Electricity purchasing and selling price conduction quantitative analysis method and device

Abstract

The invention discloses an electricity purchasing and selling price conduction quantitative analysis method and device. The method comprises the steps of initializing basic parameters of a dynamic random general equilibrium model based on historical time sequence data; constructing a three-party coupling model comprising an electricity selling company main body, a terminal user main body and a policy department main body, and setting electricity purchasing cost impact, user demand impact and policy impact as independent exogenous random processes in the three-party coupling model; logarithmic linearization processing is carried out on the calibrated three-party coupling model, and a purchase and sale electricity price description coupling equation is deduced; and generating an impulse response function according to the exogenous random process, and simulating a dynamic response track of the electricity selling price under different impacts. According to the technical scheme provided by the invention, the three-party coupling dynamic random general equilibrium model is constructed and multiple types of exogenous impacts are introduced, so that the electricity purchasing and selling price conduction coefficient can be accurately quantified and the dynamic influence of the impacts can be simulated, and thus a reliable quantitative decision basis is provided for electricity selling pricing and policy adjustment.

Description

Technical Field

[0001] This invention relates to the technical field of quantitative analysis of the electricity market, and in particular to a method and apparatus for quantitative analysis of the transmission of electricity purchase and sale prices. Background Technology

[0002] With the deepening of electricity market reform, accurately depicting the transmission mechanism of electricity purchase and sale prices has become a core issue for ensuring stable market operation, optimizing resource allocation, and implementing precise policy regulation. According to existing technology, current analytical methods in this field mainly fall into two categories: one is based on static or comparative static analysis, such as traditional cost-plus pricing models or partial equilibrium analysis. The advantage of this type of method lies in its relatively simple model construction and convenient calculation, which can roughly reflect the basic logic and transmission relationship of cost to selling price conversion. The other category is based on empirical research methods using time series, such as statistical analysis of the correlation between historical electricity price data using vector autoregression models. The advantage of this type of method is that it can capture the statistical characteristics and short-term trends of price fluctuations based on real market transaction data.

[0003] The significant limitations of existing technologies are mainly reflected in the following four aspects: First, the analytical framework lacks systematicity, often separating cost transmission, supply and demand relationships, and policy constraints, failing to establish a unified framework to integrate their interactions, leading to a one-sided understanding of the price formation mechanism. Second, existing methods lack a solid microeconomic foundation, making it difficult to deeply characterize the optimal decision-making behavior of market players such as electricity retailers (pursuing intertemporal profit maximization), end users (pursuing utility maximization), and policy departments, and their dynamic interactions in the game. Third, existing models lack dynamic adaptability, failing to effectively introduce and quantify multi-dimensional and continuous exogenous random shocks such as electricity purchase cost shocks, random fluctuations in user demand, and temporary policy interventions, thus making it difficult to simulate and predict the real-time impact of dynamic market and policy changes on price transmission. Finally, existing technologies have significant deficiencies in the quantitative precision of the transmission mechanism, often failing to accurately calculate the specific transmission coefficient of the purchase price to the retail price, the transmission lag, and the contribution of key influencing factors (such as cost markup rates and the strength of policy constraints) to the final transmission efficiency. How to properly address these problems has become an urgent issue for the industry. Summary of the Invention

[0004] This invention provides a method and apparatus for quantitative analysis of the transmission of electricity purchase and sale prices. By constructing a three-way coupled dynamic stochastic general equilibrium model and introducing multiple types of exogenous shocks, it can accurately quantify the transmission coefficient of electricity purchase and sale prices and simulate the dynamic impact of shocks, thereby providing a reliable quantitative decision-making basis for electricity pricing and policy adjustments.

[0005] According to a first aspect of the present invention, a method for quantitative analysis of the transmission of electricity purchase and sale prices is provided, the method comprising: Historical time-series data of the power market is obtained from the power trading platform and user-side metering system through the data interface. The historical time-series data includes at least one or more of the following: medium- and long-term contract power purchase price, spot market power purchase price, power sales price, user electricity consumption, and macroeconomic indicators. Based on the historical time-series data, the basic parameters of the dynamic stochastic general equilibrium model are initialized. A three-party coupling model is constructed, comprising electricity sales companies, end users, and policy departments. The pricing decision-making behavior of electricity sales companies is defined by the intertemporal profit maximization objective function, the electricity demand behavior of end users is defined by the utility maximization objective function, and the regulatory boundary of policy departments is defined by the retail electricity price constraint equation. In the three-party coupling model, the shocks of electricity purchase costs, user demand, and policy shocks are set as independent exogenous stochastic processes. The core structural parameters in the three-way coupling model are calibrated by combining the Bayesian formula with the historical time series data. The calibrated three-way coupling model is then logarithmically linearized to calculate the steady state of the system that meets the market clearing conditions and to derive the coupling equation describing the purchase and sale of electricity prices. Based on the aforementioned electricity purchase and sale price coupling equation, the transmission coefficient of the electricity purchase price to the electricity sale price is calculated, and an impulse response function is generated according to the aforementioned exogenous stochastic process to simulate the dynamic response trajectory of the electricity sale price under different shocks. Based on the transmission coefficient and the dynamic response trajectory, the electricity sale pricing strategy or policy adjustment is guided.

[0006] In one embodiment, it also includes: The pricing decision-making behavior of the electricity sales company includes: The objective function of an electricity sales company is defined as maximizing the expected discounted value of net profit in each future period, where net profit is calculated by subtracting electricity purchase cost, operating cost and policy adjustment expenses from electricity sales revenue. By setting constraints on the power purchase structure, the power purchase cost is decomposed into a weighted sum of the power purchase cost under medium- and long-term contracts and the power purchase cost in the spot market through the weight parameter ω; The objective function with respect to the electricity price R t Find the first derivative and introduce the Lagrange multiplier λ. t Characterizing the strength of policy constraints, we construct a first-order optimal condition equation: Where μ is the cost-plus rate reflecting the profit target, and P sp,t With P c,t These represent the medium- to long-term contract electricity price and the spot electricity price for period t, respectively. op Operating cost per unit.

[0007] In one embodiment, the electricity demand behavior of the end-user entity includes: Collect historical electricity consumption data and electricity price data to determine the user's baseline electricity consumption. Compared with benchmark electricity sales price ; Introducing the price elasticity of demand parameter With income elasticity parameter This quantifies users' sensitivity to price changes and income changes, respectively. Combined with the aforementioned user demand impact Construct a nonlinear electricity demand function: in, For the electricity demand in period t, and The user income and benchmark income for period t are respectively, and the nonlinear electricity demand function is used to simulate the dynamic adjustment of user electricity consumption under price and income fluctuations.

[0008] In one embodiment, the exogenous stochastic process includes: The process of defining the impact of electricity purchase costs follows a first-order autoregressive model: in, This is the cost shock persistence coefficient; The user demand shock process is defined by a first-order autoregressive model: in, This represents the persistence coefficient of the demand shock. The policy shock process is defined by a first-order autoregressive model: in, The coefficient for the persistence of policy shocks; Setting residual terms , and All of them follow a white noise distribution with a mean of 0 and a constant variance, in order to capture unpredictable instantaneous market fluctuations.

[0009] In one embodiment, the logarithmic linearization process includes: Calculate the steady-state values ​​of each variable in the three-way coupling model under no-impact conditions; The linearized form of each variable is defined as the percentage change of the variable from its steady-state value; By performing a Taylor expansion of the aforementioned three-way coupled model near the steady-state value and retaining the first-order terms, the linearized electricity purchase and sale price transmission equation of the following form is derived: in, For electricity sales price variables, For the linearization of the electricity purchase price index, the coefficients are... This represents the transmission coefficient of the electricity purchase price to the electricity sales price, which needs to be quantified.

[0010] In one embodiment, the generated impulse response function includes: Under the steady-state initial conditions of the three-way coupled model, a positive shock of unit standard deviation is applied to the white noise residual term of an exogenous random process; The electricity sales price variable is iteratively calculated over the next T time periods using the linearized electricity purchase and sale price transmission equation. A numerical sequence; Construct an impulse response trajectory diagram with time period as the horizontal axis and electricity price deviation as the vertical axis; The conduction delay index and the maximum conduction effect index are extracted from the impulse response trajectory diagram. The conduction delay is defined as the time interval from the moment the impact occurs to the time when the electricity sales price response value exceeds a preset threshold. The maximum conduction effect is defined as the absolute value of the peak value in the response trajectory sequence.

[0011] According to a second aspect of the present invention, a quantitative analysis device for the transmission of electricity purchase and sale prices is provided, comprising: The acquisition module is used to acquire historical time-series data of the power market from the power trading platform and the user-side metering system through the data interface. The historical time-series data includes at least one or more of the following: medium- and long-term contract power purchase price, spot market power purchase price, power sales price, user electricity consumption, and macroeconomic indicators. Based on the historical time-series data, the module initializes the basic parameters of the dynamic stochastic general equilibrium model. The construction module is used to build a three-party coupled model that includes electricity sales companies, end users, and policy departments. The pricing decision-making behavior of electricity sales companies is defined by the intertemporal profit maximization objective function, the electricity demand behavior of end users is defined by the utility maximization objective function, and the regulation boundary of policy departments is defined by the retail electricity price constraint equation. In the three-party coupled model, the shocks of electricity purchase costs, user demand, and policy shocks are set as independent exogenous stochastic processes. The processing module is used to calibrate the core structural parameters in the three-way coupling model by combining the Bayesian formula with the historical time series data, and to perform logarithmic linearization on the calibrated three-way coupling model, calculate the steady state of the system that meets the market clearing conditions, and derive the coupling equation describing the purchase and sale of electricity prices. The calculation module is used to calculate the transmission coefficient of the purchase price to the sales price based on the purchased electricity price coupling equation, and generate an impulse response function according to the exogenous stochastic process to simulate the dynamic response trajectory of the sales price under different shocks. Based on the transmission coefficient and the dynamic response trajectory, the module guides the adjustment of the sales pricing strategy or policy.

[0012] In one embodiment, the acquisition module, the construction module, the processing module, and the calculation module are controlled to execute any of the above-described quantitative analysis methods for the transmission of electricity purchase and sale prices.

[0013] According to a third aspect of the present invention, an electronic device is provided, comprising: a communication interface, a processor, and a memory; The memory is used to store program instructions, which, when executed by the processor that is connected to the memory via the communication interface, implement any of the above-described quantitative analysis methods for the transmission of electricity purchase and sale prices.

[0014] According to a fourth aspect of the present invention, a computer-readable storage medium is provided, on which computer program instructions are stored, which, when executed by a computer (e.g., a processor in a computer), implement any of the above-described quantitative analysis methods for the transmission of electricity purchase and sale prices.

[0015] In summary, this invention provides a method and apparatus for quantitative analysis of electricity purchase and sale price transmission. The method includes: acquiring historical time-series data of the electricity market from an electricity trading platform and a user-side metering system via a data interface. The historical time-series data includes at least one or more of the following: medium- and long-term contract purchase prices, spot market purchase prices, electricity sales prices, user electricity consumption, and macroeconomic indicators. Based on the historical time-series data, the basic parameters of a dynamic stochastic general equilibrium model are initialized. A three-way coupled model is constructed, comprising electricity sales companies, end-users, and policy departments. The pricing decision-making behavior of electricity sales companies is defined by an intertemporal profit maximization objective function, and the electricity demand behavior of end-users is defined by a utility maximization objective function. The retail electricity price constraint equation defines the regulatory boundary of the policy-making body, and sets the electricity purchase cost shock, user demand shock, and policy shock as independent exogenous stochastic processes in the three-party coupling model. The core structural parameters of the three-party coupling model are calibrated using Bayesian formulas combined with historical time-series data, and the calibrated three-party coupling model is logarithmically linearized to calculate the system steady state satisfying market clearing conditions, deriving the electricity purchase and sales price coupling equation. Based on the electricity purchase and sales price coupling equation, the transmission coefficient of the electricity purchase price to the electricity sales price is calculated, and an impulse response function is generated based on the exogenous stochastic process to simulate the dynamic response trajectory of the electricity sales price under different shocks. The transmission coefficient and dynamic response trajectory guide the adjustment of electricity sales pricing strategies or policies. The technical solution of this application, by constructing a three-party coupled dynamic stochastic general equilibrium model and introducing multiple types of exogenous shocks, overcomes the shortcomings of traditional analysis in characterizing complex market interactions. It can accurately quantify the transmission coefficient of electricity purchase and sales prices and simulate the dynamic impact of shocks, thus providing a reliable quantitative decision-making basis for electricity sales pricing and policy adjustments.

[0016] Other features and advantages of the invention will be set forth in the description which follows, and will be apparent in part from the description, or may be learned by practicing the invention. The objects and other advantages of the invention may be realized and obtained by means of the structures particularly pointed out in the written description and drawings.

[0017] The technical solution of the present invention will be further described in detail below with reference to the accompanying drawings and embodiments. Attached Figure Description

[0018] To more clearly illustrate the specific embodiments of the present invention or the technical solutions in the prior art, the drawings used in the description of the specific embodiments or the prior art will be briefly introduced below. Obviously, the drawings described below are some embodiments of the present invention. For those skilled in the art, other drawings can be obtained from these drawings without creative effort.

[0019] Figure 1A flowchart of a quantitative analysis method for the transmission of electricity purchase and sale prices is provided as an embodiment of the present invention; Figure 2 A flowchart of another quantitative analysis method for the transmission of electricity purchase and sale prices provided as an embodiment of the present invention; Figure 3 A structural diagram of a quantitative analysis device for the transmission of electricity purchase and sale prices is provided for an embodiment of the present invention; Figure 4 This is a structural diagram of an electronic device provided as an embodiment of the present invention. Detailed Implementation

[0020] The features and exemplary embodiments of various aspects of this application will be described in detail below. To make the objectives, technical solutions, and advantages of this application clearer, the application will be further described in detail below with reference to the accompanying drawings and specific embodiments. It should be understood that the specific embodiments described herein are only intended to explain this application and not to limit it. For those skilled in the art, this application can be implemented without some of these specific details. The following description of the embodiments is merely to provide a better understanding of this application by illustrating examples.

[0021] It should be noted that, in this document, relational terms such as "first" and "second" are used merely to distinguish one entity or operation from another, and do not necessarily require or imply any such actual relationship or order between these entities or operations. Furthermore, the terms "comprising," "including," or any other variations thereof are intended to cover non-exclusive inclusion, such that a process, method, article, or apparatus that comprises a list of elements includes not only those elements but also other elements not expressly listed, or elements inherent to such a process, method, article, or apparatus. Without further limitations, an element defined by the phrase "comprising..." does not exclude the presence of additional identical elements in the process, method, article, or apparatus that includes said element.

[0022] like Figure 1 As shown, this invention provides a method for quantitative analysis of the transmission of electricity purchase and sale prices, which includes: In step S11, historical time-series data of the power market is obtained from the power trading platform and the user-side metering system through the data interface. The historical time-series data includes at least one or more of the following: medium- and long-term contract power purchase price, spot market power purchase price, power sales price, user electricity consumption, and macroeconomic indicators. Based on the historical time-series data, the basic parameters of the dynamic stochastic general equilibrium model are initialized. In step S12, a three-party coupling model is constructed, which includes the main body of electricity sales companies, the main body of end users, and the main body of policy departments. The pricing decision behavior of the main body of electricity sales companies is defined by the objective function of maximizing intertemporal profits, the electricity demand behavior of the main body of end users is defined by the objective function of maximizing utility, and the regulatory boundary of the main body of policy departments is defined by the retail electricity price constraint equation. In the three-party coupling model, the shock of electricity purchase cost, user demand shock and policy shock are set as independent exogenous stochastic processes. In step S13, the core structural parameters in the three-way coupling model are calibrated by combining the Bayesian formula with the historical time series data, and the calibrated three-way coupling model is logarithmically linearized to calculate the steady state of the system that meets the market clearing conditions and derive the equation describing the electricity purchase and sale price coupling. In step S14, based on the electricity purchase and sale price coupling equation, the transmission coefficient of the electricity purchase price to the electricity sale price is calculated, and an impulse response function is generated according to the exogenous stochastic process to simulate the dynamic response trajectory of the electricity sale price under different shocks. Based on the transmission coefficient and the dynamic response trajectory, the electricity sale pricing strategy or policy adjustment is guided.

[0023] In one embodiment, it is necessary to collect historical electricity purchase and sale transaction data from electricity retailers, including time-of-use medium- and long-term contract electricity prices. Spot market electricity price Actual electricity sales price and corresponding sales volume Simultaneously, macroeconomic data needs to be integrated to obtain time series data on users' disposable income. Furthermore, relevant policy events (such as subsidy adjustments and the introduction of carbon pricing) should be identified and quantified as a basis for observing policy shocks. All data should undergo seasonal adjustment, inflation deflator, and missing value processing to form a continuous and consistent panel dataset. Based on clean data, benchmark steady-state values ​​for each variable, such as the benchmark electricity price, can be calculated. Benchmark income and benchmark electricity consumption This provides a calibration basis for subsequent modeling.

[0024] To achieve a structured characterization of market uncertainty, three exogenous stochastic shock processes need to be established and calibrated. The shock is related to electricity purchase costs. This reflects abnormal fluctuations in electricity purchase costs caused by factors such as fuel prices and supply-demand imbalances; its persistence is determined by parameters. Characterization, Impact, Innovation variance is Impact of user demand Capturing the impact of climate change, sudden changes in consumption habits, and other factors on electricity demand, parameters and Measure its persistence and volatility intensity separately. Policy shocks Characterizing unexpected regulatory policy changes, by and Definition. Initial parameters for each impact. The model can be estimated based on historical data using an autoregressive model or set using typical values ​​from the literature. The white noise residual term of the shock process ensures that the model can effectively separate predictable trends from random fluctuations.

[0025] Electricity retailers' pricing behavior follows the principle of profit maximization, with their objective function being the sum of the discounted present values ​​of expected future net profits. In the constraints, the electricity purchase cost is determined by weighting parameters. It is constructed as a weighted sum of medium- and long-term contract costs and spot market costs, i.e. By solving for the first-order optimality condition of the objective function, and introducing Lagrange multipliers to characterize the strength of policy constraints, this approach is employed. Thus, the pricing equation is obtained: ,in, For cost markup rate, Unit operating cost. End-user demand behavior is described by a non-linear function: Among them, price elasticity With income elasticity Obtained through econometric regression analysis of historical data. Policy impact is obtained through... With impact Embedded into the system, thus coupling to form a complete three-party behavior model.

[0026] To quantify the dynamic effects of price transmission, the nonlinear model needs to be transformed into a linear system. First, the steady-state equilibrium of the model under no-shock conditions is calculated to obtain the steady-state values ​​of each variable. Define any variable... The linearized form of is the percentage of its logarithm that deviates from the steady state: Subsequently, a first-order Taylor expansion of the electricity sales company's pricing equation and the user demand equation is performed at steady state, ignoring higher-order terms and substituting in the exogenous shock process. After algebraic simplification, the key linearized electricity purchase and sale price transmission equation can be derived: in, , This is a linearized variable for the composite electricity purchase price index. (Coefficient) to It is a function of structural parameters, and its specific expression is determined by the steady-state relationship and elastic parameters, for example... This directly reflects the core strength of the transmission of electricity purchase costs to electricity sales prices.

[0027] The linearized system constitutes a system of dynamic equations containing endogenous variables and exogenous shocks. Methods such as Generalized Method of Moments (GMM) or Bayesian estimation can be used to jointly estimate the structural parameters in the model based on historical data. After estimation, the parameters are substituted into the linearized equations to calculate the various transmission coefficients. The specific values. In particular, the transmission coefficient of the electricity purchase price to the electricity sales price. This can be quantified as: the percentage change in electricity retail price resulting from a 1% change in the overall electricity purchase price, assuming other factors remain constant. Furthermore, this can be further quantified by analyzing the impact... , , By applying a pulse of one standard deviation, the dynamic path of its transmission through the system to the electricity price is simulated, thereby quantifying the magnitude and time lag of the impact of different types of shocks on the final price.

[0028] This demonstration will use a provincial electricity market as an example. Assume the estimated parameters are: The conductivity coefficient was calculated. =0.82. This indicates that under this market structure, for every 1% increase in electricity purchase costs, the electricity retail price will eventually increase by 0.82%, showing a significant but not complete transmission effect. Simulating a sustained policy shock, the results show that the electricity retail price responds in the current period... Partially, the effect gradually diminishes over several periods due to demand adjustments and policy continuity. The price transmission coefficient and shock response function generated by this method can provide quantitative decision support for electricity sales companies to formulate pricing strategies, users to plan their electricity consumption, and regulatory authorities to assess policy effectiveness. This is achieved by adjusting the weights, elasticities, and shock parameters in the model.

[0029] The system connects to the trading and settlement platform of the power trading center and the user electricity consumption information collection system of the power grid company through encrypted data interfaces. The historical time-series data collected by the system covers market operation data for at least the past 60 months, specifically including: the weighted average purchase price of electricity under medium- and long-term contracts (bilateral negotiation and centralized bidding), the time-of-use average price in the spot market (day-ahead and real-time), the retail catalog price or market-based package price of end users, user electricity load data by industry and voltage level, and macroeconomic indicators such as the Consumer Price Index (CPI) and Producer Price Index (PPI) released by the National Bureau of Statistics.

[0030] During the data preprocessing stage, considering the significant seasonality of electricity data (such as peak summer / winter demand), the system employs the X-12-ARIMA seasonal adjustment algorithm to remove seasonal components from the data. Subsequently, to meet the DSGE model's requirements for steady-state deviation analysis, the system uses the Hodrick-Prescott Filter (HP filter) to decompose the time series data into trend and periodic components, retaining only the periodic component data containing economic fluctuation information. Based on this processed data, the system calibrates and initializes the basic parameters of the DSGE model, for example, setting the subjective discount factor β to 0.99 (corresponding to quarterly frequency), thereby constructing a shock-free steady-state benchmark that reflects the basic characteristics of the target electricity market.

[0031] A three-way coupled model was constructed, involving electricity sales companies, end-users, and policy departments, and the pricing decision-making behavior of electricity sales companies under multiple constraints was defined in detail. Electricity sales companies were modeled as rational economic agents in a monopolistic competition market, whose decision-making objective was to maximize the expected discounted value of net profit in each future period. The model sets constraints on the electricity purchase structure, using weight parameters... The cost of electricity purchase is broken down into a weighted sum of the cost of electricity purchase under medium- and long-term contracts and the cost of electricity purchase in the spot market, in order to reflect the actual business strategy of electricity sales companies to avoid the risk of spot price fluctuations through contract pooling.

[0032] The model's objective function, which includes price stickiness costs, is related to the electricity price. Find the first derivative and introduce the Lagrange multiplier. To characterize the shadow price intensity of government price controls or environmental assessments, the following first-order optimal condition equation is derived: in, The cost markup rate reflects the market power and profit targets of electricity sales companies; and These refer to the spot electricity price and the medium-to-long-term contract electricity price for period t, respectively. The unit operating cost covers marketing, labor, and system maintenance expenses; The existence of this indicates that when policy constraints tighten (i.e. (Increase), even if the cost of purchasing electricity remains unchanged, the optimal electricity selling price will deviate, which provides a mathematical interface for quantitative policy intervention on price transmission.

[0033] Meanwhile, this embodiment, by introducing a nonlinear electricity demand function, precisely characterizes the electricity demand behavior of end-users driven by both price signals and income changes, thus overcoming the shortcomings of traditional models that only consider rigid demand and ignore demand-side response. Users are assumed to be agents pursuing utility maximization, making electricity consumption decisions under budget constraints. The system collects historical data to determine the user's baseline electricity consumption. Compared with benchmark electricity sales price This serves as the steady-state point for the Taylor expansion. Subsequently, two key economic parameters are introduced: the price elasticity of demand parameter... (Usually negative values ​​indicate that price increases lead to reduced electricity consumption) and the income elasticity parameter (Usually taken as positive, indicating increased electricity consumption due to economic growth). Combined with user-side stochastic preference shocks. Construct a nonlinear electricity demand function of the following form: in, This represents the actual electricity demand in period t. and These represent the actual income and benchmark income of users in period t, respectively. This function not only reflects the regulatory role of the price mechanism on load but also captures the pulling effect of macroeconomic conditions on electricity consumption, enabling the model to distinguish between price-driven and income-driven load fluctuations. To simulate the dynamics and unpredictability of the real electricity market environment, this embodiment sets three independent exogenous stochastic processes in the three-way coupled model to capture market shocks from different sources. The advantage of the DSGE model lies in its dynamic nature, namely, analyzing how the system returns to steady state after being subjected to external disturbances.

[0034] The model defines the following three first-order autoregressive processes: Define the process of electricity purchase cost impact: The cost shock persistence coefficient reflects the duration of the impact of factors such as rising coal prices or the dry season for hydropower. Secondly, define the user demand impact process: This is the demand shock persistence coefficient, typically used to simulate load abrupt changes caused by extreme temperatures or public health emergencies; Finally, define the policy shock process: The policy shock persistence coefficient represents the policy inertia of adjustments to transmission and distribution prices or changes in government funds. In the above formula, the residual term... , and\ All are set to follow a white noise distribution with a mean of 0 and a constant variance, representing unpredictable instantaneous market news at each moment.

[0035] After completing the model construction and parameter setting, this embodiment calibrates the core parameters using Bayesian estimation and performs logarithmic linearization on the complex nonlinear system, thereby deriving an intuitive linearized transmission equation. Since the original three-way coupled model contains complex nonlinear functions, direct solution involves extremely high computational cost and makes it difficult to intuitively analyze the marginal effects between variables. Therefore, based on historical observation data, the system performs Markov Chain Monte Carlo (MCMC) simulation using the Metropolis-Hastings algorithm to estimate the posterior distribution of the model's deep structural parameters. A Taylor series expansion is performed near the system's steady state, retaining the first-order terms, transforming all variables into percentage changes deviating from the steady state. The following linearized electricity purchase and sale price transmission equation is derived: For the linearization of electricity sales prices, This is the linearized variable for the composite electricity purchase price index. In this equation, the coefficients... This is the transmission coefficient from the purchase price of electricity to the retail price of electricity; its value directly reflects the market's transmission efficiency. The closer it is to 1, the more completely and promptly the fluctuations in electricity purchase costs can be transmitted to end users; The smaller the value, the more likely the market has severe price stickiness or policy distortion, with costs being absorbed internally by electricity sales companies or mitigated by policy.

[0036] Based on the derived electricity purchase and sale price coupling equation, this embodiment uses an impulse response function to dynamically simulate and guide actual pricing strategies. The system simulates the dynamic response trajectory of the electricity sale price over the next T periods (e.g., 24 months) when a certain exogenous variable (e.g., the spot market electricity purchase price) experiences a positive shock of one standard deviation.

[0037] Transmission lag is defined as the time interval required from the moment of the shock (t=0) until the electricity price response reaches its peak or exceeds a preset threshold (e.g., 0.5% deviation). This helps electricity retailers predict the release point of cost pressures. The maximum transmission effect is defined as the absolute value of the peak in the response trajectory sequence, used to assess price risk exposure in extreme cases. Electricity retailers can develop package pricing strategies; for example, adjusting the contract period of fixed-price packages based on the calculated transmission lag. Policymakers can then use transmission coefficients... Assess the effectiveness of the current electricity price linkage mechanism. If it is found... A value significantly lower than the theoretical value may indicate a need to relax retail price controls or optimize the transmission mechanism of medium- and long-term contracts in order to achieve optimal allocation of electricity market resources.

[0038] The technical solution in this embodiment overcomes the shortcomings of traditional analysis in failing to characterize complex market interactions by constructing a three-way coupled dynamic stochastic general equilibrium model and introducing multiple types of exogenous shocks. It can accurately quantify the transmission coefficient of electricity purchase and sale prices and simulate the dynamic impact of shocks, thereby providing a reliable quantitative decision-making basis for electricity sales pricing and policy adjustments.

[0039] In one embodiment, such as Figure 2 As shown, the generation of the impulse response function includes the following steps S21-S24: In step S21, under the steady-state initial condition of the three-way coupled model, a positive shock of unit standard deviation is applied to the white noise residual term of a certain exogenous random process; In step S22, the electricity sales price variable for the next T time periods is iteratively calculated using the linearized electricity purchase and sale price transmission equation. A numerical sequence; In step S23, a pulse response trajectory diagram is constructed with the time period as the horizontal axis and the deviation of electricity sales price as the vertical axis; In step S24, the conduction delay index and the maximum conduction effect index are extracted from the impulse response trajectory diagram. The conduction delay is defined as the time interval from the moment the impact occurs to the time when the electricity sales price response value exceeds a preset threshold, and the maximum conduction effect is defined as the absolute value of the peak value in the response trajectory sequence. In one embodiment, the core of the dynamic simulation is based on a calibrated and linearized electricity purchase and sale price transmission model. The system places the previously established three-way coupled model under its steady-state initial conditions, i.e., all endogenous and exogenous variables are at long-term equilibrium values, and the corresponding log-linearized variables are all zero. At time t=1, the system applies a positive shock of one standard deviation to the white noise residual term of a specific exogenous stochastic process (e.g., an electricity purchase cost shock). This isolates the influence of a single shock source, ensuring that the observed dynamic response is caused only by that specific shock. Since other exogenous shocks remain zero throughout the time period, and this shock also returns to zero at time t>1, the model's dynamics depend entirely on the shock's persistence coefficient and the policy function within the model. The system recursively iterates to derive the numerical sequence of the electricity sale price variable over the next t time periods, thereby constructing the dynamic response trajectory. At time t=1, the instantaneous shock... The first-order autoregressive process of electricity purchase cost shock Affecting cost state variables The system, based on the linearized electricity purchase and sale price transmission equation and the complete state transition equation of the model, calculates the electricity sale price variable for the next t time periods. Iterative calculations are performed. This process takes into account the electricity sales company's rational predictions of future costs and policies, ensuring that the simulation results conform to the decision-making logic of maximizing intertemporal profits. The system will obtain... Numerical sequence As a result of the dynamic response, an impulse response trajectory diagram is constructed through computer graphical processing, where the time period t is the horizontal axis and the deviation of the electricity sales price is the horizontal axis. The vertical axis represents the percentage change relative to the steady-state price, providing an intuitive visualization of the price transmission process. The system extracts two key quantitative indicators from the generated impulse response trajectory diagram to guide pricing strategies and policy adjustments. The transmission lag indicator (Pass-through Lag) is defined as the time from the impact occurrence t=1 to the electricity sales price response value. The time interval between the first occurrence of a preset threshold (e.g., 0.1% of the steady-state value) or the first time a peak value is reached. The transmission lag index quantifies the lag time required for an effective price response from fluctuations in electricity purchase costs to the retail side. The maximum transmission effect index is defined as the response trajectory sequence. Peak absolute value The maximum transmission effect index quantifies the highest possible deviation of electricity prices when faced with a unit standard deviation shock, reflecting the maximum amplification or mitigation effect of market mechanisms on cost fluctuations. By extracting and analyzing these indicators, market transmission efficiency can be accurately assessed. The longer the transmission lag, the stronger the market friction or regulatory restrictions; the greater the maximum transmission effect, the higher the sensitivity of electricity prices to such shocks.

[0040] In one embodiment, Figure 3 This is a block diagram illustrating a quantitative analysis device for the transmission of electricity purchase and sale prices, according to an exemplary embodiment. Figure 3 As shown, the quantitative analysis device for the transmission of electricity purchase and sale prices includes an acquisition module 31, a construction module 32, a processing module 33, and a calculation module 34.

[0041] The acquisition module 31 is used to acquire historical time-series data of the power market from the power trading platform and the user-side metering system through the data interface. The historical time-series data includes at least one or more of the following: medium- and long-term contract power purchase price, spot market power purchase price, power sales price, user electricity consumption, and macroeconomic indicators. Based on the historical time-series data, the basic parameters of the dynamic stochastic general equilibrium model are initialized. The building module 32 is used to construct a three-party coupling model that includes the main body of electricity sales companies, the main body of end users, and the main body of policy departments. The pricing decision behavior of the main body of electricity sales companies is defined by the objective function of maximizing intertemporal profits, the electricity demand behavior of the main body of end users is defined by the objective function of maximizing utility, the regulatory boundary of the main body of policy departments is defined by the retail electricity price constraint equation, and the shock of electricity purchase cost, user demand shock and policy shock are set as independent exogenous stochastic processes in the three-party coupling model. The processing module 33 is used to calibrate the core structural parameters in the three-way coupling model by combining the Bayesian formula with the historical time series data, and to perform logarithmic linearization on the calibrated three-way coupling model, calculate the steady state of the system that meets the market clearing conditions, and derive the equation describing the electricity purchase and sale price coupling. The calculation module 34 is used to calculate the transmission coefficient of the purchase price to the sales price based on the purchase and sale price coupling equation, and generate an impulse response function according to the exogenous random process to simulate the dynamic response trajectory of the sales price under different shocks, and guide the adjustment of the sales price pricing strategy or policy based on the transmission coefficient and the dynamic response trajectory. The acquisition module 31, the construction module 32, the processing module 33, and the calculation module 34 included in the block diagram of the power purchase and sale price transmission quantitative analysis device are controlled to execute the power purchase and sale price transmission quantitative analysis method described in any of the above embodiments.

[0042] like Figure 4 As shown, the present invention provides an electronic device 400, which includes: a communication interface, a processor 401, and a memory 402; The memory 402 stores program instructions. When executed by the processor 401, which is connected to the memory 402 via the communication interface, the program instructions obtain historical time-series data of the electricity market from the electricity trading platform and the user-side metering system through the data interface. The historical time-series data includes at least one or more of the following: medium- and long-term contract electricity purchase prices, spot market electricity purchase prices, electricity sales prices, user electricity consumption, and macroeconomic indicators. Based on the historical time-series data, the basic parameters of the dynamic stochastic general equilibrium model are initialized. A three-party coupled model is constructed, comprising electricity sales companies, end-users, and policy departments. The pricing decision-making behavior of electricity sales companies is defined by an intertemporal profit maximization objective function, and the end-user's pricing decision-making behavior is defined by a utility maximization objective function. The electricity demand behavior of households is used to define the regulatory boundaries of policy departments through the retail electricity price constraint equation. In the three-party coupling model, the shocks of electricity purchase cost, user demand, and policy are set as independent exogenous stochastic processes. The core structural parameters in the three-party coupling model are calibrated by combining the Bayesian formula with the historical time series data. The calibrated three-party coupling model is then logarithmically linearized to calculate the steady state of the system that meets the market clearing conditions, and the coupling equation describing the purchase and sale of electricity prices is derived. Based on the coupling equation, the transmission coefficient of the purchase price to the sale price is calculated, and an impulse response function is generated based on the exogenous stochastic process to simulate the dynamic response trajectory of the sale price under different shocks. The transmission coefficient and the dynamic response trajectory guide the adjustment of the sale price pricing strategy or policy.

[0043] This invention provides a computer-readable storage medium storing computer program instructions. When executed by a processor, the computer program instructions acquire historical time-series data of the electricity market from an electricity trading platform and a user-side metering system via a data interface. The historical time-series data includes at least one or more of the following: medium- and long-term contract electricity purchase prices, spot market electricity purchase prices, electricity sales prices, user electricity consumption, and macroeconomic indicators. Based on the historical time-series data, the basic parameters of a dynamic stochastic general equilibrium model are initialized. A three-way coupled model is constructed, comprising electricity sales companies, end-users, and policy departments. The pricing decision-making behavior of electricity sales companies is defined by an intertemporal profit maximization objective function, and the pricing decision-making behavior of end-users is defined by a utility maximization objective function. Electricity demand behavior is analyzed using a retail electricity price constraint equation to define the regulatory boundaries of policy departments. In the three-way coupling model, the shocks to electricity purchase costs, user demand, and policy are set as independent exogenous stochastic processes. The core structural parameters of the three-way coupling model are calibrated using Bayesian formulas combined with historical time-series data. The calibrated model is then logarithmically linearized to calculate the system steady state satisfying market clearing conditions, deriving the electricity purchase and sales price coupling equation. Based on this equation, the transmission coefficient of the electricity purchase price to the sales price is calculated. An impulse response function is generated based on the exogenous stochastic process to simulate the dynamic response trajectory of the sales price under different shocks. The transmission coefficient and dynamic response trajectory guide the adjustment of electricity pricing strategies or policies.

[0044] It should be understood that the specific features, operations, and details described above regarding the method of the present invention can also be similarly applied to the apparatus and system of the present invention, or vice versa. Furthermore, each step of the method of the present invention described above can be performed by a corresponding component or unit of the apparatus or system of the present invention.

[0045] It should be understood that the various modules / units of the device of the present invention can be implemented wholly or partially through software, hardware, firmware, or a combination thereof. Each module / unit can be embedded in the processor of a computer device in hardware or firmware form or independent of the processor, or it can be stored in the memory of a computer device in software form for the processor to call to execute the operation of each module / unit. Each module / unit can be implemented as an independent component or module, or two or more modules / units can be implemented as a single component or module.

[0046] In one embodiment, a computer device is provided, including a memory and a processor. The memory stores computer instructions executable by the processor, which, when executed by the processor, instruct the processor to perform steps of the methods of embodiments of the present invention. The computer device can be broadly categorized as a server, terminal, or any other electronic device with the necessary computing and / or processing capabilities. In one embodiment, the computer device may include a processor, memory, network interface, communication interface, etc., connected via a system bus. The processor of the computer device can be used to provide the necessary computing, processing, and / or control capabilities. The memory of the computer device may include a non-volatile storage medium and internal memory. The non-volatile storage medium may store an operating system, computer programs, etc. The internal memory can provide an environment for the operation of the operating system and computer programs in the non-volatile storage medium. The network interface and communication interface of the computer device can be used to connect and communicate with external devices via a network. When the computer program is executed by the processor, it performs the steps of the methods of the present invention.

[0047] This invention can be implemented as a computer-readable storage medium storing a computer program thereon, which, when executed by a processor, causes the steps of the methods of embodiments of the invention to be performed. In one embodiment, the computer program is distributed across multiple network-coupled computer devices or processors, such that the computer program is stored, accessed, and executed in a distributed manner by one or more computer devices or processors. A single method step / operation, or two or more method steps / operations, may be executed by a single computer device or processor or by two or more computer devices or processors. One or more method steps / operations may be executed by one or more computer devices or processors, and one or more other method steps / operations may be executed by one or more other computer devices or processors. One or more computer devices or processors may execute a single method step / operation, or execute two or more method steps / operations.

[0048] It will be understood by those skilled in the art that the method steps of the present invention can be performed by a computer program instructing related hardware, such as a computer device or processor. The computer program may be stored in a non-transitory computer-readable storage medium, and its execution causes the steps of the present invention to be performed. Depending on the context, any references herein to memory, storage, databases, or other media may include non-volatile and / or volatile memory. Examples of non-volatile memory include read-only memory (ROM), programmable ROM (PROM), electrically programmable ROM (EPROM), electrically erasable programmable ROM (EEPROM), flash memory, magnetic tape, floppy disk, magneto-optical data storage device, optical data storage device, hard disk, solid-state drive, etc. Examples of volatile memory include random access memory (RAM), external cache memory, etc.

[0049] The technical features described above can be combined arbitrarily. Although not all possible combinations of these technical features are described, any combination of these technical features should be considered to be covered by this specification, provided that such combination does not contain contradictions.

[0050] Finally, it should be noted that the above embodiments are only used to illustrate the technical solutions of the present invention, and not to limit them; although the present invention has been described in detail with reference to the foregoing embodiments, those skilled in the art should understand that modifications can still be made to the technical solutions described in the foregoing embodiments, or equivalent substitutions can be made to some or all of the technical features; and these modifications or substitutions do not cause the essence of the corresponding technical solutions to deviate from the scope of the technical solutions of the embodiments of the present invention.

Claims

1. A quantitative analysis method for the transmission of electricity purchase and sale prices, characterized in that, include: Historical time-series data of the power market is obtained from the power trading platform and user-side metering system through the data interface. The historical time-series data includes at least one or more of the following: medium- and long-term contract power purchase price, spot market power purchase price, power sales price, user electricity consumption, and macroeconomic indicators. Based on the historical time-series data, the basic parameters of the dynamic stochastic general equilibrium model are initialized. A three-party coupling model is constructed, comprising electricity sales companies, end users, and policy departments. The pricing decision-making behavior of electricity sales companies is defined by the intertemporal profit maximization objective function, the electricity demand behavior of end users is defined by the utility maximization objective function, and the regulatory boundary of policy departments is defined by the retail electricity price constraint equation. In the three-party coupling model, the shocks of electricity purchase costs, user demand, and policy shocks are set as independent exogenous stochastic processes. The core structural parameters in the three-way coupling model are calibrated by combining the Bayesian formula with the historical time series data. The calibrated three-way coupling model is then logarithmically linearized to calculate the steady state of the system that meets the market clearing conditions and to derive the coupling equation describing the purchase and sale of electricity prices. Based on the aforementioned electricity purchase and sale price coupling equation, the transmission coefficient of the electricity purchase price to the electricity sale price is calculated, and an impulse response function is generated according to the aforementioned exogenous stochastic process to simulate the dynamic response trajectory of the electricity sale price under different shocks. Based on the transmission coefficient and the dynamic response trajectory, the electricity sale pricing strategy or policy adjustment is guided.

2. The quantitative analysis method for the transmission of electricity purchase and sale prices as described in claim 1, characterized in that, The pricing decision-making behavior of the electricity sales company includes: The objective function of an electricity sales company is defined as maximizing the expected discounted value of net profit in each future period, where net profit is calculated by subtracting electricity purchase cost, operating cost and policy adjustment expenses from electricity sales revenue. By setting constraints on the power purchase structure, the power purchase cost is decomposed into a weighted sum of the power purchase cost under medium- and long-term contracts and the power purchase cost in the spot market through the weight parameter ω; The objective function with respect to the electricity price R t Find the first derivative and introduce the Lagrange multiplier λ. t Characterizing the strength of policy constraints, we construct a first-order optimal condition equation: Where μ is the cost-plus rate reflecting the profit target, and P sp,t With P c,t These represent the medium- to long-term contract electricity price and the spot electricity price for period t, respectively. op Operating cost per unit.

3. The quantitative analysis method for the transmission of electricity purchase and sale prices as described in claim 2, characterized in that, The electricity demand behavior of the end-user entities includes: Collect historical electricity consumption data and electricity price data to determine the user's baseline electricity consumption. Compared with benchmark electricity sales price ; Introducing the price elasticity of demand parameter With income elasticity parameter This quantifies users' sensitivity to price changes and income changes, respectively. Combined with the aforementioned user demand impact Construct a nonlinear electricity demand function: in, For the electricity demand in period t, and The user income and benchmark income for period t are respectively, and the nonlinear electricity demand function is used to simulate the dynamic adjustment of user electricity consumption under price and income fluctuations.

4. The quantitative analysis method for the transmission of electricity purchase and sale prices as described in claim 1, characterized in that, The exogenous stochastic process includes: The process of defining the impact of electricity purchase costs follows a first-order autoregressive model: in, This is the cost shock persistence coefficient; The user demand shock process is defined by a first-order autoregressive model: in, This represents the persistence coefficient of the demand shock. The policy shock process is defined by a first-order autoregressive model: in, The coefficient for the persistence of policy shocks; Setting residual terms , and All of them follow a white noise distribution with a mean of 0 and a constant variance, in order to capture unpredictable instantaneous market fluctuations.

5. The quantitative analysis method for the transmission of electricity purchase and sale prices as described in claim 1, characterized in that, The logarithmic linearization process includes: Calculate the steady-state values ​​of each variable in the three-way coupling model under no-impact conditions; The linearized form of each variable is defined as the percentage change of the variable from its steady-state value; By performing a Taylor expansion of the aforementioned three-way coupled model near the steady-state value and retaining the first-order terms, the linearized electricity purchase and sale price transmission equation of the following form is derived: in, For electricity sales price variables, For the linearization of the electricity purchase price index, the coefficients are... This represents the transmission coefficient of the electricity purchase price to the electricity sales price, which needs to be quantified.

6. The quantitative analysis method for the transmission of electricity purchase and sale prices as described in claim 1, characterized in that, The generated impulse response function includes: Under the steady-state initial conditions of the three-way coupled model, a positive shock of unit standard deviation is applied to the white noise residual term of an exogenous random process; The electricity sales price variable is iteratively calculated over the next T time periods using the linearized electricity purchase and sale price transmission equation. A numerical sequence; Construct an impulse response trajectory diagram with time period as the horizontal axis and electricity price deviation as the vertical axis; The conduction delay index and the maximum conduction effect index are extracted from the impulse response trajectory diagram. The conduction delay is defined as the time interval from the moment the impact occurs to the time when the electricity sales price response value exceeds a preset threshold. The maximum conduction effect is defined as the absolute value of the peak value in the response trajectory sequence.

7. A quantitative analysis device for the transmission of electricity purchase and sale prices, characterized in that, include: The acquisition module is used to acquire historical time-series data of the power market from the power trading platform and the user-side metering system through the data interface. The historical time-series data includes at least one or more of the following: medium- and long-term contract power purchase price, spot market power purchase price, power sales price, user electricity consumption, and macroeconomic indicators. Based on the historical time-series data, the module initializes the basic parameters of the dynamic stochastic general equilibrium model. The construction module is used to build a three-party coupled model that includes electricity sales companies, end users, and policy departments. The pricing decision-making behavior of electricity sales companies is defined by the intertemporal profit maximization objective function, the electricity demand behavior of end users is defined by the utility maximization objective function, and the regulation boundary of policy departments is defined by the retail electricity price constraint equation. In the three-party coupled model, the shocks of electricity purchase costs, user demand, and policy shocks are set as independent exogenous stochastic processes. The processing module is used to calibrate the core structural parameters in the three-way coupling model by combining the Bayesian formula with the historical time series data, and to perform logarithmic linearization on the calibrated three-way coupling model, calculate the steady state of the system that meets the market clearing conditions, and derive the coupling equation describing the purchase and sale of electricity prices. The calculation module is used to calculate the transmission coefficient of the purchase price to the sales price based on the purchased electricity price coupling equation, and generate an impulse response function according to the exogenous stochastic process to simulate the dynamic response trajectory of the sales price under different shocks. Based on the transmission coefficient and the dynamic response trajectory, the module guides the adjustment of the sales pricing strategy or policy.

8. The quantitative analysis device for the transmission of electricity purchase and sale prices as described in claim 7, characterized in that: The acquisition module, the construction module, the processing module, and the calculation module are controlled to execute the quantitative analysis method for the transmission of electricity purchase and sale prices as described in any one of claims 1 to 6.

9. An electronic device, characterized in that, include: Communication interface, processor, memory; The memory is used to store program instructions, which, when executed by the processor that is communicatively connected to the memory via the communication interface, enable the electronic device to implement the quantitative analysis method for the transmission of electricity purchase and sale prices as described in any one of claims 1 to 6.

10. A computer-readable storage medium having program instructions stored thereon, characterized in that, When the program instructions are executed by a computer, the computer enables the computer to implement the quantitative analysis method for the transmission of electricity purchase and sale prices as described in any one of claims 1 to 6.

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