Spot goods reliability clearing and settlement method and system based on information gap theory

Abstract

The invention discloses a spot goods reliability clearing and settlement method and system based on an information gap theory, and belongs to the technical field of power market clearing optimization, and the method comprises the steps: collecting the declaration cost information of a thermal power generating unit and a renewable energy power plant, carrying out the market optimization clearing, generating a marketization starting sequence, and carrying out the optimization clearing of the marketization starting sequence; the bid-winning data is transmitted to the reliability unit combination clearing module; a reliability unit combination clearing module scheduling mechanism carries out first clearing based on the marketization starting sequence and the bid-winning data, determines a target function base value of the reliability unit combination, considers randomness of the power of the renewable energy power plant based on IGDT, carries out second clearing, and determines the target function base value of the reliability unit combination; obtaining a final thermal power generating unit starting sequence and an output fluctuation range of the renewable energy power plant; and constructing a reliable unit commitment settlement mechanism based on IGDT. According to the invention, reasonable allocation of the reliability unit combination scheduling cost between the user and the renewable energy source main body is realized, and the demand of the high renewable energy source ratio system for the capacity adequacy in the day is satisfied.

Description

Technical Field

[0001] This invention relates to the field of electricity market clearing optimization, specifically to a spot market reliability clearing and settlement method and system based on information gap theory. Background Technology

[0002] In centralized electricity spot markets, besides the market-based unit combination formed by day-ahead market clearing based on the quantity and price declarations from both generation and consumption sides, there is also a process for optimizing and adjusting unit start-up and shutdown plans to ensure sufficient intraday generation capacity. This process has different names in different markets, including Reliability Unit Commitment (RUC), Reliability Assessment and Commitment (RAC), and Residual Unit Commitment (RUC), etc., and the design elements of RUC vary in different markets. For ease of discussion, this paper will uniformly refer to it as Reliability Unit Commitment, or simply RUC.

[0003] RUC design is an indispensable part of market design, affecting spot market clearing, pricing, and settlement, and thus impacting overall market efficiency. Various independent system operators in the United States, such as the Pennsylvania-New Jersey-Maryland Interconnection (PJM), the California Independent System Operator (CAISO), the Electric Reliability Council of Texas (ERCOT), and the New York Independent System Operator (NYISO), as well as the Shandong and Gansu electricity markets in my country, have all designed corresponding RUC mechanisms based on their own specific circumstances.

[0004] Early research on RUCs primarily focused on mechanism analysis within specific markets. However, with the increasing proportion of renewable energy, system security issues have become more prominent and complex, leading to a surge in research on optimizing RUC and security-constrained unit commitment (SCUC) clearing models and algorithms. Some literature utilizes stochastic optimization and robust optimization methods to model the uncertainty of renewable energy output and incorporates this model into the RUC clearing model. The modified clearing model effectively adapts to the regulatory pressure on the system caused by fluctuations in renewable energy output. Other literature proposes a coupled clearing model of RUC and day-ahead electricity. Compared to sequential clearing models, the coupled clearing model significantly reduces upward adjustment costs in the spot market, and the operating modes of units in the day-ahead and real-time markets are more similar.

[0005] Current research on the RUC (Renewable Energy Clearing) mechanism is mostly qualitative and empirical, with limited quantitative analysis based on specific mathematical models. While some literature has conducted quantitative simulations and comparative studies of RUC models in various US markets, it lacks a systematic summary of RUC mechanisms across different markets and fails to consider my country's RUC mechanism design. Furthermore, existing RUC mechanisms that only consider user-side uncertainties are insufficient to meet the intraday available capacity requirements of large-scale renewable energy integration systems. Although some literature utilizes stochastic optimization and robust optimization methods to establish RUC clearing models considering renewable energy uncertainties, it lacks a complete RUC mechanism framework. The connection between RUC and the day-ahead market, as well as the RUC settlement system, remain unclear. Additionally, the proposed methods suffer from high computational complexity, poor interpretability, and difficulty in practical application. To ensure sufficient available capacity under high-proportion renewable energy systems, it is necessary to improve existing unit combination models and provide a complete and supporting RUC mechanism framework.

[0006] In the research of existing unit combination models, the main methods for modeling uncertainty of renewable energy can be classified as follows: (1) stochastic optimization methods, including 1. Monte Carlo simulation method, 2. point estimation method, 3. chance constraint-based method, 4. scenario-based method, (2) fuzzy optimization method, and (3) robust optimization method. Each method has its own advantages and disadvantages. A major drawback of stochastic optimization methods is that they require an accurate statistical model of the uncertain parameters. In practice, uncertainty is mainly described by applying probability distribution functions, which inevitably leads to inaccurate solutions. In addition, stochastic optimization methods usually require generating a considerable number of random scenarios, which will result in excessive computational overhead for the algorithm. If the number of random scenarios is reduced by scenario reduction methods, the effectiveness and economy of stochastic optimization methods will also be weakened. Fuzzy optimization requires the construction of fuzzy membership functions for uncertainty modeling, which are often difficult to obtain in practical applications. Compared with stochastic and fuzzy optimization methods, robust optimization only needs to determine the fluctuation range of random variables, and the overall structure is relatively simple, but the economy of the result is difficult to control. Summary of the Invention

[0007] In view of the above-mentioned problems, the present invention is proposed.

[0008] Therefore, the objective of this invention is to model and manage the uncertainties of renewable energy using the nonparametric method of Information Gap Theory (IGDT), and to construct a RUC clearing model based on the IGDT method. The IGDT method does not require input parameters such as the probability distribution of uncertain variables. Therefore, it is an effective modeling method for renewable energy uncertainties. It does not require obtaining the precise distribution of uncertain variables, nor does it require constructing stochastic scenarios to simulate the changes in uncertain variables, ensuring the effectiveness and computational efficiency of the IGDT method even in large-scale systems. Currently, this method has been widely applied in power system research.

[0009] To address the aforementioned technical problems, this invention provides the following technical solution: a spot market reliability clearing and settlement method based on information gap theory, comprising the following steps:

[0010] The system collects cost information from thermal power units and renewable energy power plants, performs market optimization and clearing, generates a market-based start-up sequence, and transmits the winning bid data to the reliability unit combination clearing module. The dispatching agency in the reliability unit combination clearing module performs the first clearing based on the market-based start-up sequence and winning bid data to determine the objective function base value for the reliability unit combination. Based on IGDT (Integrated Gas Demand Theory), considering the randomness of renewable energy power plant power output, a second clearing is performed to obtain the final start-up sequence of thermal power units and the output fluctuation range of renewable energy power plants. An IGDT-based reliability unit combination settlement mechanism is constructed to compensate for the full cost of the start-up units and calculate the targeted allocation of RUC (Revenue Limits) increase costs.

[0011] As a preferred embodiment of the spot reliability clearing and settlement method based on information gap theory described in this invention, the declared cost information includes the declared cost information of thermal power units and renewable energy power plants declared by market entities.

[0012] Information to be submitted for thermal power units includes start-up costs. Unload cost and energy cost curve C i,t ;

[0013] The application information for renewable energy power plants includes the application energy cost curve C. r,t User-reported electricity consumption benefit curve U j,t ;

[0014] The day-ahead electricity market clearing process involved market operators conducting optimized clearing based on cost information submitted by market participants. The clearing results included market-based start-up sequences. and the output of thermal power units won in the bid Renewable energy power plants win bids for power output The relevant data is transmitted to the reliability unit combination clearing module.

[0015] As a preferred embodiment of the spot reliability clearing and settlement method based on information gap theory described in this invention, the reliability unit combination clearing module includes day-ahead reliability unit combination clearing, and the dispatching agency based on market-based start-up sequence. and the output of thermal power units won in the bid Renewable energy power plants win bids for power output Load forecasting D k,t Conduct a reliability-based unit consolidation and decommissioning process;

[0016] The reliability unit group clearing process involves two clearing processes: a first clearing and a second clearing.

[0017] The first round of clearing is a deterministic optimization clearing, assuming that the daily power output of renewable energy power plants is fully controllable and that the output of power plants in the day-ahead electricity market is in line with the winning bids. Consistent, the load data in the supply and demand balance constraint is changed from the load-side user declaration value in the day-ahead electricity market clearing process to the load forecast value, and the clearing yields the base value C0 of the reliability unit combination objective function;

[0018] The second clearing process includes an optimization clearing based on IGDT to account for the uncertainty of the randomness of renewable energy power plant power output, resulting in the final thermal power unit start-up sequence μ. i,t and the fluctuation range α of the daily output of renewable energy power plants r ;

[0019] Among them, the fluctuation range of daily output of renewable energy power plants only serves a settlement purpose. When the daily output of renewable energy power plants is lower than the lower limit threshold specified in the current fluctuation range, the renewable energy power plants need to bear the corresponding RUC adjustment fee.

[0020] As a preferred embodiment of the spot reliability clearing and settlement method based on information gap theory described in this invention, the reliability unit combination clearing module further includes: setting the objective of the robust model to maximize the deviation coefficient α, calculating the total uncertainty radius φ by weighted summation of the deviation coefficients of each renewable energy power plant, constructing the IGDT reliability unit combination clearing model and setting constraints:

[0021]

[0022] Among them, s i,t and μ i,t These are the unit's startup action variables and startup state variables, respectively. i,t Let i be the electrical energy cost quoted for unit i at time t. and These represent the startup cost and no-load cost of unit i, respectively, where ε is the scaling factor, and G is the no-load cost. l-r For renewable energy power plants, the rated output P is determined. r,t For the power generation transfer distribution factor of line l, β c Let G be the robustness factor, R be the set of all thermal power generating units, N be the set of all loads, T be the set of all time periods, and P be the robustness factor. l max This represents the maximum power flow constraint value for the line.

[0023] Using the envelope model for uncertain sets In this modeling, the output fluctuation range of the renewable energy power plant is represented as:

[0024]

[0025] The IGDT reliability unit combination clearing model is simplified into a single-layer optimization model:

[0026]

[0027] Among them, P r,t The rated output of renewable energy power plant r at time t, and the rated output P of renewable energy power plant r. r,t The power generation transfer distribution factor for line l.

[0028] As a preferred embodiment of the spot reliability clearing and settlement method based on information gap theory described in this invention, the set constraints include mandatory start-up constraints for market-based generating units and safe operating constraints for generating units.

[0029] The unit operation safety constraints include upper and lower output limits, unit ramp-up constraints, minimum start-up and shutdown time constraints, and start-up and shutdown status and action variable calculation constraints.

[0030] As a preferred embodiment of the spot reliability clearing and settlement method based on information gap theory described in this invention, the construction of the reliability unit combination settlement mechanism based on IGDT includes the targeted allocation of the full cost compensation fee and RUC upward adjustment fee for the units that start up in response to the dispatching agency's dispatching instructions.

[0031] The full cost compensation fee includes the assessment of all production costs incurred by the RUC start-up unit in each period of the spot market and the total revenue obtained in the spot market, and compensation for the difference between the two, with the compensation fee settled daily and monthly.

[0032] The targeted allocation of the RUC increase fee includes the following: the party responsible for the cost is the renewable energy power plant, and users and renewable energy power plants are jointly set as the allocation objects of the RUC-related increase fee. Different calculation methods are used for the allocation amount of different allocation objects.

[0033] The total allocated amount is calculated based on the actual thermal power unit start-up sequence μ generated by the IGDT-based reliability unit combination. i,t and real-time market transaction prices and trading volume The total RUC-related increase costs are calculated as follows:

[0034]

[0035] in, The startup cost is calculated over the settlement period t. d The allocated value within, For the settlement period t d The empty cost generated inside, For the settlement period t d The energy cost generated internally, Unit i in settlement period t d Insiders should receive increased fees related to RUC; Settlement period t d The internal RUC-related expenses have been increased; Unit i in settlement period t d Insiders should receive increased fees related to RUC; For unit i in the settlement period t d The calculated value of the internal RUC fee increase can be positive or negative; This reflects the actual market transaction price at the current date. For unit i in the settlement period t dThe domestic market contributed its efforts recently; For unit i in the settlement period t d The value of the effort gained from the recent market clearing; For unit i in the settlement period t d The output value obtained from real-time market clearing within the region, T d The time set of the settlement period;

[0036] User-side allocation of total amount calculation method: In the first round of deterministic optimization and clearing of the reliability unit combination, the start-up sequence of thermal power units is determined. Based on the declared cost data of thermal power units and the start-up sequence of thermal power units after the real-time market closes. A virtual round of real-time market clearing is conducted to determine the real-time market transaction price at that moment. and trading volume Based on virtual real-time market transaction prices and volumes, the following RUC-related increase fees are calculated for deterministic and reliable unit combination models:

[0037]

[0038] in, For the user side in the settlement period t d The internally borne costs related to the RUC increase, For user i in the settlement period t d The internally borne costs related to the RUC increase; For user i in the settlement period t d The calculated value of the RUC increase cost that should be borne by the company can be positive or negative; For each unit i obtained from the determination mode reliability unit combination clearing, at time t in the settlement period. d The equivalent startup, no-load, and energy costs generated internally;

[0039] like If the RUC increase cost that the user should bear under the current calculation method is greater than the actual RUC increase cost, then the actual RUC increase cost will apply. The cost is distributed to the user side.

[0040] As a preferred embodiment of the spot reliability clearing and settlement method based on information gap theory described in this invention, the construction of the reliability unit combination settlement mechanism based on IGDT further includes the calculation method for the total amount allocated to renewable energy power plants: in, For renewable energy power plants in the settlement period t d The internally borne costs related to the RUC increase;

[0041] like at this time If the value is negative, it indicates that under current conditions, considering the reliability of IGDT unit combinations is beneficial to reducing RUC (Renewable Energy Cost) increases. In this case, renewable energy plants do not bear the cost of RUC increases.

[0042] The calculation method for the apportionment amount of each user entity on the user side is as follows: The apportionment amount of each user entity on the user side is calculated in two layers. The second layer mainly allocates all remaining unapportioned RUC reliability costs after the first layer is apportioned to the user side according to the actual electricity consumption ratio of all loads in the current settlement period.

[0043] Calculation method for the apportionment of costs among various entities in renewable energy power plants: A two-tier apportionment method is set up for the current portion of the costs. The specific two-tier apportionment method is as follows:

[0044] Each renewable energy power plant in the first tier should bear its share of the costs. The calculation method is as follows:

[0045]

[0046] in, Settlement period t d The output of the renewable energy power plant is less than the lower limit of the benchmark output. Cumulative power generation for the corresponding period;

[0047] Considering that the first-level allocation is 0 when the output of all renewable energy power plants meets the output range obtained by RUC clearing, a second-level allocation is set up. The second level will allocate the RUC increase fee of all remaining renewable energy power plants after the first level allocation to each renewable energy power plant according to the actual power generation ratio of all renewable energy power plants in the current settlement period.

[0048] Another objective of this invention is to provide a spot reliable clearing and settlement system based on information gap theory.

[0049] To solve the above-mentioned technical problems, the present invention provides the following technical solution: a spot reliability clearing and settlement system based on information gap theory, comprising: a cost acquisition module, a reliability unit combination clearing module, and a reliability unit combination settlement module;

[0050] The cost acquisition module collects cost information declared by thermal power units and renewable energy power plants, performs market optimization and clearing, generates a market-based start-up sequence, and transmits the winning bid data to the reliability unit combination clearing module.

[0051] The reliability unit combination clearing module, the scheduling mechanism of the reliability unit combination clearing module performs the first clearing based on the market-based start-up sequence and winning bid data to determine the objective function base value of the reliability unit combination, and performs the second clearing based on IGDT to consider the randomness of the power output of renewable energy power plants, so as to obtain the final start-up sequence of thermal power units and the output fluctuation range of renewable energy power plants.

[0052] The reliability unit combination settlement module constructs a reliability unit combination settlement mechanism based on IGDT, compensates for the full cost of the operating units, and calculates the targeted allocation of RUC increase costs.

[0053] The present invention provides a computer device, including a memory and a processor, wherein the memory stores a computer program, characterized in that the processor executes the computer program to implement the steps of the spot reliability clearing and settlement method based on information gap theory.

[0054] The present invention provides a computer-readable storage medium storing a computer program thereon, characterized in that, when the computer program is executed by a processor, it implements the steps of the spot reliability clearing and settlement method based on information gap theory.

[0055] The beneficial effects of this invention are as follows: This invention proposes a reliable unit combination mechanism based on information gap theory. By modeling the uncertainty of renewable energy, the proposed reliable unit combination clearing model can effectively ensure the daily capacity adequacy of the system even in high-proportion renewable energy systems. At the same time, considering the change of the responsible party for reliable unit combination, the invention achieves a reasonable allocation of the reliable unit combination scheduling cost among users and renewable energy entities by adding a virtual clearing wheel based on the principle of fairness. The effectiveness and applicability of the proposed reliable unit combination mechanism based on information gap theory and the impact of parameter selection in the information gap theory model on the overall performance of the model are verified through simulation analysis.

[0056] This paper presents a reliability-based unit combination mechanism design considering the uncertainties of renewable energy. Based on theoretical analysis and simulation analysis, the proposed IGDT-based reliability-based unit combination mechanism has the following highlights: 1. An improved basic RUC clearing model is established, considering the output uncertainty of renewable energy farms, which can meet the daily capacity adequacy requirements of systems with a high proportion of renewable energy; 2. The uncertainty modeling method of IGDT is adopted, which can quantify uncertainty when the precise probability distribution of uncertain parameters or the uncertainty interval is unknown, and has the advantages of strong applicability and high computational efficiency; 3. A settlement mechanism for RUC-related upward adjustment costs is established with renewable energy farms and users as the main stakeholders. By adding a virtual clearing round, the responsibility of renewable energy and users is divided, ensuring the fair allocation of RUC-related upward adjustment costs between renewable energy farms and users.

[0057] As the penetration rate of renewable energy gradually increases, the reliability unit combination based on deterministic optimization will find it difficult to effectively guarantee the adequacy of the system's daily available capacity under conditions of significant fluctuations in renewable energy output. Therefore, this chapter improves the Gansu RUC clearing model with superior market performance derived in Chapter 3 based on information gap theory, proposing a complete clearing process and model. Considering the change in the responsible party for the reliability unit combination, renewable energy farms are added as a contributor to the RUC-related increase costs. Based on the principle of fairness, a virtual clearing round is added to achieve a reasonable allocation of RUC-related increase costs among users and renewable energy farms. Attached Figure Description

[0058] To more clearly illustrate the technical solutions of the embodiments of the present invention, the accompanying drawings used in the description of the embodiments will be briefly introduced below. Obviously, the accompanying drawings described below are only some embodiments of the present invention. For those skilled in the art, other drawings can be obtained based on these drawings without creative effort.

[0059] Figure 1 The above is a flowchart of a spot reliability clearing and settlement method based on information gap theory, provided as an embodiment of the present invention.

[0060] Figure 2 This is a market clearing flowchart based on IGDT for a spot reliability clearing and settlement method based on information gap theory, provided as an embodiment of the present invention.

[0061] Figure 3 The system topology diagram is provided for a spot reliability clearing and settlement method based on information gap theory according to an embodiment of the present invention.

[0062] Figure 4The wind farm day-ahead electrical energy market winning bid volume provided by an embodiment of the present invention for a spot reliability clearing and settlement method based on information gap theory.

[0063] Figure 5 The deviation coefficient of a spot reliability clearing and settlement method based on information gap theory, provided as an embodiment of the present invention, varies with the robustness factor.

[0064] Figure 6 The system's time-sharing available capacity under different robustness coefficients for a spot reliability clearing and settlement method based on information gap theory, provided as an embodiment of the present invention.

[0065] Figure 7 The real-time power output curve of a wind farm is provided as an embodiment of the present invention for a spot reliability clearing and settlement method based on information gap theory.

[0066] Figure 8 This invention provides an embodiment of a spot reliability clearing and settlement method based on information gap theory, under different robustness coefficients for RUC-related upward adjustment costs.

[0067] Figure 9 This invention provides a method for spot reliability clearing and settlement based on information gap theory, which addresses the deterministic reliability of unit combination scheduling costs under different scenarios.

[0068] Figure 10 The amount to be borne by each party for the RUC-related upward adjustment cost of the spot reliability clearing and settlement method based on information gap theory provided in one embodiment of the present invention. Detailed Implementation

[0069] To make the above-mentioned objects, features, and advantages of the present invention more apparent and understandable, specific embodiments of the present invention will be described in detail below with reference to the accompanying drawings. Obviously, the described embodiments are only a part of the embodiments of the present invention, and not all of them. Based on the embodiments of the present invention, all other embodiments obtained by those skilled in the art without creative effort should fall within the protection scope of the present invention.

[0070] Example 1, referring to Figure 1 This is the first embodiment of the present invention, which provides a spot reliability clearing and settlement method based on information gap theory, including:

[0071] S1: Collect cost information submitted by thermal power units and renewable energy power plants, optimize and clear the market, generate a market-based start-up sequence, and transmit the winning bid data to the reliability unit combination clearing module.

[0072] The declared cost information includes the declared cost information of market entities for thermal power units and renewable energy power plants;

[0073] Information to be submitted for thermal power units includes start-up costs. Unload cost and energy cost curve C i,t ;

[0074] The application information for renewable energy power plants includes the application energy cost curve C. r,t User-reported electricity consumption benefit curve U j,t ;

[0075] The day-ahead electricity market clearing process involved market operators conducting optimized clearing based on cost information submitted by market participants. The clearing results included market-based start-up sequences. and the output of thermal power units won in the bid Renewable energy power plants win bids for power output The relevant data is transmitted to the reliability unit combination clearing module. Specifically, the parameters in the clearing result are obtained through optimization using the day-ahead electrical energy clearing model, and then these parameters are input into the reliability unit combination module.

[0076] Information such as market transaction prices and transaction volumes are made public on the market trading platform.

[0077] S2: The reliability unit combination clearing module dispatching agency performs the first clearing based on the market-based start-up sequence and winning bid data to determine the objective function base value of the reliability unit combination. Based on IGDT to consider the randomness of renewable energy power plant power output, it performs the second clearing to obtain the final start-up sequence of thermal power units and the output fluctuation range of renewable energy power plants.

[0078] Furthermore, following the recent elimination of unreliable unit combinations, the dispatching agency is now using market-based start-up sequences. and the output of thermal power units won in the bid Renewable energy power plants win bids for power output Load forecasting D k,t Conduct a reliability-based unit consolidation and decommissioning process;

[0079] The reliability unit group clearing process involves two clearing processes: a first clearing and a second clearing.

[0080] The first round of clearing is a deterministic optimization clearing, assuming that the daily power output of renewable energy power plants is fully controllable and that the output of power plants in the day-ahead electricity market is in line with the winning bids. The load data in the supply and demand balance constraint is changed from the load-side user declaration value in the day-ahead electricity market clearing process to the load forecast value. The clearing process yields the base value C0 of the reliability unit combination objective function. This part of the objective is to ensure that the system's start-up and shutdown plan can meet the fluctuations in system load.

[0081] The second clearing process includes an optimization clearing based on IGDT to account for the uncertainty of the randomness of renewable energy power plant power output, resulting in the final thermal power unit start-up sequence μ. i,t and the fluctuation range α of the daily output of renewable energy power plants r The goal of this section is to ensure that the system's start-up and shutdown schedules can accommodate the output fluctuations of renewable energy units.

[0082] Among them, the fluctuation range of daily output of renewable energy power plants only serves a settlement purpose. When the daily output of renewable energy power plants is lower than the lower limit threshold specified in the current fluctuation range, the renewable energy power plants need to bear the corresponding RUC adjustment fee.

[0083] It should be noted that the objective of the robust model is to maximize the deviation coefficient α, and the total uncertainty radius φ is calculated by weighted summation of the deviation coefficients of each renewable energy power plant.

[0084]

[0085] Where, ρ r The deviation coefficient α of renewable energy power plants r The weighting coefficients.

[0086] The reliability unit combination clearing module also includes setting the objective of the robust model to maximize the deviation coefficient α, calculating the total uncertainty radius φ by weighted summation of the deviation coefficients of each renewable energy power plant, constructing the IGDT reliability unit combination clearing model, and setting constraints:

[0087]

[0088] Among them, s i,t and μ i,t These are the unit's startup action variables and startup state variables, respectively. i,t Let i be the electrical energy cost quoted for unit i at time t. and These represent the startup cost and no-load cost of unit i, respectively, where ε is the scaling factor, and G is the no-load cost. l-r For renewable energy power plants, the rated output P is determined. r,t For the power generation transfer distribution factor of line l, β cLet G be the robustness factor, R be the set of all thermal power generating units, N be the set of all loads, T be the set of all time periods, and P be the robustness factor. l max This represents the maximum power flow constraint value for the line.

[0089] In the formula, s i,t and μ i,t These are the unit's startup action variables and startup state variables, respectively; C i,t Let be the electrical energy cost quoted for unit i at time t. and These represent the startup cost and no-load cost of unit i, respectively. The term serves two purposes: firstly, it acts as a proxy pricing cost, primarily by adding the unit's electrical energy cost to the objective function to estimate the system's intraday operating mode, providing support for subsequent safety verification and congestion management; secondly, it accelerates the model optimization solution speed and reduces scheduling costs during the reliability unit combination phase by clarifying the optimization direction. This term is multiplied by a small scaling factor ε to limit the impact of the cost term on the IGDT-based reliability unit combination clearing. In the formula, variable P... r,t The rated output of renewable energy power plant r at time t. l-r For renewable energy power plants, the rated output P is determined. r,t The power generation transfer distribution factor for line l. β c The robustness factor is artificially set. G represents the set of all thermal power generating units; R represents the set of all new energy generating units; N represents the set of all loads; and T represents the set of all time periods. P l max This represents the maximum power flow constraint value for the line.

[0090] It should also be noted that the set constraints include mandatory start-up constraints for market-based start-up units and unit operation safety constraints;

[0091] Market-based mandatory start-up constraints for generating units:

[0092]

[0093] in, The unit start-up state variables determined for market-oriented unit combinations.

[0094] The unit operation safety constraints include upper and lower output limits, unit ramp-up constraints, minimum start-up and shutdown time constraints, and start-up and shutdown status and action variable calculation constraints.

[0095] Output upper and lower limit constraints:

[0096]

[0097] in, and Let be the lower and upper limits of the output of unit i at time t.

[0098] Unit ramp-up constraints:

[0099]

[0100] Wherein, ΔP i U and ΔP i D Let i be the uphill and downhill ramp rates.

[0101] Minimum start-up and shutdown time constraints for the unit:

[0102]

[0103] Among them, T i U and T i D The minimum start-up and shutdown time for unit i; and Let t be the continuous start-up and shutdown time of unit i at time t.

[0104] Constraints for calculating start-up and shutdown status and action variables:

[0105]

[0106] Among them, z i,t This is the shutdown action variable for unit i.

[0107] It can be seen that the above IGDT reliability unit combination clearing model is a two-level optimization model, where the lower level represents the situation when the output P of the renewable energy power plant is reduced. r,t In the uncertain set During internal fluctuations, the cost of reliable unit combination scheduling The expected cost value (1+β) cannot be exceeded. c )C0.

[0108] Using the envelope model for uncertainty set In this modeling, the output fluctuation range of the renewable energy power plant is represented as:

[0109]

[0110] The IGDT reliability unit combination clearing model is simplified into a single-layer optimization model:

[0111]

[0112] It also satisfies the aforementioned constraints, where P r,tThe rated output of renewable energy power plant r at time t, and the rated output P of renewable energy power plant r. r,t The power generation transfer distribution factor for line l.

[0113] S3: Construct a reliability unit combination settlement mechanism based on IGDT to compensate for the full cost of the units in operation and calculate the targeted allocation of RUC increase costs.

[0114] It should be noted that this includes the full cost compensation for units started in response to dispatch instructions from the dispatching agency and the targeted allocation of RUC adjustment costs;

[0115] The full cost compensation fee includes the assessment of all production costs incurred by the RUC start-up unit in each period of the spot market and the total revenue obtained in the spot market, and compensation for the difference between the two, with the compensation fee settled daily and monthly.

[0116] However, in the targeted allocation of RUC increased costs, the IGDT-based reliability unit combination settlement mechanism, without considering the participation of virtual entities in the spot market, reduces the economic efficiency of the reliability unit combination process and increases the dispatch cost of the reliability unit combination process in order to adapt to the output fluctuations of renewable energy power plants. The party responsible for this part of the cost should be the renewable energy power plant.

[0117] The targeted allocation of the RUC increase fee includes the following: the party responsible for the cost is the renewable energy power plant, and users and renewable energy power plants are jointly set as the allocation objects of the RUC-related increase fee. Different calculation methods are used for the allocation amount of different allocation objects.

[0118] ① Calculation method for total apportionment amount:

[0119] Based on the actual thermal power unit start-up sequence μ generated by the IGDT reliability unit combination i,t and real-time market transaction prices and trading volume The total RUC-related increase costs are calculated as follows:

[0120]

[0121] in, The startup cost is calculated over the settlement period t. d The allocated value within, For the settlement period t d The empty cost generated inside, For the settlement period t d The energy cost generated internally, Unit i in settlement period t d Insiders should receive increased fees related to RUC; Settlement period t d The internal RUC-related expenses have been increased; Unit i in settlement period t d Insiders should receive increased fees related to RUC; For unit i in the settlement period t d The calculated value of the internal RUC fee increase can be positive or negative; This reflects the actual market transaction price at the current date. For unit i in the settlement period t d The domestic market contributed its efforts recently; For unit i in the settlement period t d The value of the effort gained from the recent market clearing; For unit i in the settlement period t d The output value obtained from real-time market clearing within the T region. d This is the time set of the settlement period.

[0122] ② Calculation method for total amount shared by users:

[0123] In the first round of deterministic optimization and clearing of the reliability unit combination, the start-up sequence of thermal power units was determined. Based on the declared cost data of thermal power units and the start-up sequence of thermal power units after the real-time market closes. A virtual round of real-time market clearing is conducted to determine the real-time market transaction price at that moment. and trading volume Based on virtual real-time market transaction prices and volumes, the following RUC-related increase fees are calculated for deterministic and reliable unit combination models:

[0124]

[0125] in, For the user side in the settlement period t d The internally borne costs related to the RUC increase, For user i in the settlement period t d The internally borne costs related to the RUC increase; For user i in the settlement period t d The calculated value of the RUC increase cost that should be borne by the company can be positive or negative; For each unit i obtained from the determination mode reliability unit combination clearing, at time t in the settlement period. d The equivalent startup, no-load, and energy costs generated internally.

[0126] like If the RUC increase cost that the user should bear under the current calculation method is greater than the actual RUC increase cost, then the actual RUC increase cost will apply. The cost is distributed to the user side;

[0127] ③ Calculation method for the total amount allocated to renewable energy power plants: in, For renewable energy power plants in the settlement period t d The internally borne costs related to the RUC increase;

[0128] like at this time If the value is negative, it indicates that under current conditions, considering the reliability of IGDT unit combinations is beneficial to reducing RUC (Renewable Energy Cost) increases. In this case, renewable energy plants do not bear the cost of RUC increases.

[0129] ④ Calculation method for the amount shared by each user entity on the user side: The amount shared by each user entity on the user side is calculated in two layers.

[0130] Each user in the first tier should bear their share of the costs. The calculation method is as follows:

[0131]

[0132] Where D represents the set of users participating in the spot market; The RUC Bid Cost Upliftrate is calculated as follows:

[0133]

[0134] in, For user j in settlement period t d The positive deviation of electricity consumption.

[0135] A RUC obligation is numerically equivalent to When a user actively responds to a scheduling command, resulting in a positive deviation... It is zero. The calculation method is as follows:

[0136]

[0137] in, and For user j in settlement period t d Real-time cumulative market clearing volume and day-ahead cumulative market clearing volume.

[0138] Therefore, the formula for calculating is:

[0139]

[0140] The second layer mainly allocates all remaining unallocated RUC reliability costs after the first layer's allocation to the user side according to the actual electricity consumption ratio of all loads within the settlement period.

[0141] ⑤ Calculation method for the amount of expenses shared by various entities in renewable energy power plants: A two-tier sharing method is set for the current portion of the expenses. The two-tier sharing method is as follows:

[0142] Each renewable energy power plant in the first tier should bear its share of the costs. The calculation method is as follows:

[0143]

[0144] in, Settlement period t d The output of the renewable energy power plant is less than the lower limit of the benchmark output. Cumulative power generation for the corresponding period;

[0145] Considering that the first-level allocation is 0 when the output of all renewable energy power plants meets the output range obtained by RUC clearing, a second-level allocation is set up. The second level will allocate the RUC increase fee of all remaining renewable energy power plants after the first level allocation to each renewable energy power plant according to the actual power generation ratio of all renewable energy power plants in the current settlement period.

[0146] Example 2, refer to Figures 3-10 This is the second embodiment of the present invention, which provides a spot reliability clearing and settlement method based on the information gap theory. In order to verify the beneficial effects of the present invention, scientific demonstration is carried out through experiments.

[0147] Spot market simulation was conducted using the IEEE 118-node, 54-machine model; Figure 3 In the system topology diagram, three additional wind farms, r1, r2, and r3, are added at nodes 36, 37, and 96. The load forecast and user-reported electricity consumption are calculated as known quantities. The minimum time granularity of the simulation is 1 hour, and the simulation lasts for a total of 24 hours.

[0148] The installed capacities of wind farms r1, r2, and r3 are 450MW, 400MW, and 400MW, respectively. Assuming each wind farm submits its electricity demand and bids at zero price based on load forecast data, the winning bids for the three wind farms can be obtained by running the day-ahead electricity market clearing procedure. Figure 4 As shown.

[0149] ① The impact of weighting coefficients on the reliability of IGDT unit combination scheduling results.

[0150] Set the robustness factor β c The value was 0.05. The simulation results obtained by changing the weight are shown in Table 1.

[0151] Table 1 Simulation results with different weights for 0≤α

[0152]

[0153]

[0154] Considering that when no special constraints are applied to the deviation coefficient α, i.e. when the deviation coefficient 0≤α≤1, the deviation coefficient may be 0 as shown in Table 4-3, which is unreasonable in the actual reliability unit combination clearing, the minimum deviation coefficient α for each wind farm is set to 0.1. Similarly, another round of testing is shown in Table 2.

[0155] Table 2 Simulation results for different weights with 0.1≤α≤1

[0156]

[0157] Table 2 shows different weight settings. The difference in scheduling costs between Table 1 and Table 2 is within 1.23%, while the difference is within 0.46%. Therefore, different weight settings have little impact on scheduling costs. However, because the system's sensitivity to fluctuations in various uncertain factors differs, different weight coefficient settings will also affect the uncertainty radius calculation results. Since the output curve of the wind farm r1 is closer to the system load curve, for example... Figure 4 As shown, when r1 experiences a power deficit, its impact on the system is greater than that of wind farms r2 and r3. From the perspective of longitudinal weighting results, even if wind farm r1 has a larger weight, it is difficult to achieve a larger deviation coefficient compared to the other two wind farms. In actual system operation, dispatch decision-makers should set each weight coefficient according to the actual system situation and historical experience, based on sensitivity.

[0158] Since the deviation coefficient of the wind farm is close to 0 in many cases in Table 1, it cannot be used in practice. Therefore, a minimum deviation coefficient should be set to ensure the reasonableness of the results. In this section, it is uniformly set to 0.1. After the mechanism is mature, we can also explore the method of the unit self-declaring the minimum deviation coefficient. When the lower limit of the declaration is small, the wind farm will be given a certain reward in the settlement.

[0159] ② The impact of robustness coefficient on the reliability of IGDT unit combination scheduling results

[0160] For ease of discussion, let the deviation coefficients of the three wind farms be equal, i.e., α = α r1 =α r2 =α r3 Using 0.01 as a resolution, changing the robustness factor yields the following results: Figure 5As shown in the figure, the deviation coefficient increases approximately linearly with the increase of the robustness coefficient. The larger the deviation coefficient, the more pessimistic the decision-maker's view on wind power fluctuations, and the more conservative the resulting power generation plan. At the same time, the system's robustness is also higher. This simulation result is consistent with the theoretical analysis results above.

[0161] Using 0.02 as a resolution, and selecting 6 points for the robustness coefficient from 0 to 0.1, the available system capacity is calculated as follows: Figure 6 As shown, the system becomes more conservative as the robustness coefficient increases, mainly because more units will be added during peak load periods to cope with the intraday output uncertainty of renewable energy power plants.

[0162] As the available system capacity increases during peak load periods, on the one hand, more units need to compensate in response to RUC dispatch instructions; on the other hand, due to more units being started, competition in the real-time market intensifies, and the corresponding real-time market clearing price will decrease. All of this will lead to an increase in RUC-related adjustment fees. Let's assume, for example, that the real-time output curves of the three wind farms in the real-time market are as follows: Figure 7 As shown. After performing a full-cycle simulation of the day-to-RUC-real-time spot market, the changes in RUC-related upward adjustment costs with the robustness coefficient are obtained as follows: Figure 8 As shown, the increasing trend is consistent with the aforementioned analysis results.

[0163] ③IGDT Reliability Unit Combination Calculation Performance

[0164] To verify the effectiveness of the proposed IGDT-based reliability unit combination, assuming a robustness coefficient of 0.05 and identical deviation coefficients for the three wind farms, the maximum deviation coefficient obtained from the cleared reliability unit combination is 0.19429, within the fluctuation range. A set of 1000 random output scenarios for wind farms was generated using Monte Carlo simulation. Based on the clearing and determination of the reliability of the unit combination in the random output scenario clearing mode, the distribution of scheduling costs was obtained as follows: Figure 9 As shown, all are less than (1+β) c The pessimistic cost of C0 shows that the IGDT reliability unit combination can meet the effectiveness requirements.

[0165] Based on a deterministic reliability unit combination model, a stochastic programming-based reliability unit combination model can be easily implemented. Analysis and calculations were performed using output scenarios from 10 to 20 wind farms. The calculation results of the two reliability unit combination methods considering the uncertainty of renewable energy are shown in Table 3. In the table, the expected cost of the stochastic programming reliability unit combination is the expected value of the scheduling cost across all scenarios, while the pessimistic cost is the maximum scheduling cost across all scenarios. Analysis of the table shows that the result of the stochastic unit combination depends on the number of scenarios. The more scenarios, the more accurate the description of the randomness of wind power, and the lower the expected cost. However, this also leads to a significant increase in computation time. Considering that there are far more than three wind farms in a large system, the time complexity of the stochastic optimization method is unacceptable. In contrast, IGDT improves the computational tolerance by reducing the economics of the reliability unit combination model, and can effectively achieve clearing.

[0166] Table 3 Calculation results of the two methods

[0167] Scene Calculate time (seconds) Expected Cost (RMB) Pessimistic cost (RMB) 10 182 19469861 19548115 15 283 19450288 19545203 20 359 19446526 19548779 Robust Model 33 20637371 20657568

[0168] ④IGDT Reliability Unit Combination Settlement Results

[0169] Assuming a robustness coefficient of 0.05 and identical deviation coefficients for all three wind farms, the real-time power output curves for each wind farm are as follows: Figure 7 As shown, after performing a full-cycle simulation of the day-to-RUC-real-time spot market, the total RUC-related increase cost is 419,933 yuan. Based on the known existing settlement mechanism, the RUC-related increase cost is allocated between the user side and the renewable energy power plant side. The allocation result is as follows: Figure 10 As shown, the real-time output of wind farm r1 is within the fluctuation range obtained from the IGDT reliability unit combination clearing. In this example, it does not need to bear the sharing of RUC-related upward adjustment costs. The total deviation of the real-time output of wind farms r2 and r3 being less than the fluctuation range is 7.8MW and 23.6MW, respectively. However, since the deviation of wind farm r2 occurs at 19:00 and 20:00, which is exactly the peak of RUC-related upward adjustment costs, although the total deviation is smaller, wind farm r2 bears more RUC-related upward adjustment costs.

[0170] Example 3 is the third embodiment of the present invention, which differs from the previous two embodiments in that:

[0171] If the aforementioned functions are implemented as software functional units and sold or used as independent products, they can be stored in a computer-readable storage medium. Based on this understanding, the technical solution of this invention, or the part that contributes to the prior art, or a part of the technical solution, can be embodied in the form of a software product. This computer software product is stored in a storage medium and includes several instructions to cause a computer device (which may be a personal computer, server, or network device, etc.) to execute all or part of the steps of the methods described in the various embodiments of this invention. The aforementioned storage medium includes various media capable of storing program code, such as USB flash drives, portable hard drives, read-only memory (ROM), random access memory (RAM), magnetic disks, or optical disks.

[0172] The logic and / or steps represented in the flowchart or otherwise described herein, for example, can be considered as a sequenced list of executable instructions for implementing logical functions, and can be embodied in any computer-readable medium for use by, or in conjunction with, an instruction execution system, apparatus, or device (such as a computer-based system, a processor-included system, or other system that can fetch and execute instructions from, an instruction execution system, apparatus, or device). For the purposes of this specification, "computer-readable medium" can be any means that can contain, store, communicate, propagate, or transmit programs for use by, or in conjunction with, an instruction execution system, apparatus, or device.

[0173] More specific examples of computer-readable media (a non-exhaustive list) include: electrical connections (electronic devices) having one or more wires, portable computer disk drives (magnetic devices), random access memory (RAM), read-only memory (ROM), erasable and editable read-only memory (EPROM or flash memory), fiber optic devices, and portable optical disc read-only memory (CDROM). Furthermore, computer-readable media can even be paper or other suitable media on which the program can be printed, because the program can be obtained electronically, for example, by optically scanning the paper or other medium, followed by editing, interpreting, or otherwise processing as necessary, and then stored in computer memory.

[0174] It should be understood that various parts of the present invention can be implemented in hardware, software, firmware, or a combination thereof. In the above embodiments, multiple steps or methods can be implemented in software or firmware stored in memory and executed by a suitable instruction execution system. For example, if implemented in hardware, as in another embodiment, it can be implemented using any one or a combination of the following techniques known in the art: discrete logic circuits having logic gates for implementing logical functions on data signals, application-specific integrated circuits (ASICs) having suitable combinational logic gates, programmable gate arrays (PGAs), field-programmable gate arrays (FPGAs), etc.

[0175] Example 4 is the fourth embodiment of the present invention. This embodiment provides a spot reliability clearing and settlement system based on information gap theory, including: a cost acquisition module, a reliability unit combination clearing module, and a reliability unit combination settlement module.

[0176] The cost acquisition module collects cost information declared by thermal power units and renewable energy power plants, performs market optimization and clearing, generates a market-based start-up sequence, and transmits the winning bid data to the reliability unit combination clearing module.

[0177] The reliability unit combination clearing module, the scheduling mechanism of the reliability unit combination clearing module performs the first clearing based on the market-based start-up sequence and winning bid data to determine the objective function base value of the reliability unit combination, and performs the second clearing based on IGDT to consider the randomness of the power output of renewable energy power plants, so as to obtain the final start-up sequence of thermal power units and the output fluctuation range of renewable energy power plants.

[0178] The reliability unit combination settlement module constructs a reliability unit combination settlement mechanism based on IGDT, compensates for the full cost of the operating units, and calculates the targeted allocation of RUC increase costs.

[0179] It should be noted that the above embodiments are only used to illustrate the technical solutions of the present invention and not to limit it. Although the present invention has been described in detail with reference to preferred embodiments, those skilled in the art should understand that modifications or equivalent substitutions can be made to the technical solutions of the present invention without departing from the spirit and scope of the technical solutions of the present invention, and all such modifications or substitutions should be covered within the scope of the claims of the present invention.

Claims

1. A spot reliability clearing and settlement method based on information gap theory, characterized by: include, Collect cost information submitted by thermal power units and renewable energy power plants, optimize and clear the market, generate a market-based start-up sequence, and transmit the winning bid data to the reliability unit combination clearing module; The reliability unit combination clearing module dispatching agency performs the first clearing based on the market-based start-up sequence and winning bid data to determine the objective function base value of the reliability unit combination. Based on IGDT to consider the randomness of renewable energy power plant power, it performs the second clearing to obtain the final start-up sequence of thermal power units and the output fluctuation range of renewable energy power plants. Construct a reliability unit combination settlement mechanism based on IGDT to compensate for the full cost of the units in operation and calculate the targeted allocation of RUC increase costs.

2. The spot reliability clearing and settlement method based on information gap theory as described in claim 1, characterized in that: The declared cost information includes the declared cost information of market entities for thermal power units and renewable energy power plants; Information to be submitted for thermal power units includes start-up costs. Unload cost and energy cost curve C i,t ; The application information for renewable energy power plants includes the application energy cost curve C. r,t User-reported electricity consumption benefit curve U j,t ; The day-ahead electricity market clearing process involved market operators conducting optimized clearing based on cost information submitted by market participants. The clearing results included market-based start-up sequences. and the output of thermal power units won in the bid Renewable energy power plants win bids for power output The relevant data is transmitted to the reliability unit combination clearing module.

3. The spot reliability clearing and settlement method based on information gap theory as described in claim 2, characterized in that: The reliability unit portfolio clearing module includes day-ahead reliability unit portfolio clearing, whereby the dispatching agency clears the portfolio based on a market-based start-up sequence. and the output of thermal power units won in the bid Renewable energy power plants win bids for power output Load forecasting D k,t Conduct a reliability-based unit consolidation and decommissioning process; The reliability unit group clearing process involves two clearing processes: a first clearing and a second clearing. The first round of clearing is a deterministic optimization clearing, assuming that the daily power output of renewable energy power plants is fully controllable and that the output of power plants in the day-ahead electricity market is in line with the winning bids. Consistent, the load data in the supply and demand balance constraint is changed from the load-side user declaration value in the day-ahead electricity market clearing process to the load forecast value, and the clearing yields the base value C0 of the reliability unit combination objective function; The second clearing process includes an optimization clearing based on IGDT to account for the uncertainty of the randomness of renewable energy power plant power output, resulting in the final thermal power unit start-up sequence μ. i,t and the fluctuation range α of the daily output of renewable energy power plants r ; Among them, the fluctuation range of daily output of renewable energy power plants only serves a settlement purpose. When the daily output of renewable energy power plants is lower than the lower limit threshold specified in the current fluctuation range, the renewable energy power plants need to bear the corresponding RUC adjustment fee.

4. The spot reliability clearing and settlement method based on information gap theory as described in claim 3, characterized in that: The reliability unit combination clearing module also includes setting the objective of the robust model to maximize the deviation coefficient α, calculating the total uncertainty radius φ by weighted summation of the deviation coefficients of each renewable energy power plant, constructing the IGDT reliability unit combination clearing model, and setting constraints: Among them, s i,t and μ i,t These are the unit's startup action variables and startup state variables, respectively. i,t Let i be the electrical energy cost quoted for unit i at time t. and These represent the startup cost and no-load cost of unit i, respectively, where ε is the scaling factor, and G is the no-load cost. l-r For renewable energy power plants, the rated output P is determined. r,t For the power generation transfer distribution factor of line l, β c Let G be the robustness factor, R be the set of all thermal power generating units, N be the set of all loads, T be the set of all time periods, and P be the robustness factor. l max This represents the maximum power flow constraint value for the line. Using the envelope model for uncertainty set In this modeling, the output fluctuation range of the renewable energy power plant is represented as: The IGDT reliability unit combination clearing model is simplified into a single-layer optimization model: Among them, P r,t The rated output of renewable energy power plant r at time t, and the rated output P of renewable energy power plant r. r,t The power generation transfer distribution factor for line l.

5. The spot reliability clearing and settlement method based on information gap theory as described in claim 4, characterized in that: The set constraints include mandatory start-up constraints for market-based start-up units and unit operation safety constraints. The unit operation safety constraints include upper and lower output limits, unit ramp-up constraints, minimum start-up and shutdown time constraints, and start-up and shutdown status and action variable calculation constraints.

6. The spot reliability clearing and settlement method based on information gap theory as described in claim 4, characterized in that: The aforementioned IGDT-based reliability unit combination settlement mechanism includes the targeted allocation of full-cost compensation expenses and RUC upward adjustment expenses for units that start up in response to dispatch instructions from the dispatching agency; The full cost compensation fee includes the assessment of all production costs incurred by the RUC start-up unit in each period of the spot market and the total revenue obtained in the spot market, and compensation for the difference between the two, with the compensation fee settled daily and monthly. The targeted allocation of the RUC increase fee includes the following: the party responsible for the cost is the renewable energy power plant, and users and renewable energy power plants are jointly set as the allocation objects of the RUC-related increase fee. Different calculation methods are used for the allocation amount of different allocation objects. The total allocated amount is calculated based on the actual thermal power unit start-up sequence μ generated by the IGDT-based reliability unit combination. i,t and real-time market transaction prices and trading volume The total RUC-related increase costs are calculated as follows: in, The startup cost is calculated over the settlement period t. d The allocated value within, For the settlement period t d The no-load cost generated within, C i,td For the settlement period t d The energy cost generated internally, Unit i in settlement period t d Insiders should receive increased fees related to RUC; Settlement period t d The internal RUC-related expenses have been increased; Unit i in settlement period t d Insiders should receive increased fees related to RUC; For unit i in the settlement period t d The calculated value of the internal RUC fee increase can be positive or negative; This reflects the actual market transaction price at the current date. For unit i in the settlement period t d The domestic market contributed its efforts recently; For unit i in the settlement period t d The value of the effort gained from the recent market clearing; For unit i in the settlement period t d The output value obtained from real-time market clearing within the region, T d The time set of the settlement period; The calculation method for the total amount allocated to users is as follows: In the first round of deterministic optimization and clearing of the reliability unit combination, the start-up sequence of thermal power units is determined. Based on the declared cost data of thermal power units and the start-up sequence of thermal power units after the real-time market closes. Conduct a virtual round of real-time market clearing to determine the real-time market transaction price at that time. and trading volume Based on virtual real-time market transaction prices and volumes, the following RUC-related upward adjustment fees are calculated for deterministic and reliable unit combination models: in, For the user side in the settlement period t d The internal party should bear the related RUC-related increase costs. For user i in the settlement period t d The internally borne costs related to the RUC increase; For user i in the settlement period t d The calculated value of the RUC increase cost that should be borne by the company can be positive or negative; For each unit i obtained from the determination mode reliability unit combination clearing, at time t in the settlement period. d The equivalent startup, no-load, and energy costs generated internally; like If the RUC increase cost that the user should bear under the current calculation method is greater than the actual RUC increase cost, then the actual RUC increase cost will apply. The cost is distributed to the user side.

7. The spot reliability clearing and settlement method based on information gap theory as described in claim 4, characterized in that: The proposed IGDT-based reliability unit portfolio settlement mechanism also includes the calculation method for the total amount allocated to renewable energy power plants: in, For renewable energy power plants in the settlement period t d The internally borne costs related to the RUC increase; like at this time If the value is negative, it indicates that under current conditions, considering the reliability of IGDT unit combinations is beneficial to reducing RUC (Renewable Energy Cost) increases. In this case, renewable energy plants do not bear the cost of RUC increases. The calculation method for the apportionment amount of each user entity on the user side is as follows: The apportionment amount of each user entity on the user side is calculated in two layers. The second layer mainly allocates all remaining unapportioned RUC reliability costs after the first layer is apportioned to the user side according to the actual electricity consumption ratio of all loads in the current settlement period. Calculation method for the apportionment of costs among various entities in renewable energy power plants: A two-tier apportionment method is set up for the current portion of the costs. The specific two-tier apportionment method is as follows: Each renewable energy power plant in the first tier should bear its share of the costs. The calculation method is as follows: in, Settlement period t d The output of the renewable energy power plant is less than the lower limit of the benchmark output. Cumulative power generation for the corresponding period; Considering that the first-level allocation is 0 when the output of all renewable energy power plants meets the output range obtained by RUC clearing, a second-level allocation is set up. The second level will allocate the RUC increase fee of all remaining renewable energy power plants after the first level allocation to each renewable energy power plant according to the actual power generation ratio of all renewable energy power plants in the current settlement period.

8. A spot reliability clearing and settlement system based on information gap theory, employing the spot reliability clearing and settlement method based on information gap theory as described in any one of claims 1 to 7, characterized in that, include: Cost acquisition module, reliability unit combination clearing module, and reliability unit combination settlement module; The cost acquisition module collects cost information declared by thermal power units and renewable energy power plants, performs market optimization and clearing, generates a market-based start-up sequence, and transmits the winning bid data to the reliability unit combination clearing module. The reliability unit combination clearing module, the scheduling mechanism of the reliability unit combination clearing module performs the first clearing based on the market-based start-up sequence and winning bid data to determine the objective function base value of the reliability unit combination, and performs the second clearing based on IGDT to consider the randomness of the power output of renewable energy power plants, so as to obtain the final start-up sequence of thermal power units and the output fluctuation range of renewable energy power plants. The reliability unit combination settlement module constructs a reliability unit combination settlement mechanism based on IGDT, compensates for the full cost of the operating units, and calculates the targeted allocation of RUC increase costs.

9. A computer device comprising a memory and a processor, wherein the memory stores a computer program, characterized in that, When the processor executes the computer program, it implements the steps of the spot reliability clearing and settlement method based on information gap theory as described in any one of claims 1 to 7.

10. A computer-readable storage medium having a computer program stored thereon, characterized in that, When the computer program is executed by the processor, it implements the steps of the spot reliability clearing and settlement method based on information gap theory as described in any one of claims 1 to 7.

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