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

PV ENABLED NET ZERO EV CHARGING STATION: SYSTEM DESIGN AND SIMULATION STUDY

A paradigm shift in the transportation sector is being witnessed due to resurgence of electric vehicles (EVs). They are ideally considered to be non-polluting and eco-friendly, however it has its own demerits of overloading existing grid infrastructure and, could significantly contribute towards carbon emissions depending on the source used for charging them. The ideal solution to counteract the critical shortcomings is by developing a charging infrastructure integrated with renewable energy technology. The main aim of this thesis is to design such a charging station coupled with solar energy for urban cities. Simplified EV load models are developed by considering most popular commercial EV in the market. The designed solar powered charging station is tested with the developed EV load models and, would be located in selected urban cities within Ontario. Firstly, literature review on effects of EV charging directly from grid, benefits of EV charging with renewables, and amalgamation of EV charging with Net Zero (NZ) concepts is introduced. Later, three types of system architectures are studied for solar powered charging station. Selection of architecture for this work is done considering the economics of installation, and operation. Optimization in design of solar powered charging station is presented by varying the power ratio and, obtaining the annual energy yield for different types of orientation considering all EV load models. Then, NZ Photovoltaic (PV) enabled charging station is designed and, is tested with selected load models and, energy economic analysis is done for all designs. Finally, recommendations are made encompassing the selection of net-zero based charging stations along with economic considerations and its short and long term effects on environment

IMPACT OF PLUG-IN ELECTRIC VEHICLES AND WIND GENERATORS ON HARMONIC DISTORTION OF ELECTRIC DISTRIBUTION SYSTEMS

Harmonic distortion on voltages and currents increases with the increased penetration of Plug-in Electric Vehicle (PEV) loads in distribution systems. Wind Generators (WGs), which are source of harmonic currents, have some common harmonic profiles with PEVs. Thus, WGs can be utilized in careful ways to subside the effect of PEVs on harmonic distortion. This work studies the impact of PEVs on harmonic distortions and integration of WGs to reduce it. A decoupled harmonic three-phase unbalanced distribution system model is developed in OpenDSS, where PEVs and WGs are represented by harmonic current loads and sources respectively. The developed model is first used to solve harmonic power flow on IEEE 34-bus distribution system with low, moderate, and high penetration of PEVs, and its impact on current/voltage Total Harmonic Distortions (THDs) is studied. This study shows that the voltage and current THDs could be increased upto 9.5% and 50% respectively, in case of distribution systems with high PEV penetration and these THD values are significantly larger than the limits prescribed by the IEEE standards. Next, carefully sized WGs are selected at different locations in the 34-bus distribution system to demonstrate reduction in the current/voltage THDs. In this work, a framework is also developed to find optimal size of WGs to reduce THDs below prescribed operational limits in distribution circuits with PEV loads. The optimization framework is implemented in MATLAB using Genetic Algorithm, which is interfaced with the harmonic power flow model developed in OpenDSS. The developed framework is used to find optimal size of WGs on the 34-bus distribution system with low, moderate, and high penetration of PEVs, with an objective to reduce voltage/current THD deviations throughout the distribution circuits. With the optimal size of WGs in distribution systems with PEV loads, the current and voltage THDs are reduced below 5% and 7% respectively, which are within the limits prescribed by IEEE

Optimal Design of Grid-Connected PEV Charging Systems With Integrated Distributed Resources

The penetration of plug-in electric vehicles and renewable distributed generation is expected to increase over the next few decades. Large scale unregulated deployment of either technology can have a detrimental impact on the electric grid. However, appropriate pairing of these technologies along with some storage could mitigate their individual negative impacts. This paper presents a framework and an optimization methodology for designing grid-connected systems that integrate plug-in electric vehicle chargers, distributed generation and storage. To demonstrate its usefulness, this methodology is applied to the design of optimal architectures for a residential charging case. It is shown that, given current costs, maximizing grid power usage minimizes system lifecycle cost. However, depending upon the location's solar irradiance patterns, architectures with solar photovoltaic generation can be more cost effective than architectures without. Additionally, Li-ion storage technology and micro wind turbines are not yet cost effective when compared to alternative solutions.Siemens Corporatio

Resilient Grid Operation by Integrating Photovoltaic Generation, Mobile Electric Vehicle Battery Energy Storage, and Fixed Battery Energy Storage

The widespread utilization of photovoltaic (PV) generation, and battery energy storage (BES) in mobile (Electrical Vehicles-EVs) and fixed form is bringing opportunities, as well as challenges to the power grid operation. To handle the uncertainties brought by EV BES, and PV generation, we propose to improve the system predictability through: 1) an analytical way to estimate the aggregated EVs BES power capacity for charging/discharging, considering factors such as EVs’ mobility, drivers’ stochastic behavior, energy need for future travel, etc.; and 2) an improved PV generation forecast based on Gaussian Conditional Random Fields (GCRF) method, which models both the spatial and temporal correlations in different graphs and works well even with missing or unavailable data. One of the opportunities is to improve the system flexibility by utilizing the relatively high ramping capability of mobile (EV) and fixed BES given their quite adjustable operation modes (charging and discharging). This dissertation presents a model to integrate EVs and fixed BES into the ramp market with two types of participation: a) direct participation, and b) collaboration with conventional generators. By providing the ramp service in the electricity market, EVs and fixed BES can help the power grid better handle the short term net-load variability and uncertainty. On the other hand, EVs and fixed BES do not have to charge and discharge very frequently, while their fast ramping capability can still get rewarded. Also, the limitation on the energy capacity of EVs and fixed BES can be relieved to some extent. As another opportunity, the integration of PV generation and fixed BES can also help the system in face of some unknowable uncertainties, such as the extreme event. The energy stored in the BES and generated by the PV panel can serve as the emergency power supply. Also, they are located in a more scattered manner than the conventional generators, which enables their capacity to be more accessible under the extreme conditions. This dissertation proposes an optimal allocation scheme of PV generation and fixed BES aiming at improving the system resilience, considering the unknowable nature of extreme events

Sizing Battery Energy Storage and PV System in an Extreme Fast Charging Station Considering Uncertainties and Battery Degradation

This paper presents mixed integer linear programming (MILP) formulations to obtain optimal sizing for a battery energy storage system (BESS) and solar generation system in an extreme fast charging station (XFCS) to reduce the annualized total cost. The proposed model characterizes a typical year with eight representative scenarios and obtains the optimal energy management for the station and BESS operation to exploit the energy arbitrage for each scenario. Contrasting extant literature, this paper proposes a constant power constant voltage (CPCV) based improved probabilistic approach to model the XFCS charging demand for weekdays and weekends. This paper also accounts for the monthly and annual demand charges based on realistic utility tariffs. Furthermore, BESS life degradation is considered in the model to ensure no replacement is needed during the considered planning horizon. Different from the literature, this paper offers pragmatic MILP formulations to tally BESS charge/discharge cycles using the cumulative charge/discharge energy concept. McCormick relaxations and the Big-M method are utilized to relax the bi-linear terms in the BESS operational constraints. Finally, a robust optimization-based MILP model is proposed and leveraged to account for uncertainties in electricity price, solar generation, and XFCS demand. Case studies were performed to signify the efficacy of the proposed formulations

Smart PEV Charging Station Operation and Design Considering Distribution System Impact

Penetration of plug-in electric vehicles (PEVs) into the market is expected to be large in the near future. Also, as stated by the Ontario Ministry of Transportation, the province is investing $20 million from Ontario's Green Investment Fund to build nearly 500 electric vehicle charging stations (EVCSs) at over 250 locations in Ontario by 2017. Therefore, estimating PEV charging demand at an EVCS with their complex charging behavior, their impact on the power grid, and the optimal design of EVCS need be investigated. This thesis first presents a queuing analysis based method for modeling the 24-hour charging load profile of EVCSs. The queuing model considers the arrival of PEVs as a non-homogeneous Poisson process with different arrival rates over the day; considering customer convenience and charging price as the factors that influence the hourly arrival rate of vehicles at the EVCS. One of the main contributions of the thesis is to model the PEV service time considering the state-of-charge of the battery and the effect of the battery charging behavior. The impact of PEV load models on distribution systems is studied for a deterministic case, and the impact of uncertainties is examined using the stochastic optimal power flow and Model Predictive Control approaches. The thesis presents a novel mathematical model for representing the total charging load at an EVCS in terms of controllable parameters; the load model developed using a queuing model followed by a neural network (NN). The queuing model constructs a data set of PEV charging parameters which are input to the NN to determine the controllable EVCS load model. The smart EVCS load is a function of the number of PEVs charging simultaneously, total charging current, arrival rate, and time; and various class of PEVs. The EVCS load is integrated within a distribution operations framework to determine the optimal operation and smart charging schedules of the EVCS. Objective functions from the perspective of the local distribution company (LDC) and EVCS owner are considered for studies. The performance of a smart EVCS vis-à-vis an uncontrolled EVCS is examined to emphasize the demand response (DR) contributions of a smart EVCS and its integration into distribution operations. Finally, the thesis presents the optimal design of an EVCS with the goal of minimizing the life-cycle cost, while taking into account environmental emissions. Different supply options such as renewable energy technology based and diesel generation, with realistic inputs on their physical, operating and economic characteristics are considered, in order to arrive at the optimal design of EVCS. The charging demand of the EVCS is estimated considering real drive data. Analysis is also carried out to compare the economics of a grid-connected EVCS with an isolated EVCS and the optimal break-even distance is determined. Also, the EVCS is assumed to be connected to the grid as a smart energy hub based on different supply options

Integration of EVs and DGs into the Electric Power System for Grid Modernization

Electric power systems (EPSs) are rapidly becoming more complex. Penetration of distributed generators (DGs) are increasing rapidly. Among them, DG units with intermittent renewables resources, such as solar or wind, are attracting more attention. Moreover, plug in electric vehicles (EVs) are expected to be deployed in large numbers over the next decade. These changes present opportunities as well as challenges for reliable and efficient operation of EPS. Integrating EVs in large scale, would result in over-loading of EPS. Interconnection of DGs could impact adversely on the system operation including power quality and safety of the EPS. However, due to the growing number of EVs in the system, faster charging, shorter battery reaction time, and vehicle-to-grid services, EVs could be attractive sources for system operators (SOs) to improve system reliability while creating opportunity for EV owners to gain monetary benefits. In addition, the potential benefits of DG could be sustained in avoiding or shifting investment in transmission lines and/or transformers, minimizing ohmic losses, and protecting the environment. In this dissertation, potential benefits and challenges of EVs and DGs are explored. For some potential benefits, the dissertation develops systematic frameworks, in order to facilitate integration of EVs and DGs into the EPS. Also for some challenges, the dissertation presents solutions to analyze and overcome related difficulties. To study consequences of integrating EVs, a comprehensive model of EV operation is presented. The model covers different modes of operation and includes impact of battery degradation during the operation. The model is then extended to control a large group of EVs efficiently. Several possible ancillary services which could be offered by EVs, including voltage and frequency regulation services, are discussed. Several systematic frameworks are developed to engage EVs in provision of ancillary services, from economical and technical view points. Simulation results clearly indicate EVs ability to participate in ancillary services and possible revenue stream for EV owners. In terms of DGs, the dissertation addresses a common issue in most of utility companies and that is the risk of unintentional islanding of interconnected DGs. A systematic procedure is presented in this dissertation which can detect any possible operating conditions leading to an unintentional islanding of DGs. The developed procedure can serve utility companies as an analytical tool for any interconnection study, in a timely and costly efficient manner. The procedure is not dependent on the anti-islanding schemes nor DG technologies. Simulation results of different real case studies prove the generality and practicality of the procedure