Time-sharing pricing method for multi-cycle transaction and deviation adjustment of power market in region

Abstract

The invention discloses a time-sharing pricing method for multi-cycle transaction and deviation adjustment of an electric power market in a region, and the method comprises the steps: constructing a multi-cycle collaborative transaction scale of year-month-day rolling-spot goods, and introducing a departure and use two-side deviation common-load adjustment and dynamic two-stage price limiting mechanism; and effective conversion from medium-and-long-term electric quantity value to electric power time-sharing value is realized. According to the method, on the premise that market individual competition is reserved, time-sharing price signals reflecting real supply and demand and giving consideration to efficiency and stability are formed through systematic deviation adjustment and price stability control means, and the power resource cross-cycle optimization configuration capacity and the overall market anti-risk level are improved.

Description

Technical Field

[0001] This invention relates to the technical field of electricity trading, and in particular to a time-of-use pricing method and system for multi-period trading and deviation adjustment in regional electricity markets. Background Technology

[0002] Electricity is a complex commodity with multiple characteristics, including instantaneous physical equilibrium, a unique economic cost structure, and social public service attributes. Its price formation is not only influenced by the cost of primary energy, but also by the combined effects of various factors such as supply and demand on the generation side, multi-cycle trading, system operation constraints, and market policies.

[0003] In the current electricity market system, price signals must pass through a long transmission chain of "primary energy cost - multi-cycle wholesale trading - retail pricing". Due to the relatively independent mechanisms and varying rules at each stage, the following prominent problems arise in the formation of prices across the entire chain: Fluctuations in primary energy prices, such as coal, cannot be fully mapped to medium- and long-term electricity trading prices (annual, monthly, etc.) in a quantifiable and transparent manner, resulting in a disconnect between source costs and market trading prices. The clearing rules and price structures for different cycles (annual, monthly, day-ahead, and spot) vary significantly, lacking a unified cross-cycle time-of-use price mapping model, making it difficult for the wholesale side to form continuous and consistent time-of-use price signals. Operational adjustment mechanisms such as deviation settlement and price limits are usually independent of the main price formation process; their impact is not quantified and uniformly reflected in the final electricity price, easily leading to price distortions or abnormal fluctuations in localized periods. When setting time-of-use prices or implementing cap protection at the retail side, only fragmented references to wholesale prices are often used, lacking a complete, traceable, and structured benchmark price system that spans "source-grid-load-sales".

[0004] The aforementioned problems make it difficult for the electricity market to simultaneously achieve the dual goals of "ensuring supply and stabilizing prices" and "efficient allocation." Therefore, there is an urgent need to construct an integrated time-of-use pricing method that can connect primary energy sources, wholesale markets, and retail users, achieving transparent cost transmission across the entire chain, unified price mapping across multiple cycles, and quantitative integration of regulatory impacts, in order to form stable, reliable, and interpretable time-of-use price signals. Summary of the Invention

[0005] The primary objective of this invention is to overcome the shortcomings and deficiencies of the prior art and provide a time-of-use pricing method for multi-cycle trading and deviation adjustment in the regional electricity market. This method achieves full-link, structured, and traceable transmission from primary energy costs to end-user time-of-use electricity prices by sequentially executing primary energy price linkage calculation, wholesale-side multi-cycle price fusion and stabilization, and retail-side integrated benchmark generation.

[0006] The second objective of this invention is to provide a time-of-use pricing system for multi-cycle trading and deviation adjustment in the regional electricity market.

[0007] The first objective of this invention is achieved through the following technical solution: a time-of-use pricing method for multi-period trading and deviation adjustment in the regional electricity market, comprising: A1: By combining multi-source coal price forecast data with unit standard coal consumption parameters, an annual coal-electricity linkage benchmark price is generated. By combining the actual monthly energy consumption intensity of the units with the price transmission coefficient, the monthly coal-electricity linkage transaction price is obtained, forming a primary energy time-of-use price series; A2: Based on the bilateral negotiated transaction price of coal-fired power within the region, the annual and monthly medium- and long-term contracts are weighted averaged by electricity volume to calculate the annual and monthly medium- and long-term transaction average prices. The distribution of annual and monthly contract electricity volume over time is used to construct the benchmark annual and historical monthly medium- and long-term transaction curves for the generation and consumption sides. During the day-ahead phase, rolling matching transactions and full electricity clearing are carried out from D+2 to D+4, where D refers to the electricity delivery date, resulting in time-of-use electricity volume sequences and time-of-use clearing price sequences, which constitute the day-ahead market clearing curves for the generation and consumption sides respectively. Full electricity clearing is carried out through safety-constrained economic dispatch to obtain the time-of-use electricity price at each node, the average node price of the spot market system, the weighted average price of spot electricity volume on the generation side, and the weighted average price of spot electricity volume on the consumption side. A3: Based on the benchmark year and historical monthly medium- and long-term trading curves and day-ahead market clearing curves of the power generation and consumption sides, obtain their respective actual settlement amounts; based on the weighted average spot market price corresponding to the medium- and long-term trading curves and day-ahead market clearing curves, calculate the benchmark settlement amounts for the power generation and consumption sides respectively through preset weighting; compare the actual settlement amounts with the corresponding benchmark settlement amounts to obtain the settlement amount adjustment difference, and then generate the deviation adjustment price correction amount; combine the monthly coal-electricity linkage trading price and the historical non-spot settlement average price to obtain the secondary price limit trigger value; compare the historical monthly wholesale market actual settlement weighted average price with the secondary price limit trigger value. If the former is greater than the latter, the price limit mechanism is triggered. After triggering, the original clearing price for all periods of the historical month is reduced according to the price correction ratio to obtain the price-limited corrected clearing price and the secondary price limit time-sharing price correction amount. If the former is not greater than the latter, the price limit mechanism is not triggered; combine the deviation adjustment price correction amount and the secondary price limit time-sharing price correction amount to obtain the comprehensive price correction amount; A4: By combining the average medium- and long-term transaction prices of the wholesale side in the benchmark year and the average medium- and long-term transaction prices of historical months with the spot time-of-use prices, and by decomposing and mapping the load curve of the previous complete natural year, the time-of-use reference price of the retail market is obtained; by combining the time-of-use electricity consumption sequence of the retail side, the reference price of personalized retail packages for users is obtained; and by combining the capping floating coefficient, the retail capping price for each user is obtained. A5: Based on the retail package discounted unit price and the retail capped price determined in step A4, the final retail electricity price for each retail user is determined by comparing the smaller of the two values. Finally, a multi-cycle trading and deviation adjustment time-of-use pricing dataset for the regional electricity market is compiled and output. This dataset includes: The time-of-use price series of primary energy sources generated in step A1; The multi-period trading price curves and signals generated by step A2 include medium- and long-term trading curves for the base year and historical months, day-ahead market clearing curves, and key price signals in the spot market. The overall price adjustment amount generated in step A3; The retail market time-sharing reference price sequence, personalized retail package reference price and retail cap price generated by step A4.

[0008] Furthermore, in step A2, the medium- and long-term transaction curves and actual execution curves are formed and stored independently on the generation side and the consumption side, respectively, and are collectively referred to as the generation side curve and the consumption side curve. The generation side curve records the contracts and actual electricity sold by the power generation company and the corresponding price, while the consumption side curve records the contracts and actual electricity purchased by the power user or the power sales company and the corresponding price. Together, they constitute complete transaction trajectory data on both sides of the market. The aforementioned safety-constrained economic dispatch refers to a mathematical model in the electricity spot market that optimizes power generation and consumption by maximizing the sum of consumer electricity consumption benefit functions minus the sum of producer power generation cost functions, while strictly satisfying grid safety constraints such as generator output constraints, node power balance constraints, and line transmission capacity constraints. The node marginal price generated by the solution is the time-of-use and zoned node price. D+2 to D+4 is the target period for day-ahead rolling matching transactions. This transaction is open continuously from the 2nd to the 4th day before the delivery date, allowing market participants to bid and sell hourly electricity prices for the next three days and to facilitate flexible connection between the medium- and long-term trading curve and the supply and demand of the spot market, providing market participants with curve adjustment tools. Clearing: refers to the process of determining the amount of electricity traded and the price through market rules. Electricity delivery date: refers to the date of physical delivery and financial settlement of electrical energy; Base year: refers to the complete calendar year preceding the current pricing operation, and is the data source period used to calculate the annual base fuel cost, the annual coal-electricity linkage base price, and the annual medium- and long-term average transaction price; Historical monthly period: refers to the complete calendar month preceding the current pricing operation, and is the data source period used to calculate the monthly comprehensive fuel cost, obtain the monthly medium- and long-term average transaction price, and perform deviation adjustment calculations.

[0009] Furthermore, the specific operation steps of step A1 are as follows: A11: Combining multi-source coal price forecast data with unit standard coal consumption parameters, an annual coal-electricity linkage benchmark price is generated. The calculation formula is as follows: ; In the formula: This serves as the annual benchmark price for coal-electricity price linkage. The number of coal price forecast data sources used; For the first Weights of each coal price forecast data source; For the first One coal price forecast data source provides coal price forecasts for the next year. Standard coal consumption parameters; Adjusted value for negotiated revenue; A12: Combining changes in unit operating efficiency with the price transmission coefficient, the monthly coal-electricity linkage transaction price is dynamically generated. The calculation formula is as follows: ; In the formula: This refers to the monthly coal-electricity linkage transaction price; This represents the actual monthly energy consumption intensity of the generating unit. The price transmission coefficient; by and Constitutes the primary energy price series .

[0010] Furthermore, the specific operation steps of step A2 are as follows: A21: Based on the bilateral negotiated transaction prices of coal and electricity within the region, the annual medium- and long-term average transaction prices are calculated by weighting the electricity volume. Compared with the monthly medium- and long-term average transaction price Based on the distribution of annual and monthly contracted electricity volume over time, they together constitute the benchmark annual and historical monthly medium- and long-term transaction curves for the power generation and consumption sides. ; ; In the formula: and The first The price and electricity volume of the annual medium- to long-term trading contract. This represents the total number of medium- and long-term trading contracts for the year. and The first The price and electricity volume of the monthly medium- and long-term trading contracts. This represents the total number of medium- and long-term trading contracts for the month. A22: A rolling matching trading mechanism with continuous trading on a daily basis is adopted, aiming to maximize the total market surplus. This generates hourly trading volume and hourly clearing prices for each hour from D+2 to D+4. For each trading hour of the underlying asset, all buyer quotes for that period are sorted from highest to lowest price, forming a buyer quote curve. All seller quotes are sorted from lowest to highest price, forming a seller quote curve. Starting from the highest position in the sorted list, the highest current buyer quote is matched sequentially with the lowest current seller quote, a process called quote pair matching. Matching occurs if and only if the buyer quote b is greater than or equal to the seller quote s. If a match is found, a transaction unit is formed that can be executed, until no matching unit can be found. The quote pairs, and all successfully matched quote pairs constitute the transaction set for that period: ; ; In the formula: Target period Total clearing volume; For the first The electricity consumption of each transaction unit; Target period The total number of transaction units in the transaction set; Target period A uniform clearing price; The highest bid from the buyers in the traded transactions; The lowest seller's bid in the transaction set; arranging and combining the cleared electricity volume and cleared price for each time period in chronological order constitutes the day-ahead market clearing curve for both the generation and consumption sides. The time-of-use price sequence in this day-ahead market clearing curve is denoted as { }; A23: In the spot market, the total electricity clearing on both the generation and consumption sides is executed on a rolling basis according to time periods, forming a time-of-use and zone-specific node price signal that dynamically reflects the real-time balance characteristics of electricity. The total electricity clearing is based on the following security-constrained economic dispatch, namely the SCED optimization model: Objective function: ; Generator output upper and lower limit constraints: , ; upper and lower limits of load demand power constraints , ; Node power constraints: , ; Line transmission capacity constraints: , ; In the formula: This indicates the demand-side entity, namely the load. The electricity consumption benefit function The main body of the power generation side, namely the generator. The power generation cost function; , Demand-side entities Power consumption and main body of power generation Power generation capacity; , , , These are the collections of demand-side entities, generation-side entities, nodes, and lines within the regional power system; , Generators The lower and upper limits of output; , respectively load The lower and upper limits of power consumption; , Connected to the nodes respectively The generator set and the load set; For the line Power flow, For the line The transmission limit; By solving the above-mentioned security-constrained economic dispatch, the total electricity volume on both the generation and consumption sides is cleared, generating a spot market clearing curve. From this clearing curve, the time-of-use price for each node is obtained. Calculate the following three key price signals: The arithmetic mean of the electricity prices at each node is recorded as the average node electricity price of the power system within the spot market area. : ; In the formula, The total number of nodes in the regional power system, and the number of nodes in the spot market. During the period The node electricity price is ; Weighted average price of spot electricity on the generation side : ; In the formula, For the collection of market players on the power generation side, For generator sets The node in question For generator sets During the period The winning bid volume; Weighted average price of spot electricity consumption on the electricity demand side : ; In the formula, It is a collection of market players on the electricity consumption side. For load The node in question For load During the period The winning bid volume.

[0011] Furthermore, the specific operation steps of step A3 are as follows: A31: Based on the mid-to-long-term trading curves and day-ahead market clearing curves of the generation and consumption sides, the total actual settlement funds of the generation and consumption sides are obtained. Calculate the base settlement total for the power generation and power consumption sides. , respectively denoted as and For either side The calculation formula is: ; In the formula: Indicates the power generation side. Indicates the electricity consumption side; The total electricity volume settled on the power generation or consumption side during the settlement period; It is the weighted average electricity price for medium- and long-term electricity transactions between the generation and consumption sides; It is the weighted average price of electricity in the spot market on both the generation and consumption sides; , These are the weightings for medium- and long-term electricity trading and spot trading, respectively, to meet the requirements. ; Calculate the above benchmark settlement total and the actual settlement difference to obtain the settlement fund adjustment difference for both the issuing and using sides. : ; Adjust the settlement funds difference Among the market participants on the corresponding side, the amount is allocated or shared according to their actual transaction volume, thereby generating deviation adjustment data that includes the adjustment difference, the allocation and sharing ratio, and the adjustment amount for each participant. This deviation adjustment data is a structured dataset, the core of which is represented as a set. : ; In the formula: This is a collection of amounts allocated or shared by various market participants, among which... For the first The amount of money for each market entity; This is the set of corresponding electricity allocation or sharing ratios, where For the first The electricity consumption ratio of each market entity satisfies: ; In the formula: For the first The actual transaction volume of each market participant; This represents the total number of market entities on the corresponding side. Adjust the settlement funds difference The deviation adjustment price component is obtained by converting the total settlement volume within the settlement period into unit volume, and denoted as follows: This serves as one of the deviation adjustment and correction items for the subsequent wholesale-side comprehensive time-of-use electricity price; A32: The secondary price limit mechanism is a price stabilization measure in the wholesale market. When the wholesale market price is abnormally high or significantly deviates from the reasonable range due to supply and demand fluctuations during certain settlement cycles, the secondary price limit mechanism will be triggered. The clearing price of all time periods in the market before the current month and in real time will be reduced by a uniform proportion, and a time-of-use price correction amount will be generated. This time-of-use price correction amount will then enter the time-of-use price transmission chain, affecting the comprehensive time-of-use electricity price on the wholesale side and the final retail electricity price. The secondary price limit trigger value is determined based on the monthly coal-electricity linkage transaction price and the historical average non-spot settlement price. The calculation formula is: ; In the formula, This refers to the monthly coal-electricity linkage transaction price. The preset upward floating ratio, This represents the average settlement price under historical non-spot trading models. Monthly settlement monitoring values ​​on the power generation side This represents the weighted average price of the actual settlement for the month, when the following conditions are met: If the overall price level in the wholesale market deviates from the reasonable range for the month, the secondary price limit mechanism will be triggered. After triggering, the clearing prices for all periods of the market before the end of the month and in real time will be adjusted according to the price adjustment ratio. The price is lowered, and the lowered clearing price is recorded as follows: The calculation formula is: ; The current market clearing price: ; For spot market node electricity prices: ; The aforementioned reduction resulted in a corresponding adjustment to the system average node electricity price, which serves as the standard spot price signal: ; For all settlement periods affected by price limits The second-level price limit time-sharing price adjustment amount is recorded as follows: ; ; In the formula, ; Before the price limit is implemented Clearing price; For the period after the price limit is set Adjust the clearing price; A33: Adjust the price component to reflect the deviation. Price adjustment volume with secondary price limit By overlaying the data, the wholesale side's settlement period can be obtained. Overall price adjustment: ; In the formula, Settlement period The overall price adjustment within the period, Settlement period Price component adjusted for internal deviations. Settlement period The amount of price adjustment within the secondary price limit during the time period.

[0012] Furthermore, the specific operation steps of step A4 are as follows: A41: Obtain the annual and monthly average medium- and long-term transaction prices on the wholesale side. Using the load curve of the previous complete calendar year, decompose and map it to the settlement date to obtain the annual and monthly intraday mapped price series, where the annual average medium- and long-term transaction price is... Compared with the monthly medium- and long-term average transaction price The time-sharing mapping sequences are as follows: ; ; In the formula, , These are the annual and monthly average prices of medium- and long-term electricity transactions on the wholesale side, respectively. , Each period is a separate time period within the settlement cycle. Annual time-sharing price and monthly time-sharing price; , Each period is a separate time period within the settlement cycle. Annual load decomposition weights and monthly load decomposition weights; By fusing multiple price signals—annual intraday prices, monthly intraday prices, and spot intraday prices—using weighted coefficients, a retail market intraday reference price is obtained. The calculation formula is as follows: ; In the formula: For time period Retail market time-sharing reference price; For the spot market during the period The actual clearing price; , , These are the weighting coefficients for annual, monthly, and spot prices in retail pricing, respectively, to meet the following requirements. ,and ; A42: Acquiring Specific Retail Users The time-of-use electricity consumption sequence within the settlement period, combined with the retail market time-of-use reference price generated in step A41, is used to calculate the user's average cost per kilowatt-hour, i.e., the personalized retail package reference price, with electricity consumption as the weight. The reference price for the personalized retail packages The calculation formula is: ; In the formula: For retail users Reference price for personalized retail packages, For retail users The previous complete natural year in the period Electricity consumption The total number of time periods is used to mathematically couple the user's differentiated electricity consumption behavior curves with the market time-of-use price signal, thereby quantifying the real service cost for users with different load characteristics; A43: Reference price for personalized retail packages for retail users calculated based on step A42. Combined with the preset capping upward coefficient Generate the user's retail cap price. The calculation formula is as follows: ; In the formula, This is the capping upward floating coefficient.

[0013] Furthermore, the specific operation steps of step A5 are as follows: A51: Calculate the final retail electricity price for retail users based on the integrated time-of-use pricing benchmark and user electricity consumption characteristics. It is determined by the following formula: ; In the formula: For electricity sales companies to retail users The quoted retail package price converted to a unit price; For retail users The retail cap price; among which, , Total number of retail users; A52: Finally, the output dataset is a multi-cycle trading and deviation adjustment time-of-use pricing dataset for the regional electricity market. This dataset is a structured dataset. This includes: the generated time-of-use price series of primary energy sources. The comprehensive price adjustment amount generated in step A3 Retail market intraday reference price series generated in step A4 Reference price for personalized retail packages for each retail user and retail cap price and the final retail electricity price of each retail user .

[0014] The second objective of this invention is achieved through the following technical solution: a time-of-use pricing system for multi-period trading and deviation adjustment in the regional electricity market, used to implement the aforementioned time-of-use pricing method for multi-period trading and deviation adjustment in the regional electricity market, comprising: The primary energy price calculation module is used to generate an annual coal-electricity linkage benchmark price by combining multi-source coal price forecast data and unit standard coal consumption parameters, and to obtain the monthly coal-electricity linkage transaction price by combining the actual monthly energy consumption intensity of the unit and the price transmission coefficient, so as to form a primary energy time-of-use price series. The multi-cycle trading clearing module is used to calculate the annual and monthly medium- and long-term average trading prices based on the bilateral negotiated coal and power trading prices, and to form the medium- and long-term trading curves for the benchmark year and historical months. It also forms the day-ahead market clearing curve through rolling matching transactions, and obtains the time-of-use and zoned node electricity prices through safety-constrained economic dispatch clearing, and calculates key spot market price signals. The deviation adjustment and price limit stabilization module, based on the output of the multi-cycle transaction clearing module, calculates the benchmark settlement total and the actual settlement total on both sides to obtain the deviation adjustment price component. Based on the monthly coal-electricity linkage transaction price and the historical non-spot settlement average price, it realizes the secondary price limit judgment and trigger and generates the price correction amount, and finally synthesizes the comprehensive price correction amount. The retail pricing module is used to obtain the annual and monthly medium- and long-term average transaction prices and spot time-of-use prices on the wholesale side. It generates the time-of-use reference price for the retail market by decomposing, mapping and weighting the typical load curves of history. It calculates the reference price for personalized retail packages based on the time-of-use electricity consumption sequence of retail users and generates the retail cap price by combining the capping and floating coefficient. The integrated pricing and data output module is used to receive and integrate the output data of the aforementioned modules. It determines the final retail electricity price for each retail user by comparing the discounted unit price of the retail package with the retail cap price. Finally, it generates and outputs a structured time-of-use pricing dataset that includes the time-of-use price series of primary energy, multi-period trading price curves and signals, comprehensive price correction, time-of-use reference price series of the retail market, reference price of personalized retail packages, retail cap price, and the final retail electricity price set.

[0015] Compared with the prior art, the present invention has the following advantages and beneficial effects: 1. This invention, through multi-cycle accounting of benchmark annual / monthly costs and predicted fuel costs, and a coal-fired power price linkage mechanism, scientifically and timely anchors changes in primary energy costs to the benchmark for medium- and long-term electricity trading prices, thus establishing a market-based price formation foundation that allows prices to rise and fall from the source.

[0016] 2. Based on a multi-cycle collaborative trading path of unified medium- and long-term pricing, rolling matching transactions from D+2 to D+4, and full spot market clearing, this invention provides market participants with a continuous curve adjustment tool.

[0017] 3. This invention innovatively designs a dual stabilization and control mechanism that combines the overall deviation sharing adjustment on both the generation and consumption sides with a dynamic secondary price limit based on coal-electricity linkage. While preserving individual competition and spot price signals, it achieves overall market risk sharing and extreme price suppression, balancing efficiency and stability.

[0018] 4. The present invention ultimately outputs a structured time-of-use electricity price dataset that integrates primary energy costs, multi-cycle wholesale prices, deviation adjustment amounts, and retail factors, providing a transparent and traceable integrated price benchmark across the entire chain, and offering a unified pricing reference and decision support for all aspects of the market. Attached Figure Description

[0019] Figure 1 This is an architecture diagram of the system of the present invention. Detailed Implementation

[0020] The present invention will be further described in detail below with reference to the embodiments and accompanying drawings, but the embodiments of the present invention are not limited thereto.

[0021] Example 1 This embodiment discloses a time-of-use pricing method for multi-cycle trading and deviation adjustment in the regional electricity market, the details of which are as follows: A1: By combining multi-source coal price forecast data with unit standard coal consumption parameters, an annual coal-electricity linkage benchmark price is generated. By combining the actual monthly energy consumption intensity of the units with the price transmission coefficient, the monthly coal-electricity linkage transaction price is obtained, forming a primary energy time-of-use price series; the specific operation steps are as follows: A11: Combining multi-source coal price forecast data with unit standard coal consumption parameters, an annual coal-electricity linkage benchmark price is generated. The calculation formula is as follows: ; In the formula: This serves as the annual benchmark price for coal-electricity price linkage. The number of coal price forecast data sources used; For the first Weights of each coal price forecast data source; For the first One coal price forecast data source provides coal price forecasts for the next year. Standard coal consumption parameters; Adjusted value for negotiated revenue; A12: Combining changes in unit operating efficiency with the price transmission coefficient, the monthly coal-electricity linkage transaction price is dynamically generated. The calculation formula is as follows: ; In the formula: This refers to the monthly coal-electricity linkage transaction price; This represents the actual monthly energy consumption intensity of the generating unit. The price transmission coefficient; by and Constitutes the primary energy price series .

[0022] A2: Based on the bilateral negotiated transaction price of coal-fired power within the region, the annual and monthly medium- and long-term contracts are weighted by electricity volume to calculate the annual and monthly medium- and long-term transaction average prices. The distribution of annual and monthly contract electricity volume over time is used to construct the benchmark annual and historical monthly medium- and long-term transaction curves for the generation and consumption sides. During the day-ahead phase, rolling matching transactions and full electricity clearing are conducted from D+2 to D+4, where D refers to the electricity delivery date, resulting in time-of-use electricity volume sequences and time-of-use clearing price sequences, forming the day-ahead market clearing curves for both the generation and consumption sides. Full electricity clearing is performed through safety-constrained economic dispatch, yielding the time-of-use electricity price at each node, the average node price in the spot market system, the weighted average price of spot electricity volume on the generation side, and the weighted average price of spot electricity volume on the consumption side. Specific operational steps are as follows: A21: Based on the bilateral negotiated transaction prices of coal and electricity within the region, the annual medium- and long-term average transaction prices are calculated by weighting the electricity volume. Compared with the monthly medium- and long-term average transaction price Based on the distribution of annual and monthly contracted electricity volume over time, they together constitute the benchmark annual and historical monthly medium- and long-term transaction curves for the power generation and consumption sides. ; ; In the formula: and The first The price and electricity volume of the annual medium- to long-term trading contract. This represents the total number of medium- and long-term trading contracts for the year. and The first The price and electricity volume of the monthly medium- and long-term trading contracts. This represents the total number of medium- and long-term trading contracts for the month. A22: A rolling matching trading mechanism with continuous trading on a daily basis is adopted, aiming to maximize the total market surplus. This generates hourly trading volume and hourly clearing prices for each hour from D+2 to D+4. For each trading hour of the underlying asset, all buyer quotes for that period are sorted from highest to lowest price, forming a buyer quote curve. All seller quotes are sorted from lowest to highest price, forming a seller quote curve. Starting from the highest position in the sorted list, the highest current buyer quote is matched sequentially with the lowest current seller quote, a process called quote pair matching. Matching occurs if and only if the buyer quote b is greater than or equal to the seller quote s. If a match is found, a transaction unit is formed that can be executed, until no matching unit can be found. The quote pairs, and all successfully matched quote pairs constitute the transaction set for that period: ; ; In the formula: Target period Total clearing volume; For the first The electricity consumption of each transaction unit; Target period The total number of transaction units in the transaction set; Target period A uniform clearing price; The highest bid from the buyers in the traded transactions; The lowest seller's bid in the transaction set; arranging and combining the cleared electricity volume and cleared price for each time period in chronological order constitutes the day-ahead market clearing curve for both the generation and consumption sides. The time-of-use price sequence in this day-ahead market clearing curve is denoted as { }; A23: In the spot market, the total electricity clearing on both the generation and consumption sides is executed on a rolling basis according to time periods, forming a time-of-use and zone-specific node price signal that dynamically reflects the real-time balance characteristics of electricity. The total electricity clearing is based on the following security-constrained economic dispatch, namely the SCED optimization model: Objective function: ; Generator output upper and lower limit constraints: , ; upper and lower limits of load demand power constraints , ; Node power constraints: , ; Line transmission capacity constraints: , ; In the formula: This indicates the demand-side entity, namely the load. The electricity consumption benefit function The main body of the power generation side, namely the generator. The power generation cost function; , Demand-side entities Power consumption and main body of power generation Power generation capacity; , , , These are the collections of demand-side entities, generation-side entities, nodes, and lines within the regional power system; , Generators The lower and upper limits of output; , respectively load The lower and upper limits of power consumption; , Connected to the nodes respectively The generator set and the load set; For the line Power flow, For the line The transmission limit; By solving the above-mentioned security-constrained economic dispatch, the total electricity volume on both the generation and consumption sides is cleared, generating a spot market clearing curve. From this clearing curve, the time-of-use price for each node is obtained. Calculate the following three key price signals: The arithmetic mean of the electricity prices at each node is recorded as the average node electricity price of the power system within the spot market area. : ; In the formula, The total number of nodes in the regional power system, and the number of nodes in the spot market. During the period The node electricity price is ; Weighted average price of spot electricity on the generation side : ; In the formula, For the collection of market players on the power generation side, For generator sets The node in question For generator sets During the period The winning bid volume; Weighted average price of spot electricity consumption on the electricity demand side : ; In the formula, It is a collection of market players on the electricity consumption side. For load The node in question For load During the period The winning bid volume.

[0023] A3: Based on the benchmark year and historical monthly medium- and long-term trading curves and day-ahead market clearing curves of the power generation and consumption sides, obtain their respective actual settlement amount; based on the weighted average spot market price corresponding to the medium- and long-term trading curves and day-ahead market clearing curves, calculate the benchmark settlement amount for the power generation and consumption sides respectively through preset weighting; compare the actual settlement amount with the corresponding benchmark settlement amount to obtain the settlement amount adjustment difference, and then generate the deviation adjustment price correction amount; combine the monthly coal-electricity linkage trading price with the historical non-spot settlement average price to obtain the secondary price limit trigger value; compare the historical monthly wholesale market actual settlement weighted average price with the secondary price limit trigger value. If the former is greater than the latter, the price limit mechanism is triggered. After triggering, the original clearing price for all periods of the historical month is reduced according to the price correction ratio to obtain the post-price limit correction clearing price and the secondary price limit time-sharing price correction amount. If the former is not greater than the latter, the price limit mechanism is not triggered; combine the deviation adjustment price correction amount and the secondary price limit time-sharing price correction amount to obtain the comprehensive price correction amount; the specific operation steps are as follows: A31: Based on the mid-to-long-term trading curves and day-ahead market clearing curves of the generation and consumption sides, the total actual settlement funds of the generation and consumption sides are obtained. Calculate the base settlement total for the power generation and power consumption sides. , respectively denoted as and For either side The calculation formula is: ; In the formula: Indicates the power generation side. Indicates the electricity consumption side; The total electricity volume settled on the power generation or consumption side during the settlement period; It is the weighted average electricity price for medium- and long-term electricity transactions between the generation and consumption sides; It is the weighted average price of electricity in the spot market on both the generation and consumption sides; , These are the weightings for medium- and long-term electricity trading and spot trading, respectively, to meet the requirements. ; Calculate the above benchmark settlement total and the actual settlement difference to obtain the settlement fund adjustment difference for both the issuing and using sides. : ; Adjust the settlement funds difference Among the market participants on the corresponding side, the amount is allocated or shared according to their actual transaction volume, thereby generating deviation adjustment data that includes the adjustment difference, the allocation and sharing ratio, and the adjustment amount for each participant. This deviation adjustment data is a structured dataset, the core of which is represented as a set. : ; In the formula: This is a collection of amounts allocated or shared by various market participants, among which... For the first The amount of money for each market entity; This is the set of corresponding electricity allocation or sharing ratios, where For the first The electricity consumption ratio of each market entity satisfies: ; In the formula: For the first The actual transaction volume of each market participant; This represents the total number of market entities on the corresponding side. Adjust the settlement funds difference The deviation adjustment price component is obtained by converting the total settlement volume within the settlement period into unit volume, and denoted as follows: This serves as one of the deviation adjustment and correction items for the subsequent wholesale-side comprehensive time-of-use electricity price; A32: The secondary price limit mechanism is a price stabilization measure in the wholesale market. When the wholesale market price is abnormally high or significantly deviates from the reasonable range due to supply and demand fluctuations during certain settlement cycles, the secondary price limit mechanism will be triggered. The clearing price of all time periods in the market before the current month and in real time will be reduced by a uniform proportion, and a time-of-use price correction amount will be generated. This time-of-use price correction amount will then enter the time-of-use price transmission chain, affecting the comprehensive time-of-use electricity price on the wholesale side and the final retail electricity price. The secondary price limit trigger value is determined based on the monthly coal-electricity linkage transaction price and the historical average non-spot settlement price. The calculation formula is: ; In the formula, This refers to the monthly coal-electricity linkage transaction price. The preset upward floating ratio, This represents the average settlement price under historical non-spot trading models. Monthly settlement monitoring values ​​on the power generation side This represents the weighted average price of the actual settlement for that month. When the following conditions are met: If the overall price level in the wholesale market deviates from the reasonable range for the month, the secondary price limit mechanism will be triggered. After triggering, the clearing prices for all periods of the market before the end of the month and in real time will be adjusted according to the price adjustment ratio. The price is lowered, and the lowered clearing price is recorded as follows: The calculation formula is: ; The current market clearing price: ; For spot market node electricity prices: ; The aforementioned reduction resulted in a corresponding adjustment to the system average node electricity price, which serves as the standard spot price signal: ; For all settlement periods affected by price limits The second-level price limit time-sharing price adjustment amount is recorded as follows: ; ; In the formula, ; Before the price limit is implemented Clearing price; For the period after the price limit is set Adjust the clearing price; A33: Adjust the price component to reflect the deviation. Price adjustment volume with secondary price limit By overlaying the data, the wholesale side's settlement period can be obtained. Overall price adjustment: ; In the formula, Settlement period The overall price adjustment within the period, Settlement period Price component adjusted for internal deviations. Settlement period The amount of price adjustment within the secondary price limit during the time period.

[0024] A4: Combining the average medium- and long-term transaction prices of the wholesale side in the benchmark year and the average medium- and long-term transaction prices of historical months with the spot time-of-use prices, and through the decomposition and mapping of the load curve of the previous complete calendar year, the time-of-use reference price for the retail market is obtained; combining the time-of-use electricity consumption series of the retail side, the reference price for personalized retail packages for users is obtained; combining the capping floating coefficient, the retail cap price for each user is obtained; the specific operation steps are as follows: A41: Obtain the annual and monthly average medium- and long-term transaction prices on the wholesale side. Using the load curve of the previous complete calendar year, decompose and map it to the settlement date to obtain the annual and monthly intraday mapped price series, where the annual average medium- and long-term transaction price is... Compared with the monthly medium- and long-term average transaction price The time-sharing mapping sequences are as follows: ; ; In the formula, , These are the annual and monthly average prices of medium- and long-term electricity transactions on the wholesale side, respectively. , Each period is a separate time period within the settlement cycle. Annual time-sharing price and monthly time-sharing price; , Each period is a separate time period within the settlement cycle. Annual load decomposition weights and monthly load decomposition weights; By fusing multiple price signals—annual intraday prices, monthly intraday prices, and spot intraday prices—using weighted coefficients, a retail market intraday reference price is obtained. The calculation formula is as follows: ; In the formula: For time period Retail market time-sharing reference price; For the spot market during the period The actual clearing price; , , These are the weighting coefficients for annual, monthly, and spot prices in retail pricing, respectively, to meet the following requirements. ,and ; A42: Acquiring Specific Retail Users The time-of-use electricity consumption sequence within the settlement period, combined with the retail market time-of-use reference price generated in step A41, is used to calculate the user's average cost per kilowatt-hour, i.e., the personalized retail package reference price, with electricity consumption as the weight. The reference price for the personalized retail packages The calculation formula is: ; In the formula: For retail users Reference price for personalized retail packages, For retail users The previous complete natural year in the period Electricity consumption The total number of time periods is used to mathematically couple the user's differentiated electricity consumption behavior curves with the market time-of-use price signal, thereby quantifying the real service cost for users with different load characteristics; A43: Reference price for personalized retail packages for retail users calculated based on step A42. Combined with the preset capping upward coefficient Generate the user's retail cap price. The calculation formula is as follows: ; In the formula, This is the capping upward floating coefficient.

[0025] A5: Based on the retail package discounted unit price and the retail capped price determined in step A4, the final retail electricity price for each retail user is determined by comparing the smaller of the two values. Finally, a multi-cycle trading and deviation adjustment time-of-use pricing dataset for the regional electricity market is compiled and output. This dataset includes: The time-of-use price series of primary energy sources generated in step A1; The multi-period trading price curves and signals generated by step A2 include medium- and long-term trading curves for the base year and historical months, day-ahead market clearing curves, and key price signals in the spot market. The overall price adjustment amount generated in step A3; The retail market time-sharing reference price sequence, personalized retail package reference price for each user, and retail cap price generated from step A4 are as follows: The specific operation steps are as follows: A51: Calculate the final retail electricity price for retail users based on the integrated time-of-use pricing benchmark and user electricity consumption characteristics. It is determined by the following formula: ; In the formula: For electricity sales companies to retail users The quoted retail package price converted to a unit price; For retail users The retail cap price; among which, , Total number of retail users; A52: Finally, the output dataset is a multi-cycle trading and deviation adjustment time-of-use pricing dataset for the regional electricity market. This dataset is a structured dataset. This includes: the generated time-of-use price series of primary energy sources. The comprehensive price adjustment amount generated in step A3 Retail market intraday reference price series generated in step A4 Reference price for personalized retail packages for each retail user and retail cap price and the final retail electricity price of each retail user .

[0026] In summary, the method of this invention constructs a multi-cycle coordinated trading scale encompassing annual, monthly, daily rolling, and spot markets, and introduces a shared adjustment mechanism for deviations on both the generation and consumption sides, along with a dynamic secondary price limit mechanism, to effectively convert the value of medium- and long-term electricity into the time-of-use value of electricity. While preserving individual market competition, this method, through systematic deviation adjustment and price stabilization measures, generates time-of-use price signals that reflect true supply and demand while balancing efficiency and stability, thereby enhancing the cross-cycle optimal allocation capability of electricity resources and the overall market's resilience. This method is worthy of widespread adoption.

[0027] Note: The medium- and long-term transaction curves and actual execution curves are formed and stored independently on the generation side and the consumption side, respectively, and are collectively referred to as the generation side curve and the consumption side curve. The generation side curve records the contracts and actual electricity sold by power generation companies and the corresponding prices, while the consumption side curve records the contracts and actual electricity purchased by power users or power sales companies and the corresponding prices. Together, they constitute complete transaction trajectory data on both sides of the market. Safety-constrained economic dispatch refers to a mathematical model in the electricity spot market that optimizes power generation and consumption by maximizing the sum of consumer electricity consumption benefit functions minus the sum of producer power generation cost functions, while strictly satisfying grid safety constraints such as generator output constraints, node power balance constraints, and line transmission capacity constraints. The node marginal price generated by the solution is the time-of-use and zoned node price. D+2 to D+4 is the target period for day-ahead rolling matching transactions. This transaction is open continuously from the 2nd to the 4th day before the delivery date, allowing market participants to bid and sell hourly electricity prices for the next three days and to facilitate flexible connection between the medium- and long-term trading curve and the supply and demand of the spot market, providing market participants with curve adjustment tools. Clearing: refers to the process of determining the amount of electricity traded and the price through market rules. Electricity delivery date: refers to the date of physical delivery and financial settlement of electrical energy; Base year: refers to the complete calendar year preceding the current pricing operation, and is the data source period used to calculate the annual base fuel cost, the annual coal-electricity linkage base price, and the annual medium- and long-term average transaction price; Historical monthly period: refers to the complete calendar month preceding the current pricing operation, and is the data source period used to calculate the monthly comprehensive fuel cost, obtain the monthly medium- and long-term average transaction price, and perform deviation adjustment calculations; Pricing Month: This refers to the target calendar month for which electricity delivery will occur in this pricing operation. The corresponding time-of-use electricity price sequence is one of the final outputs of this patented method.

[0028] Settlement period t: The minimum time unit for power delivery and settlement within the specified price month, which is 1 hour.

[0029] Example 2 This embodiment discloses a time-of-use pricing system for multi-period trading and deviation adjustment in a regional electricity market, used to implement the time-of-use pricing method for multi-period trading and deviation adjustment in a regional electricity market described in Embodiment 1, such as... Figure 1 As shown, it includes the following functional modules: The primary energy price calculation module is used to generate an annual coal-electricity linkage benchmark price by combining multi-source coal price forecast data and unit standard coal consumption parameters, and to obtain the monthly coal-electricity linkage transaction price by combining the actual monthly energy consumption intensity of the unit and the price transmission coefficient, so as to form a primary energy time-of-use price series. The multi-cycle trading clearing module is used to calculate the annual and monthly medium- and long-term average trading prices based on the bilateral negotiated coal and power trading prices, and to form the medium- and long-term trading curves for the benchmark year and historical months. It also forms the day-ahead market clearing curve through rolling matching transactions, and obtains the time-of-use and zoned node electricity prices through safety-constrained economic dispatch clearing, and calculates key spot market price signals. The deviation adjustment and price limit stabilization module, based on the output of the multi-cycle transaction clearing module, calculates the benchmark settlement total and the actual settlement total on both sides to obtain the deviation adjustment price component. Based on the monthly coal-electricity linkage transaction price and the historical non-spot settlement average price, it realizes the secondary price limit judgment and trigger and generates the price correction amount, and finally synthesizes the comprehensive price correction amount. The retail pricing module is used to obtain the annual and monthly medium- and long-term average transaction prices and spot time-of-use prices on the wholesale side. It generates the time-of-use reference price for the retail market by decomposing, mapping and weighting the typical load curves of history. It calculates the reference price for personalized retail packages based on the time-of-use electricity consumption sequence of retail users and generates the retail cap price by combining the capping and floating coefficient. The integrated pricing and data output module is used to receive and integrate the output data of the aforementioned modules. It determines the final retail electricity price for each retail user by comparing the discounted unit price of the retail package with the retail cap price. Finally, it generates and outputs a structured time-of-use pricing dataset that includes the time-of-use price series of primary energy, multi-period trading price curves and signals, comprehensive price correction, time-of-use reference price series of the retail market, reference price of personalized retail packages, retail cap price, and the final retail electricity price set.

[0030] The above embodiments are preferred embodiments of the present invention, but the embodiments of the present invention are not limited to the above embodiments. Any changes, modifications, substitutions, combinations, or simplifications made without departing from the spirit and principle of the present invention shall be considered equivalent substitutions and shall be included within the protection scope of the present invention.

Claims

1. A time-of-use pricing method for multi-period trading and deviation regulation in regional electricity markets, characterized in that, include: A1: Combine multi-source coal price forecast data with unit standard coal consumption parameters to generate an annual coal-electricity linkage benchmark price; By combining the actual monthly energy consumption intensity of the generating units with the price transmission coefficient, the monthly coal-power linkage transaction price is obtained, forming a primary energy time-of-use price series. A2: Based on the bilateral negotiated transaction price of coal-fired power within the region, the annual and monthly medium- and long-term contracts are weighted averaged by electricity volume to calculate the annual and monthly medium- and long-term transaction average prices. The distribution of annual and monthly contract electricity volume over time is used to construct the benchmark annual and historical monthly medium- and long-term transaction curves for the generation and consumption sides. During the day-ahead phase, rolling matching transactions and full electricity clearing are carried out from D+2 to D+4, where D refers to the electricity delivery date, resulting in time-of-use electricity volume sequences and time-of-use clearing price sequences, which constitute the day-ahead market clearing curves for the generation and consumption sides respectively. Full electricity clearing is carried out through safety-constrained economic dispatch to obtain the time-of-use electricity price at each node, the average node price of the spot market system, the weighted average price of spot electricity volume on the generation side, and the weighted average price of spot electricity volume on the consumption side. A3: Based on the medium- and long-term trading curves and day-ahead market clearing curves of the benchmark year and historical months of the power generation and power consumption sides, obtain their respective actual settlement amounts; based on the weighted average spot market price corresponding to the medium- and long-term trading curves and day-ahead market clearing curves, calculate the benchmark settlement amounts of the power generation and power consumption sides respectively through preset weights. The actual settlement amount is compared with the corresponding benchmark settlement amount to obtain the settlement amount adjustment difference, which in turn generates the deviation adjustment price correction amount. The secondary price limit trigger value is obtained by combining the monthly coal-electricity linkage transaction price with the historical non-spot settlement average price. The weighted average actual settlement price of the wholesale market in historical months is compared with the secondary price limit trigger value. If the former is greater than the latter, the price limit mechanism is triggered. After triggering, the original clearing price for all periods of that historical month is reduced according to the price correction ratio to obtain the post-price limit clearing price and the secondary price limit time-sharing price correction amount. If the former is not greater than the latter, the price limit mechanism is not triggered. The comprehensive price correction amount is obtained by combining the deviation adjustment price correction amount and the secondary price limit time-sharing price correction amount. A4: By combining the average medium- and long-term transaction prices of the wholesale side in the benchmark year and the average medium- and long-term transaction prices of historical months with the spot time-of-use prices, and by decomposing and mapping the load curve of the previous complete natural year, the time-of-use reference price of the retail market is obtained; by combining the time-of-use electricity consumption sequence of the retail side, the reference price of personalized retail packages for users is obtained; and by combining the capping floating coefficient, the retail capping price for each user is obtained. A5: Based on the retail package conversion unit price determined in step A4 and the retail cap price, the smaller value between the two is determined by comparison to determine the final retail electricity price of each retail user; finally, the multi-period transaction and deviation adjustment split-time pricing data set of the regional electricity market is collected and output, which includes: Primary energy split-time price sequence generated by step A1; Multi-period transaction price curve and signal generated by step A2, including medium and long-term transaction curve of benchmark year and historical month, day-ahead market clearing curve and spot market key price signal; The comprehensive price correction amount generated by step A3; The retail market split-time reference price sequence, the personalized retail package reference price of each user and the retail cap price generated by step A4.

2. The zonal electricity market multi-period transaction and bias adjustment time-of-use pricing method of claim 1 wherein, In step A2, the medium- and long-term transaction curves and the actual execution curves are formed and stored independently on the generation side and the consumption side, respectively, and are collectively referred to as the generation side curves and the consumption side curves. The generation side curves record the contracts and actual electricity sold by the power generation companies and the corresponding prices, while the consumption side curves record the contracts and actual electricity purchased by the power users or power sales companies and the corresponding prices. Together, they constitute complete transaction trajectory data on both sides of the market. The security-constrained economic dispatch refers to a mathematical model for optimizing the generation and consumption of power in a power spot market, with the goal of maximizing the sum of consumer benefit functions minus the sum of producer generation cost functions, while strictly satisfying the power grid safety constraints of generator unit output constraints, node power balance constraints, and line transmission capacity constraints. The node marginal price obtained from the solution of this model is the time-of-use and zoned node price. Days D+2 to D+4 are the target period for day-ahead rolling matching transactions, which are open for continuous trading from the second to fourth day before the delivery date. This allows market participants to buy and sell hourly electricity for the next three days to facilitate the flexible connection between long-term trading curves and spot market supply and demand, providing a curve adjustment tool for market participants. Clearing: refers to the process of determining the transaction volume and price through market rules; Electricity delivery date: refers to the date of physical delivery and financial settlement of electricity; Base year: refers to the previous complete natural year before the current pricing operation, which is used to calculate the annual base fuel cost, annual coal-fired power linkage benchmark price, and data source period for the average price of medium and long-term transactions; Historical monthly: refers to the previous complete natural month before the current pricing operation, which is used to calculate the monthly comprehensive fuel cost, obtain the average price of medium and long-term transactions, and calculate the data source period for deviation adjustment.

3. The zonal electricity market multi-period transaction and bias adjustment time-of-use pricing method of claim 2, wherein, The specific operation steps of step A1 are as follows: A11: Combine multi-source coal price prediction data and unit standard coal consumption parameters to generate an annual coal-fired power linkage benchmark price, with the calculation formula being: ； In the formula: is the annual coal electricity linkage benchmark price; is the number of coal price prediction data sources used; is the weight of the th coal price prediction data source; is the predicted value of the coal price of the th coal price prediction data source for the next year; is the standard coal consumption parameter; is the negotiated income adjustment value; A12: Combine unit operating efficiency changes and price transmission coefficients to dynamically generate monthly coal-fired power linkage transaction prices, with the calculation formula being: ； In the formula: is the monthly coal-electricity linkage transaction price; is the monthly actual energy consumption intensity of the unit; is the price transmission coefficient; is determined by and constitute a primary energy price sequence .

4. The zonal electricity market multi-period transaction and bias adjustment time-of-use pricing method of claim 3 wherein, The specific operation steps of step A2 are as follows: A21: Based on the bilateral negotiation transaction price of regional coal and electricity, the annual medium and long-term transaction average price is calculated by weighted average of electricity and monthly medium and long-term transaction average price , based on the distribution of annual and monthly contract electricity in time dimension, together constitute the benchmark annual and historical monthly medium and long-term transaction curve of power generation side and power consumption side; ； ； In the formulae: and are the prices and the electricity quantities of the first annual mid-and-long-term trading contracts, respectively, is the total number of annual mid-and-long-term trading contracts, and are the prices and the electricity quantities of the first monthly mid-and-long-term trading contracts, respectively, is the total number of monthly mid-and-long-term trading contracts. A22: A rolling matching trading mechanism with continuous trading on a daily basis is adopted, aiming to maximize the total market surplus. This generates hourly trading volume and hourly clearing prices for each hour from D+2 to D+4. For each trading hour of the underlying asset, all buyer quotes for that period are sorted from highest to lowest price, forming a buyer quote curve. All seller quotes are sorted from lowest to highest price, forming a seller quote curve. Starting from the highest position in the sorted list, the highest current buyer quote is matched sequentially with the lowest current seller quote, a process called quote pair matching. Matching occurs if and only if the buyer quote b is greater than or equal to the seller quote s. If a match is found, a transaction unit is formed that can be executed, until no matching unit can be found. The quote pairs, and all successfully matched quote pairs constitute the transaction set for that period: ； ； In the formula: is the total clearing electricity quantity of the target period; is the electricity quantity of the i th transaction unit; is the total number of transaction units in the transaction set of the target period; is the uniform clearing price of the target period; is the highest bid price in the transaction set; is the lowest ask price in the transaction set; arranging and combining the clearing electricity quantity and the clearing price of each period in time sequence forms the day-ahead market clearing curve of the power generation side and the power consumption side, and the time-of-use price sequence in the day-ahead market clearing curve is denoted as ;​​​​ A23: In the spot market, perform full power clearing on both sides by time period to form a time-of-use and zoned node price signal that dynamically reflects real-time power balance characteristics. The full power clearing is based on the following security-constrained economic dispatch (SCED) optimization model: Objective function: ; Generator set output upper and lower limit constraint conditions: ​​ Load demand power upper and lower limit constraints , ; Node power constraint condition: ;​ Line transmission capacity constraints: ;​ In the formula: represents the electricity benefit function of the demand side subject, i.e., the load represents the electricity generation cost function of the power generation side subject, i.e., the power generator , respectively represents the electricity power of the demand side subject and the electricity power of the power generation side subject , , , respectively represents the set of the demand side subject, the power generation side subject, the node and the line in the regional power system; , respectively represents the lower limit and the upper limit of the output of the power generator , respectively represents the lower limit and the upper limit of the electricity power of the load , respectively represents the set of the power generator and the set of the load connected to the node represents the power flow of the line represents the transmission limit of the line ​​​​​​​​ By solving the above security-constrained economic dispatch, the full electricity out-clearing of both supply and demand sides is realized, and the out-clearing curve of the spot market is generated. From the out-clearing curve, the time-of-use electricity price of each node is obtained , and the following three key price signals are calculated: Let the arithmetic average of the nodal prices of the various nodes be denoted as the average nodal price of the power system within the spot market region : ； In the formula, N is the total number of nodes in the regional power system, and the spot market node The node price at the time period is ; Weighted average price of spot electricity on the power generation side : ； In the formula, is a set of market main bodies on the power generation side, is a power generation unit is a node, is a power generation unit is the winning electric quantity in the time period . Weighted average price of spot electricity on the power consumption side : ； In the formula, is a collection of market entities on the electricity consumption side, is a load is a node, is a load is the winning electricity in the time period .

5. The zonal electricity market multi-period transaction and bias adjustment time-of-use pricing method of claim 4 wherein, The specific operation steps of step A3 are as follows: A31: Based on the medium and long term transaction curve day-ahead market clearing curve of the power generation side and the power consumption side, the actual settlement fund total of the power generation side and the power consumption side with each other is obtained , the reference settlement total of the power generation side and the power consumption side is calculated , respectively recorded as and ; for any side , the calculation formula is: ； In the formula: represents the power generation side, represents the power consumption side; is the total settlement electricity of the power generation side or the power consumption side in the settlement period; is the weighted average price of the long-term electricity energy transaction in the power generation side and the power consumption side; is the weighted average price of the electricity energy spot market in the power generation side and the power consumption side; , respectively, the long-term electricity energy transaction weight and the spot transaction weight, satisfying ; The above benchmark settlement total and actual settlement difference are calculated to obtain the respective settlement fund adjustment difference of the sending and using sides : ； adjusting the settlement fund In the corresponding side market subjects, according to the actual transaction power of each, the allocation or sharing is carried out, thereby generating the deviation adjustment data containing the adjustment difference, the allocation and sharing ratio and the adjustment amount of each subject, which is a structured data set, and the core content is represented as a set : ； In the formula: is a set of amounts allocated or shared by each market participant, wherein is the amount of the th market participant; is a set of corresponding allocated or shared electricity proportion, wherein is the electricity proportion of the th market participant, satisfying: ; In the formula: is the actual transaction electricity volume of the first market subject; is the actual transaction electricity volume of the first market subject; is the total number of corresponding side market subjects; Adjust the settlement fund to the difference Convert the total settlement electricity in the settlement period to unit electricity to obtain the deviation adjustment price component, denoted as , as one of the deviation adjustment correction terms of the subsequent wholesale side comprehensive time-of-use electricity price; A32: The two-level price limit mechanism is a price stabilization tool in the wholesale market. When the wholesale market price abnormally increases or significantly deviates from the reasonable range due to supply and demand fluctuations in certain settlement periods, the two-level price limit mechanism will be triggered, uniformly reducing the clearing prices of all time periods in the day-ahead and real-time markets by a certain proportion, and generating a time-of-use price correction amount. This time-of-use price correction amount then enters the time-of-use price transmission chain, affecting the wholesale side comprehensive time-of-use price and the final retail electricity price; The secondary limit price trigger value is determined based on the monthly coal-electricity linkage transaction price and the historical non-spot settlement average price , and the calculation formula is: ； In the formula, is a monthly coal-electricity linkage transaction price, is a preset floating ratio, is a historical settlement average price in a non-spot mode. The monthly settlement monitoring value of the power generation side represents the actual settlement weighted average price of the month, and when the following conditions are met: , it is determined that the overall price level of the wholesale market deviates from the reasonable interval, triggering the secondary price limit mechanism. After triggering, the clearing price of all time periods of the day-ahead and real-time market in the whole month is adjusted downward according to the price correction ratio , and the adjusted clearing price is recorded as . The calculation formula is: ； For day-ahead market clearing prices: ； For spot market node prices: ； The above adjustment results in a synchronous adjustment of the system average node price, which is the standard spot price signal, to: ； For all settlement periods affected by the limit price , the secondary limit price time-of-day price adjustment is ; ； wherein ; is the pre-limit period is the clearing price; is the post-limit period is the modified clearing price; A33: Bias-adjusted price component with the secondary limit price The superimposed wholesale-side integrated price correction amount at the settlement period is obtained: ​ ； In the formula, is the integrated price correction amount within the settlement period is the deviation adjustment price component within the settlement period is the integrated price correction amount within the settlement period is the deviation adjustment price component within the settlement period is the secondary limit time-of-day price correction amount within the settlement period is the secondary limit time-of-day price correction amount within the settlement period 6. The zonal electricity market multi-period transaction and bias adjustment time-of-use pricing method of claim 5 wherein, The specific operation steps of step A4 are as follows: A41: Obtain the wholesale side annual medium and long-term transaction average price and the monthly medium and long-term transaction average price, decompose and map them to the settlement day by using the load curve of the last complete natural year to obtain the annual time-sharing mapping price sequence and the monthly time-sharing mapping price sequence, wherein the time-sharing mapping sequence of the annual medium and long-term transaction average price and the monthly medium and long-term transaction average price is respectively ； ； In the formula, , are the wholesale side annual and monthly average prices of electricity energy respectively in the long-term transaction; , are the annual and monthly time-of-use mapping prices respectively in the time period of the settlement period; , are the annual and monthly load decomposition weights respectively in the time period of the settlement period. The annual time-of-use price, monthly time-of-use price, and spot time-of-use price are fused using weight coefficients to obtain a retail market time-of-use reference price, with the calculation formula being: ； In the formula: is the time period of the retail market time-sharing reference price; is the actual clearing price of the spot market in the time period ; , , are the weight coefficients of the annual, monthly, and spot price in the retail pricing, satisfying , and ; A42: Obtain specific retail user The time-of-use electricity consumption sequence in the settlement period, combined with the retail market time-of-use reference price generated in step A41, is used to calculate the average cost per kilowatt-hour of the user, i.e., the personalized retail package reference price , the calculation formula of the personalized retail package reference price is: ； In the formula: For retail users Reference price for personalized retail packages, For retail users The previous complete natural year in the period Electricity consumption The total number of time periods is used to mathematically couple the user's differentiated electricity consumption behavior curves with the market time-of-use price signal, thereby quantifying the real service cost for users with different load characteristics; A43: calculating the personalized retail package reference price of the retail user based on the calculation result of step A42 , in combination with the preset top-up floating coefficient , to generate the retail top-up price of the user , the calculation formula of which is: ； In the formula, is the top-capping floatation coefficient.

7. The zonal electricity market multi-period transaction and bias adjustment time-of-use pricing method of claim 6 wherein, The specific operation steps of step A5 are as follows: A51: calculating the final retail electricity price for the retail user based on the integrated time-of-use price benchmark and the user's electricity usage characteristics is determined by the following equation: ； In the formula: is the retail package conversion unit price reported by the electricity retailer to the retail user; is the retail user's retail cap price; wherein, , , is the total number of retail users;​ A52: Finally, the output dataset is a multi-cycle trading and deviation adjustment time-of-use pricing dataset for the regional electricity market. This dataset is a structured dataset. This includes: the generated time-of-use price series of primary energy sources. The comprehensive price adjustment amount generated in step A3 Retail market intraday reference price series generated in step A4 Reference price for personalized retail packages for each retail user and retail cap price and the final retail electricity price of each retail user .

8. A time-of-use pricing system for multi-period trading and bias adjustment in regional electricity markets, characterized by, The time-of-use pricing method for implementing multi-cycle trading and deviation adjustment in the regional electricity market as described in any one of claims 1 to 7 includes: The primary energy price calculation module is used to generate an annual coal-electricity linkage benchmark price by combining multi-source coal price forecast data and unit standard coal consumption parameters, and to obtain the monthly coal-electricity linkage transaction price by combining the actual monthly energy consumption intensity of the unit and the price transmission coefficient, so as to form a primary energy time-of-use price series. The multi-cycle trading clearing module is used to calculate the annual and monthly medium- and long-term average trading prices based on the bilateral negotiated coal and power trading prices, and to form the medium- and long-term trading curves for the benchmark year and historical months. It also forms the day-ahead market clearing curve through rolling matching transactions, and obtains the time-of-use and zoned node electricity prices through safety-constrained economic dispatch clearing, and calculates key spot market price signals. The deviation adjustment and price limit stabilization module, based on the output of the multi-cycle transaction clearing module, calculates the benchmark settlement total and the actual settlement total on both sides to obtain the deviation adjustment price component. Based on the monthly coal-electricity linkage transaction price and the historical non-spot settlement average price, it realizes the secondary price limit judgment and trigger and generates the price correction amount, and finally synthesizes the comprehensive price correction amount. The retail pricing module is used to obtain the annual and monthly medium- and long-term average transaction prices and spot time-of-use prices on the wholesale side. It generates the time-of-use reference price for the retail market by decomposing, mapping and weighting the typical load curves of history. It calculates the reference price for personalized retail packages based on the time-of-use electricity consumption sequence of retail users and generates the retail cap price by combining the capping and floating coefficient. The integrated pricing and data output module is used to receive and integrate the output data of the aforementioned modules. It determines the final retail electricity price for each retail user by comparing the discounted unit price of the retail package with the retail cap price. Finally, it generates and outputs a structured time-of-use pricing dataset that includes the time-of-use price series of primary energy, multi-period trading price curves and signals, comprehensive price correction, time-of-use reference price series of the retail market, reference price of personalized retail packages, retail cap price, and the final retail electricity price set.

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