Power grid dispatching operation optimization method considering carbon quota granularity difference in multi-system environment

Abstract

The invention provides a power grid dispatching operation optimization method considering carbon quota granularity difference in a multi-system environment, and belongs to the technical field of power grid operation. The problems of unmatched carbon quota time scales, unsmooth information transmission, insufficient equipment coordination and the like in a multi-system environment in the prior art are solved; the method comprises the following steps that a multi-time-scale optimization scheduling model used for a green certificate-carbon transaction power transmission and distribution project carbon management system is constructed, the multi-time-scale optimization scheduling model is an annual-monthly double-layer planning model, and an annual planning layer is used for realizing planning of a long-time-scale emission reduction strategy of a power transmission and distribution project acceptance enterprise; the monthly planning layer is used for realizing planning of annual to monthly time scale transaction strategies of the power transmission and distribution project acceptance enterprises; performing rolling correction on the two-stage decision of the power transmission and distribution project acceptance enterprise based on the multi-time scale optimization scheduling model; the method is used for power grid operation.

Description

TECHNICAL FIELD

[0001] The application relates to the technical field of power grid operation, in particular to a power grid dispatching and operation optimization method in a multi-system environment considering differences in carbon quota granularity. BACKGROUND

[0002] China has constructed a multi-element energy management system with the power system, carbon system and green certificate system as the core. However, there are significant differences in the time granularity of carbon quota among the three systems: the existing power grid carbon quota management technology mainly adopts a single time scale optimization method, typical schemes including a carbon quota control system based on day-ahead dispatching and a carbon emission monitoring system based on real-time dispatching. The former adopts day-ahead carbon quota optimization dispatching with a 24-hour cycle, solves the carbon emission reduction plan through mixed integer linear programming, and mainly considers power balance constraints and total carbon quota constraints; the latter adopts model predictive control or rolling optimization method with a 15-minute or 1-hour dispatching cycle, and mainly responds to carbon emission control under load changes and renewable energy fluctuations.

[0003] With the deepening of low-carbon reform in the power industry, carbon quota optimization technologies considering multi-system coordination have also appeared, such as power-carbon system coordination technology, which introduces carbon quota cost constraints in power system optimization, adopts a double-layer optimization model to handle the carbon quota coupling relationship between systems; or power-green certificate system coordination technology, which includes green certificate transaction income in the economic evaluation of carbon emission reduction of renewable energy generation, adopts a multi-objective optimization method to balance economy and environmental protection.

[0004] However, the prior art has significant technical defects. First, there is a serious mismatch in the time scale of carbon quota. The prior art generally simplifies the carbon quota of different systems to the same time scale for modeling, ignoring the inherent differences in carbon quota time granularity, such as hourly carbon emission tracking in the power system, monthly carbon quota settlement and annual compliance in the carbon system, and annual carbon emission reduction quota assessment in the green certificate system. This cannot accurately reflect the operation characteristics of carbon quota in different systems, leading to a disconnection between short-term scheduling decisions and long-term carbon emission reduction compliance targets. Second, there is a lack of multi-system carbon quota information transmission mechanism. The annual green certificate carbon quota constraint cannot be effectively transmitted to the monthly scheduling layer, and the monthly carbon quota allocation and intraday power scheduling lack coordination, resulting in a conflict between short-term optimization and long-term carbon emission reduction targets, and making it impossible to achieve global optimization under multi-system carbon quota constraints. In addition, the prior art does not adequately consider the coordinated control of emerging carbon reduction technologies such as energy storage and carbon capture, lacks coordinated operation strategies between devices, and does not fully utilize the adjustment potential of carbon reduction technologies. The device operation strategy and system carbon price signal lack effective response. Moreover, the rolling optimization of the prior art is mainly for a single time scale, lacks a carbon quota correction mechanism across time scales, and cannot dynamically adjust the long-term carbon quota strategy according to the actual system conditions, making it impossible to effectively control the cumulative effect of prediction errors. SUMMARY

[0005] To solve the problems of carbon quota time scale mismatch, poor information transmission, and insufficient device coordination in the prior art in a multi-system environment, the present application proposes a multi-system environment power grid dispatching operation optimization method considering the granularity difference of carbon quota, which provides an innovative technical solution for intelligent dispatching of power grids in a coordinated environment of power systems, carbon systems, and green certificate systems.

[0006] The technical solution adopted by the present application is as follows: a multi-system environment power grid dispatching operation optimization method considering the granularity difference of carbon quota, comprising the following steps:

[0007] Step 1: Build a multi-time scale optimal dispatching model for the green certificate-carbon trading power transmission and distribution project carbon management system. The green certificate-carbon trading power transmission and distribution project carbon management system is based on the power system, the carbon system, and the green certificate system, and is a system that integrates P2G and carbon capture technology in power transmission and distribution projects. The energy supply side of the system includes traditional power facilities and renewable energy access projects. The energy conversion and consumption devices in the system include carbon dioxide capture, methane synthesis, electrolytic water hydrogen production, and electric load output.

[0008] The green certificate-carbon trading power transmission and distribution project carbon management system is used to realize comprehensive management of power generation and emission reduction by power transmission and distribution project construction enterprises.

[0009] The multi-time scale optimization scheduling model is an annual-month dual-layer planning model, the annual planning layer is used for realizing planning of a long time scale emission reduction strategy of a power transmission and distribution project construction enterprise, and the monthly planning layer is used for realizing planning of a time scale transaction strategy of the power transmission and distribution project construction enterprise from the annual to the monthly.

[0010] Step two: based on the multi-time scale optimization scheduling model, rolling correction is performed on two-stage decisions of the power transmission and distribution project construction enterprise in the annual and monthly.

[0011] Further, the objective function of the annual planning layer is to minimize the annual total cost, including the power purchase cost, the carbon transaction cost and the green certificate transaction cost, and the constraint condition of the objective function of the annual planning layer includes: the carbon quota monthly transaction amount constraint, the carbon system compliance constraint, the carbon quota balance constraint, the green certificate quota and transaction constraint and the energy demand balance constraint.

[0012] Further, the objective function of the monthly planning layer is to minimize the monthly total cost, including the power purchase cost, the carbon transaction cost and the green certificate transaction cost; the constraint condition of the objective function of the monthly planning layer includes: the power transmission and distribution project construction enterprise bidding constraint, the carbon quota buying and selling constraint, the renewable energy power generation amount constraint, the monthly actual green certificate yield constraint, the green certificate surplus or gap constraint and the monthly green certificate and carbon balance constraint.

[0013] Further, an interfacing mechanism is arranged between the annual planning layer and the monthly planning layer, and the interfacing mechanism includes the monthly carbon quota distribution and the monthly transaction amount constraint.

[0014] Further, the step two of rolling correction of the two-stage decisions of the power transmission and distribution project construction enterprise in the annual and monthly based on the multi-time scale optimization scheduling model is as follows:

[0015] 1) initial decision stage: based on the initial annual electricity price, carbon price and green certificate price prediction, a medium and long term power system and carbon system transaction strategy is formulated, and a comprehensive optimization scheme for the whole year is formed;

[0016] 2) monthly execution stage: the medium and long term power system and carbon system transaction strategy is taken as a constraint condition, 30 times of daily decision is performed in each month, and the spot joint clearing of the power system, the carbon system and the green certificate system is realized;

[0017] 3) rolling correction stage: at the end of the month, the actual transaction result and the latest prediction are combined, the medium and long term power system and carbon system transaction strategy of the subsequent month is updated, including the power quota plan, the carbon quota plan and the green certificate transaction plan;

[0018] 4) cyclic optimization stage: based on the updated medium and long term power system and carbon system transaction strategy, the next month spot decision is performed, and cyclic iteration is performed until the end of the compliance period, and dynamic balance of the three systems is realized.

[0019] Furthermore, the monthly carbon quota trading volume constraints include maximum implementation volume constraints, minimum implementation volume constraints, and minimum threshold requirements for power transmission and distribution engineering contractors.

[0020] Furthermore, annual to monthly timescale trading strategies need to be formulated within the overall annual optimization plan, taking the medium- and long-term decisions of power transmission and distribution engineering contractors as boundary conditions for optimization in the spot market phase.

[0021] Furthermore, in the carbon management system for green certificate-carbon trading transmission and distribution projects, the traditional power facilities are thermal power units, and the objective function for the monthly planning layer is as follows:

[0022] ;

[0023] In the formula: This represents the minimum total cost objective function value for the monthly planning layer. For typical days The weights; Typical day of the month The actual number of days for this type; The hourly electricity price; The carbon price for the current month; The price of the green certificate for the current month; For power transmission and distribution engineering contractors On a typical day No. Hourly electricity trading volume; This refers to the monthly green certificate purchase volume. This represents the monthly sales volume of green certificates. This refers to the monthly carbon allowance purchase volume. This refers to the monthly carbon allowance sales volume. For smoothing terms; For smoothing coefficients;

[0024] The expression for the smoothing term is as follows:

[0025] ;

[0026] In the formula: For power transmission and distribution engineering contractors On a typical day No. The output of the thermal power unit per hour.

[0027] The advantages of this application compared to existing technologies are as follows: Firstly, it innovatively proposes a two-layer nested annual-monthly carbon quota control architecture. The annual planning layer comprehensively considers green certificate carbon quotas and carbon emission constraints to formulate annual resource allocation strategies. The monthly scheduling layer decomposes the annual carbon quota constraints into monthly targets to coordinate carbon quota allocation and power dispatch, effectively solving the problem of time-scale mismatch in carbon quotas across multiple systems. Secondly, a cross-time-scale carbon quota information transmission mechanism (i.e., a multi-time-scale optimized scheduling model) is designed to achieve top-down constraint transmission (annual carbon quota constraints → monthly targets → intraday constraints), bottom-up result feedback (intraday execution results → monthly corrections → annual adjustments), and parallel-layer coordination (real-time interaction of carbon quota information from different systems within the same time scale), avoiding the information silo problem in traditional methods. Simultaneously, a cross-time-scale rolling correction mechanism is established. By monitoring carbon quota prediction errors in real time, the optimization strategy is dynamically adjusted based on the latest information, and the constraints and objective functions at each time scale are dynamically corrected, effectively controlling the cumulative effect of prediction errors. Attached Figure Description

[0028] The following description, in conjunction with the accompanying drawings, further illustrates this application:

[0029] Fig. 1 A structural diagram of a green certificate-carbon trading transmission and distribution engineering carbon management system that integrates P2G (Power-to-Gas) and carbon capture technology in power transmission and distribution engineering, provided for embodiments of this application;

[0030] Fig. 2 This is a schematic diagram illustrating the synergistic principle of the electricity-carbon-green certificate system provided in the embodiments of this application.

[0031] Fig. 3 A timescale comparison diagram of the three systems of electricity, carbon, and green certificates provided in the embodiments of this application. Detailed Implementation

[0032] like Figs. 1 to 3 As shown, this application provides a power grid dispatching and operation optimization method under a multi-system environment considering differences in carbon quota granularity. This method can be applied to the carbon management system of green certificate-carbon trading transmission and distribution projects, which is centered on the power system, carbon system, and green certificate system. A multi-timescale optimization scheduling model for the carbon management system of green certificate-carbon trading transmission and distribution projects is constructed. The optimization scheduling problem of the carbon management system of transmission and distribution projects considering the coupling mechanism of the power-carbon-green certificate system is studied. The solution is obtained through a multi-timescale optimization framework nested between annual and monthly timescales and a rolling correction mechanism. Simulation results show that the green certificate-carbon system synergy mechanism and the multi-timescale optimization scheduling method significantly improve both carbon emission reduction effect and engineering economics, providing an effective technical path for the low-carbon transformation of the power system.

[0033] The method of this application will be described in detail below with reference to specific embodiments.

[0034] The embodiments of this application take into account the following: Fig. 1 The green certificate-carbon trading transmission and distribution engineering carbon management system shown integrates P2G and carbon capture technology with transmission and distribution engineering carbon management system. The energy supply side of the system includes traditional power facilities and renewable energy access projects, while other energy conversion and consumption equipment includes carbon dioxide capture, methane synthesis, water electrolysis for hydrogen production, and electricity load output.

[0035] In this embodiment, renewable energy is provided by photovoltaic equipment, carbon dioxide capture is provided by CCS (Carbon Capture and Storage) equipment, methane synthesis and water electrolysis for hydrogen production are provided by PSG system, and captured CO2 and generated H2 are stored by hydrogen storage equipment and carbon storage equipment. The models of each device and system constraints in the above system are as follows.

[0036] 1. Photovoltaic equipment model

[0037] The electrical energy generated by photovoltaic equipment is:

[0038] (1);

[0039] In the formula: For photovoltaic panels in Electrical energy that can be generated at any time; This refers to the total area of ​​the photovoltaic panels; For photovoltaic panel efficiency, for The amount of solar energy that can be utilized per unit area of ​​photovoltaic panels at any given time, expressed in units of... .

[0040] Rated power of photovoltaic equipment With total installation area The following relationship exists:

[0041] (2);

[0042] In the formula: For a unit area of ​​photovoltaic panel at a solar radiation level of 1 Conversion factor between output power and area Therefore, equation (1) can be transformed into:

[0043] (3).

[0044] Because the electricity generated by photovoltaic panels cannot be fully utilized, in reality Below Let the abandoned power be ,Right now:

[0045] (4).

[0046] Considering limitations such as actual site space, the maximum capacity limit for photovoltaic equipment is:

[0047] (5);

[0048] In the formula: and These are the rated power and maximum value of the photovoltaic equipment, respectively.

[0049] 2. CCS Model

[0050] The process of CCS capturing carbon dioxide emissions from thermal power units is shown in equation (6):

[0051] (6);

[0052] In the formula: For thermal power units in Carbon dioxide emissions at any given time; Emission intensity; For thermal power units in Efforts made at all times; For CCS in The amount of carbon dioxide captured at any given time; For carbon dioxide capture efficiency; This represents the actual amount collected. For dissipated components; This refers to the power consumption of the CCS. This represents the power consumption coefficient per unit CO2 capture amount.

[0053] Carbon dioxide emissions from thermal power units With effort and emission intensity Related to, catch volume Depend on and capture efficiency Decision; capture The portion used for methane synthesis is That is, the actual amount captured, with the dissipated portion being... CCS power consumption Power consumption coefficient corresponding to unit CO2 capture amount and actual catch The result of multiplication.

[0054] Thermal power units and CCS must meet the power constraints, ramp rate constraints, and other conditions shown in equations (7)-(9) during operation:

[0055] (7);

[0056] (8);

[0057] (9);

[0058] In the formula: , For thermal power units Time and The output at time -1; and These are the upper and lower limits of the output of thermal power units, respectively. and These represent the upper and lower limits of the ramp rate of thermal power units per unit time. This refers to the power consumption of the CCS. This is the rated power of the CCS.

[0059] 3. P2G model

[0060] The P2G system comprises two sub-steps: electrolysis for hydrogen production and methanation. It synthesizes green fuel methane from carbon dioxide captured by CCS and hydrogen produced by electrolysis, as shown in equations (10)-(13):

[0061] (10);

[0062] (11);

[0063] (12);

[0064] (13);

[0065] In the formula: , These are the power consumption of the electrolytic cell and the methanation equipment, respectively. This represents the total power consumption of the P2G system. For the electrolytic cell in Hydrogen production at any given time; for The amount of methane synthesized by the methanation equipment at any given time; , Hydrogen production from electrolyzers and synthetic methane The power consumption coefficient; ω represents the amount of carbon dioxide required to synthesize methane; ω is the reaction equilibrium coefficient.

[0066] To maintain stable operation, each device in the P2G system must meet the power constraints, ramp rate constraints, and other conditions shown in equations (14)-(17) during operation:

[0067] (14);

[0068] (15);

[0069] (16);

[0070] (17);

[0071] In the formula: and These are the upper and lower limits of the power consumption of the electrolytic cell; and These represent the upper and lower limits of the electrolytic cell ramp rate per unit time; and These are the upper and lower limits of the power consumption of the methanation equipment; and These represent the upper and lower limits of the ramp-up rate of the methanation equipment per unit time.

[0072] 4. Hydrogen storage equipment model

[0073] The amount of hydrogen stored in the hydrogen storage device at the current moment is related to the amount of hydrogen stored in the device at the previous moment, as well as the amount stored / released. Therefore, the amount of hydrogen stored in the hydrogen storage device can be represented as:

[0074] (18);

[0075] In the formula: and These represent the amounts of hydrogen stored in the hydrogen storage device at time t and time t-1, respectively. and These represent the amounts of hydrogen stored and removed from the hydrogen storage device at time t-1, respectively.

[0076] The following assumptions and constraints apply to hydrogen storage devices: First, the device must already contain a certain amount of hydrogen at system startup to ensure it has both storage and extraction capabilities from the outset. Second, hydrogen storage and extraction cannot occur simultaneously and must not exceed the device's maximum storage and extraction power. Finally, the stored hydrogen must be positive and must not exceed the device's design capacity. Therefore, the following assumptions apply to hydrogen storage devices:

[0077] (19);

[0078] (20);

[0079] (twenty one);

[0080] In the formula: To control the hydrogen storage and retrieval status of the hydrogen storage device at time t, a variable of 0 to 1 is used. A value of 1 represents hydrogen being stored in the hydrogen storage device, and a value of 0 represents the opposite. and These represent the maximum amount of hydrogen that can be stored and retrieved, respectively. This refers to the design capacity of the hydrogen storage device.

[0081] 5. Carbon storage equipment model

[0082] Similar to hydrogen storage devices, carbon dioxide stored in carbon storage devices can be represented as:

[0083] (twenty two);

[0084] In the formula: and These represent the amounts of carbon dioxide stored in the carbon storage device at time t and time t-1, respectively. and These represent the amounts of carbon dioxide stored and removed from the carbon storage device at time t-1, respectively.

[0085] Carbon dioxide storage constraints can be expressed as:

[0086] (twenty three);

[0087] In the formula: To control the carbon dioxide storage and retrieval status of the carbon storage device at time t, a 0-1 variable is used, where a value of 1 represents carbon dioxide being stored in the carbon storage device, and a value of 0 represents the opposite. and These represent the maximum amount of carbon dioxide that can be stored and withdrawn, respectively. The design capacity of the carbon storage equipment.

[0088] 6. Energy balance constraint

[0089] Considering the input-output balance of electrical energy throughout the entire system, relevant constraint equations are constructed. In the entire system, energy-producing equipment includes photovoltaic equipment and thermal power units, while energy-consuming equipment includes CCS equipment, electrolytic cells, and methanation equipment. Simultaneously, certain electrical load demands are also considered. Therefore, the system's energy balance is expressed by the following equation:

[0090] (twenty four);

[0091] In the formula: Let t be the electrical load demand of the system at time t.

[0092] 7. Hydrogen balance constraint

[0093] In the entire system, the equipment that can produce or output H2 includes an electrolyzer and a hydrogen storage device, while the equipment that needs to consume or input H2 includes a methanation device and a hydrogen storage device. Therefore, the hydrogen balance of the system is expressed as follows:

[0094] (25);

[0095] In the formula: For the methanation equipment in the P2G system The amount of hydrogen consumed at any given time is the amount of hydrogen required during the methanation reaction.

[0096] 8. Carbon balance constraints

[0097] In the entire system, the equipment that can produce or output CO2 includes: CCS equipment and carbon storage equipment. Equipment that needs to consume or input CO2 includes methanation equipment and carbon storage equipment. The system carbon balance can be expressed as follows:

[0098] (26).

[0099] Based on the models of each device in the above system and the relevant constraints of the system, a multi-timescale optimization scheduling model for the carbon management system of the green certificate-carbon trading transmission and distribution project is constructed.

[0100] The power system, carbon system, and green certificate system constitute the three pillars of low-carbon construction in power transmission and distribution projects, jointly promoting the optimization of project structure and the achievement of carbon emission reduction targets. For example... Fig. 2 As shown, the three systems form an interconnected operating mechanism: the power system takes real-time power balance as its core and forms electricity price signals through market mechanisms; the carbon system is based on the principle of "total control and technology optimization" and achieves carbon emission control through carbon quota trading; the green certificate system relies on the renewable energy access system to create additional environmental value for clean technologies.

[0101] The three systems differ significantly in the time dimension, such as Fig. 3 As shown, this mismatch in time scales constitutes a major challenge to the coordinated optimization of multi-stage emission reduction. The power system primarily relies on short-term monitoring, with monitoring cycles typically ranging from several hours to several days, categorized into real-time monitoring (hourly), daily statistics (daily), and phased summaries (monthly, quarterly, and yearly). The carbon system's emission reduction assessment period is usually a construction phase, but assessments can be conducted monthly, allowing transmission and distribution engineering contractors flexibility in scheduling technology implementation. The green certificate system, on the other hand, is mainly based on project cycle assessments, implementing a full-cycle evaluation system. This inconsistency in time granularity leads to difficulties in coordinating and optimizing emission reduction across stages. When making intraday emission reduction decisions during short-term construction, transmission and distribution engineering contractors must simultaneously consider monthly emission reduction technical constraints and annual effect monitoring requirements, resulting in cross-time-scale decision-making.

[0102] The specific challenges arising from the differences in time scales are mainly manifested in three aspects: First, the problem of information asymmetry in decision-making, where short-term emission reduction decisions need to be based on long-term predictions of technology costs and effectiveness assessments, and these predictions are subject to significant uncertainty; second, the problem of inconsistent system assessment frequencies, where the power system is frequently updated while the assessment of the carbon system and the effectiveness monitoring of the green certificate system are relatively lagging, leading to delays in information transmission; and third, the problem of mismatched assessment cycles, where the implementation of emission reduction measures in the power system usually takes effect immediately, while the assessment of emission reduction technologies in the carbon system and the effectiveness monitoring of the green certificate system are uniformly evaluated at the end of each phase, creating a cross-period decision-making dilemma. These differences in time scales make it difficult for traditional single-point-in-time modeling methods to accurately describe the dynamic interaction process of multiple systems, necessitating the construction of a collaborative optimization framework with time-coupled characteristics.

[0103] To address the aforementioned issues, this application proposes an innovative multi-timescale optimization scheduling model, a two-tiered annual-monthly planning model. By establishing a nested optimization structure of the entire project lifecycle (annual planning layer) and the construction phase (monthly planning layer), it effectively solves the problem of mismatched time granularity across different systems. The core innovation of this model lies in constructing an optimization framework that combines top-down and bottom-up approaches, achieving coordination and consistency between long-term planning and short-term scheduling.

[0104] The model specifically includes the following parts.

[0105] 1. Long-term (annual) emission reduction strategies

[0106] First, based on the monthly projected construction volume of power transmission and distribution engineering contractors, the initial emission reduction target allocation expected to be obtained in the monthly emission reduction technology assessment is calculated by multiplying it by the carbon emission intensity benchmark value for the corresponding project type, thereby determining the monthly projected emission reduction gap. On this basis, power transmission and distribution engineering contractors introduce a "cost-optimal" strategy, adjusting their implementation behavior according to the relative levels of the predicted emission reduction technology costs.

[0107] This long-term emission reduction strategy includes the following components.

[0108] 1) Monthly trading volume constraints for carbon allowances

[0109] From a risk management perspective, the amount of emission reduction technologies implemented by power transmission and distribution engineering contractors in a single month should not exceed a specific proportion of the overall emission reduction target. This restriction prevents the extreme situation where power transmission and distribution engineering contractors concentrate on implementing annual emission reduction measures at a certain point in time, which is conducive to the temporal dispersion of risks. Therefore, a maximum implementation amount constraint is set, as shown in equation (27). Similarly, to prevent excessively premature implementation, a maximum adjustment amount constraint is also set in equation (28), stipulating that the emission reduction measures adjusted by power transmission and distribution engineering contractors in any month shall not exceed their current adjustable total amount, ensuring the feasibility and technical stability of implementation. Emission reduction technology assessments generally have a minimum implementation unit limit, and all implementation amounts must meet this minimum threshold requirement, i.e., equation (29).

[0110] (27);

[0111] (28);

[0112] (29);

[0113] In the formula: For power transmission and distribution engineering contractors in the first Monthly carbon allowance purchases; This is the coefficient for the monthly carbon allowance trading purchase limit; The total annual carbon allowance allocated to the power transmission and distribution project contractor based on its electricity generation. For power transmission and distribution engineering contractors in the first Monthly carbon allowance sales volume; The total surplus carbon allowance for the year for companies undertaking power transmission and distribution projects. The minimum trading volume for carbon allowances is typically 1 ton.

[0114] 2) Carbon system compliance constraints

[0115] The carbon system requires that the carbon allowances held by power transmission and distribution engineering contractors at the end of the performance period must meet the coverage requirements of their actual carbon emissions, as shown in equation (30).

[0116] (30);

[0117] In the formula: Carbon emission intensity of power transmission and distribution engineering contractors; The total annual carbon allowance actually received by power transmission and distribution engineering contractors throughout the year; The sum of the net value of carbon allowance trading in the carbon system shall not be less than the actual total carbon emissions from its annual power generation; This represents the total electricity generated throughout the year. This serves as the benchmark carbon emission intensity.

[0118] 3) Carbon quota balance constraints

[0119] Equation (31) ensures that annual carbon emissions do not exceed the initial allowance plus net purchases, while the monthly carbon allowance holdings must meet the dynamic balance relationship, as shown in Equation (32).

[0120] (31);

[0121] (32);

[0122] In the formula: The estimated total annual carbon emissions (tons); In the first Monthly carbon allowance purchases; In the first Monthly carbon allowance sales volume; This refers to the carbon allowance allocated at the beginning of the year. This refers to the monthly carbon allowance allocation (broken down by year). For the first Carbon allowance holdings at the end of the month; Indexed by month.

[0123] 4) Green Certificate Quotas and Trading Constraints

[0124] This is to ensure that the annual demand for green certificates does not exceed the sum of self-produced green certificates and net purchases, while the monthly green certificate trading volume does not exceed the system limit.

[0125] (33);

[0126] (34);

[0127] In the formula: Forecast of annual green certificate production (units); The annual demand for green certificates (in units); The monthly limit for green certificate transactions (in units); In the first Monthly green certificate purchases; In the first Monthly green certificate sales volume.

[0128] 5) Energy demand balance constraints

[0129] This is used to ensure that monthly electricity demand is met.

[0130] (35);

[0131] In the formula: For the first Monthly electricity trading volume; For the first Monthly electricity demand.

[0132] 6) Objective function of annual planning

[0133] The objective function of the annual planning layer is to minimize the total annual cost, including electricity purchase cost, carbon trading cost, and green certificate trading cost.

[0134] (36);

[0135] In the formula: The total cost objective function for the annual planning layer; The benchmark electricity price (RMB / MWh); For the first Monthly carbon price (RMB / ton); For the first The price of a green certificate per month (RMB / certificate).

[0136] 2. Trading strategies on annual to monthly timescales

[0137] The annual to monthly timescale trading strategy constitutes the monthly planning layer. This layer uses the annual planning decisions as boundary conditions to optimize resource allocation and scheduling for each month. To reduce computational complexity, this layer employs a typical day representation method, selecting representative dates (such as weekdays, weekends, and days with extreme weather) for optimization and extending the results to the entire month.

[0138] The annual to monthly timescale trading strategy includes the following components.

[0139] 1) Price constraints for power transmission and distribution engineering contractors

[0140] This embodiment assumes that the power transmission and distribution engineering contractor adopts a segmented pricing method. for The first time period power transmission and distribution engineering contractor Segment price; For the winning bid volume of power transmission and distribution engineering contractors, each bid and winning bid volume must meet its upper and lower limits. Furthermore, bids must be submitted in ascending order of volume, i.e., the bids for each segment must be in descending order of volume. +1 segment price is greater than or equal to the first Price quote.

[0141] (37);

[0142] In the formula: , They are respectively the first of the power transmission and distribution engineering contractors The lower and upper limits of the price range. Index of companies undertaking power transmission and distribution projects This refers to a group of companies that undertake power transmission and distribution engineering projects.

[0143] The lower limit of the price is determined by both the cost of power generation and the cost of carbon emissions:

[0144] (38);

[0145] In the formula: The power generation cost for a certain section of the power transmission and distribution project undertaken by the company. This represents the average level of the annual carbon allowance price forecast, a figure that plays a crucial role in determining the lower limit of bids that take carbon costs into account. For power transmission and distribution engineering contractors carbon emission intensity; This serves as the benchmark carbon emission intensity. The strategy treats the expenditure on purchasing allowances in the carbon system for power generation activities as part of the production cost, incorporating it into the bidding strategy along with conventional power generation costs.

[0146] Trading strategies on annual to monthly timescales must be formulated within the framework of medium- and long-term carbon allowance trading. The medium- and long-term decisions of power transmission and distribution engineering contractors should be used as boundary conditions for optimization in the spot market phase, ensuring that their trading behavior in the secondary carbon system (i.e., monthly carbon) aligns with established medium- and long-term plans. Specifically, the cumulative monthly carbon allowance purchase volume must not be less than the monthly purchase target determined by the medium- and long-term carbon allowance trading strategy to guarantee that engineering contractors obtain sufficient carbon allowances to compensate for their anticipated shortfall. Simultaneously, it must be ensured that the carbon allowance price approaches zero when supply far exceeds demand, and that the carbon allowance price never becomes negative.

[0147] (39);

[0148] (40);

[0149] In the formula: For power transmission and distribution engineering contractors In the Daily carbon allowance purchases; In the first Daily carbon allowance sales volume; For date indexing; In the first Daily carbon allowance price; In the first The minimum price limit for daily carbon allowances.

[0150] The total amount of carbon allowances sold in a given month shall not exceed the amount of carbon allowances sold in that month as determined by the medium- and long-term carbon allowance trading strategy, ensuring that the amount of carbon allowances sold by power transmission and distribution engineering contractors is less than their carbon allowance surplus.

[0151] 2) Constraints on carbon quota purchase and sale

[0152] (41);

[0153] In the formula: This is the upper limit coefficient for monthly carbon allowance trading, ensuring that daily purchases do not exceed a certain percentage of the monthly purchase volume. This prevents power transmission and distribution engineering contractors from conducting carbon allowance trading only once a month; multiple transactions effectively spread price risk. Similar to the purchase constraint setting, daily sales do not exceed a certain percentage of the monthly sales volume. For the first Minimum coefficient for daily carbon allowance purchase; For the first Maximum coefficient for daily carbon quota purchase; For the first Minimum coefficient for selling daily carbon allowances; For the first The maximum coefficient for selling daily carbon allowances.

[0154] 3) Calculate the actual renewable energy generation, the number of green certificates, and the green certificate surplus or deficit, as shown below:

[0155] (42);

[0156] (43);

[0157] (44);

[0158] In the formula: This represents the actual monthly renewable energy generation. For typical days The representative weight; This is a typical day type; For power transmission and distribution engineering contractors On a typical day No. Hourly photovoltaic power generation capacity; Typical day of the month The actual number of days for this type; This represents the actual monthly output of green certificates. The amount of electricity generated per unit of green certificate; Green certificate surplus / shortage; This represents the actual monthly demand for green certificates.

[0159] 4) Monthly green certificates and carbon balance constraints

[0160] Ensure that the demand for green certificates is met and that monthly carbon emissions do not exceed the allowance plus net purchases.

[0161] (45);

[0162] (46);

[0163] In the formula: The actual monthly output of green certificates (in sheets). This refers to the monthly green certificate purchase volume. This represents the monthly sales volume of green certificates. The monthly demand for green certificates (in units); The monthly carbon allowance (tons) is derived from the allocation results of the annual plan; This refers to the monthly carbon allowance purchase volume. This refers to the monthly carbon allowance sales volume. This represents the actual monthly carbon emissions (in tons).

[0164] 5) Objective function of monthly planning layer

[0165] The objective function of the monthly planning layer is to minimize the total monthly cost, including electricity purchase cost, carbon trading cost, and green certificate trading cost.

[0166] (47);

[0167] In the formula: This represents the minimum total cost objective function value for the monthly planning layer. For typical days The weights; Typical day of the month The actual number of days for this type; The hourly electricity price is (RMB / MWh). The carbon price for the current month (yuan / ton); The price of the green certificate for the current month (RMB / certificate); For power transmission and distribution engineering contractors On a typical day No. Hourly electricity trading volume; For smoothing terms; This is the smoothing coefficient.

[0168] The smoothing term is introduced to improve numerical stability and reduce drastic fluctuations in decisions made between adjacent time periods.

[0169] (48);

[0170] In the formula: For power transmission and distribution engineering contractors On a typical day No. The output of the thermal power unit per hour.

[0171] In this embodiment, the multi-timescale coupled optimization model is divided into a mixed integer linear programming model in stages. The model is modeled based on MATLAB+YALMIP, and the commercial solver Gurobi is called to solve the proposed model.

[0172] 3. The two-stage rolling revision of decisions for power transmission and distribution engineering contractors throughout the year includes the following parts.

[0173] A two-tiered planning linkage mechanism was designed: an effective linkage mechanism between annual planning and monthly optimization was implemented to ensure consistency in decisions made at both time scales, including:

[0174] 1) Monthly carbon quota allocation

[0175] The annual plan allocates a total amount of carbon allowances for each month:

[0176] (49);

[0177] In the formula: This represents the total monthly carbon allowance. This refers to the monthly carbon allowance allocation (decomposed annually).

[0178] 2) Monthly transaction volume constraints

[0179] Monthly transaction volume must be consistent with the annual plan:

[0180] (50);

[0181] In the formula: For the power transmission and distribution engineering contractors identified in the annual plan In the Monthly carbon allowance purchase target; For the power transmission and distribution engineering contractors identified in the annual plan In the The monthly carbon allowance sales target.

[0182] 3) Rolling update mechanism

[0183] At the end of each month, the annual plan for the following months will be updated based on the actual transaction situation.

[0184] (51);

[0185] In the formula: For power transmission and distribution engineering contractors In the +1 to December updated annual carbon allowance purchase plan; This is an update function used to revise the carbon quota trading plan for subsequent months based on actual trading results and the latest forecasts; For power transmission and distribution engineering contractors In the +1 to December of the original annual carbon allowance purchase plan; For power transmission and distribution engineering contractors In the Actual monthly carbon allowance purchases; For power transmission and distribution engineering contractors Actual carbon emissions; For power transmission and distribution engineering contractors In the +1 to December updated annual carbon allowance sales plan; For power transmission and distribution engineering contractors In the The original annual carbon allowance sales plan will continue from January to December.

[0186] The rolling update mechanism process is as follows:

[0187] Due to discrepancies between actual construction progress and project forecasts, power transmission and distribution engineering contractors need to re-evaluate their annual and monthly decisions based on the latest forecasts and existing medium- and long-term and spot market results. The rolling update strategy for power transmission and distribution engineering contractors' decisions throughout the year is as follows:

[0188] 1) Initial decision-making stage: In the initial decision-making stage, power transmission and distribution engineering contractors formulate their own medium- and long-term power system and carbon system trading strategies based on the electricity price, carbon price and green certificate price forecasts at the beginning of the year, and form a comprehensive optimization plan for the whole year. This is the preliminary planning done by the company to achieve its own emission reduction targets and cost control.

[0189] 2) Monthly Implementation Phase: During the monthly implementation phase, the power transmission and distribution engineering contractors will use the previously established medium- and long-term power system and carbon system trading strategies as constraints, and make 30 daily decisions within each month to achieve the joint clearing of spot markets for the power system, carbon system, and green certificate system. This process is directly related to the company's monthly operating costs and resource allocation.

[0190] 3) Rolling revision phase: At the end of the month, based on actual trading results and the latest forecasts, update the medium- and long-term power system and carbon system trading strategies for subsequent months, including the power quota plan, carbon quota plan and green certificate trading plan;

[0191] 4) Cyclic Optimization Phase: In the cyclical optimization phase, power transmission and distribution engineering contractors make spot market decisions for the next month based on the updated medium- and long-term power system and carbon system trading strategies. This process iterates until the end of the compliance period, achieving dynamic equilibrium among the three systems. This process helps companies better balance costs and emission reduction targets, ensuring that all objectives are achieved within the compliance period and improving the overall operational efficiency of the company.

[0192] The above multi-timescale coupled optimization model effectively solves the problem of mismatch in time granularity of the electricity-carbon-green certificate system, achieves coordination and consistency from annual strategic planning to monthly resource allocation, and improves the carbon emission reduction effect and engineering economy of the system operation.

[0193] Finally, it should be noted that the above embodiments are only used to illustrate the technical solutions of this application, and are not intended to limit them. Although this application has been described in detail with reference to the foregoing embodiments, those skilled in the art should understand that modifications can still be made to the technical solutions described in the foregoing embodiments, or equivalent substitutions can be made to some or all of the technical features therein. Such modifications or substitutions do not cause the essence of the corresponding technical solutions to deviate from the scope of the technical solutions of the embodiments of this application.

Claims

1. A method for optimizing power grid dispatching and operation under a multi-system environment considering differences in carbon quota granularity, characterized in that: Includes the following steps: Step 1: Construct a multi-timescale optimization scheduling model for the carbon management system of green certificate-carbon trading transmission and distribution projects. The carbon management system of green certificate-carbon trading transmission and distribution projects is based on the power system, carbon system and green certificate system. It is a system that integrates P2G and carbon capture technology of transmission and distribution projects. The energy supply side of the system includes traditional power facilities and renewable energy access projects. The energy conversion and consumption equipment in the system includes carbon dioxide capture, methane synthesis, water electrolysis to produce hydrogen and electricity load output. Furthermore, the green certificate-carbon trading transmission and distribution project carbon management system is used to achieve comprehensive management of power generation and emission reduction by transmission and distribution project contractors; The multi-timescale optimization scheduling model is a two-layer annual-monthly planning model. The annual planning layer is used to plan the long-term emission reduction strategy of the power transmission and distribution engineering contractor, and the monthly planning layer is used to plan the annual to monthly timescale trading strategy of the power transmission and distribution engineering contractor. Step 2: Based on the multi-timescale optimization scheduling model, the annual and monthly two-stage decisions of the power transmission and distribution engineering contractors are rolled over and revised.

2. The method for optimizing power grid dispatching and operation under a multi-system environment considering differences in carbon quota granularity, as described in claim 1, is characterized in that: The objective function of the annual planning layer is to minimize the total annual cost, including electricity purchase cost, carbon trading cost, and green certificate trading cost. The constraints of the objective function of the annual planning layer include: monthly carbon quota trading volume constraints, carbon system compliance constraints, carbon quota balance constraints, green certificate quota and trading constraints, and energy demand balance constraints.

3. The method for optimizing power grid dispatching and operation under a multi-system environment considering differences in carbon quota granularity, as described in claim 2, is characterized in that: The objective function of the monthly planning layer is to minimize the total monthly cost, including electricity purchase cost, carbon trading cost, and green certificate trading cost. The constraints of the objective function of the monthly planning layer include: pricing constraints of transmission and distribution engineering contractors, carbon quota purchase and sale constraints, renewable energy power generation constraints, monthly actual green certificate production constraints, green certificate surplus or deficit constraints, and monthly green certificate and carbon balance constraints.

4. The method for optimizing power grid dispatching and operation under a multi-system environment considering differences in carbon quota granularity, as described in claim 3, is characterized in that: There is a linkage mechanism between the annual planning level and the monthly planning level, which includes monthly carbon quota allocation and monthly trading volume constraints.

5. The method for optimizing power grid dispatching and operation under a multi-system environment considering differences in carbon quota granularity, as described in claim 3, is characterized in that: The steps in step two for rolling adjustments to the annual and monthly decisions of power transmission and distribution engineering contractors based on a multi-time-scale optimization scheduling model are as follows: 1) Initial decision-making stage: Based on the forecasts of electricity prices, carbon prices and green certificate prices at the beginning of the year, formulate medium- and long-term trading strategies for the power system and carbon system, and form a comprehensive optimization plan for the whole year; 2) Monthly Implementation Phase: Using the medium- and long-term power system and carbon system trading strategies as constraints, 30 daily decisions will be made within each month to achieve joint clearing of spot markets for the power system, carbon system, and green certificate system; 3) Rolling revision phase: At the end of the month, based on actual trading results and the latest forecasts, update the medium- and long-term power system and carbon system trading strategies for subsequent months, including the power quota plan, carbon quota plan and green certificate trading plan; 4) Cyclic optimization phase: Based on the updated medium- and long-term power system and carbon system trading strategies, make spot decisions for the next month, iterate cyclically until the end of the performance period, and achieve dynamic equilibrium of the three systems.

6. The method for optimizing power grid dispatching and operation under a multi-system environment considering differences in carbon quota granularity, as described in claim 2, is characterized in that: The monthly carbon quota trading volume constraints include maximum implementation volume constraints, minimum implementation volume constraints, and minimum threshold requirements for power transmission and distribution engineering contractors.

7. The method for optimizing power grid dispatching and operation under a multi-system environment considering differences in carbon quota granularity, as described in claim 3, is characterized in that: Trading strategies on annual to monthly timescales need to be formulated within the overall annual optimization plan, taking the medium- and long-term decisions of power transmission and distribution engineering contractors as boundary conditions for optimization in the spot market phase.

8. The method for optimizing power grid dispatching and operation under a multi-system environment considering differences in carbon quota granularity, as described in claim 3, is characterized in that: In the carbon management system for green certificate-carbon trading transmission and distribution projects, the traditional power facilities are thermal power units. The objective function for the monthly planning layer is as follows: ； In the formula: This represents the minimum total cost objective function value for the monthly planning layer. For typical days The weights; Typical day of the month The actual number of days for this type; The hourly electricity price; The carbon price for the current month; The price of the green certificate for the current month; For power transmission and distribution engineering contractors On a typical day No. Hourly electricity trading volume; This refers to the monthly green certificate purchase volume. This represents the monthly sales volume of green certificates. This refers to the monthly carbon allowance purchase volume. This refers to the monthly carbon allowance sales volume. For smoothing terms; For smoothing coefficients; The expression for the smoothing term is as follows: ； In the formula: For power transmission and distribution engineering contractors On a typical day No. The output of the thermal power unit per hour.

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