Optimized security constrained unit commitment dispatch using linear programming for electricity markets
View Patent ↗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.
1. A computer implemented system for optimal pricing of energy and energy reserve in an electricity market of at least one market participant, said system comprising:
a data base; and
a processor configured for:
determining constraints of said at least one market participant;
receiving reserve energy bids from said market participants;
calculating, using mixed integers to represent variables, whether each reserve bid satisfies the relationship:
0 ≦p reg ( i,t )≦min { RR ( i,t ),ramp 10 ( i ),max[0,0.5·( RH ( i,t )− SS ( i,t ))]}· W ( i,t )
where p reg (i,t) is a power of regulating reserve energy for reserve eneray bid i at time step t, RR(i,t) is a regulating reserve range for the reserve energy bid i at time step t, ramp 10 (i) is a ten minute ramp capability for the reserve energy bid i at time step t; (RH(i,t) is a regulating high capacity limit for the reserve energy bid i at time step t, SS(i,t) is a self committed and scheduled capacity for the reserve energy bid i at time step t; W(i,t) is a regulating status binary variable for the reserve energy bid i at time step t;
optimizing the dispatch of energy and energy reserve responsive to reserve energy bids that satisfy said relationship considering said constraints of said at least one market participant using mixed integer linear programming techniques; and
pricing the dispatch of energy and energy reserve based on the results of said optimizing step.
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 processor is further configured for modeling linear bid curves corresponding to said reserve energy bids using a linear term and at least one associated linear equation in said mixed integer linear programming techniques.
12. The system of claim 1 , wherein said processor is further responsive to one or more cost functions associated with generation of energy over a time interval.
13. The system of claim 12 wherein said processor is further configured for minimizing said one or more cost functions.
14. The system of claim 13 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.
15. 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.
16. The system of claim 1 , wherein said processor is further responsive to one or more power functions associated with consumption of energy over a time interval.
17. The system of claim 16 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.
18. A method for optimal pricing of energy and energy reserve in an electricity market of at least one market participant, said method comprising:
determining constraints of said at least one market participant;
receiving reserve energy bids from said market participants;
calculating, using mixed integers to represent variables, whether each reserve bid satisfies the relationship:
0 ≦p reg )( i,t )≦min{ RR ( i,t ),ramp 10 ( i ),max[0,0.5·( RH ( i,t )− SS ( i,t ))]}· W ( i,t )
where p reg (i,t) is a power of regulating reserve energy for reserve energy bid i at time step t, RR(i,t) is a regulating reserve range for the reserve energy bid i at time step t, ramp 10 (i) is a ten minute ramp capability for the reserve energy bid i at time step t; (RH(i,t) is a regulating high capacity limit for the reserve energy bid i at time step t, SS(i,t) is a self committed and scheduled capacity for the reserve energy bid i at time step t; W(i,t) is a regulating status binary variable for the reserve energy bid i at time step t;
optimizing the dispatch of energy and energy reserve responsive to reserve energy bids that satisfy said relationship considering said constraints of said at least one market participant using a mixed integer linear programming technique wherein linear bid curves corresponding to the energy reserve bids are modeled by a linear term and at least one associated linear equation; and
pricing the dispatch of energy and energy reserve based on the results of said optimizing step.
19. A computer readable medium containing program instructions therein, which, when executed by a computer, causing the computer to implement a method for optimal energy and energy reserve pricing in an electricity market of at least one market participant, said method comprising:
determining constraints of at least one market participant;
receiving reserve energy bids from said market participants;
calculating, using mixed integers to represent variables, whether each reserve bid satisfies the relationship:
0 ≦p reg ( i,t )≦min{ RR ( i,t ),ramp 10 ( i ),max[0,0.5·( RH ( i,t )− SS ( i,t ))]}·W( i,t )
where p reg (i,t) is a power of regulating reserve energy for reserve energy bid i at time step t, RR(i,t) is a regulating reserve range for the reserve energy bid i at time step t, ramp 10 (i) is a ten minute ramp capability for the reserve energy bid i at time step t; (RH(i,t) is a regulating high capacity limit for the reserve energy bid i at time step t, SS(i,t) is a self committed and scheduled capacity for the reserve energy bid i at time step t; W(i,t) is a regulating status binary variable for the reserve energy bid i at time step t;
optimizing dispatch of energy and energy reserve responsive to reserve energy bids that satisfy said relationship considering said constraints of said at least one market participant using a mixed integer linear programming technique wherein linear bid curves corresponding to the energy reserve bids are modeled by a linear term and at least one associated linear equation; and
pricing the dispatch of energy and energy reserve based on the results of said optimizing step.