Power dispatching method and device based on new energy output multiple peaks and storage medium

Abstract

The invention relates to a new energy output multi-peak-based power dispatching method and device and a storage medium, which are applied to the technical field of power system optimization dispatching and comprise the following steps: establishing a wind-light-load probability density function model close to reality by acquiring historical wind-light load data; the uncertainty of uncertain variables is described by adopting a confidence gap decision theory, the condition of source load uncertainty is reflected more truly, and the fluctuation condition of the uncertain variables is represented while high fitting is ensured; meanwhile, opportunity constraints are introduced, opportunity constraint description is carried out on uncertain variables, a deterministic model is solved firstly, a target cost value under the deterministic model is obtained, then a source load uncertainty model is introduced into an optimization model, an optimal result under a limit confidence level is solved, an uncertain variable calculation rule is adopted for conversion, and a target cost value is obtained. The problem solving difficulty is simplified, and the solving accuracy of the model is improved.

Description

Technical Field

[0001] The present invention relates to the technical field of power system optimization dispatching, and in particular to a power dispatching method, device and storage medium based on multi-peak output of new energy. Background Art

[0002] With the growing demand for energy, the application of new energy has become an irreversible trend. However, the large-scale grid connection of new energy, especially wind power and photovoltaics, has brought huge challenges to the operation of the power system. Affected by environmental factors, the output of wind power and photovoltaics has strong volatility and randomness, which leads to the uncertainty of new energy output, resulting in an imbalance between supply and demand, which in turn affects the economic benefits and environmental friendliness of the system. Therefore, it is necessary to accurately assess the output uncertainty of wind power and photovoltaics so as to reasonably respond to and alleviate the adverse effects it brings.

[0003] Existing studies usually use simple standard distributions, such as Weibull distribution, Beta distribution and normal distribution, when considering renewable energy output. However, through analysis of measured data from wind farms and photovoltaic power stations, it is found that the actual renewable energy output distribution presents a multi-peak probability distribution rather than a simple unimodal distribution. Therefore, simply using the standard distribution to replace the actual renewable energy output probability distribution may cause a large deviation in the analysis results of low-carbon economic dispatch of the power system. Summary of the invention

[0004] In view of this, the purpose of the present invention is to provide a power dispatching method, device and storage medium based on multi-peak renewable energy output, so as to solve the problem in the prior art that simply using standard distribution to replace the actual renewable energy output probability distribution will cause a large deviation in the low-carbon economic dispatching analysis results of the power system.

[0005] According to a first aspect of an embodiment of the present invention, a method for dispatching electric power based on multi-peak output of renewable energy is provided, the method comprising: Acquire historical wind and solar data, and establish a multi-peak probability density function model of new energy output based on the historical wind and solar data; Obtain the expected cost with the goal of minimizing the total operating cost and carbon emission cost of the power system; Obtaining a multi-peak robust optimization model based on the multi-peak probability density function model of renewable energy output; The confidence gap decision theory is used to describe the fluctuation level of uncertain variables at the power layer and the load layer of the multi-peak robust optimization model. The confidence level of the uncertain variables is described by chance constraints, and a source-load uncertainty model considering multi-peak robust optimization is obtained. Given a preset target significance level, obtaining the limit confidence level of the uncertainty parameter fluctuation and the objective function value of the source-load uncertainty model considering multi-peak robust optimization at the preset target significance level; The source-load uncertainty model considering multi-peak robust optimization is transformed into a deterministic model considering multi-peak robust optimization; Solving the deterministic model considering multi-peak robust optimization according to the uncertain variable calculation rule and the expected cost to obtain the output of the unit in each time period; Electric power dispatch is performed according to the output of the units in each time period, the limit confidence level of the uncertain parameter fluctuation and the objective function value.

[0006] Preferably, The establishment of a multi-peak probability density function model of new energy output based on the historical wind and solar data includes: Adopting adaptive bandwidth kernel density estimation method to the historical wind-light data, establishing a distribution model close to the actual wind-light probability density function; The multi-peak characteristics of the actual probability distribution are obtained by fitting the approximate actual wind-solar probability density function distribution model, and the multi-peak probability density function model of the new energy output is established based on the multi-peak characteristics of the actual probability distribution.

[0007] Preferably, The method of obtaining the expected cost with the goal of minimizing the total operating cost of the power system and the carbon emission cost includes: A deterministic model in which the power supply side output and the load side demand are accurately predicted is established, and the objective function is to minimize the total operating cost and carbon emission cost of the power system. The expected cost of the objective function of the deterministic model in which the power supply side output and the load side demand are accurately predicted is solved under necessary constraints.

[0008] Preferably, The obtaining of a multi-peak robust optimization model based on the multi-peak probability density function model of renewable energy output includes: A day-ahead and day-intraday low-carbon dispatch optimization model is constructed with the goal of minimizing the total operating cost of the power system and the carbon emission cost. The multi-peak probability density function model of new energy output is applied to the day-ahead and day-intraday low-carbon dispatch optimization model to obtain a multi-peak robust optimization model.

[0009] Preferably, The total operation cost of the power system includes: the cost of thermal power units, wind power units, photovoltaic units and the user's phased IDR dispatching cost within the preset dispatching time; The cost of the thermal power unit includes: the operating cost and the start-up and shutdown cost of the thermal power unit; The operation cost of the thermal power unit includes: setting a power generation cost coefficient of the thermal power unit, obtaining the actual output of the thermal power unit in a preset scheduling time period, and obtaining the operation cost of the thermal power unit according to the power generation cost coefficient and the actual output of the thermal power unit; The start-stop cost of the thermal power unit includes: setting a start-stop cost coefficient of the thermal power unit, obtaining a start-stop state variable of the thermal power unit within a preset scheduling time period, and obtaining the start-stop cost of the thermal power unit according to the start-stop cost coefficient and the start-stop state variable of the thermal power unit; The wind turbine cost includes: setting a power generation cost coefficient of the wind turbine, obtaining the actual output of the wind turbine in a preset scheduling time period, and obtaining the wind turbine cost according to the power generation cost coefficient and the actual output of the wind turbine; The photovoltaic unit cost includes: setting a power generation cost coefficient of the photovoltaic unit, obtaining the actual output of the photovoltaic unit within a preset scheduling time period, and obtaining the photovoltaic unit cost according to the power generation cost coefficient and the actual output of the photovoltaic unit; The user's phased IDR scheduling cost includes: obtaining the response income of the user at each stage when performing IDR, obtaining the response status of the user at each stage within a preset time period, obtaining the response amount of the user participating in the IDR at each stage within the preset time period, and obtaining the user's phased IDR scheduling cost according to the response income of the user at each stage when performing IDR, the response status of the user at each stage within the preset time period, and the response amount of the user participating in the IDR at each stage within the preset time period; The carbon emission cost includes: obtaining the actual carbon emissions and the carbon emission target value within the preset scheduling period, obtaining the carbon trading price within the preset scheduling time period, and obtaining the carbon emission cost based on the difference between the actual carbon emissions and the carbon emission target value within the preset scheduling period and the carbon trading price.

[0010] Preferably, The necessary constraints include: power balance constraints, thermal power unit constraints, renewable energy output constraints, IDR constraints and power flow constraints; The power balance constraint includes: the actual output value of each unit in a preset time period is equal to the load value of the power demand after IDR; The thermal power unit constraints include: when the thermal power units are running, the upper and lower output limits, ramp constraints, and start and stop constraints of each thermal power unit are met; The renewable energy output constraint includes: the actual output of the wind turbine and photovoltaic units within a preset time period is not greater than their respective effective output power within the preset time period; The IDR constraints include: the response volume of users participating in the IDR within the preset time period is not greater than the maximum response volume of users within the preset time period; the response state duration of the user in each stage within the preset time period is not greater than the upper limit of the total cumulative interruption duration in each stage within the day; and the total duration of the interruption within the preset scheduling time period does not exceed the preset maximum value.

[0011] The power flow constraint includes: the input power of each node is not greater than the power transmission upper limit of the transmission line.

[0012] Preferably, The converting of the source-load uncertainty model considering multi-peak robust optimization into a deterministic model considering multi-peak robust optimization comprises: The multi-peak probability density function model of renewable energy output is segmented to form multiple single-peak models, and confidence gap decision constraints are constructed to transform the multiple single-peak models one by one, so as to transform the source-load uncertainty model considering multi-peak robust optimization into a deterministic model considering multi-peak robust optimization.

[0013] According to a second aspect of an embodiment of the present invention, there is provided a power dispatching device based on multi-peak output of renewable energy, the device comprising: The first model building module is used to obtain historical wind and solar data, and establish a multi-peak probability density function model of new energy output based on the historical wind and solar data; Expected cost acquisition module: used to obtain the expected cost with the goal of minimizing the total operating cost and carbon emission cost of the power system; The second model building module is used to obtain a multi-peak robust optimization model based on the multi-peak probability density function model of the new energy output; The third model building module is used to describe the fluctuation level of uncertain variables at the power layer and the load layer of the multi-peak robust optimization model by using the confidence gap decision theory, and to describe the confidence level of the uncertain variables by chance constraints, so as to obtain a source-load uncertainty model considering multi-peak robust optimization; The third model solving module is used to obtain the limit confidence level and the objective function value of the uncertainty parameter fluctuation of the source-load uncertainty model considering multi-peak robust optimization at the preset target significance level given a preset target significance level; A fourth model building module: used for converting the source-load uncertainty model considering multi-peak robust optimization into a deterministic model considering multi-peak robust optimization; The fourth model solving module is used to solve the deterministic model considering multi-peak robust optimization according to the uncertain variable calculation rule and the expected cost to obtain the output of the unit in each time period; Dispatching module: used to perform power dispatch according to the output of the unit in each time period, the limit confidence level of the uncertain parameter fluctuation and the objective function value.

[0014] According to a third aspect of an embodiment of the present invention, there is provided a storage medium, wherein the storage medium stores a computer program, and when the computer program is executed by a main controller, each step in the above method is implemented.

[0015] The technical solution provided by the embodiments of the present invention may have the following beneficial effects: This application establishes a wind-solar-load probability density function model that is close to reality by acquiring historical wind-solar-load data, and adopts the confidence gap decision theory to describe the uncertainty of uncertain variables, which more realistically reflects the uncertainty of source load, and represents the fluctuation of uncertain variables while ensuring a high degree of fit; at the same time, opportunity constraints are introduced to describe the uncertain variables with opportunity constraints, and the deterministic model is first solved to obtain the target cost value under the deterministic model, and then the source load uncertainty model is introduced into the optimization model to solve the optimal result under the limit confidence level, and the uncertain variable calculation rule is used for transformation, which simplifies the difficulty of problem solving and improves the accuracy of model solution.

[0016] It is to be understood that both the foregoing general description and the following detailed description are exemplary and explanatory only and are not restrictive of the invention. BRIEF DESCRIPTION OF THE DRAWINGS

[0017] The accompanying drawings, which are incorporated in and constitute a part of this specification, illustrate embodiments consistent with the invention and, together with the description, serve to explain the principles of the invention.

[0018] Figure 1 is a flow chart of a method for dispatching electric power based on multi-peak output of renewable energy according to an exemplary embodiment; Figure 2 is a system schematic diagram of a power dispatching device based on multi-peak output of renewable energy according to another exemplary embodiment; In the accompanying drawings: 1-first model building module, 2-expected cost acquisition module, 3-second model building module, 4-third model building module, 5-third model solving module, 6-fourth model building module, 7-fourth model solving module, 8-scheduling module. DETAILED DESCRIPTION

[0019] 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, the same numbers in different drawings represent the same or similar elements unless otherwise indicated. The embodiments described in the following exemplary embodiments do not represent all embodiments consistent with the present invention. Instead, they are merely examples of devices and methods consistent with some aspects of the present invention as detailed in the appended claims.

[0020] Embodiment 1 Figure 1 is a flow chart of a method for dispatching electric power based on multi-peak output of renewable energy according to an exemplary embodiment, such as Figure 1 As shown, the method includes: S1, obtaining historical wind and solar data, and establishing a multi-peak probability density function model of new energy output based on the historical wind and solar data; S2, obtaining the expected cost with the goal of minimizing the total operating cost and carbon emission cost of the power system; S3, obtaining a multi-peak robust optimization model based on the multi-peak probability density function model of renewable energy output; S4, using confidence gap decision theory to describe the fluctuation level of uncertain variables at the power layer and load layer of the multi-peak robust optimization model, describing the confidence level of uncertain variables through chance constraints, and obtaining a source-load uncertainty model considering multi-peak robust optimization; S5, given a preset target significance level, obtaining the limit confidence level and the objective function value of the uncertainty parameter fluctuation of the source-load uncertainty model considering multi-peak robust optimization at the preset target significance level; S6, converting the source-load uncertainty model considering multi-peak robust optimization into a deterministic model considering multi-peak robust optimization; S7, solving the deterministic model considering multi-peak robust optimization according to the uncertain variable calculation rule and the expected cost to obtain the output of the unit in each time period; S8, performing power dispatch according to the output of the units in each time period, the limit confidence level of the uncertain parameter fluctuation and the objective function value; It can be understood that the specific content of this embodiment is as follows: Collect historical data, and use the adaptive bandwidth kernel density estimation method to establish a distribution model that approximates the actual wind-solar probability density function for historical wind and solar data; through fitting, the actual probability distribution shows obvious multi-peak characteristics, and then the multi-peak probability density function model of new energy output is obtained; Among them, the kernel density estimation form is shown as follows:

[0021] In the formula, k ( x ) is the bandwidth h j The kernel density estimation function is M is the number of samples, each point j All have a bandwidth, so this is called adaptive or variable; k ( x) is the kernel function, which is a Gaussian kernel function; where 0≤α≤1 is the sensitivity factor, α is usually 0.5, and ω represents the bandwidth parameter; n Representation vector x Dimensions; S Indicates a n × n The positive definite covariance matrix of ; f ( x （k） ) indicates the k Sample points x （k） A function value of ; f ( x （j） ) indicates that for j Sample points x （j） The function value of ; T Represents a transpose operation; Establish a deterministic model in which the power supply output and load demand are accurately predicted; take the minimum total operating cost and carbon emission cost of the power system as the objective function, and solve the expected cost of the objective function of the deterministic model in which the power supply output and load demand are accurately predicted under necessary constraints; Wherein, the expression of the objective function is:

[0022] In the formula, C G,t , C W,t , C pv,t , C IDR,t , C carbon They are t The cost of thermal power units, wind power units, photovoltaic power generation, user phased IDR scheduling costs and carbon emission costs in different time periods. T 0 represents time; The cost of a thermal power unit consists of two parts, namely the operating cost and the start-up and shutdown cost of the thermal power unit. The operating cost of a thermal power unit is expressed by a quadratic function, and the start-up and shutdown cost coefficient of the thermal power unit is usually a constant. The cost of a thermal power unit is expressed as:

[0023] In the formula, and Respectively thermal power units in t Operating costs and start-up and shutdown costs during the period; a i , b i andc i is the power generation cost coefficient of thermal power units; Indicates that the thermal power unit is t Output during the time period; Indicates that the thermal power unit is t The start and stop state variables of the time period; N G is the number of thermal power units; t i is the start-up and shutdown cost coefficient of thermal power units; There is no fuel cost for the power generation of wind power and photovoltaic units. However, in the actual power grid, both photovoltaic power stations and wind farms are mostly owned by independent power generation groups. The power grid and the power generation group form a certain cost price through contractual agreements. The expressions for the cost of wind turbines and photovoltaic power generation are as follows:

[0024] In the formula, k W and k pv represent the power generation cost coefficients of wind turbines and photovoltaic units respectively; P W,t and P pv,t Respectively represent wind turbines and photovoltaic units in The actual output during the time period; The user's phased IDR scheduling cost is expressed as:

[0025] In the formula, r j Perform IDR for users in j The response benefit of the stage is regarded as the scheduling cost for the system; for t Users during the period i exist j The response status of the stage; N IL Indicates the total number of bins; J Indicates the number of stages, For users participating in IDR t During the period j The response of the stage; among them, and The following relations are satisfied:

[0026] In the formula, Indicates user i exist j The upper limit of the response volume of the stage; The carbon emission cost is mainly the carbon trading cost generated by the generator set participating in the carbon emission rights trading market. The baseline method is used to calculate the carbon emission quota. The carbon emission quota of the power system is proportional to the output of the generator set; the carbon emission quota is expressed as:

[0027] In the formula, E L The total carbon emission quota in a scheduling cycle, that is, the target value of carbon emissions; e is the unit electricity quota coefficient of carbon emissions in the power system; The main carbon emission cost of the power system comes from thermal power units, so the actual carbon emissions of the power system in a dispatch cycle are expressed as:

[0028] In the formula, E P Represents the total actual carbon emissions during the scheduling period; d i For thermal power units i Carbon emission coefficient; The final carbon emission cost model is as follows:

[0029] Where η is the carbon trading price; E P < E L When the power system has surplus carbon emission quotas, it can sell carbon emission rights in the carbon trading market, so that the power system can obtain certain carbon benefits; The necessary constraints include: power balance constraints, thermal power unit constraints, renewable energy output constraints, IDR constraints and power flow constraints; Power balance constraints: When the line power loss is not taken into account, the system should satisfy the requirement that the output value of each unit is equal to the load value after IDR, as follows:

[0030] Indicates the user's electricity demand. express t The load of users participating in IDR at any given moment; Constraints on thermal power units: When the thermal power unit is in operation, it should meet the upper and lower output limits, ramp constraints, and start and stop constraints, as follows:

[0031] In the formula, and Respectively represent thermal power units The lower and upper limits of output; and Respectively represent the unit Upward and downward climbing rates; and Respectively represent the unit i Minimum start and stop time; Indicates that the unit is t The number of on / off units at the moment; Indicates that the unit is k The number of on / off units at the moment; Renewable energy output constraints:

[0032] express t The effective output power of the fan at this moment, Photovoltaic unit t The effective output power of the time period; IDR Constraints:

[0033] In the formula, M j Indicates j The upper limit of the total cumulative interruption duration during the period; I i Indicates user i The maximum duration of the interruption; express t The maximum number of user responses during the time period; Power flow constraints:

[0034] In the formula, S F is the transfer factor distribution matrix; P inj Inject the power matrix into the node, which is positive for the node where the power source is located and negative for the node where the load is located; is the upper limit of line power transmission; A day-ahead and intraday low-carbon dispatch optimization model is constructed with the goal of minimizing the total operating cost and carbon emission cost of the power system. The multi-peak probability density function model of new energy output is applied to the day-ahead and intraday low-carbon dispatch optimization model to obtain a multi-peak robust optimization model. The confidence gap decision theory is used to describe the fluctuation level of uncertain variables at the power layer and load layer of the multi-peak robust optimization model. The confidence level of the uncertain variables is described by chance constraints to obtain a source-load uncertainty model considering multi-peak robust optimization. After the target significance level 1-β is given, the limit confidence level 1-α of the uncertainty parameter fluctuation and the objective function value under the uncertainty model are obtained. Among them, the confidence gap decision theory is used to describe the fluctuation level of uncertain variables at the power layer and the load layer, and the expression is:

[0035] 1-α is the uncertainty, that is, the confidence level of the uncertain variable; 1-β is the confidence level that the objective function meets the expected value; is the probability sign; U(φ,α) is the confidence uncertainty interval of the uncertain variable; represents the lower bound of an uncertain variable, represents the upper and lower bounds of uncertain variables, represents the upper bound of an uncertain variable, represents the limiting confidence level of the uncertainty parameter fluctuation, f 0 represents the objective function value, represents the target function to be solved, represents an equality constraint, represents an inequality constraint, and They are the upper and lower bounds of the uncertain variable when the confidence level is 1-α; The confidence level of uncertain variables is described by chance constraints, and the limit confidence level of uncertain parameter fluctuations is solved so that the objective function can still meet the expected optimization cost of the deterministic model in the worst case. The mathematical expression of the source-load uncertainty model considering multi-peak robust optimization is as follows:

[0036] By solving the model, we can obtain the limit confidence level 1-α of the uncertain parameter fluctuation and the objective function value under the target significance level 1-β; The multi-peak probability density function model of renewable energy output is processed in sections to form multiple single-peak models. Then, the confidence gap decision constraints are constructed and transformed one by one to transform the source-load uncertainty model considering multi-peak robust optimization into a deterministic model considering multi-peak robust optimization, specifically: The opportunity constraints are mathematically transformed as follows: , , L t are independent uncertain variables and all obey regular uncertain distribution. is regarded as a new uncertain variable, called a joint uncertain variable; let the cumulative distribution function of this uncertain variable be f , then:

[0037] F grid is a substantially measurable function. According to the properties of the distribution function, There exists a strictly monotonically increasing inverse function in the interval [0,1] that satisfies:

[0038] According to the constructed model, since the cost of wind and solar power output is lower than that of thermal power units, and wind and solar power output will reduce the carbon emissions of the system, the higher the wind and solar power forecast value, the smaller the objective function, and the larger the load, the larger the objective function. Therefore, the combined uncertain variables For uncertain variables and It is monotonically decreasing, while L t Strictly monotonically increasing, obtained by the uncertainty variable calculation rule:

[0039] Thus, the source-load uncertainty model considering multi-peak robust optimization is transformed into a deterministic model considering multi-peak robust optimization; The transformed deterministic model is solved according to the uncertainty variable calculation rule and the above-mentioned expected cost to obtain the output of the unit in each period, and the power dispatch is carried out according to the output of the unit in each period, the limit confidence level 1-α of the uncertainty parameter fluctuation and the objective function value under the uncertainty model; It is worth noting that when solving the transformed deterministic model, the system layer model is a multi-peak model, which is solved by an adaptive multi-peak differential evolution algorithm based on local binary patterns. After the solution is completed, the system layer obtains the output of each unit as basic data for solving the load layer. The load layer problem is a typical mixed integer quadratic programming problem, which is solved using the CPLEX solver of MATLAB software.

[0040] Embodiment 2 Figure 2 is a system schematic diagram of a power dispatching device based on multi-peak output of renewable energy according to another exemplary embodiment, the device comprising: The first model building module 1 is used to obtain historical wind and solar data, and establish a multi-peak probability density function model of new energy output based on the historical wind and solar data; Expected cost acquisition module 2: used to obtain the expected cost with the goal of minimizing the total operating cost and carbon emission cost of the power system; The second model building module 3 is used to obtain a multi-peak robust optimization model based on the multi-peak probability density function model of the new energy output; The third model building module 4 is used to describe the fluctuation level of uncertain variables at the power layer and the load layer of the multi-peak robust optimization model by using the confidence gap decision theory, and to describe the confidence level of the uncertain variables by chance constraints, so as to obtain a source-load uncertainty model considering multi-peak robust optimization; The third model solving module 5 is used to obtain the limit confidence level and the objective function value of the uncertainty parameter fluctuation of the source-load uncertainty model considering multi-peak robust optimization at the preset target significance level given a preset target significance level; The fourth model building module 6 is used to transform the source-load uncertainty model considering multi-peak robust optimization into a deterministic model considering multi-peak robust optimization; The fourth model solving module 7 is used to solve the deterministic model considering multi-peak robust optimization according to the uncertain variable calculation rule and the expected cost to obtain the output of the unit in each time period; The dispatching module 8 is used to perform power dispatching according to the output of the units in each time period, the limit confidence level of the uncertain parameter fluctuation and the objective function value.

[0041] Embodiment three: This embodiment provides a storage medium, wherein the storage medium stores a computer program, and when the computer program is executed by a main controller, each step in the above method is implemented; It is understandable that the storage medium mentioned above can be a read-only memory, a magnetic disk or an optical disk, etc.

[0042] It can be understood that the same or similar parts of the above embodiments can be referenced to each other, and the contents not described in detail in some embodiments can refer to the same or similar contents in other embodiments.

[0043] It should be noted that, in the description of the present invention, the terms "first", "second", etc. are only used for descriptive purposes and cannot be understood as indicating or implying relative importance. In addition, in the description of the present invention, unless otherwise specified, the meaning of "plurality" refers to at least two.

[0044] Any process or method description in a flowchart or otherwise described herein may be understood to represent a module, segment or portion of code that includes one or more executable instructions for implementing the steps of a specific logical function or process, and the scope of the preferred embodiments of the present invention includes alternative implementations in which functions may not be performed in the order shown or discussed, including performing functions in a substantially simultaneous manner or in the reverse order depending on the functions involved, which should be understood by those skilled in the art to which the embodiments of the present invention belong.

[0045] It should be understood that the various parts of the present invention can be implemented by hardware, software, firmware or a combination thereof. In the above-mentioned embodiments, multiple steps or methods can be implemented by software or firmware stored in a memory and executed by a suitable instruction execution system. For example, if implemented by hardware, as in another embodiment, it can be implemented by any one of the following technologies known in the art or their combination: a discrete logic circuit having a logic gate circuit for implementing a logic function for a data signal, a dedicated integrated circuit having a suitable combination of logic gate circuits, a programmable gate array (PGA), a field programmable gate array (FPGA), etc.

[0046] A person skilled in the art may understand that all or part of the steps in the method for implementing the above-mentioned embodiment may be completed by instructing related hardware through a program, and the program may be stored in a computer-readable storage medium, which, when executed, includes one or a combination of the steps of the method embodiment.

[0047] In addition, each functional unit in each embodiment of the present invention may be integrated into a processing module, or each unit may exist physically separately, or two or more units may be integrated into one module. The above-mentioned integrated module may be implemented in the form of hardware or in the form of a software functional module. If the integrated module is implemented in the form of a software functional module and sold or used as an independent product, it may also be stored in a computer-readable storage medium.

[0048] The storage medium mentioned above can be a read-only memory, a magnetic disk or an optical disk, etc.

[0049] In the description of this specification, the description with reference to the terms "one embodiment", "some embodiments", "examples", "specific examples", or "some examples" means that the specific features, structures, materials or characteristics described in conjunction with the embodiment or example are included in at least one embodiment or example of the present invention. In this specification, the schematic representation of the above terms does not necessarily refer to the same embodiment or example. Moreover, the specific features, structures, materials or characteristics described may be combined in any one or more embodiments or examples in a suitable manner.

[0050] Although the embodiments of the present invention have been shown and described above, it is to be understood that the above embodiments are exemplary and are not to be construed as limitations of the present invention. A person skilled in the art may change, modify, replace and vary the above embodiments within the scope of the present invention.

Claims

1. Power dispatching method based on multi-peak output of renewable energy, It is characterized in that The method comprises: Acquire historical wind and solar data, and establish a multi-peak probability density function model of new energy output based on the historical wind and solar data; Obtain the expected cost with the goal of minimizing the total operating cost and carbon emission cost of the power system; Obtaining a multi-peak robust optimization model based on the multi-peak probability density function model of renewable energy output; The confidence gap decision theory is used to describe the fluctuation level of uncertain variables at the power layer and the load layer of the multi-peak robust optimization model. The confidence level of the uncertain variables is described by chance constraints, and a source-load uncertainty model considering multi-peak robust optimization is obtained. Given a preset target significance level, obtaining the limit confidence level of the uncertainty parameter fluctuation and the objective function value of the source-load uncertainty model considering multi-peak robust optimization at the preset target significance level; The source-load uncertainty model considering multi-peak robust optimization is transformed into a deterministic model considering multi-peak robust optimization; Solving the deterministic model considering multi-peak robust optimization according to the uncertain variable calculation rule and the expected cost to obtain the output of the unit in each time period; Electric power dispatch is performed according to the output of the units in each time period, the limit confidence level of the uncertain parameter fluctuation and the objective function value.

2. The method according to claim 1, It is characterized in that The establishment of a multi-peak probability density function model of new energy output based on the historical wind and solar data includes: Adopting adaptive bandwidth kernel density estimation method to the historical wind-light data, establishing a distribution model close to the actual wind-light probability density function; The multi-peak characteristics of the actual probability distribution are obtained by fitting the approximate actual wind-solar probability density function distribution model, and the multi-peak probability density function model of the new energy output is established based on the multi-peak characteristics of the actual probability distribution.

3. The method according to claim 2, It is characterized in that The method of obtaining the expected cost with the goal of minimizing the total operating cost of the power system and the carbon emission cost includes: A deterministic model in which the power supply side output and the load side demand are accurately predicted is established, and the objective function is to minimize the total operating cost and carbon emission cost of the power system. The expected cost of the objective function of the deterministic model in which the power supply side output and the load side demand are accurately predicted is solved under necessary constraints.

4. The method according to claim 3, It is characterized in that The obtaining of a multi-peak robust optimization model based on the multi-peak probability density function model of renewable energy output includes: A day-ahead and day-intraday low-carbon dispatch optimization model is constructed with the goal of minimizing the total operating cost of the power system and the carbon emission cost. The multi-peak probability density function model of new energy output is applied to the day-ahead and day-intraday low-carbon dispatch optimization model to obtain a multi-peak robust optimization model.

5. The method according to any one of claims 3 or 4, It is characterized in that The total operation cost of the power system includes: the cost of thermal power units, wind power units, photovoltaic units and the user's phased IDR dispatching cost within the preset dispatching time; The cost of the thermal power unit includes: the operating cost and the start-up and shutdown cost of the thermal power unit; The operation cost of the thermal power unit includes: setting a power generation cost coefficient of the thermal power unit, obtaining the actual output of the thermal power unit in a preset scheduling time period, and obtaining the operation cost of the thermal power unit according to the power generation cost coefficient and the actual output of the thermal power unit; The start-stop cost of the thermal power unit includes: setting a start-stop cost coefficient of the thermal power unit, obtaining a start-stop state variable of the thermal power unit within a preset scheduling time period, and obtaining the start-stop cost of the thermal power unit according to the start-stop cost coefficient and the start-stop state variable of the thermal power unit; The wind turbine cost includes: setting a power generation cost coefficient of the wind turbine, obtaining the actual output of the wind turbine in a preset scheduling time period, and obtaining the wind turbine cost according to the power generation cost coefficient and the actual output of the wind turbine; The photovoltaic unit cost includes: setting a power generation cost coefficient of the photovoltaic unit, obtaining the actual output of the photovoltaic unit within a preset scheduling time period, and obtaining the photovoltaic unit cost according to the power generation cost coefficient and the actual output of the photovoltaic unit; The user's phased IDR scheduling cost includes: obtaining the response income of the user at each stage when performing IDR, obtaining the response status of the user at each stage within a preset time period, obtaining the response amount of the user participating in the IDR at each stage within the preset time period, and obtaining the user's phased IDR scheduling cost according to the response income of the user at each stage when performing IDR, the response status of the user at each stage within the preset time period, and the response amount of the user participating in the IDR at each stage within the preset time period; The carbon emission cost includes: obtaining the actual carbon emissions and the carbon emission target value within the preset scheduling period, obtaining the carbon trading price within the preset scheduling time period, and obtaining the carbon emission cost based on the difference between the actual carbon emissions and the carbon emission target value within the preset scheduling period and the carbon trading price.

6. The method according to claim 5, It is characterized in that The necessary constraints include: power balance constraints, thermal power unit constraints, renewable energy output constraints, IDR constraints and power flow constraints; The power balance constraint includes: the actual output value of each unit in a preset time period is equal to the load value of the power demand after IDR; The thermal power unit constraints include: when the thermal power units are running, the upper and lower output limits, ramp constraints, and start and stop constraints of each thermal power unit are met; The renewable energy output constraint includes: the actual output of the wind turbine and photovoltaic units within a preset time period is not greater than their respective effective output power within the preset time period; The IDR constraints include: the response volume of users participating in the IDR within a preset time period is not greater than the maximum response volume of users within the preset time period; the response state duration of users in each stage within the preset time period is not greater than the upper limit of the total cumulative interruption duration of each stage within the day; and the total duration of interruptions within the preset scheduling time period does not exceed the preset maximum value; The power flow constraint includes: the input power of each node is not greater than the power transmission upper limit of the transmission line.

7. The method according to claim 6, It is characterized in that The converting of the source-load uncertainty model considering multi-peak robust optimization into a deterministic model considering multi-peak robust optimization comprises: The multi-peak probability density function model of renewable energy output is segmented to form multiple single-peak models, and confidence gap decision constraints are constructed to transform the multiple single-peak models one by one, so as to transform the source-load uncertainty model considering multi-peak robust optimization into a deterministic model considering multi-peak robust optimization.

8. Power dispatching device based on multi-peak output of new energy, It is characterized in that The device comprises: The first model building module is used to obtain historical wind and solar data, and establish a multi-peak probability density function model of new energy output based on the historical wind and solar data; Expected cost acquisition module: used to obtain the expected cost with the goal of minimizing the total operating cost and carbon emission cost of the power system; The second model building module is used to obtain a multi-peak robust optimization model based on the multi-peak probability density function model of the new energy output; The third model building module is used to describe the fluctuation level of uncertain variables at the power layer and the load layer of the multi-peak robust optimization model by using the confidence gap decision theory, and to describe the confidence level of the uncertain variables by chance constraints, so as to obtain a source-load uncertainty model considering multi-peak robust optimization; The third model solving module is used to obtain the limit confidence level and the objective function value of the uncertainty parameter fluctuation of the source-load uncertainty model considering multi-peak robust optimization at the preset target significance level given a preset target significance level; A fourth model building module: used for converting the source-load uncertainty model considering multi-peak robust optimization into a deterministic model considering multi-peak robust optimization; The fourth model solving module is used to solve the deterministic model considering multi-peak robust optimization according to the uncertain variable calculation rule and the expected cost to obtain the output of the unit in each time period; Dispatching module: used to perform power dispatch according to the output of the unit in each time period, the limit confidence level of the uncertain parameter fluctuation and the objective function value.

9. A storage medium, It is characterized in that The storage medium stores a computer program, and when the computer program is executed by the main controller, each step of the power dispatching method based on multi-peak renewable energy output as described in any one of claims 1 to 7 is implemented.

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