CN109977444A - A kind of bus load prediction data modification method for generation schedule optimization - Google Patents
A kind of bus load prediction data modification method for generation schedule optimization Download PDFInfo
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Abstract
The present invention relates to a kind of bus load prediction data modification methods for generation schedule optimization；Based on being optimized by generation schedule, day part bus load modifying factor is calculated in generation schedule optimization process, bus load prediction data is effectively corrected by way of modifying factor is added；It effectively solves the problems, such as to solve to cause progress Security Checking link Load flow calculation error in generation schedule optimization process larger since bus load precision of prediction is too low；Meanwhile improving the accurate of Load flow calculation.
Description
Technical field
The present invention relates to and power dispatching automation technical field, in particular to it is a kind of for generation schedule optimization mother
Specific electric load prediction data modification method.
Background technique
Currently, bus load prediction is to supply the terminal of a relatively small power supply area by the main transformer of substation
The summation of load is the refinement of system loading.Bus load prediction is exactly the off line load of substation, is grid nodes load.
Therefore bus load prediction is prediction object with node load, and general only to carry out short-term load forecasting, prediction result mentions for power grid
Electric network swim calculating is carried out for imaginary flow data.
Since bus load prediction is using the off line load of substation as prediction object, during actual prediction
There are following difficulties:
1, bus load radix for system loading is smaller, and regularity is weak
2, it is influenced by service area's intra domain user and small power supply behavior, bus load is easy to produce mutation, and stability is poor, has
More burr；
3, data mutation caused by wrong data and changes of operating modes caused by data acquisition transmission etc., leads to data matter
It measures not high；
4, since supply district is small, the randomness of user is big, and uncertain factor is more, and the trend of load variations is unobvious；
5, the load curve diversity ratio between different buses is larger, and load variations trend is unobvious.
Security Checking link in generation schedule optimization needs to calculate electric network swim, needs accurate bus load prediction number
It is injected according to as node.However, being predicted since to will cause bus load precision of prediction not high for the above various factors with system loading
It mismatches, causes Load flow calculation error larger.
Summary of the invention
The present invention is to solve above-mentioned one or more deficiencies, and it is pre- to provide a kind of bus load for generation schedule optimization
Measured data modification method.
In order to solve the above technical problems, the technical solution adopted by the present invention is that:
A kind of bus load prediction data modification method for generation schedule optimization, comprising the following steps:
S1. generation schedule related data is obtained；
S2. security constrained economic dispatch model is established；
S3. calculating is optimized to economic load dispatching model；
S4. statistic op- timization unit gross capability, interconnection are entered power only, the prediction of original bus load always adds；
S5. bus load modifying factor is calculated；
S6. bus load prediction data is corrected.
In the above scheme, based on optimizing by generation schedule, day part mother is calculated in generation schedule optimization process
Specific electric load modifying factor effectively corrects bus load prediction data by way of modifying factor is added；Effectively solution
It certainly solves to cause to carry out Security Checking link trend meter in generation schedule optimization process since bus load precision of prediction is too low
Calculate the larger problem of error；Meanwhile improving the accurate of Load flow calculation.
Preferably, the generation schedule related data includes system loading prediction, on unit operating cost, unit output
Lower limit, unit creep speed and interconnection are entered power only.
Preferably, the objective function of the security constrained economic dispatch model is purchases strategies minimum, and formula is as follows:
Wherein: total number of units of N expression unit；T indicate considered it is total when number of segment, it is assumed that one day 96 period of consideration, then T be
96；Pi,tIndicate unit i in the power output of t period；Ci,t(Pi,t) it is unit i in the operating cost of period t, it is the more of unit output
Section linear function.
Preferably, the bound for objective function of the security constrained economic dispatch model includes system restriction and machine
Group constraint.
Preferably, the system restriction is account load balancing constraints, and the account load balancing constraints are specific as follows:
For each period t, account load balancing constraints be can be described as:
Wherein, Pi,tIndicate power output of the unit i in the t period, DtIt is predicted for the system loading of t period, TtFor the contact of t period
Line is sent into power only.
Preferably, the Unit commitment includes the constraint of unit output bound and unit ramp loss；
The unit output bound constraint is specific as follows:
The power output of unit should be within the scope of its maximum/minimum technology power output, and constraint condition can be described as:
The unit ramp loss is specific as follows:
Climbing or when lower climbing on unit, should all meet creep speed requirement；Climing constant can be described as:
Wherein, Δ Pi UFor creep speed in unit i maximum,For creep speed under unit i maximum.
Preferably, the step S3 specifically: calculating is optimized using simplex method, optimum results is obtained and protects
It deposits；Optimum results are mainly power output of each optimization unit in each period.
Preferably, the step S4 is specific as follows:
Optimize unit gross capability=all unit outputs X (1-station service power consumption rate)；
The optimization unit gross capability of t periodWherein RiFor the station service power consumption rate of unit i；
It is T that the interconnection of t period is entered power onlyt；
The bus load prediction power of t period always addsWherein b is bus serial number, NbIt is that bus is total
Number, Db,tIt is that original bus load of the bus b in the t period is predicted.
Preferably, the step S5 is specific as follows: calculating bus load modifying factor
The step S6 is specific as follows:
For every bus b in each period t, revised bus load is predicted as
Compared with prior art, the beneficial effects of the present invention are: based in the present solution, being optimized by generation schedule,
Day part bus load modifying factor is calculated in generation schedule optimization process, it is negative to bus by way of modifying factor is added
Lotus prediction data is effectively corrected；It effectively solves to cause generation schedule excellent since bus load precision of prediction is too low
The larger problem of Security Checking link Load flow calculation error is carried out during changing；Meanwhile improving the accurate of Load flow calculation.
Detailed description of the invention
Fig. 1 is a kind of flow chart of the bus load prediction data modification method for generation schedule optimization of the present invention.
Specific embodiment
Following will be combined with the drawings in the embodiments of the present invention, and technical solution in the embodiment of the present invention is purged, is complete
Site preparation description, it is clear that described embodiment is only that present invention a part is implemented to say example, instead of all the embodiments.Base
Embodiment in the present invention, it is obtained by those of ordinary skill in the art without making creative efforts it is all its
His embodiment, shall fall within the protection scope of the present invention.
A kind of bus load prediction data modification method for generation schedule optimization, flow diagram is as shown in Figure 1:
The following steps are included:
S1. generation schedule related data is obtained；
S2. security constrained economic dispatch model is established；
S3. calculating is optimized to economic load dispatching model；
S4. statistic op- timization unit gross capability, interconnection are entered power only, the prediction of original bus load always adds；
S5. bus load modifying factor is calculated；
S6. bus load prediction data is corrected.
In the present embodiment, based on optimizing by generation schedule, day part mother is calculated in generation schedule optimization process
Specific electric load modifying factor effectively corrects bus load prediction data by way of modifying factor is added；Effectively solution
It certainly solves to cause to carry out Security Checking link trend meter in generation schedule optimization process since bus load precision of prediction is too low
Calculate the larger problem of error；Meanwhile improving the accurate of Load flow calculation.
In the present embodiment, generation schedule related data includes system loading prediction, on unit operating cost, unit output
Lower limit, unit creep speed and interconnection are entered power only.
In the present embodiment, the objective function of security constrained economic dispatch model is purchases strategies minimum, and formula is as follows:
Wherein: total number of units of N expression unit；T indicate considered it is total when number of segment, it is assumed that one day 96 period of consideration, then T be
96；Pi,tIndicate unit i in the power output of t period；Ci,t(Pi,t) it is unit i in the operating cost of period t, it is the more of unit output
Section linear function.
In the present embodiment, the bound for objective function of security constrained economic dispatch model includes system restriction and machine
Group constraint.
In the present embodiment, system restriction is account load balancing constraints, and the account load balancing constraints are specific as follows:
For each period t, account load balancing constraints be can be described as:
Wherein, Pi,tIndicate power output of the unit i in the t period, DtIt is predicted for the system loading of t period, TtFor the contact of t period
Line is sent into power only.
In the present embodiment, Unit commitment includes the constraint of unit output bound and unit ramp loss；
The unit output bound constraint is specific as follows:
The power output of unit should be within the scope of its maximum/minimum technology power output, and constraint condition can be described as:
The unit ramp loss is specific as follows:
Climbing or when lower climbing on unit, should all meet creep speed requirement；Climing constant can be described as:
Wherein,For creep speed in unit i maximum,For creep speed under unit i maximum.
In the present embodiment, step S3 specifically: calculating is optimized using simplex method, optimum results is obtained and protects
It deposits；Optimum results are mainly power output of each optimization unit in each period.
In the present embodiment, step S4 is specific as follows:
Optimize unit gross capability=all unit outputs X (1-station service power consumption rate)；
The optimization unit gross capability of t periodWherein RiFor the station service power consumption rate of unit i；
It is T that the interconnection of t period is entered power onlyt；
The bus load prediction power of t period always addsWherein b is bus serial number, NbIt is that bus is total
Number, Db,tIt is that original bus load of the bus b in the t period is predicted.
In the present embodiment, step S5 is specific as follows: calculating bus load modifying factor
The step S6 is specific as follows:
For every bus b in each period t, revised bus load is predicted as
Obviously, the above embodiment of the present invention be only to clearly illustrate example of the present invention, and not be pair
The restriction of embodiments of the present invention.For those of ordinary skill in the art, may be used also on the basis of the above description
To make other variations or changes in different ways.There is no necessity and possibility to exhaust all the enbodiments.It is all this
Made any modifications, equivalent replacements, and improvements etc., should be included in the claims in the present invention within the spirit and principle of invention
Protection scope within.
Claims (9)
1. a kind of bus load prediction data modification method for generation schedule optimization, it is characterised in that: the following steps are included:
S1. generation schedule related data is obtained；
S2. security constrained economic dispatch model is established；
S3. calculating is optimized to economic load dispatching model；
S4. statistic op- timization unit gross capability, interconnection are entered power only, the prediction of original bus load always adds；
S5. bus load modifying factor is calculated；
S6. bus load prediction data is corrected.
2. the bus load prediction data modification method according to claim 1 for generation schedule optimization, feature exist
It include that system loading prediction, unit operating cost, unit output bound, unit are climbed in: the generation schedule related data
Slope rate and interconnection are entered power only.
3. the bus load prediction data modification method according to claim 1 for generation schedule optimization, feature exist
In: the objective function of the security constrained economic dispatch model is purchases strategies minimum, and formula is as follows:
Wherein: total number of units of N expression unit；T indicate considered it is total when number of segment, it is assumed that one day 96 period of considerations, then T be 96；
Pi,tIndicate unit i in the power output of t period；Ci,t(Pi,t) it is unit i in the operating cost of period t, it is the multi-section-line of unit output
Property function.
4. the bus load prediction data modification method according to claim 3 for generation schedule optimization, feature exist
In: the bound for objective function of the security constrained economic dispatch model includes system restriction and Unit commitment.
5. the bus load prediction data modification method according to claim 4 for generation schedule optimization, feature exist
In: the system restriction is account load balancing constraints, and the account load balancing constraints are specific as follows:
For each period t, account load balancing constraints be can be described as:
Wherein, Pi,tIndicate power output of the unit i in the t period, DtIt is predicted for the system loading of t period, TtInterconnection for the t period is net
It is sent into power.
6. the bus load prediction data modification method according to claim 4 for generation schedule optimization, feature exist
In: the Unit commitment includes the constraint of unit output bound and unit ramp loss；
The unit output bound constraint is specific as follows:
The power output of unit should be within the scope of its maximum/minimum technology power output, and constraint condition can be described as:
The unit ramp loss is specific as follows:
Climbing or when lower climbing on unit, should all meet creep speed requirement；Climing constant can be described as:
ΔPi D≤Pi,t-Pi,t-1≤ΔPi U (4)
Wherein, Δ Pi UFor creep speed in unit i maximum, Δ Pi DFor creep speed under unit i maximum.
7. the bus load prediction data modification method according to claim 1 for generation schedule optimization, feature exist
In: the step S3 specifically: calculating is optimized using simplex method, optimum results is obtained and saves；Optimum results master
If each optimization unit is in the power output of each period.
8. the bus load prediction data modification method according to claim 1 for generation schedule optimization, feature exist
In: the step S4 is specific as follows:
Optimize unit gross capability=all unit outputs X (1-station service power consumption rate)；
The optimization unit gross capability of t periodWherein RiFor the station service power consumption rate of unit i；
It is T that the interconnection of t period is entered power onlyt；
The bus load prediction power of t period always addsWherein b is bus serial number, NbIt is bus sum, Db,t
It is that original bus load of the bus b in the t period is predicted.
9. described in any item bus load prediction data modification methods for generation schedule optimization according to claim 1~8,
It is characterized by: the step S5 is specific as follows: calculating bus load modifying factor
The step S6 is specific as follows:
For every bus b in each period t, revised bus load is predicted as
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| CN111799798A (en) * | 2020-07-01 | 2020-10-20 | 国电南瑞科技股份有限公司 | Method and system for improving accuracy of future state load flow calculation result |
Citations (7)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN101710702A (en) * | 2009-12-03 | 2010-05-19 | 重庆大学 | Method for realizing dynamic energy-saving distribution of electrical power system |
| CN103633649A (en) * | 2013-12-19 | 2014-03-12 | 国家电网公司 | Generation method for future-state alternating-current flow of power grid |
| CN104036327A (en) * | 2014-06-20 | 2014-09-10 | 国家电网公司 | Fast bus load forecasting method for smart distribution network |
| CN104218569A (en) * | 2014-08-15 | 2014-12-17 | 国家电网公司 | Evaluative analysis method for static security check of large-scaled power grid |
| CN104538990A (en) * | 2014-12-25 | 2015-04-22 | 北京四方继保自动化股份有限公司 | Automatic generation control method for small power grid isolated network operation |
| CN105226649A (en) * | 2015-10-15 | 2016-01-06 | 中国电力科学研究院 | A kind of provincial power network power generation dispatching optimization method improved based on bus load prediction |
| US20160379143A1 (en) * | 2015-06-17 | 2016-12-29 | Capital One Services, LLC. | Systems and methods for modeling computer resource metrics |
-
2017
- 2017-12-28 CN CN201711463089.5A patent/CN109977444B/en active Active
Patent Citations (7)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN101710702A (en) * | 2009-12-03 | 2010-05-19 | 重庆大学 | Method for realizing dynamic energy-saving distribution of electrical power system |
| CN103633649A (en) * | 2013-12-19 | 2014-03-12 | 国家电网公司 | Generation method for future-state alternating-current flow of power grid |
| CN104036327A (en) * | 2014-06-20 | 2014-09-10 | 国家电网公司 | Fast bus load forecasting method for smart distribution network |
| CN104218569A (en) * | 2014-08-15 | 2014-12-17 | 国家电网公司 | Evaluative analysis method for static security check of large-scaled power grid |
| CN104538990A (en) * | 2014-12-25 | 2015-04-22 | 北京四方继保自动化股份有限公司 | Automatic generation control method for small power grid isolated network operation |
| US20160379143A1 (en) * | 2015-06-17 | 2016-12-29 | Capital One Services, LLC. | Systems and methods for modeling computer resource metrics |
| CN105226649A (en) * | 2015-10-15 | 2016-01-06 | 中国电力科学研究院 | A kind of provincial power network power generation dispatching optimization method improved based on bus load prediction |
Non-Patent Citations (2)
| Title |
|---|
| 朱泽磊: "基于母线负荷预测改进的省地协同发电计划优化方法", 《中国电机工程学报》 * |
| 杨理才: "参照系统负荷预测的母线负荷预测修正方法", 《电力系统及其自动化学报》 * |
Cited By (2)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN111799798A (en) * | 2020-07-01 | 2020-10-20 | 国电南瑞科技股份有限公司 | Method and system for improving accuracy of future state load flow calculation result |
| CN111799798B (en) * | 2020-07-01 | 2022-09-30 | 国电南瑞科技股份有限公司 | Method and system for improving accuracy of future state load flow calculation result |
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