Energy transmission scheduling method based on virtual simulation

Abstract

The invention relates to the technical field of energy dispatching, and discloses an energy transmission dispatching method based on virtual simulation, which comprises the following steps: constructing a physical visualization model according to energy core equipment, dividing a power system, a thermodynamic system and an energy station, and setting multi-energy-flow models of the power system and the thermodynamic system, setting an energy bus model of the energy station; constructing balance constraints of the power system and the thermodynamic system; constructing a cooling / heating scene, an extreme working condition scene and four types of scheduling comparison models; and constructing an objective function and a constraint condition system of the comprehensive operation cost, importing the physical visualization model to form a solvable optimization model, and solving the optimization model through CPLEX to obtain a solving result data set. According to the method, the multi-energy flow coupling model, the flexible balance and source-load co-integration constraint system and the conventional and extreme full scene are constructed, the optimal scheduling scheme with economy, safety and feasibility is output, and efficient and stable operation of the multi-energy system is supported.

Description

Technical Field

[0001] This invention relates to the field of energy dispatching technology, and in particular to an energy transmission dispatching method based on virtual simulation. Background Technology

[0002] Against the backdrop of global energy transition and multi-energy complementarity development, multi-energy systems, as the core carriers of energy supply and consumption, face complex challenges in energy transmission and scheduling. These systems often integrate multiple energy forms such as electricity and heat, involve the coordinated operation of various core energy equipment, and have special energy demands on the load side, making the dynamic balance of energy supply and demand quite difficult.

[0003] Traditional energy transmission and scheduling methods often rely on rigid real-time balance constraints, lacking the ability to explore system flexibility and adapting poorly to the volatility and randomness of renewable energy, easily leading to energy waste or supply-demand imbalances. Furthermore, existing methods often ignore the time delay and loss characteristics in multi-energy flow transmission processes, and the multi-energy flow coupling models lack sufficient refinement to accurately reflect actual energy flow patterns. In addition, traditional scheduling schemes have relatively limited scenario coverage, failing to fully consider the impact of extreme operating conditions on system operation, and the constraint system lacks characterization of long-term source-load power equilibrium relationships, resulting in insufficient safety margins. Current virtual simulation technologies in energy scheduling mostly focus on single energy systems, lacking the collaborative construction of multi-scenario and multi-comparison models, leading to a disconnect between simulation results and engineering realities, making it difficult to directly guide implementation.

[0004] Therefore, there is an urgent need for a new energy transmission and scheduling method that can achieve safe optimization and engineering implementation of scheduling schemes by constructing a refined multi-energy flow coupling model, improving the flexible constraint system, and covering the operating status of all scenarios, so as to meet the needs of efficient and stable operation of multi-energy systems. Summary of the Invention

[0005] This invention provides an energy transmission scheduling method based on virtual simulation. By constructing a multi-energy flow coupling model, a flexible balance and source-load cointegration constraint system, and normal and extreme full-scenario scenarios, it outputs an optimal scheduling scheme that combines economy, safety and feasibility, supporting the efficient and stable operation of multi-energy systems.

[0006] This invention provides an energy transmission scheduling method based on virtual simulation, comprising:

[0007] S1. Construct a physical visualization model based on the core energy equipment and divide it into power system, thermal system and energy station. Set up multi-energy flow models for the power system and thermal system, and set up energy bus model for the energy station.

[0008] S2. Construct the balance constraints of the power system and the thermal system, and use the EG two-step method to construct the linear relationship of the source-load power time series, and use the ADF test to verify the stationarity.

[0009] S3. Based on the physical visualization model and balance constraints, construct cooling / heating scenarios and extreme operating condition scenarios, and construct four types of scheduling comparison models to establish a mapping relationship between scenarios and comparison models;

[0010] S4. Construct the objective function and constraint system of comprehensive operating cost and import it into the physical visualization model to form a solvable optimization model. Solve the optimization model through CPLEX to obtain the solution result dataset.

[0011] S5. Perform physical consistency verification and engineering rationality verification based on the solution result dataset, and adjust the constraint boundary parameters of the constraint system according to the verification results, as well as optimize the objective function and calibrate the physical visualization model parameters.

[0012] Furthermore, S1 specifically includes:

[0013] S101. Construct a physical visualization model of the core energy equipment, wherein the physical visualization model establishes the connection relationship of the core energy equipment according to the actual energy flow direction; wherein, the core energy equipment includes thermal power units, CHP units, wind turbines, photovoltaic generators, electrochemical energy storage systems, high energy-consuming industrial loads, residential / public building heat loads, heat transmission pipelines, and energy conversion equipment.

[0014] S102. Set the parameters of the core energy equipment according to the physical visualization model, including the upper and lower limits of thermal power unit output, ramp rate, coal consumption coefficient, electrical output range of CHP unit, thermal output range of CHP unit, electrothermal conversion coefficient, wind power installed capacity, photovoltaic installed capacity, charge and discharge power range, rated capacity, charge and discharge efficiency, upper and lower limits of state of charge, upper and lower limits of operating power, upper limit of single-time-period adjustment power, electrolytic cell temperature constraint, industrial heating temperature demand, building heating temperature demand, and heat load power range.

[0015] S103. Divide the physical visualization model with the parameters of the energy core equipment into a power system, a thermal system and an energy station, and set up a multi-energy flow model for the power system and the thermal system, as well as an energy bus model for the energy station.

[0016] Furthermore, in S103, the power system multi-energy flow model includes:

[0017] Nodal active power balance constraints: Among them, P G,i,tFor the output of the i-th thermal power unit at time t, P CHP,t For the electrical output of the CHP unit, P WT,t P PV,t respectively contributing to wind and solar power, P BESS,ch,t P BESS,dis,t For energy storage charging and discharging power, P L,j,t Let P be the power of the j-th conventional electrical load. Al,t For high-energy-consuming industrial loads, nG represents the total number of thermal power units, and nL represents the number of conventional electrical loads;

[0018] Frequency and power difference correlation model: Where, Δf t Let ∑P be the frequency offset of the power system at time t. source,t For the total power output, P load,t For the total electrical load, K s For the unit regulating power of the power system;

[0019] The multi-energy flow model of the thermal system includes:

[0020] Relationship between heating capacity and pipe water temperature: in, The heat supplied to the CHP unit at time t, where c is the specific heat capacity of water, and G t Let t be the instantaneous flow rate of hot water in the heating pipe at time t. This refers to the water supply temperature at the pipe inlet. The return water temperature at the pipe outlet;

[0021] Pipeline heat loss model: Where, ΔQ loss,t Let λ be the heat loss of the pipe at time t, λ be the thermal conductivity of the pipe, L be the length of the pipe, and T be the heat loss of the pipe at time t. a,t For ambient temperature, d out d in These are the outer diameter and inner diameter of the pipe, respectively.

[0022] The energy bus model of the energy station includes:

[0023] Energy balance constraints: Where, Φ in For the set of energy input branches of the energy station, Φ out For the set of output energy branches, E in,i,t E out,j,t These are the input and output energy flows, respectively.

[0024] Energy conversion equipment efficiency constraint: E out,j,t =n i,j ·E in,i,t ; where n i,j Let be the conversion efficiency from input branch i to output branch j.

[0025] Furthermore, S2 specifically includes:

[0026] S201. Set a standard frequency deviation as a frequency security constraint for the power system based on the frequency and power difference correlation model of the power system.

[0027] S202. Construct a heat power balance model and a transmission delay model for the thermal system, and based on the heat power balance model, the transmission delay model, and the relationship between the heat supply and the pipe water temperature, use the average thermal perception index to set heating satisfaction as a constraint on heating satisfaction; wherein,

[0028] Thermal power balance model: Q source,t -Q load,t =ΔQ t ; where Q source,t Q represents the total heat supplied by the heat source at time t; load,t ΔQ represents the total heat load power at time t. t The difference between source and load heat power at time t;

[0029] Transmission delay model: Where ρ is the density of water and S is the cross-sectional area of ​​the pipe;

[0030] S203. Based on the constraints of the power system and the thermal system, a linear relationship between the source-load power time series is constructed using the EG two-step method. The stationarity of the power difference series is verified using ADF, and the source-load regulation strategy is dynamically adjusted based on the verification results.

[0031] Linear relationship of source-load power time series: Where t is the scheduling time, ∑P source,t Let ∑P be the total power output of the system at time t. load,t Let ΔP be the total electrical load power of the system at time t. t Q is the difference in active power between the power source and the load at time t. source,t Q is the total heat supplied by the system's heat source at time t. load,t Let ΔQ be the total heat load power of the system at time t. t The difference between the heat source and the charge power at time t.

[0032] Furthermore, S3 specifically includes:

[0033] S301. Based on the parameters of the core energy equipment, set cooling / heating scenarios and extreme operating conditions scenarios, wherein the cooling / heating scenarios include power supply side parameters, load side parameters, environmental and transmission parameters, and the extreme operating conditions scenarios include sudden change conditions in wind and solar power processing, load impact conditions, and equipment failure conditions.

[0034] S302. Based on the multi-energy flow model, energy bus model, cooling / heating scenario, and extreme operating condition scenario, four types of scheduling comparison models are constructed, and their parameters are set, including a real-time electric-thermal power balance constraint model, a flexible electric-thermal power balance constraint model, a flexible balance + source-load cointegration constraint model, and a strict flexible balance constraint model; wherein,

[0035] Real-time balance constraint model of electric and thermal power:

[0036]

[0037] Q CHP,t +Q EB,t =Q ind,t +Q res,t +Q pub,t

[0038] Flexible balance constraint model for electro-thermal power:

[0039]

[0040] Q CHP,t +Q EB,t -Q ind,t -Q res,t -Q pub,t =ΔQ t

[0041] The flexible equilibrium + source-load cointegration constraint model adopts the electric-thermal power flexible equilibrium constraint model, combined with ADF stationarity test:

[0042]

[0043] Among them, the critical value of -2.88 is the critical value of the ADF test standard, ensuring ΔP t ΔQ t It is a stationary sequence;

[0044] The strict flexible balance constraint model adopts the electro-thermal power flexible balance constraint model, and sets the tightening frequency safety constraint as follows:

[0045] S303. Establish a mapping relationship between scenario ID, model ID, and parameter set for the parameters of cooling / heating scenarios, extreme operating conditions scenarios, and the parameters of the four types of scheduling comparison models, forming a structured simulation database.

[0046] Furthermore, S4 specifically includes:

[0047] S401. Construct an objective function for the comprehensive operating cost based on cooling / heating scenarios and extreme operating conditions, specifically:

[0048] minF=F G+F DR +F BESS +F cur +F pen

[0049] Where F is the total operating cost of the system; F G Coal consumption costs for conventional thermal power units and CHP units; F DR For demand response costs of high-energy-consuming industrial loads and industrial heat loads; F BESS For the operating cost of the energy storage system; F cur The cost of penalties for curtailing wind and solar power; F pen To incur the cost of safety penalties;

[0050] S402. Integrate the parameters of the multi-energy flow model, energy bus model, balance constraint, and four types of scheduling comparison models to form a constraint system. Set the parameters of cooling / heating scenarios and extreme operating conditions to be imported into the physical visualization model to form a solvable optimization model.

[0051] S403. Based on CPLEX, construct a combination matrix of 4 scene × 4 model, call the constraints in the combination order, use the branch and bound method for iterative solution, set the convergence condition and output the optimal solution after convergence, if it does not converge, adjust the relaxation factor and solve again to form a complete solution result dataset.

[0052] Furthermore, S5 specifically includes:

[0053] S501. Perform physical consistency verification and engineering rationality verification based on the solution result dataset. If the verification fails, return to step S1 to adjust the parameter configuration.

[0054] S502. Adjust the constraint boundary parameters of the constraint system based on the verification results, and optimize the objective function and calibrate the parameters of the physical visualization model.

[0055] The present invention also provides an energy transmission scheduling device based on virtual simulation, which, based on the energy transmission scheduling method based on virtual simulation described above, comprises:

[0056] The first construction module is used to construct a physical visualization model based on the core energy equipment and divide it into power system, thermal system and energy station, set up the multi-energy flow model of the power system and thermal system, and set up the energy bus model of the energy station;

[0057] The second construction module is used to construct the balance constraints of the power system and the thermal system, and to construct the linear relationship of the source-load power time series using the EG two-step method, and to verify the stationarity using the ADF test.

[0058] The mapping module is used to construct cooling / heating scenarios and extreme operating condition scenarios based on the physical visualization model and balance constraints, as well as to construct four types of scheduling comparison models and establish the mapping relationship between scenarios and comparison models.

[0059] The solution module is used to construct the objective function and constraint system of the comprehensive operating cost and import them into the physical visualization model to form a solvable optimization model. The optimization model is solved by CPLEX to obtain the solution result dataset.

[0060] The verification module is used to perform physical consistency verification and engineering rationality verification based on the solution result dataset, and to adjust the constraint boundary parameters of the constraint system based on the verification results, as well as optimize the objective function and calibrate the parameters of the physical visualization model.

[0061] The present invention also provides a computer device, including a memory and a processor, wherein the memory stores a computer program, and the processor executes the computer program to implement the steps of the above-described method.

[0062] The present invention also provides a computer-readable storage medium having a computer program stored thereon, wherein the computer program, when executed by a processor, implements the steps of the above-described method.

[0063] The beneficial effects of this invention are as follows:

[0064] This invention ensures the accuracy of energy flow characterization by constructing a multi-energy flow coupling model including transmission delay and loss, and an energy station energy bus coupling model. A complete constraint system, built with flexible balance constraints and source-load cointegration constraints, balances system operation safety and flexibility, avoiding supply-demand imbalances and energy waste. Through comprehensive coverage of conventional cooling / heating scenarios and various extreme operating conditions, and the collaborative construction and mapping of multiple scheduling comparison models, it achieves full adaptation to different operating states and quantitative comparison of scheduling effects. Based on the solution of the comprehensive operating cost objective function and the solvable optimization model, along with triple verification, it outputs an optimal scheduling scheme that is both economical and feasible, providing strong support for the efficient and stable operation of multi-energy systems. Attached Figure Description

[0065] Figure 1 This is a schematic diagram of a method flow according to an embodiment of the present invention.

[0066] Figure 2 This is a schematic diagram of the device structure according to an embodiment of the present invention.

[0067] Figure 3 This is a schematic diagram of the internal structure of a computer device according to an embodiment of the present invention.

[0068] The realization of the objective, functional features and advantages of the present invention will be further explained in conjunction with the embodiments and with reference to the accompanying drawings. Detailed Implementation

[0069] It should be understood that the specific embodiments described herein are merely illustrative of the invention and are not intended to limit the invention.

[0070] like Figure 1 As shown, this invention provides an energy transmission scheduling method based on virtual simulation, comprising:

[0071] S1. Construct a physical visualization model based on the core energy equipment and divide it into a power system, a heating system, and an energy station. Set up multi-energy flow models for the power system and the heating system, and set up an energy bus model for the energy station. Specifically, this includes:

[0072] S101. Using MATLAB / Simulink or DIgSILENT simulation tools, construct a physical visualization model of the core energy equipment. This physical visualization model establishes the connection relationships of the core energy equipment according to the actual energy flow direction. Specifically,

[0073] Core energy equipment includes thermal power units, CHP (combined heat and power) units, wind turbines (WT), photovoltaic generators (PV), electrochemical energy storage systems (BESS), high-energy-consuming industrial loads, residential / public building heat loads, heat transmission pipelines, and energy conversion equipment (such as electric boilers and radiators).

[0074] The connection relationships of the core energy equipment are as follows:

[0075] The output terminals of the CHP unit are connected to the power bus and the heat transmission pipeline respectively; the wind turbine, photovoltaic generator, thermal power unit, and electrochemical energy storage system are connected in parallel to the power bus, which connects industrial loads and residential power loads; one end of the heat transmission pipeline is connected to the heating outlet of the CHP unit, and the other end is branched to industrial heat-consuming equipment (such as steam reactors) and building radiators; high-energy-consuming industrial loads are connected to the power bus through a dedicated interface.

[0076] S102. Set the parameters of the core energy equipment according to the physical visualization model, specifically as follows:

[0077] ① Thermal power units: upper and lower limits of output P G,min ~P G,max (Set value range 40~200MW), Climbing rate ΔP G,max (Setting range is 0.5~1.5MW / min), coal consumption coefficients a, b, c.

[0078] ②CHP Unit: Electric Output Range P CHP,min ~P CHP,max (100~250MW), thermal output range Q CHP,min ~QCHP,max (0~300MW), electrothermal conversion coefficient μ0~μ5 (μ0=0.75~0.80, μ1=0.25~0.30, μ2=7.0×10 -5 ~8.5×10 -5 (etc.), adapted to the thermal constant power operation mode.

[0079] ③ Wind turbine / photovoltaic generator: wind power installed capacity (50-200MW), photovoltaic installed capacity (30-150MW), set output fluctuation model (wind power based on Weibull distribution, photovoltaic based on Beta distribution).

[0080] ④ Electrochemical energy storage system: charge / discharge power range (20–50 MW), rated capacity (20–50 MWh), charge / discharge efficiency η ch =η dis =0.95~0.98, upper and lower limits of state of charge (SOC) min =0.1, SOC max =0.9.

[0081] ⑤ High-energy-consuming industrial loads: upper and lower limits of operating power P Al,min ~P Al,max Single-period adjustable power limit ΔP Al,up =20~25MW, ΔP Al,down =20~25MW, electrolytic cell temperature constraint T Al,min =950℃, T Al,max =970℃.

[0082] ⑥ Heat load of residential / public buildings and heat load of energy conversion equipment: industrial heating temperature demand (80~160℃), building heating temperature demand (18~24℃), heat load power range (50~250MW), set thermal inertia related parameters (building heat capacity C=1.5×10 5 ~2.0×10 5 J / (m·℃)), heat loss rate μ=2.5~3.0W / (m 2 ·℃)).

[0083] ⑦ Heat transfer pipes: length L, inner diameter d, thermal conductivity λ, hot water flow rate G, upper and lower limits of water supply temperature T start,min ~T start,max upper and lower limits of return water temperature T re_end,min ~T re_end,max .

[0084] S103. Divide the physical visualization model containing the parameters of the core energy equipment into a power system, a thermal system, and an energy station, and set up multi-energy flow models for the power system and the thermal system, as well as an energy bus model for the energy station; specifically:

[0085] ① A power system includes power generation units, energy storage units, load units, and transmission units. Power generation units include thermal power units, CHP units (electric power output), wind turbines, and photovoltaic generators, responsible for generating electricity; energy storage units include electrochemical energy storage systems, responsible for storing and releasing electrical energy; load units include high-energy-consuming industrial loads and residential / public building heat loads, responsible for consuming electrical energy; and transmission units include implicit transmission lines, responsible for transmitting electrical energy between power sources, energy storage, and loads.

[0086] The multi-energy flow model of a power system includes nodal active power balance constraints and a frequency-power difference correlation model. Specifically,

[0087] Nodal active power balance constraints:

[0088]

[0089] Among them, P G,i,t For the output of the i-th thermal power unit at time t, P CHP,t For the electrical output of the CHP unit, P WT,t P PV,t respectively contributing to wind and solar power, P BESS,ch,t P BESS,dis,t For energy storage charging and discharging power, P L,j,t Let P be the power of the j-th conventional electrical load. Al,t For high-energy-consuming industrial loads, nG represents the total number of thermal power units, and nL represents the number of conventional electrical loads. nL represents the total number of conventional electrical loads other than high-energy-consuming industrial loads, including distributed electrical loads such as residential electricity loads and public building electricity loads.

[0090] Frequency and power difference correlation model:

[0091]

[0092] Where, Δf t Let ∑P be the frequency offset of the power system at time t. source,t For total power output (including wind and solar, thermal power, CHP, and energy storage discharge), P load,t For the total electrical load (including conventional load, high-energy-consuming industrial load, and energy storage charging), K s For power system unit regulation power.

[0093] ② The thermal system includes heat source units, transmission units, and conversion and consumption units; the heat source unit includes CHP units (heat output part) and energy conversion equipment (such as electric boilers), which are responsible for heat energy production; the transmission unit includes heat transmission pipelines, which are responsible for transmitting the heat energy generated by the heat source to the load end; the conversion and consumption unit includes energy conversion equipment (such as radiators), residential / public building heat loads, and industrial heat loads of high-energy-consuming industrial loads, among which radiators realize the conversion of hot water heat energy into indoor heat, and various heat loads directly consume heat energy.

[0094] The multi-energy flow model of a thermal system includes the relationship between heat supply and pipe water temperature, the pipe heat loss model, and the hydraulic constraints (mass conservation) of the thermal system. Specifically:

[0095] Relationship between heating capacity and pipe water temperature:

[0096]

[0097] in, The heat supplied to the CHP unit at time t, where c is the specific heat capacity of water, and G t Let t be the instantaneous flow rate of hot water in the heating pipe at time t. This refers to the water supply temperature at the pipe inlet. This refers to the return water temperature at the pipe outlet.

[0098] Pipeline heat loss model:

[0099]

[0100] Where, ΔQ loss,t Let λ be the heat loss of the pipe at time t, λ be the thermal conductivity of the pipe, L be the length of the pipe, and T be the heat loss of the pipe at time t. a,t For ambient temperature, d out d in These refer to the outer diameter and inner diameter of the pipe, respectively.

[0101] Hydraulic constraints of thermal systems:

[0102] Σ s∈Γ(n) M s,n,t =0

[0103] Where Γ(n) is the set of pipes connecting node n, and M s,n,t Let be the mass flow rate of pipe s at node n at time t (inflow is positive, outflow is negative).

[0104] ③ The energy station includes an energy conversion unit and a distribution unit; the energy conversion unit includes a CHP unit (to realize cogeneration conversion), an electric boiler (to realize electricity-to-heat conversion), and a radiator (to realize heat-to-heat distribution conversion); the distribution unit distributes the converted electrical energy and heat energy to the power system and the heating system through the pipelines and lines inside the energy station.

[0105] The energy bus model of the energy station includes energy balance constraints and energy conversion equipment efficiency constraints. Specifically, the energy balance constraints are as follows:

[0106]

[0107] Where, Φ in For the energy station, the input energy branch collection (such as natural gas, electricity), Φ out For the collection of output energy branches (such as electrical energy, thermal energy), E in,i,t E out,j,t These are the input and output energy flows, respectively.

[0108] Energy conversion equipment efficiency constraints:

[0109] E out,j,t =n i,j ·E in,i,t

[0110] Where, n i,j The conversion efficiency from input branch i to output branch j (such as CHP electrical energy conversion efficiency, electric boiler thermal energy conversion efficiency).

[0111] Finally, the power system, thermal system, and energy station models were integrated through a data interface. Convergence conditions for multi-energy flow calculation were set, and the Newton-Raphson method was adopted as the multi-energy flow solution algorithm to ensure the stability and accuracy of the calculation.

[0112] S2. Construct the equilibrium constraints for the power system and the thermal system, and use the EG two-step method to construct the linear relationship of the source-load power time series, and use the ADF test to verify stationarity. Specifically, this includes:

[0113] S201. Based on the frequency and power difference correlation model of the power system, construct the frequency security constraints of the power system, specifically: standard frequency f0 = 50Hz, the allowable frequency deviation for systems with a capacity of less than 3000MW is ±0.5Hz, and the allowable deviation for systems with a capacity of 3000MW and above is ±0.2Hz.

[0114] S202. Construct a heat power balance model and a transmission delay model for the thermal system, and construct heating satisfaction constraints based on the heat power balance model, the transmission delay model, and the relationship between heat supply and pipe water temperature; specifically including:

[0115] Thermal power balance model:

[0116] Q source,t -Q load,t =ΔQ t

[0117] Among them, Q source,tQ represents the total heat supply from the heat source at time t, including the thermal output of the CHP unit, the output of the electric boiler, and the heat release power of the thermal storage device; load,t Let ΔQ be the total heat load power at time t, including industrial heat consumption, residential building heating, and public building heating power; t Let ΔQ be the difference in heat power between the source and the load at time t. t >0 indicates that excess heat is stored in pipes or the building (virtual thermal storage charging), ΔQ t <0 indicates that the virtual thermal storage releases heat to supplement the load demand.

[0118] The transmission delay model is calculated based on the pipe length and water flow velocity:

[0119]

[0120] Where ρ is the density of water and S is the cross-sectional area of ​​the pipe;

[0121] The revised heat supply model: Q actual,t =Q source,t -ΔQloss,t ensures that the constraint calculation closely matches the actual heat transfer process.

[0122] Heating satisfaction is measured using the mean thermal perception index (PMV). When the PMV index is in the range of [-1, 1], it meets the user comfort requirements, and the corresponding building room temperature constraint range is 18℃~24℃. The industrial heating temperature constraint is set according to the production process (e.g., 80~160℃).

[0123] S203. Based on the constraints of the power system and the thermal system, a linear relationship between the source-load power time series is constructed using the EG two-step method, and the source-load regulation strategy is dynamically adjusted based on the ADF test results. Specifically, this includes:

[0124] Linear relationship of source-load power time series:

[0125]

[0126] Where t is the scheduling time, ∑P source,t Let ∑P be the total power output of the system at time t. load,t Let ΔP be the total electrical load power of the system at time t. t Q is the difference in active power between the power source and the load at time t. source,t Q is the total heat supplied by the system's heat source at time t. load,t Let ΔQ be the total heat load power of the system at time t. t The difference between the heat source and the charge power at time t.

[0127] The augmented Dickey-Fowler test (ADF test) was used to analyze the power difference series. (T is the total number of time periods in the scheduling cycle) Stationarity verification is performed, with a significance level of 5%. The critical value is referenced from the ADF test standard distribution table (e.g., when T=96, the critical value is -2.88). If the test statistic is less than the critical value, the sequence is stationary and the constraints are met.

[0128] S3. Based on the multi-energy flow model, energy bus model, and balance constraints, construct cooling / heating scenarios and extreme operating condition scenarios, as well as four types of scheduling comparison models, establish the mapping relationship between scenarios and comparison models, and form a simulation structured database.

[0129] S301. Based on the parameters of the core energy equipment, set the parameters for cooling / heating scenarios and extreme operating conditions; specifically including:

[0130] ① Cooling / Heating Scenarios: Based on the parameters of the core energy equipment, set the power supply side parameters, including the output range of thermal power units, ramp rate, output range of CHP units, thermal output range of CHP units, electrothermal conversion coefficient, wind power installed capacity, photovoltaic installed capacity, wind power / photovoltaic output curves, energy storage system charge and discharge power range, energy storage system charge and discharge efficiency, energy storage system SOC range, and operating cost;

[0131] Based on the parameters of the aforementioned core energy equipment, load-side parameters are set, including electrical load (total maximum capacity, residential / public building electrical load) and heat load (total maximum capacity, building heating load, industrial heat load);

[0132] Based on the parameters of the core energy equipment, environmental and transmission parameters are set, including ambient temperature, thermal pipeline parameters (length, inner diameter, hot water flow rate, supply water temperature, return water temperature), and power system regulation parameters (unit regulation power).

[0133] The output of wind and solar power fluctuates naturally with time, with the peak period being from 10:00 to 18:00. The daytime (8:00 to 22:00) electricity load is higher than the nighttime load. The industrial heat load is stable throughout the day, while the building heat load is higher at night (22:00 to 8:00 the next day) than during the day (due to lower ambient temperature). Under the premise of meeting frequency safety (±0.5Hz) and heating satisfaction (PMV∈[-1,1]), the system operating cost (coal consumption cost + demand response cost + energy storage operation cost + wind and solar curtailment penalty cost) should be minimized.

[0134] ② Extreme operating conditions, including sudden changes in wind and solar power output, load shock conditions, and equipment failure conditions; specifically including:

[0135] a. Sudden change in wind and solar power output: The wind / solar power output changes suddenly within one dispatch period, with the change amplitude being 30% of the rated capacity, and lasts for two periods;

[0136] b. Load shock conditions: High-energy-consuming industrial loads experience sudden power fluctuations due to the anode effect, with the single-period adjustment reaching the upper limit of 25MW, or industrial heat load demand suddenly increasing by 20% (50MW) for 3 consecutive periods;

[0137] c. Equipment failure condition: One conventional thermal power unit (CON2) suddenly stopped, resulting in a sharp drop in power output of 60MW (60% of its rated capacity), lasting for 4 periods.

[0138] S302. Based on the multi-energy flow model and energy bus model, construct four types of scheduling comparison models and set their parameters, including the real-time balance constraint model of electric-thermal power, the flexible balance constraint model of electric-thermal power, the flexible balance + source-load cointegration constraint model, and the strict flexible balance constraint model.

[0139] ① Real-time balance constraint model of electric and thermal power:

[0140]

[0141] Q CHP,t +Q EB,t =Q ind,t +Q res,t +Q pub,t

[0142] Among them, P G,i,t P represents the electrical output of the i-th conventional thermal power unit at time t (i = 1, 2, 3 corresponds to 3 conventional thermal power units); CHP,t P represents the electrical output of the CHP unit at time t. WT,t P represents the output of the wind power system at time t. PV,t P represents the output of the photovoltaic system at time t. BESS,dis,t P represents the discharge power of the energy storage system at time t. Al,t P represents the power of the electrical load in the electrolytic aluminum industry at time t. load,conv,t P represents the power of a conventional electrical load (such as electricity used in residential and public buildings) at time t. BESS,ch,t Q represents the charging power of the energy storage system at time t. CHP,t Q represents the thermal output of a combined heat and power (CHP) unit at time t; EB,t Q represents the thermal output of the electric boiler at time t. ind,t Q represents the power of industrial heat load (such as heat used in production processes) at time t. res,t Q represents the power of the building's heat load (e.g., heating) at time t. pub,t This represents the power of the heat load (such as heating) of a public building at time t.

[0143] ② Flexible balance constraint model for electro-thermal power:

[0144]

[0145] Q CHP,t +Q EB,t -Q ind,t -Q res,t -Q pub,t =ΔQ t

[0146] Wherein, ΔP t ΔQ is the difference in active power between the power source and the load at time t (the difference between the power output and the electrical load), and is a system flexibility buffer variable; t The difference between the heat source and the load at time t (the difference between the heat supplied by the heat source and the heat load) is used to form a virtual heat storage buffer based on the inertia of the thermal system.

[0147] ③ Flexible equilibrium + source-load cointegration constraint model:

[0148] The flexible balance constraint model of electric-thermal power in section ② is adopted, combined with the source-load cointegration constraint (ADF stationarity test):

[0149]

[0150] The critical value of -2.88 is the critical value of the ADF test at a significance level of 5% and a sample size of 96, ensuring ΔP t ΔQ t It is a stationary sequence (with a small range of fluctuations and a mean of 0).

[0151] ④ Strictly flexible equilibrium constraint model:

[0152] The flexible balance constraint model of electro-thermal power in section ② is adopted, and the tightening frequency safety constraint is set as follows:

[0153] S303. Standardize the parameters of the cooling / heating scenario, the extreme operating condition scenario, and the parameters of the four types of scheduling comparison models according to the simulation tool data format, and unify the variable declaration rules. Finally, establish a mapping relationship between the parameters of the cooling / heating scenario, the extreme operating condition scenario, and the parameters of the four types of scheduling comparison models, forming a simulation structured database.

[0154] S4. Construct the objective function and constraint system for the comprehensive operating cost and import it into the physical visualization model to form a solvable optimization model. Solve the optimization model using CPLEX to obtain the solution result dataset; specifically including:

[0155] S401. Construct an objective function for the comprehensive operating cost based on cooling / heating scenarios and extreme operating conditions, specifically:

[0156] minF=FG +F DR +F BESS +F cur +F pen

[0157] Where F is the total operating cost of the system; F G The coal consumption cost of conventional thermal power units and CHP units, including the fuel consumption cost of conventional thermal power units and CHP units, is calculated using a secondary coal consumption characteristic model; F DR The demand response cost for high-energy-consuming industrial loads and industrial heat loads includes the adjustment costs for high-energy-consuming industrial loads and industrial heat loads; F BESS The operating cost of the energy storage system only considers the operating losses during the charging and discharging process; F cur The penalty cost for wind and solar curtailment (a penalty cost levied on unconsumed wind and solar power output to incentivize renewable energy consumption; this is a basic cost item, using the parameters from steps S2 and S3); F pen Safety penalty cost (added for extreme operating conditions, 0 for cooling / heating scenarios).

[0158] S402. Integrate the parameters of the multi-energy flow model, energy bus model, balance constraints, and four types of scheduling comparison models to form a constraint system. Set the parameters for cooling / heating scenarios and extreme operating conditions, and import them into the optimization model that can be solved by the physical visualization model.

[0159] S403. Set up the solution environment and set the objective function accuracy to 10. -6 Constraint satisfaction 10 -3 A unified solution parameter with a maximum iteration count of 10,000 was used, employing CPLEX for solving. First, initial values ​​for variables were initialized (energy storage SOC initial value 0.5, initial output of thermal power units 1.2 times minimum output, etc.). Parameter and constraint systems for cooling / heating scenarios and extreme operating conditions were imported, constructing 16 combination matrices for a 4-scenario (cooling / heating scenario + 3 extreme operating conditions) × 4 model. Constraints were sequentially called according to the combination order, and iterative solutions were performed using the branch and bound method, with the objective function change ≤ 10. -6 The convergence condition is reached by reaching the upper limit of the iteration. After convergence, the optimal solution is output. If convergence fails, the relaxation factor is adjusted and the solution is solved again. Finally, variables such as output, power difference, frequency, and room temperature of each device are stored in a structured format of scenario ID-model ID-time period t. Key indicators such as total operating cost, wind curtailment rate, and frequency overrun are calculated simultaneously to form a complete solution result dataset. The optimal scheduling scheme is formed based on the solution result dataset.

[0160] S5. Based on the solution result dataset, perform physical consistency verification and engineering rationality verification, and adjust the constraint boundary parameters of the constraint system according to the verification results, as well as optimize the objective function and calibrate the parameters of the physical visualization model. Specifically, this includes:

[0161] S501. Perform physical consistency verification and engineering rationality verification based on the solved dataset. Specifically, this includes:

[0162] ① Physical consistency verification

[0163] Ensure that the optimal solution of the optimization model conforms to the basic laws of the multi-energy flow physics model and has no abnormal results that violate energy conservation and thermal properties;

[0164] Power system energy conservation verification: For each scheduling period t, verify ∑P source,t Error ≤10 -3 MW (adjustable preset value), and frequency offset Δf t The correlation with the power difference follows the linear relationship of step S2;

[0165] Thermal balance verification of the thermal system: Based on the heat supply-water temperature relationship formula in step S1, verify Q. source,t The calculation error is ≤1%, and the pipeline heat loss Q loss,t The changing trends of the supply and return water temperature difference and ambient temperature conform to the laws of heat conduction.

[0166] Multi-energy flow coupling consistency verification: Verify the full coal consumption characteristics of the CHP unit's electrothermal output, and find no abnormalities in electrothermal output decoupling;

[0167] If the error exceeds the threshold, backtrack to the physical parameter configuration in step S1, correct it, and solve it again.

[0168] ② Verification of the rationality of the project

[0169] Equipment operation rationality: Check that the output change rate of thermal power units and CHP units is ≤ ramp rate (e.g., CON1 unit ≤ 1.5MW / min), the energy storage charging and discharging power is ≤ rated capacity (30MW), and the high energy-consuming industrial load adjustment is ≤ 25MW / time period, which meets the physical operation limits of the equipment;

[0170] Economic rationality: Under normal scenarios, the total operating cost of the system is ≤ 1.05 times that of the real-time balance constraint model of electric-thermal power. Under extreme conditions, the proportion of safety penalty cost is ≤ 10% of the total cost, and the wind curtailment rate is ≤ 5%.

[0171] S502. Adjust the constraint boundary parameters of the constraint system based on the verification results, and optimize the objective function and calibrate the parameters of the physical visualization model. Specifically, this includes:

[0172] ① Adjust the constraint boundary parameters of the constraint system:

[0173] Optimization of cointegration constraint critical value: If the flexible balance + source-load cointegration constraint model still shows non-stationarity of the difference sequence under extreme operating conditions, reduce the critical value of ADF test (e.g., adjust from -2.88 to -3.0), or narrow the power difference fluctuation range (e.g., ΔP∈[-12,12]MW);

[0174] Frequency constraint boundary optimization: If the strict flexible balance constraint model (±0.3Hz) is too uneconomical and the electric-thermal power flexible balance constraint model (±0.5Hz) is not safe enough, the allowable frequency offset range is adjusted to ±0.4Hz based on the system capacity (e.g., 3000MW) to take into account the advantages of both.

[0175] Thermal constraint optimization: If the building room temperature compliance rate is insufficient, tighten the virtual heat storage difference range (e.g., ΔQ∈[-40,40]kJ / h), or increase the standby output of the electric boiler (e.g., from 50MW to 60MW).

[0176] ② Optimize the objective function

[0177] If over-limit behaviors still occur frequently under extreme operating conditions, increase the unit penalty cost for frequency over-limit behaviors (e.g., adjust from 500 yuan / (Hz·h) to 800 yuan / (Hz·h)) and strengthen the weight of safety constraints; re-solve the optimized model to ensure that the total operating cost is reduced by ≥1% and that the safety indicators (number of over-limit behaviors, compliance rate) do not deteriorate.

[0178] ③ Calibrate the parameters of the physical visualization model

[0179] Pipeline heat loss parameter calibration: Based on the actual supply and return water temperature difference and ambient temperature monitoring data of the park's heating pipeline, the thermal conductivity λ of the pipeline insulation material is calculated (e.g., corrected from 0.1 W / (m·℃) to 0.11 W / (m·℃)).

[0180] Load characteristic parameter calibration: By recording the electrolytic aluminum production process, correct the peak and trough values ​​of the daily curve of industrial electric / heat load (e.g., correct the maximum load of high-energy-consuming industries from 400MW to 420MW);

[0181] The calibrated parameters are synchronously updated to the physical visualization model in step S1 and the optimization model in step 4 to ensure parameter consistency throughout the entire process.

[0182] like Figure 2 As shown, the present invention also provides an energy transmission scheduling device based on virtual simulation. Based on the energy transmission scheduling method based on virtual simulation described above, the device includes:

[0183] The first construction module 1 is used to construct a physical visualization model based on the energy core equipment and divide it into power system, thermal system and energy station, set up multi-energy flow models of the power system and thermal system, and set up energy bus model of the energy station;

[0184] The second construction module 2 is used to construct the balance constraints of the power system and the thermal system, and to construct the linear relationship of the source-load power time series using the EG two-step method, and to verify the stationarity using the ADF test.

[0185] The mapping module 3 is used to construct cooling / heating scenarios and extreme operating condition scenarios based on the physical visualization model and balance constraints, as well as to construct four types of scheduling comparison models and establish a mapping relationship between scenarios and comparison models.

[0186] Solving module 4 is used to construct the objective function and constraint system of comprehensive operating cost and import it into the physical visualization model to form a solvable optimization model. The optimization model is solved by CPLEX to obtain the solution result dataset.

[0187] Verification module 5 is used to perform physical consistency verification and engineering rationality verification based on the solution result dataset, and to adjust the constraint boundary parameters of the constraint system based on the verification results, as well as optimize the objective function and calibrate the parameters of the physical visualization model.

[0188] Each of the above modules is used to execute the respective steps in the above-mentioned energy transmission scheduling method based on virtual simulation. The specific implementation method is as described in the above-mentioned method embodiment, and will not be repeated here.

[0189] like Figure 3 As shown, the present invention also provides a computer device, which may be a server, and its internal structure may be as follows: Figure 3 As shown, the computer device includes a processor, memory, network interface, and database connected via a system bus. The processor provides computational and control capabilities. The memory includes non-volatile storage media and internal memory. The non-volatile storage media stores the operating system, computer programs, and database. The internal memory provides the environment for the operation of the operating system and computer programs stored in the non-volatile storage media. The database stores all data required for the process of the virtual simulation-based energy transmission scheduling method. The network interface is used for communication with external terminals via a network connection. The computer program is executed by the processor to implement the virtual simulation-based energy transmission scheduling method.

[0190] Those skilled in the art will understand that Figure 3The structure shown is merely a block diagram of a portion of the structure related to the present application and does not constitute a limitation on the computer equipment on which the present application is applied.

[0191] An embodiment of this application also provides a computer-readable storage medium storing a computer program thereon, which, when executed by a processor, implements any of the above-described energy transmission scheduling methods based on virtual simulation.

[0192] Those skilled in the art will understand that all or part of the processes in the methods of the above embodiments can be implemented by hardware related to computer program instructions. The computer program can be stored in a non-volatile computer-readable storage medium, and when executed, it can include the processes of the above method embodiments. Any references to memory, storage, databases, or other media provided in this application and used in the embodiments can include non-volatile and / or volatile memory. Non-volatile memory can include read-only memory (ROM), programmable ROM (PROM), electrically programmable ROM (EPROM), electrically erasable programmable ROM (EEPROM), or flash memory. Volatile memory can include random access memory (RAM), such as dynamic RAM (used as main storage) or static RAM (commonly used as cache memory). By way of illustration and not limitation, RAM has various forms, such as static RAM (SRAM), dynamic RAM (DRAM), synchronous DRAM (SDRAM), double data rate SDRAM (DDR SDRAM), and Rambus DRAM (RDRAM).

[0193] It should be noted that, in this document, the terms "comprising," "including," or any other variations thereof are intended to cover non-exclusive inclusion, such that a process, apparatus, article, or method that comprises a list of elements includes not only those elements but also other elements not expressly listed, or elements inherent to such process, apparatus, article, or method. Unless otherwise specified, an element defined by the phrase "comprising one..." does not exclude the presence of other identical elements in the process, apparatus, article, or method that includes that element.

[0194] The above description is merely a preferred embodiment of the present invention and does not limit the patent scope of the present invention. Any equivalent structural or procedural transformations made based on the content of the present invention's specification and drawings, or direct or indirect applications in other related technical fields, are similarly included within the patent protection scope of the present invention.

Claims

1. An energy transmission scheduling method based on virtual simulation, characterized in that, include: S1. Construct a physical visualization model based on the core energy equipment and divide it into power system, thermal system and energy station. Set up multi-energy flow models for the power system and thermal system, and set up energy bus model for the energy station. S2. Construct the balance constraints of the power system and the thermal system, and use the EG two-step method to construct the linear relationship of the source-load power time series, and use the ADF test to verify the stationarity. S3. Based on the physical visualization model and balance constraints, construct cooling / heating scenarios and extreme operating condition scenarios, and construct four types of scheduling comparison models to establish a mapping relationship between scenarios and comparison models; S4. Construct the objective function and constraint system of comprehensive operating cost and import it into the physical visualization model to form a solvable optimization model. Solve the optimization model through CPLEX to obtain the solution result dataset. S5. Perform physical consistency verification and engineering rationality verification based on the solution result dataset, and adjust the constraint boundary parameters of the constraint system according to the verification results, as well as optimize the objective function and calibrate the physical visualization model parameters.

2. The energy transmission scheduling method based on virtual simulation according to claim 1, characterized in that, S1 specifically includes: S101. Construct a physical visualization model of the core energy equipment, wherein the physical visualization model establishes the connection relationship of the core energy equipment according to the actual energy flow direction; wherein, the core energy equipment includes thermal power units, CHP units, wind turbines, photovoltaic generators, electrochemical energy storage systems, high energy-consuming industrial loads, residential / public building heat loads, heat transmission pipelines, and energy conversion equipment. S102. Set the parameters of the core energy equipment according to the physical visualization model, including the upper and lower limits of thermal power unit output, ramp rate, coal consumption coefficient, electrical output range of CHP unit, thermal output range of CHP unit, electrothermal conversion coefficient, wind power installed capacity, photovoltaic installed capacity, charge and discharge power range, rated capacity, charge and discharge efficiency, upper and lower limits of state of charge, upper and lower limits of operating power, upper limit of single-time-period adjustment power, electrolytic cell temperature constraint, industrial heating temperature demand, building heating temperature demand, and heat load power range. S103. Divide the physical visualization model with the parameters of the energy core equipment into a power system, a thermal system and an energy station, and set up a multi-energy flow model for the power system and the thermal system, as well as an energy bus model for the energy station.

3. The energy transmission scheduling method based on virtual simulation according to claim 2, characterized in that, In S103, the power system multi-energy flow model includes: Nodal active power balance constraints: Among them, P G,i,t For the output of the i-th thermal power unit at time t, P CHP,t For the electrical output of the CHP unit, P WT,t P PV,t respectively contributing to wind and solar power, P BESS,ch,t P BESS,dis,t For energy storage charging and discharging power, P L,j,t Let P be the power of the j-th conventional electrical load. Al,t For high-energy-consuming industrial loads, nG represents the total number of thermal power units, and nL represents the number of conventional electrical loads. Frequency and power difference correlation model: Where, Δf t Let ∑P be the frequency offset of the power system at time t. source,t For the total power output, P load,t For the total electrical load, K s For the unit regulating power of the power system; The multi-energy flow model of the thermal system includes: Relationship between heating capacity and pipe water temperature: in, The heat supplied to the CHP unit at time t, where c is the specific heat capacity of water, and G t Let t be the instantaneous flow rate of hot water in the heating pipe at time t. The water supply temperature at the pipe inlet. The return water temperature at the pipe outlet; Pipeline heat loss model: Where, ΔQ loss,t Let λ be the heat loss of the pipe at time t, λ be the thermal conductivity of the pipe, L be the length of the pipe, and T be the heat loss of the pipe at time t. a,t For ambient temperature, d out d in These are the outer diameter and inner diameter of the pipe, respectively. The energy bus model of the energy station includes: Energy balance constraints: Where, Φ in The set of energy input branches for the energy station, Φ out For the set of output energy branches, E in,i,t E out,j,t These are the input and output energy flows, respectively. Energy conversion equipment efficiency constraint: E out,j,t =n i,j ·E in,i,t ; where n i,j Let be the conversion efficiency from input branch i to output branch j.

4. The energy transmission scheduling method based on virtual simulation according to claim 3, characterized in that, S2 specifically includes: S201. Set a standard frequency deviation as a frequency security constraint for the power system based on the frequency and power difference correlation model of the power system. S202. Construct a heat power balance model and a transmission delay model for the thermal system, and based on the heat power balance model, the transmission delay model, and the relationship between the heat supply and the pipe water temperature, use the average thermal perception index to set heating satisfaction as a constraint on heating satisfaction; wherein, Thermal power balance model: Q source,t -Q load,t =ΔQ t ; where Q source,t Q represents the total heat supplied by the heat source at time t; load,t ΔQ represents the total heat load power at time t. t The difference between source and load heat power at time t; Transmission delay model: Where ρ is the density of water and S is the cross-sectional area of ​​the pipe; S203. Based on the constraints of the power system and the thermal system, a linear relationship between the source-load power time series is constructed using the EG two-step method. The stationarity of the power difference series is verified using ADF, and the source-load regulation strategy is dynamically adjusted based on the verification results. Linear relationship of source-load power time series: Where t is the scheduling time, ∑P source,t Let ∑P be the total power output of the system at time t. load,t Let ΔP be the total electrical load power of the system at time t. t Q is the difference in active power between the power source and the load at time t. source,t Q is the total heat supplied by the system's heat source at time t. load,t Let ΔQ be the total heat load power of the system at time t. t The difference between the heat source and the charge power at time t.

5. The energy transmission scheduling method based on virtual simulation according to claim 4, characterized in that, S3 specifically includes: S301. Based on the parameters of the core energy equipment, set cooling / heating scenarios and extreme operating conditions scenarios, wherein the cooling / heating scenarios include power supply side parameters, load side parameters, environmental and transmission parameters, and the extreme operating conditions scenarios include sudden change conditions in wind and solar power processing, load impact conditions, and equipment failure conditions. S302. Based on the multi-energy flow model, energy bus model, cooling / heating scenario, and extreme operating condition scenario, four types of scheduling comparison models are constructed, and their parameters are set, including a real-time electric-thermal power balance constraint model, a flexible electric-thermal power balance constraint model, a flexible balance + source-load cointegration constraint model, and a strict flexible balance constraint model; wherein, Real-time balance constraint model of electric-thermal power: Flexible balance constraint model for electro-thermal power: The flexible equilibrium + source-load cointegration constraint model adopts the electric-thermal power flexible equilibrium constraint model, combined with ADF stationarity test: Among them, the critical value of -2.88 is the critical value of the ADF test standard, ensuring ΔP t ΔQ t It is a stationary sequence; The strict flexible balance constraint model adopts the electro-thermal power flexible balance constraint model, and sets the tightening frequency safety constraint as follows: S303. Establish a mapping relationship between scenario ID, model ID, and parameter set for the parameters of cooling / heating scenarios, extreme operating conditions scenarios, and the parameters of the four types of scheduling comparison models, forming a structured simulation database.

6. The energy transmission scheduling method based on virtual simulation according to claim 5, characterized in that, S4 specifically includes: S401. Construct an objective function for the comprehensive operating cost based on cooling / heating scenarios and extreme operating conditions, specifically: minF=F G +F DR +F BESS +F cur +F pen Where F is the total operating cost of the system; F G Coal consumption costs for conventional thermal power units and CHP units; F DR For demand response costs of high-energy-consuming industrial loads and industrial heat loads; F BESS For the operating cost of the energy storage system; F cur The cost of penalties for curtailing wind and solar power; F pen To incur the cost of safety penalties; S402. Integrate the parameters of the multi-energy flow model, energy bus model, balance constraint, and four types of scheduling comparison models to form a constraint system. Set the parameters of cooling / heating scenarios and extreme operating conditions to be imported into the physical visualization model to form a solvable optimization model. S403. Based on CPLEX, construct a combination matrix of 4 scene × 4 model, call the constraints in the combination order, use the branch and bound method for iterative solution, set the convergence condition and output the optimal solution after convergence, if it does not converge, adjust the relaxation factor and solve again to form a complete solution result dataset.

7. The energy transmission scheduling method based on virtual simulation according to claim 6, characterized in that, S5 specifically includes: S501. Perform physical consistency verification and engineering rationality verification based on the solution result dataset. If the verification fails, return to step S1 to adjust the parameter configuration. S502. Adjust the constraint boundary parameters of the constraint system based on the verification results, and optimize the objective function and calibrate the parameters of the physical visualization model.

8. An energy transmission scheduling device based on virtual simulation, based on the energy transmission scheduling method based on virtual simulation according to any one of claims 1-7, characterized in that, The device includes: The first construction module is used to construct a physical visualization model based on the core energy equipment and divide it into power system, thermal system and energy station, set up the multi-energy flow model of the power system and thermal system, and set up the energy bus model of the energy station; The second construction module is used to construct the balance constraints of the power system and the thermal system, and to construct the linear relationship of the source-load power time series using the EG two-step method, and to verify the stationarity using the ADF test. The mapping module is used to construct cooling / heating scenarios and extreme operating condition scenarios based on the physical visualization model and balance constraints, as well as to construct four types of scheduling comparison models and establish the mapping relationship between scenarios and comparison models. The solution module is used to construct the objective function and constraint system of the comprehensive operating cost and import them into the physical visualization model to form a solvable optimization model. The optimization model is solved by CPLEX to obtain the solution result dataset. The verification module is used to perform physical consistency verification and engineering rationality verification based on the solution result dataset, and to adjust the constraint boundary parameters of the constraint system based on the verification results, as well as optimize the objective function and calibrate the parameters of the physical visualization model.

9. A computer device comprising a memory and a processor, wherein the memory stores a computer program, characterized in that, When the processor executes the computer program, it implements the steps of the method according to any one of claims 1 to 7.

10. A computer-readable storage medium having a computer program stored thereon, characterized in that, When the computer program is executed by a processor, it implements the steps of the method according to any one of claims 1 to 7.

Images