Patent Application
20060106740
This application incorporates by reference the entire disclosure of the applicants' related application entitled CREATION OF FUTURE TIME INTERVAL POWER GENERATION DATA USING HISTORICAL DATA filed concurrently herewith.
The power generation industry has been increasingly opened to free market competition. As part of this new regulatory environment, Independent Systems Operators (ISOs) have emerged. Although rules may vary in a specific ISO environment, for background purposes it is fair to say that, as part of planning for daily operation, a bidding process occurs wherein power utilities submit estimates and bids to provide power in a region for the next day. These estimates typically state the cost to generate power for the next day, and also state the seller's asking price for the next day. From these bids, a seller(s) is selected to supply power to a region for the next day. Therefore, success in the bidding process is critical to the success of a seller.
At present, these estimates or bids are generated manually by experienced employees using their personal and subjective “best guess.” Therefore, the success of the bid process varies and is dependant upon the skill and experience of the employee. Thus, a system, method, and apparatus for generating bids is needed in the power generation industry.
Methods, apparatus, and articles of manufacture such as software media for creating projected power production data are disclosed. The method may comprise storing historical heat rate data and historical process information for at least one power generation unit in a historical heat rate database. The method may also comprise retrieving the historical heat rate data from the database for a selected time interval and correcting the historical rate data using correction factors which may be based on differences between the historical process information and projected process information; and creating a projected cost or a projected price for a future time interval based on the retrieved historical heat rate data.
The following description of the figures is not intended to be, and should not be interpreted to be, limiting in any way.
As shown in one exemplary embodiment at
As shown in
Thus with above in mind, it will be discussed below how corrected day-ahead curves 30 may be generated in an exemplary embodiment.
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In
The historical heat rate curves 5 are also indexed or correlated to historical process information or conditions. As shown in
For example, there are different qualities of coal which may be used in a coal fired plant. For example, some coal performs better than other coal because it has a better heating value 14, i.e., Btu's produced per pound. For example, a more expensive pound of coal may burn at 13,000 Btu's verses a less expensive pound of coal which may burn at 9,000 Btu's. Also for example, some coal has more moisture or sulfur content than other coal. Of course there are other possible variables such as ash percentage, sulfur percentage, moisture percentage, SIP, slagging potential percentage, grind, pet coke percentage, and cost, and this not meant to be a complete list. However, the point is that the fuel quality affects heating value. Referring to
In
Continuing with the explanation of this embodiment, the retrieved historical heat rate curves 5 may be averaged together to form an averaged historical heat rate curve 24 for a projected future time interval which in this embodiment is a 1 hour interval for example from 12 A.M to 1 A.M. of the next day. This averaged historical heat rate curve 24 remains correlated to the associated historical process information, for example in this embodiment, the historical heat rate curves 5 are indexed to the following historical process information: historical ambient temperature 11, historical humidity 12, historical inlet cooling water temperature 13, and historical heating value 14 of the fuel source which in this embodiment is coal. However, because an average was taken the associated historical process information is also averaged at the same time. Thus, an averaged historical heat rate curve 24 is formed.
As shown at reference numeral 26 in
Additionally in this embodiment as shown at reference numeral 28, the corrected and averaged historical heat rate curve 28, has breakpoints 40 located at any desired megawatt levels. Furthermore, this corrected and averaged heat rate curve can then be used to calculate a cost curve and the cost of power generation using fuel cost. For example, although many formulas are possible to calculate cost, an example of one formula that computes cost as a function of load is Cost ($/hr)=Fuel Cost ($/mmBtu)×Heat Input (mmBtu/hr), where Heat Input (mmBtu/hr)=Load (MW)×Heat Rate (mmBtu/MW-hr). Fuel cost ($/mmBtu) may be stored in the historical heat rate database 20 for the time interval selected or in any storage means. Any desired megawatt levels or breakpoints 40 of cost may be taken from the curve or computed. Another example of a cost formula which could be used would also add other expenses such as the cost of emissions and fixed costs. For example, the following formula is such a formula: Cost($/hr)={Fuel Cost($/mmBtu)+NOx Price ($/lb NOx)×NOx Generation (lb NOx/mmBtu)}×Heat Input (mmBtu/hr)+Ash Costs ($/hr)+Sulfur Costs ($/hr)+Operation and Maintanence Costs ($/hr)+Fixed Costs ($/hr). Any of these added expense values could also be stored in historical heat rate curve database 20 for the time interval selected or in other storage. Thus, a resultant day-ahead cost curve 30 as shown in
A day-ahead price curve 34 may be formed from the day-ahead cost curve 30. The day-ahead price curve 34 is equal to cost plus any desired profit adjustment. The profit adjustment many take any form desired. For example a constant multiplier, may be applied equally to the day-ahead cost curve 30 or alternatively different profit adjustments with different multipliers may be used at different breakpoints 40. For example, a 100 megawatt breakpoint 40 may be selected to be associated with a lower profit adjustment (for example cost*1.2) than a 500 megawatt breakpoint 40 profit adjustment (cost*1.5) depending on the desired profit. For example, continuing with the explanation of this embodiment, as seen in
Additionally, as shown in
For a user or a power company acting as a seller, a number of advantages accrue from the above, some of which are discussed below. For example, instead of relying on a human generated best guess to forecast reasonable costs and prices for the day-ahead in order to generate a formal bid, and thus for the power company to be economically successful in bidding, actual historical heat rate data can be relied on instead. This leads to more accurate and thus more successful bidding. For example, in the embodiment above, historical heat rate data from the previous day is used and has been found to be an excellent estimator of day-ahead costs. This is simply because it has been found as a business model that conditions experienced on the day-ahead are likely to be similar to conditions experienced on the day or days before the day-ahead. Additionally, the inclusion of correction factors 26 can incorporate forecasted conditions into the projected day-ahead cost curve 30 and day-ahead price curve 34. However, as explained above, any time interval can be configured to be examined by the user at 22 in
As shown in
One of ordinary skill in the art can appreciate that a computer or other client or server device can be deployed as part of a computer network, or in a distributed computing environment. In this regard, the methods and apparatus described above and/or claimed herein pertain to any computer system having any number of memory or storage units, and any number of applications and processes occurring across any number of storage units or volumes, which may be used in connection with the methods and apparatus described above and/or claimed herein. Thus, the same may apply to an environment with server computers and client computers deployed in a network environment or distributed computing environment, having remote or local storage. The methods and apparatus described above and/or claimed herein may also be applied to standalone computing devices, having programming language functionality, interpretation and execution capabilities for generating, receiving and transmitting information in connection with remote or local services.
The methods and apparatus described above and/or claimed herein is operational with numerous other general purpose or special purpose computing system environments or configurations. Examples of well known computing systems, environments, and/or configurations that may be suitable for use with the methods and apparatus described above and/or claimed herein include, but are not limited to, personal computers, server computers, hand-held or laptop devices, multiprocessor systems, microprocessor-based systems, network PCs, minicomputers, mainframe computers, distributed computing environments that include any of the above systems or devices.
The methods described above and/or claimed herein may be described in the general context of computer-executable instructions, such as program modules, being executed by a computer. Program modules typically include routines, programs, objects, components, data structures, etc. that perform particular tasks or implement particular abstract data types. Thus, the methods and apparatus described above and/or claimed herein may also be practiced in distributed computing environments such as between different power plants or different power generator units (50-52) where tasks are performed by remote processing devices that are linked through a communications network or other data transmission medium. In a typical distributed computing environment, program modules and routines or data may be located in both local and remote computer storage media including memory storage devices. Distributed computing facilitates sharing of computer resources and services by direct exchange between computing devices and systems. These resources and services may include the exchange of information, cache storage, and disk storage for files. Distributed computing takes advantage of network connectivity, allowing clients to leverage their collective power to benefit the entire enterprise. In this regard, a variety of devices may have applications, objects or resources that may utilize the methods and apparatus described above and/or claimed herein.
Computer programs implementing the method described above will commonly be distributed to users on a distribution medium such as a CD-ROM. The program could be copied to a hard disk or a similar intermediate storage medium. When the programs are to be run, they will be loaded either from their distribution medium or their intermediate storage medium into the execution memory of the computer, thus configuring a computer to act in accordance with the methods and apparatus described above.
The term “computer-readable medium” encompasses all distribution and storage media, memory of a computer, and any other medium or device capable of storing for reading by a computer a computer program implementing the method described above.
Thus, the various techniques described herein may be implemented in connection with hardware or software or, where appropriate, with a combination of both. Thus, the methods and apparatus described above and/or claimed herein, or certain aspects or portions thereof, may take the form of program code or instructions embodied in tangible media, such as floppy diskettes, CD-ROMs, hard drives, or any other machine-readable storage medium, wherein, when the program code is loaded into and executed by a machine, such as a computer, the machine becomes an apparatus for practicing the methods and apparatus of described above and/or claimed herein. In the case of program code execution on programmable computers, the computing device will generally include a processor, a storage medium readable by the processor which may include volatile and non-volatile memory and/or storage elements, at least one input device, and at least one output device. One or more programs that may utilize the techniques of the methods and apparatus described above and/or claimed herein, e.g., through the use of a data processing, may be implemented in a high level procedural or object oriented programming language to communicate with a computer system. However, the program(s) can be implemented in assembly or machine language, if desired. In any case, the language may be a compiled or interpreted language, and combined with hardware implementations.
The methods and apparatus of described above and/or claimed herein may also be practiced via communications embodied in the form of program code that is transmitted over some transmission medium, such as over electrical wiring or cabling, through fiber optics, or via any other form of transmission, wherein, when the program code is received and loaded into and executed by a machine, such as an EPROM, a gate array, a programmable logic device (PLD), a client computer, or a receiving machine having the signal processing capabilities as described in exemplary embodiments above becomes an apparatus for practicing the method described above and/or claimed herein. When implemented on a general-purpose processor, the program code combines with the processor to provide a unique apparatus that operates to invoke the functionality of the methods and apparatus of described above and/or claimed herein. Further, any storage techniques used in connection with the methods and apparatus described above and/or claimed herein may invariably be a combination of hardware and software.
While the methods and apparatus described above and/or claimed herein have been described in connection with the preferred embodiments and the figures, it is to be understood that other similar embodiments may be used or modifications and additions may be made to the described embodiment for performing the same function of the methods and apparatus described above and/or claimed herein without deviating therefrom. Furthermore, it should be emphasized that a variety of computer platforms, including handheld device operating systems and other application specific operating systems are contemplated, especially given the number of wireless networked devices in use.
Thus, a system, method, and apparatus for generating bids for the power generation industry has been described above.
While the methods and apparatus described above and/or claimed herein are described above with reference to an exemplary embodiment, it will be understood by those skilled in the art that various changes may be made and equivalence may be substituted for elements thereof without departing from the scope of the methods and apparatus described above and/or claimed herein. In addition, many modifications may be made to the teachings of above to adapt to a particular situation without departing from the scope thereof. Therefore, it is intended that the methods and apparatus described above and/or claimed herein not be limited to the embodiment disclosed for carrying out this invention, but that the invention includes all embodiments falling with the scope of the intended claims. Moreover, the use of the term's first, second, etc. does not denote any order of importance, but rather the term's first, second, etc. are used to distinguish one element from another.
