Topics in Electromobility and Related Applications

In this thesis, we mainly discuss four topics on Electric Vehicles (EVs) in the context of smart grid and smart transportation systems. The first topic focuses on investigating the impacts of different EV charging strategies on the grid. In Chapter 3, we present a mathematical framework for formulating different EV charging problems and investigate a range of typical EV charging strategies with respect to different actors in the power system. Using this framework, we compare the performances of all charging strategies on a common power system simulation testbed, highlighting in each case positive and negative characteristics. The second topic is concerned with the applications of EVs with Vehicle-to-Grid (V2G) capabilities. In Chapter 4, we apply certain ideas from cooperative control techniques to two V2G applications in different scenarios. In the first scenario, we harness the power of V2G technologies to reduce current imbalance in a three-phase power network. In the second scenario, we design a fair V2G programme to optimally determine the power dispatch from EVs in a microgrid scenario. The effectiveness of the proposed algorithms are verified through a variety of simulation studies. The third topic discusses an optimal distributed energy management strategy for power generation in a microgrid scenario. In Chapter 5, we adapt the synchronised version of the Additive-Increase-Multiplicative-Decrease (AIMD) algorithms to minimise a cost utility function related to the power generation costs of distributed resources. We investigate the AIMD based strategy through simulation studies and we illustrate that the performance of the proposed method is very close to the full communication centralised case. Finally, we show that this idea can be easily extended to another application including thermal balancing requirements. The last topic focuses on a new design of the Speed Advisory System (SAS) for optimising both conventional and electric vehicles networks. In Chapter 6, we demonstrate that, by using simple ideas, one can design an effective SAS for electric vehicles to minimise group energy consumption in a distributed and privacy-aware manner; Matlab simulation are give to illustrate the effectiveness of this approach. Further, we extend this idea to conventional vehicles in Chapter 7 and we show that by using some of the ideas introduced in Chapter 6, group emissions of conventional vehicles can also be minimised under the same SAS framework. SUMO simulation and Hardware-In-the-Loop (HIL) tests involving real vehicles are given to illustrate user acceptability and ease of deployment. Finally, note that many applications in this thesis are based on the theories of a class of nonlinear iterative feedback systems. For completeness, we present a rigorous proof on global convergence of consensus of such systems in Chapter 2

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

Ancillary Services in Hybrid AC/DC Low Voltage Distribution Networks

In the last decade, distribution systems are experiencing a drastic transformation with the advent of new technologies. In fact, distribution networks are no longer passive systems, considering the current integration rates of new agents such as distributed generation, electrical vehicles and energy storage, which are greatly influencing the way these systems are operated. In addition, the intrinsic DC nature of these components, interfaced to the AC system through power electronics converters, is unlocking the possibility for new distribution topologies based on AC/DC networks. This paper analyzes the evolution of AC distribution systems, the advantages of AC/DC hybrid arrangements and the active role that the new distributed agents may play in the upcoming decarbonized paradigm by providing different ancillary services.Ministerio de Economía y Competitividad ENE2017-84813-RUnión Europea (Programa Horizonte 2020) 76409

Modelling scenarios for enhancing the effective implementation of secure, affordable and sustainable electricity on the Greek islands

The Greek islands’ power system is fragmented into 29 autonomous electrical systems relying on oil-fired generators to supply 82% of their electricity demand. Local power grids are only allowed to absorb a maximum renewable energy share of approximately 30% to secure the stability of the network and avoid abrupt frequency alterations. Inevitably, fossil-fuel dominated, isolated systems lead to increased generation costs, high carbon intensity and frequent power cuts. A novel integrated methodological approach has been developed to address these challenges consisting of: I) Long and short-term modelling considering interconnections and energy storage in the form of batteries versus the current energy autonomy, using the PLEXOS integrated energy model (Energy Exemplar, 2019) for a projection horizon extending between 2020 and 2040. II) ISLA demand model (Spataru, 2013), adapted to the Greek islands (ISLA_EGI), preceded by an extensive data processing, to anticipate annual demand scenarios. The two models inform each other and support the analysis of 35 scenarios. III) The development of methods to simulate electromobility in PLEXOS considering various charging strategies. This analysis contextualises the impact of innovative technologies in providing feasible solutions on the Greek islands in line with the Energy Trilemma Index (security, affordability, sustainability). It was concluded that when combining submarine interconnections and batteries (Scenario IB.x.1.0.a), generation prices were reduced by 42% at the regional and 10% at the national level compared to a BAU scenario (A.y.1.0.a), while carbon dioxide equivalent (CO2eq) emissions are reduced by 99% and 74% respectively. Also, power outage events are abolished. The benefits of a High-Efficiency demand scenario produced by ISLA_EGI show further reductions of 2.5% in emissions between 2020 and 2040. The results unveil that certain small, remote systems should remain autonomous, supported by battery storage. The operation of EVs highlights that primarily V2G scenarios and occasionally, scheduled unidirectional charging bring the ultimate benefits

Modelling District Heating in a Renewable Electricity System

With the decarbonisation of electricity generation, large scale heat pumps are becoming increasingly viable for district heating combined with thermal energy storage, district heating can provide flexibility to the electricity grid by decoupling demand from supply. This thesis examines how district heating with heat pumps and thermal energy storage can integrate with and provide a benefit to an electricity system with predominantly renewable generation. The scope of work comprises three interlinked models underpinned by the same set of meteorology data, fundamentally coupling supply and demand. First, heat load data are surveyed, and an hourly demand profile is simulated. Disaggregation of district heating loads from the national demand is accomplished via segmentation of the building stock to model heat demand at high spatiotemporal resolution. Second, a novel method of pricing hourly electricity in a zero carbon, capital-intensive renewable system with electricity storage is developed and applied to a dispatch simulation to generate hourly electricity prices. Third, a dynamic model of district heating is constructed to simulate the meeting of heat loads with different design configurations using electricity as the energy source. Model predictive control is applied with varying forecast horizons so as to minimise the cost of electricity to meet the heat demand given a time series of hourly prices and consequently optimising the capacity of thermal energy storage. It was found that a thermal energy storage capacity equivalent to 1.3% of annual demand is sufficient to minimise operating costs. Finally, the potential impact of district heating on balancing the electricity system is analysed and an equivalence between thermal and electric storage is examined. While this is highly dependent on annual conditions, it can be as much as 3.5 units of thermal storage for every unit of electrical grid storage on the system. This could potentially reduce the investment in grid storage by £36 billion, underlining the significant financial benefits of thermal storage to the whole system. The research highlights the important potential of district heating to the UK’s energy system strategy

Optimisation-based Approaches for Evaluating the Aggregation of EVs and PVs in Unbalanced Low-Voltage Networks

214 p.In the near future, it is expected that the distribution system operators face different technical challenges derived from the massification of electric mobility and renewable energy sources in the low voltage networks. The purpose of this thesis is to define different smart coordination strategies among different agents involved in the low voltage networks such as the distribution system operator, the aggregators and the end-users when significant penetration levels of these resources are adopted. New models for representing the uncertainty of the photovoltaic output power and the connection of the electric vehicles are introduced. A new energy boundary model for representing the flexibility of electric vehicles is also proposed. In combination with the above models, four optimisation models were proposed as coordination strategies into three different approaches: individual, population, and hybrid. The first model was defined at the aggregator level, whereas the other models were proposed at the distribution system operator level. Complementary experimental cases about the proposed optimisation model in the individual-based approach and the quadratic formulation in the hybrid approach for the PV power curtailment were carried out to test its response in real-time. Simulations results demonstrated that the proposed coordination strategies could effectively manage critical insertion levels of electric vehicles and photovoltaic units in unbalanced low voltage networks

Optimal coordination of electric vehicle charging and photovoltaic power curtailment in unbalanced low voltage networks: An experimental case

This study introduces a quadratic programming-based optimisation method to coordinate electric vehicle (EV) charging and photovoltaic (PV) curtailment in unbalanced low voltage (LV) networks. The proposed model is defined as a convex model that guarantees the optimal global solution of the problem avoiding the complexity of non-linear models and surpassing the limitations of local solutions derived from meta-heuristics algorithms reported in the literature. The coordination is carried out through a centralised controller installed at the header of the LV feeder. The objective of the proposed strategy is to minimise the power curtailment of all PV systems and maximise the power delivered to all EVs by optimising at every time step a suitable setpoint for the PV units and the charging rate of each EV connected without surpassing network constraints. A new energy-boundary model is also proposed to meet the energy requirements of all EVs, which is based on a recurrent function that depends on the arrival-and-desired energy states of the vehicle to compute its charging trajectory optimally. The effectiveness of the proposed coordination strategy was successfully proven through three scenarios in a laboratory environment, making use of two commercial EVs and a PV inverter in a Power Hardware-in-the-Loop setup.This work was supported by TECNALIA funding through the 2017 PhD scholarship programme. TECNALIA is a "CERVERA Technology Centre of Excellence" recognised by the Ministry of Science and Innovation. The authors also would like to thank the Basque Government (GISEL research group IT1191‐19) and the UPV/EHU (GISEL research group 18/181) for their support in this work, as well as the TU Dortmund University for allowing the use of its facilities to obtain the results described in this paper. Dr. Kalle Rauma would like to thank the support of the German Federal Ministry of Transport and Digital Infrastructure through the project Parken und Laden in der Stadt (03EMF0203). The work of Kalle Rauma was also supported by the European Union's Horizon 2020 Research and Innovation Programme through SENDER project under grant agreement no. 95775

Advanced Communication and Control Methods for Future Smartgrids

Proliferation of distributed generation and the increased ability to monitor different parts of the electrical grid offer unprecedented opportunities for consumers and grid operators. Energy can be generated near the consumption points, which decreases transmission burdens and novel control schemes can be utilized to operate the grid closer to its limits. In other words, the same infrastructure can be used at higher capacities thanks to increased efficiency. Also, new players are integrated into this grid such as smart meters with local control capabilities, electric vehicles that can act as mobile storage devices, and smart inverters that can provide auxiliary support. To achieve stable and safe operation, it is necessary to observe and coordinate all of these components in the smartgrid

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