A comprehensive day-ahead scheduling strategy for electric vehicles operation

Distribution networks are envisaged to host significant number of electric vehicles and potentially many charging stations in the future to provide charging as well as vehicle-2-grid services to the electric vehicle owners. The main goal of this study is to develop a comprehensive day-ahead scheduling framework to achieve an economically rewarding operation for the ecosystem of electric vehicles, charging stations and retailers using a comprehensive optimal charging/discharging strategy that accounts for the network constraints. To do so, an equilibrium problem is solved using a three-layer iterative optimisation problem for all stakeholders in the ecosystem. EV routing problem is solved based on a cost-benefit analysis rather than choosing the shortest route. The proposed method can be implemented as a cloud scheduling system that is operated by a non-profit entity, e.g., distribution system operators or distribution network service providers, whose role is to collect required information from all agents, perform the day-ahead scheduling, and ultimately communicate the results to relevant stakeholders. To evaluate the effectiveness of the proposed framework, a simulation study, including three retailers, one aggregator, nine charging stations and 600 electric vehicles, is designed based on real data from San Francisco, the USA. The simulation results show that the total cost of electric vehicles decreased by 17.6%, and the total revenue of charging stations and retailers increased by 21.1% and 22.6%, respectively, in comparison with a base case strategy

Autonomous Demand Side Management of Electric Vehicles

There is an error in the table of content, where publication A and B have swiched places.Demand-side management approaches that exploit the temporal flexibility of electric vehicles have attracted much attention in recent years due to the increasing market penetration. These demand-side management measures contribute to alleviating the burden on the power system, especially in distribution grids where bottlenecks are more prevalent. Electric vehicles can be defined as an attractive asset for distribution system operators, which have the potential to provide grid services if properly managed. In this thesis, first, a systematic investigation is conducted for two typically employed demand-side management methods reported in the literature: A voltage droop control-based approach and a market-driven approach. Then a control scheme of decentralized autonomous demand side management for electric vehicle charging scheduling which relies on a unidirectionally communicated grid-induced signal is proposed. In all the topics considered, the implications on the distribution grid operation are evaluated using a set of time series load flow simulations performed for representative Austrian distribution grids. Droop control mechanisms are discussed for electric vehicle charging control which requires no communication. The method provides an economically viable solution at all penetrations if electric vehicles charge at low nominal power rates. However, with the current market trends in residential charging equipment especially in the European context where most of the charging equipment is designed for 11 kW charging, the technical feasibility of the method, in the long run, is debatable. As electricity demand strongly correlates with energy prices, a linear optimization algorithm is proposed to minimize charging costs, which uses next-day market prices as the grid-induced incentive function under the assumption of perfect user predictions. The constraints on the state of charge guarantee the energy required for driving is delivered without failure. An average energy cost saving of 30% is realized at all penetrations. Nevertheless, the avalanche effect due to simultaneous charging during low price periods introduces new power peaks exceeding those of uncontrolled charging. This obstructs the grid-friendly integration of electric vehicles.publishedVersio

Optimal electric vehicle scheduling : A co-optimized system and customer perspective

Electric vehicles provide a two pronged solution to the problems faced by the electricity and transportation sectors. They provide a green, highly efficient alternative to the internal combustion engine vehicles, thus reducing our dependence on fossil fuels. Secondly, they bear the potential of supporting the grid as energy storage devices while incentivizing the customers through their participation in energy markets. Despite these advantages, widespread adoption of electric vehicles faces socio-technical and economic bottleneck. This dissertation seeks to provide solutions that balance system and customer objectives under present technological capabilities. The research uses electric vehicles as controllable loads and resources. The idea is to provide the customers with required tools to make an informed decision while considering the system conditions. First, a genetic algorithm based optimal charging strategy to reduce the impact of aggregated electric vehicle load has been presented. A Monte Carlo based solution strategy studies change in the solution under different objective functions. This day-ahead scheduling is then extended to real-time coordination using a moving-horizon approach. Further, battery degradation costs have been explored with vehicle-to-grid implementations, thus accounting for customer net-revenue and vehicle utility for grid support. A Pareto front, thus obtained, provides the nexus between customer and system desired operating points. Finally, we propose a transactive business model for a smart airport parking facility. This model identifies various revenue streams and satisfaction indices that benefit the parking lot owner and the customer, thus adding value to the electric vehicle --Abstract, page iv

Vehicle-to-grid aggregator to support power grid and reduce electric vehicle charging cost

This paper presents an optimised bidirectional Vehicle-to-Grid (V2G) operation, based on a fleet of Electric Vehicles (EVs) connected to a distributed power system, through a network of charging stations. The system is able to perform day-ahead scheduling of EV charging/discharging to reduce EV ownership charging cost through participating in frequency and voltage regulation services. The proposed system is able to respond to real-time EV usage data and identify the required changes that must be made to the day-ahead energy prediction, further optimising the use of EVs to support both voltage and frequency regulation. An optimisation strategy is established for V2G scheduling, addressing the initial battery State Of Charge (SOC), EV plug-in time, regulation prices, desired EV departure time, battery degradation cost and vehicle charging requirements. The effectiveness of the proposed system is demonstrated using a standardized IEEE 33-node distribution network integrating five EV charging stations. Two case studies have been undertaken to verify the contribution of this advanced energy supervision approach. Comprehensive simulation results clearly show an opportunity to provide frequency and voltage support while concurrently reducing EV charging costs, through the integration of V2G technology, especially during on-peak periods when the need for active and reactive power is high

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

Scheduling of EV Charging in Grid-Connected Parking Lots with Renewable Sources

The growing concern about environmental issues is leading many countries to take measures that allow a more rational energy usage and for a more sustainable future. The improvement of systems e�ciency and the use of renewable sources are some points to work on to reduce greenhouse gas emissions. That is why electric mobility is drawing the attention of companies, countries and research groups, as an important measure to face the negative consequences derived from the current energy usage. It is clear that the inclusion of electric vehicles will strongly a�ect the operation, management, and planning of current electric power systems. Firstly, an additional load will have to be considered, the electric vehicles charging. In an initial stage, when the deployment of electric vehicles is not signi�cant, special measures will not be required. However, in the future with thousands of vehicles in operation, ad-hoc electric vehicle charging can lead to line congestion or voltage limits violation. Moreover, an update of the current electric power systems regarding more advanced information and communication technologies, better metering devices, as well as the presence of more renewable sources are required for the suitable integration of electric vehicles. The increasing number of electric vehicles (EV) means there is a growing need for charging stations as well. A potential solution to address the need for charging stations is to transform traditional parking lots into smart parking lots. Due to the inherently complex and dynamic environment, a potential obstacle, from a business perspective to the process of transforming parking lots into smart parking lots is the complexity of estimating the pro�t of the smart parking lots owner and, consequently, the length of time required to recover the cost of the initial investment. We propose a simulation approach to estimate the smart parking lot owners pro�t during a certain period of time. Thus, this thesis is intended to cover the problem of signi�cant increase in electric vehicles arriving at the parking lot leading to a challenge for scheduling of vehicles for charging. The primary objective of parking lot owner is to charge more vehicles and increase pro�t. But due to stringent rules from regulators for network upgrades, increase in the number of charging slots is challenging. Installing a distributed generation like solar microgrid will bene�t from allowing many vehicles to charge at the parking lot. This thesis aims in proposing an algorithm called parking lot management system (PLMS) and charging management system (CMS) for scheduling of electric vehicles with the support of solar generation with the objective of minimizing the power drawl from the grid during high peak pricing period. Power drawl from the grid is reduced by using the solar power available. Since the power drawl from the grid is reduced, it is obvious that the pro�t of the parking lot owner is increased. scheduling is done by shifting the cars to the abundant solar power period and reducing the peaks on the grid which helps the utility operator. The proposed algorithm is simulated using MATLAB programming, and the results are presented