New energy consumption capability assessment method based on Irai random process

Abstract

The invention discloses a new energy consumption capability assessment method based on an Italian random process, and the method comprises the steps: firstly describing the fluctuation of new energy output through the Italian random process, and describing the uncertainty and time-space correlation through a stochastic differential equation; then, a maximum likelihood estimation method is adopted, the Ittan random process parameters are obtained according to new energy output historical data, and new energy output at all moments is represented by a distribution function; and finally, substituting the distribution function of the new energy output at each moment into the stochastic optimization model, solving the abandoned electric quantity of the new energy, obtaining the probability distribution of the utilization rate of the new energy according to an optimization result, and achieving the evaluation of the absorption capability of the new energy. According to the method, the uncertainty of new energy output is described by adopting the Italian random process, and the method has important significance in improving the utilization rate of new energy and guaranteeing safe and stable operation of a power system.

Description

Technical Field

[0001] The present invention belongs to the technical field of power system, and relates to the technical field of describing the randomness of new energy output by Ito random process and evaluating the new energy absorption capacity considering the uncertainty of new energy in distribution network, and in particular to a new energy absorption capacity evaluation method based on Ito random process. Background Art

[0002] In recent years, the new energy industry has developed rapidly, especially in wind and solar energy. With the advancement of technology, the installed capacity of new energy has continued to grow, becoming an important player in the field of new energy. However, against the background of the rapid increase in installed capacity of wind power and photovoltaic power, the consumption of new energy faces great difficulties. First, the intermittent and volatile nature of new energy generation is different from the stability of traditional energy. The volatility of energy sources such as wind power and photovoltaic power leads to challenges in grid dispatching and stability, which may cause safety hazards of the grid. Secondly, new energy generation is concentrated in resource-rich areas, far away from major electricity consumption areas, resulting in an imbalance between energy supply and demand. In the face of the phenomenon of "abandoning wind" and "abandoning light", it is urgent to conduct a scientific assessment of the absorption capacity of new energy. Therefore, it is particularly important to conduct a scientific and accurate assessment of the absorption capacity of new energy based on the description of the uncertainty of new energy output by the Ito stochastic process. Summary of the invention

[0003] The purpose of the present invention is to provide a new energy consumption capacity evaluation method based on Ito stochastic process in view of the shortcomings of the prior art.

[0004] The object of the present invention is achieved through the following technical scheme: a method for evaluating the new energy consumption capacity based on Ito stochastic process, comprising the following steps:

[0005] (1) Use Ito random process to describe the randomness of renewable energy output;

[0006] (2) Using the maximum likelihood estimation method, the parameters of the Ito stochastic equation are obtained based on the historical data of the output of new energy units. The new energy output distribution at the next moment is obtained from the actual value of the new energy output at the previous moment, so as to obtain the new energy output distribution at each moment;

[0007] (3) Substitute the renewable energy output described by the Ito stochastic process into the stochastic optimization model to solve the renewable energy curtailment amount and evaluate the renewable energy absorption capacity.

[0008] Furthermore, the use of the Ito random process to describe the randomness of the output of new energy specifically includes:

[0009] Ito stochastic process is a stochastic differential equation that describes randomness. This stochastic differential equation is used to describe the output of new energy units. Its expression is:

[0010] dξt =μ(ξ t ;θ)dt+σ(ξ t ;θ)dW t (1)

[0011] In the formula, ξ t is the output of the new energy unit at time t; W t is a Wiener process, which is a random process; μ(ξ t ; θ) is a parameterized drift term, reflecting ξ t The tendency to move toward its expected value; σ(ξ t ; θ) is a parameterized diffusion term, reflecting ξ t W t The influence of θ is the parameter to be determined.

[0012] Furthermore, the step (2) includes the following sub-steps:

[0013] (2.1) According to the historical output data of new energy units, the maximum likelihood estimation method is used to obtain the unknown parameter θ in the Ito random process to determine its corresponding maximum likelihood estimation target, which is expressed as:

[0014]

[0015] In the formula, L represents the maximum likelihood estimation target, x represents a random event, represents the historical output data of new energy units at each moment, T is the number of hours of historical time; Pr{·} represents the probability of occurrence;

[0016] According to the incremental independence of the Ito random process, the maximum likelihood estimation target corresponding to formula (2) is rewritten as:

[0017]

[0018] (2.2) Discretize the stochastic differential equation describing the output of the new energy unit corresponding to formula (1), and we get:

[0019] ξ t+1 =ξ t +μ(ξ t ;θ)Δt+σ(ξ t ;θ)W Δt (4)

[0020] In the formula, ξ t is the output of the new energy unit at time t, ξ t+1 is the output of the new energy unit at time t+1, Δt is the time interval, W Δt For each time interval, the normal distribution satisfies N(0,ΔtI), where I represents the identity matrix;

[0021] Based on formula (4), according to W Δt In each time interval, the normal distribution of N(0,ΔtI) is satisfied, and the output of the new energy unit at each moment and the output of the new energy unit at the previous moment satisfy the following distribution relationship:

[0022] ξ t+1 ~N(ξ t +μ(ξ t ;θ)Δt,σ(ξ t ;θ)σ(ξ t ;θ) T ) (5)

[0023] In the formula, the superscript T represents the transpose of the matrix vector;

[0024] (2.3) Based on the historical data of new energy unit output ξt and Get the following conditional probability formula:

[0025]

[0026] (2.4) Substitute the conditional probability formula corresponding to formula (6) into the maximum likelihood estimation objective corresponding to formula (3) to transform the maximum likelihood estimation objective into an optimization objective function for solving unconstrained conditions, expressed as:

[0027]

[0028] In the formula, μ and σ when L reaches the minimum value are the parameters to be estimated;

[0029] (2.5) Solve the unknown parameter θ used to represent the randomness of the new energy output: Let μ(ξ t ;θ)=-λξ t ,σ(ξ t ; θ) = σ, when L takes the minimum value, λ = λ0, σ = σ0, where λ = λ0 is the drift coefficient obtained by the objective function to be optimized according to historical data, and σ = σ0 is the diffusion coefficient obtained by the objective function to be optimized according to historical data, then the output distribution of new energy at each moment is expressed as:

[0030] dξ t =-λ0ξ t dt+σ0dW t (8)

[0031]

[0032] In the formula, when σ(ξ t ; θ)=σ0, The output of the new energy units at each moment satisfies the above normal distribution. According to the above normal distribution, its confidence interval is drawn to describe its randomness.

[0033] Furthermore, the step (3) includes the following sub-steps:

[0034] (3.1) Construct the stochastic optimization objective function under the fluctuation of renewable energy output. Specifically, substitute the renewable energy output distribution at the next moment corresponding to formula (9) into the stochastic optimization model, and determine the optimization goal as the minimum renewable energy abandonment. The corresponding stochastic optimization objective function is expressed as:

[0035]

[0036] In the formula, is the active power output of renewable energy at node i at time t, η i,t is the renewable energy curtailment rate of node i at time t, M is the total number of distribution network nodes, and B is the set of grid nodes;

[0037] (3.2) Determine the constraints of the stochastic optimization objective function, including:

[0038] ① Flow constraints:

[0039]

[0040] Where V i,t 、V j,t are the voltage amplitudes of nodes i and j at time t, R ij and X ij are the resistance and reactance on line ij, respectively, P ij,t and Q ij,t are the active load and reactive load on line ij at time t, V0 is the reference value of voltage amplitude, are the inflow and outflow active power of node i at time t, are the inflow and outflow reactive power of node i at time t, is the reactive power output of renewable energy at node i at time t, L e A collection of power grid lines;

[0041] ② Branch power constraint:

[0042]

[0043] Where P ij,max is the active power limit of line ij, Q ij,max is the reactive power limit of line ij;

[0044] ③Node voltage constraints:

[0045]

[0046] Where V i,max 、V i,min are the upper and lower bounds of the voltage amplitude at node i, respectively;

[0047] (3.3) The value of the renewable energy power abandonment rate at each node at each time is continuously adjusted, and the optimization goal is to minimize the random optimization objective function, and the renewable energy active output that meets the constraints is obtained. The random optimization result including the renewable energy power abandonment rate and the renewable energy active output is obtained, and the renewable energy absorption capacity index is used to evaluate the renewable energy absorption capacity of the random optimization result.

[0048] Furthermore, the new energy consumption capacity indicator is the new energy utilization rate, and its calculation formula is:

[0049]

[0050] In the formula, β is the utilization rate of new energy, P f is the electricity consumption of renewable energy, P q Abandoned electricity for new energy.

[0051] The beneficial effects of the present invention are as follows: the present invention uses the Ito random process to describe the uncertainty of the output of the new energy unit, characterizes the spatiotemporal correlation of the new energy output, and accurately evaluates the new energy absorption capacity by taking into account the randomness of the output of the new energy unit through a random optimization method, which is beneficial to improving the accuracy of the evaluation of the new energy absorption capacity; the present invention evaluates the new energy absorption capacity, which is of great significance to improving the utilization rate of new energy and ensuring the safe and stable operation of the power system. BRIEF DESCRIPTION OF THE DRAWINGS

[0052] Figure 1 IEEE33 node system structure diagram used in the embodiment of the present invention;

[0053] Figure 2 This is a schematic diagram of photovoltaic output under the description of Ito random process;

[0054] Figure 3 This is a schematic diagram of wind power output under the description of Ito random process;

[0055] Figure 4 It is the probability density map of the amount of abandoned electricity from renewable energy;

[0056] Figure 5 It is the probability density map of new energy utilization rate;

[0057] Figure 6 The present invention is a flow chart of the new energy consumption capacity evaluation method based on Ito stochastic process. DETAILED DESCRIPTION

[0058] Exemplary embodiments will be described in detail herein, examples of which are shown in the accompanying drawings. When the following description refers to the drawings, unless otherwise indicated, the same numbers in different drawings represent the same or similar elements. The embodiments described in the following exemplary embodiments do not represent all embodiments consistent with the present application. Instead, they are merely examples of devices and methods consistent with some aspects of the present application as detailed in the attached claims. It should be understood that the above general description and the detailed description that follows are merely exemplary and explanatory and cannot limit the present application.

[0059] The terms used in this application are for the purpose of describing specific embodiments only and are not intended to limit this application. The singular forms of "a", "said" and "the" used in this application and the appended claims are also intended to include plural forms unless the context clearly indicates other meanings. It should also be understood that the term "and / or" used herein refers to and includes any or all possible combinations of one or more associated listed items.

[0060] It should be understood that, although the term first, second, third, etc. may be used in the present application to describe various information, these information should not be limited to these terms. These terms are only used to distinguish the same type of information from each other. For example, without departing from the scope of the present application, the first information may also be referred to as the second information, and similarly, the second information may also be referred to as the first information. Depending on the context, the word "if" as used herein may be interpreted as "at the time of..." or "when..." or "in response to determination". Moreover, the term "include", "comprise" or any other variant thereof is intended to cover non-exclusive inclusion, so that the process and method including a series of elements include not only those elements, but also include other elements not clearly listed, or also include elements inherent to such process and method. In the absence of more restrictions, the elements limited by the statement "including one..." do not exclude the existence of other identical elements in the process, method, article or equipment including the elements.

[0061] The present invention is described in detail below in conjunction with the accompanying drawings. In the absence of conflict, the features of the following embodiments and implementations can be combined with each other.

[0062] Example 1

[0063] See also Figure 6 The new energy consumption capacity evaluation method based on Ito stochastic process of the present invention specifically comprises the following steps:

[0064] (1) Use Ito random process to describe the randomness of renewable energy output.

[0065] Specifically, the Ito stochastic process is a stochastic differential equation that describes randomness. The stochastic differential equation is used to describe the output of new energy units, and its expression is:

[0066] dξ t =μ(ξ t ;θ)dt+σ(ξ t ;θ)dW t (1)

[0067] In the formula, ξ t is the output of the new energy unit at time t; W t is a Wiener process, which is a random process; μ(ξ t ; θ) is a parameterized drift term, reflecting ξ t The tendency to move toward its expected value; σ(ξ t ; θ) is a parameterized diffusion term, reflecting ξ t W t The influence of θ is the parameter to be determined.

[0068] (2) The maximum likelihood estimation method is used to obtain the parameters of the Ito stochastic equation based on the historical data of the output of new energy units. The actual value of the new energy output at the previous moment is used to obtain the new energy output distribution at the next moment, so as to obtain the new energy output distribution at each moment.

[0069] (2.1) First, based on the historical output data of new energy units, the maximum likelihood estimation method is used to obtain the unknown parameter θ in the Ito random process to determine its corresponding maximum likelihood estimation target, which is expressed as:

[0070]

[0071] In the formula, L represents the maximum likelihood estimation target, x represents a random event, represents the historical output data of the new energy units at each moment, T is the number of hours of the historical time, which is 24h in this embodiment, and Pr{·} represents the probability of occurrence.

[0072] Since the Ito random process has incremental independence, the maximum likelihood estimation objective can be rewritten as:

[0073]

[0074] (2.2) Discretize the stochastic differential equation describing the output of the new energy unit corresponding to formula (1), and we get:

[0075] ξ t+1 =ξ t +μ(ξ t ;θ)Δt+σ(ξ t ;θ)W Δt (4)

[0076] In the formula, ξ t is the output of the new energy unit at time t, ξ t+1 is the output of the new energy unit at time t+1, Δt is the time interval, which is 1h in this embodiment, W Δt For each time interval, a normal distribution N(0,ΔtI) is satisfied, where I represents the identity matrix.

[0077] Because W Δt In each time interval, the normal distribution of N(0,ΔtI) is satisfied. Combined with formula (4), the output of the new energy unit at each moment and the output of the new energy unit at the previous moment satisfy the following distribution relationship:

[0078] ξ t+1 ~N(ξ t +μ(ξ t ;θ)Δt,σ(ξ t ;θ)σ(ξ t ;θ) T ) (5)

[0079] Where the superscript T represents the transpose of the matrix vector.

[0080] It can be seen from formula (5) that the output value of the new energy unit at each moment is related to the actual value of the output of the new energy unit at the previous moment, and presents a normal distribution relationship at each moment.

[0081] (2.3) Based on the historical data of new energy unit output ξt and Get the following conditional probability formula:

[0082]

[0083] (2.4) Substitute the conditional probability formula corresponding to formula (6) into the maximum likelihood estimation objective corresponding to formula (3) to transform the maximum likelihood estimation objective into an optimization objective function for solving unconstrained conditions, expressed as:

[0084]

[0085] In the formula, μ and σ when L reaches the minimum value are the parameters to be estimated.

[0086] (2.5) Solve the unknown parameter θ used to represent the randomness of the new energy output: Let μ(ξ t ;θ)=-λξ t ,σ(ξ t; θ) = σ, when L takes the minimum value, λ = λ0, σ = σ0, where λ = λ0 is the drift coefficient obtained by the objective function to be optimized according to historical data, and σ = σ0 is the diffusion coefficient obtained by the objective function to be optimized according to historical data, then the output distribution of new energy at each moment is expressed as:

[0087] dξ t =-λ0ξ t dt+σ0dW t (8)

[0088]

[0089] In the formula, when σ(ξ t ; θ)=σ0, The output of the new energy units at each moment satisfies the above normal distribution. According to the above normal distribution, its confidence interval is drawn to describe its randomness.

[0090] (3) Substitute the renewable energy output described by the Ito stochastic process into the stochastic optimization model to solve the renewable energy curtailment amount and evaluate the renewable energy absorption capacity.

[0091] (3.1) Construct the random optimization objective function under the fluctuation of renewable energy output. Specifically, after obtaining the parameters to be determined in step (2), the output value of the renewable energy unit at each moment can be expressed as a normal distribution related to the previous moment, as shown in formula (9); according to the actual output value at the previous moment, the probability distribution of the renewable energy output at the next moment is predicted by step (2), and the renewable energy output distribution at the next moment corresponding to formula (9) is substituted into the random optimization model to determine the optimization target as the minimum amount of renewable energy abandoned electricity. The corresponding random optimization objective function is expressed as:

[0092]

[0093] In the formula, is the renewable energy active power output of node i at time t, and each node satisfies the normal distribution with different expectation and variance at each time, which is a random quantity; η i,t is the renewable energy power abandonment rate of node i at time t; M is the total number of distribution network nodes; B is the set of grid nodes.

[0094] (3.2) Determine the constraints of the stochastic optimization objective function, including:

[0095] ① Flow constraints:

[0096]

[0097] Where V i,t 、V j,t are the voltage amplitudes of nodes i and j at time t, R ij and Xij are the resistance and reactance on line ij, respectively, P ij,t and Q ij,t are the active load and reactive load on line ij at time t, V0 is the reference value of voltage amplitude, are the inflow and outflow active power of node i at time t, are the inflow and outflow reactive power of node i at time t, is the reactive power output of renewable energy at node i at time t, L e A collection of power grid lines.

[0098] ② Branch power constraint:

[0099]

[0100] Where P ij,max is the active power limit of line ij, Q ij,max is the reactive power limit of line ij.

[0101] ③Node voltage constraints:

[0102]

[0103] Where V i,max 、V i,min are the upper and lower bounds of the voltage amplitude at node i, respectively.

[0104] (3.3) The value of the renewable energy power abandonment rate at each node at each time is continuously adjusted, and the optimization goal is to minimize the random optimization objective function, and the renewable energy active output that meets the constraints is obtained. The random optimization result including the renewable energy power abandonment rate and the renewable energy active output is obtained, and the renewable energy absorption capacity index is used to evaluate the renewable energy absorption capacity of the random optimization result.

[0105] Furthermore, the new energy consumption capacity indicator is the new energy utilization rate, and its calculation formula is:

[0106]

[0107]

[0108] In the formula, β is the utilization rate of new energy, P f is the electricity consumption of renewable energy, P q Substituting the new energy curtailment rate and new energy active output in the random optimization results into the above formula, the evaluation of the new energy consumption capacity can be realized.

[0109] Example 2

[0110] This example is used to illustrate the new energy consumption capacity evaluation method based on Ito stochastic process. This example is completed on a personal computer with Intel Core i5-12500H CPU @ 2.50GHz, 16.0GB memory and NVIDIA GTX 3060 GPU. The program is written in Python 3.10.9, and the optimization part uses the framework of Gurobi 10.0.3.

[0111] This embodiment uses the IEEE 33-node distribution network system as the implementation object. Figure 1 As shown in Figure 1, the IEEE 33-node distribution network system contains two photovoltaic power stations and one wind farm. The two photovoltaic power stations are connected to nodes 30 and 20 respectively, and the wind farm is connected to node 8. Figure 1 shown.

[0112] First, according to the content of step (1) in Example 1, the output of the photovoltaic power station and the wind farm is described in the form of an Ito random process.

[0113] Then, according to the method described in step (2) of Example 1, the historical output data of the photovoltaic power station and the wind farm are substituted to obtain the Ito random process parameters when the maximum likelihood estimation target takes the minimum value. After obtaining the Ito random process parameters, the distribution function of the photovoltaic power station and wind farm output at the next moment is obtained based on the actual data at the previous moment. And calculate the confidence interval at each moment, such as the photovoltaic Figure 2 As shown in Figure 3 shown.

[0114] Finally, according to the method described in step (3) of Example 1, the distribution function of the output of the photovoltaic power station and the wind farm at each moment is substituted into the stochastic optimization model, and the optimization problem is solved with the goal of minimizing the amount of abandoned power from renewable energy. The probability density function of the optimization result is as follows: Figure 4 As shown. The new energy consumption capacity is evaluated based on the optimization results. The probability density function of the evaluation results is shown as Figure 5 shown.

[0115] The above embodiments are only used to illustrate the technical solutions of the present invention, rather than to limit the same. Although the present invention has been described in detail with reference to the aforementioned embodiments, those skilled in the art should understand that the technical solutions described in the aforementioned embodiments may still be modified, or some of the technical features may be replaced by equivalents. However, these modifications or replacements do not deviate the essence of the corresponding technical solutions from the spirit and scope of the technical solutions of the embodiments of the present invention.

Claims

1. A new energy consumption capacity evaluation method based on Ito stochastic process, characterized in that: The following steps are involved: (1) Use Ito random process to describe the randomness of renewable energy output; (2) Using the maximum likelihood estimation method, the parameters of the Ito stochastic equation are obtained based on the historical data of the output of new energy units. The output distribution of new energy at the next moment is obtained from the actual value of the output of new energy at the previous moment, so as to obtain the output distribution of new energy at each moment; (3) Substitute the renewable energy output described by the Ito stochastic process into the stochastic optimization model to solve the renewable energy curtailment amount and evaluate the renewable energy absorption capacity.

2. The new energy consumption capacity evaluation method based on Ito stochastic process according to claim 1 is characterized in that: The use of the Ito random process to describe the randomness of new energy output specifically includes: Ito stochastic process is a stochastic differential equation that describes randomness. This stochastic differential equation is used to describe the output of new energy units. Its expression is: dx t =μ(ξ t ;θ)dt+σ(ξ t ;θ)dW t (1) In the formula, ξ t is the output of the new energy unit at time t; W t is a Wiener process, which is a random process; μ(ξ t ; θ) is a parameterized drift term, reflecting ξ t The tendency to move toward its expected value; σ(ξ t ; θ) is a parameterized diffusion term, reflecting ξ t W t The influence of θ is the parameter to be determined.

3. The new energy consumption capacity evaluation method based on Ito stochastic process according to claim 1 is characterized in that: The step (2) comprises the following sub-steps: (2.1) According to the historical output data of new energy units, the maximum likelihood estimation method is used to obtain the unknown parameter θ in the Ito random process to determine its corresponding maximum likelihood estimation target, which is expressed as: In the formula, L represents the maximum likelihood estimation target, x represents a random event, represents the historical output data of new energy units at each moment, T is the number of hours of historical time; Pr{·} represents the probability of occurrence; According to the incremental independence of the Ito random process, the maximum likelihood estimation target corresponding to formula (2) is rewritten as: (2.2) Discretize the stochastic differential equation describing the output of the new energy unit corresponding to formula (1), and we get: x t+1 =ξ t +μ(ξ t ;θ)Δt+σ(ξ t ;θ)W Δt (4) In the formula, ξ t is the output of the new energy unit at time t, ξ t+1 is the output of the new energy unit at time t+1, Δt is the time interval, W Δt For each time interval, the normal distribution satisfies N(0,ΔtI), where I represents the identity matrix; Based on formula (4), according to W Δt In each time interval, the normal distribution of N(0,ΔtI) is satisfied, and the output of the new energy unit at each moment and the output of the new energy unit at the previous moment satisfy the following distribution relationship: x t+1 ~N(ξ t +μ(ξ t ;θ)Δt,σ(ξ t ;θ)σ(ξ t (i) T ) (5) In the formula, the superscript T represents the transpose of the matrix vector; (2.3) According to the output of new energy units t Historical data and Get the following conditional probability formula: (2.4) Substitute the conditional probability formula corresponding to formula (6) into the maximum likelihood estimation objective corresponding to formula (3) to transform the maximum likelihood estimation objective into an optimization objective function for solving unconstrained conditions, expressed as: In the formula, μ and σ when L reaches the minimum value are the parameters to be estimated; (2.5) Solve the unknown parameter θ used to represent the randomness of the output of renewable energy: Let μ(ξ t ;θ)=-λξ t ,σ(ξ t ; θ) = σ, when L takes the minimum value, λ = λ0, σ = σ0, where λ = λ0 is the drift coefficient obtained by the objective function to be optimized according to historical data, and σ = σ0 is the diffusion coefficient obtained by the objective function to be optimized according to historical data, then the output distribution of new energy at each moment is expressed as: dξ t =-λ0ξ t dt+σ0dW t (8) In the formula, when σ(ξ t ; θ)=σ0, The output of the new energy units at each moment satisfies the above normal distribution. According to the above normal distribution, its confidence interval is drawn to describe its randomness.

4. The new energy consumption capacity evaluation method based on Ito stochastic process according to claim 1 is characterized in that: The step (3) comprises the following sub-steps: (3.1) Construct the stochastic optimization objective function under the fluctuation of renewable energy output. Specifically, substitute the renewable energy output distribution at the next moment corresponding to formula (9) into the stochastic optimization model, and determine the optimization goal as the minimum renewable energy abandonment. The corresponding stochastic optimization objective function is expressed as: In the formula, is the active power output of renewable energy at node i at time t, η i,t is the renewable energy curtailment rate of node i at time t, M is the total number of distribution network nodes, and B is the set of grid nodes; (3.2) Determine the constraints of the stochastic optimization objective function, including: ① Flow constraints: Where V i,t 、V j,t are the voltage amplitudes of nodes i and j at time t, R ij and X ij are the resistance and reactance on line ij, respectively, P ij,t and Q ij,t are the active load and reactive load on line ij at time t, V0 is the reference value of voltage amplitude, are the inflow and outflow active power of node i at time t, are the inflow and outflow reactive power of node i at time t, is the reactive power output of renewable energy at node i at time t, L e A collection of power grid lines; ② Branch power constraint: Where P ij,max is the active power limit of line ij, Q ij,max is the reactive power limit of line ij; ③Node voltage constraints: Where V i,max 、V i,min are the upper and lower bounds of the voltage amplitude at node i, respectively; (3.3) The value of the renewable energy power abandonment rate at each node at each time is continuously adjusted, and the optimization goal is to minimize the random optimization objective function, and the renewable energy active output that meets the constraints is obtained. The random optimization result including the renewable energy power abandonment rate and the renewable energy active output is obtained, and the renewable energy absorption capacity index is used to evaluate the renewable energy absorption capacity of the random optimization result.

5. The new energy consumption capacity evaluation method based on Ito stochastic process according to claim 4 is characterized in that: The new energy consumption capacity indicator is the new energy utilization rate, and its calculation formula is: In the formula, β is the utilization rate of new energy, P f is the electricity consumption of renewable energy, P q Abandoned electricity for new energy.

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