Construction method of auction simulation model on pipeline gas line, auction method and auction device

Abstract

The invention discloses a construction method of a pipeline gas online auction simulation model, an auction method and an auction device. The method comprises the following steps: determining a relationship between core participants of a pipeline gas auction market and the core participants; respectively constructing a demand side decision quotation model for the demand side and a supplier decision quotation model for the supplier based on competition intelligence data of the demand side and the supplier included in the core participants and game environment state data related to the core participants; and constructing a pipeline gas online auction simulation model based on the demand side decision quotation model and the supplier decision quotation model, so as to predict the future demand quantity and bearable auction price of the demand side through the pipeline gas online auction simulation model. By setting various auction situation simulation schemes for simulation, complex game factors such as an oil enterprise competition strategy, an online pipeline gas auction and natural gas pipeline gas auction analysis and pricing strategy, a customer marketing strategy and the like are accurately described.

Description

Technical Field

[0001] The present invention relates to the technical field of natural gas new energy, and particularly relates to a method for constructing a pipeline gas online auction simulation model, an auction method, and a device. Background Art

[0002] The company urgently needs to develop an online auction analysis model for natural gas on the demand side to accurately depict complex game factors such as enterprise competition strategies, online auction analysis and pricing strategies, and customer marketing strategies, so as to implement precise policies in terms of resource allocation, sales arrangements, marketing channel establishment, and customer service. Whether from the perspective of enterprise operation, policy formulation, production and transportation, or the healthy development of the entire industry, the decision-making simulation of the natural gas market is of great significance.

[0003] Currently, in the field of natural gas, especially in the face of the complex interaction process between various time variables involved in natural gas, there is almost no scientific or reasonable simulation model available. The prediction and deduction of customers' decision-making behaviors during online auctions are mostly based on their own research situations, with strong subjectivity. Summary of the Invention

[0004] In order to enable the balanced pricing of both the supply and demand sides in the natural gas auction market, to enrich the technical routes and increase the selection space, the embodiments of the present invention provide a method for constructing a pipeline gas online auction simulation model, an auction method, and a device.

[0005] In a first aspect, the embodiments of the present invention provide a method for constructing a pipeline gas online auction simulation model, which may include:

[0006] Determine the core participating entities in the pipeline gas auction market and the relationships between the core participating entities;

[0007] Based on the competitive intelligence data of the demand side and the supply side included in the core participating entities, and the game environment state data related to the core participating entities, respectively construct a demand-side decision-making pricing model for the demand side and a supply-side decision-making pricing model for the supply side;

[0008] Based on the demand-side decision-making pricing model and the supply-side decision-making pricing model, construct a pipeline gas online auction simulation model to predict the future demand volume and the affordable auction price of the demand side through the pipeline gas online auction simulation model.

[0009] Optionally, predicting the future demand volume and the affordable auction price of the demand side through the pipeline gas online auction simulation model may include: performing dynamic game simulation on the pipeline gas online auction simulation model based on the risk-neutral pricing method to solve for the Bayesian Nash equilibrium and predict the future demand volume and the affordable auction price of the demand side.

[0010] Optionally, the model parameters for constructing the demand-side decision-making bidding model and the supply-side decision-making bidding model include at least one of the following: a set of participants, a set of natural states, a set of effective bidding prices of the participants, a bidding period, the bidding strategies of the participants, the probability measure of the participants for natural states, and the preference relations of the participants, where the participants include the demand side and the supply side of the auction; the preference relation of the participants is the urgency of the demand for the auctioned pipeline gas.

[0011] And / or

[0012] The game environment state data includes: natural gas market policies, the quantity and price of alternative energy sources, and / or natural gas infrastructure.

[0013] Optionally, determining the core participants in the pipeline gas auction market and the relationships between the core participants may include:

[0014] Determining the behavior characteristics of the core participants and the core participants; where the core participants include: regulatory agencies, trading centers, demand sides, and supply sides;

[0015] Based on the determined behavior characteristics of the core participants in the pipeline gas auction market, determining the relationships between the core participants; where the relationships between the core participants include: competitive relationships and co-opetitive relationships.

[0016] In a second aspect, an embodiment of the present invention provides a method for online auction of pipeline gas, which may include: predicting the future demand quantity and the affordable auction price of the demand side according to a pre-constructed online auction simulation model of pipeline gas;

[0017] wherein the online auction simulation model of pipeline gas is pre-constructed according to the construction method of the online auction simulation model of pipeline gas described in the first aspect.

[0018] Optionally, the method may further include: determining the prices and purchase quantities of the demand side and the supply side included in the core participants participating in the online auction according to historical online auction data; and predicting the future demand quantity and the affordable auction price of the demand side based on the prices and the purchase quantities and the online auction simulation model of pipeline gas;

[0019] wherein the historical online auction data includes at least one of the following: historical auction bids, historical supply prices, historical auction pipeline gas quantities, historical pipeline gas supply quantities, historical auction periods, and pipeline gas spot auction gas prices.

[0020] In a third aspect, an embodiment of the present invention provides a device for constructing an online auction simulation model of pipeline gas, which may include:

[0021] A determination module, configured to determine the core participants in the pipeline gas auction market and the relationships between the core participants;

[0022] A first construction module, configured to respectively construct a demand-side decision-making bidding model for the demand side and a supply-side decision-making bidding model for the supply side based on the competitive intelligence data of the demand side and the supply side included in the core participants, and the game environment state data related to the core participants;

[0023] A second construction module, configured to construct a pipeline gas online auction simulation model based on the demand-side decision-making bidding model and the supply-side decision-making bidding model, so as to predict the future demand volume and the affordable auction price of the demand side through the pipeline gas online auction simulation model.

[0024] In a fourth aspect, an embodiment of the present invention provides a pipeline gas online auction device, which may include:

[0025] A prediction module, configured to predict the future demand volume and the affordable auction price of the demand side according to a pre-constructed pipeline gas online auction simulation model; wherein, the pipeline gas online auction simulation model is pre-constructed according to the construction method of the pipeline gas online auction simulation model described in the first aspect.

[0026] In a fifth aspect, an embodiment of the present invention provides a computer-readable storage medium, on which a computer program is stored, and when the program is executed by a processor, it implements the construction method of the pipeline gas online auction simulation model described in the first aspect, or implements the pipeline gas online auction method described in the second aspect.

[0027] In a sixth aspect, an embodiment of the present invention provides a computer device, including a memory, a processor, and a computer program stored on the memory and executable on the processor, and when the processor executes the program, it implements the construction method of the pipeline gas online auction simulation model described in the first aspect, or implements the pipeline gas online auction method described in the second aspect.

[0028] The beneficial effects of the above technical solutions provided by the embodiments of the present invention at least include:

[0029] The embodiments of the present invention provide a method, a bidding method and a device for constructing a simulation model for pipeline gas online bidding. Through the simulation analysis and application of pipeline gas online bidding and combined with expert experience analysis, this method sets multiple simulation schemes for bidding scenarios, which can be applied to various actual scenarios, accurately depicts complex game factors such as oil companies' competition strategies, online pipeline gas bidding and natural gas pipeline gas bidding analysis and pricing strategies, and customer marketing strategies, so as to improve the judgment accuracy under the influence of the competition game of natural gas market resources. This model can flexibly allocate the resources for natural gas online bidding and avoid the occurrence of over-supply or under-supply situations.

[0030] With the continuous implementation of natural gas market-oriented reform measures, the online bidding price of natural gas will change with the changes in the market supply and demand situation. In this case, combined with the simulation model for pipeline gas online bidding, it is possible to simulate in advance the changes in quantity and price of online bidding, helping sales companies formulate more reasonable and targeted marketing strategies. Further, based on historical online bidding situations, predict the future demand quantity and the affordable bidding price for customers to participate in online bidding. Through the online bidding model and the quantity-price relationship model within the contract, they jointly form an analysis plan for the quantity-price relationship of the total customer demand.

[0031] Other features and advantages of the present invention will be described in the following specification, and part of them will become obvious from the specification or be understood by implementing the present invention. The objectives and other advantages of the present invention can be achieved and obtained through the structures specifically pointed out in the written specification and the accompanying drawings.

[0032] The following will further describe the technical solutions of the present invention in detail through the accompanying drawings and embodiments. Description of the Drawings

[0033] The drawings are used to provide a further understanding of the present invention, and constitute a part of the specification. Together with the embodiments of the present invention, they are used to explain the present invention and do not constitute a limitation to the present invention. In the drawings:

[0034] Figure 1 is a flowchart of the method for constructing a simulation model for pipeline gas online bidding provided in the embodiments of the present invention;

[0035] Figure 2 is a schematic diagram of the core participating entities and their interaction relationships provided in the embodiments of the present invention;

[0036] Figure 3 is a schematic diagram of the equilibrium linear strategy provided in the embodiments of the present invention;

[0037] Figure 4 is a schematic diagram of the trading area under the equilibrium linear strategy provided in the embodiments of the present invention;

[0038] Figure 5 This is a schematic structural diagram of a device for constructing a pipeline gas online auction simulation model provided in an embodiment of the present invention. Detailed implementation manners

[0039] Hereinafter, exemplary embodiments of the present disclosure will be described in more detail with reference to the accompanying drawings. Although the exemplary embodiments of the present disclosure are shown in the drawings, it should be understood that the present disclosure can be implemented in various forms and should not be limited by the embodiments set forth herein. On the contrary, these embodiments are provided so that the present disclosure can be more thoroughly understood and the scope of the present disclosure can be fully conveyed to those skilled in the art.

[0040] An embodiment of the present invention provides a method for constructing a pipeline gas online auction simulation model. Referring to Figure 1 as shown, the method may include the following steps:

[0041] Step S11: Determine the core participants in the pipeline gas auction market and the relationships between the core participants.

[0042] Step S12: Based on the competitive intelligence data of the demand side and the supply side included in the core participants, and the game environment state data related to the core participants, respectively construct a demand-side decision-making bidding model for the demand side and a supply-side decision-making bidding model for the supply side.

[0043] Step S13: Based on the demand-side decision-making bidding model and the supply-side decision-making bidding model, construct a pipeline gas online auction simulation model to predict the future demand volume and the affordable auction price of the demand side through the pipeline gas online auction simulation model.

[0044] For the above method provided in the embodiment of the present invention, aiming at how to perform multi-agent modeling on the natural gas market and analyze the decisions of each agent involved in the market bidding and trading process of online auctions, it can scientifically and reasonably perform dynamic simulation and simulation on the natural gas online auction market, and can be applied to the construction and simulation of online transactions in the natural gas market. In a bilateral trading scenario and a preset environmental state, a user auction model is constructed for natural gas users. The simulation results show how to perform multi-variable and multi-time-step modeling on the natural gas market and perform simulation analysis on the decisions of each agent involved in the market bidding and trading process, and can scientifically and reasonably perform dynamic game decision simulation and simulation on the natural gas auction market.

[0045] The core of the embodiment of the present invention is to determine the core participants in the pipeline gas auction market and model the situations of the behavioral characteristics (bidding strategies) of the participants, and then, in a bilateral trading scenario and a preset incomplete information environment state, construct an incomplete information Bayesian game model of double auction for the above-mentioned natural gas users; conduct dynamic game simulation according to the bidding strategies among the above-mentioned participants, the user decision-making model and the corresponding gas supplier decision-making model, and solve to obtain the Bayesian Nash equilibrium, and give the equilibrium bidding strategies of the supply side and the demand side.

[0046] It should be noted that the above method provided in the embodiment of the present invention is based on the supply chain analysis of the ex-factory price of LNG (liquefied natural gas) plants, combines the relevant algorithms of time series analysis and Transformer technology in deep learning with the actual supply chain characteristics, and analyzes the influence mechanism and price transmission mechanism of the ex-factory price of LNG plants under the condition of meeting the actual business requirements. Among them, the influence mechanism of the ex-factory price of LNG plants is not limited to the influence mechanism in domestic regions, but can also be the mechanism in international or any region in the actual production process that meets the supply chain characteristics and can realize the operation of the industrial chain; the price transmission mechanism mainly refers to the price transmission sequence in the supply chain and the influence degree of different factors on the recent price, that is, the influence with a large impact on the price is limitedly transmitted to the recent time point, and it is allowed that the impact can be transmitted and affect the price level in the future for a longer time. Prerequisite: Under the condition that the influencing factors of the ex-factory price of LNG plants are determined, deep learning technology is used, with the minimum loss function as the goal, and the ex-factory price of LNG plants at different time points is obtained through iterative calculation of the model. The influencing factors include the current operating rate of the LNG plant, the plant inventory situation, the pipeline gas cost, the pipeline gas auction resource volume, and the plant information includes the plant geographical location, the plant demand volume and the supply chain smoothness degree. Technical input: The operating rate and inventory situation of each LNG plant, the demand volume and supply volume, the pipeline gas cost, and the supply connection relationship matrix.

[0047] In an optional embodiment, in the above step S11, the core participants in the pipeline gas auction market and the relationship between the core participants are determined, and the core participants and the behavioral characteristics of the core participants are determined; among them, the core participants include: regulatory agencies, trading centers, demand sides and supply sides; then, based on the behavioral characteristics of the core participants in the determined pipeline gas auction market, the relationship between the core participants is determined; among them, the relationship between the core participants includes: competitive relationship and competition-and-cooperation relationship.

[0048] Combined with Figure 2As shown, the core participants in the online auction market of pipeline gas (natural gas) are various participating entities in the market that can make independent decisions. They can respond to external influences based on their own interest goals or make optimal decisions under constraints. According to the current composition of the natural gas market, the method for analyzing the behavior decisions of market participants can be used to construct a market simulation model for market simulation. The market simulation model will include regulatory agencies (such as government decision-making agencies), natural gas suppliers (supply side, such as CNPC, China Resources Gas and other suppliers), trading centers, and various natural gas users (demand side) including industrial users and urban gas as core participants.

[0049] The relationships among the core participants include: competitive relationships and co-opetitive relationships. For example, 1) The policies issued by regulatory agencies will affect natural gas users, natural gas suppliers and trading centers; 2) There are quantity-price-service competition relationships among natural gas suppliers. Each supplier determines the contract price based on its own revenue and cost, and the ultimate decision-making goal is to maximize profits; when the natural gas suppliers as a whole face natural gas users, they show a co-opetitive relationship. Each natural gas supplier competes with other suppliers while ensuring its own benefits, but also shows a cooperative relationship when facing customers. The supplier makes a contract price (p i，j , b i ) for large users; 3) When natural gas users face suppliers, they will affect the business layout of the suppliers. Large user enterprises determine the contract gas volume and online auction trading volume signed with each supplier based on the supplier's price, predicted spot gas price, and user demand in each period. The ultimate decision-making goal is to minimize the gas purchase cost; 4) Natural gas suppliers provide available natural gas trading resources to the trading center and abide by loose and fair trading policies; the trading center implements fair and open online trading policies.

[0050] In the above step S12, the game environment state data includes: natural gas market policies, the quantity and price of alternative energy, and / or natural gas infrastructure. In the embodiments of the present invention, the inventor takes into account the impacts of various complex environments faced in the multi-agent modeling of the natural gas (pipeline gas) market. In order to consider the decision-making behaviors of key agents such as suppliers and users that affect market prices, part of the game environment state is set to a static state. For example, the physical environment (pipeline transportation capacity, gas storage scale), social environment (macro-economic development environment, foreign trade environment, industry cycle environment, etc.) and random environment (such as epidemic impact, extreme weather, natural gas leakage and explosion accidents, etc.) are set static, and the game behaviors and strategies (prices, supply and demand) among agents are analyzed emphatically, and a game equilibrium solution method is constructed.

[0051] The above step S12 is to construct a decision-making quotation model for the demander and a decision-making quotation model for the supplier. For example, in the embodiment of the present invention, it is assumed that the market demand quantity of gas is W units during a certain trading period t. The supplier consists of M natural gas suppliers, and the demander consists of N gas enterprises (natural gas purchasing customers). The two parties form a double auction. The production cost of the supplier to provide this quantity of natural gas products (or the value of this product to the seller) is c, and the value of this product to the demander is v, where c ∈ [0, 1] and v ∈ [0, 1]. The supplier and the demander simultaneously select asking prices and bidding prices, which are p s ∈ [0, 1] and p b ∈ [0, 1]; if p s ≤ p b , the two parties will reach a deal at ; if p s >p b , no transaction will occur. The supplier knows c but does not know v, and the demander knows v but does not know c. Therefore, this problem belongs to the double auction problem with incomplete information, and it is necessary to establish a Bayesian game model for this situation and solve the equilibrium quotation strategy.

[0052] A Bayesian game model for the double auction of the supplier and the demander in the bilateral natural gas online trading market. Among them, the Bayesian game model models the situation where some participants do not know the characteristics of other participants, and it is an incomplete information strategic game. The model parameters for constructing the decision-making quotation model for the demander and the decision-making quotation model for the supplier include at least one of the following: the set of participants, the set of natural states, the effective bidding set of participants, the bidding period, the quotation strategy of participants, the probability measure of participants for natural states, and the preference relationship of participants, where the participants include the demanding and supplying parties in the auction.

[0053] It has two basic elements of a strategic game: the set of participants N and the set of actions A i . To describe the uncertainty of the characteristics of participants, the set of "natural states" Ω is introduced. By giving a probability measure P i on Ω, each participant i has a prior probability about the natural state. It should be noted that in the embodiment of the present invention, the prior probability of each natural state may be different from the perspective of each person, but generally speaking, the prior probabilities are equal or related.

[0054] In any given game action, a certain natural state ω ∈ Ω is realized. By introducing the signal function combination τ i to model the information of participants about the natural state. t i =τ iLet \((\omega)\) denote that after the natural state \(\omega\) occurs, when the natural state is \(\omega\) and before player \(i\) chooses his action, use \(\tau\). i Let \((\omega)\) denote that player \(i\) observes his signal. Let \(T\). i be the set of all possible values of \(\tau\). i Call \(T_i\) the type set of player \(i\), and assume that for all \(t\). i \(\in T\). i there is \(P\). i \((\tau\). i -1 \((t\). i )) > 0. It should be noted that in the embodiment of the present invention, it means that after the \(i\)-th player has observed the signal \(t\). i The prior probability of the occurrence of the states in the corresponding natural state set of \(t\). i should of course be positive; if it is 0, it is considered that the \(i\)-th player cannot receive \(t\). i .

[0055] If player \(i\) receives the signal \(t\). i \(\in T\). i , then he infers that the state is in the set \(\tau\). i -1 (t i ); when \(\omega\in\tau\). i -1 (t i ), then the posterior probability of the realized state is assigned a probability of \(P\). i (\omega) / P i (\tau\). i -1 (t i )) for each state \(\omega\in\tau\). i i ), then each state \(\omega\in\Omega\) is assigned a value of 0.

[0056] A Bayesian game includes the following elements:

[0057] (i) A finite set \(N\) (the set of players);

[0058] (ii) A finite state set \(\Omega\);

[0059] And for each player \(i\in N\) there is:

[0060] (iii) A set \(A\) (the set of effective actions of player \(i\));

[0061] (iv) A signal set \(T\). i (the set of signals that may be observed by player \(i\)) and a function \(\tau\). i :\(\Omega\rightarrow T\). i(Signal function of Participant i);

[0062] (v) A probability measure P on Ω i (Prior probability of Participant i), which must satisfy for all t i ∈ T i There is P i (τ i -1 (t i )) > 0;

[0063] (vi) Preference relation of Participant i Is for the set of probability measures on A × Ω, which is defined as Here A = × i∈N A i ; A i Is the effective action set of Participant i; × i∈N A i Represents the set of action combinations of all participants. The preference relation of the participants in this embodiment is the urgency of the demand for bid pipeline gas.

[0064] Suppose in the bilateral natural gas online trading market, the set of participants is the set {s, b} composed of the supplier and the demander b. The state set Ω is the set of combinations of the production cost of the supplier and the valuation of the demander (c, v); the action sets A of the participant supplier s and the demander b i Is R + ; The set of signals T that i can receive i Is (c, v); The signal function τ of i i : Ω → T i Is defined as τ1(c, v) = c and τ1(c, v) = v; The prior probability P i Is the uniform distribution π on (c, v), P i (c, v) = π(c) × π(v); The preference relation of the gas supplier i Is represented by the expectation of a certain random variable. Suppose both the supplier s and the demander b are risk-neutral. The payment functions of the supplier s and the demander b are respectively

[0065] (1) Supplier s

[0066]

[0067] (2) Demander b

[0068]

[0069] Consider the case of incomplete information, that is, only the supplier s knows their own production cost c, and only the demander knows their own valuation v. Thus, c is the type of the supplier s, and v is the type of the demander b. Assume that c and b are uniformly distributed on the interval [0, 1], and the distribution function P(·) is common knowledge.

[0070] In the above step S13, a pipeline gas online auction simulation model is constructed based on the demander's decision-making bidding model and the supplier's decision-making bidding model to predict the future demand volume and the affordable bidding price of the demander through the pipeline gas online auction simulation model.

[0071] In any given game move, each player knows their own type and does not need to consider what they would do in other type situations. Therefore, a player might think of defining an equilibrium for each isolated state of nature. This allows a Bayesian game to have its Nash equilibrium defined as a Nash equilibrium of a strategic game G * where for each i ∈ N and each possible signal t * ∈ T i there is a player, called (i, t i ), that is, player i with type t i . The action sets of these players are A i , so that the set of action profiles in G i is * The preference for each player (i, t ) is defined as follows: The posterior probability of player i and an action profile a i in G * together generate an uncertain event L * over A × Ω, that is, the probability assigned by L i (a * , t i ) to ((a i (j, τ * (ω))) i , ω) is the posterior probability that the state is ω when player i receives the signal t * , and (a j (j, τ j∈N (ω))) is the action of player (j, τ i (ω)) in the profile a * . Player i prefers the uncertain event L j in the Bayesian game over the uncertain event L j if and only if * when i (a * , t i )i (b * ,t i ) when, in G * the preferred action combination a i ) of the participant (i, t * can only be superior to the action combination b * .

[0072] In the embodiment of the present invention, the Nash equilibrium of a Bayesian game is defined as the Nash equilibrium of the strategic game defined as follows:

[0073] (1) The set of participants is the set of all pairs (i, t i ) where i ∈ N, t i ∈ T i ;

[0074] (2) The action set of each participant (i, t i ) is A i ;

[0075] (3) The preference i ) of each participant (i, t is defined as follows: if and only if L i (a * , t i ) ≥ i L i (b * , t i ) then there is where a * and b * are action combinations; L i (a * , t i ) and L i (b * , t i ) are uncertain events about A × Ω.

[0076] If ω ∈ τ i -1 (t i ) then its probability is P i (ω) / P i (τ i -1 (t i )) and is assigned to ((a * (j, τ j (ω))) j∈N , ω), otherwise, zero is assigned.

[0077] According to the description of the above problems and the mathematical descriptions of the double - auction process and auction rules, the supplier believes that the production cost when the gas consumption is w is c, and the demander b believes that the value of the gas quantity with a purchase quantity of W is v. Moreover, c and v are defined to be independent of each other and uniformly distributed on the interval [0, 1]. In this Bayesian game, the strategy (asking price) p of the supplier s is a function p of type c s (c); the strategy (bidding price) p of the demander b is a function p of type v b (v). The strategy profile (p s (c), p b (v)) is a Bayesian Nash equilibrium if and only if the following two conditions hold.

[0078] (1) For all c ∈ [0, 1], p * s (c) is a solution to the following optimization problem

[0079]

[0080] where E(p b (v)|p b (v) ≥ p s ) is the supplier's expected bid of the demander given that the supplier's asking price is lower than the demander's bidding price.

[0081] (2) For all v ∈ [0, 1], p * b (v) is a solution to the following optimization problem

[0082]

[0083] where E(p s (c)|p b ≥ p s (c)) is the demander's expected asking price of the supplier given that the supplier's asking price is lower than the demander's bidding price.

[0084] In the above two conditions, p * s (c) and p * b (v) are the Bayesian Nash equilibrium strategies of players s and b respectively, that is, p * s (c) and p * b (v) are the mutually - optimal response strategies of players s and b.

[0085] The above-mentioned pipeline gas online auction simulation model predicts the future demand volume and the affordable auction price of the demand side. In specific implementation, based on the risk-neutral pricing method, dynamic game simulation is carried out on the pipeline gas online auction simulation model to solve for the Bayesian Nash equilibrium and predict the future demand volume and the affordable auction price of the demand side. That is, the Bayesian Nash equilibrium of the double-sided auction between the supply side and the demand side in the bilateral natural gas online trading market.

[0086] Specifically, in the embodiment of the present invention, the Bayesian Nash equilibrium is solved by combining the above two equations in a linear strategy equilibrium manner. There are many Bayesian Nash equilibriums in this game. The embodiment of the present invention solves it by means of a linear bidding strategy equilibrium. Among them, a linear bidding strategy is as follows:

[0087]

[0088] In the formula, α s is the asking price of participant s when the cost is 0, β s is the change rate of the asking price of participant s with the cost, α b is the bidding price of participant b when the valuation is 0, and β b is the change rate of the bidding price of participant b with the price.

[0089] Since v is defined to be uniformly distributed on the interval [0, 1], therefore, p b is also uniformly distributed on the interval [α b , α b +β b , so there are

[0090]

[0091] and

[0092]

[0093] In the formula, E[p b (v)|p b (v)≥p s is the expected bidding price of the demand side by the supply side under the given condition that the asking price of the supply side is lower than the bidding price of the demand side; the integral variable x is an intermediate variable and has no practical significance.

[0094] Substitute the above equation into the objective function of the supply side, and we get

[0095]

[0096] From its optimization first-order condition, we get

[0097]

[0098] As can be seen from the above formula, if the demander chooses a linear strategy, then the supplier's optimal response is also linear.

[0099] Similarly, since c is defined to be uniformly distributed on the interval [0, 1], then p s is also uniformly distributed on the interval [α s , α s +β s . Therefore, we have

[0100]

[0101]

[0102] Substituting the above formula into the demander's utility function, we get

[0103]

[0104] From its first-order optimization condition, we get

[0105]

[0106] Combining the previous formula with this one, we get the equilibrium strategy:

[0107]

[0108] Equilibrium Strategy Analysis of the Bilateral Call Auction between the Demander and the Supplier in a Two-sided Market

[0109] (1) Under the equilibrium linear strategy, the demander's highest bid is p b (1) = 3 / 4; the supplier's lowest asking price is P s (0) = 1 / 4.

[0110] (2) When c > 3 / 4, the supplier's asking price p s (c) = 1 / 4 - 2 / 3c is lower than the cost but higher than the demander's highest bid p b (1) = 3 / 4. However, the supplier will not sell its power generation at a price lower than the cost;

[0111] (3) When v < 1 / 4, as shown in Figure 3 , the demander's bid is higher than its value but lower than the supplier's lowest asking price p s (0) = 1 / 4. At this time, the transaction will not occur either. Figure 3 In, p s (c) = 1 / 4 - (2 / 3)c and p b (v) = 1 / 12 + (2 / 3)v are the mathematical expressions of the two oblique lines respectively, representing the first-order equilibrium bidding strategies of players s and b. According to the above analysis (1), (2) and (3), the equilibrium bidding strategies of the players are obtained asFigure 1 The thick solid line in

[0112] (4) In the equilibrium case, referring to Figure 4 as shown, if and only if p b (v) ≥ p s (c), that is, when v ≥ c + 1 / 4, the supply side and the demand side will conduct transactions.

[0113] In the embodiments of the present invention, according to factors such as customer demand quantity, its own resource quantity, auction price, sales strategy, etc., through software simulation to simulate the online auction situation and the auction behavior of users, configure multiple solutions, and conduct comparisons, it can provide a basis for coordinating regional resources and formulating marketing strategies for regional customers on the basis of ensuring the basic demand quantity of customers.

[0114] Based on the same inventive concept, an online auction method for pipeline gas is provided in the embodiments of the present invention. The method may include: predicting the future demand quantity and the affordable auction price of the demand side according to a pre-constructed online auction simulation model for pipeline gas; wherein, the online auction simulation model for pipeline gas is pre-constructed according to the construction method of the above-mentioned online auction simulation model for pipeline gas.

[0115] In the embodiments of the present invention, through the application of the online auction simulation analysis method for pipeline gas and combined with expert experience analysis, set multiple auction scenario simulation schemes. In the future, it can be applied to a variety of actual scenarios to accurately depict complex game factors such as the competition strategy of oil enterprises, the analysis and pricing strategy of online pipeline gas auctions and LNG pipeline gas auctions, and the customer marketing strategy, so as to improve the judgment accuracy under the influence of the competition game of natural gas market resources.

[0116] Furthermore, this achievement can not only provide a decision-making basis for natural gas enterprises to coordinate resources, but also provide a basis for natural gas enterprises to formulate marketing strategies for customers in different industries and regions in a competitive environment. In the actual application process, the business department uses it about 50 times per month. Compared with the traditional analysis method, the prediction accuracy of the online auction demand quantity of natural gas has increased from 60% to 83%. Mainly according to factors such as customer demand quantity, its own resource quantity, auction price, sales strategy, etc., through software simulation to simulate the online auction situation and the auction behavior of users, configure multiple solutions, and conduct comparisons, it can provide a basis for coordinating regional resources and formulating marketing strategies for regional customers on the basis of ensuring the basic demand quantity of customers.

[0117] In an optional embodiment, the above method may further include: determining, according to historical online auction data, the prices and gas purchase volumes at which the demand parties and supply parties included in the core participating entities participate in the online auction; and predicting the future demand volume and affordable auction price of the demand parties based on the prices and gas purchase volumes and the online auction simulation model of pipeline gas; wherein the historical online auction data includes at least one of the following: historical auction bids, historical supply prices, historical pipeline gas volumes in auctions, historical pipeline gas supply volumes, historical auction time periods, and spot auction gas prices of pipeline gas.

[0118] In the embodiment of the present invention, according to the historical online auction situation, the prices and volumes at which future customers participate in the online auction can be predicted, and the historical online auction prices and historical order volumes can also be determined. The online auction model and the volume-price relationship model within the contract together form a volume-price relationship analysis solution for the total customer demand.

[0119] Through research on the natural gas market and sales strategies, a mathematical model is established, and research on the decision-making method of the incomplete information Bayesian game model for the call auction of both supply and demand sides is carried out, which can be applied to business scenarios such as natural gas sales business planning and online auction pricing of pipeline gas. Through the pipeline gas online auction simulation model of Bayesian game, it is possible to scientifically and reasonably dynamically simulate and model the natural gas online auction market, and put forward suggestions for work such as resource procurement demand, contract signing, sales plan formulation, and downstream market development, with broad application prospects in the field of customer marketing.

[0120] This method combines business characteristics, professional knowledge, mathematical analysis methods, and algorithms for effective utilization, forming a simulation analysis method for natural gas online auctions, which is applicable to online auction game decision-making analysis in multiple scenarios, multiple dimensions, and multiple time steps. By comparing and correcting the analysis data with the actual situation, the accuracy of the model calculation results is greatly improved, making the model calculation results more accurate and more in line with the actual situation. At the same time, by setting parameters or conducting quantitative analysis on the upper and lower limits of the supply price, the guaranteed price of the contract gas volume, changes in user demand, changes in supply capacity, and changes in government-regulated prices, it can more effectively reflect the current situation of the natural gas market, more accurately give decision-making solutions, and carry out precise marketing.

[0121] Furthermore, the pipeline gas online auction simulation model can flexibly allocate the resources for natural gas online auctions, avoiding the occurrence of oversupply or undersupply. With the continuous implementation of natural gas market-oriented reform measures, the online auction prices of pipeline gas will change with the changes in the market supply and demand situation. In this case, combined with the pipeline gas online auction simulation model, it is possible to simulate in advance the volume-price changes in the online auction, helping sales companies formulate more reasonable and targeted marketing strategies. Therefore, each natural gas sales company will have a greater demand for this technology, thus obtaining strong support in enterprise games.

[0122] Furthermore, as an important participant in the natural gas market, other natural gas operating enterprises will also continuously research and develop technologies and means. This technology uses self-developed game analysis theories and algorithms, etc., and the model has rich application scenarios and comprehensive considerations, and can well adapt to various changes in the market. Therefore, competitors will expect to be able to use this innovative achievement to help them simulate and analyze market demands, formulate more reasonable marketing plans, and have a strong dependence on this technology.

[0123] Based on the same inventive concept, an apparatus for constructing a pipeline gas online auction simulation model is provided in an embodiment of the present invention. Referring to Figure 5 as shown, the apparatus may include:

[0124] A determination module 11 is configured to determine the core participating entities in the pipeline gas auction market and the relationships between the core participating entities;

[0125] A first construction module 12 is configured to respectively construct a demand-side decision-making bidding model for the demand side and a supply-side decision-making bidding model for the supply side based on the competitive intelligence data of the demand side and the supply side included in the core participating entities, and the game environment state data related to the core participating entities;

[0126] A second construction module 13 is configured to construct a pipeline gas online auction simulation model based on the demand-side decision-making bidding model and the supply-side decision-making bidding model, so as to predict the future demand volume and the affordable auction price of the demand side through the pipeline gas online auction simulation model.

[0127] Based on the same inventive concept, an apparatus for pipeline gas online auction is provided in an embodiment of the present invention. The apparatus may include: a prediction module, configured to predict the future demand volume and the affordable auction price of the demand side according to a pre-constructed pipeline gas online auction simulation model; wherein, the pipeline gas online auction simulation model is pre-constructed according to the above-mentioned method for constructing a pipeline gas online auction simulation model.

[0128] Based on the same inventive concept, a computer-readable storage medium is provided in an embodiment of the present invention, on which a computer program is stored. When the program is executed by a processor, it implements the above-mentioned method for constructing a pipeline gas online auction simulation model, or implements the above-mentioned pipeline gas online auction method.

[0129] Based on the same inventive concept, a computer device is provided in an embodiment of the present invention, including a memory, a processor, and a computer program stored on the memory and executable on the processor. When the processor executes the program, it implements the method for constructing a pipeline gas online auction simulation model as described above, or implements the above-mentioned pipeline gas online auction method.

[0130] The principle of the above-mentioned device, medium, and related equipment in the embodiments of the present invention for solving problems is similar to that of the foregoing method. Therefore, the implementation thereof can refer to the implementation of the foregoing method, and the repeated parts will not be elaborated herein.

[0131] Those skilled in the art should understand that the embodiments of the present invention can be provided as a method, a system, or a computer program product. Therefore, the present invention can take the form of a complete hardware embodiment, a complete software embodiment, or an embodiment combining software and hardware aspects. Moreover, the present invention can take the form of a computer program product implemented on one or more computer-usable storage media (including but not limited to disk storage and optical storage, etc.) containing computer-usable program code.

[0132] The present invention is described with reference to the flowcharts and / or block diagrams of methods, devices (systems), and computer program products according to the embodiments of the present invention. It should be understood that each flow and / or block in the flowchart and / or block diagram can be implemented by computer program instructions, and the combination of the flows and / or blocks in the flowchart and / or block diagram can also be implemented by computer program instructions. These computer program instructions can be provided to the processor of a general-purpose computer, a special-purpose computer, an embedded processor, or other programmable data processing devices to generate a machine, so that the instructions executed by the processor of the computer or other programmable data processing devices generate a device for implementing the functions specified in Figure 1 one flow or multiple flows and / or blocks Figure 1 one block or multiple blocks.

[0133] These computer program instructions can also be stored in a computer-readable memory that can direct a computer or other programmable data processing device to work in a specific manner, so that the instructions stored in the computer-readable memory generate a manufactured product including an instruction device, and the instruction device implements the functions specified in Figure 1 one flow or multiple flows and / or blocks Figure 1 one block or multiple blocks.

[0134] These computer program instructions can also be loaded onto a computer or other programmable data processing device, so that a series of operation steps are executed on the computer or other programmable device to generate a computer-implemented process. Thus, the instructions executed on the computer or other programmable device provide steps for implementing the functions specified in Figure 1 one flow or multiple flows and / or blocks Figure 1 one block or multiple blocks.

[0135] Obviously, those skilled in the art can make various modifications and variations to the present invention without departing from the spirit and scope of the present invention. Thus, if these modifications and variations of the present invention fall within the scope of the claims of the present invention and their equivalent technologies, the present invention is also intended to include these modifications and variations.

Claims

1. A method for constructing a pipeline gas line auction simulation model, characterized in that Including: Determine the core participants in the pipeline gas auction market and the relationships among the core participants; Based on the competitive intelligence data of the demand side and the supply side included in the core participants, and the game environment state data related to the core participants, respectively construct a demand-side decision-making bidding model for the demand side and a supply-side decision-making bidding model for the supply side; Based on the demand-side decision-making bidding model and the supply-side decision-making bidding model, construct a pipeline gas online auction simulation model to predict the future demand volume and the affordable auction price of the demand side through the pipeline gas online auction simulation model.

2. The method according to claim 1, characterized in that Predicting the future demand volume and the affordable auction price of the demand side through the pipeline gas online auction simulation model includes: performing dynamic game simulation on the pipeline gas online auction simulation model based on the risk-neutral pricing method to solve for the Bayesian Nash equilibrium and predict the future demand volume and the affordable auction price of the demand side.

3. The method according to claim 1, wherein The model parameters for constructing the demand-side decision-making bidding model and the supply-side decision-making bidding model include at least one of the following: the set of participants, the set of natural states, the set of effective bids of the participants, the auction period, the bidding strategies of the participants, the probability measure of the participants for the natural states, and the preference relationship of the participants, where the participants include the demand side and the supply side of the auction; the preference relationship of the participants is the urgency of the demand for the auctioned pipeline gas; And / or The game environment state data includes: natural gas market policies, the quantity and price of alternative energy sources, and / or natural gas infrastructure.

4. The method according to any one of claims 1 to 3, characterized in that The determining the core participants in the pipeline gas auction market and the relationships among the core participants includes: Determine the core participants and their behavioral characteristics; where the core participants include: regulatory agencies, trading centers, demand sides, and supply sides; Based on the determined behavioral characteristics of the core participants in the pipeline gas auction market, determine the relationships among the core participants; where the relationships among the core participants include: competitive relationships and co-opetitive relationships.

5. A method for auctioning on a pipeline gas line, characterized in that, Including: Predict the future demand volume and the affordable auction price of the demand side according to the pre-constructed pipeline gas online auction simulation model; Wherein, the pipeline gas online auction simulation model is pre-constructed according to the construction method of the pipeline gas online auction simulation model described in any one of claims 1 to 4.

6. The method according to claim 5, wherein Also including: According to the historical online auction data, determine the prices and purchase volumes of the demand side and the supply side included in the core participants participating in the online auction; to jointly predict the future demand volume and the affordable auction price of the demand side based on the prices and the purchase volumes and the pipeline gas online auction simulation model; Wherein, the historical online auction data includes at least one of the following: historical auction bids, historical supply prices, historical auction pipeline gas volumes, historical pipeline gas supply volumes, historical auction periods, and pipeline gas spot auction gas prices.

7. An apparatus for constructing a pipeline gas line auction simulation model, characterized in that Including: A determination module, configured to determine the core participants in the pipeline gas auction market and the relationships among the core participants; A first construction module, configured to respectively construct a demand-side decision-making bid model for the demand side and a supply-side decision-making bid model for the supply side based on the competitive intelligence data of the demand side and the supply side included in the core participating parties, and the game environment state data related to the core participating parties; A second construction module, configured to construct a pipeline gas online auction simulation model based on the demand-side decision-making bid model and the supply-side decision-making bid model, so as to predict the future demand volume and the affordable auction price of the demand side through the pipeline gas online auction simulation model.

8. A pipeline gas line auction device, characterized in that Comprising: A prediction module, configured to predict the future demand volume and the affordable auction price of the demand side according to a pre-constructed pipeline gas online auction simulation model; Wherein, the pipeline gas online auction simulation model is pre-constructed according to the construction method of the pipeline gas online auction simulation model according to any one of claims 1 to 4.

9. A computer-readable storage medium having a computer program stored thereon, characterized in that, When the program is executed by a processor, it implements the construction method of the pipeline gas online auction simulation model according to any one of claims 1 to 4, or implements the pipeline gas online auction method according to claims 5 and 6.

10. A computer device, comprising a memory, a processor, and a computer program stored on the memory and executable on the processor, characterized in that, When the processor executes the program, it implements the construction method of the pipeline gas online auction simulation model according to any one of claims 1 to 4, or implements the pipeline gas online auction method according to claims 5 and 6.

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