Optimized load prediction for 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 wherein load prediction is performed considering load system requirements, said system comprising: a database; and
a processor configured for:
determining constraints of said at least one market participant;
receiving thirty minute operating reserve energy bids from said market participants; calculating, using mixed integers to represent variables, whether each thirty minute operating reserve energy bid satisfies the relationship:
0 ≦p tmor ( i,t )≦min[ramp 30 ( i ), p max ( i,t )− SS ( i,t ))]·[1 −Y ( i,t )],
where p tmor (i,t) is a power of a thirty minute operating reserve for thirty minute operating reserve energy bid i at time step t, ramp 30 (i) a thirty minute ramp capability for thirty minute operating reserve energy bid i at time step t, p max (i,t) is a maximum power capability for thirty minute operating reserve energy bid i at time step t, SS(i,t) is a self committed and scheduled capacity for thirty minute operating reserve energy bid i at time step t, Y(i,t) is a status binary variable for thirty minute operating reserve energy bid i at time step t;
optimizing the dispatch of energy and energy reserve responsive to thirty minute operating reserve energy bids that satisfy said relationship and 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 the thirty minute operating 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 processor 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 pricing of energy and energy reserve in an electricity market of at least one market participant, said system comprising:
determining constraints of at least one market participant;
receiving thirty minute operating reserve energy bids from said market participants;
calculating, using mixed integers to represent variables, whether each thirty minute operating reserve energy bid satisfies the relationship:
0 ≦p tmor ( i,t )≦min[ramp 30 ( i ), p max ( i,t )− SS ( i,t ))]·[1 −Y ( i,t )],
where p tmor (i,t) is a power of a thirty minute operating reserve for thirty minute operating reserve energy bid i at time step t, ramp 30 (i) a thirty minute ramp capability for thirty minute operating reserve energy bid i at time step t, p max (i,t) is a maximum power capability for thirty minute operating reserve energy bid i at time step t, SS(i,t) is a self committed and scheduled capacity for thirty minute operating reserve energy bid i at time step t, Y(i,t) is a status binary variable for thirty minute operating reserve energy bid i at time step t;
optimizing dispatch of energy and energy reserve responsive to thirty minute operating reserve energy bids that satisfy said relationship and 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.
18. 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 thirty minute operating reserve energy bids from said market participants;
calculating, using mixed integers to represent variables, whether each thirty minute operating reserve energy bid satisfies the relationship:
0 ≦p tmor ( i,t )≦min[ramp 30 ( i ), p max ( i,t )− SS ( i,t ))]·[1 −Y ( i,t )],
where p tmor (i,t) is a power of a thirty minute operating reserve for thirty minute operating reserve energy bid i at time step t, ramp 30 (i) a thirty minute ramp capability for thirty minute operating reserve energy bid i at time step t, p max (i,t) is a maximum power capability for thirty minute operating reserve energy bid i at time step t, SS(i,t) is a self committed and scheduled capacity for thirty minute operating reserve energy bid i at time step t, Y(i,t) is a status binary variable for thirty minute operating reserve energy bid i at time step t;
optimizing dispatch of energy and energy reserve responsive to thirty minute operating reserve energy bids that satisfy said relationship and 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.