Block chain-based building integrated photovoltaic user virtual power plant day-ahead transaction method

Abstract

The invention discloses a block chain-based building integrated photovoltaic user virtual power plant day-ahead transaction method, and relates to the technical field of power markets, and the method comprises the steps: constructing a block chain implementation framework of virtual power plant transaction, dividing the interior of a virtual power plant into a demand side resource aggregation layer and a demand side resource layer, and deploying a main chain and a side chain; establishing a demand side resource model based on the demand side resource layer; setting a day-ahead virtual power plant transaction process based on the block chain implementation framework; and optimizing the day-ahead virtual power plant transaction process by using the optimization module and the demand side resource model to obtain an optimal scheme for the producer to operate the demand side resources, and completing the transaction. According to the transaction process formulated by the method, peak load shifting can be well completed.

Description

Technical Field

[0001] The present invention relates to the technical field of power market, and in particular to a day-ahead trading method for a photovoltaic building integrated user virtual power plant based on blockchain.

[0002] The invention is supported by the National Key R&D Program Project, the project name is: Virtual Power Plant Hierarchical Interaction Model and Blockchain Trusted Transaction Technology (Project No.: 2021YFB2401203) Background Art

[0003] There are a variety of demand-side resources distributed in the new power system, including renewable energy generation, energy storage systems, electric vehicles and controllable loads. As an emerging demand-side resource management method, virtual power plants aggregate these resources to form a reliable power supply, enabling them to participate in various market transactions such as electricity and ancillary services, and promote grid stability. Among them, peak load regulation is one of the most common ancillary services and has great potential in developed regions because residential loads account for 30% of total electricity consumption and include many deferrable loads.

[0004] However, the current trading framework still faces some challenges. First, the application of blockchain technology in the main-side chain interaction mode and transaction data recording has not been fully studied. Secondly, there is a lack of appropriate transaction process formulation methods for the increasing number of photovoltaic building integrated residential prosumers in modern cities. Finally, the design of smart contracts is also a key issue, including how to stipulate the transaction process, when to automatically reach a transaction, and how to plan the transaction of demand-side resources in the day-ahead market. These issues need to be further explored and resolved. Summary of the invention

[0005] In view of the challenges of the existing virtual power plant transaction framework in the application of blockchain technology, photovoltaic building integrated residential prosumer transaction process and smart contract design, the present invention is proposed.

[0006] Therefore, the problem to be solved by the present invention is how to effectively integrate and manage distributed demand-side resources in a virtual power plant environment through blockchain technology and optimization algorithms, to achieve efficient operation and trading of the power system, while balancing the overall benefits of the system with the interests of individual users, minimizing the electricity costs of individual users while completing peak shaving and valley filling for the entire system.

[0007] In order to solve the above technical problems, the present invention provides the following technical solutions:

[0008] In the first aspect, an embodiment of the present invention provides a day-ahead trading method for a photovoltaic building integrated user virtual power plant based on blockchain, which includes constructing a blockchain implementation framework for virtual power plant transactions, dividing the virtual power plant into a demand-side resource aggregation layer and a demand-side resource layer, and deploying a main chain and a side chain; establishing a demand-side resource model based on the demand-side resource layer; setting a day-ahead virtual power plant transaction process based on the blockchain implementation framework; optimizing the day-ahead virtual power plant transaction process using an optimization module and a demand-side resource model, obtaining the optimal solution for producers and consumers to operate demand-side resources, and completing the transaction.

[0009] As a preferred solution of the blockchain-based photovoltaic building integrated user virtual power plant day-ahead trading method described in the present invention, wherein: the demand-side resource model includes a load module, a battery module, a photovoltaic module, a grid power supply module and a feeder module; the demand-side resource model includes a first constraint restriction condition, a fourth constraint restriction condition, and a fifth constraint restriction condition.

[0010] As a preferred solution of the blockchain-based photovoltaic building integrated user virtual power plant day-ahead trading method of the present invention, the specific formula of the first constraint restriction condition is as follows:

[0011]

[0012] Among them, u is the producer and consumer, h is the time, Δh is the sampling period, C is the equivalent heat capacity, R is the equivalent thermal resistance, γ is the air conditioning operation mode, is the indoor temperature of prosumer u at time h, The hourly power consumption of the load-adjustable air conditioner, is the outdoor temperature of prosumer u at time h, H is the total time, and U is the total prosumer.

[0013] As a preferred solution of the day-ahead trading method of the photovoltaic building integrated user virtual power plant based on blockchain described in the present invention, the specific formula of the fourth constraint restriction condition is as follows:

[0014]

[0015] Where η is the battery charge and discharge efficiency, and are charging power and discharging power respectively, is the battery capacity, u is the producer and consumer, h is the time, SoC u,h Is the power status, is the battery capacity, H is the total time, and U is the total prosumer.

[0016] As a preferred solution of the blockchain-based photovoltaic building integrated user virtual power plant day-ahead trading method of the present invention, the specific formula of the fifth constraint restriction condition is as follows:

[0017]

[0018] Among them, θ and are the percentages that the battery can be charged and discharged, respectively. and are the lower limits of charging power and discharging power respectively, and are the upper limits of charging power and discharging power, SoC u,1 The battery power status at the initial moment, SoC u,24 The battery power status at the end time. It is a binary battery charging status indicator. Binary battery discharge status indicator, SoC u,h is the power state, u is the producer and consumer, h is the time, H is the total time, and U is the total producer and consumer.

[0019] As a preferred solution of the day-ahead trading method of the photovoltaic building integrated user virtual power plant based on blockchain described in the present invention, wherein: the day-ahead virtual power plant trading process is set based on the blockchain implementation framework, including: the distribution network operator publishes the time-of-use electricity price, which is transmitted to the entities at all levels through the main chain and the side chain; the planning results of each household energy management system are aggregated by the side chain, and then reported to the virtual power plant operator by the aggregator through the main chain to form the day-ahead net load plan; the virtual power plant operator performs peak shaving and valley filling operations based on the day-ahead net load plan for adjustment and optimization; the aggregator receives the adjusted day-ahead net load plan, optimizes and decomposes it, calculates the peak-shaving capacity and estimates the corresponding incentives; based on the decomposition results, the entities at all levels re-plan the demand-side resources, and the virtual power plant operator distributes the incentives to each producer and consumer step by step; the virtual power plant operator trades electricity with the distribution network operator at the trading center based on the demand-side resource planning results.

[0020] As a preferred solution of the blockchain-based photovoltaic building integrated user virtual power plant day-ahead trading method of the present invention, the optimization module includes an objective function, which is as follows:

[0021]

[0022] in, To charge the battery, For local consumption of photovoltaic power, Purchase electricity for the grid, is the battery discharge capacity, f u,h is the grid-fed power, Z u is the regularization term.

[0023] On the second aspect, an embodiment of the present invention provides a day-ahead trading system for a photovoltaic building integrated user virtual power plant based on blockchain, which includes a framework construction module for constructing a blockchain implementation framework for virtual power plant transactions, dividing the virtual power plant into a demand-side resource aggregation layer and a demand-side resource layer, and deploying a main chain and a side chain; a model construction module for establishing a demand-side resource model based on the demand-side resource layer; a process setting module for setting a day-ahead virtual power plant transaction process based on the blockchain implementation framework; an optimization module for optimizing the day-ahead virtual power plant transaction process using the optimization module and the demand-side resource model, obtaining the optimal solution for producers and consumers to operate demand-side resources, and completing the transaction.

[0024] In a third aspect, an embodiment of the present invention provides a computer device, including a memory and a processor, wherein the memory stores a computer program, wherein: when the computer program instructions are executed by the processor, the steps of the blockchain-based photovoltaic building integrated user virtual power plant day-ahead trading method as described in the first aspect of the present invention are implemented.

[0025] In a fourth aspect, an embodiment of the present invention provides a computer-readable storage medium having a computer program stored thereon, wherein: when the computer program instructions are executed by a processor, the steps of the blockchain-based photovoltaic building integrated user virtual power plant day-ahead trading method as described in the first aspect of the present invention are implemented.

[0026] The beneficial effects of the present invention are as follows: the present invention provides a blockchain-based photovoltaic building integrated user virtual power plant day-ahead trading framework. First, a blockchain implementation framework for virtual power plant transactions is constructed, and the virtual power plant is divided into a demand-side resource aggregation layer and a demand-side resource layer, and the main chain and the side chain are deployed; secondly, the prosumer is modeled, and the load module, battery module, photovoltaic module, grid power supply module and feeder module are constructed respectively; then the day-ahead virtual power plant transaction process is set, wherein the planning fine-tuning link is carried out for the peak-shaving process of the prosumer, and this is used as the basis for the formulation of incentives; finally, the optimization module is used to optimize the day-ahead transaction process, and the optimal solution for the prosumer to operate the demand-side resources is obtained, the optimization is completed, and the transaction is automatically concluded. Test analysis shows that the transaction process formulated by the method of the present invention can well complete peak shaving and valley filling. BRIEF DESCRIPTION OF THE DRAWINGS

[0027] In order to more clearly illustrate the technical solutions of the embodiments of the present invention, the following briefly introduces the drawings required for use in the description of the embodiments. Obviously, the drawings described below are only some embodiments of the present invention. For ordinary technicians in this field, other drawings can be obtained based on these drawings without creative work.

[0028] Figure 1A day-ahead trading method for virtual power plants of photovoltaic building integrated users based on blockchain.

[0029] Figure 2 A main-side chain architecture virtual power plant trading mechanism is proposed for the day-ahead trading method of the photovoltaic building integrated user virtual power plant based on blockchain.

[0030] Figure 3 The day-ahead virtual power plant transaction process of the photovoltaic building integrated user virtual power plant day-ahead transaction method based on blockchain.

[0031] Figure 4 Schematic diagram of the peak-shaving process of the day-ahead trading method for photovoltaic building integrated user virtual power plants based on blockchain.

[0032] Figure 5 The planning results of the day-ahead trading method for virtual power plants for photovoltaic building integrated users based on blockchain.

[0033] Figure 6 This is S3.1 (shaded part) and step S3.5 of the demand-side resource level planning of the day-ahead trading method for the photovoltaic building integrated user virtual power plant based on blockchain. DETAILED DESCRIPTION

[0034] In order to make the above-mentioned objects, features and advantages of the present invention more obvious and easy to understand, the specific implementation methods of the present invention are described in detail below in conjunction with the accompanying drawings.

[0035] In the following description, many specific details are set forth to facilitate a full understanding of the present invention, but the present invention may also be implemented in other ways different from those described herein, and those skilled in the art may make similar generalizations without violating the connotation of the present invention. Therefore, the present invention is not limited to the specific embodiments disclosed below.

[0036] Secondly, the term "one embodiment" or "embodiment" as used herein refers to a specific feature, structure, or characteristic that may be included in at least one implementation of the present invention. The term "in one embodiment" that appears in different places in this specification does not necessarily refer to the same embodiment, nor does it refer to a separate or selective embodiment that is mutually exclusive with other embodiments.

[0037] Example 1

[0038] Reference Figure 1 to Figure 6 , which is the first embodiment of the present invention, and provides a day-ahead trading method for a photovoltaic building integrated user virtual power plant based on blockchain, including:

[0039] S1: Build a blockchain implementation framework for virtual power plant transactions, divide the virtual power plant into a demand-side resource aggregation layer and a demand-side resource layer, and deploy the main chain and side chain.

[0040] Specifically, the blockchain implementation framework manages multiple BIPV residential demand-side resources, with each prosumer acting as an independent stakeholder. In order to enhance the trust between the demand-side resource aggregator and the contracted demand-side resources and simplify the transaction process, the framework integrates blockchain and multi-agent mechanisms.

[0041] Furthermore, this framework is based on the main-side chain architecture, in which the virtual power plant operator coordinates the demand-side resources through agents, trades with the distribution network in the external market, and is supervised by the power trading center. The demand-side resource layer includes photovoltaic roofs, batteries and household loads. In the demand-side resource aggregation layer, each role is assigned a main chain node, and each aggregator corresponds to a side chain. To achieve the connection between the main chain and the side chain, the framework sets up nail nodes for the aggregator. The home energy management system is designated as a light node to record load energy consumption and plan demand-side resources.

[0042] Preferably, through this structure, the present invention realizes the effective management and transaction of resources at all levels within the virtual power plant, and improves the credibility and efficiency of the system.

[0043] S2: Establish a demand-side resource model based on the demand-side resource layer.

[0044] For an aggregator, given a set U, which contains N BIPV prosumers, the demand-side resources include PV systems, batteries and loads, such as Figure 2 As shown. For virtual power plant planning at the day-ahead hour level, the planning interval is defined as h∈H={1,...,24}. Therefore, the demand-side resource model includes load module, battery module, photovoltaic module, grid power supply module and feeder module, as follows:

[0045] S2.1: Load module.

[0046] Loads can be classified into three categories: adjustable loads, transferable loads, and uncontrollable loads. Specifically, adjustable loads such as temperature control loads can be adjusted according to user preferences (temperature); transferable loads such as washing machines can be operated during user-specified time periods; and uncontrollable loads such as televisions and refrigerators cannot be adjusted or transferred.

[0047] First, take air conditioning as an example. As a typical adjustable load air conditioning, its first constraint restriction condition is as follows:

[0048]

[0049] Among them, u is the producer and consumer, h is the time, Δh is the sampling period, C is the equivalent heat capacity, R is the equivalent thermal resistance, γ is the air conditioning operation mode (positive represents cooling mode and negative represents heating mode), is the indoor temperature of prosumer u at time h, The hourly power consumption of the load-adjustable air conditioner, is the outdoor temperature of prosumer u at time h, H is the total time, and U is the total prosumer.

[0050] The indoor temperature is subject to the upper and lower limits, and the second constraint condition is obtained, as follows:

[0051]

[0052] in, and are the lower and upper limits of the indoor temperature respectively, u is the prosumer, h is the time, H is the total time, and U is the total prosumer.

[0053] Based on this, the indoor temperature discomfort C of the prosumer u u AC It can be expressed as:

[0054]

[0055] Among them, ω AC For users' sensitivity to temperature changes, is the uniform optimal indoor temperature, u is the producer and consumer, is the indoor temperature of prosumer u at time h, h is the time, H is the total time, and U is the total prosumer.

[0056] Secondly, for transferable loads, the constraints must be met: the daily power consumption of each transferable load To be set by its owner; hourly power consumption The operating mode learned from historical data should be followed, so the third constraint is obtained as follows:

[0057]

[0058]

[0059] in, For an acceptable power consumption range, is the hourly power consumption of the transferable load, is the daily power consumption of the transferable load, u is the prosumer, h is the time, H is the total time, and U is the total prosumer.

[0060] Based on this, the discomfort caused by the transfer load can be is quantized as:

[0061]

[0062] Among them, ω SUser sensitivity to shifting load, is the hourly power consumption of the transferable load, is the transferable load preferred by the user, H is the total time, and U is the total number of prosumers.

[0063] Finally, for uncontrollable loads, this method uses historical data for day-ahead planning, and its total power consumption is express.

[0064] S2.2: Battery module.

[0065] For the battery of prosumer u, the charge state is determined based on its charge and discharge state, and the fourth constraint condition is obtained, which is as follows:

[0066]

[0067] Where η is the battery charge and discharge efficiency, and are charging power and discharging power respectively, is the battery capacity, u is the producer and consumer, h is the time, SoC u,h Is the power status, is the battery capacity, H is the total time, and U is the total prosumer.

[0068] In order to ensure the safe and efficient operation of the battery, the present invention sets a fifth constraint condition for the power state and the maximum charge and discharge power, which is as follows:

[0069]

[0070] in, θ and are the percentages that the battery can be charged and discharged, respectively. and are the lower limits of charging power and discharging power respectively, and are the upper limits of charging power and discharging power, SoC u,1 The battery power status at the initial moment, SoC u,24 The battery power status at the end time. It is a binary battery charging status indicator. Binary battery discharge status indicator, SoC u,h is the power state, u is the producer and consumer, h is the time, H is the total time, and U is the total producer and consumer.

[0071] Furthermore, the battery charging and discharging cost Modeled as:

[0072]

[0073] Among them, ω B is the user sensitivity coefficient for battery cost, and are charging power and discharging power respectively, and H is the total time.

[0074] S2.3: Photovoltaic modules.

[0075] For the photovoltaic system of prosumer u, a fifth constraint is set for time h, as follows:

[0076]

[0077] in, is the total photovoltaic output, is the photovoltaic power output consumed locally, u is the prosumer, h is the time, H is the total time, and U is the total prosumers.

[0078] S2.4: Grid power supply module.

[0079] The electricity purchased from the grid by photovoltaic building integrated prosumers is Indicates that, and satisfies the sixth constraint, as follows:

[0080]

[0081] in, is the maximum power supply limited by the fuse, H is the total time, and U is the total prosumers.

[0082] The electricity charges are based on the time-of-use electricity price, which is expressed as follows:

[0083]

[0084] Among them, ρ h is the time-of-use electricity price at time h.

[0085] S2.5: Feeder module.

[0086] The photovoltaic output is fed back to the grid to generate grid feed-in revenue, which can promote the deployment of photovoltaic systems. Given a fixed grid feed-in price π, the grid feed-in revenue of the prosumer u is defined as:

[0087]

[0088] Among them, f u,h The amount of electricity fed into the grid by producers and consumers.

[0089] In addition, the amount of electricity fed into the grid cannot exceed the remaining photovoltaic output after local consumption, and the seventh constraint condition is obtained, which is as follows:

[0090]

[0091] Among them, f u,h is the grid-fed electricity, For local consumption of photovoltaic power, is the total photovoltaic output, and u is the prosumer.

[0092] S3: Set up the day-ahead virtual power plant transaction process based on the blockchain implementation framework.

[0093] Specifically, the virtual power plant power trading and peak shaving trading are as follows: Figure 3 As shown, the following steps are included:

[0094] S3.1: The distribution network operator publishes the day-ahead time-of-use electricity price and transmits it to entities at all levels through the main chain and side chain.

[0095] Specifically, the virtual power plant operator transmits the day-ahead time-of-use electricity price released by the distribution network operator on the main chain; all aggregators receive the electricity price information and broadcast it to the contracted prosumers through the side chain; the contracted user's home energy management system acts as a light node on the side chain, and makes preliminary plans for demand-side resources based on a series of smart contracts; to protect user privacy, the demand-side resource planning results are only recorded in the blocks of local light nodes; the Byzantine fault tolerance mechanism consensus conditions are set, and access to any light node requires the unanimous consent of more than 2 / 3 of the nodes on the same chain.

[0096] S3.2: The planning results of each household energy management system are aggregated through the side chain and reported to the virtual power plant operator by the aggregator through the main chain to form the overall day-ahead net load plan.

[0097] Specifically, the day-ahead net load plan for each user is calculated by accumulating the planning results of all demand-side resources of the user; to protect privacy, the user's day-ahead net load plan is reported to the aggregator through the nail node on the side chain; the aggregator further aggregates the plans of each user to form the total day-ahead net load plan at the aggregator level; and the total day-ahead net load plan at the aggregator level is uploaded to the main chain.

[0098] S3.3: Based on the overall day-ahead net load plan, the virtual power plant operator performs peak shaving and valley filling operations to adjust and optimize the overall day-ahead net load plan.

[0099] Specifically, the virtual power plant operator obtains global information through the main chain, that is, the day-ahead net load plan reported by each aggregator; calculates the total planned load at the virtual power plant level; sets the peak shaving step size, and performs the peak shaving and valley filling process at the same time: if the net load reaches a peak value at a certain moment, all peak values ​​are reduced; if the net load reaches a valley value at a certain moment, all valley values ​​are increased; it iterates the peak shaving and valley filling process until the peak-valley difference reaches the preset standard, as shown in the schematic diagram. Figure 4 shown.

[0100] S3.4: The aggregator receives the adjusted overall day-ahead net load plan, performs optimization decomposition, calculates the peak load capacity and estimates the corresponding incentives.

[0101] Specifically, the aggregator receives the fine-tuned plan; optimizes and decomposes the fine-tuned plan with the goal of minimizing the minimum mean square error between the decomposed plan and the plan reported in S3.2; calculates the total peak-shaving capacity of the virtual power plant, that is, the difference between the total plan after fine-tuning and the total plan before fine-tuning during peak and valley periods; and estimates the corresponding incentives.

[0102] S3.5: Based on the decomposition results, entities at all levels will re-plan demand-side resources, and the virtual power plant operator will distribute the calculated incentives to each producer and consumer step by step.

[0103] Specifically, the decomposed plan is distributed to the contracted users through the side chain; each home energy management system re-plans the demand-side resources based on the regularization term to match the net load issued, and obtains the final plan of all loads on the day before; the virtual power plant operator distributes the total incentive to the aggregator; the aggregator further distributes the incentive to each producer and consumer.

[0104] S3.6: The virtual power plant operator conducts electricity transactions with the distribution network operator at the trading center based on the demand-side resource planning results.

[0105] Specifically, the virtual power plant operator obtains the total net load re-planned on the day before; based on the day-ahead time-of-use electricity price, the virtual power plant operator conducts electricity transactions with the distribution network operator at the trading center.

[0106] S4: Use the optimization module and demand-side resource model to optimize the day-ahead virtual power plant transaction process, obtain the optimal solution for prosumers to operate demand-side resources, and complete the transaction.

[0107] It should be noted that in the day-ahead virtual power plant transaction, the optimization problems of S3.1 and S3.5 are solved by the optimization module. In S3.1, each producer and consumer optimizes its demand-side resource planning individually with the goal of maximizing revenue. Therefore, the eighth constraint condition represents the balance between supply and demand of electricity:

[0108]

[0109] in, is the power consumption of the air conditioner per hour, The hourly power consumption of the transferable load is The hourly power consumption of the uncontrollable load, To charge the battery, For local consumption of photovoltaic power, Purchase electricity for the grid, is the battery discharge capacity, u is the prosumer, h is the time, H is the total time, and U is the total prosumer.

[0110] It should be noted that the left side of the equal sign represents the total power consumption, and the right side of the equal sign represents the total power supply.

[0111] Furthermore, for producer-consumer u, its operating cost C u It can be expressed as:

[0112]

[0113] in, The cost of purchasing electricity, The discomfort cost caused by load transfer, is the battery charging and discharging cost, is the discomfort cost of indoor temperature, W u For feeder network revenue.

[0114] Furthermore, each prosumer u can calculate the operating cost C by the following formula: u Minimize and get the solution s u , the specific formula is as follows:

[0115]

[0116] in, To charge the battery, For local consumption of photovoltaic power, Purchase electricity for the grid, is the battery discharge capacity, f u,h The amount of electricity fed into the grid.

[0117] It should be noted that the first to eighth constraints must be met during the solution process.

[0118] In step 5, the optimization problem is similar to step 1, with the addition of a regularization term Z u , as shown below:

[0119]

[0120] Among them, ω Z The weight of the user's willingness to participate in replanning, g' u,h Indicates the power after fine-tuning.

[0121] Therefore, the re-planned objectives in S3.5 are regularized and the demand-side resource operation is further optimized:

[0122]

[0123] in, To charge the battery, For local consumption of photovoltaic power, Purchase electricity for the grid, is the battery discharge capacity, f u,h is the grid-fed power, Z u is the regularization term.

[0124] Furthermore, this embodiment also provides a day-ahead trading system for photovoltaic building integrated users' virtual power plants based on blockchain, including a framework construction module, which is used to construct a blockchain implementation framework for virtual power plant transactions, divide the virtual power plant into a demand-side resource aggregation layer and a demand-side resource layer, and deploy a main chain and a side chain; a model construction module, which is used to establish a demand-side resource model based on the demand-side resource layer; a process setting module, which is used to set the day-ahead virtual power plant transaction process based on the blockchain implementation framework; an optimization module, which is used to optimize the day-ahead virtual power plant transaction process using the optimization module and the demand-side resource model, obtain the optimal solution for the producer and consumer to operate the demand-side resources, and complete the transaction.

[0125] This embodiment also provides a computer device, which is applicable to the case of a day-ahead trading method for a photovoltaic building integrated user virtual power plant based on blockchain, including a memory and a processor; the memory is used to store computer executable instructions, and the processor is used to execute computer executable instructions to implement the day-ahead trading method for a photovoltaic building integrated user virtual power plant based on blockchain as proposed in the above embodiment.

[0126] The computer device may be a terminal, and the computer device includes a processor, a memory, a communication interface, a display screen and an input device connected via a system bus. The processor of the computer device is used to provide computing and control capabilities. The memory of the computer device includes a non-volatile storage medium and an internal memory. The non-volatile storage medium stores an operating system and a computer program. The internal memory provides an environment for the operation of the operating system and the computer program in the non-volatile storage medium. The communication interface of the computer device is used to communicate with an external terminal in a wired or wireless manner, and the wireless manner can be achieved through WIFI, an operator network, NFC (near field communication) or other technologies. The display screen of the computer device may be a liquid crystal display screen or an electronic ink display screen, and the input device of the computer device may be a touch layer covering the display screen, or a key, trackball or touchpad provided on the housing of the computer device, or an external keyboard, touchpad or mouse, etc.

[0127] This embodiment also provides a storage medium on which a computer program is stored. When the program is executed by a processor, the method for day-ahead trading of a photovoltaic building integrated user virtual power plant based on blockchain as proposed in the above embodiment is implemented.

[0128] In summary, the present invention provides a day-ahead trading framework for photovoltaic building integrated users' virtual power plants based on blockchain. First, a blockchain implementation framework for virtual power plant transactions is constructed, and the virtual power plant is divided into a demand-side resource aggregation layer and a demand-side resource layer, and the main chain and side chain are deployed; secondly, the prosumers are modeled, and the load module, battery module, photovoltaic module, grid power supply module and feeder module are constructed respectively; then the day-ahead virtual power plant transaction process is set, in which the planning fine-tuning link is carried out for the peak-shaving process of the prosumers, and this is used as the basis for the formulation of incentives; finally, the optimization module is used to optimize the day-ahead transaction process, and the optimal solution for the prosumers to operate demand-side resources is obtained. After the optimization is completed, the transaction is automatically concluded. Test analysis shows that the transaction process formulated by the method of the present invention can well complete peak shaving and valley filling.

[0129] Example 2

[0130] Reference Figure 1 to Figure 6 , which is the second embodiment of the present invention, provides a day-ahead trading method for a photovoltaic building integrated user virtual power plant based on blockchain. In order to verify the beneficial effects of the present invention, scientific demonstration is carried out through economic benefit calculation and simulation experiments.

[0131] Specifically, the present invention optimizes demand-side resource management through a series of steps to minimize electricity costs and reduce peak loads. The experiment uses 30 days of data from 8 users in the Pecan Street dataset for simulation experiments, from July 1, 2018 to July 31, 2018.

[0132] Furthermore, the parameters of each module in the demand-side resource model are set as follows: C is 0.33, R is 13.5, γ is 2.2, T ref At 25°C, 23℃, At 27℃, the peak-to-valley difference is reduced by 30%, and the step size is 0.01kWh. and is 0, and is 0.5C, η is 0.9, θ is 0.2, is 0.95, π is 0.04 USD / kWh, ρ is the electricity price (0.1129 USD / kWh from 8 pm to 5 pm the next day, 0.2499 USD / kWh from 5 pm to 8 pm), ω AC ,ω S ,ω B ,ω Z They are 0.1, 0.1, 0.01 and 1 respectively.

[0133] The experimental results show that the present invention effectively reduces the cost of electricity, especially during the peak electricity price period (5pm to 8pm), and significantly reduces electricity consumption. New energy power generation and battery discharge effectively supplement the power supply. Compared with the initial plan. S3.3 achieved a 24% reduction in peak electricity consumption, and even after considering user comfort (step S3.5), it still maintained an 18% decrease in peak-to-valley difference. The resource-level planning on the demand side also reflects the completion of peak shaving and valley filling from the equipment level while trying to meet the user's electricity comfort.

[0134] Figure 5 The net load changes of S3.1, S3.3 and S3.5 are intuitively displayed, and the optimization process is clearly presented. Figure 6 A typical day was selected to compare and illustrate the differences in demand-side resource levels between the initial plan (S3.1) and the final plan (S3.5).

[0135] In summary, the optimization method of the present invention not only reduces the overall electricity cost, but also ensures the user's electricity comfort at the equipment level, while effectively achieving the goal of peak shaving and valley filling.

[0136] It should be noted that the above embodiments are only used to illustrate the technical solutions of the present invention rather than to limit it. Although the present invention has been described in detail with reference to the preferred embodiments, those skilled in the art should understand that the technical solutions of the present invention may be modified or replaced by equivalents without departing from the spirit and scope of the technical solutions of the present invention, which should all be included in the scope of the claims of the present invention.

Claims

1. A day-ahead transaction method for a photovoltaic building integrated user virtual power plant based on blockchain, characterized in that: include, Build a blockchain implementation framework for virtual power plant transactions, divide the virtual power plant into a demand-side resource aggregation layer and a demand-side resource layer, and deploy the main chain and side chain; Establishing a demand-side resource model based on the demand-side resource layer; Setting a day-ahead virtual power plant transaction process based on the blockchain implementation framework; The optimization module and the demand-side resource model are used to optimize the day-ahead virtual power plant transaction process, obtain the optimal solution for the producer and consumer to operate the demand-side resources, and complete the transaction.

2. The method for day-ahead trading of a photovoltaic building integrated user virtual power plant based on blockchain as claimed in claim 1, characterized in that: The demand-side resource model includes a load module, a battery module, a photovoltaic module, a grid power supply module and a feeder module; the demand-side resource model includes a first constraint restriction condition, a fourth constraint restriction condition and a fifth constraint restriction condition.

3. The method for day-ahead trading of a photovoltaic building integrated user virtual power plant based on blockchain as claimed in claim 2, characterized in that: The specific formula of the first constraint condition is as follows: Among them, u is the producer and consumer, h is the time, Δh is the sampling period, C is the equivalent heat capacity, R is the equivalent thermal resistance, γ is the air conditioning operation mode, is the indoor temperature of prosumer u at time h, The hourly power consumption of the load-adjustable air conditioner, is the outdoor temperature of prosumer u at time h, H is the total time, and U is the total prosumer.

4. The method for day-ahead trading of a photovoltaic building integrated user virtual power plant based on blockchain as claimed in claim 2, characterized in that: The specific formula of the fourth constraint condition is as follows: Where η is the battery charge and discharge efficiency, and are charging power and discharging power respectively, is the battery capacity, u is the producer and consumer, h is the time, SoC u,h Is the power status, is the battery capacity, H is the total time, and U is the total prosumer.

5. The method for day-ahead trading of a photovoltaic building integrated user virtual power plant based on blockchain as claimed in claim 2, characterized in that: The specific formula of the fifth constraint condition is as follows: Among them, θ and are the percentages of the battery that can be charged and discharged, respectively. and are the lower limits of charging power and discharging power respectively, and are the upper limits of charging power and discharging power, SoC u,1 The battery power status at the initial moment, SoC u,24 The battery power status at the end time. It is a binary battery charging status indicator. Binary battery discharge status indicator, SoC u,h is the power state, u is the producer and consumer, h is the time, H is the total time, and U is the total producer and consumer.

6. The method for day-ahead trading of a photovoltaic building integrated user virtual power plant based on blockchain as claimed in claim 1, characterized in that: The day-ahead virtual power plant transaction process based on the blockchain implementation framework includes: The distribution network operator publishes the time-of-use electricity price, which is transmitted to all levels of entities through the main chain and side chain; After the planning results of each household energy management system are aggregated through the side chain, the aggregator reports them to the virtual power plant operator through the main chain to form a day-ahead net load plan; The virtual power plant operator performs peak load shaving and valley filling operations based on the day-ahead net load planning for adjustment and optimization; The aggregator receives the adjusted day-ahead net load plan, optimizes and decomposes it, calculates the peak load capacity and estimates the corresponding incentives; Based on the decomposition results, entities at all levels will re-plan demand-side resources, and the virtual power plant operator will distribute incentives to each producer and consumer step by step; Virtual power plant operators trade electricity with distribution network operators at the trading center based on the results of demand-side resource planning.

7. The method for day-ahead trading of a photovoltaic building integrated user virtual power plant based on blockchain as claimed in claim 1, characterized in that: The optimization module includes an objective function, which is as follows: in, To charge the battery, For local consumption of photovoltaic power, Purchase electricity for the grid, is the battery discharge capacity, f u,h is the grid-fed power, Z u is the regularization term.

8. A day-ahead trading system for photovoltaic building integrated users' virtual power plants based on blockchain, based on a day-ahead trading method for photovoltaic building integrated users' virtual power plants based on blockchain as claimed in any one of claims 1 to 7, characterized in that: Also includes, The framework building module is used to build a blockchain implementation framework for virtual power plant transactions, divide the virtual power plant into a demand-side resource aggregation layer and a demand-side resource layer, and deploy the main chain and side chain; A model building module, used for building a demand-side resource model based on the demand-side resource layer; A process setting module, used to set a day-ahead virtual power plant transaction process based on the blockchain implementation framework; The optimization module is used to optimize the day-ahead virtual power plant transaction process using the optimization module and the demand-side resource model, obtain the optimal solution for the producer and consumer to operate the demand-side resources, and complete the transaction.

9. A computer device comprising a memory and a processor, wherein the memory stores a computer program, characterized in that: When the processor executes the computer program, the steps of the blockchain-based photovoltaic building integrated user virtual power plant day-ahead trading method described in any one of claims 1 to 7 are implemented.

10. A computer-readable storage medium having a computer program stored thereon, characterized in that: When the computer program is executed by a processor, the steps of the day-ahead trading method for a photovoltaic building integrated user virtual power plant based on blockchain are implemented as described in any one of claims 1 to 7.

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