CHP MODELING AS A TOOL FOR ELECTRIC POWER UTILITIES TO UNDERSTAND MAJOR INDUSTRIAL CUSTOMERS

Transcription

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

2 3. Identify opportunity for cost reduction through introducing new degrees offreedom - 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: 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 thermodynamic 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. For the example case, both process and HVAC 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. 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. Ifthe 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 ifthe 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. 66

3 Figure 1: Combined Heat and Power (CHP) Model ESL-IE Fuel Fuel Fuel Fuel Fuel ommbtulh ommbtu/h ommbtu/h 72 MMBtulh ommbtulh b/h 525 Ib/h Total Steam Generation II Ib/h II Chiller oton oiblh off OkW OkW I oiblh oiblh oib/h 50 psig 391 F h-o 59414Ib/h BFW 225 F 2lb/h oiblh BFW ~~1b1 Pom", B4 Fan on 5000 Ib/h J 2591 Ib/h ~ b/h 0.40 psia ~r--olb/h Stm Users 50000lblh 360 psig 80 F Make up OkW CW oib/h BFW 160 F Condensate b/h 9877lblh Base Case 720 hr/month Heat and Power Costs OSU Electricity Usage 10,800,000 kwh/month Natural Gas 52,062 MMBtu OSU Electricity Generated 1,008,000 kwh/month Natural Gas Cost 107,248 $/month Net Electricity from OG&E 9,792,000 kwh/month --"'--- Gas Pipeline Lease 21,450 $/month RTP 0.02 $li<wh Total Gas Bill 128,698 $Imonth Electricity RTP 195,840 $/month Wamings: None 67

4 Table 1: Input data (typical) 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) Operating Data Iblh On Stream Time (billing period) o ton of refrlg PRVs Flow kwhlh Condensing Stage Pressure o kwhlh Condensate Return Flow Condensate Return Temperature Make up Water Temperature 2.00 $/MMBtu Boiler Feed Water Temperature 0.40 $/MMBlu Boller Feed Water Pressure $fmo HP Steam Pressure 0.02 $/kwh HP Steam Temperature 3.00 % LP Steam Pressure Deaerator Pressure 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 ex> '" EqUipment Perfonnance Data Boiler 10 Boiler Maximum Continuous Ratings: Boiler switches (on/off) Ib/h 40,000 40,000 40, ,000 off "SWing" on on ,000 off Boiler Blow-down % 4.00 Turbine 10 Status operating mode Steam Turbine Capacity: Maximum Extraction Flow Actual Extraction Flow Actual Power Generation EST 1 EST2 EST3 CST 4 off off on off WV Iblh N/A Iblh 0 0 Floating N/A KW Setting Units of Measurements 0 MODEL.XLS

5 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 1MW. 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 ofthe 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 ofaround $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 ofpower generation at varying gas prices, turbine loads, and other parameters Make accurate real-time decisions regarding optimum operating policy 69

6 : rn o 6.00 () c::: o :;:; 5.00 ~ Q) c::: Q) ( "'===~ OWNV 1 WNV 2WNV 3WNV 4 WNV 5WNV 6WNV Additional Power Generation in Condensing Turbines Figure 2: Marginal Cost of Incremental Power Generation (50) (100) (150) RTP ($/kwh) Figure 3: ASD Cost Savings vs. RTP 70

7 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 ofbenefits. 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 Correctly pricing steam at different pressure levels Reducing net energy costs through exploiting time-of-use electric power rates Overall planning ofthe 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 ofplant 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 "SteamTab for Excel and Lotus" brochure, ChemicaLogic, Woburn, MA, phone 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 71