A two-stage scheduling method considering Tesla valve thermal storage device for day-ahead and day-intraday

Abstract

The present invention discloses a two-stage scheduling method of the day-ahead and day-intraday considering the Tesla valve heat storage device. The present invention configures a heat storage device for the cogeneration unit, further improving the economy and low-carbon nature of the power system. After the cogeneration unit equipped with the heat storage device is adjusted, the electric-thermal coupling constraint is effectively decoupled, the adjustment flexibility of the unit is improved, the peak-shaving capacity of the system is strengthened, and the phenomenon of wind abandonment is alleviated. And through the two-stage low-carbon scheduling method of the day-ahead and day-intraday, the carbon emissions of the system are reduced while the economy is improved. The heat storage device has achieved a good improvement in heat storage and release capacity, expanded the electric-thermal operation range of the cogeneration unit, improved the electric output adjustment range of the cogeneration unit, and improved the peak-shaving capacity of the system, that is, the cogeneration unit equipped with the heat storage device participates in the two-stage low-carbon scheduling method of the day-ahead and day-intraday, which can effectively increase the consumption of new energy and reduce the system operation cost.

Description

Technical Field

[0001] The present invention belongs to the technical field of clean energy consumption, and specifically relates to a two-stage scheduling method of day-ahead and day-intraday considering a Tesla valve heat storage device. Background Art

[0002] In 2023, my country's total installed capacity of renewable energy will exceed 1.4 billion KW, accounting for more than 50% of the country's total installed capacity of power generation, and historically exceeding thermal power installed capacity. The large-scale grid connection of new energy has brought huge challenges to the safe and stable operation of the power system. At the same time, carbon emissions in the energy sector account for 87% of the national total. Therefore, my country is currently in urgent need of improving the system's absorption capacity and reducing carbon emissions. As a way of power regulation, configuring heat storage devices for cogeneration units is of great significance in improving energy utilization and reducing energy consumption.

[0003] In order to solve the above problems, it is necessary to accelerate the research and development and application of new energy, seize the opportunity of the fourth technological revolution, and improve the utilization rate of renewable energy. At the same time, building a modern energy system and improving energy efficiency are the key to solving energy dependence and non-renewable energy shortages. In order to achieve these goals, the government and enterprises need to strengthen cooperation, improve technological innovation, and comprehensively improve energy management and technical levels. Summary of the invention

[0004] In view of this, the present invention provides a two-stage scheduling method of the day-ahead and day-intraday considering the Tesla valve heat storage device. With the goal of minimizing the total system operation cost, a heat storage device is constructed and cooperates with the cogeneration unit to participate in the low-carbon scheduling of the power system, effectively reducing the amount of abandoned wind and improving the economic and low-carbon operation of the power system. Tesla valve is translated into Chinese as Tesla valve. Tesla valve is a pipeline without any moving parts inside, which can accelerate fluid flow and reduce energy loss only by its special spatial structure.

[0005] The technical solution adopted by the present invention is as follows:

[0006] The present invention provides a two-stage scheduling method considering the Tesla valve heat storage device, which is specifically implemented according to the following steps:

[0007] Step S1: establishing a heat storage device that considers the port characteristics of a multi-stage Tesla valve and the influence of the structural parameters of the Tesla valve on the port characteristics;

[0008] Step S2: Based on the optimal configuration of the heat storage device in step S1, analyzing the peak load capacity increment provided by the flexible regulation of the heat storage device for the cogeneration unit;

[0009] Step S3: Establish a two-stage day-ahead and day-intraday low-carbon dispatch model with the goal of minimizing system operation cost, and solve the model using CPLEX;

[0010] Furthermore, the step S1 of establishing a heat storage device model that takes into account the multi-stage Tesla valve port characteristics and the influence of the Tesla valve structural parameters on the port characteristics includes:

[0011] Firstly, based on the analysis of the port characteristics of Tesla valve, a heat storage device considering the port characteristics of multi-stage Tesla valve is proposed. Then, the model of the heat storage device is constructed based on the analysis of its heat transfer and flow characteristics. Finally, the relationship between the structural parameters of the heat storage device and the heat storage and release capacity is studied. Among them, the port characteristics of Tesla valve are the flow behavior of the valve fluid under specific working conditions, such as flow rate, resistance, etc. They can be obtained through experimental tests or numerical simulations. Generally, the port characteristics of the valve are evaluated by changing parameters such as fluid velocity and pressure difference and observing the flow response and resistance changes of the valve. Specifically, when the fluid flows through the Tesla valve in the forward direction, it will bypass the wing-shaped structure to generate a lateral vortex circulation, that is, secondary flow occurs at the inlet and outlet of the Tesla valve, the interaction between the fluids is enhanced, the heat exchange surface is increased, the heat transfer capacity here becomes stronger, and the acceleration effect is obtained due to the forward flow pressure. When the fluid flows through the Tesla valve in the reverse direction, it will be diverted to two paths at the bifurcation, one of which flows through the curved structure and then flows back, thereby generating a blocking effect at the confluence of the main path, resulting in an increase in the pressure head and inhibiting the forward flow of the fluid. On the other hand, during this process, the secondary flow, collision, and mixing at the inlet and outlet of the Tesla valve are relatively intense. The fluid fully flows through the curved structure and the main passage, and the flow direction changes multiple times, which increases the interaction between the fluids and the fluid disturbance effect, thereby enhancing the fluid temperature uniformity and the heat transfer capacity of the valve body, and effectively shortening the time it takes for the fluid to reach thermal stability.

[0012] Tesla valve structural parameters are structural attributes that affect valve performance, such as channel width, length, geometry, etc. These parameters can be obtained through geometric measurements during the design and manufacturing process, or simulated and optimized through computer-aided design (CAD) software.

[0013] Furthermore, the step S2 analyzes the increase in peak load regulation capacity provided by the flexible regulation of the heat storage device for the cogeneration unit based on the optimal configuration of the heat storage device in step S1, including:

[0014] Formulate corresponding operation strategies according to the maximum heat storage demand of the heat storage device during the mid-load period and the maximum heat release demand during the off-peak period to better improve the peak load regulation capacity of the system;

[0015] Furthermore, in step S3, a two-stage low-carbon scheduling model with the goal of minimizing system operation cost is established, and the model is solved by CPLEX, including:

[0016] Enable the cogeneration units equipped with heat storage devices to participate in the low-carbon dispatch of the power system. By solving equality constraints, inequality constraints and objective functions, find the optimal dispatch plan to optimize the system's operating economy and low carbon performance.

[0017] Among them, the equality constraint is: to ensure that the power system can meet the basic energy conservation and power balance during operation, and the generated optimal dispatching scheme meets the basic requirements of the system; the inequality constraint is: to ensure that the power system operates within the safe and reliable boundaries, limit the search space of the dispatching scheme, and exclude unreasonable or infeasible solutions, so as to improve the feasibility and rationality of the dispatching scheme; the objective function is: to comprehensively consider the economy, reliability and low carbon, and obtain the optimal dispatching scheme. CPLEX is a commercial software suite for solving mathematical optimization problems. It provides efficient and reliable linear programming, integer programming, mixed integer programming, quadratic programming and other optimization solvers.

[0018] The beneficial effects of the present invention are:

[0019] The present invention provides a two-stage scheduling method for the day-ahead and day-intraday considering the Tesla valve heat storage device. It is mainly aimed at the cogeneration unit of "heat-based electricity" which is subject to the restriction of giving priority to satisfying the heat load, the decline of peak-shaving capacity leads to serious wind abandonment problems, and the limited consumption of new energy. The cogeneration unit is equipped with a heat storage device, which further improves the economy and low-carbon nature of the power system. After the cogeneration unit equipped with the heat storage device is adjusted, the electricity-heat coupling constraint is effectively decoupled, the adjustment flexibility of the unit is improved, the peak-shaving capacity of the system is strengthened, the wind abandonment phenomenon is alleviated, and the total operating cost of the system is reduced while reducing carbon emissions. BRIEF DESCRIPTION OF THE DRAWINGS

[0020] Figure 1 It is a two-stage low-carbon scheduling flow chart of the day-ahead and day-intraday in the present invention.

[0021] Figure 2 It is a schematic diagram of the electric-thermal operation characteristics before and after the heat storage device is configured in the present invention. DETAILED DESCRIPTION

[0022] In order to make the purpose, technical solution and advantages of the embodiments of the present invention clearer, the technical solution in the embodiments of the present invention will be clearly and completely described below in conjunction with the drawings in the embodiments of the present invention. Obviously, the described embodiments are part of the embodiments of the present invention, not all of the embodiments. Based on the embodiments of the present invention, all other embodiments obtained by those skilled in the art without creative work are within the scope of protection of the present invention.

[0023] The present invention provides a two-stage scheduling method of day-ahead and intraday considering the Tesla valve heat storage device, wherein the day-ahead stage refers to: making plans 24 hours in advance, and the power system operator needs to carry out preparatory work such as load forecasting, power generation planning, and scheduling arrangements in the day-ahead stage to ensure that the power demand can be met in the intraday stage. This includes determining the output of the generator set, adjusting the load of the transmission line, coordinating the operation of different power plants, etc. The scheduling cycle is 24 hours, and the time scale is 1 hour. The intraday stage refers to: the power system needs to monitor load changes in real time, adjust the output of the generator set, maintain the stable operation of the power grid, etc. in the intraday stage. The power system operator may need to adjust the power generation plan according to the actual situation to deal with emergencies or load fluctuations. The scheduling cycle is 4 hours, and the time scale is 15 minutes. It is implemented specifically in the following steps:

[0024] Step S1: establishing a heat storage device that considers the port characteristics of a multi-stage Tesla valve and the influence of the structural parameters of the Tesla valve on the port characteristics;

[0025] Step S2: Based on the optimal configuration of the heat storage device in step S1, analyzing the peak load capacity increment provided by the flexible regulation of the heat storage device for the cogeneration unit;

[0026] Step S3: Establish a two-stage day-ahead and day-intraday low-carbon dispatch model with the goal of minimizing the operating cost of the cogeneration unit, and solve the model using CPLEX;

[0027] The present invention provides a two-stage low-carbon scheduling method of the day-ahead and day-intraday considering a multi-stage Tesla valve heat storage device. After the cogeneration unit is configured with a heat storage device for adjustment, it effectively decouples the electricity-heat coupling constraint, improves the adjustment flexibility of the unit, strengthens the peak-shaving capability of the system, alleviates the wind abandonment phenomenon, reduces the total operating cost of the system and reduces carbon emissions.

[0028] Specifically, the step S1 includes:

[0029] Establish a heat storage device that considers the port characteristics of multi-stage Tesla valves and the influence of Tesla valve structural parameters on port characteristics;

[0030] The performance of traditional heat storage devices is optimized by using multi-stage Tesla valves, that is, replacing all ordinary pipes inside traditional heat storage devices with pipes connected by multi-stage Tesla valves, which can reduce energy loss in the heat storage device, accelerate fluid flow, improve temperature uniformity, enhance its heat transfer capacity, and further expand the electric-thermal operation range of the cogeneration unit. This study uses the Tesla valve angle, valve distance, and distance between adjacent channels as variables to study the relationship between the structural parameters of the heat storage device and the heat storage and release capacity. Compared with ordinary pipes, multi-stage Tesla valves have a special bifurcation structure, which makes the flow fluctuation at the inlet and outlet larger when the fluid flows through the Tesla valve, resulting in stronger mixing and collision between fluids and faster local speeds, thereby enhancing the interaction between fluids, so that the fluid has higher turbulent kinetic energy, better temperature uniformity, and the ability to reach a thermally stable state faster. At the same time, when the number of Tesla valves increases, the above performance will be further improved. The pipeline formed by connecting the multi-stage Tesla valve after replacement is a pipeline formed by connecting multiple single-stage Tesla valves end to end; the multi-stage Tesla valve specifically refers to a pipeline formed by connecting multiple single-stage Tesla valves.

[0031] The calculation of heat storage / release capacity of heat storage device is as follows:

[0032]

[0033] Where: η c , η d , Γ TV,c and Γ TV,d are the energy utilization efficiency and operation state coefficient of the heat storage device during the heat storage and release process, and the heat storage time Γ TV,c =1,Γ TV,d = 0, when releasing heat, the opposite is true, Γ TV,c =0,Γ TV,d =1;c q , q, ρ are the specific volume, flow rate and density of the fluid respectively; t c and t d are the heat storage / heat release time of the heat storage device; T out and T in are the fluid outlet and inlet temperatures.

[0034] The ideal maximum temperature of Tesla valve is obtained by central composite design method:

[0035]

[0036] Where: θ T , T and ψ Tare Tesla valve angle, valve distance and distance between adjacent channels respectively, and a, b, c, d and e are first-order fitting coefficients.

[0037] Among them, the central composite design method is an experimental design method used to explore the impact of multiple factors on system response and determine the optimal working conditions. It is often used to model and optimize complex systems.

[0038] Following are the basic steps of the central composite design method:

[0039] (1) Determine factors: First, determine the key factors and their levels that affect the system response. These factors can be temperature, pressure, concentration, speed, etc.

[0040] (2) Select levels: Determine the level of each factor, which usually includes low level (-1), high level (+1), center point level (0), and possible extreme levels.

[0041] (3) Constructing the design matrix: Based on the selected levels, construct a central composite design matrix. This matrix usually uses binary coding to combine the different levels of each factor into a series of experimental points.

[0042] (4) Perform the experiment: Perform the experiment according to the test points in the design matrix. At each test point, record the value of the system response variable.

[0043] (5) Model building: Using the collected experimental data, a mathematical model is built to describe the relationship between factors and responses. Common models include linear models, quadratic models, etc.

[0044] (6) Analytical model: Analyze the established model, evaluate the impact of various factors on system response, and determine the optimal working conditions.

[0045] (7) Optimization: Use the established model to find the best working conditions to achieve the best performance of the system.

[0046] Through the above steps, the linear fitting coefficient of the equation of the relationship between the ideal maximum temperature of the Tesla valve and the angle, valve distance and distance between adjacent channels is obtained.

[0047] Specifically, step S2 includes:

[0048] On the basis of the optimal configuration of the heat storage device in step S1, the peak load regulation capacity increment provided by the flexible regulation of the heat storage device for the cogeneration unit is analyzed;

[0049] When the maximum heat storage capacity of the heat storage device during the mid-load period and the maximum heat release demand during the off-peak period are different, the operation strategy will also change accordingly. When the maximum heat storage capacity during the mid-load period is less than the maximum heat release demand during the off-peak period, the heat storage device will first store as much heat as possible during the mid-load period, and store the remaining shortfall during the peak load period to meet the peak load demand during the off-peak period; when the maximum heat storage capacity during the mid-load period is greater than the maximum heat release demand during the off-peak period, the heat storage device will fully store heat during the mid-load period to meet the peak load demand during the off-peak period, and the remaining heat will be compensated for the peak time.

[0050] Among them, the heat storage device stores as much heat energy as possible during the mid-load period. The purpose of this is to use the stored heat energy to meet energy demand during peak load periods, thereby reducing dependence on the main energy supply system and achieving energy conservation and efficient utilization. Consider whether the maximum heat storage capacity of the heat storage device is reached and whether the maximum heat storage during the mid-load period meets the maximum heat release demand during the off-peak period. The remaining shortfall refers to the part where the maximum heat storage during the mid-load period does not meet the maximum heat release demand during the off-peak period.

[0051] The peak load regulation capacity of the cogeneration unit before configuring the heat storage device is:

[0052] C0=(P max -c v1 ·H p )-max{P min -c v2 ·H L ,c m ·(H L -K)} (4)

[0053] Where: P max and P min are the maximum / minimum power generation of the unit under pure condensing conditions; c v is the reduction in electric power when additional unit heating heat is extracted under fixed steam inlet volume, c v1 and c v2 are the corresponding values ​​under maximum / minimum power output respectively; c m is the elastic coefficient of the electric heating power when the unit is running under back pressure; K is a constant; H p and H L It is the thermal load of the unit during peak and valley periods of electrical load.

[0054] The maximum amount of heat that can be stored in the heat storage device during the waist load period is:

[0055]

[0056] Where: Q TV,max is the maximum heat storage capacity of the heat storage device; H max is the maximum heating power of the unit; t Fis the duration of the waist load period; H TV,down The minimum heating power reduction value after the CHP unit is equipped with a heat storage device; H t is the heat load level at time t.

[0057] The maximum amount of heat that can be stored during peak load period is:

[0058]

[0059] Where: t P is the duration of the peak load period.

[0060] The maximum heat release demand during the trough period is:

[0061]

[0062] Where: t L is the duration of the trough period; H med is the thermal power corresponding to the minimum electrical power of the CHP unit; H TV,up The maximum increase in heating power after configuring a heat storage device for the cogeneration unit.

[0063] (1) When the maximum heat storage during the mid-load period is less than the maximum heat release demand during the off-peak period, the total peak-shaving capacity that can be increased by the system within a day is:

[0064]

[0065] (2) When the maximum heat storage during the mid-load period is greater than the maximum heat release demand during the off-peak period, the total peak-shaving capacity that can be increased by the system within a day is:

[0066]

[0067] In summary, the peak load regulation capacity of the cogeneration unit after configuring the heat storage device is:

[0068] C=C0+ΔC (10)

[0069] Specifically, in step S3,

[0070] A two-stage low-carbon scheduling model with the goal of minimizing system operation costs, day-ahead and day-intraday, was established, and the model was solved using CPLEX as follows:

[0071] In the day-ahead stage, a low-carbon dispatch model is constructed with the goal of minimizing the total cost of system operation. Its objective function is:

[0072] F1=min(C CHP +C S +C Q1 +C EB +C T +CB +C cut ) (11)

[0073] Where: F1 is the total cost of the day-ahead scheduling model; C CHP is the operating cost of the combined heat and power unit equipped with heat storage; C S is the unit start-up and shutdown cost; C Q1 C is the penalty cost for abandoning wind and solar power on the previous day; EB is the operating cost of the electric boiler; C T is the carbon trading cost; C B is the cost of purchasing electricity from the external grid; C cut Compensation cost for invoking interruptible load.

[0074] The constraints include power balance constraints, thermal storage device constraints and system spinning reserve constraints.

[0075] (1) Power balance constraint.

[0076] ① Electric power balance constraints:

[0077]

[0078] Where: P CTi,t P is the power output of the i-th cogeneration unit equipped with a heat storage device at time t; Wfj,t and P sfk,t are the predicted outputs of the j-th wind turbine and the k-th photovoltaic power station at time t; P ERo,t and P EDo,t are the charge / discharge power of the oth energy storage at time t; P B,t is the power purchased from the external grid at time t; P LDR,t P is the electric load after implementing demand response at time t; EBl,t is the power consumption of the first electric boiler at time t; P CTr,t is the total output power of the rth carbon capture unit at time t.

[0079] ② Thermal power balance constraints:

[0080]

[0081] Where: H CTi,t H is the thermal output of the i-th cogeneration unit equipped with a heat storage device at time t; EBl,t is the heating power of the first electric boiler at time t; H LDR,t is the heat load after demand response at time t.

[0082] (2) Constraints on heat storage devices.

[0083] ① Constraints on structural parameters of heat storage device.

[0084]

[0085] Where: and They are the maximum and minimum values ​​of the multi-stage Tesla valve angle, valve distance and channel distance respectively.

[0086] ②Operation constraints of heat storage devices.

[0087]

[0088] Where: Q TV,t H is the heat storage capacity of the heat storage device at time t; TV,t is the heat storage / release power of the heat storage device at time t; H TV,cmax and H TV,dmax are the maximum storage / release power of the heat storage device; Q TV,max is the maximum heat storage capacity of the heat storage device; is the remaining heat storage capacity of the heat storage device at time t; is the heat storage amount in the heat storage tank at the starting moment.

[0089] (3) System spinning reserve constraints.

[0090]

[0091] Where: P maxi and P mini are the maximum / minimum power generation of the i-th unit under pure condensing conditions; and are the maximum downward and upward climbing rates of the i-th unit at time t, respectively; and are the upper and lower spinning reserves required by the system in period t, respectively. The system spinning reserve constraint refers to a protection mechanism used to ensure that the power system can maintain stable operation when responding to emergencies. Specifically, the system spinning reserve constraint refers to a control constraint that maintains the system's rotational inertia when the system frequency drops due to emergencies such as load increase or generator failure.

[0092] In the intraday stage of this study, the low-carbon dispatch model is still constructed with the goal of minimizing the total cost of system operation. Compared with the day-ahead stage, the start and stop of the unit have been determined, so its constraints are not considered. The intraday objective function is:

[0093] F2=min(C CHP +C Q2 +C EB +C T +C B +C cut ) (twenty four)

[0094] Where: F2 is the total cost of the intraday scheduling model, C Q2 It is the penalty cost for abandoning wind and solar power within the day.

[0095] Since the time scale of the intraday scheduling phase changes from 1 hour before the day to 15 minutes, the system spinning reserve constraints change.

[0096] System spinning reserve constraints.

[0097]

[0098] Example

[0099] This study proposes a heat storage device considering multi-stage Tesla valve, analyzes the peak load capacity increment provided by the flexible regulation of the heat storage device for the CHP unit, and establishes a two-stage low-carbon scheduling model with the goal of minimizing the system operation cost. In order to verify that the two-stage low-carbon scheduling method proposed in this paper can improve the economic efficiency of system operation, the following four different scenarios are set for comparative analysis:

[0100] (1) Scenario 1: No heat storage device, no carbon capture equipment considered;

[0101] (2) Scenario 2: No heat storage device, considering carbon capture equipment;

[0102] (3) Scenario 3: There is a traditional heat storage device, and carbon capture equipment is considered;

[0103] (4) Scenario 4: With heat storage device, consider carbon capture equipment.

[0104] In the day-ahead stage, the total system operating cost, carbon emissions and wind curtailment power of Scenario 2 were reduced by RMB 7,452, 8,895 kg and 54 kW respectively compared with Scenario 1, down 4.09%, 10.20% and 5.12% year-on-year respectively, verifying the rationality of considering carbon capture equipment. Since both Scenario 1 and Scenario 2 are in the "heat-to-electricity" operation mode of the cogeneration unit, the peak-shaving capacity is poor, so the wind curtailment power is large; Scenario 3 adds a traditional heat storage device on the basis of Scenario 2, and the system cost, carbon emissions and wind curtailment are reduced by RMB 3,328 respectively , 19099Kg, and 470KW, down 1.91%, 24.40%, and 47.24% year-on-year, respectively, confirming that the system equipped with traditional heat storage devices has obvious advantages in wind curtailment; Scenario 4 replaces the traditional heat storage device of Scenario 3 with the heat storage device proposed in this study, and the system cost, carbon emissions, and wind curtailment are reduced by 4099 yuan, 37775Kg, and 525KW, down 2.39%, 63.83%, and 100% year-on-year, respectively, proving the positive significance of heat storage devices in reducing system costs, carbon emissions, and wind curtailment.

[0105] In the intraday stage, the total system operating cost of scenario four is 0.51% lower than that of scenario three, 5.88% lower than that of scenario two, and 6.47% lower than that of scenario one; the load interruption power and purchased electricity power are reduced, and carbon emissions are 7.04% lower than that of scenario three, 23.62% lower than that of scenario two, and 37.20% lower than that of scenario one; the wind curtailment power is 26.85% lower than that of scenario three, 94.55% lower than that of scenario two, and 95.39% lower than that of scenario one.

[0106] By implementing scenario 4, the regulation capacity of the system has been improved, the wind power was fully absorbed in the day-ahead stage, and the wind power abandoned during the day was only 504KW. At the same time, the total operating cost and carbon emissions decreased by 6.47% and 37.20% respectively compared with scenario 1, reducing the system's carbon emissions and improving the operating economy.

[0107] The present invention provides a two-stage scheduling method for the day-ahead and day-intraday considering the Tesla valve heat storage device. It is mainly aimed at the cogeneration unit of "heat-based electricity" which is subject to the restriction of giving priority to satisfying the heat load, the decline of peak-shaving capacity leads to serious wind abandonment problems, and the limited consumption of new energy. The cogeneration unit is equipped with a heat storage device, which further improves the economy and low-carbon nature of the power system. After the cogeneration unit equipped with the heat storage device is adjusted, the electricity-heat coupling constraint is effectively decoupled, the adjustment flexibility of the unit is improved, the peak-shaving capacity of the system is strengthened, the wind abandonment phenomenon is alleviated, and the total operating cost of the system is reduced while reducing carbon emissions.

[0108] The technical means disclosed in the scheme of the present invention are not limited to the technical means disclosed in the above-mentioned implementation mode, but also include technical schemes composed of any combination of the above-mentioned technical features. It should be pointed out that for ordinary technicians in this technical field, several improvements and modifications can be made without departing from the principle of the present invention, and these improvements and modifications are also regarded as the protection scope of the present invention.

Claims

1. A two-stage scheduling method considering the Tesla valve thermal storage device, characterized in that: The following steps are involved: Step S1: establishing a heat storage device that takes into account the port characteristics of the multi-stage Tesla valve and the influence of the Tesla valve structural parameters on the port characteristics, and optimizing the performance of the traditional heat storage device using the multi-stage Tesla valve, that is, replacing all the ordinary pipes inside the traditional heat storage device with pipes connected by the multi-stage Tesla valve; Step S2: On the basis of the optimal configuration of the heat storage device in step S1, the peak load capacity increment provided by the flexible regulation of the heat storage device for the cogeneration unit is analyzed; specifically, the operation strategy changes accordingly when the maximum heat storage of the heat storage device in the mid-load period is different from the maximum heat release demand in the off-peak period; when the maximum heat storage in the mid-load period is less than the maximum heat release demand in the off-peak period, the heat storage device first stores as much heat as possible in the mid-load period, and stores the remaining shortfall in the peak load period to meet the peak load demand in the off-peak period; when the maximum heat storage in the mid-load period is greater than the maximum heat release demand in the off-peak period, the heat storage device stores sufficient heat in the mid-load period to meet the peak load demand in the off-peak period, and the remaining heat is compensated to the peak time; Step S3: Establish a two-stage day-ahead and day-intraday low-carbon dispatch model with the goal of minimizing the operating cost of the cogeneration unit, and solve the model using CPLEX; The heat storage device has a heat storage capacity Q TV,c and heat release Q TV,d They are: Where: η c and η d is the energy utilization efficiency of the heat storage device during the heat storage and release process, Γ TV,c and Γ TV,d is the operating state coefficient of the heat storage device during the heat storage and release process, and Γ TV,c =1,Γ TV,d = 0, when releasing heat, the opposite is true, Γ TV,c =0,Γ TV,d =1;c q , q, ρ are the specific volume, flow rate and density of the fluid respectively; t c and t d are the heat storage / heat release time of the heat storage device; T out and T in are the fluid outlet temperature and inlet temperature; The ideal maximum temperature T of Tesla valve is obtained based on the central composite design method max for: T max =aθ T +bξ T +cξ T 2 +dψ T +e (3) Where: θ T , T and ψ T are Tesla valve angle, valve distance and distance between adjacent channels respectively, and a, b, c, d and e are first-order fitting coefficients.

2. A two-stage scheduling method considering the Tesla valve heat storage device before and during the day according to claim 1, characterized in that: The step S1 specifically includes: reducing energy loss in the heat storage device, accelerating fluid flow, improving temperature uniformity, enhancing its heat transfer capacity, and further expanding the electric-thermal operation range of the cogeneration unit to obtain a heat storage device; and using the Tesla valve angle, valve distance, and distance between adjacent channels as variables to study the relationship between the structural parameters of the heat storage device and the heat storage and release capacity.

3. A two-stage scheduling method considering the Tesla valve heat storage device before and during the day according to claim 1, characterized in that: The peak load capacity C0 of the cogeneration unit before configuring the heat storage device is: C0=(P max -c v1 ·H p )-max{P min -c v2 ·H L ,c m ·(H L -K)} (4) Where: P max and P min are the maximum / minimum power generation of the unit under pure condensing conditions; c v is the reduction in electric power when additional unit heating heat is extracted under fixed steam inlet volume, c v1 and c v2 are the corresponding values ​​under maximum / minimum power output respectively; c m is the elastic coefficient of the electric heating power when the unit is running under back pressure; K is a constant; H p and H L It is the heat load of the unit during the peak and valley periods of electrical load; The maximum amount of heat that can be stored in the heat storage device during the waist load period for: Where: Q TV,max H is the maximum heat storage capacity of the heat storage device; max is the maximum heating power of the cogeneration unit; t F is the duration of the waist load period; H TV,down The minimum heating power reduction value after configuring the heat storage device for the cogeneration unit; H t is the heat load level at time t; Maximum heat storage during peak load period for: Where: t P is the duration of the peak load period; Maximum heat release demand during off-peak hours for: Where: t L is the duration of the trough period; H med is the thermal power corresponding to the minimum electrical power of the cogeneration unit; H TV,up The maximum increase in heating power after configuring a heat storage device for the cogeneration unit.

4. A two-stage scheduling method considering the Tesla valve heat storage device before and during the day according to claim 3, characterized in that: (1) When the maximum heat storage capacity of the heat storage device during the mid-load period is less than the maximum heat release demand during the off-peak period, the total peak load capacity △C that can be increased by the cogeneration unit during the day is: (2) When the maximum heat storage capacity of the heat storage device during the mid-load period is greater than the maximum heat release demand during the off-peak period, the total peak load capacity △C that can be increased by the cogeneration unit during the day is: After configuring the heat storage device, the peak load capacity C of the cogeneration unit is: C=C0+ΔC (10).

5. The method of scheduling a Tesla valve thermal storage device in two stages, day-ahead and day-intraday, according to claim 1, is characterized in that: The step S3 specifically includes: In the day-ahead stage, a low-carbon dispatch model is constructed with the goal of minimizing the total operating cost of the cogeneration unit. Its objective function is: F1=min(C CHP +C S +C Q1 +C EB +C T +C B +C cut ) (11) Where: F1 is the total cost of the day-ahead scheduling model; C CHP is the operating cost of the combined heat and power unit equipped with heat storage; C S is the start-up and shutdown cost of the cogeneration unit; C Q1 C is the penalty cost for abandoning wind and solar power on the previous day; EB is the operating cost of the electric boiler; C T is the carbon trading cost; C B is the cost of purchasing electricity from the external grid; C cut Compensation cost for invoking interruptible load.

6. A two-stage scheduling method considering the Tesla valve heat storage device before and during the day according to claim 5, characterized in that: In the intraday stage, the low-carbon dispatch model is still constructed with the goal of minimizing the total operating cost of the cogeneration unit. Compared with the day-ahead stage, the start and stop of the unit have been determined, so its constraints are not considered. The intraday objective function is: F2=min(C CHP +C Q2 +C EB +C T +C B +C cut ) (12) Where: F2 is the total cost of the intraday scheduling model, C Q2 is the penalty cost for curtailing wind and solar power within the day, C EB The operating cost of the electric boiler.

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