Seminar 32 Intermediate HVAC Controls for Smart Grid...

transcript

2016 Annual Conference

St. Louis, Missouri

Donald J. ChmielewskiAssociate Professor

Illinois Institute of Technologychmielewski@iit.edu

Seminar 32 – Intermediate HVAC Controls for Smart Grid Applications

Smart Grid Coordination in Building HVAC Systems: Computational Efficiency of Constrained ELOC

Department of Chemical and Biological Engineering

Illinois Institute of Technology

Session Learning Objectives

1. Learn how EMPC can be used to coordinate building HVAC systems with dynamic electricity prices, and why EMPC with thermal energy storage requires a large prediction horizon to yield desired performance

2. Characterize buildings and heating systems in terms of ability for load shifting, and learn about strategies of rule based, predictive and optimal control

3. Understand the role of frequency regulation ancillary service in power grid operation, and the basic infrastructure setup necessary to provide frequency regulation with HVAC equipment

4. Learn about the practical control and operational considerations when retrofitting chillers to provide frequency regulation, and list factors to consider to gauge economic feasibility

ASHRAE is a Registered Provider with The American Institute of Architects Continuing Education Systems. Credit earned on completion of this program will be reported to ASHRAE Records for AIA members. Certificates of Completion for non-AIA members are available on request.

This program is registered with the AIA/ASHRAE for continuing professional education. As such, it does not include content that may be deemed or construed to be an approval or endorsement by the AIA of any material of construction or any method or manner of handling, using, distributing, or dealing in any material or product. Questions related to specific materials, methods, and services will

be addressed at the conclusion of this presentation.

Acknowledgements

Former Students:Benjamin Omell (PhD, 2013) Nation Energy Technology LabDavid Mendoza-Serrano (PhD, 2013) NalcoMing-Wei Yang (PhD, 2010) Taiwan ElectricJui-Kun Peng (PhD, 2004) Argonne National LabAmit Manthanwar (MS, 2003) Texas A&M University

Current PhD Students:Oluwasanmi Adeodu Jin Zhang

Funding:National Science Foundation (CBET-0967906 & CBET-1511925)Wanger Institute for Sustainable Engineering Research (IIT)

Outline/Agenda

• Motivation and Background

• Review of Economic Model Predictive Control (EMPC)

• Impact of Horizon Size on EMPC

• Economic Linear Optimal Control (ELOC)

• Constrained ELOC

• Impact of Horizon Size on Constrained ELOC

Generators with Dispatch Consumer DemandTransmission

Renewable Sources

Energy Storage

Smart Homes

Commercial Buildings

Smart Manufacturing

Motivation

A Dispatch Problem for Generators

Dispatch Capable

Generation Power Grid

Smart Grid Electric Power Network:

Demand

(Consumers)

Renewable

Generation

Responsive

Demand Energy Storage

Existing

Components

Expected

Future

Components

0 5 10 15 20

time (days)

Baseline

Baseline with Renewable Power

0 5 10 15 20

time (days)

Baseline with Renewable Power

Impact of Storage and DR

Incentive for Smart Grid Coordination

Operate systems to exploit these time-varying electricity prices.

170 171 172 173 174 175 176 177 178 179 180

Day of the Year

Historic electricity prices (Chicago, 2008), [PJM, 2013]

Building HVAC Example

Heat from

BuildingBuilding

Heat from

Environment

Consumption Chiller

Houston, TX (July, 2012)

Solid – Outside Temperature Dotted – Electricity Price

Heat from

BuildingBuilding

Heat from

Environment

Consumption Chiller

Heat to

Thermal

Energy Storage

Heat to

Chiller

23 24 25 26

Chiller Cooling Load (Qc)

Time (days)

23 24 25 26

Time (days)

Heat to Chiller Heat from Room

Thermal Energy Storage

Heat from

BuildingBuilding

Heat from

Environment

Consumption Chiller

Heat to

Thermal

Energy Storage

Heat to

Chiller

Heat from

BuildingBuilding

Heat from

Environment

Consumption Chiller

Heat to

Thermal Energy Storage

Heat to

Chiller

23 24 25 26

Time (days)

23 24 25 26

Time (days)

23 24 25 26

Time (days)

23 24 25 26

Time (days)

Outline/Agenda

Model Predictive Control

ikikikik

ikikikms

Niikqqq

pmsfsts

pmslikik

),,(..

),,(min||

At time the current time, i, optimize over model prediction:

Then, the manipulated variable is set as:*

|iii mm

),,(1 iiii pmsfs

Process to be Controlled

Controller

),,(min||

ikikikms

pmsgikik

Traditional vs. Economic MPC

In traditional MPC the objective is minimize deviations:

)()()()(min||

mmRmmssQssikik

In Economic MPC (EMPC) the objective is minimize expenditures:

See Rawlings et al. (2012) or Ellis et al. (2014) for additional information on EMPC

Outside

Environment

Windows

To T3T21T12 T11T11To To

Outside

Environment

5 State Building Example

|,|,|,

ikcikeP

QQTTAKTTAKT

2122111111 )()(

121112111111

TTKTTKT

12111212

21212132221

TTKTTKT

Economic Objective:

Houston, TX (July, 2012)

EMPC Simulation

Solid – EMPC with TES Dotted – EMPC without TES

EMPC Simulation with Smaller Storage

Solid – EMPC with TES Dotted – EMPC without TES

Predicting Future Disturbances

ikikikik

ikikikms

Niikqqq

pmsfsts

pmslikik

),,(..

),,(min||

Need forecasts of ikp ik for |

if Full Future Information (FFI) is not assumed

Disturbance Forecasting

Zero Future Information (ZFI) Forecasts

Assume this the present time

Disturbance Forecasting Model

Impact of Forecasting Model

4th order model

3rd order model

Incorporating a High Fidelity Forecast Model

Pseudo Future Measurement

Impact of High Fidelity Forecast Data

Pseudo Future Information (PFI) Forecasts

Impact of Forecasting on EMPC

All cases for 1.5 MWh of storage over 28 days and with EMPC horizon of 24h

Controller ExpenditurePercent

Reduction

EMPC (FFI) No TES $759 ---

EMPC (FFI) $521 31.4%

EMPC (ZFI) $556 26.7%

EMPC (PFI) $539 29.0%

Outline/Agenda

Prediction Horizon

24 hours

Model Predictive Control

ikikikik

ikikikms

Niikqqq

pmsfsts

pmslikik

),,(..

),,(min||

),,(1 iiii pmsfs

Process to be Controlled

Controller

Smaller horizon, N, will reduce computational effort!

3 4 5 6

Time (days)

Impact of Horizon Size on EMPC

Solid – 24 hr horizon Dotted – 2 hr horizon

3 4 5 6

Time (days)

Reduction

Computational

Effort (sec)

EMPC (FFI) with No TES $827 --- 13

EMPC (ZFI) N = 24 hrs $575 30.5% 13

EMPC (ZFI) N = 2 hrs $673 18.6% 7

Impact of Horizon Size on EMPC

All cases for 1.5 MWh of storage over 28 days

Outline/Agenda

Economic Linear Optimal Control (ELOC)

Steady-State

Operating

Steady-State

Operating

Optimal Steady-State

Operating Point

Expected

Dynamic

Operating

Region

Steady-State

Operating

Operating Point

Minimally

Baked-off

Operating

Expected

Dynamic

Operating

Regions

Steady-State

Operating

Operating Point

Different Controller

Tuning Values

Expected

Dynamic

Operating

Regions

Steady-State

Operating

Operating Point

Minimally

Baked-off

Operating

Different Controller

Tuning Values

Expected

Dynamic

Operating

Regions

Steady-State

Operating

Operating Point

iELOCi

Comparison of EMPC and ELOC

3 4 5 6

Time (days)

3 4 5 6-400

Time (days)

ELOC EMPC

3 4 5 6

Time (days)

ergy i

ELOC EMPC

Disturbances Simulated by Forecast Model

Both with ZFI

Comparison of EMPC and ELOC

3 4 5 6

Time (days)

3 4 5 6

Time (days)

ELOC EMPC

Disturbances taken from Historic Data

Both with ZFI

3 4 5 6

Time (days)

ELOC EMPC

Outline/Agenda

ikikik

tsPxxRuuQxxikik

..)(min||

iLQRi xLu

Predictive Form of ELOC

ikikik

iNiELOC

ikELOC

ikikELOC

tsxPxuRuxQxikik

..)(min||

iELOCi xLu

* see Chmielewski & Manthanwar (2004) for details

Linear Quadratic Regulator

Predictive Form of ELOC Constrained ELOC

ikuikxik

ikikik

iNiELOC

ikELOC

ikikELOC

tsxPxuRuxQxikik

..)(min||

Comparison of EMPC and Constrained ELOC

3 4 5 6

Time (days)

Both with ZFI

3 4 5 6

Time (days)

CELOC EMPC

3 4 5 6

Time (days)

CELOC EMPC

Outline/Agenda

Constrained ELOC and Horizon Size

3 4 5 6

Time (days)

Both with ZFI

3 4 5 6

Time (days)

CELOC, N=2 CELOC, N=24

3 4 5 6

Time (days)

ergy i

CELOC, N=2 CELOC, N=24

Reduction

Computational

Effort (sec)

EMPC (FFI) with No TES $827 --- 13

EMPC (FFI) N = 24 hrs $520 37.1% 13

EMPC (ZFI) N = 24 hrs $575 30.5% 13

EMPC (ZFI) N = 2 hrs $673 18.6% 7

Constrained ELOC (ZFI) N = 24 hrs $567 31.4% 13

Constrained ELOC (ZFI) N = 2 hrs $552 33.3% 7

All cases for 1.5 MWh of storage over 28 days

Impact of Horizon Size on Constrained ELOC

Why Does Constrained ELOC Work?

ikikik

iNiELOC

ikELOC

ikikELOC

tsxPxuRuxQxikik

..)(min||

ikuikxik

Conclusions

• EMPC with TES can reduce building HVAC expenditures significantly if under dynamic electricity prices.

• EMPC requires a large prediction horizon.

• Constrained ELOC approximates EMPC, but can use a smaller prediction horizon size.

• Disturbance model is central to Constrained ELOC.

Bibliography• D. J. Chmielewski (2014) "Special Section - Energy: Smart Grid: The Basics — What? Why? Who? How?," Chem.

Eng. Prog., vol. 110 (8), August Issue, pp. 28-34.• D. J. Chmielewski and A. M. Manthanwar (2004) "On the tuning of predictive controllers: Inverse optimality and

the minimum variance covariance constrained control problem," Ind. Eng. Chem. Res., vol. 43, pp. 7807-7814.• M. Ellis, H. Durand, and P. D. Christofides (2014) "A tutorial review of economic model predictive control

methods," J. Proc. Contr., vol. 24, pp. 1156-1178.• Ercot (2012) Historic real time data electricity prices for Houston texas. http://www.ercot.com/mktinfo/prices/• R. M. Lima, I. E. Grossmann, and Y. Jiao (2011) "Long-term scheduling of a single-unit multi-product continuous

process to manufacture high performance glass," Comp. Chem. Eng., vol. 35, pp. 554-574.• D. I. Mendoza-Serrano and D. J. Chmielewski (2012) "HVAC control using infinite-horizon economic MPC," in Proc.

of 51st IEEE Conf. Dec. Cont., Hawaii.• D. I Mendoza-Serrano and D. J Chmielewski (2014) "Smart grid coordination in building HVAC systems: EMPC and

the impact of forecasting," J. Proc. Contr., vol. 24, no. 8, pp. 1301-1310.• D. I Mendoza-Serrano and D. J Chmielewski (2015) “Smart grid coordination in building HVAC systems:

Computational efficiency of constrained economic linear optimal control” Science and Technology for the Built Environment vol. 21(6), pp 812-823

• (NCDC) National climatic data center. (2012) Hourly climate data for Houston Texas. http://www.ncdc.noaa.gov/oa/climate/climatedata.html

• B. P. Omell and D. J. Chmielewski (2013) "IGCC power plant dispatch using infinite-horizon economic model predictive control," Ind. Eng. Chem. Res., vol. 52, no. 9, pp. 3151-3164.

• J. K. Peng, A. M. Manthanwar, and D. J. Chmielewski (2005) "On the tuning of predictive controllers: The minimally backed-off operating point selection problem," Ind. Eng. Chem. Res., vol. 44, pp. 7814-7822.

• J. B. Rawlings, D. Angeli, and C. N. Bates (2012) "Fundamentals of economic model predictive control," in Proc. Of 51st IEEE Conf. Dec. Cont., Hawaii.

Questions?

Donald J. Chmielewski

chmielewski@iit.edu

ELOC Optimization Problem

Primal Problem (SDP solver)

Master Problem (BARON)

Master Problem

Primal Problem

minmax

,,,,,,

BYAXGGX

diagsqrt

mDsDqpGmBsAsts

Generalized Benders Decomposition

1YXLELOC

Seminar 32 Intermediate HVAC Controls for Smart Grid...

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