Virtual power plant multi-element flexible resource day rolling optimization scheduling method, system, equipment and medium considering random deviation

Abstract

The present application belongs to the technical field of flexible resource scheduling management, and discloses a virtual power plant multi-element flexible resource daily rolling optimization scheduling method, system, equipment and medium considering random deviation, to solve the problems of poor coordination between resources and high cost of resource regulation. The method comprises the following steps: obtaining resource configuration parameter information, a preset scheduling plan for flexible resources and corresponding response daily operation boundary information; establishing a control model for each type of flexible resource and setting a constraint condition; obtaining the quantity-price curve and adjustable interval of the flexible resource, and constructing a target function based on the same, with the minimum power tracking cost and the minimum power tracking deviation of the response scheduling plan as the target; based on the target function, a mixed integer linear programming algorithm is used to solve each control model, the corresponding power deviation correction value is obtained, and each flexible resource is scheduled based on the same; based on the operation result after scheduling at the current time, the above steps are re-executed at the next time.

Description

[0001] The present application claims priority to the invention patent application with the application date of May 19, 2025, the application number of 2025106389308, and the invention name of "Virtual power plant multi-element flexible resource day rolling optimization scheduling method, system, device and medium considering random deviation". TECHNICAL FIELD

[0002] The present application belongs to the technical field of flexible resource scheduling management, and specifically relates to a virtual power plant multi-element flexible resource day rolling optimization scheduling method, system, device and medium considering random deviation. BACKGROUND

[0003] By aggregating the demand side flexible adjustable resources to participate in the power grid demand response, it is an effective means to maintain the balance between supply and demand of the power system.

[0004] As an advanced energy management system, the virtual power plant (VPP) can integrate distributed power sources, energy storage devices, loads, and electric vehicles and other demand side adjustable resources, realize unified management and scheduling, and then participate in the power grid demand response market. Under the constraint of the demand response mechanism, the VPP needs to determine the resource scheduling strategy and capacity declaration scheme in the day-ahead response; in the day-of-response, the main goal of its operation and control is to ensure the completion of the day-ahead scheduling task according to the plan and avoid economic losses due to response deviation. Therefore, how to effectively utilize the adjustable resources inside the VPP and determine an economic and efficient scheduling plan has become a key problem to be solved in the current related technical field.

[0005] Currently, in the field of VPP regulation strategy, many domestic and foreign technologies focus on day-ahead scheduling optimization, focusing on modeling and characterization of adjustable resources such as electric vehicles, temperature-controlled loads, and distributed energy storage, as well as VPP participation in spot market, demand response market, carbon market and other multi-type market scheduling strategies. However, due to the influence of uncertain factors such as the number of electric vehicles connected to the grid, the state of the battery connected to the grid, and the prediction deviation of outdoor temperature, the actual execution effect of electric vehicles and air conditioners and other adjustment resources when executing the day-ahead scheduling plan may have significant deviation from the expected effect. To solve this problem, existing technologies use to improve the prediction accuracy of the adjustable potential of flexible resources to reduce the deviation of actual response during the day; or by reserving standby resources to reduce the response deviation caused by the deviation of the day; a model predictive control technology is also proposed to enhance the system's ability to cope with uncertain factors and improve the optimization performance of real-time scheduling strategies. However, the existing technology has obvious shortcomings in eliminating random deviations in the actual response process of the virtual power plant. On the one hand, the understanding of the adjustable capacity of multi-element resources is not comprehensive and in-depth, which leads to the inability to fully utilize the advantages of various resources in the resource allocation process, making it difficult to achieve optimal allocation of resources; on the other hand, the synergistic effect between resources is not fully considered, resulting in a lack of effective cooperation between resources during the adjustment process, which leads to inaccurate characterization of the cost of resource regulation. In addition, the existing technology has a single way of using resources, which fails to fully exploit the potential value of resources, resulting in high cost of resource regulation, affecting the economy and efficiency of the overall operation of the virtual power plant. SUMMARY

[0006] Based on the above-mentioned shortcomings and deficiencies in the prior art, one of the purposes of the present application is to at least solve one or more of the above-mentioned problems in the prior art, or in other words, one of the purposes of the present application is to provide a virtual power plant multi-element flexible resource day rolling optimization scheduling method, system, device and medium considering random deviation to achieve the purpose of improving the synergistic effect between resources and reducing the cost of resource regulation.

[0007] In order to achieve the above-mentioned purposes of the application, the following technical solutions are adopted in the present application:

[0008] In a first aspect, the present application provides a virtual power plant multi-element flexible resource day rolling optimization scheduling method considering random deviation, the flexible resources including an electric vehicle cluster, an air conditioning system and a battery, comprising the steps of:

[0009] obtaining resource configuration parameter information, a preset scheduling plan for flexible resources and response day operation boundary information corresponding to the scheduling plan, the flexible resources including an electric vehicle cluster, an air conditioning system and a battery;

[0010] Control models and constraints are established for each type of flexible resource. The control models include electric vehicle cluster charging control model, air conditioning system control model and battery charging and discharging control model.

[0011] Obtain the quantity-price curve and adjustable range of the flexible resources, and construct an objective function based on this to minimize the power tracking cost and power tracking deviation in response to the scheduling plan;

[0012] Based on the objective function, a mixed-integer linear programming algorithm is used to solve each of the control models to obtain the corresponding power deviation correction value, and each of the flexible resources is scheduled based on the power deviation correction value.

[0013] Based on the operational results after scheduling at the current moment, the above steps are re-executed at the next moment to achieve rolling optimization scheduling of diverse flexible resources of the virtual power plant.

[0014] As a preferred embodiment, the resource configuration parameter information includes the estimated adjustable capacity of the electric vehicle cluster, air conditioning system configuration parameters, and 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-network time State of charge of electric vehicle batteries when connected to the grid Battery state of charge requirements when off-grid and electric battery capacity Timetable for personnel related to building heating and cooling load and equipment timetable Indoor temperature data Outdoor temperature data Outdoor humidity data Outdoor radiation intensity .

[0015] As a preferred embodiment, 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.

[0016] The expression for the rigid electric vehicle charging power calculation model is as follows:

[0017] ,

[0018] In the formula, Indicates the first Rigid charging vehicles Power at any moment and They represent electric vehicles. battery state of charge at the grid-connected time and the target state of charge at the grid-disconnected time, denotes the battery capacity of the electric vehicle and its unit is kWh, denotes the grid-connected time of the electric vehicle and the grid-disconnected time, respectively; denotes the grid-connected time of the electric vehicle and the grid-disconnected time, respectively; denotes the grid-connected time of the electric vehicle and the grid-disconnected time, respectively;

[0019] The expression of the elastic electric vehicle daytime charging power optimization calculation model is

[0020] In the formula, denotes the charging power of the i-th elastic charging vehicle at the time t and its value satisfies the constraint of the charging power adjustment interval is the target tracking power of the electric vehicle cluster in the day-ahead scheduling plan, is the target tracking power of the electric vehicle cluster in the day-ahead scheduling plan, and represent the total number of elastic electric vehicles and rigid electric vehicles, respectively, is the daytime charging power tracking deviation.

[0021] As a preferred scheme, the constraint condition of the electric vehicle cluster charging control model is

[0022]

[0023]

[0024]

[0025]

[0026] ,

[0027] In the formula, denotes the state of charge of the i-th electric vehicle at the time t, denotes that the electric vehicle is connected to the grid but not charging, denotes that the electric vehicle is charging, is the battery SOC state of the i-th electric vehicle at the time t, is the battery SOC state of the i-th electric vehicle at the time t, is the charging efficiency of the electric vehicle, is the time step of the model, denotes the total grid-connected time of the i-th elastic electric vehicle, i.e. , ​​​​​​​​The symbol represents rounding down, represents an elastic electric vehicle The actual SOC state at the off-grid moment, the SOC state of the elastic electric vehicle at the off-grid moment is greater than , is the SOC state interval of the electric vehicle battery, is the charging power adjustment interval.

[0028] As a preferred scheme, the air conditioning system control model comprises an auto-regressive model ARX;

[0029] The expression of the auto-regressive model ARX is

[0030] ,

[0031] In the formula, represents the predicted cooling load and its unit is kW, and represents the input variable The influence coefficient of the predicted cooling load , is the historical moment data relied on for calculating the current cooling load;

[0032] The constraint condition of the auto-regressive model is

[0033]

[0034] ,

[0035] In the formula, and are the upper limit and the lower limit of the indoor temperature adjustment acceptable by the air conditioning user in the day-ahead scheduling plan, is the adjustable upper limit of the indoor temperature at the time of the day regulation correction.

[0036] As a preferred scheme, the air conditioning system control model further comprises an air conditioning system model, and the expression of the air conditioning system model is

[0037]

[0038]

[0039] ,

[0040] In the formula, is the air conditioning system capacity and its unit is kW, is the rated refrigeration coefficient of the air conditioning system, is the part load ratio of the air conditioning system, , , , respectively, are the performance coefficients of air conditioning systems.

[0041] As a preferred solution, the expression of the objective function is

[0042] ,

[0043] In the formula, is the virtual power plant day power deviation correction cost at the time moment, , , respectively, are the air conditioning electric load, the electric vehicle cluster, and the battery day regulation unit cost, and the unit is CNY / kWh, represents the optimal correction amount of the electric vehicle cluster power, represents the optimal correction amount of the air conditioning system power, represents the optimal correction amount of the battery power.

[0044] In a second aspect, the present application provides a virtual power plant multi-element flexible resource day rolling optimization scheduling system considering random deviation, which is used to realize the virtual power plant multi-element flexible resource day rolling optimization scheduling method as described in the first aspect, and comprises an information acquisition module, a model construction module, a deviation calculation module, and a rolling optimization module.

[0045] The information acquisition module is used to acquire resource configuration parameter information, a preset scheduling plan for the flexible resource, and corresponding response day operation boundary information.

[0046] The model construction module is used to respectively establish control models for various types of flexible resources and set constraint conditions, and is further used to acquire quantity-price curves and adjustable intervals of the flexible resources, and to construct an objective function with the minimum power tracking cost and the minimum power tracking deviation as targets based on the response to the scheduling plan. The control model comprises an electric vehicle cluster charging control model, an air conditioning system control model, and a battery charging and discharging control model.

[0047] The deviation calculation module solves each control model respectively based on the objective function by using a mixed integer linear programming algorithm.

[0048] The rolling optimization module schedules each flexible resource based on the power deviation correction value, and re-executes the steps of information acquisition, model construction, and deviation calculation based on the operation result after scheduling at the current time moment, so as to realize rolling optimization scheduling of the virtual power plant multi-element flexible resource.

[0049] In a third aspect, the present application provides an electronic device, which comprises a memory, a processor and a computer program, and the computer program, when executed by the processor, implements the virtual power plant multi-element flexible resource day-to-day rolling optimization scheduling method according to the first aspect.

[0050] In a fourth aspect, the present application provides a computer readable storage medium, which stores a computer program, and the computer program, when executed by a processor, implements the virtual power plant multi-element flexible resource day-to-day rolling optimization scheduling method according to the first aspect.

[0051] Compared with the prior art, the present application has the following beneficial effects:

[0052] 1. The traditional scheduling method focuses on a single flexible resource type, and it is difficult to realize the collaborative optimization between resources and the response cost is high. The present application innovatively integrates multiple flexible resources such as electric vehicle clusters, air conditioning systems and storage batteries into a unified scheduling framework, realizes the complementary use of different resources in the time, space and energy dimensions by constructing a type-specific control model and setting collaborative constraint conditions. 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.

[0053] 2. The present application adopts a day-to-day rolling optimization scheduling strategy, which adjusts the scheduling plan of the next time based on the running results of the current time, forming a closed-loop optimization mechanism. This innovation enables the virtual power plant to continuously track system state changes and quickly respond to external disturbances, significantly improving the timeliness and adaptability of the scheduling strategy, ensuring that the system always operates in an optimal state.

[0054] Further or more detailed beneficial effects will be described in the specific embodiments in conjunction with specific examples. BRIEF DESCRIPTION OF DRAWINGS

[0055] In order to more clearly illustrate the technical solutions in the embodiments of the present application or the prior art, the following will briefly introduce the drawings needed to be used in the embodiments or prior art description. Obviously, the drawings in the following description are only some embodiments of the present application, and for those skilled in the art, other drawings can also be obtained without creative labor on the basis of these drawings.

[0056] Figure 1 is a flowchart of the virtual power plant multi-element flexible resource day-to-day rolling optimization scheduling method according to the embodiments of the present application.

[0057] Figure 2 is a structural diagram of the electronic device according to the third embodiment of the present application.

[0058] Figure 3is a schematic diagram of the number of rigid charging electric vehicles newly added every 15 minutes in real time in the regulation period described in embodiment five of the present application.

[0059] Figure 4 is a schematic diagram of the day-ahead predicted outdoor temperature and the daytime real-time temperature level described in embodiment five of the present application.

[0060] Figure 5 is a schematic diagram of the daytime regulation information of the electric vehicle cluster described in embodiment five of the present application.

[0061] Figure 6 is a schematic diagram of the daytime power deviation of the electric vehicle cluster described in embodiment five of the present application.

[0062] Figure 7 is a daytime power curve of the temperature control load described in embodiment five of the present application.

[0063] Figure 8 is a schematic diagram of the daytime power deviation of the air conditioning load described in embodiment five of the present application.

[0064] Figure 9 is a schematic diagram of the daytime real-time regulation cost of the virtual power plant described in embodiment five of the present application.

[0065] Figure 10 is a schematic diagram of the daytime real-time regulation cost of the virtual power plant described in embodiment five of the present application.

[0066] Reference Signs:

[0067] 200, electronic device;

[0068] 201, processor; 202, communication bus; 203, user interface; 204, network interface; 205, memory. DETAILED DESCRIPTION

[0069] The technical solutions in the embodiments of the present application will be described clearly and completely below with reference to the drawings in the embodiments of the present application.

[0070] In the following description, a plurality of embodiments of the present application are provided, and different embodiments can be replaced or combined, so that the present application can 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, the present application should also be considered to include embodiments containing one or more of all other possible combinations of A, B, C, and D, even if the embodiment is not explicitly described in the following content.

[0071] The following description provides examples, and does not limit the scope, applicability, or examples set forth in the claims. Alterations can be made to the elements described, and functions and arrangements can be changed without departing from the scope of the inventive content. Various examples can omit, substitute, or add various procedures or components as appropriate. For instance, the methods described can be performed in an order different than described, and various steps can be added, omitted, or combined. Also, features described with respect to some examples can be combined in other examples.

[0072] In order to better understand the embodiments of the present application, before the specific embodiments of the present application are explained in detail, the application scenarios thereof are described.

[0073] The virtual power plant multi-element flexible resource day-to-day rolling optimization scheduling method described in the embodiments of the present application is applied to distributed energy system integrated management, smart grid load regulation, renewable energy consumption optimization, and power market auxiliary service participation, etc. In these scenarios, the application of the virtual power plant multi-element flexible resource day-to-day rolling optimization scheduling method aims to realize efficient collaborative scheduling of multi-element flexible resources, reduce virtual power plant operation cost and reduce deviation risk, and ensure the safety and reliability of virtual power plant operation.

[0074] Embodiment one:

[0075] As shown in Figure 1 The present embodiment provides a virtual power plant multi-element flexible resource day-to-day rolling optimization scheduling method considering random deviation, the flexible resources including an electric vehicle cluster, an air conditioning system, and a battery, comprising the steps of:

[0076] S1, acquiring resource configuration parameter information, a preset scheduling plan for the flexible resources, and corresponding response day operation boundary information thereof;

[0077] S2, respectively establishing control models and setting constraint conditions for various types of the flexible resources, the control models including establishing an electric vehicle cluster charging control model, an air conditioning system control model, and a battery charging and discharging control model;

[0078] S3, acquiring quantity-price curves and adjustable intervals of the flexible resources, and based thereon, constructing a target function taking the minimum power tracking cost and the minimum power tracking deviation as targets in response to the scheduling plan;

[0079] S4, based on the target function, respectively solving each of the control models by using a mixed integer linear programming algorithm, obtaining corresponding power deviation correction values, and based thereon, scheduling each of the flexible resources;

[0080] S5, based on the operation results after scheduling at the current time, re-executing the steps of S1 to S4 at the next time to realize rolling optimization scheduling of the virtual power plant multi-element flexible resources.

[0081] Specifically, this embodiment provides a preferred implementation, wherein the resource configuration parameter information includes the estimated adjustable capacity of the electric vehicle cluster, air conditioning system configuration parameters, and battery configuration parameters; the scheduling plan includes electric vehicle charging and discharging strategies, building indoor temperature setting strategies, and battery charging and discharging strategies; and the response day operation boundary information includes the electric vehicle grid connection time. and off-network time State of charge of electric vehicle batteries when connected to the grid Battery state of charge requirements when off-grid and electric battery capacity Timetable for personnel related to building heating and cooling load and equipment timetable Indoor temperature data Outdoor temperature data Outdoor humidity data Outdoor radiation intensity .

[0082] Specifically, this embodiment provides a preferred implementation, wherein 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;

[0083] The expression for the rigid electric vehicle charging power calculation model is as follows:

[0084] ,

[0085] In the formula, Indicates the first Rigid charging vehicles Power at any moment and They represent electric vehicles. The battery state of charge at the time of grid connection and the target state of charge at the time of grid disconnection. Indicates electric vehicles The battery capacity is expressed in kWh. and These represent the grid connection time and grid disconnection time of electric vehicles, respectively.

[0086] The expression for the daytime charging power optimization calculation model for the flexible electric vehicle is as follows:

[0087] In the formula, Indicates the first Flexible charging vehicles in The charging power at any given time and its value satisfying the charging power adjustment range. Constraints the target tracking power of the electric vehicle cluster in the day-ahead dispatching plan, and respectively represent the total number of elastic electric vehicles and rigid electric vehicles, is the charging power tracking deviation during the day.

[0088] Specifically, the embodiment provides a preferred implementation, and the constraint condition of the electric vehicle cluster charging control model is

[0089]

[0090]

[0091]

[0092]

[0093] ,

[0094] In the formula, represents the charging state of the i-th electric vehicle at the t-th moment, represents that the electric vehicle is connected to the network but not charging, represents that the electric vehicle is charging, is the battery SOC state of the i-th electric vehicle at the t-th moment, is the battery SOC state of the i-th electric vehicle at the t-th moment, is the charging efficiency of the electric vehicle, is the time step of the model, and the embodiment is preferably 15 minutes, represents the total network connection time of the i-th elastic electric vehicle, that is, , the symbol represents rounding down, represents the actual SOC state of the elastic electric vehicle at the network-off moment, and the SOC state of the elastic electric vehicle at the network-off moment is greater than , is the SOC state interval of the electric vehicle battery, is the charging power adjustment interval. More specifically, the step S4 comprises obtaining a corresponding power deviation correction value and scheduling each flexible resource based on the power deviation correction value, and the step S4 comprises calculating the temporarily called elastic charging vehicles during the day based on the elastic electric vehicle charging power optimization calculation model.

[0095] More specifically, the step S4 comprises obtaining a corresponding power deviation correction value and scheduling each flexible resource based on the power deviation correction value, and the step S4 comprises calculating the temporarily called elastic charging vehicles during the day based on the elastic electric vehicle charging power optimization calculation model.

[0096] ​​​​​​Specifically, the embodiment provides a preferred implementation, and the air conditioning system control model comprises an auto-regressive model ARX;

[0097] The expression of the auto-regressive model ARX is

[0098]

[0099] In the formula, represents a predicted cooling load, and the unit is kW, historical time data required by the ARX model for input comprises dry bulb temperature (T), relative humidity (RH), solar radiation (I), personnel occupancy schedule (O), equipment schedule (E) and indoor temperature schedule (Tin), and the unit of the indoor temperature schedule is ℃, TD RH SR OS ES RTS represents input variables of the ARX model , and the unit is ℃, represents an influence coefficient of the predicted cooling load on the predicted cooling load, is historical time data relied on for calculating a current cooling load; it is to be noted that Specific values of and are determined according to historical load data identification.

[0100] The constraint condition of the auto-regressive model is

[0101]

[0102]

[0103] In the formula, and are respectively an upper limit and a lower limit of indoor temperature regulation acceptable to an air conditioning user in a day-ahead dispatching plan, is an upper limit of indoor temperature regulation at a daytime regulation correction time, and is determined by further excavating a regulation margin acceptable to the user by means of economic incentive.

[0104] Specifically, the embodiment provides a preferred implementation, and the air conditioning system control model further comprises an air conditioning system energy efficiency model, and the expression of the air conditioning system energy efficiency model is

[0105]

[0106]

[0107]

[0108] In the formula, is an air conditioning system capacity, and the unit is kW,​​​​​​​​​​​ The rated coefficient of performance of the air conditioning system, The part load ratio of the air conditioning system, , , , The coefficient of performance of the air conditioning system, respectively, in actual use, the value of the coefficient of performance of the air conditioning system is determined according to the nameplate information of the air conditioning unit.

[0109] More specifically, the embodiment takes the real-time updated outdoor weather conditions and air conditioning usage behavior during the day as the boundary conditions, and pre-rehearses the day-ahead room temperature setting scheme to determine the daytime power tracking deviation of the air conditioning system. Based on the response power , and combined with the target response power of the air conditioning resource, to Max ensures that the power deviation is positive, because when the deviation is negative, it means that the air conditioning system needs to be adjusted downward to increase the power of the air conditioning system, that is, to make the air conditioning system use more electricity, which is not consistent with the summer demand response target. Therefore, the embodiment only considers the scenario that the air conditioning system needs to be adjusted upward to use less electricity, and the expression of the daytime power tracking deviation of the air conditioning system is:

[0110] .

[0111] More specifically, the step S4 of obtaining the corresponding power deviation correction value and scheduling each flexible resource based thereon includes the building indoor temperature reset value obtained by reversing the air conditioning system control model, and the battery charging and discharging strategy.

[0112] Specifically, the embodiment provides a preferred embodiment, and the expression of the target function is

[0113] ,

[0114] In the formula, is the virtual power plant daytime power deviation correction cost at the kth moment, , , , The air conditioning electrical load, the electric vehicle cluster, and the battery daytime regulation unit cost, respectively, and the unit is CNY / kWh, represents the optimal correction amount of the electric vehicle cluster power, represents the optimal correction amount of the air conditioning system power, represents the optimal correction amount of the battery power.

[0115] More specifically, the deviation correction amount of each type of resource needs to be equal to the power deviation quantification result of the day-ahead scheduling plan, and the expression is:

[0116] .

[0117] More specifically, the quantity-price curves of the flexible resources include quantity-price-cost models for air conditioning loads, electric vehicles, and batteries.

[0118] Considering the influence of human thermal comfort, the adjustment capacity of air conditioning resources has upper and lower limits. When the temperature comfort zone is exceeded, the required incentive cost will increase significantly. Therefore, its adjustment cost is represented by a quadratic function. The expression of the quantity-price cost model of the air conditioning load is as follows:

[0119] ,

[0120] In the formula, represents the price for calling air conditioning resources for real-time deviation elimination at time t, and and are the coefficients of the air conditioning resource quantity-price curve, which are obtained by fitting actual data. In this embodiment, they are preferably 0.00003, 0.001 and 0.6.

[0121] For electric vehicles, since the number of vehicles choosing the flexible charging strategy exhibits an "emergent" effect as the incentive level increases—that is, when the incentive reaches a certain threshold, the number of vehicles participating in the response increases exponentially—the adjustment cost of electric vehicles adopts an exponential function form. Therefore, the expression for the quantity-price cost model of the electric vehicle is:

[0122] ,

[0123] In the formula, Representing the The price is adjusted by constantly utilizing electric vehicle resources to eliminate deviations in real time. , and The coefficient for the electric vehicle resource quantity-price curve is obtained by fitting actual data in this embodiment. , and The values ​​were 0.5, 2.7, and 0.03034, respectively. It is the critical point of the electric vehicle resource quantity and price curve obtained during the fitting process. In this embodiment, its value is preferably 150 kW, which means that when the regulation capacity of electric vehicles reaches a certain threshold, the number of vehicles participating in the response increases exponentially, and the regulation cost of electric vehicles adopts the form of an exponential function.

[0124] The regulation price of a battery exhibits a stepwise relationship with the regulation quantity. As the regulation quantity increases, more power-type batteries need to be used, leading to a further increase in regulation costs. Therefore, the expression for the quantity-price cost model of the battery is:

[0125] ,

[0126] In the formula, Representing the The price that constantly utilizes battery resources to eliminate deviations in real time. , , and These are the coefficients of the battery resource quantity-price curve, obtained in this embodiment based on fitting actual data, and taking values ​​of 0.003, 0.8, 0.00867, and -0.0505 respectively. and These represent the critical point and upper limit point that lead to the battery quantity-price curve, respectively, and in this embodiment, they are taken as 150kW and 300kW, respectively.

[0127] More specifically, the adjustable range of the flexible resources includes the adjustable power range of the air conditioning load, the adjustable power range of the electric vehicle, and the adjustable power range of the battery, expressed as follows: ,

[0128] In the formula, Representing different types of resources The time deviation correction amount, where .in, The determination method varies depending on the resource: For air conditioning resources, the adjustable power range of the air conditioner is limited by the acceptable temperature range of building users; in this embodiment, the preferred upper limit of the temperature is 28°C. For electric vehicle resources, considering that subsidies can be increased to incentivize some vehicle owners to change their response intentions during daytime demand response, i.e., switch from rigid charging mode to flexible charging mode, the real-time adjustable power limit of the electric vehicle cluster is constrained by the charging load of rigid charging vehicles at the current moment. The real-time charging load of rigid electric vehicles needs to be determined based on the specific number of vehicles connected to the grid and the grid connection status. Therefore, for electric vehicles... It is dynamic, especially for batteries. The value is equal to In this embodiment, 300kW is preferred.

[0129] Example 2:

[0130] This embodiment provides a daytime rolling optimization scheduling system for multiple flexible resources in a virtual power plant that considers random deviations, used to implement the daytime rolling optimization scheduling method for multiple flexible resources in a virtual power plant as described in Embodiment 1, including: an information acquisition module, a model construction module, a deviation calculation module, and a rolling optimization module;

[0131] The information acquisition module is used to acquire resource configuration parameter information, a pre-set scheduling plan for the flexible resource, and its corresponding response day operation boundary information;

[0132] The model construction module is configured to respectively establish a control model and set a constraint condition for each type of the flexible resource, and to obtain a quantity-price curve and an adjustable interval of the flexible resource, and construct a target function aiming at minimizing a power tracking cost and minimizing a power tracking deviation in response to the scheduling plan, the control model including establishing an electric vehicle cluster charging control model, an air conditioning system control model and a battery charging and discharging control model.

[0133] The deviation calculation module is configured to respectively solve each control model based on the target function by using a mixed integer linear programming algorithm.

[0134] The rolling optimization module is configured to schedule each flexible resource based on the power deviation correction value, and to re-perform the steps of information acquisition, model construction and deviation calculation at the next time instant based on the running result after scheduling at the current time instant, so as to realize rolling optimization scheduling of the multi-element flexible resource of the virtual power plant.

[0135] Embodiment three:

[0136] As shown in Figure 2 The electronic device can include at least one processor, at least one network interface, a user interface, a memory, and at least one communication bus.

[0137] The communication bus can be used to realize the connection and communication of the above-mentioned components.

[0138] The user interface can include a key, and the optional user interface can further include a standard wired interface and a wireless interface.

[0139] The network interface can include, but is not limited to, a Bluetooth module, an NFC module, a Wi-Fi module, etc.

[0140] The processor can include one or more processing cores. The processor connects various parts in the electronic device through various interfaces and lines, executes various functions of the electronic device and processes data by running or executing instructions, programs, code sets or instruction sets stored in the memory, and calling data stored in the memory. Optionally, the processor can be implemented in at least one of the hardware forms of DSP, FPGA and PLA. The processor can integrate CPU, GPU and modem, etc. in one or several combinations. Among them, the CPU mainly processes operating systems, user interfaces and application programs, etc.; the GPU is responsible for rendering and drawing the content to be displayed on the display screen; and the modem is used for processing wireless communication. It can be understood that the above-mentioned modem can also not be integrated into the processor, but can be realized by a separate chip.

[0141] The memory may include RAM or ROM. Optionally, the memory may include a non-transitory computer-readable medium. The memory can be used to store instructions, programs, code, code sets, or instruction sets. The memory may include a program storage area and a data storage area, wherein the program storage area may store instructions for implementing an operating system, instructions for at least one function (such as touch function, sound playback function, image playback function, etc.), instructions for implementing the above-described method embodiments, etc.; the data storage area may store data involved in the above-described method embodiments, etc. Optionally, the memory may also be at least one storage device located remotely 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 rolling optimization scheduling application. The processor can be used to call the rolling optimization scheduling application stored in the memory and execute the rolling optimization scheduling steps mentioned in the foregoing embodiments.

[0142] Example 4:

[0143] This embodiment provides a computer-readable storage medium storing instructions that, when executed on a computer or processor, cause the computer or processor to perform the above-described instructions. Figure 1 One or more steps in the illustrated embodiment. If the constituent modules of the above-described electronic device are implemented as software functional units and sold or used as independent products, they can be stored in the computer-readable storage medium.

[0144] In the above embodiments, all or part of the methods can be implemented by software, hardware, firmware, or any combination thereof. When implemented by software, all or part of the methods can be implemented 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, all or part of the processes or functions described in the embodiments of the present specification are generated. 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 by 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 by wire (such as coaxial cable, optical fiber, digital subscriber line (DSL)) or wireless (such as infrared, wireless, microwave, etc.). The computer-readable storage medium can be any available medium that a computer can access or a data storage device such as a server, data center, etc. that includes one or more available media sets. The available media can be a magnetic medium (for example, a floppy disk, a hard disk, a magnetic tape), an optical medium (for example, a digital versatile disc (DVD)), or a semiconductor medium (for example, a solid state disk (SSD)) and the like.

[0145] A person of ordinary skill in the art can understand that all or part of the processes in the method of the above embodiments can be completed by instructing related hardware through a computer program, which can be stored in a computer-readable storage medium. When the program is executed, it can include the processes of the above embodiments of the method. The storage medium described above includes ROM, RAM, magnetic or optical disks, and various media that can store program codes. In the case of no conflict, the technical features in the embodiments and the implementation schemes can be combined arbitrarily.

[0146] Embodiment Five

[0147] In order to verify the effectiveness of the virtual power plant multi-element flexible resource day-to-day rolling optimization scheduling method considering random deviation described in the specification, the experiments in this embodiment are based on the actual application scenarios of the virtual power plant multi-element flexible resource day-to-day rolling optimization scheduling method.

[0148] In the experiment of the present embodiment, 50 electric vehicles have been contracted in the day-ahead, and they are connected to the grid in the regulation period according to the elastic charging mode. The number of real-time newly added rigid charging electric vehicles in the regulation period is as follows: Figure 3It is particularly pointed out that the electric vehicle information of the embodiment is generated by Monte Carlo random sampling, including the battery capacity of the electric vehicle, the grid connection SOC state, the grid connection time, the expected off-grid time, and the off-grid expected SOC state.

[0149] The embodiment experiment only considers the scenario where the day-ahead prediction level is lower than the daytime real-time temperature. The day-ahead outdoor temperature prediction and the daytime real-time temperature level are as shown in the following table. Figure 4 It is calculated that the day-ahead outdoor temperature prediction deviation range is 3% to 15%. Figure 5 The target tracking power curve of the electric vehicle cluster, the day-ahead contract electric vehicle flexible regulation load curve, and the load curve of the temporarily added rigid charging vehicle. Figure 6 The power deviation of the electric vehicle cluster executing 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 reduced to adapt to the charging load of the uncertain newly added rigid charging vehicle in the regulation period. At this time, the overall power tracking deviation of the electric vehicle cluster is 0. In the later period of the regulation period, 13:00 to 14:00, due to the connection of the uncertain rigid charging vehicle, 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 be increased. Therefore, without daytime power correction, the power deviation of the EV cluster executing the day-ahead scheduling plan is as shown in the following table. Figure 6 As shown in the following table, 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 highest deviation can reach 245%. Figure 7 The target tracking power curve of the air conditioning load and the actual power curve considering the random deviation. Due to the underestimation of the daytime outdoor temperature level in the day-ahead scheduling optimization stage, when the indoor temperature setting strategy prepared in the day-ahead is still executed, the cooling load will be increased, which in turn causes the daytime air conditioning load power to be higher than the target tracking power. Figure 8 The power deviation of the air conditioning load executing the day-ahead scheduling plan. The power tracking deviation caused by the outdoor temperature prediction deviation can be as high as 93kW, accounting for 21% of the air conditioning load power. Figure 9 The deviation correction strategy of the virtual power plant through daytime real-time regulation is as shown in the following table. Figure 10To correspond to the virtual power plant day real-time regulation cost. In the first regulation period 12:00~12:15, the overall power deviation of the virtual power plant executing the day-ahead scheduling plan is 76kW, and power needs to be adjusted downward. Through real-time deviation optimization allocation, the electric vehicle cluster needs to bear an additional 40kW of power adjustment, and the air conditioner load needs to bear an additional 36kW of power adjustment. Since the regulation cost of the battery is high under a small adjustment amount, the deviation correction resource called in this period does not consider the battery. It can also be seen that when the power deviation amount is small, the virtual power plant preferentially regulates electric vehicles and air conditioning load resources to eliminate deviation. When the power deviation amount is large, the virtual power plant preferentially regulates battery and charging vehicle resources to eliminate deviation. In addition, as the regulation period progresses, the virtual power plant daily regulation deviation correction amount increases linearly. This shows that the longer the regulation time, the greater the deviation that exists in the execution of the day-ahead scheduling plan by the virtual power plant due to the superposition of various uncertainty factors, and the greater the daily deviation correction amount needed.

[0150] Based on the above, the present embodiment verifies the effectiveness of the virtual power plant multi-element flexible resource daily rolling optimization scheduling method considering random deviation.

[0151] It should be noted that for the foregoing method embodiments, in order to simply describe, they are all expressed as a series of action combinations, but those skilled in the art should know that the present application is not limited by the action sequence described, because according to the present application, certain steps can be performed in other sequences or simultaneously. Secondly, those skilled in the art should know that the embodiments described in the specification all belong to preferred embodiments, and the actions and modules involved are not necessarily necessary for the present application.

[0152] In the above embodiments, the description of each embodiment has its own emphasis, and the parts not described in detail in a certain embodiment can be referred to the related description of other embodiments.

[0153] The above is only an exemplary embodiment of the present application, and cannot limit the scope of the present application. Any equivalent changes and modifications made in accordance with the teachings of the present application are still within the scope of the present application. Those skilled in the art will easily think of embodiments of the present application after considering the specification and practicing the disclosure herein. The present application is intended to cover any variations, uses or adaptations of the present application that follow the general principles of the present application and include common knowledge or conventional technical means in the art not disclosed by the present application. The specification and examples are only considered as exemplary, and the scope and spirit of the present application are defined by the claims.

Claims

1. A method for multi-element flexible resource day-to-day rolling optimization scheduling of a virtual power plant considering random deviations, characterized in that, The method comprises the steps of: obtaining 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 a control model for each type of flexible resource and setting a constraint condition, wherein 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; obtaining a quantity-price curve and an adjustable interval of the flexible resource, and constructing a target function based on the quantity-price curve and the adjustable interval, wherein the target function aims to minimize a power tracking cost and minimize a power tracking deviation in response to the scheduling plan; an expression of the target function is ， In the formula, is the first is the virtual power plant day power deviation correction cost at the moment, , , respectively represents the air conditioning electrical load, the electric vehicle cluster, the battery day regulation unit cost, and the unit is CNY / kWh, represents the optimal correction amount of the electric vehicle cluster power, represents the optimal correction amount of the air conditioning system power, represents the optimal correction amount of the battery power; a deviation correction amount of each type of resource needs to be equal to a power deviation quantization result of the day-ahead scheduling plan, and an expression thereof is: ， In the formula, for the daytime charging power tracking deviation of the electric vehicle cluster, for the daytime power tracking deviation of the air conditioning system; based on the target function, a mixed integer linear programming algorithm is used to solve each control model to obtain a corresponding power deviation correction value, and each flexible resource is scheduled based on the power deviation correction value; based on a running result after scheduling at a current time, the above steps are re-executed at a next time to realize rolling optimization scheduling of the virtual power plant multi-element flexible resource.

2. The method according to claim 1, wherein: the resource configuration parameter information includes an estimated adjustable capacity of the electric vehicle cluster, air conditioning system configuration parameters, and battery configuration parameters; the scheduling plan includes an electric vehicle charging and discharging strategy, a building indoor temperature setting strategy, and a battery charging and discharging strategy. The response day operation boundary information includes electric vehicle grid connection time and off-grid time , state of charge of electric vehicle battery at grid connection time and required state of charge of battery at off-grid time , and electric battery capacity , building cold and heat load related personnel schedule and equipment schedule , indoor temperature data , outdoor temperature data , outdoor humidity data , outdoor irradiance .

3. The method according to claim 2, wherein: the electric vehicle cluster charging control model includes a rigid electric vehicle charging power calculation model and an elastic electric vehicle day charging power optimization calculation model; an expression of the rigid electric vehicle charging power calculation model is ， wherein, P (t) represents the power of the rigid charging vehicle at time t, P (t) represents the power of the rigid charging vehicle at time t, Soc (t) represents the battery state of charge of the electric vehicle at time t, Soc (t) represents the battery state of charge of the electric vehicle at time t, Soc (t) represents the battery state of charge of the electric vehicle at time t, Soc (t) represents the battery state of charge of the electric vehicle at time t, C represents the battery capacity of the electric vehicle and its unit is kWh, C represents the battery capacity of the electric vehicle and its unit is kWh, T represents the on-grid time of the electric vehicle, T represents the on-grid time of the electric vehicle, The expression of the elastic electric vehicle daytime charging power optimization calculation model is , wherein, represents the charging power of the first elastic charging vehicle at the time t and the value satisfies the constraint of the charging power adjustment interval , represents the charging power of the second elastic charging vehicle at the time t and the value satisfies the constraint of the charging power adjustment interval , is the target tracking power of the electric vehicle cluster in the day-ahead scheduling plan, and respectively represent the total number of elastic electric vehicles and rigid electric vehicles, is the daytime charging power tracking deviation.

4. The method according to claim 3, wherein: a constraint condition of the electric vehicle cluster charging control model is ， wherein, represents the charging state of the th electric vehicle at time t, represents the charging state of the th electric vehicle at time t, represents that the th electric vehicle is being charged, represents the battery SOC state of the th electric vehicle at time t, represents the battery SOC state of the th electric vehicle at time t, represents the charging efficiency of the electric vehicle, represents the time step of the model, represents the total on-grid time of the th elastic electric vehicle, , represents the floor function, represents the actual SOC state of the elastic electric vehicle at the off-grid time, represents the actual SOC state of the elastic electric vehicle at the off-grid time, , represents the SOC state interval of the electric vehicle battery, represents the charging power adjustment interval.

5. The method according to claim 4, wherein: the air conditioning system control model includes an active autoregressive model ARX; an expression of the active autoregressive model ARX is ， In the formula, represents the predicted cooling load and its unit is kW, and represents the input variable on the predicted cooling load influence coefficient, is the historical time data relied on for calculating the current cooling load; a constraint condition of the active autoregressive model is ， wherein, and are the upper and lower limits of the indoor temperature adjustment acceptable to the air conditioning user in the day-ahead dispatch plan, is the upper limit of the indoor temperature adjustment at the time of the day regulation correction.

6. The method according to claim 5, wherein: the air conditioning system control model further includes an air conditioning system energy efficiency model, and an expression thereof is ， wherein, COP is the capacity of the air conditioning system and its unit is kW, COPR is the rated coefficient of performance of the air conditioning system, PLR is the part load ratio of the air conditioning system, , , , COP is the coefficient of performance of the air conditioning system, respectively.

7. A virtual power plant multi-element flexible resource day rolling optimization scheduling system considering random deviation, characterized in that, A virtual power plant multi-element flexible resource day rolling optimization scheduling method according to any one of claims 1 to 6 comprises an information acquisition module, a model construction module, a deviation calculation module, and a rolling optimization module. The information acquisition module is configured to obtain resource configuration parameter information, a preset scheduling plan for flexible resources, and response day operation boundary information corresponding to the scheduling plan. The model construction module is configured to establish a control model and set a constraint condition for each type of the flexible resource, and to obtain a quantity-price curve and an adjustable interval of the flexible resource, and to construct a target function aiming at minimizing a power tracking cost and minimizing a power tracking deviation in response to the dispatching plan, the control model including establishment of an electric vehicle cluster charging control model, an air conditioning system control model, and a battery charging and discharging control model; An expression of the target function is ， In the formula, is the first is the virtual power plant day power deviation correction cost at the moment, , , respectively represent the air conditioning electrical load, the electric vehicle cluster, and the battery day regulation unit cost, and the unit is CNY / kWh, represents the optimal correction amount of the electric vehicle cluster power, represents the optimal correction amount of the air conditioning system power, represents the optimal correction amount of the battery power; A deviation correction amount of each type of resource needs to be equal to a power deviation quantization result of the day-ahead dispatching plan, and an expression thereof is: ， In the formula, for the daytime charging power tracking deviation of the electric vehicle cluster, for the daytime power tracking deviation of the air conditioning system; The deviation calculation module is configured to solve each control model by using a mixed integer linear programming algorithm based on the target function; The rolling optimization module is configured to dispatch each flexible resource based on the power deviation correction value, and to re-perform the steps of information acquisition, model construction, and deviation calculation in the next time period based on an operation result after the dispatching in the current time period, so as to realize rolling optimization dispatching of the multi-element flexible resource of the virtual power plant.

8. A computer device comprising a memory, a processor and a computer program, characterized in that The computer program, when executed by a processor, implements the method for rolling optimization dispatching of a multi-element flexible resource of a virtual power plant in a day period according to any one of claims 1 to 6.

9. A computer-readable storage medium having stored thereon a computer program, characterized in that, The computer program, when executed by a processor, implements the method for rolling optimization dispatching of a multi-element flexible resource of a virtual power plant in a day period according to any one of claims 1 to 6.

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