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

• US 8,606,686 B1
• Filed: 03/06/2009
• Issued: 12/10/2013
• Est. Priority Date: 03/07/2008
• Status: Active Grant

• Current Assignee: MCG Versify Acquisition LLC
• Original Assignee: Versify Solutions, Inc. (MCG Energy Solutions LLC)
• Inventors: Ippolito, David, Kucharczuk, David
• Primary Examiner(s): Apple, Kirsten
• Assistant Examiner(s): GOODBODY, JOAN T
• Application Number:
  US12/399,682
• Time in Patent Office: 1,740 Days
• Field of Search: 705/37
• US Class Current: 705/37
• CPC Class Codes: G06Q 40/00 Finance; Insurance; Tax strategies; Processing of corporate or income taxes
• CPC Class Codes: G06Q 40/04 Trading; Exchange, e.g. stocks, commodities, derivatives or currency exchange
• CPC Class Codes: G06Q 40/06 Asset management; Financial planning or analysis
• CPC Class Codes: G06Q 50/06 Energy or water supply

Abstract

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.

Claims

1. A system comprising:

a processor;

a display unit; and

a non-transitory data storage device that includes instructions when executed by the processor comprising;

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 real-time power data with the LMP data for identifying causal factors; and

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

wherein the display unit displays at least one of the converted power data and the LMP data, and the correlated data of causal factors, and

wherein the data gathering unit gathers the real-time power data and the LMP data over a network to correlate the real-time power data.

2. The system of claim 1, further comprising an access unit to grant access to one or more remote users.

3. The system of claim 2, wherein the one or more remote users are power traders in at least one power trading market.

4. The system of claim 1, further comprising a remote user device.

5. The system of claim 4, wherein the display unit transmits display signals to the remote user device.

6. The system of claim 1, further comprising a price reporting unit to generate at least one price summary report.

7. The system of claim 6, wherein 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.

8. The system of claim 7, wherein 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.

9. The system of claim 1, further comprising 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.

10. The system of claim 9, wherein 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.

11. The system of claim 1, further comprising 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.

12. The system of claim 11, wherein 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.

13. The system of claim 11, wherein 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.

14. The system of claim 11, wherein the retrieved data is passed to a price reporting unit to generate at least one price look-back report according to filter criteria.

15. The system of claim 14, wherein the filter criteria includes at least a price report type.

16. The system of claim 11, further comprising 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.

17. The system of claim 11, further comprising a compare node unit to compare nodes to the retrieved data.

18. A computer-implemented method executed by one or more computer processors comprising:

a display unit; and

a non-transitory data storage device that includes instructions when executed by the one or more processors, the method comprising 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 real-time power data using the one or more computer processors with the LMP data for identifying causal factors;

storing at least the gathered real-time 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.

19. The computer-implemented method of claim 18, wherein the gathering of power data and LMP data from a plurality of remote power data sources is performed over a network.

20. The computer-implemented method of claim 18, further comprising the step of granting access to one or more remote users.

21. The computer-implemented method of claim 20, wherein the one or more remote users are power traders in at least one power trading market.

22. The computer-implemented method of claim 18, wherein the displaying step further comprises displaying signals on a remote user device.

23. The computer-implemented method of claim 22, wherein the displaying signals are transmitted to the remote user device.

24. The computer-implemented method of claim 18, further comprising the step of generating at least one price summary report.

25. The computer-implemented method of claim 24, wherein 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.

26. The computer-implemented method of claim 25, wherein 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.

27. The computer-implemented method of claim 18, further comprising 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.

28. The computer-implemented method of claim 27, wherein 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.

29. The computer-implemented method of claim 18, further comprising 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.

30. The computer-implemented method of claim 29, wherein 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.

31. The computer-implemented method of claim 29, wherein 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.

32. The computer-implemented method of claim 29, further comprising the step of passing the retrieved data and generating at least one price summary report according to filter criteria.

33. The computer-implemented method of claim 32, wherein the filter criteria includes at least a price report type.

34. The computer-implemented method of claim 29, further comprising 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.

35. The computer-implemented method of claim 29, further comprising the step of comparing nodes to the retrieved data.

36. A non-transitory 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 real-time power data and the LMP data into a common data format;

correlate real-time power data with the LMP data for identifying causal factors;

store at least the gathered real-time 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.