Virtual power plant multi-element flexible resource day-to-day rolling optimization scheduling method, system and device considering random deviation and medium
By establishing a control model and collaborative constraints for multiple flexible resources in a virtual power plant, and using a hybrid integer linear planning algorithm for optimization and scheduling, the problems of high resource regulation costs and large random deviations in virtual power plants are solved, and efficient coordinated utilization between resources and cost-effective operation of the system are achieved.
Patent Information
- Application Number
- CN202510638930.8
- Authority / Receiving Office
- CN · China
- Patent Type
- Applications(China)
- Current Assignee / Owner
- Filing Date
- 2025-05-19
- Publication Date
- 2025-06-20
- Estimated Expiration
- Not applicable · inactive patent
AI Technical Summary
The existing technology is difficult to make full use of diversified flexible resources in virtual power plants, resulting in high volume and price costs of resource regulation, and failing to effectively eliminate random deviations, affecting the balance of supply and demand of the power grid.
By establishing a control model for electric vehicle clusters, air conditioning systems and batteries, setting collaborative constraints, building an objective function to minimize power tracking costs and deviations, optimizing scheduling is used to use a hybrid integer linear planning algorithm, and adjusting the scheduling plan in real time to achieve rolling optimization scheduling of multiple flexible resources of virtual power plants.
It improves the synergistic effect between virtual power plant resources, reduces the quantity and price cost of resource regulation, significantly improves the timeliness and adaptability of the scheduling strategy, and ensures that the system always operates in the optimal state.
Smart Images
Figure CN120184950A_ABST
Abstract
Description
Technical Field
[0001] The present invention belongs to the technical field of flexible resource scheduling management, and specifically relates to a method, system, equipment and medium for daytime rolling optimization scheduling of multiple flexible resources of a virtual power plant considering random deviations. Background Art
[0002] A high proportion of renewable energy access is the guarantee for the low-carbon transformation of the power system. However, the inherent randomness and volatility of renewable energy have brought severe security and stability challenges to the supply and demand sides of the power grid. Aggregating and utilizing flexible and adjustable resources on the demand side to participate in grid demand response is an effective means to maintain the balance of supply and demand in the power system.
[0003] As an advanced energy management system, Virtual Power Plant (VPP) can integrate demand-side adjustable resources such as distributed power sources, energy storage equipment, loads, and electric vehicles to achieve unified management and scheduling, and then participate in the grid demand response market. Constrained by the demand response mechanism, VPP needs to determine the resource scheduling strategy and capacity declaration plan before the response day; on the response day, the main goal of its operation and regulation is to ensure that the day-ahead scheduling task is completed as planned and to avoid economic losses caused by response deviations. Therefore, how to effectively utilize the adjustable resources within the VPP and determine an economical and efficient scheduling plan has become a key issue that needs to be urgently solved in the current relevant technical field.
[0004] At present, in the related technologies of VPP control strategies, both domestic and foreign research mainly focuses on the optimization of the day-ahead scheduling plan, emphasizing the modeling and characterization of adjustable resources such as electric vehicles, thermostatic loads, and distributed energy storage, as well as the scheduling strategies of VPP participating in multiple types of markets such as the spot market, demand response market, and carbon market. However, affected by uncertain factors such as the number of electric vehicles connected to the grid, the connection status of batteries, and the prediction deviation of outdoor temperature, when regulating resources such as electric vehicles and air conditioners execute the day-ahead scheduling plan, the actual execution effect may deviate significantly from the expectation. To address this issue, existing technologies adopt methods such as improving the prediction accuracy of the adjustable potential of flexible resources to reduce the deviation of daytime actual response; or reserving spare resources to reduce the response deviation caused by daytime deviation disturbances; and also propose model predictive control technology to enhance the system's ability to cope with uncertain factors and improve the optimization performance of real-time scheduling strategies. However, existing technologies have obvious deficiencies in eliminating the random deviation in the actual response process of virtual power plants. On the one hand, the understanding of the adjustable capabilities of multiple resources is not comprehensive and in-depth enough, resulting in the inability to fully utilize the advantages of various resources during the resource allocation process and making it difficult to achieve the optimal allocation of resources; on the other hand, the synergistic effect between resources is not fully considered, resulting in a lack of effective cooperation and coordination among resources during the regulation process, and further leading to inaccurate characterization of the regulation cost. In addition, the existing technologies have a relatively single way of using resources, failing to fully explore the potential value of resources, resulting in high volume-price costs of resource regulation, which affects the economy and efficiency of the overall operation of virtual power plants. Summary of the Invention
[0005] Based on the above-mentioned shortcomings and deficiencies in the prior art, one of the objectives of the present invention is to at least solve one or more of the above problems existing in the prior art. In other words, one of the objectives of the present invention is to provide a day-ahead rolling optimization scheduling method, system, device, and medium for multi-flexible resources of a virtual power plant considering random deviation that meet one or more of the foregoing requirements, so as to achieve the purpose of improving the synergistic effect between resources and reducing the volume-price cost of resource regulation.
[0006] To achieve the above-mentioned invention objectives, the present invention adopts the following technical solutions: In the first aspect, the present invention provides a day-ahead rolling optimization scheduling method for multi-flexible resources of a virtual power plant considering random deviation. The flexible resources include electric vehicle clusters, air conditioning systems, and storage batteries, and the method includes the steps of: Obtaining resource configuration parameter information, a pre-set scheduling plan for flexible resources, and the corresponding response day operation boundary information for the scheduling plan, where the flexible resources include electric vehicle clusters, air conditioning systems, and storage batteries; Establishing control models for each type of the flexible resources and setting constraint conditions, where the control models include establishing a charging control model for electric vehicle clusters, an air conditioning system control model, and a charge-discharge control model for storage batteries; Obtain the quantity-price curve and adjustable range of the flexible resources, and based on this, construct an objective function aiming at minimizing the power tracking cost and power tracking deviation in response to the scheduling plan; Based on the objective function, use the mixed integer linear programming algorithm to solve each of the control models respectively, obtain the corresponding power deviation correction values, and schedule each of the flexible resources based on the power deviation correction values; Based on the operation results after scheduling at the current moment, re-execute the above steps at the next moment to achieve the rolling optimal scheduling of the multi-source flexible resources of the virtual power plant.
[0007] As a preferred solution, the resource configuration parameter information includes the estimated adjustable capacity of the electric vehicle cluster, the configuration parameters of the air conditioning system, and the configuration parameters of the storage battery; the scheduling plan includes the electric vehicle charging and discharging strategy, the building indoor temperature setting strategy, and the storage battery charging and discharging strategy; the response daily operation boundary information includes the connection time and disconnection time of the electric vehicle, the state of charge of the electric vehicle battery at the time of connection and the required state of charge of the battery at the time of disconnection as well as the electric battery capacity , the personnel schedule related to the building cooling and heating load and the equipment schedule , the indoor temperature data , the outdoor temperature data , the outdoor humidity data , the outdoor irradiation intensity .
[0008] As a preferred solution, the electric vehicle cluster charging control model includes a rigid electric vehicle charging power calculation model and an elastic electric vehicle daytime charging power optimization calculation model; The expression of the rigid electric vehicle charging power calculation model is , wherein, represents the power of the th rigid charging vehicle at the moment; The expression of the elastic electric vehicle daytime charging power optimization calculation model is , wherein, represents the power of the th elastic charging vehicle at the moment, is the target tracking power of the electric vehicle cluster in the scheduling plan.
[0009] As a preferred solution, the constraint conditions of the electric vehicle cluster charging control model are
[0010]
[0011]
[0012]
[0013] , wherein, is the battery SOC state of the th electric vehicle at the moment, is the battery SOC state of the th electric vehicle at the moment, is the charging efficiency of the electric vehicle, represents the actual SOC state of the electric vehicle at the off-grid moment, is the SOC state interval of the electric vehicle battery, is the charging power adjustment interval, represents the charging state of the th tram at the moment, represents that the tram is connected to the grid but not charging, then represents that the electric vehicle is charging.
[0014] As a preferred solution, the air-conditioning system control model includes an autoregressive model ; The expression of the autoregressive model is , wherein, represents the predicted cooling load and its unit is kW, is the historical moment data on which the current cooling / heating load depends; The constraint condition of the autoregressive model is
[0015] , wherein, and are respectively the upper and lower limits of the adjustable indoor temperature during the day-ahead scheduling stage, is the upper limit of the adjustable indoor temperature during the day-time regulation correction.
[0016] As a preferred solution, the air-conditioning system control model further includes an air-conditioning system model, and its expression is
[0017]
[0018] , wherein, is the capacity of the air - conditioning system, and its unit is kW, is the rated cooling coefficient of the air - conditioning system.
[0019] As a preferred solution, the expression of the objective function is , wherein, , , are respectively the unit cost of air - conditioning electric load, electric vehicle cluster, and battery day - time regulation, and the unit is CNY / kWh, represents the optimal power correction amount of the electric vehicle cluster, represents the optimal power correction amount of the air - conditioning system, represents the optimal power correction amount of the battery.
[0020] In a second aspect, the present invention provides a virtual power plant multi - flexible resource day - time rolling optimization scheduling system considering random deviation for implementing the virtual power plant multi - flexible resource day - time rolling optimization scheduling method as described in the first aspect, including: an information acquisition module, a model construction module, a deviation calculation module, and a rolling optimization module; The information acquisition module is used to acquire resource configuration parameter information, the preset scheduling plan for the flexible resources and the corresponding response day - time operation boundary information; The model construction module is used to respectively establish control models for various flexible resources and set constraint conditions, and is also used to acquire the quantity - price curves and adjustable intervals of the flexible resources, and based on this, construct an objective function with the minimum power tracking cost and the minimum power tracking deviation for responding to the scheduling plan. The control models include establishing an electric vehicle cluster charging control model, an air - conditioning system control model, and a battery charge - discharge control model; The deviation calculation module, based on the objective function, uses a mixed - integer linear programming algorithm to solve each control model respectively; The rolling optimization module schedules each flexible resource based on the power deviation correction value, and based on the operation result after scheduling at the current moment, re - executes the steps of information acquisition, model construction, and deviation calculation at the next moment to achieve the rolling optimization scheduling of the virtual power plant multi - flexible resources.
[0021] In a third aspect, the present invention provides an electronic device, which includes a memory, a processor, and a computer program. When the computer program is executed by the processor, it implements the method for day-ahead rolling optimal scheduling of multiple flexible resources in a virtual power plant as described in the first aspect.
[0022] In a fourth aspect, the present invention provides a computer-readable storage medium, on which a computer program is stored. When the computer program is executed by the processor, it implements the method for day-ahead rolling optimal scheduling of multiple flexible resources in a virtual power plant as described in the first aspect.
[0023] Compared with the prior art, the present invention has the following beneficial effects: 1. Traditional scheduling methods mostly focus on a single type of flexible resource and it is difficult to achieve collaborative optimization among resources. The present invention break throughly incorporates multiple flexible resources such as electric vehicle clusters, air conditioning systems, and storage batteries into a unified scheduling framework. By constructing a classification control model and setting collaborative constraint conditions, it realizes the complementary utilization of different resources in the time, space, and energy dimensions. This innovation greatly improves the overall flexibility and response speed of the virtual power plant, enabling it to more efficiently cope with load fluctuations and energy price changes.
[0024] 2. The present invention adopts a day-ahead rolling optimal scheduling strategy, which adjusts the scheduling plan for the next moment in real time based on the operation results at the current moment, forming a closed-loop optimization mechanism. This innovation enables the virtual power plant to continuously track changes in the system state, quickly respond to external disturbances, significantly improves the timeliness and adaptability of the scheduling strategy, and ensures that the system always operates in an optimal state.
[0025] Further or more detailed beneficial effects will be described in combination with specific embodiments in the specific implementation manners. BRIEF DESCRIPTION OF THE DRAWINGS
[0026] In order to more clearly illustrate the technical solutions in the embodiments of the present invention or the prior art, the following will briefly introduce the drawings required for use in the description of the embodiments or the prior art. Obviously, the following drawings are only some embodiments of the present invention. For those of ordinary skill in the art, without creative efforts, other drawings can be obtained based on these drawings.
[0027] Figure 1 is a schematic flow chart of the method for day-ahead rolling optimal scheduling of multiple flexible resources in a virtual power plant according to an embodiment of the present invention.
[0028] Figure 2 is a structural diagram of the electronic device according to Embodiment 3 of the present invention.
[0029] Figure 3 is a schematic diagram of the number of rigidly charging electric vehicles newly added every 15 minutes during the regulation period according to Embodiment 5 of the present invention.
[0030] Figure 4 It is a schematic diagram of predicting the outdoor temperature and the daytime real-time temperature level described in Embodiment 5 of the present invention.
[0031] Figure 5 It is a schematic diagram of the daytime regulation information of the electric vehicle cluster described in Embodiment 5 of the present invention.
[0032] Figure 6 It is a schematic diagram of the daytime power deviation of the electric vehicle cluster described in Embodiment 5 of the present invention.
[0033] Figure 7 It is the daytime power curve of the temperature control load described in Embodiment 5 of the present invention.
[0034] Figure 8 It is a schematic diagram of the daytime power deviation of the air-conditioning load described in Embodiment 5 of the present invention.
[0035] Figure 9 It is a schematic diagram of the real-time correction strategy for the daytime power response deviation of the virtual power plant described in Embodiment 5 of the present invention.
[0036] Figure 10 It is a schematic diagram of the daytime real-time regulation cost of the virtual power plant described in Embodiment 5 of the present invention.
[0037] Reference numerals in the drawings: 200, electronic device; 201, processor; 202, communication bus; 203, user interface; 204, network interface; 205, memory. Detailed implementation manners
[0038] Next, the technical solutions in the embodiments of the present invention will be clearly and completely described in conjunction with the accompanying drawings in the embodiments of the present invention.
[0039] In the following description, multiple embodiments of the present invention are provided, and different embodiments can be replaced or combined. Therefore, the present invention can also be considered to include all possible combinations of the same and / or different embodiments described. Thus, if one embodiment includes features A, B, and C, and another embodiment includes features B and D, then the present invention should also be considered to include embodiments containing all other possible combinations of A, B, C, and D, even though such embodiments may not be explicitly described in the following content.
[0040] The following description provides examples and does not limit the scope, applicability, or examples set forth in the claims. Changes may be made to the function and arrangement of the elements described without departing from the scope of the present disclosure. Various processes or components may be appropriately omitted, substituted, or added to each example. For example, the methods described may be performed in a different order than described, and various steps may be added, omitted, or combined. Additionally, features described with respect to some examples may be combined with other examples.
[0041] To facilitate a better understanding of the embodiments of the present invention, before explaining the specific implementation manners of the present invention in detail, its application scenarios will be described first.
[0042] The method for day-ahead rolling optimal scheduling of multi-source flexible resources of a virtual power plant described in the embodiments of this specification is applied to processes such as integrated management of distributed energy systems, load regulation of smart grids, optimization of renewable energy consumption, and participation in auxiliary services of the power market. In these scenarios, the application of the method for day-ahead rolling optimal scheduling of multi-source flexible resources of the virtual power plant aims to achieve efficient collaborative scheduling of multi-source flexible resources, reduce the operating costs of the virtual power plant, reduce deviation risks, and ensure the safety and reliability of the operation of the virtual power plant.
[0043] Embodiment 1: As Figure 1 shown, this embodiment provides a method for day-ahead rolling optimal scheduling of multi-source flexible resources of a virtual power plant considering random deviations. The flexible resources include electric vehicle clusters, air conditioning systems, and batteries, and the method includes the steps of: S1. Obtain resource configuration parameter information, a pre-set scheduling plan for the flexible resources, and its corresponding response day operation boundary information; S2. Establish control models for each type of the flexible resources respectively and set constraint conditions. The control models include establishing a charging control model for electric vehicle clusters, an air conditioning system control model, and a charge and discharge control model for batteries; S3. Obtain the quantity-price curves and adjustable intervals of the flexible resources, and based on this, construct an objective function with the minimum power tracking cost and the minimum power tracking deviation for responding to the scheduling plan; S4. Based on the objective function, use the mixed integer linear programming algorithm to solve each of the control models respectively, obtain the corresponding power deviation correction values, and schedule each of the flexible resources based on this; S5. Based on the operation results after scheduling at the current moment, re-execute the steps of S1 to S4 at the next moment to achieve the rolling optimal scheduling of multi-source flexible resources of the virtual power plant.
[0044] Specifically, this embodiment provides a preferred implementation manner. The resource configuration parameter information includes the estimated adjustable capacity of the electric vehicle cluster, the configuration parameters of the air conditioning system, and the configuration parameters of the storage battery. The scheduling plan includes the charging and discharging strategies of electric vehicles, the indoor temperature setting strategy of buildings, and the charging and discharging strategies of storage batteries. The response day operation boundary information includes the connection time of electric vehicles to the grid and the disconnection time , the state of charge of the electric vehicle battery at the time of connection to the grid and the required state of charge of the battery at the time of disconnection from the grid as well as the electric battery capacity , the schedule of personnel related to the heating and cooling load of the building and the equipment schedule , the indoor temperature data , the outdoor temperature data , the outdoor humidity data , the outdoor irradiation intensity .
[0045] Specifically, this embodiment provides a preferred implementation manner. The electric vehicle cluster charging control model includes a rigid electric vehicle charging power calculation model and an elastic electric vehicle daytime charging power optimization calculation model; The expression of the rigid electric vehicle charging power calculation model is , In the formula, represents the power of the th rigid charging vehicle at time; The expression of the elastic electric vehicle daytime charging power optimization calculation model is , in the formula, represents the power of the th elastic charging vehicle at time, is the target tracking power of the electric vehicle cluster in the scheduling plan.
[0046] Specifically, this embodiment provides a preferred implementation manner. The constraint conditions of the electric vehicle cluster charging control model are
[0047]
[0048]
[0049]
[0050] , In the formula, is the battery SOC state of the th electric vehicle at moment, is the battery SOC state of the th electric vehicle at moment, is the charging efficiency of the electric vehicle, represents the actual SOC state of the electric vehicle at the off-grid moment, is the SOC state interval of the electric vehicle battery, is the charging power adjustment interval, represents the th tram at moment of charging state, represents that the tram is connected to the grid but not charging, then represents that the electric vehicle is charging.
[0051] More specifically, the obtaining of the corresponding power deviation correction value and scheduling each of the flexible resources based on this as described in step S4 includes calculating the flexibly charged vehicles temporarily summoned during the day based on the elastic electric vehicle charging power optimization calculation model.
[0052] Specifically, this embodiment provides a preferred implementation manner, and the air conditioning system control model includes an autoregressive model ; The expression of the autoregressive model is , wherein, represents the predicted cooling load and its unit is kW, is the historical moment data on which the current cooling / heating load depends; The constraint condition of the autoregressive model is
[0053] , wherein, and are respectively the upper and lower limits of the adjustable indoor temperature during the day-ahead scheduling stage, is the upper limit of the adjustable indoor temperature during the day-time regulation correction.
[0054] Specifically, this embodiment provides a preferred implementation manner, and the air conditioning system control model further includes an air conditioning system model, and its expression is
[0055]
[0056] , In the formula, is the capacity of the air - conditioning system, and its unit is kW, is the rated cooling coefficient of the air - conditioning system.
[0057] More specifically, obtaining the corresponding power deviation correction value in step S4 and scheduling each of the flexible resources based on this includes the re - set value of the indoor temperature of the building obtained by reversing through the air - conditioning system control model, and the charge - discharge strategy of the battery.
[0058] Specifically, this embodiment provides a preferred implementation manner, and the expression of the objective function is , In the formula, , , are respectively the unit costs of the air - conditioning electrical load, the electric - vehicle cluster, and the battery's daytime regulation, and the unit is CNY / kWh, represents the optimal power correction amount of the electric - vehicle cluster, represents the optimal power correction amount of the air - conditioning system, represents the optimal power correction amount of the battery.
[0059] More specifically, the quantity - price curves of the flexible resources include the quantity - price cost model of the air - conditioning load, the quantity - price cost model of the electric vehicle, and the quantity - price cost model of the battery. The expression of the quantity - price cost model of the air - conditioning load is , the expression of the quantity - price cost model of the electric vehicle is , and the expression of the quantity - price cost model of the battery is .
[0060] More specifically, the adjustable intervals of the flexible resources include the power adjustable interval of the air - conditioning load, the power adjustable interval of the electric vehicle, and the power adjustable interval of the battery, and the expression is .
[0061] Embodiment 2: This embodiment provides a virtual - power - plant multi - flexible - resource daytime rolling optimization scheduling system considering random deviations, which is used to implement the virtual - power - plant multi - flexible - resource daytime rolling optimization scheduling method as described in Embodiment 1, and includes: an information acquisition module, a model construction module, a deviation calculation module, and a rolling optimization module; The information acquisition module is used to acquire the resource configuration parameter information, the preset scheduling plan for the flexible resources and its corresponding response - day operation boundary information; The model construction module is used to establish control models and set constraint conditions for various types of flexible resources respectively, and is also used to obtain the quantity-price curves and adjustable intervals of the flexible resources, and based on this, construct an objective function aiming at minimizing the power tracking cost and power tracking deviation in response to the scheduling plan. The control models include establishing an electric vehicle cluster charging control model, an air conditioning system control model, and a battery charge and discharge control model. The deviation calculation module uses a mixed-integer linear programming algorithm to solve each of the control models based on the objective function. The rolling optimization module schedules each flexible resource based on the power deviation correction value, and based on the operation results after scheduling at the current moment, re-executes the steps of information acquisition, model construction, and deviation calculation at the next moment to achieve the rolling optimization scheduling of the multiple flexible resources of the virtual power plant.
[0062] Embodiment 3: As Figure 2 shown, this embodiment provides an electronic device, which may include: at least one processor, at least one network interface, a user interface, a memory, and at least one communication bus.
[0063] Among them, the communication bus can be used to realize the connection and communication of the above-mentioned various components.
[0064] Among them, the user interface may include buttons, and the optional user interface may further include a standard wired interface and a wireless interface.
[0065] Among them, the network interface may but is not limited to include a Bluetooth module, an NFC module, a Wi-Fi module, etc.
[0066] Among them, the processor may include one or more processing cores. The processor uses various interfaces and lines to connect various parts within the entire electronic device, and by running or executing instructions, programs, code sets or instruction sets stored in the memory, and calling data stored in the memory, executes various functions of the electronic device and processes data. Optionally, the processor may be implemented in at least one hardware form of DSP, FPGA, and PLA. The processor may integrate one or several combinations of CPU, GPU, and modem, etc. Among them, the CPU mainly processes the operating system, user interface, and application programs, etc.; the GPU is responsible for rendering and drawing the content required to be displayed on the display screen; the modem is used to process wireless communication. It can be understood that the above-mentioned modem may also not be integrated into the processor and is implemented separately through a chip.
[0067] Among them, the memory may include RAM and may also include ROM. Optionally, the memory includes a non-transitory computer-readable medium. The memory can be used to store instructions, programs, codes, code sets or instruction sets. The memory may include a program storage area and a data storage area. Among them, the program storage area can store instructions for implementing the operating system, instructions for at least one function (such as touch function, sound playback function, image playback function, etc.), instructions for implementing the above-mentioned method embodiments, etc.; the data storage area can store the data involved in the above-mentioned method embodiments. Optionally, the memory may also be at least one storage device located far from the aforementioned processor. The memory as a computer storage medium may include an operating system, a network communication module, a user interface module, and a scroll optimization scheduling application program. The processor can be used to call the scroll optimization scheduling application program stored in the memory and execute the steps of the scroll optimization scheduling mentioned in the foregoing embodiments.
[0068] Embodiment 4: This embodiment provides a computer-readable storage medium, in which instructions are stored. When they run on a computer or a processor, the computer or the processor is caused to execute one or more of the steps in the above-mentioned Figure 1 illustrated embodiments. If the respective component modules of the above-mentioned electronic device are implemented in the form of software functional units and sold or used as independent products, they can be stored in the computer-readable storage medium.
[0069] In the above embodiments, it can be implemented in whole or in part by software, hardware, firmware, or any combination thereof. When implemented using software, it can be implemented in whole or in part in the form of a computer program product. The computer program product includes one or more computer instructions. When the computer program instructions are loaded and executed on a computer, the processes or functions described in the embodiments of this specification are generated in whole or in part. The computer can be a general-purpose computer, a special-purpose computer, a computer network, or other programmable devices. The computer instructions can be stored in a computer-readable storage medium or transmitted through the computer-readable storage medium. The computer instructions can be transmitted from one website, computer, server, or data center to another website, computer, server, or data center in a wired manner (such as coaxial cable, optical fiber, Digital Subscriber Line (DSL)) or wirelessly (such as infrared, wireless, microwave, etc.). The computer-readable storage medium can be any available medium that the computer can access or a data storage device such as a server or data center that includes one or more integrated available media. The available medium can be a magnetic medium (such as a floppy disk, hard disk, magnetic tape), an optical medium (such as a Digital Versatile Disc (DVD)), or a semiconductor medium (such as a Solid State Disk (SSD)), etc.
[0070] Those of ordinary skill in the art can understand that all or part of the processes in implementing the method of the above Embodiment 1 can be completed by instructing relevant hardware through a computer program. The program can be stored in a computer-readable storage medium. When the program is executed, it can include the processes of the embodiments of the above methods. The foregoing storage medium includes various media that can store program codes, such as ROM, RAM, magnetic disks, or optical discs. Without conflict, the technical features in this embodiment and the implementation scheme can be combined arbitrarily.
[0071] Embodiment Five: To verify the effectiveness of the method for day-ahead rolling optimal scheduling of multiple flexible resources in a virtual power plant considering random deviations described in this specification, this embodiment conducts experiments based on the actual application scenario of the method for day-ahead rolling optimal scheduling of multiple flexible resources in the virtual power plant.
[0072] In the experiment of this embodiment, 50 electric vehicles that have been contracted in advance are connected to the grid in a flexible charging manner during the grid regulation period. The number of electric vehicles with rigid charging newly added in real time every 15 minutes during the regulation period is as Figure 3As shown. It should be noted that the electric vehicle information in this embodiment is generated by Monte Carlo random sampling, including the electric vehicle battery capacity, the grid-connected SOC state, the grid-connected time, the expected off-grid time, and the expected off-grid SOC state.
[0073] This embodiment of the experiment only considers the scenario where the day-ahead prediction level is lower than the daytime real-time temperature. The day-ahead predicted outdoor temperature and the daytime real-time temperature level are as Figure 4 shown. After calculation, the deviation range of the day-ahead outdoor temperature prediction is 3% - 15%. Figure 5 are the target tracking power curve of the electric vehicle cluster, the load curve of the day-ahead contract electric vehicle flexible regulation, and the load curve of the temporarily added rigid charging vehicle. Figure 6 is the power deviation that exists when the electric vehicle cluster executes the day-ahead scheduling plan. It can be seen that from 12:00 to 13:00, the charging load of the day-ahead contract electric vehicle is continuously decreasing to adapt to the charging load of the rigid charging vehicle that is newly added uncertainly during the regulation period. At this time, the overall power tracking deviation of the electric vehicle cluster is 0. In the later stage of the regulation period, from 13:00 to 14:00, due to the connection of the uncertain rigid charging vehicle to the grid, the load of the rigid charging vehicle continues to increase and is greater than the target power value. At this time, the load of the day-ahead contract flexible charging vehicle should be minimized to reduce the power tracking deviation, but in order to ensure that the flexible charging vehicle reaches the expected SOC at the off-grid moment, its charging load has to increase. Therefore, when the day-ahead power correction is not carried out, the power deviation of the EV cluster when executing the day-ahead scheduling plan is as Figure 6 shown, that is, in the early stage of the regulation period, the overall power deviation can be minimized to 0, but as the regulation time increases, the power tracking deviation becomes larger and larger, and the maximum deviation can reach 245%. Figure 7 is the target tracking power curve of the air-conditioning load and the actual power curve considering random deviations. Since the day-ahead scheduling optimization stage underestimated the daytime outdoor temperature level, when still executing the indoor temperature setting strategy formulated in the day-ahead, it will increase the cooling load, and then cause the air-conditioning load power during the day to be higher than the target tracking power. Figure 8 is the power deviation that exists when the air-conditioning load executes the day-ahead scheduling plan. The power tracking deviation caused by the outdoor temperature prediction deviation can reach up to 93kW at most, accounting for 21% of the air-conditioning load power. Figure 9 is the deviation correction strategy of the virtual power plant through daytime real-time regulation, Figure 10It is the corresponding real-time regulation cost of the virtual power plant during the day. During the first regulation period from 12:00 to 12:15, the overall power deviation of the virtual power plant executing the day-ahead dispatch plan is 76 kW, and power reduction is required. Through the optimal allocation of real-time deviation, the electric vehicle cluster needs to additionally bear a power reduction of 40 kW, and the air-conditioning load needs to additionally bear a power reduction of 36 kW. Since the regulation cost of the battery is relatively high under a small regulation amount, the deviation correction resources called in this period do not consider the battery. It can also be seen that when the power deviation is small, the virtual power plant preferentially regulates the resources of electric vehicles and air-conditioning loads to eliminate the deviation. When the power deviation is large, the virtual power plant preferentially uses the resources of batteries and charging vehicles to eliminate the deviation. In addition, as the regulation period progresses, the day-ahead regulation deviation correction amount of the virtual power plant increases linearly. This shows that the longer the regulation time, the greater the deviation caused by the superposition of various uncertain factors in the virtual power plant's execution of the day-ahead dispatch plan, and the greater the day-ahead deviation correction amount required.
[0074] Based on the above, this embodiment verifies the effectiveness of the method for day-ahead rolling optimization dispatch of multiple flexible resources of a virtual power plant considering random deviation described in this specification.
[0075] It should be noted that for the foregoing method embodiments, for the sake of simple description, they are all expressed as a series of action combinations. However, those skilled in the art should know that the present invention is not limited by the described action sequence, because according to the present invention, certain steps can be performed in other sequences or simultaneously. Secondly, those skilled in the art should also know that the embodiments described in the specification are all preferred embodiments, and the actions and modules involved are not necessarily essential to the present invention.
[0076] In the above embodiments, the descriptions of each embodiment have their own emphases. For the parts not detailed in a certain embodiment, reference can be made to the relevant descriptions of other embodiments.
[0077] The above are only exemplary embodiments of the present invention, and the scope of the present invention cannot be limited thereby. That is, any equivalent changes and modifications made according to the teachings of the present invention still fall within the scope covered by the present invention. Those skilled in the art will easily think of other embodiments of the present invention after considering the specification and practicing the disclosure herein. The present invention aims to cover any variations, uses, or adaptive changes of the present invention, which follow the general principles of the present invention and include common general knowledge or conventional technical means in the technical field not recorded in the present invention. The specification and embodiments are only regarded as exemplary, and the scope and spirit of the present invention are defined by the claims.
Claims
1. A method for daytime rolling optimization scheduling of multiple flexible resources of virtual power plants considering random deviations, characterized in that: Includes steps: Obtain resource configuration parameter information, a preset scheduling plan for flexible resources, and response day operation boundary information corresponding to the scheduling plan, wherein the flexible resources include an electric vehicle cluster, an air conditioning system, and a battery; Establishing control models and setting constraints for each type of flexible resource, wherein the control models include establishing an electric vehicle cluster charging control model, an air conditioning system control model, and a battery charging and discharging control model; Obtaining the quantity-price curve and adjustable range of the flexible resource, and based on this, constructing an objective function with the goal of minimizing the power tracking cost and the power tracking deviation in response to the scheduling plan; Based on the objective function, a mixed integer linear programming algorithm is used to solve each of the control models respectively to obtain a corresponding power deviation correction value, and each of the flexible resources is scheduled based on the power deviation correction value; Based on the operation results after scheduling at the current moment, the above steps are re-executed at the next moment to achieve rolling optimization scheduling of multiple flexible resources of the virtual power plant.
2. According to claim 1, a method for daytime rolling optimization scheduling of multiple flexible resources of a virtual power plant considering random deviations is characterized by: The resource configuration parameter information includes the estimated adjustable capacity of the electric vehicle cluster, the air conditioning system configuration parameters and the battery configuration parameters; The scheduling plan includes electric vehicle charging and discharging strategies, building indoor temperature setting strategies and battery charging and discharging strategies; The response day operation boundary information includes the electric vehicle grid connection time and off-grid time , the state of charge of the electric vehicle battery when connected to the grid and off-grid battery state of charge And electric battery capacity , timetable for personnel related to building cooling and heating loads and equipment schedule , indoor temperature data , outdoor temperature data , Outdoor humidity data , Outdoor radiation intensity .
3. According to claim 2, a method for daytime rolling optimization scheduling of multiple flexible resources of a virtual power plant considering random deviations is characterized by: The electric vehicle cluster charging control model includes a rigid electric vehicle charging power calculation model and a flexible electric vehicle daytime charging power optimization calculation model; The expression of the rigid electric vehicle charging power calculation model is: ， In the formula, Indicates Rigid charging vehicles Power at the moment; The expression of the optimization calculation model of the flexible electric vehicle daytime charging power is: , where Indicates Flexible charging vehicles The power of the moment, Track power targets for electric vehicle fleets in dispatch plans.
4. According to claim 3, a method for daytime rolling optimization scheduling of multiple flexible resources of a virtual power plant considering random deviations is characterized by: The constraints of the electric vehicle cluster charging control model are: ， In the formula, For the Electric vehicles in The battery SOC status at the moment, For the Electric vehicles in The battery SOC status at the moment, For electric vehicle charging efficiency, Indicates the actual SOC state of the electric vehicle when it is off the grid. is the SOC state range of the electric vehicle battery, is the charging power adjustment range, Representative Tram in Charging status at all times, It means the tram is connected to the grid but not charged. It means that the electric vehicle is being charged.
5. According to claim 4, a method for daytime rolling optimization scheduling of multiple flexible resources of a virtual power plant considering random deviations is characterized by: The air conditioning system control model includes an adaptive regression model ; The origin is derived from the regression model The expression is ， In the formula, It represents the predicted cooling load and its unit is kW. The historical data on which the current cooling / heating load depends; The constraints derived from the regression model are ， In the formula, and They are the upper and lower limits of the indoor temperature that can be adjusted during the day-ahead scheduling phase. Corrects the adjustable upper limit of indoor temperature for daytime control.
6. The method for daytime rolling optimization scheduling of multiple flexible resources of a virtual power plant considering random deviation according to claim 5 is characterized by: The air conditioning system control model also includes an air conditioning system model, which is expressed as follows: ， In the formula, is the capacity of the air conditioning system and its unit is kW, Rated cooling factor for air conditioning systems.
7. The method for daytime rolling optimization scheduling of multiple flexible resources of a virtual power plant considering random deviation according to claim 6 is characterized by: The expression of the objective function is ， In the formula, , , They are the unit cost of air conditioning load, electric vehicle cluster, and battery daytime regulation, and the unit is CNY / kWh. represents the optimal correction value of electric vehicle cluster power, represents the optimal correction value of air conditioning system power, Indicates the optimal correction amount for battery power.
8. A virtual power plant multi-flexible resource daytime rolling optimization scheduling system considering random deviations, characterized in that: The method for implementing the daytime rolling optimization scheduling of multiple flexible resources of a virtual power plant as claimed in any one of claims 1 to 7 comprises: an information acquisition module, a model building module, a deviation calculation module and a rolling optimization module; The information acquisition module is used to acquire resource configuration parameter information, a preset scheduling plan for the flexible resource and its corresponding response day operation boundary information; The model building module is used to establish control models and set constraints for each type of flexible resource, and is also used to obtain the quantity-price curve and adjustable interval of the flexible resource, and based on this, construct an objective function with the goal of minimizing the power tracking cost and minimizing the power tracking deviation in response to the scheduling plan. The control model includes establishing an electric vehicle cluster charging control model, an air conditioning system control model, and a battery charging and discharging control model; The deviation calculation module uses a mixed integer linear programming algorithm to solve each of the control models based on the objective function; The rolling optimization module schedules each of the flexible resources based on the power deviation correction value, and re-executes the steps of information acquisition, model construction and deviation calculation at the next moment based on the operation results after scheduling at the current moment, so as to realize the rolling optimization scheduling of multiple flexible resources of the virtual power plant.
9. A computer device, comprising a memory, a processor and a computer program, characterized in that: When the computer program is executed by a processor, it implements the method for daily rolling optimization scheduling of multiple flexible resources of a virtual power plant as described in any one of claims 1 to 7.
10. A computer-readable storage medium having a computer program stored thereon, characterized in that: When the computer program is executed by a processor, it implements the method for daily rolling optimization scheduling of multiple flexible resources of a virtual power plant as described in any one of claims 1 to 7.