A cascade reservoir group dispatching method for clean energy consumption

Abstract

The application discloses a kind of cascade reservoir group scheduling method and system for clean energy consumption, first, obtain the long sequence of the relative error of clean energy each period output and its mean, variance, according to which the output scene set is generated by using clean energy prediction output;Second, a large-scale scene set is reduced to several typical output scenes using clustering algorithm, and then the photovoltaic output process and its occurrence probability of different scenes are obtained;Finally, construct the applicable cascade reservoir group joint scheduling model, and then solve to obtain reasonable and feasible scheduling scheme using efficient optimization algorithm.The method disclosed by the application can quickly respond to the randomness and uncertainty characteristics of the clean energy output process, effectively handle the coupling operation constraints of cascade power stations, significantly improve the model calculation efficiency and scheduling scheme quality, and is suitable for the cascade reservoir group joint scheduling problem for clean energy consumption.

Description

TECHNICAL FIELD

[0001] The application belongs to the field of reservoir optimal scheduling, and particularly relates to a cascade reservoir group scheduling method for clean energy consumption. BACKGROUND

[0002] The world is developing rapidly today, and energy development and consumption are increasing day by day. Large-scale use of non-renewable energy such as oil, coal and natural gas has brought about many ecological problems such as environmental pollution, climate change and energy crisis. In recent years, the world has been reducing the proportion of fossil energy use and vigorously developing and utilizing clean energy to effectively promote energy saving and emission reduction. However, the output of clean energy such as photovoltaic and wind power is highly volatile and random due to the influence of many environmental factors such as weather, temperature, light and wind speed, and the traditional deterministic scheduling method is difficult to respond to the prediction deviation and uncertainty of clean energy output. SUMMARY

[0003] The purpose of the present application is to provide a cascade reservoir group scheduling method and system for clean energy consumption to solve the problems in the prior art.

[0004] To achieve the above-mentioned purpose, the present application provides the following technical scheme: a cascade reservoir group scheduling method for clean energy consumption, comprising the following steps:

[0005] S1, based on the actual output and predicted output of clean energy in each period, obtaining a long sequence of clean energy output relative error and its mean and variance;

[0006] S2, based on the predicted output of clean energy in each period, obtaining a set of clean energy output scenarios;

[0007] S3, using a fuzzy clustering algorithm to reduce the set of clean energy output scenarios to a number of typical output scenarios, and then obtaining the photovoltaic output process of each typical output scenario and its occurrence probability;

[0008] S4, constructing a cascade reservoir group joint scheduling model for clean energy consumption according to the photovoltaic output process of each typical output scenario and its occurrence probability;

[0009] S5, according to the cascade reservoir group joint scheduling model for clean energy consumption, using the reservoir outflow as a decision variable to iteratively generate a number of preliminary scheduling schemes;

[0010] S6, calculating the fitness value, objective function value and penalty term value of each preliminary scheduling scheme, further obtaining the historical optimal scheduling scheme corresponding to each preliminary scheduling scheme, and obtaining a global optimal scheduling scheme according to the historical optimal scheduling scheme corresponding to each preliminary scheduling scheme;

[0011] S7, dynamically updating each preliminary scheduling scheme, if the preset maximum iteration number is reached, taking the global optimal scheduling scheme corresponding to the preset maximum iteration number as the best scheduling scheme, otherwise returning to execute step S6.

[0012] Further, the aforementioned step S1 comprises the following sub-steps:

[0013] S1.1, calculate the long sequence of relative errors of clean energy output according to the following formula:

[0014]

[0015] wherein, P real is the actual output of clean energy, P fore is the predicted output of clean energy;

[0016] S1.2, calculate the mean μ ξ and variance σ ξ of the long sequence of clean energy output relative errors ξ.

[0017] Further, the aforementioned step S2 is specifically:

[0018] According to the predicted output of clean energy in each period the set of clean energy output scenarios is obtained as follows:

[0019]

[0020] wherein, represents the k0th output scenario of clean energy; represents the value of the k0th output scenario of clean energy in the tth period;

[0021] N(μ ξ ,σ ξ ) represents a normally distributed random number with mean μ ξ and variance σ ξ ; K0 represents the number of output scenarios; T represents the calculation period.

[0022] Further, the aforementioned step S3 comprises the following sub-steps:

[0023] S3.1, use fuzzy clustering method to reduce the set of clean energy output scenarios S0 to K1 clean energy typical output scenarios S1, as follows:

[0024]

[0025] wherein, K1 << K0; represents the k1th typical output scenario of clean energy; represents the value of the k1th typical output scenario of clean energy in the tth time period; K1 represents the number of typical output scenarios;

[0026] S3.2, calculate the photovoltaic output process of each typical output scenario and its occurrence probability according to the following formula:

[0027]

[0028] wherein, represents the Euclidean distance between the output scenarios of clean energy typical output scenarios ; α represents a threshold value; represents a decision function; represents the occurrence probability of

[0029] Further, the foregoing step S4 specifically comprises: constructing a cascade reservoir group joint scheduling model for clean energy consumption, and the corresponding objective function is as follows:

[0030]

[0031] wherein, is the output of the ith reservoir in the k1th typical output scenario of clean energy in the tth time period; I represents the number of reservoirs; T represents the number of calculation time periods; is the remaining load in the k1th typical output scenario, D t is the load in the tth time period;

[0032] The cascade reservoir group joint scheduling model for clean energy consumption needs to satisfy the following constraint conditions:

[0033] water balance constraint

[0034] V i,t+1 = V i,t + [I i,t -Q i,t -S i,t ]Δt,

[0035] wherein, V i,t is the initial storage of the ith reservoir in the tth time period; I i,t is the inflow of the ith reservoir in the tth time period; Q i,t is the outflow of the ith reservoir in the tth time period; S i,t is the abandoned water flow of the ith reservoir in the tth time period.

[0036] reservoir storage constraint

[0037]

[0038] wherein, is the minimum value of the water storage of the i-th reservoir at the t-th time interval, is the maximum value of the water storage of the i-th reservoir at the t-th time interval;

[0039] discharge constraint

[0040]

[0041] wherein, is the minimum value of the discharge of the i-th reservoir at the t-th time interval, is the maximum value of the discharge of the i-th reservoir at the t-th time interval;

[0042] reservoir output constraint

[0043]

[0044] wherein, is the minimum value of the output of the i-th reservoir at the t-th time interval, is the maximum value of the output of the i-th reservoir at the t-th time interval.

[0045] Further, the aforementioned step S5 is specifically: using the reservoir discharge as a decision variable, setting a counter k3 = 1, and randomly generating M preliminary scheduling schemes in the feasible interval, then the m-th preliminary scheduling scheme X m (k3) is as follows:

[0046]

[0047] wherein, r represents a random number in the interval [0, 1] obeying uniform distribution; represents the m-th preliminary scheduling scheme X m (k3) in the m-th preliminary scheduling scheme X

[0048] Further, the aforementioned step S6 includes the following sub-steps:

[0049] S6.1, using a variable scale penalty coefficient C(k3) = exp(ζ0 x (1-ζ1)), calculating the fitness value of each preliminary scheduling scheme, as follows:

[0050]

[0051] wherein, F0[X m (k3)]、F[X m (k3)]、 represents the fitness value, the objective function value, and the penalty term value corresponding to X m (k3); C(k3) is the penalty coefficient at the k3-th iteration; ζ0, ζ1 are adjustment coefficients;

[0052] S6.2, calculate the historical optimal scheduling scheme corresponding to each preliminary scheduling scheme as follows:

[0053] PB m (k3) = argmin{PB m (k3-1), X m (k3)}, m = 1, 2, …, M; k3 = 1, 2, …, K3,

[0054] S6.3, according to the historical optimal scheduling scheme, calculate the global optimal scheduling scheme as follows:

[0055] GB(k3) = argmin{GB(k3-1), PB1(k3), PB2(k3), …, PB M (k3)}.

[0056] Further, the position of each preliminary scheduling scheme is dynamically updated in the foregoing step S7, specifically:

[0057] Position dynamic update

[0058] X m (k3+1) = w × X m (k3) + τ m (k3),

[0059] Information gradually decays

[0060] w = β0 + β1 × exp(-β2 × t / T),

[0061] Neighborhood adaptive adjustment

[0062]

[0063] wherein c1, c2 are random numbers in the interval [0, 1.5] and obeying uniform distribution; w is the adjustment weight of X m (k3); τ m (k3) is the actual adjustment value of X m (k3); χ m (k3) is the target adjustment value of X m (k3); Δ m (k3), ψ m (k3) are the upper limit and lower limit of χ m (k3) respectively; β0, β1, β2 represent dynamic adjustment coefficients.

[0064] Another aspect of the present application also provides a cascade reservoir group scheduling system for clean energy consumption, comprising:

[0065] A data preprocessing module is configured to obtain a long sequence of relative errors of clean energy output and its mean and variance based on actual output and predicted output of the clean energy in each period;

[0066] A set construction module is configured to construct a set of predicted output processes based on the predicted output of the clean energy in each period, and obtain a set of clean energy output scenarios based on the set of predicted output processes.

[0067] A model construction module is configured to construct a cascade reservoir group joint dispatching model for clean energy consumption based on the photovoltaic output process and its occurrence probability of each typical output scenario.

[0068] A dispatching scheme module comprises:

[0069] A dispatching scheme generation unit is configured to generate a plurality of preliminary dispatching schemes by taking reservoir discharge flow as a decision variable based on the cascade reservoir group joint dispatching model for clean energy consumption.

[0070] A dynamic updating unit is configured to dynamically update the preliminary dispatching schemes, and if the preset maximum iteration number is reached, the corresponding global optimal dispatching scheme at the preset maximum iteration number is taken as the best dispatching scheme, otherwise the scheme optimization unit is returned to execute.

[0071] Compared with the prior art, the present application has the following advantages and beneficial effects:

[0072] 1. The present application fully excavates the distribution law of clean energy output deviation, constructs typical output scenarios with strong representativeness, provides a practical dispatching method considering the uncertainty of clean energy output, and can solve the defects such as result distortion existing in traditional deterministic dispatching models.

[0073] 2. The present application can take into account the major demand of power grid peak shaving and clean energy consumption, fully exert the multi-dimensional compensation benefit of cascade, deeply excavate the peak shaving potential of reservoir group, and the obtained dispatching scheme is more in line with the actual operation demand, and has good engineering practicability.

[0074] 3、The high-efficiency optimization method and the variable scale constraint processing strategy have the advantages of simple structure and easy implementation, can significantly simplify the constraint processing process, enhance the convergence speed and result accuracy, significantly improve the defects of local convergence and dimension disaster faced by the existing reservoir group optimization method, and are suitable for large-scale cascade reservoir group joint scheduling problems. BRIEF DESCRIPTION OF DRAWINGS

[0075] Figure 1 A flowchart of the present application.

[0076] Figure 2 A clean energy output scenario set distribution diagram obtained by the present application.

[0077] Figure 3 A system operation scheme diagram of the present application under three different working conditions.

[0078] Figure 4 A cascade reservoir group operation scheme diagram of the present application under three different working conditions. DETAILED DESCRIPTION

[0079] In order to better understand the technical content of the present application, specific embodiments are given and described below with reference to the accompanying drawings.

[0080] Aspects of the present application are described in this detailed description and illustrated in the accompanying drawings by a number of illustrative embodiments. Embodiments of the present application are not limited to the drawings described. It should be understood that the present application is implemented by any one of the above-mentioned concepts and embodiments, and the concepts and embodiments described in detail below, because the concepts and embodiments disclosed in the present application are not limited to any embodiment. In addition, some aspects disclosed in the present application can be used alone, or in any suitable combination with other aspects disclosed in the present application.

[0081] The present application simulates the peak shaving of the Longyangxia-Laxiwa-Lijiaxia cascade reservoir group and the Longyangxia photovoltaic power station on the upper reaches of the Yellow River, in order to fully utilize the large installed capacity and high water head of the power station to improve the peak shaving capacity, alleviate the threat of clean energy such as photovoltaic power to the power system, reduce the start-stop frequency of thermal power units or low-performance hydropower units, and improve the reliability of power grid and power station operation. Therefore, for different weather conditions, according to different scheme photovoltaic prediction data, the water-photovoltaic complementary system peak shaving model is dispatched, wherein scheme one is a typical sunny day, scheme two is a typical overcast day, and scheme three is a typical rainy day.

[0082] As shown in Figure 1 The cascade reservoir group scheduling method for clean energy consumption of the present application includes the following steps:

[0083] S1, based on the actual output of clean energy in each period, the predicted output, obtain the long sequence of clean energy output relative error and its mean, variance. Specifically includes the following steps S1.1 to S1.2: S1.1, according to the following formula to calculate the long sequence of relative error of clean energy output:

[0084]

[0085] Where, P real is the actual output of clean energy, P fore is the predicted output of clean energy;

[0086] S1.2, the mean μ ξ , variance σ ξ of the long sequence of clean energy output relative error ξ is calculated. S2, according to the predicted output of clean energy in each period Obtain the set of clean energy output scenarios as follows:

[0087]

[0088] Where, represents the k0th output scenario of clean energy; represents the value of the k0th output scenario of clean energy in the t period;

[0089] N(μ ξ ,σ ξ ) represents a normally distributed random number with mean μ ξ and variance σ ξ ; K0 represents the number of output scenarios; T represents the calculation period.

[0090] S3, using fuzzy clustering algorithm to reduce the set of clean energy output scenarios to several typical output scenarios, and then obtain the photovoltaic output process of each typical output scenario and its occurrence probability. Specifically includes the following steps S3.1 to step S3.2:

[0091] S3.1, using fuzzy clustering method to reduce the set of clean energy output scenarios S0 to K1 clean energy typical output scenarios S1, as follows:

[0092]

[0093] Where, K1<<K0; represents the k1th typical output scenario of clean energy; represents the value of the k1th typical output scenario of clean energy in the t period; K1 represents the number of typical output scenarios;

[0094] S3.2, the photovoltaic output process of each typical output scenario and its occurrence probability are calculated according to the following formula:

[0095]

[0096] wherein, represents the Euclidean distance between the clean energy output scenarios typical output scenarios ; alpha represents a threshold value; represents a decision function; represents the occurrence probability of

[0097] S4, a cascade reservoir group joint scheduling model oriented to clean energy consumption is constructed, and the corresponding objective function is as follows:

[0098]

[0099] wherein, is the output of the i th reservoir at time period t in the k 1 th clean energy typical output scenario; I represents the number of reservoirs; T represents the number of calculation time periods; is the remaining load in the k 1 th typical output scenario, D t is the load at time period t;

[0100] The cascade reservoir group joint scheduling model oriented to clean energy consumption needs to meet the following constraint conditions:

[0101] water balance constraint

[0102] V i,t+1 = V i,t + [I i,t -Q i,t -S i,t ] Delta t,

[0103] wherein, V i,t is the initial storage of the i th reservoir at the t th time period; I i,t is the inflow of the i th reservoir at the t th time period; Q i,t is the outflow of the i th reservoir at the t th time period; S i,t is the abandoned water flow of the i th reservoir at the t th time period.

[0104] reservoir storage capacity constraint

[0105]

[0106] wherein, is the minimum value of the storage of the i th reservoir at the t th time period, is the maximum value of the storage of the i th reservoir at the t th time period;

[0107] Lower flow constraint

[0108]

[0109] wherein, is the minimum value of the discharge flow of the i-th reservoir at the t period, is the maximum value of the discharge flow of the i-th reservoir at the t period;

[0110] Reservoir output constraint

[0111]

[0112] wherein, is the minimum value of the output of the i-th reservoir at the t period, is the maximum value of the output of the i-th reservoir at the t period.

[0113] S5, according to the cascade reservoir group joint scheduling model facing clean energy consumption, using the reservoir discharge flow as the decision variable to iteratively generate a plurality of preliminary scheduling schemes. Specifically:

[0114] Using the reservoir discharge flow as the decision variable, let the counter k3=1, and randomly generate M preliminary scheduling schemes in the feasible interval, then the m-th preliminary scheduling scheme X m (k3) is as follows:

[0115]

[0116] wherein, r represents a random number in the interval [0,1] obeying uniform distribution; represents the m-th preliminary scheduling scheme X m (k3) in the k3-th iteration, the discharge flow of the i-th reservoir at the t period. S6, calculate the fitness value, objective function value, penalty term value of each preliminary scheduling scheme, and further obtain the historical optimal scheduling scheme corresponding to each preliminary scheduling scheme; according to the historical optimal scheduling scheme corresponding to each preliminary scheduling scheme, obtain the global optimal scheduling scheme. Specifically, it includes the following steps S6.1 to S6.3:

[0117] S6.1, using the variable scale penalty coefficient C(k3)=exp(ζ0×(1-ζ1)), calculate the fitness value of each preliminary scheduling scheme, as follows:

[0118]

[0119] wherein, F0[X m (k3)], F[X m (k3)], represents X m(k3) corresponding fitness value, objective function value, penalty term value; C(k3) is the penalty coefficient at the k3th iteration; ζ0, ζ1 are adjustment coefficients;

[0120] S6.2, calculate the corresponding historical optimal scheduling scheme of each preliminary scheduling scheme according to the following formula:

[0121] PB m (k3) = argmin{PB m (k3-1), X m (k3)}, m = 1, 2, …, M; k3 = 1, 2, …, K3,

[0122] S6.3, according to the historical optimal scheduling scheme, calculate the global optimal scheduling scheme according to the following formula:

[0123] GB(k3) = argmin{GB(k3-1), PB1(k3), PB2(k3), …, PB M (k3)}.

[0124] S7, dynamically update each preliminary scheduling scheme, if the preset maximum iteration number is reached, the global optimal scheduling scheme corresponding to the preset maximum iteration number is taken as the best scheduling scheme, otherwise return to step S6.

[0125] The position of each preliminary scheduling scheme is dynamically updated in step S7, specifically:

[0126] Position dynamic update

[0127] X m (k3+1) = w × X m (k3) + τ m (k3),

[0128] Information gradually decays

[0129] w = β0+ β1×exp(-β2×t / T),

[0130] Neighborhood adaptive adjustment

[0131]

[0132] Wherein, c1, c2 are random numbers in the interval [0, 1.5] obeying uniform distribution; w is the adjustment weight of X m (k3); τ m (k3) is the actual adjustment value of X m (k3); χ m (k3) is the target adjustment value of X m (k3); Δ m (k3), ψ m (k3) are χm (k3) upper limit, lower limit; β0, β1, β2 represent dynamic adjustment coefficients.

[0133] As Figure 2 The clean energy predicted power output scene set distribution diagram as shown in the figure, it can be seen that the predicted power output has great uncertainty, it is very necessary to consider in the model. Therefore, the fuzzy clustering algorithm is used to reduce the clean energy power output scene set to several typical power output scenes, and the photovoltaic power output process and its occurrence probability of each typical power output scene are calculated.

[0134] Figure 3 The system operation scheme diagram of the present application under three different working conditions. Figure 3 It can be seen that each reservoir cooperates with photovoltaic output, the output process trend and load demand are consistent, the residual load curve of each scheme is approximately a straight line, the average peak-valley difference of each scheme after peak shaving is less than 2.5MW, and the standard deviation is less than 1, the peak shaving effect is obvious, which indicates that the model setting of this test is reasonable, and the optimization group algorithm is effective. In addition, from table 1, it can be seen that the residual load peak-valley difference and standard deviation are the largest on sunny days and the smallest on rainy days, it can be seen that when the actual output is the predicted output, the smaller the photovoltaic output, the better the peak shaving effect, which confirms that large-scale grid-connected photovoltaic will increase the difficulty of system peak shaving and affect the safe and stable operation of the power grid.

[0135] Table 1

[0136]

[0137] Figure 4 The cascade reservoir group operation scheme diagram of the present application under three different working conditions. Figure 4 It can be seen that Longyangxia reservoir discharges and generates electricity according to its own conditions, Laxiwa reservoir undertakes the main peak shaving task, and Lijiaxia undertakes the standby power generation task. Because the capacity of Longyangxia reservoir is large, the daily water level basically remains unchanged, and the water level fluctuation range is within 0.02m during the entire dispatching period; the output change of Laxiwa reservoir is consistent with the load demand, in the early stage, the power consumption is in the low peak period, the power generation flow of Laxiwa reservoir is small, and the water head is continuously increased; in the high load peak period (11-12h) of the early stage, the high water head generated by water storage is utilized, and in the high load peak period (21-24h) of the later stage, the discharge flow is increased for power generation, and the water level almost presents a linear decreasing trend; the average output of Lijiaxia reservoir is greater than 850MW per hour, and during the entire dispatching period, it cooperates with Laxiwa reservoir for dispatching as a standby power station to ensure that the total output of the system is maximized to approach the load demand, and makes a contribution to the clean energy consumption.

[0138] While the application has been described by way of example with reference to certain embodiments thereof, it is to be understood that the application is not limited to the embodiments described above, but intrinsically extends to various modifications and changes in form and details. Therefore, the scope of the application should be determined not by the embodiments described above, but by the scope of the appended claims and their equivalents.

Claims

1. A cascade reservoir group dispatching method for clean energy consumption, characterized in that, Comprising the following steps: S1, based on the actual output of each period of clean energy, the predicted output, obtain the long sequence of relative error of clean energy output and its mean, variance; S2, based on the predicted output of each period of clean energy, obtain the clean energy output scenario set; S3, the fuzzy clustering algorithm is used to reduce the clean energy output scenario set to several typical output scenarios, and the photovoltaic output process and its occurrence probability of each typical output scenario are obtained; Including the following sub-steps: S3.1, using a fuzzy clustering method to reduce the set of clean energy output scenarios to a number of clean energy typical output scenarios as follows: ， in, ; Indicating clean energy as the first A typical scenario of exerting effort; Indicating clean energy as the first A typical output scenario in the first Values ​​for the time period; Indicates the number of typical output scenarios; S3.2, the photovoltaic output process and its occurrence probability of each typical output scenario are calculated according to the following formula: ， wherein, represents a clean energy output scenario , a typical output scenario between the Euclidean distance; represents a threshold value; represents a decision function; represents the probability of occurrence; S4, according to the photovoltaic output process and its occurrence probability of each typical output scenario, a cascade reservoir group joint scheduling model for clean energy consumption is constructed; Specifically including: constructing a cascade reservoir group joint scheduling model for clean energy consumption, the corresponding objective function is as follows: ， ， in, In the first In a typical clean energy output scenario, the first The reservoir during the period contribution; Indicates the number of reservoirs; Indicates the number of time periods used for calculation; For the first Residual load in a typical power output scenario; The load for time period t; S5, according to the cascade reservoir group joint scheduling model for clean energy consumption, the reservoir discharge flow is used as the decision variable to iteratively generate several preliminary scheduling schemes; Specifically: The reservoir outflow is used as a decision variable, and a counter is set to 0. A preliminary scheduling scheme is randomly generated in a feasible interval , and the counter is incremented by 1. In the first iteration, the first preliminary scheduling scheme is generated , and in the second iteration, the second preliminary scheduling scheme is generated . The preliminary scheduling scheme is generated as follows: ， ， wherein, represents a random number from the interval [0, 1] following a uniform distribution; represents the th preliminary scheduling scheme at the th iteration , the th reservoir at the th time period S6, the fitness value, objective function value and penalty term value of each preliminary scheduling scheme are calculated, and the historical optimal scheduling scheme corresponding to each preliminary scheduling scheme is further obtained; According to the historical optimal scheduling scheme corresponding to each preliminary scheduling scheme, the global optimal scheduling scheme is obtained; Including the following sub-steps: S6.1, Utilizing a variable scale penalty factor The fitness value of each preliminary scheduling scheme is calculated as follows: ， wherein , , denotes a corresponding fitness value, objective function value, penalty term value; is the penalty coefficient at the th iteration; , is an adjustment coefficient; S6.2, the historical optimal scheduling scheme corresponding to each preliminary scheduling scheme is calculated according to the following formula: ， S6.3, according to the historical optimal scheduling scheme, the global optimal scheduling scheme is calculated according to the following formula: ； S7, dynamically update each preliminary scheduling scheme, if the preset maximum iteration number is reached, the global optimal scheduling scheme corresponding to the preset maximum iteration number is taken as the best scheduling scheme, otherwise return to step S6; The position of each preliminary scheduling scheme is dynamically updated, specifically: Position dynamic update ， Information gradually decays ， Neighborhood adaptive adjustment ， Wherein, , is a random number obeying uniform distribution in the interval [0, 1.5]; is an adjustment weight of ; is an actual adjustment value of ; is a target adjustment value of ; , are respectively an upper limit and a lower limit of ; represents a dynamic adjustment coefficient.

2. The cascade reservoir group dispatching method for clean energy consumption according to claim 1, characterized in that, Step S1 includes the following sub-steps: S1.1, the relative error long sequence of clean energy output is calculated according to the following formula: ， wherein, the actual output of the clean energy, the predicted output of the clean energy; S1.2, calculate the mean of the long sequence of relative errors of clean energy output .​​ 3. The method of claim 2, wherein, Step S2 is specifically: According to the predicted output of the clean energy in each period , a set of clean energy output scenarios is obtained as follows: ， wherein, represents the clean energy output scenario number; represents the clean energy output scenario number in the t period; ; represents a normally distributed random number with mean and variance ; represents the number of output scenarios; represents the calculation period.

4. The method of claim 1, wherein, The cascade reservoir group joint scheduling model for clean energy consumption needs to meet the following constraint conditions: Water balance constraint , wherein V i,t is the initial storage of the i-th reservoir at the t-th time interval; I i,t is the inflow of the i-th reservoir at the t-th time interval; Q i,t is the outflow of the i-th reservoir at the t-th time interval; S i,t is the spillage of the i-th reservoir at the t-th time interval; Reservoir storage capacity constraint ， wherein, is the minimum value of the water storage of the i-th reservoir at the t period, is the maximum value of the water storage of the i-th reservoir at the t period; Discharge flow constraint ， wherein, Qmin(i) is the minimum value of the discharge of the i-th reservoir at the t period, Qmax(i) is the maximum value of the discharge of the i-th reservoir at the t period; Reservoir output constraint ， wherein, is the minimum value of the outflow of the i-th reservoir at the t period, is the maximum value of the outflow of the i-th reservoir at the t period.

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