Systems and methods for generating wind power scenarios for wind-power-integrated stochastic unit commitment problems

• US 8,949,160 B2
• Filed: 03/07/2012
• Issued: 02/03/2015
• Est. Priority Date: 03/07/2012
• Status: Expired due to Fees

• Current Assignee: International Business Machines Corporation
• Original Assignee: International Business Machines Corporation
• Inventors: Ghosh, Soumyadip, Kalagnanam, Jayant R., Koc, Ali
• Primary Examiner(s): CHANG, LI WU
• Application Number:
  US13/414,122
• Publication Number:
  US 20130238530A1
• Time in Patent Office: 1,063 Days
• US Class Current: 706/12
• CPC Class Codes: G06F 17/11 for solving equations , e.g. nonlinear equations, general mathematical optimization problems optimization specially adapted for a specific administrative, business or logistic context G06Q10/04
• CPC Class Codes: G06N 5/04 Inference or reasoning models
• CPC Class Codes: G06N 7/00 Computing arrangements based on specific mathematical models
• CPC Class Codes: G06Q 10/04 Forecasting or optimisation specially adapted for administrative or management purposes, e.g. linear programming or "cutting stock problem" market predictions or forecasting for commercial activities G06Q30/0202
• CPC Class Codes: G06Q 50/06 Energy or water supply
• CPC Class Codes: H02J 2203/20 Simulating, e g planning, reliability check, modelling or computer assisted design [CAD]
• CPC Class Codes: H02J 2300/28 The renewable source being wind energy
• CPC Class Codes: H02J 3/381 Dispersed generators
• CPC Class Codes: Y02E 10/76 Power conversion electric or electronic aspects
• CPC Class Codes: Y02E 60/00 Enabling technologies; Technologies with a potential or indirect contribution to GHG emissions mitigation
• CPC Class Codes: Y04S 40/20 Information technology specific aspects, e.g. CAD, simulation, modelling, system security

Abstract

The present disclosure relates generally to systematic algorithms (and associated systems and methods) that take a forecast model as input and produce a discrete probability distribution as output, using scenario reduction ideas from stochastic programming. In one example, an algorithm (and associated system and method) creates scenarios sequentially for each time period, leading to a scenario tree.

Claims

1. A system for providing scenario tree generation based at least in part upon scenario reduction, the system comprising one or more processor units configured for:

receiving for at least a first time period a first forecast, wherein the first forecast comprises a first set of scenarios;

reducing the first set of scenarios to a first subset of scenarios, wherein the first subset of scenarios comprises a subset of the first set of scenarios;

receiving for at least a second time period a second forecast, wherein the second forecast comprises a second set of scenarios;

reducing the second set of scenarios to a second subset of scenarios, wherein the second subset of scenarios comprises a subset of the second set of scenarios; and

generating a scenario tree based at least in part upon the first subset of scenarios and the second subset of scenarios;

wherein the scenario tree has interperiod independency;

wherein the steps of reducing comprise applying an input Ξ

to an algorithm to produce an output {tilde over (Ξ

)}; and

wherein the algorithm comprises;

2. The system of claim 1, further comprising applying the scenario tree to a stochastic unit commitment problem.

3. The system of claim 2, wherein the stochastic unit commitment problem relates to wind power generation.

4. The system of claim 3, wherein each of the first forecast and the second forecast comprises a forecast of wind speed.

5. The system of claim 1, wherein the steps are carried out in the order recited.

6. The system of claim 2, further comprising generating energy at a set of generators based upon the application of the scenario tree to the stochastic unit commitment problem.

7. The system of claim 6, further comprising generating the energy according to an optimal up and down schedule and corresponding generation amounts for the set of generators over a twenty-four-hour horizon.

8. The system of claim 7, further comprising generating the energy according to the optimal up and down schedule and the corresponding generation amounts for the set of generators over the twenty-four-hour horizon so that a total cost of generation and transmission is minimized.

9. The system of claim 8, further comprising generating the energy according to the optimal up and down schedule and the corresponding generation amounts for the set of generators over the twenty-four-hour horizon so that the total cost of generation and transmission is minimized and a set of constraints is observed.

10. The system of claim 1, wherein:

Ξ

={ξ

}_i=1^S,ξ

_iε

^T,

with marginal distributions Ξ

^t={ξ

_it}<sub>itε

It</sub>, t=1, . . . , T, n^tε

, t=1, . . . T.

11. The system of claim 1, wherein:

{tilde over (Ξ

)}={{tilde over (ξ

)}}_j=1^{{tilde over (S)}},{tilde over (ξ

)}_iε

^T,

with marginal distributions {tilde over (Ξ

)}^t={{tilde over (ξ

)}_jt}<sub>jtε

Jt</sub>, |J^t|=n^t, t=1, . . . , T.

12. A system for providing scenario tree generation based at least in part upon scenario reduction, the system comprising one or more processor units configured for:

receiving for each of a plurality of time periods a respective forecast, wherein the plurality of time periods comprise at least a first time period, at least a second time period and at least one intermediate time period, and wherein each forecast comprises a respective set of scenarios;

for each time period in sequence, reducing the respective set of scenarios to a respective subset of scenarios, wherein each subset of scenarios comprises a subset of the respective set of scenarios; and

generating a scenario tree based at least in part upon the subset of scenarios;

wherein the scenario tree has interperiod independency;

wherein the steps of reducing comprise applying an input Ξ

to an algorithm to produce an output {tilde over (Ξ

)};

wherein the algorithm comprises;

13. The system of claim 12, further comprising applying the scenario tree to a stochastic unit commitment problem.

14. The system of claim 13, wherein the stochastic unit commitment problem relates to wind power generation.

15. The system of claim 14, wherein each of the forecasts comprises a forecast of wind speed.

16. The system of claim 12, wherein the steps are carried out in the order recited.

17. The system of claim 12, wherein:

Ξ

={ξ

}_i=1^S,ξ

_iε

^T,

with marginal distributions Ξ

^t={ξ

_it}<sub>itε

It</sub>, t=1, . . . , T, n^tε

, t=1, . . . T.

18. The system of claim 12, wherein:

{tilde over (Ξ

)}={{tilde over (ξ

)}}_j=1^{{tilde over (S)}},{tilde over (ξ

)}_iε

^T,

with marginal distributions {tilde over (Ξ

)}^t={{tilde over (ξ

)}_jt}<sub>jtε

Jt</sub>, |J^t|=n^t, t=1, . . . , T.

19. A method for providing scenario tree generation based at least in part upon scenario reduction, the method comprising:

receiving for at least a first time period a first forecast, wherein the first forecast comprises a first set of scenarios;

reducing the first set of scenarios to a first subset of scenarios, wherein the first subset of scenarios comprises a subset of the first set of scenarios;

receiving for at least a second time period a second forecast, wherein the second forecast comprises a second set of scenarios;

reducing the second set of scenarios to a second subset of scenarios, wherein the second subset of scenarios comprises a subset of the second set of scenarios; and

generating a scenario tree based at least in part upon the first subset of scenarios and the second subset of scenarios;

wherein the scenario tree has interperiod independency;

wherein the steps of reducing comprise applying an input Ξ

to an algorithm to produce an output {tilde over (Ξ

)}; and

wherein;

Ξ

={ξ

}_i=1^S,ξ

_iε

^T,

with marginal distributions Ξ

^t={ξ

_it}<sub>itε

It</sub>, t=1, . . . , T, n^tε

, t=1, . . . , T,

{tilde over (Ξ

)}={{tilde over (ξ

)}}_j=1^{{tilde over (S)}},{tilde over (ξ

)}_iε

^T,

with marginal distributions {tilde over (Ξ

)}^t={{tilde over (ξ

)}_jt}<sub>jtε

Jt</sub>, |J^t|=n^t, t=1, . . . , T, and

wherein the algorithm comprises;

20. The method of claim 19, wherein the steps are carried out in the order recited.

21. An article of manufacture, comprising:

at least one non-transitory computer usable device having a computer readable program code logic tangibly embodied therein to execute at least one machine instruction in a processing unit for providing scenario tree generation based at least in part upon scenario reduction, said computer readable program code logic, when executing, performing the following steps;

receiving for at least a first time period a first forecast, wherein the first forecast comprises a first set of scenarios;

reducing the first set of scenarios to a first subset of scenarios, wherein the first subset of scenarios comprises a subset of the first set of scenarios;

receiving for at least a second time period a second forecast, wherein the second forecast comprises a second set of scenarios;

reducing the second set of scenarios to a second subset of scenarios, wherein the second subset of scenarios comprises a subset of the second set of scenarios; and

generating a scenario tree based at least in part upon the first subset of scenarios and the second subset of scenarios;

wherein the scenario tree has interperiod independency;

wherein the steps of reducing comprise applying an input Ξ

to an algorithm to produce an output {tilde over (Ξ

)}; and

wherein;

Ξ

={ξ

}_i=1^S,ξ

_iε

^T,

with marginal distributions Ξ

^t={ξ

_it}<sub>itε

It</sub>, t=1, . . . , T, n^tε

, t=1, . . . , T,

{tilde over (Ξ

)}={{tilde over (ξ

)}}_j=1^{{tilde over (S)}},{tilde over (ξ

)}_iε

^T,

with marginal distributions {tilde over (Ξ

)}^t={{tilde over (ξ

)}_jt}<sub>jtε

Jt</sub>, |J^t|=n^t, t=1, . . . , T, and

wherein the algorithm comprises;

22. The article of manufacture of claim 21, wherein the steps are carried out in the order recited.