A wind-photovoltaic-thermal storage dual-layer dispatching method considering the synergy of thermal storage retrofit and optimal energy abandonment to promote carbon emission reduction

Abstract

This invention discloses a two-tiered scheduling method for wind, solar, thermal, and energy storage that considers the synergistic effect of thermal energy storage retrofitting and optimal energy curtailment to promote carbon emission reduction. First, the operating state of thermal power units undergoing thermal energy storage retrofitting is modeled. Then, a multi-objective upper-level model is established to minimize the net load variance of the power grid and the amount of energy curtailed. Next, a carbon trading cost model is established, and based on this, a lower-level model is established with the objectives of minimizing the total system cost and carbon trading cost. Finally, the upper limit constraint on energy curtailment is appropriately modified, and the calculation is performed in the MATLAB environment using the Yalmip platform and the Gurobi solver. The model proposed in this invention can achieve both economic efficiency and low carbon emissions in the scheduling strategy, and can provide the optimal energy curtailment range that generates both economic and low carbon benefits during unit thermal energy storage retrofitting.

Description

Technical Field

[0001] This invention belongs to the field of power system optimization dispatching technology, specifically relating to a two-tier dispatching method for wind, solar, thermal, and energy storage that considers the synergistic effect of thermal energy storage retrofitting and optimal energy curtailment to promote carbon emission reduction. Background Technology

[0002] Since the national dual-carbon target was officially proposed in 2020, wind power and photovoltaic power have developed rapidly due to their low cost, mature technology, and huge development potential. However, the uncertainty of wind and solar power severely limits the absorption capacity of new energy sources. To improve the absorption level of new energy, numerous studies have utilized energy storage systems and concentrated solar power (CSP) to smooth out the wind and solar power generation curves, improving the efficiency of wind and solar absorption from the perspective of increasing power supply flexibility. However, these studies have neglected the problem of significantly increased carbon emissions from thermal power units during peak shaving. Utilizing boiler stable combustion retrofitting, rapid start-up and shutdown, and rapid ramp-up technologies to carry out deep peak shaving and flexible retrofitting of thermal power units is also an important means to improve the absorption of new energy. However, these methods are difficult to adjust the load curve from the power supply side and have not explored the optimal curtailment range that can achieve both economic efficiency and low carbon emissions when retrofitting units with thermal storage. Therefore, a wind-solar-thermal-storage dispatching model that considers the coordination between the generation and distribution ends is needed. Summary of the Invention

[0003] To address the aforementioned issues, this invention proposes a two-tiered scheduling method for wind, solar, thermal, and energy storage that considers the synergistic effect of thermal energy storage retrofitting and optimal energy curtailment to promote carbon emission reduction. This method can effectively improve energy utilization, reduce system operating costs, and decrease system carbon emissions. It achieves a significant reduction in carbon emissions during system operation while maintaining a relatively low wind and solar energy curtailment rate and operating costs.

[0004] This invention proposes a two-tiered scheduling method for wind, solar, thermal, and energy storage that considers the synergistic effect of thermal energy storage retrofitting and optimal energy curtailment to promote carbon emission reduction. The specific design scheme is as follows:

[0005] (1) Modeling the operation status of thermal power unit heat storage retrofit;

[0006] (2) Establish a multi-objective upper-level model that minimizes the net load variance of the power grid and the energy curtailment;

[0007] (3) Establish a carbon trading cost model;

[0008] (4) Establish a lower-level model with the goal of minimizing the total system cost and carbon trading cost;

[0009] (5) Adjust the upper limit constraint of energy abandonment appropriately, and calculate using the gurobi solver based on the yalmip platform in the MATLAB environment.

[0010] Furthermore, the operational status model of the thermal power unit thermal storage retrofit established in step (1) is as follows:

[0011]

[0012]

[0013]

[0014] P t coal =η ST，GEN P t ST，GEN

[0015] In the formula, The heat stored in the high-temperature thermal storage system at time t; P represents the stored heat of the high-temperature system at time t-1. tbum P represents the boiler coal combustion power during time period t. t ST，GEN η is the thermal power within the steam turbine during time period t; H P represents the heat loss rate of the high-temperature thermal storage system. t HC P t HD P t LC P t LD For high-temperature and low-temperature thermal storage systems, this refers to the stored and released thermal power; η HC η HD For the thermal efficiency of a high-temperature system; η coal For coal combustion thermal efficiency; Q represents the mass of coal consumed at time t. coal η is the calorific value of pulverized coal combustion; ST，GEN For thermoelectric conversion efficiency; P t coal To upgrade the generating unit's power output.

[0016] Furthermore, in step (2), a multi-objective function is established to minimize the net load variance of the power grid and the energy curtailment:

[0017]

[0018]

[0019]

[0020] In the formula: The net load of the receiving-end power grid during time period t, P represents the average net load of the receiving-end power grid. D,t Let ω be the initial load of the receiving-end power grid during time period t. w,t ω pv,t The wind curtailment rate and solar curtailment rate are respectively for time period t, P w,t With Ppv,t Let λ represent the wind power output and photovoltaic power output during time period t, respectively. c , λ d For energy storage charging and discharging efficiency, P c,t P d,t These represent the charging and discharging power of the energy storage system during time period t.

[0021] The upper-level model constraints mentioned in step (2), which aim to minimize the net load variance of the power grid and the curtailment, include wind and solar forecast constraints, curtailment constraints, and energy storage system constraints, as detailed below:

[0022] 0≤P w,t ≤P w,max

[0023] 0≤P pv,t ≤P pv,max

[0024] 0≤P c,t ≤P c,max

[0025] 0≤P d,t ≤P d,max

[0026] P c,t ·P d,t =0

[0027]

[0028]

[0029] Q s,max ·Q soc,min ≤Q soc,t ≤Q s,max ·Q soc,max

[0030]

[0031] In the formula: P w,max P represents the maximum power output of the wind farm during time period t. pv,max k represents the maximum output of the photovoltaic power station during time period t; w,max k represents the maximum allowable wind curtailment rate for a wind farm. pv,max P represents the maximum permissible curtailment rate for a photovoltaic power plant. c,max P represents the maximum charging power of the energy storage system. d,max Q represents the maximum discharge power of the energy storage system. s,max Q represents the maximum amount of electricity that the energy storage system can store. soc,max Q represents the upper limit of the charge ratio of the energy storage system. soc,min Q represents the lower limit of the charge ratio of an energy storage system. soc,t This represents the remaining energy storage capacity during time period t.

[0032] Furthermore, the carbon trading cost model established in step (3) is as follows:

[0033]

[0034]

[0035]

[0036] In the formula: For the initial carbon trading allowances obtained by the thermal power unit set, ω con For the collection of thermal power units, Let i be the output of thermal power unit i during time period t. It is the initial carbon emission allowance per unit of electricity generated by thermal power units; E c For the actual carbon trading allowances obtained by thermal power units, ε t,i It is the actual carbon emission quota per unit of electricity generated by thermal power units; C carbon The cost of carbon trading for thermal power units, It is the carbon trading coefficient per unit power.

[0037] Furthermore, the lower-level model in step (4) that aims to minimize the total system cost and carbon trading cost is as follows:

[0038]

[0039]

[0040] C2=K[(H s / J s )+(H N / J N )]

[0041] H s =C1q s (1-η s ) / P coal

[0042] H N =C1q N (1-η N ) / P coal

[0043]

[0044]

[0045] In the formula: C coal The price per unit of coal; This represents the start-up and shutdown status of the thermal power unit during time period t. A value of 1 indicates the unit is in operation, and a value of 0 indicates it is in shutdown. Let C1, C2, C3, and C4 represent the starting and stopping costs of unit i, respectively, and C1, C2, C3, and C4 represent the spinning reserve cost of the dispatching system, the environmental protection tax, the operating cost of the wind-solar-storage system, and the revenue from wind and solar power consumption. H represents the mass of coal consumed by unit i at time t; S With H N These are SO2 and NO emitted into the atmosphere by the generator set, respectively. x Quality and corresponding tax amount; P coal To generate electrical power for the generator; β res e is the system spin-off reserve cost factor; D e w e pv This represents the error coefficient for load, wind power, and photovoltaic output prediction. q represents the wind and solar power absorption capacity and the photovoltaic power absorption capacity during time period t, respectively; s q N SO2 and NO produced per unit of coal combustion x mass; η s η N For environmental protection equipment to desulfurize SO2 and NO x efficiency, J s With J N SO2 and NO respectively x The pollution equivalent number, K represents the tax payable per unit of pollution; μ w μ pv μ soc These represent the operating cost coefficients for wind power, photovoltaic, and energy storage systems, respectively; α w α pv α represents the environmental benefit coefficient generated by the absorption of wind and solar power. soc This refers to the environmental benefit coefficient related to energy storage systems.

[0046] The lower-level model constraints described in step (4), which aim to minimize the total system cost and carbon trading cost, include constraints on thermal power unit operation, power balance, system spinning reserve, thermal storage system, boiler coal combustion quality, steam turbine power generation, thermal storage unit output, and transmission line power, as detailed below:

[0047]

[0048]

[0049]

[0050]

[0051]

[0052]

[0053]

[0054]

[0055] 0≤P L,t ≤P L,max

[0056]

[0057]

[0058]

[0059]

[0060]

[0061]

[0062]

[0063]

[0064]

[0065]

[0066]

[0067]

[0068]

[0069] In the formula: and These represent the lower and upper limits of the output of thermal power unit i, respectively; and T represents the maximum and minimum climbing speeds of thermal power unit i at its output, respectively; on With T off These represent the maximum start-up duration and maximum shutdown duration of thermal power unit i, respectively; P w,t With P pv,t These represent the wind power output and photovoltaic power output during time period t, respectively. These are the system's positive and negative spinning reserve capacities, respectively. The stored heat of the high-temperature and low-temperature thermal storage systems at time t; These represent the minimum and maximum thermal storage capacities of the high and low temperature system during time period t, respectively. P represents the operating status of the high and low temperature thermal storage system during time period t. and These represent the system's positive and negative standby states, θ HD θ LD The maximum heat transfer coefficient at high and low temperatures, These represent the maximum heat release power at high and low temperatures, respectively. These represent the maximum and minimum coal combustion masses during time period t; P L,t Let P be the transmission power of line L during time period t. L,max This represents the maximum transmission capacity of line L; These represent the maximum and minimum power output of the steam turbine.

[0070] Furthermore, step (5) is performed in the MATLAB environment using the yalmip platform and the gurobi solver. The specific steps are as follows:

[0071] (5-1) Solve the upper-level model using gurobi based on the yalmip platform in the MATLAB environment;

[0072] (5-2) Input the net load curve obtained from the upper model into the lower model and solve it using gurobi;

[0073] (5-3) Determine whether the convergence criterion is met. If it is met, output the calculation result. If it is not met, update the upper limit of the energy abandonment rate and return to step (5-1) to solve.

[0074] (5-4) The optimal range of energy curtailment that can produce economic efficiency and low carbon emissions during thermal storage retrofitting, as well as the results of system carbon emissions and operational economy, are obtained and the experiment is concluded.

[0075] Compared with the prior art, the advantages and positive effects of the present invention are as follows:

[0076] (1) When considering the optimal wind and solar energy consumption, not only economic efficiency is considered, but also the low carbon emissions of thermal power units during peak shaving and frequency regulation.

[0077] (2) When performing optimization calculations on the renewable energy curtailment rate of the system, the impact of the curtailment rate on the system's economic efficiency and carbon emission reduction was explored.

[0078] (3) Considering the system economic dispatch during the thermal power unit thermal storage retrofit, the scope of the economic benefits and carbon emission reduction benefits brought about by the thermal storage retrofit is explained. Attached Figure Description

[0079] Figure 1 This is a structural diagram of the two-layer optimized scheduling model of the present invention;

[0080] Figure 2 This is a peak-valley analysis diagram of net load in an example of the present invention;

[0081] Figure 3 This is a diagram of the improved IEEE 30-node structure in an example of the present invention;

[0082] Figure 4 This is a load and wind power output prediction curve diagram in an example of the present invention;

[0083] Figure 5 This is a flowchart of the MATLAB-based optimized scheduling model in an example of the present invention. Detailed Implementation

[0084] The invention is further illustrated below with reference to specific embodiments and accompanying drawings. This invention proposes a two-tiered scheduling method for wind, solar, thermal, and energy storage that considers the synergistic effect of thermal energy storage retrofitting and optimal energy curtailment to promote carbon emission reduction. The structure of the two-tiered optimized scheduling model is as follows: Figure 1 As shown in the figure, the net load peak-valley analysis diagram is as follows: Figure 2 As shown, to further verify the effectiveness of the proposed method, load and wind power output prediction curves were performed on the improved IEEE 30-bus system, as shown in the figure. Figure 4 Simulation analysis was performed, and the improved IEEE 30-node architecture diagram is shown below. Figure 3 As shown, the specific implementation steps are as follows:

[0085] (1) Establish an operational status model for thermal power unit thermal storage retrofit, as detailed below:

[0086]

[0087]

[0088]

[0089] P t coal =η ST，GEN P t ST，GEN

[0090] In the formula, The heat stored in the high-temperature thermal storage system at time t; P represents the stored heat of the high-temperature system at time t-1. t burn P represents the boiler coal combustion power during time period t. t ST，GEN η is the thermal power within the steam turbine during time period t; H P represents the heat loss rate of the high-temperature thermal storage system. tHC P t HD P t LC P t LD For high-temperature and low-temperature thermal storage systems, this refers to the stored and released thermal power; η HC η HD For the thermal efficiency of a high-temperature system; η coal For coal combustion thermal efficiency; Q represents the mass of coal consumed at time t. coal η is the calorific value of pulverized coal combustion; ST，GEN For thermoelectric conversion efficiency; P t coal To upgrade the generating unit's power output.

[0091] (2) Establish an upper-level scheduling model

[0092] A multi-objective function is established to minimize the net load variance of the power grid and the energy curtailment, as follows:

[0093]

[0094]

[0095]

[0096] In the formula: The net load of the receiving-end power grid during time period t, P represents the average net load of the receiving-end power grid. D,t Let ω be the initial load of the receiving-end power grid during time period t. w,t ω pv,t The wind curtailment rate and solar curtailment rate are respectively for time period t, P w,t With P pv,t Let λ represent the wind power output and photovoltaic power output during time period t, respectively. c , λ d For energy storage charging and discharging efficiency, P c,t P d,t These represent the charging and discharging power of the energy storage system during time period t.

[0097] The upper-level model constraints, which aim to minimize the net load variance of the power grid and the curtailment of energy, include constraints on wind and solar power forecasting, energy curtailment constraints, and energy storage system constraints, as detailed below:

[0098] 0≤P w,t ≤P w,max

[0099] 0≤P pv,t ≤P pv,max

[0100] 0≤Pc,t ≤P c,max

[0101] 0≤P d,t ≤P d,max

[0102] P c,t ·P d,t =0

[0103]

[0104]

[0105] Q s,max ·Q soc,min ≤Q soc,t ≤Q s,max ·Q soc,max

[0106]

[0107] In the formula: P w,max P represents the maximum power output of the wind farm during time period t. pv,max k represents the maximum output of the photovoltaic power station during time period t; w,max k represents the maximum allowable wind curtailment rate for a wind farm. pv,max P represents the maximum permissible curtailment rate for a photovoltaic power plant. c,max P represents the maximum charging power of the energy storage system. d,max Q represents the maximum discharge power of the energy storage system. s,max Q represents the maximum amount of electricity that the energy storage system can store. soc,max Q represents the upper limit of the charge ratio of the energy storage system. soc,min Q represents the lower limit of the charge ratio of an energy storage system. soc,t This represents the remaining energy storage capacity during time period t.

[0108] (3) Establish a carbon trading cost model, as follows:

[0109]

[0110]

[0111]

[0112] In the formula: For the initial carbon trading allowances obtained by the thermal power unit set, ω con For the collection of thermal power units, Let i be the output of thermal power unit i during time period t. It is the initial carbon emission allowance per unit of electricity generated by thermal power units; E c For the actual carbon trading allowances obtained by thermal power units, εt,i It is the actual carbon emission quota per unit of electricity generated by thermal power units; C carbon The cost of carbon trading for thermal power units, It is the carbon trading coefficient per unit power.

[0113] (4) Establish a lower-level optimization scheduling model

[0114] The lower-level model, which aims to minimize the total system cost and carbon trading cost, is as follows:

[0115]

[0116]

[0117] C2=K](H s / J s )+(H N / J N )]

[0118] H s =C1q s (1-η s ) / P coal

[0119] H N =C1q N (1-η N ) / P coal

[0120]

[0121]

[0122] In the formula: C coal The price per unit of coal; This represents the start-up and shutdown status of the thermal power unit during time period t. A value of 1 indicates the unit is in operation, and a value of 0 indicates it is in shutdown. Let C1, C2, C3, and C4 represent the starting and stopping costs of unit i, respectively, and C1, C2, C3, and C4 represent the spinning reserve cost of the dispatching system, the environmental protection tax, the operating cost of the wind-solar-storage system, and the revenue from wind and solar power integration; β res e is the system spin-off reserve cost factor; D e w e pv This represents the error coefficient for load, wind power, and photovoltaic output prediction. H represents the mass of coal consumed by unit i at time t; S With H N These are SO2 and NO emitted into the atmosphere by the generator set, respectively. x Quality and corresponding tax amount; P coal To generate electrical power for the generator; q represents the wind and solar power absorption capacity and the photovoltaic power absorption capacity during time period t, respectively; s q N SO2 and NO produced per unit of coal combustion x mass; η s η N For environmental protection equipment to desulfurize SO2 and NO x efficiency, J s With J N SO2 and NO respectively x The pollution equivalent number, K represents the tax payable per unit of pollution; μ w μ pv μ soc These represent the operating cost coefficients for wind power, photovoltaic, and energy storage systems, respectively; α w α pv α represents the environmental benefit coefficient generated by the absorption of wind and solar power. soc This refers to the environmental benefit coefficient related to energy storage systems.

[0123] The lower-level model constraints, which aim to minimize the total system cost and carbon trading cost, include constraints on thermal power unit operation, power balance, system spinning reserve, thermal storage system, boiler coal combustion quality, steam turbine power generation, thermal storage unit output, and transmission line power, as detailed below:

[0124]

[0125]

[0126]

[0127]

[0128]

[0129]

[0130]

[0131]

[0132] 0≤P L,t ≤P L,max

[0133]

[0134]

[0135]

[0136]

[0137]

[0138]

[0139]

[0140]

[0141]

[0142]

[0143]

[0144]

[0145]

[0146] In the formula: and These represent the lower and upper limits of the output of thermal power unit i, respectively; and T represents the maximum and minimum climbing speeds of thermal power unit i at its output, respectively; on With T off These represent the maximum start-up duration and maximum shutdown duration of thermal power unit i, respectively; P w,t With P pv,t These represent the wind power output and photovoltaic power output during time period t, respectively. These are the system's positive and negative spinning reserve capacities, respectively. and These are respectively the system's positive rotation standby and negative standby; The stored heat of the high-temperature and low-temperature thermal storage systems at time t; These represent the minimum and maximum thermal storage capacities of the high and low temperature system during time period t, respectively. θ represents the operating status of the high and low temperature thermal storage system during time period t. HD θ LD The maximum heat transfer coefficient at high and low temperatures, These represent the maximum heat release power at high and low temperatures, respectively. These represent the maximum and minimum coal combustion masses during time period t; P L,t Let P be the transmission power of line L during time period t. L,max This represents the maximum transmission capacity of line L; These represent the maximum and minimum power output of the steam turbine.

[0147] (5) Calculate using the gurobi solver based on the yalmip platform within the MATLAB environment, such as... Figure 5 As shown, the specific steps are as follows:

[0148] (5-1) Solve the upper-level model using gurobi based on the yalmip platform in the MATLAB environment;

[0149] (5-2) Input the net load curve obtained from the upper model into the lower model and solve it using gurobi;

[0150] (5-3) Determine whether the convergence criterion is met. If it is met, output the calculation result. If it is not met, update the upper limit of the energy abandonment rate and return to step (5-1) to solve.

[0151] (5-4) The optimal range of energy curtailment that can produce economic efficiency and low carbon emissions during thermal storage retrofitting, as well as the results of system carbon emissions and operational economy, are obtained and the experiment is concluded.

[0152] To verify that considering the optimal energy curtailment and the synergistic effect of unit thermal storage retrofitting has good economic efficiency and low carbon emissions, the operating costs of the system under the following three modes are compared and analyzed.

[0153] a. A low-carbon economic dispatch model under the low-carbon trading mechanism, considering the full grid integration of wind and solar power and the thermal storage retrofit of generating units;

[0154] b. Under the low-carbon trading mechanism, a low-carbon economic dispatch model that only considers the optimal curtailment of wind and solar power;

[0155] c. A low-carbon economic dispatch model considering optimal wind and solar energy curtailment and unit thermal storage retrofitting under a low-carbon trading mechanism (the model of this invention).

[0156] Table 1 shows the system operating costs and carbon emission costs under the three modes. As can be seen from Table 1, Model 3 has the lowest total cost, reducing it by RMB 175,900 and RMB 263,200 compared to Models 1 and 2, respectively, representing reductions of 4.29% and 6.29%. This is mainly because considering optimal energy curtailment fully utilizes wind and solar power generation while mitigating large load fluctuations and reducing unit start-up and shutdown costs, resulting in reductions of RMB 25,000 and RMB 42,000 compared to Models 1 and 2, respectively. Furthermore, the thermal storage unit modification allows for secondary load adjustment, resulting in a smoother load curve, improved boiler combustion stability, reduced coal consumption per unit of power generation, and lower coal consumption for thermal power units. This also slightly reduces carbon emission costs, demonstrating that this invention can improve the low-carbon and economic efficiency of the wind-solar-thermal-storage system.

[0157] Table 1. Comparison of scheduling results for the 13 models

[0158]

[0159] Based on the yalmip platform in the MATLAB environment, the gurobi model was called to solve the model. By changing the upper limit of the curtailment constraint, it was found that the curtailment rate range that the unit thermal storage retrofit can benefit from in this example is 0-0.22.

[0160] The above embodiments are used to explain the present invention, but not to limit the present invention. Any modifications and changes made to the present invention within the spirit and scope of the claims shall fall within the protection scope of the present invention.

Claims

1. A two-tiered scheduling method for wind, solar, thermal, and energy storage that considers the synergistic effect of thermal energy storage retrofitting and optimal energy curtailment to promote carbon emission reduction, characterized in that... Includes the following steps: Step 1: Model the operating status of thermal power unit thermal storage retrofit; Step 2: Establish a multi-objective upper-level model that minimizes the net load variance of the power grid and the amount of energy curtailment; Step 3: Establish a carbon trading cost model; Step 4: Establish a lower-level model with the objective of minimizing the total system cost and carbon trading cost; Step 5: Appropriately modify the upper limit constraint of energy loss, and calculate using the gurobi solver based on the yalmip platform in the MATLAB environment; The operational status modeling of the thermal power unit thermal storage retrofit established in step 1 is as follows: ， ， ， ， In the formula, for The heat stored in the high-temperature thermal storage system at all times; for The system stores heat at high temperatures. for Periodic boiler coal combustion power; for Thermal power within the steam turbine during the time period; The heat loss rate of the high-temperature thermal storage system; , , , The stored and released heat power of high-temperature and low-temperature thermal storage systems; , Thermal efficiency of high-temperature systems; For coal combustion thermal efficiency; for The quality of coal consumed at any given time; The calorific value of pulverized coal combustion; For thermoelectric conversion efficiency; To upgrade the generator unit's power output; The multi-objective function established in step 2, which aims to minimize the net load variance of the power grid and the energy curtailment, is as follows: ， ， ， In the formula: For the receiving end power grid Net load during the period This represents the average net load of the receiving-end power grid. For the receiving end power grid Initial load for the period , They are respectively Wind curtailment rate and solar curtailment rate during different time periods and They represent Wind power output and solar power output during different time periods , For energy storage charging and discharging efficiency, , energy storage system Charge and discharge power over time period; The carbon trading cost model established in step 3 is as follows: ， ， ， In the formula: This refers to the initial carbon trading allowances obtained by thermal power units. For the collection of thermal power units, For thermal power units exist Output corresponding to the time period It is the initial carbon emission allowance per unit of electricity generated by thermal power units; The actual carbon trading allowances obtained by thermal power units. It is the actual carbon emission quota per unit of electricity generated by thermal power units; The cost of carbon trading for thermal power units, It is the carbon trading coefficient per unit power; The lower-level model in step 4, which aims to minimize the total system cost and carbon trading cost, is as follows: ， ， ， ， ， ， ， In the formula: The carbon trading costs for thermal power units. For the collection of thermal power units, The price per unit of coal; For the unit exist The quality of coal consumed at any given time; For thermal power units in The start / stop status of the time period is 1 when the unit is in running state and 0 when it is in stopping state. For the unit Start-up and shutdown costs and These are the emissions emitted into the atmosphere by the generator sets. , Quality and corresponding tax amount; , , , These represent the spinning reserve cost of the dispatching system, environmental protection tax, operating costs of the wind-solar-storage system, and revenue from wind and solar power integration, respectively; P coal To generate electrical power for the generator; The system's spin-off reserve cost coefficient; For the receiving end power grid Initial load for the period; , , This represents the error coefficient for load, wind power, and photovoltaic output prediction. , They represent Wind and solar power absorption capacity and photovoltaic power absorption capacity over a given time period; , energy storage system Charge and discharge power over time period; , The amount produced per unit of coal combustion and quality; , Desulfurization for environmental protection equipment , efficiency, and They are respectively and pollution equivalent number, This indicates the amount of tax payable by a unit of pollution; , , These represent the operating cost coefficients for wind power, photovoltaic, and energy storage systems, respectively. , This represents the environmental benefit coefficient generated by the absorption of wind and solar power. The environmental benefit coefficient related to the energy storage system. For energy storage discharge efficiency; The lower-level model constraints in step 4, which aim to minimize the total system cost and carbon trading cost, include constraints on thermal power unit operation, power balance, system spinning reserve, thermal storage system, boiler coal combustion quality, steam turbine power generation, thermal storage unit output, and transmission line power constraints, as detailed below: ， ， ， ， ， ， ， ， ， ， ， ， ， ， ， ， ， ， ， ， ， ， In the formula: and They represent thermal power units The lower and upper limits of output; and They represent thermal power units The maximum and minimum climbing speeds during power output; and thermal power units Maximum power-on duration and maximum power-off duration; and They represent Wind power output and solar power output during different time periods; , These represent the system's positive and negative spinning reserve capacities, respectively. and These are the system's positive and negative standby modes, respectively. , for The heat stored in high-temperature and low-temperature thermal storage systems; , , , They represent Minimum and maximum thermal storage capacity of the high and low temperature system during the specified time period; , , , This refers to the stored and released heat power of high-temperature and low-temperature thermal storage systems. , , , They represent Operating status of high and low temperature thermal storage systems during different time periods , The maximum heat transfer coefficient at high and low temperatures, for Periodic boiler coal combustion power , These represent the maximum heat release power at high and low temperatures, respectively. , They are respectively Maximum and minimum coal combustion mass during the time period; for Time-of-day routes Transmission power, For the line Maximum transmission capacity; , These represent the maximum and minimum power output of the steam turbine.

2. The wind-solar-thermal-storage dual-layer scheduling method for promoting carbon emission reduction by considering thermal energy storage retrofitting and optimal energy curtailment synergy as described in claim 1, characterized in that: The upper-level model constraints in step 2, which aim to minimize the net load variance of the power grid and the energy curtailment, include wind and solar forecast constraints, energy curtailment constraints, and energy storage system constraints, as detailed below: ， ， ， ， ， In the formula: express The maximum output of the wind farm during the specified time period. express The maximum output of the photovoltaic power station during the specified period; This represents the maximum allowable wind curtailment rate for a wind farm. This represents the maximum permissible curtailment rate for a photovoltaic power plant. This indicates the maximum charging power of the energy storage system. Indicates the maximum discharge power of the energy storage system; This indicates the maximum amount of electricity that the energy storage system can store. This indicates the upper limit of the charge ratio of the energy storage system. This indicates the lower limit of the charge ratio of the energy storage system. express Remaining electricity stored during the specified time period.

3. The wind-solar-thermal-storage dual-layer scheduling method for promoting carbon emission reduction by considering thermal storage retrofitting and optimal energy curtailment synergy as described in claim 1, characterized in that... Step 5, performed in the MATLAB environment using the yalmip platform, utilizes the gurobi solver for computation. The specific steps are as follows: Step 5-1: In the MATLAB environment, based on the yalmip platform, call gurobi to solve the upper-level model; Step 5-2: Input the net load curve obtained from the upper-level model into the lower-level model and solve it using gurobi. Step 5-3: Determine whether the convergence criterion is met. If it is met, output the calculation result. If the conditions are not met, update the upper limit of the energy waste rate and return to step 5-1 to solve the problem. Step 5-4: Obtain the optimal range of energy curtailment that can achieve both economic efficiency and low carbon emissions during thermal energy storage retrofitting, as well as the results of system carbon emissions and operational economy, and then conclude the process.

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