Micro-grid scheduling method, device and equipment and storage medium

Abstract

The invention discloses a micro-grid dispatching method, device and equipment and a storage medium, and relates to the technical field of micro-grid management and control, and the method comprises the steps: obtaining an energy operation model corresponding to target energy equipment in a target micro-grid; performing mixed integer programming operation based on the energy operation model to obtain a target operation curve; and performing power scheduling on the target energy equipment according to the target operation curve. According to the method, the mixed integer programming operation is performed according to the energy operation model pre-established for the target energy equipment in the micro-grid, so that the target operation curve corresponding to the optimal overall energy operation condition of the micro-grid is predicted, and power scheduling of the target energy equipment is performed based on the target operation curve. According to the invention, each energy device in the micro-grid is ensured to adaptively operate in an optimal state according to an actual condition, so that the effect of each energy device in a power system is exerted to the greatest extent, the overall economic benefit of the micro-grid is improved, and the energy consumption cost of the micro-grid is effectively reduced.

Description

Technical Field

[0001] This application relates to the field of microgrid management and control technology, and in particular to a microgrid scheduling method, device, equipment and storage medium. Background Technology

[0002] In the structure of a microgrid, since various types of energy are involved, these energy sources must be effectively dispatched and optimized during actual use.

[0003] Traditional microgrid optimization scheduling methods typically rely on empirical data to formulate scheduling strategies. However, this experience-based charging and discharging strategy cannot be adjusted according to actual conditions and often overlooks the huge economic potential of microgrids in peak shaving and valley filling.

[0004] Therefore, existing scheduling methods have failed to fully tap the economic potential of microgrids, and further improvements are needed in cost optimization. Summary of the Invention

[0005] The main purpose of this application is to provide a microgrid dispatching method, device, equipment and storage medium, which aims to solve the technical problem that the existing microgrid dispatching methods still need to be improved in terms of cost optimization.

[0006] To achieve the above objectives, this application proposes a microgrid dispatching method, which includes:

[0007] Obtain the energy operation model corresponding to the target energy equipment in the target microgrid;

[0008] Based on the energy operation model, mixed integer programming operations are performed to obtain the target operation curve;

[0009] Power scheduling is performed on the target energy equipment based on the target operating curve.

[0010] In one embodiment, the step of performing mixed-integer programming based on the energy operation model to obtain the target operating curve includes:

[0011] The microgrid dispatch output model is determined based on the energy constraint parameters of the target microgrid;

[0012] Based on the energy operation model and the microgrid scheduling output model, a mixed integer programming operation is performed to obtain the target operation curve.

[0013] In one embodiment, the energy constraint parameters include relaxation penalties and electricity purchase costs; the step of determining the microgrid dispatch output model based on the energy constraint parameters of the target microgrid includes:

[0014] The cost optimization function is determined based on the relaxation penalty of the target microgrid and the electricity purchase cost;

[0015] Obtain the operational constraints corresponding to the target energy devices in the target microgrid;

[0016] The microgrid scheduling output model is determined based on the cost optimization function and the operational constraints.

[0017] In one embodiment, the step of power scheduling of the target energy device according to the target operating curve includes:

[0018] Real-time microgrid incoming power is obtained based on a preset inspection cycle;

[0019] A preset anti-backflow test is performed based on the preset microgrid operating power range and the real-time microgrid incoming power.

[0020] Power scheduling is performed on the target energy equipment based on the test results and the target operating curve.

[0021] In one embodiment, the target energy equipment includes microgrid energy storage equipment and microgrid wind and solar equipment; the step of power scheduling of the target energy equipment based on the test results and the target operating curve includes:

[0022] When the test result shows that the real-time microgrid incoming power is less than the preset microgrid operating power range, the microgrid energy storage device is subject to discharge control.

[0023] Power scheduling is performed on the microgrid wind and solar equipment based on the energy storage discharge control results and preset scheduling strategies.

[0024] In one embodiment, the step of power scheduling of the target energy device based on the test results and the target operating curve further includes:

[0025] When the test result indicates that the real-time microgrid incoming power is within the preset microgrid operating power range, the target energy equipment is power-scheduled according to the preset scheduling strategy or the target operating curve.

[0026] In one embodiment, the step of power scheduling of the target energy device based on the test results and the target operating curve further includes:

[0027] When the test result shows that the real-time microgrid incoming power is greater than the preset microgrid operating power range, it is determined whether the target energy device has a power limitation.

[0028] If power limitations exist, power scheduling is performed on the target energy equipment according to preset power control rules and the target operating curve.

[0029] Furthermore, to achieve the above objectives, this application also proposes a microgrid dispatching device, which includes:

[0030] The energy monitoring module is used to acquire the energy operation model corresponding to the target energy equipment in the target microgrid;

[0031] The prediction module is used to perform mixed-integer programming operations based on the energy operation model to obtain the target operation curve.

[0032] The power scheduling module is used to perform power scheduling on the target energy equipment according to the target operating curve.

[0033] In addition, to achieve the above objectives, this application also proposes a microgrid scheduling device, which includes: a memory, a processor, and a microgrid scheduling program stored in the memory and executable on the processor. The microgrid scheduling program is configured to implement the steps of the microgrid scheduling method described above.

[0034] In addition, to achieve the above objectives, this application also proposes a storage medium, which is a computer-readable storage medium, storing a microgrid scheduling program. When the microgrid scheduling program is executed by a processor, it implements the steps of the microgrid scheduling method described above.

[0035] This application provides a microgrid dispatching method, apparatus, device, and storage medium. The method includes: acquiring an energy operation model corresponding to a target energy device in a target microgrid; performing mixed-integer programming operations based on the energy operation model to obtain a target operation curve; and performing power dispatching on the target energy device according to the target operation curve. Compared to existing methods that set microgrid dispatching strategies based on empirical data, this application predicts the optimal overall energy operation of the microgrid by performing mixed-integer programming operations on a pre-defined energy operation model for target energy devices such as wind, solar, and energy storage devices within the microgrid. Power dispatching of the target energy device is then performed based on the predicted target operation curve. Therefore, this application can achieve intelligent overall energy analysis through mixed-integer programming operations on the energy operation models corresponding to wind, solar, and energy storage devices in the microgrid. Based on the target operation curve, it ensures that each energy device in the microgrid adaptively operates in the optimal state according to actual conditions, thereby maximizing the role of each energy device in the power system, ultimately improving the overall economic benefits of the microgrid and effectively reducing the energy cost of the microgrid. Attached Figure Description

[0036] The accompanying drawings, which are incorporated in and form part of this specification, illustrate embodiments consistent with this application and, together with the description, serve to explain the principles of this application.

[0037] To more clearly illustrate the technical solutions in the embodiments of this application or the prior art, the drawings used in the description of the embodiments or the prior art will be briefly introduced below. Obviously, for those skilled in the art, other drawings can be obtained based on these drawings without creative effort.

[0038] Figure 1 This is a first flowchart illustrating the first embodiment of the microgrid scheduling method of this application;

[0039] Figure 2 This is a diagram of the target microgrid architecture in the first embodiment of the microgrid scheduling method of this application;

[0040] Figure 3 This is a second flowchart illustrating the first embodiment of the microgrid dispatching method of this application;

[0041] Figure 4 This is a flowchart illustrating the second embodiment of the microgrid scheduling method of this application;

[0042] Figure 5 This is a schematic diagram of the anti-backflow control process of the second embodiment of the microgrid dispatching method of this application;

[0043] Figure 6 This is a schematic diagram of the module structure of the microgrid dispatching device according to an embodiment of this application;

[0044] Figure 7 This is a schematic diagram of the equipment structure of the hardware operating environment involved in the microgrid scheduling method in this application embodiment.

[0045] The purpose, features, and advantages of this application will be further explained in conjunction with the embodiments and with reference to the accompanying drawings. Detailed Implementation

[0046] It should be understood that the specific embodiments described herein are merely illustrative of the technical solutions of this application and are not intended to limit this application.

[0047] To better understand the technical solution of this application, a detailed description will be provided below in conjunction with the accompanying drawings and specific implementation methods.

[0048] The main solution of this application is: to obtain the energy operation model corresponding to the target energy equipment in the target microgrid; to perform mixed integer programming based on the energy operation model to obtain the target operation curve; and to perform power scheduling on the target energy equipment according to the target operation curve.

[0049] Currently, traditional microgrid optimization scheduling mainly combines empirical values ​​to provide predetermined charging and discharging strategies for energy storage. However, the predetermined charging and discharging strategies have not fully tapped the economic potential of microgrid peak shaving and valley filling. Therefore, existing technologies still need to be improved in terms of optimizing microgrid scheduling costs.

[0050] This application uses mixed-integer programming to perform mixed-integer programming on a pre-defined energy operation model for target energy devices such as wind, solar, and energy storage equipment within a microgrid. This predicts the optimal overall energy operation of the microgrid and then performs power scheduling for the target energy devices based on the predicted target operation curve. Therefore, this application can achieve intelligent overall energy analysis through mixed-integer programming on the energy operation model corresponding to wind, solar, and energy storage equipment in a microgrid. Based on the target operation curve, it ensures that each energy device in the microgrid operates under optimal conditions, thereby maximizing the role of each energy device in the power system and ultimately improving the overall economic benefits of the microgrid while effectively reducing its energy costs.

[0051] It should be noted that the executing entity in this embodiment can be a microgrid dispatching system, or a computing service device with data processing, network communication, and program execution functions, such as a tablet computer, personal computer, or mobile phone, or a microgrid dispatching device capable of performing the above functions. This embodiment does not specifically limit the specific implementation. The following uses a microgrid dispatching device (hereinafter referred to as dispatching device) as the executing entity to describe this embodiment and the following embodiments.

[0052] Based on this, embodiments of this application provide a microgrid scheduling method, referring to... Figure 1 , Figure 1 This is a first flowchart illustrating the first embodiment of the microgrid scheduling method of this application.

[0053] In this embodiment, the microgrid scheduling method includes steps S10 to S30:

[0054] Step S10: Obtain the energy operation model corresponding to the target energy equipment in the target microgrid;

[0055] It should be understood that in this embodiment, the target microgrid is a grid-connected microgrid, which can be connected to the main power grid and exchange energy with it. In terms of its components, the target microgrid may include wind turbine generators, photovoltaic arrays, energy storage systems, inverters, controllers, and user loads, each with its unique function and role. A detailed architecture diagram of the target microgrid can be shown below. Figure 2 As shown, Figure 2 This is a diagram of the target microgrid architecture in the first embodiment of the microgrid scheduling method of this application. Therefore, combined with Figure 2As can be seen, in this embodiment, when the target microgrid meets the user load, excess electrical energy can be fed into the grid while satisfying tie-line constraints; when the output of the target microgrid cannot meet the user load, the main grid can be used as a backup power source, and electricity can be purchased and fed into the grid to meet the electricity demand. In order to effectively perform microgrid energy consumption analysis, this embodiment needs to model the microgrid as a whole, new energy sources such as wind power or photovoltaics, and energy storage separately to obtain the above-mentioned energy operation model.

[0056] It is readily understood that, in this embodiment, the aforementioned target energy equipment can be new energy equipment (such as wind power equipment or photovoltaic equipment) and energy storage equipment configured in the target microgrid. Therefore, assuming the target microgrid is a grid-connected wind-solar-storage microgrid, the aforementioned energy operation model can include a microgrid overall operation model, a wind power generation operation model, a photovoltaic power generation operation model, and an energy storage operation model. The microgrid overall operation model at this time can be represented as follows:

[0057] P MG =P PV +P WT +P ESS +P grid (1)

[0058] In the formula, P MG P represents the total output power of the microgrid. PV P represents the power generated by photovoltaic power generation. WT P represents the power generated by wind power. ESS P represents the power of the energy storage system. grid This indicates the power of the power grid.

[0059] It is easy to understand that when the energy storage power P ESS A value greater than zero indicates that the energy storage system is discharging; when the energy storage power P ESS A value less than zero indicates that the energy storage system is charging. When the grid output P... grid When the value is greater than zero, it indicates that the target microgrid purchases electricity from the main grid; when the grid output P grid When the value is less than zero, it indicates that the target microgrid is selling electricity to the main grid.

[0060] Therefore, as shown in the overall microgrid operation model, the target microgrid in this embodiment can achieve bidirectional energy flow through connection with the main grid. It can supply excess power to the grid and draw power from the grid when needed. This bidirectional energy exchange capability allows the microgrid to manage its energy supply more flexibly and improve energy utilization efficiency. Therefore, this embodiment needs to coordinate the output of various energy sources, especially the energy storage system, in a timely manner according to the actual grid conditions, fully utilizing and exploring the economic potential of peak shaving and valley filling in the microgrid to achieve the orderly and economical operation of the microgrid as a whole.

[0061] Furthermore, in this embodiment, the energy equipment in the target microgrid may include wind power generation equipment that uses the mechanical energy of wind to drive a generator and convert it into electrical energy. To ensure the safe and stable operation of the wind power generation equipment, the cut-in wind speed, rated wind speed, and cut-out wind speed are defined. The cut-in wind speed is the minimum wind speed required for the wind power generation equipment to generate electricity. The cut-out wind speed refers to the maximum wind speed at which the wind power generation equipment can operate. Exceeding this wind speed may damage the wind power generation equipment, requiring shutdown. The power output of the wind power generation equipment is determined by the wind speed. The relationship between the output power of the wind turbine and the wind speed, i.e., the above-mentioned wind power generation operation model, can be represented by the following piecewise function:

[0062]

[0063] In the formula, P WT The output power of the wind turbine; v, v ci v e v co These are the actual wind speed, cut-in wind speed, rated wind speed, and cut-out wind speed, respectively; P e This refers to the rated power of the wind turbine.

[0064] Furthermore, in this embodiment, the energy equipment in the target microgrid may include photovoltaic (PV) power generation equipment that converts solar energy into electrical energy. The PV power output of the photovoltaic power generation equipment is typically related to light intensity and ambient temperature; therefore, the aforementioned PV power generation operation model can be expressed as:

[0065]

[0066] In the formula, P PV N represents the output power of the photovoltaic power generation equipment at the operating point. PV P represents the number of photovoltaic panels; STC E represents the rated output power of the photovoltaic array under standard conditions. c E represents the actual solar irradiance at the operating point. STC denoted as solar irradiance under standard conditions; μ is the power temperature coefficient, with a value of -0.0043 / ℃; t c t is the operating point temperature. STC Temperature under standard conditions.

[0067] Furthermore, in this embodiment, the energy equipment in the target microgrid may also include energy storage devices that store energy in other forms. Based on the different forms of energy storage, energy storage devices can be divided into three categories: physical energy storage devices, chemical energy storage devices, and electromagnetic energy storage devices. Energy storage plays an important role in microgrids, but the selection of energy storage technology needs to be determined according to the actual needs of different application scenarios. For the target microgrid in this embodiment, energy-type energy storage devices are typically configured to achieve stable charging and discharging on an hourly basis.

[0068] It is easy to understand that, by comparing the technical characteristics of different energy storage methods, battery energy storage, due to its lower requirements for geographical conditions, higher energy conversion efficiency, and the ability of its equipment power level and discharge time to meet the energy requirements of microgrid operation, is prioritized as the energy storage system for the target microgrid in this embodiment. Taking lithium iron phosphate batteries as an example, the energy of the energy storage system is constantly changing, and the state of charge of the lithium battery is determined by its charging and discharging power. Therefore, the above energy storage operation model can be expressed as follows:

[0069]

[0070] In the formula, S OC (t) represents the SOC value of the lithium battery at time t; P LB This refers to the charging and discharging power of the lithium battery; a positive value is used during charging, and a negative value is used during discharging. (C) LB This refers to the rated capacity of the lithium battery; U LB η is the rated voltage of the lithium battery; η is the charge / discharge efficiency, which is 0.65 to 0.85 during charging and 1 during discharging; Δt is the sampling time period.

[0071] Step S20: Perform mixed integer programming based on the energy operation model to obtain the target operation curve;

[0072] It is understood that this embodiment can pre-build energy operation models corresponding to each energy device in the microgrid to determine the output factors of components such as wind, solar and energy storage in the target microgrid, and then perform mixed integer programming operations to solve the overall optimal operating conditions corresponding to different energy devices in the system, and obtain the target operating curve, so as to perform fine scheduling of the target microgrid in the future according to the target operating curve.

[0073] In one feasible implementation, refer to Figure 3 , Figure 3 This is a second flowchart illustrating the first embodiment of the microgrid dispatching method of this application. In this embodiment, step S20 may include steps A1 to A2:

[0074] Step A1: Determine the microgrid dispatch output model based on the energy constraint parameters of the target microgrid;

[0075] Step A2: Perform mixed integer programming operations based on the energy operation model and the microgrid scheduling output model to obtain the target operation curve.

[0076] It is easy to understand that in the process of microgrid dispatching, in addition to considering the energy cost of the grid, the overall reliability of the system also needs to be considered. Therefore, this embodiment can determine the planned dispatching model corresponding to the target microgrid based on the energy constraint parameters of the target microgrid, that is, the above-mentioned microgrid dispatching output model, and then perform mixed integer programming operations based on the microgrid dispatching output model and the energy operation model.

[0077] In a first feasible implementation, the energy constraint parameters include a slack penalty and the cost of electricity purchase; in this embodiment, step A1 may include steps A11 to A13:

[0078] Step A11: Determine the cost optimization function based on the relaxation penalty of the target microgrid and the electricity purchase cost;

[0079] It is easy to understand that, in this embodiment, the aforementioned relaxation penalty can be a constraint condition for the stable operation of the maintenance market corresponding to the target microgrid and the safety and stability settings of the power grid. The cost optimization function determined based on the relaxation penalty and the electricity purchase cost in this embodiment can be expressed as follows:

[0080] obj=min(ξ penalty +ξ buy (5)

[0081] In the formula, ξ penalty To relax the punishment, ξ buy This refers to the cost of purchasing electricity.

[0082] Furthermore, the slack penalty can be converted into a penalty for the curtailment of renewable energy. penalty,drop With the penalty for missing funds penalty,L It consists of two parts, and is represented as follows:

[0083] ξ penalty =ξ penalty，drop +ξ penalty,L (6)

[0084] Among them, the penalty for abandonment of renewable energy ξ penalty,drop It can be represented as:

[0085] ξ penalty,drop =L drop *λ drop (7)

[0086] In the formula, λ drop As punishment for abandoning the wind and light, L drop This represents the slack in the power curtailment constraint.

[0087] And the penalty for missing points penalty,L It can be represented as:

[0088]

[0089] In the formula, Let be the unit power supply load deficit penalty at time t. The power supply load deficit at time t is denoted by TotalHour, which represents the total scheduling period.

[0090] The aforementioned electricity purchase cost ξ buy It can be represented as:

[0091]

[0092] In the formula, The amount of electricity purchased at time t. Let t be the unit electricity purchase cost at time t.

[0093] Step A12: Obtain the operating constraints corresponding to the target energy equipment in the target microgrid;

[0094] Step A13: Determine the microgrid scheduling output model based on the cost optimization function and the operational constraints.

[0095] It is easy to understand that, in this embodiment, the above-mentioned operational constraints can be the power balance constraints and curtailment rate constraints corresponding to the target microgrid. Among them, the power balance constraint can characterize the load balance that the microgrid needs to meet at every moment during the actual operation of the microgrid in order to meet the power quality requirements, which can be expressed as follows:

[0096]

[0097] In the formula, The output of the wind power generation equipment during period t; Provide power to the photovoltaic power generation equipment during period t; Let t represent the discharge power of the energy storage device during time period t, and n represent the nth energy storage device in the target microgrid. The charging power of the energy storage device during time period t; This represents the amount of electricity wasted by wind power generation equipment during time period t. This refers to the amount of power curtailed by photovoltaic power generation equipment during time period t, which is limited by the total actual output of wind and solar power units; LOAD t The total load demand of the target microgrid during time period t; The load shedding variable is for time period t.

[0098] Among them, the photovoltaic power generation equipment is currently generating power at all times. The corresponding photovoltaic constraint can be expressed as:

[0099]

[0100] In the formula, For the output capacity of photovoltaic units, This is the ratio of the predicted output curve of the photovoltaic unit at time t to the predicted output curve at time t.

[0101] The photovoltaic curtailment constraint at this time can be expressed as:

[0102]

[0103] The wind power generation equipment is currently outputting power. The corresponding photovoltaic constraint can be expressed as:

[0104]

[0105] In the formula, For the output capacity of wind turbine units, This represents the ratio of the predicted output of the wind turbine at time t.

[0106] The wind curtailment constraint at this point can be expressed as:

[0107]

[0108] For energy storage systems, constraints typically include battery SOC (State of Charge) constraints and battery operation constraints. Specifically, to extend the lifespan of energy storage batteries and prevent damage from overcharging and discharging, battery SOC constraints are defined as setting upper and lower limits for the SOC of lithium batteries. The maximum SOC for energy-type batteries can be 0.9, and the minimum can be 0.1. Battery operation constraints, on the other hand, mean that the capacity, charging and discharging power, and number of charge / discharge cycles per day of the lithium battery energy storage system must all meet certain constraints.

[0109] It is easy to understand that if curtailment constraints are not considered, arbitrary curtailment would be allowed, which is uneconomical. Therefore, this embodiment can ensure the economic efficiency of the target microgrid operation based on curtailment rate constraints, which can be expressed as:

[0110]

[0111] 0≤L drop (16)

[0112] In the formula, DROPRATIO is the curtailment rate, and L drop This represents the relaxation amount of the curtailment rate constraint.

[0113] Therefore, after determining the energy operation model and microgrid dispatch output model corresponding to the target microgrid by formulating the above formulas (1) to (16), the steps for performing mixed integer programming in this embodiment can be as follows: Based on the energy operation model, cost optimization function and operation constraints, construct a mixed integer programming model of the microgrid planning curve. This mixed integer programming model can handle complex problems containing continuous and discrete variables, find the optimal solution under various constraints, and thus predict the best overall energy operation of the target microgrid. Then, the dispatching equipment can use the branch and bound algorithm to solve the mixed integer programming model and obtain the operation planning curve data of the target microgrid within a preset time period (e.g., the second day or the second week), including the photovoltaic power generation curve, the wind power generation curve and the energy storage SOC charging and discharging strategy curve, i.e., the above-mentioned target operation curve.

[0114] Step S30: Perform power scheduling on the target energy equipment according to the target operating curve.

[0115] It is easy to understand that once the power operation data of each energy source in the target microgrid is determined, the dispatching equipment can send the target operation curve to each energy device for execution, thereby realizing rapid coordinated control of the microgrid.

[0116] Therefore, this embodiment employs detailed energy operation models pre-developed for the target energy devices within the microgrid, such as wind and solar power generation equipment and energy storage devices. These models comprehensively consider the characteristics and operating patterns of various energy devices to ensure their accuracy and practicality. Based on this, and further incorporating the target microgrid's system operation strategy and power allocation principles, a mixed-integer programming approach is used to conduct an in-depth analysis and prediction of the overall energy operation of the microgrid. This prediction result not only considers the operating efficiency of the energy devices but also takes into account the system's stability and economy.

[0117] Based on the predicted target operating curve, we further perform power scheduling for the target energy devices. During this power scheduling process, the scheduling equipment dynamically adjusts the output power of each energy device according to the predicted target operating curve and real-time data from the target energy devices, ensuring that the microgrid can operate efficiently and economically at different times. By precisely scheduling power to effectively and economically coordinate the output of each energy device, ensuring that each device operates at its optimal operating point, we can minimize the energy costs of the microgrid and achieve reliable and economical operation of the entire system.

[0118] This embodiment provides a microgrid dispatching method, which includes: obtaining the energy operation model corresponding to the target energy equipment in the target microgrid; determining the cost optimization function based on the relaxation penalty and electricity purchase cost of the target microgrid; obtaining the operation constraints corresponding to the target energy equipment in the target microgrid; and determining the microgrid dispatching output model based on the cost optimization function and operation constraints. Mixed-integer programming is performed based on the energy operation model and the microgrid dispatching output model to obtain the target operation curve; and power dispatching is performed on the target energy equipment according to the target operation curve. This embodiment uses a pre-defined energy operation model for target energy equipment such as wind and solar power and energy storage devices in the microgrid, combined with the system operation strategy and power allocation principles of the target microgrid, to perform mixed-integer programming to predict the optimal overall energy operation of the target microgrid. Then, based on the predicted target operation curve, power dispatching is performed on the target energy equipment, thereby effectively and economically coordinating the output of each unit and minimizing the energy cost of the microgrid, achieving reliable and economical overall system operation.

[0119] Based on the first embodiment of this application, in the second embodiment of this application, the same or similar content as the first embodiment described above can be referred to the above description, and will not be repeated hereafter.

[0120] It's easy to understand that in traditional microgrid optimization scheduling, when a power surplus is detected within the microgrid, such as during periods of high photovoltaic (PV) power generation, the system takes measures to appropriately limit PV power generation to ensure that the microgrid's anti-reverse current requirements are met. However, this approach does not fully utilize the rapid adjustment capabilities of energy storage systems to supplement the anti-reverse current needs. Therefore, existing optimization scheduling methods have significant room for improvement in reducing power waste, i.e., curtailment.

[0121] To address the aforementioned issues, this embodiment combines the regulation capabilities of the energy storage system with the limiting measures of photovoltaic power generation, thereby making more effective use of renewable energy, reducing unnecessary power losses, and further improving the operating efficiency of the microgrid.

[0122] Therefore, based on the first embodiment, please refer to Figure 4 , Figure 4 This is a flowchart illustrating the second embodiment of the microgrid dispatching method of this application. In this embodiment, step S30 includes steps B1 to B3:

[0123] Step B1: Obtain the real-time microgrid incoming power based on the preset inspection cycle;

[0124] Step B2: Perform a preset anti-backflow test based on the preset microgrid operating power range and the real-time microgrid incoming power;

[0125] Step B3: Perform power scheduling on the target energy equipment based on the test results and the target operating curve.

[0126] It is understood that the dispatching equipment can obtain the electrical power received by the target microgrid from the external power grid or power source through data acquisition devices such as electricity meters, i.e., the aforementioned real-time microgrid incoming power. In this embodiment, a preset microgrid operating power range can be set in advance, and the dispatching equipment collects and judges whether the real-time incoming power of the target microgrid is within the preset microgrid operating power range according to a preset inspection cycle, so as to verify whether the target microgrid meets the anti-reverse flow control conditions. Based on the inspection results and the target operating curve, the final microgrid dispatch is performed, thereby reducing the energy cost of the microgrid while ensuring the stability of the microgrid.

[0127] In a first feasible implementation, step B3 may include steps C1 to C2:

[0128] Step C1: When the test result shows that the real-time microgrid incoming power is less than the preset microgrid operating power range, discharge control is performed on the microgrid energy storage device.

[0129] Step C2: Power scheduling of the microgrid wind and solar equipment is performed based on the energy storage discharge control results and the preset scheduling strategy.

[0130] It is important to understand that the aforementioned preset microgrid operating power range can be composed of a preset safety value and a preset hysteresis value, which can be expressed as [preset safety value, preset safety value + preset hysteresis value]. This preset safety value represents the threshold for determining whether backflow prevention is triggered. Typically, when the real-time microgrid incoming power exceeds 0, meaning the real-time incoming power is less than zero and reverses, it can be determined that backflow has occurred in the target microgrid. Therefore, setting the preset safety value to be greater than 0 allows for early triggering of strategies to adjust system power before backflow occurs.

[0131] In essence, if the dispatching equipment detects that the real-time microgrid incoming power is less than the preset microgrid operating power range (i.e., the real-time microgrid incoming power is less than the preset safety value), the dispatching equipment can execute anti-backflow control based on a preset dispatching strategy. Specifically, the dispatching equipment can manage the discharge of microgrid energy storage devices within the target energy equipment, limiting their energy storage discharge power (no action is taken if the energy storage is in charging or standby mode). In this case, the dispatching equipment can detect whether the microgrid energy storage device is in a discharging state. If it is not in a discharging state, the dispatching equipment cannot manage its discharge; in this situation, restrictions can be placed on wind and solar power equipment to implement anti-backflow control.

[0132] If the microgrid energy storage device is in a discharging state, the dispatching equipment can control the discharge of the microgrid energy storage device based on the difference between the real-time microgrid incoming power and the preset safety value, and control the real-time discharge power of the microgrid energy storage device to be reduced to the anti-reverse current safety power. The anti-reverse current safety power is equal to the real-time incoming power minus the difference between the real-time discharge power and the preset safety value.

[0133] Furthermore, if reducing the energy storage discharge power to 0 during this process still does not meet the requirements, that is, if the real-time microgrid incoming power is still less than the preset safety value, the dispatching equipment can execute a preset dispatching strategy, that is, limit the output of the photovoltaic equipment until the requirements are met.

[0134] Understandably, a larger preset safety value results in a more conservative anti-reverse current control for the microgrid, making it less prone to reverse current in the target microgrid. However, while improving the anti-reverse current effect, it also leads to more photovoltaic power being restricted and more curtailment. Conversely, if the preset safety value is adjusted to be smaller, the anti-reverse current regulation will only be triggered when the target microgrid is closer to the occurrence of reverse current. Although this can improve the utilization rate of photovoltaic power, it increases the probability of reverse current and reduces system security. Therefore, the preset safety value can be set according to the actual situation, and this embodiment does not limit its specific value.

[0135] In a second feasible implementation, step B3 may further include step D1:

[0136] Step D1: When the test result shows that the real-time microgrid incoming power is within the preset microgrid operating power range, power scheduling is performed on the target energy equipment according to the preset scheduling strategy or the target operating curve.

[0137] It is easy to understand that the aforementioned preset hysteresis value serves as a buffer parameter to avoid frequent switching between the "anti-reverse current restriction release" and "anti-reverse current power limiting" actions. Between the "preset safety value" and the "preset safety value + preset hysteresis value," the anti-reverse current strategy does not activate, and the dispatching equipment can control the target microgrid to continue operating according to the existing strategy settings. Therefore, when the dispatching equipment detects that the real-time microgrid incoming power is within the preset microgrid operating power range, the dispatching equipment may not issue any instructions, and wind, solar, and energy storage will maintain their original operation. The existing strategy corresponding to the target microgrid can be a preset dispatching strategy or a target operating curve. The preset dispatching strategy can be a fixed dispatching strategy set in advance, and the specific choice between using a preset dispatching strategy or a target operating curve to dispatch power to the target energy equipment is determined by the actual situation of the target microgrid.

[0138] It is important to understand that if the preset hysteresis value increases, the fluctuation range of real-time incoming power will correspondingly expand. This will increase the trigger threshold of the anti-reverse current restriction release mechanism, thereby reducing the trigger frequency of the mechanism and decreasing the likelihood of recurrence of reverse current. However, this will also lead to a decrease in the number of power restriction releases, thus increasing the probability of curtailment. Conversely, if the preset hysteresis value decreases, the fluctuation range of real-time incoming power will correspondingly shrink. This will lower the trigger threshold of the anti-reverse current restriction release mechanism, increase the trigger frequency of the mechanism, and increase the probability of recurrence of reverse current. However, more frequent power restriction releases will help improve the energy utilization rate of the photovoltaic system. Therefore, the preset hysteresis value can be set according to actual conditions, and this embodiment does not limit its specific value.

[0139] In a third feasible implementation, step B3 may further include steps E1 to E2:

[0140] Step E1: When the test result shows that the real-time microgrid incoming power is greater than the preset microgrid operating power range, determine whether the target energy device has a power limitation.

[0141] Step E2: If there is a power limitation, then the power of the target energy device is scheduled according to the preset power control rules and the target operating curve.

[0142] It is easy to understand that when the dispatching equipment detects that the real-time incoming power of the microgrid exceeds the preset safety value plus the preset hysteresis value, it can directly dispatch the power of the target energy equipment according to the target operating curve. However, as the aforementioned analysis shows, the dispatching equipment may impose power restrictions on microgrid energy storage equipment and / or microgrid wind and solar equipment. Therefore, the dispatching equipment can first detect whether there are power restrictions on the target energy equipment. If so, it can remove the power restrictions on the target energy equipment according to the preset power control rules.

[0143] Specifically, in this embodiment, the scheduling device can first remove the restrictions on wind and solar equipment in the target energy equipment, then remove the power restrictions on the energy storage equipment, and then read the power value set by the target operating curve after the power restrictions on the target energy equipment are removed to perform power scheduling.

[0144] In the specific implementation, refer to Figure 5 The backflow prevention and control process in this embodiment will be explained and described. Figure 5 This is a schematic diagram of the anti-backflow control process in the second embodiment of the microgrid dispatching method of this application. Figure 5 As shown, the dispatching equipment collects real-time incoming power and determines whether it is less than a preset safety value. If so, it controls the discharge of the microgrid energy storage equipment, while the microgrid wind and solar equipment operates according to the preset dispatching strategy.

[0145] If the real-time incoming power is between the preset safety value and the preset safety value plus the preset hysteresis value, the microgrid energy storage device and the microgrid wind and solar device will maintain the existing instructions and execute the original actions. The original actions may be manually set strategies or planned target operating curves.

[0146] If the real-time incoming power is greater than the preset safety value plus the preset hysteresis value, then the wind and solar restrictions and energy storage restrictions will be lifted in sequence (if any restrictions exist at this time), and the microgrid wind and solar equipment and microgrid energy storage equipment will be scheduled to operate according to the planned curve.

[0147] In summary, this embodiment proposes a microgrid coordinated optimization scheduling method that balances cost optimization and real-time backflow prevention. The method combines a protocol-based energy storage day-ahead scheduling strategy with real-time backflow prevention control, which minimizes energy costs while ensuring the safe and stable operation of the microgrid.

[0148] This embodiment discloses a method for obtaining real-time microgrid incoming power based on a preset inspection cycle; performing a preset anti-reverse current inspection based on a preset microgrid operating power range and the real-time microgrid incoming power; when the inspection result shows that the real-time microgrid incoming power is less than the preset microgrid operating power range, discharging control is implemented for the microgrid energy storage equipment; and power scheduling is performed on the microgrid wind and solar equipment based on the energy storage discharge control result and a preset scheduling strategy. When the inspection result shows that the real-time microgrid incoming power is within the preset microgrid operating power range, power scheduling is performed on the target energy equipment according to a preset scheduling strategy or target operating curve. When the inspection result shows that the real-time microgrid incoming power is greater than the preset microgrid operating power range, it is determined whether the target energy equipment has a power limitation; if a power limitation exists, power scheduling is performed on the target energy equipment according to preset power control rules and the target operating curve. This embodiment proposes a microgrid coordinated optimization scheduling method that balances cost optimization and real-time anti-reverse current, using a protocol-based day-ahead energy storage scheduling strategy and real-time anti-reverse current control to minimize energy costs while ensuring the safe and stable operation of the microgrid.

[0149] It should be noted that the above examples are only for understanding this application and do not constitute a limitation on the microgrid dispatching method of this application. Any simple modifications based on this technical concept are within the protection scope of this application.

[0150] This application also provides a microgrid dispatching device; please refer to [reference needed]. Figure 6 , Figure 6 This is a schematic diagram of the module structure of the microgrid dispatching device according to an embodiment of this application. In this embodiment, the microgrid dispatching device includes:

[0151] Energy monitoring module 601 is used to acquire the energy operation model corresponding to the target energy equipment in the target microgrid;

[0152] The prediction module 602 is used to perform mixed integer programming operations based on the energy operation model to obtain the target operation curve.

[0153] The power scheduling module 603 is used to perform power scheduling on the target energy equipment according to the target operating curve.

[0154] In one feasible implementation, in this embodiment, the prediction module 602 is further configured to determine the microgrid dispatch output model based on the energy constraint parameters of the target microgrid;

[0155] The prediction module 602 is also used to perform mixed integer programming operations based on the energy operation model and the microgrid scheduling output model to obtain the target operation curve.

[0156] In one feasible implementation, the energy constraint parameters include a relaxation penalty and the cost of purchasing electricity; in this embodiment, the prediction module 602 is further configured to determine a cost optimization function based on the relaxation penalty and the cost of purchasing electricity for the target microgrid.

[0157] The prediction module 602 is also used to obtain the operating constraints corresponding to the target energy equipment in the target microgrid;

[0158] The prediction module 602 is also used to determine the microgrid scheduling output model based on the cost optimization function and the operating constraints.

[0159] In one feasible implementation, in this embodiment, the power scheduling module 603 is further configured to obtain the real-time microgrid incoming power based on a preset inspection cycle;

[0160] The power scheduling module 603 is also used to perform a preset anti-reverse current test based on the preset microgrid operating power range and the real-time microgrid incoming power;

[0161] The power scheduling module 603 is also used to perform power scheduling on the target energy equipment based on the test results and the target operating curve.

[0162] In one feasible implementation, the target energy equipment includes microgrid energy storage equipment and microgrid wind and solar equipment; in this embodiment, the power scheduling module 603 is also used to perform discharge control on the microgrid energy storage equipment when the test result shows that the real-time microgrid incoming power is less than the preset microgrid operating power range;

[0163] The power scheduling module 603 is also used to perform power scheduling on the microgrid wind and solar equipment based on the energy storage discharge control results and preset scheduling strategies.

[0164] In a feasible implementation, in this embodiment, the power scheduling module 603 is further configured to perform power scheduling on the target energy equipment according to the preset scheduling strategy or the target operating curve when the test result indicates that the real-time microgrid incoming power is within the preset microgrid operating power range.

[0165] In a feasible implementation, in this embodiment, the power scheduling module 603 is further configured to determine whether the target energy device has a power limitation when the test result is that the real-time microgrid incoming power is greater than the preset microgrid operating power range;

[0166] The power scheduling module 603 is also used to perform power scheduling on the target energy equipment according to the preset power control rules and the target operating curve if there is a power limitation.

[0167] The microgrid dispatching device provided in this application, employing the microgrid dispatching method in the above embodiments, can solve the technical problems of microgrid dispatching. Compared with the prior art, the beneficial effects of the microgrid dispatching device provided in this application are the same as those of the microgrid dispatching method provided in the above embodiments, and other technical features in the microgrid dispatching device are the same as those disclosed in the methods of the above embodiments, and will not be repeated here.

[0168] This application provides a microgrid scheduling device, which includes: at least one processor; and a memory communicatively connected to the at least one processor; wherein the memory stores instructions executable by the at least one processor, which are executed by the at least one processor to enable the at least one processor to perform the microgrid scheduling method in Embodiment 1 above.

[0169] The following is for reference. Figure 7 The diagram illustrates a structural schematic of a microgrid dispatching device suitable for implementing embodiments of this application. The microgrid dispatching device in these embodiments may include, but is not limited to, mobile terminals such as mobile phones, laptops, digital broadcast receivers, PDAs (Personal Digital Assistants), PADs (Portable Application Description), PMPs (Portable Media Players), and vehicle terminals (e.g., vehicle navigation terminals), as well as fixed terminals such as digital TVs and desktop computers. Figure 7 The microgrid dispatching device shown is merely an example and should not impose any limitations on the functionality and scope of use of the embodiments of this application.

[0170] like Figure 7As shown, the microgrid dispatching device may include a processing unit 1001 (e.g., a central processing unit, a graphics processing unit, etc.), which can perform various appropriate actions and processes according to a program stored in a read-only memory (ROM) 1002 or a program loaded from a storage device 1003 into a random access memory (RAM) 1004. The RAM 1004 also stores various programs and data required for the operation of the microgrid dispatching device. The processing unit 1001, ROM 1002, and RAM 1004 are interconnected via a bus 1005. An input / output (I / O) interface 1006 is also connected to the bus. Typically, the following systems can be connected to the I / O interface 1006: input devices 1007 including, for example, a touchscreen, touchpad, keyboard, mouse, image sensor, microphone, accelerometer, gyroscope, etc.; output devices 1008 including, for example, a liquid crystal display (LCD), speaker, vibrator, etc.; storage devices 1003 including, for example, magnetic tape, hard disk, etc.; and communication devices 1009. Communication device 1009 allows the microgrid dispatching equipment to communicate wirelessly or wiredly with other devices to exchange data. Although the figure shows microgrid dispatching equipment with various systems, it should be understood that implementation or possession of all the systems shown is not required. More or fewer systems may be implemented alternatively.

[0171] Specifically, according to the embodiments disclosed in this application, the processes described above with reference to the flowcharts can be implemented as computer software programs. For example, an embodiment disclosed in this application includes a microgrid dispatcher product comprising a microgrid dispatcher carried on a computer-readable medium, the microgrid dispatcher containing program code for performing the methods shown in the flowcharts. In such an embodiment, the microgrid dispatcher can be downloaded and installed from a network via a communication device, or installed from storage device 1003, or installed from ROM 1002. When the microgrid dispatcher is executed by processing device 1001, it performs the functions defined in the methods of the embodiments disclosed in this application.

[0172] The microgrid dispatching equipment provided in this application, employing the microgrid dispatching method in the above embodiments, can solve the technical problem of optimizing the energy cost of microgrids. Compared with the prior art, the beneficial effects of the microgrid dispatching equipment provided in this application are the same as those of the microgrid dispatching method provided in the above embodiments, and other technical features of the microgrid dispatching equipment are the same as those disclosed in the previous embodiment method, and will not be repeated here.

[0173] It should be understood that the various parts disclosed in this application can be implemented using hardware, software, firmware, or a combination thereof. In the description of the above embodiments, specific features, structures, materials, or characteristics can be combined in any suitable manner in one or more embodiments or examples.

[0174] The above are merely specific embodiments of this application, but the scope of protection of this application is not limited thereto. Any variations or substitutions that can be easily conceived by those skilled in the art within the scope of the technology disclosed in this application should be included within the scope of protection of this application. Therefore, the scope of protection of this application should be determined by the scope of the claims.

[0175] This application provides a storage medium having computer-readable program instructions (i.e., a microgrid scheduler) stored thereon, which are used to execute the microgrid scheduling method in the above embodiments.

[0176] The storage medium provided in this application may be, for example, a USB flash drive, but is not limited to, electrical, magnetic, optical, electromagnetic, infrared, or semiconductor systems, devices, or any combination thereof. More specific examples of storage media may include, but are not limited to: electrical connections with one or more wires, portable computer disks, hard disks, random access memory (RAM), read-only memory (ROM), erasable programmable read-only memory (EPROM or flash memory), optical fiber, portable compact disk read-only memory (CD-ROM), optical storage devices, magnetic storage devices, or any suitable combination thereof. In this embodiment, the storage medium may be any tangible medium containing or storing a program that can be used by or in conjunction with an instruction execution system, system, or device. The program code contained on the storage medium may be transmitted using any suitable medium, including but not limited to: wires, optical cables, RF (Radio Frequency), etc., or any suitable combination thereof.

[0177] The aforementioned storage medium may be included in the microgrid dispatching equipment; or it may exist independently and not be assembled into the microgrid dispatching equipment.

[0178] The aforementioned storage medium carries one or more programs. When the aforementioned one or more programs are executed by the microgrid scheduling device, the microgrid scheduling device becomes: microgrid scheduling.

[0179] Microgrid dispatcher code for performing the operations of this application can be written in one or more programming languages ​​or a combination thereof. These programming languages ​​include object-oriented programming languages—such as Java, Smalltalk, and C++—as well as conventional procedural programming languages—such as C or similar languages. The program code can be executed entirely on the user's computer, partially on the user's computer, as a standalone software package, partially on the user's computer and partially on a remote computer, or entirely on a remote computer or server. In cases involving remote computers, the remote computer can be connected to the user's computer via any type of network—including a Local Area Network (LAN) or a Wide Area Network (WAN)—or can be connected to an external computer (e.g., via the Internet using an Internet service provider).

[0180] The flowcharts and block diagrams in the accompanying drawings illustrate the architecture, functionality, and operation of possible implementations of systems, methods, and microgrid dispatcher products according to various embodiments of this application. In this regard, each block in a flowchart or block diagram may represent a module, segment, or portion of code containing one or more executable instructions for implementing a specified logical function. It should also be noted that in some alternative implementations, the functions indicated in the blocks may occur in a different order than those indicated in the drawings. For example, two consecutively indicated blocks may actually be executed substantially in parallel, and they may sometimes be executed in reverse order, depending on the functions involved. It should also be noted that each block in the block diagrams and / or flowcharts, and combinations of blocks in the block diagrams and / or flowcharts, can be implemented using a dedicated hardware-based system that performs the specified function or operation, or using a combination of dedicated hardware and computer instructions.

[0181] The modules described in the embodiments of this application can be implemented in software or hardware. The names of the modules do not necessarily limit the functionality of the unit itself.

[0182] The readable storage medium provided in this application is a computer-readable storage medium that stores computer-readable program instructions (i.e., a microgrid dispatching program) for executing the above-described microgrid dispatching method, which can solve the technical problem of optimizing the energy cost of microgrids. Compared with the prior art, the beneficial effects of the storage medium provided in this application are the same as the beneficial effects of the microgrid dispatching method provided in the above embodiments, and will not be repeated here.

[0183] The above are only some embodiments of this application and do not limit the patent scope of this application. All equivalent structural transformations made under the technical concept of this application and using the contents of the specification and drawings of this application, or direct / indirect applications in other related technical fields, are included in the patent protection scope of this application.

Claims

1. A microgrid dispatching method, characterized in that, The method includes: Obtain the energy operation model corresponding to the target energy equipment in the target microgrid; Based on the energy operation model, mixed integer programming operations are performed to obtain the target operation curve; Power scheduling is performed on the target energy equipment based on the target operating curve.

2. The microgrid dispatching method as described in claim 1, characterized in that, The step of performing mixed-integer programming based on the energy operation model to obtain the target operating curve includes: The microgrid dispatch output model is determined based on the energy constraint parameters of the target microgrid; Based on the energy operation model and the microgrid scheduling output model, a mixed integer programming operation is performed to obtain the target operation curve.

3. The microgrid dispatching method as described in claim 2, characterized in that, The energy constraint parameters include relaxation penalties and electricity purchase costs; the step of determining the microgrid dispatch output model based on the energy constraint parameters of the target microgrid includes: The cost optimization function is determined based on the relaxation penalty of the target microgrid and the electricity purchase cost; Obtain the operational constraints corresponding to the target energy devices in the target microgrid; The microgrid scheduling output model is determined based on the cost optimization function and the operational constraints.

4. The microgrid dispatching method as described in claim 1, characterized in that, The step of power scheduling of the target energy equipment according to the target operating curve includes: Real-time microgrid incoming power is obtained based on a preset inspection cycle; A preset anti-backflow test is performed based on the preset microgrid operating power range and the real-time microgrid incoming power. Power scheduling is performed on the target energy equipment based on the test results and the target operating curve.

5. The microgrid dispatching method as described in claim 4, characterized in that, The target energy equipment includes microgrid energy storage equipment and microgrid wind and solar equipment; the step of power scheduling of the target energy equipment based on the test results and the target operating curve includes: When the test result shows that the real-time microgrid incoming power is less than the preset microgrid operating power range, the microgrid energy storage device is subject to discharge control. Power scheduling is performed on the microgrid wind and solar equipment based on the energy storage discharge control results and preset scheduling strategies.

6. The microgrid dispatching method as described in claim 4, characterized in that, The step of power scheduling of the target energy equipment based on the test results and the target operating curve further includes: When the test result indicates that the real-time microgrid incoming power is within the preset microgrid operating power range, the target energy equipment is power-scheduled according to the preset scheduling strategy or the target operating curve.

7. The microgrid dispatching method as described in claim 4, characterized in that, The step of power scheduling of the target energy equipment based on the test results and the target operating curve further includes: When the test result shows that the real-time microgrid incoming power is greater than the preset microgrid operating power range, it is determined whether the target energy device has a power limitation. If power limitations exist, power scheduling is performed on the target energy equipment according to preset power control rules and the target operating curve.

8. A microgrid dispatching device, characterized in that, The microgrid dispatching device includes: The energy monitoring module is used to acquire the energy operation model corresponding to the target energy equipment in the target microgrid; The prediction module is used to perform mixed-integer programming operations based on the energy operation model to obtain the target operation curve. The power scheduling module is used to perform power scheduling on the target energy equipment according to the target operating curve.

9. A microgrid dispatching device, characterized in that, The microgrid scheduling device includes: a memory, a processor, and a microgrid scheduling program stored in the memory and executable on the processor, the microgrid scheduling program being configured to implement the steps of the microgrid scheduling method as described in any one of claims 1 to 7.

10. A storage medium, characterized in that, The storage medium is a computer-readable storage medium, and the storage medium stores a microgrid scheduling program. When the microgrid scheduling program is executed by a processor, it implements the steps of the microgrid scheduling method as described in any one of claims 1 to 7.

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