Optimal dispatch method and system for a fully decentralized integrated energy system based on preset time.

Abstract

This provides an optimization dispatch method for a fully decentralized integrated energy system based on preset time settings. [Solution] The optimization dispatch method establishes an integrated energy system operation optimization model and constructs an objective function with the goal of minimizing the total cost of the integrated energy system as the dispatch problem for the integrated energy system. The objective function is subject to safe operation constraints, which include power balance constraints, power upper / lower limit constraints, and operation ramp rate constraints. The method also assumes that the communication topology of the source, load, storage, and station sides of the integrated energy system is an undirected connected graph and solves the objective function using a fully distributed optimization algorithm with a preset time based on TBG so that the integrated energy system operation optimization model converges within the preset time and obtains the optimal output strategy for the integrated energy system.

Description

[Technical Field]

[0001] (Cross-reference of related applications) This invention claims priority to the Chinese patent application (application number 202411731003.2, title "Optimal Dispatch Method and System for a Fully Distributed Integrated Energy System by Preset Time") filed with the China National Intellectual Property Administration on November 29, 2024, the entire contents of which are incorporated by reference to this invention for all purposes and constitute part of this invention.

[0002] The present invention relates to the technology of integrated energy systems, and more specifically to an optimization dispatch method and system for a fully decentralized integrated energy system based on preset time. [Background technology]

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

[0004] Unlike conventional single-energy supply systems, Integrated Energy Systems (IESs) integrate various forms of energy, such as electricity, heat, gas, and cold air. Through energy conversion and storage facilities within the system, these diverse energy sources are combined, achieving mutual complementarity and meeting the diverse energy needs of users. With advancements in communication and big data technologies, the coordination of energy generation, conversion, storage, and utilization within IESs is further strengthened. Intelligent control and coordinated management of each component enables efficient and safe management of the entire system. In the current context of rapidly developing economies and societies, where demands for energy utilization are increasing, establishing a multi-energy, economical, efficient, low-carbon, and environmentally friendly IES is of great strategic significance.

[0005] Conventional technologies often employ centralized optimization dispatch methods to address system optimization problems, such as those involving integrated energy systems. However, obtaining an optimal dispatch strategy requires all entities within the system to have access to global information. Furthermore, centralized optimization methods involve numerous communications and suffer from low network robustness, making them unsuitable for current integrated energy system development needs. Existing distributed optimization methods, on the other hand, either require only partial global information or directly apply conventional distributed optimization methods for power systems, thus failing to dynamically adapt to the different dispatch time scales of various energy sources. [Overview of the project]

[0006] To address the shortcomings of prior art, the present invention provides a fully distributed optimization dispatch method and system for integrated energy systems based on preset time, designs a fully distributed optimization algorithm based on a time-based generator (TBG), and demonstrates that the algorithm converges within the preset time using Lyapunov theory. Independent of the system's initial values ​​and parameters, with arbitrarily preset time and without the use of any global information, it is possible to find solutions for an optimization dispatch model of a thermoelectrically coupled integrated energy system including a "source-load-storage-station" configuration, encompassing equality and inequality constraints, thereby significantly improving dispatch accuracy.

[0007] To achieve the above objective, the present invention employs the following technical solutions.

[0008] In a first embodiment, the present invention provides an optimization dispatch method for a fully decentralized integrated energy system based on preset time.

[0009] A process for 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 the objective function is subject to safe operation constraints, and these safe operation constraints include power balance constraints, power upper / lower limit constraints, and operation ramp rate constraints. A fully distributed optimization dispatch method for an integrated energy system by preset time, comprising the process of solving the objective function using a fully distributed optimization algorithm by preset time based on a time-based generator to cause the integrated energy system operation optimization model to converge within a preset time, thereby outputting the optimal output strategy for the integrated energy system, 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.

[0010] In a second embodiment, the present invention provides an optimization dispatch system for a fully decentralized integrated energy system based on preset times.

[0011] A dispatch objective setting unit configured to establish an integrated energy system operation optimization model and construct an objective function with the goal of minimizing the total cost of the integrated energy system, wherein the objective function includes safe operation constraints, and the safe operation constraints include power balance constraints, power upper / lower limit constraints, and operation ramp rate constraints. A fully distributed, preset-time optimization dispatch system for an integrated energy system, comprising an optimal dispatch control unit configured to cause the integrated energy system operation optimization model to converge within a preset time, thereby outputting the optimal output strategy for the integrated energy system, by assuming that the communication topology of the source, load, storage, and station sides of the integrated energy system is an undirected connected graph and solving the objective function using a fully distributed, preset-time optimization algorithm based on a time-based generator, thereby causing the integrated energy system operation optimization model to converge within a preset time.

[0012] In a third embodiment, the present invention relates to a computer device comprising a processor and a computer-readable storage medium, A processor is suitable for running computer programs. The present invention provides a computer device that stores a computer program in the computer-readable storage medium, and when the computer program is executed by the processor, it performs an optimization dispatch method for a fully distributed integrated energy system with a preset time as described in the first aspect of the present invention.

[0013] In a fourth embodiment, the present invention provides a computer-readable storage medium in which a computer program is stored, and which is read by a processor and is suitable for executing the preset-time optimization dispatch method for a fully distributed integrated energy system described in the first embodiment of the present invention.

[0014] Compared to 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. It designs a fully distributed optimization algorithm based on a time-based generator (TBG) with preset time, and uses Lyapunov theory to prove that the algorithm converges within the preset time. Independent of the system's initial values ​​and parameters, with arbitrarily preset time, and without using any global information, it can find a solution to the optimization dispatch model of a thermoelectric integrated energy system including "source-load-storage-station," encompassing equality and inequality constraints, thereby significantly improving dispatch accuracy.

[0016] The advantages of additional embodiments of the present invention are partially shown in the following description, some of which will become apparent from the following description or will be understood through the practice of the present invention.

[0017] The accompanying drawings, which constitute part of the present invention, are used to provide a deeper understanding of the present invention. The schematic embodiments and descriptions of the present invention are for interpretation purposes only and do not unduly limit the present invention. [Brief explanation of the drawing]

[0018] [Figure 1] This is a diagram showing the configuration of the integrated energy system provided in Embodiment 1 of the present invention. [Figure 2] These are a configuration diagram and a communication network topology diagram used in the example analysis provided in Embodiment 1 of the present invention. [Figure 3] This is the thermal power response curve for a preset time of 40 s in the example analysis provided in Example 1 of the present invention. [Figure 4] This is the electrical power response curve for the preset time of 10s in the example analysis provided in Embodiment 1 of the present invention. [Figure 5] This is the thermal power supply and demand balance response curve for a preset time of 40s in the example analysis provided in Embodiment 1 of the present invention. [Figure 6] This is the electrical power response curve for a preset time of 6s in the example analysis provided in Embodiment 1 of the present invention. [Figure 7] This is the thermal power supply and demand balance response curve from 0 to 125 s in the example analysis provided in Example 1 of the present invention. [Figure 8] This is the thermal power response curve from 0 to 125 s in the example analysis provided in Example 1 of the present invention. [Figure 9] These are the simulation results of other similar algorithms in the example analysis provided in Example 1 of the present invention. [Figure 10]This is the simulation result of the algorithm proposed by the present invention in the example analysis provided in Embodiment 1 of the present invention. [Figure 11] This is a schematic diagram of an optimized dispatch system for a fully decentralized integrated energy system with preset time, provided in Embodiment 2 of the present invention. [Figure 12] This is a schematic diagram of the computer device provided in Embodiment 3 of the present invention. [Modes for carrying out the invention]

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

[0020] The following detailed descriptions are illustrative and intended to further illustrate the present invention. Unless otherwise specified, all technical and scientific terms used herein have the same meanings as those generally understood by those skilled in the art in which the present invention pertains.

[0021] Examples and features of the present invention can be combined with each other, provided that no contradiction arises.

[0022] Example 1: As described in the background technology, conventional methods are not applicable to the low-carbon and economical optimization dispatch problem of integrated energy systems that include energy with different dispatch time scales. Therefore, this implementation proposes a fully distributed optimization dispatch method for integrated energy systems using preset time scales, and includes the following:

[0023] The integrated energy system is divided into four parts according to its function: "source," "load," "storage," and "station." The specific structure is shown in Figure 1. Here, the "source" side mainly consists of 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 supplying electrical and thermal energy within the system. The "load" side includes electrical and thermal loads, with a portion of the electrical load capable of responding to demand. The "storage" side achieves the temporal transfer of electrical energy through a rational charge-discharge plan for energy storage stations. The "station" side achieves the conversion of energy forms through energy stations or combined heat and power (CHP) units, while simultaneously outputting two forms of energy: electricity and heat.

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

[0025]

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[0026]

number

[0027]

Number

[0028]

Number

[0029]

Number

Number

Number

[0030]

number

[0031]

number

[0032] The carbon emission cost of facility j is,

number

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

[0034] Low-carbon and economically optimized dispatch of integrated energy systems must satisfy the safe operation constraints of the system, and a predetermined N Power ,N Heat These represent sets of electrical energy output equipment and 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 In the case of}, the constraint can be specifically expressed as follows:

[0035] Power balance constraints are

number

number

[0036] The power upper / lower limit constraints are:

number

Number

[0037] The operation ramp rate constraint is

Number

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

Number

Number

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

[0040] Equation (19) is in the form of the sum of the cost functions of each device, and it can be seen that the constraints include equality constraints such as (14) and (15), and inequality constraints (16), (17), and (18). To facilitate the proof by the subsequent algorithm,

Number

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

[0042] For the above optimization problem, in this implementation form, a fully distributed optimization algorithm based on preset time by TBG (Time - Based Generator) is proposed to obtain the solution. Specifically, the following are included.

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

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

[0045] Lemma 1: By assumption 1,

number

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

[0047] Lemma Theorem 2: By Assumption 2,

number

number

[0048] Assumption 1 guarantees that the local information of each agent can be distributed across the entire network, and Assumption 2 guarantees 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) is subject to the following conditions:

number

[0050] Lemma 3: The following time-varying systems:

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[0051] In any initial state x(0), if the following condition is met, the system (25) converges to the preset time:

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

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[0053] Observation revealed that z i (t)∈R n i∈V is considered an auxiliary variable, and it is found that 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 handled using the penalty function method, and the penalty function is:

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[0055] And, function

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[0056] In this implementation, we provide a convergence analysis of the proposed algorithm. For the sake of analysis, regarding equation (27), first,

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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 by equation (30) the preset time t f Within this framework, a solution can be found for the distributed optimization problem, that is,

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[0059]

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

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[0061] If Assumption 1 is satisfied,

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[0062] (27) The second equation from the left

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[0063] Equilibrium point (P * ,y * ,z * ) satisfies the KKT condition, P * This can be seen as the globally optimal solution.

[0064] Next, we will 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 graph G,

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

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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),

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

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[0074] Equations (47) to (49) are combined 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 set to the system's preset time t f We have now confirmed that we can guarantee that convergence within the given range is achieved, and with that, the proof is complete.

[0080] Regarding equation (28), first, let's look at its simplified form:

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[0081] In comparison with equation (27), this algorithm restricts only the initial value of the auxiliary variable z, i.e.

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[0082] The subsequent proof is the same as that of equation (27), so we will not repeat it here.

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

number

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

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[0085] According to the KKT conditions,

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Number

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

[0087] The system contains two energy sources of heat and electricity. Since the dispatch time scales of the two energies are different, in order to preset different convergence times separately, it is necessary to decouple the coupling entities. There are two types of coupling entities in the system, namely the energy station and CHP. Among them, the energy station contains various energy conversion devices, and the output electrical power and heat power can be directly decoupled. For CHP, its output is decoupled according to the current heat-electricity ratio, and it is assumed that the unit operates in a state where the power generation amount is determined based on the heat supply load.

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

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

number

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

[0091] Table 2: Loading Subject Parameters [Table 2] The integrated energy system consists of four parts: "source-load-storage-station," encompassing two heterogeneous energy sources, heat and electricity. The "station" is considered the entity that combines these two energy sources. The two energy sources are decoupled according to the concept described above. For thermal energy, which has a relatively large dispatch time scale, its preset time t f1 Assuming =40s, for electrical energy with a relatively small dispatch time scale, its preset time t f2 Assume = 10s. 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 pump equipment, B1 and B2 are boiler equipment, and EH represents an energy station. In Figure 4, G1, G2, G3 and G4 are thermal power generation equipment, L1, L2, L3, L4, L5, L6, L7, L8, L9, L10, L11, L12, L13 and L14 are electrical load equipment, S1 and S2 are energy storage equipment, CHP1 and CHP2 are combined heat and power units, and EH represents an energy station.

[0092] The diagram shows that, after variations at different preset times, the power generated / consumed by each component in the system approaches the optimal value over time. Figure 5 shows the convergence curve of the thermal energy supply and demand balance. The diagram shows that within the preset time, it can be guaranteed that the thermal energy power supplied by each component is balanced with the thermal energy power required by the system. The dispatch results for each component are:

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[0093] To further verify that the preset time is adjustable, the preset time t of the electrical energy-related subject was determined. f3 The 6s timeframe is shortened, and its efficiency response curve is shown in Figure 6. 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, and EH represents an energy station. From the diagram, it can be seen that the power generation / consumption of each component approaches the optimal solution within 6s.

[0094] Next, the algorithm's "plug-and-play" characteristics are simulated and verified. 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 pump equipment, B1 and B2 are boiler equipment, and EH represents an energy station. From the diagram, it can be seen that within 0 to 25 seconds, the power generated / consumed by each component approaches the optimal value within a preset time of 10 seconds. If the load is disconnected or reconnected, each component 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. Even if the energy supply equipment is disconnected or reconnected, each component can converge from its original state to a new optimal value. In the cases of t=10s, 65s, and 115s, the system converges to the same state, but the initial values ​​of that convergence are different. From the above analysis, it can be seen that the algorithm has excellent "plug-and-play" characteristics and can guarantee that it approaches the optimal value within the preset time, even with different initial values ​​of the optimization variables.

[0095] When the same calculation example was analyzed using an existing preset time algorithm and equation (28) proposed in this invention, it was found that 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. From the two figures, it can be seen that both the algorithm proposed in this implementation and the existing algorithm can achieve convergence, but by adjusting the introduced correction coefficient μ, the error in the convergence result can be further reduced and the accuracy of the optimal solution can be improved.

[0096] Example 2: As shown in Figure 11, this implementation configuration is A dispatch objective setting unit configured to establish an integrated energy system operation optimization model and construct an objective function with the goal of minimizing the total cost of the integrated energy system, wherein the objective function includes safe operation constraints, and the safe operation constraints include power balance constraints, power upper / lower limit constraints, and operation ramp rate constraints. The present invention provides a fully distributed optimization dispatch system for an integrated energy system, configured to output an optimal output strategy for the integrated energy system, which is determined by solving the objective function using a fully distributed optimization algorithm with preset time based on TBG, assuming that the communication topology of the source, load, storage, and station sides of the integrated energy system is an undirected connected graph, and causing the integrated energy system operation optimization model to converge within a preset time, thereby providing an optimal dispatch control unit.

[0097] It can be understood that similar operations can be achieved without affecting the achievement of the technical effects of the embodiments of this application, since the above units can be integrated into one or more other units, either individually or as a whole, or one (or part) of these units can be further divided into several functionally smaller units. The above units are divided based on logical function. In practical applications, the function of one unit may be realized by several units, or the function of several units may be realized by one unit. In other embodiments of this application, the system may include other units. In practical applications, these functions may be realized with the assistance of other units or by the coordination of several units.

[0098] According to another embodiment of the present application, by executing a computer program (including program code) capable of executing each step according to the corresponding method described in Embodiment 1 in a general-purpose computing device (for example, a computer) including processing elements and storage elements such as a Central Processing Unit (CPU), a Random Access Memory (RAM), and a Read Only Memory (ROM), the system described in this embodiment can be constructed and the method of Embodiment 1 of the present application can be realized. The computer program may be recorded on, for example, a computer-readable recording medium, read into the above computing device via the computer-readable recording medium, and executed therein.

[0099] Embodiment 3: As shown in FIG. 12, this implementation form 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 for data transmission and reception, 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 computing core and control core of the electronic device, is suitable for executing one or more instructions, and in particular, is suitable for reading and executing one or more instructions to realize the corresponding method process or the corresponding function.

[0102] The processor 1001 is A process for 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 the objective function is subject to safe operation constraints, and these safe operation constraints include power balance constraints, power upper / lower limit constraints, and operation ramp rate constraints. Assuming that the communication topology of the source, load, storage, and station sides of the integrated energy system is an undirected connected graph, the system is configured to execute a process in which the integrated energy system operation optimization model reaches a convergence state within a preset time by solving the objective function using a fully distributed optimization algorithm with a preset time based on TBG, thereby outputting the optimal output strategy for the integrated energy system.

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

[0104] Example 4: This implementation provides a computer-readable storage medium (memory) within an electronic device for storing programs and data. It should be understood that this computer-readable storage medium may include storage media built into the electronic device, or, naturally, expandable storage media supported by the electronic device. The computer-readable storage medium provides storage space, within which the processing system of the electronic device is stored.

[0105] Furthermore, the memory space also stores one or more instructions suitable for being read and executed by the processor, and these instructions may be one or more computer programs (including program code). The computer-readable storage medium here may be high-speed RAM memory, non-volatile memory (e.g., at least one disk memory), or selectively at least one computer-readable storage medium located away from the aforementioned processor.

[0106] In one embodiment, one or more instructions are stored in the computer-readable storage medium, and the processor reads and executes one or more instructions stored in the computer-readable storage medium. A process for 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 the objective function is subject to safe operation constraints, and these safe operation constraints include power balance constraints, power upper / lower limit constraints, and operation ramp rate constraints. Assuming that the communication topology of the source, load, storage, and station sides of the integrated energy system is an undirected connected graph, the objective function is solved using a fully distributed optimization algorithm with preset time based on TBG. This process ensures that the integrated energy system operation optimization model converges within the preset time, thereby enabling the output of the optimal output strategy for the integrated energy system.

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

[0108] The above are merely preferred embodiments of the present invention and are not intended to limit it. Those skilled in the art will know that various modifications and changes can be made to the present invention. Any modifications, equivalent substitutions, improvements, etc., made within the spirit and principles of the present invention are all considered to be within the scope of protection of the present invention.

Claims

1. A computer-based optimization dispatch method for a fully distributed integrated energy system with preset time intervals, A process for 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 the objective function is subject to safe operation constraints, and the safe operation constraints include power balance constraints, power upper / lower limit constraints, and operation ramp rate constraints, where the power balance constraint is an equality constraint, and the power upper / lower limit constraints and the operation ramp rate constraints are inequality constraints. Assuming that the communication topology of the source, load, storage, and station sides of the integrated energy system is an undirected connected graph, the process involves solving the objective function using a fully distributed optimization algorithm with a preset time based on a time-based generator, thereby causing the integrated energy system operation optimization model to converge within the preset time, and thereby outputting the optimal output strategy for the integrated energy system. By introducing a time-varying gain k(t) and solving the objective function using a fully distributed algorithm with a preset time independent of any initial value, [Math DD] This is a process that assumes, [Number 62] Here, ∇C ｉ (P ｉ (t) is the gradient, P ｉ (t) is the optimization variable, representing the power of the i-th node at time t, where all power supply equipment, loads, and heat supply equipment constitute each node, and 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, [Number EE] is P ｉ which is the first derivative coefficient of P(t), where y ｉ (t), z ｉ (t), y ｊ (t), z ｊ (t) are auxiliary variables at time t, μ > 1 is an 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 represents the number of all nodes, the set of edges is E ⊆ V × V, the adjacency matrix is A = [a ｉｊ ∈ R Ｎ＊Ｎ where R represents the real number domain, and a ｉｊ is the element value of the i-th row and j-th column in the adjacency matrix A, and N ｉ is the set of adjacent nodes of node i [Number FF] is z ｉ The first derivative of (t) is the assumed process, The process involves adjusting the introduced correction coefficient μ to reduce the error in the convergence result, Penalty function method [Number 63] Using This involves handling the aforementioned inequality constraint, where τ is a very small positive constant, and g ｉ (P ｉ ) is variable P ｉ The expression includes P ｉ This represents the power of the i-th node, and is processed as follows: [Number GG] A method for optimizing and dispatching a fully distributed integrated energy system by preset time, characterized by including a process of constructing a new optimization function based on the penalty function represented by .

2. The optimization dispatch method for a fully distributed integrated energy system using a preset time according to claim 1, characterized in that the objective function minimizes the sum of the operating costs of all power supply equipment, the costs of all loads participating in demand response, and the operating costs of all heat supply equipment.

3. Time-varying system [Number HH] Considering this, here L > 0, [Mathematics II] σ∈(0,1), x ０ x is the initial state of the system, and the state variable x(t) is set to the preset time t. ｆ The final state is [σ / (1+σ)] ｌ x ０ It converges to x ０ = x(0), and κ(t) is a time-based generator. [Number JJ] κ(t) is the first derivative, In any initial state x(0), if the following conditions are met, the preset time converges, [Number 64] Includes, Here, x ＊ The optimization dispatch method for a fully decentralized integrated energy system using a preset time according to claim 1, characterized in that c is the optimal determination value, c is a positive constant, and t represents time.

4. A time-based generator κ(t) [Number 65] The following conditions are met, where t ｆ The optimization dispatch method for a fully decentralized integrated energy system using a preset time according to claim 3, characterized in that is a preset time and t represents time.

5. Using the preset time optimization dispatch method for a fully distributed integrated energy system described in any one of claims 1 to 4, A dispatch objective setting unit configured to establish an integrated energy system operation optimization model and construct an objective function with the goal of minimizing the total cost of the integrated energy system, wherein the objective function includes safe operation constraints, and the safe operation constraints include power balance constraints, power upper / lower limit constraints, and operation ramp rate constraints. A fully distributed, preset-time optimization dispatch system for an integrated energy system, characterized by including an optimal dispatch control unit configured to cause the integrated energy system operation optimization model to converge within a preset time, thereby outputting the optimal output strategy for the integrated energy system, by assuming that the communication topology of the source, load, storage, and station sides of the integrated energy system is an undirected connected graph and solving the objective function using a fully distributed, preset-time optimization algorithm based on a time-based generator.

6. A computer device including a processor and a computer-readable storage medium, A processor is suitable for running computer programs. A computer device characterized in that a computer program is stored in the computer-readable storage medium, and when the computer program is executed by the processor, an optimization dispatch method for a fully distributed integrated energy system according to a preset time as described in any one of claims 1 to 4 is performed.

7. A computer-readable storage medium is characterized in that it stores a computer program, and the computer program is read by a processor and is suitable for executing an optimization dispatch method for a fully distributed integrated energy system with a preset time according to any one of claims 1 to 4.

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