CN111625947B - Distributed energy development scale prediction method, equipment and medium - Google Patents
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Abstract
The invention discloses a method, equipment and medium for predicting the development scale of distributed energy, wherein the method comprises the following steps: determining and acquiring historical data of all distributed energy accumulation installation machines, and establishing a distributed energy technology diffusion prediction model based on the maximum market development amount according to the historical data; establishing a segmented function of the maximum market development amount of the distributed energy sources; respectively determining the leveling degree electric cost of each distributed energy source and the leveling degree electric cost of conventional thermal power, and calculating the comparable cost of each distributed energy source; determining the maximum developable amount of the corresponding distributed energy market; and determining different scene conditions, and determining the installed capacity of each distributed energy source in the corresponding scene. The invention establishes the technical diffusion model, the prediction model integrates the technical diffusion model and the leveling degree electricity cost, and can consider the dynamic comprehensive influence of various factors such as different distributed energy characteristics, development potential, project economy, matched policies and the like on the growth of the distributed energy installation.
Description
Technical Field
The invention relates to the technical field of distributed energy prediction planning, in particular to a distributed energy development scale prediction method.
Background
The typical characteristic of the distributed energy is clean, efficient, flexible and various, and the great development of the distributed energy is an effective way for adjusting the power structure of China and promoting the energy reform. The distributed energy sources at present mainly comprise distributed photovoltaic, distributed wind power and natural gas distributed energy sources, and the distributed photovoltaic and the distributed wind power sources are rich in resources, wide in application range, mature in development and large in development space; although the natural gas distributed energy is in a starting stage, the natural gas distributed energy has good peak clipping and valley filling effects, and is the best transition energy between the traditional energy and the future new energy. With policy support and technological progress, the development of distributed energy in China will steadily increase.
The distributed energy policies in China are beneficial as a whole, and various policies encourage the development of distributed energy, release the distributed energy market, reduce the admission threshold and increase the financial tax policy support. Although distributed energy has the characteristics of flexibility and high efficiency, the large-scale development of the distributed energy still faces a plurality of challenges.
Currently, no research is directed to development scale prediction of distributed energy sources. As a part of the power supply structure, the scale prediction of the distributed energy sources mainly refers to the model and thought of the power supply structure prediction technical method. However, the measurement and calculation of the development potential of the current distributed energy projects mainly aims at the natural wind and light resource quantity, and does not rise to the market development space of different distributed energy projects; the microscopic scale addressing and sizing and short-term simulation prediction mode is not suitable for macroscopic development scale prediction application; the independent application of the power structure prediction technology and method with medium-long scale including Logistic prediction model, learning curve model, scene modeling and regression analysis prediction method can not comprehensively embody the influence of policy, project economy and technical development process on the scale growth of the distributed energy installation.
In view of this, it is highly desirable to provide a method for integrating the technical diffusion model and the leveling electric cost, and considering the dynamic comprehensive influence of various factors such as different distributed energy characteristics, development potential, project economy, and matched policies on the growth of the distributed energy installation.
Disclosure of Invention
In order to solve the technical problems, the technical scheme adopted by the invention is to provide a distributed energy development scale prediction method, which comprises the following steps:
determining and acquiring historical data of all distributed energy accumulation installation machines, and establishing a distributed energy technology diffusion prediction model based on the maximum market development amount according to the historical data;
establishing a segmented function of the maximum market development capacity of the distributed energy according to the cost relation between the distributed energy and the conventional thermal power and the limitation of the policy on the development scale of the distributed energy project;
respectively determining the leveling degree electric cost of each distributed energy source and the leveling degree electric cost of conventional thermal power, and calculating the comparable cost of each distributed energy source;
determining the maximum developable amount of the corresponding distributed energy market according to the comparable cost of each distributed energy and the segmented function of the maximum market developable amount of the distributed energy;
and determining different scene conditions, and determining the installed capacity of each distributed energy source in the corresponding scene according to the distributed energy source technology diffusion prediction model.
In the method, the method specifically predicts the installed capacity of each distributed energy source in a certain future time period and specifically comprises the following steps:
s11, determining a prediction time period T, and acquiring the accumulated installed quantity of distributed energy sources at the initial moment T in the time period, the maximum technical development quantity, the leveling degree electric cost of a single distributed energy source and the leveling degree electric cost of a single conventional thermal power project;
s12, calculating comparability electricity cost of the natural gas distributed energy, and determining the maximum developable amount of the market in the current period according to a segmented function of scene conditions and the maximum market developable amount of the distributed energy;
s13, determining a predicted value of the scale of the distributed energy installation at the current moment according to the maximum development quantity of the technical diffusion model and the market at the current time period;
s14, determining a predicted value of the distributed energy leveling degree electric cost in the current period according to the predicted value of the installed scale and the current price of the natural gas;
s15, outputting the current moment distributed energy accumulation installed scale X t And a predictive value of distributed energy leveling electrical costs;
s16, judging whether T is more than or equal to T, if not, t=t+1, and turning to S12; if yes, ending.
In the method, a matlab program is used for carrying out fitting by combining Logistic, gompertz and a Bass model, a model with the minimum SSE fitting is selected as a technical diffusion model corresponding to the distributed energy source, and model parameters are determined according to fitting results.
In the above method, the distributed energy technology diffusion prediction model is specifically represented by the following formula:
X t＝ F(N t ，t)
wherein N is t The maximum market development quantity at the moment t, p and q are model parameters; in the Logistic model, p determines the shape of the curve, q represents the diffusivity of the product; in the Gompertz model, p determines the shape of a curve, and q represents the diffusivity of a product; in the bas model, p represents an innovation coefficient, and q represents an imitation coefficient.
In the above method, the piecewise function of the maximum market developable amount of the distributed energy source is as follows:
N t ＝G(N t-1 ，L t )
wherein L is t For the comparison cost of the corresponding distributed energy sources compared with the conventional thermal power, u and v are used for reflecting the limitation of the policy on the development scale of the corresponding distributed energy source project, and lambda is the cost of the distributed power sourceThe influence coefficient of the market space is determined according to the experience; n (N) max The technology for distributed power supply can be developed in quantity.
In the method, the electrical cost of the leveling degree of each distributed energy and the electrical cost of the leveling degree of the conventional thermal power are calculated by the following formula:
wherein I is 0 Representing initial investment, i.e. the overall unit cost of the project; n represents year; n represents the full life cycle; d (D) n Representing the depreciation cost of the nth year power station project; r is R n Representing the annual operating cost of the power station in the nth year; v (V) n Representing other tax such as the value-added tax of the nth year; w (W) n Representing the loan interest of the nth project; bn represents revenue from other sources in the nth year, such as renewable energy subsidies, etc.; r represents the discount rate; r is R E Representing the external factor risk cost.
In the above method, each distributed energy source is comparable to cost L t The calculation can be performed by the following formula:
L t ＝H(C t-1 )＝C t-1 /C f (4)
wherein C is t-1 C, leveling degree electric cost at the end of the t-1 period of the distributed energy source f Is the leveling degree electric cost of the conventional thermal power.
In the method, the predicted value of the current period distributed energy leveling electric cost is calculated by the following formula:
C t ＝(X t ，Y t ，P t ，t)
wherein C is 0 For the initial distributed energy leveling degree electric cost, X t Accumulating a predicted value of the installed capacity for the distributed energy source of the t year; y is Y t The technology accumulation amount of the t-th year of the distributed energy sources; k (k) l The cost of the non-research and development type input element accounts for the proportion of the corresponding distributed energy cost; a. b is an empirical parameter; p (P) t Is the price of the non-research and development type input element in the t period.
The invention also provides a computer device comprising a memory, a processor and a computer program stored in the memory and executable on the processor, the processor implementing the distributed energy development scale prediction method according to any one of the preceding claims when executing the computer program.
The invention also provides a computer readable storage medium storing a computer program, characterized in that the computer program when executed by a processor implements the distributed energy development scale prediction method according to any one of the above.
The invention establishes the technical diffusion model, considers the diffusion characteristics of the distributed energy as an innovative energy technology, integrates the technical diffusion model and the leveling degree electric cost by the prediction model, and can consider the dynamic comprehensive influence of various factors such as different distributed energy characteristics, development potential, project economy, matched policies and the like on the growth of the distributed energy installation. The method has the advantages that the defect of influence analysis of natural gas price change on installation growth in the traditional model is overcome particularly for the natural gas distributed energy; the method can be applied to scale prediction of distributed photovoltaic, distributed wind power and natural gas distributed energy installation in China, and provides a basic reference for relevant planning and design, industry enterprises and the like.
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In order to more clearly illustrate the embodiments of the present invention or the technical solutions in the prior art, the drawings that are needed in the description of the embodiments or the prior art will be briefly described, and it is obvious that the drawings in the description below are some embodiments of the present invention, and other drawings can be obtained according to the drawings without inventive effort for a person skilled in the art.
FIG. 1 is a schematic flow chart of a method for predicting the development scale of distributed energy provided by the invention;
FIG. 2 is a schematic diagram of a flow chart of the installed capacity of each distributed energy source predicting a certain time period in the future according to the present invention;
fig. 3 is a schematic diagram of a device structure provided by the present invention.
Detailed Description
The technical solutions of the present invention will be clearly and completely described in connection with the embodiments, and it is apparent that the described embodiments are some embodiments of the present invention, but not all embodiments. All other embodiments, which can be made by those skilled in the art based on the embodiments of the invention without making any inventive effort, are intended to be within the scope of the invention.
In the description of the present invention, it should be understood that the terms "center", "longitudinal", "lateral", "length", "width", "thickness", "upper", "lower", "front", "rear", "left", "right", "vertical", "horizontal", "top", "bottom", "inner", "outer", "clockwise", "counterclockwise", etc. indicate orientations or positional relationships based on the orientations or positional relationships shown in the drawings are merely for convenience in describing the present invention and simplifying the description, and do not indicate or imply that the device or element referred to must have a specific orientation, be configured and operated in a specific orientation, and thus should not be construed as limiting the present invention.
Furthermore, the terms "first," "second," and the like, are used for descriptive purposes only and are not to be construed as indicating or implying a relative importance or implicitly indicating the number of technical features indicated. Thus, a feature defining "a first" or "a second" may explicitly or implicitly include one or more of the described features. In the description of the present invention, the meaning of "a plurality" is two or more, unless explicitly defined otherwise. Furthermore, the terms "mounted," "connected," "coupled," and "connected" are to be construed broadly, and may be, for example, fixedly connected, detachably connected, or integrally connected; can be mechanically or electrically connected; can be directly connected or indirectly connected through an intermediate medium, and can be communication between two elements. The specific meaning of the above terms in the present invention will be understood in specific cases by those of ordinary skill in the art.
The invention is described in detail below with reference to the detailed description and the accompanying drawings.
As shown in fig. 1, the present invention provides a method for predicting a distributed energy development scale, comprising the steps of:
s1, determining and acquiring historical data of all distributed energy accumulation installation machines, and establishing a distributed energy technology diffusion prediction model based on the maximum market development amount according to the historical data; the history data is new annual installed capacity data and accumulated installed capacity data of each distributed power supply.
In the embodiment, selection of technical diffusion models of different distributed energy diffusion is realized based on historical data, a matlab program is used for carrying out fitting in combination with Logistic, gompertz and a Bass model, a model with the smallest fitting SSE (The sum of squares due to error, error square sum) is selected as the technical diffusion model of the corresponding distributed energy, and model parameters are determined according to fitting results; the diffusion prediction model of the distributed energy technology is specifically represented by the following formula:
X t＝ F(N t ，t)
wherein N is t The maximum potential number of the adopters in the market at the moment t is generally the maximum market development space (development amount) when the method is used for energy development, and p and q are model parameters; in the Logistic model, p determines the shape of the curve, q represents the diffusivity of the product; in the Gompertz model, p determines the shape of a curve, and q represents the diffusivity of a product; in the bas model, p represents an innovation coefficient, and q represents an imitation coefficient.
S2, establishing a segmented function of the maximum market development quantity of the distributed energy according to the cost relation between the distributed energy and the conventional thermal power and the limitation of the policy on the development scale of the distributed energy project.
In this embodiment, the maximum market development N of a distributed energy source at time t is predicted by a piecewise function t Segmentation ofIs based on L t 、u、v(v≥u>0) Is of a size of (2); wherein L is t For the comparison cost of the corresponding distributed energy sources compared with the conventional thermal power, L t The larger the distributed energy cost is, the higher the cost of the distributed energy is than that of conventional thermal power is, and the poorer the economical efficiency is. u and v are used for reflecting the limitation of the policy on the development scale of the distributed energy projects, and when u approaches infinity, the development environment is described as having absolute limitation on the development of the distributed energy, and the development space cannot be further expanded; when v approaches infinity, the development environment is extremely beneficial to the development of the distributed energy, and the scale development is not limited by the policy; according to the existing experimental study values, u and v respectively take values of 5 and 1.2 under the reference condition. Thus, the piecewise function of the maximum market exploitable amount of distributed energy is as follows:
N t ＝G(N t-1 ，L t )
wherein lambda is the influence coefficient of distributed power supply cost on the market space, and the numerical value is determined empirically; n (N) max For the technical development quantity of the distributed power supply, the distributed photovoltaic is calculated and measured according to the available roof area, the distributed wind power is calculated and measured according to the available wind resource, and the natural gas distributed energy is calculated and determined according to the installed capacity of the required scene and the area of the required scene.
S3, determining the leveling degree electric cost of each distributed energy source and the leveling degree electric cost of the conventional thermal power respectively, and calculating the comparable cost of each distributed energy source.
In this embodiment, LCOE (Levelized Cost of Energy) method is used to compare energy projects of different power generation technologies and scales.
The leveling electrical cost is effectively the ratio of the net present value of the cost to the economic time value of the energy production (economic time value). The calculation of the leveling degree electric cost of each distributed energy source and conventional thermal power is as follows:
wherein I is 0 Representing initial investment, i.e. the overall unit cost of the project; n represents year; n represents the full life cycle; d (D) n Representing the depreciation cost of the nth year power station project; r is R n Representing the annual operating cost of the power station in the nth year; v (V) n Representing other tax such as the value-added tax of the nth year; w (W) n Representing the loan interest of the nth project; bn represents revenue from other sources in the nth year, such as renewable energy subsidies, etc.; r represents the discount rate; r is R E The risk cost of the external factors is represented, and the risk cost mainly comprises the cost caused by the change of the external factors such as financial tax, financial policy and the like.
In this embodiment, the comparable cost of each distributed energy source is the ratio of the electrical cost of each distributed energy source leveling degree to the electrical cost of the leveling degree of the conventional thermal power, and in this embodiment, each distributed energy source is the current distributed photovoltaic, distributed wind power, natural gas distributed energy source, and the like.
The leveling degree electricity cost of the current distributed photovoltaic, distributed wind power and natural gas distributed energy sources is higher than that of the conventional thermal power, and along with the progress of the technology, the cost of each distributed energy source power generation cost and the cost of the conventional thermal power are gradually reduced, and the cost disadvantage is gradually reduced. t time period each distributed energy source is comparable to cost L t Can be calculated by the following formula (4):
L t ＝H(C t-1 )＝C t-1 /C f (4)
wherein C is t-1 C, leveling degree electric cost at the end of the t-1 period of the distributed energy source f Is the leveling degree electric cost of the conventional thermal power.
S4, determining the maximum developable quantity of the corresponding distributed energy market according to the sectional function of the maximum developable quantity of the distributed energy and the comparable cost of each distributed energy.
In this embodiment, the cost L of each obtained distributed energy is calculated according to step S3 t Substituting the energy into the formula (2) or substituting the energy into the corresponding maximum market development quantity of the distributed energy.
S5, determining scene conditions, and determining the installed capacity of each distributed energy source in the corresponding scene according to the distributed energy source technology diffusion prediction model.
In this embodiment, considering the influence of different scenarios on the installed capacity of each distributed energy source, for example, the dynamic change of the policy and the consumption condition may be set by the scenario, so as to simulate the comprehensive environment under the conditions of different policies, consumption and natural gas price, and the change of the environmental condition mainly acts on the parameters u and v of the piecewise function of the formula (2). The correspondence between the scene setting and the piecewise function parameter in this embodiment is shown in table 1 below.
Table 1 distributed energy scale prediction scenario set-up
Therefore, the embodiment can determine the maximum market development amount N corresponding to the corresponding distributed energy source under different scene conditions (u, v) t And combining the technical diffusion model calculation to determine the installed capacity of the corresponding distributed energy under the corresponding scene.
In the embodiment, a technical diffusion model is established, the diffusion characteristics of the distributed energy as an innovative energy technology are considered, the prediction model integrates the technical diffusion model and the leveling degree electric cost, and the dynamic comprehensive influence of various factors such as different distributed energy characteristics, development potential, project economy, matched policies and the like on the growth of the distributed energy installation can be considered. The method has the advantages that the defect of influence analysis of natural gas price change on installation growth in the traditional model is overcome particularly for the natural gas distributed energy; the method can be applied to scale prediction of distributed photovoltaic, distributed wind power and natural gas distributed energy installation in China, and provides a basic reference for relevant planning and design, industry enterprises and the like.
In this embodiment, the method can predict the installed capacity of each distributed energy source in a future period of time through the steps described above, and specifically includes the following steps:
s11, determining a prediction time period T, and acquiring the accumulated installed quantity of distributed energy sources at the initial moment T in the time period, the maximum technical development quantity, the leveling degree electric cost of a single distributed energy source and the leveling degree electric cost of a single conventional thermal power project;
s12, calculating comparability electricity cost of the natural gas distributed energy, and determining the maximum developable amount of the market in the current period according to a segmented function of scene conditions and the maximum market developable amount of the distributed energy;
s13, determining a predicted value of the scale of the distributed energy installation at the current moment according to the maximum development quantity of the technical diffusion model and the market at the current time.
S14, predicting value X according to installation scale t Current price P of natural gas t And determining a predicted value of the distributed energy leveling electric cost in the current period.
In this embodiment, the evolution of the leveling electrical cost of the distributed energy technology can calculate and predict the distributed energy cost of the current year, that is, the prediction of the next year is ready for data input. And the predicted value of the distributed energy leveling degree electric cost at the current time t is obtained by the following analysis and calculation, and the evolution process of the distributed energy leveling degree electric cost is as follows:
(1) Theory of learning
The present embodiment adopts a learning curve (experience curve) model in which the decrease in unit cost of distributed energy with the increase in accumulated output with a specific learning efficiency is a phenomenon based on empirical observation, not necessarily a natural law, and the decrease in unit cost should be regarded as a result of a long-term, dynamic co-action based on various endogenous and exogenous factors. The basic logic is as follows: the current-period distributed energy unit output leveling degree electricity cost is higher than that of the conventional energy technology; however, with the development of the former technology and the accumulation of production experience, the unit cost thereof tends to decrease. The expression of the learning curve for the distributed energy source is summarized in table 2 below.
Table 2, classification and summarization of distributed energy learning curve model
Wherein C is t C is an electric cost estimated value of t-moment distributed energy leveling degree 0 For the initial distributed energy leveling degree electric cost, X t To accumulate the output, Y t Research and development investment is accumulated or accumulated for knowledge; q (Q) t To average scale development degree, P j For the j-th input element price, a, b and c respectively represent the elastic coefficients of the corresponding elements, and d is the elastic coefficient of the non-research and development input element price of the distributed power supply.
The one-factor learning curve model describes a distributed energy level electrical cost as a function of its cumulative total yield. The two-factor learning curve isolates the pushing effect of research and development on the progress of the distributed energy technology from the increase of the accumulated output, and is a refinement and exploration on the theory of the learning curve. The average scale development degree is further added to the three-factor model. The four-factor model contains accumulated output, accumulated knowledge, scale effect and input element price factors. The price of the input element refers to the price change of certain key raw materials which cannot be explained by the inflation and are irrelevant to development (such as the price change of uranium in nuclear power and the price of natural gas in natural gas power generation). Other general input element price changes are in accordance with the annual currency expansion level, and can be corrected by considering a certain currency expansion rate; the cost reduction brought about by the development of certain key elements should be considered as the scope of influence of accumulated knowledge factors.
(2) Dynamic evolution of electrical cost for each distributed energy level
The predicted value of the distributed energy leveling electric cost in the current period is calculated by the following formula:
C t ＝(X t ，Y t ，P t ，t)
wherein X is t Accumulating a predicted value of the installed capacity for the distributed energy source of the t year; y is Y t The technology accumulation amount of the t-th year of the distributed energy sources; k (k) l The cost of the non-research and development type input element accounts for the proportion of the corresponding distributed energy cost; a. b is an empirical parameter. P (P) t The price of the non-research and development type input element in the t period is P 0 Representing the initial price of the non-research and development input element, and taking a value according to actual conditions; wherein, no corresponding element P exists in the distributed photovoltaic and the distributed wind power t Take the value of 1, k l The value is 0. For the natural gas distributed energy, the element is the price of the natural gas, P t 、k l And taking a value according to actual conditions. The values of a and b are shown in Table 3 for different distributed energy sources.
Table 3, values of the distributed energy parameters a, b
S15, outputting the current moment distributed energy accumulation installed scale X t And predictive value C of distributed energy leveling electrical cost t 。
S16, judging whether T is more than or equal to T, if not, t=t+1, and turning to S12; if yes, ending.
The implementation process of the method is described below by taking natural gas distributed energy as a case.
And (one) establishing a diffusion prediction model of the distributed energy technology.
Historical statistics of the cumulative installed scale of the natural gas distributed energy source are collected first, such as cumulative data of 2013-2018 can be collected for the natural gas distributed energy source. Assuming that the maximum technology developable amount of the technology diffusion model is unchanged between 2013 and 2018, the maximum technology developable amount N estimated according to other researches is estimated max . In this case, the time series data is combined with N max As a known quantity, fit in three technical diffusion models (Logistic, gompertz and bas models) was compared using matlab fit functionThe best effect is a Bass model, and the fitting result gives the values of parameters p and q. The development scale of the distributed energy source is predicted according to the steps S11-S16.
Firstly, setting a prediction time, if the natural gas distributed energy accumulated installed quantity is predicted to reach 2025, and the initial year is 2013, setting T to be 12, collecting model initial parameter data by taking 2013 as a time starting point, and X 0 Cumulative installed data of 2013 natural gas distributed energy sources; n (N) 0 Still N as mentioned above max ；C 0 The flattening degree electric cost of a single natural gas distributed energy source put into production in 2013 is calculated according to actual project data; c (C) f The leveling degree electric cost of a single conventional thermal power project which is put into production in 2013 is calculated according to actual project data, and is calculated according to a formula (3), wherein the leveling degree electric cost of distributed energy and the leveling degree electric cost of the single conventional thermal power project are calculated by assuming that the cost of the conventional thermal power project is unchanged for a long time.
And calculating the comparability electric cost of the natural gas distributed energy source through a formula (4) according to the calculated planeness electric cost of the distributed energy source and the planeness electric cost of a single conventional thermal power project.
Determining the maximum market development quantity N of the natural gas distributed energy in the current period according to the comparability electricity cost of the natural gas distributed energy and the current scene condition determination t 。N t According to the established distributed energy technology diffusion prediction model, the natural gas distributed energy installation scale prediction value of the current period can be obtained.
According to the obtained predicted value X of the installed scale t Current price P of natural gas t Substituting (5) to obtain a predicted value C of the electric cost of the natural gas distributed energy leveling degree in the current period t And output X t And C t 。
And finally judging whether the termination condition T is greater than or equal to T, if the termination condition T is met, ending the flow, otherwise 't=t+1', continuing to circulate the operation, and finally predicting the natural gas distributed energy accumulated installed quantity until 2025 years.
As shown in fig. 3, the present invention further provides a computer device, including a memory, a processor, and a computer program stored in the memory and capable of running on the processor, where the processor executes the computer program to implement the method for predicting the development scale of the distributed energy source in the foregoing embodiment.
The invention also provides a computer readable storage medium, on which a computer program is stored, the computer program implementing the method for training the recognition model in the above embodiment when being executed by a processor, or implementing the method for predicting the development scale of the distributed energy source in the above embodiment when being executed by a processor.
Those skilled in the art will appreciate that implementing all or part of the above described methods may be accomplished by hardware associated with computer program instructions, where the computer program may be stored on a non-transitory computer readable storage medium, which when executed may comprise the steps of the embodiments of the methods described above. Any reference to memory, storage, database, or other medium used in the various embodiments provided herein may include non-volatile and/or volatile memory. The nonvolatile memory can include Read Only Memory (ROM), programmable ROM (PROM), electrically Programmable ROM (EPROM), electrically Erasable Programmable ROM (EEPROM), or flash memory. Volatile memory can include Random Access Memory (RAM) or external cache memory. By way of illustration and not limitation, RAM is available in a variety of forms such as Static RAM (SRAM), dynamic RAM (DRAM), synchronous DRAM (SDRAM), double Data Rate SDRAM (DDRSDRAM), enhanced SDRAM (ESDRAM), synchronous Link DRAM (SLDRAM), memory bus direct RAM (RDRAM), direct memory bus dynamic RAM (DRDRAM), and memory bus dynamic RAM (RDRAM), among others.
The present invention is not limited to the above-described preferred embodiments, and any person who is informed of structural changes made under the teaching of the present invention should fall within the scope of the present invention, regardless of whether the technical solution is the same as or similar to the present invention.
Claims (7)
1. The distributed energy development scale prediction method is characterized by comprising the following steps of:
determining and acquiring historical data of all distributed energy accumulation installation machines, and establishing a distributed energy technology diffusion prediction model based on the maximum market development amount according to the historical data;
establishing a segmented function of the maximum market development capacity of the distributed energy according to the cost relation between the distributed energy and the conventional thermal power and the limitation of policies on the development scale of the distributed energy project
Respectively determining the leveling degree electric cost of each distributed energy source and the leveling degree electric cost of conventional thermal power, and calculating the comparable cost of each distributed energy source;
determining the maximum developable amount of the corresponding distributed energy market according to the comparable cost of each distributed energy and the segmented function of the maximum market developable amount of the distributed energy;
determining different scene conditions, and determining the installed capacity of each distributed energy source under the corresponding scene according to the distributed energy source technology diffusion prediction model;
specifically, the method predicts the installed capacity of each distributed energy source in a future time period and specifically comprises the following steps:
s11, determining a prediction time period T, and acquiring the accumulated installed quantity of distributed energy sources at the initial moment T in the time period, the maximum technical development quantity, the leveling degree electric cost of a single distributed energy source and the leveling degree electric cost of a single conventional thermal power project;
s12, calculating comparability electricity cost of the natural gas distributed energy, and determining the maximum developable amount of the market in the current period according to a segmented function of scene conditions and the maximum market developable amount of the distributed energy;
s13, determining a predicted value of the scale of the distributed energy installation at the current moment according to the maximum development quantity of the technical diffusion model and the market at the current time period;
s14, determining a predicted value of the distributed energy leveling degree electric cost in the current period according to the predicted value of the installed scale and the current price of the natural gas;
s15, outputting the current moment distributed energy accumulation installed scale X t And a predictive value of distributed energy leveling electrical costs;
s16, judging whether T is more than or equal to T, if not, t=t+1, and turning to S12; if yes, ending;
performing fitting by combining a matlab program with Logistic, gompertz and a Bass model, selecting a model with the minimum SSE as a technical diffusion model corresponding to the distributed energy, and determining model parameters according to a fitting result;
the diffusion prediction model of the distributed energy technology has the following specific formula:
X t＝ F(N t ，t)
wherein N is t The maximum market development quantity at the moment t, p and q are model parameters; in the Logistic model, p determines the shape of the curve, q represents the diffusivity of the product; in the Gompertz model, p determines the shape of a curve, and q represents the diffusivity of a product; in the bas model, p represents an innovation coefficient, and q represents an imitation coefficient.
2. The distributed energy development scale prediction method of claim 1, wherein the piecewise function of the maximum market developable amount of the distributed energy is as follows:
N t ＝G(N t-1 ，L t )
wherein L is t For the comparison cost of the corresponding distributed energy sources compared with the conventional thermal power, u and v are used for reflecting the limitation of the development scale of the corresponding distributed energy source project of the policy, lambda is the influence coefficient of the distributed power source cost on the market space, and the numerical value is determined empirically; n (N) max Technology for distributed power supply can be developedAmount of the components.
3. The method for predicting the development scale of distributed energy according to claim 2, wherein the electrical cost of each of the distributed energy levels and the electrical cost of the level of the conventional thermal power are calculated by the following formula:
wherein I is 0 Representing initial investment, i.e. the overall unit cost of the project; n represents year; n represents the full life cycle; d (D) n Representing the depreciation cost of the nth year power station project; r is R n Representing the annual operating cost of the power station in the nth year; v (V) n Representing other tax such as the value-added tax of the nth year; w (W) n Representing the loan interest of the nth project; bn represents revenue from other sources in the nth year, such as renewable energy subsidies, etc.; r represents the discount rate; r is R E Representing the external factor risk cost.
4. A distributed energy development scale prediction method according to claim 3 wherein each distributed energy is comparable to cost L t The calculation can be performed by the following formula:
L t ＝H(C t-1 )＝C t-1 /C f (4)
wherein C is t-1 C, leveling degree electric cost at the end of the t-1 period of the distributed energy source f Is the leveling degree electric cost of the conventional thermal power.
5. The method for predicting the development scale of distributed energy according to claim 1, wherein the predicted value of the power cost of the leveling degree of the distributed energy in the current period is calculated by the following formula:
C t ＝(X t ，Y t ，P t ，t)
wherein C is 0 For the initial distributed energy leveling degree electric cost, X t Accumulating a predicted value of the installed capacity for the distributed energy source of the t year; y is Y t The technology accumulation amount of the t-th year of the distributed energy sources; k (k) l The cost of the non-research and development type input element accounts for the proportion of the corresponding distributed energy cost; a. b is an empirical parameter; p (P) t Is the price of the non-research and development type input element in the t period.
6. A computer device comprising a memory, a processor and a computer program stored in the memory and executable on the processor, wherein the processor implements the distributed energy development scale prediction method of any one of claims 1 to 5 when the computer program is executed.
7. A computer-readable storage medium storing a computer program, wherein the computer program when executed by a processor implements the distributed energy development scale prediction method according to any one of claims 1 to 5.
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