A power grid planning method considering risk constraints under uncertain scenarios

Abstract

The application discloses a power grid planning method considering risk constraints in uncertain scenarios. The method divides statistical historical data into two parts of limit scenarios and normal scenarios, divides normal scenario historical data into multiple characteristic partitions, then generates corresponding subsets, and further constructs a multi-state model. The Markov model and Markov chain Monte Carlo simulation method are used to generate power grid uncertainty operation scenarios, and the kmeans clustering method is used to reduce scenarios to obtain power grid typical operation scenario subsets under normal state and limit state respectively. Then, a model constraint and an objective function are established, and a model is established accordingly. The model performs power grid optimization planning according to the power grid typical operation scenario subsets, and outputs power grid planning results. The application realizes optimization planning of power transmission lines, generator units and renewable energy units with known parameters, and can provide a reference for power grid planning decisions under uncertainty.

Description

TECHNICAL FIELD

[0001] The present application relates to the field of power system optimization planning, and particularly relates to a power grid planning method considering risk constraints under uncertain scenarios. BACKGROUND

[0002] The proposal of the "carbon peak and carbon neutrality" goal puts forward new requirements for the construction and development of the power system. The proportion of renewable energy in the current power system is increasing, and the load growth continues to accelerate. The uncertainty of renewable energy output and power load fluctuations brings many operation risks to the safe operation of the power grid. In order to realize the safe operation and efficient planning of the power grid, it is necessary to analyze the uncertainty characteristics of renewable energy output and power load at the corresponding time scale, and obtain typical scenarios close to the random fluctuation law on this basis, and then carry out transmission network optimization planning considering system operation in typical scenarios. The traditional planning method usually does not consider the seasonal differences of renewable energy output fluctuation and power load fluctuation at the medium and long term time scale, but directly carries out cluster analysis on the annual data to obtain typical scenarios. At the same time, the serious operation risk of the power grid mostly occurs in extreme scenarios such as high load state and low renewable energy output, low load state and high renewable energy output. Under this background, it is also necessary to include extreme scenarios into the planning model. Under this background, it is necessary to consider the time zoning of normal scenarios and the analysis of extreme scenarios, and to study the transmission network optimization planning method considering uncertainty operation scenario analysis. SUMMARY

[0003] In view of the problems in the above background art, the present application provides a power grid planning method considering risk constraints under uncertain scenarios.

[0004] The technical scheme adopted by the present application is:

[0005] The power grid planning method considering risk constraints under uncertain scenarios of the present application comprises:

[0006] Step 1: According to the extreme scenario judgment basis, the extreme scenario historical data is selected from the annual statistical historical data of renewable energy output and power load, and the remaining data is taken as normal scenario historical data;

[0007] Step 2: Obtain the characteristic parameters in different time periods of renewable energy output and power load according to the normal scenario historical data processing, and then divide the normal scenario historical data into multiple characteristic partitions in units of months according to the characteristic parameters;

[0008] Step 3: According to the characteristic sub-zones obtained in step 2, the annual statistical historical data subsets of renewable energy output and power load of each characteristic sub-zone are constructed, and then the multi-state models of renewable energy output and power load of each characteristic sub-zone are constructed, and the state transition probabilities between each state in each multi-state model are obtained by using Markov model;

[0009] Step 4: According to the multi-state models and state transition probabilities obtained in step 3, the uncertain operation scenarios of power grid under each characteristic sub-zone are simulated by using Markov chain Monte Carlo simulation method;

[0010] Step 5: The uncertain operation scenarios of power grid under each characteristic sub-zone obtained in step 4 are reduced by using kmeans clustering method, and the typical operation scenario subsets of power grid of each characteristic sub-zone are generated, and then the typical operation scenario subsets of power grid in normal operation state are obtained;

[0011] Step 6: According to the extreme scenario historical data obtained in step 1, the extreme scenario historical data subsets of renewable energy output and power load are constructed, and then the multi-state models of renewable energy output and power load under extreme scenario are constructed, and the state transition probabilities between each state in the multi-state models of renewable energy output and power load are obtained by combining Markov model processing;

[0012] Step 7: According to the multi-state models of renewable energy output and power load under extreme scenario and the state transition probabilities in step 6, the uncertain operation scenarios of power grid under extreme scenario are simulated by using Markov chain Monte Carlo simulation method, and the uncertain operation scenarios of power grid under extreme scenario are reduced by using kmeans clustering method, and the typical operation scenario subsets of power grid in extreme operation state are obtained;

[0013] Step 8: The model constraint conditions are established by combining the power grid operation constraints, the element operation constraints, the power grid load shedding risk constraints and the renewable energy curtailment risk constraints, and the objective function is constructed by taking the minimization of total cost of power grid as the optimization target, and then the power grid optimization planning model is constructed;

[0014] Step 9: According to the typical operation scenario subsets of power grid in normal operation state and extreme operation state obtained in step 5 and step 7, the model constraint conditions and the objective function in step 8, the power grid optimization planning model is used for power grid optimization planning, and the power grid planning result is obtained, which includes physical parameters such as transmission line, non-renewable energy generator and renewable energy generator.

[0015] The characteristic parameters in step 2 include the mean and variance of renewable energy output and power load in each month, which are obtained according to the following formula:

[0016]

[0017] where m represents the mth natural month, m ∈ [1, 12], D m represents the number of days in the mth month, d represents the dth natural day in the mth natural month, d ∈ [1, D m ], T represents the number of time periods in each operating scenario, t represents the tth time period in the dth natural day, t ∈ [1, T], represents the mean of the renewable energy output / power load in the mth month, represents the variance of the renewable energy output / power load in the mth month, represents the data of the renewable energy output / power load in the tth time period on the dth day in the mth month.

[0018] The division in step 2 employs a kmeans clustering method.

[0019] The multi-state model in step 3 is a state set of the renewable energy output / power load, wherein, represents the state of the i th renewable energy output / power load, K RES and K PL are the number of renewable energy output and power load states, respectively;

[0020] The state set is specifically set according to the following steps:

[0021] The minimum value and the maximum value in the annual statistical historical data of the renewable energy output / power load are respectively selected in the sub-set to construct an interval Subsequently, the interval is divided into K RES / PL sub-intervals at a certain interval, and the state represents that the renewable energy output level / power load level is between the i th interval, wherein represents the sub-set of the historical data of the renewable energy output and power load in the sub-set.

[0022] The Markov Chain Monte Carlo simulation method in steps 4 and 7 is employed to simulate and generate power grid uncertainty operating scenarios considering renewable energy output and power load fluctuations, and each power grid uncertainty operating scenario contains a renewable energy output sequence and a power load sequence for 24 hours a day.

[0023] The kmeans clustering step in steps 4 and 7 is:

[0024] 1) Set the target reduction scenario number k as the kmeans target cluster number;

[0025] 2) Select k grid uncertainty operation scenarios {C1, C2, C3, ..., C k} is used as the initial cluster center point (cluster initial center scene), and the distance from each scene to the initial center scene is obtained according to the following formula:

[0026]

[0027] Where, dis(X i ,C j ) is scene X i Go to center scene C j The distance, X i =[X i,1 ,…,X i,t ,…,X i,T ], C j =[C j,1 ,…,C j,t ,…,C j,T ], where X i,t For scene X i The data of the tth dimension in C j,t Center scene C j The data of the t-th dimension in , where T is the total data dimension of the scene data;

[0028] 3) Based on the distance from each scene to the central scene, perform the first clustering according to the scene closest to the central scene to obtain the initial clustering result;

[0029] 4) Based on the clustering results, the centers of each cluster are obtained as new cluster center scenarios;

[0030] 5) Repeated iterations are performed until the changes in the cluster center scenarios meet the convergence conditions, and the final cluster center scenarios are obtained, which in turn constitute a subset of typical power grid operation scenarios.

[0031] The extreme scenarios in step 1 include two extreme scenarios: low power load level and high renewable energy output level, and high power load level and low renewable energy output level. The extreme scenario judgment basis is set according to the following formula:

[0032]

[0033] Where, and μ RES The thresholds of the mean renewable energy output period corresponding to high renewable energy output level and low renewable energy output level, and μ PL The thresholds representing the load period means corresponding to high load level and low load level respectively.

[0034] The total cost of the power grid in step 9 includes the planning cost F inc The system operation cost F ope The objective function is set according to the following formula:

[0035] min F = min (F inc +F ope )

[0036]

[0037] In the formula, min F represents the minimum value of the total cost, the system planning cost F inc Mainly includes the planning cost of newly added transmission lines and newly added non-renewable energy generators, newly added renewable energy generators, wherein And The planning cost of candidate non-renewable energy generator i, candidate transmission line l and candidate renewable energy generator r, Ω g , Ω l And Ω r Respectively represent the set of candidate non-renewable energy generators, candidate transmission lines and candidate renewable energy generators, And Respectively represent the optimization decision variable of whether the candidate planning non-renewable energy generator g, candidate transmission line l and candidate renewable energy generator r are planned, η is the annual discount rate, y is the planning year, Φ is the set of characteristic partitions, The number of operating days contained in the s-th characteristic partition, The number of normal operation state grid operation scenarios contained in the s-th characteristic partition, c RC , And Respectively represent the unit renewable energy curtailment cost of the system, the unit load shedding cost of node i and the unit power generation cost of non-renewable energy generator g, Ξ r , Ξ b And Ξ g Respectively represent the number of renewable energy generators of the power grid, the nodes of the power grid and the non-renewable energy generators, ρ s,ω The scene probability of the ω-th scene of the s-th characteristic partition.

[0038] In step 9, the model constraint conditions include power grid operation constraints, element operation constraints, power grid load shedding risk constraints and renewable energy curtailment risk constraints:

[0039] 1) The power grid operation constraints include power grid flow balance constraints and power grid operation upper and lower limit constraints:

[0040] 1.1) The power grid flow balance constraint is set according to the following formula:

[0041]

[0042] 1.2) The upper and lower limits of grid operation are set according to the following formula:

[0043]

[0044] In the formula: Ξ l represents the existing line set of the grid, P ω,ij,t is the active power flowing through line ij at time t under scenario ω, θ ω,i,t represents the power angle of node i at time t under scenario ω, x ij represents the reactance of line ij, M l is the introduced parameter of the large M method applied to the planning decision of line l, which is actually a very large number, Λ GB , Λ RB and Λ BL are N b × N g dimensional non-renewable energy unit-node association matrix, N b × N r dimensional renewable energy unit-node association matrix, and N b × N l dimensional transmission line-node association matrix, are the non-renewable energy unit and renewable energy unit output sets at time t under scenario ω, wherein, represents the output power of non-renewable energy unit g at time t under scenario ω, represents the output power of renewable energy unit r at time t under scenario ω, and are the power load and power cut load sets at time t under scenario ω, is the transmission line power flow set at time t under scenario ω, θ i and respectively represent the upper and lower limits of the power angle of node i, represents the maximum allowable power flow of line ij, is the set of grid operation scenarios under typical normal operation of the whole year, N extr is the set of grid operation scenarios under extreme operation;

[0045] 2) The grid risk constraints include cut load risk constraints and renewable energy curtailment risk constraints:

[0046] 2.1) The cut load risk constraints are set according to the following formula:

[0047]

[0048]

[0049] 2.2) Renewable energy curtailment risk constraint is set as follows:

[0050]

[0051]

[0052] wherein: denotes the amount of load shedding at node i at time t under scenario ω, is the upper limit of tolerable load shedding rate at node i under any scenario, is the upper limit of tolerable expected load shedding rate of the whole system in characteristic partition s under normal state, is the renewable energy curtailment power of renewable energy unit r at time t under scenario ω, is the maximum available output value of renewable energy unit r at time t under scenario ω, is the upper limit of tolerable expected renewable energy curtailment rate of the whole system in characteristic partition s under normal state;

[0053] 3) Element operation constraint is set as follows:

[0054]

[0055]

[0056] wherein: is the start-stop decision variable of non-renewable energy unit g at time t under scenario ω, denotes that non-renewable energy unit g is in operation at time t under scenario ω, denotes that non-renewable energy unit g is in operation at time t+τ under scenario ω, is the start-stop decision variable of renewable energy unit r at time t under scenario ω, denotes that renewable energy unit r is in operation at time t under scenario ω, and are the lower and upper limits of output of non-renewable energy unit g, respectively, and are the downward and upward ramping constraints of non-renewable energy unit g, respectively, and are the minimum start / stop time of non-renewable energy unit g.

[0057] The beneficial effects of the present application are:

[0058] 1) The invention patent considers typical normal operating state and small probability extreme operating state. For the typical normal operating state, the characteristics of the uncertainty of renewable energy output and power load on the annual time scale are analyzed, and a quarterly division method of time series data based on characteristic indexes is proposed. The obtained division results are helpful for analyzing each quarterly division; for the small probability extreme operating state, the corresponding judgment basis of extreme scenario historical data is proposed, and the uncertainty scenario analysis is carried out based on the extreme scenario historical data. The related method can improve the fineness of uncertainty scenario analysis.

[0059] 2) The invention patent proposes an uncertainty analysis model of renewable energy output and power load based on multi-state model and Markov model. Further, Markov Monte Carlo method and kmeans scene clustering method are used to obtain power grid operation typical scene close to its random fluctuation law.

[0060] 3) The invention patent combines economic benefits and risk management requirements, and proposes a two-stage "planning-operation" power grid optimization planning model based on stochastic optimization method. The planning cost related to planning and the system operation cost related to the operation state under each uncertain operation scenario are considered, as well as various constraints including system operation, component operation and system risk constraints. The power grid optimization planning results of transmission lines and generator units considering system economy and risk management effect can be obtained. BRIEF DESCRIPTION OF DRAWINGS

[0061] Figure 1 is a method flowchart of the invention; DETAILED DESCRIPTION

[0062] The invention patent will be further described in detail below in combination with the drawings and specific embodiments:

[0063] As shown in the drawings, the specific embodiment of the power grid planning method of the invention includes the following steps: Figure 1

[0064] Step 1: In the computer, according to the extreme scenario judgment basis, the extreme scenario historical data is selected from the annual statistical historical data of renewable energy output and power load, and the remaining data is used as normal scenario historical data.

[0065] In the invention, the output and load usually refer to power.

[0066] The extreme scenarios include two types of extreme scenarios of low power load level and high renewable energy output level, and high power load level and low renewable energy output level. The extreme scenario judgment basis is constructed according to the time period mean characteristics of historical data, and is set according to the following formula:

[0067] Low power load level and high renewable energy output level,​

[0068] high power load level and low renewable energy output level,

[0069] wherein, and μ RES respectively represent the threshold of the average of renewable energy output period corresponding to high renewable energy output level and low renewable energy output level, and μ PL respectively represent the threshold of the average of load period corresponding to high load level and low load level.

[0070] The above formula shows that when the average of renewable energy output period of a day is higher than μ and the average of load period is lower than μ PL , the day can be identified as an extreme scenario of “low load level, high renewable energy output level”; similarly, when the average of renewable energy output period of a day is lower than μ RES and the average of load period is higher than μ , the day can be identified as an extreme scenario of “high load level, low renewable energy output level”.

[0071] Step 2: Obtain the characteristic parameters of renewable energy output and power load in different months of a year according to the normal scenario historical data processing, and then divide the normal scenario historical data by month according to the characteristic parameters to obtain multiple characteristic partitions with large distribution characteristic differences, each characteristic partition including the historical data of renewable energy output and power load;

[0072] wherein the division adopts a kmeans clustering method.

[0073] The characteristic parameters include the mean and variance of renewable energy output and power load of each month, which are obtained according to the following formula:

[0074]

[0075] wherein m represents the mth natural month, m ∈ [1, 12], D m represents the number of days contained in the mth month, d represents the dth natural day in the mth natural month, d ∈ [1, D m ], T represents the number of running periods under each running scenario, t represents the tth period in the dth natural day, t ∈ [1, T], represents the mean of renewable energy output / power load of the mth month, represents the variance of renewable energy output / power load of the mth month, represents the data of renewable energy output / power load of the mth month, the dth day and the tth period.

[0076] Step 3: According to the characteristics of the sub-set obtained in step 2, the annual statistical history data of renewable energy output and power load of each characteristic sub-zone is constructed, the historical data of renewable energy output and power load in a characteristic sub-zone can be divided into two sub-sets, which are the historical statistical data sub-set of renewable energy output in the corresponding month of the characteristic sub-zone and the historical statistical data sub-set of power load in the corresponding month, and then the multi-state model of renewable energy output and power load of each characteristic sub-zone is constructed, and the state transition probability between each state in each multi-state model is obtained by using Markov model;

[0077] In the application, one sub-set constructs one multi-state model, and the multi-state model defines the renewable energy output and power load state of each characteristic sub-zone.

[0078] The multi-state model in step 3 is a state set of renewable energy output / power load, Wherein, represents the i th renewable energy output / power load state, K RES and K PL are the number of states that renewable energy output and power load can be transferred to respectively;

[0079] The state set is set according to the following steps:

[0080] The minimum value and the maximum value in the annual statistical historical data of renewable energy output / power load in the sub-set are selected to construct the interval Then the interval is divided into K RES / PL sub-intervals at a certain interval, each sub-interval corresponds to a state, and the state represents that the renewable energy output level / power load level is between the i th interval, wherein represents the historical data sub-set of renewable energy output and power load in the sub-set.

[0081] Wherein represents the i th renewable energy output state performance) and (there is Wherein represents the i th power load state) to represent the state performance;

[0082] Based on the historical data of renewable energy output / power load power in the sub-set Wherein characterizes the s th renewable energy / power load historical data sub-set, and the minimum value and the maximum value in the historical data are selected to construct the interval i.e. the fluctuation range of renewable energy output / power load, which is equally divided into K RES / PL sub-ranges, and the range length is The i-th range is On this basis, the i-th state in the multi-state model represents that the renewable energy output level / power load level is between the i-th range.

[0083] In the Markov model, the state probability of renewable energy output and power load at time t is represented by respectively, where represents the probability that the renewable energy output at time t is in the i-th state, i.e. represents the probability that the power load at time t is in the i-th state, i.e. Further, the state transition matrix is where represents the transition probability of renewable energy output / power load from state i to state j at time t.

[0084] The state transition satisfies the following formula:

[0085]

[0086] where represents the state of renewable energy output / power load at time t, represents the state of renewable energy output / power load at time t, represents the state of renewable energy output / power load at time t+1;

[0087] The state transition probability in step 3 is evaluated by counting the state transition frequency and using the frequency to represent the probability:

[0088]

[0089] where represents the state transition probability of renewable energy output / power load from state i to state j at time t in the s-th characteristic range, represents the transition frequency of renewable energy output / power load from state i to state j at time t in the s-th characteristic range, represents the transition frequency of renewable energy output / power load from state i to state k at time t in the s-th characteristic range, K RES and K PLThe number of states to which renewable energy output and power load can be transferred, that is, the total number of states of the renewable energy output multi-state model and the power load multi-state model

[0090] Step 4: Based on the multi-state model and state transition probabilities obtained in Step 3, the Markov Chain Monte Carlo simulation method is used to simulate and generate transmission network uncertainty operation scenarios reflecting the fluctuations of renewable energy output and power load under various characteristic partitions;

[0091] Step 5: Use the kmeans clustering method to reduce the uncertainty operation scenarios of the transmission network under each characteristic partition obtained in step 4, generate a subset of typical operation scenarios of the transmission network for each characteristic partition, and then obtain a subset of typical operation scenarios of the power grid in a typical normal operating state;

[0092] Step 6: Based on the extreme scenario historical data obtained in step 1, a subset of the renewable energy output historical data and the power load historical data under the extreme scenario are constructed. Then, multi-state models of renewable energy output and power load under the extreme scenario are constructed respectively. The state transition probabilities between each state in the multi-state models of renewable energy output and power load are obtained by combining the Markov model.

[0093] Step 7: Based on the multi-state model of renewable energy output and power load under extreme scenarios in step 6, as well as the state transition probability, the Markov chain Monte Carlo simulation method is used to simulate and generate grid uncertainty operation scenarios under extreme scenarios. The kmeans clustering method is then used to reduce the grid uncertainty operation scenarios under extreme scenarios, obtaining a subset of typical grid operation scenarios under extreme operating states. The probability of grid uncertainty operation scenarios under extreme scenarios being selected by the model is extremely low.

[0094] In steps 4 and 7, the Markov chain Monte Carlo simulation method is used to simulate and generate transmission network uncertainty operation scenarios that take into account the fluctuations in renewable energy output and power load. Each grid uncertainty operation scenario includes a renewable energy output sequence and power load sequence for 24 hours a day.

[0095] And the kmeans clustering method steps in step 4 and step 7 are as follows:

[0096] 1) Set the number of target reduction scenarios k as the number of kmeans target clusters;

[0097] 2) Select k grid uncertainty operation scenarios {C1, C2, C3, ..., C k} is used as the initial cluster center point (cluster initial center scene), and the distance from each scene to the initial center scene is obtained according to the following formula:

[0098]

[0099] In the formula, dis(X i ,C j ) is the distance from the scene X i to the center scene C j , X i =[X i,1 ,…,X i,t ,…,X i,T ], C j =[C j,1 ,…,C j,t ,…,C j,T ], wherein X i,t is the data of the tthdimension in the scene X i , C j,t is the data of the tthdimension in the center scene C j , and T is the total data dimension of the scene data;

[0100] 3) According to the distance from each scene to the center scene, the first clustering is performed according to the closest center scene, and an initial clustering result is obtained;

[0101] 4) According to the clustering result, the center of each cluster is obtained as a new clustering center scene;

[0102] 5) Iteration is repeated until the change of the clustering center scene meets the convergence condition, and the final clustering center scene is obtained, and then a power grid typical operation scene subset is formed.

[0103] The s thcharacteristic partition generates typical scenes, and the probability of each typical scene is equal to the number of original scenes contained in the class corresponding to the clustering center, and the sum of the scene probabilities is 1.

[0104]

[0105] In the formula, p s,ω is the probability of the w thtypical scene in the s thcharacteristic partition.

[0106] Step 8: Establishing the model constraint conditions composed of the power grid operation constraint, the element operation constraint, the power grid load shedding risk constraint and the renewable energy curtailment risk constraint, constructing the objective function with the optimization target of minimizing the total cost of the power grid including operation and planning, and then constructing the two-stage “planning-operation” power grid optimization planning model based on stochastic optimization, and obtaining the planning power grid result corresponding to the minimum value of the objective function by using the power grid optimization planning model;

[0107] Step 9: Based on the typical operation scenario subsets of the power grid in typical normal operation state and extreme operation state obtained in step 5 and step 7 respectively, the model constraints and the objective function in step 8, the power grid optimization planning model is used to carry out power grid optimization planning, and the power transmission line, generator unit and other power transmission network planning results are obtained as the power grid planning method. The power grid planning results include the physical parameters of the power transmission line, generator unit and other power transmission network.

[0108] The total cost of the power grid includes the planning cost F inc and the system operation cost F ope The objective function is to minimize the total cost in the typical operation scenario, and the objective function is set according to the following formula, wherein the optimization variables include: the decision variable of whether the candidate renewable energy unit is planned The decision variable of whether the candidate non-renewable energy generator unit is planned The decision variable of whether the candidate power transmission line is planned And the power grid operation state variable under each operation scenario, wherein in the objective function And are optimization variables to be solved, and other parameters are known parameters:

[0109] min F = min (F inc +F ope )

[0110]

[0111] In the formula, min F represents the minimum value of the total cost, the system planning cost F inc mainly includes the planning cost of newly added power transmission lines and newly added non-renewable energy generator units, and newly added renewable energy units, wherein And are the planning costs of candidate non-renewable energy generator units i, candidate power transmission lines l and candidate renewable energy units r, respectively, Ω g , Ω l And Ω r respectively represent the set of candidate non-renewable energy generator units, candidate power transmission lines and candidate renewable energy units, And respectively represent the optimization decision variable of whether the candidate planning non-renewable energy generator unit g, the candidate power transmission line l and the candidate renewable energy unit r are planned, which are 0-1 variables, that is Indicates that the candidate non-renewable energy generator unit g is planned, Indicates that the candidate power transmission line l is planned, The candidate renewable energy unit r is planned. Among them, the candidate renewable energy unit is the renewable energy unit to be planned, the newly added renewable energy unit is the renewable energy unit planned in the optimization decision, and the newly added transmission line, the newly added non-renewable energy generator unit, the candidate transmission line and the candidate non-renewable energy generator unit are understood as above. η is the annual discount rate, y is the planning year, Φ is the set of characteristic partitions, is the number of operating days contained in the s-th characteristic partition, is the number of normal operating state grid operating scenarios contained in the s-th characteristic partition, RC 、 and respectively represent the unit renewable energy curtailment cost of the system, the unit load shedding cost of node i and the unit power generation cost of non-renewable energy unit g, Ξ r , Ξ b and Ξ g are the number of renewable energy units, grid nodes and non-renewable energy generators of the grid, respectively, ρ s,ω is the scene probability of the ω-th scene of the s-th characteristic partition.

[0112] Among them, the model constraint conditions include grid operation constraints, element operation constraints, grid load shedding risk constraints and renewable energy curtailment risk constraints:

[0113] 1) The grid operation constraints include grid flow balance constraints and grid operation upper and lower limit constraints:

[0114] 1.1) The grid flow balance constraint is set according to the following formula:

[0115]

[0116] 1.2) The grid operation upper and lower limit constraint is set according to the following formula:

[0117]

[0118] In the formula: Ξ l represents the set of existing lines of the grid, P ω,ij,t is the active power flowing through line ij at time t under scene ω, θ ω,i,t represents the power angle of node i at time t under scene ω, x ij represents the reactance of line ij, M l is a parameter introduced by the application of the large M method to the line l planning decision, which is a very large number, Λ GB , Λ RB and Λ BL are N b × N g dimensional non-renewable energy unit-node association matrix, Nb ×N r dimensional renewable energy unit-node incidence matrix and N b ×N l dimensional transmission line-node incidence matrix, respectively, are the non-renewable energy unit and renewable energy unit output sets at time t under scenario ω, where, denotes the output power of non-renewable energy unit g at time t under scenario ω, denotes the output power of renewable energy unit r at time t under scenario ω, and respectively, are the power load and power load shedding sets at time t under scenario ω, P ω,t = [P ω,1i,t ,…,P ω,ji,t ] is the transmission line power flow set at time t under scenario ω, θ i and respectively, denote the upper and lower limits of the power angle of node i, denotes the maximum allowable power flow of line ij, is the set of grid operation scenarios under typical normal operation state in a year, N extr is the set of grid operation scenarios under extreme operation state;

[0119] 2) The grid risk constraints include load shedding risk constraints and renewable energy curtailment risk constraints:

[0120] 2.1) The load shedding risk constraints are set according to the following formula:

[0121]

[0122] 2.2) The renewable energy curtailment risk constraints are set according to the following formula:

[0123]

[0124]

[0125] In the formula: denotes the amount of power load shedding at node i at time t under scenario ω, is the upper limit of the tolerable load shedding rate of node i under any scenario, is the upper limit of the tolerable expected load shedding rate of the system in characteristic partition s under normal state, is the new energy curtailment power of renewable energy unit r at time t under scenario ω, is the maximum output value of renewable energy unit r at time t under scenario ω, is the upper limit of the tolerable expected renewable energy curtailment rate of the whole system in characteristic partition s under normal state;

[0126] 3) The element operation constraints are set as follows:

[0127]

[0128] wherein: is the start-stop decision variable of the non-renewable energy unit g at time t under scenario ω, which is a 0-1 variable, denotes that the non-renewable energy unit g is in operation at time t under scenario ω, denotes that the non-renewable energy unit g is in operation at time t+τ under scenario ω, is the start-stop decision variable of the renewable energy unit r at time t under scenario ω, which is a 0-1 variable, denotes that the renewable energy unit r is in operation at time t under scenario ω, and are the lower and upper limits of the output of the non-renewable energy unit g, respectively, and are the downward and upward ramping constraints of the non-renewable energy unit g, respectively, and are the minimum start / stop time of the non-renewable energy unit g.

[0129] The above detailed description is used to explain and illustrate the present application, rather than to limit the present application, and any modification and change made to the present application within the spirit and protection scope of the claims shall fall into the protection scope of the present application.

[0130] The above description is only the preferred embodiment of the present application, and any equivalent change or modification made to the structure, features and principles described in the scope of the present application shall fall into the scope of the present application.

Claims

1. A power grid planning method considering risk constraints under uncertain scenarios, characterized in that, The steps include the following: Step 1: According to the extreme scenario judgment basis, the extreme scenario historical data is screened out from the annual statistical historical data of renewable energy output and power load, and the remaining data is taken as normal scenario historical data; Step 2: According to the normal scenario historical data processing, the characteristic parameters in different periods of renewable energy output and power load are obtained, and then the normal scenario historical data is divided into multiple characteristic partitions according to the characteristic parameters; Step 3: According to the characteristic partitions obtained in step 2, the sub-sets of the annual statistical historical data of renewable energy output and power load in each characteristic partition are constructed, and then the multi-state models of renewable energy output and power load in each characteristic partition are constructed, and the state transition probabilities between each state in each multi-state model are obtained by using Markov model; Step 4: According to the multi-state models and state transition probabilities obtained in step 3, the power grid uncertainty operation scenarios under each characteristic partition are simulated by using Markov chain Monte Carlo simulation method; Step 5: The power grid uncertainty operation scenarios under each characteristic partition obtained in step 4 are reduced by using kmeans clustering method, and the power grid typical operation scenario subset of each characteristic partition is generated, and then the power grid typical operation scenario subset of normal operation state is obtained; Step 6: According to the extreme scenario historical data obtained in step 1, the sub-sets of renewable energy output historical data and power load historical data under extreme scenario are constructed, and then the multi-state models of renewable energy output and power load under extreme scenario are constructed, and the state transition probabilities between each state in the multi-state models of renewable energy output and power load are obtained by combining Markov model processing; Step 7: According to the multi-state models of renewable energy output and power load under extreme scenario and the state transition probabilities in step 6, the power grid uncertainty operation scenarios under extreme scenario are simulated by using Markov chain Monte Carlo simulation method, and the power grid typical operation scenario subset of extreme operation state is obtained by reducing the power grid uncertainty operation scenarios under extreme scenario by using kmeans clustering method; Step 8: The model constraint conditions are established by establishing the power grid operation constraint, the component operation constraint, the power grid load shedding risk constraint and the renewable energy curtailment risk constraint, and the objective function is constructed by taking the minimization of the total cost of the power grid as the optimization target, and then the power grid optimization planning model is constructed; Step 9: According to the power grid typical operation scenario subsets of typical normal operation state and extreme operation state obtained in steps 5 and 7, the model constraint conditions and the objective function in step 8, the power grid optimization planning model is used for power grid optimization planning, and the power grid planning result is obtained, which includes the physical parameters of transmission line, non-renewable energy generator set and renewable energy generator set. 2.The power grid planning method considering risk constraints under uncertain scenarios according to claim 1, wherein: The characteristic parameters in step 2 include the mean and variance of renewable energy output and power load in each month, which are obtained according to the following formula: In the formula, m represents the mth natural month, m ∈ [1, 12], D m is the number of days contained in the mth month, d represents the dth natural day in the mth natural month, d ∈ [1, D m ], T is the number of running periods under each running scenario, t represents the tth period in the dth natural day, t ∈ [1, T], represents the mean value of the renewable energy output / power load of the mth month, represents the variance of the renewable energy output / power load of the mth month, represents the data of the renewable energy output / power load of the tth period on the dth day in the mth month. 3.The power grid planning method considering risk constraints under uncertain scenarios according to claim 1, characterized in that: The division in step 2 adopts kmeans clustering method.

4. The power grid planning method considering risk constraints under uncertain scenarios according to claim 1, characterized in that: The multi-state model in step 3 is a state set of renewable energy output / power load, wherein, represents the state of the i th renewable energy output / power load, K RES and K PL are the number of renewable energy output and power load states, respectively; The state set is specifically set according to the following steps: The minimum and maximum values in the annual statistical historical data of the renewable energy output / power load in the subset are selected respectively to construct intervals The intervals are then divided into K RES / PL sub-intervals at a certain interval, and the state represents that the renewable energy output level / power load level is between the i-th interval, wherein represents the historical data subset of the renewable energy output, power load in the subset.

5. The method of claim 1, wherein: The Markov Chain Monte Carlo simulation method in steps 4 and 7 is used to simulate and generate power grid uncertainty operation scenarios considering renewable energy output and power load fluctuations, and each power grid uncertainty operation scenario contains a 24-hour renewable energy output sequence and a power load sequence.

6. The method of claim 1, wherein: The kmeans clustering step in steps 4 and 7 is: 1) Set the target reduction scenario number k as the kmeans target cluster number; 2) Select k grid uncertainty operation scenarios {C1, C2, C3, ..., C k } is used as the initial center point of the cluster, and the distance from each scene to the initial center scene is obtained according to the following formula: where dis(X i ,C j ) is the distance from scene X i to center scene C j , X i =[X i,1 ,…,X i,t ,…,X i,T ], C j =[C j,1 ,…,C j,t ,…,C j,T ], where X i,t is the data of the tth dimension in scene X i , C j,t is the data of the tth dimension in center scene C j , and T is the total data dimension of the scene data. 3) According to the distance of each scenario to the center scenario, the first clustering is performed according to the nearest center scenario to obtain the initial clustering result; 4) According to the clustering result, the cluster center of each cluster is obtained as the new clustering center scenario; 5) Repeat iteration until the change of the clustering center scenario meets the convergence condition to obtain the final clustering center scenario, which further constitutes the power grid typical operation scenario subset.

7. The method of claim 1, wherein: The extreme scenarios in step 1 include two types of extreme scenarios: low power load level and high renewable energy output level, and high power load level and low renewable energy output level. The extreme scenario judgment is based on the following formula: wherein and μ RES respectively represent threshold values of the average of the renewable energy output period corresponding to the high renewable energy output level and the low renewable energy output level, and μ PL respectively represent threshold values of the average of the load period corresponding to the high load level and the low load level. 8.The power grid planning method of claim 1, wherein: The total cost of the grid in step 9 includes the planning cost F inc The total cost of the grid in step 9 includes the planning cost F ope The objective function is set according to the following formula: minF = min(F inc + F ope ) min F = min min F inc The planning cost mainly includes the planning cost of new transmission lines, new non-renewable energy generating units and new renewable energy generating units, wherein and are the planning costs of candidate non-renewable energy generating units i, candidate transmission lines l and candidate renewable energy generating units r, respectively, Ω g , Ω l and Ω r represent the sets of candidate non-renewable energy generating units, candidate transmission lines and candidate renewable energy generating units, respectively, and are the optimization decision variables indicating whether the candidate planning non-renewable energy generating units g, candidate transmission lines l and candidate renewable energy generating units r are planned, η is the annual discount rate, y is the planning year, and Φ is the set of characteristic partitions, is the number of operating days included in the s-th characteristic partition, is the number of normal operating state grid operating scenarios included in the s-th characteristic partition, c RC , and represent the unit renewable energy curtailment cost of the system, the unit load shedding cost of node i and the unit power generation cost of non-renewable energy generating unit g, respectively, Ξ r , Ξ b and Ξ g are the numbers of renewable energy generating units, grid nodes and non-renewable energy generating units of the grid, respectively, and ρ s,ω is the scenario probability of the ω-th scenario of the s-th characteristic partition. 9.The power grid planning method of claim 1, wherein: In step 9, the model constraint conditions include grid operation constraints, element operation constraints, grid load shedding risk constraints, and renewable energy curtailment risk constraints: 1) The grid operation constraints include grid power flow balance constraints and grid operation upper and lower limit constraints: 1.1) The grid power flow balance constraint is set according to the following formula: 1.2) The grid operation upper and lower limit constraint is set according to the following formula: Where: l represents the set of existing lines in the power grid, P ω,ij,t is the active power flowing through line ij at time t under scenario ω, θ ω,i,t represents the power angle of node i at time t in scenario ω, x ij Represents the reactance of line ij, M l The parameter introduced when applying the Big M method to route planning is actually a very large number, Λ GB , Λ RB and Λ BL N b ×N g Dimensional non-renewable energy unit-node association matrix, N b ×N r Dimensional renewable energy unit-node association matrix and N b ×N l The transmission line-node correlation matrix of dimension, are the output sets of non-renewable energy units and renewable energy units at time t under scenario ω, where, Pgi(t, ω) denotes the output power of non-renewable energy unit g at time t under scenario ω, Pgr(t, ω) denotes the output power of renewable energy unit r at time t under scenario ω, and Pd(t, ω) and Pd(t, ω) are the power load and power cut load set at time t under scenario ω, respectively, P ω,t = [P ω,1i,t ,…,P ω,ji,t ] are the transmission line power flow set at time t under scenario ω, θ i and φi and φi denote the upper and lower limits of the power angle of node i, respectively, φij denotes the maximum allowable power flow of line ij, N extr is the set of grid operation scenarios under typical normal operation state throughout the year; 2) The grid risk constraints include load shedding risk constraints and renewable energy curtailment risk constraints: 2.1) The load shedding risk constraint is set according to the following formula: 2.2) The renewable energy curtailment risk constraint is set according to the following formula: In the formula: represents the power load shedding amount of node i at time t under scenario ω, is the upper limit of the tolerable load shedding rate of node i under any scenario, is the upper limit of the tolerable expected load shedding rate of the system in the characteristic partition s under the normal state, is the new energy curtailment power of the renewable energy unit r at time t under scenario ω, is the maximum output value of the renewable energy unit r at time t under scenario ω, is the upper limit of the tolerable expected renewable energy curtailment rate of the whole system in the characteristic partition s under the normal state. 3) The element operation constraint is set according to the following formula: In the formula: is the start-stop decision variable of the non-renewable energy unit g at time t under scenario ω, indicates that the non-renewable energy unit g is in a running state at time t under scenario ω, indicates that the non-renewable energy unit g is in a running state at time t+τ under scenario ω, is the start-stop decision variable of the renewable energy unit r at time t under scenario ω, indicates that the renewable energy unit r is in a running state at time t under scenario ω, and are the lower and upper limits of the output of the non-renewable energy unit g, respectively, and are the downward and upward ramping constraints of the non-renewable energy unit g, respectively, and are the minimum on / off durations of the non-renewable energy unit g, respectively.

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