Power market position limit determination method and related device

Abstract

The invention discloses a position amount calculation method, and provides an electricity market position amount limit determination method and a related device in order to solve the technical problem that a fixed position amount limit system commonly adopted by an electricity market at present is difficult to adapt to remarkable time-varying volatility and cluster characteristics of electricity prices. Based on the clearing price data time sequence and the clearing price feature data time sequence of the electricity day-ahead market, the next-day lowest day-ahead clearing price is obtained through prediction and calculation, and according to the next-day lowest day-ahead clearing price, the user position limit is obtained through calculation in combination with the user medium-and-long-term contract price and the bearable maximum fund loss. According to the method, the characteristics of fluctuation clustering and peak thick tail of the electric power spot price can be effectively captured, and the potential risk loss under different confidence levels can be accurately quantified.

Description

Technical Field

[0001] This application pertains to a method for calculating open interest, specifically relating to a method for determining open interest limits in the electricity market and related apparatus. Background Technology

[0002] With the deepening of the electricity spot market construction, electricity market reform is facing new challenges and opportunities. Against this backdrop, the effective connection between medium- and long-term transactions and the spot market has become a crucial link affecting the healthy development of the electricity market. As a core tool for market participants to hedge against spot price volatility, the management of large positions in medium- and long-term transactions not only directly relates to the operational risks of market participants but also has a profound impact on the stability and liquidity of the overall market. However, the fixed position limit system currently widely used in the electricity market has significant limitations, making it difficult to adapt to the significant time-varying volatility and clustering characteristics of electricity prices due to factors such as load fluctuations, uncertainties in new energy output, and policy adjustments. This rigid system may excessively suppress trading activity and reduce market efficiency during periods of market stability; while during periods of sharp price fluctuations, it may trigger systemic risks due to excessive risk exposure.

[0003] In summary, the fixed position limit system currently widely used in the electricity market has obvious limitations. It is difficult to adapt to the significant time-varying volatility and clustering characteristics of electricity prices. This may lead to excessive suppression of trading activity and reduced market efficiency during periods of market stability, while during periods of sharp price fluctuations, it may cause systemic risks due to excessive risk exposure. Summary of the Invention

[0004] This application addresses the technical problem that the fixed position limit system commonly used in the current electricity market is difficult to adapt to the significant time-varying volatility and clustering characteristics of electricity prices, and provides a method and related apparatus for determining position limits in the electricity market.

[0005] To achieve the above objectives, this application adopts the following technical solution: Firstly, this application proposes a method for determining the electricity market holding quota, including: Obtain the time series of clearing price data for the day-ahead electricity market, and calculate the corresponding time series of clearing price characteristic data; Based on the time series of clearing price data and clearing price characteristic data of the day-ahead electricity market, the lowest day-ahead clearing price for the next day is predicted and calculated. Based on the lowest daily clearing price of the following day, combined with the user's medium- and long-term contract prices and the maximum acceptable capital loss, the user's position limit is calculated.

[0006] Furthermore, the method for calculating the user's position limit includes:

[0007] in, For the first i Individual user position limit For the first i The maximum financial loss a user can tolerate. For the first i The price of medium- to long-term contracts for individual users. This is the lowest clearing price for the following day.

[0008] Furthermore, the first i The calculation method for the total position limit for an individual user includes:

[0009] in, For the first i Total holding limit for each user Z The percentage that users can trade.

[0010] Furthermore, the method for obtaining the time series of clearing price data in the day-ahead electricity market includes: The minimum clearing price for each time period of the day-ahead electricity market is taken by date to obtain the representative clearing price for that date. The representative clearing prices are then sorted by date to obtain the time series of clearing price data for the day-ahead electricity market.

[0011] Furthermore, the method for calculating the corresponding clearing price characteristic data time series includes:

[0012] in, To clear price characteristic data time series of the first t The data corresponding to +1 day To clear the price data time series t The data corresponding to +1 day To clear the price data time series t Data corresponding to the day.

[0013] Furthermore, after obtaining the corresponding clearing price characteristic data time series through calculation, the method further includes: Perform an augmented Dickey-Fuller test on the time series of clearing price characteristic data, and / or perform an autoregressive conditional heteroscedasticity-Lagrange multiplier test on the time series of clearing price characteristic data.

[0014] Furthermore, the method shown for calculating the lowest daily clearing price for the following day includes: The conditional standard deviation for the next day was calculated as follows:

[0015] in, For the first t +1 day conditional standard deviation, This represents the long-term average variance level. Here, represents the coefficients of the ARCH term, p represents the p-parameters of the generalized autoregressive conditional heteroscedasticity model, and q represents the q-parameters of the generalized autoregressive conditional heteroscedasticity model. For the first t daily residual, The coefficient of the GARCH term. For the first t +1- j Daily conditional standard deviation; The daily risk value is calculated as follows:

[0016] in, For confidence level Below Daily risk value, For average rate of return, For confidence level Down Equidistant sites of distribution; The lowest daily clearing price for the following day is calculated as follows:

[0017] in, This is the lowest day-to-day clearing price for the following day. To clear the price data time series t Data corresponding to the day.

[0018] Secondly, this application proposes a power market position quota determination system, comprising: The data module is used to acquire the time series of clearing price data in the day-ahead electricity market and calculate the corresponding time series of clearing price characteristic data. The prediction module is used to predict and calculate the lowest day-ahead clearing price for the next day based on the time series of clearing price data and the time series of clearing price characteristic data of the day-ahead electricity market. The calculation module is used to calculate the user's position limit based on the lowest daily clearing price of the next day, combined with the user's medium- and long-term contract prices and the maximum acceptable capital loss.

[0019] Thirdly, this application proposes an electronic device, including: a memory and one or more processors; the memory is coupled to the processors; wherein the memory stores computer program code, the computer program code including computer instructions, and when the computer instructions are executed by the processor, the electronic device performs the steps of the above-described method for determining the power market position limit.

[0020] Fourthly, this application proposes a computer-readable storage medium storing a computer program that, when executed by a processor, implements the steps of the above-described method for determining the power market position limit.

[0021] Compared with the prior art, this application has the following beneficial effects: This application proposes a method for determining the position limit in the electricity market. Based on the time series of clearing price data and clearing price characteristic data of the day-ahead electricity market, the lowest day-ahead clearing price for the next day is predicted and calculated. Based on this lowest day-ahead clearing price, combined with the user's medium- and long-term contract prices and maximum tolerable capital loss, the user's position limit is calculated. This addresses the significant limitations of the currently prevalent fixed position limit system in the electricity market. This system is ill-suited to the significant time-varying volatility and clustering characteristics of electricity prices, potentially leading to excessive suppression of trading activity and reduced market efficiency during stable periods, while simultaneously causing systemic risks due to excessive risk exposure during periods of sharp price fluctuations. By establishing a scientific method for calculating position limits, this method can effectively capture the "volatile clustering" and "thick-tailed" characteristics of electricity spot prices, accurately quantify potential risk losses at different confidence levels, and help improve the risk resilience of electricity market participants in complex market environments. Furthermore, it provides theoretical basis and practical reference for regulatory agencies to improve market system design and prevent systemic financial risks, thus possessing significant theoretical value and practical significance for promoting the stable and efficient operation of the electricity market.

[0022] This application also proposes a power market position limit determination system, an electronic device, and a computer-readable storage medium, which possess all the advantages of the aforementioned power market position limit determination methods. Attached Figure Description

[0023] To more clearly illustrate the technical solutions of the embodiments of this application, the accompanying drawings used in the embodiments will be briefly introduced below. It should be understood that the following drawings only show some embodiments of this application and should not be regarded as a limitation of the scope. For those skilled in the art, other related drawings can be obtained based on these drawings without creative effort.

[0024] Figure 1This is a flowchart illustrating the first method for determining the electricity market position limit in this application. Figure 2 This is a time series diagram of clearing price data in the embodiments of this application; Figure 3 This is a time series diagram of clearing price characteristic data in the embodiments of this application; Figure 4 This is a schematic diagram of the electricity market position limit determination system of this application. Detailed Implementation

[0025] To make the objectives, technical solutions, and advantages of the embodiments of this application clearer, the technical solutions of the embodiments of this application will be clearly and completely described below with reference to the accompanying drawings. Obviously, the described embodiments are only some embodiments of this application, and not all embodiments. The components of the embodiments of this application described and shown in the accompanying drawings can generally be arranged and designed in various different configurations.

[0026] Therefore, the following detailed description of the embodiments of this application provided in the accompanying drawings is not intended to limit the scope of the claimed application, but merely to illustrate selected embodiments of the application. All other embodiments obtained by those skilled in the art based on the embodiments of this application without inventive effort are within the scope of protection of this application.

[0027] It should be noted that similar labels and letters in the following figures indicate similar items. Therefore, once an item is defined in one figure, it does not need to be further defined and explained in subsequent figures.

[0028] In the description of the embodiments of this application, it should be noted that if terms such as "upper," "lower," "horizontal," or "inner" indicate the orientation or positional relationship based on the orientation or positional relationship shown in the accompanying drawings, or the orientation or positional relationship commonly used when the product of the invention is in use, they are only for the convenience of describing this application and simplifying the description, and do not indicate or imply that the device or element referred to must have a specific orientation, or be constructed and operated in a specific orientation, and therefore should not be construed as a limitation on this application. In addition, terms such as "first" and "second" are only used to distinguish descriptions and should not be construed as indicating or implying relative importance.

[0029] Furthermore, the use of the term "horizontal" does not imply that the component must be absolutely horizontal, but rather that it can be slightly tilted. For example, "horizontal" simply means that its direction is more horizontal than "vertical," and does not mean that the structure must be completely horizontal, but can be slightly tilted.

[0030] In the description of the embodiments of this application, it should also be noted that, unless otherwise explicitly specified and limited, the terms "set," "install," "connect," and "link" should be interpreted broadly. For example, they can refer to a fixed connection, a detachable connection, or an integral connection; they can refer to a mechanical connection or an electrical connection; they can refer to a direct connection or an indirect connection through an intermediate medium; and they can refer to the internal connection of two components. Those skilled in the art can understand the specific meaning of the above terms in this application according to the specific circumstances.

[0031] Medium- and long-term (MLS) transactions, as a crucial component of the electricity market, together with the spot market, constitute a complete electricity trading system. Their effective integration directly impacts the stable operation and healthy development of the electricity market. In practice, MLS transactions are not only an important means for power generation companies, grid companies, and electricity users to ensure stable power supply, but also a core tool for mitigating the risks of drastic price fluctuations in the spot market. By signing MLS contracts in advance, market participants can lock in a portion of the electricity trading price and volume, reducing the impact of spot market price fluctuations on their operating costs or profits. Currently, with the rapid growth of new energy installed capacity, the diversification of electricity load, and the dynamic adjustment of relevant policies, the complexity and uncertainty of the electricity market have significantly increased. The scale and influence of MLS transactions are continuously expanding, and their position management has become a key link in maintaining market order and preventing systemic risks, attracting widespread attention both within and outside the industry.

[0032] In the actual implementation of medium- and long-term transactions, the core objective of position management is to safeguard the normal trading rights of market participants while preventing risks such as market manipulation and abnormal price fluctuations that may arise from large positions held by individual participants. However, the current operating environment of the electricity market exhibits significant time-varying volatility and clustering characteristics. Time-varying volatility is reflected in the real-time changes in electricity prices due to seasonal variations, intraday peak-valley load differences, and fluctuations in renewable energy output. Clustering characteristics are manifested in the fact that price fluctuations are not isolated events but often occur in clusters within specific time periods, forming continuous upward or downward trends. These characteristics place extremely high demands on position management flexibility: during periods of stable market prices and good trading order, sufficient trading space needs to be provided to market participants to stimulate market activity and improve trading efficiency; while during periods of volatile prices and high market risk, strict control of position size is necessary to avoid excessive risk exposure leading to operational risks, or even transmitting to the entire market and forming systemic risks. However, in the current market environment, different market participants have significantly different risk tolerance and trading strategies, making it difficult for a uniform position management standard to adapt to diverse market demands. This contradiction further exacerbates the difficulty of position management.

[0033] To address the risks associated with long-term positions, a fixed position limit system is commonly used as a core control measure. This system is designed to define clear boundaries for market participants' position behavior by setting explicit quantitative indicators. Its advantages lie in its simplicity, low regulatory costs, and ease of implementation, enabling the rapid establishment of basic order in position management and preventing market chaos caused by a lack of oversight. During implementation, market operators determine a unified upper limit for position sizes based on factors such as the overall market size and historical trading data. They then monitor market participants' positions in real time through the trading system. If positions exceed the limit, timely measures such as alerts, trading restrictions, and forced liquidation are taken to ensure the system's rigid implementation. In practical application, this system effectively curbed the blind expansion of positions and malicious market manipulation in the early stages of the electricity market's development, providing crucial support for a smooth market start and becoming the most widely used basic system in the current electricity market position management field.

[0034] However, the inherent limitations of this method are becoming increasingly apparent, making it difficult to adapt to the time-varying volatility and clustering characteristics of electricity prices. On the one hand, during periods of stable market prices and low trading activity, a fixed upper limit on open interest excessively restricts the trading space of market participants. On the other hand, during periods of sharp price fluctuations and concentrated release of market risks, a fixed limit is insufficient to effectively prevent risks. Furthermore, the system fails to consider individual differences among market participants, such as their risk tolerance, trading volume, and business characteristics.

[0035] Based on the above, this application proposes a method and related apparatus for determining the power market position limit. The following is a detailed description of this application in conjunction with embodiments and accompanying drawings.

[0036] like Figure 1 The diagram shown is a flowchart of the first method for determining the electricity market position limit in this application, which may include: S101: Obtain the time series of clearing price data for the day-ahead electricity market and calculate the corresponding time series of clearing price characteristic data.

[0037] The day-ahead electricity market is a market where the trading volume and price for the following day's electricity are determined through centralized bidding the day before the actual electricity trading begins. The clearing price is the transaction price determined when supply and demand in the electricity market reach equilibrium. Clearing price characteristic data, such as volatility, mean, and extreme values, are derived data extracted from the raw clearing price data that reflects price fluctuation patterns. The raw clearing price data forms the basis for reflecting market supply and demand and price fluctuations, preserving the temporal correlation characteristics of prices through time series representation. The characteristic data, on the other hand, is a refinement of the raw data, highlighting the core patterns of price fluctuations and providing effective input for subsequent price forecasting. This reduces the computational complexity of the forecasting model and makes price fluctuation patterns easier for the model to identify.

[0038] S102, based on the time series of clearing price data and clearing price characteristic data of the day-ahead electricity market, the lowest day-ahead clearing price for the next day is predicted and calculated.

[0039] By leveraging the temporal correlations and volatility patterns reflected in historical price time series data, a mapping relationship is established through forecasting, focusing on predicting the minimum clearing price. This is because the price directly relates to the risk floor of market participants; the minimum price corresponds to the maximum potential loss scenario, providing a crucial risk reference for calculating position limits. This application transforms historical price data into practically instructive next-day price predictions, clarifying the potential price risk floor faced by market participants and providing a core basis for the subsequent quantitative calculation of position limits, making the limit standards more targeted.

[0040] S103. Based on the lowest daily clearing price of the next day, combined with the user's medium- and long-term contract prices and the maximum acceptable capital loss, the user's position limit is calculated.

[0041] User position limits are the maximum number of electricity contract positions that a single market participant can hold in medium- to long-term transactions, set based on market risk conditions. The core of position limits is risk control. The minimum clearing price of the following day corresponds to the maximum potential unit electricity loss a market participant may face. Combined with the market participant's risk tolerance, a quantitative formula is used to calculate the maximum tolerable position size, ensuring that the potential loss corresponding to the position does not exceed the risk threshold, while avoiding a decline in trading activity due to excessive position restrictions. This application transforms price forecast results into an executable position management standard, achieving dynamic and quantifiable position limits. This satisfies the reasonable trading needs of market participants under different price scenarios while avoiding systemic risks from large positions through risk threshold control.

[0042] This application constructs a dynamic position management system centered on price forecasting. First, raw and characteristic data of the day-ahead clearing price in the electricity market are acquired through specialized data acquisition equipment, forming a time series to provide a foundation for subsequent analysis. Then, a price forecasting server, combined with time-series algorithms, accurately predicts the lowest day-ahead clearing price for the following day, locking in the potential risk floor for market participants. Finally, through a limit calculation terminal, combined with users' risk tolerance, the predicted price is converted into quantified position limits. This breaks the rigid constraints of traditional fixed limit systems, using market price fluctuation patterns as the core basis to achieve dynamic adaptation of position limits to market risk, while ensuring efficient and accurate operation of each stage through specialized equipment.

[0043] The following is a more detailed embodiment of this application, specifically as follows: S201: Obtain the time series of clearing price data for the day-ahead electricity market and calculate the corresponding time series of clearing price characteristic data.

[0044] The minimum clearing price for each time period of the day-ahead electricity market is taken by date to obtain the representative clearing price for that date. The representative clearing prices are then sorted by date to obtain the clearing price data time series.

[0045] The calculation method for time series data of clearing price characteristics is as follows:

[0046] Among them, the subscript The first time series of clearing price data Sun corresponds to, To clear the price data time series Data corresponding to the day, To clear the price data time series The data corresponding to +1 day To clear price characteristic data time series of the first Data corresponding to the day.

[0047] Then, by date The data is sorted to obtain a time series of clearing price characteristics.

[0048] The following tests can also be performed on the time series of clearing price characteristic data: (1) An ADF test (Augmented Dickey-Fuller test) was performed on the time series of clearing price characteristic data. The test results showed that the time series of clearing price characteristic data was stationary.

[0049] (2) The ARCH-LM test (autoregressive conditional heteroscedasticity-Lagrange multiplier test) was performed on the clearing price characteristic data time series. The test results showed that the clearing price characteristic data time series had an autoregressive conditional heteroscedasticity effect.

[0050] S202, Calculate the lowest possible daily clearing price for the next day based on clearing price data time series and clearing price characteristic data time series. .

[0051] Specifically, the following methods can be used for calculation: (1) Use the GARCH model to solve for the conditional standard deviation of the next day .

[0052] The clearing price characteristic data time series was processed using the GARCH(p,q) (Generalized Autoregressive Conditional Heteroskedasticity) model, where p and q are parameters of the GARCH(p,q) model, p is the order of the GARCH term, and q is the order of the ARCH term.

[0053] Substituting the time series of clearing price characteristic data into the following system of equations, we obtain the following fit: :

[0054]

[0055]

[0056] in, For the first Daily conditional mean No. The daily residual is... For the first The daily standardized perturbations follow an independent and identically distributed pattern with a mean of 0 and a variance of 1. For the first Daily conditional standard deviation, For the first t +1 day conditional standard deviation, This represents the long-term average variance level. The coefficient of the ARCH term. The coefficient of the GARCH term. For the first t daily residual, For the first t +1- j Daily conditional standard deviation.

[0057] The GARCH(p,q) model has passed the following tests: The standardized residual squared sequence was subjected to the Ljung-Box test, and the test results showed that the standardized residual squared sequence was uncorrelated.

[0058] (2) Calculate the lowest possible daily clearing price for the next day.

[0059] Calculate the risk value on day t:

[0060] in, The preset confidence level, For confidence level Below Daily risk value, The average rate of return is obtained from the GARCH(p,q) model. Represents confidence level Down Equivalent sites in the distribution.

[0061] Calculate the lowest possible daily clearing price for the next day:

[0062] in, This is the lowest possible clearing price for the following day.

[0063] S203, Calculate the user's position limit based on the lowest possible clearing price of the next day.

[0064] Specifically:

[0065] in, Indicates the first Individual user position limit Indicates the first The maximum financial loss a user can tolerate. Indicates the first The price of medium- to long-term contracts for individual users.

[0066] in, The value can be set according to the user's own situation. In this embodiment, it can be set to the first... 10% of each user's assets.

[0067] Specifically, a user's holdings include a tradable portion and a non-tradable portion. The electricity market only allows trading of the tradable portion of a user's holdings. The total holding limit for a user can be calculated based on the tradable portion of their holdings.

[0068] in, Indicates the first Total holding limit for each user This indicates the percentage of a user's transactions that are allowed.

[0069] The technical effects of this application are verified through the following examples.

[0070] Step S01: Obtain the time series of clearing price data for the day-ahead electricity market and calculate the corresponding time series of clearing price characteristic data.

[0071] The experimental data consisted of daily day-ahead clearing price data from a specific region in a certain country's market from July 1, 2021 to June 30, 2022. This day-ahead market cleared once per hour, resulting in 24 day-ahead clearing prices per day and a total of 8742 day-ahead clearing price data points. The minimum clearing price for each time period of the day-ahead electricity market was taken by date to obtain the representative clearing price for that date. These representative clearing prices were then sorted by date to obtain the time series of the clearing price data, as shown in Table 1.

[0072] Table 1 Time Series of Clearing Price Data

[0073] In Table 1, among the 24 day-ahead clearing prices with a date of July 1, 2021, the lowest clearing price is 25.48. Following this pattern and sorted by date, the clearing price data time series is shown in Table 1. The corresponding clearing price data time series graph is shown below. Figure 2 As shown.

[0074] To improve model accuracy, feature engineering techniques can be used to extract necessary features from the original clearing price data time series. For example, logarithmic returns can be used to construct clearing price feature data time series.

[0075] The specific method is as follows:

[0076] Among them, the subscript The first time series of clearing price data Sun corresponds to, The time series of the clearing price data is the first... Data corresponding to the day, The time series of the clearing price data is the first... The data corresponding to +1 day The time series of the clearing price characteristic data is the first... Data corresponding to the day.

[0077] By date The data is sorted to obtain the time series of the clearing price characteristics.

[0078] Taking the data in Table 1 as an example, divide the representative clearing price of 21.03 corresponding to date 2021-7-2 by the representative clearing price of 25.48 corresponding to date 2021-7-1, and then take the logarithm to obtain the clearing price characteristic data corresponding to date 2021-7-2, that is: = = -0.191944.

[0079] Following this pattern, the time series of clearing price characteristic data is obtained as shown in Table 2, and the corresponding time series graph of clearing price characteristic data is shown below. Figure 3 As shown.

[0080] Table 2 Time Series of Clearing Price Characteristics

[0081] If necessary, the time series of clearing price characteristic data can also be tested to ensure the validity of the characteristic data.

[0082] For example, the ADF (Augmented Dickey-Fuller Test) and ARCH-LM (Autoregressive Conditional Heteroscedasticity-Lagrange Multiplier Test) can be performed on time series data of clearing price characteristics. Details are as follows: An ADF (Augmented Dickey-Fuller Test) is performed on the time series of clearing price characteristic data. If the test results indicate that the time series of clearing price characteristic data is stationary, then the test is passed.

[0083] An ARCH-LM test (Autoregressive Conditional Heteroscedasticity-Lagrange Multiplier Test) is performed on the time series of clearing price characteristic data. If the test results indicate that the time series of clearing price characteristic data has an autoregressive conditional heteroscedasticity effect, then the test is passed.

[0084] Taking the data in Table 2 as an example, the ADF Test (Augmented Dickey-Fuller Test) was used to test the time series of clearing price characteristics. The null hypothesis was that the data series was non-stationary, and the alternative hypothesis was that the data series was stationary. The test results are shown in Table 3 below.

[0085] Table 3 ADF Test Results

[0086] The null hypothesis was rejected at the 1% significance level, indicating that the time series of clearing price characteristic data is stationary, and the test was passed.

[0087] Taking the data in Table 2 as an example, the ARCH-LM Test (Autoregressive Conditional Heteroscedasticity-Lagrange Multiplier Test) was used to test the time series of clearing price characteristic data. The null hypothesis was that the residual series did not have an autoregressive conditional heteroscedasticity effect, and the alternative hypothesis was that an autoregressive conditional heteroscedasticity effect existed. The test results are shown in Table 4.

[0088] Table 4. ARCH-LM test results

[0089] The null hypothesis was rejected at the 1% significance level, indicating that the time series of clearing price characteristic data has an autoregressive conditional heteroscedasticity effect, and the test was passed.

[0090] Step S02: Calculate the lowest possible daily clearing price for the next day based on the clearing price data time series and the clearing price feature data time series. .

[0091] Specific steps may include: (s-1) Solve for the conditional standard deviation of the next day using the GARCH model. .

[0092] The clearing price characteristic data time series was processed using a GARCH(p,q) (Generalized Autoregressive Conditional Heteroskedasticity) model, where p and q are parameters of the GARCH(p,q) model, p is the order of the GARCH term, and q is the order of the ARCH term. Specifically: Substituting the time series of clearing price characteristic data into the following system of equations, we obtain the following fit: ;

[0093]

[0094]

[0095] in, For the first Daily conditional mean No. The daily residual is... For the first The daily standardized perturbations follow an independent and identically distributed pattern with a mean of 0 and a variance of 1. For the first Daily conditional standard deviation, For the first t +1 day conditional standard deviation, This represents the long-term average variance level. The coefficient of the ARCH term. The coefficient of the GARCH term. For the first t daily residual, For the first t +1- j Daily conditional standard deviation.

[0096] Using the data in Table 2 as an example, we use Python programming to process the time series of clearing price characteristic data using the GARCH(p,q) model. To obtain the optimal GARCH(p,q) model, we can analyze the data using GARCH(1,1), GARCH(1,2), GARCH(2,1), and GARCH(2,2) respectively, and obtain the AIC (Akaike Information Criterion) for each of the four orders. The model order with the lowest AIC is considered optimal. The AIC information criterion is a standard for measuring the goodness of fit of a statistical model. It is based on the concept of entropy and can weigh the complexity of the estimated model against the goodness of fit of the model to the data.

[0097] In practice, using the data in Table 2 as input, the `arch` package in Python is called. When the parameters `p` and `q` are 1 and 1 respectively, the calculated AIC is -543.0738; when the parameters `p` and `q` are 1 and 2 respectively, the calculated AIC is -542.2932; when the parameters `p` and `q` are 2 and 1 respectively, the calculated AIC is -541.0738; and when the parameters `p` and `q` are 2 and 2 respectively, the calculated AIC is -540.2932. The specific results are shown in Table 5.

[0098] Table 5. AIC Calculation Results for Each Model Order

[0099] The lowest AIC value is -543.0738, corresponding to the GARCH(1,1) model. Therefore, the GARCH(1,1) model is the optimal model and will be used subsequently.

[0100] The GARCH(1,1) model is used to process the time series of clearing price characteristic data, specifically as follows:

[0101]

[0102] Since both p and q are 1, therefore:

[0103] in, For the first Daily conditional mean For the first daily residual, For the first The daily standardized perturbations follow an independent and identically distributed pattern with a mean of 0 and a variance of 1. , For the first t +1 day conditional standard deviation, This represents the long-term average variance level. The coefficient of the ARCH term. The coefficient of the GARCH term. For the first t daily residual, For the first t +1- j Daily conditional standard deviation.

[0104] In practice, using the data in Table 2 as input, with parameters p and q both set to 1, the `arch` package in Python is called to obtain the standard deviation for the next day. (The calculation results of other typical parameters include:) , , , ).

[0105] If necessary, the GARCH model can be validated to ensure its effectiveness. For example, an Ljung-Box test can be performed on the GARCH model. Details are as follows: Perform the Ljung-Box test on the standardized residual squared series. If the test result shows no correlation, the test is passed.

[0106] Taking the GARCH(1,1) model as an example, the Ljung-Box test was used to test the standardized residual squared sequences. The null hypothesis was that there was no correlation, and the alternative hypothesis was that there was correlation. The results are shown in Table 6 below. Table 6 Ljung-Box Test Results

[0107] At the 5% significance level, the null hypothesis cannot be rejected, meaning there is no correlation. This indicates that the GARCH(1,1) model meets the requirements when the lag order is 5, 10, or 15, and therefore the GARCH(1,1) model passes the test.

[0108] (s-2) Calculate the lowest possible daily clearing price for the next day.

[0109] The risk value for day t is calculated as follows:

[0110] in, The preset confidence level, For confidence level Below Daily risk value, The average rate of return is obtained from the GARCH(p,q) model. Represents confidence level Down Equivalent sites in the distribution.

[0111] Calculate the lowest possible daily clearing price for the next day:

[0112] in, This is the lowest possible clearing price for the following day.

[0113] In terms of specific implementation, , Under a 95% confidence level and a t-distribution, Then by Calculated .

[0114] Will Historical data shows that the last clearing price was Substitute The formula calculates the lowest possible daily clearing price for the following day. .

[0115] Step S03: Based on the lowest possible clearing price for the next day, calculate the user's position limit, specifically as follows:

[0116] in, Indicates the first Individual user position limit Indicates the first The maximum financial loss that each user can tolerate is set by the individual user based on their own risk tolerance. Indicates the first The price of medium- to long-term contracts for individual users.

[0117] Assume the medium- to long-term contract price for the first user The user's maximum tolerable loss is 55 (USD / MWh), which is 10% of their total assets. Assuming the user's total assets are $10 million, this is the maximum tolerable loss set by the user. It is $1 million.

[0118] Will , , Bring into From (MWh), therefore the user's holding limit is 178005.9157 (MWh).

[0119] When a user's holdings include both tradable and non-tradable portions, the user's total holding limit can be calculated using the following method:

[0120] in, Indicates the first Total holding limit for each user This indicates the percentage of a user's transactions that are allowed.

[0121] Assuming the above user tradable ratio It will be 20%. and Substitute From this, we can calculate that the total holding limit for users is 890029.5785 (MWh).

[0122] The fixed position limit system commonly used in the current electricity market has significant limitations, failing to adapt to the significant time-varying volatility and clustering characteristics of electricity prices. This can lead to excessive suppression of trading activity and reduced market efficiency during periods of market stability, while potentially triggering systemic risks due to excessive risk exposure during periods of sharp price fluctuations. This application obtains the time series of clearing price data for the day-ahead electricity market and calculates the corresponding clearing price characteristic data time series. Based on the clearing price data time series and the clearing price characteristic data time series, it calculates the lowest possible day-ahead clearing price for the next day. Finally, based on the lowest possible day-ahead clearing price for the next day, combined with the user's medium- and long-term contract prices and maximum tolerable capital loss, it calculates the user's position limit. This solves the problem that the fixed position limit system commonly used in the current electricity market is unable to adapt to the significant time-varying volatility and clustering characteristics of electricity prices. It helps improve the risk resistance capabilities of electricity market participants in complex market environments and provides theoretical basis and practical reference for regulatory agencies to improve market system design and prevent systemic financial risks. It has significant theoretical value and practical significance for promoting the stable and efficient operation of the electricity market.

[0123] like Figure 4 The diagram shown is a schematic representation of a power market position limit determination system according to this application, which may include: The data module is used to acquire the time series of clearing price data in the day-ahead electricity market and calculate the corresponding time series of clearing price characteristic data. The prediction module is used to predict and calculate the lowest day-ahead clearing price for the next day based on the time series of clearing price data and the time series of clearing price characteristic data of the day-ahead electricity market. The calculation module is used to calculate the user's position limit based on the lowest daily clearing price of the next day, combined with the user's medium- and long-term contract prices and the maximum acceptable capital loss.

[0124] It should be noted that, in the several embodiments provided in this application, it should be understood that the disclosed apparatus and methods can be implemented in other ways. For example, the system embodiments described above are merely illustrative; for instance, the division of each block is only a logical functional division, and in actual implementation, there may be other division methods. For example, multiple blocks may be combined or integrated into another device, or some features may be ignored or not executed. The modules described as separate components may or may not be physically separated. The components shown as modules may be one or more physical units, that is, they may be located in one place or distributed in multiple different places. Some or all of the modules can be selected to achieve the purpose of the solution in this embodiment according to actual needs.

[0125] Furthermore, in the various embodiments of the present invention, the modules can be integrated into one processing unit, or each module can exist physically separately, or two or more modules can be integrated into one unit. The integrated unit described above can be implemented in hardware or as a software functional unit.

[0126] This application also provides an electronic device, which may include one or more processors, memory and communication interfaces.

[0127] The memory, communication interface, and processor are coupled together. For example, the memory, communication interface, and processor can be coupled together via a bus.

[0128] The communication interface is used for data transmission with other devices. The memory stores computer program code. This computer program code includes computer instructions, which, when executed by the processor, cause the electronic device to perform the steps of the aforementioned method for determining electricity market position limits.

[0129] The processor can be a processor or controller, such as a Central Processing Unit (CPU), a general-purpose processor, a Digital Signal Processor (DSP), an Application-Specific Integrated Circuit (ASIC), a Field-Programmable Gate Array (FPGA), or other programmable logic devices, transistor logic devices, hardware components, or any combination thereof. It can implement or execute the various exemplary logic blocks, modules, and circuits described in conjunction with this disclosure. The processor can also be a combination that implements computing functions, such as a combination of one or more microprocessors, a combination of a DSP and a microprocessor, etc. The processor can be used to support an electronic device in performing the method steps provided in the above embodiments.

[0130] The bus can be a Peripheral Component Interconnect (PCI) bus or an Extended Industry Standard Architecture (EISA) bus, etc. These buses can be categorized as address buses, data buses, control buses, etc.

[0131] This application provides a computer-readable storage medium storing a computer program. When the computer program is executed by a processor, it implements the steps of the above-described method for determining the power market position limit.

[0132] The computer-readable storage media involved in this application include random access memory (RAM), memory, read-only memory (ROM), electrically programmable ROM, electrically erasable programmable ROM, registers, hard disks, removable disks, CD-ROMs, or any other form of storage media known in the art.

[0133] The above are merely preferred embodiments of this application and are not intended to limit this application. Various modifications and variations can be made to this application by those skilled in the art. Any modifications, equivalent substitutions, improvements, etc., made within the spirit and principles of this application should be included within the protection scope of this application.

Claims

1. A method for determining the power market position limit, characterized in that, include: Obtain the time series of clearing price data for the day-ahead electricity market, and calculate the corresponding time series of clearing price characteristic data; Based on the time series of clearing price data and clearing price characteristic data of the day-ahead electricity market, the lowest day-ahead clearing price for the next day is predicted and calculated. Based on the lowest daily clearing price of the following day, combined with the user's medium- and long-term contract prices and the maximum acceptable capital loss, the user's position limit is calculated.

2. The method for determining the power market position limit according to claim 1, characterized in that, The method for calculating the user's position limit includes: in, For the first i Individual user position limit For the first i The maximum financial loss a user can tolerate. For the first i The price of medium- to long-term contracts for individual users. This is the lowest clearing price for the following day.

3. The method for determining the power market position limit according to claim 2, characterized in that, No. i The calculation method for the total position limit for an individual user includes: in, For the first i Total holding limit for each user Z The percentage that users can trade.

4. The method for determining the power market position limit according to claim 1, characterized in that, The method for obtaining the time series of clearing price data for the day-ahead electricity market includes: The minimum clearing price for each time period of the day-ahead electricity market is taken by date to obtain the representative clearing price for that date. The representative clearing prices are then sorted by date to obtain the time series of clearing price data for the day-ahead electricity market.

5. The method for determining the power market position limit according to claim 1, characterized in that, The method for calculating the corresponding clearing price characteristic data time series includes: in, To clear price characteristic data time series of the first t The data corresponding to +1 day To clear the price data time series t The data corresponding to +1 day To clear the price data time series t Data corresponding to the day.

6. The method for determining the power market position limit according to claim 1, characterized in that, After obtaining the corresponding clearing price characteristic data time series through calculation, the method further includes: Perform an augmented Dickey-Fuller test on the time series of clearing price characteristic data, and / or perform an autoregressive conditional heteroscedasticity-Lagrange multiplier test on the time series of clearing price characteristic data.

7. The method for determining the power market position limit according to claim 1, characterized in that, The method shown for calculating the lowest daily clearing price for the next day includes: The conditional standard deviation for the next day was calculated as follows: in, For the first t +1 day conditional standard deviation, This represents the long-term average variance level. Here, represents the coefficients of the ARCH term, p represents the p-parameters of the generalized autoregressive conditional heteroscedasticity model, and q represents the q-parameters of the generalized autoregressive conditional heteroscedasticity model. For the first t daily residual, The coefficient of the GARCH term. For the first t +1- j Daily conditional standard deviation; The daily risk value is calculated as follows: in, For confidence level Below Daily risk value, For average rate of return, For confidence level Down Equidistant sites of distribution; The lowest daily clearing price for the following day is calculated as follows: in, This is the lowest day-to-day clearing price for the following day. To clear the price data time series t Data corresponding to the day.

8. A system for determining the quota of power market holdings, characterized in that, include: The data module is used to acquire the time series of clearing price data in the day-ahead electricity market and calculate the corresponding time series of clearing price characteristic data. The prediction module is used to predict and calculate the lowest day-ahead clearing price for the next day based on the time series of clearing price data and the time series of clearing price characteristic data of the day-ahead electricity market. The calculation module is used to calculate the user's position limit based on the lowest daily clearing price of the next day, combined with the user's medium- and long-term contract prices and the maximum acceptable capital loss.

9. An electronic device, characterized in that, include: A memory, one or more processors; the memory is coupled to the processors; wherein the memory stores computer program code, the computer program code including computer instructions, and when the computer instructions are executed by the processor, the electronic device performs the steps of the method for determining the power market position limit as described in any one of claims 1-8.

10. A computer-readable storage medium, characterized in that, The computer-readable storage medium stores a computer program that, when executed by a processor, implements the steps of the method for determining the power market position limit as described in any one of claims 1-8.

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