Aggregator multi-element electricity market electricity purchase decision-making method and system fusing user proportion and CVaR
By constructing a diversified market power purchase model and quantifying losses using CVaR, and combining a fixed single electricity price with peak-valley time-of-use pricing packages, the problem of insufficient coordination in power purchase and sale by load aggregators in a diversified power market has been solved, thereby achieving risk quantification and improved scientific decision-making.
Patent Information
- Application Number
- CN202511646482.2
- Authority / Receiving Office
- CN · China
- Patent Type
- Applications(China)
- Current Assignee / Owner
- Filing Date
- 2025-11-11
- Publication Date
- 2026-03-03
AI Technical Summary
In a diversified electricity market environment, load aggregators face problems such as insufficient coordination between power purchase and sale and lack of market risk quantification. Traditional power purchase models are difficult to balance deviation assessment risks with user demand elasticity, and lack diversified market coordination and risk quantification methods.
A diversified market electricity purchase model is constructed, which combines a fixed single electricity price and a peak-valley time-of-use electricity price retail package model. The loss in each scenario is quantified through CVaR to obtain the decision result of the benefit-risk balance and realize the electricity purchase decision.
It enhances the coordination, scientific rigor, and risk management capabilities of power purchase and sale. By coupling power purchase and sale coordination modeling with risk quantification, it achieves overall cost optimization and scientific decision-making.
Smart Images
Figure CN121599692A_ABST
Abstract
Description
Technical Field
[0001] This invention belongs to the field of electricity market technology, specifically relating to a method and system for aggregator-based multi-market electricity purchase decision-making that integrates user ratio and CVaR. Background Technology
[0002] As power market reforms deepen, load aggregators, as key hubs in the power industry chain, are facing severe challenges from diversified power purchase structures and increasingly complex risk management. Load aggregators (LAs) are professional entities that integrate dispersed power loads from industry, commerce, and residential sectors, participating in grid dispatch and power market transactions. On the one hand, with the gradual establishment of a multi-tiered power market collaborative operation mechanism, load aggregators need to coordinate power purchase strategies across multiple time and space dimensions to reduce overall power purchase costs. On the other hand, under the multiple pressures of renewable energy consumption responsibility weighting assessments and the full liberalization of the retail side, if load aggregators continue to use the traditional single power purchase model, it will be difficult to achieve an effective balance between avoiding deviation assessment risks and flexibly responding to user demand elasticity. Especially after the high proportion of renewable energy integrated into the power system, spot market price fluctuations have become more frequent, further highlighting the importance of building a diversified market trading decision-making system.
[0003] As power market reforms deepen, load aggregators, as key hubs in the power industry chain, are facing severe challenges from diversified power purchase structures and increasingly complex risk management. Load aggregators (LAs) are professional entities that integrate dispersed power loads from industry, commerce, and residential sectors to participate in grid dispatch and power market transactions. They need to address the challenges of coordinated operation of multi-tiered power markets, the assessment of renewable energy consumption responsibility weights, and the full liberalization of the retail side. Furthermore, the high proportion of renewable energy integration exacerbates spot market price volatility, leading to challenges from diversified power purchase structures and complex risk management.
[0004] Load aggregators need to coordinate multi-temporal and spatial power purchase strategies to reduce costs. Traditional single power purchase models struggle to balance deviation assessment risks with user demand elasticity, necessitating the construction of a diversified market transaction decision-making system. Current approaches primarily optimize power purchase strategies for single electricity markets, such as adjusting medium- and long-term or spot market purchase volumes based on historical data; assessing risk using simple indicators like electricity price variance; and introducing basic peak-valley time-of-use pricing packages to replace fixed prices on the retail side. However, a complete solution lacking multi-market coordination, precise risk quantification, and power purchase-sales linkage has not been formed. Existing solutions lack multi-market power purchase coordination, failing to achieve overall cost optimization; risk quantification methods are crude and unable to cover extreme price fluctuation risks; retail packages do not incorporate user selection ratios and power purchase decisions, resulting in insufficient power purchase-sales linkage; and no optimization model or intelligent solution method combining profit maximization and risk minimization has been constructed, failing to address the core issues of insufficient power purchase-sales coordination and risk quantification. The most critical technical problem is the insufficient power purchase-sales coordination and lack of market risk quantification faced by load aggregators in a diversified electricity market environment. Summary of the Invention
[0005] The purpose of this invention is to solve the problems in the prior art and provide a method and system for aggregator-based multi-market electricity purchase decision-making that integrates user ratio and CVaR.
[0006] To achieve the above objectives, the present invention employs the following technical solution: The proposed method for aggregator-based multi-market electricity purchase decision-making, which integrates user ratio and CVaR, includes the following steps: A diversified market power purchase model is constructed based on long-term electricity market, spot electricity market, green electricity market, distributed generation and energy storage leasing scenarios. Establish a retail package model for load aggregators that includes both fixed single electricity price and peak-valley time-of-use electricity price; Determine the user ratio of peak-valley time-of-use electricity pricing packages, and calculate the expected electricity sales revenue based on the user ratio and the retail package model of the load aggregator's electricity sales side. Based on the diversified market electricity purchase model and the expected electricity sales revenue, the total revenue and loss representations under all scenarios are obtained. CVaR is used to quantify the loss representations of each scenario. The loss representation results and the total revenue under all scenarios are analyzed to obtain the decision results of the revenue-risk balance, and realize diversified electricity market electricity purchase decisions.
[0007] Preferably, the construction of a diversified market power purchase model based on long-term electricity market, spot electricity market, green electricity market, distributed generation and energy storage leasing scenarios specifically includes: Scene ψ Electricity purchase costs for aggregators in the medium- and long-term trading market C B ( ψ ) represents ; Scene ψ Electricity purchase cost for aggregators in the spot market C S ( ψ ) represents ; Scene ψ Wind power purchase cost for aggregators in the green electricity trading market C W ( ψ and photovoltaic power purchase costs C PV ( ψ ) are respectively represented as and ; Scene ψ Electricity purchase cost for aggregators in the distributed generation trading market C DG ( ψ ) represents ; Scene ψ Electricity purchase cost for aggregators in the energy storage resource leasing market C ES ( ψ ) represents ;
[0008] Preferably, the establishment of a load aggregator's retail electricity package model that includes a fixed single electricity price and peak-valley time-of-use pricing specifically involves: Fixed single-price electricity package l gu Represented as ; Electricity prices for peak-valley time-of-use electricity packages Represented as ; in, For fixed electricity prices; Electricity prices for users during peak hours; The electricity price for users during normal hours; The electricity price for users during normal hours; T f During peak hours, T p This is the usual time period. T g It is the valley period.
[0009] Preferably, the proportion of users determining the peak-valley time-of-use electricity pricing package... Specifically: for:
[0010] in, g ( p This represents a collection of factors that influence a user's choice of package type.
[0011] Preferably, the calculation of expected electricity sales revenue is based on the user ratio and the electricity sales package model of the load aggregator. C R Specifically:
[0012] in, π ( ψ ) represents a scene ψ The possibility of occurrence, g ( p This represents a collection of factors that influence a user's choice of package type. express t The electrical load at any given time The electricity price for peak-valley time-of-use pricing packages. Based on the set of influencing factors g ( p The exponential transformation term of ) The electricity price is for a fixed single-price electricity package.
[0013] Preferably, the step of obtaining the total revenue and loss representation for all scenarios based on the multi-market electricity purchase model and expected electricity sales revenue results specifically includes: Scene ψ Below, the aggregator's revenue F ( ψ The expression is as follows:
[0014] Total revenue of aggregators across all scenarios F total Represented as ; The loss characterization of the aggregator is expressed as: ; in, C B ( ψ This represents the electricity purchase cost for aggregators in the medium- and long-term trading market. C S ( ψ This represents the electricity purchase cost for aggregators in the spot market. C W ( ψ This represents the cost of wind power purchase for aggregators in the green electricity trading market.C PV ( ψ The cost of purchasing electricity from photovoltaic power plants is [not specified]. C DG ( ψ This represents the electricity purchase cost for aggregators in the distributed generation trading market. C ES ( ψ This represents the electricity purchase cost for aggregators in the energy storage resource leasing market. C R For expected electricity sales revenue results; π ( ψ ) represents a scene ψ The possibility of it occurring.
[0015] Preferably, the step of using CVaR to quantify the loss representation of each scenario, analyzing the loss representation results and the total revenue under all scenarios, and obtaining the decision result of the revenue-risk balance is as follows: The maximum potential risk loss for aggregators is F VaR At a certain confidence level m The losses of the aggregator x Represented as ; The CVaR model for aggregator power trading risk is expressed as follows:
[0016] Analyzing the loss representation results and the total returns under all scenarios, the decision result balancing the return and risk is expressed as follows: ; in, F total This represents the total revenue for the aggregator across all scenarios. This represents the loss of the aggregator; r This represents the risk preference factor of the load aggregator; r The larger the value, the stronger the risk appetite of the load aggregator, i.e., the risk-seeking type; r The smaller the value, the higher the degree of risk aversion, i.e., risk-averse.
[0017] The present invention proposes a multi-asset electricity market purchasing decision system that integrates user ratio and CVaR, comprising: The electricity purchase model construction module is used to build multi-market electricity purchase models based on long-term electricity market, spot electricity market, green electricity market, distributed generation and energy storage leasing scenarios; The retail package model building module is used to establish a retail package model for load aggregators that includes a fixed single electricity price and peak-valley time-of-use electricity prices. The expected electricity sales revenue acquisition module is used to determine the user ratio of peak-valley time-of-use electricity pricing packages, and calculate the expected electricity sales revenue based on the user ratio and the retail package model of the load aggregator's electricity sales side. The electricity purchase decision output module is used to obtain the total revenue and loss representations under all scenarios based on the multi-market electricity purchase model and the expected electricity sales revenue results. It uses CVaR to quantify the loss representations of each scenario, analyzes the loss representation results and the total revenue under all scenarios, obtains the revenue-risk balance decision results, and realizes multi-market electricity purchase decisions.
[0018] A terminal device includes a memory, a processor, and a computer program stored in the memory and executable on the processor, wherein the processor executes the computer program to implement steps of an aggregator-based multi-market electricity purchase decision method that integrates user ratio and CVaR.
[0019] A computer-readable storage medium storing a computer program that, when executed by a processor, implements steps of an aggregator-based multi-market electricity purchase decision method that integrates user ratio and CVaR.
[0020] Compared with the prior art, the present invention has the following beneficial effects: This invention proposes a diversified electricity market purchase decision-making method for aggregators that integrates user ratio and CVaR. To improve the synergy between electricity purchase and sales, the method first constructs a diversified electricity purchase model covering medium- and long-term, spot, green electricity markets, and distributed generation and energy storage leasing. Simultaneously, it establishes a dual-basic retail package model with a fixed single electricity price and peak-valley time-of-use pricing, forming a matching architecture that covers multiple scenarios on the purchase side and diversifies packages on the sales side. Furthermore, it introduces a user ratio parameter for peak-valley time-of-use pricing packages, directly linking user preferences with electricity sales revenue. The expected electricity sales revenue is calculated through the sales-side model, deeply binding the allocation of electricity purchase sources with the expected electricity sales revenue, thus solving the problem of disconnect between traditional purchase and sales decisions. In addressing market risk quantification, based on a diversified electricity purchase model and expected electricity sales revenue, the total revenue and loss are calculated across all scenarios. The CVaR method is introduced to quantify loss risk under different market fluctuations, clarifying risk boundaries. Furthermore, a decision optimization model is constructed with profit maximization as its core and risk minimization as its constraint. Intelligent algorithms are used to obtain the optimal solution for revenue-risk balance, outputting electricity purchase combinations and retail pricing strategies. This addresses the blind spots caused by the lack of risk quantification in traditional decision-making. The entire process, through the coupling of purchase and sales collaborative modeling and risk quantification, achieves the organic unity of electricity purchase decisions with electricity sales revenue and risk control, improving the scientific rigor and feasibility of decision-making.
[0021] This invention proposes a diversified electricity market purchase decision system integrating user ratio and CVaR. By dividing the system into a purchase model construction module, a retail package model construction module, an expected electricity sales revenue acquisition module, and a purchase decision output module, it obtains a return-risk balance decision result, thus realizing diversified electricity market purchase decisions. The modular approach ensures that each module is independent, facilitating unified management of all modules. Attached Figure Description
[0022] To more clearly illustrate the technical solutions of the embodiments of the present invention, the accompanying drawings used in the embodiments will be briefly introduced below. It should be understood that the following drawings only show some embodiments of the present invention and should not be regarded as a limitation on the scope. For those skilled in the art, other related drawings can be obtained based on these drawings without creative effort.
[0023] Figure 1 This is a flowchart of the load aggregator multi-market power purchase decision method of the present invention.
[0024] Figure 2 This is a detailed flowchart of the method of the present invention.
[0025] Figure 3 This is a diagram illustrating the architecture of load aggregators participating in the electricity market in this invention.
[0026] Figure 4 This is a diagram of the load aggregator multi-market power purchase decision system of the present invention.
[0027] Figure 5 This is a schematic diagram of the structure of an electronic device according to the present invention. Detailed Implementation
[0028] To make the objectives, technical solutions, and advantages of the embodiments of the present invention clearer, the technical solutions of the embodiments of the present invention will be clearly and completely described below with reference to the accompanying drawings. Obviously, the described embodiments are only some embodiments of the present invention, and not all embodiments. The components of the embodiments of the present invention described and shown in the accompanying drawings can generally be arranged and designed in various different configurations.
[0029] Therefore, the following detailed description of the embodiments of the invention provided in the accompanying drawings is not intended to limit the scope of the claimed invention, but merely to illustrate selected embodiments of the invention. All other embodiments obtained by those skilled in the art based on the embodiments of the invention without inventive effort are within the scope of protection of the invention.
[0030] It should be noted that similar labels and letters in the following figures indicate similar items. Therefore, once an item is defined in one figure, it does not need to be further defined and explained in subsequent figures.
[0031] In the description of the embodiments of the present invention, it should be noted that if terms such as "upper," "lower," "horizontal," or "inner" indicate the orientation or positional relationship based on the orientation or positional relationship shown in the accompanying drawings, or the orientation or positional relationship commonly used when the product of the invention is in use, they are only for the convenience of describing the present invention and simplifying the description, and do not indicate or imply that the device or element referred to must have a specific orientation, or be constructed and operated in a specific orientation, and therefore should not be construed as a limitation of the present invention. Furthermore, terms such as "first" and "second" are only used to distinguish descriptions and should not be construed as indicating or implying relative importance.
[0032] Furthermore, the use of the term "horizontal" does not imply that the component must be absolutely horizontal, but rather that it can be slightly tilted. For example, "horizontal" simply means that its direction is more horizontal than "vertical," and does not mean that the structure must be completely horizontal, but can be slightly tilted.
[0033] The present invention will now be described in further detail with reference to the accompanying drawings: To address the problems existing in current technologies, this invention proposes a multi-market power purchase decision-making method for load aggregators. By designing a decision optimization model, it effectively improves the scientific nature of load aggregators' decision-making and risk management capabilities in a multi-market environment, and promotes their healthy development in the electricity market. Figure 1 and Figure 2 As shown, it includes the following steps: S1. Construct a diversified market power purchase model based on long-term electricity market, spot electricity market, green electricity market, distributed generation and energy storage leasing scenarios; The proposed multi-market electricity purchase model is constructed based on long-term electricity market, spot electricity market, green electricity market, distributed generation and energy storage leasing scenarios, specifically as follows: The proposed multi-market electricity purchase model is constructed based on long-term electricity market, spot electricity market, green electricity market, distributed generation and energy storage leasing scenarios, specifically as follows: Scene ψ Electricity purchase costs for aggregators in the medium- and long-term trading market C B ( ψ ) represents ; Scene ψ Electricity purchase cost for aggregators in the spot market C S ( ψ ) represents ; Scene ψ Wind power purchase cost for aggregators in the green electricity trading market C W ( ψ and photovoltaic power purchase costs C PV ( ψ ) are respectively represented as and ; Scene ψ Electricity purchase cost for aggregators in the distributed generation trading market C DG ( ψ ) represents ; Scene ψ Electricity purchase cost for aggregators in the energy storage resource leasing market C ES ( ψ ) represents ;
[0034] S2. Establish a retail package model for load aggregators that includes both fixed single electricity price and peak-valley time-of-use electricity price; The establishment of a retail electricity package model for load aggregators that includes a fixed single electricity price and peak-valley time-of-use pricing is specifically as follows: Fixed single-price electricity package l gu Represented as ; Electricity prices for peak-valley time-of-use electricity packages Represented as ; in, For fixed electricity prices; Electricity prices for users during peak hours; The electricity price for users during normal hours; The electricity price for users during normal hours; T f During peak hours, T p This is the usual time period. T g The valley period. The peak period. T f The hours are 08:00-12:00 and 17:00-21:00, during normal hours. T p The off-peak hours are 12:00-17:00 and 21:00-24:00. T g It is from 00:00 to 08:00.
[0035] S3. Determine the user ratio of peak-valley time-of-use electricity pricing packages, and calculate the expected electricity sales revenue based on the user ratio and the retail package model of the load aggregator's electricity sales side. The proportion of users who determine peak-valley time-of-use electricity pricing packages Specifically: for:
[0036] in, g ( p This represents a collection of factors that influence a user's choice of package type.
[0037] The model based on user ratio and load aggregator electricity sales side retail package calculates the expected electricity sales revenue. C R Specifically:
[0038] in, π ( ψ ) represents a scene ψ The possibility of occurrence, g ( p This represents a collection of factors that influence a user's choice of package type. express t The electrical load at any given time The electricity price for peak-valley time-of-use pricing packages. Based on the set of influencing factors g ( p The exponential transformation term of ) The electricity price is for a fixed single-price electricity package.
[0039] S4. Based on the diversified market electricity purchase model and the expected electricity sales revenue results, obtain the total revenue and loss representations under all scenarios, use CVaR to quantify the loss representations of each scenario, analyze the loss representation results and the total revenue under all scenarios, obtain the decision results of the revenue-risk balance, and realize diversified electricity market electricity purchase decisions.
[0040] The method for obtaining total revenue and loss representations for all scenarios based on a multi-market electricity purchase model and expected electricity sales revenue results is as follows: Scene ψ Below, the aggregator's revenue F ( ψ The expression is as follows:
[0041] Total revenue of aggregators across all scenarios F total Represented as ; The loss characterization of the aggregator is expressed as: ; in, C B ( ψ This represents the electricity purchase cost for aggregators in the medium- and long-term trading market. C S ( ψ This represents the electricity purchase cost for aggregators in the spot market. C W ( ψ This represents the cost of wind power purchase for aggregators in the green electricity trading market. C PV ( ψ The cost of purchasing electricity from photovoltaic power plants is [not specified]. C DG ( ψ This represents the electricity purchase cost for aggregators in the distributed generation trading market. C ES ( ψ This represents the electricity purchase cost for aggregators in the energy storage resource leasing market. C R For expected electricity sales revenue results; π ( ψ ) represents a scene ψ The possibility of it occurring.
[0042] The method employs CVaR to quantify the loss representation for each scenario, analyzes the loss representation results and the total revenue under all scenarios, and obtains the decision result of balancing revenue and risk. Specifically: The maximum potential risk loss for aggregators is F VaR At a certain confidence level m The losses of the aggregator x Represented as ; The CVaR model for aggregator power trading risk is expressed as follows:
[0043] Analyzing the loss representation results and the total returns under all scenarios, the decision result balancing the return and risk is expressed as follows: ; in, F total This represents the total revenue for the aggregator across all scenarios. This represents the loss of the aggregator; r This represents the risk preference factor of the load aggregator; r The larger the value, the stronger the risk appetite of the load aggregator, i.e., the risk-seeking type; r The smaller the value, the higher the degree of risk aversion, i.e., risk-averse.
[0044] The decision optimization model proposed in this invention is applicable to load aggregators that need to coordinate the stability of medium- and long-term contracts, the flexibility of the spot market, the responsibility for green energy consumption, and the response to user demand. Specifically, it includes independent electricity retailers with a certain market scale, integrated distribution and sales companies, and virtual power plant aggregators. The optimization algorithm can be used to solve for the optimal power purchase and sale strategy of load aggregators in the electricity trading market, and to analyze the changes in each core parameter under different user package selection ratios.
[0045] In summary, this invention analyzes the architecture of load aggregators' participation in the electricity market and studies their trading models in the medium- and long-term market, spot market, green electricity market, distributed generation market, and energy storage resource leasing market. Then, it designs two basic monthly packages for the user side: a peak-valley time-of-use pricing package considering demand response and a traditional fixed uniform pricing package. Next, based on the load aggregator's expected revenue model, it introduces the conditional value-at-risk (VaR) assessment method to construct a CVaR model for load aggregators and establishes a risk-preference-based power trading decision optimization model for load aggregators, aiming to balance maximizing the revenue and minimizing the risk for electricity retailers. This effectively improves the scientific nature of load aggregators' decision-making and risk management capabilities in a diversified market environment and promotes their healthy development in the electricity market.
[0046] The structure for load aggregators to participate in the electricity market, such as Figure 3 As shown.
[0047] On the power purchase side, the focus is on aggregators participating in five markets: the medium- and long-term trading market, the spot trading market, the green electricity trading market, the distributed generation trading market, and the energy storage resource leasing market. Medium- and long-term trading, green electricity trading, and energy storage resource leasing transactions typically involve long-term contracts, while spot trading and distributed generation trading usually involve day-ahead or intraday contracts. Long-term contracts provide aggregators with a stable base electricity supply, effectively mitigating the risk of significant price fluctuations, while also aligning with the cyclical demands of renewable energy consumption and energy storage capacity reserves. Short-term contracts, on the other hand, give aggregators the flexibility to respond to market price fluctuations, allowing them to dynamically adjust their marginal power purchase strategies to capture arbitrage opportunities. On the power sales side, two basic retail packages are designed to meet the basic electricity needs of end users. These packages serve as the main sales outlets for the electricity purchased by aggregators, aiming to ensure the stable realization of their basic electricity sales revenue.
[0048] Example 2 The present invention proposes a multi-asset electricity market purchasing decision system that integrates user ratio and CVaR, such as... Figure 4 As shown, it includes: The electricity purchase model construction module is used to build multi-market electricity purchase models based on long-term electricity market, spot electricity market, green electricity market, distributed generation and energy storage leasing scenarios; The proposed multi-market electricity purchase model is constructed based on long-term electricity market, spot electricity market, green electricity market, distributed generation and energy storage leasing scenarios, specifically as follows: Scene ψ Electricity purchase costs for aggregators in the medium- and long-term trading market C B ( ψ ) represents ; Scene ψ Electricity purchase cost for aggregators in the spot market C S ( ψ ) represents ; Scene ψ Wind power purchase cost for aggregators in the green electricity trading market C W ( ψ and photovoltaic power purchase costs C PV ( ψ ) are respectively represented as and ; Scene ψ Electricity purchase cost for aggregators in the distributed generation trading market C DG ( ψ ) represents ; Scene ψ Electricity purchase cost for aggregators in the energy storage resource leasing market C ES ( ψ ) represents ;
[0049] The retail package model building module is used to establish a retail package model for load aggregators that includes a fixed single electricity price and peak-valley time-of-use electricity prices. The establishment of a retail electricity package model for load aggregators that includes a fixed single electricity price and peak-valley time-of-use pricing is specifically as follows: Fixed single-price electricity package l gu Represented as ; Electricity prices for peak-valley time-of-use electricity packages Represented as ; in, For fixed electricity prices; Electricity prices for users during peak hours; The electricity price for users during normal hours; The electricity price for users during normal hours; T f During peak hours, T p This is the usual time period. T g It is the valley period.
[0050] The expected electricity sales revenue acquisition module is used to determine the user ratio of peak-valley time-of-use electricity pricing packages, and calculate the expected electricity sales revenue based on the user ratio and the retail package model of the load aggregator's electricity sales side. The proportion of users who determine peak-valley time-of-use electricity pricing packages Specifically: for:
[0051] in, g ( p This represents a collection of factors that influence a user's choice of package type.
[0052] The model based on user ratio and load aggregator electricity sales side retail package calculates the expected electricity sales revenue. C R Specifically:
[0053] in, π ( ψ ) represents a scene ψ The possibility of occurrence, g ( p This represents a collection of factors that influence a user's choice of package type. express t The electrical load at any given time The electricity price for peak-valley time-of-use pricing packages. Based on the set of influencing factors g ( p The exponential transformation term of ) The electricity price is for a fixed single-price electricity package.
[0054] The electricity purchase decision output module is used to obtain the total revenue and loss representations under all scenarios based on the multi-market electricity purchase model and the expected electricity sales revenue results. It uses CVaR to quantify the loss representations of each scenario, analyzes the loss representation results and the total revenue under all scenarios, obtains the revenue-risk balance decision results, and realizes multi-market electricity purchase decisions.
[0055] The method for obtaining total revenue and loss representations for all scenarios based on a multi-market electricity purchase model and expected electricity sales revenue results is as follows: Scene ψ Below, the aggregator's revenue F ( ψ The expression is as follows:
[0056] Total revenue of aggregators across all scenarios F total Represented as ; The loss characterization of the aggregator is expressed as: ; in, C B ( ψ This represents the electricity purchase cost for aggregators in the medium- and long-term trading market. C S ( ψ This represents the electricity purchase cost for aggregators in the spot market. C W ( ψ This represents the cost of wind power purchase for aggregators in the green electricity trading market. C PV ( ψ The cost of purchasing electricity from photovoltaic power plants is [not specified]. C DG ( ψ This represents the electricity purchase cost for aggregators in the distributed generation trading market. C ES ( ψ This represents the electricity purchase cost for aggregators in the energy storage resource leasing market. C R For expected electricity sales revenue results; π ( ψ ) represents a scene ψ The possibility of it occurring.
[0057] The method employs CVaR to quantify the loss representation for each scenario, analyzes the loss representation results and the total revenue under all scenarios, and obtains the decision result of balancing revenue and risk. Specifically: The maximum potential risk loss for aggregators is F VaR At a certain confidence level m The losses of the aggregator x Represented as ; The CVaR model for aggregator power trading risk is expressed as follows:
[0058] Analyzing the loss representation results and the total returns under all scenarios, the decision result balancing the return and risk is expressed as follows: ; in, F total This represents the total revenue for the aggregator across all scenarios. This represents the loss of the aggregator; r This represents the risk preference factor of the load aggregator; r The larger the value, the stronger the risk appetite of the load aggregator, i.e., the risk-seeking type; r The smaller the value, the higher the degree of risk aversion, i.e., risk-averse.
[0059] Example 3 Please see Figure 5 As shown, the present invention also provides an electronic device 100 for a multi-market electricity purchase decision method for aggregators that integrates user ratio and CVaR; the electronic device 100 includes a memory 101, at least one processor 102, a computer program 103 stored in the memory 101 and executable on the at least one processor 102, and at least one communication bus 104.
[0060] The memory 101 can be used to store the computer program 103. The processor 102 implements the steps of the aggregator-based multi-market electricity purchase decision method described in Embodiment 1 by running or executing the computer program stored in the memory 101 and calling the data stored in the memory 101. The memory 101 may mainly include a program storage area and a data storage area. The program storage area may store the operating system, at least one application program required for a function (such as sound playback function, image playback function, etc.), etc.; the data storage area may store data created based on the use of the electronic device 100 (such as audio data), etc. In addition, the memory 101 may include non-volatile memory, such as hard disk, memory, plug-in hard disk, smart media card (SMC), secure digital (SD) card, flash card, at least one disk storage device, flash memory device, or other non-volatile solid-state storage device.
[0061] The at least one processor 102 may be a Central Processing Unit (CPU), or other general-purpose processors, digital signal processors (DSPs), application-specific integrated circuits (ASICs), field-programmable gate arrays (FPGAs), or other programmable logic devices, discrete gate or transistor logic devices, discrete hardware components, etc. The processor 102 may be a microprocessor or any conventional processor. The processor 102 is the control center of the electronic device 100, connecting various parts of the electronic device 100 via various interfaces and lines.
[0062] The memory 101 in the electronic device 100 stores multiple instructions to implement a multi-aggregator electricity market purchase decision-making method that integrates user ratio and CVaR, and the processor 102 can execute the multiple instructions to achieve the following: A diversified market power purchase model is constructed based on long-term electricity market, spot electricity market, green electricity market, distributed generation and energy storage leasing scenarios. Establish a retail package model for load aggregators that includes both fixed single electricity price and peak-valley time-of-use electricity price; Determine the user ratio of peak-valley time-of-use electricity pricing packages, and calculate the expected electricity sales revenue based on the user ratio and the retail package model of the load aggregator's electricity sales side. Based on the diversified market electricity purchase model and the expected electricity sales revenue, the total revenue and loss representations under all scenarios are obtained. CVaR is used to quantify the loss representations of each scenario. The loss representation results and the total revenue under all scenarios are analyzed to obtain the decision results of the revenue-risk balance, and realize diversified electricity market electricity purchase decisions.
[0063] Example 4 If the modules / units integrated in the electronic device 100 are implemented as software functional units and sold or used as independent products, they can be stored in a computer-readable storage medium. Based on this understanding, all or part of the processes in the methods of the above embodiments can also be implemented by a computer program instructing related hardware. The computer program can be stored in a computer-readable storage medium, and when executed by a processor, it can implement the steps of the various method embodiments described above. The computer program includes computer program code, which can be in the form of source code, object code, executable files, or certain intermediate forms. The computer-readable medium can include: any entity or device capable of carrying the computer program code, a recording medium, a USB flash drive, a portable hard drive, a magnetic disk, an optical disk, a computer memory, and a read-only memory (ROM).
[0064] Those skilled in the art will understand that embodiments of the present invention can be provided as methods, systems, or computer program products. Therefore, the present invention can take the form of a completely hardware embodiment, a completely software embodiment, or an embodiment combining software and hardware aspects. Furthermore, the present invention can take the form of a computer program product embodied on one or more computer-usable storage media (including, but not limited to, disk storage, CD-ROM, optical storage, etc.) containing computer-usable program code.
[0065] This invention is described with reference to flowchart illustrations and / or block diagrams of methods, apparatus (systems), and computer program products according to embodiments of the invention. It will be understood that each block of the flowchart illustrations and / or block diagrams, and combinations of blocks in the flowchart illustrations and / or block diagrams, can be implemented by computer program instructions. These computer program instructions can be provided to a processor of a general-purpose computer, special-purpose computer, embedded processor, or other programmable data processing apparatus to produce a machine, such that the instructions, which execute via the processor of the computer or other programmable data processing apparatus, generate instructions for implementing the flowchart illustrations and / or block diagrams. Figure one One or more processes and / or boxes Figure one A device that provides the functions specified in one or more boxes.
[0066] These computer program instructions may also be stored in a computer-readable storage medium that can direct a computer or other programmable data processing device to function in a particular manner, such that the instructions stored in the computer-readable storage medium produce an article of manufacture including instruction means, which are implemented in a process Figure one One or more processes and / or boxes Figure one The function specified in one or more boxes.
[0067] These computer program instructions may also be loaded onto a computer or other programmable data processing equipment to cause a series of operational steps to be performed on the computer or other programmable equipment to produce a computer-implemented process, thereby providing instructions that execute on the computer or other programmable equipment for implementing the process. Figure one One or more processes and / or boxes Figure one The steps of the function specified in one or more boxes.
[0068] Finally, it should be noted that the above embodiments are only used to illustrate the technical solutions of the present invention and not to limit it. Although the present invention has been described in detail with reference to the above embodiments, those skilled in the art should understand that modifications or equivalent substitutions can still be made to the specific implementation of the present invention. Any modifications or equivalent substitutions that do not depart from the spirit and scope of the present invention should be covered within the scope of protection of the claims of the present invention.
Claims
1. A multi-assembly electricity market purchasing decision-making method for aggregators that integrates user ratio and CVaR, characterized in that, Includes the following steps: A diversified market power purchase model is constructed based on long-term electricity market, spot electricity market, green electricity market, distributed generation and energy storage leasing scenarios. Establish a retail package model for load aggregators that includes both fixed single electricity price and peak-valley time-of-use electricity price; Determine the user ratio of peak-valley time-of-use electricity pricing packages, and calculate the expected electricity sales revenue based on the user ratio and the retail package model of the load aggregator's electricity sales side. Based on the diversified market electricity purchase model and the expected electricity sales revenue, the total revenue and loss representations under all scenarios are obtained. CVaR is used to quantify the loss representations of each scenario. The loss representation results and the total revenue under all scenarios are analyzed to obtain the decision results of the revenue-risk balance, and realize diversified electricity market electricity purchase decisions.
2. The method for aggregator-based multi-market electricity purchase decision-making based on user ratio and CVaR as described in claim 1, characterized in that, The proposed multi-market electricity purchase model is constructed based on long-term electricity market, spot electricity market, green electricity market, distributed generation and energy storage leasing scenarios, specifically as follows: Scene ψ Electricity purchase costs for aggregators in the medium- and long-term trading market C B ( ψ ) represents ; Scene ψ Electricity purchase cost for aggregators in the spot market C S ( ψ ) represents ; Scene ψ Wind power purchase cost for aggregators in the green electricity trading market C W ( ψ and photovoltaic power purchase costs C PV ( ψ ) are respectively represented as and ; Scene ψ Electricity purchase cost for aggregators in the distributed generation trading market C DG ( ψ ) represents ; Scene ψ Electricity purchase cost for aggregators in the energy storage resource leasing market C ES ( ψ ) represents ; 3. The method for aggregator-based multi-market electricity purchase decision-making based on user ratio and CVaR as described in claim 1, characterized in that, The establishment of a retail electricity package model for load aggregators that includes a fixed single electricity price and peak-valley time-of-use pricing is specifically as follows: Fixed single-price electricity package λ gu Represented as ; Electricity prices for peak-valley time-of-use electricity packages Represented as ; in, For fixed electricity prices; Electricity prices for users during peak hours; The electricity price for users during normal hours; The electricity price for users during normal hours; T f During peak hours, T p This is the usual time period. T g It is the valley period.
4. The aggregator-based multi-market electricity purchase decision-making method based on user ratio and CVaR as described in claim 1, characterized in that, The proportion of users who determine peak-valley time-of-use electricity pricing packages Specifically: for: in, g ( p This represents a collection of factors that influence a user's choice of package type.
5. The aggregator-based multi-market electricity purchase decision-making method based on user ratio and CVaR as described in claim 1, characterized in that, The model based on user ratio and load aggregator electricity sales side retail package calculates the expected electricity sales revenue. C R Specifically: in, π ( ψ ) represents a scene ψ The possibility of occurrence, g ( p This represents a collection of factors that influence a user's choice of package type. express t The electrical load at any given time The electricity price for peak-valley time-of-use pricing packages. Based on the set of influencing factors g ( p The exponential transformation term of ) The electricity price is for a fixed single-price electricity package.
6. The aggregator-based multi-market electricity purchase decision-making method for integrating user ratio and CVaR as described in claim 1, characterized in that, The method for obtaining total revenue and loss representations for all scenarios based on a multi-market electricity purchase model and expected electricity sales revenue results is as follows: Scene ψ Below, the aggregator's revenue F ( ψ The expression is as follows: Total revenue of aggregators across all scenarios F total Represented as ; The loss characterization of the aggregator is expressed as: ; in, C B ( ψ This represents the electricity purchase cost for aggregators in the medium- and long-term trading market. C S ( ψ This represents the electricity purchase cost for aggregators in the spot market. C W ( ψ This represents the cost of wind power purchase for aggregators in the green electricity trading market. C PV ( ψ The cost of purchasing electricity from photovoltaic power plants is [not specified]. C DG ( ψ This represents the electricity purchase cost for aggregators in the distributed generation trading market. C ES ( ψ This represents the electricity purchase cost for aggregators in the energy storage resource leasing market. C R For expected electricity sales revenue results; π ( ψ ) represents a scene ψ The possibility of it occurring.
7. The aggregator-based multi-market electricity purchase decision-making method based on user ratio and CVaR as described in claim 6, characterized in that, The method employs CVaR to quantify the loss representation for each scenario, analyzes the loss representation results and the total revenue under all scenarios, and obtains the decision result of balancing revenue and risk. Specifically: The maximum potential risk loss for aggregators is F VaR At a certain confidence level μ The losses of the aggregator ξ Represented as ; The CVaR model for aggregator power trading risk is expressed as follows: Analyzing the loss representation results and the total returns under all scenarios, the decision result balancing the return and risk is expressed as follows: ; in, F total This represents the total revenue for the aggregator across all scenarios. This represents the loss of the aggregator; ρ This represents the risk preference factor of the load aggregator; ρ The larger the value, the stronger the risk appetite of the load aggregator, i.e., the risk-seeking type; ρ The smaller the value, the higher the degree of risk aversion, i.e., risk-averse.
8. A diversified electricity market purchasing decision system integrating user ratio and CVaR, characterized in that, include: The electricity purchase model construction module is used to build multi-market electricity purchase models based on long-term electricity market, spot electricity market, green electricity market, distributed generation and energy storage leasing scenarios; The retail package model building module is used to establish a retail package model for load aggregators that includes a fixed single electricity price and peak-valley time-of-use electricity prices. The expected electricity sales revenue acquisition module is used to determine the user ratio of peak-valley time-of-use electricity pricing packages, and calculate the expected electricity sales revenue based on the user ratio and the retail package model of the load aggregator's electricity sales side. The electricity purchase decision output module is used to obtain the total revenue and loss representations under all scenarios based on the multi-market electricity purchase model and the expected electricity sales revenue results. It uses CVaR to quantify the loss representations of each scenario, analyzes the loss representation results and the total revenue under all scenarios, obtains the revenue-risk balance decision results, and realizes multi-market electricity purchase decisions.
9. A terminal device, comprising a memory, a processor, and a computer program stored in the memory and executable on the processor, characterized in that, When the processor executes the computer program, it implements the steps of the aggregator-based multi-market electricity purchase decision method based on the convergence of user ratio and CVaR as described in any one of claims 1 to 7.
10. A computer-readable storage medium storing a computer program, characterized in that, When the computer program is executed by the processor, it implements the steps of the aggregator-based multi-market electricity purchase decision method for integrating user ratio and CVaR as described in any one of claims 1 to 10.