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Abstract-- This paper discusses the development of a

simulation test bed permitting the study of integrated renewable energy generators and controlled distributed heat pumps operating within distribution systems. The test bed is demonstrated in this paper by addressing the important issue of the self-regulating effect of consumer-owned air-source heat pumps on the variability induced by wind power integration, particularly when coupled with increased access to demand response realized through a centralized load control strategy. Index Terms -- Distribution power-flow analysis, wind power, distributed heat pumps, demand response, GridLAB-D.

I. INTRODUCTION OVEL modeling and simulation methods of integrated hybrid energy systems are fundamental to the development of effective operational strategies of various

distributed clean energy resources in next generation distribution systems. For example, increased penetration of wind power into distribution networks will lead to an increased requirement in co-located real-time balancing services, which is provided conventionally by different kinds of existing resources, such as thermal generators and storage. Through pairing power system operation with real-time networking capabilities, the opportunity to involve electric loads in the provision of the regulation service may in fact provide a cleaner, more efficient method to solve problems caused by renewable energy production variability [1]. The critical simulation issues include encompassing dynamic load models within distribution system models that look to investigate potential demand response (DR) control strategies aimed at the minute-to-minute time-scales over which renewable energy generators (wind, solar, etc.) display considerable variability. The focus of this paper is to introduce and assess a smart-grid community simulation platform that efficiently captures the dynamics of all participating resources within typical distribution-level power flow analysis, through coupling the powerful solution methods of GridLAB-D [2], with the data exchange and post-processing capabilities of MATLAB [3]. Dan Wang, Braydon de Wit, Simon Parkinson, Curran Crawford and Ned Djilali are with the Institute for Integrated Energy Systems, University of Victoria, Victoria, British Columbia, V8W 3P6, Canada (e-mail: danwang@uvic.ca, braydond@uvic.ca, scp@uvic.ca, curranc@uvic.ca, ndjilali@uvic.ca). David Chassin and Jason Fuller are with the Energy Science and Technology Division, Pacific Northwest National Laboratory, Richland, WA 99352 USA (e-mail: david.chassin@pnl.gov, jason.fuller@pnl.gov).

GridLAB-D is open-source software developed by the U.S. Department of Energy (DOE) at Pacific Northwest National Laboratory (PNNL), which focuses on smart distribution system simulations [2]. Particularly for distribution-level power flow calculations, GridLAB-D supplies a detailed three-phase modeling platform, and several robust power flow algorithms such as Newton-Raphson and FBS methods. It further supplies various interfaces to investigate the steady-state features of the distribution grid components, such as bus injections, line power flow, or bus voltages within time-varying simulation conditions. The purpose of this paper is to develop a more flexible and open simulation environment to investigate different load control strategies used in demand response programs. A platform is proposed that combines GridLAB-D's distribution power flow functions with an automatic central resource control (ARC) strategy and thermal dynamics modeling for distributed heat pump management [3] supported by MATLAB. The control objective of this centralized authority is to provide regulation-based ancillary services locally within the distribution system, or self-regulate the distribution feeder load. Characteristics of this smart distribution system are investigated and tested within the proposed combined simulation environment. The simulation results reveal how the conventional customer’s power consumption pattern is changed subject to control, and how energy efficiency is increased in such a distribution system. This paper proceeds as follows. The introduction of the simulation structure is given in Section II with the GUI description given in Section III. Operation of the test bed is discussed in Section IV with a preliminary test case presented and modeling results discussed in Section V. The conclusions and future directions are summarized in Section VI.

II. SIMULATION STRUCTURE OF THE TEST BED The test bed is co-simulation-based and shown in Fig#1, wherein all demand-side components are modeled and optimally controlled within the MATLAB environment, with the grid-side power flow process pursued within GridLAB-D. The system-level control can be set to achieve local control objectives at any given bus within the distribution system, or to achieve objectives aimed at controlling the conglomerate of heat pumps distributed inside the given distribution system. For example, these objectives might involve the minimization of wind fluctuations integrated into a specific bus or regulation of the unresponsive loads to minimize power losses on a given line.

A Test Bed for Self-regulating Distribution Systems: Modeling Integrated Renewable Energy and Demand Response

in the GridLAB-D/MATLAB Environment Dan Wang, Member, IEEE, Braydon de Wit, Simon Parkinson, Jason Fuller, Member, IEEE,

David Chassin, Senior Member, IEEE, Curran Crawford, Ned Djilali

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978-1-4577-2159-5/12/$31.00 ©2011 IEEE

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The test bed’s ability to integrate distribution power flow and distributed energy resource control strategies enables us to investigate a series of critical issues for smart community-based operation. Typical points of such topics include: 1) The effects of greater access to highly distributed demand

response resources on the energy efficiency and reliability of supply for power system optimal management. In this case, we are referring to DR with respect to three central control strategy options: (i) DR at the bus-level; (ii) DR at the community-level; (iii) community-based virtual power plant (VPP) modeling for interaction with transmission and sub-transmission operations.

2) The bus-aggregator model implemented within the central control strategy for DR at the bus-level, can be used to investigate the self-regulating feature of the network and to support data exchange within a community-based DR program. Meanwhile, it can be treated as the interface to reconstruct GridLAB-D's time-varying load schedule for the distribution power flow calculation.

3) The system-aggregator is modeled to observe the central control strategy for DR at the community-level, which focuses on global self-regulating features and collects data (organized power-state vector of heat pump) from the individual bus-aggregators. Meanwhile, it can be treated as the interface to support the demand dispatch through virtual power plant modeling.

4) The ability of the virtual power plant model to effectively substitute for regulation and reserve generation (in particular in relation to the integration of intermittent renewable energy resources) thereby permitting lower monetary and environmental costs.

5) The demand dispatch program is required by upper-level system operators. It integrates existing details of the lower-level systems through interfaces with system-aggregator, and is concerned with possible aggregate behavior in providing effective ancillary services to the grid.

MATALB

Thermal Dynamics Modeling Program (TDMP)

Community-based Demand Response

Program(CB-DRP)

Call

SAP

Call

TDM

P

Node-based Demand Response

Program(NB-DRP)

Bus AggregatorProgram

(BAP)

System AggregatorProgram

(SAP)

Call

BAP

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ral c

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ump)

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Climate Data Integration Program(CDIP)

Non-thermostatically Controlled Load Module

Player file

DemandDispatch Program

(DDP)

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Program(CB-VPPP)

Wind Power info

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ontr

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Developed Module Developing Module Data Flow Control Flow

MAIN_RUN Program (MRP)

Call Call Call

GUI

Third-party SupportingSoftware

“Negative”Load Object

GLM file

Uncontrolled Load info

Wind Power info

Update

Orginal

Call

Call of sub-function

Fig#1. Co-simulation structure.

6) Various potential power-level control signals can be collected from the GridLAB-D power flow calculation and analyzed within MATLAB to investigate different types of DR target designs and reconstructions. For example,

identification of power loss changes or voltage fluctuations caused by the operation of an integrated wind generation system, and control strategies to ensure that the solution are always reasonable and optimal. Bus-level aggregation and

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system-level aggregation in fact have the partial ability to minimize the power loss on the distribution lines through self-regulating features. It can also be controlled by a novel optimal central strategy of VPP which reflects the coordination between loads and distributed generation (DG), or directly using a specific hierarchical or distributed optimal control strategy. This will be the focus of future work.

7) The GridLAB-D/MATLAB hybrid platform supplies an open and user-friendly interface to allow extension to more complicated functions.

III. TEST BED GUI The operation of the GUI can be divided into three steps: 1) The main program (MRP) starts by setting up all

necessary variables for the node-based DR program (NB-DRP) which is developed based on the control strategy in [4], which will call the bus-aggregator program and heat pump thermal dynamics modeling module (TDMP) and then prompt the user for input with a GUI shown in Fig#2. The test bed integrates GridLAB-D with MATLAB to extract data for the following objects and their corresponding properties [2]:

TABLE I OBJECT AND PROPERTIES Object Corresponding properties Nodes: maximum voltage error, voltage,

current, power, and shunt Loads constant power Transformers power in, power out, power losses,

current in, current out Regulators power in, power out, power losses,

current in, current out Overhead Lines/ Underground Lines power losses

Meanwhile, the user can set up the simulation time interval and time duration information.

Fig#2. GUI to extract data for the first step

2) After Done is selected, a second GUI shown in Fig#3 will appear for selecting nodes to extract data from, based on a visible grid structure. For each node selected, a CSV file will be created containing values for node properties including maximum voltage error, voltage, current, power, and shunt.

Fig#3. GUI to extract data based on a visible grid structure for second step

3) After Done is again selected, the program will execute the co-simulation shown in Fig#4. Throughout this process, CSV files for each object that was selected in the GUI shown in Fig#2 are generated based on the desired output data. Additional CSV files of bus magnitude and phase will be created for properties containing complex values.

IV. TEST BED OPERATIONAL LOGIC The operational logic used by the test bed and can be divided into three sections including User Input, Co-simulation, and Data Output. In the case of the bus-level DR control strategy, a overview of the execution flow diagram is depicted in Fig#4, and described below:

Original GridLAB-D Modulation Information - Feeder grid layout- Initial voltage, capacity, current, and power levels

Initialization- Set 3-phase distribution topology- Set power condition at each node(thermal parameters, rated power, machine states)

If start_date == end_date

End

True False

Update GLM file, including:- Model Specifications based BAP- Recorder for each object/node selected

Call GridLAB-Dand Run GLM file

Append recorded CSV data to previous data

Increment start_date by 1 minute

Convert complex numbers to real, imaginary, magnitude, and phase

Plot data for objects/ nodes selected

MATLAB GUI- Define: Start Date, End Date, Time Interval- Select which objects to extract data from: Node, Transformer, Overhead Line, Underground Line, ect.- Select which nodes to analyze data for

Control Part:- Collect unorganized power-state vector (BAP)- Implement to demand response control (NB-DRP)

T=T+1

Original Climate Modulation Information - Outdoor temperature- Wind Speed

Thermal dynamics modeling (TDMP)

Data Output Co-simulation

User Input

T=1

Fig#4. The logic used by test bed is shown above and can be thought of in three sections including user input, a loop containing minute-by-minute GridLAB-D simulations, and data output.

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1) The modified IEEE#13 node feeder case with residential heat pumps and installed wind generator was created by MRP and is used for the power flow module of GridLAB-D. In the case of the IEEE 13 node test case, this test bed creates a co-simulation platform combined with two input files,

• IEEE_13_mod.glm: Original grid layout and parameters; • Climatedata.mat: Wind speed data and outdoor temperature

taken from [5].

2) The MATLAB GUI is started by the user at the beginning of the simulation, followed by an initialization process for the first time step, T = 1. This includes generating the building heat-pump population and allowing it to reach steady-state conditions at the initial outdoor temperature. The user is able to modify building parameters within the MATLAB script. The three-phase distribution topology and power conditions at each node are then initialized.

3) For the next simulation step (T=T+1), the bus aggregator program (BAP) is started and called by the NB-DRP. The power state vector of machines for the current time step are collected and organized to be used within the centralized comfort-constrained control method proposed in [3]. This process will determine the machine’s operational status for the next step.

4) The TDMP is formed and computes the actual power consumption for the load group.

5) BAP updates the load schedule of GridLAB-D's power flow module through a player file, which is a flexible interface to make real-time data changes for GridLAB-D's objects [2], and further prepares the necessary GLM input files for GridLAB-D.

6) MRP calls GridLAB-D to simulate the distribution feeder power flow for the T+1 interval. Once the simulation is finished, all results required are saved as CSV files and MATLAB-based plotting files.

7) The start time set by the GUI is incremented by one time step (1 minute in this paper)

8) Steps (3)–(7) are repeated until the start time is not equal to the end time. Finally, two functions of data output are performed in the test bed. GridLAB-D is used to simulate the whole distribution feeder power flow and seven file groups can be obtained depending upon specific output requirements for the slack bus, node, regulator, underground line, overhead line, load, and transformer.

For the output of plots, the user would be prompted to answer whether or not they would like to plot data including: voltage magnitudes, loads, power losses, wind power, normal load values, and the thermal dynamics values corresponding to individual heat pumps.

MAIN_RUN Program(MRP)- Calculates outputs with the heat pump controller off and on

Original GridLAB-D Modulation File File: IEEE_13_mod.glm- IEEE13 Grid layout- Original voltage, capacity, current, power levels etc...

Climate Data File: ClimateData1.mat

Load CSV filesFile: IEEE_13_load_###_properties_controlled.csvFile: IEEE_13_load_###_properties_uncontrolled.csv

Node CSV filesFile: IEEE_13_node_###_properties_controlled.csvFile: IEEE_13_node_###_properties_uncontrolled.csvFile: IEEE_13_node_###_magnitude_phase_controlled.csvFile: IEEE_13_node_###_magnitude_phase_uncontrolled.csv

Slack Bus Node CSV filesFile: IEEE_13_node_650_properties_controlled.csvFile: IEEE_13_node_650_properties_uncontrolled.csvFile: IEEE_13_node_650_magnitude_phase_controlled.csvFile: IEEE_13_node_650_magnitude_phase_uncontrolled.csv

Transformer CSV filesFile: IEEE_13_substation_transformer_properties_controlled.csvFile: IEEE_13_substation_transformer_properties_uncontrolled.csv

Regulator CSV filesFile: IEEE_13_regulator_properties_controlled.csvFile: IEEE_13_regulator_properties_uncontrolled.csv

Overhead Line CSV filesFile: IEEE_13_overhead_line_losses_controlled.csvFile: IEEE_13_overhead_line_losses_uncontrolled.csv

Underground Line CSV filesFile: IEEE_13_underground_line_losses_controlled.csvFile: IEEE_13_underground_line_losses_uncontrolled.csv

PlotsFile: IEEE_13_node_###_magnitude_phase.figFile: IEEE_13_node_650_magnitude_phase.figFile: IEEE_13_node_###_load.figFile: IEEE_13_substation_transformer_power_losses.figFile: IEEE_13_regulator_power_losses.figFile: IEEE_13_underground_line_losses.figFile: IEEE_13_overhead_line_losses.figFile: IEEE_windpower_normaload_heatpump_total.figFile: IEEE_13_slack_bus_power_in.fig

Fig#5. The program requires two input files to run and will provide output CSV files and figures corresponding to selections made by the user.

V. PRELIMINARY TEST CASE DISCUSSION

A. Test case description The IEEE 13 node test-case, as shown in Fig#6 has been implemented within the proposed test bed with comfort-constrained demand response control strategy is utilized to regulate power fluctuations from one 0.5 MW wind turbine integrated into a community consisting of 650 small-to medium-scale buildings. The locations of the buildings within the distribution system have been strategically selected to coincide with the spot-load data given for the 13-node case in [6].

646 645 632 633 634

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Fig#6. IEEE 13 node test Feeder considering heat pump management and wind power integration in MATLAB/GridLAB-D co-simulation environment

In this case, the bus-level control strategy supported by NB-DRP is used for the simulation. Three types of loads are defined: (i) a nominal uncontrolled load NP which describes all electrical energy demand that is not caused by the heat pumps, and is obtained by fitting curves to normalized data for typical

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residential houses found in [7]. Nominal load fluctuations are taken to be normally distributed, with a standard deviation equal to 10% of the off-peak levels; (ii) the heat pump load HP generated from the building-heat pump detailed load models, and (iii) the wind power input WP which is generated using a typical turbine power-curve, using wind speed data taken from [5]. A static outdoor temperature of 8 °C is input to the building models that generate the heat pump load and integrated in Climatedata.mat. For the whole feeder, GridLAB-D's load objects are used to exchange data for three different bus aggregators, the integrated heat pump models, the nominal load, and the wind power, as well as the constant power load for conventional power flow calculation. The idealized total uncontrolled load for each bus can be expressed as:

)()()()( kPkPkPkP WHNL −+= (1) To regulate the load we assume that the k+1 nominal load and wind power output are known to be extracted from Climatedata.mat and Non-thermostatically Controlled Load Modules, and then set the target heat-pump load PH

* so as to minimize deviations from the average total load over the last two sampling intervals, which is created by the NB-DRP and sent to individual load models in MATLAB [3],

)]1()([21)1()1()1(* −+++−+=+ kPkPkPkPkP LLNWH (2)

B. Results analysis Regulation results with wind integration are given in Fig#9, where two scenarios are denoted: with (controlled) and without (uncontrolled) the proposed central comfort-constrained heat pump management strategy. For the uncontrolled scenario, large load fluctuations can be observed at the slack bus (bus 650) seen in Fig#9.(C), and are ill-suited for grid-integration. This may require the use of responsive resources distributed inside the community for smoothing (capacity expansion). Applying the distributed heat pump management strategy proposed in [3], the controlled scenario shows considerable leveling is achieved, with the set-point modulation computed in real-time and sent to the thermostats, given in Fig#9.(D). As can be seen in Fig#9, these small changes to the temperature set-point partially meet the control objective, as considerable smoothing is achieved. Meanwhile the average temperature in each building remains near the customer’s desired comfort conditions over the simulation (maximum change of 0.25oC).

In order to demonstrate the effects of controlled smoothing,

the power gradient dt

dPs (kW/min) at the slack bus over a time

interval tΔ is defined as the following,

tkPkP

dtdP ss

k

s

Δ−+=

+

)()1(

)1( (3)

where sP is the power injection observed at the slack bus given in Fig#9.(C) and can be obtained by finite-differencing the profiles. The result is shown in Fig#7 as a probability density plot over the simulated time horizon. Based on the

bus-level control strategy, the whole feeder is seen to absorb larger fluctuations.

-6 -4 -2 0 2 4 6

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Fig#7. Peak normalized probability density plot of the total load power gradient of slack bus for both scenarios observed in Fig#9 (C).

Fig#8. Peak normalized probability density plot of the total load power gradient of slack bus in [4].

In comparison with the PDF plot shown in Fig#8, the whole feeder can be treated as a self-regulating community to accommodate a high penetration of wind energy, but lacks the effectiveness in filtering fluctuations observed in the case without the distribution system. This is certainly the result of the power flow constraints contained within the distribution system, and weakens the target design given in (2) due to its inability to consider power losses within the system.

Another key point obtained from these results is a change in community-based energy efficiency, which can be modeled as a function of generation GP supplied from the outside grid and

power-gradient dt

dPs caused by wind power fluctuations [8].

The self-regulating community simulated in this test bed has

the capacity to decrease dt

dPs and thus achieve higher energy

efficiency.

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0 200 400 600 800 1000 12000

500

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Fig#9.(A) The wind power data and uncontrolled normal load profiles implemented for power conditions of bus 675.

0 200 400 600 800 1000 12000

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Fig#9.(B) The total heat pump population demand implemented in the two scenarios considered for bus 675.

0 200 400 600 800 1000 12001.5

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Fig#9.(C) Power injection implemented in the two scenarios considered for Slack bus 650

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Fig#9.(D) The control signal determined in real-time and implemented in each individual thermostat of the heat pump models for bus 675

VI. CONCLUSION This paper introduces a novel platform to investigate the use of GridLAB-D/MATLAB to simulate a self-regulating distribution system. Research questions being addressed within this project are as follows: 1) The test bed exploits the abilities of GridLAB-D to simulate a wide variety of demand response programs, through data exchanged between load objects and MATLAB-based interfaces.

2) Integration of a comfort-constrained bus-level and system-level control strategy and investigate their application for increasing energy efficiency at the community-level. Future work will focus on the community-based virtual power plant program as well as more distributed energy resources and end-user appliances modeled by GridLAB-D's modules, which can be used to design more complicated control strategies and extensive customer-side data exchanges.

VII. ACKNOWLEDGMENT The authors would like to thank collaboration between

Institute for Integrated Energy Systems, University of Victoria, Canada and the Pacific Northwest National Laboratory (PNNL). Financial support from the Pacific Institute for Climate Solutions (PICS) and the NSERC Wind Energy Strategic Network (WESNet) are also gratefully acknowledged.

VIII. REFERENCES [1] D. Callaway. Tapping the energy storage potential in electric loads to

deliver load following and regulation, with application to wind energy. Energy Conversion and Management, 50:1389–1400, 2009.

[2] U.S. Department of Energy at Pacific Northwest National Laboratory. GridLAB-D, power distribution simulation software. [Online]. Available: http://www.gridlabd.org/

[3] http://www.mathworks.com/products/matlab/ [4] S. Parkinson, D. Wang, C. Crawford, N. Djilali, Comfort-constrained

Distributed Heat Pump Management, submitted for IEEE ICSGCE2011, International Conference on Smart Grid and Clean Energy Technologies, Chengdu, China, 2011

[5] University of Victoria. UVic School-based Weather Station Network Science Building Station. http//www.victoriaweather.ca/, 2010.

[6] http://ewh.ieee.org/soc/pes/dsacom/testfeeders/index.html [7] I. Knight and H. Ribberink. European and Canadian non-HVAC Electric

DHW Load Profiles for Use in Simulating the Performance of Residential Cogeneration Systems. International Energy Agency- Energy Conservation in Buildings and Community Systems Programme, 2007.

[8] Warren, Katzenstein, Jay Apt, “Air Emissions Due To Wind And Solar Power”, Environ. Sci. Technol., 2009, 43, 253–258

IX. BIOGRAPHIES

Dan Wang received the M.S. and B.S.E.E. degree from Hohai University, Nanjing, China, in 2003 and 2006, respectively and the Ph.D. degrees in power system and its automation from Tianjin Univeisity, Tianjin, China, in 2009. His previous research interests pertain mainly to distributed generation system and micro grid dynamic modeling, bulk power system stability analysis and control. In 2010, he started working as a postdoctoral fellow at the Institute for Integrated Energy Systems at the University of Victoria (IESVic) & the Pacific Institute for Climate Solutions, British Columbia, Canada. Now he is also a visiting scholar for Pacific Northwest National Laboratory (PNNL) of U.S. Department of Energy. His research currently focuses on smart grid technology implementation in power system ancillary service design, demand-side management, and large-scale renewable resources integration. Braydon de Wit Braydon de Wit is a Research Assistant at the Institute for Integrated Energy Systems whose research focuses on smart grid models and demand response technology. Braydon is a fourth-year electrical engineering student specializing in energy systems at the University of Victoria. Simon Parkinson Simon Parkinson is a graduate student researcher at the Institute for Integrated Energy Systems at the University of Victoria. His research focuses on managing large populations of electric loads to aid in the integration of renewable energy technologies. He has a B.Eng. in engineering physics from the University of Saskatchewan.

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Jason Fuller (S'08, M'10) received his B.S. degree in Physics from the University of Washington in Seattle, Washington and his M.S. degree in Electric Engineering from Washington State University. He is currently a research engineer at the Pacific Northwest National Laboratory. His main areas of interest are distribution system analysis and renewable integration. He is currently the Secretary of the Distribution System Analysis Subcommittee's Test Feeder Working Group. David Chassin (M’03, SM’05) is the manager of the Electric Power Systems Engineering group at Pacific Northwest National Laboratory and has more than 25 years of experience in the research and development of computer applications software for the architecture, engineering and construction industry. His research focuses on non-linear system dynamics, high-performance simulation and modeling of energy systems, controls, and diagnostics. He is the principle investigator and project manager of DOE's SmartGrid simulation environment, called GridLAB-D and was the architect of the Olympic Peninsula SmartGrid Demonstration's real-time pricing system. He was granted several U.S. and international patents relating to Grid Friendly(TM) appliance technology. He is a Senior Member of IEEE, a member of the NASPI Data Network Management Task Team, the Western Electric Coordinating Council (WECC) Load Modeling Task Force and Market Integration Committee, and the North American Electricity Reliability Council (NERC) Load Forecasting Work Group. Curran Crawford Dr. Crawford is an Assistant Professor in the Department of Mechanical Engineering at the University of Victoria and director of the Sustainable Systems Design Lab. His interests lie in the design of sustainable systems using multidisciplinary optimization techniques, specifically in energy-related technologies, stemming out of his graduate work on wind turbines at MIT and the University of Cambridge. He is growing this experience into other related areas including probabilistic energy systems analysis, tidal devices and PEVs. Ned Djilali Ned Djilali holds the Canada Research Chair in Energy Systems Design and Computational Modeling and is Professor of Mechanical Engineering at the University of Victoria. He has served as Director of the Institute for Integrated Energy Systems and as Interim Director of the Pacific Institute for Climate Solutions, promoting interdisciplinary research across science and technology, economics and policy. Prior to joining the University of Victoria in 1991, Dr. Djilali worked as an Aerodynamicist with the Canadair Aerospace Division of Bombardier Inc. He has published over 100 peer reviewed journal papers, and holds several patents and research awards. His current research and teaching interests include transport phenomena and sustainable energy systems, with a focus on fuel cell and hydrogen systems and on large scale grid integration of renewable energy. Dr. Djilali is a Fellow of the Canadian Academy of Engineering.