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

Abstract

The application provides a power grid dispatching operation optimization method considering granularity difference of carbon quota in a multi-system environment, and belongs to the technical field of power grid operation; solves the problems of mismatching of carbon quota time scale, poor information transmission, insufficient equipment coordination and the like in the prior art in a multi-system environment; the method comprises the following steps: constructing a multi-time scale optimization dispatching model for a green certificate-carbon trading power transmission and distribution project carbon management system; the multi-time scale optimization dispatching model is a bi-level planning model of annual and monthly; the annual planning layer is used for realizing planning of long-time scale emission reduction strategies of a power transmission and distribution project construction 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 construction enterprise; the two-stage decisions of the power transmission and distribution project construction enterprise are rolled and corrected based on the multi-time scale optimization dispatching model; and the application 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-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.

[0010] Step 2: Based on the multi-time-scale optimization scheduling model, the annual and monthly two-stage decisions of the power transmission and distribution engineering contractors are rolled over and revised.

[0011] Furthermore, 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.

[0012] Furthermore, 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.

[0013] Furthermore, a linkage mechanism is established between the annual planning level and the monthly planning level, which includes monthly carbon quota allocation and monthly trading volume constraints.

[0014] Furthermore, the steps in step two for rolling adjustments to the annual and monthly two-stage decisions of power transmission and distribution engineering contractors based on the multi-time-scale optimization scheduling model are as follows:

[0015] 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;

[0016] 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;

[0017] 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;

[0018] 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.

[0019] Further, the carbon quota monthly transaction volume constraint includes maximum implementation volume constraint, minimum implementation volume constraint and minimum threshold requirement for the power transmission and distribution project construction enterprise.

[0020] Further, the annual to monthly time scale transaction strategy needs to be formulated within the comprehensive optimization scheme for the whole year, taking the medium and long term decision of the power transmission and distribution project construction enterprise as the boundary condition for the spot phase optimization.

[0021] Further, the traditional power facility in the green certificate-carbon transaction power transmission and distribution project carbon management system is the thermal power unit, and the objective function of the monthly planning layer is as follows:

[0022] ;

[0023] In the formula: is the minimum total cost objective function value of the monthly planning layer; is the weight of the typical day ; is the actual number of days of the typical day type in the month; is the hourly electricity price; is the carbon price of the month; is the green certificate price of the month; is the power transaction volume of the power transmission and distribution project construction enterprise in the typical day at the hour; is the monthly green certificate purchase volume; is the monthly green certificate sale volume; is the monthly carbon quota purchase volume; is the monthly carbon quota sale volume; is the smoothing term; is the smoothing coefficient;

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

[0025] ;

[0026] In the formula: is the thermal power unit output of the power transmission and distribution project construction enterprise in the typical day at the 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-up 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] wherein: and are the upper and lower limits of the power consumption of the electrolyzer, respectively; and are the upper and lower limits of the ramp rate of the electrolyzer per unit time, respectively; and are the upper and lower limits of the power consumption of the methanation device, respectively; and are the upper and lower limits of the ramp rate of the methanation device per unit time, respectively.

[0072] 4. Hydrogen storage device model

[0073] The hydrogen stored in the hydrogen storage device at the current time is related to the hydrogen stored in the device at the previous time and the amount of hydrogen stored / discharged. Therefore, the hydrogen stored in the hydrogen storage device can be expressed as:

[0074] (18);

[0075] wherein: and are the amount of hydrogen stored in the hydrogen storage device at time t and time t-1, respectively; and are the amount of hydrogen stored in and discharged from the hydrogen storage device at time t-1.

[0076] The following assumptions and constraints are made for the hydrogen storage device: first, at the start of system operation, the hydrogen storage device already stores a certain amount of hydrogen to ensure that the hydrogen storage device has adjustment capabilities in both storage and discharge directions at the start time; second, hydrogen storage and discharge cannot occur simultaneously and cannot be greater than the maximum storage and discharge power of the hydrogen storage device; and finally, the stored hydrogen should be positive and cannot exceed the design capacity of the hydrogen storage device. Therefore, for the hydrogen storage device:

[0077] (19);

[0078] (20);

[0079] (21);

[0080] wherein: is a 0-1 variable to control the hydrogen storage device at time t, and the variable value is 1, which means that hydrogen is stored in the hydrogen storage device, and 0 is the opposite; and respectively, the maximum storage and removal of hydrogen; is the design capacity of the hydrogen storage device.

[0081] 5. Carbon storage device model

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

[0083] (22);

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

[0085] The carbon dioxide storage constraint can be represented as:

[0086] (23);

[0087] In the formula: is a 0-1 variable to control the carbon dioxide storage device at time t, and the variable value is 1, which means that carbon dioxide is stored in the carbon dioxide storage device, and 0 is the opposite; and respectively, the maximum storage and removal of carbon dioxide; is the design capacity of the carbon storage device.

[0088] 6. Energy balance constraint

[0089] Considering the input and output balance of the system throughout the process, the relevant constraint equation is constructed. In the entire system, the devices that produce electric energy include photovoltaic devices and thermal power units, and the devices that consume electric energy include CCS devices, electrolytic cells, methanation devices, while considering meeting certain electrical load demand. Therefore, the electric energy balance of the system is represented by the following formula:

[0090] (24);

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

[0092] 7. Hydrogen balance constraint

[0093] In the whole system, the devices that can produce or output H2 are electrolytic cell and hydrogen storage device, and the devices that need to consume or input H2 are methanation device and hydrogen storage device. Therefore, the hydrogen balance of the system is expressed as follows:

[0094] (25);

[0095] In the formula: is the hydrogen consumption amount of the methanation device in the P2G system at the time t, and is the hydrogen demand amount in the methanation reaction process.

[0096] 8, Carbon balance constraint

[0097] In the whole system, the devices that can produce or output CO2 are CCS device and carbon storage device, and the devices that need to consume or input CO2 include methanation device and carbon storage device. The carbon balance of the system can be expressed as follows:

[0098] (26).

[0099] Based on the models of the devices in the above system and the related constraints of the system, a multi-time scale optimization scheduling model of the carbon management system of the green certificate-carbon trading power transmission and distribution project is constructed.

[0100] The power system, the carbon system and the green certificate system constitute the three main pillars of the low-carbon construction of the power transmission and distribution project, and jointly promote the optimization of the project structure and the realization of the carbon emission reduction target. As shown in Fig. 2 , the three systems form a mutual linkage operation mechanism: the power system takes real-time power balance as the core, and forms the electricity price signal through the market mechanism; the carbon system is based on the principle of “total control and technical optimization”, and realizes 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 technology.

[0101] The three systems have significant differences in the time dimension, as shown in Fig. 3 , this mismatch of time scales constitutes the main challenge of multi-stage emission reduction collaborative optimization. The power system mainly monitors in the short term, and the monitoring period is usually several hours to several days, which is divided into real-time monitoring (hour level), daily statistics (day level) and stage summary (month level, quarter level and year level); the carbon system usually evaluates the emission reduction for one construction stage, but the evaluation can be carried out monthly, allowing the power transmission and distribution project construction enterprise to flexibly arrange the implementation time of the technology; the green certificate system mainly evaluates based on the project cycle, and implements the whole cycle evaluation system. This inconsistency in time granularity leads to the difficulty of cross-stage emission reduction collaborative optimization. When the power transmission and distribution project construction enterprise makes intraday emission reduction decisions in the short-term construction process, it needs to consider the monthly emission reduction technology constraints and the annual effect monitoring requirements, forming a cross-time scale decision.

[0102] ​The specific challenges brought by the time scale difference mainly manifest in three aspects: first, the problem of asymmetric decision information, the short-term emission reduction decision needs to be based on long-term prediction of technical cost and effect evaluation, while these predictions have great uncertainty; second, the problem of inconsistent evaluation frequency, the power system is frequently updated while the evaluation of carbon system and the effect monitoring of green certificate system are relatively lagging, leading to information transmission delay; third, the problem of mismatching of evaluation period, the implementation of emission reduction measures of power system usually takes effect immediately, while the evaluation of emission reduction technology of carbon system and the effect monitoring of green certificate system are unified at the end of the stage, resulting in cross-period decision-making problem. These time scale differences make it difficult for traditional single time point modeling method to accurately describe the dynamic interaction process of multi-system, and it is necessary to build a collaborative optimization framework with time coupling characteristics.

[0103] In view of the above problems, the embodiment of the application proposes an innovative multi-time scale optimization scheduling model, which is an annual-month dual-level planning model. By establishing the nested optimization structure of the whole cycle of the project (annual planning layer) - construction stage (monthly planning layer), the problem of mismatching of time granularity of different systems is effectively solved. The core innovation point of the model is to build a top-down and bottom-up combined optimization framework, realizing the coordination and consistency of long-term planning and short-term scheduling.

[0104] The model specifically includes the following parts.

[0105] 1. Long time scale (annual) emission reduction strategy

[0106] Firstly, based on the monthly expected construction amount of the power transmission and distribution engineering construction enterprise, the initial emission reduction target allocation amount expected to be obtained in the emission reduction technology evaluation is calculated by multiplying it with the carbon emission intensity benchmark value of the corresponding engineering type, and then the monthly expected emission reduction gap is determined. On this basis, the power transmission and distribution engineering construction enterprise introduces the optimization strategy of "cost optimization", and adjusts the implementation behavior according to the relative height of the emission reduction technology cost prediction value.

[0107] The long time scale emission reduction strategy includes the following parts.

[0108] 1) Carbon quota monthly transaction amount constraint

[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 constraint

[0119] Equation (31) ensures that the annual carbon emission does not exceed the initial quota plus the net purchase amount, while the carbon quota holding amount at the end of the month meets the dynamic balance relationship, as shown in Equation (32).

[0120] (31);

[0121] (32);

[0122] In the equation: is the total annual estimated carbon emission (tons); is the carbon quota purchase amount in the month; is the carbon quota sale amount in the month; is the carbon quota allocation amount at the beginning of the year; is the monthly carbon quota allocation amount (decomposed by year); is the carbon quota holding amount at the end of the month; is the month index.

[0123] 4) Green certificate quota and transaction constraint

[0124] Used to ensure that the annual green certificate demand does not exceed the self-produced green certificate plus the net purchase amount, while the monthly green certificate transaction amount does not exceed the system limit.

[0125] (33);

[0126] (34);

[0127] In the equation: is the annual estimated green certificate production (units); is the annual green certificate demand (units); is the monthly green certificate transaction limit (units); is the green certificate purchase amount in the month; is the green certificate sale amount in the month.

[0128] 5) Energy demand balance constraint

[0129] Used to ensure that the monthly power demand is met.

[0130] (35);

[0131] In the equation: is the power transaction amount in the month; the power demand of the th month.

[0132] 6) Objective function of annual planning

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

[0134] (36);

[0135] wherein: is the total cost objective function of the annual planning layer; is the benchmark electricity price (yuan / MWh); is the carbon price of the th month (yuan / ton); is the green certificate price of the th month (yuan / each).

[0136] 2. Annual-to-monthly time scale trading strategy

[0137] The annual-to-monthly time scale trading strategy is the monthly planning layer, which takes the decision results of the annual planning as the boundary conditions, and optimizes the resource allocation and scheduling in each month. In order to reduce the computational complexity, the typical day representation method is adopted, and representative dates (such as weekdays, weekends and extreme weather days) are selected for optimization, and the results are extended to the whole month.

[0138] The annual-to-monthly time scale trading strategy includes the following parts.

[0139] 1) Quotation constraints of power transmission and distribution engineering construction enterprises

[0140] This embodiment assumes that the power transmission and distribution engineering construction enterprises adopt segmented quotations, is the th segment quotation of the power transmission and distribution engineering construction enterprise in the time period; is the winning electricity quantity of the power transmission and distribution engineering construction enterprise, and each segment quotation and winning electricity quantity need to meet its upper and lower limit constraints, and the quotation needs to follow the principle from low to high, i.e. the +1th segment quotation is greater than or equal to the th segment quotation.

[0141] (37);

[0142] wherein: , are the lower limit and upper limit of the th segment quotation of the power transmission and distribution engineering construction enterprise, is the index of the power transmission and distribution engineering construction enterprise,​ A collection of power transmission and distribution construction enterprises;

[0143] The lower limit of the offer is jointly determined by the generation cost and the carbon emission cost:

[0144] (38);

[0145] wherein: is the generation cost of the power transmission and distribution construction enterprise in a certain period; represents the average level of the annual carbon quota price prediction, which plays a key role in determining the lower limit of the offer considering the carbon cost. is the carbon emission intensity of the power transmission and distribution construction enterprise ; is the benchmark carbon emission intensity. This strategy considers the expenditure of purchasing carbon quotas in the carbon system due to generation activities as a component of production cost, and includes it in the offer strategy together with the conventional generation cost.

[0146] The annual-to-monthly time scale trading strategy must be developed within the medium and long-term carbon quota trading framework, taking the medium and long-term decisions of the power transmission and distribution construction enterprise as boundary conditions for the spot phase optimization, to ensure that its trading behavior in the secondary carbon system (i.e., carbon at the monthly level) conforms to the established medium and long-term plan. Specifically, the cumulative purchase amount of monthly carbon quotas should not be less than the monthly purchase amount target determined by the medium and long-term carbon quota trading strategy, to ensure that the construction party obtains sufficient carbon quotas to make up for the expected shortfall. At the same time, it is ensured that the carbon quota price is approximately 0 when the supply is much greater than the demand, and the carbon quota price is always non-negative.

[0147] (39);

[0148] (40);

[0149] wherein: is the carbon quota purchase amount of the power transmission and distribution construction enterprise on the th day; is the carbon quota sale amount on the th day; is the date index; is the carbon quota price on the th day; is the lower limit of the carbon quota price on the th day.

[0150] When the sum of the monthly carbon quota sale amounts is not greater than the monthly carbon quota sale amount determined by the medium and long-term carbon quota trading strategy, it is ensured that the carbon quota sale amount of the power transmission and distribution construction enterprise is less than its carbon quota surplus amount.

[0151] 2) Carbon quota buying and selling constraints

[0152] (41);

[0153] In the formula: is the monthly carbon quota transaction buying upper limit coefficient, and the daily buying amount does not exceed a certain proportion of the monthly buying amount. It prevents the power transmission and distribution project construction enterprise from only conducting carbon quota transaction once a month, and effectively disperses the price risk through multiple transactions. Similar to the buying constraint setting idea, the daily selling amount does not exceed a certain proportion of the monthly selling amount. is the minimum coefficient of the th day carbon quota buying; is the maximum coefficient of the th day carbon quota buying; is the minimum coefficient of the th day carbon quota selling; is the maximum coefficient of the th day carbon quota selling.

[0154] 3) Calculate the actual renewable energy power generation, green certificate quantity, green certificate surplus or gap, as follows:

[0155] (42);

[0156] (43);

[0157] (44);

[0158] In the formula: is the monthly actual renewable energy power generation; is the representative weight of the typical day ; is the typical day type; is the photovoltaic power generation of the power transmission and distribution project construction enterprise in the typical day ; is the actual number of days of the typical day type in the month; is the monthly actual green certificate production; is the power generation corresponding to a unit of green certificate; is the green certificate surplus / gap amount; is the monthly actual green certificate demand.

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

[0160] Ensure that the green certificate demand is met, and that the monthly carbon emissions do not exceed the quota plus the net purchase amount.​

[0161] (45);

[0162] (46);

[0163] wherein: is the monthly actual green certificate production (pcs); is the monthly green certificate purchase amount; is the monthly green certificate sale amount; is the monthly green certificate demand (pcs); is the monthly carbon quota (tons), from the allocation result of annual planning; is the monthly carbon quota purchase amount; is the monthly carbon quota sale amount; is the monthly actual carbon emission (tons).

[0164] 5) Objective function of monthly planning layer

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

[0166] (47);

[0167] wherein: is the minimized total cost objective function value of monthly planning layer; is the weight of typical day ; is the actual number of typical day types in the month; is the hourly electricity price (yuan / MWh); is the carbon price in the month (yuan / ton); is the green certificate price in the month (yuan / pcs); is the power trading amount of power transmission and distribution engineering construction enterprise in typical day at hour; is the smoothing term; is the smoothing coefficient.

[0168] The smoothing term is introduced to improve numerical stability and reduce the drastic fluctuation of adjacent period decisions:

[0169] (48);

[0170] wherein: is the thermal power unit output of power transmission and distribution engineering construction enterprise in typical day at hour.

[0171] This example will establish a multi-time scale coupling optimization model in stages for mixed integer linear programming model, based on MATLAB + YALMIP modeling, calling commercial solver Gurobi to solve the proposed model.

[0172] 3, the whole year of power transmission and distribution project construction enterprise two-stage decision rolling correction, including the following parts.

[0173] The interface mechanism of bi-level programming: the effective interface mechanism between annual planning and monthly optimization is designed to ensure the consistency of two time scale decisions, including:

[0174] 1) monthly carbon quota allocation

[0175] Annual planning allocates the total amount of carbon quota for each month:

[0176] (49);

[0177] In the formula: is the total amount of monthly carbon quota; is the monthly carbon quota allocation (annual decomposition).

[0178] 2) monthly trading volume constraint

[0179] Monthly trading volume needs to be consistent with annual planning:

[0180] (50);

[0181] In the formula: is the carbon quota purchase target of the power transmission and distribution project construction enterprise in the first month of the year determined by the annual planning; is the carbon quota sale target of the power transmission and distribution project construction enterprise in the first month of the year determined by the annual planning.

[0182] 3) rolling update mechanism

[0183] After each month, update the annual planning of the subsequent month according to the actual trading

[0184] (51);

[0185] In the formula: is the updated annual carbon quota purchase plan of the power transmission and distribution project construction enterprise from the first +1 to 12 months; is the update function, which is used to correct the carbon quota trading plan of the subsequent month according to the actual trading results and the latest prediction; for power transmission and distribution engineering construction enterprises In the first +1 to 12 months of the original annual carbon quota purchase plan; for power transmission and distribution engineering construction enterprises In the first the actual carbon quota purchase amount in the month; for power transmission and distribution engineering construction enterprises the actual carbon emission amount; for power transmission and distribution engineering construction enterprises In the first +1 to 12 months of the updated annual carbon quota sale plan; for power transmission and distribution engineering construction enterprises In the first +1 to 12 months of the original annual carbon quota sale plan.

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

[0187] Due to the deviation between the actual engineering construction situation and the predicted value, the power transmission and distribution engineering construction enterprise needs to make annual and monthly two-stage decisions again according to the latest prediction, the results of the medium and long-term and spot system that have occurred. The rolling update idea of the power transmission and distribution engineering construction enterprise throughout the year is as follows:

[0188] 1) Initial decision stage: In the initial decision stage, the power transmission and distribution engineering construction enterprise, based on the electricity price, carbon price and green certificate price prediction at the beginning of the year, formulates its own medium and long-term electricity system and carbon system trading strategy, and forms a comprehensive optimization scheme for the whole year, which is the pre-planning made by the enterprise to achieve its own emission reduction target and cost control;

[0189] 2) Monthly execution stage: In the monthly execution stage, the power transmission and distribution engineering construction enterprise takes the medium and long-term electricity system and carbon system trading strategy formulated in the pre-stage as a constraint, and makes 30 daily decisions within each month to realize the joint clearing of the electricity system, carbon system and green certificate system, which directly relates to the monthly operating cost and resource allocation of the enterprise;

[0190] 3) Rolling correction stage: At the end of the month, the medium and long-term electricity system and carbon system trading strategy for the following month are updated in combination with the actual transaction results and the latest prediction, including the electricity quota plan, the carbon quota plan and the green certificate trading plan;

[0191] 4) Circulation optimization stage: In the circulation optimization stage, the power transmission and distribution engineering construction enterprise makes spot decisions for the next month based on the updated medium and long-term electricity system and carbon system trading strategy, and iterates until the end of the compliance period, realizing dynamic balance of the three systems, which can help the enterprise better balance cost and emission reduction target, ensure that each target is achieved within the compliance period, and improve the overall operating efficiency of the enterprise

[0192] Through the above multi-time scale coupling optimization model, the problem of time granularity mismatch of the electricity-carbon-green certificate system is effectively solved, the coordination from annual strategic planning to monthly resource allocation is realized, and the carbon emission reduction effect and engineering economy of system operation are improved.

[0193] Finally, it should be noted that: the above embodiments are only used to illustrate the technical solutions of the present application, but not to limit them; although the present application has been described in detail with reference to the foregoing embodiments, those skilled in the art should understand that: it can still modify the technical solutions recorded in the foregoing embodiments, or make equivalent replacement for part or all of the technical features; and these modifications or replacements do not make the essence of the corresponding technical solutions deviate from the scope of the technical solutions of the embodiments of the present 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 of 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. 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. 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. 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. Hourly output of thermal power units; Step 2: Based on the multi-time-scale 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: 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.

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 1, 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.

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 1, 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.

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 1, 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.

Images