Main-distribution integrated multi-level interactive optimization operation method, device, equipment and medium

Abstract

The invention relates to the technical field of power system optimization operation, in particular to a main and distribution integrated multi-level interactive optimization operation method, device, equipment and medium, and the method comprises the steps: building a main and distribution network multi-level collaborative optimization framework which comprises a main network scheduling layer and a distribution network scheduling layer; constructing an upper-layer main network scheduling model and a lower-layer distribution network scheduling model according to the multi-level collaborative optimization framework, and designing corresponding objective functions and constraint conditions; and solving a main and distribution network collaborative optimization problem through a synchronous alternating direction multiplier method, disassembling the overall problem into a plurality of local sub-problems, and solving the upper-layer main network scheduling model and the lower-layer distribution network scheduling model to obtain an optimized operation method. According to the main and distribution network multi-level optimization model solving method based on the synchronous alternating direction multiplier method, coordinated distribution is carried out on reactive power resource output in a power distribution network control layer, and economic operation of a power system is achieved while voltage stability is guaranteed.

Description

Technical Field

[0001] The present invention relates to the technical field of power system optimization operation, and in particular to a main-distribution integrated multi-level interactive optimization operation method, device, equipment and medium. Background Art

[0002] With the booming global economy, energy demand continues to rise, which has led to unprecedented challenges in energy supply. Distributed power generation has reduced the distribution network's dependence on the centralized power supply of the main grid, making the distribution network no longer just a transmitter and distributor of electric energy, but a key part of the power system with autonomous regulation and support capabilities.

[0003] With the access of large-scale distributed power sources to the distribution network, the uncertainty of their output has led to increased volatility in the net load of the distribution network, which has brought additional burdens to the matching of boundary power between the main and distribution networks. If the resources within the main and distribution networks cannot be effectively coordinated at this time, it will lead to system safety risks such as power imbalance and voltage over-limit in the local power grid.

[0004] How to make the main distribution network more comprehensively coordinate power demand, supply and system constraints, and ensure the safe, reliable and high-quality operation of the main distribution network is worthy of in-depth research. In recent years, the transformation of the operation mode between the main network and the distribution network has become the focus of research. In order to adapt to the needs of the new era of power grids, improve the utilization rate of renewable energy, and ensure the safe, economical and reliable operation of the power grid, a more reasonable and efficient mode - main distribution integrated operation has emerged. Under the main distribution integrated mode, how to coordinate the output of various reactive resources and achieve economic operation of the power system while ensuring voltage stability is still an urgent problem to be solved.

[0005] The information disclosed in this background technology section is only intended to deepen the understanding of the overall background technology of the present invention, and should not be regarded as acknowledging or suggesting in any form that the information constitutes the prior art already known to those skilled in the art. Summary of the invention

[0006] The present invention provides a main-distribution integrated multi-level interactive optimization operation method, device, equipment and medium, thereby effectively solving the problems in the background technology.

[0007] In order to achieve the above object, the technical solution adopted by the present invention is: a main-distribution integrated multi-level interactive optimization operation method, comprising the following steps:

[0008] Building a multi-level collaborative optimization framework for the main and distribution networks, which includes a main network dispatching layer and a distribution network dispatching layer;

[0009] According to the multi-level collaborative optimization framework, an upper-layer main network scheduling model and a lower-layer distribution network scheduling model are constructed, and corresponding objective functions and constraints are designed;

[0010] The main and distribution network coordinated optimization problem is solved by the synchronous alternating direction multiplier method. The overall problem is decomposed into several local sub-problems. The upper main network scheduling model and the lower distribution network scheduling model are solved to obtain the optimized operation method.

[0011] Furthermore, in the construction of the multi-level collaborative optimization framework of the main distribution network,

[0012] Obtain the overall system demand, balance the power load and distribute the power of the main network to ensure the stability of the main network when facing load fluctuations or external disturbances, and build the main network scheduling layer;

[0013] Obtain the dispatching instructions of the main network, perform power allocation and optimal dispatching in combination with the load demand and resource conditions of the distribution network, and build the distribution network dispatching layer.

[0014] Furthermore, the upper main grid dispatching model takes minimizing the total operation cost of the main grid as the objective function, and takes the power balance equation, the transmission capacity constraint of the transmission line, and the output constraint of the generator set as the constraint conditions;

[0015] The lower-layer distribution network scheduling model takes the minimization of the total operating cost of the distribution network as the objective function, and takes power constraints, transmission line transmission capacity constraints, and generator output constraints as constraints.

[0016] Furthermore, the objective function of the upper main network scheduling model includes:

[0017]

[0018] Where: Ω T Represents the total scheduling time; Ω G Represents the generator set in the main grid; represents the active power output by generator set i during period t; C i (·) represents the electricity production cost of generator set i;

[0019] The constraints of the upper main network scheduling model include:

[0020] Power balance equation:

[0021]

[0022] Where: Ω TDG Represents the traditional distribution network, Ω ADG represents active distribution network; Represents the total transmission power between all distribution networks and the main network during time period t;

[0023] Transmission line transmission capacity constraints:

[0024]

[0025] Where: Ω B and Ω L Represents the busbars and lines in the main grid; Represent the generator set, traditional distribution network and active distribution network respectively; S bl represents the transfer distribution factor between the active power flow of line l and the injected active power of bus b; P l L,max represents the transmission capacity of line l;

[0026] Generator output constraints:

[0027]

[0028] Where: P i G,max and P i G,min Respectively represent the upper and lower limits of the output of generator set i.

[0029] Furthermore, the objective function of the lower layer distribution network scheduling model includes:

[0030]

[0031] Where: is the generator set in the distribution network k, C i (·) represents the electricity production cost of generator set i, Ω ADG represents the active distribution network, Ω T Represents the total scheduling time, Represents the active power output by generator set i during period t;

[0032] The constraints of the lower layer distribution network scheduling model include:

[0033] Power Constraints:

[0034]

[0035] Where: and are the generator sets and power supply loads in the distribution network k; is the load power in time period t;

[0036] Transmission line transmission capacity constraints:

[0037]

[0038] Where: and are the nodes and lines of distribution network k; Supply power to line loads;

[0039] Generator output constraints:

[0040]

[0041] Where: P i G,max and P i G,min The upper and lower limits of the generator output.

[0042] Furthermore, solving the main distribution network coordinated optimization problem by using the synchronous alternating direction multiplier method comprises the following steps:

[0043] A synchronous alternating direction multiplier algorithm is used to construct Lagrangian functions for the objective functions of the upper main network scheduling model and the lower distribution network scheduling model respectively;

[0044] Through iterative calculation, the controller in the main distribution network solves the upper main network scheduling model and the lower distribution network scheduling model and the Lagrangian function in parallel, and updates the dual variables in the main distribution network;

[0045] When the boundary residual is less than or equal to the convergence parameter or the number of iterations reaches the upper limit, the iteration ends and the solution result is output.

[0046] Furthermore, the objective functions of the upper main network scheduling model and the lower distribution network scheduling model are respectively constructed to correspond to Lagrangian functions, including:

[0047] The Lagrangian function corresponding to the objective function of the main network and distribution network is set as and We can get:

[0048]

[0049] Where t is the number of iterations; i and λ j are the dual variables of the main network and the distribution network respectively; ρ is the penalty parameter;

[0050] Assume that the main network is coupled with the distribution network. and are the reference values ​​of the main network and distribution network at the t+1th iteration, respectively, and are set as:

[0051]

[0052] Furthermore, the controller in the main distribution network solves the upper main network scheduling model and the lower distribution network scheduling model and the Lagrangian function in parallel through iterative calculation, including:

[0053] In the t+1 iteration, the controller in the main distribution network solves the upper and lower layer models in parallel. as well as Decision variables, get the coupling branch state and

[0054]

[0055] The updating of the dual variables in the main distribution network includes:

[0056]

[0057] Furthermore, when the boundary residual is less than or equal to the convergence parameter or the number of iterations reaches the upper limit, the iteration ends when the following formula is satisfied;

[0058]

[0059] Where δ is the convergence parameter.

[0060] The present invention also includes a main-distribution integrated multi-level interactive optimization operation device, using the above method, the device includes:

[0061] A framework construction unit, used to build a multi-level collaborative optimization framework for the main and distribution networks, wherein the multi-level collaborative optimization framework includes a main network scheduling layer and a distribution network scheduling layer;

[0062] A modeling unit, used to construct an upper-layer main network scheduling model and a lower-layer distribution network scheduling model according to the multi-level collaborative optimization framework, and design corresponding objective functions and constraints;

[0063] The solving unit is used to solve the main distribution network coordinated optimization problem through the synchronous alternating direction multiplier method, decompose the overall problem into several local sub-problems, solve the upper main network scheduling model and the lower distribution network scheduling model, and obtain the optimized operation method.

[0064] The present invention also includes a computer device, including a memory, a processor, and a computer program stored in the memory and executable on the processor, wherein when the processor executes the computer program, the method described above is implemented.

[0065] The present invention also includes a storage medium on which a computer program is stored. When the computer program is executed by a processor, the method described above is implemented.

[0066] The beneficial effects of the present invention are as follows: by constructing a multi-level main and distribution network hierarchical distributed dispatching framework including a main power grid dispatching layer and a distribution network dispatching layer, based on an upper main grid model and a lower distribution network model, a main and distribution network multi-level optimization model solving method based on a synchronous alternating direction multiplier method is proposed, and the output of each reactive resource is coordinated and allocated at the distribution network control layer, so as to realize the economic operation of the power system while ensuring voltage stability. BRIEF DESCRIPTION OF THE DRAWINGS

[0067] In order to more clearly illustrate the embodiments of the present invention or the technical solutions in the prior art, the drawings required for use in the embodiments or the description of the prior art will be briefly introduced below. Obviously, the drawings described below are only some embodiments recorded in the present invention. For ordinary technicians in this field, other drawings can be obtained based on these drawings without paying creative work.

[0068] Figure 1 is a flow chart of the method in Example 1;

[0069] Figure 2 It is a structural schematic diagram of the device in Example 1;

[0070] Figure 3 This is a flow chart of steps 1 to 3 in Example 2;

[0071] Figure 4 It is the master-distributor coordinated scheduling framework in Example 2;

[0072] Figure 5 is the distribution network load prediction curve in Example 2;

[0073] Figure 6 is the tie line power of the main power grid and the distribution network in Example 2;

[0074] Figure 7 Comparison of network loss before and after using the double-layer optimization method in Example 2;

[0075] Figure 8 It is a structural schematic diagram of the computer device of the present invention. DETAILED DESCRIPTION

[0076] The technical solutions in the embodiments of the present invention will be described clearly and completely below in conjunction with the drawings in the embodiments of the present invention. Obviously, the described embodiments are only part of the embodiments of the present invention, rather than all the embodiments.

[0077] Embodiment 1:

[0078] like Figure 1 As shown: A main and distribution integrated multi-level interactive optimization operation method, comprising the following steps:

[0079] Build a multi-level collaborative optimization framework for the main and distribution networks, which includes the main network dispatching layer and the distribution network dispatching layer;

[0080] Construct the upper main network dispatching model and the lower distribution network dispatching model according to the multi-level collaborative optimization framework, and design the corresponding objective functions and constraints;

[0081] The main and distribution network coordinated optimization problem is solved by the synchronous alternating direction multiplier method. The overall problem is decomposed into several local sub-problems. The upper main network scheduling model and the lower distribution network scheduling model are solved to obtain the optimized operation method.

[0082] By constructing a multi-level main and distribution network hierarchical distributed dispatching framework including the main grid dispatching layer and the distribution network dispatching layer, a main and distribution network multi-level optimization model solving method based on the synchronous alternating direction multiplier method is proposed based on the upper main grid model and the lower distribution network model. The reactive resource output is coordinated and allocated at the distribution network control layer to achieve economic operation of the power system while ensuring voltage stability.

[0083] In this embodiment, a multi-level collaborative optimization framework for the main distribution network is constructed.

[0084] Obtain the overall system demand, balance the power load and distribute the power of the main network to ensure the stability of the main network when facing load fluctuations or external disturbances, and build the main network scheduling layer;

[0085] Obtain the dispatching instructions of the main network, perform power allocation and optimized dispatching based on the load demand and resource conditions of the distribution network, and build a distribution network dispatching layer.

[0086] The upper main grid dispatching model takes the minimization of the total operation cost of the main grid as the objective function, and takes the power balance equation, transmission line transmission capacity constraint, and generator output constraint as the constraint conditions;

[0087] The lower-level distribution network dispatching model takes the minimization of the total operating cost of the distribution network as its objective function, and takes power constraints, transmission line capacity constraints, and generator output constraints as constraints.

[0088] Among them, the objective functions of the upper main network scheduling model include:

[0089]

[0090] Where: Ω T Represents the total scheduling time; Ω G Represents the generator set in the main grid; represents the active power output by generator set i during period t; C i (·) represents the electricity production cost of generator set i;

[0091] The constraints of the upper main network scheduling model include:

[0092] Power balance equation:

[0093]

[0094] Where: Ω TDG Represents the traditional distribution network, Ω ADGrepresents active distribution network; Represents the total transmission power between all distribution networks and the main network during time period t;

[0095] Transmission line transmission capacity constraints:

[0096]

[0097] Where: Ω B and Ω L Represents the busbars and lines in the main grid; Represent the generator set, traditional distribution network and active distribution network respectively; S bl represents the transfer distribution factor between the active power flow of line l and the injected active power of bus b; P l L,max represents the transmission capacity of line l;

[0098] Generator output constraints:

[0099]

[0100] Where: P i G,max and P i G,min Respectively represent the upper and lower limits of the output of generator set i.

[0101] As a preferred embodiment of the above, the objective function of the lower layer distribution network scheduling model includes:

[0102]

[0103] Where: is the generator set in the distribution network k, C i (·) represents the electricity production cost of generator set i, Ω ADG represents the active distribution network, Ω T Represents the total scheduling time, Represents the active power output by generator set i during period t;

[0104] The constraints of the lower-level distribution network scheduling model include:

[0105] Power Constraints:

[0106]

[0107] Where: and are the generator sets and power supply loads in the distribution network k; is the load power in time period t;

[0108] Transmission line transmission capacity constraints:

[0109]

[0110] Where: and are the nodes and lines of distribution network k; Supply power to line loads;

[0111] Generator output constraints:

[0112]

[0113] Where: P i G,max and P i G,min The upper and lower limits of the generator output.

[0114] In this embodiment, the main distribution network coordinated optimization problem is solved by a synchronous alternating direction multiplier method, including the following steps:

[0115] The synchronous alternating direction multiplier algorithm is used to construct Lagrangian functions for the objective functions of the upper main grid dispatching model and the lower distribution grid dispatching model respectively.

[0116] Through iterative calculation, the controller in the main distribution network solves the upper main network scheduling model and the lower distribution network scheduling model and Lagrangian function in parallel, and updates the dual variables in the main distribution network;

[0117] When the boundary residual is less than or equal to the convergence parameter or the number of iterations reaches the upper limit, the iteration ends and the solution result is output.

[0118] The Lagrangian functions are constructed for the objective functions of the upper main network dispatching model and the lower distribution network dispatching model, including:

[0119] The Lagrangian function corresponding to the objective function of the main network and distribution network is set as and We can get:

[0120]

[0121] Where t is the number of iterations; i and λ j are the dual variables of the main network and the distribution network respectively; ρ is the penalty parameter;

[0122] Assume that the main network is coupled with the distribution network. and are the reference values ​​of the main network and distribution network at the t+1th iteration, respectively, and are set as:

[0123]

[0124] Through iterative calculation, the controller in the main distribution network solves the upper main network scheduling model and the lower distribution network scheduling model and Lagrangian function in parallel, including:

[0125] In the t+1 iteration, the controller in the main distribution network solves the upper and lower layer models in parallel. as well as Decision variables, get the coupling branch state and

[0126]

[0127] Update the dual variables in the main distribution network, including:

[0128]

[0129] When the boundary residual is less than or equal to the convergence parameter or the number of iterations reaches the upper limit, the iteration ends when the following formula is satisfied;

[0130]

[0131] Where δ is the convergence parameter.

[0132] like Figure 2 As shown, this embodiment also includes a main-distribution integrated multi-level interactive optimization operation device, using the above method, the device includes:

[0133] A framework construction unit is used to build a multi-level collaborative optimization framework for the main and distribution networks. The multi-level collaborative optimization framework includes a main network dispatching layer and a distribution network dispatching layer.

[0134] Modeling unit, used to build the upper main network scheduling model and the lower distribution network scheduling model according to the multi-level collaborative optimization framework, and design the corresponding objective functions and constraints;

[0135] The solving unit is used to solve the main distribution network coordinated optimization problem through the synchronous alternating direction multiplier method, decompose the overall problem into several local sub-problems, solve the upper main network scheduling model and the lower distribution network scheduling model, and obtain the optimized operation method.

[0136] Embodiment 2:

[0137] In order to make the purpose, technical solution and advantages of this embodiment more clear, the embodiment is described in detail below in conjunction with the accompanying drawings. It should be understood that the specific implementation case described here is only used to explain this embodiment and is not used to limit the embodiment.

[0138] like Figure 3As shown, this embodiment discloses a main and distribution integrated multi-level interactive optimization operation method, and the construction of the multi-level method includes the following steps: Step 1, building a multi-level collaborative optimization framework for the main and distribution networks, which is divided into two parts: the main network scheduling layer and the distribution network scheduling layer; Step 2, constructing an upper-level main network scheduling model and a lower-level distribution network scheduling model, and designing objective functions and constraints; Step 3, solving the main and distribution network collaborative optimization problem through the synchronous alternating direction multiplier method, and breaking down the overall problem into several local sub-problems, thereby reducing the computational complexity.

[0139] The construction of the main and auxiliary interactive optimization framework in step 1 includes the following steps:

[0140] Step 11, Main Network Scheduling Layer:

[0141] This layer is responsible for balancing the power load and distributing the power of the main network according to the overall needs of the system, ensuring the stability of the main network when facing load fluctuations or external disturbances.

[0142] Step 12: Distribution network scheduling layer:

[0143] At this layer, the distribution network performs local power allocation and optimized scheduling based on the dispatch instructions of the main network and its own load demand and resource conditions.

[0144] The construction of the distributed containment algorithm in step 2 includes the following steps:

[0145] Step 21: Construction of the upper mainnet model:

[0146] The upper layer takes the minimum total operating cost of the main power grid as the objective function, and its expression is:

[0147]

[0148] Where: Ω T Represents the total scheduling time; Ω G Represents the generator set in the main grid; represents the active power output by generator set i during period t; C i (·) represents the electricity production cost of generator set i.

[0149] The upper mainnet constraints include:

[0150] (1) Power balance equation;

[0151]

[0152] Where: Ω TDG Represents the traditional distribution network; and Represents the active power transmitted between the traditional distribution network k and the active distribution network k and the main grid during period t.

[0153] (2) Transmission line capacity constraints;

[0154]

[0155] Where: Ω B and Ω L Represents the busbars and lines in the main grid; Represent the generator set, traditional distribution network and active distribution network respectively; S bl represents the transfer distribution factor between the active power flow of line l and the injected active power of bus b; P l L,max Represents the transmission capacity of line l.

[0156] (3) Generator output constraints;

[0157]

[0158] Where: P i G,max and P i G,min Respectively represent the upper and lower limits of the output of generator set i.

[0159] Step 22: Lower layer distribution network scheduling model:

[0160] The lower layer takes the minimum total operating cost of the distribution network as the objective function, and its expression is:

[0161]

[0162] Where: is the generator set in the distribution network k.

[0163] The constraints include:

[0164] (1) Power constraints;

[0165]

[0166] Where: and are the generator sets and power supply loads in the distribution network k; is the load power in time period t.

[0167] (2) Transmission line capacity constraints;

[0168]

[0169] Where: and are the nodes and lines of distribution network k; Supply power to the line load.

[0170] (3) Generator output constraints;

[0171]

[0172] Where: P i G,max and P i G,min The upper and lower limits of the generator output.

[0173] The method for solving the multi-level optimization model of the main distribution network based on the synchronous alternating direction multiplier method in step 3 includes the following steps:

[0174] Step 31: Lagrangian function construction:

[0175] Using the synchronous ADMM algorithm, the Lagrangian function corresponding to the objective function of the main network and distribution network is set as

[0176] and We can get:

[0177]

[0178] Where t is the number of iterations; i and λ j are the dual variables of the main network and the distribution network respectively; ρ is the penalty parameter. Assume that the main network is coupled with the distribution network, and are the reference values ​​of the main network and distribution network at the t+1th iteration, respectively, and are set as:

[0179]

[0180] Step 32: Update the dual variables:

[0181] In the t+1 iteration, the controller in the main distribution network solves the upper and lower layer models in parallel. as well as Decision variables, get the coupling branch state and

[0182]

[0183] Update the dual variables in the main distribution network:

[0184]

[0185] Step 33: Convergence judgment:

[0186] Determine whether it has converged. When the boundary residual is less than or equal to the convergence parameter or the number of iterations reaches the upper limit, the algorithm ends. When the following formula is satisfied, the iteration ends.

[0187]

[0188] Where δ is the convergence parameter.

[0189] The following is a simulation example to verify the effectiveness of this method:

[0190] like Figure 4 As shown, according to step 1, a multi-level main and distribution network hierarchical distributed dispatching framework including the main grid dispatching layer and the distribution network dispatching layer is constructed. Based on the upper main grid model and the lower distribution network model, a main and distribution network multi-level optimization model solving method based on the synchronous alternating direction multiplier method is proposed. The reactive resource output is coordinated and allocated at the distribution network control layer to achieve economic operation of the power system while ensuring voltage stability.

[0191] The simulation verification was carried out in MATLAB, using a modified standard IEEE33 node test case. The voltage of the node system was set to 12.66kV, and the relevant research was carried out with 100MW as the benchmark power. Figure 5 The load forecast diagram of the active distribution network shown clearly depicts the load changes of the system at different time periods of the day. The tie line is an important channel connecting the main grid and the distribution network, which can realize two-way power transmission. The tie line power between the main grid and the distribution network can be obtained by iteratively solving the main grid-distribution network double-layer model, such as Figure 6 As shown. Figure 6 It can be seen that during the peak period of electricity consumption, the power of the tie line is high, and the main grid vigorously supplies power to the distribution network to support the power demand of the distribution network; during the period of low power demand, due to the reduction of power demand of the distribution network, the power transmission of the tie line also decreases. Specifically, the maximum transmission power of tie line 1 during this period reached 10.0256kW, while the maximum transmission power of tie line 2 was 7.24939kW.

[0192] Figure 7 The network loss before and after the main network-distribution network multi-objective double-layer optimization scheduling technology was compared. The results show that with the application of the double-layer optimization scheduling method, the network loss is significantly reduced. By implementing the double-layer optimization scheduling strategy, the active distribution network can optimize the output of distributed power sources, thereby significantly reducing the losses in the network and reducing the operating costs of the main network and distribution network. After adopting the main network-distribution network multi-objective double-layer optimization scheduling method, the total system cost has been significantly reduced. This proves that the hierarchical multi-objective optimization operation and scheduling method considering the main network-distribution network collaboration proposed in this paper can significantly reduce the economic cost of the power grid and has high economic benefits.

[0193] See also Figure 8A computer device 400 provided in an embodiment of the present application includes: a processor 410 and a memory 420, wherein the memory 420 stores a computer program executable by the processor 410, and when the computer program is executed by the processor 410, the above method is executed.

[0194] The embodiment of the present application further provides a storage medium 430 on which a computer program is stored. When the computer program is run by the processor 410, the above method is executed.

[0195] Among them, the storage medium 430 can be implemented by any type of volatile or non-volatile storage device or a combination thereof, such as static random access memory (SRAM), electrically erasable programmable read-only memory (EEPROM), erasable programmable read-only memory (EPROM), programmable red-only memory (PROM), read-only memory (ROM), magnetic storage, flash memory, disk or optical disk.

[0196] In the description of the present invention, the terms "first" and "second" are used for descriptive purposes only and should not be understood as indicating or implying relative importance or implicitly indicating the number of the indicated technical features. Therefore, the features defined as "first" and "second" may explicitly or implicitly include one or more of the features. "Multiple" means two or more, unless otherwise clearly and specifically defined.

[0197] In the present invention, unless otherwise clearly specified and limited, the terms "installed", "connected", "connected", "fixed" and the like should be understood in a broad sense, for example, it can be a fixed connection, a detachable connection, or an integral connection; it can be a mechanical connection or an electrical connection; it can be a direct connection or an indirect connection through an intermediate medium, it can be the internal connection of two elements or the interaction relationship between two elements. For ordinary technicians in this field, the specific meanings of the above terms in the present invention can be understood according to specific circumstances.

[0198] In the description of this specification, the description with reference to the terms "one embodiment", "some embodiments", "example", "specific example", or "some examples" etc. means that the specific features, structures, materials or characteristics described in conjunction with the embodiment or example are included in at least one embodiment or example of the present invention. In this specification, the schematic representations of the above terms do not necessarily refer to the same embodiment or example. Moreover, the specific features, structures, materials or characteristics described may be combined in any one or more embodiments or examples in a suitable manner. In addition, those skilled in the art may combine and combine the different embodiments or examples described in this specification and the features of the different embodiments or examples, unless they are contradictory.

[0199] Any process or method description in a flowchart or otherwise described herein may be understood to represent a module, segment or portion of code that includes one or more executable instructions for implementing the steps of a specific logical function or process, and the scope of the preferred embodiments of the present invention includes alternative implementations in which functions may not be performed in the order shown or discussed, including performing functions in a substantially simultaneous manner or in the reverse order depending on the functions involved, which should be understood by those skilled in the art to which the embodiments of the present invention belong.

[0200] The logic and / or steps represented in the flowchart or otherwise described herein, for example, can be considered as an ordered list of executable instructions for implementing logical functions, and can be embodied in any computer-readable medium for use by an instruction execution system, device or apparatus (such as a computer-based system, a system including a processor, or other system that can fetch instructions from an instruction execution system, device or apparatus and execute instructions), or in combination with these instruction execution systems, devices or apparatuses. For the purposes of this specification, a "computer-readable medium" can be any device that can contain, store, communicate, propagate or transmit a program for use by an instruction execution system, device or apparatus, or in combination with these instruction execution systems, devices or apparatuses. More specific examples of computer-readable media (a non-exhaustive list) include the following: an electrical connection with one or more wires (electronic devices), a portable computer disk box (magnetic device), a random access memory (RAM), a read-only memory (ROM), an erasable and programmable read-only memory (EPROM or flash memory), a fiber optic device, and a portable compact disk read-only memory (CDROM). In addition, the computer-readable medium may even be paper or other suitable medium on which the program is printed, since the program may be obtained electronically, for example, by optically scanning the paper or other medium and then editing, interpreting or processing in other suitable ways if necessary, and then stored in a computer memory.

[0201] It should be understood that the various parts of the present invention can be implemented by hardware, software, firmware or a combination thereof. In the above-mentioned embodiments, a plurality of steps or methods can be implemented by software or firmware stored in a memory and executed by a suitable instruction execution system. For example, if implemented by hardware, as in another embodiment, it can be implemented by any one of the following technologies known in the art or their combination: a discrete logic circuit having a logic gate circuit for implementing a logic function for a data signal, a dedicated integrated circuit having a suitable combination of logic gate circuits, a programmable gate array (PGA), a field programmable gate array (FPGA), etc.

[0202] A person skilled in the art may understand that all or part of the steps in the method for implementing the above-mentioned embodiment may be completed by instructing related hardware through a program, and the program may be stored in a computer-readable storage medium, which, when executed, includes one or a combination of the steps of the method embodiment.

[0203] The storage medium mentioned above may be a read-only memory, a magnetic disk or an optical disk, etc. Although the embodiments of the present invention have been shown and described above, it can be understood that the above embodiments are exemplary and cannot be understood as limiting the present invention. A person of ordinary skill in the art may change, modify, replace and modify the above embodiments within the scope of the present invention.

Claims

1. A main-distribution integrated multi-level interactive optimization operation method, characterized in that: The steps include: Building a multi-level collaborative optimization framework for the main and distribution networks, which includes a main network dispatching layer and a distribution network dispatching layer; According to the multi-level collaborative optimization framework, an upper-layer main network scheduling model and a lower-layer distribution network scheduling model are constructed, and corresponding objective functions and constraints are designed; The main and distribution network coordinated optimization problem is solved by the synchronous alternating direction multiplier method. The overall problem is decomposed into several local sub-problems. The upper main network scheduling model and the lower distribution network scheduling model are solved to obtain the optimized operation method.

2. The main-distribution integrated multi-level interactive optimization operation method according to claim 1 is characterized in that: In the multi-level collaborative optimization framework of the main distribution network, Obtain the overall system demand, balance the power load and distribute the power of the main network to ensure the stability of the main network when facing load fluctuations or external disturbances, and build the main network scheduling layer; Obtain the dispatching instructions of the main network, perform power allocation and optimal dispatching in combination with the load demand and resource conditions of the distribution network, and build the distribution network dispatching layer.

3. The main-distribution integrated multi-level interactive optimization operation method according to claim 2 is characterized in that: The upper main grid dispatching model takes the minimization of the total operation cost of the main grid as the objective function, and takes the power balance equation, the transmission capacity constraint of the transmission line, and the output constraint of the generator set as the constraint conditions; The lower-layer distribution network scheduling model takes the minimization of the total operating cost of the distribution network as the objective function, and takes power constraints, transmission line transmission capacity constraints, and generator output constraints as constraints.

4. The main-distribution integrated multi-level interactive optimization operation method according to claim 3 is characterized in that: The objective function of the upper main network scheduling model includes: Where: Ω T Represents the total scheduling time; Ω G Represents the generator set in the main grid; represents the active power output by generator set i during period t; C i (·) represents the electricity production cost of generator set i; The constraints of the upper main network scheduling model include: Power balance equation: Where: Ω TDG Represents the traditional distribution network, Ω ADG represents active distribution network; Represents the total transmission power between all distribution networks and the main network during time period t; Transmission line transmission capacity constraints: Where: Ω B and Ω L Represents the busbars and lines in the main grid; Represent the generator set, traditional distribution network and active distribution network respectively; S bl represents the transfer distribution factor between the active power flow of line l and the injected active power of bus b; P l L,max represents the transmission capacity of line l; Generator output constraints: Where: P i G,max and P i G,min Respectively represent the upper and lower limits of the output of generator set i.

5. The main-distribution integrated multi-level interactive optimization operation method according to claim 3 is characterized in that: The objective function of the lower layer distribution network scheduling model includes: Where: is the generator set in the distribution network k, C i (·) represents the electricity production cost of generator set i, Ω ADG represents the active distribution network, Ω T Represents the total scheduling time, Represents the active power output by generator set i during period t; The constraints of the lower layer distribution network scheduling model include: Power Constraints: Where: and are the generator sets and power supply loads in the distribution network k; is the load power in time period t; Transmission line transmission capacity constraints: Where: and are the nodes and lines of distribution network k; Supply power to line loads; Generator output constraints: Where: P i G,max and P i G,min The upper and lower limits of the generator output.

6. The main-distribution integrated multi-level interactive optimization operation method according to claim 1 is characterized in that: The method of solving the main distribution network coordinated optimization problem by using the synchronous alternating direction multiplier method comprises the following steps: A synchronous alternating direction multiplier algorithm is used to construct Lagrangian functions for the objective functions of the upper main network scheduling model and the lower distribution network scheduling model respectively; Through iterative calculation, the controller in the main distribution network solves the upper main network scheduling model and the lower distribution network scheduling model and the Lagrangian function in parallel, and updates the dual variables in the main distribution network; When the boundary residual is less than or equal to the convergence parameter or the number of iterations reaches the upper limit, the iteration ends and the solution result is output.

7. The main-distribution integrated multi-level interactive optimization operation method according to claim 6 is characterized in that: The objective functions of the upper main network scheduling model and the lower distribution network scheduling model are respectively constructed to correspond to Lagrangian functions, including: The Lagrangian function corresponding to the objective function of the main network and distribution network is set as and We can get: Where t is the number of iterations; i and λ j are the dual variables of the main network and the distribution network respectively; ρ is the penalty parameter; Assume that the main grid is coupled with the distribution grid. and are the reference values ​​of the main network and distribution network at the t+1th iteration, respectively, and are set as:

8. The main-distribution integrated multi-level interactive optimization operation method according to claim 7 is characterized in that: The controller in the main distribution network solves the upper main network scheduling model, the lower distribution network scheduling model and the Lagrangian function in parallel through iterative calculation, including: In the t+1 iteration, the controller in the main distribution network solves the upper and lower layer models in parallel. as well as Decision variables, get the coupling branch state x i t+1 and x j t+1 : The updating of the dual variables in the main distribution network includes:

9. The main-distribution integrated multi-level interactive optimization operation method according to claim 8 is characterized in that: When the boundary residual is less than or equal to the convergence parameter or the number of iterations reaches the upper limit, the iteration ends when the following formula is satisfied; Where δ is the convergence parameter.

10. A main and distribution integrated multi-level interactive optimization operation device, characterized in that: Using the method according to any one of claims 1 to 9, the device comprises: A framework construction unit, used to build a multi-level collaborative optimization framework for the main and distribution networks, wherein the multi-level collaborative optimization framework includes a main network scheduling layer and a distribution network scheduling layer; A modeling unit, used to construct an upper-layer main network scheduling model and a lower-layer distribution network scheduling model according to the multi-level collaborative optimization framework, and design corresponding objective functions and constraints; The solving unit is used to solve the main distribution network coordinated optimization problem through the synchronous alternating direction multiplier method, decompose the overall problem into several local sub-problems, solve the upper main network scheduling model and the lower distribution network scheduling model, and obtain the optimized operation method.

11. A computer device 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 method according to any one of claims 1 to 9 is implemented.

12. A storage medium having a computer program stored thereon, characterized in that: When the computer program is executed by a processor, the method according to any one of claims 1 to 9 is implemented.

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