Virtual power plant cluster low-carbon cooperative scheduling method, system, device and medium

Abstract

The invention provides a virtual power plant cluster low-carbon cooperative scheduling method, system and device and a medium, and the method comprises the steps: obtaining the interaction electric quantity and time-of-use electricity price of a virtual power plant cluster and a power distribution network based on a pre-constructed digital virtual power plant system facing a power system; based on the interactive electric quantity and the time-of-use electricity price, determining an optimal scheduling strategy of the virtual power plant cluster by taking the minimum operation cost as a target; making an intelligent contract of a block chain control module in the digital virtual space based on the optimal scheduling strategy; performing low-carbon cooperative scheduling on the virtual power plant cluster based on the intelligent contract in combination with real-time virtual-real information interacted by a physical entity and a digital virtual space of the power system; multi-energy complementary power dispatching is performed on the virtual power plant through the smart contract in the block chain control module, bilateral selection of low-carbon cooperative dispatching is realized, configuration and operation of the virtual power plant are adjusted in real time based on interactive real-time virtual and real information in the virtual power plant system, and low-carbon cooperative operation of the virtual power plant is realized.

Description

Technical Field

[0001] The present invention relates to the technical field of virtual power plants, and in particular to a low-carbon collaborative scheduling method, system, equipment and medium for a virtual power plant cluster. Background Art

[0002] Building a new power system that is clean, flexible, digital, and intelligent is an important development direction in the energy field. However, with the development and utilization of a high proportion of new energy, the operating characteristics and stable form of the power system have changed significantly. The improvement of the system's regulation and support capabilities faces many constraints, and the safe and stable operation of the system faces great risks and challenges. It is urgent to tap into the massive heterogeneous demand-side resources with high flexibility and enhance the system's flexible regulation capabilities. On this basis, virtual grid technology can give full play to its advantages. Based on advanced control, metering, and communication technologies, virtual power plants can effectively aggregate massive and diverse distributed resources on the demand side, achieve efficient scheduling and management of energy, and provide the necessary flexibility support for the power system.

[0003] The new electricity market reform and the development of new Internet technology, communication technology and smart power technology have provided new ideas for the transformation of virtual power plant technology to the digitalization of power systems. Through the above technologies, virtual power plants can efficiently aggregate distributed new energy power generation systems, various energy storage systems and various adjustable and controllable loads to achieve coordinated scheduling and optimized operation of various distributed energy equipment within the power system.

[0004] At present, research on the operation and control of virtual power plants mainly focuses on the operation and control strategies of virtual power plants that aggregate multiple DERs (Distributed Energy Resources). Due to the different characteristics and operation modes of energy resources and equipment of various virtual power plants, integrating these resources into a unified virtual power plant requires complex management and optimization methods. Therefore, the existing centralized dispatching system is difficult to adapt to the real-time coordinated control of multiple virtual power plants at the equipment level, that is, it is difficult to perform reasonable real-time dispatch of multiple virtual power plants, resulting in reduced resource utilization and economic benefits in the power system, and thus making it impossible to achieve low-carbon operation of virtual power plant clusters. Summary of the invention

[0005] In order to overcome the above-mentioned deficiencies of the prior art, the present invention proposes a low-carbon collaborative scheduling method for a virtual power plant cluster, comprising:

[0006] Based on the pre-built digital virtual power plant system for power systems, the interactive power volume between the virtual power plant cluster and the distribution network and the time-of-use electricity price are obtained;

[0007] Based on the interactive electricity volume between the virtual power plant cluster and the distribution network and the time-of-use electricity price, the optimal dispatching strategy of the virtual power plant cluster is determined with the minimum operating cost as the goal;

[0008] Formulate a smart contract for a blockchain control module in a digital virtual space based on the optimal scheduling strategy;

[0009] Based on the smart contract, and in combination with the real-time virtual-real information of the physical entity and digital virtual space interaction of the power system, low-carbon collaborative scheduling of multiple virtual power plants in the virtual power plant cluster is performed;

[0010] The digital virtual power plant system for the electric power system is constructed based on a digital virtual space and physical entities of the electric power system, and the physical entities of the electric power system include the virtual power plant cluster and the distribution network.

[0011] Optionally, the digital virtual power plant system for the power system includes the following construction process:

[0012] Based on the static data of the physical entities in the power system, a data middle platform of the digital virtual space is built to form an initial database of the digital virtual space;

[0013] Based on the digital description of the parameters and performance of the physical entity, forming an operation database of the physical entity;

[0014] Combining the initial database, the operating database and the twin data model of the physical entity to form the twin data of the digital virtual space;

[0015] Constructing a digital twin of the digital virtual space based on a physical model of the physical entity, operating data, and a simulation model simulating the behavior and performance of the physical entity;

[0016] Combining the data middle platform, the twin data, the digital twin and the intelligent application of the physical entity, the digital virtual space is constructed, and the intelligent application includes the blockchain control module;

[0017] The digital virtual power plant system for the power system is constructed based on the digital virtual space and the physical entity of the power system.

[0018] Optionally, based on the smart contract, in combination with real-time virtual-real information of the interaction between the physical entity and the digital virtual space of the power system, low-carbon collaborative scheduling of multiple virtual power plants in the virtual power plant cluster includes:

[0019] Using the real-time virtual-real information of the interaction between the physical entity and the digital virtual space of the power system, the operation of the virtual power plant cluster in the physical entity is controlled and the configuration of the virtual power plant cluster in the physical entity is adjusted;

[0020] Based on the smart contract, combined with the low-carbon collaborative interaction mode between the virtual power plant and the distribution network in the blockchain control module, and the low-carbon collaborative interaction mode between the virtual power plant and other virtual power plants, real-time power dispatch is performed for multiple virtual power plants in the virtual power plant cluster.

[0021] Optionally, the controlling the operation of the virtual power plant cluster in the physical entity and adjusting the configuration of the virtual power plant cluster in the physical entity by using the real-time virtual-real information of the interaction between the physical entity and the digital virtual space of the power system includes:

[0022] Using the digital twin in the digital virtual space to obtain actual operating data of the physical entity of the power system, and performing digital twin mapping in combination with the twin data in the digital virtual space to obtain a twin data model of the physical entity;

[0023] Storing the twin data model and the actual operation data in the twin data, and updating the twin data in the digital virtual space;

[0024] Using the digital twin to obtain static data of the physical entity, predicting the state of the physical entity based on the static data and the actual operation data, and obtaining a prediction result;

[0025] and determining operation control information of the physical entity based on the real-time simulation result of the digital twin, and controlling the operation of the virtual power plant cluster in the physical entity based on the operation control information;

[0026] Utilize the intelligent application in the digital virtual space to perform intelligent analysis based on the updated twin data and the prediction results, determine the configuration adjustment information of the physical entity based on the results of the intelligent analysis, and adjust the configuration of the virtual power plant cluster in the physical entity based on the configuration adjustment information.

[0027] Optionally, the determining of the optimal dispatching strategy of the virtual power plant cluster based on the interactive power volume between the virtual power plant cluster and the distribution network and the time-of-use electricity price with the minimum operating cost as the goal includes:

[0028] For each of the virtual power plants, the load power balance constraint condition of the virtual power plant is constructed according to the total power output of the photovoltaic system in the virtual power plant, the power mutually called by the photovoltaic systems of the virtual power plant and other virtual power plants, the interactive power between the virtual power plant and the distribution network, the charging power of the battery in the virtual power plant and the local load;

[0029] Constructing load power balance constraints of the virtual power plant cluster based on the total power output of the photovoltaic system in each virtual power plant, the charge and discharge power of the battery, and the local load;

[0030] Based on the interactive electricity volume and time-of-use electricity price of each virtual power plant and the distribution network, an objective function is constructed with the goal of minimizing the operating cost of the virtual power plant cluster;

[0031] Based on the objective function, the load power balance constraints of each virtual power plant and the load power balance constraints of the virtual power plant cluster, an optimal scheduling strategy for the coordinated scheduling of the virtual power plants is obtained.

[0032] Optionally, the objective function is:

[0033]

[0034] in, It represents the interactive electricity between virtual power plant i and distribution network during collaborative operation; represents the time-of-use electricity price that virtual power plant i buys from the distribution network during period t; and is a complementary coefficient, which is determined based on the power interaction between the virtual power plant i and the distribution network; represents the time-of-use electricity price of virtual power plant i selling electricity to the distribution network in period t; E represents the mathematical expectation; T represents the total number of decision periods t; N represents the total number of virtual power plants.

[0035] Optionally, the load power balance constraint condition of the virtual power plant is:

[0036]

[0037] in, represents the total power output of the photovoltaic system of virtual power plant i; N represents the total number of virtual power plants; represents the power dispatched from the PV system of virtual power plant i to virtual power plant j; represents the power dispatched from the PV system of virtual power plant j to virtual power plant i; It represents the interactive electricity between virtual power plant i and distribution network during collaborative operation; represents the charging power of the battery in virtual power plant i; represents the local load in virtual power plant i.

[0038] Optionally, the load power balance constraint condition of the virtual power plant cluster is:

[0039]

[0040] in, represents the total power output of the PV system of virtual power plant i; represents the discharge power of the battery of virtual power plant i; I d，i represents the discharge state of the battery in virtual power plant i; represents the charging power of the battery of virtual power plant i; c，i represents the charging state of the battery in the virtual power plant i; Indicates the power loss during the battery charging and discharging process; The local load in virtual power plant i; N represents the total number of virtual power plants.

[0041] Optionally, before obtaining the interactive power quantity and time-of-use electricity price between the virtual power plant cluster and the distribution network in the power system, the method further includes:

[0042] Creating a Docker container instance in a cloud-edge collaborative control module of the digital virtual space;

[0043] Performing online detection, computational analysis, and collaborative control functional design on the container instance to obtain a container instance with completed functional design;

[0044] The container instance that has completed the functional design is used for online monitoring and computational analysis of the operating status of the digital virtual power plant system.

[0045] Optionally, based on the smart contract, in combination with real-time virtual-real information of the interaction between the physical entity and the digital virtual space of the power system, low-carbon collaborative scheduling of multiple virtual power plants in the virtual power plant cluster includes:

[0046] Based on the container instance with completed functional design and the smart contract, combined with the real-time virtual-real information of the interaction between the physical entity and the digital virtual space of the power system, low-carbon collaborative scheduling of multiple virtual power plants in the virtual power plant cluster is jointly performed.

[0047] Optionally, creating a Docker container instance in the cloud-edge collaborative control module of the digital virtual space includes:

[0048] Encapsulating a container image in the cloud-edge collaborative control module;

[0049] The Docker agent in the cloud-edge collaborative control module is used to create a Docker container instance based on the container image and the user's scheduling, computing and analysis request for the virtual power plant.

[0050] Based on the same inventive concept, the present invention proposes a virtual power plant cluster low-carbon collaborative dispatching system, including: a digital virtual space and a physical entity of a power system;

[0051] The physical entities of the power system include a virtual power plant cluster and a distribution network;

[0052] The digital virtual space is used to obtain the interactive electricity volume and time-of-use electricity price between the virtual power plant cluster and the distribution network in the power system; and based on the interactive electricity volume and time-of-use electricity price between the virtual power plant cluster and the distribution network, the optimal scheduling strategy of the virtual power plant cluster is determined with the minimum operating cost as the goal, and a smart contract is formulated based on the optimal scheduling strategy; it is also used to perform low-carbon collaborative scheduling of multiple virtual power plants in the virtual power plant cluster based on the smart contract, combined with the real-time virtual and real information of the interaction between the physical entity of the power system and the digital virtual space.

[0053] Optionally, the digital virtual space includes digital twins, twin data, smart applications and a data middle platform that are communicatively connected to each other;

[0054] The data middle platform is built based on the static data of physical entities in the power system to form an initial database;

[0055] The twin data includes the initial database, the operation database of the physical entity and the twin data model of the physical entity;

[0056] The digital twin is constructed based on the physical model, operation data and simulation model of the physical entity that simulates the behavior and performance of the physical entity; it is used to obtain the actual operation data of the physical entity, and perform digital twin mapping in combination with the twin data to obtain the twin data model of the physical entity, store the twin data model and the actual operation data in the twin data, and update the twin data; it is also used to obtain the static data of the physical entity, predict the state of the physical entity based on the static data and the actual operation data, and obtain the prediction result; and determine the operation control information of the physical entity based on its own real-time simulation results, and control the operation of the virtual power plant cluster in the physical entity based on the operation control information;

[0057] The intelligent application is used to perform intelligent analysis based on the updated twin data and the prediction results, determine configuration adjustment information of the physical entity based on the results of the intelligent analysis, and adjust the configuration of the virtual power plant cluster in the physical entity based on the configuration adjustment information.

[0058] Optionally, the smart application includes a blockchain control module:

[0059] Used to formulate a smart contract based on the optimal scheduling strategy;

[0060] Based on the smart contract, combined with the low-carbon collaborative interaction mode between the virtual power plant and the distribution network in the blockchain control module, and the low-carbon collaborative interaction mode between the virtual power plant and other virtual power plants, real-time power dispatch is performed for multiple virtual power plants in the virtual power plant cluster.

[0061] Optionally, the digital virtual space also includes a cloud-edge collaborative control module:

[0062] Used to create a Docker container instance, and perform online detection, computational analysis, and collaborative control functional design on the container instance to obtain a container instance with completed functional design;

[0063] The container instance having completed functional design is used for online monitoring and computational analysis of the operating status of the physical entities of the digital virtual space and the power system;

[0064] The container instance that completes the functional design is also used to combine the smart contract and the real-time virtual-real information of the interaction between the physical entity and the digital virtual space of the power system to jointly perform low-carbon collaborative scheduling of multiple virtual power plants in the virtual power plant cluster.

[0065] Optionally, the cloud-edge collaborative control module is further used to:

[0066] Package container images;

[0067] Using a Docker agent to create a Docker container instance based on the container image and a user's request for scheduling, computing and analyzing the virtual power plant;

[0068] The container instance is subjected to online detection, computational analysis and collaborative control functional design to obtain a container instance with completed functional design.

[0069] In another aspect, the present application further provides an electronic device, comprising: at least one processor and a memory; the memory and the processor are connected via a bus;

[0070] The memory is used to store one or more programs;

[0071] When the one or more programs are executed by the at least one processor, a low-carbon collaborative scheduling method for a virtual power plant cluster as described above is implemented.

[0072] On the other hand, the present application also provides a computer-readable storage medium having an execution program stored thereon, and when the execution program is executed, a low-carbon collaborative scheduling method for a virtual power plant cluster as described above is implemented.

[0073] Compared with the closest prior art, the present invention has the following beneficial effects:

[0074] The present invention provides a method, system, device and medium for low-carbon collaborative scheduling of a virtual power plant cluster, including: based on a pre-built digital virtual power plant system for the power system, obtaining the interactive power and time-of-use electricity price between the virtual power plant cluster and the distribution network; based on the interactive power and time-of-use electricity price between the virtual power plant cluster and the distribution network, determining the optimal scheduling strategy of the virtual power plant cluster with the minimum operating cost as the goal; formulating a smart contract of a blockchain control module in a digital virtual space based on the optimal scheduling strategy; based on the smart contract, combining the real-time virtual-real information of the interaction between the physical entity of the power system and the digital virtual space, performing low-carbon collaborative scheduling of multiple virtual power plants in the virtual power plant cluster; the present invention combines multiple virtual power plants Aggregate analysis, through the pre-built digital virtual power plant system for the power system, tap the potential of multi-energy complementary synergy of virtual power plants and release the flexibility of cluster regulation; use the smart contract containing the optimal scheduling strategy in the blockchain control module to perform real-time power scheduling of multiple virtual power plants for multi-energy complementarity, and adjust the configuration and operation of virtual power plants in real time based on the real-time virtual and real information interacting in the virtual power plant system, so as to jointly realize the coordinated operation of virtual power plants, improve the utilization rate of various energy sources, reduce carbon emissions, and realize the low-carbon operation of virtual power plant clusters; at the same time, in the digital virtual power plant system, by analyzing the optimal scheduling strategy, the economic benefits of the virtual power plant cluster operation process are taken into account, unnecessary benefit calculations are avoided, and operating costs are reduced. BRIEF DESCRIPTION OF THE DRAWINGS

[0075] Figure 1 A schematic diagram of a flow chart of a low-carbon collaborative scheduling method for a virtual power plant cluster provided by the present invention;

[0076] Figure 2 A schematic diagram of a low-carbon collaborative scheduling architecture for a virtual power plant cluster provided by the present invention;

[0077] Figure 3 A schematic diagram of the structure of the distribution network system of the virtual power plant cluster provided by the present invention;

[0078] Figure 4 A schematic diagram of the structure of the blockchain control module provided by the present invention;

[0079] Figure 5 A schematic diagram of the docker container operation architecture provided by the present invention;

[0080] Figure 6 The present invention provides a schematic structural diagram of an electronic device. DETAILED DESCRIPTION

[0081] The specific implementation modes of the present invention are further described in detail below with reference to the accompanying drawings.

[0082] Example 1

[0083] The present invention provides a virtual power plant cluster low-carbon collaborative scheduling method, such as Figure 1 As shown, including:

[0084] S1. Based on the pre-built digital virtual power plant system for the power system, the interactive power volume between the virtual power plant cluster and the distribution network and the time-of-use electricity price are obtained;

[0085] S2. Based on the interactive electricity volume between the virtual power plant cluster and the distribution network and the time-of-use electricity price, determine the optimal dispatching strategy of the virtual power plant cluster with the minimum operating cost as the goal;

[0086] S3. Formulate a smart contract for a blockchain control module in a digital virtual space based on the optimal scheduling strategy;

[0087] S4. Based on the smart contract, combined with the real-time virtual-real information of the physical entity and digital virtual space interaction of the power system, low-carbon collaborative scheduling of multiple virtual power plants in the virtual power plant cluster is performed;

[0088] The digital virtual power plant system for the electric power system is constructed based on a digital virtual space and physical entities of the electric power system, and the physical entities of the electric power system include the virtual power plant cluster and the distribution network.

[0089] In step S1, S1-1, building a digital virtual power plant system for the power system requires synchronous planning of the digital virtual space and the physical entities of the power system.

[0090] Starting from the modeling stage (modeling is to establish a digital virtual space, the digital virtual power plant system consists of digital virtual space and physical entities, such as Figure 2 As shown, digital virtual space is Figure 2 In the operation process, simulation, knowledge, application and other related models and management data are continuously imported to continuously improve the data middle platform database. Figure 2 The intelligent application services in the virtual power plant can realize decision support and optimized management of the virtual power plant. For the demand-side resources that have been built and put into use, they are incorporated into the digital virtual power plant system through digital modeling and deployment of IoT facilities. Through intelligent perception and data collection, the information center data platform is enriched. The demand-side resources include power generation resources such as distributed photovoltaic systems on the distribution network side, as well as flexible loads such as household / public electric vehicle charging piles, air conditioners, water heaters, and other electricity / gas / cold / heat loads, that is, the physical entity of the virtual power plant.

[0091] In terms of optimized operation, digital virtual space and physical entities realize twin parallelism and virtual-real interaction through efficient connection and real-time transmission. With the help of IoT intelligent perception and real-time information collection technology, "from real to virtual" is realized. The physical entity of the virtual power plant and the digital virtual space realize virtual-real iteration through the feedback mechanism. On this basis, the support of the intelligent decision-making platform and real-time optimization operation control are used to realize "from virtual to real".

[0092] Based on the above requirements, a digital virtual power plant system including a digital virtual space and a physical entity of a virtual power plant is constructed. The digital virtual space includes a digital twin, twin data, intelligent applications, and a data middle platform; as shown in the following formula:

[0093] DTVPPS={P VPP ,DT,DT Data ,CN,DT&S}

[0094] Among them, DTVPPS stands for Digital Virtual Power Plant System (Digital Twin Virtual Power Plant System is also called Digital Twin Virtual Power Plant System); VPP Represents the physical entity of the virtual power plant (Physical Virtual Power Plant); DT stands for Digital Twin (Digital Twin); DT Data It represents twin data (Digital Twindata); CN represents the communication connection (Connection) between modules; DT&S represents the intelligent application services (Digital Twin&Services) provided by the digital virtual power plant system.

[0095] like Figure 2 As shown, the physical entities of the virtual power plant include personnel behavior and management, electricity market, distribution network operation, virtual power plant layer, user layer and equipment layer, among which the distribution network operation is the distribution network layer. In this application, the low-carbon coordinated dispatching between the virtual power plant in the virtual power plant layer and the distribution network in the distribution network layer is mainly studied, and other layers are not analyzed in depth.

[0096] Specifically, the digital virtual power plant system for the power system includes the following construction process:

[0097] Based on the static data of the physical entities in the power system, a data middle platform of the digital virtual space is built to form an initial database of the digital virtual space;

[0098] Based on the digital description of the parameters (characteristics, behaviors and formation process) and performance of the physical entity, an operation database of the physical entity is formed; for example, by using digital technologies such as smart sensors and the Internet of Things, the characteristics, behaviors, formation process and performance of physical objects such as demand-side resources and markets are digitally described to establish a historical and real-time operation database;

[0099] Combining the initial database, the operating database and the twin data model of the physical entity to form the twin data of the digital virtual space;

[0100] Constructing a digital twin of the digital virtual space based on a physical model of the physical entity, operating data, and a simulation model simulating the behavior and performance of the physical entity;

[0101] Combining the data middle platform, the twin data, the digital twin and the intelligent application of the physical entity, the digital virtual space is constructed, and the intelligent application includes the blockchain control module;

[0102] The digital virtual power plant system for the power system is constructed based on the digital virtual space and the physical entities of the power system, and the twin data, smart applications (smart application services) and digital twins are interconnected.

[0103] S1-2. Virtual power plants can be coordinated and controlled based on energy Internet information and communication technologies to achieve efficient and flexible use of distributed energy and energy storage devices. Distribution network systems containing virtual power plants, such as Figure 3 The distribution network system is a part of the physical entity of the power system, wherein the distribution network system includes multiple virtual power plants connected to the distribution network, such as Figure 3 As shown in the figure, it is a schematic diagram of the structure of the distribution network system. Each virtual power plant is connected to the distribution network power network ( Figure 3 solid line in), and the communication network ( Figure 3 The dotted line in the figure is connected to the dispatching control center of the distribution network for power call and information transmission. In this embodiment, the new energy source selected by the virtual power plant is a distributed photovoltaic power generation system, such as Figure 3 As shown in the figure, each virtual power plant is mainly composed of a distributed photovoltaic power generation system, a storage battery, a local load and a virtual power plant control and dispatching center. AC / DC represents the conversion between alternating current and direct current.

[0104] When operating independently, each virtual power plant only interacts with the distribution network. The electricity generated by the distributed photovoltaic power generation system can be used to meet local loads, charge batteries, and sell to the distribution network for profit. At the same time, in addition to meeting local loads, batteries can also store and release electricity through interaction with the distribution network. Local loads can buy electricity from the distribution network during peak hours to meet demand. When operating in a coordinated manner, in addition to interacting with the distribution network, each virtual power plant can also interact with each other. In addition to calling its own distributed photovoltaic power and energy storage system, the virtual power plant can also use the photovoltaic power and energy storage devices of other virtual power plants. Compared with the independent operation state, each virtual power plant has more new energy photovoltaic power supply during the coordinated operation. At the same time, it can use larger capacity energy storage batteries to store electricity. Therefore, the coordinated operation makes the entire distribution network system more flexible and can achieve efficient use of distributed photovoltaic power generation systems and energy storage equipment.

[0105] For a distribution network system containing multiple virtual power plants, if the interaction between virtual power plants is not considered, the photovoltaic power generation and energy storage devices of the system as a whole cannot be efficiently utilized. Therefore, in order to achieve the coordinated scheduling of virtual power plant clusters, it is necessary to consider the interconnection status of multiple virtual power plants. The optimization operation goal of each virtual power plant in the distribution network system is to minimize the local operating cost, that is, the optimal operation decision of a single virtual power plant within the decision cycle is usually the local optimal solution of the overall operation decision of the virtual power plant cluster. In a distribution network system containing multiple virtual power plants, in multiple operation decision cycles, the output power of each distributed photovoltaic power generation system is only affected by the ambient temperature and radiation intensity.

[0106] Therefore, based on the digital virtual power plant system, the interactive electricity volume and time-of-use electricity price between the virtual power plant cluster and the distribution network are obtained to analyze and calculate the optimal solution for minimizing the operating cost of the virtual power plant cluster.

[0107] In step S2, based on the interactive electricity volume between the virtual power plant cluster and the distribution network and the time-of-use electricity price, the optimal scheduling strategy of the virtual power plant cluster is determined with the minimum operating cost as the goal.

[0108] S2-1. For each of the virtual power plants, the load power balance constraint conditions of the virtual power plant are constructed according to the total power output by the photovoltaic system in the virtual power plant, the power mutually called upon by the photovoltaic systems of the virtual power plant and other virtual power plants, the interactive power between the virtual power plant and the distribution network, the charging power of the batteries in the virtual power plant and the local load.

[0109] During collaborative operation, based on the comprehensive operation information of the distribution network, the photovoltaic power of the virtual power plant can be sold to the distribution network, and can also be called by other virtual power plants to meet local loads or charge batteries. Based on the power interaction between the photovoltaic system in the virtual power plant and the distribution network, as well as the power interaction with other virtual power plants in the distribution network system, the total power output of the photovoltaic system of the virtual power plant is obtained. The calculation formula for the total power output of the photovoltaic cell in the i-th virtual power plant in the t period (t represents the t-th decision cycle) is:

[0110]

[0111] in, represents the total power output of the PV system of virtual power plant i; represents the electricity sold by the photovoltaic system of virtual power plant i to the distribution network, g represents electricity sold, and pv represents the photovoltaic system; represents the electricity of the PV system of virtual power plant i that is called by other virtual power plants j to meet the load, and N represents the total number of virtual power plants; represents the power of the PV system of virtual power plant i that is used by other virtual power plants j to charge batteries, b represents the called power; I c，j It indicates the charging status of the battery in other virtual power plants j. Its value is 1 or 0, 1 indicates the charging status, and 0 indicates the uncharging status.

[0112] During coordinated operation, the storage battery of virtual power plant i can not only be charged with power from the photovoltaic system of the virtual power plant where it is located and charged with power purchased from the distribution network, but can also be charged with power from the excess photovoltaic systems of other virtual power plants. At the same time, the power of the storage battery in virtual power plant i can also be called by other virtual power plants to meet the load demand. In time period t, the calculation formula for the charging and discharging power of the storage battery is:

[0113]

[0114] in, represents the charging power of the battery of virtual power plant i; represents the electricity purchased by virtual power plant i from the distribution grid for battery charging; represents the power that virtual power plant i calls from the photovoltaic system of other virtual power plant j for battery charging; N represents the total number of virtual power plants; I c，i Indicates the charging state of the battery in virtual power plant i. Its value is 1 or 0. 1 indicates the charging state and 0 indicates the uncharging state. Here, 1 is taken; represents the discharge power of the battery of virtual power plant i; It indicates that the battery of virtual power plant i is used by virtual power plant j to meet the power demand of load; I represents the electricity sold to the distribution grid from the battery of virtual power plant i; d，i Indicates the discharge status of the battery in virtual power plant i. Its value can be 0 or 1. 0 means not in the discharge state, and 1 means in the discharge state. Here, 1 is taken.

[0115] During coordinated operation, the load power balance constraint of virtual power plant i in period t is:

[0116]

[0117] in, represents the total power output of the photovoltaic system of virtual power plant i; N represents the total number of virtual power plants; represents the power dispatched from the PV system of virtual power plant i to virtual power plant j; represents the power dispatched from the photovoltaic system of virtual power plant j to virtual power plant i, i, j represents the power dispatch relationship from virtual power plant i to j, and j, i represents the power dispatch relationship from virtual power plant j to i; It represents the interactive power between the virtual power plant i and the distribution network during collaborative operation, and sb represents the storage battery; represents the charging power of the battery in virtual power plant i; represents the local load in virtual power plant i.

[0118] s2-2. Construct the load power balance constraint condition of the virtual power plant cluster based on the total power output of the photovoltaic system in each of the virtual power plants, the charging and discharging power of the battery, and the local load.

[0119] During coordinated operation, in period t, the calculation formula for the interactive power between virtual power plant i and the distribution network is:

[0120]

[0121] in, It represents the interactive electricity between virtual power plant i and distribution network during collaborative operation; I represents the power of the battery of virtual power plant i being called by virtual power plant j to meet the load demand; d，i Represents the discharge state of the battery in virtual power plant i; I c，i represents the charging state of the battery in the virtual power plant i; It indicates that virtual power plant i calls for electricity from the photovoltaic system of other virtual power plant j for battery charging; N indicates the total number of virtual power plants.

[0122] From the perspective of the distribution network, in period t, the load power balance constraint of N virtual power plants (i.e., the balance constraint of the virtual power plant cluster) is:

[0123]

[0124] in, represents the total power output of the PV system of virtual power plant i; represents the discharge power of the battery of virtual power plant i; I d，i represents the discharge state of the battery in virtual power plant i; represents the charging power of the battery of virtual power plant i; c，i represents the charging state of the battery in the virtual power plant i; Indicates the power loss during the battery charging and discharging process; The local load in virtual power plant i; N represents the total number of virtual power plants.

[0125] S2-3. Based on the interactive electricity volume and time-of-use electricity price between each virtual power plant and the distribution network, an objective function is constructed with the goal of minimizing the operating cost of the virtual power plant cluster.

[0126] The output power of the photovoltaic system (distributed photovoltaic power generation system) of each virtual power plant in the distribution network is affected by the random changes of radiation and temperature and is a random variable. At the same time, the local load of each virtual power plant is also a random quantity. Therefore, the expected cost of N virtual power plants in the distribution network system within T decision cycles is determined to be the minimum according to the following objective function:

[0127]

[0128] in, It represents the interactive electricity between virtual power plant i and distribution network during collaborative operation; represents the time-of-use electricity price that virtual power plant i buys from the distribution network during period t; and is the complementary coefficient, which takes 1 and 0 respectively according to the power interaction between the virtual power plant i and the distribution network (i.e., if power is dispatched from the virtual power plant to the distribution network, w b Take 1, w s If it is negative, it takes 0 and 1 respectively; It represents the time-of-use electricity price of virtual power plant i selling electricity to the distribution network in period t, E represents the mathematical expectation; T represents the total number of decision periods t; N represents the total number of virtual power plants.

[0129] S2-4. Based on the objective function, the load power balance constraints of each virtual power plant and the load power balance constraints of the virtual power plant cluster, the optimal scheduling strategy for the coordinated scheduling of the virtual power plants is obtained.

[0130] The optimal dispatching strategy aims to minimize the cost of the virtual power plant purchasing electricity from the distribution network. At the same time, by optimizing the operation of the virtual power plant, efficient utilization of photovoltaic power generation and energy storage equipment in the distribution network system can be achieved.

[0131] In step S3, a smart contract of the blockchain control module in the digital virtual space is formulated based on the optimal scheduling strategy.

[0132] In step S4, based on the smart contract and combined with the real-time virtual-real information of the interaction between the physical entity and the digital virtual space of the power system, low-carbon collaborative scheduling is performed on multiple virtual power plants in the virtual power plant cluster.

[0133] S4-1. Utilize the real-time virtual-real information of the interaction between the physical entity and the digital virtual space of the power system to control the operation of the virtual power plant cluster in the physical entity and adjust the configuration of the virtual power plant cluster in the physical entity.

[0134] like Figure 2 As shown, the actual operation data of the physical entity of the power system is obtained by using the digital twin in the digital virtual space, and digital twin mapping is performed in combination with the twin data in the digital virtual space (a complete digital twin mapping is formed through data integration and simulation operation), so as to obtain a twin data model of the physical entity;

[0135] Storing the twin data model and the actual operation data in the twin data, and updating the twin data in the digital virtual space;

[0136] The digital twin is used to obtain static data of the physical entity, and the state of the physical entity is predicted based on the static data and the actual operation data (i.e., the future state of the physical entity is evolved and predicted through real-time simulation) to obtain a prediction result; and the prediction result is displayed through the smart application to the user in a visual manner;

[0137] Determine the operation control information of the physical entity based on the real-time simulation result of the digital twin, and control the operation of the virtual power plant cluster in the physical entity based on the operation control information (i.e. Figure 2 holographic control in );

[0138] The intelligent application in the digital virtual space is used to perform intelligent analysis based on the updated twin data and the prediction results, determine the configuration adjustment information of the physical entity based on the results of the intelligent analysis, and adjust the configuration of the virtual power plant cluster in the physical entity based on the configuration adjustment information. The intelligent application is also used to receive feedback results after the physical entity is adjusted, and provide intelligent application services for safety warning and fault diagnosis, while supporting the safe and stable operation of the physical entity of the virtual power plant.

[0139] Through efficient connection and real-time transmission between physical entities and digital virtual space, coordinated scheduling of various parts of the virtual power plant, feedback control of physical entities and iterative growth of simulation models in digital twins can be achieved; the iterative growth of simulation models in digital twins includes: intelligent application services receive real-time data from physical entities, analyze and process them, and then optimize the simulation model in the digital twin based on the analysis results to improve its accuracy; the digital twin itself will also adjust and optimize its own simulation model based on the received physical entity data.

[0140] S4-2. Based on the smart contract, combined with the low-carbon collaborative interaction mode between the virtual power plant and the distribution network in the blockchain control module, and the low-carbon collaborative interaction mode between the virtual power plant and other virtual power plants, real-time power dispatch is performed for multiple virtual power plants in the virtual power plant cluster.

[0141] Among them, the blockchain control module is the control part of the digital virtual power plant system that uses blockchain technology. Regional chain technology is similar to a distributed ledger, which has the characteristics of decentralization, immutability, and traceability. Through a distributed consensus mechanism, data is identified and verified to ensure the authenticity and integrity of the data. According to different usage scenarios, blockchains are divided into three categories: 1) Public chain: In the public chain, all nodes can participate in decision-making. 2) Alliance chain: Alliance chain is mainly used in specific groups or organizations, and a part of the nodes are selected as memory points. When the block is generated, the memory points participate in the decision-making together, and other non-memory nodes can trade, but have no recording rights. 3) Private chain: Private chain is controlled by private institutions and only uses blockchain technology as an accounting tool. The right to account belongs to private institutions and is not open to the public. Private chains and alliance chains have reduced the degree of openness and decentralization, which does not conform to the core concept of blockchain "decentralization". Even the read and write permissions of the database are limited to local nodes.

[0142] In this solution, the data function part of the digital virtual power plant system is optimized based on blockchain technology, the dispatching and computing capabilities of low-carbon operation are strengthened, and a low-carbon collaborative dispatching mode between virtual power plant clusters and between virtual power plants and distribution networks is constructed. And by combining the digital twin virtual power plant system, the problems faced by virtual power plants are solved.

[0143] A blockchain control module with the following functional modes is constructed in the intelligent application of the digital virtual power plant system:

[0144] a) Smart contracts: Improve transaction settlement efficiency and stimulate user response enthusiasm.

[0145] The blockchain control module can realize automatic execution of transaction settlement and improve transaction management efficiency. In virtual power plants, the automatic execution feature makes the profit distribution process more transparent and faster. Distributed energy owners can automatically redistribute profits after making profits. The automated mechanism shortens the period for distributed energy owners to recover funds, stimulates the enthusiasm of participants, and promotes the consumption of new energy.

[0146] b) Collective maintenance: decentralized data management to increase the utilization ratio of distributed energy.

[0147] The blockchain control module allows each node to participate in the block writing process and has the authority to access block data without relying on a centralized control center. In the virtual power plant data management process, decentralized data management allows distributed power sources to participate in the reading, writing and maintenance of the system, thereby increasing the participation of distributed power sources. The decentralized management model not only reduces the risk of a single point of failure, but also improves the stability and reliability of the entire system.

[0148] c) Information security: Ensure transaction security and establish an honest energy market.

[0149] Blockchain technology makes secure transactions a reality in the energy sector. Compared with traditional transaction management models, virtual power plants have higher security risks in data transmission and storage. The use of blockchain technology can effectively prevent information leakage and identity disguise, and improve the security of virtual power plant systems. At the same time, the transparency and traceability of blockchain technology make the transaction process more open and fair, which is conducive to establishing an honest energy market.

[0150] d) Decentralization: Adapt to the decentralized control model and reduce system operating costs.

[0151] The decision of the blockchain control module is jointly executed by all nodes, which reflects the characteristics of decentralization. Virtual power plants contain a variety of distributed energy sources, which are suitable for switching from traditional centralized control mode to decentralized control mode. Through blockchain technology, flexible scheduling and optimal configuration of distributed energy can be achieved to improve energy utilization. In addition, the decentralized management model helps to reduce the operating cost of the energy system and promote the development of green energy.

[0152] In summary, the application of blockchain technology in digital virtual power plant systems has significant advantages. Through the characteristics of smart contracts, collective maintenance, information security and decentralization, blockchain technology provides strong support for the development of virtual power plants. In the future, with the continuous maturity and popularization of blockchain technology, virtual power plants will play a more important role in the global energy field.

[0153] like Figure 4The figure shows the application of blockchain technology in this solution, that is, the low-carbon collaborative dispatching mode of virtual power plant clusters provided by the blockchain control module; through the established carbon reduction benefit mechanism, the comprehensive benefits before and after the virtual power plant interaction are calculated, that is, the comprehensive benefits before and after the operation of the virtual power plant before and after the optimal dispatching strategy is put into dispatch using smart contracts. Two levels of low-carbon operation modes of virtual power plant clusters are considered, one is the friendly low-carbon collaborative interaction mode between virtual power plants and distribution networks, and the other is the low-carbon collaborative interaction mode between virtual power plant clusters.

[0154] Specifically, based on the smart contract, combined with the low-carbon collaborative interaction mode between the virtual power plant and the distribution network in the blockchain control module, and the low-carbon collaborative interaction mode between the virtual power plant and other virtual power plants, real-time power dispatch is performed on multiple virtual power plants in the virtual power plant cluster. Among them, the smart contract includes the optimal dispatch strategy; in the friendly interaction mode between the virtual power plant and the distribution network, through the two-way selection between the virtual power plant and the distribution network and the smart contract, the metering of power transactions is open and transparent, which can stimulate user participation; in the low-carbon collaborative interaction mode between the virtual power plant clusters, through the two-way selection between the virtual power plant and other virtual power plants and the smart contract, a long-term power purchase agreement is reached, and real-time power transaction settlement is achieved, shortening the capital recovery cycle.

[0155] In this solution, before obtaining the interactive electricity volume and time-of-use electricity price between the virtual power plant cluster and the distribution network in the power system, that is, when building a digital virtual power plant system for the power system, it also includes: creating a Docker container instance in the cloud-edge collaborative control module of the digital virtual space; performing online detection, computational analysis and collaborative control functional design on the container instance to obtain a container instance with completed functional design.

[0156] That is, Docker container technology is used in the low-carbon collaborative scheduling of virtual power plants, and the container instance that completes the functional design is used to perform online monitoring and computational analysis on the operating status of the digital virtual power plant system. Docker is a lightweight virtualization application system that can be regarded as a small computing system that completes single computing analysis. Docker has further improved the file encapsulation system, making it faster and lighter than ordinary virtual machines, and the same server resources can run more applications. Due to its lightweight resource requirements, the server resources of the cloud-edge system can be used as a unified resource pool for scheduling. Another major advantage of Docker is that it runs relatively independently, is not restricted by the local operating system, and has a low workload for publishing, modifying, and testing applications, and is easy to maintain and low-cost. Docker containers are the main carrier for realizing the advantages of the cloud-edge collaborative system of virtual power plants. Through Docker, efficient use of the massive computing resources of the cloud-edge collaborative system is achieved to meet the computing and analysis requirements required for virtual power plant scheduling.

[0157] like Figure 5 As shown, this is the operating architecture of Docker. The Docker container image is encapsulated by the cloud system, that is, the container image is encapsulated in the cloud-edge collaborative control module. Both the cloud and edge systems can run the container and perform computing and analysis. The cloud-edge system shares the virtual power plant scheduling application Docker container image. The cloud or edge virtual power plant scheduling related computing and analysis request is first sent to the Docker agent. The Docker agent selects the Docker container image according to the request content and dynamically creates a container instance (that is, the Docker agent in the cloud-edge collaborative control module is used to create a Docker container instance based on the container image and the user's scheduling computing request for the virtual power plant). Figure 5 The computing container 1, computing container 2, ..., computing container M in the container are sent to the user through the Docker agent. The container directory mounts the Volume (data volume) to read the updates of the computing-related models in the maintenance power grid analysis model database, and update the models in the container instance. In the cloud-edge collaborative control module, it supports the parallel operation of multiple users and multiple container instances, regularly queries the container status and releases the occupied containers.

[0158] Functional design is the basis of container design. In order to ensure the safe operation of the virtual power plant itself and provide good interactive and coordinated support for the digital virtual power plant system, the functional division of container design is as follows.

[0159] 1) Online monitoring: The operating status of the connected virtual power plant control unit is monitored online through data stream interaction with the virtual power plant cluster. The operating status includes the continuity, reliability, cumulative adjustment capacity, cumulative adjustment time and operating status of the data transmission channel. The virtual power plant control unit is in the digital virtual space, and controls the operation of the virtual power plant cluster through the virtual power plant control unit.

[0160] 2) Load forecasting: Forecast the load of the connected virtual power plant control unit, train the forecasting model through artificial intelligence algorithms and collected data, and create the corresponding load forecasting container image.

[0161] 3) Dispatch control in non-spot market: Determine the overall control target based on the day-ahead and intraday peak shaving and valley filling demands released by the dispatcher, and allocate the overall control target to the virtual power plant control unit through the edge cluster based on information such as the maximum adjustable power curve and the minimum adjustable power curve for execution.

[0162] 4) Collaborative control: According to the quotation of the virtual power plant control unit, the adjustable capacity is called in sequence from low to high until the demand issued by the virtual power plant dispatch control center is met. The regulation demand issued by the cloud control application the day before mainly includes the day-ahead planning curve of the virtual power plant control unit, and the regulation demand issued within the day mainly includes the intraday regulation instruction of the virtual power plant control unit.

[0163] 5) Dispatching control in the spot market: The dispatching control function of the spot market scenario of the connected virtual power plant control unit can receive the spot market clearing results and send them to the virtual power plant control unit through the edge cluster for execution according to the clearing results.

[0164] 6) Baseline load formulation: Provide the baseline load and actual response capacity of each virtual power plant control unit for settlement. The actual response capacity is determined by the difference between the baseline load and the actual measured load. The baseline load formulation can be divided into three types: weekdays, weekends and holidays. The same type of daily load that does not participate in the response is selected as the load sample, and the average value is calculated to obtain the baseline load.

[0165] In this solution, the container services in the digital virtual power plant system mainly include power forecasting, power allocation, power trading and grid static security analysis. Among them, power forecasting includes the forecasting of system power, bus and controllable power units, and grid static security analysis includes flow calculation, short circuit calculation, network loss calculation, stability calculation, etc. There are many calculation strategies for power forecasting, power allocation and power trading, and various types of Docker containers are used to correspond to various power forecasting, power allocation and power trading strategies. That is, the functional design of online detection, calculation analysis and collaborative control of the container instance is performed to obtain a container instance that completes the functional design, including:

[0166] The multiple internal blocks in the container instance are constructed in the following manner to realize the functional design of online monitoring, computational analysis and collaborative control of the container instance:

[0167] Constructing a model database: constructing a model database including multiple power equipment models and parameters in the virtual power plant in the internal block, specifically including the power equipment models required for power forecasting, power distribution, power trading and grid static security analysis, mainly including generator, transformer, load and other models and capacity, power line models and impedance parameters. For the load forecast of the virtual power plant, it also includes wind turbine model, photovoltaic power generation model, power-to-gas model, etc.

[0168] Establish parameter files: Establish files of parameters required for calculation and analysis during the operation of virtual power plants, including parameters required for calculations such as power forecasting, power allocation, power trading, and grid static security analysis, such as active and reactive power convergence criteria, maximum number of iterations, objective function constraints, economic benefit costs, and wind power / photovoltaic unit maintenance costs.

[0169] Design a Protobuf (a data serialization protocol) communication program: for the executable program caprotobuf (an executable program), implement encoding and decoding according to the pre-defined Protobuf structured data format.

[0170] Design calculation programs: including various calculation programs such as power prediction, power allocation, power trading and grid static security analysis. Different calculation strategies of the same type are independent of each other. The calculation program is used to perform power prediction and power trading calculations during the operation of the virtual power plant based on the data in the model database and parameter files, combined with the calculation model in the calculation program. The power prediction model adopts a machine learning algorithm, and the machine learning algorithm required for the power prediction calculation program is obtained through sample training in the cloud.

[0171] Build dependent dynamic libraries: including power grid section dynamic library, topology analysis dynamic library, power flow calculation dynamic library, and machine learning model library, which are used for topology analysis, power flow calculation, and training learning during the operation of virtual power plants.

[0172] Based on the container instance with completed functional design and the smart contract, combined with the real-time virtual-real information of the interaction between the physical entity and the digital virtual space of the power system, low-carbon collaborative scheduling of multiple virtual power plants in the virtual power plant cluster is jointly performed.

[0173] In this plan, the physical entity and digital virtual space of the virtual power plant are planned synchronously, the data middle platform of the digital virtual space is constructed, and a static attribute database is formed. Based on the physical entity and digital virtual space of the power system, the overall architecture of the collaborative dispatching system of the multi-energy flow virtual power plant, namely, the digital virtual power plant system, is constructed; a decentralized low-carbon collaborative management platform based on a consensus mechanism is established, and a low-carbon collaborative dispatching mode of a virtual power plant cluster for multi-energy supply and demand is proposed based on regional chain technology, namely, the collaborative interaction mode provided by the blockchain control module in the digital virtual space; the functions of the collaborative dispatching system of the virtual power plant (namely, the Docker container instance) are designed, and the functional modules include online monitoring, load forecasting, dispatching control under non-spot markets, collaborative control, and dispatching control under spot markets; a collaborative dispatching system for virtual power plant clusters is developed based on Docker container technology, and the real-time virtual-real information of the interaction between the physical entity and digital virtual space of the power system is combined with the smart contract to jointly perform low-carbon collaborative dispatching of virtual power plant clusters.

[0174] In this solution, the pre-built digital virtual power plant system is used to explore the potential of multi-energy complementary synergy of the virtual power plant, release the flexibility of cluster regulation, and realize the coordinated operation of the virtual power plant equipment layer based on the flexible scheduling of distributed resources, thereby improving the system efficiency and providing an effective solution for the digitalization of virtual power plants. At the same time, it fully considers the application of intelligent interactive modes in digital virtual space, reduces the management cost of the system, avoids unnecessary benefit calculations, and has good numerical stability and robustness. In addition, through the lightweight Docker container technology, it can realize the efficient use of massive computing resources of the low-carbon collaborative system, meet the computing and analysis needs of the virtual power plant cluster to achieve low-carbon collaboration, and has important practical application value.

[0175] At the same time, due to the decentralized, open, transparent and tamper-proof characteristics of regional chain technology, it can realize bilateral selection of low-carbon collaborative dispatching (i.e. two-way dispatching of electricity between virtual power plants and distribution networks), and can efficiently utilize the massive computing resources of low-carbon collaborative systems.

[0176] In order to improve the control of virtual power plant clusters and realize the low-carbon utilization of distributed resources, the present invention designs a low-carbon collaborative scheduling method and system for virtual power plant clusters. By establishing the overall architecture of the multi-energy flow virtual power plant collaborative scheduling system, the problem that the current centralized scheduling system cannot adapt to the coordinated real-time control of multiple virtual power plants at the equipment level is solved, and the key requirements of the new digital power system in terms of fast communication, computing and analysis, data storage, frequency and voltage regulation, and power market platform are met. At the same time, based on Docker container technology, efficient utilization of the massive computing resources of the low-carbon collaborative system is achieved, meeting the computing and analysis requirements required for the virtual power plant cluster to achieve low-carbon collaboration.

[0177] Example 2

[0178] Based on the same inventive concept, the present invention also provides a virtual power plant cluster low-carbon collaborative dispatching system, such as Figure 2 As shown, it includes: digital virtual space and physical entities of power system;

[0179] The physical entities of the power system include a virtual power plant cluster and a distribution network;

[0180] The digital virtual space is used to obtain the interactive electricity volume and time-of-use electricity price between the virtual power plant cluster and the distribution network in the power system; and based on the interactive electricity volume and time-of-use electricity price between the virtual power plant cluster and the distribution network, the optimal scheduling strategy of the virtual power plant cluster is determined with the minimum operating cost as the goal, and a smart contract is formulated based on the optimal scheduling strategy; it is also used to perform low-carbon collaborative scheduling of multiple virtual power plants in the virtual power plant cluster based on the smart contract, combined with the real-time virtual and real information of the interaction between the physical entity of the power system and the digital virtual space.

[0181] In a possible implementation, the digital virtual space includes digital twins, twin data, smart applications, and a data middle platform that are interconnected and communicate with each other;

[0182] The data middle platform is built based on the static data of physical entities in the power system to form an initial database;

[0183] The twin data includes the initial database, the operation database of the physical entity and the twin data model of the physical entity;

[0184] The digital twin is constructed based on the physical model, operation data and simulation model of the physical entity that simulates the behavior and performance of the physical entity; it is used to obtain the actual operation data of the physical entity, and perform digital twin mapping in combination with the twin data to obtain the twin data model of the physical entity, store the twin data model and the actual operation data in the twin data, and update the twin data; it is also used to obtain the static data of the physical entity, predict the state of the physical entity based on the static data and the actual operation data, and obtain the prediction result; and determine the operation control information of the physical entity based on its own real-time simulation results, and control the operation of the virtual power plant cluster in the physical entity based on the operation control information;

[0185] The intelligent application is used to perform intelligent analysis based on the updated twin data and the prediction results, determine configuration adjustment information of the physical entity based on the results of the intelligent analysis, and adjust the configuration of the virtual power plant cluster in the physical entity based on the configuration adjustment information.

[0186] In one possible implementation, the above-mentioned intelligent application includes a blockchain control module:

[0187] Used to formulate a smart contract based on the optimal scheduling strategy;

[0188] Based on the smart contract, combined with the low-carbon collaborative interaction mode between the virtual power plant and the distribution network in the blockchain control module, and the low-carbon collaborative interaction mode between the virtual power plant and other virtual power plants, real-time power dispatch is performed for multiple virtual power plants in the virtual power plant cluster.

[0189] In a possible implementation, the digital virtual space further includes a cloud-edge collaborative control module:

[0190] Used to create a Docker container instance, and perform online detection, computational analysis, and collaborative control functional design on the container instance to obtain a container instance with completed functional design;

[0191] The container instance having completed functional design is used for online monitoring and computational analysis of the operating status of the physical entities of the digital virtual space and the power system;

[0192] The container instance that completes the functional design is also used to combine the smart contract and the real-time virtual-real information of the interaction between the physical entity and the digital virtual space of the power system to jointly perform low-carbon collaborative scheduling of multiple virtual power plants in the virtual power plant cluster.

[0193] In a possible implementation, the cloud-edge collaborative control module is further used to:

[0194] Package container images;

[0195] Using a Docker agent to create a Docker container instance based on the container image and a user's request for scheduling, computing and analyzing the virtual power plant;

[0196] The container instance is subjected to online detection, computational analysis and collaborative control functional design to obtain a container instance with completed functional design.

[0197] Example 3

[0198] like Figure 6 As shown, the present invention also provides an electronic device, which may be a computer device, a single-chip device, an intelligent mobile device, etc. The electronic device in this embodiment may include a processor, a memory, a transceiver component, etc. The memory, the processor, and the transceiver component are connected via a bus; the memory may be used to store an execution program, and an exemplary execution program may include instructions; the processor is used to execute the instructions stored in the memory. The memory may also be used to store data, which may be called and / or modified when the instructions are executed.

[0199] The processor may be a central processing unit (CPU), or other general-purpose processors, digital signal processors (DSP), application-specific integrated circuits (ASIC), field-programmable gate arrays (FPGA) or other programmable logic devices, discrete gate or transistor logic devices, discrete hardware components, etc. It is the computing core and control core of the terminal, which is suitable for implementing one or more instructions, specifically suitable for loading and executing one or more instructions in a storage medium to implement corresponding method processes or corresponding functions, so as to realize the steps of a low-carbon collaborative scheduling method for a virtual power plant cluster in the above-mentioned embodiment.

[0200] Example 4

[0201] Based on the same inventive concept, the present invention also provides a readable storage medium, specifically an electronic device readable storage medium (Memory), which is a memory device in an electronic device for storing programs and data. It can be understood that the storage medium here can include both built-in storage media in electronic devices and extended storage media supported by electronic devices. The storage medium provides a storage space, which stores the operating system of the terminal. In addition, one or more instructions suitable for being loaded and executed by a processor are also stored in the storage space, and these instructions can be one or more execution programs (including program codes). It should be noted that the storage medium here can be a high-speed RAM memory or a non-volatile memory, such as at least one disk memory. The processor loads and executes one or more instructions stored in the storage medium, which can implement the steps of a low-carbon collaborative scheduling method for a virtual power plant cluster in the above embodiment.

[0202] Those skilled in the art will appreciate that embodiments of the present invention may be provided as methods, systems, or computer program products. Therefore, the present invention may take the form of a complete hardware embodiment, a complete software embodiment, or an embodiment combining software and hardware. Moreover, the present invention may take the form of a computer program product implemented on one or more computer-usable storage media (including but not limited to disk storage, CD-ROM, optical storage, etc.) containing computer-usable program code.

[0203] The present invention is described with reference to flowcharts and / or block diagrams of methods, devices (systems), and computer program products according to embodiments of the present invention. It should be understood that each process and / or block in the flowchart and / or block diagram, as well as the combination of processes and / or blocks in the flowchart and / or block diagram, can be implemented by computer program instructions. These computer program instructions can be provided to a processor of a general-purpose computer, a special-purpose computer, an embedded processor, or other programmable data processing device to produce a machine, so that the instructions executed by the processor of the computer or other programmable data processing device generate instructions for implementing the processes in the flowchart and / or block diagram. Figure 1 A process or multiple processes and / or boxes Figure 1 A device that provides the functions specified in a block or multiple blocks.

[0204] These computer program instructions may also be stored in a computer-readable memory capable of directing a computer or other programmable data processing device to operate in a specific manner, so that the instructions stored in the computer-readable memory produce an article of manufacture comprising an instruction device, which implements the process Figure 1 A process or multiple processes and / or boxes Figure 1 A function specified in one or more boxes.

[0205] These computer program instructions can also be loaded onto a computer or other programmable data processing device so that a series of operating steps are executed on the computer or other programmable device to produce a computer-implemented process, thereby providing instructions for implementing the process. Figure 1 A process or multiple processes and / or boxes Figure 1 The steps for the functions specified in one or more boxes.

[0206] Finally, it should be noted that the above embodiments are only used to illustrate the technical solutions of the present invention rather than to limit its protection scope. Although the present invention has been described in detail with reference to the above embodiments, ordinary technicians in the relevant field should understand that after reading the present invention, those skilled in the art can still make various changes, modifications or equivalent substitutions to the specific implementation methods of the application, but these changes, modifications or equivalent substitutions are all within the protection scope of the claims to be approved.

Claims

1. A low-carbon collaborative scheduling method for a virtual power plant cluster, characterized in that: include: Based on the pre-built digital virtual power plant system for power systems, the interactive power volume between the virtual power plant cluster and the distribution network and the time-of-use electricity price are obtained; Based on the interactive electricity volume between the virtual power plant cluster and the distribution network and the time-of-use electricity price, the optimal dispatching strategy of the virtual power plant cluster is determined with the minimum operating cost as the goal; Formulate a smart contract for a blockchain control module in a digital virtual space based on the optimal scheduling strategy; Based on the smart contract, and in combination with the real-time virtual-real information of the physical entity and digital virtual space interaction of the power system, low-carbon collaborative scheduling of multiple virtual power plants in the virtual power plant cluster is performed; The digital virtual power plant system for the electric power system is constructed based on a digital virtual space and physical entities of the electric power system, and the physical entities of the electric power system include the virtual power plant cluster and the distribution network.

2. The method according to claim 1, characterized in that The digital virtual power plant system for power systems includes the following construction process: Based on the static data of the physical entities in the power system, a data middle platform of the digital virtual space is built to form an initial database of the digital virtual space; Based on the digital description of the parameters and performance of the physical entity, forming an operation database of the physical entity; Combining the initial database, the operating database and the twin data model of the physical entity to form the twin data of the digital virtual space; Constructing a digital twin of the digital virtual space based on a physical model of the physical entity, operating data, and a simulation model simulating the behavior and performance of the physical entity; Combining the data middle platform, the twin data, the digital twin and the intelligent application of the physical entity, the digital virtual space is constructed, and the intelligent application includes the blockchain control module; The digital virtual power plant system for the power system is constructed based on the digital virtual space and the physical entity of the power system.

3. The method according to claim 2, characterized in that Based on the smart contract, the real-time virtual-real information of the interaction between the physical entity and the digital virtual space of the power system is combined to perform low-carbon collaborative scheduling of multiple virtual power plants in the virtual power plant cluster, including: Using the real-time virtual-real information of the interaction between the physical entity and the digital virtual space of the power system, the operation of the virtual power plant cluster in the physical entity is controlled and the configuration of the virtual power plant cluster in the physical entity is adjusted; Based on the smart contract, combined with the low-carbon collaborative interaction mode between the virtual power plant and the distribution network in the blockchain control module, and the low-carbon collaborative interaction mode between the virtual power plant and other virtual power plants, real-time power dispatch is performed for multiple virtual power plants in the virtual power plant cluster.

4. The method according to claim 3, characterized in that The method of utilizing the real-time virtual-real information of the interaction between the physical entity and the digital virtual space of the power system to control the operation of the virtual power plant cluster in the physical entity and adjust the configuration of the virtual power plant cluster in the physical entity includes: Using the digital twin in the digital virtual space to obtain actual operating data of the physical entity of the power system, and performing digital twin mapping in combination with the twin data in the digital virtual space to obtain a twin data model of the physical entity; Storing the twin data model and the actual operation data in the twin data, and updating the twin data in the digital virtual space; Using the digital twin to obtain static data of the physical entity, predicting the state of the physical entity based on the static data and the actual operation data, and obtaining a prediction result; and determining operation control information of the physical entity based on the real-time simulation result of the digital twin, and controlling the operation of the virtual power plant cluster in the physical entity based on the operation control information; Utilize the intelligent application in the digital virtual space to perform intelligent analysis based on the updated twin data and the prediction results, determine the configuration adjustment information of the physical entity based on the results of the intelligent analysis, and adjust the configuration of the virtual power plant cluster in the physical entity based on the configuration adjustment information.

5. The method according to claim 1, characterized in that The method of determining the optimal dispatching strategy of the virtual power plant cluster based on the interactive power volume between the virtual power plant cluster and the distribution network and the time-of-use electricity price with the minimum operating cost as the goal includes: For each of the virtual power plants, the load power balance constraint condition of the virtual power plant is constructed according to the total power output of the photovoltaic system in the virtual power plant, the power mutually called by the photovoltaic systems of the virtual power plant and other virtual power plants, the interactive power between the virtual power plant and the distribution network, the charging power of the battery in the virtual power plant and the local load; Constructing load power balance constraints of the virtual power plant cluster based on the total power output of the photovoltaic system in each virtual power plant, the charge and discharge power of the battery, and the local load; Based on the interactive electricity volume and time-of-use electricity price of each virtual power plant and the distribution network, an objective function is constructed with the goal of minimizing the operating cost of the virtual power plant cluster; Based on the objective function, the load power balance constraints of each virtual power plant and the load power balance constraints of the virtual power plant cluster, an optimal scheduling strategy for the coordinated scheduling of the virtual power plants is obtained.

6. The method according to claim 5, characterized in that The objective function is: in, It represents the interactive electricity between virtual power plant i and distribution network during collaborative operation; represents the time-of-use electricity price that virtual power plant i buys from the distribution network during period t; and is a complementary coefficient, which is determined based on the power interaction between the virtual power plant i and the distribution network; represents the time-of-use electricity price of virtual power plant i selling electricity to the distribution network in period t; E represents the mathematical expectation; T represents the total number of decision periods t; N represents the total number of virtual power plants.

7. The method according to claim 5, characterized in that The load power balance constraint of the virtual power plant is: in, represents the total power output of the photovoltaic system of virtual power plant i; N represents the total number of virtual power plants; represents the power dispatched from the PV system of virtual power plant i to virtual power plant j; represents the power dispatched from the PV system of virtual power plant j to virtual power plant i; It represents the interactive electricity between virtual power plant i and distribution network during collaborative operation; represents the charging power of the battery in virtual power plant i; represents the local load in virtual power plant i.

8. The method according to claim 5, characterized in that The load power balance constraint condition of the virtual power plant cluster is: in, represents the total power output of the PV system of virtual power plant i; represents the discharge power of the battery of virtual power plant i; I d,i represents the discharge state of the battery in virtual power plant i; represents the charging power of the battery of virtual power plant i; I c,i represents the charging state of the battery in the virtual power plant i; Indicates the power loss during the battery charging and discharging process; The local load in virtual power plant i; N represents the total number of virtual power plants.

9. The method according to claim 1, characterized in that Before obtaining the interactive power quantity and time-of-use electricity price between the virtual power plant cluster and the distribution network in the power system, the method further includes: Creating a Docker container instance in a cloud-edge collaborative control module of the digital virtual space; Performing online detection, computational analysis, and collaborative control functional design on the container instance to obtain a container instance with completed functional design; The container instance that has completed the functional design is used for online monitoring and computational analysis of the operating status of the digital virtual power plant system.

10. The method according to claim 9, characterized in that Based on the smart contract, the real-time virtual-real information of the interaction between the physical entity and the digital virtual space of the power system is combined to perform low-carbon collaborative scheduling of multiple virtual power plants in the virtual power plant cluster, including: Based on the container instance with completed functional design and the smart contract, combined with the real-time virtual-real information of the interaction between the physical entity and the digital virtual space of the power system, low-carbon collaborative scheduling of multiple virtual power plants in the virtual power plant cluster is jointly performed.

11. The method according to claim 9, characterized in that The step of creating a Docker container instance in the cloud-edge collaborative control module of the digital virtual space includes: Encapsulating a container image in the cloud-edge collaborative control module; The Docker agent in the cloud-edge collaborative control module is used to create a Docker container instance based on the container image and the user's scheduling, computing and analysis request for the virtual power plant.

12. A virtual power plant cluster low-carbon collaborative dispatching system, characterized in that: include: Digital virtual space and the physical entity of the power system; The physical entities of the power system include a virtual power plant cluster and a distribution network; The digital virtual space is used to obtain the interactive electricity volume and time-of-use electricity price between the virtual power plant cluster and the distribution network in the power system; and based on the interactive electricity volume and time-of-use electricity price between the virtual power plant cluster and the distribution network, the optimal scheduling strategy of the virtual power plant cluster is determined with the minimum operating cost as the goal, and a smart contract is formulated based on the optimal scheduling strategy; it is also used to perform low-carbon collaborative scheduling of multiple virtual power plants in the virtual power plant cluster based on the smart contract, combined with the real-time virtual and real information of the interaction between the physical entity of the power system and the digital virtual space.

13. The system of claim 12, wherein: The digital virtual space includes digital twins, twin data, intelligent applications and data middle platforms that are interconnected and communicate with each other; The data middle platform is built based on the static data of physical entities in the power system to form an initial database; The twin data includes the initial database, the operation database of the physical entity and the twin data model of the physical entity; The digital twin is constructed based on the physical model, operation data and simulation model of the physical entity that simulates the behavior and performance of the physical entity; it is used to obtain the actual operation data of the physical entity, and perform digital twin mapping in combination with the twin data to obtain the twin data model of the physical entity, store the twin data model and the actual operation data in the twin data, and update the twin data; it is also used to obtain the static data of the physical entity, predict the state of the physical entity based on the static data and the actual operation data, and obtain the prediction result; and determine the operation control information of the physical entity based on its own real-time simulation results, and control the operation of the virtual power plant cluster in the physical entity based on the operation control information; The intelligent application is used to perform intelligent analysis based on the updated twin data and the prediction results, determine configuration adjustment information of the physical entity based on the results of the intelligent analysis, and adjust the configuration of the virtual power plant cluster in the physical entity based on the configuration adjustment information.

14. The system of claim 13, wherein: The smart application includes a blockchain control module: Used to formulate a smart contract based on the optimal scheduling strategy; Based on the smart contract, combined with the low-carbon collaborative interaction mode between the virtual power plant and the distribution network in the blockchain control module, and the low-carbon collaborative interaction mode between the virtual power plant and other virtual power plants, real-time power dispatch is performed for multiple virtual power plants in the virtual power plant cluster.

15. The system of claim 12, wherein: The digital virtual space also includes: a cloud-edge collaborative control module, Used to create a Docker container instance, and perform online detection, computational analysis, and collaborative control functional design on the container instance to obtain a container instance with completed functional design; The container instance having completed functional design is used for online monitoring and computational analysis of the operating status of the physical entities of the digital virtual space and the power system; The container instance that completes the functional design is also used to combine the smart contract and the real-time virtual-real information of the interaction between the physical entity and the digital virtual space of the power system to jointly perform low-carbon collaborative scheduling of multiple virtual power plants in the virtual power plant cluster.

16. The system of claim 15, wherein: The cloud-edge collaborative control module is also used for: Package container images; Using a Docker agent to create a Docker container instance based on the container image and a user's request for scheduling, computing and analyzing the virtual power plant; The container instance is subjected to online detection, computational analysis and collaborative control functional design to obtain a container instance with completed functional design.

17. An electronic device, characterized in that: include: at least one processor and memory; The memory and the processor are connected via a bus; The memory is used to store one or more programs; When the one or more programs are executed by the at least one processor, a low-carbon collaborative scheduling method for a virtual power plant cluster as described in any one of claims 1 to 11 is implemented.

18. A computing device readable storage medium, characterized in that: An execution program is stored thereon, and when the execution program is executed, a low-carbon collaborative scheduling method for a virtual power plant cluster as described in any one of claims 1 to 11 is implemented.

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