CN-122022370-A - Virtual power plant resource optimal configuration method and system

CN122022370ACN 122022370 ACN122022370 ACN 122022370ACN-122022370-A

Abstract

The invention relates to the technical field of power plant resource optimization, in particular to a virtual power plant resource optimal allocation method and system. The method comprises the steps of collecting resource characteristic parameters of various resources in a virtual power plant, calculating energy storage following coefficients, mobile energy storage availability and response matching degree based on the resource characteristic parameters to serve as cooperative measurement indexes, collecting the cooperative measurement indexes to construct time sequence curves, calculating phase differences between every two of the three time sequence curves to obtain a first phase difference, a second phase difference and a third phase difference, judging the resource cooperative state type of the virtual power plant according to symbol combinations and amplitudes of the three phase differences, and identifying configuration defect resource types based on the resource cooperative state type. According to the invention, the dynamic response characteristics of the energy storage system, the electric automobile and the controllable load resources are quantitatively analyzed through the energy storage following coefficient, the mobile energy storage availability and the response matching degree, and the advance or retard states of each resource in different time periods can be identified in real time.

Inventors

WU ZHENGHUA
HUA YUESHEN
DAI RUIXIN
ZHU QI
SUN BO

Assignees

国网上海市电力公司

Dates

Publication Date: 20260512
Application Date: 20260225

Claims (10)

1. The virtual power plant resource optimal allocation method is characterized by comprising the following steps of: Step S1, collecting resource characteristic parameters of various resources in a virtual power plant; step S2, calculating an energy storage following coefficient, a mobile energy storage availability and a response matching degree based on the resource characteristic parameters to serve as a cooperative measurement index; s3, acquiring cooperative measurement indexes to construct a time sequence curve, and calculating phase differences between every two of the three time sequence curves to obtain a first phase difference, a second phase difference and a third phase difference; s4, judging the resource coordination state type of the virtual power plant according to the symbol combinations and the amplitudes of the three phase differences, and identifying the configuration defect resource category based on the resource coordination state type; And S5, adjusting the resource allocation proportion aiming at the allocation defect resource category to reduce the phase difference amplitude, and outputting a resource optimal allocation scheme and an equivalent adjustment capacity.
2. The virtual power plant resource optimization configuration method according to claim 1, wherein the step S1 includes the steps of: Step S11, reading the SOC values of the energy storage system according to fixed time intervals, and continuously reading the preset number of SOC values to form an SOC data sequence; Step S12, calculating root mean square of adjacent numerical value differences in the SOC data sequence to serve as the SOC fluctuation rate of the energy storage system; step S13, counting the number of electric vehicles in charge at the current moment in the coverage area of the virtual power plant, dividing the number of electric vehicles by the geographic area of the coverage area, and generating electric vehicle access density; and S14, reading the SOC values of the batteries of all the charged electric vehicles, and averaging to obtain the average SOC of the electric vehicles.
3. The virtual power plant resource optimization configuration method according to claim 2, wherein the step S1 further comprises the steps of: Step S15, transmitting a power adjustment test instruction to preselected controllable load equipment, and recording a transmission time as a starting time; Step S16, continuously monitoring the actual power change of the controllable load equipment, and recording response time when the power change amount reaches the rated power preset proportion; s17, acquiring prediction data of a new energy power prediction system accessed by a virtual power plant, and reading time intervals of two adjacent prediction time points in the prediction data as a new energy prediction error time window; And step S18, storing the SOC fluctuation rate, the new energy output fluctuation rate, the electric vehicle access density, the electric vehicle average SOC, the load response delay time and the new energy prediction error time window in a correlated manner with the time stamp to obtain the resource characteristic parameters.
4. A virtual power plant resource optimization configuration method according to claim 3, wherein step S2 comprises the steps of: S21, dividing the SOC fluctuation rate of the energy storage system by the new energy output fluctuation rate to obtain an original value of an energy storage following coefficient when the new energy output fluctuation rate in the resource characteristic parameters is larger than a minimum fluctuation threshold; s22, when the fluctuation rate of the new energy output is smaller than or equal to the minimum fluctuation threshold, setting the energy storage following coefficient as a default original value of the energy storage following coefficient; And S23, judging whether the original value of the energy storage following coefficient is in a preset effective range, taking the upper limit value when the original value exceeds the upper limit, taking the lower limit value when the original value is lower than the lower limit value, and keeping unchanged when the original value is in the range to obtain the energy storage following coefficient.
5. The method for optimizing configuration of resources of a virtual power plant according to claim 4, wherein step S2 further comprises the steps of: Step S24, multiplying the electric vehicle access density and the average SOC of the electric vehicle to obtain an electric vehicle resource product; Step S25, obtaining the capacity of a total assembly machine of the virtual power plant, dividing the product of the electric automobile resources by the capacity of the total assembly machine to obtain the availability of mobile energy storage; Step S26, dividing the load response delay time by the new energy prediction error time window to obtain response matching degree when the new energy prediction error time window is not zero, and setting the response matching degree to be a preset default matching degree value when the new energy prediction error time window is zero; and step S27, storing the energy storage following coefficient, the mobile energy storage availability and the response matching degree in association with the time stamp to form a cooperative measurement index.
6. The method for optimizing configuration of virtual power plant resources according to claim 5, wherein step S3 comprises the steps of: S31, reading the cooperative measurement indexes in a continuous time window, and respectively extracting an energy storage following coefficient sequence, a mobile energy storage availability sequence and a response matching degree sequence according to the arrangement of time stamps; Step S32, extracting the maximum value and the minimum value in each sequence, and calculating the difference value between the maximum value and the minimum value as a numerical range; Step S33, respectively carrying out smoothing treatment on the normalized numerical value sequences to obtain three smoothing sequences, wherein the smoothing treatment is specifically carried out on each data point, arithmetic average is carried out on each preset number of adjacent points before and after each data point, and the average value is taken as a smoothing value of the point; Step S34, respectively drawing time sequence curves by taking the time stamp as an abscissa and the numerical values of the three smooth sequences as an ordinate, and marking the time sequence curves as a response matching degree curve, an energy storage following coefficient curve and a mobile energy storage availability curve; And step S35, calculating phase differences among the response matching degree curve, the energy storage following coefficient curve and the mobile energy storage availability curve to obtain a first phase difference, a second phase difference and a third phase difference.
7. The method for optimizing configuration of virtual power plant resources according to claim 6, wherein step S35 includes the steps of: Step S351, selecting an energy storage following coefficient curve and a mobile energy storage availability curve as a first group of curve pairs to be compared, and translating one curve left and right on a time axis to obtain a translation quantity value representing the time relationship of the two curves; Step S352, setting a search range of time offset, wherein the range is from a negative maximum offset value to a positive maximum offset value, and the maximum offset value is determined according to a preset proportion of the length of the time window; Step S353, traversing the translation data in the search range, and calculating the sum of products of corresponding points in the overlapping time periods of the two sequences for each translation to obtain a similarity value.
8. The virtual power plant resource optimization configuration method according to claim 7, wherein step S35 further comprises the steps of: Step S354, using the translation quantity corresponding to the maximum value in the similarity value as the time offset between the energy storage following coefficient curve and the movable energy storage availability curve; step S355, judging the sign and absolute value amplitude of the time offset, and recording the sign and absolute value amplitude as a first phase difference; step 356, selecting an energy storage following coefficient curve and a response matching degree curve as a second group of curve pairs to be compared, and obtaining a second phase difference by the same method; and S357, selecting the movable energy storage availability curve and the response matching degree curve as a third group of curve pairs to be compared, and obtaining a third phase difference by the same method.
9. The virtual power plant resource optimization configuration method according to claim 8, wherein step S4 includes the steps of: Step S41, taking the upper quartile of the first phase difference, the second phase difference and the third phase difference as a phase difference judging threshold; step S42, comparing the amplitudes of the three phase differences with a phase difference judging threshold value respectively, and marking the phase difference as a remarkable phase difference when the amplitude of the phase difference is larger than the threshold value; Step S43, counting the number of the obvious phase differences, judging that the resource coordination state type is synchronous coordination type when the number is zero, judging that the resource coordination state type is local imbalance type when the number is one, and judging that the resource coordination state type is overall imbalance type when the number is two or three; and S44, identifying the configuration defect resource category based on the resource collaboration state type.
10. A virtual power plant resource optimal allocation system for performing the virtual power plant resource optimal allocation method of claim 1, the virtual power plant resource optimal allocation system comprising: the resource characteristic acquisition module is used for acquiring resource characteristic parameters of various resources in the virtual power plant; the cooperative measurement calculation module is used for calculating an energy storage following coefficient, a mobile energy storage availability and a response matching degree based on the resource characteristic parameters to serve as cooperative measurement indexes; the phase difference analysis module is used for acquiring the cooperative measurement indexes to construct a time sequence curve, and calculating phase differences between every two of the three time sequence curves to obtain a first phase difference, a second phase difference and a third phase difference; The collaborative state identification module is used for judging the resource collaborative state type of the virtual power plant according to the symbol combination and the amplitude of the three phase differences, and identifying the configuration defect resource category based on the resource collaborative state type; And the optimal configuration decision module is used for adjusting the resource configuration proportion aiming at the configuration defect resource category to reduce the phase difference amplitude, and outputting a resource optimal configuration scheme and an equivalent adjustment capacity.

Description

Virtual power plant resource optimal configuration method and system Technical Field The invention relates to the technical field of power plant resource optimization, in particular to a virtual power plant resource optimal allocation method and system. Background And the distributed energy source is monitored, predicted and coordinated in real time by utilizing technologies such as cloud computing, big data, artificial intelligence, edge control and the like, so that the distributed energy source has scheduling and response capacities equivalent to those of a conventional large-scale power plant in function. The virtual power plant realizes output prediction, load response scheduling and energy storage optimization control of each resource through the energy management platform, so that peak regulation, frequency modulation, demand side response and auxiliary service transaction are participated in the power market by the identity of a unified body. In the existing virtual power plant operation practice, obvious response difference and time sequence mismatch problems exist among various internal resources. For example, although an energy storage system has a quick response capability, a charging and discharging strategy is often asynchronous with new energy output fluctuation, an electric automobile is used as mobile energy storage, the time-space distribution of access of the electric automobile is random, the resource availability is difficult to quantify, and an adjustable resource at a load side is often unable to be dynamically matched with new energy change due to a large response delay. These factors lead to the phenomenon of "collaborative imbalance" of virtual power plants in overall scheduling-i.e., failure to form effective timing coordination among resources, affecting the capacity and economy of the scheduling. Under the background of uncertainty of new energy output and randomness of electric automobile access, the traditional method cannot quantitatively evaluate the matching relation among the energy storage device, the mobile energy storage and the load response, and also lacks an analysis mechanism for multi-source dynamic phase difference characteristics, so that the resource configuration process lacks real-time adaptability and refined basis. Disclosure of Invention Based on this, the present invention needs to provide a method and a system for optimizing and configuring resources of a virtual power plant, so as to solve at least one of the above technical problems. In order to achieve the above purpose, the virtual power plant resource optimization configuration method comprises the following steps: Step S1, collecting resource characteristic parameters of various resources in a virtual power plant; step S2, calculating an energy storage following coefficient, a mobile energy storage availability and a response matching degree based on the resource characteristic parameters to serve as a cooperative measurement index; s3, acquiring cooperative measurement indexes to construct a time sequence curve, and calculating phase differences between every two of the three time sequence curves to obtain a first phase difference, a second phase difference and a third phase difference; s4, judging the resource coordination state type of the virtual power plant according to the symbol combinations and the amplitudes of the three phase differences, and identifying the configuration defect resource category based on the resource coordination state type; And S5, adjusting the resource allocation proportion aiming at the allocation defect resource category to reduce the phase difference amplitude, and outputting a resource optimal allocation scheme and an equivalent adjustment capacity. The invention also provides a virtual power plant resource optimal allocation system for executing the virtual power plant resource optimal allocation method, wherein the virtual power plant resource optimal allocation system comprises the following components: the resource characteristic acquisition module is used for acquiring resource characteristic parameters of various resources in the virtual power plant; the cooperative measurement calculation module is used for calculating an energy storage following coefficient, a mobile energy storage availability and a response matching degree based on the resource characteristic parameters to serve as cooperative measurement indexes; the phase difference analysis module is used for acquiring the cooperative measurement indexes to construct a time sequence curve, and calculating phase differences between every two of the three time sequence curves to obtain a first phase difference, a second phase difference and a third phase difference; The collaborative state identification module is used for judging the resource collaborative state type of the virtual power plant according to the symbol combination and the amplitude of the three phase differences, and identifying the confi