Power transmission capacity distribution method and device of power system

Abstract

The invention discloses a power transmission capacity distribution method and device for a power system, and the method comprises the steps: obtaining data samples of uncertainty factors in the power system, carrying out the clustering processing of the data samples, and obtaining an uncertainty set which describes the value range of the uncertainty factors; obtaining unit operation data in the power system, and obtaining unit operation constraints according to the unit operation data; establishing a two-stage robust optimization model of power transmission capacity distribution according to the uncertainty set and unit operation constraints; and solving the two-stage robust optimization model by adopting an imprecise column and constraint generation algorithm to obtain a power transmission capacity distribution scheme. And meanwhile, an imprecise column and constraint generation algorithm is introduced to accelerate solution, so that the problems of complex optimization model, large calculation amount and the like existing in transmission capacity distribution of the novel power system are solved, and the algorithm solution is accelerated while the requirement of transmission capacity distribution under the background of construction of the novel power system is met.

Description

Technical Field

[0001] The present invention relates to the technical field of power system dispatching, and in particular to a method and device for allocating power transmission capacity of a power system. Background Art

[0002] As power resources and load demand are distributed inversely, in order to break the barriers to resource allocation and achieve more optimized resource allocation, cross-provincial and cross-regional power transmission and power trading are needed. At the same time, in order to achieve the dual carbon goals, renewable energy with volatility, intermittency and uncertainty characteristics will be embedded in the new power system in large quantities.

[0003] At present, the existing cross-provincial and cross-regional economic dispatch considering uncertainty factors has the following challenges: 1) Due to the shortage of trading channel resources between certain key regions (provinces) and the inadequacy of the cross-provincial and cross-regional electricity trading mechanism, problems such as excessively high transmission prices, insufficient external electricity resources and restrictions on transmission channels within the province have arisen. 2) At the same time, the overall utilization rate of inter-regional (provincial) trading channels is low and certain key transmission channels are blocked. 3) The development of new power systems has exacerbated the severity of the problem. The increase in randomness and uncertainty in the state of the power system has led to insufficient resources for inter-provincial trading channels. 4) In the cross-provincial and cross-regional trading environment, new and old market players have many different needs for the allocation mechanism of transmission capacity and the cross-provincial and cross-regional electricity trading mechanism.

[0004] In addition, with the increasing proportion of renewable energy generation, the large-scale access of emerging loads such as electric vehicles, and the continuous expansion of cross-regional and cross-provincial transactions, the multi-region economic dispatch model considering the allocation of transmission capacity is increasing. The most common solution method for the two-stage economic dispatch problem of day-ahead and intraday under the operation of the intra-provincial and inter-provincial markets is the C&CG (Column-and-Constraint Generation) algorithm. However, under the framework of the traditional C&CG algorithm, the scale of the main problem gradually increases with the iteration process, which increases the computational complexity in the existing application context. Summary of the invention

[0005] The technical problem to be solved by the present invention is that the allocation of power transmission capacity in the power system has the problems of complex optimization model and large amount of calculation.

[0006] In order to solve the above technical problems, the technical solution adopted by the present invention is:

[0007] A method for allocating transmission capacity of a power system, comprising:

[0008] Acquire data samples of uncertainty factors in the power system, and perform clustering processing on the data samples to obtain an uncertainty set describing the value range of the uncertainty factors;

[0009] Acquire unit operation data in the power system, and obtain unit operation constraints according to the unit operation data;

[0010] Establishing a two-stage robust optimization model for transmission capacity allocation according to the uncertainty set and unit operation constraints;

[0011] An inexact column and constraint generation algorithm is used to solve the two-stage robust optimization model and obtain a transmission capacity allocation scheme.

[0012] In order to solve the above technical problems, another technical solution adopted by the present invention is:

[0013] A power system transmission capacity allocation device, comprising:

[0014] A first acquisition module is used to acquire data samples of uncertainty factors in the power system, and perform clustering processing on the data samples to obtain an uncertainty set describing the value range of the uncertainty factors;

[0015] A second acquisition module is used to acquire the unit operation data in the power system and obtain the unit operation constraints according to the unit operation data;

[0016] A model building module, used for building a two-stage robust optimization model for transmission capacity allocation according to the uncertainty set and unit operation constraints;

[0017] The solution module is used to solve the two-stage robust optimization model by adopting an inexact column and constraint generation algorithm to obtain a transmission capacity allocation plan.

[0018] In order to solve the above technical problems, another technical solution adopted by the present invention is:

[0019] An electronic device comprises a memory, a processor and a computer program stored in the memory and executable on the processor, wherein the processor implements the various steps in the above-mentioned method for allocating transmission capacity of a power system when executing the computer program.

[0020] In order to solve the above technical problems, another technical solution adopted by the present invention is:

[0021] A computer storage medium stores a computer program, which, when executed by a processor, implements the various steps of the above-mentioned method for allocating power transmission capacity of a power system.

[0022] The beneficial effects of the present invention are as follows: an uncertainty set is constructed through data samples of uncertainty factors in the power system, and unit operation constraints are constructed according to unit operation data, and then a two-stage robust optimization model for transmission capacity allocation is established according to the uncertainty set and the unit operation constraints, that is, the model takes into account the uncertainty factors and unit operation conditions that affect the transmission capacity allocation results in the new power system, and introduces an inexact column and constraint generation algorithm to accelerate the solution. The algorithm can adaptively adjust the iteration mode according to the iteration results, and reasonably allocate computing resources in the iteration process to improve the solution rate, thereby solving the problems of complex optimization models and large calculation amount in the transmission capacity allocation of the new power system, thereby accelerating the algorithm solution while meeting the needs of transmission capacity allocation in the context of new power system construction. BRIEF DESCRIPTION OF THE DRAWINGS

[0023] Figure 1 A flowchart of a method for allocating transmission capacity of a power system according to an embodiment of the present invention;

[0024] Figure 2 A flowchart of solving an inexact column and constraint generation algorithm in a power system transmission capacity allocation method in an embodiment of the present invention;

[0025] Figure 3 The present invention is a schematic diagram of the structure of a power system transmission capacity allocation device in an embodiment of the present invention. DETAILED DESCRIPTION

[0026] In order to explain the technical content, achieved objectives and effects of the present invention in detail, the following is an explanation in combination with the implementation modes and the accompanying drawings.

[0027] Please refer to Figure 1 , a method for allocating transmission capacity of a power system, comprising:

[0028] Acquire data samples of uncertainty factors in the power system, and perform clustering processing on the data samples to obtain an uncertainty set describing the value range of the uncertainty factors;

[0029] Acquire unit operation data in the power system, and obtain unit operation constraints according to the unit operation data;

[0030] Establishing a two-stage robust optimization model for transmission capacity allocation according to the uncertainty set and unit operation constraints;

[0031] An inexact column and constraint generation algorithm is used to solve the two-stage robust optimization model and obtain a transmission capacity allocation scheme.

[0032] The beneficial effects of the present invention are as follows: an uncertainty set is constructed through data samples of uncertainty factors in the power system, and unit operation constraints are constructed according to unit operation data, and then a two-stage robust optimization model for transmission capacity allocation is established according to the uncertainty set and the unit operation constraints, that is, the model takes into account the uncertainty factors and unit operation conditions that affect the transmission capacity allocation results in the new power system, and introduces an inexact column and constraint generation algorithm to accelerate the solution. The algorithm can adaptively adjust the iteration mode according to the iteration results, and reasonably allocate computing resources in the iteration process to improve the solution rate, thereby solving the problems of complex optimization models and large calculation amount in the transmission capacity allocation of the new power system, thereby accelerating the algorithm solution while meeting the needs of transmission capacity allocation in the context of new power system construction.

[0033] Furthermore, the data samples include accidental factor samples and non-accidental factor samples;

[0034] Clustering is performed on the data samples to obtain an uncertainty set describing the value range of the uncertainty factor, including:

[0035] Clustering the accidental factor samples in a random optimization manner to obtain an uncertainty set of accidental factors;

[0036] Clustering the non-accidental factor samples by a robust optimization method to obtain a non-accidental factor uncertainty set;

[0037] The uncertainty set of the uncertainty factor value range is obtained according to the accidental factor uncertainty set and the non-accidental factor uncertainty set.

[0038] From the above description, it can be seen that by distinguishing data samples into accidental factor samples and non-accidental factor samples, and processing the accidental factor samples and non-accidental factor samples in different ways, the uncertainty sets corresponding to different samples can be obtained more accurately.

[0039] Further, the accidental factors include component outage, and the accidental factor samples include historical data of the unit and data related to the power generation unit;

[0040] The clustering process of the accidental factor samples by random optimization includes:

[0041] Based on the generator capacity interruption probability table and in combination with the unit historical data and the power generation unit related data, the capacity interruption probability of the component outage combination with different capacity combinations is obtained;

[0042] The capacity interruption probabilities of all component outage combinations are integrated to obtain the uncertainty set of accidental factors.

[0043] From the above description, it can be seen that by adopting a modeling method based on the capacity interruption probability table, by inputting the historical data of the unit and the relevant data of the power generation unit, the capacity interruption probability of the component outage combination with different capacity combinations is obtained with the corresponding probability, thereby obtaining an accurate set of accidental factor uncertainties.

[0044] Furthermore, the non-accidental factor uncertainty set includes:

[0045]

[0046] In the formula, Ψ represents the uncertainty set of non-accidental factors, It represents the predicted positive deviation value of the output of each type of unit at time t; Indicates the minimum deviation of the output forecast of each type of unit; It is a 0-1 variable, indicating that the corresponding non-accidental factors are in different scenarios considered in robust optimization; α represents the positive deviation value that may appear in the prediction of robust optimization.

[0047] From the above description, it can be seen that the non-accidental factors are described by the predicted positive deviation value of the output of each type of unit, the minimum deviation of the output prediction of each type of unit, the variable parameters and the predicted positive deviation value, which can accurately describe the impact of non-accidental factors on the unit output in different scenarios; at the same time, for the modeling of various power generation entities that seriously interfere with the transmission capacity allocation results, the robust optimization method is adopted to consider the worst output fluctuation situation, and the balance between the economy and safety of the optimization model can be flexibly adjusted by changing the robustness parameters in the model.

[0048] Further, the unit operation data includes cascade hydropower unit data; the unit operation constraints include hydropower unit operation constraints;

[0049] The obtaining of the unit operation constraint according to the unit operation data comprises:

[0050] Constructing the operation constraints of the hydropower unit according to the hydropower unit data;

[0051] The operating constraints of the hydropower unit include water balance constraints, total discharge flow constraints, power discharge flow constraints, abandoned water discharge flow constraints, forebay water level constraints, equation constraints between forebay water level and reservoir capacity, equation constraints between tailwater water level and discharge volume, head constraints and discharge power constraints.

[0052] From the above description, it can be seen that by constructing constraints including water balance constraints, total discharge flow constraints, and power discharge flow constraints through hydropower unit data, the operation of the hydropower unit can be accurately described, thereby improving the accuracy of subsequent solutions.

[0053] Furthermore, the unit operation data includes wind turbine unit data and photovoltaic unit data;

[0054] The obtaining of the unit operation constraint according to the unit operation data comprises:

[0055]

[0056] in, and They represent the upper and lower limits of the power of the i-th wind turbine located at the n-node in the m-region respectively; and They represent the upper and lower limits of the power of the ith PV unit located at the nth node in the mth region respectively; and They respectively represent the output of the i-th wind turbine and the i-th photovoltaic unit located at the n-node in the m-region.

[0057] From the above description, it can be seen that by constructing wind turbine operation constraints and photovoltaic unit operation constraints through wind turbine unit data and photovoltaic unit data, the operation conditions of wind turbine units and photovoltaic units can be accurately described, thereby improving the accuracy of subsequent solutions.

[0058] Furthermore, the two-stage robust optimization model for transmission capacity allocation is established according to the uncertainty set and the unit operation constraints, including:

[0059] Construct an optimization model for transmission capacity allocation in the day-ahead market stage;

[0060] Construct an optimization model for transmission capacity allocation in the intraday market stage;

[0061] The transmission capacity allocation optimization model in the day-ahead market stage is taken as the main problem, and the transmission capacity allocation optimization model in the intraday market stage is taken as the sub-problem, thereby obtaining the two-stage robust optimization model.

[0062] From the above description, it can be seen that by constructing the transmission capacity allocation optimization model in the day-ahead market stage as the main problem, and constructing the transmission capacity allocation optimization model in the intraday market stage as the sub-problem, that is, considering the differentiated market transaction demands and output uncertainty characteristics of various new market entities and traditional market entities in the model, the demand for transmission capacity allocation under the new power system can be met.

[0063] Furthermore, the transmission capacity allocation optimization model in the day-ahead market stage is a two-layer model;

[0064] The two-stage robust optimization model for transmission capacity allocation is established according to the uncertainty set and the unit operation constraints, including:

[0065] The transmission capacity allocation optimization model in the day-ahead market stage is transformed into a single-layer model by using KKT condition or dual transformation method and big M method;

[0066] The two-stage robust optimization model is obtained based on the converted transmission capacity allocation optimization model in the day-ahead market stage and the transmission capacity allocation optimization model in the intraday market stage.

[0067] From the above description, it can be seen that the KKT condition and the big M method are used to transform the two-layer day-ahead market stage transmission capacity allocation optimization model into a single-layer mixed integer linear programming problem, so that it can be solved using a commercial solver, simplifying the solution process.

[0068] Further, obtaining the two-stage robust optimization model includes:

[0069]

[0070] stAy≥d;

[0071]

[0072] Where x and y represent the decision variables of the intraday market stage and the day-ahead market stage respectively; c is the parameter of the day-ahead market stage; b is the parameter of the intraday market stage; u is the uncertainty parameter of the intraday stage, which is characterized by the uncertainty set Ψ; A and d are the parameter matrices of the day-ahead scheduling stage in the compact form, G, h, E and M are the parameter matrices of the intraday market stage in the compact form; F(y,u) is the feasible domain of x under the forecast deviation.

[0073] From the above description, it can be seen that based on the two-stage robust optimization model constructed above, an inexact column and constraint generation algorithm is used to accelerate the solution of the two-stage robust optimization model. The algorithm adaptively adjusts the iteration mode according to the iteration results and reasonably allocates computing resources in the iteration process to improve the solution rate.

[0074] Another embodiment of the present invention provides a power system transmission capacity allocation device, comprising:

[0075] A first acquisition module is used to acquire data samples of uncertainty factors in the power system, and perform clustering processing on the data samples to obtain an uncertainty set describing the value range of the uncertainty factors;

[0076] A second acquisition module is used to acquire the unit operation data in the power system and obtain the unit operation constraints according to the unit operation data;

[0077] A model building module, used for building a two-stage robust optimization model for transmission capacity allocation according to the uncertainty set and unit operation constraints;

[0078] The solution module is used to solve the two-stage robust optimization model by using an inexact column and constraint generation algorithm to obtain a transmission capacity allocation plan.

[0079] Furthermore, the data samples include accidental factor samples and non-accidental factor samples;

[0080] Clustering is performed on the data samples to obtain an uncertainty set describing the value range of the uncertainty factor, including:

[0081] Clustering the accidental factor samples in a random optimization manner to obtain an uncertainty set of accidental factors;

[0082] Clustering the non-accidental factor samples by a robust optimization method to obtain a non-accidental factor uncertainty set;

[0083] The uncertainty set of the uncertainty factor value range is obtained according to the accidental factor uncertainty set and the non-accidental factor uncertainty set.

[0084] Further, the accidental factors include component outage, and the accidental factor samples include historical data of the unit and data related to the power generation unit;

[0085] The clustering process of the accidental factor samples by random optimization includes:

[0086] Based on the generator capacity interruption probability table and in combination with the unit historical data and the power generation unit related data, the capacity interruption probability of the component outage combination with different capacity combinations is obtained;

[0087] The capacity interruption probabilities of all component outage combinations are integrated to obtain the uncertainty set of accidental factors.

[0088] Furthermore, the non-accidental factor uncertainty set includes:

[0089]

[0090] In the formula, Ψ represents the uncertainty set of non-accidental factors, It represents the predicted positive deviation value of the output of each type of unit at time t; Indicates the minimum deviation of the output forecast of each type of unit; It is a 0-1 variable, indicating that the corresponding non-accidental factors are in different scenarios considered in robust optimization; α represents the positive deviation value that may appear in the prediction of robust optimization.

[0091] Further, the unit operation data includes cascade hydropower unit data; the unit operation constraints include hydropower unit operation constraints;

[0092] The obtaining of the unit operation constraint according to the unit operation data comprises:

[0093] Constructing the operation constraints of the hydropower unit according to the hydropower unit data;

[0094] The operating constraints of the hydropower unit include water balance constraints, total discharge flow constraints, power discharge flow constraints, abandoned water discharge flow constraints, forebay water level constraints, equation constraints between forebay water level and reservoir capacity, equation constraints between tailwater water level and discharge volume, head constraints and discharge power constraints.

[0095] Furthermore, the unit operation data includes wind turbine unit data and photovoltaic unit data;

[0096] The obtaining of the unit operation constraint according to the unit operation data comprises:

[0097]

[0098] in, and They represent the upper and lower limits of the power of the i-th wind turbine located at the n-node in the m-region respectively; and They represent the upper and lower limits of the power of the ith PV unit located at the nth node in the mth region respectively; and They respectively represent the output of the i-th wind turbine and the i-th photovoltaic unit located at the n-node in the m-region.

[0099] Furthermore, the two-stage robust optimization model for transmission capacity allocation is established according to the uncertainty set and the unit operation constraints, including:

[0100] Construct an optimization model for transmission capacity allocation in the day-ahead market stage;

[0101] Construct an optimization model for transmission capacity allocation in the intraday market stage;

[0102] The transmission capacity allocation optimization model in the day-ahead market stage is used as the main problem of the model, and the transmission capacity allocation optimization model in the intraday market stage is used as the sub-problem of the model, so as to obtain the two-stage robust optimization model.

[0103] Furthermore, the transmission capacity allocation optimization model in the day-ahead market stage is a two-layer model;

[0104] The two-stage robust optimization model for transmission capacity allocation is established according to the uncertainty set and the unit operation constraints, including:

[0105] The transmission capacity allocation optimization model in the day-ahead market stage is transformed into a single-layer model by using KKT condition or dual transformation method and big M method;

[0106] The two-stage robust optimization model is obtained based on the converted transmission capacity allocation optimization model in the day-ahead market stage and the transmission capacity allocation optimization model in the intraday market stage.

[0107] Further, obtaining the two-stage robust optimization model includes:

[0108]

[0109] stAy≥d;

[0110]

[0111] Where x and y represent the decision variables of the intraday market stage and the day-ahead market stage respectively; c is the parameter of the day-ahead market stage; b is the parameter of the intraday market stage; u is the uncertainty parameter of the intraday stage, which is characterized by the uncertainty set Ψ; A and d are the parameter matrices of the day-ahead scheduling stage in the compact form, G, h, E and M are the parameter matrices of the intraday market stage in the compact form; F(y,u) is the feasible domain of x under the forecast deviation.

[0112] Another embodiment of the present invention provides an electronic device, including a memory, a processor, and a computer program stored in the memory and executable on the processor, wherein the processor implements the various steps in the above-mentioned method for allocating transmission capacity of a power system when executing the computer program.

[0113] Another embodiment of the present invention provides a computer storage medium having a computer program stored thereon, wherein the computer program, when executed by a processor, implements the various steps of the above-mentioned method for allocating transmission capacity of a power system.

[0114] The power system transmission capacity allocation method and device provided by the present invention can be applied to the power system transmission capacity allocation scenario, which is described below through specific implementation methods:

[0115] Embodiment 1

[0116] Please refer to Figure 1 , a method for allocating transmission capacity of a power system, comprising:

[0117] S1. Obtain data samples of uncertainty factors in the power system, and cluster the data samples to obtain an uncertainty set that describes the value range of the uncertainty factors; in the new power system, the uncertainty factors include accidental factors and non-accidental factors; this embodiment uses random optimization modeling and robust optimization modeling to model the above two types of uncertainty factors respectively, and obtains the uncertainty set of the value range of the uncertainty factors based on the accidental factor uncertainty set and the non-accidental factor uncertainty set.

[0118] Among them, the accidental events related to the failure of power system components are called accidental uncertainty factors. In this embodiment, the shutdown of the generator set is used as the accidental uncertainty factor to model it.

[0119] The modeling method based on the Capacity Outage Probability Table is adopted. The historical data and information related to the power generation unit are input. By calculating all possible power generation unit combinations and their corresponding outage states one by one, the outage probability distribution table under each capacity combination is obtained, that is, the uncertainty set of accidental factors is obtained. This table reflects the outage probability under different capacity levels in the system, which is usually calculated based on historical outage data or equipment performance data.

[0120] As for the rapidly developing renewable energy in the new power system, the volatility and intermittency of its power generation are called non-accidental factors. In this embodiment, wind turbines and photovoltaic units are modeled as accidental uncertainty factors. Taking wind turbines and photovoltaic units as examples, it is assumed that the predicted output and load demand prediction deviations satisfy the Gaussian distribution with a variance of Ω. The Monte Carlo sampling method is used to sample and generate a large number of prediction deviation scenarios, and then the Fast Forward Selection Algorithm (FFSA) is used to reduce the scenarios to obtain N Ψ A typical scenario is formed based on which the uncertainty set Ψ is formed to describe the range of values ​​of the accidental uncertainty relationship parameters:

[0121]

[0122] In the formula, Ψ represents the uncertainty set of non-accidental factors, It represents the predicted positive deviation value of the output of each type of unit at time t; Indicates the minimum deviation of the output forecast of each type of unit; is a 0-1 variable, indicating that the corresponding non-accidental factors are in different scenarios considered in robust optimization, for example, when When , it means that the corresponding uncertainty factor is in the worst scenario considered in robust optimization; when When , it means that the corresponding uncertainty factor is not in the worst scenario considered in robust optimization; α represents the positive deviation value that may appear in the prediction of robust optimization.

[0123] S2. Acquire the unit operation data in the power system, and obtain the unit operation constraints according to the unit operation data; the unit operation data includes cascade hydropower unit data, wind power unit data and photovoltaic unit data;

[0124] Among them, the operating characteristics constraints of cascade hydropower units are as follows:

[0125] (1) Water balance constraints

[0126]

[0127] Among them, V m,n,i,t represents the total water volume of the i-th cascade hydropower station located at the n-node in the m-region during period t; R m,n,i,t represents the local runoff of the ith cascade hydropower station located at node n in region m during period t; the subscript g represents the upstream hydropower plant; t g,m,n,i represents the water transfer period between the i-th cascade hydropower station located at the n-th node in the m-region and its g-th upstream hydropower station; represents the total discharge of the gth upstream hydropower station of the i-th cascade hydropower station located at the nth node in the m-region; Q m,n,i,t represents the total discharge of the i-th cascade hydropower station located at the n-node in the m-region during the period t; Ω m,n,i represents the set of adjacent upstream hydropower plants of the ith cascade hydropower station located at node n in region m; Δt represents the scheduling step; V m,n,i represents the number of units of the i-th cascade hydropower station located at the n-node in the m-region; and S m,n,i,t They represent the discharge flow and abandoned discharge flow of the ith cascade hydropower station located at node n in region m during period t. The parameter 3600 in the formula indicates that the dispatching step is in hours. After deleting 3600, the dispatching step is in seconds. m,n,i,t , and Q m,n,i,t The units cooperate with each other.

[0128] (2) Total leakage flow constraint

[0129]

[0130] in, and They represent the maximum total flow and minimum total flow of the i-th cascade hydropower station located at the n-node in the m-region respectively; I m,n Represents the number of cascade hydropower stations located in n nodes of region m.

[0131] (3) Discharge flow constraints

[0132]

[0133] in, and They represent the maximum discharge flow and minimum discharge flow of the i-th cascade hydropower station located at node n in region m, respectively.

[0134] (3) Discharge flow constraints

[0135]

[0136] in, and They respectively represent the maximum and minimum water discharge of the i-th cascade hydropower station located at the n-node in the m-region.

[0137] (4) Forebay water level constraints

[0138]

[0139] z m,n,i,init =Z m,n,i,1 ;

[0140] z m,n,i,end =Z m,n,i,T+1 ;

[0141] Among them, Z m,n,i,t represents the water level of the forebay of the i-th cascade hydropower station located at the n-node in the m-region during period t; and They represent the maximum forebay water level and the minimum forebay water level of the ith cascade hydropower station located at node n in region m; z m,n,i,init and z m,n,i,end They represent the initial forebay water level and the final forebay water level of the i-th cascade hydropower station located at node n in region m during period t.

[0142] (5) Equality constraint between forebay water level and reservoir capacity

[0143]

[0144] in, It represents the relationship function between the reservoir capacity of the i-th cascade hydropower station located at the n-node in the m-region and the water level in the forebay.

[0145] (6) Equality constraint between tailwater level and discharge

[0146]

[0147] in, It represents the relationship between the total flow and tailwater level of the ith cascade hydropower station located at the nth node in the mth region; It represents the tailwater level of the i-th cascade hydropower station located at node n in region m at time t.

[0148] (7) Head constraint

[0149]

[0150] Among them, H m,n,i,t It represents the water head of the i-th cascade hydropower station located at the n-node in the m-region at time t.

[0151] (8) Discharge power constraint

[0152]

[0153] in, represents the discharge power of the i-th cascade hydropower station located at the n-node in the m-region at time t; and They represent the upper and lower limits of the discharge power of the i-th cascade hydropower station located at the n-node in the m-region respectively; It represents the relationship between the power generation, total flow and water head of the i-th cascade hydropower station located at the n-node in the m-region.

[0154] The operating characteristics of wind turbines and photovoltaic units are constrained as follows:

[0155]

[0156] in, and They represent the upper and lower limits of the power of the i-th wind turbine located at the n-node in the m-region respectively; and They represent the upper and lower limits of the power of the ith PV unit located at the nth node in the mth region respectively; and They respectively represent the output of the i-th wind turbine and the i-th photovoltaic unit located at the n-node in the m-region.

[0157] S3. A two-stage robust optimization model for transmission capacity allocation is established based on the uncertainty set and unit operation constraints. Taking the two-stage day-ahead and intraday transmission capacity coordinated robust optimization model as an example, the model is as follows:

[0158] S31. Construct a transmission capacity allocation optimization model in the day-ahead market stage to describe the inter-region (province) and intra-region (province) transmission capacity allocation in the day-ahead market stage:

[0159]

[0160]

[0161] The above two-layer model is transformed into a single-layer mixed integer linear programming problem using KKT conditions and the big M method, so that it can be solved using a commercial solver. The transformed single-layer model is as follows:

[0162]

[0163]

[0164] In the formula, T represents the total time period set; M represents the power purchase area set; N m represents the set of dispatching nodes in the mth power purchasing area; S represents the set of power selling provinces; N s represents the node set of the s-th electricity sales province; Represents the output of a single wind turbine. Indicates the output of a single photovoltaic unit, Indicates the output of a single hydropower unit; L m represents the set of key transmission lines in the mth power purchasing area; L represents the set of inter-provincial interconnection lines; T DA Indicates the day-ahead clearing period collection; and They represent the wind turbine set, photovoltaic set and hydropower set at the nth node in the mth power purchasing area respectively; subscript l represents the lth inter-provincial connection line or provincial key line; subscript t represents time period t; subscript m represents power purchasing area m; subscript n represents the nth dispatching node in a power purchasing area or a power selling area; subscript s represents the sth power selling area; Q n,m,P () represents the electric energy quotation function of the nth node unit in the mth power purchasing area. In this embodiment, a quadratic function is used as the unit quotation function; and Respectively indicate the upper and lower reserve quotations; P is the interval electricity purchase price of the mth electricity purchase area in period t; n,m,t It represents the electric energy output of the conventional unit at the nth node in the mth power purchasing area in the tth period; and They represent the upper and lower reserve capacities of the units at the nth node in the mth power purchasing area during the tth period respectively; is the interval power purchase demand of the mth power purchase area in the tth period. This model assumes that the interval power purchase quantity is connected from an equivalent node in the power purchase area; and They represent the wind turbine output and photovoltaic power output of the nth node in the mth power purchasing area in the tth period respectively; D n,m,t represents the load of the nth node in the mth power purchasing area in the tth period; SF l,n,m represents the element in the lth row and nth column of the power transfer factor matrix of the mth power purchasing area; represents the maximum remaining available transmission capacity of the lth key transmission line in the mth power purchasing area; SF l,m represents the power transfer factor of the purchased electricity from the mth power purchasing area to line l; and They represent the upper and lower limits of the conventional unit output at the nth node in the mth power purchasing area; P n,m,U and P n,m,D They represent the limits of the upward and downward climbing rates of the conventional units at the nth node in the mth power purchasing area respectively; represents the electric energy transmitted by the generator set of the nth node in the sth electricity selling province to the mth electricity purchasing province in the tth period; Q n,s,t represents the bid price of the unit at the nth node in the sth electricity sales province in the tth period; represents the inter-provincial transmission price of electricity from the s-th electricity-selling province to the m-th electricity-purchasing province during the t-th period; SF s,n,m,l P represents the power flow impact factor on line l for the transaction pair in which the generator set at the nth node in the sth power sales area transmits electric energy to the mth power purchase area. For a DC line, the power flow impact factor is 1 (in this embodiment, it is assumed that the power flow direction is fixed, from the set power sales area to the power purchase area); l ATC represents the maximum remaining available transmission capacity of the inter-provincial interconnection lines; ω l,t represents the dual multiplier of the corresponding constraint; and Indicates the upper and lower limits of the output of the unit at the nth node in the sth power sales area; and represents the dual multiplier of the corresponding constraint; f n,s,m represents the power transmission loss coefficient of the tie line between the nth node in the sth power sales area and the mth power purchase area; the power purchase price of the power purchase area m in the tth period of the interval market It is the marginal price of the interval market node, that is, the dual variable λ corresponding to the constraint m,t .

[0165] S32. Construct an optimization model for transmission capacity allocation in the intraday market stage;

[0166]

[0167] Where, T IT Indicates the intraday clearing period collection; and They represent the upward adjustment cost and downward adjustment cost of the node n generator set in the daily period when purchasing electricity to save m respectively; and They represent the penalty cost coefficients of wind abandonment, solar abandonment and load reduction of electricity purchase province m at node n respectively; and They represent the increase and decrease of the output of the generating unit at node n in period t when purchasing electricity to save m respectively; and They represent the wind turbine abandoned wind volume, photovoltaic abandoned light volume and load reduction volume of the electricity purchase saving m at node n during period t; and They represent the predicted positive deviation values ​​of wind turbine output, photovoltaic generator output and load at node n for electricity purchase saving m during period t; ε t , and γ t,m,l is the dual multiplier of the corresponding constraint; η W , η PV and ηL They respectively represent the maximum wind power abandonment ratio, the maximum solar power abandonment ratio and the maximum load reduction ratio.

[0168] S33. Taking the transmission capacity allocation optimization model in the day-ahead market stage as the main problem of the model, and taking the transmission capacity allocation optimization model in the intraday market stage as the sub-problem of the model, the above two-stage robust optimization model is constructed into the compact form of the following formula:

[0169]

[0170] stAy≥d;

[0171]

[0172] In the formula, x and y represent the decision variables of the intraday market stage and the day-ahead market stage, respectively. The decision variables of the intraday market stage include and The decision variables in the day-ahead market phase include P n,m,t , and c is the parameter of the day-ahead market stage; b is the parameter of the intraday market stage; u is the uncertainty parameter of the intraday stage, characterized by the uncertainty set Ψ; A and d are the parameter matrices of the day-ahead scheduling stage in the compact form, G, h, E and M are the parameter matrices of the intraday market stage in the compact form; F(y,u) is the feasible domain of x under the forecast deviation.

[0173] S4. Use the inexact column and constraint generation algorithm to solve the two-stage robust optimization model and obtain the transmission capacity allocation plan; Among them, the inexact C&CG algorithm is mainly used for the scenario where the optimization problem is too large and the solution speed is slow. When the main problem introduces too many constraints during the iteration process, the solution speed will be significantly reduced. Therefore, compared with the conventional C&CG algorithm, the inexact C&CG algorithm solves the main problem to a suboptimal rather than optimal state for fast solution, and adds a buffer layer in the cyclic iteration process. Different cycle stages will be entered according to different solution states to form a fast solution state;

[0174] Please refer to Figure 2 , the solution steps of the inexact C&CG algorithm are as follows:

[0175] (1) Solving the main problem

[0176] Set a relative optimality gap Under this condition, solve the master problem (MP):

[0177]

[0178] In the formula, δ represents the value of the second-stage objective function, and the main problem satisfies the following constraints:

[0179] Ay ≥ d;

[0180] δ≥b T x z ;

[0181]

[0182] In the formula, the superscript z represents the zth iteration, Represents the lower bound constraint of the main problem; Solve MP to obtain the optimal decision y in the day-ahead scheduling stage z+1 , and the corresponding y can be obtained in the solver z+1 The upper and lower bounds of z+1 and L z+1 , if L z +1 satisfy This indicates that L z+1 As the effective lower bound, it is set as the termination judgment index of this round of iteration, that is, Finally, U z+1 Assign to In the next main problem, the iteration speed of the lower bound is improved.

[0183] (2) Solving subproblems

[0184]

[0185] In the iterative process, the optimal transmission capacity allocation decision in the day-ahead stage is transferred to the intraday stage model to solve the above sub-problems. For the two-layer model of the sub-problem, the KKT condition is also used to convert the two-layer model into a single-layer model. It will not be repeated here, and the converted single-layer sub-problem model is directly given:

[0186]

[0187]

[0188]

[0189] Where, Ε represents the set of all dual multipliers in the constraints of the intraday scheduling optimization model; and v t,m,l They represent the 0-1 auxiliary variables introduced to linearize the nonlinear optimization model; and M t,m,l They represent the maximum constants introduced by linearizing the nonlinear optimization model; SF l,n,m,W , SF l,n,m,PV and SFl,n,m,L They respectively represent the power transfer factor matrix related to wind turbines, the power transfer factor matrix related to photovoltaic turbines, and the power transfer factor matrix related to adjustable load reduction.

[0190] Based on the above formula, we can get the optimal decision x for the intraday scheduling stage: z+1 , and update the upper bound UB of the model:

[0191] UB z+1 =min(UB z ,c T y z+1 +b T x z+1 );

[0192] (3) Cycle state selection

[0193] First, calculate the basis for determining the termination of the iteration:

[0194]

[0195] If the above formula is established, the algorithm terminates and the transmission capacity allocation result is obtained, otherwise the loop state judgment is performed. When the above formula is established, it indicates that the upper and lower bound iterations of the two-stage robust optimization model are close to the optimal. At this time, the solution accuracy of the main problem should be improved, and the solution mode that has always remained in the suboptimal state of the main problem should be changed; if the above formula is not established, it indicates that the model is still far from the optimal solution during the iteration process, and more new scenarios need to be added (update the upper bound after solving the subproblem and add the worst scenario to the main problem) and continue to keep the main problem in the mode of solving to the suboptimal state to ensure a higher solution speed.

[0196] Embodiment 2

[0197] Please refer to Figure 3 , a power system transmission capacity allocation device, comprising: a first acquisition module, used to acquire data samples of uncertainty factors in the power system, and cluster the data samples to obtain an uncertainty set describing the value range of the uncertainty factors; a second acquisition module, used to acquire unit operation data in the power system, and obtain unit operation constraints based on the unit operation data; a model building module, used to establish a two-stage robust optimization model for transmission capacity allocation based on the uncertainty set and the unit operation constraints; a solution module, used to solve the two-stage robust optimization model using an inexact column and constraint generation algorithm to obtain a transmission capacity allocation plan.

[0198] Furthermore, the data samples include accidental factor samples and non-accidental factor samples;

[0199] Clustering is performed on the data samples to obtain an uncertainty set describing the value range of the uncertainty factor, including:

[0200] Clustering the accidental factor samples in a random optimization manner to obtain an uncertainty set of accidental factors;

[0201] Clustering the non-accidental factor samples by a robust optimization method to obtain a non-accidental factor uncertainty set;

[0202] The uncertainty set of the uncertainty factor value range is obtained according to the accidental factor uncertainty set and the non-accidental factor uncertainty set.

[0203] Further, the accidental factors include component outage, and the accidental factor samples include historical data of the unit and data related to the power generation unit;

[0204] The clustering process of the accidental factor samples by random optimization includes:

[0205] Based on the generator capacity interruption probability table and in combination with the unit historical data and the power generation unit related data, the capacity interruption probability of the component outage combination with different capacity combinations is obtained;

[0206] The capacity interruption probabilities of all component outage combinations are integrated to obtain the uncertainty set of accidental factors.

[0207] Furthermore, the non-accidental factor uncertainty set includes:

[0208]

[0209] In the formula, Ψ represents the uncertainty set of non-accidental factors, It represents the predicted positive deviation value of the output of each type of unit at time t; Indicates the minimum deviation of the output forecast of each type of unit; It is a 0-1 variable, indicating that the corresponding non-accidental factors are in different scenarios considered in robust optimization; α represents the positive deviation value that may appear in the prediction of robust optimization.

[0210] Further, the unit operation data includes cascade hydropower unit data; the unit operation constraints include hydropower unit operation constraints;

[0211] The obtaining of the unit operation constraint according to the unit operation data comprises:

[0212] Constructing the operation constraints of the hydropower unit according to the hydropower unit data;

[0213] The operating constraints of the hydropower unit include water balance constraints, total discharge flow constraints, power discharge flow constraints, abandoned water discharge flow constraints, forebay water level constraints, equation constraints between forebay water level and reservoir capacity, equation constraints between tailwater water level and discharge volume, head constraints and discharge power constraints.

[0214] Furthermore, the unit operation data includes wind turbine unit data and photovoltaic unit data;

[0215] The obtaining of the unit operation constraint according to the unit operation data comprises:

[0216]

[0217] in, and They represent the upper and lower limits of the power of the i-th wind turbine located at the n-node in the m-region respectively; and They represent the upper and lower limits of the power of the ith PV unit located at the nth node in the mth region respectively; and They respectively represent the output of the i-th wind turbine and the i-th photovoltaic unit located at the n-node in the m-region.

[0218] Furthermore, the two-stage robust optimization model for transmission capacity allocation is established according to the uncertainty set and the unit operation constraints, including:

[0219] Construct an optimization model for transmission capacity allocation in the day-ahead market stage;

[0220] Construct an optimization model for transmission capacity allocation in the intraday market stage;

[0221] The transmission capacity allocation optimization model in the day-ahead market stage is used as the main problem of the model, and the transmission capacity allocation optimization model in the intraday market stage is used as the sub-problem of the model, so as to obtain the two-stage robust optimization model.

[0222] Furthermore, the transmission capacity allocation optimization model in the day-ahead market stage is a two-layer model;

[0223] The two-stage robust optimization model for transmission capacity allocation is established according to the uncertainty set and the unit operation constraints, including:

[0224] The transmission capacity allocation optimization model in the day-ahead market stage is transformed into a single-layer model by using KKT condition or dual transformation method and big M method;

[0225] The two-stage robust optimization model is obtained based on the converted transmission capacity allocation optimization model in the day-ahead market stage and the transmission capacity allocation optimization model in the intraday market stage.

[0226] Further, obtaining the two-stage robust optimization model includes:

[0227]

[0228] stAy≥d;

[0229]

[0230] Where x and y represent the decision variables of the intraday market stage and the day-ahead market stage respectively; c is the parameter of the day-ahead market stage; b is the parameter of the intraday market stage; u is the uncertainty parameter of the intraday stage, which is characterized by the uncertainty set Ψ; A and d are the parameter matrices of the day-ahead scheduling stage in the compact form, G, h, E and M are the parameter matrices of the intraday market stage in the compact form; F(y,u) is the feasible domain of x under the forecast deviation.

[0231] Embodiment 3

[0232] An electronic device comprises a memory, a processor and a computer program stored in the memory and executable on the processor, wherein when the processor executes the computer program, each step of the method for allocating transmission capacity of a power system as described in the first embodiment is implemented.

[0233] Embodiment 4

[0234] A computer storage medium stores a computer program, which, when executed by a processor, implements each step of the method for allocating power transmission capacity of a power system as described in the first embodiment.

[0235] In summary, the power system transmission capacity allocation method and device provided by the present invention consider the differentiated market transaction demands and output uncertainty characteristics of various new market entities and traditional market entities in the model, and consider the resource complementarity between various market entities to meet the demand for transmission capacity allocation under the new power system; and in the model, according to different types of uncertainty sources, respectively adopt corresponding uncertainty modeling methods, for modeling various power generation entities that seriously interfere with the transmission capacity allocation results, adopt a robust optimization method to consider the worst output fluctuation situation, and can flexibly adjust the balance between the economy and safety of the optimization model by changing the robustness parameters in the model; at the same time, the non-precise C&CG algorithm is used to accelerate the solution of the two-stage robust optimization model, the algorithm adaptively adjusts the iteration mode according to the iteration results, and improves the solution rate by reasonably allocating computing resources during the iteration process.

[0236] Those skilled in the art will appreciate that the embodiments of the present application can be provided as methods, systems, or computer program products. Therefore, the present application can adopt the form of complete hardware embodiments, complete software embodiments, or embodiments in combination with software and hardware. Moreover, the present application can adopt the form of a computer program product implemented on one or more computer-usable storage media (including but not limited to disk storage, CD-ROM, optical storage, etc.) that contain computer-usable program code. The scheme in the embodiments of the present application can be implemented in various computer languages, for example, object-oriented programming language Java and literal scripting language JavaScript, etc.

[0237] The present application is described with reference to the flowcharts and / or block diagrams of the methods, devices (systems), and computer program products according to the embodiments of the present application. It should be understood that each process and / or box in the flowchart and / or block diagram, as well as the combination of the processes and / or boxes in the flowchart and / or block diagram, can be implemented by computer program instructions. These computer program instructions can be provided to a processor of a general-purpose computer, a special-purpose computer, an embedded processor, or other programmable data processing device to generate a machine, so that the instructions executed by the processor of the computer or other programmable data processing device generate instructions for implementing the processes in the flowchart and / or block diagram. Figure 1 A process or multiple processes and / or boxes Figure 1 A device that provides the functions specified in a block or multiple blocks.

[0238] These computer program instructions may also be stored in a computer-readable memory capable of directing a computer or other programmable data processing device to operate in a specific manner, so that the instructions stored in the computer-readable memory produce an article of manufacture comprising an instruction device, which implements the process Figure 1 A process or multiple processes and / or boxes Figure 1 A function specified in one or more boxes.

[0239] These computer program instructions can also be loaded onto a computer or other programmable data processing device so that a series of operating steps are executed on the computer or other programmable device to produce a computer-implemented process, thereby providing instructions for implementing the process. Figure 1 A process or multiple processes and / or boxes Figure 1 The steps for the functions specified in one or more boxes.

[0240] Although the preferred embodiments of the present application have been described, those skilled in the art may make other changes and modifications to these embodiments once they have learned the basic creative concept. Therefore, the appended claims are intended to be interpreted as including the preferred embodiments and all changes and modifications falling within the scope of the present application.

[0241] Obviously, those skilled in the art can make various changes and modifications to the present application without departing from the spirit and scope of the present application. Thus, if these modifications and variations of the present application fall within the scope of the claims of the present application and their equivalents, the present application is also intended to include these modifications and variations.

Claims

1. A method for allocating power transmission capacity of a power system, characterized in that: include: Acquire data samples of uncertainty factors in the power system, and perform clustering processing on the data samples to obtain an uncertainty set describing the value range of the uncertainty factors; Acquire unit operation data in the power system, and obtain unit operation constraints according to the unit operation data; Establishing a two-stage robust optimization model for transmission capacity allocation according to the uncertainty set and unit operation constraints; An inexact column and constraint generation algorithm is used to solve the two-stage robust optimization model and obtain a transmission capacity allocation scheme.

2. A method for allocating power transmission capacity of a power system according to claim 1, characterized in that: The data samples include accidental factor samples and non-accidental factor samples; Clustering is performed on the data samples to obtain an uncertainty set describing the value range of the uncertainty factor, including: Clustering the accidental factor samples in a random optimization manner to obtain an uncertainty set of accidental factors; Clustering the non-accidental factor samples by a robust optimization method to obtain a non-accidental factor uncertainty set; The uncertainty set of the uncertainty factor value range is obtained according to the accidental factor uncertainty set and the non-accidental factor uncertainty set.

3. A method for allocating power transmission capacity of a power system according to claim 2, characterized in that: The accidental factors include component outages, and the accidental factor samples include historical data of the unit and data related to the power generation unit; The clustering process of the accidental factor samples by random optimization includes: Based on the generator capacity interruption probability table and in combination with the unit historical data and the power generation unit related data, the capacity interruption probability of the component outage combination with different capacity combinations is obtained; The capacity interruption probabilities of all component outage combinations are integrated to obtain the uncertainty set of accidental factors.

4. A method for allocating power transmission capacity of a power system according to claim 2, characterized in that: The non-accidental factor uncertainty set includes: In the formula, Ψ represents the uncertainty set of non-accidental factors, It represents the predicted positive deviation value of the output of each type of unit at time t; Indicates the minimum deviation of the output forecast of each type of unit; It is a 0-1 variable, indicating that the corresponding non-accidental factors are in different scenarios considered in robust optimization; α represents the positive deviation value that may appear in the prediction of robust optimization.

5. A method for allocating power transmission capacity of a power system according to claim 1, characterized in that: The unit operation data includes cascade hydropower unit data; the unit operation constraints include hydropower unit operation constraints; The obtaining of the unit operation constraint according to the unit operation data comprises: Constructing the operation constraints of the hydropower unit according to the hydropower unit data; The operating constraints of the hydropower unit include water balance constraints, total discharge flow constraints, power discharge flow constraints, abandoned water discharge flow constraints, forebay water level constraints, equation constraints between forebay water level and reservoir capacity, equation constraints between tailwater water level and discharge volume, head constraints and discharge power constraints.

6. A method for allocating power transmission capacity of a power system according to claim 1, characterized in that: The unit operation data includes wind turbine unit data and photovoltaic unit data; The obtaining of the unit operation constraint according to the unit operation data comprises: in, and They represent the upper and lower limits of the power of the i-th wind turbine located at the n-node in the m-region respectively; and They represent the upper and lower limits of the power of the ith PV unit located at the nth node in the mth region respectively; and They respectively represent the output of the i-th wind turbine and the i-th photovoltaic unit located at the n-node in the m-region.

7. A method for allocating power transmission capacity of a power system according to claim 1, characterized in that: The two-stage robust optimization model for transmission capacity allocation is established according to the uncertainty set and the unit operation constraints, including: Construct an optimization model for transmission capacity allocation in the day-ahead market stage; Construct an optimization model for transmission capacity allocation in the intraday market stage; The transmission capacity allocation optimization model in the day-ahead market stage is used as the main problem of the model, and the transmission capacity allocation optimization model in the intraday market stage is used as the sub-problem of the model, so as to obtain the two-stage robust optimization model.

8. A method for allocating power transmission capacity of a power system according to claim 7, characterized in that: The transmission capacity allocation optimization model in the day-ahead market stage is a two-layer model; The two-stage robust optimization model for transmission capacity allocation is established according to the uncertainty set and the unit operation constraints, including: The transmission capacity allocation optimization model in the day-ahead market stage is transformed into a single-layer model by using KKT condition or dual transformation method and big M method; The two-stage robust optimization model is obtained based on the converted transmission capacity allocation optimization model in the day-ahead market stage and the transmission capacity allocation optimization model in the intraday market stage.

9. A method for allocating power transmission capacity of a power system according to claim 8, characterized in that: The obtaining of the two-stage robust optimization model comprises: Where x and y represent the decision variables of the intraday market stage and the day-ahead market stage respectively; c is the parameter of the day-ahead market stage; b is the parameter of the intraday market stage; u is the uncertainty parameter of the intraday stage, which is characterized by the uncertainty set Ψ; A and d are the parameter matrices of the day-ahead scheduling stage in the compact form, G, h, E and M are the parameter matrices of the intraday market stage in the compact form; F(y,u) is the feasible domain of x under the forecast deviation.

10. A power system transmission capacity allocation device, characterized in that: include: A first acquisition module is used to acquire data samples of uncertainty factors in the power system, and perform clustering processing on the data samples to obtain an uncertainty set describing the value range of the uncertainty factors; A second acquisition module is used to acquire the unit operation data in the power system and obtain the unit operation constraints according to the unit operation data; A model building module, used for building a two-stage robust optimization model for transmission capacity allocation according to the uncertainty set and unit operation constraints; The solution module is used to solve the two-stage robust optimization model by using an inexact column and constraint generation algorithm to obtain a transmission capacity allocation plan.

11. A power system transmission capacity allocation device according to claim 10, characterized in that: The data samples include accidental factor samples and non-accidental factor samples; Clustering is performed on the data samples to obtain an uncertainty set describing the value range of the uncertainty factor, including: Clustering the accidental factor samples in a random optimization manner to obtain an uncertainty set of accidental factors; Clustering the non-accidental factor samples by a robust optimization method to obtain a non-accidental factor uncertainty set; The uncertainty set of the uncertainty factor value range is obtained according to the accidental factor uncertainty set and the non-accidental factor uncertainty set.

12. A power system transmission capacity allocation device according to claim 11, characterized in that: The accidental factors include component outages, and the accidental factor samples include historical data of the unit and data related to the power generation unit; The clustering process of the accidental factor samples by random optimization includes: Based on the generator capacity interruption probability table and in combination with the unit historical data and the power generation unit related data, the capacity interruption probability of the component outage combination with different capacity combinations is obtained; The capacity interruption probabilities of all component outage combinations are integrated to obtain the uncertainty set of accidental factors.

13. The power system transmission capacity allocation device according to claim 11, characterized in that: The non-accidental factor uncertainty set includes: In the formula, Ψ represents the uncertainty set of non-accidental factors, It represents the predicted positive deviation value of the output of each type of unit at time t; Indicates the minimum deviation of the output forecast of each type of unit; It is a 0-1 variable, indicating that the corresponding non-accidental factors are in different scenarios considered in robust optimization; α represents the positive deviation value that may appear in the prediction of robust optimization.

14. The power system transmission capacity allocation device according to claim 10, characterized in that: The unit operation data includes cascade hydropower unit data; the unit operation constraints include hydropower unit operation constraints; The obtaining of the unit operation constraint according to the unit operation data comprises: Constructing the operation constraints of the hydropower unit according to the hydropower unit data; The operating constraints of the hydropower unit include water balance constraints, total discharge flow constraints, power discharge flow constraints, abandoned water discharge flow constraints, forebay water level constraints, equation constraints between forebay water level and reservoir capacity, equation constraints between tailwater water level and discharge volume, head constraints and discharge power constraints.

15. The power system transmission capacity allocation device according to claim 10, characterized in that: The unit operation data includes wind turbine unit data and photovoltaic unit data; The obtaining of the unit operation constraint according to the unit operation data comprises: in, and They represent the upper and lower limits of the power of the i-th wind turbine located at the n-node in the m-region respectively; and They represent the upper and lower limits of the power of the ith PV unit located at the nth node in the mth region respectively; and They respectively represent the output of the i-th wind turbine and the i-th photovoltaic unit located at the n-node in the m-region.

16. The power system transmission capacity allocation device according to claim 10, characterized in that: The two-stage robust optimization model for transmission capacity allocation is established according to the uncertainty set and the unit operation constraints, including: Construct an optimization model for transmission capacity allocation in the day-ahead market stage; Construct an optimization model for transmission capacity allocation in the intraday market stage; The transmission capacity allocation optimization model in the day-ahead market stage is used as the main problem of the model, and the transmission capacity allocation optimization model in the intraday market stage is used as the sub-problem of the model, so as to obtain the two-stage robust optimization model.

17. A power system transmission capacity allocation device according to claim 16, characterized in that: The transmission capacity allocation optimization model in the day-ahead market stage is a two-layer model; The two-stage robust optimization model for transmission capacity allocation is established according to the uncertainty set and the unit operation constraints, including: The transmission capacity allocation optimization model in the day-ahead market stage is transformed into a single-layer model by using KKT condition or dual transformation method and big M method; The two-stage robust optimization model is obtained based on the converted transmission capacity allocation optimization model in the day-ahead market stage and the transmission capacity allocation optimization model in the intraday market stage.

18. A power system transmission capacity allocation device according to claim 17, characterized in that: The obtaining of the two-stage robust optimization model comprises: Where x and y represent the decision variables of the intraday market stage and the day-ahead market stage respectively; c is the parameter of the day-ahead market stage; b is the parameter of the intraday market stage; u is the uncertainty parameter of the intraday stage, which is characterized by the uncertainty set Ψ; A and d are the parameter matrices of the day-ahead scheduling stage in the compact form, G, h, E and M are the parameter matrices of the intraday market stage in the compact form; F(y,u) is the feasible domain of x under the forecast deviation.

19. An electronic device comprising a memory, a processor, and a computer program stored in the memory and executable on the processor, characterized in that: When the processor executes the computer program, each step of the method for allocating transmission capacity of a power system as described in any one of claims 1 to 9 is implemented.

20. A computer storage medium having a computer program stored thereon, characterized in that: When the computer program is executed by a processor, each step of the method for allocating transmission capacity of a power system as described in any one of claims 1 to 9 is implemented.

Images