A probability evaluation method for sending power of a water and wind complementary system

Abstract

A kind of probability evaluation method of water, wind and light complementary system sending power, comprising the following steps: step 1: based on the water, wind and light complementary system sending power under the typical wind light uncertain scene, the Cornish-Fisher series method is used to build the display mathematical expression of sending power quantile;Step 2: establish water, wind and light complementary system sending power probability evaluation model;Step 3: the multiple iteration solution of linear model is converted into highly nonlinear model to obtain the probability evaluation result of water, wind and light complementary system sending power.The method can maximize the complementary flexibility of water, wind and light system, to evaluate the upper and lower limit of each time section sending power of system with certain confidence, the obtained sending power boundary can be used for system to report power adjustable range to power grid, and as the reference basis for power grid to formulate day-ahead generation dispatching instruction, so that water, wind and light system can meet the execution of power generation plan of power grid with greater probability under the premise of considering wind light prediction error.

Description

TECHNICAL FIELD

[0001] The present application relates to the technical field of power grid operation control, in particular to a probability evaluation method for sending power of a water-wind-solar complementary system. BACKGROUND

[0002] With the acceleration of low-carbon transformation of power, renewable energy power generation such as wind power, photovoltaic power and hydropower is developing rapidly. With hydropower as the leader, a multi-energy complementary system of cascade water-wind-solar is formed.

[0003] The bundling and sending mode of water-wind-solar multi-energy complementation is an effective means for developing wind and light resources widely recognized in recent years. Hydropower has the characteristics of large capacity and flexible operation, while wind and light have the characteristics of large reserves and short construction period. The use of existing sending channels of hydropower for "bundling" and sending can fully play the technical and economic characteristics of different types of power sources. The mode of hydropower as the main power and wind power and photovoltaic power as the auxiliary power makes the hydropower units with regulation capacity participate in peak shaving and smooth the output fluctuation of new energy, which helps to alleviate the phenomenon of insufficient electricity in dry season and to a certain extent alleviates the situation of "difficult to consume" of new energy. In dry season, hydropower units mainly bear peak load or waist load as peak shaving capacity; in flood season, they mainly bear base load to avoid water abandonment.

[0004] However, wind and light energy are affected by natural climate conditions, and their output power shows strong randomness, volatility and intermittency. Accurate prediction of wind and light power is impossible, resulting in strong uncertainty of the bundled sending power of the water-wind-solar complementary system, which makes the system sending power deviate greatly from the power generation plan required by the power grid and is subject to examination. Therefore, an effective interaction mode between the water-wind-solar complementary system and the power grid needs to be designed. On the basis of considering the uncertainty of wind and light and effectively coordinating water regulation and electricity regulation, the multi-resource flexible complementary characteristics of the water-wind-solar system are fully utilized to improve the reliability of the system's power supply support capability.

[0005] The "Quantification of Hydropower Flexibility and Promotion Method under Large-scale Wind-solar Grid Connection Conditions" proposes a method for quantifying the supply capacity of hydropower flexibility considering availability and responding to the prediction error of new energy. By embedding the flexible supply and demand relationship constraint into the day-ahead dispatching model, the flexibility supply capacity of hydropower is recognized, and the system has a large adjustment margin to cope with the randomness of wind and light power generation. However, the premise of quantifying the flexibility supply capacity of hydropower is that the system receives the power generation dispatching instruction from the power grid, and then obtains it after internal reservoir dispatching decision, which leads to the inability to realize effective coordination between reservoir dispatching and power grid dispatching, resulting in resource waste. Moreover, the water-wind-solar complementary system cannot provide reference for the power grid to formulate power generation dispatching instructions, and there is still a risk that the system sending power cannot meet the requirements of the power grid. SUMMARY

[0006] The object of the present application is to solve the technical problems that the existing water power flexibility supply capacity quantification is based on the premise that the system receives the power grid generation dispatching instruction, and is obtained after internal reservoir scheduling decision, which leads to the failure to realize the effective coordination of reservoir scheduling and power grid scheduling, resulting in resource waste, and the water, wind and light complementary system cannot provide reference basis for the power grid to formulate the generation dispatching instruction in advance, and the system sending power still cannot meet the requirements of the power grid, and a water, wind and light complementary system day-ahead sending power probability evaluation method considering wind and light uncertainty is provided.

[0007] In order to solve the above technical problems, the technical scheme adopted by the present application is:

[0008] A probability evaluation method for sending power of a water, wind and light complementary system, comprising the following steps:

[0009] Step 1: based on the sending power of the water, wind and light complementary system under the typical wind and light uncertainty scene, a Cornish-Fisher series method is used to construct a display mathematical expression of the sending power quantile;

[0010] Step 2: based on the constructed display mathematical expression of the sending power quantile, with the maximum range of the upper and lower quantiles of the system sending power at each time under a certain confidence as the target, a water, wind and light complementary system sending power probability evaluation model is established;

[0011] Step 3: for the nonlinear Cornish-Fisher constraint in the probability evaluation model, a small step iteration solving method based on chain structure data flow is proposed, and the highly nonlinear model is converted into multiple iteration solving of linear model, so as to obtain the probability evaluation result of the sending power of the water, wind and light complementary system.

[0012] In step 1, when constructing the display mathematical expression of the sending power quantile, the following substeps are adopted:

[0013] Step 1-1: based on the scene method, the uncertainty of wind and light output is discretized into N s typical wind and light uncertainty scenes;

[0014] Step 1-2: a group of one-dimensional arrays composed of the sending power of the water, wind and light complementary system under N s scenarios at t time is obtained, specifically Where P 0,t is the sending power of the system at t time, P 0,1,t , P 0,2,t , are the sending power of the system under scene 1, scene 2 and scene N s at t time, and N s is the total number of scenes;

[0015] Step 1-3: The Cornish-Fisher series method is used to establish the mathematical relationship between the sending power and its quantile.

[0016] In step 1-3, the Cornish-Fisher method with cumulative amount of 5 orders is used to establish the mathematical relationship between the sending power and its quantile, and the specific expression is as follows:

[0017]

[0018]

[0019]

[0020]

[0021]

[0022]

[0023]

[0024] wherein, P 0,s,t is the system sending power under the scene s at time t; μ P0,t , σ P0,t is the mean and standard deviation of the system sending power P0 at time t under all scenes; μ P0,t，3 , μ P0,t,4 , μ P0,t,5 are the normalized 3rd, 4th, and 5th order origin moments of the system sending power P0 at time t; ξ q is the quantile of the standard Gaussian distribution corresponding to the probability q; based on the given probability q, is the normalized quantile of the system sending power P0 at time t, F p0,t,q is the denormalized quantile of the system sending power P0 at time t under the probability q.

[0025] In step 2, the established water, wind and light complementary system sending power probability evaluation model is as follows:

[0026] (1) Objective function

[0027] Based on the explicit mathematical expression of the system sending power quantile constructed in step 1, the objective function of the probability evaluation model of the water, wind and light complementary system sending power is to maximize the upper and lower quantile range of the system sending power at each time under a certain confidence:

[0028]

[0029] wherein, T is the total number of scheduling periods; is the upper and lower quantile range of the system sending power at time t under the probability qmax The denormalized quantile of the system's output power; For time period t, the probability is q min The denormalized quantile of the system's output power;

[0030] (2) Constraints

[0031] 1) Water balance constraint

[0032]

[0033] In the formula, V s,i,t+1 V s,i,t These represent the reservoir capacities of the i-th hydropower station in scenario s at times t+1 and t, respectively; s,i,t Let Q be the inflow rate of the i-th hydropower station in scenario s at time t; s,i,t Let τ be the outflow from the i-th hydropower station in scenario s at time t; Δt be the scheduling time interval; τ be the outflow from the reservoir. i-1 The water flow delay time from the (i-1)th hydropower station to the ith hydropower station; Considering the water flow delay in scenario s, the (i-1)th hydropower station in the time period t-τ i-1 Traffic; R s,i,t Let be the interval flow between power station i-1 and power station i at time t in scenario s; Let be the power generation flow of the i-th hydropower station in scenario s at time t; Let be the water discharge flow of the i-th hydropower station in scenario s during time period t;

[0034] 2) Reservoir water level constraints

[0035]

[0036] In the formula, Z represents the inlet water level of the i-th hydropower station at time t in scenario s; i,t , These are the lower limit and upper limit of the water level of the i-th hydropower station at time t, respectively. The lower limit of the water level is often taken as the dead water level, the lower limit of the dispatching, etc., and the upper limit of the water level is taken as the flood limit water level, the normal high water level, or the upper limit of the dispatching, etc. This constraint limits the fluctuation range of the water level.

[0037] 3) Initial and final water level constraints

[0038]

[0039]

[0040] In the formula, These are the initial water level and the control water level at the end of the scheduling period, respectively. respectively, are the scheduling decision water levels of the ith hydroelectric power station at time 1 and time T under scenario s; and is the allowable scheduling final water level deviation to avoid affecting the water quantity of the next scheduling period;

[0041] 4) Outflow constraint

[0042]

[0043] wherein Q s,i,t is the outflow of the ith hydroelectric power station at time period t under scenario s; and Q i,t , are the lower and upper limits of the outflow of the ith hydroelectric power station at time period t, respectively; the lower limit of the outflow is usually determined by the ecological water demand, and the upper limit of the outflow is determined by the discharge capacity of the reservoir, which ensures the normal range of the discharge flow of the reservoir;

[0044] 5) Power output constraint

[0045]

[0046] wherein P is the power output of the ith hydroelectric power station at time period t under scenario s; are the lower and upper limits of the power output of the ith hydroelectric power station at time period t, respectively;

[0047] 6) Water level-storage capacity constraint

[0048]

[0049] wherein f i ZV (·) is a water level-storage capacity nonlinear relationship curve function of the reservoir where the ith hydroelectric power station is located;

[0050] 7) Tailwater level-discharge flow constraint

[0051]

[0052] wherein f i ZQ (·) is a tailwater level-discharge flow nonlinear relationship curve function of the ith hydroelectric power station; is the tailwater level of the ith hydroelectric power station at time period t under scenario s;

[0053] 8) Unit power output constraint

[0054]

[0055] wherein P s,i,n,t , P i,n is the power output of the nth unit of the ith hydroelectric power station at time period t under scenario s and the upper and lower limits thereof; and us,i,n,t is the on-off state variable of the nth unit of power plant i in time period t under scenario s, taking 1 to represent the on state and 0 to represent the off state, and the output of power plant i can be represented as:

[0056]

[0057] where N i represents the number of units of power plant i;

[0058] 9) Unit power generation flow constraint

[0059]

[0060] where is the power generation flow of the nth unit of power plant i in time period t under scenario s and its upper and lower limits; and the power generation flow of power plant i can be represented as:

[0061]

[0062] 10) Unit vibration zone constraint

[0063]

[0064] where is the upper and lower limit of the output of the kth vibration zone of the nth unit of power plant i;

[0065] 11) Unit on-off duration constraint

[0066]

[0067] where is the start-up operation variable of the nth unit of power plant i in time period t, taking 1 to represent start-up operation; is the shutdown operation variable of the nth unit of power plant i in time period t, taking 1 to represent shutdown operation; are the minimum on-off durations of the nth unit of power plant i, respectively; is the maximum on-off times of the nth unit of power plant i in the dispatching cycle; u s,i,n,t-1 is the on-off state variable of the nth unit of power plant i in time period t-1 under scenario s, and λ is the time index variable;

[0068] 12) Unit ramp constraint

[0069] -△P i,n ≤P s,i,n,t+1 -P s,i,n,t ≤△P i,n

[0070] where △P i,nP represents the climbing ability of the nth generating unit of power station i. s,i,n,t+1 This refers to the output of the nth generating unit of power station i in scenario s during time period t+1;

[0071] 13) Generating head constraints of the unit

[0072]

[0073] In the formula, H s,i,n,t , Let t represent the generating head and head loss of the nth generating unit of power plant i in scenario s during time period t. This refers to the water level in front of the dam of the i-th hydropower station in scenario s at time t-1;

[0074] 14) Head loss function constraints

[0075]

[0076] In the formula, a i b i Let i be the head loss coefficient and loss constant of power station i;

[0077] 15) Unit dynamic characteristic constraints

[0078]

[0079] In the formula, Let be the nonlinear relationship function of output-head-flow rate of the nth generating unit of power station i;

[0080] 16) Power balance constraints

[0081]

[0082] In the formula, P 0,s,t P represents the channel output power during time period t in scenario s. PV,s,t , These represent the power output and abandoned power of photovoltaic systems in scenario s during time period t, respectively; P WT,s,t , These represent the power output and abandoned power of wind power in scenario s during time period t, respectively; P L,s,t For scenario s, time period t is the domain load, and I refers to the total number of hydropower stations;

[0083] 17) Constraints on power and water wastage

[0084]

[0085] In the formula, Pr{·} is the confidence expression; η is the confidence level;

[0086] 18) Transmission channel constraints

[0087]

[0088] wherein, is the upper limit of the power of the transmission channel;

[0089] 19) Nonlinear Cornish-Fisher constraints

[0090] The specific expression of the Cornish-Fisher constraint is the mathematical relationship between the sending power and its quantile proposed in steps 1-3, and the Cornish-Fisher constraint establishes the mathematical relationship between the sending power and its quantile of the system under each scenario.

[0091] In step 3, for the nonlinear Cornish-Fisher constraint in the sending power probability evaluation model of the water-wind-solar complementary system, a small-step iteration solving method based on chain structure data flow is proposed, which converts the highly nonlinear model into multiple iteration solving of linear model to obtain the probability evaluation result of the sending power of the water-wind-solar complementary system; including the following steps:

[0092] Step 3-1: initialize the iteration number k = 0;

[0093] Step 3-2: obtain the feasible solution of the sending power of the water-wind-solar complementary system in the dispatching period under the typical wind-solar uncertainty scenario in the kth iteration, and calculate the quantile of the sending power in the kth iteration using the Cornish-Fisher series method, the probability q max and q min The quantile of the sending power of the system at time t is and

[0094] Step 3-3: based on the data flow of the chain structure, derive the partial derivative function of the sending power quantile and calculate the partial derivative coefficient of the kth iteration:

[0095]

[0096]

[0097]

[0098]

[0099]

[0100]

[0101]

[0102] wherein, respectively, are the de-normalized quantile, normalized quantile, normalized 5th order central moment, normalized 4th order central moment, normalized 3rd order central moment, increment of mean and standard deviation of the system output power at time t under probability q at the kth iteration; is the increment of the system output power at time t under scenario s at the kth iteration; the feasible solution of the system output power at the kth iteration in step 3-2 is substituted into, and each partial derivative coefficient can be obtained;

[0103] Step 3-4: Set the small step change range of the output power, based on the partial derivative function of the output power quantile derived in step 3-3 and the calculation result of the partial derivative coefficient at the kth iteration, the following linearized Cornish-Fisher constraint is obtained:

[0104]

[0105]

[0106]

[0107]

[0108]

[0109]

[0110]

[0111]

[0112]

[0113] wherein, δ is the limit of the small step change range of the output power; respectively, are the de-normalized quantile of the output power of the hydro- wind- solar complementary system at time t under probability q at the kth iteration and the k+1th iteration; respectively, are the normalized quantile of the output power of the hydro- wind- solar complementary system at time t under probability q at the kth iteration and the k+1th iteration; respectively, are the normalized 5th order central moment of the output power of the hydro- wind- solar complementary system at time t under probability q at the kth iteration and the k+1th iteration; respectively, are the normalized 4th order central moment of the output power of the hydro- wind- solar complementary system at time t under probability q at the kth iteration and the k+1th iteration; respectively, are the normalized 3rd order central moment of the output power of the hydro- wind- solar complementary system at time t under probability q at the kth iteration and the k+1th iteration; respectively are the standard deviation of the water, wind and solar complementary system output power at time t in the kth iteration and the k+1th iteration; respectively are the mean of the water, wind and solar complementary system output power at time t in the kth iteration and the k+1th iteration; it should be noted that the remaining nonlinear constraints in the model, such as the water level-storage capacity constraint, the unit vibration zone constraint, the unit dynamic characteristic constraint and the like, can be linearized by using the existing technology, and the present application does not make too much description;

[0114] Step 3-5: processing and solving the water, wind and solar complementary system output power probability evaluation model in the kth iteration, and judging whether the model converges or not;

[0115] Step 3-6: when the model iteration solving is completed, the obtained is the probability evaluation result of the water, wind and solar system output power.

[0116] In step 3-5, whether the model converges or not is judged, and the specific steps are as follows:

[0117] Step 3-5-1: all nonlinear Cornish-Fisher constraints in the water, wind and solar complementary system output power probability evaluation model are replaced by the linearized Cornish-Fisher constraints obtained in step 3-4, and are all regarded as decision variables, then the water, wind and solar complementary system output power probability evaluation model at this time becomes a mixed integer linear programming model;

[0118] Step 3-5-2: in order to gradually increase the quantile range under the probability q max and q min in the iteration process, the following constraints are added to the mixed integer linear programming model obtained in step 3-5-1:

[0119]

[0120]

[0121] wherein, respectively are the quantiles of the system output power at time t in the kth iteration under the probability q max and q min , which are known quantities and have been obtained in step 3-2; respectively represent the quantiles of the water, wind and solar complementary system output power at time t in the k+1th iteration under the probability q max and q min , both of which are the model variables to be solved in the kth iteration;

[0122] Step 3-5-3: The model obtained in step 3-5-2 is solved by using the Gurobi solver, and the water-wind-solar complementary system sending power at time t in scenario s at the k+1th iteration is obtained To avoid the error caused by linearization, the actual quantile is further updated by using the mathematical relationship between the sending power and its quantile proposed in step 1-3 The actual quantile is further updated by using the mathematical relationship between the sending power and its quantile proposed in step 1-3

[0123] Step 3-5-4: It is judged whether the model converges or not. When any one of the following two conditions is met, the model converges, and the iteration solving ends; otherwise, k=k+1 is set, and step 3-2 is returned.

[0124] Condition 1: If the absolute value deviation of the sending power quantile results obtained in the adjacent two iterations under the probability q max and q min is less than the allowable error ε, that is,

[0125] Condition 2: The iteration number k reaches the maximum iteration number K.

[0126] A small-step iteration solving method based on chain structure data flow, which converts a highly nonlinear model into a linear model for multiple iteration solving to obtain the probability evaluation result of the water-wind-solar complementary system sending power; specifically including the following steps:

[0127] Step S1: The iteration number k is initialized as k=0;

[0128] Step S2: The feasible solution of the water-wind-solar complementary system sending power at the kth iteration in the dispatching cycle under the typical wind-solar uncertainty scenario is obtained, and the Cornish-Fisher series method is used to calculate the quantile of the sending power at the kth iteration, that is, the quantile of the system sending power at time t under the probability q max and q min is and

[0129] Step S3: Based on the chain structure data flow, the partial derivative function of the sending power quantile is derived, and the partial derivative coefficient of the kth iteration is calculated:

[0130]

[0131]

[0132]

[0133]

[0134]

[0135]

[0136]

[0137] wherein, are the de-normalized quantile, normalized quantile, normalized 5th order central moment, normalized 4th order central moment, normalized 3rd order central moment, mean and increment of standard deviation of the system output power at time t under probability q at the kth iteration, respectively; is the increment of the system output power at time t under scenario s at the kth iteration; substituting the feasible solution of the system output power at the kth iteration in step S2, the partial derivative coefficients of each term can be obtained;

[0138] Step S4: setting the small step change range of the output power, based on the partial derivative function of the output power quantile derived in step S3 and the calculation results of the partial derivative coefficients at the kth iteration, the following linearized Cornish-Fisher constraint is obtained:

[0139]

[0140]

[0141]

[0142]

[0143]

[0144]

[0145]

[0146]

[0147]

[0148] wherein, δ is the small step change range limit of the output power; are the de-normalized quantile of the water, wind and solar complementary system output power at time t under probability q at the kth iteration and the k+1th iteration, respectively; are the normalized quantile of the water, wind and solar complementary system output power at time t under probability q at the kth iteration and the k+1th iteration, respectively; are the normalized 5th order central moment of the water, wind and solar complementary system output power at time t under probability q at the kth iteration and the k+1th iteration, respectively; respectively, normalized 4th order central moment of the water-wind-solar complementary system output power at time t under probability q in the kth iteration and the k+1th iteration; respectively, normalized 3rd order central moment of the water-wind-solar complementary system output power at time t under probability q in the kth iteration and the k+1th iteration; respectively, standard deviation of the water-wind-solar complementary system output power at time t in the kth iteration and the k+1th iteration; respectively, mean value of the water-wind-solar complementary system output power at time t in the kth iteration and the k+1th iteration; it should be noted that the remaining nonlinear constraints in the model, such as water level-storage capacity constraint, unit vibration zone constraint, unit dynamic characteristic constraint, etc., can be linearized by using existing technologies, and the present application does not make too much description;

[0149] Step S5: processing and solving the water-wind-solar complementary system output power probability evaluation model in the kth iteration, and judging whether the model converges or not;

[0150] Step S6: when the iteration solving of the model is finished, the obtained is the probability evaluation result of the water-wind-solar system output power.

[0151] In step S5, whether the model converges or not is judged, and the specific steps are as follows:

[0152] Step S5-1: all nonlinear Cornish-Fisher constraints in the water-wind-solar complementary system output power probability evaluation model are replaced by the linearized Cornish-Fisher constraints obtained in step S4, and are all regarded as decision variables, then the probability evaluation model of the water-wind-solar complementary system output power becomes a mixed integer linear programming model;

[0153] Step S5-2: in order to gradually increase the quantile range of the system output power under probability q max and q min in the iteration process, the following constraints are added to the mixed integer linear programming model obtained in step S5-1:

[0154]

[0155]

[0156] wherein, respectively, quantile of the system output power at time t under probability q max and q min in the kth iteration, which is a known quantity and has been obtained in step S2; respectively, quantile of the system output power at time t under probability q max and qmin The quantile of the water-wind-solar complementary system sending power, both are the model variables of the kth iteration;

[0157] Step S5-3: solving the model obtained in step S5-2 by using a Gurobi solver, so as to obtain the water-wind-solar complementary system sending power at time t under scenario s in the k+1th iteration In order to avoid the error caused by linearization, further use The actual quantile is updated through the mathematical relationship between the sending power and the quantile

[0158] Step S5-4: judging whether the model converges or not, when any one of the following two conditions is met, the model converges, and the iteration solving ends; otherwise, let k=k+1, and return to step S2;

[0159] Condition 1: if the probability q max And q min The absolute value deviation of the sending power quantile results obtained by the adjacent two iterations is less than the allowable error ε, that is

[0160] Condition 2: the iteration number k reaches the maximum iteration number K.

[0161] An electronic device, comprising a memory, a processor, the processor and the memory are connected through a communication bus, the memory is used for storing a computer program, the computer program is executed by the processor to realize the method as claimed in claim 1 to claim 8.

[0162] Compared with the prior art, the present application has the following technical effects:

[0163] 1) The present application can solve the technical problem of the existing water-wind-solar complementary system and the fuzzy interaction mode of the power grid, compared with the prior art, the present patent realizes the probability evaluation of the water-wind-solar complementary system sending power under the premise of considering the wind-solar uncertainty and the reservoir regulation capacity, and the evaluation result can be used for reporting to the power grid dispatching department as the basis for formulating the power grid dispatching instruction, so as to realize the coordinated cooperation of water regulation and power regulation, and improve the resource utilization rate;

[0164] 2) The present application provides an interaction mode of the water-wind-solar complementary system and the power grid, that is, the system reports the sending power interval to the power grid dispatching department by evaluating the adjustable range of the sending power, and the power grid dispatching department formulates and issues a feasible power generation plan according to the evaluation result, so as to realize the effective cooperation of power regulation and water regulation;

[0165] 3) This invention proposes a probabilistic evaluation method for the power output of a hydro-wind-solar system. This method can fully explore the complementary characteristics of the system and maximize the boundary range of the system's power output with a large confidence level while meeting constraints such as reservoir scheduling, safe operation of generating units, and water and electricity curtailment, thereby ensuring the reliable power supply support capability of the system.

[0166] 4) This invention proposes a small-step iterative model solving method, which transforms the solving of highly nonlinear models into iterative solving of linearized models, thereby improving the model solving efficiency;

[0167] 5) This invention can maximize the complementary flexibility of hydro-wind-solar systems, evaluate the upper and lower limits of the power output of the system at each time section with a certain confidence level, and the obtained power output boundary can be used by the system to report the adjustable power range to the grid and serve as a reference for the grid to formulate day-ahead power generation dispatch instructions, so as to realize the interaction between hydro-wind-solar systems and the grid, and enable hydro-wind-solar systems to meet the grid's power generation plan with a large confidence level under the premise of considering the uncertainty of wind and solar power. Attached Figure Description

[0168] The present invention will be further described below with reference to the accompanying drawings and embodiments:

[0169] Figure 1 This is a flowchart of the probability assessment method for the output power of a water-wind-solar system according to an embodiment of the present invention;

[0170] Figure 2 This is a topology diagram of a water-wind-solar hybrid system according to an embodiment of the present invention;

[0171] Figure 3 This is a schematic diagram of a typical wind power output uncertainty scenario according to an embodiment of the present invention;

[0172] Figure 4 This is a schematic diagram of a typical photovoltaic power output uncertainty scenario according to an embodiment of the present invention;

[0173] Figure 5 These are schematic diagrams of the system output power in 10 scenarios according to embodiments of the present invention;

[0174] Figure 6 This is a schematic diagram of the adjustable power range of the hydro-wind-solar system under a 90% confidence level according to an embodiment of the present invention;

[0175] Figure 7 This is a violin plot showing the probability distribution of the power output of the underwater wind and solar system at time 35 in an embodiment of the present invention. Detailed Implementation

[0176] This invention provides a probabilistic method for evaluating the transmitted power of a hydro-wind-solar hybrid system, comprising the following steps:

[0177] Step 1: Based on the typical wind and light uncertain scene, the Cornish-Fisher series method is used to construct the explicit mathematical expression of the sending power quantile of the water, wind and light complementary system;

[0178] Step 2: Based on the constructed explicit mathematical expression of the sending power quantile, the upper and lower quantile range of the system sending power at each time under a certain confidence is maximized as the target, and a probability evaluation model of the water, wind and light complementary system sending power is established;

[0179] Step 3: For the nonlinear Cornish-Fisher constraint in the probability evaluation model, a small step iterative solution method based on chain structure data flow is proposed, which converts the highly nonlinear model into multiple iterative solutions of linear model to obtain the probability evaluation result of the water, wind and light complementary system sending power.

[0180] In step 1, when constructing the explicit mathematical expression of the sending power quantile, the following substeps are adopted:

[0181] Step 1-1: Based on the scene method, the uncertainty of wind and light output is discretized into N s typical wind and light uncertain scenes;

[0182] Step 1-2: Obtain a one-dimensional array composed of the sending power of the water, wind and light complementary system under N s scenarios at t time, which is where P 0,t is the sending power of the system at t time, are the sending power of the system under scenario 1, scenario 2 and scenario N s at t time, and N s is the total number of scenarios;

[0183] Step 1-3: The Cornish-Fisher series method is used to establish the mathematical relationship between the sending power and its quantile.

[0184] In step 1-3, the Cornish-Fisher method with 5-order cumulative quantity is used to establish the mathematical relationship between the sending power and its quantile, and the specific expression is as follows:

[0185]

[0186]

[0187]

[0188]

[0189]

[0190]

[0191]

[0192] where P 0,s,t is the system output power at time t under scenario s; μ P0,t , σ P0,t are the mean and standard deviation of the system output power P0at time t under all scenarios; μ P0,t，3 , μ P0,t,4 , μ P0,t,5 are the normalized 3rd, 4th, and 5th order central moments of the system output power P0at time t; ξ q is the quantile of the standard Gaussian distribution corresponding to the probability q; based on a given probability q, is the normalized quantile of the system output power P0at time t, F p0,t,q is the denormalized quantile of the system output power P0at time t under the probability q.

[0193] In step 2, the established probability evaluation model of the water-wind-solar complementary system output power is specifically:

[0194] (1) Objective function

[0195] Based on the explicit mathematical expression of the system output power quantile constructed in step 1, the objective function of the probability evaluation model of the water-wind-solar complementary system output power is to maximize the upper and lower quantile range of the system output power at each time under a certain confidence level:

[0196]

[0197] where T is the total number of scheduling periods; is the denormalized quantile of the system output power at time t under the probability q max ; and is the denormalized quantile of the system output power at time t under the probability q min ; and

[0198] (2) Constraint conditions

[0199] 1) Water balance constraint

[0200]

[0201] where V s,i,t+1 and V s,i,t are the storage capacities of the i-th hydropower station at t+1 and t under scenario s; I s,i,t is the inflow of the i-th hydropower station at t under scenario s; Q s,i,t is the outflow of the i-th hydropower station at t under scenario s; and Δt is the scheduling time interval.i-1 is the water flow lag time from the i-1th hydropower station to the ith hydropower station; is the flow of the i-1th hydropower station at time period t-τ under scenario s; i-1 is the flow of the i-1th hydropower station at time t under scenario s; s,i,t is the interval flow between the i-1th hydropower station and the ith hydropower station at time t under scenario s; is the power generation flow of the ith hydropower station at time t under scenario s; is the abandoned water flow of the ith hydropower station at time period t under scenario s;

[0202] 2) Reservoir water level constraint

[0203]

[0204] In the formula, is the water level before the dam of the ith hydropower station at time t under scenario s; i,t , are the lower limit and the upper limit of the water level of the ith hydropower station at time t, respectively, the lower limit of the water level is usually the dead water level, the lower limit of the dispatch, etc., and the upper limit of the water level is the flood control water level, the normal high water level, or the upper limit of the dispatch, etc., and the constraint limits the fluctuation range of the water level;

[0205] 3) Initial and final water level constraint

[0206]

[0207]

[0208] In the formula, are the initial water level and the final control water level of the dispatch period, respectively; are the dispatch decision water levels of the ith hydropower station at time 1 and time T under scenario s, respectively; and △Z is the allowed deviation of the final water level of the dispatch period to avoid affecting the water quantity of the next dispatch period;

[0209] 4) Outflow constraint

[0210]

[0211] In the formula, Q s,i,t is the outflow of the ith hydropower station at time period t under scenario s; Q i,t , are the lower limit and the upper limit of the outflow of the ith hydropower station at time period t, respectively; the lower limit of the outflow is usually determined by the ecological water demand, and the upper limit of the outflow is determined by the discharge capacity of the reservoir, and the constraint ensures the normal range of the discharge flow of the reservoir;

[0212] 5) Power station output constraint

[0213]

[0214] where, is the output of the ith hydro power plant at time period t under scenario s; are the lower and upper bounds of the output of the ith hydro power plant at time period t, respectively;

[0215] 6) Water level-storage capacity constraint

[0216]

[0217] where, f i ZV is the water level-storage capacity nonlinear relationship curve function of the reservoir where the power plant i is located;

[0218] 7) Tailwater level-discharge constraint

[0219]

[0220] where, f i ZQ is the tailwater level-discharge nonlinear relationship curve function of the power plant i; is the tailwater level of the power plant i at time period t under scenario s;

[0221] 8) Unit output constraint

[0222]

[0223] where, P s,i,n,t , P i,n is the output of the nth unit of the power plant i at time period t under scenario s and its upper and lower bounds; u s,i,n,t is the on-off state variable of the nth unit of the power plant i at time period t under scenario s, taking 1 to represent the on state and 0 to represent the off state, then the output of the power plant i can be represented as:

[0224]

[0225] where, N i represents the number of units of the power plant i;

[0226] 9) Unit generation flow constraint

[0227]

[0228] where, is the generation flow of the nth unit of the power plant i at time period t under scenario s and its upper and lower bounds; then the generation flow of the power plant i can be represented as:

[0229]

[0230] 10) Unit vibration zone constraints

[0231]

[0232] where, are the upper and lower output limits of the kth vibration zone of the nth unit of power plant i;

[0233] 11) Unit start-up and shut-down duration constraints

[0234]

[0235] where, is the start-up operation variable of the nth unit of power plant i at time period t, taking 1 to represent start-up operation; is the shut-down operation variable of the nth unit of power plant i at time period t, taking 1 to represent shut-down operation; are the minimum start-up and shut-down durations of the nth unit of power plant i, respectively; is the maximum start-up times of the nth unit of power plant i within the scheduling period; u s,i,n,t-1 is the start-up and shut-down state variable of the nth unit of power plant i at time period t-1 under scenario s, and λ is the time index variable;

[0236] 12) Unit ramping constraints

[0237] -△P i,n ≤P s,i,n,t+1 -P s,i,n,t ≤△P i,n

[0238] where, △P i,n is the ramping capability of the nth unit of power plant i, P s,i,n,t+1 refers to the output of the nth unit of power plant i at time period t+1 under scenario s;

[0239] 13) Unit generation water head constraints

[0240]

[0241] where, H s,i,n,t , is the generation water head and water head loss of the nth unit of power plant i at time period t under scenario s, refers to the dam front water level of the ith hydropower station at t-1 time under scenario s;

[0242] 14) Water head loss function constraints

[0243]

[0244] where, a i , b iThe head loss coefficient and loss constant of the power station i;

[0245] 15) Unit power characteristics constraints

[0246]

[0247] In the formula, The output-head-flow nonlinear relationship function of the nth unit of the power station i;

[0248] 16) Power balance constraints

[0249]

[0250] In the formula, P 0,s,t The channel sending power of the time period t under the scenario s; P PV,s,t , The output and curtailment of photovoltaic in the time period t under the scenario s; P WT,s,t , The output and curtailment of wind power in the time period t under the scenario s; P L,s,t The in-domain load of the time period t under the scenario s, I refers to the total number of hydropower stations;

[0251] 17) Curtailed power and curtailed water constraints

[0252]

[0253] In the formula, Pr{·} is a confidence expression; η is a confidence level;

[0254] 18) Transmission channel constraints

[0255]

[0256] In the formula, The upper limit of the transmission channel power;

[0257] 19) Nonlinear Cornish-Fisher constraints

[0258] The specific expression of the Cornish-Fisher constraint is the mathematical relationship between the sending power and its quantile proposed in steps 1-3, and the Cornish-Fisher constraint establishes the mathematical relationship between the system sending power and its quantile under each scenario.

[0259] In step 3, for the nonlinear Cornish-Fisher constraint in the sending power probability evaluation model of the water-wind-solar complementary system, a small-step iteration solving method based on chain structure data flow is proposed, which converts the highly nonlinear model into multiple iteration solving of linear models to obtain the probability evaluation result of the sending power of the water-wind-solar complementary system; including the following steps:

[0260] Step 3-1: initialize iteration number k = 0;

[0261] Step 3-2: obtain the feasible solution of the kth iteration of the dispatching period of the water-wind complementary system under the typical wind light uncertainty scenario, and calculate the quantile of the dispatching power at time t under the probability q max and q min The quantile of the system dispatching power at time t under the probability q and

[0262] Step 3-3: based on the data flow of the chain structure, derive the partial derivative function of the quantile of the dispatching power and calculate the partial derivative coefficient of the kth iteration:

[0263]

[0264]

[0265]

[0266]

[0267]

[0268]

[0269]

[0270] wherein, are the de-normalized quantile, normalized quantile, normalized 5th order central moment, normalized 4th order central moment, normalized 3rd order central moment, increment of the mean and standard deviation of the system dispatching power at time t under the probability q in the kth iteration; is the increment of the system dispatching power at time t under the scenario s in the kth iteration; by substituting the feasible solution of the kth iteration of the system dispatching power in step 3-2, the partial derivative coefficients of each item can be obtained;

[0271] Step 3-4: set the small step change range of the dispatching power, based on the partial derivative function of the quantile of the dispatching power derived in step 3-3 and the calculation results of the partial derivative coefficients of the kth iteration, the following linearized Cornish-Fisher constraint is obtained:

[0272]

[0273]

[0274]

[0275]

[0276]

[0277]

[0278]

[0279]

[0280]

[0281] wherein, δ is the range limit of the small step change of the sending power; respectively, are the de-normalized quantile of the sending power of the water-wind-solar complementary system at time t under probability q in the kth iteration and the k+1th iteration; respectively, are the normalized quantile of the sending power of the water-wind-solar complementary system at time t under probability q in the kth iteration and the k+1th iteration; respectively, are the normalized 5th order central moment of the sending power of the water-wind-solar complementary system at time t under probability q in the kth iteration and the k+1th iteration; respectively, are the normalized 4th order central moment of the sending power of the water-wind-solar complementary system at time t under probability q in the kth iteration and the k+1th iteration; respectively, are the normalized 3rd order central moment of the sending power of the water-wind-solar complementary system at time t under probability q in the kth iteration and the k+1th iteration; respectively, are the standard deviation of the sending power of the water-wind-solar complementary system at time t in the kth iteration and the k+1th iteration; respectively, are the mean of the sending power of the water-wind-solar complementary system at time t in the kth iteration and the k+1th iteration; it is to be noted that the remaining nonlinear constraints in the model, such as the water level-storage capacity constraint, the unit vibration zone constraint, the unit dynamic characteristic constraint, etc., can be linearized by using the existing technology, and the present application does not make too many explanations;

[0282] Step 3-5: processing and solving the water-wind-solar complementary system sending power probability evaluation model in the kth iteration, and judging whether the model converges or not;

[0283] Step 3-6: when the model iteration solving is finished, the obtained is the probability evaluation result of the water-wind-solar system sending power.

[0284] In step 3-5, whether the model converges or not is judged, and the specific steps are as follows:

[0285] Step 3-5-1: Replace all the nonlinear Cornish-Fisher constraints in the probabilistic assessment model of the water-wind-solar complementary system output power with the linearized Cornish-Fisher constraints obtained in step 3-4, and add the following constraints to the mixed integer linear programming model obtained in step 3-5-1:

[0286] Step 3-5-2: To gradually increase the quantile range at probabilities q max and q min in the iteration process, the following constraints are added to the mixed integer linear programming model obtained in step 3-5-1:

[0287]

[0288]

[0289] where, are the quantiles of the system output power at time t under probabilities q max and q min , respectively, which are known quantities and have been obtained in step 3-2; are the quantiles of the water-wind-solar complementary system output power at time t under probabilities q max and q min , respectively, both of which are variables to be solved in the kth iteration model;

[0290] Step 3-5-3: The model obtained in step 3-5-2 is solved using the Gurobi solver, and the water-wind-solar complementary system output power at time t under scenario s in the (k+1)th iteration is obtained. To avoid errors caused by linearization, the actual quantiles are updated using the mathematical relationship between the output power and its quantiles proposed in step 1-3.

[0291] Step 3-5-4: Determine whether the model converges. If either of the following two conditions is met, the model converges and the iteration is terminated; otherwise, set k = k + 1 and return to step 3-2.

[0292] Condition 1: If the absolute value deviation of the output power quantiles obtained in the adjacent two iterations under probabilities q max and q min is less than the allowable error ε, i.e.,

[0293] Condition 2: The iteration number k reaches the maximum iteration number K.

[0294] The application also comprises a small-step iteration solving method based on chain structure data flow, which converts a highly nonlinear model into multiple iteration solving of linear model to obtain the probability evaluation result of the water-wind-solar complementary system sending power; specifically comprising the following steps:

[0295] Step S1: initialize iteration number k=0;

[0296] Step S2: obtain the feasible solution of the kth iteration of the water-wind-solar complementary system sending power in the dispatching period under the typical wind-solar uncertainty scenario, and calculate the quantile of the sending power at the kth iteration by using the Cornish-Fisher series method, the quantile of the sending power at time t under the probability q max and q min The quantile of the sending power at time t under the probability q and

[0297] Step S3: based on the chain structure data flow, derive the partial derivative function of the sending power quantile and calculate the partial derivative coefficient of the kth iteration:

[0298]

[0299]

[0300]

[0301]

[0302]

[0303]

[0304]

[0305] wherein, are the de-normalized quantile, normalized quantile, normalized 5th order origin moment, normalized 4th order origin moment, normalized 3rd order origin moment, increment of the mean and standard deviation of the sending power of the system at time t under the probability q at the kth iteration; is the increment of the sending power of the system at time t under the scenario s at the kth iteration; by substituting the feasible solution of the kth iteration of the sending power of the system in step S2, the partial derivative coefficients can be obtained;

[0306] Step S4: set the small-step change range of the sending power, based on the partial derivative function of the sending power quantile derived in step S3 and the calculation result of the kth iteration partial derivative coefficient, obtain the following linearized Cornish-Fisher constraint:

[0307]

[0308]

[0309]

[0310]

[0311]

[0312]

[0313]

[0314]

[0315]

[0316] wherein, δ is the range limit of the small step change of the sending power; respectively, are the de-normalized quantile of the sending power of the water-wind-photovoltaic complementary system at time t under probability q in the kth iteration and the k+1th iteration; respectively, are the normalized quantile of the sending power of the water-wind-photovoltaic complementary system at time t under probability q in the kth iteration and the k+1th iteration; respectively, are the normalized 5th order central moment of the sending power of the water-wind-photovoltaic complementary system at time t under probability q in the kth iteration and the k+1th iteration; respectively, are the normalized 4th order central moment of the sending power of the water-wind-photovoltaic complementary system at time t under probability q in the kth iteration and the k+1th iteration; respectively, are the normalized 3rd order central moment of the sending power of the water-wind-photovoltaic complementary system at time t under probability q in the kth iteration and the k+1th iteration; respectively, are the standard deviation of the sending power of the water-wind-photovoltaic complementary system at time t in the kth iteration and the k+1th iteration; respectively, are the mean of the sending power of the water-wind-photovoltaic complementary system at time t in the kth iteration and the k+1th iteration; it is to be noted that the remaining nonlinear constraints in the model, such as the water level-storage capacity constraint, the unit vibration zone constraint, the unit dynamic characteristic constraint, etc., can be linearized by using the existing technology, and the present application does not make too many explanations;

[0317] Step S5: processing and solving the water-wind-photovoltaic complementary system sending power probability evaluation model in the kth iteration, and judging whether the model converges or not;

[0318] Step S6: when the model iteration solving is finished, the obtained is the probability evaluation result of the water-wind-photovoltaic system sending power.

[0319] In step S5, it is judged whether the model converges, and the specific steps are as follows:

[0320] Step S5-1: all nonlinear Cornish-Fisher constraints in the power output probability evaluation model of the water-wind-solar complementary system are replaced by the linearized Cornish-Fisher constraints obtained in step S4, and are all regarded as decision variables, at this time the probability evaluation model of the power output of the water-wind-solar complementary system becomes a mixed integer linear programming model;

[0321] Step S5-2: in order to gradually increase the quantile range under the probabilities q max and q min in the iteration process, the following constraints are added to the mixed integer linear programming model obtained in step S5-1:

[0322]

[0323]

[0324] wherein, and are the quantiles of the power output of the system at time t under the probabilities q max and q min in the kth iteration, and are known quantities, which have been obtained in step S2; and respectively represent the quantiles of the power output of the water-wind-solar complementary system at time t under the probabilities q max and q min in the k+1th iteration, and both are the to-be-solved variables of the model in the kth iteration;

[0325] Step S5-3: the model obtained in step S5-2 is solved by using the Gurobi solver, so that the power output of the water-wind-solar complementary system at time t under the scenario s in the k+1th iteration In order to avoid the error caused by linearization, the actual quantile is further updated by using through the mathematical relationship between the power output and the quantile of the power output

[0326] Step S5-4: it is judged whether the model converges, when any one of the following two conditions is met, the model converges, and the iteration solving ends; otherwise, let k=k+1, and return to step S2.

[0327] Condition 1: if the absolute value deviation of the power output quantile results obtained by adjacent two iterations under the probabilities q max and q min is less than the allowable error ε, that is,

[0328] Condition 2: the iteration number k reaches the maximum iteration number K.

[0329] An electronic device includes a processor, a communication interface, a memory and a communication bus, wherein the processor, the communication interface and the memory complete the communication among each other through the communication bus. The processor can call the logical instructions in the memory to execute a probability evaluation method of sending power of a water, wind and light complementary system and a small step iteration solving method based on a chain structure data flow.

[0330] In addition, the logical instructions in the memory can be realized in the form of a software functional unit and sold or used as an independent product, and can be stored in a computer readable storage medium. Based on such understanding, the technical solutions of the present application can be embodied in the form of a software product. The computer software product is stored in a storage medium and includes a plurality of instructions for causing a computer device (which can be a personal computer, a server or a network device, etc.) to execute all or part of the steps of the method described in the present application. The storage medium includes a U disk, a mobile hard disk, a read-only memory (ROM), a random access memory (RAM), a magnetic disk or an optical disk, and various media that can store program codes.

[0331] On the other hand, the present application also provides a computer program product, which includes a computer program, the computer program can be stored on a non-transitory computer readable storage medium, and the computer program is executed by a processor, so that the computer can execute the probability evaluation method of sending power of a water, wind and light complementary system and the small step iteration solving method based on a chain structure data flow.

[0332] In another aspect, the present application also provides a non-transitory computer readable storage medium, which stores a computer program, and the computer program is executed by a processor to implement the probability evaluation method of sending power of a water, wind and light complementary system and the small step iteration solving method based on a chain structure data flow.

[0333] Embodiment:

[0334] A probability evaluation method of sending power of a water, wind and light complementary system considering wind and light uncertainty, the overall process is as shown in Figure 1 The system topology corresponding to the embodiment is as shown in Figure 2 The system includes 4 hydropower stations with 15 units and 1 wind farm and 1 photovoltaic power station. The dispatching cycle is 1d and the dispatching period is 15min. The characteristic parameters of each cascade hydropower station are as shown in Table 1. Other model parameters are set as follows: the capacity of the wind farm and the photovoltaic power station is 550MW and 500MW respectively; the capacity of each power transmission section is 1500MW, 1500MW, 1000MW and 4000MW.

[0335] Table 1. Characteristic parameters of hydropower stations

[0336]

[0337] A power probability assessment method for hydro-wind-solar hybrid systems that takes into account wind and solar uncertainties was implemented for this system. The method includes the following steps:

[0338] 1) Based on the power output of a hydro-wind-solar hybrid system under typical uncertain wind and solar scenarios, the Cornish-Fisher series method is used to construct an explicit mathematical expression for the power output quantiles.

[0339] Since there are prediction errors in the day-ahead power forecasts for wind and solar power, the uncertainty of wind and solar power output is discretized into N based on the scenario method. s A typical scene with uncertain landscape, such as Figure 3 The following are 10 typical scenarios of uncertain wind power output, such as... Figure 4 The diagram shows 10 typical scenarios with uncertain photovoltaic output. At time t, the power output of the hydro-wind-solar hybrid system under different scenarios can be represented by a one-dimensional array, i.e. Where P 0,t Let P be the output power of the system at time t. 0,1,t P 0,2,t , These are Scene 1, Scene 2, and Scene N, respectively. s The system output power, N s The total number of scenarios is given. The mathematical relationship between transmitted power and its quantiles is established using the Cornish-Fisher method of 5th order cumulant, as shown below:

[0340]

[0341]

[0342]

[0343]

[0344]

[0345]

[0346]

[0347] Among them, P 0,s,t Power is delivered to the system in scenario s; μ P0,t σ P0,t The mean and standard deviation of the system output power at time t for all scenarios; μ P0,t，3, μ P0,t,4 , μ P0,t,5 are normalized 3rd, 4th, 5th order central moments respectively; ξ q is the quantile of the standard Gaussian distribution corresponding to the probability q; based on the given probability q, is the normalized quantile, F p0,t,q is the de-normalized quantile.

[0348] Taking time 15 as an example, the system sending power under 10 scenarios is as shown in Table 1, and the Cornish-Fisher calculation results are as shown in Table 2, where q is taken as 0.05 and 0.95 respectively. Figure 5

[0349] Table 2 Cornish-Fisher calculation results at time 15

[0350]

[0351] 2) A day-ahead sending power probability evaluation model of the water-wind-solar complementary system is established, and the upper and lower boundaries of the system sending power at each time section are maximized with a certain confidence as follows:

[0352] (1) Objective function

[0353] By using the complementary characteristics of wind power, photovoltaic power and hydropower output, a probability evaluation model of the sending capacity of the water-wind-solar complementary system is established, and the evaluation model maximizes the joint sending range of the system at each time section with a certain confidence interval as the target:

[0354]

[0355] Wherein, T is the total number of scheduling periods; is the quantile of the system sending power at time t under the probability q max ; is the quantile of the system sending power at time t under the probability q min . In the embodiment, q max is 0.95, and q min is 0.05.

[0356] (2) Constraint condition

[0357] 1) Water balance constraint

[0358]

[0359] Wherein, V s,i,t+1 , V s,i,t are the storage capacities of the i th hydropower station at t+1 and t time under the scenario s; I s,i,t is the inflow of the i th hydropower station at time t under the scenario s; Q s,i,t ​Let τ be the outflow from the i-th hydropower station in scenario s during time period t; Δt is the scheduling time interval; τ is the outflow from the reservoir. i-1 The water flow delay time from the (i-1)th hydropower station to the ith hydropower station; Considering the water flow delay in scenario s, the (i-1)th hydropower station in the time period t-τ i-1 Traffic; R s,i,t Let t be the interval flow between power station i-1 and power station i in scenario s during time period t; Let be the power generation flow of the i-th hydropower station in scenario s during time period t; Let be the water discharge flow of the i-th hydropower station in scenario s during time period t.

[0360] 2) Reservoir water level constraints

[0361]

[0362] In the formula, Z represents the inlet water level of the i-th hydropower station at time t in scenario s; i,t , These are the lower and upper limits of the water level at time t for the i-th hydropower station, respectively. The lower limit of the water level is often taken as the dead water level, the lower limit of the dispatching system, etc., while the upper limit of the water level is taken as the flood control limit, the normal high water level, or the upper limit of the dispatching system, etc. This constraint limits the fluctuation range of the water level.

[0363] 3) Initial and final water level constraints

[0364]

[0365]

[0366] In the formula, These represent the initial water level and the control water level at the end of the scheduling period, respectively; the allowable deviation of the water level at the end of the scheduling period is to avoid affecting the water volume in the next scheduling cycle.

[0367] 4) Outbound flow constraints

[0368]

[0369] In the formula, Q s,i,t Let Q be the outflow from the i-th hydropower station in scenario s during time period t; i,t , These represent the lower and upper limits of the outflow from the i-th hydropower station during time period t, respectively. The lower limit of the outflow is usually determined by the ecological water demand, while the upper limit is determined by the reservoir's discharge capacity. This constraint ensures that the reservoir's outflow falls within the normal range.

[0370] 5) Power plant output constraints

[0371]

[0372] where, is the output of the ith hydro power plant at time period t under scenario s; are the lower and upper bounds of the output of the ith hydro power plant at time period t, respectively.

[0373] 6) Water level-storage capacity constraint

[0374]

[0375] where, f i ZV is the water level-storage capacity nonlinear relationship curve function of the reservoir where the power plant i is located.

[0376] 7) Tailwater level-discharge constraint

[0377]

[0378] where, f i ZQ is the tailwater level-discharge nonlinear relationship curve function of the power plant i; is the tailwater level of the power plant i at time period t under scenario s.

[0379] 8) Unit output constraint

[0380]

[0381] where, P s,i,n,t , P i,n is the output of the nth unit of the power plant i at time period t under scenario s and its upper and lower bounds; u s,i,n,t is the on-off state variable of the nth unit of the power plant i at time period t under scenario s, taking 1 to represent the on state and 0 to represent the off state, then the output of the power plant i can be represented as:

[0382]

[0383] where, N i represents the number of units of the power plant i.

[0384] 9) Unit generation flow constraint

[0385]

[0386] where, is the generation flow of the nth unit of the power plant i at time period t under scenario s and its upper and lower bounds; then the generation flow of the power plant i can be represented as:

[0387]

[0388] 10) Unit vibration zone constraints

[0389]

[0390] where, is the upper and lower output limit of the kth vibration zone of the nth unit of power plant i.

[0391] 11) Unit start-up and shut-down duration constraints

[0392]

[0393] where, is the start-up operation variable of the nth unit of power plant i at time period t, taking 1 to represent start-up operation; is the shut-down operation variable of the nth unit of power plant i at time period t, taking 1 to represent shut-down operation; are the minimum start-up and shut-down duration of the nth unit of power plant i, respectively; is the maximum start-up times of the nth unit of power plant i within the dispatching cycle.

[0394] 12) Unit ramping constraints

[0395]

[0396] where, ΔP i,n is the ramping capability of the nth unit of power plant i.

[0397] 13) Unit power generation head constraints

[0398]

[0399] where, H s,i,n,t , is the power generation head and head loss of the nth unit of power plant i at time period t under scenario s.

[0400] 14) Head loss function constraints

[0401]

[0402] where, a i , b i are the head loss coefficient and loss constant of power plant i.

[0403] 15) Unit power characteristics constraints

[0404]

[0405] where, is the power-head-flow nonlinear relationship function of the nth unit of power plant i.

[0406] 16) Power balance constraint

[0407]

[0408] where P 0,s,t is the channel delivered power at time period t under scenario s; P PV,s,t , are the power output and curtailment of photovoltaic at time period t under scenario s, respectively; P WT,s,t , are the power output and curtailment of wind power at time period t under scenario s, respectively; P L,s,t is the in-domain load at time period t under scenario s.

[0409] 17) Power curtailment and water curtailment constraint

[0410]

[0411] where Pr{·} is the confidence expression; η is the confidence level.

[0412] 18) Transmission channel constraint

[0413]

[0414] where is the upper limit of transmission channel power.

[0415] 19) Cornish-Fisher constraint

[0416] 3) A small step iteration model solving method is proposed, which converts the highly nonlinear model into a linear model for multiple iteration solving, and the specific steps are as follows:

[0417] 3.1) Initialize the iteration number k = 0;

[0418] 3.2) Obtain the feasible solution of the delivered power of the water-wind-solar complementary system in the dispatching period under the typical wind-solar uncertainty scenario in the kth iteration, and calculate the quantile of the kth iteration using the Cornish-Fisher series method, and the initial quantile of the system delivered power at time t under the probability q is

[0419] 3.3) Based on the chain structure of data flow, the partial derivative function of the delivered power quantile is derived and the partial derivative coefficient of the kth iteration is calculated:

[0420]

[0421]

[0422]

[0423]

[0424]

[0425]

[0426]

[0427] wherein, are the de-normalized quantile, normalized quantile, normalized 5th order central moment, normalized 4th order central moment, normalized 3rd order central moment, mean and standard deviation of the system output power at the kth iteration, respectively; is the increment of the system output power at time t under scenario s at the kth iteration; substituting the feasible solution of the system output power at the kth iteration in S4.2, the partial derivative coefficients can be obtained.

[0428] 3.4) setting the small step change range of the output power, based on the obtained partial derivative coefficients, linearizing the Cornish-Fisher constraint:

[0429]

[0430]

[0431]

[0432]

[0433]

[0434]

[0435]

[0436]

[0437]

[0438] wherein, δ is the small step change range limit of the output power. It should be noted that the remaining nonlinear constraints in the model, such as the water level-storage capacity constraint, the unit vibration zone constraint, the unit dynamic characteristic constraint and the like, can be linearized by the existing technology, and the present application does not make too much description.

[0439] 3.5) replacing the Cornish-Fisher constraint in the probability evaluation model of the water, wind and light complementary system output capacity with the S4.3 constraint, and solving the model by using the Gurobi solver, the system output power can be obtained Further, the step 1) is used to calculate and update If If the establishment or iteration number k reaches the maximum iteration number K, the solution ends; otherwise, let k = k + 1, and return to step 3.2).

[0440] The model solution result is as shown in Figure 6 The adjustable interval of the system output power can be obtained under the 90% confidence interval, and the system can report the interval to the power grid dispatching department. The power grid dispatching department formulates a power generation dispatching plan to meet the power grid operation demand within the interval, so as to realize the interaction between the water, wind and light system and the power grid. As an example, the probability distribution of the system output power with the change of the iteration number is as shown in Figure 7 It can be seen from the figure that with the increase of the iteration number, the distribution range of the system output power expands, which verifies the effectiveness of the proposed model.

Claims

1. A probabilistic method for evaluating the transmitted power of a hydro-wind-solar hybrid system, characterized in that, Includes the following steps: Step 1: Based on the power output of the hydro-wind-solar hybrid system under typical wind and solar uncertain scenarios, the Cornish-Fisher series method is used to construct an explicit mathematical expression for the power output quantiles; Step 2: Based on the constructed mathematical expression of the quantile of the transmitted power, with the goal of maximizing the range of the upper and lower quantiles of the transmitted power at each time point under a certain confidence level, establish a probability assessment model for the transmitted power of the hydro-wind-solar hybrid system. Step 3: To address the nonlinear Cornish-Fisher constraint in the probabilistic evaluation model, a small-step iterative solution method based on chain-structured data flow is proposed. This method transforms the highly nonlinear model into a linear model through multiple iterative solutions, thereby obtaining the probabilistic evaluation results of the power output of the hydro-wind-solar hybrid system. In step 3, a small-step iterative solution method based on chain-structured data flow is proposed, including the following steps: Step 3-1: Initialize the iteration count k=0; Step 3-2: Obtain the feasible solution for the power output of the hydro-wind-solar hybrid system in the k-th iteration within the scheduling cycle under typical wind-solar uncertainty scenarios, and calculate the quantile of the power output in the k-th iteration using the Cornish-Fisher series method. t At any moment in probability and The quantile of the power output from the lower system is and ; Step 3-3: Based on the data flow of the chain structure, derive the partial derivative function of the output power quantile and calculate the partial derivative coefficient of the k-th iteration: ； ； ； ； ； ； ； in, , , , , , , At the k-th iteration, t The denormalized quantiles, normalized quantiles, normalized 5th-order raw moments, normalized 4th-order raw moments, normalized 3rd-order raw moments, and the increments of the mean and standard deviation of the system's output power at time probability q. For the k-th iteration, t The increment of the system's output power under time scenario s; for t The denormalized quantile of the system output power P0 at probability q at any given time. For all scenarios t Timing system output power P The mean of 0, For all scenarios t Timing system output power P 0 standard deviation, for t Timing system output power P Normalized quantiles of 0 for t Timing system output power P The normalized third-order raw moment of 0, for t Timing system output power P The normalized fourth-order raw moment of 0, for t Timing system output power P The normalized 5th-order raw moment of 0, for t The system output power at time s; by substituting the feasible solution of the system output power in the kth iteration of step 3-2 into it, the partial derivative coefficients of each term can be obtained; Step 3-4: Set the range of small step size variation of the transmitted power. Based on the partial derivative function of the transmitted power quantile derived in Step 3-3 and the calculation results of the partial derivative coefficient of the kth iteration, obtain the linearized Cornish-Fisher constraint. Steps 3-5: Process and solve the power probability assessment model of the hydro-wind-solar hybrid system in the kth iteration, and determine whether the model has converged; Steps 3-6: When the model iteration solution is completed, the obtained... , This is the probability assessment result of the power output of the water-wind-solar system.

2. The method according to claim 1, characterized in that, In step 1, the following sub-steps are used when constructing the explicit mathematical expression for the transmitted power quantiles: Step 1-1: Discretize the uncertainty of wind and solar power output based on the scenario method. A typical landscape with uncertainties; Step 1-2: Obtain in t time, A one-dimensional array consisting of the transmitted power of a hydro-wind-solar hybrid system in a given scenario, specifically: ,in for t The power output of the time system , , They are respectively t Scene 1, Scene 2 and Scene The system output power is below Total number of scenes; Steps 1-3: Use the Cornish-Fisher series method to establish the mathematical relationship between the transmitted power and its quantiles.

3. The method according to claim 2, characterized in that, In steps 1-3, the Cornish-Fisher method with 5th order cumulants is used to establish the mathematical relationship between the transmitted power and its quantiles. The specific expression is as follows: ； ； ； ； ； ； ； in, for t System output power in time scenario s; , For all scenarios t The mean and standard deviation of the system output power P0 at any given time; , , They are respectively t The normalized 3rd, 4th, and 5th order raw moments of the system output power P0 at any given time; Let be the quantile of the standard Gaussian distribution corresponding to probability q; given probability q, for t The normalized quantile of the system output power P0 at any given time. for t The denormalized quantile of the system output power P0 at probability q at any given time.

4. The method according to claim 2 or 3, characterized in that, In step 2, the established probability assessment model for the transmitted power of the hydro-wind-solar hybrid system is as follows: (1) Objective function: Based on the explicit mathematical expression of the system's transmitted power quantiles constructed in step 1, the objective function of the established probabilistic evaluation model for the transmitted power of the hydro-wind-solar hybrid system is to maximize the range of the upper and lower quantiles of the system's transmitted power at a certain confidence level. ； in, This represents the total number of scheduling periods; for t At any moment in probability The denormalized quantile of the system's output power; for t Time period in probability The denormalized quantile of the system's output power; (2) Constraints: 1) Water balance constraints: ； In the formula, , Scenes s Next i A hydropower station t +1 and t Storage capacity at any given time; For the scene s Next i A hydropower station t Inbound traffic at any given time; For the scene s Next i A hydropower station t Real-time outbound flow; The scheduling time interval; For from the first i -1 hydropower station to the first i The water flow stagnation time at the hydropower station; For the scene s After considering the water flow lag time, the first i -1 hydropower station during the period Traffic; For the scene s Lower power station i -1 and power station i Between t Interval flow at any given time; For the scene s Next i A hydropower station t Power generation flow rate at any given moment; For the scene s Next i The hydropower station during the period t The discharge flow rate; 2) Reservoir water level constraints: ； In the formula, For the scene s Next i A hydropower station t The water level in front of the dam at any given time; , The first i A hydropower station t The lower and upper limits of water level at any given time are defined. The lower limit is usually the dead water level or the lower limit of the dispatching system, while the upper limit is the flood control limit, the normal high water level, or the upper limit of the dispatching system. This constraint limits the range of water level fluctuations. 3) Initial and final water level constraints: ； In the formula, , These are the initial water level and the control water level at the end of the scheduling period, respectively. , These represent the scheduling decision water levels of the i-th hydropower station in scenario s at time 1 and time T, respectively. To allow for a certain level deviation at the end of the scheduling period, so as to avoid affecting the water volume in the next scheduling cycle; 4) Outbound flow constraints: ； In the formula, For the scene s Next i The hydropower station during the period t Outbound flow; , The first i The hydropower station during the period t The lower limit and upper limit of the outflow from the reservoir; the lower limit of the outflow is usually determined by the ecological water demand, while the upper limit of the outflow is determined by the reservoir's discharge capacity. This constraint ensures the normal range of the reservoir's discharge flow. 5) Power plant output constraints: ； In the formula, For the scene s Next i The hydropower station during the period t contribution; , The first i The hydropower station during the period t The lower and upper limits of output; 6) Water level-reservoir capacity constraints: ； In the formula, For power station i The nonlinear relationship curve function between water level and reservoir capacity of the reservoir is as follows; 7) Tailwater level - discharge capacity constraint: ； In the formula, For power station i The nonlinear relationship curve function between tailwater level and discharge capacity; For the scene s Lower power station i During the period t The tailwater level; 8) Unit output constraints: ； In the formula, , , For the nth generating unit of power station i in scenario s during time period t The output and its upper and lower limits; For the nth generating unit of power station i in scenario s during time period t Let the start-up and stop-down state variables be denoted as 1 for start-up and 0 for stop-down. Then the output of power station i can be expressed as: ； In the formula, Indicates power station i The number of generating units; 9) Unit power generation flow constraints: ； In the formula, , , For the nth generating unit of power station i in scenario s during time period t Given the power generation flow rate and its upper and lower limits, the power generation flow rate of power station i can be expressed as: ； 10) Unit vibration zone constraints: ； In the formula, , For power station i The n The first of the Taiwanese unit k The upper and lower limits of output in each vibration zone; 11) Unit start-up and shutdown duration constraints: ； In the formula, For power station i The n Taiwanese crew during the period t The startup operation variable, where 1 indicates a startup operation; For power station i The n Taiwanese crew during the period t The shutdown operation variable, where 1 indicates a shutdown operation; , Power plants i The n Minimum start-up and shutdown durations for each unit; For power station i The n The maximum number of times a generator unit can be started within a scheduling cycle; For the nth generating unit of power station i in scenario s during time period t -1 is the start / stop status variable. For time-stamped variables; 12) Unit ramp-up constraints: ； In the formula, For power station i The n The hill-climbing ability of the Taiwanese generator set. This refers to the nth generating unit of power station i in scenario s during time period t +1 effort; 13) Generating head constraints of the unit: ； In the formula, , For the scene s Lower power station i The n Taiwanese crew during the period t The power generation head and head loss, Refers to the scene s Next i A hydropower station t Water level in front of the dam at time -1; 14) Head loss function constraints: ； In the formula, , For power station i Head loss coefficient and loss constant; 15) Unit dynamic characteristic constraints: ； In the formula, For power station i The n Nonlinear relationship function of power output-head-flow rate of the Taiwanese generator unit; 16) Power balance constraints: ； In the formula, For scene s, time period t The channel output power; , For example, photovoltaics in scenario s during time period t The output and the amount of power wasted; , Wind power in scenario s for different time periods t The output and the amount of power wasted; For scene s, time period t Intra-domain load, This refers to the total number of hydroelectric power stations; 17) Constraints on power and water wastage: ； In the formula, This is the confidence expression; The confidence level; 18) Transmission channel constraints: ； In the formula, This is the upper limit of the power transmission channel; 19) Nonlinear Cornish-Fisher constraints: The specific expression of the Cornish-Fisher constraint is the mathematical relationship between the transmitted power and its quantile proposed in steps 1-3. The Cornish-Fisher constraint establishes the mathematical relationship between the system's transmitted power and its quantile in various scenarios.

5. The method according to claim 4, characterized in that, In steps 3-5, the model is determined to have converged. The specific steps are as follows: Step 3-5-1: Replace all nonlinear Cornish-Fisher constraints in the power output probability assessment model of the hydro-wind-solar hybrid system with the linearized Cornish-Fisher constraints obtained in Step 3-4, and... , , , , , , , If all are considered as decision variables, then the probabilistic evaluation model of the power output of the hydro-wind-solar hybrid system becomes a mixed integer linear programming model. Step 3-5-2: To gradually increase the probability during the iteration process and To determine the quantile range below, add the following constraints to the mixed-integer linear programming model obtained in step 3-5-1: ； ； in, , When it is the kth iteration t At any moment in probability and The quantile of the power output by the lower system is a known quantity, which has been obtained in step 3-2; , These represent the (k+1)th iteration. t At any moment in probability and The quantiles of the power output of the hydro-wind hybrid system are both variables to be determined in the model during the k-th iteration. Step 3-5-3: Solve the model obtained in step 3-5-2 using the Gurobi solver to obtain the solution for the (k+1)th iteration. t Power output of the hydro-wind-solar hybrid system under scenario s To avoid errors caused by linearization, further utilization... The actual quantiles are updated using the mathematical relationship between the transmitted power and its quantiles proposed in steps 1-3. , ; Step 3-5-4: Determine if the model has converged iteratively. If either of the following two conditions is met, the model has converged and the iterative solution ends; otherwise, let k = k + 1 and return to step 3-2. Condition 1: If in probability and The absolute value deviation of the output power quantile results obtained in two consecutive iterations is less than the allowable error. ,Right now ; Condition 2: The number of iterations k reaches the maximum number of iterations K.

6. The method according to claim 1, characterized in that, In steps 3-4, the linearized Cornish-Fisher constraints are obtained as follows: ； ； ； ； ； ； ； ； ； in, To limit the range of small step changes in the output power; , For the k-th iteration and the (k+1)-th iteration, respectively, t Denormalized quantile of the power output of the hydro-wind-solar hybrid system at time probability q; , For the k-th iteration and the (k+1)-th iteration, respectively, t Normalized quantile of the power output of the hydro-wind-solar hybrid system at time probability q; , For the k-th iteration and the (k+1)-th iteration, respectively, t Normalized fifth-order original moment of the power output of the hydro-wind-solar hybrid system at time probability q; , For the k-th iteration and the (k+1)-th iteration, respectively, t Normalized fourth-order original moment of the power output of the hydro-wind-solar hybrid system at time probability q; , For the k-th iteration and the (k+1)-th iteration, respectively, t Normalized third-order moment of the power output of the hydro-wind-solar hybrid system at time probability q; , For the k-th iteration and the (k+1)-th iteration, respectively, t Standard deviation of the power output of the hydro-wind-solar hybrid system at any given time; , For the k-th iteration and the (k+1)-th iteration, respectively, t The average power output of the hydro-wind-solar hybrid system at any given time; For the first k +1 iterations t System output power in scenario s at time t For the first k During the next iteration t The system output power in scenario s.

7. A small-step iterative solution method based on a chain-structured data flow, characterized in that, The method transforms a highly nonlinear model into a linear model through multiple iterative solutions to obtain a probabilistic assessment of the transmitted power of the hydro-wind-solar hybrid system; specifically, it includes the following steps: Step S1: Initialize the number of iterations k=0; Step S2: Obtain the feasible solution for the power output of the hydro-wind-solar hybrid system in the k-th iteration within the scheduling cycle under typical wind-solar uncertainty scenarios, and calculate the quantile of the power output in the k-th iteration using the Cornish-Fisher series method. t At any moment in probability and The quantile of the power output from the lower system is and ; Step S3: Based on the data flow of the chain structure, derive the partial derivative function of the output power quantile and calculate the partial derivative coefficients of the k-th iteration: ； ； ； ； ； ； ； in, , , , , , , At the k-th iteration, t The denormalized quantiles, normalized quantiles, normalized 5th-order raw moments, normalized 4th-order raw moments, normalized 3rd-order raw moments, and the increments of the mean and standard deviation of the system's output power at time probability q. For the k-th iteration, t The increment of the system's output power under time scenario s; for t The denormalized quantile of the system output power P0 at probability q at any given time. For all scenarios t Timing system output power P The mean of 0, For all scenarios t Timing system output power P 0 standard deviation, for t Timing system output power P Normalized quantiles of 0 for t Timing system output power P The normalized third-order raw moment of 0, for t Timing system output power P The normalized fourth-order raw moment of 0, for t Timing system output power P The normalized 5th-order raw moment of 0, for t The system output power at time s; by substituting the feasible solution of the system output power in the kth iteration of step S2 into it, the partial derivative coefficients of each term can be obtained; Step S4: Set the range of small step size variation of the transmitted power. Based on the partial derivative function of the transmitted power quantile derived in step S3 and the calculation results of the partial derivative coefficient of the kth iteration, obtain the linearized Cornish-Fisher constraint. Step S5: Process and solve the power output probability assessment model of the hydro-wind-solar hybrid system in the kth iteration, and determine whether the model has converged; Step S6: When the model iteration solution is completed, the obtained... , This is the probability assessment result of the power output of the water-wind-solar system.

8. The method according to claim 7, characterized in that, In step S4, the linearized Cornish-Fisher constraints are obtained as follows: ； ； ； ； ； ； ； ； ； in, To limit the range of small step changes in the output power; , For the k-th iteration and the (k+1)-th iteration, respectively, t Denormalized quantile of the power output of the hydro-wind-solar hybrid system at time probability q; , For the k-th iteration and the (k+1)-th iteration, respectively, t Normalized quantile of the power output of the hydro-wind-solar hybrid system at time probability q; , For the k-th iteration and the (k+1)-th iteration, respectively, t Normalized fifth-order original moment of the power output of the hydro-wind-solar hybrid system at time probability q; , For the k-th iteration and the (k+1)-th iteration, respectively, t Normalized fourth-order original moment of the power output of the hydro-wind-solar hybrid system at time probability q; , For the k-th iteration and the (k+1)-th iteration, respectively, t Normalized third-order moment of the power output of the hydro-wind-solar hybrid system at time probability q; , For the k-th iteration and the (k+1)-th iteration, respectively, t Standard deviation of the power output of the hydro-wind-solar hybrid system at any given time; , For the k-th iteration and the (k+1)-th iteration, respectively, t The average power output of the hydro-wind-solar hybrid system at any given time. For the first k +1 iterations t System output power in scenario s at time t For the first k During the next iteration t The system output power in scenario s.

9. An electronic device, characterized in that, It includes a memory and a processor, with the processor and memory connected via a communication bus. The memory is used to store computer programs, and when the computer programs are executed by the processor, they implement the method as described in claim 1, 2, 3, 5, or 6.

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