Source network load storage economic dispatching method considering multi-partition risk in extreme weather

Abstract

The invention discloses a source network load storage economic dispatching method considering multi-partition risk in extreme weather, which comprises the following steps: representing a plurality of substations in a power grid system as nodes to obtain a plurality of nodes; generating a typical section according to historical data, and generating a node load shedding risk distribution matrix Msr and a node wind and light abandoning risk distribution matrix Mcr based on the typical section; constructing a target optimization function and a constraint condition of Msr and Mcr based on a multivariable conditional value-at-risk formula, and solving to obtain a load shedding expected value-at-risk and a confidence coefficient of the plurality of nodes and a wind and light abandoning expected value-at-risk and a confidence coefficient of the plurality of nodes; and constructing an objective function of the source network load storage economic dispatching model, and optimizing reserve capacity constraints in constraint conditions of the objective function according to a solving result to obtain the source network load storage economic dispatching model. The invention relates to the technical field of power systems, and solves the technical problems that the risk assessment of the power system is inaccurate and the economy is difficult to guarantee.

Description

Technical Field

[0001] The present invention belongs to the field of power systems and relates to economic dispatch technology, specifically to a source-grid-load-storage economic dispatch method that takes into account multi-zone risks under extreme weather conditions. Background Art

[0002] In order to cope with extreme weather conditions, measures can be taken to ensure power supply security in all links of "source, grid, load and storage". On the power supply side, this can be achieved by increasing the spare capacity of adjustable generator sets; on the grid side, cross-regional power transmission can be achieved through the "big grid"; on the demand side, demand-side response is one of the measures to increase power supply security in extreme weather; on the energy storage side, emergency response capabilities can be improved by building energy storage facilities.

[0003] Extreme weather will bring greater uncertainty to the output and load characteristics of renewable energy, thus posing greater challenges to the safe and stable operation of the power grid. Existing economic dispatch methods for sources, grids, loads and storage mostly use methods based on CVaR (Conditional Value-at-Risk) to quantify uncertainty risks, but these methods have certain limitations. The optimization dispatch model based on the single variable CVaR lacks in terms of the differences and correlations in the risk values ​​of load shedding and wind and solar abandonment at different nodes. For example, in actual power grids, the distribution of renewable energy, load composition and correlation with other nodes in various regions are complex and diverse, and it is difficult for a single variable CVaR to fully and accurately describe this complex situation.

[0004] The risk situation faced by different regions is affected by many factors, such as geographical environment, energy resource distribution, electricity demand structure, etc. Some regions may have abundant wind energy resources but low load demand, while other regions may have peak load periods and low renewable energy output periods. These factors lead to the diversified and interrelated characteristics of risks between nodes. The single variable CVaR model cannot fully consider these factors, making the risk assessment inaccurate, which in turn affects the scientificity and rationality of scheduling decisions. Summary of the invention

[0005] The present invention aims to solve at least one of the technical problems existing in the prior art; to this end, the present invention proposes an economic dispatch method for source, grid, load and storage taking into account multi-zone risks under extreme weather conditions, which is used to solve the technical problems of inaccurate risk assessment, unreasonable resource allocation and difficulty in ensuring economic efficiency caused by the existing methods ignoring the differences and correlations in the risk values ​​of load shedding and wind and solar power abandonment in different zones.

[0006] To achieve the above object, a first aspect of the present invention provides a source-grid-load-storage economic dispatch method considering multi-zone risks under extreme weather conditions, comprising:

[0007] S1, several substations in the power grid system are represented as nodes to obtain several nodes;

[0008] S2, generating a typical section according to historical data, and generating a node risk allocation matrix based on the typical section to obtain a node risk allocation matrix NRAM; wherein the node risk allocation matrix NRAM includes a node load shedding risk allocation matrix Msr and a node wind and solar abandonment risk allocation matrix Mcr, and the historical data includes the flow direction, power generation, power generation and power generation load data of each node in the power grid during the historical time period;

[0009] S3, based on the multivariate conditional risk value formula, construct the objective optimization function and constraint conditions of Msr, and solve the expected risk value and confidence of load shedding of several nodes;

[0010] S4, based on the multivariate conditional risk value formula, construct the objective optimization function and constraint conditions of Mcr, and solve the expected risk value and confidence level of abandoned wind and solar power at several nodes;

[0011] S5, constructing the objective function of the source-grid-load-storage economic dispatch model, and optimizing the spare capacity constraint in the constraint conditions of the objective function according to the expected risk value and confidence of load shedding of the several nodes and the expected risk value and confidence of wind and solar power abandonment, to obtain the source-grid-load-storage economic dispatch model.

[0012] Based on the above technical process, the method of the present invention adopts the multivariate conditional risk value theory to construct the target optimization function and constraint conditions of Msr and Mcr, comprehensively considers the relationship between factors such as the uncertainty of new energy output and load fluctuation among multiple nodes, and considers the correlation between the uncertainty of new energy power generation and load changes, accurately quantifies the risk value of load shedding and the risk value of abandoned wind and solar power at each node, ensures the power balance and equipment operation safety of the system during the optimization process, enhances the ability of the power system to cope with complex uncertainties under extreme weather conditions, and achieves multiple goals of economic and reliable operation and improved efficiency of new energy utilization.

[0013] Furthermore, the objective optimization function and constraint conditions of constructing Msr include:

[0014] The objective optimization function of Msr is: in, represents the node expected load shedding risk allocation matrix, u sd* represents the expected risk value of load shedding of several nodes, p sd* represents the expected confidence of load shedding of several nodes, u sd represents the load shedding risk value of several nodes, p sd represents the load shedding confidence of several nodes, m represents the node, Indicates the load shedding weight, M sr is the r×r node load shedding risk allocation matrix, and the elements in the matrix are d mn , represents the load shedding risk allocation ratio from node m to node n, represents the risk value of load shedding, N MC Indicates the number of samples for Monte Carlo sampling; represents the load shedding risk confidence level, ξ mk represents the sample of the kth net load disturbance, represents the risk cost matrix of load shedding, T represents the transposition operation;

[0015] The constraints of Msr are: Among them, p m represents the risk confidence of node m, p represents the overall risk confidence of several nodes, Indicates the maximum load shedding among several nodes.

[0016] Furthermore, the objective optimization function and constraint conditions of constructing Mcr include:

[0017] The objective optimization function of Mcr is: in, represents the node expected wind and solar abandonment risk allocation matrix, u cu* represents the expected risk value of abandoned wind and solar power at several nodes, p cu* represents the expected confidence of abandoned wind and solar power at several nodes, u cu represents the risk value of abandoned wind and solar power at several nodes, p cu represents the confidence level of wind and solar power abandonment of several nodes, m represents the node, represents the risk weight of abandoned wind and solar power, The risk value of abandoned wind and solar power is Indicates the risk confidence of abandoned wind and solar power, represents the risk cost matrix of wind and solar curtailment, and T represents the transposition operation;

[0018] The constraints of Mcr are: in, Indicates the maximum amount of abandoned wind power among several nodes.

[0019] Furthermore, the objective function of the source-grid-load-storage economic dispatch model is: In the formula, C t represents the total cost of the power grid system, t∈[1,T] represents the power generation time, represents the power generation cost of the adjustable generator set, represents the start-stop cost of the adjustable generator set, It represents the penalty cost of abandoning wind and solar power generation units. represents the penalty cost of load shedding.

[0020] Furthermore, the The calculation method is: in and is the power generation cost parameter, P t represents the output decision variable of the adjustable generator set, and N represents the number of adjustable generator sets, and i represents adjustable generator set i.

[0021] Furthermore, the The calculation method is: in, and They represent the start and stop operation indicator variables of the i-th adjustable generator set at time t, and Represents the start-stop cost of the i-th adjustable generator set at time t.

[0022] Furthermore, the The calculation method is: Among them, τ curt represents the penalty cost coefficient for abandoning wind and solar power, It represents the abandoned wind and solar power of renewable energy generator set j at time t, and M represents the number of renewable energy generator sets.

[0023] Furthermore, the The calculation method is: Among them, m represents a node, Q represents a set of nodes, represents the l-th level load shedding cost of node m, It represents the lth level load shedding of node m at time t.

[0024] Furthermore, the constraints of the objective function include demand-side response constraints, reserve capacity constraints, power balance constraints, adjustable generator set constraints, energy storage state constraints, and wind and solar power abandonment constraints.

[0025] Furthermore, the demand side response constraint is: Among them, m represents the node, t represents the time, Indicates the proportion of loads at different levels l at different times, Represents the deterministic input of the source-grid-load-storage economic dispatch model;

[0026] The spare capacity constraint is: and Obey Gaussian distribution Among them, Pr[] represents conditional probability, m represents node, t represents time, j represents new energy generator set, and They represent the expected risk value of load shedding and wind power curtailment, and They represent the expected confidence of load shedding and wind power abandonment, Indicates the net load, Indicates the sum of the maximum outputs of the connected and turned-on new energy generators. Indicates the minimum output of the connected and turned-on new energy generator sets. represents the mean value of net load power forecast, represents the mean value of load power forecast, represents the power prediction mean of the new energy generator set j, represents the variance of the net load power forecast, represents the variance of load power forecast, represents the variance of the power prediction of the new energy generator j;

[0027] The power balance constraint is: and Among them, m and n represent nodes, t represents time, and i represents an adjustable generator set. Indicates the power flow, S m represents the set of energy storage devices connected to node m, represents the charging and discharging power of energy storage unit k, and When charging, When is discharge, P i,t represents the output power of the adjustable generator set, and Ε[] represents the random variable The expectation, θ m,t With θ n,t They represent the voltage phase angles of node m and node n, respectively, and x mn represents the reactance of the branch between node m and node n, represents the maximum transmission power of the branch between node m and node n;

[0028] The adjustable generator set constraints include:

[0029] The output constraint of the adjustable generator set is: Among them, u i,t ∈{0,1} represents the switch state of the adjustable generator set i at time t, 0 represents off, 1 represents on, [·] + =max[·,0], and They represent the lower and upper limits of the output of the adjustable generator set i respectively;

[0030] The climbing constraint of the adjustable generator set is: Among them, P i,t represents the output power of adjustable generator set i, and They represent the positive climbing limit and negative climbing limit of the adjustable generator set i respectively;

[0031] The energy storage state constraint is: in, and They represent the energy storage charging power and energy storage discharging power of energy storage device k at time t, and They represent the upper limit of charging speed and discharging speed of energy storage device k respectively, and S represents the number of energy storage devices;

[0032] The wind and solar power abandonment constraints are: in, Represents the output power of new energy generator set j.

[0033] Compared with the prior art, the present invention has the following beneficial effects:

[0034] The multivariate conditional value at risk (MCVaR) takes into account the risks of load shedding and wind and solar power abandonment at different nodes. Under extreme weather conditions, the uncertainty of new energy output and load characteristics increases. Traditional methods are difficult to accurately deal with the differences and correlations in risk values ​​at different nodes. MCVaR can effectively characterize the risks at different nodes, making risk assessment more comprehensive and accurate.

[0035] Through optimization, the risk allocation matrix with the minimum comprehensive risk of each node is obtained, namely the node load shedding risk allocation matrix (NSRAM) and the node wind and solar curtailment risk allocation matrix (NCRAM), which helps to reasonably allocate the risks caused by load fluctuations, dynamically adjust the load shedding amount according to the situation of each node, minimize the total risk of the system, and ensure the stable operation of the system under uncertainty. For example, part of the risk of nodes with high load shedding costs can be transferred to nodes with lower costs to reduce the overall risk cost. BRIEF DESCRIPTION OF THE DRAWINGS

[0036] In order to more clearly illustrate the embodiments of the present invention or the technical solutions in the prior art, the drawings required for use in the embodiments or the description of the prior art will be briefly introduced below. Obviously, the drawings described below are only some embodiments of the present invention. For ordinary technicians in this field, other drawings can be obtained based on these drawings without paying creative work.

[0037] Figure 1 A flow chart of a source-grid-load-storage economic dispatching method considering multi-zone risks under extreme weather conditions provided by the present invention;

[0038] Figure 2 The invention provides a startup schedule for each unit obtained by applying a source-grid-load-storage economic dispatch method considering multi-zone risks under extreme weather conditions. DETAILED DESCRIPTION

[0039] The technical solution of the present invention will be clearly and completely described below in conjunction with the embodiments. Obviously, the described embodiments are only part of the embodiments of the present invention, not all of the embodiments. Based on the embodiments of the present invention, all other embodiments obtained by ordinary technicians in this field without creative work are within the scope of protection of the present invention.

[0040] See also Figure 1-Figure 2 The first embodiment of the present invention provides a source-grid-load-storage economic dispatch method considering multi-zone risks under extreme weather conditions, including:

[0041] S1, several substations in the power grid system are represented as nodes to obtain several nodes;

[0042] S2, generating a typical section according to the historical data, and generating a node risk allocation matrix based on the typical section to obtain a node risk allocation matrix NRAM; wherein the node risk allocation matrix NRAM includes a node load shedding risk allocation matrix Msr and a node wind and solar abandonment risk allocation matrix Mcr; wherein the historical data represents the flow direction, power generation, power generation and power generation load data of each node in the power grid during the historical time period;

[0043] S3, based on the multivariate conditional risk value formula, construct the objective optimization function and constraint conditions of Msr, and solve the expected risk value and confidence of load shedding of several nodes;

[0044] S4, based on the multivariate conditional risk value formula, construct the objective optimization function and constraint conditions of Mcr, and solve the expected risk value and confidence level of abandoned wind and solar power at several nodes;

[0045] S5, constructing the objective function of the source-grid-load-storage economic dispatch model, and optimizing the spare capacity constraint in the constraint conditions of the objective function according to the expected risk value and confidence of load shedding of the several nodes and the expected risk value and confidence of wind and solar power abandonment, to obtain the source-grid-load-storage economic dispatch model.

[0046] In this implementation, the node load shedding risk allocation matrix Msr and the node wind and solar abandonment risk allocation matrix Mcr are introduced to obtain the node risk allocation matrix, and the risk of the net load of each node is allocated to the node with flexible resources. Through NRAM, the risks caused by load fluctuations can be reasonably allocated, and the load shedding strategy of each node can be optimized, thereby minimizing the total risk of the system. This matrix not only reflects the load distribution of each node in the system, but also can dynamically adjust the load shedding amount under different circumstances to ensure that the system can maintain stable operation when dealing with uncertainty.

[0047] Specifically, firstly, several substations in the power grid system are represented as nodes to obtain several nodes;

[0048] Then, a typical section is generated according to the historical data, and a node risk allocation matrix is ​​generated based on the typical section to obtain a node risk allocation matrix NRAM; wherein the historical data includes but is not limited to the flow direction, power generation, power generation and power generation load data of each node in the power grid during the historical time period;

[0049] For a typical section, the risk allocation matrix needs to consider the conditional risk value of multiple nodes, so the optimization problem is constructed based on the MCVaR (multivariate conditional value at risk) theory. For load shedding risk, the loss function can be defined as: Among them, g() represents the loss function, represents the weight of node m, represents the load shedding risk value of node m, represents the load shedding confidence of node m, represents the risk cost matrix of load shedding, T represents the transposition operation, ξ mk represents the sample of the kth net load disturbance of computing node m, [·] + =max(0,·);

[0050] Then, Monte Carlo sampling can be used to simplify the expression of the load shedding objective function as follows:

[0051] Among them, g'() represents, N MC Table Design Monte Carlo Sampling Sample Number;

[0052] Therefore, the solution process of Msr is summarized as the following optimization problem:

[0053] The objective optimization function is in, represents the node expected load shedding risk allocation matrix, u sd* represents the expected risk value of load shedding of several nodes, p sd* represents the expected confidence of load shedding of several nodes, u sdrepresents the load shedding risk value of several nodes, p sd represents the load shedding confidence of several nodes, m represents the node, M sr is the r×r node load shedding risk allocation matrix, and the elements in the matrix are d mn , represents the load shedding risk allocation ratio from node m to node n, N MC Indicates the number of samples for Monte Carlo sampling; Indicates the load shedding risk confidence level;

[0054] The constraints are , where p m represents the risk confidence of node m, p represents the overall risk confidence of several nodes, Indicates the maximum load shedding among several nodes;

[0055] Then, the expected risk value u of load shedding of several nodes is obtained by solving sd* and confidence level p sd* ;

[0056] Similarly, Mcr can also be modeled and solved using the above method, and the objective optimization function and constraint conditions of Mcr are obtained as follows:

[0057] Mcr's objective optimization function: in, represents the node expected wind and solar abandonment risk allocation matrix, u cu* represents the expected risk value of abandoned wind and solar power at several nodes, p cu* represents the expected confidence of abandoned wind and solar power at several nodes, u cu represents the risk value of abandoned wind and solar power at several nodes, p cu represents the confidence level of wind and solar power abandonment of several nodes, m represents the node, represents the risk weight of abandoned wind and solar power, The risk value of abandoned wind and solar power is Indicates the risk confidence of abandoned wind and solar power, represents the risk cost matrix of wind and solar curtailment, and T represents the transposition operation;

[0058] The constraints of Mcr are: in, Indicates the maximum amount of abandoned wind and solar power in several nodes;

[0059] Similarly, the expected risk value u of abandoned wind and solar power at several nodes is obtained by solving cu* and confidence level p cu* .

[0060] Under complex working conditions such as extreme weather, the objective optimization function of Msr and the objective optimization function of Mcr can accurately obtain the optimal allocation of uncertainties at each node. Specifically, the risk of load shedding at each node depends on the upward fluctuation of the net load, while the risk of abandoning wind and solar power is determined by the downward fluctuation of the net load. In terms of load shedding, the Msr matrix can allocate the risk of load shedding to nodes with a total load greater than 0. For a certain load node, its load shedding risk mainly comes from two aspects: one is the risk caused by the actual output of the new energy node being lower than the predicted value; the other is the risk caused by the load node itself being higher than the predicted value. Msr has a key advantage, which is that it can realize the transfer of load shedding between different nodes. For example, assuming that the probability of the load of node A exceeding the predicted value is high, and its load shedding cost is also high, while the situation of node B is just the opposite. In this case, with the help of the optimization method, part of the load shedding risk value of node A can be transferred to node B, thereby reducing the overall load shedding risk cost, so that the system can allocate risks more flexibly and efficiently when dealing with load fluctuations under extreme weather, and ensure the stable operation of the power system.

[0061] Next, the objective function of the source-grid-load-storage economic dispatch model is constructed, and the reserve capacity constraint in the constraint conditions of the objective function is optimized based on the expected risk value and confidence of load shedding of several nodes and the expected risk value and confidence of wind and solar power abandonment of several nodes, so as to obtain the final source-grid-load-storage economic dispatch model.

[0062] Specifically, the objective function of the economic dispatch model of the unit combination usually includes the power generation cost of the adjustable units, the start-up and shutdown costs, the penalty costs of wind and solar power abandonment, and load shedding. Therefore, the total cost of the power grid system is: In the formula, C t represents the total cost of the power grid system, t∈[1,T] represents the power generation time, represents the power generation cost of the adjustable generator set, represents the start-stop cost of the adjustable generator set, It represents the penalty cost of abandoning wind and solar power generation units. represents the penalty cost of load shedding;

[0063] in, is the power generation cost in the period t∈[1,T], which is usually a quadratic function of the adjustable unit output: in and is the power generation cost parameter, P t represents the output decision variable of the adjustable generator set, and N represents the number of adjustable generator sets, i represents adjustable generator set i;

[0064] Adjustable start-stop cost of generator sets The expression is: in, and They represent the start and stop operation indicator variables of the i-th adjustable generator set at time t, and represents the start-stop cost of the i-th adjustable generator set at time t;

[0065] The calculation method is: Among them, τ curt represents the penalty cost coefficient for abandoning wind and solar power, represents the abandoned wind and solar power of renewable energy generator set j at time t, and M represents the number of renewable energy generator sets;

[0066] Non-interruptible loads correspond to extremely high load shedding costs Other interruptible but non-transferable loads correspond to different load shedding costs. To simplify the model, the load shedding costs of interruptible loads are divided into three levels: therefore The penalty cost of load shedding is calculated as: Among them, m represents a node, Q represents a set of nodes, represents the load shedding cost at level l, Represents the load shedding amount of node m at time t.

[0067] The constraints of the source-grid-load-storage economic dispatch model include demand-side response constraints, reserve capacity constraints, power balance constraints, adjustable generator constraints, energy storage state constraints, and wind and solar power curtailment constraints.

[0068] Specifically, the demand-side response constraints are: in, Indicates the proportion of loads at different levels l at different times, Represents the deterministic input of the source-grid-load-storage economic dispatch model;

[0069] In order to overcome the uncertainty of negative renewable energy output and load, the reserve capacity of adjustable generator sets can be increased to maintain the stable operation of the system. The reserve capacity needs to take into account the uncertainty of load and renewable energy output. Therefore, the reserve capacity constraint is: and Obey Gaussian distribution Where Pr[] represents conditional probability, m represents node, t represents time, and j represents new energy generator set. and Indicates the risk value of load shedding and wind and solar curtailment, and Indicates the confidence level of load shedding and wind and solar curtailment, Indicates the net load, Represents the sum of the maximum outputs of the turned-on renewable energy generators connected to node m, It represents the minimum sum of the outputs of the new energy generators connected to node m. represents the mean value of net load power forecast, represents the mean value of load power forecast, represents the power prediction mean of the new energy generator j at node m, represents the variance of the net load power forecast, represents the variance of load power forecast, represents the variance of the new energy power prediction;

[0070] In the UHV transmission line, the resistance is much smaller than the reactance. Therefore, the power flow of the node m,n∈Q in the UHV network during the period t can be approximately expressed by the injection of active power as follows: θ m,t With θ n,t They represent the voltage phase angles of node m and node n at time t, respectively, and x mn represents the reactance of the branch between node m and node n, represents the maximum transmission power of the branch between node m and node n. At this time, the power balance constraint is: and in, Indicates the power flow, S m represents the set of energy storage devices connected to node m, represents the charging and discharging power of energy storage unit k, and When charging, When is discharge, P i,t represents the output power of the adjustable generator set, and Ε[] represents the random variable expectations;

[0071] The startup state of the adjustable generator set can be represented by 0-1, that is, u i,t ∈{0,1} represents the switch state of generator set i at time t, 0 means shutdown and 1 means startup. and It can be expressed as: in,[·] + =max[·,0], initial state and It can be determined according to the actual situation;

[0072] The constraints of adjustable generator sets mainly include the maximum / minimum output constraints and ramp constraints of the sets. Among them, the maximum / minimum output constraints of the sets can be expressed as: in, and They represent the lower and upper limits of the output of the adjustable generator set i respectively;

[0073] The climbing constraint of the adjustable generator set is: Among them, P i,t represents the output power of adjustable generator set i, and They represent the positive climbing limit and negative climbing limit of the adjustable generator set i respectively;

[0074] Energy storage state constraints include energy storage capacity constraints, maximum charge and discharge power constraints, etc. Here, the energy storage charging power of energy storage device k∈S in time period t is and discharge power are decision variables respectively, and the energy storage capacity is defined as an auxiliary variable, so the real-time energy of energy storage device k is:

[0075] in, They represent the charging and discharging efficiency respectively, and S represents the number of energy storage devices;

[0076] The charge and discharge state constraints of the energy storage device are: in, and They represent the upper limit of charging speed and the upper limit of discharging speed of energy storage device k respectively;

[0077] The constraints for curtailing wind and solar power are: in, Represents the output power of new energy generator set j.

[0078] In this embodiment, it is assumed that the number of generator sets is N=20, the number of nodes is r=5, and the load shedding and wind power abandonment weights of each node are

[0079] First, Monte Carlo sampling is performed based on the probability distribution of typical sections in historical data, with the number of sampling being N. MC , and obtain the net load sample geometry matrix; then the net load sample geometry matrix is ​​used as input, and the optimal load shedding risk allocation method is obtained through the objective optimization function and constraint conditions of Msr At the same time, the expected risk value u of load shedding at each node is obtained sd* and load shedding expected confidence p sd* As shown in the following table:

[0080] node Expected risk value of load shedding (MW) Load shedding confidence 1 106.56 98.00％ 2 32.12 99.90％ 3 20.34 98.91％ 4 2.62 99.99％ 5 32.31 96.70％

[0081] Then, the net load sample geometry matrix is ​​used as input, and the optimal wind and solar abandonment risk allocation method is obtained through the objective optimization function and constraint conditions of Mcr. At the same time, the expected risk value u of abandoned wind and solar power at each node is obtained cu* and the expected confidence level of abandoned wind power p cu* As shown in the following table:

[0082] node Expected risk value of abandoned wind and solar power (MW) Confidence level of wind power abandonment expectations 1 20.12 98.00％ 2 30.65 99.90％ 3 3.98 98.91％ 4 67.22 99.99％ 5 30.21 96.70％

[0083] Then u sd* and p sd* and u cu* and p cu* As input, the forecast data of renewable energy output and load are input, and according to the objective function and constraints of the source-grid-load-storage economic dispatch model, the optimization dispatch model is solved to obtain the startup and operation mode u of the adjustable generator set. i,t and P t , tidal power Wait for the results.

[0084] Part of the data in the above formula is calculated by removing the dimension and taking its numerical value. The formula is a formula closest to the actual situation obtained by software simulation of a large amount of collected data; the preset parameters and preset thresholds in the formula are set by technical personnel in this field according to actual conditions or obtained through simulation of a large amount of data.

[0085] Working principle of the present invention:

[0086] Firstly, several substations in the power grid system are represented as nodes to determine the research object and provide a basic framework for subsequent analysis;

[0087] Then, a typical section is generated based on historical data (covering the flow direction, power generation, power generation and power load data of each node in the power grid during the historical period);

[0088] Next, a node risk allocation matrix NRAM is generated based on typical sections. The matrix includes the node load shedding risk allocation matrix Msr and the node wind and solar abandonment risk allocation matrix Mcr. NRAM can reasonably allocate risks, optimize load shedding strategies, dynamically adjust load shedding amounts, and ensure stable operation of the system when dealing with uncertainties.

[0089] Construct the objective optimization function and constraint conditions of Msr, solve the expected risk value and confidence of load shedding of several nodes, provide a basis for subsequent decision-making, and identify high-risk nodes;

[0090] Construct the target optimization function of Mcr and solve the expected risk value and confidence of abandoned wind and solar power at several nodes to provide data support for the reasonable arrangement of wind and solar power generation resources and the balanced utilization of new energy;

[0091] Construct the objective function of the source-grid-load-storage economic dispatch model, including the power generation cost of adjustable units, start-up and shutdown costs, wind and solar abandonment and load shedding penalty costs, etc., and comprehensively consider the costs of all links in the power system;

[0092] According to the expected risk value and confidence of node load shedding and wind and solar abandonment, the reserve capacity constraint in the constraint condition of the optimization objective function is introduced into the opportunity constraint to consider the uncertainty of load and new energy output;

[0093] Finally, a source-grid-load-storage economic dispatch model is obtained, which improves the system's ability to cope with extreme weather uncertainties, ensures the safe, stable and economical operation of the system, and achieves optimal resource allocation.

[0094] The above embodiments are only used to illustrate the technical method of the present invention rather than to limit it. Although the present invention has been described in detail with reference to the preferred embodiments, those skilled in the art should understand that the technical method of the present invention may be modified or replaced by equivalents without departing from the spirit and scope of the technical method of the present invention.

Claims

1. A source-grid-load-storage economic dispatch method considering multi-zone risks under extreme weather conditions, characterized in that: include: S1, several substations in the power grid system are represented as nodes to obtain several nodes; S2, generating a typical section according to historical data, and generating a node risk allocation matrix based on the typical section to obtain a node risk allocation matrix NRAM; wherein the node risk allocation matrix NRAM includes a node load shedding risk allocation matrix Msr and a node wind and solar abandonment risk allocation matrix Mcr, and the historical data includes the flow direction, power generation, power generation and power generation load data of each node in the power grid during the historical time period; S3, based on the multivariate conditional risk value formula, construct the objective optimization function and constraint conditions of Msr, and solve the expected risk value and confidence of load shedding of several nodes; S4, based on the multivariate conditional risk value formula, construct the objective optimization function and constraint conditions of Mcr, and solve the expected risk value and confidence level of abandoned wind and solar power at several nodes; S5, constructing the objective function of the source-grid-load-storage economic dispatch model, and optimizing the spare capacity constraint in the constraint conditions of the objective function according to the expected risk value and confidence of load shedding of the several nodes and the expected risk value and confidence of wind and solar power abandonment, to obtain the source-grid-load-storage economic dispatch model.

2. According to claim 1, a source-grid-load-storage economic dispatch method considering multi-zone risks under extreme weather conditions is characterized in that: The objective optimization function and constraint conditions for constructing Msr include: The objective optimization function of Msr is: in, represents the node expected load shedding risk allocation matrix, u sd* represents the expected risk value of load shedding of several nodes, p sd* represents the expected confidence of load shedding of several nodes, u sd represents the load shedding risk value of several nodes, p sd represents the load shedding confidence of several nodes, m represents the node, Indicates the load shedding weight, M sr is the r×r node load shedding risk allocation matrix, and the elements in the matrix are d mn , represents the load shedding risk allocation ratio from node m to node n, represents the risk value of load shedding, N MC Indicates the number of samples for Monte Carlo sampling; represents the load shedding risk confidence level, ξ mk represents the sample of the kth net load disturbance, represents the risk cost matrix of load shedding, T represents the transposition operation; The constraints of Msr are: Among them, p m represents the risk confidence of node m, p represents the overall risk confidence of several nodes, Indicates the maximum load shedding among several nodes.

3. According to claim 1, a source-grid-load-storage economic dispatch method considering multi-zone risks under extreme weather conditions is characterized in that: The objective optimization function and constraint conditions for constructing Mcr include: The objective optimization function of Mcr is: in, represents the node expected wind and solar abandonment risk allocation matrix, u cu* represents the expected risk value of abandoned wind and solar power at several nodes, p cu* represents the expected confidence of abandoned wind and solar power at several nodes, u cu represents the risk value of abandoned wind and solar power at several nodes, p cu Indicates the confidence level of wind and solar power abandonment of several nodes, represents the risk weight of abandoned wind and solar power, The risk value of abandoned wind and solar power is Indicates the risk confidence of abandoned wind and solar power, represents the risk cost matrix of wind and solar curtailment, and T represents the transposition operation; The constraints of Mcr are: in, Indicates the maximum amount of abandoned wind power among several nodes.

4. According to claim 1, a source-grid-load-storage economic dispatch method considering multi-zone risks under extreme weather conditions is characterized in that: The objective function of the source-grid-load-storage economic dispatch model is: In the formula, C t represents the total cost of the power grid system, t∈[1,T] represents the power generation time, represents the power generation cost of the adjustable generator set, represents the start-stop cost of the adjustable generator set, It represents the penalty cost of abandoning wind and solar power generation units. represents the penalty cost of load shedding.

5. The method for economic dispatch of source, grid, load and storage considering multi-zone risks under extreme weather conditions according to claim 4, characterized in that: Said The calculation method is: in and is the power generation cost parameter, t represents time, P t represents the output decision variable of the adjustable generator set, and N represents the number of adjustable generator sets, and i represents the adjustable generator set.

6. The method for economic dispatch of source, grid, load and storage considering multi-zone risks under extreme weather conditions according to claim 4, characterized in that: Said The calculation method is: in, and They represent the operation indicator variables of starting and stopping the i-th adjustable generator set at time t, and represents the cost of starting and stopping the i-th adjustable generator set at time t.

7. The method for economic dispatch of source, grid, load and storage considering multi-zone risks under extreme weather conditions according to claim 4, characterized in that: Said The calculation method is: Among them, τ curt represents the penalty cost coefficient for abandoning wind and solar power, It represents the abandoned wind and solar power of renewable energy generator set j at time t, and M represents the number of renewable energy generator sets.

8. The method for economic dispatch of source, grid, load and storage considering multi-zone risks under extreme weather conditions according to claim 4, characterized in that: Said The calculation method is: Among them, Q represents the set of nodes, represents the l-th level load shedding cost of node m, It represents the l-th level load shedding of node m at time t.

9. The method for economic dispatch of source, grid, load and storage considering multi-zone risks under extreme weather conditions according to claim 1, characterized in that: The constraints of the objective function include demand-side response constraints, reserve capacity constraints, power balance constraints, adjustable generator set constraints, energy storage state constraints, and wind and solar power abandonment constraints.

10. The method for economic dispatch of source, grid, load and storage considering multi-zone risks under extreme weather conditions according to claim 9, characterized in that: The demand side response constraints are: in, Indicates the proportion of loads at different levels l at different times, Represents the deterministic input of the source-grid-load-storage economic dispatch model; The spare capacity constraint is: and Obey Gaussian distribution Among them, Pr[] represents the conditional probability, j represents the new energy generator set, and They represent the expected risk value of load shedding and wind power curtailment, and They represent the expected confidence of load shedding and wind power abandonment, Indicates the net load, Indicates the sum of the maximum outputs of the connected and turned-on new energy generators. Indicates the minimum output sum of the connected and turned-on new energy generator sets. represents the mean value of net load power forecast, represents the mean value of load power forecast, represents the power prediction mean of the new energy generator set j, represents the variance of the net load power forecast, represents the variance of load power forecast, represents the power prediction variance of the new energy generator set j; The power balance constraint is: and Among them, m and n represent nodes. Indicates the power flow, S m represents the set of energy storage devices connected to node m, represents the charging and discharging power of energy storage unit k, and When charging, When is discharge, P i,t represents the output power of the adjustable generator set, and Ε[] represents the random variable The expectation, θ m,t With θ n,t They represent the voltage phase angles of node m and node n, respectively, and x mn represents the reactance of the branch between node m and node n, represents the maximum transmission power of the branch between node m and node n; The adjustable generator set constraints include: The output constraint of the adjustable generator set is: Among them, u i,t ∈{0,1} represents the switch state of the adjustable generator set i at time t, 0 represents off, 1 represents on, [·] + =max[·,0], and They represent the lower and upper limits of the output of the adjustable generator set i respectively; The climbing constraint of the adjustable generator set is: in, and They represent the positive climbing limit and negative climbing limit of the adjustable generator set i respectively; The energy storage state constraint is: Where k represents the energy storage device, and Represent the energy storage charging power and energy storage discharging power respectively, and They represent the upper limit of charging speed and the upper limit of discharging speed respectively, and S represents the number of energy storage devices; The wind and solar power abandonment constraints are: in, Represents the output power of new energy generator set j.

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