggregation and flexible demand management of buildings, energy storage units and EVs. The operation of the LEMS was demonstrated through simulation scenarios using real data from a commercial building facility in Manchester. To fully understand the EV integration consequences, the behaviour of the EV drivers and its impact on the road transport and electric power system has been studied. A multi-agent simulation model was developed to simulate the charging and routing behaviour of the EV drivers. The EV drivers were simulated as autonomous agents in a complex environment consisted of an electric power and road transport network. Different behavioural profiles were considered to describe the way an EV driver deals with the everyday challenges

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

A cloud-based energy management system for building managers

A Local Energy Management System (LEMS) is described to control Electric Vehicle charging and Energy Storage Units within built environments. To this end, the LEMS predicts the most probable half hours for a triad peak, and forecasts the electricity demand of a building facility at those times. Three operational algorithms were designed, enabling the LEMS to (i) flatten the demand profile of the building facility and reduce its peak, (ii) reduce the demand of the building facility during triad peaks in order to reduce the Transmission Network Use of System (TNUoS) charges, and (iii) enable the participation of the building manager in the grid balancing services market through demand side response. The LEMS was deployed on over a cloud-based system and demonstrated on a real building facility in Manchester, UK

Scalable local energy management systems

Commercial buildings have been identified as a major contributor of total global energy consumption. Mechanisms for collecting data about energy consumption patterns within buildings, and their subsequent analysis to support demand estimation (and reduction) remain important research challenges, which have already attracted considerable work. We propose a cloud based energy management system that enables such analysis to scale to both increasing data volumes and number of buildings. We consider both energy consumption and storage to support: (i) flattening the peak demand of commercial building(s); (ii) enable a “cost reduction” mode where the demand of a commercial building is reduced for those hours when a “triad peak” is expected; and (iii) enables a building manager to participate in grid balancing services market by means of demand response. The energy management system is deployed on a cloud infrastructure that adapts the number of computational resources needed to estimate potential demand, and to adaptively run multiple what-if scenarios to choose the most optimum configuration to reduce building energy demand

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

Predicting the energy demand of buildings during triad peaks in GB

A model-based approach is described to forecast triad periods for commercial buildings, using a multi-staged analysis that takes a number of different data sources into account, with each stage adding more accuracy to the model. In the first stage, a stochastic model is developed to calculate the probability of having a “triad” on a daily and half-hourly basis and to generate an alert to the building manager if a triad is detected. In the second stage, weather data is analysed and included in the model to increase its forecasting accuracy. In the third stage, an ANN forecasting model is developed to predict the power demand of the building at the periods when a “triad” peak is more likely to occur. The stochastic model has been trained on “triad” peak data from 1990 onwards, and validated against the actual UK “triad” dates and times over the period 2014/2015. The ANN forecasting model was trained on electricity demand data from six commercial buildings at a business park for one year. Local weather data for the same period were analysed and included to improve model accuracy. The electricity demand of each building on an actual “triad” peak date and time was predicted successfully, and an overall forecasting accuracy of 97.6% was demonstrated for the buildings being considered in the study. This measurement based study can be generalised and the proposed methodology can be translated to other similar built environment

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

Schedule, distribution and distach algorithms for electric vehicle load based on economic and network criteria

170170 σ.Η διαρκής ανάπτυξη και εξέλιξη της ηλεκτροκίνησης φέρνει τους εμπλεκόμενους φορείς στον χώρο των Συστημάτων Ηλεκτρικής Ενέργειας αντιμέτωπους με νέες προκλήσεις, καθώς το προφίλ του ηλεκτρικού φορτίου αλλάζει, και τίθενται νέες παράμετροι. Τα πρώτα χρόνια το ποσοστό διείσδυσης αναμένεται να είναι σχετικά μικρό, και να μην επηρεάσει αισθητά την λειτουργία του δικτύου. Με τον αριθμό των ηλεκτρικών οχημάτων όμως σταδιακά να αυξάνεται, υπάρχει κίνδυνος να εμφανιστούν προβλήματα σε βασικά χαρακτηριστικά του δικτύου όπως τάσεις ζυγών και φόρτιση γραμμών. Επομένως κρίνεται απαραίτητη η ανάπτυξη συστημάτων ελέγχου κατάλληλων για την αντιμετώπισή τους. Σκοπός της διπλωματικής αυτής είναι η ανάπτυξη ενός ολοκληρωμένου μοντέλου ελέγχου της φόρτισης των ηλεκτρικών οχημάτων. Αναπτύσσονται και παρουσιάζονται αλγόριθμοι για τον προγραμματισμό, την κατανομή και την διαχείριση του φορτίου των οχημάτων με στόχο την ελαχιστοποίηση του κόστους. Στην συνέχεια οι αλγόριθμοι αυτοί επεκτείνονται έτσι ώστε να λαμβάνεται υπόψη η επίδραση της φόρτισης των ηλεκτρικών οχημάτων στην λειτουργία του δικτύου. Ακολουθούν προσομοιώσεις σε ένα ρεαλιστικό αστικό δίκτυο διανομής και μελετάται η συνεισφορά της χρήσης αυτών στα τεχνικά και ποιοτικά χαρακτηριστικά λειτουργίας του δικτύου, όπως τάσεις ζυγών, φόρτιση γραμμών και μετασχηματιστών, και απώλειες δικτύου. Για την ανάπτυξη των αλγορίθμων χρησιμοποιείται το προγραμματιστικό περιβάλλον του MATLAB, ενώ η προσομοίωση στο δίκτυο πραγματοποιείται με την βοήθεια του λογισμικού EUROSTAG. Στο κεφάλαιο 1 γίνεται μια σύντομη ανασκόπηση της υπάρχουσας τεχνολογίας των ηλεκτρικών οχημάτων και των υποδομών φόρτισης, καθώς και των προτύπων που έχουν καθιερωθεί για την σύνδεση αυτών με το Σύστημα Ηλεκτρικής Ενέργειας. Στο κεφάλαιο 2 περιγράφονται οι φάσεις διείσδυσης των οχημάτων και τα νέα επιχειρηματικά μοντέλα που προκύπτουν. Στο κεφάλαιο 3 επεξηγούνται αναλυτικά οι διαδικασίες ελέγχου που προτείνονται και οι αλγόριθμοι που αναπτύχθηκαν γι’ αυτό το σκοπό, ενώ στο κεφάλαιο 4 εμφανίζονται τα αποτελέσματα της προσομοίωσης αυτών σε ένα υπαρκτό δίκτυο διανομής. Τέλος, στο κεφάλαιο 5 συνοψίζονται τα καταληκτικά συμπεράσματα, όπως προκύπτουν από την μελέτη της επίδρασης της φόρτισης ηλεκτρικών οχημάτων στην λειτουργία του δικτύου.The continuous growth and evolve of vehicle electrification causes the electric power systems to confront new challenges, since the load profile changes, and new parameters are being set. Initially, the EV uptake is expected to be relatively small, and the network operation will not be considerably affected. However, with the number of EVs gradually rising, problems may occur in technical characteristics of the network, like bus voltages and line congestion. Therefore, it is necessary to develop new EV management systems so as to prevent such phenomena. This diploma thesis intends to propose a complete model for the control of EV charging. Algorithms for schedule, distribution and management of EV load are presented, focusing on minimizing the energy cost. Then, these algorithms are being modified so as the effect on network’s parameters is taken into account. With the simulations that follow on a real urban distribution network, we have the chance to study the contribution of their use in the technical and quality specifications of the network, like bus voltages, line and transformer loading, and losses. The algorithms are developed in MATLAB programming language, whilst the network simulation is carried out with the software EUROSTAG. In chapter 1, one can find a brief overview of existing EV and charging infrastructure technologies as well as the basic established standards for their connection with electric power systems. In chapter 2, the penetration phases and the new business models that arise, are described. In chapter 3, there is a detailed analysis of the proposed control procedures and the algorithms developed for this purpose, while in chapter 4 the simulation results are shown. Finally, in chapter 5, the comparative conclusions from the study of EV charging and its effect on the network are summarized.Χαράλαμπος Ε. Μαρμαρά