Power distribution network reconstruction method and device based on demand side response and rotary power flow controller

Abstract

The embodiment of the invention provides a power distribution network reconstruction method and device based on a demand side response and a rotating power flow controller. The power distribution network reconstruction method based on the demand side response and the rotating power flow controller comprises the steps of constructing a demand side response and rotating power flow controller model under power distribution network reconstruction; determining an optimization target, and constructing a power distribution network reconstruction model based on the demand side response, the rotation power flow controller model and the optimization target; and converting the power distribution network reconstruction model into a mixed integer second-order cone programming problem, and solving the converted model to obtain a power distribution network reconstruction strategy. Constraint is converted by adopting a second-order cone model, so that a non-convex power distribution network reconstruction model is converted into a mixed integer second-order cone programming problem, and then a solver is adopted for solving. The effects of power flow regulation and control of the rotary power flow controller and demand side response peak clipping and valley filling are considered, overload of a part of lines can be relieved, the voltage quality is improved, and node voltage fluctuation and system network loss are reduced.

Description

Technical Field

[0001] The embodiments of this specification relate to the technical field of distribution network reconstruction, and in particular to a distribution network reconstruction method based on demand-side response and a rotating power flow controller. Background Art

[0002] Distribution network reconstruction is a technical means to improve the reliability and economy of the system by optimizing the topology of the distribution system. At present, China is vigorously promoting the application of a high proportion of renewable energy power generation. Electric vehicles, photovoltaic power generation, wind turbines and various energy storage devices have been widely connected to the distribution network. These factors have led to a continuous increase in the penetration rate of distributed power sources in the distribution system. However, with the large-scale access of these uncertain distributed power sources, the active reconstruction of the distribution system faces increasingly severe challenges, and more efficient strategies are needed to cope with the complexity and uncertainty of system operation.

[0003] Flexible interconnection devices based on power electronic devices, such as smart soft switches and energy routers, have become important technologies in the optimization and regulation of modern distribution networks. Smart soft switches can achieve closed-loop operation of distribution networks, balance power between feeders, and achieve power flow regulation and power mutual assistance between distribution networks by precisely controlling the converters on both sides. However, smart soft switches rely on fully controlled power electronic conversion devices, which leads to great challenges in terms of floor space, investment and maintenance costs.

[0004] As a flexible AC transmission device based on a rotating phase-shifting transformer, the rotating power flow controller has significant advantages in inter-feeder power regulation. The rotating power flow controller can achieve continuous and precise power regulation, has low cost, simple control method, and exhibits strong shock resistance and excellent stability. At present, there is insufficient comprehensive research on the impact of high proportion of new energy, demand-side response and rotating power flow controller on distribution network reconstruction. In addition, in terms of distribution network reconstruction algorithm, although heuristic algorithms have been widely used in distribution network reconstruction problems, the results based on heuristic algorithms may be local optimal solutions and cannot guarantee global optimality. Therefore, the problem of distribution network reconstruction under the comprehensive influence of multiple factors needs to be solved urgently. Summary of the invention

[0005] In view of this, the embodiments of this specification provide a distribution network reconstruction method based on demand side response and rotating power flow controller. One or more embodiments of this specification also relate to a distribution network reconstruction device based on demand side response and rotating power flow controller, a computing device, a computer readable storage medium and a computer program to solve the technical defects existing in the prior art.

[0006] According to the first aspect of the embodiments of this specification, a distribution network reconstruction method based on demand-side response and a rotating power flow controller is provided, including: Construct a demand-side response and rotating power flow controller model under distribution network reconstruction; Determine the optimization objective, and based on the demand-side response, the rotating power flow controller model, and the optimization objective, construct a distribution network reconstruction model; Convert the distribution network reconstruction model into a mixed-integer second-order cone programming problem, and solve the converted model to obtain a distribution network reconstruction strategy; Based on the demand-side response, the rotating power flow controller model, and the optimization objective, constructing a distribution network reconstruction model includes: Determine the first constraint condition based on the demand-side response; Determine the second constraint condition based on the rotating power flow controller model; Based on the first constraint condition, the second constraint condition, and the optimization objective, construct a distribution network reconstruction model.

[0007] In a possible implementation manner, determining the optimization objective includes: Determine the optimization objective based on the network loss cost, the wind curtailment cost, the PV curtailment cost, and the switch cost.

[0008] In a possible implementation manner, determining the first constraint condition based on the demand-side response includes:

[0009] Where: is the electricity price elasticity coefficient of the user at time t; is the change in electricity consumption demand before and after implementing demand response at time t; is the change in electricity price before and after implementing demand response at time t; and are the load amounts at time t before and after implementing demand response respectively; and are the electricity prices before and after implementing demand response at time t respectively; and are the peak and valley electricity prices of the load respectively; and are the time intervals to which the peak and valley electricity prices belong respectively; and are the lower and upper limits of the electricity price after implementing demand response at time t respectively; T is the total number of divided time periods.

[0010] In a possible implementation manner, determining the second constraint condition based on the rotating power flow controller model includes:

[0011] Where: , and , are the active and reactive powers transmitted by the power flow controller respectively; and are respectively the power losses of the power flow controller connected to both ends of i , j node; and are respectively the reactive power constraint coefficients at both ends of the i , j of the power flow controller; is the capacity of the power flow controller.

[0012] In a possible implementation, an optimization objective is determined based on network loss cost, wind curtailment cost, PV curtailment cost, and switching cost, including:

[0013] where: C is the total cost of the distribution network reconfiguration model; is the length of each time period; is the set of all branches with tie lines in the distribution system; is the set of nodes in the distribution system where the rotating power flow controller is connected; is the set of nodes in the distribution system where wind turbines are connected; is the set of nodes in the distribution system where PVs are connected; is the current flowing through branch ij at time t; is the equivalent resistance of branch ij; is the active power loss of the rotating power flow controller at node i at time t; and are respectively the powers generated by the wind turbine and PV at node i at time t; and are respectively the actual powers of the wind turbine and PV connected to the grid at node i at time t; and are 0-1 variables, representing the opening and closing status of branch ij in the initial network state and after distribution network reconfiguration respectively. The value equal to 1 indicates that branch ij is closed, and the value equal to 0 indicates that branch ij is open; , , , and are respectively the unit prices of network loss cost, switching cost, wind curtailment cost, PV curtailment cost, and operating cost of the rotating power flow controller.

[0014] In a possible implementation, the distribution network reconfiguration model is transformed into a mixed-integer second-order cone programming problem, and the transformed model is solved to obtain a distribution network reconfiguration strategy, including: By adopting a second-order cone model to transform the constraints of the distribution network reconstruction model, a mixed-integer second-order cone programming problem is obtained; The transformed model is solved by using an objective solver to obtain a distribution network reconstruction strategy.

[0015] According to the second aspect of the embodiments of this specification, a distribution network reconstruction device based on demand-side response and a rotating power flow controller is provided, including: A data construction module configured to construct a demand-side response and rotating power flow controller model under distribution network reconstruction; A model construction module configured to determine an optimization objective and construct a distribution network reconstruction model based on the demand-side response, rotating power flow controller model, and optimization objective; A model solving module configured to transform the distribution network reconstruction model into a mixed-integer second-order cone programming problem and solve the transformed model to obtain a distribution network reconstruction strategy.

[0016] According to the third aspect of the embodiments of this specification, a computing device is provided, including: A memory and a processor; The memory is used to store computer-executable instructions, and the processor is used to execute the computer-executable instructions. When the computer-executable instructions are executed by the processor, the steps of the above-mentioned distribution network reconstruction method based on demand-side response and rotating power flow controller are implemented.

[0017] According to the fourth aspect of the embodiments of this specification, a computer-readable storage medium is provided, which stores computer-executable instructions. When the instructions are executed by a processor, the steps of the above-mentioned distribution network reconstruction method based on demand-side response and rotating power flow controller are implemented.

[0018] According to the fifth aspect of the embodiments of this specification, a computer program is provided. When the computer program is executed on a computer, the computer is made to execute the steps of the above-mentioned distribution network reconstruction method based on demand-side response and rotating power flow controller.

[0019] The embodiments of this specification provide a distribution network reconstruction method and device based on demand-side response and a rotating power flow controller. The distribution network reconstruction method based on demand-side response and a rotating power flow controller includes: constructing a demand-side response and rotating power flow controller model under distribution network reconstruction; determining an optimization objective, and constructing a distribution network reconstruction model based on the demand-side response, the rotating power flow controller model, and the optimization objective; converting the distribution network reconstruction model into a mixed-integer second-order cone programming problem, and solving the converted model to obtain a distribution network reconstruction strategy. By using a second-order cone model to transform the constraints, the non-convex distribution network reconstruction model is transformed into a mixed-integer second-order cone programming problem, and then a solver is used for solving. The second-order cone optimization method calculates the iteration direction and solution step size based on linear transformation and duality theory, can achieve fast convergence of the second-order cone optimization problem, and can ensure the optimality of the solution. Thus, this solution considers the power flow regulation of the rotating power flow controller and the peak shaving and valley filling of the demand-side response, can relieve the overload of some lines, improve the voltage quality, reduce the node voltage fluctuation and system network loss. At the same time, adopting a distribution network reconstruction strategy considering demand response and a rotating power flow controller can effectively improve the new energy consumption capacity of the distribution system, flatten the load peak-valley difference, and enhance the economic efficiency of the distribution network operation. BRIEF DESCRIPTION OF THE DRAWINGS

[0020] Figure 1 is a flowchart of a distribution network reconstruction method based on demand-side response and a rotating power flow controller provided by an embodiment of this specification; Figure 2 is a topological structure diagram of a rotating power flow controller of a distribution network reconstruction method based on demand-side response and a rotating power flow controller provided by an embodiment of this specification; Figure 3 is an IEEE 33-node distribution system diagram of a distribution network reconstruction method based on demand-side response and a rotating power flow controller provided by an embodiment of this specification; Figure 4 is a schematic diagram of load power before and after implementing demand response of a distribution network reconstruction method based on demand-side response and a rotating power flow controller provided by an embodiment of this specification; Figure 5 is a schematic diagram of the power transmitted by a rotating power flow controller of a distribution network reconstruction method based on demand-side response and a rotating power flow controller provided by an embodiment of this specification; Figure 6 is a schematic structural diagram of a distribution network reconstruction device based on demand-side response and a rotating power flow controller provided by an embodiment of this specification; Figure 7 is a structural block diagram of a computing device provided by an embodiment of this specification. DETAILED DESCRIPTION OF THE EMBODIMENTS

[0021] Numerous specific details are set forth in the following description to provide a thorough understanding of the present specification. However, the present specification can be implemented in many other ways different from those described herein, and those skilled in the art can make similar extensions without departing from the connotation of the present specification. Therefore, the present specification is not limited by the specific implementations disclosed below.

[0022] The terms used in one or more embodiments of the present specification are for the purpose of describing specific embodiments only and are not intended to limit one or more embodiments of the present specification. The singular forms "a" and "the" used in one or more embodiments of the present specification and the appended claims are also intended to include the plural forms unless the context clearly indicates otherwise. It should also be understood that the term "and / or" used in one or more embodiments of the present specification refers to and includes any or all possible combinations of one or more of the associated listed items.

[0023] It should be understood that although the terms first, second, etc. may be used in one or more embodiments of the present specification to describe various information, such information should not be limited to these terms. These terms are only used to distinguish the same type of information from each other. For example, without departing from the scope of one or more embodiments of the present specification, the first may also be referred to as the second, and similarly, the second may also be referred to as the first. Depending on the context, the word "if" as used herein may be interpreted as "when" or "while" or "in response to determining".

[0024] In the present specification, a method for reconfiguring a distribution network based on demand-side response and a rotating power flow controller is provided. The present specification also relates to a device for reconfiguring a distribution network based on demand-side response and a rotating power flow controller, a computing device, and a computer-readable storage medium, which will be described in detail one by one in the following embodiments.

[0025] See Figure 1 , Figure 1 , which shows a flowchart of a method for reconfiguring a distribution network based on demand-side response and a rotating power flow controller according to an embodiment of the present specification, specifically including the following steps.

[0026] Step 101: Construct a model of demand-side response and a rotating power flow controller under distribution network reconfiguration.

[0027] In practical applications, demand-side response often adopts the form of time-of-use electricity prices to guide users' electricity consumption behaviors, thereby transferring some loads during peak hours to off-peak hours to achieve the purpose of peak shaving, valley filling, and load balancing.

[0028] See Figure 2, the rotating power flow controller mainly consists of two rotating phase-shifting transformers. The rotor winding is wound with a large number of coils. In actual production, the rotor winding is connected in parallel to the line as the primary winding, and the stator side is connected in series to the line as the secondary winding. Driven by the servo motor, p the RPST stator winding of the pair of poles rotates a certain mechanical angle, and a mechanical angle of the axis is generated between the stator and rotor windings, and then the electrical angle between the windings also changes. Based on the principle of electromagnetic induction, the rotation of the rotor angles of the two RPSTs synthesizes a stator voltage with a certain amplitude and an adjustable phase angle of 360° and injects it into the line, realizing the continuous voltage regulation of the rotating power flow controller and smoothly and continuously realizing the function of controlling the line power.

[0029] RPST1 and RPST2 are two rotating phase-shifting transformers respectively, and are the voltages at both ends of the line connected by the rotating power flow controller, that is, the sending-end voltage and the receiving-end voltage respectively, is the output voltage of the rotating power flow controller, and are the rotation angles of the two RPSTs respectively. By only changing the magnitude of the rotor angle, the magnitude of the voltage vector of the rotating power flow controller connected in series to the line can be changed, thus realizing smooth voltage regulation.

[0030] The sending-end voltage generates a parallel voltage of the rotor winding through the rotor winding, and generates and stator-side voltages on the stator winding side through the principle of electromagnetic induction. By adjusting the rotation angles , a series output voltage with a certain amplitude and an adjustable phase angle of 360° is obtained.

[0031] Step 102: Determine the optimization objective. Based on the demand-side response, the rotating power flow controller model, and the optimization objective, construct a distribution network reconfiguration model.

[0032] In a possible implementation, determining the optimization objective includes: determining the optimization objective based on the network loss cost, the wind curtailment cost, the photovoltaic curtailment cost, and the switch cost.

[0033] In practical applications, the economy of the distribution network reconfiguration is characterized by taking the sum of minimizing the network loss, wind curtailment, photovoltaic curtailment, the loss of the rotating power flow controller, and the switch cost as the objective function.

[0034] Specifically, determining the optimization objective based on the network loss cost, the wind curtailment cost, the photovoltaic curtailment cost, and the switch cost includes:

[0035] Where: C is the total cost of the distribution network reconfiguration model; is the length of each time period; is the set of all branches including tie lines in the distribution system; is the set of nodes where the rotating power flow controller is connected in the distribution system; is the set of nodes where wind turbines are connected in the distribution system; is the set of nodes where photovoltaic panels are connected in the distribution system; is the current flowing through branch ij at time t; is the equivalent resistance of branch ij; is the active power loss of the rotating power flow controller at node i at time t; and are the powers generated by the wind turbine and photovoltaic panel at node i at time t, respectively; and are the actual powers of the wind turbine and photovoltaic panel connected to the grid at node i at time t, respectively; and are 0-1 variables, representing the opening and closing status of branch ij in the initial network state and after the distribution network reconfiguration, respectively. A value of 1 indicates that branch ij is closed, and a value of 0 indicates that branch ij is open; , , , and are the unit prices of network loss cost, switch cost, wind curtailment cost, PV curtailment cost, and the operation cost of the rotating power flow controller, respectively.

[0036] In a possible implementation, based on demand response, the rotating power flow controller model, and the optimization objective, a distribution network reconfiguration model is constructed, including: determining the first constraint condition based on demand response; determining the second constraint condition based on the rotating power flow controller model; and constructing the distribution network reconfiguration model based on the first constraint condition, the second constraint condition, and the optimization objective.

[0037] In practical applications, the price-incentive type load is regulated based on the electricity price elasticity coefficient to achieve peak shaving and valley filling of the electricity load, and the change in load demand after implementing time-of-use electricity price is determined according to the electricity demand elasticity coefficient of the region. The elasticity coefficient of the electricity load can be expressed as the percentage change in the user's electricity demand caused by the change in electricity price within a certain period; the load constraint considering demand response is shown in the following formula: Specifically, determining the first constraint condition based on demand response includes:

[0038] Where: is the electricity price elasticity coefficient of the user at time t; The change in electricity demand before and after implementing demand response at time t; The change in electricity price before and after implementing demand response at time t; and are the load amounts at time t before and after implementing demand response, respectively; and are the electricity prices before and after implementing demand response at time t, respectively; and are the peak and valley electricity prices of the load, respectively; and are the time intervals to which the peak and valley electricity prices belong, respectively; and are the lower and upper limits of the electricity price after implementing demand response at time t, respectively; T is the total number of divided time periods.

[0039] Furthermore, in the case of high - proportion new - energy access, considering the control of the power flow controller for distribution network reconfiguration, the constraint conditions of the rotating power flow controller are shown as follows: In a possible implementation, determining the second constraint condition based on the rotating power flow controller model includes:

[0040] Among them: , and , are the active and reactive powers transmitted by the power flow controller, respectively; and are respectively the power losses of the power flow controllers connected to both ends of i , j nodes; and are respectively the reactive - power constraint coefficients at both ends of the i , j ends of the power flow controller; is the capacity of the power flow controller.

[0041] Step 103: Convert the distribution network reconfiguration model into a mixed - integer second - order cone programming problem, and solve the converted model to obtain the distribution network reconfiguration strategy.

[0042] In a possible implementation, converting the distribution network reconfiguration model into a mixed - integer second - order cone programming problem and solving the converted model to obtain the distribution network reconfiguration strategy includes: converting the constraints of the distribution network reconfiguration model by using the second - order cone model to obtain a mixed - integer second - order cone programming problem; using an objective solver to solve the converted model to obtain the distribution network reconfiguration strategy.

[0043] In practical applications, some of the constraints in the power flow constraints and the rotating power flow controller constraints are non-linear models. These constraints are strongly non-convex, difficult to solve, have low solution efficiency, and cannot directly use CPLEX to solve the model. By using the second-order cone model to transform the constraints, the non-convex distribution network reconstruction model is transformed into a mixed-integer second-order cone programming problem, and then a commercial solver is used for solving. The second-order cone optimization method calculates the iteration direction and the solution step size based on linear transformation and duality theory, which can achieve the rapid convergence of the second-order cone optimization problem and ensure the optimality of the solution.

[0044] In one embodiment, the IEEE 33-node distribution system is used as an example for analysis. The structure of the system is as Figure 3 shown. Its base voltage is 12.66 kV, the base power is 1 MW, node 1 is the slack node, the maximum active load of the system is 3715 kW, and the maximum reactive load is 2300 kVar; energy storage is connected at nodes 17 and 25, and a rotating power flow controller is connected between nodes 12 and 22, with a capacity of 0.3 MW.

[0045] According to the current peak-valley electricity price policy of a certain city, the peak electricity consumption period is set to 08:00 - 22:00, and the valley period is set to 22:00 - 08:00 the next day.

[0046] Using YALMIP and CPLEX to solve the model, when the new energy penetration rate is 70%, the total cost of network loss cost, wind curtailment cost, PV curtailment cost, and switching cost of the IEEE 33-node distribution system within a day is 1538.1 yuan. The strategy considering demand response and rotating power flow controller is adopted, and 70% of the new energy with the penetration rate is completely consumed by the distribution system load. The switches of lines L7-8, L13-14, L27-28, L8-21, and L9-15 are disconnected from the system.

[0047] Figure 4 are the load curves before and after implementing demand response; after adopting the demand response strategy, part of the load is transferred from the peak electricity consumption period (08:00 - 22:00) to the valley period (22:00 - 08:00 the next day); the load peak-valley difference before adopting the demand response strategy is 0.533 (per unit value), and the load peak-valley difference after adopting the demand response strategy is 0.332 (per unit value). After adopting the demand response strategy, the load peak-valley difference has been improved to a certain extent compared with that before the demand response.

[0048] Figure 5The figure shows the active power curve transmitted by the rotating power flow controller; the rotating power flow controller controls the active power transmitted by the line to minimize the network loss of the distribution system. Since there is a photovoltaic connection at port 2 (node 22) of the rotating power flow controller, more active power is transmitted by the rotating power flow controller during the peak photovoltaic power generation period (9:00 - 15:00), and less active power is transmitted during the low photovoltaic power generation period (20:00 - 5:00 the next day).

[0049] The embodiment of this specification provides a distribution network reconstruction method and device based on demand-side response and a rotating power flow controller. The distribution network reconstruction method based on demand-side response and a rotating power flow controller includes: constructing a demand-side response and rotating power flow controller model under distribution network reconstruction; determining an optimization objective, and based on the demand-side response, the rotating power flow controller model, and the optimization objective, constructing a distribution network reconstruction model; transforming the distribution network reconstruction model into a mixed-integer second-order cone programming problem, and solving the transformed model to obtain a distribution network reconstruction strategy. By using the second-order cone model to transform the constraints, the non-convex distribution network reconstruction model is transformed into a mixed-integer second-order cone programming problem, and then a solver is used to solve it. The second-order cone optimization method calculates the iteration direction and solution step size based on linear transformation and duality theory, which can achieve the fast convergence of the second-order cone optimization problem and ensure the optimality of the solution.

[0050] Corresponding to the above method embodiment, this specification also provides an embodiment of a distribution network reconstruction device based on demand-side response and a rotating power flow controller. Figure 6 The figure shows a schematic structural diagram of a distribution network reconstruction device based on demand-side response and a rotating power flow controller provided by an embodiment of this specification. As Figure 6 shown, the device includes: A data construction module 601, configured to construct a demand-side response and rotating power flow controller model under distribution network reconstruction; A model construction module 602, configured to determine an optimization objective, and based on the demand-side response, the rotating power flow controller model, and the optimization objective, construct a distribution network reconstruction model; A model solving module 603, configured to transform the distribution network reconstruction model into a mixed-integer second-order cone programming problem, and solve the transformed model to obtain a distribution network reconstruction strategy.

[0051] In a possible implementation, determining the optimization objective includes: Determining the optimization objective based on network loss cost, wind curtailment cost, photovoltaic curtailment cost, and switching cost.

[0052] In a possible implementation, determining the first constraint condition based on the demand-side response includes:

[0053] Among them: is the electricity price elasticity coefficient of the user at time t; is the change in electricity consumption demand before and after implementing demand response at time t; is the change in electricity price before and after implementing demand response at time t; and are the load amounts at time t before and after implementing demand response respectively; and are the electricity prices before and after implementing demand response at time t respectively; and are the peak and valley electricity prices of the load respectively; and are the time intervals to which the peak and valley electricity prices belong respectively; and are the lower and upper limits of the electricity price after implementing demand response at time t respectively; T is the total number of divided time periods.

[0054] In a possible implementation manner, the second constraint condition is determined based on the rotating power flow controller model, including:

[0055] Among them: 、 and 、 are the active and reactive powers transmitted by the power flow controller respectively; and are respectively the power losses of the power flow controller connected to both ends of i 、 j nodes; and are respectively the reactive power constraint coefficients at both ends of the i 、 j of the power flow controller; is the capacity of the power flow controller.

[0056] In a possible implementation manner, the optimization objective is determined based on the network loss cost, wind curtailment cost, PV curtailment cost and switching cost, including:

[0057] Among them: C is the total cost of the distribution network reconfiguration model; is the length of each time period; is the set of all branches with tie lines in the distribution system; is the set of nodes in the distribution system where the rotating power flow controller is connected; is the set of nodes in the distribution system where wind turbines are connected; is the set of nodes with PV access in the distribution system; is the current flowing through branch ij at time t; is the equivalent resistance of branch ij; is the active power loss of the rotating power flow controller at node i at time t; and are the powers generated by the wind turbine and PV at node i at time t respectively; and are the actual powers of the wind turbine and PV connected to the grid at node i at time t respectively; and are 0-1 variables, representing the opening and closing status of branch ij in the initial state of the network and after the distribution network reconstruction respectively. The value equal to 1 means branch ij is closed, and the value equal to 0 means branch ij is open; 、 、 、 and are the unit prices of network loss cost, switch cost, wind curtailment cost, PV curtailment cost and the operation cost of the rotating power flow controller respectively.

[0058] In a possible implementation, the distribution network reconstruction model is transformed into a mixed-integer second-order cone programming problem, and the transformed model is solved to obtain the distribution network reconstruction strategy, including: The constraints of the distribution network reconstruction model are transformed by using the second-order cone model to obtain a mixed-integer second-order cone programming problem; The transformed model is solved by using an objective solver to obtain the distribution network reconstruction strategy.

[0059] The embodiments of this specification provide a distribution network reconstruction method and device based on demand response and rotating power flow controller. The distribution network reconstruction device based on demand response and rotating power flow controller includes: constructing a demand response and rotating power flow controller model under distribution network reconstruction; determining an optimization objective, and constructing a distribution network reconstruction model based on the demand response, the rotating power flow controller model and the optimization objective; transforming the distribution network reconstruction model into a mixed-integer second-order cone programming problem, and solving the transformed model to obtain the distribution network reconstruction strategy. By using the second-order cone model to transform the constraints, the non-convex distribution network reconstruction model is transformed into a mixed-integer second-order cone programming problem, and then a solver is used for solving. The second-order cone optimization method calculates the iteration direction and solution step size based on linear transformation and duality theory, can achieve the fast convergence of the second-order cone optimization problem, and can ensure the optimality of the solution.

[0060] The above is a schematic solution of a distribution network reconstruction device based on demand-side response and a rotating power flow controller according to this embodiment. It should be noted that the technical solution of the distribution network reconstruction device based on demand-side response and a rotating power flow controller belongs to the same concept as the above-mentioned technical solution of the distribution network reconstruction method based on demand-side response and a rotating power flow controller. For the details not described in the technical solution of the distribution network reconstruction device based on demand-side response and a rotating power flow controller, reference can be made to the description of the technical solution of the distribution network reconstruction method based on demand-side response and a rotating power flow controller.

[0061] Figure 7 The structural block diagram of a computing device 700 provided according to an embodiment of this specification is shown. The components of the computing device 700 include, but are not limited to, a memory 710 and a processor 720. The processor 720 is connected to the memory 710 through a bus 730, and a database 750 is used to store data.

[0062] The computing device 700 further includes an access device 740, and the access device 740 enables the computing device 700 to communicate via one or more networks 760. Examples of these networks include the Public Switched Telephone Network (PSTN), Local Area Network (LAN), Wide Area Network (WAN), Personal Area Network (PAN), or a combination of communication networks such as the Internet. The access device 740 may include one or more of any type of wired or wireless network interfaces (for example, a network interface card (NIC)), such as an IEEE 802.11 Wireless Local Area Network (WLAN) wireless interface, a Worldwide Interoperability for Microwave Access (Wi-MAX) interface, an Ethernet interface, a Universal Serial Bus (USB) interface, a cellular network interface, a Bluetooth interface, and a Near Field Communication (NFC).

[0063] In an embodiment of this specification, the above components of the computing device 700 and Figure 7 other components not shown therein may also be connected to each other, for example, through a bus. It should be understood that Figure 7The block diagram of the computing device shown is for illustrative purposes only and is not a limitation on the scope of this specification. Those skilled in the art can add or replace other components as needed.

[0064] The computing device 700 can be any type of stationary or mobile computing device, including mobile computers or mobile computing devices (e.g., tablet computers, personal digital assistants, laptop computers, notebook computers, netbooks, etc.), mobile phones (e.g., smartphones), wearable computing devices (e.g., smartwatches, smart glasses, etc.) or other types of mobile devices, or stationary computing devices such as desktop computers or personal computers (PCs, Personal Computers). The computing device 700 can also be a mobile or stationary server.

[0065] Among them, the processor 720 is used to execute the following computer-executable instructions, which when executed by the processor implement the steps of the above-mentioned distribution network reconstruction method based on demand-side response and a rotating power flow controller. The above is a schematic solution of a computing device in this embodiment. It should be noted that the technical solution of this computing device and the technical solution of the above-mentioned distribution network reconstruction method based on demand-side response and a rotating power flow controller belong to the same concept. For the details not described in detail in the technical solution of the computing device, reference can be made to the description of the technical solution of the above-mentioned distribution network reconstruction method based on demand-side response and a rotating power flow controller.

[0066] An embodiment of this specification also provides a computer-readable storage medium, which stores computer-executable instructions that, when executed by a processor, implement the steps of the above-mentioned distribution network reconstruction method based on demand-side response and a rotating power flow controller.

[0067] The above is a schematic solution of a computer-readable storage medium in this embodiment. It should be noted that the technical solution of this storage medium and the technical solution of the above-mentioned distribution network reconstruction method based on demand-side response and a rotating power flow controller belong to the same concept. For the details not described in detail in the technical solution of the storage medium, reference can be made to the description of the technical solution of the above-mentioned distribution network reconstruction method based on demand-side response and a rotating power flow controller.

[0068] An embodiment of this specification also provides a computer program, where when the computer program is executed on a computer, the computer is made to execute the steps of the above-mentioned distribution network reconstruction method based on demand-side response and a rotating power flow controller.

[0069] The above is a schematic solution of a computer program according to this embodiment. It should be noted that the technical solution of this computer program and the technical solution of the above-mentioned distribution network reconstruction method based on demand-side response and a rotating power flow controller belong to the same concept. For the details not described in detail in the technical solution of the computer program, reference can be made to the description of the technical solution of the above-mentioned distribution network reconstruction method based on demand-side response and a rotating power flow controller.

[0070] The above has described specific embodiments of this specification. Other embodiments are within the scope of the appended claims. In some cases, the actions or steps recited in the claims may be performed in a different order than in the embodiments and still achieve the desired result. Additionally, the processes depicted in the figures do not necessarily require the particular order or sequential order shown to achieve the desired result. In certain embodiments, multitasking and parallel processing are also possible or may be advantageous.

[0071] The computer instructions include computer program code, and the computer program code may be in the form of source code, object code, an executable file, or some intermediate form, etc. The computer-readable medium may include: any entity or device capable of carrying the computer program code, a recording medium, a USB flash drive, a mobile hard disk, a magnetic disk, an optical disk, a computer memory, a read-only memory (ROM, Read-Only Memory), a random access memory (RAM, Random Access Memory), an electrical carrier signal, a telecommunication signal, and a software distribution medium, etc. It should be noted that the content included in the computer-readable medium may be appropriately increased or decreased according to the requirements of legislation and patent practice within the jurisdiction. For example, in some jurisdictions, according to legislation and patent practice, the computer-readable medium does not include electrical carrier signals and telecommunication signals.

[0072] It should be noted that for the foregoing method embodiments, for the sake of simplicity of description, they are all expressed as a series of action combinations. However, those skilled in the art should know that the embodiments of this specification are not limited by the described order of actions, because according to the embodiments of this specification, certain steps may be performed in other orders or simultaneously. Secondly, those skilled in the art should also know that the embodiments described in the specification are all preferred embodiments, and the actions and modules involved are not necessarily essential to the embodiments of this specification.

[0073] In the above embodiments, the descriptions of the various embodiments have their own focuses. For the parts not described in detail in a certain embodiment, reference can be made to the relevant descriptions of other embodiments.

[0074] The preferred embodiments of the present specification disclosed above are only used to help explain the present specification. The alternative embodiments do not describe all the details in detail, nor do they limit the invention to the specific embodiments described. Obviously, many modifications and changes can be made according to the content of the embodiments of the present specification. These embodiments are selected and specifically described in this specification in order to better explain the principles and practical applications of the embodiments of the present specification, so that those skilled in the art can well understand and utilize this specification. This specification is only limited by the claims and their full scope and equivalents.

Claims

1. A distribution network reconstruction method based on demand-side response and a rotating power flow controller, characterized in that Including: Constructing a demand - side response and a rotating power flow controller model under distribution network reconfiguration; Determining an optimization objective, and constructing a distribution network reconfiguration model based on the demand - side response, the rotating power flow controller model, and the optimization objective; Converting the distribution network reconfiguration model into a mixed - integer second - order cone programming problem, and solving the converted model to obtain a distribution network reconfiguration strategy; The constructing a distribution network reconfiguration model based on the demand - side response, the rotating power flow controller model, and the optimization objective includes: Determining a first constraint condition based on the demand - side response; Determining a second constraint condition based on the rotating power flow controller model; Constructing a distribution network reconfiguration model based on the first constraint condition, the second constraint condition, and the optimization objective.

2. The method according to claim 1, wherein The determining an optimization objective includes: Determining an optimization objective based on network loss cost, wind curtailment cost, photovoltaic curtailment cost, and switch cost.

3. The method according to claim 1, wherein The determining a first constraint condition based on the demand - side response includes: Wherein: is the electricity price elasticity coefficient of the user at time t; is the change in electricity consumption demand before and after the implementation of demand response at time t; is the change in electricity price before and after the implementation of demand response at time t; and are the load amounts at time t before and after the implementation of demand response respectively; and are the electricity prices before and after the implementation of demand response at time t respectively; and are the peak and valley electricity prices of the load respectively; and are the time intervals to which the peak and valley electricity prices belong respectively; and are the lower and upper limits of the electricity price after the implementation of demand response at time t respectively; T is the total number of divided time periods.

4. The method according to claim 1, characterized in that, The determining a second constraint condition based on the rotating power flow controller model includes: Wherein: and and and are the active and reactive powers transmitted by the power flow controller respectively; and are the power losses of the power flow controller connected to both ends of i and j nodes respectively; and are the reactive power constraint coefficients at both ends of the i and j of the power flow controller respectively; is the capacity of the power flow controller.

5. The method according to claim 2, wherein The determining an optimization objective based on network loss cost, wind curtailment cost, photovoltaic curtailment cost, and switch cost includes: Where: C is the total cost of the distribution network reconfiguration model; is the length of each time period; is the set of all branches with tie lines in the distribution system; is the set of nodes in the distribution system where the rotating power flow controller is connected; is the set of nodes in the distribution system where wind turbines are connected; is the set of nodes in the distribution system where photovoltaic panels are connected; is the current flowing through branch ij at time t; is the equivalent resistance of branch ij; is the active power loss of the rotating power flow controller at node i at time t; and are the powers generated by the wind turbine and photovoltaic panel at node i at time t, respectively; and are the actual powers of the wind turbine and photovoltaic panel connected to the grid at node i at time t, respectively; and are 0-1 variables, representing the opening and closing status of branch ij in the initial network state and after distribution network reconfiguration, respectively. A value of 1 indicates that branch ij is closed, and a value of 0 indicates that branch ij is open; , , , and are the unit prices of network loss cost, switch cost, wind curtailment cost, PV curtailment cost, and operating cost of the rotating power flow controller, respectively.

6. The method according to claim 1, wherein The converting the distribution network reconfiguration model into a mixed - integer second - order cone programming problem, and solving the converted model to obtain a distribution network reconfiguration strategy includes: Converting the constraints of the distribution network reconfiguration model by using a second - order cone model to obtain a mixed - integer second - order cone programming problem; Using an objective solver to solve the converted model to obtain a distribution network reconfiguration strategy.

7. A distribution network reconstruction device based on demand side response and a rotating power flow controller, which is used to implement the steps of the distribution network reconstruction method based on demand side response and a rotating power flow controller according to any one of claims 1 to 6, and is characterized in that, Including: A data construction module configured to construct a demand - side response and a rotating power flow controller model under distribution network reconfiguration; A model construction module configured to determine an optimization objective, and construct a distribution network reconfiguration model based on the demand - side response, the rotating power flow controller model, and the optimization objective; A model solving module configured to convert the distribution network reconfiguration model into a mixed - integer second - order cone programming problem, and solve the converted model to obtain a distribution network reconfiguration strategy.

8. A computing device, characterized in that, Including: A memory and a processor; The memory is used to store computer - executable instructions, and the processor is used to execute the computer - executable instructions. When the computer - executable instructions are executed by the processor, the steps of the distribution network reconfiguration method based on demand - side response and rotating power flow controller according to any one of claims 1 to 6 are implemented.

9. A computer - readable storage medium storing computer - executable instructions, which when executed by a processor implement the steps of the distribution network reconfiguration method based on demand - side response and rotating power flow controller according to any one of claims 1 to 6.

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