Adjustable resource unified data external characteristic modeling, regulation and control method and system for various transaction scenes

Abstract

The invention discloses an adjustable resource unified data external characteristic modeling, regulation and control method and system oriented to various transaction scenes. The method comprises the following steps: collecting real-time data of an internal adjustable resource of a virtual power plant; the climbing time, the duration, the response capacity and the responsible frequency are quantized and defined as a group of external characteristic parameters, an adjustable resource data external characteristic model is constructed, adjustable resources are classified according to the external characteristic parameters of the model, and an adjustable resource characteristic library is established; in combination with the response characteristic and the load state of each adjustable resource, generating an optimal regulation and control strategy meeting the regulation and control requirements, and allocating a corresponding regulation and control instruction to each adjustable resource; each adjustable resource adjusts power output according to the regulation and control instruction, and meanwhile, the calling cost of each adjustable resource is calculated; in combination with the real-time state and the adjustment cost minimization target, the regulation sequence and proportion are optimized; according to the method, the economy and flexibility of the virtual power plant in multi-scene regulation can be effectively improved, and meanwhile, the accuracy of power grid frequency modulation response and the system stability are enhanced.

Description

TECHNICAL FIELD

[0001] The present application relates to modeling and regulation methods and systems, in particular to a unified data external characteristic modeling and regulation method and system for various trading scenarios. BACKGROUND

[0002] With the access of various resources such as distributed energy, controllable load and electric vehicles to the power grid, virtual power plant (VPP) as a platform for aggregating and scheduling various adjustable resources has gradually highlighted its importance in promoting clean energy consumption, improving grid regulation capacity and meeting power system frequency regulation requirements. Virtual power plant aggregates a large number of distributed resources to provide services such as frequency regulation and energy balance for the power grid, and has become a core component of modern energy systems.

[0003] However, virtual power plants face many challenges in actual operation. With the gradual transformation of the electricity market to the spot market, the market has higher requirements for the response speed, regulation capacity and frequency regulation accuracy of virtual power plants; virtual power plants contain various types of resources, and the response time, duration and regulation capacity of each resource are different, which brings difficulties to fine scheduling and control.

[0004] The existing technology mainly relies on scenario-based empirical data for regulation, lacks a unified modeling method based on the data external characteristics and dynamic response capabilities of different types of adjustable resources, and is difficult to adapt to various regulation scenarios and implement flexible regulation strategies. SUMMARY

[0005] The purpose of the present application is to provide a unified data external characteristic modeling and regulation method for various trading scenarios to classify and model adjustable resource characteristics, solve the problem of resource diversity and complex external characteristic modeling in current virtual power plant regulation, improve the accuracy of regulation response and control, realize cross-scenario optimization and control, and on the other hand, provide a unified data external characteristic modeling and regulation system for various trading scenarios.

[0006] Technical solution: The adjustable resource unified data external characteristic modeling and regulation method provided by the present application comprises the following steps:

[0007] Collecting real-time data of ramp-up time, duration, response capacity and response frequency of adjustable resources inside the virtual power plant, the adjustable resources including photovoltaic, energy storage system, electric vehicle charging pile and user flexible load;

[0008] Quantitatively defining the ramp-up time, duration, response capacity and response frequency as a group of external characteristic parameters to construct an adjustable resource data external characteristic model, classifying the adjustable resources according to the model external characteristic parameters, and establishing an adjustable resource feature library;

[0009] predicting short-term power output of each adjustable resource in combination with historical data and environmental parameters;

[0010] calculating adjustment margin of each adjustable resource according to load shedding ratio based on the prediction result of the short-term power output;

[0011] generating optimal control strategy meeting frequency regulation demand in combination with external characteristic parameters of each adjustable resource in the adjustable resource characteristic library, the adjustment margin and load state, and distributing corresponding control instructions to each adjustable resource;

[0012] adjusting power output of each adjustable resource according to the control instructions, while calculating calling cost of each adjustable resource;

[0013] optimizing control sequence and ratio in combination with real-time state and calling cost minimization target.

[0014] Preferably, the virtual power plant comprises an adjustable resource response control center, a photovoltaic system and a Boost converter, an energy storage system and an energy storage converter, an electric vehicle charging pile and a converter, a user flexible load and a load bus, a data monitoring and feedback module.

[0015] The adjustable resource response control center is used to receive control instructions issued by the upper-level power grid, generate optimal control strategy and distribute power control instructions in combination with response characteristics and load state of each adjustable resource; the photovoltaic system realizes dynamic power control through the Boost converter; the energy storage system optimizes charging and discharging power in combination with state of charge through the energy storage converter; the electric vehicle charging pile adjusts charging power through the converter; the user flexible load adjusts reduction ratio through the load bus; the data monitoring and feedback module is used to collect real-time operation data of each adjustable resource in real time and upload to the adjustable resource response control center to optimize the control strategy.

[0016] Preferably, the adjustable resource characteristic library comprises ramping time, duration, response capacity and adjustable response frequency characteristics; the ramping time refers to the shortest time for a device to fully reach the output state required by an instruction after receiving the instruction from the system; the duration refers to the time for a response resource to fully reach the output state required by an instruction after responding to the instruction; the response capacity refers to the capacity of an adjustable resource that can be responded to after the system issues a response instruction; and the adjustable response frequency refers to the maximum number of times an adjustable resource is allowed to be called within a set entire scheduling period T=24h.

[0017] Preferably, power output of each adjustable resource is controlled by dynamically adjusting load shedding ratio, and the photovoltaic load shedding operation formula is represented as:

[0018] P pv _shed=k pv *P pv ;

[0019] where Pshed represents the power of the photovoltaic working in shed state, kshed represents the shed coefficient, Pshed(t) represents the power prediction curve of the photovoltaic on the previous day. pv pv where kshed represents the shed coefficient, Pshed(t) represents the power prediction curve of the photovoltaic on the previous day. pv

[0020] Preferably, the formula of the calling cost of each adjustable resource is represented as:

[0021]

[0022] where x represents the cost required for calling the resource in the ith 15 min, C represents the calling cost of the photovoltaic, C represents the calling cost of the energy storage system, C represents the calling cost of the electric vehicle charging pile, C represents the calling cost of the user flexible load, and i∈(1, 2, 3…96).

[0023] Preferably, the target function of the power output of each adjustable resource according to the control instruction is:

[0024]

[0025] where F represents the calling cost, T represents a scheduling period, ΔPshed represents the adjustment amount of the photovoltaic, ΔP represents the adjustment amount of the energy storage system, ΔP represents the adjustment amount of the electric vehicle charging pile, and ΔP represents the adjustment amount of the user flexible load. pv es ev user

[0026] The constraint condition is: ΔPshed(t) + ΔP(t) + ΔP(t) + ΔP(t) + ΔP(t) = D(t). pv es ev user

[0027] -down_margin(t)≤ΔP(t)≤up_margin(t);

[0028] ΔP(t+1)-ΔP(t)≤ramp_rate_p(t);

[0029] where D(t) represents the calling demand, down_margin(t) and up_margin(t) respectively represent the down margin and the up margin of the adjustable resource at the t period, and ramp_rate_p(t) represents the maximum ramping ability of the adjustable resource at the t period.

[0030] ​​​​​​​​​​​​​​The adjustable resource unified data external characteristic modeling and regulation system comprises a scheduling layer, a control layer and a resource layer;

[0031] The scheduling layer is used for analyzing load demand, market electricity price and adjustable resource state, formulating an optimal regulation strategy, optimizing market participation strategy of the virtual power plant according to price fluctuation and demand signal, dynamically adjusting the regulation strategy, decomposing the regulation strategy into specific regulation instructions and issuing the regulation instructions to the control layer.

[0032] The control layer is used for receiving the regulation instructions issued by the scheduling layer, distributing tasks to each adjustable resource after analysis, allocating regulation tasks according to the characteristics and state of each adjustable resource, collecting real-time operation data of each adjustable resource, integrating historical data and environmental parameters, establishing an adjustable resource characteristic curve, predicting future load demand and adjustable resource response capacity based on environmental parameters and historical operation records.

[0033] The resource layer comprises a photovoltaic system, an energy storage system, an electric vehicle charging pile and user flexible load adjustable resources, and dynamically adjusts power output according to the specific regulation instructions of the control layer and uploads power change, equipment load and abnormal information key data to the control layer.

[0034] Preferably, the control layer further comprises real-time monitoring of the operation state of the adjustable resources, detection of power deviation, delayed response abnormal conditions, triggering of emergency plans and dynamic adjustment of the regulation strategy or activation of standby resources.

[0035] Advantages: Compared with the prior art, the present application has the following remarkable advantages: 1. Through dynamic regulation of photovoltaic, energy storage system, electric vehicle charging pile and user flexible load adjustable resources, efficient participation in frequency modulation service of adjustable resources is realized, which can significantly smooth the load fluctuation of the power grid, improve the response speed and economy; 2. Based on real-time load demand and adjustable resource characteristics, the load reduction coefficient and call cost are flexibly adjusted, which can effectively reduce the peak-valley difference, reduce the regulation cost, realize accurate flexible load management and optimize the load reduction effect; 3. Through time-sharing and resource-sharing regulation, the system stability and energy saving effect can be effectively improved, which provides strong support for load management of smart grid. BRIEF DESCRIPTION OF DRAWINGS

[0036] Figure 1 It is a method architecture schematic diagram of the present application;

[0037] Figure 2 It is an adjustable resource participation regulation service process schematic diagram of the present application;

[0038] Figure 3 It is an adjustable resource call cost schematic diagram of the present application;

[0039] Figure 4The demand curve diagram is called for the application;

[0040] Figure 5 The curve diagram before and after the output correction of all adjustable resources of the application is shown. DETAILED DESCRIPTION

[0041] The technical solutions of the application are further described below in combination with the drawings.

[0042] The unified data external characteristic modeling and regulation method of adjustable resources for various transaction scenarios includes the following steps:

[0043] The internal adjustable resources of the virtual power plant include photovoltaic systems, energy storage systems, electric vehicle charging piles, and user flexible loads.

[0044] The virtual power plant architecture includes an adjustable resource response control center, photovoltaic systems and Boost converters, energy storage systems and energy storage converters, electric vehicle charging piles and converters, user flexible loads and load buses, and a data monitoring and feedback module.

[0045] The adjustable resource response control center receives the regulation instructions issued by the upper-level power grid, combines the response characteristics of each adjustable resource and the load state, dynamically generates the optimal regulation strategy and distributes the power regulation instructions, and issues the corresponding power regulation instructions to the photovoltaic, energy storage systems, electric vehicle charging piles, and user flexible loads. At the same time, the adjustable resource response control center optimizes the regulation sequence and proportion of the adjustable resources according to the changes in the calling cost, resource response speed, and real-time electricity price, to realize the satisfaction of regulation demand and the minimization of regulation cost. The introduction of the adjustable resource response control center enables the virtual power plant to quickly and accurately respond to regulation service demand, which can significantly improve the dynamic adjustment efficiency and economic benefits of the system.

[0046] The photovoltaic system realizes accurate power control through the Boost converter. When the system issues regulation instructions, the Boost converter is responsible for adjusting the photovoltaic output according to the instructions to ensure that the photovoltaic system can quickly respond and stably output.

[0047] The energy storage system controls the charge and discharge power of the energy storage unit through the energy storage converter, and optimizes the output adjustment in combination with the current load demand and the energy storage state.

[0048] The electric vehicle charging pile adopts the charging pile converter for power control, receives and executes the regulation instructions issued by the system, and dynamically adjusts the charging power.

[0049] The user flexible load load bus performs unified power control, and the load bus is responsible for coordinating the operating states of various flexible loads to respond to the load regulation demand of the power grid.

[0050] The data monitoring and feedback module collects real-time operation data of each adjustable resource, uploads to the control center to optimize the control strategy, and realizes intelligent control and economic benefit improvement of the virtual power plant in various trading scenarios.

[0051] S1, the adjustable resource response control center quantitatively defines the ramping time, duration, response capacity and response frequency as a set of external characteristic parameters by collecting real-time data of the adjustable resources in the virtual power plant, to construct an adjustable resource data external characteristic model, classifies the adjustable resources according to the model external characteristic parameters, establishes an adjustable resource characteristic library, and the adjustable resource characteristics of the adjustable resource characteristic library are as follows:

[0052] The ramping time τ of each adjustable resource res , which refers to the shortest time for the response resource to fully reach the output state required by the instruction after the device receives the instruction from the system;

[0053] The duration T of each adjustable resource last , which refers to the time for the response resource to fully reach the output state required by the instruction after responding to the system instruction;

[0054] The response capacity P of each adjustable resource res , which refers to the capacity that the adjustable resource can respond after the system issues a response instruction;

[0055] The response frequency f of each adjustable resource res , which refers to the maximum number of times the adjustable resource is allowed to be called within a set entire scheduling period T = 24h.

[0056] S2, the adjustable resource response control center cleans and processes the historical data of the adjustable resources, removes abnormal data, fills in missing values, ensures data integrity, combines external factors such as weather and electricity price, predicts the photovoltaic power P pv , energy storage power P es , electric vehicle charging pile power P ev , for user flexible load, based on historical electricity consumption habits, electricity price and frequency modulation demand, predicts the response behavior of the user, and generates a predicted flexible load curve P user .

[0057] S3, calculate the load change rate of each adjustable resource, generate a ramping capacity curve, and analyze the load adjustment speed and response characteristics of each adjustable resource.

[0058] The calculation and analysis of ramping capability is to divide 24 hours a day into 96 time periods, each time period being 15 minutes, for analyzing the load change rate of each adjustable resource. The ramping capability of photovoltaic, energy storage system, electric vehicle charging pile and user flexible load in each 15 minutes is evaluated by calculating the power change amount of adjacent time periods. In addition, the time sequence curve of ramping capability shows the dynamic response trend of each adjustable resource in a day, which provides an important basis for optimizing the control strategy and ensuring system stability.

[0059] Taking photovoltaic as an example, the calculation formula of photovoltaic ramping capability is:

[0060] ramp_rate_pv(t)=(P pv (t+1)-P pv (t));

[0061] Wherein, ramp_rate_pv represents the ramping capability of photovoltaic, P pv represents the power of photovoltaic.

[0062] S4, in order to realize the demand of peak clipping and valley filling and dynamic frequency regulation, the system optimizes the power output of each adjustable resource by dynamically adjusting the load shedding rate. According to the adjustability of each adjustable resource and the current load demand, the load shedding operation is maintained, and the power output of photovoltaic, energy storage system, electric vehicle charging pile and user flexible load is reduced at the load peak period according to the real-time control instruction. The load shedding operation controls the output of each resource by setting different reduction ratios.

[0063] (1) Taking photovoltaic as an example, the load shedding operation formula is represented as: P pv _shed=k pv *P pv ;

[0064] In the formula, P pv _shed represents the power of photovoltaic working in load shedding state; k pv represents the load shedding coefficient; P pv represents the power prediction curve of photovoltaic of the previous day.

[0065] (2) Taking photovoltaic as an example, the calculation formula of adjustment margin is:

[0066] up_margin_pv=P pv _shed;

[0067] down_margin_pv=P pv -P pv _shed;

[0068] In the formula, up_margin_pv and down_margin_pv represent the up-regulation margin and down-regulation margin of photovoltaic respectively; p pvp represents the predicted power of the photovoltaic on the previous day. pv _shed represents the power of the photovoltaic load shedding state.

[0069] The cost of the adjustable resource receiving regulation instructions depends on the type of the adjustable resource to achieve a balance between economy and responsiveness. The up-regulation margin is the power difference between the load shedding state and the previous day's predicted state of the adjustable resource, indicating that each adjustable resource can further adjust the output power in the upward direction; and the down-regulation margin indicates the remaining reduction space of the adjustable resource in the load shedding state.

[0070] The regulation margin of the photovoltaic depends on the load shedding coefficient of the photovoltaic, which considers factors such as real-time light intensity, environmental temperature, etc., and dynamically adjusts to achieve a higher new energy consumption rate. Generally, the load shedding coefficient is valued between 0 and 1, used to regulate the reduction proportion of photovoltaic power generation. When the light intensity is high or the temperature is suitable, the photovoltaic output is more sufficient, to avoid putting pressure on the power grid, a lower load shedding coefficient is selected to appropriately reduce the output, thereby relieving the peak load pressure. In the case of low light or high temperature affecting component efficiency, the load shedding coefficient can take a high value close to 1 to reduce the reduction and try to maintain the power generation output to improve the new energy consumption level. By comprehensively considering external factors such as light, temperature, etc., the load shedding coefficient is dynamically adjusted to achieve friendly support for the power grid and maximize the renewable energy utilization rate.

[0071] The regulation margin of the energy storage system depends on the load shedding coefficient of the energy storage system, which is dynamically adjusted according to the key characteristics of the energy storage battery to achieve efficient and sustainable operation. The selection of the load shedding coefficient considers factors such as the current state of charge (SOC) of the battery, the charge and discharge rate limit, temperature sensitivity, and aging state, etc. When the SOC is low, the load shedding coefficient tends to be low to reduce the discharge reduction, prevent deep discharge, and prolong the battery life; when the SOC is high and the load demand is low, the load shedding coefficient will increase to promote the energy storage into the power storage mode. When the charge and discharge rate approaches the battery's rated upper limit, the system will increase the load shedding coefficient to limit the power output and avoid excessive temperature rise or stress.

[0072] The load shedding coefficient of the electric vehicle charging pile is dynamically adjusted according to factors such as the urgency of the user's charging demand, the current state of charge (SOC) of the battery, historical charging data prediction, and user charging preferences, etc., to achieve intelligent electric vehicle charging pile load shedding control. The load shedding coefficient is valued between 0 and 1, controlling the reduction proportion of the charging power. By combining the user's charging preferences, such as users who prefer to charge at night and have ample time, the system will prioritize a lower load shedding coefficient during the peak load period of the power grid, reducing the charging power during the peak load period and charging during the period of lower load.

[0073] The load shedding coefficient of the user flexible load is dynamically adjusted based on the load type, user behavior preference, current grid state, and electricity price fluctuation, etc. to ensure that the basic demand of the user is met while achieving system load balancing. The load shedding coefficient is valued between 0 and 1, and is used to control the reduction proportion of the flexible load in the peak or valley period. For non-critical or delayable flexible load (such as electric water heater, air conditioner, etc.), the system will select a lower load shedding coefficient during the load peak period to reduce its operating power.

[0074] S5, the cost of receiving the regulation instruction of the adjustable resource depends on the type of the adjustable resource, to balance the economy and responsiveness. For photovoltaic power generation, the call cost mainly reflects the potential power generation income lost due to power adjustment, so the call cost of photovoltaic is higher in the period when the electricity price is higher, and relatively lower in the period when the electricity price is lower. The call cost of the energy storage system considers the charge and discharge cycle life of the battery, the power conversion efficiency and the aging cost of the battery:

[0075] (1) The call cost vector of photovoltaic is:

[0076] In the formula, represents the call cost of photovoltaic, i∈(1, 2, 3, …, 96).

[0077] (2) The call cost vector of the energy storage system is:

[0078] In the formula, represents the call cost of the energy storage system, i∈(1, 2, 3, …, 96).

[0079] (3) The call cost vector of the electric vehicle charging pile is:

[0080] In the formula, represents the call cost of the electric vehicle charging pile, i∈(1, 2, 3, …, 96).

[0081] (4) The call cost vector of the user flexible load is:

[0082] In the formula, represents the call cost of the user flexible load, i∈(1, 2, 3, …, 96).

[0083] The adjustable resources (photovoltaic, energy storage system, electric vehicle charging pile and user flexible load) participate in the frequency modulation service in a flexible manner by dynamically adjusting the power correction amount through the regulation instruction to meet the up-regulation or down-regulation demand of the power grid in different time periods. The core of the strategy is to set the up-regulation or down-regulation power target of the resource in a specific time period according to the load fluctuation and real-time frequency modulation demand of the power grid. The system increases the power output in the up-regulation time period and reduces the output in the down-regulation time period, and decomposes the total frequency modulation capacity to each adjustable resource to realize the frequency modulation effect, and the objective function is:

[0084]

[0085] In the formula, F represents the calling cost (adjustment cost), T represents a scheduling period, ΔP pv , ΔP es , ΔP ev , ΔP user respectively represent the adjustment amount of photovoltaic, energy storage system, electric vehicle charging pile and user flexible load.

[0086] The constraint condition is: ΔP pv (t)+ΔP es (t)+ΔP ev (t)+ΔP user (t)=D(t);

[0087] In the formula, D(t) is the adjustment demand.

[0088] -down_margin(t)≤ΔP(t)≤up_margin(t);

[0089] In the formula, down_margin(t) and up_margin(t) are respectively the down-regulation margin and the up-regulation margin of the adjustable resource at the t period.

[0090] ΔP(t+1)-Δp(t)≤ramp_rate_p(t);

[0091] In the formula, ramp_rate_p(t) is the maximum ramping capability of the adjustable resource at the t period.

[0092] S6, distribute the regulation demand to each adjustable resource, optimize the regulation sequence and proportion in combination with the real-time state and the minimum adjustment cost target, each adjustable resource adjusts the power output according to the instruction, forms a closed-loop control logic, and calculates the calling cost of each adjustable resource.

[0093] The adjustable resource unified data external characteristic modeling and regulation system facing multiple transaction scenarios is divided into three levels from top to bottom, including the scheduling layer, the control layer and the resource layer, each level faces different functional requirements, and supports flexible access and plug and play of multiple adjustable resources.

[0094] Through the hierarchical architecture of the virtual power plant, various adjustable resources such as photovoltaic systems, energy storage systems, electric vehicle charging piles and user flexible loads are integrated to realize unified optimization control and information interaction. The hierarchical architecture design of the system can facilitate power regulation of the virtual power plant facing various trading scenarios, while achieving the goals of adjustable resource collaborative complementary control and low-cost regulation, ensuring efficient integration and intelligent control of various adjustable resources, as follows:

[0095] The dispatch layer is responsible for analyzing information such as load demand, market electricity price and resource state, and formulating optimal control strategies to meet the requirements of the spot market and auxiliary service market. This layer ensures the economic efficiency and effectiveness of control by comprehensively considering response speed, capacity limit and dynamic characteristics.

[0096] The dispatch layer interfaces with the spot market and auxiliary service market, optimizes market participation strategies of the virtual power plant according to price fluctuations and demand signals, maximizes profits, and dynamically adjusts control strategies to adapt to market demand. The global strategy is decomposed into specific control instructions, which are issued to the control layer as operation requirements.

[0097] The control layer receives control instructions from the dispatch layer, analyzes and distributes tasks to various adjustable resources, flexibly allocates control tasks according to the characteristics and state of adjustable resources, and ensures accurate execution of instructions and efficient response of resources.

[0098] The control layer collects real-time operation data of various adjustable resources (such as power output, response speed and load change), and is responsible for the integration and prediction of historical data. Through data cleaning and processing, the adjustable resource characteristic curve is established, and based on environmental parameters (such as weather, price fluctuations) and historical operation records, future load demand and resource response capacity are predicted.

[0099] The control layer monitors the real-time operation state of the adjustable resources, detects abnormal conditions such as power deviation and delayed response, triggers emergency plans in a timely manner, dynamically adjusts control strategies or activates backup resources, and ensures the safe and stable operation of the system.

[0100] The resource layer includes various adjustable resources such as photovoltaic systems, energy storage systems, electric vehicle charging piles and user flexible loads, supports flexible access and plug-and-play, and realizes unified management through standardized interfaces.

[0101] The resource layer dynamically adjusts power output according to specific instructions from the control layer. Photovoltaic systems accurately control power through inverters, energy storage systems adjust charge and discharge power according to state of charge, electric vehicle charging piles adjust charging power, and user flexible loads optimize operation through load buses.

[0102] The resource layer monitors the running state in real time, uploads key data (such as power change, device load, abnormal information) to the control layer, and ensures the accuracy of regulation and control execution and the optimization of scheduling strategy.

[0103] Simulation example:

[0104] In this example, a virtual power plant is taken as an example, and the scene covers photovoltaic power generation system, energy storage system, electric vehicle charging pile and user flexible load and other various adjustable resources. The performance of the system in peak load shifting, dynamic regulation and cost optimization is verified by using the matlab simulation tool.

[0105] In this example, the reduction degree of photovoltaic, energy storage system, electric vehicle charging pile and flexible load is set to 60%, 80%, 90% and 90% respectively.

[0106] The simulation results show that: the adjustable resource response control center receives the adjustment demand of the upper power grid, and reduces 100kW between 12:45-13:30 and increases 50kW between 20:00-21:30, and calls the output of each adjustable resource combination to achieve the adjustment demand according to the principle of calling the lowest cost, and the total cost required for the final regulation and control service is 73.8708 yuan, verifying the effectiveness of the method proposed in the patent.

[0107] Those skilled in the art should understand that the embodiments of the present application can be provided as a method, a system, or a computer program product. Therefore, the present application can take the form of a complete hardware embodiment, a complete software embodiment, or an embodiment combining software and hardware aspects. Moreover, the present application can take the form of a computer program product implemented on one or more computer usable storage media (including but not limited to magnetic disk storage, CD-ROM, optical storage, etc.) containing computer usable program code. The solutions in the embodiments of the present application can be implemented in various computer languages, such as object-oriented programming language Java and interpreted scripting language JavaScript.

[0108] The present application is described with reference to flowcharts and / or block diagrams according to the method, device (system) and computer program product of the embodiments of the present application. It should be understood that each flow and / or block in the flowchart and / or block diagram, and the combination of the flows and / or blocks in the flowchart and / or block diagram can be realized by computer program instructions. These computer program instructions can be provided to the processor of a general-purpose computer, a special-purpose computer, an embedded processor or other programmable data processing device to produce a machine, so that the instructions executed by the processor of the computer or other programmable data processing device produce a machine that implements the functions specified in the flowchart and / or block diagram. Figure 1 The functions specified in one flow or multiple flows and / or blocks Figure 1 The device for the functions specified in one block or multiple blocks.

[0109] These computer program instructions can also be stored in a computer readable memory that can direct a computer or other programmable data processing apparatus to function in a particular manner, such that the instructions stored in the computer readable memory produce an article of manufacture including instructions which implement the Figure 1 function specified in the flow or flows and / or blocks Figure 1 of the block or blocks.

[0110] These computer program instructions can also be loaded onto a computer or other programmable data processing apparatus to cause a series of operational steps to be performed on the computer or other programmable apparatus to produce a computer implemented process such that the instructions that are executed on the computer or other programmable apparatus provide steps for implementing the Figure 1 function specified in the flow or flows and / or blocks Figure 1 of the block or blocks.

[0111] Although preferred embodiments of the application have been described herein, substitutions and alterations are possible in view of the disclosure of this application without departing from the spirit and scope of the present application. Therefore, it is the intent of the appended claims to cover all such variations and modifications as come within the scope of the present application. 1. A method for providing a user with a plurality of services, comprising the steps of:

[0112] It will be apparent to those skilled in the art that various modifications and variations can be made to the present application without departing from the spirit or scope of the application. Thus, it is intended that the present application cover modifications and variations of this application provided they come within the scope of the appended claims and their equivalents.

Claims

1. A method for modeling and regulating unified data external characteristics of adjustable resources for multiple transaction scenarios, characterized in that, The method comprises the following steps: Collecting real-time data of ramping time, duration, response capacity and response frequency of adjustable resources inside a virtual power plant, the adjustable resources including photovoltaic, energy storage system, electric vehicle charging pile and user flexible load; Quantitatively defining the ramping time, duration, response capacity and response frequency as a group of external characteristic parameters to construct an external characteristic model of adjustable resource data, classifying the adjustable resources according to the model external characteristic parameters and establishing an adjustable resource feature library; Predicting short-term power output of each adjustable resource in combination with historical data and environmental parameters; Calculating the adjustment margin of each adjustable resource according to the load shedding ratio based on the prediction result of the short-term power output; Generating an optimal regulation strategy meeting the frequency regulation demand in combination with the external characteristic parameters of each adjustable resource in the adjustable resource feature library, the adjustment margin and load state, and distributing corresponding regulation instructions to each adjustable resource; Adjusting power output of each adjustable resource according to the regulation instruction, and simultaneously calculating the calling cost of each adjustable resource; Optimizing the regulation sequence and ratio in combination with real-time state and the minimum calling cost target.

2. The adjustable resource unified data external data characteristic modeling and regulation method according to claim 1, characterized in that, The virtual power plant comprises an adjustable resource response control center, photovoltaic system and Boost converter, energy storage system and energy storage converter, electric vehicle charging pile and converter, user flexible load and load bus, data monitoring and feedback module; The adjustable resource response control center is used for receiving regulation instructions issued by the upper-level power grid, generating an optimal regulation strategy and distributing power regulation instructions in combination with the response characteristics and load state of each adjustable resource; the photovoltaic system realizes dynamic power control through the Boost converter; the energy storage system optimizes charging and discharging power in combination with the state of charge through the energy storage converter; the electric vehicle charging pile adjusts charging power through the converter; the user flexible load adjusts the reduction ratio through the load bus; and the data monitoring and feedback module is used for collecting real-time operation data of each adjustable resource in real time and uploading the real-time operation data to the adjustable resource response control center to optimize the regulation strategy.

3. The adjustable resource unified data offload characteristic modeling and regulation method according to claim 1, characterized in that, The adjustable resource feature library comprises ramping time, duration, response capacity and response frequency adjustable resource characteristics; the ramping time refers to the shortest time for a device to completely reach the output state required by an instruction after receiving the instruction from a system; the duration refers to the time for a response resource to completely reach the output state required by an instruction after responding to the instruction; the response capacity refers to the capacity of an adjustable resource that can be responded to after a system issues a response instruction; and the response frequency refers to the maximum number of times an adjustable resource is allowed to be called within a set entire scheduling period T=24h.

4. The adjustable resource unified data offload characteristic modeling and regulation method according to claim 1, characterized in that, The power output of each adjustable resource is controlled by dynamically adjusting the load shedding ratio, and the photovoltaic load shedding operation formula is represented as: P pv _shed=k pv *P pv ; In the formula, P pv _shed represents the power of the photovoltaic system operating in off-load mode, k pv P represents the load reduction factor. pv This represents the power forecast curve for photovoltaic power from the previous day.

5. The adjustable resource unified data offload characteristic modeling and regulation method according to claim 1, characterized in that, The calling cost of each adjustable resource is calculated according to the formula represented as: where x represents the cost required for the i th 15 min to call resources, represents the calling cost of photovoltaic, represents the calling cost of energy storage system, represents the calling cost of electric vehicle charging pile, represents the calling cost of user flexible load, i∈(1,2,3…96).

6. The adjustable resource unified data offload characteristic modeling and regulation method according to claim 1, characterized in that, The objective function for the power output of each adjustable resource to be adjusted according to the regulation instruction is represented as: Where F represents the call cost, T represents a scheduling period, ΔP pv , ΔP es , ΔP ev , ΔP user respectively represent the adjustment amount of photovoltaic, energy storage system, electric vehicle charging pile and user flexible load; The constraint is: ΔP pv (t) + ΔP es (t) + ΔP ev (t) + ΔP user (t) = D(t); -down_margin(t)≤ΔP(t)≤up_margin(t); ΔP(t+1)-ΔP(t)≤ramp_rate_p(t); Wherein, D(t) represents the calling demand, down_margin(t) and up_margin(t) represent the down margin and up margin of the adjustable resource at the t period respectively, and ramp_rate_p(t) represents the maximum ramping ability of the adjustable resource at the t period.

7. A unified data external characteristic modeling and control system for adjustable resources in multiple transaction scenarios, characterized in that, The scheduling layer, the control layer and the resource layer are included. The scheduling layer is configured to analyze load demand, market electricity price and adjustable resource state, generate an optimal regulation strategy, optimize market participation strategy of the virtual power plant according to price fluctuation and demand signal, dynamically adjust the regulation strategy, decompose the regulation strategy into specific regulation instructions, and deliver the regulation instructions to the control layer. The control layer is configured to receive the regulation instructions delivered by the scheduling layer, distribute tasks to each adjustable resource after analysis, allocate regulation tasks according to adjustable resource characteristics and load state, collect real-time operation data of each adjustable resource, integrate historical data and environmental parameters, establish adjustable resource characteristic curve, predict future load demand and adjustable resource response ability based on environmental parameters and historical operation records. The resource layer includes photovoltaic, energy storage system, electric vehicle charging pile and user flexible load adjustable resource, dynamically adjusts power output according to specific regulation instructions of the control layer, and uploads power change, equipment load and abnormal key data to the control layer.

8. The adjustable resource unified data offload modeling and regulation system of claim 7, wherein, The control layer further includes real-time monitoring of adjustable resource operation state, detection of power deviation, delayed response abnormality, triggering of emergency plan, and dynamic adjustment of regulation strategy or activation of standby resource.

9. A computer readable storage medium having stored thereon a computer program, characterized in that, The computer program is executed by the processor to realize the adjustable resource unified data external characteristic modeling and regulation method in any one of claims 1 to 6.

10. An electronic device comprising a memory, a processor, and a computer program stored on the memory and executable on the processor, characterized in that, The processor executes the computer program to realize the adjustable resource unified data external characteristic modeling and regulation method in any one of claims 1 to 6.

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