A high proportion of renewable energy power grid scheduling method based on information gap decision theory

Abstract

This invention discloses a high-proportion renewable energy grid dispatching method based on information gap decision theory, comprising: establishing an optimization model based on information gap decision theory; establishing a mathematical model of a hydro / solar / wind integrated dispatching system; and, considering the uncertainties in wind and solar power output, establishing a high-proportion renewable energy grid dispatching model based on information gap decision theory, according to the optimization model based on information gap decision theory and the mathematical model of the hydro / solar / wind integrated dispatching system. This invention effectively quantifies the uncertainty of wind and solar power output in the hydro-wind-solar integrated system, ensuring that the current dispatching cost of the system does not exceed the cost under the influence of a certain participating factor, thus achieving maximum tolerance for output changes and providing a relationship between cost and uncertainty radius for different types of decision-makers.

Description

Technical Field

[0001] This invention relates to a high-proportion renewable energy grid dispatching method based on information gap decision theory, belonging to the field of power system dispatching. Background Technology

[0002] Energy has become an irreplaceable material foundation for human production and daily life, thanks to advancements in productivity. To a certain extent, the level of energy utilization can reflect a country's comprehensive strength. my country possesses diverse energy sources and abundant reserves. Among these, fossil fuels are primarily coal, with proven coal reserves ranking third in the world; hydropower resources theoretically rank first globally; and wind energy resources are also among the world's richest. According to the "National Wind Energy Resource Assessment Results (2014)," my country's wind energy-rich areas are mainly distributed in the Northwest, North China, Northeast, and East China regions. The actual exploitable wind energy potential at a height of only 10 meters is 2.53 × 10⁻⁶. 11 W; In terms of solar energy, over 60% of the country has more than 2000 hours of sunshine per year and an annual radiation of 5000 MJ / m². 2 The above-mentioned geographical conditions provide favorable resources for utilizing solar energy. Under these conditions, the conversion and efficient utilization of energy are crucial.

[0003] With the large-scale construction and grid connection of new energy units such as wind and solar power, the penetration rate of new energy in the power grid continues to increase. The uncontrollable output of new energy units will have a certain impact on power grid dispatch. A major challenge in multi-energy joint dispatch is how to handle the difficulty in accurately predicting the output of new energy. The essence of the difficulty in predicting the output of new energy is the uncertainty of new energy output. Currently, there are two main methods for handling the uncertainty of new energy output: physical prediction methods and statistical methods. However, these two methods are poorly adapted to the joint dispatch of power grid systems with a high proportion of renewable energy (high proportion means that green energy output accounts for more than 60% in a power system dispatch problem, and this proportion may be even higher in the Yunnan power grid, even reaching 80%). Without historical data, it is impossible to accurately predict the output of new energy units; if physical parameter prediction methods that do not require historical data are used, the impact of the uncertainty of new energy itself cannot be considered, and the calculation is complex. Therefore, the optimization problem involving new energy units essentially needs to solve the problem of how to measure the uncertainty of new energy units and how to select optimization strategies.

[0004] Based on current research findings, these methods all require knowledge of the uncertainties in order to obtain the probability distribution, membership function, and bounded uncertainty set. However, these conditions are difficult to achieve under Knight's uncertainty (a risk that cannot be measured or whose probability cannot be calculated). Summary of the Invention

[0005] This invention provides a high-proportion renewable energy grid dispatching method based on information gap decision theory. This method examines the characteristics of uncertainty from a non-probabilistic perspective and establishes an IGBT optimization model under risk avoidance decision and risk seeking decision.

[0006] The technical solution of this invention is: a high-proportion renewable energy grid dispatching method based on information gap decision theory, comprising:

[0007] Establish an optimization model for information gap decision theory;

[0008] Establish a mathematical model for a water / solar / wind integrated scheduling system;

[0009] Based on the information gap decision theory optimization model and the mathematical model of the hydro / solar / wind joint dispatch system, and considering the uncertainty of wind and solar power output, a high-proportion renewable energy grid dispatch model based on information gap decision theory is established.

[0010] Establish an optimization model based on information gap decision theory, specifically as follows:

[0011] 1) Establish a system model, as shown below:

[0012]

[0013] In the formula, f(x,α) is the objective function, x is an uncertain parameter, and α is the decision variable; H(x,α) and G(x,α) are the equality and inequality constraints, respectively, and G... min G max These represent the minimum and maximum values ​​of the inequality constraints, respectively.

[0014] 2) Establish an uncertainty model, as shown below:

[0015]

[0016] In the formula: γ represents the fluctuation range of the uncertain parameter x, i.e., the uncertainty radius, and γ≥0, x * It is the predicted value of x; U(γ,x) * ) indicates that the deviation is not greater than γx * The set of all x;

[0017] 3) Establish optimization decision-making, including two types: risk avoidance decision-making and risk seeking decision-making; both optimization decisions are based on the basic model, as shown in Equation (1). Consider the system model that is not affected by uncertain factors, solve Equation (1), and assume that the solution is r0, which is the basic solution of the system model without considering the influence of uncertain factors.

[0018] Risk aversion decision-making:

[0019]

[0020] Equation (3) means maximizing the degree of fluctuation of the uncertainty radius to obtain the greatest robustness; where: This indicates the degree of fluctuation of the uncertainty radius γ under risk-averse decision-making; r c This represents the acceptable preset target value; δ is the user participation factor.

[0021] Risk-based decision making:

[0022]

[0023] Equation (4) means minimizing the degree of uncertainty fluctuation to obtain the greatest opportunity; where: r represents the degree of fluctuation of the uncertainty radius γ under risk-seeking decision-making. w This indicates the expected return.

[0024] A mathematical model for a water / solar / wind integrated scheduling system is established, specifically as follows:

[0025] 1) The objective function of the mathematical model of the joint scheduling system is:

[0026]

[0027] In the formula, F1 is the system electricity purchase cost, and α k,t Let P represent the electricity price of energy k at time t. i,t This represents the power demand at time t;

[0028] 2) The water flow time delay τ can be approximated as:

[0029] τ=1 / 3600*ΔX(i,j) / u (6)

[0030] In the formula, ΔX(i,j) represents the cross-sectional distance between upstream hydropower station i and downstream hydropower station j, and u represents the average cross-sectional velocity of the water flow; since the outflow M ht There is a certain mathematical relationship between M and the average cross-sectional velocity u and the cross-sectional area A, namely M ht =Au, therefore equation (6) can be written as:

[0031] τ=1 / 3600*AΔX(i,j) / M ht (7)

[0032] 3) Constraints of the model:

[0033] System constraints:

[0034] P D,t -P hi,t -P PV,t -PWT,t -P i,t =0 (8)

[0035] In the formula: P D,t P represents the load at time t; hi,t P represents the output of hydropower station i at time t; PV,t P represents the output of the photovoltaic power station at time t; WT,t This represents the power output of the wind farm at time t.

[0036] Hydropower unit constraints:

[0037]

[0038] In the formula: V hi,t V hi,t-τ Let I represent the reservoir capacity of hydropower station i at times t and t-τ, respectively; τ represents the water flow time delay; hi,t Q represents the natural inflow of hydropower station i at time t; hi,t S represents the power generation flow of hydropower station i at time t; hi,t P represents the discharge flow rate of hydropower station i at time t; hi,tmin P hi,tmax Let represent the lower and upper limits of the output of hydropower station i at time t, respectively;

[0039] Wind turbine constraints:

[0040]

[0041] In the formula: P WT,t P WT,t-1 P represents the power generation of the wind turbine at times t and t-1; WT,up P WT,down P represents the uphill and downhill gradeability of the wind turbine. WT,tmin P WT,tmax These represent the lower and upper limits of the wind turbine's output at time t, respectively.

[0042] Constraints of photovoltaic units:

[0043] P PV,tmin ≤P PV,t ≤P PV,tmax (11)

[0044] In the formula: P PV,tmax P PV,tmin These represent the upper and lower limits of the photovoltaic unit's output, respectively.

[0045] Without considering the uncertainty of wind and solar power, the basic mathematical model of the joint scheduling system is Equation (5) and Equation (8)-(11). By combining them, we can set the basic solution as C0.

[0046] The aforementioned optimization model based on information gap decision theory and the mathematical model of the hydro / solar / wind joint dispatch system, considering the uncertainty of wind and solar power output, establishes a high-proportion renewable energy grid dispatch model based on information gap decision theory, specifically as follows:

[0047] 1) Uncertainty model of wind turbine output:

[0048]

[0049] In the formula: P represents the output power of the wind turbine at time t. WT,t The predicted value; β W Indicates the degree of fluctuation in wind power output; Represents the uncertain parameter P WT,t The fluctuation range; that is, the actual value of the wind turbine output will fall within the range of... Within the range;

[0050] 2) Uncertainty model for photovoltaic output:

[0051]

[0052] In the formula: P represents the output power of the photovoltaic unit at time t. PV,t The predicted value; γ P Indicates the degree of fluctuation in photovoltaic power; Represents the uncertain parameter P PV,t The fluctuation range; that is, the actual value of photovoltaic output will fall within the range of [missing information]. In the interval;

[0053] 3) Handling of uncertain models:

[0054]

[0055] In the formula: Ψ represents the uncertainty after normalization; λ and μ represent the weighting coefficients affecting wind turbines and photovoltaics, respectively;

[0056] 4) Establish an IGDT optimization model under risk-averse decision-making:

[0057] maxΨ

[0058]

[0059] In the formula: C0 is the basic cost obtained by solving the basic model; C1 is the cost acceptable to the decision-maker under risk-averse decision; σ0 is the user participation factor under risk-averse decision; combining formula (5) with formulas (8), (9), (10), (11), (12), (13), (14), (15) gives the IGDT optimization model under risk-averse decision;

[0060] 5) Establish an IGDT optimization model under risk-seeking decision-making:

[0061] minΨ

[0062]

[0063] In the formula: C0 is the basic cost obtained by solving the basic model; C2 is the cost acceptable to the decision-maker under the opportunity-seeking strategy; σ1 is the user participation factor under the risk-seeking decision; combining equation (5) with equations (8), (9), (10), (11), (12), (13), (14), and (16) yields the IGDT optimization model under the risk-seeking decision.

[0064] The beneficial effects of this invention are:

[0065] 1. The scheduling strategy proposed in this invention is more effective and convenient than the prior art, requires no historical data, and can select an appropriate optimization strategy based on the uncertainty interval and the decision-maker's decision preferences, and formulate a matching optimization scheduling scheme based on the decision-maker's acceptable cost and risk tolerance.

[0066] 2. The uncertainty of new energy output is represented by the "gap" method. During the optimization process, appropriate solution software and algorithms can be selected according to the characteristics of the mathematical model.

[0067] 3. It effectively quantifies the uncertainty of wind and solar power output in the hydro-wind-solar combined system, ensuring that the current scheduling cost of the system is not higher than the cost under the influence of a certain participating factor, and achieves the maximum tolerance for output changes, providing a relationship between cost and uncertainty radius for different types of decision-makers. Attached Figure Description

[0068] Figure 1 It is a power output curve of a wind farm;

[0069] Figure 2 This is a graph showing the output of a photovoltaic power station.

[0070] Figure 3 This is a graph showing the relationship between cost and uncertainty radius under risk aversion strategies;

[0071] Figure 4 This is a graph showing the relationship between cost and radius of uncertainty under a risk-seeking strategy;

[0072] Figure 5 This is a graph showing the relationship between the downside of risk aversion strategies, the proportion of light, and the system's uncertainty radius;

[0073] Figure 6 This is a graph showing the relationship between the risk-seeking strategy's disadvantage, the proportion of light, and the system's uncertainty radius. Detailed Implementation

[0074] The invention will be further described below with reference to the accompanying drawings and embodiments, but the scope of the invention is not limited to the description.

[0075] Example 1: As Figure 1-6 As shown, a high-proportion renewable energy grid dispatching method based on information gap decision theory includes: establishing an optimization model of information gap decision theory; establishing a mathematical model of a hydroelectric / solar / wind joint dispatching system; and, considering the uncertainty of wind and solar power output, establishing a high-proportion renewable energy grid dispatching model based on information gap decision theory, according to the optimization model of information gap decision theory and the mathematical model of the hydroelectric / solar / wind joint dispatching system.

[0076] Optionally, an optimization model based on information gap decision theory can be established, specifically as follows:

[0077] 1) Establish a system model, as shown below: The system model is a general mathematical model that considers equality / inequality constraints;

[0078]

[0079] In the formula, f(x,α) is the objective function, x is an uncertain parameter, and α is the decision variable; H(x,α) and G(x,α) are the equality and inequality constraints, respectively, and G... min G max These represent the minimum and maximum values ​​of the inequality constraints, respectively.

[0080] 2) Establish an uncertainty model, as shown below:

[0081] There are roughly three uncertainty models to choose from in IGDT: the envelope-bound model, the ellipsoid-bound model, and the weighted-mean-squared-error model. This invention adopts the envelope-bound model, as follows:

[0082]

[0083] In the formula: γ represents the fluctuation range of the uncertain parameter x, i.e., the uncertainty radius, and γ≥0, x * It is the predicted value of x; U(γ,x) * ) indicates that the deviation is not greater than γx * The set of all x;

[0084] 3) Establish optimization decision-making, including two types: risk avoidance (robust) decision-making and risk seeking (opportunity) decision-making; both optimization decisions are based on the basic model, as shown in Equation (1). Consider the system model that is not affected by uncertain factors (or assume accurate prediction), solve Equation (1), and assume that the solution is r0, where r0 is the basic solution of the system model without considering the influence of uncertain factors.

[0085] Risk aversion decision-making:

[0086] Risk-averse (robust) decision-making often studies the relationship between the system's resilience and the costs required when the radius of uncertainty increases (uncertainty is harmful to decision-makers), aiming to maximize the change in the radius of uncertainty in order to ensure the minimum target is achieved even when the impact of uncertainty is at its maximum.

[0087]

[0088] Equation (3) means maximizing the degree of fluctuation of the uncertainty radius to obtain the greatest robustness; where: This indicates the degree of fluctuation of the uncertainty radius γ under risk-averse decision-making; r c This represents the acceptable preset target value, or the severity of the scenario; δ is the user participation factor, which is subjectively determined by the decision-maker and can be understood as the extra cost the decision-maker is willing to pay to achieve this goal.

[0089] Risk-averse decision-making typically assumes that the actual volatility is (1+γ); The larger the value, the stronger the system's ability to resist fluctuations or avoid risks, but it will increase the system's costs and lead to a deterioration in economic efficiency;

[0090] Risk-based decision making:

[0091] Risk-seeking (opportunity) decisions represent "the decision-maker's most fervent desire for returns." Generally, research shows that when the radius of uncertainty decreases (uncertainty is favorable to the decision-maker), the decision-maker's profit is no less than the maximum return under the influence of the participation factor.

[0092]

[0093] Equation (4) means minimizing the degree of uncertainty fluctuation to obtain the greatest opportunity; where: r represents the degree of fluctuation of the uncertainty radius γ under risk-seeking decision-making. w This indicates the expected return.

[0094] In the case of uncertain volatility, risk-seeking models typically assume that the actual volatility is (1-γ); The larger the value, the greater the potential gains, but the greater the risks the system faces. Adopting a risk-seeking model means accepting the potential risks, and the gains often outweigh the risk costs.

[0095] Optionally, a mathematical model for a water / solar / wind joint scheduling system is established, specifically as follows:

[0096] 1) The objective function of the mathematical model of the joint scheduling system is:

[0097]

[0098] In the formula, based on the load size, the hourly rate is used as the optimization scale. Under the condition of renewable energy output, the system electricity purchase cost is preferred as the objective function; F1 is the system electricity purchase cost, α k,t P represents the electricity price of energy k at time t. i,t This represents the power demand at time t;

[0099] 2) The water flow time delay characteristics are approximated using a functional relationship; ignoring the flow smoothing phenomenon, the water flow time delay τ can be approximated as:

[0100] τ=1 / 3600*ΔX(i,j) / u (6)

[0101] In the formula, ΔX(i,j) represents the cross-sectional distance between upstream hydropower station i and downstream hydropower station j, and u represents the average cross-sectional velocity of the water flow; since the outflow M ht There is a certain mathematical relationship between M and the average cross-sectional velocity u and the cross-sectional area A, namely M ht =Au, therefore equation (6) can be written as:

[0102] τ=1 / 3600*AΔX(i,j) / M ht (7)

[0103] 3) Constraints of the model:

[0104] System constraints

[0105] To simplify calculations, this invention treats network losses as a constant and includes them within the system load; the power balance constraint of the power system can be expressed as:

[0106] P D,t -P hi,t -P PV,t -P WT,t -P i,t =0 (8)

[0107] In the formula: P D,t P represents the load at time t; hi,t P represents the output of hydropower station i at time t; PV,tP represents the output of the photovoltaic power station at time t; WT,t This represents the power output of the wind farm at time t.

[0108] Hydropower unit constraints:

[0109] Because hydropower can be started and stopped flexibly, it can often be put into operation within tens of seconds; therefore, the gradient constraint of the hydropower unit is not considered here.

[0110]

[0111] In the formula: V hi,t V hi,t-τ Let I represent the reservoir capacity of hydropower station i at times t and t-τ, respectively; τ represents the water flow time delay; hi,t Q represents the natural inflow of hydropower station i at time t; hi,t S represents the power generation flow of hydropower station i at time t; hi,t P represents the discharge flow rate of hydropower station i at time t; hi,tmin P hi,tmax Let represent the lower and upper limits of the output of hydropower station i at time t, respectively;

[0112] Wind turbine constraints:

[0113]

[0114] In the formula: P WT,t P WT,t-1 P represents the power generation of the wind turbine at times t and t-1; WT,up P WT,down P represents the uphill and downhill gradeability of the wind turbine. WT,tmin P WT,tmax These represent the lower and upper limits of the wind turbine's output at time t, respectively.

[0115] Constraints of photovoltaic units:

[0116] P PV,tmin ≤P PV,t ≤P PV,tmax (11)

[0117] In the formula: P PV,tmax P PV,tmin These represent the upper and lower limits of the photovoltaic unit's output, respectively.

[0118] Without considering the uncertainty of wind and solar power, the basic mathematical model of the joint scheduling system is Equation (5) and (8)-(11). By combining them, we can set the basic solution as C0.

[0119] Optionally, the step of establishing a high-proportion renewable energy grid dispatch model based on information gap decision theory, considering the uncertainty of wind and solar power output, and optimizing the model and the mathematical model of the joint dispatch system including water / solar / wind power, specifically involves:

[0120] 1) Uncertainty model of wind turbine output:

[0121]

[0122] In the formula: P represents the output power of the wind turbine at time t. WT,t The predicted value; β W Indicates the degree of fluctuation in wind power output; Represents the uncertain parameter P WT,t The fluctuation range; that is, the actual value of the wind turbine output will fall within the range of... Within the range;

[0123] 2) Uncertainty model for photovoltaic output:

[0124]

[0125] In the formula: P represents the output power of the photovoltaic unit at time t. PV,t The predicted value; γ P Indicates the degree of fluctuation in photovoltaic power; Represents the uncertain parameter P PV,t The fluctuation range; that is, the actual value of photovoltaic output will fall within the range of [missing information]. In the interval;

[0126] 3) Handling of uncertain models:

[0127] The weighted normalization method is used to integrate two uncertainties into one influencing factor, and the influence of new energy fluctuations on the joint system is changed by changing the weight. This process can solve the problem that information gap decision theory is difficult to handle when solving problems with multiple uncertainties due to the limitations of the theory itself.

[0128]

[0129] In the formula: Ψ represents the uncertainty after normalization; λ and μ represent the weighting coefficients affecting wind turbines and photovoltaics, respectively;

[0130] 4) When the output of new energy sources fluctuates in a direction unfavorable to decision-makers, hydropower alone cannot compensate for the power deficit caused by insufficient wind and solar power output, and decision-makers will need to bear more costs; establish an IGDT optimization model under risk-averse decision-making:

[0131] maxΨ

[0132]

[0133] In the formula: C0 is the basic cost obtained by solving the basic model; C1 is the cost acceptable to the decision-maker under the risk aversion strategy; σ0 is the user participation factor, which is determined by the decision-maker; combining formula (5) with formulas (8), (9), (10), (11), (12), (13), (14), (15) gives the IGDT optimization model under risk aversion decision-making.

[0134] 5) When the output of new energy sources fluctuates in a direction favorable to decision-makers, the electricity generated by cascade hydropower units, wind power, and photovoltaic units can meet the electricity needs of more users; establish an IGDT optimization model under risk-seeking decision-making:

[0135] minΨ

[0136]

[0137] In the formula: C0 is the basic cost obtained by solving the basic model; C2 is the cost acceptable to the decision-maker under the opportunity-seeking strategy; σ1 is the user participation factor, which is determined by the decision-maker; combining equation (5) with equations (8), (9), (10), (11), (12), (13), (14), and (16) gives the IGDT optimization model under the risk-seeking decision.

[0138] The following specific experimental data are given:

[0139] Different risk-averse strategies are employed within the same day to control the proportion of wind and solar power in the grid to be equal. By changing the dispatch costs borne by decision-makers, the maximum tolerance of the system to fluctuations in wind and solar power output is sought based on these costs. The relationship between cost and the radius of uncertainty (i.e., uncertainty) is as follows: Figure 3 As shown. By Figure 3 It can be seen that when adopting a risk-averse strategy, the scheduling cost that decision-makers can bear will increase with the increase of the participating factors, and the system's tolerance for the uncertainty radius of wind and solar power output will also increase under a larger scheduling cost.

[0140] Different risk-seeking strategies are employed within the same day to control the proportion of wind and solar power in the grid to be equal. By changing the dispatch costs borne by decision-makers, the maximum tolerance of the system to fluctuations in wind and solar power output is sought based on these costs. The relationship between cost and uncertainty radius is as follows: Figure 4 As shown. By Figure 4 It can be seen that when choosing a risk-seeking strategy, as the participation factor increases, the scheduling cost borne by the decision-maker will decrease accordingly. At this time, the uncertainty radius of the system indicates the degree to which the actual output of wind and solar power is higher than the predicted value.

[0141] Using risk-avoidance strategies within the same characteristic day and maintaining consistent scheduling costs, the impact of wind and solar power weighting coefficients on system tolerance during weight normalization is investigated by varying the wind and solar power ratios. The relationship between the wind and solar power ratios and the system's uncertainty radius is as follows: Figure 5 As shown, the weighting coefficient λ of the wind turbine is used as the horizontal axis. From... Figure 5 It can be seen that when choosing risk avoidance strategies in all four seasons, the system’s tolerance for the radius of uncertainty decreases continuously as the weight of wind turbines increases. From the perspective of system operation, the reason for this is that wind power has a larger installed capacity and a larger proportion than photovoltaic power. Its output fluctuations will have a greater impact on the system, which leads to a decrease in the system’s tolerance.

[0142] Using a risk-seeking strategy with the same control scheduling costs within the same characteristic day, the impact of wind and solar weighting coefficients on system tolerance during weight normalization is studied by changing the wind and solar ratios. The relationship between the wind and solar ratios and the system uncertainty radius is as follows: Figure 6 As shown, the weighting coefficient λ of the wind turbine is used as the horizontal axis. From... Figure 6 As can be seen, when choosing risk-based strategies under the four seasonal characteristics, the uncertainty radius of system profitability increases as the proportion of wind turbine weight gradually increases. From the perspective of system operation, this is because the greater the proportion of wind power, the greater the impact of its volatility will be compared to the impact of reduced photovoltaic volatility. This means that increasing wind power may bring lower costs to decision-makers.

[0143] The specific embodiments of the present invention have been described in detail above with reference to the accompanying drawings. However, the present invention is not limited to the above embodiments. Within the scope of knowledge possessed by those skilled in the art, various changes can be made without departing from the spirit of the present invention.

Claims

1. A high-proportion renewable energy grid dispatching method based on information gap decision theory, characterized in that: include: Establish an optimization model for information gap decision theory; Establish a mathematical model for a water / solar / wind integrated scheduling system; Based on the information gap decision theory optimization model and the mathematical model of the joint dispatch system of water / solar / wind, and considering the uncertainty of wind and solar power output, a high-proportion renewable energy grid dispatch model based on information gap decision theory is established. A mathematical model for a water / solar / wind integrated scheduling system is established, specifically as follows: 1) The objective function of the mathematical model of the joint scheduling system is: In the formula, F1 is the system electricity purchase cost, and α k,t Let P represent the electricity price of energy k at time t. i,t This represents the power demand at time t; 2) The water flow time delay τ is: τ=1 / 3600*AΔX(i,j) / M ht (7) In the formula, ΔX(i,j) represents the cross-sectional distance between upstream hydropower station i and downstream hydropower station j; M ht Indicates outflow rate; A represents the water flow area; 3) Constraints of the model: System constraints: P D,t -P hi,t -P PV,t -P WT,t -P i,t =0(8) In the formula: P D,t P represents the load at time t; hi,t P represents the output of hydropower station i at time t; PV,t P represents the output of the photovoltaic power station at time t; WT,t This represents the power output of the wind farm at time t. Hydropower unit constraints: In the formula: V hi,t V hi,t-τ Let I represent the reservoir capacity of hydropower station i at times t and t-τ, respectively; τ represents the water flow time delay; hi,t Q represents the natural inflow of hydropower station i at time t; hi,t S represents the power generation flow of hydropower station i at time t; hi,t P represents the discharge flow rate of hydropower station i at time t; hi,tmin P hi,tmax Let represent the lower and upper limits of the output of hydropower station i at time t, respectively; Wind turbine constraints: In the formula: P WT,t P WT,t-1 P represents the power generation of the wind turbine at times t and t-1; WT,up P WT,down P represents the uphill and downhill gradeability of the wind turbine. WT,tmin P WT,tmax These represent the lower and upper limits of the wind turbine's output at time t, respectively. Constraints of photovoltaic units: P PV,tmin ≤P PV,t ≤P PV,tmax (11) In the formula: P PV,tmax P PV,tmin These represent the upper and lower limits of the photovoltaic unit's output, respectively. Without considering the uncertainty of wind and solar power, the basic mathematical model of the joint scheduling system is Equation (5) and Equation (8)-(11). By combining them, we can set the basic solution as C0.

2. The high-proportion renewable energy grid dispatching method based on information gap decision theory according to claim 1, characterized in that: Establish an optimization model based on information gap decision theory, specifically as follows: 1) Establish a system model, as shown below: In the formula, f(x,α) is the objective function, x is an uncertain parameter, and α is the decision variable; H(x,α) and G(x,α) are the equality and inequality constraints, respectively, and G... min G max These represent the minimum and maximum values ​​of the inequality constraints, respectively. 2) Establish an uncertainty model, as shown below: In the formula: γ represents the fluctuation range of the uncertain parameter x, i.e., the uncertainty radius, and γ≥0, x * It is the predicted value of x; U(γ,x) * ) indicates that the deviation is not greater than γx * The set of all x; 3) Establish optimization decision-making, including two types: risk avoidance decision-making and risk seeking decision-making; both optimization decisions are based on the basic model, as shown in Equation (1). Consider the system model that is not affected by uncertain factors, solve Equation (1), and assume that the solution is r0, which is the basic solution of the system model without considering the influence of uncertain factors. Risk-averse decision-making: Equation (3) means maximizing the degree of fluctuation of the uncertainty radius to obtain the greatest robustness; where: This indicates the degree of fluctuation of the uncertainty radius γ under risk-averse decision-making; r c This represents the acceptable preset target value; δ is the user participation factor. Risk-based decision making: Equation (4) means minimizing the degree of uncertainty fluctuation to obtain the greatest opportunity; where: r represents the degree of fluctuation of the uncertainty radius γ under risk-seeking decision-making. w This indicates the expected return.

3. The high-proportion renewable energy grid dispatching method based on information gap decision theory according to claim 1, characterized in that: The aforementioned optimization model based on information gap decision theory and the mathematical model of the hydro / solar / wind joint dispatch system, considering the uncertainty of wind and solar power output, establishes a high-proportion renewable energy grid dispatch model based on information gap decision theory, specifically as follows: 1) Uncertainty model of wind turbine output: In the formula: P represents the output power of the wind turbine at time t. WT,t The predicted value; β W Indicates the degree of fluctuation in wind power output; Represents the uncertain parameter P WT,t The fluctuation range; that is, the actual value of the wind turbine output will fall within the range of... Within the range; 2) Uncertainty model for photovoltaic output: In the formula: P represents the output power of the photovoltaic unit at time t. PV,t The predicted value; γ P Indicates the degree of fluctuation in photovoltaic power; Represents the uncertain parameter P PV,t The fluctuation range; that is, the actual value of photovoltaic output will fall within the range of [missing information]. In the interval; 3) Handling of uncertain models: In the formula: Ψ represents the uncertainty after normalization; λ and μ represent the weighting coefficients affecting wind turbines and photovoltaics, respectively; 4) Establish an IGDT optimization model under risk-averse decision-making: maxΨ In the formula: C0 is the basic cost obtained by solving the basic model; C1 is the cost acceptable to the decision-maker under risk-averse decision; σ0 is the user participation factor under risk-averse decision; combining formula (5) with formulas (8), (9), (10), (11), (12), (13), (14), (15) gives the IGDT optimization model under risk-averse decision; 5) Establish an IGDT optimization model under risk-seeking decision-making: minΨ In the formula: C0 is the basic cost obtained by solving the basic model; C2 is the cost acceptable to the decision-maker under the opportunity-seeking strategy; σ1 is the user participation factor under the risk-seeking decision; combining equation (5) with equations (8), (9), (10), (11), (12), (13), (14), and (16) yields the IGDT optimization model under the risk-seeking decision.

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