Program Topic: EV Growth, Impact & Integration
- F4-1 – Friday 15:20-17:00
- 15:20 – A Novel Feature Fitting Simulation Algorithm for Estimating Electric Vehicle Demand
- 15:40 – Propagation of Electrical Disturbances to Automotive Batteries in Vehicle-to-Grid Context
- 16:00 – Funding an Electric Vehicle Charging Infrastructure From Associated Health Benefits
- 16:20 – The Effect of PEV Uncontrolled and Smart Charging on Distribution System Planning
- 16:40 – Integration of Electric Vehicles Into a Smart Power Grid: A Technical Review
F4-1 – Friday 15:20-17:00
15:20 A Novel Feature Fitting Simulation Algorithm for Estimating Electric Vehicle Demand
Electric vehicles will drive the future, therefore forecasting and simulating ‘transportation electrification’ demand over the coming years has become important for utilities. Ever since electricity was commercialized there has been a need for demand forecasting and simulation because electricity provider’s ability to produce energy far exceeds their ability to store energy. This is an industry worth billions of dollars and therefore even a marginal improvement in the way it’s predicted can have a great impact. Demand forecasting is critical for unit commitment and broadly effects the wholesale electricity market price. With the addition of transportation electrification this process has become even more challenging. Traditional ways are hard to model and computationally intensive, nowadays this type of problem is of great interest in the field of machine learning as well, because of the availability of large datasets from utilities. However datasets for transportation electrification still remain a huge challenge therefore more work needs to be done in forecasting electric vehicular loads. This paper tackles this new problem and presents a new method called Feature Fitting Simulation Algorithm to estimate electric vehicle charging demand profiles. The simulation was performed on MATLAB and Excel using various tools and functions to ensure the algorithms run on optimum efficiency. The novel feature of the algorithm is its hybrid structure of considering both historical and simulation data for temporal predication, secondly it introduces two key variables scaling and sensitivity to better control the time series output. FFSA is vetted against machine learning algorithms and the results indicate a better performance achieved by FFSA.
15:40 Propagation of Electrical Disturbances to Automotive Batteries in Vehicle-to-Grid Context
This paper investigates the effects of disturbances originating in the electric grid as well as residential appliance inrush currents on the integrity of battery packs in electric vehicles that are connected to the grid or a residence for the purpose of V2G or V2H service. Simulation results show that the effect on battery capacity loss was negligible. The large size of an automotive battery pack allows it to easily withstand the levels of current caused by typical grid based disturbances and appliance inrush currents. Thus, power grid disturbances as they exist, need not be considered a reason to refrain from employing an electric vehicle for V2G or V2H service.
16:00 Funding an Electric Vehicle Charging Infrastructure From Associated Health Benefits
This report looks at the health benefits associated with driving an electric vehicle, and determines the cost effectiveness of using these benefits to fund the expansion of electric vehicle charging infrastructure between 2016 and 2021. Damages related to poor air quality such as sick days and increased chronic illness were quantified, and reductions in instances of these issues resulted in a benefit that can be reaped by governments and municipalities. The result indicate that the benefits associated with improved air quality in general far exceed the costs of installing additional charging stations. The findings indicate that the overall outlook for future expansion of charging stations can be borne by the improved air quality provided by an increasing movement away from traditional internal combustion engine vehicles and towards electrified mobility options.
16:20 The Effect of PEV Uncontrolled and Smart Charging on Distribution System Planning
This paper presents a planning model for distribution systems considering various energy supply options such as distributed generation (DG), substations, and feeders. In addition, the impact of Plug-in-Electric Vehicle (PEV) uncontrolled and smart charging loads on the plan outcome is evaluated. A new optimal power flow (OPF) based optimization model is proposed to schedule PEV uncontrolled and smart charging loads. Test results are presented to demonstrate the effectiveness of the proposed model. The results show that PEV charging loads significantly affects the plan outcomes.
16:40 Integration of Electric Vehicles Into a Smart Power Grid: A Technical Review
Electrification of a transportation system is one of the most promising alternatives to mitigate the dependency of urban life to fossil fuels. However, introducing a large number of grid-connected vehicles reveals technical problems affecting the entire power system specially the low voltage distribution power grid. In this context, this paper presents a review of technical challenges associated with the integration of Vehicle-to-Grids (V2Gs). These challenges could be studied in several subsections of a power system such as the operation of power electronics equipment, supply-demand imbalance, and impacts on voltage and frequency. In addition, to clarify the concept of smart grid in a power system, a new definition of a smart power grid in the sector of power distribution is elaborated considering the effects of V2Gs. This developed definition specifies that the penetration of V2Gs, in fact, establishes an opportunity for implementing the smart power distribution through offering renewable energy storages, two-way communication, and reactive and active power injections to the grid. This review may be regarded as an important basis for the investigation of future challenges in the integration of V2Gs into a smart grid.