NCAPPA Conference - 2016

Steve Sharpe, PE, CEM

Energy Manager

The University of North Carolina at Wilmington

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Agenda

� Overview of UNCW CREST Research Park/MARBIONC Building

� Design Intent for Plant Operations

� Chiller Plant Controls & Optimization

� Optimization – How do we know it works?

� Value of Continuous Commissioning & Preventative Maintenance

� Q&A

Facility Overview

UNCW CREST Research Park/MARBIONC Building

• Biotechnology research centers the park offers new flexible research laboratories for lease

• Park consists of 3 main buildings:

– Center for Marine Science

– Shellfish Research Hatchery

– MARBIONC Building

MARBIONC

• 69,000 Sq. Ft. Facility

• $30M built with ARRA funds

• LEED Silver

• 365 day operation - Zero downtime

Energy & Design Intent for Chiller Plant Operations

� High energy performance system – Energy efficiency

� Superior level of redundancy – 24/7/365 operations

� Assured reliability for Critical Cooling - A large fresh air requirement meant the

applied design must perform well under all conditions

� Verifiable results – Track and measure progress

� Optimized Operations - High performance components do not operate at peak

performance by themselves

� Optimized Operations - Individual equipment requirements as well as a total system

level requirements

• Design engineer to develop sequence of operation � Simple

sequence that doesn’t optimize the operation.

• Previous optimization software installation was a ‘black box’ � No

verification that it was actually optimizing. This system was removed

and replaced when it went down.

• What was needed was a system that would:

• Provide optimization

• Makes sense

• Not be a black box

• Be verifiable

Lessons Learned - Past Attempts at Optimization

for Chiller Plant Operations

2x 750Ton Mag Bearing Oil Free Chillers2x 750Ton Mag Bearing Oil Free Chillers

2x Variable Primary Chw Pumps2x Variable Primary Chw Pumps

1x Low Approach Tower with large flow range1x Low Approach Tower with large flow range

Variable speed condenser water pumpingVariable speed condenser water pumping

HVAC System optimization/ condition monitoringHVAC System optimization/ condition monitoring

The Solution – Overview of Chiller Plant Equipment

Optimization, Automated Reporting & Condition Monitoring

Kiltech - CPECS Optimization System

� Handles plant control:

� Hard wired input/ outputs.

� Communication to chillers, VFD’s, air handlers, BAS and BTU meters.

� Delivers total system energy approach sequence of operations.

� Includes full measurement & verification system.

� Inbuilt enterprise SQL database stores 7yrs data onsite and can be backed up offsite.

The System Approach

� High performance components do not necessarily operate at peak

performance by themselves.

� Control strategy needs to understand individual equipment

requirements as well as a total system level energy consumption.

� Air side integration a must � The left hand needs to know what the

right hand is doing!

Brute Force Optimization strategy or BFO for short:

� This approach is equipment model driven and is customized to the plant and operations.

� At the heart of the software is an algorithm that is computing all possible chiller, pump and cooling tower sequencing permutations, modified flows, set points and load limits.

� These calculations find the combination of equipment and speeds that result in the lowest kW input and/or the lowest instantaneous cost of production (driven by a cost of operation goal seek algorithm as an electric device minimal kW search).

The System Approach

The System Approach

Brute Force

Optimizer

Chw & Cw Pumps

Cooling Towers

Optimization - Strategies Applied

� Variable primary pumping control with reset based on weighted AHU & FCU valve position.

� Optimized cooling tower temperature control that utilizes chiller load data and actual cooling tower off design correlations (merkelequations).

� Optimized sequencing of chillers & pumps using 15min buffered data and actual equipment models.

� Variable speed condenser water control for part load optimization.

� Chilled water reset based on AHU fan power, discharge air set point and percentage cooling valve position.

Variable Primary Pumping ΔP Reset

� Looks at all the AHU & FCU valves in the system.

� Attempt to maintain valves between 80% and 90% open.

� Moves the system curve continuously!

� Resets must take place at a controlled rate and upper and lower limits need to be set.

� Error conditions such as a stuck open valve need to be detected and handled.

� Should be implemented along side an air side optimization program that includes AHU condition monitoring.

Variable Primary Pumping ΔP Reset

Maintain at 80% to 90%

Via dp reset strategy.

Variable Primary Pumping ΔP Reset

Reduced head – moving system curve

limits the system valve throttling and

unnecessary pump work.

New System Curve

Optimized Tower Control

� Optimized approach accepts that tower

approach at constant load varies with wet

bulb temperature.

� Airs ability to take on heat decreases as

wet bulb temperature decreases.

� Use of constant wet bulb offset results in

non optimal control on in many instances.

� Wet bulb calculation relies on RH% & dry

bulb temperature measurements.

� Commercial grade sensors (capacitive or

resistive type) frequently lose

calibration or delivered accuracy is less

than acceptable error for low approach

tower selections

Optimized Tower Control

Finding the Energy Sweet Spot

Optimized Chiller Sequencing

� When applied to variable speed chillers algorithm needs to accept

that chiller performance on a VFD centrifugal varies greatly with lift

(CWRT – CHWST). Constantly changing sequence point!

� Compressor operation at higher lift have smaller operating envelopes

(compressors run closer to the surge line).

� Optimized sequencing needs to look at load, condenser water flow

rate and lift.

Optimized Chiller Sequencing

� CPECS uses a multivariate regression model to predict chiller

performance based on:

� CHWST

� CWST

� CWRT

� Power Input

� Evaporator and Condenser Approach temperatures.

CAPFT = a + b*CHWS + c*CHWS^2 + d*CWRT + e*CWRT^2 +

f*CHWS*CWRT

EIRFT = a + b*CHWS + c*CHWS^2 + d*CWRT + e*CWRT^2 +

f*CHWS*CWRT

EIRFPLR = a + b*PLR + c*PLR^2 + d*dT + e*dT^2 + f*PLR*dT

Typical Chiller Performance Model at fixed leaving

chilled water temperature

Modified Model – Accepts variable CW flow &

changing CHWST

• 27 coefficients

• Requires additional modeling

and data handling tools

• Each different chiller must be

modeled.

• CPECS currently has models

for:

• Turbocor based chillers

• Many york chillers (Const

Speed & VFD)

• Carrier VFD machines

• Trane VFD machines

Optimized Chiller Sequencing – Putting the

chiller performance models to work

Optimized Chiller Sequencing

� How does it work?

� 15min rolling averaged conditions are used in an iterative

algorithm that seeks to select the right number of chillers that

satisfy the cooling load at the lowest kW input.

� Minimum chiller flows must be taken into account.

� Additional loads such as dedicated primary pumps or condenser

water pumps should be included.

� Predicted demand and time of day useful to avoid short cycling

Variable Speed Condenser Water Pumping

� Needs to be carefully analyzed against load profile prior to selection of strategy.

� 30% of surveyed applications should not adopt this.

� Needs to be made in conjunction with tower control optimization strategy.

� 15% error on flow can result in operation less efficient than constant speed pumping.

� Tower minimum flow (fill coverage) needs to be closely adhered to.

� Chiller minimum flow and fouling avoidance needs to be taken into account – know your condenser design tube velocity.

Variable Speed Condenser Water Pumping

� VFD chiller performance changes rapidly with lift!

� Lift is based on leaving condenser water temperature not entering –

low condenser water temperature at low flow might get you the same

result as full flow with a higher temperature (less fan power).

� Algorithm seeks to achieve lowest:

CHILLER KW + TOWER KW + COND PUMP KW

Adaptive Chilled Water SP Reset

� Chilled water reset is adaptively reset based on an energy trade off

algorithm between AHU fan energy and chiller energy.

� Such a strategy requires full integration from the central plant to the

AHU and AHU variable speed drive.

� Initial set point typically set to 2F above base set point, adaptive

algorithm takes effect after 20min of plant operation.

Putting it all together

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Optimization – How do we know it works?

� Continuous feedback – self-monitoring

� Displayed target values.

� Visual Real-time Comparison to baseline performance models.

� Reporting with traceable accuracies.

� Beware of the “green washers”

Optimization – How do we know it works?

UNCW MARBIONC Results

� Energy saved over baseline to date (Placeholder) as of Jan 1 2013

when system started

� Financial figures (Placeholder)

� Reporting overview & demo

� Live demonstration

Optimization Issues

� Complex algorithms and equipment models that are not necessarily

accessible to the controls community.

� Additional engineering costs to implement.

� Relatively new market with few players:

� Tom Hartman

� Taylor Engineering

� Philip Kennedy

� Burt Rishell

� Siemens demand flow

� Most commercial optimization providers have adopted a “black box”

approach to chilled water plant and air side optimization as a means

to protect their investment. Some vendors even requesting NDA’s just

to talk about their product.

� Black box approach has led to skepticism and limited adoption in the

field.

� Industry wide adoption has the potential to reduce building HVAC

energy use by as much as 24% worldwide.

� Legislation, education and greater number of open publications

required to promote technologies/ engineering methods.

Optimization Issues

Keeping the performance up

• Most buildings continuously evolve in terms of use and function

• Consequently, while commissioning an existing HVAC system to the design intent

generally improves comfort and efficiency significantly, it is still likely to result in a

sub-optimal system set-up.

• Continuous commissioning - explicitly considers current functions and uses of the

building and requirements on its systems and attempts to optimize the efficiency of

the systems for the current application.

• Examples: Six buildings on the Texas Tech University campus, several buildings at

Penn State University, the major buildings on the Texas A&M University campus, the

Texas A&M International campus at Laredo, Brooke Army Medical Center, and others

� 15 to 25% annual energy savings from retrofitted buildings, from newer

buildings, or from older, existing buildings.

Value of Continuous Commissioning

Value of Preventative Maintenance

Jones Lang LaSalle surveyed approximately 12 percent of the company’s entire

portfolio of 119 million square feet. For this 14 million square feet of mixed property

types:

Source: Jones Lang LaSalle. ‘Determining the Economic Value of Preventative Maintenance’

Value of Preventative Maintenance

Source: Jones Lang LaSalle. ‘Determining the Economic Value of Preventative Maintenance’

Steve Sharpe PE, CEM

Energy Manager

The University of North Carolina at

Wilmington

Phone: 910-962-7454

Email: sharpes@uncw.edu

Thank you.

Questions?

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