Fully distributed optimization operation method and system for multiple producers and consumers without coordination subject

Abstract

The present invention discloses a method and system for fully distributed optimization operation of multiple producers and consumers without a coordinating subject, wherein the method includes: constructing a mathematical model of the internal resources of the producers and consumers; constructing a centralized optimization model for the end-to-end interactive operation of multiple producers and consumers; decoupling the interactive power of each producer and consumer according to the energy interactive flow direction of each region, setting virtual nodes and global variables; constructing a distributed optimization framework for the end-to-end interactive operation of multiple producers and consumers, and performing interactive iterative solution. The present invention takes into account the physical network constraints within multiple producers and consumers, decouples the power of each producer and consumer according to the energy interactive flow direction of each region, sets virtual nodes and global variables, establishes a distributed optimization framework based on the dual decomposition principle and the alternating direction multiplier method, and can ensure the feasibility of the final transaction result while achieving efficient energy utilization of multiple producers and consumers. The present invention can be widely used in the field of power markets.

Description

Technical Field

[0001] The present invention relates to the field of power market, and in particular to a fully distributed optimization operation method and system for multiple producers and consumers without a coordination subject. Background Art

[0002] With the widespread access of distributed generators (DG), energy storage devices (ES) and flexible loads (FL) to the power grid, traditional power grid loads are bound to transform from a single consumer identity to a prosumer identity with both consumption and production capabilities. The resulting problems such as bidirectional power flows have led to the continuous increase in the network complexity of the power system, and the supervision and control of the power system are facing severe challenges.

[0003] To this end, numerous scholars have conducted extensive and in-depth research on transactive energy (TE) as a control and dispatch mechanism for achieving economical and secure operation of power systems. Transactive energy optimization and dispatch mechanisms can be preliminarily categorized into three models based on whether a centralized coordination center controls them: fully centralized dispatch, weakly centralized dispatch, and fully distributed dispatch. Regional pricing mechanisms for transactive energy can also be broadly divided into two categories: unified price clearing and end-to-end autonomous pricing by producers and consumers. Currently, fully centralized and unified price clearing are relatively mature transactive mechanisms, but these models require the centralized optimization module to possess comprehensive information about all objects in the optimization area topology.

[0004] Obviously, with the continuous development of distributed energy in the power grid, it is unrealistic to fully grasp the power grid information. At the same time, the continuous increase in the number of producers and consumers is likely to cause the optimization problem to explode in dimension and become difficult to solve. Therefore, the distributed solution method is a major trend in the future development of energy interaction. Among them, the end-to-end transaction mechanism can fully consider the demand-side resources, meet the local balance of supply and demand power, and the local consumption of new energy, which is more valuable for research than the direct interaction between producers and consumers and the power grid. However, the current research on interactive energy mechanisms based on end-to-end transactions has the following problems. (1) It generally relies on a third-party energy interaction platform to coordinate the information of various producers and consumers and update prices; (2) The end-to-end transaction price of producers and consumers uses a unified pricing method, ignoring the differences in the willingness of different producers and consumers to pay for electricity, which does not conform to the actual market rules; (3) It rarely considers the constraints of the actual physical network, or uses external network managers to limit the flow, which is difficult to apply to actual large-scale networks. Therefore, it is urgent to find a fully distributed end-to-end energy interactive pricing mechanism that considers the constraints of the physical network. Summary of the Invention

[0005] In order to solve one of the technical problems existing in the prior art to at least a certain extent, the purpose of the present invention is to provide a fully distributed optimization operation method and system for multiple producers and consumers without a coordination subject.

[0006] The technical solution adopted in the present invention is:

[0007] A fully distributed optimization operation method for multiple producers and consumers without a coordination subject comprises the following steps:

[0008] Constructing a mathematical model of internal resources of a prosumer, wherein the mathematical model of internal resources of the prosumer includes at least one of a distributed unit model, an energy storage model, a new energy resource model, or a flexible load model;

[0009] Build a centralized optimization model for end-to-end interaction between multiple producers and consumers;

[0010] According to the energy interaction flow in each area, the interaction power of each producer and consumer is decoupled, and virtual nodes and global variables are set;

[0011] Build a distributed optimization framework for end-to-end interactive operation among multiple producers and consumers, and perform interactive iterative solutions.

[0012] Furthermore, the expression of the distributed unit model is:

[0013]

[0014] Where, represents the cost of the distributed generation unit of the i-th prosumer at time t; represents the active power output of the distributed generator set of the i-th prosumer at the t-th moment; represents the reactive power output of the distributed generator set of the i-th prosumer at the t-th moment; Indicates the upper limit of active output, indicates the lower limit of active output; Indicates the upper limit of reactive power output, Indicates the lower limit of reactive power output; β DG,a ,β DG,b ,β DG,c Represents the cost coefficient of distributed generation;

[0015] The expression of the energy storage model is:

[0016]

[0017] Where, represents the lower limit constraint of charging power, represents the upper limit constraint of charging power; represents the lower limit constraint of the discharge power, represents the upper limit constraint of the discharge power; represents the charging power of ESS at time t, represents the discharge power of ESS at time t; Indicates the state of charge; η ESS,c represents the charging efficiency of ESS, η ESS,d Represents the discharge efficiency of ESS; represents the aging cost in the ESS operating cost; β ESS represents the degradation cost coefficient;

[0018] The expression of the new energy resource model is:

[0019]

[0020] Where, represents the actual photovoltaic output of the i-th prosumer; represents the actual output of wind power of the i-th prosumer at time t; and represents the maximum output prediction value of photovoltaic at time t; represents the maximum output forecast value of wind power at time t;

[0021] The expression of the flexible load model is:

[0022]

[0023] Where, represents the user satisfaction loss cost of prosumer i at time t; β FL Represents the cost coefficient of regulating flexible load; P i FL (t) represents the flexible load adjustment amount; represents the lower limit of the flexible load regulation of prosumer i at time t, represents the upper limit of the regulation of the flexible load of prosumer i at time t.

[0024] Furthermore, the centralized optimization model for the end-to-end interactive operation of multiple producers and consumers takes into account the physical network constraints and network tax costs of the interactions of multiple producers and consumers, the balance of power purchase and sale, and the overall power balance constraints.

[0025] Furthermore, the physical network constraint of the interaction between multiple prosumers is expressed as follows:

[0026]

[0027] Where B I,j represents the node set injected into node j along the reference direction of the tidal flow, B s,j represents the set of nodes flowing out of node j along the reference direction of the tidal flow; Indicates branch l i,j The active power, Indicates branch l i,j Reactive power; Indicates node B j The active power, Indicates node B j Reactive power; among them, the branch power needs to meet the upper and lower limits of the power flow constraints; represents the resistance of the branch, Indicates the reactance of the branch; The voltage of node j must meet the upper and lower limits of the node voltage. restrictions;

[0028] The expressions for the grid access tax and fee costs, the purchase and sale power balance, and the overall power balance constraint for multi-producer consumers are as follows:

[0029]

[0030] Where, represents the network tax cost paid by prosumer i when interacting with prosumers at time t, represents the electricity purchase and sales cost of prosumer i interacting with the upper grid at time t; represents the amount of electricity purchased by prosumer i from prosumer j, represents the amount of electricity sold by prosumer i to prosumer j; β tax Indicates the network tax rate; P j buyFromG (t) represents the amount of electricity purchased by prosumer i from the upper grid, represents the amount of electricity sold by prosumer i to the upper grid; represents the price of electricity purchased by prosumer i from the upper grid, P represents the price of electricity sold by prosumer i to the upper grid; the total amount of electricity purchased and sold by prosumer i at time t is P i buy (t), To express, N MG is the total number of prosumers.

[0031] Furthermore, the centralized optimization model for the end-to-end interactive operation of multiple prosumers takes minimizing the sum of the operating costs of all prosumers as the optimization goal. The expression of the optimization objective function of the centralized optimization model is as follows:

[0032]

[0033] Where H represents the total number of time periods in a day. represents the tax cost for grid usage, Represents the cost of electricity purchase and sale with the power grid.

[0034] Furthermore, the interactive power of each producer and consumer is decoupled according to the interactive energy flow direction of each region, and virtual nodes and global variables are set, including:

[0035] Based on the energy interaction flow in each region, the branch tearing method is used to decouple the interactive power of each producer and consumer. Virtual nodes and global variables are set. By adding virtual nodes on the connection lines between the entities, the two common constraints of the coupled power flow constraint and the purchase and sale power balance constraint are transformed to achieve entity decoupling.

[0036] The expression of the transformed constraint is as follows:

[0037]

[0038] Where, represents the power dummy variable introduced between the i-th prosumer and the j-th prosumer; represents the voltage dummy variable introduced between the i-th prosumer and the j-th prosumer; It represents the voltage value on the i and j sides of the microgrid after decomposing the connecting lines.

[0039] Furthermore, the distributed optimization framework for end-to-end interactive operation of multiple producers and consumers includes:

[0040] The Lagrange dual decomposition and alternating direction multiplier method are used to further decouple the dummy variables;

[0041] Among them, after decomposing the transformed constraints through Lagrangian dual decomposition, the decomposition results are as follows:

[0042]

[0043] C ALL represents the sum of all costs, ρ represents the step size of the alternating multiplier method, represents the Lagrange multiplier at the voltage equality constraint.

[0044] Furthermore, the decomposition result is solved by the alternating direction multiplication method, and the solution is as follows:

[0045]

[0046]

[0047]

[0048] Where γ represents the feasible range of power P.

[0049] Furthermore, the interactive iterative solution includes:

[0050] Initialize the virtual global variable S (0) and the price multiplier λ (0) , set k = 0;

[0051] The global variable S that interacts between the producer and consumer i and the associated subject (k) and the price multiplier λ (k) , determine the optimal operation strategy P (k+1) ;

[0052] The optimal operation strategy P for the interaction between the producer and consumer i and the associated subjects (k+1) , and update the global variable S (k+1) ;

[0053] The optimal operation strategy P for the interaction between the producer and consumer i and the associated subjects (k+1) With the global variable S (k+1) , and update the multiplier λ (k+k) ;

[0054] Determine whether the interactive iteration has converged. If so, output the calculation results; otherwise, continue the iterative calculation.

[0055] The convergence criteria are as follows:

[0056]

[0057] Among them, ε is the preset threshold.

[0058] Another technical solution adopted in the present invention is:

[0059] A fully distributed optimization operation system with multiple producers and consumers without a coordination subject, comprising:

[0060] at least one processor;

[0061] at least one memory for storing at least one program;

[0062] When the at least one program is executed by the at least one processor, the at least one processor implements the above method.

[0063] The beneficial effects of the present invention are: the present invention takes into account the physical network constraints within multiple producers and consumers, decouples the power of each producer and consumer according to the energy interaction flow in each area, sets virtual nodes and global variables, and establishes a distributed optimization framework based on the dual decomposition principle and the alternating direction multiplier method, which can ensure the feasibility of the final transaction results while achieving effective energy utilization of multiple producers and consumers. BRIEF DESCRIPTION OF THE DRAWINGS

[0064] In order to more clearly illustrate the embodiments of the present invention or the technical solutions in the prior art, the following introduction is made to the drawings of the embodiments of the present invention or the related technical solutions in the prior art. It should be understood that the drawings introduced below are only for the convenience of clearly describing some embodiments of the technical solutions of the present invention. For those skilled in the art, other drawings can be obtained based on these drawings without any creative work.

[0065] Figure 1 This is a flowchart of the steps of a fully distributed optimization operation method for multiple producers and consumers without a coordination subject in an embodiment of the present invention. DETAILED DESCRIPTION

[0066] The embodiments of the present invention are described in detail below, examples of which 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 only used to explain the present invention and are not to be construed as limiting the present invention. The step numbers in the following embodiments are provided for ease of explanation only and do not limit the order of the steps. The order of execution of the steps in the embodiments can be adaptively adjusted according to the understanding of those skilled in the art.

[0067] In the description of the present invention, it should be understood that descriptions involving orientations, such as up, down, front, back, left, right, etc., indicating orientations or positional relationships, are based on the orientations or positional relationships shown in the accompanying drawings. They are only for the convenience of describing the present invention and simplifying the description, and do not indicate or imply that the device or element referred to must have a specific orientation, be constructed and operated in a specific orientation. Therefore, they cannot be understood as limitations on the present invention.

[0068] In the description of the present invention, "several" means one or more, "many" means more than two, "greater than," "less than," and "exceed" are understood to exclude the number itself, while "above," "below," and "within" are understood to include the number itself. The use of "first" and "second" in the description is solely for the purpose of distinguishing technical features and should not be construed as indicating or implying relative importance, implicitly specifying the number of the indicated technical features, or implicitly specifying the order of the indicated technical features.

[0069] In the description of the present invention, unless otherwise clearly defined, terms such as setting, installing, and connecting should be understood in a broad sense, and technicians in the relevant technical field can reasonably determine the specific meanings of the above terms in the present invention based on the specific content of the technical solution.

[0070] Example 1

[0071] like Figure 1 As shown, this embodiment provides a fully distributed optimization operation method for multiple producers and consumers without a coordination subject, including the following steps:

[0072] S101. Construct a mathematical model of the internal resources of producers and consumers.

[0073] Among them, the mathematical model of the internal resources of the prosumer includes a distributed unit model, an energy storage model, a new energy resource model, and a flexible load model.

[0074] The expression of the distributed unit model is:

[0075]

[0076] Where, represents the cost of the distributed generation unit of the i-th prosumer at time t; represents the active power output of the distributed generator set of the i-th prosumer at the t-th moment; represents the reactive power output of the distributed generator set of the i-th prosumer at the t-th moment; Indicates the upper limit of active output, indicates the lower limit of active output; Indicates the upper limit of reactive power output, Indicates the lower limit of reactive power output; β DG,a ,β DG,b ,β DG,c Represents the cost coefficient of distributed power generation.

[0077] The expression of the energy storage model is:

[0078]

[0079] Where, represents the lower limit constraint of charging power, represents the upper limit constraint of charging power; represents the lower limit constraint of the discharge power, represents the upper limit constraint of the discharge power; represents the charging power of ESS at time t, represents the discharge power of ESS at time t; Indicates the state of charge; η ESS,c represents the charging efficiency of ESS, η ESS,d Represents the discharge efficiency of ESS; represents the aging cost in the ESS operating cost; β ESS represents the degradation cost coefficient.

[0080] The expression of the new energy resource model is:

[0081]

[0082] Where, represents the actual photovoltaic output of the i-th prosumer; represents the actual output of wind power of the i-th prosumer at time t; and represents the maximum output prediction value of photovoltaic at time t; It represents the maximum output forecast value of wind power at the tth moment.

[0083] The expression of the flexible load model is:

[0084]

[0085] Where, represents the user satisfaction loss cost of prosumer i at time t; β FL Represents the cost coefficient of regulating flexible load; P i FL (t) represents the flexible load adjustment amount; represents the lower limit of the flexible load regulation of prosumer i at time t, represents the upper limit of the regulation of the flexible load of prosumer i at time t.

[0086] S102. Build a centralized optimization model for end-to-end interactive operation among multiple producers and consumers.

[0087] Construct a centralized optimization model for the end-to-end interactive operation of multiple producers and consumers, taking into account the physical network constraints and network tax costs of the interaction between multiple producers and consumers, the balance of power purchase and sale, and the overall power balance constraints.

[0088] The physical network constraints for multi-prosumer interactions are as follows:

[0089]

[0090] Among them, B I,j ,B S,j They represent the node sets that flow into and out of node j along the tidal reference direction. Respectively represent branch l i,j With Node B j Active and reactive power, of which the branch power needs to meet the upper and lower limits of the power flow The resistance and reactance of the branch are expressed as Refers to the voltage of node j, which also needs to meet the node voltage upper and lower limits restrictions.

[0091] The grid access tax and fee costs for multiple producers and consumers, the balance of purchased and sold power, and the overall power balance constraints are as follows:

[0092] Considering that the purchase and sale of electricity in the network can occur between individual prosumers or between prosumers and the upper power grid, as for the exchange of electricity between entities, the ownership of the network belongs to the power grid. Therefore, considering that each entity needs to pay the corresponding network tax (borne equally by the buyer and seller) according to the exchange power during the exchange process, we can get

[0093]

[0094] Where, represents the grid access tax cost paid by prosumer i for interacting with other prosumers at time t, as well as the electricity purchase and sales cost for interacting with the upper power grid, where They represent the amount of electricity purchased and sold by prosumer i to prosumer j, β tax It represents the network tax rate. i buyFromG (t), They represent the amount of electricity purchased and sold by prosumer i to the upper grid and the price of electricity purchased and sold. The total amount of electricity purchased and sold by prosumer i at time t is P i buy (t), To express, N MG Refers to the total number of producers and consumers.

[0095] In order to promote the local consumption of new energy, this embodiment does not set an upper limit on the amount of electricity that can be exchanged between producers and consumers. is the user load value of prosumer i at time t, so the power purchase and sales power constraints and the final power balance constraints of each prosumer can be obtained as follows:

[0096]

[0097] The day-ahead optimization is performed with the goal of minimizing the sum of the operating costs of all prosumers. The centralized optimization objective function is constructed as follows:

[0098]

[0099] Where H represents the total number of time periods in a day. represents the tax cost for grid usage, Represents the cost of electricity purchase and sale with the power grid.

[0100] S103. Decouple the interactive power of each producer and consumer based on the interactive energy flow in each region, and set virtual nodes and global variables.

[0101] Based on the energy interaction flow in each region, the branch tearing method is introduced to decouple the interactive power of each producer and consumer. Virtual nodes and global variables are set. By adding virtual nodes on the connection lines between the entities, the two common constraints of coupled power flow constraints and power purchase and sales balance constraints are transformed to achieve entity decoupling. The transformed constraints are as follows:

[0102]

[0103] Where, represents the power dummy variable introduced between the i-th prosumer and the j-th prosumer; represents the voltage dummy variable introduced between the i-th prosumer and the j-th prosumer; It represents the voltage value on the i and j sides of the microgrid after decomposing the connecting lines.

[0104] S104. Build a distributed optimization framework for end-to-end interactive operation among multiple producers and consumers, and perform interactive iterative solutions.

[0105] The distributed optimization framework for end-to-end interaction among multiple producers and consumers includes the use of Lagrangian dual decomposition and alternating direction multiplier method to further decouple virtual variables.

[0106] Using Lagrange dual decomposition to process Equation (9), the results are as follows:

[0107]

[0108] The alternating direction multiplier method is used to solve Equation (10), and the results are as follows:

[0109]

[0110]

[0111]

[0112] Decoupling Equation (12) is the main difficulty in achieving fully distributed optimization. In fact, it can be proved that the variable S only needs the marginal power purchase and sale of adjacent entities to complete the update. The optimization process of Equation (12) is equivalent to the following equation:

[0113]

[0114]

[0115] Among them, in step S104, the interactive iterative solution step is performed, which specifically includes steps A1-A5:

[0116] A1. Initialize virtual global variable S (0) and the price multiplier λ(0) , set k=0.

[0117] A2. Update of the operation strategy of each producer and consumer: Producer and consumer i interacts with related entities S (k) and λ (k) , solve equation (11) to formulate the optimal operation strategy P (k+1) .

[0118] A3. Update of global variables of each producer and consumer: Producer and consumer i interacts with related entities P (k+1) Update the global variable S using equations (14) and (15) (k+1) .

[0119] A4. Update of interaction multipliers of each producer and consumer: The interaction between producer and consumer i and its associated subjects P (k+1) With S (k+1) Calculate equation (13) and update the multiplier λ (k+1) .

[0120] A5. Convergence judgment: The convergence criterion is set as follows

[0121]

[0122] If converged, output the result; otherwise, go to step A2 to continue iterative calculation.

[0123] Example 2

[0124] This embodiment provides a fully distributed optimization operation method for multiple producers and consumers without a coordinating entity, including the following steps:

[0125] S201. Establish a mathematical model of the internal resources of producers and consumers.

[0126] S202. Construct a centralized optimization model for end-to-end interactive operation among multiple producers and consumers.

[0127] S203. According to the energy interaction flow direction of each region, the branch tearing method is introduced to decouple the interaction power of each producer and consumer, and virtual nodes and global variables are set.

[0128] S204. Build a distributed optimization framework for end-to-end interactive operation among multiple producers and consumers, and perform interactive iterative solutions.

[0129] The fully distributed optimization operation method for multiple producers and consumers without a coordinating entity is specifically described as follows:

[0130] In this embodiment, electric vehicles are considered as resources within each prosumer entity, and the model is expressed as follows:

[0131]

[0132] Where, e is the number of electric vehicles, e e,t is the battery capacity of the e-th electric vehicle at the beginning of period t, η is the charging efficiency, p e,k represents the charging power of the e-th electric vehicle in time period k, e e,t,max 、e e,t,min They are the upper and lower bounds of the electric vehicle energy at time t; the upper bound of energy means that the electric vehicle is charged at maximum power to e after being connected to the grid. exp The energy change curve during this period; the lower boundary of energy represents the energy change curve of delayed charging after connection until the moment of leaving the device reaches the user's expected energy value; e exp The battery power level when the user wants to leave, usually the maximum power level of the battery; e,t,max 、p e,t,min are the upper and lower limit constraints of the charging power of the e-th electric vehicle in period t, p max Indicates the maximum charging power affected by the properties of the charging pile and the electric vehicle itself; t end Indicates the end period.

[0133] Apart from this, the other contents in the second embodiment are the same as those in the first embodiment.

[0134] Example 3

[0135] This embodiment provides a fully distributed optimization operation method for multiple producers and consumers without a coordinating entity, including the following steps:

[0136] S301. Establish a mathematical model of the internal resources of producers and consumers.

[0137] S302. Construct a centralized optimization model for end-to-end interactive operation among multiple producers and consumers.

[0138] S303. According to the energy interaction flow direction of each region, the branch tearing method is introduced to decouple the interaction power of each producer and consumer, and virtual nodes and global variables are set.

[0139] S304. Build a distributed optimization framework for end-to-end interactive operation among multiple producers and consumers, and perform interactive iterative solutions.

[0140] The fully distributed optimization operation method for multiple producers and consumers without a coordinating entity is specifically described as follows:

[0141] In this embodiment, the reciprocating cooling unit is considered as a resource within each prosumer entity, and its model is represented as follows:

[0142]

[0143] Where, represents the cost associated with the reciprocating cooling unit of the i-th prosumer; αRC ,β RC and η RC As its coefficient aging cost coefficient, use thermal satisfaction cost and conversion efficiency coefficient; and They represent the input power and output power of the reciprocating cooling unit at time t, respectively, and represents the expected heat demand of prosumers; and They respectively represent the upper and lower limits of the input electric power of the reciprocating cooling unit.

[0144] Apart from this, the other contents in the third embodiment are the same as those in the first embodiment.

[0145] Example 4

[0146] This embodiment provides a fully distributed optimization operation method for multiple producers and consumers without a coordinating entity, including the following steps:

[0147] S401. Establish a mathematical model of the internal resources of producers and consumers.

[0148] S402. Construct a centralized optimization model for end-to-end interactive operation among multiple producers and consumers.

[0149] S403. According to the energy interaction flow direction of each region, the branch tearing method is introduced to decouple the interaction power of each producer and consumer, and virtual nodes and global variables are set.

[0150] S404. Build a distributed optimization framework for end-to-end interactive operation among multiple producers and consumers, and perform interactive iterative solutions.

[0151] The fully distributed optimization operation method for multiple producers and consumers without a coordinating entity is specifically described as follows:

[0152] In this embodiment, the day-ahead demand response is considered as a resource within each prosumer entity, and its model is expressed as follows:

[0153]

[0154] Where, represents the capacity of the i-th microgrid participating in demand response at time t; N k is the total number of demand response levels; P i,k (t) represents the capacity of participating in the kth response level; Z i,k (t) is a 0-1 variable, p k ,P U,k They represent the corresponding compensation price and response capacity upper limit under the k-th level demand response.

[0155] Apart from this, the other contents in the fourth embodiment are the same as those in the first embodiment.

[0156] This embodiment also provides a fully distributed optimization operation system for multiple producers and consumers without a coordination subject, including:

[0157] at least one processor;

[0158] at least one memory for storing at least one program;

[0159] When the at least one program is executed by the at least one processor, the at least one processor implements the following Figure 1 The method shown.

[0160] A fully distributed optimization operation system for multiple producers and consumers without a coordination subject in this embodiment can execute a fully distributed optimization operation method for multiple producers and consumers without a coordination subject provided by the method embodiment of the present invention, can execute any combination of implementation steps of the method embodiment, and has the corresponding functions and beneficial effects of the method.

[0161] The present application also discloses a computer program product or computer program, which includes computer instructions stored in a computer-readable storage medium. A processor of a computer device can read the computer instructions from the computer-readable storage medium, and the processor executes the computer instructions, so that the computer device performs Figure 1 The method shown.

[0162] In some optional embodiments, the function / operation mentioned in the block diagram may not occur in the order mentioned in the operation diagram. For example, depending on the function / operation involved, the two boxes shown in succession can actually be executed substantially simultaneously or the boxes can sometimes be executed in reverse order. In addition, the embodiment presented and described in the flow chart of the present invention is provided in an exemplary manner for the purpose of providing a more comprehensive understanding of the technology. The disclosed method is not limited to the operation and logic flow presented herein. Optional embodiments are contemplated in which the order of the various operations is changed and the sub-operations described as a part of a larger operation are performed independently.

[0163] Furthermore, although the present invention is described in the context of functional modules, it should be understood that, unless otherwise indicated, one or more of the functions and / or features described may be integrated into a single physical device and / or software module, or one or more functions and / or features may be implemented in separate physical devices or software modules. It will also be understood that a detailed discussion of the actual implementation of each module is not necessary for understanding the present invention. More specifically, given the properties, functions, and internal relationships of the various functional modules in the devices disclosed herein, the actual implementation of the module will be understood within the ordinary skill of an engineer. Therefore, a person skilled in the art using ordinary skill will be able to implement the present invention set forth in the claims without undue experimentation. It will also be understood that the specific concepts disclosed are merely illustrative and are not intended to limit the scope of the present invention, which is determined by the full scope of the appended claims and their equivalents.

[0164] If the functions are implemented in the form of software functional units and sold or used as independent products, they can be stored in a computer-readable storage medium. Based on this understanding, the technical solution of the present invention, or the part that contributes to the prior art, or the part of the technical solution, can be embodied in the form of a software product. The computer software product is stored in a storage medium and includes several instructions for enabling a computer device (which can be a personal computer, a server, or a network device, etc.) to execute all or part of the steps of the method described in each embodiment of the present invention. The aforementioned storage medium includes various media that can store program codes, such as a USB flash drive, a mobile hard disk, a read-only memory (ROM), a random access memory (RAM), a magnetic disk, or an optical disk.

[0165] The logic and / or steps represented in the flowcharts or otherwise described herein, for example, can be considered as an ordered list of executable instructions for implementing the logical functions, and can be embodied in any computer-readable medium for use by, or in conjunction with, an instruction execution system, apparatus, or device (e.g., a computer-based system, a system including a processor, or other system that can fetch and execute instructions from an instruction execution system, apparatus, or device). For purposes of this specification, a "computer-readable medium" can be any device that can contain, store, communicate, propagate, or transport a program for use by, or in conjunction with, an instruction execution system, apparatus, or device.

[0166] More specific examples (a non-exhaustive list) of computer-readable media include the following: an electrical connection with one or more wires (electronic devices), a portable computer disk cartridge (magnetic devices), 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 disc 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, followed by editing, deciphering, or processing in another suitable manner as necessary, and then stored in a computer memory.

[0167] It should be understood that various parts of the present invention can be implemented using hardware, software, firmware, or a combination thereof. In the above-described embodiments, multiple steps or methods can be implemented using software or firmware stored in a memory and executed by a suitable instruction execution system. For example, if implemented using hardware, as in another embodiment, any one of the following technologies known in the art or a combination thereof can be used: a discrete logic circuit having a logic gate circuit for implementing a logic function on a data signal, an application-specific integrated circuit having a suitable combination of logic gate circuits, a programmable gate array (PGA), a field programmable gate array (FPGA), etc.

[0168] In the above description of this specification, reference to the terms "one embodiment / example," "another embodiment / example," or "certain embodiments / examples" 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 invention. In this specification, the schematic representation of the above terms does not necessarily refer to the same embodiment or example. Moreover, the specific features, structures, materials, or characteristics described may be combined in any suitable manner in any one or more embodiments or examples.

[0169] While embodiments of the present invention have been shown and described, it will be appreciated by those skilled in the art that various changes, modifications, substitutions, and variations may be made to the embodiments without departing from the principles and spirit of the invention, and that the scope of the invention is defined by the claims and their equivalents.

[0170] The above is a specific description of the preferred implementation of the present invention, but the present invention is not limited to the above embodiments. Those skilled in the art can make various equivalent modifications or substitutions without violating the spirit of the present invention. These equivalent modifications or substitutions are all included in the scope defined by the claims of this application.

Claims

1. A fully distributed optimization operation method for multiple producers and consumers without a coordinating subject, characterized in that: The following steps are involved: Constructing a mathematical model of internal resources of a prosumer, wherein the mathematical model of internal resources of the prosumer includes a distributed unit model, an energy storage model, a new energy resource model, and a flexible load model; Build a centralized optimization model for end-to-end interaction between multiple producers and consumers; According to the energy interaction flow in each area, the interaction power of each producer and consumer is decoupled, and virtual nodes and global variables are set; Build a distributed optimization framework for end-to-end interaction between multiple producers and consumers, and perform interactive iterative solutions; The centralized optimization model for the end-to-end interaction of multiple prosumers takes into account the physical network constraints and network tax costs of the interaction of multiple prosumers, the balance of power purchase and sale, and the overall power balance constraints; The above method decouples the interactive power of each producer and consumer based on the energy interaction flow direction of each area, and sets virtual nodes and global variables, including: Based on the energy interaction flow in each region, the branch tearing method is used to decouple the interactive power of each producer and consumer. Virtual nodes and global variables are set. By adding virtual nodes on the connection lines between each subject, the two common constraints of coupled power flow constraints and power purchase and sales balance constraints are transformed to achieve subject decoupling. The expression of the transformed constraint is as follows: Where, represents the power dummy variable introduced between the i-th prosumer and the j-th prosumer; represents the voltage dummy variable introduced between the i-th prosumer and the j-th prosumer; It represents the voltage value of the i-side and j-side of the microgrid after decomposing the connecting line; represents the amount of electricity sold by prosumer i to prosumer j, It represents the amount of electricity purchased by prosumer i from the upper grid.

2. The method for fully distributed optimization operation of multiple producers and consumers without a coordinating subject according to claim 1 is characterized in that: The expression of the distributed unit model is: Where, represents the cost of the distributed generation unit of the i-th prosumer at time t; represents the active power output of the distributed generator set of the i-th prosumer at the t-th moment; represents the reactive power output of the distributed generator set of the i-th prosumer at the t-th moment; Indicates the upper limit of active output, indicates the lower limit of active output; Indicates the upper limit of reactive power output, Indicates the lower limit of reactive power output; β DG,a ,β DG,b ,β DG,c Represents the cost coefficient of distributed generation; The expression of the energy storage model is: Where, represents the lower limit constraint of charging power, represents the upper limit constraint of charging power; represents the lower limit constraint of the discharge power, represents the upper limit constraint of the discharge power; represents the charging power of ESS at time t, represents the discharge power of ESS at time t; Indicates the state of charge; η ESS,c represents the charging efficiency of ESS, η ESS,d Represents the discharge efficiency of ESS; represents the aging cost in the ESS operating cost; β ESS represents the degradation cost coefficient; The expression of the new energy resource model is: Where, represents the actual photovoltaic output of the i-th prosumer; represents the actual output of wind power of the i-th prosumer at time t; and represents the maximum output prediction value of photovoltaic at time t; represents the maximum output forecast value of wind power at time t; The expression of the flexible load model is: Where, represents the user satisfaction loss cost of prosumer i at time t; β FL Represents the cost coefficient of regulating flexible load; P i FL (t) represents the flexible load adjustment amount; represents the lower limit of the flexible load regulation of prosumer i at time t, represents the upper limit of the regulation of the flexible load of prosumer i at time t.

3. The method for fully distributed optimization operation of multiple producers and consumers without a coordinating subject according to claim 1 is characterized in that: The physical network constraints for the interaction of multiple prosumers are expressed as follows: Where B I,j represents the node set injected into node j along the reference direction of the tidal flow, B S,j represents the set of nodes flowing out of node j along the reference direction of the tidal flow; Indicates branch l i,j The active power, Indicates branch l i,j Reactive power; Indicates node B j The active power, Indicates node B j Reactive power; among them, the branch power needs to meet the upper and lower limits of the power flow constraints; represents the resistance of the branch, Indicates the reactance of the branch; The voltage of node j must meet the upper and lower limits of the node voltage. restrictions; The expressions for the grid access tax and fee costs, the purchase and sale power balance, and the overall power balance constraint for multi-producer consumers are as follows: Where, represents the network tax cost paid by prosumer i when interacting with prosumers at time t, represents the electricity purchase and sales cost of prosumer i interacting with the upper grid at time t; represents the amount of electricity purchased by prosumer i from prosumer j, represents the amount of electricity sold by prosumer i to prosumer j; β tax Indicates the over-the-net tax rate; represents the amount of electricity purchased by prosumer i from the upper grid, represents the amount of electricity sold by prosumer i to the upper grid; represents the price of electricity purchased by prosumer i from the upper grid, represents the price of electricity sold by prosumer i to the upper grid; the total amount of electricity purchased and sold by prosumer i at time t is To express, N MG is the total number of prosumers.

4. The method for fully distributed optimization operation of multiple producers and consumers without a coordinating subject according to claim 1 is characterized in that: The centralized optimization model for the end-to-end interactive operation of multiple prosumers is optimized to minimize the sum of the operating costs of all prosumers. The optimization objective function of the centralized optimization model is expressed as follows: Where H represents the total number of time periods in a day. represents the tax cost for grid usage, Represents the cost of electricity purchase and sale with the power grid.

5. The method for fully distributed optimization operation of multiple producers and consumers without a coordinating subject according to claim 1 is characterized in that: The distributed optimization framework for building end-to-end interactive operations among multiple producers and consumers includes: The Lagrange dual decomposition and alternating direction multiplier method are used to further decouple the dummy variables; Among them, after decomposing the transformed constraints through Lagrangian dual decomposition, the decomposition results are as follows: Among them, C ALL represents the sum of all costs, ρ represents the step size of the alternating multiplier method, represents the Lagrange multiplier at the voltage equality constraint.

6. The method for fully distributed optimization operation of multiple producers and consumers without a coordinating subject according to claim 5 is characterized in that: The decomposition result is solved by the alternating direction multiplication method, and the solution is as follows: Where γ represents the feasible range of power P.

7. The method for fully distributed optimization operation of multiple producers and consumers without a coordinating subject according to claim 1 is characterized in that: The interactive iterative solution includes: Initialize the virtual global variable S (0) and the price multiplier λ (0) , set k = 0; The global variable S that interacts between the producer and consumer i and the associated subject (k) and the price multiplier λ (k) , determine the optimal operation strategy P (k+1) ; The optimal operation strategy P for the interaction between the producer and consumer i and the associated subjects (k+1) , and update the global variable S (k+1) ; The optimal operation strategy P for the interaction between the producer and consumer i and the associated subjects (k+1) With the global variable S (k+1) , and update the multiplier λ (k+1) ; Determine whether the interactive iteration has converged. If so, output the calculation results; otherwise, continue the iterative calculation. The convergence criteria are as follows: Among them, ε is the preset threshold.

8. A fully distributed optimization operation system with multiple producers and consumers without a coordinating subject, characterized by: include: at least one processor; at least one memory for storing at least one program; When the at least one program is executed by the at least one processor, the at least one processor implements the method according to any one of claims 1 to 7.

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