New energy scheduling system based on wind-solar complementation

Abstract

The invention discloses a new energy scheduling system based on wind-solar complementation, and belongs to the technical field of grid-connected scheduling, and the system specifically comprises the steps: obtaining the predicted output power of a new energy power station, and determining the target output power of the new energy power station according to the predicted output power; presetting a monitoring period, drawing a time-varying curve of the output power of the new energy power station, obtaining all coordinate points with the generated power being the target output power in the changing curve, and marking the coordinate points as reference points; dividing the change curve into a plurality of sub-curves by taking any adjacent reference point as a division point, and calculating a risk coefficient of the new energy power station according to the plurality of sub-curves; and obtaining a deviation value of the new energy power station, determining the grid-connected new energy power station according to the risk coefficient and the deviation value Pc, and performing three-phase division on all the grid-connected new energy power stations, thereby reducing the three-phase load imbalance of the power grid during grid connection of the new energy power station, and improving the operation stability and the operation efficiency of the power grid.

Description

Technical Field

[0001] The present invention relates to the technical field of grid-connected dispatching, and in particular to a new energy dispatching system based on wind-solar complementarity. Background Art

[0002] As traditional fossil energy sources such as coal and oil are becoming increasingly depleted, and environmental pollution is becoming increasingly serious, and as the global demand for renewable energy continues to increase, wind power and photovoltaic power generation, as important forms of renewable energy, have become important energy supply methods. However, since wind power and photovoltaic power generation are usually affected by meteorological factors and have the characteristics of randomness, intermittency and volatility, the energy generated is uncertain, which brings challenges to the safe and stable operation of the power grid.

[0003] With the increase in the number of new energy power stations in the region, especially when multiple new energy power stations are connected to the grid at the same time, the instantaneous voltage rise will cause instantaneous voltage shock to the large power grid, which poses a serious safety hazard. On the one hand, power electronic conversion devices are usually used in microgrids as interfaces with distributed power sources. Power electronic devices are more susceptible to grid harmonics, increasing additional losses in power generation, transmission, and distribution of the power system, affecting the abnormal operation of electrical equipment. On the other hand, due to the uncertainty and time-varying nature of the three-phase voltage connected to the microgrid, the three-phase voltage of the grid is unbalanced. The unbalanced voltage will increase the energy loss of the microgrid system, causing fluctuations in the grid voltage, reducing the grid's operating efficiency, and increasing line losses and equipment losses. Summary of the invention

[0004] The purpose of the present invention is to provide a new energy dispatching system based on wind-solar complementarity to solve the following technical problems:

[0005] With the increase in the number of new energy power stations in the region, especially when multiple new energy power stations are connected to the grid at the same time, the instantaneous voltage rise will cause instantaneous voltage shock to the large power grid, which poses a serious safety hazard. On the one hand, power electronic conversion devices are usually used in microgrids as interfaces with distributed power sources. Power electronic devices are more susceptible to grid harmonics, increasing additional losses in power generation, transmission, and distribution of the power system, affecting the abnormal operation of electrical equipment. On the other hand, due to the uncertainty and time-varying nature of the three-phase voltage connected to the microgrid, the three-phase voltage of the grid is unbalanced. The unbalanced voltage will increase the energy loss of the microgrid system, causing fluctuations in the grid voltage, reducing the grid's operating efficiency, and increasing line losses and equipment losses.

[0006] The purpose of the present invention can be achieved through the following technical solutions:

[0007] A new energy dispatching system based on wind-solar complementarity, a data acquisition module, used to obtain the predicted output power of the new energy power station, and determine the target output power P' of the new energy power station according to the predicted output power;

[0008] A data analysis module is used to preset a monitoring period H, periodically obtain the output power P of the new energy power station within the monitoring period, and draw a curve f(t) of the output power of the new energy power station changing with time, obtain all coordinate points with a power generation of P' in the change curve f(t) and mark them as reference points D1, D2, ..., Dn; D = (ti, P'), where ti is the time corresponding to the i-th reference point;

[0009] Taking any adjacent reference point as a segmentation point, the change curve P(t) is segmented into several sub-curves, any sub-curve is selected and the segmentation value C=P'-max(f(t)) of the sub-curve is calculated, t∈[ti,ti+1], wherein ti and ti+1 respectively represent the starting time point and the ending time point of the sub-curve segment, if the segmentation value C is greater than 0, the sub-curve is calibrated as a positive curve, if the segmentation value C is less than 0, the sub-curve is calibrated as a negative curve; the risk coefficient K of the new energy power station is calculated according to the positive curve and the negative curve;

[0010] The result generation module is used to obtain the deviation value Pc of the new energy power station, determine the grid-connected power of all grid-connected new energy power stations according to the risk coefficient K and the deviation value Pc, and divide all grid-connected new energy power stations into three phases according to the grid-connected power.

[0011] As a further solution of the present invention: in the data analysis module, the specific calculation process of the risk coefficient K is:

[0012]

[0013] Among them, Tz is the time length corresponding to any of the positive curves, Tf is the time length corresponding to any of the negative curves; M is the number of all positive curves, m is the number of all negative curves; Czq is the evaluation score corresponding to any positive curve; Cfu is the evaluation score corresponding to any negative curve.

[0014] As a further solution of the present invention: the specific calculation process of the evaluation score is:

[0015]

[0016] As a further solution of the present invention: in the result generation module, the specific calculation process of the deviation value is:

[0017] Pc=Czq-Cfu;

[0018] Among them, Czq is the evaluation score corresponding to any positive curve; Cfu is the evaluation score corresponding to any negative curve.

[0019] As a further solution of the present invention: it also includes determining the grid-connected new energy power station, and the specific process of determining the grid-connected new energy power station is:

[0020] If Pc is less than 0, the new energy power station is marked as a new energy power station to be connected to the grid; if Pc is greater than 0, according to the calculation formula The preferred score is calculated. If the preferred score is greater than or equal to the preset threshold, the new energy power station is marked as a grid-connected new energy power station; if the preferred score is greater than or equal to the preset threshold, the new energy power station is marked as a new energy power station to be connected to the grid, where K' is the preset standard risk coefficient and α is the preset correction coefficient.

[0021] As a further solution of the present invention: in the result generation module, the calculation process of the grid-connected power is:

[0022] With the deviation value as the horizontal axis and the risk coefficient as the vertical axis, an evaluation coordinate system is constructed to generate feature points corresponding to all grid-connected renewable energy power stations, calculate the Euclidean distance I between all feature points, generate the Euclidean distance set U, set the clustering control radius R, take any feature point as the center, calculate the feature point density P within the control radius R, obtain the mean feature point density within the control radius R of all feature points, and mark the mean feature point density as MinP; for any feature point, if P is greater than MinP, a category cluster is generated with the feature point as the core point, and all adjacent feature points within the radius R are classified into the category cluster to obtain several category clusters; if there is a non-core point feature set whose density P within the control radius R is also greater than MinP, the category cluster generated by the non-core point is merged with the original category cluster to generate several category clusters;

[0023] With the core point in each category cluster as the center and R as the radius, several subclusters are generated. The degree of aggregation of each subcluster is calculated and marked as DP. The calculation formula of DP is:

[0024]

[0025] Where z represents the number of feature points in the cluster, I represents the Euclidean distance, z0 represents the core point of the cluster, and v0 represents any feature point in the cluster;

[0026] In each category cluster, the subcluster with the smallest DP value is selected as the representative cluster of the category cluster, and a representative set of each category cluster is generated. The mean value Pc' of any horizontal axis coordinate of the corresponding feature point of any representative set is obtained. According to the calculation formula P 并 =Pc' / H The grid-connected power of the new energy power station corresponding to this category cluster is calculated.

[0027] As a further solution of the present invention: the process of setting the cluster control radius R is:

[0028] Taking any feature point as the center, calculate the Euclidean distance between the feature point and any other feature point, sum each Euclidean distance to get u, and get the control radius R based on the value u. The calculation formula is as follows:

[0029]

[0030]

[0031] Among them, u is the sum of the Euclidean distance data values ​​of all feature points, and I is the Euclidean distance between any feature points.

[0032] As a further solution of the present invention: the total grid-connected power W per unit time of the corresponding new energy power station in all category clusters is obtained, the phase standard value W / 3 is set, the phase sequence combination of several category clusters is performed by exhaustive method, the deviation value of each phase load in any phase sequence combination and the standard load value W / 3 is calculated, the evaluation score of each phase sequence combination is calculated according to the deviation value, and the phase sequence combination with the smallest evaluation score is selected as the phase sequence allocation of the current category cluster;

[0033] Evaluation score = (X1w-W / 3)2+(X2w-W / 3)2+(X3w-W / 3)2;

[0034] Among them, X1w is the grid-connected power of phase X1 corresponding to any phase sequence combination, X2w is the grid-connected power of phase X2 corresponding to the phase sequence combination, and X3w is the grid-connected power of phase X3 corresponding to the phase sequence combination.

[0035] Beneficial effects of the present invention:

[0036] The present invention first obtains the predicted output power of the new energy power station, and determines the target output power of the new energy power station according to the predicted output power, which is the basis for subsequent analysis. By presetting the monitoring period and drawing the output power change curve according to the target output power, the reference point is determined by the target output power, which provides a benchmark point for further analysis and can reflect the actual output power trend of the new energy power station; then the segmentation value is calculated and the positive curve and the negative curve are determined; the preferred score is calculated according to the positive curve, and the difference score is calculated according to the negative curve. It should be noted that the larger the preferred score, the more the new energy power station meets the grid-connected demand, and the larger the difference score, the poorer the output effect of the actual new energy power station, and the less it meets the grid-connected demand; it is worth noting that the longer the duration corresponding to the positive curve, the more time the output effect of the new energy power station is greater than the target level in the actual process, which can reflect that the new energy power station meets the grid-connected demand; the longer the duration corresponding to the negative curve, the more time the new energy power station meets the grid-connected demand; The more time the output effect is less than the target level, the less the new energy power station meets the grid connection requirements. Finally, the risk coefficient and the deviation value are calculated comprehensively, and the grid-connected new energy power stations are screened out according to the risk coefficient and the deviation value. Since the three phases connected to the microgrid are uncertain and time-varying, the three-phase voltage of the power grid is unbalanced. The unbalanced voltage will increase the energy loss of the microgrid system, thereby causing fluctuations in the power grid voltage, reducing the operating efficiency of the power grid, and increasing line losses and equipment losses. Therefore, the present invention uses the deviation value as the horizontal axis and the risk coefficient as the vertical axis to construct an evaluation coordinate system, generate feature points corresponding to all grid-connected new energy power stations, calculate the Euclidean distance between the feature points, and set the clustering control radius according to the Euclidean distance, so that all new energy power stations can be divided into several representative category clusters, and the three-phase phase sequence of the representative category clusters is divided and the grid-connected phase of each new energy power station is determined according to the division results, thereby reducing the imbalance of the three-phase load of the power grid when connected to the grid and improving the stability of the power grid operation. BRIEF DESCRIPTION OF THE DRAWINGS

[0037] The present invention will be further described below in conjunction with the accompanying drawings.

[0038] Figure 1 It is a schematic diagram of the process of a new energy dispatching system based on wind-solar complementarity of the present invention. DETAILED DESCRIPTION

[0039] The following will be combined with the drawings in the embodiments of the present invention to clearly and completely describe the technical solutions in the embodiments of the present invention. Obviously, the described embodiments are only part of the embodiments of the present invention, not all of the embodiments. Based on the embodiments of the present invention, all other embodiments obtained by ordinary technicians in this field without creative work are within the scope of protection of the present invention.

[0040] See also Figure 1 As shown, the present invention is a new energy dispatching system based on wind-solar complementarity, comprising:

[0041] A data acquisition module, used to acquire the predicted output power of the new energy power station, and determine the target output power P' of the new energy power station according to the predicted output power;

[0042] A data analysis module is used to preset a monitoring period H, periodically obtain the output power P of the new energy power station within the monitoring period, and draw a curve f(t) of the output power of the new energy power station changing with time, obtain all coordinate points with a power generation of P' in the change curve f(t) and mark them as reference points D1, D2, ..., Dn; D = (ti, P'), where ti is the time corresponding to the i-th reference point;

[0043] Taking any adjacent reference point as a segmentation point, the change curve P(t) is segmented into several sub-curves, any sub-curve is selected and the segmentation value C=P'-max(f(t)) of the sub-curve is calculated, t∈[ti,ti+1], wherein ti and ti+1 respectively represent the starting time point and the ending time point of the sub-curve segment, if the segmentation value C is greater than 0, the sub-curve is calibrated as a positive curve, if the segmentation value C is less than 0, the sub-curve is calibrated as a negative curve; the risk coefficient K of the new energy power station is calculated according to the positive curve and the negative curve;

[0044] The result generation module is used to obtain the deviation value Pc of the new energy power station, determine the grid-connected power of all grid-connected new energy power stations according to the risk coefficient K and the deviation value Pc, and divide all grid-connected new energy power stations into three phases according to the grid-connected power.

[0045] The present invention first obtains the predicted output power of the new energy power station, and determines the target output power of the new energy power station according to the predicted output power, which is the basis for subsequent analysis. By presetting the monitoring period and drawing the output power change curve according to the target output power, the reference point is determined by the target output power, which provides a benchmark point for further analysis and can reflect the actual output power trend of the new energy power station; then the segmentation value is calculated and the positive curve and the negative curve are determined; the preferred score is calculated according to the positive curve, and the difference score is calculated according to the negative curve. It should be noted that the larger the preferred score, the more the new energy power station meets the grid connection requirements, and the larger the difference score, the poorer the output effect of the actual new energy power station, and the less it meets the grid connection requirements; it is worth noting that the longer the duration corresponding to the positive curve, the more time the output effect of the new energy power station is greater than the target level in the actual process, which can reflect that the new energy power station meets the grid connection requirements; the longer the duration corresponding to the negative curve, the worse the new energy power station is in actual The more time the output effect is less than the target level in the process, the less the new energy power station meets the grid connection requirements. Finally, the risk coefficient and deviation value are calculated comprehensively, and the grid-connected new energy power stations are screened out according to the risk coefficient and deviation value. Due to the uncertainty and time-varying nature of the three-phase connected to the microgrid, the three-phase voltage of the power grid is unbalanced. The unbalanced voltage will increase the energy loss of the microgrid system, thereby causing fluctuations in the power grid voltage, reducing the operating efficiency of the power grid, and increasing line loss and equipment loss. The evaluation coordinate system is constructed with the deviation value as the horizontal axis and the risk coefficient as the vertical axis to generate the corresponding feature points of all grid-connected new energy power stations. By calculating the Euclidean distance between the feature points and setting the clustering control radius according to the Euclidean distance, all new energy power stations can be divided into several representative category clusters. By dividing the three-phase phase sequence of the representative category clusters and determining the access phase of each new energy power station according to the division results, the imbalance of the three-phase load of the power grid is reduced and the stability of the power grid operation is improved.

[0046] In another preferred embodiment of the present invention, in the data analysis module, the specific calculation process of the risk coefficient K is:

[0047]

[0048] Among them, Tz is the time length corresponding to any of the positive curves, Tf is the time length corresponding to any of the negative curves; M is the number of all positive curves, m is the number of all negative curves; Czq is the evaluation score corresponding to any positive curve; Cfu is the evaluation score corresponding to any negative curve.

[0049] In another preferred embodiment of the present invention, the specific calculation process of the evaluation score is:

[0050]

[0051] In another preferred embodiment of the present invention, in the result generation module, the specific calculation process of the deviation value is:

[0052] Pc=Czq-Cfu;

[0053] Among them, Czq is the evaluation score corresponding to any positive curve; Cfu is the evaluation score corresponding to any negative curve.

[0054] In another preferred embodiment of the present invention, it further includes determining a grid-connected new energy power station, and the specific process of determining the grid-connected new energy power station is:

[0055] If Pc is less than 0, the new energy power station is marked as a new energy power station to be connected to the grid; if Pc is greater than 0, according to the calculation formula The preferred score is calculated. If the preferred score is greater than or equal to the preset threshold, the new energy power station is marked as a grid-connected new energy power station; if the preferred score is greater than or equal to the preset threshold, the new energy power station is marked as a new energy power station to be connected to the grid, where K' is the preset standard risk coefficient and α is the preset correction coefficient.

[0056] In another preferred embodiment of the present invention, in the result generation module, the calculation process of the grid-connected power is:

[0057] With the deviation value as the horizontal axis and the risk coefficient as the vertical axis, an evaluation coordinate system is constructed to generate feature points corresponding to all grid-connected renewable energy power stations, calculate the Euclidean distance I between all feature points, generate the Euclidean distance set U, set the clustering control radius R, take any feature point as the center, calculate the feature point density P within the control radius R, obtain the mean feature point density within the control radius R of all feature points, and mark the mean feature point density as MinP; for any feature point, if P is greater than MinP, a category cluster is generated with the feature point as the core point, and all adjacent feature points within the radius R are classified into the category cluster to obtain several category clusters; if there is a non-core point feature set whose density P within the control radius R is also greater than MinP, the category cluster generated by the non-core point is merged with the original category cluster to generate several category clusters;

[0058] With the core point in each category cluster as the center and R as the radius, several subclusters are generated. The degree of aggregation of each subcluster is calculated and marked as DP. The calculation formula of DP is:

[0059]

[0060] Where z represents the number of feature points in the cluster, I represents the Euclidean distance, z0 represents the core point of the cluster, and v0 represents any feature point in the cluster;

[0061] In each category cluster, the subcluster with the smallest DP value is selected as the representative cluster of the category cluster, and a representative set of each category cluster is generated. The mean value Pc' of any horizontal axis coordinate of the corresponding feature point of any representative set is obtained. According to the calculation formula P 并 =Pc' / H The grid-connected power of the new energy power station corresponding to this category cluster is calculated.

[0062] In another preferred embodiment of the present invention, the process of setting the cluster control radius R is:

[0063] Taking any feature point as the center, calculate the Euclidean distance between the feature point and any other feature point, sum each Euclidean distance to get u, and get the control radius R based on the value u. The calculation formula is as follows:

[0064]

[0065] Among them, u is the sum of the Euclidean distance data values ​​of all feature points, and I is the Euclidean distance between any feature points.

[0066] In another preferred embodiment of the present invention, the total grid-connected power W per unit time of the corresponding new energy power station in all category clusters is obtained, the phase standard value W / 3 is set, and the phase sequence combination of several category clusters is performed by exhaustive method, and the deviation value of each phase load in any phase sequence combination from the standard load value W / 3 is calculated, and the evaluation score of each phase sequence combination is calculated according to the deviation value, and the phase sequence combination with the smallest evaluation score is selected as the phase sequence allocation of the current category cluster;

[0067] Evaluation score = (X1w-W / 3)2+(X2w-W / 3)2+(X3w-W / 3)2;

[0068] Among them, X1w is the grid-connected power of phase X1 corresponding to any phase sequence combination, X2w is the grid-connected power of phase X2 corresponding to the phase sequence combination, and X3w is the grid-connected power of phase X3 corresponding to the phase sequence combination.

[0069] The above is a detailed description of an embodiment of the present invention, but the content is only a preferred embodiment of the present invention and cannot be considered to limit the scope of implementation of the present invention. All equivalent changes and improvements made within the scope of the present invention should still fall within the scope of the patent coverage of the present invention.

Claims

1. A new energy dispatching system based on wind-solar complementarity, characterized in that: include: A data acquisition module, used to acquire the predicted output power of the new energy power station, and determine the target output power P' of the new energy power station according to the predicted output power; A data analysis module is used to preset a monitoring period H, periodically obtain the output power P of the new energy power station within the monitoring period, and draw a curve f(t) of the output power of the new energy power station changing with time, obtain all coordinate points with a power generation of P' in the change curve f(t) and mark them as reference points D1, D2, ..., Dn; D = (ti, P'), where ti is the time corresponding to the i-th reference point; Taking any adjacent reference point as a segmentation point, the change curve f(t) is segmented into several sub-curves, any sub-curve is selected and the segmentation value C=P'-max(f(t)) of the sub-curve is calculated, t∈[ti,ti+1], wherein ti and ti+1 respectively represent the starting time point and the ending time point of the sub-curve segment, if the segmentation value C is greater than 0, the sub-curve is calibrated as a positive curve, if the segmentation value C is less than 0, the sub-curve is calibrated as a negative curve; the risk coefficient K of the new energy power station is calculated according to the positive curve and the negative curve; The result generation module is used to obtain the deviation value Pc of the new energy power station, determine the grid-connected power of all grid-connected new energy power stations according to the risk coefficient K and the deviation value Pc, and divide all grid-connected new energy power stations into three phases according to the grid-connected power.

2. A new energy dispatching system based on wind-solar complementarity according to claim 1, characterized in that: In the data analysis module, the specific calculation process of the risk coefficient K is: Among them, Tz is the time length corresponding to any of the positive curves, Tf is the time length corresponding to any of the negative curves; M is the number of all positive curves, m is the number of all negative curves; Czq is the evaluation score corresponding to any positive curve; Cfu is the evaluation score corresponding to any negative curve.

3. A new energy dispatching system based on wind-solar complementarity according to claim 2, characterized in that: The specific calculation process of the evaluation score is as follows:

4. The new energy dispatching system based on wind-solar complementarity according to claim 1 is characterized in that: In the result generation module, the specific calculation process of the deviation value is: Pc=Czq-Cfu; Among them, Czq is the evaluation score corresponding to any positive curve; Cfu is the evaluation score corresponding to any negative curve.

5. The new energy dispatching system based on wind-solar complementarity according to claim 1 is characterized in that: The result generation module also includes determining the grid-connected new energy power station. The specific process of determining the grid-connected new energy power station is as follows: If Pc is less than 0, the new energy power station is marked as a new energy power station to be connected to the grid; if Pc is greater than 0, according to the calculation formula The preferred score is calculated. If the preferred score is greater than or equal to the preset threshold, the new energy power station is marked as a grid-connected new energy power station; if the preferred score is greater than or equal to the preset threshold, the new energy power station is marked as a new energy power station to be connected to the grid, where K' is the preset standard risk coefficient and α is the preset correction coefficient.

6. The new energy dispatching system based on wind-solar complementarity according to claim 1 is characterized in that: In the result generation module, the calculation process of the grid-connected power is: With the deviation value as the horizontal axis and the risk coefficient as the vertical axis, an evaluation coordinate system is constructed to generate feature points corresponding to all grid-connected renewable energy power stations, calculate the Euclidean distance I between all feature points, generate the Euclidean distance set U, set the clustering control radius R, take any feature point as the center, calculate the feature point density P within the control radius R, obtain the mean feature point density within the control radius R of all feature points, and mark the mean feature point density as MinP; for any feature point, if P is greater than MinP, a category cluster is generated with the feature point as the core point, and all adjacent feature points within the radius R are classified into the category cluster to obtain several category clusters; if there is a non-core point feature set whose density P within the control radius R is also greater than MinP, the category cluster generated by the non-core point is merged with the original category cluster to generate several category clusters; With the core point in each category cluster as the center and R as the radius, several subclusters are generated. The degree of aggregation of each subcluster is calculated and marked as DP. The calculation formula of DP is: Where z represents the number of feature points in the cluster, I represents the Euclidean distance, z0 represents the core point of the cluster, and v0 represents any feature point in the cluster; In each category cluster, the subcluster with the smallest DP value is selected as the representative cluster of the category cluster, and a representative set of each category cluster is generated. The mean value Pc' of any horizontal axis coordinate of the corresponding feature point of any representative set is obtained. According to the calculation formula P 并 =Pc' / H The grid-connected power of the new energy power station corresponding to this category cluster is calculated.

7. A new energy dispatching system based on wind-solar complementarity according to claim 6, characterized in that: The process of setting the cluster control radius R is: Taking any feature point as the center, calculate the Euclidean distance between the feature point and any other feature point, sum each Euclidean distance to get u, and get the control radius R based on the value u. The calculation formula is as follows: Among them, u is the sum of the Euclidean distance data values ​​of all feature points, and I is the Euclidean distance between any feature points.

8. The new energy dispatching system based on wind-solar complementarity according to claim 6 is characterized in that: Obtain the total grid-connected power W per unit time of the corresponding new energy power stations in all category clusters, set the phase standard value W / 3, use the exhaustive method to combine the phase sequence of several category clusters, calculate the deviation value of each phase load in any phase sequence combination from the standard load value W / 3, calculate the evaluation score of each phase sequence combination based on the deviation value, and select the phase sequence combination with the smallest evaluation score as the phase sequence allocation of the current category cluster; Evaluation score = (X1w-W / 3)2+(X2w-W / 3)2+(X3w-W / 3)2; Among them, X1w is the grid-connected power of phase X1 corresponding to any phase sequence combination, X2w is the grid-connected power of phase X2 corresponding to the phase sequence combination, and X3w is the grid-connected power of phase X3 corresponding to the phase sequence combination.

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