CN109617048A - Electric Power Network Planning typical scene choosing method based on multiobjective linear programming - Google Patents
Electric Power Network Planning typical scene choosing method based on multiobjective linear programming Download PDFInfo
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- H—ELECTRICITY
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Abstract
The present invention relates to a kind of Electric Power Network Planning typical scene choosing method based on multiobjective linear programming, on the basis of constructing typical day evaluation index system, building optimizes typical day preference pattern and Electric Power Network Planning typical scene choosing method, comprising: typical day evaluation index system；It constructs the fuzzy solution of the typical day Selection Model multiobjective linear programming two stages based on mixing shaping linear programming: typical day Selection Model being solved using two stages fuzzy programming solving method.
Description
Technical Field
The invention relates to a power grid planning typical scene selection method based on multi-objective linear programming.
Background
The permeability of distributed power sources in a power grid is continuously improved, and the influence degree on the power grid is also continuously increased, so that the operation regulation and planning design of the power distribution network considering various distributed power sources becomes a current research hotspot, but because the data volume of load points, wind, light and other renewable energy sources in the power grid is large, the calculation scale required by planning design analysis and scheduling strategy formulation is large, and in order to meet the calculation efficiency and the integrity of data in calculation, a large amount of data needs to be compressed, so that the problem of selection in a typical day is involved
The existing selection of typical daily scenes mainly adopts a clustering method, so more researches are focused on the improvement of a clustering algorithm, and a high-efficiency algorithm is developed by combining technologies such as big data, parallel computation and the like to obtain a better result; the other type is that a typical scene is selected by a sampling method for a large number of generated scenes; the other type is to search for a typical scene through algorithms such as forward search and backward search of a heuristic algorithm, and the improved method is to optimize by defining a new distance index and a search flow generally, so that the algorithm effect is improved, but the method is essentially not different from the search of a k-means clustering algorithm. For the selection problem of the typical scene and the typical day, firstly, how to judge the quality of the selection result should be specified, and then how to select should be considered, however, the existing literature does not solve the problem of the evaluation index, and the selection result is almost evaluated through the distance index.
In recent years, certain research results have been obtained for the selection problem of typical scenes and typical days, but the existing method still has certain defects and shortcomings:
(1) the existing documents do not solve the problem of evaluation indexes, only evaluate the selection result through the distance indexes, and lack comprehensive basis.
(2) Most of the existing selection methods for typical day scenes adopt a clustering method, so that more research focuses on the improvement of a clustering algorithm.
Disclosure of Invention
Aiming at the problems, the invention aims to overcome the defects of a single typical day selection means, and an optimized typical day selection model and an optimized typical day selection algorithm are constructed on the basis of constructing a typical day evaluation index system, wherein the technical scheme is as follows:
a power grid planning typical scene selection method based on multi-objective linear programming is used for constructing an optimized typical day selection model and a power grid planning typical scene selection method on the basis of constructing a typical day evaluation index system, and comprises the following steps:
s1) typical daily evaluation index system
1) Statistical index
The annual total load electric quantity deviation delta C represents the total load electric quantity sigma omega after the typical day is calculated through weightingd·CdTotal load capacity C with original datayearRelative error of (2):
in the above formula, ωdWeight coefficient, C, representing typical day ddTotal electrical load capacity of the whole day, C, representing typical day dyearRepresenting the total annual load capacity, and D representing the set of all typical days;
the annual load power distribution deviation Δ P represents the total load capacity calculated by weighting for each period of a typical dayAnd the total amount of the historical load at the momentAverage value of relative error of (a):
in the above formula, D0Representing the set of all historical dates in the raw data,indicating the original load power value at time t on date d,representing the load power value at the tth moment of a typical day d;
the annual resource total deviation Delta S represents the total resource amount Sigma omega after the typical day is calculated by weightingd·SdAnd the total amount S of resources in the original datayearRelative error of (2); wherein SdRepresents the total amount of resources, S, for a typical day dyearRepresenting the total annual resource amount;
the annual resource distribution deviation aw represents the total resource amount calculated by weighting for each period of the typical dayAnd the total amount of historical resources at the momentAverage value of relative error of; wherein,the raw asset value representing the date d at time t,to representResource value at time tth on typical day d;
2) timing indicator
Typical day-around data density is represented by the number of data points within a cutoff distance:
IS＝{1,2,…,card(D0)}
in the above formula, dijRespectively representing the distance between the ith and jth typical day data vectors, the Euclidean distance, d, is adopted in the inventioncDenotes the truncation distance, ISRepresenting a set of metrics;
the typical daily radiance radius is defined by using distance, if the typical day i is the global maximum data density data point, the radiance radius is the distance between the point and the global farthest point, otherwise, the radiance radius is the distance between the point and the adjacent closest data point with greater data density:
in the above formula, the first and second carbon atoms are,the index set representing the ith typical day is composed of labels of individuals with higher surrounding data density;
the peak load deviation Δ L represents the maximum load value in a typical day at the same timeMaximum load value at time corresponding to historical dataRelative error of (2);
the peak resource deviation Delta S represents the maximum resource value in a typical day at the same timeMaximum resource value corresponding to time in historical dataRelative error of (2);
maximum deviation of time-interval power change rateReflecting the maximum load change power in a certain period of time in a typical dayAnd the maximum change value in the historical dataRelative error of (2);
maximum deviation of time-interval resource change rateReflecting the maximum resource variation value for a period of time on a typical dayAnd the maximum change value in the historical dataRelative error of (2);
power change rate coverage over time periodsReflecting the maximum load variation power for a certain period of time on a typical dayRelative position in historical data variance:
time-phased resource change rate coverageReflecting the maximum resource variation value for a certain period of time in a typical day.Relative position in historical data variation value, form same time period power change rate coverage
S2), constructing a typical day selection model based on mixed shaping linear programming
Optimization objective z1Represents typical days of selection, z2Error, z, representing the total load demand and total resource volume after a typical day has been calculated by weighting3Representing the total typical data density around the day, z4Represents the total typical daily radiation radius:
in the above formula, uiBinary variable, u, representing typical day picksi1 denotes that the i-th day is a typical day, n denotes the total number of days, and ρ ═ ρ1,ρ2,…,ρn]δ＝[δ1,δ2,…,δn]The columns in the matrix A represent load data and resource data of one day, and b represents the total amount of load and resource at the same time:
the optimization variables include a weight variable wiAnd binary variable ui：
The constraint conditions comprise (1) constraint of typical day weights through binary variables, and zero setting of the weights if the typical day weights are not, 2) representation of sum of all typical day weights as total days N in historical data, (3) representation of load or resource deviation of each time interval to enable the total deviation to be controlled within a certain range, α is a proportionality coefficient, (4) setting of lower limit of typical day days, extreme constraint can be set through constraint making time, (5) representation of non-negative real numbers of typical day weights, and (6) representation of variable uiIs a binary variable;
s3) Multi-object Linear programming two-stage fuzzy solution
And solving the typical day selection model by adopting a two-stage fuzzy programming solution.
Due to the adoption of the technical scheme, compared with the prior art, the invention has the following advantages:
(1) according to the method, an evaluation index system for evaluating the quality of a selection result of a typical day is established, then a mixed shaping linear programming model is established according to the evaluation index, the total deviation, the distribution error and the like of resources and loads are minimized under the condition of selecting the minimum number of days through optimal configuration weight, and the typical day has better representativeness.
(2) The optimization method provided by the invention can solve the optimization result of the weight, so that the error in the aspects of total amount and distribution deviation is smaller than that of the traditional clustering algorithm, and the linear programming model can flexibly set various extreme constraint conditions such as extreme value deviation, fluctuation deviation and typical days, so that the selection result meets the expected effect and the use condition.
Drawings
FIG. 1 is a diagram of a typical daily-selected composite assessment index structure according to the present invention.
FIG. 2 is a month resource and load data of an embodiment.
FIG. 3 is a decision graph based on density and radius of radiation for an embodiment.
Fig. 4 is a diagram of raw data corresponding to a typical data center of an embodiment.
FIG. 5 is a decision position diagram of the selection result of MILP in the embodiment
FIG. 6 is a comparison of typical day selection results for the examples
FIG. 7 is a time-phased peak load deviation plot of an embodiment
FIG. 8 is a time-sliced peak resource deviation statistical chart of an embodiment
The reference numbers in the figures illustrate: 1 black color: the k-means method; 2 red line: the MILP method; 3, blue line: MILP2 method
Table 1 shows the extreme values of the respective targets during the single-target planning according to the embodiment of the present invention.
Table 2 is a date when each time period of the embodiment of the present invention satisfies the deviation threshold.
Table 3 is a comparison of the results of the solution for the extreme and the infinite constraints for the examples of the present invention.
Table 4 shows the comparison of the indexes of the extreme-constrained MILP and the k-means method for the line with extreme constraint and no extreme constraint in the example of the invention.
Detailed Description
A large amount of information in the historical data comprises resource and load total amount information, distribution information, time sequence change information and extreme scene information, a typical day selection comprehensive evaluation index model comprising statistical indexes and time sequence indexes is constructed, and the selection effect of the typical day is comprehensively evaluated in multiple aspects. And a multi-target mixed shaping linear programming model is constructed by combining the statistical indexes and the time sequence indexes, so that under the condition of reducing the days of the typical day, each index is optimally used as a target constructed by a typical day selection model.
According to the invention, an optimized typical day selection model and an optimized typical day selection algorithm are constructed on the basis of constructing a typical day evaluation index system. The invention is described in detail below with reference to the figures and examples.
1. Method of implementation
1) Comprehensive evaluation index system
The large amount of information present in the historical data includes resource and load total information, distribution information, timing variation information, and extreme scenario information. Therefore, in order to comprehensively evaluate the selection effect of the typical day from multiple aspects, the invention constructs a model for selecting the comprehensive evaluation index in the typical day, which comprises the statistical index and the time sequence index, and the structure diagram of the model is shown in fig. 1.
The establishment of the statistical indexes mainly considers that the total amount and distribution of loads and resources relate to the investment benefit calculation of the distributed power supply and the power distribution network, so that the statistical indexes of historical data and typical daily data are kept within a certain error range.
All year roundThe total load electric quantity deviation delta C represents the total load electric quantity sigma omega after the typical day is calculated through weightingd·CdTotal load capacity C with original datayearRelative error of (2):
in the above formula, ωdWeight coefficient, C, representing typical day ddTotal electrical load capacity of the whole day, C, representing typical day dyearRepresenting the total annual charge capacity and D representing the set of all typical days.
The annual load power distribution deviation Δ P represents the total load capacity calculated by weighting for each period of a typical dayAnd the total amount of the historical load at the momentAverage value of relative error of (a):
in the above formula, D0Representing the set of all historical dates in the raw data,indicating the original load power value at time t on date d,representing the load power value at time tth on typical day d.
The annual resource total deviation Delta S represents the total resource amount Sigma omega after the typical day is calculated by weightingd·SdAnd the total amount S of resources in the original datayearRelative error of (2). Wherein SdRepresents the total amount of resources on a typical day d，SyearRepresenting the total annual resource amount.
The annual resource distribution deviation aw represents the total resource amount calculated by weighting for each period of the typical dayAnd the total amount of historical resources at the momentRelative error average of. Wherein,the raw asset value representing the date d at time t,representing the resource value at time tth on typical day d.
Secondly, selecting a typical day with a large amount of time series data requires selecting a situation which has high typical degree and is frequently generated in actual operation, so that the invention constructs indexes of typical day surrounding data density and typical day radiation radius reflecting the typical degree of the typical day. In addition, the time sequence index is established by considering the conditions of holidays, severe weather and the like in actual operation, so that the peak resource and peak load deviation is established, and the description effect of a typical day on an extreme scene is reflected. Due to the fact that time sequence fluctuation exists between resources and loads, the running frequency of a power distribution network, the unit climbing slope, the maximum charge and discharge power of an energy storage device and the like are affected, the typical daily reflected fluctuation rate and the extreme fluctuation rate existing in original data are kept within a certain error range.
Typical day-around data density is represented by the number of data points within a cutoff distance:
IS＝{1,2,…,card(D0)}
in the above formula, dijRespectively representing the distance between the ith and jth typical day data vectors, the Euclidean distance, d, is adopted in the inventioncDenotes the truncation distance, ISA set of metrics is represented.
The typical daily radiance radius is defined by using distance, if the typical day i is the global maximum data density data point, the radiance radius is the distance between the point and the global farthest point, otherwise, the radiance radius is the distance between the point and the adjacent closest data point with greater data density:
in the above formula, the first and second carbon atoms are,the index set indicating the ith typical day is composed of labels of other individuals whose surrounding data density is greater than that of the index set.
The peak load deviation Δ L represents the maximum load value in a typical day at the same timeMaximum load value at time corresponding to historical dataRelative error of (2).
The peak resource deviation Delta S represents the maximum resource value in a typical day at the same timeMaximum resource value corresponding to time in historical dataRelative error of (2).
Maximum deviation of time-interval power change rateReflecting the maximum load change power in a certain period of time in a typical dayAnd the maximum change value in the historical dataRelative error of (2).
Maximum deviation of time-interval resource change rateReflecting the maximum resource variation value for a period of time on a typical dayAnd the maximum change value in the historical dataRelative error of (2).
Power change rate coverage over time periodsReflecting the maximum load variation power for a certain period of time on a typical dayRelative position in historical data variance:
time-phased resource change rate coverageReflecting the maximum resource variation value for a certain period of time in a typical day.Relative position in historical data variation value, form same time period power change rate coverage
2) Typical day selection model
The typical day selection mainly aims at selecting as few typical days as possible to replace a large amount of original data, so that under the condition of reducing the number of typical days as much as possible, each index is optimally used as the basis for constructing a typical day selection model.
Optimization objective z1Represents typical days of selection, z2Error, z, representing the total load demand and total resource volume after a typical day has been calculated by weighting3Representing the total typical data density around the day, z4Represents the total typical daily radiation radius:
in the above formula, uiBinary variable, u, representing typical day picksi1 denotes that the i-th day is a typical day, n denotes the total number of days, and ρ ═ ρ1,ρ2,…,ρn]δ＝[δ1,δ2,…,δn]Each column of the a matrix represents load data and resource data for each day, and b represents the total amount of load and resource at the same time:
the optimization variables include a weight variable wiAnd binary variable ui：
The constraint conditions comprise (1) constraint of typical day weights through binary variables, and zero setting of the weights if the typical day weights are not, 2) representation of sum of all typical day weights as total days N in historical data, (3) representation of load or resource deviation of each time interval to enable the total deviation to be controlled within a certain range, α is a proportionality coefficient, (4) setting of lower limit of typical day days, extreme constraint can be set through constraint making time, (5) representation of non-negative real numbers of typical day weights, and (6) representation of variable uiAs binary variables
3) Multi-objective linear programming solution
And solving the multi-target linear programming model by using a two-stage fuzzy programming solution.
2. Case analysis
1) Typicality index validity verification
The case data is shown in fig. 2, which is data of wind speed, light intensity, local power load, resource and load per unit measured in a certain place of Xining.
In order to verify the effectiveness of the proposed indexes of the data density around the typical day and the radiation radius of the typical day, a schematic diagram as shown in fig. 3 is constructed by taking the data density as an abscissa and the radiation radius as an ordinate, and a point set close to the upper right corner in the schematic diagram reflects that the data has high density and large radiation radius, is the central point of a large amount of data and has strong typicality, so the upper right corners p1 and p2 are selected as representatives; the upper left point indicates that the data has small surrounding density values and large radius of radiation, which represents an extreme case of outliers, and therefore the upper left points p3 and p4 are selected for comparison. The raw data plots for p 1-p 4 are shown in FIG. 4.
As can be seen from fig. 5, the typical pattern can be better reflected by the typical daily ambient data density and the typical daily radiance radius. The data wind speed, illumination intensity and load demand in the typical data p1 and p2 are all around the average value of the overall data, while the wind speed in p3 and p4 representing extreme scenes is large and the illumination resources are insufficient, which indicates that the weather condition is not good, and the weather condition may be cloudy or rainy. The coverage status of the selected typical day is rich, so that the typical index can better reflect the typical degree of the data.
2) Typical day selection without extreme constraints
Table 1 in order to set the maximum reference actual value of the objective function as the expected value, the maximum value of Obj1 for typical days of the day is set as the boundary value.
Table 3 shows the two-stage solution results of the fuzzy programming method in the present invention under two conditions, i.e., the minimum extreme constraint of the windage resource and the minimum extreme constraint of the non-windage resource. The two-stage solution results are the same, which indicates that the results are effective. The data of 12 th day with most typicality is endowed with the maximum weight value, the rationality and the representativeness of the selection result are ensured, the data of 73 th day and 90 th day with unobvious typicality is endowed with smaller weight values, the selection result contains both typical data and atypical data, and the practicability and the rationality of the selection result of the typical day are ensured through reasonable weight value distribution. Typical daily results selected in example MILP are shown in fig. 5.
Table 4 shows the comparison of various indexes of a typical daily method selected by a multi-objective mixed shaping linear programming (MILP) and a k-means method in the invention. It can be seen that in the aspects of load and total resource amount, the weight coefficients of the clustering method are positive integers, and the method provided by the invention optimizes the real values of the weights, so that the error in the aspect of total amount statistics is smaller, and the precision can be generally improved by about 10 times. The distribution error of the visible resource and the load is smaller than the result of the k-means method by setting the error constraint of each time interval. The method is characterized in that no mandatory constraint is set when indexes such as resource and load fluctuation coverage rate and extreme maximum deviation exist, due to the existence of the constraint of time-divided total deviation, partial extreme scenes exist in a selection result, and it can be seen that the deviation of the MILP method except for loads is large, the illumination coverage fluctuation rate in the aspect of the resource and load coverage rate is basically equal to that of the k-means method, the deviation of the resource and load fluctuation peak value is superior, and the extreme value of a typical day selection result is closer to the extreme value of original data, so that the extreme condition existing in reality can be reflected better.
3) Extreme constraint imposed typical day selection
The deviation of the minimum wind speed value under the condition of endless terminal constraint is large, and the minimum wind speed value extreme constraint can be added, because the deviation is in the range of 11: 00-13: the illumination resources are sufficient in the 00 time period, the size of the wind resources mainly depends on the states of power grid operation, energy storage scheduling and the like, so that the relative deviation of the minimum value of the wind speed in the specified time period is less than 1, and the date numbers respectively meeting the set thresholds corresponding to the three moments are given in table 2.
The results of the two-stage planning after the wind speed minimum extreme constraint is added are given in table 3. Considering that the most representative day 12 still occupies the main weight after the extreme index, the extreme at day 58 satisfies the constraint of the minimum wind speed, and the planning result includes the day and is assigned with smaller weight. The typical index of the endless constraint condition in table 4 is decreased to further reduce the total deviation and distribution deviation, which are all better than the k-means algorithm. The coverage rate of the illumination resources is further improved, and the fluctuation peak value deviation is further reduced.
Fig. 6 shows the k-means algorithm and the raw data of the mlp 2 selection result after the extreme constraint is added, and it can be seen that the extreme constraint of the present invention makes the distribution of wind resources wider, and the scenes of the maximum wind resources and the minimum wind resources are closer to the extreme values in the raw data.
FIG. 7 compares the time-phased peak load deviations of the three methods, and MILP2 represents the case of windage speed constraint, and it can be seen that the peak load deviation is reduced after the windage speed constraint, but the three cases are basically equivalent in general, and the peak load error can be kept within a small range.
FIG. 8 compares the time-share peak resource deviation in three cases, and the deviation between the maximum and minimum values of the visible light resource can be obtained by the MILP2 of the method of the present invention with a deviation value smaller than that of k-means. It can be seen from fig. 8(4) that the minimum deviation of the wind resource is greatly reduced as a result of the selection after the minimum constraint of the wind speed is added, the deviation values at three noon moments are all within 1, and the errors in other aspects are basically unchanged when the minimum deviation is unconstrained.
TABLE 1
| Target | max | min |
| Typical days of day Obj1 | 15 | 3 |
| Obj2 Total resource/load bias Rate | 0 | 0.1539 |
| Overall density of Obj3 | 2022.6740 | 256.4277 |
| Overall radius of radiation of Obj4 | 171.4572 | 23.9521 |
TABLE 2
TABLE 3
TABLE 4
| Each index | k-means | MILP | MILP2 |
| Deviation of load capacity | 0.017084 | 0.001108 | 0.00047 |
| Deviation of illumination resources | 0.067755 | 0.002045 | 0.000186 |
| Deviation of wind resources | 0.074744 | 0.006427 | 0.002769 |
| Error of load distribution | 0.011262 | 0.017118 | 0.00963 |
| Illumination resource distribution error | 0.067755 | 0.034254 | 0.038195 |
| Wind resource distribution error | 0.080398 | 0.032887 | 0.039933 |
| Load fluctuation coverage rate | 0.988406 | 0.999034 | 0.999034 |
| Peak deviation of load fluctuation | 0.207638 | 0.039367 | 0.039367 |
| Fluctuating coverage of illumination | 0.997585 | 0.987923 | 0.993237 |
| Peak deviation of light fluctuation | 0.04228 | 0.190987 | 0.139173 |
| Wind speed fluctuation coverage | 0.987923 | 0.994203 | 0.994203 |
| Peak deviation of wind speed fluctuation | 0.588168 | 0.509592 | 0.509592 |
Claims (1)
1. A power grid planning typical scene selection method based on multi-objective linear programming is used for constructing an optimized typical day selection model and a power grid planning typical scene selection method on the basis of constructing a typical day evaluation index system, and comprises the following steps:
s1) typical daily evaluation index system
1) Statistical index
The annual total load electric quantity deviation delta C represents the total load electric quantity sigma omega after the typical day is calculated through weightingd·CdTotal load capacity C with original datayearRelative error of (2):
in the above formula, ωdWeight coefficient, C, representing typical day ddTotal electrical load capacity of the whole day, C, representing typical day dyearRepresenting the total annual load capacity, and D representing the set of all typical days;
the annual load power distribution deviation Δ P represents the total load capacity calculated by weighting for each period of a typical dayAnd the total amount of the historical load at the momentAverage value of relative error of (a):
in the above formula, D0Representing the set of all historical dates in the raw data,indicating the original load power value at time t on date d,representing the load power value at the tth moment of a typical day d;
the annual resource total deviation Delta S represents the total resource amount Sigma omega after the typical day is calculated by weightingd·SdAnd the total amount S of resources in the original datayearRelative error of (2); wherein SdRepresents the total amount of resources, S, for a typical day dyearRepresenting the total annual resource amount;
the annual resource distribution deviation aw represents the total resource amount calculated by weighting for each period of the typical dayAnd the total amount of historical resources at the momentAverage value of relative error of; wherein,the raw asset value representing the date d at time t,a resource value representing the typical day, d, time t;
2) timing indicator
Typical day-around data density is represented by the number of data points within a cutoff distance:
IS＝{1,2,…,card(D0)}
in the above formula, dijRespectively representing the distance between the ith and jth typical day data vectors, the Euclidean distance, d, is adopted in the inventioncDenotes the truncation distance, ISRepresenting a set of metrics;
the typical daily radiance radius is defined by using distance, if the typical day i is the global maximum data density data point, the radiance radius is the distance between the point and the global farthest point, otherwise, the radiance radius is the distance between the point and the adjacent closest data point with greater data density:
in the above formula, the first and second carbon atoms are,the index set representing the ith typical day is composed of labels of individuals with higher surrounding data density;
the peak load deviation Δ L represents the maximum load value in a typical day at the same timeMaximum load value at time corresponding to historical dataRelative error of (2);
the peak resource deviation Delta S represents the maximum resource value in a typical day at the same timeMaximum resource value corresponding to time in historical dataRelative error of (2);
maximum deviation of time-interval power change rateReflecting the maximum load change power in a certain period of time in a typical dayAnd the maximum change value in the historical dataRelative error of (2);
maximum deviation of time-interval resource change rateTypical day of the reactionMaximum resource change value of a certain period of timeAnd the maximum change value in the historical dataRelative error of (2);
power change rate coverage over time periodsReflecting the maximum load variation power for a certain period of time on a typical dayRelative position in historical data variance:
time-phased resource change rate coverageReflecting the maximum resource variation value for a certain period of time in a typical day.Relative position in historical data variation value, form same time period power change rate coverage
S2), constructing a typical day selection model based on mixed shaping linear programming
Optimization objective z1Represents typical days of selection, z2Error, z, representing the total load demand and total resource volume after a typical day has been calculated by weighting3Representing the total typical data density around the day, z4Represents the total typical daily radiation radius:
in the above formula, uiBinary variable, u, representing typical day picksi1 denotes that the i-th day is a typical day, n denotes the total number of days, and ρ ═ ρ1,ρ2,…,ρn]δ＝[δ1,δ2,…,δn]The columns in the matrix A represent load data and resource data of one day, and b represents the total amount of load and resource at the same time:
the optimization variables include a weight variable wiAnd binary variable ui：
The constraint conditions comprise (1) constraint of typical day weights through binary variables, and zero setting of the weights if the typical day weights are not, 2) representation of sum of all typical day weights as total days N in historical data, (3) representation of load or resource deviation of each time interval to enable the total deviation to be controlled within a certain range, α is a proportionality coefficient, (4) setting of lower limit of typical day days, extreme constraint can be set through constraint making time, (5) representation of non-negative real numbers of typical day weights, and (6) representation of variable uiIs a binary variable;
s3) Multi-object Linear programming two-stage fuzzy solution
And solving the typical day selection model by adopting a two-stage fuzzy programming solution.
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| CN113807563A (en) * | 2021-07-28 | 2021-12-17 | 国网能源研究院有限公司 | Multi-station fusion optimization method considering operating characteristics of different functional modules |
| CN114219235A (en) * | 2021-11-29 | 2022-03-22 | 浙江大学 | Typical day selection and working condition set construction method of micro-energy network based on wavelet transformation |
| CN114819429A (en) * | 2021-01-18 | 2022-07-29 | 天津大学 | Optimization-based typical daily design boundary extraction method for comprehensive energy system |
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| CN114819429A (en) * | 2021-01-18 | 2022-07-29 | 天津大学 | Optimization-based typical daily design boundary extraction method for comprehensive energy system |
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| CN114219235A (en) * | 2021-11-29 | 2022-03-22 | 浙江大学 | Typical day selection and working condition set construction method of micro-energy network based on wavelet transformation |
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