System and method for gathering and performing complex analyses on power data from multiple remote sources

The invention encompasses data analytics, and more specifically, encompasses the efficient gathering and management of data, and the execution of data analysis solutions on complex power and pricing.

FIELD OF THE INVENTION

The invention encompasses data analytics, and more specifically, encompasses the efficient gathering and management of data, and the execution of data analysis solutions on complex power and pricing.

DISCUSSION OF THE RELATED ART

In the power trading markets, power traders demand accurate, powerful, robust, and reliable data analysis systems. Power markets are comprised of Independent System Operators (ISO) and Regional Transmission Organizations (RTO), each of which is responsible for a specific geographical region that receives power transmission from one or more power generators. Each ISO/RTO is comprised of one or more regional power market, which can be considered as “assigned” to a subset geographical power region of its parent set's (i.e., ISO/RTO) region. Moreover, one or more power “nodes” exist within each of the regional power markets and, accordingly, belong to that specific regional power market. Within each ISO/RTO regional power market, power prices are: (a) established, (b) tracked, and (c) published according to supply and demand fundamentals, as power is traded and eventually generated. For each power node within a specific ISO/RTO regional power market, power prices vary individually. Therefore, for purposes of the power trading markets, power is typically traded on an hourly basis at each node, in a dutch-type auction market. Moreover, there is also a “day ahead” (DA) market that allows traders to bid/offer power into the market on a DA basis. With respect to the DA market, the ISO/RTO for the specific geographical region that is affected by one or more given DA trades determines the final DA price for each power node on which a bid/offer is placed. Subsequently, on an hourly basis, each ISO/RTO alerts each power trader who placed a bid/offer into its market(s) as to which DA trades were executed.

As for the operation of the actual power generation markets, which significantly affects the decisions made by power traders as well as the trading positions they choose to exercise, power generation may become disrupted in real time due to multiple factors. Some of these factors include, for example, power congestion, weather-related conditions, unexpected generator/transmission outages, or even differences between forecasted and actual power demand (i.e., power “load”). These factors taken in the aggregate, or individually, can easily disrupt the power grid. Therefore, as a direct proximate cause, these factors have a significant impact on power traders' decisions to exercise certain trade and/or develop certain trading strategies. Furthermore, whether the impact on the power grid is positive or negative, its root cause(s) simply translates into a proportionally weighed causal factor that “drives one or more power nodes' prices.

Additionally, every power node in an ISO/RTO market is assigned an actual “real time” (RT) price. Similar to the way in which DA trading is performed, the RT prices are also capable of being traded in a real time market (sometimes referred to as a “spot market”), as prices are published hourly by the appropriate ISO/RTO. Coupled together, the DA and RT prices are commonly known as Locational Marginal Pricing (LMP) data. LMP data is considered to be vital for power traders engaged in active trading, as well as for developing trading strategies, across various ISO/RTO markets. Accordingly, correlation between LMP data, power data, and the causal factors affecting at least this data from a recent or historical data analysis perspective would provide power traders valuable insight into the market. From a historical data perspective, the correlations between power data and LMP data in the power trading markets, according to causal factors, would assist the power trader in determining how the market would react in similar situations in the future because power trading markets tend to mimic their past/historical performance(s) when the same/similar causal factors are presented.

However, current data analysis systems that are tailored towards power traders for use in the power or energy trading markets only offer very limited data analysis capabilities. First, current data analysis systems do not make use of the full set of available power and price data. Moreover, these systems operate in a static manner and do not support dynamic data gathering, management, and analysis methodologies. Second, current data analysis systems also do not have the capability to gather, manage, and analyze data such that certain cause and effect scenarios can be determined accurately. For example, using current systems, power data such as power pricing data from the various markets cannot be managed and analyzed, in view of usage, congestion, weather-related conditions, and transmission outages, such that cause and effect factors are properly linked and identified to inform power traders to make appropriate market decisions.

SUMMARY OF THE INVENTION

Accordingly, the invention encompasses systems and methods for gathering and performing analyses on power data from multiple remote sources that substantially obviates one or more problems due to limitations and disadvantages of the related art.

An encompassed feature of the invention is a powerful, efficient, and robust power data management and analysis capabilities to allow power traders to make well-informed, confident trades, as well as to develop similar trading strategies.

Another encompassed feature of the invention is an efficient power data management solution that seamlessly retrieves, formats, and analyzes large quantities of power data from many remote sources, and provides various reports.

Additional features and advantages of the invention will be set forth in the description which follows, and in part will be apparent from the description, or may be learned by practice of the invention. The embodiments and other advantages of the invention will be realized and attained by the structure particularly pointed out in the written description and claims hereof as well as the appended drawings.

To achieve these and other advantages and in accordance with the purpose of the invention, as embodied and broadly described, the systems and methods for gathering and performing complex analyses on power data from multiple remote sources includes a system, including a data gathering unit to gather power data and locational marginal pricing (LMP) data from a plurality of remote power data sources and to convert the power data and the LMP data into a common data format; a data analysis unit to correlate the power data with the LMP data based on causal factors; a database to store at least the gathered power data and the LMP data, the converted power data and the LMP data, and the correlated data of causal factors; and a display unit to display at least one of the converted power data and the LMP data, and the correlated data of causal factors.

Another illustrative embodiment of the invention encompasses methods including the steps of: gathering power data and LMP data from a plurality of remote power data sources and converting the power data and the LMP data into a common data format; correlating the power data with the LMP data based on causal factors; storing at least the gathered power data and the LMP data, the converted power data and the LMP data, and the correlated data of causal factors; and displaying at least one of the converted power data and the LMP data, and the correlated data of causal factors.

DETAILED DESCRIPTION OF THE INVENTION

The invention encompasses a system including:

a data gathering unit to gather power data and locational marginal pricing (LMP) data from a plurality of remote power data sources and to convert the power data and the LMP data into a common data format;

a data analysis unit to correlate the power data with the LMP data for identifying causal factors;

a database to store at least the gathered power data and the LMP data, the converted power data and the LMP data, and the correlated data of causal factors; and

a display unit to display at least one of the converted power data and the LMP data, and the correlated data of causal factors.

In certain illustrative embodiments, the data gathering unit gathers power data and LMP data over a network.

In certain illustrative embodiments, the system further comprises an access unit to grant access to one or more remote users.

In certain illustrative embodiments, the one or more remote users are power traders in at least one power trading market.

In certain illustrative embodiments, the system further comprises a remote user device.

In certain illustrative embodiments, the display unit transmits display signals to the remote user device.

In certain illustrative embodiments, the system further comprises a price reporting unit to generate at least one price summary report.

In certain illustrative embodiments, the price summary report is presented based on filter criteria, the filter criteria including at least one of a price report type, start date, end date, day type, hour type, independent system operator (ISO) type, node type, delta value, standard deviation value, rank-by value, and LMP type.

In certain illustrative embodiments, the system further comprises a chart analysis unit to compare at least one of the converted power data and the LMP data and the correlated data of causal factors and to generate at least one chart analysis report.

In certain illustrative embodiments, the chart analysis report is generated based on filter criteria, the filter criteria including at least one of a start date, an end date, a day type, a hour type, a time zone type, a plurality of node types, a plurality of ISO/zone types, a plurality of weather types, and a compare nodes type.

In certain illustrative embodiments, the system further comprises a price look-back unit to retrieve the converted power data and the LMP data, and the correlated data of causal factors according to at least look-back criteria.

In certain illustrative embodiments, the look-back criteria includes at least one of a temperature value, a load value, an outage value, an ISO type, a weather type, a forecast date, a day value, a hour type, a matching hour value, a start date, and an end date.

In certain illustrative embodiments, the look-back criteria includes at least one of a fuel index type, a fuel index price, a constraint type, and a transmission outage type.

In certain illustrative embodiments, the retrieved data is passed to a price reporting unit to generate at least one price look-back report according to filter criteria.

In certain illustrative embodiments, the filter criteria includes at least a price report type.

In certain illustrative embodiments, the system further comprises a look-back results unit to display the retrieved data according to at least one of a date value, a day value, an average load forecast value, an outage value, an actual outage value, an average temperature value, a spot fuel value, and a price report type.

In certain illustrative embodiments, the system further comprises a compare node unit to compare nodes to the retrieved data.

In certain illustrative embodiments, the price report type includes at least one of a scouting summary, scouting detail, hourly spread, hourly averages, node ranking, top nodes, LMP breakdown, day ahead (DA) constraints, constraint frequency, weather forecast, weather forecast versus actual forecast, fuel prices, financial transmission rights (FTR) monthly auction, RSG/OP reserves, and transmission outages.

In another embodiment, the invention encompasses a computer-implemented method, including the steps of:

gathering power data and locational marginal pricing (LMP) data from a plurality of remote power data sources and converting the power data and the LMP data into a common data format;

correlating the power data with the LMP data for identifying causal factors;

storing at least the gathered power data and the LMP data, the converted power data and the LMP data, and the correlated data of causal factors; and

displaying at least one of the converted power data and the LMP data, and the correlated data of causal factors.

In certain illustrative embodiments, the gathering of power data and LMP data from a plurality of remote power data sources is performed over a network.

In certain illustrative embodiments, the computer-implemented further comprises the step of granting access to one or more remote users.

In certain illustrative embodiments, the one or more remote users are power traders in at least one power trading market.

In certain illustrative embodiments, the displaying step further comprises displaying signals on a remote user device.

In certain illustrative embodiments, the displaying signals are transmitted to the remote user device.

In certain illustrative embodiments, the computer-implemented method further comprises the step of generating at least one price summary report.

In certain illustrative embodiments, the price summary report is generated based on filter criteria, the filter criteria including at least one of a price report type, start date, end date, day type, hour type, independent system operator (ISO) type, node type, delta value, standard deviation value, rank-by value, and LMP type.

In certain illustrative embodiments, the computer-implemented method further comprises the step of comparing at least one of the converted power data and the LMP data and the correlated data of causal factors and generating at least one chart analysis report.

In certain illustrative embodiments, the chart analysis report is generated based on filter criteria, the filter criteria including at least one of a start date, an end date, a day type, a hour type, a time zone type, a plurality of node types, a plurality of ISO/zone types, a plurality of weather types, and a compare nodes type.

In certain illustrative embodiments, the computer-implemented method further comprises the step of retrieving the converted power data and the LMP data, and the correlated data of causal factors according to at least look-back criteria.

In certain illustrative embodiments, the look-back criteria includes at least one of a temperature value, a load value, an outage value, an ISO type, a weather type, a forecast date, a day value, a hour type, a matching hour value, a start date, and an end date.

In certain illustrative embodiments, the look-back criteria includes at least one of a fuel index type, a fuel index price, a constraint type, and a transmission outage type.

In certain illustrative embodiments, the computer-implemented method further comprises the step of passing the retrieved data and generating at least one price summary report according to filter criteria.

In certain illustrative embodiments, the filter criteria includes at least a price report type.

In certain illustrative embodiments, the computer-implemented method further comprises the step of displaying the retrieved data according to at least one of a date value, a day value, an average load forecast value, an outage value, an actual outage value, an average temperature value, a spot fuel value, and a price report type.

In certain illustrative embodiments, the computer-implemented method further comprises the step of comparing nodes to the retrieved data.

In certain illustrative embodiments, the price report type includes at least one of a scouting summary, scouting detail, hourly spread, hourly averages, node ranking, top nodes, LMP breakdown, day ahead (DA) constraints, constraint frequency, weather forecast, weather forecast versus actual forecast, fuel prices, financial transmission rights (FTR) monthly auction. RSG/OP reserves, and transmission outages.

In another embodiment, the invention encompasses a computer-readable storage medium, storing one or more programs configured for execution by one or more processors, the one or more programs comprising instructions to:

gather power data and locational marginal pricing (LMP) data from a plurality of remote power data sources and convert the power data and the LMP data into a common data format;

correlate the power data with the LMP data for identifying causal factors;

store at least the gathered power data and the LMP data, the converted power data and the LMP data, and the correlated data of causal factors; and

display at least one of the converted power data and the LMP data, and the correlated data of causal factors.

Reference will now be made in detail to the embodiments of the invention, examples of which are illustrated in the accompanying drawings. Wherever possible, like reference numbers will be used for like elements.

FIG. 1is a block diagram illustrating an exemplary embodiment of the system and data flow architecture of the present invention. As illustrated inFIG. 1, the exemplary embodiment includes data processing layers and communication interface layers. The data processing layers may, for example, include a remote power data sources layer20, a system layer30(comprising at least one or more of a computer server, a database, and a network communication device) and a power trading market layer40. Further, the communication interface layer may include local area networks (LAN) or wide area networks (WAN)50and60. However, other data and communication layers may be used without departing from the scope of the invention.

As further illustrated inFIG. 1, the remote power data sources layer20includes several power and price data stores21-25. Each of the data stores21-25contains power and price data from a specific Independent System Operator (ISO) or Regional Transmission Organization (RTO) power market. For example, the MISO21data store contains power and price data from the Midwest ISO; the PJM22data store contains power and price data from the Pennsylvania, New Jersey, and Maryland ISO; the NEISO23data store contains power and price data from the New England ISO; the NYISO24data store contains power and price data from the New York ISO; and, the RTO25data store contains power and price data from an RTO. Each of these data stores21-25is publicly accessible and may be queried directly, or via a local or wide area network50and/or60.

Because the data formats (also referred to as data schemas) of the data stores21-25may not be consistent among the data stores21-25, the data gathering unit31implements a plurality of customized routines to convert the power and price data into at least one common data format. To accurately complete the data formatting routine, the data gathering unit31is regularly updated with the latest customized routines that include data format changes on each data store. Accordingly, the data gathering unit31normalizes data from the disparate remote data sources for efficient data handling by other units within the system layer30. Additionally data gathering unit31acts as a layer of abstraction that insulates/encapsulates the other units from having to undergo changes as data stores may edit their respective data formats. The data gathering unit31may also implement the actual data format changes that should be made as data stores edit their respective data formats. Alternatively, the data gathering unit31may invoke outside procedures, which are updated as any edits are made to the data formats. Further, the outside procedures may be executed via the master data store32, or any other data store that supports the procedures' successful execution. The data gathering unit31may, in some instances, execute on an hourly basis because the ISO/RTO markets publish and/or update their data at such time intervals. In a one day period the ISO/RTO markets could publish and/or update their data—in different data formats—at least twenty-four (24) times; this frequency factor when multiplied by the number of ISO/RTO markets, five (5) of which are shown in the exemplary embodiment ofFIG. 1, is an example of the operational frequency of the data gathering unit31. For example, according to this sample frequency scenario as applied to the exemplary embodiment, the data gathering unit31would execute one hundred-twenty (120) different times in just a one day period. If either the frequency of the data's publications/updates is increased (i.e., bi-hourly or even more frequent), or the number of accessed ISO/RTO markets is increased, the data gathering unit31would have to execute at frequency intervals paralleling those of the ISO/RTO markets' publications/updates.

After the data gathering unit31gathers and normalizes the data from the data stores21-25, the gathered power and price data converted to a common data format are sent from the data gathering unit31to the master data store32for storage and later retrieval/query access by other units of the system layer30. The master data store32may be implemented using any type of data base management system (DBMS) such as, for example, SQL Server™, Oracle™, or Accessr™. However, other DBMS or data storage solutions (e.g., files, memory, etc.) may be used without departing from the scope of the invention.

The master data store32includes power data, price data (including locational marginal pricing (LMP) data), and causal factors data obtained from the various remote power data sources20. In an exemplary embodiment, the gathered power data and price data from each ISO/RTO region may be logically/physically stored together, while the same data may be stored separately from another ISO/RTO region's data. Furthermore, with each of the various ISO/RTO databases, the power and price data is partitioned according to a month of the year to which it applies. As a consequence of this multi-tiered data organization and storage scheme, the queries applied against each tier of data may only traverse a specific ISO/RTO region's data according to a specific month of the year, and not necessarily the entire depth and breadth of ISO/RTO region data that is available on the master data store32. Of course, one of ordinary skill in the art will recognize that queries (like those written using the structure query language, SQL) can be broadened or narrowed accordingly and do not have to conform to this specific implementation. For example, SQL or other types of queries may be written in such a way as to combine months or ISO/RTO regions/markets in order to broaden the scope of their data coverage. Similarly, the queries may also be limited to narrower subsets of data within a month, regional power markets within an ISO/RTO region/market, and/or even to specific power nodes within regional power markets; these queries may also be further executed alone or in combination with other query elements for access to an even narrower set of data, as long as those other elements are defined and available in the database schema.

As depicted in the exemplary embodiment ofFIG. 1, the access unit33and the data analysis unit34interface with the power and price data stores21-25or, more generally, the remote power data sources20. As opposed to maintaining independent and/or separate connections to each one of the remote power data sources20, which may also be available locally rather than remotely, and switching between each connection according to whichever data store21-25is actively being queried (whether serially or in parallel), the exemplary embodiment maintains the master data store32in a manner that eliminates this problem. The master data store32may contain stored procedures that are customized for execution against each power and price data store21-25(each of which might have different data formats). As a result, any subsequent unit within the system30may query the master data store32, regardless of the power and price data store21-25from which information is sought. The master data store's32stored procedures may be executed either directly from the master data store32against each of the remote power data sources20, or via the data gathering unit31; another layer of data abstraction may be added such that the stored procedures are executed neither through the master data store32nor the data gathering unit31, but rather via another unit or DBMS. The master data store32of the system30may serve as the physical and/or logical single access point to the one or more power and price data stores21-25. Thus, the access unit33and the data analysis unit34may maintain one connection to the master data store32, without the need for maintaining multiple and/or separate connections to, generally, the remote power data sources20or, specifically, each of the power and price data stores21-25.

As further illustrated in the exemplary embodiment ofFIG. 1, the data analysis unit34maintains several data flow interfaces/connection with several other units/data store(s). The data analysis unit34maintains a data flow interface/connection with the master data store32, the access unit33, and the display unit35. The data gathering unit32, although not shown in the exemplary embodiment ofFIG. 1, may also maintain a data flow interface connection with remote power data sources20, and/or another data store, in parallel or separate from the master data store32.

The data analysis unit34performs analyses on power data and price data to correlate them individually or in the aggregate with one or more causal factors. It is these analyses and other similar ones that are processed by the data analysis unit34, verified against a power trader user's43access permission(s), and may then be sent via either the access unit33or display unit35to the power trader access unit41. The transmission of the results of these analyses, and similar ones, may be performed directly, or over a LAN or WAN60(whether wired or wireless). Once transmitted to the one or more power trader access units41and to the power trader user(s)43in one or more power trading markets40, the results may then be displayed over a processing/display device42like a laptop, PDA, mobile telephone, or other similar processing/display device, capable of running a power trader access unit41and/or capable of receiving the results from the access unit33or display unit35. A power trader access unit41may be an internet browser like those offered by Microsoft, Netscape, or Mozilla (e.g., Internet Explorer™, Navigator™, Firefox™), a standalone application, or may be a port capable of sending and receiving data, whether that port be a direct-connection type port, or a network-connection type port. In accordance with the exemplary embodiment ofFIG. 1, the access unit33first receives signals from the one or more power trader access units41and determines whether the power trader43may or may not be provided access to the features of the system30. Whether the power trader43or remote user is granted access or not, the power trader43or remote user receives the appropriate access signals sent from the access unit33and received by the power trader access unit41. Similarly, the display unit35also transmits the appropriate display signals, often through the access unit33and then to the power trading access unit41, according to whether the power trader is granted access or not. The power trader43or remote user subsequently learns of a permission status via the specific processing/display device42or other remote user device that might be used. The specific implementation and connection of the power trader access unit41, with the units in the system30, will be understood by one of ordinary skill in the art as not limiting the scope of the features embodied by the unit, or those it may interface with.

Further, as illustrated in the exemplary embodiment ofFIG. 1, the data flow interface/connection that the data analysis unit34maintains with the master data store32acts as a source data stream from which the commonly formatted power and price data, which results from the operation(s) of the data gathering unit31, is received. Once received, the commonly formatted power and price data (including LMP data), is analyzed according to data processing algorithms that correlate the commonly formatted power and price data with LMP data for identifying causal factors. These causal factors include, but are not limited to, the following type(s) of power data: power usage, congestion, weather-related conditions (e.g., temperature, dew point, and/or relative humidity), transmission outages, peak power data, off-peak power data, binding constraints, fuel price(s), and/or time zone. The correlation operation(s) that are performed by the algorithms may be performed on one or more ISO/RTO regions/markets, which may further include many regions and, for example, hundreds or thousands of power nodes, or the correlation may be performed on a narrower set of data like that of one or a couple of nodes; either range of processing is fully supported by the data analysis unit34and its algorithms. Through the execution of the data analysis unit34and its algorithmically determined correlations, power traders43in one or more power trading markets40are able to execute power trades, as well as develop similar power trading strategies, with the important advantage of realizing the causal factors or cause-and-effect scenarios that correspond to specific conditions in ISO/RTO regions/markets, and/or power nodes. Moreover, as described below in more detail and illustrated in the exemplary embodiments ofFIGS. 2-11, through the execution of the data analysis unit34and its algorithmically determined correlations, power traders43in one or more power trading markets40are further presented with a variety of options/tools for selecting the various ways to customize their queries and to receive the corresponding results through several kinds of reports. Thus, the system30operating as a whole offers a very dynamic, customized, power trader-friendly environment, while concurrently managing uncommonly formatted data from the remote power data sources20, and executing algorithms to aid the user in identifying cause-and-effect scenarios.

Power traders43in one or more power trading markets40are capable of accessing several features through the power trader access unit41as it interfaces with the access unit33and/or the display unit35. The features are specifically aimed at customizing queries against the master data store32, selecting one of several types of features for customizing the queries, and receiving several kinds of reports for display via the power trader access unit41and a processing/display device42.

The features for customizing queries and the kinds of reports are embodied inFIGS. 2-11. Moreover, the features may be categorized into the following types: price reporting, chart analysis, and price-look back. First, the price reporting feature, which is implemented by the price reporting unit, generates one or more price summary reports according to user-selected filter criteria. Second, the chart analysis feature, which is implemented by the chart analysis unit, compares one or more of the common format power data and price data (including LMP data), as well as the correlated data, including causal factor(s) data, according to user-selected filter criteria. Third, the price look-back feature, which is implemented by a price look-back unit, retrieves and analyzes certain of the common format power data and price data, including LMP data, as well as the correlated data (including causal factor(s) data), according to user-selected look-back criteria, and generates one or more price look-back reports.

FIG. 2is an exemplary embodiment of a price summary report100of the price reporting feature, over a power user access interface105. As illustrated inFIG. 2, the price summary report100is comprised of a plurality of filter criteria that may be selected by a power trader user43. The filter criteria permit a power trader user43to customize the price summary report100such that the user can view the most relevant cause-and-effect scenarios for the user's trading strategies. The filter criteria include, but are not limited to, the following: a price report type110, a start date111, an end date112, a day type113, a hour type114, an independent system operator (ISO) type115, a node type116, a delta value117, a standard deviation value118, a rank-by value119, and an LMP type120. One or many of the filter criteria may be chosen. Further, the price report type110includes, but is not limited to, the following report types: scouting summary125, scouting detail130, hourly spread135, hourly averages140, node ranking145, top nodes150, LMP breakdown155, day ahead (DA) constraints160, constraint frequency165, weather forecast170, weather forecast versus actual forecast175, fuel prices180, financial transmission rights (FTR) monthly auction185, RSG/OP reserves190, and transmission outages195. Once selected by the power trader user43, and either an executable button like the search button121is invoked or automatic execution occurs, the filter criteria is/are sent to the price reporting unit for processing by the appropriate data analysis unit34algorithm(s). Subsequent to the algorithm's(s') processing of the appropriate commonly formatted power and price data with LMP data, including causal factors, and the selected filter criteria, the resulting data is sent from the price reporting unit to either the access unit33and/or display unit35for subsequent transmission to the power trader access unit41and a processing/display device42. Then, at that time or at a later point in time the power trader43may read and analyze the displayed resulting data in order to determine and/or analyze prospective power trades and power trading strategies according to the resulting data. In addition, the resulting data is displayed to the power trader43based on the selected price report type110, each of which may display a different set and/or subset(s) of the resulting data. Each power trader43, based on user preference(s), may freely choose the price report type110that best suits preferred power trading needs and strategies. Moreover, the same price report may show anything, for example, from a price for a given hour averaged over a specific timeframe, to all of the prices averaged over the same timeframe.

An exemplary embodiment of each of the price report types, listed above, is illustrated inFIGS. 2(a)-(o), respectively. Many of the price report types (like, for example, scouting summary125, as shown inFIG. 2(a)) also contain a “drill-down” feature122that may, for example, provide hourly detail power and price data about a specific power node.

FIG. 3illustrates an exemplary embodiment of the DA, RT, and load details power node report200, over a power user access interface105. This report is the first of several types of reports, which are illustrated inFIGS. 4-7(described below), that allow power trader users43to quickly navigate from the more general price report types (described above) to these specific reports with the capability to be able to analyze finite hourly level detail(s). For example, these reports are capable of displaying data that may be analyzed (in at least graph or tabular format) based on a range as specific as five (5) minute intervals for past and current hours. The report illustrated inFIG. 3is capable of displaying, for example, hourly level data for a specific, user selected date interval, day(s), and hour(s). Hourly price data is displayed by default both graphically and in tabular format. The user has the capability to change the presented data by selecting from the number of options205available like: DA, RT, and Delta. These options205are further displayed according to, for example: price, loss, congestion, standard deviation. 15DMA, 30DMA, 45DMA, RSG, ISO Load, ISO Outage, weather locale, temperature, dew point, and relative humidity. Once the data is presented at least based on these default or custom capabilities, for example, via the power trader access unit41and a processing/display device42, the power trader user43may view the data to determine cause-and-effect scenarios.

FIG. 4illustrates an exemplary embodiment of the DA, RT, and load averages power node report300, over a power user access interface105. This report shares many of the characteristics that define the details power node report200. In addition, the report has the capability to present, for example, hourly level data that is averaged for each hour of the day (i.e., he1, he2, . . . he24) over a user selected time interval.

FIG. 5illustrates an exemplary embodiment of the DA, RT, and load statistics power node report400, over a power user access interface105. This report also shares many of the characteristics that define the details power node report200. In addition, however, the report has the capability to present, for example, key price statistics405over the past one or more years, and/or over a user selected time interval. The report is capable of presenting, for example, the following key price statistics405: minimum price, maximum price, 15-day average, 30-day average, and 45-day average.

FIG. 6illustrates an exemplary embodiment of the LMP frequency distribution power node report500, over a power user access interface105. This report also shares many of the characteristics that define the details power node report200. In addition, however, the report has the capability to present, for example, the percentage of time that LMP data meets specific price ranges.

FIG. 7illustrates an exemplary embodiment of the DA and RT correlation power node report600, over a power user access interface105. This report also shares many of the characteristics that define the details power node report200. In addition, however, the report has the capability to present, for example, a user-selected comparison of a specific power node against all other power nodes, or a specific subset of power nodes, within an ISO/RTO region market. Moreover, the user is presented with at least two options, one for computing an r-value correlation coefficient605, and another for comparing an LMP price610on an hour-by-hour basis. The computation of an r-value correlation coefficient uses a standard r-value calculation and may be performed by the data analysis unit34, a stored procedure of the master data store32, or another unit or data store of the system30. The r-value correlation coefficient calculations may be used by power trader users43to identify power nodes with similar trends, as well as potential hedging opportunities. As for the other option, comparing an LMP price, the comparison is performed between a price for each hour to every other power node, or a specific subset of power nodes, within an ISO/RTO region/market; it may also compute the average difference between them.

FIG. 8illustrates an exemplary embodiment of the ISO footprint report700, over a power user access interface105. This report also shares many of the characteristics that define the details power node report200. Specifically, this report provides the capability to analyze a specific ISO/RTO region's/market's load data as it compares to outage data705.

FIG. 9illustrates an exemplary embodiment of the report preferences unit800, over a power user access interface105. Specifically, this report provides the capability to set specific report preferences like, for example: from-to date range805, power node type810. ISO-RTO type815, day type820, and hour(s) type825. The preferences may be saved by the user and/or loaded from an earlier time when they were saved. In addition, once preferences are saved and/or loaded they may be subsequently reflected in the reports.

FIG. 10illustrates an exemplary embodiment of a chart analysis power node report900of the chart analysis feature, over a power user access interface105. As illustrated inFIG. 10, the chart analysis report is comprised of a plurality of filter criteria905that may be selected by a power trader user43. The filter criteria905permit a power trader user43to customize the chart analysis power node report900such that user can compare nodes and view the most relevant cause-and-effect scenarios for the trading strategies. The filter criteria generally include common format power data and price data (including LMP data), as well as the correlated data including causal factor(s). Specifically, the filter criteria include, but are not limited to, the following: a start date910, an end date915, a day type920, a hour type925, a time zone type930, a plurality of node types935, a plurality of ISO/zone types940, a plurality of weather types945, and a compare nodes type950. The exemplary embodiment illustrated inFIG. 10shows that up to five (5) power nodes may be compared simultaneously. In other embodiments, however, the user may be able to select more or less power nodes. Furthermore, users may also choose to include load and outage data, as well as weather conditions data. Again, the exemplary embodiment illustrated inFIG. 10illustrates that data from up to two (2) ISO/RTO regions/markets could be selected, as well as weather conditions data from up to two (2) cities. In other embodiments, however, the user may be able to select more or less ISO/RTO regions/markets, as well as weather conditions data from more or less cities.

FIG. 11illustrates an exemplary embodiment of a price look-back report1000of the price look-back feature, over a power user access interface105. The price look-back feature permits a power trader user43to invoke the retrieval and analysis of certain of the common format power data and price data (including LMP data), as well as the correlated data including causal factor(s), according to user-selected look-back criteria, and to generate one or more price look-back reports1000. As illustrated inFIG. 11, the look-back criteria1005include, but are not limited to, for example: fuel index type1010, fuel index price1015, constraint type1020, and transmission outage type1025. Further, as also illustrated inFIG. 11, the look-back criteria1005include, but are not limited to, for example: temperature value1030, load value1035, outage value1040, ISO type1040, weather type1050, forecast date1055, day value1060, hour type1065, matching hour value1070, start date1075, and end date1080. Specific tolerance ranges may be selected for one or more of the look-back criteria. Once the user has selected the look-back criteria, and the data is retrieved and analyzed by the price look-back feature, it is passed on to a look-back results unit or displayed directly. The retrieved and analyzed data is that which corresponds to the specific criteria selected; the data may be for corresponding days, other time frames, or based on one or more other metrics. The look-back results unit implements a look-back results feature that displays the retrieved and analyzed data according to, for example, one or more of the following criteria: date value1085, day value1090, average load forecast value1095, outage value2000, actual outage value2005, average temperature value2010, spot fuel value2015, and price report type2020. At this point in time, the user may invoke the price reporting feature on certain of the retrieved and analyzed data displayed by the look-back results feature. The user may also retrieve LMP data for specific power nodes for the retrieved and analyzed data. If the user invokes the price reporting feature, the user may then also choose one or more of the price report types110(discussed above) and generate one or more price summary reports, accordingly. In addition to, or instead of, invoking the price reporting feature on certain of the retrieved and analyzed data displayed by the look-back results feature, the user may also choose to invoke the compare node feature, which is implemented by the compare node unit, in order to compare one or more specific power nodes to the retrieved and displayed data.

The features and capabilities of the price look-back feature, as well as some of the other features of the exemplary embodiment, provide power trader users43with powerful tools to at least retrieve and analyze the most proper and accurate historical power and price data, which serve as a reliable indicator for today's power prices and market performance. In addition, power trader users43are also able to at least analyze accurate cause-and-effect scenarios that permit the traders to make confident, informed trades, as well as to develop successful power market trading strategies.

FIG. 12illustrates an exemplary embodiment of a five-minute ticker report1100interface, over a power user access interface105. As illustrated inFIG. 12, the five-minute ticker report may comprise price node summary charts1105,1110. The five-minute ticker report is a user-customizable report that provides power trader users43with the ability to identify a list of nodes1135for which they would like to track data such as average RT price1140and DA price1145, according to, for example, five-minute intervals (e.g., every 5 minutes, 10 minutes, 15 minutes, and so on). Other data may also be reported1110such as, for example, constraints1140affecting DA pricing of one or more ISOs1115, as well as the start time1125and end time1130of such constraints1140. The five-minute report may be implemented by the price reporting unit of the price reporting feature.

FIG. 13illustrates an exemplary embodiment of a daily market summary report1200interface. As illustrated inFIG. 13, the daily market summary report1205may comprise an ISO-level summary1210and a node details-level summary1215. The daily market summary report1205may be transmitted daily to power trader users43via, for example, email or other communication means. The report1205may be transmitted at any time after the next day's DA prices are available from one or more ISOs/RTOs. The report1205may provide power trader users43with a summary or snapshot of one or more corresponding power markets. For example, the report120may provide DA and RT prices for average peak times1220and average off-peak times1225. The report1205may be implemented by the price reporting unit of the price reporting feature.

The exemplary embodiments described herein not only manage, track, and analyze power and pricing data, but the analysis capabilities aid power traders in determining the causal factors that drive specific ISO/RTO power trading markets. In fact, a system and method are provided for, among other things, evaluating supply and demand fundamentals, power data, pricing data, causal factors, and determining a “real time” (RT) price.

It will be apparent to those skilled in the art that various modifications and variations can be made in the system and method for gathering and performing complex analyses on power data from multiple remote sources, of the present invention, without departing form the spirit or scope of the invention. Thus, it is intended that the invention cover the modifications and variations of this invention provided they come within the scope of the appended claims and their equivalents.