Park comprehensive energy transient optimization control method and system based on digital twinning

Abstract

The application provides a park comprehensive energy transient optimization control method and system based on digital twinning, analyzes transient processes of an electric power system and a heat system, establishes objective functions F1 and F2 considering factors such as stability, overshoot and regulation time, and solves the objective functions by using a genetic algorithm NSGA-III, compared with other solving algorithms, the solving accuracy and efficiency are improved, and finally, the electric-heat system combined transient optimization control of the PIES system is realized.

Description

Technical Field

[0001] This invention belongs to the technical field of integrated energy optimization control, and in particular relates to a method and system for transient optimization control of integrated energy in industrial parks based on digital twins. Background Technology

[0002] The statements in this section are merely background information related to the present invention and do not necessarily constitute prior art.

[0003] Most research on integrated energy systems remains at the level of mathematical modeling, simulation, and analysis, failing to establish more accurate simulation models and faster information exchange channels to achieve more accurate data analysis and result verification. Currently, a new round of technological revolution and industrial transformation is accelerating, with the rapid development of next-generation information technologies such as big data, cloud computing, the Internet of Things, mobile internet, artificial intelligence, blockchain, and 5G. The digital economy is constantly changing human production and lifestyles. The speed, scope, and depth of the digital economy's development are unprecedented, making it a key force in reorganizing global resources, reshaping the global economic structure, and altering the global competitive landscape. Digital twin technology makes data, information, and scenarios more process-oriented, visual, and three-dimensional, effectively promoting the development of the digital economy. Modeling, simulation, and analysis of integrated energy systems based on digital twin technology has become a popular research direction.

[0004] On the other hand, PIES (Park Integrated Energy System) is a unique integrated energy system, similar to a "microgrid" on the user side. Because it can fully absorb local renewable energy sources (RES) and meet diverse user energy demands, PIES has been widely promoted and applied. However, the integration of a high proportion of renewable energy and controllable loads in the system significantly increases the uncertainty of system operation. To quickly track system fluctuations and accurately control operating equipment, transient operation optimization and control of PIES considering dynamic response performance has become an important research topic in the current research community.

[0005] The inventors discovered that existing technologies are mostly mathematical modeling methods for integrated energy systems with low accuracy. They can only perform optimal scheduling of steady-state systems, making it difficult to analyze transient processes. Furthermore, the scheduling timescales are long, resulting in significant errors. In addition, the objective functions for optimal scheduling of integrated energy systems mentioned in existing patents are mostly common economic and carbon emission targets. They are often based on predicted data of distributed generation equipment and loads to optimize the output of simulated equipment, which is a steady-state analysis method of the system. Summary of the Invention

[0006] To overcome the shortcomings of the existing technologies, this invention provides a transient optimization control method and system for integrated energy systems in industrial parks based on digital twins. This invention analyzes the transient processes of both the power system and the thermal system, and establishes objective functions F1 and F2 that consider factors such as stability, overshoot, and settling time. These functions are solved using the NSGA-III genetic algorithm, which improves the accuracy and efficiency of the solution compared to other algorithms. Ultimately, this invention achieves joint transient optimization control of the electric and thermal systems of the PIES system.

[0007] To achieve the above objectives, one or more embodiments of the present invention provide the following technical solution: a digital twin-based integrated energy transient optimization control method for industrial parks, comprising:

[0008] Construct digital twin simulation models corresponding to the energy equipment in the park's integrated energy system;

[0009] A first objective function is established based on the transient process stability and overshoot of the power system in the integrated energy system, and a second objective function is established based on the transient process stability and settling time of the thermal system in the integrated energy system, thus forming a multi-objective optimization function;

[0010] The optimal control parameters are obtained by optimizing the multi-objective optimization function.

[0011] The optimal control parameters are applied to the actual integrated energy system of the park based on digital twin technology.

[0012] A second aspect of the present invention provides a digital twin-based integrated energy transient optimization control system for industrial parks, comprising:

[0013] The model building module is configured to: build digital twin models corresponding to the energy equipment in the park's integrated energy system;

[0014] The objective function establishment module is configured to establish a multi-objective optimization function with the optimization objectives being the maximum adjustment capacity of renewable energy equipment in different operating modes and the minimum adjustment time of different heating equipment in different operating modes in the integrated energy system.

[0015] The objective function solving module is configured to: optimize and solve the multi-objective optimization function to obtain the optimal control parameters;

[0016] The optimized control module is configured to apply optimal control parameters to the actual integrated energy system of the park based on digital twin technology.

[0017] A third aspect of the present invention provides a computer-readable storage medium for storing computer instructions, which, when executed by a processor, perform the steps described in the above method.

[0018] A fourth aspect of the present invention provides an electronic device including a memory and a processor, and computer instructions stored in the memory and running on the processor, wherein the computer instructions, when executed by the processor, perform the steps described in the above method.

[0019] The above one or more technical solutions have the following beneficial effects:

[0020] This invention utilizes digital twin technology to design a virtual simulation system for a park's integrated energy system, essentially performing mechanistic modeling, which significantly improves the model accuracy of the simulation system. Simultaneously, the virtual simulation system interacts with the actual physical system in real time, achieving a precise characterization of the actual physical system and facilitating subsequent system analysis, design optimization, and verification.

[0021] This invention uses a multi-objective genetic algorithm to iterate continuously in order to optimize the transient processes of highly coupled power and thermal systems in a park's integrated energy system. This can improve the stability of the transient processes of the electrothermal system, reduce overshoot, reduce settling time, and improve computational efficiency. It is especially suitable for the transient operation optimization of a park's integrated energy system.

[0022] Advantages of additional aspects of the invention will be set forth in part in the description which follows, and in part will be obvious from the description, or may be learned by practice of the invention. Attached Figure Description

[0023] The accompanying drawings, which form part of this invention, are used to provide a further understanding of the invention. The illustrative embodiments of the invention and their descriptions are used to explain the invention and do not constitute an improper limitation of the invention.

[0024] Figure 1 This is a schematic diagram of the integrated energy system of the park in Embodiment 1 of the present invention;

[0025] Figure 2 This is a schematic diagram of the digital twin technology configuration of the integrated energy system in the park according to Embodiment 1 of the present invention;

[0026] Figure 3 This is a flowchart of the multi-objective genetic algorithm optimization process in Embodiment 1 of the present invention;

[0027] Figure 4 This is a schematic diagram of the iterative calculation of the multi-objective genetic algorithm in Embodiment 1 of the present invention. Detailed Implementation

[0028] It should be noted that the following detailed descriptions are exemplary and intended to provide further illustration of the invention. Unless otherwise specified, all technical and scientific terms used herein have the same meaning as commonly understood by one of ordinary skill in the art to which this invention pertains.

[0029] It should be noted that the terminology used herein is for the purpose of describing particular implementations only and is not intended to limit the exemplary implementations of the present invention.

[0030] Where there is no conflict, the embodiments and features in the embodiments of the present invention can be combined with each other.

[0031] Example 1

[0032] like Figures 1-4 As shown, this embodiment discloses a digital twin-based integrated energy transient optimization control method for industrial parks, including:

[0033] Construct digital twin simulation models corresponding to the energy equipment in the park's integrated energy system;

[0034] A first objective function is established based on the transient process stability and overshoot of the power system in the integrated energy system, and a second objective function is established based on the transient process stability and settling time of the thermal system in the integrated energy system, thus forming a multi-objective optimization function;

[0035] The optimal control parameters are obtained by optimizing the multi-objective optimization function.

[0036] The optimal control parameters are applied to the actual integrated energy system of the park based on digital twin technology.

[0037] In this embodiment, Figure 3 This diagram illustrates the composition of the park's integrated energy system, which includes renewable energy equipment such as photovoltaic (PV) and wind turbines (WT); combined heat and power (CHP) and heating equipment such as gas turbines (GT), heat recovery boilers (HR), gas boilers (GB), and electric boilers (EB); and energy storage equipment such as batteries and thermal storage tanks. The actual PIES system is complex, with multiple energy sources coupled together. To reduce modeling difficulty, mathematical modeling is typically used. However, this method creates a simple system model with low accuracy, limiting its application to steady-state analysis. For better analysis of the system's transient processes, more precise modeling is needed. Combining digital twin technology with a mechanistic model can achieve both accurate characterization of the actual physical system and real-time two-way information interaction, such as... Figure 1 As shown.

[0038] In this embodiment, a virtual simulation model, or mechanism model, of the actual physical system is built in simulation software to accurately depict the integrated energy system (PIES) of the park. Simultaneously, data from the actual system is input into the simulation software in real time, enabling information exchange between the two and completing the construction of the digital twin model.

[0039] During the transient processes of PIES (Power Systems Instructions for Estimation), the power system responds extremely quickly, while the thermal system exhibits significant inertia and delay during dynamic processes such as start-up, shutdown, and load changes. This results in longer settling times, adversely affecting the control quality of the system's power supply process. Establishing objective functions that include stability and settling time for both subsystems can optimize the transient processes of the power system, achieving stable operation and reducing overshoot; it can also optimize the transient processes of the thermal system, reducing settling time.

[0040] like Figure 3 As shown in the figure, the digital twin-based integrated energy transient optimization control method for industrial parks in this embodiment specifically includes:

[0041] S1: Set the operating parameters of the simulation software: set the running time to 1 hour, the sampling time to 1 minute (i.e., update the controlled variables of the controlled object every minute), the prediction period to 15 minutes, and the control period to 10 minutes; set the parameters to be optimized for the PIES virtual simulation model power system: the active and reactive power control errors ΔP(t) and ΔQ(t) of the RES equipment in different operating modes are within the range of [0~10%], and the deviations of the voltage amplitude and frequency of the RES equipment from the rated values ​​ΔU(t) and ΔH(t) in different operating modes are within the range of [0~5%]; set the parameters to be optimized for the PIES virtual simulation model thermal system: the temperature deviation coefficient matrix Temp of the heating equipment i in operating mode j. i,j The flow deviation coefficient matrix of the pipeline of heating equipment i under working mode j. i,j Set the parameters to be optimized for the energy storage device in the PIES virtual simulation model: initial state of the battery h = 0, energy storage variation range hb = [0.2~0.8], initial state of the thermal storage tank k = 0, energy storage variation range kb = [0.2~0.8]; set the iteration optimization number Gen = 100 for the multi-objective genetic algorithm NSGA-Ⅲ; set the remaining parameters according to the given values ​​of the actual physical equipment and the load prediction values.

[0042] S2: Start the PIES virtual simulation model in the simulation software.

[0043] S3: For each control cycle, real-time monitoring of data such as voltage, current, operating mode, and active and reactive power of the RES equipment; real-time monitoring of data such as operating status, supply and return water temperature, pipeline flow rate, and heat supply of the heating equipment; real-time monitoring of data such as operating status, capacity, output power, and heat supply of the energy storage equipment; all data are collected and stored immediately.

[0044] S4: Based on load changes, calculate the active and reactive power control errors ΔP(t) and ΔQ(t) of the RES equipment in the current operating mode for each sampling period within the control cycle; the deviations ΔU(t) and ΔH(t) of the voltage amplitude and frequency of the RES equipment from their rated values ​​in the current operating mode; and the temperature deviation coefficient matrix Temp of the heating equipment in the current operating mode. *i,*j The flow deviation coefficient matrix of the heating equipment under the current operating mode. *i,*j The initial state h of the battery and the battery capacity hb; the initial state k of the thermal storage tank and the thermal storage tank capacity kb.

[0045] S5: Based on its calculation results, the simulation system calculates and analyzes parameters such as system stability, settling time, overshoot, and error magnitude.

[0046] S6: During this control cycle, control parameters that meet the initial constraints of equipment output and operation in the virtual simulation model are sent to the controller to control the power subsystem and thermal subsystem to adjust the equipment.

[0047] The constraints are set for different devices and the entire PIES system when building the virtual simulation model, such as energy balance constraints, device output constraints, and energy storage constraints. The constraints are shown below:

[0048] (1) Energy balance constraint

[0049] The energy balance constraints are as follows:

[0050] P grid (t)+P PV (t)+P WT (t)+P GT (t)=P eload (t)+P EB (t) (1)

[0051] In the formula, each element from left to right represents the power purchased by the power grid at time t, the power generated by photovoltaic power generation, the power generated by wind turbines, the power generated by gas boilers, the power of electrical loads, and the power consumed by electric boilers.

[0052] The thermal balance constraints are as follows:

[0053] P HRh(t)+P GBh (t)+P EBh (t)=P hload (t) (2)

[0054] In the formula, each element from left to right represents the heat generation power of the waste heat boiler, the heat generation power of the gas boiler, the heat generation power of the electric boiler, and the heat load power at time t.

[0055] The natural gas balance constraints are as follows:

[0056] L Gas (t)=L gGT (t)+L gGB (t) (3)

[0057] In the formula, L Gas (t) represents the natural gas supply at time t; L gGT (t) represents the gas consumption of the gas turbine at time t; L gGB (t) represents the gas consumption of the gas boiler at time t.

[0058] (2) Equipment output constraints

[0059] 0≤P x (t)≤P x,max (4)

[0060] |P x (t)-P x (t-1)|≤ΔP x (t) (5)

[0061] In the formula, P x (t) represents the output of device x at time t; P x,max ΔP represents the upper limit of the output power of device x. x (t) represents the ramp rate limit of device x at time t; x represents the device type.

[0062] (3) Constraints of energy storage devices

[0063] 0≤P s,c (t)≤P s,cmax (6)

[0064] 0≤P s,d (t)≤P s,dmax (7)

[0065] P s,c (t)P s,d (t)=0 (8)

[0066] O s,min ≤O s (t)≤O s,max (9)

[0067] O s (0) = O s (T) (10)

[0068] In the formula, P s,cmax and P s,dmax This refers to the upper limit of the energy storage and dissipation power of energy storage devices; O s,max O s,min P represents the upper and lower limits of the energy storage state of the energy storage device at time t; T is the scheduling period; P s,c P s,d Let be the energy storage power and energy release power at time t, respectively.

[0069] S7: Repeat steps S2 to S6 in the next control cycle until the model runs out.

[0070] In this embodiment, in order to enable PIES to operate stably and respond quickly under load and renewable energy equipment (RES) output changes, to control the output of each device in the system stably and efficiently, and to stabilize the system voltage amplitude and frequency at near the rated value quickly, thereby reducing the adjustment time of the thermal system.

[0071] Using the active and reactive power control errors ΔP(t) and ΔQ(t) of the RES equipment, the deviations of voltage amplitude and frequency from the rated values ​​ΔU(t) and ΔH(t), the switching state h of the battery, and the battery capacity hb as optimization variables [ΔP(t)ΔQ(t)ΔU(t)ΔH(t) h hb], the objective function F1 for transient process stability and overshoot of the PIES power system was established.

[0072] The objective function F1 for the transient process stability and overshoot of a power system is defined as the maximum value of the regulation capability of multiple RES devices under different operating modes:

[0073]

[0074] |L i,j (t)|=|[ΔP i,j (t)ΔQ i,j (t)ΔU i,j (t)ΔH i,j (t)] T | (12)

[0075] Where N and M are the number of RES devices and their respective operating modes; t is the simulation time; T 0,j T 1,jThese represent the start and end times of the calculation for the RES device in operating mode j, respectively; R is the weighting coefficient matrix, with all elements in the matrix set to 1 by default. R represents the varying degrees of importance placed on the control error of different devices under different operating modes; |L i,j (t)| represents the error matrix of the i-th RES device at time t in operating mode j, ΔP i,j (t) and ΔQ i,j (t) represents the control error of the active and reactive power of the i-th RES device at time t in operating mode j, ΔU i,j (t) and ΔH i,j (t) represents the voltage amplitude and frequency deviation of the i-th RES device in operating mode j at time t, respectively, from the rated value; h(t) represents the switching state of the battery at time t; hb(t) represents the capacity of the battery at time t.

[0076] The temperature deviation coefficient matrix of the heating equipment. *i,*j Flow deviation coefficient matrix of heating equipment *i,*j The on / off state k of the thermal storage tank and the capacity kb of the thermal storage tank are used as optimization variables. *i,*j Flow *i,*j [k kb], and established the objective function F2 for the transient process stability and settling time of the PIES thermodynamic system;

[0077] The objective function F2 for the transient process stability and settling time of a thermal system is defined as the minimum settling time of different heating equipment under different operating modes:

[0078]

[0079] Where N and M represent the number of heating devices and their respective operating modes; T 0,j T 1,j These are the start and end times of the calculation for the heating equipment in working mode j, respectively. Let be the temperature deviation coefficient matrix of the i-th heating device at time t in operating mode j. Let be the pipeline flow deviation coefficient matrix of the i-th heating device at time t in working mode j; k(t) is the on / off state of the heat storage tank at time t; kb(t) is the capacity of the heat storage tank at time t.

[0080] In this embodiment, the multi-objective genetic algorithm NSGA-Ⅲ is used to solve the optimal solution sets of objective functions F1 and F2 respectively, with the number of iterations being Gen+1. If the number of iterations of the genetic algorithm is less than 100, the above steps S2 to S6 are repeated. After reaching the maximum number of iterations, the optimal solution set is screened using a multi-objective decision model based on Nash equilibrium points, the optimal compromise solution is selected, and the optimal control parameters are applied to the actual physical system.

[0081] like Figure 4 As shown, the optimal solution sets for objective functions F1 and F2 are obtained using the multi-objective genetic algorithm NSGA-Ⅲ, specifically:

[0082] Step 1: Directly adopt the objective functions F1 and F2 of the optimization problem as the fitness functions of individuals in the population, and stipulate that the smaller the fitness function, the stronger the survival ability of the population and the easier it is to reproduce the next generation;

[0083] Step 2: When the algorithm makes a selection, it randomly selects a number of individuals each time (in this embodiment, the number is set to half the population size), and the individual with the highest fitness is inherited as the next generation. The best individual in the current population is excluded from the genetic operation. After crossover, mutation and other operations are completed in the current generation, it is used to replace the individual with the worst fitness in the current generation.

[0084] Step 3: During the crossover process, all paired individuals are randomly paired, then the exchange positions are randomly set among the paired individuals, and then the paired individuals exchange some information with each other according to the exchange probability.

[0085] Step 4: Using polynomial mutation, certain gene values ​​change into other alleles according to a certain probability distribution, thereby producing individuals with different genotypes. This mutation method maintains population diversity with a high mutation rate in the early stage of population reproduction, avoiding getting trapped in local optima. In the middle and late stages, the mutation rate is reduced to prevent the solution from going far away from the optimal solution and to ensure stability.

[0086] Step 5: Determine if the number of iterations has been reached. If the number of iterations has not been reached, repeat steps 1 to 4. If the number of iterations has been reached, output the optimal solution.

[0087] Most existing technologies only perform transient analysis on power systems, requiring only that the voltage frequency be stable and the output active and reactive power meet requirements. Because thermodynamic systems have a larger response time scale and differ significantly from power systems, they are not analyzed. This invention analyzes the transient processes of both power and thermodynamic systems, establishing objective functions F1 and F2 that consider factors such as stability, overshoot, and settling time. These functions are solved using the NSGA-III genetic algorithm, which improves accuracy and efficiency compared to other algorithms, ultimately achieving joint transient optimization control of the electrothermal system within the PIES system.

[0088] Example 2

[0089] The purpose of this embodiment is to provide a digital twin-based integrated energy transient optimization control system for industrial parks, including:

[0090] The model building module is configured to: build digital twin models corresponding to the energy equipment in the park's integrated energy system;

[0091] The objective function establishment module is configured to establish a multi-objective optimization function with the optimization objectives being the maximum adjustment capacity of renewable energy equipment in different operating modes and the minimum adjustment time of different heating equipment in different operating modes in the integrated energy system.

[0092] The objective function solving module is configured to: optimize and solve the multi-objective optimization function to obtain the optimal control parameters;

[0093] The optimized control module is configured to apply optimal control parameters to the actual integrated energy system of the park based on digital twin technology.

[0094] Example 3

[0095] The purpose of this embodiment is to provide a computing device, including a memory, a processor, and a computer program stored in the memory and executable on the processor, wherein the processor executes the program to implement the steps of the above-described method.

[0096] Example 4

[0097] The purpose of this embodiment is to provide a computer-readable storage medium.

[0098] A computer-readable storage medium having a computer program stored thereon, which, when executed by a processor, performs the steps of the above-described method.

[0099] The steps and methods involved in the apparatuses of Embodiments 2, 3, and 4 above correspond to those in Embodiment 1. For specific implementation details, please refer to the relevant description section of Embodiment 1. The term "computer-readable storage medium" should be understood as a single medium or multiple media including one or more instruction sets; it should also be understood as including any medium capable of storing, encoding, or carrying an instruction set for execution by a processor and enabling the processor to perform any of the methods in this invention.

[0100] Those skilled in the art will understand that the modules or steps of the present invention described above can be implemented using general-purpose computer devices. Optionally, they can be implemented using computer-executable program code, thereby allowing them to be stored in a storage device for execution by a computer device, or they can be fabricated as separate integrated circuit modules, or multiple modules or steps can be fabricated as a single integrated circuit module. The present invention is not limited to any particular combination of hardware and software.

[0101] While the specific embodiments of the present invention have been described above in conjunction with the accompanying drawings, this is not intended to limit the scope of protection of the present invention. Those skilled in the art should understand that various modifications or variations that can be made by those skilled in the art without creative effort based on the technical solutions of the present invention are still within the scope of protection of the present invention.

Claims

1. A method for transient optimization control of integrated energy in a park based on digital twins, characterized in that, include: Construct digital twin simulation models corresponding to the energy equipment in the park's integrated energy system; The first objective function is established based on the transient process stability and overshoot of the power system in the integrated energy system, and the second objective function is based on the transient process stability and settling time of the thermal system in the integrated energy system, thus forming a multi-objective optimization function. The first objective function is defined as the maximum value of the regulation capability of multiple renewable energy devices under different operating modes, using the active and reactive power of the renewable energy devices to control the error. P(t), Q(t), the deviation of voltage amplitude and frequency from the rated values. U(t), H(t) and the switching state of the battery Battery capacity As an optimization variable [ P(t) Q(t) U(t) H(t) Establish the first objective function; First objective function : ； ； in, and These refer to the number of renewable energy devices and their respective operating modes; For simulation time; , Renewable energy equipment in different operating modes The calculation starts and ends at the specified time. This is the weighting coefficient matrix, with the default element being 1. R represents the different levels of importance placed on the control error of different devices under different operating modes. for Time of the first A renewable energy device in operating mode The error matrix below, and They are respectively Time of the first A renewable energy device in operating mode Control error of active and reactive power. and They are respectively Time of the first A renewable energy device in operating mode The deviation of the voltage amplitude and frequency from the rated value; for Monitor the on / off status of the battery at all times; for The capacity of the battery at all times; The second objective function is defined as the minimum adjustment time of different heating devices under different operating modes, using the temperature deviation coefficient matrix of the heating devices. Pipeline flow deviation coefficient matrix of heating equipment The on / off status of the thermal storage tank Thermal storage tank capacity As an optimization variable [ Establish a second objective function; Second objective function : ； in, and These refer to the number of heating equipment and the number of their respective operating modes; , The heating equipment is in different working modes. The calculation starts and ends at the specified time. for Time of the first Each heating device is in working mode The temperature deviation coefficient matrix below, for Time of the first Each heating device is in working mode The pipeline flow deviation coefficient matrix is ​​as follows; for The on / off status of the thermal storage tank at all times; for The capacity of the thermal storage tank at all times; The optimal control parameters are obtained by optimizing the multi-objective optimization function. Optimal control parameters are applied to the actual integrated energy system of the park based on digital twin technology; The digital twin-based integrated energy transient optimization control method for the park uses the multi-objective genetic algorithm NSGA-Ⅲ to solve the multi-objective optimization function and obtain the optimal solution set; The optimal control parameters are obtained by filtering the optimal solution set based on the Nash equilibrium point multi-objective decision model.

2. The method for transient optimization control of integrated energy in a park based on digital twins as described in claim 1, characterized in that, The park's integrated energy system includes renewable energy equipment, combined heat and power (CHP) and heating equipment, and energy storage equipment.

3. The method for transient optimization control of integrated energy in a park based on digital twins as described in claim 1, characterized in that, This also includes setting parameters to be optimized for the simulation model, including: active and reactive power control errors of renewable energy equipment under different operating modes. , Within the range of [0~10%], the deviation of voltage amplitude and frequency of renewable energy equipment from the rated values ​​under different operating modes. , Within the range of [0~5%], heating equipment In work mode Temperature deviation coefficient matrix and heating equipment In work mode The pipeline flow deviation coefficient matrix and the initial state of the battery. Energy storage variation range The initial state of the thermal storage tank Energy storage variation range .

4. The method for transient optimization control of integrated energy in a park based on digital twins as described in claim 1, characterized in that, In the multi-objective genetic algorithm, the fitness function is a first objective function and a second objective function.

5. A digital twin-based integrated energy transient optimization control system for industrial parks, employing the digital twin-based integrated energy transient optimization control method for industrial parks as described in any one of claims 1-4, characterized in that, include: The model building module is configured to: build digital twin models corresponding to the energy equipment in the park's integrated energy system; The objective function establishment module is configured to establish a multi-objective optimization function with the optimization objectives being the maximum adjustment capacity of renewable energy equipment in different operating modes and the minimum adjustment time of different heating equipment in different operating modes in the integrated energy system. The objective function solving module is configured to: optimize and solve the multi-objective optimization function to obtain the optimal control parameters; The optimized control module is configured to apply optimal control parameters to the actual integrated energy system of the park based on digital twin technology.

6. A computer-readable storage medium having a computer program stored thereon, characterized in that, When the program is executed by the processor, it implements the steps in the digital twin-based transient optimization control method for integrated energy in a park as described in any one of claims 1-4.

7. A processing apparatus, comprising a memory, a processor, and a computer program stored in the memory and executable on the processor, characterized in that, When the processor executes the program, it implements the steps in the digital twin-based integrated energy transient optimization control method for parks as described in any one of claims 1-4.

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