Virtual power plant planning method and system based on flexibility vacancy partition evaluation

Abstract

The invention provides a virtual power plant planning method and system based on flexibility vacancy partition evaluation. The method comprises the steps that node flexibility supply and power fluctuation quantity are calculated; determining tie line transmission capability constraints, and constructing a flexibility mutual aid scheme optimization model; solving the model to obtain a flexibility mutual aid scheme; calculating the adjustment demand of each partition in combination with a node flexibility supply and demand and flexibility mutual aid scheme; constructing a controllable resource physical characteristic model, simulating a resource operation state and an adjustment capability, and calculating a proportional relation between the adjustment capability and the capacity based on a simulation result; obtaining different aggregation modes and aggregation difficulties, calculating a unit capacity aggregation cost coefficient, and constructing a virtual power plant cost model; and constructing an optimal configuration model by taking meeting of adjustment requirements of each partition as a constraint and taking the minimum total aggregation cost as a target, and solving to obtain the virtual power plant aggregation resource type and capacity of each partition. Based on the method, the invention also provides a virtual power plant planning system. According to the invention, rapid and accurate optimization planning of the virtual power plant is realized.

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 shortage zoning assessment. Background Art

[0002] Under the current trend of energy structure transformation and widespread application of renewable energy, renewable energy such as wind power and photovoltaics has been developed on a large scale. The power generation output of renewable energy such as wind power and photovoltaics is significantly intermittent, volatile and uncertain, which leads to the new power system showing significant "source-load" dual uncertainty characteristics, bringing significant challenges to the power balance and safe and stable operation of the power system. The replacement of traditional units such as thermal power by renewable energy sources with strong randomness and weak controllability has led to an increase in the regulation demand of the power system and a decrease in the regulation capacity. It is difficult to adapt to the development of the new situation by relying solely on the power regulation capacity to meet the regulation demand of the power system. It is necessary to vigorously tap the load-side resources to participate in the interaction of the power system. As a mainstream technical form of load-side resource aggregation and control, virtual power plants can aggregate widely distributed controllable resources on the load side into a unified whole to participate in the interaction of the power grid. However, the geographical distribution characteristics of controllable resources on the load side lead to the need for reasonable planning of virtual power plants according to resource distribution and grid regulation needs, that is, scientifically aggregate controllable resources of appropriate capacity corresponding to types in different regions to achieve the matching of the spatiotemporal characteristics of grid regulation demand and virtual power plant regulation capacity, so as to achieve scientific utilization of load-side resources and promote the consumption of renewable energy.

[0003] In summary, as a new type of power system, virtual power plants are still in the small-scale development stage. Virtual power plant resource aggregation is in the bottom-up self-organization stage, that is, virtual power plants aggregate all controllable resources in the jurisdiction without distinguishing resource types, capacities and regulation characteristics. With the advancement of the construction scale of virtual power plants, the disorderly development model will face interactive efficiency challenges: first, the construction location of virtual power plants may deviate from the location with large grid regulation demand, resulting in the virtual power plant regulation capacity being limited by the grid transmission capacity and unable to be fully utilized; second, the regulation characteristics of virtual power plants depend on the type of aggregated resources, and the duration and time of the regulation capacity of virtual power plants may deviate from the duration and time period of the grid regulation demand, resulting in the problem that they cannot be regulated when they are needed and are not needed when they have the regulation capacity. Therefore, how to ensure the matching of the spatiotemporal characteristics of regulation demand and regulation resources, and optimize the configuration of virtual power plant access areas, capacities and aggregated resources are still urgent problems to be solved. Summary of the invention

[0004] In order to solve the above technical problems, the present invention proposes a virtual power plant planning method and system based on flexibility shortage zoning assessment. Based on the flexibility calculation model, the grid zoning regulation demand can be quantitatively assessed, and the regulation characteristics of various controllable resources can be further combined. On the basis of considering the matching of grid regulation demand and regulation resource spatiotemporal characteristics, the virtual power plant access area, capacity and aggregated resource type can be optimally configured to achieve fast and accurate optimization planning of virtual power plants.

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

[0006] 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 interconnection line, and build a flexibility mutual assistance scheme optimization model with the goal of minimizing the flexibility deficit of the entire network based on the transmission capacity constraints of the interconnection line; obtain a flexibility mutual assistance scheme that matches the current moment demand by solving the flexibility mutual assistance scheme optimization model; calculate the regulation demand of each grid zone by combining the node flexibility supply and demand with the flexibility mutual assistance scheme;

[0007] Construct 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 capabilities 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 construct 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, an optimization configuration model of the virtual power plant 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, comprising 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-storage characteristics of each node; determine the transmission capacity constraints of the interconnection line, and build a flexibility mutual assistance scheme optimization model with the goal of minimizing the flexibility shortage of the entire network based on the transmission capacity constraints of the interconnection line; obtain a flexibility mutual assistance scheme that matches the current moment demand by solving the flexibility mutual assistance scheme optimization model; and calculate the regulation demand of each partition of the power grid 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 capabilities 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 constraint of meeting the regulation needs of each grid zone and the goal of minimizing the total aggregation cost of the virtual power plant, and uses linear programming theory to solve the aggregation resource type and capacity of the virtual power plant in each zone.

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

[0014] The effects provided in the content 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 shortage zone assessment, the method comprising the following steps: calculating node flexibility supply and power fluctuation based on source-load-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 shortage 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 zone of the power grid 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 durations 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 zone of the power grid 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 shortage zoning assessment, a virtual power plant planning system based on flexibility shortage zoning assessment is also proposed. The present invention can consider the source-load characteristics of each power system partition and the flexibility mutual assistance capability of the partition interconnection line, quantitatively evaluate the regulation requirements of different interactive scenarios such as peak regulation and frequency regulation in each power grid partition, and at the same time consider the regulation characteristics and aggregation cost differences of virtual power plants with different adjustable resources, so as to achieve the matching of regulation requirements and resource spatiotemporal characteristics in the virtual power plant planning process.

[0016] The present invention adopts parameterization to describe the differences in grid zone 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 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 The adjustment demand zoning quantification process based on flexibility supply and demand analysis in Example 1 of the present invention;

[0019] Figure 3 A modeling process for control 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 Embodiment 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 aims to solve 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 firstly uses a flexibility calculation model to analyze the flexibility supply and demand of each zone of the power system, considers the existing flexibility resources of each zone, source-load changes and transmission capacity constraints of the interconnection lines between zones, quantitatively evaluates the flexibility margin and shortage of each zone, and forms the regulation demand of each zone; then, based on the virtual power plant refined production simulation tool, calculates the differences in performance indicators of virtual power plants that aggregate different resources in terms of response speed, adjustable capacity, regulation cost and duration, and forms a parameter description of the regulation characteristics of different types of virtual power plants; finally, constructs an optimization configuration model for virtual power plants in power system zones with the constraint of satisfying the flexibility shortage of each zone and the goal of minimizing the aggregation cost, and quickly solves the optimization configuration model to form the optimal configuration plan of the virtual power plant.

[0025] Figure 1 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 deficit of the entire network; a flexibility mutual assistance scheme that matches the current moment's demand is obtained by solving the flexibility mutual assistance scheme optimization model; 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 plan 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, the historical statistical data and operating data of the power forecast errors of loads and wind and solar power sources are used as input. For each node, a probability distribution model of the power forecast errors of the connected loads and wind and solar power sources is constructed to calculate the volatility under the specified probability. The power changes of the loads and wind and solar power sources at each moment are calculated based on the flexibility analysis theory by combining the volatility and operating data. The upward / downward flexibility supply of thermal power and energy storage is calculated by combining the source and storage installed capacity parameters and operating data.

[0029] Through investigation, the load of each node in 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 value of the 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 predicted output curve of wind and solar power sources.

[0030] Calculate the node flexibility supply and power fluctuation based on the source-load-storage characteristics of each node; determine the node load fluctuation rate based on the acquired node load power prediction error historical data in the process of determining the transmission capacity constraints of the interconnection line; 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;

[0031] Read the historical data of the current node load power forecast error, draw the forecast error frequency distribution histogram, and use normal distribution, k2 distribution, t distribution and other distribution forms to fit the probability distribution, and select the distribution form with the smallest fitting error as the forecast error probability distribution density function. Set the forecast error probability value that needs to be dealt with, and find the error value when the cumulative probability is equal to the probability that needs to be dealt with as the volatility that needs to be dealt with.

[0032] The present invention describes in detail the process of distribution fitting with normal distribution, and the fitting and volatility determination methods of other distribution forms are similar. The scope of protection of the present invention is not limited to the probability distribution listed in Example 1, and those skilled in the art can make reasonable choices according to actual conditions.

[0033] Obtain the historical data of node load power prediction error, draw the prediction error histogram and use normal distribution for distribution fitting, and determine the parameters of the normal distribution of the current node load prediction error by maximum likelihood estimation method: 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 of power forecast error of wind and solar power sources at the current node, draw a frequency distribution histogram of the forecast error, and use normal distribution, k2 distribution, t distribution and other distribution forms to fit the probability distribution, and select the distribution form with the smallest fitting error as the forecast error probability distribution density function. Set the forecast error probability value that needs to be dealt with, and find the error value when the cumulative probability is equal to the probability that needs to be dealt with as the volatility that needs to be dealt with.

[0036] The present invention describes in detail the process of distribution fitting with normal distribution, and the fitting and volatility determination methods of other distribution forms are similar. The scope of protection of the present invention is not limited to the probability distribution listed in Example 1, and those skilled in the art can make reasonable choices according to actual conditions.

[0037] Obtain the historical data of power prediction error of wind and solar power sources at the node, draw the prediction error histogram and use normal distribution for distribution fitting, and determine the parameters of normal distribution of wind and solar prediction error at the current node by maximum likelihood estimation method: mean and variance; based on the 3σ principle, according to the prediction error with a probability of 99.7% as the criterion, calculate the fluctuation rate of wind and solar power sources 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; Vj is the photovoltaic fluctuation rate of node j; σ Wj is the standard deviation of wind power prediction error at node j; Vj is the standard deviation of the PV prediction error at node j.

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

[0041] Obtain node load curve data, and at any moment, consider the maximum load fluctuation that needs to be dealt with, 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 value of the load increase power change, which is:

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

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

[0048] For 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's upward power change is calculated using the maximum value of the load's upward power change and the maximum value of the wind-solar upward power change; the maximum value of the node's downward power change is calculated using the maximum value of the load's downward power change and the maximum value of the wind-solar downward power change;

[0050] Obtain node wind power curve data, and at any moment, consider the maximum wind power fluctuation that needs to be dealt with, 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 value of the wind power increase power change, which is:

[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 down-regulation power change, specifically:

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

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

[0058]

[0059]

[0060] in, represents the upper limit of PV fluctuation of node j at time t; represents the lower limit of photovoltaic fluctuation of 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 value of the photovoltaic power reduction change:

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

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

[0065]

[0066]

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

[0068] Read the composition parameters and operation 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 operation data includes the output data of the thermal power unit.

[0069] Combined with the parameters and operation data of the thermal power unit, the flexibility of up and down adjustment provided by the thermal power unit at node j is calculated by considering the ramp rate and output range of thermal power, which is as follows:

[0070]

[0071]

[0072] in, is the upward climbing rate of conventional thermal power units; is the downward climbing rate of conventional thermal power units; Provide the maximum technical contribution to 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 indicates the maximum value of power that can be increased by the thermal power unit of node j at time t in the time scale of τ; It indicates the maximum value of power reduction that the thermal power unit at node j can achieve at time t in the time scale of τ.

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

[0074] Combined with energy storage parameters and operating data, considering the maximum charge and discharge power constraints and state of charge constraints of 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 power of the energy storage device at node j at time t; represents the maximum power 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 at the energy storage device of node j at time t.

[0078] Adding up the flexibility provided by thermal power and energy storage, and adding down the 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] According to the power adjustment change of node flexibility demand and flexibility supply, calculate the flexibility deficit / surplus of node j, and calculate the upward flexibility surplus and upward flexibility deficit of node j at time t according to the upward flexibility supply of node j at time t and the maximum value of the upward power change of node j at time t;

[0083] The downward flexibility margin and downward flexibility deficit of node j at time t are calculated according to 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; It 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, go to step 1.2. Otherwise, read the next node for analysis and increase the node number by 1, then go to step 3.

[0090] Step 1.2, taking the node flexibility net power adjustment change and the flexibility supply timing curve as input, for each moment, determine whether there is a power shortage based on the system flexibility supply and demand situation, and when there is a flexibility shortage, build an upward / downward flexibility mutual assistance optimization model that considers the transmission capacity of the interconnection line and the adjustable range of the node, and determine the flexibility mutual assistance plan that matches the current moment 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 flexibility demand and supply curve of the system.

[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 the flexibility supply Worst case flexibility shortfall

[0095] The specific calculation process is:

[0096]

[0097]

[0098]

[0099]

[0100] in, 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 downward flexibility shortfall of the system at time t.

[0105] Based on the positive and negative flexibility shortages, the flexibility shortage status is marked, specifically:

[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] According to the connection between network nodes and branches and their positive direction relationship with branch current, the system branch and node association matrix C is constructed;

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

[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 shortage at the current moment as the criteria to form a node set

[0121] Taking the power increase 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 deficit 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] In the formula, h is the upward flexibility of the mutual aid output of node j at time t; 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 l transmission capacity, N l is the set of tie lines whose transmission capacity needs to be considered; Represents the upward adjustment of the flexibility shortage node set; Represents an increase in the set of nodes with rich flexibility.

[0124] Solve the mutual assistance optimization model for upward 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 The node with reduced flexibility at the current moment is selected as the criterion 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] In the formula, 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 adjust the flexibility shortage node set downward; To reduce the flexibility of the rich node set.

[0133] Solve the upward or downward flexibility mutual assistance optimization model 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 aid curve are used as input to calculate the power grid zone flexibility deficit, and the flexibility deficit is counted to form the characteristic index of each zone flexibility deficit, and the zone power grid regulation demand is quantified. 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 calculated to calculate the flexibility requirement for the partition to increase / decrease power, which is:

[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 partition and the flexibility transmitted through the line are summed to calculate the upward / downward flexibility supply of the partition, 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 difference between the supply and demand of the zone flexibility is taken as the positive value to calculate the zone flexibility shortage of zone r, which is:

[0148]

[0149]

[0150] in, To take into account the upward flexibility deficit of zone r at time t after the node flexibility supply and demand and line flexibility complement each other; To take into account the downward flexibility deficit of zone r at time t after the node flexibility supply and demand and line flexibility complement each other;

[0151] right and Perform statistical analysis to characterize the grid regulation requirements as average values ​​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 of each grid zone is divided into different time scales to obtain the peak load demand and frequency regulation demand of each grid zone. In this application, the first time scale is 1 hour and the second time scale is 1 minute.

[0156] Steps 1.1 to 1.4 are performed 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 of each grid zone. and FM Adjust demand.

[0157] In Step 2, construct 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 capabilities 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;

[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 operating status and regulation capacity of different types of resources are simulated using the refined operation simulation program, 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 3A modeling process for control characteristics of virtual power plants with different types of controllable resources in Example 1 of the present invention;

[0160] In Step 2.1: Through investigation and analysis, the key factors affecting the adjustable capacity of each controllable resource are analyzed, including the distribution of resource physical and user behavior parameters, meteorological condition parameters, 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 upper and lower limits of indoor temperature; meteorological condition parameters include outdoor temperature, etc.

[0162] If the controllable resource is an electric vehicle, the physical parameters mainly consider 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 charge and discharge efficiency, etc.; the user behavior parameters include the time of use and disconnection from the grid, the expected SOC value when off the 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 physical parameters to be considered include the charging and discharging power of the energy storage system, the rated capacity of the battery, and the charging and discharging efficiency, without user behavior parameters and 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 air conditioning, the air conditioning compressor load percentage control signal, room temperature as the state variable, and outdoor temperature as the input variable are used to construct the house temperature change process equation and air conditioning control law equation according to the principle of spatial thermal balance to form an air conditioning physical model. When the first-order temperature change equation is used to describe the room temperature change law, and the air conditioner adopts hysteresis control, the physical model is as follows, and other second-order temperature change 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 load percentage of the air conditioner compressor; when m(t) = 0-1, it is a variable frequency air conditioner that can be continuously adjusted; 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 an air conditioner; ε is a very small time lag.

[0171] If the controllable resource is an electric vehicle, the percentage of the electric vehicle 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 according to the energy integration principle. When the off-grid time and the expected state of charge are used to describe the user behavior, the physical model is as follows. The complex form considering the relationship between the power battery capacity and the charge and discharge rated power and the rate can be expanded on this basis. The physical model of the electric vehicle is:

[0172]

[0173] in, for i EV The state of charge of electric vehicles; For the i EV The charging and discharging power of electric vehicles; for i EV Rated capacity of electric vehicle battery; After the adjustment is completed, EV The operating power of electric vehicles; is the electric vehicle adjustment duration; 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, the natural conditions such as wind speed, light, and temperature are used as inputs, and the generator power is used as output. Its physical model is constructed based on the conversion characteristics of wind power and photovoltaic units. Assuming that the wind power generation power is linearly related to the cubic wind speed, the physical model of distributed wind power is:

[0175]

[0176] in, For the i WT Output power of the typhoon generator set at time t; For the i WT Typhoon turbine rated power; For the i WT Wind speed of typhoon generator set; The i 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 charging and discharging power is used as the input variable. The physical model of distributed energy storage is constructed according to the energy integration principle; its 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 refrigeration energy efficiency coefficient Air conditioner rated power Indoor and outdoor temperature With the indoor temperature upper and lower limits Indoor temperature setting value

[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 the rated power of distributed wind 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 the maximum and minimum output of distributed power sources

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

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

[0190] The power constraint and energy constraint are considered comprehensively to calculate the regulation capacity of each resource unit. Taking the indoor temperature as the state variable, the calculation process of the regulation capacity of the air conditioner includes:

[0191]

[0192]

[0193]

[0194]

[0195]

[0196]

[0197] in, It is the reciprocal of the time constant of room temperature change; is the total electrical energy that the room can store; The ratio of the temperature setting interval to the temperature change time; For the i AC Maximum indoor temperature of air conditioner; For the i AC Minimum indoor temperature of air conditioner; For the i AC Air conditioning set temperature; For the i AC Air conditioning operating power limit; For the i AC The lower limit of air conditioner operating power; For the i AC The temperature status of the air conditioner 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 battery; Adjust duration for electric vehicles.

[0201] Using the intermediate variable 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 battery of the electric vehicle 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 The upper limit of the state of charge of the battery of an electric vehicle; For the i EV Maximum charging power for electric vehicles.

[0206] Calculate the adjustable power and adjustable power of the electric vehicle at the current moment:

[0207]

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

[0209] The calculation process of the adjustable capacity of distributed power sources 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 generation at time t; for i DE Maximum output of distributed power sources; for i DE The minimum output of a distributed power source; for i DE The output of each distributed power source at time t;

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

[0214]

[0215]

[0216] in, For the i DES The ability 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 resources 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 unit at time t; The capacity of each type of resource unit at time t is lowered; N type is 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] . Production simulation, 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] Statistics of the adjustable capacity change curve with an adjustment time of 1 hour are collected to calculate the statistical index of the adjustable capacity;

[0227]

[0228] Increase the capacity for peak load regulation of various types of resources; The downward regulation capacity for each type of resource;

[0229] Comparing the statistical index of the regulation capacity on the 1h time scale with the aggregate capacity, the peak regulation aggregate capacity coefficient is obtained:

[0230] In the formula, The upper aggregate capacity coefficient for each type of resource peak load scenario, It is the aggregate capacity coefficient of each type of resource peak load 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] In the formula, Provides frequency modulation capabilities for various types of resources, Provides the ability to adjust the frequency of each type of resource;

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

[0235] in, It is the upper aggregation capacity coefficient in each type of resource frequency modulation scenario; It is the lower aggregate capacity coefficient in each type of 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 aggregation cost per unit capacity of different controllable resources.

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

[0238]

[0239] Among them, K AC I is the unit capacity aggregation cost coefficient of air conditioning; AC Fixed investment for the 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 for the interactive data between the control platform software and the charging infrastructure. The former is a fixed investment that is independent of the aggregate capacity, and the latter is a variable investment that is proportional to the aggregate 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 control platform; c EV Construction cost for the communication module.

[0243] If the controllable resource is a distributed power source, the investment includes the communication module for data interaction between the control platform software and the distributed power generation equipment. The former is a fixed investment that is independent of the aggregated capacity, and the latter is a variable investment that is 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 control platform; cDE Construction cost for 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 that is independent of the aggregate capacity, and the latter is a variable investment that is proportional to the aggregate 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 control platform; c DES Construction cost for the communication module.

[0249] In Step 3, 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, and 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 Embodiment 1 of the present invention; input And the unit capacity aggregation cost coefficient; with the minimum total aggregation cost of demand-side resources as the goal, and 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 the medium air conditioner; 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 sources; For partition Ω r The upper limit of the capacity that can be aggregated by distributed energy storage; For partition Ω r Aggregate capacity of energy storage; For partition Ω r Aggregate capacity of electric vehicles; For partition Ω r Aggregate capacity of distributed generation.

[0257] In the peak load scenario, considering the aggregation of energy storage, electric vehicles and distributed power sources to meet the peak load 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] The above optimization model is solved by CPLEX or gurobi solver to obtain the aggregate capacity of each type of resource in each partition virtual power plant. and That 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, and quantitatively assess the regulation needs of different interactive scenarios such as peak regulation and frequency regulation in each zone of the power grid. At the same time, it takes into account the regulation characteristics of virtual power plants that aggregate different adjustable resources and the differences in aggregation costs, so as to achieve matching of regulation needs with the spatiotemporal characteristics of resources in the virtual power plant planning process.

[0265] Embodiment 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 a virtual power plant planning method based on flexibility shortage zoning evaluation proposed in Example 1 of the present invention, a virtual power plant planning system based on flexibility shortage zoning evaluation is also proposed. Figure 5 A schematic diagram of a virtual power plant planning system based on flexibility shortage zoning assessment proposed in Embodiment 2 of the present invention, the system comprising 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 interconnection line, and build a flexibility mutual assistance scheme optimization model with the goal of minimizing the flexibility shortage of the entire network based on the transmission capacity constraints of the interconnection line; obtain a flexibility mutual assistance scheme that matches the current moment demand by solving the flexibility mutual assistance scheme optimization model; combine the node flexibility supply and demand with the flexibility mutual assistance scheme to calculate the regulation demand of each partition of the power grid;

[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 capabilities 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;

[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 consider the source-load characteristics of each power system zone and the flexibility mutual assistance capabilities of the zone interconnection lines, quantitatively assess the regulation needs of different interactive scenarios such as peak regulation and frequency regulation in each zone of the power grid, and at the same time consider the regulation characteristics of virtual power plants that aggregate different adjustable resources and the differences in aggregation costs, so as to achieve matching of regulation needs with the spatiotemporal characteristics of resources in the virtual power plant planning process.

[0272] Embodiment 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, thereby achieving efficient generation of planning schemes.

[0273] The description of the relevant parts of a 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 a 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 describes the specific implementation of the present invention in conjunction with the accompanying drawings, it is not intended to limit the scope of protection of the present invention. For those skilled in the art, other different forms of modifications or deformations can be made on the basis of the above description. It is not necessary and impossible to list all the implementation methods here. On the basis of the technical solution of the present invention, various modifications or deformations that can be made by those skilled in the art without creative work 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 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 interconnection line, and build a flexibility mutual assistance scheme optimization model with the goal of minimizing the flexibility deficit of the entire network based on the transmission capacity constraints of the interconnection line; obtain a flexibility mutual assistance scheme that matches the current moment demand by solving the flexibility mutual assistance scheme optimization model; calculate the regulation demand of each grid zone by combining the node flexibility supply and demand with the flexibility mutual assistance scheme; Construct 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 capabilities 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, an optimization configuration model of the virtual power plant 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 node flexibility supply and power fluctuation are calculated based on the source-load-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; For the maximum load fluctuation rate of the node that needs to be dealt with at any moment, determine the maximum load and minimum load that may appear at the next moment; calculate the difference between the maximum load that may appear 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 appear at the current moment and the next moment to form the maximum load decrease power change; For 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 parameters and operation data of the thermal power unit, the flexibility of up and down adjustment provided by the thermal power unit at node j is calculated by considering the ramp rate and output range of thermal power, which is as follows: in, is the upward climbing rate of conventional thermal power units; is the downward climbing rate of conventional thermal power units; Provide the maximum technical contribution to 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 indicates the maximum value of power that can be increased by the thermal power unit of node j at time t in the time scale of τ; It indicates the maximum value of power that can be reduced by the thermal power unit of node j at time t in the time scale of τ; Combined with energy storage parameters and operating data, considering the maximum charge and discharge power constraints and state of charge constraints of energy storage, the up and down adjustment flexibility provided by the energy storage at node j is calculated; specifically: 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 power of the energy storage device at node j at time t; represents the maximum power 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; represents the maximum value of the power that can be reduced at the energy storage device of node j at time t; Adding up the flexibility provided by thermal power and energy storage, and adding down the flexibility provided by thermal power and energy storage, we get: in, represents the upward flexibility supply of node j at time t; represents the downward flexibility supply of node j at time t; Calculate the upward flexibility margin and upward flexibility deficit of node j at time t according to the upward flexibility supply of node j at time t and the maximum value of the upward power change of node j at time t; The downward flexibility margin and downward flexibility deficit of node j at time t are calculated according to the downward flexibility supply of node j at time t and the maximum downward power change of node j at time t.

3. A virtual power plant planning method based on flexibility deficit zoning assessment according to claim 2, characterized in that: The process of constructing a flexibility mutual assistance scheme optimization model with the goal of minimizing the flexibility deficit of the entire network based on the tie line transmission capacity constraint 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 lower 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 the flexibility supply Flexibility requirements will be increased and increase flexibility supply Worst case flexibility shortfall Flexibility requirements will be lowered and lowering the flexibility supply Worst case flexibility shortfall Based on the positive and negative flexibility shortages, the flexibility shortage status is marked, specifically: Among them, Statefle(t) is the flexibility state of the system at time t, where 0 represents no shortage; 1 represents an upward shortage; 2 represents a downward shortage; Construct the system branch and node association matrix C; construct the line admittance diagonal matrix A=diag(1 / x l ); Each element in the matrix A is the reciprocal of the reactance of line l, i.e. 1 / x l ;x l is the reactance of line l; The branch power transfer distribution factor matrix H is calculated using C and A as follows: H=[diag(1 / x l )C]{C T diag(1 / x l )C} -1 ; Among them, H is an L×N dimensional matrix; C T is the transposed matrix of matrix C; If Statefle(t) is 0, check whether all moments have been traversed; 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 shortage at the current moment as the criteria to form a node set Taking the power increase 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 deficit of the system after transmission flexibility as the objective function, a mutual optimization model for increase flexibility is constructed, which is as follows: In the formula, h is the upward flexibility of the mutual aid output of node j at time t; l,j is the element in the lth row and jth column of the matrix H; ΔP l up (t) is the power change of line 1 at time t; P l (t) is the original transmission power of line l at time t; is the upper limit of line l transmission capacity, N l is the set of tie lines whose transmission capacity needs to be considered; Represents the upward adjustment of the flexibility shortage node set; Represents the upward adjustment of the flexibility-rich node set; Solve the mutual assistance optimization model for upward flexibility to obtain the mutual assistance plan, and calculate the change in line flexibility supply obtained by mutual assistance at each node: in, is the change in flexibility supply at node j caused by flexibility mutual assistance at time t; The change in the downward flexibility supply of node j caused by the flexibility mutual assistance at time t; Then check if all moments are traversed; If Statefle(t) is 2, To select the nodes with sufficient flexibility at the current moment, a node set is formed. by The node with reduced flexibility at the current moment is selected as the criterion 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: In the formula, 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; Represents the set of nodes for downward adjustment of flexibility shortage; Represents the downward adjustment of the flexibility-rich node set; Solve the upward or downward flexibility mutual assistance optimization model 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.

4. A virtual power plant planning method based on flexibility deficit zoning assessment according to claim 3, characterized in that: The process of calculating the regulation demand of each grid zone according to the flexibility mutual assistance scheme includes: 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 region to be analyzed r=1; The maximum sum of the net power increase / decrease changes of all nodes in the partition is calculated to calculate the flexibility requirement for the partition to increase / decrease power, which is: in, is the upward flexibility demand of partition r at time t; is the downward flexibility demand of partition r at time t; The flexibility supply of all nodes in the partition and the flexibility transmitted through the line are summed to calculate the upward / downward flexibility supply of the partition, specifically: in, is the upward flexibility supply of partition r at time t; is the downward flexibility supply of partition r at time t; The difference between the supply and demand of the zone flexibility is taken as the positive value to calculate the zone flexibility shortage of zone r, which is: in, To take into account the upward flexibility deficit of zone r at time t after the node flexibility supply and demand and line flexibility complement each other; To take into account the downward flexibility deficit of zone r at time t after the node flexibility supply and demand and line flexibility complement each other; right and Perform statistical analysis to characterize the grid regulation requirements as average values ​​over the entire operating cycle: in, Adjust the demand for partition r upward; Adjust the demand downward for partition r; The peak load and frequency regulation demands of each power grid zone are obtained by calculating the flexibility deficit of each zone at different time scales.

5. 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 include air conditioners, electric vehicles, distributed power sources, and distributed energy storage; The construction process of the air conditioning physical model includes: taking 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 according to the principle of spatial thermal balance to form the air conditioning physical model; The construction process of the electric vehicle physical model includes: taking the percentage of the electric vehicle power battery as the state variable and the charging and discharging power as the input variable, and constructing the electric vehicle physical model according to the energy integration principle; The construction process of the physical model of distributed power sources includes: taking natural conditions as input and generator power as output, and constructing the physical model of distributed power sources according to the conversion characteristics of wind power and photovoltaic units; The construction process of distributed energy storage includes: taking the battery power percentage as the state variable and the charging and discharging power as the input variable, and constructing a distributed energy storage physical model based on the energy integration principle.

6. A virtual power plant planning method based on flexibility deficit zoning assessment according to claim 5, characterized in that: The simulation tool is used to simulate the operation status and regulation capability of various types of resources. The process of calculating the proportional relationship between regulation capability and regulation capacity under different regulation durations based on the simulation results includes: Monte Carlo simulation is used to generate parameters corresponding to each resource monomer, and the parameters corresponding to each resource monomer are cyclically substituted into the physical model for state update, and the monomer power at each moment is calculated to form a power baseline for each type of resource monomer; The power constraint and energy constraint are considered comprehensively to calculate the regulation capacity of each resource unit. Taking the indoor temperature as the state variable, the calculation process of the regulation capacity of the air conditioner includes: in, It is the reciprocal of the time constant of room temperature change; is the total electrical energy that the room can store; The ratio of the temperature setting interval to the temperature change time; For the i AC Maximum indoor temperature of air conditioner; For the i AC Minimum indoor temperature of air conditioner; For the i AC Air conditioning set temperature; For the i AC Air conditioning operating power limit; For the i AC The lower limit of air conditioner operating power; For the i AC The temperature status of the air conditioner at time t; 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 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 battery; Adjusting duration for electric vehicles; Using the intermediate variable Calculate the minimum charging power of the electric vehicle at the current moment: 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 battery of the electric vehicle 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; The maximum charging power of electric vehicles is: In the formula, For the i EV The upper limit of the state of charge of the battery of an electric vehicle; For the i EV Maximum charging power of electric vehicles; Calculate the adjustable power and adjustable power of the electric vehicle at the current moment: For the i EV The electric vehicle has the ability to increase its capacity at time t; For the i EV The down-regulation capacity of electric vehicles at time t; The calculation process of the adjustable capacity of distributed power sources includes: 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 generation at time t; for i DE Maximum output of distributed power sources; for i DE The minimum output of a distributed power source; for i DE The output of each distributed power source at time t; The regulation capability of distributed energy storage is expressed as: in, For the i DES The ability 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; The total adjustment capacity of each type of resources is: 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 unit at time t; The capacity of each type of resource unit at time t is lowered; N type is the number of aggregated monomers of each type of resource; i∈(i AC ,i EV ,i DE ,i DES ).

7. A virtual power plant planning method based on flexibility deficit zoning assessment according to claim 6, 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: Among them, E type Aggregate capacity for each type of resource; P itype,r The rated power of any type of resource unit; The adjustable capacity variation curve of the first time scale is counted to calculate the statistical index of the adjustable capacity; in, Increase the capacity for peak load regulation of various types of resources; The downward regulation capacity for each type of resource; Comparing the statistical index of the first time scale regulation capacity with the aggregate capacity, we can get the peak regulation aggregate capacity coefficient: In the formula, The upper aggregate capacity coefficient for each type of resource peak load scenario, It is the lower aggregate capacity coefficient under each type of resource peak load scenario; Statistically analyzing the adjustable capacity change curve at the second time scale and calculating the statistical index of the adjustable capacity; In the formula, Provides frequency modulation capabilities for various types of resources, Provides the ability to adjust the frequency of each type of resource; Comparing the second time scale regulation capability statistical indicator aggregation capacity, we can get the frequency regulation aggregation capacity coefficient: in, It is the upper aggregation capacity coefficient in each type of resource frequency modulation scenario; It is the lower aggregate capacity coefficient in each type of resource frequency modulation scenario.

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

9. A virtual power plant planning method based on flexibility deficit zoning assessment according to claim 8, 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 demand of each grid zone as a constraint, and using linear programming theory to solve 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 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: in, For partition Ω r The upper limit of the aggregated capacity of the medium air conditioner; 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 sources; For partition Ω r The upper limit of the capacity that can be aggregated by distributed energy storage; For partition Ω r Aggregate capacity of energy storage; For partition Ω r Aggregate capacity of electric vehicles; For partition Ω r Aggregate capacity of distributed generation; In the peak load scenario, considering the aggregation of energy storage, electric vehicles and distributed power sources to meet the peak load 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.

10. A virtual power plant planning system based on flexibility deficit zoning assessment, characterized in that: It includes partition quantization 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-storage characteristics of each node; determine the transmission capacity constraints of the interconnection line, and build a flexibility mutual assistance scheme optimization model with the goal of minimizing the flexibility shortage of the entire network based on the transmission capacity constraints of the interconnection line; obtain a flexibility mutual assistance scheme that matches the current moment demand by solving the flexibility mutual assistance scheme optimization model; and calculate the regulation demand of each partition of the power grid 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 capabilities 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 constraint of meeting the regulation needs of each grid zone and the goal of minimizing the total aggregation cost of the virtual power plant, and uses linear programming theory to solve the aggregation resource type and capacity of the virtual power plant in each zone.

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