A multi-time scale power market coupling dispatch method

Abstract

The application discloses a multi-time scale power market coupling clearing method, and belongs to the field of power transaction. The method covers the difference settlement mechanism of electricity into the renewable energy long-term market transaction constraint, and reflects the dynamic execution process of the annual mechanism electricity through the monthly rolling update. The method can realize the time sequence coupling simulation among the annual, monthly and spot markets, accurately represents the transmission influence of the new energy difference settlement mechanism on the long-term transaction boundary, the spot market clearing result, the unit dispatching plan and the settlement result, thereby reasonably verifies the influence of different strategies on the clearing rationality and the renewable energy curtailment rate, reduces the renewable energy curtailment rate through reasonable strategies, improves the power system operation efficiency, improves the future renewable energy popularization and penetration rate, and provides technical support for the power market operation analysis, market rule checking and dispatching decision under the condition of high proportion of new energy.

Description

Technical Field

[0001] This invention belongs to the field of electricity trading, and more specifically, relates to a multi-timescale electricity market coupled clearing method. Background Technology

[0002] With the continuous growth of installed capacity of new energy sources, the proportion of wind and solar power generation in the power system is constantly increasing. New energy power generation is characterized by low marginal cost, strong output volatility, and uneven temporal distribution. In the context of an increasingly complex electricity market and the coupling of multiple time scales—annual, monthly, and spot trading—with market participants continuously engaging in annual medium- and long-term, monthly, and spot trading, the allocation of different annual-monthly-spot position ratios directly determines the overall supply-demand balance, price fluctuation path, and new energy consumption potential of the electricity market. Without prior analysis of electricity trading conditions, market participants and dispatching agencies are easily caught in a dilemma: "signing too many annual and monthly medium- and long-term contracts, blocking spot consumption potential; signing too few contracts, facing high price risks." Furthermore, new energy power plants may experience significant power curtailment. Therefore, accurate simulation of electricity trading is of great importance. By systematically simulating electricity trading decisions before actual operation and predicting power flow, price signals, and new energy curtailment rates in advance, quantitative support can be provided for market participants to optimize trading decisions and for regulators to improve market mechanisms, ultimately leading to improved power system operating efficiency and a steady increase in renewable energy penetration.

[0003] Existing electricity market clearing simulation methods primarily target single spot markets. Examples include Chinese patents such as "CN121836962B: An Adaptive Accelerated Solution Method and System for Electricity Spot Market Clearing" and "CN119048145B: A Cross-Regional Electricity Reserve Market Clearing Method Considering Transmission Line Reserve Capacity." However, these methods struggle to explicitly describe the temporal dependencies between annual medium- and long-term transactions, monthly medium- and long-term transactions, and spot market transactions. Furthermore, markets at different time scales are not independent: the results of annual medium- and long-term transactions directly determine the tradable space in the monthly market, while the cumulative annual and monthly contracted electricity further influences the remaining settlement electricity and bidding incentives in the spot market. Because existing simulation methods generally sever the coupling relationships between these three markets, failing to integrate annual contract decomposition, monthly rolling adjustments, and spot market clearing into a unified framework, significant deviations arise between the simulation methods and actual market operations, resulting in low accuracy of the simulation results. Furthermore, existing methods typically do not include the electricity covered by the renewable energy price difference settlement mechanism in the tradable range of the renewable energy medium- and long-term market. This makes it difficult to accurately simulate the crowding-out effect of the mechanism's electricity on market-based trading space. They also fail to update the monthly effective mechanism electricity based on the remaining mechanism electricity and historically settled mechanism electricity, thus failing to reflect the dynamic changes in mechanism settlement throughout the year. Moreover, they do not form a closed loop between spot market clearing, actual mechanism settlement electricity updates, and technical type reference price calculations. This makes it difficult to accurately assess the impact of the price difference settlement mechanism on spot prices, dispatch results, and settlement composition under conditions of high renewable energy, further affecting the accuracy of simulation results. Summary of the Invention

[0004] To address the aforementioned deficiencies or improvement needs of existing technologies, this invention provides a multi-timescale coupled electricity market clearing method. This method can reasonably verify the impact of different strategies on the rationality of clearing and the curtailment rate of renewable energy, thereby reducing the curtailment rate of renewable energy through reasonable strategies, improving the operating efficiency of the power system, and increasing the future popularization and penetration rate of renewable energy.

[0005] To achieve the above objectives, according to a first aspect of the present invention, a multi-timescale electricity market coupling clearing method considering a renewable energy price difference settlement mechanism is provided, comprising: S1 generates time-of-use electricity-price pairs for the annual medium- and long-term market within the scope of the electricity volume that various market participants can declare in the annual medium- and long-term market. These pairs are then substituted into the electricity medium- and long-term market clearing model to obtain the annual medium- and long-term market clearing electricity volume and clearing price for various market participants. Among them, the types of market participants include thermal power units, renewable energy units, and load-side entities. S2 breaks down the annual medium- and long-term market clearing volume of various market participants into monthly volumes, thus obtaining the monthly breakdown of annual contracts for various market participants. S3, for the current month m, calculate the effective mechanism power coverage ratio of each renewable energy unit in the current month's monthly trading by using the annual mechanism power limit of each renewable energy unit, the actual mechanism settlement power completed in the previous month, and the predicted power generation of the current month; where, at the initialization, m=1, and the actual mechanism settlement power completed by each renewable energy unit in the previous month is 0. S4 generates monthly medium- and long-term market time-of-use electricity-price pairs within the declared electricity volume range of various market participants in the monthly medium- and long-term market. These pairs are then substituted into the electricity medium- and long-term market clearing model to obtain the monthly medium- and long-term market clearing electricity volume and clearing price for various market participants. S5, after adding the monthly breakdown of annual contract electricity volume of various market participants with the monthly medium and long-term market clearing electricity volume to obtain the cumulative medium and long-term contract electricity volume of various market participants in the current month, decompose it to each operating day and time period of the current month to calculate the average settlement price of each market participant in each operating day and time period of the current month. S6. For each operating day of the current month, substitute the segmented bids of each generating unit, the load demand of each node and the transmission network parameters into the day-ahead spot market clearing model to obtain the spot market clearing power of each type of market participant at each bidding stage on each operating day, so as to update the actual mechanism settlement electricity of each renewable energy unit in the current month, thereby calculating the price difference settlement amount of each renewable energy unit in the current month. S7 updates the current month to m+1, returns to S3, and continues until all months are sequentially traversed. Based on the calculation results, the annual power generation profit of thermal power generation companies and renewable energy power generation companies, and the annual electricity purchase cost of electricity sales companies are obtained.

[0006] According to a second aspect of the present invention, an electronic device is provided, comprising: a computer-readable storage medium and a processor; The computer-readable storage medium is used to store executable instructions; The processor is configured to read executable instructions stored in the computer-readable storage medium and execute the method as described in the first aspect.

[0007] According to a third aspect of the invention, a computer-readable storage medium is provided, the computer-readable storage medium storing computer instructions for causing a processor to perform the method as described in the first aspect.

[0008] According to a fourth aspect of the invention, a computer program product is provided, comprising a computer program or instructions that, when executed by a processor, implement the method described in the first aspect.

[0009] In summary, compared with the prior art, the above-described technical solutions conceived by this invention can achieve the following beneficial effects: The method provided by this invention, through unified modeling of the electricity covered by the mechanism, the tradable range of the medium- and long-term market, the transfer of annual to monthly contract positions, the transfer of monthly to day-ahead contract positions, the clearing of network constraints in the spot market, and the reference price and price difference settlement results for technology types, enables regulatory agencies, grid companies, power trading institutions, and market participants to simulate the market operation status under different renewable energy penetration rates, different medium- and long-term trading coverage levels, and different combinations of mechanism electricity volume and mechanism electricity price in advance. This provides a quantitative basis for market rule verification and system operation decisions. The electricity covered by the price difference settlement mechanism is incorporated into the medium- and long-term market trading constraints of renewable energy, and the dynamic execution process of the annual mechanism electricity volume is reflected through monthly rolling updates. It can achieve time-series coupling simulation between the annual, monthly, and spot markets, accurately characterize the transmission impact of the renewable energy price difference settlement mechanism on the medium- and long-term trading boundaries, spot clearing results, unit dispatching plans, and settlement results, thereby reasonably verifying the impact of different strategies on the rationality of clearing and the renewable energy curtailment rate. By using reasonable strategies, the renewable energy curtailment rate can be reduced, the power system operation efficiency can be improved, and the future popularization and penetration rate of renewable energy can be increased. This provides technical support for power market operation analysis, market rule verification, and dispatching decisions under high-proportion renewable energy conditions. Attached Figure Description

[0010] Figure 1 This is a schematic diagram of a multi-timescale market coupling clearing method for the actual operation of a power system, provided in an embodiment of the present invention.

[0011] Figure 2 This is a schematic diagram illustrating the price level under the change in the proportion of electricity generated by the mechanism, as provided in an embodiment of the present invention.

[0012] Figure 3 The monthly reference price distribution chart for wind power and photovoltaic power under different mechanism power ratios is provided for embodiments of the present invention.

[0013] Figure 4 This is a schematic diagram illustrating the composition of load-side settlement prices under different electricity ratio mechanisms, as provided in embodiments of the present invention.

[0014] Figure 5 This is a distribution chart of spot clearing prices for 8760 time periods throughout the year under different electricity ratios based on different mechanisms, provided as an embodiment of the present invention. Detailed Implementation

[0015] To make the objectives, technical solutions, and advantages of this invention clearer, the invention will be further described in detail below with reference to the accompanying drawings and embodiments. It should be understood that the specific embodiments described herein are merely illustrative and not intended to limit the invention. Furthermore, the technical features involved in the various embodiments of this invention described below can be combined with each other as long as they do not conflict with each other.

[0016] This invention provides a multi-timescale market-coupled clearing method for the actual operation of power systems, integrating renewable energy price difference settlement mechanisms, power system operation constraints, medium- and long-term contract position transfer, and spot market scheduling into a unified calculation framework. Figure 1 As shown, it includes: S1 generates time-of-use electricity-price pairs for the annual medium- and long-term market within the scope of the electricity volume that various market participants can declare in the annual medium- and long-term market. These pairs are then substituted into the electricity medium- and long-term market clearing model to obtain the annual medium- and long-term market clearing electricity volume and clearing price for various market participants. Among them, the types of market participants include thermal power units, renewable energy units, and load-side entities. S2 breaks down the annual medium- and long-term market clearing volume of various market participants into monthly volumes, thus obtaining the monthly breakdown of annual contracts for various market participants. S3, for the current month m, calculate the effective mechanism power coverage ratio of each renewable energy unit in the current month's monthly trading by using the annual mechanism power limit of each renewable energy unit, the actual mechanism settlement power completed in the previous month, and the predicted power generation of the current month; where, at the initialization, m=1, and the actual mechanism settlement power completed by each renewable energy unit in the previous month is 0. S4 generates monthly medium- and long-term market time-of-use electricity-price pairs within the declared electricity volume range of various market participants in the monthly medium- and long-term market. These pairs are then substituted into the electricity medium- and long-term market clearing model to obtain the monthly medium- and long-term market clearing electricity volume and clearing price for various market participants. S5, the monthly breakdown of annual contract electricity volume of various market participants and the monthly medium- and long-term market clearing electricity volume are added together to obtain the cumulative medium- and long-term contract electricity volume of various market participants in the current month, and then the volume is broken down to each operating day and time period in the current month to calculate the average settlement price of each market participant in each operating day and time period in the current month. S6. For each operating day of the current month, substitute the segmented bids of each generating unit, the load demand of each node and the transmission network parameters into the day-ahead spot market clearing model to obtain the spot market clearing power of each type of market participant at each bidding stage on each operating day, so as to update the actual mechanism settlement electricity of each renewable energy unit in the current month, thereby calculating the price difference settlement amount of each renewable energy unit in the current month. S7 updates the current month to m+1, returns to S3, and continues until all months are sequentially traversed. Based on the calculation results, the annual power generation profit of thermal power generation companies and renewable energy power generation companies, and the annual electricity purchase cost of electricity sales companies are obtained.

[0017] Specifically, the method provided by this invention is based on a multi-timescale coupled electricity market clearing strategy that takes into account the renewable energy price difference settlement mechanism, a lower-level electricity market clearing model, and an upper-level market participant trading decision model. This strategy has three characteristics: First, it incorporates the annual medium- and long-term, monthly medium- and long-term, and spot markets into a unified time-series trading framework, describing the impact of previous market trading results on subsequent market settlement boundaries through top-down contract decomposition; second, it incorporates the electricity covered by the price difference settlement mechanism into the renewable energy medium- and long-term market trading constraints, and reflects the dynamic execution process of the annual mechanism electricity through monthly rolling updates; and third, it integrates market-based trading revenue and price difference settlement amount into the power generation enterprise profit function.

[0018] The following sections will introduce the renewable energy price difference settlement mechanism model, the lower-level electricity market clearing model, and the upper-level market participant transaction decision model.

[0019] 1. Renewable Energy Price Difference Settlement Mechanism Model For renewable energy units The annual mechanism power coverage ratio is defined as follows: The annual limit for total electricity consumption is The settlement price is For the first of the year In a given month, the mechanism-mandated electricity coverage ratio shall not exceed the annual mechanism-mandated electricity coverage ratio, and the cumulative mechanism-mandated electricity for the whole year shall not exceed the annual mechanism-mandated electricity cap. Based on the mechanism-mandated electricity settled in previous months and the projected power generation for the current month, the effective mechanism-mandated electricity coverage ratio referenced for monthly transactions of renewable energy units is expressed as follows: (1) In the formula, For renewable energy units In the The coverage ratio of effective mechanisms to be referenced during monthly transactions; For the months The summation index variable; For renewable energy units In the The actual monthly settlement of electricity volume (also known as the actual settlement of electricity volume). For renewable energy units In the Monthly period The predicted power generation. Equation (1) shows that when the annual surplus mechanism power is sufficient, the first The monthly coverage ratio will still be implemented according to the annual mechanism; when the remaining mechanism power is insufficient to cover the corresponding proportion of the predicted power generation for the month, the monthly effective mechanism coverage ratio will be reduced accordingly. This is a period for clearing out the spot market.

[0020] Complete the first After the spot market clears, the actual electricity settlement mechanism will be updated based on the actual power generation of renewable energy units: (2) In the formula, For the first Monthly running days collection; For renewable energy units In the Monthly operating days Time period No. The spot market clearing power of the quoted segment. Equation (2) shows that the monthly settlement electricity of renewable energy units is determined by two conditions: first, the product of the actual power generation in the current month and the annual mechanism coverage ratio; second, the remaining annual mechanism electricity after deducting the electricity settled in previous months. When the actual power generation of renewable energy units in previous months is high, the annual mechanism electricity may be consumed in advance, and the effective mechanism coverage ratio in subsequent months will decrease accordingly. Through the above monthly rolling update process, the dynamic execution mode of the price difference settlement mechanism in the annual operation cycle is characterized.

[0021] The price difference settlement mechanism is based on the difference between the mechanism settlement price and the market reference price, and settles the electricity covered by the mechanism monthly. For generating units included in the renewable energy price difference settlement mechanism, their market reference price is determined according to the weighted average price of all units of the same technology category in the spot market for the current month. Therefore, there are differences in the market reference price for generating units of different technology categories such as wind power and photovoltaic power. Renewable Energy Generating Units The technology category belongs to the number The weighted average price of the spot market in the month Represented as: (3) In the formula, Indicates with the unit A collection of units belonging to the same technology category. For the summation index of renewable energy units; For renewable energy units Node In the Monthly operating days Time period The spot market price.

[0022] renewable energy units In the Monthly price difference settlement amount Represented as: (4) Equations (1) to (4) together constitute the monthly rolling accounting model of the renewable energy price difference settlement mechanism. Subsequently, in the multi-level electricity market two-layer game model, the mechanism coverage ratio will be incorporated into the medium- and long-term market transaction boundary of renewable energy, and the price difference settlement amount will be incorporated into the main revenue function of renewable energy entities.

[0023] 2. Lower-level electricity market clearing model (1) Medium- and long-term electricity market clearing model Both annual and monthly medium- and long-term markets adopt centralized trading and a unified marginal clearing mechanism. Power generation companies and electricity sales companies submit time-of-use electricity volume-price pairs respectively. Under the condition of meeting supply and demand balance, the electricity market operator determines the medium- and long-term market clearing volume and unified marginal clearing price for each entity with the goal of maximizing social welfare.

[0024] To provide a unified format for describing the annual and monthly medium- to long-term markets, a transaction phase index is introduced. .when When it refers to the medium- to long-term market, This represents the medium- to long-term market over a given month. Further define the time index. :when hour, This indicates the period of the annual trading year. ;when hour, , indicating the first Monthly trading hours .

[0025] In annual and monthly medium- to long-term centralized transactions, market operators organize market clearing with the goal of maximizing social welfare. Their objective function is: (5) In the formula, , and They represent thermal power units Renewable energy units and load-side main body In the medium and long term market Time period Market clearing volume; , and They represent thermal power units Renewable energy units and load-side main body In the medium and long term market Time period The declared price.

[0026] The clearing volume of each market participant shall not exceed the corresponding declared volume, subject to the following constraints: (6) (7) (8) In the formula, , and They represent thermal power units Renewable energy units and load-side main body In the medium and long term market Time period The declared electricity volume.

[0027] During each medium- and long-term market settlement period, the supply-side clearing electricity volume and the load-side purchasing electricity volume should remain in balance, subject to the following constraints: (9) Equations (5) to (9) constitute a unified centralized clearing model for the annual and monthly medium- and long-term markets, which determines the clearing volume and price based on the electricity-price pairs submitted by both supply and demand sides. In solving this model, the dual variable of equation (9) is denoted as... This indicates the medium- to long-term market. During the delivery period A unified marginal clearing price.

[0028] (2) Spot Market Clearing Model The lower-level spot market model on each operating day The market clearing process aims to minimize total reporting costs, and its objective function is as follows: (10) In the formula This is a collection of periods for clearing out the spot market; and They are respectively a collection of renewable energy units and a collection of thermal power units; This is a set of price ranges for thermal power units; For thermal power units exist Monthly operating days Time period No. Clearing power of each bidding segment; and Renewable energy units and thermal power units exist Month Day 1 of operation The declared price for each declaration segment; The unit is the length of a time period.

[0029] Each node must satisfy power balance constraints in each time period: (11) In the formula, and Access nodes A collection of thermal power units and a collection of renewable energy units; For nodes A set of connected nodes; For nodes To the node exist Monthly operating days Time period Transmission line power flow; For nodes exist Monthly operating days Time period The load demand. In solving this problem, the dual variable corresponding to equation (11) is denoted as... , representing a node exist Monthly operating days Time period The marginal price at the spot market node.

[0030] The clearing power of thermal power units and renewable energy units in each spot market session must not exceed the available declared capacity of the corresponding bidding segment. Spot market quotations are submitted on a daily basis, and the same tiered bidding segment capacity is used for each session within the same operating day. For renewable energy units, since their available output varies with the time of day, the available declared capacity for each bidding segment must also be based on the available capacity factor declared for the corresponding time session. The conversion is performed. The corresponding constraints are: (12) (13) In the formula, and thermal power units and renewable energy units exist Monthly operating days No. The application capacity for each quotation segment; For renewable energy units exist Monthly operating days Time period The declared capacity factor.

[0031] The power flow of the line should meet the transmission capacity constraints: (14) (15) In the formula, and They are nodes To node The lower and upper limits of line power flow.

[0032] The lower-level spot market linear programming model, composed of equations (10) to (15), can obtain the cleared power of generating units, power flow, and spot market clearing price after providing the piecewise bidding curves of each power generation enterprise. The above results will be fed back to the upper-level market entity trading decision model to calculate the revenue of power generation enterprises and constitute the market environment for renewable energy entities to optimize their bidding decisions.

[0033] 3. Transaction decision-making model of upper-level market participants (1) Considering the medium- and long-term market quotation constraints of the renewable energy price difference settlement mechanism To ensure stable power supply and control the spot market risk exposure of market participants, medium- and long-term markets typically set corresponding requirements on the contract ratios of market participants. Annual medium- and long-term markets constrain the proportion of electricity traded by market participants in their total annual power generation; monthly medium- and long-term markets further constrain the cumulative annual and monthly medium- and long-term trading ratios on top of the annual contracts.

[0034] The lower and upper limits for the proportion of medium- and long-term market transactions are defined as follows: (16) In the formula, and These are the lower and upper limits for the proportion of medium- and long-term market transactions each year; and These represent the lower and upper limits of the cumulative medium- and long-term transaction ratio after completing the monthly transactions.

[0035] make This indicates the type of market entity, corresponding to thermal power units, renewable energy units, and load-side entities, respectively. (Using an index...) Indexes referring to various market entities, i.e., when hour, Index for thermal power units ;when hour, Index for renewable energy ;when hour, For load index The cumulative medium- and long-term electricity volumes declared by each entity at different transaction stages are expressed as follows: (17) In the formula, for Category of market entities In the medium and long term market The declared electricity volume submitted during the specified time period; for Category of market entities Annual medium- and long-term market The time period clearing power is broken down to the first Monthly contracted electricity volume; for Category of market entities In the Monthly submissions of medium- and long-term market data Electricity consumption reported during specific time periods. Therefore, when hour, This indicates the annual medium- and long-term market declaration electricity volume; when hour, This represents the sum of the annual contract breakdown and the monthly market-reported electricity volume.

[0036] For thermal power units, their cumulative medium- and long-term declared electricity volume is directly determined by the prescribed medium- and long-term transaction ratio: (18) In the formula, For thermal power units During the transaction phase Time period The corresponding predicted power generation.

[0037] For renewable energy units, the electricity covered by the mechanism is included in the prescribed medium- and long-term trading ratio, and therefore needs to be deducted from the medium- and long-term trading ratio. The lower and upper limits of the medium- and long-term trading ratio for renewable energy units, after adjustment by the price difference settlement mechanism, are as follows: (19) (20) (twenty one) In the formula, Effective Mechanism Electricity Coverage Ratio: In annual transactions, the annual mechanism electricity coverage ratio. In monthly transactions, the rolling monthly mechanism electricity coverage ratio is defined by equation (1). .

[0038] The cumulative long-term declared electricity volume of renewable energy units meets the constraints: (twenty two) In the formula, For renewable energy units During the transaction phase Time period The corresponding predicted power generation.

[0039] The scale of medium- and long-term electricity purchases on the load side should be matched with the scale of tradable electricity on the supply side. Based on the medium- and long-term trading boundaries for thermal power units and renewable energy units, the lower and upper limits of the medium- and long-term electricity purchase ratio on the load side are expressed as follows: (twenty three) (twenty four) In the formula, For load During the transaction phase Time period The corresponding predicted electricity consumption.

[0040] The cumulative medium- and long-term electricity declared by the load-side entities should meet the following constraints: (25) The prices submitted for medium- and long-term market transactions should meet the upper and lower limits of market prices. (26) In the formula, and respectively medium and long term markets The lower and upper limits of the declared price.

[0041] (2) Decomposition Model of Medium and Long-Term Contracts in the Electricity Market The annual medium-to-long-term market, the monthly medium-to-long-term market, and the spot market have different time scales. Contracts formed after the annual market clearing need to be decomposed into monthly contracts first. After the monthly market transactions are completed, the annual and monthly contracts are further decomposed into the spot market for each trading day. Through this top-down contract decomposition process, a temporal coupling relationship is established between the results of preceding market transactions and the boundaries of subsequent market transactions.

[0042] make This indicates the generator set type. For generator sets, the annual medium- to long-term market is in the [time period]. The cleared electricity volume is broken down into monthly figures according to the monthly forecast power generation curve: (27) In the formula, for Type of unit During the medium to long term of the year The amount of electricity cleared; for Type of unit Annual contract broken down to the first Monthly period The amount of electricity; for Type of unit Year-round The predicted power generation; for Type of unit No. Monthly period The predicted power generation.

[0043] Since this study focuses on the hierarchical transmission relationship of medium- and long-term contracts in a multi-tiered electricity market, and does not involve specific contract matching between power generators and users or differentiated load-side decomposition strategies, for the sake of model simplification, it is assumed that the contract decomposition of each load-side entity is based on the average distribution of electricity traded on the supply side. The annual contract monthly decomposition relationship of the load-side entities is expressed as follows: (28) In the formula, For load Time period Annual contract broken down to the first Electricity purchase volume after one month; For load During the medium to long term of the year Electricity purchased; For the annual trading period of the supply side Cumulative transaction volume; For the annual trading period of the supply side Decomposed to the first The cumulative transaction volume for the month. and The calculation formulas are as follows: (29) (20) After the monthly medium- and long-term market clearing is completed, the annual contract allocation volume and the monthly contract clearing volume together constitute the medium- and long-term contract coverage: (31) In the formula, for Category Main In the Monthly period The cumulative medium- and long-term contracted electricity volume; for Category Main No. Monthly period Monthly medium- and long-term market clearing volume.

[0044] For generating units, the cumulative medium- and long-term contracts are further broken down into individual operating days based on the projected power generation: (32) In the formula, for Type of unit Decomposed to the first Monthly operating days Time period The cumulative medium- and long-term contracted electricity volume; for Type of unit No. Monthly operating days Time period The predicted power generation.

[0045] The cumulative medium- and long-term contracts on the load side are broken down into operating days according to the average distribution of the electricity traded on the supply side: (33) In the formula, For the medium- and long-term trading period on the supply side Cumulative transaction volume; For the medium- and long-term trading period on the supply side Decomposed to the operating day Cumulative transaction volume. and The calculation formulas are as follows: (34) (35) After obtaining the cumulative medium- and long-term contracted electricity volume, the average medium- and long-term settlement price for market participants is: (36) In the formula, for Type body exist Monthly operating days Time period The average settlement price of electricity covered by medium- and long-term contracts; For the medium to long term market period of the year A unified marginal clearing price; For the first Monthly medium- to long-term market period A unified marginal clearing price.

[0046] Equations (27) to (36) constitute a top-down decomposition process for medium- and long-term contracts. The annual contract is first decomposed into monthly contracts, and the annual and monthly cumulative contracts are then further decomposed into daily operating days and spot market periods. Through this process, the annual and monthly transaction results are transformed into the spot market settlement boundary and affect the electricity exposure of market participants in the spot market.

[0047] (3) Current spot market price constraints The upper-level bidding strategy must first meet the declared capacity constraints. For thermal power units and renewable energy units, the upper and lower limits of the declared capacity for each bidding segment are as follows: (37) (38) In the formula, For thermal power units The installed capacity, For renewable energy units The installed capacity.

[0048] The sum of the quoted capacities of each section of the generating unit equals its installed capacity, subject to the following constraint: (39) (40) The segmented bidding prices for both thermal power units and renewable energy units must meet the upper and lower limits of market quotations and non-decreasing constraints. (41) (42) In the formula, and These represent the lower and upper limits of the spot market price for thermal power units, respectively. and These represent the lower and upper limits of spot market prices for renewable energy generating units, respectively.

[0049] To depict the restrictions imposed by regulatory requirements on physical retention behavior, the capacity factor for renewable energy applications is subject to upper and lower limits for each time period, and the physical retention volume for each time period is subject to the maximum physical retention rate. constraint: (43) (44) In the formula, For renewable energy units exist Monthly operating days Time period The predicted capacity factor. The upper-level bidding strategy optimization model composed of equations (37) to (44) describes the decision-making process of power generation companies to optimize the declared capacity and declared price based on the feedback of the lower-level market clearing.

[0050] (4) Market objective function considering renewable energy price difference settlement The annual objective function of market participants is jointly determined based on the decomposition results of medium- and long-term contracts, the average medium- and long-term settlement price, the clearing results of the spot market, and the renewable energy price difference settlement mechanism. Power generation companies aim to maximize annual power generation profits, while power sales companies aim to minimize annual power purchase costs.

[0051] The actual electricity generation of thermal power units, renewable energy units, and load-side entities in the spot market at different times can be defined as follows: (45) (46) (47) The medium- and long-term electricity volumes submitted by market participants are subject to equations (18), (22), and (25), but their actual cleared volume is also affected by market supply and demand and the bidding behavior of other participants. To reduce the deviation of the actual cleared volume from the prescribed trading ratio in the medium- and long-term market, a cumulative medium- and long-term contract deviation penalty is introduced. Category Main Its cumulative medium- and long-term contract deviation electricity volume for: (48) In the formula, This refers to the number of months within a year; and They are respectively Category Main No. Monthly period The lower and upper limits of cumulative medium- and long-term contracted electricity volume; for Category Main No. Monthly period The cumulative actual cleared volume in the medium- and long-term market. When the cumulative medium- and long-term contract volume is within the specified range, the deviation volume is zero.

[0052] For thermal power generation companies and renewable energy power generation companies, their annual profit maximization objective functions are as follows: (49) (50) In the formula, and thermal power companies and renewable energy companies Total annual profit; For thermal power units No. Monthly operating days Revenue from electricity generation; For renewable energy units No. Monthly operating days Market-based power generation revenue; Renewable energy units as defined in equation (4) No. Monthly price difference settlement amount; This is the cumulative penalty coefficient for deviations in medium- and long-term contracts.

[0053] thermal power units In the Monthly operating days The returns are: (51) In the formula, For thermal power units The marginal cost of generating electricity; the first term represents the revenue corresponding to the electricity covered by medium- and long-term contracts; the second term represents the revenue corresponding to the remaining electricity settled in the spot market.

[0054] Since the marginal cost of generating electricity from renewable energy units such as wind power and solar power is close to zero, their daily market revenue is expressed as: (52) For electricity sales companies ,make This represents the set of loads it serves. The electricity sales company aims to minimize its annual electricity purchase cost; its objective function is expressed as: (53) In the formula, For electricity sales companies Total annual electricity purchase cost; For load No. The monthly cost of electricity. The formula for calculation is: (54) In the formula, For load The node is located at Monthly operating days Time period The first item represents the spot market price; the second item represents the electricity purchase cost corresponding to the electricity volume covered by the medium- and long-term contracts; the third item represents the settlement cost corresponding to the remaining electricity volume purchased in the spot market.

[0055] The method provided by this invention uses a preset market operating cycle as the calculation scope and performs market clearing step by step according to the actual trading sequence of the annual medium- and long-term market, the monthly medium- and long-term market, and the day-ahead spot market. It incorporates the electricity volume covered by the new energy price difference settlement mechanism, the mechanism settlement price, medium- and long-term contract positions, and the spot market operating results into a unified calculation process. Through the monthly rolling update of the mechanism's electricity volume and the top-down decomposition of medium- and long-term contract positions, a temporal coupling relationship is established between the results of preceding transactions and the boundaries of subsequent transactions. The method provided by this invention will be further explained below using a complete year as an example.

[0056] S1 generates time-of-use electricity-price pairs for the annual medium- and long-term market within the scope of the electricity volume that various market participants can declare in the annual medium- and long-term market. These pairs are then substituted into the electricity medium- and long-term market clearing model to obtain the annual medium- and long-term market clearing electricity volume and clearing price for various market participants. Among them, the types of market participants include thermal power units, renewable energy units, and load-side entities.

[0057] Specifically, before step S1, power system operation data and market rule parameters are acquired in advance, and the computing environment is initialized: First, the power system operation data, market entity parameters, market rule parameters, and new energy price difference settlement mechanism parameters are obtained through the data reading module.

[0058] Power system operation data includes: grid node set, transmission line connection relationship, upper and lower limits of transmission capacity of each transmission line, time-of-use load demand of each node, installed capacity of thermal power units, marginal generation cost of thermal power units, installed capacity of renewable energy units, and predicted capacity factors of renewable energy units such as wind power and photovoltaic power.

[0059] Market participant parameters include: the attribution relationship between generator sets and power generation companies, the attribution relationship between loads and electricity sales companies, the annual forecasted power generation or power consumption of each participant, the monthly forecasted power generation or power consumption, as well as the day-ahead forecasted power generation, forecasted power consumption, and forecasted renewable energy capacity factor.

[0060] Market rule parameters include: annual upper and lower limits for medium- and long-term market trading ratios, upper and lower limits for cumulative medium- and long-term market trading ratios after completing monthly trading, upper and lower limits for medium- and long-term market bid prices, upper and lower limits for spot market bid prices, number of spot market quotation segments, and maximum physical retention rate of renewable energy.

[0061] The parameters of the new energy price difference settlement mechanism include: the annual mechanism power coverage ratio of each renewable energy unit, the annual mechanism power total upper limit, and the mechanism settlement price.

[0062] After completing the data reading, initialize the month index to month 1, initialize the cumulative settlement electricity of each renewable energy unit in the preceding months to 0, and establish a data structure for storing the annual medium- and long-term market clearing results, monthly medium- and long-term market clearing results, day-ahead spot market clearing results, rolling update results of mechanism electricity, and settlement results of market participants.

[0063] In step S1, the annual medium- and long-term market transaction boundaries are determined, and the annual medium- and long-term market clearing is performed.

[0064] Specifically, before the annual medium- and long-term market opens, the range of electricity that various market entities can declare in the annual medium- and long-term market is determined based on market rule parameters, the annual forecasted power generation, and the annual forecasted electricity consumption of various market entities.

[0065] For thermal power units, the upper and lower limits of their annual medium- and long-term market declaration electricity volume are calculated according to formula (18).

[0066] For renewable energy units, the annual mechanism electricity coverage ratio is used as the effective mechanism electricity coverage ratio for the annual trading phase. The upper and lower limits of the annual medium- and long-term market declaration electricity after the price difference settlement mechanism are calculated according to formulas (19) to (22). Since the mechanism-covered electricity is included in the prescribed medium- and long-term trading ratio, when the mechanism coverage ratio increases, the range of electricity available for market-based annual medium- and long-term trading for renewable energy units will be reduced accordingly.

[0067] For the load side, the upper and lower limits of the annual medium- and long-term market electricity purchase volume are calculated according to the tradable scale of thermal power units and renewable energy units on the supply side, in accordance with formulas (23) to (25).

[0068] Each market participant's annual medium- and long-term market bid price should meet the upper and lower price limits specified in formula (26). Market participants generate annual medium- and long-term market time-of-use electricity-price pairs within the corresponding feasible range and submit them to the market operator. In one implementation, market participant bids can be provided by an external bid strategy generation module; when reproducing a given market scenario, preset bid data can also be read directly.

[0069] The market operator inputs the annual medium- and long-term market time-of-use electricity and price pairs submitted by each market participant into the centralized trading model composed of equations (5) to (9), and calls the linear programming solver to calculate the annual medium- and long-term market cleared electricity for each thermal power unit, renewable energy unit and load-side entity, as well as the unified marginal clearing price corresponding to each delivery period.

[0070] S2 breaks down the annual medium- and long-term market clearing volume of various market participants into monthly volumes, thus obtaining the monthly breakdown of annual contracts for various market participants.

[0071] Specifically, in step S2, the annual medium- and long-term market contract positions are broken down into monthly positions. That is, after the annual medium- and long-term market clearing is completed, the contract positions formed in the annual medium- and long-term market are broken down into monthly positions according to the monthly forecast curve, providing initial contract boundaries for monthly medium- and long-term market trading.

[0072] For thermal power units and renewable energy units, according to formula (27), the annual medium- and long-term market clearing power is decomposed into each month according to the proportion of the monthly forecast power generation of each unit to the annual forecast power generation.

[0073] For the load side, according to equations (28) to (30), the annual medium- and long-term market purchase volume is decomposed into each month according to the monthly distribution of the annual transaction volume on the supply side.

[0074] After the decomposition is completed, the monthly annual contract decomposition electricity volume of each market participant and the corresponding annual medium- and long-term market unified marginal clearing price are stored in the monthly transaction status data to provide input for subsequent monthly medium- and long-term market clearing.

[0075] S3, for the current month m, calculate the effective mechanism power coverage ratio of each renewable energy unit in the current month's monthly trading by using the annual mechanism power limit of each renewable energy unit, the actual mechanism settlement power completed in the previous month, and the predicted power generation of the current month.

[0076] Specifically, in step S3, the effective mechanism power coverage ratio for the current month is calculated on a rolling basis. That is, for the current month, before the monthly medium- and long-term market opens, the effective mechanism power coverage ratio for each renewable energy unit during the monthly trading is calculated according to formula (1) based on the annual upper limit of the total mechanism power volume, the annual mechanism power coverage ratio, the actual mechanism settlement power volume completed in the previous month, and the predicted power generation for the current month.

[0077] When the remaining annual reserve power is sufficient, the effective reserve power coverage ratio for the current month equals the annual reserve power coverage ratio. When the remaining annual reserve power is insufficient to cover the reserve power corresponding to the projected power generation for the current month, the effective reserve power coverage ratio decreases accordingly.

[0078] This step uses projected power generation to determine the tradable boundaries before the commencement of monthly medium- and long-term trading. The actual settlement volume will be updated based on the actual cleared power generation after all spot market transactions are completed before the current month's due date.

[0079] S4 generates monthly medium- and long-term market time-of-use electricity-price pairs within the declared electricity volume range for various market participants in the monthly medium- and long-term market. These pairs are then substituted into the electricity medium- and long-term market clearing model to obtain the monthly medium- and long-term market clearing electricity volume and clearing price for various market participants.

[0080] Specifically, in step S4, the monthly medium- and long-term market transaction boundaries are determined, and monthly medium- and long-term market clearing is performed. That is, based on the annual contract monthly breakdown results obtained in step S2, the current month's effective mechanism electricity coverage ratio obtained in step S3, and the current month's predicted power generation and predicted electricity consumption, the scope of electricity that each market participant can declare in the monthly medium- and long-term market is determined.

[0081] For thermal power units, the monthly medium- and long-term market electricity volume range is calculated based on the monthly breakdown of the annual contract volume, according to formulas (17) and (18).

[0082] For renewable energy units, according to formulas (19) to (22), the effective mechanism electricity coverage ratio for the current month is deducted from the prescribed cumulative medium- and long-term transaction ratio, and the upper and lower limits of the monthly medium- and long-term market declaration electricity after the price difference settlement mechanism is adjusted are calculated.

[0083] For the load side, the upper and lower limits of its monthly medium- and long-term market purchase volume are determined according to the scale of tradable electricity on the supply side, in accordance with formulas (23) to (25).

[0084] Each market participant generates a monthly medium- and long-term market time-of-use electricity-price pair within the constraints described above. The market operator inputs the corresponding quotes into the centralized trading model composed of equations (5) to (9) and calls the linear programming solver to calculate the monthly medium- and long-term market clearing electricity and unified marginal clearing price for each market participant in the current month.

[0085] S5 adds up the monthly breakdown of annual contracts for various market participants with the monthly clearing of medium- and long-term market transactions to obtain the cumulative medium- and long-term contract volume for each market participant in the current month. It then breaks down the volume to each operating day and time period in the current month to calculate the average settlement price for each market participant in each operating day and time period in the current month. Specifically, in step S5, the cumulative medium- and long-term contract positions are formed and allocated to each operating day. That is, after the monthly medium- and long-term market clearing is completed in the current month, the monthly allocated electricity of the annual contract is added to the monthly medium- and long-term market clearing electricity, and the cumulative medium- and long-term contract electricity of each market participant in the current month is calculated according to formula (31).

[0086] For thermal power units and renewable energy units, according to formula (32), the cumulative medium and long-term contract power is decomposed into each operating day and each spot market period of the current month according to the proportion of the day-ahead forecast power generation of each unit to the monthly forecast power generation.

[0087] For the load side, according to equations (33) to (35), based on the day-ahead distribution of the cumulative medium- and long-term contracted electricity volume on the supply side, the cumulative medium- and long-term purchased electricity volume is decomposed into each operating day and each spot market period of the current month.

[0088] Based on the annual medium- and long-term market clearing price, the monthly medium- and long-term market clearing price, and the decomposed electricity volume corresponding to the two types of contracts, the average medium- and long-term settlement price of each market participant on each operating day and in each time period is calculated according to formula (36).

[0089] Through the above process, the contract positions formed in the annual and monthly medium-to-long-term markets are converted into the settlement boundary of the day-ahead spot market.

[0090] S6. For each operating day of the current month, substitute the segmented bids of each generating unit, the load demand of each node, and the transmission network parameters into the day-ahead spot market clearing model to obtain the spot market clearing power of each type of market participant at each bidding stage on each operating day, so as to update the actual mechanism settlement electricity of each renewable energy unit in the current month, thereby calculating the price difference settlement amount of each renewable energy unit in the current month.

[0091] Specifically, step S6 includes: S61 generates day-ahead spot market quotes and executes spot market clearing daily, including: For each operating day of the current month, read the corresponding node load demand, renewable energy capacity factor forecast, transmission network parameters, and day-ahead spot market quotations for each generating unit.

[0092] Thermal power units and renewable energy units shall submit their day-ahead spot market declaration capacity and declaration price in a segmented bidding manner. The declared capacity of each bidding segment shall meet the upper and lower limit constraints and total capacity constraints specified in Equations (37) to (40), and the declared price of each bidding segment shall meet the upper and lower limit constraints and non-decreasing constraints specified in Equations (41) and (42).

[0093] For renewable energy units, the declared capacity factor and physical retention ratio for each time period should also be checked according to formulas (43) and (44) to ensure that the declared capacity does not exceed the predicted available output and the physical retention behavior does not exceed the preset maximum physical retention rate.

[0094] After completing the quotation verification, the segmented quotations of each generating unit, the load demand of nodes and the transmission network parameters are input into the day-ahead spot market clearing model composed of equations (10) to (15), and the linear programming solver is called to clear the market with the goal of minimizing the total bidding cost.

[0095] For each operating day, the clearing power of each thermal power unit and renewable energy unit in each bidding segment, the power flow of each transmission line, and the marginal price of each spot market node are stored. According to equations (45) to (47), the actual spot market electricity volume of each market participant in each time period is further summarized.

[0096] Complete the day-ahead spot market clearing for all operating days of the current month in the manner described above.

[0097] S62 updates the actual electricity consumption settled under the current mechanism for the month and calculates the price difference settlement amount, including: After clearing all day-ahead spot markets for the current month, the actual settlement electricity of each renewable energy unit for the current month is updated according to formula (2) based on the actual cleared electricity generation of the renewable energy units in the current month.

[0098] The actual electricity volume settled under the mechanism is subject to two conditions: first, the product of the actual monthly power generation and the annual mechanism electricity coverage ratio; and second, the remaining annual mechanism electricity volume after deducting the mechanism electricity volume settled in previous months. This restriction ensures that the total annual mechanism electricity volume settled does not exceed the annual mechanism electricity volume limit.

[0099] Subsequently, according to wind power, photovoltaic or other preset renewable energy technology categories, the actual power generation of units of the same technology category and the marginal price of spot market nodes are summarized, and the weighted average reference price of spot market corresponding to each technology category in the current month is calculated according to formula (3).

[0100] Based on the mechanism settlement price, the technical category reference price, and the actual settlement amount of the mechanism electricity in the current month, calculate the price difference settlement amount for each renewable energy unit in the current month according to formula (4).

[0101] The actual settled electricity volume of the current month is added to the historical settled electricity volume of the mechanism, and the accumulated settled electricity volume is used as the input for calculating the effective mechanism electricity coverage ratio in the next month.

[0102] S7 updates the current month to m+1, returns to S3, and continues until all months are sequentially traversed. Based on the calculation results, the annual power generation profit of thermal power generation companies and renewable energy power generation companies, and the annual electricity purchase cost of electricity sales companies are obtained.

[0103] Specifically, in step S7, the annual revenue, electricity purchase cost, and medium- and long-term contract deviations of market participants are calculated. That is, after completing the calculations for all months in sequence (i.e., executing S3 to S6 for each month in sequence), the economic indicators of each market participant are summarized based on the annual medium- and long-term market clearing results, the monthly medium- and long-term market clearing results, the day-ahead spot market clearing results, and the price difference settlement results.

[0104] First, the deviation between the cumulative medium- and long-term contract volume of each market participant and the prescribed trading range is calculated according to formula (48). When the actual cleared cumulative medium- and long-term contract volume is within the prescribed range, the deviation is 0; when the actual cleared volume exceeds the prescribed range, the excess portion is included in the deviation penalty.

[0105] For thermal power generation companies, their annual power generation profit is calculated according to equations (49) and (51). The corresponding revenue includes the revenue generated from the electricity covered by medium- and long-term contracts and the revenue generated from the remaining electricity settled in the spot market, minus marginal power generation costs and medium- and long-term contract deviation penalties.

[0106] For renewable energy power generation companies, their annual power generation profit is calculated according to equations (50) and (52). The corresponding revenue includes the market-based revenue generated from the electricity covered by medium- and long-term contracts, the market-based revenue generated from the electricity settled in the remaining spot market, and the price difference settlement amount generated by the new energy price difference settlement mechanism, minus the deviation penalty of medium- and long-term contracts.

[0107] For electricity sales companies, their annual electricity purchase cost is calculated according to equations (53) and (54). The corresponding cost includes the electricity purchase cost corresponding to the electricity volume covered by medium- and long-term contracts, the settlement cost corresponding to the remaining electricity volume purchased in the spot market, and the medium- and long-term contract deviation penalty.

[0108] Finally, the results of the multi-timescale coupled electricity market clearing are output, including: 1. Annual medium- and long-term market clearing volume and unified marginal clearing price for all participants; 2. Monthly clearing volume of various market participants in the medium and long term and unified marginal clearing price; 3. Annual contract monthly breakdown results, cumulative medium- and long-term contract positions, and day-ahead spot market settlement boundaries; 4. Unit clearing power, transmission line power flow, and nodal marginal price for each operating day and time period; 5. Monthly effective mechanism power coverage ratio, actual mechanism settlement power, reference price for technology category, and settlement amount of price difference for each renewable energy unit; 6. Annual profits of each power generation company, annual electricity purchase costs of each electricity sales company, and the deviation in medium- and long-term contract electricity volume for each market participant; 7. Statistical indicators used to characterize the market's operating status, including spot market price distribution, the proportion of periods with low or negative prices, the proportion of various types of settlement electricity volume, and the proportion of settlement amount for price differences.

[0109] The method provided by this invention can simulate the continuous operation of the annual medium- and long-term market, the monthly medium- and long-term market, and the day-ahead spot market according to the actual transaction sequence, and update the settlement electricity of the renewable energy mechanism on a rolling basis according to the actual spot market clearing results. This method integrates the renewable energy price difference settlement mechanism, the transmission of medium- and long-term contracts, and the physical operation constraints of the power system into a unified calculation framework, and can be used to analyze the impact of different renewable energy penetration rates, different medium- and long-term transaction coverage levels, and different combinations of mechanism electricity and mechanism settlement prices on market operation results.

[0110] Example 1 A case study was conducted using data from a high-proportion renewable energy power system, with the transmission topology simplified to a single-price node. Thermal power units were categorized into large, medium, and small types based on installed capacity, as shown in Table 1. The total installed capacity of thermal power units was 41,400 MW. Renewable energy units included wind turbines and photovoltaic (PV) units. Based on the high-proportion renewable energy scenario, wind and PV installed capacities were set at 119,947 MW and 141,965 MW, respectively. Each type of unit was evenly allocated to 10 power generation companies according to its installed capacity. System load and wind / solar capacity factors used actual data from the entire year.

[0111] Table 1 Parameter Table of Thermal Power Units

[0112] This example illustrates the impact of changes in the electricity proportion under the Contract for Difference (CFD) mechanism on the coupled clearing and settlement results of the electricity market across multiple time scales under conditions of high renewable energy integration. The upper and lower limits of the annual medium- and long-term market electricity proportion are set at 35% and 40%, respectively; the upper and lower limits of the annual monthly cumulative medium- and long-term market electricity proportion are set at 45% and 55%, respectively. Five simulation scenarios are used: 0%, 20%, 40%, 60%, and 80% of the CFD electricity proportion, with a CFD electricity price of 332.9 yuan / MWh. The upper and lower limits of the medium- and long-term market transaction price are set at 332.9 yuan / MWh and 499.3 yuan / MWh, respectively, while the upper and lower limits of the spot market quote are set at -200 yuan / MWh and 1000 yuan / MWh, respectively.

[0113] Figure 2 The price level is given under the change of the proportion of electricity generated by the mechanism. Figure 2 As can be seen, the average price of annual contracts remained relatively stable near the lower limit of the medium- and long-term market price, while the average price of monthly contracts was mainly distributed within the range of 337-344 yuan / MWh. Compared with the medium- and long-term contract prices, the average spot price decreased significantly due to the increase in the proportion of randomized electricity generation, falling from 200.60 yuan / MWh in the 0% scenario to 98.54 yuan / MWh in the 80% scenario. Figure 2 It can be seen that, under the condition of high proportion of renewable energy access, the change in the proportion of electricity generated by the mechanism is mainly reflected in the spot price level and the spot settlement results.

[0114] Figure 3 The monthly reference price distributions for wind power and solar power under different power generation mechanisms are presented respectively. Figure 3The box plot in the image represents the distribution range of the 12-month reference price, with the scatter plot representing the annual average reference price and the horizontal dashed line representing the settlement price under the CfD mechanism. The wind power results show that as the proportion of CfD-based electricity increases, the monthly reference price for wind power generally decreases. The annual average reference price for wind power decreased from RMB 118.84 / MWh in the 0% scenario to RMB 3.04 / MWh in the 80% scenario. The decrease in photovoltaic reference prices is more significant, with the annual average reference price falling from RMB 50.91 / MWh in the 0% scenario to -RMB 78.27 / MWh in the 80% scenario. Figure 3 It can be seen that under the condition of high proportion of new energy access, the spot reference prices of wind power and photovoltaic are significantly lower than the settlement price of CfD mechanism, and the photovoltaic reference price is more likely to enter the negative range.

[0115] Figure 4 The composition of the load-side settlement price is given under the 20%, 40%, 60%, and 80% electricity consumption ratios. Figure 4 The bar chart in the image represents the settlement components of wind and solar power under the mechanism, while the black scatter dots represent settlement prices in the multi-tiered market. As the proportion of electricity generated under the mechanism increases, the settlement component under the mechanism increases significantly: the total settlement component under the mechanism increases from 32.4 yuan / MWh in the 20% scenario to 200.6 yuan / MWh in the 80% scenario. Specifically, the settlement component for wind power increases from 19.0 yuan / MWh to 115.6 yuan / MWh, and the settlement component for solar power increases from 13.4 yuan / MWh to 85.0 yuan / MWh. Meanwhile, the settlement price in the load-side market decreases from 237.4 yuan / MWh in the 20% scenario to 105.4 yuan / MWh in the 80% scenario. Figure 4 It can be seen that the mechanism increases the share of price difference settlement that the load side needs to bear, while the market settlement price formed by the spot and medium- and long-term markets decreases. The two together constitute the final settlement result of the load side.

[0116] Figure 5The distribution of spot clearing prices for 8760 time periods throughout the year under different electricity quotas under different mechanisms is presented. In the horizontal stacked bars, each segment represents the percentage of time periods corresponding to a price range. The first range represents negative price periods (less than 0 yuan / MWh), and the second range represents periods with prices between 0 and 100 yuan / MWh, including periods with a price of 0 yuan / MWh. In the 0% mechanism electricity price scenario, negative price periods account for 13.2%, the 0-100 yuan / MWh range accounts for 41.7%, the 300-400 yuan / MWh range accounts for 17.1%, and the high-price range of 600-1000 yuan / MWh accounts for 13.5%. As the CfD mechanism electricity quota increases, the percentage of negative price periods generally rises, reaching 28.3%, 32.2%, and 39.3% in the 40%, 60%, and 80% scenarios, respectively. Meanwhile, the 0-100 yuan / MWh range still maintains a high percentage, indicating that under high-proportion renewable energy access conditions, spot prices are at low or negative levels for a large number of time periods. Figure 5 It is evident that the proportion of CfD (Consumer-Fuel-Demand) electricity not only affects the average spot price but also alters the annual distribution structure of spot prices. Under conditions of high renewable energy grid connection, a higher CfD proportion of electricity will cause spot prices to concentrate more in the low-price and negative-price ranges, thus reflecting the impact of periods of abundant renewable energy output on spot market prices.

[0117] This invention provides an electronic device, including: a computer-readable storage medium and a processor; The computer-readable storage medium is used to store executable instructions; The processor is configured to read executable instructions stored in the computer-readable storage medium and execute the method as described in any of the above embodiments.

[0118] This invention provides a computer-readable storage medium storing computer instructions that cause a processor to perform the method described in any of the above embodiments.

[0119] This invention provides a computer program product, including a computer program or instructions, which, when executed by a processor, implement the method described in any of the above embodiments.

[0120] Those skilled in the art will readily understand that the above description is merely a preferred embodiment of the present invention and is not intended to limit the present invention. Any modifications, equivalent substitutions, and improvements made within the spirit and principles of the present invention should be included within the scope of protection of the present invention.

Claims

1. A multi-timescale electricity market coupling clearing method that takes into account the settlement mechanism for renewable energy price differences, characterized in that, include: S1 generates time-of-use electricity-price pairs for the annual medium- and long-term market within the scope of the electricity volume that various market participants can declare in the annual medium- and long-term market. These pairs are then substituted into the electricity medium- and long-term market clearing model to obtain the annual medium- and long-term market clearing electricity volume and clearing price for various market participants. Among them, the types of market participants include thermal power units, renewable energy units, and load-side entities. S2 breaks down the annual medium- and long-term market clearing volume of various market participants into monthly volumes, thus obtaining the monthly breakdown of annual contracts for various market participants. S3, for the current month m, calculate the effective mechanism power coverage ratio of each renewable energy unit in the current month's monthly trading by using the annual mechanism power limit of each renewable energy unit, the actual mechanism settlement power completed in the previous month, and the predicted power generation of the current month; where, at the initialization, m=1, and the actual mechanism settlement power completed by each renewable energy unit in the previous month is 0. S4 generates monthly medium- and long-term market time-of-use electricity-price pairs within the declared electricity volume range of various market participants in the monthly medium- and long-term market. These pairs are then substituted into the electricity medium- and long-term market clearing model to obtain the monthly medium- and long-term market clearing electricity volume and clearing price for various market participants. S5, after adding the monthly breakdown of annual contract electricity volume of various market participants with the monthly medium- and long-term market clearing electricity volume to obtain the cumulative medium- and long-term contract electricity volume of various market participants in the current month, decompose it to each operating day and time period of the current month, and calculate the average settlement price of each market participant in each operating day and time period of the current month. S6. For each operating day of the current month, substitute the segmented bids of each generating unit, the load demand of each node and the transmission network parameters into the day-ahead spot market clearing model to obtain the spot market clearing power of each type of market participant at each bidding stage on each operating day, so as to update the actual mechanism settlement electricity of each renewable energy unit in the current month, thereby calculating the price difference settlement amount of each renewable energy unit in the current month. S7 updates the current month to m+1, returns to S3, and continues until all months are sequentially traversed. Based on the calculation results, the annual power generation profit of thermal power generation companies and renewable energy power generation companies, and the annual electricity purchase cost of electricity sales companies are obtained.

2. The method as described in claim 1, characterized in that, In step S1, the range of electricity that the thermal power unit can declare in the annual medium- and long-term market is as follows: in, and These represent the lower and upper limits for the proportion of medium- and long-term market transactions each year. For thermal power units During the annual trading period The corresponding predicted power generation, For thermal power units In the medium and long term market The declared electricity volume submitted during the specified time period A collection of thermal power units; The range of electricity that the renewable energy units can declare in the annual medium- and long-term market is as follows: in, , , For new energy units During the annual trading period The corresponding predicted power generation, For renewable energy units The annual mechanism for electricity coverage ratio, For new energy units In the medium and long term market The declared electricity volume submitted during the specified time period A collection of new energy generating units; The range of electricity that the load-side entity can declare in the annual medium- and long-term market is as follows: in, For the load side main body In the medium and long term market The declared electricity volume submitted during the specified time period For the load-side main body assembly, For the load side main body During the annual trading period The corresponding predicted electricity consumption, , .

3. The method as described in claim 2, characterized in that, In step S3, the effective mechanism electricity coverage ratio referenced by each renewable energy unit during the current month's monthly trading is as follows: in, For renewable energy units In the The effective power coverage ratio referenced during monthly transactions For renewable energy units The annual limit on total electricity consumption. For renewable energy units In the The actual monthly electricity consumption is settled through a mechanism. For renewable energy units The actual electricity volume settled through the mechanism in previous months. For renewable energy units In the Monthly period The predicted power generation.

4. The method as described in claim 3, characterized in that, In step S4, the declared electricity volume range for the thermal power unit in the monthly medium- and long-term market is as follows: in, and These represent the lower and upper limits of the cumulative medium- and long-term transaction ratio after completing the monthly transactions. For thermal power units In the medium and long term market The time period clearing power is broken down to the first Monthly contracted electricity volume For thermal power units In the Monthly submissions of medium- and long-term market data Electricity consumption reported during specific time periods For thermal power units In the Monthly trading hours The corresponding predicted power generation; The range of electricity that the renewable energy units can declare in the monthly medium- and long-term market is as follows: in, For new energy units In the medium and long term market The time period clearing power is broken down to the first Monthly contracted electricity volume For new energy units In the Monthly submissions of medium- and long-term market data Electricity consumption reported during specific time periods , , For new energy units In the Monthly trading hours The corresponding predicted power generation; The declared electricity volume range for the load-side entity in the monthly medium- and long-term market is as follows: in, For the load side main body In the Monthly trading hours The corresponding predicted electricity consumption, For the load side main body In the Monthly submissions of medium- and long-term market data Electricity purchase during specific time periods For the load side main body In the medium and long term market The time period clearing power is broken down to the first Monthly electricity purchase volume , .

5. The method as described in claim 4, characterized in that, In step S6, according to the formula Update the actual settlement electricity volume of each renewable energy unit for the current month; according to the formula Calculate the price difference settlement amount for each renewable energy unit in the current month; in, For renewable energy units In the The actual monthly settlement mechanism for electricity consumption. For the first Monthly running days collection; For renewable energy units In the Monthly operating days Time period No. The spot market clearing power of the quoted price range. This is a collection of periods for clearing out the spot market. For the set of renewable energy price ranges, The time interval between adjacent time periods; For renewable energy units In the The monthly price difference settlement amount, For renewable energy units In the The actual monthly electricity consumption is settled through a mechanism. For mechanism settlement price, , Indicates the relationship with renewable energy units A collection of new energy generating units belonging to the same technology category. To The summation index of renewable energy units in the data. For renewable energy units Node In the Monthly operating days Time period The spot market price, For renewable energy units In the Monthly operating days Time period No. The clearing power of the spot market in the quoted price range.

6. The method as described in claim 5, characterized in that, In step S7, the formula for calculating the annual power generation profit of the thermal power generation enterprise is as follows: in, For thermal power enterprises Annual power generation profit thermal power units In the Monthly operating days The benefits, For thermal power enterprises A collection of thermal power units, This is the cumulative penalty coefficient for deviations in medium- and long-term contracts. For thermal power units The cumulative deviation of medium- and long-term contract electricity volume. This refers to the number of months within a year; The formula for calculating the annual power generation profit of the renewable energy power generation enterprise is as follows: in, For renewable energy companies Annual power generation profits for renewable energy companies A collection of renewable energy sources, For renewable energy units No. Monthly operating days Market-based power generation revenue For renewable energy units No. Monthly price difference settlement amount For renewable energy units The cumulative deviation of electricity volume in medium and long-term contracts; The formula for calculating the annual electricity purchase cost of the electricity sales company is as follows: in, For electricity sales companies Annual electricity purchase cost, as the main load body No. Monthly electricity purchase cost, For electricity sales companies The set of service load subjects, as the main load body The cumulative deviation of medium- and long-term contract electricity volume.

7. An electronic device, characterized in that, include: Computer-readable storage media and processors; The computer-readable storage medium is used to store executable instructions; The processor is configured to read executable instructions stored in the computer-readable storage medium and execute the method as described in any one of claims 1-6.

8. A computer-readable storage medium, characterized in that, The computer-readable storage medium stores computer instructions for causing a processor to perform the method as described in any one of claims 1-6.

9. A computer program product, comprising a computer program or instructions, characterized in that, When the computer program or instructions are executed by a processor, they implement the method as described in any one of claims 1-6.

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