Campus Energy Management

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insight. agility. incuity

Campus Energy Management

IDEA 2008

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Presentation Team

•

Incuity Software

– Doug Lawson, President

– Don Lawson, Imaginary Assistant

•

Rice University

– Eric Valentine, Energy Management Coordinator

– Mark Gardner, Deep Thinker

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Proven Solution that Saves Energy

• Holistic and pre-emptive approach to campus

energy management.

• Proven savings of up to 23% of total energy bill.

• Based on prediction, intelligence and costing.

• Low capital cost with a rapid return on investment.

• Saves money through:

– Smarter central plant management

– Detection of abnormal building behavior – Improved operational planning

– Enabling better maintenance decisions – Providing detailed management insight

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Unique Methodology

4 Buildings Buildings Plants Plants Entire Campus Entire Campus

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Silos of disparate data

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Silos of disparate data

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Records

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Silos of disparate data

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Unified Production Model

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Extensible and based on proven platform

• Every campus has important differences and

priorities so an open solution that can be modified by end-users is needed

• IncuityCEM is built on IncuityEMI, the award

winning platform used in hundreds of demanding Industrial applications

• If you can use Excel you can customize your

application in almost any manner

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Example – Rice University

Example

–

Rice University

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The Campus

• 4.1 Million Square Feet • 83 Buildings

• Cogeneration (7.5 MW) • Electricity – 16 Megawatts • Steam – 130K lbs/hour

• Chilled Water - 14,000 Tons • $800M expansion

– New District Cooling Plant

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The Key Questions

• Are the buildings behaving?

– If not, what are the cost implications

• What are the financial implications?

• On target with carbon footprint?

• Is utility purchasing meeting plan?

• How to Operate the plant?

– Run the co-generators? – Which chillers?

– Boiler configuration? – Power level?

– Export electricity?

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The Variables

• Demand prediction

– Weather (Forecast data)

– Campus Events (Campus ERP) – Student Calendar (Facilities DB)

• Cost prediction

– Real time electrical market (Utility) – Campus electrical contract (Financial) – Natural gas price (Utility)

• Capability Prediction – Control Systems – Maintenance System • Buildings – Building automation – Metering systems

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Example – Real time effective costs

Example

–

Real time effective costs

15 Actual cost of Actual cost of steam steam Spike in Market Spike in Market Clearing Price of Clearing Price of electricity electricity

More than 70 factors More than 70 factors affect cost

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Example – A managed building

Example

–

A managed building

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Accurate Future Demand Reports

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A typical building operating ABOVE expectations

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Poorly behaving (Systems placed in By-Pass)

Poorly behaving (Systems placed in By

-

Pass)

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The Outcome

• Saving $3.5M/year in energy costs

– Identified control system malfunctions

– Improved sequencing and scheduling

– Optimized plant line-ups

– Negotiated better utility procurement