Low-carbon dispatching method and system for power grid cross-region interconnection system

Abstract

This invention discloses a low-carbon dispatching method and system for inter-regional power grid interconnection systems, relating to the field of low-carbon power dispatching technology. The method includes: acquiring operating cost data and risk measurement cost data of the inter-regional power grid interconnection system; constructing a low-carbon operation optimization model based on the operating cost data and the risk measurement cost data; determining the objective function and constraints of the low-carbon operation optimization model with the goal of minimizing the total cost of the inter-regional power grid interconnection system; and solving the objective function based on the constraints to obtain the optimal low-carbon dispatching strategy for the inter-regional power grid interconnection system. This invention enables low-carbon dispatching of the power output of each unit in the sending and receiving end systems of the inter-regional power grid interconnection system and the DC tie-line transmission plan, thereby promoting the consumption of renewable energy and reducing wasted power and system carbon emissions.

Description

Technical Field

[0001] This invention relates to the field of low-carbon power dispatching technology, and in particular to a low-carbon dispatching method and system for inter-regional power grid interconnection systems. Background Technology

[0002] In order to uphold the development concept of a low-carbon and green energy system, we are currently actively promoting energy transformation and upgrading, and driving the large-scale grid connection of renewable energy. However, in the "Three Norths" region (Northeast, North, and Northwest China), although renewable energy is abundant, local load demand is limited, making it difficult to absorb renewable energy locally, and highlighting the problems of wind and solar curtailment. Summary of the Invention

[0003] The purpose of this invention is to provide a low-carbon dispatching method and system for inter-regional grid interconnection systems, so as to promote the consumption of renewable energy and reduce the amount of abandoned electricity and system carbon emissions.

[0004] To achieve the above objectives, the present invention provides the following solution:

[0005] A low-carbon dispatching method for a power grid inter-regional interconnection system, the method comprising:

[0006] Obtain operating cost data and risk measurement cost data for the inter-regional power grid interconnection system; the operating cost data includes: operating costs of thermal power units at the sending and receiving ends, operating costs of solar thermal power plants, operation and maintenance costs of wind power plants, operating costs of electric heating devices, carbon trading costs of the inter-regional interconnection system, and green certificate trading costs of the inter-regional interconnection system; the risk measurement cost data includes: confidence level, loss function, and loss threshold;

[0007] A low-carbon operation optimization model is constructed based on the operating cost data and the risk measurement cost data.

[0008] With the goal of minimizing the total cost of the inter-regional power grid interconnection system, the objective function and constraints of the low-carbon operation optimization model are determined.

[0009] The objective function is solved according to the constraints to obtain the optimal low-carbon scheduling strategy for the inter-regional grid interconnection system. The optimal low-carbon scheduling strategy is used to schedule the power output of each unit in the sending and receiving end system of the inter-regional grid interconnection system and the DC tie line transmission plan.

[0010] Optionally, the objective function is:

[0011] minF=F XY +C WCVaR ;

[0012] Where F is the total cost of the cross-regional interconnection system; F XY For operating costs; C WCVaRThe cost is used to measure risk; minF represents the goal of minimizing the total cost of the cross-regional interconnection system.

[0013] Optionally, the operating cost is:

[0014]

[0015] Among them, C G Operating costs of thermal power units at both the sending and receiving ends; C CSP C is the operating cost of a solar thermal power plant; W C is the operation and maintenance cost of wind farms; EH For the operating cost of electric heating devices; For the carbon trading costs of inter-regional interconnection systems; C GC Costs associated with cross-regional interconnection system green certificate transactions.

[0016] Optionally, the cost of the risk measurement is:

[0017]

[0018] Where β is the confidence level; Let be the loss function, representing the loss value of the receiving system under scenario λ; κ is the loss threshold; ρ (y) for The probability density function of the prediction error of wind power output in the sending-end system; W is the set of probability distributions of the prediction error of wind power output in the sending-end system; R is the set of real numbers; N G denoted as the number of scenes; e is a unit vector.

[0019] Optionally, the constraints include: power balance constraints, solar thermal power plant operation constraints, and DC tie line operation constraints.

[0020] Optionally, the power balance constraint is:

[0021] Where, N S P represents the total number of thermal power units at the sending end. G,i,t N represents the output of the i-th sending-end thermal power unit during time period t; W P represents the total number of wind farms. W,i,t N represents the power output of the i-th wind farm during time period t; CSP P represents the total number of solar thermal power plants. CSP,i,t Let be the output of the i-th solar thermal power plant during time period t; P represents the load of the sending-end system during time period t; dc,t Let t be the power transmitted through the tie line during time period t.

[0022] Optionally, the operating constraints of the solar thermal power plant include: power generation system output constraints and power generation system ramping constraints;

[0023] The output constraint of the power generation system is:

[0024] in, This is the upper limit of the output of a solar thermal power plant; This represents the lower limit of the output of a solar thermal power plant; P CSP,i,t u represents the output of the i-th solar thermal power plant during time period t; i,t This represents the operating status of the i-th thermal power unit during time period t.

[0025] The ramping constraint of the power generation system is:

[0026] in, This represents the maximum upward ramp rate for the power generation stage of a solar thermal power plant. P represents the maximum downward ramp rate of the power generation stage in a solar thermal power plant. CSP,i,t-1 Let be the output of the i-th solar thermal power plant during time period t-1.

[0027] Optionally, the DC tie line operation constraints include: upper and lower limits of DC tie line output power, power transmission direction adjustment constraints, number of DC tie line power adjustment constraints, DC tie line output adjustment rate constraints, power transmission constraints, and peak shaving margin constraints of the receiving-end system.

[0028] The upper and lower limits of the output power of the DC tie line are constrained as follows:

[0029] in, P represents the upper limit of the DC tie line transmission power; dc This represents the lower limit of the DC tie-line transmission power; P dc,t The power transmitted through the tie line during time period t;

[0030] The power transmission direction adjustment constraint is:

[0031] in, The DC tie line transmission power is adjusted upward during time period t. The DC tie line transmission power is adjusted downward during time period t; This refers to the upward adjustment of DC tie-line transmission power during time period t+1. This refers to the downward adjustment of DC tie-line transmission power during time period t+1. xt The state of the DC tie line transmission power during time period t;

[0032] The constraint on the number of DC tie line power adjustment cycles is:

[0033] Where T is the total scheduling period; S is the maximum number of adjustments allowed for the DC converter within the scheduling cycle;

[0034] The output adjustment rate constraint of the DC tie line is:

[0035] in, δ dc This is the minimum transmission adjustment for the DC tie line; P represents the maximum transmission regulation of the DC tie line. dc,t-1 The power transmitted through the tie line during time period t-1;

[0036] The power transmission constraint is:

[0037] Where, ρ dc The permissible deviation rate for exchanging electrical energy on a DC tie line; Q dc Planned day-ahead power transmission for DC tie lines;

[0038] The peak-shaving margin constraint of the receiving-end system is:

[0039] in, This represents the peak margin of the receiving system during time period t. This represents the downscaling margin of the receiving system during time period t.

[0040] Optionally, the step of solving the objective function based on the constraints to obtain the optimal low-carbon dispatch strategy for the inter-regional grid interconnection system specifically includes:

[0041] Linearize the nonlinear terms in the objective function to obtain a mixed-integer linear programming expression;

[0042] Solving the mixed-integer linear programming expression based on the constraints yields the optimal low-carbon scheduling strategy for the inter-regional power grid interconnection system.

[0043] This invention also provides a low-carbon dispatching system for inter-regional power grid interconnection, the system corresponding to the above-described method, the system comprising:

[0044] The data acquisition unit is used to acquire operating cost data and risk measurement cost data of the inter-regional power grid interconnection system. The operating cost data includes: operating costs of thermal power units at the sending and receiving ends, operating costs of solar thermal power plants, operation and maintenance costs of wind power plants, operating costs of electric heating devices, carbon trading costs of the inter-regional interconnection system, and green certificate trading costs of the inter-regional interconnection system. The risk measurement cost data includes: confidence level, loss function, and loss threshold.

[0045] The model building unit is used to build a low-carbon operation optimization model based on the operating cost data and the risk measurement cost data.

[0046] The objective function and constraint determination unit is used to determine the objective function and constraints of the low-carbon operation optimization model with the goal of minimizing the total cost of the inter-regional grid interconnection system.

[0047] The solution unit is used to solve the objective function according to the constraints to obtain the optimal low-carbon scheduling strategy for the inter-regional grid interconnection system; the optimal low-carbon scheduling strategy is used to schedule the power output of each unit in the sending and receiving end system of the inter-regional grid interconnection system and the DC tie line transmission plan.

[0048] According to specific embodiments provided by the present invention, the present invention discloses the following technical effects:

[0049] This invention provides a low-carbon dispatching method and system for inter-regional power grid interconnection systems. Based on the inter-regional interconnection system under the green certificate-carbon joint trading mechanism, it introduces risk measurement cost, constructs a low-carbon operation optimization model, and determines the objective function and constraints of the low-carbon operation optimization model with the goal of minimizing the total cost of the inter-regional power grid interconnection system. Finally, it solves for the optimal low-carbon dispatching strategy of the inter-regional power grid interconnection system, which can perform low-carbon dispatching of the output of each unit in the sending and receiving end system of the inter-regional power grid interconnection system and the DC tie line transmission plan, thereby promoting the consumption of renewable energy and reducing the amount of abandoned electricity and the system's carbon emissions. Attached Figure Description

[0050] To more clearly illustrate the technical solutions in the embodiments of the present invention or the prior art, the drawings used in the embodiments will be briefly introduced below. Obviously, the drawings described below are only some embodiments of the present invention. For those skilled in the art, other drawings can be obtained based on these drawings without creative effort.

[0051] Figure 1 A flowchart of the low-carbon dispatching method for inter-regional power grid interconnection system provided by the present invention;

[0052] Figure 2 Schematic diagram of a combined wind-solar-thermal-thermal power generation system at the sending end;

[0053] Figure 3 A block diagram of a low-carbon dispatching system for inter-regional power grid interconnection provided by this invention;

[0054] Figure 4 A schematic diagram showing the predicted values ​​of wind power output, solar intensity, and load demand;

[0055] Figure 5 This is a schematic diagram showing the unit output and green certificate purchase volume under Example 1;

[0056] Figure 6 This is a schematic diagram showing the unit output and green certificate purchase volume under Example 2;

[0057] Figure 7 This is a diagram showing the comparison of system carbon emissions under Example 1 and Example 2;

[0058] Figure 8 A comparative diagram of the tie-line transmission plans for examples 2 and 3;

[0059] Figure 9 This is a schematic diagram comparing the peak adjustment margin of the receiving-end system in examples 2 and 3.

[0060] Figure 10 This is a schematic diagram comparing the peak-shaving margin of the receiving-end system in Examples 2 and 3.

[0061] Figure 11 The scheduling comparison results are shown at confidence levels of 0.93 and 0.97.

[0062] Symbol explanation: Data acquisition unit—301, model construction unit—302, objective function and constraint determination unit—303, solution unit—304. Detailed Implementation

[0063] The technical solutions of the embodiments of the present invention will be clearly and completely described below with reference to the accompanying drawings. Obviously, the described embodiments are only some embodiments of the present invention, and not all embodiments. Based on the embodiments of the present invention, all other embodiments obtained by those skilled in the art without creative effort are within the scope of protection of the present invention.

[0064] The purpose of this invention is to provide a low-carbon dispatching method and system for inter-regional grid interconnection systems, so as to promote the consumption of renewable energy and reduce the amount of abandoned electricity and system carbon emissions.

[0065] To make the above-mentioned objects, features and advantages of the present invention more apparent and understandable, the present invention will be further described in detail below with reference to the accompanying drawings and specific embodiments.

[0066] Figure 1 A flowchart illustrating the low-carbon dispatching method for inter-regional power grid interconnection systems provided by this invention. Figure 1 As shown, the method includes:

[0067] Step S101: Obtain the operating cost data and risk measurement cost data of the inter-regional grid interconnection system; the operating cost data includes: the operating cost of thermal power units at the sending and receiving ends, the operating cost of solar thermal power plants, the operation and maintenance cost of wind power plants, the operating cost of electric heating devices, the carbon trading cost of the inter-regional interconnection system, and the green certificate trading cost of the inter-regional interconnection system; the risk measurement cost data includes: confidence level, loss function, and loss threshold.

[0068] Step S102: Construct a low-carbon operation optimization model based on the operating cost data and the risk measurement cost data.

[0069] Step S103: With the goal of minimizing the total cost of the inter-regional grid interconnection system, determine the objective function and constraints of the low-carbon operation optimization model.

[0070] Step S104: Solve the objective function according to the constraints to obtain the optimal low-carbon scheduling strategy for the inter-regional grid interconnection system; the optimal low-carbon scheduling strategy is used to schedule the power output of each unit in the sending and receiving end system of the inter-regional grid interconnection system and the DC tie line transmission plan.

[0071] The steps described above will be discussed in detail below:

[0072] 1. In step 101, the modeling of the inter-regional power grid interconnection system includes:

[0073] 1.1 Combined wind-solar-thermal power generation at the sending end

[0074] In environments rich in renewable energy sources at the sending end, constructing a combined wind-solar-thermal-thermal power generation system, such as... Figure 2 As shown. In addition to supplying power to the sending-end load, the surplus electrical energy of the sending-end power generation system is transmitted to the receiving-end system via a DC tie line.

[0075] The sending-end system is centered around a solar thermal power plant equipped with an electric heating device. The solar thermal power plant consists of three parts: a solar collector, a power generation system, and a thermal storage system. At the solar collector, solar energy is converted into heat energy, which is then transferred by heating a heat-conducting medium. This transferred heat energy is then converted into electrical energy at the power generation stage, completing the solar-thermal-electric conversion. The thermal storage system allows for bidirectional energy flow with the heat-conducting medium, and the stored heat energy can be readily used by the power generation system. The thermal power obtained through the solar collector is:

[0076] Q C,t =η SF S SF D t (1)

[0077] In the formula: Q C,t η represents the thermal power obtained in the heat collection process during time period t; SF For light-to-thermal conversion efficiency; S SF D is the area of ​​the light field; t t represents the direct solar radiation index during time period t.

[0078] The thermal power obtained through the collection stage can be directly used for power generation, or it can be stored in the thermal storage stage and supplied to the power generation stage during peak load periods. Therefore, the power generation capacity of a CSP (Concentrated Solar Power Plant) is:

[0079]

[0080] In the formula: t represents the CSP power generation during time period t; μ represents the thermoelectric conversion efficiency. The heat collected during time period t is supplied to the power generation stage. The heat stored in the thermal energy storage stage is supplied to the power generation stage during time period t; η SG This refers to the heat conversion efficiency between the thermal storage and power generation stages.

[0081] The electric heating device installed at the thermal storage stage of a concentrated solar power (CSP) plant can convert a portion of wind power into heat energy and store it in the thermal storage stage. Based on this characteristic, during periods of high wind power generation, the electric heating device can convert surplus wind energy into heat energy and store it in the thermal storage stage; while during peak load periods, the heat energy stored in the thermal storage stage is converted into electricity through the power generation stage, increasing the power generation of the CSP plant and simultaneously promoting wind power absorption and achieving energy time-shifting. This energy time-shifting characteristic creates a complementary effect between wind and CSP, while the addition of thermal power units further enhances system operational stability and ensures sufficient power supply.

[0082] 1.2 Receiving-end system and its peak-shaving margin modeling

[0083] The receiving-end system consists of thermal power units and DC feed-in links, which work together to meet the load demand of the receiving-end system. Due to the low ramp rate of thermal power units and the fixed DC feed-in power within a single time period, the peak-shaving flexibility of the receiving-end system is insufficient. Therefore, it is necessary to reserve a certain peak-shaving margin for the receiving-end system.

[0084] The maximum peak-shaving capacity of the receiving-end system is determined by the daily maximum load of the receiving-end system and the spinning reserve rate of the receiving-end system, as shown in equation (3).

[0085]

[0086] In the formula: This represents the maximum peak-shaving capacity of the receiving-end system. ε represents the maximum daily load of the receiving-end system; ε represents the spinning reserve rate of the receiving-end system.

[0087] The DC tie line and the receiving-end thermal power units work together to fill the peak-shaving capacity of the receiving-end system. Therefore, the maximum output power and minimum technical output of the receiving-end thermal power units can be determined by the maximum peak-shaving capacity of the receiving-end system.

[0088]

[0089]

[0090] In the formula: This represents the maximum output power of the receiving-end thermal power unit. Minimum technical output for receiving-end thermal power units; N represents the transmission power of the tie line at the time of maximum daily load. R The number of receiving-end thermal power units; u i This refers to the start-stop state (i.e., operating state) of thermal power unit i; υ represents the maximum output power of thermal power unit i (i.e., the i-th thermal power unit); υ represents the peak shaving depth of the thermal power unit.

[0091] Based on the maximum and minimum output of the receiving-end thermal power units, the positive and negative peak-shaving capacities of the receiving-end system are obtained:

[0092]

[0093]

[0094] In the formula: and These represent the positive and negative peak-shaving capacities of the receiving-end system during time period t; P dc,t Let t be the power transmitted through the tie line during time period t.

[0095] To ensure the normal and stable operation of the receiving-end system, a certain peak-shaving margin needs to be reserved to ensure that the peak-shaving capacity of the receiving-end system can always meet its peak-shaving needs. The upper and lower peak-shaving margins of the receiving-end system are:

[0096]

[0097]

[0098] In the formula: and These represent the upper and lower peak shaving margins of the receiving system during time period t; P l,t Let t be the load of the receiving-end system during time period t; σ is the minimum margin rate of the receiving-end system.

[0099] 1.3 DC tie line operation mode

[0100] DC tie lines flexibly control DC transmission power by manipulating converter trigger pulses and changing the tap changer positions within the station. To fully utilize the flexible control characteristics of DC tie lines, this invention treats the DC tie line transmission power as an optimizable variable, comprehensively considering the operating conditions of the sending and receiving units for integrated scheduling, and ensuring the feasibility of the transmission plan through operational constraints. Building upon previous research, this invention considers the impact of peak-shaving margin constraints of the receiving system on the operation of DC tie lines, further optimizing the DC tie line transmission power.

[0101] 2. Furthermore, this invention shifts the purchaser of green certificates from electricity sales companies to thermal power units. Renewable energy units, as the main producers of green certificates, benefit by selling green certificates to thermal power units. In order to obtain the carbon emission reduction benefits from purchasing green certificates, thermal power units will prioritize the dispatch of this renewable energy source to reduce system carbon emissions and increase the carbon quota of thermal power units, thereby increasing the demand for green certificate purchases.

[0102] The green certificate-carbon joint trading model constructed in this invention, with thermal power units as green certificate purchasers, includes:

[0103] 2.1 Green Certificate Trading Model for Cross-Regional Interconnection System

[0104] Under the joint trading mechanism, the sending and receiving thermal power units, as green certificate purchasers, need to purchase a corresponding number of green certificates from the sending renewable energy units to reduce carbon emissions. The renewable energy units, in turn, obtain additional revenue through green certificate trading as a reward for their contribution to environmental protection.

[0105] The cost for thermal power units at both the sending and receiving ends to purchase green certificates from renewable energy units at the sending end is:

[0106]

[0107] In the formula: Cost of purchasing green certificates for thermal power units at both the sending and receiving ends; N S The total number of thermal power units at the sending end is T; the total dispatching time period is T. Purchase green certificates for the i-th sending-end thermal power unit within time period t; Purchase green certificates for the j-th receiving-end thermal power unit within time period t; λ GC This refers to the price at which green certificates are traded.

[0108] Revenue from the sale of green certificates by renewable energy generating units at the sending end is:

[0109]

[0110] Where: N W and N CSP These represent the total number of wind power plants and solar thermal power plants, respectively; P W,i,t For the i-th wind farm to generate power during time period t; P CSP,j,t θ represents the output of the j-th solar thermal power plant during time period t; θ represents the green certificate quota ratio of the renewable energy power plant.

[0111] 2.2 Carbon Trading Model for Inter-regional Interconnection Systems

[0112] Under the joint trading mechanism, to obtain the carbon emission reduction benefits from green certificates, the sending and receiving end thermal power units will prioritize the dispatch of this renewable energy source as clean energy output from the thermal power units. The purchased green certificates reduce the system's carbon emissions while simultaneously increasing the carbon quota of the thermal power units. Therefore, under the green certificate-carbon joint trading mechanism, the carbon quota of the sending and receiving end systems is:

[0113]

[0114]

[0115] In the formula: and Carbon quotas for the sending and receiving systems, respectively; Q i,t and Q j,t α represents the power generation of thermal power units i and j during time period t; GC χ² is the conversion factor for converting green certificates into carbon allowances. i and χ j These are the carbon allowances for unit electricity consumption of thermal power units i and j, respectively.

[0116] The carbon emissions of the sending and receiving end system are determined by the output of its thermal power units, as shown in equation (14).

[0117]

[0118] In the formula: Carbon emissions of the sending / receiving system; E i (Q i,t ) represents the carbon emissions of thermal power unit i during time period t; c 0,i c 1,i and c 2,i These are the first carbon emission coefficient, the second carbon emission coefficient, and the third carbon emission coefficient for thermal power unit i, respectively.

[0119] This invention employs a tiered carbon trading mechanism to calculate the carbon trading costs of the sending and receiving systems. The tiered carbon trading mechanism divides carbon emissions into multiple tiers; the higher the emission level in a tier, the higher the carbon trading price, and the greater the system's costs. The tiered carbon trading costs are as follows:

[0120]

[0121] In the formula: α represents the tiered carbon trading cost; p is the base price for carbon trading; l is the length of the carbon emission range; α c Price growth rate; E0 represents carbon emissions, and E0 represents carbon allowance.

[0122] In step 102, this invention, based on the green certificate-carbon joint trading mechanism, introduces the worst-case conditional value at risk (WCVaR) theory to assess the risk measurement cost caused by uncertainty when only partial probability information of random variables is known, and establishes a low-carbon operation optimization model for the cross-regional interconnection system. The low-carbon operation optimization model includes two parts: operating costs and risk measurement costs.

[0123] 3. In step 103, the present invention aims to minimize the total cost of the cross-regional interconnection system and determines the objective function and constraints of the low-carbon operation optimization model.

[0124] 3.1 Objective Function

[0125] The objective function of the low-carbon operation optimization model is:

[0126] min F = F XY +C WCVaR (16)

[0127] In the formula: F represents the total cost of the cross-regional interconnection system; F XY For operating costs; C WCVaR The cost is used to measure risk; min F represents the objective of minimizing the total cost of the cross-regional interconnection system.

[0128] 3.1.1 Operating Costs

[0129] The operating costs of a cross-regional interconnection system include system operating costs.

[0130] System operating costs include the operating costs of thermal power units, solar thermal power plants with electric heating devices, and wind power plants, as well as the costs of green certificate-carbon joint trading. The system operating cost expression is as follows:

[0131]

[0132] The above items are as follows:

[0133]

[0134] In the formula: C G To account for the operating costs of thermal power units at both the sending and receiving ends, a i b i and c i These are the first, second, and third coal cost coefficients for the i-th thermal power unit, respectively. Let u be the start-up and shutdown cost of the i-th thermal power unit. i,t This represents the operating state (start-up / shutdown state) of the i-th thermal power unit during time period t, with a value of 1 when the unit is running and 0 when it is stopped; C CSP For the operating costs of a solar thermal power plant, KCSP The unit power generation cost of a solar thermal power plant C represents the start-up and shutdown cost of the i-th solar thermal power plant; W For wind farm operation and maintenance costs, K W C represents the unit operation and maintenance cost of a wind farm; EH For the operating cost of the electric heating device, K EH The unit operating cost of the electric heating device, The electrical power absorbed by the electric boiler from the wind power plant during time period t; To reduce the carbon trading costs of inter-regional interconnection systems, For the carbon trading costs of the sending-end system, For the carbon trading costs of the receiving-end system; C GC Costs associated with cross-regional interconnection system green certificate transactions.

[0135] 3.1.2 Risk Measurement Costs

[0136] WCVaR is defined as follows in discrete scenarios:

[0137]

[0138] In the formula: β is the confidence level; x is the decision variable; κ is the loss threshold; ρ(y) is the probability density function of the random variable; W is a set of probability distributions with known partial information; y λ Let f(x,y) be a random variable in scenario λ. λ ) represents the loss value under scenario λ; R is the set of real numbers; N G denoted as the number of scenes; e is a unit vector.

[0139] The uncertainty of inter-regional interconnection systems mainly stems from the prediction error of wind power output in the sending-end system, i.e., the difference between the actual wind power output and the predicted wind power output. When the prediction error ΔP W,t When the value is less than 0, if the positive rotational reserve capacity of the sending-end system cannot compensate for this power loss, it will result in a loss of load; when the prediction error ΔP W,t When the value is greater than 0, if the negative rotation reserve of the sending-end system is insufficient, it will result in power curtailment losses.

[0140] The system loss value is then:

[0141]

[0142] In the formula: The loss value of the receiving system during time period t; and These represent the positive and negative spinning reserve capacities of the receiving-end system during time period t, respectively. PL Penalty for unit cost under load; c EL The unit cost of power curtailment penalty.

[0143] Therefore, the risk measurement cost of the cross-regional interconnection system is

[0144]

[0145] Where: loss function Let represent the loss value of the receiving-end system under scenario λ, where the decision variable is the predicted value of the wind power output of the sending-end system, and the random variable is the prediction error of the wind power output of the sending-end system. The prediction error of the wind power output of the sending-end system is equal to the difference between the actual value and the predicted value of the wind power output of the sending-end system; ρ(y) is... The probability density function of the prediction error of wind power output in the sending-end system; W is the set of probability distributions of the prediction error of wind power output in the sending-end system.

[0146] 3.2 Constraints

[0147] In this invention, the constraints include: power balance constraints, solar thermal power plant operation constraints, and DC tie line operation constraints.

[0148] 3.2.1 Power Balance Constraints

[0149] The power balance constraints include power balance constraints for the sending-end system and power balance constraints for the receiving-end system.

[0150] Taking the sending-end system as an example, the power balance constraint of the sending-end system is:

[0151]

[0152] Where: N S P represents the total number of thermal power units at the sending end. G,i,t N represents the output of the i-th sending-end thermal power unit during time period t; W P represents the total number of wind farms. W,i,t N represents the power output of the i-th wind farm during time period t; CSP P represents the total number of solar thermal power plants. CSP,i,t Let be the output of the i-th solar thermal power plant during time period t; P represents the load of the sending-end system during time period t; dc,t Let t be the power transmitted through the tie line during time period t.

[0153] 3.2.2 Operational Constraints of Solar Thermal Power Plants

[0154] The operational constraints of the solar thermal power plant include: power generation system output constraints and power generation system ramping constraints.

[0155] ①Power generation system output constraints:

[0156]

[0157] In the formula: and These represent the upper and lower limits of the output of a solar thermal power plant.

[0158] ② Power generation system ramping constraints:

[0159]

[0160] In the formula: and These represent the maximum upward and downward ramp rates for the power generation stage of a solar thermal power plant.

[0161] 3.2.3 DC tie-line operating constraints

[0162] The DC tie line operation constraints include: upper and lower limits of DC tie line output power, power transmission direction adjustment constraints, number of DC tie line power adjustment constraints, DC tie line output adjustment rate constraints, power transmission constraints, and peak shaving margin constraints of the receiving-end system.

[0163] ① DC tie line output power upper and lower limit constraints:

[0164]

[0165] In the formula: and P dc These are the upper and lower limits of the transmission power of the DC tie line.

[0166] ② Transmission power direction adjustment constraints:

[0167] The direction of power transmission through DC tie lines cannot be reversed in adjacent time periods.

[0168]

[0169] In the formula: and These represent the upward and downward adjustment states of the DC tie line transmission power during time period t.

[0170] ③ DC tie-line power adjustment frequency constraint:

[0171]

[0172] In the formula: S is the maximum number of adjustments allowed for the DC converter within the scheduling cycle.

[0173] ④ DC tie line output adjustment rate constraint:

[0174]

[0175] In the formula: δ dc and These are the minimum and maximum transmission adjustment amounts for DC tie lines, respectively.

[0176] ⑤ Power transmission constraints:

[0177]

[0178] In the formula: ρ dc Q represents the permissible deviation rate of energy exchanged on a DC tie line; dc This indicates the planned day-ahead power transmission for the DC tie line.

[0179] ⑥ Peak-shaving margin constraints of the receiving-end system:

[0180]

[0181] Equation (30) ensures the sufficiency of peak shaving in the receiving-end system.

[0182] 4. In step 104, solving the objective function based on the constraints to obtain the optimal low-carbon dispatch strategy for the inter-regional power grid interconnection system specifically includes:

[0183] Step 1041: Linearize the nonlinear terms in the objective function to obtain a mixed-integer linear programming expression.

[0184] Step 1042: Solve the mixed-integer linear programming expression according to the constraints to obtain the optimal low-carbon scheduling strategy for the inter-regional grid interconnection system.

[0185] Specifically, the low-carbon operation optimization model for cross-regional interconnection systems constructed in this invention is a mixed integer nonlinear model, therefore the nonlinear terms need to be linearized.

[0186] Equations (14) and (18) contain squared terms, which are processed using piecewise linearization. The linearization process is as follows:

[0187] Step 1: Based on the required precision, select Q+1 segmentation points [r1, r2, ..., r Q+1 The original function is divided into Q intervals.

[0188] Step 2: Add Q+1 continuous auxiliary variables [w1, w2, ..., w Q+1 ] and Q binary auxiliary variables [z1, z2, ..., z Q ], and satisfy the following formula:

[0189]

[0190] Step 3: Replace the nonlinear function terms with linear expressions as shown in equations (32) and (33), respectively:

[0191]

[0192]

[0193] Equation (21) involves a min-max problem, and the linearization process is as follows:

[0194] First, to ensure the reliability of the probability of each output scenario, the scenario probability is placed into a box-shaped uncertainty set, i.e.

[0195]

[0196] In the formula: The probability of contributing to renewable energy; It is an uncertain set; The reference probability distribution for renewable energy output; ε is the perturbation variable; ε and These are the lower and upper limits of the disturbance variable, respectively.

[0197] To simplify the expression, an auxiliary variable v is introduced. λ =[f(x,y λ )-κe] + Equation (21) can be transformed into:

[0198]

[0199] In the formula: k * (v) is the optimal solution of the linear programming equation (36).

[0200]

[0201] The dual form of equation (36) is:

[0202]

[0203] In the formula: u, ζ, ω are the first dual variable, the second dual variable, and the third dual variable, respectively.

[0204] Therefore, using the Lagrange duality principle, equation (21) in the discrete scenario will be transformed into the following mixed-integer linear programming problem:

[0205]

[0206] After linearization, the optimization of the model is transformed into a mixed-integer linear programming problem, which is solved using Yalmip and the Gurobi solver. The optimal low-carbon scheduling strategy for the inter-regional interconnection system obtained from the solution is used to schedule the output of each unit in the sending and receiving end systems and the DC tie line transmission plan, thereby increasing the demand for green certificate purchases, promoting the consumption of renewable energy, and reducing the amount of abandoned electricity and the system's carbon emissions.

[0207] This invention also provides a low-carbon dispatching system for inter-regional power grid interconnection, which corresponds to the method described above. Figure 3 This is a block diagram of the low-carbon dispatching system for the inter-regional power grid interconnection system provided by the present invention. (See diagram below.) Figure 3 As shown, the system includes:

[0208] The data acquisition unit 301 is used to acquire operating cost data and risk measurement cost data of the inter-regional interconnection system of the power grid; the operating cost data includes: operating cost of thermal power units at the sending and receiving ends, operating cost of solar thermal power plants, operation and maintenance cost of wind power plants, operating cost of electric heating devices, carbon trading cost of inter-regional interconnection system, and green certificate trading cost of inter-regional interconnection system; the risk measurement cost data includes: confidence level, loss function, and loss threshold.

[0209] The model building unit 302 is used to build a low-carbon operation optimization model based on the operating cost data and the risk measurement cost data.

[0210] The objective function and constraint determination unit 303 is used to determine the objective function and constraints of the low-carbon operation optimization model with the goal of minimizing the total cost of the inter-regional grid interconnection system.

[0211] The solution unit 304 is used to solve the objective function according to the constraints to obtain the optimal low-carbon scheduling strategy for the inter-regional grid interconnection system; the optimal low-carbon scheduling strategy is used to schedule the power output of each unit in the sending and receiving end system of the inter-regional grid interconnection system and the DC tie line transmission plan.

[0212] The invention will now be discussed in further detail with specific examples.

[0213] The example in this invention is based on the 2025 power grid plan of a province in Northwest China. The installed capacity of renewable energy in the sending-end system accounts for 51.2%, making it a typical high-proportion renewable energy system. The maximum transmission power of the interconnection line is 4000MW. Forecast values ​​for wind power output, solar irradiance, and load demand are provided below. Figure 4 The receiving-end system's spinning reserve ratio ε is set at 5%, and the minimum margin ratio σ is set at 1%. The green certificate trading price is set at 100 yuan / MW; the carbon trading base price p is set at 100 yuan / t; the carbon emission range length l is 4000t; and the price growth rate α is... c It is 25%; the green certificate to carbon quota transfer coefficient α GC The confidence level is 0.8, the renewable energy quota ratio θ of new energy power plants is 0.2; the confidence level β is 0.9, and the unit costs of curtailment penalty and load shedding penalty are 140 yuan / MW and 700 yuan / MW, respectively.

[0214] To verify the effectiveness and feasibility of the model constructed in this invention, the following three simulation examples are set up for verification:

[0215] Example 1: The operation mode of the tie line does not take into account the impact of the peak-shaving margin of the receiving system; the green certificate-carbon joint trading mechanism is considered, in which the electricity sales company is the green certificate purchaser.

[0216] Example 2: The operation mode of the tie line does not take into account the impact of the peak-shaving margin of the receiving system; the green certificate-carbon joint trading mechanism is considered, with thermal power units as green certificate purchasers.

[0217] Example 3: The operation mode of the tie line is considered in light of the peak-shaving margin of the receiving system; the green certificate-carbon joint trading mechanism is considered with thermal power units as green certificate purchasers.

[0218] Figure 5 The results of unit output and green certificate purchase amount are given for Example 1. Figure 6 The results for unit output and green certificate purchases under Example 2 are presented. Figure 5 It can be seen that since the peak output of wind power and the peak demand of the sending and receiving ends do not occur simultaneously, wind power output rises to a high level during the periods of 1:00-6:00 and 20:00-24:00. Solar thermal power plants reduce output or even shut down to make room for wind power to be fed into the grid. At this time, the electricity demand at the sending and receiving ends is low. The dispatch model, considering the start-up and shutdown costs of thermal power units and solar thermal power plants, will abandon some wind power to ensure its own economic optimization. During the period of 15:00-18:00, wind power output is low. Solar thermal power plants rely on the heat energy stored in the thermal storage system to increase unit output to compensate for the wind power output shortfall. At this time, the evening peak electricity demand arrives, and the system simultaneously increases the output of thermal power units at both the sending and receiving ends to ensure system supply and demand balance.

[0219] like Figure 6 As shown, by considering the green certificate-carbon joint trading mechanism with thermal power units as green certificate purchasers, the green certificates purchased by the sending and receiving thermal power units can serve as part of the carbon allowance, thereby reducing the system's carbon trading costs. This increases the demand for green certificates, with the purchase volume increasing from 37,123 MW before optimization to 52,073 MW. The system's renewable energy consumption level has significantly improved, with the renewable energy consumption rate rising from 90.96% before optimization to 94.88%. Furthermore, during peak renewable energy output periods, the sending and receiving thermal power units promote renewable energy consumption by purchasing green certificates, which also alleviates the curtailment phenomenon. The curtailed power volume decreased from 10,698 MW before optimization to 6,114 MW, a decrease of 42.85%. At the same time, the output of thermal power units also decreased accordingly, down 4.51% year-on-year. Therefore, it can be seen that the green certificate-carbon joint trading mechanism with thermal power units as green certificate purchasers both increases the demand for green certificates and reduces the output of thermal power units.

[0220] Figure 7 This shows the changes in system carbon emissions under Case 1 and Case 2. From... Figure 7It can be seen that during the periods of 1:00-11:00 and 20:00-24:00, compared with Example 1, the carbon emissions in Example 2 are significantly reduced. The carbon emissions decreased from 127,216 tons before optimization to 119,578 tons, a decrease of 6.04%. The main reason is that the system has abundant renewable energy during this period. Through the green certificate-carbon joint trading mechanism with thermal power units as green certificate purchasers, the sending and receiving thermal power units purchased a large number of green certificates, which promoted the output of wind power plants and solar thermal power plants, improved the level of renewable energy consumption, and alleviated the curtailment of electricity during this period. The increase in renewable energy output means a decrease in the output of thermal power units at the sending and receiving ends. Therefore, the carbon emissions are significantly reduced.

[0221] However, during the 12:00-18:00 period, the carbon emissions of the system under Example 2 did not decrease significantly, and in some periods, the carbon emissions were even higher than those in Example 1. This is mainly because the system's renewable energy output is low and the load demand is high during this period, resulting in almost no power curtailment and no significant room for improvement in renewable energy consumption. In addition, under the green certificate-carbon joint trading mechanism with thermal power units as green certificate purchasers, the output of concentrated solar power (CSP) plants increases during periods of high renewable energy generation, leading to a decrease in heat storage during this period. Consequently, CSP plants experience a decrease in output, while thermal power units experience an increase in output. However, based on the above analysis, the green certificate-carbon joint trading mechanism with thermal power units as green certificate purchasers achieves the goal of low carbon emissions and reduces the system's carbon emissions.

[0222] Figure 8 This is a comparison diagram of the DC tie-line transmission plans under Examples 2 and 3. From... Figure 8 As can be seen from the data, compared to Example 2, the DC tie line transmission plan in Example 3 can better match the load change trend of the receiving system due to the consideration of the peak-shaving margin of the receiving system. During the periods of 3:00-5:00, 6:00-10:00, and 11:00-19:00, the load demand of the receiving system shows an increasing trend, and its DC tie line is adjusted to increase the transmission power. During the periods of 1:00-2:00, 20:00-22:00, and 23:00-24:00, the load of the receiving system shows a decreasing trend, and its DC tie line is adjusted to reduce the transmission power.

[0223] Figure 9 This is a comparison chart of the peak margin of the receiving-end system under examples 2 and 3. Figure 10 This is a comparison chart of the peak-shaving margin of the receiving-end system in Examples 2 and 3. From... Figure 9 and Figure 10As can be seen, during the 6:00-7:00 period, the receiving-end system has insufficient downward peak-shaving margin. This is because the load on the receiving-end system is low during this period, while the renewable energy output of the sending-end system is high. Since the tie line does not consider the peak-shaving constraints of the receiving-end system, the output power of the tie line is high, resulting in the supply of the receiving-end system exceeding the demand, and the system's downward peak-shaving capacity is insufficient. Conversely, during the 15:00-18:00 period, the receiving-end system has insufficient upward peak-shaving margin. This is because the load on the receiving-end system is high during this period, but the renewable energy output of the sending-end system is low, resulting in low output power of the tie line. With demand far exceeding supply, the system's upward peak-shaving capacity is insufficient.

[0224] When the receiving-end system experiences insufficient peak-shaving margins, the system needs to start and stop some units to meet supply and demand balance, as the controllable units in the receiving-end system include thermal power units and concentrated solar power plants, thus increasing system operating costs. As shown in Table 1, compared to Example 2, Example 3 reduced system operating costs by 4.34%, reduced wind curtailment by 82MW, and reduced carbon emissions by 649 tons. The above analysis indicates that the tie-line operation mode considering the peak-shaving margin of the receiving-end system can reduce system operating costs while ensuring the absorption of renewable energy in the sending-end system.

[0225] Table 1 Comparison of Key System Indicators in Examples 2 and 3

[0226]

[0227] To illustrate the impact of confidence level on scheduling schemes, Figure 11 The report presents a comparison of overall dispatch results at confidence levels of 0.93 and 0.97. It's clear that higher confidence levels correspond to more conservative dispatch strategies. Compared to the dispatch scheme at a confidence level of 0.93, the scheme at 0.97 tends to use more costly, controllable units such as thermal power plants and concentrated solar power (CSP) plants for supply and demand balance, resulting in an increase of 4150MW in thermal power generation and 617MW in CSP generation. Conversely, wind power generation decreases, while wind curtailment increases by 1438MW. In actual dispatch, lower-risk operating modes are used when system reliability is prioritized, while higher-risk operating modes can be selected when renewable energy consumption is prioritized.

[0228] The beneficial effects of the low-carbon dispatching method and system for inter-regional power grid interconnection of the present invention are reflected in:

[0229] (1) The power transmitted across the DC tie line is used as an optimization variable. A DC tie line power optimization model is created in the form of operation constraints. An operation constraint that considers the peak-shaving margin of the receiving system is added to the optimization model. While ensuring the flexible regulation of the DC tie line, the peak-shaving pressure of the receiving system is alleviated.

[0230] (2) In response to the problem of weak demand for green certificates, a green certificate-carbon joint trading model is proposed, in which thermal power units are green certificate buyers. Thermal power units increase their carbon quotas by purchasing green certificates, thereby reducing carbon trading costs. This move promotes the development of the green certificate trading mechanism and increases the demand for green certificates.

[0231] (3) Introducing the worst-case risk theory and considering the impact of the uncertainty of wind power output in the sending system makes the system operation more robust.

[0232] The various embodiments in this specification are described in a progressive manner, with each embodiment focusing on its differences from other embodiments. Similar or identical parts between embodiments can be referred to interchangeably. For the systems disclosed in the embodiments, since they correspond to the methods disclosed in the embodiments, the descriptions are relatively simple; relevant parts can be referred to the method section.

[0233] Specific examples have been used to illustrate the principles and implementation methods of this invention. The descriptions of the above embodiments are only for the purpose of helping to understand the core ideas of this invention. Furthermore, those skilled in the art will recognize that, based on the ideas of this invention, there will be changes in the specific implementation methods and application scope. Therefore, the content of this specification should not be construed as a limitation of this invention.

Claims

1. A low-carbon dispatching method for a power grid inter-regional interconnection system, characterized in that, The method includes: Obtain operating cost data and risk measurement cost data for the inter-regional power grid interconnection system; the operating cost data includes: operating costs of thermal power units at the sending and receiving ends, operating costs of solar thermal power plants, operation and maintenance costs of wind power plants, operating costs of electric heating devices, carbon trading costs of the inter-regional interconnection system, and green certificate trading costs of the inter-regional interconnection system; the risk measurement cost data includes: confidence level, loss function, and loss threshold; A low-carbon operation optimization model is constructed based on the operating cost data and the risk measurement cost data. With the goal of minimizing the total cost of the inter-regional power grid interconnection system, the objective function and constraints of the low-carbon operation optimization model are determined. The objective function is: minF=F XY +C WCVaR ; Where F is the total cost of the cross-regional interconnection system; F XY For operating costs; C WCVaR To measure the cost of risk; minF represents the objective of minimizing the total cost of the cross-regional interconnection system; The objective function is solved according to the constraints to obtain the optimal low-carbon scheduling strategy for the inter-regional grid interconnection system; the optimal low-carbon scheduling strategy is used to schedule the power output of each unit in the sending and receiving end system of the inter-regional grid interconnection system and the DC tie line transmission plan. The loss value of the receiving-end system in the inter-regional power grid interconnection system during time period t is: in: The loss value of the receiving system during time period t; and These represent the positive and negative spinning reserve capacities of the receiving-end system during time period t; c PL Penalty for unit cost under load; c EL The unit cost of power curtailment penalty; ΔP W,t Prediction error of wind power output in the sending-end system; The cost of the risk measurement is: Where β is the confidence level; Let be the loss function, representing the loss value of the receiving system under scenario λ. κ is the loss threshold; ρ(y) is The probability density function of the prediction error of wind power output in the sending-end system; W is the set of probability distributions of the prediction error of wind power output in the sending-end system; R is the set of real numbers; N G is the number of scenes; e is the unit vector; The constraints include: power balance constraints, solar thermal power plant operation constraints, and DC tie line operation constraints. The DC tie line operation constraints include: upper and lower limits of DC tie line output power, power transmission direction adjustment constraints, number of DC tie line power adjustment constraints, DC tie line output adjustment rate constraints, power transmission constraints, and peak shaving margin constraints of the receiving end system. The peak-shaving margin constraint of the receiving-end system is: in, This represents the peak margin of the receiving system during time period t. This represents the peak margin of the receiving system during time period t. Maximum peak shaving capacity of receiving-end system in: This represents the maximum peak-shaving capacity of the receiving-end system. ε represents the maximum daily load of the receiving-end system; ε represents the spinning reserve rate of the receiving-end system. The maximum output power and minimum technical output of the receiving-end thermal power unit are: in: This represents the maximum output power of the receiving-end thermal power unit. Minimum technical output for receiving-end thermal power units; This refers to the transmission power of the tie line at the time of maximum daily load. N represents the maximum peak-shaving capacity of the receiving-end system. R The number of receiving-end thermal power units; u i This refers to the start-stop state (i.e., operating state) of thermal power unit i; υ represents the maximum output power of thermal power unit i (i.e., the i-th thermal power unit); υ represents the peak shaving depth of the thermal power unit. The positive and negative peak-shaving capacities of the receiving-end system are: in: and These represent the positive and negative peak-shaving capacities of the receiving-end system during time period t; P dc,t The power transmitted through the tie line during time period t; This represents the maximum output power of the receiving-end thermal power unit. Minimum technical output for receiving-end thermal power units; The peak shaving margins of the receiving-end system are: in: and These represent the upper and lower peak shaving margins of the receiving system during time period t; and These represent the positive and negative peak-shaving capacities of the receiving-end system during time period t; P l,t Let t be the load of the receiving-end system during time period t; σ is the minimum margin rate of the receiving-end system.

2. The low-carbon dispatching method for inter-regional power grid interconnection systems according to claim 1, characterized in that, The operating cost is: F XY =C G +C CSP +C W +C EH +C CO2 +C GC ; Among them, C G Operating costs of thermal power units at both the sending and receiving ends; C CSP C is the operating cost of a solar thermal power plant; W C is the operation and maintenance cost of wind farms; EH C is the operating cost of the electric heating device; CO2 For the carbon trading costs of inter-regional interconnection systems; C GC Costs associated with cross-regional interconnection system green certificate transactions.

3. The low-carbon dispatching method for inter-regional power grid interconnection systems according to claim 1, characterized in that, The power balance constraint is: Where, N S P represents the total number of thermal power units at the sending end. G,i,t N represents the output of the i-th sending-end thermal power unit during time period t; W P represents the total number of wind farms. W,i,t N represents the power output of the i-th wind farm during time period t; CSP P represents the total number of solar thermal power plants. CSP,i,t Let be the output of the i-th solar thermal power plant during time period t; P represents the load of the sending-end system during time period t; dc,t Let t be the power transmitted through the tie line during time period t.

4. The low-carbon dispatching method for inter-regional power grid interconnection systems according to claim 1, characterized in that, The operational constraints of the solar thermal power plant include: power generation system output constraints and power generation system ramping constraints; The output constraint of the power generation system is: in, This is the upper limit of the output of a solar thermal power plant; This represents the lower limit of the output of a solar thermal power plant; P CSP,i,t u represents the output of the i-th solar thermal power plant during time period t; i,t This represents the operating status of the i-th thermal power unit during time period t. The ramping constraint of the power generation system is: in, This represents the maximum upward ramp rate for the power generation stage of a solar thermal power plant. P represents the maximum downward ramp rate of the power generation stage in a solar thermal power plant. CSP,i,t-1 Let be the output of the i-th solar thermal power plant during time period t-1.

5. The low-carbon dispatching method for inter-regional power grid interconnection systems according to claim 1, characterized in that, The DC tie line operation constraints include: upper and lower limits of DC tie line output power, power transmission direction adjustment constraints, number of DC tie line power adjustment constraints, DC tie line output adjustment rate constraints, power transmission constraints, and peak shaving margin constraints of the receiving end system. The upper and lower limits of the output power of the DC tie line are constrained as follows: in, This is the upper limit of the transmission power of the DC tie line; P dc This represents the lower limit of the DC tie-line transmission power; P dc,t The power transmitted through the tie line during time period t; The power transmission direction adjustment constraint is: in, The DC tie line transmission power is adjusted upward during time period t. The DC tie line transmission power is adjusted downward during time period t; This refers to the upward adjustment of DC tie-line transmission power during time period t+1. The DC tie line transmission power is adjusted downwards during time period t+1; x t The state of the DC tie line transmission power during time period t; The constraint on the number of DC tie line power adjustment cycles is: Where T is the total scheduling period; S is the maximum number of adjustments allowed for the DC converter within the scheduling cycle; The output adjustment rate constraint of the DC tie line is: in, δ dc This is the minimum transmission adjustment for the DC tie line; P represents the maximum transmission regulation of the DC tie line. dc,t-1 The power transmitted through the tie line during time period t-1; The power transmission constraint is: (1-ρ dc )Q dc ≤∑P dc,t ≤(1+ρ dc )Q dc Where, ρ dc The permissible deviation rate for exchanging electrical energy on a DC tie line; Q dc The planned power transmission for the DC tie line.

6. The low-carbon dispatching method for inter-regional power grid interconnection systems according to claim 1, characterized in that, The step of solving the objective function based on the constraints to obtain the optimal low-carbon dispatch strategy for the inter-regional power grid interconnection system specifically includes: Linearize the nonlinear terms in the objective function to obtain a mixed-integer linear programming expression; Solving the mixed-integer linear programming expression based on the constraints yields the optimal low-carbon scheduling strategy for the inter-regional power grid interconnection system.

7. A low-carbon dispatching system for inter-regional power grid interconnection, characterized in that, The system includes: The data acquisition unit is used to acquire operating cost data and risk measurement cost data of the inter-regional power grid interconnection system. The operating cost data includes: operating costs of thermal power units at the sending and receiving ends, operating costs of solar thermal power plants, operation and maintenance costs of wind power plants, operating costs of electric heating devices, carbon trading costs of the inter-regional interconnection system, and green certificate trading costs of the inter-regional interconnection system. The risk measurement cost data includes: confidence level, loss function, and loss threshold. The model building unit is used to build a low-carbon operation optimization model based on the operating cost data and the risk measurement cost data. The objective function and constraint determination unit is used to determine the objective function and constraints of the low-carbon operation optimization model with the goal of minimizing the total cost of the inter-regional grid interconnection system. The objective function is: minF=F XY +C WCVaR ; Where F is the total cost of the cross-regional interconnection system; F XY For operating costs; C WCVaR To measure the cost of risk; minF represents the objective of minimizing the total cost of the cross-regional interconnection system; The solution unit is used to solve the objective function according to the constraints to obtain the optimal low-carbon scheduling strategy for the inter-regional grid interconnection system; the optimal low-carbon scheduling strategy is used to schedule the power output of each unit in the sending and receiving end system of the inter-regional grid interconnection system and the DC tie line transmission plan. The loss value of the receiving-end system in the inter-regional power grid interconnection system during time period t is: in: The loss value of the receiving system during time period t; and These represent the positive and negative spinning reserve capacities of the receiving-end system during time period t; c PL Penalty for unit cost under load; c EL The unit cost of power curtailment penalty; ΔP W,t Prediction error of wind power output in the sending-end system; The cost of the risk measurement is: Where β is the confidence level; Let be the loss function, representing the loss value of the receiving system under scenario λ. κ is the loss threshold; ρ(y) is The probability density function of the prediction error of wind power output in the sending-end system; W is the set of probability distributions of the prediction error of wind power output in the sending-end system; R is the set of real numbers; N G is the number of scenes; e is the unit vector; The constraints include: power balance constraints, solar thermal power plant operation constraints, and DC tie line operation constraints. The DC tie line operation constraints include: upper and lower limits of DC tie line output power, power transmission direction adjustment constraints, number of DC tie line power adjustment constraints, DC tie line output adjustment rate constraints, power transmission constraints, and peak shaving margin constraints of the receiving end system. The peak-shaving margin constraint of the receiving-end system is: in, This represents the peak margin of the receiving system during time period t. This represents the peak margin of the receiving system during time period t. Maximum peak shaving capacity of receiving-end system in: This represents the maximum peak-shaving capacity of the receiving-end system. ε represents the maximum daily load of the receiving-end system; ε represents the spinning reserve rate of the receiving-end system. The maximum output power and minimum technical output of the receiving-end thermal power unit are: in: This represents the maximum output power of the receiving-end thermal power unit. Minimum technical output for receiving-end thermal power units; This refers to the transmission power of the tie line at the time of maximum daily load. N represents the maximum peak-shaving capacity of the receiving-end system. R The number of receiving-end thermal power units; u i This refers to the start-stop state (i.e., operating state) of thermal power unit i; υ represents the maximum output power of thermal power unit i (i.e., the i-th thermal power unit); υ represents the peak shaving depth of the thermal power unit. The positive and negative peak-shaving capacities of the receiving-end system are: in: and These represent the positive and negative peak-shaving capacities of the receiving-end system during time period t; P dc,t The power transmitted through the tie line during time period t; This represents the maximum output power of the receiving-end thermal power unit. Minimum technical output for receiving-end thermal power units; The peak shaving margins of the receiving-end system are: in: and These represent the upper and lower peak shaving margins of the receiving system during time period t; and These represent the positive and negative peak-shaving capacities of the receiving-end system during time period t; P l,t Let t be the load of the receiving-end system during time period t; σ is the minimum margin rate of the receiving-end system.

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