Comprehensive energy system multi-objective optimization method and system based on fractional order PID

Abstract

The invention discloses an integrated energy system multi-objective optimization method and system based on fractional order PID, and relates to the technical field of integrated energy management. The method comprises the following steps: acquiring operation parameters of each park of a to-be-optimized integrated energy system, and establishing an internal coupling model of the integrated energy system; determining a multi-objective function of each park according to an internal coupling model of the integrated energy system; according to system characteristics of different parks, a distributed algorithm based on fractional order PID is utilized to perform multi-objective function solution, and by adjusting adjustable parameters of fractional order, under the condition that convergence of the distributed algorithm is guaranteed, each park reaches the fastest convergence speed, and auxiliary variables are interacted among different parks in the iteration process. According to the method, the parameters in the model can be adjusted according to the characteristics of different systems on the premise of ensuring information security, and the operation efficiency of the model in a heterogeneous system is improved while constraints are met.

Description

Technical Field

[0001] The present invention relates to the technical field of integrated energy management, and in particular, to a multi-objective optimization method and system for an integrated energy system based on fractional-order PID. Background Technique

[0002] The statements in this part only provide background technical information related to the present invention and do not necessarily constitute prior art.

[0003] In response to the collaborative optimization challenges brought about by the large-scale regional interconnection of the park integrated energy system, the distributed method provides a flexible and efficient local decision-making mechanism, which can meet the needs of privacy protection and is applicable to large-scale systems. At the same time, introducing the distributed method into the optimization management scheme of the multi-park integrated energy system can coordinate the collaborative complementarity and coupling response characteristics among multiple types of energy, give full play to the interactive sharing advantages between different parks, and maximize the overall benefits of the system.

[0004] Considering that there are multiple heterogeneous energy subsystems (such as electricity, heat, cold, etc.) and different economic objectives (such as cost, efficiency, carbon emissions, etc.) in the integrated energy system, the multi-objective optimization problem has been introduced into the optimization management scheme of the multi-park integrated energy system. While coordinating the operation of each subsystem, the global improvement of system performance is achieved. Nowadays, although significant progress has been made in the theory of multi-objective distributed optimization algorithms, there are still many limitations and challenges in practical applications. On the one hand, most current algorithms rely too much on the central coordinator or global information sharing, and are easily affected by problems such as communication failures and data leakage during actual operation. How to improve the computational efficiency and real-time performance of the algorithm while ensuring convergence is a difficult problem. On the other hand, when dealing with the trade-off between objectives, existing algorithms usually rely on subjective weight setting and prior knowledge, and it is difficult to generate a uniformly distributed Pareto front, thus affecting the comprehensiveness of decision-making.

[0005] Due to the significant non-local characteristics of the fractional derivative, it not only depends on the current state during the calculation process, but also comprehensively considers the historical state of the variable. This characteristic endows the fractional derivative with stronger memory ability and dynamic response ability, enabling the optimization algorithm to efficiently explore the optimal solution in a wider spatial dimension, thus accelerating the overall optimization process. Compared with traditional integer-order methods, the fractional derivative can avoid falling into local minima in complex optimization problems and accelerate convergence to the global optimal solution. Therefore, the application of fractional order in multi-objective distributed algorithms is becoming more and more extensive.

[0006] In existing research, multi-objective distributed optimization algorithms provide new possibilities for the optimization of integrated energy systems at the theoretical level. However, they still face many challenges in terms of algorithm performance, practical applicability, etc. Researchers do not consider the differences between different parks and cannot comprehensively consider economy, low carbon, and high efficiency, which seriously affects the operating economy and efficiency of multi-park integrated energy systems. At the same time, affected by load and renewable energy output, the operating environment of integrated energy systems is full of uncertainties. When facing a dynamic environment, the real-time response ability is weak and it cannot meet the requirements of real-time optimization of integrated energy systems. Summary of the Invention

[0007] Aiming at the deficiencies of the existing technology, the purpose of the present invention is to provide a multi-objective optimization method and system for an integrated energy system based on fractional-order PID, which uses a fractional-order PID distributed algorithm to improve the convergence speed and global search ability of the model. On the premise of ensuring information security, the parameters in the model can be adjusted according to the characteristics of different systems, and while meeting the constraints, the operating efficiency of the model in heterogeneous systems can be improved.

[0008] To achieve the above object, the present invention is realized through the following technical solutions:

[0009] The first aspect of the present invention provides a multi-objective optimization method for an integrated energy system based on fractional-order PID, including the following steps:

[0010] Obtain the operating parameters of each park in the integrated energy system to be optimized, and establish an internal coupling model of the integrated energy system;

[0011] Determine the multi-objective function of each park according to the internal coupling model of the integrated energy system;

[0012] According to the system characteristics of different parks, use a distributed algorithm based on fractional-order PID to solve the multi-objective function. Among them, by adjusting the adjustable parameters of the fractional order, under the condition of ensuring the convergence of the distributed algorithm, the fastest convergence speed is achieved for each park, and auxiliary variables are exchanged between different parks during the iteration process.

[0013] Further, the specific steps for establishing the internal coupling model of the integrated energy system are:

[0014] According to the operating parameters of the power system and the operating parameters of the thermal system, establish a power system model and a thermal system model respectively;

[0015] Realize the internal coupling of the integrated energy system through cogeneration equipment and CHP units.

[0016] Further, the objectives of the multi-objective function include economic objectives, low-carbon objectives, and efficiency objectives.

[0017] Furthermore, the constraints of the integrated energy system include the global balance constraints of electric energy and heat energy, the upper and lower limits of the output of each device, and the ramp constraints.

[0018] Furthermore, the construction process of the distributed algorithm for fractional-order PID includes:

[0019] Construct a directed graph composed of multiple nodes and edges;

[0020] Establish an adjacency matrix according to the relationship between nodes, which is used to characterize the in-degree and out-degree of nodes;

[0021] Introduce adjustable coefficients and auxiliary variables, and calculate the objective function of each park according to the in-degree and out-degree of nodes.

[0022] Furthermore, the specific steps of using the distributed algorithm based on fractional-order PID to solve the multi-objective function are as follows:

[0023] Set the output of each device and initialize the auxiliary variables;

[0024] Iteratively solve the objective function according to the distributed algorithm of fractional-order PID. Among them, each node performs local update according to the fractional gradient descent method until the fractional-order vector approaches 0, the algorithm converges, and the optimal solution is obtained;

[0025] Output the optimized scheduling result.

[0026] Furthermore, the auxiliary variables do not contain privacy information.

[0027] The second aspect of the present invention provides a multi-objective optimization system for an integrated energy system based on fractional-order PID, including:

[0028] A data acquisition module, configured to acquire the operation parameters of each park of the integrated energy system to be optimized and establish an internal coupling model of the integrated energy system;

[0029] An objective function construction module, configured to determine the multi-objective function of each park according to the internal coupling model of the integrated energy system;

[0030] A function solving module, configured to solve the multi-objective function by using the distributed algorithm based on fractional-order PID according to the system characteristics of different parks. Among them, by adjusting the adjustable parameters of the fractional order, under the condition of ensuring the convergence of the distributed algorithm, each park reaches the fastest convergence speed, and auxiliary variables are exchanged between different parks during the iteration process.

[0031] In a third aspect of the present invention, a medium is provided, on which a program is stored, and when the program is executed by a processor, the steps in the multi-objective optimization method for an integrated energy system based on fractional-order PID as described in the first aspect of the present invention are implemented.

[0032] In a fourth aspect of the present invention, a device is provided, including a memory, a processor, and a program stored on the memory and executable on the processor. When the processor executes the program, the steps in the multi-objective optimization method for an integrated energy system based on fractional-order PID as described in the first aspect of the present invention are implemented.

[0033] The above one or more technical solutions have the following beneficial effects:

[0034] The present invention discloses a multi-objective optimization method and system for an integrated energy system based on fractional-order PID, and constructs a multi-objective distributed optimization solution model. By adjusting the optimization algorithm according to the change of the network topology structure, the single-point failure problem that is extremely likely to occur in the centralized method is solved, and the system has higher robustness. At the same time, the multi-objectives are closer to the complex situations in reality, and the problem that the single objective cannot take into account economy, low carbon and high efficiency is solved.

[0035] The present invention also introduces a fractional-order proportional-integral-derivative (PID) distributed optimization algorithm. This algorithm combines the control concepts of fractional order and PID. Compared with the existing PI algorithm, it improves the convergence speed of the algorithm and enhances the rapid response ability to external environment changes. At the same time, the fractional-order derivative has stronger memory ability and dynamic response ability, increasing the degree of freedom and flexibility of the algorithm, and can simultaneously adapt to the characteristics of heterogeneous parks and coordinate the interactive operation between parks.

[0036] The present invention considers the physical pipeline transportation cost and pipeline energy loss of power transmission and heat transfer between different parks, and selects the efficiency of each park as one of the objective functions. The transmission path with lower loss and lower cost is preferentially selected. It can not only ensure the transmission of energy quality, reduce the energy loss of irreversible processes, but also promote the multi-energy coordination of the integrated energy system.

[0037] The advantages of the additional aspects of the present invention will be partially given in the following description, partially will become obvious from the following description, or will be understood through the practice of the present invention. BRIEF DESCRIPTION OF THE DRAWINGS

[0038] The accompanying drawings forming a part of the present invention are used to provide a further understanding of the present invention. The schematic embodiments of the present invention and their descriptions are used to explain the present invention and do not constitute an improper limitation to the present invention.

[0039] Figure 1 It is a flow chart of the fractional-order PID algorithm in the first embodiment of the present invention;

[0040] Figure 2 This is the communication topology structure of the example in Embodiment 1 of the present invention;

[0041] Figure 3 This is a schematic diagram for comparing the convergence speeds of different examples in Embodiment 1 of the present invention;

[0042] Figure 4 This is a schematic diagram of the 24-hour multi-objective optimal scheduling result of Park 1 in Embodiment 1 of the present invention;

[0043] Figure 5 This is a schematic diagram of the 24-hour multi-objective optimal scheduling result of Park 2 in Embodiment 1 of the present invention;

[0044] Figure 6 This is a schematic diagram of the 24-hour multi-objective optimal scheduling result of Park 3 in Embodiment 1 of the present invention;

[0045] Figure 7 This is a schematic diagram of the 24-hour multi-objective optimal scheduling result of Park 4 in Embodiment 1 of the present invention. Detailed implementation manners

[0046] It should be noted that the following detailed description is exemplary and is intended to provide further illustration of the present invention. Unless otherwise specified, all technical and scientific terms used herein have the same meaning as commonly understood by those of ordinary skill in the technical field to which the present invention belongs.

[0047] It should be noted that the terms used herein are only for describing specific implementation manners and are not intended to limit the exemplary embodiments according to the present invention. As used herein, unless the context clearly indicates otherwise, the singular form is also intended to include the plural form. In addition, it should be understood that when the terms "comprise" and / or "include" are used in this specification, they indicate the presence of features, steps, operations, devices, components, and / or combinations thereof;

[0048] Embodiment 1:

[0049] Embodiment 1 of the present invention provides a multi-objective optimization method for an integrated energy system based on fractional-order PID. By introducing a fractional-order proportional-integral-derivative (PID) distributed optimization algorithm, the distributed optimization problem between different parks in the integrated energy system is solved. As Figure 1 shown, first, the output x of each device is set i , and the auxiliary variables of the algorithm are initialized. The multi-objective functions are set, including objective functions K1, K2, and K3. The fractional-order PID algorithm is used for iterative solution. During this period, the auxiliary variables without privacy information are exchanged between parks, and the iterative variables are updated until the fractional order is zero, and the optimization result is output to schedule the integrated energy system.

[0050] Specifically, it includes the following steps:

[0051] Step 1: Obtain the operation parameters of each park in the integrated energy system to be optimized, and establish an internal coupling model of the integrated energy system.

[0052] Step 1.1: Establish a power system model and a thermal system model respectively according to the operation parameters of the power system and the thermal system.

[0053] Specifically, obtain the operation parameters of each park in the integrated energy system to be optimized. Based on the characteristics of each park, build a power system model and a thermal system model respectively on the simulation platform.

[0054] Step 1.2: Achieve the internal coupling of the integrated energy system through energy conversion by cogeneration equipment and CHP units.

[0055] Step 2: Determine the multi-objective functions of each park according to the internal coupling model of the integrated energy system.

[0056] Step 2.1: Determine the multi-objective functions. Among them, the objectives of the multi-objective functions include economic objectives, low-carbon objectives, and efficiency objectives. Since the loads and equipment parameters of each park are different, the coefficients in each objective function will also vary.

[0057] Step 2.1.1: Economic objective function K1: Aiming at the lowest total operation cost of the park integrated energy system, the expression is as follows:

[0058]

[0059] In the formula, F 1 is the operation cost of a single park, C(P t cg ) is the operation cost of the traditional generator at time t, and P t cg represents the power generation of the traditional generator at time t; C(P t rg ) is the operation cost of the renewable energy generator at time t, and P t rg represents the power generation of the renewable energy generator at time t; is the operation cost of the gas boiler at time t, represents the heat production of the gas boiler at time t; is the operation cost of the cogeneration equipment at time t, and are respectively used to represent the power generation and heat production of the cogeneration equipment at time t; t chp 、 represents the heat production of the cogeneration equipment at time t; is the penalty term function. By adjusting the penalty term coefficient, the problem of the coupling between the power generation and heat production in the cogeneration equipment is solved.

[0060] Step 2.1.2: Low-carbon objective function K2: Build a low-carbon model with the goal of minimizing the carbon trading cost of the park's integrated energy system:

[0061]

[0062] In the formula, F 2 is the carbon trading cost of a single park, To fix the carbon trading price, is the carbon emission quota of the park, λ e , h are the carbon quota coefficients per unit of electricity and heat production, c eh is the conversion coefficient of electricity and heat; is the actual carbon emissions, ε e , ε h are the carbon emission coefficients per unit of electricity and heat production, respectively.

[0063] Step 2.1.3: Efficiency objective function K2: based on the park's integrated energy system The goal is to minimize efficiency and cost. Efficiency Model:

[0064]

[0065]

[0066] In the formula, F 3 For a single park Efficiency cost, η E for Efficiency, E out,e For electrical load E out,h Heat load E in,e For power consumption E in,g To purchase gas

[0067] Step 2.2: Transform the multi-objective problem. Determine the preference index based on a set of multi-objective functions to eliminate the reliance on prior knowledge and transform the multi-objective problem into a single-objective problem for solution.

[0068] The formula is as follows:

[0069]

[0070] in, is the park objective function, F i kis the k-th objective function of park i, is the multi-objective preference index, is the ideal decision variable, is the optimal value of the sum of the objective functions under the constraints.

[0071] Step 2.3: Determine the multi-objective function constraints. The integrated energy system constraints include global balance constraints for electrical and thermal energy, upper and lower output limits for each device, and ramp constraints.

[0072]

[0073] Where P load,t 、H load,t are the electrical load and thermal load of the system at time t, respectively.

[0074] Step 3: According to the system characteristics of different parks, a distributed algorithm based on fractional-order PID is used to solve the multi-objective function.

[0075] Among them, by adjusting the adjustable parameter α of the fractional order, each park can achieve the fastest convergence speed while ensuring the convergence of the distributed algorithm. During the iterative process, auxiliary variables that do not contain privacy information are interacted between different parks.

[0076] In this embodiment, auxiliary variables are calculated variables that do not affect the actual state of the system during the algorithm solution process. The main function of the auxiliary variables is to assist in the implementation of the algorithm but does not change the core semantics of the algorithm.

[0077] Privacy information refers to sensitive data in each park system that should not be known to other participants, such as model parameters, user load, etc.

[0078] Compared with the centralized method, this embodiment has stronger privacy protection characteristics, is suitable for large-scale systems, and can more effectively cope with system changes and local data uncertainties.

[0079] Step 3.1: Construct a distributed algorithm framework for fractional-order PID. Specifically, determine the correlation matrix of the multi-park integrated energy system based on the interaction relationships between different parks, and then substitute the obtained park objective function into the algorithm for iterative solution.

[0080] Step 3.1.1: Create a directed graph consisting of multiple nodes and edges.

[0081] In an integrated energy system, the structure of the communication network can be modeled and represented by a directed graph G = (V, E, A) consisting of multiple nodes and edges.

[0082] Step 3.1.2: Create an adjacency matrix based on the relationships between nodes to represent the in-degree and out-degree of the nodes.

[0083] Here, V = {v ij | i = 1, ..., n; j = 1, ..., m} represents the set of nodes (or participants), represents the set of communication edges, and A = {a ij} ∈ R n×n is the adjacency matrix representing the relationships between nodes. In this directed graph, the total number of nodes is defined as N. In the context of a directed graph, if a node V i can send (or V j receive) information to another node V j , then we say that V i is the out (or in) neighbor node of V j . Specifically, if there exists an edge from V i to V j , then in the adjacency matrix a ij = 1, and in the opposite case, a ij = 0. Similarly, the sets of in-neighbors and out-neighbors of node j are respectively defined as and Correspondingly, the in-degree and out-degree of a node are respectively defined by and . In addition, the Laplacian matrix of the graph is defined as L = D - A, where D is a diagonal matrix whose diagonal elements are equal to the in-degrees of the respective nodes.

[0084] Step 3.1.3: Introduce adjustable coefficients and auxiliary variables, and calculate the objective function of each park according to the in-degree and out-degree of the nodes.

[0085]

[0086] In the formula, is the fractional-order calculation, and α is an adjustable parameter from 0 to 1; is the objective function of each park, x i is the decision variable of agent i, d is the global constraint; λ and ξ are both auxiliary variables of the distributed algorithm, used to satisfy the global resource constraint and share information with neighbor nodes respectively; c1, c2, c3, c4, c5 and p are all adjustable coefficients of the algorithm.

[0087] Step 3.2: Use the distributed algorithm based on fractional-order PID to solve the multi-objective function.

[0088] Step 3.2.1: Set the output of each device and initialize the auxiliary variables.

[0089] Step 3.2.2: During the iteration process, iteratively solve the objective function according to the distributed algorithm of fractional-order PID.

[0090] Among them, each node performs local updates according to the fractional gradient descent method, and the specific steps are as follows:

[0091] First, calculate the fractional derivative in this iteration, and then substitute it into the corresponding formula for update. Until the fractional vector approaches 0, the algorithm converges to obtain the Pareto optimal solution. Output the optimized scheduling result. The formula is as follows:

[0092]

[0093] In the formula, x k+1 is the variable of the (k + 1)-th iteration, x k is the variable of the k-th iteration, and Δt is the step size.

[0094] The present invention is mainly divided into three parts: the power system and thermal system models, the multi-objective function model, and the fractional PID distributed algorithm framework. The power system and thermal system models convert energy through combined heat and power equipment - CHP units, supply energy to the electrical load and thermal load respectively, and at the same time interact electrical energy and thermal energy between different parks to meet the load demand. The multi-objective function model comprehensively considers the equipment operation cost, carbon trading cost and efficiency to achieve the purpose of simultaneously considering the economy, low carbon and high energy utilization efficiency of the system. Due to the memory effect and global characteristics of the fractional derivative, the fractional PID distributed algorithm can improve the convergence speed and global search ability of the algorithm. On the premise of ensuring information security, the parameters in the algorithm can be adjusted according to the characteristics of different systems, and while meeting the constraints, the operation efficiency of the algorithm in heterogeneous systems can be improved.

[0095] To prove the effectiveness of the present invention, the present invention takes four interconnected parks with different system structures and functional attributes as examples, named Park 1, Park 2, Park 3 and Park 4 respectively, to verify the effectiveness and real-time performance of the proposed model and method. The park equipment mainly includes traditional generators (CG), renewable energy generators (RG), combined heat and power equipment (CHP), and gas production equipment (GB). At the same time, the energy load can be divided into electrical load (PL) and thermal load (HL). The objective functions are the economic index, environmental protection index and energy efficiency. The scheduling period is 1 day, and the scheduling time interval is 1 h, that is, T = 24. The model structure and the communication topology structure between its energy supply equipment are as Figure 2 shown.

[0096] Figure 3 For the comparison of the iteration situations of the traditional PI distributed algorithm and the distributed algorithm based on fractional PID, it can be seen that, compared with the traditional PI distributed algorithm, the distributed algorithm based on fractional PID has a faster convergence speed and a smaller static error.

[0097] Taking Park 1 as an example, Table 1 shows the output results of single-point equipment in Park 1 for each calculation example. From Table 1, it can be seen that the results of Calculation Example 2 using the fractional-order PID algorithm are basically the same as those of the centralized algorithm in Calculation Example 1, proving the effectiveness and correctness of the fractional-order PID algorithm.

[0098] Table 1. Output Results of Single-Point Equipment in Park 1 for Each Calculation Example

[0099] Equipment Name Example 1 Centralized Algorithm Example 2 Fractional-order PID Algorithm Traditional Generator 143.33 143.44 Renewable Generator 400 400 Power Generation by CHP Equipment 109.69 109.71 Gas Boiler 787.47 787.46 Heat Generation by CHP Equipment 164.53 164.85

[0100] The 24-hour calculation results of the multi-objective optimization of the integrated energy system based on the fractional-order PID algorithm are as Figure 4 、 Figure 5 、 Figure 6 and Figure 7 shown. Due to the existence of the two objective functions of low carbon and efficiency, compared with the single-objective optimization dispatch that only considers economy, the power load supply tends to be more powered by renewable energy generators. At the same time, because the energy loss in the pipeline transmission process is considered in the objective function, the interaction of electric energy and heat energy between parks tends to supply energy directly from the park with more production capacity to the park with less production capacity, avoiding the situation of the interaction energy passing through other parks.

[0101] Example 2:

[0102] The second embodiment of the present invention provides a multi-objective optimization system for an integrated energy system based on fractional-order PID, including:

[0103] A data acquisition module configured to acquire the operation parameters of each park of the integrated energy system to be optimized and establish an internal coupling model of the integrated energy system;

[0104] An objective function construction module configured to determine the multi-objective functions of each park according to the internal coupling model of the integrated energy system;

[0105] A function solving module configured to solve the multi-objective functions by using a distributed algorithm based on fractional-order PID according to the system characteristics of different parks. Among them, by adjusting the adjustable parameters of the fractional order, under the condition of ensuring the convergence of the distributed algorithm, the fastest convergence speed is achieved for each park, and auxiliary variables are interacted between different parks during the iteration process.

[0106] Example 3:

[0107] The third embodiment of the present invention provides a medium on which a program is stored, and when the program is executed by a processor, it implements the steps in the multi-objective optimization method of the integrated energy system based on fractional-order PID as described in the first embodiment of the present invention.

[0108] Example 4:

[0109] Embodiment 4 of the present invention provides a device, including a memory, a processor, and a program stored on the memory and executable on the processor. When the processor executes the program, the steps in the multi-objective optimization method of the integrated energy system based on fractional-order PID described in Embodiment 1 of the present invention are implemented.

[0110] The steps involved in Embodiments 2, 3, and 4 above correspond to those in Method Embodiment 1. For specific implementation manners, reference may be made to the relevant description part of Embodiment 1.

[0111] Those skilled in the art should understand that the above-mentioned modules or steps of the present invention can be implemented by a general-purpose computer device. Optionally, they can be implemented by program codes executable by the computing device, so that they can be stored in the storage device and executed by the computing device, or they can be separately fabricated into individual integrated circuit modules, or multiple modules or steps among them can be fabricated into a single integrated circuit module for implementation. The present invention is not limited to any specific combination of hardware and software.

[0112] Although the specific implementation manners of the present invention have been described above in conjunction with the accompanying drawings, they are not limitations on the protection scope of the present invention. Those skilled in the art should understand that, based on the technical solutions of the present invention, various modifications or deformations that can be made without creative efforts by those skilled in the art are still within the protection scope of the present invention.

Claims

1. A multi-objective optimization method for integrated energy systems based on fractional-order PID, characterized in that, It includes the following steps: Obtain the operation parameters of each park in the integrated energy system to be optimized, and establish an internal coupling model of the integrated energy system; Determine the multi-objective functions of each park according to the internal coupling model of the integrated energy system; According to the system characteristics of different parks, use the distributed algorithm based on fractional-order PID to solve the multi-objective functions. Among them, by adjusting the adjustable parameters of the fractional order, under the condition of ensuring the convergence of the distributed algorithm, the fastest convergence speed can be achieved for each park, and auxiliary variables are interacted between different parks during the iteration process.

2. The multi-objective optimization method for integrated energy systems based on fractional-order PID according to claim 1, characterized in that The specific steps for establishing the internal coupling model of the integrated energy system are as follows: Establish a power system model and a thermal system model respectively according to the operation parameters of the power system and the thermal system; Realize the internal coupling of the integrated energy system through the combined heat and power equipment and CHP units.

3. The multi-objective optimization method for integrated energy systems based on fractional-order PID according to claim 1, characterized in that The objectives of the multi-objective function include economic objectives, low-carbon objectives and efficiency objectives.

4. The multi-objective optimization method for an integrated energy system based on fractional-order PID according to claim 1, characterized in that, The constraints of the integrated energy system include the global balance constraints of electric energy and heat energy, the upper and lower limits of the output of each device, and the ramp constraint.

5. The multi-objective optimization method for integrated energy systems based on fractional-order PID according to claim 1, characterized in that, The construction process of the distributed algorithm based on fractional-order PID includes: Establish a directed graph composed of multiple nodes and edges; Establish an adjacency matrix according to the relationship between nodes, which is used to characterize the in-degree and out-degree of nodes; Introduce adjustable coefficients and auxiliary variables, and calculate the objective functions of each park according to the in-degree and out-degree of nodes.

6. The multi-objective optimization method for integrated energy systems based on fractional-order PID according to claim 5, characterized in that, The specific steps for using the distributed algorithm based on fractional-order PID to solve the multi-objective functions are as follows: Set the output of each device and initialize the auxiliary variables; Iteratively solve the objective function according to the distributed algorithm based on fractional-order PID. Among them, each node performs local update according to the fractional gradient descent method until the fractional-order vector approaches 0, the algorithm converges, and the optimal solution is obtained; Output the optimized scheduling result.

7. The multi-objective optimization method for integrated energy systems based on fractional-order PID according to claim 1, characterized in that, The auxiliary variables do not contain privacy information.

8. Multi-objective optimization system for integrated energy system based on fractional-order PID, characterized in that, It includes: A data acquisition module configured to obtain the operation parameters of each park in the integrated energy system to be optimized and establish an internal coupling model of the integrated energy system; An objective function construction module configured to determine the multi-objective functions of each park according to the internal coupling model of the integrated energy system; A function solving module configured to use the distributed algorithm based on fractional-order PID to solve the multi-objective functions according to the system characteristics of different parks. Among them, by adjusting the adjustable parameters of the fractional order, under the condition of ensuring the convergence of the distributed algorithm, the fastest convergence speed can be achieved for each park, and auxiliary variables are interacted between different parks during the iteration process.

9. A computer-readable storage medium, characterized in that, Among them, multiple instructions are stored, and the instructions are suitable for being loaded and executed by the processor of the terminal device to perform the multi-objective optimization method of the integrated energy system based on fractional-order PID according to any one of claims 1-7.

10. A terminal device, characterized in that, It includes a processor and a computer-readable storage medium. The processor is used to implement each instruction; the computer-readable storage medium is used to store multiple instructions, and the instructions are suitable for being loaded and executed by the processor to perform the multi-objective optimization method of the integrated energy system based on fractional-order PID according to any one of claims 1-7.

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