Patent Application
20160371405
This disclosure relates to power generation, and more particularly, to systems and methods of forecasting power plant performance.
Energy traders typically carry out bidding in the marketplace based on power consumption forecasts and the corresponding power generation forecasts. These forecasts can consider a number of different factors, some of which are variable in nature. Among these variable factors can be the predicted performance efficiency of one or more power plants over a future period of time and the effects of weather on these power plants during this future period of time. In many traditional situations, the amount of power that is bid into the market for a power plant (and the associated fuel consumption to operate the power plant) is based on the intuition and experience of a plant manager. While this knowledge may have been built up by the plant manager after many years of service, there is also a natural tendency on the part of the plant manager to hedge his or her bets in order to avoid being caught short of fuel needs to operate the power plant. Unfortunately, erroneous forecasts based on such hedging can prove very expensive, because changing the amount of power generation of a power plant by even a few additional megawatts per hour can add up to a significant unrealized profit to the owner of the power plant.
Even where human forecasts are eliminated so as to avoid issues such as hedging, many alternative traditional forecasts based on computer-based solutions are also susceptible to errors, at least some of which may be attributable to inadequate modeling procedures and/or inaccurate assumptions.
A power generation plant typically includes a number of complex machines such as for example, a turbine, a compressor, a combustor, and a controller. Each of these complex machines can include a large number of stationary as well as moving parts. Some of these moving parts deteriorate upon prolonged usage. As a result, the amount of power generated by the power plant can deteriorate over time. Such deterioration can be addressed by replacing working parts that have suffered an unacceptable level of wear and tear, and/or by replacing defective parts that have become unsuitable for use. In some situations even after replacement of such faulty parts, the level of power generated by the power plant can be less than optimal, especially when compared to a level of power that was generated by the power plant at an earlier instant in time, say for example, when the power plant was first placed in service.
Unfortunately, comparing a level of performance of a power plant at any given instant in time against a level of performance of the power plant when the power plant is operated under optimal conditions is a complicated and difficult procedure due to a number of reasons. For example, it is often difficult to accurately characterize and quantify various operational parameters of the power plant especially when these operational parameters are constantly changing. The difficulty can not only be attributed to factors such as the complex interdependency between various structural components of the power plant, but also due to other factors such as the weather for example, which can play a significant role in terms of power generation and power consumption.
More specifically, it is often difficult to create an accurate reference model of a power plant that can be used to assess the performance of the power plant at any later instant in time after the power plant has been placed in service. In one traditional approach, power plant performance data is accumulated over a period of time, for example weeks, months or years. The accumulated “historic” data can be used to generate a “reference” power plant simulation model that is representative of an averaged approximation of the power plant over a variety of operating conditions over the period of time. However, it can be understood that various anomalies may have occurred during the period of time that the performance data was accumulated, especially when the period of time is very long (extending over several years, for example). These anomalies can result in the “reference” power plant simulation model being improperly skewed and not accurately reflecting the characteristics of the power plant, say for example, after some sub-optimal parts have been replaced recently.
The shortcoming in the time-averaged approach for collecting data may be addressed to some extent by generating a plant simulation model using data that is collected in real time. However, a plant simulation model based on real-time data collection suffers from certain shortcomings when this plant simulation model is used in a non-comparative stand-alone manner that does not take into consideration other types of information. For example, a plant simulation model based on real-time data may be used to evaluate a set of predictions or estimates provided by a human being. Such an evaluation may not only overlook the fact that the power plant may be operating sub-optimally when the real time data is collected, but also that the predictions and estimates provided by the human being have been historically unreliable and inaccurate.
Embodiments of the disclosure are generally directed to systems and methods for forecasting power plant performance.
According to one example embodiment of the disclosure, a method can include providing in a computer system, a calibrated physics-based simulation model of a power plant. The calibrated physics-based simulation model is used to generate training data for effectuating a surrogate neural network model. Furthermore, a periodic performance index is received in the computer. The periodic performance index, which is indicative of dynamic changes in one or more operating parameters of the power plant, is processed in combination with the surrogate neural network model to forecast one or more performance parameters of the power plant.
According to another example embodiment of the disclosure, a system can include a computer that is configured to use a calibrated physics-based simulation model to generate training data. The training data is used by the computer to effectuate a surrogate neural network model. Furthermore, the computer is configured to receive a periodic performance index. The periodic performance index, which is indicative of dynamic changes in one or more operating parameters of the power plant, is processed in combination with the surrogate neural network model by the computer for forecasting one or more performance parameters of the power plant.
According to yet another example embodiment of the disclosure, a computer-readable storage medium can be provided. The computer-readable storage medium has stored thereon, instructions executable by a computer for performing operations that can include using a calibrated physics-based simulation model to generate training data. The training data is used to effectuate a surrogate neural network model. The surrogate neural network model is processed in combination with a periodic performance index, which is indicative of dynamic changes in one or more operating parameters of the power plant, to forecast one or more performance parameters of the power plant.
Other embodiments and aspects of the disclosure will become apparent from the following description taken in conjunction with the following drawings.
Having thus described the disclosure in general terms, reference will now be made to the accompanying drawings, which are not necessarily drawn to scale, and wherein:
The disclosure now will be described more fully hereinafter with reference to the accompanying drawings, in which example embodiments of the disclosure are shown. This disclosure may, however, be embodied in many different forms and should not be construed as limited to the example embodiments set forth herein; rather, these embodiments are provided so that this disclosure will satisfy applicable legal requirements. Like numbers refer to like elements throughout.
Turning to
One example of an intrinsic factor that can affect the power generation by the power plant 110 is the structural condition of some parts of a power generating machine. For example, one or more fins of a turbine blade may have deteriorated over a period of time such that an amount of airflow through the turbine has reduced gradually over this period of time. The reduced airflow can cause a drop in the amount of generated power. Similarly, the quality of the fuel provided to operate a compressor may be poor at any one time and better at another time, thus resulting in a variance in the amount of power generated by using the compressor.
One example of an extrinsic factor that can influence the performance of the power plant 110 is indicated in
Such intrinsic and extrinsic factors can be taken into consideration for accurate forecasting of the performance of the power plant 110. Further, the performance of the power plant 110 can be monitored on an ongoing regular basis so as to obtain performance data that is up-to-date. This monitoring functionality may be carried out by the performance monitoring computer system 120. There are many different systems and methods that can be employed to execute the functionality of the performance monitoring computer system 120. In one exemplary implementation, plant data is obtained from the power plant 110 on the basis of a sampling routine having a desired level of periodicity. A few non-exhaustive list of repetitive sampling routines can include a first sampling routine referred to herein as a sub-hourly sampling routine where plant data samples are obtained at “x” minutes intervals every hour throughout each day, a second sampling routine that is an hourly sampling routine, a third sampling routine that is a daily sampling routine, a fourth sampling routine that is a weekly sampling routine, and a fifth sampling routine that is a monthly sampling routine.
The plant data samples obtained via one or more of these sampling routines are evaluated in the performance monitoring computer system 120 by using a computer simulation model of the power plant and the results of the evaluations are provided to the performance forecasting computer system 125. When the sampling routine is of the order of “x” minutes intervals throughout each day, or on a daily basis, for example, the evaluation results generated by the performance monitoring computer system 120 based on such recent historical data, provide a granular level of information that greatly exceeds the level of information derived by many traditional systems that employ historical data extending over much longer periods such as months or years. Understandably, using older historical data fails to provide accurate forecasting results as the power plant may not be at the same level of performance as when the data accumulation was begun. This problem is compounded if the forecasting does not use reference parameters associated with a power plant generating an optimal level of power at an earlier instant, such as when the power plant was first placed in service.
In the exemplary implementation shown in
The performance forecasting computer system 125 uses the periodic performance index to forecast the performance of the power plant 110 as will be described in further detail using
The calibrated physics-based simulation plant model 205 can be used to generate training data that is provided to a surrogate neural network plant model 210. As the name suggests, the surrogate neural network plant model 210 can be viewed as a surrogate representation of the power plant 110 by way of the calibrated physics-based simulation plant model 205. Various load parameters can be applied to the surrogate neural network plant model 210 in order to generate various evaluation plant models. Some or all of the various load parameters that are applied to the surrogate neural network plant model 210 can be different from the OEM-oriented load parameters applied to the calibrated physics-based simulation plant model 205. One or more of the evaluation plant models generated by the surrogate neural network plant model 210 can be used as one or more calibrated reference templates by the combiner 215.
The combiner 215 processes the calibrated reference templates in combination with the periodic performance index that is provided to the combiner 215 by the performance monitoring computer system 120 (shown in
The operations carried out by the forecast developer 225 generally involve sweeps of various load fractions over each forecast period (for example, an hour-by-hour forecast). The sweeps can range from 50% to 100% of a base load placed upon the power plant 110. In one example implementation, the sweeps are directed at evaluating the effect of various loads upon the incremental heat rate (IHR) of the power plant 110.
The evaluation results can be provided in the form of an IHR curve, which is useful in making decisions to perform remedial action upon the power plant 110 if such remedial action is desired. IHR can be generally defined as an amount of fuel required to produce the next megawatt (MW) of output from the power plant 110 and has to be monotonically increasing in nature. Calculating an IHR can be carried out by minimizing a mean square error of the heat rate derived from the surrogate neural network plant model 210 and the heat rate derived from various points on a proposed IHR curve under the constraint that each IHR point be greater than the one before.
Attention is now drawn to
The memory 425 can be used to store program instructions that are loadable and executable by the processor 405, as well as to store data generated during the execution of these programs. Depending on the configuration and type of the computer 400, the memory 425 can be volatile (such as random access memory (RAM)) and/or non-volatile (such as read-only memory (ROM), flash memory, etc.). In some embodiments, the memory devices can also include additional removable storage 430 and/or non-removable storage 435 including, but not limited to, magnetic storage, optical disks, and/or tape storage. The disk drives and their associated computer-readable media can provide non-volatile storage of computer-readable instructions, data structures, program modules, and other data for the devices. In some implementations, the memory 425 can include multiple different types of memory, such as static random access memory (SRAM), dynamic random access memory (DRAM), or ROM.
The memory 425, the removable storage 430, and the non-removable storage 435 are all examples of computer-readable storage media. For example, computer-readable storage media can include volatile and non-volatile, removable and non-removable media implemented in any method or technology for storage of information such as computer-readable instructions, data structures, program modules or other data. Additional types of computer storage media that can be present include, but are not limited to, programmable random access memory (PRAM), SRAM, DRAM, RAM, ROM, electrically erasable programmable read-only memory (EEPROM), flash memory or other memory technology, compact disc read-only memory (CD-ROM), digital versatile discs (DVD) or other optical storage, magnetic cassettes, magnetic tapes, magnetic disk storage or other magnetic storage devices, or any other medium which can be used to store the desired information and which can be accessed by the devices. Combinations of any of the above should also be included within the scope of computer-readable media.
Computer 400 can also include one or more communication connections 410 that can allow a control device (not shown) to communicate with devices or equipment capable of communicating with the computer 400. The connections can be established via various data communication channels or ports, such as USB or COM ports to receive cables connecting the control device to various other devices on a network. In one embodiment, the control device can include Ethernet drivers that enable the control device to communicate with other devices on the network. According to various embodiments, communication connections 410 can be established via a wired and/or wireless connection on the network.
The computer 400 can also include one or more input devices 415, such as a keyboard, mouse, pen, voice input device, and touch input device. It can further include one or more output devices 420, such as a display, printer, and speakers.
In other embodiments, however, computer-readable communication media can include computer-readable instructions, program modules, or other data transmitted within a data signal, such as a carrier wave, or other transmission. As used herein, however, computer-readable storage media do not include computer-readable communication media.
Turning to the contents of the memory 425, the memory 425 can include, but is not limited to, an operating system (OS) 426 and one or more application programs or services for implementing the features and aspects disclosed herein. Such applications or services can include a performance forecasting module 427. In one embodiment, the performance forecasting module 427 can be implemented by software that is provided in configurable control block language and is stored in non-volatile memory. When executed by the processor 405, the performance forecasting module 427 implements the various functionalities and features described in this disclosure.
References are made herein to block diagrams of systems, methods, and computer program products according to example embodiments of the disclosure. It will be understood that at least some of the blocks of the block diagrams, and combinations of blocks in the block diagrams, respectively, can be implemented at least partially by computer program instructions. These computer program instructions can be loaded onto a general purpose computer, special purpose computer, special purpose hardware-based computer, or other programmable data processing apparatus to produce a machine, such that the instructions which execute on the computer or other programmable data processing apparatus create means for implementing the functionality of at least some of the blocks of the block diagrams, or combinations of blocks in the block diagrams discussed.
These computer program instructions can also be stored in a computer-readable memory that can direct a computer or other programmable data processing apparatus to function in a particular manner, such that the instructions stored in the computer-readable memory produce an article of manufacture including instruction means that implement the function specified in the block or blocks. The computer program instructions can also be loaded onto a computer or other programmable data processing apparatus to cause a series of operational elements to be performed on the computer or other programmable apparatus to produce a computer implemented process such that the instructions that execute on the computer or other programmable apparatus provide elements for implementing the functions specified in the block or blocks.
One or more components of the systems and one or more elements of the methods described herein can be implemented through an application program running on an operating system of a computer. They also can be practiced with other computer system configurations, including hand-held devices, multiprocessor systems, microprocessor based, or programmable consumer electronics, mini-computers, mainframe computers, etc.
Application programs that are components of the systems and methods described herein can include routines, programs, components, data structures, etc. that implement certain abstract data types and perform certain tasks or actions. In a distributed computing environment, the application program (in whole or in part) can be located in local memory, or in other storage. In addition, or in the alternative, the application program (in whole or in part) can be located in remote memory or in storage to allow for circumstances where tasks are performed by remote processing devices linked through a communications network.
Many modifications and other embodiments of the example descriptions set forth herein to which these descriptions pertain will come to mind having the benefit of the teachings presented in the foregoing descriptions and the associated drawings. Thus, it will be appreciated the disclosure may be embodied in many forms and should not be limited to the example embodiments described above. Therefore, it is to be understood that the disclosure is not to be limited to the specific embodiments disclosed and that modifications and other embodiments are intended to be included within the scope of the appended claims. Although specific terms are employed herein, they are used in a generic and descriptive sense only and not for purposes of limitation.
