Virtual power plant adjustable resource configuration method and system and medium

Abstract

The invention discloses an adjustable resource allocation method and system for a virtual power plant and a medium, and belongs to the technical field of power system planning and optimization. Comprising the following steps: constructing an ordered sequence based on annual hourly power load data, constructing an empirical cumulative distribution function, and obtaining minimum construction capacity thresholds corresponding to different coverage probabilities through an inverse function of the empirical cumulative distribution function; determining the annual total cost under each threshold, drawing a total cost-coverage probability curve, and determining a construction capacity candidate interval by taking the lowest coverage probability requirement and the highest bearable cost as boundaries; solving the optimal construction capacity under the constraint of operation and reliability by taking the minimization of annual total cost as a target, and allocating the optimal construction capacity to adjustable resources of an air conditioner, a battery charging and swapping unit, an energy storage unit, a data center and an industrial interruptible load according to technical and economic characteristics, so as to complete the optimal configuration of a virtual power plant resource structure; according to the invention, scientific planning of the virtual power plant construction capacity and reasonable configuration of adjustable resources are realized.

Description

Technical Field

[0001] This invention relates to the field of power system planning and optimization technology, and in particular to a method, system and medium for adjustable resource allocation in a virtual power plant. Background Technology

[0002] With the deepening of the construction of new power systems, the high proportion of renewable energy connected to the grid has brought significant challenges of randomness and volatility to the power system, placing higher demands on the grid's flexible adjustment capabilities. Against this backdrop, virtual power plants, as an advanced technology that aggregates distributed energy resources, energy storage systems, and diverse adjustable loads, have become a key means to improve system flexibility and reliability. Their core task is to maximize overall operational economic benefits by optimizing the allocation of internal adjustable resources, while ensuring regional power supply and demand balance.

[0003] Currently, the planning and resource allocation methods for virtual power plants typically rely on historical peak loads or a few typical daily scenarios for capacity calculation, and then allocate various adjustable resources based on empirical proportions. This method has significant limitations: firstly, it fails to fully utilize the complete probabilistic distribution information of the 8760 hours of load data throughout the year, thus failing to accurately quantify the actual coverage capacity of the annual load under different construction capacities, easily leading to an imbalance between the economy and reliability of capacity planning; secondly, resource allocation is often disconnected from the overall probabilistic reliability objectives of the system, lacking a refined model for synergistic optimization of the technical and economic characteristics of different types of adjustable resources, thereby limiting the overall regulation efficiency of the virtual power plant.

[0004] While existing research has yielded significant results in short-term optimized operation and market trading strategies for virtual power plants, a systematic and quantitative method remains lacking in its long-term planning phase. This method addresses how to determine the construction capacity based on the annual load probability distribution and, based on this, collaboratively optimize the allocation ratio of various types of adjustable resources. This gap in the planning stage exposes virtual power plants to risks of suboptimal resource allocation and a difficulty in balancing economic efficiency and reliability during actual construction and operation. Summary of the Invention

[0005] The purpose of this invention is to provide a method for adjusting resource allocation in a virtual power plant, which realizes the scientific planning of the construction capacity of the virtual power plant and the rational allocation of adjustable resources.

[0006] To address the aforementioned technical problems, embodiments of the present invention provide a method for adjustable resource allocation in a virtual power plant, comprising the following steps: Obtain the hourly electricity load of the region to be priced throughout the year, and sort it in ascending order to obtain an ordered sequence of electricity load. An empirical cumulative distribution function (CDF) is constructed based on an ordered sequence of power loads; the minimum construction capacity threshold of virtual power plants corresponding to different coverage probabilities is obtained through the inverse function of the CDF. Determine the annualized total cost of the virtual power plant corresponding to each minimum capacity threshold, and plot the total cost-coverage probability curve with coverage probability as the horizontal axis and annualized total cost as the vertical axis. Based on the total cost-coverage probability curve, construct a candidate interval for the construction capacity. The lower boundary of the candidate interval is the construction capacity corresponding to the minimum coverage probability required by a certain region, and the upper boundary of the candidate interval is the construction capacity corresponding to the highest annualized cost that a certain region can afford. With the premise of maximizing coverage probability as a constraint and minimizing the annualized total cost of the virtual power plant as the core objective, under the constraints of virtual power plant operation and reliability, the optimal construction capacity is determined from the candidate interval, and the optimal construction capacity is allocated to adjustable resources according to the optimal ratio.

[0007] In some optional embodiments, the empirical cumulative distribution function (CDF) is formulated as follows: In the formula, This is an estimate of the empirical cumulative distribution function (CDF). Pre-set the construction capacity for the virtual power plant. This represents the total number of hourly electricity load samples throughout the year. The hourly power load corresponds to a certain moment; the empirical cumulative distribution function CDF is monotonically increasing and has a value range of [0,1], which can intuitively reflect the coverage of the virtual power plant's power supply capacity as the construction capacity changes.

[0008] In some optional embodiments, the formula for obtaining the minimum construction capacity threshold of the virtual power plant corresponding to different coverage probabilities is as follows: In the formula, It is the inverse function of the CDF. The minimum construction capacity threshold for virtual power plants under different coverage probability requirements. For different coverage probabilities.

[0009] In some optional embodiments, the objective function expression is as follows: maximizing coverage probability is a given constraint, and minimizing the annualized total cost of the virtual power plant is the core objective. ; In the formula, To comprehensively optimize the objective function; The weighting coefficient adjusts the priority between coverage probability and annualized total cost; The annualized total cost of the virtual power plant; For coverage probability, Capacity for building virtual power plants; In the formula, The construction investment cost (ten thousand yuan) is determined based on the ratio of unit construction capacity to resource allocation. The capital recovery factor and the discount rate are mentioned. Lifespan Year; For operation and maintenance costs; To adjust compensation costs; For communication and management costs; The revenue consists of capacity revenue, call revenue, and availability revenue. In the formula, For coverage probability, To build capacity for virtual power plants, For indicator functions, This represents the hourly power load at a given moment.

[0010] In some optional embodiments, the virtual power plant operation and reliability constraints include adjustable resource capacity allocation constraints, virtual power plant real-time power balance constraints, energy storage full-cycle energy balance constraints, energy storage charge and discharge state mutual exclusion constraints, adjustable resource operation boundary constraints, load coverage probability constraints, and virtual power plant construction capacity boundary constraints.

[0011] In some alternative embodiments, the adjustable resources include air conditioning loads, charging and battery swapping stations, novel energy storage, adjustable data center loads, and industrial interruptible loads.

[0012] Embodiments of the present invention also provide a virtual power plant adjustable resource optimization system, comprising: The data acquisition module is used to obtain the hourly electricity load of the region to be priced throughout the year, and sort it in ascending order to obtain an ordered sequence of electricity load. The capacity threshold calculation module is used to construct an empirical cumulative distribution function (CDF) based on an ordered sequence of power loads; and to obtain the minimum construction capacity threshold of the virtual power plant corresponding to different coverage probabilities through the inverse function of the CDF. Candidate Interval Determination Module: Used to determine the annualized total cost of virtual power plants corresponding to each minimum capacity threshold, plotting a total cost-coverage probability curve with coverage probability as the horizontal axis and annualized total cost as the vertical axis; constructing candidate intervals for construction capacity based on the total cost-coverage probability curve, wherein the lower boundary of the candidate interval is the construction capacity corresponding to the minimum coverage probability required by a certain region, and the upper boundary of the candidate interval is the construction capacity corresponding to the highest annualized cost that a certain region can afford; Adjustable resource optimization allocation module: Under the premise of maximizing coverage probability and with the core objective of minimizing the annualized total cost of the virtual power plant, under the constraints of virtual power plant operation and reliability, it determines the optimal construction capacity from the candidate interval and allocates the optimal construction capacity to the adjustable resources according to the optimal ratio.

[0013] Embodiments of the present invention also provide a computer device, including: at least one processor; and a memory communicatively connected to the at least one processor; wherein the memory stores instructions executable by the at least one processor, the instructions being executed by the at least one processor to enable the at least one processor to perform the above-described virtual power plant adjustable resource configuration method.

[0014] Embodiments of the present invention also provide a computer-readable storage medium storing a computer program, which, when run by a processor, is capable of executing the above-described virtual power plant adjustable resource allocation method.

[0015] The adjustable resource allocation method for virtual power plants provided by this invention has at least the following beneficial effects: This invention establishes a scientific decision-making mechanism based on probability coverage by constructing an annual load experience cumulative distribution function and calculating quantile capacity. This effectively overcomes the limitations of traditional planning methods that rely on single peak loads or typical daily curves, significantly improving the accuracy of capacity allocation and avoiding problems of investment redundancy or insufficient capacity. By plotting the total cost-coverage probability curve and constructing capacity candidate intervals, reliability requirements and investment capabilities are quantified into clear dual-boundary constraints, providing an intuitive quantitative basis for planning decisions and achieving an optimal balance between economy and reliability. On this basis, by comprehensively considering the technical and economic characteristics of various resources such as air conditioning loads, charging and swapping stations, new energy storage, data centers, and industrial interruptible loads, the invention achieves refined configuration and collaborative optimization of resource structures, improving the overall flexibility and operational efficiency of virtual power plants. Furthermore, this method framework has good universality and scalability, and can adapt to the differentiated needs of different regions by adjusting basic parameters, providing a scientific, reliable, and flexible solution for virtual power plant planning in regional power grids. Attached Figure Description

[0016] The accompanying drawings, which are included to provide a further understanding of the invention and form part of this invention, illustrate exemplary embodiments of the invention and are used to explain the invention, but do not constitute an undue limitation of the invention. In the drawings:

[0017] Figure 1 This is a flowchart of a virtual power plant adjustable resource allocation method according to an embodiment of the present invention; Figure 2This is a general flowchart of a virtual power plant adjustable resource allocation method according to an embodiment of the present invention; Figure 3 This is a schematic diagram of the empirical cumulative distribution function (CDF) curve and quantiles provided according to an embodiment of the present invention; Figure 4 This is a diagram illustrating the trade-off between coverage and annualized total cost according to an embodiment of the present invention; Figure 5 This is a schematic diagram of an adjustable resource ratio provided according to an embodiment of the present invention; Figure 6 This is a schematic diagram of the energy storage SOC change and charge / discharge mutual exclusion relationship provided according to an embodiment of the present invention. Detailed Implementation

[0018] To make the objectives, technical solutions, and advantages of this invention clearer, the technical solutions of this invention will be clearly and completely described below in conjunction with specific embodiments and corresponding drawings. Obviously, the described embodiments are only a part of the embodiments of this invention, and not all of them. Based on the embodiments of this invention, all other embodiments obtained by those skilled in the art without creative effort are within the scope of protection of this invention.

[0019] One embodiment of the present invention relates to a method for adjustable resource allocation in a virtual power plant. The implementation details of the method for adjustable resource allocation in a virtual power plant in this embodiment are described in detail below. The following implementation details are provided for ease of understanding and are not necessary for implementing this solution.

[0020] The specific process of the virtual power plant adjustable resource allocation method in this embodiment can be described as follows: Figure 1 As shown, it includes: Step 101: Obtain the hourly electricity load of the region to be priced throughout the year, and sort it in ascending order to obtain an ordered sequence of electricity loads; First, obtain 8760 hours of annual load data for a specific region, sourced from regional dispatch centers, historical operation monitoring databases, etc. Data dimensions should include auxiliary features such as time, load, and holiday markers to comprehensively depict the seasonality and randomness of electricity demand.

[0021] Before model calculation, the input data needs to undergo complete quality checks and preprocessing: Completeness check: Ensure that the data covers 8760 hours throughout the year, and use the mean of the same hour within the week to imput missing periods; Consistency check: Includes time series continuity, abnormal peak detection, and unit system unification; Abnormal data processing: Correct abrupt peaks or troughs using a local weighted regression smoothing algorithm; Data standardization: Use MW as the unit of measurement for all power data, and standardize the time step to 1 hour.

[0022] Simultaneously, parameter libraries were established for five categories of adjustable resources, including air conditioning load, charging and battery swapping stations, new energy storage, data centers, and industrial interruptible loads. The parameters cover key indicators such as construction capacity limits, rated power, operating costs, and availability, providing physical boundary conditions for subsequent optimization.

[0023] The preprocessed hourly load values ​​for the whole year are sorted in ascending order to obtain an ordered load sequence: ,in ; Step 102: Construct an empirical cumulative distribution function (CDF) based on the ordered sequence of power loads; obtain the minimum construction capacity threshold of the virtual power plant corresponding to different coverage probabilities through the inverse function of the CDF. The empirical cumulative distribution function (CDF) formula is as follows: In the formula, This is an estimate of the empirical cumulative distribution function (CDF). Pre-set the construction capacity for the virtual power plant. This represents the total number of hourly electricity load samples throughout the year. The hourly power load corresponds to a certain moment; the empirical cumulative distribution function CDF is monotonically increasing and has a value range of [0,1], which can intuitively reflect the coverage of the virtual power plant's power supply capacity as the construction capacity changes.

[0024] Based on the empirical cumulative distribution function CDF, the load loss probability (LOLP) index is calculated, which is defined as follows: This indicator represents the available capacity of the system. The probability that the annual load exceeds the built capacity is considered the risk level of insufficient power supply. The LOLP curve monotonically decreases with increasing built capacity, reflecting the trend that reliability increases with the scale of construction. By comprehensively analyzing the functional shapes of empirical CDF and LOLP, the probabilistic characteristics of system supply and demand balance can be obtained, seeking a balance between reliability and economy.

[0025] The formulas for the minimum construction capacity threshold of virtual power plants under different coverage probability requirements are as follows: In the formula, It is the inverse function of the CDF. The minimum construction capacity threshold for virtual power plants under different coverage probability requirements. For different coverage probabilities.

[0026] Step 103: Determine the annualized total cost of the virtual power plant corresponding to each minimum capacity threshold. Plot the total cost-coverage probability curve with coverage probability as the horizontal axis and annualized total cost as the vertical axis. Construct a candidate interval for construction capacity based on the total cost-coverage probability curve. The lower boundary of the candidate interval is the construction capacity corresponding to the minimum coverage probability required by a certain region, and the upper boundary of the candidate interval is the construction capacity corresponding to the highest annualized cost that a certain region can afford. The annualized total cost comprises construction investment cost, operation and maintenance cost, adjustment and compensation cost, and communication management cost.

[0027] At the planning level, there is a clear nonlinear relationship between coverage and construction capacity: as construction capacity increases, the improvement in system reliability slows down, while investment costs increase linearly or superlinearly. To balance these two factors, this invention introduces a reliability-economic trade-off analysis mechanism, determining the reasonable range of capacity values ​​by jointly evaluating the annualized total cost curve and the CDF curve. Specifically: ① The low coverage range (10%–40%) corresponds to a planning approach prioritizing economic efficiency. At this point, the virtual power plant construction capacity is relatively small, and investment and operating costs are at a low level. However, it may not be able to fully meet demand during peak load periods, and the system faces certain power supply risks. This is suitable for areas with small load fluctuations or external peak-shaving support. ② The medium coverage range (40%–70%) reflects the balance point between economic efficiency and reliability. Virtual power plants can cover most of the annual load demand while maintaining a high investment utilization rate. This range usually corresponds to the inflection point of the annualized total cost curve, i.e., the area where the cost increase rate is similar to the reliability improvement rate, making it the most engineering-feasible construction capacity segment. ③ The high coverage range (70%–90%) embodies a planning approach prioritizing reliability, suitable for scenarios with high renewable energy penetration or strict system safety requirements. At this point, the virtual power plant capacity is sufficient to cover periods of extreme high load, but investment costs increase significantly. If the capacity is further increased to near 100% coverage, although the power supply risk is almost eliminated, the return on investment decreases significantly, and the economic advantage is no longer available.

[0028] The total cost-coverage probability curve typically exhibits a gradual increase followed by a steeper one, with the inflection point representing the capacity range where economic efficiency and reliability achieve a dynamic balance. By calculating the annualized total cost under different coverage rates, the location with the minimum cost change rate or the maximum marginal benefit can be identified as the recommended construction capacity. In model implementation, this invention calculates different... Minimum annualized total cost under the value and based on capacity quantiles Establish joint analysis relationships using these as independent variables. Define the candidate interval for construction capacity as:

[0029] in, and These correspond to the maximum and minimum reliability levels that the plan can accept. Generally, this invention recommends setting the candidate range to 3%–10% of the predicted maximum load, which ensures both economy and high system stability.

[0030] Step 104: With the premise of maximizing coverage probability as a constraint and minimizing the annualized total cost of the virtual power plant as the core objective, under the constraints of virtual power plant operation and reliability, the optimal construction capacity is determined from the candidate interval, and the optimal construction capacity is allocated to the adjustable resources according to the optimal ratio.

[0031] Adjustable resources include air conditioning loads, charging and battery swapping stations, new energy storage, adjustable loads for data centers, and interruptible loads for industries.

[0032] The objective function expression is: ; In the formula, To comprehensively optimize the objective function; The weighting coefficient adjusts the priority between coverage probability and annualized total cost; The annualized total cost of the virtual power plant; For coverage probability, Capacity for building virtual power plants; In the formula, The construction investment cost (ten thousand yuan) is determined based on the ratio of unit construction capacity to resource allocation. The capital recovery factor and the discount rate are mentioned. Lifespan Year; For operation and maintenance costs; To adjust compensation costs; For communication and management costs; The revenue consists of capacity revenue, call revenue, and availability revenue. In the formula, For coverage probability, To build capacity for virtual power plants, For indicator functions, This represents the hourly power load at a given moment.

[0033] In the formula, To cover construction investment costs, Based on unit capacity cost, Let be the investment capacity coefficient for the i-th type of adjustable resource.

[0034] In the formula, is the capital recovery factor, i is the discount rate, and n is the operating life of the virtual power plant.

[0035] In the formula, For communication and management costs, Based on unit capacity cost, Let be the unit operation and maintenance cost coefficient for the i-th type of adjustable resource.

[0036] In the formula, To adjust compensation costs, Based on unit capacity cost, For annual electricity purchases, Let be the electricity purchase allocation coefficient for the i-th type of adjustable resource.

[0037] In the formula, The revenue consists of capacity revenue, call revenue, and availability revenue. For capacity revenue, For the purpose of generating revenue, The unit price for capacity revenue, To determine the unit price for revenue collection; The total electricity consumption for the year is calculated based on the load gap. For usability benefits, Unit price is compensated for availability; The number of available hours for the virtual power plant throughout the year is calculated based on the non-zero load gap periods.

[0038] The various operational and reliability constraints of the virtual power plant include adjustable resource capacity allocation constraints, virtual power plant real-time power balance constraints, energy balance constraints throughout the energy storage cycle, mutual exclusion constraints of energy storage charging and discharging states, adjustable resource operation boundary constraints, load coverage probability constraints, and virtual power plant construction capacity boundary constraints.

[0039] The adjustable resource capacity allocation constraint is: The real-time power balance constraints for the virtual power plant are: The energy balance constraint for the entire life cycle of energy storage is: The mutual exclusion constraint for energy storage charge and discharge states is: Adjustable resource operation boundary constraints: The load coverage probability constraint is: The boundary constraints for the construction capacity of the virtual power plant are: The optimization results of this invention are complete and multi-dimensional, specifically including: the optimal construction capacity and its corresponding annualized total cost, providing a direct basis for investment decisions; the optimal allocation ratio of various adjustable resources, clarifying the collaborative structure of air conditioning load, charging and swapping stations, new energy storage, adjustable data center load, and interruptible industrial load; system reliability index analysis, covering target coverage and actual load loss probability assessment; and multi-dimensional visualization results such as empirical CDF curves, cost-coverage relationship curves, resource structure ratio diagrams, and energy storage SOC curves, intuitively displaying probability distribution, economic trade-offs, resource allocation, and operational status. Through the complete technical process from in-depth load data analysis to optimal resource allocation, this invention provides a scientific and reliable overall planning scheme for virtual power plant construction. Furthermore, this methodological framework has good versatility and adaptability; it can be flexibly applied to different regional power grid structures and development needs by only adjusting basic input parameters, providing effective technical support for differentiated planning of regional power grids.

[0040] Another embodiment of the present invention relates to a virtual power plant adjustable resource optimization system. The implementation details of the virtual power plant adjustable resource optimization system of this embodiment are described in detail below. The following implementation details are provided for ease of understanding and are not necessary for implementing this solution.

[0041] Specifically, the data acquisition module is used to acquire the hourly power load of a certain region throughout the year and sort it in ascending order to obtain an ordered sequence of power loads; The capacity threshold calculation module is used to construct an empirical cumulative distribution function (CDF) based on an ordered sequence of power loads. The CDF represents the probability that the virtual power plant's preset construction capacity covers the annual power load. The module calculates the inverse function of the CDF to obtain the minimum construction capacity threshold of the virtual power plant corresponding to different coverage probabilities. Candidate interval determination module: Calculate the annualized total cost of the virtual power plant corresponding to each capacity threshold, and plot the total cost-coverage probability curve with coverage probability as the horizontal axis and annualized total cost as the vertical axis; construct candidate intervals for construction capacity based on the total cost-coverage probability curve, wherein the lower boundary of the candidate interval is the construction capacity corresponding to the minimum coverage probability required by a certain region, and the upper boundary of the candidate interval is the construction capacity corresponding to the highest annualized cost that a certain region can bear; Adjustable resource optimization allocation module: With the premise of maximizing coverage probability as a constraint and minimizing the annualized total cost of the virtual power plant as the core objective, under the constraints of virtual power plant operation and reliability, it determines the optimal construction capacity from the candidate interval and allocates the optimal construction capacity to the adjustable resources according to the optimal ratio.

[0042] The overall flowchart of the virtual power plant adjustable resource allocation method is as follows: Figure 2 As shown in the diagram, the system comprises three main modules: data input and probabilistic modeling, optimization calculation, and result output and verification. First, the data input module acquires annual load data. Then, the probabilistic modeling phase uses an empirical cumulative distribution function to analyze the probabilistic characteristics of the load and determine capacity thresholds for different reliability levels. Next, the optimization module takes the target coverage rate and the construction capacity range as input. Based on the objective of minimizing the annualized total cost, it considers the operational characteristics and cost parameters of multiple adjustable resources and uses a mixed-integer programming method to solve the problem. The output module provides the optimal construction capacity, resource structure ratio, and corresponding reliability indicators. Finally, the optimized construction capacity is evaluated using these three indicators. If satisfactory, the optimized capacity is output; otherwise, the relevant parameters in the model are readjusted for further optimization.

[0043] Example 1: Before constructing a virtual power plant capacity optimization scheme, its core objective must be clearly defined: to meet the regional power load coverage needs with the lowest annualized cost, while integrating adjustable resources such as wind power, photovoltaics, and energy storage. Virtual power plants need to balance power supply reliability and economy through accurate load forecasting and capacity configuration, with the empirical cumulative distribution function (CDF) being a key analytical tool.

[0044] This analysis is based on 8760 hours of annual electricity load data (unit: MW) for a certain region. An empirical CDF curve was constructed after sorting the data in ascending order. The empirical cumulative distribution function (CDF) curve and quantile diagram are shown below. Figure 3As shown in the figure, the cumulative probability distribution of the load is obtained by sorting the annual load data, and the capacity of each quantile is marked. The horizontal axis of the cumulative probability distribution curve represents the load capacity, and the vertical axis represents the cumulative probability of load occurrence at or below that capacity. The quantiles in the figure represent the load capacity corresponding to different coverage targets. Through these quantiles, we can determine the capacity to meet the load demand for specific periods throughout the year, avoiding over-design or waste of resources. This figure intuitively presents the statistical characteristics of the load and provides a quantitative basis for capacity decisions.

[0045] This visually demonstrates the coverage capacity of virtual power plants for the entire year's load under different capacities. The green dashed line in the figure marks the P90 quantile (2033.7MW), indicating that when the constructed capacity is 2033.7MW, it can meet 90% of the annual load demand; the red dashed line marks the P30 quantile (1453.7MW), corresponding to a 30% coverage probability, providing multi-scenario reference thresholds for capacity planning.

[0046] Example 2: This invention analyzes the capacity threshold corresponding to 8760 hours of electricity load data in a certain region throughout the year, and combines economic models such as the initial construction investment of virtual power plants (including the construction cost of various adjustable resources), annual operation and maintenance costs, and resource adjustment compensation costs to calculate the annualized total cost corresponding to different load coverage rates, and finally draws this relationship diagram.

[0047] The diagram showing the trade-off between coverage and annualized total cost is as follows: Figure 4 As shown in the figure, the trade-off between cost and reliability in the construction scale of a virtual power plant is illustrated. In the figure, coverage rate is the independent variable, representing the proportion of load that the virtual power plant capacity can cover. The vertical axis represents the annualized total cost, encompassing construction investment, operating costs, regulation compensation, and communication management costs. As coverage rate increases, system reliability improves, but the annualized total cost also increases because increased construction capacity is needed to meet higher reliability targets. This trade-off diagram allows planners to determine the optimal construction capacity of the virtual power plant based on economic objectives and reliability requirements.

[0048] The “P10 Economic Priority Point” marked in red in the diagram corresponds to a scenario with a load coverage rate of 10%: the annualized total cost is 43.17 million yuan, and the matching construction capacity is 1227.1MW. This is suitable for scenarios with extremely high cost control priorities and low requirements for power supply coverage. The “P90 Stability Priority Point” marked in green corresponds to a scenario with a load coverage rate of 90%: the annualized total cost rises to 910.8 million yuan, and the matching construction capacity is 2353.7MW. This is suitable for scenarios with extremely high power supply reliability priorities and greater cost flexibility.

[0049] Example 3: This resource allocation analysis is based on the optimal construction capacity of the virtual power plant (combined with the results of the load coverage and cost trade-off mentioned above), while also taking into account the technical characteristics of various adjustable resources (such as the response speed of air conditioning load and the capacity stability of new energy storage) and economic costs (such as the operation and maintenance costs of charging and swapping stations and the adjustment and compensation costs of industrial loads), and finally determines the proportion scheme of various resources.

[0050] An illustration of the adjustable resource ratio is shown below. Figure 5 As shown in the diagram, the resource allocation of a virtual power plant typically includes adjustable resources such as air conditioning load, charging and battery swapping stations, new energy storage, data centers, and industrial loads. By solving the optimization model, the optimal power proportion of each type of resource is obtained. This diagram presents the proportion of each type of resource in a pie chart format, allowing planners to intuitively understand the percentage of each resource in the total construction capacity. During the optimization process, the proportion of different resources will be adjusted according to fluctuations in load demand and changes in economic requirements. Through this diagram, the resource allocation of the virtual power plant is clearly displayed, ensuring that resources can be coordinated and scheduled most effectively to achieve optimal system performance.

[0051] Figure 4 The chart comprises both a pie chart and a bar chart, showcasing the proportional distribution of resources. The pie chart presents the overall proportions: air conditioning load accounts for 31%, industrial load for 29%, charging and battery swapping stations for 16%, new energy storage for 14%, and photovoltaic power plants for 10%. The bar chart on the right uses "proportion (%)" as the vertical axis and resource type as the horizontal axis, providing a clearer comparison of the proportions of various resources. Among them, air conditioning load has the highest proportion (31%), due to its fast response speed and low adjustment cost, making it the core resource for short-term load regulation. Industrial load (29%) relies on a larger adjustment range to support medium- and long-term load balance. New energy storage (14%) and photovoltaic power plants (10%) serve as supplements, providing capacity reserves and clean energy, respectively. Charging and battery swapping stations (16%) achieve coordinated regulation of the transportation and power systems.

[0052] Example 4: After the virtual power plant completes the resource structure configuration, it is necessary to clarify the core operational objectives of the energy storage module: under the premise of ensuring the safe lifespan of energy storage, match the hourly load adjustment needs of the virtual power plant through reasonable charging and discharging strategies, and at the same time maintain the energy storage state of charge (SOC) within a safe range. The "Schematic diagram of energy storage SOC change and charging and discharging power mutual exclusion" is a key tool for monitoring and optimizing the energy storage operation status.

[0053] This energy storage operation analysis is based on the 25-hour hourly load demand of a virtual power plant (matching the regional load characteristics mentioned above), combined with the safety operation constraints of energy storage (such as SOC upper and lower limits, charging and discharging power thresholds), and synchronously records the SOC changes and charging and discharging power status of the energy storage, and finally draws this schematic diagram.

[0054] A schematic diagram of energy storage SOC changes and charge / discharge mutual exclusion relationship is shown below. Figure 6 As shown in the figure, the SOC variation on a typical day is illustrated. The charging period is during the low-load valley period from 1:00 to 6:00, and the discharging period is during the peak period from 18:00 to 21:00, reflecting the role of energy storage in peak shaving and valley filling. The charging and discharging processes are subject to mutual exclusion constraints, meaning that the energy storage system cannot perform charging and discharging operations simultaneously. By introducing binary variable constraints, it is ensured that the energy storage system completes charging or discharging operations within a reasonable time window, maximizing resource utilization, optimizing energy storage resource scheduling strategies, and ensuring the overall reliability and economy of the virtual power plant.

[0055] Taking the 2023 load data of a certain region as an example, optimization solutions were implemented using the MATLAB platform. First, an empirical CDF curve was generated using a sorting function; second, the LOLP (Lowest Limit of Capacity) was calculated and combined with the target coverage rate to determine the capacity range; the capacity was scanned within the coverage range of 10%–90%; then, the mixed integer programming model was solved using YALMIP and GUROBI, outputting the optimal construction capacity, resource allocation, SOC curve, and annualized total cost. The final results show that the optimal point corresponds to a capacity of 2273.4MW, approximately 6% of the predicted maximum load, with an annualized total cost of 897.54 million yuan, demonstrating a good balance between reliability and economy.

[0056] The steps of the various methods described above are only for clarity. In practice, they can be combined into one step or some steps can be split into multiple steps. As long as they include the same logical relationship, they are all within the protection scope of this invention. Adding insignificant modifications or introducing insignificant designs to the algorithm or process, without changing the core design of the algorithm and process, are also within the protection scope of this invention.

[0057] Another embodiment of the present invention relates to a computer-readable storage medium storing a computer program. When executed by a processor, the computer program implements the method embodiments described above.

[0058] That is, those skilled in the art will understand that all or part of the steps in the methods of the above embodiments can be implemented by a program instructing related hardware. This program is stored in a storage medium and includes several instructions to cause a device (which may be a microcontroller, chip, etc.) or processor to execute all or part of the steps of the methods described in the various embodiments of the present invention. The aforementioned storage medium includes various media capable of storing program code, such as USB flash drives, portable hard drives, read-only memory (ROM), random access memory (RAM), magnetic disks, or optical disks.

[0059] Those skilled in the art will understand that the above embodiments are specific embodiments for implementing the present invention, and in practical applications, various changes can be made to them in form and detail without departing from the spirit and scope of the present invention.

Claims

1. A method for adjustable resource allocation in a virtual power plant, characterized in that, The method includes: Obtain the hourly electricity load of the region to be priced throughout the year, and sort it in ascending order to obtain an ordered sequence of electricity load. An empirical cumulative distribution function (CDF) is constructed based on an ordered sequence of power loads; the minimum construction capacity threshold of virtual power plants corresponding to different coverage probabilities is obtained through the inverse function of the CDF. Determine the annualized total cost of the virtual power plant corresponding to each minimum capacity threshold, and plot the total cost-coverage probability curve with coverage probability as the horizontal axis and annualized total cost as the vertical axis. Based on the total cost-coverage probability curve, construct a candidate interval for the construction capacity. The lower boundary of the candidate interval is the construction capacity corresponding to the minimum coverage probability required by a certain region, and the upper boundary of the candidate interval is the construction capacity corresponding to the highest annualized cost that a certain region can afford. With the premise of maximizing coverage probability as a constraint and minimizing the annualized total cost of the virtual power plant as the core objective, under the constraints of virtual power plant operation and reliability, the optimal construction capacity is determined from the candidate interval, and the optimal construction capacity is allocated to adjustable resources according to the optimal ratio.

2. The virtual power plant adjustable resource allocation method as described in claim 1, characterized in that, The empirical cumulative distribution function (CDF) formula is as follows: In the formula, This is an estimate of the empirical cumulative distribution function (CDF). Pre-set the construction capacity for the virtual power plant. This represents the total number of hourly electricity load samples throughout the year. The hourly power load corresponding to a certain moment. The function is an indicator function; the empirical cumulative distribution function (CDF) is monotonically increasing and has a value range of [0,1], which can intuitively reflect the coverage of the virtual power plant's power supply capacity as the construction capacity changes.

3. The virtual power plant adjustable resource allocation method as described in claim 1, characterized in that, The formula for obtaining the minimum construction capacity threshold of virtual power plants corresponding to different coverage probabilities is as follows: In the formula, It is the inverse function of the CDF. The minimum construction capacity threshold for virtual power plants under different coverage probability requirements. For different coverage probabilities.

4. The virtual power plant adjustable resource allocation method as described in claim 1, characterized in that, The annualized total cost comprises construction investment cost, operation and maintenance cost, adjustment and compensation cost, and communication management cost.

5. The virtual power plant adjustable resource allocation method as described in claim 1, characterized in that, The objective function, which takes maximizing coverage probability as a constraint and minimizing the annualized total cost of the virtual power plant as its core objective, is expressed as follows: ； In the formula, To comprehensively optimize the objective function; The weighting coefficient adjusts the priority between coverage probability and annualized total cost; The annualized total cost of the virtual power plant; For coverage probability, Capacity for building virtual power plants; In the formula, The construction investment cost (ten thousand yuan) is determined based on the ratio of unit construction capacity to resource allocation. The capital recovery factor and the discount rate are mentioned. Lifespan Year; For operation and maintenance costs; To adjust compensation costs; For communication and management costs; The revenue consists of capacity revenue, call revenue, and availability revenue. In the formula, For coverage probability, To build capacity for virtual power plants, For indicator functions, This represents the hourly power load at a given moment.

6. The virtual power plant adjustable resource allocation method as described in claim 1, characterized in that, The constraints on the operation and reliability of the virtual power plant include adjustable resource capacity allocation constraints, real-time power balance constraints of the virtual power plant, energy balance constraints throughout the energy storage cycle, mutual exclusion constraints of energy storage charging and discharging states, operational boundary constraints of adjustable resources, load coverage probability constraints, and construction capacity boundary constraints of the virtual power plant.

7. The virtual power plant adjustable resource allocation method as described in claim 1, characterized in that, The adjustable resources include air conditioning loads, charging and battery swapping stations, new energy storage, adjustable data center loads, and industrial interruptible loads.

8. A virtual power plant adjustable resource optimization system, characterized in that, The system includes: The data acquisition module is used to obtain the hourly electricity load of the region to be priced throughout the year, and sort it in ascending order to obtain an ordered sequence of electricity load. The capacity threshold calculation module is used to construct an empirical cumulative distribution function (CDF) based on an ordered sequence of power loads; and to obtain the minimum construction capacity threshold of the virtual power plant corresponding to different coverage probabilities through the inverse function of the CDF. Candidate Interval Determination Module: Used to determine the annualized total cost of virtual power plants corresponding to each minimum capacity threshold, plotting a total cost-coverage probability curve with coverage probability as the horizontal axis and annualized total cost as the vertical axis; constructing candidate intervals for construction capacity based on the total cost-coverage probability curve, wherein the lower boundary of the candidate interval is the construction capacity corresponding to the minimum coverage probability required by a certain region, and the upper boundary of the candidate interval is the construction capacity corresponding to the highest annualized cost that a certain region can afford; Adjustable resource optimization allocation module: Under the premise of maximizing coverage probability and with the core objective of minimizing the annualized total cost of the virtual power plant, under the constraints of virtual power plant operation and reliability, it determines the optimal construction capacity from the candidate interval and allocates the optimal construction capacity to the adjustable resources according to the optimal ratio.

9. A computer system, characterized in that, include: At least one processor; And a memory communicatively connected to the at least one processor; wherein the memory stores instructions executable by the at least one processor, the instructions being executed by the at least one processor to enable the at least one processor to perform the virtual power plant adjustable resource configuration method as described in any one of claims 1 to 7.

10. A computer-readable storage medium, characterized in that, The computer-readable storage medium stores a computer program that, when executed by a processor, is capable of performing the virtual power plant adjustable resource allocation method as defined in any one of claims 1 to 7.

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