CN111260158A - Market multi-interest main body transaction behavior modeling method based on internal interactive cooperation - Google Patents
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Abstract
The invention discloses a market multi-interest main body transaction behavior modeling method based on internal interactive cooperation, which is characterized in that an energy hub basic framework of a market multi-interest main body is constructed based on the basic principle of hub input and output; respectively carrying out independent modeling analysis on multi-benefit subjects interactively cooperating in the energy concentrator, namely carrying out modeling analysis on an electric benefit subject, a gas benefit subject and a cogeneration benefit subject; establishing a coupling matrix to represent the incidence relation among all benefit subjects; and analyzing and calculating the obtained optimal conclusion through the coupling matrix, and guiding the reasonable distribution of the multi-benefit subjects according to the optimal conclusion. According to the invention, through quantifying the energy flow of the power connecting line and the natural gas pipeline between the system and the main network, the electric heating load variation trend is respectively analyzed, and an optimal conclusion is obtained under the constraint of an objective function.
Description
Technical Field
The invention belongs to the technical field of multi-energy main body cooperation management, and particularly relates to a market multi-benefit main body transaction behavior modeling method based on internal interactive cooperation.
Background
Energy is the basis for human survival and development and is the life line of national economy. Renewable energy is a great measure for improving the energy structure of China, promoting environmental protection and maintaining the sustainable development of society and economy. In the process of considering energy development, electric energy is no longer in a single energy form, and multiple energy forms such as gas heat greatly influence the proportion of electric energy in an energy structure. However, the development of gas-heat energy has far influence on the environment beyond electric energy, so that a multi-energy system formed by reasonably planning and designing electricity, heat and gas and optimizing operation provides an important solution for realizing the change of the energy field. When each energy source participates in the market, the market behavior of each energy source is restricted by other energy source forms. Each energy form is an independent benefit main body, and the operation and optimization of the power distribution network are inevitably influenced by other energy forms under the framework of mainly electric energy, so that the influence on the electricity benefit main body under the condition that a distribution network area taking an intelligent park and the like as a typical demonstration area is considered, and the coupling of various energy forms is considered. The existing literature is used for researching a combined dispatching model of energy systems such as electricity-gas, electricity-heat and the like. The multi-energy system has complex space-time characteristics, and has different research methods for different time scales and space scales.
On the spatial scale, the research of the multi-energy system can follow 'multi-energy element-multi-energy element integration-multi-energy network', and the space is divided into regional multi-energy systems which mainly adopt electric heating coupling and electric coupling; on the time scale, the research of the multi-energy system can be divided into dynamic problems on short time scales such as seconds and minutes, and dynamic problems on time scales such as hours, days and months. The main body of the multi-energy system includes not only single elements such as a thermal power plant, an electric boiler, etc., but also a combination of these elements, for example, a commercial building may include various elements such as a distributed natural gas power generation device, a distributed photovoltaic device, an electric boiler, a heat pump, etc. Therefore, it is necessary to study the integration characteristics of the device based on the characteristics of the device, and in academia, the integration of the multi-energy device is generally called energy hub. The main body of the multi-energy system comprises single elements such as a thermal power plant, an electric boiler and the like, and more, the single elements are higher-level packaging and combination of the elements, the elements are mutually coupled, the connection relationship is complex, and the analysis is often difficult. In fact, the most critical to a system is its input-output characteristics, i.e., the "external port" characteristics. For a multi-energy system, the problem to be solved is how to equate the coupling conversion relationship of internal elements and establish an external end port model for mapping the input and output of electricity, gas and heat. Under the concept of the energy hub, energy hubs are produced. The method can improve the flexibility of the energy system and can be widely applied to the problems of system planning, optimal power flow, demand side management, optimal operation and the like, the concept of optimal operation is mostly appeared in the field of power system analysis, and the method is popularized to the optimal operation of a multi-energy hybrid system. On the basis, the multi-energy-form participation market behavior is defined as participation of a plurality of interest-related bodies in the market, the power distribution network region is mainly researched, and how the multi-interest body internally couples and interactively cooperates to guide the market behavior is influenced, and the economy of the system is influenced.
Therefore, it is necessary to provide a modeling method for trading behavior of market multi-interest main body based on internal interactive collaboration.
Disclosure of Invention
The invention aims to provide a market multi-benefit-subject trading behavior modeling method based on internal interactive cooperation, aims to construct three different benefit subjects based on an energy hub and taking coordination and complementation of electricity, heat and gas as an example in a power distribution network area, and provides the market multi-benefit-subject trading behavior modeling method based on the internal interactive cooperation. Through quantifying the energy flow of the power connecting line and the natural gas pipeline between the system and the main network, including the output characteristics of each device in the hub in different time periods, a corresponding coupling matrix model is established, and the multi-dimensional characteristics of three stakeholders are modeled and analyzed. Meanwhile, the market behaviors of multi-benefit subjects are guided by considering the demand response characteristics of loads in multiple periods, analyzing the change trend of the connection electric load and the heat load of the energy hub and the electric purchasing condition and calculating the total electric purchasing cost. And finally, taking 3 typical parks in the multi-interest main body hub system as an example, the superiority of the distribution network multi-interest main body market trading mechanism based on the internal interactive cooperation of the energy hub is demonstrated.
In order to achieve the purpose, the technical scheme adopted by the invention is as follows:
the market multi-interest main body trading behavior modeling method based on internal interactive collaboration comprises the following steps:
step 1: constructing an energy hub basic framework of a market multi-interest main body based on the input and output basic principle of the hub;
step 2: respectively carrying out independent modeling analysis on the multi-benefit main bodies interactively cooperated in the energy hub in the step 1, namely carrying out modeling analysis on an electric benefit main body, a gas benefit main body and a cogeneration benefit main body;
and step 3: establishing a coupling matrix to represent the incidence relation among all the benefit bodies in the step 2;
and 4, step 4: estimating the photovoltaic and fan output according to historical data, measuring the tie line power of the system and calculating the net exchange power; measuring gas flow data of a natural gas pipeline, and setting a target function to calculate the gas and electricity acquisition cost; analyzing the change trend of the thermoelectric load according to the response change; and (4) analyzing and calculating the obtained optimal conclusion through the coupling matrix in the step (3), and guiding the reasonable distribution of the multi-benefit subjects according to the optimal conclusion.
Further, in step 1, the basic framework of the energy hub is as follows:
in the formula (1), the vector
And
representing inputs and outputs of different energies of a single hub, matrix lambdamnUsed as a coupling matrix describing the relationship between the input and the output, where the value is related to the conversion efficiency of the power supply device.
Further, the modeling analysis of the electric interest body in the step 2 specifically comprises:
the electric interest main body comprises photovoltaic, wind power generation, comprehensive dispatching of energy storage equipment and load demand response;
for photovoltaic and wind power generation, a random planning method is selected, and the uncertainty of a distributed power supply is described by using a probability density distribution function, wherein the photovoltaic output obeys beta distribution, and the fan output obeys two-parameter Weibu distribution;
for the comprehensive scheduling of the energy storage device, the energy storage device can be flexibly called in a scheduling period, and a dynamic model of the energy storage device can be expressed as follows:
in the formula (3), x is a binary variable and represents the charge-discharge state of the energy storage device at the ith day t, 1 is in a working state, 0 is in a non-working state,
in order to charge and discharge power in real time,
is a charge-discharge power limit; in the formula (4), Ei,0，Ei,24Representing the stored energy at the beginning and end of the energy storage device; es,t,jReal-time charge;
limiting the real-time charge capacity of the equipment;
the maximum charge capacity of the equipment; ei,tRepresenting the electric quantity stored by the energy storage equipment at adjacent moments, α and β are charge and discharge efficiencies, and are both set to be 0.9;
for the load demand response, the PBDR is selected as a research object in the load demand response project, the multi-interest subject is built, and the user is guided to carry out load distribution by responding the time-of-use electricity price signal in real time; the dual electricity price mechanism of the time-of-use electricity price and the real-time electricity price is selected, a user responds to the time-of-use electricity price and can participate in the PBDR project according to the real-time electricity price, and load demand calculation after PBDR is realized can be represented as follows:
in the formula:
in order to implement the load after PBDR,
initial load before PBDR implementation; t is a total scheduling period, and s and T are sub-periods; e.g. of the typestThe value is the price elastic coefficient, and s-t is the self elastic coefficient, otherwise, the value is the cross elastic coefficient;
to implement the real-time electricity rates after PBDR,
initial time-of-use electricity prices before PBDR is implemented; the real-time electricity price can be calculated according to the following formula:
in the formula:
is the total load demand for a day;
is the average load of a day.
Further, the modeling analysis on the gas benefit subject in the step 2 specifically comprises:
the interest subject of the gas boiler takes the gas boiler as a considered object, and the model of the interest subject of the gas boiler is represented by the coupling relation of the heating power and the gas consumption in unit time:
in the formula, muGB,thRepresents the electrical conversion efficiency;
the heat supply power of the gas-fired boiler is increased,
is the amount of natural gas consumed per unit time.
Further, the modeling analysis of the cogeneration interest body in the step 2 specifically comprises:
the combined heat and power benefit agent takes natural gas as a consideration object, and the power supply and heat supply quantity of the combined heat and power benefit agent is expressed as follows:
in the formula:
the electric quantity and the heating quantity of the cogeneration unit generated at the first day t,
is the consumption of natural gas per unit time, muCHP,ele，μCHP,thThe gas-electricity conversion efficiency and the gas-heat conversion efficiency are respectively.
Further, the coupling matrix in step 3 is specifically:
by modeling the coupling relationship among the internal devices of the multi-interest main body, the analysis of the coupling relationship among the multi-interest main body can be further expressed as follows:
in the middle of the above formula, the compound has the following structure,
the total electric load demand at the time t of the first day;
the total heat load demand at the time t of the first day;
storing power for the heat storage device in real time;
purchasing electric power from the side of the power grid;
outputting power for the distributed power supply;
is the total demand response;
the method comprises the following steps of (1) generating electric quantity and heating quantity for a cogeneration unit at t time of the first day;
the heat is generated by the gas boiler;
outputting the total electric quantity for the concentrator;
outputting the total heat for the concentrator;
inputting the electric quantity of a power grid for the concentrator;
converting heat into electricity;
generating heat for a steam boiler; mu.sCHP,eleμGB,thAnd muCHP,thThe electric, electric and gas-heat conversion efficiencies are respectively 0.3, 0.4 and 0.9.
The beneficial technical effects of the invention are as follows: (1) the method comprises the steps of constructing an internal coupling interaction model of a multi-benefit main body based on an energy hub, establishing a definite coupling matrix, quantifying the energy flow of a power connecting line and a natural gas pipeline between a system and a main network, analyzing the variation trend of electric heating loads respectively, and obtaining an optimal conclusion under the constraint of an objective function.
(2) An electricity price type demand response model is introduced, an electricity price mechanism is fully responded, distributed energy storage is matched, a typical region of a power distribution network is taken as an example, and the specific influence of a market mechanism on an electric heating load under the coupling condition of a multi-benefit subject is researched.
Drawings
FIG. 1 is a block diagram of a multi-stakeholder hub architecture according to an embodiment of the invention.
Fig. 2 is a schematic diagram illustrating an electrical load variation of the hub according to an embodiment of the present invention.
Fig. 3 is a schematic diagram illustrating a thermal load variation of the hub according to an embodiment of the present invention.
Fig. 4 is a schematic diagram showing the change of the purchased power under two scenarios according to the embodiment of the present invention.
Fig. 5 is a schematic diagram showing the change of the gas purchase amount under two scenarios according to the embodiment of the invention.
Fig. 6 is a schematic diagram showing the electrical load change before and after the PBDR is implemented in the hub 1 according to the embodiment of the present invention.
Fig. 7 is a schematic diagram showing the electrical load change before and after the PBDR implementation of the hub 2 according to the embodiment of the present invention.
Fig. 8 is a schematic diagram showing the electrical load change before and after the PBDR implementation of the hub 3 according to the embodiment of the present invention.
Fig. 9 shows a probability density diagram of photovoltaic contribution for an embodiment of the present invention.
Fig. 10 shows a probability distribution diagram of photovoltaic contribution for an embodiment of the present invention.
FIG. 11 is a probability density diagram of fan output according to an embodiment of the present invention.
FIG. 12 is a schematic illustration of a probability distribution of fan output according to an embodiment of the present invention.
FIG. 13 is a flowchart illustrating the overall steps of an embodiment of the present invention.
Detailed Description
The technical solutions in the embodiments of the present invention will be clearly and completely described below with reference to fig. 1 to 13 of the present invention, and it is obvious that the described embodiments are only a part of the embodiments of the present invention, and not all of the embodiments. 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.
Example (b):
as shown in fig. 13, the modeling method for trading behavior of multi-benefit agent in market based on internal interactive collaboration comprises the following steps:
step 1: constructing an energy hub basic framework of a market multi-interest main body based on the input and output basic principle of the hub;
step 2: respectively carrying out independent modeling analysis on the multi-benefit main bodies interactively cooperated in the energy hub in the step 1, namely carrying out modeling analysis on an electric benefit main body, a gas benefit main body and a cogeneration benefit main body;
and step 3: establishing a coupling matrix to represent the incidence relation among all the benefit bodies in the step 2;
and 4, step 4: estimating the photovoltaic and fan output according to historical data, measuring the tie line power of the system and calculating the net exchange power; measuring gas flow data of a natural gas pipeline, and setting a target function to calculate the gas and electricity acquisition cost; analyzing the change trend of the thermoelectric load according to the response change; and (4) analyzing and calculating the obtained optimal conclusion through the coupling matrix in the step (3), and guiding the reasonable distribution of the multi-benefit subjects according to the optimal conclusion.
The energy hub is not only a connection center between various energy forms and different types of loads, but also comprises the transmission, conversion and storage of energy of different forms, and has good control characteristics. The problem of weak correlation between the power grid and the micro-grid is solved to a certain extent. As a virtual control platform, the system can monitor the running states of the micro-grid and the power distribution network in real time, respond to the change of distributed energy output and optimize various load requirements in time. The diversity of distributed energy sources dictates that multi-stakeholders must include a variety of energy supply equipment. The basic framework of a multi-benefit-subject hub is designed by integrating benefit subjects such as electric power, heat, gas and the like into research objects, and is shown in figure 1.
For a single multi-benefit agent hub (MSH), the multi-port network should include photovoltaic, wind turbines, cogeneration units, gas boilers, transformers, and energy storage systems, among other functional units. For a single input-output energy conversion device, the coupling relationship between the input and output is typically described by a coupling matrix in the following specific form:
in the formula (1), the vector
And
representing inputs and outputs of different energies of a single hub, matrix lambdamnUsed as a coupling matrix describing the relationship between the input and the output, where the value is related to the conversion efficiency of the power supply device.
A. Electric benefits subject
For the power benefit agent, the comprehensive scheduling and load demand response of photovoltaic, wind power generation and energy storage equipment are mainly considered. In order to solve the uncertainty of photovoltaic and fan output, a stochastic programming method is selected, and the uncertainty of a distributed power supply is described by using a probability density distribution function (CDF), wherein the photovoltaic output obeys beta distribution, and the fan output obeys wibu distribution of two parameters. The detailed figures are shown in detail in fig. 9-12.
(1) Energy storage model
The energy storage device can be flexibly called in the scheduling period, and the dynamic model can be expressed as follows:
in the formula (3), x is a binary variable and represents the charge-discharge state of the energy storage device at the ith day t, 1 is in a working state, 0 is in a non-working state,
in order to charge and discharge power in real time,
is a charge-discharge power limit; in the formula (4), Ei,0，Ei,24Representing the stored energy at the beginning and end of the energy storage device; es,t,jReal-time charge;
limiting the real-time charge capacity of the equipment;
the maximum charge capacity of the equipment; ei,tRepresenting the electric quantity stored by the energy storage equipment at adjacent moments, α and β are charge and discharge efficiencies, and are both set to be 0.9;
for the load demand response, the PBDR is selected as a research object in the load demand response project, the multi-interest subject is built, and the user is guided to carry out load distribution by responding the time-of-use electricity price signal in real time; the dual electricity price mechanism of the time-of-use electricity price and the real-time electricity price is selected, a user responds to the time-of-use electricity price and can participate in the PBDR project according to the real-time electricity price, and load demand calculation after PBDR is realized can be represented as follows:
in the formula:
in order to implement the load after PBDR,
initial load before PBDR implementation; t is a total scheduling period, and s and T are sub-periods; e.g. of the typestThe value is the price elastic coefficient, and s-t is the self elastic coefficient, otherwise, the value is the cross elastic coefficient;
to implement the real-time electricity rates after PBDR,
initial time-of-use electricity prices before PBDR is implemented; the real-time electricity price can be calculated according to the following formula:
in the formula:
is the total load demand for a day;
is the average load of a day.
B. Gas benefit agent
The gas boiler benefit body (GBS) is mainly typified by a gas boiler. They usually only switch gas and warm air. The model is represented by the coupling relation between the heat supply power and the gas consumption in unit time:
in the formula, muGB,thRepresents the electrical conversion efficiency;
the heat supply power of the gas-fired boiler is increased,
is the amount of natural gas consumed per unit time.
C. Combined heat and power benefit agent
For the benefit subject of cogeneration, natural gas is used as the main energy of the thermal power generating unit, and has the unique advantages of high power generation efficiency, comprehensive utilization of energy and the like. The power supply and heat supply are expressed as:
in the formula:
the electric quantity and the heating quantity of the cogeneration unit generated at the first day t,
is the consumption of natural gas per unit time, muCHP,ele，μCHP,thThe gas-electricity conversion efficiency and the gas-heat conversion efficiency are respectively.
D. Hub internal coupling relationship
By modeling the coupling relationship among the internal devices of the multi-interest main body, the analysis of the coupling relationship among the multi-interest main body can be further expressed as follows:
in the upper type，
The total electric load demand at the time t of the first day;
the total heat load demand at the time t of the first day;
storing power for the heat storage device in real time;
purchasing electric power from the side of the power grid;
outputting power for the distributed power supply;
is the total demand response;
the method comprises the following steps of (1) generating electric quantity and heating quantity for a cogeneration unit at t time of the first day;
the heat is generated by the gas boiler;
outputting the total electric quantity for the concentrator;
outputting the total heat for the concentrator;
inputting the electric quantity of a power grid for the concentrator;
converting heat into electricity;
generating heat for a steam boiler; mu.sCHP,eleμGB,thAnd muCHP,thThe electric, electric and gas-heat conversion efficiencies are respectively 0.3, 0.4 and 0.9.
Simulation analysis
Three hub hubs are taken as an example, the rationality of the scheme is verified, three different parks are included, and the influence of multi-dimensional characteristics of multi-benefit subjects on gas and power purchasing optimization under different conditions is discussed. The scheduling period is about 25 days, i is 25, T is 24 hours, the time step is 1 hour, and the internal parameters of each hub are identical. The thermal load and electrical load values of different hubs are shown in the figure, and a probability density function curve of an output is obtained by inputting historical data. See figures 2-4 for details. In addition, other power limits are shown in table 2, and the time-of-use electricity rates are shown in table 3. For better results, two basic scenarios were set for comparison. Firstly, the system does not contain a demand response item, and only carries out gas-electricity consumption analysis without relating to the movement of user-side resources; the other scene is to introduce a demand corresponding item, because the user's electricity utilization behavior can be adjusted through the real-time response to the electricity price signal, the user side resource can be fully adjusted, and the peak-load and valley-load adjusting effect is better under the stimulation of the time-of-use electricity price. The resulting pattern can be contrasted sharply.
TABLE 2 electric quantity and Natural gas quantity Transmission constraints
TABLE 3 time-of-use price for buying and selling electricity
For case 1, which contains all of the stakeholders in the hub, but no demand response items, the CDF curve for the gas and electricity purchases in this case is shown in fig. 5. As can be clearly seen from the figure, the purchase amount of the natural gas is between 0kW and 9000kW, the purchase amount reaches a peak value around 7500kW, and the probability value reaches 1. Meanwhile, the electricity purchasing power is between 0 and 5000kw, the electricity purchasing quantity reaches a peak value at about 4500kw, and the probability value is 1. Due to the uncertainty of the distributed power generation, when the photovoltaic and the fan can not meet the basic load requirement of urban domestic electricity, the requirements of electricity and heat load can be adjusted by purchasing electricity and gas, and the constraint of the limit of power transmission power needs to be met.
For case 2, we can see that the rising trend of the CDF curve is similar to case 1. But the amount of electricity and gas purchased is significantly reduced due to the implementation of the PBDR project. This is because the PBDR reduces the power purchase from the distribution network in the garden to some extent by actively guiding the power utilization behavior of the user, and has good peak load regulation and valley filling capability. On the basis of case 1, the gas supply pressure of the natural gas system is also reduced. The improvement of the energy supply proportion of the power system is beneficial to improving the economic operation level of the whole system and relieving the power supply pressure of the system.
FIGS. 6-8 show the change in internal load capacity of a single hub before and after the implementation of a PBDR project. Therefore, after the PBDR project is implemented, the load curve has good variation trend in different peak-valley periods, and the good characteristic that PBDR has peak clipping and valley filling is reflected. Different hub load values are different, but the curves have similar basic trends, which shows that the advantages of PBDR in system operation are particularly obvious. Further, since the reduction in the electric load affects the purchase amount of natural gas to some extent, the dependence on natural gas is also reduced, and thus the cost influence due to the internal coupling characteristics of the multi-benefit agent is easily obtained.
The gas and electricity procurement costs for the hub are shown in table 1 below. It can be seen by comparison that the gas and electricity procurement costs are higher for case 1 than for case 2. The main reason is that the implementation of the PBDR project actively guides the electricity utilization behavior of users according to the time-of-use electricity price, and reduces the running cost of the whole system and the power supply pressure of each concentrator.
TABLE 1 comparison of cost calculations for each part under different conditions
The method mainly researches multi-dimensional feature modeling of multi-interest main bodies in a park, and the multi-dimensional feature modeling comprises multi-period operation characteristics, internal coupling characteristics and space characteristics, provides the concept of a multi-interest related person concentrator system, and constructs a multi-energy coupling model in a concentrator. How multi-benefit agents interact with the power distribution network through internal interactive cooperation is analyzed. Finally, the effectiveness of the trading behavior of the distribution network multi-interest main body based on internal interactive cooperation is illustrated through a calculation example. Meanwhile, the PBDR project is used as a comparison object, so that the implementation of the PBDR can improve the economy of system operation and relieve the power supply pressure of a distribution network.
In the description of the present invention, it is to be understood that the terms "counterclockwise", "clockwise", "longitudinal", "lateral", "up", "down", "front", "back", "left", "right", "vertical", "horizontal", "top", "bottom", "inner", "outer", etc., indicate orientations or positional relationships based on those shown in the drawings, and are used for convenience of description only, and do not indicate or imply that the referenced devices or elements must have a particular orientation, be constructed and operated in a particular orientation, and thus, are not to be considered as limiting.
Claims (6)
1. The market multi-interest main body trading behavior modeling method based on internal interactive collaboration is characterized by comprising the following steps of:
step 1: constructing an energy hub basic framework of a market multi-interest main body based on the input and output basic principle of the hub;
step 2: respectively carrying out independent modeling analysis on the multi-benefit main bodies interactively cooperated in the energy hub in the step 1, namely carrying out modeling analysis on an electric benefit main body, a gas benefit main body and a cogeneration benefit main body;
and step 3: establishing a coupling matrix to represent the incidence relation among all the benefit bodies in the step 2;
and 4, step 4: estimating the photovoltaic and fan output according to historical data, measuring the tie line power of the system and calculating the net exchange power; measuring gas flow data of a natural gas pipeline, and setting a target function to calculate the gas and electricity acquisition cost; analyzing the change trend of the thermoelectric load according to the response change; and (4) analyzing and calculating the obtained optimal conclusion through the coupling matrix in the step (3), and guiding the reasonable distribution of the multi-benefit subjects according to the optimal conclusion.
2. The internal interactive collaboration based modeling method for trading behavior of multi-interest main body in market according to claim 1, wherein in step 1, the energy hub basic framework is as follows:
in the formula (1), the vector
And
representing inputs and outputs of different energies of a single hub, matrix lambdamnUsed as a coupling matrix describing the relationship between the input and the output, where the value is related to the conversion efficiency of the power supply device.
3. The market multi-interest-principal trading behavior modeling method based on internal interactive collaboration as claimed in claim 2, wherein the modeling analysis of the electric interest principal in the step 2 is specifically as follows:
the electric interest main body comprises photovoltaic, wind power generation, comprehensive dispatching of energy storage equipment and load demand response;
for photovoltaic and wind power generation, a random planning method is selected, and the uncertainty of a distributed power supply is described by using a probability density distribution function, wherein the photovoltaic output obeys beta distribution, and the fan output obeys two-parameter Weibu distribution;
for the comprehensive scheduling of the energy storage device, the energy storage device can be flexibly called in a scheduling period, and a dynamic model of the energy storage device can be expressed as follows:
in the formula (3), x is a binary variable and represents the charge-discharge state of the energy storage device at the ith day t, 1 is in a working state, 0 is in a non-working state,
in order to charge and discharge power in real time,
is a charge-discharge power limit; in the formula (4), Ei,0，Ei,24Representing the stored energy at the beginning and end of the energy storage device; es,t,jReal-time charge;
limiting the real-time charge capacity of the equipment;
the maximum charge capacity of the equipment; ei,tRepresenting the electric quantity stored by the energy storage equipment at adjacent moments, α and β are charge and discharge efficiencies, and are both set to be 0.9;
for the load demand response, the PBDR is selected as a research object in the load demand response project, the multi-interest subject is built, and the user is guided to carry out load distribution by responding the time-of-use electricity price signal in real time; the dual electricity price mechanism of the time-of-use electricity price and the real-time electricity price is selected, a user responds to the time-of-use electricity price and can participate in the PBDR project according to the real-time electricity price, and load demand calculation after PBDR is realized can be represented as follows:
in the formula:
in order to implement the load after PBDR,
initial load before PBDR implementation; t is a total scheduling period, and s and T are sub-periods; e.g. of the typestThe value is the price elastic coefficient, and s-t is the self elastic coefficient, otherwise, the value is the cross elastic coefficient;
to implement the real-time electricity rates after PBDR,
initial time-of-use electricity prices before PBDR is implemented; the real-time electricity price can be calculated according to the following formula:
in the formula:
is the total load demand for a day;
is the average load of a day.
4. The internal interactive collaboration-based market multi-interest entity trading behavior modeling method according to claim 3, wherein the modeling analysis on the gas interest entity in the step 2 specifically comprises the following steps:
the interest subject of the gas boiler takes the gas boiler as a considered object, and the model of the interest subject of the gas boiler is represented by the coupling relation of the heating power and the gas consumption in unit time:
in the formula, muGB,thRepresents the electrical conversion efficiency;
the heat supply power of the gas-fired boiler is increased,
is the amount of natural gas consumed per unit time.
5. The internal interactive collaboration-based market multi-interest entity trading behavior modeling method according to claim 4, wherein the modeling analysis on the cogeneration interest entity in the step 2 is specifically as follows:
the combined heat and power benefit agent takes natural gas as a consideration object, and the power supply and heat supply quantity of the combined heat and power benefit agent is expressed as follows:
in the formula:
the electric quantity and the heating quantity of the cogeneration unit generated at the first day t,
is the consumption of natural gas per unit time, muCHP,ele，μCHP,thThe gas-electricity conversion efficiency and the gas-heat conversion efficiency are respectively.
6. The internal interactive collaboration-based market multi-interest entity trading behavior modeling method according to claim 5, wherein the coupling matrix in the step 3 is specifically:
by modeling the coupling relationship among the internal devices of the multi-interest main body, the analysis of the coupling relationship among the multi-interest main body can be further expressed as follows:
in the middle of the above formula, the compound has the following structure,
the total electric load demand at the time t of the first day;
the total heat load demand at the time t of the first day;
storing power for the heat storage device in real time;
purchasing electric power from the side of the power grid;
outputting power for the distributed power supply;
is the total demand response;
the method comprises the following steps of (1) generating electric quantity and heating quantity for a cogeneration unit at t time of the first day;
the heat is generated by the gas boiler;
outputting the total electric quantity for the concentrator;
outputting the total heat for the concentrator;
inputting the electric quantity of a power grid for the concentrator;
converting heat into electricity;
generating heat for a steam boiler; mu.sCHP,eleμGB,thAnd muCHP,thThe electric, electric and gas-heat conversion efficiencies are respectively 0.3, 0.4 and 0.9.
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