Source network load storage cooperative scheduling method and related equipment

Abstract

The invention provides a source network load storage cooperative scheduling method and related equipment. The method comprises the following steps: acquiring flexible load data and electric vehicle data; performing first scheduling processing on the flexible load based on a scheduling strategy preset by the flexible load data; performing second scheduling processing on the electric vehicle based on a scheduling strategy preset by the electric vehicle data; determining an objective function value based on the first scheduling processing and the second scheduling processing; determining the first scheduling processing and the second scheduling processing corresponding to the minimum value of the objective function value; performing scheduling processing on the flexible load based on the first scheduling processing; and carrying out scheduling processing on the electric vehicle based on the second scheduling processing. According to the embodiment of the invention, multi-energy optimal scheduling can be realized, and the power grid stability and the new energy utilization efficiency are improved.

Description

Technical Field

[0001] This application relates to the field of resource scheduling technology, and in particular to a source-grid-load-storage coordinated scheduling method and related equipment. Background Technology

[0002] In existing technologies, flexible interaction between generation and load resources and the dispatch system is gradually being achieved by coordinating resources on the generation and demand sides. Based on a cloud-edge collaborative dispatch framework, the upper-level dispatch is responsible for the overall optimized dispatch of multiple energy systems, while the lower-level dispatch transmits demand information to the upper-level dispatch center through comprehensive management of controllable load-side equipment, thereby achieving extensive collaborative interaction between power generation, grid, load, and storage. Compared with traditional systems, this dispatch method integrates resources on the generation and load sides. The upper-level dispatch center transmits price signals to lower-level equipment through market mechanisms, thereby indirectly controlling the operation of lower-level equipment and improving the safety, economy, and environmental friendliness of power grid operation.

[0003] However, existing technologies have some shortcomings in practical applications. Due to the strong randomness and volatility of new energy sources, and the relatively dispersed distribution of demand-side loads, existing technologies lack sufficient information collection and analysis capabilities when processing large-scale data, making it difficult to efficiently extract and filter user behavior characteristics. Furthermore, existing grid-load interaction models have high requirements for response speed and capacity, while existing scheduling technologies still fall short in terms of real-time performance and dynamic coordination capabilities, resulting in the grid's stability failing to meet requirements under new energy grid integration conditions. Especially with the widespread adoption of new energy vehicles, vehicle-grid interaction participates in grid scheduling as a flexible resource, but existing technologies have not yet effectively formulated strategies to fully leverage the bidirectional role of electric vehicles, making it difficult to maximize the technical effect of source-grid-load-storage coordinated scheduling. Summary of the Invention

[0004] In view of this, the purpose of this application is to propose a source-grid-load-storage coordinated scheduling method and related equipment.

[0005] To achieve the above objectives, this application provides a source-grid-load-storage coordinated scheduling method, comprising: Acquire flexible load data and electric vehicle data; The flexible load is first scheduled based on the pre-set scheduling strategy of the flexible load data. The electric vehicles are subjected to a second scheduling process based on the preset scheduling strategy of the electric vehicle data. The objective function value is determined based on the first scheduling process and the second scheduling process; Determine the first scheduling process and the second scheduling process corresponding to the minimum value of the objective function; The flexible load is scheduled based on the first scheduling process; The electric vehicle is scheduled based on the second scheduling process.

[0006] In one possible implementation, the scheduling power of the flexible load is expressed by the following formula:

[0007] in, Indicates the load compensation amount. , , Indicates the power variation factor. Indicates the power of the transferable load. This indicates the power of the load that can be moved. This indicates the power that can be reduced to reduce the load. Indicates the transferable time period. Indicates the load cycle that can be shifted. This indicates the period during which the load can be reduced.

[0008] In one possible implementation, the constraints of the pre-set scheduling strategy for the flexible load data are expressed by the following formula:

[0009] in, Indicates a transitional state. Indicates a state that can be translated. This indicates the schedulable state, with 1 indicating occurrence and 0 indicating non-occurrence. Indicates the translation time. This indicates a reduction in time. Indicates the number of reductions. This represents the minimum value of transferable load power. This represents the maximum transferable load power. This represents the minimum power of the load that can be moved. This represents the maximum power of the load that can be moved. This represents the minimum amount of load power that can be reduced. This indicates the maximum amount of load power that can be reduced.

[0010] In one possible implementation, the method further includes: In response to the scheduling of the flexible load, compensation is performed; The compensation is expressed by the following formula:

[0011] in, This indicates compensation for transferable loads. This indicates compensation for loads that can be shifted. This indicates compensation that can reduce the load. , , This represents the compensation coefficient.

[0012] In one possible implementation, the constraints of the preset scheduling strategy for the electric vehicle data are expressed by the following formula:

[0013] in, This indicates the lowest charge level of the battery. This represents the current charging state of the electric vehicle battery at time t. This indicates the highest charge level of the battery. This represents the minimum voltage at a power grid node. This represents the maximum value of the voltage at a power grid node. This indicates the grid connection status at time t, where 1 indicates grid connection and 0 indicates otherwise. This indicates the minimum energy capacity of an electric vehicle battery. This indicates the maximum energy capacity of an electric vehicle battery. This represents the energy level of the electric vehicle battery at time t. Indicates charging efficiency. Indicates discharge efficiency. Let represent the charging power of the electric vehicle at time t. This represents the discharge power of the electric vehicle at time t. Indicates a time interval.

[0014] In one possible implementation, the objective function is expressed as follows:

[0015] in, This indicates the charging, discharging, operation, and maintenance costs of energy storage facilities. Indicates the proportion of new energy sources. This indicates the penalty cost for abandoning wind and solar power. This indicates the revenue from carbon trading and carbon capture systems. This indicates voltage deviation.

[0016] Based on the same inventive concept, embodiments of this application also provide a source-grid-load-storage coordinated scheduling device, comprising: The acquisition module is configured to acquire flexible load data and electric vehicle data; The first scheduling module is configured to perform a first scheduling process on the flexible load based on a preset scheduling strategy of the flexible load data. The second scheduling module is configured to perform a second scheduling process on the electric vehicles based on a preset scheduling strategy for the electric vehicle data. The objective function calculation module is configured to determine the objective function value based on the first scheduling process and the second scheduling process; The determination module is configured to determine the first scheduling process and the second scheduling process corresponding to the minimum value of the objective function; The first processing module is configured to perform scheduling processing on the flexible load based on the first scheduling processing; The second processing module is configured to perform scheduling processing on the electric vehicle based on the second scheduling processing.

[0017] Based on the same inventive concept, embodiments of this application also provide an electronic device, including a memory, a processor, and a computer program stored in the memory and executable on the processor, wherein the processor executes the program to implement the source-grid-load-storage coordinated scheduling method as described in any of the above claims.

[0018] Based on the same inventive concept, embodiments of this application also provide a non-transitory computer-readable storage medium storing computer instructions for causing the computer to execute any of the source-grid-load-storage coordinated scheduling methods described above.

[0019] Based on the same inventive concept, this application also provides a computer program product, which includes computer program instructions, the computer instructions being used to cause the computer program product to execute any of the source-grid-load-storage coordinated scheduling methods described above.

[0020] As can be seen from the above, the source-grid-load-storage coordinated scheduling method and related equipment provided in this application acquire flexible load data and electric vehicle data; perform a first scheduling process on the flexible load based on a preset scheduling strategy of the flexible load data; perform a second scheduling process on the electric vehicles based on a preset scheduling strategy of the electric vehicle data; determine an objective function value based on the first and second scheduling processes; determine the first and second scheduling processes corresponding to the minimum value of the objective function value; perform scheduling processing on the flexible load based on the first scheduling process; and perform scheduling processing on the electric vehicles based on the second scheduling process. This application's embodiments, by optimizing the scheduling strategies for flexible loads and electric vehicles, achieve coordinated scheduling of source-grid-load-storage, significantly improving the operating efficiency and stability of the power system. By acquiring flexible load data and electric vehicle data, and combining them with preset scheduling strategies to perform separate scheduling processes on flexible loads and electric vehicles, their flexibility and controllability can be fully utilized. Based on this, through the optimization of the objective function, the optimal scheduling scheme is determined, enabling the scheduling of flexible loads and electric vehicles to more accurately meet the needs of the power system, reducing system operating costs, and improving the capacity for renewable energy absorption. This application, in the process of flexible load dispatching, accurately models the power of transferable, shiftable, and reduceable loads and introduces multiple constraints to ensure the practical feasibility of the dispatching scheme. Simultaneously, a compensation mechanism is used to compensate for the dispatching behavior of flexible loads, further enhancing user participation and the feasibility of dispatching. Furthermore, for electric vehicle dispatching, multiple constraints are set, including charging and discharging power, electricity trading costs, and battery loss rates. Combined with objective function optimization, this better coordinates the charging and discharging behavior of electric vehicles, promoting efficient vehicle-grid interaction. The objective function comprehensively considers factors such as energy storage facility operation and maintenance costs, the proportion of new energy sources, the cost of wind and solar curtailment penalties, carbon trading revenue, and voltage deviation, fully reflecting comprehensive optimization. Through the method of this application, efficient coordination between flexible loads, electric vehicles, and grid resources can be achieved, effectively mitigating the impact of the randomness and volatility of new energy sources on the grid, improving dispatching flexibility and the utilization efficiency of new energy sources. At the same time, it reduces wind and solar curtailment, lowers operating costs, and enhances overall economic benefits through carbon trading and carbon capture revenue, promoting the construction and development of a new type of electricity based on new energy sources. Attached Figure Description

[0021] To more clearly illustrate the technical solutions in this application or related technologies, the drawings used in the description of the embodiments or related technologies will be briefly introduced below. Obviously, the drawings described below are only embodiments of this application. For those skilled in the art, other drawings can be obtained based on these drawings without creative effort.

[0022] Figure 1 This is a schematic diagram of the source-grid-load-storage coordinated scheduling method according to an embodiment of this application; Figure 2 This is a schematic diagram of the source-grid-load-storage coordinated scheduling device according to an embodiment of this application; Figure 3 This is a schematic diagram of the electronic device structure according to an embodiment of this application. Detailed Implementation

[0023] To make the objectives, technical solutions, and advantages of this application clearer, the following detailed description is provided in conjunction with specific embodiments and the accompanying drawings.

[0024] It should be noted that, unless otherwise defined, the technical or scientific terms used in the embodiments of this application should have the ordinary meaning understood by one of ordinary skill in the art to which this application pertains. The terms "first," "second," and similar terms used in the embodiments of this application do not indicate any order, quantity, or importance, but are merely used to distinguish different components. Terms such as "comprising" or "including" mean that the element or object preceding the word encompasses the elements or objects listed after the word and their equivalents, without excluding other elements or objects. Terms such as "connected" or "linked" are not limited to physical or mechanical connections, but can include electrical connections, whether direct or indirect. Terms such as "upper," "lower," "left," and "right" are only used to indicate relative positional relationships; when the absolute position of the described object changes, the relative positional relationship may also change accordingly.

[0025] It is understood that before using the technical solutions of the various embodiments in this disclosure, users will be informed of the type, scope of use, and usage scenarios of the personal information involved in an appropriate manner, and user authorization will be obtained.

[0026] For example, upon receiving a user's active request, a prompt message is sent to the user to explicitly inform them that the requested operation will require the acquisition and use of the user's personal information. This allows the user to independently choose, based on the prompt message, whether to provide personal information to the software or hardware such as electronic devices, applications, servers, or storage media performing the operations of this disclosed technical solution.

[0027] As an optional but not limited implementation, in response to a user's active request, sending a prompt message to the user can be done via a pop-up window, where the prompt message can be presented in text format. Furthermore, the pop-up window can also include a selection control allowing the user to choose "agree" or "disagree" to provide personal information to the electronic device.

[0028] It is understood that the above notification and user authorization process are merely illustrative and do not constitute a limitation on the implementation of this disclosure. Other methods that comply with relevant laws and regulations may also be applied to the implementation of this disclosure.

[0029] As described in the background section, existing technologies coordinate resources on the generation and demand sides, achieving collaborative interaction between power generation, grid, load, and storage based on a cloud-edge collaborative scheduling framework. The upper-level scheduling is responsible for overall optimization, while the lower-level scheduling manages controllable equipment on the load side and indirectly controls equipment operation through market mechanisms, thereby improving the safety and economy of power grid operation. However, due to the randomness and volatility of new energy sources and the dispersed nature of load distribution, existing technologies are insufficient in large-scale data processing and user behavior analysis, making it difficult to meet the demands for real-time and dynamic adjustment. Furthermore, with the widespread adoption of new energy vehicles, while the flexible resources of vehicle-grid interaction can participate in power grid scheduling, existing technologies cannot fully leverage their bidirectional effects, making it difficult to achieve the optimal effect of collaborative scheduling of power generation, grid, load, and storage.

[0030] Based on the above considerations, this application proposes a source-grid-load-storage coordinated scheduling method, which involves acquiring flexible load data and electric vehicle data; performing a first scheduling process on the flexible load based on a preset scheduling strategy of the flexible load data; performing a second scheduling process on the electric vehicles based on a preset scheduling strategy of the electric vehicle data; determining an objective function value based on the first and second scheduling processes; determining the first and second scheduling processes corresponding to the minimum value of the objective function; performing scheduling processing on the flexible load based on the first scheduling process; and performing scheduling processing on the electric vehicles based on the second scheduling process. This application achieves coordinated scheduling of source, grid, load, and storage by optimizing the scheduling strategies for flexible loads and electric vehicles, significantly improving operational efficiency and stability. By acquiring flexible load and electric vehicle data and combining preset scheduling strategies and objective function optimization, it accurately meets the needs of the power system, effectively reducing operating costs and improving the capacity for renewable energy absorption. This application ensures the feasibility of the scheduling scheme by accurately modeling and setting constraints for transferable, shiftable, and reduceable loads, and improves user participation through a compensation mechanism. Simultaneously, it sets multiple constraints for electric vehicles to coordinate charging and discharging behavior and promote efficient vehicle-grid interaction. The objective function comprehensively considers operation and maintenance costs, the proportion of renewable energy, penalties for wind and solar curtailment, carbon trading revenue, and voltage deviation, achieving a comprehensive optimization of economic efficiency, environmental friendliness, and stability. This application effectively mitigates the impact of renewable energy volatility on the power grid, improves dispatch flexibility and renewable energy utilization efficiency, reduces wind and solar curtailment, lowers costs and enhances economic benefits, and contributes to the construction of a new power system dominated by renewable energy.

[0031] The technical solutions of the embodiments of this application will be described in detail below through specific examples.

[0032] refer to Figure 1 The source-grid-load-storage coordinated scheduling method of this application includes the following steps: Step S101: Obtain flexible load data and electric vehicle data; Step S102: Perform the first scheduling process on the flexible load based on the preset scheduling strategy of the flexible load data; Step S103: Perform a second scheduling process on the electric vehicles based on the preset scheduling strategy of the electric vehicle data; Step S104: Determine the objective function value based on the first scheduling process and the second scheduling process; Step S105: Determine the first scheduling process and the second scheduling process corresponding to the minimum value of the objective function; Step S106: Perform scheduling processing on the flexible load based on the first scheduling processing; Step S107: Perform scheduling processing on the electric vehicle based on the second scheduling processing.

[0033] For step S101, acquire flexible load data and electric vehicle data.

[0034] In this embodiment, step S101 involves acquiring flexible load data and electric vehicle data to collect basic information for coordinated scheduling of power generation, grid, load, and storage. Flexible load data includes information on transferable loads, shiftable loads, and loads that can be reduced, such as the load's power variation factor, compensation amount, and corresponding dispatchable time interval. Through smart meters and a data acquisition system, detailed operating parameters of various flexible loads are recorded, including minimum and maximum load power, transfer time, and shift cycle. Simultaneously, user electricity consumption patterns and load elasticity are analyzed to accurately match user load demands. Furthermore, information such as the response speed, duration, and reliability of flexible loads is collected to provide a basis for the formulation and optimization of subsequent scheduling strategies.

[0035] Electric vehicle (EV) data includes parameters such as charging / discharging capacity, battery capacity, state of charge (SOC), charging / discharging efficiency, loss rate, and electricity purchase / sale costs. Through unified management of EV clusters, information such as daily load power, charging time, and grid connection status is obtained. Combined with dynamic charging / discharging data, the EVs' ability to participate in grid dispatch as distributed energy storage units is clarified. Simultaneously, considering the operating characteristics of EVs, battery voltage, node voltage range, and charging / discharging power constraints are recorded to ensure that vehicles meet safety and economic requirements during dispatch. This data acquisition provides comprehensive support for subsequent coordinated dispatching based on flexible loads and EVs, further promoting optimized operation.

[0036] For step S102, the flexible load is subjected to a first scheduling process based on the scheduling strategy preset by the flexible load data.

[0037] In some embodiments, the scheduling power of the flexible load is expressed by the following formula:

[0038] in, Indicates the load compensation amount. , , Indicates the power variation factor. Indicates the power of the transferable load. This indicates the power of the load that can be moved. This indicates the power that can be reduced to reduce the load. Indicates the transferable time period. Indicates the load cycle that can be shifted. This indicates the period during which the load can be reduced.

[0039] In some embodiments, the constraints of the pre-set scheduling strategy for the flexible load data are expressed by the following formula:

[0040] in, Indicates a transitional state. Indicates a state that can be translated. This indicates the schedulable state, with 1 indicating occurrence and 0 indicating non-occurrence. Indicates the translation time. This indicates a reduction in time. Indicates the number of reductions. This represents the minimum value of transferable load power. This represents the maximum transferable load power. This represents the minimum power of the load that can be moved. This represents the maximum power of the load that can be moved. This represents the minimum amount of load power that can be reduced. This indicates the maximum amount of load power that can be reduced.

[0041] In this embodiment, for step S102, the flexible loads are subjected to a first scheduling process based on a pre-set scheduling strategy using flexible load data. Specifically, the scheduling power of flexible loads includes three types: transferable loads, shiftable loads, and loads that can be reduced. During implementation, the power of these three types of loads is processed according to the flexible load data, combined with the pre-set scheduling strategy and demand, to ensure the stable operation of the power system.

[0042] In load transfer scheduling, load operating periods can be adjusted within a certain time interval. For example, the operating time of certain loads is shifted to periods of lower demand, while ensuring that the transferred load power meets the minimum and maximum limits set in the scheduling strategy. Load shifting involves moving the entire operating cycle of the load to other time periods, and during the adjustment process, it is necessary to ensure that the shifted load power still meets the preset power range requirements. For load reduction scheduling, peak shaving is achieved by temporarily reducing or interrupting the operation of some loads during the reduction period, while meeting the limits on the number of reductions and the minimum reduction time requirements to minimize the impact on users' normal electricity experience.

[0043] During scheduling, the actual operating status of the load is monitored in real time to ensure that the adjustment of the scheduled power meets the preset constraints. For example, the power of transferable loads must fluctuate between their minimum and maximum values ​​while meeting the constraints of the transfer time range; the power of loads that can be shifted must also be kept within a set range and strictly adhere to the shift cycle requirements; the power of loads that can be reduced must meet the reduction conditions, ensuring that the reduction time and number of reductions do not exceed the specified range. In addition, by setting the scheduling status parameters of flexible loads (such as α, β, and γ), this application can flexibly determine and control whether scheduling occurs, thereby achieving precise management of different types of loads.

[0044] In this embodiment, through the first scheduling process of flexible loads, the load pressure on the power grid during a specific period is effectively alleviated. Simultaneously, the scheduling strategy ensures the rationality and feasibility of load adjustments, avoiding excessive interference with user electricity demand. The entire scheduling process strictly adheres to the constraints in the flexible load data and scheduling strategy, ensuring the safety and economy of this application's operation.

[0045] In one feasible embodiment, in a power consumption scenario of a commercial area, the load data of this area indicates that some loads have high flexibility and can achieve peak shaving and valley filling effects through reasonable scheduling. First, based on the type and characteristics of user loads, the loads in this area are divided into three categories: transferable loads, shiftable loads, and loads that can be reduced.

[0046] For transferable loads, this application selects to schedule the central air conditioning system in commercial areas. Due to the flexibility of central air conditioning operating times, this application devises a strategy to shift the operating time of the air conditioning cooling load from peak hours to off-peak hours. For example, the air conditioning is turned on in advance for pre-cooling when the grid load is low in the morning, while the air conditioning load is reduced during peak electricity demand in the afternoon, thereby reducing the pressure on the grid while ensuring that the indoor temperature remains within a comfortable range.

[0047] For loads that can be shifted, this application addresses the advertising displays and some non-critical lighting equipment in the shopping mall. These loads are less time-sensitive and can be adjusted to operate during lower evening hours. In this way, peak-hour electricity demand is reduced while ensuring that the user's basic business operations are not affected.

[0048] For load reduction, this application selects to temporarily shut down landscape lighting and some decorative electrical appliances. These devices are interrupted during peak load periods, and since their impact on the overall function of the commercial area is minimal, reducing these loads will not significantly disrupt users' daily activities.

[0049] During the scheduling process, this application strictly adheres to the constraints of the flexible load scheduling strategy. For example, the transfer time range of transferable loads must be within a set interval, and the power of the transferred loads must meet their maximum and minimum limits; the operating cycle of transferable loads must conform to a preset time window; and the number of load reductions must be lower than the set maximum number to ensure that the user's electricity experience is not excessively affected.

[0050] Through the implementation of the above-mentioned dispatching procedures, peak loads in the commercial area were effectively reduced, and the pressure on the power grid was significantly lowered. At the same time, the dispatching strategy ensured that users' core needs were met, without significantly impacting their normal business operations. The entire process was strictly carried out according to flexible load data and preset dispatching strategies, demonstrating the important role of flexible load dispatching in power system optimization.

[0051] Furthermore, in some embodiments, the method further includes: In response to the scheduling of the flexible load, compensation is performed; The compensation is expressed by the following formula:

[0052] in, This indicates compensation for transferable loads. This indicates compensation for loads that can be shifted. This indicates compensation that can reduce the load. , , This represents the compensation coefficient.

[0053] In this embodiment, the compensation mechanism is divided into three types based on the type of flexible load: transferable load compensation, shiftable load compensation, and load reduction compensation. This application determines the specific compensation amount by calculating the power change of the flexible load after scheduling processing and combining it with a preset compensation coefficient.

[0054] For compensation of transferable loads, this application calculates the compensation amount based on the power variation of the transferred loads. Adjusting the operating hours of the transferred loads from peak to off-peak periods alleviates peak load pressure on the power grid while still meeting users' electricity needs. In the compensation calculation process, this application calculates the economic compensation received by users during the dispatching process based on the total power of the transferred loads and the compensation coefficient.

[0055] For compensation of load shifting, this application evaluates the adjustment of the shifting load's operating cycle. Using the total power of the shifted load and the corresponding compensation coefficient, this application calculates the compensation amount received by the user for cooperating with dispatch. Load shifting has a relatively small impact on user activities, but effectively helps the power grid achieve optimized load time distribution; therefore, the compensation mechanism can further encourage users to actively participate in load shifting management.

[0056] For compensation of load reduction, this application provides compensation to users based on the power and duration of the load reduction. Load reduction typically involves the temporary interruption of non-essential electrical equipment, which may impact users' electricity experience. Therefore, the compensation mechanism needs to fully consider users' losses and ensure that the compensation amount can make up for the inconvenience caused by load reduction. In calculating the compensation amount, this application uses the total power of the reduced load and the compensation coefficient to determine the user's compensation benefit.

[0057] The compensation coefficient can be flexibly adjusted for the three load types mentioned above based on load characteristics, scheduling strategies, and user participation. For example, a higher compensation coefficient can be set for load types with a greater impact to increase users' willingness to cooperate. Furthermore, this application can dynamically adjust the compensation amount based on user response speed and scheduling effectiveness. For instance, additional compensation rewards can be given to users with fast response times and good execution results, thereby further incentivizing users to actively participate in flexible load scheduling.

[0058] In this embodiment, by compensating for transferable loads, shiftable loads, and loads that can be reduced, this application effectively enhances users' enthusiasm for participating in flexible load dispatch while ensuring grid operating efficiency. The introduction of the compensation mechanism not only compensates for potential losses incurred by users due to their cooperation with dispatch but also provides direct economic benefits to users, further consolidating the positive interactive relationship between users and the grid. The entire compensation process is strictly executed according to preset formulas and compensation strategies, ensuring that the calculation of compensation amounts is reasonable and transparent, while simultaneously meeting users' fairness requirements and grid stability requirements.

[0059] In one practical embodiment, a commercial area participated in flexible load dispatching to support grid operation. During the dispatching process, some of the area's loads were categorized into three types: transferable loads, shiftable loads, and loads that could be reduced, and each type was dispatched accordingly. To protect the interests of users, this application provides economic compensation to users after dispatching is completed, based on the dispatching strategy and actual power changes.

[0060] In this embodiment, the central air conditioning system of the commercial area is classified as a transferable load. This application employs a scheduling strategy to shift the operating hours of the central air conditioning system from the afternoon peak grid load to the morning off-peak load, significantly reducing the electricity demand of the commercial area during peak hours while maintaining comfortable indoor temperatures. After scheduling is completed, this application compensates the commercial area based on the power changes of the central air conditioning system, ensuring that users receive a reasonable economic return for any inconvenience caused by adjusting air conditioning operating times.

[0061] Meanwhile, large advertising displays and some non-critical lighting equipment within the shopping mall were classified as relocatable loads. This application employs a scheduling strategy to shift the operating cycles of these loads to the lower load periods in the evening. Although the operating times of the advertising displays and non-critical lighting equipment changed after the adjustment, it did not significantly impact the mall's normal operations. After the scheduling was completed, this application provides corresponding compensation to the mall based on the power changes of these loads to mitigate any potential impacts from the load adjustment.

[0062] In addition, the landscape lighting and some decorative electrical appliances in the commercial area were classified as load-reducible. This application temporarily shut down these devices during peak grid load periods, thereby further reducing the overall load pressure on the area. Although the temporary shutdown of the landscape lighting and decorative electrical appliances had a relatively small impact on the overall function of the commercial area, this application still provided economic compensation based on the reduction of these loads to protect the interests of users and enhance their willingness to participate in grid dispatch.

[0063] Throughout the implementation process, the compensation mechanism was designed to fully consider the potential impact of different types of load dispatch on users, ensuring that the compensation amount could cover potential losses and provide reasonable economic returns. By compensating for transferable loads, shiftable loads, and loads that can be reduced in sequence, this application successfully achieved optimized management of the power grid load while effectively enhancing the enthusiasm of users to cooperate with the power grid. The entire process strictly followed the dispatch strategy and compensation rules, ensuring the rationality and transparency of compensation, while reflecting the fairness and operability of flexible load dispatch.

[0064] Furthermore, in step S103, a second scheduling process is performed on the electric vehicle based on a pre-set scheduling strategy for the electric vehicle data.

[0065] In some embodiments, the constraints of the preset scheduling strategy for the electric vehicle data are expressed by the following formula:

[0066] in, This indicates the lowest charge level of the battery. This represents the current charging state of the electric vehicle battery at time t. This indicates the highest charge level of the battery. This represents the minimum voltage at a power grid node. This represents the maximum value of the voltage at a power grid node. This indicates the grid connection status at time t, where 1 indicates grid connection and 0 indicates otherwise. This indicates the minimum energy capacity of an electric vehicle battery. This indicates the maximum energy capacity of an electric vehicle battery. This represents the energy level of the electric vehicle battery at time t. Indicates charging efficiency. Indicates discharge efficiency. Let represent the charging power of the electric vehicle at time t. This represents the discharge power of the electric vehicle at time t. Indicates a time interval.

[0067] In some embodiments, the dispatchable capacity of electric vehicles for:

[0068] The benefits for electric vehicle owners are:

[0069] The charging time for an electric vehicle is:

[0070] The daily load power of n electric vehicles charging haphazardly is:

[0071] in, Indicates the rated battery capacity. This represents the electricity price at time t when the electric vehicle is discharging. This represents the price at which the owner purchases electricity when charging an electric vehicle. Indicates battery degradation rate. This indicates the efficiency of the electric vehicle charging system.

[0072] In this embodiment, for step S103, a second scheduling process is performed on the electric vehicle using a pre-set scheduling strategy based on the electric vehicle data. As a mobile load, the charging and discharging behavior of electric vehicles can be flexibly managed, providing important support for power grid load optimization and scheduling. Based on the electric vehicle's charging and discharging data and the pre-set scheduling strategy, parameters such as the electric vehicle's dispatchable capacity, charging time, owner benefits, and overall charging load power are accurately calculated and optimized.

[0073] During the scheduling process, this application first monitors the current charging state of the electric vehicle battery in real time based on the battery's state constraints. The battery's charging state must be maintained between the minimum and maximum charging levels to ensure that the electric vehicle can meet the owner's basic driving needs while participating in scheduling. Simultaneously, this application also monitors the grid node voltage to ensure that the electric vehicle's charging and discharging does not affect the grid's voltage stability. By setting grid connection status parameters, this application can flexibly determine whether the electric vehicle is in a grid-connected state and perform corresponding charging and discharging scheduling.

[0074] In this embodiment, the dispatchable capacity of the electric vehicle is calculated based on its battery's state of charge and rated capacity. This application determines the electric vehicle's charging and discharging capacity within a specific time period by evaluating the battery's energy level and charging / discharging efficiency, while dynamically adjusting the dispatch of the electric vehicle in conjunction with grid load demand. In this process, this application fully considers the variations in the electric vehicle's charging and discharging power, ensuring that the dispatching results meet the grid's needs and protect the interests of vehicle owners.

[0075] Furthermore, this application also calculates the benefits for vehicle owners. During the dispatching process, the benefits for electric vehicle owners mainly include the income from selling electricity to the grid during discharge and the costs incurred in purchasing electricity during charging. This application calculates the total benefits for vehicle owners participating in dispatching by comprehensively considering grid electricity prices, charging prices, and battery degradation rates, and compensates for battery degradation through a reasonable compensation mechanism, further enhancing the enthusiasm of vehicle owners to participate in dispatching.

[0076] Regarding the calculation of charging time, this application determines the time required for an electric vehicle to complete charging based on the initial and target energy levels of the electric vehicle battery, combined with the charging power and the efficiency of the charging system. This calculation result can help car owners rationally schedule charging time and also provide a reference for the overall scheduling of this application.

[0077] For situations involving multiple electric vehicles charging haphazardly, this application also assesses and calculates the overall daily load power. With n electric vehicles participating in charging, this application accumulates the charging power of each electric vehicle to obtain the total load power of the haphazard charging. This assessment not only reflects the impact of electric vehicle charging on the overall grid load but also provides data support for further optimizing charging scheduling. By monitoring and analyzing the daily load power, this application can rationally schedule the charging and discharging times of electric vehicles, avoiding excessive grid load or reduced operating efficiency due to haphazard charging.

[0078] Throughout the dispatching process, this application rationally arranges the charging and discharging behavior of electric vehicles based on the constraints and optimization objectives of the dispatching strategy, while ensuring that parameters such as battery charging status, grid node voltage, and system efficiency meet preset requirements. Through optimization of the dispatchable capacity of electric vehicles, owner benefits, charging time, and overall load power, this application achieves dynamic management of grid load and efficient utilization of electric vehicle resources. The entire dispatching process not only safeguards the interests of electric vehicle owners but also effectively improves the operational stability and economy of the power grid, providing reliable technical support for the implementation of flexible load dispatching.

[0079] In one specific embodiment, multiple electric vehicles within a community participate in flexible load dispatching of the power grid. To achieve this goal, this application performs a second dispatching process on these vehicles based on their charging and discharging data and a preset dispatching strategy. During the dispatching process, this application first monitors the battery status of each electric vehicle in real time to ensure that the battery charging level remains within the allowable range. For example, if an electric vehicle's battery is currently at 50% charge when participating in dispatching, this application formulates a corresponding dispatching plan based on preset minimum 30% and maximum 90% charging level limits to ensure that the vehicle can still meet the daily travel needs of the owner after dispatching.

[0080] Meanwhile, this application analyzes the voltage conditions of power grid nodes within the community to ensure that the charging and discharging of electric vehicles will not affect the voltage stability of the power grid. For example, when an electric vehicle is scheduled to charge during peak hours, this application detects that the node voltage is close to the lower limit during that period and then adjusts its charging time to a period with lower power grid load, thereby avoiding voltage anomalies.

[0081] During the dispatching process, this application also dynamically calculates the dispatchable capacity of each electric vehicle. For example, if an electric vehicle has a battery with a rated capacity of 60 kWh and is currently charging at 50%, this application determines that its discharge capacity is 15 kWh by analyzing data such as the battery's charge and discharge efficiency and remaining charge, and incorporates this data into the overall dispatching plan to meet the grid's dispatching needs.

[0082] Furthermore, to incentivize vehicle owners to participate in dispatching, this application calculates their revenue during the dispatching process based on a revenue model for electric vehicle owners. For example, an electric vehicle might purchase 10 kWh of electricity at a low price during off-peak hours and then sell 8 kWh of that electricity back to the grid at a higher price during peak hours. This application considers battery depreciation costs when calculating revenue, providing vehicle owners with a reasonable economic return and thus increasing their willingness to participate.

[0083] In this embodiment, the present application also optimizes the charging time of electric vehicles. For example, if the owner of an electric vehicle plans to use the vehicle the next morning, the present application calculates the time required for the vehicle to complete charging based on its current battery level and target battery level, combined with the efficiency of the charging equipment, and allocates charging tasks to the vehicle during periods of low grid load, thus meeting the owner's vehicle usage needs while avoiding load pressure during peak hours.

[0084] For situations where multiple electric vehicles participate in the dispatching process simultaneously within a community, this application coordinates and manages the overall charging and discharging behavior. For example, during a peak evening period, ten electric vehicles in the community plan to charge. If disorderly charging by all vehicles could lead to excessive grid load, this application uses a dispatching strategy to rationally arrange the charging times of the vehicles, prioritizing some vehicles to complete charging before the peak evening period and postponing the charging tasks of other vehicles to the less loaded nighttime hours. Simultaneously, two electric vehicles have high battery levels; this application dispatches them to discharge into the grid during the peak evening period to alleviate peak-hour load pressure and bring additional benefits to the vehicle owners.

[0085] Throughout the scheduling process, this application strictly adheres to preset constraints and scheduling strategies to ensure that each electric vehicle meets the requirements of its battery status, grid conditions, and owner needs when participating in scheduling. By optimizing the charging and discharging behavior and timing of electric vehicles, this application successfully achieves dynamic management of grid load, effectively avoiding situations of excessively high or low load. Simultaneously, vehicle owners receive reasonable economic returns, and the normal use of their vehicles is not affected. This embodiment fully demonstrates the application value of flexible load scheduling in electric vehicle scenarios, providing reference experience for its further widespread adoption.

[0086] Furthermore, in step S104, the objective function value is determined based on the first scheduling process and the second scheduling process.

[0087] In some embodiments, the objective function is expressed by the following formula:

[0088] in, This indicates the charging, discharging, operation, and maintenance costs of energy storage facilities. Indicates the proportion of new energy sources. This indicates the penalty cost for abandoning wind and solar power. This indicates the revenue from carbon trading and carbon capture systems. This indicates voltage deviation.

[0089] The following formulas represent the following: the operation and maintenance costs of energy storage facilities (charging and discharging), the proportion of new energy sources, the cost of penalties for wind and solar curtailment, the revenue from carbon trading and carbon capture systems, and voltage deviation:

[0090] in, This represents the charging and discharging cost of energy storage facilities per unit power. This represents the power generation of the new energy source at time t; Indicates the total power generation of the entire network; , Indicates the amount of wind and solar power curtailed; , This represents the cost of curtailing wind and solar power per unit of power. , This represents the carbon gain per unit power and the cost of the carbon capture system; Represents a node i exist t Voltage amplitude at any given moment; This represents the rated voltage amplitude of node i.

[0091] In this embodiment, for step S104, the objective function value is further determined by combining the results of the first and second scheduling processes to achieve the overall optimized scheduling objective. The design of the objective function comprehensively considers multiple factors, including the charging and discharging operation and maintenance costs of energy storage facilities, the proportion of new energy sources, the cost of wind and solar curtailment penalties, the revenue of carbon trading and carbon capture systems, and voltage deviation. The trade-off optimization of these factors can effectively improve the economy, environmental protection, and stability of power grid operation.

[0092] In the objective function, the charging, discharging, and operation and maintenance costs of energy storage facilities are a significant factor affecting operation. This application analyzes the charging and discharging power of energy storage facilities and calculates their operation and maintenance costs over different time periods to ensure that energy storage facilities can operate at the lowest cost while meeting grid dispatch requirements. The proportion of renewable energy is an important indicator for measuring the efficiency of clean energy utilization. This application analyzes the ratio of renewable energy generation to the total grid generation in different time periods, aiming to maximize the utilization rate of renewable energy during dispatching, reduce dependence on traditional fossil fuels, and promote the popularization and application of green energy.

[0093] Furthermore, the penalty cost for wind and solar curtailment carries a certain weight in the objective function. This application statistically analyzes the curtailed wind and solar power output in different time periods and calculates the resulting penalty costs to guide dispatch strategies and minimize the waste of renewable energy. For example, when wind and solar power generation exceed load demand in a certain period, this application prioritizes the use of energy storage facilities to store the surplus electricity, or utilizes the orderly charging of electric vehicles to absorb excess renewable energy generation, thereby minimizing the probability of wind and solar curtailment and reducing related penalty costs.

[0094] Carbon trading and carbon capture system revenue are also important components of the objective function optimization. This application evaluates the changes in carbon trading revenue under different dispatch strategies by calculating the carbon revenue per unit power and the cost of the carbon capture system, ensuring that this application can meet environmental goals while achieving economic benefits. For example, during periods when renewable energy accounts for a higher proportion, this application prioritizes the dispatch of renewable energy power generation, thereby reducing carbon emissions and increasing carbon trading revenue.

[0095] Voltage deviation, as a crucial indicator affecting grid operational stability, is also incorporated into the objective function for optimization. This application monitors the voltage amplitude of each node, calculates its deviation from the rated voltage, and integrates this voltage deviation as one of the constraints into the dispatch strategy. For example, when the voltage deviation of a certain node is large, this application adjusts the charging and discharging power of energy storage facilities or regulates the charging and discharging behavior of electric vehicles to reduce the voltage deviation and ensure the stability and safety of grid operation.

[0096] By designing the aforementioned objective function, this application comprehensively considers factors such as economy, environmental protection, and stability, dynamically adjusting the usage of various resources during the dispatching process to achieve efficient utilization of energy storage facilities and new energy sources, while ensuring the safe and reliable operation of the power grid. Ultimately, the optimized objective function provides a scientific basis for power grid dispatching, helping this application maximize its overall benefits.

[0097] In one specific embodiment, a regional power grid system is undergoing flexible load dispatch optimization to achieve a comprehensive improvement in economy, environmental protection, and grid stability. The regional power grid incorporates energy storage facilities, wind power, and photovoltaic power resources, and a certain number of electric vehicles are also involved in the dispatch. To determine the optimal dispatch strategy, this application further optimizes the objective function value based on the results of the first and second dispatch processes.

[0098] During the scheduling process, the charging and discharging operation and maintenance costs of energy storage facilities are one of the primary factors considered in this application. For example, when daytime photovoltaic power generation is high, this application prioritizes charging of energy storage facilities to store excess renewable energy generation and reduce curtailment. Simultaneously, to reduce the operation and maintenance costs of energy storage facilities, this application schedules charging tasks for energy storage facilities during periods of lower electricity prices, while avoiding overuse during high-load periods, thereby achieving economic optimization.

[0099] Increasing the proportion of renewable energy is another important optimization objective of this application. During a daily scheduling cycle, the power output of wind and solar power varies depending on weather and time of day. This application monitors renewable energy generation in real time and maximizes its proportion by prioritizing renewable energy generation. For example, when wind power generation is higher at night, this application schedules electric vehicle charging to absorb excess wind energy, while simultaneously reducing the use of traditional thermal power generation, thereby improving the utilization efficiency of renewable energy.

[0100] To further reduce the penalty costs associated with wind and solar curtailment, this application dynamically adjusts the power output of wind and solar power at different times. For example, when solar power generation reaches its peak at midday, this application found that some solar power generation exceeds load demand, potentially leading to curtailment. To reduce penalty costs, this application arranges for energy storage facilities to perform rapid charging, and simultaneously transfers excess solar power generation to other areas with lower loads through inter-regional power dispatch, ensuring full utilization of new energy power generation.

[0101] Carbon trading and carbon capture system revenue is one of the environmental indicators optimized in this application. During dispatching, this application prioritizes low-carbon renewable energy power generation resources to reduce carbon emissions and increase carbon trading revenue. For example, during morning and evening peak hours, this application meets load demand by dispatching wind and solar power generation, while reducing the use of traditional fossil fuel power generation and lowering carbon emissions. This application also calculates the changes in carbon revenue under different dispatching strategies to ensure that environmental goals are achieved while optimizing economics.

[0102] Voltage deviation is a key factor affecting the stability of power grid operation. During a certain implementation period, this application found that the voltage deviation at some nodes was significant, potentially impacting the safe operation of the power grid. To address this issue, this application adjusts the node voltage in a timely manner by regulating the charging and discharging power of energy storage facilities and the charging behavior of electric vehicles. For example, when the voltage deviation at a node exceeds the allowable range, this application arranges for energy storage facilities to discharge to supplement the voltage demand of that node, thereby controlling the voltage deviation within a reasonable range and ensuring the stability of the power grid.

[0103] Through this specific embodiment, this application comprehensively considers multiple factors such as the operation and maintenance costs of energy storage facilities (charging and discharging), the proportion of new energy sources, the cost of wind and solar curtailment penalties, the revenue from carbon trading and carbon capture systems, and voltage deviation, and dynamically optimizes the objective function value. Ultimately, this application achieves an overall improvement in the economy, environmental friendliness, and stability of power grid operation, while meeting multiple needs of users and the power grid, and providing strong support for the practical application of flexible load dispatching.

[0104] Furthermore, for steps S105, S106, and S107, the first scheduling process and the second scheduling process corresponding to the minimum value of the objective function are determined; the flexible load is scheduled based on the first scheduling process; and the electric vehicle is scheduled based on the second scheduling process.

[0105] In this embodiment, for steps S105, S106, and S107, the minimum value of the objective function is determined, and the corresponding first and second scheduling processes are optimized to maximize the overall benefits of this application. First, based on the design of the objective function, this application incorporates factors such as the operation and maintenance costs of energy storage facilities (charging and discharging), the proportion of new energy sources, the cost of wind and solar curtailment penalties, the revenue from carbon trading and carbon capture systems, and voltage deviation into the scope of scheduling optimization. By dynamically calculating the objective function value, this application determines the scheduling scheme that minimizes the objective function value, ensuring comprehensive optimization of economic efficiency, environmental protection, and grid stability.

[0106] In the first dispatching process, this application optimizes the dispatching of flexible loads to achieve overall grid load balance. During dispatching, this application, based on the optimization results of the objective function, schedules energy storage facilities to charge during periods of low load and discharge during periods of high load, effectively alleviating grid load pressure. Simultaneously, this application rationally allocates electricity demand within the region, prioritizing the allocation of surplus renewable energy generation to areas with lower loads, reducing the cost of cross-regional dispatching. Furthermore, this application monitors voltage deviations at grid nodes in real time and dynamically adjusts these deviations through the dispatching of energy storage facilities, ensuring grid operational stability.

[0107] In the second scheduling process, this application optimizes the scheduling of electric vehicles to achieve efficient utilization of renewable energy generation and dynamic balance of grid load. Based on the optimization results of the objective function, this application schedules electric vehicles to charge during periods of high wind and solar power generation, thereby absorbing excess renewable energy generation. Simultaneously, this application dynamically adjusts the charging and discharging behavior of electric vehicles according to their charging needs and battery status. For example, during a peak period, this application schedules some electric vehicles to discharge to supplement the grid's load demand and alleviate peak-hour load pressure. Furthermore, to encourage vehicle owners to participate in scheduling, this application, combined with the optimization results of the objective function, provides vehicle owners with a reasonable benefit model, ensuring that they receive economic returns while participating in scheduling.

[0108] By employing an optimized scheduling scheme based on the first and second scheduling processes, this application achieves efficient utilization of energy storage facilities and electric vehicles, while simultaneously improving the utilization rate of new energy power generation, reducing wind and solar curtailment, and lowering the overall operating cost of the power grid. Ultimately, this application achieves an optimal balance between economic and environmental benefits while meeting user electricity demand and ensuring grid operational stability, providing a scientific basis and practical support for the practical application of flexible load dispatching.

[0109] In one specific embodiment, the power grid of a certain urban area is connected to a large number of energy storage facilities, wind power generation, and photovoltaic power generation resources, while multiple groups of electric vehicles participate in flexible load dispatch. In order to achieve the economy, environmental protection, and stability of power grid operation, this application comprehensively analyzes the first and second dispatch processes based on the optimization results of the objective function value, and determines the dispatch scheme corresponding to the minimum objective function value, thereby completing the optimized dispatch of flexible loads and electric vehicles.

[0110] In the first dispatch process, energy storage facilities in the region are prioritized to respond to the grid's load demand. For example, when photovoltaic power generation reaches its peak at noon, this application finds that the grid load is at a low level, which may lead to some photovoltaic power being curtailed. To avoid curtailment, this application arranges energy storage facilities to store excess photovoltaic power, and simultaneously, based on the optimization results of the objective function, allocates charging tasks to the lowest-cost energy storage devices, reducing overall operation and maintenance costs. In the evening, when the grid load is high, the energy storage facilities discharge according to the dispatch plan to supplement the actual electricity demand of the grid and alleviate load pressure during peak hours. Furthermore, this application monitors the voltage status of each node in the grid in real time and finds that large voltage deviations at some nodes may affect grid stability. To solve this problem, this application dispatches energy storage facilities to improve the voltage levels of relevant nodes, ultimately controlling the voltage deviation within a reasonable range and ensuring the safe operation of the grid.

[0111] In the second scheduling process, this application optimizes the charging and discharging behavior of electric vehicles. For example, during periods of high wind power generation at night, this application schedules some electric vehicles for charging to fully absorb excess wind power and reduce wind curtailment. Simultaneously, this application dynamically adjusts the time and power allocation of charging tasks based on the battery status of electric vehicles and the travel needs of vehicle owners. For instance, for electric vehicles needed for morning use, this application schedules them for charging at night to ensure sufficient battery power; while for electric vehicles parked for extended periods, this application schedules their charging during periods of high wind power generation to maximize the utilization efficiency of new energy sources. During periods of high grid load at night, this application also schedules some electric vehicles for discharging, transferring the stored energy from their batteries to the grid to alleviate load pressure during peak hours. To incentivize vehicle owners to participate in scheduling, this application, based on the optimization results of the objective function, provides reasonable economic returns for participating vehicle owners, ensuring both the owners' benefits and achieving overall optimization of grid scheduling.

[0112] Through this specific embodiment, this application optimizes the first and second scheduling processes based on minimizing the objective function value, achieving efficient scheduling of energy storage facilities and electric vehicles. Ultimately, this application successfully improves the utilization rate of new energy power generation, significantly reduces wind and solar curtailment, lowers grid operating costs, maintains grid stability and security, and provides a scientific optimization method and practical experience for the practical application of flexible load dispatch.

[0113] As can be seen from the above embodiments, the source-grid-load-storage coordinated scheduling method described in this application acquires flexible load data and electric vehicle data; performs a first scheduling process on the flexible load based on a preset scheduling strategy of the flexible load data; performs a second scheduling process on the electric vehicles based on a preset scheduling strategy of the electric vehicle data; determines an objective function value based on the first and second scheduling processes; determines the first and second scheduling processes corresponding to the minimum value of the objective function value; performs scheduling processing on the flexible load based on the first scheduling process; and performs scheduling processing on the electric vehicles based on the second scheduling process. This application achieves a comprehensive improvement in the economy, environmental protection, and stability of power grid operation through efficient scheduling of flexible loads and electric vehicles. First, by acquiring flexible load data and electric vehicle data, this application can comprehensively grasp the real-time status of dispatchable resources in the power grid, providing accurate basic information for subsequent scheduling optimization. The comprehensiveness and real-time nature of the flexible load data and electric vehicle data allow the scheduling strategy to be flexibly adjusted according to actual needs, enhancing the adaptability and responsiveness of this application.

[0114] Secondly, based on the preset scheduling strategies for flexible load data and electric vehicle data, this application performs first and second scheduling processes for flexible loads and electric vehicles respectively. This hierarchical scheduling strategy can fully consider the different characteristics of flexible loads and electric vehicles. For example, the scheduling of flexible loads focuses more on load balance and grid stability, while the scheduling of electric vehicles focuses more on the absorption of new energy sources and user demand. Through hierarchical optimization, this application can ensure the efficient utilization of various resources in scheduling, avoiding resource waste and unnecessary scheduling costs.

[0115] After completing the first and second scheduling processes, this application further calculates the objective function value based on the combined scheduling results of both. The objective function design comprehensively considers multiple indicators, including the operation and maintenance costs of energy storage facilities, the proportion of new energy sources, the cost of wind and solar curtailment penalties, the revenue from carbon trading and carbon capture systems, and voltage deviation, thus evaluating grid operation from multiple dimensions. Through optimized calculation of the objective function value, this application can select the optimal scheme among different scheduling strategies, achieving global optimization in terms of economy, environmental protection, and stability.

[0116] Furthermore, this application optimizes the first and second scheduling processes by determining the minimum value of the objective function, ensuring the global optimality of the scheduling scheme. Based on the optimization results, this application further processes the scheduling of flexible loads and electric vehicles to ensure that the final scheduling scheme not only meets real-time grid demand but also balances economic benefits and environmental goals. For example, during peak load periods, this application effectively reduces grid load pressure through the discharge of energy storage facilities and electric vehicles. Simultaneously, during periods of high renewable energy generation, it achieves efficient renewable energy consumption by scheduling the electricity consumption of flexible loads and the charging tasks of electric vehicles.

[0117] The technical benefits of this application are reflected in several aspects. First, by optimizing the scheduling of flexible loads and electric vehicles, the utilization rate of new energy power generation in the power grid is significantly improved, wind and solar curtailment is reduced, and the efficient utilization and green development of new energy are promoted. Second, by rationally arranging the charging and discharging behavior of energy storage facilities and the charging and discharging tasks of electric vehicles, the operating costs of the power grid are reduced. Furthermore, by optimizing the voltage deviation index in the objective function, this application can effectively maintain the operational stability of the power grid and improve overall safety and reliability. Finally, the implementation of this application provides a scientific and effective scheduling method for power grid operation, achieving an organic combination of economy, environmental protection, and stability, and providing technical support and theoretical basis for the practical application of flexible loads and electric vehicles.

[0118] It should be noted that the method in this embodiment can be executed by a single device, such as a computer or server. The method can also be applied in a distributed scenario, where multiple devices cooperate to complete the task. In such a distributed scenario, one of these devices may execute only one or more steps of the method in this embodiment, and the multiple devices will interact with each other to complete the method described.

[0119] It should be noted that the above description describes some embodiments of this application. Other embodiments are within the scope of the appended claims. In some cases, the actions or steps recorded in the claims can be performed in a different order than that shown in the above embodiments and still achieve the desired result. Furthermore, the processes depicted in the drawings do not necessarily require a specific or sequential order to achieve the desired result. In some embodiments, multitasking and parallel processing are also possible or may be advantageous.

[0120] Based on the same inventive concept, corresponding to any of the above embodiments, this application also provides a source-grid-load-storage coordinated scheduling device.

[0121] refer to Figure 2 The source-grid-load-storage coordinated scheduling device includes: Acquisition module 21 is configured to acquire flexible load data and electric vehicle data; The first scheduling module 22 is configured to perform a first scheduling process on the flexible load based on a preset scheduling strategy of the flexible load data; The second scheduling module 23 is configured to perform a second scheduling process on the electric vehicle based on a preset scheduling strategy for the electric vehicle data. The objective function calculation module 24 is configured to determine the objective function value based on the first scheduling process and the second scheduling process; The determination module 25 is configured to determine the first scheduling process and the second scheduling process corresponding to the minimum value of the objective function; The first processing module 26 is configured to perform scheduling processing on the flexible load based on the first scheduling processing; The second processing module 27 is configured to perform scheduling processing on the electric vehicle based on the second scheduling processing.

[0122] For ease of description, the above devices are described in terms of function, divided into various modules. Of course, in implementing this application, the functions of each module can be implemented in one or more software and / or hardware.

[0123] The apparatus described above is used to implement the corresponding source-grid-load-storage coordinated scheduling method in any of the foregoing embodiments, and has the beneficial effects of the corresponding method embodiments, which will not be repeated here.

[0124] Based on the same inventive concept, corresponding to the methods of any of the above embodiments, this application also provides an electronic device, including a memory, a processor, and a computer program stored in the memory and executable on the processor, wherein the processor executes the program to implement the source-grid-load-storage coordinated scheduling method described in any of the above embodiments.

[0125] Figure 3 This embodiment illustrates a more specific hardware structure of an electronic device, which may include a processor 1010, a memory 1020, an input / output interface 1030, a communication interface 1040, and a bus 1050. The processor 1010, memory 1020, input / output interface 1030, and communication interface 1040 are interconnected internally via the bus 1050.

[0126] The processor 1010 can be implemented using a general-purpose CPU (Central Processing Unit), microprocessor, application-specific integrated circuit (ASIC), or one or more integrated circuits, and is used to execute relevant programs to implement the technical solutions provided in the embodiments of this specification.

[0127] The memory 1020 can be implemented in the form of ROM (Read Only Memory), RAM (Random Access Memory), static storage device, dynamic storage device, etc. The memory 1020 can store the operating system and other applications. When the technical solutions provided in the embodiments of this specification are implemented by software or firmware, the relevant program code is stored in the memory 1020 and is called and executed by the processor 1010.

[0128] The input / output interface 1030 is used to connect input / output modules to realize information input and output. Input / output modules can be configured as components within the device (not shown in the figure) or externally connected to the device to provide corresponding functions. Input devices may include keyboards, mice, touchscreens, microphones, various sensors, etc., while output devices may include displays, speakers, vibrators, indicator lights, etc.

[0129] The communication interface 1040 is used to connect a communication module (not shown in the figure) to enable communication between this device and other devices. The communication module can communicate via wired means (such as USB, Ethernet cable, etc.) or wireless means (such as mobile network, WIFI, Bluetooth, etc.).

[0130] Bus 1050 includes a pathway for transmitting information between various components of the device, such as processor 1010, memory 1020, input / output interface 1030, and communication interface 1040.

[0131] It should be noted that although the above-described device only shows the processor 1010, memory 1020, input / output interface 1030, communication interface 1040, and bus 1050, in specific implementations, the device may also include other components necessary for normal operation. Furthermore, those skilled in the art will understand that the above-described device may only include the components necessary for implementing the embodiments of this specification, and not necessarily all the components shown in the figures.

[0132] The electronic devices described above are used to implement the corresponding source-grid-load-storage coordinated scheduling method in any of the foregoing embodiments, and have the beneficial effects of the corresponding method embodiments, which will not be repeated here.

[0133] Based on the same inventive concept, corresponding to the methods of any of the above embodiments, this application also provides a non-transitory computer-readable storage medium that stores computer instructions for causing the computer to execute the source-grid-load-storage coordinated scheduling method as described in any of the above embodiments.

[0134] The computer-readable medium of this embodiment includes permanent and non-permanent, removable and non-removable media, and information storage can be implemented by any method or technology. Information can be computer-readable instructions, data structures, program modules, or other data. Examples of computer storage media include, but are not limited to, phase-change memory (PRAM), static random access memory (SRAM), dynamic random access memory (DRAM), other types of random access memory (RAM), read-only memory (ROM), electrically erasable programmable read-only memory (EEPROM), flash memory or other memory technologies, CD-ROM, digital versatile optical disc (DVD) or other optical storage, magnetic tape, magnetic magnetic disk storage or other magnetic storage devices, or any other non-transfer medium that can be used to store information accessible by a computing device.

[0135] The computer instructions stored in the storage medium of the above embodiments are used to cause the computer to execute the source-grid-load-storage coordinated scheduling method as described in any of the above embodiments, and have the beneficial effects of the corresponding method embodiments, which will not be repeated here.

[0136] Based on the same inventive concept, corresponding to the source-grid-load-storage coordinated scheduling method described in any of the above embodiments, this disclosure also provides a computer program product, which includes computer program instructions. In some embodiments, the computer program instructions can be executed by one or more processors of a computer to cause the computer and / or the processors to execute the source-grid-load-storage coordinated scheduling method. Corresponding to the execution entity for each step in each embodiment of the source-grid-load-storage coordinated scheduling method, the processor executing the corresponding step can belong to the corresponding execution entity.

[0137] The computer program product of the above embodiments is used to cause the computer and / or the processor to execute the source-grid-load-storage coordinated scheduling method as described in any of the above embodiments, and has the beneficial effects of the corresponding method embodiments, which will not be repeated here.

[0138] Those skilled in the art should understand that the discussion of any of the above embodiments is merely exemplary and is not intended to imply that the scope of this application (including the claims) is limited to these examples; within the framework of this application, the technical features of the above embodiments or different embodiments can also be combined, the steps can be implemented in any order, and there are many other variations of different aspects of the embodiments of this application as described above, which are not provided in the details for the sake of brevity.

[0139] Additionally, to simplify the description and discussion, and to avoid obscuring the embodiments of this application, the well-known power / ground connections to integrated circuit (IC) chips and other components may or may not be shown in the provided drawings. Furthermore, the apparatus may be shown in block diagram form to avoid obscuring the embodiments of this application, and this also takes into account the fact that the details of the implementation of these block diagram apparatuses are highly dependent on the platform on which the embodiments of this application will be implemented (i.e., these details should be fully understood by those skilled in the art). While specific details (e.g., circuits) have been set forth to describe exemplary embodiments of this application, it will be apparent to those skilled in the art that the embodiments of this application can be implemented without these specific details or with variations thereof. Therefore, these descriptions should be considered illustrative rather than restrictive.

[0140] Although this application has been described in conjunction with specific embodiments thereof, many substitutions, modifications, and variations of these embodiments will be apparent to those skilled in the art from the foregoing description. For example, other memory architectures (e.g., dynamic RAM (DRAM)) may be used with the embodiments discussed.

[0141] The embodiments of this application are intended to cover all such substitutions, modifications, and variations that fall within the broad scope of the appended claims. Therefore, any omissions, modifications, equivalent substitutions, improvements, etc., made within the spirit and principles of the embodiments of this application should be included within the protection scope of this application.

Claims

1. A source-grid-load-storage coordinated scheduling method, characterized in that, include: Acquire flexible load data and electric vehicle data; The flexible load is first scheduled based on the pre-set scheduling strategy of the flexible load data. The electric vehicles are subjected to a second scheduling process based on the preset scheduling strategy of the electric vehicle data. The objective function value is determined based on the first scheduling process and the second scheduling process; Determine the first scheduling process and the second scheduling process corresponding to the minimum value of the objective function; The flexible load is scheduled based on the first scheduling process; The electric vehicle is scheduled based on the second scheduling process.

2. The method according to claim 1, characterized in that, The scheduling power of the flexible load is expressed by the following formula: ； in, Indicates the load compensation amount. , , Indicates the power variation factor. Indicates the power of the transferable load. This indicates the power of the load that can be moved. This indicates the power that can be reduced to reduce the load. Indicates the transferable time period. Indicates the load cycle that can be shifted. This indicates the period during which the load can be reduced.

3. The method according to claim 2, characterized in that, The constraints of the pre-set scheduling strategy for the flexible load data are expressed by the following formula: ； in, Indicates a transitional state. Indicates a state that can be translated. This indicates the schedulable state, with 1 indicating occurrence and 0 indicating non-occurrence. Indicates the translation time. This indicates a reduction in time. Indicates the number of reductions. This represents the minimum value of transferable load power. This represents the maximum transferable load power. This represents the minimum power of the load that can be moved. This represents the maximum power of the load that can be moved. This represents the minimum amount of load power that can be reduced. This indicates the maximum amount of load power that can be reduced.

4. The method according to claim 3, characterized in that, The method further includes: In response to the scheduling of the flexible load, compensation is performed; The compensation is expressed by the following formula: ； in, This indicates compensation for transferable loads. This indicates compensation for loads that can be shifted. This indicates compensation that can reduce the load. , , This represents the compensation coefficient.

5. The method according to claim 1, characterized in that, The constraints of the preset scheduling strategy for the electric vehicle data are expressed by the following formula: ； in, This indicates the lowest charge level of the battery. This represents the current charging state of the electric vehicle battery at time t. This indicates the highest charge level of the battery. This represents the minimum voltage at a power grid node. This represents the maximum value of the voltage at a power grid node. This indicates the grid connection status at time t, where 1 indicates grid connection and 0 indicates otherwise. This indicates the minimum energy capacity of an electric vehicle battery. This indicates the maximum energy capacity of an electric vehicle battery. This represents the energy level of the electric vehicle battery at time t. Indicates charging efficiency. Indicates discharge efficiency. Let represent the charging power of the electric vehicle at time t. This represents the discharge power of the electric vehicle at time t. Indicates a time interval.

6. The method according to claim 1, characterized in that, The objective function is expressed by the following formula: ； in, This indicates the charging, discharging, operation, and maintenance costs of energy storage facilities. Indicates the proportion of new energy sources. This indicates the penalty cost for abandoning wind and solar power. This indicates the revenue from carbon trading and carbon capture systems. This indicates voltage deviation.

7. A source-grid-load-storage coordinated scheduling device, characterized in that, include: The acquisition module is configured to acquire flexible load data and electric vehicle data; The first scheduling module is configured to perform a first scheduling process on the flexible load based on a preset scheduling strategy of the flexible load data; The second scheduling module is configured to perform a second scheduling process on the electric vehicles based on a preset scheduling strategy for the electric vehicle data. The objective function calculation module is configured to determine the objective function value based on the first scheduling process and the second scheduling process; The determination module is configured to determine the first scheduling process and the second scheduling process corresponding to the minimum value of the objective function; The first processing module is configured to perform scheduling processing on the flexible load based on the first scheduling processing; The second processing module is configured to perform scheduling processing on the electric vehicle based on the second scheduling processing.

8. An electronic device comprising a memory, a processor, and a computer program stored in the memory and executable on the processor, characterized in that, When the processor executes the computer program, it implements the method as described in any one of claims 1 to 6.

9. A non-transitory computer-readable storage medium storing computer instructions, characterized in that, The computer instructions are used to cause the computer to perform the method according to any one of claims 1 to 6.

10. A computer program product comprising computer program instructions that, when executed on a computer, cause the computer to perform the method as described in any one of claims 1 to 6.

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