Cascade pre-flood energy storage risk analysis and control method for hydro-wind-solar complementary system

Abstract

The present invention belongs to the field of power generation scheduling of power systems. Disclosed is a cascade pre-flood energy storage risk analysis and control method for a hydro-wind-solar complementary system. The method comprises: using pre-flood energy storage as a constraint to establish a dry-season drawdown model and a flood-season water storage scheduling rule, so as to determine a simulation operation criterion; constructing a set of dry-season power shortage, flood-season water abandonment, insufficient year-end energy storage and wind-solar power curtailment indexes, so as to quantify multi-stage and multi-source operation risks; and coupling Monte Carlo and fuzzy membership degrees to characterize multi-dimensional uncertainty scenarios and probabilities thereof, and using simulation analysis to determine quantitative relationships between pre-flood energy storage and hydro-wind-solar complementary benefits, risk probabilities and risk losses. Results of instance analysis of a hydro-wind-solar complementary system in a certain extremely-large drainage basin show that the present invention can comprehensively describe risks in terms of occurrence probabilities and losses, and a verification result shows that by means of accurate risk description and reasonable energy storage control, the system power generation is increased by 580 million kWh, and the risk loss is reduced by 42% on average, and therefore the present invention exhibits good practicability.

Description

Risk analysis and control method for pre-flood energy storage of cascade hydro-wind-solar complementary system TECHNICAL FIELD

[0001] The present application belongs to the field of power system dispatching, and relates to a risk analysis and control method for pre-flood energy storage of cascade hydro-wind-solar complementary system. BACKGROUND

[0002] Taking full advantage of China's hundreds of thousands of kilowatts of hydropower advantage to develop hydro-wind-solar complementation is a realistic and reliable choice for China to relieve the flexibility demand of wind and solar and promote wind and solar consumption at present. However, in the complementary operation, how to coordinate the energy storage arrangement of cascade hydropower stations, especially large-scale controlled power stations, will greatly affect the compensation support of hydropower to new energy power and electricity. There are mainly three kinds of traditional hydro-wind-solar system long-term dispatching operation methods: 1) optimizing the long-term energy storage arrangement of hydropower from the perspective of long-term complementary electricity fluctuation and system benefit; 2) considering the short-term demand and short-term operation characteristics of new energy to improve the reliability of long-term planning; 3) introducing risk indicators as optimization criteria to reduce the risk of long-term planning. At present, long-term planning mostly focuses on single or a few risk sources in risk control (Wang J, Zhao ZP, Cheng CT, et al. Research on cascade hydro-wind-solar complementary dispatching rules coupled with output damage depth and power abandonment criteria [J]. Journal of Hydraulic Engineering, 2023, 54(12): 1415-1429.), while the actual operation of hydro-wind-solar complementary system faces complex multi-source risks, including system power shortage (Guo Y, Ming B, Huang Q, et al. Risk-averse day-ahead generation scheduling of hydro-wind-photovoltaic complementary systems considering the steady requirement of power delivery [J]. Applied Energy, 2022, 309: 118467.), new energy power abandonment (Ming B, Li Y, Liu P, et al. Research on long-term optimal scheduling of hydro-solar complementary system considering nested short-term power abandonment risk [J]. Journal of Hydraulic Engineering, 2021, 52(06): 712-722.), water abandonment of hydropower stations (Cao R, Cheng CT, Shen JJ, et al. Long-term power generation scheduling method of power station considering water storage period water abandonment risk. Journal of Hydraulic Engineering, 2021. 52(10): 1193-1203.), unreasonable cascade energy storage control (Niu WJ, Wu XY, Feng ZK, et al. Optimal scheduling method for cascade hydropower station group energy storage control. Proceedings of the Chinese Society of Electrical Engineering, 2017. 37(11): 3139-3147+3369.), etc.; and the current methods rarely consider the risk balance control demand in actual production and operation. Therefore, it is particularly necessary to further quantify the multiple risks of hydro-wind-solar complementary operation and propose a risk control method suitable for it.

[0003] The pre-flood energy storage control of the cascade hydropower station is a key task of long-term scheduling, directly affecting the available water in dry season and the storage in flood season. The whole year can be divided into two main stages with the pre-flood as the time node: the drawdown period (about January to June) and the storage adjustment period (about July to December). Due to the seasonal characteristics of runoff and wind-solar energy, the operation characteristics of different stages conflict with the control requirements of pre-flood energy storage control of cascade hydropower station. Unreasonable control is prone to key risks such as power shortage in dry season, water abandonment in flood season, wind-solar power abandonment and unreasonable storage control at the end of the year. Therefore, accurately grasping the relationship between pre-flood energy storage and various operation risks is beneficial to the balanced control of multiple risks of complementary systems.

[0004] In view of the above problems, the application provides a water, wind and light complementary system cascade pre-flood energy storage risk analysis and control method, and applies it to a large basin water, wind and light complementary system as an engineering background. The results show that the application can effectively quantify the fine relationship between cascade flood season energy storage and various risks of the complementary system, effectively reduce the system risk and reduce the operation loss of water, wind and light complementation in practical application.

[0005] SUMMARY

[0006] The technical problem to be solved by the application is to provide a water, wind and light complementary system cascade pre-flood energy storage risk analysis and control method to depict various risks and losses faced by pre-flood energy storage, quantitatively analyze the multi-dimensional operation risk of the water, wind and light complementary system, and reasonably control the pre-flood energy storage of the cascade hydropower station.

[0007] The technical scheme of the application is as follows:

[0008] A water, wind and light complementary system cascade pre-flood energy storage risk analysis and control method, comprising the following steps:

[0009] (1) A dry season drawdown optimization model is constructed with the cascade pre-flood energy storage, dry season monthly electricity control and conventional water power scheduling restrictions as constraints to obtain the optimal dry season drawdown scheme. The objective function of the dry season drawdown optimization model is as follows:

[0010] In the formula, E is the expected power generation under each scenario; J, T1 and N represent the runoff uncertainty scenario, the number of dry season scheduling periods and the set of power stations respectively, and j, t and n are the corresponding set elements; P j is the jth scenario probability; Ph j,n,t is the output of power station n in scenario j at the tth period; Pwp j,t is the wind-solar output of scenario j at the tth period; and △t is the time length (s) of the period.

[0011] The pre-flood energy storage constraint and the dry season monthly electricity control constraint are as follows:

[0012] 1) Step flood storage constraint

[0013] wherein: E is the pre-flood storage of power station n; E tar E is the target pre-flood storage; Vn is the reservoir capacity of power station n at the end of the period; V is the total reservoir capacity of power stations upstream of power station n, is the set of power stations upstream of power station n; η n is the average water consumption rate of power station n;

[0014] 2) Monthly power generation control constraint during dry season

[0015] wherein: K t is the control proportion of power generation during dry season (historical statistics) at time period t; ξ is the control error;

[0016] The Gurobi solver is used as the modeling and solving platform, the nonlinear constraints in the dry season drawdown optimization model are linearized by using Python programming and pyomo modeling language, and are converted into a mixed integer linear programming for solving;

[0017] (2) Constructing the storage dispatching rule, including the five-section hedging dispatching rule and the step output allocation method, the step output allocation method adopts the K value judgment method;

[0018] (2.1) Establishing the hedging dispatching rule: the five-section hedging dispatching rule is constructed by using the Python-pyomo modeling program to establish the parameter linear optimization method to determine the monthly time period output of the water-wind-solar complementary system, and the principle is shown in Figure 1. In the figure, OAGD is the three-section dispatching rule of the water-wind-solar complementary system, wherein OA is the guaranteed output section; AG is the guaranteed output section; and GD is the increased output section. The hedging dispatching rule of the present application adds B point and C point between AG and GD sections on the basis of the three-section dispatching rule, to establish the hedging dispatching rule OABCD containing the BC hedging section, wherein the horizontal coordinates of the intersection points of the BC section with AG and GD are parameters a and b. The determination method of the parameters a and b is obtained by optimizing the historical water-wind-solar output results, and the specific expression is as follows:

[0019] wherein: k and d are the slope and intercept of the BC section curve; E t is the average available energy of the historical water-wind-solar at time period t; T2 is the number of dispatching time periods in the storage adjustment period; P t is the historical average output corresponding to time period t; since BC intersects with AB section and GD section, a and b are calculated by combining the expressions of AB section and GD section as follows:

[0020] Pmax M is the maximum output of the water-wind-solar complementary system; P f is the guaranteed output of the water-wind-solar complementary system; is the available storage of the cascade hydropower stations (excluding dead storage); and△m is the number of hours in a month.

[0021] (2.2) Cascade output distribution: After obtaining the monthly period output of the water-wind-solar complementary system in step (2.1), the remaining output after deducting the wind-solar output is distributed among the cascade hydropower stations. The K value discrimination method is used to determine the order of water storage and water supply of the cascade hydropower stations. The hydropower station with a larger K value is first stored when water storage occurs, and the hydropower station with a smaller K value is first supplied with water when water supply occurs. The discrimination formula K n of the hydropower station n is calculated as follows:

[0022] △E x represents the energy increase of the hydropower station n and the downstream hydropower stations due to the water storage△V n of the hydropower station n; wherein, is the water energy increase of the hydropower station n due to the increase of the water head, W n is the current storage of the hydropower station n; is the energy increase of the downstream hydropower stations due to the water storage△V n of the hydropower station n, represents the set of downstream hydropower stations of the hydropower station n;△V n is the unit water storage of the hydropower station n;η k is the average water consumption rate of the hydropower station k from the initial water level to the water storage level;△E z represents the energy increase of the upstream hydropower stations due to the water storage△V n of the hydropower station n; represents the set of upstream hydropower stations of the hydropower station n; V k represents the water storage of the upstream hydropower station k above the dead storage, W k is the interval inflow of the hydropower station k; is the relationship function between the unit storage change and the water consumption rate change of the hydropower station n when the storage is V n ;

[0023] (3) Fuzzy theory is used to represent the high-dimensional and multiple uncertain probabilities of water-wind-solar;

[0024] (3.1) Assuming that the prediction error of the runoff and the wind-solar new energy output is a fuzzy variable, and the prediction error distribution conforms to the Cauchy distribution, the membership function expression of the runoff and the wind-solar new energy output prediction error ε is:

[0025] Where EP and EN are the statistical average of positive and negative errors in the runoff and wind power output uncertainty set, and σ is the weight;

[0026] (3.2) Import the historical prediction and measured data of long series of runoff and wind power output from Excel files using the Python programming language. Use the Python-Scipy library-cauchy.fit function to fit the Cauchy distribution parameters to the calculated prediction error data.

[0027] (3.3) Express the comprehensive membership between the runoff and wind power output of each power station in the same period and between the runoff and wind power output of each power station in different periods, and define the following two types of fuzzy relations:

[0028] The fuzzy relation between the runoff and wind power output of each power station in the same period is as follows: h1(Q1 t ,Q2 t ,...,Qn t ,Pwp t )=min{f(Q1 t ),f(Q2 t ),...,f(Qn t ),f(Pwp t )} (11)

[0029] Where Q1 t , Q2 t ,..., Qn t are the inflow or interval flow of power station 1, 2, …, n in period t; Pwp t is the output of wind power in period t; and f(·) is the corresponding membership function.

[0030] The fuzzy relation between the runoff and wind power output of each power station in different periods is expressed as follows: h3(Pwp1,Pwp2,...,Pwp T )=min{f(Pwp1),f(Pwp2),...,f(Pwp T )} (13)

[0031] Where h2(·) and h3(·) are the fuzzy relations between the runoff in different periods and the wind power output in different periods, i.e., the comprehensive membership; is the runoff of power station n in periods 1, 2, …, T; Pwp1, Pwp2, …, Pwp T is the output of wind power in periods 1, 2, …, T;

[0032] (4) Select the key risk indicators of the flood season and dry season, and establish the flood-dry key risk indicator set of the water-wind-solar complementary system, which is as follows:

[0033] (4.1) Risk of power shortage in dry season R s is

[0034] wherein: is the risk of power shortage in scenario i, with 1 for existing risk and 0 for non-existing risk; P i,t is the system power output value in scenario i in time period t; I is the total number of simulated scenarios, T1 is the total number of time periods in dry season, P f is the guaranteed power output of the water-wind-solar complementary system;

[0035] (4.2) Risk of water spillage in flood season R w is

[0036] wherein: S i is the water spillage in scenario i; Sd is the water spillage control threshold; spill i,t is the water spillage in scenario i in time period t;

[0037] (4.3) Risk of insufficient storage at the end of the year R e is

[0038] wherein: Eend i is the storage at the end of the year in scenario i; E min is the minimum storage requirement at the end of the year for the system, and a storage lower than this is regarded as insufficient storage;

[0039] (4.4) Risk of wind-solar power curtailment R c is curtail i,t = f(Ph i,t )t∈T (21)

[0040] wherein: C i is the wind-solar power curtailment in scenario i; Cd is the power curtailment control threshold for the cascade hydropower station; curtail i,t is the wind-solar power curtailment in scenario i in time period t; f(·) is the power curtailment function of the water-wind-solar complementary system; Ph i,t is the hydropower output in scenario i in time period t; T is the total number of time periods in a year;

[0041] The risk loss value calculation methods of the risks of power shortage in dry season, water spillage in flood season, insufficient storage at the end of the year and wind-solar power curtailment in each index scenario are as follows:

[0042] where: is the index loss value of the water-wind complementary system in the dry season power shortage, flood season water abandonment, end of year energy storage deficiency and wind-solar power abandonment; μ i,t is the fuzzy membership degree of scenario i in the t period; μ i is the comprehensive fuzzy membership degree of the i scene;

[0043] (5) Quantification of the risk of dry season power shortage, flood season water abandonment, wind-solar power abandonment and end of year energy storage deficiency

[0044] The runoff prediction error and wind-solar power output prediction error are recorded as fuzzy variables Without considering the difference between wind and solar, wind power and photovoltaic are collectively referred to as new energy; the risk quantification steps are as follows:

[0045] (5.1) According to the historical runoff, new energy output prediction and measured data, the parameters of the runoff and wind-solar output membership function are calculated using Python-Scipy library-cauchy.fit function, and then the fuzzy membership function of runoff prediction error and new energy output prediction error is determined 1≤m≤12, 1≤z≤Z, z represents the uncertainty variable of runoff or wind-solar output, and m represents the month;

[0046] (5.2) According to the error membership function of inflow and new energy output, a series of real numbers are randomly generated using the Monte Carlo simulation method in the Python-random program package and the corresponding membership degree k=1, 2,..., N, N is the number of random numbers;

[0047] (5.3) Simulation operation; in the dry season, from the beginning of the year, according to the dry season drawdown scheme, monthly simulation is carried out by using constant water level calculation; for any month m, a series of According to the initial and final water level in the scheme, a series of total output of the complementary system Ps m,k and dry season wind-solar power abandonment and determine the membership degree If the final water level in the scheme is not feasible, the water level obtained by simulation is used instead of the final water level in the scheme for subsequent simulation calculation; in the flood season, five-stage scheduling rules are used for monthly simulation; from the beginning of the flood season, a series of For any scenario get the end of year energy storage Eend k , flood season wind-solar power abandonment and flood season water abandonment S k and the corresponding membership degree ml represents the starting month of the flood season, and and Add up to get the annual wind and light abandoned electricity under the scene k;

[0048] (5.4) According to step (3), the risk probability and risk loss of the risk indexes of dry season power shortage, flood season water abandonment, end-of-year energy storage deficiency and wind and light abandoned electricity are calculated;

[0049] (5.5) For other cascade pre-flood storage, repeat (5.2)-(5.6), and finally obtain the refined relationship between cascade storage and dry season power shortage, flood season water abandonment, end-of-year energy storage deficiency and wind and light abandoned electricity risk.

[0050] Compared with the prior art, the present application has the beneficial effects: the refined relationship between cascade pre-flood storage of water, wind and light complementary system and multi-dimensional operation risk and system benefit can be obtained, which is beneficial for dispatchers to accurately grasp the possible risk and potential maximum loss of cascade pre-flood storage decision under future water, wind and light multiple uncertainties, so as to carry out multi-dimensional risk control, obtain risk balance and cascade pre-flood storage control scheme considering benefit. BRIEF DESCRIPTION OF DRAWINGS

[0051] Fig. 1 is a schematic diagram of five-stage dispatching rules of water, wind and light complementary system;

[0052] Fig. 2 is a VaR schematic diagram of risk loss distribution;

[0053] Fig. 3 is a relationship diagram of pre-flood storage and each risk probability; wherein, (1) is dry season power shortage risk; (2) is end-of-year energy storage deficiency risk; (3) is flood season water abandonment risk; (4) is wind and light abandoned electricity risk;

[0054] Fig. 4 is a dry season power shortage loss distribution and risk value; wherein, (1) is normal year-pre-flood storage 7; (2) is normal year-pre-flood storage 8; (3) is dry year-pre-flood storage 9; (4) is dry year-pre-flood storage 4; (5) is dry year-pre-flood storage 5; (6) is dry year-pre-flood storage 6; (7) is dry year-pre-flood storage 7; (8) is dry year-pre-flood storage 8; (9) is dry year-pre-flood storage 9;

[0055] Fig. 5 is system power generation under different pre-flood storage; wherein, (1) is wet year; (2) is normal year; (3) is dry year. DETAILED DESCRIPTION

[0056] The specific embodiments of the present application will be further described in combination with the drawings and technical solutions.

[0057] A water, wind and light complementary system cascade pre-flood storage risk analysis and control method, comprising the following steps:

[0058] (1) With the constraints of pre-flood energy storage, monthly power control in dry season and conventional restrictions of water and electricity dispatching, the dry season drawdown optimization model is constructed to obtain the optimal dry season drawdown scheme. The objective function of the dry season drawdown optimization model is as follows:

[0059] In the formula, E is the expected power generation under each scenario; J, T1, N represent the runoff uncertainty scenario, the number of dry season dispatching periods and the set of power stations respectively, and j, t, n are the corresponding set elements; P j is the jth scenario probability; Ph j,n,t is the output of power station n in the tth period of scenario j; Pwp j,t is the wind and solar output in the tth period of scenario j;△t is the time length of the period (s);

[0060] The pre-flood energy storage constraint and the monthly power control constraint in dry season are as follows:

[0061] 1) Pre-flood energy storage constraint of cascade

[0062] In the formula: is the pre-flood energy storage of power station n; E tar is the target pre-flood energy storage; is the reservoir capacity of power station n at the end of the period; is the total reservoir capacity of the upstream power stations of power station n, is the set of upstream power stations of power station n; η n is the average water consumption rate of power station n;

[0063] 2) Monthly power control constraint in dry season

[0064] In the formula: K t is the control proportion of power generation in the tth period in dry season (historical statistics); ξ is the control error;

[0065] The Gurobi solver is used as the modeling and solving platform. Python programming and pyomo modeling language are used to linearize the nonlinear constraints in the dry season drawdown optimization model, and the linear programming is converted into a mixed integer linear programming for solving;

[0066] (2) The storage dispatching rules are constructed, including five-stage hedging dispatching rules and cascade output allocation method. The K value discrimination method is used in the cascade output allocation method;

[0067] (2.1) Establishing hedging scheduling rules: a parameter linear optimization method is used to establish a five-section hedging scheduling rule by using Python-pyomo modeling program to determine the monthly time period output of the water-wind-solar complementary system, and the principle is shown in FIG. 1. In the figure, OAGD is a three-section scheduling rule of the water-wind-solar complementary system, wherein OA is a guaranteed output section below the output; AG is a guaranteed output section; and GD is an increased output section. The hedging scheduling rule of the present application adds B point and C point between AG and GD sections on the basis of the three-section scheduling rule, to establish a hedging scheduling rule OABCD containing BC hedging section, wherein the horizontal coordinates of the intersection points of BC section with AG and GD are parameters a and b. The determination method of parameters a and b is obtained by optimization according to the historical output results of water-wind-solar, and the specific expression is as follows:

[0068] In the formula, k and d are the slope and intercept of the BC section curve; E t is the average available energy of the historical water-wind-solar at t period; T2 is the number of scheduling periods in the water storage adjustment period; P t is the historical average output corresponding to the t period; since BC intersects with AB section and GD section, a and b are calculated by combining the expressions of AB section and GD section as follows:

[0069] In the formula, P M is the maximum output of the water-wind-solar complementary system; P f is the guaranteed output of the water-wind-solar complementary system; is the available storage capacity of the cascade hydropower station when full storage (removing dead storage); and △m is the number of hours in a month.

[0070] (2.2) Cascade output distribution: after obtaining the monthly time period output of the water-wind-solar complementary system in step (2.1), the remaining output after deducting the wind-solar output is distributed among the cascades, and the K value discrimination method is used to determine the water storage order of the cascades, and the hydropower station with large K value stores water first when storing water, and the hydropower station with small K value supplies water first when supplying water; the discrimination formula K n value of the hydropower station n is calculated as follows:

[0071] In the formula, △E x represents the energy increased in the hydropower station n and the downstream hydropower station due to the water storage △V n of the hydropower station n, wherein, is the water energy increment of the hydropower station n due to the increase of water head caused by water storage, W n is the current storage capacity of the hydropower station n; is the energy increased in the downstream hydropower station due to the water storage △V n of the hydropower station n, V n is the unit storage of power station n; η k is the average water consumption rate of power station k from the initial water level to the storage water level; △E z represents the storage △V n of power station n V k represents the storage of power station k above the dead storage, W k is the interval inflow of power station k; is the relationship function of unit storage change and water consumption rate change when the storage of power station n is V n ;

[0072] (3) Fuzzy theory is used to represent the high-dimensional and multiple uncertain probabilities of water, wind and light;

[0073] (3.1) Assuming that the prediction error of runoff and wind and light new energy output is a fuzzy variable, and the prediction error distribution conforms to Cauchy distribution, the membership function expression of runoff and wind and light new energy output prediction error ε is:

[0074] In the formula: EP and EN are the statistical average values of positive and negative errors in the uncertain set of runoff and wind power output, and σ is the weight;

[0075] (3.2) The Python programming language is used to import the long series of historical prediction and measured data of runoff and wind and light new energy output from Excel files, and the Python-Scipy library-cauchy.fit function is used to fit the Cauchy distribution parameters of the calculated prediction error data;

[0076] (3.3) The comprehensive membership between the runoff and wind and light output of each power station in the same period and the runoff and wind and light output of each power station between different periods is expressed, and the following two types of fuzzy relations are defined:

[0077] The fuzzy relation between the runoff and wind and light output of each power station in the same period is as follows: h1(Q1 t ,Q2 t ,...,Qn t ,Pwp t )=min{f(Q1 t ),f(Q2 t ),...,f(Qn t ),f(Pwp t )} (36)

[0078] In the formula: Q1 t ,Q2 t ,...,Qn tQi,t is the inflow or interflow of power station i at time period t; Pwp t is the output of wind and solar at time period t; f(·) is the corresponding membership function;

[0079] The fuzzy relations of the inflows of each power station and the outputs of wind and solar between different time periods are expressed as follows: h3(Pwp1,Pwp2,...,Pwp T )=min{f(Pwp1),f(Pwp2),...,f(Pwp T )} (38)

[0080] In the formula, h2(·) and h3(·) are the fuzzy relations between the inflows of different time periods and the fuzzy relations between the outputs of wind and solar of different time periods, i.e. the comprehensive membership degrees; is the inflow of power station n at time period 1, 2, …, T; Pwp1, Pwp2, …, Pwp T is the output of wind and solar at time period 1, 2, …, T;

[0081] (4) Key risk indicators in the flood season and the dry season are selected, and a set of key risk indicators of the water-wind-solar complementary system in the flood season and the dry season is established, which is specifically as follows:

[0082] (4.1) Dry season power shortage risk R s is

[0083] In the formula, is the risk description value of power shortage in scenario i, and the risk is 1 when there is a risk and 0 when there is no risk; P i,t is the system output value of scenario i at time period t; I is the total number of simulated scenarios, T1 is the total number of time periods in the dry season, P f is the guaranteed output of the water-wind-solar complementary system;

[0084] (4.2) Flood season water spill risk R w is

[0085] In the formula, S i is the water spill amount of scenario i; Sd is the water spill control threshold; spill i,t is the water spill amount of scenario i at time period t;

[0086] (4.3) Year-end energy storage shortage risk R e is

[0087] In the formula, Eend i is the year-end energy storage of scenario i; E minis the minimum storage requirement at the end of the year, and less than this is considered insufficient storage;

[0088] (4.4) Wind and light curtailment risk R c is curtail i,t = f(Ph i,t )t∈T (46)

[0089] In the formula: C i is the wind and light curtailment of scenario i; Cd is the curtailment control threshold of cascade hydropower stations; curtail i,t is the wind and light curtailment of scenario i in the tth period; f(·) is the curtailment function of the water-wind-light complementary system; Ph i,t is the water power output of scenario i in the tth period; T is the number of periods in a year;

[0090] The risk loss value calculation methods of dry season power shortage, flood season water curtailment, end-of-year insufficient storage, and wind and light curtailment in each index scenario are as follows:

[0091] In the formula: is the index loss value of the water-wind-light complementary system dry season power shortage, flood season water curtailment, end-of-year insufficient storage, and wind and light curtailment; μ i,t is the fuzzy membership degree of scenario i in the tth period; μ i is the comprehensive fuzzy membership degree of the ith scenario;

[0092] (5) Quantification of dry season power shortage, flood season water curtailment, wind and light curtailment, and end-of-year insufficient storage risk

[0093] The runoff prediction error and wind and light output prediction error are recorded as fuzzy variables Without considering the difference between wind and light, wind power and photovoltaic are collectively referred to as new energy; the risk quantification steps are as follows:

[0094] (5.1) According to the historical runoff, new energy output prediction and measured data, the parameters of the runoff and wind and light output membership functions are calculated using the Python-Scipy library-cauchy.fit function, and then the fuzzy membership degree functions of the runoff prediction error and new energy output prediction error are determined respectively 1≤m≤12, 1≤z≤Z, z represents the runoff or wind and light output uncertainty variable, and m represents the month;

[0095] (5.2) According to the error membership degree functions of inflow and new energy output, a series of real numbers are randomly generated using the Monte Carlo simulation method in the Python-random program package and the corresponding membership k = 1, 2,..., N, N is the number of random numbers;

[0096] (5.3) Simulate the operation; in the dry season, from the beginning of the year, according to the dry season drawdown scheme, monthly simulation is carried out by constant water level calculation; for any month m, a series of According to the initial and final water levels in the scheme, a series of total complementary system outputs Ps m,k and dry season wind and light abandoned electricity and determine the membership If the final water level in the scheme is not feasible, the water level obtained by simulation is used to replace the final water level in the scheme for subsequent simulation calculation; in the flood season, five-stage scheduling rules are used for monthly simulation; from the beginning of the flood season, a series of For any scenario get the end-of-year storage Eend k , flood season wind and light abandoned electricity and flood season abandoned water S k and the corresponding membership ml represents the starting month of the flood season, and and are added to obtain the annual wind and light abandoned electricity under scenario k;

[0097] (5.4) According to step (3), calculate the risk probability and risk loss of the indexes of dry season power shortage, flood season abandoned water, end-of-year storage deficiency, and wind and light abandoned electricity;

[0098] (5.5) For other pre-flood storage of cascade, repeat (5.2)-(5.6), and finally obtain the refined relationship between cascade storage and the risks of dry season power shortage, flood season abandoned water, end-of-year storage deficiency, and wind and light abandoned electricity.

[0099] The present application is verified by taking a water and wind light complementary base in the downstream of a certain large basin as an example. The cascade power stations select XW, MW, NZD and JH four reservoirs. According to the planning, the wind power and photovoltaic are respectively set as 570 MW and 4497 MW. Three typical level years of abundant, normal and dry are selected as different frequency years in the future to test the effectiveness of the present application. Since the regulation capacity of MW and JH is relatively weak, according to the historical operation data, the monthly water level is given in the long term, and the annual initial water level of XW and NZD is set as 1230 m and 805 m. According to the change rule of flood and dry in the downstream of the basin, the energy storage month before the flood is selected as July. Referring to the scheduling operation water level process of XW and NZD, the lowest drawdown water level interval of XW is set as [1166, 1186.5], and the interval of NZD is set as [765, 797.6]. After calculation, the lowest pre-flood energy storage of the cascade is 131.5 billion kWh, and the highest is 201.3 billion kWh. In the energy storage interval, 9 typical pre-flood energy storages are evenly dispersed according to the step of 8.8 billion kWh, and the energy storage numbers 1 to 9 are analyzed for risk and benefit. The lowest energy storage demand of the cascade at the end of the year is 221.3 billion kWh. According to the relevant policy and historical operation experience, the water and electricity abandonment thresholds are set as 6% and 10%.

[0100] Figure 3 shows the relationship between the pre-flood energy storage of the cascade and the risk occurrence probability. It can be seen that in the abundant water year, there is almost no shortage of energy storage at the end of the year and power shortage in the dry season, but there is a relatively large risk of water and light electricity abandonment. Under the highest pre-flood energy storage, the risks of water and light electricity abandonment are 0.54 and 0.84 respectively. On the contrary, in the dry water year, there is no risk of water and light electricity abandonment, but the energy storage is too high, the water is insufficient in the dry season, and there is a relatively large risk of power shortage. The risk probability corresponding to the maximum pre-flood energy storage is 1. In addition, if the pre-flood energy storage is too low, the water storage is insufficient in the flood season in the dry water year, and the power station often cannot meet the minimum energy storage at the end of the year. The risks of energy storage shortage corresponding to the pre-flood energy storages 1, 2 and 3 are 0.306, 0.175 and 0.095 respectively. In the normal water year, the energy storage at the end of the year can almost meet the requirement, and the risk can be ignored. However, the pre-flood energy storage should not be too high, and the pre-flood energy storages 7, 8 and 9 will bring a considerable risk of power shortage in the dry season, which are 0.085, 0.204 and 0.52 respectively. With the increase of the pre-flood energy storage, the water abandonment in the flood season increases significantly, especially after more than 5 energy storages. Compared with the pre-flood energy storage, the risk of electricity abandonment increases with the increase of the pre-flood energy storage, but it is not significant. The risk ranges of the two are [0.02, 0.34] and [0.04, 0.15] respectively.

[0101] According to the risk relationship diagram of figure 3, the decision maker can make a decision under a certain risk tolerance. For example, assuming that the tolerance is 20%, in the abundant water year, the pre-flood energy storage should be controlled below the energy storage 4 as far as possible to avoid the high risk of water and light electricity abandonment. In the normal water year, the pre-flood energy storage can be appropriately increased, but it should not be higher than the energy storage 6 to avoid the high risk of water abandonment. In the dry water year, the pre-flood energy storage can be balanced between the energy storages 2-5. In addition, the comprehensive determination of the pre-flood energy storage can also refer to the subsequent risk loss and benefit analysis.

[0102] The risk loss distribution and the risk value (confidence level of 0.95) of the risk probability greater than 0.05 are further counted. As can be seen from FIG. 4 and Table 1, under the same pre-flood storage, the greater the inflow, the lower the risk value of power shortage in dry season, and the higher the risk value of water abandonment in flood season. In addition, the greater the risk, the higher the corresponding risk loss, that is, the greater the risk value. Taking the power shortage loss in dry season of the dry year in FIG. 4 as an example, the maximum output loss corresponding to the storage 4 is 42.5 MW, and the maximum output loss corresponding to the storage 9 is 423.6 MW, which is nearly ten times different. However, the relationship between the risk probability and the risk value is not linearly positively correlated. For example, in the wet year, the water abandonment probability of the pre-flood storage 5 to 9 is 0.12 (see FIG. 1), but the risk values are almost equal (see Table 2). Such inconsistent relationship can provide a certain decision-making space for decision-makers, and further illustrates the significance and necessity of comprehensively representing the risk by the risk probability and the risk loss, and verifies the effectiveness and comprehensiveness of the risk quantification of the present application.

[0103] Table 1 Risk value of water abandonment

[0104] Table 2 Risk value of water abandonment

[0105] FIG. 5 is the system power generation under different pre-flood storages. With the increase of the pre-flood storage, the water consumption in dry season decreases, the water storage in flood season decreases, the power generation water head and the power generation water increase, therefore, the power generation in dry season decreases with the increase of the pre-flood storage, and the power generation in flood season increases with the increase of the pre-flood storage. The overall power generation in the wet year and the dry year decreases with the increase of the pre-flood storage. This is because, in the wet year, too high pre-flood storage increases the water abandonment loss, and reduces the power generation benefit; and in the dry year, the lower the pre-flood storage, the greater the system water consumption for power generation, and the greater the power generation benefit. The benefit analysis can make up for the insufficient information of the pre-flood storage only considering the risk analysis. For example, it is mentioned above that the pre-flood storage in the normal year needs to be controlled as much as possible below the storage 6. As can be seen from FIG. 5, the benefit of the storage 5 is the highest, and the risk value is relatively small (see Table 1 and Table 2), and the difference is not large, therefore, the pre-flood storage should be controlled at the storage 5.

[0106] In order to further verify the effectiveness of the present application, the system benefit and risk results of a long series are simulated and compared with other ways. The commonly used long-term random expected scheduling model is adopted for comparison. First, the decision of the pre-flood storage of the scheduling personnel is simulated according to the benefit and risk analysis results, and then the pre-flood storage of the cascade is obtained by solving the comparison model. The results of the two are shown in Table 3.

[0107] Table 3 Pre-flood storage decision scheme

[0108] On the basis of the determined pre-flood energy storage, the long-term scheduling process of both is simulated. The operation in the drawdown period adopts the uniform drawdown rule, and the operation in the storage adjustment period adopts the aforementioned proposed storage scheduling rule. After years of simulation, the index results of both are shown in Table 4. It can be seen that the scheduling results of the pre-flood energy storage selected by the application are better in power generation, power shortage in dry season, wind and light power curtailment and flood season water curtailment, and the average risk loss is reduced by 42% except that the pre-flood energy storage is slightly smaller than the comparative method at the end of the year. This is because the pre-flood energy storage selected by the application avoids the above risks, performs better at the multi-year level, and verifies the effectiveness and practicality of the application in actual scheduling work.

[0109] Table 4 Comparison of simulation indexes

Claims

1. A water-wind complementary system cascade flood pre-accumulation risk analysis and control method, characterized in that, The method comprises the following steps: (1) With the constraints of the step flood storage, the monthly electricity control in dry season, and the conventional restrictions of water and electricity dispatching, the dry season drawdown optimization model is constructed to obtain the optimal dry season drawdown scheme. The objective function of the dry season drawdown optimization model is as follows: In the formula, E is the expected power generation under each scenario; J, T1, and N respectively represent the runoff uncertainty scenario, the number of dry period scheduling periods, and the set of power stations, j, t, and n are the corresponding set elements; P j is the jth scenario probability; Ph j,n,t is the output of power station n corresponding to the tth period of scenario j; Pwp j,t is the wind-solar output of the tth period of scenario j; and △t is the time length (s) corresponding to the period. The flood season energy storage constraints and the dry season monthly power control constraints are as follows: 1) Stepwise pre-flood energy containment In the formulae: To store energy before the flood season for power station n; E tar To store energy before the flood season for the target; to the end of the period; the total storage of the power plants upstream of the power plant n, is the set of upstream power stations of the power station n; η n is the average water consumption rate of the power plant n; 2) monthly power control constraints during dry season In the formula, K t is the control proportion of power generation in the dry season for the period t; and ξ is the control error. Taking Gurobi solver as the modeling and solving platform, the nonlinear constraints in the dry season drawdown optimization model are linearized by using Python programming and pyomo modeling language, and are converted into a mixed integer linear programming for solving; (2) The storage scheduling rules are constructed, including five-stage hedging scheduling rules and a cascade output allocation method, and the K value discrimination method is adopted in the cascade output allocation method; (2.1) Establishing the hedging scheduling rule: the five-section hedging scheduling rule is established by using the Python-pyomo modeling program to build a parameter linear optimization method to determine the monthly time period output of the water-wind-solar complementary system; wherein, the OAGD is the three-section scheduling rule of the water-wind-solar complementary system, wherein the OA section is the below-output section for ensuring the output, the AG section is the output section for ensuring the output, and the GD section is the section for increasing the output; on the basis of the three-section scheduling rule, the B point and the C point are added between the AG section and the GD section respectively to establish the hedging scheduling rule OABCD containing the BC hedging section, wherein the horizontal coordinates of the intersection points of the BC section with the AG section and the GD section are parameters a and b; the determination method of the parameters a and b is obtained according to the historical output results of water, wind and solar, and the specific expressions are as follows: where k and d are the slope and intercept of the BC segment curve; E t is the average available energy of the t-period historical water power; T2 is the number of dispatch periods of the water storage adjustment period; P t is the historical average output of the corresponding t-period; due to the intersection of the BC segment with the AB segment and the GD segment, the expressions of the joint AB segment and the GD segment are calculated by the following formula: where: P M is the maximum power of the hydro-wind-solar complementary system; P f is the guaranteed power of the hydro-wind-solar complementary system; is the available energy storage when the cascade power stations are full storage; and Δm is the number of hours in a month; (2.2) Stepwise output distribution: After obtaining the monthly period output of the water-wind-solar complementary system in step (2.1), the remaining output after deducting the wind-solar output is distributed among the steps, and the K value discrimination method is used to determine the water supply and storage order of the steps. When storing water, the power station with a large K value stores water first; when supplying water, the power station with a small K value supplies water first; the discriminant K n value of power station n is calculated as follows: where: ΔE x represents the increased energy of the power station n and the downstream hydropower stations due to the water storage of the power station n ΔV n wherein, is the water energy increment caused by the increase of the water storage head of power station n, W n is the current reservoir capacity of power station n; is the downstream water power station and thus the power station n water storage AV n and the increased energy, denotes the set of power stations downstream of power station n; AV n is the unit storage of power station n; η k is the average water consumption rate of power station k from the initial water level to the storage water level; AE z denotes the storage AV n causes the upstream hydropower station to increase the energy; denotes the set of upstream power plants of power plant n; V k denotes the water storage above the dead storage of upstream power plant k, W k is the inflow to the interval of power plant k; is the function of the relationship between the unit storage variation and the water consumption rate variation when the power station n has a storage capacity of V n . (3) The fuzzy theory is adopted to represent the high-dimensional and multiple uncertain probabilities of water, wind and light; (3.1) Assuming that the prediction error of runoff and wind-solar new energy output is a fuzzy variable, and the prediction error distribution conforms to the Cauchy distribution, the membership function expression of the prediction error ε of runoff and wind-solar new energy output is: In the formula, EP and EN are statistical average values of positive and negative errors in the runoff and wind and light output uncertainty set, and σ is a weight; (3.2) The Python programming language is used to import the historical prediction and measured data of long series runoff and wind and light new energy output from the Excel file, and the cauchy.fit function of the Python-Scipy library is used to fit the calculation obtained prediction error data to the Cauchy distribution parameters; (3.3) The comprehensive membership degrees between the runoff and wind and light outputs of each power station in the same period and between the runoff and wind and light outputs of each power station in different periods are expressed, and the following two types of fuzzy relations are defined: The fuzzy relation between the runoff and wind and light outputs of each power station in the same period is as follows: h1(Q1 t ,Q2 t ,...,Qn t ,Pwp t ) = min {f(Q1 t ), f(Q2 t ),..., f(Qn t ), f(Pwp t )} (11) where Q1 t Q2 t ,...,Qn t are the inflow or interzonal flow of power plants 1, 2,...,n at time period t, respectively; Pwp t is the output of wind and solar power at time period t; and f(·) is the corresponding membership function. The fuzzy relationship between the runoff of each power station and the wind-solar output in different time periods is expressed as follows: h3(Pwp1, Pwp2,..., Pwp T ) = min {f(Pwp1), f(Pwp2),..., f(Pwp T )} (13) In the formula: h2(·) and h3(·) are the fuzzy relations between the runoff of different time periods and the fuzzy relations between the wind-solar power output of different time periods, i.e. the comprehensive membership degrees; Pwp1, Pwp2,..., PwpTare the wind power outputs at time periods 1, 2,..., T; and T Pwp1, Pwp2,..., PwpTare the wind power outputs at time periods 1, 2,..., T; and (4) The key risk indicators of the flood season and the dry season are selected, and the key risk indicator set of the water, wind and light complementary system in the flood season and the dry season is established, and the specific indicators are as follows: (4.1) Risk of electricity shortage in the dry season R s To In the formulae: is the output shortage risk characterization value under scenario i, the risk exists is 1, and the risk does not exist is 0; P i,t is the system output value of scenario i in the t period; I is the total number of simulated scenarios, T1 is the total number of periods in the dry season, P f is the guaranteed output of the water-wind complementary system; (4.2) Flood season water abandonment risk R w To where: S i is the amount of water spilled for scenario i; Sd is the spill control threshold; spill i,t is the amount of water spilled for scenario i at time period t; (4.3) end-of-year energy deficit risk R e To where: Eend i is the end-of-year storage under scenario i; E min is the minimum end-of-year storage requirement for the system, below which the storage is considered to be insufficient. (4.4) wind and light electricity abandonment risk R c for curtail i,t = f(Ph i,t ) t e T (21) In the formula: C i Cd represents the curtailment of wind and solar power in scenario i; Cd is the curtailment control threshold for cascade hydropower stations; curtail i,t It represents the curtailment of wind and solar power in scenario i at time t; f(·) is the curtailment function of the hydro-wind-solar hybrid system; Ph i,t It is a scene i No. t Hydropower output during a given time period; T represents the number of time periods throughout the year. The risk loss value calculation method of the dry season power shortage, flood season water abandonment, insufficient energy storage at the end of the year, and wind and light abandonment under each index scenario is as follows: In the formulae: is the index loss value of water and wind complementary system in dry season power shortage, flood season water abandonment, end of year energy storage deficiency and wind and light abandonment; μ i,t is the fuzzy membership degree of scene i in the t period; μ i is the comprehensive fuzzy membership degree of the i scene; (5) Quantification of dry season power shortage, flood season water abandonment, wind and light power abandonment and insufficient energy storage at the end of the year The runoff prediction error, wind power and solar power output prediction error are denoted as fuzzy variables Without considering the difference between wind and light, the wind power and photovoltaic power are collectively referred to as new energy; the risk quantification steps are as follows: (5.1) According to the historical runoff, the predicted and measured data of new energy output, the parameters of the runoff and wind-solar output membership functions are calculated by using Python-Scipy library-cauchy.fit function, and then the fuzzy membership functions of runoff prediction error and new energy output prediction error are determined respectively z represents the runoff or wind and light output uncertainty variable, and m represents the month; (5.2) According to the error membership function of inflow and new energy output, a series of real numbers are randomly generated by using the Monte Carlo simulation method in the Python-random package and the corresponding membership N is the number of random numbers; (5.3) Perform simulation run; in dry season, from the beginning of the year, according to the dry season drawdown scheme, use fixed water level calculation to perform monthly simulation; for any month m, generate a series of According to the initial and final water levels in the scheme, a series of complementary systems Total output Ps m,k And dry season wind light abandon electricity and determine the membership If the final water level in the scheme is not feasible, the water level obtained by simulation is used to replace the final water level in the scheme for subsequent simulation calculation; in the flood season, five-stage dispatching rules are used for monthly simulation; from the beginning of the flood season, a series of For any scenario Eend is obtained k , flood season wind light abandoned electricity and flood season water abandonment S k and corresponding membership ml represents the month of the beginning of the flood season, will and are added to obtain the annual wind and light power abandonment under the scene k. (5.4) According to step (3), the risk probability and risk loss of the dry season power shortage, the flood season water abandonment, the insufficient energy storage at the end of the year and the wind and light power abandonment indicators are calculated; (5.5) For other cascade flood season energy storage, steps (5.2)-(5.6) are repeated, and finally the fine relationship between the cascade energy storage and the dry season power shortage, the flood season water abandonment, the insufficient energy storage at the end of the year and the wind and light power abandonment risk is obtained.

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