Optimized dispatch method and system for fully distributed integrated energy system with preset time

Abstract

A method for optimizing dispatching of a fully distributed integrated energy system with preset times is provided. [Solution] The optimization dispatch method establishes an integrated energy system operation optimization model and constructs an objective function as a dispatch problem for the integrated energy system with the goal of minimizing the total cost of the integrated energy system. Safe operation constraints are set in the objective function, including a power balance constraint, a power upper / lower limit constraint, and an operation ramp rate constraint. The method also assumes that the communication topology of the integrated energy system's source side, load side, storage side, and station side is an undirected connected graph. A fully distributed optimization algorithm based on TBG with a preset time is used to solve the objective function, so that the integrated energy system operation optimization model reaches a convergence state within a preset time, and an optimal output strategy for the integrated energy system is obtained.

Description

[Technical Field]

[0001] (CROSS-REFERENCE TO RELATED APPLICATIONS) This invention claims priority to a Chinese patent application filed with the State Intellectual Property Office of the People's Republic of China on November 29, 2024 (application number 202411731003.2, invention title: "Optimized dispatch method and system for fully distributed integrated energy system with preset time"), the entire contents of which are incorporated by reference into and constitute a part of the present invention for all purposes.

[0002] The present invention relates to the technical field of integrated energy systems, and more particularly to a method and system for optimizing dispatch of a fully distributed integrated energy system with preset times. [Background technology]

[0003] The statements in this section provide only background information related to the present invention and do not necessarily constitute prior art.

[0004] Unlike traditional single-energy supply systems, integrated energy systems (IES) integrate various energy forms, such as electricity, heat, gas, and cold, through energy conversion and energy storage equipment within the system. These energy sources are combined to complement each other and meet the diverse energy needs of users. With the development of communication and big data technologies, the integration of energy generation, conversion, storage, utilization, and other components in IESs is becoming increasingly stronger. Intelligent control and collaborative management of each component enables efficient and safe management of the entire system. With the current rapid development of the economy and society and increasing demands for energy utilization, establishing an IES that combines multiple energy sources, is economical, efficient, low-carbon, and environmentally friendly.

[0005] In the prior art, centralized optimization dispatch methods are often used to solve optimization problems in systems such as integrated energy systems. To obtain an optimal dispatch strategy, all parties in the system must have access to global information. Furthermore, centralized optimization methods require a high number of communications and poor network robustness, making them unable to meet the current needs of integrated energy system development. However, existing distributed optimization methods require partial global information or directly apply traditional distributed optimization methods for power systems, making them unable to dynamically adapt to the dispatch time scales of different energy sources. Summary of the Invention

[0006] To solve the deficiencies of the prior art, the present invention provides a method and system for optimizing dispatch of a fully distributed integrated energy system with preset time. A fully distributed optimization algorithm with preset time based on a time-based generator (TBG) is designed, and Lyapunov theory is used to prove that the algorithm converges within the preset time. The solution can be obtained for an optimization dispatch model of a thermoelectric integrated energy system including a "source-load-storage-station" system, including equality and inequality constraints, without relying on the initial values ​​and parameters of the system, with the time arbitrarily preset, and without using any global information, thereby significantly improving the accuracy of dispatch.

[0007] To achieve the above objectives, the present invention adopts the following technical solutions:

[0008] In a first aspect, the present invention provides a method for optimizing dispatch of a fully distributed integrated energy system with preset times.

[0009] A process of establishing an integrated energy system operation optimization model and constructing an objective function with the goal of minimizing the total cost of the integrated energy system, wherein safe operation constraints are set in the objective function, and the safe operation constraints include a power balance constraint, a power upper / lower limit constraint, and an operation ramp rate constraint; and a process of assuming that the communication topology of the source side, load side, storage side, and station side of the integrated energy system is an undirected connected graph, and solving the objective function using a fully distributed optimization algorithm with a preset time based on a time-based generator, thereby making the integrated energy system operation optimization model reach a convergence state within the preset time, thereby outputting an optimal output strategy for the integrated energy system.

[0010] In a second aspect, the present invention provides an optimized dispatch system for a fully distributed integrated energy system with preset times.

[0011] A dispatch target setting unit configured to establish an integrated energy system operation optimization model and construct an objective function with the goal of minimizing a total cost of the integrated energy system, wherein a safe operation constraint is set in the objective function, and the safe operation constraint includes a power balance constraint, a power upper / lower limit constraint, and an operation ramp rate constraint; and an optimal dispatch control unit configured to assume that the communication topology of the source side, load side, storage side, and station side of the integrated energy system is an undirected connected graph, and to solve the objective function using a fully distributed optimization algorithm with a preset time based on a time-based generator, thereby making the integrated energy system operation optimization model reach a convergence state within a preset time, thereby outputting an optimal output strategy for the integrated energy system.

[0012] In a third aspect, the present invention provides a computer apparatus including a processor and a computer-readable storage medium, a processor adapted to execute a computer program; The computer-readable storage medium stores a computer program, which, when executed by the processor, implements the method for optimizing dispatching of a fully distributed, integrated energy system according to a preset time as described in the first aspect of the present invention.

[0013] In a fourth aspect, the present invention provides a computer-readable storage medium having stored thereon a computer program, the computer program being suitable, when read by a processor, for executing the method for optimizing dispatching of a fully distributed, integrated energy system according to a preset time according to the first aspect of the present invention.

[0014] Compared with the prior art, the beneficial effects of the present invention are as follows:

[0015] This invention divides a thermoelectric integrated energy system into four functional parts: "source-load-storage-station," and establishes a low-carbon economic dispatch model for it. A fully distributed optimization algorithm with preset time based on a time-based generator (TBG) is designed, and Lyapunov theory is used to prove that the algorithm converges within the preset time. This solution for the optimization dispatch model of a thermoelectric integrated energy system including "source-load-storage-station," incorporating equality and inequality constraints, can be obtained without relying on the initial values ​​and parameters of the system, the time can be arbitrarily preset, and no global information is used, significantly improving dispatch accuracy.

[0016] Advantages of additional aspects of the invention will be set forth in part in the description that follows, and in part will be obvious from the description, or may be learned through practice of the invention.

[0017] The accompanying drawings, which form a part of this specification, are intended to provide a further understanding of the present invention, and the schematic embodiments and description thereof are intended to provide an understanding of the present invention and are not intended to unduly limit the present invention. [Brief explanation of the drawings]

[0018] [Figure 1] 1 is a configuration diagram of an integrated energy system provided in a first embodiment of the present invention. [Figure 2] 1 is a configuration diagram and a communication network topology diagram in an example analysis provided in Example 1 of the present invention. FIG. [Figure 3] 1 is a thermal power response curve for a preset time of 40 seconds in an example analysis provided in Example 1 of the present invention. [Figure 4] 1 is an electrical power response curve when the preset time is 10 seconds in an example analysis provided in Example 1 of the present invention. [Figure 5] 10 is a thermal power supply and demand balance response curve when the preset time is 40 seconds in the example analysis provided in Example 1 of the present invention. [Figure 6] 10 is an electrical power response curve when the preset time is 6 seconds in an example analysis provided in Example 1 of the present invention. [Figure 7] 1 is a thermal power supply and demand balance response curve from 0 to 125 s in an example analysis provided in Example 1 of the present invention. [Figure 8] 1 is a thermal power response curve from 0 to 125 seconds in an example analysis provided in Example 1 of the present invention. [Figure 9] 10 shows the simulation results of other similar algorithms in the example analysis provided in Example 1 of the present invention. [Figure 10]1 shows the simulation results of the algorithm proposed by the present invention in an example analysis provided in Example 1 of the present invention. [Figure 11] FIG. 10 is a schematic diagram of an optimization dispatch system for a fully distributed integrated energy system according to a preset time provided in Example 2 of the present invention. [Figure 12] FIG. 10 is a schematic diagram of a computer device provided in Example 3 of the present invention. DETAILED DESCRIPTION OF THE INVENTION

[0019] The present invention will be further described below in combination with the accompanying drawings and embodiments.

[0020] The following detailed description is for illustrative purposes only and is intended to further explain the present invention. Unless otherwise specified, all technical and scientific terms used herein have the same meaning as commonly understood by those skilled in the art to which this invention belongs.

[0021] Unless a contradiction occurs, the embodiments of the present invention and the features of the embodiments may be combined with each other.

[0022] Example 1: As described in the background art, conventional methods cannot be applied to the low-carbon and economical optimization dispatch problem of an integrated energy system including energies with different dispatch time scales. In light of this, this embodiment proposes an optimization dispatch method for a fully distributed integrated energy system with a preset time, which includes the following:

[0023] An integrated energy system can be divided into four parts according to their functions: "source," "load," "storage," and "station." The specific structure is shown in Figure 1. Here, the "source" side mainly includes two parts: a power supply unit and a heat supply unit. The power source includes thermal power generation and renewable energy, and the heat source includes a boiler and a heat pump, which are responsible for the supply of electrical energy and thermal energy within the system. The "load" side includes electrical loads and thermal loads, and some of the electrical loads can respond to demand. The "storage" side realizes the temporal transfer of electrical energy through a rational charging and discharging plan of the energy storage station. The "station" side realizes energy form conversion through an energy station or a combined heat and power (CHP) unit, while outputting two forms of energy: electricity and heat.

[0024] To ensure that each part of the integrated energy system operates in a low-carbon and economical manner, a low-carbon and economical operation model is established. The optimized objective function can be expressed as follows:

[0025]

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[0026]

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[0027]

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[0028]

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[0029]

number

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[0030]

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[0031]

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[0032] The carbon emission cost of facility j is

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[0033] In this implementation, F represents the total cost of the IES, and F P ,F H ,F ES ,F EH ,F L represent the costs on the power source side, heat source side, "storage" side, "station" side, and "load" side, respectively. N RE ,N G ,N B ,NP ,N ES ,N EH ,N CHP ,N L respectively represent the number of new energy power generation facilities, the number of thermal power generation facilities, the number of boiler facilities, the number of heat pump facilities, the number of energy storage facilities, the number of energy stations, the number of combined heat and power units, and the number of loads.

[0034] Low-carbon and economical optimal dispatch of integrated energy systems must satisfy the system's safety operation constraints and achieve a given N Power ,N Heat represent a set of electrical energy output equipment and a set of thermal energy output equipment, respectively, P i ∈{P i,RE ,P j.G ,P s,ES ,P m1,EH ,P n1,CHP}, H j ∈{P k.B ,P l.P ,P m2,EH ,P m2,EH ,P n2,CHP}, the constraint can be specifically expressed as follows:

[0035] The power balance constraints are

number

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[0036] Power upper / lower constraints are

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[0037] The operating ramp rate constraint is

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[0038] Based on the above model, the low-carbon and economic dispatch problem of the integrated energy system is

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[0039] Here, equations (14) and (15) are equality constraints, and equations (16) to (18) are inequality constraints.

[0040] Equation (19) is the sum of the cost functions of each device, and the constraints include equality constraints such as (14) and (15) and inequality constraints (16), (17), and (18). To simplify the proof by the following algorithm,

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[0041] Note: Equation (20) is a simplified version of equation (19) for the convenience of subsequent proof. Equation (19) is the sum of the objective functions of all equipment, and N in equation (20) is the total number of equipment. The equality constraints in equation (20) are simplified versions of (14) and (15), and the inequality constraints are simplified versions of (16) to (18).

[0042] For the above optimization problem, in this implementation, a fully distributed optimization algorithm with preset time based on TBG (Time-Based Generator) is proposed to solve the problem. Specifically, it includes the following:

[0043] The network communication topology diagram of this implementation is represented by G={V,E,A}, where the set of nodes is V={1,2,3,...,N}, the set of edges is E⊆V×V, and the adjacency matrix of the graph G is A=[a ij ]∈R N×N If (i,j)∈E, then a ij >0, otherwise a ij = 0. The set of neighboring nodes of node i is N i ={j∈V:a ij >0}, and the Laplacian matrix of a graph G is L = DA, where D = diag{d1,d2,d3,...,d N} is the degree matrix of the graph, and d i denotes the degree of node i.

[0044] Assumption 1: An undirected graph G is connected.

[0045] Lemma 1: From Assumption 1,

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[0046] Assumption 2: C i (P i ):R n →R is ω i -strongly convex, continuously differentiable, and locally m i -Lipschitz gradient, ω i and m i are all positive constants.

[0047] Lemma 2: From Assumption 2,

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[0048] Assumption 1 ensures that each agent's local information can be distributed throughout the network, and assumption 2 ensures that the cost function is smooth, strongly convex, and that an optimal point exists in the function.

[0049] The introduction of a time-based generator κ(t) satisfies the following conditions:

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[0050] Lemma 3: For the following time-varying system:

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[0051] For any initial state x(0), the system (25) will converge in preset time if the following conditions are met:

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[0052] To solve the problem in equation (20), we introduce a time-varying gain k(t) using the TBG technique. First, we propose a fully distributed algorithm with a preset time that does not depend on any initial value:

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[0053] By observation, z i (t)∈R n ,i∈V is an auxiliary variable, and its initial value can be determined in advance. Therefore, the initial value of the variable is

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[0054] The inequality constraint in equation (20) is processed using the penalty function method, and the penalty function is

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[0055] And the function

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[0056] In this implementation, we provide a convergence analysis of the proposed algorithm. For the sake of convenience, we first calculate Equation (27) as follows:

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[0057] During the ceremony,

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[0058] Lemma 4: If both Assumption 1 and Assumption 2 are satisfied, then the preset time t f The distributed optimization problem can be solved within a

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[0059]

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[0060] Proof of Lemma 4: First, the optimal solution P * Prove that there exists a (P * ,y * ,z * ) is the equilibrium point of (27), the following conditions:

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[0061] If assumption 1 is met,

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[0062] In the second equation of (27), from the left

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[0063] Equilibrium point (P * ,y * ,z * ) satisfies the KKT conditions, and P * is found to be a global optimum.

[0064] Next, we analyze the convergence of the system based on Lyapunov theory.

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[0065]

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[0066] According to the properties of the Laplacian matrix of a graph G,

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[0067] Based on the orthogonal edge exchange above, we need to prove that χ can converge within a preset time. The following Lyapunov function:

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[0068]

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[0069] According to (38)-(41),

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[0070] According to Lemma 1 and Lemma 2,

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[0071] According to Young's inequality,

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[0072] Substituting equations (44) and (45) into equation (43), we get

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[0073] According to equations (22) and (20),

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[0074] Equations (47) to (49) are converted into equation (50):

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[0075] According to equations (42) and (43),

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[0076] Next, Lemma 1 and the Comparison Principle:

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[0077] According to (42),

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[0078] t≧t f In this case, k(t)=0,

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[0079] The algorithm is based on the system preset time t f We can guarantee that we achieve convergence within , and this completes the proof.

[0080] Regarding equation (28), first consider its concise form:

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[0081] Compared to equation (27), the algorithm only constrains the initial value of the auxiliary variable z, i.e.

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[0082] The subsequent proof is similar to equation (27) and will not be repeated here.

[0083] When considering inequality constraints in the system, the penalty function is set to Equation (29) and the new optimization function:

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[0084] Equation (57) is clearly a strongly convex function. The unique optimal solutions when inequality constraints are considered and when inequality constraints are ignored are respectively

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[0085] According to the KKT conditions,

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[0086] The calculation example configuration diagram and communication network topology are shown in Figure 2. G1, G2, G3, and G4 are thermal power generation facilities, L1, L2, L3, L4, L5, L6, L7, L8, L9, L10, L11, L12, L13, and L14 are electrical load facilities, S1 and S2 are energy storage facilities, CHP1 and CHP2 are combined heat and power units, P1 and P2 are heat pump facilities, B1 and B2 are boiler facilities, H1, H2, H3, H4, H5, and H6 are thermal load facilities, PV represents photovoltaic facilities, and WT represents wind power generation facilities. The numbers 1 to 30 on the left represent 30 system nodes, and the numbers 1 to 14 on the right represent 14 system nodes.

[0087] The system includes two energy sources, heat and electricity, and because the dispatch time scales of the two energies are different, the coupling entities must be decoupled in order to separately preset different convergence times. The system includes two types of coupling entities: an energy station and a CHP. The energy station includes various energy conversion devices, and its output electrical power and thermal power can be directly decoupled. As for the CHP, its output is decoupled according to the current heat-to-power ratio, and the unit is assumed to operate in a state where it determines the amount of power generation based on the heat supply load.

[0088] The algorithm-related parameters are σ=10 -5 , τ=0.001, ξ=100,

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[0089] The physical parameters of each entity are set in Tables 1 and 2. The carbon emission coefficient is α = 0.06, and the initial value of the auxiliary variable is y i (0)=0, z i(0)=0,

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[0090] Table 1: Energy supplier parameters (where P i (0) is the initial power of node i) [Table 1]

[0091] Table 2: Load-based parameters [Table 2] The integrated energy system includes four parts: "Source-Load-Storage-Station" that encompass two different energy sources, heat and electricity. Among them, "Station" is the entity that combines the two energy sources, heat and electricity. The two energy sources are decoupled according to the concept in the previous paragraph. For thermal energy, which has a relatively large dispatch time scale, the preset time t f1 = 40 s, whereas for electrical energy, which has a relatively small dispatch time scale, the preset time t f2 Assume that τ = 10 s. The power response curves are shown in Figures 3 and 4. In Figure 3, CHP1 and CHP2 are combined heat and power units, P1 and P2 are heat pumps, B1 and B2 are boilers, and EH is an energy station. In Figure 4, G1, G2, G3, and G4 are thermal power plants, L1, L2, L3, L4, L5, L6, L7, L8, L9, L10, L11, L12, L13, and L14 are electrical loads, S1 and S2 are energy storage facilities, CHP1 and CHP2 are combined heat and power units, and EH is an energy station.

[0092] From the figure, we can see that after fluctuations at different preset times, the generated / consumed power of each entity in the system approaches the optimal value as time passes. Figure 5 shows the convergence curve of the thermal energy supply and demand balance. From the figure, we can see that within the preset time, the thermal energy power supplied by each entity can be guaranteed to be balanced with the thermal energy power required by the system. The dispatching results of each entity are as follows:

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[0093] To further verify that the preset time is adjustable, the preset time t f3 The efficiency response curve is shown in Figure 6. G1, G2, G3, and G4 are thermal power plants, L1, L2, L3, L4, L5, L6, L7, L8, L9, L10, L11, L12, L13, and L14 are electrical loads, S1 and S2 are energy storage facilities, CHP1 and CHP2 are combined heat and power units, and EH is an energy station. The diagram shows that the power generated and consumed by each entity approaches the optimal solution within 6 seconds.

[0094] Next, we simulate and verify the "plug-and-play" characteristics of the algorithm. The simulation results are shown in Figures 7 and 8. In Figure 8, CHP1 and CHP2 are combined heat and power units, P1 and P2 are heat pumps, B1 and B2 are boilers, and EH is an energy station. From the diagram, we can see that within 0 to 25 seconds, the generated / consumed power of each entity approaches the optimal value within a preset time of 10 seconds. When a load is disconnected or reconnected, each entity can converge from its original state to a new optimal value according to the preset time. The preset time can also be reset according to actual needs. When an energy supply facility is disconnected or reconnected, each entity can also converge from its original state to a new optimal value. Whether t=10 seconds, 65 seconds, or 115 seconds, the system converges to the same state, but the initial values ​​for convergence are different. From the above analysis, we can see that the algorithm has excellent "plug-and-play" characteristics and can guarantee that the optimal value can be approached within the preset time even with different initial values ​​of the optimization variables.

[0095] When the same calculation example was analyzed using the existing preset time algorithm and the equation (28) proposed in this invention, when μ=1.65, the initial values ​​of the variables were P1(0)=40, P2(0)=35, P1(0)=45, P1(0)=40, y i (0)=0, z i (0)=0, i=1,2,...,4, δ0=145. The simulation results are shown in Figures 9 and 10. P1, P2, P3, and P4 are power generation units, respectively. From the two figures, we can see that the algorithm proposed in this implementation and the existing algorithm can both achieve convergence, but by adjusting the introduced correction coefficient μ, the error of the convergence result can be reduced and the accuracy of the optimal solution can be improved.

[0096] Example 2: As shown in FIG. 11, this implementation is A dispatch target setting unit configured to establish an integrated energy system operation optimization model and construct an objective function with the goal of minimizing a total cost of the integrated energy system, wherein a safe operation constraint is set in the objective function, and the safe operation constraint includes a power balance constraint, a power upper / lower limit constraint, and an operation ramp rate constraint; The present invention provides an optimization dispatch system for a fully distributed integrated energy system with a preset time, including an optimal dispatch control unit configured to assume that the communication topology of the source side, load side, storage side, and station side of the integrated energy system is an undirected connected graph, and solve the objective function using a fully distributed optimization algorithm with a preset time based on TBG, thereby making the integrated energy system operation optimization model reach a convergence state within a preset time, and thereby output an optimal output strategy for the integrated energy system.

[0097] It can be understood that the above units can be integrated into one or more other units, either separately or as a whole, or one (or some) of the units can be further divided into multiple functionally smaller units, thereby achieving similar operations without affecting the achievement of the technical effects of the embodiments of the present application. The above units are divided based on logical functions. In actual applications, the function of one unit may be realized by multiple units, or the functions of multiple units may be realized by one unit. In other embodiments of the present application, the system may include other units. In actual applications, these functions may be realized with the assistance of other units or through cooperation of multiple units.

[0098] According to another embodiment of the present application, a system described in this embodiment can be constructed and the method of Example 1 of the present application can be realized by executing a computer program (including program code) capable of executing each step of the corresponding method described in Example 1 in a general-purpose computing device (e.g., a computer) including processing elements and storage elements such as a central processing unit (CPU), random access memory (RAM), and read only memory (ROM). The computer program may be recorded in a computer-readable recording medium, for example, and read into and executed in the computing device via the computer-readable recording medium.

[0099] Example 3: 12, the present embodiment provides an electronic device including a processor 1001, a communication interface 1002, and a computer-readable storage medium 1003. Here, the processor 1001, the communication interface 1002, and the computer-readable storage medium 1003 may be connected via a bus or other means.

[0100] Here, the communication interface 1002 is used to send and receive data, the computer-readable storage medium 1003 is stored in the memory of the electronic device, the computer-readable storage medium 1003 is used to store a computer program, the computer program includes program instructions, and the processor 1001 is used to execute the program instructions stored in the computer-readable storage medium 1003.

[0101] The processor 1001 (or central processing unit (CPU)) is the computational and control core of an electronic device, and is suitable for executing one or more instructions, and in particular for reading and executing one or more instructions to implement a corresponding method process or a corresponding function.

[0102] The processor 1001 A process of establishing an integrated energy system operation optimization model and constructing an objective function with the goal of minimizing the total cost of the integrated energy system, wherein safe operation constraints are set in the objective function, and the safe operation constraints include a power balance constraint, a power upper / lower limit constraint, and an operation ramp rate constraint; The system is configured to execute a process in which the communication topology of the source side, load side, storage side, and station side of the integrated energy system is assumed to be an undirected connected graph, and the objective function is solved using a fully distributed optimization algorithm with a preset time based on TBG, so that the integrated energy system operation optimization model reaches a convergence state within a preset time, thereby outputting an optimal output strategy for the integrated energy system.

[0103] The detailed process is as introduced in Example 1 and will not be repeated here.

[0104] Example 4: This embodiment provides a computer-readable storage medium (memory) that is a storage device in an electronic device for storing programs and data. It can be understood that the computer-readable storage medium here may include a storage medium built into the electronic device, and may also include an expansion storage medium supported by the electronic device. The computer-readable storage medium provides a storage space in which a processing system of the electronic device is stored.

[0105] The storage space may also store one or more instructions suitable for being read and executed by the processor, which may be one or more computer programs (including program code). Note that the computer-readable storage medium here may be a high-speed RAM memory, a non-volatile memory (e.g., at least one disk memory), or, optionally, at least one computer-readable storage medium located remotely from the processor.

[0106] In one embodiment, the computer-readable storage medium has one or more instructions stored thereon, and the processor reads and executes the one or more instructions stored on the computer-readable storage medium to: A process of establishing an integrated energy system operation optimization model and constructing an objective function with the goal of minimizing the total cost of the integrated energy system, wherein safe operation constraints are set in the objective function, and the safe operation constraints include a power balance constraint, a power upper / lower limit constraint, and an operation ramp rate constraint; Assuming that the communication topology of the source side, load side, storage side, and station side of the integrated energy system is an undirected connected graph, a fully distributed optimization algorithm with a preset time based on TBG is used to solve the objective function, so that the integrated energy system operation optimization model reaches a convergence state within a preset time, thereby realizing a process of outputting the optimal output strategy of the integrated energy system.

[0107] The detailed process is as introduced in Example 1 and will not be repeated here.

[0108] The above is only a preferred embodiment of the present invention and is not intended to limit the present invention. Those skilled in the art can make various modifications and changes to the present invention. Any modifications, equivalent replacements, improvements, etc. made within the spirit and principle of the present invention are all intended to be included in the protection scope of the present invention.

Claims

1. 1. A computer-implemented method for optimizing dispatch of a fully distributed integrated energy system according to a preset time, comprising: A process of establishing an integrated energy system operation optimization model and constructing an objective function with the goal of minimizing the total cost of the integrated energy system, wherein a safe operation constraint is set in the objective function, and the safe operation constraint includes a power balance constraint, a power upper / lower limit constraint, and an operation ramp rate constraint, where the power balance constraint is an equality constraint, and the power upper / lower limit constraint and the operation ramp rate constraint are inequality constraints; Assuming that the communication topology of the source side, load side, storage side, and station side of the integrated energy system is an undirected connected graph, and solving the objective function using a fully distributed optimization algorithm with a preset time based on a time-based generator, so that the integrated energy system operation optimization model reaches a convergence state within the preset time, thereby outputting an optimal output strategy of the integrated energy system; Introducing a time-varying gain k(t) and solving the objective function using a fully distributed algorithm with a preset time that does not depend on any initial value; [Number DD] A process that assumes that [Number 62] Here, ∇C ｉ (P ｉ (t)) is the gradient, and P ｉ (t) is the optimization variable, which represents the power of the i-th node at time t. All power supply facilities, loads, and heat supply facilities constitute each node. The total number of nodes is N. C ｉ (P ｉ (t)) represents the cost corresponding to the power of the i-th node at time t, [Math EE] Is P ｉ is the first derivative of (t), and y ｉ (t), z ｉ (t), y ｊ (t), z ｊ (t) is the auxiliary variable at time t, μ>1 is the equality constraint correction coefficient, the network communication topology diagram is represented by G = {V, E, A}, the set of nodes is V = {1, 2, 3,..., N}, N is the number of all nodes, the set of edges is E ⊆ V × V, and the adjacency matrix is ​​A = [a ｉｊ ]∈R Ｎ＊Ｎ where R represents the real number domain and a ｉｊ is the element value in the i-th row and j-th column of the adjacency matrix A, and N ｉ is the set of neighbors of node i, [Number FF] haz ｉ the assumed process, which is the first derivative of (t); a process of reducing the error of the convergence result by adjusting the introduced correction coefficient μ; Penalty Function Method [Number 63] Using processing the inequality constraints, where τ is a very small positive constant and g ｉ (P ｉ ) is the variable P ｉ is a formula including P ｉ represents the power of the i-th node, [Number GG] and constructing a new optimization function based on the penalty function expressed by:

2. 2. The optimization dispatch method for a fully distributed integrated energy system according to a preset time as described in claim 1, wherein the objective function is to minimize the sum of the operating costs of all electric power supply facilities, the cost of all loads participating in demand response, and the operating costs of all heat supply facilities.

3. Time-varying systems [Number HH] where L>0, [Mathematics II] , σ∈(0,1), and x ０ is the initial state of the system, and the state variable x(t) is ｆ Final state within [σ / (1+σ)] ｌ x ０ converges to x ０ = x(0), κ(t) is a time-based generator, [Number JJ] is the first derivative of κ(t), At any initial state x(0), if the following condition is satisfied, the preset time will converge: [Number 64] Including, Here, x ＊ The optimization dispatch method for a fully distributed integrated energy system according to preset time as claimed in claim 1, characterized in that: is an optimal decision value, c is a positive constant, and t represents time.

4. The time-based generator κ(t) is [Number 65] where t ｆ The optimization dispatch method for a fully distributed integrated energy system according to a preset time as claimed in claim 3, wherein: is a preset time, and t represents time.

5. Utilizing the method for optimizing dispatch of a fully distributed integrated energy system according to a preset time as claimed in any one of claims 1 to 4, A dispatch target setting unit configured to establish an integrated energy system operation optimization model and construct an objective function with the goal of minimizing a total cost of the integrated energy system, wherein a safe operation constraint is set in the objective function, and the safe operation constraint includes a power balance constraint, a power upper / lower limit constraint, and an operation ramp rate constraint; and an optimal dispatch control unit configured to assume that the communication topology of the source side, load side, storage side, and station side of the integrated energy system is an undirected connected graph, and to solve the objective function using a fully distributed optimization algorithm with a preset time based on a time-based generator, thereby making the integrated energy system operation optimization model reach a convergence state within a preset time, and thereby output an optimal output strategy for the integrated energy system.

6. A computing device including a processor and a computer-readable storage medium, a processor adapted to execute a computer program; The computer-readable storage medium stores a computer program, and when the computer program is executed by the processor, the computer device performs the method for optimizing dispatching of a fully distributed, integrated energy system using a preset time according to any one of claims 1 to 4.

7. A computer-readable storage medium having a computer program stored therein, the computer program being suitable for being read by a processor and for executing the method for optimizing dispatching of a fully distributed, integrated energy system according to a preset time as claimed in any one of claims 1 to 4.

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