Virtual power plant power transaction method and device

Abstract

The invention provides a virtual power plant power transaction method and device, and the method is applied to a server, and the method comprises the steps: executing a resource aggregation operation for an aggregable resource in a target region; executing a resource evaluation operation on the aggregable resources of each resource type to determine a corresponding evaluation result; constructing an optimal scheduling model corresponding to the virtual power plant based on the evaluation result; obtaining current electricity price curve data and power grid load prediction data of the electricity market in real time; substituting the current electricity price curve data into the first sub-objective function, and substituting the power grid load prediction data into the second sub-objective function so as to update the model parameters of the optimization scheduling model; and solving the updated optimal scheduling model by adopting an optimization algorithm to determine a power transaction strategy. Thus, the optimization scheduling model is established through resource aggregation and evaluation to determine the power transaction strategy, the efficiency and the income of the power transaction of the virtual power plant are improved, and the accuracy and the timeliness of strategy formulation are ensured by a real-time data acquisition and model parameter updating mechanism.

Description

Technical Field

[0001] This application relates to the technical field of virtual power plants, and particularly to a virtual power plant power trading method and device. Background Art

[0002] In today's energy field, with the continuous advancement of the construction of the new power system, virtual power plant technology has shown significant value in power market trading and grid flexible regulation due to its ability to efficiently aggregate and coordinately regulate distributed energy, and has gradually become an important support for energy transformation. By integrating diversified resources, virtual power plants form market entities with large-scale regulation capabilities, and have prominent economic and environmental benefits in participating in power spot trading, ancillary service markets, and demand-side response.

[0003] However, traditional virtual power plant trading strategies still have significant limitations in terms of resource value assessment and dynamic optimization. Most of the trading strategies applied by traditional virtual power plants adopt static aggregation and single economic objective optimization, resulting in difficulties in balancing the economic benefits and technical feasibility of resource combinations. In addition, most strategies adopt an offline optimization mode, making it difficult to respond in a timely manner to the price dynamics of the power market and the real-time changes in the grid operation status, resulting in limited robustness and economy of trading strategies, and restricting the comprehensive competitiveness of virtual power plants in complex market environments. Summary of the Invention

[0004] This application proposes a virtual power plant power trading method and device, aiming to significantly improve the efficiency and revenue of virtual power plant power trading. Through accurate resource assessment and optimization scheduling models, the maximization of economic benefits and the minimization of operating costs are achieved. The real-time data acquisition and model parameter update mechanism ensure the accuracy and timeliness of strategy formulation.

[0005] In a first aspect, an embodiment of this application provides a virtual power plant power trading method, which is applied to a server. The method includes:

[0006] Performing a resource aggregation operation on aggregable resources within a target area, where the aggregable resources include distributed resources, energy storage resources, and controllable load resources;

[0007] Performing a resource assessment operation on the aggregable resources of each resource type to determine the corresponding assessment results. The resource assessment operation is used to quantify the economic value and technical applicability of the aggregable resources participating in power trading through preset assessment indicators corresponding to each resource type;

[0008] Construct an optimal scheduling model corresponding to a virtual power plant based on multiple evaluation results. The optimal scheduling model includes a first sub-objective function and a second sub-objective function. The first sub-objective function includes the power generation revenue corresponding to distributed resources, the energy storage revenue corresponding to energy storage resources, and the controllable load revenue corresponding to controllable load resources. The first sub-objective function is used to maximize the sum of the power generation revenue, the energy storage revenue, and the controllable load revenue. The second sub-objective function is used to minimize the penalty cost of the power balance deviation and the power quality over-limit generated by the virtual power plant during operation.

[0009] Obtain the current electricity price curve data and grid load forecast data of the power market in real time; and,

[0010] Substitute the current electricity price curve data into the first sub-objective function, and substitute the grid load forecast data into the second sub-objective function to update the model parameters of the optimal scheduling model.

[0011] Use a preset optimization algorithm to solve the updated optimal scheduling model to determine the power trading strategy.

[0012] In a second aspect, an embodiment of the present application provides a virtual power plant power trading device, which is applied to a server. The device includes:

[0013] A resource integration unit for performing a resource aggregation operation on aggregable resources in a target area. The aggregable resources include distributed resources, energy storage resources, and controllable load resources;

[0014] A resource evaluation unit for performing a resource evaluation operation on aggregable resources of each resource type to determine the corresponding evaluation results. The resource evaluation operation is used to quantify the economic value and technical applicability of aggregable resources participating in power trading through preset evaluation indicators corresponding to each resource type;

[0015] A model construction unit for constructing an optimal scheduling model corresponding to a virtual power plant based on multiple evaluation results. The optimal scheduling model includes a first sub-objective function and a second sub-objective function. The first sub-objective function includes the power generation revenue corresponding to distributed resources, the energy storage revenue corresponding to energy storage resources, and the controllable load revenue corresponding to controllable load resources. The first sub-objective function is used to maximize the sum of the power generation revenue, the energy storage revenue, and the controllable load revenue. The second sub-objective function is used to minimize the penalty cost of the power balance deviation and the power quality over-limit generated by the virtual power plant during operation.

[0016] A data acquisition unit for obtaining the current electricity price curve data and grid load forecast data of the power market in real time;

[0017] A model update unit for substituting the current electricity price curve data into the first sub-objective function and substituting the grid load forecast data into the second sub-objective function to update the model parameters of the optimal scheduling model.

[0018] A policy generation unit, which is configured to solve the updated optimal scheduling model by using a preset optimization algorithm to determine a power trading policy.

[0019] In a third aspect, an embodiment of the present application provides a server, including a processor, a memory, and one or more programs. The one or more programs are stored in the memory and are configured to be executed by the processor. The programs include instructions for performing the steps in the first aspect of the embodiments of the present application.

[0020] In a fourth aspect, an embodiment of the present application provides a computer-readable storage medium, on which a computer program / instruction is stored. When the computer program / instruction is executed by a processor, the steps in the first aspect of the embodiments of the present application are implemented.

[0021] In a fifth aspect, an embodiment of the present application provides a computer program product, including a computer program / instruction. When the computer program / instruction is executed by a processor, some or all of the steps described in the first aspect of the embodiments of the present application are implemented.

[0022] It can be seen that in the embodiments of the present application, the server performs resource aggregation and resource evaluation on aggregable resources in the target area, and then constructs an optimal scheduling model of the virtual power plant corresponding to the target area, realizing the maximization of economic benefits and the minimization of operating costs. In addition, the server also ensures the accuracy and timeliness of policy formulation based on the real-time data acquisition and model parameter update mechanism. BRIEF DESCRIPTION OF THE DRAWINGS

[0023] 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 required for the description of the embodiments or the prior art. Obviously, the following drawings are only some embodiments of the present application. For those of ordinary skill in the art, other drawings can be obtained based on these drawings without creative efforts.

[0024] Figure 1 is a structural block diagram of a power trading system provided by an embodiment of the present application;

[0025] Figure 2 is a schematic flowchart of a virtual power plant power trading method provided by an embodiment of the present application;

[0026] Figure 3 is a schematic flowchart of another virtual power plant power trading method provided by an embodiment of the present application;

[0027] Figure 4 is a schematic flowchart of another virtual power plant power trading method provided by an embodiment of the present application;

[0028] Figure 5 It is a schematic flowchart of another virtual power plant power trading method provided by an embodiment of the present application;

[0029] Figure 6 It is a block diagram of the functional units of a virtual power plant power trading device provided by an embodiment of the present application;

[0030] Figure 7 It is a block diagram of the functional units of another virtual power plant power trading device provided by an embodiment of the present application;

[0031] Figure 8 It is a block diagram of the structure of a server provided by an embodiment of the present application. Detailed implementation manners

[0032] In order to enable those skilled in the art to better understand the solution of the present application, the technical solutions in the embodiments of the present application will be clearly and completely described below with reference to the accompanying drawings in the embodiments of the present application. Obviously, the described embodiments are only a part of the embodiments of the present application, rather than all the embodiments. Based on the embodiments in the present application, all other embodiments obtained by those of ordinary skill in the art without creative efforts shall fall within the protection scope of the present application.

[0033] The terms "first", "second", etc. in the specification and claims of the present application and the above-mentioned drawings are used to distinguish different objects, rather than to describe a specific order. In addition, the terms "include" and "have" and any variations thereof are intended to cover non-exclusive inclusions. For example, a process, method, system, product or device that includes a series of steps or units is not limited to the listed steps or units, but may optionally further include unlisted steps or units, or may optionally further include other steps or units inherent to these processes, methods, products or devices.

[0034] Referring to "embodiment" herein means that a specific feature, structure or characteristic described in connection with the embodiment may be included in at least one embodiment of the present application. The phrase appears in various places in the specification and does not necessarily refer to the same embodiment, nor is it an independent or alternative embodiment mutually exclusive with other embodiments. Those skilled in the art will explicitly and implicitly understand that the embodiments described herein may be combined with other embodiments.

[0035] Please refer to Figure 1 , Figure 1 It is a block diagram of a power trading system provided by an embodiment of the present application. As Figure 1As shown in the figure, the power trading system 100 includes a server 110, a virtual power grid 120, and an external interaction side 130. The server 110 is used to control the power resources in the virtual power grid 120 and perform corresponding data processing to generate corresponding intraday power trading strategies or day-ahead power trading strategies, and then interact with the external interaction side 130 to complete power trading. The virtual power grid 120 integrates fragmented resources into an equivalently flexible and controllable power plant by means of intelligent algorithms and communication technologies, realizes coordinated operation with the power grid, participates in power market trading, peak shaving and frequency modulation services, etc., ultimately improves energy utilization efficiency, reduces energy consumption costs, and helps the large-scale consumption of renewable energy. Among them, the data processing process in the virtual power grid 120 is all controlled by the server 110, including power resource aggregation, power resource regulation, power resource trading, and so on. The aggregable resources controlled by the virtual power grid 120 include distributed resources 121, controllable load resources 122, and energy storage resources 123. The external interaction side 130 includes a power grid 131 and a power market 132. The server 110 controls the virtual power grid 120 to achieve complementary operation with the power grid 131. During the peak load of the power grid 131, the virtual power grid 120 can release energy storage or reduce controllable loads to relieve the congestion of transmission and distribution lines; during the low valley, it can absorb excess renewable energy and reduce wind and light curtailment. In addition, it can also provide a safety buffer for the power grid 131. Through its fast regulation ability, the virtual power grid 120 can suppress the intermittent fluctuations of distributed energy (such as sudden drops in photovoltaic power), and reduce the fault risk of the power grid 131. The server 110 controls the virtual power grid 120 to participate in transactions with the power market 132 and provide services for the power market 132, so as to provide auxiliary services such as frequency modulation and reserve capacity to the market and obtain service fees (such as frequency modulation signal tracking, fast ramp response). In addition, through flexible resource aggregation, the server 110 enables the virtual power grid 120 to quickly fill the market supply-demand gap, suppress sharp fluctuations in electricity prices, improve market operation efficiency, and achieve market balance. The server 110 can control one or more virtual power grids 120 and interact with the external interaction side 130 to conduct corresponding power trading.

[0036] Based on this, the embodiments of the present application provide a power trading method for a virtual power plant. The embodiments of the present application will be described in detail below with reference to the accompanying drawings.

[0037] Embodiment 1. The context framework of the power trading method for the virtual power plant in the embodiments of the present application will be described below.

[0038] Please refer to Figure 2 , Figure 2 which is a schematic flowchart of a power trading method for a virtual power plant provided by an embodiment of the present application. The method is applied to the server 110 in the power trading system 100; the method includes:

[0039] Step S201: Perform resource aggregation operations on aggregable resources within the target area.

[0040] Among them, the aggregable resources include distributed resources, energy storage resources, and controllable load resources.

[0041] Among them, the server 110 performs resource aggregation operations on the aggregable resources in the virtual power grid 120 within the target area to achieve subsequent unified regulation and resource collaborative optimization, improving energy utilization efficiency and power grid stability. Among them, the distributed resources 121 cover intermittent power sources such as distributed photovoltaics and wind power. By aggregating, the output fluctuations are smoothed. The energy storage resources 123 include battery energy storage, supercapacitors, etc. After aggregation, the charging and discharging power and time are dynamically coordinated. The controllable load resources 122 include industrial flexible loads, air-conditioning clusters, etc. By aggregating, the power consumption timing and power threshold are adjusted.

[0042] Among them, the resource aggregation operation can specifically include the following processes: 1. Resource access, complete resource data collection and status monitoring through communication protocols; 2. Feature modeling, establish a resource dynamic model based on parameters such as output / load curves and response rates; 3. Collaborative optimization, with economy, low carbon, or grid demand as the goal, generate an aggregation scheduling strategy through a distributed algorithm; 4. Instruction issuance, send power setting values or start / stop instructions to each resource terminal and track the execution feedback in real time.

[0043] Step S202: Perform resource evaluation operations on the aggregable resources of each resource type to determine the corresponding evaluation results.

[0044] Among them, the resource evaluation operation is used to quantify the economic value and technical applicability of aggregable resources participating in power transactions through the preset evaluation indicators corresponding to each resource type.

[0045] Step S203: Construct an optimization scheduling model for the virtual power plant based on multiple evaluation results.

[0046] Among them, the optimization scheduling model includes a first sub-objective function and a second sub-objective function. The first sub-objective function includes the power generation income corresponding to distributed resources, the energy storage income corresponding to energy storage resources, and the controllable load income corresponding to controllable load resources. The first sub-objective function is used to maximize the sum of the power generation income, energy storage income, and controllable load income. The second sub-objective function is used to minimize the penalty cost of power balance deviation and power quality overlimit generated during the operation of the virtual power plant.

[0047] Among them, obtain the model parameters of the aggregable resources corresponding to each resource type in the virtual power grid based on multiple evaluation results. Specifically, the model parameters corresponding to distributed resources: master the rated power of each distributed generation device The real-time power generation power P under different environments gen,i(t), the power generation stability index is the power standard deviation σ gen,i (t), the volatility VR gen,i , and the power quality parameter, i.e., the voltage deviation ΔU gen,i (t), the frequency deviation Δf gen,i (t). The model parameters corresponding to the energy storage resources: the rated capacity of the energy storage device The charge-discharge efficiency, i.e., the charge efficiency η c,j , the discharge efficiency η d,j , the response speed index, i.e., the average response time t resp,j . The model parameters corresponding to the controllable load resources: the adjustment range of the controllable load, the lower limit the upper limit The adjustment speed, i.e., the average adjustment time t adj,k and the adjustment flexibility R flex,k .

[0048] In a possible embodiment, an optimization scheduling model corresponding to a virtual power plant is constructed based on multiple evaluation results, including: taking the evaluation results of distributed resources, the evaluation results of energy storage resources, and the evaluation results of controllable load resources as input parameters of the first sub-objective function; constructing the first sub-objective function based on the input parameters; determining the first penalty cost according to the difference between the voltage deviation and the first preset threshold and the preset first penalty coefficient, and determining the second penalty cost according to the difference between the frequency deviation and the second preset threshold and the preset second penalty coefficient, and determining the third penalty cost according to the difference between the harmonic distortion rate and the third preset threshold and the preset third penalty coefficient; and determining the fourth penalty cost according to the real-time power generation power corresponding to the distributed resources, the charge-discharge power corresponding to the energy storage resources, the actual adjustment power of the controllable load resources, and the power interacted with the power grid obtained in real time; constructing the second sub-objective function according to the first penalty cost, the second penalty cost, the third penalty cost, and the fourth penalty cost; and performing multi-objective joint modeling on the first sub-objective function and the second sub-objective function to generate the optimization scheduling model.

[0049] Among them, the power generation revenue is calculated by multiplying the real-time electricity price in the power market by the real-time power generation power of the distributed resources. The real-time power generation power is determined according to the power generation power curve of the distributed resources and the current system time. The energy storage revenue is calculated by superimposing the charge-discharge revenue and the ancillary service revenue under the peak-valley electricity price difference strategy. The charge-discharge revenue is determined according to the charge-discharge efficiency of the energy storage resources, the energy storage capacity, the valley electricity price, and the peak electricity price in the power market. The controllable load revenue is calculated by multiplying the actual adjustment power by the preset subsidy unit price. The actual adjustment power is determined according to the power adjustment range and the power adjustment success rate of the controllable load resources.

[0050] Among them, the data processing performed by the technical steps of this embodiment includes constructing sub-objective functions for maximizing the benefits of various resources (i.e., the first sub-objective function), constructing a sub-objective function for the stable operation of the power system (i.e., the second sub-objective function), and constructing an optimal scheduling model.

[0051] Specifically, the data processing process for constructing the sub-objective function for maximizing the benefits of various resources includes constructing power generation benefits (i.e., distributed resource benefits), constructing energy storage benefits, and constructing controllable load benefits, and constructing the first sub-objective function based on power generation benefits, energy storage benefits, and controllable load benefits.

[0052] Furthermore, the specific calculation process of power generation benefits is as follows:

[0053] Let the market electricity price (i.e., the real-time electricity price of the above-mentioned power market) change with time as C e (t), the power generation power of the i-th distributed energy generation unit at time t is P gen,i (t) (i.e., the above-mentioned real-time power generation power, where the real-time power generation power is determined according to the power generation power curve corresponding to the distributed resources and the current system time), and the power generation benefit R gen The calculation formula is:

[0054] R gen =∑i∑ t P gen,i (t)×C e (t).

[0055] The objective function aims to maximize this benefit value, that is, it is hoped that the distributed energy generates electricity at full capacity during high electricity price periods, reasonably adjusts the power generation power during low electricity price periods, and reduces unnecessary power generation costs.

[0056] Furthermore, the specific calculation process of energy storage benefits is as follows:

[0057] Let the charge and discharge power of the energy storage device at time t be P stoj (t), positive for discharging and negative for charging; the low valley electricity price is The peak electricity price is The unit benefit of ancillary services is C aux , and the capacity of ancillary services provided (i.e., the above-mentioned energy storage capacity) is Q aux,j (t). The energy storage benefit R sto The calculation formula is as follows:

[0058]

[0059] In the above formula, the first item in the brackets is the profit from discharging, the second item is the charging cost, and the third item is the ancillary service income; let the energy storage device seize the peak-valley electricity price difference and accurately participate in ancillary services to maximize the benefit. Among them, is the profit from discharging, is the charging cost, and the difference between the discharging profit and the charging cost is used to characterize the charging and discharging profit under the peak-valley electricity price difference strategy, C aux ×Q aux,j (t) is the ancillary service income (i.e., the above-mentioned ancillary service profit).

[0060] Furthermore, the specific calculation process of the controllable load profit is as follows:

[0061] Let the unit subsidy obtained by the user due to load regulation be C sub , and the regulation power of the kth controllable load at time t be P load,k (t), and the calculation formula of the controllable load profit R load is:

[0062] R load =∑ k ∑ t P load,k (t)×C sub .

[0063] When the model expects the controllable load to be regulated by the power grid, more subsidies can be obtained through efficient cooperation with the instructions.

[0064] Furthermore, the specific calculation process of constructing the first sub-objective function based on the power generation profit, energy storage profit, and controllable load profit is as follows:

[0065] The calculation formula of the first sub-objective function is:

[0066] Z1 = w1R gen +w2R sto +w3R load .

[0067] Among them, w1, w2, and w3 are weight coefficients, which are used to balance the importance of different resource profits in the total objective and can be flexibly adjusted according to factors such as the cost investment and market potential of the resources. Specifically, in terms of cost investment, factors such as the initial investment cost, operation and maintenance cost, and service life of various resources need to be considered. Resources with greater investment recovery pressure can appropriately increase their weights to ensure the interests of investors. In terms of market potential, the market potential of distributed energy depends on local electricity price policies and renewable energy subsidies; the market potential of energy storage resources is related to the peak-valley electricity price difference and the price of the ancillary service market; the market potential of controllable loads is related to the demand response subsidy policy and the power grid peak shaving demand. Resources with greater market potential should be given higher weights. The weights need to meet the following constraint conditions:

[0068]

[0069] Specifically, the data processing process for constructing the sub-objective function for the stable operation of the power system (i.e., the second sub-objective function) includes setting power balance constraints, setting power quality constraints, and constructing the second sub-objective function based on the power balance constraints and power quality constraints.

[0070] Furthermore, the specific calculation process for setting the power balance constraints is as follows:

[0071] At any given time, the power generation, energy storage, and power consumption within the virtual power plant should achieve balance to avoid power deficits or surpluses from affecting the stability of the power grid. Let the power exchanged with the power grid be P grid (t), and the power balance formula is:

[0072] ∑ i P gen,i (t) + ∑ j P sto,j (t) = ∑ k P load,k (t) + P hrid (t).

[0073] A deviation penalty term can be set in the above objective function. When there is a power imbalance, a certain cost penalty is imposed to prompt the dispatching strategy to meet the power balance requirements. For example, let the power deviation penalty coefficient be C p , the power imbalance amount be |ΔP(t)|, and the fourth penalty cost Cost p The calculation formula: Cost p = C p ×∑ t |ΔP(t)|.

[0074] Furthermore, the specific calculation process for setting the power quality constraints is as follows:

[0075] The purpose is to maintain stable voltage, frequency, and compliance with harmonic content. Let the allowable range of voltage deviation be [ΔU min , ΔU max , the allowable range of frequency deviation be [Δf min , Δf max , and the upper limit of the total harmonic distortion rate be THD limit . Penalties are imposed for exceeding the range. Let the voltage deviation penalty coefficient (i.e., the first penalty coefficient) be C U , the frequency deviation penalty coefficient (i.e., the second penalty coefficient) be C f , and the harmonic distortion penalty coefficient (i.e., the third penalty coefficient) be C h . The penalty cost calculation formula is as follows:

[0076] Cost U = C U ×∑ i ∑ tmax(0,|ΔU gen,i (t)|-ΔU max ), where Cost U is the first penalty cost;

[0077] Cost f = C f ×∑ i ∑ t max(0,|Δfg gen,i (t)|-Δf max ), where Costf f is the second penalty cost;

[0078] Cost h = C h ×∑ i ∑ t max(0,THD gen,i (t)-THD limit ), where Cost h is the second penalty cost.

[0079] Furthermore, the specific calculation process for constructing the second sub-objective function based on the power balance constraint and the power quality constraint is as follows:

[0080] Z2 = Cost p + Cost U + Cost f + Cost h , where Z2 is the second sub-objective function.

[0081] Furthermore, based on the specific calculation processes of the above first sub-objective function Z1 and second sub-objective function Z2, a multi-objective joint modeling is performed on the first sub-objective function Z1 and the second sub-objective function Z2 to generate the optimization scheduling model F. F can be understood as a multi-objective function constructed based on the first sub-objective function Z1 and the second sub-objective function Z2. The calculation formula of F is as follows:

[0082]

[0083] where: W Z1 and W Z2 are the weights of the revenue objective and the stable operation objective respectively, and the weight values are determined according to the focus of the virtual power plant operation.

[0084] It can be seen that in this embodiment, the optimized scheduling model constructs an economic objective by integrating the power generation benefits of distributed resources, the charge and discharge benefits of energy storage, and the regulation benefits of controllable loads, and constructs a safety objective by introducing the penalty costs of voltage / frequency deviation, harmonic distortion rate, and grid interaction power, so as to achieve the multi-objective collaborative optimization of economic optimum and grid stability. The model quantifies the resource scheduling potential and constraint conditions based on real-time electricity prices, peak-valley price differences, and dynamic equipment evaluation parameters, and uses a multi-objective algorithm to balance the maximization of benefits and the minimization of power quality risks, supporting the virtual power plant to dynamically adjust the output strategy between the power market and grid demand, improving the renewable energy consumption rate, reducing the grid volatility, and ensuring the robustness and compliance of the aggregated resource scheduling.

[0085] Step S204, obtain the current electricity price curve data of the power market and the grid load forecast data in real time.

[0086] Among them, the current electricity price curve data of the power market refers to a data set reflecting the electricity price fluctuations at different times in a specific time period (such as intraday or real-time) in the power trading market, which is usually dynamically generated by factors such as supply and demand relationships, market bidding, and policy regulation. The grid load forecast data refers to the data of the total electricity demand of the grid and its spatio-temporal distribution in a future time period generated by a forecast model based on parameters such as historical load, weather, economic activities, and user behavior, and is used to support the scheduling and resource planning of the power system.

[0087] Step S205, substitute the current electricity price curve data into the first sub-objective function and substitute the grid load forecast data into the second sub-objective function to update the model parameters of the optimized scheduling model.

[0088] Among them, substituting the current electricity price curve data into the first sub-objective function means substituting the newly obtained electricity price C e (t) corresponding to each time period t into the power generation benefits, energy storage benefits, and controllable load benefits, so that the model can calculate the potential benefits of distributed energy generation according to the latest electricity price. Similarly, in the calculation of energy storage benefits, the part related to the peak-valley electricity price difference is updated to ensure that the charge and discharge strategies of energy storage devices can adapt to the electricity price changes. In addition, since the electricity price change will affect the economic value of different resources, adjust the optimization directions of decision variables such as the power generation power of distributed resources, the charge and discharge power of energy storage, and the power of controllable loads. For example, when the electricity price rises, the model will tend to increase the power generation power of distributed energy and the discharge power of energy storage devices during the optimization process, and at the same time appropriately reduce the power of controllable loads to increase the electricity sales revenue. By modifying the coefficients or weights in the objective function and constraint conditions, the model optimizes the scheduling plan in a more favorable direction.

[0089] Among them, substituting the power grid load forecasting data into the second sub-objective function means adding a margin or risk factor to the power balance constraint according to the uncertainty of load forecasting. For example, if the uncertainty of load forecasting is high, a certain reserve capacity is set to cope with possible load peaks and ensure the power balance of the power system.

[0090] It can be seen that this step constructs an economic objective by integrating the power generation benefits of distributed resources, the peak-valley arbitrage and ancillary service benefits of energy storage, and the regulation benefits of controllable loads, and constructs a safety objective by combining the penalty costs of voltage / frequency deviation, harmonic distortion rate, and grid interaction power over-limit, achieving the multi-objective dynamic balance of economic optimization of virtual power plant multi-resource collaborative scheduling and minimizing grid risks. Based on real-time electricity prices, equipment evaluation parameters, and grid operation constraints, the model quantifies the resource regulation potential and safety boundaries, and generates a scheduling strategy that takes into account both revenue maximization and power quality guarantee through a multi-objective optimization algorithm, significantly improving the renewable energy consumption rate, reducing grid volatility, and enhancing the adaptability and robustness of the virtual power plant between the power market and grid demand.

[0091] Step S206, use a preset optimization algorithm to solve the updated optimal scheduling model to determine the power trading strategy.

[0092] Embodiment 2. The virtual power plant power trading method in the embodiments of the present application is described below in combination with the resource evaluation method for different resource types.

[0093] Please refer to Figure 3 , Figure 3 which is a schematic flowchart of another virtual power plant power trading method provided by the embodiments of the present application. This method is applied to the Figure 1 shown server 110. As Figure 3 shown, this method includes the following steps:

[0094] Step S301, perform a resource aggregation operation on the aggregable resources in the target area.

[0095] Among them, the aggregable resources include distributed resources, energy storage resources, and controllable load resources.

[0096] Step S302, perform a resource evaluation operation on the distributed resources to determine the first evaluation result.

[0097] Specifically, the specific operation of performing a resource evaluation operation on the distributed resources is as follows:

[0098] In a possible embodiment, if the resource type being currently processed is a distributed resource, a resource evaluation operation is performed on the aggregable resources of each resource type to determine the corresponding evaluation result (i.e., the first evaluation result), including: obtaining a set of generated power corresponding to the distributed resource collected at preset time intervals; based on the generated power data and weather condition information, using the least squares method to fit a power-light intensity curve or a power-wind speed curve to generate a generated power prediction model for the distributed resource; calculating the power standard deviation through the time series corresponding to the generated power data; calculating the voltage deviation, frequency deviation, and total harmonic distortion rate of the distributed resource based on the generated power data; and generating an evaluation result corresponding to the distributed resource according to the generated power prediction model, power standard deviation, voltage deviation, frequency deviation, and total harmonic distortion rate.

[0099] Among them, the set of generated power contains generated power data and weather condition information. Each generated power data corresponds to a weather condition information, and the weather condition information includes light intensity information and wind speed information. The (first) evaluation result is used to characterize the power generation stability, power quality, and generated power curve of the distributed resource.

[0100] Among them, the implementation order of the server for the aggregation and evaluation operation of distributed resources in the virtual power grid of the target area is: first, screen the distributed energy resources in the target area, including solar photovoltaic, wind energy, biomass energy, etc., and select the resources that meet the access requirements of the virtual power plant according to factors such as geographical location, installed capacity, and access conditions. Then, sign an access agreement with the owners of the distributed energy resources and install equipment to connect with the resources to achieve resource aggregation; after the resource aggregation is completed, obtain the set of generated power corresponding to the aggregated distributed resources. The set of generated power is used to characterize the generated power data under different weather conditions collected at preset time intervals, and the server performs a resource evaluation operation according to the set of generated power.

[0101] Furthermore, the resource evaluation operation includes three types of evaluation operations, specifically the generated power curve evaluation, power generation stability evaluation, and power quality evaluation.

[0102] Among them, the process of the generated power curve evaluation is as follows: for solar photovoltaic power generation, according to the collected data, use the least squares method to fit a power-light intensity curve, and the linear fitting formula is P = aG + b, where P is the generated power, G is the light intensity, and a and b are fitting coefficients. By fitting a large amount of data, the generated power curves in different seasons and different time periods can be obtained. And for wind power generation, fit a power-wind speed curve, and the linear fitting formula is P = av d, where v is the wind speed, and a and d are coefficients. A piecewise function considering the cut-in wind speed, rated wind speed, and cut-out wind speed is used to accurately describe the power curve. A power generation prediction model is generated through the power generation curves corresponding to the above two different types of distributed resources.

[0103] Among them, the process of evaluating power generation stability includes: setting the power generation time series as P1, P2,..., P n , with a time interval of Δt. Calculate the average power: Then calculate the power standard deviation The smaller the power standard deviation, the higher the power generation stability of the distributed resources in the virtual power grid. The volatility VR gen,i can also be used for evaluation.

[0104] Among them, the process of evaluating power quality is to evaluate by calculating the voltage deviation, frequency deviation, and total harmonic distortion rate. The calculation formula corresponding to the voltage deviation is as follows:

[0105] where the collected voltage data is U, and the rated voltage is U N ;

[0106] The calculation formula corresponding to the frequency deviation is as follows:

[0107] Δf = f - f N , where the collected actual frequency is f, and the rated frequency is f N ;

[0108] The calculation process of the total harmonic distortion rate is: perform a fast Fourier transform on the collected voltage or current signal to obtain each harmonic component U n , and the calculation formula for the voltage signal is as follows:

[0109] where U1 is the fundamental voltage amplitude;

[0110] Similarly, the current signal has a similar calculation method.

[0111] It can be seen that in this embodiment, the server realizes multi-dimensional analysis of the power generation potential, stability, and grid connection compatibility of distributed resources by establishing a weather-related power generation prediction model, quantifying power volatility (standard deviation), and evaluating power quality indicators such as voltage / frequency deviation and harmonic distortion rate, thereby improving the power generation prediction accuracy, identifying the risk of output fluctuation, screening high-quality controllable resources, providing a basis for dynamic optimization of the aggregation system, reducing the impact of intermittent power sources on the power grid, and supporting safe and economic refined dispatching decisions.

[0112] Step S303: Perform a resource evaluation operation on the energy storage resources to determine the second evaluation result.

[0113] Specifically, the specific operations for performing a resource evaluation operation on energy storage resources are as follows:

[0114] In a possible embodiment, if the resource type being currently processed is an energy storage resource; a resource evaluation operation is performed on the aggregable resources of each resource type to determine the corresponding evaluation result (i.e., the second evaluation result), including: using the constant current discharge method for the energy storage devices within the target area corresponding to the energy storage resource to determine the actual available capacity of the energy storage devices; sending charge and discharge commands to the energy storage devices to control the energy storage devices to perform charge and discharge operations, and collecting the actual response times of the energy storage devices when performing charge and discharge operations; determining the command response time based on the actual response time and the command sending time of the charge and discharge commands; and, obtaining the charging input electric energy and the actual stored electric energy of the energy storage devices during the charging process, and obtaining the initial stored electric energy and the discharge output electric energy of the energy storage devices during the discharge process; determining the charging efficiency of the energy storage devices based on the ratio of the charging input electric energy to the actual stored electric energy, and determining the discharge efficiency of the energy storage devices based on the ratio of the discharge output electric energy to the initial stored electric energy; generating an evaluation result corresponding to the energy storage resource based on the actual available capacity, the command response time, the charging efficiency, and the discharge efficiency.

[0115] Among them, the (second) evaluation result is used to characterize the charge and discharge efficiency, response speed, and energy storage capacity of the energy storage resource.

[0116] Among them, the specific implementation method for the server to perform a resource aggregation operation on the energy storage resource is to select appropriate energy storage devices according to different types of energy storage devices in the market, including electrochemical energy storage, mechanical energy storage, etc., according to the application scenarios of the virtual power plant. Connect the energy storage devices to the control system of the virtual power plant, install the devices, and obtain information such as the power, voltage, current, charge and discharge status of the energy storage devices in real time.

[0117] Furthermore, the resource evaluation operation of the server for the energy storage resource specifically includes an energy storage capacity evaluation, a charge and discharge efficiency evaluation, and a response speed evaluation.

[0118] Specifically, the specific implementation method of the energy storage capacity evaluation is: obtaining its rated capacity C from the technical parameter manual of the energy storage device N , with the unit of ampere-hour (Ah) or kilowatt-hour (kWh). For battery energy storage, C N = I N × t N , where I N is the rated current, and t NRated discharge time. For energy storage devices in the target area corresponding to the energy storage resources, the actual available capacity of all energy storage devices is regularly tested using the constant current discharge method. Discharge at a constant current I and record the discharge time t. Then the actual capacity C = I × t. It should be noted that for lithium-ion batteries, the influence of factors such as temperature and charge-discharge rate on the capacity also needs to be considered. The actual capacity of lithium-ion batteries is significantly affected by temperature and charge-discharge rate: high temperature (>40°C) accelerates aging and briefly increases the capacity, while low temperature (<0°C) reduces the available capacity due to the decrease in ion activity; high-rate discharge (>1C) reduces the actual released capacity due to the polarization effect. The test needs to be carried out at the standard temperature (25 ± 5°C), and a dynamic correction model is established based on the real-time temperature and charge-discharge rate. Or, it can be expressed by the formula C = C N ×f(T, C R ) where f(T, C R ) is a function of temperature T and charge-discharge rate C R used to correct the actual capacity.

[0119] Specifically, the specific implementation method for evaluating the charge-discharge efficiency is as follows: Send charge-discharge commands to the energy storage device to control the energy storage device to perform charging and discharging operations. During the charging process, record the electrical energy E c-in input during charging and the electrical energy E c-out actually stored in the battery. The charging efficiency During the discharging process, record the initial electrical energy E d-in stored in the battery and the electrical energy E d-out output during discharging. The discharging efficiency

[0120] Specifically, the specific implementation method for evaluating the response speed is as follows: Send charge-discharge commands from the control platform of the virtual power plant to the energy storage device, and record the time interval t from the issuance of the command to the actual achievement of the specified power output or input by the energy storage device as the response time. Conduct multiple tests and take the average value to measure the response speed.

[0121] It can be seen that in this embodiment, the server calibrates the actual available capacity of the energy storage device through the constant current discharge method, and combines the dynamic tests of the charge-discharge command response time and charge-discharge efficiency (charging input / stored electrical energy ratio, discharging output / initial energy storage ratio) to achieve multi-dimensional quantitative analysis of the response speed, energy conversion loss, and capacity authenticity of the energy storage resources. Thus, high-response and low-loss high-quality devices are screened, providing an evaluation basis for the real-time charge-discharge potential, command following accuracy, and cycle life of the aggregation system, supporting the formulation of the optimal energy storage configuration and scheduling strategy in scenarios such as peak shaving and valley filling, frequency regulation, etc., and improving the system economy and operation reliability.

[0122] Step S304, perform a resource evaluation operation on the controllable load resources to determine the third evaluation result.

[0123] Specifically, the specific operations for performing resource evaluation operations on controllable load resources are as follows:

[0124] In a possible embodiment, if the resource type being currently processed is a controllable load resource; perform resource evaluation operations on the aggregable resources of each resource type to determine the corresponding evaluation results (i.e., the third evaluation results), including: obtaining the rated power and device type of each controllable load device in the target area; determining the sub-power adjustment range of each controllable load device according to the rated power and the power adjustment ratio; adding up the upper limit and lower limit of the adjustable power of each controllable load device to determine the total power adjustment range of the controllable load resources in the target area; sending power adjustment instructions to each controllable load device in the target area to control the power adjustment of the controllable load device, and collecting the power adjustment time of each controllable load device in real time; calculating the overall adjustment speed of multiple controllable load devices according to the preset device power weight and power adjustment time; and determining the proportion of the number of devices that successfully respond to the power adjustment instructions within a preset period; generating the evaluation results corresponding to the controllable load resources according to the total power adjustment range, the overall adjustment speed, and the device number proportion.

[0125] Among them, the device type is used to indicate the power adjustment ratio of the corresponding controllable load device, the sub-power adjustment range is used to indicate the upper limit and lower limit of the adjustable power of the corresponding controllable load device, and the (third) evaluation results are used to characterize the power adjustment range, power adjustment speed, and power adjustment success rate of the controllable load resources.

[0126] Among them, the specific implementation method of the server for controllable resource aggregation includes conducting a general survey of various types of electrical loads in the target area, including lighting systems and air conditioning systems in commercial buildings, motor equipment in industrial enterprises, smart home appliances in residential households, etc. For non-smart devices, carry out intelligent transformation, install intelligent controllers or intelligent sockets so that they can receive control instructions from the virtual power plant. Sign agreements with load users to clarify the rights and obligations of both parties.

[0127] Furthermore, the resource evaluation operations of the server for controllable resources specifically include adjustment range evaluation, adjustment speed evaluation, and adjustment flexibility evaluation.

[0128] Among them, the specific implementation method of the adjustment range evaluation is to obtain its rated power P of the device from the technical parameters of the device r , for some devices, the adjustment range may be a certain proportion of the rated power. Therefore, the server also needs to obtain the device type to determine the power adjustment ratio to determine the sub-power adjustment range of each controllable load device. For example, the adjustment range of a smart air conditioner may be between 30% and 100% of the rated power, that is, the lower limit of the adjustable power is 0.3Pr , the adjustable power upper limit is P r . For a load group composed of multiple controllable load devices, the total adjustment range is the superposition of the adjustment ranges of each device. Suppose there are n controllable load devices in total, and the lower limit of the corresponding total power adjustment range is The upper limit is

[0129] Among them, the specific implementation method of adjusting speed evaluation is to record the time required for a single controllable load device to adjust from one power state P1 to another power state P2, which is used as a measure of the adjustment speed. For a group of controllable load devices, the overall adjustment speed can be calculated by statistically analyzing the adjustment speeds of different devices and combining their weights in the load group. For example, for two controllable load devices A and B, their adjustment speeds are t A and t B , and the device power weights are w A and w B , then the overall adjustment speed

[0130] Specifically, the acquisition of device power weights is usually based on the following methods: 1. Based on the rated power ratio. The weight is determined by the ratio of the rated power of the device to the total rated power of the load group. The larger the power of the device, the more significant its impact on the overall adjustment ability. 2. Based on the adjustment range weight. If the adjustment ranges of devices (such as the minimum value Pmin and the maximum value Pmax of adjustable power) are different, the weights can be dynamically adjusted according to their adjustment capabilities. The wider the adjustment range of the device, the higher the weight, reflecting its flexibility value. 3. Based on historical response data, the weights are dynamically corrected according to the historical response performance of the device (such as adjustment speed, success rate). For example: wi = α · rated power ratio + β · historical success rate, where α and β are adjustment coefficients used to balance power capacity and actual response reliability. 4. Agreement or market rules. In the access agreement signed between the virtual power plant and the resource owner, the weight allocation rules can be clearly defined. For example, the weights of high-value user devices are preferentially guaranteed, or dynamically adjusted according to market incentive mechanisms. In practical applications, the weights may be determined by integrating the above multiple factors through optimization algorithms (such as the analytic hierarchy process) or negotiation mechanisms to ensure fairness and efficiency. The reasonable design of weights can encourage resource owners to actively participate in regulation and optimize the dispatching strategy of the virtual power plant.

[0131] Among them, the specific implementation method of adjusting flexibility evaluation is to count the number m of controllable load devices that can respond and adjust power in a timely manner according to the command within a certain period of time. The total number of devices is n, then the response rate Other indicators can also be considered, such as the average response delay time, the number of successful responses, etc., to comprehensively evaluate the adjustment flexibility.

[0132] It can be seen that in this embodiment, the server determines the total power adjustment range of individuals and regions through the rated power and type classification of devices, and combines the real-time tests of the response time, success rate (proportion of device quantity), and weighted overall adjustment speed of the power adjustment instruction to quantify the flexible adjustment potential, response real-time performance, and execution reliability of controllable load resources, so as to screen high-precision adjustment and fast-response high-quality load clusters, and provide key parameters such as dynamically adjustable capacity, instruction following ability, and device coordination stability for scenarios such as demand response and frequency modulation auxiliary services, support the precise matching of power grid-side supply and demand fluctuations, improve the robustness and economy of load-side resource aggregation control, and reduce the risks of regulation delay and execution deviation.

[0133] Step S305, construct an optimization scheduling model corresponding to the virtual power plant based on multiple evaluation results.

[0134] Among them, the multiple evaluation results include a first evaluation result, a second evaluation result, and a third evaluation result. The optimization scheduling model includes a first sub-objective function and a second sub-objective function. The first sub-objective function includes the power generation income corresponding to distributed resources, the energy storage income corresponding to energy storage resources, and the controllable load income corresponding to controllable load resources. The first sub-objective function is used to maximize the sum of the power generation income, energy storage income, and controllable load income, and the second sub-objective function is used to minimize the penalty cost of power balance deviation and power quality over-limit generated during the operation of the virtual power plant.

[0135] Step S306, obtain the current electricity price curve data and power grid load forecast data of the electricity market in real time.

[0136] Step S307, substitute the current electricity price curve data into the first sub-objective function, and substitute the power grid load forecast data into the second sub-objective function to update the model parameters of the optimization scheduling model.

[0137] Step S308, use a preset optimization algorithm to solve the updated optimization scheduling model to determine the power trading strategy.

[0138] Embodiment 3. The virtual power plant power trading method in the embodiments of the present application will be described below in combination with the generation of the power trading strategy.

[0139] Please refer to Figure 4 , Figure 4 which is a schematic flowchart of another virtual power plant power trading method provided by the embodiments of the present application. This method is applied to the Figure 1 shown server 110. As Figure 4 shown, this method includes the following steps:

[0140] Step S401, perform a resource aggregation operation on the aggregable resources within the target area.

[0141] Among them, the aggregable resources include distributed resources, energy storage resources, and controllable load resources.

[0142] Step S402: Perform a resource evaluation operation on the aggregable resources of each resource type to determine the corresponding evaluation results.

[0143] Among them, the resource evaluation operation is used to quantify the economic value and technical applicability of the aggregable resources participating in power trading through the preset evaluation indicators corresponding to each resource type.

[0144] Step S403: Construct an optimization scheduling model corresponding to the virtual power plant based on multiple evaluation results.

[0145] Among them, the optimization scheduling model includes a first sub-objective function and a second sub-objective function. The first sub-objective function includes the power generation revenue corresponding to the distributed resources, the energy storage revenue corresponding to the energy storage resources, and the controllable load revenue corresponding to the controllable load resources. The first sub-objective function is used to maximize the sum of the power generation revenue, the energy storage revenue, and the controllable load revenue. The second sub-objective function is used to minimize the penalty cost of the power balance deviation and the power quality over-limit generated during the operation of the virtual power plant.

[0146] Step S404: Real-time obtain the current electricity price curve data and grid load forecast data of the power market.

[0147] Step S405: Substitute the current electricity price curve data into the first sub-objective function and substitute the grid load forecast data into the second sub-objective function to update the model parameters of the optimization scheduling model.

[0148] Step S406: Solve the updated optimization scheduling model through an optimization algorithm to determine the power generation plan of the distributed resources.

[0149] Among them, the power generation plan is used to indicate the electricity sales declaration volume corresponding to the virtual power plant in the target area during each time period. The charge and discharge strategy is used to indicate the charging time period and discharging time period of the energy storage device corresponding to the energy storage resources. The power regulation plan is used to indicate the power reduction instruction and power restoration instruction for the controllable load device. The power reduction instruction is used to indicate the corresponding controllable load device to release the operating power. The power restoration instruction is used to indicate the corresponding controllable load device to restore the operating power.

[0150] Among them, after adjusting the model parameters through the above steps, this step can further select a suitable optimization algorithm, such as linear programming, dynamic programming, particle swarm optimization algorithm (PSO), or genetic algorithm (GA), etc. The optimization algorithm re-searches for the optimal scheduling scheme under the new parameters and constraint conditions, that is, re-determines the power generation plan, the energy storage device scheduling strategy, and the controllable load regulation plan to adapt to market changes.

[0151] Exemplarily, when the applicable scenario is an optimization problem where both the objective function and constraints are linear (such as resource allocation, cost minimization), linear programming can be selected as the optimization algorithm. With the goal of minimizing the power generation cost and satisfying the upper and lower limits of the unit output and the load balance constraint, specifically, the simplex method or the interior point method is used to iterate at the vertices of the feasible region to find the optimal solution that extremizes the objective function. When the applicable scenario is a multi-stage decision-making problem with optimal substructure and overlapping subproblems (such as energy storage scheduling under time-of-use electricity prices), dynamic programming can be selected as the optimization algorithm. The optimization mechanism is to decompose the problem into recursive stages, use the state transition equation and the Bellman equation to record the local optimal solutions, and deduce the global optimal strategy reversely. For example, for the optimization of the charging and discharging plan of energy storage devices within 24 hours, each period's decision affects the subsequent benefits. When the applicable scenario is a non-linear, non-convex or high-dimensional problem (such as a scheduling model with wind and solar uncertainties), particle swarm optimization can be selected as the optimization algorithm. The optimization mechanism is that the particle swarm simulates the foraging of bird flocks, updates the speed and position by tracking the individual historical best (pBest) and the global best (gBest), and gradually approaches the global optimum. For example, in the multi-objective optimization of a virtual power plant, the output of distributed power sources and the charging and discharging power of energy storage are dynamically adjusted.

[0152] Step S407: Dynamically adjust the power generation plan, charging and discharging strategy, and power regulation plan according to the real-time grid load fluctuations to determine the power trading strategy.

[0153] Among them, the power trading strategy includes the day-ahead electricity trading strategy and the intra-day electricity trading strategy. The intra-day electricity trading strategy determines the power generation power for each period according to the power generation plan output by the above-mentioned optimized scheduling model. If the model shows that the power generation power of a distributed photovoltaic power station is abundant in a certain period of the next day, combined with the market electricity price forecast at this time, a power selling declaration is submitted to the electricity spot market. According to the energy storage device scheduling strategy output by the scheduling model, a peak-valley electricity price arbitrage plan is formulated. During the low electricity price period, arrange for the energy storage device to be fully charged; during the high electricity price period, discharge and sell electricity. When determining the charging and discharging power and electricity volume, fully consider the performance of the energy storage device, such as the charging and discharging efficiency and the rated capacity, to avoid overcharging and over-discharging and shortening the life.

[0154] Furthermore, through step S407, it is possible to analyze abnormal fluctuations such as sudden increases or decreases in electricity prices and sudden changes in grid loads according to market electricity prices, grid loads, distributed energy generation, and energy storage device status, quickly compare the preset scenarios and response strategies of the optimized scheduling model, and fine-tune the trading plan to determine the day-ahead electricity trading strategy. According to the controllable load regulation plan output by the scheduling model and the real-time grid load, flexibly control the controllable load. During the peak grid load period, increase the load regulation intensity and cut off non-essential electricity consumption; during the low load period, relax the regulation limit and utilize the low-price electricity.

[0155] It can be seen that in this embodiment, the server dynamically solves the multi-resource collaborative scheduling model through a preset optimization algorithm, generates accurate plans for distributed power output, energy storage charging and discharging, and load regulation in real time, and flexibly adjusts the strategy according to the grid load fluctuation, so as to achieve the rapid response matching between the power generation side and the demand side. Through the integrated optimization of the economic benefit target and the voltage / frequency safety constraint, while maximizing the market benefits of the virtual power plant (such as electricity price arbitrage and ancillary services), it effectively suppresses the impact of load fluctuation on the grid, improves the renewable energy consumption rate and equipment utilization rate, and ensures the robustness of the power trading strategy in a complex market environment and the safety of grid operation.

[0156] Embodiment 4. The virtual power plant power trading method in the embodiments of the present application will be described below in combination with value contribution coordination.

[0157] Please refer to Figure 5 , Figure 5 which is a schematic flowchart of another virtual power plant power trading method provided by the embodiments of the present application. This method is applied to the Figure 1 server 110 shown in Figure 5 As shown in

[0158] Step S501, perform a resource aggregation operation on the aggregable resources within the target area.

[0159] Among them, the aggregable resources include distributed resources, energy storage resources, and controllable load resources.

[0160] Step S502, perform a resource evaluation operation on the aggregable resources of each resource type to determine the corresponding evaluation results.

[0161] Among them, the resource evaluation operation is used to quantify the economic value and technical applicability of the aggregable resources participating in power trading through the preset evaluation indicators corresponding to each resource type.

[0162] Step S503, construct an optimization scheduling model corresponding to the virtual power plant based on multiple evaluation results.

[0163] Among them, the optimization scheduling model includes a first sub-objective function and a second sub-objective function. The first sub-objective function includes the power generation income corresponding to the distributed resources, the energy storage income corresponding to the energy storage resources, and the controllable load income corresponding to the controllable load resources. The first sub-objective function is used to maximize the sum of the power generation income, the energy storage income, and the controllable load income. The second sub-objective function is used to minimize the penalty cost of the power balance deviation and the power quality over-limit generated by the virtual power plant during operation.

[0164] Step S504, obtain the current electricity price curve data and grid load forecast data of the power market in real time.

[0165] Step S505: Substitute the current electricity price curve data into the first sub-objective function and the power grid load forecast data into the second sub-objective function to update the model parameters of the optimal scheduling model.

[0166] Step S506: Solve the updated optimal scheduling model using a preset optimization algorithm to determine the power trading strategy.

[0167] Step S507: Determine the user set in the virtual power plant, and based on the Shapley value corresponding to each resource provider and the power trading strategy, determine the profit distribution strategy.

[0168] Among them, the user set includes multiple resource providers corresponding to each resource type, and the Shapley value is used to quantify and reflect the average marginal contribution of each resource provider to the overall revenue in all coalition combinations.

[0169] The specific implementation manner of Step S507 is as follows:

[0170] In a possible embodiment, determine the user set in the virtual power plant; calculate the revenue characteristic function values under different coalition combinations according to the updated optimal scheduling model; for each resource provider, perform a weighted average of the marginal contributions in all coalition combinations to obtain the Shapley value; based on the Shapley value corresponding to each resource provider and the power trading strategy, determine the profit distribution strategy.

[0171] Among them, the user set includes multiple resource providers corresponding to each resource type, the coalition combination consists of at least one resource provider, and the revenue characteristic function value is used to represent the total revenue that the corresponding coalition combination can obtain in participating in the power trading of the virtual power plant; for each resource provider, calculate the marginal contribution in each coalition combination, where the marginal contribution refers to the difference between the total revenue that can be obtained after the currently processed resource provider joins the corresponding coalition combination and the total revenue before joining, and the Shapley value is used to quantify and reflect the average marginal contribution of each resource provider to the overall revenue in all coalition combinations.

[0172] Taking energy storage resources as an example, after participating in the alliance, energy storage increases its revenue through low charging and high discharging, and its contribution is the difference in revenue before and after joining. By traversing all possible resource combinations and calculating the average contribution value of each resource through weighting, the total profit is finally distributed to each resource party according to the contribution ratio to ensure the fairness of distribution. For flexible resources (such as fast-response energy storage), due to their stronger ability to improve the revenue of the alliance, their Shapley values are higher. For intermittent resources (such as photovoltaic), their contributions need to be evaluated in combination with the stability of power output. In actual cases, this method increases the profit share of energy storage and controllable loads, encourages them to optimize their response strategies, while ensuring the reasonable revenue of basic resources such as photovoltaic, enhancing the internal cooperation efficiency and long-term cooperation stability of the virtual power plant, and providing a reliable distribution mechanism for the market-oriented operation of complex energy aggregations.

[0173] It can be seen that in this embodiment, the server quantifies the marginal contribution of each resource provider to the total revenue of the virtual power plant in different alliance combinations through the Shapley value, and determines the profit distribution strategy based on the weighted average of the marginal contributions, solving the problem of fair revenue distribution when multi-type resources participate in power trading collaboratively. By traversing all possible alliance combinations and calculating the revenue characteristic function values, it accurately depicts the value contributions of differentiated resources such as distributed resources, energy storage, and controllable loads, ensuring that the distribution results meet individual rationality and alliance stability, encouraging resource providers to actively optimize their output strategies to improve the overall revenue, while enhancing the cooperation efficiency and market competitiveness of the virtual power plant's resource aggregation.

[0174] It can be seen that in this application, the server realizes the maximization of economic revenue and the minimization of operating costs through an accurate resource evaluation and optimization scheduling model. Also, the server ensures the accuracy and timeliness of strategy formulation through a real-time data acquisition and model parameter update mechanism. Also, by quantifying the marginal contribution of each resource provider to the total revenue of the virtual power plant in different alliance combinations through the Shapley value, and determining the profit distribution strategy based on the weighted average of the marginal contributions, the problem of fair revenue distribution when multi-type resources participate in power trading collaboratively is solved.

[0175] The following is the device embodiment of this application. The device embodiment of this application and the method embodiment of this application belong to the same concept and are used to execute the method described in the embodiment of this application. For the convenience of description, only the parts related to the device embodiment of this application are shown in the device embodiment of this application. For the specific technical details not disclosed, please refer to the description of the method embodiment of this application, and details will not be repeated here.

[0176] A virtual power plant power trading device provided by an embodiment of this application is applied to Figure 1The server 110 in the power trading system 100 shown. Specifically, the virtual power plant power trading device is used to execute the steps performed by the server 110 in the above virtual power plant power trading method. The virtual power plant power trading device provided in the embodiments of the present application may include modules corresponding to the respective steps.

[0177] In the embodiments of the present application, the virtual power plant power trading device may be divided into functional modules according to the above method examples. For example, each functional module may be divided corresponding to each function, or two or more functions may be integrated into one processing module. The above integrated module may be implemented in the form of hardware or in the form of a software functional module. The division of modules in the embodiments of the present application is illustrative, merely a logical function division, and there may be other division methods in actual implementation.

[0178] In the case of dividing each functional module corresponding to each function, Figure 6 is a block diagram of the composition of the functional units of a virtual power plant power trading device provided in the embodiments of the present application; the virtual power plant power trading device is applied to Figure 1 the server 110 in the power trading system 100 shown. The virtual power plant power trading device 60 includes: a resource integration unit 601, configured to perform a resource aggregation operation on aggregable resources in a target area, where the aggregable resources include distributed resources, energy storage resources, and controllable load resources; a resource evaluation unit 602, configured to perform a resource evaluation operation on aggregable resources of each resource type to determine corresponding evaluation results, and the resource evaluation operation is used to quantify the economic value and technical applicability of aggregable resources participating in power trading through preset evaluation indicators corresponding to each resource type; a model construction unit 603, configured to construct an optimal scheduling model corresponding to the virtual power plant based on multiple evaluation results, where the optimal scheduling model includes a first sub-objective function and a second sub-objective function, the first sub-objective function includes the power generation income corresponding to distributed resources, the energy storage income corresponding to energy storage resources, and the controllable load income corresponding to controllable load resources, the first sub-objective function is used to maximize the sum of the power generation income, energy storage income, and controllable load income, and the second sub-objective function is used to minimize the penalty cost of power balance deviation and power quality over-limit generated during the operation of the virtual power plant; a data acquisition unit 604, configured to acquire current electricity price curve data and grid load forecast data of the power market in real time; a model update unit 605, configured to substitute the current electricity price curve data into the first sub-objective function and substitute the grid load forecast data into the second sub-objective function to update the model parameters of the optimal scheduling model; a strategy generation unit 606, configured to use a preset optimization algorithm to solve the updated optimal scheduling model to determine a power trading strategy.

[0179] In a possible embodiment, if the resource type being currently processed is a distributed resource; in terms of performing a resource evaluation operation on the aggregable resources of each resource type to determine the corresponding evaluation result, the resource evaluation unit 602 is specifically configured to: obtain a set of generated power corresponding to the distributed resource collected at a preset time interval, the set of generated power including generated power data and weather condition information, each generated power data corresponding to a weather condition information, and the weather condition information including light intensity information and wind speed information; based on the generated power data and the weather condition information, use the least squares method to fit a power-light intensity curve or a power-wind speed curve to generate a generated power prediction model for the distributed resource; calculate the power standard deviation through the time series corresponding to the generated power data; calculate the voltage deviation, frequency deviation, and total harmonic distortion rate of the distributed resource according to the generated power data; and generate an evaluation result corresponding to the distributed resource according to the generated power prediction model, the power standard deviation, the voltage deviation, the frequency deviation, and the total harmonic distortion rate, where the evaluation result is used to characterize the power generation stability, power quality, and generated power curve of the distributed resource.

[0180] In a possible embodiment, if the resource type being currently processed is a energy storage resource; in terms of performing a resource evaluation operation on the aggregable resources of each resource type to determine the corresponding evaluation result, the resource evaluation unit 602 is specifically configured to: adopt a constant current discharge method for the energy storage devices in the target area corresponding to the energy storage resource to determine the actual available capacity of the energy storage devices; send charge and discharge instructions to the energy storage devices to control the energy storage devices to perform charge and discharge operations, and collect the actual response times of the energy storage devices when performing charge and discharge operations; determine the instruction response time according to the actual response time and the instruction sending time of the charge and discharge instructions; and obtain the charging input electric energy and the actual stored electric energy of the energy storage devices during the charging process, and obtain the initial stored electric energy and the discharge output electric energy of the energy storage devices during the discharging process; determine the charging efficiency of the energy storage devices based on the ratio of the charging input electric energy to the actual stored electric energy, and determine the discharging efficiency of the energy storage devices based on the ratio of the discharge output electric energy to the initial stored electric energy; and generate an evaluation result corresponding to the energy storage resource according to the actual available capacity, the instruction response time, the charging efficiency, and the discharging efficiency, where the evaluation result is used to characterize the charge and discharge efficiency, response speed, and energy storage capacity of the energy storage resource.

[0181] In a possible embodiment, if the resource type being currently processed is a controllable load resource; in terms of performing a resource evaluation operation on the aggregable resources for each resource type to determine the corresponding evaluation result, the resource evaluation unit 602 is specifically configured to: obtain the rated power and device type of each controllable load device in the target area, where the device type is used to indicate the power adjustment ratio of the corresponding controllable load device; determine the sub-power adjustment range of each controllable load device according to the rated power and the power adjustment ratio, where the sub-power adjustment range is used to indicate the upper limit and lower limit of the adjustable power of the corresponding controllable load device; superimpose the upper limit and lower limit of the adjustable power of each controllable load device to determine the total power adjustment range of the controllable load resources in the target area; send a power adjustment instruction to each controllable load device in the target area to control the controllable load device to perform power adjustment, and collect the power adjustment time of each controllable load device in real time; calculate the overall adjustment speed of multiple controllable load devices according to the preset device power weight and power adjustment time; and determine the proportion of the number of devices that successfully respond to the power adjustment instruction within a preset period; generate an evaluation result corresponding to the controllable load resource according to the total power adjustment range, the overall adjustment speed, and the proportion of the number of devices, where the evaluation result is used to characterize the power adjustment range, power adjustment speed, and power adjustment success rate of the controllable load resource.

[0182] In a possible embodiment, in terms of constructing an optimization scheduling model corresponding to a virtual power plant based on multiple evaluation results, the model construction unit 603 is specifically configured to: use the evaluation results of distributed resources, the evaluation results of energy storage resources, and the evaluation results of controllable load resources as input parameters of the first sub-objective function; construct the first sub-objective function based on the input parameters, where the power generation revenue is calculated by multiplying the real-time electricity price in the power market by the real-time power generation power of the distributed resources, and the real-time power generation power is determined according to the power generation power curve of the distributed resources and the current system time. The energy storage revenue is calculated by superimposing the charge and discharge revenue and the ancillary service revenue under the peak-valley electricity price difference strategy. The charge and discharge revenue is determined according to the charge and discharge efficiency of the energy storage resources, the energy storage capacity, the valley electricity price, and the peak electricity price in the power market. The controllable load revenue is calculated by multiplying the actual regulation power by the preset subsidy unit price, and the actual regulation power is determined according to the power regulation range and the power regulation success rate of the controllable load resources; determine the first penalty cost according to the difference between the voltage deviation and the first preset threshold and the preset first penalty coefficient, determine the second penalty cost according to the difference between the frequency deviation and the second preset threshold and the preset second penalty coefficient, and determine the third penalty cost according to the difference between the harmonic distortion rate and the third preset threshold and the preset third penalty coefficient; and determine the fourth penalty cost according to the real-time power generation power corresponding to the distributed resources, the charge and discharge power corresponding to the energy storage resources, the actual regulation power of the controllable load resources, and the grid interaction power obtained in real time; construct the second sub-objective function according to the first penalty cost, the second penalty cost, the third penalty cost, and the fourth penalty cost; perform multi-objective joint modeling on the first sub-objective function and the second sub-objective function to generate an optimization scheduling model.

[0183] In a possible embodiment, in terms of using a preset optimization algorithm to solve the updated optimization scheduling model to determine a power trading strategy, the strategy generation unit 606 is specifically configured to: solve the updated optimization scheduling model through the optimization algorithm to determine the power generation plan of the distributed resources, the charge and discharge strategy of the energy storage resources, and the power regulation plan of the controllable load resources; dynamically adjust the power generation plan, the charge and discharge strategy, and the power regulation plan according to the grid load fluctuations obtained in real time to determine the power trading strategy, where the power generation plan is used to indicate the electricity sales declaration volume corresponding to the virtual power plant in the target area in each time period, the charge and discharge strategy is used to indicate the charging time period and the discharging time period of the energy storage device corresponding to the energy storage resources, the power regulation plan is used to indicate the power reduction instruction and the power restoration instruction for the controllable load device, the power reduction instruction is used to indicate the corresponding controllable load device to release the operating power, and the power restoration instruction is used to indicate the corresponding controllable load device to restore the operating power.

[0184] In a possible embodiment, the policy generation unit 606 is further specifically configured to: determine a set of users in the virtual power plant, where the set of users includes multiple resource providers corresponding to each resource type; calculate the revenue characteristic function values under different coalition combinations according to the updated optimal scheduling model, where the coalition combinations are composed of at least one resource provider, and the revenue characteristic function value is used to characterize the total revenue that the corresponding coalition combination can obtain in participating in the power transaction of the virtual power plant; for each resource provider, calculate the marginal contribution in each coalition combination, where the marginal contribution refers to the difference between the total revenue that can be obtained after the currently processed resource provider joins the corresponding coalition combination and the total revenue before joining; for each resource provider, perform a weighted average of the marginal contributions in all coalition combinations to obtain the Shapley value, where the Shapley value is used to quantify and reflect the average marginal contribution of each resource provider to the overall revenue in all coalition combinations; and determine the profit distribution policy based on the Shapley value corresponding to each resource provider and the power transaction policy.

[0185] In the case of adopting an integrated unit, as Figure 7 shown, Figure 7 is a block diagram of the functional units of another virtual power plant power trading device provided by an embodiment of the present application. In Figure 7 , the virtual power plant power trading device 60 includes: a processing module 720 and a communication module 710. The processing module 720 is used to control and manage the operations of the virtual power plant power trading device 60. For example, the steps of the resource integration unit 601, the resource evaluation unit 602, the model construction unit 603, the data acquisition unit 604, the model update unit 605, and the policy generation unit 606, and / or for performing other processes of the technologies described herein. The communication module 710 is used to support the interaction between the virtual power plant power trading device and other devices. As Figure 7 shown, the virtual power plant power trading device may include a storage module 730, and the storage module 730 is used to store the program code and data of the virtual power plant power trading device.

[0186] Among them, the processing module 720 may be a processor or a controller. For example, it may be a central processing unit (CPU), a general-purpose processor, a digital signal processor (DSP), an ASIC, an FPGA, or other programmable logic devices, transistor logic devices, hardware components, or any combination thereof. It can implement or execute various exemplary logic blocks, modules, and circuits described in combination with the disclosure of the present application. The processor may also be a combination that implements computing functions, such as a combination of one or more microprocessors, a combination of a DSP and a microprocessor, and so on. The communication module 710 may be a transceiver, an RF circuit, or a communication interface, etc. The storage module 730 may be a memory.

[0187] Among them, all relevant contents of each scenario involved in the above method embodiments can be cited in the function descriptions of the corresponding functional modules, and will not be elaborated here. The above virtual power plant power trading device 60 can execute the above Figure 2 shown virtual power plant power trading method.

[0188] The above embodiments can be implemented in whole or in part by software, hardware, firmware, or any combination thereof. When implemented using software, the above embodiments can be implemented in whole or in part in the form of a computer program product. The computer program product includes one or more computer instructions or computer programs. When the computer instructions or computer programs are loaded or executed on a computer, the processes or functions according to the embodiments of the present application are generated in whole or in part. The computer can be a general-purpose computer, a special-purpose computer, a computer network, or other programmable devices. The computer instructions can be stored in a computer-readable storage medium, or transmitted from one computer-readable storage medium to another. For example, the computer instructions can be transmitted from one website, computer, server, or data center to another website, computer, server, or data center in a wired or wireless manner. The computer-readable storage medium can be any available medium that the computer can access, or a data storage device such as a server or data center that includes one or more collections of available media. The available medium can be a magnetic medium (such as a floppy disk, hard disk, or magnetic tape), an optical medium (such as a DVD), or a semiconductor medium. The semiconductor medium can be a solid-state drive.

[0189] Figure 8 is a structural block diagram of a server provided by an embodiment of the present application. As Figure 8 shown, the server 110 can include one or more of the following components: a processor 810, and a memory 820 coupled to the processor 810. The memory 820 can store one or more computer programs 821, and the one or more computer programs 821 can be configured to be executed by one or more processors 810 to implement the methods described in the above embodiments. The server here is the server 110 in the above embodiments.

[0190] The processor 810 may include one or more processing cores. The processor 810 connects various parts within the entire server 110 through various interfaces and lines, and executes various functions of the server 110 and processes data by running or executing instructions, programs, code sets, or instruction sets stored in the memory 820, and by calling the data stored in the memory 820. Optionally, the processor 810 may be implemented in at least one hardware form of digital signal processing (DSP), field-programmable gate array (FPGA), or programmable logic array (PLA). The processor 810 may integrate one or a combination of several of a central processing unit (CPU), a graphics processing unit (GPU), and a modem, etc. Among them, the CPU mainly processes the operating system, user interface, application programs, etc.; the GPU is responsible for rendering and drawing the display content; the modem is used to process wireless communications. It can be understood that the above-mentioned modem may not be integrated into the processor 810 and may be implemented separately through a communication chip.

[0191] The memory 820 may include random access memory (RAM) and may also include read-only memory (ROM). The memory 820 can be used to store instructions, programs, code, code sets, or instruction sets. The memory 820 may include a program storage area and a data storage area. Among them, the program storage area may store instructions for implementing the operating system, instructions for implementing at least one function (such as touch function, sound playback function, image playback function, etc.), instructions for implementing each of the above method embodiments, etc. The data storage area may also store data created during the use of the server 110.

[0192] It can be understood that the server 110 may include more or fewer structural elements than those shown in the above structural block diagram, which will not be limited here.

[0193] The embodiments of the present application also provide a computer storage medium, on which computer programs / instructions are stored, and when the computer programs / instructions are executed by a processor, they implement some or all of the steps of any of the methods described in the above method embodiments.

[0194] The embodiments of the present application also provide a computer program product. The computer program product includes a non-transitory computer-readable storage medium storing a computer program, and the computer program is operable to cause a computer to execute some or all of the steps of any one of the methods described in the foregoing method embodiments.

[0195] It should be understood that in various embodiments of the present application, the sequence numbers of the foregoing processes do not mean the order of execution. The execution order of each process should be determined according to its function and internal logic, and should not constitute any limitation to the implementation process of the embodiments of the present application.

[0196] In several embodiments provided by the present application, it should be understood that the disclosed methods, devices, and systems can be implemented in other ways. For example, the device embodiments described above are merely illustrative; for example, the division of units is only a logical function division, and there may be other division methods in actual implementation; for example, multiple units or components can be combined or integrated into another system, or some features can be ignored or not executed. Another point, the displayed or discussed mutual coupling, direct coupling, or communication connection may be through some interfaces, and the indirect coupling or communication connection of devices or units may be in electrical, mechanical, or other forms.

[0197] The units described as separate components may or may not be physically separated, and the components displayed as units may or may not be physical units, that is, they may be located in one place, or may be distributed to multiple network units. Some or all of the units can be selected according to actual needs to achieve the purpose of the solution of this embodiment.

[0198] In addition, in each embodiment of the present invention, the functional units can be integrated in a processing unit, or each unit can be physically included separately, or two or more units can be integrated in one unit. The above-mentioned integrated unit can be implemented in the form of hardware, or in the form of hardware plus software functional units.

[0199] The integrated units implemented in the form of software functional units can be stored in a computer-readable storage medium. The above-mentioned software functional units stored in a storage medium include several instructions for causing a computer device (which can be a personal computer, a server, or a network device, etc.) to execute some steps of the methods according to various embodiments of the present invention. The foregoing storage medium includes: USB flash drives, mobile hard disks, magnetic disks, optical disks, volatile memories or non-volatile memories. Among them, the non-volatile memory can be a read-only memory (ROM), a programmable ROM (PROM), an erasable programmable ROM (EPROM), an electrically erasable programmable ROM (EEPROM), or a flash memory. The volatile memory can be a random access memory (RAM), which is used as an external cache. By way of example but not limitation, many forms of random access memory (RAM) are available, such as static random access memory (SRAM), dynamic random access memory (DRAM), synchronous dynamic random access memory (SDRAM), double data rate synchronous dynamic random access memory (DDR SDRAM), enhanced synchronous dynamic random access memory (ESDRAM), synchronous link dynamic random access memory (SLDRAM), and direct rambus random access memory (DR RAM), etc., and various media that can store program codes.

[0200] Although the present invention is disclosed as above, the present invention is not limited thereto. Any person skilled in the art can easily think of changes or substitutions without departing from the spirit and scope of the present invention, and can make various changes and modifications, including combinations of the above different functions and implementation steps, including software and hardware implementation manners, all within the protection scope of the present invention.

Claims

1. A virtual power plant power trading method, characterized in that, Applied to a server, the method includes: Performing a resource aggregation operation on aggregable resources within a target area, where the aggregable resources include distributed resources, energy storage resources, and controllable load resources; Performing a resource evaluation operation on the aggregable resources of each resource type to determine corresponding evaluation results. The resource evaluation operation is used to quantify the economic value and technical applicability of the aggregable resources participating in power trading through preset evaluation indicators corresponding to each resource type; Constructing an optimal scheduling model corresponding to a virtual power plant based on the multiple evaluation results. The optimal scheduling model includes a first sub-objective function and a second sub-objective function. The first sub-objective function includes the power generation revenue corresponding to the distributed resources, the energy storage revenue corresponding to the energy storage resources, and the controllable load revenue corresponding to the controllable load resources. The first sub-objective function is used to maximize the sum of the power generation revenue, the energy storage revenue, and the controllable load revenue. The second sub-objective function is used to minimize the penalty cost of power balance deviation and power quality over-limit generated during the operation of the virtual power plant; Obtaining the current electricity price curve data and grid load forecast data of the power market in real time; and Substituting the current electricity price curve data into the first sub-objective function and substituting the grid load forecast data into the second sub-objective function to update the model parameters of the optimal scheduling model; Using a preset optimization algorithm to solve the updated optimal scheduling model to determine a power trading strategy.

2. The method according to claim 1, characterized in that, If the resource type currently being processed is the distributed resource; The performing a resource evaluation operation on the aggregable resources of each resource type to determine corresponding evaluation results includes: Obtaining a set of power generation powers corresponding to the distributed resources collected at preset time intervals. The set of power generation powers includes power generation power data and weather condition information. Each power generation power data corresponds to a piece of weather condition information, and the weather condition information includes light intensity information and wind speed information; Based on the power generation power data and the weather condition information, using the least squares method to fit a power-light intensity curve or a power-wind speed curve to generate a power generation power prediction model corresponding to the distributed resources; Calculating the power standard deviation through the time series corresponding to the power generation power data; Calculating the voltage deviation, frequency deviation, and total harmonic distortion rate of the distributed resources according to the power generation power data; Generating an evaluation result corresponding to the distributed resources based on the power generation power prediction model, the power standard deviation, the voltage deviation, the frequency deviation, and the total harmonic distortion rate. The evaluation result is used to characterize the power generation stability, power quality, and power generation power curve of the distributed resources.

3. The method according to claim 1, characterized in that, If the resource type currently being processed is the energy storage resource; The performing a resource evaluation operation on the aggregable resources of each resource type to determine corresponding evaluation results includes: Adopting a constant current discharge method for energy storage devices within the target area corresponding to the energy storage resources to determine the actual available capacity of the energy storage devices; Send charge and discharge commands to the energy storage device to control the energy storage device to perform charging and discharging operations, and collect the actual response times of the energy storage device for performing the charging and discharging operations; Determine the command response time based on the actual response time and the command sending time of the charge and discharge command; and, obtain the charging input electric energy and the actual stored electric energy of the energy storage device during the charging process, and obtain the initial stored electric energy and the discharge output electric energy of the energy storage device during the discharging process; Determine the charging efficiency of the energy storage device based on the ratio of the charging input electric energy to the actual stored electric energy, and determine the discharging efficiency of the energy storage device based on the ratio of the discharge output electric energy to the initial stored electric energy; Generate an evaluation result corresponding to the energy storage resource according to the actual available capacity, the command response time, the charging efficiency, and the discharging efficiency, where the evaluation result is used to characterize the charge and discharge efficiency, response speed, and energy storage capacity of the energy storage resource.

4. The method according to claim 1, wherein If the resource type currently being processed is the controllable load resource; Performing a resource evaluation operation on the aggregable resources for each resource type to determine the corresponding evaluation result, including: Obtain the device rated power and device type of each controllable load device in the target area, where the device type is used to indicate the power adjustment ratio of the corresponding controllable load device; Determine the sub-power adjustment range of each controllable load device according to the device rated power and the power adjustment ratio, where the sub-power adjustment range is used to indicate the adjustable power upper limit and the adjustable power lower limit of the corresponding controllable load device; Superimpose the adjustable power upper limit and the adjustable power lower limit of each controllable load device to determine the total power adjustment range of the controllable load resources in the target area; Send power adjustment commands to each controllable load device in the target area to control the controllable load device to perform power conditioning, and collect the power adjustment time of each controllable load device in real time; Calculate the overall adjustment speed of multiple controllable load devices according to the preset device power weight and the power adjustment time; and, determine the proportion of the number of devices that successfully respond to the power adjustment command within a preset time period; Generate an evaluation result corresponding to the controllable load resource according to the total power adjustment range, the overall adjustment speed, and the device number proportion, where the evaluation result is used to characterize the power adjustment range, power adjustment speed, and power adjustment success rate of the controllable load resource.

5. The method according to any one of claims 1-4, characterized in that, The constructing an optimal scheduling model corresponding to the virtual power plant based on the multiple evaluation results includes: Use the evaluation results of the distributed resources, the evaluation results of the energy storage resources, and the evaluation results of the controllable load resources as input parameters of the first sub-objective function; Based on the input parameters, construct the first sub-objective function, where the power generation revenue is calculated by multiplying the real-time electricity price of the power market by the real-time power generation power of the distributed resources. The real-time power generation power is determined according to the power generation power curve of the distributed resources and the current system time. The energy storage revenue is calculated by superimposing the charge-discharge revenue and the ancillary service revenue under the peak-valley electricity price difference strategy. The charge-discharge revenue is determined according to the charge-discharge efficiency of the energy storage resources, the energy storage capacity, the low-valley electricity price and the peak electricity price of the power market. The controllable load revenue is calculated by multiplying the actual regulation power by the preset subsidy unit price. The actual regulation power is determined according to the power regulation range and the power regulation success rate of the controllable load resources; Determine the first penalty cost according to the difference between the voltage deviation and the first preset threshold and the preset first penalty coefficient, determine the second penalty cost according to the difference between the frequency deviation and the second preset threshold and the preset second penalty coefficient, and determine the third penalty cost according to the difference between the harmonic distortion rate and the third preset threshold and the preset third penalty coefficient; and, Determine the fourth penalty cost according to the real-time power generation power corresponding to the distributed resources, the charge-discharge power corresponding to the energy storage resources, the actual regulation power of the controllable load resources, and the grid interaction power obtained in real time; Construct the second sub-objective function according to the first penalty cost, the second penalty cost, the third penalty cost and the fourth penalty cost; Perform multi-objective joint modeling on the first sub-objective function and the second sub-objective function to generate the optimal scheduling model.

6. The method according to claim 5, wherein Solving the updated optimal scheduling model by using a preset optimization algorithm to determine the power trading strategy, including: Solving the updated optimal scheduling model by using the optimization algorithm to determine the power generation plan of the distributed resources, the charge-discharge strategy of the energy storage resources, and the power regulation plan of the controllable load resources; Dynamically adjust the power generation plan, the charge-discharge strategy and the power regulation plan according to the grid load fluctuations obtained in real time to determine the power trading strategy, where the power generation plan is used to indicate the electricity sales declaration volume corresponding to the virtual power plant in the target area in each time period, the charge-discharge strategy is used to indicate the charging time period and the discharging time period of the energy storage device corresponding to the energy storage resources, the power regulation plan is used to indicate the power reduction instruction and the power restoration instruction for the controllable load device, the power reduction instruction is used to indicate the corresponding controllable load device to release the operating power, and the power restoration instruction is used to indicate the corresponding controllable load device to restore the operating power.

7. The method according to claim 6, characterized in that The method further includes: Determine the user set in the virtual power plant, where the user set includes multiple resource providers corresponding to each resource type; Calculate the value of the revenue characteristic function for different coalition combinations according to the updated optimized scheduling model. The coalition combinations are composed of at least one of the resource providers, and the value of the revenue characteristic function is used to characterize the total revenue that the corresponding coalition combination can obtain in participating in the power transaction of the virtual power plant; For each of the resource providers, calculate the marginal contribution in each of the coalition combinations. The marginal contribution refers to the difference between the total revenue that can be obtained after the currently processed resource provider joins the corresponding coalition combination and the total revenue before joining; For each of the resource providers, perform a weighted average of the marginal contributions in all of the coalition combinations to obtain the Shapley value. The Shapley value is used to quantify and reflect the average marginal contribution of each resource provider to the overall revenue in all of the coalition combinations; Based on the Shapley value corresponding to each resource provider and the power trading strategy, determine the profit distribution strategy.

8. A virtual power plant power trading device, characterized in that, Applied to a server, the device includes: A resource integration unit for performing a resource aggregation operation on aggregable resources within a target area. The aggregable resources include distributed resources, energy storage resources, and controllable load resources; A resource evaluation unit for performing a resource evaluation operation on the aggregable resources of each resource type to determine the corresponding evaluation results. The resource evaluation operation is used to quantify the economic value and technical applicability of the aggregable resources participating in power transactions through preset evaluation indicators corresponding to each resource type; A model construction unit for constructing an optimized scheduling model corresponding to the virtual power plant based on the multiple evaluation results. The optimized scheduling model includes a first sub-objective function and a second sub-objective function. The first sub-objective function includes the power generation revenue corresponding to the distributed resources, the energy storage revenue corresponding to the energy storage resources, and the controllable load revenue corresponding to the controllable load resources. The first sub-objective function is used to maximize the sum of the power generation revenue, the energy storage revenue, and the controllable load revenue. The second sub-objective function is used to minimize the penalty cost of power balance deviation and power quality over-limit generated during the operation of the virtual power plant; A data acquisition unit for real-time acquiring the current electricity price curve data and grid load forecast data of the power market; A model update unit for substituting the current electricity price curve data into the first sub-objective function and substituting the grid load forecast data into the second sub-objective function to update the model parameters of the optimized scheduling model; A strategy generation unit for using a preset optimization algorithm to solve the updated optimized scheduling model to determine the power trading strategy.

9. A server, characterized in that, Includes a processor, a memory, a communication interface, and one or more programs. The one or more programs are stored in the memory and are configured to be executed by the processor. The programs include instructions for performing the steps in the method according to any one of claims 1-7.

10. A computer-readable storage medium, characterized in that, Store a computer program for electronic data exchange, wherein the computer program causes a computer to execute the method according to any one of claims 1-7.

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