A virtual power plant planning method and system based on flexibility shortage zoning assessment

Abstract

The present invention proposes a virtual power plant planning method and system based on flexibility shortage zone assessment, which includes: calculating node flexibility supply and power fluctuation; determining the transmission capacity constraints of the interconnection line, and constructing a flexibility mutual assistance scheme optimization model; solving the model to obtain a flexibility mutual assistance scheme; combining the node flexibility supply and demand with the flexibility mutual assistance scheme to calculate the regulation demand of each zone; constructing a controllable resource physical characteristic model, simulating the resource operation status and regulation capacity, and calculating the regulation capacity and capacity ratio relationship based on the simulation results; obtaining different aggregation methods and aggregation difficulties, calculating the unit capacity aggregation cost coefficient, and constructing a virtual power plant cost model; constructing an optimization configuration model with the constraint of meeting the regulation demand of each zone and the goal of minimizing the total aggregation cost, and solving to obtain the aggregation resource type and capacity of each zone virtual power plant. Based on this method, a virtual power plant planning system is also proposed. The present invention realizes fast and accurate optimization planning of virtual power plants.

Description

Technical Field

[0001] The present invention belongs to the technical field of virtual power plant planning, and in particular relates to a virtual power plant planning method and system based on flexibility deficiency zoning assessment. Background Art

[0002] With the current trend of energy restructuring and the widespread adoption of renewable energy, renewable energy sources such as wind power and photovoltaics have experienced large-scale development. The significant intermittency, volatility, and uncertainty of wind and photovoltaic power generation output leads to significant "source-load" uncertainty in the new power system, posing significant challenges to the power system's power balance and safe and stable operation. The replacement of traditional units such as thermal power with highly random and weakly controllable renewable energy sources has led to increased power system regulation demands and reduced regulation capacity. Relying solely on power source regulation capacity to meet power system regulation needs is insufficient to adapt to the new developments. It is necessary to vigorously tap into load-side resources to participate in power system interactions. Virtual power plants, a mainstream technology for load-side resource aggregation and control, can aggregate widely distributed controllable resources on the load side into a unified entity to participate in grid interaction. However, the geographical distribution of load-side controllable resources necessitates rational planning of virtual power plants based on resource distribution and grid regulation needs. Specifically, appropriate controllable resources of appropriate capacity and type should be scientifically aggregated across different regions to match grid regulation needs with the spatiotemporal characteristics of virtual power plant regulation capacity, thereby achieving scientific utilization of load-side resources and promoting renewable energy consumption.

[0003] In summary, virtual power plants, as a new type of power system, are still in the small-scale development stage. Virtual power plant resource aggregation is in a bottom-up self-organizing stage, meaning that virtual power plants aggregate all controllable resources within their jurisdiction without distinguishing between resource type, capacity, or regulation characteristics. As virtual power plant construction scales up, the disorderly development model will face interactive efficiency challenges: First, the location of virtual power plants may deviate from locations with high grid regulation demand, resulting in the virtual power plant's regulation capacity being limited by the grid's transmission capacity and unable to be fully utilized; second, the regulation characteristics of virtual power plants depend on the type of aggregated resources. The duration and time period of the virtual power plant's regulation capacity may deviate from the duration and time period of grid regulation demand, resulting in the inability to regulate when needed and the lack of regulation when available. Therefore, how to ensure the matching of regulation demand with the spatiotemporal characteristics of regulation resources and optimize the allocation of virtual power plant access areas, capacity, and aggregated resources remain urgent issues. Summary of the Invention

[0004] To address the aforementioned technical issues, this paper proposes a virtual power plant planning method and system based on flexibility deficit zoning assessment. This method quantitatively assesses grid regulation requirements based on a flexibility calculation model. Furthermore, it integrates the regulation characteristics of various controllable resources. This method optimizes the virtual power plant's access region, capacity, and aggregated resource types, taking into account the matching of grid regulation requirements with the spatiotemporal characteristics of regulation resources. This enables rapid and accurate virtual power plant planning.

[0005] A virtual power plant planning method based on flexibility deficit zoning assessment includes the following steps:

[0006] Calculate node flexibility supply and power fluctuation based on the source, load, and storage characteristics of each node; determine the transmission capacity constraints of the tie line, and based on the tie line transmission capacity constraints, construct a flexibility mutual assistance solution optimization model with the goal of minimizing the flexibility deficit of the entire network; solve the flexibility mutual assistance solution optimization model to obtain a flexibility mutual assistance solution that matches the current demand; and calculate the regulation demand of each grid zone by combining the node flexibility supply and demand with the flexibility mutual assistance solution;

[0007] Build models of the physical characteristics and user behavior of different types of controllable resources, use simulation tools to simulate the operating status and regulation capabilities of various types of resources, and calculate the proportional relationship between regulation capability and regulation capacity under different regulation durations based on the simulation results. Obtain different types of aggregation methods and aggregation difficulties, calculate the unit capacity aggregation cost coefficient, and build a virtual power plant cost model.

[0008] With the constraint of meeting the regulation needs of each grid zone and the goal of minimizing the total aggregation cost of the virtual power plant, a virtual power plant optimization configuration model is constructed. The linear programming theory is used to solve the aggregation resource type and capacity of the virtual power plant in each zone.

[0009] A virtual power plant planning system based on flexibility deficit zoning assessment includes a zoning quantification module, a model building module, and an optimization configuration module;

[0010] The partition quantification module is used to calculate the node flexibility supply and power fluctuation based on the source, load and storage characteristics of each node; determine the transmission capacity constraint of the tie line, and build a flexibility mutual assistance scheme optimization model based on the tie line transmission capacity constraint with the goal of minimizing the flexibility deficit of the entire network; obtain a flexibility mutual assistance scheme that matches the current demand by solving the flexibility mutual assistance scheme optimization model; and calculate the regulation demand of each grid partition by combining the node flexibility supply and demand with the flexibility mutual assistance scheme;

[0011] The model building module is used to obtain the physical characteristics and user behavior models of different types of controllable resources, use simulation tools to simulate the operating status and regulation capabilities of various types of resources, and calculate the proportional relationship between regulation capability and regulation capacity under different regulation durations based on the simulation results; obtain different types of aggregation methods and aggregation difficulties, calculate the unit capacity aggregation cost coefficient, and build a virtual power plant cost model;

[0012] The optimization configuration module is used to construct a virtual power plant optimization configuration model with the goal of minimizing the total aggregation cost of the virtual power plant, and to use linear programming theory to solve the aggregation resource type and capacity of the virtual power plant in each partition.

[0013] To achieve the above object, the present invention adopts the following technical solutions:

[0014] The effects provided in the summary of the invention are only the effects of the embodiments, not all the effects of the invention. One of the above technical solutions has the following advantages or beneficial effects:

[0015] The present invention proposes a virtual power plant planning method and system based on flexibility deficit zoning assessment, the method comprising the following steps: calculating node flexibility supply and power fluctuation based on source, load and storage characteristics of each node; determining the transmission capacity constraint of the interconnection line, and constructing a flexibility mutual assistance scheme optimization model with the goal of minimizing the flexibility deficit of the entire network based on the interconnection line transmission capacity constraint; obtaining a flexibility mutual assistance scheme that matches the current moment demand by solving the flexibility mutual assistance scheme optimization model; calculating the regulation demand of each grid zone by combining node flexibility supply and demand with the flexibility mutual assistance scheme; constructing physical characteristics and user behavior models of different types of controllable resources, using simulation tools to simulate the operating status and regulation capacity of various types of resources, and calculating the proportional relationship between regulation capacity and regulation capacity under different regulation times based on the simulation results; obtaining different types of aggregation methods and aggregation difficulties, calculating the unit capacity aggregation cost coefficient, and constructing a virtual power plant cost model; constrained by meeting the regulation demand of each grid zone and with the goal of minimizing the total aggregation cost of the virtual power plant, constructing a virtual power plant optimization configuration model, and using linear programming theory to solve the aggregated resource type and capacity of the virtual power plant in each zone. Based on a virtual power plant planning method based on flexibility deficit zoning assessment, a virtual power plant planning system based on flexibility deficit zoning assessment is also proposed. This method considers the source-load characteristics of each power system zone and the flexibility mutual assistance capabilities of the zone's interconnection lines, quantitatively assessing the regulation needs of each grid zone for different interactive scenarios such as peak shaving and frequency regulation. It also considers the regulation characteristics and aggregation cost differences of virtual power plants that aggregate different adjustable resources, achieving a match between regulation needs and resource spatiotemporal characteristics during virtual power plant planning.

[0016] The present invention uses parameterization to describe the differences in grid zoning regulation requirements and virtual power plant regulation characteristics, avoiding large-scale power system operation simulation of the entire system, which can significantly reduce the complexity of the planning model and achieve efficient generation of planning schemes. BRIEF DESCRIPTION OF THE DRAWINGS

[0017] Figure 1 This is a flow chart of a virtual power plant planning method based on flexibility shortage zoning assessment proposed in Example 1 of the present invention;

[0018] Figure 2 This is the adjustment demand zoning quantification process based on flexibility supply and demand analysis in Example 1 of the present invention;

[0019] Figure 3 The modeling process for the regulation characteristics of virtual power plants with different types of controllable resources in Example 1 of the present invention;

[0020] Figure 4 This is a flowchart of optimizing the configuration of a virtual power plant with minimum aggregation cost in Example 1 of the present invention;

[0021] Figure 5 This is a schematic diagram of a virtual power plant planning system based on flexibility shortage zoning assessment proposed in Example 2 of the present invention. DETAILED DESCRIPTION

[0022] Example 1

[0023] Embodiment 1 of the present invention proposes a virtual power plant planning method based on flexibility deficiency zoning assessment, which addresses the problem that the partitioned access of wind and solar renewable energy to the power system leads to temporal and spatial differences in system regulation demand.

[0024] The present invention first uses a flexibility calculation model to analyze the flexibility supply and demand of each partition of the power system, considers the existing flexibility resources of each partition, source-load changes and the transmission capacity constraints of the interconnection lines between partitions, quantitatively evaluates the flexibility margin and shortage of each partition, and forms the regulation demand of each partition; then, based on the virtual power plant refined production simulation tool, calculates the performance index differences of virtual power plants that aggregate different resources in terms of response speed, adjustable capacity, regulation cost and duration, and forms a description of the regulation characteristic parameters of different types of virtual power plants; finally, constructs an optimal configuration model for virtual power plants in power system partitions with the constraint of meeting the flexibility shortage of each partition and the goal of minimizing the aggregation cost, and quickly solves the optimal configuration plan of the virtual power plant through the optimization configuration model.

[0025] Figure 1 This is a flow chart of a virtual power plant planning method based on flexibility shortage zoning assessment proposed in Example 1 of the present invention;

[0026] In Step 1, the node flexibility supply and power fluctuation are calculated based on the source, load and storage characteristics of each node; the transmission capacity constraint of the interconnection line is determined, and based on the transmission capacity constraint of the interconnection line, a flexibility mutual assistance scheme optimization model is constructed with the goal of minimizing the flexibility shortage of the entire network; the flexibility mutual assistance scheme optimization model is solved to obtain a flexibility mutual assistance scheme that matches the current demand; and the regulation demand of each grid zone is calculated according to the flexibility mutual assistance scheme.

[0027] Taking the system composition parameters and operation data as input, the net load power change and flexibility supply of each node are first calculated according to the source, load and storage parameters and operation data of each node. Then, the optimal mutual assistance scheme for upward / downward flexibility is determined considering the maximum transmission capacity constraint of the interconnection line. Finally, the flexibility supply and demand of each node in the zone and the mutual assistance of flexibility between zones are comprehensively considered to form the upward / downward flexibility shortage curve of each zone, and the flexibility shortage is statistically analyzed to form the flexibility shortage of each zone, which is the characteristic indicator of the regulation demand of each zone. Figure 2 This is the adjustment demand zoning quantification process based on flexibility supply and demand analysis in Example 1 of the present invention.

[0028] In step 1.1, using historical statistical data and operational data of power forecast errors of loads, wind and solar power sources as input, a probability distribution model of the power forecast errors of the connected loads, wind and solar power sources is constructed for each node, and the volatility under a specified probability is calculated. The volatility is combined with operational data to calculate the power changes of loads and wind and solar power sources at each moment based on flexibility analysis theory. The upward / downward flexibility supply of thermal power and energy storage is calculated by combining source and storage installed capacity parameters with operational data.

[0029] Through investigation, the load of each node of the target system, the composition parameters of wind and solar power sources, the historical data of power prediction errors and the operating data are obtained; the composition parameters include the maximum load of each node, the installed capacity of wind and solar power sources, the historical data value of power prediction errors, the time series data sequence of load and wind and solar power output prediction errors, and the operating data include the load curve and the wind and solar power output prediction curve.

[0030] Calculate the node flexibility supply and power fluctuation based on the source, load and storage characteristics of each node; determine the node load fluctuation rate based on the obtained historical data of node load power forecast error in the process of determining the transmission capacity constraint of the interconnection line; determine the node wind and solar power source fluctuation rate based on the obtained historical data of node wind and solar power source power forecast error;

[0031] Read the historical data of the current node's load power forecast error, plot a frequency distribution histogram of the forecast error, and fit the probability distribution using various distribution forms, such as normal distribution, k² distribution, and t distribution. Select the distribution form with the smallest fitting error as the forecast error probability distribution density function. Set the forecast error probability value to be addressed, and find the error value when the cumulative probability equals the required probability as the volatility to be addressed.

[0032] The present invention describes in detail the process of distribution fitting using a normal distribution. The fitting and volatility determination methods for other distribution forms are similar. The scope of protection of the present invention is not limited to the probability distributions listed in Example 1. Those skilled in the art can make reasonable choices based on actual circumstances.

[0033] Obtain the historical data of node load power forecast error, draw the forecast error histogram and use the normal distribution to fit the distribution. Use the maximum likelihood estimation method to determine the parameters of the normal distribution of the current node load forecast error: mean and variance. Based on the 3σ principle, with the prediction error of 99.7% probability as the criterion, calculate the current node load fluctuation rate; ε Lj =3σ Lj ;(1)

[0034] Where j is the node number; ε Lj is the load fluctuation rate of node j; σ Lj is the standard deviation of the load forecast error at node j.

[0035] Read the historical data on power forecast errors for the wind and solar power sources at the current node, plot a frequency distribution histogram of the forecast errors, and fit the probability distribution using various distribution forms, such as the normal distribution, k² distribution, and t distribution. Select the distribution form with the smallest fitting error as the forecast error probability distribution density function. Set the forecast error probability value to be addressed, and find the error value when the cumulative probability equals the required probability as the volatility to be addressed.

[0036] The present invention describes in detail the process of distribution fitting using a normal distribution. The fitting and volatility determination methods for other distribution forms are similar. The scope of protection of the present invention is not limited to the probability distributions listed in Example 1. Those skilled in the art can make reasonable choices based on actual circumstances.

[0037] Obtain the historical data of the power forecast error of the wind and solar power sources at the node, draw a histogram of the forecast error and use the normal distribution for distribution fitting. Determine the parameters of the normal distribution of the wind and solar forecast error at the current node: mean and variance by the maximum likelihood estimation method. Based on the 3σ principle and the 99.7% probability forecast error as the criterion, calculate the fluctuation rate of the wind and solar power source at the current node; ε Wj =3σ Wj ;(2)

[0038] εVj =3σ Vj ; (3)

[0039] Among them, ε Wj is the wind power fluctuation rate at node j; ε Vj is the photovoltaic fluctuation rate of node j; σ Wj is the standard deviation of wind power prediction error at node j; σ Vj is the standard deviation of the photovoltaic prediction error at node j.

[0040] Considering the maximum load fluctuation rate of the node that needs to be addressed at any moment, determine the maximum load and minimum load that may occur at the next moment; calculate the difference between the maximum load that may occur at the next moment and the current load to form the maximum load increase power change; calculate the difference between the minimum load that may occur at the current moment and the next moment to form the maximum load decrease power change;

[0041] Obtain node load curve data. For any moment, consider the maximum load fluctuation that needs to be handled and calculate the possible maximum and minimum loads. Specifically:

[0042]

[0043]

[0044] in, represents the upper limit of the load fluctuation of node j at time t; represents the lower limit of load fluctuation of node j at time t; P Load,j (t) represents the actual load value of node j at time t;

[0045] The difference between the maximum possible load at the next moment and the current load is calculated to form the maximum load increase power change, specifically:

[0046] The difference between the minimum possible load at the current moment and the next moment is calculated to form the maximum load reduction power change, specifically:

[0047] in, is the maximum value of the load increase power change of node j at time t; is the maximum power reduction change of the load of node j at time t; τ represents the time scale.

[0048] Considering the maximum wind and solar power fluctuation rate of the node that needs to be dealt with at any moment, determine the maximum wind and solar output and the minimum wind and solar output that may appear at the next moment; calculate the difference between the maximum wind and solar output that may appear at the next moment and the current wind and solar output to form the maximum wind and solar power increase change; calculate the difference between the minimum wind and solar output that may appear at the current moment and the next moment to form the wind and solar power decrease change;

[0049] The maximum value of the node power increase change is calculated by using the maximum value of the load power increase change and the maximum value of the wind and solar power power increase change; the maximum value of the node power decrease change is calculated by using the maximum value of the load power decrease change and the maximum value of the wind and solar power power decrease change;

[0050] Obtain node wind power curve data. For any moment, consider the maximum wind power fluctuation that needs to be addressed and calculate the maximum and minimum wind power output that may occur at the next moment. Specifically:

[0051]

[0052]

[0053] in, is the upper limit of wind power fluctuation at node j at time t; is the lower limit of wind power fluctuation at node j at time t; P W,j (t) is the actual wind power output of node j at time t;

[0054] The difference between the maximum possible wind power output at the next moment and the current wind power output is calculated to form the maximum wind power increase change, specifically:

[0055] The difference between the minimum possible wind power output at the current moment and the next moment is calculated to form the wind power downward power change, specifically:

[0056] in, represents the maximum value of wind power increase change at node j at time t; represents the maximum value of wind power reduction change at node j at time t;

[0057] Obtain the node photovoltaic curve data, and at any moment, consider the maximum photovoltaic fluctuation that needs to be dealt with, and calculate the maximum and minimum photovoltaic output that may occur at the next moment, specifically:

[0058]

[0059]

[0060] in, represents the upper limit of photovoltaic fluctuation of node j at time t; represents the lower limit of photovoltaic fluctuation at node j at time t, P V,j (t) represents the actual photovoltaic output of node j at time t.

[0061] Calculate the difference between the maximum possible photovoltaic output at the next moment and the current photovoltaic output to form the maximum photovoltaic power increase change:

[0062] Calculate the difference between the minimum possible photovoltaic output at the current moment and the next moment to form the maximum photovoltaic power reduction change:

[0063] in, represents the maximum value of the photovoltaic power increase change at node j at time t; It represents the maximum value of the photovoltaic power reduction change at node j at time t.

[0064] Taking into account the maximum values of load, wind and solar power upward and downward power changes, the maximum value of net load power change at node j is calculated, specifically:

[0065]

[0066]

[0067] in, Indicates the maximum value of the power change at node j at time t, It represents the maximum power reduction change of node j at time t.

[0068] Read the composition parameters and operating data of the thermal power unit connected to the node, where the composition parameters include the maximum and minimum technical output and up and down climbing parameters of the thermal power unit, and the operating data includes the output data of the thermal power unit.

[0069] Combined with the thermal power unit parameters and operating data, the ramp rate and output range of thermal power are considered to limit the calculation of the up and down adjustment flexibility provided by the thermal power unit at node j, specifically:

[0070]

[0071]

[0072] in, is the upward climbing rate of conventional thermal power units; is the downward ramp rate of conventional thermal power units; Provide the maximum technical output for thermal power units; The minimum technical output of thermal power units; P g,j (t) is the output of the thermal power unit at time t; It represents the maximum value of power that can be increased by the thermal power unit at node j at time t under the time scale τ; It represents the maximum value of power reduction that the thermal power unit at node j can achieve at time t in the time scale τ.

[0073] Read the configuration parameters and operating data of the energy storage connected to the node. The configuration parameters include the maximum charge and discharge power of the energy storage and the maximum and minimum state of charge. The operating data includes the energy storage output data and state of charge change data.

[0074] Combining energy storage parameters with operational data, and considering the maximum charge and discharge power constraints and state of charge constraints of the energy storage, the up and down adjustment flexibility provided by the energy storage at node j is calculated; specifically:

[0075]

[0076]

[0077] in, represents the maximum discharge power of the energy storage device at node j; P ess,j,ch (t) represents the actual charging power of the energy storage device at node j at time t; P ess,j,dis (t) represents the actual discharge power of the energy storage device at node j at time t; η ch is the rated charging efficiency of the energy storage device; η dis is the rated discharge efficiency of the energy storage device; E j (t) represents the amount of electricity in the energy storage device at node j at time t; represents the maximum capacity of the energy storage device at node j at time t; represents the minimum power of the energy storage device at node j at time t; represents the maximum adjustable power of the energy storage device at node j at time t; It represents the maximum value of the power that can be reduced by the energy storage device at node j at time t.

[0078] Adding the upward flexibility provided by thermal power and energy storage, and adding the downward flexibility provided by thermal power and energy storage, we get:

[0079]

[0080]

[0081] in, represents the upward flexibility supply of node j at time t; represents the downward flexibility supply of node j at time t.

[0082] Calculate the flexibility deficit / surplus of node j based on the power adjustment change of node flexibility demand and flexibility supply. Calculate the flexibility surplus and flexibility deficit of node j at time t based on the flexibility supply of node j at time t and the maximum power adjustment change of node j at time t.

[0083] The downward flexibility margin and downward flexibility deficit of node j at time t are calculated based on the downward flexibility supply of node j at time t and the maximum downward power change of node j at time t; specifically:

[0084]

[0085]

[0086]

[0087]

[0088] in, represents the upward flexibility margin of node j at time t; represents the downward flexibility margin of node j at time t; represents the flexibility shortfall of node j at time t; represents the downward flexibility shortfall of node j at time t; N is the total number of nodes.

[0089] Check whether all nodes have been analyzed. If so, proceed to step 1.2. Otherwise, read the next node for analysis and increase the node number by 1, then proceed to step 3.

[0090] Step 1.2: Using the net power adjustment change of node flexibility and the flexibility supply timing curve as input, determine whether there is a power shortage at each moment based on the system flexibility supply and demand. If there is a flexibility shortage, build an upward / downward flexibility mutual assistance optimization model that considers the transmission capacity of the tie line and the node adjustable range. Determine the flexibility mutual assistance solution that matches the current demand through model solution.

[0091] Taking the maximum power adjustment change of node flexibility demand in step 1.1 and node flexibility supply as input, calculate the total system flexibility demand and supply curves.

[0092] The total flexibility demand of the system is calculated by taking the maximum value of the power adjustment change of the node flexibility demand as input; the flexibility demand includes the upward flexibility demand and downward adjustment of flexibility requirements

[0093] The total system flexibility supply is calculated with the node flexibility supply as input; the flexibility supply includes the upward flexibility supply and lowering flexibility supply

[0094] Flexibility requirements will be increased and increase flexibility supply Worst case flexibility shortfall Flexibility requirements will be lowered and lowering flexibility supply Worst case flexibility shortfall

[0095] The specific calculation process is:

[0096]

[0097]

[0098]

[0099]

[0100] in, is the upward flexibility requirement of the system at time t; is the downward flexibility requirement of the system at time t; The upward flexibility supply for the system at time t; The downward flexibility supply for the system at time t;

[0101] The flexibility deficit curve of the system is calculated by subtracting the flexibility supply from the flexibility demand:

[0102]

[0103]

[0104] in, is the upward flexibility shortfall of the system at time t; is the system's downward flexibility shortfall at time t.

[0105] Based on the positive or negative flexibility shortfall, the flexibility shortfall status is marked as follows:

[0106]

[0107] Among them, Statefle(t) is the flexibility state of the system at time t, where 0 represents no shortage, 1 represents an upward shortage, and 2 represents a downward shortage.

[0108] Construct the system branch and node correlation matrix C based on the connection between network nodes and branches and their relationship with the positive direction of branch power flow;

[0109]

[0110] Where L is the total number of lines; l represents the lth line, l = 1, 2, ..., L; n = 1, 2, ..., j, ..., N; the value rules for each element of C are:

[0111]

[0112] c l,j Represents the j-th node element of line l;

[0113] According to the physical parameters of line impedance, the line admittance diagonal matrix A=diag(1 / x l ) is in the form of:

[0114]

[0115] Each element in the matrix A is the reciprocal of the reactance of line l, that is, 1 / x l ;x l is the reactance of line 1.

[0116] The branch power transfer distribution factor matrix H is calculated using C and A as follows:

[0117] H=[diag(1 / x l )C]{C T diag(1 / x l )C} -1 ; (38)

[0118] Among them, H is an L×N dimensional matrix; C T is the transposed matrix of matrix C.

[0119] If Statefle(t) is 0, check whether all moments have been traversed;

[0120] If Statefle(t) is 1, then To select the nodes with sufficient flexibility at the current moment for the criteria, a node set is formed. by Filter the nodes with increased flexibility at the current moment as the criteria to form a node set

[0121] Taking the power increase amount of all nodes as the variable to be optimized, the line transmission power and the node increase range as constraints, and the minimum increase flexibility shortfall of the system after transmission flexibility as the objective function, a mutual optimization model for increase flexibility is constructed, which is as follows:

[0122]

[0123] Where, is the upward flexibility of the mutual aid output of node j at time t; h l,j is the element in the lth row and jth column of the matrix H; is the power change of line l at time t; P l (t) is the original transmission power of line l at time t; is the upper limit of line 1 transmission capacity, N l is the set of tie lines whose transmission capacity needs to be considered; Represents the increase in flexibility shortage node set; Represents an increase in the set of nodes with rich flexibility.

[0124] Solve the mutual assistance optimization model for increasing flexibility to obtain the mutual assistance plan, and calculate the change in line flexibility supply obtained by mutual assistance at each node:

[0125]

[0126]

[0127] in, is the change in flexibility supply at node j caused by flexibility mutual assistance at time t; The change in flexibility supply at node j caused by flexibility mutual assistance at time t.

[0128] Then check if all moments are traversed;

[0129] If Statefle(t) is 2, To select the nodes with sufficient flexibility at the current moment, a node set is formed. by Filter the nodes with reduced flexibility at the current moment as the criteria to form a node set

[0130] Taking the power reduction amount of all nodes as the variable to be optimized, the line transmission power and the node reduction range as constraints, and the minimum system reduction flexibility deficit after transmission flexibility as the objective function, a mutual optimization model for reduction flexibility is constructed, which is as follows:

[0131]

[0132] Where, is the downward flexibility of the mutual aid input of node j at time t; ΔP l dn (t) is the power change of line l at time t; To lower the flexibility shortage node set; To reduce the flexibility of the rich node set.

[0133] Solve the mutual assistance optimization model for upward or downward flexibility to obtain the mutual assistance plan, and calculate the line flexibility supply obtained through line transmission:

[0134]

[0135]

[0136] Then check whether all moments have been traversed until all moments have been traversed.

[0137] In step 1.3, the node flexibility supply and net load power change time series curve and the zone flexibility mutual benefit curve are used as input to calculate the zone flexibility deficit of the power grid. The flexibility deficit is statistically analyzed to form a characteristic index of the flexibility deficit of each zone, thus completing the quantification of the zone grid regulation demand. The specific steps are as follows:

[0138] Divide the target system into r partitions Ω1, Ω2…Ω according to administrative regions r ;Ω1 is the first partition;Ω r is the rth partition; let the area to be analyzed r=1;

[0139] The maximum sum of the net power increase / decrease changes of all nodes in the partition is used to calculate the partition's power increase / decrease flexibility requirement, specifically:

[0140]

[0141]

[0142] in, is the upward flexibility demand of partition r at time t; is the downward flexibility demand of partition r at time t;

[0143] The flexibility supply of all nodes in the zone and the flexibility transmitted via the line are summed to calculate the upward / downward flexibility supply of the zone, specifically:

[0144]

[0145]

[0146] in, is the upward flexibility supply of partition r at time t; is the downward flexibility supply of partition r at time t;

[0147] The flexibility shortage of zone r is calculated by taking the difference between the zone flexibility supply and demand and taking the positive value, which is:

[0148]

[0149]

[0150] in, To take into account the upward flexibility shortfall of zone r at time t after the node flexibility supply and demand and line flexibility complement each other; This is the downward flexibility shortfall of zone r at time t after taking into account the mutual benefit between node flexibility supply and demand and line flexibility;

[0151] right and Perform statistical analysis and characterize the grid regulation demand as average value over the entire operating cycle:

[0152]

[0153]

[0154] in, Adjust the demand for partition r upward; Adjust the demand downward for partition r;

[0155] In step 1.4, the flexibility deficit is divided into different time scales to obtain the peak load and frequency regulation requirements of each grid zone. In this application, the first time scale is 1 hour and the second time scale is 1 minute.

[0156] Perform steps 1.1 to 1.4 for the 1h and 1min time scales respectively to calculate the hourly and minute-level flexibility deficits of each grid zone, i.e., the peak load regulation of each grid zone. and FM Adjust demand.

[0157] In Step 2, we build models of the physical characteristics and user behavior of different types of controllable resources, use simulation tools to simulate the operating status and regulation capabilities of each type of resource, and calculate the proportional relationship between regulation capability and regulation capacity under different regulation durations based on the simulation results. We also obtain different types of aggregation methods and aggregation difficulties, calculate the unit capacity aggregation cost coefficient, and build a virtual power plant cost model.

[0158] Based on the physical characteristics of different types of controllable resources and user behavior models, a refined operation simulation program is developed as a tool; the refined operation simulation program is used to simulate the operating status and regulation capacity of different types of resources, and the proportional relationship between regulation capacity and regulation capacity under different regulation times (i.e., aggregation capacity coefficient) is calculated based on the simulation results; different types of aggregation methods and difficulties are investigated, the unit capacity aggregation cost coefficient is calculated, and a virtual power plant cost model is constructed.

[0159] Figure 3The modeling process for the regulation characteristics of virtual power plants with different types of controllable resources in Example 1 of the present invention;

[0160] In Step 2.1: Research and analyze the key factors influencing the adjustable capacity of each controllable resource, including the distribution of resource physical and user behavior parameters, meteorological conditions, etc.

[0161] If the controllable resource is air conditioning, the physical parameters mainly include the energy efficiency ratio, rated power, room thermal resistance, heat capacity parameters, and air conditioning temperature control dead zone of the air conditioning system; user behavior parameters include user set temperature, indoor temperature, and allowable indoor temperature upper and lower limits; meteorological condition parameters include outdoor temperature, etc.

[0162] If the controllable resource is an electric vehicle, the physical parameters mainly include the rated capacity and rated operating power of the electric vehicle battery, the upper and lower limits of SOC operation, the maximum / minimum charge and discharge power, and the charge and discharge efficiency; the user behavior parameters include the time between on-grid and off-grid connection, the expected SOC value when off-grid, etc.; there are no meteorological condition parameters;

[0163] If the controllable resource is a distributed power source, the physical parameters mainly consider the installed capacity and rated power of the power source; in terms of meteorological condition parameters, solar power generation mainly considers light intensity, temperature, etc.; wind power generation mainly considers factors such as wind speed.

[0164] If the controllable resource is distributed energy storage, the main considerations are physical parameters such as the energy storage system's charging and discharging power, battery rated capacity, and charging and discharging efficiency, without user behavior parameters or meteorological condition parameters.

[0165] In step 2.2, a refined production simulation program is developed based on the physical characteristics of each controllable resource and the user behavior model.

[0166] If the controllable resource is an air conditioner, the air conditioner compressor load percentage is used as the control signal, the room temperature is used as the state variable, and the outdoor temperature is used as the input variable. Based on the principle of spatial thermal balance, the room temperature variation equation and the air conditioner control law equation are constructed to form the air conditioner physical model. When a first-order temperature variation equation is used to describe the room temperature variation, and the air conditioner adopts hysteresis control, the physical model is as follows. Other second-order temperature variation equations and PI control laws are similar to the following form.

[0167] The physical model of the air conditioner is:

[0168]

[0169]

[0170] in, For the i ACThe percentage of load on the air conditioner compressor; when m(t) = 0-1, it is a continuously adjustable variable frequency air conditioner; when m(t) = 0 or 1, it is a fixed frequency air conditioner that can only be started and stopped; for i AC The indoor temperature state variable of the air conditioner at time t; For the i AC The outdoor temperature of the air conditioner at time t; for i AC The set temperature of the air conditioner at time t; For the i AC Air conditioner temperature control dead zone; For the i AC Equivalent heat capacity of air conditioner (kJ / ℃); For the i AC Equivalent thermal resistance of air conditioner (℃ / kW); For the i AC Refrigeration energy efficiency coefficient of air conditioner; For the i AC The rated power of the air conditioner; ε is a very small time lag.

[0171] If the controllable resource is an electric vehicle, the percentage of the electric vehicle's power battery is used as the state variable, and the charge and discharge power is used as the input variable. The physical model of the electric vehicle is constructed based on the energy integration principle. When the time between off-grid connection and the expected state of charge is used to describe user behavior, the physical model is as follows. This can be expanded to include more complex forms that consider the relationship between the power battery capacity and the charge and discharge rated power and rate. The physical model of the electric vehicle is:

[0172]

[0173] in, for i EV State of charge of electric vehicles; For the i EV Charging and discharging power of electric vehicles; for i EV Rated capacity of electric vehicle batteries; After the adjustment is completed, EV Operating power of electric vehicles; is the adjustment time of the electric vehicle; t0 is the time when the adjustment command is issued; For the i EV Rated efficiency of electric vehicle batteries; The time for charging and grid connection; Off-grid time for charging.

[0174] If the controllable resource is a distributed power source, with natural conditions such as wind speed, sunlight, and temperature as input and generator power as output, its physical model is constructed based on the conversion characteristics of wind power and photovoltaic units. Assuming that wind power is linearly related to the cubic power of wind speed, the physical model of distributed wind power is:

[0175]

[0176] in, For the i WT Output power of typhoon turbine generator set at time t; For the i WT Typhoon turbine rated power; For the i WT Typhoon turbine wind speed; are the i-th WT The cut-in wind speed, cut-out wind speed and rated wind speed of typhoon turbines.

[0177] Assuming that the conversion characteristics of the photovoltaic unit are the photoelectric effect equation, the distributed photovoltaic physical model is:

[0178]

[0179] is the i-th PV Output power of photovoltaic units; is the i-th PV Irradiation intensity of photovoltaic units; For the i PV The maximum output power of a photovoltaic unit under the preset test conditions; G STC is the light intensity under the preset test conditions; k is the power temperature coefficient; For the i PV The operating temperature of the photovoltaic unit components; T f is the reference temperature.

[0180] If the controllable resource is distributed energy storage, the battery power percentage is used as the state variable and the charge and discharge power is used as the input variable. A distributed energy storage physical model is constructed based on the energy integration principle. The physical model is:

[0181]

[0182] in, is the i-th DES Energy storage state of charge; is the i-th DES Energy storage charging power; is the i-th DES Energy storage discharge power; For the i DES Rated capacity of energy storage batteries; For the i DES Rated charging efficiency of each energy storage battery; For the i DES Rated discharge efficiency of each energy storage battery; For the i DES Maximum charging power of distributed energy storage; For the i DES The maximum discharge power of a distributed energy storage.

[0183] Based on the distribution of physical and user behavior parameters of various types of controllable resources, Monte Carlo simulation is used to generate the corresponding parameters of each resource unit. During the simulation process, multiple groups of parameter samples are generated through random sampling.

[0184] Based on the distribution of physical and user behavior parameters of various types of controllable resources, Monte Carlo simulation is used to generate the corresponding parameters of each resource unit. During the simulation process, multiple groups of parameter samples are generated through random sampling.

[0185] If the controllable resource is air conditioning, the generated parameters include equivalent heat capacity Thermal resistance Air conditioning and refrigeration energy efficiency coefficient Air conditioner rated power Indoor and outdoor temperature and the upper and lower limits of indoor temperature Indoor temperature set point

[0186] If the controllable resource is an electric vehicle, the generated parameters include the time when the electric vehicle enters the grid t iEV,in , the time when electric vehicles leave the grid Electric vehicle rated power Charge and discharge efficiency Electric vehicle battery rated capacity Maximum and minimum state of charge of electric vehicles Maximum charge and discharge power The minimum state of charge that an electric vehicle must achieve when it is off-grid After the adjustment is completed, EV The operating power of electric vehicles.

[0187] If the controllable resource is a distributed power source, the generated parameters include the cut-in of distributed wind power. Cut out Rated wind speed and distributed wind power rated power Distributed photovoltaic generation parameters include maximum output power Power temperature coefficient k, photovoltaic module operating temperature With reference temperature T f , light intensity GSTC ; Generate maximum and minimum output of distributed power

[0188] If the controllable resource is distributed energy storage, the generated parameters include the rated power of the energy storage battery Charge and discharge efficiency Maximum charge and discharge power of energy storage Maximum and minimum state of charge for energy storage and

[0189] Substitute the parameters of each type of controllable resource unit into the physical model for status update, calculate the power of each unit at each moment, and form the power baseline of each type of resource unit and

[0190] The regulation capacity of each resource unit is calculated by comprehensively considering power and energy constraints. Taking indoor temperature as the state variable, the air conditioner regulation capacity calculation process includes:

[0191]

[0192]

[0193]

[0194]

[0195]

[0196]

[0197] in, is the reciprocal of the time constant of room temperature change; is the total electrical energy that the room can store; Set the ratio of the temperature range to the temperature change time ratio; For the i AC Maximum temperature of air-conditioned room; For the i AC Minimum temperature of air-conditioned room; For the i AC Air conditioning set temperature; For the i AC Air conditioner operating power limit; For the i AC Air conditioner operating power lower limit; For the i AC Air conditioner temperature status at time t; Adjust the time for the air conditioner.

[0198] Taking the state of charge of the electric vehicle battery as the state variable, calculate the intermediate variable

[0199]

[0200] in, For the i EV The expected minimum state of charge of an electric vehicle when it is off-grid; For the i EV Rated charging efficiency of electric vehicles; For the i EV Maximum charging power of electric vehicles; For the i EV The time when electric vehicles are off the grid; For the i EV The time when the electric vehicle adjustment instruction is issued; For the i EV Rated capacity of electric vehicle batteries; Adjust duration for electric vehicles.

[0201] Using the intermediate variables Calculate the minimum charging power of the electric vehicle at the current moment:

[0202]

[0203] in, For the i EV Maximum discharge power of electric vehicles; For the i EV Rated discharge efficiency of electric vehicles; For the i EV The state of charge of the electric vehicle battery at the time when the electric vehicle adjustment command is issued; t0 is the time when the command is issued; For the i EV The lower limit of the state of charge of the battery of an electric vehicle; For the i EV Minimum charging power for electric vehicles;

[0204] The maximum charging power of electric vehicles is:

[0205] in, For the i EV Upper limit of state of charge of electric vehicle battery; For the i EV The maximum charging power of an electric vehicle.

[0206] Calculate the current power that can be increased and decreased by the electric vehicle:

[0207]

[0208] For the i EV The ability of electric vehicles to increase at time t; For the i EV The down-regulation capability of electric vehicles at time t;

[0209] The calculation process of the adjustable capacity of distributed power generation includes:

[0210]

[0211]

[0212] in, for i DE The regulation capacity of a distributed power source at time t; for i DE The down-regulation capability of a distributed power source at time t; for i DE Maximum output of a distributed power source; for i DE Minimum output of a distributed power source; for i DE The output of each distributed power source at time t;

[0213] The regulation capability expression of distributed energy storage is:

[0214]

[0215]

[0216] in, For the i DES The capacity of distributed energy storage to increase at time t; For the i DES The down-regulation capability of a distributed energy storage at time t; For the i DES A maximum state of charge limit for distributed energy storage; For the i DES A minimum state of charge limit for distributed energy storage;

[0217] The total adjustment capacity of each type of resource is:

[0218]

[0219] in, Increase the total capacity of each type of resource; The total downward adjustment capacity for each type of resource; Increase the capacity of any type of resource at time t; The capacity of each type of resource unit at time t is lowered; N typeis the number of aggregated monomers of each type of resource; i∈(i AC ,i EV ,i DE ,i DES ).

[0220] In step 2.3, for any type of controllable resource, change the aggregate capacity and perform multiple refinements.

[0221] Through production simulation, we can obtain the time-varying curves of different adjustable capacities under different polymerization capacities;

[0222] The process of obtaining different types of aggregation methods and aggregation difficulties, calculating the unit capacity aggregation cost coefficient, and building a virtual power plant cost model includes:

[0223] Calculate the aggregate capacity as follows:

[0224]

[0225] Among them, E type Aggregate capacity for each type of resource; The rated power of any type of resource unit;

[0226] Calculate the statistical index of the adjustable capacity by taking statistics of the adjustable capacity change curve with an adjustment time of 1 hour;

[0227]

[0228] Increase the capacity for peak shaving of various types of resources; The downward regulation capacity of various types of resources;

[0229] Comparing the 1h timescale regulation capability statistical index with the aggregate capacity, the peak regulation aggregate capacity coefficient is obtained:

[0230] Where, The aggregate capacity coefficient for each type of resource peak load scenario, The aggregate capacity coefficient for each type of resource peak-shaving scenario;

[0231] Statistics of the adjustable capacity change curve with an adjustment time of 1 minute are collected to calculate the statistical index of the adjustable capacity;

[0232]

[0233] Where, Provides frequency modulation capabilities for various types of resources, Provides the ability to adjust the frequency of various types of resources;

[0234] Comparing the 1-minute time scale regulation capability statistical indicator aggregation capacity, we can obtain the frequency modulation aggregation capacity coefficient:

[0235] in, The upper aggregation capacity coefficient for each type of resource frequency modulation scenario; It is the lower aggregate capacity coefficient in each resource frequency modulation scenario.

[0236] Investigate the aggregation methods of various types of controllable resources, analyze the fixed investment and variable investment required for aggregation, and calculate the unit capacity aggregation cost of different controllable resources.

[0237] If the controllable resource is air conditioning, the investment includes the control platform software and the monitoring data acquisition terminal. The former is a fixed investment that is unrelated to the aggregated capacity, while the latter is a variable investment that is proportional to the aggregated capacity. The unit capacity aggregation cost coefficient of air conditioning is:

[0238]

[0239] Among them, K AC is the unit capacity aggregation cost coefficient of air conditioning; I AC Fixed investment for the management and control platform; c AC Invest in monitoring data collection terminals;

[0240] If the controllable resource is an electric vehicle, the investment includes the construction cost of the communication module that controls the data exchange between the platform software and the charging infrastructure. The former is a fixed investment that is unrelated to the aggregated capacity, while the latter is a variable investment that is proportional to the aggregated capacity. The unit capacity aggregation cost coefficient of electric vehicles is:

[0241]

[0242] Among them, K EV I is the aggregate cost coefficient per unit capacity of electric vehicles; EV Fixed investment for the management and control platform; c EV Construction cost of the communication module.

[0243] If the controllable resource is a distributed power source, the investment includes the communication module for data exchange between the control platform software and the distributed power generation equipment. The former is a fixed investment unrelated to the aggregated capacity, while the latter is a variable investment proportional to the aggregated capacity. The unit capacity aggregation cost coefficient of the distributed power source is:

[0244]

[0245] Among them, K DE I is the aggregated cost coefficient of distributed power unit capacity; DE Fixed investment for the management and control platform; c DEConstruction cost for the communication module;

[0246] If the controllable resource is distributed energy storage, the investment includes the communication module for data exchange between the control platform software and the energy storage control system. The former is a fixed investment unrelated to the aggregated capacity, while the latter is a variable investment proportional to the aggregated capacity. The unit capacity aggregation cost coefficient of distributed energy storage is:

[0247]

[0248] Among them, K DES I is the aggregated cost coefficient of distributed energy storage per unit capacity; DES Fixed investment for the management and control platform; c DES Construction cost of the communication module.

[0249] In Step 3, a virtual power plant optimization configuration model is constructed with the constraint of meeting the regulation needs of each grid zone and the goal of minimizing the total aggregation cost of the virtual power plant. The linear programming theory is used to solve the aggregation resource type and capacity of the virtual power plant in each zone.

[0250] Figure 4 This is a flowchart of optimizing the configuration of a virtual power plant with minimum aggregation cost in Example 1 of the present invention; input and the unit capacity aggregation cost coefficient; with the goal of minimizing the total aggregation cost of demand-side resources, and with the upper limit of the aggregated capacity of various resources and the requirements of peak load and frequency regulation in each zone as constraints, an optimization configuration model is constructed. The objective function and capacity upper limit constraints are as follows:

[0251]

[0252]

[0253]

[0254]

[0255]

[0256] in, For partition Ω r The upper limit of the aggregated capacity of medium air conditioners; For partition Ω r The upper limit of the aggregated capacity of electric vehicles in China; Partition Ω r The upper limit of the aggregated capacity of distributed power generation; For partition Ω r The upper limit of the aggregated capacity of distributed energy storage; For partition Ω r Aggregate capacity of energy storage; For partition Ωr Aggregate capacity of electric vehicles; For partition Ω r Aggregate capacity of distributed generation.

[0257] In the peak load regulation scenario, considering the combination of energy storage, electric vehicles and distributed power sources to meet the peak load regulation demand, the constraints are:

[0258]

[0259]

[0260] In the frequency regulation scenario, the aggregation of distributed power sources, energy storage, electric vehicles, and temperature control loads is considered to meet the frequency regulation requirements:

[0261]

[0262]

[0263] Use CPLEX or gurobi solver to solve the above optimization model and obtain the aggregate capacity of each type of resource in each partition virtual power plant. and This is the optimized configuration result of the virtual power plant.

[0264] A virtual power plant planning method based on flexibility deficit zoning assessment proposed in Example 1 of the present invention can take into account the source-load characteristics of each power system zone and the flexibility mutual assistance capabilities of the zone interconnection lines, quantitatively evaluate the regulation needs of different interactive scenarios such as peak regulation and frequency regulation in each power grid zone, and at the same time take into account the regulation characteristics and aggregation cost differences of virtual power plants that aggregate different adjustable resources, so as to achieve matching of regulation needs with the spatiotemporal characteristics of resources in the virtual power plant planning process.

[0265] Example 1 of the present invention proposes a virtual power plant planning method based on flexibility deficiency zoning assessment, which uses parameterization to describe the differences in grid zoning regulation requirements and virtual power plant regulation characteristics, avoids large-scale power system operation simulation of the entire system, and can significantly reduce the complexity of the planning model and achieve efficient generation of planning schemes.

[0266] Example 2

[0267] Based on the virtual power plant planning method based on flexibility deficiency zoning assessment proposed in Example 1 of the present invention, a virtual power plant planning system based on flexibility deficiency zoning assessment is also proposed. Figure 5 This is a schematic diagram of a virtual power plant planning system based on flexibility deficit zoning assessment proposed in Example 2 of the present invention, which includes a zoning quantification module, a model building module, and an optimization configuration module;

[0268] The partition quantification module is used to calculate the node flexibility supply and power fluctuation based on the source, load and storage characteristics of each node; determine the transmission capacity constraints of the tie line, and based on the tie line transmission capacity constraints, construct a flexibility mutual assistance scheme optimization model with the goal of minimizing the flexibility deficit of the entire network; by solving the flexibility mutual assistance scheme optimization model, a flexibility mutual assistance scheme that matches the current demand is obtained; and combine the node flexibility supply and demand with the flexibility mutual assistance scheme to calculate the regulation demand of each grid partition;

[0269] The model building module is used to obtain the physical characteristics and user behavior models of different types of controllable resources, use simulation tools to simulate the operating status and regulation capabilities of various types of resources, and calculate the proportional relationship between regulation capability and regulation capacity under different regulation durations based on the simulation results. It also obtains different types of aggregation methods and aggregation difficulties, calculates the unit capacity aggregation cost coefficient, and constructs a virtual power plant cost model.

[0270] The optimization configuration module is used to construct a virtual power plant optimization configuration model with the constraint of meeting the regulation needs of each grid zone and the goal of minimizing the total aggregation cost of the virtual power plant. The linear programming theory is used to solve the aggregation resource type and capacity of the virtual power plant in each zone.

[0271] A virtual power plant planning system based on flexibility deficit zoning assessment proposed in Example 2 of the present invention can take into account the source-load characteristics of each power system zone and the flexibility mutual assistance capabilities of the zone interconnection lines, quantitatively evaluate the regulation needs of different interactive scenarios such as peak regulation and frequency regulation in each power grid zone, and at the same time take into account the regulation characteristics and aggregation cost differences of virtual power plants that aggregate different adjustable resources, so as to achieve matching of regulation needs with the spatiotemporal characteristics of resources in the virtual power plant planning process.

[0272] Example 2 of the present invention proposes a virtual power plant planning system based on flexibility deficiency zoning assessment, which uses parameterization to describe the differences in grid zoning regulation requirements and virtual power plant regulation characteristics, avoids large-scale power system operation simulation of the entire system, and can significantly reduce the complexity of the planning model and achieve efficient generation of planning schemes.

[0273] The description of the relevant parts of the virtual power plant planning system based on flexibility deficiency zoning assessment provided in Example 2 of the present application can be found in the detailed description of the corresponding parts of the virtual power plant planning method based on flexibility deficiency zoning assessment provided in Example 1 of the present application, and will not be repeated here.

[0274] Although the above description is of specific embodiments of the present invention in conjunction with the accompanying drawings, it does not limit the scope of protection of the present invention. For those skilled in the art, other different forms of modifications or variations can be made based on the above description. It is not necessary and impossible to list all embodiments here. Based on the technical solution of the present invention, various modifications or variations that can be made by those skilled in the art without expending creative effort are still within the scope of protection of the present invention.

Claims

1. A virtual power plant planning method based on flexibility deficit zoning assessment, characterized in that: The following steps are involved: Calculate node flexibility supply and power fluctuation based on the source, load, and storage characteristics of each node; determine the transmission capacity constraints of the tie line, and based on the tie line transmission capacity constraints, construct a flexibility mutual assistance solution optimization model with the goal of minimizing the flexibility deficit of the entire network; solve the flexibility mutual assistance solution optimization model to obtain a flexibility mutual assistance solution that matches the current demand; and calculate the regulation demand of each grid zone by combining the node flexibility supply and demand with the flexibility mutual assistance solution; Calculate node flexibility supply and power fluctuation based on the source, load and storage characteristics of each node; The process of determining the transmission capacity constraints of the tie line includes: Determine the node load fluctuation rate based on the acquired node load power prediction error historical data; determine the node wind and solar power source fluctuation rate based on the acquired node wind and solar power source power prediction error historical data; Considering the maximum load fluctuation rate of the node that needs to be addressed at any moment, determine the maximum load and minimum load that may occur at the next moment; calculate the difference between the maximum load that may occur at the next moment and the current load to form the maximum load increase power change; calculate the difference between the minimum load that may occur at the current moment and the next moment to form the maximum load decrease power change; Considering the maximum wind and solar power fluctuation rate of the node that needs to be dealt with at any moment, determine the maximum wind and solar output and the minimum wind and solar output that may appear at the next moment; calculate the difference between the maximum wind and solar output that may appear at the next moment and the current wind and solar output to form the maximum wind and solar power increase change; calculate the difference between the minimum wind and solar output that may appear at the current moment and the next moment to form the wind and solar power decrease change; The maximum value of the node power increase change is calculated by using the maximum value of the load power increase change and the maximum value of the wind and solar power power increase change; the maximum value of the node power decrease change is calculated by using the maximum value of the load power decrease change and the maximum value of the wind and solar power power decrease change; Combined with the thermal power unit parameters and operating data, the calculation node is considered to limit the thermal power ramp rate and output range. Thermal power units provide flexibility in terms of up and down regulation, specifically: ； ； in, is the upward climbing rate of conventional thermal power units; is the downward ramp rate of conventional thermal power units; Provide the maximum technical output for thermal power units; The minimum technical output of thermal power units; for The output of thermal power units at each moment; Indicates Nodes under time scale Thermal power units in The maximum power value can be adjusted at any time; Indicates Nodes under time scale Thermal power units in The maximum power value can be adjusted down at any time; Combined with energy storage parameters and operating data, considering the maximum charge and discharge power constraints and charge state constraints of energy storage, the node Energy storage provides flexibility in terms of up and down regulation; specifically: ； ； in, Representation node Maximum discharge power of the energy storage device; Representation node Energy storage device Actual charging power at all times; Representation node Energy storage device Actual discharge power at any moment; is the rated charging efficiency of the energy storage device; is the rated discharge efficiency of the energy storage device; Representation node Energy storage device The amount of electricity at the moment; Representation node Energy storage device Maximum power at the moment; Representation node Energy storage device Minimum power at the moment; Representation node Energy storage device The maximum value of the power that can be increased at any time; Representation node Energy storage device The maximum value of power that can be adjusted downward at the moment; Adding the upward flexibility provided by thermal power and energy storage, and adding the downward flexibility provided by thermal power and energy storage, we get: ； ； in, Representation node exist The upward flexibility supply at all times; Representation node exist The supply of downward flexibility at all times; According to the node exist Increased flexibility supply and nodes at all times exist The maximum value calculation node of the power increase change at time exist The upward flexibility margin and upward flexibility shortfall at any given time; According to the node exist Moment-by-moment flexibility supply and nodes exist The maximum value calculation node of the power reduction change at time exist The downward flexibility margin and downward flexibility shortfall at any given moment; Build models of the physical characteristics and user behavior of different types of controllable resources, use simulation tools to simulate the operating status and regulation capabilities of various types of resources, and calculate the proportional relationship between regulation capability and regulation capacity under different regulation durations based on the simulation results. Obtain different types of aggregation methods and aggregation difficulties, calculate the unit capacity aggregation cost coefficient, and build a virtual power plant cost model. With the constraint of meeting the regulation needs of each grid zone and the goal of minimizing the total aggregation cost of the virtual power plant, a virtual power plant optimization configuration model is constructed. The linear programming theory is used to solve the aggregation resource type and capacity of the virtual power plant in each zone.

2. A virtual power plant planning method based on flexibility deficit zoning assessment according to claim 1, characterized in that: The process of constructing a flexibility mutual assistance solution optimization model based on the tie line transmission capacity constraint with the goal of minimizing the flexibility deficit of the entire network includes: The total flexibility demand of the system is calculated by taking the maximum value of the power adjustment change of the node flexibility demand as input; the flexibility demand includes the upward flexibility demand and downward adjustment of flexibility requirements ; The total system flexibility supply is calculated with the node flexibility supply as input; the flexibility supply includes the upward flexibility supply and lowering flexibility supply ; Flexibility requirements will be increased and increase flexibility supply Worst case flexibility shortfall ; flexibility requirements will be lowered and lowering flexibility supply Worst case flexibility shortfall ; Based on the positive or negative flexibility shortfall, the flexibility shortfall status is marked as follows: ； in, for The flexibility status of the system at that moment, where 0 represents no vacancies; 1 represents upward vacancies; and 2 represents downward vacancies. Constructing the system branch and node association matrix ; According to the physical parameters of line impedance, construct the line admittance diagonal matrix = ;matrix Each element in the value is a line The reciprocal of reactance, i.e. ; For the line reactance; use and Calculate the branch power transfer distribution factor matrix for: ； in, for × dimensional matrix; is a matrix The transposed matrix of if If it is 0, check whether all moments are traversed; if If is 1, To select the nodes with sufficient flexibility at the current moment for the criteria, a node set is formed. ;by Filter the nodes with increased flexibility at the current moment as the criteria to form a node set ; Taking the power increase amount of all nodes as the variable to be optimized, the line transmission power and the node increase range as constraints, and the minimum increase flexibility shortfall of the system after transmission flexibility as the objective function, a mutual optimization model for increase flexibility is constructed, which is as follows: ； Where, for Time Node Flexibility in adjusting mutual aid output upwards; is a matrix Line 1 Column elements; for Timeline Power variation; for Timeline The original transmission power; For the line Transmission capacity limit, is the set of tie lines whose transmission capacity needs to be considered; Represents the increase in flexibility shortage node set; Represents an increase in the set of flexibility-rich nodes; Solve the mutual assistance optimization model for increasing flexibility to obtain the mutual assistance plan, and calculate the change in line flexibility supply obtained by mutual assistance at each node: ； ； in, for Nodes caused by the mutual assistance of time flexibility Adjusting upwards the change in flexibility supply; for Nodes caused by the mutual assistance of time flexibility Adjusting downwards for changes in flexibility supply; Then check if all moments are traversed; if is 2, To select the nodes with sufficient flexibility at the current moment, a node set is formed. ;by Filter the nodes with reduced flexibility at the current moment as the criteria to form a node set ; Taking the power reduction amount of all nodes as the variable to be optimized, the line transmission power and the node reduction range as constraints, and the minimum system reduction flexibility deficit after transmission flexibility as the objective function, a mutual optimization model for reduction flexibility is constructed, which is as follows: ； Where, for Time Node flexibility in adjusting down the mutual aid input; for Timeline The power change; Represents the set of nodes that reduce the flexibility shortage; Represents the downward adjustment of the flexibility-rich node set; Solve the mutual assistance optimization model for upward or downward flexibility to obtain the mutual assistance plan, and calculate the line flexibility supply obtained through line transmission: ； ； Then check whether all moments have been traversed until all moments have been traversed.

3. A virtual power plant planning method based on flexibility deficit zoning assessment according to claim 2, characterized in that: The process of calculating the regulation demand of each grid zone according to the flexibility mutual assistance scheme includes: The target system is divided into two categories according to administrative regions: partitions , … ; For the first partition; For the Partition; Area to be analyzed ; The maximum sum of the net power increase / decrease changes of all nodes in the partition is used to calculate the partition's power increase / decrease flexibility requirement, specifically: ； ； in, for Time partition upward flexibility needs; for Time partition downward flexibility needs; The flexibility supply of all nodes in the zone and the flexibility transmitted via the line are summed to calculate the upward / downward flexibility supply of the zone, specifically: ； ； in, for Time partition The upward adjustment of flexibility supply; for Time partition The downward adjustment of flexibility supply; Calculate the partition by taking the difference between the supply and demand of the partition flexibility and taking the positive value. Flexibility zone vacancies, specifically: ； ； in, To take into account the interaction between node flexibility supply and demand and line flexibility Time partition the shortfall in upward flexibility; To take into account the interaction between node flexibility supply and demand and line flexibility Time partition the shortfall in downward adjustment flexibility; right and Perform statistical analysis and characterize the grid regulation demand as average value over the entire operating cycle: ； ； in, For partition Increase demand; For partition Adjust demand downwards; The peak-shaving demand and frequency-regulating demand of each power grid zone are obtained by calculating the flexibility deficit of each zone at different time scales.

4. A virtual power plant planning method based on flexibility deficit zoning assessment according to claim 1, characterized in that: The process of constructing physical characteristics and user behavior models of different types of controllable resources includes: controllable resources including air conditioners, electric vehicles, distributed power sources, and distributed energy storage; The construction process of the air conditioning physical model includes: using the air conditioning compressor load percentage as the control signal, the room temperature as the state variable, and the outdoor temperature as the input variable, and constructing the house temperature change process equation and the air conditioning control law equation based on the spatial heat balance principle to form the air conditioning physical model; The process of constructing the electric vehicle physical model includes: using the percentage of electric vehicle power battery as the state variable and the charge and discharge power as the input variable, and constructing the electric vehicle physical model according to the energy integration principle; The construction process of the distributed power generation physical model includes: taking natural conditions as input and generator power as output, and constructing the distributed power generation physical model according to the conversion characteristics of wind power and photovoltaic units; The construction process of distributed energy storage includes: using the battery power percentage as the state variable and the charge and discharge power as the input variable, and constructing a distributed energy storage physical model based on the energy integration principle.

5. A virtual power plant planning method based on flexibility deficit zoning assessment according to claim 4, characterized in that: The process of using simulation tools to simulate the operating status and regulation capabilities of various types of resources and calculating the relationship between regulation capabilities and regulation capacity ratios under different regulation durations based on the simulation results includes: Monte Carlo simulation is used to generate parameters corresponding to each resource unit, and the parameters corresponding to each resource unit are cyclically substituted into the physical model to update the state, and the power of each unit at each moment is calculated to form a power baseline for each type of resource unit; The regulation capacity of each resource unit is calculated by comprehensively considering power and energy constraints. Taking indoor temperature as the state variable, the air conditioner regulation capacity calculation process includes: ； ； ； ； ； ； in, is the reciprocal of the time constant of room temperature change; is the total electrical energy that the room can store; Set the ratio of the temperature range to the temperature change time ratio; For the Maximum temperature of air-conditioned room; For the Minimum temperature of air-conditioned room; For the Air conditioning set temperature; For the Air conditioner operating power limit; For the Air conditioner operating power lower limit; For the air conditioner Temperature status at all times; Adjust the time for the air conditioner; Taking the state of charge of the electric vehicle battery as the state variable, calculate the intermediate variable : ； in, For the The expected minimum state of charge of an electric vehicle when it is off-grid; For the Rated charging efficiency of electric vehicles; For the Maximum charging power of electric vehicles; For the The time when electric vehicles are off the grid; For the The time when the electric vehicle adjustment instruction is issued; For the Rated capacity of electric vehicle batteries; Adjusting duration for electric vehicles; Using the intermediate variables Calculate the minimum charging power of the electric vehicle at the current moment: ； in, For the Maximum discharge power of electric vehicles; For the Rated discharge efficiency of electric vehicles; For the The state of charge of the electric vehicle battery at the time when the electric vehicle adjustment instruction is issued; The moment the instruction is issued; For the The lower limit of the state of charge of the battery of an electric vehicle; For the Minimum charging power for electric vehicles; The maximum charging power of electric vehicles is: ； Where, For the Upper limit of state of charge of electric vehicle battery; For the Maximum charging power of electric vehicles; Calculate the current power that can be increased and decreased by the electric vehicle: ； For the Electric vehicles Always improve your ability; For the Electric vehicles Always adjust your capabilities; The calculation process of the adjustable capacity of distributed power generation includes: ； ； in, for Distributed Power Generation Always improve your ability; for Distributed Power Generation Always adjust your capabilities; for Maximum output of a distributed power source; for Minimum output of a distributed power source; for Distributed Power Generation The amount of effort at any given moment; The regulation capability expression of distributed energy storage is: ； ； in, For the Distributed energy storage Always improve your ability; For the Distributed energy storage Always adjust your capabilities; For the A maximum state of charge limit for distributed energy storage; For the A minimum state of charge limit for distributed energy storage; The total adjustment capacity of each type of resource is: ； in, Increase the total capacity of each type of resource; The total downward adjustment capacity for each type of resource; Any type of resource unit Always improve your ability; For each type of resource monomer Always adjust your capabilities; Aggregate the number of entities for each type of resource; .

6. A virtual power plant planning method based on flexibility deficit zoning assessment according to claim 5, characterized in that: The process of obtaining different types of aggregation methods and aggregation difficulties, calculating the unit capacity aggregation coefficient, and building a virtual power plant cost model includes: Calculate the aggregate capacity as follows: ； in, Aggregate capacity for each type of resource; The rated power of any type of resource unit; Calculate the statistical index of the adjustable capacity by performing statistics on the adjustable capacity change curve of the first time scale; ； in, Increase the capacity for peak shaving of various types of resources; The downward regulation capacity of various types of resources; Comparing the first time scale regulation capability statistical index with the aggregate capacity, we can obtain the peak regulation aggregate capacity coefficient: ； Where, The aggregate capacity coefficient for each type of resource peak load scenario, The aggregate capacity coefficient for each type of resource peak load scenario; Calculate the statistical index of the adjustable capacity by taking statistics of the adjustable capacity change curve of the second time scale; ； Where, Provides frequency modulation capabilities for various types of resources, Provides the ability to adjust the frequency of various types of resources; Comparing the second time scale regulation capability statistical indicator, the aggregate capacity, we can obtain the frequency regulation aggregate capacity coefficient: ； in, The upper aggregation capacity coefficient for each type of resource frequency modulation scenario; It is the lower aggregate capacity coefficient in each resource frequency modulation scenario.

7. A virtual power plant planning method based on flexibility deficit zoning assessment according to claim 6, characterized in that: The unit capacity aggregation cost coefficient of the air conditioner is: ； in, is the unit capacity aggregation cost coefficient of air conditioning; Fixed investment for the control platform; Invest in monitoring data collection terminals; The aggregate cost coefficient per unit capacity of electric vehicles is: ； in, is the aggregate cost coefficient per unit capacity of electric vehicles; Fixed investment for the control platform; Construction cost for the communication module; The aggregate cost coefficient of distributed power supply per unit capacity is: ； in, is the aggregated cost coefficient of distributed power unit capacity; Fixed investment for the control platform; Construction cost for the communication module; The aggregate cost coefficient of distributed energy storage per unit capacity is: ； in, is the aggregated cost coefficient per unit capacity of distributed energy storage; Fixed investment for the control platform; Construction cost of the communication module.

8. A virtual power plant planning method based on flexibility deficit zoning assessment according to claim 7, characterized in that: The process of constructing a virtual power plant optimization configuration model with the goal of minimizing the total aggregation cost of the virtual power plant and satisfying the regulation requirements of each grid zone as a constraint, and using linear programming theory to solve the process of obtaining the aggregated resource type and capacity of the virtual power plant in each zone includes: enter 、 , and unit capacity aggregation cost coefficient; With the goal of minimizing the total aggregation cost of demand-side resources, and with the upper limit of the aggregated capacity of various resources and the requirement of peak load and frequency regulation in each zone as constraints, an optimization configuration model is constructed. The objective function and capacity upper limit constraints are as follows: ； ； ； ； ； in, For partition The upper limit of the aggregated capacity of medium air conditioners; For partition The upper limit of the aggregated capacity of electric vehicles in China; Partition The upper limit of the aggregated capacity of distributed power generation; For partition The upper limit of the aggregated capacity of distributed energy storage; For partition Aggregate capacity of energy storage; For partition Aggregate capacity of electric vehicles; For partition Aggregate capacity of distributed generation; In the peak load regulation scenario, considering the combination of energy storage, electric vehicles and distributed power sources to meet the peak load regulation demand, the constraints are: ； ； In the frequency regulation scenario, the aggregation of distributed power sources, energy storage, electric vehicles, and temperature control loads is considered to meet the frequency regulation requirements: ； ； The linear programming theory is used to solve the aggregated resource type and capacity of the virtual power plant in each partition, which is the optimal configuration result of the virtual power plant.

9. A virtual power plant planning system based on flexibility deficiency zoning assessment, used to execute a virtual power plant planning method based on flexibility deficiency zoning assessment according to any one of claims 1 to 8, characterized in that: Includes partition quantification module, model building module and optimization configuration module; The partition quantification module is used to calculate the node flexibility supply and power fluctuation based on the source, load and storage characteristics of each node; determine the transmission capacity constraint of the tie line, and build a flexibility mutual assistance scheme optimization model based on the tie line transmission capacity constraint with the goal of minimizing the flexibility deficit of the entire network; obtain a flexibility mutual assistance scheme that matches the current demand by solving the flexibility mutual assistance scheme optimization model; and calculate the regulation demand of each grid partition by combining the node flexibility supply and demand with the flexibility mutual assistance scheme; The model building module is used to obtain the physical characteristics of different types of controllable resources and user behavior models, use simulation tools to simulate the operating status and regulation capabilities of various types of resources, and calculate the proportional relationship between regulation capability and regulation capacity under different regulation durations based on the simulation results; Obtain different types of aggregation methods and aggregation difficulties, calculate the unit capacity aggregation cost coefficient, and build a virtual power plant cost model; The optimization configuration module is used to construct a virtual power plant optimization configuration model with the goal of minimizing the total aggregation cost of the virtual power plant, and to use linear programming theory to solve the aggregation resource type and capacity of the virtual power plant in each partition.

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