ESL-IE-97-04-12
CHP MODELING AS A TOOL FOR ELECTRIC POWER UTILITIES
TO UNDERSTAND MAJOR INDUSTRIAL CUSTOMERS
Jimmy D. Kwnana / Francisco J. Alanis
Principal Engineers
Linnhoff March, Inc.
Houston, TX
Tony Swad
Senior Engineer
Oklahoma Gas & Electric Co.
Oklahoma City, OK
Jigar V. Shah
Consultant
EPRI Chemical & Petroleum Office
Pittsburgh, PA
ABSTRACT
In the face of pending deregulation, many electric utilities are struggling to retain major industrial customers. The strategies for retaining customers, especially those with cogeneration options, include:
• superior customer service and innovative contract terms
• identifying cost-effective alternatives to cogeneration that are in fact better for the customer and what alternatives there might be to offering discounted rates (eg. ASDs) in the first place. The objective, of course, is to minimize utility revenue loss, while keeping the customer happy.
The revenue erosion problem for electric power utilities has been in the making for some time, ever since deregulation set in. About five years ago, there were a series of papers (Refs 1, 2, 3, and 4] that began to deal with this issue. Although the concept was similar to the one presented in this paper, the focus was a bit different. The CHP simulation model was written in Fortran, and an optimization package
(LINDO) was used to find the least-eost design and operating policy. This was a labor-intensive effort, requiring very high skill level, and turned out to be too expensive for general application.
• treating potential cogeneration candidates as partners in a "distributed generation and supply" chain.
The first step in understanding the available options and appropriate strategy is to properly understand the customers' thermal and electric energy needs, and the existing Combined Heat and
Power (CHP) system. This paper outlines an approach for developing such models at low cost, and using them as a tool towards the aforementioned goal.
With recent advances in spreadsheet software capability, and the availability of stearn properties
"add-in" packages, it has become possible to accomplish essentially the same objectives at much lower cost. The new strategy is to use a simplified spreadsheet simulation model to calculate the exact break-even point for the customer's make-versus-buy or stay-versus-go decision. The way this approach has been used by OG&E is illustrated through an actual case study, funded in part by EPRI's Customer services division.
BACKGROUND
In the approaching era of deregulation, the established electric utility companies face stiff competition for large base-load power consumers.
Many utilities are enticing such customers to stay with them by offering steeply discounted rates on long-term contracts. While this may be a successful short-term load retention strategy, the consequence is long-term revenue loss.
In this paper, we are outlining an alternate strategy by which the electric utility can make better decisions about how much it can afford to give up, and when. We show that once you have understood the customer's energy system it is possible to calculate exactly how big a rate discount will be required to swing the customer's stay/go decision,
PROPOSED APPROACH
There are four key elements in the proposed new approach:
1. Construct a simulation model of the customer's
Combined Heat and Power (CHP) system, using an electronic spreadsheet linked to a steam properties database.
2. Test sensitivity of customer economics to key variables such as hardware, operating/control policies, and contract provisions. This is done by simulating various alternative scenarios.
Proceedings from the Nineteenth Industrial Energy Technology Conference, Houston, TX, April 23-24, 1997
65

ESL-IE-97-04-12
3. Identify opportunity for cost reduction through introducing new degrees of freedom - whether physical (eg. new hardware, controls) or contractual (it may be necessary to consider innovative rate structures and schedules)
4. Work with the customer's engineers to jointly develop the optimum solution
CASE STUDY
Oklahoma Gas and Electric Company (OG&E) has introduced a real-time pricing (RTP) option within its service territory. One of its largest customers had expressed interest in using this option, but was reluctant to make a decision until the full implications were better understood
OG&E used the CHP modeling approach to effectively resolve this customer's concerns. Model development and related sensitivity studies were carried out with four interrelated objectives in mind:
For the example case, both process and HV AC heating needs are provided by steam withdrawn from the extraction steam turbines (ESTs). Normally, only one extraction turbine is needed to supply the heating steam demand. When this demand exceeds the maximum allowable extraction flow, the pressure reducing valves (pRV) are opened. In most cases, the PRY flow is so small that bringing another extraction turbine on line is not justified.
The performance of the turbines was coded into the model by using data provided by the client.
These performance data determine the limits within which the turbines can operate and include power generation at different extraction and condensing flows, and steam conditions in and out of the turbine.
First, the extraction flow is determined as described in the next paragraph. The condensing stage flow is back-calculated from the power output specified from the turbine. A warning flag prevents the user from specifying operating conditions which cannot be handled by the turbine.
• to provide a tool to assist in allocating costs between steam and power generation.
• to calculate the true cost of running each of the steam turbine drives associated with the boiler feed water (BFW) pumps, boiler # 4 fan and the
1200 ton steam-driven chiller.
• to make an economic comparison of the various drive options for the 1200 ton chiller (viz. steam turbine, synchronous and induction ASD motors).
• to facilitate the customer's decision-making process for selecting between generating electricity in-house and buying electricity at
OG&E's RTP rates.
A schematic representation of the CHP system is shown in Figure 1. The model was developed using an electronic spreadsheet (Excel 5) with an add-in software package for calculating steam thermo­ dynamic properties and turbine performance.
Several such packages are commercially available
[Ref 5, 6].
For accurate simulation, the computational logic coded into the model must follow actual operating policies and equipment performance constraints. It is therefore important to understand the system extremely well.
In order to solve the steam balance, there must be one "floating" value for each header. The key header in this model is the 50 psig header. The model was set up such that the extraction flow from
EST -3 floats, and the let-down flow through the
PRVs is user-specified, which mimics normal operating practice. If the actual steam demand exceeds the maximum extraction flow of one of the turbines, the user has the choice of adding another turbine or increasing PRY flow.
Boilers B3 and B4 usually operate from the end of September through May, and boilers B1 and B5 operate from June through September. The user selects which boilers are to be used by typing "on" or "off'in the "Input Data" worksheet (Table 1).
The program incorporates a correlation for boiler fuel usage as a function of steam generation. This is based on regression of the best available correlation for boiler efficiency.
In addition to modeling the physical equipment and operation, it is important to model the contract provisions for purchase of fuel and electricity. Fuel and pipeline lease costs are a function of the boiler fuel load. Normally, electricity costs would be calculated exactly the same as if the customer were actually buying that amount of power. Fuel and electricity costs are then summed to give a total cost for operation of the boiler plant and associated utility system during the billing period.
Proceedings from the Nineteenth Industrial Energy Technology Conference, Houston, TX, April 23-24, 1997
66

Figure 1: Combined Heat and Power (CHP) Model
ESL-IE-97-04-12
Fuel o
MMBtulh
Fuel o
MMBtu/h
Fuel o
MMBtu/h
Fuel
72 MMBtulh
Fuel o
MMBtulh
1
570381b/h
525 Ib/h
50 psig
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I o
Iblh
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5000 Ib/h
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225 F
360 psig
~
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Total Steam Generation
II
57038 Ib/h
II
Chiller o ton o
Iblh
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BFW o
Ib/h
Stm Users
50000lblh
160 F Condensate
425001b/h
80 F Make up
9877lblh
Base Case 720 hr/month
Heat and Power Costs
Natural Gas 52,062 MMBtu
Natural Gas Cost 107,248 $/month
Gas Pipeline Lease 21,450
$/month
Total Gas Bill 128,698 $Imonth
Wamings: None
OSU Electricity Usage 10,800,000 kWh/month
OSU Electricity Generated 1,008,000 kWh/month
Net Electricity from OG&E 9,792,000 kWh/month
RTP 0.02 $lI<wh
Electricity Bill @ RTP 195,840 $/month
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Steam and Power Needs
Steam for Heating Duties
Steam Driven Chiller Load
Site Electrical Demand
Chilled Water Plant Electricity Demand
Natural Gas and Electricity Costs
Purchased gas
Transmission charge (pipeline lease)
Minimum Pipeline Lease
RTP Electricity Price
Local Tax (Fuel only)
Table 1: Input data (typical)
Operating Data
50000 Iblh On Stream Time (billing period) o ton of refrlg PRVs Flow
15000 kwhlh o kwhlh
Condensing Stage Pressure
Condensate Return Flow
Condensate Return Temperature
2.00 $/MMBtu
0.40 $/MMBlu
25000 $fmo
0.02 $/kwh
3.00 %
Make up Water Temperature
Boiler Feed Water Temperature
Boller Feed Water Pressure
HP Steam Pressure
HP Steam Temperature
LP Steam Pressure
Deaerator Pressure
EqUipment Perfonnance Data
Boiler 10
Boiler Maximum Continuous Ratings:
Boiler switches (on/off)
Boiler Blow-down
Turbine 10
Status operating mode
Steam Turbine Capacity:
Maximum Extraction Flow
Actual Extraction Flow
Actual Power Generation
Setting Units of Measurements
Ib/h
%
WV
Iblh
Iblh
KW
0
81
40,000 off
4.00
EST 1 off
1500
50000
0
1000
82
40,000
"SWing"
83
40,000 on
84
125,000 on
85
100,000 off
EST2 off
1500
50000
0
0
EST3 on
1500
55000
Floating
1400
CST 4 off
5000
N/A
N/A
5000
720 hr/month o
Ib/h
0.4 PSIA
85 %
160 F
80 F
225 F
360 pslg
250 psig
590 F
50 psig
18 psla
Proceedings from the Nineteenth Industrial Energy Technology Conference, Houston, TX, April 23-24, 1997
MODEL.XLS

ESL-IE-97-04-12
The procedure for detennining the best operating strategy under the RTF program was as follows:
1. A base case is established. This is done by running the model with the expected steam and electricity hourly usage to calculate the base case fuel and electricity bills.
2. One of the parameters in the base case is then perturbed by a small amount. For example, power generation from the condensing turbine might be increased by 1 MW. The model is then run again.
3. The fuel and electricity bills, and the total operating costs, for the two cases are compared.
The difference between the total operating costs of the two cases reflects the benefit (or penalty) of the change that has been made.
The variation of marginal power generation cost with turbine load for two typical gas prices is shown in Figure 2. It was found that the average cost of power generation drops approximately 17% as the condensing turbine load increases from 1 to 5 MW.
This reflects increased turbine efficiency at higher loadings. Obviously, when the RTF price is below the break-even cost of generation, there would be no incentive to generate power. Conversely, at an RTF price above the break-even, there is strong incentive to generate power.
Similar calculations were carried out, using the
ClIP model, to evaluate the economics of replacing the existing steam turbine drive on the 1200 ton chiller compressor with an electric motor equipped with an ASD drive. The following assumptions were made:
• a (typical) motor efficiency of 94%
• Induction ASD cost of $150 $/HP
• Induction ASD installation cost of $1 00 IHP
• Induction ASD motor cost of $80 /HP
• Synchronous ASD cost of $300 /HP
• Synchronous ASD installation cost of $200 /HP
In Figure 3, the total operating cost savings for an ASD motor compared to the steam turbine drive are plotted against RTF for three different refrigeration loads. The motor turns out to offer net savings of around $90,000 per year (assuming 2000 hr/yr operation) when the RTP is 2 centslkwh.
Clearly, the optimum customer strategy is to have a dual drive compressor, with the motor being used during off-peak. rates and the turbine being used during on-peak. rates. The capital cost of adding an
ASD/motor was estimated at $350,000, yielding a simple payback of 4 years at an RTF rate of 2 clkwh.
The results obtained from the simulation study indicate that
• The customer will obtain maximum benefit from
OG&E's RTF program by maximizing in-house power generation in their existing condensing turbine when the RTP price is greater than the computed break-even generating cost.
• For RTF prices less than break-even, the customer's condensing turbine should not be operated. The model can be used to re-assess the break-even price as it changes with the economic environment (eg. gas prices, contract terms).
• Under all realistic RTP prices, the customer should continue to drive the BFW pumps and
Boiler # 4 fan with back pressure steam turbines.
• Based on projected gas prices and RTF rates, installing an induction ASD on the 1200 ton chiller will pay back in less than 4 years.
However, the economics are also sensitive to the refrigeration load on the chiller and the number of operating hours per year. The break even point (fuel savings vs. electricity cost) occurs at an average RTF of7.5 centslkwh.
BENEFITS TO CUSTOMER
The customer derived a number of benefits from the ClIP model. They now have a tool that enables them to
• Quickly and easily evaluate economics of alternate system configurations, hardware, control strategies, and contract options from their energy suppliers
• Compute break-even cost of power generation at varying gas prices, turbine loads, and other parameters
• Make accurate real-time decisions regarding optimum operating policy
Proceedings from the Nineteenth Industrial Energy Technology Conference, Houston, TX, April 23-24, 1997
69

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Figure 2: Marginal Cost of Incremental Power Generation
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150
100
50
(50)
(100)
(150)
RTP ($/kWh)
Figure 3: ASD Cost Savings vs. RTP
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Specifically, the study helped this customer make appropriate decisions regarding the operating policies for their existing extraction and condensing turbines, and to evaluate the economics of using induction motors in place of stearn turbine drives.
The decision to participate in OG&E's new RTP option was confirmed once the magnitude of potential cost savings became clear.
BENEFITS TO OG&E
OG&E too derived a number of benefits. First,
OG&E has helped to improve the financial health and well-being of its customer, the company's most valuable asset. The customer has optimized its participation in the RTP program, which has certain revenue benefits to OG&E as well.
Second, OG&E has cemented its relationship with a large and valued customer. Although hard to quantify, one need only compare the relative economics of retaining an existing customer versus acquiring a new one.
Third, OG&E has improved its understanding of the technical, economic and social issues that drive customer decisions. Because OG&E strives to excel in its chosen markets by exceeding customer expectations, rather than merely meeting minimum customer needs, this enhanced understanding will help shape future customer service initiatives and marketing priorities.
Finally, OG&E has identified additional opportunities for customer system (hardware) improvements as a result of this work, which should further enhance the customer's perception of OG&E as a value-added energy supplier and potential energy partner for the future.
CONCLUSION
The experience with this study highlights the usefulness of a simulation model to understand and quantify the interactions between the various components of a customer's CHP system. In several previous EPRI/utility projects, such models provided an important basis for helping the customer make appropriate decisions with respect to issues such as
• Electric versus non-electric drives
• Assessment of cogeneration options
• Avoiding capital investment in new boiler capacity by saving steam through heat recovery and by switching to electric drives
• Correctly pricing steam at different pressure levels
• Reducing net energy costs through exploiting time-of-use electric power rates
• Overall planning of the site utility system to accommodate future production plans
As competition in the energy utility business intensifies, large base-loaded power consumers are expected to be offered a proliferation of energy pricing options; the ability to accurately model their economic impact will become more and more critical as a decision-support tool. Such models can also be used by electric power utilities to evaluate innovative contract terms for optimum rate design.
REFERENCES
1. Kumana, J D and R Nath, "Demand Side
Dispatching, Part 1 - A Novel Approach for
Industrial Load Shaping Applications", IETC
Proceedings (March 93)
2. R Nath, D A Cerget, and E T Henderson,
"Demand Side Dispatching, Part 2 - An
Industrial Application", IETC Proceedings
(March 93)
3. R Nath and J D Kumana, "NOx Dispatching in
Plant Utility Systems using Existing Software
Tools", IETC Proceedings (April 92)
4. R Nath, J D KUJIl3I13, and J F Holiday,
"Optimum Dispatching of Plant Utility Systems to Minimize Cost and Local NOx Emissions",
Proceedings of the ASME Industrial Power
Conference, New Orleans (March 92), Vol 17, p59
5. "STEAM for Microsoft Excel" brochure,
LinnhoffMarch Inc, Houston, TIC, phone 713­
787-6861
6. "SteamTab for Excel and Lotus" brochure,
ChemicaLogic, Woburn, MA, phone 617-938­
7722
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