A method for identifying key operational risks of integrated water, wind and solar power generation in a river basin

Abstract

The present invention belongs to the technical field of integrated water, wind and solar power operation in a river basin, and relates to a method for identifying key operational risks of integrated water, wind and solar power in a river basin. First, based on the complex coordinated operation requirements such as the demand for comprehensive utilization of hydropower, multi-level scheduling, multiple time scales, and the complementarity of water, wind and solar power, a cascade hydropower operation risk system and seven quantitative indicators for constructing the system are constructed; secondly, an annual 8760-h panoramic time series operation simulation model of the water, wind and solar power clean energy base in the river basin is constructed to compare and calculate the change level of cascade hydropower operation risk before and after the access of new energy; finally, a risk ranking method coupling TOPSIS and AHP is proposed to eliminate the differences in units and magnitudes of different risk indicators and identify key risks. Taking the clean energy base in the lower reaches of the Jinsha River as an application example, the distribution characteristics of various risks are revealed, and it is found that the risks of hydropower abandonment, output fluctuation, and peak-shaving pressure are more sensitive to the access of new energy, and the sensitivity of risks to seasons and new energy installations is explored.

Description

Technical Field

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

[0002] River basin integrated clean energy bases primarily rely on hydropower bases and their transmission channels, integrating a reasonable scale of wind and solar power generation projects to achieve integrated resource allocation, dispatching, and power consumption. Essentially, these systems are coupled heterogeneous energy systems with complex, nonlinear spatiotemporal correlations. This requires comprehensive, multi-dimensional coordination across multiple utilization requirements, including flood control, power generation, navigation, ecology, and water supply; across multiple long-term, medium-term, and short-term timescales; across multiple dispatch levels within the river basin's centralized control and power grid; and across multiple power sources, including wind power, photovoltaic power, and hydropower. This fundamentally changes the operational approach of cascade hydropower. How to balance these various operational requirements, construct a risk indicator system for cascade hydropower operations tailored to river basin integrated water, wind, and solar power, and identify key sensitive risks, is a primary challenge urgently needed in the operational and risk management of integrated water, wind, and solar power in river basins.

[0003] The operational risks of integrated hydropower, wind, and solar power generation in a river basin are adverse consequences caused by exogenous uncertainties. Research on the operational risks of integrated hydropower, wind, and solar power generation in a river basin can be divided into two main categories. The first emphasizes the control of established conventional risks during the development of multi-energy complementary integrated operation plans. This literature explicitly incorporates risk control constraints or objectives into integrated hydropower, wind, and solar power generation scheduling models to achieve risk-inclusive scheduling. The second category focuses on constructing a set of operational risk indicators. Common risks include load loss and load shedding, production deficit, unit vibration zone crossing, and discharge fluctuation risk. These indicators are then quantitatively analyzed using time-series production simulations. The first category of research essentially subjectively identifies the risk items to be controlled and uses optimization modeling to produce optimized operational results that control these risks. However, this research lacks a basis for selecting the control risks and suffers from a degree of subjective assumptions. The second category of research often involves fewer than three risk indicators and lacks in-depth research on the relationships between these indicators. Currently, there is a lack of research that comprehensively and uniformly reveals the relative importance of various operational risks. Summary of the Invention

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

[0005] The technical solution of the present invention:

[0006] A method for identifying key operational risks of integrated water, wind and solar power generation in a river basin includes the following steps:

[0007] Step (1) Constructing a risk index system for the complementary 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, to construct an operational risk assessment index system for cascade hydropower stations under the integration of water, wind and solar power.

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

[0009] (1.1) Hydropower output fluctuation index:

[0010] (1)

[0011] Where, Indicates extremely poor; represents the output fluctuation value of hydropower station h between period t and period t+2, MW; It 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] Where, represents the reservoir water level fluctuation value of hydropower station h between period t and period t+2, m; It 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] Where, represents the reservoir water level fluctuation value of hydropower station h between period t and period t+2, m; represents the tailwater level of hydropower station h during period t, in m.

[0018] (1.4) Crossing vibration zone index:

[0019] (4)

[0020] Where, It represents the rate at which the hydropower station h crosses 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] Where, represents the head obstruction depth of hydropower station h at 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) Discarded water indicators:

[0025] (6)

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

[0027] (1.7) Peak load regulation pressure index:

[0028] (7)

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

[0030] Step (2) construct a multi-stage DC transmission cascade hydropower joint operation simulation model.

[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 water abandonment; 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 water abandonment.

[0035] (2.2) Constraints:

[0036] 1) Hydraulic constraints include:

[0037] Water balance constraints:

[0038] (10)

[0039] Where, , 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 hydropower stations directly upstream of hydropower station h; Indicates upstream power station The power generation flow in period t is: Indicates upstream power station The abandoned water flow in time period t; Indicates that 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 time 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 at time period t, in 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 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, in MW; and They represent the initial and final storage capacities of reservoir h, and They represent the initial and final storage capacity limits of reservoir h, in ten thousand m 3 .

[0057] 2) Constraints on 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] Where, It represents the output of unit u in period t, MW; represents the hydropower generation flow of unit u during period t; represents the average daily water consumption rate of power station h on day d, m 3 / kWh.

[0062] Unit start and stop status 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: Indicates that unit u starts up at the beginning of period t. It means that unit u has no startup action at the beginning of period t; Indicates that unit u shuts down at the beginning of period t. Indicates that unit u is in no-operation 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 hydropower generation flow of unit u in period t respectively; Represents the output ramp of unit u during period t, in MW.

[0076] Unit start-up and shutdown 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-solar 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); represents the load shape factor value of the DC transmission line of the hydro-wind-solar hybrid power station (h,s) during period t; It represents the transmission power of the DC transmission line of the hydro-wind-solar hybrid power station (h,s) in time period t, MW.

[0085] Transmission load constraints of hydro-wind-solar hybrid power stations:

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

[0087] (31)

[0088] (32)

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

[0090] Constraints on hydropower retention in hydro-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. The retained power needs to meet a certain ratio requirement, which is expressed as follows:

[0092] (33)

[0093] (34)

[0094] Where, represents the output of hydropower station h in time period t; Represents the index set of water-wind-solar hybrid power stations contained in the hydropower station h; and They represent the retained provincial output and DC output of the hydro-wind-solar hybrid power station (h, s) in time period t, in 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 seasons:

[0096] When a hydropower station is used to transmit electricity during the dry season, the distribution of retained electricity must be balanced. The formula is as follows:

[0097] (35)

[0098] (36)

[0099] Where, 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 hydro-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 flood season:

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

[0102] (37)

[0103] (38)

[0104] Where, It represents the distribution ratio coefficient of the electricity output from the hydro-wind-solar hybrid power station (h,s) during the flood season; It represents the proportional coefficient of the retained 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 during dry season replacement:

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

[0107] (39)

[0108] (40)

[0109] Where, and They represent the retained provincial output and the output transmitted via DC power of the hydro-wind-solar hybrid power station (h, s) in period t respectively; It represents the proportional coefficient of the retained electricity distribution of the hydro-wind-solar hybrid power station (h,s) during the dry season; It represents the distribution ratio coefficient of the electricity output of the hydro-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 period h, MWh.

[0110] Constraints on the allocation coefficients of flood and dry seasons:

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

[0112] (41)

[0113] (42)

[0114] Where, 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 curtailing water and renewable energy power, 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) at time period t, in MW; and They represent the wind power curtailment and photovoltaic power curtailment of the hydro-wind-solar hybrid power station (h,s) in time period t, in MW; represents the water discharge of hydropower station h in period t, m 3 / s; is the penalty coefficient for water abandonment.

[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 the framework agreement:

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

[0128] 4) Operational constraints of hydropower, wind power and solar power complementarity:

[0129] (45)

[0130] (46)

[0131] (47)

[0132] (48)

[0133] (49)

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

[0135] Where, 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, in MW; represents the average wind speed of the hydro-wind-solar hybrid power station (h,s) in time period t, m / s; are 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); Represents 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 stations 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 plants, respectively. Solving these two models using gurobi in Python 3.10 yielded 8760 hours of refined, long-term operational samples for the joint dispatch of cascade hydropower stations and the integrated operation of multiple DC water, wind, and solar power plants, respectively. The time series calculation results for each risk indicator in step (1) were quantified based on these 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 of the power plant level into the cascade system level risk. By comparing the operation risks of cascade hydropower stations and multi-DC wind and solar 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] Operational risk identification is based on the quantitative statistics of risks in the operational samples. After solving the simulation models of steps (2) and (3), the operational samples are obtained. Risk quantitative statistics are performed based on the risk indicators of step (1) and the two statistical perspectives of risk probability and risk expectation. To further characterize the operational risk sensitivity of cascade hydropower after the integration and integrated operation of wind and solar renewable energy, the risk sensitivity is quantified using the expected risk growth rate within different time periods.

[0141] 1) Risk Probability :

[0142] (50)

[0143] 2) Risk Expectancy :

[0144] (51)

[0145] Where, 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) (i.e., 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.

[0146] (4.2) Risk identification:

[0147] Based on the statistical results of risk quantification, this paper proposes a risk sensitivity aggregation ranking technique that couples the subjective and objective weighting method based on an improved AHP with TOPSIS to achieve risk identification. The subjective and objective weighting method based on the improved AHP derives a power plant risk control weight matrix based on the key operating parameters of the cascade hydropower station. TOPSIS uses the risk sensitivity of risk quantification statistics as input and assigns risk identification weights based on the power plant risk control weight matrix. This aggregates individual hydropower operation risks into the overall cascade hydropower operation risk, and provides an operational risk sensitivity score at the cascade level.

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

[0149] This paper proposes a subjective and objective weighting method based on a modified AHP to assign risk control weights to cascade power plants. Unlike existing subjective and objective weighting methods, this method overcomes the subjectivity inherent in subjective weighting methods while also avoiding the requirement of a uniform dimension for the evaluation indicator set required by objective weighting methods. The detailed steps are as follows.

[0150] ① A hierarchical evaluation model consisting of a target layer, a criterion layer, and a decision layer was constructed. The target layer aimed to allocate risk control weights for power plants; the criterion layer included six criteria: installed capacity, guaranteed output, available storage capacity, maximum power generation flow, maximum outflow flow, and basin area; and the decision layer was the power plant under study.

[0151] 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.

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

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

[0154] ⑤Calculate 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 of 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 the method.

[0156] (52)

[0157] (53)

[0158] (54)

[0159] Where, 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.

[0160] (55)

[0161] (56)

[0162] (57)

[0163] (58)

[0164] 2) Top-of-the-line solution distance method (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 solves the problem of sensitivity aggregation ranking 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 with seven risk indicators in a river basin, according to the simulation results of the simulation model constructed in steps (2) and (3) of the present invention, the full-year 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 the risk 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 Calculate the operational risk sensitivity matrix considering the hydropower risk control weights , as in formula (60);

[0168] ③Objects at 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 of 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 formula (61)-(63) . The larger the value, the more serious the deterioration of the risk indicator. It is a key risk faced by cascade hydropower after the integration of wind and solar new 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 r-th risk from the optimal risk score, represents the distance between the rth risk item and the worst risk score, Represents the relative importance score of the rth risk.

[0176] The 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, the 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 It 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 basin. DETAILED DESCRIPTION

[0180] The present invention will be further described below with reference to implementation cases.

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

[0182] The present invention was tested by taking the water-wind-solar integrated clean energy base in the lower Jinsha River basin as the research object. Figure 3As shown, the integrated hydropower, wind, and solar power clean energy base in the lower Jinsha River basin is being constructed around three giant cascade hydropower stations: Wudongde, Baihetan, and Xiluodu. The base has a total installed hydropower capacity of 38.8 million kW. Of this, 10.486 million kW of wind and solar power are planned to be connected to the right bank of Wudongde, the left bank of Baihetan, and the left bank of Xiluodu. Once connected to the grid, the wind and solar power stations will be transmitted together with the hydropower to Guangdong, Jiangsu, and other provinces via ultra-high voltage direct current (UHVDC) interconnection lines. Because the integrated hydropower, wind, and solar power clean energy base in the lower Jinsha River basin is still in the planning and construction phase, it is difficult to assess the new risks that the giant hydropower stations will face once renewable energy is added to the grid. Therefore, this example uses the integrated hydropower, wind, and solar power clean energy base in the lower Jinsha River basin as a simulation case to identify key operational risks for integrated hydropower, wind, and solar power in the basin. This provides support for a multi-dimensional risk control process for the Jinsha River cascade hydropower operations.

[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 indicators:

[0188] Head obstruction depth index:

[0189] Discarded water index:

[0190] Peak shaving pressure indicators:

[0191] Where, Indicates extremely poor; represents the output fluctuation value of hydropower station h between period t and period t+2, MW; It 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 period t and period t+2, m; It 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 period t and period t+2, m; It represents the tailwater level of hydropower station h during period t, in m. It represents the rate at which the hydropower station h crosses 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 at 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 waste water, 10,000 m 3 ; represents the average water discharge of hydropower station h in period t, m 3 / s. represents the peak-to-valley difference in hydropower load of the hydro-wind-solar hybrid power station s on day d, in MW; represents the hydropower load of the hydro-wind-solar hybrid power station s in time period t, MW; Indicates the total number of water, wind and solar hybrid power stations in the basin's clean energy base; Indicates the total number of time periods.

[0192] Step (2): Construct and solve the simulation model of multi-stage DC transmission cascade hydropower joint operation.

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

[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 water abandonment; 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 water abandonment.

[0197] Constraints: These include 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] Where, , 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 of reservoir h in period t, respectively, 3 / s; represents the direct upstream power station of hydropower station h, represents the set of hydropower stations directly upstream of hydropower station h; Indicates upstream power station The power generation flow in period t is: Indicates upstream power station The abandoned water flow in time period t; Indicates that 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 at time period t, in 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, in 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 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 capacities of reservoir h, and They represent the initial and final storage capacity limits of reservoir h, in ten thousand m 3 .

[0214] 2) Constraints on hydropower units include:

[0215] Power characteristics of hydropower units:

[0216]

[0217] Unit start and stop status constraints:

[0218]

[0219]

[0220]

[0221] Unit operating vibration zone constraints:

[0222]

[0223] Unit operating boundaries:

[0224]

[0225]

[0226]

[0227] Unit start-up and shutdown duration:

[0228]

[0229]

[0230] Where, It represents the output of unit u in period t, MW; represents the hydropower generation flow of unit u during 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: Indicates that unit u starts up at the beginning of period t. It means that unit u has no startup action at the beginning of period t; Indicates that unit u shuts down at the beginning of period t. Indicates that unit u is in no-operation 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 hydropower generation flow of unit u in period t respectively; Represents the output ramp of unit u during period t, in 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] Transmission load constraints of hydro-wind-solar hybrid power stations:

[0235]

[0236]

[0237] Constraints on hydropower retention in hydro-wind-solar hybrid power stations:

[0238]

[0239]

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

[0241]

[0242]

[0243] Constraints on the distribution of electricity delivered and retained during 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 the allocation coefficients of flood and dry seasons:

[0250] Xiluodu is constrained by 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); represents the load shape factor value of the DC transmission line of the hydro-wind-solar hybrid power station (h,s) during period t; It represents the transmission power of the DC transmission line of the hydro-wind-solar hybrid power station (h,s) in time period t, MW. represents the DC output of the hydro-wind-solar hybrid power station (h,s) in time period t; It represents the capacity of the DC transmission line of the hydro-wind-solar hybrid power station (h,s) in time period t, in MW. represents the output of hydropower station h in time period t; Represents the index set of water-wind-solar hybrid power stations contained in the hydropower station h; and They represent the retained provincial output and DC output of the hydro-wind-solar hybrid power station (h, s) in time period t, in 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. 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 hydro-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 output from the hydro-wind-solar hybrid power station (h,s) during the flood season; It represents the proportional coefficient of the retained 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 via DC power of the hydro-wind-solar hybrid power station (h, s) in period t respectively; It represents the proportional coefficient of the retained electricity distribution of the hydro-wind-solar hybrid power station (h,s) during the dry season; It represents the distribution ratio coefficient of the electricity output of the hydro-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 period 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 perform model optimization and solution.

[0258] Step (3): Construct and solve the simulation model of multi-DC transmission cascade hydropower joint operation.

[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 renewable 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) at time period t, in MW; and They represent the wind power curtailment and photovoltaic power curtailment of the hydro-wind-solar hybrid power station (h,s) in time period t, in MW; represents the water discharge of hydropower station h in period t, m 3 / s; is the penalty coefficient for water abandonment.

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

[0264] Operation constraints of hydropower, wind power and solar power complementarity:

[0265]

[0266]

[0267]

[0268]

[0269]

[0270] Where, 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, in MW; represents the average wind speed of the hydro-wind-solar hybrid power station (h,s) in time period t, m / s; are 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); Represents 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 installation 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 and solar integrated clean energy base in the lower reaches of the Jinsha River.

[0275] Gurobi10.0.1 is called through Python3.10 to perform model optimization and solution.

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

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

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

[0279] Risk Probability :

[0280] Risk Expectation :

[0281] Where, 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 for various operational risk indicators at each hydropower station before and after the cascade hydropower station was connected to renewable energy. It can be seen that after the planned connection to renewable energy, the expected values ​​of the three operational risks of output fluctuation, water curtailment, and peak-shaving pressure all increased at the three cascade hydropower stations in the basin. The expected growth rates of the remaining operational risks for the remaining three hydropower stations were not uniform, with both positive and negative increases occurring simultaneously. Therefore, it is difficult to directly determine the key operational risks of the cascade hydropower station in the basin after the connection to renewable energy.

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

[0286] Based on the statistical results of risk quantification, the invention proposes a risk sensitivity aggregation ranking technique that couples the subjective and objective weighting method based on an improved AHP with TOPSIS to achieve risk identification. The subjective and objective weighting method based on the improved AHP derives a power plant risk control weight matrix based on the key operating parameters of the cascade hydropower station. TOPSIS uses the risk sensitivity of risk quantification statistics as input and assigns risk identification weights based on the power plant risk control weight matrix. This aggregates individual hydropower operation risks into the overall cascade hydropower operation risk, and provides an operational 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 consisting of a target layer, a criterion layer, and a decision layer. The target layer is the weight allocation of power plant risk control; the criterion layer contains six 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. For example, in this embodiment, Wudongde, Baihetan, and Xiluodu are taken as the research objects, and the decision layer contains three items, namely, Wudongde, Baihetan, and Xiluodu. Figure 2 ;

[0289] 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. Unlike 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 formula, where 1-6 represent the installed capacity, available capacity, maximum power generation flow, maximum outflow flow, upstream basin area and guaranteed output criterion in order.

[0290]

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

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

[0293]

[0294]

[0295] All passed the consistency test.

[0296] ⑤Calculate 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 of 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 index of the hydropower station object, with values ​​of 1, 2, and 3; symbol , which means the index of the hydropower station object, with values ​​of 1, 2, and 3; symbol , meaning the judgment criterion index, with values ​​of 1, 2, 3, 4, 5, 6; symbol , which means risk indicator index, with values ​​of 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) Top-of-the-line solution distance method (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 , which enables TOPSIS to 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 full-year expected values ​​of the seven risks in the water-wind-solar multi-energy complementary operation risk indicator system in step (1) are calculated respectively, and the risk growth rate after the new energy is connected relative to the risk before the connection is obtained. These values ​​are used as the sensitivity of the seven risks after the new energy is connected, forming a 7×3 hydropower operation risk sensitivity matrix. , the calculation results are as follows (unit is %);

[0306]

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

[0308]

[0309] ③Objects at 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 of a hydropower station object and the worst risk indicator vector .

[0310] ④ Passing (Where: represents the distance of the r-th risk from the optimal risk score, represents the distance between the rth risk item and 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 the value, the more serious the deterioration of the risk indicator. It is a key risk faced by cascade hydropower after the integration of wind and solar new energy, and it needs to be paid attention to and controlled.

[0311] Table 2 shows the relative importance scores (X) for various risk indicators. The larger the X value, the greater the risk's criticality. Table 2 shows that after renewable energy integration, the relative importance scores (X) for peak load regulation, output fluctuation, and cascade water abandonment are significantly higher than for other risk indicators. This means that peak load regulation and output fluctuation risks are key risk indicators after renewable energy integration. This is primarily due to the combined effects of renewable energy and load, which led to significant deterioration in operational indicators at Wudongde, Baihetan, and Xiluodu. The output fluctuation risk at Xiluodu increased by 129.37%, while the peak load risk increased by more than 100% (Table 1). Further comparison of operational risk changes in cascade hydropower stations before and after renewable energy integration reveals that the aforementioned two key operational risks, including the risk of water abandonment, all increased at each station. While reservoir water level fluctuation and tailwater level fluctuation increased at Wudongde, the risks at the downstream Xiluodu and Baihetan decreased. Meanwhile, the risk of head obstruction remained unchanged, indicating that renewable energy integration has little impact on water level fluctuations both in front of and behind the reservoir dams. As for the risk of crossing the vibration zone, Wudongde was 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. Due to the connection between water and electricity, hydropower stations can adjust water levels through "water abandonment regulation", which becomes 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 by: The steps include: Step (1) Constructing a risk indicator system for the operation of water, wind and solar power multi-energy complementarity: 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 regulation pressure index, to construct an operation risk evaluation indicator 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: Where, R(·) represents the range; Nwave h，t represents the output fluctuation value of hydropower station h between period t and period t+2, MW; N h,t represents the reservoir water level of hydropower station h at the beginning of period t, m; (1.2) Reservoir water level fluctuation index: Where, Zwave h，t represents the reservoir water level fluctuation value of hydropower station h between time period t and time period t+2, m; Z h，t represents the reservoir water level of hydropower station h at the beginning of period t, m; (1.3) Tail water level fluctuation index: Where, Zdwave h，t represents the reservoir water level fluctuation value of hydropower station h between period t and period t+2, m; represents the tailwater level of hydropower station h during period t, m; (1.4) Crossing vibration zone index: Where, 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; U h represents the total number of units in the hydropower station h, The value is in the range [0, 100%]; (1.5) Head obstruction depth index: Where, represents the head obstruction depth of hydropower station h at time period t, m; represents the design generating head of the hydropower station h, m; H h，t represents the generating head of hydropower station h in time period t, m; (1.6) Amount of discarded water: Where VW h It represents the hydropower station h in the risk inspection period T s Daily hydropower waste water, 10,000 m 3 ; QW h，t represents the average water discharge of hydropower station h in period t, m 3 / s; (1.7) Peak load regulation index: peaking s，d =R({NH s,t |s=1,...,S;t=1,...,T d }) (7) Where, peaking s,d represents the peak-to-valley difference in hydropower load of the hydropower-wind-solar hybrid power station s on day d, MW; NH s，t represents the hydropower load of the hydropower station s in time period t, in MW; S represents the total number of hydropower stations in the clean energy base of the basin; T d Indicates the total number of time periods; Step (2) construct a multi-stage DC transmission cascade hydropower joint operation simulation model: (2.1) Objective function: max(F1-F2) (8) Where: F1 is the total power generation of the water, wind and solar multi-energy complementary system in the basin, MW; F2 is the system water abandonment penalty; H, S h , T represents the set of cascade power stations, the set of sub-plants of power station h, and the set of dispatching periods respectively; ph h，s,t represents the output of the hydro-wind-solar hybrid power station (h, s) in time period t, MW; qw h，t represents the water discharge of hydropower station h in period t, m 3 / s; β is the penalty coefficient for water abandonment; (2.2) Constraints: 1) Hydraulic constraints include: Water balance constraints: v h，t =v h，t-1 +(qi h，t -qp h，t -qw h，t )3600 / 10000 (10) Where, qi h，t ,qp h，t and qw h，t They represent the inflow, power generation and abandoned water flow of reservoir h in period t, respectively. 3 / s;v h，t represents the storage capacity of reservoir h in time period t, in ten thousand m 3 ; Hydraulic connection constraints: Among them, qn h，t represents the interval flow of reservoir h in time period t, m 3 / s; uh represents the power station directly upstream of the hydropower station h, UH h represents the set of hydropower stations directly upstream of the hydropower station h; qp uh，t represents the power generation flow of the upstream power station uh in time period t, qw uh,t represents the water discharge of the upstream power station uh in time period t; dt uh，h represents the water flow delay time from the upstream power station uh to the downstream power station h, h; Hydrological basic curve: Among them, v h，t represents the storage capacity of reservoir h in time period t, qs h，t represents the outflow of reservoir h in time period t; and They represent the water level-storage capacity curve and tailwater level-discharge curve of reservoir h respectively; h，t and zd h，t They represent the reservoir water level and tailwater level of reservoir h in time period t, m; Head constraint: Among them, H h，t represents the hydraulic head of reservoir h at time period t, m; Hydraulic boundary conditions: 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 ;qp h,t +qw h,t Indicates outbound flow; and They represent the upper and lower limits of the outflow of reservoir h in time period t, m 3 / s;qp s，t 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 hydropower generation flow of the hydro-wind-solar hybrid power station s in time period t, m 3 / s;ph s，t 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; v h，0 and v h，T They represent the initial and final storage capacities of reservoir h, and They represent the initial and final storage capacity limits of reservoir h, in ten thousand m 3 ; 2) Constraints on 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: ph u，t ·wcr h，d =3600·qp u，t (20) Where, ph u，t represents the output of unit u during period t, MW; qp u，t represents the hydropower generation flow of unit u during period t; wcr h,d represents the average daily water consumption rate of power station h on day d, m 3 / kWh; Unit start and stop status constraints: x u,t+1 -y u，t+1 =I u,t+1 -I u，t (21) x u，t +y u，t ≤1 (22) x u，t ,y u，t ,I u，t ∈[0,1] (23) Where: x u,t and y u，t They represent the startup and shutdown actions of unit u at the beginning of period t: x u，t =1 means that the unit u starts at the beginning of period t, x u,t =0 means that the unit u has no startup action at the beginning of period t; u，t =1 means that unit u is shut down at the beginning of period t, y u,t =0 means that the unit u has no shutdown action at the beginning of period t; I u，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: Where: and They represent the lower and upper limits of the output of unit u in the nth feasible region in period t, in MW; the unit operation boundary is: 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 hydropower generation flow of unit u in period t respectively; represents the output ramp of unit u during period t, MW; Unit start-up and shutdown duration: Where: and They represent the minimum duration of startup and shutdown 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-solar and solar-power clusters. The formula is as follows: Where: h，s represents the load shape curve scaling control variable of the hydro-wind-solar hybrid power station (h, s); represents the load shape factor value of the DC transmission line of the hydro-wind-solar hybrid power station (h, s) during period t; load h,s,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; Transmission load constraints of hydro-wind-solar hybrid power stations: The total output of a hydropower, wind-solar hybrid power station should balance the corresponding receiving-end load demand, as expressed in the following formula: Where, represents the DC output of the hydro-wind-solar hybrid power station (h,s) in time period t; represents the capacity of the DC transmission line of the hydro-wind-solar hybrid power station (h,s) in time period t, MW; Constraints on hydropower retention in hydro-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. The retained power needs to meet a certain ratio requirement, which is expressed as follows: Where, ph h，t represents the output of hydropower station h in time period t; s h Represents the index set of water-wind-solar hybrid power stations contained in the hydropower station h; and They represent the retained provincial output and DC output of the hydro-wind-solar hybrid power station (h, s) in time period t, in 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; Constraints on the distribution of electricity delivered and retained during dry seasons: When a hydropower station is used to transmit electricity during the dry season, the distribution of retained electricity must be balanced. The formula is as follows: Where, represents the total amount of electricity retained in the province by hydropower station h during the dry season, MWh; represents the power transmission ratio of the hydro-wind-solar hybrid power station (h,s) during the dry season; Δt represents the power generation period; T dry Indicates the total number of dry season periods; Constraints on the distribution of electricity delivered and retained during flood season: When a hybrid hydropower station transmits electricity during the flood season, the distribution of retained electricity must be balanced. The formula is as follows: Where, It represents the distribution ratio coefficient of the electricity output from the hydro-wind-solar hybrid power station (h,s) during the flood season; T represents the proportional coefficient of the retained electricity distribution of the hydro-wind-solar hybrid power station (h,s) during the flood season; wet Indicates the total number of flood season periods; Constraints on the distribution of electricity delivered and retained during dry season replacement: When delivering electricity under the dry season Xiluo replacement condition, the distribution of retained electricity must be balanced, and the formula is as follows: Where, and They represent the retained provincial output and the output transmitted via DC power of the hydro-wind-solar hybrid power station (h, s) in period t respectively; It represents the proportional coefficient of the retained electricity distribution of the hydro-wind-solar hybrid power station (h,s) during the dry season; represents the distribution ratio coefficient of the electricity output of the hydro-wind-solar hybrid power station (h,s) during the dry season; ph h，t represents the output of hydropower station h in time period t; represents the total electricity retained by the hydropower station during the dry season h, MWh; Constraints on the allocation coefficients of flood and dry seasons: The allocation coefficient of a hybrid hydropower station during flood and dry seasons must meet certain constraints, as shown in the following formula: Where, It represents the distribution ratio coefficient of the electricity output from the hydro-wind-solar hybrid power station (h,s) during the flood season; Step (3) Constructing 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 curtailing water and renewable energy power, as shown below; max(F1-F2-F3) (43) Where: F1 is the total power generation of the water, wind and solar multi-energy complementary system in the basin; F2 is the penalty for hydropower abandonment in the system; F3 is the penalty for wind power and photovoltaic abandonment in the system; ph h，s，t , and They represent the hydropower output, wind power absorptive output and photovoltaic absorptive output of the hydro-wind-solar hybrid power station (h, s) at time period t, in MW; and They represent the wind power curtailment and photovoltaic power curtailment of the hydro-wind-solar hybrid power station (h,s) in time period t, MW; qw h，t represents the water discharge of hydropower station h in period t, m 3 / s; β is the penalty coefficient for abandoning water; (3.2) Constraints: 1) Hydraulic constraints: Same as formulas (10)-(19); 2) Constraints on hydropower units: Same as formulas (20)-(29); 3) Constraints on power distribution ratio in the framework agreement: Same as formula (30), (32)-(42); 4) Operational constraints of hydropower, wind power and solar power complementarity: Equations (46) and (47) are the empirical formulas for the theoretical output of wind power and photovoltaic power, respectively; Equations (48) and (49) represent the constraints on the priority consumption of new energy, controlling the total curtailment rate of new energy within 3%; Where, They represent the installed capacity of wind power and photovoltaic power in the hydro-wind-solar hybrid power station (h, s), MW; are the theoretical wind power output and theoretical photovoltaic output of the hydro-wind-solar hybrid power station (h, s) in time period t, in MW; v h，s，t represents the average wind speed of the hydro-wind-solar hybrid power station (h,s) in time period t, m / s; are 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; R h，s，t , T h，s，t are the average solar radiation intensity and the average surface temperature of the solar cell array in the hydro-wind-solar hybrid power station (h, s) during time period t; R stc ,T stc They represent the solar radiation intensity and temperature under standard conditions respectively; α represents the power temperature coefficient of the solar cell; Step (4) Identification of key operational risks of cascade hydropower projects with 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. After solving the simulation model of step (2) and step (3), the operation sample is obtained. 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 and integrated operation of wind and solar new energy, the risk sensitivity is quantified based on the risk expectation growth rate in different time periods. 1) Risk probability P x : 2) Risk Expectancy E x : Where, T S represents the number of time periods in the risk assessment cycle; x h,t 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) (i.e., 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; 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 the improved AHP, the risk control weight matrix of the power station is derived based on the key operating parameters of the cascade hydropower station. TOPSIS uses the risk sensitivity of risk quantification statistics as input and assigns risk identification weights based on the power station risk control weight matrix. It aggregates the single hydropower operation risk into the overall operation risk of the cascade hydropower station and gives the operation risk sensitivity score at the cascade level. 1) Subjective and objective weighting method based on improved AHP; ① A hierarchical evaluation model consisting of a target layer, a criterion layer, and a decision layer was constructed. The target layer aimed to allocate risk control weights for power plants; the criterion layer included six criteria: installed capacity, guaranteed output, available storage capacity, maximum power generation flow, maximum outflow flow, and basin area; and the decision layer was the power plant under study. ② Construct the decision-making layer judgment matrix, as shown in formula (52); b kij 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 the judgment matrix set B={B k |k=1, 2, ... m}; ④ Perform consistency check on each judgment matrix in the judgment matrix set. The consistency check formula is shown in Equations (53) and (54); ⑤Calculate the maximum characteristic root λ of each judgment matrix in the judgment matrix set kmax The corresponding eigenvector U k We can get formula (55), and further get the weight matrix W of the decision object under criterion k according to formulas (56) and (57): k ,in Further construct the weight matrix set W={W k |k=1, 2, ... m}; ⑥ According to the operator's attention to each indicator of the indicator layer, the reference weight vector of the weight matrix set is given as Δ={δ k |k=1,2,…m}, and finally the power plant risk control weight matrix ΔW is obtained according to formula (58); R CR =I CI / I RI (53) Where, I CI is the consistency index; I RI is the average random consistency index, and its value is related to the order of matrix B; max is the maximum eigenvalue of the judgment matrix B; R CR Represents the consistency ratio, when R CR When <0.1, the judgment matrix B meets the consistency requirement, otherwise the judgment matrix is ​​reconstructed; IN k =[in k1 ,in k2 ,…,in kn ] T (55) 2) TOPSIS (topic-splitting distance method); In order to reflect the integrity of the operational risk of the cascade basin, the power plant risk control weight matrix ΔW is introduced to enable TOPSIS to solve the sensitivity aggregation ranking problem of multiple risk indicators in a multi-object system. The algorithm flow is as follows: ① For a basin cascade hydropower system including n hydropower stations and considering l risk indicators, based on the simulation results of the simulation model constructed in steps (2) and (3) of the present invention, the full-year expected values ​​of the seven risks in the water-wind-solar multi-energy complementary operation risk indicator system in step (1) are statistically calculated, and the expected risk growth rate after the new energy is connected relative to the risk before the connection is obtained. These values ​​are respectively used as the sensitivity of the seven risks after the new energy is connected, forming a 7×n hydropower operation risk sensitivity matrix Z, as shown in formula (59); ②Introduce the power plant risk control weight matrix ΔW and calculate the hydropower operation risk sensitivity matrix Z considering the hydropower risk control weight. * , as shown in formula (60); ③ The weighted characteristic index set of hydropower station object i 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 of n hydropower station objects and the worst risk indicator vector ④ The sensitivity ranking score X of the operation risk of the cascade hydropower system in the basin after the installation of new energy is calculated through equations (61)-(63); Where: represents the distance of the r-th risk from the optimal risk score, represents the distance between the rth risk and the worst risk score, X r Represents the relative importance score of the rth risk.

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