Optimized transmission and load security constrained unit commitment dispatch using linear programming for electricity markets

• US 7,349,883 B2
• Filed: 04/08/2003
• Issued: 03/25/2008
• Est. Priority Date: 03/10/2003
• Status: Expired due to Fees

• Current Assignee: Siemens AG
• Original Assignee: Siemens Power Transmission & Distribution Incorporated (Siemens AG)
• Inventors: Bjelogrlic, Milan, Ristanovic, Petar
• Primary Examiner(s): Borissov; Igor N.
• Application Number:
  US10/409,562
• Publication Number:
  US 20040181421A1
• Time in Patent Office: 1,813 Days
• Field of Search: 705/412, 705/37, 705/400, 705/1, 705/7, 705/8, 705/10, 705/26, 705/27, 700/286, 700/291
• US Class Current: 705/37
• CPC Class Codes: G06Q 30/0283 Price estimation or determination
• CPC Class Codes: G06Q 30/08 Auctions
• CPC Class Codes: G06Q 40/04 Trading; Exchange, e.g. stocks, commodities, derivatives or currency exchange
• CPC Class Codes: H02J 3/003 Load forecast, e.g. methods or systems for forecasting future load demand
• CPC Class Codes: H02J 3/008 involving trading of energy or energy transmission rights
• CPC Class Codes: Y02B 70/3225 Demand response systems, e.g. load shedding, peak shaving
• CPC Class Codes: Y04S 10/50 Systems or methods supporting the power network operation or management, involving a certain degree of interaction with the load-side end user applications
• CPC Class Codes: Y04S 20/222 Demand response systems, e.g. load shedding, peak shaving
• CPC Class Codes: Y04S 50/10 Energy trading, including energy flowing from end-user application to grid
• CPC Class Codes: Y04S 50/14 Marketing, i.e. market research and analysis, surveying, promotions, advertising, buyer profiling, customer management or rewards

Abstract

The present invention is a method for optimizing security constrained unit commitment in the day ahead wholesale electricity market using mixed integer linear programming techniques. The wholesale electricity market uniquely requires the submission of offers to supply energy and ancillary services at stated prices, as well as bids to purchase energy, and known operating and security constraints. The present invention address the above noted needs by providing a SCUC engine to support and implement the requirements via a computer system implementation.

Claims

1. A computer implemented system for optimal energy and energy reserve dispatching in an electricity market of market participants, said system comprising:

a database; and

a processor configured;

inputting constraints of the market participants;

receiving energy and energy reserve bids from the market participants;

defining piecewise linear bid cost curves having respective breakpoints corresponding to the energy and energy reserve bids;

converting the piecewise linear bid cost curves to one or more cost variables associated with a set of linear equations according to the following relationship;

c_com(i,t)=Σ

_kc_com^pl(i,t,k)·

ω

_com^pl(i,t,k),

wherein c_com(i,t) is a cost variable for an energy commodity bid i at time step t;

k is a breakpoint of a piecewise linear bid cost curve;

c_com^pl(i,t,k) is a piecewise linear bid cost curve breakpoint for energy commodity bid i at time step t at breakpoint k;

ω

_com^pl (i,t,k) is a weighting variable associated with the piecewise linear bid cost curve commodity bid i at time step t at breakpoint k;

calculating an optimal dispatch of energy and energy reserve responsive to the cost variable considering said constraints of the market participants using mixed integer linear programming techniques;

pricing the optimal dispatch of energy and energy reserve considering said constraints; and

providing the pricing to the market participants.

2. The system of claim 1, wherein said constraint is a market participant energy limit.

3. The system of claim 1, wherein said constraint is a load energy limit.

4. The system of claim 1, wherein said constraint is a market participant regulation availability.

5. The system of claim 1, wherein said constraint is a market participant regulation range.

6. The system of claim 1, wherein said constraint is a market participant spinning reserve limit.

7. The system of claim 1, wherein said constraint is a load spinning reserve limit.

8. The system of claim 1, wherein said constraint is a market participant non-spinning reserve limit.

9. The system of claim 1, wherein said constraint is a market participant capacity limit.

10. The system of claim 1, wherein said constraint is a load capacity limit.

11. The system of claim 1, wherein said calculating an optimal dispatch is further responsive to one or more cost functions associated with generation of energy over a time interval.

12. The system of claim 11 wherein said calculating an optimal dispatch further comprises minimizing said one or more cost functions.

13. The system of claim 12 wherein said one or more cost functions comprise at least one of a start up cost, a no load cost, a cost of the commodity energy, and a cost of commodity regulating reserve energy.

14. The system of claim 1 wherein said energy reserve comprises at least one of a ten minute spinning reserve, a ten-minute non-spinning reserve, and a thirty minute operating reserve.

15. The system of claim 1, wherein said pricing is further responsive to one or more power functions associated with consumption of energy over a time interval.

16. The system of claim 15 wherein said one or more power functions comprise at least one of a power of commodity energy, a power penalty factor, a power of price sensitive load, a power penalty factor of the price dependent load, and a power of price non-sensitive load.

17. A method for optimal energy and energy reserve dispatching in an electricity market of market participants, said method comprising:

inputting constraints of the market participants;

receiving energy and energy reserve bids from the market participants;

defining piecewise linear bid cost curves having respective breakpoints corresponding to the energy and energy reserve bids;

converting the piecewise linear bid cost curves to one or more cost variables associated with a set of linear equations according to the following relationship;

c_com(i,t)=Σ

_kc_com^pl(i,t,k)·

ω

_com^pl(i,t,k),

wherein c_com(i,t) is a cost variable for an energy commodity bid i at time step t;

k is a breakpoint of a piecewise linear bid cost curve;

c_com^pl(i,t,k) is a piecewise linear bid cost curve breakpoint for energy commodity bid i at time step t at breakpoint k;

ω

_com^pl(i,t,k) is a weighting variable associated with the piecewise linear bid cost curve commodity bid i at time step t at breakpoint k;

calculating an optimal dispatch of energy and energy reserve responsive to the cost variable considering said constraints of the market participants using a mixed integer linear programming technique;

pricing the optimal dispatch of energy and energy reserve; and

providing the pricing to the market participants.

18. A computer readable medium containing program instructions recorded therein, which, when executed by a computer, causing the computer to implement a method for optimal energy and energy reserve dispatching in an electricity market of market participants, said method comprising:

inputting constraints of the market participants;

receiving energy and energy reserve bids from the market participants;

defining piecewise linear bid cost curves having respective breakpoints corresponding to the energy and energy reserve bids;

converting the piecewise linear bid cost curves to one or more cost variables associated with a set of linear equations according to the following relationship;

c_com(i,t)=Σ

_kc_com^pl(i,t,k)·

ω

_com^pl(i,t,k),

wherein c_com(i,t) is a cost variable for an energy commodity bid i at time step t;

k is a breakpoint of a piecewise linear bid cost curve;

c_com^pl(i,t,k) is a piecewise linear bid cost curve breakpoint for energy commodity bid i at time step t at breakpoint k;

ω

_com^pl(i,t,k) is a weighting variable associated with the piecewise linear bid cost curve commodity bid i at time step t at breakpoint k;

calculating an optimal dispatch of energy and energy reserve responsive to the cost variable considering said constraints of the market participants using a mixed integer linear programming technique;

pricing the optimal dispatch of energy and energy reserve considering said constraints; and

providing the pricing to the market participants.