Multi-virtual power plant multi-time scale cooperative scheduling method and system based on block chain

Abstract

The invention provides a block chain-based multi-virtual power plant multi-time scale cooperative scheduling method and system, and the method comprises the following operations: distributed resource modeling operation, which is used for constructing a time-phased operation model of each distributed resource; day-ahead scheduling operation: generating a contract power operation plan of each virtual power plant on the scheduling day through cross-power-plant transaction matching and power grid-power plant transaction matching before the start of each scheduling day; intra-day real-time scheduling operation: scheduling distributed resources in each virtual power plant to run according to a contract power operation plan in each scheduling day, and performing real-time compensation on operation errors; transaction settlement and data evidence storage operation: carrying out settlement on cross-power-plant transaction and power grid-power-plant transaction of each virtual power plant, and carrying out blockchain-based evidence storage on data generated by day-ahead scheduling operation and intra-day real-time scheduling operation. According to the technical scheme, cooperative scheduling of distributed resources among a plurality of virtual power plants can be realized.

Description

Technical Field

[0001] This application belongs to the field of new energy technology and relates to distributed photovoltaic grid-connected intelligent control technology. Specifically, it provides a blockchain-based multi-virtual power plant multi-timescale collaborative scheduling method and system. Background Technology

[0002] In recent years, the global energy system has been accelerating its transition to distributed renewable energy. In my country, the construction of new power systems, represented by distributed photovoltaics, has also made great progress. As a key carrier for aggregating distributed resources, the market size of virtual power plants is also expanding.

[0003] A Virtual Power Plant (VPP) is an energy management system that integrates distributed energy resources through a cloud platform and intelligent control system, coordinates and controls them, and participates in the electricity market and grid operation as a whole. With multiple VPPs participating in the electricity market as independent entities, the intermittency and load fluctuations of distributed energy sources lead to a surge in system balancing pressure. Although VPPs can participate in market transactions by coordinating internal resources, three major bottlenecks still exist in multi-VPP collaborative scenarios: 1) Most VPP control mechanisms focus on optimizing a single VPP, lacking cross-entity, multi-timescale collaborative strategies, resulting in low overall market efficiency; 2) Centralized trading platforms have single-point-of-failure risks, and cross-VPP trading processes are opaque, making it difficult to establish multilateral trust; 3) Rigid control strategies cannot balance the operational autonomy and differentiated constraints of each VPP, lacking flexible mechanisms to cope with source-load uncertainties.

[0004] Therefore, it is necessary to improve the existing multi-virtual power plant grid connection and dispatch scheme in order to effectively enhance the overall efficiency and stability of distributed resource grid connection of multi-virtual power plants. Summary of the Invention

[0005] This application provides a blockchain-based multi-virtual power plant multi-timescale collaborative scheduling method through embodiments, used for collaborative scheduling of a multi-virtual power plant grid connection framework. The multi-virtual power plant grid connection framework consists of an application layer and a network layer. The application layer includes a resource modeling module, and day-ahead scheduling, intraday scheduling, and evidence storage modules corresponding to each virtual power plant. The network layer includes a main chain and side chains corresponding to each virtual power plant. The main chain includes power trading center nodes and virtual power plant aggregator nodes, and each side chain is linked to the main chain. The collaborative scheduling method includes the following operations: Distributed resource modeling operations construct time-segmented operation models for each distributed resource based on the equipment operation characteristics of the distributed resources controlled by each virtual power plant. The daytime dispatch operation, before the start of each dispatch day, generates the contracted power operation plan for each virtual power plant for that dispatch day based on the time-of-use operation model of each distributed resource and the time-of-use electricity price of the power grid through cross-power plant transaction matching between each virtual power plant and grid-power plant transaction matching between each virtual power plant and the power grid. Intraday real-time dispatching operations are performed, and during each dispatching day, the distributed resources in each virtual power plant are dispatched to operate according to the contracted power operation plan, and real-time compensation is made for operating errors. The transaction settlement and data storage operations, based on the results of intraday real-time dispatch operations, settle inter-power plant transactions and grid-power plant transactions for each virtual power plant, and store the data generated by day-ahead dispatch operations and intraday real-time dispatch operations on a blockchain-based basis.

[0006] This application also provides a blockchain-based multi-virtual power plant multi-timescale collaborative scheduling system through embodiments, used to implement the aforementioned collaborative scheduling method. It consists of an application layer and a network layer. The application layer includes a resource modeling module, as well as a day-ahead scheduling module, an intraday scheduling module, and a certificate storage module corresponding to each virtual power plant. The network layer includes a main chain and side chains corresponding to each virtual power plant. The main chain includes power trading center nodes and virtual power plant aggregator nodes. Each side chain is linked to the main chain.

[0007] The blockchain-based multi-virtual power plant multi-timescale collaborative scheduling method and system provided in this application first matches the most economically efficient power trading opportunities among multiple virtual power plants through inter-power plant transaction matching during the day-ahead scheduling phase. This achieves peak-valley complementarity among multiple virtual power plants containing different types of distributed resources. Then, the remaining power purchase / sale plans of each virtual power plant that cannot achieve inter-power plant transaction matching are aggregated and traded with the power grid in a unified manner. This effectively avoids the problems of low overall market efficiency and high comprehensive energy costs for users caused by the inability to achieve collaborative scheduling when each virtual power plant trades with the power grid and connects to the grid independently. This effectively improves the overall efficiency and stability of grid connection of various distributed resources of multiple virtual power plants. Attached Figure Description

[0008] Figure 1 This is a schematic diagram of the framework of a blockchain-based multi-virtual power plant multi-timescale collaborative scheduling system provided in the embodiments of this application; Figure 2 This is a schematic diagram of the architecture of a blockchain-based multi-virtual power plant multi-timescale collaborative scheduling system provided in the embodiments of this application; Figure 3 This is a flowchart of a blockchain-based multi-virtual power plant multi-timescale collaborative scheduling method provided in the embodiments of this application; Figure 4This is a schematic diagram of the peak-valley distribution of net power load for multiple virtual power plants in one embodiment; Figure 5 This is a schematic diagram illustrating the time-of-use electricity price fluctuations of a power grid in one embodiment; Figure 6 This is a flowchart of power plant transaction matching provided according to an embodiment of this application. Detailed Implementation

[0009] The present application will now be further described based on preferred embodiments and with reference to the accompanying drawings.

[0010] In the description of the embodiments of this application, it should be noted that if terms such as "upper," "lower," "inner," or "outer" are used to indicate the orientation or positional relationship based on the orientation or positional relationship shown in the accompanying drawings, or the orientation or positional relationship commonly used when the product of this application is in use, they are only for the convenience of describing this application and simplifying the description, and do not indicate or imply that the device or element referred to must have a specific orientation, or be constructed and operated in a specific orientation, and therefore should not be construed as a limitation on this application. In addition, in the description of this application, in order to distinguish different units, the terms "first," "second," etc. are used in this specification, but these are not limited by the manufacturing order, nor should they be construed as indicating or implying relative importance. Their names may differ in the detailed description and claims of this application.

[0011] The vocabulary used in this specification is for illustrative purposes and is not intended to limit the scope of this application. It should also be noted that, unless otherwise expressly specified and limited, the terms "set," "connected," and "linked" should be interpreted broadly. For example, they can refer to a fixed connection, a detachable connection, or an integral connection; they can refer to a mechanical connection, a direct connection, or an indirect connection via an intermediate medium; or they can refer to the internal communication between two components. Those skilled in the art will understand the specific meaning of these terms in this application.

[0012] Figure 1 This is a virtual power plant grid-connected architecture for controlling the power operation of various distributed resources, such as... Figure 1 As shown, distributed resources refer to various distributed generation units, distributed energy storage units, and distributed load units distributed within a certain spatial range. Distributed generation units can be, for example, distributed photovoltaic equipment, wind turbines, and other power generation devices; distributed energy storage units can be, for example, distributed large-capacity batteries, energy storage stations, and other energy storage devices capable of charging and discharging electricity; distributed load units can be various electrical appliances such as air conditioners in individual households, or central air conditioning, lighting, and other electrical appliances with independent power supply circuits in various commercial buildings.

[0013] To enable grid-connected control of distributed resources with different power generation, storage, and consumption characteristics, several distributed resources can be grouped together according to parameters such as region, power, and storage capacity. A virtual power plant can then be used as a unified control platform for grid-connected regulation. The control unit of the virtual power plant includes at least a trading module, a dispatching module, and a data storage module. The trading module aggregates the output and load information of each distributed resource and conducts power purchase and sale transactions with the grid to generate power trading contracts for each dispatching day. The dispatching module aims to schedule each distributed resource to fulfill its power trading contract for the dispatching day by issuing control commands to each resource and monitoring its operational status. The data storage module typically uses trusted data storage mechanisms such as blockchain to store grid trading contract data, control commands from each distributed resource, and operational status data.

[0014] As the scale of distributed resources expands, constraints such as computing power, communication latency, management boundaries, and market mechanisms generally necessitate increasing the number of virtual power plants to form a multi-virtual power plant grid-connected control structure. However, the increase in the number of virtual power plants also increases the difficulty of coordinated scheduling among them. As analyzed in the background section, existing technologies mostly focus on internal optimization scheduling within a single virtual power plant, lacking research on cross-virtual power plant coordinated scheduling. Furthermore, when each virtual power plant individually trades electricity with the grid, its bargaining power is insufficient, making it difficult to achieve the overall optimal power purchase / sale scheme. When conducting cross-virtual power plant transactions, the transaction process lacks transparency, making it difficult to establish multilateral trust. In addition, due to the lack of a flexible scheduling mechanism to address source-load uncertainty, rigid control strategies alone cannot simultaneously consider the operational autonomy and differentiated constraints of each virtual power plant.

[0015] To address the aforementioned issues, some embodiments of this application provide a blockchain-based multi-virtual power plant multi-timescale collaborative scheduling system, which is used for collaborative scheduling of the grid-connected architecture of multiple virtual power plants. Figure 2 This is a schematic diagram of the framework of the collaborative scheduling system provided according to some embodiments of this application.

[0016] like Figure 2 As shown, the collaborative scheduling system consists of an application layer and a network layer. The application layer includes a resource modeling module, a day-ahead scheduling module, an intraday scheduling module, an economic incentive mechanism module, and a record-keeping module. The number of day-ahead scheduling modules and intraday real-time scheduling compensation modules is the same as the number of virtual power plants. Each day-ahead scheduling module and intraday real-time scheduling compensation module corresponds to one virtual power plant, enabling internal scheduling within each virtual power plant. In the embodiments of this application, the internal scheduling of each virtual power plant includes at least the following operations: A1. Based on the equipment operation characteristics of each distributed resource contained in the virtual power plant, generate a bidding plan for power trading and report it to the network layer before the start of the scheduling day; A2. Formulate the operation plan for each distributed resource on the dispatch day based on the signed power trading contract, and dispatch each distributed resource to execute the plan in real time; A3. Store evidence of the data generated during the process of virtual power plants generating electricity trading bids and executing electricity operation plans.

[0017] The network layer adopts a main-sidechain collaborative blockchain network architecture for cross-power plant collaborative scheduling among multiple virtual power plants. In the embodiments of this application, cross-power plant collaborative scheduling includes at least the following operations: B1. Based on the power trading bidding plans submitted by each virtual power plant, cross-power plant trading matching is carried out among the virtual power plants, and cross-power plant trading contracts are generated based on the successfully matched portions. B2. Aggregate the unsuccessful cross-power plant transactions in the power trading bidding plans of various virtual power plants, conduct transactions with the power grid, and generate power grid-power plant inter-transaction contracts; B3. To preserve evidence of all types of generated power trading contracts and their execution status, including inter-power plant trading contracts and grid-power plant trading contracts.

[0018] Specifically, see Figure 2 The main chain consists of a power trading center node and several virtual power plant aggregator nodes. Each virtual power plant aggregator node is associated with (or corresponds to) a virtual power plant and is responsible for aggregating the distributed resource information of its associated virtual power plant to formulate a bidding plan for power trading on behalf of that virtual power plant. The power trading center node performs cross-power plant transaction matching between virtual power plants and grid-power plant transaction matching between virtual power plants and the power grid based on the bidding plans of each virtual power plant, thereby generating cross-power plant transaction contracts and grid-power plant transaction contracts.

[0019] On the sidechain, each virtual power plant aggregator node independently deploys and manages a sidechain. Each distributed resource in its corresponding virtual power plant accesses the sidechain through a proxy node. The proxy node is used to receive the equipment operation data of each distributed resource and to issue control commands to each distributed resource.

[0020] Furthermore, each sidechain interacts with the main chain through anchor nodes. The distributed ledger operates independently on either the main chain or the sidechain, ensuring that information is shared only with authorized entities within the same blockchain, thereby protecting privacy. On both the main chain and each sidechain, the power trading center nodes, virtual power plant aggregator nodes, and agent nodes for each distributed resource all synchronize their ledgers through the PBFT protocol, ensuring the consistency of the global transaction ledger. The aforementioned blockchain-based data storage methods are already known to those skilled in the art. Without departing from the above technical approach, those skilled in the art can flexibly choose various blockchain platforms with a main-chain-sidechain architecture to complete the construction of the network layer.

[0021] Through the above architecture, each virtual power plant can comprehensively evaluate the bids of other virtual power plants and the grid bids, and choose to conduct cross-virtual power transactions among virtual power plant aggregators or grid-power plant power transactions with the grid based on the evaluation results. This enables the coordinated matching of power trading opportunities among multiple virtual power plants, effectively expanding the range of choices available to each virtual power plant when formulating its operation plan. Compared with the method of only conducting power transactions between virtual power plants in the grid, it can achieve a better global coordinated dispatch effect.

[0022] The following, in conjunction with the accompanying drawings, provides a detailed explanation of the specific workflows of the equipment control layer and the collaborative scheduling layer.

[0023] Figure 3 This is a flowchart of a blockchain-based multi-virtual power plant multi-timescale collaborative scheduling method according to some embodiments of this application. The method can... Figure 2 The collaborative scheduling system shown is implemented.

[0024] like Figure 3 As shown, the method includes the following operations: Operation 1: Distributed resource modeling operation. Based on the equipment operation characteristics of the distributed resources controlled by each virtual power plant, construct a time-sharing operation model for each distributed resource. Operation 2, Day-ahead scheduling operation: Before the start of each scheduling day, based on the time-of-use operation model of each distributed resource and the time-of-use electricity price of the power grid, the contract power operation plan of each virtual power plant for that scheduling day is generated through cross-power plant transaction matching between each virtual power plant and grid-power plant transaction matching between each virtual power plant and the power grid. Operation 3: Intraday real-time dispatching operation. During each dispatching day, the distributed resources in each virtual power plant are dispatched to operate according to the contracted power operation plan, and real-time compensation is made for operating errors. Operation 4, transaction settlement and data storage, involves settling inter-power plant transactions and grid-power plant transactions based on the results of intraday real-time dispatch operations, and storing the data generated by day-ahead dispatch operations and intraday real-time dispatch operations on a blockchain-based basis.

[0025] The following section provides a detailed explanation of the specific implementation of this collaborative scheduling method.

[0026] Distributed resource modeling operations Operation 1 is used to construct time-segmented operation models for each distributed resource, providing a basis for subsequent day-ahead scheduling operations. In some embodiments, Operation 1 can be executed by the resource modeling module in the application layer. The time-segmented operation model modeling operation for each distributed resource can be a one-time operation, such as performing a unified time-segmented operation model modeling operation for all distributed resources during system initialization, and performing a separate modeling operation for each newly added distributed resource during subsequent system operation; or it can be repeated, such as updating the time-segmented operation model of each distributed resource based on its operating characteristics on the scheduling day at the end of each scheduling day (in the embodiments of this application, a scheduling day refers to the repeated cycle of two-stage coordinated scheduling of each virtual power plant, including day-ahead scheduling operations and intraday real-time scheduling operations. Generally, the scheduling day coincides with a calendar day, that is, each calendar day has 24 hours as a scheduling day).

[0027] Preferably, the time-sharing operation model of each distributed resource divides the operation into running segments according to equal time intervals, dividing the 24 hours of the day into segments. A runtime segment (e.g., with a runtime segment length of 1 hour). It also describes the characteristics of power consumption or output in each time period.

[0028] For example, when the distributed resource is a distributed generation unit such as a photovoltaic (PV) system, its corresponding time-of-use operation model is the time-of-use output model of the PV system, used to characterize the power generation of the PV system in each operating period. Similarly, when the distributed resource is a distributed energy storage unit such as a rechargeable battery, its time-of-use operation model is the change model of the battery's charging and discharging capacity in each operating period, used to characterize the amount of electricity discharged by the distributed energy storage unit to the distributed load unit, or the amount of electricity charged from the distributed generation unit, in each operating period. Furthermore, when the distributed resource is a distributed load unit such as an air conditioner, its corresponding time-of-use operation model is used to characterize the electricity consumption of the distributed load unit in each operating period.

[0029] In some specific embodiments, operation one includes the following steps: S11, Establish the time-segmented operation model corresponding to the distributed generation unit.

[0030] Taking distributed photovoltaic (PV) equipment as an example, the process of constructing a time-sharing operation model for distributed power generation units is explained. Since the output of distributed PV equipment is directly related to the intensity of solar irradiance, its operation process can be represented by the physical model as follows: (1), (1) In the formula, for The predicted power generation of this distributed generation unit (i.e., distributed photovoltaic equipment) during the specified time period. for The predicted average power generation of the distributed generation equipment during the specified time period; This represents the total number of power generation units in the distributed power generation system. For the first The area of ​​each power generation unit, express The intensity of light irradiance at any given moment; This refers to the photoelectric conversion efficiency.

[0031] The power generation model of the distributed photovoltaic (PV) system shown in equation (1) is applicable to clear weather. When there are clouds above the PV system, the solar irradiance is affected by external factors such as the thickness of the clouds, which can easily lead to deviations in the PV output prediction. Therefore, in some preferred embodiments, the cloud thickness can be discretized into... common The actual power generation of photovoltaics can be represented by the predicted output and the change in power generation caused by cloud shading.

[0032] Specifically, the photovoltaic power output variation process is set as a Markov process, and its variation range can be discretized as follows: If the power generation of the distributed generation equipment changes within a certain state level, then... The stay time follows the parameter: If the photovoltaic output follows an exponential distribution and the transition probability follows a discrete Gaussian distribution centered on itself, then the corrected photovoltaic output can be described as the following time-segmented operation model: (2), In the formula, express The actual power generation of the distributed photovoltaic equipment during the specified period. This indicates that the distributed generation equipment is in the time period The level of power generation change status, For this distributed power generation equipment in The period is in a state The corresponding change in power generation can be determined based on statistics of the historical power generation of distributed generation equipment and cloud thickness.

[0033] S12, Establish a time-segmented operation model for the distributed energy storage unit.

[0034] In some specific embodiments, the distributed energy storage unit can be used according to the time period. Net electricity change Construct the following time-segmented operation model: (3), in, The rated capacity of the distributed energy storage unit. and Distributed energy storage units in Average charging power and average discharging power over the time period and These represent the charging efficiency and discharging efficiency of the distributed energy storage unit, respectively. , Distributed energy storage units in The state of charge at the start and end of the time period.

[0035] In addition, distributed energy storage devices also need to meet energy constraints, power constraints, and charge / discharge state constraints: (4), in, , These are the minimum and maximum charging power of the energy storage device, respectively. , These are the minimum and maximum discharge power of the energy storage device, respectively. , These are 0-1 variables representing the charging and discharging states of the energy storage device, respectively. A value of 0 indicates a non-operating state, and a value of 1 indicates an operating state. For example... Indicates standby mode. This indicates that the device is in one of the states of charging or discharging.

[0036] S13, Establish the time-segmented operation model corresponding to the distributed load unit.

[0037] Based on load operation and controllability characteristics, distributed load units can be further divided into three types: adjustable loads, transferable loads, and interruptible loads. Adjustable loads do not change the operating time, but can change the power consumption by adjusting the power or operating mode, such as household air conditioners. Transferable load units can change the power consumption time, but the total power consumption is fixed, such as household new energy electric vehicle charging piles. Interruptible loads can completely stop the power consumption to reduce the total power consumption, such as non-critical lighting and some commercial air conditioners.

[0038] For adjustable loads (such as household air conditioners), the following time-of-use operation model can be established: (5), (6), in, express Electricity consumption of household air conditioners during different time periods This indicates the air conditioner's operating mode, and its value is either 1 or 2. Indicates refrigeration. Indicates heating. This represents the equivalent heat capacity of the space in which a household air conditioner operates. , They represent Temperature of the space where the household air conditioner is applied at the start and end of the time period. and These represent the lower and upper limits of the building's internal temperature, respectively.

[0039] It should be understood that the time-sharing operation model shown in equation (5) corresponds to the case where the adjustable load is a household air conditioner and heat exchange with the outside is not considered. When establishing a time-sharing operation model for a household air conditioner, those skilled in the art can also consider the heat exchange process between the indoor space and the outside to obtain a more accurate time-sharing operation model for the household air conditioner. In addition, when the adjustable load is other equipment, the corresponding electricity consumption for time-sharing operation can be established by analyzing the energy conversion process during its operation. The specific expression.

[0040] For transferable loads, their equipment operation model can be represented by the energy conservation constraints and energy boundary constraints that govern changes in energy consumption: (7), (8), in, Indicates transferable load in Electricity consumption during a certain period This represents the total electricity consumption of transferable loads on a single dispatch day. , These represent the transferable loads at... The lower and upper limits of electricity consumption for each time period.

[0041] For interruptible loads, power consumption is zero during interruption, but the number of interruptions must be satisfied. Therefore, its equipment operation model can be represented as follows: (9), (10) in, Indicates interruptible load in The state of the time period This indicates that the interruptible load can be shut down. This indicates that the load can be interrupted. This indicates the rated power consumption of interruptible loads. This indicates the maximum number of interruptible loads per day.

[0042] Coordinated scheduling and data storage across multiple virtual power plants and time scales In the embodiments of this application, the coordinated scheduling between multiple virtual power plants and the power grid is performed at multiple time scales, including day-ahead scheduling operations (operation two) and intraday real-time scheduling operations (operation three). The day-ahead scheduling operation is performed before the start of each scheduling day, while the intraday real-time scheduling operation continues throughout each scheduling day. Clearly, the day-ahead scheduling operation and the intraday real-time scheduling operation are performed cyclically on a 24-hour period.

[0043] Furthermore, data generated during day-ahead and intraday real-time scheduling operations, such as the operation plans of various distributed resources, power trading contracts, and the actual operating status of various distributed resources (reflecting the execution of power trading contracts), will also be recorded by the nodes involved in the data in their blockchain ledgers through a blockchain consensus mechanism, thereby achieving blockchain-based data notarization. As mentioned above, in the embodiments of this application, the network layer adopts a main-sidechain architecture. Those skilled in the art can comprehensively consider factors such as the scale and transaction volume of virtual power plants and distributed resources, and select a suitable blockchain platform with a main-sidechain architecture, such as Ardor or Hyperledger Fabric, to build the network layer for power transaction settlement and trusted data notarization operations.

[0044] In some embodiments of this application, the application layer and network layer may implement day-ahead scheduling operations through the following steps: Step S21: The day-ahead scheduling module formulates the day-ahead initial power operation plan for each virtual power plant.

[0045] Specifically, before the start of each scheduling day, the power trading center node obtains the grid time-of-use electricity price from the grid, including the grid time-of-use electricity sales price. Time-of-use electricity pricing with the power grid And it is broadcast on the main blockchain, and received by various virtual power plant aggregator nodes. , Broadcast to the corresponding sidechain.

[0046] The day-ahead dispatch module corresponding to each virtual power plant receives data from the broadcast of the power trading center node. and The system obtains the time-sharing operation models of each distributed resource from the resource modeling module. Based on the objective function shown in (11), and with the goal of minimizing its own cost, it searches for the optimal power generation, power consumption, and power change of each distributed resource in each time period to obtain the optimal... Each virtual power plant submits its initial day-ahead power operation plan to its corresponding virtual power plant aggregator node: (11), in, For total cost, , , , , , These include the electricity purchase cost of the virtual power plant, the adjustable load deviation cost, the transferable load deviation cost, the interruptible load deviation cost, the operating cost of the distributed energy storage unit, and the electricity sales revenue of the virtual power plant. This indicates that in each runtime segment ( (Total number of runtime segments for each scheduling day), the amount of electricity purchased by the virtual power plant from external sources. The total power generation of each distributed generation unit The total power consumption of each adjustable load Total electricity consumption of all transferable loads Total power consumption of each interruptible load The sum of the changes in the amount of electricity in each distributed energy storage unit And the electricity sold by virtual power plants to the outside world. The decision set constituted by this.

[0047] Table 1 below illustrates the expressions and meanings of the above cost and benefit items: Table 1 (11) Meaning of Cost and Benefit Items Clearly, the optimal solution is found through the search. This refers to the initial day-ahead power operation plan of the virtual power plant. Simultaneously, according to this plan, the required power generation, power consumption, and power change of each distributed resource at each time period are also determined. Therefore, the day-ahead scheduling module can further optimize the power generation, power consumption, and power change of each operating segment. , , , and The loads are allocated to each distributed generation unit, distributed energy storage unit, adjustable load, transferable load, and interruptible load, thereby forming the day-ahead initial power operation plan for each distributed resource.

[0048] In addition, the scheduling module will In , The electricity purchase demand and available electricity capacity for each time period are sent to their corresponding virtual power plant aggregator nodes.

[0049] Step S22: The virtual power plant aggregator node generates a bidding plan for the virtual power plant based on the day-ahead initial power operation plan.

[0050] The virtual power plant aggregator node received the day-ahead dispatch module report. , Subsequently, based on the matching and execution status of the corresponding virtual power plants' power trading contracts prior to this coordinated dispatch, the time-of-use power purchase price for this coordinated dispatch process is determined. Time-of-use electricity pricing This allows us to obtain the bidding plan of the corresponding virtual power plant during this collaborative dispatch process. .

[0051] After each virtual power plant aggregator node generates a bidding plan for this collaborative scheduling process, it sends it to the power trading center node via the main chain.

[0052] Step S23: The power trading center node performs cross-power plant transaction matching between virtual power plants based on the peak-valley characteristics of the bidding plans of each virtual power plant.

[0053] Each virtual power plant exhibits different peak-valley characteristics because the types and capacities of its distributed resources (power generation, energy storage, and load) vary. Figure 4 This illustrates, in a specific embodiment, the peak-valley characteristics exhibited by the bidding plans of three virtual power plants, such as... Figure 4 As shown, virtual power plant A contains a large number of distributed generation devices, and its peak-valley characteristics show that it outputs more power during the sunshine period and there is no obvious peak in electricity consumption throughout the day; virtual power plant B is located in a commercial area and contains more interruptible loads, showing obvious peak in electricity consumption during working hours; virtual power plant C is located in a residential area, and some users have installed small distributed generation devices, showing obvious residential electricity consumption characteristics.

[0054] Figure 5 The chart shows the time-of-use electricity price and purchase price curves based on the power grid. Figure 4 and Figure 5It can be observed that different virtual power plants have complementary peak and valley characteristics. Therefore, when each virtual power plant can conduct cross-power plant electricity trading at a price that is more favorable than the grid electricity price, it can not only effectively reduce the overall operating cost and increase the revenue of multiple virtual power plants, but also effectively avoid the problem of increased grid connection fluctuations caused by the grid and multiple distributed resources in the same region simultaneously conducting reverse operations of selling / purchasing electricity.

[0055] In some specific embodiments, after receiving the bidding plans sent by various virtual power plant aggregator nodes, the power trading center node, through... Figure 5 The collaborative matching algorithm shown searches for the most economical electricity trading opportunities among various virtual power plants, thereby generating cross-power plant trading contracts.

[0056] like Figure 6 As shown, the specific process for cross-power plant transaction matching is as follows: (1) Each virtual power plant ( The power purchase plan in the bidding plan (i.e.) (Part of) based on its electricity purchase price Sort from highest to lowest, and then list the electricity sales plans (i.e., the bidding plans of each virtual power plant) in their respective bid plans. (Partial) based on its electricity sales price Sort from lowest to highest; (2) Match the top-ranked power purchase plan with the top-ranked power sales plan. If the power sales price is lower than the power purchase price, the match is successful. The transaction volume is the minimum of the two parties' tradable volumes. (3) Subtract the transaction volume of the previous round from the transaction volume of the successfully matched buyer and seller. If one party has exhausted its volume, remove it from the list. (4) Repeat the above steps until no bidding plan is found that satisfies the requirement that the electricity sales price is lower than the electricity purchase price.

[0057] The aforementioned cross-power plant transaction matching operation applies to each time period of the dispatch day. The process is carried out, ultimately resulting in inter-power plant trading contracts for the entire scheduling day, encompassing all time periods of the scheduling day. This refers to the electricity volume, transaction price, and information of the trading parties involved in power transactions between various virtual power plants.

[0058] Step S24: The power trading center node summarizes the bidding plans of each virtual power plant that have not been matched through cross-power plant transactions, and uniformly matches them with the power grid for inter-power plant transactions.

[0059] Specifically, after completing the cross-power plant transaction matching operation, the power trading center node summarizes the parts of the bidding plans of each virtual power plant that have not achieved cross-power plant transaction matching, obtains the aggregated remaining power purchase demand and power sales demand of each virtual power plant in each time period, and then performs grid-power plant inter-transaction matching with the power grid to generate grid-power plant inter-transaction contracts.

[0060] In the process of matching power grid-power plant transactions between power trading center nodes and the power grid, the purchasing and selling capabilities of various virtual power plants are aggregated and traded uniformly with the power grid. This significantly enhances their bargaining power with the power grid, enabling them to offer prices lower than those quoted by the grid. , Better pricing generates grid-power plant trading contracts.

[0061] Step S25: The day-ahead scheduling module generates a contract power execution plan for the virtual power plant based on the inter-power plant transaction contract and the grid-power plant inter-transaction contract.

[0062] The power trading center nodes decompose inter-power plant trading contracts and grid-power plant inter-trading contracts according to the virtual power plants involved in the contracts. These contracts are then sent to the corresponding day-ahead scheduling modules through the respective virtual power plant aggregator nodes. The day-ahead scheduling modules then determine the required trading time periods based on the received contracts. Total electricity purchased The total amount of electricity generated by each distributed generation unit The total electricity consumption required by each adjustable load. The total electricity consumption required by each transferable load. The total power consumption required for each interruptible load. The total change in electricity that each distributed energy storage unit needs to achieve. And the total amount of electricity that the virtual power plant needs to sell. This allows us to obtain the contracted power operation plan of the virtual power plant on the dispatch day. .

[0063] Because power trading matching may fail, the contract power operation plan generated by the day-ahead scheduling module may be affected. Compared with the initial power operation plan of the day There may be discrepancies; therefore, the day-ahead scheduling module generates the contract power operation plan. Then, according to equation (13), with the objective of minimizing the difference between each distributed resource operating according to the contracted power operation plan (including generation, charging, discharging, and consumption) and operating according to the initial day-ahead power operation plan, the contracted power operation plan for each distributed resource in each time period is determined. : (12) in, For each distributed resource At various times The required operating volume (i.e., power generation, charging / discharging, and power consumption) is based on the initial power operation plan. For each distributed resource At various times The amount of electricity to be operated according to the contracted power operation plan.

[0064] In some preferred embodiments, to balance economic efficiency with the matching degree of power plan execution, day-ahead dispatching operations also include the following steps: In step S26, the day-ahead scheduling module fine-tunes the contracted power operation plan for each adjustable load using a comfort weight based on reputation value.

[0065] Specifically, the day-ahead scheduling module uses equation (14) as the search target to search for each adjustable load. The adjusted power operation plan : (14) in, , Adjustable load At various times Actual and expected values ​​of operational indicators For example, when the adjustable load is set as a preset standard for deviation from the target index. When it is an indoor air conditioner, , Each time period The actual indoor temperature and setting temperature , It can be set to a temperature difference value such as 2℃ or 3℃, which is sufficient to make the indoor temperature perceived by the user not reach the set temperature; For adjustable load The credit rating can be adjusted based on the load. A credit score of 0 to 1 is assigned to a user based on their completion of the contracted power operation plan within the timeframe preceding the scheduling date. This is the baseline value for the weighting.

[0066] Observing equation (14), we can see that The user comfort weights, based on reputation values, are used to balance grid commands and user preferences. The higher the reputation value of an adjustable load, the higher the proportion of penalties for failing to meet operational targets. This means that for adjustable loads with high reputation, the tendency is to ignore electricity consumption deviations and aim to achieve the desired operational targets. Conversely, the lower the reputation value of an adjustable load, the lower the proportion of penalties for failing to meet operational targets. At this point, the proportion of penalties for deviations between actual electricity consumption and contractually planned electricity consumption gradually increases. For adjustable loads with low reputation, a comprehensive balance needs to be struck between achieving the desired targets to reduce penalties and increasing penalties due to electricity consumption deviations. By fine-tuning the contracts for each adjustable load using the objective function of equation (14), the overall electricity consumption can remain within the contractual plan while prioritizing the achievement of targets for adjustable loads with high reputation.

[0067] After completing the above-mentioned day-ahead scheduling operations, the intraday scheduling module of each virtual power plant controls the operating status of each distributed resource according to the contract power operation plan after the start of the scheduling day. At the same time, it performs real-time compensation for the operating errors generated by each distributed resource in the process of executing the contract power operation plan. This operation is called intraday real-time scheduling operation.

[0068] Since the generation and consumption units in the distributed resources of the virtual power plant perform unidirectional energy conversion, while the distributed energy storage unit undertakes bidirectional energy conversion of discharge and charge, the intraday dispatch module uses the additional charging and discharging capacity of the distributed energy storage unit to compensate for the operating errors of the distributed generation and load units, based on the charging and discharging capacity of the distributed energy storage unit in accordance with the contract power operation plan.

[0069] In some preferred embodiments of this application, the intraday scheduling module of each virtual power plant can perform real-time compensation for operational errors through the following steps: Step S31: Track and summarize the operational errors of each distributed generation device and distributed load on the power execution plan in real time; Step S32: Track and summarize the additional charging and discharging capacity of each distributed energy storage device in real time, provided that the power execution plan is completed; Step S33: Based on the additional charging and discharging capacity of each distributed energy storage device, compensate for the operating errors of each distributed generation device and distributed load. The compensation order is determined based on the credit rating of each distributed generation device and distributed load.

[0070] The specific embodiments of this application have been described in detail above. For those skilled in the art, several improvements and modifications can be made to this application without departing from the principle of this application, and these improvements and modifications also fall within the protection scope of the claims of this application.

Claims

1. A blockchain-based multi-virtual power plant multi-timescale collaborative scheduling method for collaborative scheduling of a multi-virtual power plant grid connection framework. The multi-virtual power plant grid connection framework consists of an application layer and a network layer, wherein... The application layer includes a resource modeling module, as well as a day-ahead scheduling module, an intraday scheduling module, and a certificate storage module corresponding to each virtual power plant. The network layer includes a main chain and side chains corresponding to each virtual power plant. The main chain includes power trading center nodes and virtual power plant aggregator nodes. Each side chain is linked to the main chain. Its features include the following operations: Distributed resource modeling operations construct time-segmented operation models for each distributed resource based on the equipment operation characteristics of the distributed resources controlled by each virtual power plant. The daytime dispatch operation, before the start of each dispatch day, generates the contracted power operation plan for each virtual power plant on that dispatch day based on the time-sharing operation model of each distributed resource and the time-sharing electricity price of the power grid through cross-power plant transaction matching between each virtual power plant and grid-power plant transaction matching between each virtual power plant and the power grid. Intraday real-time dispatching operations are performed, and during each dispatching day, the distributed resources in each virtual power plant are dispatched to operate according to the contracted power operation plan, and real-time compensation is made for operating errors. The transaction settlement and data storage operations, based on the results of intraday real-time dispatch operations, settle inter-power plant transactions and grid-power plant transactions for each virtual power plant, and store the data generated by day-ahead dispatch operations and intraday real-time dispatch operations on a blockchain-based basis.

2. The blockchain-based multi-virtual power plant multi-timescale collaborative scheduling method according to claim 1, characterized in that, The time-sharing operation model of the distributed resources includes: The time-segmented operation model of distributed generation units is used to characterize the power generation of distributed generation units in each operating segment. The time-segmented operation model of distributed energy storage units is used to characterize the power changes of distributed energy storage units in different operating periods. The time-segmented operation model of the distributed load unit is used to characterize the power consumption of the distributed load unit in each operating segment. The types of the distributed load unit include adjustable loads that do not change the power consumption time but can change the power consumption by adjusting the power or operating mode, transferable loads that can change each power consumption time but have a fixed total power consumption, and interruptible loads that can completely suspend power consumption to reduce the total power consumption.

3. The blockchain-based multi-virtual power plant multi-timescale collaborative scheduling method according to claim 2, characterized in that, The day-ahead scheduling operation includes the following steps performed before each scheduling day: S21, The day-ahead dispatch module formulates the initial day-ahead power operation plan for the virtual power plant; S22, the virtual power plant aggregator node generates a bidding plan for the virtual power plant based on the day-ahead initial power operation plan; S23, the power trading center node performs cross-power plant transaction matching between virtual power plants based on the peak-valley characteristics of the bidding plans of each virtual power plant; S24, the power trading center node summarizes the bidding plans of each virtual power plant that have not been matched through cross-power plant transactions, and uniformly matches them with the power grid for power grid-power plant transactions. S25, the day-ahead dispatch module generates a contract power operation plan for the virtual power plant on the dispatch day based on the inter-power plant transaction contract and the grid-power plant inter-transaction contract.

4. The blockchain-based multi-virtual power plant multi-timescale collaborative scheduling method according to claim 3, characterized in that, The day-ahead module formulates the initial day-ahead power operation plan for the virtual power plant, including the following operations: The day-ahead dispatch module corresponding to each virtual power plant searches for the optimal solution based on the objective function shown in the following formula, with the goal of minimizing its own cost. Each virtual power plant submits its initial day-ahead power operation plan to its corresponding virtual power plant aggregator node: ， in, For total cost, , , , , , These include the electricity purchase cost of the virtual power plant, the adjustable load deviation cost, the transferable load deviation cost, the interruptible load deviation cost, the operating cost of the distributed energy storage unit, and the electricity sales revenue of the virtual power plant. For each runtime segment Electricity purchased from external sources by virtual power plants The total power generation of each distributed generation unit The total power consumption of each adjustable load Total electricity consumption of all transferable loads Total power consumption of each interruptible load The sum of the changes in the amount of electricity in each distributed energy storage unit And the electricity sold by virtual power plants to the outside world. The decision set constituted, and and The electricity prices are determined based on the time-of-use electricity sales price and the time-of-use electricity purchase price of the power grid, respectively.

5. The blockchain-based multi-virtual power plant multi-timescale collaborative scheduling method according to claim 4, characterized in that, The day-ahead module formulates the initial day-ahead power operation plan for the virtual power plant, and also includes the following operations: The search yielded the initial power operation plan for the virtual power plant. Subsequently, the scheduling module further optimized the scheduling of each runtime segment. , , , and The power is allocated to each distributed generation unit, distributed energy storage unit, adjustable load, transferable load, and interruptible load to form the day-ahead initial power operation plan for each distributed resource.

6. The blockchain-based multi-virtual power plant multi-timescale collaborative scheduling method according to claim 4, characterized in that, The virtual power plant aggregator node received the day-ahead dispatch module report. , Subsequently, based on the matching and execution status of the corresponding virtual power plants' power trading contracts prior to this coordinated dispatch, their time-of-use power purchase price and time-of-use power sales price during this coordinated dispatch process are determined, thereby obtaining the corresponding virtual power plants' bidding plans during this coordinated dispatch process.

7. The blockchain-based multi-virtual power plant multi-timescale collaborative scheduling method according to claim 3, characterized in that, After receiving the bidding plans from various virtual power plant aggregator nodes, the power trading center node uses a collaborative matching algorithm to select the appropriate time period for each scheduling day. Generating cross-power plant trading contracts, the collaborative matching algorithm includes the following steps: The power purchase plans in the bidding plans of each virtual power plant are sorted from high to low according to their power purchase price, and the power sales plans in the bidding plans of each virtual power plant are sorted from low to high according to their power sales price. The top-ranked electricity purchase plan is matched with the top-ranked electricity sales plan. If the electricity sales price is lower than the electricity purchase price, the match is successful, and the transaction volume is the minimum of the transaction volumes available to both parties. Subtract the tradable power of the successfully matched buyer and seller from the power of the previous round of transactions. If one party has exhausted its power, remove it from the list. Repeat the above steps until no bidding plan is found that satisfies the requirement that the electricity sales price is lower than the electricity purchase price.

8. The blockchain-based multi-virtual power plant multi-timescale collaborative scheduling method according to claim 3, characterized in that, The day-ahead scheduling module determines the contract power operation plan of the virtual power plant based on the received transaction contract, and determines the contract power operation plan of each distributed resource in each time period with the goal of minimizing the difference between each distributed resource operating according to the contract power operation plan and operating according to the initial day-ahead power operation plan.

9. The blockchain-based multi-virtual power plant multi-timescale collaborative scheduling method according to claim 8, characterized in that, The day-ahead scheduling operation also includes: The current dispatch module assigns a comfort weight to each adjustable load unit based on its reputation value. Contracted power operation plan Fine-tuning is performed, and the adjusted power operation plan is obtained by searching for the target using the following formula. : ， in, , Adjustable load At various times Actual and expected values ​​of operational indicators The preset standard for indicator deviation. For adjustable load credibility This is the baseline value for the weighting.

10. The blockchain-based multi-virtual power plant multi-timescale collaborative scheduling method according to claim 2, characterized in that, The intraday scheduling module has additional charging and discharging capabilities based on the charging and discharging capacity specified in the contracted power operation plan of the distributed energy storage unit, and compensates for the operating errors of the distributed generation unit and the distributed load unit.

11. A blockchain-based multi-virtual power plant multi-timescale collaborative scheduling system, used to implement the collaborative scheduling method described in claim 1, comprising an application layer and a network layer, wherein, The application layer includes a resource modeling module, as well as a day-ahead scheduling module, an intraday scheduling module, and a certificate storage module corresponding to each virtual power plant. The network layer includes a main chain and side chains corresponding to each virtual power plant. The main chain includes power trading center nodes and virtual power plant aggregator nodes, and each side chain is linked to the main chain.

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