A method and system for optimal operation of a multi-terminal flexible power distribution network containing microgrids

Abstract

The application discloses a kind of multi-terminal flexible power distribution network optimization operation method and system containing micro-grid, it is related to power distribution network operation optimization technical field, including acquisition multi-terminal flexible power distribution network parameter, according to the multi-terminal flexible power distribution network parameter constructs multi-terminal flexible power distribution network model;Solve the lower layer of the multi-terminal flexible power distribution network model, obtain the scheduling scheme of each unit of micro-grid;According to the scheduling scheme, solve the upper layer of the multi-terminal flexible power distribution network model, obtain power distribution network power purchase plan, and optimize power distribution network based on the scheduling scheme and the power purchase plan.The application is based on micro-grid technology and flexible interconnection technology constructs multi-terminal flexible power distribution network model, model upper layer is power distribution network, with minimum operating loss as objective function, model lower layer is micro-grid with minimum operating cost as objective function.Under the premise of considering system safe operation constraint, joint optimization is solved, can effectively consume distributed renewable energy, realize the economic safe operation of multi-terminal flexible power distribution network.

Description

Technical Field

[0001] The present invention relates to the technical field of distribution network operation optimization, and in particular to a method and system for optimizing the operation of a multi-terminal flexible distribution network including a microgrid. Background Art

[0002] Flexible interconnectors, based on advanced power electronics technology, are a key link in integrating diverse distributed renewable energy sources into distribution networks. These devices transform the traditional distribution network's single-ended, radial power supply model into a multi-terminal, closed-loop power supply model, transforming it into a flexible distribution network and enabling continuous and flexible power flow regulation. However, as the number of flexible interconnectors increases, accurately coordinating the transmission power of these interconnectors becomes crucial for improving the operational reliability of flexible distribution networks.

[0003] Flexible distribution networks integrating large amounts of renewable energy can effectively alleviate power supply pressure, regulate system power flows, and achieve load balancing across lines. However, the randomness and intermittent nature of distributed renewable energy pose significant challenges to power system operation, primarily manifesting in complex and volatile system power flows, voltage overshooting, and reduced power quality. Consequently, many distribution networks prefer to increase operating costs rather than integrate distributed renewable energy, resulting in significant resource waste.

[0004] Distributed renewable energy resources are characterized by sparse spatial distribution and large installed capacity, placing higher demands on the dispatching capabilities of flexible distribution networks. Furthermore, distributed renewable energy sources have varying output characteristics. How to accommodate these diverse distributed renewable energy sources through the output of controllable units within the system is a key issue and one of the challenges in solving the model. Therefore, ensuring the efficient management and absorption of distributed renewable energy resources through flexible distribution networks is a pressing issue for the safe and economic operation of distribution networks. Summary of the Invention

[0005] In view of the problems existing in the existing flexible distribution network, the present invention is proposed.

[0006] Therefore, the problem to be solved by the present invention is to achieve efficient management and consumption of distributed renewable energy through a flexible distribution network.

[0007] In order to solve the above technical problems, the present invention provides the following technical solutions:

[0008] In a first aspect, an embodiment of the present invention provides a method for optimizing operation of a multi-terminal flexible distribution network including a microgrid, which includes:

[0009] Collecting multi-terminal flexible distribution network parameters, and constructing a multi-terminal flexible distribution network model according to the multi-terminal flexible distribution network parameters;

[0010] Solving the lower layer of the multi-terminal flexible distribution network model to obtain a scheduling plan for each unit of the microgrid;

[0011] According to the scheduling scheme, the upper layer of the multi-terminal flexible distribution network model is solved to obtain a power purchase plan for the distribution network, and the distribution network is optimized based on the scheduling scheme and the power purchase plan.

[0012] As a preferred solution of the multi-terminal flexible distribution network optimization operation method containing microgrids described in the present invention, wherein: the multi-terminal flexible distribution network model includes an upper-layer distribution network, a lower-layer microgrid and safe operation constraints;

[0013] The objective function of the microgrid is to minimize the operating cost, as shown in the following formula:

[0014] min f=C MG =C DG +C ESS +C DNtoMG +C DR

[0015]

[0016] Among them, C MG is the total operating cost of the microgrid; C DG is the power generation cost of distributed controllable power supply; C ESS is the charging and discharging cost of energy storage; C DNtoMG is the electricity purchase cost of the microgrid; C DR is the cost of demand response; N T is the scheduling period; is the cost coefficient of distributed controllable power supply; P DG,t is the generated power of the distributed controllable power supply at time t; is the energy storage charging and discharging cost coefficient; η is the energy storage charging and discharging efficiency; and are the discharge power and charging power of energy storage at time t respectively; and are the unit electricity prices of the microgrid for purchasing and selling electricity; P MG,t is the electricity purchase amount at time t, when P MG,t ≥0 indicates electricity purchase, otherwise it indicates electricity sale; is the cost coefficient of demand response; ΔP load,t is the transferable load at time t.

[0017] As a preferred solution of the method for optimizing the operation of a multi-terminal flexible distribution network containing a microgrid according to the present invention, the distribution network takes minimizing the operating loss as the objective function, as shown in the following formula:

[0018]

[0019]

[0020] Among them, P loss is the operating loss of the system; is the operating loss of the distribution network; is the operating loss of the medium voltage DC bus; is the operating loss of MMC; N is the number of nodes in the distribution network; M is the number of nodes in the DC line; R ij is the line resistance between node i and node j; P ij,t and Q ij,t are the active power and reactive power flowing from node i to node j at time t; V i,t is the voltage amplitude of node i at time t; R kl is the line resistance between node k and node l; P kl,t is the active power flowing from node k to node l at time t; V k,t is the voltage amplitude of node k at time t; N MMC is the number of MMCs; is the loss coefficient of the sth MMC; and are the active power and reactive power absorbed by the sth MMC from the access node at time t, respectively.

[0021] As a preferred solution of the method for optimizing operation of a multi-terminal flexible distribution network including a microgrid according to the present invention, wherein: the distribution network and the microgrid are interconnected via a 10kV medium voltage DC bus;

[0022] The distribution network is connected to the upper power grid and interconnected with the medium voltage DC bus through a modular multilevel converter MMC;

[0023] The microgrid is interconnected with the medium voltage DC bus through a flexible interconnection device.

[0024] As a preferred solution of the multi-terminal flexible distribution network optimization operation method containing a microgrid according to the present invention, the safe operation constraints include distributed controllable power supply operation constraints, energy storage operation constraints, microgrid power exchange constraints, demand response operation constraints, AC distribution network power flow equation constraints, DC line power flow equation constraints and MMC operation constraints;

[0025] The distributed controllable power supply operation constraints are as follows:

[0026]

[0027] in, and The upper and lower limits of the distributed controllable power supply ramp rate; and It is the upper and lower limits of the active power of the distributed controllable power supply.

[0028] As a preferred solution of the method for optimizing the operation of a multi-terminal flexible distribution network containing a microgrid according to the present invention, the energy storage operation constraint is as follows:

[0029]

[0030]

[0031] Among them, E ESS is the maximum charge and discharge power of energy storage; U ESS,t When it is 1, it means charging, and when it is 0, it means discharging; and is the maximum and minimum value of the remaining energy storage capacity; E 0,n is the capacity of energy storage at the initial moment;

[0032] The AC distribution network power flow equation constraint is as follows:

[0033]

[0034] Among them, P j,t and Q j,t are the active power and reactive power of the net load of node j at time t; I ij,t is the current amplitude between node i and node j at time t; P m,t is the active load of node m at time t; and are the active load and reactive load of node j at time t respectively.

[0035] As a preferred solution of the method for optimizing the operation of a multi-terminal flexible distribution network containing a microgrid according to the present invention, the demand response operation constraint is a transferable demand response, and the load transferred by the user should satisfy the total load demand within the scheduling period. At the same time, the load is only allowed to be transferred in or out, and is not allowed to exceed the maximum transferable load.

[0036] In a second aspect, an embodiment of the present invention provides a multi-terminal flexible distribution network optimization operation system including a microgrid, which includes:

[0037] The model building module is used to collect multi-terminal flexible distribution network parameters and build a multi-terminal flexible distribution network model including microgrids based on the collected parameters;

[0038] The microgrid optimization module is used to solve the microgrid model of the multi-terminal flexible distribution network, obtain the optimal scheduling plan of each unit in the microgrid, and the amount of electricity purchased by the microgrid;

[0039] The distribution network optimization module is used to solve the upper-layer distribution network model of the multi-terminal flexible distribution network based on the power purchase amount output by the microgrid optimization module, and obtain the optimal scheduling plan of the distribution network.

[0040] In a third aspect, an embodiment of the present invention provides a computer device comprising a memory and a processor, wherein the memory stores a computer program, wherein: when the processor executes the computer program, it implements any step of the above-mentioned method for optimizing the operation of a multi-terminal flexible distribution network containing a microgrid.

[0041] In a fourth aspect, an embodiment of the present invention provides a computer-readable storage medium having a computer program stored thereon, wherein: when the computer program is executed by a processor, any step of the above-mentioned method for optimizing the operation of a multi-terminal flexible distribution network containing a microgrid is implemented.

[0042] The beneficial effect of the present invention is that a multi-terminal flexible distribution network model is constructed based on microgrid technology and flexible interconnection technology. The upper layer of the model is the distribution network, with the minimum operating loss as the objective function; the lower layer of the model is the microgrid, with the minimum operating cost as the objective function. Under the premise of considering the system's safe operation constraints, the particle swarm optimization algorithm is used to jointly optimize and solve the upper and lower layer models to obtain the power purchase plan of the distribution network and the scheduling plan of the microgrid, which can effectively absorb distributed renewable energy and realize the economic and safe operation of the multi-terminal flexible distribution network. At the same time, the present invention solves the model by partitioning and jointly solving, the calculation process is simple, and it has high engineering practical value. BRIEF DESCRIPTION OF THE DRAWINGS

[0043] To more clearly illustrate the technical solutions of the embodiments of the present invention, the following briefly introduces the drawings required for describing the embodiments. Obviously, the drawings described below are only some embodiments of the present invention. Those skilled in the art can also derive other drawings based on these drawings without inventive effort. Among them:

[0044] Figure 1 Flowchart of the optimized operation method for a multi-terminal flexible distribution network containing microgrids.

[0045] Figure 2 Schematic diagram of the structure of a multi-terminal flexible distribution network containing microgrids and an optimization operation method of a multi-terminal flexible distribution network containing microgrids.

[0046] Figure 3 The load curve diagram, distributed photovoltaic power and distributed wind power diagram before and after optimization of the multi-terminal flexible distribution network optimization operation method containing microgrids.

[0047] Figure 4 This is the optimized microgrid dispatching scheme diagram for the optimal operation method of the multi-terminal flexible distribution network containing microgrids.

[0048] Figure 5 This is a diagram of the power distribution of MMC transmitted to the microgrid partition after optimization of the multi-terminal flexible distribution network optimization operation method containing microgrids. DETAILED DESCRIPTION

[0049] To make the above-mentioned objects, features, and advantages of the present invention more clearly understood, the following detailed description of the specific embodiments of the present invention is given in conjunction with the accompanying drawings. It is obvious that the described embodiments are only part of the embodiments of the present invention, not all of them. Based on the embodiments of the present invention, all other embodiments obtained by ordinary persons in this field without creative work should fall within the scope of protection of the present invention.

[0050] In the following description, many specific details are set forth to facilitate a full understanding of the present invention. However, the present invention may also be implemented in other ways different from those described herein. Those skilled in the art may make similar generalizations without violating the connotation of the present invention. Therefore, the present invention is not limited to the specific embodiments disclosed below.

[0051] Secondly, the term "one embodiment" or "embodiment" herein refers to a specific feature, structure, or characteristic that may be included in at least one implementation of the present invention. The phrase "in one embodiment" appearing in various places throughout this specification does not necessarily refer to the same embodiment, nor does it refer to a separate or selective embodiment that is mutually exclusive of other embodiments.

[0052] The present invention is described in detail with reference to schematic diagrams. For ease of illustration, cross-sectional views of device structures may be partially enlarged and not to scale when describing embodiments of the present invention. Furthermore, the schematic diagrams are merely illustrative and should not limit the scope of the present invention. Furthermore, in actual production, the three-dimensional dimensions of length, width, and depth should be included.

[0053] In the description of the present invention, it should be noted that the terms "upper, lower, inner, and outer" and other references to orientations or positional relationships are based on the orientations or positional relationships shown in the accompanying drawings and are intended solely to facilitate and simplify the description of the present invention. They are not intended to indicate or imply that the devices or components referred to must have, be constructed, or operate in a specific orientation, and therefore should not be construed as limitations on the present invention. Furthermore, the terms "first, second, or third" are used for descriptive purposes only and should not be construed as indicating or implying relative importance.

[0054] In this disclosure, unless otherwise specified or limited, the terms "mounted," "connected," and "connected" should be interpreted broadly. For example, they may refer to fixed, removable, or integral connections. They may also refer to mechanical, electrical, or direct connections, indirect connections through an intermediary, or internal communication between two components. Those skilled in the art will understand the specific meanings of these terms in this disclosure.

[0055] Example 1

[0056] Reference Figures 1 to 5 , which is the first embodiment of the present invention, provides a method for optimizing operation of a multi-terminal flexible distribution network including a microgrid, comprising:

[0057] S1: Collect multi-terminal flexible distribution network parameters and build a multi-terminal flexible distribution network model according to the multi-terminal flexible distribution network parameters;

[0058] Extract distribution network data, microgrid data, and load point data, and summarize system information, including: distribution network line parameters, load conditions, distributed photovoltaic power, distributed wind power, and operating constraints of each unit;

[0059] Based on the multi-terminal flexible distribution network parameters provided above, a multi-terminal flexible distribution network model including a microgrid is constructed, including: setting the distribution network to minimize operating losses as the objective function, setting the microgrid to minimize operating costs as the objective function, and considering the system safety operation constraints;

[0060] The multi-terminal flexible distribution network model with microgrids is divided into upper and lower layers. The upper layer is the distribution network, consisting of a modified IEEE 33-node standard system; the lower layer is the microgrid, consisting of distributed controllable power sources, energy storage, demand response, photovoltaic power, and wind power. The distribution network and microgrid are interconnected via a 10kV medium-voltage DC bus. The distribution network connects to the upper grid at node 1 and is interconnected to the medium-voltage DC bus via two 1MW modular multilevel converters (MMCs), connected to nodes 18 and 22, respectively. The microgrid is interconnected to the medium-voltage DC bus via a flexible interconnection device.

[0061] Microgrids are primarily composed of distributed controllable power sources, energy storage, demand response, photovoltaic power, and wind power. They meet their own load demands through coordinated scheduling of various units within the system and support from the upper-level distribution network. The optimization goal of a microgrid is to maximize the absorption of distributed renewable energy while minimizing its own operating costs, while ensuring safe system operation. Therefore, operating costs include the generation costs of distributed controllable power sources, the charging and discharging costs of energy storage, the electricity purchase costs of the microgrid, and the costs of demand response:

[0062] min f=C MG =C DG +CESS +C DNtoMG +C DR

[0063]

[0064]

[0065] Among them, C MG is the total operating cost of the microgrid; C DG is the power generation cost of distributed controllable power supply; C ESS is the charging and discharging cost of energy storage; C DNtoMG is the electricity purchase cost of the microgrid; C DR is the cost of demand response; N T is the scheduling period; is the cost coefficient of distributed controllable power supply; P DG,t is the generated power of the distributed controllable power supply at time t; is the energy storage charging and discharging cost coefficient; η is the energy storage charging and discharging efficiency; and are the discharge power and charging power of energy storage at time t respectively; and are the unit electricity prices of the microgrid for purchasing and selling electricity; P MG,t is the electricity purchase amount at time t, when P MG,t ≥0 indicates electricity purchase, otherwise it indicates electricity sale; is the cost coefficient of demand response; ΔP load,t is the transferable load at time t.

[0066] The distribution network objective function is expressed as follows:

[0067] The distribution network is the upper layer of the model. A modified IEEE 33-node standard system is selected as the distribution network, which meets its own load requirements with the support of the upper power grid. The optimization goal of the microgrid is to minimize system operating losses while ensuring safe system operation. Therefore, operating losses include the operating losses of the distribution network, the medium-voltage DC bus, and the MMC:

[0068]

[0069] Among them, P loss is the operating loss of the system; is the operating loss of the distribution network; is the operating loss of the medium voltage DC bus; is the operating loss of MMC; N is the number of nodes in the distribution network; M is the number of nodes in the DC line; R ij is the line resistance between node i and node j; P ij,t and Qij,t are the active power and reactive power flowing from node i to node j at time t; V i,t is the voltage amplitude of node i at time t; R kl is the line resistance between node k and node l; P kl,t is the active power flowing from node k to node l at time t; V k,t is the voltage amplitude of node k at time t; N MMC is the number of MMCs; is the loss coefficient of the sth MMC; and are the active power and reactive power absorbed by the sth MMC from the access node at time t, respectively.

[0070] Furthermore, the system safety operation constraints are constructed, and the constraints are expressed as follows:

[0071] 1) Operational constraints of distributed controllable power sources

[0072]

[0073]

[0074] 2) Energy storage operation constraints

[0075]

[0076] 3) Microgrid power exchange constraints

[0077]

[0078] 4) Demand response operation constraints

[0079] Considering transferable demand response, the load transferred by users should satisfy the total load demand within the scheduling period. At the same time, the load is only allowed to be transferred in or out, and is not allowed to exceed the maximum transferable load.

[0080]

[0081] 5) AC distribution network power flow equation constraints

[0082] The AC distribution network power flow is described using the DisFlow branch power flow method.

[0083]

[0084]

[0085] 6) DC line power flow equation constraints

[0086]

[0087] 7)MMC operation constraints

[0088]

[0089] in, and The upper and lower limits of the distributed controllable power supply ramp rate; and is the upper and lower limits of the active power of the distributed controllable power supply; E ESS is the maximum charge and discharge power of energy storage; U ESS,t When it is 1, it means charging, and when it is 0, it means discharging; and is the maximum and minimum value of the remaining energy storage capacity; E 0,n is the capacity of energy storage at the initial moment; and are the lower and upper limits of the amount of electricity that can be purchased by the microgrid respectively; and are the load after response and the load before response at time t respectively; τ is the range of load variation allowed at each moment; X ij is the line impedance between node i and node j; P j,t and Q j,t are the active power and reactive power of the net load of node j at time t; I ij,t is the current amplitude between node i and node j at time t; P m,t is the active load of node m at time t; and are the active load and reactive load of node j at time t respectively; and are the lower and upper limits of the reactive power absorbed by the sth MMC from the access node; S MMCs is the capacity of the sth MMC; P MMG,t is the active power absorbed by the microgrid from the medium voltage DC bus at time t; is the active power flowing from the sth MMC to the medium voltage DC bus at time t.

[0090] S2: Solve the lower layer of the multi-terminal flexible distribution network model to obtain the scheduling plan of each unit of the microgrid;

[0091] The microgrid includes distributed controllable power sources, energy storage, demand response, photovoltaic and wind power. By coordinating the distributed controllable power sources, energy storage and microgrid power purchase, distributed renewable energy is absorbed. The microgrid parameters are input and the particle swarm optimization algorithm is used to solve the microgrid model. The results after the transferable demand response optimizes the load curve are as follows: Figure 3As shown in Figure 1, through the incentive of time-of-use electricity prices, users shift part of their load during peak hours to off-peak hours, effectively alleviating power supply pressure. The peak-to-valley difference in load before optimization was 844kW, and after optimization it was 745kW, a reduction of 11.73%. The scheduling results of distributed controllable power supply, energy storage, and microgrid power purchase are shown in Figure 1. Figure 4 As shown, the microgrid's current is positive when purchasing electricity and negative when selling it; it is positive when the energy storage is charging and negative when discharging. Through the coordinated scheduling of distributed controllable power sources, energy storage, and microgrid power purchases, distributed renewable energy is effectively absorbed, and excess power is transmitted to the distribution network through flexible interconnection devices and MMCs, increasing microgrid revenue while alleviating load pressure on the distribution network.

[0092] S3: According to the scheduling plan, solve the upper layer of the multi-terminal flexible distribution network model to obtain a power purchase plan for the distribution network, and optimize the distribution network based on the scheduling plan and the power purchase plan.

[0093] The distribution network is based on the power purchase plan of the microgrid, with the objective function of minimizing the system operation loss. The line parameters and system load of the distribution network are input, and the particle swarm optimization algorithm is used to solve the distribution network model. The power distribution of the two MMCs is as follows: Figure 5 As shown, the power transmitted from the distribution network to the microgrid is positive, while the power transmitted from the microgrid to the distribution network is negative. Because the parameters of the distribution network lines are the same, the loads vary, resulting in different power transmissions from the MMCs. Since the load on the line containing MMC#2 is higher than that on the line containing MMC#1, power transmission from the distribution network to the microgrid is primarily through MMC#1, while power transmission from the microgrid to the distribution network is primarily through MMC#2. This effectively achieves line load balancing and reduces line operating losses.

[0094] In summary, the use of particle swarm optimization algorithm to solve the operation optimization problem of multi-terminal flexible distribution network containing microgrids can effectively absorb distributed renewable energy and reduce the operating loss of the distribution system, thereby obtaining the optimal multi-terminal flexible distribution network scheduling scheme containing microgrids. It has a certain guiding role in accelerating the construction of multi-terminal flexible distribution network containing microgrids in actual engineering practice.

[0095] Furthermore, this embodiment also provides a multi-terminal flexible distribution network optimization operation system including a microgrid, including:

[0096] A model building module is used to collect multi-terminal flexible distribution network parameters and build a multi-terminal flexible distribution network model including a microgrid based on the collected parameters;

[0097] The microgrid optimization module is used to solve the microgrid model of the lower layer of the multi-terminal flexible distribution network, obtain the optimal scheduling plan for each unit in the microgrid, and the power purchase amount of the microgrid.

[0098] The distribution network optimization module is used to solve the upper-layer distribution network model of the multi-terminal flexible distribution network based on the power purchase amount output by the microgrid optimization module, and obtain the optimal scheduling plan of the distribution network.

[0099] This embodiment also provides a computer device, which is suitable for the case of a multi-terminal flexible distribution network optimization operation method including a microgrid, and includes a memory and a processor; the memory is used to store computer-executable instructions, and the processor is used to execute computer-executable instructions to implement the multi-terminal flexible distribution network optimization operation method including a microgrid as proposed in the above embodiment.

[0100] The computer device may be a terminal, comprising a processor, a memory, a communication interface, a display screen and an input device connected via a system bus. The processor of the computer device is used to provide computing and control capabilities. The memory of the computer device comprises a non-volatile storage medium and an internal memory. The non-volatile storage medium stores an operating system and a computer program. The internal memory provides an environment for the operation of the operating system and the computer program in the non-volatile storage medium. The communication interface of the computer device is used to communicate with an external terminal in a wired or wireless manner, and the wireless manner may be achieved through WIFI, an operator network, NFC (near field communication) or other technologies. The display screen of the computer device may be a liquid crystal display or an electronic ink display screen, and the input device of the computer device may be a touch layer covering the display screen, or a button, trackball or touchpad provided on the housing of the computer device, or an external keyboard, touchpad or mouse, etc.

[0101] This embodiment also provides a storage medium having a computer program stored thereon, which, when executed by a processor, implements the method for optimizing the operation of a multi-terminal flexible distribution network including a microgrid as proposed in the above embodiment.

[0102] The storage medium proposed in this embodiment and the data storage method proposed in the above embodiment belong to the same inventive concept. Technical details not fully described in this embodiment can be found in the above embodiment, and this embodiment has the same beneficial effects as the above embodiment.

[0103] Example 2

[0104] This is the second embodiment of the present invention, which provides a method for optimizing the operation of a multi-terminal flexible distribution network including a microgrid. In order to verify the beneficial effects of the present invention, scientific demonstration is carried out through economic benefit calculation and simulation experiments.

[0105] In order to verify the effectiveness and superiority of the invented method, this embodiment conducts a simulation test on an IEEE 33-node standard distribution network including a microgrid.

[0106] Configure the computer hardware environment and install MATLAB R2022a software.

[0107] Establish an IEEE 33-node standard distribution network model, including line parameters, node loads, etc.

[0108] Two 1MW MMCs are set at nodes 18 and 22 for interconnection with the microgrid medium-voltage DC bus.

[0109] A microgrid model is constructed, which includes a 100kW distributed controllable power supply, a 200kWh energy storage system, a 100kW photovoltaic power plant, a 50kW wind power plant and a transferable demand response load.

[0110] Set system parameters: distributed controllable power generation cost coefficient 0.5 yuan / kWh, energy storage charging and discharging cost coefficient 0.1 yuan / kWh, energy storage efficiency 0.9, electricity purchase price 0.6 yuan / kWh, electricity selling price 0.4 yuan / kWh, demand response cost coefficient 0.8 yuan / kWh.

[0111] Implementation process:

[0112] The distribution network, microgrid and load point data are extracted to build a multi-terminal flexible distribution network model including microgrid. The distribution network target is set to minimize operating losses, and the microgrid target is set to minimize operating costs, and various operating constraints are considered.

[0113] Solve the microgrid model and obtain the optimal distributed controllable power supply, energy storage and power purchase scheduling plan through the particle swarm optimization algorithm to absorb renewable energy and smooth the load curve.

[0114] According to the microgrid power purchase plan, the distribution network model is solved, and the optimal power allocation scheme of MMC is obtained through the particle swarm optimization algorithm to achieve the minimum operation loss of the distribution network.

[0115] Record the dispatch values ​​and distribution network operation data of each participant, generate data tables and analysis results, as shown in the following table:

[0116] Table 1 Experimental data table

[0117]

[0118]

[0119] As can be seen from the table above, by solving the two-layer optimization of the multi-terminal flexible distribution network model, it is possible to reasonably arrange the output of each device in the microgrid, effectively absorb distributed renewable energy, and control the electricity purchase cost of the microgrid.

[0120] Renewable energy consumption: Both photovoltaic and wind power are fully utilized during the day. For example, between 10:00 AM and 4:00 PM, photovoltaic output ranges from 62.5 to 86.7 kWh, and wind output ranges from 25.2 to 36.3 kWh. The microgrid utilizes energy storage and demand response to adjust the load curve, enabling greater utilization of renewable energy.

[0121] Internal coordination within the microgrid: Distributed controllable power sources, energy storage systems, and power purchases work together to reduce operating costs. For example, between 7:00 PM and 9:00 PM, the controllable power sources are largely shut down, allowing the energy storage system to discharge significantly while simultaneously selling electricity to the distribution network. This avoids peak power purchases from the grid and reduces operating costs.

[0122] Reduced distribution network losses: By optimizing the distribution network model and rationally allocating power transmission between the two MMCs, load balancing is achieved, keeping overall distribution network line losses low. Data shows that the maximum line loss in the distribution network is 16.3 kWh, accounting for only approximately 2.5% of the total load.

[0123] It should be noted that the above embodiments are only used to illustrate the technical solutions of the present invention and are not intended to limit the present invention. Although the present invention has been described in detail with reference to the preferred embodiments, those skilled in the art should understand that the technical solutions of the present invention may be modified or replaced by equivalents without departing from the spirit and scope of the technical solutions of the present invention, which should all be included in the scope of the claims of the present invention.

Claims

1. A method for optimizing the operation of a multi-terminal flexible distribution network including a microgrid, characterized by: include, Collecting multi-terminal flexible distribution network parameters, and constructing a multi-terminal flexible distribution network model according to the multi-terminal flexible distribution network parameters; Solving the lower layer of the multi-terminal flexible distribution network model to obtain a scheduling plan for each unit of the microgrid; According to the scheduling plan, solving the upper layer of the multi-terminal flexible distribution network model to obtain a power purchase plan for the distribution network, and optimizing the distribution network based on the scheduling plan and the power purchase plan; The multi-terminal flexible distribution network model includes an upper-layer distribution network, a lower-layer microgrid, and safe operation constraints; The objective function of the microgrid is to minimize the operating cost, as shown in the following formula: min f=C MG =C DG +C ESS +C DNtoMG +C DR Among them, C MG is the total operating cost of the microgrid; C DG is the power generation cost of distributed controllable power supply; C ESS is the charging and discharging cost of energy storage; C DNtoMG is the electricity purchase cost of the microgrid; C DR is the cost of demand response; N T is the scheduling period; is the cost coefficient of distributed controllable power supply; P DG,t is the generated power of the distributed controllable power supply at time t; is the energy storage charging and discharging cost coefficient; η is the energy storage charging and discharging efficiency; and are the discharge power and charging power of energy storage at time t respectively; and are the unit electricity prices of the microgrid for purchasing and selling electricity; P MG,t is the electricity purchase amount at time t, when P MG,t ≥0 indicates electricity purchase, otherwise it indicates electricity sale; is the cost coefficient of demand response; ΔP load,t is the transferable load at time t.

2. The method for optimizing operation of a multi-terminal flexible distribution network including a microgrid according to claim 1, wherein: The distribution network takes the minimum operating loss as the objective function, as shown in the following formula: Among them, P loss is the operating loss of the system; is the operating loss of the distribution network; is the operating loss of the medium voltage DC bus; is the operating loss of MMC; N is the number of nodes in the distribution network; M is the number of nodes in the DC line; R ij is the line resistance between node i and node j; P ij,t and Q ij,t are the active power and reactive power flowing from node i to node j at time t; V i,t is the voltage amplitude of node i at time t; R kl is the line resistance between node k and node l; P kl,t is the active power flowing from node k to node l at time t; V k,t is the voltage amplitude of node k at time t; N MMC is the number of MMCs; is the loss coefficient of the sth MMC; and are the active power and reactive power absorbed by the sth MMC from the access node at time t, respectively.

3. The method for optimizing operation of a multi-terminal flexible distribution network including a microgrid according to claim 1 or 2, characterized in that: The distribution network and the microgrid are interconnected via a 10kV medium voltage DC bus; The distribution network is connected to the upper power grid and interconnected with the medium voltage DC bus through a modular multilevel converter MMC; The microgrid is interconnected with the medium voltage DC bus through a flexible interconnection device.

4. The method for optimizing operation of a multi-terminal flexible distribution network including a microgrid according to claim 3, wherein: The safety operation constraints include distributed controllable power supply operation constraints, energy storage operation constraints, microgrid power exchange constraints, demand response operation constraints, AC distribution network power flow equation constraints, DC line power flow equation constraints and MMC operation constraints; The distributed controllable power supply operation constraints are as follows: in, and The upper and lower limits of the distributed controllable power supply ramp rate; and It is the upper and lower limits of the active power of the distributed controllable power supply.

5. The method for optimizing operation of a multi-terminal flexible distribution network including a microgrid according to claim 4, wherein: The energy storage operation constraints are shown in the following formula: Among them, E ESS is the maximum charge and discharge power of energy storage; U ESS,t When it is 1, it means charging, and when it is 0, it means discharging; and is the maximum and minimum value of the remaining energy storage capacity; E 0,n is the capacity of energy storage at the initial moment; The AC distribution network power flow equation constraint is as follows: Among them, P j,t and Q j,t are the active power and reactive power of the net load of node j at time t; I ij,t is the current amplitude between node i and node j at time t; P m,t is the active load of node m at time t; and are the active load and reactive load of node j at time t respectively.

6. The method for optimizing operation of a multi-terminal flexible distribution network including a microgrid according to claim 5, wherein: The demand response operation constraint is a transferable demand response. The load transferred by the user should satisfy the total load demand within the scheduling period. The load is only allowed to be transferred in or out at the same time, and is not allowed to exceed the maximum transferable load.

7. A multi-terminal flexible distribution network optimization operation system including a microgrid, based on the multi-terminal flexible distribution network optimization operation method including a microgrid according to any one of claims 1 to 6, characterized in that: include, The model building module is used to collect multi-terminal flexible distribution network parameters and build a multi-terminal flexible distribution network model including microgrids based on the collected parameters; The microgrid optimization module is used to solve the microgrid model of the multi-terminal flexible distribution network, obtain the optimal scheduling plan of each unit in the microgrid, and the amount of electricity purchased by the microgrid; The distribution network optimization module is used to solve the upper distribution network model of the multi-terminal distribution network according to the purchased electricity output by the power grid optimization module, and obtain the optimal scheduling plan for the distribution network.

8. A computer device comprising a memory and a processor, wherein the memory stores a computer program, wherein: When the processor executes the computer program, the steps of the method for optimizing operation of a multi-terminal flexible distribution network containing a microgrid are implemented as described in any one of claims 1 to 6.

9. A computer-readable storage medium having a computer program stored thereon, characterized in that: When the computer program is executed by a processor, the steps of the method for optimizing operation of a multi-terminal flexible distribution network containing a microgrid according to any one of claims 1 to 6 are implemented.

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