Dynamic Modeling, Simulation and Control of a Small Wind-Fuel Cell Hybrid Energy System for Stand-Alone Applications

G

raduate Student Seminar : Master of Engineering June 29, 2004

Mohammad Jahangir Khan

[email protected] Faculty of Engineering & Applied Science Electrical Engineering 1

Outline

     

Introduction

• Renewable Energy, Hybrid & Stand-alone Power Sources • Emerging Technologies, Scope of Research

Pre-feasibility Study

• Load, Resource, Technology Options • Sensitivity & Optimization Results

Model Formulation

• Wind Energy Conversion System, Fuel Cell System, Electrolyzer, Power Converter • System Integration

Simulation Results

• Random Wind Variation • Step Response

Conclusion

2

• •

Canada and the Global Energy Scenario

At present, proportion of renewable energy in the global energy mix is about 14 % only.

Various environmental regulations and protocols aim at increasing this ratio towards 50% by 2050.

Source: German Advisory Council on Global Change Introduction 3

• •

In Canada, utilization of renewable resources is less than 1 % (excluding hydroelectricity) Vast wind energy potential is mostly unexplored.

Source: The Conference Board of Canada Introduction Source: Natural Resources Canada 4

Emerging Technologies in Energy Engineering

• •

Wind and Solar energy technologies are the forerunners Hydrogen based energy conversion bears good potential Source: Worldwatch Institute Introduction Source: Plug Power Inc., NY 5

Hybrid Energy Systems in Stand-alone Applications

• • • •

Energy from a renewable source depends on environmental conditions In a Hybrid Energy System, a renewable source is combined with energy storage and secondary power source(s). Mostly used in off-grid/remote applications Could be tied with a distributed power generation network.

Introduction 6

• • • • • •

Wind-Fuel Cell Hybrid Energy System

A wind turbine works as a primary power source Availability of wind energy is of intermittent nature Excess energy could be used for hydrogen production by an electrolyzer During low winds, a fuel-cell delivers the electrical energy using the stored hydrogen Radiated heat could be used for space heating Power converters and controllers are required to integrate the system Introduction 7

Scope of Research



Q1. Is a wind-fuel cell hybrid energy system feasible for a given set of conditions?

• •

Pre-feasibility Study Site: St. John’s, Newfoundland.



Q2. What are the alternatives for building and testing a HES, provided component cost is very high and technology risk is substantial?

• •

Computer aided modeling System integration and performance analysis through simulation Introduction 8

Pre-feasibility Study

Investigation of technology options, configurations and economics using

:

• • • • • •

Electrical load profile Availability of renewable resources Cost of components (capital, O&M) Technology alternatives Economics & constraints HOMER (optimization software) 9

HOMER Implementation

• • • •

St. John’s, Newfoundland Renewable (wind/solar) & non renewable (Diesel generator) sources Conventional (Battery) & non conventional (Hydrogen) energy storage Sensitivity analysis with wind data, solar irradiation, fuel cell cost & diesel price.

Pre-feasibility Study 10

Electrical Load

• • • • •

A typical grid connected home may consume around 50 kWh/d (peak 15 kW) A HES is not suitable for such a large load Off-grid/remote homes should be designed with energy conservation measures A house with 25 kWh/d (4.73 kW peak) is considered Actual data is scaled down Pre-feasibility Study Source: Newfoundland Hydro 11

• •

Renewable Resources

Hourly wind data for one year at St. John’s Airport.

Average wind speed in St. John’s is around 6.64 m/s.

• •

Hourly solar data for one year at St. John’s Airport.

Average solar irradiation in St. John’s is around 3.15 kWh/d/m 2 .

Pre-feasibility Study 12

Components

• • • • • • •

Wind turbine Solar array Fuel cell Diesel generator Electrolyzer Battery Power converter Pre-feasibility Study 13

Sensitivity Results

• •

At present, a wind/diesel/battery system is the most economic solution Solar energy in Newfoundland is not promising Pre-feasibility Study 14

• •

A wind/fuel cell/diesel/battery system would be feasible if the fuel cell cost drops around 65%.

A wind/fuel cell HES would be cost-effective if the fuel cell cost decreases to 15% of its present value Pre-feasibility Study 15

Optimization Results

Considering :

• • •

wind speed = 6.64 m/s solar irradiation = 3.15 kWh/m 2 /d Diesel price = 0.35 $/L The optimum solutions are: Pre-feasibility Study 16

Wind-Fuel Cell System Optimization

Pre-feasibility Study 17

Model Formulation

Models Developed for:

• • • •

Wind Turbine (7.5 kW): Bergey Excel-R PEM Fuel Cell (3.5 kW): Ballard MK5-E type Electrolyzer (7.5 kW): PHOEUBS type Power Converters (3.5 kW)

Approach:

• • •

Empirical & physical relationships used Components are integrated into a complete system through control and power electronic interfaces Simulation done in MATLAB-Simulink ® 18

Wind Energy Conversion System (WECS)

     Small wind turbine: BWC Excel-R type Wind field

• •

Rotor aerodynamics Spatial Filter Induction Lag PM DC generator

• •

Controller Reference speed generator Fuzzy logic controller Model Formulation 19

Small WECS

Power in the wind: Captured power:

P wind



1



A wt V 3 wind 2 P a



C p 1



A wt V 3 eff 2

Power Diameter Hub-height Control/Regulation Over-speed Protection Generator Application

50 W ~ 10 KW 1 ~ 7 m ~ 30 m Stall, Yaw, Pitch, Variable speed Horizontal/Vertical furling DC, Permanent Magnet Alternator Stand-alone, Grid connections Model Formulation 20

Small WECS Model Formulation

Wind Field

V wind



V turb



V avg dV turb dt

 

1 T v V turb



m wind ( t )

Spatial Filter & Induction Lag

V filt V wind 0 .

43795 s = 0 .

1918 s 2



1 .



1 1598 .

s 4142



1 .

4142

PM DC Generator

T l



k



I a r E a



k



r



V t _ wt T a



T l



E a



J



L a d



r dt dI a dt

 

R a I a B

 Model Formulation

V eff V filt



a i s



s



1



1 1



1

21

Controller Design

Control Problem

I.

II.

Below rated wind speed:

Extract maximum available power

Near-rated wind speed:

Maintain constant rated power

III.

Over-rated wind speed :

Decrease rotor speed (shut-down)

I II III

 

Control method

 A PD-type fuzzy logic controller (FLC) is employ Reference rotor speed is estimated from rotor torque Difference in actual & ref. Speed is used to control the dump load Model Formulation 22

   

Determination of Ref. Rotor Speed

Rotor torque is assumed available Below rated reference rotor speed: 

ref



T a ' k T



k w T a '

Near-rated conditions: 

ref

 

ro

Over-rated reference rotor speed: 

ref



P max T a '

Model Formulation 23

Design of Fuzzy Logic Controller

A PD type FLC is used for the whole range of wind variation

Variable Identification:

Error & Rate of change of error

Fuzzification:

Five Gaussian membership functions for all variables

Rules of inference:

Fuzzy Associative Memory

Defuzzification:

Centroid method (Mamdani) Model Formulation 24

Summary

    Dynamic model of a Small wind turbine (BWC Excel-R type) Wind field, Rotor aerodynamics, PM DC generator Controller (Reference speed generator, Fuzzy logic controller) Mechanical sensorless control (rotor torque assumed estimable) Model Formulation 25

Fuel Cell System

      PEM fuel cell: Ballard MK5-E type Empirical & physical expressions Electrochemistry Dynamic energy balance Reactant flow Air flow controller Model Formulation 26

 

PEM Fuel Cells

Polymer membrane is sandwiched between two electrodes, containing a gas diffusion layer (GDL) and a thin catalyst layer. H 2 H 2 O 2 O 2 Conductive plates Flow channels Gas diffusion layer Catalyst later Electrolyte Electric load H 2 The membrane-electrode assembly (MEA) is pressed by two conductive plates containing channels to allow reactant flow.

FuelI In O 2 H 2 2e Load Positive Ion Oxidant in 1/2O 2 H 2 O Negative Ion H 2 O Depleted Fuel Anode Cathode Electrolyte Depleted oxidant Model Formulation 27

  

Fuel Cell Model Formulation

Electrochemical Model

 Cell voltage & Stack voltage:

V cell V stack

 

E Nernst N fc V cell

 

act

 

ohmic

Open circuit voltage:

E Nernst = 1 .

229



8 .

5 × 10 3 (T fc 298 .

15 )+ RT fc 2 F ln



p ' H 2

 

O 2 0 .

5

 Activation overvoltage:

V act

  

act dV act dt I fc = C dl V act R act C dl E Nernst C dl R act I fc R int + V cell -

Ohmic overvoltage 

ohmic



fc R int

Model Formulation 28

Reactant Flow Model

 Performance depends on oxygen, hydrogen & vapor pressure  Anode & Cathode flow models determine reactant pressures  Ideal gas law equations and principles of mole conservation are employed

V RT dP g dt • m out = = • m in k(P g • m out P amb ± ) I nF

Model Formulation 29

Thermal Model

 Fuel cell voltage depends on stack temperature   Stack temperature depends on load current, cooling, etc.

Total power (from hydrogen) = Electrical output + Cooling + Surface Loss + Stack Heating  A first order model based on stack heat capacity is used

C t _ fc dT fc ' dt = • Q stack_ fc C t _ fc dT fc ' dt = P tot _ fc



P fc



• Q cool _ fc



• Q loss _ fc

Total power Surface heat loss Stack heating Electric power Cooling system heat removal Model Formulation 30

Summary

   Dynamic model of a PEM fuel cell (Ballard MK5-E type) Electrochemical, thermal and reactant flow dynamics included Model shows good match with test results Model Formulation 31

Electrolyzer

    Alkaline Electrolyzer: PHOEBUS type Empirical & physical expressions Electrochemistry Dynamic energy balance Model Formulation 32

Alkaline Electrolyzer

  Aqueous KOH is used as electrolyte Construction similar to fuel cell Model Formulation 33

Electrolyzer Model Formulation

Electrochemical Model

 Cell voltage:

U cell



U rev



r 1



r 2 T elz A elz I elz



s log t 1



t 2 / T elz



t 3 / T elz 2 A elz I elz



1

 Faraday efficiency: 

F



f 1



I



elz



I / elz A elz /



2 A elz



2 f 2

 Hydrogen production: 

n H 2

 

F N elz zF I elz

Thermal Model

C t _ elz dT elz dt = • Q stack_ elz • Q gen _ elz



• Q stack_ elz



• Q cool _ elz



• Q loss _ elz

Model Formulation 34

• • • •

Power Electronic Converters

•

Variable DC output of the Wind turbine/Fuel cell is interfaced with a 200 V DC bus Load voltage: 120 V, 60Hz Steady state modeling of DC-DC converters Simplified inverter model coupled with LC filter PID controllers used Model Formulation 35

Power Converter Models

 WECS Buck-Boost Converter

V bus V t _ wt



1 D wt



D wt

 Inverter, Filter & R-L Load  Fuel Cell Boost Converter

V bus V stack



1



1 D fc

Model Formulation 36

System Integration

Wind-fuel cell system interconnection Model Formulation Power flow control Start Wind Power Load Power Wind Power-Load Power Deficit Power Fuel Cell N Positive Y Excess Power Electrolzyer End 37

MATLAB-Simulink

®

Simulation

38

  

Simulation

Simulation time = 15 seconds Constant temperature in fuel cell & electrolyzer assumed

• • •

Step changes in Wind speed Load resistance Hydrogen pressure Simulation 39

Results

System response with random wind

Results 40

WECS performance (step response)

Results 41

Power balance (step response)

Results 42

Fuel cell performance (step response)

Results 43

Electrolyzer performance (step response)

Results 44

Power converter performance (step response)

Results 45

Summary

   Highest settling time for the wind turbine Controlled operation of the wind turbine, fuel cell, electrolyzer and power converter found to be satisfactory Coordination of power flow within the system achieved 2.5

2 1.5

1 0.5

0 WECS Fuel Cell Component Power Converter 46

Contributions

 For a stand-alone residential load in St. John’s, consuming 25 kWh/d (4.73 kW peak) a pre-feasibility study is carried out.  A mathematical model of wind-fuel cell energy system is developed, simulated and presented. The wind turbine model employs a concept of mechanical sensorless FLC.

 The PEM fuel cell model unifies the electrochemical, thermal and reactant flow dynamics.

 A number of papers generated through this work. Explored fields include:

•

Wind resource assessment

• • •

Fuel cell modeling Grid connected fuel cell systems Small wind turbine modeling 47

Conclusions

 A wind-fuel cell hybrid energy system would be cost effective if the fuel cell cost reduces to 15% of its current price. Cost of energy for such a system would be around $0.427/kWh.

 Performance of the system components and control methods were found to be satisfactory.  Improvement in relevant technologies and reduction in component cost are the key to success of alternative energy solutions.

48

Further Work

     Development of a faster model for investigating variations in system temperature and observing long term performance (daily yearly).

Inclusion of various auxiliary devices into the fuel cell and electrolyzer system.

Use of stand-by batteries Research into newer technologies such as, low speed wind turbines, reversible fuel cell etc.

Comprehensive study of relevant power electronics and controls 49

Acknowledgement

      Faculty of Engineering & Applied Science, MUN.

School of Graduate Studies, MUN.

NSERC Environment Canada Dr. M. T. Iqbal.

Drs. Quaicoe, Jeyasurya, Masek, and Rahman.

Thank You

For your attention & presence

Questions/Comments

50