Optimal dispatching method and terminal of urban integrated energy system based on consensus variables

Abstract

The present invention discloses a method and terminal for optimizing and dispatching an urban integrated energy system based on consensus variables, respectively constructs mathematical models of each energy system and a corresponding coupling system, and obtains an optimized dispatching model of the urban integrated energy system; introduces consensus variables to decouple the optimized dispatching model of the urban integrated energy system, respectively constructs a sub-optimal dispatching model of each energy system and a corresponding coupling system, and optimizes each sub-optimal dispatching model in parallel; calculates the original residual and the dual residual after each optimization, and if both the original residual and the dual residual satisfy the convergence error, the current optimization result is used as the optimized dispatching scheme of the urban integrated energy system, otherwise the parameters are updated, and the optimization is continued until both the original residual and the dual residual satisfy the convergence error. The present invention solves the optimized dispatching problem of the urban integrated energy system through a distributed optimized dispatching method, thereby efficiently obtaining the optimized dispatching scheme of the urban integrated energy system.

Description

Technical Field

[0001] The present invention relates to the technical field of energy optimization, and in particular to an urban integrated energy system optimization scheduling method and terminal based on consensus variables. Background Art

[0002] Clean production, safe transmission, optimal distribution and economic use of energy are important ways to achieve the goals of "carbon neutrality and carbon peak". By effectively integrating existing resources and facilities, building an urban integrated energy system that covers multiple energy couplings such as power distribution systems and gas distribution systems has become one of the important means to achieve efficient energy utilization within urban areas.

[0003] The optimization and dispatching of traditional energy systems usually adopts a centralized approach, that is, all kinds of operating parameters involved in the system need to be uploaded to a unified dispatching center, and the operating mode is determined after centralized optimization. Considering that the urban integrated energy system involves multiple energy systems such as the power distribution system and the gas distribution system, the centralized optimization and dispatching method has certain shortcomings: on the one hand, there are difficulties in information exchange between different energy systems. For example, the equipment parameters and topological structure of the power distribution system are core commercial secrets, and information sharing cannot be achieved from a security perspective; on the other hand, with the increase in system scale, the model dimension and computational overhead of centralized optimization and dispatching grow exponentially, and the computational efficiency decreases. Summary of the invention

[0004] The technical problem to be solved by the present invention is to provide a method and terminal for optimizing and dispatching an urban integrated energy system based on consensus variables, so as to efficiently obtain an optimized dispatching plan for the urban integrated energy system.

[0005] In order to solve the above technical problems, the technical solution adopted by the present invention is:

[0006] The urban integrated energy system optimization dispatching method based on consensus variables includes the following steps:

[0007] S1. Build mathematical models of each energy system and the corresponding coupling system respectively to obtain the optimal dispatch model of the urban integrated energy system;

[0008] S2. Introduce consensus variables to decouple the optimization scheduling model of the urban integrated energy system, respectively construct sub-optimization scheduling models for each energy system and the corresponding coupling system, and optimize each of the sub-optimization scheduling models in parallel;

[0009] S3. After each optimization, the primal residual and the dual residual are calculated. If both the primal residual and the dual residual satisfy the convergence error, the current optimization result is used as the optimal scheduling plan for the urban integrated energy system. Otherwise, the iteratively updated consensus variables are substituted as input parameters into the sub-optimal scheduling model of each energy system, and the optimization is continued until both the primal residual and the dual residual satisfy the convergence error.

[0010] In order to solve the above technical problems, another technical solution adopted by the present invention is:

[0011] The urban integrated energy system optimization dispatching terminal based on consensus variables includes a memory, a processor, and a computer program stored in the memory and executable on the processor. When the processor executes the computer program, the following steps are implemented:

[0012] S1. Build mathematical models of each energy system and the corresponding coupling system respectively to obtain the optimal dispatch model of the urban integrated energy system;

[0013] S2. Introduce consensus variables to decouple the optimization scheduling model of the urban integrated energy system, respectively construct sub-optimization scheduling models for each energy system and the corresponding coupling system, and optimize each of the sub-optimization scheduling models in parallel;

[0014] S3. After each optimization, the primal residual and the dual residual are calculated. If both the primal residual and the dual residual satisfy the convergence error, the current optimization result is used as the optimal scheduling plan for the urban integrated energy system. Otherwise, the iteratively updated consensus variables are substituted as input parameters into the sub-optimal scheduling model of each energy system, and the optimization is continued until both the primal residual and the dual residual satisfy the convergence error.

[0015] The beneficial effects of the present invention are: the urban comprehensive energy system optimization scheduling method and terminal based on consensus variables can flexibly split the optimization scheduling problem of the urban comprehensive energy system into multiple sub-optimization problems according to the number of operating entities through a distributed optimization scheduling method, and make full use of the computing power of the existing scheduling platform of each energy subsystem to the maximum extent, avoid the interaction of a large number of operating parameters between different energy systems, ensure the information security of each operating entity, and obtain the optimization scheduling plan of the urban comprehensive energy system, thereby improving the computing efficiency and efficiently obtaining the optimization scheduling plan of the urban comprehensive energy system. BRIEF DESCRIPTION OF THE DRAWINGS

[0016] Figure 1 It is a schematic diagram of the main process of the urban integrated energy system optimization scheduling method based on consensus variables according to an embodiment of the present invention;

[0017] Figure 2 A schematic diagram of the architecture of an urban integrated energy system involved in an embodiment of the present invention;

[0018] Figure 3 It is a schematic diagram of the architecture of a two-stage coupling system involved in an embodiment of the present invention;

[0019] Figure 4 A schematic diagram of a specific process of a method for optimizing and scheduling an urban integrated energy system based on consensus variables according to an embodiment of the present invention;

[0020] Figure 5 This is a diagram of optimization scheduling results of a coupling system involved in an embodiment of the present invention;

[0021] Figure 6 A schematic diagram of changes in the original residual and the dual residual involved in an embodiment of the present invention;

[0022] Figure 7 It is a structural schematic diagram of a city integrated energy system optimization scheduling terminal based on consensus variables according to an embodiment of the present invention.

[0023] Description of labels:

[0024] 1. Urban integrated energy system optimization dispatching terminal based on consensus variables; 2. Processor; 3. Memory. DETAILED DESCRIPTION

[0025] In order to explain the technical content, achieved objectives and effects of the present invention in detail, the following is an explanation in combination with the implementation modes and the accompanying drawings.

[0026] Please refer to Figures 1 to 6 , the urban integrated energy system optimization dispatching method based on consensus variables includes the following steps:

[0027] S1. Build mathematical models of each energy system and the corresponding coupling system respectively to obtain the optimal dispatch model of the urban integrated energy system;

[0028] S2. Introduce consensus variables to decouple the optimization scheduling model of the urban integrated energy system, respectively construct sub-optimization scheduling models for each energy system and the corresponding coupling system, and optimize each of the sub-optimization scheduling models in parallel;

[0029] S3. After each optimization, the primal residual and the dual residual are calculated. If both the primal residual and the dual residual satisfy the convergence error, the current optimization result is used as the optimal scheduling plan for the urban integrated energy system. Otherwise, the iteratively updated consensus variables are substituted as input parameters into the sub-optimal scheduling model of each energy system, and the optimization is continued until both the primal residual and the dual residual satisfy the convergence error.

[0030] From the above description, it can be seen that the beneficial effects of the present invention are: through the distributed optimization scheduling method, the optimization scheduling problem of the urban comprehensive energy system can be flexibly divided into multiple sub-optimization problems according to the number of operating entities, and the computing power of the existing scheduling platform of each energy subsystem can be fully utilized to the maximum extent, avoiding the interaction of a large number of operating parameters between different energy systems, ensuring the information security of each operating entity, and obtaining the optimization scheduling plan of the urban comprehensive energy system, thereby improving the computing efficiency and efficiently obtaining the optimization scheduling plan of the urban comprehensive energy system.

[0031] Furthermore, the energy system includes a power distribution system and a gas distribution system.

[0032] Furthermore, the step S1 specifically includes the following steps:

[0033] S11, constructing the active power equation, reactive power equation and voltage equation of the power distribution system as shown in the following equations:

[0034]

[0035] Where l is the branch number of the distribution system, n is the node number of the distribution system, P is the active power, I is the line current, R is the line resistance, Q is the reactive power, X is the line reactance, and U is the node voltage;

[0036] The node voltage constraint, branch current constraint and system power purchase constraint of the power distribution system are obtained as follows:

[0037]

[0038] Where U min is the lower limit of node voltage, U max is the upper limit of node voltage, I max is the branch current limit, P elec is the power purchased by the distribution system, P min is the lower limit of the power purchased by the distribution system, P max The upper limit of the power purchased by the power distribution system;

[0039] S12. Constructing the pipeline flow equation of the gas distribution system is shown as follows:

[0040]

[0041] Where pl is the pipeline number, m is the node number of the gas distribution system, F is the pipeline gas flow rate, s(·) is the sign function, k pl is the pipeline fluid parameter, p is the air pressure of the gas distribution system;

[0042] The node pressure constraint, system gas purchase constraint and piecewise linearization constraint of the gas distribution system are obtained as shown in the following formula:

[0043]

[0044] In the formula, p min is the lower limit of node pressure, p max is the upper limit of node pressure, F gas F is the gas purchase volume of the gas distribution system, min F is the lower limit of gas purchase for the gas distribution system, max is the upper limit of gas purchase for the gas distribution system, δ pl,h is the piecewise linearization state variable, h is the linear segment number, f pl,h is the value of the variable within the segment, f min is the lower limit of the segment, f max is the upper limit of the segment, and H is the total number of segments;

[0045] S13, constructing the energy coupling relationship of the two types of coupling systems corresponding to the power distribution system and the gas distribution system as shown in the following formula:

[0046]

[0047] Where I is the number of the first type of coupling system, II is the number of the second type of coupling system, and L e is the output side electrical load of the coupling system, L h is the heat load on the output side, P e is the input side electric power of the coupling system, P g is the gas load on the input side, v is the distribution coefficient, η T is the power transformer efficiency, η CAC is the central air conditioning efficiency, η ge is the power generation efficiency of the cogeneration unit, η gh is the heat generation efficiency of the cogeneration unit, η GB The heat generation efficiency of the gas boiler;

[0048] The input side power constraints of the two types of coupling systems are shown as follows:

[0049]

[0050] Where P e,min is the lower limit of the input power, P e,max is the upper limit of the input power, P g,min is the lower limit of gas power on the input side, P g,max is the upper limit of gas power on the input side;

[0051] S14. The optimization scheduling target of the urban integrated energy system is to optimize the operating cost. The optimization scheduling model of the urban integrated energy system is as follows:

[0052]

[0053] In the formula, OC is the operating cost, t is the number of the optimized scheduling period, T is the total number of optimized scheduling periods, C elec,t is the electricity price in the corresponding period, C gas,t is the gas price in the corresponding period.

[0054] From the above description, it can be seen that the distribution system model is composed of active power equations, reactive power equations and voltage equations, and the constraints include node voltage constraints, branch current constraints and system power purchase constraints; the gas distribution system is composed of pipeline flow equations, and the constraints include node gas pressure constraints, system gas purchase constraints and linear simulation constraints; the coupling system is composed of energy coupling matrices, and the constraints include input side electric power constraints and input side gas power constraints, so as to quickly and accurately construct mathematical models of the distribution system, gas distribution system and corresponding coupling system, so as to obtain the optimization scheduling model of urban integrated energy system.

[0055] Furthermore, the step S2 specifically includes the following steps:

[0056] S21. Construct the consensus variable matrix as shown below:

[0057]

[0058] Where P e,grid is the active load matrix of nodes in which the power distribution system and the coupling system are interconnected, P e,con is the electric power consensus variable matrix, P e,hub is the matrix of the electric power on the input side of the coupling system, P g,grid is the node gas load matrix of the gas distribution system and the coupling system that are interconnected, P g,con is the gas power consensus variable matrix, P g,hub A matrix formed by the gas power at the input side of the coupling system;

[0059] S22, respectively constructing the augmented Lagrangian objective functions of the power distribution system, the gas distribution system and the coupling system as shown in the following formula:

[0060]

[0061] Where, L e is the Lagrangian objective function of the power distribution system, λ e is the Lagrange multiplier matrix of the power distribution system, ρ is the iteration step size, L g is the Lagrangian objective function of the valve train, λ g is the Lagrange multiplier matrix of the gas distribution system, L hub is the Lagrangian objective function of the coupled system, λhub is the Lagrangian multiplier matrix of the coupled system, that is, the optimization scheduling problem of the urban integrated energy system is decoupled into sub-optimization problems of each energy system, the objective function is the augmented Lagrangian function of each energy system, and the constraints are the equality and inequality equations corresponding to the mathematical models of each energy system.

[0062] From the above description, it can be seen that by introducing consensus variables, the optimization scheduling problem of the urban integrated energy system is split, and sub-optimization scheduling models of the power distribution system, gas distribution system and coupling system are constructed respectively. Each sub-optimization problem is solved independently to provide a distributed optimization scheduling method to improve computational efficiency.

[0063] Furthermore, the step S3 specifically includes the following steps:

[0064] S31. Update the consensus variable according to the following formula:

[0065]

[0066] In the formula, k is the iteration round, N e is the number of distribution system nodes connected to the electric power consensus variable, N g is the number of gas distribution system nodes associated with the gas power consensus variable, N hub is the number of coupled system nodes associated with the electric power and gas power consensus variables;

[0067] S32. Calculate the original residual according to the following formula:

[0068]

[0069] In the formula, r is the original residual;

[0070] S33. Calculate the dual residual according to the following formula:

[0071]

[0072] Where s is the dual residual;

[0073] S34. Determine whether both the original residual and the dual residual satisfy the convergence error. If so, use the current optimization result as the optimization scheduling plan for the urban integrated energy system. Otherwise, substitute the iteratively updated consensus variables as input parameters into the sub-optimization scheduling model of each energy system, and continue to optimize until both the original residual and the dual residual satisfy the convergence error.

[0074] From the above description, it can be seen that the consensus variables, Lagrange multipliers, iteration step size and other algorithm parameters are updated according to the calculation results of each sub-optimization problem, and on this basis, the iterative primal residual and dual residual are calculated as the basis for judging whether the final result is obtained, so as to obtain the optimal optimization scheduling plan for the urban integrated energy system.

[0075] Please refer to Figure 7 , an urban integrated energy system optimization dispatching terminal based on consensus variables, comprising a memory, a processor, and a computer program stored in the memory and executable on the processor, wherein the processor implements the following steps when executing the computer program:

[0076] S1. Build mathematical models of each energy system and the corresponding coupling system respectively to obtain the optimal dispatch model of the urban integrated energy system;

[0077] S2. Introduce consensus variables to decouple the optimization scheduling model of the urban integrated energy system, respectively construct sub-optimization scheduling models for each energy system and the corresponding coupling system, and optimize each of the sub-optimization scheduling models in parallel;

[0078] S3. After each optimization, the primal residual and the dual residual are calculated. If both the primal residual and the dual residual satisfy the convergence error, the current optimization result is used as the optimal scheduling plan for the urban integrated energy system. Otherwise, the iteratively updated consensus variables are substituted as input parameters into the sub-optimal scheduling model of each energy system, and the optimization is continued until both the primal residual and the dual residual satisfy the convergence error.

[0079] From the above description, it can be seen that the beneficial effects of the present invention are: through the distributed optimization scheduling method, the optimization scheduling problem of the urban comprehensive energy system can be flexibly divided into multiple sub-optimization problems according to the number of operating entities, and the computing power of the existing scheduling platform of each energy subsystem can be fully utilized to the maximum extent, avoiding the interaction of a large number of operating parameters between different energy systems, ensuring the information security of each operating entity, and obtaining the optimization scheduling plan of the urban comprehensive energy system, thereby improving the computing efficiency and efficiently obtaining the optimization scheduling plan of the urban comprehensive energy system.

[0080] Furthermore, the energy system includes a power distribution system and a gas distribution system.

[0081] Furthermore, the step S1 specifically includes the following steps:

[0082] S11, constructing the active power equation, reactive power equation and voltage equation of the power distribution system as shown in the following equations:

[0083]

[0084] Where l is the branch number of the distribution system, n is the node number of the distribution system, P is the active power, I is the line current, R is the line resistance, Q is the reactive power, X is the line reactance, and U is the node voltage;

[0085] The node voltage constraint, branch current constraint and system power purchase constraint of the power distribution system are obtained as follows:

[0086]

[0087] Where U min is the lower limit of node voltage, U max is the upper limit of node voltage, I max is the branch current limit, P elec is the power purchased by the distribution system, P min is the lower limit of the power purchased by the distribution system, P max The upper limit of the power purchased by the power distribution system;

[0088] S12. Constructing the pipeline flow equation of the gas distribution system is shown as follows:

[0089]

[0090] Where pl is the pipeline number, m is the node number of the gas distribution system, F is the pipeline gas flow rate, s(·) is the sign function, k pl is the pipeline fluid parameter, p is the air pressure of the gas distribution system;

[0091] The node pressure constraint, system gas purchase constraint and piecewise linearization constraint of the gas distribution system are obtained as shown in the following formula:

[0092]

[0093] In the formula, p min is the lower limit of node pressure, p max is the upper limit of node pressure, F gas F is the gas purchase volume of the gas distribution system, min F is the lower limit of gas purchase for the gas distribution system, max is the upper limit of gas purchase for the gas distribution system, δ pl,h is the piecewise linearization state variable, h is the linear segment number, f pl,h is the value of the variable within the segment, f min is the lower limit of the segment, f max is the upper limit of the segment, and H is the total number of segments;

[0094] S13, constructing the energy coupling relationship of the two types of coupling systems corresponding to the power distribution system and the gas distribution system as shown in the following formula:

[0095]

[0096] Where I is the number of the first type of coupling system, II is the number of the second type of coupling system, and L e is the output side electrical load of the coupling system, L h is the heat load on the output side, P e is the input side electric power of the coupling system, P g is the gas load on the input side, v is the distribution coefficient, η T is the power transformer efficiency, η CAC is the central air conditioning efficiency, η ge is the power generation efficiency of the cogeneration unit, η gh is the heat generation efficiency of the cogeneration unit, η GB The heat generation efficiency of the gas boiler;

[0097] The input side power constraints of the two types of coupling systems are shown as follows:

[0098]

[0099] Where P e,min is the lower limit of the input power, P e,max is the upper limit of the input power, P g,min is the lower limit of gas power on the input side, P g,max is the upper limit of gas power on the input side;

[0100] S14. The optimization scheduling target of the urban integrated energy system is to optimize the operating cost. The optimization scheduling model of the urban integrated energy system is as follows:

[0101]

[0102] In the formula, OC is the operating cost, t is the number of the optimized scheduling period, T is the total number of optimized scheduling periods, C elec,t is the electricity price in the corresponding period, C gas,t is the gas price in the corresponding period.

[0103] From the above description, it can be seen that the distribution system model is composed of active power equations, reactive power equations and voltage equations, and the constraints include node voltage constraints, branch current constraints and system power purchase constraints; the gas distribution system is composed of pipeline flow equations, and the constraints include node gas pressure constraints, system gas purchase constraints and linear simulation constraints; the coupling system is composed of energy coupling matrices, and the constraints include input side electric power constraints and input side gas power constraints, so as to quickly and accurately construct mathematical models of the distribution system, gas distribution system and corresponding coupling system, so as to obtain the optimization scheduling model of urban integrated energy system.

[0104] Furthermore, the step S2 specifically includes the following steps: The step S2 specifically includes the following steps:

[0105] S21. Construct the consensus variable matrix as shown below:

[0106]

[0107] Where P e,grid is the active load matrix of nodes in which the power distribution system and the coupling system are interconnected, P e,con is the electric power consensus variable matrix, P e,hub is the matrix of the electric power on the input side of the coupling system, P g,grid is the node gas load matrix of the gas distribution system and the coupling system that are interconnected, P g,con is the gas power consensus variable matrix, P g,hub A matrix formed by the gas power at the input side of the coupling system;

[0108] S22, respectively constructing the augmented Lagrangian objective functions of the power distribution system, the gas distribution system and the coupling system as shown in the following formula:

[0109]

[0110] Where, L e is the Lagrangian objective function of the power distribution system, λ e is the Lagrange multiplier matrix of the distribution system, ρ is the iteration step size, L g is the Lagrangian objective function of the valve train, λ g is the Lagrange multiplier matrix of the gas distribution system, L hub is the Lagrangian objective function of the coupled system, λ hub is the Lagrangian multiplier matrix of the coupled system, that is, the optimization scheduling problem of the urban integrated energy system is decoupled into sub-optimization problems of each energy system, the objective function is the augmented Lagrangian function of each energy system, and the constraints are the equality and inequality equations corresponding to the mathematical models of each energy system.

[0111] From the above description, it can be seen that by introducing consensus variables, the optimization scheduling problem of the urban integrated energy system is split, and sub-optimization scheduling models of the power distribution system, gas distribution system and coupling system are constructed respectively. Each sub-optimization problem is solved independently to provide a distributed optimization scheduling method to improve computational efficiency.

[0112] Furthermore, the step S3 specifically includes the following steps:

[0113] S31. Update the consensus variable according to the following formula:

[0114]

[0115] In the formula, k is the iteration round, N e is the number of distribution system nodes connected to the electric power consensus variable, N g is the number of gas distribution system nodes associated with the gas power consensus variable, N hub is the number of coupled system nodes associated with the electric power and gas power consensus variables;

[0116] S32. Calculate the original residual according to the following formula:

[0117]

[0118] In the formula, r is the original residual;

[0119] S33. Calculate the dual residual according to the following formula:

[0120]

[0121] Where s is the dual residual;

[0122] S34. Determine whether both the original residual and the dual residual satisfy the convergence error. If so, use the current optimization result as the optimization scheduling plan for the urban integrated energy system. Otherwise, substitute the iteratively updated consensus variables as input parameters into the sub-optimization scheduling model of each energy system, and continue to optimize until both the original residual and the dual residual satisfy the convergence error.

[0123] From the above description, it can be seen that the consensus variables, Lagrange multipliers, iteration step size and other algorithm parameters are updated according to the calculation results of each sub-optimization problem, and on this basis, the iterative primal residual and dual residual are calculated as the basis for judging whether the final result is obtained, so as to obtain the optimal optimization scheduling plan for the urban integrated energy system.

[0124] Please refer to Figures 1 to 6 , Embodiment 1 of the present invention is:

[0125] The urban integrated energy system optimization dispatching method based on consensus variables includes the following steps:

[0126] S1. Build mathematical models of each energy system and the corresponding coupling system respectively to obtain the optimal dispatch model of the urban integrated energy system;

[0127] like Figure 2 As shown, in this embodiment, the energy system includes a power distribution system and a gas distribution system, combined with Figure 3 It can be seen that the power distribution system and the gas distribution system have two types of coupling systems, each of which has two coupling systems. Figure 4 It can be seen that step S1 specifically includes the following steps:

[0128] S11. The active power equation, reactive power equation and voltage equation of the power distribution system are constructed as follows:

[0129]

[0130] Where l is the branch number of the distribution system, n is the node number of the distribution system, P is the active power, I is the line current, R is the line resistance, Q is the reactive power, X is the line reactance, and U is the node voltage;

[0131] The node voltage constraints, branch current constraints and system power purchase constraints of the distribution system are shown as follows:

[0132]

[0133] Where U min is the lower limit of node voltage, U max is the upper limit of node voltage, I max is the branch current limit, P elec is the power purchased by the distribution system, P min is the lower limit of the power purchased by the distribution system, P max The upper limit of the power purchased by the power distribution system;

[0134] S12. Construct the pipeline flow equation of the gas distribution system as shown below:

[0135]

[0136] Where pl is the pipeline number, m is the node number of the gas distribution system, F is the pipeline gas flow rate, s(·) is the sign function, k pl is the pipeline fluid parameter, p is the air pressure of the gas distribution system;

[0137] The node pressure constraints, system gas purchase constraints and piecewise linearization constraints of the gas distribution system are shown as follows:

[0138]

[0139] In the formula, p min is the lower limit of node pressure, p max is the upper limit of node pressure, F gas F is the gas purchase volume of the gas distribution system, min F is the lower limit of gas purchase for the gas distribution system, max is the upper limit of gas purchase for the gas distribution system, δ pl,h is the piecewise linearization state variable, h is the linear segment number, f pl,h is the value of the variable within the segment, f min is the lower limit of the segment, f max is the upper limit of the segment, and H is the total number of segments;

[0140] S13. The energy coupling relationship between the two types of coupling systems corresponding to the power distribution system and the gas distribution system is shown in the following formula:

[0141]

[0142] Where I is the number of the first type of coupling system, II is the number of the second type of coupling system, and L e is the output side electrical load of the coupling system, L h is the heat load on the output side, P e is the input side electric power of the coupling system, P g is the gas load on the input side, v is the distribution coefficient, η T is the power transformer efficiency, η CAC is the central air conditioning efficiency, η ge is the power generation efficiency of the cogeneration unit, η gh is the heat generation efficiency of the cogeneration unit, η GB The heat generation efficiency of the gas boiler;

[0143] The input side power constraints of the two types of coupled systems are shown as follows:

[0144]

[0145] Where P e,min is the lower limit of the input power, P e,max is the upper limit of the input power, P g,min is the lower limit of gas power on the input side, P g,max is the upper limit of gas power on the input side;

[0146] S14. The optimization dispatching target of the urban integrated energy system is to optimize the operating cost. The optimization dispatching model of the urban integrated energy system is as follows:

[0147]

[0148] In the formula, OC is the operating cost, t is the number of the optimized scheduling period, T is the total number of optimized scheduling periods, C elec,t is the electricity price in the corresponding period, C gas,t is the gas price in the corresponding period.

[0149] S2. Introduce consensus variables to decouple the optimal dispatch model of the urban integrated energy system, build sub-optimal dispatch models for each energy system and the corresponding coupled system, and optimize each sub-optimal dispatch model in parallel;

[0150] In this embodiment, step S2 specifically includes the following steps:

[0151] S21. Construct the consensus variable matrix as shown below:

[0152]

[0153] Where P e,grid is the active load matrix of nodes in which the distribution system and the coupling system are interconnected, P e,con is the electric power consensus variable matrix, P e,hub is the matrix of the electric power on the input side of the coupling system, P g,grid is the node gas load matrix of the gas distribution system and the coupling system, P g,con is the gas power consensus variable matrix, P g,hub is the matrix formed by the gas power on the input side of the coupling system;

[0154] Among them, the node active load matrix is ​​composed of all interconnected node active loads of the distribution system, the matrix composed of the input side electric power of the coupling system is composed of all input side electric powers of the coupling system, the node gas load matrix is ​​composed of all interconnected node gas loads of the gas distribution system, the matrix composed of the input side electric power of the coupling system is composed of all input side electric powers of the coupling system, and the matrix composed of the input side gas power of the coupling system is composed of all input side gas powers of the coupling system.

[0155] S22. The augmented Lagrangian objective functions of the power distribution system, gas distribution system and coupling system are constructed as follows:

[0156]

[0157] Where, L e Augmented Lagrangian objective function for distribution system, λ e is the Lagrange multiplier matrix of the distribution system, ρ is the iteration step size, L g is the Lagrangian objective function of the valve train, λ g is the Lagrange multiplier matrix of the valve train, L hub is the Lagrangian objective function for the coupled system, λ hub It is the Lagrangian multiplier matrix of the coupled system, that is, the optimization scheduling problem of the urban integrated energy system is decoupled into sub-optimization problems of each energy system. The objective function is the augmented Lagrangian function of each energy system, and the constraints are the equality and inequality equations corresponding to the mathematical model of each energy system.

[0158] The optimized scheduling results can be referred to Figure 5 .

[0159] S3. After each optimization, the primal residual and the dual residual are calculated. If both the primal residual and the dual residual satisfy the convergence error, the current optimization result is used as the optimal scheduling plan for the urban integrated energy system. Otherwise, the iteratively updated consensus variables are substituted as input parameters into the sub-optimal scheduling model of each energy system, and the optimization is continued until both the primal residual and the dual residual satisfy the convergence error.

[0160] In this embodiment, step S3 specifically includes the following steps:

[0161] S31. Update the consensus variable according to the following formula:

[0162]

[0163] In the formula, k is the iteration round, N e is the number of distribution system nodes connected to the electric power consensus variable, N g is the number of gas distribution system nodes associated with the gas power consensus variable, N hub is the number of coupled system nodes associated with the electric power and gas power consensus variables;

[0164] S32. Calculate the original residual according to the following formula:

[0165]

[0166] In the formula, r is the original residual;

[0167] S33. Calculate the dual residual according to the following formula:

[0168]

[0169] Where s is the dual residual;

[0170] S34. Determine whether both the original residual and the dual residual satisfy the convergence error. If so, use the current optimization result as the optimal scheduling plan for the urban integrated energy system. Otherwise, substitute the iteratively updated consensus variables as input parameters into the sub-optimal scheduling model of each energy system, and continue to optimize until both the original residual and the dual residual satisfy the convergence error.

[0171] That is Figure 6 As shown in the figure, when the number of iterations is 141, both the original residual and the dual residual are less than the convergence error. The optimization result at this time is the optimal scheduling plan for the urban integrated energy system.

[0172] Please refer to Figure 7 , Embodiment 2 of the present invention is:

[0173] The urban integrated energy system optimization and dispatching terminal 1 based on consensus variables includes a memory 3, a processor 2, and a computer program stored in the memory 3 and executable on the processor 2. When the processor 2 executes the computer program, the steps of the above-mentioned embodiment 1 are implemented.

[0174] To sum up, the urban integrated energy system optimization and scheduling method and terminal based on consensus variables provided by the present invention can flexibly split the optimization and scheduling problem of the urban integrated energy system into multiple sub-optimization problems according to the number of operating entities through a distributed optimization and scheduling method, and make full use of the computing power of the existing scheduling platform of each energy subsystem to the maximum extent, avoid the interaction of a large number of operating parameters between different energy systems, ensure the information security of each operating entity, and obtain the optimization scheduling plan of the urban integrated energy system, thereby improving the computing efficiency and efficiently obtaining the optimization scheduling plan of the urban integrated energy system.

[0175] The above descriptions are merely embodiments of the present invention and are not intended to limit the patent scope of the present invention. Any equivalent transformations made using the contents of the present invention's specification and drawings, or directly or indirectly applied in related technical fields, are also included in the patent protection scope of the present invention.

Claims

1. An optimal dispatching method for urban integrated energy system based on consensus variables, characterized in that: Includes steps: S1. Construct mathematical models of each energy system and the corresponding coupling system respectively to obtain an optimal dispatching model of the urban comprehensive energy system, wherein the energy system includes a power distribution system and a gas distribution system, and the power distribution system and the gas distribution system have two types of coupling systems, wherein each type of coupling system has two; S2. Introduce consensus variables to decouple the optimization scheduling model of the urban integrated energy system, respectively construct sub-optimization scheduling models for each energy system and the corresponding coupling system, and optimize each of the sub-optimization scheduling models in parallel: S21. Construct the consensus variable matrix as shown below: Where P e,grid is the active load matrix of nodes in which the power distribution system and the coupling system are interconnected, P e,con is the electric power consensus variable matrix, P e,hub is the matrix of the electric power on the input side of the coupling system, P g,grid is the node gas load matrix of the gas distribution system and the coupling system that are interconnected, P g,con is the gas power consensus variable matrix, P g,hub A matrix formed by the gas power at the input side of the coupling system; S22, respectively constructing the augmented Lagrangian objective functions of the power distribution system, the gas distribution system and the coupling system as shown in the following formula: Where, L e is the Lagrangian objective function of the power distribution system, λ e is the Lagrange multiplier matrix of the power distribution system, ρ is the iteration step size, L g is the Lagrangian objective function of the valve train, λ g is the Lagrange multiplier matrix of the gas distribution system, L hub is the Lagrangian objective function of the coupled system, λ hub is the Lagrangian multiplier matrix of the coupled system, that is, the optimization scheduling problem of the urban integrated energy system is decoupled into sub-optimization problems of each energy system, the objective function is the augmented Lagrangian function of each energy system, and the constraint conditions are the equality and inequality equations corresponding to the mathematical model of each energy system; S3. After each optimization, the primal residual and the dual residual are calculated. If both the primal residual and the dual residual satisfy the convergence error, the current optimization result is used as the optimal dispatching scheme for the urban integrated energy system. Otherwise, the iteratively updated consensus variables are substituted as input parameters into the sub-optimal dispatching model of each energy system, and the optimization is continued until both the primal residual and the dual residual satisfy the convergence error: S31. Update the consensus variable according to the following formula: In the formula, k is the iteration round, N e is the number of distribution system nodes connected to the electric power consensus variable, N g is the number of nodes in the gas distribution system associated with the gas power consensus variable, N hub is the number of coupled system nodes associated with the electric power and gas power consensus variables; S32. Calculate the original residual according to the following formula: In the formula, r is the original residual; S33. Calculate the dual residual according to the following formula: Where s is the dual residual; S34, determine whether the original residual and the dual residual both meet the convergence error. If so, use the current optimization result as the optimal scheduling plan for the urban integrated energy system. Otherwise, substitute the iteratively updated consensus variables as input parameters into the sub-optimal scheduling models of each energy system, and continue to optimize until the original residual and the dual residual both meet the convergence error. When the number of iterations is ≥141, the original residual and the dual residual are both less than the convergence error. The optimization result at this time is the optimal scheduling plan for the urban integrated energy system. The step S1 specifically includes the following steps: S11, constructing the active power equation, reactive power equation and voltage equation of the power distribution system as shown in the following equations: Where l is the branch number of the distribution system, n is the node number of the distribution system, P is the active power, I is the line current, R is the line resistance, Q is the reactive power, X is the line reactance, and U is the node voltage; The node voltage constraint, branch current constraint and system power purchase constraint of the power distribution system are obtained as follows: Where U min is the lower limit of node voltage, U max is the upper limit of node voltage, I max is the branch current limit, P elec is the power purchased by the distribution system, P min is the lower limit of the power purchased by the distribution system, P max The upper limit of the power purchased by the power distribution system; S12. Constructing the pipeline flow equation of the gas distribution system is shown as follows: Where pl is the pipeline number, m is the node number of the gas distribution system, F is the pipeline gas flow rate, s(·) is the sign function, k pl is the pipeline fluid parameter, p is the air pressure of the gas distribution system; The node pressure constraint, system gas purchase constraint and piecewise linearization constraint of the gas distribution system are obtained as shown in the following formula: In the formula, p min is the lower limit of node pressure, p max is the upper limit of node pressure, F gas F is the gas purchase volume of the gas distribution system, min F is the lower limit of gas purchase for the gas distribution system, max is the upper limit of gas purchase for the gas distribution system, δ pl,h is the piecewise linearization state variable, h is the linear segment number, f pl,h is the value of the variable within the segment, f min is the lower limit of the segment, f max is the upper limit of the segment, and H is the total number of segments; S13, constructing the energy coupling relationship of the two types of coupling systems corresponding to the power distribution system and the gas distribution system as shown in the following formula: Where I is the number of the first type of coupling system, II is the number of the second type of coupling system, and L e is the output side electrical load of the coupling system, L h is the heat load on the output side, P e is the input side electric power of the coupling system, P g is the gas load on the input side, v is the distribution coefficient, η T is the power transformer efficiency, η CAC is the central air conditioning efficiency, η ge is the power generation efficiency of the cogeneration unit, η gh is the heat generation efficiency of the cogeneration unit, η GB The heat generation efficiency of the gas boiler; The input side power constraints of the two types of coupling systems are shown as follows: Where P e,min is the lower limit of the input power, P e,max is the upper limit of the input power, P g,min is the lower limit of gas power on the input side, P g,max is the upper limit of gas power on the input side; S14. The optimization scheduling target of the urban integrated energy system is to optimize the operating cost. The optimization scheduling model of the urban integrated energy system is as follows: In the formula, OC is the operating cost, t is the number of the optimized scheduling period, T is the total number of optimized scheduling periods, C elec,t is the electricity price in the corresponding period, C gas,t is the gas price in the corresponding period; Through the distributed optimization scheduling method, the optimization scheduling problem of the urban integrated energy system is flexibly divided into multiple sub-optimization problems according to the number of operating entities, so as to make the best use of the computing power of the existing scheduling platform of each energy subsystem and avoid the interaction of a large number of operating parameters between different energy systems.

2. An urban integrated energy system optimization dispatching terminal based on consensus variables, comprising a memory, a processor, and a computer program stored in the memory and executable on the processor, characterized in that: When the processor executes the computer program, the following steps are implemented: S1. Construct mathematical models of each energy system and the corresponding coupling system respectively to obtain an optimal dispatching model of the urban comprehensive energy system, wherein the energy system includes a power distribution system and a gas distribution system, and the power distribution system and the gas distribution system have two types of coupling systems, wherein each type of coupling system has two; S2. Introduce consensus variables to decouple the optimization scheduling model of the urban integrated energy system, respectively construct sub-optimization scheduling models for each energy system and the corresponding coupling system, and optimize each of the sub-optimization scheduling models in parallel: S21. Construct the consensus variable matrix as shown below: Where P e,grid is the active load matrix of nodes in which the power distribution system and the coupling system are interconnected, P e,con is the electric power consensus variable matrix, P e,hub is the matrix of the electric power on the input side of the coupling system, P g,grid is the node gas load matrix of the gas distribution system and the coupling system that are interconnected, P g,con is the gas power consensus variable matrix, P g,hub A matrix formed by the gas power at the input side of the coupling system; S22, respectively constructing the augmented Lagrangian objective functions of the power distribution system, the gas distribution system and the coupling system as shown in the following formula: Where, L e is the Lagrangian objective function of the power distribution system, λ e is the Lagrange multiplier matrix of the power distribution system, ρ is the iteration step size, L g is the Lagrangian objective function of the valve train, λ g is the Lagrange multiplier matrix of the gas distribution system, L hub is the Lagrangian objective function of the coupled system, λ hub is the Lagrangian multiplier matrix of the coupled system, that is, the optimization scheduling problem of the urban integrated energy system is decoupled into sub-optimization problems of each energy system, the objective function is the augmented Lagrangian function of each energy system, and the constraint conditions are the equality and inequality equations corresponding to the mathematical model of each energy system; S3. After each optimization, the primal residual and the dual residual are calculated. If both the primal residual and the dual residual satisfy the convergence error, the current optimization result is used as the optimal dispatching scheme for the urban integrated energy system. Otherwise, the iteratively updated consensus variables are substituted as input parameters into the sub-optimal dispatching model of each energy system, and the optimization is continued until both the primal residual and the dual residual satisfy the convergence error: S31. Update the consensus variable according to the following formula: In the formula, k is the iteration round, N e is the number of distribution system nodes connected to the electric power consensus variable, N g is the number of gas distribution system nodes associated with the gas power consensus variable, N hub is the number of coupled system nodes associated with the electric power and gas power consensus variables; S32. Calculate the original residual according to the following formula: In the formula, r is the original residual; S33. Calculate the dual residual according to the following formula: Where s is the dual residual; S34, determine whether the original residual and the dual residual both meet the convergence error. If so, use the current optimization result as the optimal scheduling plan for the urban integrated energy system. Otherwise, substitute the iteratively updated consensus variables as input parameters into the sub-optimal scheduling models of each energy system, and continue to optimize until the original residual and the dual residual both meet the convergence error. When the number of iterations is ≥141, the original residual and the dual residual are both less than the convergence error. The optimization result at this time is the optimal scheduling plan for the urban integrated energy system. The step S1 specifically includes the following steps: S11, constructing the active power equation, reactive power equation and voltage equation of the power distribution system as shown in the following equations: Where l is the branch number of the distribution system, n is the node number of the distribution system, P is the active power, I is the line current, R is the line resistance, Q is the reactive power, X is the line reactance, and U is the node voltage; The node voltage constraint, branch current constraint and system power purchase constraint of the power distribution system are obtained as follows: Where U min is the lower limit of node voltage, U max is the upper limit of node voltage, I max is the branch current limit, P elec is the power purchased by the distribution system, P min is the lower limit of the power purchased by the distribution system, P max The upper limit of the power purchased by the power distribution system; S12. Constructing the pipeline flow equation of the gas distribution system is shown as follows: Where pl is the pipeline number, m is the node number of the gas distribution system, F is the pipeline gas flow rate, s(·) is the sign function, k pl is the pipeline fluid parameter, p is the air pressure of the gas distribution system; The node pressure constraint, system gas purchase constraint and piecewise linearization constraint of the gas distribution system are obtained as shown in the following formula: In the formula, p min is the lower limit of node pressure, p max is the upper limit of node pressure, F gas F is the gas purchase volume of the gas distribution system, min F is the lower limit of gas purchase for the gas distribution system, max is the upper limit of gas purchase for the gas distribution system, δ pl,h is the piecewise linearization state variable, h is the linear segment number, f pl,h is the value of the variable within the segment, f min is the lower limit of the segment, f max is the upper limit of the segment, and H is the total number of segments; S13, constructing the energy coupling relationship of the two types of coupling systems corresponding to the power distribution system and the gas distribution system as shown in the following formula: Where I is the number of the first type of coupling system, II is the number of the second type of coupling system, and L e is the output side electrical load of the coupling system, L h is the heat load on the output side, P e is the input side electric power of the coupling system, P g is the gas load on the input side, v is the distribution coefficient, η T is the power transformer efficiency, η CAC is the central air conditioning efficiency, η ge is the power generation efficiency of the cogeneration unit, η gh is the heat generation efficiency of the cogeneration unit, η GB The heat generation efficiency of the gas boiler; The input side power constraints of the two types of coupling systems are shown as follows: Where P e,min is the lower limit of the input power, P e,max is the upper limit of the input power, P g,min is the lower limit of gas power on the input side, P g,max is the upper limit of gas power on the input side; S14. The optimization scheduling target of the urban integrated energy system is to optimize the operating cost. The optimization scheduling model of the urban integrated energy system is as follows: In the formula, OC is the operating cost, t is the number of the optimized scheduling period, T is the total number of optimized scheduling periods, C elec,t is the electricity price in the corresponding period, C gas,t is the gas price in the corresponding period; Through the distributed optimization scheduling method, the optimization scheduling problem of the urban integrated energy system is flexibly divided into multiple sub-optimization problems according to the number of operating entities, so as to make the best use of the computing power of the existing scheduling platform of each energy subsystem and avoid the interaction of a large number of operating parameters between different energy systems.

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