Multi-energy capacity configuration method and system

Abstract

The invention relates to a multi-energy capacity configuration method, and the method comprises the steps: obtaining the operation data of each energy power station of a power system, obtaining the output power of each energy power station based on the operation data, determining a typical day based on a preset rule, and carrying out the configuration of the multi-energy capacity according to the typical day and the output power in combination with the operation constraint condition of the power system. And constructing a multi-scene power generation model, constructing a power system revenue model according to the multi-scene power generation model and the revenue parameters and cost parameters of the energy power stations, and generating a target capacity configuration strategy through the power system revenue model by taking maximization of the sum of comprehensive revenue of the energy power stations as a target. According to the invention, the problem of resource waste caused by unreasonable capacity configuration of the multi-scene power system is solved. On the premise of ensuring that system safety and stability constraints are met, renewable energy capacity configuration is optimized, and the new energy consumption capacity is improved.

Description

Technical Field

[0001] The present application relates to the field of power system control, and in particular to a multi-energy capacity configuration method and system. Background Art

[0002] Clean and low-carbon power industry is an inevitable requirement for achieving sustainable social and economic development. At present, the proportion of low-carbon energy such as photovoltaic power stations and wind farms in the power system is increasing. Unlike traditional power sources, photovoltaic and wind power have low carbon emissions, but they are intermittent, highly volatile, reverse peak-shaving, and poor controllability. With the increase in grid-connected capacity, the impact of photovoltaic power stations and wind farms on the system is becoming more and more obvious. The capacity configuration strategy of the power system for multi-scenario power generation is unreasonable, which easily leads to waste of resources.

[0003] At present, no effective solution has been proposed to address the problem of resource waste caused by unreasonable capacity configuration of power systems in multiple scenarios in related technologies. Summary of the invention

[0004] The embodiments of the present application provide a multi-energy capacity configuration method, system, electronic device and storage medium to at least solve the problem of resource waste caused by unreasonable capacity configuration of multi-scenario power systems in related technologies.

[0005] In a first aspect, an embodiment of the present application provides a multi-energy capacity configuration method, the method comprising:

[0006] Acquire operation data of each energy power station in the power system, and obtain output power of each energy power station based on the operation data;

[0007] Determine a typical day based on preset rules, and construct a multi-scenario power generation model according to the typical day and the output power, and in combination with constraints on the operation of the power system;

[0008] Constructing a power system revenue model based on the multi-scenario power generation model and the revenue parameters and cost parameters of each energy power station;

[0009] With the goal of maximizing the sum of the comprehensive benefits of the energy power stations, a target capacity configuration strategy is generated through the power system benefit model.

[0010] In some embodiments, obtaining the output power of each energy power station based on the operation data includes:

[0011] Based on the wind turbine power model, determining the output power of the wind turbine according to the wind turbine rated power, the wind turbine real-time wind speed, the wind turbine cut-in wind speed, the wind turbine cut-out wind speed and the rated wind speed in the operating data; and / or

[0012] Based on the photovoltaic power model, determining the output power of the photovoltaic power station according to the real-time light intensity, photovoltaic rated power and rated light intensity in the operating data; and / or

[0013] Based on the centralized photovoltaic power station model, determining the output power of the centralized photovoltaic power station according to the solar rated power, rated light intensity, photothermal efficiency and thermoelectric conversion efficiency in the operating data; and / or

[0014] Based on the energy storage power model, the output power of the energy storage power station is determined according to the energy status at the last moment in the operation data and the charging efficiency of the energy storage power station.

[0015] In some embodiments, the wind turbine power model includes:

[0016]

[0017] Among them, P win,t is the output power of the wind turbine, P win,N is the rated power of the fan, v is the real-time wind speed of the fan, v ci is the wind turbine cut-in wind speed, v co is the fan cut-out wind speed, v N is the rated wind speed.

[0018] In some embodiments, the photovoltaic power model includes:

[0019]

[0020] Among them, P pho,t is the output power of the photovoltaic power station, P pho,N is the photovoltaic rated power, γ is the real-time light intensity, γ N is the rated light intensity.

[0021] In some embodiments, the centralized photovoltaic power station model includes:

[0022]

[0023] Among them, P hea,t is the output power of the centralized photovoltaic power station, P hea,N is the solar rated power, γ is the real-time light intensity, γ N is the rated light intensity, η p,h is the photothermal efficiency, η h,e is the thermoelectric conversion efficiency.

[0024] In some embodiments, the energy storage power model includes:

[0025]

[0026] Among them, P sto,t is the output power of the energy storage power station, P sto,t-1 is the output power of the energy storage power station at the previous moment, η sto is the charging efficiency, and Δ(t) is the time difference between the previous moment and the current moment.

[0027] In some embodiments, constructing a power system revenue model according to the multi-scenario power generation model and the revenue parameters and cost parameters of each energy power station includes:

[0028] Based on the multi-scenario power generation model, and the income parameters and cost parameters of each energy power station, the power sales income, equipment depreciation value, investment cost, operation and maintenance cost, and power purchase cost of each energy power station are obtained;

[0029] The power system profit model is constructed based on the electricity sales revenue, the equipment depreciation value, the investment cost, the operation and maintenance cost and the electricity purchase cost.

[0030] In some of the embodiments, the constraints on the operation of the power system include: ramp constraints, capacity constraints, section constraints and unit output constraints.

[0031] In a second aspect, an embodiment of the present application provides a multi-energy capacity configuration system, the system comprising: a data processing module, a power model building module, a revenue model building module and a strategy generation module, wherein:

[0032] The data processing module is used to obtain the operating data of each energy power station in the power system, and obtain the output power of each energy power station based on the operating data;

[0033] The power model building module is used to determine a typical day based on a preset rule, and build a multi-scenario power generation model according to the typical day and the output power, in combination with the constraints of the power system operation;

[0034] The revenue model building module is used to build a power system revenue model based on the multi-scenario power generation model and the revenue parameters and cost parameters of each energy power station;

[0035] The strategy generation module is used to generate a target capacity configuration strategy through the power system revenue model with the goal of maximizing the sum of the comprehensive benefits of the energy power stations.

[0036] In a third aspect, an embodiment of the present application provides a computer device, comprising a memory, a processor, and a computer program stored in the memory and executable on the processor, wherein when the processor executes the computer program, the multi-energy capacity configuration method as described in the first aspect above is implemented.

[0037] In a fourth aspect, an embodiment of the present application provides a computer-readable storage medium having a computer program stored thereon, which, when executed by a processor, implements the multi-energy capacity configuration method as described in the first aspect above.

[0038] Compared with the related art, the multi-energy capacity configuration method provided in the embodiment of the present application obtains the operating data of each energy power station in the power system, obtains the output power of each energy power station based on the operating data, determines the typical day based on the preset rules, and constructs a multi-scenario power generation model based on the typical day and the output power, and combines the constraints of the power system operation. According to the multi-scenario power generation model, as well as the income parameters and cost parameters of each energy power station, a power system revenue model is constructed. With the goal of maximizing the sum of the comprehensive benefits of each energy power station, a target capacity configuration strategy is generated through the power system revenue model, which solves the problem of resource waste caused by unreasonable capacity configuration of the multi-scenario power system. On the premise of ensuring that the system safety and stability constraints are met, the renewable energy capacity configuration is optimized and the new energy absorption capacity is improved. BRIEF DESCRIPTION OF THE DRAWINGS

[0039] The drawings described herein are used to provide a further understanding of the present application and constitute a part of the present application. The illustrative embodiments of the present application and their descriptions are used to explain the present application and do not constitute an improper limitation on the present application. In the drawings:

[0040] Figure 1 is a flow chart of a multi-energy capacity configuration method according to an embodiment of the present application;

[0041] Figure 2 is a typical scenario power output curve diagram according to an embodiment of the present application;

[0042] Figure 3 It is a comparison diagram of energy proxy benefits of various schemes according to an embodiment of the present application;

[0043] Figure 4 is a comparison chart of new energy consumption of various schemes according to an embodiment of the present application;

[0044] Figure 5 is a structural block diagram of a multi-energy capacity configuration system according to an embodiment of the present application;

[0045] Figure 6 It is a schematic diagram of the internal structure of an electronic device according to an embodiment of the present application. DETAILED DESCRIPTION

[0046] In order to make the purpose, technical solutions and advantages of the present application clearer, the present application is described and illustrated below in conjunction with the accompanying drawings and embodiments. It should be understood that the specific embodiments described herein are only used to explain the present application and are not intended to limit the present application. Based on the embodiments provided in the present application, all other embodiments obtained by ordinary technicians in the field without making creative work are within the scope of protection of the present application.

[0047] Obviously, the drawings described below are only some examples or embodiments of the present application. For ordinary technicians in this field, the present application can also be applied to other similar scenarios based on these drawings without creative work. In addition, it can also be understood that although the efforts made in this development process may be complicated and lengthy, for ordinary technicians in this field related to the content disclosed in this application, some changes in design, manufacturing or production based on the technical content disclosed in this application are just conventional technical means, and should not be understood as insufficient content disclosed in this application.

[0048] Reference to "embodiments" in this application means that a particular feature, structure, or characteristic described in conjunction with the embodiments may be included in at least one embodiment of the present application. The appearance of the phrase in various locations in the specification does not necessarily refer to the same embodiment, nor is it an independent or alternative embodiment that is mutually exclusive with other embodiments. It is explicitly and implicitly understood by those of ordinary skill in the art that the embodiments described in this application may be combined with other embodiments without conflict.

[0049] Unless otherwise defined, the technical terms or scientific terms involved in this application should be understood by people with ordinary skills in the technical field to which this application belongs. The words "one", "a", "a", "the" and the like involved in this application do not indicate a quantitative limitation, and may represent the singular or plural. The terms "include", "comprise", "have" and any of their variations involved in this application are intended to cover non-exclusive inclusions; for example, a process, method, system, product or device that includes a series of steps or modules (units) is not limited to the listed steps or units, but may also include steps or units that are not listed, or may also include other steps or units inherent to these processes, methods, products or devices. The words "connect", "connected", "coupled" and the like involved in this application are not limited to physical or mechanical connections, but may include electrical connections, whether direct or indirect. The "multiple" involved in this application refers to two or more. "And / or" describes the association relationship of associated objects, indicating that there may be three relationships, for example, "A and / or B" can represent: A exists alone, A and B exist at the same time, and B exists alone. The character " / " generally indicates that the objects before and after are in an "or" relationship. The terms "first", "second", "third", etc. involved in this application are only used to distinguish similar objects and do not represent a specific ordering of the objects.

[0050] This embodiment provides a multi-energy capacity configuration method. Figure 1 is a flow chart of a multi-energy capacity configuration method according to an embodiment of the present application. Figure 1 As shown, the process includes the following steps:

[0051] Step S101, acquiring the operating data of each energy station in the power system, and obtaining the output power of each energy station based on the operating data.

[0052] It should be noted that energy power stations include but are not limited to wind power, photovoltaic, hydropower, thermal power, centralized photovoltaic and energy storage power stations.

[0053] In some embodiments, obtaining the output power of each energy power station based on the operation data in step S101 includes:

[0054] Step S1011, based on the wind turbine power model, the output power of the wind turbine is determined according to the wind turbine rated power, the wind turbine real-time wind speed, the wind turbine cut-in wind speed, the wind turbine cut-out wind speed and the rated wind speed in the operating data.

[0055] According to the Weibull distribution, the wind speed probability density function model is established:

[0056]

[0057] Where: v is the real-time wind speed, k and c are the shape parameter and scale parameter respectively.

[0058] According to the wind speed probability density function model and the relationship between the output power of the wind turbine and the wind speed, a wind turbine power model is established:

[0059]

[0060] Among them, P win,t is the output power of the wind turbine, P win,N is the rated power of the fan, v is the real-time wind speed of the fan, v ci is the wind turbine cut-in wind speed, v co is the fan cut-out wind speed, v N is the rated wind speed.

[0061] Step S1012, based on the photovoltaic power model, according to the real-time light intensity, photovoltaic rated power and rated light intensity in the operation data, the output power of the photovoltaic power station is determined.

[0062] Establish the probability density function of the Beta distribution of light intensity γ within a certain period of time:

[0063]

[0064] According to the light intensity probability density function model and the relationship between photovoltaic output power and light intensity, a photovoltaic power model is established:

[0065]

[0066] Among them, P pho,t is the output power of the photovoltaic power station, P pho,N is the photovoltaic rated power, γ is the real-time light intensity, γ N is the rated light intensity.

[0067] Step S1013, based on the centralized photovoltaic power station model, according to the solar energy rated power, rated light intensity, photothermal efficiency and thermoelectric conversion efficiency in the operation data, the output power of the centralized photovoltaic power station is determined.

[0068] According to the relationship between the output power and light intensity of the centralized photovoltaic power station, a centralized photovoltaic power station model is constructed:

[0069]

[0070] Among them, P hea,t is the output power of the centralized photovoltaic power station, P hea,N is the solar rated power, γ is the real-time light intensity, γ N is the rated light intensity, η p,h is the photothermal efficiency, η h,e is the thermoelectric conversion efficiency.

[0071] Step S1014, based on the energy storage power model, according to the energy status at the last moment in the operation data and the charging efficiency of the energy storage power station, determine the output power of the energy storage power station.

[0072] The power of the energy storage battery at time t is not only related to the supply and demand relationship at time t, but also to its energy status at the previous moment. When the system is fully charged, the battery is charged with an efficiency of ; when the system is insufficient, it will be discharged.

[0073] Energy storage power model:

[0074]

[0075] Among them, P sto,t is the output power of the energy storage power station, P sto,t-1 is the output power of the energy storage power station at the previous moment, η sto is the charging efficiency, Δ(t) is the time difference between the previous moment and the current moment.

[0076] Continue to refer to Figure 1 After obtaining the output power of each energy power station, execute step S102.

[0077] Step S102, determining a typical day based on preset rules, and constructing a multi-scenario power generation model according to the typical day and output power, and in combination with constraints on power system operation.

[0078] According to time characteristics and seasonal differences, several typical days of the year are selected, and a multi-scenario power generation model is established based on the typical days:

[0079]

[0080] Where P is the installed capacity of the equipment, n is the total number of typical scenarios, and P i is the power generation on the ith typical working day, η i is the probability of the ith typical day.

[0081] Optionally, P i =P win,i +P pho,i +P hyd,i +P the,i +P hea,i +P sto,i , where P win,i , P pho,i , P hyd,i , P the,i , P hea,i , P sto,i They are the power generation of wind power, photovoltaic power, hydropower, thermal power, centralized photovoltaic power and energy storage on the i-th typical day.

[0082] In some of the embodiments, the constraints on the operation of the power system include: ramp constraints, capacity constraints, section constraints, and unit output constraints.

[0083] Hill climbing constraints include:

[0084] ΔP t up ≤P t the,maxup +P t hyd,maxup +P t sto,maxout

[0085] Among them, ΔP t up is the climbing power at time t; P t the,max up , P t h,max up are the maximum ramping power of thermal power and hydropower at time t respectively; P t sto,max out is the maximum output power of the energy storage at time t.

[0086] ΔP t down ≤P t the,maxdown +P t hyd,maxdown +P t sto,maxin

[0087] Among them, ΔP t down is the load reduction power at time t; P t the,max down , P t h,max down are the maximum load reduction power of thermal power and hydropower at time t; P t sto,max in is the maximum charging power of the energy storage system at time t.

[0088] Capacity constraints include:

[0089]

[0090] Among them, P win , P pho , P hyd , P the , P hea , P sto They are wind power, photovoltaic power, hydropower, thermal power, centralized photovoltaic power, and energy storage power station installed capacity; P win,max , P pho,max , Phyd,max , P the,max , P hea,max , P sto,max They are the maximum installed capacities of wind power, photovoltaic power, hydropower, thermal power, centralized photovoltaic power and energy storage power stations.

[0091] Section constraints include:

[0092]

[0093] Among them, P l is the transmission power on the line section l; P section，max is the maximum transmission power of the line.

[0094] Unit output constraint: In order to improve the consumption of renewable energy wind power and photovoltaic power while ensuring that the conventional power supply constraints are met, a dispatching strategy that prioritizes the consumption of wind power and photovoltaic power is adopted in the multi-scenario model. t+1 up >P t+1 the ,max up +P t+1 hyd ,max up , then wind power and photovoltaic power should be abandoned in advance at time t, where ΔP t+1 up represents the total ramp power requirement of the system at time t+1, P t+1 the,max up and P t+1 hyd,max up They represent the maximum thermal power and hydropower ramping power of the system at time t+1 respectively.

[0095] Step S103, constructing a power system revenue model based on the multi-scenario power generation model and the revenue parameters and cost parameters of each energy power station.

[0096] In some embodiments, step S103 specifically includes:

[0097] Step S1031, based on the multi-scenario power generation model, as well as the income parameters and cost parameters of each energy power station, obtain the power sales income, equipment depreciation value, investment cost, operation and maintenance cost and power purchase cost of each energy power station.

[0098] The electricity sales revenue of each energy power station C S The calculation formula is as follows:

[0099] C S =(1+α-β)*R*P sel

[0100] Among them, R is the electricity price; P selis the electricity sales volume; α is the policy subsidy coefficient; β is the environmental pollution coefficient.

[0101] Equipment depreciation of each energy power station C D The calculation formula is as follows:

[0102]

[0103] Among them, P re is the unit capacity; D is the recovery income corresponding to unit capacity; L is the unit service life; r is the discount rate.

[0104] Investment cost of each energy power station C V The calculation formula is as follows:

[0105]

[0106] Among them, P is the installed capacity of the unit; V is the investment cost per unit capacity.

[0107] Operation and maintenance costs of each energy power station C M The calculation formula is as follows:

[0108] C M =P*M

[0109] Among them, M is the unit capacity operation and maintenance cost of the unit, and P is the installed capacity of the unit.

[0110] For the electricity purchase cost C P :

[0111] Taking power constraint as flexibility constraint, the total output of multi-energy system is uncertain and may not meet the load demand. If the total output is greater than the load, it will lead to wind power and solar power abandonment; if the system power supply is insufficient, it is necessary to purchase electricity from the grid, and the total C P Cost as electricity purchase P g (t), that is

[0112] C P =f(P g (t))

[0113] Among them, C P Allocate to each power source according to capacity ratio, taking wind power purchase cost as an example:

[0114]

[0115] Step S1032, constructing a power system profit model based on electricity sales revenue, equipment depreciation value, investment cost, operation and maintenance cost, and electricity purchase cost.

[0116] With the goal of maximizing the comprehensive benefits of various power agency entities, a power system benefit model is constructed:

[0117]

[0118] Step S104, with the goal of maximizing the sum of the comprehensive benefits of each energy power station, a target capacity configuration strategy is generated through a power system benefit model.

[0119] When optimizing capacity configuration, sensors are used to obtain information from the external environment, and corresponding rules are selected from the behavioral rule library, which are compared with the target benefits to maximize benefits. Specifically, it includes:

[0120] Step S1041, input the environmental impact factor, the environmental impact factor E is E = [e1, e2, L, e i L] column vector, the dimension of which is the sum of the number of environmental impact factors. i is the integer encoding of the environmental impact factor i.

[0121] Step S1042, change the controllable factor S, the controllable factor S is a form of S = [s1, s2, L, s i L], whose dimension is the sum of the number of controllable factors. i is the integer encoding of the controllable factor i.

[0122] Step S1043, establishing behavior rules:

[0123]

[0124] Among them, E k According to the rules, R k Environmental information obtained from the outside world; S jk Expressed with probability δ jk Behavioral measures taken, that is, adaptive adjustments to controllable factors. jk satisfy:

[0125]

[0126] Step S1044, behavior modification rule: If the information obtained from the external environment is E k , then according to the probability S jk Select δ jk As the corresponding behavior metric. If the objective benefit function value increases, then δ jk The value increases, otherwise δ jk The value decreases, which represents the process of experience accumulation and improved adaptability.

[0127] If all the objective benefit function values ​​in the power system revenue model converge, the target capacity allocation strategy (Pwin , P pho , P hyd , P the , P hea , P sto target configuration strategy); otherwise, return to step S1043 to continue iterative calculation.

[0128] Through the above steps, the operation data of each energy power station in the power system is obtained, the output power of each energy power station is obtained based on the operation data, the typical day is determined based on the preset rules, and a multi-scenario power generation model is constructed based on the typical day and the output power, combined with the constraints of the power system operation. According to the multi-scenario power generation model, as well as the income parameters and cost parameters of each energy power station, a power system revenue model is constructed. With the goal of maximizing the sum of the comprehensive benefits of each energy power station, a target capacity configuration strategy is generated through the power system revenue model, which solves the problem of resource waste caused by unreasonable capacity configuration of the multi-scenario power system. On the premise of ensuring that the system safety and stability constraints are met, the capacity configuration of renewable energy is optimized and the new energy absorption capacity is improved.

[0129] In a specific embodiment, the system voltage is 330kV, the power is 100MVA, and the discount rate is 0.12. Based on the theory of complex adaptive systems, each power source is selected as a research agent, and the capacity of each power source is used as a controllable factor. Factors such as wind speed, light intensity, climbing and descending are set as environmental variables for each agent. Each power supply entity generates and changes its own behavior rules based on the input of its own environmental variable value and the benefit value obtained after changing the capacity, so that the capacity value of each power supply entity converges.

[0130] The capacity variation range is selected to be 5MW, the crossover rate is 0.7, and the variation rate is 0.3 to maximize the benefits of each power source entity at the same time. Table 1 is a power capacity configuration scheme according to an embodiment of the present application. As shown in Table 1, the table shows 5 different feasible schemes and a CAS scheme obtained through screening.

[0131] Table 1

[0132]

[0133] The above six power supply structure configuration schemes are subjected to 100 time series multi-scenario power outputs for 8760 hours per year. Figure 2 is a typical scenario power output curve diagram according to an embodiment of the present application, and Table 2 is a new energy consumption table according to an embodiment of the present application.

[0134] Table 2

[0135]

[0136]

[0137] The benefits and new energy consumption of each power agent in the six scenarios are normalized and compared. Figure 3 is a comparison chart of energy proxy benefits of various schemes according to an embodiment of the present application, Figure 4 This is a comparison chart of new energy consumption of various schemes according to an embodiment of the present application.

[0138] It should be noted that the steps shown in the above process or the flowchart in the accompanying drawings can be executed in a computer system such as a set of computer executable instructions, and although a logical order is shown in the flowchart, in some cases, the steps shown or described can be executed in an order different from that shown here.

[0139] This embodiment also provides a multi-energy capacity configuration system, which is used to implement the above embodiments and preferred implementations, and will not be repeated here. As used below, the terms "module", "unit", "subunit", etc. can implement a combination of software and / or hardware for a predetermined function. Although the devices described in the following embodiments are preferably implemented in software, the implementation of hardware, or a combination of software and hardware, is also possible and conceivable.

[0140] Figure 5 is a structural block diagram of a multi-energy capacity configuration system according to an embodiment of the present application, such as Figure 5 As shown, the system includes: a data processing module 51, a power model building module 52, a profit model building module 53 and a strategy generating module 54.

[0141] The data processing module 51 is used to obtain the operating data of each energy station in the power system, and obtain the output power of each energy station based on the operating data.

[0142] The power model building module 52 is used to determine a typical day based on preset rules, and to build a multi-scenario power generation model according to the typical day and output power, combined with constraints on power system operation.

[0143] The revenue model building module 53 is used to build a power system revenue model based on the multi-scenario power generation model and the revenue parameters and cost parameters of each energy power station.

[0144] The strategy generation module 54 is used to generate a target capacity configuration strategy through a power system revenue model with the goal of maximizing the sum of the comprehensive benefits of each energy power station.

[0145] In some of the embodiments, the data processing module 51 includes: a wind turbine module, a photovoltaic module, a solar module and an energy storage module.

[0146] A wind turbine module, for determining the output power of the wind turbine based on the wind turbine power model and according to the wind turbine rated power, wind turbine real-time wind speed, wind turbine cut-in wind speed, wind turbine cut-out wind speed and rated wind speed in the operating data; and / or

[0147] A photovoltaic module for determining the output power of the photovoltaic power station based on a photovoltaic power model according to the real-time light intensity, the photovoltaic rated power and the rated light intensity in the operating data; and / or

[0148] A solar module for determining the output power of a concentrated photovoltaic power station based on a concentrated photovoltaic power station model and according to the solar rated power, rated light intensity, photothermal efficiency and thermoelectric conversion efficiency in the operating data; and / or

[0149] The energy storage module is used to determine the output power of the energy storage power station based on the energy storage power model, according to the energy status at the previous moment in the operation data and the charging efficiency of the energy storage power station.

[0150] In some embodiments, the wind turbine power model includes:

[0151]

[0152] Among them, P win,t is the output power of the wind turbine, P win,N is the rated power of the fan, v is the real-time wind speed of the fan, v ci is the wind turbine cut-in wind speed, v co is the fan cut-out wind speed, v N is the rated wind speed.

[0153] In some embodiments, the photovoltaic power model includes:

[0154]

[0155] Among them, P pho,t is the output power of the photovoltaic power station, P pho,N is the photovoltaic rated power, γ is the real-time light intensity, γ N is the rated light intensity.

[0156] In some embodiments, the centralized photovoltaic power plant model includes:

[0157]

[0158] Among them, P hea,t is the output power of the centralized photovoltaic power station, P hea,N is the solar rated power, γ is the real-time light intensity, γ N is the rated light intensity, η p,h is the photothermal efficiency, η h,e is the thermoelectric conversion efficiency.

[0159] In some embodiments, the energy storage power model includes:

[0160]

[0161] Among them, P sto,t is the output power of the energy storage power station, P sto,t-1 is the output power of the energy storage power station at the previous moment, η sto is the charging efficiency, Δ(t) is the time difference between the previous moment and the current moment.

[0162] In some embodiments, the revenue model building module 53 includes: a cost module and a model generation module.

[0163] The cost module is used to obtain the electricity sales revenue, equipment depreciation value, investment cost, operation and maintenance cost and electricity purchase cost of each energy power station based on the multi-scenario power generation model and the income parameters and cost parameters of each energy power station.

[0164] The model generation module is used to construct a power system profit model based on electricity sales revenue, equipment depreciation value, investment cost, operation and maintenance cost, and electricity purchase cost.

[0165] In some of the embodiments, the constraints on the operation of the power system include: ramp constraints, capacity constraints, section constraints, and unit output constraints.

[0166] Through the above system, the data processing module 51 obtains the operating data of each energy station in the power system, and obtains the output power of each energy station based on the operating data. The power model construction module 52 determines the typical day based on the preset rules, and constructs a multi-scenario power generation model based on the typical day and the output power, combined with the constraints of the power system operation. The revenue model construction module 53 constructs the power system revenue model based on the multi-scenario power generation model, as well as the revenue parameters and cost parameters of each energy station. The strategy generation module 54 takes the maximization of the sum of the comprehensive benefits of each energy station as the goal, and generates the target capacity configuration strategy through the power system revenue model, which solves the problem of resource waste caused by unreasonable capacity configuration of the multi-scenario power system. Under the premise of ensuring that the system safety and stability constraints are met, the renewable energy capacity configuration is optimized and the new energy absorption capacity is improved.

[0167] It should be noted that the above modules can be functional modules or program modules, and can be implemented by software or hardware. For modules implemented by hardware, the above modules can be located in the same processor; or the above modules can be located in different processors in any combination.

[0168] This embodiment further provides an electronic device, including a memory and a processor, wherein the memory stores a computer program, and the processor is configured to run the computer program to execute the steps in any one of the above method embodiments.

[0169] Optionally, the electronic device may further include a transmission device and an input / output device, wherein the transmission device is connected to the processor, and the input / output device is connected to the processor.

[0170] Optionally, in this embodiment, the processor may be configured to perform the following steps through a computer program:

[0171] S1, obtaining the operating data of each energy power station in the power system, and obtaining the output power of each energy power station based on the operating data.

[0172] S2, based on the preset rules, determines the typical day, and builds a multi-scenario power generation model according to the typical day and output power, combined with the constraints of the power system operation.

[0173] S3, constructs a power system revenue model based on the multi-scenario power generation model and the revenue parameters and cost parameters of each energy power station.

[0174] S4, with the goal of maximizing the sum of the comprehensive benefits of each energy power station, generates a target capacity configuration strategy through the power system benefit model.

[0175] It should be noted that the specific examples in this embodiment can refer to the examples described in the above embodiments and optional implementation modes, and this embodiment will not be described in detail here.

[0176] In one embodiment, Figure 6 is a schematic diagram of the internal structure of an electronic device according to an embodiment of the present application, such as Figure 6 As shown, an electronic device is provided, which may be a server, and its internal structure diagram may be as shown in Figure 6 As shown. The electronic device includes a processor, a memory, a network interface and a database connected through a system bus. Among them, the processor of the electronic device is used to provide computing and control capabilities. The memory of the electronic device includes a non-volatile storage medium and an internal memory. The non-volatile storage medium stores an operating system, a computer program and a database. The internal memory provides an environment for the operation of the operating system and the computer program in the non-volatile storage medium. The database of the electronic device is used to store data. The network interface of the electronic device is used to communicate with an external terminal through a network connection. When the computer program is executed by the processor, a multi-energy capacity configuration method is implemented.

[0177] Those skilled in the art will understand that Figure 6The structure shown in the figure is merely a block diagram of a partial structure related to the scheme of the present application, and does not constitute a limitation on the electronic device to which the scheme of the present application is applied. The specific electronic device may include more or fewer components than shown in the figure, or combine certain components, or have a different arrangement of components.

[0178] Those skilled in the art can understand that all or part of the processes in the above-mentioned embodiment methods can be completed by instructing the relevant hardware through a computer program, and the computer program can be stored in a non-volatile computer-readable storage medium. When the computer program is executed, it can include the processes of the embodiments of the above-mentioned methods. Among them, any reference to memory, storage, database or other media used in the embodiments provided in this application can include non-volatile and / or volatile memory. Non-volatile memory may include read-only memory (ROM), programmable ROM (PROM), electrically programmable ROM (EPROM), electrically erasable programmable ROM (EEPROM) or flash memory. Volatile memory may include random access memory (RAM) or external cache memory. As an illustration and not limitation, RAM is available in many forms, such as static RAM (SRAM), dynamic RAM (DRAM), synchronous DRAM (SDRAM), double data rate SDRAM (DDRSDRAM), enhanced SDRAM (ESDRAM), synchronous link (Synchlink) DRAM (SLDRAM), memory bus (Rambus) direct RAM (RDRAM), direct memory bus dynamic RAM (DRDRAM), and memory bus dynamic RAM (RDRAM).

[0179] Those skilled in the art should understand that the various technical features of the above-described embodiments may be arbitrarily combined. To make the description concise, not all possible combinations of the various technical features in the above-described embodiments are described. However, as long as there is no contradiction in the combination of these technical features, they should be considered to be within the scope of this specification.

[0180] The above-mentioned embodiments only express several implementation methods of the present application, and the descriptions thereof are relatively specific and detailed, but they cannot be understood as limiting the scope of the invention patent. It should be pointed out that, for a person of ordinary skill in the art, several variations and improvements can be made without departing from the concept of the present application, and these all belong to the protection scope of the present application. Therefore, the protection scope of the patent of the present application shall be subject to the attached claims.

Claims

1. A multi-energy capacity configuration method, characterized in that: The method comprises: Acquire operation data of each energy power station in the power system, and obtain output power of each energy power station based on the operation data; Determine a typical day based on preset rules, and construct a multi-scenario power generation model according to the typical day and the output power, and in combination with constraints on the operation of the power system; Constructing a power system revenue model based on the multi-scenario power generation model and the revenue parameters and cost parameters of each energy power station; With the goal of maximizing the sum of the comprehensive benefits of the energy power stations, a target capacity configuration strategy is generated through the power system benefit model.

2. The method according to claim 1, characterized in that: The obtaining the output power of each energy power station based on the operation data comprises: Based on the wind turbine power model, determining the output power of the wind turbine according to the wind turbine rated power, the wind turbine real-time wind speed, the wind turbine cut-in wind speed, the wind turbine cut-out wind speed and the rated wind speed in the operating data; and / or Based on the photovoltaic power model, determining the output power of the photovoltaic power station according to the real-time light intensity, the photovoltaic rated power and the rated light intensity in the operating data; and / or Based on the centralized photovoltaic power station model, determining the output power of the centralized photovoltaic power station according to the solar rated power, rated light intensity, photothermal efficiency and thermoelectric conversion efficiency in the operating data; and / or Based on the energy storage power model, the output power of the energy storage power station is determined according to the energy status at the last moment in the operation data and the charging efficiency of the energy storage power station.

3. The method according to claim 2, characterized in that The wind turbine power model includes: Among them, P win,t is the output power of the wind turbine, P win,N is the rated power of the fan, v is the real-time wind speed of the fan, v ci is the wind turbine cut-in wind speed, v co is the fan cut-out wind speed, v N is the rated wind speed.

4. The method according to claim 2, characterized in that: The photovoltaic power model includes: Among them, P pho,t is the output power of the photovoltaic power station, P pho,N is the photovoltaic rated power, γ is the real-time light intensity, γ N is the rated light intensity.

5. The method according to claim 2, characterized in that: The centralized photovoltaic power station model includes: Among them, P hea,t is the output power of the centralized photovoltaic power station, P hea,N is the solar rated power, γ is the real-time light intensity, γ N is the rated light intensity, η p,h is the photothermal efficiency, η h,e is the thermoelectric conversion efficiency.

6. The method according to claim 2, characterized in that The energy storage power model includes: Among them, P sto,t is the output power of the energy storage power station, P sto,t-1 is the output power of the energy storage power station at the previous moment, η sto is the charging efficiency, and Δ(t) is the time difference between the previous moment and the current moment.

7. The method according to claim 1, characterized in that The constructing of the power system revenue model according to the multi-scenario power generation model and the revenue parameters and cost parameters of each energy power station includes: Based on the multi-scenario power generation model, and the income parameters and cost parameters of each energy power station, the power sales income, equipment depreciation value, investment cost, operation and maintenance cost, and power purchase cost of each energy power station are obtained; The power system profit model is constructed based on the electricity sales revenue, the equipment depreciation value, the investment cost, the operation and maintenance cost and the electricity purchase cost.

8. The method according to claim 1, characterized in that: The constraints for the operation of the power system include: ramp constraints, capacity constraints, section constraints and unit output constraints.

9. A multi-energy capacity configuration system, characterized in that: The system includes: a data processing module, a power model building module, a revenue model building module and a strategy generation module, wherein: The data processing module is used to obtain the operating data of each energy power station in the power system, and obtain the output power of each energy power station based on the operating data; The power model building module is used to determine a typical day based on a preset rule, and build a multi-scenario power generation model according to the typical day and the output power, in combination with the constraints of the power system operation; The revenue model building module is used to build a power system revenue model based on the multi-scenario power generation model and the revenue parameters and cost parameters of each energy power station; The strategy generation module is used to generate a target capacity configuration strategy through the power system revenue model with the goal of maximizing the sum of the comprehensive benefits of the energy power stations.

10. An electronic device comprising a memory, a processor, and a computer program stored in the memory and executable on the processor, characterized in that: When the processor executes the computer program, the multi-energy capacity configuration method according to any one of claims 1 to 8 is implemented.

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