Net Zero Challenges and Opportunities...Net Zero Challenges and Opportunities: The Role of Modeling...

Assistant Secretary of the Army (Installations, Energy & Environment)

Net Zero Challenges and Opportunities:

The Role of Modeling in Net Zero Installation Planning

ERDC/CERL, Champaign, IL

Dr. Mike Case

[email protected]

Army Net Zero Installation Workshop

18-20 Jan 2012


What is a model?

• Simplified representation intended to explain workings of real world system – Back of envelope – Spreadsheet – Supercomputer

• “A scientific theory should be as simple as possible, but no simpler.” – Albert Einstein – often interpreted as

a criticism of over-simplified models of nature.


Opinions Vary

• “… lies, damn lies, and models.” – With apologies to Samuel Clemens

• “… essentially, all models are wrong, but some are useful.” – George Box

Take away – work with experienced teams who know the strengths and weaknesses of the models they use.


Decision Analytic Framework

• What do you need to succeed? – List of coordinated, phased projects

• Master plan, roadmap – Performance, cost/benefit analysis, life cycle cost. – Risks – Stakeholders

• Don’t forget the qualitative criteria!

– Communications (MARKETING) – Resourcing Strategies

Alternatives Electric Site

[MWhr] Nat Gas Site

[MWhr] Total Site [MWhr] % Savings

Site Total Source Fuel [MWhr]

% Savings Source

Base Case 37,960 32,166 70,126 160,561

CHP Engines 0 67,222 67,222 4% 70,583 56%


Why use models? (continued)

• How can models be useful for NZI planning? – Description

• (e.g., estimate building loads) – Prediction

• (e.g., predict performance of Measure) – Optimization

• (e.g., select best solution(s))


• Energy Plan • Projects • Sequence • Schedule • Costs • Risk •DD1391

NZI-E Optimization Process

1 Setup

Site Data

2 Optimize Building

Energy Efficiency Measures

(EEMS)

3 Optimize

District/Cluster Solutions

4 Support Net Zero Planning

with Justification & Documentation

• Building • Geography • Utilities • Cost Data • Water Facility

Simulation

Installation Simulation

6

Iterate over

Building EEMS


• EW2 Plan • Projects • Sequence • Schedule • Costs • Risk •DD1391

NZI Planning Cycle

1 Setup

Site Data

2 Minimize

loads

3 Optimize

and integrate across Installation

and region

4 Update plans

with Justification & Documentation

• Building • Geography • Utilities • Cost Data • Water • Waste • Greenhouse Gas

Execute, Track, Measure


Descriptive Model

• NZI Load Estimation • Measured data best • Usually need to estimate using models • EnergyPlus, FEDS, DOE2/eQuest, Water

Balance, SWARS,etc. • Make sure model handles resolution

required (e.g., 8760 hours)


Inventory/Classify Building Types


Run baseline to determine loads/flows


Review/Calibrate Baseline


Review Load Profiles


Predictive Model

• Model Measures to be applied

• Energy, Water, Waste

• Best Practices, Technology, Behavior COF

TEMF

UEPH


Review Measures to be Modeled


Simulation Predicts Performance & Cost


Compare Alternatives - Decision Criteria


Supporting Graphics


Supporting Data For Decision Criteria


Optimization

• Best Measures • Sizes • Operation Strategies • Lowest Cost • Models –

• NZI Optimization Tool, POLIS, REO, FEDS, MODEST, MSW (Solid Waste), DSS (Water)


NZI Optimization Tool • Installation/Regional • Regionally appropriate measures/technology • Optimizes clusters • Cluster level demands can include heating,

cooling, electric, critical electric, water, waste, etc.

• Developed by United Technologies under contract to Army

• Prototyped at Ft. Carson, Ft. Bragg, Ft. Sill • Currently being applied to USMA


Define System and Options


Apply Optimization Approach

• Apply optimization model • Linear Programming

commonly used – Scalable – Accurate enough for first cut – 8760 hours (1 year) common

• Limitations – Non-linearities – Highly coupled systems


Reporting

Plan

What’s behind the NZI Optimization Tool

24

Formulate Optimization Problem Find Integrated Solutions

PV1

PV2

ES

DCB

CV

ACB

WT

EH

B1

B2

HX

EC

ABC

ORC

MT

MT

MT

MT

MT

MT

MT

MT

MT

MT

AC - supply:

Grid

AC - consume

Heating

Natural gas

Heating

Natural gas

Heating

Waste heat

Heating

Cooling

Waste heat

Cooling

Waste heat Natural gas

Bio gas

Natural gas

Bio gas

Natural gas

Bio gas

Natural gas

Bio gas

Natural gas

Bio gas

Natural gas

Bio gas

Natural gas

Bio gas

Natural gas

Bio gas

Natural gas

Bio gas

Natural gas

Bio gas

DC - supply

DC - supply

DC - supply

DC - consume DC - consume: conversion

DC - supply: conversion AC - supply: conversion

AC - consume: conversion AC - consumeAC - consume

AC - supply

Waste heatWaste heat

Waste heat Waste heat

AC - supplyWaste heat

Waste heat





















Low Fidelity Device & Flow

Model

Weather Data

Load Profiles

Automatic Code Generation

Economic Information

CPLEX

Solver

‘N’ Promising Configurations

“Superstructure” Feasible

Equipment Configurations ACB

B1

HX

EC

ABC

ORC

MT

MT

MT

MT

AC - supply: Grid

AC - consume

Natural gas

Heating

Waste heat

Heating

Cooling

Waste heat

Cooling


Bio gas

Natural gas

Bio gas

Natural gas

Bio gas

Natural gas

Bio gas

AC - consume












ACB

B1

HX

EC

ABC

ORC

MT

MT

MT

MT

AC - supply: Grid

AC - consume

Natural gas

Heating

Waste heat

Heating

Cooling

Waste heat

Cooling


Bio gas

Natural gas

Bio gas

Natural gas

Bio gas

Natural gas

Bio gas

AC - consume












ACB

B1

HX

EC

ABC

ORC

MT

MT

MT

MT

AC - supply: Grid

AC - consume

Natural gas

Heating

Waste heat

Heating

Cooling

Waste heat

Cooling


Bio gas

Natural gas

Bio gas

Natural gas

Bio gas

Natural gas

Bio gas

AC - consume












ACB

B1

HX

EC

ABC

ORC

MT

MT

MT

MT

AC - supply: Grid

AC - consume

Natural gas

Heating

Waste heat

Heating

Cooling

Waste heat

Cooling


Bio gas

Natural gas

Bio gas

Natural gas

Bio gas

Natural gas

Bio gas

AC - consume












System Visualization

LinearSystem Model in AMPL

Mixed Integer Linear Programming

Optimization

Min l(x,y) s.t. f(x,y)=0 g(x,y)>=0

Framework developed by United Technologies Research Center (UTRC) under contract to ERDC – Simplified Diagram


• Available Now

– NZE- I Energy Master Planning – Facility Modeling and EEM analysis – Clustering analysis using Subject Matter Expert teams

• Virtual NZI-E Testbed

– January 2012 – Building Module – 8 building types and ~ 14 EEM packages available

– August 2012 – Clustering Module – August 2013 – Renewables and Storage Module

• Combined Energy, Water, Waste modeling

– Start rollout of selected water modules this year

25

Capability Schedule


Conclusion

• People matter • Real world

experience counts • Know the question • Use experienced

modelers • Know and question

assumptions • Peer review results


[email protected]

Net Zero Challenges and Opportunities...Net Zero Challenges and Opportunities: The Role of Modeling...

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