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Modelling Energy Demand for a Fleet of Hydrogen-Electric Vehicles Interacting with a Clean Energy Hub, International Conference on Hydrogen Production (ICH2P) 2009

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    Notes on slide 1

    Good morning, and welcome to my presentation.My name is Faraz Syed and I’m from the Green Energy & Fuel Cell Lab at the University of Waterloo. I’m a Masters student there under the supervision of Dr. Michael Fowler, and my presentation today will be about vehicle fleet modelling.A lot of work has been done in modelling individual hybrid vehicles to optimize their fuel efficiency (and this has helped in their success), but we are starting to get to the point where we want to know what will happen when thousands, or possibly millions, of electric-drive vehicles start plugging in to the grid. What changes need to be made to accommodate them? To answer that, I created a model with about 4,000 plug-in hydrogen fuel cell vehicles. These vehicles travel during the day and charge at night, and the electricity they receive comes from 10 wind turbines.

    We build energy hub models in our group. Some past papers from our group have even analyzed a network of energy hubs for optimal power flow.This is in general what they look like. Remember that they are an interface between supply and demand. For the purposes of this presentation, my only supply is wind power, and my only demand is transportation.The previous work has even tried to model energy hub performance connected to transportation, but used assumed load profiles. I create a vehicle fleet model to generate a realistic profile.(explain diagram)

    I’ve assumed that there each vehicle has a very simple behaviour … from 12 am to 7 am (the off peak hours) they charge and refuel, and between the charging periods we have travelling periods, which is where the daily travel happens. This is really simple behaviour, but I chose to start here because this is the desired behaviour for most of the PHEVs. Future work would focus on probability-based, or stochastic, charging, where time-of-use curves would generate random charging events based on a probability distribution.

    This graph shows what happens to the ESS between charging and travelling periods, and exposes another assumption … all travel occurs at once.Change to show non-complete depletion (possibly copy a MATLAB figure)

    Talk about refueling! Maybe have a cycle chart showing daily operation between charging & travelling

    Vehicle parameters loosely based on plug-in fuel cell Chevy Volt concept unveiled by GM in Shanghai, 2007

    Rerun with variable electricity supply!

    All of this work should help us understand the impact these technologies will have on our energy system as they begin to penetrate the market.

    So on to the outline of my presentation. I’ll give a quick introduction to provide a context for my work, and then I’ll jump into Model Development, after which I will present the results of a sample case, and then end the presentation with Conclusions

    One of the research themes in our group is to look at energy systems with a holistic view … that is, to look at the entire energy life cycle, and how emerging technologies shape it.The electricity sector is changing. New technologies are having an effect on the electricity sector, and I’m going to mention a few of these:On the supply side, we’re seeing an increased use of distributed power generation (i.e. not big power plants), and we’re seeing increasing use of renewable energy, which is an intermittent source.On the demand side, obviously we’re seeing increased demand due to population growth, but we’re also going to see increasing electricity demand for transportation.

    I think we all know that hydrogen has a big role to play in shaping the future. There are lots of benefits, but I’ll go over the important ones for this presentation. (go over slide)

    Hydrogen-based transportationWhen I say “increasing electricity demand for transportation”, I’m talking about the evolution of the vehicle. Most automotive experts agree that there is a progression between different types of advanced vehicles, and they follow this path.With this evolution, transportation will have strong interactions with the electricity sector.

    Hydrogen-based transportationWhen I say “increasing electricity demand for transportation”, I’m talking about the evolution of the vehicle. Most automotive experts agree that there is a progression between different types of advanced vehicles, and they follow this path.With this evolution, transportation will have strong interactions with the electricity sector.

    Hydrogen-based transportationWhen I say “increasing electricity demand for transportation”, I’m talking about the evolution of the vehicle. Most automotive experts agree that there is a progression between different types of advanced vehicles, and they follow this path.With this evolution, transportation will have strong interactions with the electricity sector.

    Hydrogen-based transportationWhen I say “increasing electricity demand for transportation”, I’m talking about the evolution of the vehicle. Most automotive experts agree that there is a progression between different types of advanced vehicles, and they follow this path.With this evolution, transportation will have strong interactions with the electricity sector.

    So this is going to lead to a new integrated energy system, and electricity and hydrogen will be the primary energy carriers in this system. Everything is going to demand either electricity, or hydrogen.This … the integrated energy system, is our focus. We want to know what it will look like. It’s going to be very complex … first of all transportation will be part of the mix, because it’s going to use a lot of electricity and hydrogen. So sizing is one issue. Secondly, there will be a lot of communication and demand management back and forth to minimize demand variability, and this will affect your sizing. So this is an iterative process.

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    Modelling Energy Demand for a Fleet of Hydrogen-Electric Vehicles Interacting with a Clean Energy Hub, International Conference on Hydrogen Production (ICH2P) 2009 - Presentation Transcript

    1. Modelling Energy Demand for a Fleet of Hydrogen-Electric Vehicles Interacting with a Clean Energy Hub Fowler, David Wan, Yaser Faraz Syed*, Michael Maniyali Green Energy & Fuel Cell Lab, Chemical Engineering University of Waterloo International Conference on Hydrogen Production, Oshawa. May 3rd – 6th 2009
    2. Presentation Outline 1. Introduction 2. Model Development 3. Results 4. Conclusions 5. Future Work 2
    3. Introduction • Electricity: changes on supply-side: • Increasing use of distributed power generation • Increasing use of renewable energy (intermittent) • Electricity: changes on demand-side: • Anticipated population growth • Increasing electricity demand for transportation 3
    4. Role of Hydrogen • Hydrogen will be important for supply- and demand-side issues • Supply side: • Electricity storage for peak load shaving & renewable enabling • Demand side: • Hydrogen-based transportation (reduced impact, increased energy security) 4
    5. Electrification of Transportation Conventional Vehicles PFCVs • Hydrogen PHEVs & electricity • Electricity, some HEVs gasoline • Gasoline CVs • Gasoline 5
    6. Electrification of Transportation Hybrid Electric Vehicles PFCVs • Hydrogen PHEVs & electricity • Electricity, some HEVs gasoline • Gasoline CVs • Gasoline 6
    7. Electrification of Transportation Plug-in Hybrid Electric Vehicles PFCVs • Hydrogen PHEVs & electricity • Electricity, some HEVs gasoline • Gasoline CVs • Gasoline 7
    8. Electrification of Transportation Plug-in Fuel Cell Vehicles PFCVs • Hydrogen PHEVs & electricity • Electricity, some HEVs gasoline • Gasoline CVs • Gasoline 8
    9. Integrated Energy System & Hubs • New integrated energy system (also called the hydrogen economy) likely • Energy hubs will form interface between supply & demand to provide: • Electricity storage for peak load shaving & renewable enabling • Demand-side management (e.g. PHEV charging) 9
    10. Model Development: Clean Energy Hub Electricity Supply Electricity system (10 wind turbines @ 20MW total capacity) E  H2 H2  E Vehicle fleet H2 storage (4,000 vehicles) Legend Hydrogen system Electricity Hydrogen Clean Energy Hub Energy Demand Schematic of systems and energy interactions in the clean energy hub model 10
    11. Model Logic for Clean Energy Hub If Supply > Demand If Demand > Supply Store excess Generate electricity electricity as hydrogen from hydrogen 11
    12. Model Development: Fleet • Fleet consists of 4,000 hydrogen-electric vehicles • Bottom-up approach: individual vehicle actions were simulated • Review of existing vehicle models (e.g. PSAT & CRUISE): • each component is modelled • intended for vehicle designers • too complex & computationally expensive 12
    13. Vehicle architecture represented in PSAT 13
    14. Fleet Model: Architecture • Developed simplified vehicle architecture • Designed to be generic & applicable to variety of real vehicle architectures • Two (2) energy inputs (electricity & hydrogen) • Two (2) energy storage devices (ESS & HSS) • Two (2) energy conversion devices 14
    15. E  KE Electricity system ESS Wheels H2  KE Hydrogen system HSS Vehicle Legend Electricity Hydrogen Kinetic Energy Simplified vehicle architecture used for fleet model 15
    16. Fleet Model: Architecture • Electricity Storage System (ESS) parameters: • Capacity [kWh] • State-of-charge (SOC) [%] • Hydrogen Storage System (HSS) parameters: • Capacity [kg] • Amount stored [kg] 16
    17. Charge depleting Charge sustaining ESS State of Charge (%) HSS storage (kg) Distance travelled Energy usage during travel modes 17
    18. Fleet Model: Daily Operation Charging Travelling period period 12 am – 7 8 am – 11 pm am 18
    19. Fleet Model: Daily Operation • Charging period is modelled every time- step (1 hr) • Travelling period is modelled over entire period 19
    20. travelling charging travelling charging 100% SOC [%] 50% 0% 0 6 12 18 24 30 36 42 48 54 Simulation Time [h] Charging & travelling period demonstration 20
    21. Smart Charging/Charging Strategy • Different charging strategies can be used: • Full-power charging • Minimum-power charging • Full-power charging is simplest, limited only by charging station power • Minimum-power charging targets full ESS charging over entire charging period 21
    22. Fleet Model: Travel Simulation Energy Daily travel depletion distance function • Daily travel distance (i.e. driver behaviour) is an input • Stochastic model in place of actual data • Gaussian distribution with mean of 30 km & standard deviation of 1 km 22
    23. START NO Does the given travel distance exceed Deplete the ESS accordingly the charge depleting range? YES Deplete the ESS completely and subtract charge depleting range from travel distance NO Does remaining distance exceed the Deplete the HSS accordingly charge sustaining range? YES Deplete the HSS completely and subtract charge sustaining range from travel distance Return total distance travelled STOP Energy depletion function for ESS & HSS 23
    24. Other Model Parameters Parameters for an individual fuel cell electric vehicle Parameter Value Unit ESS capacity 10 kWh ESS initial SOC 100 % HSS capacity 4 kg HSS initial mass 4 kg Charge-depleting electricity 6.5 km/kWh consumption Charge-sustaining 70 km/kg hydrogen consumption Maximum charging station 1.65 kW power 24
    25. Simulation • Simulation run for 7-day period in January • Two scenarios compared: • Case A: Full-power charging • Case B: Minimum-power charging 25
    26. 8.0 7.0 6.0 5.0 Power [MW] 4.0 3.0 2.0 1.0 0.0 12 AM 1 AM 2 AM 3 AM 4 AM 5 AM 6 AM Simulation Time [h] Full-power charging Minimum-power charging Wind Power Sample case electricity demand and supply profiles during charging 26
    27. Simulation Results • Demand exceeded supply in both scenarios • hydrogen system filled the supply deficit to ensure supply reliability. • Effect of switching from full-power to minimum-power charging strategy: • 14.6% decrease in peak demand • 40.8% decrease in supply deficit • electricity generation capacity of hydrogen system can be reduced by up to 40.8% as well 27
    28. Conclusions • Developed a “bottom-up” fleet model for hydrogen-electric vehicles (PFCVs) • Model output: fleet load profile (electricity & hydrogen) • Demonstrated smart-charging simulation through charging strategy 28
    29. Future Work • PHEV fleet modelling: gasoline instead of hydrogen • Per-hour travel modelling • Need better driver behaviour data • Stochastic charging: no fixed charging period for fleet 29
    30. Thank You Questions? Faraz Syed Chemical Engineering University of Waterloo f2syed@uwaterloo.ca

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