Coordinated scheduling method and device for multi-energy complementary system and storage medium

Abstract

The invention discloses a coordinated scheduling method and device for a multi-energy complementary system, and a storage medium. The method comprises the steps of obtaining pre-scheduling data of multiple historical periods and pre-scheduling data of a next period chained on multiple target nodes in the multi-energy complementary system from a block chain; wherein the pre-scheduling data is data obtained by predicting power supply and demand conditions of all subjects in the multi-energy complementary system in each time period by a plurality of target nodes by using respective prediction models; the plurality of target nodes comprise a photovoltaic node, a wind power node, a thermal power node, an energy storage node and a power grid electricity purchasing node; determining scheduling data of all main bodies in the multi-energy complementary system in the next time period based on the pre-scheduling data of the plurality of historical time periods and the pre-scheduling data of the next time period; and uploading the scheduling data to the block chain, and performing coordinated scheduling on the power resources of the plurality of target nodes according to the scheduling data in the next time period.

Description

Technical Field

[0001] The present application relates to the field of electric power technology, and in particular to a coordinated scheduling method, device and storage medium for a multi-energy complementary system. Background Art

[0002] With the development of energy internet and the increase in the proportion of renewable energy, how to efficiently manage and dispatch different types of energy supply and demand has become a key issue. The traditional single energy system management method is difficult to adapt to the complexity and uncertainty of modern energy networks, especially when it comes to intermittent energy sources such as solar energy and wind energy. The output of these energy sources is affected by weather conditions and is highly volatile and unpredictable, which poses a challenge to the stable operation of the power grid. In order to improve the overall efficiency and reliability of the energy system, a multi-energy complementary system has emerged. This system aims to achieve optimal configuration and dynamic balance of energy by integrating multiple energy forms, such as photovoltaic power generation, wind power generation, thermal power generation, energy storage systems, and power grid purchases. However, due to the large differences in the characteristics of each energy node and the complex interactions between them, how to effectively coordinate the scheduling between these nodes has become an urgent problem to be solved.

[0003] The existing multi-energy complementary system scheduling method is mainly based on a centralized control strategy. The core of this strategy is to establish a centralized scheduling organization, which is responsible for collecting real-time data of all energy nodes in the entire system, including but not limited to power generation, demand, energy storage status and other information. Based on this data, the central scheduling organization uses optimization algorithms, such as linear programming, dynamic programming, etc., to formulate a global scheduling plan, aiming to balance supply and demand, maximize system efficiency or minimize costs. However, this centralized scheduling method has the following limitations:

[0004] 1. Information islands and global optimization problems: Due to the lack or inefficiency of data sharing platforms, there is a lack of effective communication between energy nodes, resulting in information isolation. This makes it difficult for central dispatch agencies to obtain comprehensive and real-time data, which affects their ability to formulate global optimal dispatch plans. Incomplete local information may lead to suboptimal solutions for the overall dispatch strategy. In some cases, dispatch decisions may be out of touch with actual demand due to information delays or errors.

[0005] 2. Vulnerability of centralized control: Under the centralized control strategy, the central node becomes the key point of the system, which carries the heavy responsibility of data collection, processing and decision-making. Once the central node fails or suffers a malicious attack, the normal operation of the entire system will be seriously threatened, and may even cause the system to crash. In addition, the decision-making process of the central node may become inefficient due to information overload or processing capacity limitations, further exacerbating the problem of system response speed and stability.

[0006] 3. Unfair and inefficient resource allocation: Resource allocation is determined by the central node. Regions or nodes far from the center may not be able to obtain necessary resource support in a timely manner due to delays or distortions in information transmission. This not only leads to unfair resource allocation, but may also cause resource waste or shortages, reducing the operating efficiency of the entire system. At the same time, the decision of the central node may be too idealistic and fail to fully consider the actual conditions and needs of the edge nodes, further weakening the flexibility and adaptability of scheduling.

[0007] 4. Information security risks: During the data storage process, since all key data are concentrated in the central node, once the security protection measures of the data storage facilities fail, the risk of data tampering, leakage or loss will increase significantly, which poses a major threat to the stability of the system and user trust.

[0008] There are problems in the existing multi-energy complementary system scheduling methods, such as information islands and global optimization difficulties, over-reliance on centralized scheduling, and data security risks. No effective solution has been proposed so far. Summary of the invention

[0009] The embodiments of the present disclosure provide a coordinated scheduling method, device and storage medium for a multi-energy complementary system, so as to at least solve the technical problems of information islands and global optimization problems, over-reliance on centralized scheduling and data security risks in the scheduling method of the multi-energy complementary system in the prior art.

[0010] According to one aspect of an embodiment of the present disclosure, a coordinated scheduling method for a multi-energy complementary system is provided, wherein the subjects in the multi-energy complementary system include photovoltaic nodes, wind power nodes, thermal power nodes, power consumption nodes, energy storage nodes and power grid power purchasing nodes, and the coordinated scheduling method includes: obtaining pre-scheduling data of multiple historical time periods and pre-scheduling data of the next time period on the chain of multiple target nodes in the multi-energy complementary system from the blockchain; wherein the pre-scheduling data is data obtained by predicting the power supply and demand conditions of all subjects in the multi-energy complementary system in each time period by using their own prediction models at multiple target nodes; the multiple target nodes include photovoltaic nodes, wind power nodes, thermal power nodes, energy storage nodes and power grid power purchasing nodes; based on the pre-scheduling data of multiple historical time periods and the pre-scheduling data of the next time period, determining the scheduling data of all subjects in the multi-energy complementary system in the next time period; uploading the scheduling data to the blockchain, and coordinating and scheduling the power resources of the multiple target nodes according to the scheduling data in the next time period.

[0011] According to another aspect of the embodiments of the present disclosure, a storage medium is further provided. The storage medium includes a stored program, wherein the above method is executed by a processor when the program is running.

[0012] According to another aspect of the embodiment of the present disclosure, a coordinated scheduling device for a multi-energy complementary system is also provided, wherein the subjects in the multi-energy complementary system include photovoltaic nodes, wind power nodes, thermal power nodes, power consumption side nodes, energy storage nodes and power grid power purchasing nodes, and the coordinated scheduling device includes: a data acquisition module, used to obtain pre-scheduling data of multiple historical time periods and pre-scheduling data of the next time period on the chain of multiple target nodes in the multi-energy complementary system from the blockchain; wherein the pre-scheduling data is data obtained after multiple target nodes use their respective prediction models to predict and optimize the power supply and demand conditions of all subjects in the multi-energy complementary system in each time period; the multiple target nodes include photovoltaic nodes, wind power nodes, thermal power nodes, energy storage nodes and power grid power purchasing nodes; a data determination module, used to determine the scheduling data of all subjects in the multi-energy complementary system in the next time period based on the pre-scheduling data of multiple historical time periods and the pre-scheduling data of the next time period; a coordinated scheduling module, used to chain the scheduling data to the blockchain, and coordinate the scheduling of power resources of multiple target nodes in the next time period according to the scheduling data.

[0013] According to another aspect of the embodiment of the present disclosure, a coordination and scheduling device for a multi-energy complementary system is also provided, wherein the subjects in the multi-energy complementary system include photovoltaic nodes, wind power nodes, thermal power nodes, power consumption nodes, energy storage nodes and power grid purchase nodes, and the coordination and scheduling device includes a processor; and a memory connected to the processor, for providing the processor with instructions for processing the following processing steps: obtaining pre-scheduling data of multiple historical time periods and pre-scheduling data of the next time period on the chain of multiple target nodes in the multi-energy complementary system from the blockchain; wherein the pre-scheduling data is data obtained after multiple target nodes use their respective prediction models to predict and optimize the power supply and demand conditions of all subjects in the multi-energy complementary system in each time period; the multiple target nodes include photovoltaic nodes, wind power nodes, thermal power nodes, energy storage nodes and power grid purchase nodes; based on the pre-scheduling data of multiple historical time periods and the pre-scheduling data of the next time period, determine the scheduling data of all subjects in the multi-energy complementary system in the next time period; chain the scheduling data to the blockchain, and coordinate and schedule the power resources of multiple target nodes according to the scheduling data in the next time period.

[0014] In this application, firstly, the pre-scheduling data of multiple target nodes on the chain and the pre-scheduling data of the next period are obtained from the blockchain. Through the blockchain technology, the data of all participating nodes in the multi-energy complementary system can be ensured to be transparent and accessible on the chain, thereby breaking the information island and providing a data basis for global optimization. In addition, all participating nodes can use their own prediction models, combined with historical data, to accurately predict the power supply and demand of future periods, and share their predicted power supply and demand through the blockchain, so that the pre-processing and prediction of the data are completed independently by each participating node, reducing the dependence on a single centralized dispatching center. Then, based on the pre-scheduling data and the pre-scheduling data of the next period, a dispatching data that comprehensively considers the power supply and demand of all subjects in the entire multi-energy complementary system is determined to achieve global optimization and ensure the balanced distribution of energy. Finally, the dispatching data is put on the chain and the power resources of multiple target nodes are coordinated and dispatched according to the dispatching data in the next period, which ensures the openness and transparency of the dispatching data, so that all participating nodes can verify the fairness and rationality of the dispatching, ensure that all participating nodes act according to a unified dispatching plan, and improve the operating efficiency and stability of the entire system. Therefore, this application effectively solves the technical problems existing in the prior art of multi-energy complementary system scheduling methods, such as information islands and global optimization problems, over-reliance on centralized scheduling, and data security risks, by combining blockchain technology and distributed pre-scheduling mechanism. BRIEF DESCRIPTION OF THE DRAWINGS

[0015] The drawings described herein are used to provide a further understanding of the present disclosure and constitute a part of the present application. The illustrative embodiments of the present disclosure and their descriptions are used to explain the present disclosure and do not constitute an improper limitation on the present disclosure. In the drawings:

[0016] Figure 1 is a hardware structure block diagram of a computing device for implementing the method according to Embodiment 1 of the present application;

[0017] Figure 2a is a schematic diagram of a coordination and scheduling platform of a multi-energy complementary system according to Example 1 of the present application;

[0018] Figure 2b This is a schematic diagram of the association relationship between all entities and the blockchain in the multi-energy complementary system described in Example 1 of the present application;

[0019] Figure 3 is a flow chart of the coordinated scheduling method of the multi-energy complementary system according to Example 1 of the present application;

[0020] Figure 4 is a schematic diagram of a coordination and scheduling device for a multi-energy complementary system according to Embodiment 2 of the present application; and

[0021] Figure 5It is a schematic diagram of the coordination and scheduling device of the multi-energy complementary system described in Example 3 of the present application. DETAILED DESCRIPTION

[0022] In order to enable those skilled in the art to better understand the technical solutions of the present disclosure, the technical solutions in the embodiments of the present disclosure will be clearly and completely described below in conjunction with the drawings in the embodiments of the present disclosure. Obviously, the described embodiments are only embodiments of a part of the present disclosure, not all of the embodiments. Based on the embodiments in the present disclosure, all other embodiments obtained by ordinary technicians in this field without making creative work should fall within the scope of protection of the present disclosure.

[0023] It should be noted that the terms "first", "second", etc. in the specification and claims of the present disclosure and the above-mentioned drawings are used to distinguish similar objects, and are not necessarily used to describe a specific order or sequence. It should be understood that the data used in this way can be interchangeable where appropriate, so that the embodiments of the present disclosure described herein can be implemented in an order other than those illustrated or described herein. In addition, the terms "including" and "having" and any variations thereof are intended to cover non-exclusive inclusions, for example, a process, method, system, product, or device that includes a series of steps or units is not necessarily limited to those steps or units that are clearly listed, but may include other steps or units that are not clearly listed or inherent to these processes, methods, products, or devices.

[0024] Example 1

[0025] According to this embodiment, a method embodiment of a coordinated scheduling method for a multi-energy complementary system is provided. It should be noted that the steps shown in the flowchart of the accompanying drawings can be executed in a computer system such as a set of computer executable instructions, and although a logical order is shown in the flowchart, in some cases, the steps shown or described can be executed in an order different from that shown here.

[0026] The method embodiment provided in this embodiment can be executed in a mobile terminal, a computer terminal, a server or a similar computing device. Figure 1 FIG. 1 shows a hardware structure block diagram of a computing device for implementing a coordinated scheduling method for a multi-energy complementary system. Figure 1 As shown, the computing device may include one or more processors (the processor may include but is not limited to a processing device such as a microprocessor MCU or a programmable logic device FPGA), a memory for storing data, a transmission device for communication functions, and an input / output interface. The memory, the transmission device, and the input / output interface are connected to the processor via a bus. In addition, it may also include: a display, a keyboard, and a cursor control device connected to the input / output interface. A person skilled in the art can understand that Figure 1 The structure shown is only for illustration and does not limit the structure of the above electronic device. Figure 1 More or fewer components as shown, or with Figure 1 Different configurations are shown.

[0027] It should be noted that the one or more processors and / or other data processing circuits described above may generally be referred to herein as "data processing circuitry". The data processing circuitry may be embodied in whole or in part as software, hardware, firmware, or any other combination thereof. In addition, the data processing circuitry may be a single independent processing module, or may be incorporated in whole or in part into any of the other components of the computing device. As involved in the embodiments of the present disclosure, the data processing circuitry acts as a processor control (e.g., selection of a variable resistor terminal path connected to an interface).

[0028] The memory can be used to store software programs and modules of application software, such as the program instructions / data storage device corresponding to the coordinated scheduling method of the multi-energy complementary system in the embodiment of the present disclosure. The processor executes various functional applications and data processing by running the software programs and modules stored in the memory, that is, the coordinated scheduling method of the multi-energy complementary system of the above-mentioned application program is realized. The memory may include a high-speed random access memory, and may also include a non-volatile memory, such as one or more magnetic storage devices, flash memory, or other non-volatile solid-state memory. In some instances, the memory may further include a memory remotely arranged relative to the processor, and these remote memories may be connected to the computing device via a network. Examples of the above-mentioned network include, but are not limited to, the Internet, an intranet, a local area network, a mobile communication network, and combinations thereof.

[0029] The transmission device is used to receive or send data via a network. The specific example of the above network may include a wireless network provided by a communication provider of the computing device. In one example, the transmission device includes a network adapter (Network Interface Controller, NIC), which can be connected to other network devices through a base station so as to communicate with the Internet. In one example, the transmission device can be a radio frequency (Radio Frequency, RF) module, which is used to communicate with the Internet wirelessly.

[0030] The display may be, for example, a touch screen liquid crystal display (LCD) that may enable a user to interact with a user interface of the computing device.

[0031] It should be noted that, in some optional embodiments, the above Figure 1The computing device shown may include hardware elements (including circuits), software elements (including computer code stored on a computer-readable medium), or a combination of both hardware elements and software elements. Figure 1 This is merely one example of a particular embodiment and is intended to illustrate the types of components that may be present in the computing devices described above.

[0032] This technical solution can be used to realize the dispatch of regional power grids, specifically, the regional power grid can be, for example, a power grid in a city area. The regional power grid is powered by a multi-energy complementary system, which can accommodate multiple energy resource inputs and integrate multiple energy forms, such as photovoltaic power generation, wind power generation, thermal power generation, energy storage systems, and power grid purchases, to achieve optimal configuration and dynamic balance of energy.

[0033] Figure 2a is a schematic diagram of a coordination and scheduling platform of a multi-energy complementary system according to this embodiment, Figure 2b This is a schematic diagram of the relationship between all entities in the multi-energy complementary system and the blockchain. Figure 2a and Figure 2b As shown, the coordination and scheduling platform includes: a multi-energy complementary system, a blockchain and a scheduling node. All entities in the multi-energy complementary system and the scheduling node are nodes on the blockchain. The entities in the multi-energy complementary system include photovoltaic nodes, wind power nodes, thermal power nodes, energy storage nodes, power load nodes and power grid purchase nodes. Among them, the photovoltaic node is associated with multiple photovoltaic units (for example, photovoltaic units 1~I), the wind power node is associated with multiple wind turbines (for example, wind turbines 1~J), the thermal power node is associated with multiple thermal power units (for example, thermal power units 1~L), the energy storage node is associated with multiple energy storage units (for example, energy storage units 1~M), and the power load nodes include, but are not limited to, factory production line nodes, charging pile nodes, and electric hydrogen production nodes. The energy storage node can obtain and store electricity from the local power grid during the off-peak period, and supply electricity to the local power grid during the peak period to balance the power supply load of the local power grid. The power grid purchase node can purchase electricity from the State Grid Corporation when the local power grid's spontaneous power output is insufficient to achieve power supplement.

[0034] In the blockchain network, multiple types of free transactions can occur, and all transactions are confirmed and maintained by all nodes of the network, and the security and validity of transactions and information are guaranteed through the consensus mechanism. At the same time, all transaction data in the network is stored in encrypted form on all nodes of the network, and reasonable mechanism design is used to ensure that the system is traceable and stable without the need for a central agency.

[0035] In this embodiment, the coordinated scheduling process of the multi-energy complementary system is divided into two levels, that is, the multiple target nodes participating in the scheduling are first distributed pre-scheduled, and then the calling node generates a scheduling data according to the pre-scheduled data generated by each target node, so that the multiple target nodes participating in the scheduling can be coordinated and scheduled according to the scheduling data to achieve the balance of power consumption and power supply of the regional power grid power load. Among them, the multiple target nodes participating in the scheduling include photovoltaic nodes, wind power nodes, thermal power nodes, energy storage nodes and power grid purchase nodes. Scheduling thermal power nodes is mainly reflected in pre-scheduling the power generation power of multiple thermal power units 1~L associated with the thermal power node. Scheduling energy storage nodes is mainly reflected in pre-scheduling the charging power and discharging power of multiple energy storage units 1~M associated with the energy storage node. Scheduling photovoltaic nodes is mainly reflected in pre-scheduling the power supply power of multiple photovoltaic units 1~I associated with the photovoltaic node. Scheduling wind power nodes is mainly reflected in pre-scheduling the power supply power of multiple wind turbines 1~J associated with the wind power node.

[0036] The power generation side of the local power grid has multiple thermal power units, multiple wind power units, multiple photovoltaic units, and multiple energy storage units. The load side of the local power grid is mainly industrial energy, that is, the production lines of multiple factories on the energy consumption side. The power consumption side of the local power grid is mainly hydrogen production, charging piles, etc. The trading business entities of the local power grid are:

[0037] (1) Power generation sector: thermal power nodes, wind power nodes, and photovoltaic nodes. Thermal power nodes are mainly used to generate electricity and supply power to the local power grid;

[0038] (2) Electricity-consuming departments: factory production line nodes, purchasing electricity from power generation nodes and the State Grid;

[0039] (3) Energy storage department: energy storage node, which obtains and stores electricity from the local power grid during off-peak hours and supplies electricity to the local power grid during peak hours to balance the power supply load of the local power grid;

[0040] (4) Charging pile node: used to charge electric vehicles;

[0041] (5) Electricity-generated hydrogen production node;

[0042] (6) Dispatching node: Provides power monitoring, command and dispatching services;

[0043] (7) Grid power purchase node: When power generation output is insufficient, electricity is purchased from the State Grid.

[0044] The coordination and dispatching platform of the multi-energy complementary system should realize the following five functions:

[0045] (1) Accurately record the power, output, and load of each entity in the multi-energy complementary system with time tags to form a time-stamped power list;

[0046] (2) Formulate energy prices for each entity (wind, solar, and thermal) on the power generation side, and generate accurate and reliable electronic bills based on the electricity bill;

[0047] (3) Based on the power generation and consumption, a thermal map of the local power grid is formed, and ultimately a single map of the power in the grid is achieved;

[0048] (4) Form a model and mechanism that records unique, clear and cannot be tampered with. Participating entities can be expanded, and servers and application terminals can be set up in each entity to form an extended application of a decentralized model;

[0049] (5) Allowing each subject to publish demand, that is, allowing authorized login personnel to publish the energy supply demand or energy consumption demand of this node.

[0050] Among them, the specific transaction functions cover the following elements:

[0051] (1) Trading goods: Electricity quantity (year, month, day + real-time adjustment)

[0052] (2) Accounting content: transaction parties, electricity prices, electricity consumption, time, etc.

[0053] (3) Trading unit: Energy unit under the account

[0054] (4) Transaction subjects: units to which the unit belongs and multi-party interactions.

[0055] In order to realize the above functions, each subject in the multi-energy complementary system needs to have the following data support:

[0056] (1) Thermal power nodes: output, power, start and stop status of each unit, power and power of the booster station outgoing line;

[0057] (2) Wind power node: wind turbine start and stop status, output and power of each wind turbine. Output power and power of booster station;

[0058] (3) Photovoltaic node: inverter circuit breaker status, inverter power, and power. Booster station outgoing line power and power;

[0059] (4) Energy storage node: charging and discharging status, charging and discharging power, and charging and discharging capacity;

[0060] (5) Electricity-generated hydrogen production node: load power and electricity;

[0061] (6) Charging pile node: charging pile status (standby, charging), charging power, and power;

[0062] (7) Electricity-generated hydrogen production node: load power and electricity quantity.

[0063] Under the above operating environment, according to the first aspect of this embodiment, a coordinated scheduling method for a multi-energy complementary system is provided. The method comprises: Figure 2a The coordinated scheduling platform implementation shown in . Figure 3 A schematic diagram showing the process of the method is shown in FIG. Figure 3 As shown, the method includes:

[0064] S102: Obtaining pre-dispatching data of multiple historical periods and pre-dispatching data of the next period from the blockchain of multiple target nodes in the multi-energy complementary system; wherein the pre-dispatching data is data obtained by predicting the power supply and demand of all entities in the multi-energy complementary system in each period by using their respective prediction models; the multiple target nodes include photovoltaic nodes, wind power nodes, thermal power nodes, energy storage nodes and power grid purchase nodes;

[0065] S104: Determine the scheduling data of all entities in the multi-energy complementary system in the next period based on the pre-scheduling data of multiple historical periods and the pre-scheduling data of the next period;

[0066] S106: Upload the scheduling data to the blockchain, and coordinate the scheduling of power resources of multiple target nodes according to the scheduling data in the next period.

[0067] Specifically, refer to Figure 2a and Figure 2b As shown in the figure, the multiple target nodes involved in the scheduling in the multi-energy complementary system include photovoltaic nodes, wind power nodes, thermal power nodes, energy storage nodes and power grid purchase nodes. Each target node can use its own prediction model to predict the power supply or power consumption of all entities in the multi-energy complementary system in the next period, and determine the pre-scheduling data for the next period based on the prediction results and upload them to the blockchain. Taking the photovoltaic node as an example, the photovoltaic node uses its own prediction model to predict the photovoltaic power supply power of the photovoltaic node, the wind power supply power of the wind power node, the thermal power supply power of the thermal power node, the charging and discharging power of the energy storage node (positive for discharge, negative for charging), the power grid transmission power of the power grid purchase node (i.e., the power transmitted from the national power grid) and the power consumption of the power load node in the next period, and obtains the pre-scheduling data for the next period and uploads the pre-scheduling data to the blockchain. Similarly, the wind power node, thermal power node, energy storage node and power grid purchase node also use their own prediction models to predict the pre-scheduling data for the next period and upload them to the blockchain.

[0068] Thus, in the process of coordinating and dispatching the regional power grid, the dispatching node can obtain the pre-dispatching data of multiple historical periods and the pre-dispatching data of the next period on the chain of multiple target nodes in the multi-energy complementary system from the blockchain (corresponding to step S102). Blockchain technology can ensure that the data of all participating nodes in the multi-energy complementary system are transparent and accessible on the chain, thereby breaking the information island and providing a data basis for subsequent global optimization. In addition, all target nodes can use their own prediction models to accurately predict the power supply and demand in future periods, and share their predicted power supply and demand through the blockchain, so that the data preprocessing and prediction are completed independently by each participating node, reducing the dependence on a single centralized dispatching center.

[0069] Then, the scheduling node determines the scheduling data of all entities in the multi-energy complementary system in the next time period based on the pre-scheduling data of multiple historical time periods and the pre-scheduling data of the next time period (corresponding to step S104). Specifically, the scheduling node generates a comprehensive scheduling plan (i.e., the scheduling data of the next time period) by comprehensively analyzing the pre-scheduling data of multiple historical time periods and the pre-scheduling data of the next time period, which is used to guide the energy allocation in the next time period. Thereby, it is ensured that the determined scheduling data comprehensively considers the historical supply and demand data of all entities in the multi-energy complementary system and the power supply and demand situation in the future time period, realizes global optimization, and provides a basis for the subsequent balanced allocation of energy.

[0070] Finally, the dispatching node uploads the dispatching data to the chain, and coordinates the dispatching of power resources of multiple target nodes according to the dispatching data in the next period (corresponding to step S106). Thus, the transparency, traceability and security of the dispatching data are ensured, so that all target nodes can verify the fairness and rationality of the dispatching, ensure that all target nodes act according to the unified dispatching plan, and improve the operating efficiency and stability of the entire system.

[0071] As described in the background technology, the existing multi-energy complementary system scheduling method is mainly based on a centralized control strategy. The core of this strategy is to establish a centralized scheduling organization, which is responsible for collecting real-time data of all energy nodes in the entire system, including but not limited to power generation, demand, energy storage status and other information. Based on these data, the central scheduling organization uses optimization algorithms, such as linear programming, dynamic programming, etc., to formulate a global scheduling plan, aiming to balance supply and demand, maximize system efficiency or minimize costs. However, this centralized scheduling method has problems such as information islands and global optimization problems, over-reliance on centralized scheduling, and data security risks.

[0072] In view of this, according to this embodiment, firstly, the pre-scheduling data of multiple target nodes on the chain and the pre-scheduling data of the next period are obtained from the blockchain. Through the blockchain technology, the data of all participating nodes in the multi-energy complementary system can be ensured to be transparent and accessible on the chain, thereby breaking the information island and providing a data basis for global optimization. In addition, all participating nodes can use their own prediction models, combined with historical data, to accurately predict the power supply and demand of the future period, and share their predicted power supply and demand through the blockchain, so that the pre-processing and prediction of the data are completed independently by each participating node, reducing the dependence on a single centralized dispatching center. Then, based on the pre-scheduling data and the pre-scheduling data of the next period, a dispatching data that comprehensively considers the power supply and demand of all subjects in the entire multi-energy complementary system is determined to achieve global optimization and ensure the balanced distribution of energy. Finally, the dispatching data is put on the chain and the power resources of multiple target nodes are coordinated and dispatched according to the dispatching data in the next period, ensuring the openness and transparency of the dispatching data, so that all participating nodes can verify the fairness and rationality of the dispatching, ensuring that all participating nodes act according to a unified dispatching plan, and improving the operating efficiency and stability of the entire system. Therefore, this application effectively solves the technical problems existing in the prior art of multi-energy complementary system scheduling methods, such as information islands and global optimization problems, over-reliance on centralized scheduling, and data security risks, by combining blockchain technology and distributed pre-scheduling mechanism.

[0073] Optionally, multiple target nodes determine the pre-scheduling data for the next time period through the following steps: each target node obtains the pre-scheduling data for the current time period on the chain of multiple target nodes from the blockchain, obtains the demand information for the next time period on the chain of all entities in the multi-energy complementary system from the blockchain, and obtains the environmental information for the next time period; each target node constructs a vector based on the pre-scheduling data for the current time period, the demand information for the next time period, and the environmental information for the next time period; each target node inputs the vector into its own pre-trained prediction model, predicts the power supply and demand situation of all entities in the multi-energy complementary system in the next time period, and obtains the initial pre-scheduling data for the next time period; the photovoltaic node and the wind power node directly upload the initial pre-scheduling data to the blockchain; the thermal power node, the energy storage node, and the power grid purchasing node optimize the initial pre-scheduling data according to the actual situation of each associated unit or unit, and upload the optimized pre-scheduling data to the blockchain.

[0074] In this embodiment, when each target node determines the pre-scheduling data for the next time period, it can first obtain the pre-scheduling data for the current time period on the chain of multiple target nodes from the blockchain, obtain the demand information for the next time period on the chain of all entities in the multi-energy complementary system from the blockchain, and obtain the environmental information for the next time period. Taking the photovoltaic node as an example, the photovoltaic node first obtains the environmental information for the next time period, the pre-scheduling data corresponding to the current time period, and the demand information for the next time period, so as to predict the photovoltaic power supply power, wind power supply power, thermal power supply power, energy storage charging and discharging power (positive for discharge, negative for charging), power grid transmission power (i.e., the power transmitted from the national power grid), and load power consumption in the next time period. Among them, the environmental information can be, for example, θ1~θ N , respectively representing temperature, humidity, cloud cover and other information. The scheduling data for the current period includes: photovoltaic power supply power X a0 、Wind power supply power X b0 、Thermal power supply power X c0 、Energy storage charging and discharging power X d0 , Grid transmission power X e0 And the load power X f0 The demand information for the next period includes: photovoltaic energy demand D a 、Wind power supply demandD b 、Demand for thermal power supplyD c 、Energy storage supply demand D d , power grid energy demand D e and the energy demand of the load D f .

[0075] Then, the PV node constructs a vector (θ1~θ N ,X a0 ~X e0 ,D a ~D f ), input your own prediction model to predict the photovoltaic power supply power X in the next period a1 、Wind power supply power X b1 、Thermal power supply power X c1 、Energy storage charging and discharging power X d1 , Grid transmission power X e1 And the load power X f1 The prediction model may be a neural network prediction model, for example, multiple neural network-based prediction models may be used to make predictions based on the vector. Thus, the pre-scheduling data (X) for the next time period is predicted. a1 ~X f1 ) and upload it to the blockchain.

[0076] For wind power nodes, thermal power nodes, energy storage nodes and power grid purchase nodes, similar methods as photovoltaic nodes can be used for prediction, so as to predict the pre-dispatch data for the next period and upload it to the blockchain. Among them, the pre-dispatch data for the next period predicted by the wind power node is (X a2 ~X f2 ) indicates that the thermal power node predicts the pre-dispatching data for the next period of time (X a3 ~X f3 ) indicates that the energy storage node predicts the pre-dispatching data for the next period of time (X a4 ~X f4 ) indicates that the power grid purchasing node predicts the pre-dispatching data for the next period of time (X a5 ~X f5 )express.

[0077] refer to Figure 2b As shown in the figure, photovoltaic nodes, wind power nodes, thermal power nodes and energy storage nodes are all associated with multiple units or units, so these nodes can obtain the initial pre-dispatch data for the next period based on the prediction, and perform internal pre-dispatch for each unit or unit, thereby optimizing the initial pre-dispatch data. However, due to the poor controllability of photovoltaic power generation equipment and wind power equipment, photovoltaic nodes and wind power nodes only perform predictions without performing actual internal pre-dispatch operations. The internal pre-dispatch operation is performed by thermal power nodes, energy storage nodes and power grid purchase nodes. Therefore, on this basis, the thermal power nodes, energy storage nodes and power grid purchase nodes optimize each unit or unit associated with the node respectively, thereby performing internal pre-dispatch, and based on the results of the internal pre-dispatch, propose the final pre-dispatch data for the regional power grid (i.e., the optimized pre-dispatch data) and upload the data to the blockchain.

[0078] It should be noted that in the process of dispatching the regional power grid, photovoltaic nodes, wind power nodes, thermal power nodes and energy storage nodes only provide their own overall data to the blockchain, and also accept the overall dispatch of the regional power grid for this node. In the overall dispatching process of the regional power grid, when each target node uploads data to the blockchain, only the overall power supply power of the node is uploaded. Taking the thermal power node as an example, the thermal power node is associated with thermal power units 1 to L. After the thermal power node predicts the initial pre-dispatch data for the next period, the power supply power of each thermal power unit 1 to L can be determined according to the optimization algorithm to perform internal pre-dispatch. After that, the thermal power node will upload the overall power supply power (that is, the sum of the power supply power of each thermal power unit 1 to L) to the chain.

[0079] In this way, each target node can make personalized predictions based on its own characteristics and conditions, ensuring that the pre-scheduling data uploaded to the chain by each target node is targeted and practical. In addition, some target nodes can perform internal pre-scheduling based on the preliminary pre-scheduling data obtained by their own predictions according to the actual situation of their own equipment, so as to be closer to the actual operation situation, thereby obtaining optimized pre-scheduling data and uploading it to the chain, ensuring that the pre-scheduling data uploaded to the chain by each target node is more in line with the actual capabilities of each node, avoiding over-scheduling or under-scheduling.

[0080] Optionally, the thermal power nodes, energy storage nodes and power purchase nodes of the power grid optimize the initial pre-dispatching data according to the actual conditions of the units or units associated with them, including: the thermal power nodes, energy storage nodes and power purchase nodes of the power grid determine the constraints and objective functions of the units or units associated with them according to the actual conditions of the units or units associated with them; the thermal power nodes, energy storage nodes and power purchase nodes of the power grid determine the power supply power of the units or units associated with them in the next time period by using the particle swarm optimization algorithm based on their constraints and objective functions; the thermal power nodes, energy storage nodes and power purchase nodes of the power grid determine the total power supply power of their respective nodes in the next time period based on the power supply power of the units or units associated with them in the next time period; the thermal power nodes, energy storage nodes and power purchase nodes of the power grid replace the power supply power of the corresponding nodes in the initial pre-dispatching data predicted by them with the total power supply power of their respective nodes in the next time period to obtain the optimized pre-dispatching data.

[0081] Specifically, the initial pre-dispatch data obtained by thermal power node prediction is (X a3 , X b3 , X c3 , X d3 , X e3 , X f3 ), refer to Figure 2b As shown in the figure, since the thermal power node is associated with thermal power units 1 to L, the thermal power node is based on the initial pre-dispatch data (X a3 ~X f3 ), and perform internal pre-dispatch for each thermal power unit 1 to L, specifically:

[0082] (1) The thermal power node determines the constraints related to the power supply of each thermal power unit 1 to L.

[0083] First, assume that the power supply of each thermal power unit is X c31 ~X c3L , according to the preset maximum power X of each thermal power unit c31Max ~X c3LMax and minimum power X c31Min ~X c3LMin Set the first constraint, that is, X c31Min≦X c31 ≦X c31Max , X c32Min ≦X c32 ≦X c32Max , X c3LMin ≦X c3L ≦X c3LMax ;

[0084] Next, set the second constraint, X c31 +X c32 +X c33 +...+X c3L ≥X f3 -X a3 -X b3 -X d3 -X e3 ;

[0085] (2) According to the principle of minimum cost, determine the objective function F(X c31 ~X c3L ): F(X c31 ~X c3L )=S c31 (X c31 )+S c32 (X c32 )+...+S c3L (X c3L );

[0086] Among them, S c31 (), S c32 (),...S c3L () is the cost function corresponding to each thermal power unit;

[0087] (3) Based on the above constraints and objective function, the particle swarm optimization algorithm is used to determine the power supply power X of each thermal power unit. c31 ~X c3L , X c31 +X c32 +X c33 +...+X c3L＝ X' c3 ;

[0088] (4) The initial pre-scheduling data (X a3 , X b3 , X c3 , X d3 , X e3 , X f3 ) c3 Replace with X' c3 , thus obtaining the optimized pre-scheduling data (X a3 , X b3 , X' c3, X d3 , X e3 , X f3 ).

[0089] In this embodiment, the energy storage node also uses the same method as the thermal power node to perform internal pre-scheduling to determine the optimized pre-scheduling data (X a4 , X b4 , X c4 , X' d4 , X e4 , X f4 ).

[0090] In this embodiment, the power grid purchasing node is based on the initial pre-dispatching data (X a5 , X b5 , X c5 , X d5 , X e5 , X f5 ), power transmission power of the power grid X e5 Perform internal pre-scheduling, specifically:

[0091] (1) Assume that the power transmission power of the power grid is X e5 , we can first calculate the maximum power X of the power transmission power of the pre-set grid e5Max and the minimum power 0 sets the constraint condition, 0≦X e5 ≦X e5Max ; Secondly, set constraints, X e5 ≥X f5 -X a5 -X b5 -X c5 -X d5 ;

[0092] (2) According to the principle of minimum cost, determine the objective function F(X e5 )=S e5 (X e5 );

[0093] Among them, S e5 () is the cost function corresponding to the power transmission power of the power grid.

[0094] (3) Based on the above constraints and objective function, the genetic algorithm is used to optimize and determine the power transmission power X' e5 ;

[0095] (4) The initial pre-scheduling data (X a5 , X b5 , X c5 , X d5 , X e5 , X f5 ) e5 Replace with X'e5 , thus obtaining the optimized pre-scheduling data (X a5 , X b5 , X c5 , X d5 , X' e5 , X f5 ).

[0096] Therefore, thermal power nodes, energy storage nodes and power grid purchase nodes can set constraints and objective functions according to the characteristics of their own units or units, ensuring that the optimization process fully considers the physical limitations of the equipment and operational goals, such as cost minimization, emission control, etc. In addition, the optimized pre-dispatch data is closer to the actual operation situation, realizing refined management and system coordination, and improving the accuracy and reliability of dispatch.

[0097] Optionally, based on the pre-scheduling data of multiple historical time periods and the pre-scheduling data of the next time period, the scheduling data of all entities in the multi-energy complementary system in the next time period is determined, including: determining the weight values ​​of multiple target nodes based on the pre-scheduling data of multiple historical time periods; based on the weight values, performing weighted summation of the pre-scheduling data of the next time period predicted by the multiple target nodes to obtain the scheduling data of all entities in the multi-energy complementary system in the next time period.

[0098] Specifically, the pre-dispatching data of each target node on the chain is weighted and summed to determine the dispatching data of the regional power grid (X a , X b , X c , X d , X e , X f ). The weight value of each target node can be determined based on the deviation of the pre-dispatch data of each node in the previous pre-dispatch, that is, the weight value of each target node is determined by calculating the deviation between the pre-dispatch data and the dispatch data of multiple historical periods. Among them, the weight values ​​corresponding to the photovoltaic node, wind power node, thermal power node, energy storage node and power grid purchase node are ω1, ω2, ω3, ω4, and ω5 respectively. Then, the dispatch data (X) of the next period is calculated by the following formula a , X b , X c , X d , X e , X f ):

[0099] (X a , X b , X c , X d , X e , X f )=ω1(X a1 , X b1 , Xc1 , X d1 , X e1 , X f1 )+ω2(X a2 , X b2 , X c2 , X d2 , X e2 , X f2 )+ω3(X a3 , X b3 , X' c3 , X d3 , X e3 , X f3 )+ω4(X a4 , X b4 , X c4 , X' d4 , X e4 , X f4 )+ω5(X a5 , X b5 , X c5 , X d5 , X' e5 , X f5 )

[0100] Thus, by weighted summation, more emphasis is placed on the prediction results of target nodes with excellent historical performance, which reduces the uncertainty in scheduling decisions and improves the scientificity and rationality of decisions. The introduction of weight values ​​helps to disperse risks and avoid over-reliance on the prediction of a single node. Moreover, this process encourages each target node to improve the accuracy of predictions in order to obtain a higher weight value, which promotes healthy competition and cooperation among the various subjects in the multi-energy complementary system. In this way, not only the accuracy and efficiency of scheduling decisions are improved, but also by promoting the collaborative cooperation of various subjects in the system, it provides strong support for building a more intelligent, efficient and stable multi-energy complementary system.

[0101] Optionally, based on the pre-scheduling data of multiple historical time periods, determining the weight values ​​of multiple target nodes includes: obtaining the pre-scheduling data of multiple historical time periods and the corresponding scheduling data from the blockchain; for each target node, calculating the error between the pre-scheduling data of each historical time period predicted by the target node and the corresponding scheduling data by the following formula:

[0102]

[0103] In the formula, E ij is the error of the i-th target node in the j-th historical scheduling process; (X iaj , X ibj , X icj , X idj , X iej , Xifj ) is the pre-scheduling data of the jth historical period predicted by the i-th target node, X iaj The photovoltaic power supply power predicted for the i-th target node in the j-th historical period, X ibj The wind power supply power predicted for the i-th target node in the j-th historical period, X icj The thermal power supply power predicted for the i-th target node in the j-th historical period, X idj The energy storage charging and discharging power predicted for the i-th target node in the j-th historical period, X iej The grid transmission power predicted for the i-th target node in the j-th historical period, X ifj The load power consumption of the jth historical period predicted by the i-th target node, where i = a~e, a represents the photovoltaic node, b represents the wind power node, c represents the thermal power node, d represents the energy storage node, and e represents the power grid purchasing node; the dispatch data of the jth historical period is (X aj , X bj , X cj , X dj , X ej , X fj ), X aj is the photovoltaic power supply power in the jth historical period, X bj is the wind power supply power in the jth historical period, X cj is the thermal power supply power in the jth historical period, X dj is the energy storage charging and discharging power in the jth historical period, X ej is the power transmission power of the power grid in the jth historical period, X fj is the load power consumption in the jth historical period;

[0104] For each target node, calculate the average error between the pre-scheduled data and the corresponding scheduled data for all historical periods predicted by the target node

[0105]

[0106] The softmax function is used to map the average errors of all target nodes to values ​​in the interval [0,1], and the mapped values ​​are determined as the weight values ​​of the corresponding target nodes.

[0107] Specifically, based on the pre-scheduling data of multiple historical periods, the weight values ​​of multiple target nodes are determined, which specifically includes the following steps:

[0108] (1) First, the pre-scheduling data uploaded to the chain by each node in the previous pre-scheduling can be obtained from the blockchain, and the scheduling data uploaded to the chain by the scheduling node in the previous pre-scheduling can be obtained;

[0109] (2) For each target node, calculate the error between the pre-scheduling data of the node in each historical scheduling and the scheduling data uploaded by the scheduling node;

[0110]

[0111] In the formula, E ij is the error of the i-th target node in the j-th historical scheduling process; (X iaj , X ibj , X icj , X idj , X iej , X ifj ) is the pre-scheduling data of the jth historical period predicted by the i-th target node, X iaj The photovoltaic power supply power predicted for the i-th target node in the j-th historical period, X ibj The wind power supply power predicted for the i-th target node in the j-th historical period, X icj The thermal power supply power predicted for the i-th target node in the j-th historical period, X idj The energy storage charging and discharging power predicted for the i-th target node in the j-th historical period, X iej The grid transmission power predicted for the i-th target node in the j-th historical period, X ifj The load power consumption of the jth historical period predicted by the i-th target node, where i = a~e, a represents the photovoltaic node, b represents the wind power node, c represents the thermal power node, d represents the energy storage node, and e represents the power grid purchasing node; the dispatch data of the jth historical period is (X aj , X bj , X cj , X dj , X ej , X fj ), X aj is the photovoltaic power supply power in the jth historical period, X bj is the wind power supply power in the jth historical period, X cj is the thermal power supply power in the jth historical period, X dj is the energy storage charging and discharging power in the jth historical period, X ej is the power transmission power of the power grid in the jth historical period, X fj is the load power consumption in the jth historical period;

[0112] (3) For each target node, calculate the average error between the pre-scheduling data of the node and the scheduling data uploaded by the scheduling node during the historical scheduling process of all sessions.

[0113]

[0114] (4) Use the softmax function to map the average error of all target nodes to a value in the interval [0,1], and determine the mapped value as the weight ω of the corresponding node i ;

[0115] (5) Based on the weight ω of each target node i , perform weighted summation of the pre-dispatching data on all target nodes to determine the dispatching data of the regional power grid (X a , X b , X c , X d , X e , X f ).

[0116] Therefore, by calculating the error, the prediction performance of each target node can be quantified, providing an objective basis for the subsequent determination of the weight value. Calculating the error for each historical period and each node separately helps to deeply analyze the performance of each node under different conditions and provide detailed data support for optimizing the scheduling strategy. The calculation of the average error takes into account the performance of the node in multiple historical periods, which can more comprehensively reflect its long-term prediction ability and help identify nodes with high stability and reliability. Through the average error, the prediction performance of different nodes can be compared fairly, avoiding deviations caused by abnormal performance in individual periods. The softmax function helps to smooth the error, avoid uneven weight distribution caused by extreme error values, improve the stability and rationality of the weight value, so that nodes with better prediction performance will obtain higher weight values, reflecting the relative importance and contribution of each node in coordinated scheduling.

[0117] Optionally, the power resources of multiple target nodes are coordinated and scheduled in the next time period according to the scheduling data, including: the multiple target nodes obtain the scheduling data of the next time period from the blockchain; the multiple target nodes redetermine their respective power supply power, charging and discharging power or transmission power in the next time period according to the scheduling data.

[0118] Specifically, each target node in the multi-energy complementary system (such as photovoltaic nodes, wind power nodes, thermal power nodes, energy storage nodes, power grid purchase nodes and power consumption nodes) obtains the scheduling data for the next period from the blockchain. These data include the overall power supply demand, power supply plan and expected contribution of each node in the next period. Through the blockchain, each node can obtain system information fairly and transparently, which promotes efficient sharing of information and helps to achieve overall optimization of the system. After obtaining the scheduling data for the next period, each target node recalculates and adjusts its power supply power, charging and discharging power or transmission power in the next period based on these data, combined with its own operating status and capabilities, to ensure that the adjustment of each node can not only meet the requirements of system scheduling, but also maximize its own benefits or minimize costs.

[0119] Therefore, each node can make personalized adjustments based on the scheduling data, which can better match the system requirements, while taking into account the optimal use of its own resources, improving the flexibility and efficiency of scheduling. Through the self-adjustment of each node, the system can achieve more efficient resource allocation and coordination, reduce unnecessary energy waste, and improve the overall energy utilization efficiency. Each node makes autonomous decisions based on scheduling data, which enhances the autonomy of the system, reduces dependence on central scheduling, and improves the robustness and risk resistance of the system. In this way, not only the operating efficiency and stability of the multi-energy complementary system are improved, but also by promoting information sharing and autonomous decision-making, it provides strong support for building a more intelligent, flexible and efficient energy system.

[0120] Optionally, the method also includes: the thermal power nodes, energy storage nodes and power grid purchasing nodes use a particle swarm optimization algorithm to redetermine the power supply power of each associated unit or unit in the next time period based on the redetermined power supply power, charging and discharging power or transmission power in the next time period and the constraints and objective functions of each associated unit or unit.

[0121] Specifically, the thermal power node can be based on the re-determined power supply power X' for the next period c3 , the constraints and objective functions related to the power supply of each thermal power unit 1 to L are determined by re-determining the power supply of each associated thermal power unit in the next period of time in the following way:

[0122] (1) The thermal power node is based on the dispatch data (X a , X b , X c , X d , X e , X f ), and redetermine the total power supply power for the next period as X c Assume that the power supply of thermal power units 1 to L in the next period is X' c31 ~X'c3L , X' c31 +X' c32 +X' c33 +…+X' c3L =X c First, the maximum power X of each thermal power unit can be set in advance. c31Max ~X c3LMax and minimum power X c31Min ~X c3LMin Set the first constraint. That is, X c31Min ≦X' c31 ≦X c31Max , X c32Min ≦X' c32 ≦X c32Max , X c3LMin ≦X' c3L ≦X c3LMax Next, set the second constraint, X' c31 +X' c32 +X' c33 +···+X' c3L ≥X f -X a -X b -X d -X e ;

[0123] (2) According to the principle of minimum cost, the objective function is redefined as F(X' c31 ~X' c3L ), F(X' c31 ~X' c3L )=S c31 (X' c31 )+S c32 (X' c32 )+...+S c3L (X' c3L );

[0124] Among them, S c31 (), S c32 (),...S c3L () is the cost function corresponding to each thermal power unit.

[0125] (3) Based on the re-determined constraints and objective function, the particle swarm optimization algorithm is used to determine the power supply X' of each thermal power unit. c31 ~X' c3L .

[0126] The energy storage node also uses the same method as the thermal power node to perform the second internal pre-dispatch to determine the charging and discharging power X' of each energy storage unit 1~M d41 ~X'd4M The power grid purchasing node is based on the dispatch data (X a , X b , X c , X d , X e , X f ), and redetermine the power transmission power of the next period as X e .

[0127] Therefore, by considering the constraints and objective functions based on the re-determined power supply, and using the particle swarm optimization algorithm, not only the operating efficiency and economy of thermal power nodes, energy storage nodes and power grid purchase nodes are improved, but also through personalized optimization and global optimization, a solid foundation is laid for building a more intelligent, flexible and efficient multi-energy complementary system. In this way, not only the efficient sharing of information and autonomous decision-making are promoted, and the autonomy and risk resistance of the coordinated scheduling scheme are enhanced, but also through parallel processing and highly adaptable optimization algorithms, the scheme is ensured to achieve the optimal allocation and coordination of resources in the face of complex and changing environments.

[0128] In addition, according to this embodiment, a storage medium is also provided, wherein the storage medium includes a stored program, wherein when the program is run, a processor executes any one of the above methods.

[0129] In this application, firstly, the pre-scheduling data of multiple target nodes on the chain and the pre-scheduling data of the next period are obtained from the blockchain. Through the blockchain technology, the data of all participating nodes in the multi-energy complementary system can be ensured to be transparent and accessible on the chain, thereby breaking the information island and providing a data basis for global optimization. In addition, all participating nodes can use their own prediction models, combined with historical data, to accurately predict the power supply and demand of future periods, and share their predicted power supply and demand through the blockchain, so that the pre-processing and prediction of the data are completed independently by each participating node, reducing the dependence on a single centralized dispatching center. Then, based on the pre-scheduling data and the pre-scheduling data of the next period, a dispatching data that comprehensively considers the power supply and demand of all subjects in the entire multi-energy complementary system is determined to achieve global optimization and ensure the balanced distribution of energy. Finally, the dispatching data is put on the chain and the power resources of multiple target nodes are coordinated and dispatched according to the dispatching data in the next period, which ensures the openness and transparency of the dispatching data, so that all participating nodes can verify the fairness and rationality of the dispatching, ensure that all participating nodes act according to a unified dispatching plan, and improve the operating efficiency and stability of the entire system. Therefore, this application effectively solves the technical problems existing in the prior art of multi-energy complementary system scheduling methods, such as information islands and global optimization problems, over-reliance on centralized scheduling, and data security risks, by combining blockchain technology and distributed pre-scheduling mechanism.

[0130] It should be noted that, for the above-mentioned method embodiments, for the sake of simplicity, they are all described as a series of action combinations, but those skilled in the art should know that the present invention is not limited by the described action sequence, because according to the present invention, certain steps can be performed in other sequences or simultaneously. Secondly, those skilled in the art should also know that the embodiments described in the specification are all preferred embodiments, and the actions and modules involved are not necessarily required by the present invention.

[0131] Example 2

[0132] Figure 4 The structural schematic diagram of the coordinated scheduling device 400 of the multi-energy complementary system according to the present embodiment is shown. The coordinated scheduling device 400 includes: a data acquisition module 410, which is used to obtain the pre-scheduling data of multiple historical time periods and the pre-scheduling data of the next time period on the chain of multiple target nodes in the multi-energy complementary system from the blockchain; wherein the pre-scheduling data is the data obtained after the multiple target nodes use their respective prediction models to predict and optimize the power supply and demand of all entities in the multi-energy complementary system in each time period; the multiple target nodes include photovoltaic nodes, wind power nodes, thermal power nodes, energy storage nodes and power grid purchase nodes; a data determination module 420, which is used to determine the scheduling data of all entities in the multi-energy complementary system in the next time period based on the pre-scheduling data of multiple historical time periods and the pre-scheduling data of the next time period; a coordinated scheduling module 430, which is used to chain the scheduling data to the blockchain, and coordinate the scheduling of the power resources of multiple target nodes according to the scheduling data in the next time period.

[0133] Optionally, multiple target nodes determine the pre-scheduling data for the next time period through the following steps: each target node obtains the pre-scheduling data for the current time period on the chain of multiple target nodes from the blockchain, obtains the demand information for the next time period on the chain of all entities in the multi-energy complementary system from the blockchain, and obtains the environmental information for the next time period; each target node constructs a vector based on the pre-scheduling data for the current time period, the demand information for the next time period, and the environmental information for the next time period; each target node inputs the vector into its own pre-trained prediction model, predicts the power supply and demand situation of all entities in the multi-energy complementary system in the next time period, and obtains the initial pre-scheduling data for the next time period; the photovoltaic node and the wind power node directly upload the initial pre-scheduling data to the blockchain; the thermal power node, the energy storage node, and the power grid purchasing node optimize the initial pre-scheduling data according to the actual situation of each associated unit or unit, and upload the optimized pre-scheduling data to the blockchain.

[0134] Optionally, the thermal power nodes, energy storage nodes and power purchase nodes of the power grid optimize the initial pre-dispatching data according to the actual conditions of the units or units associated with them, including: the thermal power nodes, energy storage nodes and power purchase nodes of the power grid determine the constraints and objective functions of the units or units associated with them according to the actual conditions of the units or units associated with them; the thermal power nodes, energy storage nodes and power purchase nodes of the power grid determine the power supply power of the units or units associated with them in the next time period by using the particle swarm optimization algorithm based on their constraints and objective functions; the thermal power nodes, energy storage nodes and power purchase nodes of the power grid determine the total power supply power of their respective nodes in the next time period based on the power supply power of the units or units associated with them in the next time period; the thermal power nodes, energy storage nodes and power purchase nodes of the power grid replace the power supply power of the corresponding nodes in the initial pre-dispatching data predicted by them with the total power supply power of their respective nodes in the next time period to obtain the optimized pre-dispatching data.

[0135] Optionally, the data determination module 420 is specifically used to: determine the weight values ​​of multiple target nodes based on the pre-scheduling data of multiple historical time periods; based on the weight values, perform weighted summation on the pre-scheduling data of the next time period predicted by the multiple target nodes to obtain the scheduling data of all entities in the multi-energy complementary system in the next time period.

[0136] Optionally, based on the pre-scheduling data of multiple historical time periods, determining the weight values ​​of multiple target nodes includes: obtaining the pre-scheduling data of multiple historical time periods and the corresponding scheduling data from the blockchain; for each target node, calculating the error between the pre-scheduling data of each historical time period predicted by the target node and the corresponding scheduling data by the following formula:

[0137]

[0138] In the formula, E ij is the error of the i-th target node in the j-th historical scheduling process; (X iaj , X ibj , X icj , X idj , X iej , X ifj ) is the pre-scheduling data of the jth historical period predicted by the i-th target node, X iaj The photovoltaic power supply power predicted for the i-th target node in the j-th historical period, X ibj The wind power supply power predicted for the i-th target node in the j-th historical period, X icj The thermal power supply power predicted for the i-th target node in the j-th historical period, X idj The energy storage charging and discharging power predicted for the i-th target node in the j-th historical period, X iejThe grid transmission power predicted for the i-th target node in the j-th historical period, X ifj The load power consumption of the jth historical period predicted by the i-th target node, where i = a~e, a represents the photovoltaic node, b represents the wind power node, c represents the thermal power node, d represents the energy storage node, and e represents the power grid purchasing node; the dispatch data of the jth historical period is (X aj , X bj , X cj , X dj , X ej , X fj ), X aj is the photovoltaic power supply power in the jth historical period, X bj is the wind power supply power in the jth historical period, X cj is the thermal power supply power in the jth historical period, X dj is the energy storage charging and discharging power in the jth historical period, X ej is the power transmission power of the power grid in the jth historical period, X fj is the load power consumption in the jth historical period;

[0139] For each target node, calculate the average error between the pre-scheduled data and the corresponding scheduled data for all historical periods predicted by the target node

[0140]

[0141] The softmax function is used to map the average errors of all target nodes to values ​​in the interval [0,1], and the mapped values ​​are determined as the weight values ​​of the corresponding target nodes.

[0142] Optionally, the coordination scheduling module 430 is specifically used for: multiple target nodes obtain scheduling data for the next time period from the blockchain; and multiple target nodes redetermine their respective power supply power, charging and discharging power, or transmission power in the next time period based on the scheduling data.

[0143] Optionally, the coordination and scheduling device 400 also includes a secondary scheduling module, which is used for: thermal power nodes, energy storage nodes and power grid purchasing nodes to redetermine the power supply power of each associated unit or unit in the next time period based on the redetermined power supply power, charging and discharging power or transmission power in the next time period and the constraints and objective functions of each associated unit or unit, using a particle swarm optimization algorithm.

[0144] Thus, according to this embodiment, firstly, the pre-scheduling data of multiple target nodes on the chain and the pre-scheduling data of the next period are obtained from the blockchain. Through the blockchain technology, the data of all participating nodes in the multi-energy complementary system can be ensured to be transparent and accessible on the chain, thereby breaking the information island and providing a data basis for global optimization. In addition, all participating nodes can use their own prediction models, combined with historical data, to accurately predict the power supply and demand of the future period, and share their predicted power supply and demand through the blockchain, so that the pre-processing and prediction of the data are completed independently by each participating node, reducing the dependence on a single centralized dispatching center. Then, based on the pre-scheduling data and the pre-scheduling data of the next period, a dispatching data that comprehensively considers the power supply and demand of all subjects in the entire multi-energy complementary system is determined to achieve global optimization and ensure the balanced distribution of energy. Finally, the dispatching data is put on the chain and the power resources of multiple target nodes are coordinated and dispatched according to the dispatching data in the next period, ensuring the openness and transparency of the dispatching data, so that all participating nodes can verify the fairness and rationality of the dispatching, ensuring that all participating nodes act according to a unified dispatching plan, and improving the operating efficiency and stability of the entire system. Therefore, this application effectively solves the technical problems existing in the prior art of multi-energy complementary system scheduling methods, such as information islands and global optimization problems, over-reliance on centralized scheduling, and data security risks, by combining blockchain technology and distributed pre-scheduling mechanism.

[0145] Example 3

[0146] Figure 5 The coordinated scheduling device 500 of the multi-energy complementary system according to the present embodiment is shown, the subjects in the multi-energy complementary system include photovoltaic nodes, wind power nodes, thermal power nodes, power consumption nodes, energy storage nodes and power grid purchase nodes, and the coordinated scheduling device 500 includes: a processor 510; and a memory 520, connected to the processor 510, for providing the processor 510 with instructions for processing the following processing steps: obtaining pre-scheduling data of multiple historical time periods and pre-scheduling data of the next time period on the chain of multiple target nodes in the multi-energy complementary system from the blockchain; wherein the pre-scheduling data is data obtained after the multiple target nodes use their respective prediction models to predict and optimize the power supply and demand conditions of all subjects in the multi-energy complementary system in each time period; the multiple target nodes include photovoltaic nodes, wind power nodes, thermal power nodes, energy storage nodes and power grid purchase nodes; based on the pre-scheduling data of multiple historical time periods and the pre-scheduling data of the next time period, determining the scheduling data of all subjects in the multi-energy complementary system in the next time period; uploading the scheduling data to the blockchain, and coordinating and scheduling the power resources of the multiple target nodes according to the scheduling data in the next time period.

[0147] Optionally, multiple target nodes determine the pre-scheduling data for the next time period through the following steps: each target node obtains the pre-scheduling data for the current time period on the chain of multiple target nodes from the blockchain, obtains the demand information for the next time period on the chain of all entities in the multi-energy complementary system from the blockchain, and obtains the environmental information for the next time period; each target node constructs a vector based on the pre-scheduling data for the current time period, the demand information for the next time period, and the environmental information for the next time period; each target node inputs the vector into its own pre-trained prediction model, predicts the power supply and demand situation of all entities in the multi-energy complementary system in the next time period, and obtains the initial pre-scheduling data for the next time period; the photovoltaic node and the wind power node directly upload the initial pre-scheduling data to the blockchain; the thermal power node, the energy storage node, and the power grid purchasing node optimize the initial pre-scheduling data according to the actual situation of each associated unit or unit, and upload the optimized pre-scheduling data to the blockchain.

[0148] Optionally, the thermal power nodes, energy storage nodes and power purchase nodes of the power grid optimize the initial pre-dispatching data according to the actual conditions of the units or units associated with them, including: the thermal power nodes, energy storage nodes and power purchase nodes of the power grid determine the constraints and objective functions of the units or units associated with them according to the actual conditions of the units or units associated with them; the thermal power nodes, energy storage nodes and power purchase nodes of the power grid determine the power supply power of the units or units associated with them in the next time period by using the particle swarm optimization algorithm based on their constraints and objective functions; the thermal power nodes, energy storage nodes and power purchase nodes of the power grid determine the total power supply power of their respective nodes in the next time period based on the power supply power of the units or units associated with them in the next time period; the thermal power nodes, energy storage nodes and power purchase nodes of the power grid replace the power supply power of the corresponding nodes in the initial pre-dispatching data predicted by them with the total power supply power of their respective nodes in the next time period to obtain the optimized pre-dispatching data.

[0149] Optionally, based on the pre-scheduling data of multiple historical time periods and the pre-scheduling data of the next time period, the scheduling data of all entities in the multi-energy complementary system in the next time period is determined, including: determining the weight values ​​of multiple target nodes based on the pre-scheduling data of multiple historical time periods; based on the weight values, performing weighted summation of the pre-scheduling data of the next time period predicted by the multiple target nodes to obtain the scheduling data of all entities in the multi-energy complementary system in the next time period.

[0150] Optionally, based on the pre-scheduling data of multiple historical time periods, determining the weight values ​​of multiple target nodes includes: obtaining the pre-scheduling data of multiple historical time periods and the corresponding scheduling data from the blockchain; for each target node, calculating the error between the pre-scheduling data of each historical time period predicted by the target node and the corresponding scheduling data by the following formula:

[0151]

[0152] In the formula, E ij is the error of the i-th target node in the j-th historical scheduling process; (X iaj , X ibj , X icj , X idj , X iej , X ifj ) is the pre-scheduling data of the jth historical period predicted by the i-th target node, X iaj The photovoltaic power supply power predicted for the i-th target node in the j-th historical period, X ibj The wind power supply power predicted for the i-th target node in the j-th historical period, X icj The thermal power supply power predicted for the i-th target node in the j-th historical period, X idj The energy storage charging and discharging power predicted for the i-th target node in the j-th historical period, X iej The grid transmission power predicted for the i-th target node in the j-th historical period, X ifj The load power consumption of the jth historical period predicted by the i-th target node, where i = a~e, a represents the photovoltaic node, b represents the wind power node, c represents the thermal power node, d represents the energy storage node, and e represents the power grid purchasing node; the dispatch data of the jth historical period is (X aj , X bj , X cj , X dj , X ej , X fj ), X aj is the photovoltaic power supply power in the jth historical period, X bj is the wind power supply power in the jth historical period, X cj is the thermal power supply power in the jth historical period, X dj is the energy storage charging and discharging power in the jth historical period, X ej is the power transmission power of the power grid in the jth historical period, X fj is the load power consumption in the jth historical period;

[0153] For each target node, calculate the average error between the pre-scheduled data and the corresponding scheduled data for all historical periods predicted by the target node

[0154]

[0155] The softmax function is used to map the average errors of all target nodes to values ​​in the interval [0,1], and the mapped values ​​are determined as the weight values ​​of the corresponding target nodes.

[0156] Optionally, the power resources of multiple target nodes are coordinated and scheduled in the next time period according to the scheduling data, including: the multiple target nodes obtain the scheduling data of the next time period from the blockchain; the multiple target nodes redetermine their respective power supply power, charging and discharging power or transmission power in the next time period according to the scheduling data.

[0157] Optionally, the memory 520 is also used to provide the processor 510 with instructions for processing the following processing steps: the thermal power nodes, energy storage nodes and power grid purchasing nodes use a particle swarm optimization algorithm to redetermine the power supply power of each associated unit or unit in the next time period based on the redetermined power supply power, charging and discharging power or transmission power in the next time period and the constraints and objective functions of each associated unit or unit.

[0158] Thus, according to this embodiment, firstly, the pre-scheduling data of multiple target nodes on the chain and the pre-scheduling data of the next period are obtained from the blockchain. Through the blockchain technology, the data of all participating nodes in the multi-energy complementary system can be ensured to be transparent and accessible on the chain, thereby breaking the information island and providing a data basis for global optimization. In addition, all participating nodes can use their own prediction models, combined with historical data, to accurately predict the power supply and demand of the future period, and share their predicted power supply and demand through the blockchain, so that the pre-processing and prediction of the data are completed independently by each participating node, reducing the dependence on a single centralized dispatching center. Then, based on the pre-scheduling data and the pre-scheduling data of the next period, a dispatching data that comprehensively considers the power supply and demand of all subjects in the entire multi-energy complementary system is determined to achieve global optimization and ensure the balanced distribution of energy. Finally, the dispatching data is put on the chain and the power resources of multiple target nodes are coordinated and dispatched according to the dispatching data in the next period, ensuring the openness and transparency of the dispatching data, so that all participating nodes can verify the fairness and rationality of the dispatching, ensuring that all participating nodes act according to a unified dispatching plan, and improving the operating efficiency and stability of the entire system. Therefore, this application effectively solves the technical problems existing in the prior art of multi-energy complementary system scheduling methods, such as information islands and global optimization problems, over-reliance on centralized scheduling, and data security risks, by combining blockchain technology and distributed pre-scheduling mechanism.

[0159] The serial numbers of the above embodiments of the present invention are only for description and do not represent the advantages or disadvantages of the embodiments.

[0160] In the above embodiments of the present invention, the description of each embodiment has its own emphasis. For parts that are not described in detail in a certain embodiment, reference can be made to the relevant descriptions of other embodiments.

[0161] In the several embodiments provided in this application, it should be understood that the disclosed technical content can be implemented in other ways. Among them, the device embodiments described above are only schematic. For example, the division of the units is only a logical function division. There may be other division methods in actual implementation. For example, multiple units or components can be combined or integrated into another system, or some features can be ignored or not executed. Another point is that the mutual coupling or direct coupling or communication connection shown or discussed can be through some interfaces, indirect coupling or communication connection of units or modules, which can be electrical or other forms.

[0162] The units described as separate components may or may not be physically separated, and the components shown as units may or may not be physical units, that is, they may be located in one place or distributed on multiple network units. Some or all of the units may be selected according to actual needs to achieve the purpose of the solution of this embodiment.

[0163] In addition, each functional unit in each embodiment of the present invention may be integrated into one processing unit, or each unit may exist physically separately, or two or more units may be integrated into one unit. The above-mentioned integrated unit may be implemented in the form of hardware or in the form of software functional units.

[0164] If the integrated unit is implemented in the form of a software functional unit and sold or used as an independent product, it can be stored in a computer-readable storage medium. Based on this understanding, the technical solution of the present invention, in essence, or the part that contributes to the prior art, or all or part of the technical solution can be embodied in the form of a software product, and the computer software product is stored in a storage medium, including a number of instructions for a computer device (which can be a personal computer, a server or a network device, etc.) to perform all or part of the steps of the method described in each embodiment of the present invention. The aforementioned storage medium includes: U disk, read-only memory (ROM, Read-Only Memory), random access memory (RAM, RandomAccess Memory), mobile hard disk, magnetic disk or optical disk and other media that can store program codes.

[0165] The above is only a preferred embodiment of the present invention. It should be pointed out that for ordinary technicians in this technical field, several improvements and modifications can be made without departing from the principle of the present invention. These improvements and modifications should also be regarded as the scope of protection of the present invention.

Claims

1. A coordinated scheduling method for a multi-energy complementary system, characterized in that: The main bodies in the multi-energy complementary system include photovoltaic nodes, wind power nodes, thermal power nodes, power consumption side nodes, energy storage nodes and power grid purchase nodes, and the coordinated scheduling method includes: Obtaining pre-scheduling data of multiple historical time periods and pre-scheduling data of the next time period on the blockchain of multiple target nodes in the multi-energy complementary system; wherein the pre-scheduling data is data obtained by the multiple target nodes using their respective prediction models to predict the power supply and demand conditions of all entities in the multi-energy complementary system in each time period; the multiple target nodes include photovoltaic nodes, wind power nodes, thermal power nodes, energy storage nodes and power grid purchase nodes; Determine the scheduling data of all the subjects in the multi-energy complementary system in the next time period based on the pre-scheduling data of the multiple historical time periods and the pre-scheduling data of the next time period; The scheduling data is uploaded to the blockchain, and the power resources of the multiple target nodes are coordinated and scheduled according to the scheduling data in the next time period.

2. The method according to claim 1, characterized in that The multiple target nodes determine the pre-scheduled data for the next period by the following steps: Each target node obtains the pre-scheduling data of the current time period on the blockchain of the multiple target nodes, obtains the demand information of the next time period on the blockchain of all the subjects in the multi-energy complementary system, and obtains the environmental information of the next time period; Each target node constructs a vector based on the pre-scheduling data of the current period, the demand information of the next period and the environmental information of the next period; Each target node inputs the vector into its own pre-trained prediction model, predicts the power supply and demand of all entities in the multi-energy complementary system in the next period, and obtains the initial pre-scheduling data for the next period; The photovoltaic node and the wind power node directly upload the initial pre-scheduling data to the blockchain; the thermal power node, the energy storage node and the power grid purchasing node optimize the initial pre-scheduling data according to the actual conditions of each associated unit or unit, and upload the optimized pre-scheduling data to the blockchain.

3. The method according to claim 4, characterized in that The thermal power node, the energy storage node and the power grid purchasing node optimize the initial pre-dispatching data according to the actual conditions of the respective associated units or units, including: The thermal power node, the energy storage node and the power grid purchasing node determine the constraints and objective functions of the respective associated units or units according to the actual conditions of the respective associated units or units; The thermal power node, the energy storage node and the power grid purchasing node determine the power supply power of each associated unit or unit in the next period of time based on their respective constraints and objective functions using a particle swarm optimization algorithm; The thermal power node, the energy storage node and the power grid purchasing node determine the total power supply power of their respective nodes in the next time period based on the power supply power of each associated unit or unit in the next time period; The thermal power node, the energy storage node and the power grid purchasing node replace the power supply power of the corresponding node in the initial pre-scheduling data predicted by each node with the total power supply power of each node in the next time period to obtain optimized pre-scheduling data.

4. The method according to claim 1, characterized in that The determining, based on the pre-scheduling data of the plurality of historical time periods and the pre-scheduling data of the next time period, the scheduling data of all the subjects in the multi-energy complementary system in the next time period comprises: Determining weight values ​​of the plurality of target nodes based on the pre-scheduling data of the plurality of historical time periods; Based on the weight value, weighted sum is performed on the pre-scheduling data of the next time period predicted by the multiple target nodes to obtain the scheduling data of all the subjects in the multi-energy complementary system in the next time period.

5. The method according to claim 4, characterized in that The determining, based on the pre-scheduling data of the multiple historical time periods, weight values ​​of the multiple target nodes comprises: Obtaining pre-scheduling data and corresponding scheduling data for the multiple historical time periods from the blockchain; For each target node, the error between the pre-scheduled data and the corresponding scheduled data for each historical period predicted by the target node is calculated by the following formula: In the formula, E ij is the error of the i-th target node in the j-th historical scheduling process; (X iaj , X ibj , X icj , X idj , X iej , X ifj ) is the pre-scheduling data of the jth historical period predicted by the i-th target node, X iaj The photovoltaic power supply power predicted for the i-th target node in the j-th historical period, X ibj The wind power supply power predicted for the i-th target node in the j-th historical period, X icj The thermal power supply power predicted for the i-th target node in the j-th historical period, X idj The energy storage charging and discharging power predicted for the jth historical period by the i-th target node, X iej The grid transmission power predicted for the i-th target node in the j-th historical period, X ifj The load power consumption of the jth historical period predicted by the i-th target node, where i = a~e, a represents the photovoltaic node, b represents the wind power node, c represents the thermal power node, d represents the energy storage node, and e represents the power grid purchasing node; the dispatch data of the jth historical period is (X aj , X bj , X cj , X dj , X ej , X fj ), X aj is the photovoltaic power supply power in the jth historical period, X bj is the wind power supply power in the jth historical period, X cj is the thermal power supply power in the jth historical period, X dj is the energy storage charging and discharging power in the jth historical period, X ej is the power transmission power of the power grid in the jth historical period, X fj is the load power consumption in the jth historical period; For each target node, calculate the average error between the pre-scheduled data and the corresponding scheduled data for all historical periods predicted by the target node The softmax function is used to map the average errors of all target nodes to values ​​in the interval [0,1], and the mapped values ​​are determined as the weight values ​​of the corresponding target nodes.

6. The method according to claim 1, characterized in that The coordinating and scheduling the power resources of the plurality of target nodes according to the scheduling data in the next time period includes: The multiple target nodes obtain the scheduling data for the next period from the blockchain; The multiple target nodes redetermine their respective power supply power, charge / discharge power or transmission power in the next time period according to the scheduling data.

7. The method according to claim 1, characterized in that Also includes: The thermal power node, the energy storage node and the power grid purchasing node redetermine the power supply power of each associated unit or unit in the next time period based on the redetermined power supply power, charging and discharging power or transmission power in the next time period and the constraints and objective functions of each associated unit or unit, using a particle swarm optimization algorithm.

8. A storage medium, characterized in that: The storage medium includes a stored program, wherein when the program is run, the processor executes the method according to any one of claims 1 to 7.

9. A coordination and scheduling device for a multi-energy complementary system, characterized in that: The main bodies in the multi-energy complementary system include photovoltaic nodes, wind power nodes, thermal power nodes, power consumption side nodes, energy storage nodes and power grid purchase nodes, and the coordination and scheduling device includes: A data acquisition module, used to obtain from the blockchain the pre-scheduling data of multiple historical periods and the pre-scheduling data of the next period of multiple target nodes in the multi-energy complementary system; wherein the pre-scheduling data is the data obtained after the multiple target nodes use their respective prediction models to predict and optimize the power supply and demand conditions of all entities in the multi-energy complementary system in each period; the multiple target nodes include photovoltaic nodes, wind power nodes, thermal power nodes, energy storage nodes and power grid purchase nodes; A data determination module, configured to determine the scheduling data of all entities in the multi-energy complementary system in the next time period based on the pre-scheduling data of the multiple historical time periods and the pre-scheduling data of the next time period; A coordination and scheduling module is used to upload the scheduling data to the blockchain and coordinate and schedule the power resources of the multiple target nodes according to the scheduling data in the next time period.

10. A coordination and scheduling device for a multi-energy complementary system, characterized in that: The main bodies in the multi-energy complementary system include photovoltaic nodes, wind power nodes, thermal power nodes, power consumption side nodes, energy storage nodes and power grid purchase nodes, and the coordination and scheduling device includes: Processor; and A memory, connected to the processor, configured to provide the processor with instructions for processing the following processing steps: Obtaining from the blockchain the pre-scheduling data of multiple historical periods and the pre-scheduling data of the next period of multiple target nodes in the multi-energy complementary system; wherein the pre-scheduling data is the data obtained after the multiple target nodes use their respective prediction models to predict and optimize the power supply and demand conditions of all entities in the multi-energy complementary system in each period; the multiple target nodes include photovoltaic nodes, wind power nodes, thermal power nodes, energy storage nodes and power grid purchase nodes; Determine the scheduling data of all the subjects in the multi-energy complementary system in the next time period based on the pre-scheduling data of the multiple historical time periods and the pre-scheduling data of the next time period; The scheduling data is uploaded to the blockchain, and the power resources of the multiple target nodes are coordinated and scheduled according to the scheduling data in the next time period.

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