Power dispatching method and device, computer equipment, storage medium and computer program product

Abstract

The invention relates to a power dispatching method and device, computer equipment, a storage medium and a computer program product. Relates to the technical field of power systems. The method comprises the following steps: obtaining an adjustable interval of a building load in an electric power to-be-dispatched area, a fluctuation output rule of distributed photovoltaic, an electric vehicle dynamic feasible region fused with a charging intention of a user and a state change rule of an energy storage system, so as to carry out modeling on electric power use conditions of the building load, the distributed photovoltaic, the electric vehicle and the energy storage system; obtaining a comprehensive model comprising a plurality of sub-models; constructing a scheduling optimization model based on the dynamic electricity price information acquired in real time and the comprehensive model, and determining a preliminary power scheduling scheme based on the scheduling optimization model; and dynamically adjusting the initial power dispatching scheme based on a preset rolling optimization mechanism, and continuously updating a charging and discharging strategy, adjustable load adjustment and an electricity purchasing and selling decision to obtain a target power dispatching scheme for power dispatching. By adopting the method, the power dispatching efficiency can be improved.

Description

Technical Field

[0001] This application relates to the field of power system technology, and in particular to a power dispatching method, apparatus, computer equipment, storage medium, and computer program product. Background Technology

[0002] When resource aggregators integrate various resources within a region, including building loads, renewable energy, energy storage devices, and electric vehicles, into a unified entity for electricity market transactions and demand response, the diverse physical characteristics of these different resource types, especially the significant uncertainty in electric vehicle user charging behavior influenced by price signals, personal preferences, and other factors, result in a highly coupled and dynamically stochastic aggregated load. Therefore, quantifying the impact of user behavior uncertainty on resource adjustability and coordinating the power boundaries, timing constraints, and operational flexibility of multiple resource types have become unresolved issues.

[0003] Current methods for addressing these issues primarily involve decentralized modeling and independent scheduling of various resources, treating user behavior for resources such as electric vehicles as deterministic or simplified parameters. This approach fails to accurately reflect the collaborative compensation relationships between resources and makes it even more difficult to characterize the scheduling risks arising from random fluctuations in user preferences. Furthermore, existing models focus on updating and calculating prices and physical loads, lacking a systematic description of user behavior uncertainty, the continuity of resource states within rolling windows, and short-cycle decision-making mechanisms based on risk preferences. Consequently, the overall power dispatching efficiency remains low. Summary of the Invention

[0004] Therefore, it is necessary to provide a power dispatching method, apparatus, computer equipment, computer-readable storage medium, and computer program product to address the aforementioned technical problem of low power dispatching efficiency.

[0005] Firstly, this application provides a power dispatching method, including:

[0006] The adjustable range of building load, the fluctuation output pattern of distributed photovoltaic, the dynamic feasible domain of electric vehicles that integrates user charging intentions, and the state change pattern of energy storage system are obtained within the power dispatch area. In order to model the power usage of the building load, the distributed photovoltaic, the electric vehicles, and the energy storage system, a comprehensive model including multiple sub-models is obtained.

[0007] Based on the real-time acquired dynamic electricity price information and the comprehensive model, a scheduling optimization model is constructed, and a preliminary power dispatching scheme is determined based on the scheduling optimization model.

[0008] The preliminary power dispatching scheme is dynamically adjusted based on a preset rolling optimization mechanism, and the charging and discharging strategy, adjustable load regulation, and power purchase and sale decisions are continuously updated to obtain the target power dispatching scheme for power dispatching.

[0009] In one embodiment, constructing a scheduling optimization model based on real-time acquired dynamic electricity price information and the comprehensive model includes: acquiring operating costs and transaction costs based on the real-time acquired dynamic electricity price information, wherein the operating costs include the photovoltaic curtailment cost of the distributed photovoltaic system, the operation and maintenance cost of the energy storage system, the charging cost of the electric vehicle, and the electricity purchase cost of the area to be scheduled; acquiring preset constraints based on the comprehensive model; wherein the preset constraints include energy balance constraints, power boundary constraints, and state of charge constraints for each device in the area to be scheduled; wherein the devices include the building load, the distributed photovoltaic system, the electric vehicle, and the energy storage system; and constructing the scheduling optimization model based on the operating costs, the transaction costs, and the preset constraints.

[0010] In one embodiment, the step of dynamically adjusting the preliminary power dispatch scheme based on a preset rolling optimization mechanism, continuously updating the charging and discharging strategy, adjustable load regulation, and power purchase and sale decisions to obtain a target power dispatch scheme for power dispatch, includes: collecting real-time updated operating data in each rolling dispatch cycle based on a preset rolling optimization mechanism; the operating data includes the actual photovoltaic output of the distributed photovoltaic system, the actual value of the building load, the real-time electricity price of the electricity market in the area to be dispatched, and the real-time access status of the electric vehicles; obtaining the deviation between the operating data and the preliminary power dispatch scheme, and adjusting the charging and discharging strategy, adjustable load regulation, and power purchase and sale decisions based on the deviation to obtain a target power dispatch scheme for power dispatch.

[0011] In one embodiment, the power dispatching method further includes: establishing a building load mathematical model based on a benchmark load curve generated from historical load data of buildings in the area to be dispatched, the allowable fluctuation range of the building load, response time characteristics, and industry experience parameters, as the first sub-model among multiple sub-models.

[0012] In one embodiment, the power dispatching method further includes: establishing a photovoltaic output prediction model for the distributed photovoltaic system based on historical irradiance, weather forecast information, and photovoltaic module parameters, as the second sub-model among multiple sub-models.

[0013] In one embodiment, the power dispatching method further includes: determining the intensity of a user's willingness to adjust charging under different price signals based on the correlation between the user's charging willingness and the price signal; determining the minimum and maximum allowable power of the electric vehicle at different times based on the pre-acquired grid connection time, off-grid expected power, and battery safety operation requirements of the electric vehicle; and constructing an electric vehicle power model as the third sub-model among multiple sub-models based on the minimum and maximum allowable power of the electric vehicle at different times, the power constraints, rate regulation constraints, and state of charge constraints of the electric vehicle in charging and discharging states.

[0014] Secondly, this application also provides a power dispatching device, comprising:

[0015] The model building module is used to obtain the adjustable range of building load, the fluctuation output law of distributed photovoltaic, the dynamic feasible domain of electric vehicles that integrates users' charging intentions, and the state change law of energy storage system in the power dispatching area, so as to model the power usage of the building load, the distributed photovoltaic, the electric vehicles, and the energy storage system, and obtain a comprehensive model including multiple sub-models.

[0016] The preliminary scheme determination module is used to construct a scheduling optimization model based on the real-time acquired dynamic electricity price information and the comprehensive model, and to determine a preliminary power dispatch scheme based on the scheduling optimization model.

[0017] The target scheme determination module is used to dynamically adjust the preliminary power dispatch scheme based on a preset rolling optimization mechanism, and continuously update the charging and discharging strategy, adjustable load regulation and power purchase and sale decisions to obtain the target power dispatch scheme for power dispatch.

[0018] Thirdly, this application also provides a computer device, the computer device including a memory and a processor, the memory storing a computer program, and the processor executing the computer program to perform the following steps:

[0019] The adjustable range of building load, the fluctuation output pattern of distributed photovoltaic, the dynamic feasible domain of electric vehicles that integrates user charging intentions, and the state change pattern of energy storage system are obtained within the power dispatch area. In order to model the power usage of the building load, the distributed photovoltaic, the electric vehicles, and the energy storage system, a comprehensive model including multiple sub-models is obtained.

[0020] Based on the real-time acquired dynamic electricity price information and the comprehensive model, a scheduling optimization model is constructed, and a preliminary power dispatching scheme is determined based on the scheduling optimization model.

[0021] The preliminary power dispatching scheme is dynamically adjusted based on a preset rolling optimization mechanism, and the charging and discharging strategy, adjustable load regulation, and power purchase and sale decisions are continuously updated to obtain the target power dispatching scheme for power dispatching.

[0022] Fourthly, this application also provides a computer-readable storage medium having a computer program stored thereon, the computer program performing the following steps when executed by a processor:

[0023] The adjustable range of building load, the fluctuation output pattern of distributed photovoltaic, the dynamic feasible domain of electric vehicles that integrates user charging intentions, and the state change pattern of energy storage system are obtained within the power dispatch area. In order to model the power usage of the building load, the distributed photovoltaic, the electric vehicles, and the energy storage system, a comprehensive model including multiple sub-models is obtained.

[0024] Based on the real-time acquired dynamic electricity price information and the comprehensive model, a scheduling optimization model is constructed, and a preliminary power dispatching scheme is determined based on the scheduling optimization model.

[0025] The preliminary power dispatching scheme is dynamically adjusted based on a preset rolling optimization mechanism, and the charging and discharging strategy, adjustable load regulation, and power purchase and sale decisions are continuously updated to obtain the target power dispatching scheme for power dispatching.

[0026] Fifthly, this application also provides a computer program product, which includes a computer program that, when executed by a processor, performs the following steps:

[0027] The adjustable range of building load, the fluctuation output pattern of distributed photovoltaic, the dynamic feasible domain of electric vehicles that integrates user charging intentions, and the state change pattern of energy storage system are obtained within the power dispatch area. In order to model the power usage of the building load, the distributed photovoltaic, the electric vehicles, and the energy storage system, a comprehensive model including multiple sub-models is obtained.

[0028] Based on the real-time acquired dynamic electricity price information and the comprehensive model, a scheduling optimization model is constructed, and a preliminary power dispatching scheme is determined based on the scheduling optimization model.

[0029] The preliminary power dispatching scheme is dynamically adjusted based on a preset rolling optimization mechanism, and the charging and discharging strategy, adjustable load regulation, and power purchase and sale decisions are continuously updated to obtain the target power dispatching scheme for power dispatching.

[0030] The aforementioned power dispatching methods, devices, computer equipment, storage media, and computer program products, during the power dispatching process, first acquire the adjustable range of building loads within the power dispatching area, the fluctuating output patterns of distributed photovoltaic systems, the dynamic feasible domain of electric vehicles integrating user charging intentions, and the state change patterns of energy storage systems. This allows for the modeling of the power usage of building loads, distributed photovoltaic systems, electric vehicles, and energy storage systems, resulting in a comprehensive model including multiple sub-models. Based on real-time acquired dynamic electricity price information and the comprehensive model, a dispatching optimization model is constructed, and a preliminary power dispatching scheme is determined based on the dispatching optimization model. The preliminary power dispatching scheme is then dynamically adjusted based on a preset rolling optimization mechanism, continuously updating charging and discharging strategies, adjustable load regulation, and power purchase and sale decisions to obtain a target power dispatching scheme for power dispatching. In the above process, by integrating the adjustable range of building load, the fluctuation law of distributed photovoltaic, the dynamic feasible domain of electric vehicles, and the state change law of energy storage system, a multi-sub-model and a comprehensive model are constructed, providing reliable data and model support for dispatch optimization. Subsequently, based on real-time dynamic electricity price and the comprehensive model, a dispatch optimization model is constructed, which can generate a preliminary dispatch plan. Then, through a preset rolling optimization mechanism, the charging and discharging strategy, adjustable load adjustment, and power purchase and sale decision are dynamically adjusted to achieve full-time dynamic adaptation of the dispatch plan. This effectively reduces the pressure of peak-valley difference in the power grid, maximizes the utilization of electricity price difference revenue, reduces the user's electricity purchase cost and the power grid operation cost, enhances the economy and stability of the power system, and further achieves the goal of improving power dispatch efficiency. Attached Figure Description

[0031] To more clearly illustrate the technical solutions in the embodiments or related technologies of this application, the accompanying drawings used in the description of the embodiments or related technologies will be briefly introduced below. Obviously, the accompanying drawings described below are only some embodiments of this application. For those skilled in the art, other drawings can be obtained based on these drawings without creative effort.

[0032] Figure 1 This is a flowchart illustrating a power dispatching method in one embodiment;

[0033] Figure 2 This is a flowchart illustrating the process of constructing a scheduling optimization model in a power dispatching method according to one embodiment;

[0034] Figure 3 This is a flowchart illustrating the process of obtaining a target power dispatch scheme in a power dispatching method in one embodiment;

[0035] Figure 4 This is a flowchart illustrating the power dispatching steps in one embodiment;

[0036] Figure 5This is a structural block diagram of a power dispatching device in one embodiment;

[0037] Figure 6 This is an internal structural diagram of a computer device in one embodiment. Detailed Implementation

[0038] To make the objectives, technical solutions, and advantages of this application clearer, the following detailed description is provided in conjunction with the accompanying drawings and embodiments. It should be understood that the specific embodiments described herein are merely illustrative and not intended to limit the scope of this application.

[0039] In recent years, with the large-scale integration of distributed photovoltaic power, electric vehicle charging infrastructure, and user-side energy storage systems, resource aggregators have played an increasingly important role in the market. Resource aggregators typically participate in electricity market transactions and demand response by integrating various resources within a region, including building loads, renewable energy, energy storage devices, and electric vehicles, as a whole. However, due to the diverse physical characteristics of different resource types, especially the significant uncertainty in electric vehicle user charging behavior influenced by price signals, personal preferences, and other factors, the aggregated load exhibits highly coupled and dynamically stochastic characteristics. Against this backdrop, accurately quantifying the impact of user behavior uncertainty on resource adjustability, coordinating the power boundaries, timing constraints, and operational flexibility of various resources, and constructing a unified model applicable to day-ahead optimization and intraday rolling scheduling have become crucial for resource aggregators to achieve economic operation and market bidding.

[0040] In current research and engineering applications, common methods often model and schedule various resources independently and in a decentralized manner, generally treating user behavior for resources such as electric vehicles as deterministic or simplified parameters. This fails to accurately reflect the collaborative compensation relationships between resources and makes it even more difficult to characterize the scheduling risks caused by random fluctuations in user intentions. Furthermore, existing methods struggle to coordinate and update scheduling results when market prices or equipment operating states change. While intraday rolling optimization mechanisms have been proposed, existing models primarily focus on updating prices and physical loads, lacking a systematic description of user behavior uncertainty, the continuity of resource states within the rolling window, and short-cycle decision-making mechanisms based on risk preferences. This makes it difficult to balance economy and reliability when rolling scheduling faces multiple uncertainties in practice. Therefore, the current power dispatching efficiency remains low.

[0041] To address the aforementioned problem of low power dispatch efficiency, in one embodiment, such as Figure 1As shown, a power dispatching method is provided. This embodiment illustrates the method applied to a terminal. It is understood that this method can also be applied to a server, and further to a system including both a terminal and a server, and is implemented through interaction between the terminal and the server. In this embodiment, the method includes the following steps:

[0042] Step S102: Obtain the adjustable range of building load, the fluctuation output pattern of distributed photovoltaic, the dynamic feasible domain of electric vehicles that integrates user charging intentions, and the state change pattern of energy storage system within the power dispatch area, so as to model the power usage of building load, distributed photovoltaic, electric vehicles and energy storage system, and obtain a comprehensive model including multiple sub-models.

[0043] Among them, the power dispatching area is a specific geographical area or power grid zone that requires overall allocation and operation control of power resources. The power dispatching area can include multiple power sources, loads and storage entities such as building loads, distributed photovoltaics, electric vehicles, and energy storage systems, and is the target of power dispatching. The adjustable range of building loads is the power range and time interval within which the building's power load can be increased or decreased while meeting the normal production and living needs of users. The fluctuation output law of distributed photovoltaics is the fluctuation characteristics and trend of distributed photovoltaic power generation as the light intensity, weather and other natural conditions change, which can be used to predict the peak and valley periods of photovoltaic power generation. User charging willingness is the preference of electric vehicle users for charging time and charging method based on their own travel needs, charging costs and other factors, and is an influencing factor in determining the dynamic feasible domain of electric vehicles.

[0044] More specifically, the state change law of the energy storage system refers to the characteristics of the energy storage system's state of charge, charging and discharging power, energy loss and other parameters changing over time during the charging and discharging process; the sub-model is an independent mathematical model built based on the operating characteristics and parameters of four main entities in the power dispatch area: building load, distributed photovoltaic, electric vehicle and energy storage system; the comprehensive model is a unified model that integrates the above four sub-models and establishes the power coupling relationship and interaction mechanism between the main entities to reflect the overall operating characteristics of source, load and storage in the area.

[0045] Step S104: Based on the real-time acquired dynamic electricity price information and comprehensive model, construct a scheduling optimization model, and determine a preliminary power dispatching scheme based on the scheduling optimization model.

[0046] Among them, dynamic electricity price information is time-of-use electricity price data generated in real time by the electricity market based on supply and demand, grid operation status, etc., and dynamic electricity price information generally fluctuates dynamically over time; the dispatch optimization model is an optimization model built on the basis of the comprehensive model, guided by dynamic electricity price, and combined with grid security constraints, renewable energy consumption targets, etc., and is a model that maximizes dispatch efficiency under the premise of satisfying various constraints; the preliminary power dispatch scheme is the initial dispatch strategy obtained based on the dispatch optimization model, which includes the charging and discharging plans of each entity, load adjustment schemes, power purchase and sale arrangements, etc.

[0047] Step S106: Based on the preset rolling optimization mechanism, the preliminary power dispatching scheme is dynamically adjusted, and the charging and discharging strategy, adjustable load regulation and power purchase and sale decision are continuously updated to obtain the target power dispatching scheme for power dispatching.

[0048] Among them, the rolling optimization mechanism is a dynamic adjustment mechanism based on real-time data feedback. It mainly updates the source-load-storage operation status and electricity price information in the region according to a preset time period to iteratively revise the initial dispatch plan; the charging and discharging strategy is a charging and discharging time and power allocation plan for energy storage systems and electric vehicles; the adjustable load adjustment is the power increase or decrease operation of adjustable resources such as building loads according to the dispatch plan; the power purchase and sale decision is the decision on the time and amount of power to be purchased or sold based on real-time electricity prices and the power supply and demand situation in the region; the target power dispatch plan is the final dispatch plan obtained after adjustment by the rolling optimization mechanism.

[0049] The aforementioned power dispatching method first obtains the adjustable range of building loads, the fluctuating output patterns of distributed photovoltaic systems, the dynamic feasible domain of electric vehicles (EVs) incorporating user charging intentions, and the state change patterns of energy storage systems within the power dispatching area. This allows for the modeling of power usage by building loads, distributed photovoltaic systems, EVs, and energy storage systems, resulting in a comprehensive model that includes multiple sub-models. Based on real-time dynamic electricity price information and the comprehensive model, a dispatching optimization model is constructed, and a preliminary power dispatching scheme is determined based on this model. Finally, the preliminary power dispatching scheme is dynamically adjusted based on a pre-set rolling optimization mechanism, continuously updating charging and discharging strategies, adjustable load regulation, and power purchase and sale decisions to obtain a target power dispatching scheme for power dispatching. In the above process, by integrating the adjustable range of building load, the fluctuation law of distributed photovoltaic, the dynamic feasible domain of electric vehicles, and the state change law of energy storage system, a multi-sub-model and a comprehensive model are constructed, providing reliable data and model support for dispatch optimization. Subsequently, based on real-time dynamic electricity price and the comprehensive model, a dispatch optimization model is constructed, which can generate a preliminary dispatch plan. Then, through a preset rolling optimization mechanism, the charging and discharging strategy, adjustable load adjustment, and power purchase and sale decision are dynamically adjusted to achieve full-time dynamic adaptation of the dispatch plan. This effectively reduces the pressure of peak-valley difference in the power grid, maximizes the utilization of electricity price difference revenue, reduces the user's electricity purchase cost and the power grid operation cost, enhances the economy and stability of the power system, and further achieves the goal of improving power dispatch efficiency.

[0050] In one exemplary embodiment, such as Figure 2 As shown, a scheduling optimization model is constructed based on real-time acquired dynamic electricity price information and a comprehensive model, including: Step S202, obtaining operating costs and transaction costs based on real-time acquired dynamic electricity price information, where operating costs include the cost of photovoltaic curtailment of distributed photovoltaic power, the operation and maintenance costs of energy storage systems, the charging costs of electric vehicles, and the electricity purchase costs of the area to be dispatched; Step S204, obtaining preset constraints based on the comprehensive model, where preset constraints include energy balance constraints, power boundary constraints, and state of charge constraints for each device in the area to be dispatched; the devices include building loads, distributed photovoltaic power, electric vehicles, and energy storage systems; Step S206, constructing a scheduling optimization model based on operating costs, transaction costs, and preset constraints.

[0051] Among them, operating costs are the total costs incurred by various source-load-storage devices in the power dispatching area during the power dispatching process; transaction costs are the various costs incurred when the dispatching area and the external power grid conduct power purchase and sale transactions in the power market environment, which may include transaction fees, metering service fees, etc.; photovoltaic curtailment costs are the economic losses incurred by distributed photovoltaic power stations due to their output exceeding the load absorption capacity and grid acceptance limit in the area, forcing them to reduce their power generation; energy storage system operation and maintenance costs are the various expenses incurred by the energy storage system in maintaining the normal operation of the system during the charging and discharging cycle, such as equipment maintenance fees, battery depreciation fees, operation and maintenance personnel labor costs, monitoring system operation fees, etc.; electric vehicle charging costs are the total costs incurred to complete the charging process of electric vehicles; and power purchase costs in the dispatching area are the costs incurred when the power in the dispatching area cannot meet the load demand, and power is purchased from the external power grid.

[0052] Furthermore, the preset constraints are conditions set in advance before constructing the scheduling optimization model; the energy balance constraints are the energy conservation relationship that must be maintained between the total power generation and purchased power of the power to be dispatched area in any scheduling period and the total load power consumption, external power sales, and energy storage system charging and discharging in the area; the power boundary constraints are the maximum power operating limits set for various equipment such as building loads, distributed photovoltaics, electric vehicles, and energy storage systems; and the state of charge constraints are the state of charge (SOC) operating range restrictions set for energy storage systems and electric vehicle batteries, which may include upper and lower thresholds for the state of charge.

[0053] In this embodiment, operating costs and transaction costs are obtained based on dynamic electricity price information. At the same time, the three core constraints of energy balance, power boundary, and state of charge are clearly defined based on the comprehensive model, which ensures the rigor of the scheduling optimization model. This not only effectively reduces the photovoltaic curtailment rate, but also smooths the peak-valley difference of the power grid by reasonably controlling the charging and discharging of energy storage and the charging behavior of electric vehicles, reduces regional electricity purchase costs and transaction costs, improves the safety and stability of power system operation, and provides reliable support for efficient regional power scheduling.

[0054] In one embodiment, such as Figure 3As shown, the preliminary power dispatching scheme is dynamically adjusted based on a preset rolling optimization mechanism, and the charging and discharging strategy, adjustable load regulation, and power purchase and sale decisions are continuously updated to obtain the target power dispatching scheme for power dispatching. The steps include: Step S302, based on the preset rolling optimization mechanism including the set rolling dispatching cycle, collecting real-time updated operating data in each rolling dispatching cycle; the operating data includes the actual output of distributed photovoltaic power, the actual value of building load, the real-time electricity price of the electricity market in the area to be dispatched, and the real-time access status of electric vehicles; Step S304, obtaining the deviation between the operating data and the preliminary power dispatching scheme, and adjusting the charging and discharging strategy, adjustable load regulation, and power purchase and sale decisions based on the deviation to obtain the target power dispatching scheme for power dispatching.

[0055] Among them, the rolling dispatch cycle is the time interval for implementing the rolling optimization mechanism, and it is the core parameter for dividing the dynamic adjustment period of the dispatch plan; real-time operation data is dynamic data collected from the source-load-storage equipment operation status and the electricity market in the power waiting area within each rolling dispatch cycle; actual photovoltaic output is the real-time power generation of distributed photovoltaic power stations within the dispatch cycle; actual building load is the real-time power consumption of building electrical equipment within the dispatch cycle; real-time electric vehicle access status is real-time status information such as whether electric vehicles are connected to charging facilities, the number of connected vehicles, and the duration of connection within the dispatch cycle; and operation data deviation is the difference between the real-time operation data collected within the rolling dispatch cycle and the predicted data used when formulating the preliminary dispatch plan.

[0056] In this embodiment, by setting a rolling scheduling cycle, operational data such as actual photovoltaic output, actual building load, real-time electricity price, and electric vehicle access status are accurately collected in each cycle, enabling timely acquisition of real-time equipment operation data. By comparing the real-time operation data with the preset data of the preliminary scheduling plan, the deviation between the two is quantitatively analyzed, and the charging and discharging strategy, adjustable load regulation, and power purchase and sale decisions are adjusted, effectively solving the problem of the disconnect between the traditional static scheduling plan and the actual operating conditions. Furthermore, it can also correct scheduling deviations in real time, reduce photovoltaic curtailment losses, and reduce power purchase costs.

[0057] Furthermore, in one embodiment, the power dispatching method further includes: establishing a mathematical model of building load based on a benchmark load curve generated from historical load data of buildings within the dispatching area, the allowable fluctuation range of building load, response time characteristics, and industry experience parameters, as the first sub-model among multiple sub-models.

[0058] Among them, historical load data refers to the electricity load data recorded by buildings in the power dispatch area during past operating cycles; the benchmark load curve is a curve generated based on the historical load data of buildings through statistical analysis, trend fitting, and other methods, reflecting the time-varying pattern of typical building electricity load; the allowable fluctuation range of building load is the limit range within which the building's electricity load can be adjusted upward or downward under the premise of meeting normal production and living electricity needs; the response time characteristic is the time characteristic of the building load from the start of load adjustment to reaching the target load value after receiving the dispatch instruction, which can include parameters such as load adjustment response delay and load change rate; industry experience parameters are parameters with industry universality formed through long-term practice in the field of building power load modeling and dispatch, which can include load characteristic coefficients, equipment operating efficiency coefficients, and load adjustment potential coefficients for different types of buildings; and the first sub-model is one of the sub-models in the construction system of the comprehensive model of the power dispatch area.

[0059] In this embodiment, a benchmark load curve is generated by using historical building load data. Combined with allowable fluctuation range, response time characteristics, and industry experience parameters, a building load mathematical model is established as the first sub-model of the comprehensive model. This model can provide accurate load forecasting and control basis for regional power dispatching and improve the utilization efficiency of load-side resources.

[0060] In an exemplary embodiment, the power dispatching method further includes: establishing a photovoltaic output prediction model for distributed photovoltaics based on historical irradiance, weather forecast information, and photovoltaic module parameters, as the second sub-model among multiple sub-models.

[0061] Among them, historical irradiance is the solar radiation energy received per unit area in the area where the distributed photovoltaic power station is located over a period of time; weather forecast information is short-term or ultra-short-term weather forecast data provided by meteorological departments or professional meteorological service agencies for the area where the photovoltaic power station is located, which may include key meteorological elements such as light intensity, cloud cover, temperature, and wind speed; photovoltaic module parameters can be the technical parameters of the core equipment such as photovoltaic panels and inverters that constitute the distributed photovoltaic power station, including, for example, the rated power, photoelectric conversion efficiency, temperature coefficient, and maximum power point tracking range of the photovoltaic modules; the second sub-model is one of the sub-models of the comprehensive model of the power dispatching area.

[0062] In this embodiment, a photovoltaic power output prediction model is established as the second sub-model of the comprehensive model by using historical irradiance, weather forecast information, photovoltaic module parameters, historical operating patterns of photovoltaic power generation, external environmental influencing factors and inherent characteristics of equipment. This model can accurately predict the output change trend of photovoltaic power plants, improving prediction accuracy and fluctuation prediction accuracy.

[0063] In one embodiment, the power dispatching method further includes: determining the intensity of a user's willingness to adjust charging under different price signals based on the correlation between the user's charging willingness and the price signal; determining the minimum and maximum allowable power of the electric vehicle at different times based on the pre-acquired grid connection time, off-grid expected power, and battery safety operation requirements of the electric vehicle; and constructing an electric vehicle power model as the third sub-model among multiple sub-models based on the minimum and maximum allowable power of the electric vehicle at different times, the power constraints, rate regulation constraints, and state of charge constraints of the electric vehicle in charging and discharging states.

[0064] Among them, price signals are price-related information that guides users' electricity consumption behavior in the electricity market; the intensity of charging adjustment willingness is the degree to which electric vehicle users are willing to adjust their original charging plans when they receive different price signals, which can be expressed in the form of quantitative coefficients or levels; grid connection time is the specific time point when an electric vehicle connects to the charging facilities in the area to be dispatched and establishes a power connection with the regional power system; the expected off-grid charge is the state of charge that electric vehicle users expect their batteries to reach when they plan to leave the charging facilities; the minimum allowable charge is the minimum battery charge that electric vehicles are allowed at different times, determined based on battery safety operation requirements, users' expected off-grid charge, and travel needs; the maximum allowable charge is the maximum battery charge that electric vehicles are allowed at different times, determined based on battery safety operation requirements and the power limitations of charging facilities; the third sub-model is one of the sub-models of the comprehensive model of the power dispatch area.

[0065] In this embodiment, by combining the correlation between price signals and users' charging intentions, the intensity of charging adjustment intentions can be accurately quantified. At the same time, by integrating core constraints such as grid connection time, off-grid expected capacity, and battery safety requirements, a third sub-model is established. This not only ensures users' travel needs and battery operation safety, but also provides accurate electric vehicle-side data support for power dispatch.

[0066] This application provides a power dispatching method. To better understand the process of the above power dispatching method, in conjunction with... Figure 4 As shown below, the specific process of a power dispatching method according to this application is described in detail, including the following steps:

[0067] Step S402: Based on the operational characteristics of multiple resource types, establish an aggregated regional system model to achieve a unified representation of building load, photovoltaic output, and electric vehicle load considering user charging intentions; Step S404: Based on the regional system, establish a day-ahead optimization scheduling model to obtain the power purchase and sale strategy, energy storage charging and discharging plan, and adjustable load adjustment scheme to form a baseline scheduling result; Step S406: Introduce a rolling optimization mechanism to dynamically correct the day-ahead scheduling scheme and update the energy storage scheduling, electric vehicle charging and discharging strategy, and power purchase and sale decision in real time.

[0068] In this system, the aggregated regional system model corresponds to the comprehensive model, the baseline dispatching result corresponds to the preliminary power dispatching scheme, the dynamically revised day-ahead dispatching scheme, and the real-time updated energy storage dispatching, electric vehicle charging and discharging strategies, and the strategies generated after power purchase and sale decisions correspond to the target power dispatching scheme. Before designing the collaborative dispatching and optimization strategies on the resource aggregator side, it is necessary to accurately model the operating characteristics of the main energy consumption and power generation resources within the aggregated region. Resource aggregators typically manage multiple types of typical energy consumers, including building loads, distributed photovoltaics, and electric vehicle charging facilities. Among these, building loads are the most basic and stable source of electricity demand in the region, exhibiting obvious intraday periodicity and seasonality. Distributed photovoltaics, as a major form of renewable energy access, has output characteristics that fluctuate with changes in weather conditions, making it an important factor affecting regional power balance. Electric vehicle charging loads, on the other hand, possess strong adjustability and mobility, and their charging demand has significant flexibility in terms of time and power, serving as an important adjustment resource for resource aggregators. Therefore, to ensure the accuracy of subsequent day-ahead optimization and rolling scheduling models, a building load model, a distributed photovoltaic model, and an electric vehicle charging model are constructed to uniformly represent the energy consumption behavior, output characteristics, and adjustability of these three typical resources. Building loads typically consist of multiple loads such as lighting, air conditioning, office equipment, heating, and ventilation, exhibiting obvious periodicity and stability. Their load curves can be obtained by statistically analyzing historical load data from recent days. To more accurately reflect the operational characteristics and adjustability of building loads, a baseline load curve is constructed based on historical data, and a mathematical model of building load is established by combining the allowable fluctuation range of the load, response time characteristics, and industry experience parameters.

[0069] Furthermore, the predicted load of the building at time t Formula (1) is obtained by averaging the load curves of the most recent N days: In the formula, The actual building load on day d at time t; For the historical days used to calculate the baseline; Formula (2): In the formula, This is the actual load on the building at time t. and These are the minimum and maximum load power, respectively. Since the loads of air conditioning, ventilation, etc., cannot increase or decrease abruptly, the rate of change of building load must satisfy formula (3): In the formula, The maximum acceptable ramp capacity for building load; to support demand response and market participation, building load can be decomposed into rigid load and adjustable load, as shown in formula (4): In the formula, It is a rigid load that is not adjustable and must be satisfied; This is an adjustable load. The adjustable load must not exceed the adjustable upper limit, as shown in formula (5): In the formula, This represents the maximum adjustable capacity.

[0070] Furthermore, distributed photovoltaic (PV) power output, as the most important renewable energy source for aggregators, exhibits typical randomness and intermittency. To accurately reflect PV power generation capacity during scheduling and optimization, it is necessary to establish a PV power output prediction model based on historical irradiance, weather forecast information, and PV module parameters. This model can not only provide the expected value of PV power generation but also describe the range of its upper and lower boundaries using uncertainty scenario generation methods. Multi-scenario PV power output sequences can be generated using Monte Carlo, GAN, Copula, and other methods as inputs to the optimization model to improve robustness. The model should also meet the following constraints, as shown in formula (6): ,in, It is the output of photovoltaic power generation at time t; and These represent the minimum and maximum power outputs of photovoltaic power generation, respectively.

[0071] In addition, with the continuous growth of the number of electric vehicles in the areas covered by aggregators, their charging behavior has become an important factor affecting the load curve. Electric vehicle loads have characteristics such as random arrival, uncertain dwell time, adjustable charging power, and diversified charging demand, making them one of the most flexible and adjustable resources in the region. Under the coordination of resource aggregators, electric vehicles can not only meet the travel needs of individual users, but also participate in energy management by optimizing charging and discharging strategies, thereby achieving peak shaving and valley filling, reducing electricity purchase costs, and improving overall regulation capabilities.

[0072] Therefore, when constructing the aggregated region system model, it is necessary to establish a mathematical model that can reflect the behavioral characteristics of electric vehicles. The charging state of electric vehicles is used to describe the real-time energy state, and the specific formula is as follows: Formula (7): In the formula, and They represent in and The state of charge (SOC) of an electric vehicle at any time. It is the self-discharge coefficient; and These are the charging efficiency and discharging efficiency of electric vehicles, respectively. and These represent the actual power at the charging and discharging moments of the electric vehicle, respectively; the relevant physical constraints to be satisfied include the following model (8):

[0073]

[0074]

[0075]

[0076]

[0077]

[0078]

[0079] in, and These represent the maximum power of the electric vehicle in charging and discharging states, respectively. and These represent the electric vehicle at time [time]. and The charging power; and These represent the electric vehicle at time [time]. and The discharge power; and These represent the minimum downward adjustment rate and the maximum upward adjustment rate of electric vehicles, respectively. and These represent the minimum and maximum permissible state of charge (SOC) of an electric vehicle, respectively. and Boolean variables for charging and discharging electric vehicles.

[0080] Furthermore, to more accurately characterize the actual adjustability of electric vehicles in real-time scheduling, a user response willingness model is introduced. The charging willingness of electric vehicle users is closely related to the charging price signal. Based on the Weber-Fechner law, the charging willingness of the i-th electric vehicle in response to the charging price signal... Response rate This can be expressed as formula (9): In the formula, Price signals for EV charging; The charging response rate of the i-th EV represents the strength of a user's willingness to accept adjustments to the charging plan at a given price. Let be the response coefficient of the i-th EV user. Let be the response constant of the i-th EV user; An embedded virtual battery model is used to dynamically calculate the power and energy feasible domain boundary with charging as the guide, specifically represented by the following model (10).

[0081]

[0082]

[0083]

[0084]

[0085] In the formula, This represents the minimum allowable battery level for the vehicle at time t, considering grid connection time, minimum charging power, and battery safety lower limit. For the i-th electric vehicle at the grid connection time The initial energy of the battery. The grid connection time of the i-th electric vehicle; The scheduling time interval; The minimum permissible charge level is the lowest energy limit that the battery can operate safely or that is psychologically acceptable to the user. This represents the maximum allowable battery level for the vehicle at time t, taking into account the user's willingness to charge, the desired off-grid charge level, and the battery safety limit. For the i-th electric vehicle at the off-grid time The desired battery energy output is determined by the user's travel needs; The maximum permissible charge level for safe battery operation; This represents the lower limit of power allowed at time t to ensure energy continuity. At time t, the maximum allowable charging power after considering user preferences and energy continuity.

[0086] More importantly, after obtaining an aggregated regional system model that includes multiple resources such as photovoltaic power generation, energy storage systems, electric vehicles, and building loads, it is necessary to further construct an economic optimization model for resource aggregators. This model is used to determine the power allocation and power purchase and sale strategies for various types of equipment during future dispatch cycles. With the goal of minimizing system operating costs and electricity market transaction costs, the costs of photovoltaic curtailment, energy storage system operation, electric vehicle charging, and local power purchase are uniformly quantified. Combined with the energy balance constraints, power boundary constraints, and SOC state constraints of each device, a day-ahead dispatch optimization model is formed. Through this model, the day-ahead dispatch scheme that minimizes the overall economic cost during the dispatch cycle can be obtained under the premise of satisfying the operating rules of the equipment and the grid interface conditions.

[0087] Among them, transaction costs are used to characterize the expenses incurred by resource aggregators in purchasing and selling electricity in the electricity market; system operating costs are used to represent the related expenses incurred during the operation and maintenance of energy storage systems and electric vehicle charging stations. Based on the above objectives, a corresponding optimization model can be further constructed as shown in (11): In the formula, Cost of solar power curtailment; and These represent the operating costs of the energy storage system and the electric vehicle charging station, respectively. The cost of charging electric vehicles; The cost of purchasing electricity from the local power grid is represented by the following model (12):

[0088]

[0089] In the formula, The unit cost of curtailed photovoltaic power; and These are the unit operating costs of energy storage systems and charging stations, respectively. The unit charging cost for electric vehicles; and These represent the unit electricity price for purchasing electricity from the grid and selling electricity to the grid, respectively.

[0090] Furthermore, in addition to the constraints of the energy storage system and the electric vehicle itself, its charging and discharging power should also meet the following constraints, as shown in model (13): ; ,in, and These represent the start and end times in the day-ahead scheduling model, respectively.

[0091] Finally, based on the day-ahead optimized dispatch model, a rolling optimization mechanism is further introduced to address real-time uncertainties such as fluctuations in renewable energy output, changes in user behavior, and random fluctuations in market prices. Rolling optimization continuously acquires the latest operational information on a shorter timescale, including real-time renewable energy output, updated forecasts of building and electric vehicle loads, real-time electricity market prices, and the latest SOC (State of Charge) of the energy storage system. The optimization model is re-solved in each rolling cycle, enabling the dispatch strategy to continuously improve. Based on these real-time inputs, the day-ahead dispatch plan is dynamically adjusted without altering the overall dispatch framework. Key decision variables such as charging and discharging power, adjustable load adjustment, electric vehicle charging arrangements, and power purchase and sale strategies are updated on a rolling basis, thereby correcting deviations between day-ahead dispatch and actual operating conditions. Rolling optimization can promptly adjust the dispatch scheme when market prices change or resource status is updated, minimizing operating costs, efficiently utilizing renewable energy, and ensuring stable operation of the user-side system. This allows resource aggregators to maintain dynamic optimization capabilities of their dispatch strategies during real-time operation, providing higher economic efficiency and operational reliability for the entire system.

[0092] Among them, the objective of intraday rolling optimization is to minimize the total cost of photovoltaic, electric vehicles and their charging stations, energy storage systems, and resource aggregators and electricity market transactions. Its objective function can be expressed as follows (14):

[0093]

[0094]

[0095] in, and These represent the power purchased and the power sold between the resource aggregator and the power grid, respectively. This is the scheduling deviation penalty coefficient, used to control the cost weight of the deviation between the day-ahead plan and real-time execution; Electricity purchased from the power grid, Electricity sold to the power grid.

[0096] Resource aggregators also need to meet the following constraints (15):

[0097]

[0098]

[0099]

[0100] in, and These represent the maximum amount of electricity that a resource aggregator can purchase from the main grid and the maximum amount of electricity it can sell to the main grid, respectively.

[0101] The electric vehicle's battery capacity must also meet the following constraints (16):

[0102]

[0103] in, Let t be the remaining battery power of the electric vehicle at time t.

[0104] Therefore, in the above embodiments, by establishing a comprehensive model of building load, photovoltaic power generation, electric vehicles and energy storage systems that take into account the dynamic feasible domain of user willingness, and designing a scheduling strategy that combines day-ahead planning and intraday risk perception rolling adjustment mechanism, it is possible to achieve adaptive coordinated operation among multiple types of resources in uncertain environments, dynamically update the operating status, reduce market deviation electricity, improve overall economy and scheduling reliability, and provide robust, efficient and risk-controllable technical support for resource aggregators to participate in the electricity market.

[0105] Furthermore, the power dispatching method of this application is applicable to user-side energy consumption scenarios consisting of multiple adjustable resources such as building load, distributed photovoltaic, electric vehicles, and energy storage systems. First, it constructs a building load model, a photovoltaic power generation model, and an electric vehicle charging behavior model that considers the uncertainty of user charging intentions, uniformly describing the power boundaries, timing constraints, and adjustability of multiple resource types. Then, based on dynamic electricity price information and resource operation characteristics, a day-ahead optimization model is established to obtain the optimal power purchase and sale plan and internal resource coordination strategy for future dispatch cycles. Further, during the real-time operation phase, combining the latest electricity price, photovoltaic output, building load, and electric vehicle access status, a rolling optimization mechanism is used to dynamically adjust the day-ahead plan, achieving continuous updates to charging and discharging strategies, adjustable load regulation, and power purchase and sale decisions. This effectively improves the economic efficiency and dispatch coordination capabilities of resource aggregators, enhances the utilization level of renewable energy, and strengthens overall operating efficiency, providing an efficient and reliable optimization method for user-side multi-resource collaborative management.

[0106] It should be understood that although the steps in the flowcharts of the embodiments described above are shown sequentially according to the arrows, these steps are not necessarily executed in the order indicated by the arrows. Unless explicitly stated herein, there is no strict order restriction on the execution of these steps, and they can be executed in other orders. Moreover, at least some steps in the flowcharts of the embodiments described above may include multiple steps or multiple stages. These steps or stages are not necessarily completed at the same time, but can be executed at different times. The execution order of these steps or stages is not necessarily sequential, but can be performed alternately or in turn with other steps or at least some of the steps or stages of other steps.

[0107] Based on the same inventive concept, this application also provides a power dispatching device for implementing the power dispatching method described above. The solution provided by this device is similar to the solution described in the above method; therefore, the specific limitations in one or more power dispatching device embodiments provided below can be found in the limitations of the power dispatching method described above, and will not be repeated here.

[0108] In one exemplary embodiment, such as Figure 5 As shown, a power dispatching device is provided, including: a model building module 501, a preliminary scheme determination module 502, and a target scheme determination module 503, wherein:

[0109] The model building module 501 is used to obtain the adjustable range of building load, the fluctuation output law of distributed photovoltaic, the dynamic feasible domain of electric vehicles that integrates users' charging intentions, and the state change law of energy storage system in the power dispatching area, so as to model the power usage of building load, distributed photovoltaic, electric vehicles and energy storage system, and obtain a comprehensive model including multiple sub-models.

[0110] The preliminary scheme determination module 502 is used to construct a scheduling optimization model based on real-time acquired dynamic electricity price information and a comprehensive model, and to determine a preliminary power dispatch scheme based on the scheduling optimization model.

[0111] The target scheme determination module 503 is used to dynamically adjust the preliminary power dispatch scheme based on a preset rolling optimization mechanism, and continuously update the charging and discharging strategy, adjustable load regulation and power purchase and sale decision to obtain the target power dispatch scheme for power dispatch.

[0112] Furthermore, in one embodiment, the model building module 501 is also used to obtain operating costs and transaction costs based on real-time acquired dynamic electricity price information. The operating costs include the cost of photovoltaic curtailment of distributed photovoltaic power, the operation and maintenance costs of energy storage systems, the charging costs of electric vehicles, and the electricity purchase costs of the area to be dispatched. The module also obtains preset constraints based on a comprehensive model. The preset constraints include energy balance constraints, power boundary constraints, and state of charge constraints for each device in the area to be dispatched. The devices include building loads, distributed photovoltaic power, electric vehicles, and energy storage systems. Based on the operating costs, transaction costs, and preset constraints, a dispatch optimization model is constructed.

[0113] Furthermore, in one embodiment, the scheme determination module 502 is also used to collect real-time updated operating data in each rolling scheduling cycle based on the preset rolling optimization mechanism including a set rolling scheduling cycle; the operating data includes the actual photovoltaic output of distributed photovoltaic, the actual value of building load, the real-time electricity price of the electricity market in the area to be dispatched, and the real-time access status of electric vehicles; obtain the deviation between the operating data and the preliminary power dispatch scheme, and adjust the charging and discharging strategy, adjustable load adjustment, and power purchase and sale decision based on the deviation to obtain the target power dispatch scheme for power dispatch.

[0114] Furthermore, in one embodiment, the model building module 501 is also used to establish a mathematical model of building load based on the benchmark load curve generated from the historical load data of buildings in the area to be scheduled, the allowable fluctuation range of building load, response time characteristics, and industry experience parameters, as the first sub-model among multiple sub-models.

[0115] Furthermore, in one embodiment, the model building module 501 is also used to build a photovoltaic output prediction model for distributed photovoltaics based on historical irradiance, weather forecast information and photovoltaic module parameters, as the second sub-model among multiple sub-models.

[0116] Furthermore, in one embodiment, the model building module 501 is also used to determine the intensity of a user's willingness to adjust charging under different price signals based on the correlation between the user's charging willingness and the price signal; to determine the minimum and maximum allowable power of the electric vehicle at different times based on the pre-acquired grid connection time, off-grid expected power, and battery safety operation requirements of the electric vehicle; and to construct an electric vehicle power model as the third sub-model among multiple sub-models based on the minimum and maximum allowable power of the electric vehicle at different times, the power constraints, rate regulation constraints, and state of charge constraints of the electric vehicle in charging and discharging states.

[0117] Each module in the aforementioned power dispatching device can be implemented entirely or partially through software, hardware, or a combination thereof. These modules can be embedded in or independent of the processor in a computer device, or stored in the memory of a computer device as software, so that the processor can call and execute the corresponding operations of each module.

[0118] In one exemplary embodiment, a computer device is provided, which may be a server, and its internal structure diagram may be as follows: Figure 6 As shown, this computer device includes a processor, memory, input / output (I / O) interfaces, and a communication interface. The processor, memory, and I / O interfaces are connected via a system bus, and the communication interface is also connected to the system bus via the I / O interfaces. The processor provides computational and control capabilities. The memory includes non-volatile storage media and internal memory. The non-volatile storage media stores the operating system, computer programs, and a database. The internal memory provides the environment for the operation of the operating system and computer programs stored in the non-volatile storage media. The database stores power dispatching data. The I / O interfaces are used for exchanging information between the processor and external devices. The communication interface is used for communication with external terminals via a network connection. When the computer program is executed by the processor, it implements a power dispatching method.

[0119] Those skilled in the art will understand that Figure 6The structure shown is merely a block diagram of a portion of the structure related to the present application and does not constitute a limitation on the computer device to which the present application is applied. Specific computer devices may include more or fewer components than those shown in the figure, or combine certain components, or have different component arrangements.

[0120] In one exemplary embodiment, a computer device is provided, including a memory and a processor, wherein the memory stores a computer program, and the processor executes the computer program to implement the steps in the above-described method embodiments.

[0121] In one embodiment, a computer-readable storage medium is provided having a computer program stored thereon, which, when executed by a processor, implements the steps in the above method embodiments.

[0122] In one embodiment, a computer program product is provided, including a computer program that, when executed by a processor, implements the steps in the above method embodiments.

[0123] It should be noted that the user information (including but not limited to user device information, user personal information, etc.) and data (including but not limited to data used for analysis, data stored, data displayed, etc.) involved in this application are all information and data authorized by the user or fully authorized by all parties, and the collection, use and processing of the relevant data must comply with relevant regulations.

[0124] Those skilled in the art will understand that all or part of the processes in the above embodiments can be implemented by a computer program instructing related hardware. The computer program can be stored in a non-volatile computer-readable storage medium. When executed, the computer program can include the processes of the embodiments described above. Any references to memory, databases, or other media used in the embodiments provided in this application can include at least one of non-volatile and volatile memory. Non-volatile memory can include read-only memory (ROM), magnetic tape, floppy disk, flash memory, optical memory, high-density embedded non-volatile memory, resistive random access memory (ReRAM), magnetic random access memory (MRAM), ferroelectric random access memory (FRAM), phase change memory (PCM), graphene memory, etc. Volatile memory can include random access memory (RAM) or external cache memory, etc. By way of illustration and not limitation, RAM can take many forms, such as Static Random Access Memory (SRAM) or Dynamic Random Access Memory (DRAM). The databases involved in the embodiments provided in this application may include at least one type of relational database and non-relational database. Non-relational databases may include, but are not limited to, blockchain-based distributed databases. The processors involved in the embodiments provided in this application may be general-purpose processors, central processing units, graphics processing units, digital signal processors, programmable logic devices, quantum computing-based data processing logic devices, etc., and are not limited to these.

[0125] The technical features of the above embodiments can be combined in any way. For the sake of brevity, not all possible combinations of the technical features in the above embodiments are described. However, as long as there is no contradiction in the combination of these technical features, they should be considered to be within the scope of this specification.

[0126] The embodiments described above are merely illustrative of several implementation methods of this application, and while the descriptions are specific and detailed, they should not be construed as limiting the scope of this patent application. It should be noted that those skilled in the art can make various modifications and improvements without departing from the concept of this application, and these all fall within the protection scope of this application. Therefore, the protection scope of this application should be determined by the appended claims.

Claims

1. A power dispatching method, characterized by, The method includes: The adjustable range of building load, the fluctuation output pattern of distributed photovoltaic power, the dynamic feasible domain of electric vehicles that integrates user charging intentions, and the state change pattern of energy storage system are obtained within the power dispatch area. In order to model the power usage of the building load, the distributed photovoltaic power, the electric vehicles, and the energy storage system, a comprehensive model including multiple sub-models is obtained. Based on the real-time acquired dynamic electricity price information and the comprehensive model, a scheduling optimization model is constructed, and a preliminary power dispatching scheme is determined based on the scheduling optimization model. The preliminary power dispatching scheme is dynamically adjusted based on a preset rolling optimization mechanism, and the charging and discharging strategy, adjustable load regulation, and power purchase and sale decisions are continuously updated to obtain the target power dispatching scheme for power dispatching.

2. The method of claim 1, wherein, The scheduling optimization model, constructed based on real-time acquired dynamic electricity price information and the comprehensive model, includes: Based on the real-time acquired dynamic electricity price information, the operating costs and transaction costs are obtained. The operating costs include the photovoltaic curtailment cost of the distributed photovoltaic system, the operation and maintenance cost of the energy storage system, the charging cost of the electric vehicle, and the electricity purchase cost of the area to be dispatched. Preset constraints are obtained based on the comprehensive model; the preset constraints include energy balance constraints, power boundary constraints, and state of charge constraints for each device in the area to be scheduled; the devices include the building load, the distributed photovoltaic system, the electric vehicle, and the energy storage system; The scheduling optimization model is constructed based on the operating cost, the transaction cost, and the preset constraints.

3. The method of claim 1, wherein, The step of dynamically adjusting the preliminary power dispatching scheme based on a preset rolling optimization mechanism, continuously updating the charging and discharging strategy, adjustable load regulation, and power purchase and sale decisions, to obtain a target power dispatching scheme for power dispatching includes: Based on the preset rolling optimization mechanism, which includes a set rolling scheduling cycle, real-time updated operating data is collected in each rolling scheduling cycle. The operating data includes the actual photovoltaic output of the distributed photovoltaic system, the actual value of the building load, the real-time electricity price of the electricity market in the area to be scheduled, and the real-time access status of the electric vehicle. The deviation between the operational data and the preliminary power dispatching scheme is obtained, and the charging and discharging strategy, adjustable load regulation, and power purchase and sale decisions are adjusted based on the deviation to obtain the target power dispatching scheme for power dispatching.

4. The method of claim 1, wherein, The method further includes: Based on the baseline load curve generated from the historical load data of buildings in the area to be scheduled, the allowable fluctuation range of the building load, the response time characteristics, and industry experience parameters, a mathematical model of building load is established as the first sub-model among multiple sub-models.

5. The method of claim 1, wherein, The method further includes: Based on historical irradiance, weather forecast information, and photovoltaic module parameters, a photovoltaic output prediction model for the distributed photovoltaic system is established as the second sub-model among multiple sub-models.

6. The method of claim 1, wherein, The method further includes: Based on the correlation between users' charging intentions and price signals, the strength of users' willingness to adjust their charging under different price signals is determined; Based on the pre-acquired grid connection time, off-grid expected power, and battery safety operation requirements of the electric vehicle, the minimum and maximum allowable power of the electric vehicle at different times are determined. Based on the minimum and maximum allowable power of the electric vehicle at different times, the power constraints, rate regulation constraints, and state of charge constraints of the electric vehicle in charging and discharging states, an electric vehicle power model is constructed as the third sub-model among the multiple sub-models.

7. A power scheduling device, characterized by The device includes: The model building module is used to obtain the adjustable range of building load, the fluctuation output law of distributed photovoltaic, the dynamic feasible domain of electric vehicles that integrates users' charging intentions, and the state change law of energy storage system in the power dispatching area, so as to model the power usage of the building load, the distributed photovoltaic, the electric vehicles, and the energy storage system, and obtain a comprehensive model including multiple sub-models. The preliminary scheme determination module is used to construct a scheduling optimization model based on the real-time acquired dynamic electricity price information and the comprehensive model, and to determine a preliminary power dispatch scheme based on the scheduling optimization model. The target scheme determination module is used to dynamically adjust the preliminary power dispatch scheme based on a preset rolling optimization mechanism, and continuously update the charging and discharging strategy, adjustable load regulation and power purchase and sale decision to obtain the target power dispatch scheme for power dispatch.

8. A computer device comprising a memory and a processor, the memory storing a computer program, characterized in that, When the processor executes the computer program, it implements the steps of the method according to any one of claims 1 to 6.

9. A computer-readable storage medium having stored thereon a computer program, characterized in that, When the computer program is executed by a processor, it implements the steps of the method according to any one of claims 1 to 6.

10. A computer program product comprising a computer program, characterized in that, When the computer program is executed by a processor, it implements the steps of the method according to any one of claims 1 to 6.

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