CN117057831A - Green evidence market and electric power market collaborative modeling method based on system dynamics - Google Patents
Green evidence market and electric power market collaborative modeling method based on system dynamics Download PDFInfo
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Abstract
The invention relates to a green certificate market and electric power market collaborative modeling method based on system dynamics, which is used for determining a functional relation of participation of green certificate transaction in an electric power market according to a linking mechanism between green certificate transaction and an electric power transaction market main body. According to the green certificate market and electric power market collaborative modeling method based on system dynamics, by means of a linking mechanism between a green certificate transaction and an electric power transaction market main body, a causal relation graph of key factors of the green certificate market and the electric power market is built, a system flow graph of collaborative development of the green certificate market and the electric power market is built, a system dynamics model of collaborative development of the green certificate market and the electric power market is built, key factors of influence of collaborative development of the green certificate market and the electric power market on a power supply structure are analyzed, and the coupling action mechanism of the green certificate transaction and the electric power transaction is analyzed based on the linking mechanism of the system dynamics research on the two, and on the basis, a collaborative development model of the two is built, and influence of the system factors on the power supply structure is analyzed.
Description
Technical Field
The invention relates to the technical field of renewable energy sources, in particular to a green certificate market and electric power market collaborative modeling method based on system dynamics.
Background
Under the background of global energy conservation and emission reduction, green power certificate transaction derived from renewable energy quota system becomes an important point of development and research of various countries. The foreign green card trade market tends to be perfect after more than ten years of development, has a sound market trade mechanism and supervision means, has good market development, forms a stable price mechanism and has flexible market self-regulation capability.
At present, the green card trade is still in the primary development stage in China, the market scale is delayed and can not be developed, but in the future, china needs to form a complete green card trade market. The green electric power certificate transaction has great influence on the power generation side and the power utilization side of the electric network in China, and particularly on the electric network currently under the reform of the electric power market. However, the research on the collaborative development of the green evidence market and the electric power market is little at present, and the action mechanism and the influence of the collaborative development of the green evidence market and the electric power market are not focused yet.
In addition, the system dynamics is based on the causal relation of each factor and a feedback loop, and the feedback structural relation among variables in the system is analyzed to study the overall behavior of the system. The green certificate transaction and the power transaction are complex dynamic systems, and relate to factors such as renewable energy quota system, power price, green certificate demand and the like. The system dynamics is taken as a method for comprehensively, dynamically and quantitatively describing the problems, and the influence of each factor on the electric power market and the power supply structure after the collaborative development of two markets can be comprehensively shown.
Therefore, the invention starts from a double-carbon target, analyzes the coupling action mechanism of the green card transaction and the electric power transaction based on the system dynamics research, constructs a collaborative development model of the two on the basis, and analyzes the influence of system factors on the power supply structure.
Disclosure of Invention
Accordingly, the present invention is directed to a method for collaborative modeling of green license market and electric power market based on system dynamics, which solves one of the above drawbacks.
The invention is realized by the following technical scheme:
a green evidence market and electric power market collaborative modeling method based on system dynamics comprises the following specific steps:
1) Determining a functional relation of the green certificate transaction participating in the power market according to a linking mechanism between the green certificate transaction and a power transaction market main body;
2) Determining key factors of a system dynamics model according to the functional relation, and further constructing a causal relation diagram of key factors of a green evidence market and an electric power market;
3) Constructing a system flow diagram of the collaborative development of the green certificate market and the electric power market according to the key factors, the related factors influencing the key factors and the causal relationship diagram;
4) On the basis of the system flow diagram, a system dynamics model of the collaborative development of the green certificate market and the electric power market is established;
5) And simulating by using Vensim software according to the constructed system dynamics model, and analyzing key factors of the influence of the collaborative development of the green evidence market and the electric power market on the power supply structure.
Further, the key factors include: renewable energy installation proportion, renewable energy quota proportion, renewable energy power generation amount and power demand increase rate;
the functional relation comprises: consumer utility function, renewable energy production cost function, coal-to-electricity production cost function, green license price and electric power price function relationship, carbon emission function, carbon quota index function;
the consumer utility function is:
U(Q)＝εQ 2 +ζQ+δ
wherein U (Q) is a utility function; epsilon, zeta and delta are parameters;
the renewable energy production cost function is as follows:
wherein c R The production cost of renewable energy power is high; q R Generating energy for renewable energy sources; beta R 、And lambda (lambda) R Is a parameter;
the coal electricity production cost function is as follows:
wherein c F The coal electricity production cost is; q F The energy generation capacity is the traditional energy; beta F 、And lambda (lambda) F Is a parameter;
the green certificate price and the electric power price function relation is as follows:
wherein ρ is g (ρ e ) The price is green;
the carbon emission function is:
wherein,is carbon emission; η (eta) 1 、η 2 Is the carbon emission coefficient; />A trade price for carbon;
the carbon quota index function is:
wherein,is a carbon quota index; alpha is carbon quota; mu is the emission reduction rate of the coal power plant quotient; q F Generating energy for traditional energy (coal electricity); />Is the carbon emissions trading volume.
Further, the related factors of the key factors include the following:
the relevant factors of the renewable energy installation proportion comprise: renewable energy sources are installed in an installation machine, a traditional energy source reconstruction machine, a traditional energy source installation proportion and a traditional energy source generating capacity;
the related factors of the renewable energy quota ratio comprise: quota growth rate, power price, green certificate price, green power plant manufacturer profit margin and renewable energy installation construction;
the related factors of the renewable energy power generation amount comprise: the method comprises the steps of issuing a green license to a generator, holding the green license by the generator, holding the green license by a power grid, predicting sales volume of the green license, excess demand of the green license, carbon emission volume and carbon quota;
the related factors of the power demand growth rate include: the power price demand elasticity, the green license of the uploading, the expected purchase amount of the green license, the renewable energy installation construction and the traditional energy installation construction.
Further, the system dynamics model for the collaborative development of the green evidence market and the electric power market comprises the following steps: a state variable equation, a rate variable equation, an auxiliary set variable equation and an exogenous variable equation;
the state variable equation is as follows:
ρ gc ＝ρ g0 +ρ Q
wherein ρ is gc For green license price change, units: a meta-element; ρ g0 Initial price for green license, units: element/individual; ρ gc For the green evidence excess demand, units: a plurality of;
wherein c R The renewable energy is installed in units of: billions/KW; c R0 The initial installed amount of renewable energy is as follows: billions/KW; μ is the installed proportion of renewable energy sources; ρ g0 Initial price for green license, units: element/individual; ρ g The price of green card is: element/individual; v is the average annual utilization hour of renewable energy, unit: hours/year;
the rate variable equation is:
wherein ρ is R The number of green certificates is the number of green certificates held by a power producer, and the unit is: a plurality of; q R The renewable energy generating capacity is as follows: trillion KW.h;
the auxiliary variable equation is as follows:
wherein q R The renewable energy generating capacity is as follows: trillion KW.h; c R The renewable energy is installed in units of: billions/KW; v is the average annual utilization hour of renewable energy, unit: hours/year;
the exogenous variable equation:
wherein Q is e As power demand, units: trillion KW.h; q (Q) 0 As an initial value of power demand, unit: trillion KW.h; q (Q) up Is the rate of increase of power demand;
T QR ＝T QR0 +T up
wherein T is QR Is the quota ratio; t (T) QR0 The quota ratio is an initial value; t (T) up Is the quota growth rate.
Further, the system dynamics model for the collaborative development of the green evidence market and the electric power market comprises the following steps: exogenous variables, state variables, and constants;
the exogenous variables include: a power demand;
the state variables include: green license price, carbon trade price, renewable energy installation quantity and traditional energy installation quantity;
the constants include: the power demand growth rate, the renewable energy quota proportion, the renewable energy installation proportion and the renewable energy average annual utilization hour.
Further, the influence of key factors in the green evidence market and electric power market collaborative development model on the power supply structure comprises: renewable energy quota system, renewable energy installation proportion and power demand growth rate;
the specific content of the influence of the renewable energy quota system on the power supply structure comprises the following steps: simulating and analyzing the influence of different quota system targets on the traditional energy generating capacity and the renewable energy generating capacity by using Vensim software;
the specific contents of the influence of the renewable energy installation proportion on the power supply structure include: simulating and analyzing the influence of different renewable energy installation proportions on renewable energy generating capacity and renewable energy generating capacity duty ratio by using Vensim software;
the specific content of the influence of the power demand growth rate on the power supply structure comprises the following steps: and simulating and analyzing the influence of different power demand growth rates on the installed capacity of the renewable energy source by using Vensim software.
The invention has the beneficial effects that:
according to the green certificate market and electric power market collaborative modeling method based on system dynamics, by means of a linking mechanism between a green certificate transaction and an electric power transaction market main body, a causal relation graph of key factors of the green certificate market and the electric power market is built, a system flow graph of collaborative development of the green certificate market and the electric power market is built, a system dynamics model of collaborative development of the green certificate market and the electric power market is built, key factors of influence of collaborative development of the green certificate market and the electric power market on a power supply structure are analyzed, and the coupling action mechanism of the green certificate transaction and the electric power transaction is analyzed based on the linking mechanism of the system dynamics research on the two, and on the basis, a collaborative development model of the two is built, and influence of the system factors on the power supply structure is analyzed.
Additional advantages, objects, and features of the invention will be set forth in part in the description which follows and in part will become apparent to those having ordinary skill in the art upon examination of the following or may be learned from practice of the invention. The objects and other advantages of the invention may be realized and obtained by means of the instrumentalities and combinations particularly pointed out in the specification.
Drawings
FIG. 1 is a flow chart of a system dynamics-based collaborative development analysis method for green evidence market and electric power market provided by the invention;
FIG. 2 is a causal graph of green license transaction market and electric power market coupling provided by an embodiment of the present invention;
FIG. 3 is a system flow diagram of the collaborative development of the green license market and the electric power market according to an embodiment of the present invention;
FIG. 4 is a graph showing simulation results of the change of the conventional energy power generation ratio with time at different renewable energy quota ratios according to the embodiment of the present invention;
FIG. 5 is a graph showing simulation results of the change of the renewable energy power generation ratio with time under different renewable energy quota ratios according to the embodiment of the invention;
FIG. 6 is a graph showing simulation results of the power generation of renewable energy sources with time at different power demand growth rates according to an embodiment of the present invention;
FIG. 7 is a graph showing simulation results of the change of the renewable energy power generation ratio with time at different power demand growth rates according to the embodiment of the present invention;
FIG. 8 is a graph showing simulation results of the change of the renewable energy installation amount with time under different renewable energy installation ratios according to the embodiment of the invention.
Detailed Description
For the purpose of making the objects, technical solutions and advantages of the embodiments of the present invention more apparent, the technical solutions of the embodiments of the present invention will be clearly and completely described below with reference to the accompanying drawings in the embodiments of the present invention, and it is apparent that the described embodiments are some embodiments of the present invention, but not all embodiments of the present invention. The components of the embodiments of the present invention generally described and illustrated in the figures herein may be arranged and designed in a wide variety of different configurations.
Thus, the following detailed description of the embodiments of the invention, as presented in the figures, is not intended to limit the scope of the invention, as claimed, but is merely representative of selected embodiments of the invention. 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.
It should be noted that: like reference numerals and letters denote like items in the following figures, and thus once an item is defined in one figure, no further definition or explanation thereof is necessary in the following figures.
In the foregoing description of the invention, it should be noted that the azimuth or positional relationship indicated by the terms "one side", "the other side", etc. are based on the azimuth or positional relationship shown in the drawings, or the azimuth or positional relationship in which the inventive product is conventionally put in use, are merely for convenience of describing the invention and simplifying the description, and are not indicative or implying that the apparatus or element to be referred to must have a specific azimuth, be configured and operated in a specific azimuth, and therefore should not be construed as limiting the invention. Furthermore, the terms "first," "second," and the like, are used merely to distinguish between descriptions and should not be construed as indicating or implying relative importance.
Furthermore, the terms "identical" and the like do not denote that the components are identical, but rather that there may be minor differences. The term "perpendicular" merely means that the positional relationship between the components is more perpendicular than "parallel" and does not mean that the structure must be perfectly perpendicular, but may be slightly tilted.
Referring to fig. 1-8, the present invention provides a technical solution: the embodiment of the invention discloses a green evidence market and electric power market collaborative modeling method based on system dynamics under a double-carbon target, which comprises the following steps:
step one: according to a linking mechanism between the green certificate transaction and the main body of the electric power transaction market, determining the balance condition of the green certificate transaction participating in the electric power market, and further constructing a causal relationship diagram of the green certificate market and the electric power market key factors by determining key factors of a system dynamics model according to the balance condition;
step two: according to the key factors and the causal relationship graph, constructing a system flow graph of the collaborative development of the green certificate market and the electric power market;
thirdly, establishing a system dynamics model of the collaborative development of the green certificate market and the electric power market on the basis of a system flow diagram;
step four: and simulating by using Vensim software according to the constructed system dynamics model, and analyzing key factors of the influence of the collaborative development of the green evidence market and the electric power market on the power supply structure.
Further, key factors of the system dynamics model in the first step include: renewable energy installation proportion, renewable energy quota proportion, renewable energy power generation amount and power demand increase rate;
further, the functional relationship in the first step includes: consumer utility function, renewable energy production cost function, coal-to-electricity production cost function, green license price and electric power price function relationship, carbon emission function, carbon quota index function;
the consumer utility function is:
U(Q)＝εQ 2 +ζQ+δ (1)
wherein U (Q) is a utility function; epsilon, zeta and delta are parameters.
The renewable energy production cost function of (2) is:
wherein c R The production cost of renewable energy power is high; q R Generating energy for renewable energy sources; beta R 、And lambda (lambda) R Is a parameter.
The coal electricity production cost function of (2) is as follows:
wherein c F The coal electricity production cost is; q F Generating energy for traditional energy (coal electricity); beta F 、And lambda (lambda) F Is a parameter.
The green certificate price and the electric power price function relation is as follows:
wherein ρ is g (ρ e ) Is green price.
The carbon emission function is:
wherein,is carbon emission; η (eta) 1 、η 2 Is the carbon emission coefficient; />For carbon trade prices.
The carbon quota index function is:
wherein,is a carbon quota index; alpha is carbon quota; mu is the emission reduction rate of the coal power plant quotient; q F Generating energy for traditional energy (coal electricity); />Is the carbon emissions trading volume.
Further, in the second step, the correlation factors of the key factors of the green evidence market and the electric power market collaborative modeling method based on system dynamics under the double-carbon target comprise the following contents:
wherein, the relevant factors of renewable energy installation proportion include: renewable energy sources are installed in an installation machine, a traditional energy source reconstruction machine, a traditional energy source installation proportion and a traditional energy source generating capacity;
wherein, the relevant factors of renewable energy quota proportion include: quota growth rate, power price, green certificate price, green power plant manufacturer profit margin and renewable energy installation construction;
wherein, the associated factors of renewable energy power generation include: the method comprises the steps of issuing a green license to a generator, holding the green license by the generator, holding the green license by a power grid, predicting sales volume of the green license, excess demand of the green license, carbon emission volume and carbon quota;
the correlation factors of the power demand growth rate include: the power price demand elasticity, the green license of the uploading, the expected purchase amount of the green license, the construction of a renewable energy installation and the construction of a traditional energy installation;
further, the system dynamics model of the collaborative development of the green certificate market and the electric power market in the third step comprises the following steps: a state variable equation, a rate variable equation, an auxiliary set variable equation and an exogenous variable equation;
the state variable equation of (2) is:
ρ gc ＝ρ g0 +ρ Q (7)
wherein ρ is gc For green license price change, units: a meta-element; ρ g0 Initial price for green license, units: element/individual; ρ gc For the green evidence excess demand, units: a plurality of;
wherein c R The renewable energy is installed in units of: billions/KW; c R0 The initial installed amount of renewable energy is as follows: billions/KW; μ is the installed proportion of renewable energy sources; ρ g0 Initial price for green license, units: element/individual; ρ g The price of green card is: element/individual; v is the average annual utilization hour of renewable energy, unit: hours/year;
the rate variable equation for (2) is:
wherein ρ is R The number of green certificates is the number of green certificates held by a power producer, and the unit is: tensioning; q R The renewable energy generating capacity is as follows: trillion KW.h;
the auxiliary variable equation of (2) is:
wherein q R The renewable energy generating capacity is as follows: trillion KW.h; c R The renewable energy is installed in units of: billions/KW; v is the average annual utilization hour of renewable energy, unit: hours/year;
exogenous variable equation:
wherein Q is e As power demand, units: trillion KW.h; q (Q) 0 As an initial value of power demand, unit: trillion KW.h; q (Q) up Is the rate of increase of power demand;
T QR ＝T QR0 +T up (12)
wherein T is QR Is the quota ratio; t (T) QR0 The quota ratio is an initial value; t (T) up Is the quota growth rate;
further, the system dynamics model of the collaborative development of the green certificate market and the electric power market in the third step comprises the following steps: exogenous variables, state variables, and constants;
wherein the exogenous variables include: electric power demand
Wherein the state variables include: green license price, carbon trade price, renewable energy installation quantity and traditional energy installation quantity
Wherein the constant comprises: the power demand growth rate, the renewable energy quota proportion, the renewable energy installation proportion and the renewable energy average annual utilization hour;
further, the influence of key factors in the green evidence market and electric power market collaborative development model in the fourth step on the power supply structure comprises: renewable energy quota ratio, renewable energy installation ratio and power demand growth rate;
the specific content of the influence of the renewable energy quota proportion on the power supply structure comprises the following steps: simulating and analyzing the influence of different quota system targets on the traditional energy generating capacity and the renewable energy generating capacity by using Vensim software;
the specific content of the influence of the renewable energy installation proportion on the power supply structure comprises the following steps: simulating and analyzing the influence of different renewable energy installation proportions on renewable energy generating capacity and renewable energy generating capacity duty ratio by using Vensim software;
the specific content of the influence of the power demand growth rate on the power supply structure comprises the following steps: simulating and analyzing the influence of different power demand growth rates on the installed capacity of the renewable energy sources by using Vensim software;
the implementation content of the fourth step is as follows:
and the system dynamics software Vensim is adopted to simulate and analyze the influence of the collaborative development of the green evidence market and the electric power market on the power supply structure.
The model in the invention is based on 2020 national green evidence market and electric power market related data, and is simulated by Vensim software, the set running time unit is month, the duration is 100 months, and the time period is 2021-2028 years.
The initial data in the model are:
| parameters (parameters) | Assignment of value |
| Rate of increase of power demand | 3.1％ |
| Quota targets | 20％ |
| Quota growth rate | 1.6％ |
| Initial value of renewable energy installation proportion | 29.8％ |
| Average annual renewable energy utilization hour | 1796.05 hours/year |
| Initial value of power demand | 7.51 trillion kW.h |
| Initial price of green certificate | 0.25 yuan/kW.h |
| Initial value of renewable energy installation | 9.34 billion kW |
| Traditional energy installation | 12.45 trillion kW |
| Carbon trade price | 49 yuan/ton |
Along with the continuous increase of renewable energy quota system targets, the model is simulated by Vensim software, and based on the renewable energy quota system targets (20%) in 2030 of China, the quota system targets are respectively set to be 19% (reduced by 1%), 20% (2030 target) and 21% (increased by 1%), simulation is carried out, and the simulation results of the power generation ratio of the traditional energy and the renewable energy under three situations are respectively shown in fig. 4 and 5;
along with the increase of the power demand increase rate, the model is simulated by Vens im software, and the simulation results of the power generation amount and the power generation amount ratio of the renewable energy sources under three situations are respectively shown in fig. 6 and 7, wherein the power demand increase rate is set to be 2.8 percent (3 percent reduction), 3.1 percent (data in 2020) and 3.4 percent increase by taking the 2020 power demand increase rate as a reference;
as the renewable energy installation proportion increases, the model is simulated by Vens im software, and based on the 2025 renewable energy installation proportion as an expected target (50%), the renewable energy installation proportion is set to be 45%, 50% and 55%, respectively, and simulation results of the renewable energy installation amount under three situations are shown in fig. 8.
While the applicant has described and illustrated the embodiments of the present invention in detail in connection with the drawings of the specification, it should be understood by those skilled in the art that the above embodiments are only preferred embodiments of the present invention, and the detailed description is provided to assist the reader in better understanding the spirit of the invention, and does not limit the scope of the invention, but any modification or variation based on the spirit of the invention should fall within the scope of the invention
Finally, it is noted that the above embodiments are only for illustrating the technical solution of the present invention and not for limiting the same, and although the present invention has been described in detail with reference to the preferred embodiments, it should be understood by those skilled in the art that modifications and equivalents may be made thereto without departing from the spirit and scope of the technical solution of the present invention, which is intended to be covered by the scope of the claims of the present invention.
Claims (6)
1. A green evidence market and electric power market collaborative modeling method based on system dynamics is characterized by comprising the following specific steps:
1) Determining a functional relation of the green certificate transaction participating in the power market according to a linking mechanism between the green certificate transaction and a power transaction market main body;
2) Determining key factors of a system dynamics model according to the functional relation, and further constructing a causal relation diagram of key factors of a green evidence market and an electric power market;
3) Constructing a system flow diagram of the collaborative development of the green certificate market and the electric power market according to the key factors, the related factors influencing the key factors and the causal relationship diagram;
4) On the basis of the system flow diagram, a system dynamics model of the collaborative development of the green certificate market and the electric power market is established;
5) And simulating by using Vensim software according to the constructed system dynamics model, and analyzing key factors of the influence of the collaborative development of the green evidence market and the electric power market on the power supply structure.
2. The system dynamics-based green evidence market and electric power market collaborative modeling method according to claim 1, wherein:
the key factors include: renewable energy installation proportion, renewable energy quota proportion, renewable energy power generation amount and power demand increase rate;
the functional relation comprises: consumer utility function, renewable energy production cost function, coal-to-electricity production cost function, green license price and electric power price function relationship, carbon emission function, carbon quota index function;
the consumer utility function is:
U(Q)＝εQ 2 +ζQ+δ
wherein U (Q) is a utility function; epsilon, zeta and delta are parameters;
the renewable energy production cost function is as follows:
wherein c R The production cost of renewable energy power is high; q R Generating energy for renewable energy sources; beta R 、And lambda (lambda) R Is a parameter;
the coal electricity production cost function is as follows:
wherein c F The coal electricity production cost is; q F The energy generation capacity is the traditional energy; beta F 、And lambda (lambda) F Is a parameter;
the green certificate price and the electric power price function relation is as follows:
wherein ρ is g (ρ e ) The price is green;
the carbon emission function is:
wherein,is carbon emission; η (eta) 1 、η 2 Is the carbon emission coefficient; />A trade price for carbon;
the carbon quota index function is:
wherein,is a carbon quota index; alpha is carbon quota; mu is the emission reduction rate of the coal power plant quotient; q F Generating energy for traditional energy (coal electricity);is the carbon emissions trading volume.
3. The system dynamics-based green evidence market and electric power market collaborative modeling method according to claim 1, wherein: the related factors of the key factors comprise the following contents:
the relevant factors of the renewable energy installation proportion comprise: renewable energy sources are installed in an installation machine, a traditional energy source reconstruction machine, a traditional energy source installation proportion and a traditional energy source generating capacity;
the related factors of the renewable energy quota ratio comprise: quota growth rate, power price, green certificate price, green power plant manufacturer profit margin and renewable energy installation construction;
the related factors of the renewable energy power generation amount comprise: the method comprises the steps of issuing a green license to a generator, holding the green license by the generator, holding the green license by a power grid, predicting sales volume of the green license, excess demand of the green license, carbon emission volume and carbon quota;
the related factors of the power demand growth rate include: the power price demand elasticity, the green license of the uploading, the expected purchase amount of the green license, the renewable energy installation construction and the traditional energy installation construction.
4. The system dynamics-based green evidence market and electric power market collaborative modeling method according to claim 1, wherein: the system dynamics model for the collaborative development of the green evidence market and the electric power market comprises the following components: a state variable equation, a rate variable equation, an auxiliary set variable equation and an exogenous variable equation;
the state variable equation is as follows:
ρ gc ＝ρ g0 +ρ Q
wherein ρ is gc For green license price change, units: a meta-element; ρ g0 Initial price for green license, units: element/individual; ρ gc For the green evidence excess demand, units: a plurality of;
wherein c R The renewable energy is installed in units of: billions/KW; c R0 The initial installed amount of renewable energy is as follows: billions/KW; μ is the installed proportion of renewable energy sources; ρ g0 Initial price for green license, units: element/individual; ρ g The price of green card is: element/individual; v is the average annual utilization hour of renewable energy, unit: hours/year;
the rate variable equation is:
wherein ρ is R The number of green certificates is the number of green certificates held by a power producer, and the unit is: a plurality of; q R The renewable energy generating capacity is as follows: trillion KW.h;
the auxiliary variable equation is as follows:
wherein q R The renewable energy generating capacity is as follows: trillion KW.h; c R The renewable energy is installed in units of: billions/KW; v is the average annual utilization hour of renewable energy, unit: hours/year;
the exogenous variable equation:
wherein Q is e As power demand, units: trillion KW.h; q (Q) 0 As an initial value of power demand, unit: trillion KW.h; q (Q) up Is the rate of increase of power demand;
T QR ＝T QR0 +T up
wherein T is QR Is the quota ratio; t (T) QR0 The quota ratio is an initial value; t (T) up Is the quota growth rate.
5. The system dynamics-based green evidence market and electric power market collaborative modeling method according to claim 1, wherein: the system dynamics model for the collaborative development of the green evidence market and the electric power market comprises the following components: exogenous variables, state variables, and constants;
the exogenous variables include: a power demand;
the state variables include: green license price, carbon trade price, renewable energy installation quantity and traditional energy installation quantity;
the constants include: the power demand growth rate, the renewable energy quota proportion, the renewable energy installation proportion and the renewable energy average annual utilization hour.
6. The system dynamics-based green evidence market and electric power market collaborative modeling method according to claim 1, wherein: the influence of key factors in the green evidence market and electric power market collaborative development model on the power supply structure comprises the following steps: renewable energy quota system, renewable energy installation proportion and power demand growth rate;
the specific content of the influence of the renewable energy quota system on the power supply structure comprises the following steps: simulating and analyzing the influence of different quota system targets on the traditional energy generating capacity and the renewable energy generating capacity by using Vensim software;
the specific contents of the influence of the renewable energy installation proportion on the power supply structure include: simulating and analyzing the influence of different renewable energy installation proportions on renewable energy generating capacity and renewable energy generating capacity duty ratio by using Vensim software;
the specific content of the influence of the power demand growth rate on the power supply structure comprises the following steps: and simulating and analyzing the influence of different power demand growth rates on the installed capacity of the renewable energy source by using Vensim software.
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