Optimal bidding strategies of an electric vehicle aggregator in an electricity market

In recent years Electric Vehicles (EVs) are getting more importance as future transport systems, due to the increase of the concerns relevant to the greenhouse gases emission and the use fossil fuel. The management of the charging and discharging process of EVs could provide new business model for participating in the electricity markets. Moreover, vehicle to grid systems have the potential of increasing utility system flexibility. This thesis develops some models for the optimal integration of the EVs in the electricity market. In particular, the thesis focuses on the optimal bidding strategy of an EV aggregator participating to both the day ahead market and the secondary reserve market. The aggregator profit is maximized taking into account the energy balance equation, as well as the technical constraints of energy settlement, power supply and state of charge of the EVs. The results obtained by using the GAMS (General Algebraic Modelling System) environment are presented and discussed.

Intelligent electric vehicle heuristic for energy resource management using simulated annealing

The smart grid concept appears as a suitable solution to guarantee the power system operation in the new electricity paradigm with electricity markets and integration of large amounts of Distributed Energy Resources (DERs). Virtual Power Player (VPP) will have a significant importance in the management of a smart grid. In the context of this new paradigm, Electric Vehicles (EVs) rise as a good available resource to be used as a DER by a VPP. This paper presents the application of the Simulated Annealing (SA) technique to solve the Energy Resource Management (ERM) of a VPP. It is also presented a new heuristic approach to intelligently handle the charge and discharge of the EVs. This heuristic process is incorporated in the SA technique, in order to improve the results of the ERM. The case study shows the results of the ERM for a 33-bus distribution network with three different EVs penetration levels, i. e., with 1000, 2000 and 3000 EVs. The results of the proposed adaptation of the SA technique are compared with a previous SA version and a deterministic technique.

Smart Electric Vehicle Charging System

In this work is proposed the design of a system to create and handle Electric Vehicles (EV) charging procedures, based on intelligent process. Due to the electrical power distribution network limitation and absence of smart meter devices, Electric Vehicles charging should be performed in a balanced way, taking into account past experience, weather information based on data mining, and simulation approaches. In order to allow information exchange and to help user mobility, it was also created a mobile application to assist the EV driver on these processes. This proposed Smart ElectricVehicle Charging System uses Vehicle-to-Grid (V2G) technology, in order to connect Electric Vehicles and also renewable energy sources to Smart Grids (SG). This system also explores the new paradigm of Electrical Markets (EM), with deregulation of electricity production and use, in order to obtain the best conditions for commercializing electrical energy.

Electric vehicle scenario simulator tool for smart grid operators

This paper presents a simulator for electric vehicles in the context of smart grids and distribution networks. It aims to support network operator´s planning and operations but can be used by other entities for related studies. The paper describes the parameters supported by the current version of the Electric Vehicle Scenario Simulator (EVeSSi) tool and its current algorithm. EVeSSi enables the definition of electric vehicles scenarios on distribution networks using a built-in movement engine. The scenarios created with EVeSSi can be used by external tools (e.g., power flow) for specific analysis, for instance grid impacts. Two scenarios are briefly presented for illustration of the simulator capabilities.

An optimal scheduling problem in distribution networks considering V2G

This paper addresses the problem of energy resource scheduling. An aggregator will manage all distributed resources connected to its distribution network, including distributed generation based on renewable energy resources, demand response, storage systems, and electrical gridable vehicles. The use of gridable vehicles will have a significant impact on power systems management, especially in distribution networks. Therefore, the inclusion of vehicles in the optimal scheduling problem will be very important in future network management. The proposed particle swarm optimization approach is compared with a reference methodology based on mixed integer non-linear programming, implemented in GAMS, to evaluate the effectiveness of the proposed methodology. The paper includes a case study that consider a 32 bus distribution network with 66 distributed generators, 32 loads and 50 electric vehicles.

The electric vehicle integration into the power system: an application to the portuguese case

Electric vehicles (EV) offer a great potential to address the integration of renewable energy sources (RES) in the power grid, and thus reduce the dependence on oil as well as the greenhouse gases (GHG) emissions. The high share of wind energy in the Portuguese energy mix expected for 2020 can led to eventual curtailment, especially during the winter when high levels of hydro generation occur. In this paper a methodology based on a unit commitment and economic dispatch is implemented, and a hydro-thermal dispatch is performed in order to evaluate the impact of the EVs integration into the grid. Results show that the considered 10 % penetration of EVs in the Portuguese fleet would increase load in 3 % and would not integrate a significant amount of wind energy because curtailment is already reduced in the absence of EVs. According to the results, the EV is charged mostly with thermal generation and the associated emissions are much higher than if they were calculated based on the generation mix.

Multi-agent management system for electric vehicle charging

This paper proposes an implementation, based on a multi-agent system, of a management system for automated negotiation of electricity allocation for charging electric vehicles (EVs) and simulates its performance. The widespread existence of charging infrastructures capable of autonomous operation is recognised as a major driver towards the mass adoption of EVs by mobility consumers. Eventually, conflicting requirements from both power grid and EV owners require automated middleman aggregator agents to intermediate all operations, for example, bidding and negotiation, between these parts. Multi-agent systems are designed to provide distributed, modular, coordinated and collaborative management systems; therefore, they seem suitable to address the management of such complex charging infrastructures. Our solution consists in the implementation of virtual agents to be integrated into the management software of a charging infrastructure. We start by modelling the multi-agent architecture using a federated, hierarchical layers setup and as well as the agents' behaviours and interactions. Each of these layers comprises several components, for example, data bases, decision-making and auction mechanisms. The implementation of multi-agent platform and auctions rules, and of models for battery dynamics, is also addressed. Four scenarios were predefined to assess the management system performance under real usage conditions, considering different types of profiles for EVs owners', different infrastructure configurations and usage and different loads on the utility grid (where real data from the concession holder of the Portuguese electricity transmission grid is used). Simulations carried with the four scenarios validate the performance of the modelled system while complying with all the requirements. Although all of these have been performed for one charging station alone, a multi-agent design may in the future be used for the higher level problem of distributing energy among charging stations. Copyright (c) 2014 John Wiley & Sons, Ltd.

Simulated Annealing Approach Applied to the Energy Resource Management Considering Demand Response for Electric Vehicle

The aggregation and management of Distributed Energy Resources (DERs) by an Virtual Power Players (VPP) is an important task in a smart grid context. The Energy Resource Management (ERM) of theses DERs can become a hard and complex optimization problem. The large integration of several DERs, including Electric Vehicles (EVs), may lead to a scenario in which the VPP needs several hours to have a solution for the ERM problem. This is the reason why it is necessary to use metaheuristic methodologies to come up with a good solution with a reasonable amount of time. The presented paper proposes a Simulated Annealing (SA) approach to determine the ERM considering an intensive use of DERs, mainly EVs. In this paper, the possibility to apply Demand Response (DR) programs to the EVs is considered. Moreover, a trip reduce DR program is implemented. The SA methodology is tested on a 32-bus distribution network with 2000 EVs, and the SA results are compared with a deterministic technique and particle swarm optimization results.

Dynamic range prediction for an electric vehicle

Electric Vehicles (EVs) have limited energy storage capacity and the maximum autonomy range is strongly dependent of the driver's behaviour. Due to the fact of that batteries cannot be recharged quickly during a journey, it is essential that a precise range prediction is available to the driver of the EV. With this information, it is possible to check if the desirable destination is achievable without a stop to charge the batteries, or even, if to reach the destination it is necessary to perform an optimized driving (e.g., cutting the air-conditioning, among others EV parameters). The outcome of this research work is the development of an Electric Vehicle Assistant (EVA). This is an application for mobile devices that will help users to take efficient decisions about route planning, charging management and energy efficiency. Therefore, it will contribute to foster EVs adoption as a new paradigm in the transportation sector.

On-board electric vehicle battery charger with enhanced V2H operation mode

This paper proposes an on-board Electric Vehicle (EV) battery charger with enhanced Vehicle-to-Home (V2H) operation mode. For such purpose was adapted an on-board bidirectional battery charger prototype to allow the Grid-to-Vehicle (G2V), Vehicle-to-Grid (V2G) and V2H operation modes. Along the paper are presented the hardware topology and the control algorithms of this battery charger. The idea underlying to this paper is the operation of the on-board bidirectional battery charger as an energy backup system when occurs a power outages. For detecting the power outage were compared two strategies, one based on the half-cycle rms calculation of the power grid voltage, and another in the determination of the rms value based in a Kalman filter. The experimental results were obtained considering the on-board EV battery charger under the G2V, V2G, and V2H operation modes. The results show that the power outage detection is faster using a Kalman filter, up to 90% than the other strategy. This also enables a faster transition between operation modes when a power outage occurs.

Smart charging management for electric vehicle battery chargers

This paper proposes a smart battery charging strategy for Electric Vehicles (EVs) targeting the future smart homes. The proposed strategy consists in regulate the EV battery charging current in function of the total home current, aiming to prevent overcurrent trips in the main switch breaker. Computational and experimental results were obtained under real-time conditions to validate the proposed strategy. For such purpose was adapted a bidirectional EV battery charger prototype to operate in accordance with the aforementioned strategy. The proposed strategy was validated through experimental results obtained both in steady and transient states. The results show the correct operation of the EV battery charger even under heavy load variations.

A case study on the conversion of an internal combustion engine vehicle into an electric vehicle

This paper presents the conversion process of a traditional Internal Combustion Engine vehicle into an Electric Vehicle. The main constitutive elements of the Electric Vehicle are presented. The developed powertrain uses a three-phase inverter with Field Oriented Control and space vector modulation. The developed on-board batteries charging system can operate in Grid-to-Vehicle and Vehicle-to-Grid modes. The implemented prototypes were tested, and experimental results are presented. The assembly of these prototypes in the vehicle was made in accordance with the Portuguese legislation about vehicles conversion, and the main adopted solutions are presented.

System Investigation for Hybrid Electric Vehicle

A hybrid electric vehicle is a fast-growing concept in the field of vehicle industry. Nowadays two global problems make manufactures to develop such systems. These problems are: the growing cost of a fuel and environmental pollution. Also development of controlled electric drive with high control accuracy and reliability allows improving of vehicle drive characteristics. The objective of this Diploma Thesis is to investigate the possibilities of electrical drive application for new principle of parallel hybrid vehicle system. Electric motor calculations, selection of most suitable control system and other calculations are needed. This work is not final work for such topic. Further investigation with more precise calculations, modeling, measurements and cost calculations are needed to answer the question if such system is efficient.

Marketing Plan of Electric Vehicle Charging Solutions to USA

The purpose of the Thesis was to evaluate the business environment of electrical vehicle charging equipment (EVSE) in USA, find the key issues of entering the US EVSE markets and to form a marketing plan for possible market entry. The external market research analyzed the environment, customers, competitors and demand of EVSEs. In the internal analysis the focus was on resources and capabilities, offering, performance, business relations and US related issues. The research about the business environment was done using already available information from market studies and seminars. In external analysis there were also two semi-structured interviews from market experts used. The internal analysis was done mostly by interviews, but also company’s internal data sources were used. The interviews were semi-constructed and included eight interviewees from each part of EVSE value chain. The research findings were analyzed using SWOT analysis, which was converted to a so called TOWS matrix for extracting strategies. As a result of the Thesis, valuable information about the US markets and their requirements for EVSEs was gained. By matching the strengths of the case company and market requirements, customer segmentation and targeting were done and a marketing plan was constructed for the case company to help their management to evaluate the feasibility of possible market entry and investments to USA.

Design and Control of Permanent Magnet Synchronous Machine for Hybrid Electric Vehicle

This master’s thesis mainly focuses on the design requirements of an Electric drive for Hybrid car application and its control strategy to achieve a wide speed range. It also emphasises how the control and performance requirements are transformed into its design variables. A parallel hybrid topology is considered where an IC engine and an electric drive share a common crank shaft. A permanent magnet synchronous machine (PMSM) is used as an electric drive machine. Performance requirements are converted into Machine design variables using the vector model of PMSM. Main dimensions of the machine are arrived using analytical approach and Finite Element Analysis (FEA) is used to verify the design and performance. Vector control algorithm was used to control the machine. The control algorithm was tested in a low power PMSM using an embedded controller. A prototype of 10 kW PMSM was built according to the design values. The prototype was tested in the laboratory using a high power converter. Tests were carried out to verify different operating modes. The results were in agreement with the calculations.