Optimized scheduling method and system for preset-time fully-distributed integrated energy system

Abstract

The invention belongs to the technical field of comprehensive energy systems. The invention provides a preset time fully distributed integrated energy system optimization scheduling method and system, the problem of integrated energy system scheduling is solved by taking the minimum total cost as an objective function, the objective function is configured with safe operation constraints, and the safe operation constraints comprise a power balance constraint, a power upper and lower limit constraint and a slope rate constraint; and assuming that the communication topologies of the source side, the load side, the storage side and the station side are undirected connected graphs, and solving the target function based on a TBG preset time fully distributed optimization algorithm, so that the integrated energy system reaches a convergence state within a preset time, and an optimal output strategy of the integrated energy system is obtained. The method does not depend on an initial value and parameters of the system, and can solve the optimal scheduling model of the comprehensive energy system containing the source-load-storage-station and containing the equality and inequality constraints without using any global information at any preset time, so that the scheduling precision is improved.

Description

Technical Field

[0001] The present invention relates to the technical field of integrated energy systems, and in particular to a method and system for optimizing and scheduling a fully distributed integrated energy system with a preset time. Background Art

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

[0003] The Integrated Energy System (IES) is different from the traditional single energy supply system. It integrates multiple energy forms such as electricity, heat, gas, and cold. Through the energy conversion and energy storage equipment in the system, various energies are coupled to achieve the complementary and mutual assistance of different energies to meet the various energy needs of users. With the development of communication technology and big data technology, the connection between energy generation, conversion, storage, and utilization in IES is further strengthened. Through intelligent control and collaborative management of each link, the overall efficient and safe management and control of the system can be achieved. In the context of the rapid development of today's economy and society, which has put forward higher requirements for energy utilization, it is of great strategic significance to establish a multi-energy collaborative, economical, efficient, low-carbon and environmentally friendly IES.

[0004] In the prior art, centralized optimization and scheduling methods are often used to optimize the optimization of integrated energy systems. Each subject in the system needs to master the global information in order to obtain the optimization scheduling strategy. In addition, the centralized optimization method has a large number of communications and poor robustness of the communication network, which can no longer meet the needs of the current development of integrated energy systems. The existing distributed optimization methods either require partial global information or directly apply the distributed optimization methods of traditional power systems, which cannot dynamically meet the scheduling time scales of different energy sources. Summary of the invention

[0005] In order to address the deficiencies in the prior art, the present invention provides a preset time fully distributed integrated energy system optimization scheduling method and system, designs a preset time fully distributed optimization algorithm based on a time base generator (TBG), and uses Lyapunov theory to prove that the algorithm converges within the preset time, is independent of the system initial values ​​and parameters, can arbitrarily preset the time, and solves the optimization scheduling model of a thermoelectric coupled integrated energy system including "source-load-storage-station" containing equality and inequality constraints without using any global information, thereby greatly improving the scheduling accuracy.

[0006] In order to achieve the above object, the present invention adopts the following technical solution: In a first aspect, the present invention provides a method for optimizing and scheduling a fully distributed integrated energy system with a preset time.

[0007] A method for optimizing and dispatching a fully distributed integrated energy system with a preset time, comprising the following processes: The total cost minimization is used as the objective function as the integrated energy system scheduling problem, and the objective function is configured with safe operation constraints, and the safe operation constraints include: power balance constraints, power upper and lower limit constraints and running slope rate constraints; Assuming that the communication topology of the source side, load side, storage side and station side is an undirected connected graph, the objective function is solved by a preset time fully distributed optimization algorithm based on the time base generator, so that the integrated energy system reaches a convergence state within the preset time, and the optimal output strategy of the integrated energy system is obtained.

[0008] In a second aspect, the present invention provides a preset time fully distributed integrated energy system optimization and scheduling system.

[0009] A preset time fully distributed integrated energy system optimization dispatching system, comprising: The scheduling target setting unit is configured to: take the minimum total cost as the objective function as the comprehensive energy system scheduling problem, and the objective function is configured with a safe operation constraint, and the safe operation constraint includes: a power balance constraint, a power upper and lower limit constraint, and a running slope constraint; The optimal scheduling control unit is configured as follows: assuming that the communication topology of the source side, load side, storage side and station side is an undirected connected graph, solving the objective function based on a preset time fully distributed optimization algorithm of a time base generator, so that the integrated energy system reaches a convergence state within a preset time, and obtains the optimal output strategy of the integrated energy system.

[0010] In a third aspect, the present invention provides a computer device, comprising: a processor and a computer-readable storage medium; a processor adapted to execute a computer program; A computer-readable storage medium, wherein a computer program is stored in the computer-readable storage medium, and when the computer program is executed by the processor, the preset time fully distributed integrated energy system optimization scheduling method as described in the first aspect of the present invention is implemented.

[0011] In a fourth aspect, the present invention provides a computer-readable storage medium storing a computer program, wherein the computer program is suitable for being loaded by a processor and executing the preset time fully distributed integrated energy system optimization scheduling method as described in the first aspect of the present invention.

[0012] Compared with the prior art, the present invention has the following beneficial effects: The present invention divides the thermoelectric coupling integrated energy system into four parts of "source-load-storage-station" according to its functions, establishes its low-carbon economic scheduling model, designs a preset time fully distributed optimization algorithm based on a time base generator (TBG), and uses Lyapunov theory to prove that the algorithm converges within the preset time, does not depend on the initial values ​​and parameters of the system, can arbitrarily preset the time, and solves the optimization scheduling model of the thermoelectric coupling integrated energy system containing "source-load-storage-station" with equality and inequality constraints without using any global information, thereby greatly improving the scheduling accuracy.

[0013] Advantages of additional aspects of the present invention will be given in part in the following description, and in part will become obvious from the following description, or will be learned through practice of the present invention. BRIEF DESCRIPTION OF THE DRAWINGS

[0014] The accompanying drawings in the specification, which constitute a part of the present invention, are used to provide a further understanding of the present invention. The exemplary embodiments of the present invention and their descriptions are used to explain the present invention and do not constitute improper limitations on the present invention.

[0015] Figure 1 A structural diagram of an integrated energy system provided in Example 1 of the present invention; Figure 2 The structural diagram and communication network topology diagram in the example analysis provided in Embodiment 1 of the present invention; Figure 3 The thermal power response curve when the preset time is 40s in the example analysis provided in Example 1 of the present invention; Figure 4 This is the electric power response curve when the preset time is 10s in the example analysis provided in Example 1 of the present invention; Figure 5 The thermal power supply and demand balance response curve when the preset time is 40s in the example analysis provided in Example 1 of the present invention; Figure 6 This is the electric power response curve when the preset time is 6s in the example analysis provided in Example 1 of the present invention; Figure 7 The thermal power supply and demand balance response curve of 0-125s in the example analysis provided in Example 1 of the present invention; Figure 8 The thermal power response curve of 0-125s in the example analysis provided in Example 1 of the present invention; Fig. 9 The simulation results of other similar algorithms in the example analysis provided in Example 1 of the present invention; Fig.10 The simulation results of the algorithm proposed by the present invention in the example analysis provided in Example 1 of the present invention; Fig.11A schematic diagram of a preset time fully distributed integrated energy system optimization scheduling system provided in Example 2 of the present invention; Fig.12 A schematic diagram of a computer device provided in Example 3 of the present invention. DETAILED DESCRIPTION

[0016] The present invention will be further described below in conjunction with the accompanying drawings and embodiments.

[0017] It should be noted that the following detailed descriptions are exemplary and are intended to provide further explanation of the present invention. Unless otherwise specified, all technical and scientific terms used herein have the same meanings as those commonly understood by those skilled in the art to which the present invention belongs.

[0018] In the absence of conflict, the embodiments of the present invention and the features of the embodiments may be combined with each other.

[0019] Embodiment 1: As described in the background technology, the traditional method cannot be applied to the low-carbon economic optimization scheduling problem of the integrated energy system containing energy with different scheduling time scales. In view of this, this implementation method proposes a preset time fully distributed integrated energy system optimization scheduling method, including the following contents: The integrated energy system can be divided into four parts according to its function: "source", "load", "storage" and "station". The specific structure is as follows: Figure 1 As shown. The "source" side mainly includes two parts: the power supply unit and the heat supply unit. The power source includes thermal power generation and renewable energy, and the heat source includes boilers and heat pumps, which are responsible for the supply of electricity and heat energy in the system; the "load" side includes electric load and heat load, and part of the electric load can respond to demand; the "storage" side realizes the time transfer of electric energy through the reasonable charging and discharging plan of the energy storage station; the "station" side realizes the conversion of energy forms through the energy station or the combined heat and power unit CHP, and outputs both electric and thermal energy.

[0020] In order to ensure the coordinated low-carbon economic operation of various parts of the integrated energy system, a low-carbon economic operation model is established. The optimization objective function can be expressed as follows: (1); (2); (3); (4); In the formula, are the cost coefficients, is the power of new energy power generation equipment i, is the power of thermal power generation equipment j, For boiler k of power.

[0021] (5); In the formula, , is the cost coefficient, The heat pump outputs thermal power. is the carbon emission cost of the heat pump, is the electrothermal conversion coefficient.

[0022] (6); In the formula, is the operating cost of the energy storage device s, is the cost coefficient, is the charging and discharging power of the energy storage device s.

[0023] (7); In the formula, The cost of powering the energy station, Cost of heating the energy station, is the energy cost of the combined heat and power unit, Power supply to the energy station, Heating power for energy stations, Provides heating power for the combined heat and power unit.

[0024] (8); (9); (10); In the formula, is the cost coefficient, , , They are the carbon emission cost of powering the energy station, the carbon emission cost of heating the energy station, and the overall carbon emission cost of the cogeneration unit.

[0025] (11); Where: is the cost of load w participating in demand response, is demand response power; (12); Where: is the carbon emission intensity, is the cost coefficient.

[0026] equipment j Carbon emission costs: (13); Where: is the carbon emission correlation coefficient of equipment j, is the carbon emission power of thermal power generation equipment j.

[0027] In this implementation, F Indicates the overall cost of IES; They represent the costs of the power supply side, heat source side, "storage" side, "station" side and "load" side respectively. They respectively represent the number of new energy power generation equipment, the number of thermal power generation equipment, the number of boiler equipment, the number of heat pump equipment, the number of energy storage equipment, the number of energy stations, the number of cogeneration units and the number of loads.

[0028] The low-carbon economic optimization dispatch of the integrated energy system needs to meet the safe operation constraints of the system. They represent the collection of electrical energy output devices and thermal energy output devices respectively. , , then the constraint can be specifically expressed as: Power balance constraints: (14); (15); in, They represent the overall electrical load and thermal load demand of the system respectively. They are electricity supply power, demand response power and heating power respectively.

[0029] Power upper and lower limit constraints: (16); (17); in, Respectively represent the power output device i The minimum and maximum power output, , indicating the electrical load w The minimum and maximum values ​​of the demand response power.

[0030] Slope constraint: (18); in, Indicates the device i The climbing rate, For equipment i Output power at the last moment The minimum and maximum output power of device i respectively.

[0031] Based on the above model, the low-carbon economic dispatch problem of the integrated energy system can be expressed as follows: (19); in, , , are the operating costs of all power supply equipment, the cost of all loads participating in demand response, and the operating costs of all heating equipment. Represents the total number of power supply devices, i Representative i Power supply equipment, represents the total number of loads, For the load, Represents the total number of heating equipment, Representative j A heating device.

[0032] Among them, equations (14) and (15) are equality constraints, and equations (16)-(18) are inequality constraints.

[0033] It can be seen that equation (19) is the sum of the cost functions of each device, and the constraints include equality constraints such as (14) and (15), as well as inequality constraints (16), (17), and (18). In order to facilitate the proof of the subsequent algorithm, it is further expressed as follows: (20); Where N is the number of all nodes in the system. Contains variables The expression of , , , , , is an n-dimensional vector space, R is the set of real numbers, and Separate i The load demand and the total load demand, P represents the total system power, Represents the total system cost.

[0034] Note: Formula (20) is a simplified form of Formula (19) for the convenience of subsequent proof. Formula (19) is the sum of the objective functions of all devices, and N in Formula (20) is the total number of all devices. The equality constraints in Formula (20) are simplified forms of (14) and (15), and the inequality constraints are simplified forms of (16)-(18).

[0035] In view of the above optimization problem, this implementation proposes a preset time fully distributed optimization algorithm based on TBG (time base generator) to solve it, specifically, including: The network communication topology diagram of this implementation method is used Represents that the node set , the set of edges is ,picture G The adjacency matrix of ,if ,but ,otherwise .node i The set of adjacent nodes is ,picture G The Laplace matrix of ,in is the degree matrix of the graph, Representation Node i The degree.

[0036] Assumption 1: Undirected Graph G are connected.

[0037] Lemma 1: By assumption 1, we can get: , (twenty one); in, They are L The second smallest eigenvalue and The largest eigenvalue.

[0038] Assumption 2: yes - Strongly convex and continuously differentiable functions with local -Lipschitz gradient, and All are normal numbers.

[0039] Lemma 2: By assumption 2, we can get: (twenty two); (twenty three); In the formula, is the gradient operator, , is a positive constant, and Q is a variable in the feasible region.

[0040] Assumption 1 ensures that the local information of each agent can be dispersed to the entire network, and Assumption 2 ensures the smoothness, strong convexity and optimality of the cost function.

[0041] Introducing the Time Base Generator The following conditions must be met: , (twenty four); in, is the preset time.

[0042] Lemma 3: Consider the following time-varying system: (25); in, , , , is the initial state of the system, and the state variable Set the preset time Converges to the final state , For n-dimensional vector space, the definition of preset time convergence is given: For any initial state When the following conditions are met, the system (25) achieves the preset time convergence: (26); in, is the optimal decision value, is a very small normal number.

[0043] To solve the problem in equation (20), the TBG technique is used to introduce a time-varying gain , firstly propose a preset time fully distributed algorithm that does not rely on any initial value: (27); in, In formula (25), we define is the gradient, is the optimization variable, is the ; represents the cost corresponding to the power of the i-th node at time t; for The first derivative of ; for t Auxiliary variables of time; is the equality constraint correction coefficient, is an auxiliary variable, is the element value of the i-th row and j-th column in the adjacency matrix A, is the set of neighbor nodes of node i; for The first derivative of for The first derivative of .

[0044] Discovered through observation As an auxiliary variable, its initial value can be determined in advance, so it is assumed that the initial value of the variable satisfies On this basis, a preset time fully distributed algorithm with less interactive information between agents is proposed: (28); Compared with formula (27), formula (28) needs to ensure that the initial value of the auxiliary variable meets certain conditions, and the agents do not need to exchange information about the auxiliary variable. This algorithm reduces the amount of communication, but the conditions are more stringent.

[0045] For the inequality constraint in formula (20), the penalty function method is used to process it, and the penalty function is constructed as follows: (29); In the formula, is a very small normal number.

[0046] And construct the function: ,in .

[0047] This implementation gives the convergence analysis of the proposed algorithm. For equation (27), in order to facilitate analysis, it is first rewritten into a compact form as follows: (30); in, , , , , , here is to convert the algebraic expression above into a matrix expression, that is, a compact form, , , They are , , The first derivative of .

[0048] Lemma 4: When Assumptions 1 and 2 are satisfied, in equation (30) we can Solve the distributed optimization problem internally, that is, satisfy: (31); in, represents the time t , , , , is the initial value of the Lyapunov function defined later, is the Lyapunov function, represents the optimal value of P, express The optimal value of the transpose of express The optimal value of the transpose of express The optimal value of the transpose of .

[0049] (32); (33); in, , , , and are all constants.

[0050] Proof of Lemma 4: First we prove that there is an optimal solution ,make is the equilibrium point of (27), then the following conditions are satisfied: (34); When Assumption 1 is satisfied, we can get , substituting into the first equation of (34), we can obtain: (35); Multiply the second equation in (27) by , we can get: (36); It can be seen that the balance point Satisfies the KKT condition, and It is the global optimal solution.

[0051] Next, based on the Lyapunov theory, we analyze the convergence of the system. , , , then (30) can be transformed into: (37); in, , for The first derivative of for The first derivative of for The first derivative of .

[0052] Perform the following orthogonal transformation: ,in, , , is an orthogonal matrix, , , .

[0053] According to the figure G Laplace matrix properties, , , then system (37) can be rewritten into two subsystems: (38); (39); Based on the above positive exchange edges, we need to prove Able to converge within a preset time. Consider the following Lyapunov function: (40); (41); in, .

[0054] make , we can get: (42); According to (38)-(41), we get: (43); According to Lemma 1 and Lemma 2, we can get: (44); According to Young's inequality, we can get: (45); Substitute equation (44) and equation (45) into equation (43): (46); According to formula (22) and formula (20), we can get: (47); (48); (49); Substitute equation (47) - equation (49) into equation (50): (50); According to formula (42) and formula (43), we can get: (51); in, is the coefficient.

[0055] Then invoke Lemma 1 and the comparison principle: (52); According to (42), we can get: (53); when hour, , we can get: (54).

[0056] It can be seen that this algorithm can ensure that the system The inner realization converges, and the proof is complete.

[0057] For formula (28), we first give its compact form: (55); in, for in vector form.

[0058] Compared with formula (27), this algorithm only considers the auxiliary variables The initial value of is constrained, that is, , then multiply the third equation of equation (55) by , can be obtained , so we have: (56); in, Auxiliary variables The value of the i-th element in at time t.

[0059] The subsequent proof is similar to that of formula (27) and will not be repeated here.

[0060] When considering inequality constraints in the system, the penalty function is set by (29) and a new optimization function is constructed: (57); Equation (57) is obviously a strongly convex function. Let , Respectively represent the unique optimal solution considering inequality constraints and not considering inequality constraints, then we can get the following relationship: (58); in, for The optimal value of N is the number of nodes in the system, , .

[0061] According to the KKT conditions, we can know that: (59); in, for The feasible domain of , , is a variable value in the feasible region that satisfies the Slater condition. , the optimal solution of formula (57) can be used as the suboptimal solution of the original problem, so the formulas (27) and (28) are Replace it with (57), and when Choose enough hours to ensure .

[0062] The example structure diagram and communication network topology are as follows: Figure 2 As shown, 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 electric load equipment, S1 and S2 are energy storage equipment, CHP1 and CHP2 are cogeneration units, P1 and P2 are heat pump equipment, B1 and B2 are boiler equipment, H1, H2, H3, H4, H5, and H6 are heat load equipment, PV stands for photovoltaic equipment, WT stands for wind power generation equipment, the numbers 1-30 on the left stand for 30 system nodes, and the numbers 1-14 on the right stand for 14 system nodes.

[0063] Since the system contains two types of energy, thermal power, and the time scales of the two energy scheduling are different, in order to preset different convergence times, the coupling subjects need to be decoupled. There are two types of coupling subjects in the system: energy stations and CHP. The energy station contains a variety of energy conversion equipment, and its output electric power and thermal power can be directly decoupled; for CHP units, its output is decoupled according to the current thermal-electric ratio, assuming that the unit works in a state of heat-to-electricity.

[0064] Algorithm related parameters , , , construct TBG as shown in (60).

[0065] (60); The physical parameter settings of each subject are shown in Table 1 and Table 2. Carbon emission correlation coefficient , initial value of auxiliary variable , , , total heat load , total power load .

[0066] Table 1: Energy supply parameters, including: Pi(0) is the initial power of node i

[0067] Table 2: Load body parameters

[0068] The integrated energy system consists of four parts: "source-load-storage-station", including two heterogeneous energy sources, heat and electricity. The "station" is the main body of coupling the two energy sources of heat and electricity. According to the ideas in the previous article, the two energy sources have been decoupled. For thermal energy with a larger scheduling time scale, it is assumed that its preset time , and the time scale of power dispatch is smaller, assuming that the preset time The power response curve is as follows: Figure 3 and Figure 4 shown. Figure 3 In the figure, CHP1 and CHP2 are combined heat and power units, P1 and P2 are heat pump equipment, B1 and B2 are boiler equipment, and EH stands for energy station; Figure 4 In the figure, 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 electric load equipment, S1 and S2 are energy storage equipment, CHP1 and CHP2 are cogeneration units, and EH stands for energy station.

[0069] It can be seen from the figure that after fluctuations of different preset times, the generated / consumed power of each subject in the system can tend to the optimal value over time. Figure 5 The convergence curve of thermal energy supply and demand balance is given. It can be seen from the figure that within the preset time, the thermal energy power supplied by each subject can be guaranteed to be balanced with the thermal energy power required by the system, and the scheduling results of each subject are: (61); All of them are within the physical constraints, which proves the feasibility of the proposed method.

[0070] In order to further verify that the preset time is adjustable, the preset time of the power-related subjects is reduced , its efficiency response curve is as follows Figure 6 As shown in the figure, 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 electric load equipment, S1 and S2 are energy storage equipment, CHP1 and CHP2 are cogeneration units, and EH represents energy station. It can be seen from the figure that the generation / consumption power of each subject tends to the optimal solution within 6s.

[0071] Next, the "plug and play" feature of the algorithm is simulated and verified. The simulation results are as follows: Figure 7 , Figure 8 shown. Figure 8 In the figure, CHP1 and CHP2 are cogeneration units, P1 and P2 are heat pump equipment, B1 and B2 are boiler equipment, and EH represents the energy station. As can be seen from the figure, within 0-25s, the power generation / consumption of each entity tends to the optimal value within the preset time of 10s. When the load is cut off or reconnected, each entity can converge to the new optimal value from the original state according to the preset time. And the preset time can be reset according to actual needs. When the energy supply equipment is cut off or reconnected, each entity can also converge to the new optimal value from the original state. The systems all converge to the same state, but their initial values ​​of convergence are different. The above analysis shows that the algorithm has good "plug and play" characteristics and can ensure that under different initial values ​​of optimization variables, they tend to the optimal value within the preset time.

[0072] The same example is analyzed using the existing preset time algorithm and the formula (28) proposed in the present invention. The initial values ​​of the variables are , , , , , , , The simulation results are as follows: Fig. 9 and Fig.10 As shown in the figure, P1, P2, P3 and P4 are power generation units respectively. From the two figures, it can be seen that the algorithm proposed in this implementation method and the existing algorithm can achieve convergence, but by adjusting the correction coefficient introduced ,The error of the convergence result can be smaller and the accuracy of the optimal solution can be improved.

[0073] Embodiment 2: like Fig.11 As shown, this implementation method provides a preset time fully distributed integrated energy system optimization scheduling system, including: The scheduling target setting unit is configured to: take the minimum total cost as the objective function as the comprehensive energy system scheduling problem, and the objective function is configured with a safe operation constraint, and the safe operation constraint includes: a power balance constraint, a power upper and lower limit constraint, and a running slope constraint; The optimal scheduling control unit is configured as follows: assuming that the communication topology of the source side, load side, storage side and station side is an undirected connected graph, solving the objective function based on the preset time fully distributed optimization algorithm of TBG, so that the integrated energy system reaches a convergence state within the preset time, and obtains the optimal output strategy of the integrated energy system.

[0074] It is understandable that the above-mentioned units can be separately or completely combined into one or several other units to constitute, or one (some) of the units can be further divided into multiple smaller units in function to constitute, which can achieve the same operation without affecting the realization of the technical effects of the embodiments of the present application. The above-mentioned units are divided based on logical functions. In practical applications, the functions of one unit can also be implemented by multiple units, or the functions of multiple units can be implemented by one unit. In other embodiments of the present application, the system may also include other units. In practical applications, these functions can also be implemented with the assistance of other units, and can be implemented by the collaboration of multiple units.

[0075] According to another embodiment of the present application, the system described in this embodiment can be constructed, and the method of Example 1 of the present application can be implemented by running a computer program (including program code) capable of executing the steps involved in the corresponding method described in Example 1 on a general-purpose computing device such as a computer, which includes processing elements and storage elements such as a central processing unit (CPU), random access memory (RAM), and read-only memory (ROM). The computer program can be recorded on, for example, a computer-readable recording medium, and loaded into the above-mentioned computing device through the computer-readable recording medium and run therein.

[0076] Embodiment 3: like Fig.12 As shown, this implementation provides an electronic device, which includes a processor 1001, a communication interface 1002, and a computer-readable storage medium 1003. The processor 1001, the communication interface 1002, and the computer-readable storage medium 1003 may be connected via a bus or other means.

[0077] Among them, the communication interface 1002 is used to receive and send data, the computer-readable storage medium 1003 can be 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.

[0078] The processor 1001 (or CPU (Central Processing Unit)) is the computing core and control core of the electronic device, which is suitable for implementing one or more instructions, and specifically suitable for loading and executing one or more instructions to implement corresponding method processes or corresponding functions.

[0079] The processor 1001 is configured to execute the following process: The total cost minimization is used as the objective function as the integrated energy system scheduling problem, and the objective function is configured with safe operation constraints, and the safe operation constraints include: power balance constraints, power upper and lower limit constraints and running slope rate constraints; Assuming that the communication topology of the source side, load side, storage side and station side is an undirected connected graph, the objective function is solved based on the preset time fully distributed optimization algorithm of TBG, so that the integrated energy system reaches a convergence state within the preset time, and the optimal output strategy of the integrated energy system is obtained.

[0080] The detailed process is described in Example 1 and will not be repeated here.

[0081] Embodiment 4: This implementation provides a computer-readable storage medium (Memory), which is a memory device in an electronic device for storing programs and data. It is understandable that the computer-readable storage medium here can include both built-in storage media in the electronic device and, of course, extended storage media supported by the electronic device. The computer-readable storage medium provides a storage space that stores the processing system of the electronic device.

[0082] In addition, the storage space also stores one or more instructions suitable for being loaded and executed by the processor, and these instructions may be one or more computer programs (including program codes). It should be noted that the computer-readable storage medium here may be a high-speed RAM memory, or a non-volatile memory, such as at least one disk memory; optionally, it may also be at least one computer-readable storage medium located away from the aforementioned processor.

[0083] In one embodiment, the computer-readable storage medium stores one or more instructions; the processor loads and executes the one or more instructions stored in the computer-readable storage medium to implement the following process: The total cost minimization is used as the objective function as the integrated energy system scheduling problem, and the objective function is configured with safe operation constraints, and the safe operation constraints include: power balance constraints, power upper and lower limit constraints and running slope rate constraints; Assuming that the communication topology of the source side, load side, storage side and station side is an undirected connected graph, the objective function is solved based on the preset time fully distributed optimization algorithm of TBG, so that the integrated energy system reaches a convergence state within the preset time, and the optimal output strategy of the integrated energy system is obtained.

[0084] The detailed process is described in Example 1 and will not be repeated here.

[0085] The above description is only a preferred embodiment of the present invention and is not intended to limit the present invention. For those skilled in the art, the present invention may have various modifications and variations. Any modification, equivalent replacement, improvement, etc. made within the spirit and principle of the present invention shall be included in the protection scope of the present invention.

Claims

1. A method for optimizing and scheduling a fully distributed integrated energy system with a preset time, characterized in that: The process includes: The total cost minimization is used as the objective function as the integrated energy system scheduling problem, and the objective function is configured with safe operation constraints, and the safe operation constraints include: power balance constraints, power upper and lower limit constraints and running slope rate constraints; Assuming that the communication topology of the source side, load side, storage side and station side is an undirected connected graph, the objective function is solved by a preset time fully distributed optimization algorithm based on the time base generator, so that the integrated energy system reaches a convergence state within the preset time, and the optimal output strategy of the integrated energy system is obtained.

2. The preset time fully distributed integrated energy system optimization scheduling method according to claim 1, characterized in that: The objective function is: minimizing 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 heating equipment.

3. The preset time fully distributed integrated energy system optimization scheduling method according to claim 1 or 2, characterized in that: Consider the following time-varying system: ,in, , , , is the initial state of the system, and the state variable Set the preset time Converges to the final state , , is the time base generator, for The first derivative of for The first derivative of ; For any initial state , the preset time converges when the following conditions are met, including: ,in, is the optimal decision value, is a normal number, Represents time.

4. The preset time fully distributed integrated energy system optimization scheduling method according to claim 3 is characterized in that: Introducing time-varying gain , solving the objective function by a preset time fully distributed algorithm that does not rely on any initial value, including: ； in, is the gradient, To optimize the variable, represents the power of the i-th node at time t. All power supply equipment, loads and heating equipment constitute each node, and the total number of nodes is N; represents the cost corresponding to the power of the i-th node at time t; for The first derivative of ; are auxiliary variables at time t, is the correction coefficient of the equality constraint, and the network communication topology is Represents a node set , N represents the number of all nodes, and the set of edges is , the adjacency matrix , R represents the field of real numbers, is the element value of the i-th row and j-th column in the adjacency matrix A, is the set of neighbor nodes of node i; for The first derivative of for The first derivative of .

5. The preset time fully distributed integrated energy system optimization scheduling method according to claim 3, characterized in that: Introducing time-varying gain , solving the objective function by a preset-time fully distributed algorithm that does not depend on any initial value, assuming ,include: ； in, is the gradient, To optimize the variable, represents the power of the i-th node at time t. All power supply equipment, loads and heating equipment constitute each node, and the total number of nodes is N; represents the cost corresponding to the power of the i-th node at time t; for The first derivative of ; for t Auxiliary variables at time, is the correction coefficient of the equality constraint, and the network communication topology is Represents a node set , N represents the number of all nodes, and the set of edges is , the adjacency matrix , R represents the field of real numbers, is the element value of the i-th row and j-th column in the adjacency matrix A, is the set of neighbor nodes of node i; for The first derivative of for The first derivative of .

6. The preset time fully distributed integrated energy system optimization scheduling method according to claim 3, characterized in that: Time base generator The following conditions must be met: , ,in, For preset time, Represents time.

7. The preset time fully distributed integrated energy system optimization scheduling method according to claim 1 or 2, characterized in that: The penalty function method is used to process the inequalities in the safety operation constraints.

8. A preset time fully distributed integrated energy system optimization scheduling system, characterized in that: include: The scheduling target setting unit is configured to: take the minimum total cost as the objective function as the comprehensive energy system scheduling problem, and the objective function is configured with a safe operation constraint, and the safe operation constraint includes: a power balance constraint, a power upper and lower limit constraint, and a running slope constraint; The optimal scheduling control unit is configured as follows: assuming that the communication topology of the source side, load side, storage side and station side is an undirected connected graph, solving the objective function based on a preset time fully distributed optimization algorithm of a time base generator, so that the integrated energy system reaches a convergence state within a preset time, and obtains the optimal output strategy of the integrated energy system.

9. A computer device, characterized in that: include: A processor and a computer readable storage medium; a processor adapted to execute a computer program; A computer-readable storage medium, wherein a computer program is stored in the computer-readable storage medium, and when the computer program is executed by the processor, the preset time fully distributed integrated energy system optimization scheduling method as described in any one of claims 1 to 7 is implemented.

10. A computer-readable storage medium, characterized in that: The computer-readable storage medium stores a computer program, which is suitable for being loaded by a processor and executing the preset time fully distributed integrated energy system optimization scheduling method as described in any one of claims 1 to 7.

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