Cascade water scenery long-term complementary dispatching plan optimization method, electronic device and computer program product

Abstract

The application relates to the technical field of new energy, and provides a long-term complementary scheduling optimization method for cascade water, wind and light, an electronic device and a computer program product. The method comprises the following steps: quantifying multiple uncertainties of new energy output and runoff to obtain a quantification result; a double-layer recursive optimization model comprising a planning stage and an adjustment stage is constructed based on the quantification result, wherein the double-layer recursive optimization model takes maximization of long-term power generation of the cascade water, wind and light as an optimization target, the planning stage is defined to generate an initial scheduling plan, the adjustment stage is used for dynamically correcting a planning output deviation based on the quantification result, and state information coupling between the two stages is realized through a transfer variable; the double-layer recursive optimization model is iteratively solved to output an optimized scheduling plan meeting a constraint condition. The long-term scheduling problem under multiple uncertainties of water, wind and light is solved, the limitation that a traditional deterministic model cannot dynamically correct a plan is broken through, and the robustness of the scheduling plan and the new energy consumption capacity are significantly improved.

Description

Technical Field

[0001] The present application relates to the field of new energy technology, and specifically to a method for optimizing a long-term complementary scheduling plan for cascaded hydropower, wind power, and solar power, an electronic device, and a computer program product. Background Art

[0002] my country boasts a high installed capacity of wind and solar power. With a high proportion of renewable energy connected to the grid, the intermittent, random, and seasonal characteristics of wind and solar power generation have become increasingly prominent. This has significantly reshaped the power structure of my country's power system and significantly increased the difficulty of accommodating renewable energy. The lack of long-term, large-scale, flexible regulation capacity has gradually become a common challenge facing my country's power grid. Exploiting the flexible regulation capabilities of various power sources has become an important means of addressing this challenge. Pending major breakthroughs in key technologies such as the safety and affordability of new energy storage, fully leveraging the resource advantages of my country's large hydropower bases, with their large scale and strong regulation capabilities, will become an important way to support the large-scale centralized consumption of wind and solar power, particularly in controlled reservoir power stations with annual or multi-year regulation capabilities.

[0003] However, how to implement long-term scheduling of large-scale river basin integrated water, wind and solar energy bases is affected by many factors such as multiple uncertainties in runoff and wind and solar power, and transmission channels. It is essentially a high-dimensional multi-stage stochastic optimization problem, which puts higher requirements on the long-term scheduling plan of cascade hydropower.

[0004] How to fully consider the above factors in formulating a long-term complementary scheduling plan for cascaded hydropower, wind power and solar power is a difficult problem that needs to be solved urgently. Summary of the Invention

[0005] In view of this, the embodiments of the present application provide a method for optimizing the long-term complementary scheduling plan of cascaded water, wind and solar power, an electronic device and a computer program product, which can effectively solve the problem of difficulty in formulating a reasonable long-term complementary scheduling plan caused by multiple uncertainties of water, wind and solar power, and deduce a long-term complementary scheduling plan for cascaded water, wind and solar power, thereby solving the problem of long-term scheduling operation of cascaded water, wind and solar power.

[0006] A first aspect of the embodiments of the present application provides a method for optimizing a long-term complementary scheduling plan for cascaded hydropower, wind power, and solar power, including:

[0007] Multiple uncertainties are quantified for renewable energy output and runoff to obtain quantitative results;

[0008] Based on the quantified results, a two-layer recursive optimization model is constructed, which includes a planning stage and an adjustment stage. The two-layer recursive optimization model takes maximizing the long-term power generation of cascade hydropower, wind power, and solar power as the optimization goal. The planning stage is defined to generate an initial scheduling plan. The adjustment stage dynamically corrects the planned output deviation based on the quantified results, and realizes state information coupling between the two stages by transferring variables.

[0009] The two-layer recursive optimization model is solved iteratively to output an optimized scheduling plan that meets the constraints.

[0010] A second aspect of the embodiments of the present application provides a device for optimizing a long-term complementary scheduling plan for cascaded hydropower, wind power, and solar power, including:

[0011] Quantification module, used to quantify multiple uncertainties of renewable energy output and runoff to obtain quantitative results;

[0012] An optimization model module is configured to construct a two-layer recursive optimization model comprising a planning stage and an adjustment stage based on the quantified results, wherein the two-layer recursive optimization model takes maximizing the long-term power generation of cascade hydropower, wind power, and solar power as an optimization objective, defines a planning stage for generating an initial scheduling plan, and an adjustment stage for dynamically correcting planned output deviations based on the quantified results, and realizes state information coupling between the two stages by transferring variables;

[0013] The solution module is used to iteratively solve the two-layer recursive optimization model and output an optimized scheduling plan that meets the constraint conditions.

[0014] A third aspect of an embodiment of the present application provides an electronic device, including a processor, a memory, and a computer program stored in the memory and runnable on the processor. When the processor executes the computer program, the electronic device implements the method for optimizing the long-term complementary scheduling plan of cascaded hydropower, wind power, and solar power as provided in the first aspect of the embodiment of the present application.

[0015] A fourth aspect of the embodiments of the present application provides a computer program product, including a computer program. When the computer program is executed, the method according to the first aspect of the embodiments of the present application is executed.

[0016] The first aspect of the embodiment of the present application provides an optimization method for the long-term complementary scheduling plan of cascade hydropower, wind power and solar power, which obtains quantitative results by quantifying multiple uncertainties of renewable energy output and runoff; constructs a two-layer recursive optimization model including a planning stage and an adjustment stage based on the quantitative results, wherein the two-layer recursive optimization model takes maximizing the long-term power generation of cascade hydropower, wind power and solar power as the optimization goal, defines the planning stage to generate an initial scheduling plan, and the adjustment stage dynamically corrects the planned output deviation based on the quantitative results, and realizes the coupling of state information between the two stages by transferring variables; iteratively solves the two-layer recursive optimization model to output an optimized scheduling plan that meets the constraints. Through uncertainty quantification, model construction, and iterative solution, the long-term scheduling problem of hydropower, wind power and solar power under multiple uncertainties is systematically solved, breaking through the limitation of traditional deterministic models that cannot dynamically correct plans, and significantly improving the robustness of the scheduling plan and the new energy absorption capacity.

[0017] It can be understood that the beneficial effects of the second to fourth aspects mentioned above can be found in the relevant description of the first aspect mentioned above, and will not be repeated here. BRIEF DESCRIPTION OF THE DRAWINGS

[0018] In order to more clearly illustrate the technical solutions in the embodiments of the present application, the following briefly introduces the drawings required for use in the embodiments or descriptions of the prior art. Obviously, the drawings described below are only some embodiments of the present application. For ordinary technicians in this field, other drawings can be obtained based on these drawings without any creative work.

[0019] Figure 1 This is a flow chart of a method for optimizing a long-term complementary scheduling plan for cascaded hydropower, wind power, and solar power provided in one embodiment of the present application;

[0020] Figure 2 It is a schematic diagram of the process of optimizing long-term output planning of hydropower, wind power and solar power, the process of real output simulation and the boundary of optimized output fluctuation;

[0021] Figure 3 This is a schematic diagram of the output deviation rate, end-of-period energy storage, and overall power generation of 1000 sets of real scheduling simulation results;

[0022] Figure 4 This is a schematic diagram of the structure of a long-term complementary scheduling plan optimization device for cascaded hydropower, wind power, and solar power provided in an embodiment of the present application;

[0023] Figure 5 It is a structural diagram of an electronic device provided in an embodiment of the present application. DETAILED DESCRIPTION

[0024] In the following description, specific details such as specific system structures and techniques are provided for purposes of illustration rather than limitation to facilitate a thorough understanding of the embodiments of the present application. However, it will be apparent to those skilled in the art that the present application may be implemented in other embodiments without these specific details. In other cases, detailed descriptions of well-known systems, devices, circuits, and methods are omitted to avoid obscuring the description of the present application with unnecessary detail.

[0025] It should be understood that when used in the present specification and the appended claims, the term "comprising" indicates the presence of described features, integers, steps, operations, elements and / or components, but does not preclude the presence or addition of one or more other features, integers, steps, operations, elements, components and / or collections thereof.

[0026] It will also be understood that the term "and / or" used in this specification and the appended claims refers to and includes any and all possible combinations of one or more of the associated listed items.

[0027] Reference within the specification of this application to "one embodiment" or "some embodiments" means that a particular feature, structure, or characteristic described in connection with the embodiment is included in at least one embodiment of the application. The appearances of the phrase "in one embodiment" or "in some embodiments" in various places within specifications are not necessarily all referring to the same embodiment, however, are meant to signify that "one or more, but not all embodiments" of the application so described are contemplated to develop the application. The terms "including," "comprising," "having," and variations thereof, are meant to encompass the items listed thereafter and equivalents thereof as well as additional items.

[0028] As Figure 1 shown, the step S101 to S103 of the long-term complementary scheduling plan optimization method for cascade water, wind and light provided by the embodiments of the application includes the following steps:

[0029] Step S101, multiple uncertainty quantification is performed on new energy output and runoff to obtain a quantification result.

[0030] Step S102, a double-layer recursive optimization model including a planning stage and an adjustment stage is constructed based on the quantification result, wherein the double-layer recursive optimization model takes the maximization of long-term power generation of cascade water, wind and light as an optimization objective, defines the planning stage to generate an initial scheduling plan, the adjustment stage dynamically corrects the planned output deviation based on the quantification result, and realizes the coupling of state information between the two stages through a transfer variable.

[0031] Step S103, the double-layer recursive optimization model is iteratively solved to output an optimized scheduling plan meeting the constraint condition.

[0032] The embodiments of the application perform multiple uncertainty quantification on new energy output and runoff to obtain a quantification result, construct a double-layer recursive optimization model including a planning stage and an adjustment stage based on the quantification result, wherein the double-layer recursive optimization model takes the maximization of long-term power generation of cascade water, wind and light as an optimization objective, defines the planning stage to generate an initial scheduling plan, the adjustment stage dynamically corrects the planned output deviation based on the quantification result, and realizes the coupling of state information between the two stages through a transfer variable, and iteratively solves the double-layer recursive optimization model to output an optimized scheduling plan meeting the constraint condition. Through uncertainty quantification, model construction and iterative solution, the long-term scheduling problem under the multiple uncertainty of water, wind and light is systematically solved, the limitation that the traditional deterministic model cannot dynamically correct the plan is broken through, and the robustness and new energy consumption capacity of the scheduling plan are significantly improved.

[0033] In one embodiment, multiple uncertainty quantification is performed on new energy output and runoff to obtain a quantification result, including:

[0034] Step S201, based on the ARMA model, the new energy output data is subjected to stationarity test and parameter calibration, and new energy output deviation white noise is generated.

[0035] In application, new energy historical data is collected, 8760 long sequence data is processed into monthly scale long sequence data, and ADF test is used to test the stationarity of monthly scale long sequence data; autocorrelation function is used to calculate the AR order p, partial autocorrelation function is used to determine the order q of MA, and the parameters of the ARMA model corresponding to the order p and q are deduced, and the specific model can be specifically expressed as the following equation:

[0036]

[0037] Among them, represents the AR term, represents the white noise term, is a constant term, is new energy output.

[0038] Step S202, based on the Markov chain, the historical runoff deviation rate is subjected to state division, and runoff white noise is obtained.

[0039] Step S203, the new energy output deviation white noise and the runoff white noise are coupled through Cartesian product, a multiple uncertainty joint probability distribution for representing the multiple uncertainty state transition process of new energy output and runoff is generated, and is output as a quantitative result.

[0040] The embodiment of the application uses ARMA and Markov chain to respectively quantify new energy and runoff uncertainty, and generates a joint probability distribution through Cartesian product coupling, realizes multiple source uncertainty coupling modeling for the first time, avoids error accumulation caused by traditional method of separate processing, and provides high-precision input for optimization model.

[0041] In one embodiment, based on the Markov chain, the historical runoff deviation rate is subjected to state division, and runoff white noise is obtained, including:

[0042] Step S2021, the deviation rate of each month of the historical runoff data from the multi-year average value is divided into a plurality of continuous intervals, and each interval corresponds to a discrete state;

[0043] Step S2022, based on the division interval, the state transition frequency between adjacent monthly stages is counted, and a state transition probability matrix of the Markov chain is constructed;

[0044] Step S2023, according to the state transition probability matrix, a runoff white noise sequence representing runoff uncertainty is generated through random simulation.

[0045] ​In the application, the embodiment of the present application collects historical runoff data, calculates the deviation between the mean runoff value and the historical value for each month, constructs a historical deviation data set, and then deduces the deviation rate of each month as the basis for dividing the Markov state; each Markov state is associated with an integer, and the specific association relationship is 0-25% interval, 25%-50% interval, 50%-75% interval, 75%-100% interval corresponds to 1, 2, 3, and 4 respectively, that is, based on the month as the scale, there are 12 stages in total. Except for the first stage, each stage has 4 states, for a total of 45 state nodes; based on the Markov states of the above-mentioned stages, the state transition probability matrix of adjacent stages is calculated.

[0046] In the application, the Cartesian product calculation method is used to couple the runoff white noise and the renewable energy output deviation white noise, and the multiple uncertainty state transition process of renewable energy output and runoff is derived, thereby quantifying its uncertainty.

[0047] The embodiment of the present application is based on the state division and transition probability matrix of the Markov chain, discretizes the continuous runoff deviation into a finite state space, effectively captures the time-dependent characteristics of the runoff, is more adaptable to the long-term fluctuation law of the runoff than the traditional statistical model, and enhances the reliability of the runoff prediction.

[0048] In one embodiment, the objective function of the two-level recursive optimization model is:

[0049] ;

[0050] Among them, t and m are the indexes of time period and hydropower station respectively; T and M are the sets of time period and hydropower station respectively; represents the output of hydropower station m in stage 0 and period t; represents the abandoned electricity generated by hydropower station m abandoning water in stage 0; represents the power curtailment caused by hydropower station m abandoning water in stage s; represents the output of renewable energy in period t during stage 0; Indicates the time corresponding to time period t; represents the power abandonment penalty coefficient; Indicates the output deviation between the adjustment stage and the planning stage of the comprehensive output of water, wind and solar power; Represents the expected function.

[0051] In the application, the above stochastic optimization model is constructed with the maximum long-term power generation of cascade hydropower, wind power and solar power as the optimization goal. The model consists of 13 stages, the 0th stage is the planning stage, and the remaining 12 stages are plan adjustment stages.

[0052] In one embodiment, the output deviation between the hydropower, wind power and solar power combined output adjustment phase and the planned phase Calculated by the following formula:

[0053] ;

[0054] ;

[0055] in, Indicates the fluctuation range of output deviation allowed between the adjustment stage and the planning stage; represents the output of hydropower station m in the sth stage of the adjustment phase; It represents the output of new energy in the sth stage of the adjustment phase.

[0056] In one embodiment, the constraints of the two-level recursive optimization model include:

[0057] Planning stage constraints and adjustment stage constraints;

[0058] The constraints in the planning stage and the adjustment stage include water balance constraints, hydropower and new energy output constraints, water abandonment constraints, transmission channel constraints and boundary constraints.

[0059] In the application, the planning stage constraints are as follows:

[0060] Water balance constraints:

[0061] ;

[0062] ;

[0063] ;

[0064] in, , They represent the storage capacity of power station m at time t and time t+1 in the sth stage respectively, Indicates the inflow flow, Indicates outbound flow, Indicates the evaporation rate, represents the power generation flow, It means discarding water. Indicates natural storage, Indicates the outflow from the upstream power station.

[0065] Hydropower and new energy output constraints:

[0066] ;

[0067] ;

[0068] in, Indicates water consumption rate; Indicates new energy output; represents the output of the hydropower station in the tth period of the planning stage s; It indicates the output of new energy in the tth period of the planning stage s.

[0069] Water abandonment constraints:

[0070] ;

[0071] in, Indicates power curtailment caused by water curtailment.

[0072] Outbound channel constraints:

[0073] ;

[0074] in, , They represent the minimum output limit and channel capacity limit of the channel respectively.

[0075] Boundary constraints:

[0076] ;

[0077] ;

[0078] ;

[0079] ;

[0080] ;

[0081] ;

[0082] in, and Respectively represent the lower and upper limits of power generation flow; Represent the minimum flow requirements for navigation, agricultural water use, and water supply respectively; Indicates the upper limit of outbound flow; , Respectively represent the lower and upper limits of storage capacity; Respectively represent the initial and final storage capacity; and They represent the lower and upper limits of hydropower output respectively.

[0083] In the application, the constraints in the adjustment phase are as follows:

[0084] The adjustment phase consists of stages 1-12. Its water balance constraints, hydropower and renewable energy output constraints, water abandonment constraints, transmission channel constraints, and boundary constraints are consistent with those in the planning phase. The main difference is that runoff and renewable energy output are random variables. The specific constraints are as follows:

[0085] ;

[0086] ;

[0087] in, is the runoff random variable; is the random disturbance of runoff; is a new energy random variable; It is a random disturbance of new energy; is the runoff of hydropower station m at stage s; It is the output of new energy in the sth stage of the adjustment phase.

[0088] Therefore, the output deviation between the adjustment stage and the planning stage can be further expressed as:

[0089] ;

[0090] .

[0091] In one embodiment, iteratively solving a two-level recursive optimization model to output an optimized scheduling plan that satisfies the constraints includes:

[0092] Step S301: Decompose the two-layer recursive optimization model into a planning stage and multiple adjustment stage sub-problems.

[0093] In application, decomposing the two-level recursive optimization model into planning stage and multiple adjustment stage sub-problems includes defining the model essence in the two-level recursive optimization model as a multi-stage stochastic programming problem. Specifically:

[0094] The two-level recursive optimization model is defined as the following equation:

[0095] ;

[0096] ;

[0097] ;

[0098] ;

[0099] in, Represents state variables; represent the control variables and transfer variables in the planning stage respectively; represents the benefit function of the planning stage; represents the state variables and control variables in the adjustment stage; represents the function related to the end-of-period energy storage; 、 、 and Represents the matrix related to the corresponding state variables and control variables; represents the feasible solution set of stage s; represents the random variable associated with the white noise of natural runoff and wind and solar power output; represents the white noise of natural runoff and wind and solar output; is a unified state variable; represents the benefit function; represents the matrix coefficients corresponding to the state variables of the sth stage; represents the matrix coefficients corresponding to the state variables of the s-1th stage; represents the matrix coefficients corresponding to the control variables; represents the matrix coefficients corresponding to the transferred variables; represents the state variable of the s-1th stage; represents the matrix coefficients corresponding to the control variables in the planning stage; represents the matrix coefficients corresponding to the transfer variables in the planning stage; represents the random variable in the planning stage.

[0100] The above problem is essentially a multi-stage stochastic programming problem. However, in addition to the state variables and control variables, the above model has transfer variables that need to be transferred from the planning stage to the adjustment stage. It is difficult to directly convert the Bellman equation into an SDDP model for solution. In order to use an improved SDDP method for efficient solution, the specific solution steps include steps S302 to S305:

[0101] Step S302: Establish the association relationship between the state variables and the control variables between stages through dynamic programming recursive equations.

[0102] In the application, based on the dynamic programming Bellman equation, the multi-stage stochastic programming problem is decomposed into a two-level recursive problem:

[0103] ;

[0104] ;

[0105] in, represents the expected value function of stage 1; represents the value function of the sth stage; represents the expected value function of the s+1th stage.

[0106] Step S303: introduce auxiliary state variables to replace transfer variables, establish a variable transfer relationship between the planning stage and the adjustment stage, and convert the multi-stage optimization problem into a dynamic programming problem in a unified state space.

[0107] In the application, introduce auxiliary state variables Instead of passing variables, the above problem can be further expressed as:

[0108] ;

[0109] In the application, further introduce unified state variables Update the value function as follows:

[0110] ;

[0111] in, represents the transfer variable of the s-1th stage; represents the value function of the sth stage after the introduction of unified state variables; represents the value function of the sth stage before the introduction of the unified state variable; represents the benefit function of the sth stage after the introduction of unified state variables; represents the value function of the sth stage before the introduction of the unified state variable; represents the matrix coefficient corresponding to the unified state variable introduced in the sth stage; represents the matrix coefficient corresponding to the unified state variable introduced in the s-1th stage; represents the matrix coefficients corresponding to the control variables; Represents the unified state variable corresponding to the s-1th stage.

[0112] Step S304: Duality theory is introduced into the nonlinear constraints related to state transition in the two-level recursive optimization model, and the constraints are relaxed to the objective function through Lagrange multipliers to obtain a mixed integer linear programming model.

[0113] In the application, define , then the adjustment phase can be further expressed as:

[0114] ;

[0115] The above model only contains state variables and control variables, and the final model is converted to:

[0116] ;

[0117] in, represents the expected value function after the introduction of unified state variables; Represents the benefit function after the introduction of unified state variables; represents the planning stage control variables after the introduction of unified state variables; represents the matrix coefficients corresponding to the unified state variables; Represents the matrix coefficients corresponding to the control variables in the planning stage after the introduction of unified state variables.

[0118] In the application, the duality theory is introduced for the constraints related to state transition, including the output of new energy, storage capacity and auxiliary state variables, which can be specifically expressed as follows:

[0119] ;

[0120] ;

[0121] ;

[0122] ;

[0123] ;

[0124] ;

[0125] in, 、 、 、 They represent the dual variables of the corresponding state variables respectively; is the AR term coefficient; Represents the MA coefficient, specifically ; is the white noise of renewable energy output in period t-1; is the storage capacity of hydropower station m at stage s; is the storage capacity of hydropower station m at stage s-1; is the inflow of hydropower station m at stage s; is the outflow of hydropower station m at stage s; is the evaporation amount of hydropower station m in the sth stage.

[0126] In the application, the deviation constraint strategy for the simulation planning stage is as follows:

[0127] ;

[0128] ;

[0129] ;

[0130] in, Output auxiliary variable indicating the deviation between the adjusted output and the planned output;

[0131] In one embodiment, the relaxed objective function is:

[0132] ;

[0133] Among them, t and m are the indexes of time period and hydropower station respectively; T and M are the sets of time period and hydropower station respectively; represents the output of hydropower station m in stage 0 and period t; represents the abandoned electricity generated by hydropower station m abandoning water in stage 0; represents the power curtailment caused by hydropower station m abandoning water in stage s; represents the output of renewable energy in period t during stage 0; Indicates the time corresponding to time period t; represents the power abandonment penalty coefficient; represents the expected function; Output auxiliary variable representing the deviation between the adjusted output and the planned output.

[0134] The embodiment of the present application uses auxiliary variables The nonlinear deviation penalty term is transformed into a linearly separable form, and the complex objective function is efficiently solved through mixed integer programming.

[0135] Step S305: Iteratively solve the mixed integer linear programming model and output the optimal scheduling plan.

[0136] The embodiments of the present application embed the complex coupling relationship of transfer variables into the dynamic programming framework through variable expansion and duality theory, solving the technical bottleneck of traditional SDDP that cannot handle cross-stage transfer variables, and significantly improving the solution efficiency and convergence stability of high-dimensional optimization problems.

[0137] It should be understood that the size of the serial numbers of the steps in the above embodiments does not mean the order of execution. The execution order of each process should be determined by its function and internal logic, and should not constitute any limitation on the implementation process of the embodiments of this application.

[0138] The method of the embodiment of the present application is described as follows using a specific project as an example:

[0139] The effectiveness of the proposed method was verified using a cascade hydropower, wind, and solar power complex in southwest my country as an example. This complex comprises two hydropower stations, A and B, with a total installed capacity of 10,050 MW. Existing wind and solar power stations total nearly 4,000 MW, and an external transmission channel capacity of 10,000 MW. Based on this engineering context, the proposed method was applied to verify its effectiveness and feasibility. An improved SDDP-based stochastic optimization algorithm for the cascade hydropower, wind, and solar power system was used for efficient solution (i.e., step S103). The algorithm was developed using JuMP in Julia and solved using Gurobi 10.0. All programs were run on a personal computer equipped with an Intel Core i7-9750H CPU with a 2.6 GHz CPU and 16.0 GB of RAM. Iterations were terminated after 6,000 iterations.

[0140] The results are analyzed as follows:

[0141] In order to compare the effectiveness of the proposed hydro-wind-solar complementary scheduling model (IPA-SDDP), the hydro-wind-solar deterministic scheduling model (DOM) was set as a comparison model, and the scheduling plans derived from the two models were applied to random simulations to verify the advantages and disadvantages of the scheduling plans. The simulation scenarios were set to 1000 groups, that is, 1000 groups of runoff and renewable energy output scenarios were randomly generated as scheduling plan inputs for simulation. Figure 2 The figure shows the optimized long-term output planning process of hydropower, wind power and solar power, the actual output simulation process and the optimized output fluctuation boundary. It can be seen from the figure that all output simulation processes of the proposed model IPA-SDDP are included in the optimized output fluctuation boundary, while DOM exceeds the allowable output fluctuation boundary from March to December. The simulated output process deviates greatly from the planned output process. This shows that the proposed model can give full play to the long-term flexibility of hydropower, improve the feasibility of long-term scheduling plan, and promote the consumption of new energy. Figure 3 The output deviation rate, end-of-period energy storage, and overall power generation of 1,000 sets of real scheduling simulation results are shown respectively. Figure 3 (a) It can be seen that the monthly output deviation rate of the proposed model IPA-SDDP is generally lower than that of the DOM model, and is less than 10%; Figure 3 (b) shows the results of 1000 scheduling simulations at the end of December. It can be seen from the figure that IPA-SDDP is significantly higher than the DOM model, with an average of 3.087 billion m 3 , the latter is only 2.409 billion m 3 ; Figure 3 (c) is the average power generation of all simulation results in each month. The proposed model IPA-SDDP is slightly lower than DOM, mainly because DOM does not consider the uncertainty of runoff and renewable energy output.

[0142] Overall, the model can derive a long-term complementary scheduling plan for water, wind and solar power that takes into account the multiple uncertainties of water, wind and solar power, and can fully tap the temporal and spatial complementarity potential of water, wind and solar power, improve the feasibility of cascade hydropower generation plans, and support the large-scale and efficient consumption of new energy.

[0143] The present application also provides an apparatus for optimizing a long-term complementary scheduling plan for cascaded hydropower, wind-solar power systems, configured to execute the steps in the aforementioned method for optimizing a long-term complementary scheduling plan for cascaded hydropower, wind-solar power systems. The apparatus can be a virtual appliance within an electronic device, executed by a processor within the electronic device, or it can be the electronic device itself.

[0144] like Figure 4As shown, the device 100 for optimizing the long-term complementary scheduling plan of cascade hydropower, wind power, and solar power provided in the embodiment of the present application includes:

[0145] Quantification module 101, used to quantify multiple uncertainties of renewable energy output and runoff to obtain quantitative results;

[0146] Optimization model module 102 is used to construct a two-level recursive optimization model consisting of a planning phase and an adjustment phase based on the quantification results. The two-level recursive optimization model takes maximizing the long-term power generation of cascade hydropower, wind power, and solar power as the optimization goal. The planning phase defines the generation of an initial scheduling plan. The adjustment phase dynamically corrects planned output deviations based on the quantification results, and achieves state information coupling between the two phases by transferring variables.

[0147] The solution module 103 is used to iteratively solve the two-layer recursive optimization model and output an optimized scheduling plan that meets the constraint conditions.

[0148] In application, each module in the cascade hydropower, wind power and solar power long-term complementary scheduling plan optimization device can be a software program module, or can be implemented through different logic circuits integrated in the processor, or can be implemented through multiple distributed processors.

[0149] like Figure 5 As shown, the embodiment of the present application further provides an electronic device 200, including: at least one processor 201 ( Figure 5 Only one processor is shown in the figure), a memory 202, and a computer program 203 stored in the memory 202 and executable on at least one processor 201. When the processor 201 executes the computer program 203, the steps in the above-mentioned method embodiments are implemented.

[0150] In applications, electronic devices may include, but are not limited to, processors and memories. Those skilled in the art will appreciate that Figure 5 The electronic device is merely an example and does not limit the electronic device. The electronic device may include more or fewer components than shown in the figure, or may include a combination of certain components or different components.

[0151] In applications, a processor may be a central processing unit (CPU), other general-purpose processors, a digital signal processor (DSP), an application-specific integrated circuit (ASIC), a field-programmable gate array (FPGA), other programmable logic devices, discrete gate or transistor logic devices, discrete hardware components, etc. A general-purpose processor may be a microprocessor or any conventional processor.

[0152] In applications, in some embodiments, memory can be an internal storage unit of an electronic device, such as a hard drive or memory. In other embodiments, memory can also be an external storage device of the electronic device, such as a plug-in hard drive, a Smart Media Card (SMC), a Secure Digital (SD) card, or a flash memory card. Furthermore, memory can include both internal storage units and external storage devices. Memory is used to store operating systems, application programs, boot loaders, data, and other programs, such as computer program code. Memory can also be used to temporarily store data that has been output or is about to be output.

[0153] It should be noted that the information interaction, execution process, etc. between the above-mentioned devices / units are based on the same concept as the method embodiment of this application. Their specific functions and technical effects can be found in the method embodiment section and will not be repeated here.

[0154] Those skilled in the art can clearly understand that, for the convenience and brevity of description, only the division of the above-mentioned functional units and modules is used as an example for illustration. In actual applications, the above-mentioned functions can be distributed and completed by different functional units and modules as needed, that is, the internal structure of the device can be divided into different functional units or modules to complete all or part of the functions described above. The functional units and modules in the embodiment can be integrated into one processing unit, or each unit can exist physically alone, or two or more units can be integrated into one unit. The above-mentioned integrated unit can be implemented in the form of hardware or in the form of software functional units. In addition, the specific names of the functional units and modules are only for the convenience of distinguishing each other, and are not used to limit the scope of protection of this application. The specific working process of the units and modules in the above-mentioned system can refer to the corresponding process in the aforementioned method embodiment, and will not be repeated here.

[0155] An embodiment of the present application further provides a computer-readable storage medium, which stores a computer program. When the computer program is executed by a processor, the steps in the above-mentioned method embodiments can be implemented.

[0156] An embodiment of the present application provides a computer program product, including a computer program. When the computer program product runs on an electronic device, the electronic device can implement the steps in the above-mentioned various method embodiments when executing the computer program product.

[0157] If the integrated unit is implemented as a software functional unit and sold or used as a standalone product, it can be stored in a computer-readable storage medium. Based on this understanding, the present application can implement all or part of the process steps in the above-mentioned method embodiments by using a computer program to instruct the relevant hardware. The computer program can be stored in a computer-readable storage medium. When executed by a processor, the computer program can implement the steps of each of the above-mentioned method embodiments. The computer program includes computer program code, which can be in source code form, object code form, executable file, or some intermediate form. Computer-readable media can include at least: any entity or device capable of carrying computer program code to a device / electronic device, recording media, computer memory, read-only memory (ROM), random access memory (RAM), electric carrier signals, telecommunication signals, and software distribution media. Examples include USB flash drives, removable hard drives, magnetic disks, or optical disks. In some jurisdictions, based on legislation and patent practice, computer-readable media cannot be electric carrier signals or telecommunication signals.

[0158] In the above embodiments, the description of each embodiment has its own focus. For parts that are not described or recorded in detail in a certain embodiment, reference can be made to the relevant description of other embodiments.

[0159] Those skilled in the art will appreciate that the units and algorithm steps of each example described in conjunction with the embodiments disclosed herein can be implemented in electronic hardware, or a combination of computer software and electronic hardware. Whether these functions are performed in hardware or software depends on the specific application and design constraints of the technical solution. Professional and technical personnel can use different methods to implement the described functions for each specific application, but such implementation should not be considered beyond the scope of this application.

[0160] In the embodiments provided in this application, it should be understood that the disclosed devices and methods can be implemented in other ways. For example, the device embodiments described above are merely schematic. For example, the division of modules or units is only a logical function division. In actual implementation, there may be other division methods, such as multiple units or components can be combined or integrated into another system, or some features can be ignored or not executed. Another point is that the mutual coupling or direct coupling or communication connection shown or discussed can be through some interfaces, indirect coupling or communication connection of devices or units, which can be electrical, mechanical or other forms.

[0161] Units described as separate components may or may not be physically separate, and components shown as units may or may not be physical units, that is, they may be located in one place or distributed across multiple network units. Some or all of these units may be selected to achieve the purpose of this embodiment according to actual needs.

[0162] The above embodiments are only used to illustrate the technical solutions of the present application, rather than to limit them. Although the present application has been described in detail with reference to the aforementioned embodiments, those skilled in the art should understand that they can still modify the technical solutions described in the aforementioned embodiments, or make equivalent replacements for some of the technical features therein. These modifications or replacements do not deviate the essence of the corresponding technical solutions from the spirit and scope of the technical solutions of the various embodiments of the present application, and should all be included in the scope of protection of the present application.

Claims

1. A method for optimizing the long-term complementary scheduling plan of cascade hydropower, wind power and solar power, characterized in that: include: Multiple uncertainties are quantified for renewable energy output and runoff to obtain quantitative results; Based on the quantified results, a two-layer recursive optimization model is constructed, which includes a planning stage and an adjustment stage. The two-layer recursive optimization model takes maximizing the long-term power generation of cascade hydropower, wind power, and solar power as the optimization goal. The planning stage is defined to generate an initial scheduling plan. The adjustment stage dynamically corrects the planned output deviation based on the quantified results, and realizes state information coupling between the two stages by transferring variables. Iteratively solving the two-layer recursive optimization model and outputting an optimized scheduling plan that meets the constraints; The multiple uncertainties of renewable energy output and runoff are quantified to obtain quantitative results, including: Based on the ARMA model, the stationarity test and parameter calibration of the renewable energy output data are carried out to generate the renewable energy output deviation white noise; The historical runoff deviation rate is divided into states based on the Markov chain to obtain the runoff white noise; The new energy output deviation white noise and the runoff white noise are coupled through a Cartesian product to generate a multiple uncertainty joint probability distribution for characterizing the multiple uncertainty state transition process of new energy output and runoff, which is output as a quantization result.

2. The method for optimizing the long-term complementary scheduling plan of cascaded hydropower, wind power and solar power as claimed in claim 1, characterized in that: The Markov chain-based state division of the historical runoff deviation rate to obtain runoff white noise includes: The deviation rate between the measured values ​​of each month and the multi-year mean in the historical runoff data is divided into multiple continuous intervals, each interval corresponds to a discrete state; Based on the divided intervals, the state transition frequency between adjacent monthly stages is counted to construct the state transition probability matrix of the Markov chain; According to the state transition probability matrix, a runoff white noise sequence representing runoff uncertainty is generated through random simulation.

3. The method for optimizing the long-term complementary scheduling plan of cascade hydropower, wind power and solar power as claimed in claim 1, characterized in that: The objective function of the two-layer recursive optimization model is: Among them, t and m are the indexes of time period and hydropower station respectively; T and M are the sets of time period and hydropower station respectively; represents the output of hydropower station m in stage 0 and period t; represents the abandoned electricity generated by hydropower station m abandoning water in stage 0; represents the power curtailment caused by hydropower station m abandoning water in stage s; represents the output of renewable energy in period t of stage 0; Δt represents the time corresponding to period t; φ represents the power curtailment penalty coefficient; Indicates the output deviation between the adjustment stage and the planning stage of the comprehensive output of water, wind and solar power; Represents the expected function.

4. The method for optimizing the long-term complementary scheduling plan of cascade hydropower, wind power and solar power as claimed in claim 3, characterized in that: The output deviation between the adjustment stage and the planning stage of the comprehensive output of water, wind and solar power Calculated by the following formula: Among them, OVR represents the fluctuation range of output deviation allowed between the adjustment stage and the planning stage; represents the output of hydropower station m in the sth stage of the adjustment phase; It represents the output of new energy in the sth stage of the adjustment phase.

5. The method for optimizing the long-term complementary scheduling plan of cascade hydropower, wind power and solar power as claimed in claim 1, characterized in that: The constraints of the two-layer recursive optimization model include: Planning stage constraints and adjustment stage constraints; The constraints in the planning stage and the constraints in the adjustment stage both include water balance constraints, hydropower and new energy output constraints, water abandonment constraints, transmission channel constraints and boundary constraints.

6. The method for optimizing the long-term complementary scheduling plan of cascade hydropower, wind power and solar power as claimed in claim 1, characterized in that: The iteratively solving the two-layer recursive optimization model and outputting an optimized scheduling plan that satisfies the constraints includes: Decomposing the two-level recursive optimization model into a planning phase and multiple adjustment phase sub-problems; The relationship between state variables and control variables between stages is established through stochastic dynamic programming recursive equations; Auxiliary state variables are introduced to replace transfer variables, and the variable transfer relationship between the planning stage and the adjustment stage is established, so that the multi-stage optimization problem is converted into a stochastic dynamic programming problem in a unified state space. The duality theory is introduced into the nonlinear constraints related to the state transition in the two-level recursive optimization model, and the constraints are relaxed to the objective function through Lagrange multipliers to obtain a mixed integer linear programming model; The mixed integer linear programming model is iteratively solved to output an optimal scheduling plan.

7. The method for optimizing the long-term complementary scheduling plan of cascade hydropower, wind power and solar power as claimed in claim 6, characterized in that: The relaxed objective function is: Among them, t and m are the indexes of time period and hydropower station respectively; T and M are the sets of time period and hydropower station respectively; represents the output of hydropower station m in stage 0 and period t; represents the abandoned electricity generated by hydropower station m abandoning water in stage 0; represents the power curtailment caused by hydropower station m abandoning water in stage s; represents the output of renewable energy in period t of stage 0; Δt represents the time corresponding to period t; φ represents the power curtailment penalty coefficient; represents the expected function; Output auxiliary variable representing the deviation between the adjusted output and the planned output.

8. An electronic device, characterized in that: The electronic device comprises a processor, a memory, and a computer program stored in the memory and executable on the processor, wherein when the processor executes the computer program, the electronic device implements the method according to any one of claims 1 to 7.

9. A computer program product, characterized in that The invention comprises a computer program, which, when being executed, enables the method according to any one of claims 1 to 7 to be performed.

Images