| Date post: | 10-Jun-2015 |
| Category: |
Engineering |
| Upload: | zondits |
| View: | 52 times |
| Download: | 1 times |
Main Headquarters: 120 Water Street, Suite 350, North Andover, MA 01845 With offices in: NY, ME, TX, CA, OR
www.ers-inc.com
IDENTIFYING HIGH VALUE CHPUSING A LOW-COST METHODOLOGY
By: Susan Haselhorst
Background
Legislation in Massachusetts in 2008 Almost tripled energy efficiency goals
for the mandated electric company run energy efficiency programs
CHP envisioned providing 20% of the C&I electric portfolio saving
• New measure for the program administrators
How to prevent this?
100%
80%
60%
40%
20%
0%
20%
40%
60%
80%
100%
Age
0Ag
e 1
Age
2Ag
e 3
Age
6Ag
e 0
Age
1Ag
e 2
Age
3Ag
e 0
Age
1Ag
e 2
Age
3Ag
e 4
Age
5Ag
e 6
Age
7Ag
e 0
Age
1Ag
e 2
Age
3Ag
e 4
Age
5Ag
e 6
Age
0Ag
e 1
Age
2Ag
e 3
Age
4Ag
e 5
Age
6Ag
e 7
FC GT ICE MT ALL
← O
ff
On→
Capacity factor Unused capacity while on Off> 3 day duration
Off1 to 3 day duration
Off<1 day duration
Better CHP Program Design
Thermal following Driven by energy efficiency, not grid
benefits Combined annual efficiency >60% Cost-benefit screen requires 5000
FLH+ Educated consumer
Rigorous technical assistance Discourages exporting Long term O&M contracts
Who are the good customers?
CHP Market Characterization KEMA evaluation team Itron conducted qualitative
assessment ERS conducted the quantitative
assessment NGRID study manager
04/13/2023
Inputs C&I Monthly gas bills
115,000 accounts Service town Some business type
Hourly weather data Gas & electric commodity
cost Electric distribution rates Average CHP performance
and cost, by size range Existing distributed
generation lists Base load shape library
Outputs For each gas account
Optimum thermal following CHP size
Annual kWh generated Annual fuel fired Annual useful heat System cost O&M cost Payback
Aggregated accounts By Program Administrator By customer type By CHP size range Lead lists of specific
customers
CUSTOMER CHP POTENTIAL MODEL
6
Under the Hood
1. Use bills to quantify the base and weather sensitive load
2. 8760 hourly analysis using weather and library of base load profiles
3. Selecting an optimally sized system
From gas bills
Highly certain of base and weather sensitive load magnitudes
WS Load Profile
Distribute the annual load through the heating season using hourly weather data
Base load served by hot water
Building type inferred from SIC or name of the account Discount a portion of base usage Assign an hourly profile
Optimized CHP Size – Downsize
Building location
Use location Identify program administrator Located in municipal service territory Exclude sites where interconnect is difficult
Model Weaknesses
Overstate potential Site already generates electricity Facility CHP potential is limited by electrical
usage Average pricing doesn’t screen expensive sites About 25% of heating systems not conducive
Understate potential Emerging technologies aren’t captured Additional potential with absorption cooling Doesn’t capture oil-fired potential
Right perspective for a lead list
A few good units ….
By System Size
By Building Type
Examine impact of assumptions
What if …
17
Payback - Years Num AcctsPct Change
from Baseline Capacity (kW)Pct Change
from BaselineAverage System
Size kWPayback <3 199 -86% 157,644 -67% 791 Payback <4 1,153 -21% 378,265 -20% 328 Baseline for payback <5 years 1,464 0% 475,167 0% 325 Payback <6 1,882 29% 556,145 17% 295 Payback <8 2,515 72% 660,168 39% 263
Interesting Findings
Car washes perceived as good CHP candidates Car washes, however, do not
use much gas Wash water is not heated
Mismatch between electric and thermal loads Manual check of 127accounts Insufficient electrical use at
half of the viable residential sites
Rich and immediately useable results Lead lists Market focus
Technique could be adapted Improved estimates of economic
potential Could test other screening models Assess other gas measures
Conclusions
19
