New energy power generation priority scheduling method in electric power spot market

Abstract

The invention relates to the technical field of electric power system operation and control, in particular to a new energy power generation priority scheduling method in an electric power spot market, and the method comprises the steps: determining a cluster scale corresponding to a collection node according to a stable threshold value when the stable threshold value is in a preset stable threshold value range; determining a target power grid state according to the load fluctuation value and the power generation fatigue value, determining a cluster division coefficient of each sink node according to the target power grid state, and determining a preset selection mode according to the cluster division coefficient; determining a power generation state according to the margin evaluation value and the electric quantity leakage value, and determining a power prediction mode according to the power generation state; under the preset dispatching condition, power dispatching analysis is conducted on the power generation area according to the expected power value, cluster division can be conducted on the basis that the deviation between the actual power generation amount and the expected strategy is reduced, and the new energy power dispatching difficulty is reduced.

Description

Technical Field

[0001] The present invention relates to the field of power system operation and control technology, and in particular to a new energy power generation priority dispatching method in a power spot market. Background Art

[0002] With the rapid growth of renewable energy power generation, the participation of renewable energy power generation in the electricity spot market is also getting higher and higher. In the process of renewable energy scheduling and power generation forecasting, only the node power limit is considered. However, due to the intermittent and instability of renewable energy power generation, there is a deviation between the actual power generation and the expected strategy, which leads to the inability to schedule renewable energy power according to actual needs in actual operation. In the process of renewable energy power generation scheduling, each gathering node corresponds to several power generation areas. Due to the intermittent and volatile nature of renewable energy power generation, the power generation characteristics of each power generation area are different, which increases the complexity of scheduling. Therefore, how to cluster the power generation areas corresponding to each gathering node on the basis of reducing the deviation between the actual power generation and the expected strategy to reduce the difficulty of renewable energy power scheduling is a technical problem that needs to be solved urgently by technical personnel in this field.

[0003] Chinese patent publication number CN118381038A discloses a reactive voltage control method for a new energy field grid connection point, including: based on the established equivalent model of the power generation area and the central point voltage reference value issued by the AVC master station, determine the voltage reference value of the power generation area export bus, and calculate the command value of the wind turbine constant voltage control according to the active power output of the power generation area, so that the measured values ​​of the power generation area export voltage and reactive power value meet the constraint conditions, and realize the control of the voltage of the new energy power station grid connection point. It can be seen that the above technical solution has the following problems: the calculation method of the command value is single, and it is impossible to adaptively adjust the calculation method of the command value according to the actual state of the power generation area, and the degree of difference between each power generation area when connected to the power grid is not considered, resulting in a deviation between the actual power generation and the expected strategy, and the power generation area corresponding to each collection node is not clustered, resulting in greater difficulty in the dispatch of new energy power. Summary of the invention

[0004] To this end, the present invention provides a method for preferential dispatching of renewable energy power generation under the electricity spot market, so as to overcome the problems that the calculation method of the command value in the prior art is single, the calculation method of the command value cannot be adaptively adjusted according to the actual state of the power generation area, and the degree of difference between the power generation areas when they are connected to the power grid is not considered, resulting in a deviation between the actual power generation and the expected strategy, and the power generation areas corresponding to each aggregation node are not clustered, resulting in greater difficulty in dispatching renewable energy power.

[0005] To achieve the above object, the present invention provides a method for preferentially dispatching renewable energy power generation in a power spot market, comprising:

[0006] Determine a stability threshold value according to a wind power reduction reference value and a power conflict value, and when the stability threshold value is within a preset stability threshold value range, determine a cluster size corresponding to the aggregation node according to the stability threshold value;

[0007] Determine the target grid state according to the load fluctuation value and the power generation fatigue value, and determine the cluster division coefficient of each aggregation node as a balance coefficient or a regional correlation according to the target grid state, and determine the preset selection method as selecting the same number of reference power generation areas at both ends of the reference sequence or selecting the reference power generation areas in the reference sequence in descending order of regional correlation according to the cluster division coefficient;

[0008] Determine the power generation state according to the margin assessment value and the power leakage value, and determine the power forecasting method according to the power generation state, that is, determine the control factor according to the power generation area category and determine the power expectation value according to the control factor, or determine the optimization method according to the access difference;

[0009] The optimization method includes determining the expected power value according to the influence coefficient, and determining the expected power value according to the adjacent historical power values;

[0010] The power generation area category is determined according to the power generation reference value and the node threshold;

[0011] Under the preset dispatching conditions, power dispatching analysis is performed for the power generation area based on the expected power value.

[0012] Furthermore, the cluster size corresponding to the aggregation node is positively correlated with the stability threshold of the power generation area corresponding to the aggregation node.

[0013] Furthermore, if the target grid state is that the load fluctuation value is greater than or equal to the preset load fluctuation value or the power generation fatigue value is greater than or equal to the preset power generation fatigue value, the cluster division basis is the balance coefficient;

[0014] If the target grid state is that the load fluctuation value is less than the preset load fluctuation value and the power generation fatigue value is less than the preset power generation fatigue value, the cluster division basis is the regional correlation;

[0015] The balance coefficient is determined according to the ratio of the stability threshold to the expected power value.

[0016] Furthermore, when clustering is performed for a single sink node, the sink node is recorded as a target sink node, the standard power generation area corresponding to the target sink node is recorded as a reference power generation area, and the reference power generation areas are sorted in descending order of clustering coefficients and recorded as a reference sequence;

[0017] Select a preset number of reference power generation areas in the reference sequence in sequence according to a preset selection method and divide them into node clusters until the number of reference power generation areas in the node cluster reaches the cluster size, which is recorded as the end of the division of a node cluster, and continue to divide the reference power generation areas that are not recorded as node clusters until all reference power generation areas are recorded as node clusters;

[0018] If the cluster division coefficient is a balance coefficient, the default selection method is to select the same number of reference power generation areas at both ends of the reference sequence;

[0019] If the cluster division coefficient is the regional correlation, the default selection method is to select the reference power generation area in the reference sequence in descending order of regional correlation;

[0020] The standard power generation area is a power generation area directly connected to the collection node.

[0021] Furthermore, the power generation fatigue value is determined according to the unit load factor and the turbulence influence coefficient;

[0022] The unit load factor is the average value of the reference torsional stress of each wind turbine generator corresponding to a power generation area, and the reference torsional stress is the maximum torsional stress monitored by the torque sensor placed on the main shaft of a single wind turbine generator in the current monitoring period;

[0023] The method for confirming the turbulence influence coefficient is that, for a wind turbine, the line connecting the point where the impeller of the wind turbine is farthest from the ground and the point where the impeller is closest to the ground is divided into N equal parts, the wind speed detected at each divided point in the current monitoring period is recorded as the reference wind speed, and the maximum reference wind speed corresponding to a single divided point is recorded as the extreme wind speed. The turbulence influence coefficient is the difference between the maximum and minimum values ​​of the extreme wind speeds corresponding to each divided point.

[0024] Further, when the power generation state is that the margin evaluation value is greater than or equal to the preset margin evaluation value and the power leakage value is less than the preset power leakage value, the control factor is determined according to the power generation area category and the power expectation value is determined according to the control factor;

[0025] The power expectation value is positively correlated with the control factor.

[0026] Further, when the power generation area category is a type of power generation area whose power generation reference value is less than a preset power generation reference value or whose node threshold is less than a preset node threshold, its control factor is set to the preset reference value;

[0027] The preset reference value is any constant within a first numerical range.

[0028] Further, when the power generation area category is that the power generation reference value is greater than or equal to the preset power generation reference value and the node threshold is greater than or equal to the preset node threshold, the control factor is determined according to the node threshold and the power generation reference value;

[0029] The control factor is positively correlated with the node threshold, and the control factor is positively correlated with the power generation reference value.

[0030] Furthermore, when the power generation state is that the margin evaluation value is less than the preset margin evaluation value or the power leakage value is greater than or equal to the preset power leakage value, and the access difference is greater than or equal to the preset access difference, the power expectation value is determined according to the influence coefficient;

[0031] The influence coefficient is positively correlated with the expected value of electric power.

[0032] Furthermore, when the power generation state is that the margin evaluation value is less than the preset margin evaluation value or the power leakage value is greater than or equal to the preset power leakage value, and the access difference is less than the preset access difference, the power expectation value is determined according to the adjacent historical power values;

[0033] The adjacent historical power values ​​are positively correlated with the expected power values.

[0034] Compared with the prior art, the beneficial effect of the present invention lies in that, in the technical scheme of the present invention, the stability threshold is used to effectively reflect the stability of power generation in each power generation area, and then the cluster size corresponding to the aggregation node is determined according to the stability threshold of the power generation area corresponding to the aggregation node, so that the determination of the cluster size is more in line with the actual application scenario, and then the power resources can be allocated and scheduled more reasonably, reducing the difficulty of scheduling new energy power.

[0035] Furthermore, in the present invention, the target grid state is determined according to the load fluctuation value and the power generation fatigue value, and the load fluctuation value and the power generation fatigue value are used to effectively reflect the operating stability of the target grid. Different cluster division coefficients are adaptively selected according to the target grid state, so that the selection of cluster division coefficients is more in line with the actual application scenario, and then the preset selection method is determined according to the cluster division coefficient, which avoids the problem of large fluctuations in the power generation dispatched to the aggregation node causing grid failure, helps to achieve the optimal configuration of new energy power resources, and then can more reasonably allocate new energy power resources to avoid power waste and imbalance between supply and demand.

[0036] Furthermore, when the cluster division coefficient in the present invention is a balance coefficient, the balance coefficient is used to effectively reflect the degree of balance between the power generation of the power generation area and the stability of the power generation in the power generation area, and then the node cluster is divided through a preset selection method. By dividing the power generation areas with large differences in balance coefficients, the stability of the aggregation node is guaranteed when the stability of the target power grid is poor, thereby avoiding the problem of large fluctuations in the power generation dispatched to the aggregation node leading to power grid failure. When the cluster division coefficient is regional correlation, when the stability of the target power grid is good, the power generation areas with large regional correlation are divided into node clusters, which is conducive to combining power generation areas with similar power generation characteristics. When the power supply and demand changes, the node clusters can coordinate with each other to maintain the stable operation of the power system.

[0037] Furthermore, in the present invention, the power generation state is determined according to the margin assessment value and the power leakage value, and the node margin situation of the aggregation node transmitted from each power generation area to the power grid and the power waste situation of the power generation area are effectively reflected through the margin assessment value and the power leakage value. Then, different power forecasting methods are adaptively selected according to the power generation state, so that the selection of power forecasting method is more in line with the actual application scenario, avoiding the problem of large deviation between actual power generation and expected strategy, which is conducive to more flexible response to power grid demand under different power generation states, and then scheduling new energy power according to actual demand.

[0038] Furthermore, in the present invention, the category of the power generation area is determined according to the power generation reference value and the node threshold. The power generation reference value and the node threshold reflect the upward regulation capacity of the power generation area, and then the control factor is determined according to the category of the power generation area, so that the selection of the control factor is more in line with the actual application scenario, and different power generation areas can be controlled differently, and then the expected value of electricity is determined according to the control factor, which avoids the problem that the power grid cannot flexibly adjust the output of each power generation area when responding to emergencies. During the peak period of power demand, the power generation area with strong upward regulation capacity can be preferentially dispatched to meet the power grid demand. During the trough period of power demand, the output of the power generation area with strong upward regulation capacity can be reduced to avoid waste of resources, thereby improving the accuracy and flexibility of power grid dispatching, and then reasonably dispatching new energy power according to actual needs.

[0039] Furthermore, the present invention effectively reflects the degree of difference in the ability of each power generation area to access the power grid through the access difference, and then determines the expected value of electricity based on the access difference, avoiding the problem of a single method of calculating the command value in the prior art and the inability to adaptively adjust the method of calculating the command value based on the access difference. It can optimize the allocation of power resources, help ensure the stable operation of the power grid and improve the reliability of power supply, and then reduce the deviation between the actual power generation and the expected strategy to reasonably dispatch new energy power. BRIEF DESCRIPTION OF THE DRAWINGS

[0040] Figure 1 It is a schematic diagram of the method for prioritizing dispatching new energy power generation in the electricity spot market of the present invention;

[0041] Figure 2 A flow chart of determining the cluster division coefficient of each aggregation node according to the target power grid state of the present invention;

[0042] Figure 3 A flow chart of determining a control factor according to a power generation area category according to the present invention;

[0043] Figure 4 The present invention is a flow chart of determining a power prediction method according to power generation status. DETAILED DESCRIPTION

[0044] In order to make the objects and advantages of the present invention more clearly understood, the present invention is further described below in conjunction with embodiments; it should be understood that the specific embodiments described herein are only used to explain the present invention and are not used to limit the present invention.

[0045] The preferred embodiments of the present invention are described below with reference to the accompanying drawings. It should be understood by those skilled in the art that these embodiments are only used to explain the technical principles of the present invention and are not intended to limit the protection scope of the present invention.

[0046] It should be noted that, in the description of the present invention, terms such as "up", "down", "left", "right", "inside" and "outside" indicating directions or positional relationships are based on the directions or positional relationships shown in the drawings. This is merely for the convenience of description and does not indicate or imply that the device or element must have a specific orientation, be constructed and operated in a specific orientation. Therefore, it cannot be understood as a limitation on the present invention.

[0047] In addition, it should be noted that in the description of the present invention, unless otherwise clearly specified and limited, the terms "installed", "connected", and "connected" should be understood in a broad sense, for example, it can be a fixed connection, a detachable connection, or an integral connection; it can be a mechanical connection or an electrical connection; it can be a direct connection, or it can be indirectly connected through an intermediate medium, or it can be the internal communication of two components. For those skilled in the art, the specific meanings of the above terms in the present invention can be understood according to specific circumstances.

[0048] See also Figures 1 to 4 As shown, the present invention provides a method for preferentially dispatching renewable energy power generation in a power spot market, comprising:

[0049] Determine a stability threshold value according to a wind power reduction reference value and a power conflict value, and when the stability threshold value is within a preset stability threshold value range, determine a cluster size corresponding to the aggregation node according to the stability threshold value;

[0050] Determine the target grid state according to the load fluctuation value and the power generation fatigue value, and determine the cluster division coefficient of each aggregation node as a balance coefficient or a regional correlation according to the target grid state, and determine the preset selection method as selecting the same number of reference power generation areas at both ends of the reference sequence or selecting the reference power generation areas in the reference sequence in descending order of regional correlation according to the cluster division coefficient;

[0051] Determine the power generation state according to the margin assessment value and the power leakage value, and determine the power forecasting method according to the power generation state, that is, determine the control factor according to the power generation area category and determine the power expectation value according to the control factor, or determine the optimization method according to the access difference;

[0052] The optimization method includes determining the expected power value according to the influence coefficient, and determining the expected power value according to the adjacent historical power values;

[0053] The power generation area category is determined according to the power generation reference value and the node threshold;

[0054] Under the preset dispatching conditions, power dispatching analysis is performed for the power generation area based on the expected power value.

[0055] In the present invention, the power generation area information is first obtained for subsequent analysis. The power generation area information includes the number of power generation areas, the number of wind turbines in each power generation area, the wind speed in the power generation area, etc. A single power generation area includes a number of wind turbines for wind power generation. The target power grid is a power grid connected to new energy electricity. The present invention is applied to power generation scheduling of an electric power grid system. The power generation area access method of the electric power grid system is multi-level access, that is, any level corresponds to a collection node. The collection node is used to collect the electric energy generated by the power generation area connected to the node and the electric energy transmitted by other collection nodes connected to the node and transmit them to the power grid or other collection nodes that are closer to the power grid than this node.

[0056] The target grid state includes a first target grid state and a second target grid state. The first target grid state is that the load fluctuation value is less than a preset load fluctuation value and the power generation fatigue value is less than a preset power generation fatigue value. The second target grid state is that the load fluctuation value is greater than or equal to the preset load fluctuation value or the power generation fatigue value is greater than or equal to the preset power generation fatigue value.

[0057] The power generation state includes a first power generation state and a second power generation state. The first power generation state is that the margin assessment value is greater than or equal to the preset margin assessment value and the power leakage value is less than the preset power leakage value. The second power generation state is that the margin assessment value is less than the preset margin assessment value or the power leakage value is greater than or equal to the preset power leakage value.

[0058] The power generation area categories include Class I power generation areas and Class II power generation areas. Class I power generation areas are areas where the power generation reference value is less than the preset power generation reference value or the node threshold is less than the preset node threshold. Class II power generation areas are areas where the power generation reference value is greater than or equal to the preset power generation reference value and the node threshold is greater than or equal to the preset node threshold.

[0059] The preset dispatching condition is that power dispatching is required, and the power dispatching analysis for the power generation area according to the power expectation value includes: recording the collection node whose distance from the power consumption area is less than the preset distance as the dispatching collection node, recording the standard power generation area corresponding to the reference collection node as the pre-dispatching area, and for a single dispatching collection node, recording the dispatching collection node as the target dispatching collection node, determining the dispatching ratio corresponding to the target dispatching collection node according to the power consumption evaluation value, and determining the number of dispatching node clusters corresponding to the target dispatching collection node according to the dispatching ratio, recording the standard power generation area corresponding to the dispatching node cluster as the dispatching area, and the power connected to the power grid in the single dispatching area is the same as the corresponding power expectation value; wherein, the power consumption area is the area where power dispatching is required, the power demand of the power consumption area is the power required by the power consumption area in a single monitoring cycle, the distance between the power consumption area and the collection node is the shortest distance between the center position of the power consumption area and the single collection node, and the center position of the power consumption area is the center of the circumscribed circle of the power consumption area; the value of the preset distance can be determined according to the actual application scenario, the greater the user's demand for new energy power, the greater the value of the preset distance, and a value of the preset distance is provided, and the preset distance is 20km;

[0060] The dispatch ratio is positively correlated with the power consumption assessment value. The number of dispatch node clusters corresponding to the target dispatch aggregation node = dispatch ratio × the total number of node clusters corresponding to the target dispatch aggregation node. It can be understood that if the calculated number of dispatch node clusters corresponding to the target dispatch aggregation node is a non-integer, the number of dispatch node clusters is the larger integer value of the two integers adjacent to the non-integer.

[0061] Electricity consumption evaluation value = electricity demand of the electricity consumption area × first coefficient - distance between the electricity consumption area and the target scheduling aggregation node × second coefficient. The values ​​of the first coefficient and the second coefficient can be obtained by the user through deep learning convolutional neural network learning for historical records that meet user needs. It can be understood that the present invention reflects the degree of influence of the proportion of scheduling node clusters corresponding to the aggregation node on the electricity demand through the electricity consumption evaluation value. The user can use the historical records that meet user needs for deep learning to determine the electricity demand of the electricity consumption area and the distance between the electricity consumption area and the target scheduling aggregation node for the proportion of scheduling node clusters corresponding to the aggregation node on the electricity demand, and then select the values ​​of the first coefficient and the second coefficient accordingly, and provide a value of the first coefficient and the second coefficient, the first coefficient is 0.6, and the second coefficient is 0.4.

[0062] The present invention obtains the expected power value of each power generation area through different power prediction methods and different optimization methods, so that users can transmit the power of the power generation area according to the expected power value for dispatching to the power consumption area, thereby reducing the deviation between the power dispatched to the power grid and the expected strategy, and improving the accuracy and flexibility of power grid dispatch.

[0063] The present invention is provided with a continuously cyclic monitoring cycle, and the data status is determined once at the end of each monitoring cycle. The duration of the monitoring cycle can be set according to the needs of the user. The greater the user's demand for the accuracy of data status monitoring, the shorter the monitoring cycle duration. A value of the monitoring cycle duration is provided, and the monitoring cycle duration is 30 minutes.

[0064] In the present invention, several historical records are correspondingly provided, and any historical record records the load fluctuation value, power generation fatigue value, power generation reference value, node threshold and access difference in at least one historical process of renewable energy power generation scheduling, and each historical record corresponds to a qualified mark, which records the degree of deviation between the actual power generation and the expected strategy and whether the difficulty of renewable energy power scheduling meets user needs. The qualified mark can be recorded manually.

[0065] Stability threshold = power conflict value - wind reduction reference value, the wind reduction reference value is the average of the wind reduction means of each power generation area corresponding to a collection node, and the wind reduction mean is the average of the wind reduction values ​​of each wind turbine corresponding to the power generation area; wind reduction value = angle deviation coefficient + wind speed difference coefficient, the angle deviation coefficient is positively correlated with the angle deviation value, and the wind speed difference coefficient is positively correlated with the wind speed difference; the angle deviation value is the angle between the wind direction and the wind wheel at the end of the current monitoring period, the wind speed difference = the absolute value of the difference between the maximum wind speed extreme value and the minimum wind speed extreme value / maximum reference wind speed, the wind direction is determined by a wind direction sensor, and the model of the wind direction sensor is not specifically restricted, and users can choose according to actual needs.

[0066] The method for confirming the power conflict value is to record each power generation area corresponding to a collection node as a reference detection area, detect the low moment corresponding to each reference detection area in the current monitoring cycle, and the low moment is the moment when the power generation of a reference detection area is the smallest in the current monitoring cycle. The shortest time that can include the low moment corresponding to each reference detection area is recorded as the power conflict value.

[0067] The values ​​within the preset stability threshold range are all greater than the preset stability threshold. The value of the preset stability threshold can be determined by the user according to the actual application scenario. The greater the user's demand for clustering the power generation area corresponding to the aggregation node, the smaller the value of the preset stability threshold. A value of the preset stability threshold is provided. The preset stability threshold is the average value of the stability threshold corresponding to the historical records that can meet the user's needs.

[0068] Specifically, the cluster size corresponding to the aggregation node is positively correlated with the stability threshold of the power generation area corresponding to the aggregation node.

[0069] The cluster size is the maximum allowed number of power generation areas contained in any node cluster, where the maximum allowed number is an even number.

[0070] Specifically, if the target grid state is that the load fluctuation value is greater than or equal to the preset load fluctuation value or the power generation fatigue value is greater than or equal to the preset power generation fatigue value, the cluster division basis is the balance coefficient;

[0071] If the target grid state is that the load fluctuation value is less than the preset load fluctuation value and the power generation fatigue value is less than the preset power generation fatigue value, the cluster division basis is the regional correlation;

[0072] The balance coefficient is determined according to the ratio of the stability threshold to the expected power value.

[0073] Among them, it can be understood that the present invention monitors the power transmitted to the user by the target power grid in real time in each monitoring cycle, and calculates the values ​​corresponding to the power transmitted to the user by the target power grid monitored in the current monitoring cycle. The calculation formula of the load fluctuation value σ is: M is the number of values ​​corresponding to the amount of electricity transmitted from the target power grid to the user during the current monitoring period, x i is the value corresponding to the amount of electricity transmitted from the i-th target power grid to the user monitored in the current monitoring period, and μ is the average amount of electricity transmitted from the target power grid to the user monitored in the current monitoring period.

[0074] The values ​​of the preset load fluctuation value and the preset power generation fatigue value can be determined by the user according to the actual application scenario. The greater the user's demand for stable operation of the target power grid, the smaller the values ​​of the preset load fluctuation value and the preset power generation fatigue value. A value of a preset load fluctuation value and a preset power generation fatigue value is provided, and the detection cluster division method is a historical record divided according to the regional correlation. The average value of the load fluctuation value corresponding to the historical record that can meet the user's needs is recorded as the preset load fluctuation value, and the average value of the power generation fatigue value corresponding to the historical record that can meet the user's needs is recorded as the preset power generation fatigue value.

[0075] Balance coefficient = stability threshold / power expectation;

[0076] The regional correlation degree is the absolute value of the difference between the regional reference values ​​corresponding to the two standard power generation areas, and the regional reference value = power generation area scale coefficient + generator distribution coefficient; the power generation area scale coefficient is the number of wind turbines corresponding to a power generation area, and the generator distribution coefficient is confirmed in that, for a power generation area, the power generation area is recorded as a reference power generation area, and the generator distribution coefficient = the number of densely packed wind turbines in the reference power generation area / the number of all wind turbines in the reference power generation area. The method for confirming densely packed wind turbines is that, for a single wind turbine in the reference power generation area, the wind turbine is recorded as a target wind turbine, and other wind turbines in the reference power generation area that do not contain the target wind turbine are recorded as reference wind turbines, and the target wind turbine is detected. The shortest distance between each reference wind turbine generator, the difference between the maximum value and the minimum value of each shortest distance is recorded as the reference difference, and the average value of the reference difference corresponding to each wind turbine generator in the reference power generation area is recorded as the generator distribution coefficient; it can be understood that the power generation area scale coefficient will affect the flow distribution of the target power grid, and thus affect the stability of the target power grid. The larger the generator distribution coefficient, the more interference there is between the wind turbines, resulting in greater turbulence intensity, and then the regional reference value is used to reflect the wind stability of each power generation area, and then the power generation areas with large regional correlation are divided into node clusters, which is conducive to combining power generation areas with similar power generation characteristics. When the supply and demand of electricity changes, the node clusters can coordinate with each other to maintain the stable operation of the power system.

[0077] Specifically, when clustering a single sink node, the sink node is recorded as the target sink node, the standard power generation area corresponding to the target sink node is recorded as the reference power generation area, and the reference power generation areas are sorted in descending order of clustering coefficients and recorded as a reference sequence;

[0078] Select a preset number of reference power generation areas in the reference sequence in sequence according to a preset selection method and divide them into node clusters until the number of reference power generation areas in the node cluster reaches the cluster size, which is recorded as the end of the division of a node cluster, and continue to divide the reference power generation areas that are not recorded as node clusters until all reference power generation areas are recorded as node clusters;

[0079] If the cluster division coefficient is a balance coefficient, the default selection method is to select the same number of reference power generation areas at both ends of the reference sequence;

[0080] If the cluster division coefficient is the regional correlation, the default selection method is to select the reference power generation area in the reference sequence in descending order of regional correlation;

[0081] The standard power generation area is a power generation area directly connected to the collection node.

[0082] It can be understood that when clustering the reference power generation areas not recorded as node clusters, if the number of reference power generation areas is smaller than the cluster size, all reference power generation areas not recorded as node clusters are recorded as one node cluster.

[0083] Specifically, the power generation fatigue value is determined according to the unit load factor and the turbulence influence coefficient;

[0084] The unit load factor is the average value of the reference torsional stress of each wind turbine generator corresponding to a power generation area, and the reference torsional stress is the maximum torsional stress monitored by the torque sensor placed on the main shaft of a single wind turbine generator in the current monitoring period;

[0085] The method for confirming the turbulence influence coefficient is that, for a wind turbine, the line connecting the point where the impeller of the wind turbine is farthest from the ground and the point where the impeller is closest to the ground is divided into N equal parts, the wind speed detected at each divided point in the current monitoring period is recorded as the reference wind speed, and the maximum reference wind speed corresponding to a single divided point is recorded as the extreme wind speed. The turbulence influence coefficient is the difference between the maximum and minimum values ​​of the extreme wind speeds corresponding to each divided point.

[0086] Among them, power generation fatigue value = unit load factor + turbulence influence coefficient, the torque sensor is placed at any position of the main shaft of a single wind turbine, and the turbulence influence coefficient = the maximum value of the wind speed extreme values ​​corresponding to each equally divided point - the minimum value of the wind speed extreme values ​​corresponding to each equally divided point. The value of N can be determined by the user according to user needs. The greater the accuracy of the user's determination of the turbulence influence coefficient, the greater the value of N. A value of N is provided, N is 15, the wind speed of each equally divided point is measured by a wind speed sensor, and there is no specific restriction on the models of the torque sensor and the wind speed sensor. Users can choose according to actual needs.

[0087] Specifically, when the power generation state is that the margin assessment value is greater than or equal to the preset margin assessment value and the power leakage value is less than the preset power leakage value, the control factor is determined according to the power generation area category and the power expectation value is determined according to the control factor;

[0088] The power expectation value is positively correlated with the control factor.

[0089] Among them, the margin assessment value is the minimum value of the node reference values ​​corresponding to each aggregation node in the current monitoring period, the node reference value = (maximum power of the transmission path - current power of the transmission path) / maximum power of the transmission path, the maximum power of the transmission path is the target maximum power that can be transmitted in the current monitoring period, and the target maximum power can be determined by the line dynamic load capacity assessment model based on the climate environment, tower structure and conductor characteristics; the current power of the transmission path is determined by the power calculation under the preset fault scenario, and the power calculation can use the Newton-Raphson method or the PQ decomposition method, which is easy for technicians in this field to understand and will not be elaborated in detail.

[0090] The power leakage value is the average of the wind abandonment rates of each monitoring period before the current monitoring period. For a single monitoring period, the monitoring period is recorded as the target monitoring period. The wind abandonment rate of the target monitoring period = wind abandonment power / actual power generation capacity. Wind abandonment power = actual power generation capacity - power connected to the grid in each power generation area during the target monitoring period. The actual power generation capacity is the total power generated by each power generation area during the target monitoring period.

[0091] The values ​​of the preset margin assessment value and the preset power leakage value can be determined by the user according to the actual application scenario. It can be understood that the smaller the value of the preset margin assessment value and the larger the value of the preset power leakage value, the greater the demand for reasonable scheduling of new energy electricity in power generation areas with high risk of wind and power abandonment. Provided are the values ​​of the preset margin assessment value and the preset power leakage value, the preset margin assessment value is 40%, and the preset power leakage value is 10%.

[0092] Specifically, when the power generation area category is a power generation area whose power generation reference value is less than a preset power generation reference value or whose node threshold is less than a preset node threshold, its control factor is set to a preset reference value;

[0093] The preset reference value is any constant within a first numerical range.

[0094] Wherein, the power generation reference value = wind shear coefficient - wind reduction coefficient, the wind shear coefficient is positively correlated with the wind shear mean value, and the wind reduction coefficient is positively correlated with the wind reduction mean value; the wind shear mean value is the average value of the wind shear values ​​of each wind turbine corresponding to the power generation area; the wind shear value is the average value of each wind speed extreme value corresponding to a wind turbine, which is recorded as the wind shear value;

[0095] The method for confirming the node threshold is as follows: for a power generation area, the power generation area is recorded as a target power generation area, the sink nodes corresponding to the transmission path of the target power generation area are detected, and the average value of the node reference values ​​corresponding to each sink node is recorded as the node threshold;

[0096] The values ​​of the preset power generation reference value and the preset node threshold value can be determined by the user according to the actual application scenario. The greater the user's demand for reasonable scheduling of new energy power, the smaller the value of the preset power generation reference value and the smaller the value of the preset node threshold value. A value of the preset power generation reference value and the preset node threshold value is provided, and the historical records of the control factor being set as the preset reference value are detected. The average value of the power generation reference value corresponding to each power generation area in the historical records that can meet the user's needs is recorded as the preset power generation reference value, and the average value of the node threshold value corresponding to each power generation area in the historical records that can meet the user's needs is recorded as the preset node threshold value;

[0097] The values ​​of the first numerical range are determined by simulating the access of the power generation area to the power grid through power system simulation software, and a first numerical range is provided. The values ​​in the first numerical range are all greater than or equal to 1.0 and less than or equal to 1.1.

[0098] Specifically, when the power generation area category is that the power generation reference value is greater than or equal to the preset power generation reference value and the node threshold is greater than or equal to the preset node threshold, the control factor is determined according to the node threshold and the power generation reference value;

[0099] The control factor is positively correlated with the node threshold, and the control factor is positively correlated with the power generation reference value.

[0100] Among them, control factor = (node ​​threshold / preset node threshold) × threshold coefficient + (power generation reference value / preset power generation reference value) × power generation coefficient. The values ​​of threshold coefficient and power generation coefficient can be obtained by users through deep learning convolutional neural network for historical records that can meet user needs. It can be understood that the present invention reflects the degree of influence of the power generation area on the path passed in the process of transmitting electricity to the power grid through the control factor. The user can use deep learning to obtain the influence of the threshold coefficient and the power generation coefficient on the path passed in the process of transmitting electricity from the power generation area to the power grid through historical records that can meet user needs, and then select the values ​​of threshold coefficient and power generation coefficient accordingly. Among them, threshold coefficient + power generation coefficient = 1, and a value of threshold coefficient and power generation coefficient is provided, the threshold coefficient is 0.5, and the power generation coefficient is 0.5.

[0101] Specifically, when the power generation state is that the margin assessment value is less than the preset margin assessment value or the power leakage value is greater than or equal to the preset power leakage value, and the access difference is greater than or equal to the preset access difference, the power expectation value is determined according to the influence coefficient;

[0102] The influence coefficient is positively correlated with the expected value of electric power.

[0103] Wherein, access difference = (maximum value of access reference value corresponding to each reference area - minimum value of access reference value corresponding to each reference area) / maximum value of access reference value corresponding to each reference area, and the reference area is each standard power generation area corresponding to a single collection node;

[0104] Access reference value = wind speed change value + harmonic current, wind speed change value = maximum value of wind shear mean values ​​in each reference area - minimum value of wind shear mean values ​​in each reference area, harmonic current is the harmonic current generated by a single reference area during the current monitoring period, and the harmonic current can be measured by Fourier transform method, harmonic analyzer method or embedded device method. Users can choose according to actual needs. This is easy for technical personnel in this field to understand and will not be elaborated in detail.

[0105] Influence coefficient = limit reference value × (adjacent historical power value / total historical power value), limit reference value = 0.95 × target maximum power that can be transmitted by the transmission path where the sink node is located in the current monitoring period, adjacent historical power value is the historical power value corresponding to the reference period, the reference period is the monitoring period before the current monitoring period and adjacent to the current monitoring period, for a single monitoring period, the monitoring period is recorded as the target monitoring period, the historical power value corresponding to the target monitoring period = the actual power generation capacity of the target monitoring period - the wind power abandoned in the target monitoring period, the total historical power value is the sum of the historical power values ​​corresponding to each monitoring period before the current monitoring period;

[0106] The value of the preset access difference can be determined by the user according to the actual application scenario. The greater the user's demand for the prediction accuracy of the expected power value, the smaller the value of the preset access difference. A value of the preset access difference is provided, and the historical records of determining the expected power value based on the influence coefficient are monitored. The average value of the access difference corresponding to the historical records that can meet the user's needs is recorded as the preset access difference.

[0107] Specifically, when the power generation state is that the margin assessment value is less than the preset margin assessment value or the power leakage value is greater than or equal to the preset power leakage value, and the access difference is less than the preset access difference, the power expectation value is determined according to the adjacent historical power values;

[0108] The adjacent historical power values ​​are positively correlated with the expected power values.

[0109] Among them, the adjacent historical power value is the amount of electricity connected to the grid during adjacent monitoring periods;

[0110] The adjacent monitoring period is a monitoring period that is located before the current monitoring period and is adjacent to the current monitoring period.

[0111] So far, the technical solutions of the present invention have been described in conjunction with the preferred embodiments shown in the accompanying drawings. However, it is easy for those skilled in the art to understand that the protection scope of the present invention is obviously not limited to these specific embodiments. Without departing from the principle of the present invention, those skilled in the art can make equivalent changes or substitutions to the relevant technical features, and the technical solutions after these changes or substitutions will fall within the protection scope of the present invention.

[0112] The above description is only a preferred embodiment of the present invention and is not intended to limit the present invention. For those skilled in the art, the present invention may have various modifications and variations. Any modification, equivalent replacement, improvement, etc. made within the spirit and principle of the present invention shall be included in the protection scope of the present invention.

Claims

1. A method for preferentially dispatching renewable energy power generation in a power spot market, characterized in that: include: Determine a stability threshold value according to a wind power reduction reference value and a power conflict value, and when the stability threshold value is within a preset stability threshold value range, determine a cluster size corresponding to the aggregation node according to the stability threshold value; Determine the target grid state according to the load fluctuation value and the power generation fatigue value, and determine the cluster division coefficient of each aggregation node as a balance coefficient or a regional correlation according to the target grid state, and determine the preset selection method as selecting the same number of reference power generation areas at both ends of the reference sequence or selecting the reference power generation areas in the reference sequence in descending order of regional correlation according to the cluster division coefficient; Determine the power generation state according to the margin assessment value and the power leakage value, and determine the power forecasting method according to the power generation state, that is, determine the control factor according to the power generation area category and determine the power expectation value according to the control factor, or determine the optimization method according to the access difference; The optimization method includes determining the expected power value according to the influence coefficient, and determining the expected power value according to the adjacent historical power values; The power generation area category is determined according to the power generation reference value and the node threshold; Under the preset dispatching conditions, power dispatching analysis is performed for the power generation area based on the expected power value.

2. The method for prioritizing dispatching new energy power generation in the electricity spot market according to claim 1 is characterized in that: The cluster size corresponding to the aggregation node is positively correlated with the stability threshold of the power generation area corresponding to the aggregation node.

3. The method for prioritizing dispatching new energy power generation in the electricity spot market according to claim 2 is characterized in that: If the target grid state is that the load fluctuation value is greater than or equal to the preset load fluctuation value or the power generation fatigue value is greater than or equal to the preset power generation fatigue value, the cluster division basis is the balance coefficient; If the target grid state is that the load fluctuation value is less than the preset load fluctuation value and the power generation fatigue value is less than the preset power generation fatigue value, the cluster division basis is the regional correlation; The balance coefficient is determined according to the ratio of the stability threshold to the expected power value.

4. The method for prioritizing dispatching new energy power generation in the electricity spot market according to claim 3 is characterized in that: When clustering a single sink node, the sink node is recorded as the target sink node, the standard power generation area corresponding to the target sink node is recorded as the reference power generation area, and the reference power generation areas are sorted in descending order of clustering coefficients and recorded as a reference sequence; Select a preset number of reference power generation areas in the reference sequence in sequence according to a preset selection method and divide them into node clusters until the number of reference power generation areas in the node cluster reaches the cluster size, which is recorded as the end of the division of a node cluster, and continue to divide the reference power generation areas that are not recorded as node clusters until all reference power generation areas are recorded as node clusters; If the cluster division coefficient is a balance coefficient, the default selection method is to select the same number of reference power generation areas at both ends of the reference sequence; If the cluster division coefficient is the regional correlation, the default selection method is to select the reference power generation area in the reference sequence in descending order of regional correlation; The standard power generation area is a power generation area directly connected to the collection node.

5. The method for prioritizing dispatching new energy power generation in the electricity spot market according to claim 4 is characterized in that: The power generation fatigue value is determined according to the unit load factor and the turbulence influence coefficient; The unit load factor is the average value of the reference torsional stress of each wind turbine generator corresponding to a power generation area, and the reference torsional stress is the maximum torsional stress monitored by the torque sensor placed on the main shaft of a single wind turbine generator in the current monitoring period; The method for confirming the turbulence influence coefficient is that, for a wind turbine, the line connecting the point where the impeller of the wind turbine is farthest from the ground and the point where the impeller is closest to the ground is divided into N equal parts, the wind speed detected at each divided point in the current monitoring period is recorded as the reference wind speed, and the maximum reference wind speed corresponding to a single divided point is recorded as the extreme wind speed. The turbulence influence coefficient is the difference between the maximum and minimum values ​​of the extreme wind speeds corresponding to each divided point.

6. The method for prioritizing dispatching new energy power generation in the electricity spot market according to claim 5, characterized in that: When the power generation state is that the margin assessment value is greater than or equal to the preset margin assessment value and the power leakage value is less than the preset power leakage value, the control factor is determined according to the power generation area category and the power expectation value is determined according to the control factor; The power expectation value is positively correlated with the control factor.

7. The method for prioritizing dispatching new energy power generation in the electricity spot market according to claim 6, characterized in that: When the power generation area category is a power generation area whose power generation reference value is less than the preset power generation reference value or whose node threshold is less than the preset node threshold, its control factor is set to the preset reference value; The preset reference value is any constant within a first numerical range.

8. The method for prioritizing dispatching new energy power generation in the electricity spot market according to claim 7, characterized in that: When the power generation area category is that the power generation reference value is greater than or equal to the preset power generation reference value and the node threshold is greater than or equal to the preset node threshold, the control factor is determined according to the node threshold and the power generation reference value; The control factor is positively correlated with the node threshold, and the control factor is positively correlated with the power generation reference value.

9. The method for prioritizing dispatching new energy power generation in the electricity spot market according to claim 8, characterized in that: When the power generation state is that the margin assessment value is less than the preset margin assessment value or the power leakage value is greater than or equal to the preset power leakage value, and the access difference is greater than or equal to the preset access difference, the power expectation value is determined according to the influence coefficient; The influence coefficient is positively correlated with the expected value of electric power.

10. The method for prioritizing dispatching new energy power generation in the electricity spot market according to claim 9, characterized in that: When the power generation state is that the margin assessment value is less than the preset margin assessment value or the power leakage value is greater than or equal to the preset power leakage value, and the access difference is less than the preset access difference, the power expectation value is determined according to the adjacent historical power values; The adjacent historical power values ​​are positively correlated with the expected power values.

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