# Presentation: 734078

## Craig Christensen

- Center for Buildings and Thermal Systems, NREL
**Scott Horowitz, Adam Courtney, Todd Givler**
- Building Systems Program, University of Colorado
**Greg Barker**
- Mountain Energy Partnership

**Notes:**
Good afternoon! And good morning to those in Colorado.

## Outline 

- Objective
- Conceptual Approach
- Optimization Methodology
- Validation & Benefits
- Conclusions
- Software Demo

**Notes:**
Today, I will discuss BEopt, in terms of:
Objective 
Approach 
Methodology 
Validation 
Conclusions 
And a look at the user interface 

## Objective

-  (with minimum energy-related  annual costs)
-  on the path to zero net energy

**Notes:**
We have developed BEopt, with support from the U.S. DOE, to find optimized building designs on the path to  Zero Energy Homes.

## Conceptual Approach 

**Notes:**
I will describe what we mean by the path to zero energy.

## The Path to Zero Net Energy 

- 0

- 500

- 1,000

- 1,500

- 2,000

- 2,500

- 0%

- 100%

- Source Energy Savings (%)

- Mortgage + Energy Costs ($/year)

- mortgage

- utility bills

- cash flow

- 1

- The Path to Zero Net Energy

**Notes:**
The ‘path to zero net energy’ can be shown conceptually on this type of graph 
with energy savings on the x-axis 
and energy-related costs on the y-axis.

Currently BEopt shows the sum of the mortgage payment (the increment to cover energy improvements) and utility bills, plus replacement costs (discounted) for measures with lifetimes less than the analysis period. This is essentially a adjusted 1st -year energy-related cashflow.  These terms are most ‘real’ in the residential market.

We have avoided economic metrics such as NPV or LCOE (which involve discounting, a concept foreign to homebuyers and builders), but will add them as options in BEopt.

For the base-case building, the annual costs are entirely due to utility bills.

## The Path to Zero Net Energy

- 0

- 500

- 1,000

- 1,500

- 2,000

- 2,500

- 0%

- 100%

- Source Energy Savings (%)

- mortgage

- utility bills

- cash flow

- 1

- 2

- Mortgage + Energy Costs ($/year)

**Notes:**
... from the base case, 
there are some no-cost or low-cost options available for saving energy, 
so the cash flow curve follows the utility bill cost line.

... as the building design is further improved,
utility bills are reduced, while there are increased mortgage payments to pay for the EEM’s (or options as we call them in BEopt).

Annual costs are at a minimum at Point 2 which, in terms of our economic metric, is a global optimum for the homeowner.  

The slope of the cash flow curve represents the net marginal cost of saved energy.  So, at point 2, the cost of the last unit of saved energy just equals the cost of buying energy from the utility. 

However, one may desire to go further, for non-economic reasons.

## The Path to Zero Net Energy

- 0

- 500

- 1,000

- 1,500

- 2,000

- 2,500

- 0%

- 100%

- Source Energy Savings (%)

- mortgage

- utility bills

- cash flow

- 1

- 2

- 3

- Mortgage + Energy Costs ($/year)

**Notes:**
... continuing on, mortgage payments increase more rapidly (due to diminishing returns on EEM’s).

At point 3, the net marginal cost of EE (indicated by the slope of the curve) equals the net cost of PV power.(click)
If we continue to add EEMs beyond point 3, the cash flow curve would trend upward with an increasingly steep slope.
Therefore, beyond this point, it is more cost-effective to invest in PV than to do more EE. 

## The Path to Zero Net Energy

- 0

- 500

- 1,000

- 1,500

- 2,000

- 2,500

- 0%

- 100%

- Source Energy Savings (%)

- mortgage

- utility bills

- cash flow

- 1

- 2

- 3

- 4

- Mortgage + Energy Costs ($/year)

**Notes:**
...as the size of the PV system is increased, ZNE is eventually achieved.

At this point, utility bills are zero (at least in theory, with net metering) and the annual energy-related costs are comprised entirely of mortgage payments for the EE and PV.

A potential advantage to the homeowner is that susceptibility to future rate increases and utility price volatility is eliminated.

## The Path to Zero Net Energy

- 0

- 500

- 1,000

- 1,500

- 2,000

- 2,500

- 0%

- 100%

- Source Energy Savings (%)

- Mortgage + Energy Costs ($/year)

**Notes:**
...as the size of the PV system is increased, ZNE is eventually achieved.

At this point, utility bills are zero (at least in theory, with net metering) and the annual energy-related costs are comprised entirely of mortgage payments for the EE and PV.

A potential advantage to the homeowner is that susceptibility to future rate increases and utility price volatility is eliminated.

## The Path to Zero Net Energy

- 0

- 500

- 1,000

- 1,500

- 2,000

- 2,500

- 0%

- 100%

- Source Energy Savings (%)

- Mortgage + Energy Costs ($/year)

- Higher Utility Rates

**Notes:**
...as the size of the PV system is increased, ZNE is eventually achieved.

At this point, utility bills are zero (at least in theory, with net metering) and the annual energy-related costs are comprised entirely of mortgage payments for the EE and PV.

A potential advantage to the homeowner is that susceptibility to future rate increases and utility price volatility is eliminated.

## The Path to Zero Net Energy 

- 0

- 500

- 1,000

- 1,500

- 2,000

- 2,500

- 0%

- 100%

- Source Energy Savings (%)

- Mortgage + Energy Costs ($/year)

- The Path to Zero Net Energy

**Notes:**

So, that is the concept for the path to zero net energy

But we have to find the path - the lower boundary of cost/performance results for many possible building designs 

## Optimization Methodology 

**Notes:**
To do this, we use optimization

## Simulation / Optimization Software

- DOE2

- TRNSYS

-  SDHW

- PV

- TMY2

- Heating
- Cooling
- Lighting
- Appliances

- Optimal
- Building
- Designs

*BEopt*

**Notes:**
BEopt, a meta program, calls DOE2 and TRNSYS annual, hourly simulations, evaulating a series of building designs using a optimization search technique 

BE-Opt is actually designed to work with any simulation program that uses text-based input files, and we are currently adapting it to work with Energy-Plus.

## Sequential Search Technique

- Source Energy Savings (%)

- Total Annual Costs ($/year)

- 1

- 2

- 3

- 4

**Notes:**
We propose what might be called a “steepest coordinate” method, which is related to the “sequential analysis” approach described by David Springer of DEG and I believe is similar to what is used in the EnergyGauge FL software.  

Click From the starting point, Click  Simulations are performed for each individual option, inserted by itself into the base building, 

Click and the measure with the steepest slope is selected,

Click With this measure incorporated into the building, another set of simulations is performed,

Click And the next best measure is selected, and so on. 

## Large-Step Special Case

- Source Energy Savings (%)

- Total Annual Costs ($/year)

- 1 (A1, B1)

- 2 (A1, B2)

-  (A2, B1)

**Notes:**
There are some special cases where the standard sequential search technique may fail to find certain optimal points.

For example, here BEopt would first select point 2 

Then in the next iteration, 

## Large-Step Special Case

- Source Energy Savings (%)

- Total Annual Costs ($/year)

- 1 (A1, B1)

- 2 (A1, B2)

- 3 (A2, B2)

- 3’ (A2, B1)

## Large-Step Special Case

- Source Energy Savings (%)

- Total Annual Costs ($/year)

- 1 (A1, B1)

- 2 (A1, B2)

- 3 (A2, B2)

- 3’ (A2, B1)

##  Invest/Divest Special Case

- Source Energy Savings (%)

- Total Annual Costs ($/year)

- 1 (A1, B1)

- 2 (A1, B2)

- 3 (A1, B3)

-  (A0, B3)

-  Invest/Divest Special Case

##  Invest/Divest Special Case

- Source Energy Savings (%)

- Total Annual Costs ($/year)

- 1 (A1, B1)

- 2 (A1, B2)

- 3 (A1, B3)

- 3’ (A0, B3)

-  Invest/Divest Special Case

##  Invest/Divest Special Case

- Source Energy Savings (%)

- Total Annual Costs ($/year)

- 1 (A1, B1)

- 2 (A1, B2)

- 3 (A1, B3)

- 3’ (A0, B3)

-  Invest/Divest Special Case

## Positive Interaction Special Case

- Source Energy Savings (%)

- Total Annual Costs ($/year)

- 1 (A0, B1)

- (A1, B2)

- (A2, B1)

- 2 (A1, B1)

- 3

## Positive Interaction Special Case

- Source Energy Savings (%)

- Total Annual Costs ($/year)

- 1 (A0, B1)

- (A1, B2)

-  (A2, B1)

- 2 (A1, B1)

- 3’ (A2, B2)

- 3

## Positive Interaction Special Case

- Source Energy Savings (%)

- Total Annual Costs ($/year)

- 1 (A0, B1)

- (A1, B2)

- (A2, B1)

- 2 (A1, B1)

- 3’ (A2, B2)

- 3

## Validation & Benefits 

**Notes:**
So, the question is how well does this optimization work? And what are the benefits? 

## Slide 25

**Notes:**
In this example, we have 75 options in 14 categories.

## Slide 26

**Notes:**
Here are the optimization results, resulting from approximately 750 simulations. 

## Slide 27

**Notes:**
For validation, we completed approximately 3⁄4 million simulations – using distributed computing with a pool of about 30 PC’s at NREL.

As you can see, the optimization did a good job of finding the optimal path 

## 75 options/simulations

- Source Energy Savings (%)

- Total Annual Costs ($/year)

**Notes:**
We conducted an exercise, using simulations similarLy to how they are often used in non-optimization energy analysis. 

We performed simulations with individual options as in first optimization iteration.

The results were rank-ordered based on cost-effectiveness.

And the results, were assumed to be additive ...

## Slide 29

**Notes:**
This works reasonably well up to about 25% energy savings, 

But beyond this point, performance is significantly over-estimated, 
because negative interactions and diminishing returns are not properly accounted for.

## Slide 30

**Notes:**
Next we were a little smarter and, as each option was added, it was re-simulated in the presence of the options previously added in.

In this case, interactions are accounted for and predictions are reasonable up to about 40% energy savings,   

but the method fails to find higher-energy-savings optimal building designs up to 50% or more, as found by the optimization method .

## Conclusions 

## Current BEopt Capabilities

- Runs detailed hour-by-hour simulations:  DOE2 and TRNSYS
- Accurately accounts for interactions across categories (e.g., glass type and HVAC)
- Evaluates realistic (discrete) options for building efficiency and solar water heating
- Finds and stores optimal designs over the entire range of energy savings from base case to zero net energy
- Identifies near-optimal alternative designs
- Includes a detailed user-accessible options library
- Automatically implements the BA Benchmark

**Notes:**
By way of conclusions, I have simply listed current BEopt capabilities.