A water-light complementary power generation scheduling chart compiling method considering risk and benefit

Abstract

The application provides a water-light complementary power generation scheduling method considering risk and benefit, and belongs to the technical field of water-light complementary technology.The method comprises the following steps: obtaining input and water level boundary conditions of a water-light complementary power generation system day-ahead generation plan compilation model; constructing a water-light complementary power generation system day-ahead generation plan compilation model based on the risk of power generation abandonment and load loss of the water-light complementary power generation system; solving the water-light complementary power generation system day-ahead generation plan compilation model, extracting a risk rate function of the water-light complementary power generation system based on the solving result; embedding the risk rate function into a water-light complementary medium and long term scheduling graph optimization model, and solving the optimal scheduling graph of the water-light complementary power generation system.The method considers the risk of the water-light complementary power generation system in short-term operation induced by the uncertainty of photovoltaic output prediction, the long-term risk resistance of the water-light complementary power generation system, and the long-term complementarity of water and light resources, so that the scheduling graph compiled by the method considers risk and benefit.

Description

Technical Field

[0001] This invention relates to the field of hydro-solar hybrid technology, specifically to a method for compiling a hydro-solar hybrid power generation scheduling diagram that balances risk and benefit, and a computer-readable storage medium. Background Technology

[0002] With the escalating global energy crisis, deteriorating ecological environment, and increasing impacts of climate change, accelerating the development and utilization of renewable energy to achieve a green and low-carbon transformation of the energy system is a major strategic measure to ensure future energy security and address global climate change. Due to the strong randomness, intermittency, and volatility of photovoltaic power generation, and its lack of adjustability, direct grid connection poses a threat to the safe and stable operation of the power grid. Complementing photovoltaic power generation with flexible and rapidly adjustable hydropower is an effective way to solve its grid connection problem. However, due to the significant uncertainty in photovoltaic output forecasting, the operation of the hydro-photovoltaic complementary power generation system may face the risks of power curtailment and load shedding. How to formulate hydro-photovoltaic complementary power generation dispatch rules that balance risks and benefits has become a hot topic in the field of hydro-photovoltaic complementary dispatch.

[0003] Existing research on multi-energy complementary scheduling has focused on two main approaches. First, it constructs a day-ahead power generation planning model for hydro-solar complementary power generation systems, considering the uncertainties in the hydro-solar forecasting process and aiming to optimize peak-shaving capacity and minimize the risk rate of the complementary system. Second, it constructs a medium- to long-term optimal scheduling model for hydro-solar complementary systems, with the primary objective of maximizing power generation efficiency, and solves for the optimal scheduling rules (scheduling function / scheduling diagram). However, the results of medium- to long-term scheduling serve as boundary conditions for short-term scheduling. Medium- to long-term scheduling rules that solely focus on maximizing efficiency often result in poor hydropower regulation capacity in certain scenarios, leading to higher operational risks. Therefore, how to consider short-term operational risks when constructing a medium- to long-term optimal scheduling model for hydro-solar complementary systems, improve the system's resilience in the medium to long term, and formulate a hydro-solar complementary scheduling diagram that balances risk and efficiency is a pressing issue for practical operation and scheduling. Summary of the Invention

[0004] The purpose of this invention is to provide a method for compiling a hydro-solar hybrid power generation dispatch diagram that takes into account both risks and benefits, as well as a computer-readable storage medium, so as to at least solve the problems in the existing hydro-solar hybrid medium- and long-term optimization dispatch models that do not consider short-term operational risks and medium- and long-term risk resistance capabilities.

[0005] To achieve the above objectives, the first aspect of the present invention provides a method for compiling a hydro-solar hybrid power generation dispatching diagram that balances risk and benefit, comprising:

[0006] Determine the input and water level boundary conditions for the day-ahead power generation planning model of the hydro-solar hybrid power generation system;

[0007] Based on the risks of power curtailment and load loss in hydro-solar hybrid power generation systems, a day-ahead power generation planning model for hydro-solar hybrid power generation systems is constructed.

[0008] Solve the day-ahead power generation planning model of the hydro-solar hybrid power generation system, and extract the risk rate function of the hydro-solar hybrid power generation system based on the solution results;

[0009] By embedding the risk rate function into the medium- and long-term scheduling diagram optimization model of the hydro-solar hybrid power generation system, the optimal scheduling diagram of the hydro-solar hybrid power generation system is solved.

[0010] This method considers the risks of short-term operation such as power curtailment and load shedding that occur during the operation of the hydro-solar hybrid power generation system, and incorporates these risks into the optimization model of the medium- and long-term dispatch diagram of the hydro-solar hybrid system, resulting in a new optimization model of the medium- and long-term dispatch diagram of the hydro-solar hybrid system. This ensures that the dispatch diagram compiled by this method can take into account both benefits and long-term risk resistance.

[0011] Optionally, the inputs for obtaining the day-ahead power generation plan preparation model of the hydro-solar hybrid power generation system include:

[0012] The predicted photovoltaic output process, runoff process, and grid load process for the next day are used as inputs for the day-ahead power generation planning model of the hydro-solar hybrid power generation system.

[0013] Because there is uncertainty in photovoltaic power output prediction, a distribution function is used to fit the prediction error during the prediction of the next day's photovoltaic power output. The Akaike Information Criterion is used to test the goodness of fit and determine the probability distribution function type and parameters that the prediction error follows. Based on this probability distribution function, the Latin hypercube sampling method is used to generate multiple photovoltaic power output prediction scenarios.

[0014] Optionally, the water level boundary conditions for the day-ahead power generation planning model of the hydro-solar hybrid power generation system are determined, including:

[0015] A deterministic optimization scheduling model for a hydro-solar hybrid power generation system is constructed, and the model is solved to obtain the daily water level process in a typical resource year. The daily water level process in a typical resource year is then used as the water level boundary condition for the day-ahead power generation plan preparation model of the hydro-solar hybrid power generation system.

[0016] Optionally, the construction of the deterministic optimization scheduling model for the hydro-solar hybrid power generation system includes:

[0017] Three typical resource years—abundant, average, and dry—are selected. Within each typical resource year, the scheduling period is defined as a day. A deterministic optimization scheduling model for the hydro-solar hybrid power generation system is constructed with the maximum power generation and the highest power generation guarantee rate of the hydro-solar hybrid power generation system as the objective function.

[0018] Optionally, solving the deterministic optimization scheduling model of the hydro-solar hybrid power generation system includes:

[0019] The daily runoff process and daily photovoltaic output process are used as inputs to the deterministic optimal scheduling model of the hydro-solar hybrid power generation system, and the water level at the end of each time period is used as the decision variable of the deterministic optimal scheduling model of the hydro-solar hybrid power generation system. The dynamic programming algorithm is used to solve the problem.

[0020] In constructing and solving the deterministic optimization scheduling model of the hydro-solar hybrid power generation system in this method, considering the short-term operational risks, the scheduling period is based on the day, and the model also uses the daily runoff process and the daily photovoltaic output process as inputs.

[0021] Optionally, the method for solving the day-ahead power generation planning model of the hydro-solar hybrid power generation system, and extracting the risk rate function of the hydro-solar hybrid power generation system based on the solution results, includes:

[0022] Under all daily scenarios encompassing three typical resource years (abundant, average, and dry), the model inputs are runoff process, grid load process, and photovoltaic output process. The time period is in hours, and the output of the hydro-solar hybrid power generation system in each time period is the decision variable. The multi-objective cuckoo algorithm is used to solve the day-ahead power generation planning model of the hydro-solar hybrid power generation system. The calculation results are statistically generated into a short-term operation dataset of the hydro-solar hybrid power generation system. The risk rate function of the hydro-solar hybrid power generation system is extracted from this dataset.

[0023] Optionally, the risk function can be extracted in the following ways:

[0024] A scatter plot was drawn with the hydropower output sequence as the independent variable and the comprehensive risk rate sequence as the dependent variable.

[0025] After excluding outliers in the scatter plot, fit the upper and lower envelopes of the scatter plot respectively;

[0026] The median line of the upper and lower envelopes is taken as the risk rate fitting curve. After extracting the coordinate point sequence corresponding to the risk rate fitting curve, the risk rate function of the hydro-solar hybrid power generation system is obtained by fitting using the multivariate nonlinear regression method.

[0027] Optionally, the step of nesting the risk rate function into the medium-to-long-term scheduling diagram optimization model for hydro-solar hybrid power generation to solve for the optimal scheduling diagram of the hydro-solar hybrid power generation system includes:

[0028] By embedding the risk rate function into the medium- and long-term scheduling optimization model of hydro-solar hybrid power generation, and taking the month as the scheduling period, a new medium- and long-term scheduling optimization model of hydro-solar hybrid power generation is constructed that comprehensively considers benefits and risks. The monthly runoff and monthly photovoltaic output sequence are used as model inputs, and the node coordinates of the upper and lower scheduling lines and the parameters of the scheduling function are used as decision variables. The multi-objective cuckoo algorithm is used to optimize the decision variables, and the optimal form and parameters of the medium- and long-term scheduling diagram of the hydro-solar hybrid power generation system are obtained.

[0029] In constructing a new medium- and long-term scheduling optimization model for hydro-solar hybrid power generation, this method takes into account the issue of medium- and long-term risk resistance. Therefore, a risk rate function is coupled to the original model, and the monthly scheduling period is used as the model input, with monthly runoff and monthly photovoltaic power output sequences as the model input, in order to achieve medium- and long-term risk resistance.

[0030] A second aspect of the present invention provides a computer-readable storage medium storing computer instructions that, when executed on a computer, cause the computer to perform the method described in the first aspect.

[0031] This invention considers the risks of short-term operation of a hydro-solar hybrid power generation system caused by the uncertainty of photovoltaic output prediction, and couples the risks into the medium- and long-term scheduling diagram optimization model of the hydro-solar hybrid system. It can formulate a medium- and long-term scheduling diagram that takes into account both risks and benefits to guide the actual operation of the hydro-solar hybrid power generation system.

[0032] Other features and advantages of the embodiments of the present invention will be described in detail in the following detailed description section. Attached Figure Description

[0033] The accompanying drawings are provided to further illustrate embodiments of the present invention and form part of the specification. They are used together with the following detailed description to explain the embodiments of the present invention, but do not constitute a limitation thereof. In the drawings:

[0034] Figure 1 This is a flowchart of a method for compiling a hydro-solar hybrid power generation dispatching diagram that takes into account both risks and benefits, provided by one embodiment of the present invention.

[0035] Figure 2 This is a schematic diagram of the medium- and long-term scheduling of water-solar hybrid systems provided in one embodiment of the present invention. Detailed Implementation

[0036] The specific embodiments of the present invention will be described in detail below with reference to the accompanying drawings. It should be understood that the specific embodiments described herein are for illustration and explanation only and are not intended to limit the present invention.

[0037] Figure 1 This is a flowchart of a method for compiling a hydro-solar hybrid power generation dispatching diagram that balances risk and benefit, provided by one embodiment of the present invention. Figure 1 As shown, this invention provides a method for compiling a hydro-solar hybrid power generation dispatch map that balances risk and benefit. The method includes:

[0038] S1: Construct a deterministic optimization scheduling model for the hydro-solar hybrid power generation system and obtain the water level boundary conditions for the day-ahead power generation plan preparation model of the hydro-solar hybrid power generation system.

[0039] In this embodiment, the process of constructing a deterministic optimization scheduling model for a hydro-solar hybrid power generation system specifically includes:

[0040] Using frequency analysis, typical resource years with annual average power output frequencies of 25%, 50%, and 75% were selected as dry, average, and abundant resource years, respectively. Within the typical resource years, the scheduling period was set on a daily basis, and the objective functions were the maximum power generation and the highest power generation guarantee rate of the hydro-solar hybrid power generation system. A deterministic optimization scheduling model for the hydro-solar hybrid power generation system was then constructed.

[0041] After constructing a deterministic optimal scheduling model for the hydro-solar hybrid power generation system, the water level boundary conditions for the day-ahead power generation plan preparation model of the hydro-solar hybrid power generation system are obtained by solving the deterministic optimal scheduling model of the hydro-solar hybrid power generation system. The specific solution process includes:

[0042] Daily runoff process Q day Solar power output process The model input is the water level at the end of each time period, which is the model decision variable. Dynamic programming (DP) is used to solve the problem, yielding the daily water level process for each typical year, which serves as the boundary condition for the day-ahead power generation planning model. The recursive formula for DP is as follows:

[0043]

[0044] In equation (1), f t (·) and f t+1 (·) represent the expected power generation of the hydro-solar hybrid power generation system during time period t and the remaining time period, respectively; This represents the power generation of the hydro-solar hybrid power generation system when the initial water level of the reservoir in time period t is the kth discrete scenario, the water level at the end of the time period is the lth discrete scenario, and the photovoltaic output is the uth discrete scenario. This represents the power generation of the hydro-solar hybrid power generation system during the remaining time period when the initial water level of the reservoir is the l-th discrete scenario and the photovoltaic output is the v-th discrete scenario at time t+1.

[0045] S2: Predict the photovoltaic output process, runoff process, and grid load process for the next day, and use them as inputs for the day-ahead power generation planning model of the hydro-solar hybrid power generation system.

[0046] Considering the uncertainty in photovoltaic power output prediction, this embodiment employs eight different theoretical distribution functions (extreme value distribution, exponential distribution, gamma distribution, generalized extreme value distribution, log-normal distribution, normal distribution, Weibull distribution, and generalized Pareto distribution) to fit the prediction error of the photovoltaic power output process. The Akaike information criterion method is used to perform a goodness-of-fit test to determine the optimal probability distribution function type and parameters that the prediction error follows. Based on the probability distribution function type and parameters of the photovoltaic power output process prediction, the Latin hypercube sampling method is used to generate N sets of photovoltaic power output process prediction scenarios. p .

[0047]

[0048] In equation (2), N p For the set of N simulated photovoltaic power output scenarios; For the predicted photovoltaic power output process; e p The prediction error of the photovoltaic power output process is obtained by using Latin hypercube sampling.

[0049] S3: Considering the risks of power curtailment and load shedding in the hydro-solar hybrid power generation system induced by the uncertainty of the photovoltaic output prediction process, a day-ahead power generation planning model for the hydro-solar hybrid power generation system is constructed.

[0050] S4: Solve the day-ahead power generation planning model of the hydro-solar hybrid power generation system, and extract the risk rate function of the hydro-solar hybrid power generation system based on the solution results.

[0051] Specifically, in this embodiment, the solution process for the day-ahead power generation planning model of the hydro-solar hybrid power generation system includes:

[0052] In S1, under all daily scenarios for each typical year, the model inputs are runoff process, load process, and N sets of photovoltaic output process prediction scenarios. The time period is in hours, and the output of the hydro-solar hybrid power generation system in each time period is the decision variable. The multi-objective cuckoo algorithm is used to solve the day-ahead power generation planning model for the hydro-solar hybrid power generation system. The results are statistically analyzed to generate a short-term operation dataset for the hydro-solar hybrid power generation system. The risk rate function of the hydro-solar hybrid power generation system is extracted from this short-term operation dataset using the following method:

[0053] (1) Using the hydropower output sequence as the independent variable and the comprehensive risk rate sequence as the dependent variable, draw a scatter plot;

[0054] (2) After excluding outliers in the scatter plot, fit the upper and lower envelopes of the scatter plot respectively;

[0055] (3) Take the median line of the upper and lower envelope as the risk rate fitting curve, extract the corresponding coordinate point sequence of the risk rate fitting curve, and use the multivariate nonlinear regression method to fit the risk rate function of the hydro-solar hybrid power generation system.

[0056] S5: The risk rate function is nested within the medium- to long-term optimal scheduling model for hydro-solar hybrid power generation. A new model is constructed, taking a monthly scheduling period, to comprehensively consider both benefits and risks. Based on this new model, the optimal scheduling diagram for the hydro-solar hybrid power generation system is solved.

[0057] The solution process includes:

[0058] First, determine the basic form of the medium- and long-term scheduling diagram for hydro-solar hybrid systems, such as... Figure 2 As shown, the scheduling diagram is divided into three parts by two upper and lower scheduling lines, from top to bottom: the increased output zone, the standard output zone, and the reduced output zone. The decision variable for each zone, hydropower output, is a linear function of available energy AE. Available energy AE equals the current available energy of the reservoir (power generation corresponding to available reservoir capacity) plus the predicted photovoltaic power. The node coordinates (t,x) and (t,y) of the upper and lower scheduling lines, as well as the parameters (a,b) of the scheduling function, are the optimization variables for the medium- and long-term scheduling diagram optimization model of hydropower-solar hybrid power generation. To ensure that the upper and lower scheduling lines do not intersect, a penalty function method is used. The form of the penalty function is as follows:

[0059]

[0060] In equation (3), g(x) i ,y i ) represents the penalty function for node coordinates.

[0061] Then, taking the month as the scheduling period, the monthly runoff and monthly photovoltaic output sequence as model inputs, and the node coordinates (t,x) and (t,y) of the upper and lower scheduling lines and the parameters (a,b) of the scheduling function as decision variables, the multi-objective cuckoo algorithm is used to optimize the decision variables, and the optimal form and parameters of the long-term scheduling diagram of the hydro-solar complementary power generation system are obtained.

[0062] Specifically, in this embodiment, to ensure that the daily water level process for each typical resource year can be obtained through the deterministic optimization scheduling model of the hydro-solar hybrid power generation system, the objective function of the deterministic optimization scheduling model of the hydro-solar hybrid power generation system includes the following:

[0063] Objective function 1: Maximum power generation of the hydro-solar hybrid power generation system

[0064]

[0065] Objective function 2: Maximum power generation guarantee rate of the hydro-solar hybrid power generation system

[0066]

[0067] In equations (3) and (4), EP represents the total power generation during the system dispatch period; i and I are the medium- and long-term dispatch period number and the total dispatch period, respectively; P i h P i s Let ΔT be the actual average power output of hydropower and the average power output of photovoltaic power station in the i-th time period; i The dispatch period is long; ER is the system's power generation guarantee rate; #(P i h +P i s ≥P firm This represents the number of periods during the entire scheduling period where the net output of the complementary system is higher than the guaranteed output.

[0068] The constraints of the deterministic optimal scheduling model for the hydro-solar hybrid power generation system are as follows:

[0069] Constraint 1: Water Balance Constraint

[0070] V i+1 =V i, +(Q in,i -Q out,i )ΔT i (5)

[0071] Constraint 2: Storage Capacity Constraint

[0072] V min ≤V i ≤V max (6)

[0073] Constraint 3: Downflow constraint

[0074] Q out,min ≤Q out,i ≤Q out,max (7)

[0075] Constraint 4: Hydropower Station Output Constraints

[0076]

[0077] Constraint 5: Characteristic Constraints of Hydropower Stations

[0078] Z i =f zv (V i (9)

[0079] Z tail,i =f zq (Q e,i +Qs,i (10)

[0080] In equations (5) to (10): V i Q represents the reservoir capacity of the hydropower station during the i-th time period; in,i and Q out,i Inflow and outflow to the reservoir during period i; V min and V max These are the lower and upper limits of the reservoir capacity for hydropower stations, respectively; Q out,min and Q out,max These are the minimum and maximum discharge flows of the hydropower station, respectively. and These are the lower and upper limits of the hydropower station's output, respectively; Z tail,i Let be the tailwater level of the hydropower station during the i-th time period.

[0081] Specifically, in this embodiment, the objective function of the day-ahead power generation planning model for the hydro-solar hybrid power generation system is as follows:

[0082] Objective function 1: Minimum overall risk rate of power generation plan

[0083]

[0084]

[0085]

[0086] To balance the system's peak-shaving performance and overall benefits, objective functions 2 and 3 are as follows:

[0087] Objective function 2: Minimum standard deviation of residual load in the power grid

[0088]

[0089] Objective function 3: Maximum power generation of the hydro-solar hybrid power generation system

[0090]

[0091] In equations (11) to (15), R is the overall risk rate of the hydro-solar hybrid power generation system; and These represent the curtailment rate and load shedding rate for time period t under the i-th photovoltaic power output prediction scenario; ΔN t The difference between the actual output of the hydro-solar hybrid power station and the planned power generation. and These represent the hydropower and photovoltaic output for time period t under the i-th photovoltaic output prediction scenario; N plan The power generation plan for the hydro-solar hybrid power generation system; p iThe probability of scenario i occurring in photovoltaic power output prediction; std(x) represents the standard deviation of variable x; L re L and Q represent the surplus load and grid load of the power grid, respectively; k represents the output coefficient of the hydropower station; Q t ΔH represents the hydropower station's water diversion flow rate for power generation during time period t; t This represents the hydroelectric head of the power station during time period t; This indicates the output of the photovoltaic power station during time period t.

[0092] The constraints of the day-ahead power generation planning model for a hydro-solar hybrid power generation system are as follows:

[0093] Constraint 1: Long-distance power transmission stability constraint

[0094] Long-distance power transmission requires a smooth, stepped power curve.

[0095]

[0096] In equation (16): P s Let be the output power (MW) during the s-th stable operating period of the system; tc is the time point at which the output changes in the stepped transmission curve. The derived day-ahead power generation plan can be expressed as:

[0097]

[0098] In equation (17): S is the number of steps of the power transmission curve.

[0099] Constraint 2: Conveying capacity constraint

[0100]

[0101] In equation (18), Tc max This represents the maximum transmission capacity of the power transmission channel.

[0102] Constraint 3: Reservoir Characteristic Constraints

[0103] Characterizing water level-reservoir capacity curve

[0104] Z i =f zv (V i (19)

[0105] Z tail,i =f zq (Q e,i +Q s,i (20)

[0106] In equations (19) and (20): f ZV (·) indicates the relationship between the water level in front of the dam and the reservoir capacity; f Zq(·) indicates the relationship between the tailwater level and the reservoir discharge flow; Z tail,i Let be the tailwater level of the hydropower station during the i-th time period.

[0107] Constraint 4: Water Balance Constraint

[0108] Characterizes the continuity of water flow. The change in reservoir capacity is equal to the difference between the inflow and outflow of water.

[0109] V t+1 =V t +3600(I t -Q t -EI t )ΔT; (21)

[0110] In equation (21): I t and Q t These represent the inflow and outflow from the reservoir at time t, respectively; EI t The evaporation and seepage loss of the reservoir during time period t.

[0111] Constraint 5: Storage Capacity Constraint

[0112] It represents the range of changes in reservoir capacity, and the reservoir capacity at each moment must be within a certain allowable range.

[0113] V min ≤V t ≤V max ; (twenty two)

[0114] In equation (22): V min and V max These represent the lower and upper limits of the storage capacity, respectively.

[0115] Constraint 6: Medium- and Long-Term Water Level Boundary Conditions

[0116] The power generation plan for the hydro-solar hybrid power generation system is formulated under all daily scenarios in typical years of abundant, flat and dry periods. The final water level of each daily scenario should meet the water level boundary conditions obtained by solving the deterministic optimization scheduling model in S1.

[0117]

[0118] In equation (23): Z i,end This represents the final water level in the power generation planning process under the i-th daily scenario; Let ΔZ represent the water level at the end of the i-th time period obtained from the deterministic optimization scheduling model, and let ΔZ represent the maximum allowable error in the water level at the end.

[0119] Specifically, in this embodiment, the objective function of the new long-term scheduling graph optimization model for water-solar hybrid systems includes:

[0120] Objective function 1: Maximum power generation:

[0121]

[0122] Objective function 2: Maximum power generation guarantee rate:

[0123]

[0124] Objective function 3: Minimize the overall risk rate:

[0125]

[0126] In equations (24) to (26): EP is the total power generation during the complementary system dispatch period; i and I are the medium- and long-term dispatch period number and the total dispatch period, respectively; P i h P i s Let ΔT be the average power output of hydropower and the average power output of photovoltaic power station in the i-th time period; i The dispatch period is long; ER is the generation guarantee rate of the complementary system; #(P i h +P i s ≥P firm ) represents the number of times the net output of the complementary system exceeds the guaranteed output during the entire scheduling period; RI represents the comprehensive risk rate of the hydro-solar complementary power station; R(·) represents the risk rate function constructed in step S3.

[0127] The constraints of the medium- and long-term scheduling optimization model for water-solar hybrid systems include:

[0128] Constraint 1: Water Balance Constraint

[0129] V i+1 =V i, +(Q in,i -Q out,i )ΔT i (27)

[0130] Constraint 2: Storage Capacity Constraint

[0131] V min ≤V i ≤V max (28)

[0132] Constraint 3: Downflow constraint

[0133] Q out,min ≤Q out,i ≤Q out,max (29)

[0134] Constraint 4: Hydropower Station Output Constraints

[0135]

[0136] Constraint 5: Characteristic Constraints of Hydropower Stations

[0137] Z i =f zv (V i (31)

[0138] Z tail,i =f zq (Q e,i +Q s,i (32)

[0139] Constraint 6: Output Range Constraint

[0140] N min ≤N1≤N2≤N3≤N max (33)

[0141] In equations (27) to (33), V i Q represents the reservoir capacity of the hydropower station during the i-th time period; in,i and Q out,i Inflow and outflow to the reservoir during period i; V min and V max These are the lower and upper limits of the reservoir capacity for hydropower stations, respectively; Q out,min and Q out,max These are the minimum and maximum discharge flows of the hydropower station, respectively. and These are the lower and upper limits of the hydropower station's output, respectively; Z tail,i N represents the tailwater level of the hydropower station during the i-th time period; min To optimize the lower limit of output; N1, N2, and N3 represent the optimized output in the areas of reduced output, guaranteed output, and increased output, respectively; N max To optimize the upper limit of output.

[0142] This invention also provides a computer-readable storage medium storing computer instructions. When these computer instructions are executed on a computer, the computer performs a method for compiling a hydro-solar hybrid power generation dispatching diagram that balances risk and benefit, as provided in this embodiment.

[0143] Those skilled in the art will understand that all or part of the steps in the methods of the above embodiments can be implemented by a program instructing related hardware. This program is stored in a storage medium and includes several instructions to cause a microcontroller, chip, or processor to execute all or part of the steps of the methods described in the various embodiments of the present invention. The aforementioned storage medium includes various media capable of storing program code, such as a USB flash drive, portable hard drive, read-only memory (ROM), random access memory (RAM), magnetic disk, or optical disk.

[0144] The optional embodiments of the present invention have been described in detail above with reference to the accompanying drawings. However, the embodiments of the present invention are not limited to the specific details described above. Within the scope of the technical concept of the embodiments of the present invention, various simple modifications can be made to the technical solutions of the embodiments of the present invention, and these simple modifications all fall within the protection scope of the embodiments of the present invention. It should also be noted that the various specific technical features described in the above specific embodiments can be combined in any suitable manner without contradiction. To avoid unnecessary repetition, the embodiments of the present invention will not further describe the various possible combinations.

[0145] Furthermore, various different embodiments of the present invention can be combined in any way, as long as they do not violate the spirit of the embodiments of the present invention, they should also be regarded as the content disclosed by the embodiments of the present invention.

Claims

1. A method for compiling a hydro-solar hybrid power generation dispatch diagram that balances risk and benefit, characterized in that, include: To determine the input and water level boundary conditions for the day-ahead power generation planning model of the hydro-solar hybrid power generation system; specifically, to predict the photovoltaic output process, runoff process, and grid load process for the next day, which will serve as the input for the day-ahead power generation planning model of the hydro-solar hybrid power generation system. Based on the risks of power curtailment and load loss in hydro-solar hybrid power generation systems, a day-ahead power generation planning model for hydro-solar hybrid power generation systems is constructed. A day-ahead power generation planning model for a hydro-solar hybrid power generation system is solved, and a risk rate function for the system is extracted based on the solution. Specifically, under all day scenarios in three typical resource years (abundant, average, and dry), the model is solved using runoff, grid load, and photovoltaic output processes as inputs, with hourly intervals and the output of the hydro-solar hybrid power generation system in each interval as the decision variable. The multi-objective Cuckoo algorithm is used to solve the day-ahead power generation planning model for the hydro-solar hybrid power generation system. The calculation results are statistically analyzed to generate a short-term operation dataset for the hydro-solar hybrid power generation system, and the risk rate function is extracted based on this dataset. The objective function of the day-ahead power generation planning model for the aforementioned hydro-solar hybrid power generation system includes: the minimum comprehensive risk rate of the power generation plan, which is expressed as: , , ;in, R The overall risk rate of the hydro-solar hybrid power generation system, and The first i Under the photovoltaic power output forecast scenario t Period-based curtailment rate and load shedding rate Predicting scenarios for photovoltaic power output i The probability of occurrence The difference between the actual output and the planned power generation of the hydro-solar hybrid power station. and The first i Under the photovoltaic power output forecast scenario t Hydropower and solar power output during certain periods This dataset contains the power generation plan of a hydro-solar hybrid power generation system during time period t. The risk rate function of the system is extracted from this dataset, specifically including: plotting a scatter plot with the hydropower output sequence as the independent variable and the comprehensive risk rate sequence as the dependent variable; after removing outliers from the scatter plot, fitting the upper and lower envelopes of the scatter plot; taking the median of the upper and lower envelopes as the risk rate fitting curve; extracting the coordinate point sequence corresponding to the risk rate fitting curve; and using a multivariate nonlinear regression method to fit the risk rate function of the hydro-solar hybrid power generation system. ; The optimal scheduling diagram of the hydro-solar hybrid power generation system is obtained by nesting the risk rate function into a medium-to-long-term scheduling diagram optimization model. Specifically, the model is constructed using a monthly scheduling period, comprehensively considering both benefits and risks. Monthly runoff and monthly photovoltaic output sequences are used as model inputs, and the node coordinates of the upper and lower scheduling lines and the parameters of the scheduling function are used as decision variables. A multi-objective cuckoo algorithm is employed to optimize the decision variables, yielding the optimal form and parameters of the medium-to-long-term scheduling diagram of the hydro-solar hybrid power generation system. The objective function of the medium-to-long-term scheduling diagram optimization model includes: maximum power generation, expressed as... And the maximum power generation guarantee rate, expressed as And the lowest overall risk rate is expressed as: ,in, This represents the total power generation during the complementary system's dispatch period. and These are the medium- and long-term scheduling period numbers and the total scheduling period, respectively. , For the first i Average power output of hydropower and average power output of photovoltaic power stations during the same period. Due to the long scheduling period, For the power generation guarantee rate of the complementary system, This refers to the number of times during the entire scheduling period when the net output of the complementary system exceeds the guaranteed output. The overall risk rate of a hydro-solar hybrid power station, The risk rate function of the obtained hydro-solar hybrid power generation system is fitted.

2. The method for compiling a hydro-solar hybrid power generation dispatching diagram that balances risk and benefit according to claim 1, characterized in that, Determine the water level boundary conditions for the day-ahead power generation planning model of the hydro-solar hybrid power generation system, including: A deterministic optimization scheduling model for a hydro-solar hybrid power generation system was constructed and solved to obtain the daily water level process in a typical resource year. The daily water level process in a typical resource year was then used as the water level boundary condition for the day-ahead power generation plan preparation model of the hydro-solar hybrid power generation system.

3. The method for compiling a hydro-solar hybrid power generation dispatching diagram that balances risk and benefit according to claim 2, characterized in that, The construction of the deterministic optimization scheduling model for the hydro-solar hybrid power generation system includes: Three typical resource years—abundant, average, and dry—are selected. Within each typical resource year, the scheduling period is defined as a day. A deterministic optimization scheduling model for the hydro-solar hybrid power generation system is constructed with the maximum power generation and the highest power generation guarantee rate of the hydro-solar hybrid power generation system as the objective function.

4. The method for compiling a hydro-solar hybrid power generation dispatching diagram that balances risk and benefit according to claim 2, characterized in that, The solution to the deterministic optimization scheduling model of the hydro-solar hybrid power generation system includes: The daily runoff process and daily photovoltaic output process are used as inputs to the deterministic optimal scheduling model of the hydro-solar hybrid power generation system, and the water level at the end of each time period is used as the decision variable of the deterministic optimal scheduling model of the hydro-solar hybrid power generation system. The dynamic programming algorithm is used to solve the problem.

5. The method for compiling a hydro-solar hybrid power generation dispatching diagram that balances risk and benefit according to claim 1, characterized in that, In the process of predicting the photovoltaic power output for the next day, various theoretical probability distribution functions are used to fit the photovoltaic power output prediction error. The Akaike Information Criterion method is used to test the goodness of fit and determine the probability distribution function type and parameters that the prediction error follows. Based on this probability distribution function, the Latin hypercube sampling method is used to generate multiple photovoltaic power output prediction scenarios.

6. A computer-readable storage medium, characterized in that, The computer-readable storage medium stores computer instructions that, when executed on a computer, cause the computer to perform the method according to any one of claims 1-5.

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