A method and system for virtual power plant peak shaving optimization scheduling

Abstract

The application provides a virtual power plant peak shaving optimization scheduling method and system, and belongs to the field of power dispatching; solves the problem of low virtual power plant peak shaving scheduling efficiency; specifically as follows: obtaining power generation data, energy storage data and historical power consumption data, calculating expected power consumption; obtaining power transmission power and residual power, and combining the expected power consumption to adjust the power generation power once; determining the maximum power consumption time of each municipal district, adjusting the voltage phase angle and the current phase angle; obtaining the actual power consumption and the actual residual power, and judging whether the actual residual power is sufficient; if sufficient, regulating and controlling the discharge power and the discharge time of the power storage device; if insufficient, adjusting the power generation power twice; the application obtains, analyzes and processes the relevant power consumption data of the target area, combines the relevant facilities of the virtual power plant, and adjusts the adjustable load of each county and district to improve the power utilization rate.

Description

Technical Field

[0001] This invention relates to a method and system for peak shaving and optimization scheduling of virtual power plants, and pertains to the field of power dispatching. Background Technology

[0002] Existing methods or systems for optimizing the scheduling of virtual power plants have the following shortcomings:

[0003] Data uncertainty: There are bottlenecks in the accuracy of current new energy forecasts. For example, the current photovoltaic power forecast error rate is still 8% to 12%, which leads to a deviation between actual dispatch demand and the planned dispatch demand. At the same time, the actual response rate of some adjustable loads using existing virtual power plant dispatch methods also deviates from the expected value, resulting in a poor user experience.

[0004] High complexity of technical integration: Existing virtual power plant scheduling requires the integration of various heterogeneous resources, including different types of power generation units, energy storage systems and loads. These resources have different characteristics, communication protocols and control methods. Achieving unified scheduling of these resources requires solving complex technical integration problems, including challenges in data acquisition, communication networks, and control strategies. Existing systems often face problems such as inconsistent interfaces and inconsistent data formats, which increases the difficulty and cost of implementation.

[0005] Insufficient user participation: The effectiveness of virtual power plant peak shaving and optimization scheduling largely depends on the level of participation of end users; however, due to the lack of sufficient incentives and convenient participation channels, many potential users are taking a wait-and-see attitude towards virtual power plant projects. Summary of the Invention

[0006] To address the shortcomings of existing technologies, the present invention aims to provide a method and system for peak shaving optimization scheduling of virtual power plants, thereby solving the problem of low peak shaving scheduling efficiency in virtual power plants.

[0007] To achieve the above objectives, the present invention provides a technical solution as follows: A method for peak-shaving optimization scheduling of virtual power plants includes:

[0008] Step S1: Obtain power generation data and energy storage data; obtain the number of municipal districts in the target area, and obtain historical electricity consumption data for the rigid load and adjustable load corresponding to each municipal district; perform time-series analysis on the historical electricity consumption data, and calculate the expected electricity consumption of the rigid load and adjustable load for each municipal district;

[0009] Step S2: Construct the power characteristic function of the baseload power station; obtain the transmission power of the power grid and the remaining power of all energy storage devices, and adjust the power generation of the baseload power station once by combining the power characteristic function and the expected power consumption of the rigid load and adjustable load of each municipality; determine the maximum power consumption time of each municipality based on the rigid load of each municipality, and adjust the voltage phase angle and current phase angle of the power grid to each municipality based on the maximum power consumption time;

[0010] Step S3: Construct the heating characteristic function of the energy storage device; obtain the actual power consumption of the adjustable load and the actual remaining power of the energy storage device in real time; determine whether the actual remaining power is sufficient based on the actual power consumption; if sufficient, adjust the discharge power and discharge time of each energy storage device according to the heating characteristic function and the actual power consumption, without adjusting the power generation of the base load power station; if insufficient, make a secondary adjustment to the power generation of the base load power station according to the actual power consumption.

[0011] Furthermore, the specific steps for calculating the expected electricity consumption of rigid loads and adjustable loads are as follows:

[0012] Obtain the number of municipal districts, mn;

[0013] Obtain the electricity consumption (lr) of the rigid load of the first municipal district from 0:00 on day 1 to 23:00 on day 3. （1,0） ~lr （3,23） ; Electricity consumption of adjustable load la （1,0） ~la （3,23） ;

[0014] For lr （1,0） ~lr （3,23） and la （1,0） ~la （3,23） Perform time-series analysis to calculate the expected electricity consumption qr of the rigid load in the first municipal district. （1,0） ~qr （1,23） And the expected power consumption qa of adjustable load （1,0） ~qa （1,23） ;

[0015] Calculate the expected electricity consumption qr for the rigid load corresponding to the 2nd to mnth municipal districts. （2,0） ~qr （2,23） to qr （mn,0） ~qr （mn,23） ;

[0016] Expected power consumption qa for adjustable load （2,0） ~qa （2,23） to qa （mn,0） ~qa （mn,23） .

[0017] Furthermore, the qr（1,0） ~qr （1,23） and qa （1,0） ~qa （1，23） The calculation steps are as follows:

[0018] Use ADF detection to sequentially process lr （1,0）～ lr （3,23） Perform differential detection to obtain the difference order dr （1） dr （2） and dr （3） ;

[0019] Determine dr （1） dr （2） and dr （3） Are the three equal?

[0020] If they are equal, then for lr （1,0） ~lr （1,23） Perform d-order differencing and process with ACF and PACF to obtain the autoregressive parameter p and the moving average parameter q;

[0021] Calculate the autocorrelation coefficients φ from order 1 to order p. （1） ~φ （p） and the moving average coefficients θ of orders 1 to q （1） ~θ （q） ;

[0022] Calculate lr （3,0） ~lr （3,23） The average value of tlr;

[0023] Calculate lr （3,0） ~lr （3,23） White noise ε （0） ~ε （23） ;

[0024] Let flr be the expected electricity consumption of the rigid load at time t one day from now. （t） ;

[0025] Let the autoregressive coefficient of the lth order be φ. （l） Let the rigid load electricity consumption on the 3rd day (t-l) be lr. （t－l） Let the moving average coefficient of the s-th order be θ. （s） Let the rigid load power consumption on the 3rd day (t-s) be lr. （t－s） ,lr （t－s） The corresponding white noise is ε （t－s） ;

[0026] Construct formula A-5:

[0027] ;

[0028] Based on formula A-5, perform differential inverse operation to estimate the expected electricity consumption qr of the rigid load of the first municipal district from 0:00 to 23:00 on the next day. （1，0） ~qr （1,23） ;

[0029] If dr （1） dr （2） and dr （3） If the three are not equal, then construct the sample set Xe. （0） ~Xe （23） Using a Gaussian kernel as the function and a support vector regression algorithm, the expected electricity consumption qr of the rigid load in the first municipal district from 0:00 to 23:00 on the next day is estimated. （1，0） ~qr （1,23） ;

[0030] According to la （1,0） ~la （1,23） Calculate the expected electricity consumption qa of the adjustable load from 0:00 to 23:00 on the next day. （1,0） ~qa （1，23） .

[0031] Furthermore, the specific steps for adjusting the power generation capacity are as follows:

[0032] Based on multinomial regression, the power characteristic function of the first to the dy-th generators is calculated with the fuel consumption of each generator as the dependent variable and the average power generation as the independent variable, resulting in the function f. （1） ~f （dy） ;

[0033] Obtain the transmission power Pin of the power grid; obtain the remaining power aeq of all energy storage devices;

[0034] Obtain the expected electricity consumption qr of the rigid load of all municipal districts. （1,0） ~qr （mn,23） ; Expected power consumption qa for adjustable load （2,0） ~qa （mn,23） ;

[0035] Calculate qr （1,0） ~qr （mn,23） with qa （2,0） ~qa （mn,23） The sum of aqq;

[0036] Obtain the actual power generation of the first to the dy generators in the baseload power station, and calculate the expected transmission power Pqn of the power grid.

[0037] Compare the values ​​of Pin and Pqn, and adjust the power generation capacity accordingly.

[0038] If Pin < Pqn, then let pk be the generating power of the first to the dy generators in the base load power station after one adjustment. （1） ~pk （dy） ;

[0039] Let pk （1） ~pk （dy） The derivative of the corresponding generator power characteristic function is f 、 （1） ~f 、 （dy） ;

[0040] Define formula B-1:

[0041] ;

[0042] Calculate pk using the Lagrange algorithm. （1） ~pk （dy） The value is used to adjust the power generation capacity of the baseload power station.

[0043] If Pin≥Pqn, no processing is performed, and the power grid and the transmission model of the municipal area are defined, defining the corresponding constraints of current, power, impedance and voltage.

[0044] Furthermore, the specific steps for defining the constraints corresponding to current, power, impedance, and voltage are as follows:

[0045] Let the power transmission capacity of the power grid be Pw and the transmission voltage be Uw; let the power consumption of the city district be Pp and the voltage be Up.

[0046] Let the branch current be I. （1） and I （4） The main circuit current is I （2） I （3） and I （5） Let the equivalent impedance be Z and the equivalent admittance be Y. （1） and Y （2） ; where Y （1） and Y （2） The values ​​are all Yy;

[0047] Definition I （1） ~I （5） Current constraint:

[0048] ;

[0049] Define the power constraints for Pw, Pp, and Yy:

[0050] ;

[0051] Define the impedance constraint of Z:

[0052] ;

[0053] Define the voltage constraints for Pw, Uw, Pp, and Up:

[0054] .

[0055] Furthermore, the specific steps for adjusting the voltage phase angle and the current phase angle are as follows:

[0056] Adjust the voltage phase angle and current phase angle corresponding to the 1st to mnth municipal districts;

[0057] Obtain the expected electricity consumption qr of the rigid load of the first municipal district. （1，0） ~qr （1,23） ;

[0058] Extract qr （1，0） ~qr （1,23） The maximum value qr （1，tim） Extract the maximum power consumption time (tim);

[0059] Extract the expected electricity consumption qa of the adjustable load of the first municipal district at time tim. （1，tim） ;

[0060] Extract the expected electricity consumption qr of the rigid load of the 2nd to mnth municipal districts at time tim. （2，tim） ~qr （mn，tim） The expected power consumption qa of the adjustable load （2，tim） ~qa （mn，tim） ;

[0061] Obtain transmission voltage Uwi （1） Voltage phase angle βu and current phase angle βi;

[0062] Obtain the actual electricity consumption of the rigid load in the first municipal district at time (ho-1) jr （ho-1） and expected power consumption qr （ho-1） ; Calculate the proportionality coefficient br;

[0063] Obtain the power factor angles of the 2nd to mnth municipal districts, and get μ. （2） ~μ （mn） ;

[0064] Compare the magnitude of br with 1, and adjust the power factor angles of the 2nd to mnth municipalities or the voltage phase angle βu and current phase angle βi during power transmission in the 1st municipality;

[0065] Adjust the voltage phase angle and current phase angle corresponding to the 2nd to mnth municipal districts.

[0066] Furthermore, the specific steps for adjusting the power factor angles of the 2nd to mnth municipal districts are as follows:

[0067] If br > 1, then adjust the power factor angle of the power grid supplying power to the 2nd to mnth municipal districts to obtain μa. （2） ~μa （mn） ;

[0068] Obtain the actual electricity consumption of the adjustable load in the first municipal district at (ho-1) ja （ho-1） ;

[0069] Calculate the electricity consumption rate Ppi of the first municipal district at (ho-1);

[0070] Ppi, Pwi （1） and Uwi （1） Substitute the values ​​into the voltage constraints in reverse to calculate the equivalent received voltage Upi; then substitute Upi into the current constraints, power constraints, and impedance constraints in sequence to calculate the equivalent impedance Zi. （1） ;

[0071] Calculate the equivalent impedance Zi of the 2nd to mnth municipal districts. （2） ~Zi （mn） ;

[0072] Calculate Zi （1） ~Zi （mn） The inverse ratio is used to obtain the diversion ratio iz for the 1st to the mnth municipal districts. （1） ~iz （mn） ;

[0073] According to br calculation aiz （1） ;

[0074] Let the diversion ratio of the v-th and mn-th municipal districts be iz. （v） The changed split ratio is biz （v） Define the calculation formula B-2-1:

[0075] ;

[0076] Calculate the diversion ratio (biz) after changes in the 2nd to the mnth municipal districts. （2） ~biz （mn） ;

[0077] Calculate aiz （1） ~biz （mn） The ratio of oz is used to obtain the secondary diversion ratio of the first to the mnth municipal districts. （1） ~oz （mn） ;

[0078] Calculate the power factor angle θp of the first municipal district;

[0079] Let the secondary diversion ratio of the v-th municipal district be oz. （v） Let μa be the power factor angle of the adjusted power grid supplying power to the v-th municipal district. （v） Define the calculation formula B-3-1:

[0080] ;

[0081] Calculate μa （2） ~μa （mn） .

[0082] Furthermore, the specific steps for adjusting the power factor angles of the 2nd to mnth municipal districts also include:

[0083] If br < 1, then adjust the power factor angle of the power grid supplying power to the 2nd to mnth municipal districts to obtain μb. （2） ~μb （mn） ;

[0084] Calculate the diversion ratio iz for the 1st to the mnth municipal districts. （1） ~iz （mn） and aiz （1） ;

[0085] Let the diversion ratio of the v-th and mn-th municipal districts after the change be denoted as ciz. （v） Define calculation formula B-2-2:

[0086] ;

[0087] According to formula B-2-2, calculate the diversion ratio ciz after the changes in the 2nd to the mnth municipal districts. （2） ~ciz （mn） ;

[0088] Calculate aiz （1） ~ciz （mn） The ratio of the two values ​​is used to obtain the secondary diversion ratio pz for the 1st to the mnth municipal districts. （1） ~pz （mn） ;

[0089] Let pz be the secondary diversion ratio of the vth municipal district. （v） Let the power factor angle of the adjusted power grid supplying power to the v-th municipal district be μb. （v） ; Define calculation formula B-3-2:

[0090] ;

[0091] Calculate μb （2） ~μb （mn） .

[0092] Furthermore, the specific steps for adjusting βu and βi are as follows:

[0093] If br = 1, only change the voltage phase angle βu and current phase angle βi of the first municipal district to obtain βu （1） and βi （1） ;

[0094] Obtain the power generation Pjw of the base load power station and calculate qr （1，tim） ~qr （mn，tim） and the sum qqa of qa （1，tim） ~qa （mn，tim） ;

[0095] Obtain the transmission power Pwi from the power supply grid to the 2nd to the mnth municipal districts （2） ~Pwi （mn） ; Calculate the sum aPw of Pwi （1） ~Pwi （mn） ;

[0096] Judge whether it holds;

[0097] If it holds, do not change the values of βu and βi;

[0098] If it does not hold, calculate the voltage division ratio of the 1st to the mnth municipal districts to obtain iw （1） ~iw （mn） ;

[0099] Calculate the ratio za of the total expected power consumption （2） ~za （mn） ; Calculate the sum aza of za （2） ~za （mn） ;<00008(此处原文有误,推测应为 )

[0100] Calculate the sum aqr of qr （1，tim） ~qr （mn，tim） ;

[0101] Calculate the remaining allocable power req;

[0102] Let the power consumption of the rigid load in the vth municipal district be qr （v，tim） , the allocated power be rrq （v） , and the ratio of the total expected power consumption be za （v） : Define the calculation formula B - 4:

[0103] ;

[0104] Calculate the allocable power rrq of the 2nd to the mnth municipal districts （2） ~rrq （mn） ;

[0105] Calculate the second partial voltage ratio to obtain tw （1） ~tw （mn） ;

[0106] Let θa denote the power factor angle of the first municipal district after the change, and let μ denote the power factor angle of the vth municipal district. （v） The voltage divider ratio is denoted as iw （v） The second partial voltage ratio is denoted as tw （v） Calculate the value of θa:

[0107] ;

[0108] Compare the magnitudes of θa and θp to determine βu. （1） and βi （1） The value;

[0109] If θa ≥ θp, then βu （1） for: ,βi （1） for: ;

[0110] If θa < θp, then βu （1） for: ,βi （1） for: .

[0111] A peak-shaving optimization scheduling system for virtual power plants includes:

[0112] Data acquisition module: used to acquire power generation data from distributed generation equipment and energy storage data from energy storage devices; acquire the number of municipal districts in the target area, and acquire historical electricity consumption data of rigid load and adjustable load corresponding to each municipal district;

[0113] Data processing module: Used for time-series analysis of historical electricity consumption data, calculating the expected electricity consumption of rigid and adjustable loads in each municipal district; constructing the power characteristic function of the baseload power station; obtaining the transmission power of the power grid and the remaining energy of all energy storage devices, and adjusting the power generation of the baseload power station based on the power characteristic function and the expected electricity consumption of rigid and adjustable loads in each municipal district; determining the maximum electricity consumption time of each municipal district based on the rigid load, and adjusting the voltage phase angle and current phase angle of the power grid to each municipal district based on the maximum electricity consumption time;

[0114] The optimized scheduling module is used to construct the heating characteristic function of the energy storage device; obtain the actual power consumption of the adjustable load and the actual remaining power of the energy storage device in real time; determine whether the actual remaining power is sufficient based on the actual power consumption; if sufficient, adjust the discharge power and discharge time of each energy storage device according to the heating characteristic function and the actual power consumption, without adjusting the power generation of the base load power station; if insufficient, make a secondary adjustment to the power generation of the base load power station according to the actual power consumption.

[0115] Continuous control module: used to update power generation data and energy storage data, and to synchronously regulate the power generation of the baseload power station, the voltage phase angle and current phase angle of each municipality, and the discharge power and discharge time of the energy storage device.

[0116] Compared with the prior art, the beneficial effects of the present invention are:

[0117] Improving power utilization: This invention integrates intermittent power sources into a controllable whole by aggregating the power generation equipment and energy storage devices corresponding to virtual power plants in the target area; at the same time, this invention can predict future power consumption changes in the target area based on historical power consumption data and coordinate other controllable resources for complementarity, thereby effectively improving power utilization; research shows that after adopting peak-shaving and optimized scheduling of virtual power plants, the renewable energy absorption rate of the regional power grid can be increased by 15%-25%.

[0118] Reduce the cost of peak shaving in the power grid: Traditional peak shaving mainly relies on the start-up and shutdown of coal-fired units or the operation of gas-fired units, which is costly; while this invention, by calling on the distributed energy storage of virtual power plants, can carry out peak shaving according to the actual electricity demand of the target area, which can reduce the dependence on traditional peak shaving units.

[0119] Enhancing the flexibility of power system operation: This invention enables virtual power plants to perform peak shaving according to the actual operation of the power grid through real-time monitoring and rapid response, providing flexible peak shaving services to the power grid. At the same time, during the peak shaving operation, this invention adjusts the power distribution of the power grid in the target area by adjusting the power factor angle, and then supplements the power of the target area through the energy storage device, realizing the coordinated optimization of source, grid, load and storage, and promoting the optimal allocation of energy resources. Attached Figure Description

[0120] Other features, objects, and advantages of the invention will become more apparent from the following detailed description of non-limiting embodiments with reference to the accompanying drawings:

[0121] Figure 1 This is a schematic diagram of the method of the present invention;

[0122] Figure 2 This is a schematic diagram of the system of the present invention;

[0123] Figure 3 This is a schematic diagram of the power transmission model of the present invention. Detailed Implementation

[0124] To make the above-mentioned objects, features and advantages of the present invention more apparent and understandable, the present invention will be further described in detail below with reference to the accompanying drawings and specific embodiments.

[0125] Please see Figure 1 A method for peak shaving optimization scheduling of virtual power plants includes:

[0126] Step S1: Obtain the power generation data of the distributed generation equipment (corresponding to the virtual power plant) and the energy storage data of the energy storage device; obtain the number of municipal districts in the target area, and obtain the historical electricity consumption data of the rigid load and adjustable load (last three days) for each municipal district; perform time-series analysis on the historical electricity consumption data, and calculate the expected electricity consumption of the rigid load and adjustable load for each municipal district;

[0127] Power generation data includes: the number of distributed generation devices and the actual power generation of each distributed generation device;

[0128] Energy storage data includes: the energy storage capacity, maximum charging power, and maximum discharging power of the energy storage device;

[0129] It should be noted that, in this invention, "target area" refers to a city-level region where the present invention (a method and system for peak shaving and optimization scheduling of virtual power plants) is used for power regulation.

[0130] It should be noted that a Virtual Power Plant (VPP) is a technological model that uses information and communication technologies and software systems to aggregate dispersed, small-scale distributed energy resources (DERs) to form a "virtual" power system that can operate collaboratively; unlike traditional power plants, virtual power plants do not have fixed, large-scale physical facilities.

[0131] Distributed power generation equipment: refers to green energy power generation equipment, such as photovoltaic, small wind power, biomass energy, etc.

[0132] Energy storage system: refers to energy storage devices used in homes or businesses, such as BYD Battery-Box, Huawei LUNA2000, Tesla Powerwall, etc.

[0133] Rigid load: refers to electrical equipment or institutions whose power consumption time or power consumption cannot be adjusted, such as hospitals, government buildings, traffic lights, etc.

[0134] Adjustable load: refers to power-consuming equipment or mechanisms whose power consumption time or power consumption can be adjusted, such as street lighting, industrial production lines, electric vehicle charging stations, etc.

[0135] "Baseload power plant" refers to a power plant that is used to ensure the basic electricity needs of a target area and operates 24 hours a day without interruption, such as a nuclear power plant or a large fuel power plant.

[0136] The specific steps of step S1 are as follows:

[0137] Obtain the number of municipal districts (mn) in the target region;

[0138] Obtain the electricity consumption (lr) of the rigid load in the first municipal district at 0:00, 1:00 and up to 23:00 on the first day. （1,0） ,lr （1,1） ~lr （1,23） ; Electricity consumption of adjustable load la （1,0） ,la （1,1） ~la （1,23） ;

[0139] Electricity consumption of rigid load at 0:00, 1:00, and up to 23:00 on the second day. （2,0） ,lr （2,1） ~lr （2,23） ; Electricity consumption of adjustable load la （2,0） ,la （2,1） ~la （2,23） ;

[0140] Electricity consumption of rigid load at 0:00, 0:00, and 23:00 on the third day. （3,0） ,lr （3,1） ~lr （3,23） ; Electricity consumption of adjustable load la （3,0） ,la （3,1） ~la （3,23） ;

[0141] For lr （1,0） ~lr （3,23） and la （1,0） ~la （3,23） Perform time-series analysis to calculate the expected electricity consumption qr of the rigid load corresponding to the first municipal district at 0:00, 1:00, and up to 23:00 on the next day. （1,0） qr （1,1） ~qr （1,23） And the expected power consumption qa of adjustable load （1,0） , qa （1,1） ~qa （1,23） ;

[0142] Use ADF detection to sequentially process lr （1,0） ~lr （1,23） ,lr （2,0）） ~lr （2,23） and lr （3,0） ~lr （3,23） Perform differential detection to obtain the difference order dr（1） dr （2） and dr （3） ;

[0143] Determine dr （1） dr （2） and dr （3） Are the three equal?

[0144] If dr （1） dr （2） and dr （3） If all three are equal, then for lr （1,0） ~lr （1,23） Perform d-order differencing and process with the autocorrelation function (ACF) and partial autocorrelation function (PACF) to obtain the autoregressive parameter p and the moving average parameter q (the range of values ​​for d, p, and q are all [1, 24), and the values ​​of (24-p) and (24-q) are both greater than or equal to 1).

[0145] Calculate the autocorrelation coefficients φ for the 1st, 2nd, and up to the pth order. （1） φ （2） ~φ （p） and the moving average coefficients θ of orders 1 to q （1） θ （2） ~θ （q） ;

[0146] According to lr （1,0） ,lr （1,1） ~lr （1,23） Calculate the autocorrelation coefficient φ （1，1） φ （1，2） ~φ （1，p） and the moving average coefficients θ of orders 1 to q （1，1） θ （1，2） ~θ （1，q） ;

[0147] Calculate lr （1,0） ~lr （1,23） The average value of alar;

[0148] {lr （1,0） ~lr （1,23）} as sequence T;

[0149] Calculate the differences from order 1 to p corresponding to sequence T to obtain sequence T. （1） ~T （p） ;

[0150] Calculate the sequence T with respect to the sequence T （1） ~T （p） The autocovariance is obtained by γ. （1） ~γ （p） ;

[0151] Construct a (1×p) matrix B （φ） : ;

[0152] Construct a (1×p) matrix B （1） : ;

[0153] Construct a (p×p) matrix B （2） : ; where matrix B （2） All elements on the diagonal are alar; Transform sequence T with respect to sequence T （1） ~T （p-1） autocovariance γ （1） ~γ （p-1） symmetrically arranged in matrix B （2） Both sides of the diagonal;

[0154] Calculate φ （1，1） ~φ （1，p） Value: Where -1 represents the inverse of the matrix, and * represents matrix multiplication;

[0155] Calculate the moving average coefficients θ from the 1st to the qth order. （1，1） ~θ （1，q） ;

[0156] Let the expected autoregressive sliding coefficient of the k-th order be qrm. （k） The value of k ranges from 1 to q;

[0157] Compare the magnitudes of p and q, and define the expected autoregressive sliding coefficient qrm. （k） By applying mathematical constraints, we obtain formula A-1 or formula A-2:

[0158] If p ≥ q, then define formula A-1:

[0159] ; where φ （1，i） (lr) （1,0） ~lr （1,23） The corresponding autocorrelation coefficient of order i, where i ranges from 1 to p; γ （k-i） This represents the autocovariance between sequence T and the "(k-i)th order difference of sequence T";

[0160] If p < q, then define formula A-2:

[0161] ;

[0162] Where, θ （1，k） (lr) （1,0） ~lr （1,23） (Corresponding) k-th order moving average coefficient;

[0163] θ （1，j） (lr) （1,0） ~lr （1,23） The corresponding moving average coefficient of order j; the value of j ranges from 1 to (q-k); θ （1，（j+k）） (lr) （1,0） ~lr （1,23） The corresponding (j+k)th order moving average coefficient;

[0164] Let the autoregressive sliding coefficient of the kth order be rm. （k） Define formula A-3:

[0165] ;

[0166] Define function Mi(θ) （1，k） ), thus obtaining formula A-4:

[0167] ;

[0168] Based on formula A-4, the gradient descent algorithm iterates through the function Mi(θ). （1，k） ), making the function Mi(θ) （1，k） When the output value of is minimized, θ is obtained. （1，1） ~θ （1，q） ;

[0169] Repeated calculation of φ （1，1） ~φ （1，p） With θ （1，1） ~θ （1，q） The calculation steps are based on lr （2,0） ,lr （2,1） ~lr （2,23） Calculate φ （2，1） φ （2，2） ~φ （2，p） With θ （2，1） θ （2，2） ~θ （2，q） ;

[0170] According to lr （3,0） ,lr （3,1） ~lr （3,23） Calculate φ （3，1） φ （3，2） ~φ （3，p） With θ （3，1） θ （3，2） ~θ （3，q） ;

[0171] Calculate φ （1，1） φ （2，1） and φ （3，1） The average value, as φ （1） ;

[0172] Calculate φ （1，3） φ （2，2） and φ （3，2） The average value, as φ （2） ;

[0173] And so on, calculate φ （1，q） φ （2，q） and φ （3，q） The average value, as φ （q） ;

[0174] Calculate θ （1，1） θ （2，1） and θ （3，1） The average value, as θ （1） ;

[0175] Calculate θ （1，3） θ （2，2） and θ （3，2） The average value, as θ （2） ;

[0176] And so on, calculate θ （1，q） θ （2，q） and θ （3，q） The average value, as θ （q） ;

[0177] Calculate lr （3,0） ,lr （3,1） ~lr （3,23） The average value of tlr;

[0178] Calculate lr （3,0） ~lr （3,23） White noise ε （0） ~ε （23） ; where ε （1） =lr （3,0） -tlr;ε （1） =lr （1,1） -tlr; and so on, ε （23） =lr （3,23） -tlr;

[0179] Let flr be the expected electricity consumption of the rigid load at time t one day from now. （t） Where t≤d;

[0180] Let the autoregressive coefficient of the lth order be φ. （l） Let the rigid load electricity consumption on the 3rd day (t-l) be lr. （t－l） Where, the value of l ranges from 1 to p;

[0181] Let the moving average coefficient of the s-th order be θ. （s） Let the rigid load power consumption on the 3rd day (t-s) be lr.（t－s） ,lr （t－s） The corresponding white noise is ε （t－s） Where s takes values ​​from 1 to q; ε （t－s） ∈{ε （0） ~ε （23）};

[0182] Construct formula A-5:

[0183] ;

[0184] Based on formula A-5, perform differential inverse operation to estimate the expected electricity consumption qr of the rigid load of the first municipal district at 0:00, 1:00, and up to 23:00 on the next day. （1，0） qr （1，1） ~qr （1,23） ;

[0185] If dr （1） dr （2） and dr （3） If the three are not equal, then construct the sample set Xe. （0） Xe （1） ~Xe （23） Using a Gaussian kernel as the function and a support vector regression algorithm, the expected electricity consumption qr of the rigid load of the first municipal district at 0:00, 01:00, and up to 23:00 on the next day is estimated. （1，0） qr （1，1） ~qr （1,23） ;

[0186] Among them, the sample set Xe （0） For {lr （1,0） ,lr （2,0） ,lr （3,0）};

[0187] Sample set Xe （1） For {lr （1,1） ,lr （2,1） ,lr （3,1）};

[0188] And so on, sample set Xe （23） For {lr （1,23） ,lr （2,23） ,lr （3,23）};

[0189] Repeat qr （1，0） qr （1，1） ~qr （1,23） The calculation steps are based on la. （1,0） ,la （1,1） ~la （1,23）Calculate the expected electricity consumption qa of the adjustable load corresponding to 0:00, 1:00, and up to 23:00 on the next day. （1,0） , qa （1,1） ~qa （1，23） ;

[0190] Repeated calculation of qr （1,0） qr （1,1） ~qr （1,23） and qa （1,0） , qa （1,1） ~qa （1,23） The calculation steps are as follows: calculate the rigid load qr corresponding to the 2nd to mnth municipal districts at 0:00, 1:00, and up to 23:00 on the next day. （2,0） qr （2,1） ~qr （2,23） to qr （mn,0） qr （mn,1） ~qr （mn,23） ;

[0191] Expected power consumption qa for adjustable load （2,0） , qa （2,1） ~qa （2,23） to qa （mn,0） , qa （mn,1） ~qa （mn,23） .

[0192] Step S2: Construct the power characteristic function of the baseload power station; obtain the transmission power of the power grid and the remaining power of all energy storage devices, and combine the power characteristic function with the expected power consumption of the rigid load and adjustable load of each municipality to adjust the power generation of the baseload power station; determine the maximum power consumption time of each municipality based on the rigid load of each municipality, and adjust the voltage phase angle and current phase angle of the power grid to each municipality based on the maximum power consumption time to achieve peak-shaving power consumption;

[0193] The specific steps of step S2 are as follows:

[0194] Get the total number of generators (dy) in the baseload power station (target area); get the average power generation and fuel consumption per hour for each generator (last three days) (if the baseload power station is a hydroelectric generator or a geothermal generator, then "fuel consumption per hour of baseload power station" means: the average speed of the generator blades per hour of the baseload power station), as historical data;

[0195] Based on multinomial regression, the NumPy library is used to calculate the power characteristic function of the 1st, 2nd, up to the dyth generator (with fuel consumption of each generator as the dependent variable and average power generation as the independent variable), resulting in the function f. （1） f （2） ~f （dy） ;

[0196] It should be noted that because a power station can choose from a variety of different generator models, the consumables used by each generator may be different, that is, the "fuel consumption of each generator in the baseload power station" is different. Different fuel consumption will bring different dimensions. Therefore, in the process of calculating the "power characteristic function", this invention only provides the idea and cannot give a unified and definite mathematical expression.

[0197] Get the power transmission power Pin of the power grid; get the number of energy storage devices st, and get the remaining power rq of the 1st, 2nd, up to the stth energy storage devices. （1） ,rq （2） ~rq （st） ; Calculate rq （1） ~rq （st） The sum of aeq;

[0198] Obtain the expected electricity consumption (qr) of the rigid load corresponding to the entire municipal districts at 0:00, 01:00, and up to 23:00 on the next day. （1,0） ~qr （mn,23） ; Expected power consumption qa for adjustable load （2,0） ~qa （mn,23） ;

[0199] Calculate qr （1,0） ~qr （mn,23） with qa （2,0） ~qa （mn,23） The sum of aqq;

[0200] Obtain the actual power generation of the 1st, 2nd, up to the dyth generator in the baseload power station, and get pi. （1） pi （2） ~pi （dy） ; Calculate pi （1） ~pi （dy） and ap;

[0201] Calculate the expected transmission power Pqn of the power grid: ;

[0202] Compare the values ​​of Pin and Pqn to determine whether to adjust the power generation capacity of the baseload power station.

[0203] If Pin < Pqn, then the power generation capacity of the baseload power station will be adjusted once;

[0204] If Pin ≥ Pqn, then no action is taken;

[0205] The power generation capacity of the baseload power plant will be adjusted once;

[0206] Assume that after one adjustment, the base load power station has generators numbered 1, 2, up to dy, with a generating capacity of pk.（1） PK （2） ~pk （dy） ;

[0207] Let pk （1） PK （2） ~pk （dy） The derivative of the power characteristic function of the corresponding 1st, 2nd, and up to the dyth generator is f. 、 （1） f 、 （2） ~f 、 （dy） ;

[0208] By defining the constraints, we obtain formula B-1:

[0209] ;

[0210] Based on formula B-1, the Lagrange algorithm is used to calculate pk. （1） PK （2） ~pk （dy） The value;

[0211] Please see Figure 3 Define the power grid and the transmission model of the city's jurisdiction, and define the corresponding constraints of current, power, impedance and voltage;

[0212] Let the power transmission capacity of the power grid be Pw and the transmission voltage be Uw; let the power consumption of the city district be Pp and the voltage be Up.

[0213] Let the branch current be I. （1） and I （4） The main circuit current is I （2） I （3） and I （5） Let the equivalent impedance be Z and the equivalent admittance be Y. （1） and Y （2） ; where Y （1） and Y （2） The values ​​are all Yy;

[0214] Definition I （1） ~I （5） Current constraint:

[0215] ;

[0216] Define the power constraints for Pw, Pp, and Yy:

[0217] ;

[0218] Define the impedance constraint of Z:

[0219] ;

[0220] Define the voltage constraints for Pw, Uw, Pp, and Up:

[0221] ;

[0222] Adjust the voltage phase angle and current phase angle corresponding to the 1st to mnth municipal districts;

[0223] Obtain the expected electricity consumption qr of the rigid load of the first municipal district. （1，0） ~qr （1,23） ;

[0224] Extract qr （1，0） ~qr （1,23） The maximum value qr （1，tim） Extract qr （1，tim） The (time) subscript tim; (if qr （1，0） ~qr （1,23） If there are multiple corresponding maximum values, then tim represents "qr". （1，0） ~qr （1,23） The maximum index in "maximum value"; for example: qr （1，0） 5, qr （1，1） 2, qr （1，2） 5, qr （1，3） 5, qr （1，4） If qr is 4, then tim is 3; that is, qr （1，0） ~qr （1，4） The maximum value in the value is 5. Among the expected electricity consumption of rigid loads with a value of 5, the largest time index is 3, so tim is 3;).

[0225] The value of tim is taken as the maximum power consumption time; the value of tim ranges from 1 to 23.

[0226] Extract the expected electricity consumption qa of the adjustable load of the first municipal district at time tim. （1，tim） ;

[0227] Extract the expected electricity consumption qr of the rigid load of the 2nd, 3rd, up to the mnth municipal district at time tim. （2，tim） qr （3，tim） ~qr （mn，tim） The expected power consumption qa of the adjustable load （2，tim） , qa （3，tim） ~qa （mn，tim） ;

[0228] Get the current time as ho hours mt minutes, and get the transmission voltage Uwi. （1） Voltage phase angle βu and current phase angle βi;

[0229] Obtain the actual electricity consumption of the rigid load in the first municipal district at time (ho-1) jr （ho-1） and expected power consumption qr （ho-1） ; where qr （ho-1） ∈{qr （1,0） ~qr （1,23）};

[0230] Calculate the proportionality coefficient br: br = jr （ho-1） / qr （ho-1） ;

[0231] Obtain the power factor angle (i.e., the difference between the voltage phase angle and the current phase angle) of the 2nd to mnth municipal districts, and get μ. （2） ~μ （mn） ;

[0232] Compare the magnitude of br with 1, and adjust the power factor angles of the 2nd to mnth municipalities or the voltage phase angle βu and current phase angle βi during power transmission in the 1st municipality;

[0233] If br > 1, then adjust the power factor angle of the power grid supplying power to the 2nd to mnth municipal districts to obtain μa. （2） ~μa （mn） ;

[0234] Obtain the actual electricity consumption of the adjustable load in the first municipal district at (ho-1) ja （ho-1） ;

[0235] Calculate the electricity consumption rate Ppi of the first municipal district at (ho-1):

[0236] ;

[0237] Using Ppi as Pp, Pwi （1） As Pw, Uwi （1） Substituting Uw into the voltage constraint in reverse, we calculate the equivalent receiving voltage Upi (Upi is the smallest significant real root) of the first municipality at (ho-1); substituting Upi into the current constraint, power constraint, and impedance constraint in sequence, we calculate the equivalent impedance Zi of the first municipality. （1） ;

[0238] Repeat Zi （1） The calculation process involves calculating the equivalent impedance Zi of the 2nd to mnth municipal districts. （2） ~Zi （mn） ;

[0239] Calculate Zi （1） Zi （2） :~:Zi （mn） The inverse ratio is used to obtain the diversion ratio iz for the 1st to the mnth municipal districts.（1） :iz （2） :~:iz （mn） ;

[0240] Calculate iz （1） The changed value (i.e., increasing iz) （1） (value), get aiz （1） :

[0241] ;

[0242] Let the diversion ratio of the v-th and mn-th municipal districts be iz. （v） The changed split ratio is biz （v） Define the calculation formula B-2-1:

[0243] Where, the value of v ranges from 2 to mn;

[0244] According to formula B-2-1, calculate the diversion ratio (biz) after the changes in the 2nd to the mnth municipal districts. （2） ~biz （mn） ;

[0245] Calculate aiz （1） :biz （2） :~:biz （mn） The ratio of oz is used to obtain the secondary diversion ratio of the first to the mnth municipal districts. （1） oz （2） :~:oz （mn） ;

[0246] Calculate the power factor angle θp of the first municipal district: θp = βu - βi;

[0247] Let the secondary diversion ratio of the v-th municipal district be oz. （v） Let μa be the power factor angle of the adjusted power grid supplying power to the v-th municipal district. （v） Define the calculation formula B-3-1:

[0248] ;

[0249] Calculate μa according to formula B-3-1. （2） ~μa （mn） ;

[0250] If br < 1, then adjust the power factor angle of the power grid supplying power to the 2nd to mnth municipal districts to obtain μb. （2） ~μb （mn） ;

[0251] Repeat the above calculation "iz （1） :iz （2） :~:iz（mn） The steps are as follows: calculate the diversion ratio iz for the 1st to the mnth municipal districts. （1） :iz （2） :~:iz （mn） ;

[0252] Calculate iz （1） The changed value (i.e., the decrease of iz) （1） (value), get aiz （1） :

[0253] ;

[0254] Let the diversion ratio of the v-th and mn-th municipal districts after the change be denoted as ciz. （v） Define calculation formula B-2-2:

[0255] ;

[0256] According to formula B-2-2, calculate the diversion ratio ciz after the changes in the 2nd to the mnth municipal districts. （2） ~ciz （mn） ;

[0257] Calculate aiz （1） :ciz （2） :~:ciz （mn） The ratio of the two values ​​is used to obtain the secondary diversion ratio pz for the 1st to the mnth municipal districts. （1） :pz （2） :~:pz （mn） ;

[0258] Let pz be the secondary diversion ratio of the vth municipal district. （v） Let the power factor angle of the adjusted power grid supplying power to the v-th municipal district be μb. （v） ; Define calculation formula B-3-2:

[0259] ;

[0260] Calculate μb according to formula B-3-2. （2） ~μb （mn） ;

[0261] If br = 1, then only the voltage phase angle βu and current phase angle βi of the first municipal district are changed to obtain βu. （1） and βi （1） ;

[0262] Obtain the power generation Pjw of the baseload power station (at ho time mt minute), and calculate qr. （1，tim） ~qr （mn，tim） and QA （1，tim） ~qa （mn，tim） The sum of qqa;

[0263] Obtain the transmission power Pwi of the power supply grid to the 2nd to the mnth municipal districts at (ho:mt). （2） ~Pwi （mn） ; Calculate Pwi （1） ~Pwi （mn） and get the sum aPw;

[0264] Judge whether it holds;

[0265] If it holds, do not change the values of βu and βi, that is, βu （1） =βu, βi （1） =βi;

[0266] If it does not hold, obtain the transmission power Pwi of the power supply grid to the 2nd to the mnth municipal districts at the current time (i.e., at ho:mt). （2） ~Pwi （mn） ;

[0267] Calculate the ratio of Pwi （1） :Pwi （2） :~:Pwi （mn） as the voltage division ratio of the 1st to the mnth municipal districts, and get iw （1） :iw （2） :~:iw （mn） ;

[0268] Calculate the total expected power consumption zq of the 1st municipal district at tim （1） :zq （1） =qr （1，tim） +qa （1，tim） ;

[0269] The total expected power consumption zq of the 2nd municipal district （2） :zq （2） =qr （2，tim） +qa （2，tim） ;

[0270] And so on, the total expected power consumption zq of the mnth municipal district （mn） :zq （mn） =qr （mn，tim） +qa （mn，tim） ;

[0271] Calculate the ratio of zq （2） :~:zq （mn） to obtain the ratio za of the total expected power consumption of the 2nd to the mnth municipal districts[[ID=8​​​​​​​

[0272] Calculate qr （1，tim） ~qr （mn，tim） and aqr;

[0273] Based on the expected electricity consumption of the rigid and adjustable loads in the first municipal district, calculate the remaining allocable electricity to obtain req:

[0274] ;

[0275] Let qr be the electricity consumption of the rigid load in the v-th municipal district at time tim. （v，tim） Let the allocated electricity for the vth municipality be rrq. （v） The ratio of total expected electricity consumption is za （v） Define the calculation formula B-4:

[0276] ;

[0277] Based on formula B-4, the allocable electricity for the 2nd to mnth municipal districts is calculated, yielding rrq. （2） ~rrq （mn） ;

[0278] Calculate (qr) （v，tim） +qa （v，tim） ):rrq （2） :~:rrq （mn） The ratio of is used as the secondary partial pressure ratio for the 1st to the mnth municipal districts to obtain tw （1） :tw （2） :~:tw （mn） ;

[0279] Calculate the power factor angle θp of the first municipal district: θp = βu - βi;

[0280] Let θa denote the power factor angle of the first municipal district after the change, and let μ denote the power factor angle of the vth municipal district. （v） The voltage divider ratio is denoted as iw （v） The second partial voltage ratio is denoted as tw （v） Calculate the value of θa:

[0281] ;

[0282] Compare the magnitudes of θa and θp to determine βu. （1） and βi （1） The value;

[0283] If θa ≥ θp, then βu （1） The value is: ,βi （1） The value is: ;

[0284] If θa < θp, then βu （1） The value is: ,βi （1） The value is: ;

[0285] Repeat the same steps to adjust the voltage phase angle and current phase angle of the first to the mnth municipal districts, and adjust the voltage phase angle and current phase angle of the second to the mnth municipal districts.

[0286] Step S3: Construct the heating characteristic function of the energy storage device; obtain the actual power consumption of the adjustable load (target area) and the actual remaining power of the energy storage device in real time; determine whether the actual remaining power is sufficient based on the actual power consumption; if sufficient, adjust the discharge power and discharge time of each energy storage device according to the heating characteristic function and the actual power consumption, without adjusting the power generation of the base load power station; if insufficient, make a secondary adjustment to the power generation of the base load power station according to the actual power consumption.

[0287] The discharge power and heat generation of all energy storage devices are obtained within a certain time period. Using the discharge power and discharge time of the energy storage devices as independent variables and the heat generation as the dependent variable, a multinomial regression is used to construct the heat generation characteristic function of each energy storage device.

[0288] Determine whether the actual remaining power is sufficient based on the actual power consumption.

[0289] If sufficient, prioritize energy storage devices with a large remaining power and a large maximum charging power and storage capacity as the preferred devices.

[0290] Based on the heating characteristic function of the power distribution device and the actual power consumption, and without exceeding the maximum discharge power of the preferred device, the Lagrange algorithm is used to regulate the discharge power and discharge time of each energy storage device to minimize the heat generation of all preferred devices; without adjusting the power generation of the baseload power station.

[0291] If insufficient, repeat step S2 to adjust the power generation of the baseload power station once, and adjust the power generation of the baseload power station a second time according to the actual electricity consumption.

[0292] Please see Figure 2 A peak-shaving optimization scheduling system for virtual power plants includes:

[0293] Data acquisition module: used to acquire power generation data of distributed generation equipment (corresponding to the virtual power plant) and energy storage data of energy storage devices; acquire the number of municipal districts in the target area, and acquire historical electricity consumption data of rigid load and adjustable load (last three days) for each municipal district;

[0294] The data processing module is used to perform time-series analysis on historical electricity consumption data, calculate the expected electricity consumption of rigid and adjustable loads in each municipal district; construct the power characteristic function of the baseload power station; obtain the transmission power of the power grid and the remaining energy of all energy storage devices, and adjust the power generation of the baseload power station based on the power characteristic function and the expected electricity consumption of rigid and adjustable loads in each municipal district; determine the maximum electricity consumption time of each municipal district based on the rigid load, and adjust the voltage phase angle and current phase angle of the power grid to each municipal district based on the maximum electricity consumption time to achieve peak-shaving electricity consumption.

[0295] The optimized scheduling module is used to construct the heating characteristic function of the energy storage device; obtain the actual power consumption of the adjustable load (target area) and the actual remaining power of the energy storage device in real time; determine whether the actual remaining power is sufficient based on the actual power consumption; if sufficient, adjust the discharge power and discharge time of each energy storage device according to the heating characteristic function and the actual power consumption, without adjusting the power generation of the base load power station; if insufficient, make a secondary adjustment to the power generation of the base load power station according to the actual power consumption.

[0296] Continuous control module: used to update power generation data and energy storage data, and to synchronously regulate the power generation of the baseload power station, the voltage phase angle and current phase angle of each municipality, and the discharge power and discharge time of the energy storage device.

[0297] The above formulas are all dimensionless calculations. The formulas are derived from software simulations using a large amount of collected data to obtain the most recent real-world results. The preset parameters in the formulas are set by those skilled in the art according to the actual situation. For example, there are weighting coefficients and proportional coefficients. The values ​​set are to quantify each parameter to obtain a specific value, which is convenient for subsequent comparison. The values ​​of the weighting coefficients and proportional coefficients are only required to not affect the proportional relationship between the parameters and the quantified values.

[0298] Finally, it should be noted that the above-described embodiments are merely specific implementations of the present invention, used to illustrate the technical solutions of the present invention, and not to limit it. The scope of protection of the present invention is not limited thereto. Although the present invention has been described in detail with reference to the foregoing embodiments, those skilled in the art should understand that any person skilled in the art can still modify or easily conceive of changes to the technical solutions described in the foregoing embodiments within the technical scope disclosed in the present invention, or make equivalent substitutions for some of the technical features; and these modifications, changes, or substitutions do not cause the essence of the corresponding technical solutions to deviate from the spirit and scope of the technical solutions of the embodiments of the present invention, and should all be covered within the scope of protection of the present invention. Therefore, the scope of protection of the present invention should be determined by the scope of the claims.

Claims

1. A method for peak-shaving optimization scheduling of virtual power plants, characterized in that, The scheduling method includes: Acquire power generation and energy storage data; obtain the number of municipal districts in the target area, and obtain historical electricity consumption data for the rigid and adjustable loads corresponding to each municipal district; perform time-series analysis on the historical electricity consumption data, and calculate the expected electricity consumption of the rigid and adjustable loads for each municipal district; Construct the power characteristic function of the baseload power station; obtain the transmission power of the power grid and the remaining power of all energy storage devices, and combine the power characteristic function with the expected power consumption of the rigid load and adjustable load of each municipality to adjust the power generation of the baseload power station; determine the maximum electricity consumption time of each municipality based on the rigid load of each municipality, and adjust the voltage phase angle and current phase angle of the power grid to each municipality based on the maximum electricity consumption time; Construct a heating characteristic function for the energy storage device; obtain the actual power consumption of the adjustable load and the actual remaining power of the energy storage device in real time; determine whether the actual remaining power is sufficient based on the actual power consumption; if sufficient, adjust the discharge power and discharge time of each energy storage device according to the heating characteristic function and the actual power consumption, without adjusting the power generation of the base load power station; if insufficient, make a secondary adjustment to the power generation of the base load power station according to the actual power consumption.

2. The method for peak shaving and optimized scheduling of virtual power plants according to claim 1, characterized in that, The specific steps for calculating the expected power consumption of the rigid load and the adjustable load are as follows: Get the number of municipal districts mn; get the electricity consumption lr of the rigid load of the first municipal district from 0:00 on day 1 to 23:00 on day 3. （1,0） ~lr （3,23） ; Electricity consumption of adjustable load la （1,0） ~la （3,23） ; For lr （1,0） ~lr （3,23） and la （1,0） ~la （3,23） Perform time-series analysis to calculate the expected electricity consumption qr of the rigid load in the first municipal district. （1,0） ~qr （1,23） And the expected power consumption qa of adjustable load （1,0） ~qa （1,23） ; Calculate the expected electricity consumption qr for the rigid load corresponding to the 2nd to mnth municipal districts. （2,0） ~qr （2,23） to qr （mn,0） ~qr （mn,23） ; Expected power consumption qa for adjustable load （2,0） ~qa （2,23） to qa （mn,0） ~qa （mn,23） .

3. The method for peak shaving and optimized scheduling of a virtual power plant according to claim 2, characterized in that, The qr （1,0） ~qr （1,23） and qa （1,0） ~qa （1，23） The calculation steps are as follows: Use ADF detection to sequentially process lr （1,0）～ lr （3,23） Perform differential detection to obtain the difference order dr （1） dr （2） and dr （3） ; Determine dr （1） dr （2） and dr （3） Are the three equal? If they are equal, then for lr （1,0） ~lr （1,23） Perform d-order differencing and process with ACF and PACF to obtain the autoregressive parameter p and the moving average parameter q; where the range of values ​​for d, p and q are all [1, 24), and the values ​​of (24-p) and (24-q) are both greater than or equal to 1; Calculate the autocorrelation coefficients φ from order 1 to order p. （1） ~φ （p） and the moving average coefficients θ of orders 1 to q （1） ~θ （q） ; Calculate lr （3,0） ~lr （3,23） The average value tlr; calculate lr （3,0） ~lr （3,23） White noise ε （0） ~ε （23） Let flr be the expected electricity consumption of the rigid load at time t one day from now. （t） ; Let the autoregressive coefficient of the lth order be φ. （l） Let the rigid load electricity consumption on the 3rd day (t-l) be lr. （t－l） Let the moving average coefficient of the s-th order be θ. （s） Let the rigid load power consumption on the 3rd day (t-s) be lr. （t－s） ,lr （t－s） The corresponding white noise is ε （t－s） ; Construct formula A-5: ； Based on formula A-5, perform differential inverse operation to estimate the expected electricity consumption qr of the rigid load of the first municipal district from 0:00 to 23:00 on the next day. （1，0） ~qr （1,23） ; If dr （1） dr （2） and dr （3） If the three are not equal, then construct the sample set Xe. （0） ~Xe （23） Using a Gaussian kernel as the function and a support vector regression algorithm, the expected electricity consumption qr of the rigid load in the first municipal district from 0:00 to 23:00 on the next day is estimated. （1，0） ~qr （1,23） ; According to la （1,0） ~la （1,23） Calculate the expected electricity consumption qa of the adjustable load from 0:00 to 23:00 on the next day. （1,0） ~qa （1，23） .

4. The method for peak shaving and optimized scheduling of virtual power plants according to claim 1, characterized in that, The specific steps for adjusting the power generation capacity are as follows: Based on multinomial regression, the power characteristic function of the first to the dy-th generators is calculated with the fuel consumption of each generator as the dependent variable and the average power generation as the independent variable, resulting in the function f. （1） ~f （dy） ; Obtain the transmission power Pin of the power grid; obtain the remaining power aeq of all energy storage devices; Obtain the expected electricity consumption qr of the rigid load of all municipal districts. （1,0） ~qr （mn,23） ; Expected power consumption qa for adjustable load （1,0） ~qa （mn,23） ; Calculate qr （1,0） ~qr （mn,23） with qa （1,0） ~qa （mn,23） The sum of aqq; Obtain the actual generating power of generators 1 to dy in the baseload power station, and calculate the expected transmission power Pqn of the power grid: ； Compare the values ​​of Pin and Pqn, and adjust the power generation capacity accordingly. If Pin < Pqn, then let pk be the generating power of the first to the dy generators in the base load power station after one adjustment. （1） ~pk （dy） ; Let pk （1） ~pk （dy） The derivative of the corresponding generator power characteristic function is f 、 （1） ~f 、 （dy） ; Define formula B-1: ； Calculate pk using the Lagrange algorithm. （1） ~pk （dy） The value is used to adjust the power generation capacity of the baseload power station. If Pin≥Pqn, no processing is performed, and the power grid and the transmission model of the municipal area are defined, defining the corresponding constraints of current, power, impedance and voltage.

5. The method for peak shaving and optimized scheduling of a virtual power plant according to claim 4, characterized in that, The specific steps for defining the corresponding constraints for current, power, impedance, and voltage are as follows: Let the power of the power grid be Pw and the voltage be Uw; let the power of the municipal district be Pp and the voltage be Up. Let the branch current be I. （1） and I （4） The main circuit current is I （2） I （3） and I （5） Let the impedance be Z and the admittance be Y. （1） and Y （2） ; where Y （1） and Y （2） The values ​​are all Yy; Definition I （1） ~I （5） Current constraint: ； Define the power constraints for Pw, Pp, and Yy: ； Define the impedance constraint of Z: ； Define the voltage constraints for Pw, Uw, Pp, and Up: 。 6. The method for peak shaving and optimized scheduling of a virtual power plant according to claim 1, characterized in that, The specific steps for adjusting the voltage phase angle and current phase angle are as follows: Adjust the voltage phase angle and current phase angle corresponding to the 1st to mnth municipal districts; Obtain the expected electricity consumption qr of the rigid load of the first municipal district. （1，0） ~qr （1,23） ; Extract qr （1，0） ~qr （1,23） The maximum value qr （1，tim） ; Extract the maximum power consumption time tim; The value range of tim is 1 to 24; Extract the expected electricity consumption qa of the adjustable load of the first municipal district at time tim. （1，tim） ; Extract the expected electricity consumption qr of the rigid load of the 2nd to mnth municipal districts at time tim. （2，tim） ~qr （mn，tim） The expected power consumption qa of the adjustable load （2，tim） ~qa （mn，tim） ; Obtain transmission voltage Uwi （1） Voltage phase angle βu and current phase angle βi; Obtain the actual electricity consumption of the rigid load in the first municipal district at time (ho-1) jr （ho-1） and expected power consumption qr （ho-1） ; Calculate the proportionality coefficient br: br = jr （ho-1） / qr （ho-1） ; Obtain the power factor angles of the 2nd to mnth municipal districts, and get μ. （2） ~μ （mn） ; Compare the magnitude of br with 1, and adjust the power factor angles of the 2nd to mnth municipalities or the voltage phase angle βu and current phase angle βi during power transmission in the 1st municipality; Adjust the voltage phase angle and current phase angle corresponding to the 2nd to mnth municipal districts.

7. The method for peak shaving and optimized scheduling of a virtual power plant according to claim 6, characterized in that, The specific steps for adjusting the power factor angles of the 2nd to mnth municipal districts are as follows: If br > 1, then adjust the power factor angle of the power grid supplying power to the 2nd to mnth municipal districts to obtain μa. （2） ~μa （mn） ; Obtain the actual electricity consumption of the adjustable load in the first municipal district at (ho-1) ja （ho-1） ; Calculate the electricity consumption rate Ppi of the first municipal district at (ho-1): ； Ppi, Pwi （1） and Uwi （1） Substitute the values ​​into the voltage constraints in reverse to calculate the equivalent received voltage Upi; then substitute Upi into the current constraints, power constraints, and impedance constraints in sequence to calculate the equivalent impedance Zi. （1） Among them, Pwi （1） This indicates the power transmission capacity of the power grid supplying electricity to the first municipal district; Calculate the equivalent impedance Zi of the 2nd to mnth municipal districts. （2） ~Zi （mn） ; Calculate Zi （1） ~Zi （mn） The inverse ratio is used to obtain the diversion ratio iz for the 1st to the mnth municipal districts. （1） ~iz （mn） ; According to br calculation aiz （1） : ; Let the diversion ratio of the vth municipal district be iz. （v） The changed split ratio is biz （v） Where, the value range of v is 2 to mn; the calculation formula B-2-1 is defined as follows: ； Calculate the diversion ratio (biz) after changes in the 2nd to the mnth municipal districts. （2） ~biz （mn） ; Calculate aiz （1） ~biz （mn） The ratio of oz is used to obtain the secondary diversion ratio of the first to the mnth municipal districts. （1） ~oz （mn） ; Calculate the power factor angle θp of the first municipal district: θp = βu - βi; Let the secondary diversion ratio of the vth municipal district be oz. （v） Let μa be the power factor angle of the adjusted power grid supplying power to the v-th municipal district. （v） ; ； Calculate μa （2） ~μa （mn） .

8. The method for peak shaving and optimized scheduling of a virtual power plant according to claim 7, characterized in that, The specific steps for adjusting the power factor angles of the 2nd to mnth municipal districts also include: If br < 1, then adjust the power factor angle of the power grid supplying power to the 2nd to mnth municipal districts to obtain μb. （2） ~μb （mn） ; Calculate the diversion ratio iz for the 1st to the mnth municipal districts. （1） ~iz （mn） and aiz （1） ; Let the diversion ratio of the v-th municipal district after the change be ciz. （v） Define calculation formula B-2-2: ； According to formula B-2-2, calculate the diversion ratio ciz after the changes in the 2nd to the mnth municipal districts. （2） ~ciz （mn） ; Calculate aiz （1） :ciz （2） :~:ciz （mn） The ratio of the two values ​​is used to obtain the secondary diversion ratio pz for the 1st, 2nd, and mnth municipal districts. （1） :pz （2） :~:pz （mn） ; Let pz be the secondary diversion ratio of the vth municipal district. （v） Let the power factor angle of the adjusted power grid supplying power to the v-th municipal district be μb. （v） ; ； Calculate μb （2） ~μb （mn） .

9. A method for peak shaving and optimized scheduling of virtual power plants according to claim 7, characterized in that, The specific steps for adjusting βu and βi are as follows: If br = 1, then only the voltage phase angle βu and current phase angle βi of the first municipal district are changed to obtain βu. （1） and βi （1） ; Obtain the power generation capacity Pjw of the baseload power station and calculate qr. （1，tim） ~qr （mn，tim） and QA （1，tim） ~qa （mn，tim） The sum of qqa; Obtain the transmission power Pwi of the power grid supplying electricity to the 2nd to mnth municipal districts. （2） ~Pwi （mn） ; Calculate Pwi （1） ~Pwi （mn） and aPw; judge Is it valid? If true, then the values ​​of βu and βi remain unchanged; If this is not true, then calculate the pressure ratio values ​​of the 1st to the mnth municipalities to obtain iw. （1） ~iw （mn） ; Calculate the ratio za of the total expected power consumption （2） ~ za （mn） ; Calculate za （2） ~ za （mn） and the sum aza of them; Calculate qr （1，tim） ~qr （mn，tim） and aqr; Calculate the remaining allocable power req: ； Let qr be the electricity consumption of the rigid load in the v-th municipal district. （v，tim） The allocated power is rrq （v） The ratio of total expected electricity consumption is za （v）； Where, the value range of v is 2 to mn; the calculation formula B-4 is defined as follows: ； Calculate the allocatable electricity rrq for the 2nd to mnth municipal districts. （2） ~rrq （mn） ; Calculate the second partial voltage ratio to obtain tw （1） ~tw （mn） ; Let θa denote the power factor angle of the first municipal district after the change, and let μ denote the power factor angle of the vth municipal district. （v） The voltage divider ratio is denoted as iw （v） The second partial voltage ratio is denoted as tw （v） Calculate the value of θa: ； Obtain the power factor angle θp of the first municipal district, compare the magnitudes of θa and θp, and determine βu. （1） and βi （1） The value; If θa ≥ θp, then βu （1） for: ,βi （1） for: ; If θa < θp, then βu （1） for: ,βi （1） for: .

10. A peak-shaving optimization scheduling system for virtual power plants, applicable to the peak-shaving optimization scheduling method for virtual power plants as described in any one of claims 1-9, characterized in that, The system includes: Data acquisition module: used to acquire power generation data from distributed generation equipment and energy storage data from energy storage devices; acquire the number of municipal districts in the target area, and acquire historical electricity consumption data of rigid load and adjustable load corresponding to each municipal district; Data processing module: Used for time-series analysis of historical electricity consumption data, calculating the expected electricity consumption of rigid and adjustable loads in each municipal district; constructing the power characteristic function of the baseload power station; obtaining the transmission power of the power grid and the remaining energy of all energy storage devices, and adjusting the power generation capacity of the baseload power station based on the power characteristic function and the expected electricity consumption of rigid and adjustable loads in each municipal district; determining the maximum electricity consumption time of each municipal district based on the rigid load, and adjusting the voltage phase angle and current phase angle of the power grid to each municipal district based on the maximum electricity consumption time; The optimized scheduling module is used to construct the heating characteristic function of the energy storage device; obtain the actual power consumption of the adjustable load and the actual remaining power of the energy storage device in real time; determine whether the actual remaining power is sufficient based on the actual power consumption; if sufficient, adjust the discharge power and discharge time of each energy storage device according to the heating characteristic function and the actual power consumption, without adjusting the power generation of the base load power station; if insufficient, make a secondary adjustment to the power generation of the base load power station according to the actual power consumption. Continuous control module: used to update power generation data and energy storage data, and synchronously regulate the power generation of the baseload power station, the voltage phase angle and current phase angle of each municipality, and the discharge power and discharge time of the energy storage device.

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