A method, device, equipment and storage medium for optimizing dispatching of power system

Abstract

The present application discloses an optimized dispatching method, device, equipment and storage medium for an electric power system, the method comprising: obtaining a reservoir hydropower model; for each moment, determining a constraint expression of baseload power based on the operating information and information relationship of each cascade hydropower station combined with a power allocation model of regulated power and baseload power, the power allocation model comprising a regulated power allocation coefficient and a baseload power allocation coefficient; determining an expected target and an expected value of baseload power based on the constraint expression of each baseload power combined with a constraint set; determining a target uncertainty coefficient in combination with an optimized dispatching model, and realizing re-optimization of regulated power and baseload power. The problem of unreasonable dispatching in the dispatching process of the electric power system is solved, the operating efficiency of cascade hydropower is effectively improved, and the current operating target of the system is guaranteed to achieve maximum tolerance for uncertain changes in power on the basis of meeting a certain expected level, and the power fluctuation of the system is effectively smoothed.

Description

Technical Field

[0001] The present invention relates to the technical field of hydropower dispatching, and in particular to an optimization dispatching method, device, equipment and storage medium for a power system. Background Art

[0002] With the development of society, the problems of world energy security and environmental degradation have become increasingly prominent, and renewable energy generation represented by water, wind and solar power generation has developed rapidly. Hydropower is the main form of renewable energy generation. As a good regulated power source, hydropower is often used to smooth the fluctuations of wind and solar power generation and system peak regulation, but hydropower is affected by natural conditions and limited by prediction technology. Considering the uncertainty of hydropower, rationally optimizing the scheduling of hydropower is conducive to the improvement of water resource utilization, hydropower generation efficiency and stable operation of the power grid. Affected by environmental factors, the runoff water, lag time, environmental flow demand, etc. of cascade hydropower in scheduling and operation are random. If these uncertain factors are used as determined parameters, the optimization scheme obtained will deviate from reality. Most of the existing technologies directly consider the randomness of cascade hydropower operation from the aspect of water uncertainty, ignoring the influence of uncertain factors such as water flow lag time and environmental flow demand. If all uncertain factors are considered at the same time, an uncertain model is established for each influencing factor, which will undoubtedly increase the difficulty of solving the model. Therefore, optimizing the scheduling of hydropower considering uncertain factors has become a problem to be solved. Summary of the invention

[0003] The present invention provides an optimized dispatching method, device, equipment and storage medium for an electric power system, so as to solve the problem of unreasonable dispatching of the electric power system and realize optimized dispatching of the electric power system.

[0004] According to one aspect of the present invention, there is provided a method for optimizing dispatching of a power system, comprising:

[0005] Acquire a reservoir hydropower model, wherein the reservoir hydropower model includes operation information of at least one cascade hydropower station in at least one scheduling period and information relationship between each of the operation information, wherein the operation information includes power generation, and the scheduling period includes at least one time;

[0006] At each moment, a constraint expression of base load power is determined according to the operation information and information relationship of each of the cascade hydropower stations in combination with a power allocation model of regulation power and base load power, wherein the power allocation model includes a regulation power allocation coefficient and a base load power allocation coefficient;

[0007] Determine the expected target and the expected value of the baseload power according to the constraint expressions of each baseload power in combination with the constraint set;

[0008] The target uncertainty coefficient is determined based on the expected target, the expected value of baseload power, the constraint expression of baseload power, the constraint set and the predetermined optimization scheduling model, and the generated power is optimally scheduled according to the target uncertainty coefficient to achieve the optimal allocation of the regulating power and the baseload power.

[0009] According to another aspect of the present invention, there is provided an optimization dispatching device for a power system, comprising:

[0010] A model acquisition module, used to acquire a reservoir hydropower model, wherein the reservoir hydropower model includes operation information of at least one cascade hydropower station in at least one scheduling period and information relationship between each of the operation information, wherein the operation information includes power generation, and the scheduling period includes at least one time;

[0011] A constraint expression determination module is used to determine the constraint expression of the base load power at each moment according to the operation information and information relationship of each of the cascade hydropower stations combined with the power allocation model of the regulation power and the base load power, wherein the power allocation model includes a regulation power allocation coefficient and a base load power allocation coefficient;

[0012] An expectation determination module, configured to determine an expected target and an expected value of baseload power according to the constraint expressions of each baseload power combined with a constraint set;

[0013] A coefficient determination module is used to determine the target uncertainty coefficient based on the expected target, the expected value of baseload power, the constraint expression of baseload power, the constraint set and the predetermined optimization scheduling model, and optimize the generation power according to the target uncertainty coefficient to achieve the optimal allocation of the regulation power and the baseload power.

[0014] According to another aspect of the present invention, an electronic device is provided, the electronic device comprising:

[0015] at least one processor; and

[0016] a memory communicatively connected to the at least one processor; wherein,

[0017] The memory stores a computer program executable by the at least one processor, and the computer program is executed by the at least one processor so that the at least one processor can execute the method for optimizing scheduling of the power system described in any embodiment of the present invention.

[0018] According to another aspect of the present invention, a computer-readable storage medium is provided, wherein the computer-readable storage medium stores computer instructions, and the computer instructions are used to enable a processor to implement the power system optimization scheduling method described in any embodiment of the present invention when executed.

[0019] The technical solution of the embodiment of the present application is to obtain a reservoir hydropower model, wherein the reservoir hydropower model includes the operation information of at least one cascade hydropower station in at least one scheduling period and the information relationship of each operation information, wherein the operation information includes the generated power, and the scheduling period includes at least one moment; for each moment, a constraint expression of baseload power is determined according to the operation information and information relationship of each cascade hydropower station combined with a power allocation model of regulating power and baseload power, wherein the power allocation model includes a regulating power allocation coefficient and a baseload power allocation coefficient; a desired target is determined according to the constraint expression of each baseload power combined with a constraint set and the expected value of base load power; based on the expected target, the expected value of base load power, the constraint expression of base load power, the constraint set and the predetermined optimization scheduling model, the target uncertainty coefficient is determined, and the power generation power is optimized and scheduled according to the target uncertainty coefficient to achieve the optimal allocation of the regulation power and base load power, solving the problem of unreasonable scheduling in the power system scheduling process, and the constraint expression of base load power is determined by the power allocation model. The power allocation model includes the regulation power allocation coefficient and the base load power allocation coefficient. The allocation coefficient is introduced to set the allocation coefficient for the regulation power and the base load power respectively to achieve the reasonable division of the generating power. According to the constraint expression of base load power combined with the constraint set, the expected target and the expected value of base load power are determined, and the target uncertainty coefficient is further determined. The power generation power is optimized and scheduled according to the target uncertainty coefficient, and the power generation power is reasonably divided into base load power and regulation power, effectively improving the operation efficiency of cascade hydropower, ensuring that the current operation target of the system achieves the maximum tolerance for power uncertainty changes on the basis of meeting a certain expected level, and effectively smoothing the system power fluctuation.

[0020] It should be understood that the contents described in this section are not intended to identify the key or important features of the embodiments of the present invention, nor are they intended to limit the scope of the present invention. Other features of the present invention will become easily understood through the following description. BRIEF DESCRIPTION OF THE DRAWINGS

[0021] In order to more clearly illustrate the technical solutions in the embodiments of the present invention, the following briefly introduces the drawings required for use in the description of the embodiments. Obviously, the drawings described below are only some embodiments of the present invention. For ordinary technicians in this field, other drawings can be obtained based on these drawings without creative work.

[0022] Figure 1 is a flow chart of an optimization dispatching method for a power system provided according to Embodiment 1 of the present invention;

[0023] Figure 2 It is a schematic diagram of the structure of each cascade hydropower in a reservoir hydropower model provided according to the first embodiment of the present invention;

[0024] Figure 3 is a flow chart of an optimization dispatching method for a power system provided according to a second embodiment of the present invention;

[0025] Figure 4a is an example diagram of optimal values ​​of base load power allocation factors of different hydropower stations provided according to the second embodiment of the present invention;

[0026] Figure 4b is an example diagram of optimal values ​​of regulating power allocation factors of different hydropower stations provided according to the second embodiment of the present invention;

[0027] Figure 5a is a schematic diagram of changes in equivalent electric quantity of a traditional hydropower dispatching model without considering power allocation factors provided according to the second embodiment of the present invention;

[0028] Figure 5b It is a schematic diagram of the change of the total water storage equivalent electricity of cascade hydropower in an optimization scheduling model provided according to the second embodiment of the present invention;

[0029] Figure 6a This is an example diagram showing an optimization scheduling result provided according to the second embodiment of the present invention;

[0030] Figure 6b This is an example diagram showing a traditional deterministic optimization scheduling result provided according to the second embodiment of the present invention;

[0031] Figure 7 is a schematic diagram of the structure of an optimization dispatching device for a power system provided according to Embodiment 3 of the present invention;

[0032] Figure 8 It is a structural schematic diagram of an electronic device for implementing the optimization dispatching method of the power system according to an embodiment of the present invention. DETAILED DESCRIPTION

[0033] In order to enable those skilled in the art to better understand the scheme of the present invention, the technical scheme in the embodiments of the present invention will be clearly and completely described below in conjunction with the drawings in the embodiments of the present invention. Obviously, the described embodiments are only part of the embodiments of the present invention, not all of the embodiments. Based on the embodiments of the present invention, all other embodiments obtained by ordinary technicians in this field without creative work should fall within the scope of protection of the present invention.

[0034] It should be noted that the terms "first", "second", etc. in the specification and claims of the present invention and the above-mentioned drawings are used to distinguish similar objects, and are not necessarily used to describe a specific order or sequence. It should be understood that the data used in this way can be interchanged where appropriate, so that the embodiments of the present invention described herein can be implemented in an order other than those illustrated or described herein. In addition, the terms "including" and "having" and any variations thereof are intended to cover non-exclusive inclusions, for example, a process, method, system, product or device that includes a series of steps or units is not necessarily limited to those steps or units that are clearly listed, but may include other steps or units that are not clearly listed or inherent to these processes, methods, products or devices.

[0035] Embodiment 1

[0036] Figure 1 A flowchart of a method for optimizing and dispatching a power system is provided for the first embodiment of the present invention. This embodiment is applicable to dispatching a power system. The method can be executed by an optimization and dispatching device for a power system. The optimization and dispatching device for a power system can be implemented in the form of hardware and / or software. The optimization and dispatching device for a power system can be configured in electronic devices such as computers. Figure 1 As shown, the method includes:

[0037] S101. Acquire a reservoir hydropower model, where the reservoir hydropower model includes operation information of at least one cascade hydropower station in at least one scheduling cycle and information relationship between each operation information, the operation information includes power generation, and the scheduling cycle includes at least one time.

[0038] In this embodiment, the reservoir hydropower model can be specifically understood as a hydropower station water volume coupling model, which can describe the relationship between each cascade hydropower station and the model of hydropower information. The scheduling cycle can be understood as the time period for optimizing scheduling. For example, the scheduling cycle can be 24 time intervals a day, and the scheduling cycle includes 24 moments. The operation information can be specifically understood as the information involved in the operation of the hydropower station, such as the power generation power, the power generation head of the cascade hydropower station, the power generation efficiency, the power generation flow, etc. There is a certain correlation between each piece of operation information, that is, there is an information relationship between the operation information. The information relationship of the operation information is determined accordingly after the reservoir hydropower model is determined. Specifically, a reservoir hydropower model is pre-constructed, and a reservoir hydropower model is constructed according to the water conservancy connection between each cascade hydropower station. The operation information of each cascade hydropower station and the information relationship between each piece of operation information can be determined through the reservoir hydropower model.

[0039] For example, Figure 2 This is a schematic diagram of the structure of each cascade of hydropower in a reservoir hydropower model provided in an embodiment of the present application. The following types of information can be regarded as operation information. Figure 2 Taking the series-parallel cascade hydropower generation model as an example, the model includes four cascade hydropower stations 21, each of which includes a reservoir 211 and a power generation device 212, and the power generation device is used to generate alternating current. Figure 2 The meaning of the parameters in V j,t is the reservoir water volume of the j-th hydropower station at time t, Q j,t is the power generation flow of the j-th hydropower station at time t, which is used for power generation. Other similar parameters will not be described in detail, and those skilled in the art will know them. Then the power generation power of the cascade hydropower station is It can be expressed as:

[0040]

[0041] Among them, H j,t is the generating head of the j-th hydropower station at time t; η j is the power generation efficiency of the j-th hydropower station; Q j,t is the power generation flow of the j-th hydropower station at time t. Among them:

[0042] Q j,t =V j,t-1 -V j,t +R j,t -ΔI j,t

[0043] Among them, V j,t-1 is the reservoir water volume of the j-th hydropower station at time t-1; ΔI j,t is the amount of water discharged from the j-th reservoir at time t; R j,t is the inflow flow of the j-th hydropower station at time t. Among them:

[0044]

[0045] Among them, I j,t is the natural water inflow of the j-th reservoir at time t; They are the j-1st and j-2nd hydropower stations at t-τ j-1 , t-τ j-2 The power generation flow at the moment; τ j-1 , τ j-2 are the water flow lag time from the j-1st and j-2nd level reservoirs to the jth level reservoir respectively; The j-1st and j-2nd level reservoirs are at t-τ j-1 , t-τ j-2 The amount of water discarded at a time; j is a 0-1 variable. When the j-th reservoir is the confluence point of the j-1st and j-2nd reservoirs, a j =1, otherwise a j =0.

[0046] S102. At each moment, a constraint expression of base load power is determined according to the operation information and information relationship of each cascade hydropower station in combination with a power allocation model of regulation power and base load power, wherein the power allocation model includes a regulation power allocation coefficient and a base load power allocation coefficient.

[0047] In this embodiment, the regulating power is used to smooth the system power fluctuations to meet the system load demand; the baseload power is used to meet the water demand for reservoir safety, cascade basin navigation, and upstream and downstream ecology. The power allocation model can be specifically understood as a mathematical model for calculating how to allocate regulating power and baseload power. The constraint expression of baseload power is used to express the relationship between baseload power and the operating information of cascade hydropower, and the baseload power can be constrained by the operating information. The regulating power allocation coefficient can be specifically understood as the allocation coefficient of the generated power used as the regulating power; the baseload power allocation coefficient can be specifically understood as the allocation coefficient of the generated power used as the baseload power.

[0048] The constraint expression of the corresponding base load power is calculated for each moment, and a power allocation model for allocating regulating power and base load power is pre-built. When constructing the power allocation model, in order to improve the operating efficiency of cascade hydropower, the regulating power allocation coefficient and the base load power allocation coefficient are introduced. The operating information of each cascade hydropower station and the information relationship between the operating information are respectively substituted into the power allocation model for calculation to obtain the constraint expression of the base load power of each cascade hydropower station. The constraint expression of base load power is the relationship between base load power and each operating information. When calculating the constraint expression, the operating information used may need to be determined based on other operating information that has a certain correlation with it. Therefore, the information relationship needs to be used when determining the constraint expression.

[0049] S103: Determine an expected target and an expected value of baseload power according to the constraint expressions of each baseload power in combination with a constraint set.

[0050] In this embodiment, the constraint set can be specifically understood as the constraints of various types of data involved in the operation of the cascade hydropower station, for example, the constraints of the generating head (which can be the value range of the generating head); the expected target is obtained by optimizing the deterministic model, and the expected target in this application is the maximum generating head, the minimum water abandonment and the base load power as the optimization target, and the optimal value obtained. The expected value of the base load power is the value of the base load power corresponding to the expected target of the optimal solution when calculating it.

[0051] Specifically, a constraint set is pre-set, a relationship between the baseload power and each operation information is determined according to a constraint expression of the baseload power, a constraint of each operation information is determined according to the constraint set, an expected target is calculated in combination with the constraint expression of the baseload power, and an expected value of the baseload power corresponding to the expected target is determined.

[0052] S104, determining a target uncertainty coefficient based on the expected target, the expected value of baseload power, the constraint expression of baseload power, the constraint set and a predetermined optimization scheduling model, and optimizing the generation power according to the target uncertainty coefficient to achieve optimal allocation of regulation power and baseload power.

[0053] In this embodiment, the optimization dispatch model can be specifically understood as a mathematical model for performing robust optimization dispatch of the power system, a model constructed based on base load power uncertainty. The target uncertainty coefficient can be specifically understood as a coefficient for determining the base load power fluctuation amount.

[0054] Specifically, an optimization scheduling model is constructed in advance. The optimization scheduling model in this application is constructed based on the uncertainty of hydropower base load power, and the optimization scheduling model includes uncertainty coefficients. The expected target, the expected value of base load power, the constraint expression of base load power, the constraint set and the predetermined optimization scheduling model are used to determine the target uncertainty coefficient as the constraint conditions of the optimization scheduling model, and the optimal solution of the uncertainty coefficient in the optimization scheduling model is calculated, and the optimal solution of the uncertainty coefficient is used as the target uncertainty coefficient. The fluctuation range of the base load power is determined according to the target uncertainty coefficient, and the generated power is divided into base load power and regulation power according to the fluctuation range of the base load power for corresponding output, so as to achieve the optimal allocation of regulation power and base load power.

[0055] This application determines the relationship between various operating information by constructing a reservoir hydropower model, deriving the relationship between parameters through combining power allocation models, optimal scheduling models, etc., determining the value range of the data according to the constraint set, and further finding the optimal solution based on the value range to obtain the target uncertainty coefficient and achieve optimal scheduling.

[0056] The technical solution of the embodiment of the present invention obtains a reservoir hydropower model, where the reservoir hydropower model includes the operation information of at least one cascade hydropower station within at least one scheduling period and the information relationship between the operation information, the operation information includes the power generation power, and the scheduling period includes at least one moment; for each moment, according to the operation information and information relationship of each cascade hydropower station, combined with the power distribution model of the regulating power and the base load power, the constraint expression of the base load power is determined, and the power distribution model includes a regulating power distribution coefficient and a base load power distribution coefficient; according to the constraint expressions of each base load power, combined with the constraint set, the expected target and the expected value of the base load power are determined; based on the expected target, the expected value of the base load power, the constraint expression of the base load power, the constraint set and a pre-determined optimal scheduling model, the target uncertainty coefficient is determined, and the power generation power is optimized and scheduled according to the target uncertainty coefficient to achieve the optimal distribution of the regulating power and the base load power, solving the problem of unreasonable scheduling in the power system scheduling process. By using the power distribution model to determine the constraint expression of the base load power, the power distribution model includes a regulating power distribution coefficient and a base load power distribution coefficient, and by introducing the distribution coefficients, the distribution coefficients are set for the regulating power and the base load power respectively to achieve the reasonable division of the power generation power. According to the constraint expression of the base load power, combined with the constraint set, the expected target and the expected value of the base load power are determined, and then the target uncertainty coefficient is further determined. By optimizing and scheduling the power generation power through the target uncertainty coefficient, the power generation power is reasonably divided into the base load power and the regulating power, effectively improving the operation efficiency of the cascade hydropower, ensuring that the current operation target of the system realizes the maximum tolerance for the uncertain change of the power on the basis of meeting a certain expected level, and effectively suppressing the power fluctuation of the system.

[0057] Embodiment 2

[0058] Figure 3 It is a flowchart of an optimal scheduling method for a power system provided by Embodiment 2 of the present invention, and this embodiment is refined on the basis of the above embodiment. As Figure 3 shown, the method includes:

[0059] S201. Obtain a reservoir hydropower model, where the reservoir hydropower model includes the operation information of at least one cascade hydropower station within at least one scheduling period and the information relationship between the operation information, the operation information includes the power generation power, and the scheduling period includes at least one moment.

[0060] S202. For each moment, determine the operation parameters of each cascade hydropower station.

[0061] In this embodiment, the operating parameters can be specifically understood as parameter information during the operation of the cascade hydropower station. The operating parameters can be fixed values ​​determined according to actual engineering requirements, such as power generation constants, maximum power generation, system load, etc. For each moment, the operating parameters of each cascade hydropower station during operation are determined respectively, wherein various types of parameters in the operating parameters can be preset and directly obtained during calculation.

[0062] S203, bringing the operation information, information relationship and operation parameters of each cascade hydropower station into the power calculation formula in the power allocation model of the regulation power and the base load power, and obtaining the constraint expression of the base load power.

[0063] The power allocation model of the present application includes multiple power calculation formulas, which are used to calculate the expression relationship between various operating information and other variables involved in the operation process, determine the constraints between various operating information through the information relationship between various operating information, and determine the constraints through the relationship between the operating information and other variables. The power allocation model is pre-constructed, and the various power calculation formulas in the power allocation model are determined. The operating information, information relationship, and operating parameter information of each cascade hydropower station are introduced into each power calculation formula. The constraint expression of the base load power is determined through calculation, and the constraint relationship between the base load power and other information is expressed through the constraint expression.

[0064] Optionally, the power calculation formula in the power allocation model includes:

[0065]

[0066]

[0067]

[0068]

[0069]

[0070] Q b,j,t =β j Cap j ;

[0071] Q a,j,t +Q b,j,t =Q j,t ;

[0072]

[0073] Among them, Q j,t is the power generation flow of the j-th hydropower station at time t; Q a,j,t is the power generation flow used to adjust power at the j-th hydropower station at time t; Qb,j,t is the power generation flow used for base load power of the j-th hydropower station at time t; μ j is the power generation constant of cascade hydropower; Cap j is the maximum power generation capacity of the j-th hydropower station, P m,t is the remaining power; P L,t is the system load; P w,i,t is the power of wind power station i at time t; N H is the number of hydropower stations; N W is the number of wind power stations; α j is the regulating power allocation coefficient of the j-th hydropower station; β j is the base load power allocation coefficient of the j-th hydropower station, is the power generation capacity of the j-th hydropower station at time t; is the base load power of the j-th hydropower station at time t; is the regulated power of the j-th hydropower station at time t;

[0074] Q j,t and is the operation information, μ j , Cap j , P L,t , P w,i,t 、N H and N W For running parameters.

[0075] P m,t , α j , β j Parameters such as are intermediate variables involved in the calculation process.

[0076] By converting the relationship through the above calculation formula, an expression between the baseload power and other information can be obtained, that is, the constraint expression of the baseload power.

[0077] S204: Obtain a deterministic optimization model.

[0078] In this embodiment, the deterministic optimization model can be specifically understood as an optimization scheduling model based on the deterministic construction of hydropower base load power. The deterministic optimization model is pre-constructed and stored, and the deterministic optimization model includes a calculation formula for calculating the expected value. The deterministic optimization model in the embodiment of the present application takes the maximum power generation head and the minimum abandoned water load base load power as the expectation. When performing optimization scheduling, the deterministic optimization model is directly obtained.

[0079] S205 , performing calculations according to the constraint expressions of each base load power, the constraint set, and the calculation formula of the deterministic optimization model to determine the maximum expectation.

[0080] The calculation formula of the deterministic optimization model takes the expected value as the calculation target, and takes the constraint expression and constraint set of the base load power as constraints. The calculation is performed according to the calculation formula of the deterministic optimization model to obtain the maximum expectation and the values ​​of each parameter when the maximum expectation is calculated.

[0081] S206: Determine the maximum expectation as the expected target, and determine the baseload power corresponding to the maximum expectation as the baseload power expected value.

[0082] The maximum expectation is taken as the expected target, and when determining the maximum expectation, the value of the baseload power is taken, and this value is taken as the expected value of the baseload power.

[0083] Optionally, the calculation formula of the deterministic optimization model is:

[0084]

[0085] stC;

[0086] Among them, f is the expectation, T is the scheduling period, is the base load power of the j-th hydropower station at time t, H j,t is the generating head of the j-th hydropower station at time t; ΔI j,t is the amount of water discharged from the j-th reservoir at time t; 1 and γ 2 is a constant; N H is the number of hydropower stations; C is the constraint set;

[0087] The constraint set includes at least H j,t Constraints and ΔI j,t constraint.

[0088] This application takes the maximum generating head, minimum water abandonment and base load power of the cascade hydropower station as the optimization objectives, takes the generating head minus the water abandonment and base load power as the expectation, takes the maximum expectation as the target expectation, and takes the value of the base load power when the expectation takes the maximum value as the expected value of the base load power.

[0089] S207: Substitute the expected target, the expected value of baseload power, the constraint expression of baseload power and the constraint set into the calculation formula of the optimization scheduling model for solution to determine the maximum value of the uncertainty coefficient.

[0090] In this embodiment, there are multiple calculation formulas of the optimization scheduling model, and the calculation formula of the optimization scheduling model includes an uncertainty coefficient. The uncertainty coefficient is introduced when constructing the optimization scheduling model, and the upper and lower limits of the base load power fluctuation are determined by the uncertainty coefficient. The expected target, the expected value of the base load power, the constraint expression of the base load power and the constraint set are used as constraints, and the maximum value of the uncertainty coefficient, that is, the maximum value of the uncertainty coefficient, is calculated by the calculation formula of the optimization scheduling model.

[0091] S208. Determine the maximum value of the uncertainty coefficient as the target uncertainty coefficient.

[0092] Optionally, the calculation formula of the optimization scheduling model includes:

[0093] maxθ;

[0094] stf ≥ (1-σ)f 0 ;

[0095]

[0096]

[0097]

[0098] C;

[0099] Among them, θ is the uncertainty coefficient, f 0 is the expected target, σ is the expected deviation coefficient; f is the expectation; T is the scheduling period, H j,t is the generating head of the j-th hydropower station at time t; ΔI j,t is the amount of water discharged from the j-th reservoir at time t; 1 and γ 2 is a constant; N H is the number of hydropower stations; and is the Lagrange multiplier; for In vector form, is the expected value of base load power; C is the constraint set.

[0100] It is important to know that in the calculation formula of the optimization scheduling model, f 0 ,σ,T,γ 1 , γ 2 、N H 、N W , C are all known quantities, θ, H j,t , ΔI j,t is the decision variable, contains n elements, where n is related to the number of time intervals in the scheduling cycle. For example, if the scheduling cycle is 24 time intervals per day, then n=24.

[0101] The optimization scheduling model in this application is determined as follows:

[0102] Taking the maximum generating head, minimum abandoned water volume and base load power of cascade hydropower as the optimization objectives, under the constraint set C, with the help of information gap decision theory, a robust optimization dispatch model of the power system based on the uncertainty of hydropower base load power is established, as shown in the following formula:

[0103]

[0104] Among them, σ is the expected deviation coefficient, f 0 is the expected target; f is the expectation, T is the scheduling period, is the base load power of the j-th hydropower station at time t, H j,t is the generating head of the j-th hydropower station at time t; ΔI j,t is the amount of water discharged from the j-th reservoir at time t; 1 and γ 2 is a constant; N H is the number of hydropower stations; C is the constraint set.

[0105] With the help of duality theory, the Lagrangian function is constructed to transform the above optimization scheduling model into a semi-definite programming problem for solution. The specific model is shown in the following formula:

[0106]

[0107] This application uses the information gap decision theory to establish a base load power uncertainty model to describe the randomness of hydropower operation, and further quantifies the impact of uncertain factors on system operation. Combined with the regulation characteristics of cascade hydropower and controllable loads to smooth wind power fluctuations and peak-to-valley shifting, a robust optimization scheduling model for hydropower and controllable load coordination considering base load power uncertainty is established, and an optimization scheduling scheme with robust feasibility is obtained.

[0108] S209: Optimize the scheduling of the generated power according to the target uncertainty coefficient to achieve optimal allocation of the regulated power and the base load power.

[0109] Optionally, optimizing the scheduling of power generation according to the target uncertainty coefficient can be optimized as follows: determining the upper and lower limits of the baseload power according to the target uncertainty coefficient and the expected value of the baseload power; and dividing the power generation power into baseload power and regulation power according to the upper and lower limits of the baseload power.

[0110] The upper and lower limits of baseload power fluctuation are determined according to the target uncertainty coefficient, and the upper and lower limits of baseload power are determined according to the expected value of baseload power and the upper and lower limits of baseload power fluctuation.

[0111] Exemplarily, the present application provides a calculation formula:

[0112]

[0113] in, is the expected value of base load power; δ j,t is the fluctuation value of base load power; is the lower limit of the base load power fluctuation value of cascade hydropower station j at time t, is the upper limit of the base load power fluctuation value of cascade hydropower station j at time t.

[0114] The optimal range of base load power is determined according to the upper and lower limits of base load power, and the base load power is allocated according to this range. The remaining power is the regulating power. The generated power is divided into base load power and regulating power according to the optimal scheduling method. The cascade hydropower stations are controlled to output the corresponding power according to the above optimal scheduling method within the corresponding scheduling period, so as to achieve optimal scheduling of the power system.

[0115] Optionally, the constraint set includes: power balance constraint, generating head constraint, generating flow constraint, reservoir water balance constraint, reservoir water storage capacity constraint, water abandonment constraint, reservoir initial storage capacity and final storage capacity constraint, environmental flow constraint, water level constraint and controllable load regulation power constraint.

[0116] This application simulates the operation of a hydropower station through a model, and calculates the relationship between various parameters and information through multiple model simulations to obtain an expression, determines the value range of different information through a constraint set, solves the expression according to the value range of the information, and obtains the optimal solution for the hydropower operation.

[0117] The embodiment of the present invention provides an optimization dispatching method for an electric power system, which solves the problem of unreasonable dispatching in the dispatching process of the electric power system. The constraint expression of the base load power is determined by a power distribution model. The power distribution model includes a regulating power distribution coefficient and a base load power distribution coefficient. The distribution coefficient is introduced to set the distribution coefficients for the regulating power and the base load power respectively, so as to realize the reasonable division of the generating power. According to the constraint expression of the base load power combined with the constraint set, the expected target and the expected value of the base load power are determined, and the target uncertainty coefficient is further determined. The generated power is optimally dispatched by the target uncertainty coefficient, and the generated power is reasonably divided into the base load power and the regulating power, so as to effectively improve the operation efficiency of the cascade hydropower and smooth the system power fluctuation.

[0118] For example, a system consisting of four cascade hydropower stations, one wind farm, and one movable load is used as an example, and the dispatching period is 24 time intervals per day. By optimizing the above model, the following results are obtained:

[0119] (1) The base load power allocation factor is an indirect index to measure the ability of cascade hydropower to regulate wind power fluctuations. As the base load power allocation factor decreases, more water storage capacity can be allocated to compensate for wind power. However, too small a base load factor value may lead to insufficient base load generation and low water utilization efficiency. Figure 4aAn example diagram of the optimal value of the base load power allocation factor of different hydropower stations provided in an embodiment of the present application is shown in FIG. Figure 4b This is an example diagram of the optimal value of the power allocation factor for different hydropower stations provided in the embodiment of the present application. Figure 4a As shown in the figure, since the base load factor is mainly determined by the reservoir regulation capacity, the base load factor of hydropower 3 and 4 is greater than that of hydropower 1 and 2. In addition, affected by the change of reservoir capacity and the change of water inflow in different seasons, the base load factor in the flood season is greater than that in the dry season. From the perspective of the overall hydropower energy availability, it is effective to assign an appropriate base load factor for each hydropower.

[0120] The power allocation factor represents the coordinated power allocation between cascade hydropower stations to track the net load. The traditional method generally allocates the overall demand of the system to each hydropower station according to the proportion of the hydropower station's power generation capacity. However, the allocation ratio of different reservoirs is affected by factors such as water inflow at different times, water storage demand and environmental flow demand. Figure 4b As shown in the figure, in the dry season, the distribution factor of hydropower 3 is the largest. The main reason is that the head of hydropower 3 is higher in the current scheduling period. Allocating more power demand to hydropower 3 can improve the operating efficiency of the entire system. In addition, hydropower 1 and hydropower 2 have low heads at this time, and their operating efficiency is low. They play a more important role in water storage regulation while meeting the system regulation requirements. Hydropower 4 has the largest water storage capacity in the flood season, so a larger power demand is allocated to hydropower 3 to improve the operating efficiency of hydropower.

[0121] Figure 5a A schematic diagram of changes in equivalent electric quantity of a traditional hydropower dispatching model that does not consider power allocation factors provided in an embodiment of the present application; Figure 5b A schematic diagram of the changes in the total water storage equivalent electricity of cascade hydropower in an optimized scheduling model provided in an embodiment of the present application. Using the traditional scheduling method, the total water storage equivalent electricity of cascade hydropower varies from 58.55 to 137.18 MWh, and it varies from 87.4 to 133.6 MWh after using the scheduling method proposed in this application. As can be seen from the figure, after adopting the optimized scheduling method provided in this application, the changes in the equivalent electricity of cascade hydropower storage are more stable, and a high storage capacity level is maintained during peak load periods, which effectively guarantees the operating space of the system and improves the power generation efficiency of cascade hydropower. Thereby reducing the implicit investment in unit expansion and maintenance due to load growth in the system.

[0122] Figure 6a This is an example diagram showing the results of an optimized scheduling when the expected deviation coefficient σ = 0.2. Figure 6b This is a sample diagram showing the results of a traditional deterministic optimization scheduling. Figure 6b compared to, Figure 6aThe base load power of cascade hydropower is reduced, and the regulating power is increased. This is because when considering the uncertainty of the base load power of cascade hydropower, the model provided in this application will allocate as little power demand as possible to the base load power under the condition of meeting the safe operation of cascade hydropower, and increase the regulating power to cope with the uncertainty of cascade hydropower and wind power changes. Therefore, the scheduling scheme of this application has a certain robustness and is more in line with the actual situation.

[0123] From the above results, it can be seen that this application has a significant effect on improving the system's ability to cope with uncertain fluctuations in wind power, increasing wind power absorption, and improving the operating efficiency of cascade hydropower.

[0124] Embodiment 3

[0125] Figure 7 This is a schematic diagram of the structure of an optimization dispatching device for a power system provided in Embodiment 3 of the present invention. Figure 7 As shown, the device includes: a model acquisition module 31, a constraint expression determination module 32, an expectation determination module 33 and a coefficient determination module 34.

[0126] The model acquisition module 31 is used to acquire a reservoir hydropower model, wherein the reservoir hydropower model includes operation information of at least one cascade hydropower station in at least one scheduling period and information relationship between the operation information, wherein the operation information includes power generation, and the scheduling period includes at least one time;

[0127] A constraint expression determination module 32 is used to determine the constraint expression of the base load power at each moment according to the operation information and information relationship of each cascade hydropower station combined with the power allocation model of the regulation power and the base load power, wherein the power allocation model includes a regulation power allocation coefficient and a base load power allocation coefficient;

[0128] An expectation determination module 33, configured to determine an expected target and an expected value of baseload power according to the constraint expressions of each baseload power combined with a constraint set;

[0129] The coefficient determination module 34 is used to determine the target uncertainty coefficient based on the expected target, the expected value of baseload power, the constraint expression of baseload power, the constraint set and the predetermined optimization scheduling model, and optimize the generation power according to the target uncertainty coefficient to achieve the optimal allocation of the regulation power and the baseload power.

[0130] The embodiment of the present application provides an optimized dispatching device for an electric power system, which solves the problem of unreasonable dispatching in the dispatching process of the electric power system. The constraint expression of the base load power is determined by the power distribution model. The power distribution model includes a regulating power distribution coefficient and a base load power distribution coefficient. The distribution coefficient is introduced to set the distribution coefficients for the regulating power and the base load power respectively, so as to realize the reasonable division of the generating power. According to the constraint expression of the base load power combined with the constraint set, the expected target and the expected value of the base load power are determined, and the target uncertainty coefficient is further determined. The generated power is optimally dispatched by the target uncertainty coefficient, and the generated power is reasonably divided into the base load power and the regulating power, so as to effectively improve the operation efficiency of the cascade hydropower and smooth the system power fluctuation.

[0131] Optionally, the constraint expression determination module 32 includes:

[0132] A parameter determination unit, used to determine the operating parameters of each of the cascade hydropower stations;

[0133] The constraint expression determination unit is used to bring the operation information, information relationship and operation parameters of each cascade hydropower station into the power calculation formula in the power allocation model of the regulating power and base load power to obtain the constraint expression of the base load power.

[0134] Optionally, the power calculation formula in the power allocation model includes:

[0135]

[0136]

[0137]

[0138]

[0139]

[0140] Q b,j,t =β j Cap j ;

[0141] Q a,j,t +Q b,j,t =Q j,t ;

[0142]

[0143] Among them, Q j,t is the power generation flow of the j-th hydropower station at time t; Q a,j,t is the power generation flow used to adjust power at the j-th hydropower station at time t; Q b,j,tis the power generation flow used for base load power of the j-th hydropower station at time t; μ j is the power generation constant of the cascade hydropower station; Cap j is the maximum power generation capacity of the j-th hydropower station, P m,t is the remaining power; P L,t is the system load; P w,i,t is the power of wind power station i at time t; N H is the number of hydropower stations; N W is the number of wind power stations; α j is the regulating power allocation coefficient of the j-th hydropower station; β j is the base load power allocation coefficient of the j-th hydropower station, is the power generation capacity of the j-th hydropower station at time t; is the base load power of the j-th hydropower station at time t; is the regulated power of the j-th hydropower station at time t;

[0144] Q j,t and is the operation information, μ j , Cap j , P L,t , P w,i,t 、N H and N W For running parameters.

[0145] Optionally, the expectation determination module 33 includes:

[0146] A deterministic model acquisition unit, used for acquiring a deterministic optimization model;

[0147] A maximum expectation determination unit, configured to determine the maximum expectation by performing calculations according to the constraint expressions of each base load power, the constraint set and the calculation formula of the deterministic optimization model;

[0148] The expectation determination unit is used to determine the maximum expectation as the expected target, and determine the baseload power corresponding to the maximum expectation as the baseload power expected value.

[0149] Optionally, the calculation formula of the deterministic optimization model is:

[0150]

[0151] stC;

[0152] Among them, f is the expectation, T is the scheduling period, is the base load power of the j-th hydropower station at time t, H j,t is the generating head of the j-th hydropower station at time t; ΔI j,t is the amount of water discharged from the j-th reservoir at time t;1 , and γ 2 is a constant; N H is the number of hydropower stations; C is the constraint set;

[0153] The constraint set includes at least H j,t Constraints and ΔI j,t constraint.

[0154] Optionally, the coefficient determination module 34 includes:

[0155] A maximum value determination unit, used to bring the expected target, the expected value of baseload power, the constraint expression of baseload power and the constraint set into the calculation formula of the optimization scheduling model for solution, and determine the maximum value of the uncertainty coefficient;

[0156] The coefficient determination unit is used to determine the maximum value of the uncertainty coefficient as the target uncertainty coefficient.

[0157] Optionally, the calculation formula of the optimization scheduling model includes:

[0158] maxθ;

[0159] stf ≥ (1-σ)f 0 ;

[0160]

[0161]

[0162]

[0163] C;

[0164] Among them, θ is the uncertainty coefficient, f 0 is the expected target, σ is the expected deviation coefficient; f is the expectation; T is the scheduling period, H j,t is the generating head of the j-th hydropower station at time t; ΔI j,t is the amount of water discharged from the j-th reservoir at time t; 1 and γ 2 is a constant; N H is the number of hydropower stations; and is the Lagrange multiplier; for In vector form, is the expected value of base load power; C is the constraint set.

[0165] Optionally, the constraint set includes: power balance constraint, power generation head constraint, power generation flow constraint, reservoir water balance constraint, reservoir water storage capacity constraint, water abandonment constraint, reservoir initial storage capacity and final storage capacity constraint, environmental flow constraint, water level constraint and controllable load regulation power constraint.

[0166] The power system optimization dispatching device provided in the embodiment of the present invention can execute the power system optimization dispatching method provided in any embodiment of the present invention, and has the corresponding functional modules and beneficial effects of the execution method.

[0167] Embodiment 4

[0168] Figure 8 A schematic diagram of the structure of an electronic device 40 that can be used to implement an embodiment of the present invention is shown. The electronic device is intended to represent various forms of digital computers, such as laptop computers, desktop computers, workstations, personal digital assistants, servers, blade servers, mainframe computers, and other suitable computers. The electronic device can also represent various forms of mobile devices, such as personal digital processing, cellular phones, smart phones, wearable devices (such as helmets, glasses, watches, etc.) and other similar computing devices. The components shown herein, their connections and relationships, and their functions are merely examples and are not intended to limit the implementation of the present invention described and / or required herein.

[0169] like Figure 8 As shown, the electronic device 40 includes at least one processor 41, and a memory connected to the at least one processor 41, such as a read-only memory (ROM) 42, a random access memory (RAM) 43, etc., wherein the memory stores a computer program that can be executed by at least one processor, and the processor 41 can perform various appropriate actions and processes according to the computer program stored in the read-only memory (ROM) 42 or the computer program loaded from the storage unit 48 to the random access memory (RAM) 43. In the RAM 43, various programs and data required for the operation of the electronic device 40 can also be stored. The processor 41, the ROM 42, and the RAM 43 are connected to each other through a bus 44. An input / output (I / O) interface 45 is also connected to the bus 44.

[0170] A number of components in the electronic device 40 are connected to the I / O interface 45, including: an input unit 46, such as a keyboard, a mouse, etc.; an output unit 47, such as various types of displays, speakers, etc.; a storage unit 48, such as a disk, an optical disk, etc.; and a communication unit 49, such as a network card, a modem, a wireless communication transceiver, etc. The communication unit 49 allows the electronic device 40 to exchange information / data with other devices through a computer network such as the Internet and / or various telecommunication networks.

[0171] The processor 41 may be a variety of general and / or special processing components with processing and computing capabilities. Some examples of the processor 41 include, but are not limited to, a central processing unit (CPU), a graphics processing unit (GPU), various dedicated artificial intelligence (AI) computing chips, various processors running machine learning model algorithms, digital signal processors (DSPs), and any appropriate processors, controllers, microcontrollers, etc. The processor 41 executes the various methods and processes described above, such as an optimization scheduling method for a power system.

[0172] In some embodiments, the optimization scheduling method of the power system may be implemented as a computer program, which is tangibly contained in a computer-readable storage medium, such as a storage unit 48. In some embodiments, part or all of the computer program may be loaded and / or installed on the electronic device 40 via the ROM 42 and / or the communication unit 49. When the computer program is loaded into the RAM 43 and executed by the processor 41, one or more steps of the optimization scheduling method of the power system described above may be performed. Alternatively, in other embodiments, the processor 41 may be configured to execute the optimization scheduling method of the power system in any other appropriate manner (e.g., by means of firmware).

[0173] Various implementations of the systems and techniques described above herein can be implemented in digital electronic circuit systems, integrated circuit systems, field programmable gate arrays (FPGAs), application specific integrated circuits (ASICs), application specific standard products (ASSPs), systems on chips (SOCs), load programmable logic devices (CPLDs), computer hardware, firmware, software, and / or combinations thereof. These various implementations can include: being implemented in one or more computer programs that can be executed and / or interpreted on a programmable system including at least one programmable processor, which can be a special purpose or general purpose programmable processor that can receive data and instructions from a storage system, at least one input device, and at least one output device, and transmit data and instructions to the storage system, the at least one input device, and the at least one output device.

[0174] Computer programs for implementing the methods of the present invention may be written in any combination of one or more programming languages. These computer programs may be provided to a processor of a general-purpose computer, a special-purpose computer, or other programmable data processing device, so that when the computer program is executed by the processor, the functions / operations specified in the flow chart and / or block diagram are implemented. The computer program may be executed entirely on the machine, partially on the machine, partially on the machine and partially on a remote machine as a stand-alone software package, or entirely on a remote machine or server.

[0175] In the context of the present invention, a computer-readable storage medium may be a tangible medium that may contain or store a computer program for use by or in combination with an instruction execution system, device or equipment. A computer-readable storage medium may include, but is not limited to, electronic, magnetic, optical, electromagnetic, infrared, or semiconductor systems, devices or equipment, or any suitable combination of the foregoing. Alternatively, a computer-readable storage medium may be a machine-readable signal medium. A more specific example of a machine-readable storage medium may include an electrical connection based on one or more lines, a portable computer disk, a hard disk, a random access memory (RAM), a read-only memory (ROM), an erasable programmable read-only memory (EPROM or flash memory), an optical fiber, a portable compact disk read-only memory (CD-ROM), an optical storage device, a magnetic storage device, or any suitable combination of the foregoing.

[0176] To provide interaction with a user, the systems and techniques described herein may be implemented on an electronic device having: a display device (e.g., a CRT (cathode ray tube) or LCD (liquid crystal display) monitor) for displaying information to the user; and a keyboard and a pointing device (e.g., a mouse or trackball) through which the user can provide input to the electronic device. Other types of devices may also be used to provide interaction with the user; for example, the feedback provided to the user may be any form of sensory feedback (e.g., visual feedback, auditory feedback, or tactile feedback); and input from the user may be received in any form (including acoustic input, voice input, or tactile input).

[0177] The systems and techniques described herein may be implemented in a computing system that includes backend components (e.g., as a data server), or a computing system that includes middleware components (e.g., an application server), or a computing system that includes frontend components (e.g., a user computer with a graphical user interface or a web browser through which a user can interact with implementations of the systems and techniques described herein), or a computing system that includes any combination of such backend components, middleware components, or frontend components. The components of the system may be interconnected by any form or medium of digital data communication (e.g., a communication network). Examples of communication networks include: a local area network (LAN), a wide area network (WAN), a blockchain network, and the Internet.

[0178] A computing system may include a client and a server. The client and the server are generally remote from each other and usually interact through a communication network. The client and server relationship is generated by computer programs running on the corresponding computers and having a client-server relationship with each other. The server may be a cloud server, also known as a cloud computing server or cloud host, which is a host product in the cloud computing service system to solve the defects of difficult management and weak business scalability in traditional physical hosts and VPS services.

[0179] It should be understood that the various forms of processes shown above can be used to reorder, add or delete steps. For example, the steps described in the present invention can be executed in parallel, sequentially or in different orders, as long as the desired results of the technical solution of the present invention can be achieved, and this document does not limit this.

[0180] The above specific implementations do not constitute a limitation on the protection scope of the present invention. It should be understood by those skilled in the art that various modifications, combinations, sub-combinations and substitutions can be made according to design requirements and other factors. Any modification, equivalent substitution and improvement made within the spirit and principle of the present invention should be included in the protection scope of the present invention.

Claims

1. An optimal dispatching method for a power system, It is characterized in that include: Acquire a reservoir hydropower model, wherein the reservoir hydropower model includes operation information of at least one cascade hydropower station in at least one scheduling period and information relationship between each of the operation information, wherein the operation information includes power generation, and the scheduling period includes at least one time; At each moment, a constraint expression of base load power is determined according to the operation information and information relationship of each of the cascade hydropower stations in combination with a power allocation model of regulation power and base load power, wherein the power allocation model includes a regulation power allocation coefficient and a base load power allocation coefficient; Determine the expected target and the expected value of the baseload power according to the constraint expressions of each baseload power in combination with the constraint set; Determine a target uncertainty coefficient based on the expected target, the expected value of baseload power, the constraint expression of baseload power, the constraint set and a predetermined optimization scheduling model, and optimize the generation power according to the target uncertainty coefficient to achieve the optimal allocation of the regulation power and the baseload power; The step of optimizing the power generation according to the target uncertainty coefficient to achieve the optimal allocation of the regulation power and the base load power includes: Determining upper and lower limits of base load power according to the target uncertainty coefficient and the expected value of base load power; dividing the generated power into base load power and regulating power according to the upper and lower limits of base load power; The step of determining the expected target and the expected value of the baseload power according to the constraint expressions of each baseload power in combination with the constraint set includes: Obtain a deterministic optimization model; Performing calculations based on the constraint expressions of each base load power, the constraint set, and the calculation formula of the deterministic optimization model to determine the maximum expectation; Determine the maximum expectation as the expected target, and determine the baseload power corresponding to the maximum expectation as the baseload power expected value; Wherein, the calculation formula of the deterministic optimization model is: stC; Among them, f is the expectation, T is the scheduling period, is the base load power of the j-th hydropower station at time t, H j,t is the generating head of the j-th hydropower station at time t; ΔI j,t is the amount of water discharged from the j-th reservoir at time t; 1 and γ 2 is a constant; N H is the number of hydropower stations; C is the constraint set; The constraint set includes at least H j,t Constraints and ΔI j,t constraint.

2. The method according to claim 1, It is characterized in that The constraint expression for base load power is determined based on the operation information and information relationship of each cascade hydropower station in combination with the power allocation model of the regulation power and the base load power, including: Determining the operating parameters of each of the cascade hydropower stations; The operation information, information relationship and operation parameters of each of the cascade hydropower stations are introduced into the power calculation formula in the power allocation model of the regulating power and base load power to obtain a constraint expression of the base load power.

3. The method according to claim 2, It is characterized in that The power calculation formula in the power allocation model includes: Q b,j,t =β j Chapter j ; Q a,j,t +Q b,j,t =Q j,t ; Among them, Q j,t is the power generation flow rate of the j-th hydropower station at time t; Q a,j,t is the power generation flow rate used for regulating power of the j-th hydropower station at time t; Q b,j,t is the power generation flow rate used for base load power of the j-th hydropower station at time t; μ j is the power generation constant of cascade hydropower stations; Cap j is the maximum power generation of the j-th hydropower station, P m,t is the surplus power; P L,t is the system load; P w,i,t is the power of wind power station i at time t; N H is the number of hydropower stations; N W is the number of wind power stations; α j is the regulating power distribution coefficient of the j-th hydropower station; β j is the base load power distribution coefficient of the j-th hydropower station, is the power generation of the j-th hydropower station at time t; is the base load power of the j-th hydropower station at time t; is the regulating power of the j-th hydropower station at time t; Q j,t and is the operation information, μ j , Cap j , P L,t , P w,i,t 、N H and N W For running parameters.

4. The method according to claim 1, It is characterized in that The determining of the target uncertainty coefficient based on the expected target, the expected value of baseload power, the constraint expression of baseload power, the constraint set and the predetermined optimization scheduling model includes: The expected target, the expected value of base load power, the constraint expression of base load power and the constraint set are brought into the calculation formula of the optimization scheduling model for solving, and the maximum value of the uncertainty coefficient is determined; The maximum value of the uncertainty coefficient is determined as the target uncertainty coefficient.

5. The method according to claim 4, It is characterized in that The calculation formula of the optimization scheduling model includes: maxθ; s.t.f≥(1-σ)f 0 ; C； Among them, θ is the uncertainty coefficient, f 0 is the expected target, σ is the expected deviation coefficient; f is the expectation; T is the scheduling period, H j,t is the generating head of the j-th hydropower station at time t; ΔI j,t is the amount of water discharged from the j-th reservoir at time t; 1 and γ 2 is a constant; N H is the number of hydropower stations; and is the Lagrange multiplier; for In vector form, is the expected value of base load power; C is the constraint set.

6. The method according to any one of claims 1 to 5, It is characterized in that The constraint set includes: power balance constraint, power generation head constraint, power generation flow constraint, reservoir water balance constraint, reservoir water storage capacity constraint, water abandonment constraint, reservoir initial storage capacity and final storage capacity constraint, environmental flow constraint, water level constraint and controllable load regulation power constraint.

7. An optimization dispatching device for a power system, It is characterized in that include: A model acquisition module, used to acquire a reservoir hydropower model, wherein the reservoir hydropower model includes operation information of at least one cascade hydropower station in at least one scheduling period and information relationship between each of the operation information, wherein the operation information includes power generation, and the scheduling period includes at least one time; A constraint expression determination module is used to determine the constraint expression of the base load power at each moment according to the operation information and information relationship of each of the cascade hydropower stations combined with the power allocation model of the regulation power and the base load power, wherein the power allocation model includes a regulation power allocation coefficient and a base load power allocation coefficient; An expectation determination module, configured to determine an expected target and an expected value of baseload power according to the constraint expressions of each baseload power combined with a constraint set; A coefficient determination module, used to determine a target uncertainty coefficient based on the expected target, the expected value of baseload power, the constraint expression of baseload power, the constraint set and the predetermined optimization scheduling model, and optimize the generation power according to the target uncertainty coefficient to achieve the optimal allocation of the regulation power and the baseload power; The step of optimizing the power generation according to the target uncertainty coefficient to achieve the optimal allocation of the regulation power and the base load power includes: Determining upper and lower limits of base load power according to the target uncertainty coefficient and the expected value of base load power; dividing the generated power into base load power and regulating power according to the upper and lower limits of base load power; Wherein, the expectation determination module includes: A deterministic model acquisition unit, used for acquiring a deterministic optimization model; A maximum expectation determination unit, configured to determine the maximum expectation by performing calculations according to the constraint expressions of each base load power, the constraint set and the calculation formula of the deterministic optimization model; an expectation determination unit, configured to determine the maximum expectation as an expected target, and determine a baseload power corresponding to the maximum expectation as a baseload power expected value; Wherein, the calculation formula of the deterministic optimization model is: stC; Among them, f is the expectation, T is the scheduling period, is the base load power of the j-th hydropower station at time t, H j,t is the generating head of the j-th hydropower station at time t; ΔI j,t is the amount of water discharged from the j-th reservoir at time t; 1 and γ 2 is a constant; N H is the number of hydropower stations; C is the constraint set; The constraint set includes at least H j,t Constraints and ΔI j,t constraint.

8. An electronic device, It is characterized in that The electronic device comprises: at least one processor; and a memory communicatively connected to the at least one processor; wherein, The memory stores a computer program executable by the at least one processor, and the computer program is executed by the at least one processor so that the at least one processor can execute the method for optimizing scheduling of a power system according to any one of claims 1 to 6.

9. A computer-readable storage medium, It is characterized in that The computer-readable storage medium stores computer instructions, and the computer instructions are used to enable a processor to implement the power system optimization scheduling method according to any one of claims 1 to 6 when executed.

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