Wind-solar-water storage multi-energy complementary optimization scheduling method considering uncertainty

Abstract

The invention discloses a wind-light-water storage multi-energy complementation optimization scheduling method considering uncertainty. The method comprises the following steps: describing the uncertainty of wind and light output in a wind-light-water storage multi-energy complementation system by using a multi-scene method based on Latin hypercube sampling and a synchronous back substitution reduction method, and outputting a typical scene set; processing two uncertain variables of wind and light output in the typical scene set by using a distributed robust optimization scheduling method based on multiple discrete scenes to construct an uncertain set; according to the uncertainty set, constructing a wind-light-water-storage multi-energy complementary robust optimization scheduling model based on uncertainty; and solving the constructed wind-light-water-storage multi-energy complementary robust optimization scheduling model based on the uncertainty so as to output an optimal scheduling scheme. According to the method, wind, light and water resource characteristics and complementary characteristics among resources are analyzed, and model solving is more efficient and faster through the method.

Description

Technical Field

[0001] The present invention relates to the field of wind, solar, and water storage technology, and in particular to a wind, solar, and water storage multi-energy complementary optimization scheduling method taking uncertainty into consideration. Background Art

[0002] In today's rapidly developing economy, environmental pollution and energy shortage are two inevitable problems that are also plaguing the overall development of society. Vigorously developing clean energy power generation methods such as wind power, photovoltaics, and hydropower is now also a hot research direction at home and abroad. Photovoltaic and wind power are restricted by geographical location, seasonal conditions, etc., and have disadvantages such as volatility and intermittency. It is difficult for a single photovoltaic, wind power, hydropower or wind-solar complementary system to guarantee sufficient power generation in a certain area, so it is difficult to popularize on a large scale. The energy model of the multi-energy complementary system can effectively solve the above problems. Hydropower has great advantages in peak-shaving and frequency regulation performance, with the advantages of flexible start-up and fast regulation speed. Combining the respective characteristics of photovoltaic and wind power, and operating in a complementary manner with hydropower, it can improve the grid's ability to absorb photovoltaic and wind power. In addition, with the continuous expansion of photovoltaic and wind power installed capacity and the development of electrochemical energy storage technology, energy storage power stations with charging and discharging functions have been applied to the coordinated operation of multi-energy complementary power stations, making the coordinated operation of multi-energy complementary power stations more flexible and effectively reducing power abandonment. Therefore, studying the optimal operation and scheduling method of wind-solar-hydro-storage multi-energy complementarity has very important theoretical and practical significance in increasing the safety and economy of the power system and improving the efficiency of new energy utilization.

[0003] As the installed capacity of new energy power stations such as wind power and photovoltaic power stations increases, how to coordinate the operation of various power stations to transmit high-quality electricity to the power grid is the trend and difficulty of the current research on multi-energy complementary power stations. At present, the complementary systems studied by most scholars mainly tend to be wind-solar complementary systems. The methods for dealing with the uncertainty problems of complementary systems mostly use random optimization, fuzzy programming and robust optimization methods, while the model solution uses the traditional Benders decomposition algorithm.

[0004] With a high proportion of new energy sources connected to the power grid, the uncertainty of their output has a greater impact on the power system. The wind-solar complementary system is difficult to cope with the uncertainty brought by wind power and photovoltaics, and cannot guarantee the transmission of high-quality electricity to the power grid, reducing the impact on the stability of the power system. Therefore, it is necessary to introduce hydropower and energy storage with flexible start-up and fast adjustment speed into the wind-solar complementary system to smooth the uncertainty of the complementary system. In addition, the traditional uncertainty analysis method random optimization is difficult to obtain an accurate probability distribution, and robust optimization is too conservative. It is urgent to adopt a better method to analyze the uncertainty of the complementary system.

[0005] Photovoltaic and wind power are restricted by geographical location, seasonal conditions and other constraints, and have disadvantages such as volatility and intermittency. A single photovoltaic, wind power, hydropower or wind-solar complementary system cannot guarantee sufficient power generation in a certain area, and therefore is difficult to popularize on a large scale. The energy model of a multi-energy complementary system can effectively solve the above problems. Summary of the invention

[0006] The present invention aims to solve one of the technical problems in the related art at least to a certain extent.

[0007] To this end, the present invention proposes a wind-solar-water-storage multi-energy complementary optimization scheduling method considering uncertainty, analyzes the characteristics of wind, light, and water resources and the complementary characteristics between resources, and introduces hydropower generation and energy storage batteries to existing wind and photovoltaic power stations to build a wind-light-water-storage multi-energy complementary system. Considering the uncertainty of wind and solar output, the distributed blue bar optimization method is used to construct a wind-light-water-storage multi-energy complementary optimization scheduling model with the minimum net load fluctuation and the maximum clean energy power generation as the optimization objectives, and it is proposed to use the C&CG decomposition algorithm to solve the wind-light-water-storage multi-energy complementary model, so that the model solution is more efficient and faster.

[0008] Another object of the present invention is to propose a wind, solar, water and storage multi-energy complementary optimization scheduling system that takes uncertainty into account.

[0009] To achieve the above objectives, the present invention proposes, on one hand, a wind, solar, water and storage multi-energy complementary optimization scheduling method considering uncertainty, comprising:

[0010] A multi-scenario method based on Latin hypercube sampling and synchronous back-substitution reduction is used to describe the uncertainty of wind and solar output in the wind-solar-hydro-storage multi-energy complementary system and output a typical scenario set.

[0011] The two uncertain variables of wind and solar output in the typical scenario set are processed by using the distributed robust optimization scheduling method based on multiple discrete scenarios to construct an uncertainty set;

[0012] According to the uncertainty set, a wind-solar-hydro-storage multi-energy complementary robust optimization scheduling model based on uncertainty is constructed;

[0013] The constructed uncertainty-based wind-solar-hydro-storage multi-energy complementary robust optimization scheduling model is solved to output the optimal scheduling plan.

[0014] To achieve the above-mentioned purpose, the present invention proposes, on the other hand, a wind, solar, water and storage multi-energy complementary optimization scheduling system considering uncertainty, comprising:

[0015] Uncertainty description module, which is used to describe the uncertainty of wind and solar output in the wind-solar-hydro-storage multi-energy complementary system using a multi-scenario method based on Latin hypercube sampling and synchronous back-substitution reduction method to output a typical scenario set;

[0016] An uncertainty set construction module is used to process the two uncertain variables of wind and solar output in the typical scenario set and construct an uncertainty set by using a distributed robust optimization scheduling method based on multiple discrete scenarios;

[0017] An optimization scheduling model construction module is used to construct an uncertainty-based wind-solar-hydro-storage multi-energy complementary robust optimization scheduling model according to the uncertainty set;

[0018] The optimization scheduling model solving module is used to solve the uncertainty-based wind-solar-hydro-storage multi-energy complementary robust optimization scheduling model to output the optimal scheduling plan.

[0019] The wind, solar, water and storage multi-energy complementary optimization scheduling method and system considering uncertainty in the embodiments of the present invention analyzes the characteristics of wind, solar and water resources and the complementary characteristics between the resources, introduces hydropower generation and energy storage batteries into the existing wind and solar power stations to build a wind, solar, water and storage multi-energy complementary system, and constructs a wind, solar, water and storage multi-energy complementary optimization scheduling model considering the uncertainty of wind and solar output, so as to efficiently solve the model.

[0020] Additional aspects and advantages of the present invention will be given in part in the following description and in part will be obvious from the following description, or will be learned through practice of the present invention. BRIEF DESCRIPTION OF THE DRAWINGS

[0021] The above and / or additional aspects and advantages of the present invention will become apparent and easily understood from the following description of the embodiments in conjunction with the accompanying drawings, in which:

[0022] Figure 1 is a flow chart of a wind, solar, water and storage multi-energy complementary optimization scheduling method considering uncertainty according to an embodiment of the present invention;

[0023] Figure 2 is a specific implementation flow chart of a wind, solar, water and storage multi-energy complementary optimization scheduling method considering uncertainty according to an embodiment of the present invention;

[0024] Figure 3 is a typical scene generation flow chart based on LHS according to an embodiment of the present invention;

[0025] Figure 4 is a flowchart of solving the distributed robust optimization model according to an embodiment of the present invention;

[0026] Figure 5 It is a structural diagram of a wind, solar, hydropower and storage multi-energy complementary optimization scheduling system considering uncertainty according to an embodiment of the present invention. DETAILED DESCRIPTION

[0027] It should be noted that, in the absence of conflict, the embodiments of the present invention and the features in the embodiments can be combined with each other. The present invention will be described in detail below with reference to the accompanying drawings and in combination with the embodiments.

[0028] In order to enable those skilled in the art to better understand the scheme of the present invention, the technical scheme in the embodiments of the present invention will be clearly and completely described below in conjunction with the drawings in the embodiments of the present invention. Obviously, the described embodiments are only part of the embodiments of the present invention, not all of the embodiments. Based on the embodiments of the present invention, all other embodiments obtained by ordinary technicians in this field without creative work should fall within the scope of protection of the present invention.

[0029] The following describes the wind, solar, hydropower and storage multi-energy complementary optimization scheduling method and system considering uncertainty proposed in an embodiment of the present invention with reference to the accompanying drawings.

[0030] Figure 1 is a flow chart of a wind, solar, water and storage multi-energy complementary optimization scheduling method considering uncertainty according to an embodiment of the present invention. Figure 1 As shown, including:

[0031] S1, using a multi-scenario method based on Latin hypercube sampling and synchronous back-substitution reduction method to describe the uncertainty of wind and solar output in the wind-solar-hydro-storage multi-energy complementary system and output a typical scenario set;

[0032] S2, using a distributed robust optimization scheduling method based on multiple discrete scenarios to process the two uncertain variables of wind and solar output in the typical scenario set to construct an uncertainty set;

[0033] S3, constructing a wind-solar-hydro-storage multi-energy complementary robust optimization scheduling model based on uncertainty according to the uncertainty set;

[0034] S4, solve the constructed uncertainty-based wind-solar-hydro-storage multi-energy complementary robust optimization scheduling model to output the optimal scheduling plan.

[0035] The specific implementation steps of the present invention are shown in 2.

[0036] (1) Describe the uncertainty of wind and solar output in the wind, solar, hydro and storage multi-energy complementary system.

[0037] (2) A distributed robust optimization scheduling method based on multiple discrete scenarios is adopted to process the two uncertain variables of wind and solar output and construct an uncertainty set.

[0038] (3) Construct a robust optimization scheduling model for wind-solar-hydro-storage multi-energy complementarity taking into account uncertainty.

[0039] (4) Solve the constructed wind-solar-hydro-storage multi-energy complementary robust optimization scheduling model considering uncertainty.

[0040] In one embodiment of the present invention, the uncertainty of wind and solar output in a wind-solar-hydropower-storage multi-energy complementary system is described. The following steps are included:

[0041] The multi-scenario technology based on Latin hypercube sampling and synchronous back-substitution reduction is used for processing. The specific process is as follows: Figure 3 shown.

[0042] In one embodiment of the present invention, a distributed robust optimization scheduling method based on multiple discrete scenarios is adopted. First, two uncertain variables, wind and solar output, are processed to construct an uncertainty set. The specific construction process is as follows.

[0043] Assume that there are M actual scenarios, which are satisfied by screening M1, M2, ..., M Ns A finite discrete scenario, constructing a theoretical distribution of uncertain variables in The confidence set under different θ values ​​is established by the following formula:

[0044]

[0045] Where: s is the relevant probability under scenario s; N s is the total number of scenes; Ω ρ is s feasible domain; ρ is the element ρ s vector; θ1, θ ∞ They are the probability allowable deviation limits under the corresponding constraints of 1-norm and ∞-norm respectively; the confidence set is constructed based on the given confidence level and amount of historical data:

[0046]

[0047] Let the right side of the inequality be equal to α1 and α ∞ , then it is converted into:

[0048]

[0049] It can be seen from equations (4-11) to (4-12) that the value of θ decreases as the amount of historical data increases. When the amount of historical data increases to ∞, θ1 and θ ∞ is 0, that is, the probability distribution deviation is 0, which means that the initial distribution is the true distribution.

[0050] In one embodiment of the present invention, a wind-solar-hydro-storage multi-energy complementary robust optimization scheduling model considering uncertainty is constructed, which includes the following steps:

[0051] The objective function of the scheduling model constructed by the present invention is:

[0052] 1) Minimum net load variance

[0053] In order to make the multi-energy complementary system better track the load curve, under the condition of full consumption of wind and solar power, the output of hydropower and energy storage is adjusted to minimize the fluctuation of the net load curve as much as possible, and the minimum net load variance is taken as the first objective function of the wind-solar-hydropower-storage joint optimization scheduling model, that is,

[0054]

[0055] Where: f1 is the variance of net load; T is the dispatch period; P t is the net load at time t; P is the average value of the net load; P l,t is the initial load of the system at time t; P w,t is the wind power output at time t; P pv,t is the photovoltaic power output at time t; P h,t is the hydropower output at time t; P s,t The energy storage output is at time t.

[0056] 2) Clean energy has the largest total power generation

[0057] In order to give full play to the advantages of new energy, low carbon and environmental protection, and realize large-scale consumption of clean energy, wind and solar power are given priority for full consumption under the condition of meeting the grid constraints of the power system and the output constraints of wind and solar power. The focus is on optimizing the hydropower output in each period, while smoothing the fluctuations of wind and solar power and load, and increasing the power generation as much as possible to meet more load demands. The maximum total power generation of wind, solar and hydropower is the second objective function of the wind-solar-hydro-storage joint optimization scheduling model, that is,

[0058]

[0059] Where: f2 is the total power generation of wind, solar and hydropower

[0060] 3.3.2 Constraints

[0061] Wind-solar-hydro-storage combined output constraints:

[0062] P min,t ≤P w,t +P pv,t +P h,t +P s,t ≤P max,t (7)

[0063] Where: P min,t is the minimum total output of water, wind, solar and energy storage at time t, set to 0; P max,t is the maximum total output of water, wind, solar and energy storage at time t, set to Pl,t Since wind and solar power output is fully consumed first, the combined output requirements are met by adjusting the output of hydropower and energy storage, and the situation where the system load is less than the wind and solar power output is not considered for the time being.

[0064] Wind and solar power output constraints:

[0065] The model uses hydropower and energy storage to adjust the fluctuations of wind and solar power, giving priority to absorbing the full amount of wind and solar power output. According to the output forecast value:

[0066]

[0067]

[0068] Where: are the predicted values ​​of wind power output and photovoltaic output respectively.

[0069] Hydropower output constraints:

[0070] When performing short-term dispatching of a hydropower station, it is only necessary to comprehensively consider the upper and lower limits of the hydropower station's output, water balance constraints, water level constraints, and reservoir capacity constraints:

[0071] 0≤P h,t ≤P h,max (10)

[0072] 3600(Q in,t -Q h,t )Δt=ΔQ h,t (11)

[0073] Z h,min ≤Z h,t ≤Z h,max (12)

[0074] V h,min ≤V h,t ≤V h,max (13)

[0075] Where: P h,max is the maximum output power of the hydropower station during period t; Q in,t is the inflow of the hydropower station during period t; Q h,t is the change in the reservoir capacity of the hydropower station during period t; Z h,max , Z h,min V is the highest and lowest water level of the hydropower station; h,max 、V h,min It is the maximum and minimum storage capacity of the hydropower station.

[0076] Energy storage constraints:

[0077] Energy storage system charge state constraints:

[0078]

[0079] SOC min ≤SOC t ≤SOC max (15)

[0080] Energy storage charging and discharging constraints:

[0081]

[0082]

[0083]

[0084] Where: SOC t SOC is the state of charge of the energy storage system at time t, that is, the ratio of the remaining capacity of the energy storage device to the total capacity; min SOC is the lower limit of the state of charge. max is the upper limit of the state of charge; η c is the charging efficiency; η d is the discharge efficiency; E s is the rated value of the energy storage system; is the charging power at time t; are the minimum and maximum values ​​of the charging power respectively; is the discharge efficiency at time t; are the minimum and maximum values ​​of discharge efficiency respectively; Δt is the duration of each period, which is 1h; ε t It takes 0 or 1. When it is 1, the energy storage system discharges, and when it is 0, the energy storage system charges.

[0085] For the convenience of solving, the weighted method is used to transform multiple objectives into a single objective:

[0086] minαf1-βf2(19)

[0087] Where α and β are the weighted coefficients of each objective function.

[0088] When multiple scenarios are used to describe the uncertainty of wind and solar power, the scheduling model can be expressed in the following uncertain form:

[0089]

[0090] Where: x is the discrete decision variable of the first stage, x = [ε t ] T ;y s is the second-stage continuous decision variable, c is the coefficient column vector corresponding to the objective function (19); A, C, D, d, and h are the coefficient matrices or constant column vectors corresponding to the variables in the model constraints. In formula (21), the first row represents the inequality constraints in the model, including formulas (10), (12) to (13), and (15) to (17); the second row corresponds to formula (7).

[0091] Based on the method described in formula (15), the uncertainty of wind and solar output is processed, and the data-driven distributed robust optimization scheduling model is obtained as follows:

[0092]

[0093] In one embodiment of the present invention, the constructed wind-solar-hydro-storage multi-energy complementary robust optimization scheduling model considering uncertainty is solved, and the specific process is as follows:

[0094] First, linearize the constraints:

[0095] The comprehensive norm constraint in formula (1) is an absolute value constraint, which is linearized using formula (23):

[0096]

[0097] Where: and are the probability distribution ρ of scene s respectively s Relative to ρ s0 The positive and negative offset of and are respectively s A 0-1 flag that generates positive or negative offsets.

[0098] The present invention uses the C&CG decomposition algorithm to solve the model. The main problem of the C&CG algorithm can be expressed as:

[0099]

[0100] The C&CG algorithm divides the model into the main problem (MP) and sub-problems (SP) for iterative solution. The main problem is as follows:

[0101]

[0102] Where: m is the number of iterations

[0103] The first stage variable x * After the main problem is solved, the sub-problem is given according to the given x * Find the worst probability distribution, return to the main problem, and provide an upper bound:

[0104]

[0105] The solution flow chart is shown in Figure 4, and the specific solution steps are as follows:

[0106] Step 1: Set the initial probability distribution and initial upper and lower bounds UB = ∞, LB = 0, and set the number of iterations to 1;

[0107] Step 2: By solving MP, we can get the optimal solution (x * ,χ), and update the lower bound value max{LB,χ} according to the obtained result;

[0108] Step 3: Solve the first-stage variable x obtained by solving the main problem * Bring in the sub-problem to solve and find the worst scenario probability distribution And find the objective function value f(x * ), and according to the obtained result, the upper bound min{UB,f(x * )} to update;

[0109] Step 4: If UB-LB < ε, it means that the threshold requirement is met, the iteration stops, and the optimal solution x is returned * On the contrary, update Add constraints to the main problem and return to step 2 to continue solving.

[0110] According to the wind, solar, water and storage multi-energy complementary optimization scheduling method considering uncertainty of the present invention, the present invention analyzes the characteristics of wind, light and water resources and the complementary characteristics between the resources, introduces hydropower generation and energy storage batteries into the existing wind and solar power stations to construct a wind, solar, water and storage multi-energy complementary system, and constructs a wind, solar, water and storage multi-energy complementary optimization scheduling model considering the uncertainty of wind and solar output, which gives full play to the characteristics of flexible operation and good power regulation performance of hydropower and energy storage batteries, makes up for the insufficient output of wind and solar power, and smoothes the impact of the access of new energy such as wind and solar power on the stability of the power grid. At the same time, it effectively responds to the uncertainty of wind and solar output prediction, reduces the risk of power abandonment and load loss of the complementary system, and ensures the stable operation of the complementary system.

[0111] The beneficial effects of the present invention are:

[0112] 1. The new energy utilization model of wind, solar, hydropower and energy storage with multiple complementary energies can give full play to the characteristics of hydropower and energy storage batteries with flexible operation and good power regulation performance. It can not only make up for the insufficient output of wind and solar power, but also smooth out the impact of the access of new energy such as wind and solar power on the stability of the power grid.

[0113] 2. The robust optimization method combines the advantages of stochastic optimization and robust optimization. It adopts the method of finding the decision results under the worst probability distribution of uncertain parameters. Its optimization results perform well in terms of economy and conservatism. It can give full play to the flexibility of hydropower stations and energy storage stations, improve the peak-shaving capacity of complementary systems, effectively deal with the uncertainty of wind and solar output forecasts, reduce the risks of power abandonment and load loss in complementary systems, and ensure the stable operation of complementary systems.

[0114] 3. The proposed C&CG decomposition algorithm is used to solve the wind-solar-hydro-storage multi-energy complementary model. The main-subproblem framework is applied, and the variables and constraints related to the subproblems are continuously introduced into the solution process of the main problem, which significantly improves the expected strength of the lower bound of the objective function value, effectively reduces the number of iterations, and makes the model solution more efficient and faster.

[0115] In order to implement the above embodiment, Figure 5 As shown, this embodiment also provides a wind, solar, water and storage multi-energy complementary optimization scheduling system 10 considering uncertainty, including:

[0116] The uncertainty description module 100 is used to describe the uncertainty of wind and solar output in the wind-solar-water-storage multi-energy complementary system by using a multi-scenario method based on Latin hypercube sampling and synchronous back-substitution reduction method to output a typical scenario set;

[0117] The uncertainty set construction module 200 is used to process the two uncertain variables of wind and solar output in the typical scenario set and construct an uncertainty set by using a distributed robust optimization scheduling method based on multiple discrete scenarios;

[0118] An optimization scheduling model construction module 300 is used to construct an uncertainty-based wind-solar-hydro-storage multi-energy complementary robust optimization scheduling model according to the uncertainty set;

[0119] The optimization scheduling model solving module 400 is used to solve the constructed uncertainty-based wind-solar-hydro-storage multi-energy complementary robust optimization scheduling model to output the optimal scheduling plan.

[0120] According to the wind, solar, hydropower and water storage multi-energy complementary optimization scheduling system considering uncertainty in the embodiment of the present invention, the present invention analyzes the characteristics of wind, solar and water resources and the complementary characteristics between the resources, introduces hydropower generation and energy storage batteries into the existing wind and solar power stations to construct a wind, solar, hydropower and water storage multi-energy complementary system, and constructs a wind, solar, hydropower and water storage multi-energy complementary optimization scheduling model considering the uncertainty of wind and solar power output, fully giving play to the characteristics of flexible operation and good power regulation performance of hydropower and energy storage batteries, making up for the insufficient output of wind and solar power, and smoothing the impact of the access of new energy such as wind and solar power on the stability of the power grid. At the same time, it effectively responds to the uncertainty of wind and solar power output prediction, reduces the risk of power abandonment and load loss of the complementary system, and ensures the stable operation of the complementary system.

[0121] In the description of this specification, the description with reference to the terms "one embodiment", "some embodiments", "example", "specific example", or "some examples" etc. means that the specific features, structures, materials or characteristics described in conjunction with the embodiment or example are included in at least one embodiment or example of the present invention. In this specification, the schematic representations of the above terms do not necessarily refer to the same embodiment or example. Moreover, the specific features, structures, materials or characteristics described may be combined in any one or more embodiments or examples in a suitable manner. In addition, those skilled in the art may combine and combine the different embodiments or examples described in this specification and the features of the different embodiments or examples, without contradiction.

[0122] In addition, the terms "first" and "second" are used for descriptive purposes only and should not be understood as indicating or implying relative importance or implicitly indicating the number of the indicated technical features. Therefore, the features defined as "first" and "second" may explicitly or implicitly include at least one of the features. In the description of the present invention, the meaning of "plurality" is at least two, such as two, three, etc., unless otherwise clearly and specifically defined.

Claims

1. A wind, solar, water and storage multi-energy complementary optimization scheduling method considering uncertainty, characterized in that: include: A multi-scenario method based on Latin hypercube sampling and synchronous back-substitution reduction is used to describe the uncertainty of wind and solar output in the wind-solar-hydro-storage multi-energy complementary system and output a typical scenario set. The two uncertain variables of wind and solar output in the typical scenario set are processed by using the distributed robust optimization scheduling method based on multiple discrete scenarios to construct an uncertainty set; According to the uncertainty set, a wind-solar-hydro-storage multi-energy complementary robust optimization scheduling model based on uncertainty is constructed; The constructed uncertainty-based wind-solar-hydro-storage multi-energy complementary robust optimization scheduling model is solved to output the optimal scheduling plan.

2. The method according to claim 1, characterized in that The two uncertain variables of wind and solar output in the typical scenario set are processed by using the distributed robust optimization scheduling method based on multiple discrete scenarios to construct an uncertain set, including: Assume that there are M actual scenarios, which are satisfied by screening of A finite discrete scenario, constructing a theoretical distribution of uncertain variables in The confidence set under different θ values ​​is established by the following formula: Where: s is the relevant probability under scenario s; N s is the total number of scenes; Ω ρ is s feasible domain; ρ is the element ρ s vector; θ1, θ ∞ They are the probability allowable deviation limits under the corresponding constraints of 1-norm and ∞-norm respectively; the confidence set is constructed based on the given confidence level and amount of historical data: Let the right side of the inequality be equal to α1 and α ∞ , then it is converted into:

3. The method according to claim 1, characterized in that According to the uncertainty set, a wind-solar-hydro-storage multi-energy complementary robust optimization scheduling model based on uncertainty is constructed, including: The objective function of building a scheduling model is: 1) Minimum net load variance The minimum net load variance is taken as the objective function of the wind-solar-hydro-storage joint optimization scheduling model, that is: Where: f1 is the variance of net load; T is the dispatch period; P t is the net load at time t; is the average value of the net load; P l,t is the initial load of the system at time t; P w,t is the wind power output at time t; P pv,t is the photovoltaic power output at time t; P h,t is the hydropower output at time t; P s,t The energy storage output is at time t; 2) Clean energy has the largest total power generation The maximum total power generation of wind, solar, and hydropower is taken as the objective function of the wind-solar-hydropower-storage joint optimization scheduling model, that is, Where: f2 is the total power generation of wind, solar and hydropower; Constraints Wind-solar-hydro-storage combined output constraints: P min,t ≤P w,t +P pv,t +P h,t +P s,t ≤P max,t (7) Where: P min,t is the minimum total output of water, wind, solar and energy storage at time t, set to 0; P max,t is the maximum total output of water, wind, solar and energy storage at time t, set to P l,t ; Wind and solar power output constraints: The model uses hydropower and energy storage to adjust the fluctuations of wind and solar power, giving priority to absorbing the full amount of wind and solar power output. According to the output forecast value: Where: are the predicted values ​​of wind power output and photovoltaic output respectively; Hydropower output constraints: When conducting short-term dispatching of hydropower stations, it is sufficient to comprehensively consider the upper and lower limits of the hydropower station's output, water balance constraints, water level constraints, and reservoir capacity constraints: 0≤P h,t ≤P h,max (10) 3600(Q in,t -Q h,t )Δt=ΔQ h,t (11) WITH h,min ≤Z h,t ≤Z h,max (12) In h,min ≤V h,t ≤V h,max (13) Where: P h,max is the maximum output power of the hydropower station during period t; Q in,t is the inflow of the hydropower station during period t; Q h,t is the change in the reservoir capacity of the hydropower station during period t; Z h,max , Z h,min V is the highest and lowest water level of the hydropower station; h,max 、V h,min is the maximum and minimum storage capacity of the hydropower station; Energy storage constraints: Energy storage system charge state constraints: SOC min ≤SOC t ≤SOC max (15) Energy storage charging and discharging constraints: Where: SOC t SOC is the state of charge of the energy storage system at time t, that is, the ratio of the remaining capacity of the energy storage device to the total capacity; min SOC is the lower limit of the state of charge. max is the upper limit of the state of charge; η c is the charging efficiency; η d is the discharge efficiency; E s is the rated value of the energy storage system; is the charging power at time t; are the minimum and maximum values ​​of the charging power respectively; is the discharge efficiency at time t; are the minimum and maximum values ​​of discharge efficiency respectively; Δt is the duration of each period, which is 1h; ε t It takes 0 or 1. When it is 1, the energy storage system discharges, and when it is 0, the energy storage system charges; Use weighted method to transform multiple objectives into a single objective: minαf1-βf2 (19)In the formula, α and β are the weighting coefficients of each objective function; When multiple scenarios are used to describe the uncertainty of wind and solar power, the scheduling model is expressed in the following uncertain form: Where: x is the discrete decision variable of the first stage, x = [ε t ] T ;y s is the second-stage continuous decision variable, c is the coefficient column vector corresponding to the objective function (19); A, C, D, d, h are the coefficient matrices or constant column vectors corresponding to the variables in the model constraints; in (21), the first row represents the inequality constraints in the model, including (10), (12) to (13), (15) to (17); the second row corresponds to (7); Based on formula (15), the uncertainty of wind and solar power output is processed, and the data-driven distributed robust optimization scheduling model is obtained as follows:

4. The method according to claim 2, characterized in that: Solve the uncertainty-based wind-solar-hydro-storage multi-energy complementary robust optimization scheduling model to output the optimal scheduling plan, including: Linearize the constraints: The comprehensive norm constraint in formula (1) is an absolute value constraint, which is linearized using formula (23): Where: and are the probability distribution ρ of scene s respectively s Relative to ρ s0 The positive and negative offset of and are respectively s A 0-1 sign that generates positive and negative offsets; The C&CG decomposition algorithm is used to solve the model; the main problem of the C&CG algorithm is expressed as: The C&CG algorithm divides the model into the main problem MP and the sub-problem SP for iterative solution. The main problem is as follows: Where: m is the number of iterations; The first stage variable x * After the main problem is solved, the sub-problem is given according to the given x * Find the worst probability distribution, return to the main problem, and provide an upper bound:

5. The method according to claim 4, characterized in that The method further comprises: Set the initial probability distribution and initial upper and lower bounds UB = ∞, LB = 0, and set the number of iterations to 1; By solving MP, we get the optimal solution (x * ,χ), and update the lower bound value max{LB,χ} according to the obtained result; The first-stage variable x obtained by solving the main problem * Bring in the sub-problem to solve and find the worst scenario probability distribution And find the objective function value f(x * ), and according to the obtained result, the upper bound min{UB,f(x * )} to update; If UB-LB < ε, it means that the threshold requirement is met, the iteration stops, and the optimal solution x is returned. * On the contrary, update Add constraints to the main problem and continue to solve the MP.

6. A wind, solar, water and storage multi-energy complementary optimization scheduling system considering uncertainty, characterized in that: include: Uncertainty description module, which is used to describe the uncertainty of wind and solar output in the wind-solar-hydro-storage multi-energy complementary system using a multi-scenario method based on Latin hypercube sampling and synchronous back-substitution reduction method to output a typical scenario set; An uncertainty set construction module is used to process the two uncertain variables of wind and solar output in the typical scenario set and construct an uncertainty set by using a distributed robust optimization scheduling method based on multiple discrete scenarios; An optimization scheduling model construction module is used to construct an uncertainty-based wind-solar-hydro-storage multi-energy complementary robust optimization scheduling model according to the uncertainty set; The optimization scheduling model solving module is used to solve the uncertainty-based wind-solar-hydro-storage multi-energy complementary robust optimization scheduling model to output the optimal scheduling plan.

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