River basin water-wind-solar integrated key operation risk identification method

Abstract

The invention belongs to the technical field of watershed water-wind-light integrated operation, and relates to a watershed water-wind-light integrated key operation risk identification method. The method comprises the following steps: firstly, starting from complex coordinated operation requirements such as hydroelectric comprehensive utilization requirements, multi-level scheduling, multiple time scales and water-wind-light complementation, constructing a cascade hydroelectric operation risk system and constructing seven quantitative indexes of the system; secondly, constructing a watershed water-wind-light clean energy base annual 8760h panoramic time sequence operation simulation model, and comparing and calculating cascade hydropower operation risk change levels before and after new energy access; and finally, proposing a risk sorting method coupling TOPSIS and AHP, eliminating different risk index units and magnitude differences, and identifying key risks. By taking a clean energy base at the downstream of the Jinsha River as an application example, various risk distribution characteristics are disclosed, water and electricity waste water, output fluctuation and peak regulation pressure risks are found to be more sensitive to new energy access, and the sensitivity of the risks to seasons and new energy installation is explored and analyzed.

Description

Technical Field

[0001] The present invention belongs to the technical field of integrated water-wind-solar operation in a river basin, and relates to a method for identifying key operational risks of integrated water-wind-solar operation in a river basin. Background Art

[0002] The basin water, wind and solar integrated clean energy base mainly relies on the hydropower base and its transmission channels, and integrates a certain scale of wind and solar new energy power generation projects within a reasonable range to achieve integrated resource allocation, dispatching operation and power consumption. Its essence is a giant system of heterogeneous energy coupling with complex nonlinear spatiotemporal correlation characteristics, which requires multi-dimensional global coordination considerations such as flood control-power generation-navigation-ecology-water supply, long-term-medium-term-short-term multi-time scales, basin centralized control-grid multi-dispatch levels, wind power-photovoltaic-hydropower multi-power sources, etc. This fundamentally changes the dispatching and operation mode of cascade hydropower. How to take into account the operation requirements of all aspects to build a cascade hydropower operation risk indicator system for basin water, wind and solar integration and identify key sensitive risks is the primary problem that needs to be solved in the dispatching and operation and risk control of basin water, wind and solar integration.

[0003] The risk of integrated water, wind and solar power operation in a river basin is an adverse consequence caused by exogenous uncertainties. Research on the risk of integrated water, wind and solar power operation in a river basin can be divided into two categories. One category emphasizes the control of established conventional risks in the process of preparing multi-energy complementary integrated operation plans. This type of literature explicitly incorporates risk control constraints or risk control targets into the integrated water, wind and solar power dispatch model to achieve the preparation of dispatch plans that take into account risks. The other category focuses on building a set of operational risk indicators. Common risks include load loss and load shedding risks, production deficit risks, unit crossing vibration zone risks, and discharge fluctuation risks. Time series production simulation is used to quantify and analyze risk indicators. The former type of research is essentially a subjective presentation of risk items that need to be controlled, and the optimized operation results of controlling this risk are given through optimization modeling. This type of research does not provide the basis for selecting the control risk and has certain subjective and arbitrary defects. The risk indicators involved in the second type of research are often within 3 items, and the relationship between different risk indicators has not been deeply studied. At present, there is still a lack of research results that can comprehensively and uniformly reveal the relative importance of various operational risks. Summary of the invention

[0004] Under the background of integrated water-wind-solar power construction in river basins, the present invention aims at the difficult problem of how to construct a cascade hydropower operation risk system for integrated water-wind-solar power construction in river basins and identify key risks therefrom. A method for identifying key operation risks of integrated water-wind-solar power construction in river basins is proposed. The method can be used to solve the key problems that need to be urgently solved in the scheduling and operation of clean energy bases of water, wind and solar power in some river basins.

[0005] The technical solution of the present invention:

[0006] A method for identifying key operational risks of water-wind-solar integration in a river basin includes the following steps:

[0007] Step (1) Constructing a risk index system for the operation of water, wind and solar power: Considering seven indicators, namely, hydropower output fluctuation index, reservoir water level fluctuation index, tail water level fluctuation index, crossing vibration zone index, head obstruction depth index, abandoned water volume index and peak regulation pressure index, a risk evaluation index system for the operation of cascade hydropower stations under the integration of water, wind and solar power is constructed.

[0008] The definitions of each indicator are as follows:

[0009] (1.1) Hydropower output fluctuation index:

[0010] (1)

[0011] In the formula, Indicates extremely poor; represents the output fluctuation value of hydropower station h between period t and period t+2, MW; Represents the reservoir water level of hydropower station h at the beginning of period t, m.

[0012] (1.2) Reservoir water level fluctuation index:

[0013] (2)

[0014] In the formula, represents the reservoir water level fluctuation value of hydropower station h between time period t and time period t+2, m; Represents the reservoir water level of hydropower station h at the beginning of period t, m.

[0015] (1.3) Tail water level fluctuation index:

[0016] (3)

[0017] In the formula, represents the reservoir water level fluctuation value of hydropower station h between time period t and time period t+2, m; It represents the tailwater level of hydropower station h in period t, m.

[0018] (1.4) Cross-vibration zone indicators:

[0019] (4)

[0020] In the formula, It represents the rate of hydropower station h crossing the vibration zone between time periods t and t+1, %; Indicates whether the unit u of the hydropower station h crosses the vibration zone between time period t and time period t+1, 1 means it crosses the vibration zone, and 0 means it does not cross the vibration zone; represents the total number of units in the hydropower station h, The value is in the range of [0,100%].

[0021] (1.5) Head obstruction depth index:

[0022] (5)

[0023] In the formula, represents the head obstruction depth of hydropower station h in time period t, m; represents the design generating head of the hydropower station h, m; represents the generating head of hydropower station h in time period t, m.

[0024] (1.6) Amount of abandoned water:

[0025] (6)

[0026] In the formula, Indicates the hydropower station h in the risk investigation period Daily hydropower wastewater, 10,000 m 3 ; represents the average water discharge of hydropower station h in time period t, m 3 / s.

[0027] (1.7) Peak load regulation index:

[0028] (7)

[0029] In the formula, represents the peak-to-valley difference of hydropower load of the hydro-wind-solar hybrid power station s on day d, MW; represents the hydropower load of the hydro-wind-solar hybrid power station s in time period t, MW; It represents the total number of water-wind-solar hybrid power stations in the basin’s clean energy base; Indicates the total number of time periods.

[0030] Step (2) construct a simulation model for the joint operation of multiple DC transmission cascade hydropower plants.

[0031] (2.1) Objective function:

[0032] (8)

[0033] (9)

[0034] Where: is the total power generation of the water, wind and solar multi-energy complementary system in the basin, MW; Penalty for system abandonment of water; They represent the set of cascade power stations, the set of sub-plants of power station h, and the set of dispatching periods respectively; represents the output of the hydro-wind-solar hybrid power station (h,s) in time period t, MW; represents the water discharge of hydropower station h in period t, m 3 / s; is the penalty coefficient for abandoning water.

[0035] (2.2) Constraints:

[0036] 1) Hydraulic constraints include:

[0037] Water balance constraints:

[0038] (10)

[0039] In the formula, , and They represent the inflow, power generation and abandoned water flow of reservoir h in period t, respectively. 3 / s; represents the storage capacity of reservoir h in time period t, in ten thousand m 3 .

[0040] Hydraulic connection constraints:

[0041] (11)

[0042] in, represents the interval flow of reservoir h in time period t, m 3 / s; represents the direct upstream power station of hydropower station h, represents the set of direct upstream power stations of hydropower station h; Indicates upstream power station The power generation flow in time period t is: Indicates upstream power station The abandoned water flow in time period t; Indicates that from the upstream power station The water flow delay time to the downstream power station is h.

[0043] Hydrological basic curve:

[0044] (12)

[0045] (13)

[0046] in, represents the storage capacity of reservoir h in time period t, represents the outflow of reservoir h in period t; and They represent the water level-storage capacity curve and tailwater level-discharge curve of reservoir h respectively; and They represent the reservoir water level and tailwater level of reservoir h in time period t, m.

[0047] Head constraint:

[0048] (14)

[0049] in, represents the hydraulic head of reservoir h in period t, m.

[0050] Hydraulic boundary conditions:

[0051] (15)

[0052] (16)

[0053] (17)

[0054] (18)

[0055] (19)

[0056] in, and They represent the upper and lower limits of the storage capacity of reservoir h in time period t, in ten thousand m 3 ; Indicates outbound flow; and They represent the upper and lower limits of the outflow of reservoir h in time period t, m 3 / s; represents the hydropower generation flow of the hydro-wind-solar hybrid power station s in time period t, and They represent the upper and lower limits of the hydropower generation flow of the hydro-wind-solar hybrid power station s in time period t, m 3 / s; represents the hydropower output of the hydro-wind-solar hybrid power station s in time period t, and They represent the upper and lower limits of the hydropower output of the hydro-wind-solar hybrid power station s in time period t, MW; and They represent the initial and final storage capacity of reservoir h, and They represent the initial and final storage capacity limits of reservoir h, m 3 .

[0057] 2) Constraints of hydropower units include:

[0058] Power characteristics of hydropower units:

[0059] Since the model simulation cycle is daily, the fixed water consumption rate within the day is used for output calculation:

[0060] (20)

[0061] In the formula, It represents the output of unit u in period t, MW; represents the hydropower generation flow of unit u in period t; represents the average daily water consumption rate of power station h on day d, m 3 / kWh.

[0062] Unit start and stop state constraints:

[0063] (twenty one)

[0064] (twenty two)

[0065] (twenty three)

[0066] Where: and They represent the startup and shutdown actions of unit u at the beginning of period t respectively: It means that the unit u starts up at the beginning of period t. It means that the unit u has no startup action at the beginning of period t; Indicates that unit u shuts down at the beginning of period t. It means that the unit u is in non-operating action at the beginning of period t; Indicates the on / off status of unit u in time period t, 1 means on and 0 means off.

[0067] Unit operating vibration zone constraints:

[0068] To prevent the unit from operating in the vibration zone, the feasible zone of the unit is limited according to the water head. The formula is as follows:

[0069] (twenty four)

[0070] Where: and They represent the lower and upper limits of the output of unit u in the nth feasible zone during period t, in MW.

[0071] Unit operating boundaries:

[0072] (25)

[0073] (26)

[0074] (27)

[0075] Where: and They represent the upper and lower limits of the output of unit u in period t respectively; and They represent the upper and lower limits of the hydropower generation flow of unit u in period t respectively; Represents the output ramp of unit u during period t, MW.

[0076] Unit start and stop duration:

[0077] (28)

[0078] (29)

[0079] Where: and They represent the minimum startup duration and shutdown duration of unit u respectively.

[0080] 3) The power distribution ratio constraints in the framework agreement include:

[0081] DC transmission load step shape factor constraints:

[0082] Due to the operating power limitation of the DC tie line, a step-shaped coefficient constraint is proposed for the complementary output of the hydropower, wind and solar power clusters. The formula is as follows:

[0083] (30)

[0084] Where: represents the load shape curve scaling control variable of the hydro-wind-solar hybrid power station (h, s); It represents the load shape factor value of the DC transmission line of the water-wind-solar hybrid power station (h,s) during period t; It represents the transmission power of the DC interconnection line of the hydro-wind-solar hybrid power station (h,s) in time period t, MW.

[0085] Load constraints for hydro-wind-solar hybrid power stations:

[0086] The total output of the hydro-wind-solar hybrid power station should balance the corresponding receiving-end load demand, as shown in the following formula:

[0087] (31)

[0088] (32)

[0089] In the formula, represents the output of the hydro-wind-solar hybrid power station (h,s) at time period t through direct current transmission; It represents the capacity of the DC transmission line of the hydro-wind-solar hybrid power station (h,s) in time period t, MW.

[0090] Hydropower retention constraints for water-wind-solar hybrid power stations:

[0091] Part of the hydropower generated by the hydro-wind-solar hybrid power station needs to be retained in the province, and the retained power needs to meet a certain ratio requirement, as shown in the following formula:

[0092] (33)

[0093] (34)

[0094] In the formula, represents the output of hydropower station h in time period t; Represents the index set of the water-wind-solar hybrid power stations contained in the hydropower station h; and They represent the retained provincial output and the output transmitted through direct current of the hydro-wind-solar hybrid power station (h, s) in time period t, MW; It represents the proportion of hydropower required to be retained by the hydro-wind-solar hybrid power station (h,s) on day d to the total hydropower generation.

[0095] Constraints on the distribution of electricity delivered and retained during dry season:

[0096] When the hydropower station is transported out during the dry season, the distribution of retained electricity should be balanced, and the formula is as follows:

[0097] (35)

[0098] (36)

[0099] In the formula, It represents the total amount of electricity retained in the province by hydropower station h during the dry season, MWh; It represents the distribution ratio coefficient of the electricity output of the water-wind-solar hybrid power station (h,s) during the dry season; Indicates the power generation period; Indicates the total number of dry season periods.

[0100] Constraints on the distribution of electricity delivered and retained during the flood season:

[0101] When the hybrid hydropower station is transmitting electricity during the flood season, the distribution of retained electricity must be balanced, and the formula is as follows:

[0102] (37)

[0103] (38)

[0104] In the formula, It represents the distribution ratio coefficient of the electricity transmitted by the hydro-wind-solar hybrid power station (h,s) during the flood season; It represents the proportion coefficient of reserved electricity distribution of the hydro-wind-solar hybrid power station (h,s) during the flood season; Indicates the total number of flood season periods.

[0105] Constraints on the distribution of electricity delivered and retained under dry season replacement conditions:

[0106] When the electricity is delivered under the dry season Xiluo replacement condition, the distribution of retained electricity should be balanced, and the formula is as follows:

[0107] (39)

[0108] (40)

[0109] In the formula, and They represent the retained provincial output and the output transmitted through direct current of the hydro-wind-solar hybrid power station (h, s) in time period t respectively; It represents the proportion coefficient of reserved electricity distribution of the water-wind-solar hybrid power station (h,s) during the dry season; It represents the distribution ratio coefficient of the electricity output of the water-wind-solar hybrid power station (h,s) during the dry season; represents the output of hydropower station h in time period t; It represents the total electricity retained by the hydropower station during the dry season h, MWh.

[0110] Constraints on flood and dry season allocation coefficients:

[0111] The allocation coefficient of the hybrid hydropower station in the flood and dry seasons must meet certain constraints, and the formula is as follows:

[0112] (41)

[0113] (42)

[0114] In the formula, It represents the distribution ratio coefficient of the electricity transmitted from the hydro-wind-solar hybrid power station (h,s) during the flood season.

[0115] Step (3) construct a multi-DC water, wind and solar integrated operation simulation model.

[0116] (3.1) Objective function:

[0117] Maximize the total power generation of the cascade hydro-wind-solar system and introduce penalties for water abandonment and renewable energy abandonment, as shown below.

[0118] (43)

[0119] (44)

[0120] Where: The total power generation of the water, wind and solar multi-energy complementary system in the basin; Penalties for hydropower abandonment in the system; Penalties for system wind and photovoltaic power curtailment. , and They represent the hydropower output, wind power absorptive output and photovoltaic absorptive output of the hydro-wind-solar hybrid power station (h, s) in time period t, MW; and They represent the wind power curtailment and PV power curtailment of the hydro-wind-solar hybrid power station (h, s) in time period t, MW; represents the water discharge of hydropower station h in period t, m 3 / s; is the penalty coefficient for abandoning water.

[0121] (3.2) Constraints:

[0122] 1) Hydraulic constraints:

[0123] Same as formulas (10)-(19).

[0124] 2) Constraints on hydropower units:

[0125] Same as formulas (20)-(29).

[0126] 3) Constraints on power distribution ratio in framework agreement:

[0127] Same as formula (30), (32)-(42).

[0128] 4) Operation constraints of water-wind-solar hybrid:

[0129] (45)

[0130] (46)

[0131] (47)

[0132] (48)

[0133] (49)

[0134] Formulas (46) and (47) are empirical formulas for the theoretical output of wind power and photovoltaic power, respectively. Formulas (48) and (49) represent the constraints on the priority consumption of new energy, controlling the total abandonment rate of new energy within 3%.

[0135] In the formula, They represent the installed capacity of wind power and photovoltaic power in the hydro-wind-solar hybrid power station (h, s), MW; They represent the theoretical wind power output and theoretical photovoltaic output of the hydro-wind-solar hybrid power station (h, s) in time period t, MW; represents the average wind speed of the hydro-wind-solar hybrid power station (h,s) in time period t, m / s; They represent the cut-in wind speed, cut-out wind speed and rated wind speed of the wind turbine in the hydro-wind-solar hybrid power station (h, s), m / s. They represent the average solar radiation intensity (W / m 2 ) and the average surface temperature of the solar cell array (°C); Respectively represent the solar radiation intensity under standard conditions (1000W / m 2 ) and temperature (25℃); Indicates the power temperature coefficient of the solar cell (-0.35% / °C).

[0136] Step (4) Identification of key operational risks of cascade hydropower projects with integrated water, wind and solar power in the basin.

[0137] In steps (2) and (3), simulation optimization models were constructed for the joint dispatch of cascade hydropower stations and the integrated operation of multiple DC water, wind and solar power transmission. The two models were solved by calling gurobi with python3.10 to obtain 8760h of refined and long-term operation samples of the joint dispatch of cascade hydropower stations and the integrated operation of multiple DC water, wind and solar power transmission. The time series calculation results of each risk indicator in step (1) were quantified based on the two sample sets.

[0138] Step (4) converts the time series calculation results of the risk indicators obtained above into risk probability and expectation, and further converts the risk aggregation at the power plant level into cascade system level risk. By comparing the operation risks of cascade hydropower stations and multi-DC wind and solar power integrated systems, the cascade system operation risk sensitivity aggregation ranking is performed to complete the cascade operation risk identification, which is convenient for reducing the risk control scale and achieving accurate risk control. The specific steps are as follows:

[0139] (4.1) Risk quantification statistics:

[0140] Operation risk identification is based on the risk quantification statistics in the operation sample. The operation sample is obtained by solving the simulation model of step (2) and step (3). The risk quantification statistics are performed based on the risk indicators of step (1) and the two statistical perspectives of risk probability and risk expectation. In order to further characterize the risk sensitivity of cascade hydropower operation after the integration of wind and solar energy and integrated operation, the risk sensitivity is quantified based on the risk expectation growth rate in different time periods.

[0141] 1) Risk Probability :

[0142] (50)

[0143] 2) Risk Expectation :

[0144] (51)

[0145] In the formula, Indicates the number of time periods within the risk assessment cycle; represents the risk value of hydropower station h in time period t, m; Indicates whether the risk x of hydropower station h occurs in period t. The risk x refers to whether the seven indicators in step (1), namely, the hydropower output fluctuation index, reservoir water level fluctuation index, tail water level fluctuation index, crossing vibration zone index, head obstruction depth index, abandoned water volume index and peak regulation pressure index, exceed the threshold. It is a Boolean variable, 0 means no risk occurs, 1 means risk occurs; It represents the quantitative value of the risk x of hydropower station h in period t.

[0146] (4.2) Risk identification:

[0147] Based on the risk quantification statistical results, the present invention proposes a risk sensitivity aggregation ranking technology that couples the subjective and objective weighting method based on improved AHP and TOPSIS to achieve risk identification. The subjective and objective weighting method based on improved AHP is used to obtain the power station risk control weight matrix based on the key operating parameters of cascade hydropower; TOPSIS uses the risk sensitivity of risk quantification statistics as input, and allocates risk identification weights based on the power station risk control weight matrix, aggregates single hydropower operation risks into the overall operation risks of cascade hydropower, and gives operation risk sensitivity scores at the cascade level.

[0148] 1) Subjective and objective weighting method based on improved AHP.

[0149] The present invention proposes a subjective and objective weighting method based on improved AHP to allocate the risk control weights of cascade power stations. Different from the existing subjective and objective weighting method, the method proposed in the present invention not only overcomes the problem of excessive subjectivity of the subjective weighting method, but also avoids the requirement of uniform dimension for the evaluation object index set of the objective weighting method. The detailed steps of the method are as follows.

[0150] ① Construct a hierarchical evaluation model including target layer, criterion layer and decision layer. The target layer is the weight allocation of power plant risk control; the criterion layer contains 6 criteria, namely installed capacity, guaranteed output, available storage capacity, maximum power generation flow, maximum outflow flow and basin area; the decision layer is the power plant object under study;

[0151] ②Construct the decision-making layer judgment matrix, as shown in formula (52). It represents the importance of decision i relative to decision j under the kth criterion. Different from the traditional subjective weighting method, this method objectively gives the judgment matrix through the criterion parameters.

[0152] ③According to the 6 criteria of the criterion layer, execute ② and construct a judgment matrix set ;

[0153] ④ Perform consistency check on each judgment matrix in the judgment matrix set. The consistency check formula is shown in equations (53) and (54);

[0154] ⑤ Calculate the maximum eigenvalue of each judgment matrix in the judgment matrix set The corresponding eigenvector We get formula (55), and further get the criterion based on formulas (56) and (57): The weight matrix of the decision object ,in . We can further construct a weight matrix set ;

[0155] ⑥ According to the operator's attention to each indicator in the indicator layer, the reference weight vector of the weight matrix set is given According to formula (58), the power plant risk control weight matrix is ​​finally obtained: . This is the subjective part of this method.

[0156] (52)

[0157] (53)

[0158] (54)

[0159] In the formula, is the consistency indicator; is the average random consistency index, and its value is related to the order of matrix B; is the maximum eigenvalue of the judgment matrix B. Represents the consistency ratio, when , the judgment matrix B meets the consistency requirement, otherwise the judgment matrix is ​​reconstructed.

[0160] (55)

[0161] (56)

[0162] (57)

[0163] (58)

[0164] 2) Topology of solutions (TOPSIS).

[0165] In order to reflect the integrity of the cascade basin operation risk, the power plant risk control weight matrix is ​​introduced. , so that TOPSIS can solve the sensitivity aggregation ranking problem of multiple risk indicators in a multi-object system. The algorithm flow of the present invention is given as follows.

[0166] ①For a hydropower station objects, considering For a cascade hydropower system in a river basin with seven risk indicators, according to the simulation results of the simulation model constructed in steps (2) and (3) of the present invention, the annual expected values ​​of the seven risks in the risk indicator system of water, wind and solar multi-energy complementary operation in step (1) are statistically calculated, and the risk growth rate after the new energy is connected relative to that before the connection is obtained, which are respectively used as the sensitivity of the seven risks after the new energy is connected, forming Hydropower Operation Risk Sensitivity Matrix , as shown in formula (59);

[0167] ②Introducing the power plant risk control weight matrix , and the hydropower operation risk sensitivity matrix The operational risk sensitivity matrix considering the hydropower risk control weights is calculated , as formula (60);

[0168] ③ Objects in hydropower stations The weighted feature index set In the above equation, the minimum value is extracted as the optimal risk indicator. , extract the maximum value as the worst risk indicator ,get The optimal risk index vector for a hydropower station object and the worst risk indicator vector .

[0169] ④ The sensitivity ranking score of the operation risk of the cascade hydropower system in the basin after the installation of new energy is calculated by formulas (61)-(63) . The larger it is, the more serious the deterioration of the risk indicator. It is the key risk faced by cascade hydropower after the integration of wind and solar energy, and it needs to be paid attention to and controlled.

[0170] (59)

[0171] (60)

[0172] (61)

[0173] (62)

[0174] (63)

[0175] Where: represents the distance of the rth risk from the optimal risk score, represents the distance of the rth risk from the worst risk score, Represents the relative importance score of the rth risk.

[0176] Beneficial effects of the invention: The present invention is oriented to the actual engineering demands of the integrated water, wind and solar clean energy base in the river basin. Starting from the multi-dimensional global coordinated operation requirements, it constructs a multi-dimensional system operation risk evaluation index system with multiple utilization demands, multiple time scales, multiple scheduling levels and multiple power supply objects. On the basis of constructing a long-term refined operation simulation model of the integrated water, wind and solar clean energy base, it proposes a relative importance ranking method for hydropower operation risks after new energy access, identifies the sensitive weak links of many differentiated operation risks, and provides support for the integrated water, wind and solar scheduling and targeted risk management. Relying on the actual project of the water, wind and solar clean energy base in the lower reaches of the Jinsha River, method verification and risk analysis were carried out. BRIEF DESCRIPTION OF THE DRAWINGS

[0177] Figure 1 It is a schematic diagram of the risk analysis of cascade hydropower operation under the integrated operation of water, wind and solar energy complementarity in the basin.

[0178] Figure 2 This is a hierarchical diagram of the subjective and objective weighting method based on the improved AHP.

[0179] Figure 3 It is a schematic diagram of cascade hydropower operation for the integration of water, wind and solar power in the river basin. DETAILED DESCRIPTION

[0180] The present invention is further described below in conjunction with implementation examples.

[0181] The overall process of the present invention is as follows Figure 1 shown.

[0182] The water-wind-solar integrated clean energy base in the lower reaches of the Jinsha River was used as the research object to test the present invention. Figure 3As shown in the figure, the water-wind-solar-integrated clean energy base in the lower reaches of the Jinsha River is constructed around the three giant cascade hydropower stations of Wudongde, Baihetan and Xiluodu in the lower reaches of the Jinsha River, with a total installed capacity of 38.8 million kW of hydropower. Among them, the right bank of Wudongde, the left bank of Baihetan and the left bank of Xiluodu plan to access a total of 10.486 million kW of wind and solar installed capacity. After the wind and solar power stations are connected to the grid, they will be sent to Guangdong, Jiangsu and other provinces together with the hydropower through the UHV DC interconnection line. Since the water-wind-solar-integrated clean energy base in the lower reaches of the Jinsha River is still in the planning and construction stage, it is difficult to grasp the new risks faced by the giant hydropower station after the participation of new energy in the external transmission. Therefore, this embodiment takes the water-wind-solar-integrated clean energy base in the lower reaches of the Jinsha River as a simulation case, and identifies the key operation risks of hydropower facing the water-wind-solar integration of the basin, providing support for the multi-dimensional risk control sequence faced by the operation of the Jinxia cascade hydropower.

[0183] Step (1): Construct a risk indicator system.

[0184] Hydropower output fluctuation indicators:

[0185] Reservoir water level fluctuation index:

[0186] Tail water level fluctuation index:

[0187] Crossing vibration zone indicator:

[0188] Head obstruction depth indicator:

[0189] Discarded water index:

[0190] Peak load pressure index:

[0191] In the formula, Indicates extremely poor; represents the output fluctuation value of hydropower station h between period t and period t+2, MW; Represents the reservoir water level of hydropower station h at the beginning of period t, m. represents the reservoir water level fluctuation value of hydropower station h between time period t and time period t+2, m; Represents the reservoir water level of hydropower station h at the beginning of period t, m. represents the reservoir water level fluctuation value of hydropower station h between time period t and time period t+2, m; It represents the tailwater level of hydropower station h in period t, m. It represents the rate of hydropower station h crossing the vibration zone between time periods t and t+1, %; Indicates whether the unit u of the hydropower station h crosses the vibration zone between time period t and time period t+1, 1 means it crosses the vibration zone, and 0 means it does not cross the vibration zone; represents the total number of units in the hydropower station h, The value is in the range of [0,100%]. represents the head obstruction depth of hydropower station h in time period t, m; represents the design generating head of the hydropower station h, m; represents the generating head of hydropower station h in time period t, m. Indicates the hydropower station h in the risk investigation period Daily hydropower wastewater, 10,000 m 3 ; represents the average water discharge of hydropower station h in time period t, m 3 / s. represents the peak-to-valley difference of hydropower load of the hydro-wind-solar hybrid power station s on day d, MW; represents the hydropower load of the hydro-wind-solar hybrid power station s in time period t, MW; It represents the total number of water-wind-solar hybrid power stations in the basin’s clean energy base; Indicates the total number of time periods.

[0192] Step (2): Construct and solve a simulation model for the joint operation of multiple DC transmission cascade hydropower plants.

[0193] Objective function: maximize the total hydropower generation and introduce overall cascade water abandonment penalty.

[0194]

[0195]

[0196] Where: is the total power generation of the water, wind and solar multi-energy complementary system in the basin, MW; Penalty for system abandonment of water; They represent the set of cascade power stations, the set of sub-plants of power station h, and the set of dispatching periods respectively; represents the output of the hydro-wind-solar hybrid power station (h,s) in time period t, MW; represents the water discharge of hydropower station h in period t, m 3 / s; is the penalty coefficient for abandoning water.

[0197] Constraints: including hydraulic constraints, hydropower unit constraints, and power distribution ratio constraints in the framework agreement. The mathematical expression of the constraint model is as follows:

[0198] 1) Hydraulic constraints include:

[0199] Water balance constraints:

[0200]

[0201] Hydraulic connection constraints:

[0202] Hydrological basic curve:

[0203]

[0204]

[0205] Head constraint:

[0206]

[0207] Hydraulic boundary conditions:

[0208]

[0209]

[0210]

[0211]

[0212]

[0213] In the formula, , and They represent the inflow, power generation and abandoned water flow of reservoir h in period t, respectively. 3 / s; represents the storage capacity of reservoir h in time period t, in ten thousand m 3 . and They represent the interval flow and outflow flow of reservoir h in time period t, respectively, 3 / s; represents the direct upstream power station of hydropower station h, represents the set of direct upstream power stations of hydropower station h; Indicates upstream power station The power generation flow in time period t is: Indicates upstream power station The abandoned water flow in time period t; Indicates that from the upstream power station The water flow delay time to the downstream power station is h. and They represent the water level-storage capacity curve and tailwater level-discharge curve of reservoir h respectively; and They represent the reservoir water level and tailwater level of reservoir h in time period t, m. represents the hydraulic head of reservoir h in period t, m. and They represent the upper and lower limits of the storage capacity of reservoir h in time period t, in ten thousand m 3 ; Indicates outbound flow; and They represent the upper and lower limits of the outflow of reservoir h in time period t, m 3 / s; represents the hydropower output of the hydro-wind-solar hybrid power station s in time period t, and They represent the upper and lower limits of the hydropower output of the hydro-wind-solar hybrid power station s in time period t, MW; represents the hydropower generation flow of the hydro-wind-solar hybrid power station s in time period t, and They represent the upper and lower limits of the hydropower generation flow of the hydro-wind-solar hybrid power station s in time period t, m 3 / s. and They represent the initial and final storage capacity of reservoir h, and They represent the initial and final storage capacity limits of reservoir h, m 3 .

[0214] 2) Constraints of hydropower units include:

[0215] Power characteristics of hydropower units:

[0216]

[0217] Unit start and stop state constraints:

[0218]

[0219]

[0220]

[0221] Unit operating vibration zone constraints:

[0222]

[0223] Unit operating boundaries:

[0224]

[0225]

[0226]

[0227] Unit start and stop duration:

[0228]

[0229]

[0230] In the formula, It represents the output of unit u in period t, MW; represents the hydropower generation flow of unit u in period t; represents the average daily water consumption rate of power station h on day d, m 3 / kWh. and They represent the startup and shutdown actions of unit u at the beginning of period t respectively: It means that the unit u starts up at the beginning of period t. It means that the unit u has no startup action at the beginning of period t; Indicates that unit u shuts down at the beginning of period t. It means that the unit u is in non-operating action at the beginning of period t; Indicates the on / off status of unit u in time period t, 1 means on and 0 means off. and They represent the lower and upper limits of the output of unit u in the nth feasible zone during period t, in MW. and They represent the upper and lower limits of the output of unit u in period t respectively; and They represent the upper and lower limits of the hydropower generation flow of unit u in period t respectively; Represents the output ramp of unit u during period t, MW. and They represent the minimum startup duration and shutdown duration of unit u respectively.

[0231] 3) The power distribution ratio constraints in the framework agreement include:

[0232] DC transmission load step shape factor constraints:

[0233]

[0234] Load constraints for hydro-wind-solar hybrid power stations:

[0235]

[0236]

[0237] Hydropower retention constraints for water-wind-solar hybrid power stations:

[0238]

[0239]

[0240] Constraints on the distribution of electricity delivered and retained during dry season:

[0241]

[0242]

[0243] Constraints on the distribution of electricity delivered and retained during the flood season:

[0244]

[0245]

[0246] The constraints on the distribution of electricity delivered and retained under the Xiluodu replacement condition during the dry season:

[0247]

[0248]

[0249] Constraints on flood and dry season allocation coefficients:

[0250] Xiluodu is constrained according to the replacement conditions, and the allocation coefficients of other hybrid hydropower stations in flood and dry seasons must meet certain constraints. The formula is as follows:

[0251]

[0252]

[0253] Where: represents the load shape curve scaling control variable of the hydro-wind-solar hybrid power station (h, s); It represents the load shape factor value of the DC transmission line of the water-wind-solar hybrid power station (h,s) during period t; It represents the transmission power of the DC interconnection line of the hydro-wind-solar hybrid power station (h,s) in time period t, MW. represents the output of the hydro-wind-solar hybrid power station (h,s) at time period t through direct current transmission; It represents the capacity of the DC transmission line of the hydro-wind-solar hybrid power station (h,s) in time period t, MW. represents the output of hydropower station h in time period t; Represents the index set of the water-wind-solar hybrid power stations contained in the hydropower station h; and They represent the retained provincial output and the output transmitted through direct current of the hydro-wind-solar hybrid power station (h, s) in time period t, MW; It represents the proportion of hydropower required to be retained by the hydro-wind-solar hybrid power station (h,s) on day d to the total hydropower generation. It represents the total amount of electricity retained in the province by hydropower station h during the dry season, MWh; It represents the distribution ratio coefficient of the electricity output of the water-wind-solar hybrid power station (h,s) during the dry season; Indicates the power generation period; Indicates the total number of dry season periods. It represents the distribution ratio coefficient of the electricity transmitted by the hydro-wind-solar hybrid power station (h,s) during the flood season; It represents the proportion coefficient of reserved electricity distribution of the hydro-wind-solar hybrid power station (h,s) during the flood season; Indicates the total number of flood season periods. and They represent the retained provincial output and the output transmitted through direct current of the hydro-wind-solar hybrid power station (h, s) in time period t respectively; It represents the proportion coefficient of reserved electricity distribution of the water-wind-solar hybrid power station (h,s) during the dry season; It represents the distribution ratio coefficient of the electricity output of the water-wind-solar hybrid power station (h,s) during the dry season; represents the output of hydropower station h in time period t; It represents the total electricity retained by the hydropower station during the dry season h, MWh.

[0254] The input data of the model include historical observation data and basic data of hydropower stations.

[0255] (1) Historical observation data: Collect hourly natural runoff data of the lower reaches of the Jinsha River in 2021.

[0256] (2) Basic data of hydropower stations: relevant data of Wudongde, Baihetan and Xiluodu hydropower stations in the lower reaches of the Jinsha River, including installed capacity data, upper and lower limits of reservoir capacity, upper and lower limits of power generation flow, initial and final reservoir capacity, transmission channel capacity, and load distribution ratio of multiple power grids.

[0257] Gurobi10.0.1 is called through Python3.10 to optimize and solve the model.

[0258] Step (3): Construct and solve a simulation model for the joint operation of multiple DC transmission cascade hydropower plants.

[0259] The objective function is to maximize the total power generation of the cascade hydro-wind-solar system and introduce penalties for abandoned water and new energy power:

[0260]

[0261]

[0262] Where: The total power generation of the water, wind and solar multi-energy complementary system in the basin; Penalties for hydropower abandonment in the system; Penalties for system wind and photovoltaic power curtailment. , and They represent the hydropower output, wind power absorptive output and photovoltaic absorptive output of the hydro-wind-solar hybrid power station (h, s) in time period t, MW; and They represent the wind power curtailment and PV power curtailment of the hydro-wind-solar hybrid power station (h, s) in time period t, MW; represents the water discharge of hydropower station h in time period t, m 3 / s; is the penalty coefficient for abandoning water.

[0263] The constraints are based on the model in step (2) and supplemented with the water-wind-solar hybrid operation constraints:

[0264] Operation constraints of water-wind-solar hybrid:

[0265]

[0266]

[0267]

[0268]

[0269]

[0270] In the formula, They represent the installed capacity of wind power and photovoltaic power in the hydro-wind-solar hybrid power station (h, s), MW; They represent the theoretical wind power output and theoretical photovoltaic output of the hydro-wind-solar hybrid power station (h, s) in time period t, MW; represents the average wind speed of the hydro-wind-solar hybrid power station (h,s) in time period t, m / s; They represent the cut-in wind speed, cut-out wind speed and rated wind speed of the wind turbine in the hydro-wind-solar hybrid power station (h, s), m / s. They represent the average solar radiation intensity (W / m 2 ) and the average surface temperature of the solar cell array (°C); Respectively represent the solar radiation intensity under standard conditions (1000W / m 2 ) and temperature (25℃); Indicates the power temperature coefficient of the solar cell (-0.35% / °C).

[0271] The input data of the model include historical observation data, basic data of hydropower stations, and wind and solar power installed capacity planning data.

[0272] (1) Historical observation data: Collect hourly natural runoff data, wind speed data, solar radiation intensity data, and air temperature data in the lower reaches of the Jinsha River in 2021.

[0273] (2) Basic data of hydropower stations: The relevant data of Wudongde, Baihetan and Xiluodu hydropower stations in the lower reaches of the Jinsha River are from China Yangtze Power Co., Ltd., including installed capacity data, upper and lower limits of reservoir capacity, upper and lower limits of power generation flow, initial and final reservoir capacity, transmission channel capacity, and load distribution ratio of multiple power grids;

[0274] (3) Wind and solar power installed capacity planning data: Collect wind power and photovoltaic power installed capacity planning data and integration methods for the proposed urban water-wind-solar integrated clean energy base in the lower reaches of the Jinsha River.

[0275] Gurobi10.0.1 is called through Python3.10 to optimize and solve the model.

[0276] Step (4): Identification of key operational risks of cascade hydropower projects with integrated water, wind and solar power systems in the basin.

[0277] (4.1) Complete the risk quantification statistics of each hydropower station:

[0278] The 8760-hour refined and long-term operation samples of the cascade hydropower joint dispatch and multi-DC external transmission water, wind and solar power integrated operation obtained in steps (2) and (3) are used to quantify the seven risk indicators in step (1). Then, the risk expectation and risk probability statistical methods are used to obtain the long-term risk statistical calculation results as shown in Table 2.

[0279] Risk Probability :

[0280] Risk Expectation :

[0281] In the formula, Indicates the number of time periods within the risk assessment cycle; represents the risk value of hydropower station h in time period t, m; Indicates whether the risk x of hydropower station h occurs in period t, that is, whether the seven indicators of hydropower output fluctuation index, reservoir water level fluctuation index, tailwater level fluctuation index, crossing vibration zone index, head obstruction depth index, abandoned water volume index and peak regulation pressure index exceed the threshold. It is a Boolean variable, 0 means no risk occurs, 1 means risk occurs; It represents the quantitative value of the risk x of hydropower station h in period t.

[0282] Table 1 Statistics of risk expectation and risk expectation growth rate of cascade hydropower stations

[0283]

[0284] Table 1 shows the expected risk quantification results of various operational risk indicators of each power station before and after the cascade hydropower is connected to renewable energy. It can be seen that after the connection to renewable energy according to the planned plan, the expected values ​​of the three operational risks of output fluctuation, water abandonment and peak load pressure of the three cascade hydropower stations in the basin all increased; the growth rates of the remaining operational risk expectations of the other three power stations were not uniform, with both positive and negative growth. Therefore, it is difficult to directly determine the key operational risks of the cascade hydropower in the basin after the connection to renewable energy.

[0285] (4.2) Risk identification based on risk quantification statistics:

[0286] Based on the risk quantification statistical results, the invention proposes a risk sensitivity aggregation ranking technology that couples the subjective and objective weighting method based on improved AHP and TOPSIS to achieve risk identification. The subjective and objective weighting method based on improved AHP is used to obtain the power station risk control weight matrix based on the key operating parameters of cascade hydropower; TOPSIS uses the risk sensitivity of risk quantification statistics as input, and allocates risk identification weights based on the power station risk control weight matrix, aggregates single hydropower operation risks into the overall operation risks of cascade hydropower, and gives an operation risk sensitivity score at the cascade level.

[0287] 1) Subjective and objective weighting method based on improved AHP.

[0288] ① Construct a hierarchical evaluation model including target layer, criterion layer and decision layer, where the target layer is the risk control weight allocation of power stations; the criterion layer contains 6 criteria, namely installed capacity, guaranteed output, available storage capacity, maximum power generation flow, maximum outflow flow and basin area; the decision layer is the power station object under study. For example, in this embodiment, Wudongde, Baihetan and Xiluodu are taken as the research objects, and the decision layer contains 3 items, namely Wudongde, Baihetan and Xiluodu, see Figure 2 ;

[0289] ②Construct the decision-making layer judgment matrix, as shown in formula (52). It represents the importance of decision i relative to decision j under the kth criterion. Different from the traditional subjective weighting method, this method objectively gives the judgment matrix through the criterion parameters. The relative importance matrix results of the six criteria are shown in the following formulas, where 1-6 represent installed capacity, available capacity, maximum power generation flow, maximum outflow flow, upstream basin area and guaranteed output criterion respectively.

[0290]

[0291] ③According to the 6 criteria of the criterion layer, execute ② and construct a judgment matrix set ;

[0292] ④Perform consistency check on each judgment matrix in the judgment matrix set. The consistency check formula is as follows;

[0293]

[0294]

[0295] All passed the consistency check.

[0296] ⑤ Calculate the maximum eigenvalue of each judgment matrix in the judgment matrix set The corresponding eigenvector , further based on and Get the guidelines The weight matrix of the decision object ,in The decision object weight matrices of the six criteria are calculated as follows:

[0297]

[0298] We can further construct a weight matrix set ;

[0299] ⑥ According to the operator's attention to each indicator in the indicator layer, the reference weight vector of the weight matrix set is given , according to the formula Finally, the power plant risk control weight matrix is ​​obtained This is the subjective part of the method, the matrix The calculation results are as follows.

[0300]

[0301] Process value: In the formulas involved in the above steps, is the consistency indicator; is the average random consistency index, and its value is related to the order of matrix B; is the maximum eigenvalue of the judgment matrix B. Represents the consistency ratio, when When , the judgment matrix B meets the consistency requirement, otherwise the judgment matrix is ​​reconstructed. When the matrix order is 1 or 2, Take 0; when the matrix order is 3, Take 0.58; when the matrix order is 4, Take 0.9; when the matrix order is 5, Take 1.21; when the matrix order is 6, Take 1.24.

[0302] The meanings and values ​​of the symbols involved in the above process are as follows: Symbol , which means the hydropower station object index, with values ​​of 1, 2, 3; symbol , which means the hydropower station object index, with values ​​of 1, 2, 3; symbol , meaning the judgment criterion index, with values ​​of 1, 2, 3, 4, 5, 6; symbol , which means the risk index index, and its value is 1, 2, 3, 4, 5, 6, 7; symbol , which means the number of hydropower station objects, and its value is 3; symbol , which means the number of judgment criteria, is 6; symbol , which means the risk index number, and its value is 7.

[0303] 2) Topology of solutions (TOPSIS).

[0304] TOPSIS was first proposed in 1981. It can judge the relative merits of each attribute in a single system containing multiple attributes and give a quantitative score. In order to reflect the integrity of the cascade basin operation risk, the present invention introduces the power plant risk control weight matrix , so that TOPSIS can solve the sensitivity aggregation ranking problem of multiple risk indicators in a multi-object system. The algorithm flow of the present invention is given as follows.

[0305] ① For a basin cascade hydropower system including three hydropower stations and considering seven risk indicators, according to the simulation results of the multi-DC transmission cascade hydropower joint operation simulation model constructed in steps (2) and (3) of this patent, the annual expected values ​​of the seven risks in the risk indicator system of water, wind and solar multi-energy complementary operation in step (1) are respectively calculated, and the risk growth rate after the access of new energy relative to that before the access is obtained, which are respectively used as the sensitivity of the seven risks after the access of new energy, forming a 7×3 hydropower operation risk sensitivity matrix , the calculation results are as follows (unit: %);

[0306]

[0307] ②Introducing the power plant risk control weight matrix , and the hydropower operation risk sensitivity matrix The operational risk sensitivity matrix considering the hydropower risk control weights is calculated , the calculation results are as follows (unit: %);

[0308]

[0309] ③ Objects in hydropower stations The weighted feature index set In the above equation, the minimum value is extracted as the optimal risk indicator. , extract the maximum value as the worst risk indicator ,get The optimal risk index vector for a hydropower station object and the worst risk indicator vector .

[0310] ④ Passing (Where: represents the distance of the rth risk from the optimal risk score, represents the distance of the rth risk from the worst risk score, Represents the relative importance score of the rth risk. ) Calculate the sensitivity ranking score of the basin cascade hydropower system operation risk after the installation of new energy The calculation results are shown in Table 2 below. The larger it is, the more serious the deterioration of the risk indicator. It is the key risk faced by cascade hydropower after the integration of wind and solar energy, and it needs to be paid attention to and controlled.

[0311] Table 2 shows the relative importance score X of each risk indicator. The larger the X value, the greater the risk criticality. It can be seen from Table 2 that after the access of new energy, the relative importance score X of peak load regulation, output fluctuation and cascade water abandonment is significantly higher than other risk indicators. That is to say, after the access of new energy, the peak load regulation and output fluctuation risks are two key risk indicators. The main reason is that under the joint action of new energy and load, the two operating indicators of Wudongde, Baihetan and Xiluodu have deteriorated seriously. Among them, the output fluctuation risk growth rate of Xiluodu reached 129.37%, and the peak load risk growth rate was all greater than 100% (Table 1). Further comparison of the change level of operation risk of cascade hydropower stations before and after the access of new energy shows that the above two key operation risks of each power station, including the risk of water abandonment, have all increased; the two indicators of reservoir water level fluctuation and tailwater level fluctuation in Wudongde have increased, while the risks of downstream Xiluodu and Baihetan have decreased. At the same time, there is no obvious change in the risk of head obstruction, indicating that the access of new energy has little effect on the water level fluctuation before and after the reservoir dam; as for the risk of crossing the vibration zone, Wudongde is significantly affected. In summary, power generation risks such as output fluctuations and peak-shaving pressure are directly and significantly affected by the intermittent, volatile and random nature of renewable energy output; and due to the existence of hydropower and electricity connections, hydropower stations can adjust water levels through "water abandonment regulation", which has become a "mitigation measure" for reservoir risks such as water level fluctuations and head obstruction that are affected by the access of renewable energy.

[0312] Table 2 Ranking of relative importance scores of system risks

[0313]

Claims

1. A method for identifying key operational risks of integrated water, wind and solar power in a river basin, characterized in that: The steps include: Step (1) Constructing a risk index system for the operation of water, wind and solar power: Considering seven indicators, namely, hydropower output fluctuation index, reservoir water level fluctuation index, tailwater level fluctuation index, crossing vibration zone index, head obstruction depth index, abandoned water volume index and peak load pressure index, to construct an operation risk evaluation index system for cascade hydropower stations under the integration of water, wind and solar power; The definitions of each indicator are as follows: (1.1) Hydropower output fluctuation index: （1） In the formula, Indicates extremely poor; It represents the hydropower station h in time period t and time period Output fluctuation value between, MW; represents the reservoir water level of hydropower station h at the beginning of period t, m; (1.2) Reservoir water level fluctuation index: （2） In the formula, represents the reservoir water level fluctuation value of hydropower station h between time period t and time period t+2, m; represents the reservoir water level of hydropower station h at the beginning of period t, m; (1.3) Tail water level fluctuation index: （3） In the formula, represents the reservoir water level fluctuation value of hydropower station h between time period t and time period t+2, m; represents the tailwater level of hydropower station h during period t, m; (1.4) Cross-vibration zone indicators: （4） In the formula, It represents the rate of hydropower station h crossing the vibration zone between time periods t and t+1, %; Indicates whether the unit u of the hydropower station h crosses the vibration zone between time period t and time period t+1, 1 means it crosses the vibration zone, and 0 means it does not cross the vibration zone; represents the total number of units in the hydropower station h, The value is in the range of [0,100%]; (1.5) Head obstruction depth index: （5） In the formula, represents the head obstruction depth of hydropower station h in time period t, m; represents the design generating head of the hydropower station h, m; represents the generating head of hydropower station h in time period t, m; (1.6) Amount of abandoned water: （6） In the formula, Indicates the hydropower station h in the risk investigation period Daily hydropower wastewater, 10,000 m 3 ; represents the average water discharge of hydropower station h in time period t, m 3 / s; (1.7) Peak load regulation index: （7） In the formula, represents the peak-to-valley difference of hydropower load of the hydro-wind-solar hybrid power station s on day d, MW; represents the hydropower load of the hydropower-wind-solar hybrid power station s in time period t, MW; It represents the total number of water-wind-solar hybrid power stations in the basin’s clean energy base; Indicates the total number of time periods; Step (2) Construct a simulation model for the joint operation of multiple DC transmission cascade hydropower stations: (2.1) Objective function: （8） （9） Where: is the total power generation of the water, wind and solar multi-energy complementary system in the basin, MW; Penalty for system abandonment of water; They represent the set of cascade power stations, the set of sub-plants of power station h, and the set of dispatching periods respectively; represents the output of the hydro-wind-solar hybrid power station (h,s) in time period t, MW; represents the water discharge of hydropower station h in time period t, m 3 / s; is the penalty coefficient for abandoning water; (2.2) Constraints: 1) Hydraulic constraints include: Water balance constraints: （10） In the formula, , and They represent the inflow, power generation and abandoned water flow of reservoir h in period t, respectively. 3 / s; represents the storage capacity of reservoir h in time period t, in ten thousand m 3 ; Hydraulic connection constraints: （11） in, represents the interval flow of reservoir h in time period t, m 3 / s; represents the direct upstream power station of hydropower station h, represents the set of direct upstream power stations of hydropower station h; Indicates upstream power station The power generation flow in time period t is: Indicates upstream power station The abandoned water flow in time period t; Indicates that from the upstream power station The water flow delay time to the downstream power station is h; Hydrological basic curve: （12） （13） in, represents the storage capacity of reservoir h in time period t, represents the outflow of reservoir h in period t; and They represent the water level-storage capacity curve and tailwater level-discharge curve of reservoir h respectively; and They represent the reservoir water level and tailwater level of reservoir h in time period t, m; Head constraint: （14） in, represents the hydraulic head of reservoir h in time period t, m; Hydraulic boundary conditions: （15） （16） （17） （18） （19） in, and They represent the upper and lower limits of the storage capacity of reservoir h in time period t, in ten thousand m 3 ; Indicates outbound flow; and They represent the upper and lower limits of the outflow of reservoir h in time period t, m 3 / s; represents the hydropower generation flow of the hydro-wind-solar hybrid power station s in time period t, and They represent the upper and lower limits of the hydropower generation flow of the hydro-wind-solar hybrid power station s in time period t, m 3 / s; represents the hydropower output of the hydro-wind-solar hybrid power station s in time period t, and They represent the upper and lower limits of the hydropower output of the hydro-wind-solar hybrid power station s in time period t, MW; and They represent the initial and final storage capacity of reservoir h, and They represent the initial and final storage capacity limits of reservoir h, m 3 ; 2) Constraints of hydropower units include: Power characteristics of hydropower units: Since the model simulation cycle is daily, the fixed water consumption rate within the day is used for output calculation: （20） In the formula, It represents the output of unit u in period t, MW; represents the hydropower generation flow of unit u in period t; represents the average daily water consumption rate of power station h on day d, m 3 / kWh; Unit start and stop state constraints: （21） （22） （23） Where: and They represent the startup and shutdown actions of unit u at the beginning of period t respectively: It means that the unit u starts up at the beginning of period t. It means that the unit u has no startup action at the beginning of period t; Indicates that unit u shuts down at the beginning of period t. It means that the unit u is in non-operating action at the beginning of period t; Indicates the on / off status of unit u in time period t, 1 means on, 0 means off; Unit operating vibration zone constraints: To prevent the unit from operating in the vibration zone, the feasible zone of the unit is limited according to the water head. The formula is as follows: （24） Where: and They represent the lower and upper limits of the output of unit u in the nth feasible zone in period t, MW; Unit operating boundaries: （25） （26） （27） Where: and They represent the upper and lower limits of the output of unit u in period t respectively; and They represent the upper and lower limits of the hydropower generation flow of unit u in period t respectively; represents the output ramp of unit u during period t, MW; Unit start and stop duration: （28） （29） Where: and They represent the minimum startup duration and shutdown duration of unit u respectively; 3) The power distribution ratio constraints in the framework agreement include: DC transmission load step shape factor constraints: Due to the operating power limitation of the DC tie line, a step-shaped coefficient constraint is proposed for the complementary output of the hydropower, wind and solar power clusters. The formula is as follows: （30） Where: represents the load shape curve scaling control variable of the hydro-wind-solar hybrid power station (h, s); It represents the load shape factor value of the DC transmission line of the hydro-wind-solar hybrid power station (h,s) during period t; represents the transmission power of the DC transmission line of the hydro-wind-solar hybrid power station (h,s) in time period t, MW; Load constraints for hydro-wind-solar hybrid power stations: The total output of the hydro-wind-solar hybrid power station should balance the corresponding receiving-end load demand, as shown in the following formula: （31） （32） In the formula, represents the output of the hydro-wind-solar hybrid power station (h,s) at time period t through direct current transmission; represents the capacity of the DC transmission line of the hydro-wind-solar hybrid power station (h,s) in time period t, MW; Hydropower retention constraints for water-wind-solar hybrid power stations: Part of the hydropower generated by the hydro-wind-solar hybrid power station needs to be retained in the province, and the retained power needs to meet a certain ratio requirement, as shown in the following formula: （33） （34） In the formula, represents the output of hydropower station h in time period t; Represents the index set of the water-wind-solar hybrid power stations contained in the hydropower station h; and They represent the retained provincial output and DC transmission output of the hydro-wind-solar hybrid power station (h, s) in time period t, MW; It represents the proportion of the hydroelectricity that the hydro-wind-solar hybrid power station (h,s) requires to be retained on day d to the total hydroelectricity generation; Constraints on the distribution of electricity delivered and retained during dry season: When the hydropower station is transported out during the dry season, the distribution of retained electricity should be balanced, and the formula is as follows: （35） （36） In the formula, It represents the total amount of electricity retained in the province by hydropower station h during the dry season, MWh; It represents the distribution ratio coefficient of the electricity output of the water-wind-solar hybrid power station (h,s) during the dry season; Indicates the power generation period; Indicates the total number of dry season periods; Constraints on the distribution of electricity delivered and retained during the flood season: When the hybrid hydropower station is transmitting electricity during the flood season, the distribution of retained electricity must be balanced, and the formula is as follows: （37） （38） In the formula, It represents the distribution ratio coefficient of the electricity transmitted by the hydro-wind-solar hybrid power station (h,s) during the flood season; It represents the proportion coefficient of reserved electricity distribution of the hydro-wind-solar hybrid power station (h,s) during the flood season; Indicates the total number of flood season periods; Constraints on the distribution of electricity delivered and retained under dry season replacement conditions: When the electricity is delivered under the dry season Xiluo replacement condition, the distribution of retained electricity should be balanced, and the formula is as follows: （39） （40） In the formula, and They represent the retained provincial output and the output transmitted through direct current of the hydro-wind-solar hybrid power station (h, s) in time period t respectively; It represents the proportion coefficient of reserved electricity distribution of the water-wind-solar hybrid power station (h,s) during the dry season; It represents the distribution ratio coefficient of the electricity output of the water-wind-solar hybrid power station (h,s) during the dry season; represents the output of hydropower station h in time period t; It represents the total electricity retained by the hydropower station during the dry season h, MWh; Constraints on flood and dry season allocation coefficients: The allocation coefficient of the hybrid hydropower station in the flood and dry seasons must meet certain constraints, and the formula is as follows: （41） （42） In the formula, It represents the distribution ratio coefficient of the electricity transmitted by the hydro-wind-solar hybrid power station (h,s) during the flood season; Step (3) Construct a multi-DC water, wind and solar integrated operation simulation model: (3.1) Objective function: Maximize the total power generation of the cascade hydro-wind-solar system and introduce penalties for water abandonment and renewable energy abandonment, as shown below; （43） （44） Where: The total power generation of the water, wind and solar multi-energy complementary system in the basin; Penalties for hydropower abandonment in the system; Penalties for system wind and PV curtailment; , and They represent the hydropower output, wind power absorptive output and photovoltaic absorptive output of the hydro-wind-solar hybrid power station (h, s) in time period t, MW; and They represent the wind power curtailment and PV power curtailment of the hydro-wind-solar hybrid power station (h, s) in time period t, MW; represents the water discharge of hydropower station h in time period t, m 3 / s; is the penalty coefficient for abandoning water; (3.2) Constraints: 1) Hydraulic constraints: Same as formula (10)-(19); 2) Constraints on hydropower units: Same as formula (20)-(29); 3) Constraints on power distribution ratio in framework agreement: Same as formula (30), (32)-(42); 4) Operation constraints of water-wind-solar hybrid: （45） （46） （47） （48） （49） Formulas (46) and (47) are empirical formulas for the theoretical output of wind power and photovoltaic power, respectively; Formulas (48) and (49) represent the constraints on the priority consumption of new energy, controlling the total abandonment rate of new energy within 3%; In the formula, They represent the installed capacity of wind power and photovoltaic power in the hydro-wind-solar hybrid power station (h, s), MW; They represent the theoretical wind power output and theoretical photovoltaic output of the hydro-wind-solar hybrid power station (h, s) in time period t, MW; represents the average wind speed of the hydro-wind-solar hybrid power station (h,s) in time period t, m / s; They represent the cut-in wind speed, cut-out wind speed and rated wind speed of the wind turbine in the hydro-wind-solar hybrid power station (h, s), m / s; They represent the average solar radiation intensity and the average surface temperature of the solar cell array in the photovoltaic power station in the water-wind-solar hybrid power station (h, s) during time period t; Respectively represent the solar radiation intensity and temperature under standard conditions; Represents the power temperature coefficient of the solar cell; Step (4) Identification of key operational risks of cascade hydropower for integrated water, wind and solar power in the basin: (4.1) Risk quantification statistics: Operation risk identification is based on the risk quantification statistics in the operation sample. The operation sample is obtained by solving the simulation model of step (2) and step (3). The risk quantification statistics are performed based on the risk indicators of step (1) and the two statistical perspectives of risk probability and risk expectation. In order to further characterize the risk sensitivity of cascade hydropower operation after the integration of wind and solar energy and integrated operation, the risk sensitivity is quantified based on the risk expectation growth rate in different time periods. 1) Risk Probability : （50） 2) Risk Expectation : （51） In the formula, Indicates the number of time periods within the risk assessment cycle; represents the risk value of hydropower station h in time period t, m; Indicates whether the risk x of hydropower station h occurs in period t. The risk x refers to whether the seven indicators in step (1), namely, the hydropower output fluctuation index, reservoir water level fluctuation index, tail water level fluctuation index, crossing vibration zone index, head obstruction depth index, abandoned water volume index and peak regulation pressure index, exceed the threshold. It is a Boolean variable, 0 means no risk occurs, 1 means risk occurs; represents the quantitative value of the risk x of hydropower station h in period t; (4.2) Risk identification: Based on the subjective and objective weighting method of improved AHP, the risk control weight matrix of power stations is obtained based on the key operating parameters of cascade hydropower. TOPSIS uses the risk sensitivity of risk quantification statistics as input, allocates risk identification weights based on the power station risk control weight matrix, aggregates single hydropower operation risks into the overall operation risks of cascade hydropower, and gives operation risk sensitivity scores at the cascade level. 1) Subjective and objective weighting method based on improved AHP; ① Construct a hierarchical evaluation model including target layer, criterion layer and decision layer. The target layer is the weight allocation of power plant risk control; the criterion layer contains 6 criteria, namely installed capacity, guaranteed output, available storage capacity, maximum power generation flow, maximum outflow flow and basin area; the decision layer is the power plant object under study; ② Construct the decision-making layer judgment matrix, as shown in formula (52); It represents the importance of decision i relative to decision j under the kth criterion. Different from the traditional subjective weighting method, this method objectively gives the judgment matrix through the criterion parameters; ③According to the 6 criteria of the criterion layer, execute ② and construct a set of judgment matrices ; ④ Perform consistency check on each judgment matrix in the judgment matrix set. The consistency check formula is shown in equations (53) and (54); ⑤ Calculate the maximum eigenvalue of each judgment matrix in the judgment matrix set The corresponding eigenvector We get formula (55), and further get the criterion based on formulas (56) and (57): The weight matrix of the decision object ,in ; Further construct the weight matrix set ; ⑥ According to the operator's attention to each indicator in the indicator layer, the reference weight vector of the weight matrix set is given According to formula (58), the power plant risk control weight matrix is ​​finally obtained: ; （52） （53） （54） In the formula, is the consistency indicator; is the average random consistency index, and its value is related to the order of matrix B; is the maximum eigenvalue of the judgment matrix B; Represents the consistency ratio, when When , the judgment matrix B meets the consistency requirement, otherwise the judgment matrix is ​​reconstructed; （55） （56） （57） （58） 2) TOPSIS (the distance method between superior and inferior solutions); In order to reflect the integrity of the cascade basin operation risk, the power plant risk control weight matrix is ​​introduced. , so that TOPSIS can solve the sensitivity aggregation ranking problem of multiple risk indicators in a multi-object system; the algorithm flow is as follows; ①For a hydropower station objects, considering For a cascade hydropower system in a river basin with seven risk indicators, according to the simulation results of the simulation model constructed in steps (2) and (3) of the present invention, the annual expected values ​​of the seven risks in the risk indicator system of water, wind and solar multi-energy complementary operation in step (1) are statistically calculated, and the risk growth rate after the new energy is connected relative to that before the connection is obtained, which are respectively used as the sensitivity of the seven risks after the new energy is connected, forming Hydropower Operation Risk Sensitivity Matrix , as shown in formula (59); ②Introducing the power plant risk control weight matrix , and the hydropower operation risk sensitivity matrix The operational risk sensitivity matrix considering the hydropower risk control weights is calculated , as formula (60); ③ Objects in hydropower stations The weighted feature index set In the above equation, the minimum value is extracted as the optimal risk indicator. , extract the maximum value as the worst risk indicator ,get The optimal risk index vector for a hydropower station object and the worst risk indicator vector ; ④ The sensitivity ranking score of the operation risk of the cascade hydropower system in the basin after the installation of new energy is calculated by formulas (61)-(63) ; （59） （60） （61） （62） （63） Where: represents the distance of the rth risk from the optimal risk score, represents the distance of the rth risk from the worst risk score, Represents the relative importance score of the rth risk.

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