An iterative method and terminal for configuration-operation collaborative optimization model of energy system

Abstract

An iterative method and terminal for an energy system configuration-operation collaborative optimization model includes the following steps: In the configuration-operation collaborative optimization model, the lower-level operation optimization model obtains an optimized operation scheme based on the random configuration scheme obtained from the upper-level configuration optimization model; heuristic rules are used to optimize the redundancy of various capacity data, and the required number of equipment units corresponding to each type of capacity data is updated respectively; the updated required number of equipment units is stored and the capacity optimization data is summarized to form an optimized configuration scheme under the optimized operation scheme; the optimized configuration scheme is fed back to the upper-level configuration optimization model for calculation. This invention establishes a comprehensive energy system configuration-operation collaborative optimization model to achieve rapid iteration; at the same time, by introducing heuristic rules, it eliminates the problem of equipment capacity redundancy that may exist in the configuration scheme input in the model, accelerates the upper-level optimization configuration, and improves the solution speed of the optimized configuration scheme set of the comprehensive energy system configuration-operation collaborative optimization model.

Description

Technical Field

[0001] This invention belongs to the research field of multi-objective optimization adjustment methods, and specifically relates to an iterative method and terminal for an energy system configuration-operation collaborative optimization model. Background Technology

[0002] In recent years, with the increasing environmental requirements of the state for industry, most industries plan their production based on both economic and environmental indicators. The economic objective is to minimize the sum of annualized investment cost, annualized operation and maintenance cost, and annualized operating cost, while the environmental objective is to minimize carbon emissions. Given these multiple objectives, establishing a multi-objective optimization and adjustment model for rational planning is crucial.

[0003] In industrial production planning, two distinct research directions are typically considered: the configuration scheme and operational optimization of integrated energy systems. These two approaches are interconnected and mutually influential. The configuration scheme guides the development of operational plans, while the operational status verifies the configuration scheme. Furthermore, integrated energy systems exhibit diverse load types and complex coupling mechanisms. To adapt to numerous operational scenarios, considering operational optimization during configuration optimization allows the system to fully leverage the coupling and complementarity of various energy sources, providing more realistic operational parameters and resulting in more accurate results.

[0004] In existing technologies, the integrated energy system synergistic optimization problem that simultaneously considers configuration and operation is mostly solved by modeling configuration and operation separately, establishing a two-level nonlinear model, and using nonlinear algorithms to solve configuration and operation iteratively in sequence. For complex synergistic optimization problems, the solution speed is relatively slow due to the large number of iterations between the upper and lower levels. In addition, the obtained random configuration scheme may contain obvious equipment capacity redundancy, which wastes resources and further slows down the solution speed. Summary of the Invention

[0005] The technical problem to be solved by this invention is to provide an iterative method and terminal for an energy system configuration-operation collaborative optimization model, which considers multiple energy conversion and storage devices, establishes a two-level planning model, and applies heuristic rules in the model to solve the device capacity redundancy problem, thereby accelerating the iteration speed.

[0006] To solve the above-mentioned technical problems, the technical solution adopted by the present invention is as follows:

[0007] An iterative method for an energy system configuration-operation co-optimization model, comprising the following steps:

[0008] S1. In the configuration-run collaborative optimization model, the lower-level run optimization model generates an optimized run plan based on the random configuration plan obtained from the upper-level configuration optimization model.

[0009] S2. Optimize the redundancy of various capacity data using heuristic rules, and update the required number of devices for each type of capacity data respectively;

[0010] S3. Store the updated number of required devices and summarize the capacity optimization data to form an optimized configuration scheme under the optimized operation scheme;

[0011] S4. Feed back the optimized configuration scheme to the upper-level configuration optimization model for calculation.

[0012] To solve the above-mentioned technical problems, another technical solution adopted by the present invention is as follows:

[0013] An iterative terminal for an energy system configuration-operation co-optimization model includes a memory, a processor, and a computer program stored in the memory and executable on the processor. When the processor executes the computer program, it implements the steps in the iterative method for the energy system configuration-operation co-optimization model described above.

[0014] The beneficial effects of this invention are as follows: It provides an iterative method and terminal for an energy system configuration-operation collaborative optimization model, establishes an integrated energy system configuration-operation collaborative optimization model, and achieves rapid iteration; at the same time, by introducing heuristic rules, it eliminates the problem of equipment capacity redundancy that may exist in the configuration schemes input in the model, accelerates the iterative process of the upper-level configuration optimization model, accelerates the convergence process of the energy system configuration-operation collaborative optimization model to obtain the optimal solution set of the optimized configuration scheme, and improves the solution speed of the integrated energy system configuration-operation collaborative optimization model for the optimized configuration scheme set. Attached Figure Description

[0015] Figure 1 This is a flowchart illustrating an iterative method for an energy system configuration-operation collaborative optimization model according to a certain embodiment of the present invention.

[0016] Figure 2 This is a flowchart of the redundancy optimization of energy storage batteries based on heuristic rules in an iterative method of an energy system configuration-operation collaborative optimization model according to a certain embodiment of the present invention.

[0017] Figure 3 This is a flowchart of the redundancy optimization of biogas tanks based on heuristic rules in an iterative method of an energy system configuration-operation collaborative optimization model according to a certain embodiment of the present invention.

[0018] Figure 4 This is a flowchart of the redundancy optimization of a gas turbine based on heuristic rules in an iterative method of an energy system configuration-operation collaborative optimization model according to a certain embodiment of the present invention.

[0019] Figure 5This is a flowchart of the redundancy optimization of a biogas furnace based on heuristic rules in an iterative method of an energy system configuration-operation collaborative optimization model according to a certain embodiment of the present invention.

[0020] Figure 6 This is a flowchart of redundancy optimization for electric boilers based on heuristic rules in an iterative method of an energy system configuration-operation collaborative optimization model according to a certain embodiment of the present invention.

[0021] Figure 7 This is a schematic diagram of an iterative terminal for an energy system configuration-operation collaborative optimization model according to a certain embodiment of the present invention;

[0022] Label Explanation:

[0023] 1. An iterative terminal for an energy system configuration-operation collaborative optimization model; 2. A processor;

[0024] 3. Storage. Detailed Implementation

[0025] To explain in detail the technical content, objectives, and effects of the present invention, the following description is provided in conjunction with the embodiments and accompanying drawings.

[0026] Please refer to Figures 1 to 6 An iterative method for an energy system configuration-operation co-optimization model, comprising the following steps:

[0027] S1. In the configuration-run collaborative optimization model, the lower-level run optimization model generates an optimized run plan based on the random configuration plan obtained from the upper-level configuration optimization model.

[0028] S2. Optimize the redundancy of various capacity data using heuristic rules, and update the required number of devices for each type of capacity data respectively;

[0029] S3. Store the updated number of required devices and summarize the capacity optimization data to form an optimized configuration scheme under the optimized operation scheme;

[0030] S4. Feed back the optimized configuration scheme to the upper-level configuration optimization model for calculation.

[0031] The working principle of this invention is as follows: It provides an iterative method and terminal for an energy system configuration-operation co-optimization model, establishing a comprehensive energy system configuration-operation co-optimization model to achieve rapid iteration; simultaneously, by introducing heuristic rules, it eliminates potential equipment capacity redundancy issues in the input configuration schemes of the model, accelerating the iteration process of the upper-level configuration optimization model, speeding up the convergence process of the energy system configuration-operation co-optimization model in obtaining the optimal solution set of the optimized configuration schemes, and improving the solution speed of the comprehensive energy system configuration-operation co-optimization model for solving the optimized configuration scheme set.

[0032] Further, step S2 specifically includes:

[0033] S21. Select any type of unoptimized capacity data;

[0034] S22. Using the working capacity of the device corresponding to the capacity data as an indicator, optimize the capacity data using heuristic rules, and update the number of required devices corresponding to the capacity data.

[0035] S23. Return to step S21 until all the capacity data has been optimized for redundancy.

[0036] As described above, when multiple types of capacity data exist, redundancy optimization is performed on each type of capacity data separately. The optimization metric is the working capacity of the device corresponding to the capacity data, and heuristic rules are used for optimization. Once one type of capacity data is optimized, the required number of devices is updated, and the next type of capacity data is optimized, until all capacity data has completed redundancy optimization.

[0037] Further, step S22 specifically includes:

[0038] S221. Detect the number n of devices corresponding to the current capacity data. If nj = 1, determine that the devices corresponding to the capacity data are not redundant, and regard nj as the required number of devices corresponding to the capacity data, and proceed to step S23; otherwise, proceed to the next step.

[0039] S222. Detect the working efficiency of the device. If the maximum working efficiency satisfies:

[0040]

[0041] If the capacity data is determined to be non-redundant, nj is considered as the number of required devices corresponding to the capacity data, and the process proceeds to step S23; otherwise, j is set to j+1, and the process returns to step S221.

[0042] Where j is the iteration factor, and its initial value is 0;

[0043] n represents the number of devices corresponding to the current capacity data;

[0044] P max To achieve the maximum operating efficiency of the equipment;

[0045] P0 is the preset operating efficiency of the equipment;

[0046] m is the influence factor corresponding to the current capacity data.

[0047] As described above, the working principle of this process is as follows: First, it is determined whether the number of devices corresponding to the capacity data satisfies n=1. If it does, it indicates that the minimum requirement of the device has been met. If not, the process proceeds to the redundancy determination step, which corresponds to step S221. When the number of devices is not 1, the process proceeds to the redundancy determination of the device's working efficiency. For different capacity data, the corresponding device types are also different. The working efficiency is verified, which can be either the total working efficiency of all devices of the same type or the average working efficiency. Therefore, m is introduced as the influencing factor corresponding to the current capacity data. When the working efficiency of the current device falls within the above range, it indicates that there is no redundancy. If it does not fall within this range, it means that if one device of this type is removed, the working efficiency can still meet the constraint requirements. This proves that there is redundancy at this time, and the number of devices needs to be reduced. The iterative calculation continues, which corresponds to step S222.

[0048] Specifically, in one embodiment of the present invention, the devices corresponding to different types of capacity data in the integrated energy system are exemplified as follows:

[0049] The biogas system contains the following equipment: energy storage battery, biogas tank, gas turbine, biogas stove, electric boiler, etc.

[0050] The petrochemical system contains the following equipment: separation towers, heat exchangers, hydrogenation reactors, oil storage tanks, circulating fluidized beds, cyclone separators, etc.

[0051] Furthermore, step S2220 is included between steps S221 and S222:

[0052] S2220. If it is detected that the device corresponding to the current capacity data is limited by the energy state, the capacity data is first optimized using heuristic rules with the energy state as an indicator, and then proceeded to the next step after optimization is completed; otherwise, proceed directly to the next step.

[0053] As described above, in an energy system, different capacity data correspond to different devices. Sometimes, the devices are not only limited by their working efficiency, but also by their energy state. Therefore, after verifying the initial number of devices, the energy state of the devices corresponding to the capacity data is optimized for redundancy. For example, the energy storage battery and biogas tank in the biogas system mentioned above.

[0054] Further, step S2220 specifically includes:

[0055] S22201. Detect the energy state of the device. If the actual minimum ratio of the energy state satisfies E... min =x, and nj is regarded as the number of required devices corresponding to the capacity data, and proceed to step S23; otherwise, proceed to step S22202;

[0056] S22202, If the maximum ratio of the energy states satisfies:

[0057]

[0058] If nj is considered as the number of required devices corresponding to the capacity data, proceed to step S23; otherwise, proceed to step S222.

[0059] Among them, E min The minimum ratio of actual energy states;

[0060] x is the minimum preset ratio of energy states;

[0061] E max The maximum ratio of the actual energy state;

[0062] y is the maximum preset ratio of energy states.

[0063] As described above, the system has a minimum preset ratio and a maximum preset ratio for energy state. When the energy state of a device falls below the minimum preset ratio, it stops working. The actual minimum and maximum ratios of the device's energy state are compared with the aforementioned preset ratios based on the judgment conditions, and redundancy optimization is then performed. Specifically, when the actual minimum ratio of the device's energy state satisfies E... min =x, which means that the minimum preset ratio limit device cannot continue to work at this time. If the number of devices is reduced, the minimum ratio of the actual energy state will be further reduced. If it continues to work, it will destroy the original operation mode. This indicates that after the energy state is determined, the number of devices is not redundant. This corresponds to step S12201.

[0064] When E min When x is not equal to 0, the determination of whether the actual energy state falls within the above range is based on the following principle: calculating the maximum ratio change of the normal energy state that the capacity data should have when one device is subtracted, and using this as the judgment condition; if it falls within the range... If this range is within the range, it means that the maximum ratio of the actual energy state at this time is reasonable and the number of devices is not redundant; otherwise, proceed to the next step, which corresponds to step S12202.

[0065] Specific examples are as follows: In one embodiment of the present invention, the device corresponding to the capacity data is an energy storage battery in a biogas system. The energy state of the energy storage battery is its battery capacity (SOC). The system has preset minimum and maximum energy ratios of x = 0.1 and y = 0.9, respectively, which constrains the energy storage battery SOC to vary within the range of [0.1, 0.9]. If the minimum SOC value of the energy storage battery is SOCbesmin = 0.1, then it is possible that the SOC constraint prevents the energy storage battery from continuing to discharge. If the number of energy storage batteries is reduced, causing the lower limit of the energy storage battery discharge to shift downward, allowing it to continue discharging, then the original operating mode is disrupted, indicating a lack of redundancy. If the minimum SOC value of the energy storage battery, SOCbesmin, is greater than 0.1, reducing the number of energy storage batteries to lower the discharge limit will not affect the original operation mode, indicating that further evaluation is needed. The SOC constraint limits the energy storage battery SOC to vary within the range of [0.1, 0.9]. If one energy storage battery is removed, the SOC constraint for the corresponding (n-1) energy storage batteries will be that the maximum SOC value, SOCbesmax, is less than or equal to 0.9*(n-1) / n. If the maximum SOC value, SOCbesmax, is not within the range of (0.9*(n-1) / n, 0.9], it indicates that the capacity of the n energy storage batteries is redundant.

[0066] Please refer to Figure 7 An iterative terminal 1 for an energy system configuration-operation collaborative optimization model includes a memory 3, a processor 2, and a computer program stored in the memory and executable on the processor 2. When the processor 2 executes the computer program, it implements the iterative method and terminal for any of the above-described energy system configuration-operation collaborative optimization models.

[0067] This invention provides an iterative method and terminal for an energy system configuration-operation collaborative optimization model, mainly applied to multi-objective optimization and adjustment in energy systems. The following embodiments illustrate this method:

[0068] Embodiment 1 of the present invention is as follows:

[0069] Please refer to Figures 1 to 6 An iterative method for an energy system configuration-operation co-optimization model, comprising the following steps:

[0070] S1. In the configuration-run collaborative optimization model, the lower-level run optimization model generates an optimized run plan based on the random configuration plan obtained from the upper-level configuration optimization model.

[0071] S2. Optimize the redundancy of various capacity data using heuristic rules, and update the required number of devices for each type of capacity data respectively.

[0072] S3. Store the updated number of required devices and summarize the capacity optimization data to form an optimized configuration scheme under the optimized operation scheme;

[0073] S4. Feed the optimized configuration scheme back to the upper-level configuration optimization model for calculation.

[0074] In this embodiment, an iterative method for an energy system configuration-operation collaborative optimization model is provided. This method establishes an integrated energy system configuration-operation collaborative optimization model to achieve rapid iteration. At the same time, by introducing heuristic rules, the method eliminates the problem of equipment capacity redundancy that may exist in the configuration schemes input into the model, accelerates the upper-level optimization configuration, and improves the solution speed of the optimized configuration scheme set of the integrated energy system configuration-operation collaborative optimization model.

[0075] Embodiment 2 of the present invention is as follows: Please refer to Figures 1 to 6 Based on Example 1, step S2 specifically includes:

[0076] S21. Select any type of unoptimized capacity data;

[0077] S22. Using the working capacity of the equipment corresponding to the capacity data as an indicator, optimize the capacity data using heuristic rules, and update the number of required equipment corresponding to the capacity data.

[0078] S23. Return to step S21 until all capacity data has been optimized for redundancy.

[0079] In this embodiment, when multiple types of capacity data exist, redundancy optimization is performed on each type of capacity data. The optimization metric is the working capacity of the device corresponding to the capacity data, and heuristic rules are used for optimization. Once one type of capacity data is optimized, the required number of devices is updated, and the next type of capacity data is optimized, until all capacity data has completed redundancy optimization.

[0080] Embodiment 3 of the present invention is as follows: Please refer to Figures 1 to 6 Based on Example 2, S221, detect the number n of devices corresponding to the current capacity data. If nj = 1, determine that the devices corresponding to the capacity data are not redundant, regard nj as the required number of devices corresponding to the capacity data, and proceed to step S23; otherwise, proceed to the next step.

[0081] S222. The working efficiency of the testing equipment, if the maximum working efficiency satisfies:

[0082]

[0083] If the capacity data is determined to be non-redundant, nj is regarded as the number of required devices corresponding to the capacity data, and the process proceeds to step S23; otherwise, j is set to j+1, and the process returns to step S221.

[0084] Where j is the iteration factor, and its initial value is 0;

[0085] n represents the number of devices corresponding to the current capacity data;

[0086] P max To achieve the maximum operating efficiency of the equipment;

[0087] P0 is the preset operating efficiency of the equipment;

[0088] m is the influence factor corresponding to the current capacity data.

[0089] As described above, the working principle of this process is as follows: First, it is determined whether the number of devices corresponding to the capacity data satisfies n=1. If it does, it indicates that the minimum requirement of the device has been met. If not, the process proceeds to the redundancy determination step, which corresponds to step S221. When the number of devices is not 1, the process proceeds to the redundancy determination of the device's working efficiency. For different capacity data, the corresponding device types are also different. The working efficiency is verified, which can be either the total working efficiency of all devices of the same type or the average working efficiency. Therefore, m is introduced as the influencing factor corresponding to the current capacity data. When the working efficiency of the current device falls within the above range, it indicates that there is no redundancy. If it does not fall within this range, it means that if one device of this type is removed, the working efficiency can still meet the constraint requirements. This proves that there is redundancy at this time, and the number of devices needs to be reduced. The iterative calculation continues, which corresponds to step S222.

[0090] Specifically, in this embodiment, the equipment corresponding to different types of capacity data in the integrated energy system is exemplified as follows:

[0091] The biogas system contains the following equipment: energy storage battery, biogas tank, gas turbine, biogas stove, electric boiler, etc.; correspondingly, the process of redundancy optimization using heuristic rules is as follows:

[0092] (1) Energy storage battery: The working efficiency of the energy storage battery is judged based on the charging power Pch and the discharging power Pdis. The influence factor m of the energy storage battery is 5. If the maximum value of the charging power Pch and the maximum value of the discharging power Pdis simultaneously satisfy:

[0093] Pch max ∈((n-1)P0) / 5,nP0 / 5]&Pdis max ∈((n-1)P0 / 5,nP0 / 5]

[0094] If so, it is determined that there is no redundancy; otherwise, it means that the n energy storage batteries are redundant at this time, and iterative optimization is required according to the above formula.

[0095] (2) Biogas Tank: The working efficiency of the biogas tank is judged based on the exhaust volume. The influence factor m of the gas storage tank is 6. If the maximum absolute value of the exhaust volume is Vgs max satisfy:

[0096] Vgs max ∈((n-1)P0) / 6,nP0 / 6]

[0097] If so, it is determined that there is no redundancy; this means that n biogas tanks are redundant at this time, and iterative optimization is needed according to the above formula.

[0098] (3) Gas turbine: The efficiency of a gas turbine is judged based on its output electrical power. The influence factor m of the gas turbine is 1. If the maximum output electrical power of the gas turbine is Pchp max satisfy:

[0099] Pchp max ∈((n-1)P0),nP0]

[0100] If so, it is determined that there is no redundancy; this means that there is redundancy in n gas turbines at this time, and iterative optimization is required according to the above formula.

[0101] (4) Biogas Stove: The efficiency of a gas turbine is judged based on its output thermal power. The influence factor m of the biogas stove is 1. If the maximum output thermal power of the biogas stove is Pf max satisfy:

[0102] Pf max ∈((n-1)P0),nP0]

[0103] If so, it is determined that there is no redundancy; this means that there is redundancy in n biogas digesters at this time, and iterative optimization is required according to the above formula.

[0104] (5) Electric boiler: The working efficiency of an electric boiler is judged based on its output heat power. The influence factor m of the biogas boiler is 1. If the maximum output heat power Pb of the biogas boiler is... max satisfy:

[0105] Pb max ∈((n-1)P0),nP0]

[0106] If so, then it is determined that there is no redundancy; this means that there is redundancy in n electric boilers at this time, and iterative optimization is required according to the above formula.

[0107] Embodiment four of the present invention is as follows: Please refer to Figures 1 to 6 Based on Example 3,

[0108] Step S2220 is also included between steps S221 and S222:

[0109] S2220. If it is detected that the device corresponding to the current capacity data is limited by the state of energy, the capacity data is first optimized using heuristic rules with the state of energy as an indicator. After optimization, proceed to the next step; otherwise, proceed directly to the next step.

[0110] S22201. Detect the energy state of the equipment. If the actual minimum ratio of the energy state satisfies E... min =x, treat nj as the required number of equipment units corresponding to the capacity data, and proceed to step S23; otherwise, proceed to step S22202;

[0111] S22202. If the maximum ratio of energy states satisfies:

[0112]

[0113] If nj is considered as the number of required devices corresponding to the capacity data, proceed to step S13; otherwise, proceed to step S222.

[0114] Among them, E min The minimum ratio of actual energy states;

[0115] x is the minimum preset ratio of energy states;

[0116] E max The maximum ratio of the actual energy state;

[0117] y is the maximum preset ratio of energy states.

[0118] In this embodiment, different capacity data correspond to different devices. Sometimes, the devices are not only limited by their working efficiency, but also by their energy state. Therefore, after verifying the initial number of devices, the energy state of the devices corresponding to the capacity data is optimized for redundancy. For example, the energy storage battery and biogas tank in the biogas system mentioned above.

[0119] For example: The capacity data corresponds to the energy storage battery in the biogas system. The energy state of the energy storage battery is its capacity (SOC). The system has preset minimum and maximum energy ratios of x = 0.1 and y = 0.9, respectively, which constrains the energy storage battery SOC to vary within the range of [0.1, 0.9]. If the minimum SOC value of the energy storage battery is SOCbesmin = 0.1, then it is possible that the SOC constraint prevents the energy storage battery from continuing to discharge. If the number of energy storage batteries is reduced, causing the lower limit of the energy storage battery discharge to shift downward, allowing it to continue discharging, then the original operating mode is disrupted, indicating a lack of redundancy. If the energy storage battery SOC... The minimum value SOCbesmin > 0.1. If reducing the number of energy storage batteries lowers the discharge limit of the energy storage batteries without affecting the original operation mode, it indicates that further evaluation is needed. The constraint of energy storage battery SOC limits the SOC of the energy storage batteries to vary within the range of [0.1, 0.9]. If one energy storage battery is removed, the SOC constraint for the corresponding (n-1) energy storage batteries is that the maximum capacity of the energy storage batteries SOCbesmax ≤ 0.9*(n-1) / n. If SOCbesmax is not in the range of (0.9*(n-1) / n, 0.9], it indicates that the capacity of n energy storage batteries is redundant. The optimization of biogas digester energy state redundancy is the same as above.

[0120] Embodiment five of the present invention is as follows: Please refer to Figures 1 to 6 Based on Example 4,

[0121] The configuration-running co-optimization model is calculated using the NSGA-II algorithm.

[0122] In this embodiment, the sources of various capacity data are: the initial population of capacity data in the integrated energy system configuration-operation collaborative optimization model constructed by the NSGA-II algorithm and the random configuration scheme of the upper-level configuration optimization model; after obtaining the configuration scheme, it is sent to the lower-level operation optimization model in the NSGA-II algorithm, and the lower-level configuration optimization model is rapidly iterated by applying heuristic rules to form the optimal optimization scheme, which is then returned to the upper-level configuration optimization model for further iterative calculation.

[0123] Specifically, in this embodiment, the integrated energy system configuration-operation collaborative optimization model is a two-layer configuration-operation collaborative optimization model. The upper-layer configuration optimization model is a multi-objective optimization method based on the NSGA-II algorithm to obtain the Pareto optimal solution set. It establishes two objective functions that satisfy both economic and environmental objectives: minimizing the sum of annualized investment cost, annualized operation and maintenance cost, and annualized operating cost as the economic objective, and minimizing carbon emissions as the environmental objective, to obtain an optimized configuration scheme set. The annualized operating cost and carbon emissions are obtained through feedback from the lower-layer optimization operation model. In the upper-layer configuration optimization model, during the initial iteration, 2poplength initial configuration scheme populations are randomly generated according to constraints, and a maximum iteration count n is set. In subsequent iterations, the parent population containing poplength excellent configuration schemes retained from the previous iteration is merged with the child population containing poplength configuration schemes randomly generated according to constraints to form a new initial population. In the lower-level optimization model, based on each random configuration scheme obtained from the initial population in the upper level, the optimal operation scheme is obtained with the goal of minimizing the annualized operating cost. The annualized operating cost and carbon emissions are then fed back to the upper level to obtain the economic and environmental target values ​​of this random configuration scheme. By non-dominated sorting of the economic and environmental target values ​​of all initial populations, before reaching the maximum number of iterations n, an elite retention strategy is used to retain poplength excellent individuals as the parent population, which then enters the next iteration of the upper-level configuration optimization model. When the maximum number of iterations n is reached, the optimal solution set of the optimized configuration scheme is obtained.

[0124] Embodiment six of the present invention is as follows: Please refer to Figure 7 An iterative terminal 1 for an energy system configuration-operation co-optimization model includes a memory 3, a processor 2, and a computer program stored in the memory and executable on the processor. When the processor executes the computer program, it implements the steps in the iterative method of an energy system configuration-operation co-optimization model according to any of the above embodiments.

[0125] In summary, this invention provides an iterative method and terminal for an energy system configuration-operation collaborative optimization model. Based on the NSGA-II algorithm, it establishes a comprehensive energy system configuration-operation collaborative optimization model to achieve rapid iteration. At the same time, by introducing heuristic rules, it eliminates the problem of equipment capacity redundancy that may exist in the configuration schemes input into the model, accelerates the upper-level optimization configuration, and improves the solution speed of the optimized configuration scheme set of the comprehensive energy system configuration-operation collaborative optimization model.

[0126] The above description is merely an embodiment of the present invention and does not limit the patent scope of the present invention. Any equivalent modifications made based on the content of the present invention specification and drawings, or direct or indirect applications in related technical fields, are similarly included within the patent protection scope of the present invention.

Claims

1. An iterative method for an energy system configuration-operation collaborative optimization model, characterized in that: Including the following steps: S1. In the configuration-run collaborative optimization model, the lower-level run optimization model generates an optimized run plan based on the random configuration plan obtained from the upper-level configuration optimization model. S2. Optimize the redundancy of various capacity data using heuristic rules, and update the required number of devices for each type of capacity data, specifically: S21. Select any type of unoptimized capacity data; S22. Using the working capacity of the devices corresponding to the capacity data as an indicator, optimize the capacity data using heuristic rules, and update the required number of devices corresponding to the capacity data, specifically as follows: S221. Detect the number of devices corresponding to the current capacity data. If satisfied If the capacity data is determined to be non-redundant, then... If the required number of devices is considered to correspond to the capacity data, proceed to step S23; otherwise, proceed to the next step. S222. Detect the working efficiency of the device. If the maximum working efficiency satisfies: Then determine that the device corresponding to the capacity data is not redundant, and... If the required number of devices is considered to correspond to the capacity data, proceed to step S23; otherwise, set... Return to step S221; wherein is an iteration factor, initially set to 0; n CurrentCapacityData corresponds to the number of devices; For maximum efficiency of the device; preset working efficiency of the device; an impact factor corresponding to the current capacity data; S23. Return to step S21 until all the capacity data has been optimized for redundancy; S3. Store the updated number of required devices and summarize the capacity optimization data to form an optimized configuration scheme under the optimized operation scheme; S4. Feed the optimized configuration scheme back to the upper-level configuration optimization model for calculation. 2.The iterative method of claim 1, wherein: The step S2220 is also included between steps S221 and S222: S2220. If it is detected that the device corresponding to the current capacity data is limited by the state of energy, the capacity data is first optimized using heuristic rules with the state of energy as an indicator. After the optimization is completed, proceed to the next step. Otherwise, proceed directly to the next step.

3. The iterative method of claim 2, wherein: The specific steps of S2220 are as follows: S22201. Detect the energy state of the device; if the actual minimum ratio of the energy state satisfies... ,Will The number of required devices corresponding to the capacity data is considered, and the process proceeds to step S23. Otherwise, proceed to step S22202; S22202, If the maximum ratio of the energy states satisfies: Then The number of required devices corresponding to the capacity data is considered, and the process proceeds to step S23. Otherwise, proceed to step S222; wherein, is the minimum ratio of the actual energy state; a minimum preset ratio of energy state; the maximum ratio of the actual energy state; is the maximum preset ratio for the energy state.

4. The iterative method of claim 1, wherein: The upper-level configuration optimization model of the configuration-operation collaborative optimization model is a multi-objective optimization method based on the non-dominated sorting genetic algorithm NSGA-II to obtain the Pareto optimal solution set. It establishes economic objective function and environmental objective function, with the goal of minimizing the sum of annualized investment cost, annualized operation and maintenance cost, and annualized operating cost, and the goal of minimizing carbon emissions, to obtain an optimal configuration scheme set. Among them, the annualized operating cost and carbon emissions are obtained through feedback from the lower-level optimized operating model; In the first iteration, 2poplength initial configuration schemes are randomly generated according to the constraints, and the maximum number of iterations n is set. In non-initial iterations, the parent population containing poplength excellent configuration schemes retained from the previous iteration is merged with the offspring population containing poplength configuration schemes randomly generated according to constraints to form a new initial population.

5. The iterative method of claim 4, wherein: Step S1 specifically includes: The lower-level operation optimization model obtains an optimized operation scheme based on each random configuration scheme obtained from the upper-level initial population, with the goal of minimizing the annualized operating cost. Step S4 further includes the following steps: The upper-level configuration optimization model receives the optimized configuration scheme, corresponding annualized operating cost, and carbon emissions from the lower-level operation optimization model. It calculates the economic and environmental target values ​​based on the economic and environmental objective functions, performs non-dominated sorting, and determines whether the preset maximum number of iterations has been reached. If so, the optimal solution set of the optimized configuration scheme is obtained; otherwise, poplength excellent individuals are retained as the parent population through an elite retention strategy, and the model enters the next iteration of the upper-level configuration optimization model.

6. An iteration terminal of a configuration-operation collaborative optimization model of an energy system, characterized in that: It includes a memory, a processor, and a computer program stored in the memory and executable on the processor, wherein the processor executes the computer program to implement the steps in the iterative method of the energy system configuration-operation cooperative optimization model according to any one of claims 1-5.

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