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

Abstract

The application discloses a photovoltaic building integrated user virtual power plant day-ahead transaction method based on a block chain, relates to the technical field of power markets, and comprises the following steps: constructing a block chain implementation framework of a virtual power plant transaction, dividing 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 an optimization module and the demand side resource model, obtaining an optimal scheme of a producer and consumer operation demand side resource, and completing the transaction. The transaction process formulated by the method can well complete peak clipping and valley filling.

Description

TECHNICAL FIELD

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

[0002] The present application is supported by the National Key Research and Development Program Project, and the project name is: Virtual Power Plant Hierarchical Interaction Mode and Blockchain Trustworthy Trading Technology (Project Number: 2021YFB2401203) BACKGROUND

[0003] In the new power system, there are various demand-side resources, including new energy generation, energy storage systems, electric vehicles, and controllable loads. Virtual power plants, as an emerging demand-side resource management approach, aggregate these resources to form a reliable power supply, enabling them to participate in various market transactions such as electricity and ancillary services, and promoting grid stability. Among them, peak shaving service is one of the most common ancillary services, which has great potential in developed areas, as residential loads account for 30% of total power consumption and contain many postponable loads.

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

[0005] In view of the challenges of the existing virtual power plant trading framework in the application of blockchain technology, the trading process of photovoltaic building integrated residential producers and consumers, and the design of smart contracts, the present application is proposed.

[0006] Therefore, the problem to be solved by the present application is how to effectively integrate and manage distributed demand-side resources in the virtual power plant environment through blockchain technology and optimization algorithms, achieve efficient operation and trading of the power system, balance the overall system benefit and individual user benefit, and minimize the individual user's electricity cost while achieving the overall peak shaving and valley filling of the system.

[0007] To solve the above technical problems, the present application provides the following technical solutions:

[0008] In a first aspect, the embodiments of the present application provide a method for day-ahead transaction of a BIP user virtual power plant based on a blockchain, which comprises constructing a blockchain implementation framework for virtual power plant transaction, 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 by using an optimization module and the demand side resource model to obtain an optimal scheme of demand side resource operation of producers and consumers, and completing the transaction.

[0009] As a preferred scheme of the method for day-ahead transaction of a BIP user virtual power plant based on a blockchain, the demand side resource model comprises a load module, a battery module, a photovoltaic module, a grid power supply module, and a feeder module; the demand side resource model comprises a first constraint condition, a fourth constraint condition, and a fifth constraint condition.

[0010] As a preferred scheme of the method for day-ahead transaction of a BIP user virtual power plant based on a blockchain, the specific formula of the first constraint condition is as follows:

[0011]

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

[0013] As a preferred scheme of the method for day-ahead transaction of a BIP user virtual power plant based on a blockchain, the specific formula of the fourth constraint condition is as follows:

[0014]

[0015] wherein η is a battery charging and discharging efficiency, and are charging power and discharging power, respectively, is a battery capacity, u is a producer or consumer, h is a time, SoC u,h is a state of charge, is a battery capacity, H is a total time, and U is a total producer or consumer.

[0016] As a preferred scheme of the method for day-ahead transaction of a BIP user virtual power plant based on a blockchain, the specific formula of the fifth constraint condition is as follows:

[0017]

[0018] wherein, θ and are the percentage of battery power that can be charged and discharged, respectively, and are the lower limits of charging and discharging power, respectively, and are the upper limits of charging and discharging power, respectively, SoC u,1 is the initial state of charge of the battery, SoC u,24 is the final state of charge of the battery, is a binary battery charging status indicator, is a binary battery discharging status indicator, SoC u,h is the state of charge, u is the producer or consumer, h is the time, H is the total time, and U is the total producer or consumer.

[0019] As a preferred scheme of the photovoltaic building integrated user virtual power plant day-ahead transaction method based on the blockchain of the application, wherein: the day-ahead virtual power plant transaction 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 each level of subject through the main chain and the side chain; the planning results of each household energy management system are aggregated through the side chain, and then reported to the virtual power plant operator through the main chain by the aggregator, to form a day-ahead net load planning; the virtual power plant operator adjusts and optimizes based on the day-ahead net load planning; the aggregator receives the adjusted day-ahead net load planning, optimizes and decomposes, calculates the peak shaving capacity and estimates the corresponding incentive; based on the decomposition results, each level of subject re-plans the demand side resource, and the virtual power plant operator distributes the incentive to each producer or consumer; the virtual power plant operator trades electricity with the distribution network operator at the transaction center according to the demand side resource planning results.

[0020] As a preferred scheme of the photovoltaic building integrated user virtual power plant day-ahead transaction method based on the blockchain of the application, wherein: the optimization module includes a target function, which is specifically as follows:

[0021]

[0022] wherein, is the battery charging power, is the local consumption of photovoltaic output, is the grid purchase power, is the battery discharging power, f u,h is the grid feeding power, and Z u is the regularization term.

[0023] Secondly, embodiments of the present invention provide a blockchain-based day-ahead trading system for building-integrated photovoltaic (BIPV) user virtual power plants. This system includes a framework construction module for building a blockchain implementation framework for virtual power plant trading, dividing the virtual power plant into a demand-side resource aggregation layer and a demand-side resource layer, and deploying a main chain and side chains; a model construction module for establishing a demand-side resource model based on the demand-side resource layer; a process setting module for setting the day-ahead virtual power plant trading process based on the blockchain implementation framework; and an optimization module for optimizing the day-ahead virtual power plant trading process using the optimization module and the demand-side resource model to obtain the optimal solution for producers and consumers to operate demand-side resources and complete the transaction.

[0024] Thirdly, embodiments of the present invention provide 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, they implement 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.

[0025] Fourthly, embodiments of the present invention provide a computer-readable storage medium having a computer program stored thereon, wherein: when the computer program instructions are executed by a processor, they implement 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.

[0026] The beneficial effects of this invention are as follows: This invention provides a blockchain-based framework for day-ahead trading of building-integrated photovoltaic (BIPV) user virtual power plants. First, a blockchain implementation framework for virtual power plant trading is constructed, dividing the virtual power plant internally into a demand-side resource aggregation layer and a demand-side resource layer, and deploying a main chain and side chains. Second, prosumers are modeled, constructing load modules, battery modules, photovoltaic modules, grid power supply modules, and feeder modules respectively. Then, the day-ahead virtual power plant trading process is set, wherein the planning and fine-tuning stage targets the peak-shaving process of prosumers, and uses this as the basis for incentive formulation. Finally, the day-ahead trading process is optimized using an optimization module to obtain the optimal solution for prosumers to operate demand-side resources; upon optimization, the transaction is automatically completed. Testing and analysis show that the trading process formulated by the method of this invention can effectively achieve peak shaving and valley filling. Attached Figure Description

[0027] To more clearly illustrate the technical solutions of the embodiments of the present invention, the accompanying drawings used in the description of the embodiments will be briefly introduced below. Obviously, the accompanying drawings described below are only some embodiments of the present invention. For those skilled in the art, other drawings can be obtained based on these drawings without creative effort.

[0028] Figure 1A blockchain-based photovoltaic building integrated user virtual power plant day-ahead transaction method.

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

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

[0031] Figure 4 A peak shaving process schematic diagram of the blockchain-based photovoltaic building integrated user virtual power plant day-ahead transaction method.

[0032] Figure 5 Planning results of the blockchain-based photovoltaic building integrated user virtual power plant day-ahead transaction method.

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

[0034] In order to make the above-mentioned objects, features and advantages of the present application more apparent and comprehensible, the specific embodiments of the present application will be described in detail below with reference to the accompanying drawings.

[0035] In the following description, a lot of specific details are set forth in order to provide a thorough understanding of the present application, but the present application can also be implemented in other ways different from the description, and those skilled in the art can make similar generalizations without departing from the essence of the present application, so the present application is not limited to the specific embodiments disclosed below.

[0036] Secondly, the "one embodiment" or "embodiment" referred to herein means that the specific features, structures or characteristics can be included in at least one implementation of the present application. "In one embodiment" appearing in different places in the specification does not mean the same embodiment, nor is it an independent or alternative embodiment that excludes other embodiments.

[0037] Embodiment 1

[0038] Reference Figures 1-6 For the first embodiment of the present application, the embodiment provides a blockchain-based photovoltaic building integrated user virtual power plant day-ahead transaction method, comprising,

[0039] S1: 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.

[0040] Specifically, the blockchain implementation framework manages multiple building integrated photovoltaic residential demand side resources, and each producer and consumer is an independent stakeholder. In order to improve the trust between the demand side resource aggregator and the signed demand side resource and simplify the transaction process, the framework integrates the blockchain and the multi-agent mechanism.

[0041] Further, the framework is based on a main side chain architecture, in which the virtual power plant operator coordinates the demand side resources through an agent, 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. In order to realize the connection between the main chain and the side chain, the framework sets a peg node for the aggregator. The home energy management system is designated as a light node, which is used to record load energy consumption and plan demand side resources.

[0042] Preferably, through this structure, the present application realizes the effective management and transaction of resources at each level in 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 building integrated photovoltaic producers and consumers in total, the demand side resources include photovoltaic systems, batteries and loads, as shown in Figure 2 For day-ahead hourly virtual power plant planning, the planning interval is defined as h∈H={1,...,24}. Therefore, the demand side resource model includes a load module, a battery module, a photovoltaic module, a grid power supply module and a feeder module, as follows:

[0045] S2.1: Load module.

[0046] The load can be classified into three categories: adjustable load, transferable load and uncontrollable load. Specifically, the adjustable load such as temperature control load can be adjusted according to user preference (temperature); the transferable load such as washing machine can be run at the user specified time period; and the uncontrollable load such as television, refrigerator, etc. cannot be adjusted or transferred.

[0047] First, taking air conditioner as an example, as a typical adjustable load air conditioner, its first constraint condition is as follows:

[0048]

[0049] Where 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 conditioner operating mode (positive for cooling mode and negative for heating mode), Th (h) is the indoor temperature of prosumer u at time h, The hourly power consumption of adjustable load air conditioner, Th (h) 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 limited by the upper and lower limits, and the second constraint condition is obtained, which is as follows:

[0051]

[0052] wherein, and Th (h) 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.

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

[0054]

[0055] wherein, AC ω is the sensitivity of the user to temperature change, Th (h) 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 the transferable load, the constraint condition needs to meet: the daily power consumption of each transferable load needs to be set by the owner; the hourly power consumption should follow the operation mode learned from historical data, and thus the third constraint condition is obtained, which is as follows:

[0057]

[0058]

[0059] wherein, is the 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 C caused by the transferable load is quantified as:

[0061]

[0062] wherein, SSensitivity of users to shifting load, Hourly electricity consumption of the shiftable load, Preferred shiftable load of users, H is the total time, and U is the total consumer.

[0063] Finally, for the uncontrollable load, the method uses historical data for day-ahead planning, and the total electricity consumption is represented by .

[0064] S2.2: Battery module.

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

[0066]

[0067] where η is the battery charging and discharging efficiency, and are the charging power and discharging power, is the battery capacity, u is the consumer, h is the time, SoC u,h is the state of charge, is the battery capacity, H is the total time, and U is the total consumer.

[0068] In order to ensure the safe and efficient operation of the battery, the fifth constraint condition is set for the state of charge and the maximum charging and discharging power, as follows:

[0069]

[0070] where, θ and are the percentage of the battery that can be charged and discharged, and are the lower limits of the charging power and discharging power, and are the upper limits of the charging power and discharging power, SoC u,1 is the initial time battery state of charge, SoC u,24 is the end time battery state of charge, is a binary battery charging state indicator, is a binary battery discharging state indicator, SoC u,h is the state of charge, u is the consumer, h is the time, H is the total time, and U is the total consumer.

[0071] Further, the battery charging and discharging cost is modeled as:

[0072]

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

[0074] S2.3: Photovoltaic module.

[0075] For the prosumer u, the fifth constraint condition is set for the time h, as follows:

[0076]

[0077] where is the total photovoltaic output, is the locally consumed photovoltaic output, u is the prosumer, h is the time, and H is the total time.

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

[0079] The power purchased by the BIPV prosumer from the grid is represented by and satisfies the sixth constraint condition, as follows:

[0080]

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

[0082] The electricity fee is established based on the time-of-use electricity price, as follows:

[0083]

[0084] where ρ h is the time-of-use electricity price at time h.

[0085] S2.5: Grid feeding module.

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

[0087]

[0088] where f u,h is the grid feeding electricity of the prosumer.

[0089] Moreover, the grid feeding electricity cannot exceed the remaining photovoltaic output after local consumption, resulting in the seventh constraint condition, as follows:

[0090]

[0091] wherein f u,h is the grid power, is the local PV power, is the total PV power, and u is the producer or consumer.

[0092] S3: Implementing a day-ahead VPP transaction process based on the blockchain framework.

[0093] Specifically, the day-ahead VPP energy transaction and peak shaving transaction, as shown in Figure 3 , includes the following steps:

[0094] S3.1: The distribution network operator publishes a day-ahead time-of-use price, which is transmitted to each subject through the main chain and the side chain.

[0095] Specifically, the virtual power plant operator transmits the day-ahead time-of-use price published by the distribution network operator on the main chain; all aggregators receive the price information and broadcast it to the contracted producers and consumers through the side chain; the home energy management system of the contracted user serves as a light node on the side chain, and preliminarily plans the demand-side resources according to a series of smart contracts; to protect user privacy, the demand-side resource planning results are only recorded in the local light node block; a Byzantine fault-tolerant mechanism consensus condition is set, and more than 2 / 3 of the same chain nodes need to agree before accessing any light node.

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

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

[0098] S3.3: The virtual power plant operator adjusts and optimizes the overall day-ahead net load planning based on the overall day-ahead net load planning.

[0099] Specifically, the virtual power plant operator obtains global information, i.e., the day-ahead net load planning reported by each aggregator, through the main chain; calculates the total planning load at the virtual power plant level; sets a peak shaving step, and simultaneously performs the peak shaving and valley filling processes: if the net load reaches a peak value at a certain time, all peak values are reduced; if the net load reaches a valley value at a certain time, all valley values are increased; the peak shaving and valley filling processes are iteratively executed until the peak-valley difference reaches a preset standard, as shown in Figure 4 .

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

[0101] Specifically, the aggregator receives the fine-tuned schedule; performs optimization decomposition on the fine-tuned schedule to minimize the least square error between the decomposed schedule and the reported schedule in S3.2; calculates the total virtual power plant peak shaving capacity, which is the difference between the fine-tuned total schedule and the pre-fine-tuned total schedule in peak and valley periods; and estimates the corresponding incentive.

[0102] S3.5: Based on the decomposition results, each level of subject re-plans the demand side resource, and the virtual power plant operator distributes the calculated incentive to each producer and consumer.

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

[0104] S3.6: The virtual power plant operator trades electricity with the distribution network operator in the trading center according to the demand side resource planning results.

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

[0106] S4: The optimization module and the demand side resource model are used to optimize the day-ahead virtual power plant trading process to obtain the optimal scheme of the producer and consumer operating the demand side resource, 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 the revenue. Therefore, the eighth constraint condition represents the balance between supply and demand of electricity:

[0108]

[0109] wherein, is the hourly power consumption of the air conditioner, is the hourly power consumption of the transferable load, is the hourly power consumption of the uncontrollable load, is the battery charging power, is the local consumption of photovoltaic output, is the grid power purchase amount, is the total battery discharge power, u is the prosumer, h is the time, H is the total time, and U is the total prosumers.

[0110] Note that the left side of the equation represents the total power consumption, and the right side of the equation represents the total power supply.

[0111] Further, for each prosumer u, the operation cost C u can be expressed as:

[0112]

[0113] where, is the cost of purchasing electricity, is the cost of discomfort caused by shifting loads, is the cost of battery charging and discharging, is the cost of indoor temperature discomfort, W u is the revenue from feeding the grid.

[0114] Further, each prosumer u minimizes the operation cost C u to obtain the solution s u , and the specific formula is as follows:

[0115]

[0116] where, is the battery charging power, is the locally consumed photovoltaic output, is the grid electricity purchase power, is the battery discharge power, f u,h is the grid feeding power.

[0117] Note that the first constraint condition to the eighth constraint condition need to be met during the solving process.

[0118] In step 5, the optimization problem is similar to step 1, and a regularization term Z u is added, as shown in the following formula:

[0119]

[0120] where, ω Z represents the weight of the user's willingness to participate in the re-planning, g' u,h represents the fine-tuned power.

[0121] Therefore, the goal of the re-planning in S3.5 is regularized, and the demand-side resource operation is further optimized:

[0122]

[0123] where, charging power of the battery, locally consumed photovoltaic output, purchased power from the grid, discharging power of the battery, u,h fed-in power, u regularization term.

[0124] Further, the embodiment also provides a blockchain-based photovoltaic building integrated user virtual power plant day-ahead transaction system, which comprises a framework modeling module, a model modeling module, a process setting module, and an optimization module.

[0125] The embodiment also provides a computer device suitable for the blockchain-based photovoltaic building integrated user virtual power plant day-ahead transaction method, which comprises a memory and a processor.

[0126] The computer device can be a terminal, and the computer device comprises a processor, a memory, a communication interface, a display screen, and an input device which are connected through a system bus. The processor of the computer device is configured to provide computing and control capabilities. The memory of the computer device comprises 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 running the operating system and the computer program in the non-volatile storage medium. The communication interface of the computer device is configured to perform wired or wireless communication with external terminals. The wireless communication can be achieved through WIFI, an operator network, NFC (near field communication), or other technologies. The display screen of the computer device can be a liquid crystal display screen or an electronic ink display screen. The input device of the computer device can be a touch layer overlaid on the display screen, or a key, a trackball, or a touchpad arranged on the shell of the computer device. In addition, the input device can also be an external keyboard, a touchpad, a mouse, or the like.

[0127] The embodiment also provides a storage medium having a computer program stored thereon, and the program is executed by a processor to implement the blockchain-based photovoltaic building integrated user virtual power plant day-ahead transaction method.

[0128] In summary, the application provides a photovoltaic building integrated user virtual power plant day-ahead transaction framework based on a blockchain. First, a blockchain implementation framework for virtual power plant transactions is constructed, the virtual power plant is divided into a demand side resource aggregation layer and a demand side resource layer, and a main chain and a side chain are deployed. Second, producers and consumers are modeled, and a load module, a battery module, a photovoltaic module, a grid power supply module, and a feeder module are constructed. Third, a day-ahead virtual power plant transaction process is set, in which the planning fine-tuning link is for the peak shaving process of the producers and consumers, and is used as the basis for developing incentives. Finally, an optimization module is used to optimize the day-ahead transaction process, and the optimal solution for the producers and consumers to operate the demand side resources is obtained. The transaction is automatically completed after optimization. Test analysis shows that the transaction process developed by the application can well complete peak clipping and valley filling.

[0129] Embodiment 2

[0130] Reference Figures 1-6 For the second embodiment of the application, the embodiment provides a photovoltaic building integrated user virtual power plant day-ahead transaction method based on a blockchain. In order to verify the beneficial effects of the application, economic benefit calculation and simulation experiments are used for scientific demonstration.

[0131] Specifically, the application optimizes demand side resource management through a series of steps to minimize electricity costs and achieve peak clipping and valley filling. The experiment uses 30-day data of 8 users in the Pecan Street dataset for simulation experiments, from July 1, 2018 to July 31, 2018.

[0132] Further, 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 is 25℃, is 23℃, is 27℃, the peak-valley difference of peak clipping and valley filling is reduced by 30%, the step size is 0.01kWh, and are 0, and are 0.5C, η is 0.9, θ is 0.2, is 0.95, π is 0.04USD / kWh, ρ is the electricity price (0.1129USD / kWh from 8pm to 5pm the next day, and 0.2499USD / kWh from 5pm to 8pm), ω AC , ω S , ω B , ω Z are 0.1, 0.1, 0.01, and 1, respectively.

[0133] The experimental results show that the application effectively reduces the electricity cost, especially significantly reduces the electricity consumption during the peak period of electricity price (from 5 pm to 8 pm), and the new energy generation and battery discharge effectively supplement the power supply. Compared with the initial planning, S3.3 realizes a 24% reduction in the peak electricity consumption, and even after considering the user comfort (step S3.5), it still maintains an 18% reduction in the peak-valley difference. The planning at the level of demand-side resources also reflects that the peak shaving and valley filling are completed on the premise of trying to meet the user's electricity comfort at the device level.

[0134] Figure 5 The net load changes of S3.1, S3.3 and S3.5 are directly displayed, and the optimization process is clearly presented. Figure 6 Then a typical day is selected, and the differences between the initial planning (S3.1) and the final planning (S3.5) at the level of demand-side resources are compared and displayed.

[0135] In summary, the optimization method of the application not only realizes the reduction of the overall electricity cost, but also guarantees the electricity comfort of the user at the device level, and effectively completes the peak shaving and valley filling target.

[0136] It should be noted that the above embodiments are only used to illustrate the technical solutions of the application and not to limit it. Although the application has been described in detail with reference to the preferred embodiments, it should be understood by those skilled in the art that the technical solutions of the application can be modified or replaced equivalently without departing from the spirit and scope of the technical solutions of the application, and they should be covered in the scope of the claims of the application.

Claims

1. A blockchain-based method for day-ahead trading of virtual power plants for building-integrated photovoltaics (BIPV) users, characterized by: include, A blockchain implementation framework for virtual power plant transactions is constructed, which divides the virtual power plant into a demand-side resource aggregation layer and a demand-side resource layer, and deploys the main chain and side chains. A demand-side resource model is established based on the aforementioned demand-side resource layer; The day-ahead virtual power plant trading process is set up based on the aforementioned blockchain implementation framework; The day-ahead virtual power plant trading process is optimized using the optimization module and the demand-side resource model to obtain the optimal solution for producers and consumers to operate demand-side resources and complete the transaction. The demand-side resource model includes load modules, battery modules, photovoltaic modules, grid power supply modules, and feeder modules; the demand-side resource model includes a first constraint, a fourth constraint, and a fifth constraint. The specific formula for the first constraint condition is as follows: Where u represents the producer-consumer ratio, h represents the time interval, Δh represents the sampling period, C represents the equivalent heat capacity, R represents the equivalent thermal resistance, and γ represents the air conditioning operation mode. Let u be the indoor temperature of the consumer at time h. Hourly power consumption of an adjustable load air conditioner Let u be the outdoor temperature at time h. For the total time, For total producers and consumers; The specific formula for the fourth constraint is as follows: Where η is the battery charge / discharge efficiency. and These are charging power and discharging power, respectively. Where u represents battery capacity, h represents time, and SoC represents the time period. u,h Battery status. For battery capacity, For the total time, For total producers and consumers; The specific formula for the fifth constraint is as follows: in, θ and These represent the percentages of the battery's charge and discharge capabilities, respectively. and These are the lower limits of charging power and discharging power, respectively. and These are the upper limits for charging power and discharging power, respectively, for the SoC. u,1 The SoC represents the initial battery state. u,24 This indicates the battery's state of charge at the end of the process. A binary battery charging status indicator. For binary battery discharge status indication, SoC u,h Here, u represents the power status, h represents the time, and u represents the power producer / consumer. For the total time, For total producers and consumers; The optimization module includes the objective function, as follows: in, Charge the battery. To contribute to the local consumption of photovoltaic power. Purchase electricity for the power grid f is the battery discharge capacity. u,h For grid power supply, Z u This is a regularization term.

2. The blockchain-based method for day-ahead trading of building-integrated photovoltaic (BIPV) user virtual power plants as described in claim 1, characterized in that: The day-ahead virtual power plant transaction process based on the blockchain implementation framework includes: Distribution network operators publish time-of-use electricity prices, which are then transmitted to various levels of entities through the main chain and side chains. The planning results of each household energy management system are aggregated through the sidechain and then reported by the aggregator to the virtual power plant operator through the main chain to form the day-ahead net load plan; Virtual power plant operators adjust and optimize their operations by performing peak shaving and valley filling based on day-ahead net load planning; Aggregators receive the adjusted day-ahead net load plan, perform optimization decomposition, calculate peak-shaving capacity, and estimate corresponding incentives. Based on the decomposition results, entities at all levels will re-plan demand-side resources, and virtual power plant operators will allocate incentives to each producer and consumer at each level. Virtual power plant operators trade electricity with distribution network operators at the trading center based on demand-side resource planning results.

3. A blockchain-based day-ahead trading system for building-integrated photovoltaic (BIPV) user virtual power plants, based on the blockchain-based day-ahead trading method for BIPV user virtual power plants as described in any one of claims 1 to 2, characterized in that: It also includes, The framework building module is used to build a blockchain implementation framework for virtual power plant transactions. It divides the virtual power plant into a demand-side resource aggregation layer and a demand-side resource layer, and deploys the main chain and side chains. The model building module is used to build a demand-side resource model based on the demand-side resource layer. The process setting module is used to set the day-ahead virtual power plant trading 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 to obtain the optimal solution for producers and consumers to operate demand-side resources and complete the transaction.

4. 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, it implements the steps of the day-ahead trading method for the blockchain-based photovoltaic building integrated user virtual power plant as described in any one of claims 1 to 2.

5. A computer-readable storage medium having a computer program stored thereon, characterized in that: When the computer program is executed by the processor, it implements the steps of the day-ahead trading method for the blockchain-based photovoltaic building integrated user virtual power plant as described in any one of claims 1 to 2.

Images