CN113222233A - Natural gas multi-main-body energy game analysis method and system - Google Patents
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Abstract
The invention provides a natural gas multi-main-body energy game analysis method and a system, wherein the method comprises the following steps: analyzing evaluation indexes of all the main bodies; determining the game relation among the subjects according to the analysis result; and analyzing the game process among the subjects and the conditions and influencing factors when the equilibrium state is reached according to the game relation. The method can simulate the game relation and the game state among the oligopeptides in the natural gas market, intuitively and accurately describe the resource quantity fluctuation state and the price adjustment strategy in each oligopolistic game process through a mathematical model, and further analyze the influence of external environment change on the natural gas energy main game strategy.
Description
Technical Field
The invention belongs to the field of multi-subject energy planning, and particularly relates to a natural gas multi-subject energy game analysis method and system.
Background
The natural gas market in China is still in the stage of monopoly competition, and enterprises can adopt different competitive behaviors due to different market structures, so that different market effects can be generated inevitably. The research on the market competition and the game mechanism of the natural gas short-head enterprise has stronger practical significance. At present, most scholars research on natural gas oligopolistic enterprises is mainly carried out on the premise of static, complete information and single strategy, and then the complete information is supposed not to exist in reality, so that the decision of the scholars is carried out in a limited manner; meanwhile, due to the conditions that the management layer judges the market situation, and whether the production scale and the information of the enterprises are sufficiently symmetrical or not, the enterprises have different expectations, so that the natural gas oligopolistic enterprises actively participate in the market, develop an optimized and feasible strategy and realize the maximization of the profits per se are a very complex process.
At present, the research on the game of the energy main bodies in the natural gas market is mainly carried out on the premise of static state, complete information and a single strategy, the game relation and state change among the energy main bodies are difficult to be fully reflected, and the research result is over-ideal.
Disclosure of Invention
Aiming at the problems, the invention provides a natural gas multi-main-body energy game analysis method and system, which can simulate the game relation among natural gas energy main bodies, analyze and obtain conditions and influence factors when the whole system reaches a balanced state, and further research the influence of the decision of an enterprise on the whole market under different expected decision rules.
A natural gas multi-subject energy gaming analysis method, the method comprising the steps of:
analyzing evaluation indexes of all the main bodies;
determining the game relation among the subjects according to the analysis result;
and analyzing the game process among the subjects and the conditions and influencing factors when the equilibrium state is reached according to the game relation.
Preferably, the multi-agent includes a resource supply end agent, a resource demand end agent, a government or regulatory body, and a natural gas energy source substitution end agent.
Preferably, the evaluation index of the resource supply terminal main body is evaluated from three aspects of sales volume, market share and benefit; evaluating the evaluation index of the resource demand side main body from the aspect of resource cost; the government or regulatory bodies are used to regulate market prices and gate house prices.
Preferably, the main gaming relations are as follows: the resource supply end main body acquires the number of users, the gas consumption demand and the bearable price information, and transmits the updated information to the resource demand end main body after analyzing and processing the information by a price adjustment economic method; the resource demand end main body obtains updated user quantity, gas consumption demand and bearable price information provided by the resource supply end main body, determines the gas consumption demand and the resource supplier by optimizing resource configuration, and then transmits the information to the resource supplier; the government and the supervision department control the price of the natural gas within a specified range and then feed back the price to the resource supply end main body and the resource demand end main body; wherein there is a contention relationship between the resource-supplying end bodies.
Preferably, the method for analyzing the game process among the subjects and the conditions and influencing factors when the equilibrium state is reached is as follows:
respectively initializing an environment variable, a resource demand end main body variable and a resource supply end main body variable;
the resource supply end main body acquires market information and determines or updates a sale target according to the market information;
the resource supplier end main body calls a supplier optimization model to optimize a marketing strategy, if the price variation amplitude of all resource suppliers is smaller than or equal to a preset value a, the game state reaches a balanced state, the analysis is terminated, and balanced state information is output; if the price variation of the resource supplier is larger than the preset value a, the resource supply end main body transmits price, resource quantity and supply and demand coefficient information to the resource demand end main body;
after the resource demand end main bodies acquire the price, the resource amount and the supply and demand coefficient information of each resource supply end main body, calling a user optimization model to optimize resource allocation, and if the variation range of the demand amount of all the resource demand end main bodies is less than or equal to a preset value b, enabling the game state to reach a balanced state, terminating analysis and outputting balanced state information; if the variation amplitude of the demand quantity of the resource demand end main body is larger than the preset value b, the resource demand end main body feeds back demand information to the market and the resource supply end main body;
the resource supply end main body acquires the demand information, the competitor information and the market information, and carries out market evaluation by combining a sales target;
and determining or updating a sales target according to the market evaluation result, and iterating and circulating.
Preferably, the environmental variables include alternative energy prices, pipe network transport capacity constraints; the user subject variables comprise user quantity, gas consumption demand and price bearing capacity; the resource supply end main body variable comprises a resource amount and a cost;
the preset value a is 3%, and the preset value b is 3%;
the demand information includes own gas demand and resource suppliers.
Preferably, the resource demand end main body comprises a city gas company and a direct supply user; the city gas company comprises a city gas user group and a single city gas user, and the direct supply users comprise a direct supply user group and a single direct supply user; the method is based on the following assumptions:
dividing direct supply user groups and city gas user groups according to cities, setting the upper limit of the natural gas resource amount of each city, and simulating the gas supply capacity of a pipe network company;
the demand of a single city gas user is determined, the city gas user of the minimum unit is not simulated and modeled in the model, but the gas utilization rule of the city gas user is analyzed by using a probability theory and mathematical statistics method, the city gas company and the direct supply user are simulated and modeled by adopting a scatter point distribution mode, and the natural gas demand of each user is set according to data of probability statistics;
after the price is adjusted, the resource supply end main body firstly transmits the price information to the government, and after the government approves no problem, the price information is pushed to the market;
when the resource supply end main body evaluates the marketing condition, the ratio of the resource quantity to the demand quantity is used as a coefficient for evaluating the resource supply tension degree, when the coefficient is less than 1, the coefficient represents that the supply is less than the demand, the supplier feeds back a signal that the resource is abundant to the market, and when the coefficient is more than 1, the coefficient represents that the supply is more than the demand, the supplier feeds back a signal that the resource is not abundant to the market;
the user puts forward the consideration of the resource purchasing intention: whether the price is acceptable, whether the production and operation have no alternative energy, whether other surrounding users use the natural gas, and whether the natural gas is urgently used;
the user puts forward the consideration of contract signing intention: whether the price is superior, whether it is below the average price of the market, whether there are other more cost-effective resources, which resources are used by other surrounding users, whether there is an urgency to use natural gas.
Preferably, the system comprises the following modules:
the evaluation module is used for analyzing evaluation indexes of the main bodies;
the determining module is used for determining the game relation among the main bodies;
and the analysis module is used for analyzing the game process among the main bodies and the conditions and the influence factors when the balance state is achieved.
Preferably, the multi-agent includes a resource supply end agent, a resource demand end agent, a government or regulatory body, and a natural gas energy source substitution end agent.
Preferably, the evaluation indexes of the evaluation module are specifically: evaluating the evaluation index of the resource supply terminal main body from three aspects of sales volume, market share and benefit; evaluating the evaluation index of the resource demand side main body from the aspect of resource cost; the government or regulatory department is used for regulating market price and gate station price;
the determining module is configured to determine a game relationship among the subjects, specifically: the resource supply end main body acquires the number of users, the gas consumption demand and the bearable price information, and transmits the updated information to the resource demand end main body after analyzing and processing the information by a price adjustment economic method; the resource demand end main body obtains updated user quantity, gas consumption demand and bearable price information provided by the resource supply end main body, determines the gas consumption demand and the resource supplier by optimizing resource configuration, and then transmits the information to the resource supplier; the government and the supervision department control the price of the natural gas within a specified range and then feed back the price to the resource supply end main body and the resource demand end main body; wherein a contention relationship exists between the resource provider entities;
the analysis module is used for analyzing the game process among the main bodies and specifically comprises the following conditions and influence factors when the balance state is achieved: respectively initializing an environment variable, a resource demand end main body variable and a resource supply end main body variable;
the resource supply end main body acquires market information and determines or updates a sale target according to the market information;
the resource supplier end main body calls a supplier optimization model to optimize a marketing strategy, if the price variation amplitude of all resource suppliers is smaller than or equal to a preset value a, the game state reaches a balanced state, the analysis is terminated, and balanced state information is output; if the price variation of the resource supplier is larger than the preset value a, the resource supply end main body transmits price, resource quantity and supply and demand coefficient information to the resource demand end main body;
after the resource demand end main bodies acquire the price, the resource amount and the supply and demand coefficient information of each resource supply end main body, calling a user optimization model to optimize resource allocation, and if the variation range of the demand amount of all the resource demand end main bodies is less than or equal to a preset value b, enabling the game state to reach a balanced state, terminating analysis and outputting balanced state information; if the variation amplitude of the demand quantity of the resource demand end main body is larger than the preset value b, the resource demand end main body feeds back demand information to the market and the resource supply end main body;
the resource supply end main body acquires the demand information, the competitor information and the market information, and carries out market evaluation by combining a sales target;
and determining or updating a sales target according to the market evaluation result, and iterating and circulating.
The invention has the following beneficial effects: (1) the factor graph theory is applied for the first time to carry out simulation modeling on the game relation of the natural gas energy main body, and the influence of urban gas companies, direct supply users, supervision agencies and alternative energy sources on the game relation is further considered on the basis of the limited game; (2) the energy game relation is systematically analyzed by applying a system dynamics theory in the whole dimension and the individual dimension: on the market overall level, the influence of the external environment on the market game state is fully considered, the key influence factors are quantitatively analyzed, and a market dynamics response model is established; on the energy main body level, carrying out weight analysis on market state parameters and external environment influence factors, analyzing the influence of the market state parameters and the external environment influence factors on an energy main body according to the influence degree and the reaction sensitivity, and establishing an energy main body dynamic response model; (3) according to the characteristics of gas purchasing behaviors of users, the users are divided into two categories, namely urban gas companies and direct supply users, the market reaction characteristics of each category of users are clustered and analyzed, and a differentiated model parameter database is established; (4) the energy game state is researched from two levels of a space dimension and a time dimension: in the space dimension, the influence of gas using behaviors between the user and surrounding users is considered, and the tendency and the crowd psychology of the user are simulated; in the time dimension, the energy game process can be continuously carried out, and when the game state reaches the Nash equilibrium condition, the single main body parameter change breaks the Nash equilibrium state, so that the game finds a new Nash equilibrium state point again.
In conclusion, the method can simulate the game relation and the game state among the oligopeptides in the natural gas market, intuitively and accurately describe the resource quantity fluctuation state and the price adjustment strategy in each oligopolistic game process through a mathematical model, and further analyze the influence of external environment change on the natural gas energy main game strategy. By analyzing the system dynamics characteristics expressed by the natural gas market for realizing the decision-making target, the system can provide technical support for enterprise decision-making in the natural gas market in China. The technical scheme of the invention is the comprehensive application of various game relation theories, and comprises a plurality of game research theories such as a behavior strategy, a Cohn's theorem, an incomplete information game, a main body communication mechanism, a sub-game perfect balance and a repeated game, the research result is closer to the actual game state of an energy main body, and each main body is a mixed strategy game carried out in the incomplete information state, so that the game is more in line with the actual situation and can better serve the behavior decision of the energy main body.
Additional features and advantages of the invention will be set forth in the description which follows, and in part will be obvious from the description, or may be learned by practice of the invention. The objectives and other advantages of the invention will be realized and attained by the structure particularly pointed out in the written description and claims hereof as well as the appended drawings.
Drawings
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 those skilled in the art can also obtain other drawings according to the drawings without creative efforts.
Fig. 1 shows a flow chart of a method for analyzing a game relationship according to an embodiment of the present invention.
Detailed Description
In order to make the objects, technical solutions and advantages of the embodiments of the present invention clearer, the technical solutions in the embodiments of the present invention will be clearly and completely described below with reference to the drawings in the embodiments of the present invention, and it is obvious 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.
Examples
A natural gas multi-subject energy gaming analysis method, the multi-subjects including a resource supply subject, a resource demand subject, a government or regulatory authority, and a natural gas energy replacement subject, the method comprising the steps of:
analyzing evaluation indexes of all the main bodies; evaluating the evaluation index of the resource supply terminal main body from three aspects of sales volume, market share and benefit; evaluating the evaluation index of the resource demand side main body from the aspect of resource cost; the government or regulatory department is used for regulating market price and gate station price;
determining the game relation among the subjects according to the analysis result; the main game relations are as follows: the resource supply end main body acquires the number of users, the gas consumption demand and the bearable price information, and transmits the updated information to the resource demand end main body after analyzing and processing the information by a price adjustment economic method; the resource demand end main body obtains updated user quantity, gas consumption demand and bearable price information provided by the resource supply end main body, determines the gas consumption demand and the resource supplier by optimizing resource configuration, and then transmits the information to the resource supplier; the government and the supervision department control the price of the natural gas within a specified range and then feed back the price to the resource supply end main body and the resource demand end main body; wherein a contention relationship exists between the resource provider entities;
analyzing the game process among the main bodies and the conditions and influencing factors when the balance state is achieved according to the game relation; the specific method is shown in figure 1:
respectively initializing environment variables (including alternative energy price and pipe network transmission capacity constraint), main body variables of a resource demand end (including user quantity, gas demand and price bearing capacity) and main body variables of a resource supply end (including resource quantity, cost and the like);
the resource supply end main body acquires market information and determines or updates a sale target according to the market information;
the resource supplier end main body calls a supplier optimization model to optimize a marketing strategy, if the price variation amplitude of all resource suppliers is less than or equal to 3 percent, the game state reaches a balanced state, the analysis is terminated, and balanced state information is output; if the price variation of the resource supplier is more than 3%, the resource supply end main body transmits price, resource amount and supply and demand coefficient information to the resource demand end main body;
after the resource demand end main bodies acquire the price, the resource amount and the supply and demand coefficient information of each resource supply end main body, calling a user optimization model to optimize resource allocation, and if the variation range of the demand amount of all the resource demand end main bodies is less than or equal to 3%, the game state reaches a balanced state, the analysis is terminated, and balanced state information is output; if the variation amplitude of the demand quantity of the resource demand end main body is larger than 3%, the resource demand end main body feeds back demand information to the market and the resource supply end main body;
the resource supply end main body acquires the demand information (including self gas consumption demand and resource suppliers), competitor information and market information, and carries out market evaluation by combining with a sales target;
and determining or updating a sales target according to the market evaluation result, and iterating and circulating.
Further, the resource demand end main body comprises an urban gas company and a direct supply user; the city gas company comprises a city gas user group and a single city gas user, and the direct supply users comprise a direct supply user group and a single direct supply user; the method is based on the following assumptions:
dividing direct supply user groups and city gas user groups according to cities, setting the upper limit of the natural gas resource amount of each city, and simulating the gas supply capacity of a pipe network company;
the demand of a single city gas user is determined, the city gas user of the minimum unit is not simulated and modeled in the model, but the gas utilization rule of the city gas user is analyzed by using a probability theory and mathematical statistics method, the city gas company and the direct supply user are simulated and modeled by adopting a scatter point distribution mode, and the natural gas demand of each user is set according to data of probability statistics;
after the price is adjusted, the resource supply end main body firstly transmits the price information to the government, and after the government approves no problem, the price information is pushed to the market;
when the resource supply end main body evaluates the marketing condition, the ratio of the resource quantity to the demand quantity is used as a coefficient for evaluating the resource supply tension degree, when the coefficient is less than 1, the coefficient represents that the supply is less than the demand, the supplier feeds back a signal that the resource is abundant to the market, and when the coefficient is more than 1, the coefficient represents that the supply is more than the demand, the supplier feeds back a signal that the resource is not abundant to the market;
the user puts forward the consideration of the resource purchasing intention: whether the price is acceptable, whether the production and operation have no alternative energy, whether other surrounding users use the natural gas, and whether the natural gas is urgently used;
the user puts forward the consideration of contract signing intention: whether the price is superior, whether it is below the average price of the market, whether there are other more cost-effective resources, which resources are used by other surrounding users, whether there is an urgency to use natural gas.
According to the natural gas multi-subject game analysis method, the invention also provides a natural gas multi-subject energy game analysis system, wherein the multi-subjects comprise a resource supply subject, a resource demand subject, a government or regulatory department and a natural gas energy substitute subject, and the system comprises the following modules:
the evaluation module is used for analyzing evaluation indexes of the main bodies; the evaluation indexes of the evaluation module are specifically as follows: evaluating the evaluation index of the resource supply terminal main body from three aspects of sales volume, market share and benefit; evaluating the evaluation index of the resource demand side main body from the aspect of resource cost; the government or regulatory body is used for regulating market price and gate station price
The determining module is used for determining the game relationship among the main bodies, and specifically comprises the following steps: the resource supply end main body acquires the number of users, the gas consumption demand and the bearable price information, and transmits the updated information to the resource demand end main body after analyzing and processing the information by a price adjustment economic method; the resource demand end main body obtains updated user quantity, gas consumption demand and bearable price information provided by the resource supply end main body, determines the gas consumption demand and the resource supplier by optimizing resource configuration, and then transmits the information to the resource supplier; the government and the supervision department control the price of the natural gas within a specified range and then feed back the price to the resource supply end main body and the resource demand end main body; wherein a contention relationship exists between the resource provider entities;
the analysis module is used for analyzing the game process among the main bodies and the conditions and the influence factors when the balance state is achieved, and specifically comprises the following steps: respectively initializing an environment variable, a resource demand end main body variable and a resource supply end main body variable;
the resource supply end main body acquires market information and determines or updates a sale target according to the market information;
the resource supplier end main body calls a supplier optimization model to optimize a marketing strategy, if the price variation amplitude of all resource suppliers is less than or equal to 3 percent, the game state reaches a balanced state, the analysis is terminated, and balanced state information is output; if the price variation of the resource supplier is more than 3%, the resource supply end main body transmits price, resource amount and supply and demand coefficient information to the resource demand end main body;
after the resource demand end main bodies acquire the price, the resource amount and the supply and demand coefficient information of each resource supply end main body, calling a user optimization model to optimize resource allocation, and if the variation range of the demand amount of all the resource demand end main bodies is less than or equal to 3%, the game state reaches a balanced state, the analysis is terminated, and balanced state information is output; if the variation amplitude of the demand quantity of the resource demand end main body is larger than 3%, the resource demand end main body feeds back demand information to the market and the resource supply end main body;
the resource supply end main body acquires the demand information, the competitor information and the market information, and carries out market evaluation by combining a sales target;
and determining or updating a sales target according to the market evaluation result, and iterating and circulating.
To sum up, in the market starting phase: at a resource supply end, a natural gas resource supplier discloses resource information, including natural gas resource supply quantity and natural gas selling price; at the resource demand end, a user generates natural gas resource demand according to production and living needs of the user, and combines gas supply information of resource suppliers acquired from the market to compare and select natural gas resources and provide gas purchase demand for specified resource suppliers.
In the market gaming stage: the stage is characterized in that market price fluctuation is large, user demand changes obviously, and at a resource supply end, a natural gas resource supplier obtains natural gas supply and demand information from the market, wherein the natural gas supply and demand information comprises the total demand of users, user types, average market prices, and resource supply and supply prices of competitors. Then, overall evaluation is carried out on the market supply and demand condition, the self-operation condition, the competitor competition strategy and the like, price and policy adjustment is carried out according to the evaluation result, and the updated resource information and policy information are disclosed to the market; at the resource demand end, the user calls a resource configuration optimization model according to the latest resource information and policy information acquired from the market, optimizes the gas purchase demand again, and puts forward the gas purchase demand to the specified resource supplier. The process will iterate over a period of time to allow the market to compete fully.
In the market stable phase: this stage is characterized by less market price fluctuations and less user demand variations. At a resource supply end, after a natural gas resource supplier adjusts the price and policy according to market information, the natural gas selling price is basically kept unchanged or the variation range is very small; at the resource demand end, after a user calls a resource configuration optimization model according to natural gas resource supply information in the market, the gas purchase demand is basically unchanged. The user and the resource provider start to sign a gas purchase contract.
Although the present invention has been described in detail with reference to the foregoing embodiments, it will be understood by those of ordinary skill in the art that: the technical solutions described in the foregoing embodiments may still be modified, or some technical features may be equivalently replaced; and such modifications or substitutions do not depart from the spirit and scope of the corresponding technical solutions of the embodiments of the present invention.
Claims (10)
1. A natural gas multi-subject energy game analysis method is characterized by comprising the following steps:
analyzing evaluation indexes of all the main bodies;
determining the game relation among the subjects according to the analysis result;
and analyzing the game process among the subjects and the conditions and influencing factors when the equilibrium state is reached according to the game relation.
2. The natural gas multi-agent energy gaming analysis method of claim 1, wherein the multi-agents include resource supply agent agents, resource demand agent agents, government or regulatory bodies, and natural gas energy replacement agent agents.
3. The natural gas multi-subject energy game analysis method according to claim 2, wherein evaluation indexes of the resource supply subject are evaluated from three aspects of sales, market share and benefit; evaluating the evaluation index of the resource demand side main body from the aspect of resource cost; the government or regulatory bodies are used to regulate market prices and gate house prices.
4. The natural gas multi-subject energy game analysis method of claim 2, wherein the subject game relationships are: the resource supply end main body acquires the number of users, the gas consumption demand and the bearable price information, and transmits the updated information to the resource demand end main body after analyzing and processing the information by a price adjustment economic method; the resource demand end main body obtains updated user quantity, gas consumption demand and bearable price information provided by the resource supply end main body, determines the gas consumption demand and the resource supplier by optimizing resource configuration, and then transmits the information to the resource supplier; the government and the supervision department control the price of the natural gas within a specified range and then feed back the price to the resource supply end main body and the resource demand end main body; wherein there is a contention relationship between the resource-supplying end bodies.
5. The method for analyzing the natural gas multi-subject energy game as claimed in claim 2, wherein the method for analyzing the game process among the subjects and the conditions and influencing factors when the equilibrium state is reached is as follows:
respectively initializing an environment variable, a resource demand end main body variable and a resource supply end main body variable;
the resource supply end main body acquires market information and determines or updates a sale target according to the market information;
the resource supplier end main body calls a supplier optimization model to optimize a marketing strategy, if the price variation amplitude of all resource suppliers is smaller than or equal to a preset value a, the game state reaches a balanced state, the analysis is terminated, and balanced state information is output; if the price variation of the resource supplier is larger than the preset value a, the resource supply end main body transmits price, resource quantity and supply and demand coefficient information to the resource demand end main body;
after the resource demand end main bodies acquire the price, the resource amount and the supply and demand coefficient information of each resource supply end main body, calling a user optimization model to optimize resource allocation, and if the variation range of the demand amount of all the resource demand end main bodies is less than or equal to a preset value b, enabling the game state to reach a balanced state, terminating analysis and outputting balanced state information; if the variation amplitude of the demand quantity of the resource demand end main body is larger than the preset value b, the resource demand end main body feeds back demand information to the market and the resource supply end main body;
the resource supply end main body acquires the demand information, the competitor information and the market information, and carries out market evaluation by combining a sales target;
and determining or updating a sales target according to the market evaluation result, and iterating and circulating.
6. The natural gas multi-principal energy gaming analysis method of claim 5, wherein the environmental variables include alternative energy prices, pipe network transport capacity constraints; the user subject variables comprise user quantity, gas consumption demand and price bearing capacity; the resource supply end main body variable comprises a resource amount and a cost;
the preset value a is 3%, and the preset value b is 3%;
the demand information includes own gas demand and resource suppliers.
7. The natural gas multi-subject energy game analysis method of any one of claims 2-6, wherein the resource demand end subjects include city gas companies and direct supply users; the city gas company comprises a city gas user group and a single city gas user, and the direct supply users comprise a direct supply user group and a single direct supply user; the method is based on the following assumptions:
dividing direct supply user groups and city gas user groups according to cities, setting the upper limit of the natural gas resource amount of each city, and simulating the gas supply capacity of a pipe network company;
the demand of a single city gas user is determined, the city gas user of the minimum unit is not simulated and modeled in the model, but the gas utilization rule of the city gas user is analyzed by using a probability theory and mathematical statistics method, the city gas company and the direct supply user are simulated and modeled by adopting a scatter point distribution mode, and the natural gas demand of each user is set according to data of probability statistics;
after the price is adjusted, the resource supply end main body firstly transmits the price information to the government, and after the government approves no problem, the price information is pushed to the market;
when the resource supply end main body evaluates the marketing condition, the ratio of the resource quantity to the demand quantity is used as a coefficient for evaluating the resource supply tension degree, when the coefficient is less than 1, the coefficient represents that the supply is less than the demand, the supplier feeds back a signal that the resource is abundant to the market, and when the coefficient is more than 1, the coefficient represents that the supply is more than the demand, the supplier feeds back a signal that the resource is not abundant to the market;
the user puts forward the consideration of the resource purchasing intention: whether the price is acceptable, whether the production and operation have no alternative energy, whether other surrounding users use the natural gas, and whether the natural gas is urgently used;
the user puts forward the consideration of contract signing intention: whether the price is superior, whether it is below the average price of the market, whether there are other more cost-effective resources, which resources are used by other surrounding users, whether there is an urgency to use natural gas.
8. A natural gas multi-subject energy gaming analysis system, the system comprising the following modules:
the evaluation module is used for analyzing evaluation indexes of the main bodies;
the determining module is used for determining the game relation among the main bodies;
and the analysis module is used for analyzing the game process among the main bodies and the conditions and the influence factors when the balance state is achieved.
9. The natural gas multi-agent energy gaming analysis system of claim 8, wherein the multi-agents include resource supply agent agents, resource demand agent agents, government or regulatory authorities, and natural gas energy replacement agent agents.
10. The natural gas multi-principal energy gaming analysis system of claim 9,
the evaluation indexes of the evaluation module are specifically as follows: evaluating the evaluation index of the resource supply terminal main body from three aspects of sales volume, market share and benefit; evaluating the evaluation index of the resource demand side main body from the aspect of resource cost; the government or regulatory department is used for regulating market price and gate station price;
the determining module is configured to determine a game relationship among the subjects, specifically: the resource supply end main body acquires the number of users, the gas consumption demand and the bearable price information, and transmits the updated information to the resource demand end main body after analyzing and processing the information by a price adjustment economic method; the resource demand end main body obtains updated user quantity, gas consumption demand and bearable price information provided by the resource supply end main body, determines the gas consumption demand and the resource supplier by optimizing resource configuration, and then transmits the information to the resource supplier; the government and the supervision department control the price of the natural gas within a specified range and then feed back the price to the resource supply end main body and the resource demand end main body; wherein a contention relationship exists between the resource provider entities;
the analysis module is used for analyzing the game process among the main bodies and specifically comprises the following conditions and influence factors when the balance state is achieved: respectively initializing an environment variable, a resource demand end main body variable and a resource supply end main body variable;
the resource supply end main body acquires market information and determines or updates a sale target according to the market information;
the resource supplier end main body calls a supplier optimization model to optimize a marketing strategy, if the price variation amplitude of all resource suppliers is smaller than or equal to a preset value a, the game state reaches a balanced state, the analysis is terminated, and balanced state information is output; if the price variation of the resource supplier is larger than the preset value a, the resource supply end main body transmits price, resource quantity and supply and demand coefficient information to the resource demand end main body;
after the resource demand end main bodies acquire the price, the resource amount and the supply and demand coefficient information of each resource supply end main body, calling a user optimization model to optimize resource allocation, and if the variation range of the demand amount of all the resource demand end main bodies is less than or equal to a preset value b, enabling the game state to reach a balanced state, terminating analysis and outputting balanced state information; if the variation amplitude of the demand quantity of the resource demand end main body is larger than the preset value b, the resource demand end main body feeds back demand information to the market and the resource supply end main body;
the resource supply end main body acquires the demand information, the competitor information and the market information, and carries out market evaluation by combining a sales target;
and determining or updating a sales target according to the market evaluation result, and iterating and circulating.
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Citations (6)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN107172606A (en) * | 2017-06-29 | 2017-09-15 | 南京邮电大学 | A kind of distribution method of green energy resource joint spectrum shared in the wireless network |
| CN109034563A (en) * | 2018-07-09 | 2018-12-18 | 国家电网公司 | Multi-subject game incremental power distribution network source-network-load collaborative planning method |
| CN109286187A (en) * | 2018-10-19 | 2019-01-29 | 国网宁夏电力有限公司经济技术研究院 | A kind of microgrid towards multiagent balance of interest economic load dispatching method a few days ago |
| CN109919452A (en) * | 2019-02-15 | 2019-06-21 | 三峡大学 | A kind of electric power based on multi-agent Game-Gas Comprehensive energy resource system joint planing method |
| CN110210712A (en) * | 2019-05-05 | 2019-09-06 | 三峡大学 | It is a kind of to consider uncertain and multi-agent Game integrated energy system planing method |
| CN111062514A (en) * | 2019-11-14 | 2020-04-24 | 国网能源研究院有限公司 | Power system planning method and system |
-
2021
- 2021-04-30 CN CN202110481570.7A patent/CN113222233B/en active Active
Patent Citations (6)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN107172606A (en) * | 2017-06-29 | 2017-09-15 | 南京邮电大学 | A kind of distribution method of green energy resource joint spectrum shared in the wireless network |
| CN109034563A (en) * | 2018-07-09 | 2018-12-18 | 国家电网公司 | Multi-subject game incremental power distribution network source-network-load collaborative planning method |
| CN109286187A (en) * | 2018-10-19 | 2019-01-29 | 国网宁夏电力有限公司经济技术研究院 | A kind of microgrid towards multiagent balance of interest economic load dispatching method a few days ago |
| CN109919452A (en) * | 2019-02-15 | 2019-06-21 | 三峡大学 | A kind of electric power based on multi-agent Game-Gas Comprehensive energy resource system joint planing method |
| CN110210712A (en) * | 2019-05-05 | 2019-09-06 | 三峡大学 | It is a kind of to consider uncertain and multi-agent Game integrated energy system planing method |
| CN111062514A (en) * | 2019-11-14 | 2020-04-24 | 国网能源研究院有限公司 | Power system planning method and system |
Non-Patent Citations (1)
| Title |
|---|
| 张培鑫: "基于多主体动态博弈的天然气定价模型研究", 《中国优秀硕士学位论文全文数据库 工程科技Ⅰ辑》, pages 019 - 10 * |
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