CN111625947A - Distributed energy development scale prediction method, equipment and medium - Google Patents
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Abstract
The invention discloses a method, a device and a medium for predicting the development scale of distributed energy resources, wherein the method comprises the following steps: determining and obtaining accumulated installed historical data of each distributed energy source, and establishing a distributed energy technology diffusion prediction model based on the maximum market exploitability amount according to the historical data; establishing a sectional function of the maximum market exploitability of the distributed energy; respectively determining the leveling power consumption cost of each distributed energy and the leveling power consumption cost of conventional thermal power, and calculating the comparable cost of each distributed energy; determining a corresponding maximum exploitable amount of a distributed energy market; and determining different scene conditions, and determining the installed capacity of each distributed energy resource under the corresponding scene. The method establishes the technical diffusion model, and the prediction model integrates the technical diffusion model and the levelization power cost, so that the dynamic comprehensive influence of various factors such as different distributed energy characteristics, development potential, project economy, matching policy and the like on the increase of the distributed energy installation machine can be considered.
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 distributed energy is characterized by cleanness, high efficiency, flexibility and diversity, and the vigorous development of the distributed energy is an effective way for adjusting the electric power structure of China and promoting the energy reformation. At present, distributed energy mainly comprises distributed photovoltaic, distributed wind power and natural gas distributed energy, and the distributed photovoltaic and the distributed wind power have the advantages of rich resources, wide application range, mature development and larger 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 functions and is the best transition energy between the traditional energy and the future new energy. With the support of policies and the progress of technologies, the development of distributed energy in China will steadily increase.
The distributed energy policy in China is good in whole, each policy encourages the development of distributed energy, the distributed energy market is released, the admission threshold is reduced, and the supporting force of the finance and tax policy is increased. Despite the flexible and efficient features of distributed energy, the scale development of distributed energy still faces many challenges.
Currently, no research is directly focused on the development scale prediction of distributed energy. As a part of a power supply structure, the scale prediction of the distributed energy mainly refers to a model and thought of a power supply structure prediction technical method. However, the measurement and calculation of the development potential of the current distributed energy project mainly aims at the natural wind and light resource amount, and does not rise to the market development space of different distributed energy projects; the micro-scale locating, sizing and short-term simulation prediction mode is not suitable for macro development scale prediction application; the medium-long-term scale power supply structure prediction technology and method comprise a Logistic prediction model, a learning curve model, scenario modeling and regression analysis prediction method which are independently applied, and the influences of policies, project economy and technology development processes on the increase of the installed scale of the distributed energy sources cannot be comprehensively embodied.
In view of the above, a method for providing a dynamic comprehensive influence of various factors, such as a technology diffusion model, a levelization power cost, and consideration of different distributed energy characteristics, development potential, project economy, and matching policy, on the increase of distributed energy installation is needed.
Disclosure of Invention
In order to solve the technical problem, the technical scheme adopted by the invention is to provide a distributed energy development scale prediction method, which comprises the following steps:
determining and obtaining accumulated installed historical data of each distributed energy source, and establishing a distributed energy technology diffusion prediction model based on the maximum market exploitability amount according to the historical data;
according to the cost relationship between the distributed energy and the conventional thermal power and the limit of a policy on the development scale of the distributed energy project, establishing a sectional function of the maximum market developability of the distributed energy;
respectively determining the leveling power consumption cost of each distributed energy and the leveling power consumption cost of conventional thermal power, and calculating the comparable cost of each distributed energy;
determining the maximum exploitable amount of the corresponding distributed energy market according to the comparable cost of each distributed energy and the sectional function of the maximum market exploitable amount of the distributed energy;
and determining different scene conditions, and determining the installed capacity of each distributed energy resource under the corresponding scene according to the distributed energy technology diffusion prediction model.
In the method, specifically predicting installed capacity of each distributed energy source in a future certain time period specifically includes the following steps:
s11, determining a prediction time period T, and acquiring the accumulated machine loading amount, the maximum technology developability amount, the leveling electricity consumption cost of a single distributed energy source and the leveling electricity consumption cost of a single conventional thermal power project of the distributed energy source at the initial time T in the time period;
s12, calculating the comparable electricity cost of the natural gas distributed energy, and determining the maximum exploitable amount of the market at the current time according to the scene condition and the sectional function of the maximum exploitable amount of the market of the distributed energy;
s13, determining a predicted value of the installed scale of the distributed energy at the current moment according to the technology diffusion model and the maximum exploitable amount of the market at the current time period;
s14, determining a predicted value of the electric cost of the distributed energy standardization in the current time period according to the predicted value of the installed scale and the current price of the natural gas;
s15, outputting the accumulated installed scale X of the distributed energy at the current momenttAnd a predicted value of the electric cost of the distributed energy leveling degree;
s16, judging whether T is larger than or equal to T, if not, changing T to T +1, and turning to S12; if yes, the process is ended.
In the method, fitting is carried out by using a matlab program in combination with Logistic, Gompertz and Bass models, the model with the minimum fitting SSE is selected as a technical diffusion model corresponding to the distributed energy, and model parameters are determined according to a fitting result.
In the above method, the distributed energy technology diffusion prediction model is specifically as follows:
Xt＝F(Nt，t)
in the formula, NtThe maximum market development amount at the moment t, and p and q are model parameters; in the Logistic model, p determines the shape of the curve, and q represents the diffusivity of the product; in the Gompertz model, p determines the shape of the curve, and q represents the diffusivity of the product; in the Bass model, p represents innovation coefficients and q represents simulation coefficients.
In the above method, the segmented function of the maximum market exploitability of the distributed energy source is as follows:
Nt＝G(Nt-1，Lt)
in the formula, LtFor the comparative cost of corresponding distributed energy compared with conventional thermal power, u and v are used for embodying the limit of the policy corresponding to the development scale of the distributed energy project, lambda is the influence coefficient of the cost of the distributed power supply on the market space of the distributed power supply, and the numerical value is determined according to experience; n is a radical ofmaxIs a technology exploitable amount of distributed power.
In the above method, the leveling power cost of each distributed energy source and the leveling power cost of the conventional thermal power are calculated by the following formula:
in the formula I0Representing the initial investment, namely the whole unit cost of the project; n represents the year; n represents the full life cycle; dnRepresenting the depreciation cost of the power station project in the nth year; rnRepresenting the annual operating cost of the power station in the nth year; vnOther taxes such as the value-added tax of the nth year are represented; wnIndicating the interest of the n-th project loan; bn representing other sources of year nIncome, such as renewable energy subsidies and the like; r represents the discount rate; rERepresenting the external factor risk cost.
In the above method, each distributed energy resource may have a cost LtCan be calculated using the following formula:
Lt＝H(Ct-1)＝Ct-1/Cf(4)
in the formula, Ct-1For the flat electric cost at the end of the t-1 period of the distributed energy, CfThe electricity consumption cost is leveled for the conventional thermal power.
In the above method, the predicted value of the current period distributed energy leveling power cost is calculated by the following formula:
Ct＝(Xt，Yt，Pt，t)
in the formula, C0For the initial distributed energy leveling of kilowatt-hour costs, XtAccumulating the predicted value of the installed capacity for the distributed energy in the t year; y istTechnology accumulation amount of the distributed energy source in the t year; k is a radical oflThe cost of the non-research and development type input elements accounts for the proportion of the cost of the corresponding distributed energy; a. b is an empirical parameter; ptThe price of the non-research and development type input element at the t stage.
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, wherein the processor executes the computer program to implement the distributed energy development scale prediction method according to any one of the above.
The present invention also provides 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 the above.
The method establishes a technology diffusion model, considers the diffusion characteristics of the distributed energy as an innovative energy technology, synthesizes the technology diffusion model and the levelization power cost through a prediction model, and can consider the dynamic comprehensive influence of various factors such as different distributed energy characteristics, development potential, project economy, matching policy and the like on the increase of the distributed energy installation. The method makes up the deficiency of the influence of the traditional model on the natural gas price change on the installed growth particularly for the natural gas distributed energy; the method can be applied to the scale prediction of distributed photovoltaic, distributed wind power and natural gas distributed energy installation in China, and provides basic reference for related planning design, industrial 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 used in the description of the embodiments or the prior art will be briefly described below, and it is obvious that the drawings in the following description are some embodiments of the present invention, and other drawings can be obtained by those skilled in the art without creative efforts.
Fig. 1 is a schematic flow chart of a distributed energy development scale prediction method provided by the present invention;
fig. 2 is a schematic diagram illustrating a flow of installed capacity of each distributed energy source for predicting a future time period according to the present invention;
fig. 3 is a schematic structural diagram of the apparatus provided in the present invention.
Detailed Description
The technical solutions of the present invention will be described clearly and completely with reference to the following embodiments, and it should be understood that the described embodiments are some, but not all, embodiments of the present invention. All other embodiments, which can be derived by a person skilled in the art from the embodiments given herein without making any creative effort, shall fall within the protection scope of the present invention.
In the description of the present invention, it is to 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", and the like, indicate orientations and positional relationships based on those shown in the drawings, and are used only for convenience of description and simplicity of description, and do not indicate or imply that the device or element being referred to must have a particular orientation, be constructed and operated in a particular orientation, and thus, should not be considered as limiting the present invention.
Furthermore, the terms "first", "second" and "first" are used for descriptive purposes only and are not to be construed as indicating or implying relative importance or implicitly indicating the number of technical features indicated. Thus, features defined as "first", "second", may explicitly or implicitly include one or more of the described features. In the description of the present invention, "a plurality" means two or more unless specifically defined otherwise. Furthermore, the terms "mounted," "connected," and "connected" are to be construed broadly and may, for example, be fixedly connected, detachably connected, or integrally connected; can be mechanically or electrically connected; they may be connected directly or indirectly through intervening media, or they may be interconnected between two elements. The specific meanings of the above terms in the present invention can be understood in specific cases to those skilled in the art.
The invention is described in detail below with reference to specific embodiments and the accompanying drawings.
As shown in fig. 1, the present invention provides a method for predicting the development scale of distributed energy resources, which comprises the following steps:
s1, determining and acquiring accumulated installed historical data of each distributed energy source, and establishing a distributed energy technology diffusion prediction model based on the maximum market exploitability amount according to the historical data; the historical data includes annual newly-added installed capacity data, accumulated installed capacity data and the like of each distributed power supply.
In The embodiment, The technical diffusion model selection of different distributed energy diffusion is realized based on historical data, fitting is carried out by using a matlab program in combination with Logistic, Gompertz and Bass models, The model with The minimum fitting SSE (The sum of square errors) is selected as The technical diffusion model corresponding to The distributed energy, and model parameters are determined according to The fitting result; the distributed energy technology diffusion prediction model is specifically as follows:
Xt＝F(Nt，t)
in the formula, NtThe maximum potential number of the adopters in the market at the time t is generally referred to as 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, and q represents the diffusivity of the product; in the Gompertz model, p determines the shape of the curve, and q represents the diffusivity of the product; in the Bass model, p represents innovation coefficients and q represents simulation coefficients.
And S2, establishing a segmented function of the maximum market exploitability amount of the distributed energy according to the cost relation between the distributed energy and the conventional thermal power and the limit of the policy on the development scale of the distributed energy project.
In the embodiment, the maximum market exploitability N of a certain distributed energy source at the time t is predicted through a sectional functiontThe basis of segmentation is Lt、u、v(v≥u>0) The size of (d); wherein L istFor the comparative cost of the corresponding distributed energy compared to conventional thermal power, LtThe larger the distributed energy source cost, the higher the conventional thermal power cost, and the poorer the economical efficiency. u and v are used for reflecting the limit of the policy on the development scale of the corresponding distributed energy project, and when u approaches infinity, the development environment has absolute limit on the development of the distributed energy, and the development space cannot be further expanded; when v approaches infinity, the development environment is extremely favorable for the development of distributed energy, and the scale development is not limited by policies; according to the prior research experience value, u and v respectively take values of 5 and 1.2 under the reference condition. Thus, the segmented function for the maximum market exploitable amount of distributed energy sources is as follows:
Nt＝G(Nt-1，Lt)
in the formula, lambda is an influence coefficient of the cost of the distributed power supply on the market space of the distributed power supply, and the numerical value is determined according to experience; n is a radical ofmaxThe distributed photovoltaic power generation system is a technology exploitable amount of a distributed power supply, distributed photovoltaic power generation is calculated and determined according to available roof area, distributed wind power generation is calculated and determined according to available wind resources, and natural gas distributed energy is calculated and determined according to the machine-installable capacity and the required scene area of a required scene.
And S3, respectively determining the leveling power consumption cost of each distributed energy source and the leveling power consumption cost of the conventional thermal power, and calculating the comparable cost of each distributed energy source.
In this embodiment, an lcoe (leveled Cost of energy) method is used to compare energy projects of different power generation technologies and scales.
The normalized electricity cost is actually the ratio of the net present value of the cost to the economic time value (ecomictime value) of the energy production. The calculation of the leveling power cost of each distributed energy and the conventional thermal power is as follows:
in the formula I0Representing the initial investment, namely the whole unit cost of the project; n represents the year; n represents the full life cycle; dnRepresenting the depreciation cost of the power station project in the nth year; rnRepresenting the annual operating cost of the power station in the nth year; vnOther taxes such as the value-added tax of the nth year are represented; wnIndicating the interest of the n-th project loan; bn represents the income of other sources in the nth year, such as renewable energy subsidies and the like; r represents the discount rate; rEThe risk cost of the external factors is represented, and mainly comprises the cost caused by the change of the external factors such as fiscal taxes, financial policies and the like.
In this embodiment, the comparable cost of each distributed energy is a ratio of the leveling power cost of each distributed energy to the leveling power cost of the conventional thermal power, and each distributed energy in this embodiment is a current distributed photovoltaic, distributed wind power, natural gas distributed energy, and the like.
The leveling power cost of the current distributed photovoltaic, distributed wind power and natural gas distributed energy is higher than that of the conventional thermal power, along with the progress of the technology, the power generation cost of each distributed energy and the cost of the conventional thermal power are gradually reduced, and the cost disadvantage is gradually reduced. Comparable cost L of each distributed energy source in t periodtCan be calculated by the following formula (4):
Lt＝H(Ct-1)＝Ct-1/Cf(4)
in the formula, Ct-1For the flat electric cost at the end of the t-1 period of the distributed energy, CfThe electricity consumption cost is leveled for the conventional thermal power.
And S4, determining the maximum exploitable amount of the corresponding distributed energy market according to the comparative cost of each distributed energy and the sectional function of the maximum market exploitable amount of the distributed energy.
In this embodiment, the comparable cost L of each distributed energy resource obtained by calculation according to step S3tAnd substituting the maximum market exploitability amount of the corresponding distributed energy into the formula (2).
And S5, determining scene conditions, and determining installed capacity of each distributed energy resource under the corresponding scene according to the distributed energy technology diffusion prediction model.
In this embodiment, considering the influence of different scenarios on the installed capacity of each distributed energy resource, for example, dynamic changes of policy and consumption conditions can be set through a scenario to simulate a comprehensive environment under different policy, consumption, and natural gas price conditions, and the changes of environmental conditions mainly act on the parameters u and v of the piecewise function of the equation (2). The corresponding relationship between the scene setting and the piecewise function parameter in this embodiment is shown in table 1 below.
Table 1 distributed energy dimensioning scenario setup
Therefore, the embodiment can determine the maximum market exploitable corresponding to the corresponding distributed energy resources under different scene conditions (u, v)Quantity NtAnd the installed capacity of the corresponding distributed energy under the corresponding scene can be determined by combining the calculation of the technical diffusion model.
In the embodiment, a technology diffusion model is established, the diffusion characteristic of the distributed energy as an innovative energy technology is considered, the prediction model integrates the technology diffusion model and the levelization power cost, and the dynamic comprehensive influence of various factors such as the characteristics of different distributed energy, development potential, project economy, matching policy and the like on the increase of the distributed energy installation machine can be considered. The method makes up the deficiency of the influence of the traditional model on the natural gas price change on the installed growth particularly for the natural gas distributed energy; the method can be applied to the scale prediction of distributed photovoltaic, distributed wind power and natural gas distributed energy installation in China, and provides basic reference for related planning design, industrial enterprises and the like.
In this embodiment, the installed capacity of each distributed energy resource in a future time period can be predicted through the above steps, and the method specifically includes the following steps:
s11, determining a prediction time period T, and acquiring the accumulated machine loading amount, the maximum technology developability amount, the leveling electricity consumption cost of a single distributed energy source and the leveling electricity consumption cost of a single conventional thermal power project of the distributed energy source at the initial time T in the time period;
s12, calculating the comparable electricity cost of the natural gas distributed energy, and determining the maximum exploitable amount of the market at the current time according to the scene condition and the sectional function of the maximum exploitable amount of the market of the distributed energy;
and S13, determining the predicted value of the installed scale of the distributed energy at the current moment according to the technology diffusion model and the maximum exploitable amount of the market at the current time.
S14, predicting value X according to installed scaletCurrent price of natural gas PtAnd determining a predicted value of the electric cost of the distributed energy standardization level in the current time period.
In this embodiment, the evolution of the normalized electricity consumption cost of the distributed energy technology can calculate and predict the cost of the distributed energy in the current year, that is, the prediction in the next year is ready for data input. And the predicted value of the electric cost of the distributed energy standardization at the current moment t is obtained by analyzing and calculating as follows, and the evolution process of the electric cost of the distributed energy standardization is as follows:
(1) theory of learning
The present embodiment studies the distributed energy technology using a learning curve (experience curve) model in which the unit cost of distributed energy decreases with the increase of the accumulated yield with a specific learning efficiency, which is a phenomenon based on empirical observation, rather than an inevitable natural law, and the decrease of the unit cost should be regarded as a result of long-term, dynamic co-action based on various endogenous and exogenous factors. The basic logic is as follows: the current distributed energy unit output standard electric power cost is higher than that of the conventional energy technology; however, as the former technology is developed and the production experience is accumulated, the unit cost thereof is in a downward trend. The expressions for the learning curve of the distributed energy resource are summarized in table 2 below.
TABLE 2 Classification summary of distributed energy learning curve models
Wherein, CtNormalizing the kilowatt-hour cost estimation value for distributed energy at time t, C0For the initial distributed energy leveling of kilowatt-hour costs, XtTo accumulate the yield, YtInvestments for knowledge accumulation or accumulated research and development; qtTo average extent of scale development, PjFor the j-th input element price, a, b, 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 single factor learning curve model describes a distributed energy leveling power cost as a function of its cumulative total output. The two-factor learning curve separates the promotion effect of research and development on the progress of the distributed energy technology from the increase of the accumulated yield, and is a refinement and exploration of the learning curve theory. The mean scale development degree was further added to the three-factor model. The four-factor model contains factors of accumulative yield, accumulative knowledge, scale effect and investment element price. The input element price refers to some key raw material price changes (such as the price change of uranium in nuclear power and the price of natural gas in natural gas power generation) which cannot be explained by currency expansion and are not related to research and development. The price variation of other general input elements meets the inflation level of the through-put in the whole year, and can be corrected by considering a certain inflation rate of the through-put; the cost reduction brought about by the development of certain key elements is considered as the influence range of accumulated knowledge factors.
(2) Dynamic evolution of leveled kilowatt-hour cost of distributed energy resources
The predicted value of the electric cost of the distributed energy leveling degree in the current time period is calculated by the following formula:
Ct＝(Xt，Yt，Pt，t)
in the formula, XtAccumulating the predicted value of the installed capacity for the distributed energy in the t year; y istTechnology accumulation amount of the distributed energy source in the t year; k is a radical oflThe cost of the non-research and development type input elements accounts for the proportion of the cost of the corresponding distributed energy; a. b is an empirical parameter. PtFor non-research and development type input element, price at t stage, P0Representing the initial non-research and development input element price, and taking a value according to the actual situation; wherein, there is no corresponding element P in both distributed photovoltaic and distributed wind powertA value of 1, klThe value is 0. For natural gas distributed energy, the factor is the natural gas price, Pt、klAnd taking values according to actual conditions. The values of a and b for different distributed energy sources are shown in table 3.
TABLE 3 values of the distributed energy parameters a, b
S15, output whenFront-time distributed energy accumulation installation scale XtAnd a predicted value C of the electric cost of the distributed energy levelingt。
S16, judging whether T is larger than or equal to T, if not, changing T to T +1, and turning to S12; if yes, the process is ended.
The implementation process of the method is described below by taking natural gas distributed energy as an example.
And (I) describing a process for establishing a distributed energy technology diffusion prediction model.
Historical statistical data of the natural gas distributed energy accumulation installed scale is collected firstly, for example, for the natural gas distributed energy, the accumulated data of 2013 and 2018 can be collected currently. Assuming that the maximum technology exploitability quantity of the technology diffusion model is unchanged between 2013 and 2018 years, the maximum technology exploitability quantity N is estimated according to other researchesmax. In this case, the time series data is combined with NmaxAs a known quantity, the fitting function of matlab is utilized, the model with the best fitting effect among three technical diffusion models (Logistic, Gompertz and Bass models) is compared to be the Bass model, and values of parameters p and q are given according to the fitting result. The distributed energy development scale is predicted according to the above steps S11-S16.
Firstly, setting a prediction time, if the accumulated loading amount of the natural gas distributed energy resources until 2025 years is predicted and the initial year is 2013, setting T to be 12, and collecting initial parameter data of a model by taking 2013 as a time starting point, X0Accumulating the installed data of the natural gas distributed energy in 2013; n is a radical of0Is still the aforementioned Nmax；C0The normalized electricity consumption cost of the single natural gas distributed energy produced in 2013 is measured and calculated according to actual project data; cfThe average power consumption cost of a single conventional thermal power project put into production in 2013 is calculated according to the formula (3), the average power consumption cost of the distributed energy and the average power consumption cost of the single conventional thermal power project are assumed to be unchanged for a long time because the conventional thermal power is developed to be mature according to actual project data.
And calculating the comparable electricity consumption cost of the natural gas distributed energy source through a formula (4) according to the obtained standard electricity consumption cost of the distributed energy source and the standard electricity consumption cost of a single conventional thermal power project.
Determining the maximum market exploitable amount N of the natural gas distributed energy in the current time period according to the comparable electricity cost of the natural gas distributed energy and the current scene conditiont。NtThe natural gas distributed energy installed scale prediction value in the current time period can be obtained according to the established distributed energy technology diffusion prediction model.
According to the obtained machine scale predicted value XtCurrent price of natural gas PtSubstituting formula (5) to obtain the predicted value C of the electric cost of the natural gas distributed energy standardization level in the current time periodtAnd output XtAnd Ct。
And finally, judging a termination condition, judging whether T is greater than or equal to T, if the condition is met, ending the process, otherwise, continuing to circulate the operation, and finally predicting the natural gas distributed energy accumulated loading amount until 2025 years.
As shown in fig. 3, the present invention further provides a computer device, which includes a memory, a processor, and a computer program stored in the memory and executable on the processor, and the processor executes the computer program to implement the distributed energy resource development scale prediction method in the foregoing embodiments.
The present invention also provides a computer-readable storage medium on which a computer program is stored, the computer program, when being executed by a processor, implementing the recognition model training method in the above-described embodiment, or the computer program, when being executed by the processor, implementing the distributed energy development scale prediction method in the above-described embodiment.
It will be understood by those skilled in the art that all or part of the processes of the methods of the embodiments described above can be implemented by hardware associated with instructions of a computer program, which can be stored in a non-volatile computer-readable storage medium, and when executed, can include processes of the embodiments of the methods described above. Any reference to memory, storage, database, or other medium used in the embodiments provided herein may include non-volatile and/or volatile memory, among others. Non-volatile 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), Rambus Direct RAM (RDRAM), direct bus dynamic RAM (DRDRAM), and memory bus dynamic RAM (RDRAM).
The present invention is not limited to the above-mentioned preferred embodiments, and any structural changes made under the teaching of the present invention shall fall within the protection scope of the present invention, which has the same or similar technical solutions as the present invention.
Claims (10)
1. A distributed energy development scale prediction method is characterized by comprising the following steps:
determining and obtaining accumulated installed historical data of each distributed energy source, and establishing a distributed energy technology diffusion prediction model based on the maximum market exploitability amount according to the historical data;
according to the cost relationship between the distributed energy and the conventional thermal power and the limit of a policy on the development scale of the distributed energy project, establishing a sectional function of the maximum market developability of the distributed energy;
respectively determining the leveling power consumption cost of each distributed energy and the leveling power consumption cost of conventional thermal power, and calculating the comparable cost of each distributed energy;
determining the maximum exploitable amount of the corresponding distributed energy market according to the comparable cost of each distributed energy and the sectional function of the maximum market exploitable amount of the distributed energy;
and determining different scene conditions, and determining the installed capacity of each distributed energy resource under the corresponding scene according to the distributed energy technology diffusion prediction model.
2. The method according to claim 1, wherein the step of predicting the installed capacity of each of the distributed energy resources at a future time period comprises the steps of:
s11, determining a prediction time period T, and acquiring the accumulated machine loading amount, the maximum technology developability amount, the leveling electricity consumption cost of a single distributed energy source and the leveling electricity consumption cost of a single conventional thermal power project of the distributed energy source at the initial time T in the time period;
s12, calculating the comparable electricity cost of the natural gas distributed energy, and determining the maximum exploitable amount of the market at the current time according to the scene condition and the sectional function of the maximum exploitable amount of the market of the distributed energy;
s13, determining a predicted value of the installed scale of the distributed energy at the current moment according to the technology diffusion model and the maximum exploitable amount of the market at the current time period;
s14, determining a predicted value of the electric cost of the distributed energy standardization in the current time period according to the predicted value of the installed scale and the current price of the natural gas;
s15, outputting the accumulated installed scale X of the distributed energy at the current momenttAnd a predicted value of the electric cost of the distributed energy leveling degree;
s16, judging whether T is larger than or equal to T, if not, changing T to T +1, and turning to S12; if yes, the process is ended.
3. The distributed energy resource development scale prediction method according to claim 1 or 2, characterized in that fitting is carried out by using matlab program in combination with Logistic, Gompertz and bases models, the model with the minimum fitting SSE is selected as the technology diffusion model corresponding to the distributed energy resource, and the model parameters are determined according to the fitting result.
4. The method according to claim 3, wherein the distributed energy technology spread prediction model is specified by the following equation:
Xt＝F(Nt，t)
in the formula, NtThe maximum market development amount at the moment t, and p and q are model parameters; in the Logistic model, p determines the shape of the curve, and q represents the diffusivity of the product; in the Gompertz model, p determines the shape of the curve, and q represents the diffusivity of the product; in the Bass model, p represents innovation coefficients and q represents simulation coefficients.
5. The method of forecasting the development scale of distributed energy according to claim 4, wherein the segmented function of the maximum market exploitable amount of distributed energy is given by:
Nt＝G(Nt-1，Lt)
in the formula, LtFor the comparative cost of corresponding distributed energy compared with conventional thermal power, u and v are used for embodying the limit of the policy corresponding to the development scale of the distributed energy project, lambda is the influence coefficient of the cost of the distributed power supply on the market space of the distributed power supply, and the numerical value is determined according to experience; n is a radical ofmaxIs a technology exploitable amount of distributed power.
6. The method according to claim 5, wherein the normalized electricity cost of each distributed energy and the normalized electricity cost of conventional fossil power are calculated by the following formula:
in the formula I0Representing the initial investment, namely the whole unit cost of the project; n represents the year; n represents the full life cycle; dnRepresenting the depreciation cost of the power station project in the nth year; rnRepresenting the annual operating cost of the power station in the nth year;Vnother taxes such as the value-added tax of the nth year are represented; wnIndicating the interest of the n-th project loan; bn represents the income of other sources in the nth year, such as renewable energy subsidies and the like; r represents the discount rate; rERepresenting the external factor risk cost.
7. The method of forecasting distributed energy resource development scale of claim 6, wherein each distributed energy resource has a comparable cost LtCan be calculated using the following formula:
Lt＝H(Ct-1)＝Ct-1/Cf(4)
in the formula, Ct-1For the flat electric cost at the end of the t-1 period of the distributed energy, CfThe electricity consumption cost is leveled for the conventional thermal power.
8. The method according to claim 2, wherein the predicted value of the electric cost of distributed energy leveling for the current period is calculated by the following formula:
Ct＝(Xt，Yt，Pt，t)
in the formula, C0For the initial distributed energy leveling of kilowatt-hour costs, XtAccumulating the predicted value of the installed capacity for the distributed energy in the t year; y istTechnology accumulation amount of the distributed energy source in the t year; k is a radical oflThe cost of the non-research and development type input elements accounts for the proportion of the cost of the corresponding distributed energy; a. b is an empirical parameter; ptThe price of the non-research and development type input element at the t stage.
9. 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 evolution scale prediction method of any one of claims 1 to 8 when executing the computer program.
10. A computer-readable storage medium storing a computer program, wherein the computer program, when executed by a processor, implements the distributed energy resource development scale prediction method according to any one of claims 1 to 8.
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