Hierarchical contract model construction method and device for virtual power plant value network

Abstract

The embodiment of the application provides a hierarchical contract model construction method and device of a virtual power plant value network, which comprises the following steps: establishing a virtual power plant value network; the virtual power plant value network comprises at least one of the following entities: an auxiliary service market entity, a virtual power plant entity, an energy domain entity, an energy information domain cross-domain entity and an information resource request entity; according to the transaction relationship between the entities in the virtual power plant value network, an energy transaction income model and an information transaction income model in the virtual power plant value network are established; and according to the energy transaction income model and the information transaction income model, a hierarchical contract model of the virtual power plant value network is established; the hierarchical contract model of the virtual power plant value network is used for maximizing the energy transaction income and the information transaction income in the virtual power plant value network. The method of the embodiment of the application realizes win-win value gain of the energy domain and the information domain.

Description

Technical Field

[0001] This invention relates to the field of energy information technology, and in particular to a method and apparatus for constructing a hierarchical contract model for a virtual power plant value network. Background Technology

[0002] Virtual power plants highly integrate energy and information technology, relying on the rapid interaction of energy flow and information flow to break through the traditional energy production and utilization methods, and provide effective control means for the large-scale deployment and operation of various new power sources and loads.

[0003] However, current virtual power plants lack a comprehensive energy-information cross-domain interaction value assessment system, and lack effective characterization and representation of the value flow of various roles and entities in the energy and information domains within the virtual power plant. Therefore, how to establish a virtual power plant energy-information trading model to achieve value representation and quantitative assessment of the dual-flow interaction of energy and information, and realize value gains for multiple entities, is a problem that urgently needs attention from those skilled in the art. Summary of the Invention

[0004] To address the problems in the prior art, embodiments of the present invention provide a method and apparatus for constructing a hierarchical contract model for a virtual power plant value network.

[0005] Specifically, the embodiments of the present invention provide the following technical solutions:

[0006] In a first aspect, embodiments of the present invention provide a method for constructing a hierarchical contract model for a virtual power plant value network, including:

[0007] A virtual power plant value network is established; the virtual power plant value network includes at least one of the following entities: ancillary service market entity, virtual power plant entity, energy domain entity, energy information domain cross-domain entity, and information resource request entity; energy trading relationships and / or information trading relationships exist between the entities in the virtual power plant value network;

[0008] Based on the transaction relationships between entities in the virtual power plant value network, an energy transaction revenue model and an information transaction revenue model are established in the virtual power plant value network. The energy transaction revenue model is used to characterize the revenue from energy transactions in the virtual power plant value network, and the information transaction revenue model is used to characterize the revenue from information transactions in the virtual power plant value network.

[0009] A hierarchical contract model for the virtual power plant value network is established based on the energy trading revenue model and the information trading revenue model. The hierarchical contract model for the virtual power plant value network is used to maximize the energy trading revenue and information trading revenue in the virtual power plant value network.

[0010] Furthermore, there is an energy trading relationship between the virtual power plant entity and the energy domain entity and the cross-domain entity of the energy information domain;

[0011] There is an information transaction relationship between the cross-domain entity in the energy information domain and the entity requesting the information resources.

[0012] Furthermore, the step of establishing an energy transaction revenue model and an information transaction revenue model in the virtual power plant value network based on the transaction relationships between entities in the virtual power plant value network includes:

[0013] The energy trading revenue model in the virtual power plant value network is established using the following formula:

[0014]

[0015] Among them, U VPP α(t) represents the revenue from energy transactions between the virtual power plant entity and the cross-domain entity; α(t) represents the unit price at which the virtual power plant entity sells energy to the ancillary services market; δ(t) represents the unit price at which the virtual power plant entity pays the cross-domain entity in the energy information domain for regulated energy. This represents the actual electricity consumption of cross-domain entity m in the energy information domain during the time period t. This represents the baseline energy consumption of cross-domain entity m in the energy information domain during the time period t.

[0016] The following formula is used to establish an information transaction revenue model in the virtual power plant value network:

[0017]

[0018] in, This represents the revenue generated from information transactions between cross-domain entities in the energy information domain and entities requesting information resources; x m,n (t) represents the matching relationship between cross-domain entity m and computation task n; w m,n (t) represents the benefit obtained by cross-domain entity m in computation task n.

[0019] Furthermore, the hierarchical contract model construction method for the virtual power plant value network also includes:

[0020] Based on the hierarchical contract model of the virtual power plant value network, target information is determined; the target information includes at least one of the following: energy transaction prices between virtual power plant entities and cross-domain entities in the energy information domain in the virtual power plant value network, matching relationships of computing tasks between cross-domain entities in the energy information domain and information resource request entities, computing resources allocated to cross-domain entities in the energy information domain, and the cost corresponding to each computing task.

[0021] Based on the energy transaction price between the virtual power plant entity and the cross-domain entity of the energy information domain in the proposed power plant value network in the target information, the matching relationship of the computing tasks between the cross-domain entity of the energy information domain and the information resource request entity, the computing resources allocated to the cross-domain entity of the energy information domain and the cost corresponding to each computing, the maximum revenue of energy transactions and the maximum revenue of information transactions in the virtual power plant value network are determined.

[0022] Furthermore, after establishing the virtual power plant value network, it also includes:

[0023] Obtain the target error; the target error includes at least one of the following: the error between the energy regulation amount uploaded by the distributed resource entity and the actual energy regulation amount of the distributed resource entity, the error between the predicted energy regulation amount of the distributed resource entity and the actual energy regulation amount of the distributed resource entity, and the error between the predicted energy regulation amount of the distributed resource entity and the predicted energy regulation amount when the timeliness of data analysis is insufficient; the distributed resource entity includes the energy domain entity and the cross-domain entity of the energy information domain;

[0024] Based on the target error, the revenue of the virtual power plant entity in the virtual power plant value network is updated.

[0025] Secondly, embodiments of the present invention also provide a hierarchical contract model construction apparatus for a virtual power plant value network, comprising:

[0026] The first establishment module is used to establish a virtual power plant value network; the virtual power plant value network includes at least one of the following entities: ancillary service market entity, virtual power plant entity, energy domain entity, energy information domain cross-domain entity, and information resource request entity; energy trading relationships and / or information trading relationships exist between the entities in the virtual power plant value network;

[0027] The second module is used to establish an energy transaction revenue model and an information transaction revenue model in the virtual power plant value network based on the transaction relationships between entities in the virtual power plant value network. The energy transaction revenue model is used to characterize the revenue of energy transactions in the virtual power plant value network, and the information transaction revenue model is used to characterize the revenue of information transactions in the virtual power plant value network.

[0028] The third module is used to establish a hierarchical contract model for the virtual power plant value network based on the energy trading revenue model and the information trading revenue model; the hierarchical contract model for the virtual power plant value network is used to maximize the energy trading revenue and information trading revenue in the virtual power plant value network.

[0029] Thirdly, embodiments of the present invention also provide an electronic device, including a memory, a processor, and a computer program stored in the memory and executable on the processor, wherein when the processor executes the program, it implements the hierarchical contract model construction method for the virtual power plant value network as described in the first aspect.

[0030] Fourthly, embodiments of the present invention also provide a non-transitory computer-readable storage medium storing a computer program thereon, wherein the computer program, when executed by a processor, implements the hierarchical contract model construction method for the virtual power plant value network as described in the first aspect.

[0031] Fifthly, embodiments of the present invention also provide a computer program product, including a computer program that, when executed by a processor, implements the hierarchical contract model construction method for the virtual power plant value network as described in the first aspect.

[0032] The hierarchical contract model construction method and apparatus for virtual power plant value networks provided in this invention construct a virtual power plant value network comprising ancillary service market entities, virtual power plant entities, energy domain entities, cross-domain entities in the energy and information domains, and information resource request entities. This allows for precise modeling of the energy-information dual-domain value flow among multiple entities within the virtual power plant value network. Furthermore, based on the energy and information transaction relationships between entities within the virtual power plant value network, an energy transaction revenue model and an information transaction revenue model are established. This enables precise quantification and characterization of the energy-information dual-domain value gains among various entities within the virtual power plant value network, thereby accurately determining the energy and information transaction revenues of the entities within the virtual power plant value network. Finally, based on the energy and information transaction revenue models, a hierarchical contract model for the virtual power plant value network is established to maximize the energy and information transaction revenues within the virtual power plant value network. This achieves a combined increase in the revenue of virtual power plant entities in the energy domain and the revenue of computing and communication nodes in the information domain, better realizing a win-win value gain for both the energy and information domains. Attached Figure Description

[0033] To more clearly illustrate the technical solutions in this invention or the prior art, the drawings used in the description of the embodiments or the prior art will be briefly introduced below. Obviously, the drawings described below are some embodiments of this invention. For those skilled in the art, other drawings can be obtained from these drawings without creative effort.

[0034] Figure 1 This is a flowchart illustrating the hierarchical contract model construction method for a virtual power plant value network provided in this embodiment of the invention.

[0035] Figure 2 This is a schematic diagram of the virtual power plant value network provided in an embodiment of the present invention;

[0036] Figure 3 This is a schematic diagram of energy information interaction provided in an embodiment of the present invention;

[0037] Figure 4 This is a schematic diagram of the hierarchical contract model of the virtual power plant value network provided in this embodiment of the invention;

[0038] Figure 5 This is a schematic diagram of the structure of the hierarchical contract model construction device for the virtual power plant value network provided in an embodiment of the present invention;

[0039] Figure 6 This is a schematic diagram of the structure of the electronic device provided in an embodiment of the present invention. Detailed Implementation

[0040] 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 with reference to the accompanying drawings. Obviously, the described embodiments are only some, not all, of the embodiments of this invention. All other embodiments obtained by those skilled in the art based on the embodiments of this invention without creative effort are within the scope of protection of this invention.

[0041] The method of this invention can be applied to energy information technology scenarios to realize the construction of a hierarchical contract model for virtual power plant value networks.

[0042] In related technologies, a comprehensive value assessment system for cross-domain energy-information interaction has not yet been established, and there is a lack of effective characterization and representation of the value flow of various roles and entities in the energy and information domains within a virtual power plant. Therefore, how to establish a virtual power plant energy-information trading model to achieve value representation and quantitative assessment of the dual-flow interaction of energy and information, and realize value gains for multiple entities, is a problem that urgently needs attention from those skilled in the art.

[0043] The hierarchical contract model construction method for the virtual power plant value network of this invention constructs a virtual power plant value network that includes ancillary service market entities, virtual power plant entities, energy domain entities, cross-domain entities in the energy and information domains, and information resource request entities. This allows for precise modeling of the energy-information dual-domain value flow among multiple entities within the virtual power plant value network. Furthermore, based on the energy and information transaction relationships between entities in the virtual power plant value network, an energy transaction revenue model and an information transaction revenue model are established. This enables precise quantification and characterization of the energy-information dual-domain value gains among various entities in the virtual power plant value network, thus accurately determining the energy and information transaction revenues of the entities within the virtual power plant value network. Finally, based on the energy and information transaction revenue models, a hierarchical contract model for the virtual power plant value network is established to maximize the energy and information transaction revenues, achieving a combined increase in the revenue of virtual power plant entities in the energy domain and the revenue of computing and communication nodes in the information domain, ultimately realizing a win-win value gain for both the energy and information domains.

[0044] The following is combined Figures 1-6 The technical solution of the present invention will be described in detail with reference to specific embodiments. The following specific embodiments can be combined with each other, and the same or similar concepts or processes may not be described again in some embodiments.

[0045] Figure 1 This is a flowchart illustrating an embodiment of the hierarchical contract model construction method for a virtual power plant value network provided by this invention. Figure 1 As shown, the method provided in this embodiment includes:

[0046] Step 101: Establish a virtual power plant value network; the virtual power plant value network includes at least one of the following entities: ancillary service market entity, virtual power plant entity, energy domain entity, energy information domain cross-domain entity, and information resource request entity; energy trading relationships and / or information trading relationships exist between the entities in the virtual power plant value network;

[0047] Specifically, in related technologies, a comprehensive value assessment system for cross-domain energy-information interaction has not yet been established, and there is a lack of effective characterization and representation of the value flow of various roles and entities in the energy and information domains within a virtual power plant. Therefore, how to establish a virtual power plant energy-information trading model to achieve value representation and quantitative assessment of the dual-flow interaction of energy and information, and to realize value gains for multiple entities, is a problem that urgently needs attention from those skilled in the art. Furthermore, existing technologies, for integrated energy systems, propose value propositions for integrated energy systems based on user needs, and then focus on constructing a value network model of the integrated energy system based on system value form, value creation, and value transfer. However, these existing value network models constructed from an energy perspective cannot be directly applied to virtual power plants because virtual power plants achieve a deeper interaction between energy and information. On the one hand, virtual power plants rely on data analysis technology in the information domain to achieve precise and efficient control; on the other hand, large computing and communication nodes participate in the control of virtual power plants as large, flexible loads, realizing value interaction between the energy and information domains.

[0048] To address the aforementioned issues, this invention constructs a virtual power plant value network comprising ancillary service market entities, virtual power plant entities, energy domain entities, cross-domain entities in the energy information domain, and information resource request entities. Within this virtual power plant value network, entities engage in both energy and information transactions, thereby accurately modeling the energy-information dual-domain value flow and value gain among multiple entities within the virtual power plant value network.

[0049] In one embodiment, an energy trading relationship exists between the virtual power plant entity and the energy domain entity, as well as between the energy information domain cross-domain entities;

[0050] There is an information transaction relationship between cross-domain entities in the energy information domain and entities requesting information resources.

[0051] Specifically, in this embodiment of the invention, a virtual power plant value network is constructed, comprising ancillary service market entities, virtual power plant entities, energy domain entities, energy information domain cross-domain entities, and information resource request entities. This allows for precise modeling of the energy-information dual-domain value flow among multiple entities within the virtual power plant value network. The virtual power plant value network includes energy transaction relationships between virtual power plant entities and energy domain entities and energy information domain cross-domain entities, and information transaction relationships between energy information domain cross-domain entities and information resource request entities. In other words, the energy information domain cross-domain entities in the virtual value network possess the energy regulation capabilities required by the energy grid, while also possessing the communication capabilities required by the information network. In the energy information domain, cross-domain entities have both energy and information trading relationships within the virtual power plant value network. For example, data centers are both large loads in the energy network and important computing nodes in the information network; electric vehicles are not only spatially flexible distributed resources in the energy network, but also, with the development of vehicle networking and autonomous driving technologies, are gradually becoming flexible communication and computing nodes in the information network; communication base stations, as widely distributed flexible loads in the energy network, are becoming increasingly dense with the development of 5G technology, resulting in a sharp increase in power consumption, and are also important communication and computing nodes in the information network.

[0052] For example, such as Figure 2 The virtual power plant value network shown can better achieve win-win value gains through cross-domain energy-information value interaction. The interaction process involving the virtual power plant mainly includes four steps: 1) The virtual power plant issues dispatch instructions and incentive information to distributed resources. The dispatch instructions specify the desired operating power value for each load, and the incentive information specifies the reward that distributed resources can receive for each kilowatt-hour regulated. Distributed resources include energy entities and cross-domain entities; 2) Each distributed resource responds to the received instructions and incentive information, providing ancillary services such as peak shaving and peak regulation to the main grid by changing its own electricity consumption or generation behavior, and uploading relevant measurement data, such as electricity consumption, to the virtual power plant platform; 3) The virtual power plant management platform calculates the regulation amount of distributed resources and reports it to the ancillary service market. It also analyzes the reported data to determine the dispatch instructions and incentive information for the next time slot; 4) The ancillary service market pays the virtual power plant corresponding rewards based on the reported regulation amount. The virtual power plant also shares a portion of the received rewards with distributed resources to incentivize them to participate more in the management of the virtual power plant. In addition, cross-domain entities such as data centers, communication base stations, and electric vehicles can also provide communication and computing services to information resource requesters and generate revenue.

[0053] Optionally, in this embodiment of the invention, economic, social, and environmental value can be generated through the interaction process between various entities in the virtual power plant value network. From an economic perspective, the virtual power plant and the distributed resources participating in its regulation can obtain economic benefits from the ancillary services market. Furthermore, data centers, electric vehicles, and communication base stations, as new entities spanning the energy and information domains, can obtain economic benefits by providing their own communication and computing resources in addition to participating in power dispatch. From an environmental perspective, the virtual power plant can integrate various forms of energy on the energy input side, achieving coordinated interaction and mutual conversion of energy, reducing primary energy consumption, and decreasing emissions of harmful gases. From a social perspective, the participation of virtual power plants in ancillary services such as peak shaving can help the power grid achieve more flexible demand-side dispatch, improve the absorption of renewable energy, and enable the rapid development of low-cost new energy systems.

[0054] The virtual power plant value network constructed in this embodiment of the invention enables an interactive network formed by information resource users, energy production systems, and energy storage systems through certain value connections. Optionally, the virtual power plant value network constructed in this embodiment of the invention participates in the ancillary services market by aggregating resources such as electric vehicles, data centers, communication base stations, distributed energy, hybrid energy storage, and electricity load, and interacts with the main power grid in real time. Since electric vehicles, data centers, and communication base stations have both energy domain and information domain characteristics, they can be regarded as energy-information cross-domain entities. Optionally, the entities in the virtual power plant value network constructed in this embodiment of the invention include at least one of the following:

[0055] (1) Ancillary services market operators

[0056] Ancillary service market operators refer to dispatch centers that issue instructions to virtual power plants, power generation companies, grid operators, and power users in order to maintain the safe and stable operation of the power system and ensure power quality, in addition to normal power generation, transmission, and use. Ancillary services include: primary frequency regulation, automatic generation control (AGC), peak shaving, reactive power regulation, reserve, and black start services.

[0057] (2) Virtual power plant entity

[0058] A virtual power plant is a unified and coordinated management system that integrates distributed generation, energy storage, and load equipment, centered around electricity, and relies on modern intelligent control technologies such as real-time metering and data communication. A virtual power plant can act as an intermediary between distributed resources and the ancillary services market, representing distributed resource owners in executing market clearing results and facilitating energy trading.

[0059] (3) Distributed energy

[0060] Distributed energy is an energy supply method built at the user end, including gas fuel, photovoltaic and wind power generation, such as distributed photovoltaic panels installed on residential roofs, and wind turbines and photovoltaic power generation equipment installed in smart parks.

[0061] (4) Hybrid energy storage

[0062] Hybrid energy storage includes facilities such as thermal storage tanks, cold storage tanks, thermal storage electric boilers, and energy storage batteries. Due to the excellent peak-shaving and valley-filling performance of thermal storage electric boilers, this section will use them as an example. During off-peak hours, the thermal storage electric boiler is turned on to heat water and store it in a tank. During peak hours, the boiler is turned off, and the hot water in the storage tank is used for heating, achieving the goal of using all off-peak electricity (full thermal storage) or partially using off-peak electricity (semi-thermal storage) for heating.

[0063] (5) Electrical load

[0064] Electricity loads include industrial loads and residential loads, which participate in virtual power plant dispatch through their electricity consumption. Some electricity loads can be replaced by other energy sources, such as heat energy replacing electric heaters for heating.

[0065] (6) Cross-domain entities

[0066] Cross-domain entities refer to communication and computing nodes in information networks and large loads and energy storage resources in energy networks. They can generate revenue by providing communication and computing services in information networks, or by providing power regulation capabilities to virtual power plants in energy networks. Several typical cross-network entities mainly include fixed communication and computing nodes, such as data centers and communication base stations, as well as mobile communication and computing nodes, such as electric vehicles.

[0067] (7) Data Center

[0068] Data centers contain massive amounts of servers and communication equipment, providing powerful computing and storage services to users of information resources. As a large workload, data centers can provide a degree of regulation, much like virtual power plants, by adjusting their computing tasks. Simultaneously, data centers can access a portion of their backup power systems to participate in grid dispatch without affecting mission-critical workloads, and the heat from their cooling systems can be used for heating or to power hot water systems and boilers.

[0069] (8) Communication base station

[0070] While providing massive amounts of high-quality communication load services, communication base stations have gradually developed into an important emerging load in power distribution networks. During operation, intelligent control technology can be used to schedule and manage the power supply and consumption equipment in the base stations, fully leveraging their interactive response potential and providing considerable flexibility support to the power grid.

[0071] (9) Electric vehicles

[0072] Electric vehicles, with the help of charging stations, can achieve bidirectional power flow with the power grid. They can both draw power from the grid (charging) and send power back to the grid (discharging), providing ancillary services such as improving power quality and absorbing renewable energy. Simultaneously, electric vehicles can provide latency-sensitive and computationally-intensive computing services to smart terminals in the power grid, and can also act as relay nodes to assist the power grid communication system in data uploading and distribution.

[0073] (10) Information resource users

[0074] Information resource users refer to users who need to use computing or communication resources such as data centers, base stations, and electric vehicles, including but not limited to smart grid terminals and individual users.

[0075] Optionally, the entities in the virtual power plant value network constructed in this embodiment of the invention have the following value interactions:

[0076] (1) Energy resource trading

[0077] Ancillary Services Market Operators - Virtual Power Plants: Virtual power plants participate in the ancillary services market by aggregating resources such as controllable loads, distributed energy storage, electric vehicles (charging piles, charging and battery swapping stations), data centers, and communication base stations. The ancillary services market operator provides funding, and virtual power plants pre-declare the amount of electricity they can participate in. The ancillary services market operator settles revenue with the virtual power plants based on their actual participation level. Ancillary service revenue is related to the actual electricity participated in, market clearing, and clearing prices. Virtual power plants settle revenue with their internal participating resources based on their participation level, achieving a two-way interaction between electricity and economic value.

[0078] Virtual Power Plant - Distributed Resources: When a virtual power plant needs to provide ancillary services such as peak shaving and demand response, distributed resources respond according to the dispatch instructions issued by the virtual power plant and obtain corresponding revenue from it. Considering the bidirectional power supply characteristics of electric vehicles, when electric vehicles lack power, they need to purchase power from charging piles or charging stations. When the virtual power plant needs to provide ancillary services such as peak shaving and demand response, the electric vehicles or electric vehicle aggregators that have submitted applications need to charge / discharge in response and obtain corresponding revenue from the virtual power plant. Data centers need to purchase large amounts of electricity to support a large number of computing services. At the same time, data centers can choose to postpone computing tasks to respond to virtual power plant dispatch and obtain revenue, or use part of their backup power system for grid dispatch and obtain revenue. Communication base stations need to purchase large amounts of electricity to support the massive communication load services of communication networks. At the same time, the energy-consuming or energy-supplying equipment of communication base stations can participate in grid dispatch to obtain revenue. Ultimately, this achieves bidirectional interaction of economic value among multiple energy sources (electricity and heat).

[0079] (2) Information resource transactions

[0080] Information resource users – cross-domain entities: Information resource users (such as mobile phones, IoT monitoring devices, and other smart terminals) can purchase computing resources from data centers and electric vehicles to provide computing services. Communication base stations and electric vehicles can provide communication services. Among them, fixed cross-network entities such as data centers and communication base stations often have greater communication and computing capabilities, providing relatively stable communication and computing services for information resource users. Mobile cross-network entities such as electric vehicles have greater flexibility, providing more spatially flexible communication and computing services.

[0081] The virtual power plant value network constructed in this embodiment of the invention can better achieve win-win value gains through cross-domain value interaction between energy and information. For example, Figure 3 As shown, the cross-domain value interaction between energy and information mainly includes: 1) Cross-domain entities, acting as communication and computing nodes, provide communication, computing, and accurate and timely data prediction services for virtual power plant scheduling; 2) Cross-domain entities, acting as large loads and energy storage, participate in virtual power plant regulation, providing large-scale power consumption regulation capabilities for the energy domain. Ultimately, through the deep integration of cross-domain entities and the energy domain, the benefits of virtual power plants in the energy domain and the benefits of computing and communication nodes in the information domain can be jointly enhanced.

[0082] Step 102: Based on the transaction relationships between entities in the virtual power plant value network, establish an energy transaction revenue model and an information transaction revenue model in the virtual power plant value network; the energy transaction revenue model is used to characterize the revenue of energy transactions in the virtual power plant value network; the information transaction revenue model is used to characterize the revenue of information transactions in the virtual power plant value network.

[0083] Specifically, in this embodiment of the invention, after constructing a virtual power plant value network, an energy transaction revenue model and an information transaction revenue model are established based on the energy transaction and information transaction relationships between entities in the virtual power plant value network. The energy transaction revenue model is used to characterize the revenue from energy transactions in the virtual power plant value network; the information transaction revenue model is used to characterize the revenue from information transactions in the virtual power plant value network. This achieves accurate quantification and characterization of the dual-domain value gain of energy and information between entities in the virtual power plant value network. Furthermore, based on this accurate quantification and characterization of the dual-domain value gain of energy and information between entities in the virtual power plant value network, the energy transaction revenue and information transaction revenue in the virtual power plant value network can be accurately determined, better realizing a win-win value gain.

[0084] Step 103: Establish a hierarchical contract model for the virtual power plant value network based on the energy trading revenue model and the information trading revenue model; the hierarchical contract model for the virtual power plant value network is used to maximize the energy trading revenue and information trading revenue in the virtual power plant value network.

[0085] Specifically, this invention establishes energy transaction revenue models and information transaction revenue models in a virtual power plant value network based on the energy and information transaction relationships between entities in the virtual power plant value network. This enables the precise quantification and characterization of the dual-domain value gains of energy and information among various entities in the virtual power plant value network. After accurately characterizing the energy and information transaction revenues of the main entities in the virtual power plant value network, a hierarchical contract model for the virtual power plant value network can be established based on the energy and information transaction revenue models. This maximizes the energy and information transaction revenues in the virtual power plant value network, achieving a joint improvement in the revenues of virtual power plant entities in the energy domain and the revenues of computing and communication nodes in the information domain.

[0086] The method described in the above embodiments constructs a virtual power plant value network comprising ancillary service market entities, virtual power plant entities, energy domain entities, cross-domain entities in the energy information domain, and information resource request entities. This allows for precise modeling of the energy-information dual-domain value flow among multiple entities within the virtual power plant value network. Furthermore, based on the energy and information transaction relationships between entities within the virtual power plant value network, an energy transaction revenue model and an information transaction revenue model are established. This enables precise quantification and characterization of the energy-information dual-domain value gains among various entities within the virtual power plant value network, thereby accurately determining the energy and information transaction revenues of the entities within the virtual power plant value network. Finally, based on the energy and information transaction revenue models, a hierarchical contract model for the virtual power plant value network is established to maximize the energy and information transaction revenues within the virtual power plant value network. This achieves a combined increase in the revenue of virtual power plant entities in the energy domain and the revenue of computing and communication nodes in the information domain, ultimately realizing a win-win value gain for both the energy and information domains.

[0087] In one embodiment, based on the transaction relationships between entities in the virtual power plant value network, an energy transaction revenue model and an information transaction revenue model are established in the virtual power plant value network, including:

[0088] The energy trading revenue model in the virtual power plant value network is established using the following formula:

[0089]

[0090] Among them, U VPP(t) represents the revenue from energy transactions between the virtual power plant entity and the cross-domain entity; α(t) represents the unit price at which the virtual power plant entity sells energy to the ancillary services market entity; δ(t) represents the unit price at which the virtual power plant entity pays the cross-domain entity in the energy information domain for the controlled energy; This represents the actual electricity consumption of cross-domain entity m in the energy information domain during the time period t. This represents the baseline energy consumption of cross-domain entity m in the energy information domain during the time period t.

[0091] The following formula is used to establish an information transaction revenue model in the virtual power plant value network:

[0092]

[0093] in, This represents the revenue generated from information transactions between cross-domain entities in the energy information domain and entities requesting information resources; x m,n (t) represents the matching relationship between cross-domain entity m and computation task n; w m,n (t) represents the benefit obtained by cross-domain entity m in computation task n.

[0094] Specifically, based on the energy and information trading relationships between entities in the virtual power plant value network, an energy trading revenue model and an information trading revenue model are established within the virtual power plant value network. The energy trading revenue model represents the revenue from energy transactions within the virtual power plant value network, while the information trading revenue model represents the revenue from information transactions. This achieves precise quantification and representation of the dual-domain value gains in energy and information between entities within the virtual power plant value network. Furthermore, based on this precise quantification and representation of the dual-domain value gains in energy and information between entities within the virtual power plant value network, the energy trading revenue and information trading revenue of the main entities in the virtual power plant value network can be accurately determined, better realizing a win-win value gain.

[0095] For example, the energy trading revenue model in the virtual power plant value network can be established using the following formula:

[0096]

[0097] Among them, U VPP (t) represents the revenue from energy transactions between the virtual power plant entity and the cross-domain entity; α(t) represents the unit price at which the virtual power plant entity sells energy to the ancillary services market entity; δ(t) represents the unit price at which the virtual power plant entity pays the cross-domain entity in the energy information domain for the controlled energy; This represents the actual electricity consumption of cross-domain entity m in the energy information domain during the time period t. This represents the baseline energy consumption of cross-domain entity m in the energy information domain during the time period t.

[0098] The following formula is used to establish an information transaction revenue model in the virtual power plant value network:

[0099]

[0100] in, This represents the revenue generated from information transactions between cross-domain entities in the energy information domain and entities requesting information resources; x m,n (t) represents the matching relationship between cross-domain entity m and computation task n; w m,n (t) represents the benefit obtained by cross-domain entity m in computation task n.

[0101] For example, such as Figure 4 As shown, the virtual power plant value network constructed in this invention comprises a system consisting of a Virtual Power Plant (VPP), an Edge Server Cluster (ESC), and a Computation Task Requester (CTR). The cross-domain entity, taking the Edge Server Cluster as an example, provides computing services to the Computation Task Requester in the information domain and participates in the regulation of the virtual power plant in the energy domain, providing it with power regulation capabilities. The entire time span is divided into continuous time slots Γ = {1,…,t,…,T}. The ESC can generate revenue by trading regulation amounts with the VPP and providing computing resources to the CTR. The ESC consists of M Edge Servers, represented as... CTR consists of N requesters and is represented as The interaction process of the VPP-ESC-CTR system mainly includes four steps: 1) The VPP sends an energy contract to the ESC, which specifies the revenue the ESC can obtain for each kilowatt-hour regulated; 2) A computational contract is formulated between the ESC and CTR to determine the allocated computing resources and the remuneration to be paid by the ESC, while also obtaining the matching strategy between the arriving tasks and edge servers; 3) The ESC uploads its regulation amount to the VPP, and the VPP aggregates the regulation amounts from all servers and sends it to the energy market; 4) The energy market pays the VPP remuneration based on the value of the VPP's regulation amount and the market mechanism determined by the day-ahead market, the VPP pays the ESC remuneration according to the formulated energy contract, and the CTR pays the ESC remuneration according to the formulated computational contract. In addition, the ESC needs to pay the electricity bill to the main power grid. Specifically, based on the transaction relationships between entities in the virtual power plant value network, the energy transaction revenue model and information transaction revenue model in the virtual power plant value network are established as follows:

[0102] (1) Energy Trading Revenue Model

[0103] Specifically, the energy trading process involves the Virtual Power Provider (VPP) generating revenue by aggregating and selling its electricity consumption to the energy market, and sharing profits with the Energy Controller (ESC) according to the energy contract between the VPP and the ESC. The VPP's utility is defined as the difference between its energy market revenue and the ESC's payments; the energy trading revenue model is expressed as follows:

[0104]

[0105] in, This represents the actual power consumption of server m in time slot t. This represents the baseline energy consumption of server m in time slot t, which is the power consumption if the service does not participate in VPP regulation. The difference between the actual power consumption and the baseline power consumption is the user's adjustment amount, expressed as... Here we assume a peak-shaving scenario, which means adjusting users to reduce their electricity consumption, therefore ΔE m α(t) > 0. α(t) represents the unit price at which the VPP sells its regulation volume to the ancillary services market, which is determined the previous day. The utility of the ESC participating in the VPP's energy trading is defined as:

[0106]

[0107] (2) Information Transaction Revenue Model

[0108]

[0109] Where v m,n (t) represents the ratio of computing resources allocated to each computing task, w m,n (t) represents the cost that each task should pay for ESC. Furthermore, the matching strategy between the server and the computation tasks is defined as x. m,n (t), x m,n (t) = 1 indicates that a computation contract was established between requester n's computation task and server m in time slot t, meaning that requester n's computation task was assigned to server m in time slot t. In the t-th time slot, the task generated by the n-th computation task requester is represented as A. n (t), the length of the task (in millions of instructions) is expressed as l n (t). Using millions of instructions per second (MIPS) as a measure of computing speed, the computing speed of the m-th server can be expressed as:

[0110]

[0111] Wherein, CPI represents the average number of clock cycles per instruction processed by the data center for a task process, and F... m This represents the CPU frequency (clock cycles per second) of the m-th data center.

[0112] Since the computing power of a single server is limited, assume that each server is a queue subsystem used to buffer tasks. Let QX m (t) represents the length of tasks queued in the subsystem of server m in time slot t. Assume QX m (0) = 0 and the queue length is QX m (t) is calculated as follows:

[0113]

[0114] Among them, the binary variable x m,n (t) indicates whether the computation task generated by requester n is assigned to server m in time slot t, where Δt represents the time slot length, and v m,n (t) represents the assignment to computation task A. n The computational resources of (t) and server m. We assume that each server executes only one task at a time, and executes queues QX sequentially. m All tasks in (t). Therefore, v m,n (t)×S m ×Δt is the number of instructions executed by server m within time slot t. This queue system needs to be stable to avoid a sharp increase in the number of computations waiting to be executed. The stability of the queue is defined as:

[0115]

[0116] In addition, we use QT m (t) represents the execution queue QX m The time required for the existing tasks in (t):

[0117]

[0118] Based on the above analysis, it can be concluded that if task S n If time slot t is allocated to server m, then its total computation time is the waiting time in the queue plus the task execution time, as follows:

[0119]

[0120] You can adjust v m,n (t) Change the server's operating power

[0121]

[0122] Among them, E min,m This refers to the server's energy consumption when it is idle, E. max,m It is the server energy consumption when the server is fully utilized. PUE is the power efficiency ratio, which is determined by statistical records of the ratio of data center facility power to computing power consumption.

[0123] CTRs have varying computational latency sensitivities, but they don't disclose their latency requirements for computational tasks because they expect the fastest possible computational service. This behavior reduces the flexibility of ESC power conditioning. The solution is to incentivize CTRs and model the interaction between CTRs and ESCs as a contract based on an inverse selection model. This encourages CTRs to truthfully report their latency requirements and maximizes the utility of both parties. Based on the inverse selection model, CTRs are categorized into several types according to their computational latency sensitivity. The CTR type is defined as computational latency sensitivity θ. i , satisfying θ1<…<θ I I is the number of possible user types. A larger θ i This indicates that users are more time-sensitive. A user's utility function is the computational service received minus the cost paid:

[0124]

[0125] CTR utility follows the law of diminishing returns, which leads to a correlation between realizable income and type θ. i The ratio is logarithmically proportional to the actual total computation time. Individual rationality (IR) and incentive compatibility (IC) constraints are integrated into the adverse selection model of contract design. The IR constraint ensures that each CTR always receives a different type of non-negative utility, i.e.:

[0126]

[0127] The IC constraint indicates that each CTR can only be achieved by selecting a type θ that is suitable for it. i Contracts to maximize their own utility

[0128]

[0129] (3) Hierarchical contract model of virtual power plant value network

[0130] The energy trading revenue model (energy contract) and the information trading revenue model (computation contract) are coupled because the ESC needs to coordinate the computation task scheduling strategy with the CTR based on the incentive information provided by the VPP. The total utility of the ESC is the revenue from energy trading and the revenue from providing computing services minus the electricity cost.

[0131]

[0132] Here, β represents the unit price of electricity. However, the two benefits of ESC are conflicting. ESC gains more revenue by reducing power consumption, but this reduces the revenue from providing computing services. To maximize the total profit of VPP-ESC-CTR in energy trading and providing computing services, we model the hierarchical contracts in VPP-ESC-CTR as follows:

[0133]

[0134] The hierarchical contract model for the virtual power plant value network needs to consider the stability of the computational task queues on each server and the computational latency requirements of CTR constraints. The hierarchical contract model for the virtual power plant value network can be represented as the following two coupled problems, thereby maximizing the benefits of the virtual power plant and cross-domain entities:

[0135] 1) Maximizing VPP revenue: Maximizing the long-run average profit of the VPP:

[0136]

[0137] stδ min ≤δ(t)≤δ max

[0138] 2) Maximizing ESC Profits: Maximizing the long-run average profit of the ESC.

[0139]

[0140]

[0141]

[0142]

[0143]

[0144]

[0145] The method described in the above embodiments establishes an energy transaction revenue model and an information transaction revenue model within the virtual power plant value network based on the energy transaction and information transaction relationships between entities in the virtual power plant value network. The energy transaction revenue model characterizes the revenue from energy transactions within the virtual power plant value network, while the information transaction revenue model characterizes the revenue from information transactions within the virtual power plant value network. This achieves precise quantification and characterization of the dual-domain value gain between entities in the virtual power plant value network. Furthermore, based on this precise quantification and characterization of the dual-domain value gain between entities in the virtual power plant value network, the energy transaction revenue and information transaction revenue of the entities within the virtual power plant value network can be accurately determined, thus better realizing a win-win value gain.

[0146] In one embodiment, the method for constructing a hierarchical contract model for a virtual power plant value network further includes:

[0147] Based on the hierarchical contract model of the virtual power plant value network, target information is determined; the target information includes at least one of the following: the energy transaction price between the virtual power plant entity and the cross-domain entity of the energy information domain in the virtual power plant value network, the matching relationship of computing tasks between the cross-domain entity of the energy information domain and the information resource request entity, the computing resources allocated to the cross-domain entity of the energy information domain, and the cost corresponding to each computing task.

[0148] Based on the energy transaction price between the virtual power plant entity and the cross-domain entity of the energy information domain in the proposed power plant value network in the target information, the matching relationship of the computing tasks between the cross-domain entity of the energy information domain and the information resource request entity, the computing resources allocated to the cross-domain entity of the energy information domain and the cost corresponding to each computing, determine the maximum revenue of energy transactions and the maximum revenue of information transactions in the virtual power plant value network.

[0149] Specifically, after establishing a hierarchical contract model for the virtual power plant value network based on the energy trading revenue model and the information trading revenue model, the target information can be determined based on the hierarchical contract model of the virtual power plant value network. Then, based on the energy trading price between the virtual power plant entity and the cross-domain entity in the energy information domain in the virtual power plant value network, the matching relationship of the computing tasks between the cross-domain entity in the energy information domain and the information resource request entity, the computing resources allocated to the cross-domain entity in the energy information domain, and the cost corresponding to each computing, the maximum revenue of energy trading and the maximum revenue of information trading in the virtual power plant value network can be determined. This achieves a joint improvement in the revenue of the virtual power plant entity in the energy domain and the revenue of the computing and communication nodes in the information domain, and better realizes the win-win value gain of the energy domain and the information domain.

[0150] Optionally, after establishing a hierarchical contract model for the virtual power plant value network based on the energy trading revenue model and the information trading revenue model—that is, after representing the hierarchical contract model of the virtual power plant value network as a coupled problem of maximizing VPP revenue p0 and maximizing ESC revenue p1—problems P0 and P1 are mutually coupled. The decision variables of P0 exist in the objective function of P1, and the decision variables of P1 exist in the objective function of P0. In this embodiment of the invention, based on the Lyapunov principle, the target information is determined according to the hierarchical contract model of the virtual power plant value network; specifically as follows:

[0151] To decouple the dependency of problem P1 on future variables, we use Lyapunov optimization to study the balance between ESC profit and computation task queue stability. The Lyapunov function and Lyapunov drift are defined as follows:

[0152]

[0153]

[0154] According to Lyapunov optimization theory, the execution queue stability constraint is equivalent to minimizing the drift Δ(QX(t)). Optimizing the objective function using constraints is equivalent to optimizing "drift plus penalty", defined as follows:

[0155]

[0156] Here, the parameter V≥0 serves as the penalty weight, representing the importance of the objective function relative to the constraints. This objective function includes variables at time t+1, so an upper bound on the objective function is needed to decouple the optimization objective from future information. Simplifying by squaring both sides of the equation for the queue QX(t), we get:

[0157]

[0158] Problem P1 can be transformed into P2

[0159]

[0160]

[0161]

[0162]

[0163]

[0164] Among them, O m,n The expression for (t) is

[0165]

[0166] Then, problem P2 can be broken down into two subproblems, P3 and P4. Problem P3 is to solve the problem when computation task A... n (t) is assigned to server m, and the ESC and CTR contracts are executed.

[0167]

[0168]

[0169]

[0170]

[0171] Problem P4, based on the solutions obtained from P3, yields the matching decision x between each computational task and each server across all contracts. m,n (t)

[0172]

[0173]

[0174] In problem P3, there are also nonlinear IR and IC constraints. Based on relevant papers on contract theory, the IR and IC constraints can be transformed into:

[0175]

[0176]

[0177] Therefore, problem P3 can be further transformed into:

[0178]

[0179]

[0180]

[0181]

[0182] In summary, we need to solve three subproblems: P0, P4, and P5.

[0183] This invention employs the DQN-HC (Deep Q-learning Network based HierarchicalContracting) algorithm to jointly solve the subproblems P0, P5, and P6 of the energy contract between VPP and ESC, the computation contract between ESC and CTR, and the matching between computation tasks and servers. Specifically, the DQN-HC algorithm consists of three modules. The computation contract design module determines the computation contract after inputting δ(t). The computation task and server matching module is used to determine the matching result x between each computation task and server. m,n The energy contract design module (t) determines the adjustment amount δ(t) based on the ESC. These three modules operate sequentially and iteratively, solved using a DQN. The DQN's state transitions are driven by the computation of the contract and the matching results, training the DQN to obtain the energy contract decision strategy. After determining the energy contract, the ESC then obtains information about x. m,n (t), Contract-based computational task scheduling decisions. Among them,

[0184] The computational contract design module is used to solve problem P5, assuming computational task A. n When (t) is assigned to the m-th server, the computational contract can be obtained by solving the iterative Lagrange method for the interaction between each server and the computational task.

[0185] Computational task and server matching module: based on the solution obtained Each CTR is based on its own type θ i Choose a contract that suits you and get O m,n The value of (t) is determined, and then problem P4 is solved using the KM (Kuhn Munkres) matching algorithm. Specifically, a weighted undirected graph is constructed with all edge servers and assigned tasks, where the weights of edge connection nodes m and n are the negatives of the objective function in problem P4. m,n =-O m,n (t), because the KM algorithm is maximum weight matching. The indices of the edge servers and computing tasks, as well as the edge weights of the constructed graph, are input into the KM algorithm to obtain the matching strategy x. m,n (t).

[0186] Energy Contract Design Module: Problem P0 is modeled as a Markov decision process, and δ(t) is obtained by solving the DQN algorithm, where the action is the energy contract a. t =δ(t), the adjustment s at state ESC t =ΔE(t), the reward function is the objective function r(s) of problem P0. t,a t )= U iPP (t). After multiple iterations of training with the DQN algorithm, the optimal δ(t) can be explored to maximize VPP gains.

[0187] Given an energy contract choice strategy π, it can be represented by the Q function in state s. t Take action a t Long-term average return of VPP:

[0188]

[0189] Here, γ represents the discount factor used to balance immediate and long-term rewards. This invention obtains the optimal action that maximizes the Q-value by training a DQN network. The Q-function can be evaluated as Q(s,a)≈Q′(s,a,θ), where Q′ is trained to approximate the true Q-value.

[0190] Specifically, the training process of the DQN-HC algorithm is as follows: First, the parameters of the Q-network and the experience pool R are randomly initialized. In each round of training, action a is selected based on the current policy and exploration noise. t Then, the corresponding reward r is obtained through state transition. t and the state s at the next moment t+1 The state transition process is obtained through the computational contract design module and the computational task and server matching module. DQN will obtain the state (a t ,s t ,r t ,s t+1 The data is stored in an experience pool. Then, DQN randomly selects a subset of data from the experience pool for training to update the network parameters while minimizing the following loss function:

[0191]

[0192] in, This is the target network. After K rounds of iteration, the optimal energy contract selection strategy will be obtained.

[0193] The proposed method was verified through simulation experiments. In the simulation settings, the arrival rate of the computation task follows an average rate of... The exponential distribution is used, with each server's frequency set to 2.5 GHz and CPI to 2.5. The PUE is set to 2.1, and the idle and peak power of each server are set to 60W and 300W, respectively. The energy contract value range is set to [$30 / MWh, $40 / MWh], and the energy market remuneration price for VPPs is set to $40 / MWh. The type of each CTR is set to θ. i∈{19,20,21} and all CTRs are assumed to follow a uniform distribution.

[0194] (1) Analysis of task scheduling results

[0195] First, the performance of contract-based computational task scheduling is evaluated. The computational resources allocated to each type of CTR and the associated costs are determined, since θ1 < θ2 < ... < θ5, type θ5 CTRs are the most latency-sensitive. Results show that more resources are allocated to latency-sensitive CTRs, validating that the ESC can provide computational services based on the different computational latency requirements of CTRs. Furthermore, the proposed contract-based resource allocation scheme is verified to address the information asymmetry problem caused by CTR selfishness. For different types of contracts corresponding to different types of CTRs, the results show that only by selecting the appropriate contract type can the CTR achieve maximum utility. For example, the utility of type θ2 CTRs can be maximized by selecting a contract of type θ2. Therefore, computational contracts can reveal the actual computational latency sensitivity of CTRs, which can provide greater flexibility for the ESC to adjust power consumption behavior based on the excitation signal of the VPP.

[0196] Then, the performance of the proposed algorithm was analyzed with different Lyapunov penalty parameters V ∈ [0.5, 1]. When V is small, the average queue backlog and ESC profit are small. This is because a smaller parameter V allows the algorithm to focus more on minimizing the queue length rather than maximizing the ESC profit. Therefore, when V is small, the ESC system is stable and can provide long-term computation services for CTR without congestion. As V increases, the average queue backlog and ESC profit gradually increase, which may increase the instability of the task queue and make ESC more profitable. In practical applications, a moderate V should be set to achieve a balance between the ESC profit and the length of the computation task queue.

[0197] (2) Analysis of Energy Contract Formulation Results

[0198] The convergence of rewards in the proposed DQN-HC algorithm was further analyzed. Initially, due to limited knowledge, undesirable behaviors with low returns were selected. With each successive iteration, the agent learns through trial and error to find actions that yield higher returns. Finally, the rewards converge to their maximum value, resulting in the optimal energy contract optimization strategy for ESC. The impact of the discount factor γ was also analyzed. If γ = 0.1, the trained policy is concerned with instantaneous returns rather than managing long-term profits, leading to a final maximum return lower than when γ = 0.9. Therefore, long-term returns need to be considered in energy contract optimization.

[0199] (3) VPP and ESC revenue analysis

[0200] Finally, the proposed scheme was validated to improve VPP and ESC returns compared to other benchmark methods. Two benchmark methods were used for VPP return comparison: 1) the average method (AVG), which uses the average of the maximum and minimum energy contract values ​​as a fixed energy contract; and 2) the prediction-based DQN (P-DQN), which uses a deep neural network to predict the ESC adjustment amount before optimizing the energy contract using DQN. The proposed DQN-HC algorithm significantly increases VPP returns compared to other benchmarks because it integrates the ESC computation task scheduling strategy into the VPP energy contract optimization process. Compared to prediction-based energy contract optimization methods, this method can more accurately model the relationship between the optimal energy contract and the ESC adjustment amount.

[0201] In addition, two baseline methods were used to compare the benefits of ESC: 1) Static Method: ESC does not participate in VPP but manages computational tasks as usual. 2) Best Effort Method: ESC participates in VPP to the best of its ability, selectively delaying some computational tasks according to the needs of ESC. Compared with the baseline methods, the proposed DQN-HC algorithm can increase the benefits of ESC. Therefore, for ESC, participation in VPP can increase benefits, and the DQN-HC algorithm is suitable for scheduling its computational tasks when ESC is managed by VPP.

[0202] In summary, the hierarchical contract model constructed in this invention can promote mutual benefit and win-win results between virtual power plants and cross-network entities.

[0203] The method described above, after establishing a hierarchical contract model for the virtual power plant value network based on the energy trading revenue model and the information trading revenue model, can determine target information based on the hierarchical contract model of the virtual power plant value network. Then, based on the energy trading price δ(t) between the virtual power plant entity and the cross-domain entity in the energy information domain within the target information's virtual power plant value network, and the matching relationship x between the cross-domain entity in the energy information domain and the information resource request entity, the method can determine the target information. m,n (t), the computing resources allocated to cross-domain entities in the energy information domain and the cost w for each computation. m,n (t) determines the maximum revenue from energy transactions and the maximum revenue from information transactions in the virtual power plant value network, thereby achieving a joint increase in the revenue of virtual power plant entities in the energy domain and the revenue of computing and communication nodes in the information domain, and better realizing the win-win value gain of the energy domain and the information domain.

[0204] In one embodiment, the method for constructing a hierarchical contract model for a virtual power plant value network further includes: determining the revenue of virtual power plant entities in the virtual power plant value network based on at least one of the following:

[0205] The energy transaction price provided by the ancillary service market entity to the virtual power plant entity within the target time period; the energy regulation volume won by the virtual power plant entity in the ancillary service market entity within the target time period; the remuneration paid by the virtual power plant entity to the distributed resource entity within the target time period; the energy regulation capacity provided by the distributed resource entity within the target time period; the deviation between the actual regulation volume and the winning regulation volume that the ancillary service market entity can tolerate; the distributed resource entity includes energy domain entities and cross-domain entities of the energy information domain.

[0206] Specifically, in this embodiment of the invention, after constructing a virtual power plant value network that includes ancillary service market entities, virtual power plant entities, energy domain entities, cross-domain entities in the energy information domain, and information resource request entities, the energy-information dual-domain value flow among multiple entities in the virtual power plant value network is accurately modeled. This allows for the quantification and characterization of the energy-information win-win driven virtual power plant value network, quantitative analysis of the benefits of different roles in the energy-information domain and the value of the entire value network, and determination of the benefits of each entity in the virtual power plant value network.

[0207] Optionally, the revenue representation of a single entity in the virtual power plant value network is as follows:

[0208] (1) Revenue of the virtual power plant entity

[0209] Virtual power plant (VPS) operators generate revenue by aggregating distributed resources and selling regulation capacity to the energy market. While receiving revenue, they must also distribute it to the aggregated distributed resources. In the actual market mechanism, VPSs report their regulation volume for the following day. Revenue is settled based on the final regulation volume won in the energy market. For each settlement period, the deviation between the actual regulation volume and the won regulation volume must be within a certain range to receive revenue; otherwise, no settlement is made. If the deviation is within the range, a penalty fee is charged to users based on the deviation amount. The revenue of VPS operators is as follows:

[0210]

[0211] Where, α t ΔE represents the clearing price offered to virtual power plant operators in the t-slot energy market. n,t U represents the adjustment capability provided by the nth distributed resource within time slot t. n,t ΔE represents the revenue allocated by the virtual power plant in time slot t to the nth distributed resource. r,tThis represents the amount of regulation won by the virtual power plant in the energy market during the time period t, and ∈ represents the deviation between the actual tolerable regulation amount stipulated by the energy market and the winning regulation amount.

[0212] (2) Benefits of distributed entity resources

[0213] (2a) Benefits of distributed resources (energy entities) in the pure energy domain

[0214] Because thermal storage electric boilers have significant regulation capabilities, this invention uses a thermal storage electric boiler as an example to characterize the revenue of distributed resources in the pure energy domain. The total revenue of the nth thermal storage electric boiler... The revenue U obtained from participating in the regulation of virtual power plants n,t Subtract the operation and maintenance costs C of the thermal storage electric boiler n,t :

[0215]

[0216]

[0217] Specifically, the revenue obtained by the thermal storage electric boiler from the virtual power plant is the revenue from the regulation amount multiplied by the settlement price, minus the penalty cost for the actual regulation amount deviating from the required regulation amount in the instruction. n,t ≤E n,max .

[0218] (2b) Benefits of distributed resources (cross-domain entities) in the energy-information cross-domain domain

[0219] Distributed resources spanning energy and information domains primarily refer to resources that possess both the energy regulation capabilities required by the energy grid and the communication and computing resources needed by the information network. For example, data centers are both large loads in the energy grid and important computing nodes in the information network; electric vehicles are spatially flexible distributed resources in the energy grid, and with the development of vehicle-to-everything (V2X) and autonomous driving technologies, they are gradually becoming flexible communication and computing nodes in the information network; communication base stations, as widely distributed flexible loads in the energy grid, are becoming increasingly dense with the development of 5G technology, leading to a sharp increase in power consumption, and they are also important communication and computing nodes in the information network. The benefits of these energy-information cross-domain distributed resources can be expressed as:

[0220]

[0221] Among them, U n (t) represents the revenue that the nth cross-domain resource obtains from the virtual power plant within time slot t, V n(t) represents the revenue obtained by the nth cross-domain resource through communication and computing tasks within time slot t. A m,n Let A(t) be a binary variable. m,n (t) = 1 indicates that within time slot t, the nth cross-domain resource provides information services to the mth information resource user. m,n (t) represents the payment that information resource users need to pay for the service, F n (t) represents the electricity cost that the nth cross-domain resource needs to pay within time slot t.

[0222] (3) Information resource users

[0223] Users of information resources benefit from satisfying their computational needs while also incurring certain costs. To more efficiently meet the diverse information resource needs of different users, we categorize them based on their experimental sensitivity to computational tasks, θ. i Indicates user type, User type meets θ i A larger value indicates that the user is more sensitive to latency in computational tasks, meaning that more computing resources need to be allocated to that user to meet their needs. Therefore, the benefit to the information resource user can be expressed as:

[0224]

[0225] Among them, T n,m (t) represents the time required for the m-th user's computing task to complete on the n-th data center based on the allocation decision of computing resources within time slot t, W n,m (t) represents the payment that the m-th user needs to make to the n-th data center.

[0226] Optionally, the global network revenue representation of the virtual power plant value network is as follows:

[0227] Optionally, the virtual power plant and distributed resource value interaction model, as shown in Table 1, is mainly characterized from the dimensions of energy domain objective function, information domain objective function, energy domain constraints, information domain constraints, and other costs.

[0228] Table 1 Energy-Information Win-Win Interaction Optimization Model

[0229]

[0230]

[0231] By combining different optimization objectives and constraints in Table 1, a decision model for optimizing the value gain of virtual power plants can be constructed to obtain the optimal virtual power plant scheduling decision, ultimately realizing the value gain of virtual power plants. As shown in Table 2, this invention measures the value generated by the virtualized power plant value network from three aspects: economic value, social value, and environmental value.

[0232] Table 2 Value Characterization of Virtualized Power Plant Value Network

[0233]

[0234]

[0235] The method described in the above embodiments, by constructing a virtual power plant value network that includes ancillary service market entities, virtual power plant entities, energy domain entities, cross-domain entities in the energy information domain, and information resource request entities, achieves accurate modeling of the energy-information dual-domain value flow among multiple entities in the virtual power plant value network. This allows for the quantification and characterization of the energy-information win-win driven virtual power plant value network, quantitative analysis of the benefits of different roles in the energy-information domain and the value of the entire value network, and accurate determination of the benefits of each entity in the virtual power plant value network.

[0236] In one embodiment, after establishing the virtual power plant value network, the method further includes:

[0237] Obtain the target error; the target error includes at least one of the following: the error between the energy regulation amount uploaded by the distributed resource entity and the actual energy regulation amount of the distributed resource entity, the error between the predicted energy regulation amount of the distributed resource entity and the actual energy regulation amount of the distributed resource entity, and the error between the predicted energy regulation amount of the distributed resource entity and the predicted energy regulation amount when the timeliness of data analysis is insufficient; the distributed resource entity includes energy domain entities and cross-domain entities of energy information domain.

[0238] Based on the target error, the revenue of virtual power plant entities in the virtual power plant value network is updated.

[0239] In this embodiment of the invention, to accurately determine the revenue of virtual power plant entities in the virtual power plant value network, a target error is first obtained. The target error includes at least one of the following: the error between the energy regulation amount uploaded by the distributed resource entity and the actual energy regulation amount of the distributed resource entity; the error between the predicted energy regulation amount of the distributed resource entity and the actual energy regulation amount of the distributed resource entity; and the error between the predicted energy regulation amount of the distributed resource entity and the predicted energy regulation amount when the timeliness of data analysis is insufficient. The distributed resource entity includes energy domain entities and cross-domain entities of the energy information domain. Based on the obtained target error, the revenue of virtual power plant entities in the virtual power plant value network can be updated, thereby accurately determining the revenue of virtual power plant entities in the virtual power plant value network.

[0240] Optionally, when smart terminals in a virtual power plant collect data, system malfunctions or human tampering may occur, leading to missing or abnormal data in the reported data. The accuracy of the reported data affects the value gain of the virtual power plant's value network. On one hand, if the virtual power plant operator reports inaccurate regulation volumes to the energy market, the energy market will penalize the virtual power plant after data auditing reveals the error. On the other hand, if the regulation volume uploaded by distributed resources is greater than the actual regulation volume, the virtual power plant will distribute more revenue to the distributed resources, thus reducing the virtual power plant's profits. Conversely, if the regulation volume uploaded by distributed resources is less than the actual regulation volume, the distributed resources will not receive their due revenue, reducing their incentive to participate in the virtual power plant.

[0241] In this embodiment of the invention, after obtaining the error between the energy regulation amount uploaded by the distributed resource entity and the actual energy regulation amount of the distributed resource entity, the revenue of the virtual power plant entity in the virtual power plant value network is updated in the following way to accurately determine the revenue of the virtual power plant entity in the virtual power plant value network:

[0242] ΔE n '(t) represents the adjustment amount for uploading the nth distributed resource in the t-th time slot, ΔE n (t) represents the actual adjustment amount for the nth distributed resource in time slot t. Due to system failure or human tampering, the uploaded adjustment amount and the actual adjustment amount are inconsistent. n (t)=ΔE n (t)- ΔE n ′(t) represents the error between the uploaded adjustment amount and the actual adjustment amount. This represents the revenue of a virtual power plant assuming no data acquisition errors. The relationship between the actual revenue gained by a virtual power plant due to discrepancies between the uploaded and actual regulation volumes and the data accuracy error can be expressed as:

[0243]

[0244] Therefore, after obtaining the error between the energy regulation amount uploaded by the distributed resource entity and the actual energy regulation amount of the distributed resource entity, the revenue of the virtual power plant entity in the virtual power plant value network can be updated, and the revenue of the virtual power plant entity in the virtual power plant value network can be accurately determined.

[0245] Optionally, the most critical task in a virtual power plant (VPS) is to predict users' regulation capabilities. On one hand, it's necessary to predict the total regulation volume of the VPS for the following day and report this predicted value to the energy market. On the other hand, when the VPS performs real-time instruction decomposition within a day, it needs to issue dispatch instructions to each distributed resource. To maximize the VPS's profits, it's also necessary to predict the regulation capability of each distributed resource in a future time slot to decide how much regulation should be issued to different distributed resources in the current time slot. Therefore, accurate real-time prediction of user regulation capabilities is key to maximizing the VPS's profits. To this end, we further quantify the impact of the accuracy of user regulation capability prediction on the VPS's revenue.

[0246] In this embodiment of the invention, after obtaining the error between the predicted energy regulation amount of the distributed resource entity and the actual energy regulation amount of the distributed resource entity, the revenue of the virtual power plant entity in the virtual power plant value network is updated in the following way to accurately determine the revenue of the virtual power plant entity in the virtual power plant value network:

[0247] Assuming ΔE′(t) is the predicted overall regulation of the virtual power plant in time slot t, and ΔE(t) is the actual regulation of the virtual power plant in time slot t, then e(t) = ΔE(t) - ΔE′(t) represents the error between the predicted regulation and the actual regulation. Assuming the virtual power plant issues dispatch instructions to each user based on the predicted values... The revenue of a virtual power plant represents the ideal scenario where data prediction has no error. The relationship between the overall revenue and error of the virtual power plant value network can be expressed as follows:

[0248]

[0249] Therefore, after obtaining the error between the predicted energy regulation amount of the distributed resource entity and the actual energy regulation amount of the distributed resource entity, the revenue of the virtual power plant entity in the virtual power plant value network can be updated, and the revenue of the virtual power plant entity in the virtual power plant value network can be accurately determined.

[0250] Optionally, the timeliness of data analysis affects the value gain of the virtual power plant's value network. When the virtual power plant issues real-time dispatch instructions to distributed resources, it needs to issue refined dispatch instructions based on real-time adjustment quantity prediction results to maximize the virtual power plant's benefits. If the distributed resource adjustment quantity prediction result for the next time slot is not obtained in time within the specified time slot for generating the dispatch instruction, the virtual power plant will make dispatch instruction decisions based on a known value (such as the adjustment quantity of the distributed resource in the previous time slot, the average value of the adjustment quantity, etc.). This will result in the instructions issued to the distributed resources not accurately matching their actual adjustment capabilities, causing losses to the distributed resources' revenue, and also preventing the virtual power plant from obtaining the desired adjustment quantity, thus affecting the virtual power plant's revenue.

[0251] In this embodiment of the invention, after obtaining the error between the predicted energy regulation amount of the distributed resource entity and the predicted energy regulation amount corresponding to insufficient timeliness of data analysis, the revenue of the virtual power plant entity in the virtual power plant value network is updated in the following way to accurately determine the revenue of the virtual power plant entity in the virtual power plant value network:

[0252] ΔE n ′(t) represents the predicted adjustment amount for the nth distributed resource in time slot t, ΔE n (t) represents the actual adjustment amount for the nth distributed resource in time slot t. When the predicted value cannot be obtained in real time within the required time slot, the adjustment amount prediction result of the previous time slot is used instead. The error in the predicted adjustment amount value required for decision-making and scheduling instructions due to failure to meet computational timeliness requirements is expressed as e. n (t)=ΔE n (t-1)-ΔE n The time required to predict the adjustment is expressed as T′(t). actual The virtual power plant dispatch mechanism requires the prediction of regulation volume in T req If the predicted regulation value of the nth distributed resource is not obtained in time slot t, the virtual power plant will not receive the desired regulation value. The revenue of a virtual power plant is represented by the timeliness of data prediction meeting the requirements of the dispatching process. The relationship between the actual revenue of a virtual power plant and the timeliness of the regulation quantity prediction can be expressed as follows:

[0253]

[0254] Therefore, after obtaining the error between the predicted energy regulation amount of the distributed resource entity and the predicted energy regulation amount when the data analysis timeliness is insufficient, the revenue of the virtual power plant entity in the virtual power plant value network can be updated, and the revenue of the virtual power plant entity in the virtual power plant value network can be accurately determined.

[0255] In order to accurately determine the revenue of virtual power plant entities in the virtual power plant value network, the method of the above embodiments first obtains a target error. The target error includes at least one of the following: the error between the energy regulation amount uploaded by the distributed resource entity and the actual energy regulation amount of the distributed resource entity; the error between the predicted energy regulation amount of the distributed resource entity and the actual energy regulation amount of the distributed resource entity; and the error between the predicted energy regulation amount of the distributed resource entity and the predicted energy regulation amount when the timeliness of data analysis is insufficient. The distributed resource entity includes energy domain entities and cross-domain entities of energy information domain. Based on the obtained target error, the revenue of virtual power plant entities in the virtual power plant value network can be updated, thereby accurately determining the revenue of virtual power plant entities in the virtual power plant value network.

[0256] The following describes the hierarchical contract model construction device for virtual power plant value networks provided by the present invention. The hierarchical contract model construction device for virtual power plant value networks described below can be referred to in correspondence with the hierarchical contract model construction method for virtual power plant value networks described above.

[0257] Figure 5 This is a schematic diagram of the hierarchical contract model construction device for a virtual power plant value network provided by the present invention. The hierarchical contract model construction device for a virtual power plant value network provided in this embodiment includes:

[0258] The first establishment module 710 is used to establish a virtual power plant value network; the virtual power plant value network includes at least one of the following entities: ancillary service market entity, virtual power plant entity, energy domain entity, energy information domain cross-domain entity, and information resource request entity; energy trading relationships and / or information trading relationships exist between the entities in the virtual power plant value network;

[0259] The second module 720 is used to establish an energy transaction revenue model and an information transaction revenue model in the virtual power plant value network based on the transaction relationships between entities in the virtual power plant value network. The energy transaction revenue model is used to characterize the revenue of energy transactions in the virtual power plant value network; the information transaction revenue model is used to characterize the revenue of information transactions in the virtual power plant value network.

[0260] The third module 730 is used to establish a hierarchical contract model for the virtual power plant value network based on the energy trading revenue model and the information trading revenue model; the hierarchical contract model for the virtual power plant value network is used to maximize the energy trading revenue and information trading revenue in the virtual power plant value network.

[0261] Optionally, there may be energy trading relationships between virtual power plant entities and energy domain entities, as well as cross-domain entities in the energy information domain;

[0262] There is an information transaction relationship between cross-domain entities in the energy information domain and entities requesting information resources.

[0263] Optionally, the second establishment module 720 is specifically used to: establish an energy trading revenue model in the virtual power plant value network using the following formula:

[0264]

[0265] Among them, U VPP (t) represents the revenue from energy transactions between the virtual power plant entity and the cross-domain entity; α(t) represents the unit price at which the virtual power plant entity sells energy to the ancillary services market entity; δ(t) represents the unit price at which the virtual power plant entity pays the cross-domain entity in the energy information domain for the controlled energy; This represents the actual electricity consumption of cross-domain entity m in the energy information domain during the time period t. This represents the baseline energy consumption of cross-domain entity m in the energy information domain during the time period t.

[0266] The following formula is used to establish an information transaction revenue model in the virtual power plant value network:

[0267]

[0268] in, This represents the revenue generated from information transactions between cross-domain entities in the energy information domain and entities requesting information resources; x m,n (t) represents the matching relationship between cross-domain entity m and computation task n; w m,n (t) represents the benefit obtained by cross-domain entity m in computation task n.

[0269] Optionally, the third establishment module 730 is specifically used to: determine target information based on the hierarchical contract model of the virtual power plant value network; the target information includes at least one of the following: the energy transaction price between the virtual power plant entity and the cross-domain entity of the energy information domain in the virtual power plant value network, the matching relationship of computing tasks between the cross-domain entity of the energy information domain and the information resource request entity, the computing resources allocated to the cross-domain entity of the energy information domain, and the cost corresponding to each computing task;

[0270] Based on the energy transaction price between the virtual power plant entity and the cross-domain entity of the energy information domain in the proposed power plant value network in the target information, the matching relationship of the computing tasks between the cross-domain entity of the energy information domain and the information resource request entity, the computing resources allocated to the cross-domain entity of the energy information domain and the cost corresponding to each computing, determine the maximum revenue of energy transactions and the maximum revenue of information transactions in the virtual power plant value network.

[0271] Optionally, the first establishment module 710 is specifically used to: determine the revenue of a virtual power plant entity in the virtual power plant value network based on at least one of the following:

[0272] The energy transaction price provided by the ancillary service market entity to the virtual power plant entity within the target time period; the energy regulation volume won by the virtual power plant entity in the ancillary service market entity within the target time period; the remuneration paid by the virtual power plant entity to the distributed resource entity within the target time period; the energy regulation capacity provided by the distributed resource entity within the target time period; the deviation between the actual regulation volume and the winning regulation volume that the ancillary service market entity can tolerate; the distributed resource entity includes energy domain entities and cross-domain entities of the energy information domain.

[0273] Optionally, the first establishment module 710 is specifically used for: obtaining the target error; the target error includes at least one of the following: the error between the energy regulation amount uploaded by the distributed resource entity and the actual energy regulation amount of the distributed resource entity, the error between the predicted energy regulation amount of the distributed resource entity and the actual energy regulation amount of the distributed resource entity, and the error between the predicted energy regulation amount of the distributed resource entity and the predicted energy regulation amount when the timeliness of data analysis is insufficient; the distributed resource entity includes energy domain entities and cross-domain entities of energy information domain;

[0274] Based on the target error, the revenue of virtual power plant entities in the virtual power plant value network is updated.

[0275] The apparatus of this invention is used to execute the method in any of the foregoing method embodiments, and its implementation principle and technical effect are similar, so they will not be described again here.

[0276] Figure 6The example illustrates the physical structure of an electronic device, which may include: a processor 810, a communications interface 820, a memory 830, and a communication bus 840, wherein the processor 810, the communications interface 820, and the memory 830 communicate with each other through the communication bus 840. The processor 810 can call logical instructions in the memory 830 to execute a method for constructing a hierarchical contract model for a virtual power plant value network. This method includes: establishing a virtual power plant value network; the virtual power plant value network includes at least one of the following entities: ancillary service market entity, virtual power plant entity, energy domain entity, energy information domain cross-domain entity, and information resource request entity; energy trading relationships and / or information trading relationships exist between the entities in the virtual power plant value network; based on the trading relationships between the entities in the virtual power plant value network, an energy trading revenue model and an information trading revenue model are established in the virtual power plant value network; the energy trading revenue model is used to characterize the revenue from energy trading in the virtual power plant value network; the information trading revenue model is used to characterize the revenue from information trading in the virtual power plant value network; a hierarchical contract model for the virtual power plant value network is established based on the energy trading revenue model and the information trading revenue model; the hierarchical contract model for the virtual power plant value network is used to maximize the energy trading revenue and information trading revenue in the virtual power plant value network.

[0277] Furthermore, the logical instructions in the aforementioned memory 830 can be implemented as software functional units and, when sold or used as independent products, can be stored in a computer-readable storage medium. Based on this understanding, the technical solution of the present invention, essentially, or the part that contributes to the prior art, or a part of the technical solution, can be embodied in the form of a software product. This computer software product is stored in a storage medium and includes several instructions to cause a computer device (which may be a personal computer, server, or network device, etc.) 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.

[0278] On the other hand, the present invention also provides a computer program product, the computer program product comprising a computer program stored on a non-transitory computer-readable storage medium, the computer program comprising program instructions, which, when executed by a computer, enable the computer to execute the hierarchical contract model construction method for a virtual power plant value network provided by the above methods. This method comprises: establishing a virtual power plant value network; the virtual power plant value network comprising at least one of the following entities: ancillary service market entity, virtual power plant entity, energy domain entity, energy information domain cross-domain entity, and information resource request entity; energy trading relationships and / or information trading relationships existing between the entities in the virtual power plant value network; establishing an energy trading revenue model and an information trading revenue model in the virtual power plant value network based on the trading relationships between the entities in the virtual power plant value network; the energy trading revenue model characterizing the revenue from energy trading in the virtual power plant value network; the information trading revenue model characterizing the revenue from information trading in the virtual power plant value network; establishing a hierarchical contract model for the virtual power plant value network based on the energy trading revenue model and the information trading revenue model; and maximizing the energy trading revenue and information trading revenue in the virtual power plant value network using the hierarchical contract model for the virtual power plant value network.

[0279] In another aspect, the present invention also provides a non-transitory computer-readable storage medium storing a computer program thereon, which, when executed by a processor, implements a method for constructing a hierarchical contract model for a virtual power plant value network as described above. This method includes: establishing a virtual power plant value network; the virtual power plant value network includes at least one of the following entities: ancillary service market entity, virtual power plant entity, energy domain entity, energy information domain cross-domain entity, and information resource request entity; energy trading relationships and / or information trading relationships exist between the entities in the virtual power plant value network; based on the trading relationships between the entities in the virtual power plant value network, an energy trading revenue model and an information trading revenue model are established in the virtual power plant value network; the energy trading revenue model is used to characterize the revenue from energy trading in the virtual power plant value network; the information trading revenue model is used to characterize the revenue from information trading in the virtual power plant value network; a hierarchical contract model for the virtual power plant value network is established based on the energy trading revenue model and the information trading revenue model; the hierarchical contract model for the virtual power plant value network is used to maximize the energy trading revenue and information trading revenue in the virtual power plant value network.

[0280] The device embodiments described above are merely illustrative. The units described as separate components may or may not be physically separate. The components shown as units may or may not be physical units; that is, they may be located in one place or distributed across multiple network units. Some or all of the modules can be selected to achieve the purpose of this embodiment according to actual needs. Those skilled in the art can understand and implement this without any creative effort.

[0281] Through the above description of the embodiments, those skilled in the art can clearly understand that each embodiment can be implemented by means of software plus necessary general-purpose hardware platforms, and of course, it can also be implemented by hardware. Based on this understanding, the above technical solutions, in essence or the part that contributes to the prior art, can be embodied in the form of a software product. This computer software product can be stored in a computer-readable storage medium, such as ROM / RAM, magnetic disk, optical disk, etc., and includes several instructions to cause a computer device (which may be a personal computer, server, or network device, etc.) to execute the methods described in the various embodiments or some parts of the embodiments.

[0282] 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 them; although the present invention has been described in detail with reference to the foregoing embodiments, those skilled in the art should understand that modifications can still be made to the technical solutions described in the foregoing embodiments, or equivalent substitutions can be made to some of the technical features; and these modifications or substitutions do not cause the essence of the corresponding technical solutions to deviate from the spirit and scope of the technical solutions of the embodiments of the present invention.

Claims

1. A method for constructing a hierarchical contract model for a virtual power plant value network, characterized in that, include: Establish a virtual power plant value network; The virtual power plant value network includes at least one of the following entities: ancillary service market entity, virtual power plant entity, energy domain entity, energy information domain cross-domain entity, and information resource request entity; energy trading relationships and / or information trading relationships exist between the entities in the virtual power plant value network. Based on the transaction relationships between entities in the virtual power plant value network, an energy transaction revenue model and an information transaction revenue model are established within the virtual power plant value network. The energy transaction revenue model characterizes the revenue from energy transactions within the virtual power plant value network; the information transaction revenue model characterizes the revenue from information transactions within the virtual power plant value network. Establishing these models based on the transaction relationships between entities in the virtual power plant value network includes: The energy trading revenue model in the virtual power plant value network is established using the following formula: ； in, α(t) represents the revenue from energy transactions between the virtual power plant entity and the cross-domain entity; α(t) represents the unit price at which the virtual power plant entity sells energy to the ancillary services market entity. This represents the unit price of regulated energy paid by the virtual power plant entity to cross-domain entities in the energy information domain; Represents cross-domain entities in the energy information domain. In the Actual electricity consumption during the time period Represents cross-domain entities in the energy information domain. In the Baseline energy consumption over the time period; The following formula is used to establish an information transaction revenue model in the virtual power plant value network: ； in, This refers to the revenue generated from information transactions between cross-domain entities in the energy information domain and entities requesting information resources. This represents the matching relationship between cross-domain entity m and computation task n; This represents the benefit that cross-domain entity m obtains in computation task n; A hierarchical contract model for the virtual power plant value network is established based on the energy trading revenue model and the information trading revenue model. The hierarchical contract model for the virtual power plant value network is used to maximize the energy trading revenue and information trading revenue in the virtual power plant value network.

2. The method for constructing a hierarchical contract model for a virtual power plant value network according to claim 1, characterized in that, The virtual power plant entity has an energy transaction relationship with the energy domain entity and the cross-domain entity of the energy information domain; There is an information transaction relationship between the cross-domain entity in the energy information domain and the entity requesting the information resources.

3. The method for constructing a hierarchical contract model for a virtual power plant value network according to claim 1, characterized in that, Also includes: Based on the hierarchical contract model of the virtual power plant value network, target information is determined; the target information includes at least one of the following: energy transaction prices between virtual power plant entities and cross-domain entities in the energy information domain in the virtual power plant value network, matching relationships of computing tasks between cross-domain entities in the energy information domain and information resource request entities, computing resources allocated to cross-domain entities in the energy information domain, and the cost corresponding to each computing task. Based on the energy transaction price between the virtual power plant entity and the cross-domain entity of the energy information domain in the proposed power plant value network in the target information, the matching relationship of the computing tasks between the cross-domain entity of the energy information domain and the information resource request entity, the computing resources allocated to the cross-domain entity of the energy information domain and the cost corresponding to each computing, the maximum revenue of energy transactions and the maximum revenue of information transactions in the virtual power plant value network are determined.

4. The method for constructing a hierarchical contract model for a virtual power plant value network according to claim 3, characterized in that, Also includes: Determine the revenue of a virtual power plant entity in the virtual power plant value network based on at least one of the following: The energy transaction price provided by the ancillary service market entity to the virtual power plant entity within the target time period; the energy regulation volume won by the virtual power plant entity in the ancillary service market within the target time period; the remuneration paid by the virtual power plant entity to the distributed resource entity within the target time period; the energy regulation capacity provided by the distributed resource entity within the target time period; the deviation between the actual regulation volume and the winning regulation volume that the ancillary service market can tolerate; the distributed resource entity includes the energy domain entity and the cross-domain entity of the energy information domain.

5. The method for constructing a hierarchical contract model for a virtual power plant value network according to claim 4, characterized in that, After establishing the virtual power plant value network, it also includes: Obtain the target error; the target error includes at least one of the following: the error between the energy regulation amount uploaded by the distributed resource entity and the actual energy regulation amount of the distributed resource entity, the error between the predicted energy regulation amount of the distributed resource entity and the actual energy regulation amount of the distributed resource entity, and the error between the predicted energy regulation amount of the distributed resource entity and the predicted energy regulation amount when the timeliness of data analysis is insufficient; the distributed resource entity includes the energy domain entity and the cross-domain entity of the energy information domain; Based on the target error, the revenue of the virtual power plant entity in the virtual power plant value network is updated.

6. A hierarchical contract model construction device for a virtual power plant value network, characterized in that, include: The first module is used to establish a virtual power plant value network. The virtual power plant value network includes at least one of the following entities: ancillary service market entity, virtual power plant entity, energy domain entity, energy information domain cross-domain entity, and information resource request entity; energy trading relationships and / or information trading relationships exist between the entities in the virtual power plant value network. The second module is used to establish an energy transaction revenue model and an information transaction revenue model in the virtual power plant value network based on the transaction relationships between entities in the virtual power plant value network. The energy transaction revenue model characterizes the revenue from energy transactions in the virtual power plant value network; the information transaction revenue model characterizes the revenue from information transactions in the virtual power plant value network. The process of establishing the energy transaction revenue model and the information transaction revenue model in the virtual power plant value network based on the transaction relationships between entities in the virtual power plant value network includes: The energy trading revenue model in the virtual power plant value network is established using the following formula: ； in, α(t) represents the revenue from energy transactions between the virtual power plant entity and the cross-domain entity; α(t) represents the unit price at which the virtual power plant entity sells energy to the ancillary services market entity. This represents the unit price of regulated energy paid by the virtual power plant entity to cross-domain entities in the energy information domain; Represents cross-domain entities in the energy information domain. In the Actual electricity consumption during the time period Represents cross-domain entities in the energy information domain. In the Baseline energy consumption over the time period; The following formula is used to establish an information transaction revenue model in the virtual power plant value network: ； in, This refers to the revenue generated from information transactions between cross-domain entities in the energy information domain and entities requesting information resources. This represents the matching relationship between cross-domain entity m and computation task n; This represents the benefit that cross-domain entity m obtains in computation task n; The third module is used to establish a hierarchical contract model for the virtual power plant value network based on the energy trading revenue model and the information trading revenue model; the hierarchical contract model for the virtual power plant value network is used to maximize the energy trading revenue and information trading revenue in the virtual power plant value network.

7. An electronic 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 program, it implements the hierarchical contract model construction method for the virtual power plant value network as described in any one of claims 1 to 5.

8. A non-transitory computer-readable storage medium having a computer program stored thereon, characterized in that, When executed by a processor, the computer program implements the hierarchical contract model construction method for the virtual power plant value network as described in any one of claims 1 to 5.

9. A computer program product having executable instructions stored thereon, characterized in that, When executed by the processor, this instruction causes the processor to implement the hierarchical contract model construction method for the virtual power plant value network as described in any one of claims 1 to 5.

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