A DISTRIBUTED INTELLIGENT AUTOMATED DEMAND RESPONSE BUILDING MANAGEMENT SYSTEM RESPONSE TO THE

DEPARTMENT OF ENERGY FUNDING OPPORTUNITY NO. DE-FOA-000115 “RECOVERY ACT: ADVANCED ENERGY EFFICIENT BUILDING TECHNOLOGIES”

PRINCIPAL INVESTIGATORS: PROF. DAVID AUSLANDER DEPARTMENT OF MECHANICAL ENGINEERING, U.C. BERKELEY PROF. DAVID CULLER DEPARTMENT OF ELECTRICAL ENGINEERING AND COMPUTER SCIENCE, U.C. BERKELEY PROF. PAUL K. WRIGHT DIRECTOR, CENTER FOR INFORMATION TECHNOLOGY RESEARCH IN THE INTEREST OF SOCIETY (CITRIS) AND THE DEPARTMENT OF MECHANICAL ENGINEERING, U.C. BERKELEY DR. YAN LU SIEMENS CORPORATE RESEARCH INC MR. THOMAS GRUENEWALD, SIEMENS CORPORATE RESEARCH INC. MS. MARY ANN PIETTE LAWRENCE BERKELEY NATIONAL LABORATORY DR. GARY L. BALDWIN, PROJECT MANAGER U.C. BERKELEY AND CITRIS DATE: 18 AUGUST 2009

Table of Contents......................................................................................................................................i List of Tables............................................................................................................................................ii List of Figures...........................................................................................................................................iii List of Acronyms......................................................................................................................................iv

TABLE OF CONTENTS TECHNICAL DISCUSSION 1. TECHNICAL MERIT AND APPROACH...........................................................................................1 1.1 Project Description, Goals and Objectives...................................................................................1 1.2 Relationship to Announcement Objectives..................................................................................2 1.3 Current State of Knowledge or Technology................................................................................3 1.4 Project Performance Goals..........................................................................................................4 1.5 Work Plan....................................................................................................................................5 1.6 Labor Hours................................................................................................................................10 1.7 Project Schedule and Milestones................................................................................................11 1.8 Travel..........................................................................................................................................12 1.9 Project Risks...............................................................................................................................12 2. ENERGY, ENVIRONMENTAL AND ECONOMIC BENEFITS.....................................................14 2.1 Energy Savings...........................................................................................................................14 2.2 Environmental Benefits..............................................................................................................16 2.3 Economic Impacts......................................................................................................................16 3. PARTICIPANT ROLES, CAPABILITIES AND INDUSTRY EXPERIENCE................................18 3.1 Organizational Qualifications and Industry Experience.............................................................18 3.2 Personnel Qualifications and Experience...................................................................................19 3.3 Organizational Structure.............................................................................................................22 3.4 Facilities and Equipment............................................................................................................23 4. COMMERCIALIZATION AND MARKET POTENTIAL...............................................................24 4.1 Commercialization Strategy.......................................................................................................24 4.2 Market Potential.........................................................................................................................25 A. STATEMENT OF PROJECT OBJECTIVES....................................................................................A1 B. KEY PERSONNEL RESUMES.........................................................................................................B1 C. LETTERS OF COMMITMENT.........................................................................................................C1 D. BIBLIOGRAPHY & REFERENCES CITED ...................................................................................D1

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List of Tables Table 1: Annual Performance Goals ………………………………………………………..5 Table 2: Project Labor Hours………………………………………………………………..10 Table 3: Project Travel Plans………………………………………………………………..12 Table 4: Benefits of Demand Response……………………………………………………..14 Table 5: National Total Energy Savings with DIADR………………………………………16

ii

List of Figures Figure 1: Architecture for DIADR…………………………………………………………..6 Figure 2: Relationship between energy efficiency, time of use optimization, and fast DR…14 Figure 3. Office building electricity use with and without ADR, Martinez, CA…………….15

iii

List of Acronyms ADR: Automated Demand Response AHU: Air Handling Unit ASHRAE: American Society for Heating, Refrigeration, and Air Conditioning Engineers BTP: Building Technology Program CHP: Combined Heat and Power CITRIS: Center for Information Technology Research in the Interest of Society DG: Distributed generation DIADR: Distributed Intelligent Automated Demand Response DR: Demand Response DRAS: Demand Response Automation Server DRRC: Demand Response Research Center EMCS: Energy Management and Control System EPRI: Electric Power Research Institute GW: giga-Watts HVAC: Heating, Ventilation, and Air Conditioning ISO: Independent System Operator kW: kilo-Watt LBNL: Lawrence Berkeley National Laboratory MMBtu: Millions of British Thermal Units NIST: National Institutes for Standards and Technology OpenADR: an open, two way communications system for DR reliability and price signals SBT: Siemens Building Technologies SCR: Siemens Corporate Research SOA: Service-Oriented Architecture UCB: University of California at Berkeley VAV: Variable air volume iv

1. TECHNICAL MERIT AND APPROACH 1.1 Project Description, Goals and Objectives This project proposes to develop a Distributed Intelligent Automated Demand Response (DIADR) management system with intelligent optimization and control algorithms for demand management taking into account a multitude of factors: comfort, HVAC, lighting, and other building systems, climate, and usage/occupancy patterns. The goal is to demonstrate an innovative DR management system on a typical commercial building to achieve 30% demand reduction while still maintaining the building as a healthy, productive, and comfortable environment for the building occupants. The key challenge to meet such an aggressive goal is to provide the DR ability to address more and more complex building systems. Reliance upon a centralized building energy management or pre‐programmed controllers to take action based on a demand response signal can result in a loss in the system responsiveness to the dynamic changes of energy price, occupancy patterns and load requirements.. In this proposal, we describe a distributed intelligent demand response management system which -

is a mission based, re-configurable system to switch load shedding strategies based on building operation status and demand response signal;

-

is more responsive: these nodes (sensors, meters & sub-meters with embedded microprocessors) will take on actuation and system control functionality as well;

-

embeds as much autonomy as possible in local sections of the network to enable distributed optimization and control functions. To meet the challenge of increasing local autonomy we will work on ‘light-weight’ service-oriented architectures adapted to this unique environment. Defining the local functionality and how it interacts with the global system (e.g. cost of power from utility) is a prime research topic. As an important step towards realizing our vision of implementing DIADR strategies as part of a smart-grid enabling technology, we will first show case the system on the UCB CITRIS Building. 1

1.2 Relationship to Announcement Objectives This project addresses Technical Subtopic 1.2: Advanced Whole-Building Control Systems and Information Technology, by developing an innovative whole-building demand response management system to manage and control low-energy buildings, maximize energy savings and peak demand reductions, and enable two-way communication with the electrical grid. These advanced whole-building control systems are capable of automatically detecting and configuring building subsystems that have conforming “plug and play” capabilities consistent with our service-oriented-architecture. The success of the proposed innovative will support the DOE Building Technology Program (BTP) research goals towards achieving net-zero energy buildings by reducing energy demand while still meeting building comfort requirements. The decreased energy use results in fewer greenhouse gas emissions and less energy imports which will meet DOE ARRA second and fifth ARRA priorities for Clean, Secure Energy and Climate Change. In addition, the Science and Discovery goal will be met with our innovative building control technologies and energy efficiency systems for both new and existing residential and commercial buildings. The autonomous energy control provides building operations with the ability to monitor occupancy patterns, detect anomalies, and results in reduced building lifecycle cost. These added together will contribute to building a more sustainable society to meet the third DOE ARRA priority, Economic Prosperity. 1.3 Current State of Knowledge or Technology Building Automation Systems: Up-to-date measuring and control technology combined with a building automation and control system is seen today as the basis for good building performance, saving between 5 and 15 percent of overall building energy consumption. Building automation systems, which are present in more than half of all buildings in the U.S. larger than 100,000 square feet, have become the accepted technology used in controlling HVAC and other systems in most new commercial and institutional buildings. Existing buildings can be retrofitted, a change that has been shown to provide economically beneficial improvements in energy efficiency and occupant comfort. Although most building automation 2

systems are designed primarily for HVAC control, many incorporate additional functions. Automated Demand Response (ADR): Demand response is the process of managing energy use dynamically through cooperation between power customers, their electric utility, and the electric system’s operator (the independent system operator, or ISO). When the electrical grid is near capacity for any reason—for example, when too many air conditioners start laboring on a hot summer’s day—the ISO informs electric utilities and power consumers. Automated demand response takes this manual interaction to a new level by cutting out the necessity of human intervention, improving reliability and consistency. Having the building automation system handling the DR requests gives the building owner and the facility management an additional level of control. Building control strategies for ADR Current control technology and strategy for ADR is relying on centralized building energy management or pre‐programmed controllers to take action based on a demand response signal. In such a pre-configured ADR enabled EMS/controller, it is often seen that the service levels of HVAC and lighting system are hardcoded for DR events, e.g. temperature set points are changed; lighting levels are reduced.[Piette, Gridwise 2008], However, distributed intelligent control can easily handle the combinatorial complexity of modern buildings in real time. With today’s wired/wireless sensor technology, demand response can be embedded autonomously in local sections of the network to enable distributed optimization and control functions. The distributed DR control will be fully scalable to additional building subsystems such as fire and smoke control, and even energy generation and storage systems. Service Oriented Architecture (SOA) and OpenADR SOA separates functions into distinct units (services), which can be distributed over a network and can be combined and reused to create applications. These services communicate with each other by passing data from one service to another, or by coordinating an activity between two or more services, which enables a more responsive, flexible distributed control framework. Many think of SOA only as Web services, The same concepts – not necessarily the implementation – can be applied to automation and control. We will use Open Automated 3

Demand Response (OpenADR) at the top level provide an open, two way communications system for DR reliability and price signals. OpenADR is one of the key Smart Grid standards supported by NIST to help automate DR. It is a non-proprietary standards-based communications data model that provides for utility, aggregator, and independent system operators to provide electronic price, reliability, and DR event signals that can be linked directly to customer facility energy management and related building control systems. The proposed distributed intelligent Automated Demand Response utilizes a real-time service oriented architecture developed by Siemens to integrate ADR into a hierarchical building automation system. It will leverage not only the information the building automation system has available e.g. actual values of inside/outside temperature, energy consumption of the major systems in the building, but also the implemented control strategies. 1.4 Project Performance Goals

For the proposed project, we are going to develop a distributed intelligent control strategy for load management and control for automated demand response. Due to the autonomous nature of local control, the system will be more responsive to the dynamic changes such as energy price, occupancy patterns, load requirements as well as weather conditions and our objective is to meet 30% peak load reduction while maintaining productivity. The baseline for the 30% reduction will be a critical energy usage day for which the outside temperature at the test site is at least 95 degrees F.

The proposed project includes two phase, and each one will run for 12 months. Our annual performance goals for Year 1 and Year 2 are shown in the table below:

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Table 1. Annual Performance Goals

Year

Yearly Performance Goal

Year 1

Local DIADR--Room level integration with light, HVAC, lab loads and possibly battery charge system to meet 30% demand reduction at DR event for ancillary services demand response

Year 2

DIADR for UCB CITRIS Building integration to meet 30% demand reduction at DR event for DayAhead demand response, Day-of demand response and ancillary services demand response

1.5 Work Plan Objectives This project proposes to develop a distributed automated Demand Response management system to achieve 30% peak demand reduction while still maintaining the building as a healthy, productive, and comfortable environment for the building occupants.

Scope of Work: We will first develop a complete multi-tier demand response management architecture, followed by the design of a distributed intelligent control strategy for load management and control to respond to a DR event. We will validate the distributed intelligent load control strategy in a lab environment. Finally, we will integrate such a distributed intelligent automated demand response management system into an existing Siemens-controlled building, the UCB CITRIS Building, to show 30% peak demand reduction.

Project Task Description:

Phase I (12 months) Research & development

Task 1.0 Project Management Plan A Project Management Plan will be developed in compliance with the Statement of Project Objectives, Appendix A, of the FOA. Task 2.0 DIADR functional requirements and architecture 5

Figure 1

For this task, first we will make a survey of current building control strategies for ADR, test bed designs for ADR and previous related research, following by defining test scenarios to meet our performance goal in Table 1. Then we will create functional requirements for the DIADR system and decide the kinds of metering, types of loads to curtail, communication protocols, etc. After that we will identify types of equipment, simulation tools for lab environment testing. With defined test scenarios and hardware selection, we will come up with architecture to describe DIADR management system. Figure 1 shows a preliminary architecture for such a DIADR management System. It is a multi-tier building DR management integrated with a hierarchical building automation system: -

The DR module at the top tier is part of the building management system, which will act as energy service interface to interact with the electric grid to perform effective energy conservation and management. We plan to make the interface OpenADR enabled. The OpenADR tests will 6

include a variety of signals from day ahead, day of, and very fast DR. A set of building response strategies will be developed for these different time scales of response. -

The second tier is subsystem level DR manager, for subsystems including building units (Floors/HVAC regions), central controlled power loads (security system, fire system, elevators) and onsite generation where it exists (CHP Units, solar thermal, solar generation, absorption chiller etc).

-

The lowest tier DR manager is the leaf manager which corresponds to local control (rooms/zones) level in building automation system. Each of the local control system has a terminal controller which is able to control different loads, include heating/cooling/ventilation through VAV boxes, fan coil unit, lighting and lab equipment, etc. The terminal controller is also the host of the leaf DR manager. A simplified model of the building mass and control strategies will be developed using the EnergyPlus based DR Quick Assessment Tool to evaluate DR strategies, including precooling. .

-

Within each local control, radio devices are deployed for temperature/humidity/illumination measurements and load control. To achieve reliable and timely communication an embedded real time service oriented architecture is used as middleware.

Task 3.0 Develop Service Oriented Architecture for distributed intelligent ADR management System In this task we will develop a service-oriented architecture as middleware for DIADR management. The middleware will be built on modules (objects) associated with building services that can be delivered or curtailed (heating, cooling, ventilation: set point adjustment, supply air temperature adjustment, chilled water temperature adjustment, lighting etc.). Modules can act on measured or estimated energy and costs to deliver their associated services in response to load requirements, occupancy and weather changes. We plan to develop the SOA based on Siemens existing embedded service-oriented architecture. Task 4.0 Building Management System OpenADR Integration

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Under this task, we will enhance the existing Siemens Building Management System –Apogee-- to be Open ADR ready. We plan to interface the BMS with an OpenADR client to OpenADR DRAS server located at the LBNL lab. The OpenADR tests will include a variety of signals from day ahead, day of, and very fast DR. These different signals and building response strategies provide different value to the utilities, ISO, and grid managers.

Task 5.0 Demand Response Algorithm Development In this task we will develop the key technology required for the DIADR system. We will start the research and development process by defining a base line, centralized load management DR algorithm. Then we will develop a distributed load management (e.g., agent-based) algorithm. The algorithms we need to develop include the distributed control and optimization strategy and functionality scoping. Specifically, our DR optimization will be able to consider a variety of loads, such as HVAC, lighting, energy storage, etc.. An agent, for example, could react to DR events using market mechanisms by bidding either for energy service or load shedding if economic incentive is given. The agent can be hosted at the subsystem level as well as field device level. The agent can sense, derive, plan and execute autonomously. We will validate our algorithms first with simulations where the occupancy and weather changes can be emulated. Task 6.0 DIADR local control testing in a lab environment In this task we will do local control (room level) ADR testing by implementing DIADR in a test room (lab) environment. The controllers and sensors provided by Siemens Technology will be installed and commissioned with Siemens SOA technology. DIADR will be deployed to manage lighting, HVAC control, lab electricity loads and potentially a battery charge system. A short term DR event will be triggered to study the response of the distributed intelligent system under different weather conditions and energy use patterns and validate the 30% demand reduction. Sub-task 6.1 Local Control DIADR Integration We will design the testbed architecture with test scenarios specified first. Then our team from SBT will install and configure the equipment for the test environment in UCB. Siemens Corporate Research will 8

integrate Siemens SOA into the local control system. After that our DIADR algorithms will be integrated into the testbed. Sub-task 6.2 Test and Optimization DIADR control strategy will be tested for local control in the lab environment. Functionality Scoping will be optimized regarding to the control capability of each distributed intelligent component. Sub-task 6.3 Room Demonstrator We will demonstrate DIADR to DOE. And this will conclude our Phase I work. Phase II (12 months) Implementation & validation Task 7.0 DIADR Building Integration In this task, DIADR will be deployed to the whole building scale. Several local controls will be integrated to the subsystem level. Furthermore, several subsystems, including AHU zones, central controlled loads, and building energy storage systems will be integrated with DIADR too. Finally, the top tier DR module will be connected to the OpenADR DRAS server. Task 8.0 Enhanced Scale Testing An intensive test will be made for the whole building integrated DIADR system. Test scenarios will be defined based on the setting of the UCB CITRIS building. A test plan will be created and mock tests will be carried out on this enhanced scale of DIADR system. System improvement will be made and tested. Task 9.0 Commercialization strategy and Plan In this task, we will make a lifecycle cost analysis for DIADR and its commercialization potential will be studied. We will also construct a customer value proposition for the proposed technology and SBT will make a commercialization plan out of the advanced control strategy for ADR. Task 10.0 Building Demonstrator In the final demo we are going to brief the DOE. This task will include two stages: preparation stage and presentation stage. Task 11.0 Project Report 9

Y2

1

0.5

1

1

1.5

0.5

5.7 1.5

4

2

1

3

1

3 4

5 4.1 0.5

2

Intern Student

2

Engineer

1

3

3

Project Management

12.0 Project Report

1

0.5

1

2.5

0.5

CoPI

0.5

0.5

1.0

0.5

0.5

0.5

0.5

0.5 1

0.5

App. Engineer (SBT)

Siemens Corporate Research (person month)

11.0 Building Demonstrator

8.0 Advanced Automated DR Building Integration (Tier 1-3) 9.0 Enhanced Scale Testing (Tier 1-3) 10.0 Commercialization Plan

5.0 Demand Response Algorithm Development 6.0 DIADR local control (Tier3) testing in a lab environment 7.0 Project Management

4.0 BMS Open ADR Integration

2.0 Functional Requirements and Architecture Design 3.0 Develop SOA for DIADR

Y1 1.0 Project Management

Project Task

1

1

1

1

PI

1

1

2

0.4

2

2.4

Professor Project Management

2

6

5

4

5

3.5

12

5

5

Graduate Student Research -ers

University California Berkeley (person month)

0.15

0.16

0.15

0.16

CoPI

1.73

0.57

1.17

Sr. Scientific Research Associate

2.4

0.8

1.6

Research Associate

LBNL

6.9

2.28

4.62

Graduate Student Research Associate

In the last task of the proposed project, we will a submit project Report to DOE. A journal/conference

publication will be prepared and presented.

1.6 Labor Hours

Table 2. Labor Hours

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1.7 Project Schedule and Milestones

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1.8 Travel Table 3. Travel Plans Trip Purpose Kick Off Annual Status Demonstration of Siemens SOA on selected hardware platform Control Algorithm in Room Test OpenADR / BMS Kick Off Enhanced Scale Test Debriefing Annual project briefings at NETL Annual project briefings at NETL Kick Off meeting at NETL Total

Origin

Destination

# of Trips

# of Duratio People n in MD

Berkeley, CA Princeton, NJ

Morgantown, Pittsburg Berkeley, CA

1 2

2 3

3 3

2,780 10,140

Princeton, NJ

Berkeley, CA

1

3

3

5,070

Princeton, Princeton, Princeton, Princeton,

Berkeley, Berkeley, Berkeley, Berkeley,

CA CA CA CA

1 1 1 1

1 3 2 3

7 3 7 3

2,810 5,070 5,620 5,070

Princeton, NJ

Morgantown, Pittsburg

2

1

2

2,820

Berkeley, CA Princeton, NJ

Morgantown, Pittsburg Morgantown, Pittsburg

2 1

4 1

2 2

8,880 1,410 49,670

NJ NJ NJ NJ

1.9 Project Risks At each task and sub-task in the project, a careful analysis will be performed to ascertain risks to completion of the task/sub-task and the impact of obstacles to the overall mission of the project. We delineate the major risks in the table below, each tied to a critical milestone/decision point, and briefly describe the mitigation strategy.

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Risk Factor

M itigation Strate gy

We base our development on leveraging extensive previous work on DR. If the initial DIADR algorithm falls short of Algorithm's ability to demonstrate achievement of 30% peak demand predicting a 30% practical peak demand reduction, we will re-evaluate the algorithm design, and/or, based on theory, reduction through simulation. establish a new target goal for peak demand reduction.

Algorithm's testing in laboratory environment

If the laboratory tests of the algorithm(s) do not confirm the predictions of the simulations, after exploring all sources of incompatibility with our assumptions, we will try the algorithm on a known-good test system at Siemens Building Technologies to determine sources of errors. Necessary changes will be made to the algorithm.

The Building Management System in the CITRIS Building at U.C. Berkeley is a Siemens system. The successes of Complexities of integration of overcoming the previous two risk factors would then DIADR management system into a indicate an incorrect installation or other site-specific laboratory environment anomaly in the CITRIS Building laboratory that must be explored, understood, and corrected. The additional complexities of embracing the BMS of an entire building require careful and comprehensive understanding; if the problems lie in the hardware, a redesign of the interface electronics and/or middleware may Scaling of DIADR hardward and be required. If the problems lie in the software's inability to software system to a whole-building embrace the complexities of an entire building, we will at U.C. Berkeley need to dissect the algorithm(s) and/or perhaps temporarily scale the control back to a point where the 30% goals can be met. This would be followed by corrections to the software and a redeployment to the whole-building scale.

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2. ENERGY, ENVIRONMENTAL AND ECONOMIC BENEFITS 2.1 Energy Savings The primary objective of this project is to help reduce Electric Peak demand and provide day-ahead, dayof, and fast demand response. Figure 2 below shows the relationship between energy efficiency, time of use optimization, and day-ahead, day of- and fast-demand response. There are numerous complex value streams associated with DR, as summarized in Table 4.

In price

response programs customers typically see higher on-peak prices 50-100 hours per year. The tradeoff for facility engineers is to understand their electric load shapes and the costs and benefits of

Figure 2.

modifying end-use services to obtain cost savings.

In terms of reliability

benefits, the marginal savings of an extra kW include not only the on-peak price but also the expected value of potential

outage

costs,

therefore

revealing large benefits to the customer and society. As a result, the value of DR depends on complex interactions of many factors such as generation capacity, transmission, end-use intensity, weather, programs and tariffs as well as financial program incentives. TABLE 4: Benefits of DR Reliability of the System: Poor power quality and power interruptions are estimated to cost $100 billion to the nation every year [3]. DR enhances electric system reliability Reduction of Costs: DR implementation can lower costs for generation, transmission and distribution charges and help reduce wholesale market prices Efficient Markets: It is estimated that a 10% reduction in electricity demand in California may reduce wholesale price spikes 50% [4]. When customers change their electricity usage behavior and reduce or shift on-peak usage and costs to off-peak periods, it results in more efficient use of the electric system. Risk Management: Prices in wholesale markets vary from day to day, and hour to hour. DR reduces suppliers’ and customers’ risk in

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the market. DR can especially help manage risks by being available, reliable, modular and dispatchable. Environmental Impact: Demand response can help reduce environmental burdens placed on the air, land and water by reducing or delaying new power plant developments and by allowing the use of the current generation capacity more effectively. These benefits are highly regional and can be large in some areas and negligible in others. Customer Service: DR helps customers understand and better manage their loads and reduce electricity bills. Market Power Mitigation: DR programs help relieve market power of traditional and new energy suppliers especially, when there are tight supplies and/or transmission constraints that might lead to market power

A recent national assessment of DR for FERC suggested that the U.S. could obtain over 180 GW of DR with aggressive deployment of DR systems. This technology will be key in achieving those aggressive targets. About 1/3 of this may be achieved by the commercial sector. Commercial buildings are a major contributor to summer peak demand. Recent estimates suggest that commercial buildings account for 330 GW which is 45% of the total for the entire U.S. summer peak demand (Kiliccote, et al, 2006). To address this large load, studies on automated DR in 28 commercial buildings in California indicate that a shortterm reduction of five to ten percent of the peak summer electric demand is feasible in many buildings with existing EMCS, accounting for between 5 and 10 GW (Kiliccote et al, 2006). Research in California has shown that the application of advanced DR has spilled over to daily time of use and basic energy efficiency. LBNL has demonstrated Figure 3.

reductions in peak electric use of 1020%

in

dozens

of

commercial

buildings. This research will develop standard tools for achieving those savings in plug and play platforms with advanced controls. Figure 3 illustrates a DR event based on resetting zone temperatures. Typical savings are 0.5 to 1 W/sqft.

The

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U.S. average office building uses about 6 W/sqft. The estimated national total energy savings calculation, shown in Table 5 below, is based on the information supplied in Appendix A of the FOA, in addition to information from the 2008 Buildings Energy Data Book - US DOE, page 3-4. The results take into consider energy for heating, cooling, ventilation, and lighting (fluorescent). % Energy Savings Over  National Energy  Potential Market  Typical New  Consumption for  Penetration (%) Technology End‐Use Category

2%

2.53 quads

33%

National Total  Energy Savings

Table 5.

0.017 quads

2.2 Environmental Benefits Demand Response provides environmental benefits by reducing the emissions of generation plants during peak periods (“peakers”). It also provides overall conservation effects, both directly from demand response load reductions (that are not made up at another time) and indirectly from increased customer awareness of their energy usage and costs. In California alone, the California Environmental Protection Agency estimates that the implementation of existing DR technology will eliminate the need to build five new electricity generation plants that would have been needed by 2013. This reduces the amount of CO2 emitted into the atmosphere by approximately 9 million tonnes each year, the equivalent of removing approximately 1.3 million mid-sized passenger cars from California’s roads. By employing automated demand response, we expect a direct environmental benefit of this project based on 17000 MMBtu of electricity savings translate to 270,000 kg of CO2. Additional savings are likely in areas where the night time electricity is cleaner than day time electricity because many DR strategies use pre-cooling to shift loads from peak to off-peak times. 2.3 Economic Impacts Reports by McKinsey and others show that the U.S. economy has the potential to reduce annual nontransportation energy consumption by roughly 23 percent by 2020, eliminating more than $1.2 trillion in 16

waste – well beyond the $520 billion upfront investment (not including program costs) that would be required. The current cost of a peaking turbine is about $100/kW. With a sample value of 60 GW, this DR for the commercial sector provides a tremendous value to the economy by reducing the need for new peaker plants, plus improving the grid reliability and deferring the need for new transmission and distribution systems. Bringing new controls technology into buildings will also create new jobs and stimulate local mechanical and controls contractors. In addition to the economic benefits outlined above, new job creation within the existing corporate infrastructure will be augmented by new start-ups, a process that has already begun. The track record from U.C. alone is rich with examples: 1. Golden Power Mfg., and subsidiary Radio Thermostat Co. of America, has been working with CITRIS technology and the California Energy Commission on Radio Data System thermostats for several years. 2.

Adura Technologies is a clean energy technology company that emerged from the Center for the Built Environment at U.C. Berkeley. It applies low-power wireless mesh networking technology to building automation.

3. Arch Rock is a pioneer in IP-based wireless sensor network technology and provides products that bridge the physical and digital worlds. Prof. David Culler, a co-PI on this proposal, was one of the founders of the company. 4. Sentilla Corporation spun out of U.C. Berkeley and provides demand-side energy management solutions for data centers, commercial and industrial facilities. 5. Federspiel Controls also emerged from U.C. Berkeley. It develops data center solutions to address the high energy costs associated with data center cooling.

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6. Wireless Industrial Technologies, founded by co-PI Paul Wright, makes wireless sensor networks to apply wireless technology and energy scavenging to heavy industries. 3. PARTICIPANT ROLES, CAPABILITIES AND INDUSTRY EXPERIENCE The team will consist of experts from University of California at Berkley (UCB), Siemens Corporate Research (SCR, Princeton, NJ), Lawrence Berkeley National Lab (LBNL). UCB will be the prime contractor, and supervise the entire project and is responsible for all the deliverables. 3.1 Organizational Qualifications and Industry Experience Siemens Corporate Research (SCR) is a U.S. company and the only Siemens research facility in the U.S., provides its U.S. businesses with research expertise in specialized areas. Located in Princeton, NJ, SCR’s technology focus is reflected in research being conducted across the company. There are approximately 280 research professionals and scientists at the Princeton, NJ facility. The SCR Automation and Control (AC) Department, with which the PI and key team member are affiliated, is one of the largest departments within SCR. It works actively with Siemens Operating Groups, including Siemens Building Technology, Siemens Energy & Automation, to increase their technological and competitive edge and achieving time-to-market.

Areas of focus include high

performance building, industrial control system architecture, network management and service-oriented communication, control system safety, control network security and smart wireless sensor networking and RFID technologies etc. In addition, SCR/AC also works on government contracted research. An active project,

funded by DOE/OE, is titled “Protecting Power Grid from Cyber Attacks.” SCR/AC has

successfully demonstrated its technical and management capability through these contracted research activities. Siemens Building Technologies is a leader in the multi-billion dollar North American market for products, systems and services that automate facility infrastructures and ensure their energy efficiency.

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3.2 Personnel Qualifications and Experience The following table lists the primary principle and key members in the project. PRINCIPAL & KEY TEAM MEMBERS LOCATION

TITLE

ROLE

UC Berkeley

Professor

Advanced building control strategy, system architecture

UC Berkeley

Professor and Director of CITRIS

Energy monitoring systems, wireless sensor networks, DR

Siemens Corporate Research

Research Scientist

Advanced building control strategy, system architecture

Siemens Corporate Research

Project Manager

High Performance building, Building information model

Siemens Building Technology

Senior Principal Scientist

Building Automation / Management Systems

Professor

Advanced architectures for wireless sensor networks and controls

LBNL

Staff Scientist

Two-way demand response automation systems, energy analysis

LBNL

Senior Scientific Engineering Associate

Two-way demand response automation systems, energy analysis

UC Berkeley and CITRIS

Director of Special Projects in Energy

Project Management

(% TIME ON PROJECT) Dr. David Auslander (17%) Dr. Paul Wright (8.5%) Dr. Yan Lu (25%) Mr. Thomas Gruenewald (25%) Mr. Pornsak Sonkakul (100%) Dr. David Culler UC Berkeley (8.5%)

Ms. Mary Ann Piette (10%)

Ms. Sila Kiliccote (10%) Dr. Gary L. Baldwin (25%)

19

The Primary Investigator of this proposed work will be Dr. David Auslander. He is Professor of the Graduate School, Mechanical Engineering Department, University of California at Berkeley. He has also served as Associate Dean and Acting Dean of the College of Engineering. He has interests in dynamic systems and control. His research and teaching interests include mechatronics and real time software, energy control systems, and mechanical control. Current projects in these areas are building energy control, design methodology for real time control software for mechanical systems, satellite attitude control, simulation methods for constrained mechanical systems, and engineering curriculum development. He consults in industrial servo control systems and other control and computer applications. He is co-founder and senior technical consultant to Berkeley Process Control, Inc. (now a part of Moog, Inc.), a company specializing in industrial machine control. His undergraduate studies were at the Cooper Union and his graduate studies were at MIT, both in Mechanical Engineering. He has been the recipient of numerous awards including the Education Award of the American Automatic Control Council, the Control Practice Award of the Dynamic Systems and Control Division of ASME, and is a Fellow of the ASME.

The Co-PIs of this project will be Professors Paul K. Wright and David E. Culler. Dr. Wright is the Director of CITRIS -- the Center for Information Technology in the Interests of Society. He is a professor in the mechanical engineering department, and holds the A. Martin Berlin Chair. He is also a co-director of the Berkeley Manufacturing Institute (BMI) and co-director of the Berkeley Wireless Research Center (BWRC). From 1995 to 2005 was the co-chair of the Management of Technology Program. He is a member of the National Academy of Engineering; a Fellow of the American Society of Mechanical Engineers; and a Fellow of the Society of Manufacturing Engineers. Dr. Wright’s research interests

include energy scavenging and storage; smart materials; design of wireless sensor systems. Application areas include: energy efficiency and demand response; first responder applications;

20

medical products. He is credited for the invention of the first open-architecture control of manufacturing. David Culler is a Professor of Computer Science at the University of California, Berkeley, CTO of Arch Rock Corporation, and Associate CIO for the College of Engineering. Professor Culler received his B.A. from U.C. Berkeley in 1980, and M.S. and Ph.D. from MIT in 1985 and 1989. He has been on the faculty at Berkeley since 1989, where he holds the Howard Friesen Chair. He is a member of the National Academy of Engineering, an ACM Fellow, an IEEE Fellow and was selected for ACMs Sigmod Outstanding Achievement Award, Scientific American's 'Top 50 Researchers', and Technology Review's '10 Technologies that Will Change the World'. He received the NSF Presidential Young Investigators award in 1990 and the NSF Presidential Faculty Fellowship in 1992. He was the Principal Investigator of the DARPA Network Embedded Systems Technology project that created the open platform for wireless sensor networks based on TinyOS, and was the founding Director of Intel Research, Berkeley. He has served on Technical Advisory Boards for several companies, including Inktomi, ExpertCity (now CITRIX on-line), and DoCoMo USA. The PI from Siemens Corporate Research Inc will be Dr. Yan Lu from SCR, who is well versed in the management of research projects. Dr. Lu has worked on and successfully delivered several government funded projects in the areas of robust and survivable complex systems, including Robust Tape Transport System project funded by NIST ATP Program, Survivable Ship Engineering Control System project funded by ONR and DOE funded Securing Power Grid Security from Cyber Attacks project. Her past research activities included fault diagnosis and intelligent control of highly-distributed ship chilled water supplying system and an integrated security solution for power automation system. Currently Dr. Lu also works on a corporate funded distributed energy resource project and renewable energy, including wind power generation, solar energy systems, and fuel cells. Dr. Lu obtained her Ph.D. from Electrical and Computer Engineering Department of Carnegie Mellon University.

21

The PI from Lawrence Berkeley National Laboratory is Mary Ann Piette. Ms. Piette is a Staff Scientist and the Research Director of the Demand Response Research Center. She is the Deputy of the Building Technologies Department. She has been at LBNL since 1983 and has extensive experience evaluating the performance of energy efficiency and demand response in large facilities.

The Demand Response

Research Center is a 5-year old Center to plan, manage, conduct and disseminate DR research for the California Energy Commission.

Ms. Piette completed her undergraduate work at UC Berkeley in

Physical Science. She has a Master’s of Science Degree in Mechanical Engineering from UC Berkeley and a Licentiate in Building Services Engineering from the Chalmers University of Technology in Sweden.

Ms. Sila Kiliccote is the key member working on this project from LBNL. Sila Kiliccote is a Senior Scientific Engineering Associate in the Building Technologies Department at Lawrence Berkeley National Laboratory. She has been a part of the Automated Demand Response team developing an automated demand response communication infrastructure, integrating it with building control systems and working with stakeholders to standardize the information model. Her areas of interest include characterization of buildings and demand reduction, demand responsive lighting systems, building systems integration and feedback for demand-side management. She has a master’s degree in Building Science from Carnegie Mellon University and a Bachelor of Science in Electrical Engineering from University of New Hampshire.

3.3 Organizational Structure Prof. Auslander is the overall PI of the project. He will act as an architect of the DIADR system as well as the lead academic researcher for this proposed project. Technically his team will mainly contribute to the advanced DIADR algorithms development and DIADR test in lab environment. Prof. Wright’s team will mainly contribute to the DIADR integrated building architecture design and testing. Prof. Culler’s team will contribute to wireless technology for local control. 22

Program  Manager  (DOE)

Siemens

Corporate

Research’s

main

contribution includes DIADR architecture Wright (UCB)

Auslander (UCB)

Culler  (UCB)

Baldwin (CITRIS)

design,

agent-based

distributed

ADR

control strategy development, integrating Piette (LBNL)

Lu (SCR)

Kiliccote (LBNL)

Gruenewald Ersch Bhatt (SCR)

Songkakul (SBT)

Siemens SOA into the test environment and both operation and testing of DIADR integrated local DR control and building

DR management. Siemens Building Technology will provide application engineering work to support our integration and provide technical consultation on implementing DIADR on Siemens Apogee system. LBNL’s key contribution will be integrating OpenADR to Building Management System and supporting Prof. Auslander on developing centralized optimization for ADR. 3.4 Facilities and Equipment CITRIS Building: Vision: Creation of a “Living Laboratory” building in which demand response, pervasive sensing, ambient computing, and smart materials can continue to reduce the energy footprint of a building while offering the highest quality environment for the productivity of its occupants. Infrastructure already available: The Siemens Building Automation System (BAS) is being used to control the operation of Sutardja Dai Hall, the home of the new CITRIS1 headquarters building on the UC Berkeley campus. The building opened on February 27, 2009. The BAS monitors and controls all building HVAC and is under local control of CITRIS staff enabling the expansions described below. The BAS is also connected to a Preventative Maintenance Software (PM) system. This database contains all building mechanical and AV

1

CITRIS is the Center for Information Technology in the Interests of Society. The planning for CITRIS began in 1999-20001 and it soon became one of the four California Institutes for Science and Innovation (CISIs), California centers of excellence.. It serves 4 UC campuses (Berkeley, Davis, Merced and Santa Cruz).

23

equipment information. A separately-controlled, confined space in the CITRIS building will serve as the laboratory test space for initial algorithm testing, as outlined in Task 6 above. Work in progress – sub-metering and pervasive sensing with WSNs: CITRIS staff are presently adding a variety of energy efficiency ‘fit-outs’ to this basic system. Work is in progress on sub-metering equipment so that the usage on each floor of the 7-story building can be monitored independently. These networks will allow us to execute a variety of local controls for lighting and HVAC. We estimate that these additional ‘fit-outs’ are adding ~$200,000 of value (in time-and-materials) as ‘add-ons’ to the original BAS. 4. COMMERCIALIZATION AND MARKET POTENTIAL 4.1 Commercialization Strategy We will conduct an extensive lifecycle cost analysis and make customer value propositions, tariff designs, and benefits analyses that allow economic integration of DIADR with utility operations. The value propositions will be expanded to include environmental and societal impacts as well as direct economic benefits. By including a few “early adopters” in the development of the commercial value proposition, we will increase the chances that DIADR will be acceptable and useful. Siemens Building Technology will be the key adopter of the innovation resulted from the proposed technology. Meanwhile, other building technology vendors have the access to the technology to make a packaged product. This project will be included in the LBNL DR Research Center (DRRC) briefings that are provided to electric utilities around the U.S., as well as control companies and large building owners. The DRRC provides case studies and technical reports to ASHRAE, NIST, EPRI, DOE and other organizations interested in advanced energy management and Smart Grid applications in commercial buildings. Furthermore, we will develop the value propositions that include environmental and societal impacts, tariff designs, and benefits analyses that allow economic integration of customer participation (DSM) and 24

response with utility operations. The industry partners of both CITRIS and the affiliate CBE on the UCB campus will be briefed regularly on the progress of the project. Many of the industry partners are from the controls and HVAC equipment industries, such as Honeywell, United Technologies, Siemens, York, etc. As such, these industry partners will be keenly interested in the project and will be in position to adopt the technologies of the research to improve their respective products. Research results will also be disseminated through traditional publication of research papers in industry conferences/tradeshows, standards organizations, and journals, as well as public workshops held at CITRIS and LBNL. Additionally, both CITRIS and LBNL have relationships with two of California’s regulatory agencies, the California Energy Commission and the California Public Utilities Commission. Finally, the UCB campus has always been an incubator of startup companies as previously mentioned. It is the intent of the project to encourage and facilitate the commercialization of the technologies developed in the project through the formation of startup companies. 4.2 Market Potential The market potential will be driven by the cost-effectiveness of the DIADR Systems. In addition to the high efficiency of the DIADR System, the project team considers the following factors to be key contributors to making the DIADR System extremely cost-effective. 1. The pervasive adoption of low-cost wireless sensors by the building/energy/environment controls industry in the coming years. 2. The enhancement of building/energy/environment control systems with the latest web-based service oriented architecture (SOA) technologies; the same technology that is central to DIADR. 3. New and retrofit construction that will utilize the new SOA enhanced building / energy / environment control systems, in line with a national trend towards dynamic pricing of electrical power. The project team sees these factors as continually increasing the cost effectiveness of DIADR with time and estimates the utilization of DIADR can reach as high as 33% of new and retrofit construction per year. 25

Appendix A1: Statement of Project Objectives (SOPO)

STATE OF PROJECT OBJECTIVES “A DISTRIBUTED INTELLIGENT AUTOMATED DEMAND RESPONSE BUILDING MANAGEMENT SYSTEM” A. Objectives This project proposes to develop a Distributed Intelligent Automated Demand Response (DIADR) management system for buildings to achieve 30% peak demand reduction while still maintaining the building as a healthy, productive, and comfortable environment for the building occupants. The objectives for Phase 1 (to be completed in the first year) are to create the functional requirements for the DIADR system and to design a Service-Oriented Architecture to address those requirements. This phase will also include extensive test design and optimization of the system at the laboratory level. In Phase 2 (to be completed in the second year), we will deploy the DIADR system at the whole-building scale. Demonstrations (to the DOE and industry), the development of commercialization plans, and the delivery of a final report will also take place in this phase. B. Scope of Work We will develop a complete, multi-tier demand response management architecture, known as a Distributed Intelligent Automated Demand Response (DIADR), followed by the design of a distributed intelligent control strategy for load management and control to respond to a DR event. We will validate the distributed intelligent load control strategy in a lab environment and integrate such a distributed intelligent automated demand response management system in an existing Siemens technology-based building, the U.C. Berkeley CITRIS Building (Sutardja Dai Hall), to showcase at least a 30% peak demand reduction using OpenADR, an open, two way communications system for DR reliability and price signals. The objectives of the first phase of the project will be met by careful analyses of the weaknesses of current building control strategies for ADR and the creation of new functional requirements for the DIADR system. By adapting the existing Siemens Building Management System – Apogee – to OpenADR, we will leverage and enhance existing technology to address the greater demands of DIADR. The demonstration portions of the project are key to its success and ultimate commercialization. Therefore, we will start with Siemens Buildings Technologies hardware and middleware and demonstrate the new architecture in a laboratory environment. We will meet the objectives of the second phase of the project by deploying the hardware and middleware to an entire building on the U.C. Berkeley campus, Sutardja Dai Hall,. C. Tasks to be Performed Phase 1: Research and Development (12 months) Task 1.0 – Project Management Plan Executive Summary: This project proposes to develop a Distributed Intelligent Automated Demand Response (DIADR) management system with intelligent optimization and control algorithms for demand management,

taking into account a multitude of factors affecting cost: comfort, HVAC, lighting, and other building systems, climate, and usage/occupancy patterns. The goal is to demonstrate an innovative DR management system on a typical commercial building to achieve 30% demand reduction while still maintaining the building as a healthy, productive, and comfortable environment for the building occupants. The key challenge to meet such an aggressive goal is to provide the DR ability to address more and more complex building systems that include a variety of loads, multiple generations, and uncertain environment requirements. Reliance upon a centralized building energy management or pre‐programmed controllers to take action based on a demand response signal can result in a loss in the system responsiveness to the dynamic changes of energy price, occupancy patterns, load requirements as well as weather conditions. In this proposal, we describe a distributed intelligent demand response management system which accomplishes the following: -

is a mission-based system, with re-configurability to switch load shedding strategies based on building operation status and demand response signal; is more responsive by looking beyond just sensing and metering: those nodes (sensors, meters & sub-meters with embedded microprocessors) will take on actuation and system control functionality as well; embeds as much autonomy as possible in local sections of the network to enable distributed optimization and control functions. Agent-based control is going to be studied as it applies to this particular domain.

To meet the challenge of increasing local autonomy we will work on ‘light-weight’ service-oriented architectures adapted to this unique environment. Defining the local functionality and how it interacts with the global system (e.g. cost of power from utility) is a prime research topic. As an important step towards realizing our vision of implementing DIADR strategies as part of a smart-grid enabling technology, we will first showcase the system on U.C. Berkeley CITRIS Building. Risk Management: At each task and sub-task in the project, a careful analysis will be performed to ascertain risks to completion of the task/sub-task and the impact of obstacles to the overall mission of the project. We quantify the major risks in the table below and briefly describe the mitigation strategy.

Risk Factor

M itigation Strate gy

We base our development on leveraging extensive previous Algorithm's ability to demonstrate work on DR. If the initial DIADR algorithm falls short of achievement of 30% peak demand predicting a 30% practical peak demand reduction, we will re-evaluate the algorithm design, and/or, based on theory, reduction through simulation. establish a new target goal for peak demand reduction.

Algorithm's testing in laboratory environment

If the laboratory tests of the algorithm(s) do not confirm the predictions of the simulations, after exploring all sources of incompatibility with our assumptions, we will try the algorithm on a known-good test system at Siemens Building Technologies to determine sources of errors. Necessary changes will be made to the algorithm.

The Building Management System in the CITRIS Building at U.C. Berkeley is a Siemens system. The successes of Complexities of integration of overcoming the previous two risk factors would then DIADR management system into a indicate an incorrect installation or other site-specific laboratory environment anomaly in the CITRIS Building laboratory that must be explored, understood, and corrected. The additional complexities of embracing the BMS of an entire building require careful and comprehensive understanding; if the problems lie in the hardware, a redesign of the interface electronics and/or middleware may Scaling of DIADR hardward and be required. If the problems lie in the software's inability to software system to a whole-building embrace the complexities of an entire building, we will at U.C. Berkeley need to dissect the algorithm(s) and/or perhaps temporarily scale the control back to a point where the 30% goals can be met. This would be followed by corrections to the software and a redeployment to the whole-building scale.

Milestone Log:

Phase

Milestone

Completion Date

1

Research and Development Functional requirements and architecture design for 30% peak demand reduction Development of Service-oriented archtecture BMS OpenADR Integration Demand Response algorithm development DIADR local control testing (lab enviroment)

30-Jan-11 30-Mar-10 30-Apr-10 30-May-10 30-Sep-10 30-Jan-11

2

Implementation and Validation Advanced automated DR building integration: 100% installation at U.C. Berkeley Enhanced (whole-building) scale testing at U.C. Berkeley Commercializatio plan 100% completion Complete system demonstration for DOE and industry: 30% peak demand reduction demo'd. Final project report delivered to DOE

31-Dec-11 30-Apr-11 30-Sep-11 30-Sep-11 31-Oct-11 31-Dec-11

Funding and Costing Profiles:

Project Funding Profile (excludes overhead) University of California, Berkeley Auslander, David Wright, Paul Culler, David Baldwin, Gary GSR

Period 1

Period 2

$ $ $ $ $

40,352 22,614 17,829 55,475 125,328

$ $ $ $ $

41,967 23,519 18,542 55,475 133,265

Siemens Corporate Research Lu, Yan Ersch, Florian Gruenewald, Thomas Bhatt, Pranav Intern

$ $ $ $ $

34,748 86,314 35,931 6,213 35,000

$ $ $ $ $

34,748 86,314 35,931 6,213 35,000

Lawrence Berkeley National Lab Piette, Mary A. Kiliccote, Sila Research Associate GSRA

$ $ $ $

4,904 18,095 12,536 15,238

$ $ $ $

5,202 19,288 13,369 15,697

Total

$

510,576 $

524,530

Project Costing Profile (excludes overhead) Oct-09 $ 68,818 Nov-09 $ 68,818 Dec-09 $ 68,818 Jan-10 $ 68,818 Feb-10 $ 68,818 Mar-10 $ 68,818 Apr-10 $ 68,818 May-10 $ 68,818 Jun-10 $ 68,818 Jul-10 $ 68,818 Aug-10 $ 68,818 Sep-10 $ 68,813 Total $ 825,811

Project Timeline:

Success Criteria at Decision Points:

Decision Point 1. After DR algorithm development has been finished, the project team will demonstrate, through simulations, the extent to which 30% peak demand reduction is possible and practical. Decision Point 2. After the DIADR algorithm has been developed, it will be tested for local room control in a laboratory environment. If this is not successful, the team will need to revisit the

fundamentals of the algorithm and its simulation results, modifying where necessary to achieve the goal of 30% reduction with a practical implementation, followed by re-testing. Decision Point 3. After integration with Tier 1, Tier 2 and Tier 3 in the DIADR overall architecture, in lab environment, the team will need to evaluate the efficacy of the entire design and assess its suitability for the next phase of deployment; that is, scalability to a whole-building environment. Decision Point 4. After scaling to initial installation in the CITRIS building (Sutardja Dai Hall), further testing will be needed to a) confirm proper operation, and b) ultimately, confirm that 30% peak demand reduction can be achieved in this whole-building test bed.

Task 2.0 - Functional Requirements and Architecture Survey current building control strategies for ADR and define test bed designs to meet the performance goal of 30% peak demand reduction. Create the functional requirements for DIADR; design an architecture to describe the DIADR management system. Task 3.0 – Develop Service-Oriented Architecture for Distributed Intelligent ADR Management System. Develop a service-oriented architecture as middleware for DIADR management, built on modules (objects) associated with building services that can be delivered or curtailed (heating, cooling, ventilation: set point adjustment, supply air temperature adjustment, chilled water temperature adjustment, lighting etc.). Sub-task 3.1 SOA Development Develop the SOA based on Siemens existing embedded service-oriented architecture. Task 4.0 – Building Management System OpenADR Integration Enhance the existing Siemens Building Management System – Apogee – to be OpenADR ready. Interface the Building Management System with an OpenADR client to OpenADR DRAS server located in the LBNL lab. Task 5.0 – Demand Response Algorithm Development Develop the key technologies required for the DIADR system. Define a base line, centralized load management optimization algorithm. Then develop a distributed load management (e.g., agent-based) algorithm, including the distributed control and optimization strategy and functionality scoping. Task 6.0 – DIADR Local Control Testing in Lab Environment Test local control (room level) ADR by implementing DIADR in a lab environment. The controllers and sensors provided by Siemens Technology will be installed and commissioned with Siemens SOA technology and deployed to manage light control, HVAC control, lab electricity loads and potentially a battery charge system.

Sub-task 6.1 Local Control DIADR Integration Design the testbed architecture with test scenarios specified first. The team from SBT will install and configure the equipment for a lab environment in UCB. Siemens Corporate Research will integrate Siemens SOA into the local control system. Integrate the DIADR algorithms into the testbed. Sub-task 6.2 Test and Optimization The DIADR control strategy will be tested for local control in the lab environment. Functionality Scoping will be optimized regarding to the control capability of each distributed intelligent component. Sub-task 6.3 Room Demonstrator Demonstrate DIADR to DOE. This will conclude our Phase I work. Phase 2: Implementation and Validation (12 months) Task 7.0 – DIADR Building Integration In this task, DIADR will be deployed to the whole building scale. Several local controls will be integrated to subsystem level. Furthermore, several subsystems, including AHU zones, central controlled loads, building energy storage systems and on-site generation will be integrated with DIADR too. Finally, the top tier DR module will be integrated to OpenADR DRAS server. Task 8.0 – Enhanced Scale Testing An intensive test will be made for the whole building integrated DIADR system. Test scenarios will be defined based on the setting of UCB CITRIS building. A test plan will be created and mock tests will be carried out on this enhanced scale of DIADR system. System improvement will be made and tested. Task 9.0 – Commercialization Strategy and Plan In this task, we will make a lifecycle cost analysis for DIADR and its commercialization potential will be studied. We will also construct a customer value proposition for the proposed technology and SBT will make a commercialization plan out of the advanced control strategy for ADR. Task 10.0 – Building Demonstrator In the final demo we are going to brief the DOE. This task will include two stages: preparation stage and presentation stage. Task 11.0 – Project Report In the last task of the proposed project, we will a submit project Report to DOE. A journal/conference publication will be prepared and presented.

D. Deliverables 1. Task 1: Deliver Project Management Plan to DOE 2. Task 2: Deliver to DOE a report on the functional requirements for the DIADR system 3. Task 3: Deliver to DOE a report that describes an appropriate Service-oriented Architecture for the DIADR system 4. Task 4: Deliver to DOE a report on the success of interfacing the Building Management System with an OpenADR client. 5. Task 5: Deliver to DOE a report to describe a Centralized Load Management algorithm and demonstrate its operation 6. Task 6: Deliver to DOE a report describing the outcome of the first DIADR experiments in a laboratory environment. 7. Task 7: Deliver to DOE a report on the success of porting and enhancing the DIADR system to a whole-building environment at U.C. Berkeley 8. Task 8: Deliver to DOE a report on the outcome of tests on the functionality of the DIADR system at the U.C. Berkeley installation. 9. Task 9: Deliver to DOE a Commercialization Plan for introduction of the DIADR technology into industry. 10. Task 10: On-site demonstration for the DOE at U.C. Berkeley of the DIADR system in operation and the 30% peak demand reduction. 11. Task 11: Deliver to DOE a final report on the project and publish the results at an appropriate conference and in its proceedings. E. Briefings / Technical Presentations We will provide all relevant personnel for the project kick-off meeting, to be held in Pittsburgh or Morgantown, and at that meeting present the Project Management Plan. We will provide all appropriate budget briefings, progress reports, demonstrations, and final reports, as outlined in the sections above.

Appendix B1: Key Personnel Resumes/Biographical Sketches

David M. Auslander Professor of the Graduate School, Mechanical Engineering University of California at Berkeley Mechanical Engineering Department, University of California, Berkeley, CA 94720-1740, USA 510-642-4930, 510-643-5599(fax) [email protected], Employment University of California at Berkeley, Associate Dean for Student Affairs and Research, College of Engineering, 1999 – 2005, Acting Dean, College of Engineering, 2002-03 University of California at Berkeley, Professor of the Graduate School: joined faculty in 1966 - present Northern Research and Development Corp., Cambridge, MA: Senior Engineer, 1961-64 Education BSME SM ScD

The Cooper Union, 1961 Massachusetts Institute of Technology, 1964 Massachusetts Institute of Technology, 1966

Areas of Research Control system design and analysis, real time software methodology, bioengineering, mechatronics, motion control, energy management systems, dynamic system modeling and simulation Visiting Positions University of Tokyo, Center for Advanced Studies of the National Polytechnic Institute (Mexico), Princeton University, University of Sydney, École Nationale Supérieure des Arts et Métier (Paris), École Polytechnique Federale de Lausanne Teaching Teach classes in mechatronics, real time software, feedback control systems; Developed mechatronics sequence (undergraduate and graduate courses in real time software, digital and analog electronics, mechanical system control); Co-Developed new introductory feedback control course; Developed measurement and instrumentation course; Authored and co-authored textbooks in these areas; Developed robotics-mechatronics course at Univ. of Calif., Merced Professional Activities International Federation of Automatic Control (IFAC) 1996 Triennial Congress, Conf. Mgr. ASME Dynamics Systems and Control Division, chair 1981, member of various committees ASME Transactions, J. Dynamic Systems, Measurement and Control, Editor 1981-86 Consulting: Berkeley Process Control (motion control, co-founder of company), legal consulting Honors Louis Levy Award (best paper), Franklin Institute (twice) Fellow of the American Society of Mechanical Engineers Education Award, Dynamic Systems and Control Division, ASME Education Award, American Automatic Control Council Control Practice Award, Dynamic Systems and Control Division, ASME Donald P.Eckman Award of the Instrumentation, Systems, and Automation Society (ISA) Mechatronics and Embedded Systems and Applications (MESA), ASME/IEEE, Career Award Relevant Publications 1. Peffer, T, E. Arens, X. Chen, J Jang, D. Auslander, “A Tale of Two Houses: the Human Dimension of Demand Response Enabling Technology from a Case Study of an Adaptive Wireless Thermostat,” American Council for an Energy Efficient Economy (ACEEE), Energy Efficiency in Buildings, Pacific Grove, CA, Aug., 2008.

2.

3.

4.

5.

Chen, X, J. Jang, D. Auslander, T. Peffer, E. Arens, ” Demand Response-Enabled Residential Thermostat Controls,” American Council for an Energy Efficient Economy (ACEEE), ACEEE Summer Study on Energy Efficiency in Buildings, Pacific Grove, CA, Aug., 2008. Burke, W., D.M. Auslander, “Robust Control of Residential Demand Response Network with Low Bandwidth Input,” ASME Dynamic Systems and Control Conference, Ann Arbor, MI, Oct., 2008. M. Najafi, D. Auslander, P. Bartlett, P. Haves, "Overcoming the Complexity of Diagnostic Problems due to Sensor Network Architecture", Eleventh International Conference on Intelligent Systems and Controls (ISC 2008), Orlando, Florida, November 2008. M. Najafi, D. Auslander, P. Bartlett, P. Haves, "Fault Diagnostics and Supervised Testing: How Fault Diagnostic tools can be Proactive?", Eleventh International Conference on Intelligent Systems and Controls (ISC 2008), Orlando, Florida, November 2008.

Synergistic Activities 1) My career work has been in the area of controls, with a wide variety of applications. Energy management fits well with this and allows for the use of a broad array of control techniques. 2) I have done extensive work on enabling technologies for demand response in residences. This work was sponsored by the Calif. Energy Commission and dealt with distributed energy control in residences. 3) I have worked on a reference design for programmable communicating thermostats (PCTs) and am currently working on a reference design for a residential and small commercial energy gateway system. In both cases, these projects allow the Calif. Energy Commission to better understand technologies that will have to be deployed in large numbers. 4) I am working on fault detection and analysis in commercial HVAC systems. A substantial fraction of immediate energy savings can come from automated fault analysis so that systems work properly. Current and Pending Support 1. “Software for Sensor Data Interpretation in Buildings,” Lawrence Berkeley Nat’l Lab, Dec. 08 – May, 10, $76,595, 1 month of PI support 2. “Development of an HVAC Load Model for Aggregates of Homes,” California Institute for Energy Efficiency, Nov. 08 – Dec. 09, $116, 759, 1 month of PI support 3. “Demand Response Systemic Control Future Work,” Lawrence Berkeley Nat’l Lab, Sept., 07 – Aug. 09, $46,500, 0.5 month PI support 4. “Fault Diagnostics in HVAC Systems,” Lawrence Berkeley Nat’l Lab, Aug. 05-Sept. 09, $146,727, 1 month PI support. 5. “Reference Designs for Energy Management Communications,” Calif. Energy Commission, April 09 – Mar. 10, $100,000, 1 month PI support. 6. (Pending) “Modeling, the Development of Load Control Strategies and the Integration of Electric Generators Driven by Renewable Resources,” California Institute for Energy Efficiency, July 09 – June 10, $100,000, 1 month PI support. 7. (Pending) “Distributed Intelligent Automated Demand Response (DIADR) Building Management System,” US DOE, October 09 – September 2011, $1,987,675, 2 summer mos.

Collaborators and Co-editors: Arens, Edward., Dep’t of Architecture, Univ of Calif, Berkeley, Haves, Phillip., Lawrence Berkeley National Lab (LBNL), Hofmann, Ronald, Consultant, Lin, Robert, Space Sciences Lab, Univ. of Calif., Berkeley, Pankow, David, Space Sciences Lab, Univ. of Calif., Berkeley, Sohn, Michael, Lawrence Berkeley National Lab (LBNL), Wright, Paul, Mechanical Engineering, Univ. of Calif., Berkeley, White, Richard, EECE, Univ. of Calif., Berkeley Graduate and Postdoctoral Advisors and Advisees: Chen, Xue, Microsoft Corp., Hemmi, Kazuo, Siebold University of Nagasaki, Jang, Jaehwi, Samsung Corp., Kim, Jong Hak, Corning, Corp., Peffer, Therese, Univ. of Calif., Berkeley, Tang, Shan, Western Digital Corp., Ullrich, Peter, retired

Paul K. Wright PROFESSIONAL PREPARATION University of Birmingham, England

Industrial Metallurgy

B.Sc. 1968

University of Birmingham, England

Industrial Metallurgy

Ph.D. 1971

APPOINTMENTS 2007-present Member, National Academy of Engineering 2006-present

Director, Center for Information Technology in the Interests of Society (CITRIS)

1999-present

Co-Director, Berkeley Manufacturing Institute, UC Berkeley

1999-present

Co-Director, Berkeley Wireless Research Center

1991-present

The A. Martin Berlin Professor of Mechanical Engineering, UC at Berkeley

1987-1991 1979-1987 1978 1975-1978 1972-1975

Professor of Computer Science and Director of the Robotics and Manufacturing Research Laboratory, Courant Institute of Mathematical Sciences, New York University Professor of Mechanical Engineering and The Robotics Institute, Carnegie-Mellon University, Pittsburgh, PA Research Associate in Physics, Cavendish Laboratory, University of Cambridge, England Senior Lecturer, Dept. of Mech. Eng., Univ. of Auckland, New Zealand Research Engineer, Dept. of Sci. and Ind. Res., Auckland, New Zealand

SELECTED PUBLICATIONS •

5 publications most closely related to the proposed project

P. Mitcheson, E.K. Reilly, T. Toh, P.K.Wright and E. Yeatman, “Performance limits of three MEMS inertial energy generator transduction types,” Journal of Micromechanics and Microengineering (Institute of Physics), Volume 17, (2007), pp. S211-S216. N. Ota and P.K. Wright, “Trends in Wireless Sensor Networks for Manufacturing,” International Journal of Manufacturing Research (IJMR, Volume 1, 2006, pp. 3-13. M.H. Schneider, J.W. Evans, P.K.Wright and D. Ziegler, “Designing a thermoelectrically powered wireless sensor network for monitoring aluminum smelters,” Proceedings of the Institute of Mechanical Engineers, Part E: Process Mechanical Engineering, Volume 220, 2006, JPME67, pp. 181-190. S. Roundy, and P.K. Wright, “A Piezoelectric Vibration based Generator for Wireless Electronics,” Smart Materials and Structures, Volume 13, 2004, pp. 1131-1142. S. Roundy, B. Otis, Y-H. Chee, J.M. Rabaey, and P.K. Wright, 2003. “A 1.9GHz RF Transmit Beacon using Environmentally Scavenged Energy,” 2003, ISPLED 2003, Seoul Korea, August 25 - 27, 2003. Winner of a Best Paper Award •

Other significant publications

J. Wilson, V. Bhargava, A. Redfern, and P.K. Wright, “A Wireless Sensor Network and Incident Command Interface for Urban Firefighting”, The 4th Annual International Conference on Mobile and Ubiquitous Systems: Computing, Networking, and Services, August 6-10, 2007, Philadelphia, Pennsylvania, USA. W. Watts, M. Koplow, A. Redfern, and P.K. Wright, “Application of Multizone HVAC Control Using Wireless Sensor Networks and Actuating Vent Registers”, Proceedings of the 2007 International Conference for Enhanced Building Operations, October 31-November 2, 2007 S. Roundy, P.K. Wright and J. Rabaey, “Energy Scavenging for Wireless Sensor Networks with Special Focus on Vibrations”, Kluwer Academic Publishers, published in 2004, 1- 212 pages, ISBN Number 14020-7663-0

P.K. Wright, 21st Century Manufacturing, Prentice Hall, Upper Saddle River, NJ. August 2000, 1-510 pages. ISBN Number 0-13-095601-5 E.M. Trent and P.K.Wright, Metal Cutting, 4th Edition, Butterworth Heinemann, Newton MA. January 2000, 1- 446 pages. ISBN Number 0-7506-7069-XD SYNERGISTIC ACTIVITIES • Energy Scavenging and Storage for Wireless Sensor Networks was first funded by NSF, and now by the California Energy Commission (CEC). In the CEC funded research with Prof. Arens and others, the larger societal goal is to reduce electricity use, especially during peak-loads, in buildings. The study began by inventing more robust and lower power Wireless Sensor Networks (WSNs) to monitor and control temperatures and comfort in buildings. Yet, WSNs were, and still are, hampered by the use of replaceable batteries. Work thus focused on scavenging and storage, which has matured from macro scale devices to work on vibration-scavenging devices at the MEMS scale. Pneumatic dispenser printing of millimeter scale batteries and super-capacitors is also in progress. • In previous years, Dr. Wright directed (with Professor Sequin from Computer Science) the CyberCut project that began in the 1990s. The National Science Foundation grants during the period were called CyberCut, MOSIS++; Agent-based Precision Manufacturing; and an Investigation of Design for Manufacturability Metrics and Methods. The key intellectual idea was to equip ‘upstream’ designers with knowledge of the ‘downstream’ manufacturing equipment and the geometrical constraints of tools and equipment. CURRENT PROPOSALS Source of Support: UC CIEE, Total Award Amount: $5,485,813, 03/01/03 – 09/15/09 Person-Months per Year Committed to the Project: Cal: 0.00 Acad: 0.00 Sumr: 1.00 Source of Support: UC CIEE, Total Award Amount: $1,500,000, 06/22/07-06/30/10 Person-Months per Year Committed to the Project: Cal: 0.00 Acad: 0.00 Sumr: 1.00 Source of Support: LBNL, Total Award Amount: $220,295, 04/01/08-09/30/10 Person-Months per Year Committed to the Project: Cal: 0.00 Acad: 0.00 Sumr: 0.00 PENDING PROPOSALS Source of Support: NSF, Total Award Amount: $10,000,000, 07/15/09-07/14/14 Person-Months per Year Committed to the Project: Cal: 0.00 Acad: 0.00 Sumr: 0.00 Source of Support: Cal RA CEC, Total Award Amount: $255,545, 07/1/09-06/30/10 Person-Months per Year Committed to the Project: Cal: 0.00 Acad: 0.00 Sumr: 0.00 Source of Support: California HealthCare Foundation, Total Award Amount: $54,769, 05/26/09-08/25/09 Person-Months per Year Committed to the Project: Cal: 0.00 Acad: 0.00 Sumr: 0.00 Source of Support: DOE, Total Award Amount: $1,250,000, 06/1/09-05/31/14 Person-Months per Year Committed to the Project: Cal: 0.00 Acad: 0.00 Sumr: 0.00 Source of Support: Siemens, Total Award Amount: $217,971, 03/1/09-03/31/10 Person-Months per Year Committed to the Project: Cal: 0.00 Acad: 0.00 Sumr: 0.00 Source of Support: NIST, Total Award Amount: $1,500,000, 05/1/10-04/30/13 Person-Months per Year Committed to the Project: Cal: 0.00 Acad: 0.00 Sumr: 1.00

Source of Support: US DOE, Total Award Amount: $1,987,675, 10/01/09-09/30/11 Person-Months per Year Committed to the Project: Cal: 0.00 Acad: 0.00 Sumr: 1.00

David E. Culler, Professor, Computer Science Division --- EECS, University of California, Berkeley, CA 510-643-7572, [email protected], http://www.cs.berkeley.edu/~culler Education Massachusetts Institute of Technology, Cambridge MA Ph.D. in Computer Science, June 1989., S.M. in EE&CS, Jan. 1985. University of California, Berkeley CA, A.B. in Mathematics, August 1980, cum laude. APPOINTMENTS University of California, Dept. of Electrical Engineering and Computer Science, Professor College of Engineering Chief Information Officer Chair, UCB Conflict of Interest Committee (7/09 – now), Campus Technology Council Arch Rock Corporation [5/05 – now] CoFounder, Chairman and CTO Intel Research, Berkeley [6/2001 --- 8/2003] Founding Laboratory Director National Energy Research Scientific Computing Center, Berkeley CA [5/96 --- now] . AWARDS AND HONORS ACM Sigmobile Outstanding Achievement Award, 2007, Harold Friesen Chair of Electrical Engineering and Computer Sciences, 2006, National Academy of Engineering Member, 2005, IEEE Fellow, 2006, ACM Fellow, 2003, Scientific American top 50 Researchers 2003, Technology Review 10 Emerging Technologies that Will Change the World, 2003. National Science Foundation Presidential Faculty Fellowship in Engineering, 1992. National Science Foundation Presidential Young Investigator Award, 1990. RELATED PUBLICATIONS Design and Implementation of a High-Fidelity AC Metering Network, Xiaofan Jiang, Stephen Dawson-Haggerty, Prabal Dutta, and David Culler, 8th international conference on Information processing in sensor networks, (IPSN SPOTS 2009) March 2009 (to appear). IP is dead, long live IP for wireless sensor networks, J. Hui and D. Culler, 6th ACM conference on Embedded network sensor systems, Nov. 2008. An Architecture for Local Energy Generation, Distribution, and Sharing, Mike M. He, Evan M. Reutzel, Xiaofan Jiang, Randy H. Katz, Seth R. Sanders, David E. Culler, Ken Lutz IEEE Conference on Global Sustainable Energy Infrastructure (Energy2030′08), Nov. 2008. Transmission of IPv6 Packets over IEEE 802.15.4 Networks, G. Montenegro,N. Kushalnagar,J. Hui, and D. Culler, IETF RFC 4944, September 2007. Design, Modeling, and Capacity Planning for Micro-Solar Power Sensor Networks, Jay Taneja, Jaein Jeong and David Culler , 7th international conference on Information processing in sensor networks, (IPSN SPOTS 2008) April 2008 Other Significant Publications The Emergence of a Networking Primitive in Wireless Sensor Networks , Philip Levis, Eric Brewer, David Culler, David Gay, Samuel Madden, Neil Patel, Joe Polastre, Scott Shenker, Robert Szewczyk, and Alec Woo, Communications of the ACM, Volume 51, Issue 7, July 2008. Trickle: A Self-Regulating Algorithm for Code Propagation and Maintenance in Wireless Sensor Networks, Philip Levis, Neil Patel, David Culler, and Scott Shenker, NSDI 2004 (best paper) SPINS: Security Protocols for Sensor Networks. Adrian Perrig, Robert Szewczyk, Victor Wen, David Culler, J.D. Tygar. Mobicom 2001. (award paper). The Ninja architecture for robust Internet-scale systems and services, Steven D. Gribble, Matt Welsh, Rob von Behren, Eric A. Brewer, David Culler, N. Borisov, S. Czerwinski, R. Gummadi, J. Hill, A. Joseph, R.H. Katz, Z.M. Mao, S. Ross, and B. Zhao, Journal of Computer Networks, Volume 35, Issue 4, March 2001. (award paper).

System Architecture Directions for Networked Sensors, J. Hill, R. Szewcyk, A. Woo, D. Culler, S. Hollar, K. Pister, 9th International Conference on Architectural Suport for Programming Languages and Operating Systems, Nov. 2000, pp. 93-104 Parallel Computer Architecture: A Hardware/Software Approach, David E. Culler and Jaswinder Pal Singh with Anoop Gupta, Morgan-Kaufmann Inc., 1999. A Case for NOW (Networks of Workstations), T. Anderson, D. Culler, and D. Patternson IEEE Micro, Feb. 1995 (award paper) LogP: Towards a Realistic Model of Parallel Computation, D. Culler, R. Karp, D. Patterson, A. Sahay, K. Schauser, E. Santos, R. Subramonian, and T. von Eicken. Proceedings of the Fourth ACM SIGPLAN Symposium on Principles and Practice of Parallel Programming, May 1993 (Award Paper). Active Messages: a Mechanism for Integrated Communication and Computation, T. von Eicken, D. Culler, K. Schauser, and S. Goldstein. Proceedings of the 19th International Symposium on Computer Architecture, Gold Coast, AUS, May 1992 (Award Paper). SYNERGISTIC ACTIVITIES IETF ROLL – Chairing IETF working group on routing over low power and lossy networks (ROLL) which focuses on embedded wireless networks for industrial processes, building controls, home automation, and urban management. Also active in IETF 6 LOWPAN. Lead the worldwide TinyOS open source community developing wireless sensor networks and the Berkeley Wireless Embedded Systems lab which has designed five generations of motes. UCB Associate CIO and COE CIO leading efforts to utilize IT to dramatically reduce building energy consumption. Current: Title: CRI: Scalable Wireless Embedded Sensor Network Evaluation Facility Source of Support: National Science Foundation; Total Award Amount: $1,343,017 Location of Project: UC Berkeley; Person-months Per Year Committed to Project: Sumr: 0.2 Title: Toward a Petabyte Storage Infrastructure Source of Support: National Science Foundation; Total Award Amount: $1,800,000 Location of Project: UC Berkeley; Person-months Per Year Committed to Project: 0.00 Pending: Title: LoCal - A Radical Distributed Architecture for Local Energy Generation, Distribution, and Sharing Source of Support: National Science Foundation; Total Award Amount: $10,000 Location of Project: UC Berkeley; Person-months Per Year Committed to Project: 0.00 Title: Distributed Intelligent Automated Demand Response (DIADR) Building Management System Source of Support: US DOE; Total Award Amount: $1,997,456 Location of Project: UC Berkeley; Person-months Per Year Committed to Project: Sumr: 2.0 Collaborators in the Last 48 Months Not Listed Above: Wei Hong , Larry Peterson, Deborah Estrin PhD Students Advised: William Kramer, Jonathan Hui, Sukun Kim, Kamin Whitehouse, Robert Szewczyk, Phillip Levis, Joe Polastre, Alec Woo, Frederick Wong, Jason Hill. Matt Welsh, Philip Buonadonna, Brent Chun, Alan Mainwaring, Richard Martin, Andrea Dusseau, Steven Lumetta, Seth Goldstein, Thorsten von Eicken, Klaus Schauser, Bruce Holmer / PhD Advisor: Prof. Arvind (MIT)

Dr. Gary L. Baldwin received his B.S., M.S., and Ph.D. degrees in Electrical Engineering and Computer Science from the University of California, Berkeley, in 1966, 1967, and 1970, respectively. Dr. Baldwin has extensive experience in the corporate arena, holding technical and management positions at Bell Telephone Laboratories in Holmdel, New Jersey, and HewlettPackard Laboratories in Palo Alto, California, where his research involved microwave and optical devices. He served for twelve years as the Director of the Solid-State Technology Laboratory and was a member of the senior management team of HP Labs. A former Acting Assistant Professor of Electrical Engineering at UC Berkeley, Dr. Baldwin has served at U.C. Berkeley as the Executive Director of the Gigascale Silicon Research Center (1999-2003), the Executive Director of CITRIS (2003-2008), and currently serves as the CITRIS Director of Special Projects in Energy and the Environment. Publications: None Synergistic Activities: None Current and Pending Support: Source of Support: US Department of Energy National Energy Technology Laboratory Total Award Amount: $1,997,456; 10/01/09-09/30/11 Person-Months per Year Committed to the Project: Cal: 3.00 Acad: 0.00 Sumr: 0.00 Collaborators and Co-editors: None Graduate and Postdoctoral Advisors and Advisees: None

T H O M A S   G R U E N E W A L D (Co‐PI)    +1(609)734‐3546    PROFILE 

EXPERIENCE  10/2008 – present 

10‐2005 – 10/2008 

09/2004 – 06/2006 

08/2003 – 09/2004 

04/2003 – 07/2003 

01/2002 – 03/2003 

[email protected]  15 years of experience in all phases of the software development thru life cycle and is  successfully managing and mentoring a global team of 10 people. Deep experience in  embedded system programming and strong multi project management skills and accustomed  to meet tight deadlines.    Siemens Corporate Research, Princeton, NJ  Project Manager – High Performance Building  Leading a global team of 10 located in China, Germany and US of research scientists and  software engineers and interns in the R&D areas High Performance Buildings, an innovation  project of Siemens Corporate Research in partnership with Siemens Building Technology and  universities, which focuses on aspects of high performance and green buildings in the area of  BIM Integrated Engineering, Energy Simulation, Smart Grid Integration and Building as a  Manufacturing Plant. Results of the project are directly applicable to current and future  products and solutions.    Siemens Corporate Research, Princeton, NJ  Project Manager – Network Management & Control Systems Platform (SCADA)  Lead  global  team  of  20  located  in  India,  Germany  and  US  of  software  engineers  and  contractors  and  interns  in  terms  of  the  service  oriented  platform  of  the  Managed  Object  Component which allows to build a custom object model using a configuration,  a software  container  to  host  disparate  (C,  C++,  Java  and  C#)  software  components  and  let  them  communicate  which  each  other,  and  a  Common  Name  Service  and  System  Management  Components.    Siemens Corporate Research, Princeton, NJ  Project Manager – Simatic Failsafe and Simatic FM458 Turbine Controller integration into  SPPA‐T3000  Integration of Simatic Failsafe software component and Simatic FM458 turbine controller  software component into Siemens Power Plant Automation T3000 using Siemens  Automation API and Siemens OpenES interfaces. Invented software which allows seamless  integration of regular client software into a multi user client‐server environment with zero  code change. Therefore a window remoting tool was developed to remote defined window  to connected T3000 clients. Development of a dialog acknowledger tool to automatically  acknowledge popping windows on the server in a controlled manner.  Wrote  invention  disclosure  of  new  way  of  window  remoting  for  seamless  integration  into  software products with zero code change. Lead a group of 4 people. (C/C++, C#, Java)    Siemens Corporate Research, Princeton, NJ  Project Lead – Simatic Integration into Next Generation Workbench Framework  Designed  and  developed  feasibility  study  for  future  Simatic  workbench.  Integration  of  existing  Simatic  SKA  editor,  Simatic  Step7  block  functionality  and  Simotion  CamTool  and  Simatic  Motion  Control  Chart  into  the  next  generation  workbench  by  reusing  code.  Developed  multi  screen  support  on  application  level  for  the  workbench.  Created  and  presented  demos  to  the  Siemens  architecture  team.  Leading  Project  group  of  4  people  in  average. (C/C++, C#).    Siemens Corporate Research, Princeton, NJ  Project Lead – Automation & Control Studies  Developed  demo  of  motion control functionality  in WinAC  with Simotion, Easy  Motion  and  OSB motion controller (C/C++). Development prototype and design of object oriented motion  control  interface  and  language  to  transform  object  motion  to  actuator  control.  Design  and  implementation  of  a  pure  Java  Configuration  Applet  for  a  web  based  PLC  (ISAC)  using  web  based HTML/XML protocol.    Siemens Energy & Automation, Princeton, NJ 

10/1996 – 12/2001 

04/1996 – 09/2001 

10/1996 – 03/1999 

  EDUCATION 

PATENT   

Software Engineer – WinAC Product Development  Ported  WinAC  to  VxWorks  using  Tornado.  Redesigned  S7  protocol  server  for  WinAC  V7  architecture.  Developed  S7Server  as  an  independent  component  in  WinAC  by  using  and  defining the TPI and V7 interface of the WinAC CPU core component. Implementing Simatic  S7Server communication protocol and services (OVS, TIS, PBK, Diagnostic, BZUE) in C/C++.     Siemens Energy & Automation, Princeton, NJ  Software Engineer – Web Based Unified Panel for Simatic WinAC Software Logic Controller   Developed  web  based  unified  software  Java  applet  panel  using  an  event  driven  HTTP/XML  protocol  to  control  to  control  the  WinAC.  The  Simatic  WinAC  is  a  PC  based  software  logic  controller.    Siemens Software House, Erlangen, Germany  Software Engineer – Embedded Real Time System Motion Controller Development  Software development for embedded motion control product OSB (Open Software Box) with  real  time  operating  systems  (RMOS,  QNX,  WindowsNT  with  real  time  extension).  Further  development  and  function  expansion  of  the  OSB  startup  tool.  Execution  of  OSB  software  tests  to  ensure  quality  of  product.  Porting  of  OSB  to  Windows  Real  Time  eXtension  (RTX).  Design and implementation of Real Time Java extension for OSB motion controller for RMOS  and Windows NT.    Siemens Software House, Erlangen, Germany  Software Engineer – Video Conferencing Tool for ATM Networks & TFA Software for ISDN  Telephones  Developed  multimedia  video  conferencing  system  software  featuring  ATM  (Asynchronous  Transfer Modus) networks on Windows with Visual C++. Integration of Microsoft NetMeeting  using DCOM interfaces. Development of TFA Software (Telephone Feature Access) for ISDN  telephones.  Bachelor in Automation Engineering and Automation Process Control, 1996  Siemens Technical Academy, Erlangen, Germany     Software Container For Disparate Software Components   (#2007P21381 US, 60/976.812)  New way of enabling software components that are implemented in different  technologies, e.g. C++, Java or Microsoft .NET, to be loaded into one process and  communication with each other.   

Invention Disclosure: Dynamic Emergency Evacuation Route Planning Based on  Building Information Model (BIM) (#2008E21326 US)  Dynamic evacuation route planning (DERP) based on building formation model and run‐ time building fire monitoring is proposed for emergency evacuation in the event of a  fire, explosion, spill or other emergency using BIM.   

TRAINING 

PUBLICATIONS  CURRENT AND  PENDING SUPPORT  SYNERGISTIC  ACTIVITIES   

Invention Disclosure: Integration of Interoperable Building Model Into Engineering  Systems (#2009E09165 US)  The proposed invention of using vendor independent IFC in building model is designed  to automate and reduce the engineering effort. This solution eliminates the errors that  are prone to occur when the same effort is performed manually.     Experienced Managers  Information Security  SCRUM software development process  None  None  Project lead of the High Performance Building project. (please see experience) 

Yan Lu 5 Banff Dr. West Windsor, NJ 08550 609-306-9293 [email protected] -------------------------------------------------------------------------------------------Education •

Carnegie Mellon University, Pittsburgh, PA, 1997 – 2001 PhD in ECE Dept. GPA 4.0/4.0. Thesis title: Advanced servo control for tape transport system • Tsinghua University, Beijing, P.R. China, 1994-1997 M.S. in Automation Department. Thesis title: Process control of large-delay system via Neural Networks • Tsinghua University, Beijing, P.R.China, 1989-1994 B.E. in Automation Department. Thesis title: Computer control for a guide aligning system with CCD sensors Research Experience •

2004-present, Research Scientist, Simens Corporate Research , Princeton, NJ Working on creating innovative working solutions for automatic manufacturing system, energy control system and building automation system. Research Projects include ™ Co-PI for DOE funded project--Protecting Intelligent Distributed Power grid from Cyber Attacks : Working on a multi-layer security framework for protecting power T&D (transmission and distribution) automation systems against cyber attacks. ™ Technical Lead for Office of Naval Research funded Project--Reconfigurable, Cooperative, Optimized Vital resource Demonstrator, Working on developing a resilient and survivable shipboard engineering control system (ECS) at Naval Surface Warefare Center Carderock Division(NSWCCD) Philadelphia test sites with o Distributed intelligence with reconfigurability o Multilayer agent-based decision-making o Enhanced survivable communication.. ™ Corporated funded research projects including o Developed Agent-based distributed control system based on Siemens controllers: Agent has model-based diagnosis capability; Agent has direct control on Simens control devices; Agents communictae through JADE (FIPA compliant); The system was applied to Honolic manufacturing system, proactive agent-based system applied to Ship Auxilliry System control with model-based planning o High Performance Building: Building automation; building information model; Building energy simulation; Function Model based building control system fault diagnosis and recovery; Tactical Building information and control system for first responders. Developed Building information based first responder decision support software. o Motion control algorithm development: Control object motion by translating object motion to actuator control; distributed motion control algorithm development; Distriuted motion planning o Distributed energy resource control and integration; Vitual power plant scheduling and optimization; Function Model based wind turbine design and production modeling.

•

2002-2004, Research Technical Staff, Seagate Research Center, Seagate Technology, Pittsburgh, PA Responsible for high TPI tracking capability development on a spinstand tester. Responsible for investigating the dynamics of the flexible, multi-stage, non-linear structures of the magnetic head actuator and providing insight for the mechanical design. Responsible for synthesizing controllers to stabilize the head positioning system and minimize disturbance induced position errors. Also responsible for conducting research into advancd control schemes, like MIMO (multi-input, multi output) design and H-infinity design, for the future architecture of magnetic recording servo system. Responsible for position error signal analysis to get good position feedback for the closed-loop head positioning system, including servo pattern study and encoding scheme improvement. Select Publications • •

• • • • • • •

D. Wei, Y. Lu, P. Skare, M. Jafari, K. Rhode and M. Muller, Power Infrastructure Security: Fundamental Insights of Potential Cyber Attacks and Their Impacts on the Power Grid, Workshop on Future Directions in Cyber-physical Systems Security, July 22-24, Newark, NJ. P. Zhao, Y. Lu, M. A. Jafari & D. Golmohammadi A Multi-Criteria Economic Evaluation Framework for Control System Configuration – Framework and Case Study, Proceeding of 2nd IFAC Workshop on dependable Control of Discrete Systems, June 10-12, 2009, Bari, Italy. Y. Lu, F. Ferrese and M. Labouliere, Anti-Threat Mobile Agent-based Ship Freshwater Cooling System, American Society of Naval Engineer Automation & Controls Symposium 2007. J. Dang, Y. Lu, P. Zhao and M. Jafari, Wholesale Power Trading through Concurrent Multiple-Issue Negotiation, accepted by Transaction of the Institute of Measurement and Control, 2008. Y. Lu, M. Jafari, Distributed Intelligent Industrial Automation – Issues and Challenges, Issue No. 1, Automation Technology in Practice 2007. P. Zhao, Y. Lu, Towards Dependable Distributed Systems - An Online Framework for Fault Detection and Avoidance, IFAC Workshop on Dependable Control of Discrete Systems, July 2007. Y. Lu, M Karaman, Robust attenuation of disturbances beyond open-loop bandwidth using phase-stabilization and H infinity Loop-shaping for disk drive, ISPS 2004. M. Karaman and Y. Lu, Comparison of Suspension Based and Slider Based Microactuators for Track Following Performance, AMC2004. Y. Lu and S. Panda, Tutorial on Control System Design for Tape Drives, American Control Conference 2003.

Current and Pending Support ™ Co-PI for DOE funded project--Protecting Intelligent Distributed Power grid from Cyber Attacks Total 1.7 million from DOE from 08/07-11/09, contributing 4 months per calendar year to the project ™ PI from SCR for ONR funded project—Anti-threat Ship Control System Subcontracted by John Hopkins University/Applied Physics Lab. Total amount 600K for 3 years from 01/01/09-12/31/11, contributing 2 month per calendar year to the project

Pranav Bhatt 8, Princeton Highlands Blvd, Princeton, NJ 08540 Cell: (732) 887 4405 ● Email: [email protected]

TECHNICAL PROFICIENCY

LANGUAGES/TECHNOLOGY: C,C++,VISUAL C++ (MFC,ATL/COM) EMBEDDED VISUAL C++, C#, VISUAL BASIC, ACTION SCRIPT 3.0, FLASH SIMULATOR: PLCSIM, SINGULATOR SOFTWARE: STEP 7, PROTOOL PRO, CLEARCASE, MS VISIO, MS OFFICE XP/2005 DEVELOPMENT ENVIRONMENT: MS VISUAL STUDIO 2005, MICROSOFT PLATFORM BUILDER 5.0 OS: WINDOWS XP, WINDOWS CE 3.0/4.0 /5.0

RESPONSIBILITIES/QUALIFICATIONS • • • • • • • • • •

•

Possesses expertise in Object Oriented Development and C++/VC++/Embedded VC++ development Strong experience in Win32 application development using Sockets, Shared Memory, Multi Threading. Experienced in SOA application development Execute projects in entirety from gathering requirements, to design, to implementation and testing to delivery phase Develop functional requirements, design documents and user documentation Write/Contribute to proposals to acquire projects from Government and other Siemens Business Units Implement prototypes and provide feasibility reports Coordinate and execute projects by leading small teams Mentor, monitor and provide support to new hires and interns Provide support for Siemens products to internal/external Siemens customers Strong understanding of Simatic Soft PLCs and SCADA system (Human Machine Interface) software to achieve totally integrated solutions

PROFESSIONAL EXPERIENCE SIEMENS CORPORATE RESEARCH, PRINCETON, NJ SOFTWARE DEVELOPER, JUNE 2000 – PRESENT Project: Role: Technology: Description:

SOA Framework for Embedded Devices ( Jan 2009 to Present) Lead Developer C, C++, C# Middleware facilitating communication and integration of embedded devices in to MES/SCADA system

Responsibilities: ƒ ƒ ƒ ƒ

Project: Role: Technology: Description: Responsibilities: ƒ ƒ ƒ

Key contributor in design and development of the framework Development of core components providing diagnostic, C++ wrapper for interoperability Providing architecture/technical support to customers developing their object model to utilize the framework better Conduct workshops to educate the customer about proper usage of the framework and its fundamentals Network Management & Control System (May 2007 to Jan 2009) Lead Developer/Sub-Project Lead C++, C#, Java, MFC System Management using Services Based Communication Development of a core service used in several Siemens products Key Developer for the project possessing in-depth knowledge of core components Design, develop & coordinate the service implementation to completion

ƒ ƒ ƒ ƒ ƒ Project: Technology: Role: Description: Responsibilities: ƒ ƒ ƒ Project: Technologies: Role: Description:

Conduct workshops to provide knowledge and guidance to NM&CS users Coordinate with other project members/leads to ensure timely delivery of components Generate test cases & provide unit tests developed components Provide design and implementation specification and user documentation M&C service is designed to adapt different user models as per Business Unit requirements Simatic WinCC Prototype (February 2006 to January 2007) C++, COM, MFC Developer Feasibility Study and Migration Strategy for WinCC to Port to Service Based Communication Ported existing product to NM&CS based platform with minimal to no changes to the existing components Feasibility study proved valuable to split a monolithic client/server based architectures to modular service based architecture Coordinated the project along with 3 other developers Multiple Projects (June 2000 to July 2006) C++, Managed C++, C#, VC++, VB, MFC, ATL/COM Developer Several projects including Singulator, PLCSim, Software PLC, SDI, ActiveX Control development etc.

Responsibilities: • • • • • •

Contributed in design & implementation of an SDI application using MFC/ATL/COM which provides unified interface Soft PLCs and reduced source maintenance significantly. Involved in Soft PLC development for embedded device (Simatic Multi Panel, Windows CE 3.0) and provided integrated solutions with HMI (Embedded VC++). Designed and implemented Tuning Panel and PLC ActiveX Controls to support existing applications and reduced user configuration. Developed applications providing web based (HTTP) communication. Ported Soft PLCs on Pocket PC (MIPS, ARM, and SH3). Responsible for prototype development, prepare and present demos to internal/external Siemens customers

EDUCATION & CREDENTIALS Degree / Certificate

University / Institute

GPA

Year

B.S Computer Engineering

Rutgers University, New Brunswick, NJ

3.16

12/2000

PUBLICATIONS: NONE SYNERGISTIC ACTIVITIES: NONE COLLABORATORS/COEDITORS: NONE GRADUATE & POST DOCTORAL ADVISORS/ADVISEES: NONE

Pornsak Songkakul, D.Eng, LEED-AP Siemens Building Technologies, Inc. 1000 Deerfield Parkway, Buffalo Grove, IL 60089 (847) 941 5642 [email protected] Summary: Dr. Pornsak Songkakul received a D. Eng. in Mechanical Engineering in 1987 from Texas A&M University. He is a member of the System Application department of Siemens Building Technologies, Inc. He has 20 years of experiences in developing products and applications for HVAC systems, controls, energy efficiency and optimization techniques, renewable energy technologies, wireless communication, LEED-NC and LEED-EB certification processes. He has also extensive knowledge of commercial building construction industry, HVAC and controls systems design practices. He is also a LEED-AP Education: 1981 Chulalongkorn University, Bangkok, Thailand, B.Eng. Mechanical Engineering 1983 Texas A&M, College Station, TX, M. Eng.Mechanical Engineering 1987 Texas A&M, College Station, TX, D. Eng Mechanical Engineering Positions and Employment: 1981 HVAC Design Engineer, Allied Engineering Company, Bangkok, Thailand 1985 – 1986 Research Intern, Systems and Controls Department, Johnson Controls, Inc., Milwaukee, Wisconsin 1986 - 1987 Research Associate, Mechanical Engineering Department, Texas A&M University, College Station, Texas 1987 - 1992 Sr. Research Scientist, Knowledge Engineering Department, Johnson Controls, Inc., Milwaukee, Wisconsin 1992 – 1994 Team Leader, Controls Group Research Department, Johnson Controls, Inc., Milwaukee, Wisconsin 1995 – 1999 Member of the Corporate Advanced Systems Engineering Department, Landis & Gyr Technology Innovation Corp., Buffalo Grove, Illinois 1999 – 2005 Senior Principal Engineer, System Applications, Siemens Building Technology, Building Automation, Buffalo Grove, Illinois 2005 - Present: Senior Principal Engineer, High Performance Building Systems and Wireless Solutions, Siemens Building Technology, HVAC Products, Buffalo Grove, Illinois Patents: 2007 U.S. Patent No. 7,164,972: Method and Apparatus for Representing Building System, January 16, 2007

Synergistic Activities: Technical committee member: American Society of Heating, Refrigerating, and AirConditioning Engineers, ASHRAE Member: Continental Automated Building Associations, CABA Member: ZigBee Alliance Technical committee member: Organization for the Advancement of Structured Information Standards, OASIS Publications: None Post-Doctoral: None

Siemens Corporate Research, Inc. 755 College Road East Princeton, NJ 08540 +1 (609) 734 6563 [email protected]

Florian Ersch  Professional Experience  10/2006 – present

Software Engineer at Siemens Corporate Research in Princeton, NJ • Development of prototypes related to Building Information Modeling. • Developer for a runtime platform core, specifically an object repository within the core. • Development of a configuration tool for a runtime platform core. • All activities included supervising interns and/or contractors.

08/2006 – 10/2006

Diploma Thesis at Siemens Corporate Technology in Munich, Germany • Topic: “Towards a Methodology for Entity-Driven Management of Software Development"

05/2005 – 11/2005

Intern at Siemens Corporate Research in Princeton, NJ • Developer for a runtime platform core.

10/2003 – 08/2004

Intern at Siemens Communication in Munich, Germany • Development a prototype for DRM protected mobile content.

Education  10/2002 – 10/2006

Student at Munich University of Applied Sciences • Department of Computer Science • Degree: Diplom Informatik (FH)

09/2004 – 05/2005

Exchange Student at California Polytechnic State University at San Luis Obispo, CA • Department of Computer Science

Skills  Language

German (Native) English (Fluent)

Programming

Java, .net (C#), C++, UML, XML, HTML Visual Studio, Eclipse

Software Engineering

Strong background in: • Software Architecture and Object Modeling • Programming

Publications:  None

Synergistic Activities:  None

Current and Pending Support:  None

BIOGRAPHICAL SKETCH and OTHER SUPPORT NAME Mary Ann Piette

POSITION TITLE Deputy Group Leader/Staff Scientist Lawrence Berkeley National Laboratory

EDUCATION and TRAINING

INSTITUTION AND LOCATION

DEGREE (if applicable)

University of California, Berkeley

B. A.

University of California, Berkeley

M. S.

University of Technology, Gothenburg, Sweden

Licentiate

YEAR(s)

FIELD OF STUDY

1983 Physical Science, Emphasis in Energy Studies 1988 Mechanical Engineering, Emphasis in Heat Transfer 1992 Building Services Engineering

Research and Professional Experience:

Mary Ann Piette is a Staff Scientist at Lawrence Berkeley National Laboratory and the Research Director of the PIER Demand Response Research Center. She is the Deputy Head of the Building Technologies Dept. She has been at LBNL since 1983 and has extensive experience evaluating the performance of energy efficiency and demand response in large facilities. She has authored about 100 papers on efficiency and demand response. The PIER Demand Response Research Center is a 5-year old Center to plan, manage, conduct and disseminate DR research for the California Energy Commission. In 2006 Ms Piette received the Benner Award at the National Conference on Building Commissioning for contributions to making commissioning “business as usual”. Oct 1998 - Present, Deputy Group Leader/Staff Scientist Building Technologies Department, Lawrence Berkeley National Laboratory Research Director, CEC PIER Demand Response Research Center Responsible for overall Center planning, management, and R&D, both technical and financial management of research projects scoped at $13 M for five years. Identify and develop opportunities for new research and collaboration. Colead Commercial Building Systems Group, organize and lead multi-year research activities to analyze performance data (measured, simulated, and forecasted) on the energy performance and cost-effectiveness of technologies, equipment, systems, and designs in new and existing commercial buildings. Develop new technologies and strategies for energy savings. Jan. 1991 - Oct. 1998, Staff Scientist Energy Analysis Program, Lawrence Berkeley National Laboratory Conducted research similar to above projects while part of Energy Analysis Program. May 1989 - Nov. 1990, Visiting Researcher, Chalmers University of Technology, Gothenburg, Sweden Sabbatical with the International Energy Agency's Center for the Analysis and Dissemination of Demonstrated Energy Technologies (CADDET). Performance analysis of diurnal thermal storage systems and controls for electric load management in commercial buildings. Assisted in the design of CADDET analysis projects and data compilation activities. May 1989 - Dec. 1983 Senior Research Associate, Center for Building Sciences, Lawrence Berkeley Laboratory Responsibility for data collection and analysis of the commercial segment of the Buildings Energy-Use Compilation and Analysis (BECA) project that evaluates conservation and load-shaping techniques in new and retrofitted buildings.

Performed technology assessments of thermal cool storage and energy-efficient lighting technologies, emphasizing physical and economic characteristics. June 1983 - Dec. 1983, Technical Assistant 2, Energy-Efficient Buildings Program, Lawrence Berkeley Laboratory Identified, obtained, and evaluated data on the performance and cost-effectiveness of energy saving measures in new and retrofitted commercial buildings. Publications:

Electronic copies of these publications are posted at drrc.lbl.gov/publications. M.A. Piette, G. Ghatikar, S. Kiliccote, E. Koch, D. Hennage P. Palensky and C. McParland. Open Automated Demand Response Communications Specification (Version 1.0). LBNL-1779E. November 2008. California Energy Commission, PIER Program. CEC-500-2009-063. April 2009. M.A.Piette, M.A., S. Kiliccote, and G. Ghatikar. Linking Continuous Energy Management and Open Automated Demand Response. Presented at the Grid Interop Forum, Atlanta, GA, November 11-13, 2008. LBNL-1361E. November 2008. E. Koch and M.A. Piette. Scenarios for Consuming Standardized Automated Demand Response Signals. Presented at the Grid Interop Forum, Atlanta, GA, November 11-13, 2008. LBNL-1362E. November 2008. S. Kiliccote, S and M.A. Piette. Automation of Capacity Bidding with an Aggregator using Open Automated Demand Response.Lawrence Berkeley National Laboratory. DRRC Report. CEC-500-208-059. October 2008 S. Kiliccote, M.A. Piette, G. Wikler, J. Prijyanonda, and A. Chiu. Installation and Commissioning Automated Demand Response Systems. Proceedings, 16th National Conference on Building Commissioning, Newport Beach, CA, April 22-24, 2008. LBNL-187E. April 2008. G. Wikler, A. Chiu, M.A. Piette, S. Kiliccote, D.Hennage, and C. Thomas. Enhancing Price Response Programs through Auto-DR: California's 2007 Implementation Experience. Proceedings, 18th National Energy Services Conference and Exposition, Clearwater Beach, FL, January 28-31, 2008. LBNL-212E. January 2008. K. Coughlin, M.A. Piette, C. Goldman and S. Kiliccote. Estimating Demand Response Load Impacts: Evaluation of Baseline Load Models for Non-Residential Building in California. LBNL-63728. January 2008. J. Han and M.A.Piette. Solutions for Summer Electric Power Shortages: Demand Response and its Applications in Air Conditioning and Refrigerating Systems. Lawrence Berkeley National Laboratory. DRRC Report. LBNL-63806. January 2008. N.A. Motegi, M.A. Piette, D.S. Watson,, S. Kiliccote, P Xu. Introduction to Commercial Building Control Strategies and Techniques for Demand Response. Report for the California Energy Commission, PIER. LBNL59975. May 2007. M.A. Piette, D.S. Watson, N.A. Motegi, S. Kiliccote, P. Xu. Automated Critical Peak Pricing Field Tests: 2006 Pilot Program Description and Results. LBNL 62218. Report for the California Energy Commission, PIER and PG&E Emerging Technologies Program. May 2007. Current and Pending Support E214EE (Mary Ann Piette) California Energy Commission Demand Response Research Center Role: PI

4/1/2004 – 3/31/2011 $2.5M/Year

8 personmonths/yr

E838EE (Mary Ann Piette) Bonneville Power Administration Auto-Open Demand Response Technology Demonstration Project Role: PI

10/31/2008 – 12/31/2009 $518K

1 person-month/yr

BIOGRAPHICAL SKETCH and OTHER SUPPORT NAME Sila Kiliccote

POSITION TITLE Senior Scientific Engineering Associate Lawrence Berkeley National Laboratory

EDUCATION and TRAINING

INSTITUTION AND LOCATION

DEGREE (if applicable)

University of New Hampshire

B. S.

Carnegie Mellon University

M. S.

Carnegie Mellon University

M.S.

YEAR(s)

FIELD OF STUDY

1994 Electrical Engineering, Minor in Illumination and Optical Engineering 1995 Master of Building Science 2000 Master of Industrial Administration (equivalent of an MBA), on leave

Research and Professional Experience:

Sila Kiliccote is a Senior Scientific Engineering Associate in the Building Technologies Department at Lawrence Berkeley National Laboratory. She has been a part of the Automated Demand Response team since 2004. Her areas of interest include characterization of buildings and demand reduction, demand responsive lighting systems, building systems integration and feedback for demand-side management. July 2007 - Present, Senior Scientific Engineer Associate June 2004 – July 2007, Scientific Engineer Associate Demand Response Research Center and Lighting Resaerch Group,Building Technologies Department, Lawrence Berkeley National Laboratory • Automated Demand Response and OpenADR development, field testing and implementation studies • Participating Load Pilot –individual sites’ participation into California’s wholesale market using OpenADR.. • DR Potential Assessment of UC Merced campus (thermal energy storage) • Northwest OpenADR Technology Demonstration – DR potential of winter morning peaking commercial buildings. • Small Commercial DR Pilot Study • Establishing links between Energy Efficiency and Demand Response (DR) • New York Times Headquarter Building – Lighting and DR evaluations 1995 - 1999, Research Assistant Center for Building Performance and Diagnostics (CBPD) Department of Architecture, Carnegie Mellon University • Assistant Project Manager, Intelligent Workplace (IW), Pittsburgh, PA. • Design Reviewer, U.S. Embassy in Berlin, Berlin, Germany. • Integrated System Consulting, LG Kangnam Tower, Seoul, Korea. • Teaching, Building Physics, Department of Architecture. Publications:

Electronic copies of these publications are posted at drrc.lbl.gov/publications. M.A. Piette, G. Ghatikar, S. Kiliccote, E. Koch, D. Hennage P. Palensky and C. McParland. Open Automated Demand Response Communications Specification (Version 1.0). LBNL-1779E. November 2008. California Energy Commission, PIER Program. CEC-500-2009-063. April 2009. M.A.Piette, M.A., S. Kiliccote, and G. Ghatikar. Linking Continuous Energy Management and Open Automated Demand Response. Presented at the Grid Interop Forum, Atlanta, GA, November 11-13, 2008. LBNL-1361E. November 2008. S. Kiliccote, S and M.A. Piette. Automation of Capacity Bidding with an Aggregator using Open Automated Demand Response.Lawrence Berkeley National Laboratory. DRRC Report. CEC-500-208-059. October 2008 S. Kiliccote, M.A. Piette, G. Wikler, J. Prijyanonda, and A. Chiu. Installation and Commissioning Automated Demand Response Systems. Proceedings, 16th National Conference on Building Commissioning, Newport Beach, CA, April 22-24, 2008. LBNL-187E. April 2008. G. Wikler, A. Chiu, M.A. Piette, S. Kiliccote, D.Hennage, and C. Thomas. Enhancing Price Response Programs through Auto-DR: California's 2007 Implementation Experience. Proceedings, 18th National Energy Services Conference and Exposition, Clearwater Beach, FL, January 28-31, 2008. LBNL-212E. January 2008. K. Coughlin, M.A. Piette, C. Goldman and S. Kiliccote. Estimating Demand Response Load Impacts: Evaluation of Baseline Load Models for Non-Residential Building in California. LBNL-63728. January 2008. N.A. Motegi, M.A. Piette, D.S. Watson,, S. Kiliccote, P Xu. Introduction to Commercial Building Control Strategies and Techniques for Demand Response. Report for the California Energy Commission, PIER. LBNL59975. May 2007. Rubinstein, F.M., and S. Kiliccote. Demand Responsive Lighting: A Scoping Study. Lawrence Berkeley National Laboratory. DRRC Report. LBNL-62226. January 2007 M.A. Piette, D.S. Watson, N.A. Motegi, S. Kiliccote, P. Xu. Automated Critical Peak Pricing Field Tests: 2006 Pilot Program Description and Results. LBNL 62218. Report for the California Energy Commission, PIER and PG&E Emerging Technologies Program. May 2007. Eleanor S. Lee, Glenn D. Hughes, Robert D. Clear, Luís L. Fernandes, Sila Kiliccote, Mary Ann Piette, Francis M. Rubinstein, Stephen E. Selkowitz.Daylighting the New York Times Headquarters Building: Final Report: Commissioning Daylighting Systems and Estimation of Demand Response. Lawrence Berkeley National Laboratory, Berkeley, CA. Draft report published on the project website: http://windows.lbl.gov/comm_perf/nyt_pubs.html Kiliccote S., Piette M.A. and Hansen D., Advanced Control and Communication Technologies for Energy Efficiency and Demand Response., Proceedings of Second Carnegie Mellon Conference in Electric Power Systems: Monitoring, Sensing, Software and Its Valuation for the Changing Electric Power Industry, Pittsburgh PA. January 2006. Current and Pending Support E214EE (Mary Ann Piette, PI) California Energy Commission Demand Response Research Center Role: Technical Lead, multiple research tasks

4/1/2004 – 3/31/2011 $2.5M/Year

3 personmonths/yr

E838EE (Mary Ann Piette, PI) Bonneville Power Administration Auto-Open Demand Response Technology Demonstration Project Role: Technical Lead

10/31/2008 – 12/31/2009 $518K

3 personmonths/yr

E732EE (Mary Ann Piette, PI) California Institute for Energy and the Environment AutoCPP Pilot – Commercial Buildings III (PG&E) Role: Technical Lead

6/24/2008 – 12/31/2009 $472.7K

3 personmonths/yr

Identification of Potential Conflicts of Interest or Bias in Selection of Reviewers: LBNL: Collaborators and Co-editors: Roger Levy, Levy Associates Ron Hofmann, CIEE Dan Hennage, Ed Koch, Akuacom Greg Wikler, Global Energy Partners

Appendix C1: Commitment Letters

Appendix D: Bibliography and References 1. “Linking Continuous Energy Management and Open Automated Demand Response,” Mary Ann Piette, Sila Kiliccote, and Girish Ghatikar, Lawrence Berkeley National Laboratory, GridWise Interop Forum, 2008 2. “Advanced Control Technologies and Strategies Linking Demand Response and Energy Efficiency,” Sila Kiliccote and Mary Ann Piette, Lawrence Berkeley National Laboratory, ICEBO 2005 Conference Paper September 1, 2005