Wind-solar collaborative optimization method and system considering distributed new energy wide-area complementation

Abstract

The invention discloses a wind-light collaborative optimization method and system considering distributed new energy wide area complementation, the method is based on distributed new energy wind power and photovoltaic output joint probability distribution, a wind-light ratio optimization model is constructed, the model takes the maximum wind-light complementation characteristic considering source load matching as an objective function, and a wind-light ratio optimization model is constructed. Building area constraint, integer constraint and renewable energy output permeability constraint considering distributed new energy wide area complementation characteristics are taken as constraint conditions; therefore, a new energy capacity matching problem considering a single distributed new energy wind-solar complementary characteristic is expanded into a new energy capacity matching problem considering distributed new energy wide-area complementation and wind-solar complementation, so that the space-time complementation characteristic of wind and solar can be utilized to a greater extent, and the new energy output fluctuation can be stabilized; the peak regulation pressure of large-scale new energy access is reduced, the source load matching can be improved, the safe and stable operation of a power system is promoted, and the consumption of wind and light resources is improved.

Description

Technical Field

[0001] The present invention relates to the technical field of power system expansion planning, and in particular to a wind-solar collaborative optimization method and system taking into account the wide-area complementarity of distributed new energy. Background Art

[0002] Vigorously developing new energy industries represented by wind power and photovoltaics and building a new power system with new energy as the main body are of great significance to the clean and low-carbon transformation of energy and the realization of carbon peak and carbon neutrality goals. Due to the large fluctuations in wind and photovoltaic power generation output, it is easy to have an adverse effect on the safe and stable operation of the power system, thereby limiting the consumption of wind and solar resources. Therefore, optimizing the ratio of wind and solar capacity and reducing the fluctuation of new energy output through the complementary characteristics of wind and solar are important means to maintain the stability of the power system and promote the consumption and development of new energy power.

[0003] The analysis and evaluation of wind-solar complementary characteristics are the premise and basis for complementary planning and operation. Most of the existing methods only consider the wind-solar complementary characteristics of a single distributed new energy source to optimize the wind-solar capacity ratio, that is, only consider the wind-solar complementary characteristics of relatively small-scale and relatively independent new energy power generation facilities (such as a single wind turbine or a small solar power generation system) to optimize the wind-solar capacity ratio, and have not yet considered the wide-area complementary characteristics between multiple distributed new energy sources. Compared with a single distributed new energy source, wide-area distributed new energy refers to a collection of multiple distributed new energy power generation facilities within a larger geographical range. They may be distributed in different regions, but together they constitute a regional new energy power generation network, with large spatial differences, and more significant complementary characteristics of wind and solar resources.

[0004] Therefore, how to propose a wind-solar coordinated optimization configuration method that takes into account the wide-area complementarity of distributed new energy, so as to achieve wind-solar complementarity to a greater extent, smooth out the fluctuations in new energy output, achieve a scientific ratio of wind and solar capacity, and promote the consumption of wind and solar resources, is an urgent problem to be solved by technical personnel in this field. Summary of the invention

[0005] In order to solve the problems existing in the prior art, the present invention proposes a method and system for the coordinated optimization configuration of wind and solar power considering the wide-area complementarity of distributed new energy. The method expands the new energy capacity ratio problem considering the complementary characteristics of wind and solar power of a single distributed new energy to the new energy capacity ratio problem considering the wide-area complementarity of distributed new energy and the complementarity of wind and solar power. It can make greater use of the spatiotemporal complementarity of wind and solar power, smooth out the fluctuation of new energy output, reduce the peak-shaving pressure of large-scale new energy access, promote the safe and stable operation of the power system, and improve the absorption of wind and solar resources.

[0006] The technical solution of the present invention is as follows:

[0007] A wind-solar collaborative optimization method considering distributed new energy wide-area complementarity, comprising:

[0008] Based on the power curves of wind power generation equipment and photovoltaic power generation equipment, the joint probability distribution of distributed new energy wind power and photovoltaic output is established;

[0009] Based on the joint probability distribution of the distributed new energy wind power and photovoltaic output, a wind-solar ratio optimization model is constructed. The wind-solar ratio optimization model takes the maximum wind-solar complementary characteristics considering source-load matching as the objective function, and takes the construction area constraint, integer constraint and renewable energy output penetration rate constraint considering the distributed new energy wide-area complementary characteristics as the constraint conditions;

[0010] The wind-solar ratio optimization model is solved to obtain a wind-solar ratio optimization solution that takes into account distributed new energy wide-area complementarity and wind-solar complementarity.

[0011] Furthermore, the expression of the joint probability distribution of the distributed new energy wind power and photovoltaic output is as follows:

[0012]

[0013] In the formula, represents the wind power output rate of the i-th distributed renewable energy; represents the photovoltaic output rate of the i-th distributed renewable energy; F(P i WS ,P i SS ) represents the joint probability distribution of the wind and solar power output rate of the i-th distributed renewable energy; F(P i WS ) represents the marginal distribution of wind power output rate of the i-th distributed renewable energy; F(P i SS ) represents the marginal distribution of the regional photovoltaic output rate of the i-th distributed renewable energy; C represents the Copula function;

[0014] The marginal distribution expressions of wind power and photovoltaic output rates of the i-th distributed new energy are as follows:

[0015]

[0016] In the formula, f(P i WS ) represents the kernel density estimation of the probability density function obeyed by the wind power output rate of the i-th distributed renewable energy; f(P i SS ) represents the kernel density estimation of the probability density function obeyed by the photovoltaic output rate of the i-th distributed renewable energy; t represents the t-th moment;

[0017] The kernel density estimation expression of the probability density function obeyed by the wind power and photovoltaic output rates of the i-th distributed new energy is as follows:

[0018]

[0019] Where h represents bandwidth, which is a non-negative smoothing parameter; n represents the number of samples; Representation and Sample points with the same distribution; Representation and Sample points of the same distribution; K(x) represents the Gaussian kernel function;

[0020] The expression of the wind power output rate function of the i-th distributed renewable energy source is as follows:

[0021]

[0022] In the formula, v i (t) represents the wind speed of the i-th distributed renewable energy source at time t; v s represents the cut-in wind speed of the i-th distributed renewable energy wind turbine; represents the utilization coefficient of the i-th distributed renewable energy wind energy; represents the swept area of ​​the impeller of the i-th distributed renewable energy wind turbine; ρ i represents the air density of the i-th distributed new energy source; v r represents the rated wind speed of the ith distributed renewable energy wind turbine; v0 represents the cut-out wind speed of the ith distributed renewable energy wind turbine;

[0023] The expression of the photovoltaic output rate function of the i-th distributed new energy source is as follows:

[0024]

[0025] In the formula, represents the solar irradiance of the i-th distributed renewable energy source at time t; G r represents the standard solar irradiance required for the solar cell to generate electricity at rated power; γ represents the power temperature coefficient of the solar cell; T i (t) represents the ambient temperature of the i-th distributed renewable energy source at time t; T r Indicates the standard ambient temperature of the solar cell working at rated power; η i Represents the loss correction coefficient of the i-th distributed renewable energy.

[0026] Furthermore, the objective function is expressed as:

[0027]

[0028] Where M represents the wind-solar complementary characteristics considering source-load matching; σ L represents the standard deviation of the load in the study area (which can be obtained by calculating the regional load data); σ Le Represents the standard deviation of the equivalent load in the study area.

[0029] Furthermore, the expression of equivalent load in the study area is as follows:

[0030]

[0031] Where, L e (t) represents the equivalent load demand at time t in the study area; L(t) represents the load demand at time t in the study area; P(t) represents the total output of distributed renewable energy;

[0032] The expression of the total output of the distributed new energy is as follows:

[0033]

[0034] In the formula, represents the installed capacity of the i-th distributed renewable energy wind power; Represents the installed capacity of the i-th distributed renewable energy photovoltaic system.

[0035] Furthermore, the expression of the construction area constraint is:

[0036]

[0037] Where P wu Represents the single unit capacity of wind power; P su Represents the single-unit capacity of solar cells; S wu Indicates the area occupied by a single wind power unit; S su Indicates the area occupied by a single photovoltaic unit; represents the maximum construction area coefficient of wind power of the i-th distributed renewable energy; S represents the maximum photovoltaic construction area coefficient of the i-th distributed new energy; i Represents the research area of ​​the i-th distributed renewable energy source.

[0038] Furthermore, the expression of the integer constraint is:

[0039]

[0040] Where n is the ratio of the installed capacity of the ith distributed new energy photovoltaic power plant to the single-unit capacity of the solar cell; m is the ratio of the installed capacity of the ith distributed new energy wind power plant to the single-unit capacity of the wind power plant; n and m are non-negative integers.

[0041] Furthermore, the expression of the renewable energy output penetration rate constraint is:

[0042]

[0043] Where r represents the renewable energy penetration requirement; Δ t Indicates the time interval; H indicates the total duration.

[0044] A wind-solar collaborative optimization system considering distributed new energy wide-area complementarity, comprising a joint probability distribution building module, a model building module and a model solving module;

[0045] The joint probability distribution establishment module is used to establish a joint probability distribution of distributed new energy wind power and photovoltaic output based on the power curves of wind power generation equipment and photovoltaic power generation equipment;

[0046] The model building module is used to build a wind-solar ratio optimization model based on the joint probability distribution of the distributed new energy wind power and photovoltaic output. The wind-solar ratio optimization model takes the maximum wind-solar complementary characteristics considering source-load matching as the objective function, and takes the construction area constraint, integer constraint and renewable energy output penetration rate constraint considering the distributed new energy wide-area complementary characteristics as the constraint conditions;

[0047] The model solving module is used to solve the wind-solar ratio optimization model to obtain a wind-solar ratio optimization solution that takes into account distributed new energy wide-area complementarity and wind-solar complementarity.

[0048] An electronic device comprises a memory and a processor, wherein the memory stores a computer program, and the processor is used to call and run the computer program stored in the memory to execute any of the methods described above.

[0049] A computer-readable storage medium stores a computer program, wherein the computer program implements the steps of any of the above methods when executed by a processor.

[0050] Compared with the prior art, the present invention has the following beneficial effects:

[0051] The present invention proposes a wind-solar collaborative optimization method and system considering distributed new energy wide-area complementarity. The method constructs a wind-solar ratio optimization model based on the joint probability distribution of distributed new energy wind power and photovoltaic output. The model takes the maximization of the wind-solar complementary characteristics considering source-load matching as the objective function, and takes the construction area constraint, integer constraint and renewable energy output penetration rate constraint considering the distributed new energy wide-area complementarity characteristics as the constraint conditions. In this way, the new energy capacity ratio problem considering the wind-solar complementary characteristics of a single distributed new energy is expanded to the new energy capacity ratio problem considering the distributed new energy wide-area complementarity and wind-solar complementarity. It can make greater use of the spatiotemporal complementarity characteristics of wind and solar, smooth out the fluctuation of new energy output, reduce the peak-shaving pressure of large-scale new energy access, and improve source-load matching, promote the safe and stable operation of the power system, and improve the consumption of wind and solar resources. BRIEF DESCRIPTION OF THE DRAWINGS

[0052] Figure 1 This is a flow chart of the wind-solar collaborative optimization method considering the wide-area complementarity of distributed new energy in the embodiment. DETAILED DESCRIPTION

[0053] In order to make the purpose, technical solutions and advantages of the embodiments of the present invention clearer, the technical solutions 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 part of the embodiments of the present invention, not all of the embodiments. Generally, the components of the embodiments of the present invention described and shown in the drawings here can be arranged and designed in various different configurations.

[0054] Embodiment 1:

[0055] The present invention provides a wind-solar collaborative optimization method considering distributed new energy wide-area complementarity, such as Figure 1 As shown, including:

[0056] S1. Based on the power curves of wind power generation equipment and photovoltaic power generation equipment, establish the joint probability distribution of distributed new energy wind power and photovoltaic output;

[0057] S2. Based on the joint probability distribution of distributed new energy wind power and photovoltaic output, a wind-solar ratio optimization model is constructed. The wind-solar ratio optimization model takes the maximum wind-solar complementary characteristics considering source-load matching as the objective function, and takes the construction area constraint, integer constraint and renewable energy output penetration rate constraint considering the distributed new energy wide-area complementary characteristics as the constraint conditions;

[0058] S3. Solve the wind-solar ratio optimization model to obtain a wind-solar ratio optimization solution that takes into account the wide-area complementarity of distributed new energy and the wind-solar complementarity.

[0059] Embodiment 2:

[0060] This embodiment is further designed on the basis of the first embodiment in that: in this embodiment, the expression of the joint probability distribution of the distributed new energy wind power and photovoltaic output is as follows:

[0061]

[0062] In the formula, represents the wind power output rate of the i-th distributed renewable energy; represents the photovoltaic output rate of the i-th distributed renewable energy; F(P i WS ,P i SS ) represents the joint probability distribution of the wind and solar power output rate of the i-th distributed renewable energy; F(P i WS ) represents the marginal distribution of wind power output rate of the i-th distributed renewable energy; F(P i SS ) represents the marginal distribution of the regional photovoltaic output rate of the i-th distributed renewable energy; C represents the Copula function;

[0063] The marginal distribution expressions of wind power and photovoltaic output rates of the i-th distributed renewable energy are as follows:

[0064]

[0065] In the formula, f(P i WS ) represents the kernel density estimation of the probability density function obeyed by the wind power output rate of the i-th distributed renewable energy; f(P i SS ) represents the kernel density estimation of the probability density function obeyed by the photovoltaic output rate of the i-th distributed renewable energy; t represents the t-th moment;

[0066] The kernel density estimation expression of the probability density function obeyed by the wind power and photovoltaic output rate of the i-th distributed renewable energy is as follows:

[0067]

[0068] Where h represents bandwidth, which is a non-negative smoothing parameter; n represents the number of samples; Representation and Sample points with the same distribution; Representation and Sample points of the same distribution; K(x) represents the Gaussian kernel function;

[0069] The expression of the wind power output rate function of the i-th distributed renewable energy is as follows:

[0070]

[0071] In the formula, v i (t) represents the wind speed of the i-th distributed renewable energy source at time t; v s represents the cut-in wind speed of the i-th distributed renewable energy wind turbine; represents the utilization coefficient of the i-th distributed renewable energy wind energy; represents the swept area of ​​the impeller of the i-th distributed renewable energy wind turbine; ρ i represents the air density of the i-th distributed new energy source; v r represents the rated wind speed of the ith distributed renewable energy wind turbine; v0 represents the cut-out wind speed of the ith distributed renewable energy wind turbine;

[0072] The expression of the photovoltaic output rate function of the i-th distributed renewable energy is as follows:

[0073]

[0074] In the formula, represents the solar irradiance of the i-th distributed renewable energy source at time t; G r represents the standard solar irradiance required for the solar cell to generate electricity at rated power; γ represents the power temperature coefficient of the solar cell; T i (t) represents the ambient temperature of the i-th distributed renewable energy source at time t; T r Indicates the standard ambient temperature of the solar cell working at rated power; η i Represents the loss correction coefficient of the i-th distributed renewable energy.

[0075] Embodiment three:

[0076] This embodiment is further designed on the basis of the second embodiment in that: the expression of the objective function in this embodiment is:

[0077]

[0078] Where M represents the wind-solar complementary characteristics considering source-load matching; σ L represents the standard deviation of the load in the study area (which can be obtained by calculating the regional load data); σ Le Represents the standard deviation of the equivalent load in the study area.

[0079] Embodiment 4:

[0080] This embodiment is further designed on the basis of the first embodiment in that: the expression of equivalent load in the study area in this embodiment is as follows:

[0081]

[0082] Where, L e(t) represents the equivalent load demand at time t in the study area; L(t) represents the load demand at time t in the study area; P(t) represents the total output of distributed renewable energy;

[0083] The expression of the total output of distributed new energy is as follows:

[0084]

[0085] In the formula, represents the installed capacity of the i-th distributed renewable energy wind power; Represents the installed capacity of the i-th distributed renewable energy photovoltaic system.

[0086] Embodiment five:

[0087] This embodiment is further designed on the basis of the fourth embodiment in that: the expression of the construction area constraint in this embodiment is:

[0088]

[0089] Where P wu Represents the single unit capacity of wind power; P su Represents the single-unit capacity of solar cells; S wu Indicates the area occupied by a single wind power unit; S su Indicates the area occupied by a single photovoltaic unit; represents the maximum construction area coefficient of wind power of the i-th distributed renewable energy; S represents the maximum photovoltaic construction area coefficient of the i-th distributed new energy; i Represents the research area of ​​the i-th distributed renewable energy source.

[0090] Embodiment six:

[0091] This embodiment is further designed on the basis of the fifth embodiment in that the expression of the integer constraint in this embodiment is:

[0092]

[0093] Where n is the ratio of the installed capacity of the ith distributed new energy photovoltaic power plant to the single-unit capacity of the solar cell; m is the ratio of the installed capacity of the ith distributed new energy wind power plant to the single-unit capacity of the wind power plant; n and m are non-negative integers.

[0094] Embodiment seven:

[0095] This embodiment is further designed on the basis of the sixth embodiment in that: the expression of renewable energy output penetration rate constraint in this embodiment is:

[0096]

[0097] Where r represents the renewable energy penetration requirement; Δ t Indicates the time interval; H indicates the total duration.

[0098] Embodiment eight:

[0099] A wind-solar collaborative optimization system considering distributed new energy wide-area complementarity of the present invention comprises a joint probability distribution establishment module, a model construction module and a model solution module; wherein,

[0100] A joint probability distribution establishment module is used to establish a joint probability distribution of distributed new energy wind power and photovoltaic output based on the power curves of wind power generation equipment and photovoltaic power generation equipment;

[0101] The model building module is used to build a wind-solar ratio optimization model based on the joint probability distribution of distributed new energy wind power and photovoltaic output. The wind-solar ratio optimization model takes the maximum wind-solar complementary characteristics considering source-load matching as the objective function, and takes the construction area constraint, integer constraint and renewable energy output penetration rate constraint considering the wide-area complementary characteristics of distributed new energy as the constraint conditions;

[0102] The model solving module is used to solve the wind-solar ratio optimization model and obtain a wind-solar ratio optimization solution that takes into account the wide-area complementarity of distributed new energy and wind-solar complementarity.

[0103] Embodiment nine:

[0104] An electronic device of the present invention includes a memory and a processor, wherein the memory stores a computer program, and the processor is used to call and run the computer program stored in the memory to execute the method described in any of the above embodiments.

[0105] A computer-readable storage medium of the present invention stores a computer program, and when the computer program is executed by a processor, the steps of the method described in any of the above embodiments are implemented.

[0106] Embodiment ten:

[0107] A wind-solar collaborative optimization method considering distributed new energy wide-area complementarity of the present invention comprises the following specific steps:

[0108] A. Through the power curves of wind power and photovoltaic power generation equipment, the wind, light and other resource data are converted into wind power and photovoltaic output data, and a joint probability distribution model of wind and light output of distributed new energy is established based on an optimal Copula function. Specifically including:

[0109] A01. Establish output rate functions of wind power and photovoltaic power of distributed new energy. Specifically, in the embodiment of the present invention, the output rate functions of wind power and photovoltaic power are expressed as follows:

[0110] The expression of the wind power output rate function of the i-th distributed renewable energy is as follows:

[0111]

[0112] In the above formula, represents the wind power output rate of the i-th distributed renewable energy source, v i (t) represents the wind speed of the i-th distributed renewable energy source at time t, v s represents the cut-in wind speed of the i-th distributed renewable energy wind turbine, represents the i-th distributed renewable energy wind energy utilization coefficient, represents the swept area of ​​the impeller of the i-th distributed renewable energy wind turbine, ρ i represents the air density of the i-th distributed new energy source, v r It represents the rated wind speed of the ith distributed renewable energy wind turbine generator set, and v0 represents the cut-out wind speed of the ith distributed renewable energy wind turbine generator set.

[0113] The expression of the photovoltaic output rate function of the i-th distributed renewable energy is as follows:

[0114]

[0115] In the above formula, represents the photovoltaic output rate of the i-th distributed renewable energy, represents the solar irradiance of the i-th distributed renewable energy source at time t, G r represents the standard solar irradiance required for the solar cell to generate electricity at rated power, γ represents the power temperature coefficient of the solar cell, T i (t) represents the ambient temperature of the i-th distributed renewable energy source at time t, T r Indicates the standard ambient temperature of the solar cell working at rated power, η i Represents the loss correction coefficient of the i-th distributed renewable energy.

[0116] A02. Estimate the probability density function obeyed by the wind power and photovoltaic output rates of distributed new energy. Since wind and light resource data have great uncertainty and randomness, in order to ensure the accuracy of model solution, the kernel density estimation method in non-parametric estimation is used to calculate the marginal distribution of wind power and photovoltaic output. Specifically in the embodiment of the present invention, the kernel density estimation expression of the probability density function obeyed by the wind power and photovoltaic output rates of the i-th distributed new energy is as follows:

[0117]

[0118] In the above formula, f(P i WS) represents the kernel density estimation of the probability density function obeyed by the wind power output rate of the i-th distributed renewable energy, f(P i SS ) represents the kernel density estimation of the probability density function obeyed by the photovoltaic output rate of the i-th distributed renewable energy, h represents the bandwidth, which is a non-negative smoothing parameter, n represents the number of samples, Representation and Sample points of the same distribution, Representation and Sample points with the same distribution, K(x) represents the Gaussian kernel function.

[0119] A03. Establish the marginal distribution of wind power and photovoltaic output rates of distributed new energy. Specifically, in the embodiment of the present invention, the marginal distribution expression of wind power and photovoltaic output rates of the i-th distributed new energy is as follows:

[0120]

[0121] In the above formula, F(P i WS ) represents the marginal distribution of wind power output rate of the i-th distributed renewable energy source, F(P i SS ) represents the marginal distribution of the regional photovoltaic output rate of the i-th distributed renewable energy, and t represents the t-th moment.

[0122] A04. Establish a joint probability distribution model of wind power and photovoltaic output of distributed new energy. Specifically, in the embodiment of the present invention, the joint probability distribution model of wind and photovoltaic output is expressed as follows:

[0123]

[0124] In the above formula, F(P i WS ,P i SS ) represents the joint probability distribution of the wind and solar power output rate of the i-th distributed renewable energy, and C represents an optimal Copula function.

[0125] After obtaining the joint probability distribution model of wind power and photovoltaic output of distributed renewable energy, the embodiment of the present invention further performs subsequent step B.

[0126] B. Based on the joint probability distribution model of wind and solar output, an objective function considering the wind-solar complementary characteristics of source-load matching is established:

[0127] Based on the new energy ratio optimization model of distributed new energy wide-area complementarity considering source-load matching, in order to reduce the regulation pressure of the power system, the ideal condition for optimizing the new energy ratio is that the total output of distributed new energy can follow the fluctuation of load to avoid the increase of the difficulty of peak regulation of the power grid. Therefore, an objective function of wind-solar complementarity maximizing source-load matching is established; specifically in the embodiment of the present invention, the expression of the objective function is as follows:

[0128]

[0129] In the above formula, M represents the wind-solar complementary characteristics considering source-load matching, σ L represents the standard deviation of the load in the study area (which can be obtained by calculating the regional load data), σ Le Represents the standard deviation of the equivalent load in the study area.

[0130] The expression of equivalent load in the study area is as follows:

[0131]

[0132] In the above formula, L e (t) represents the equivalent load demand in the study area at time t, L(t) represents the load demand in the study area at time t, and P(t) represents the total output of distributed renewable energy.

[0133] The expression of the total output of the distributed new energy is as follows:

[0134]

[0135] In the above formula, represents the installed capacity of the i-th distributed renewable energy wind power, Represents the installed capacity of the i-th distributed renewable energy photovoltaic system.

[0136] After obtaining the objective function of the wind-solar complementary characteristics taking into account source-load matching, the embodiment of the present invention further performs subsequent step C.

[0137] C. Based on the distributed new energy wide-area complementary characteristics considering source-load matching, a new energy ratio optimization model and constraint conditions for the distributed new energy wide-area complementary considering source-load matching are established, including construction area constraints, integer constraints and renewable energy output penetration rate constraints. The specific expressions are as follows:

[0138] The land occupation of wind power and photovoltaic units should not exceed the prescribed construction area. The construction area constraint expression is as follows:

[0139]

[0140] In the above formula, P wu Represents the single unit capacity of wind power, Psu Represents the single-unit capacity of solar cells, S wu Indicates the area occupied by a single wind turbine, S su Indicates the area occupied by a single photovoltaic unit, represents the maximum construction area coefficient of wind power for the i-th distributed renewable energy source, represents the maximum photovoltaic construction area coefficient of the i-th distributed renewable energy, S i Represents the research area of ​​the i-th distributed renewable energy source.

[0141] The installed capacity of wind power and photovoltaic power should be an integer multiple of the actual single-unit capacity of wind power and photovoltaic power. The integer constraint expression is as follows:

[0142]

[0143] In the above formula, n and m are non-negative integers.

[0144] The wind and solar power output of distributed renewable energy should meet the planned green electricity requirements and reach the specified renewable energy penetration rate. The penetration rate constraint expression is as follows:

[0145]

[0146] In the above formula, r represents the renewable energy penetration requirement; Δ t Indicates the time interval; H indicates the total duration.

[0147] After obtaining the new energy ratio optimization model of distributed new energy wide-area complementarity considering source-load matching, the embodiment of the present invention further performs subsequent step D.

[0148] D. Solve the new energy ratio optimization model of distributed new energy wide-area complementarity considering source-load matching, and obtain a new energy capacity optimization configuration plan taking into account distributed new energy wide-area complementarity and wind-solar complementarity.

[0149] The above are only preferred embodiments of the present invention and are not intended to limit the present invention. For those skilled in the art, the present invention may have various modifications and variations. Any modification, equivalent replacement, improvement, etc. made within the spirit and principle of the present invention shall be included in the protection scope of the present invention.

Claims

1. A wind-solar collaborative optimization method considering distributed new energy wide-area complementarity, characterized in that: include: Based on the power curves of wind power generation equipment and photovoltaic power generation equipment, the joint probability distribution of distributed new energy wind power and photovoltaic output is established; Based on the joint probability distribution of the distributed new energy wind power and photovoltaic output, a wind-solar ratio optimization model is constructed. The wind-solar ratio optimization model takes the maximum wind-solar complementary characteristics considering source-load matching as the objective function, and takes the construction area constraint, integer constraint and renewable energy output penetration rate constraint considering the distributed new energy wide-area complementary characteristics as the constraint conditions; The wind-solar ratio optimization model is solved to obtain a wind-solar ratio optimization solution that takes into account distributed new energy wide-area complementarity and wind-solar complementarity.

2. The wind-solar collaborative optimization method considering distributed new energy wide-area complementarity according to claim 1 is characterized in that: The expression of the joint probability distribution of the distributed new energy wind power and photovoltaic output is as follows: In the formula, represents the wind power output rate of the i-th distributed renewable energy; represents the photovoltaic output rate of the i-th distributed renewable energy; F(P i WS ,P i SS ) represents the joint probability distribution of the wind and solar power output rate of the i-th distributed renewable energy; F(P i WS ) represents the marginal distribution of wind power output rate of the i-th distributed renewable energy; F(P i SS ) represents the marginal distribution of the regional photovoltaic output rate of the i-th distributed renewable energy; C represents the Copula function; The marginal distribution expressions of wind power and photovoltaic output rates of the i-th distributed new energy are as follows: In the formula, f(P i WS ) represents the kernel density estimation of the probability density function obeyed by the wind power output rate of the i-th distributed renewable energy; f(P i SS ) represents the kernel density estimation of the probability density function obeyed by the photovoltaic output rate of the i-th distributed renewable energy; t represents the t-th moment; The kernel density estimation expression of the probability density function obeyed by the wind power and photovoltaic output rates of the i-th distributed new energy is as follows: Where h represents bandwidth, which is a non-negative smoothing parameter; n represents the number of samples; Representation and Sample points with the same distribution; Representation and Sample points of the same distribution; K(x) represents the Gaussian kernel function; The expression of the wind power output rate function of the i-th distributed renewable energy source is as follows: In the formula, v i (t) represents the wind speed of the i-th distributed renewable energy source at time t; v s represents the cut-in wind speed of the i-th distributed renewable energy wind turbine; represents the utilization coefficient of the i-th distributed renewable energy wind energy; represents the swept area of ​​the impeller of the i-th distributed renewable energy wind turbine; ρ i represents the air density of the i-th distributed new energy source; v r represents the rated wind speed of the ith distributed renewable energy wind turbine; v0 represents the cut-out wind speed of the ith distributed renewable energy wind turbine; The expression of the photovoltaic output rate function of the i-th distributed new energy source is as follows: In the formula, represents the solar irradiance of the i-th distributed renewable energy source at time t; G r represents the standard solar irradiance required for the solar cell to generate electricity at rated power; γ represents the power temperature coefficient of the solar cell; T i (t) represents the ambient temperature of the i-th distributed renewable energy source at time t; T r Indicates the standard ambient temperature of the solar cell working at rated power; η i Represents the loss correction coefficient of the i-th distributed renewable energy.

3. The wind-solar collaborative optimization method considering distributed new energy wide-area complementarity according to claim 2 is characterized in that: The expression of the objective function is: Where M represents the wind-solar complementary characteristics considering source-load matching; σ L represents the standard deviation of the load in the study area (which can be obtained by calculating the regional load data); σ Le Represents the standard deviation of the equivalent load in the study area.

4. The wind-solar collaborative optimization method considering distributed new energy wide-area complementarity according to claim 3 is characterized in that: The expression of equivalent load in the study area is as follows: Where, L e (t) represents the equivalent load demand at time t in the study area; L(t) represents the load demand at time t in the study area; P(t) represents the total output of distributed renewable energy; The expression of the total output of the distributed new energy is as follows: In the formula, represents the installed capacity of the i-th distributed renewable energy wind power; Represents the installed capacity of the i-th distributed renewable energy photovoltaic system.

5. The wind-solar collaborative optimization method considering distributed new energy wide-area complementarity according to claim 4 is characterized in that: The expression of the construction area constraint is: Where P wu Represents the single unit capacity of wind power; P su Represents the single-unit capacity of solar cells; S wu Indicates the area occupied by a single wind power unit; S su Indicates the area occupied by a single photovoltaic unit; represents the maximum construction area coefficient of wind power of the i-th distributed renewable energy; S represents the maximum photovoltaic construction area coefficient of the i-th distributed new energy; i Represents the research area of ​​the i-th distributed renewable energy source.

6. The wind-solar collaborative optimization method considering distributed new energy wide-area complementarity according to claim 5 is characterized in that: The expression of the integer constraint is: In the formula, n is the ratio of the installed capacity of the i-th distributed new energy photovoltaic system to the single-unit capacity of the solar cell; m is the ratio of the installed capacity of the i-th distributed renewable energy wind power to the single unit capacity of wind power; n and m are non-negative integers.

7. The wind-solar collaborative optimization method considering distributed new energy wide-area complementarity according to claim 6 is characterized in that: The expression of the renewable energy output penetration rate constraint is: Where r represents the renewable energy penetration requirement; Δ t Indicates the time interval; H indicates the total duration.

8. A wind-solar collaborative optimization system considering distributed new energy wide-area complementarity, characterized in that: It includes a joint probability distribution building module, a model building module and a model solving module; The joint probability distribution establishment module is used to establish a joint probability distribution of distributed new energy wind power and photovoltaic output based on the power curves of wind power generation equipment and photovoltaic power generation equipment; The model building module is used to build a wind-solar ratio optimization model based on the joint probability distribution of the distributed new energy wind power and photovoltaic output. The wind-solar ratio optimization model takes the maximum wind-solar complementary characteristics considering source-load matching as the objective function, and takes the construction area constraint, integer constraint and renewable energy output penetration rate constraint considering the distributed new energy wide-area complementary characteristics as the constraint conditions; The model solving module is used to solve the wind-solar ratio optimization model to obtain a wind-solar ratio optimization solution that takes into account distributed new energy wide-area complementarity and wind-solar complementarity.

9. An electronic device, characterized in that: The electronic device comprises a memory and a processor, wherein the memory stores a computer program, and the processor is configured to call and run the computer program stored in the memory to execute the method according to any one of claims 1 to 7.

10. A computer-readable storage medium storing a computer program, characterized in that: When the computer program is executed by a processor, the steps of the method as claimed in any one of claims 1 to 7 are implemented.

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