Multi-element flexible load scheduling method, device and equipment based on controllable margin

Abstract

The invention provides a multi-element flexible load scheduling method, device and equipment based on controllable margin, and relates to the technical field of power grid scheduling, and the method comprises the steps: calculating the power margin, time margin and energy margin of each flexible load in a target power grid; the flexible load comprises an air conditioning load, an electric vehicle load and an energy storage load; according to the power margin, the time margin and the energy margin of each flexible load, calculating the controllable margin of each flexible load, and screening out a plurality of adjustable flexible loads from all flexible loads of the target power grid; selecting at least one target node and a target flexible load in each target node from the plurality of nodes based on the corresponding relationship between the plurality of adjustable flexible loads and the nodes and the target adjustment power of the target power grid; and obtaining a load scheduling scheme of the target power grid according to the target adjustment power and the operation parameters of each target flexible load. According to the method, the flexible load can be quantified, stable operation of the target power grid is guaranteed, and regulation and control failure is avoided.

Description

Technical Field

[0001] This invention relates to the field of power grid dispatching technology, and in particular to a multi-element flexible load dispatching method, apparatus and equipment based on controllable margin. Background Technology

[0002] With the widespread integration of new energy sources such as wind power and photovoltaics into the power grid, the load-side structure is undergoing profound changes, with the scale of flexible loads such as air conditioning, electric vehicles, and distributed energy storage growing rapidly. These flexible loads can dynamically adjust electricity consumption periods and amounts, enabling cross-period power transfer and improving the flexibility of grid regulation. To adapt to the flexibility of these flexible loads, the power system will gradually develop towards large-scale and refined operation, transforming from a traditional power supply model to an intelligent power supply model.

[0003] Existing technologies typically improve the intelligence level of power systems by using data acquisition devices, power electronic devices, smart metering terminals, system monitoring platforms, and information and communication infrastructure. They also achieve the transformation of power supply modes by establishing a collaborative interaction mechanism among power sources, grids, loads, and storage, enabling convenient access for new energy units such as air conditioning loads, electric vehicles, and energy storage devices, and increasing the participation of flexible load resources in scheduling.

[0004] However, due to the dispersed nature of flexible loads and the inability of existing technologies to quantify them, existing load regulation methods may fail, affecting the stability of power system operation. Summary of the Invention

[0005] This invention provides a method, apparatus, and equipment for multi-element flexible load dispatching based on controllable margin, in order to address the problem that flexible loads are characterized by dispersion and that existing technologies cannot quantify these flexible loads, which may lead to control failures in existing load regulation methods and affect the stability of power system operation.

[0006] In a first aspect, embodiments of the present invention provide a multi-element flexible load scheduling method based on controllable margin, comprising: Calculate the power margin, time margin, and energy margin of each flexible load in the target power grid; where flexible loads include air conditioning loads, electric vehicle loads, and energy storage loads; Based on the power margin, time margin, and energy margin of each flexible load, the controllability margin of each flexible load is calculated, and based on the controllability margin of each flexible load, multiple adjustable flexible loads are selected from all flexible loads of the target power grid. Based on the correspondence between multiple adjustable flexible loads and nodes, and the target adjustment power of the target power grid, at least one target node and the target flexible load within each target node are selected from among the multiple nodes. The target regulating power and the operating parameters of each target flexible load are input into the load dispatching model to obtain the load dispatching scheme of the target power grid.

[0007] Secondly, embodiments of the present invention provide a multi-element flexible load dispatching device based on controllable margin, comprising: The calculation module is used to calculate the power margin, time margin, and energy margin of each flexible load in the target power grid; where flexible loads include air conditioning loads, electric vehicle loads, and energy storage loads. The screening module is used to calculate the controllability margin of each flexible load based on its power margin, time margin, and energy margin, and to screen out multiple adjustable flexible loads from all flexible loads in the target power grid based on the controllability margin of each flexible load. The selection module is used to select at least one target node and the target flexible load within each target node from among multiple nodes based on the correspondence between multiple adjustable flexible loads and nodes and the target regulating power of the target power grid. The scheduling module is used to input the target regulating power and the operating parameters of each target flexible load into the load scheduling model to obtain the load scheduling scheme of the target power grid.

[0008] Thirdly, embodiments of the present invention provide an electronic device, including a memory and a processor, wherein the memory stores a computer program, and the processor executes the computer program to implement the method described in the first aspect or any possible implementation thereof.

[0009] In this embodiment of the invention, the power margin, time margin, and energy margin of each flexible load such as air conditioning load, electric vehicle load, and energy storage load in the target power grid are calculated. Based on these margins, the controllability margin of each flexible load is calculated, enabling the unification of the quantification standard for flexible loads and achieving quantification of flexible loads. Based on the controllability margin of each flexible load, multiple adjustable flexible loads are selected from all flexible loads in the target power grid. According to the node corresponding to each adjustable flexible load and the target regulation power of the target power grid, at least one target node and target flexible loads within each target node are determined. This approach considers both the target regulation power and the controllability margin of the flexible load when determining the target flexible loads, ensuring that each target flexible load can participate in load regulation. The target regulation power and the load regulation power of each target flexible load are then input into the load scheduling model to obtain the load scheduling scheme for the target power grid. Combining the target regulation power with the load scheduling model generates a scheduling scheme that ensures the stable operation of the target power grid, prevents regulation failures, and significantly improves the utilization efficiency of flexible load resources. Attached Figure Description

[0010] Figure 1 This is a flowchart illustrating the implementation of the multi-element flexible load scheduling method based on controllable margin provided in this embodiment of the invention. Figure 2 This is a flowchart illustrating the implementation of step S110 of the multi-element flexible load scheduling method based on controllable margin provided in this embodiment of the invention. Figure 3 This is a schematic diagram of the charging behavior of an electric vehicle based on a multi-element flexible load scheduling method with controllable margin, as provided in an embodiment of the present invention. Figure 4 This is a schematic diagram of multi-timescale scheduling of the multi-element flexible load scheduling method based on controllable margin provided in the embodiments of the present invention; Figure 5 This is a schematic diagram of the time mechanism at different time scales of the multi-dimensional flexible load scheduling method based on controllable margin provided in the embodiments of the present invention; Figure 6 This is a flowchart illustrating the implementation of step S130 of the multi-element flexible load scheduling method based on controllable margin provided in this embodiment of the invention. Figure 7 This is a schematic diagram illustrating the indoor temperature variation pattern during air conditioning operation using a multi-element flexible load scheduling method based on controllable margin, as provided in an embodiment of the present invention. Figure 8 This is a schematic diagram of the structure of the multi-element flexible load scheduling device based on controllable margin provided in an embodiment of the present invention; Figure 9 This is a schematic diagram of an electronic device provided in an embodiment of the present invention. Detailed Implementation

[0011] The embodiments of the present invention will now be described in detail with reference to the accompanying drawings.

[0012] See Figure 1 The document illustrates a flowchart of the implementation of the multi-element flexible load scheduling method based on controllable margin provided in an embodiment of the present invention, which is described in detail below: Step S110: Calculate the power margin, time margin, and energy margin of each flexible load in the target power grid; where flexible loads include air conditioning loads, electric vehicle loads, and energy storage loads.

[0013] In some embodiments, air conditioning loads, electric vehicle loads, and energy storage loads in the target power grid are considered flexible loads because they can be controlled during load and dispatch operations. Power margin reflects the safe distance between the current operating power of the flexible load and its power limit, and is used to assess the instantaneous regulation capability of the flexible load. Time margin quantifies the theoretical maximum duration for which the flexible load can maintain its current operating condition; it represents the controllable time for the flexible load to potentially participate in grid dispatch and needs to be dynamically adjusted based on user behavior patterns. Energy margin characterizes the total energy regulation that the flexible load can provide during the load dispatch cycle, and its calculation method varies depending on the characteristics of the flexible load.

[0014] See Figure 2 The specific processing method of the above step S110 includes steps S1101-S1103, and the specific content is as follows: Step S1101: Determine the real-time operating power of each air conditioning load based on the constraints of each air conditioning load in the target power grid. Calculate the power margin, time margin, and energy margin of each air conditioning load based on its real-time operating power, rated operating power, indoor temperature, outdoor temperature, set temperature, and energy efficiency ratio.

[0015] In some embodiments, the constraints on the air conditioning load are used to represent the relationship between the operating state of the air conditioning load and the indoor ambient temperature. Based on the constraints on the air conditioning load, the real-time operating power of each air conditioning load can be determined. The constraints on the air conditioning load are as follows:

[0016]

[0017] in, Indoor temperature, The lower limit of acceptable temperature. The upper limit of acceptable temperature, This refers to the operating status of the air conditioning load. The air inertia coefficient characterizes the air's heat storage capacity. The threshold temperature dead zone for fixed-frequency air conditioners Set the temperature for the air conditioner.

[0018] In one possible implementation, step S1101 is specifically processed as follows: For each air conditioning load, the following steps are performed: the ratio of the real-time operating power of the air conditioning load to its rated operating power is determined as the power margin of the air conditioning load; the product of the real-time operating power of the air conditioning load and a preset standardized time base is determined as the real-time load of the air conditioning load, and the ratio of the load adjustment amount of the air conditioning load to the real-time load amount is determined as the time margin of the air conditioning load; the energy storage state variable of the air conditioning load is calculated according to the calculation formula of the indoor temperature, set temperature, energy efficiency ratio, and energy storage state variable of the air conditioning load; the maximum adjustable capacity of the air conditioning load is calculated according to the calculation formula of the outdoor temperature, set temperature, energy efficiency ratio, and maximum adjustable capacity of the air conditioning load; the ratio of the energy storage state variable of the air conditioning load to the maximum adjustable capacity is determined as the energy margin of the air conditioning load.

[0019] In some embodiments, the formula for calculating the power margin of the air conditioning load is:

[0020] in, Let be the real-time operating power of the air conditioning load at time t; This refers to the rated operating power of the air conditioning load.

[0021] The formula for calculating the time margin of air conditioning load is:

[0022] in, Let t be the real-time operating power of the air conditioning load. The preset standardized time base can typically be set to 24 hours, but other durations are also possible. The total available capacity of the air conditioning load can be extracted from the operating parameters of the air conditioner.

[0023] It should be noted that for air conditioning loads, when the temperature setpoint is increased, the air conditioning compressor shuts down, resulting in an interruption of cooling output; this process is equivalent to the energy release phase of an energy storage system. Conversely, when the setpoint is decreased, the air conditioner continues to operate at its rated power to replenish cooling; this process is equivalent to the energy storage phase of an energy storage system. Under dynamic ambient temperature conditions, the virtual energy storage capacity exhibited by the air conditioning system can be quantified using the following model:

[0024]

[0025] Based on the above capacity model, the energy margin index for air conditioning load is defined as follows:

[0026] In the formula: Let be the energy storage state variable of the air conditioning system at time t; For the system's maximum adjustable energy capacity, when When the value is 0, the device is powered off, the system exits the virtual energy storage operation mode, and it lacks demand response capability. Let t be the indoor temperature at time t, in °C. The outdoor temperature at time t is expressed in °C. The equivalent heat capacity of a building, in J / ℃, reflects its overall heat storage capacity. The energy efficiency ratio (EER) of an air conditioner is the ratio of cooling capacity to input power. This indicates the on / off status of the air conditioner; 1 means it's running, and 0 means it's off. The highest indoor temperature, The lowest indoor temperature, and The value is preset.

[0027] For air conditioning loads, their operating characteristics have a natural transition threshold. When the operating conditions approach this threshold, the state of the air conditioning equipment is susceptible to unexpected changes due to external disturbances. Direct control of the air conditioning equipment at this time will lead to a shortened adjustment duration and a diminished adjustment effect. Therefore, it is necessary to establish a state-aware adjustment margin design mechanism and construct an energy margin assessment model based on the differences in real-time operating conditions of the equipment. The energy storage margin range of the air conditioning load is between 0 and 1. When the room temperature approaches the preset upper limit, the remaining adjustable capacity of the air conditioning unit in the shutdown state approaches zero. This state is similar to an energy storage device entering deep discharge; however, for continuously operating air conditioning units, this operating condition corresponds to a larger power adjustment space, and its equivalent charging reserve capacity is in the peak region.

[0028] Step S1102: Based on the constraints of each electric vehicle load in the target power grid, determine the preset usage time of each electric vehicle load, and calculate the power margin, time margin, and energy margin of each electric vehicle load based on the preset usage time, real-time charging power, rated charging power, upper limit of SOC, lower limit of SOC, and real-time SOC.

[0029] In some embodiments, the constraints on the load of the electric vehicle are:

[0030]

[0031] in, and These are the upper and lower limits of the battery charge capacity for electric vehicles. This refers to the state of charge of an electric vehicle. This is the lower limit of the charging curve. In addition to the upper limit of the charging curve, electric vehicles also have time constraints, namely, the time period during which the electric vehicle needs to be charged.

[0032] It should be noted that, see Figure 3 , Figure 3 In China, the state of charge of electric vehicles Its charging state is near the upper limit of the charging curve. It will change from 1 to 0, indicating that charging has stopped; and when the state of charge approaches the lower limit of the charging curve, It will change from 0 to 1, indicating that charging has started.

[0033] In one possible implementation, step S1102 is specifically processed as follows: For each electric vehicle load, the following steps are performed: the ratio of the real-time charging power to the rated charging power of the electric vehicle load is determined as the power margin of the electric vehicle load; the ratio of the preset usage time of the electric vehicle load to the preset standardized time base is determined as the first ratio; the product of the real-time operating power and the standardized time base of the electric vehicle load is determined as the real-time load of the electric vehicle load; and the ratio of the available total capacity of the electric vehicle load to the real-time load is determined as the second ratio; the minimum value between the first ratio and the second ratio is determined as the time margin of the electric vehicle load; and the energy margin of the electric vehicle load is calculated according to the SOC upper limit, SOC lower limit, real-time SOC and the first energy margin calculation formula.

[0034] In some embodiments, the power margin of the electric vehicle load is:

[0035] In the formula: Let t be the real-time charging power of the electric vehicle load. The rated charging power for electric vehicle loads.

[0036] The time margin of electric vehicle load is:

[0037] In the formula: The total available capacity of the electric vehicle load can be extracted from the real-time operating data of the electric vehicles. It serves as a standardized time reference for normalization. The system presets the usage time for users, which is the difference between the latest time a user sets to disconnect from the network and the current time. The first ratio, The second ratio, This represents the real-time load. The time margin quantifies the theoretical maximum duration for which the equipment can maintain its current operating conditions. It represents the controllable time during which the load can potentially participate in grid dispatch and needs to be dynamically adjusted based on user behavior patterns.

[0038] When modeling and analyzing the charging behavior of electric vehicles, a two-way energy interaction model can be constructed by drawing on the experience of air conditioning load. Based on this, the energy margin index of electric vehicles is defined as:

[0039] When an electric vehicle is charging, its energy margin can be represented by its relative position to preset charging upper and lower limit curves. This reflects the amount of electricity the electric vehicle can charge or discharge at the current moment. The state of charge of the electric vehicle. The average charging curve, Let t be the upper limit of the charging curve at time t. This represents the lower bound of the charging curve at time t.

[0040] Step S1103: Calculate the power margin, time margin, and energy margin of each energy storage load based on its real-time output power, rated output power, and SOC.

[0041] In some embodiments, the energy storage load is an energy storage device in the target power grid. When charging, the energy storage device needs to obtain energy from the target power grid. When discharging, the energy storage device can deliver energy to the target power grid to power the equipment in the grid.

[0042] In one possible implementation, step S1103 is specifically processed as follows: For each energy storage load, the following steps are performed: the ratio of the real-time output power of the energy storage load to the rated output power is determined as the power margin of the energy storage load; the product of the real-time output power of the energy storage load and a preset standardized time base is determined as the real-time load of the energy storage load; the ratio of the load adjustment amount of the energy storage load to the real-time load amount is determined as the time margin of the energy storage load; the energy margin of the energy storage load is calculated according to the SOC and the second energy margin calculation formula of the energy storage load.

[0043] In some embodiments, the power margin of the energy storage load is:

[0044] In the formula: Let be the real-time output power of the energy storage load at time t; This is the rated output power of the energy storage load.

[0045] The time margin of energy storage load is:

[0046] in, Let t be the real-time operating power of the energy storage load. The preset standardized time base can typically be set to 24 hours, but other durations are also possible. This represents the total available capacity of the energy storage load; the total available capacity of the energy storage load can be directly extracted from the operating data of the energy storage equipment.

[0047] The energy state margin of energy storage loads is quantitatively assessed using a state of charge (SOC) model:

[0048] In the formula: Let be the state of charge of the energy storage device at time t. When... When the system enters charging mode, it prioritizes charging tasks for energy storage units with lower states of charge; when When this occurs, the system switches to discharge mode, prioritizing the discharge operation of energy storage devices with higher states of charge; for critical states... The calculation method for the energy state margin of energy storage devices needs to be determined in conjunction with the specific requirements of the power grid dispatch instructions. That is, based on the adjustment requirements issued by the power grid, the range of energy that each flexible load can provide or absorb under the current operating state is assessed, thereby dynamically determining its energy margin.

[0049] Step S120: Calculate the controllability margin of each flexible load based on its power margin, time margin, and energy margin, and select multiple adjustable flexible loads from all flexible loads in the target power grid based on the controllability margin of each flexible load.

[0050] In some embodiments, the controllable margin is used to uniformly quantify flexible loads of different load types. The value of the controllable margin is between 0 and 1. An interval can be set according to historical scheduling experience. When the controllable margin is in this interval, it indicates that the flexible load can participate in scheduling. This interval can usually be set to (0.1, 0.9), or it can be set to other values, without specific limitations here.

[0051] In one possible implementation, step S120 is specifically processed as follows: the power margin, time margin, and energy margin of each flexible load are weighted and summed according to the preset weights corresponding to the load type of the flexible load to obtain the controllable margin of each flexible load; the flexible loads whose controllable margins are within the preset range are determined as the adjustable flexible loads of the target power grid.

[0052] In some embodiments, the importance of power margin, time margin, and energy margin varies for different load types. Therefore, when determining the weights of power margin, time margin, and energy margin for different load types, the load type of flexible loads needs to be considered. For example, for air conditioning loads, since air conditioning has high power requirements, the weight corresponding to power margin can be set to 0.5, the weight corresponding to time margin to 0.2, and the weight corresponding to energy margin to 0.3. The formula for calculating controllability margin is:

[0053]

[0054] In the formula: , , These are weighting coefficients, representing the importance of different regulatory dimensions; , , These are power margin, time margin, and energy margin, respectively.

[0055] Step S130: Based on the correspondence between multiple adjustable flexible loads and nodes, and the target adjustment power of the target power grid, select at least one target node and the target flexible load within each target node from among the multiple nodes.

[0056] In some embodiments, a node is located in the target power grid and is a key connection unit for power transmission and distribution. It is a centralized connection point for various power equipment and power loads, responsible for collecting the power output from the power source and distributing it to each load as needed. At the same time, it maintains the stability of electrical parameters such as voltage and frequency, and connects multiple components, including power supply / transmission side equipment such as generators, transmission lines, and transformers, as well as various power loads such as industrial, residential, and commercial loads. In other words, each node in the target power grid corresponds to multiple flexible loads. These flexible loads may be of the same type or different types. For example, node 1 corresponds to 10 air conditioning loads, and node 2 corresponds to 5 air conditioning loads, 3 electric vehicle loads, and 1 energy storage load.

[0057] It should be noted that when dispatching the target power grid, three dispatching schemes can be employed: day-ahead dispatching, intraday rolling dispatching, and real-time coordination. (See also...) Figure 4 and Figure 5It is known that day-ahead dispatch uses a 1-hour timescale to formulate a day-ahead dispatch plan covering a 24-hour cycle. The main decision variables of the day-ahead dispatch scheme include the start-stop combination strategy of conventional generator units, the baseline response of PDR loads, and the call plan for Class A IDR loads. The day-ahead dispatch results are input into the intraday rolling optimization stage in the form of deterministic parameters. The day-ahead dispatch model, based on the short-term forecast results of renewable energy power generation and user load electricity consumption, combined with the day-ahead optimization dispatch model, and taking into account the start-stop combination strategy of conventional generator units, the baseline response of PDR loads, Class A IDR loads, and the charging and discharging of pumped storage stations, etc., obtains the day-ahead dispatch instructions. The timescale of the day-ahead dispatch instructions is 1 hour, which means that the day-ahead dispatch instructions need to be updated every hour.

[0058] The intraday rolling optimization uses a 15-minute timescale, dividing one hour into four cycles for rolling optimization. Its core functions include ultra-short-term power forecasting and correction for new energy power generation units, optimization of the charging and discharging power curves of electrochemical energy storage systems, and dynamic scheduling of Class B IDR resources. While maintaining key parameters such as unit combinations and load baselines determined by the day-ahead scheduling, the intraday optimization compensates for forecast errors through a rolling correction mechanism. The intraday rolling optimization model, based on ultra-short-term forecasts of new energy power generation and user load consumption, and considering factors such as new energy unit output plans, Class B IDR load scheduling, and the charging and discharging amounts of electrochemical energy storage stations, derives rolling optimization instructions. The timescale for these instructions is 15 minutes, meaning that the rolling optimization instructions need to be updated every 15 minutes.

[0059] The real-time coordination phase, also known as the load regulation phase, adopts a 5-minute time scale and performs rolling optimization with a 15-minute cycle. Its main mechanisms include using the intraday optimized structure as a reference, implementing rapid power balance correction, and compensating for network transmission delays and equipment response deviations. Based on real-time data of renewable energy generation power and real-time user load power consumption, real-time net load power data is obtained. Then, based on the real-time net load power data and the predicted load power data obtained from the intraday rolling phase, the real-time unbalanced power is calculated and determined as the target power.

[0060] See Figure 6 The specific processing method of step S130 above includes steps S1301-S1303, the details of which are as follows: Step S1301: Calculate the adjustable power of each node based on the correspondence between multiple adjustable flexible loads and nodes and the preset power calculation formula.

[0061] In some embodiments, each node may correspond to multiple adjustable flexible loads of different load types, and the preset power calculation formula for each load type is different. Load types include air conditioning type, electric vehicle type, and energy storage type.

[0062] For air conditioning systems, the air conditioning load is considered a "thermal energy storage" system. The model is based on the Equivalent Thermal Parameter (ETP) method, which analogizes the change in indoor building temperature to the dynamic process of an RC circuit. Cooling power is considered a current source, the indoor-outdoor temperature difference a voltage source, and building thermal resistance and thermal capacity are equivalent to resistance and capacitance.

[0063]

[0064]

[0065] In the formula: The indoor temperature at time (t+1), in °C. Let t be the indoor temperature at time t, in °C. The outdoor temperature at time t is expressed in °C. The air inertia coefficient characterizes the air's heat storage capacity; Equivalent thermal resistance of a building, unit: ℃ / kW, reflects the characteristics of heat transfer resistance; The equivalent heat capacity of a building, in J / ℃, reflects its overall heat storage capacity. is the heterothermal coefficient, which describes nonlinear heat transfer effects; The energy efficiency ratio (EER) of an air conditioner is the ratio of cooling capacity to input power. This is the simulation time step; Let t be the thermal power of the air conditioner. This indicates the on / off status of the air conditioner; 1 means it's running, and 0 means it's off.

[0066] The indoor temperature variation pattern can be obtained by solving the first-order differential equation. For example... Figure 7 As shown, by adjusting the upper and lower limits of the temperature setpoint, the air conditioner can reduce or increase power consumption while ensuring basic user comfort. This quantitative model transforms the fuzzy concept of thermal comfort into a precise and schedulable energy storage capacity.

[0067] Adjusting the preset temperature parameters of an air conditioning unit changes its operating state, and this state switching directly causes fluctuations in power consumption. In a given node, if there are n air conditioning units, these units are divided into... There are several control groups. As the next control cycle begins, the system always maintains a balance: one group of air conditioners is on while another is off, ensuring that the number of air conditioners started and stopped remains equal in each cycle. Based on this alternating operation mechanism, a mathematical expression for the load regulation capacity of the air conditioning cluster can be derived:

[0068]

[0069] In the formula: and These represent the lower and upper limits of the adjustable power when all air conditioning loads within a node participate in grid demand response at time t, respectively. For groups that are currently shut down, For groups that are currently active, This refers to the rated power of the air conditioner.

[0070] This model achieves refined control of the air conditioning unit's operating status through dynamic adjustment of the temperature setpoint, providing a quantifiable control boundary for load regulation in the power system.

[0071] For electric vehicles, multiple electric vehicles corresponding to a node can be considered as a distributed "electric energy storage" system. Assume that the electric vehicles operate within a preset time window... [In-process orderly charging is performed, and the initial state of charge upon connection to the grid is] When charging is completed, the battery must reach a state of charge sufficient to meet the travel needs of the next day. The average charging power of electric vehicles within this time window can be expressed as:

[0072] In the formula: For electric vehicle charging efficiency; This represents the total battery capacity of the electric vehicle.

[0073] When electric vehicles are in the time window [ [With constant power] When performing ordered charging, a standardized charging curve can be constructed:

[0074] Electric vehicle charging can employ a square wave charging method, and the charging behavior of an electric vehicle is as follows: Figure 3 As shown.

[0075] The dispatchable capacity of a single electric vehicle is its battery capacity, while the dispatchable capacity of a node's electric vehicle type is the sum of the available capacities of all connected EVs. Its adjustable power and energy boundaries are clearly defined, allowing direct participation in grid dispatch. Under the aggregated electric vehicle control framework, once the charging power boundary conditions are set, each individual electric vehicle will follow predetermined boundary constraints to form a uniquely determined charging trajectory. By dynamically adjusting the power boundary threshold, real-time control of the charging behavior of all electric vehicles within the node can be achieved, i.e., real-time control of the electric vehicle load. Specifically, for the n electric vehicles corresponding to a node, the charging process begins immediately after the vehicle completes grid connection, and its operation will continue until the preset termination conditions are met. Until then:

[0076]

[0077] In the formula: , The lower and upper limits of the adjustable power that can be controlled in response to load control for all electric vehicle loads in the node; Let t represent the charging and discharging behavior of the j-th electric vehicle.

[0078] For energy storage types, distributed energy storage systems (such as lithium batteries) are themselves real energy storage units, making their models more straightforward. For battery energy storage systems, a dynamic charging and discharging model is constructed, ignoring the impact of battery self-discharge on the long-term energy storage state and assuming that the charging and discharging efficiency remains constant during the scheduling cycle.

[0079]

[0080] In the formula: For energy storage devices The state of charge at any given moment; , These are the charge / discharge efficiency coefficients of the battery; The rated capacity of the equipment; Let t be the net power of the energy storage device interacting with the grid.

[0081] To extend battery cycle life and ensure operational safety, energy storage systems must adhere to dual operational constraints: state of charge (SOC) constraints and power flow constraints. The SOC constraint requires that battery capacity be strictly controlled within a safe operating range, while the power flow constraint requires that charging and discharging power not exceed limits.

[0082]

[0083] In the formula: , These are safety features designed to prevent over-discharge and suppress overcharge. The threshold value depends on the characteristics of the cell materials and the control strategy. The maximum allowable charge and discharge power is determined by factors such as battery internal resistance and temperature rise characteristics.

[0084] For an aggregate consisting of n independent energy storage devices corresponding to a node, it can be equivalent to a single virtual energy storage device in day-ahead optimization scheduling, and its core parameters are determined by the following rules:

[0085]

[0086] In the formula: Let be the charge capacity of the i-th energy storage device. Let i be the charging and discharging power of the i-th energy storage device. This represents the adjustable power of all energy storage loads corresponding to this node.

[0087] It should be noted that when calculating the adjustable power of each node, the adjustable power of each load type corresponding to that node is added together to obtain the adjustable power of that node. Since power can be adjusted upwards or downwards at any given time to achieve peak shaving and valley filling, each node will have two adjustable power values. When the power is adjusted downwards, that is, when it is necessary to reduce the power consumed by each flexible load in the grid, the adjustable power is: + - When power is adjusted upwards, that is, when it is necessary to increase the power consumed by each flexible load in the grid, the adjustable power is: + + .

[0088] Step S1302: Based on the target regulating power of the target power grid and the adjustable power of each node, determine at least one target node and the node regulating power of each target node; wherein, the target regulating power is the difference between the predicted load of the target power grid at the current moment and the real-time load of the target power grid in the load forecast result of the target power grid; the sum of the node regulating power of each target node is greater than or equal to the target regulating power of the target power grid.

[0089] In some embodiments, when the target regulation power of the target power grid is such that the power needs to be adjusted downwards, it is necessary to use... + - To determine the target node, when the target regulation power of the target power grid is such that the power needs to be adjusted upwards, it is necessary to use... + + To determine the target nodes, each node should be arranged in descending order of adjustable power. If the sum of the adjustable powers of the first *a* nodes is greater than the target adjustable power, and the sum of the adjustable powers of the first *a-1* nodes is less than the target adjustable power, then the first *a* nodes are designated as target nodes, where *a* is a positive integer greater than 1. After selecting *a* nodes, the adjustable power of each of the first *a-1* nodes is determined as the node adjustable power of that node. The difference between the target adjustable power and the sum of the adjustable powers of each of the first *a-1* nodes is determined as the node adjustable power of the *a*-th node.

[0090] Step S1303: For each target node, if the node regulation power of the target node is equal to the adjustable power of the target node, then all adjustable flexible loads in the target node are determined as target flexible loads; if the node regulation power of the target node is less than the adjustable power of the target node, then at least one target flexible load in the target node is determined based on the node regulation power and the load regulation power of each adjustable flexible load in the target node.

[0091] In some embodiments, since the adjustable power of each target node is the upper limit of the adjustment that the target node can participate in, when the node adjustment power of the target node is equal to the adjustable power of the target node, it is necessary to use each adjustable flexible load corresponding to the target node for adjustment in order to meet the adjustment conditions. Therefore, all adjustable flexible loads within the target node need to be identified as target flexible loads. When the node adjustment power of the target node is less than the adjustable power of the target node, it means that selecting a portion of the adjustable loads from the target node is sufficient to meet the node adjustment power requirement, and it is not necessary for all adjustable loads within the target node to operate. Therefore, at least one adjustable flexible load needs to be selected from the target node as a target flexible load.

[0092] It should be noted that when the adjustable flexible load is an air conditioning load, the load regulation power of the adjustable flexible load is the difference between the rated operating power and the real-time operating power; when the adjustable flexible load is an electric vehicle load, the load regulation power of the adjustable flexible load is the difference between the rated charging power and the real-time charging power; when the adjustable flexible load is an energy storage load, the load regulation power of the adjustable flexible load is the difference between the rated output power and the real-time output power.

[0093] In one possible implementation, step S1303 is specifically processed as follows: sort each adjustable flexible load in the target node according to the order of controllability margin from largest to smallest; calculate the difference between the sum of the load regulation power of the Nth adjustable flexible load to the Mth adjustable flexible load and the node regulation power; where N is a positive integer, M is a positive integer, and N≤M; determine the Nth adjustable flexible load to the Mth adjustable flexible load with the smallest difference as the target flexible load in the target node.

[0094] In some embodiments, all loads within the target node are arranged in descending order of controllability margin, with adjustable flexible loads having larger controllability margins given priority for adjustment. Besides controllability margin, it is also necessary to ensure that the difference between the sum of the adjustment power of the selected multiple target flexible loads and the node adjustment power of the target node is minimized. Therefore, an optimization objective function is needed to select the adjustable flexible loads. The optimization objective function is:

[0095] In the formula: This represents the regulating power of the h-th target flexible load in the target node, where M is the number of target flexible loads in the target node.

[0096] Step S140: Input the target regulating power and the operating parameters of each target flexible load into the load dispatching model to obtain the load dispatching scheme of the target power grid.

[0097] In some embodiments, the inputs to the load dispatching model include the operating parameters of each flexible load (such as real-time power, SOC, temperature, controllability margin, and user constraints) and dispatching instructions issued by the power grid. The model is built based on historical operating data and dispatching results, formed by fitting the operating characteristics and dispatching behavior of the flexible loads. The output of the model is the load dispatching scheme for the target power grid, including the power allocation results for each node and its corresponding flexible load.

[0098] By calculating the power margin, time margin, and energy margin of each flexible load (such as air conditioning load, electric vehicle load, and energy storage load) in the target power grid, and based on these margins, the controllability margin of each flexible load is calculated. This allows for the standardization of flexible load quantification through controllability margins, enabling the quantification of flexible loads. Based on the controllability margin of each flexible load, multiple adjustable flexible loads are selected from all flexible loads in the target power grid. Then, based on the node corresponding to each adjustable flexible load and the target regulation power of the target power grid, at least one target node and the target flexible loads within each target node are determined. This approach considers both the target regulation power and the controllability margin of the flexible loads when determining target flexible loads, ensuring that each target flexible load can participate in load regulation. The target regulation power and the load regulation power of each target flexible load are then input into the load dispatching model to obtain the load dispatching scheme for the target power grid. Combining the target regulation power with the load dispatching model generates a dispatching scheme that ensures the stable operation of the target power grid, prevents regulation failures, and significantly improves the utilization efficiency of flexible load resources.

[0099] It should be understood that the sequence number of each step in the above embodiments does not imply the order of execution. The execution order of each process should be determined by its function and internal logic, and should not constitute any limitation on the implementation process of the embodiments of the present invention.

[0100] The following are device embodiments of the present invention. For details not described in detail, please refer to the corresponding method embodiments described above.

[0101] Figure 8 A schematic diagram of a multi-element flexible load dispatching device based on controllable margin provided in an embodiment of the present invention is shown. For ease of explanation, only the parts related to the embodiment of the present invention are shown, and are described in detail below: like Figure 8 As shown, the multi-element flexible load dispatching device 8 based on controllable margin includes: The calculation module 81 is used to calculate the power margin, time margin, and energy margin of each flexible load in the target power grid; whereby flexible loads include air conditioning loads, electric vehicle loads, and energy storage loads. The screening module 82 is used to calculate the controllability margin of each flexible load based on the power margin, time margin and energy margin of each flexible load, and to screen out multiple adjustable flexible loads from all flexible loads of the target power grid based on the controllability margin of each flexible load. Module 83 is selected to select at least one target node and the target flexible load within each target node based on the correspondence between multiple adjustable flexible loads and nodes and the target regulating power of the target power grid. The scheduling module 84 is used to input the target regulating power and the operating parameters of each target flexible load into the load scheduling model to obtain the load scheduling scheme of the target power grid.

[0102] In one possible implementation, the calculation module 81 is specifically used for: determining the real-time operating power of each air conditioning load based on the constraints of each air conditioning load in the target power grid; calculating the power margin, time margin, and energy margin of each air conditioning load based on the real-time operating power, rated operating power, indoor temperature, outdoor temperature, set temperature, and energy efficiency ratio of each air conditioning load; determining the preset usage time of each electric vehicle load based on the constraints of each electric vehicle load in the target power grid; and calculating the power margin, time margin, and energy margin of each electric vehicle load based on the preset usage time, real-time charging power, rated charging power, upper limit of SOC, lower limit of SOC, and real-time SOC of each electric vehicle load; and calculating the power margin, time margin, and energy margin of each energy storage load based on the real-time output power, rated output power, and SOC of each energy storage load in the target power grid.

[0103] In one possible implementation, the calculation module 81 is further configured to: for each air conditioning load, perform the following steps: determine the power margin of the air conditioning load as the ratio of its real-time operating power to its rated operating power; determine the real-time load of the air conditioning load as the product of its real-time operating power and a preset standardized time base, and determine the time margin of the air conditioning load as the ratio of its load adjustment amount to its real-time load amount; calculate the energy storage state variable of the air conditioning load according to the calculation formulas for its indoor temperature, set temperature, energy efficiency ratio, and energy storage state variable; calculate the maximum adjustable capacity of the air conditioning load according to the calculation formulas for its outdoor temperature, set temperature, energy efficiency ratio, and maximum adjustable capacity; and determine the energy margin of the air conditioning load as the ratio of its energy storage state variable to its maximum adjustable capacity.

[0104] In one possible implementation, the calculation module 81 is further configured to: for each electric vehicle load, perform the following steps: determine the ratio of the real-time charging power to the rated charging power of the electric vehicle load as the power margin of the electric vehicle load; determine the ratio of the preset usage time of the electric vehicle load to the preset standardized time base as a first ratio; determine the product of the real-time operating power of the electric vehicle load and the standardized time base as the real-time load of the electric vehicle load; and determine the ratio of the load adjustment amount of the electric vehicle load to the real-time load as a second ratio; determine the minimum value between the first ratio and the second ratio as the time margin of the electric vehicle load; and calculate the energy margin of the electric vehicle load according to the SOC upper limit, SOC lower limit, real-time SOC and the first energy margin calculation formula.

[0105] In one possible implementation, the calculation module 81 is further configured to: for each energy storage load, perform the following steps: determine the ratio of the real-time output power of the energy storage load to the rated output power as the power margin of the energy storage load; determine the product of the real-time output power of the energy storage load and a preset standardized time base as the real-time load of the energy storage load, and determine the ratio of the load adjustment amount of the energy storage load to the real-time load amount as the time margin of the energy storage load; calculate the energy margin of the energy storage load according to the SOC and the second energy margin calculation formula of the energy storage load.

[0106] In one possible implementation, the screening module 82 is specifically used to: weight and sum the power margin, time margin, and energy margin of each flexible load according to the preset weight corresponding to the load type of the flexible load to obtain the controllable margin of each flexible load; and determine the flexible loads whose controllable margins are within the preset range as the adjustable flexible loads of the target power grid.

[0107] In one possible implementation, module 83 is selected and specifically used for: dividing each adjustable flexible load according to nodes, and calculating the adjustable power of each node according to a preset power calculation formula; determining at least one target node and the node adjustment power of each target node according to the target adjustment power of the target power grid and the adjustable power of each node; wherein, the target adjustment power is the difference between the predicted load of the target power grid at the current moment and the real-time load of the target power grid in the load prediction result of the target power grid; the sum of the node adjustment power of each target node is greater than or equal to the target adjustment power of the target power grid; for each target node, if the node adjustment power of the target node is equal to the adjustable power of the target node, then all adjustable flexible loads in the target node are determined as target flexible loads; if the node adjustment power of the target node is less than the adjustable power of the target node, then at least one target flexible load in the target node is determined according to the node adjustment power and the load adjustment power of each adjustable flexible load in the target node.

[0108] In one possible implementation, module 83 is further configured to: sort each adjustable flexible load in the target node in descending order of controllability margin; calculate the difference between the sum of the load regulation power of the Nth adjustable flexible load to the Mth adjustable flexible load and the node regulation power; where N is a positive integer, M is a positive integer, and N≤M; and determine the Nth adjustable flexible load to the Mth adjustable flexible load with the smallest difference as the target flexible load in the target node.

[0109] Figure 9 This is a schematic diagram of an electronic device provided in an embodiment of the present invention. For example... Figure 9 As shown, the electronic device 9 of this embodiment includes a processor 90 and a memory 91. The memory 91 stores a computer program 92. When the processor 90 executes the computer program 92, it implements the steps in the various method embodiments described above. Alternatively, when the processor 90 executes the computer program 92, it implements the functions of each module / unit in the various device embodiments described above.

[0110] For example, computer program 92 may be divided into one or more modules / units, which are stored in memory 91 and executed by processor 90 to complete the present invention. The one or more modules / units may be a series of computer program instruction segments capable of performing a specific function, which describe the execution process of computer program 92 in electronic device 9.

[0111] Electronic device 9 may include, but is not limited to, processor 90 and memory 91. Those skilled in the art will understand that... Figure 9This is merely an example of electronic device 9 and does not constitute a limitation on electronic device 9. It may include more or fewer components than shown, or combine certain components, or different components. For example, electronic device 9 may also include input / output devices, network access devices, buses, etc.

[0112] For the sake of simplicity and clarity, only the above-described functional modules / units are used as examples. In practical applications, the functions described above can be assigned to different functional modules / units as needed. These modules / units can be implemented in hardware, software, or a combination of both.

[0113] In the above embodiments, the descriptions of each embodiment have their own emphasis. Parts not detailed or described in a particular embodiment can be referred to in the relevant descriptions of other embodiments. Unless otherwise specified or in conflict with logic, the terminology and / or descriptions between different embodiments are consistent and can be referenced interchangeably. Technical features in different embodiments can be combined to form new embodiments based on their inherent logical relationships.

[0114] The above embodiments are only used to illustrate the technical solutions of the present invention, and are not intended to limit it. Although the present invention has been described in detail with reference to the foregoing embodiments, those skilled in the art should understand that modifications can still be made to the technical solutions described in the foregoing embodiments, or equivalent substitutions can be made to some of the technical features. Such modifications or substitutions do not cause the essence of the corresponding technical solutions to deviate from the spirit and scope of the technical solutions of the embodiments of the present invention, and should all be included within the protection scope of the present invention.

Claims

1. A multi-element flexible load scheduling method based on controllable margin, characterized in that, include: Calculate the power margin, time margin, and energy margin of each flexible load in the target power grid; wherein, the flexible load includes air conditioning load, electric vehicle load, and energy storage load; Based on the power margin, time margin, and energy margin of each flexible load, the controllability margin of each flexible load is calculated, and based on the controllability margin of each flexible load, multiple adjustable flexible loads are selected from all flexible loads of the target power grid. Based on the correspondence between multiple adjustable flexible loads and nodes, and the target adjustment power of the target power grid, at least one target node and the target flexible load within each target node are selected from among the multiple nodes. The target regulating power and the operating parameters of each target flexible load are input into the load dispatching model to obtain the load dispatching scheme of the target power grid.

2. The multi-element flexible load scheduling method based on controllable margin according to claim 1, characterized in that, The calculation of the power margin, time margin, and energy margin of each flexible load in the target power grid includes: Based on the constraints of each air conditioning load in the target power grid, determine the real-time operating power of each air conditioning load. Based on the real-time operating power, rated operating power, indoor temperature, outdoor temperature, set temperature and energy efficiency ratio of each air conditioning load, calculate the power margin, time margin and energy margin of each air conditioning load. Based on the constraints of each electric vehicle load in the target power grid, the preset usage time of each electric vehicle load is determined, and based on the preset usage time, real-time charging power, rated charging power, upper limit of SOC, lower limit of SOC and real-time SOC of each electric vehicle load, the power margin, time margin and energy margin of each electric vehicle load are calculated. Based on the real-time output power, rated output power, and SOC of each energy storage load in the target power grid, calculate the power margin, time margin, and energy margin of each energy storage load.

3. The multi-element flexible load scheduling method based on controllable margin according to claim 2, characterized in that, The calculation of power margin, time margin, and energy margin for each air conditioning load based on its real-time operating power, rated operating power, indoor temperature, outdoor temperature, set temperature, and energy efficiency ratio includes: For each air conditioning load, perform the following steps: The ratio of the real-time operating power of the air conditioning load to the rated operating power is determined as the power margin of the air conditioning load. The product of the real-time operating power of the air conditioning load and the preset standardized time base is determined as the real-time load of the air conditioning load, and the ratio of the load adjustment amount of the air conditioning load to the real-time load amount is determined as the time margin of the air conditioning load. Calculate the energy storage state variables of the air conditioning load based on the indoor temperature, set temperature, energy efficiency ratio, and energy storage state variables calculation formula. Calculate the maximum adjustable capacity of the air conditioning load based on the outdoor temperature, set temperature, energy efficiency ratio, and maximum adjustable capacity calculation formula. The ratio of the energy storage state variable of the air conditioning load to the maximum adjustable capacity is determined as the energy margin of the air conditioning load.

4. The multi-element flexible load scheduling method based on controllable margin according to claim 2, characterized in that, The calculation of power margin, time margin, and energy margin for each electric vehicle load, based on the preset usage time, real-time charging power, rated charging power, upper limit of SOC, lower limit of SOC, and real-time SOC, includes: For each electric vehicle load, perform the following steps: The ratio of the real-time charging power to the rated charging power of the electric vehicle load is determined as the power margin of the electric vehicle load. The ratio of the preset usage time of the electric vehicle load to the preset standardized time base is determined as the first ratio; the product of the real-time operating power of the electric vehicle load and the standardized time base is determined as the real-time load of the electric vehicle load; and the ratio of the load adjustment amount of the electric vehicle load to the real-time load is determined as the second ratio. The minimum value between the first ratio and the second ratio is determined as the time margin of the electric vehicle load; The energy margin of the electric vehicle load is calculated based on the formulas for the upper limit of SOC, lower limit of SOC, real-time SOC, and first energy margin of the electric vehicle load.

5. The multi-element flexible load scheduling method based on controllable margin according to claim 2, characterized in that, The calculation of the power margin, time margin, and energy margin of each energy storage load based on its real-time output power, rated output power, and SOC in the target power grid includes: For each energy storage load, perform the following steps: The ratio of the real-time output power of the energy storage load to its rated output power is determined as the power margin of the energy storage load. The product of the real-time output power of the energy storage load and the preset standardized time base is determined as the real-time load of the energy storage load, and the ratio of the load adjustment amount of the energy storage load to the real-time load amount is determined as the time margin of the energy storage load. The energy margin of the energy storage load is calculated based on the SOC and second energy margin calculation formulas.

6. The multi-element flexible load scheduling method based on controllable margin according to claim 1, characterized in that, The step of dividing multiple adjustable flexible loads according to nodes and determining at least one target node and the target flexible load within each target node based on the target regulating power of the target power grid includes: Each adjustable flexible load is divided into nodes, and the adjustable power of each node is calculated according to the preset power calculation formula. Based on the target regulating power of the target power grid and the adjustable power of each node, at least one target node and the node regulating power of each target node are determined; wherein, the target regulating power is the difference between the predicted load of the target power grid at the current moment and the real-time load of the target power grid in the load forecast result of the target power grid; the sum of the node regulating powers of each target node is greater than or equal to the target regulating power of the target power grid; For each target node, if the node regulation power of the target node is equal to the adjustable power of the target node, then all adjustable flexible loads within the target node are identified as target flexible loads; if the node regulation power of the target node is less than the adjustable power of the target node, then at least one target flexible load in the target node is identified based on the node regulation power and the load regulation power of each adjustable flexible load in the target node.

7. The multi-element flexible load scheduling method based on controllable margin according to claim 6, characterized in that, The step of determining at least one target flexible load in the target node based on the node adjustment power and the load adjustment power of each adjustable flexible load in the target node includes: Sort each adjustable flexible load in the target node in descending order of controllability margin; Calculate the difference between the sum of the load regulation power of the Nth adjustable flexible load to the Mth adjustable flexible load and the node regulation power; where N is a positive integer, M is a positive integer, and N≤M; The Nth adjustable flexible load to the Mth adjustable flexible load with the smallest difference is determined as the target flexible load in the target node.

8. The multi-element flexible load scheduling method based on controllable margin according to claim 1, characterized in that, The process involves calculating the controllability margin of each flexible load based on its power margin, time margin, and energy margin, and then selecting multiple adjustable flexible loads from all flexible loads in the target power grid based on the controllability margin of each flexible load. This includes: The power margin, time margin, and energy margin of each flexible load are weighted and summed according to the preset weights corresponding to the load type of the flexible load to obtain the controllable margin of each flexible load. Flexible loads with controllable margins within a preset range are defined as adjustable flexible loads of the target power grid.

9. A multi-element flexible load dispatching device based on controllable margin, characterized in that, include: The calculation module is used to calculate the power margin, time margin, and energy margin of each flexible load in the target power grid; wherein, the flexible load includes air conditioning load, electric vehicle load, and energy storage load; The screening module is used to calculate the controllability margin of each flexible load based on the power margin, time margin, and energy margin of each flexible load, and to screen out multiple adjustable flexible loads from all flexible loads of the target power grid based on the controllability margin of each flexible load. The selection module is used to select at least one target node and the target flexible load within each target node from among multiple nodes based on the correspondence between multiple adjustable flexible loads and nodes and the target regulating power of the target power grid. The scheduling module is used to input the target regulating power and the operating parameters of each target flexible load into the load scheduling model to obtain the load scheduling scheme of the target power grid.

10. An electronic device, characterized in that, It includes a memory and a processor, the memory storing a computer program, and the processor executing the computer program to implement the method as described in any one of claims 1 to 8.

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