Distributed optimization method, device, equipment and storage medium for integrated energy system

Abstract

The present application relates to the technical field of energy system optimization and scheduling, and in particular to a distributed optimization method, device, equipment and storage medium for an integrated energy system, the method comprising: establishing a distributed optimization scheduling model for multiple interconnected energy hubs based on the multi-energy conversion constraints within the energy hub and the multi-energy network operation constraints of the integrated energy system of multiple interconnected energy hubs; optimizing the distributed optimization scheduling model for multiple interconnected energy hubs based on the homomorphic encryption algorithm and the alternating direction multiplier method to obtain the optimization result; controlling the output of each capacity unit in the region of the integrated energy system and the operating state of each node of the multi-energy network to achieve the best state according to the interaction between the optimization result, the information layer and the physical layer, and sharing the optimal energy among multiple regions of the integrated energy system. Thus, the problem of privacy leakage in the process of information interaction between interconnected energy hubs is solved, the convergence of the proposed algorithm is guaranteed, and the optimal energy sharing of multiple energy sources is realized, which has good application value.

Description

Technical Field

[0001] The present application relates to the technical field of energy system optimization and scheduling, and in particular to a distributed optimization method, device, equipment and storage medium for an integrated energy system. Background Art

[0002] In response to the challenges brought by global climate change, most countries have reached a consensus on achieving carbon neutrality by the middle of this century. In recent years, new energy represented by wind power and photovoltaics has developed rapidly. According to statistics, the national installed power generation capacity in 2021 is about 2.38 billion kilowatts, a year-on-year increase of 7.9%. Among them, the installed capacity of wind power is about 330 million kilowatts, a year-on-year increase of 16.6%; the installed capacity of solar power generation is about 310 million kilowatts, a year-on-year increase of 20.9%. However, my country's current energy structure is still dominated by thermal power, and fast, flexible and low-carbon regulation resources are relatively insufficient. It is difficult to effectively respond to the challenges of stable operation of the system and supply and demand balance brought about by large-scale and intermittent new energy access. The way of relying solely on limited regulation resources in various links of the power system to maintain the safe supply of system energy is gradually unsustainable. Building a comprehensive energy system under the goal of carbon neutrality has become an important means to solve the problem of energy supply and demand balance in the future.

[0003] An integrated energy system generally refers to a coordinated system of multiple energy sources such as coal, oil, natural gas, electricity, and heat energy in a certain region in multiple links such as production, transmission, conversion, storage, and consumption, across energy forms and multiple spatial and temporal scales. Compared with the traditional power system, the "integration" of the integrated energy system is mainly manifested in the following aspects: (1) The energy supply structure has changed from being dominated by coal-fired units with controllable continuous output to being dominated by new energy power generation units with strong uncertainty and weak controllability, and a mode of rapid and flexible adjustment of multiple controllable units or energy hubs; (2) The load characteristics have changed from the traditional single, rigid, and pure consumption characteristics to diversified, flexible, and both production and consumption characteristics; (3) The energy network form has changed from the traditional one-way, step-by-step transmission and distribution to a two-way, multi-energy flow multi-energy network (including power network, heat network, and natural gas network, etc.); (4) The operating characteristics have changed from the traditional power system "source follows load" supply and demand balance adjustment mode and the large power grid integrated control mode to the cross-energy form source, network, load, storage multi-link coordinated interaction mode and multi-regional autonomous coordinated control mode. The above-mentioned changes are driving the integrated energy system to transform from traditional centralized optimization scheduling and management to a more flexible distributed multi-energy energy sharing and collaborative interaction mode. In order to promote efficient and safe interaction of energy information and achieve multi-party distributed optimal coordination, it is necessary to consider how to coordinate the supply and demand relationship of multi-party energy hubs and multi-energy energy sharing in an orderly manner. Its essence is a hierarchical distributed optimization problem.

[0004] The related technologies mainly include target cascade analysis method, generalized Benders decomposition method, master-slave game method, alternating direction multiplier method, etc.

[0005] However, the related technology requires the exchange of primitive variables and dual variables between multiple energy hubs in the process of solving optimization problems, which will lead to serious privacy leakage problems, allowing opponents to maliciously infer energy consumption patterns and management information in the region, and may cause further economic losses. Summary of the invention

[0006] The present application provides a distributed optimization method, device, equipment and storage medium for an integrated energy system to solve the problem of privacy leakage in the process of information interaction between interconnected energy hubs. Distributed consistency consensus can be reached without the two interacting parties sharing their respective sensitive information in plain text, thereby ensuring the convergence of the proposed algorithm and realizing optimal energy sharing of multiple energy sources.

[0007] The first aspect of the present application provides a distributed optimization method for an integrated energy system, wherein the integrated energy system includes a vertically layered information layer and a physical layer, wherein the method includes the following steps:

[0008] Based on the multi-energy conversion constraints within the energy hub and the multi-energy network operation constraints of the integrated energy system of multiple interconnected energy hubs, a distributed optimization scheduling model for multiple interconnected energy hubs is established;

[0009] Based on the homomorphic encryption algorithm and the alternating direction multiplier method, the distributed optimization scheduling model of the plurality of interconnected energy hubs is optimized to obtain an optimization result; and

[0010] According to the optimization result and the interactive relationship between the information layer and the physical layer, the output of each production unit in the region of the integrated energy system and the operating status of each node in the multi-energy network are controlled to reach the optimal state, and the optimal energy among multiple regions of the integrated energy system is shared.

[0011] Optionally, the establishing of a distributed optimization scheduling model for multiple interconnected energy hubs includes:

[0012] Based on the preset multi-energy network standardized modeling formula, a distributed optimization scheduling model of multiple interconnected energy hubs is established, wherein the multi-energy network standardized modeling formula is:

[0013]

[0014]

[0015]

[0016]

[0017]

[0018]

[0019] Among them, min C(P n ) is to minimize the total operating cost of the rth region of the integrated energy system, N r is the total number of energy hubs in the region, P n is the matrix of energy output of all units in the nth energy hub in the region; a n , b n , c n is the fuel cost coefficient matrix, X n , Y n , Z n are the multi-energy input correlation matrix, output correlation matrix and energy conversion matrix, V n is the correlation matrix connecting the energy hub port and the multi-energy network branch, R is the total number of regions in the integrated energy system, V in,n and V out,n are the energy hub entrance and exit multi-energy association matrices, U in,m and U out,m are the multi-energy energy flow matrices on the mth branch connected to the entrance and exit of the energy hub, To represent the set of branches connected to the nth energy hub, L n is the multi-energy load matrix of the nth energy hub, and They represent the upper bounds of the inlet and outlet multi-energy flows on the mth branch; M r is the total number of multi-energy network branches in the rth region, indicating the energy exchange relationship between two adjacent regions r and l, where U r→l represents the energy flowing from region r to region l, U l→r represents the energy flowing from region l to region r, is the set of regions adjacent to r.

[0020] Optionally, the multi-energy energy flow matrix on the mth branch connected to the energy hub outlet is:

[0021]

[0022] Where u represents the energy flow on the network branch, the superscript i→j represents the energy flow from node i to node j, and represents the adjacency relationship between node i and node j. The subscripts e, h, and g represent the power network, district heating network, and natural gas network, respectively.

[0023] Optionally, for the power network, a Distflow model is used to represent the transmission relationship of electric energy between interconnected energy hubs, wherein the Distflow model is:

[0024]

[0025]

[0026]

[0027]

[0028]

[0029] in, The active power injected into the node, The reactive power injected into the node, is the adjacency relationship between node i and node j, r i→j is the branch resistance, x i→j is the branch reactance; V i is the voltage at node i, V j is the voltage at node j, is the branch current.

[0030] For the district heating network, a quality-quantity joint regulation model is used to represent the heat energy transfer process, wherein the quality-quantity joint regulation model is:

[0031]

[0032]

[0033]

[0034]

[0035]

[0036]

[0037]

[0038]

[0039]

[0040] in, is the heat supply of the heat source node, κ is the power and energy unit conversion factor, C w is the specific heat capacity of water, is the water supply of the heat source node, is the inlet temperature of the heat source node connected to the thermal pipeline, is the mixed temperature of the return water from the thermal pipeline back to the heat source node, is the amount of water flowing into node i, is the amount of pipe water flowing from node i to node j, is the inlet temperature of node i, is the inlet temperature of node j, is the current ambient temperature, λ is the thermal pipe resistance coefficient, ρ i→j is the pipe length, L h，i is the thermal load of node i, is the outlet temperature of node i, is the return water mixing temperature at node i.

[0041] For the natural gas pipeline network, the Weymouth steady-state gas flow model is used to represent the natural gas transportation process, wherein the Weymouth steady-state gas flow model is:

[0042]

[0043]

[0044]

[0045]

[0046]

[0047]

[0048] in, is the natural gas energy flow, GHV is the calorific value of natural gas, is the natural gas flow on the pipeline, Consumption of fuel gas compressor, is the gas compressor consumption coefficient, π i is the air pressure at node i, π j is the air pressure at node j, ξ i→j is the pipe resistance coefficient.

[0049] Optionally, the calculation method of the alternating direction multiplier method is:

[0050] Converting the distributed optimization scheduling model of the plurality of interconnected energy hubs into a standard form of an alternating direction multiplier method to obtain a converted distributed optimization scheduling model of the plurality of interconnected energy hubs;

[0051] Solving the distributed optimization scheduling model of the transformed multiple interconnected energy hubs by using an initialized Lagrange multiplier matrix to obtain a first optimal energy flow, and updating coupling terms in the distributed optimization scheduling model of the transformed multiple interconnected energy hubs to obtain a first updated coupling term;

[0052] The distributed optimization scheduling model of the multiple interconnected energy hubs after the transformation is solved by using the initialized Lagrange multiplier matrix and the first updated coupling term to obtain the second optimal energy flow, and update the coupling term to obtain the second updated coupling term:

[0053] According to the first updated coupling term and the second updated coupling term, it is determined whether the original and dual residuals are within the tolerance range. If not, the initialized Lagrangian multiplier matrix is ​​updated, and the distributed optimization scheduling model of the transformed multiple interconnected energy hubs is solved according to the updated initialized Lagrangian multiplier matrix until the preset iteration conditions are met.

[0054] Optionally, the calculation method of the homomorphic encryption algorithm is:

[0055] Based on the first key calculation formula, the first prime number and the second prime number of the same bit length are calculated to obtain a public key of the encryption key;

[0056] Based on the Euler inertia function, calculation is performed according to the first prime number and the second prime number to obtain a private key of the encryption key.

[0057] Optionally, the distributed optimization scheduling model of the plurality of interconnected energy hubs is optimized based on the homomorphic encryption algorithm and the alternating direction multiplier method to obtain an optimization result, including:

[0058] The first region encrypts the first region coupling item using the public key corresponding to the first region to obtain the first region ciphertext, and sends the first region ciphertext to the second region;

[0059] The second region encrypts the second region coupling item by using the public key corresponding to the second region to obtain a second region ciphertext;

[0060] The second region calculates a ciphertext deviation and a ciphertext weighted deviation of a second region weighted value by using the first region ciphertext and the second region ciphertext;

[0061] The first region decrypts the ciphertext weighted deviation of the weighted value of the second region using the private key corresponding to the first region, and weights the decryption result using the weighted value of the first region to obtain an update variable, so as to update the initialized Lagrange multiplier matrix through the update variable.

[0062] A second aspect of the present application provides a distributed optimization device for an integrated energy system, wherein the integrated energy system includes a vertically layered information layer and a physical layer, wherein the device includes:

[0063] A model building module is used to build a distributed optimization scheduling model for multiple interconnected energy hubs based on the multi-energy conversion constraints within the energy hub and the multi-energy network operation constraints of the integrated energy system of multiple interconnected energy hubs;

[0064] an optimization module, configured to optimize the distributed optimization scheduling model of the plurality of interconnected energy hubs based on a homomorphic encryption algorithm and an alternating direction multiplier method to obtain an optimization result; and

[0065] A control module is used to control the output of each production unit in the region of the integrated energy system and the operating status of each node in the multi-energy network to achieve the optimal state according to the optimization result and the interactive relationship between the information layer and the physical layer, so as to share the optimal energy among multiple regions of the integrated energy system.

[0066] Optionally, the model building module includes:

[0067] Based on the preset multi-energy network standardized modeling formula, a distributed optimization scheduling model of multiple interconnected energy hubs is established, wherein the multi-energy network standardized modeling formula is:

[0068]

[0069]

[0070]

[0071]

[0072]

[0073]

[0074] Among them, min C(P n ) is to minimize the total operating cost of the rth region of the integrated energy system, N r is the total number of energy hubs in the region, P n is the matrix of energy output of all units in the nth energy hub in the region; a n , b n , c n is the fuel cost coefficient matrix, X n , Y n , Z n are the multi-energy input correlation matrix, output correlation matrix and energy conversion matrix, V nis the correlation matrix connecting the energy hub port and the multi-energy network branch, R is the total number of regions in the integrated energy system, V in,n and V out,n are the energy hub entrance and exit multi-energy association matrices, U in,m and U out,m are the multi-energy energy flow matrices on the mth branch connected to the entrance and exit of the energy hub, To represent the set of branches connected to the nth energy hub, L n is the multi-energy load matrix of the nth energy hub, and They represent the upper bounds of the inlet and outlet multi-energy flows on the mth branch; M r is the total number of multi-energy network branches in the rth region, indicating the energy exchange relationship between two adjacent regions r and l, where U r→l represents the energy flowing from region r to region l, U l→r represents the energy flowing from region l to region r, is the set of regions adjacent to r.

[0075] Optionally, the multi-energy energy flow matrix on the mth branch connected to the energy hub outlet is:

[0076]

[0077] Where u represents the energy flow on the network branch, the superscript i→j represents the energy flow from node i to node j, and represents the adjacency relationship between node i and node j. The subscripts e, h, and g represent the power network, district heating network, and natural gas network, respectively.

[0078] Optionally, for the power network, a Distflow model is used to represent the transmission relationship of electric energy between interconnected energy hubs, wherein the Distflow model is:

[0079]

[0080]

[0081]

[0082]

[0083]

[0084] in, The active power injected into the node, The reactive power injected into the node, is the adjacency relationship between node i and node j, ri→j is the branch resistance, x i→j is the branch reactance; V i is the voltage at node i, V j is the voltage at node j, is the branch current.

[0085] For the district heating network, a quality-quantity joint regulation model is used to represent the heat energy transfer process, wherein the quality-quantity joint regulation model is:

[0086]

[0087]

[0088]

[0089]

[0090]

[0091]

[0092]

[0093]

[0094]

[0095] in, is the heat supply of the heat source node, κ is the power and energy unit conversion factor, C w is the specific heat capacity of water, is the water supply of the heat source node, is the inlet temperature of the heat source node connected to the thermal pipeline, is the mixed temperature of the return water from the thermal pipeline back to the heat source node, is the amount of water flowing into node i, is the amount of pipe water flowing from node i to node j, is the inlet temperature of node i, is the inlet temperature of node j, is the current ambient temperature, λ is the thermal pipe resistance coefficient, ρ i→j is the pipe length, L h,i is the thermal load of node i, is the outlet temperature of node i, is the return water mixing temperature at node i.

[0096] For the natural gas pipeline network, the Weymouth steady-state gas flow model is used to represent the natural gas transportation process, wherein the Weymouth steady-state gas flow model is:

[0097]

[0098]

[0099]

[0100]

[0101]

[0102]

[0103] in, is the natural gas energy flow, GHV is the calorific value of natural gas, is the natural gas flow on the pipeline, Consumption of fuel gas compressor, is the gas compressor consumption coefficient, π i is the air pressure at node i, π j is the air pressure at node j, ξ i→j is the pipe resistance coefficient.

[0104] Optionally, the calculation method of the alternating direction multiplier method is:

[0105] Converting the distributed optimization scheduling model of the plurality of interconnected energy hubs into a standard form of an alternating direction multiplier method to obtain a converted distributed optimization scheduling model of the plurality of interconnected energy hubs;

[0106] Solving the distributed optimization scheduling model of the transformed multiple interconnected energy hubs by using an initialized Lagrange multiplier matrix to obtain a first optimal energy flow, and updating coupling terms in the distributed optimization scheduling model of the transformed multiple interconnected energy hubs to obtain a first updated coupling term;

[0107] The distributed optimization scheduling model of the multiple interconnected energy hubs after the transformation is solved by using the initialized Lagrange multiplier matrix and the first updated coupling term to obtain the second optimal energy flow, and update the coupling term to obtain the second updated coupling term:

[0108] According to the first updated coupling term and the second updated coupling term, it is determined whether the original and dual residuals are within the tolerance range. If not, the initialized Lagrangian multiplier matrix is ​​updated, and the distributed optimization scheduling model of the transformed multiple interconnected energy hubs is solved according to the updated initialized Lagrangian multiplier matrix until the preset iteration conditions are met.

[0109] Optionally, the calculation method of the homomorphic encryption algorithm is:

[0110] Based on the first key calculation formula, the first prime number and the second prime number of the same bit length are calculated to obtain a public key of the encryption key;

[0111] Based on the Euler inertia function, calculation is performed according to the first prime number and the second prime number to obtain a private key of the encryption key.

[0112] Optionally, the optimization module includes:

[0113] A first encryption unit, wherein the first region encrypts the first region coupling item by using a public key corresponding to the first region to obtain a first region ciphertext, and sends the first region ciphertext to the second region;

[0114] A second encryption unit, wherein the second region encrypts the second region coupling item by using a public key corresponding to the second region to obtain a second region ciphertext;

[0115] A calculation unit, wherein the second region calculates a ciphertext deviation and a ciphertext weighted deviation of a second region weighted value by using the first region ciphertext and the second region ciphertext;

[0116] A decryption unit, wherein the first region decrypts the ciphertext weighted deviation of the weighted value of the second region using the private key corresponding to the first region, and weights the decryption result using the weighted value of the first region to obtain an update variable, so as to update the initialized Lagrange multiplier matrix through the update variable.

[0117] The third aspect of the present application provides an electronic device, comprising: a memory, a processor, and a computer program stored in the memory and executable on the processor, wherein the processor executes the program to implement the distributed optimization method of the integrated energy system as described in the above embodiment.

[0118] The fourth aspect of the present application provides a computer-readable storage medium having a computer program stored thereon, which is executed by a processor to implement the distributed optimization method of the integrated energy system as described in the above embodiment.

[0119] Therefore, the embodiments of the present application have the following beneficial effects:

[0120] The distributed optimization scheduling model of multiple interconnected energy hubs in the embodiment of the present application not only considers the information interaction between multiple energy hubs, but also considers the refined model of multi-energy network operation, including power network, regional heating network and natural gas network. On this basis, in order to avoid the problem of privacy leakage in the process of information interaction between interconnected energy hubs, the embodiment of the present application combines the homomorphic encryption algorithm with the alternating direction multiplier method, and designs a privacy-preserving distributed optimal energy flow optimization scheduling method, which is used to reach a distributed consistency consensus when the interacting parties do not share their sensitive information in plain text, thereby ensuring the convergence of the proposed algorithm, realizing multi-energy optimal energy sharing, and having high application value.

[0121] Additional aspects and advantages of the present application will be given in part in the description below, and in part will become apparent from the description below, or will be learned through the practice of the present application. BRIEF DESCRIPTION OF THE DRAWINGS

[0122] The above and / or additional aspects and advantages of the present application will become apparent and easily understood from the following description of the embodiments in conjunction with the accompanying drawings, in which:

[0123] Figure 1 A flowchart of a distributed optimization method for an integrated energy system provided according to an embodiment of the present application;

[0124] Figure 2 A flowchart of a distributed optimization method for an integrated energy system provided according to an embodiment of the present application;

[0125] Figure 3 A schematic diagram of a comprehensive energy management system structure provided according to an embodiment of the present application;

[0126] Figure 4 A schematic diagram of an integrated energy management system provided according to an embodiment of the present application;

[0127] Figure 5 A schematic diagram of an iterative result of inter-region encrypted weighted deviation information (ciphertext) provided according to an embodiment of the present application;

[0128] Figure 6 A schematic diagram of a result of multi-energy energy sharing between regions in a comprehensive energy system provided according to an embodiment of the present application;

[0129] Figure 7 A block diagram of a distributed optimization device for an integrated energy system provided according to an embodiment of the present application;

[0130] Figure 8 A schematic diagram of an electronic device provided according to an embodiment of the present application. DETAILED DESCRIPTION

[0131] The embodiments of the present application are described in detail below, and examples of the embodiments are shown in the accompanying drawings, wherein the same or similar reference numerals throughout represent the same or similar elements or elements having the same or similar functions. The embodiments described below with reference to the accompanying drawings are exemplary and are intended to be used to explain the present application, and should not be construed as limiting the present application.

[0132] The following describes the distributed optimization method, device, equipment and storage medium of the integrated energy system of the embodiment of the present application with reference to the accompanying drawings. In view of the privacy leakage problem in the information interaction process between interconnected energy hubs mentioned in the above background technology, the present application provides a distributed optimization method for an integrated energy system, in which a distributed optimization scheduling model of multiple interconnected energy hubs is established based on the multi-energy conversion constraints within the energy hub and the multi-energy network operation constraints of the integrated energy system of multiple interconnected energy hubs, and based on the homomorphic encryption algorithm and the alternating direction multiplier method, the distributed optimization scheduling model of multiple interconnected energy hubs is optimized to obtain the optimization result, and the output of each capacity unit in the region of the integrated energy system and the operation state of each node of the multi-energy network are controlled according to the interaction relationship between the optimization result, the information layer and the physical layer to achieve the optimal state, and share the optimal energy between multiple regions of the integrated energy system. Thus, the privacy leakage problem in the information interaction process between interconnected energy hubs is solved, and a distributed consistency consensus can be reached without sharing the plain text of each sensitive information between the interacting parties, thereby ensuring the convergence of the proposed algorithm and realizing the optimal energy sharing of multiple energy sources.

[0133] Specifically, Figure 1 A flow chart of a distributed optimization method for an integrated energy system provided in an embodiment of the present application.

[0134] like Figure 1 As shown, the distributed optimization method of the integrated energy system includes the following steps:

[0135] In step S101, a distributed optimization scheduling model of multiple interconnected energy hubs is established based on the multi-energy conversion constraints within the energy hub and the multi-energy network operation constraints of the integrated energy system of multiple interconnected energy hubs.

[0136] Specifically, a distributed optimization scheduling model describing multiple interconnected energy hubs is established, and the expression is as follows:

[0137]

[0138]

[0139]

[0140]

[0141]

[0142]

[0143] Wherein, formula (1) is to minimize the total operating cost of the rth region of the integrated energy system, N r is the total number of energy hubs in the region; P n is the matrix of energy output of all units in the nth energy hub in the region; a n , b n , c n is the fuel cost coefficient matrix; Formula (2) describes the relationship between the multi-energy input and output equations of the nth energy hub, X n , Y n , Z n are the multi-energy input correlation matrix, output correlation matrix and energy conversion matrix, V n is the correlation matrix connecting the energy hub port and the multi-energy network branch, R is the total number of regions in the integrated energy system; V in,n and V out,n are the energy hub entrance and exit multi-energy association matrices, respectively. Equations (3) and (4) describe the equation relationship between the two, respectively. in,m and U out,m are the multi-energy energy flow matrices on the mth branch connected to the entrance and exit of the energy hub, To represent the set of branches connected to the nth energy hub, L n is the multi-energy load matrix of the nth energy hub; in formula (5) and They represent the upper bounds of the inlet and outlet multi-energy flows on the mth branch; M r is the total number of multi-energy network branches in the rth region; Formula (6) represents the energy exchange relationship between two adjacent regions r and l, where U r→l represents the energy flowing from region r to region l, U l→r represents the energy flowing from region l to region r, is the set of regions adjacent to r.

[0144] There is usually energy loss in the multi-energy network. The operation constraints of the multi-energy network should be considered in the distributed optimization scheduling process of multiple interconnected energy hubs.

[0145]

[0146] Where u represents the energy flow on the network branch, the superscript i→j represents the energy flow from node i to node j, and represents the adjacency relationship between node i and node j, and the subscripts e, h, and g represent the power network, the district heating network, and the natural gas network, respectively; the specific steps are as follows:

[0147] For the power grid, the Distflow model is used to describe the transmission relationship of electric energy between interconnected energy hubs, as follows:

[0148]

[0149]

[0150]

[0151]

[0152]

[0153] Among them, equations (7) and (8) respectively represent the relationship between the active power and reactive power injected into the node, and are the active power and reactive power injected into the node respectively, V n The elements in represent the adjacency relationship between node i and node j, r i→j and x i→j represent the branch resistance and reactance respectively; Equation (9) describes the node voltage V i and branch current Satisfies Ohm's law; Formula (10) describes the node power balance relationship; Formula (11) describes the upper and lower boundary constraints of each variable;

[0154] For the district heating network, the quality-quantity joint regulation model is used to describe the heat energy transfer process, as follows:

[0155]

[0156]

[0157]

[0158]

[0159]

[0160]

[0161]

[0162]

[0163]

[0164] Wherein, formula (12) describes the heat supply of the heat source node κ is the power and energy unit conversion factor, C w is the specific heat capacity of water, is the water supply of the heat source node, and are the inlet temperature of the heat source node connected to the thermal pipeline and the return water mixed temperature from the thermal pipeline back to the heat source node; Formula (13) describes the equation relationship of the water supply of the heat source node, where and are the water volume flowing into node i and the pipe water volume flowing from node i to node j respectively; Formula (14) describes the inlet temperature of adjacent nodes i and j and The equation relationship between is the current ambient temperature, λ and ρ i→j are the thermal pipe resistance coefficient and the pipe length respectively; Formula (15) describes the equation relationship of the pipe heat flow, where L h,i is the thermal load of node i; based on Kirchhoff's law, equation (16) describes the inlet and outlet temperatures of node i Equation (17) describes the return water mixing temperature of node i. Formula (18) describes the upper and lower boundary constraints of each variable.

[0165] For the natural gas pipeline network, the Weymouth steady-state gas flow model is used to describe the natural gas transmission process, as follows:

[0166]

[0167]

[0168]

[0169]

[0170]

[0171]

[0172] Wherein, formula (19) describes the natural gas energy flow GHV is the calorific value of natural gas, is the natural gas flow on the pipeline; Equation (20) describes the consumption of the gas compressor driving the natural gas transportation The equation relationship is is the gas compressor consumption coefficient, πi is the node gas pressure; Equations (21) and (22) together describe the equation relationship between the natural gas flow and flow direction of adjacent nodes i and j, where ξ i→j is the pipeline resistance coefficient; Formula (23) describes the upper and lower boundary constraints of each variable.

[0173] In step S102, based on the homomorphic encryption algorithm and the alternating direction multiplier method, the distributed optimization scheduling model of multiple interconnected energy hubs is optimized to obtain the optimization result.

[0174] Optionally, in some embodiments, the calculation method of the alternating direction multiplier method is: converting the distributed optimization scheduling model of multiple interconnected energy hubs into the standard form of the alternating direction multiplier method to obtain the converted distributed optimization scheduling model of multiple interconnected energy hubs; using the initialized Lagrangian multiplier matrix to solve the converted distributed optimization scheduling model of multiple interconnected energy hubs to obtain the first optimal energy flow, and updating the coupling terms in the converted distributed optimization scheduling model of multiple interconnected energy hubs to obtain the first updated coupling term; using the initialized Lagrangian multiplier matrix and the first updated coupling term to solve the converted distributed optimization scheduling model of multiple interconnected energy hubs to obtain the second optimal energy flow, and update the coupling terms to obtain the second updated coupling term: according to the first updated coupling term and the second updated coupling term, determine whether the original and dual residuals are within the tolerance range, if not, update the initialized Lagrangian multiplier matrix, and solve the converted distributed optimization scheduling model of multiple interconnected energy hubs according to the updated initialized Lagrangian multiplier matrix until the preset iteration condition is met.

[0175] Optionally, in some embodiments, the calculation method of the homomorphic encryption algorithm is: based on the first key calculation formula, the first prime number and the second prime number of the same bit length are calculated to obtain the public key of the encryption key; based on the Euler inertia function, the first prime number and the second prime number are calculated to obtain the private key of the encryption key.

[0176] Optionally, in some embodiments, based on the homomorphic encryption algorithm and the alternating direction multiplier method, the distributed optimization scheduling model of multiple interconnected energy hubs is optimized to obtain an optimization result, including: the first region uses the public key corresponding to the first region to encrypt the first region coupling item to obtain the first region ciphertext, and sends the first region ciphertext to the second region; the second region uses the public key corresponding to the second region to encrypt the second region coupling item to obtain the second region ciphertext; the second region uses the first region ciphertext and the second region ciphertext to calculate the ciphertext deviation and the ciphertext weighted deviation of the second region weighted value; the first region uses the private key corresponding to the first region to decrypt the ciphertext weighted deviation of the second region weighted value, and uses the first region weighted value to weight the decryption result to obtain an updated variable, so as to update the initialized Lagrange multiplier matrix through the updated variable.

[0177] Specifically, the distributed optimization scheduling model P1 of multiple interconnected energy hubs is transformed into the standard form P2 of the alternating direction multiplier method;

[0178]

[0179] Subject to constraints (2)-(23), where λ r→l is the Lagrange multiplier matrix, and ρ is the quadratic penalty coefficient matrix.

[0180] In order to reach a consistent consensus on multi-energy energy sharing, in each iterative solution process, the alternating direction multiplier method requires all regions or energy hubs to share local information with adjacent entities, which will lead to privacy leakage problems. Based on the homomorphic encryption method of computational complexity theory of mathematical problems, the homomorphic encrypted data is processed to obtain an output, and this output is decrypted, and the result is consistent with the output result obtained by processing the unencrypted original data using the same method. In order to avoid privacy leakage problems in the process of information interaction between interconnected energy hubs, the embodiment of the present application combines the homomorphic encryption algorithm with the alternating direction multiplier method, and designs a distributed optimization method for an integrated energy system, which is used to reach a distributed consistent consensus when the interacting parties do not share their respective sensitive information in plain text, thereby ensuring the convergence of the proposed algorithm and realizing optimal energy sharing of multiple energy sources; the specific steps are as follows:

[0181] Implementation of the alternating direction multiplier method; the specific steps are as follows:

[0182] Let k denote the number of iterations, and the region r is initialized with the Lagrange multiplier matrix λ r→l (k), solve the distributed optimization scheduling problem P2 of multiple interconnected energy hubs described by equation (24), obtain the optimal energy flow and update the coupling term U r→l (k+1):

[0183]

[0184] The Lagrange multiplier matrix λ is initialized in region l using r→l (k) and U from region r r→l (k+1), solve the distributed optimization scheduling problem P2 of multiple interconnected energy hubs described by equation (24), obtain the optimal energy flow and update the coupling term U l →r (k+1):

[0185]

[0186] Update the coupling terms according to equations (25) and (26), and iterate until the primal and dual residuals are within the tolerance range:

[0187]

[0188]

[0189] Otherwise, update the Lagrange multiplier matrix as follows:

[0190] λ r→l (k+1)=λ r→l (k)+τρ(U r→l (k+1)-U l→r (k+1)) (29)

[0191] Repeat the above steps until the stopping criteria of equations (27) and (28) are met.

[0192] Implementation of homomorphic encryption algorithm; the specific steps are as follows:

[0193] Generate key k pub =(x,y) and k pri =(σ,μ):

[0194] Choose two large prime numbers p and q of the same bit length and calculate x = p·q, y = x+1 and Euler's inertia function σ = φ(x) = (p-1)·(q-1); μ = φ(x) -1 mod x represents the modular inverse of the function φ(x); output public key k pub =(x,y), private key k pri =(σ,μ);

[0195] Encryption process c = ε(i):

[0196] Select random number Among them, gcd is the greatest common divisor; generate ciphertext c = y i ·r x mod x 2 ,in

[0197] Decryption process i=D(c):

[0198] Define the divisibility function Generate plaintext i=T(c σ mod x 2 )·μ mod x;

[0199] The above homomorphic encryption process has the characteristics of additive homomorphism and multiplicative homomorphism, that is,

[0200] c1·c2=ε(i1)·ε(i2)=ε(i1+i2) (30)

[0201] ε(i) k=ε(ki) (31)

[0202] Implementation of privacy-preserving distributed optimal energy flow optimization scheduling method; Figure 2 As shown, the specific steps are as follows:

[0203] Step 1: Region r uses public key k pub,r The coupling term U r→l (k) Encryption:

[0204] U r→l (k) = ε(U r→l (k)) (32)

[0205] Region r will use the public key k pub,r and encrypted information ε(U r→l (k)) is sent to the adjacent area l.

[0206] Step 2: Region l uses public key k pub,l The coupling term U l→r (k) Encryption is performed in parallel with step 1:

[0207] U l→r (k) = ε(U l→r (k)) (33)

[0208] Step 3: Region l uses the ciphertext to calculate the deviation:

[0209] ε(U r→l (k)-U l→r (k))=ε(U r→l (k))·ε(U l→r (k)) (34)

[0210] Region l uses ciphertext to calculate a l (k) Weighted bias:

[0211]

[0212] And the deviation information ε(a l (k)·(U r→l (k)-U l→r (k))) is sent to region r.

[0213] Step 4: Region r uses private key k pri,r Decrypt the information received from area l and obtain a l (k)·(U r→l (k)-U l→r (k)), and further utilize a r (k) Weighting:

[0214] τρ·(Ur→l (k)-U l→r (k)) = a r (k)·a l (k)·(U r→l (k)-U l→r (k)) (36)

[0215] Therefore, τρ=a r (k)·a l (k) = a r→l =a l→r ; On this basis, equation (29) is further used to update the dual variables.

[0216] Step 5: Thus, the true state information of region r and region l after the k+1th iteration can be obtained by calculating equations (25) and (26); when the stopping criteria of equations (27) and (28) are met, the iteration is terminated.

[0217] In step S103, the output of each production unit in the region of the integrated energy system and the operating status of each node in the multi-energy network are controlled to reach the optimal state according to the optimization results, the interactive relationship between the information layer and the physical layer, and the optimal energy is shared among multiple regions of the integrated energy system.

[0218] Step 6: The optimization results are sent to the physical layer controllers in each region. The output of each production unit in the region and the operating status of each node in the multi-energy network are controlled according to the optimization results to achieve optimal energy sharing among multiple regions of the integrated energy system.

[0219] In order to enable those skilled in the art to further understand the distributed optimization method of the integrated energy system of the embodiment of the present application, it is elaborated in detail below in conjunction with specific embodiments.

[0220] like Figure 3 As shown, the embodiment of the present application also provides an integrated energy management system based on multi-party secure computing for implementing the distributed optimization method of the integrated energy system of the embodiment of the present application.

[0221] Specifically, the integrated energy management system is a hierarchical system, which is vertically divided into an information layer and a physical layer.

[0222] The information layer consists of a two-level structure of a central server and regional servers. Each regional server is used to collect real-time operating information of multi-energy networks and equipment in the region, and uploads the key information required by the distributed optimization algorithm to the central server in the form of ciphertext through homomorphic encryption technology. The central server executes the distributed optimization algorithm and minimizes the deviation, and sends the optimization results to each regional server in the form of ciphertext. Each regional server obtains the plaintext optimization results through homomorphic decryption, and further sends them to the physical layer controller of each region. According to the optimization results, the output of each production unit in the region and the operating status of each node in the multi-energy network are controlled to achieve optimal energy sharing among multiple regions of the integrated energy system.

[0223] The physical layer consists of multiple regional interconnected energy hubs, multi-energy networks, and equipment controllers. The energy hubs include multiple production units, such as gas turbines, gas boilers, and cogeneration. The multi-energy network includes power networks, regional heating networks, and natural gas networks, providing physical support for multi-energy flows. The equipment controllers include multiple production unit controllers and multi-energy network controllers, which are used to track the optimization results of the information layer in real time.

[0224] Furthermore, if Figure 4 As shown, the embodiment of the present application is based on a comprehensive energy system consisting of a 3-machine 9-node power system, a 9-node regional heating system and a 9-node natural gas system, which is mainly divided into 3 areas. Area 1 is the main energy supplier of the system, and is configured with natural gas sources, photovoltaic power stations, wind farms, gas turbines and gas boilers, and is directly connected to the large power grid. Areas 2 and 3 are configured with cogeneration units, such as Figure 4 As shown. Based on the proposed privacy-preserving distributed optimal energy flow optimization scheduling method, it can converge after 30 iterations. The iterative results of the encrypted weighted deviation information (ciphertext) between regions are shown as follows: Figure 5 As shown in the figure, homomorphic encryption can successfully conceal the original information. Although the iteration deviation converges after 30 iterations, the corresponding ciphertext is still a random number, so it can effectively protect the privacy of multi-party information interaction. Figure 6 The results of multi-energy sharing among three regions are given, among which Figure 6 (a) is the result of power exchange, Figure 6 (b) is the result of heat exchange, Figure 6 (c) is the result of natural gas exchange. It can be seen that the multi-energy energy sharing among the three regions has good convergence performance within a limited number of iterations, which verifies the practicality of the proposed method. In addition, the physical layer controller of the integrated energy management system can track the information layer optimization results in real time, and finally realize the optimal energy sharing among multiple regions of the integrated energy system.

[0225] According to the distributed optimization method of the integrated energy system proposed in the embodiment of the present application, a distributed optimization scheduling model of multiple interconnected energy hubs is established based on the multi-energy conversion constraints within the energy hub and the multi-energy network operation constraints of the integrated energy system of multiple interconnected energy hubs, and the distributed optimization scheduling model of multiple interconnected energy hubs is optimized based on the homomorphic encryption algorithm and the alternating direction multiplier method to obtain an optimized result. According to the optimization result, the interactive relationship between the information layer and the physical layer, the output of each production unit in the region of the integrated energy system and the operating status of each node in the multi-energy network are controlled to achieve the optimal state, and the optimal energy between multiple regions of the integrated energy system is shared. Thus, the problem of privacy leakage in the process of information interaction between interconnected energy hubs is solved, and a distributed consistency consensus can be reached without the two interacting parties sharing their respective sensitive information in plain text, thereby ensuring the convergence of the proposed algorithm and realizing the optimal energy sharing of multiple energy sources.

[0226] Next, the distributed optimization device of the integrated energy system proposed in accordance with the embodiment of the present application will be described with reference to the accompanying drawings.

[0227] Figure 7 It is a block diagram of a distributed optimization device for an integrated energy system according to an embodiment of the present application.

[0228] like Figure 7 As shown, the distributed optimization device 10 of the integrated energy system includes: a model building module 100, an optimization module 200 and a control module 300.

[0229] The model building module 100 is used to establish a distributed optimization scheduling model of multiple interconnected energy hubs based on the multi-energy conversion constraints within the energy hub and the multi-energy network operation constraints of the integrated energy system of multiple interconnected energy hubs;

[0230] The optimization module 200 is used to optimize the distributed optimization scheduling model of multiple interconnected energy hubs based on the homomorphic encryption algorithm and the alternating direction multiplier method to obtain an optimization result; and

[0231] The control module 300 is used to control the output of each production unit in the region of the integrated energy system and the operating status of each node in the multi-energy network to achieve the optimal state according to the optimization results, the interactive relationship between the information layer and the physical layer, and share the optimal energy among multiple regions of the integrated energy system.

[0232] Optionally, in some embodiments, the model building module 100 includes:

[0233] Based on the preset standardized modeling formula of multi-energy network, a distributed optimization scheduling model of multiple interconnected energy hubs is established, where the standardized modeling formula of multi-energy network is:

[0234]

[0235]

[0236]

[0237]

[0238]

[0239]

[0240] Among them, min C(P n ) is to minimize the total operating cost of the rth region of the integrated energy system, N r is the total number of energy hubs in the region, P n is the matrix of energy output of all units in the nth energy hub in the region; a n , b n , c n is the fuel cost coefficient matrix, X n , Y n , Z n are the multi-energy input correlation matrix, output correlation matrix and energy conversion matrix, V n is the correlation matrix connecting the energy hub port and the multi-energy network branch, R is the total number of regions in the integrated energy system, V in,n and V out,n are the energy hub entrance and exit multi-energy association matrices, U in,m and U out,m are the multi-energy energy flow matrices on the mth branch connected to the entrance and exit of the energy hub, To represent the set of branches connected to the nth energy hub, L n is the multi-energy load matrix of the nth energy hub, and They represent the upper bounds of the inlet and outlet multi-energy flows on the mth branch; M r is the total number of multi-energy network branches in the rth region, indicating the energy exchange relationship between two adjacent regions r and l, where U r→l represents the energy flowing from region r to region l, U l→r represents the energy flowing from region l to region r, is the set of regions adjacent to r.

[0241] Optionally, in some embodiments, the multi-energy energy flow matrix on the mth branch connected to the energy hub outlet is:

[0242]

[0243] Where u represents the energy flow on the network branch, the superscript i→j represents the energy flow from node i to node j, and represents the adjacency relationship between node i and node j. The subscripts e, h, and g represent the power network, district heating network, and natural gas network, respectively.

[0244] Optionally, for the power network, a Distflow model is used to represent the transmission relationship of electric energy between interconnected energy hubs, where the Distflow model is:

[0245]

[0246]

[0247]

[0248]

[0249]

[0250] in, The active power injected into the node, The reactive power injected into the node, is the adjacency relationship between node i and node j, r i→j is the branch resistance, x i→j is the branch reactance; V i is the voltage at node i, V j is the voltage at node j, is the branch current.

[0251] For the district heating network, the quality-quantity joint regulation model is used to represent the heat energy transfer process, where the quality-quantity joint regulation model is:

[0252]

[0253]

[0254]

[0255]

[0256]

[0257]

[0258]

[0259]

[0260]

[0261] in, is the heat supply of the heat source node, κ is the power and energy unit conversion factor, C w is the specific heat capacity of water, is the water supply of the heat source node, is the inlet temperature of the heat source node connected to the thermal pipeline, is the mixed temperature of the return water from the thermal pipeline back to the heat source node, is the amount of water flowing into node i, is the amount of pipe water flowing from node i to node j, is the inlet temperature of node i, is the inlet temperature of node j, is the current ambient temperature, λ is the thermal pipe resistance coefficient, ρ i→j is the pipe length, L h,i is the thermal load of node i, is the outlet temperature of node i, is the return water mixing temperature at node i.

[0262] For the natural gas pipeline network, the Weymouth steady-state gas flow model is used to represent the natural gas transmission process, where the Weymouth steady-state gas flow model is:

[0263]

[0264]

[0265]

[0266]

[0267]

[0268]

[0269] in, is the natural gas energy flow, GHV is the calorific value of natural gas, is the natural gas flow on the pipeline, Consumption of fuel gas compressor, is the gas compressor consumption coefficient, π i is the air pressure at node i, π j is the air pressure at node j, ξ i→j is the pipe resistance coefficient.

[0270] Optionally, in some embodiments, the calculation method of the alternating direction multiplier method is:

[0271] The distributed optimization scheduling model of multiple interconnected energy hubs is converted into a standard form of the alternating direction multiplier method to obtain a converted distributed optimization scheduling model of multiple interconnected energy hubs;

[0272] Using the initialization Lagrange multiplier matrix to solve the transformed distributed optimization scheduling model of the multiple interconnected energy hubs, obtain the first optimal energy flow, and update the coupling terms in the transformed distributed optimization scheduling model of the multiple interconnected energy hubs to obtain the first updated coupling terms;

[0273] The distributed optimization scheduling model of multiple interconnected energy hubs after the initialization of the Lagrange multiplier matrix and the first updated coupling term solution is used to obtain the second optimal energy flow, and the coupling term is updated to obtain the second updated coupling term:

[0274] According to the first updated coupling term and the second updated coupling term, it is determined whether the primal and dual residuals are within the tolerance range. If not, the initialized Lagrangian multiplier matrix is ​​updated, and the distributed optimization scheduling model of the transformed multiple interconnected energy hubs is solved according to the updated initialized Lagrangian multiplier matrix until the preset iteration conditions are met.

[0275] Optionally, in some embodiments, the calculation method of the homomorphic encryption algorithm is:

[0276] Based on the first key calculation formula, the first prime number and the second prime number of the same bit length are calculated to obtain a public key of the encryption key;

[0277] Based on the Euler inertia function, a calculation is performed according to the first prime number and the second prime number to obtain a private key of the encryption key.

[0278] Optionally, in some embodiments, the optimization module 200 includes:

[0279] The first encryption unit encrypts the first region coupling item by using the public key corresponding to the first region, obtains the first region ciphertext, and sends the first region ciphertext to the second region;

[0280] The second encryption unit encrypts the second region coupling item by the second region using the public key corresponding to the second region to obtain the second region ciphertext;

[0281] A calculation unit, wherein the second region calculates a ciphertext deviation and a ciphertext weighted deviation of a second region weighted value by using the first region ciphertext and the second region ciphertext;

[0282] The decryption unit decrypts the ciphertext weighted deviation of the weighted value of the second area by the first area using the private key corresponding to the first area, and weights the decryption result by the weighted value of the first area to obtain an update variable, so as to update the initialized Lagrange multiplier matrix through the update variable.

[0283] It should be noted that the aforementioned explanation of the embodiment of the distributed optimization method for the integrated energy system is also applicable to the distributed optimization device for the integrated energy system of this embodiment, and will not be repeated here.

[0284] According to the distributed optimization device of the integrated energy system proposed in the embodiment of the present application, a distributed optimization scheduling model of multiple interconnected energy hubs is established based on the multi-energy conversion constraints within the energy hub and the multi-energy network operation constraints of the integrated energy system of multiple interconnected energy hubs, and the distributed optimization scheduling model of multiple interconnected energy hubs is optimized based on the homomorphic encryption algorithm and the alternating direction multiplier method to obtain an optimized result. According to the optimization result, the interactive relationship between the information layer and the physical layer, the output of each production unit in the region of the integrated energy system and the operating status of each node in the multi-energy network are controlled to achieve the optimal state, and the optimal energy between multiple regions of the integrated energy system is shared. Thus, the problem of privacy leakage in the process of information interaction between interconnected energy hubs is solved, and a distributed consistency consensus can be reached without the two interacting parties sharing their respective sensitive information in plain text, thereby ensuring the convergence of the proposed algorithm and realizing the optimal energy sharing of multiple energy sources.

[0285] Figure 8 A schematic diagram of the structure of an electronic device provided in an embodiment of the present application. The electronic device may include:

[0286] A memory 801 , a processor 802 , and a computer program stored in the memory 801 and executable on the processor 802 .

[0287] When the processor 802 executes the program, the distributed optimization method of the integrated energy system provided in the above embodiment is implemented.

[0288] Furthermore, the electronic device further comprises:

[0289] The communication interface 803 is used for communication between the memory 801 and the processor 802 .

[0290] The memory 801 is used to store computer programs that can be executed on the processor 802 .

[0291] The memory 801 may include a high-speed RAM memory, and may also include a non-volatile memory (non-volatile memory), such as at least one disk memory.

[0292] If the memory 801, the processor 802 and the communication interface 803 are implemented independently, the communication interface 803, the memory 801 and the processor 802 can be connected to each other through a bus and communicate with each other. The bus can be an Industry Standard Architecture (ISA) bus, a Peripheral Component (PCI) bus or an Extended Industry Standard Architecture (EISA) bus. The bus can be divided into an address bus, a data bus, a control bus, etc. For ease of representation, Figure 8 Only one thick line is used in the diagram, but this does not mean that there is only one bus or only one type of bus.

[0293] Optionally, in a specific implementation, if the memory 801, the processor 802 and the communication interface 803 are integrated on a chip, the memory 801, the processor 802 and the communication interface 803 can communicate with each other through an internal interface.

[0294] The processor 802 may be a central processing unit (CPU), or an application specific integrated circuit (ASIC), or one or more integrated circuits configured to implement the embodiments of the present application.

[0295] An embodiment of the present application also provides a computer-readable storage medium having a computer program stored thereon, which, when executed by a processor, implements the above-mentioned distributed optimization method for the integrated energy system.

[0296] In the description of this specification, the description with reference to the terms "one embodiment", "some embodiments", "example", "specific example", or "some examples" etc. means that the specific features, structures, materials or characteristics described in conjunction with the embodiment or example are included in at least one embodiment or example of the present application. In this specification, the schematic representations of the above terms do not necessarily refer to the same embodiment or example. Moreover, the specific features, structures, materials or characteristics described may be combined in any one or N embodiments or examples in a suitable manner. In addition, those skilled in the art may combine and combine the different embodiments or examples described in this specification and the features of the different embodiments or examples, without contradiction.

[0297] In addition, the terms "first" and "second" are used for descriptive purposes only and should not be understood as indicating or implying relative importance or implicitly indicating the number of technical features indicated. Therefore, a feature defined as "first" or "second" may explicitly or implicitly include at least one of the features. In the description of this application, "N" means at least two, such as two, three, etc., unless otherwise clearly and specifically defined.

[0298] Any process or method description in a flowchart or otherwise described herein may be understood to represent a module, fragment or portion of code comprising one or more executable instructions for implementing the steps of a custom logical function or process, and the scope of the preferred embodiments of the present application includes alternative implementations in which functions may not be performed in the order shown or discussed, including performing functions in a substantially simultaneous manner or in reverse order depending on the functions involved, which should be understood by technicians in the technical field to which the embodiments of the present application belong.

[0299] The logic and / or steps represented in the flowchart or otherwise described herein, for example, can be considered as an ordered list of executable instructions for implementing logical functions, and can be embodied in any computer-readable medium for use by an instruction execution system, device or apparatus (such as a computer-based system, a system including a processor, or other system that can fetch instructions from an instruction execution system, device or apparatus and execute instructions), or in combination with these instruction execution systems, devices or apparatuses. For the purpose of this specification, "computer-readable medium" can be any device that can contain, store, communicate, propagate or transmit a program for use by an instruction execution system, device or apparatus, or in combination with these instruction execution systems, devices or apparatuses. More specific examples of computer-readable media (a non-exhaustive list) include the following: an electrical connection with one or N wirings (electronic devices), a portable computer disk box (magnetic device), a random access memory (RAM), a read-only memory (ROM), an erasable and programmable read-only memory (EPROM or flash memory), a fiber optic device, and a portable compact disk read-only memory (CDROM). In addition, the computer-readable medium may even be paper or other suitable medium on which the program is printed, since the program may be obtained electronically, for example, by optically scanning the paper or other medium and then editing, interpreting or processing in other suitable ways if necessary, and then stored in a computer memory.

[0300] It should be understood that the various parts of the present application can be implemented by hardware, software, firmware or a combination thereof. In the above-mentioned embodiment, the N steps or methods can be implemented by software or firmware stored in a memory and executed by a suitable instruction execution system. For example, if implemented by hardware, as in another embodiment, it can be implemented by any one of the following technologies known in the art or their combination: a discrete logic circuit having a logic gate circuit for implementing a logic function for a data signal, a dedicated integrated circuit having a suitable combination of logic gate circuits, a programmable gate array (PGA), a field programmable gate array (FPGA), etc.

[0301] A person skilled in the art may understand that all or part of the steps in the method for implementing the above-mentioned embodiment may be completed by instructing related hardware through a program, and the program may be stored in a computer-readable storage medium, which, when executed, includes one or a combination of the steps of the method embodiment.

[0302] In addition, each functional unit in each embodiment of the present application may be integrated into a processing module, or each unit may exist physically separately, or two or more units may be integrated into one module. The above-mentioned integrated module may be implemented in the form of hardware or in the form of a software functional module. If the integrated module is implemented in the form of a software functional module and sold or used as an independent product, it may also be stored in a computer-readable storage medium.

[0303] The storage medium mentioned above may be a read-only memory, a magnetic disk or an optical disk, etc. Although the embodiments of the present application have been shown and described above, it can be understood that the above embodiments are exemplary and cannot be understood as limiting the present application. A person of ordinary skill in the art may change, modify, replace and modify the above embodiments within the scope of the present application.

Claims

1. A distributed optimization method for an integrated energy system, characterized in that: The integrated energy system comprises a vertically layered information layer and a physical layer, wherein the method comprises the following steps: Based on the multi-energy conversion constraints within the energy hub and the multi-energy network operation constraints of the integrated energy system of multiple interconnected energy hubs, a distributed optimization scheduling model for multiple interconnected energy hubs is established; Based on the homomorphic encryption algorithm and the alternating direction multiplier method, the distributed optimization scheduling model of the plurality of interconnected energy hubs is optimized to obtain an optimization result; and According to the optimization result, the interactive relationship between the information layer and the physical layer, the output of each production unit in the region of the integrated energy system and the operating state of each node in the multi-energy network are controlled to reach the optimal state, and the optimal energy among multiple regions of the integrated energy system is shared, wherein: The distributed optimization scheduling model for establishing multiple interconnected energy hubs includes: Based on the preset multi-energy network standardized modeling formula, a distributed optimization scheduling model of multiple interconnected energy hubs is established, wherein the multi-energy network standardized modeling formula is: Among them, min C(P n ) is to minimize the total operating cost of the rth region of the integrated energy system, N r is the total number of energy hubs in the region, P n is the matrix of energy output of all units in the nth energy hub in the region; a n , b n , c n is the fuel cost coefficient matrix, X n , Y n , Z n are the multi-energy input correlation matrix, output correlation matrix and energy conversion matrix, V n is the correlation matrix connecting the energy hub port and the multi-energy network branch, R is the total number of regions in the integrated energy system, V in,n and V out,n are the energy hub entrance and exit multi-energy association matrices, U in,m and U out,m are the multi-energy energy flow matrices on the mth branch connected to the entrance and exit of the energy hub, To represent the set of branches connected to the nth energy hub, L n is the multi-energy load matrix of the nth energy hub, and They represent the upper bounds of the inlet and outlet multi-energy flows on the mth branch; M r is the total number of multi-energy network branches in the rth region, indicating the energy exchange relationship between two adjacent regions r and l, where U r→l represents the energy flowing from region r to region l, U l→r represents the energy flowing from region l to region r, is the set of regions adjacent to r; The calculation method of the alternating direction multiplier method is: Converting the distributed optimization scheduling model of the plurality of interconnected energy hubs into a standard form of an alternating direction multiplier method to obtain a converted distributed optimization scheduling model of the plurality of interconnected energy hubs; Solving the distributed optimization scheduling model of the transformed multiple interconnected energy hubs by using an initialized Lagrange multiplier matrix to obtain a first optimal energy flow, and updating coupling terms in the distributed optimization scheduling model of the transformed multiple interconnected energy hubs to obtain a first updated coupling term; The distributed optimization scheduling model of the multiple interconnected energy hubs after the transformation is solved by using the initialized Lagrange multiplier matrix and the first updated coupling term to obtain the second optimal energy flow, and update the coupling term to obtain the second updated coupling term: According to the first updated coupling term and the second updated coupling term, it is determined whether the primal and dual residuals are within a tolerance range; if not, the initialized Lagrangian multiplier matrix is ​​updated, and the distributed optimization scheduling model of the transformed multiple interconnected energy hubs is solved according to the updated initialized Lagrangian multiplier matrix until a preset iteration condition is met; The calculation method of the homomorphic encryption algorithm is: Based on the first key calculation formula, the first prime number and the second prime number of the same bit length are calculated to obtain a public key of the encryption key; Based on the Euler inertia function, calculation is performed according to the first prime number and the second prime number to obtain a private key of the encryption key.

2. The method according to claim 1, characterized in that The multi-energy energy flow matrix on the mth branch connected to the energy hub outlet is: Where u represents the energy flow on the network branch, the superscript i→j represents the energy flow from node i to node j, and represents the adjacency relationship between node i and node j. The subscripts e, h, and g represent the power network, district heating network, and natural gas network, respectively.

3. The method according to claim 2, characterized in that For the power network, the Distflow model is used to represent the transmission relationship of electric energy between interconnected energy hubs, wherein the Distflow model is: in, The active power injected into the node, The reactive power injected into the node, is the adjacency relationship between node i and node j, r i→j is the branch resistance, x i→j is the branch reactance; V i is the voltage at node i, V j is the voltage at node j, l i→i is the branch current; For the district heating network, a quality-quantity joint regulation model is used to represent the heat energy transfer process, wherein the quality-quantity joint regulation model is: in, is the heat supply of the heat source node, κ is the power and energy unit conversion factor, C w is the specific heat capacity of water, is the water supply of the heat source node, is the inlet temperature of the heat source node connected to the thermal pipeline, is the mixed temperature of the return water from the thermal pipeline back to the heat source node, is the amount of water flowing into node i, is the amount of pipe water flowing from node i to node j, is the inlet temperature of node i, is the inlet temperature of node j, is the current ambient temperature, λ is the thermal pipe resistance coefficient, ρ i→j is the pipe length, is the thermal load of node i, is the outlet temperature of node i, is the return water mixing temperature of node i; For the natural gas pipeline network, the Weymouth steady-state gas flow model is used to represent the natural gas transportation process, wherein the Weymouth steady-state gas flow model is: in, is the natural gas energy flow, GHV is the calorific value of natural gas, is the natural gas flow on the pipeline, Consumption of fuel gas compressor, is the gas compressor consumption coefficient, π i is the air pressure at node i, π j is the air pressure at node j, ξ i→j is the pipe resistance coefficient.

4. The method according to claim 1, characterized in that The distributed optimization scheduling model of the plurality of interconnected energy hubs is optimized based on the homomorphic encryption algorithm and the alternating direction multiplier method to obtain the optimization results, including: The first region encrypts the first region coupling item using the public key corresponding to the first region to obtain the first region ciphertext, and sends the first region ciphertext to the second region; The second region encrypts the second region coupling item by using the public key corresponding to the second region to obtain a second region ciphertext; The second region calculates a ciphertext deviation and a ciphertext weighted deviation of a second region weighted value by using the first region ciphertext and the second region ciphertext; The first region decrypts the ciphertext weighted deviation of the weighted value of the second region using the private key corresponding to the first region, and weights the decryption result using the weighted value of the first region to obtain an update variable, so as to update the initialized Lagrange multiplier matrix through the update variable.

5. A distributed optimization device for an integrated energy system, characterized in that: The integrated energy system comprises a vertically layered information layer and a physical layer, wherein the device comprises: A model building module is used to build a distributed optimization scheduling model for multiple interconnected energy hubs based on the multi-energy conversion constraints within the energy hub and the multi-energy network operation constraints of the integrated energy system of multiple interconnected energy hubs; an optimization module, configured to optimize the distributed optimization scheduling model of the plurality of interconnected energy hubs based on a homomorphic encryption algorithm and an alternating direction multiplier method to obtain an optimization result; and A control module is used to control the output of each production unit in the region of the integrated energy system and the operating state of each node of the multi-energy network to achieve the optimal state according to the optimization result, the interactive relationship between the information layer and the physical layer, and share the optimal energy among multiple regions of the integrated energy system, wherein: The model building module comprises: Based on the preset multi-energy network standardized modeling formula, a distributed optimization scheduling model of multiple interconnected energy hubs is established, wherein the multi-energy network standardized modeling formula is: Among them, minC(P n ) is to minimize the total operating cost of the rth region of the integrated energy system, N r is the total number of energy hubs in the region, P n is the matrix of energy output of all units in the nth energy hub in the region; a n , b n , c n is the fuel cost coefficient matrix, X n , Y n , Z n are the multi-energy input correlation matrix, output correlation matrix and energy conversion matrix, V n is the correlation matrix connecting the energy hub port and the multi-energy network branch, R is the total number of regions in the integrated energy system, V in,n and V out,n are the energy hub entrance and exit multi-energy association matrices, U in,m and U out,m are the multi-energy energy flow matrices on the mth branch connected to the entrance and exit of the energy hub, To represent the set of branches connected to the nth energy hub, L n is the multi-energy load matrix of the nth energy hub, and They represent the upper bounds of the inlet and outlet multi-energy flows on the mth branch; M r is the total number of multi-energy network branches in the rth region, indicating the energy exchange relationship between two adjacent regions r and l, where U r→l represents the energy flowing from region r to region l, U l→r represents the energy flowing from region l to region r, is the set of regions adjacent to r; The calculation method of the alternating direction multiplier method is: Converting the distributed optimization scheduling model of the plurality of interconnected energy hubs into a standard form of an alternating direction multiplier method to obtain a converted distributed optimization scheduling model of the plurality of interconnected energy hubs; Solving the distributed optimization scheduling model of the transformed multiple interconnected energy hubs by using an initialized Lagrange multiplier matrix to obtain a first optimal energy flow, and updating coupling terms in the distributed optimization scheduling model of the transformed multiple interconnected energy hubs to obtain a first updated coupling term; The distributed optimization scheduling model of the multiple interconnected energy hubs after the transformation is solved by using the initialized Lagrange multiplier matrix and the first updated coupling term to obtain the second optimal energy flow, and update the coupling term to obtain the second updated coupling term: According to the first updated coupling term and the second updated coupling term, it is determined whether the primal and dual residuals are within a tolerance range; if not, the initialized Lagrangian multiplier matrix is ​​updated, and the distributed optimization scheduling model of the transformed multiple interconnected energy hubs is solved according to the updated initialized Lagrangian multiplier matrix until a preset iteration condition is met; The calculation method of the homomorphic encryption algorithm is: Based on the first key calculation formula, the first prime number and the second prime number of the same bit length are calculated to obtain a public key of the encryption key; Based on the Euler inertia function, calculation is performed according to the first prime number and the second prime number to obtain a private key of the encryption key.

6. The device according to claim 5, characterized in that The multi-energy energy flow matrix on the mth branch connected to the energy hub outlet is: Where u represents the energy flow on the network branch, the superscript i→j represents the energy flow from node i to node j, and represents the adjacency relationship between node i and node j. The subscripts e, h, and g represent the power network, district heating network, and natural gas network, respectively.

7. The device according to claim 6, characterized in that For the power network, the Distflow model is used to represent the transmission relationship of electric energy between interconnected energy hubs, wherein the Distflow model is: in, The active power injected into the node, The reactive power injected into the node, is the adjacency relationship between node i and node j, r i→j is the branch resistance, x i→j is the branch reactance; V i is the voltage at node i, V j is the voltage at node j, l i→j is the branch current; For the district heating network, a quality-quantity joint regulation model is used to represent the heat energy transfer process, wherein the quality-quantity joint regulation model is: in, is the heat supply of the heat source node, κ is the power and energy unit conversion factor, C w is the specific heat capacity of water, is the water supply of the heat source node, is the inlet temperature of the heat source node connected to the thermal pipeline, is the mixed temperature of the return water from the thermal pipeline back to the heat source node, is the amount of water flowing into node i, is the amount of pipe water flowing from node i to node j, is the inlet temperature of node i, is the inlet temperature of node j, is the current ambient temperature, λ is the thermal pipe resistance coefficient, ρ i→j is the pipe length, is the thermal load of node i, is the outlet temperature of node i, is the return water mixing temperature of node i; For the natural gas pipeline network, the Weymouth steady-state gas flow model is used to represent the natural gas transportation process, wherein the Weymouth steady-state gas flow model is: in, is the natural gas energy flow, GHV is the calorific value of natural gas, is the natural gas flow on the pipeline, Consumption of fuel gas compressor, is the gas compressor consumption coefficient, π i is the air pressure at node i, π j is the air pressure at node j, ξ i→j is the pipe resistance coefficient.

8. The device according to claim 5, characterized in that The distributed optimization scheduling model of the plurality of interconnected energy hubs is optimized based on the homomorphic encryption algorithm and the alternating direction multiplier method to obtain the optimization results, including: The first region encrypts the first region coupling item using the public key corresponding to the first region to obtain the first region ciphertext, and sends the first region ciphertext to the second region; The second region encrypts the second region coupling item by using the public key corresponding to the second region to obtain a second region ciphertext; The second region calculates a ciphertext deviation and a ciphertext weighted deviation of a second region weighted value by using the first region ciphertext and the second region ciphertext; The first region decrypts the ciphertext weighted deviation of the weighted value of the second region using the private key corresponding to the first region, and weights the decryption result using the weighted value of the first region to obtain an update variable, so as to update the initialized Lagrange multiplier matrix through the update variable.

9. An electronic device, characterized in that: include: A memory, a processor, and a computer program stored in the memory and executable on the processor, wherein the processor executes the program to implement the distributed optimization method for the integrated energy system as described in any one of claims 1 to 4.

10. A computer-readable storage medium having a computer program stored thereon, characterized in that: The program is executed by a processor to implement the distributed optimization method of the integrated energy system as described in any one of claims 1 to 4.

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