Rolling p2p power distribution information interaction method between micro-grid intelligent buildings

Abstract

This invention relates to the field of microgrid-based intelligent building optimization and operation technology, and particularly to a rolling P2P distributed energy information interaction method among microgrid-based intelligent buildings. The steps include: establishing a feasible domain model for the flexible resources of intelligent buildings, including a distributed photovoltaic power generation prediction interval model, an energy storage system charging and discharging power interval model, and a flexible load power adjustment interval model; establishing a P2P energy sharing aggregation interval model and a rolling P2P energy sharing framework; and establishing and solving a microgrid-based intelligent building distributed information interaction scheduling model, including constructing the objective function and constraints of the microgrid economic scheduling model, transforming the microgrid-based intelligent building distributed information interaction problem, and the solution process. This method fully explores the scheduling potential of flexible resources within buildings, improves the flexibility of coordinated scheduling, enhances the enthusiasm of buildings to participate in P2P energy interaction, and increases the self-consumption level of distributed energy, effectively solving the problem of large-scale intelligent building optimization iteration.

Description

Technical Field

[0001] This invention relates to the field of microgrid multi-intelligent building optimization operation technology, and in particular to a rolling P2P distributed power information interaction method among microgrid intelligent buildings. Background Technology

[0002] With the rapid development of distributed generation technology and the popularization of renewable energy, traditional centralized power systems are gradually transforming into distributed power systems. Microgrids, as an important distributed energy system, can achieve local self-sufficiency and optimized allocation of electricity, improving energy utilization efficiency and power supply reliability. Against this backdrop, power demand management and supply-demand balance among smart buildings, which possess both production and consumption attributes, have become key issues. P2P power sharing among multiple smart buildings can not only optimize power resource allocation but also enhance the initiative of smart buildings in participating in the market and the local consumption of distributed energy.

[0003] When addressing the increasingly large-scale P2P power sharing problem in microgrids and smart buildings, traditional centralized methods suffer from drawbacks such as large communication data volumes, low robustness, and privacy concerns, failing to meet the demands of distribution network optimization and dispatch in a power market environment. Distributed optimization, through local information and neighbor communication principles, can achieve optimized dispatch of the entire system, offering greater privacy and timeliness, and aligning with the overall trend of power system decentralization.

[0004] Although existing technologies employ distributed methods that differ from traditional centralized methods, distributed scheduling methods such as consensus algorithms, game theory, and ADMM require each subject to solve optimization sub-problems in a pre-defined order and communicate the decision results between subjects during the iterative solution process. This results in problems such as slow computation speed and poor convergence. Summary of the Invention

[0005] The purpose of this invention is to overcome the shortcomings of existing technologies and propose a rolling P2P distributed power information interaction method among microgrid smart buildings, which can improve the flexibility of coordinated scheduling of multiple smart buildings in a microgrid.

[0006] To achieve the above objectives, the present invention adopts the following specific technical solution:

[0007] The microgrid-based intelligent building inter-building rolling P2P distributed power information interaction method provided by the present invention includes the following steps:

[0008] S1. Establish a feasible domain model for the flexibility resources of intelligent buildings, including a distributed photovoltaic power generation prediction range model, an energy storage system charging and discharging power range model, and a flexible load power adjustment range model.

[0009] S2. Construct a rolling P2P power sharing strategy for intelligent building aggregated power range, including establishing a P2P power sharing aggregated range model and establishing a rolling P2P power sharing framework.

[0010] S3. Establish and solve the distributed information interaction scheduling model for microgrid intelligent buildings, including constructing the objective function and constraints of the microgrid economic scheduling model, transforming the distributed information interaction problem of microgrid intelligent buildings, and the solution process.

[0011] Furthermore, in step S1, the expression for the distributed photovoltaic power generation prediction interval model is:

[0012] ;

[0013] In the formula, Indicates buildings exist The feasible domain for flexible photovoltaic output during different time periods For buildings exist Solar power output forecast for the period For buildings exist Actual output of photovoltaic power during the period and These represent the confidence levels for photovoltaic output being lower than and higher than the predicted value, respectively. and These represent the confidence levels, respectively. and The quantiles for probability prediction.

[0014] Furthermore, in step S1, the expression for the energy storage system's charge / discharge power range model is:

[0015] ;

[0016] In the formula, Indicates buildings exist The flexibility and feasible domain of time-of-use energy storage systems and Representing buildings exist The charging and discharging power of the time-limited energy storage system and Buildings The upper limit of energy storage charging and discharging power. Indicates buildings exist Energy storage capacity value for a given time period and These represent the charging and discharging efficiency of the energy storage system. and Buildings The maximum and minimum values ​​of the state of charge of the energy storage system.

[0017] Furthermore, in step S1, the expression for the flexible load power adjustment interval model is:

[0018] ;

[0019] In the formula, Indicates buildings exist The feasible range for flexibility of time-based flexible loads. Indicates buildings exist Time-based flexible load power, and Buildings exist Maximum and minimum values ​​after time-period load adjustment.

[0020] Furthermore, in step S2, the expression for the P2P energy sharing aggregation interval model is as follows:

[0021] ;

[0022] In the formula, The aggregated power range for P2P energy sharing after Minkowski summation; For the first The feasible domain space of demand-side resources; The Minkowski summation symbol; Indicates buildings exist Net P2P interaction power during the period Indicates buildings exist Purchase power during the time period Indicates buildings exist Sales power during a given time period; For the first The power value of demand-side resources; the aggregated P2P interaction power feasible domain is represented by the power range form, thereby quantifying the flexibility and control capability of resources, so that distributed resources in the building can participate in the transaction equally;

[0023] ;

[0024] ;

[0025] In the formula, This represents a Boolean variable. To ensure that the purchase and sale of electricity in the building do not occur simultaneously, the power purchased is set to 1, and the power sold is set to 0. and Representing buildings exist Power size for buying and selling during specific time periods; and Representing buildings exist The maximum power purchased and sold during a given time period;

[0026] Buildings During the scheduling period The feasible range for internal transaction power is:

[0027] ;

[0028] ;

[0029] ;

[0030] ;

[0031] In the formula: and Buildings exist Maximum and minimum power limits for sales volume during specific time periods; and Buildings exist Maximum and minimum power limits for the amount of goods purchased during a given time period.

[0032] Furthermore, in step S2, the rolling P2P energy sharing framework is as follows:

[0033] Intelligent buildings describe their feasible domain based on their resource scheduling flexibility, and aggregate them into a whole through Minkowski theory during P2P energy interaction. Participating in rolling P2P power sharing in the form of aggregated power ranges, among which, This indicates the length of the prediction time range, dividing the entire optimization cycle of the system into... Each time period; P2P energy interaction during electricity delivery period Previous Open, and before the delivery of electricity Closed; the P2P energy interaction period can be represented as During this process, each building, based on its own feasible aggregation range and electricity purchase and sale strategy, seeks a matching trading partner on the microgrid P2P interactive platform to negotiate and ultimately complete the electricity purchase and sale; after the electricity is delivered, each building manages its energy during the designated period. The system continuously acquires new forecast information and updates its own demand situation to reduce the impact of uncertainties and dynamically adjusts its aggregated power range to participate in the next period's energy interaction; at this time, the rolling range moves forward to the next operating period. Energy Management Start Time The above operations are repeated iteratively until the requirements of all smart buildings within the microgrid are met.

[0034] Furthermore, in step S3, the objective function of the microgrid economic scheduling model is as follows:

[0035] ;

[0036] ;

[0037] In the formula: For microgrid smart buildings within the predicted time range Total internal operating costs Indicates buildings Within the forecast time range Total internal operating costs For buildings exist The cost of P2P electricity exchange incurred when purchasing electricity from other buildings during a certain period. For buildings exist Time-of-use demand response cost For buildings exist Energy storage operation and maintenance costs during different time periods For buildings exist The cost of power interaction with the main network during specific time periods. To measure the cost of risk; and Representing buildings exist Time period to building The price of buying and selling power; and Representing buildings exist Time period to building The size of the power purchased and sold; For buildings Demand response coefficient For buildings exist Ideal power consumption during a given time period This represents the operation and maintenance cost coefficient for energy storage systems. and They represent Time-of-use electricity pricing and grid connection pricing for buildings; and Representing buildings exist The time period is determined by the power of buying and selling on the main network; Confidence level; As an auxiliary variable, the optimal value is ; Number of scenes; For buildings exist Cost loss value for the period;

[0038] The uncertainty of photovoltaic output can lead to different revenue levels for buildings in P2P electricity transactions, depending on the power sold. or At that time, it will result in a loss of profit; when or At that time, it will lead to losses in electricity purchase costs; the uncertainty of photovoltaic output will cause losses to the building P2P power interaction. for:

[0039] ;

[0040] In the formula, The unit cost is penalized for risk.

[0041] Furthermore, in step S3, the constraints include intelligent building power balance constraints, main grid power purchase and sales constraints, and P2P power sharing constraints.

[0042] The power balance constraint for intelligent buildings is to ensure power balance within each building during the power sharing process. Ignoring network losses, this constraint must be satisfied, and its expression is as follows:

[0043] ;

[0044] The main grid power purchase and sale constraint allows buildings to sell surplus electricity when photovoltaic power generation is in surplus or purchase electricity from the main grid when there is a power shortage. The range of changes in the purchased and sold electricity volume is expressed as follows:

[0045] ;

[0046] The P2P power sharing constraint is the inter-building P2P power sharing power constraint, and its expression is as follows:

[0047] .

[0048] Furthermore, in step S3, the transformation of the distributed information interaction problem in the microgrid smart building is as follows:

[0049] The microgrid P2P interaction platform retains the transaction electricity information of all buildings. Each building updates its own electricity purchase and sale strategy based on the transaction electricity of other buildings. The transaction power is used as a consistency variable for inter-building P2P transactions. After determining their electricity purchase and sale demand through internal energy management, buildings upload the pre-transaction electricity range within the predicted time frame to the communication network via BEMS. The microgrid of the smart building adopts an undirected graph model. Description; in which Let represent the set of each building node. The P2P interaction power is represented by the node injection power. The communication links in the inter-node communication network are considered undirected edges. Then, the edge-node association matrix of the communication topology graph is... It can be represented as:

[0050] ;

[0051] In the formula, Indicates by building and buildings The communication link formed by the undirected graph and the Laplace matrix. It is a symmetric positive semi-definite matrix, and its first... The line represents the first The cumulative gain generated when a smart building node disturbs the interaction power of all other nodes;

[0052] Using P2P interaction power as a consistency variable between buildings, while also satisfying the overall power balance condition of the microgrid, the consistency variable matrix is ​​as follows:

[0053] ;

[0054] Interaction pair-interaction coefficient matrix and power balance condition for a single scheduling period:

[0055] ;

[0056] ;

[0057] In the formula, Indicates the time range of the forecast Internal battery level interaction information; Indicates in Time Period Buildings To the building Interactive power consumption; in buildings and buildings The interaction pairs involved buildings and buildings During interaction The value is 1 for all other cases and 0 for the rest. ,in Indicates the Kronecker product. It is the identity matrix;

[0058] The microgrid intelligent building scheduling problem is transformed into a matrix form, using auxiliary variables. The Lagrange function is constructed as follows:

[0059] ;

[0060] In the formula, Indicate to remove Smart buildings within the post-microgrid The objective function for the time period; Let represent the decision variables in microgrid intelligent building scheduling, where Indicates buildings Decision variables for each period within the forecast timeframe; and These are the coefficient matrix and the parameter matrix, respectively; Indicates buildings exist Vector form of decision variables over a given period; The Lagrange multiplier, representing the balance of power interaction between buildings, can also represent price information when buildings engage in peer-to-peer (P2P) interactions. Represents the Lagrange multipliers of consistency variables; ; ; Indicates penalties for violating consistency constraints; The feasible region of the building local decision variables is represented by equations (12)-(16); Indicates the penalty coefficient;

[0061] Indicates buildings The group of adjacent buildings, Indicates at least Secondary information interaction and buildings Establish a network of buildings for communication. and The definition of information exchange content between buildings is:

[0062] ;

[0063] In the formula: information This includes information on electricity purchase and sale and power consumption; This indicates information about penalties for power convergence between buildings; Represents the building at the initial moment To the building Transmitted information on the amount of electricity purchased and sold, and power output; Indicates the initial building To the building The message conveyed is one of punishment for conformity; Indicates the first During the next iteration Zhonglouyu Local electricity purchase and sales data and power information; Indicates the first During iteration Zhonglouyu Information on penalties for local conformity.

[0064] Furthermore, in step S3, during the solution process, the building... According to the Pre-traded electricity information in the next iteration Update its own decision variables Then calculate the Lagrange multiplier variables. As the power of P2P interaction, the expression is as follows:

[0065] ;

[0066] ;

[0067] In the formula: Indicates the number of iterations; This represents the value of the variable that minimizes the objective function;

[0068] For each building Gradually avoid duplicate data collection during information exchange. Information in the building Transmitted to buildings In addition to its own information, the information also includes The information; through a distributed information exchange process, the computation process can be decomposed into sub-problems involving N buildings; let As a new dual iterative variable, update the pre-traded electricity information. The inter-building solution process based on information interaction is as follows:

[0069] ;

[0070] ;

[0071] In the formula, Indicates the number of information exchanges;

[0072] Buildings After determining its own P2P purchase and sale electricity volume, it reaches consistency with other buildings by iterating its P2P interaction power.

[0073] Using residuals as the convergence criterion, the convergence accuracy is... The residual is defined as:

[0074] ;

[0075] The solution process steps are as follows:

[0076] Step 1: Initialize the internal interaction power of each building interaction pair Develop pre-purchased electricity sales and energy storage charging and discharging plans for each building. ;

[0077] Step 2: Each building's BEMS adjusts its power based on the initial interaction power. The interaction power of each interaction pair is updated through the information exchange strategy to obtain interaction power information. ;

[0078] Step 3: Determine if the convergence condition is met. If not, return to Step 2 until the convergence condition is met.

[0079] The present invention can achieve the following technical effects:

[0080] 1. The microgrid smart building inter-building rolling P2P power distributed information interaction method provided by the present invention is based on the Minkowski summation to perform equal aggregation of distributed resources within the building to obtain the feasible region of P2P power interaction, and participate in inter-building power interaction in the form of aggregation intervals, which fully explores the scheduling potential of flexible resources within the building and improves the flexibility of microgrid multi-smart building coordinated scheduling.

[0081] 2. To address the coordination and optimization problem of power interaction among smart buildings, a rolling P2P energy interaction framework is proposed. While considering the risk costs in P2P power interaction, it reduces the overall operating cost of the microgrid. It balances the economics of system operation, improves the enthusiasm of smart buildings to participate in P2P power interaction, and enhances the self-consumption level of distributed energy resources.

[0082] 3. The proposed distributed information interaction optimization strategy enables multiple smart buildings in a microgrid to exchange only expected transaction volume information while simultaneously solving their own optimization problems in parallel, demonstrating good compatibility with the rolling P2P energy sharing approach. This strategy avoids problems such as high computational pressure and privacy leakage, improves the convergence speed of distributed information interaction, has good scalability, and can effectively solve the optimization iteration problem of large-scale smart buildings. Attached Figure Description

[0083] Figure 1 This is a schematic diagram of the microgrid multi-intelligent building operation framework provided in an embodiment of the present invention.

[0084] Figure 2 This is a schematic diagram of an interval rolling P2P power sharing framework provided according to an embodiment of the present invention.

[0085] Figure 3This is a schematic diagram of the adjustable power domain of the photovoltaic building 1 aggregation zone according to an embodiment of the present invention;

[0086] Figure 4 This is a schematic diagram of the optimized scheduling results of photovoltaic building 1 in Example 1 provided by the present invention;

[0087] Figure 5 This is a schematic diagram of the optimized scheduling results of photovoltaic building 1 in Example 2 provided by the present invention;

[0088] Figure 6 This is a schematic diagram of the optimized scheduling results of photovoltaic building 1 in Example 3 provided by the present invention.

[0089] Figure 7 This is a schematic diagram of the P2P interactive power formation process provided in an embodiment of the present invention.

[0090] Figure 8 This is a schematic diagram of the consistency error convergence curve provided in an embodiment of the present invention.

[0091] Figure 9 This is a schematic diagram illustrating the number of iterations for different building sizes according to an embodiment of the present invention. Detailed Implementation

[0092] In the following description, embodiments of the invention will be described with reference to the accompanying drawings. In the description below, the same modules are denoted by the same reference numerals. Where the same reference numerals are used, their names and functions are also the same. Therefore, their detailed description will not be repeated.

[0093] To make the objectives, technical solutions, and advantages of this invention clearer, the invention will be further described in detail below with reference to the accompanying drawings and specific embodiments. It should be understood that the specific embodiments described herein are merely illustrative of the invention and do not constitute a limitation thereof.

[0094] This invention provides a microgrid-based intelligent building rolling P2P power sharing distributed information interaction method, including the following:

[0095] 1. Construct a microgrid-based intelligent building operation framework.

[0096] Consider a microgrid system composed of multiple intelligent buildings, interconnected by power and communication networks. Each building in the microgrid is considered a node based on these communication connections. The operational framework of the microgrid multi-intelligent building system, based on the bidirectional flow of electrical energy and information, is shown in the attached figure. Figure 1 As shown.

[0097] Each building is equipped with an Energy Storage System (ESS), photovoltaic (PV) and other renewable energy generation, as well as flexible resources such as elastic loads (EL). These flexible loads reduce the power supply pressure on the distribution network to the microgrid system by shifting electricity demand from peak to off-peak periods. The smart buildings participate in rolling P2P energy sharing through a microgrid P2P interaction platform for the purchase and sale of electricity. Simultaneously, each building is equipped with a Building Energy Management System (BEMS) to control its internal flexible resources.

[0098] During the rolling P2P power sharing phase, the smart building, based on the PV power generation prediction interval and its own distributed resource optimization, uploads the pre-interaction power interval within the prediction time domain to the communication network through BEMS, updates the P2P interaction power and feeds it back to BEMS, optimizes the distributed resources within the prediction time domain, and reaches an equilibrium state when the interaction power no longer changes, thus obtaining the optimal value of P2P interaction power between buildings. The power purchase and sale are delivered on the microgrid P2P interaction platform and the calculation for the next time period is performed.

[0099] 2. Establish a rolling P2P power sharing model for aggregated power ranges in intelligent buildings.

[0100] 2.1 Establish a feasible domain model for the flexibility resources of intelligent buildings, including a distributed photovoltaic power generation prediction range model, an energy storage system charging and discharging power range model, and a flexible load power adjustment range model.

[0101] 2.1.1 Distributed Photovoltaic Power Generation Prediction Interval Model:

[0102] The electricity for P2P interaction between smart buildings comes from distributed photovoltaic (PV) power, and the uncertainty of PV output can be modeled using prediction intervals at different confidence levels. First, the quantiles of the predicted PV output at different confidence levels are calculated using probability distributions. Then, the interval containing the PV output caused by the prediction error is represented as follows:

[0103] (1)

[0104] In the formula, Indicates buildings exist The feasible domain for flexible photovoltaic power output during specific time periods; For buildings exist Solar power output forecast for the specified time period; For buildings exist Actual photovoltaic output during the period; and These represent the confidence levels for photovoltaic output being lower than and higher than the predicted value, respectively. and These represent the confidence levels, respectively. and The quantiles for probability prediction.

[0105] 2.1.2 Model of charging and discharging power range of energy storage system:

[0106] The charging and discharging power of energy storage systems in smart buildings can be adjusted according to a predetermined plan. The power adjustment range of the energy storage system is deterministic, but it is affected by the energy storage capacity and the upper limit of the charging and discharging power. Therefore, the charging and discharging power range of the energy storage system can be expressed as:

[0107] (2)

[0108] In the formula: Indicates buildings exist The feasible domain for flexibility of time-of-use energy storage systems; and Representing buildings exist The charging and discharging power of the time-limited energy storage system; and Buildings The upper limit of energy storage charging and discharging power; Indicates buildings exist Energy storage capacity value for a given time period; and These are the charging and discharging efficiencies of the energy storage system; and Buildings The maximum and minimum values ​​of the state of charge of the energy storage system.

[0109] 2.1.3 Flexible load power adjustment range model:

[0110] Based on the power demand in P2P power sharing, flexible loads in smart buildings can adjust their power consumption within a certain range to achieve economical building operation. The power adjustment range of flexible loads can be expressed as:

[0111] (3)

[0112] In the formula: Indicates buildings exist The feasible range for flexibility of time-based flexible loads; Indicates buildings exist Flexible load power over time periods; and Buildings exist Maximum and minimum values ​​after time-period load adjustment.

[0113] 2.2 Establish a P2P power sharing aggregation interval model.

[0114] Influenced by the photovoltaic power output prediction range, the P2P interaction power between buildings also fluctuates within a certain range. Smart buildings are composed of various distributed resources aggregated equally. Using the Minkowski summation theory, the photovoltaic power generation prediction range and the controllable flexible resource power range are effectively integrated, and the P2P interaction power range of smart buildings is quantified as follows:

[0115] ; (4)

[0116] In the formula: The aggregated power range for P2P energy sharing after Minkowski summation; For the first The feasible domain space of demand-side resources; The Minkowski summation symbol; Indicates buildings exist Net P2P interaction power during the period Indicates buildings exist Purchase power during the time period Indicates buildings exist Sales power during a given time period. For the first The power values ​​of various demand-side resources are used. The aggregated P2P interaction power feasible region is represented by a power range, thereby quantifying the resource flexibility and control capability, enabling multiple types of small-capacity and dispersed distributed resources within the building to participate in transactions equally.

[0117] The integrated P2P power sharing zone, while satisfying the aforementioned feasible domain space, should also meet the following power constraint:

[0118] (5)

[0119] (6)

[0120] In the formula, This represents a Boolean variable. To ensure that the purchase and sale of electricity in the building do not occur simultaneously, the power purchased is set to 1, and the power sold is set to 0. and Representing buildings exist Power size for buying and selling during specific time periods; and Representing buildings exist The maximum value of power purchased and sold during a given time period.

[0121] In the process of modeling the P2P power sharing aggregation interval, considering that the photovoltaic output interval is represented at a certain confidence level, the building is derived based on equation (4). During the scheduling period The feasible range for internal transaction power is:

[0122] (7)

[0123] (8)

[0124] (9)

[0125] (10)

[0126] In the formula: and Buildings exist Maximum and minimum power limits for sales volume during specific time periods; and Buildings exist Maximum and minimum power limits for the amount of goods purchased during a given time period.

[0127] 2.3 Establish a rolling P2P power sharing framework.

[0128] Intelligent buildings describe their feasible domain based on their resource scheduling flexibility, and aggregate them into a whole through Minkowski theory during P2P energy sharing. Participating in rolling P2P energy exchange in the form of aggregated power ranges, as shown in the attached figure. Figure 2 As shown. Among them, This indicates the length of the prediction time range, dividing the entire optimization cycle of the system into... This refers to a specific time period. P2P energy interaction occurs during the electricity delivery period. Previous Open, and before the delivery of electricity Closed. Therefore, the P2P energy interaction period can be represented as... During this process, each building, based on its own feasible aggregation range and electricity purchase / sale strategy, seeks and negotiates with suitable trading partners on the microgrid P2P interactive platform to ultimately complete the electricity purchase / sale. After the electricity is delivered, each building manages its electricity usage during designated energy management periods. The system continuously acquires new forecast information and updates its own demand situation to reduce the impact of uncertainties, dynamically adjusting its aggregated power range to participate in the next energy interaction period. At this time, the rolling range moves forward to the next operating period. Energy Management Start Time The above operation is repeated, iterating continuously until the requirements of all smart buildings within the microgrid are met.

[0129] 3. Establish and solve the distributed information interaction scheduling model for microgrid-based intelligent buildings.

[0130] 3.1 Objective function of microgrid economic scheduling model.

[0131] While considering the building's own load adjustment and the operation and maintenance costs of the energy storage system, it is also necessary to consider the risk costs brought by the uncertainty of photovoltaic output to P2P power interaction. Therefore, the total operating cost function of the microgrid multi-smart building is:

[0132] (11)

[0133] (12)

[0134] In the formula: For microgrid smart buildings in the prediction time range Total internal operating costs; Indicates buildings Within the forecast time range Total internal operating costs; For buildings exist The cost of P2P electricity exchange generated from purchasing electricity from other buildings during a given period; For buildings exist Time-of-use demand response costs; For buildings exist Energy storage operation and maintenance costs over time periods; For buildings exist Cost of power interaction with the main network during specific time periods; To measure the cost of risk. and Representing buildings exist Time period to building The price of buying and selling power; and Representing buildings exist Time period to building The size of the power purchased and sold; For buildings Demand response coefficient; For buildings exist Ideal power consumption during a given time period; This represents the operation and maintenance cost coefficient for energy storage systems. and They represent Time-of-use electricity pricing and grid connection pricing for buildings; and Representing buildings exist The time period is determined by the power of buying and selling on the main network; Confidence level; As an auxiliary variable, the optimal value is... ; Number of scenes; For buildings exist Cost loss value for a given period.

[0135] The uncertainty of photovoltaic output can lead to varying levels of return for buildings in P2P electricity transactions, posing a certain degree of risk. When selling power... or At that time, it will result in a loss of profit; when or This will result in losses in electricity purchase costs. Under a certain confidence level, the uncertainty in photovoltaic output will lead to losses in building-to-building (P2P) electricity interaction. for:

[0136] (13)

[0137] In the formula: The unit cost is penalized for risk.

[0138] 3.2 Constraints.

[0139] 3.2.1 Power balance constraints in intelligent buildings:

[0140] During power sharing, to ensure power balance within each building, and neglecting network losses, the following constraints must be met:

[0141] (14)

[0142] 3.2.2 Constraints on Main Grid Electricity Purchase and Sale:

[0143] Buildings are permitted to sell surplus electricity during periods of photovoltaic power generation surplus or purchase electricity from the main grid during periods of power shortage. However, the amount of electricity purchased and sold at any given dispatch time should not be too high and should vary within a defined range.

[0144] (15)

[0145] In the formula: and Representing buildings exist The maximum power sold to the main network during a given time period.

[0146] 3.2.3 Constraints of P2P Energy Sharing:

[0147] The power exchange between buildings for P2P electrical energy should meet the following constraints:

[0148] ; (16)

[0149] 3.3 Transformation of distributed information interaction issues in microgrid-based intelligent buildings.

[0150] This section sets up a microgrid P2P interaction platform that retains the interactive electricity consumption information of all buildings. Each building updates its own electricity purchase and sale strategy based on the transaction electricity consumption of other buildings. Interactive power is used as a consistency variable for inter-building P2P transactions. After determining their electricity purchase and sale demand through internal energy management, buildings upload the pre-traded electricity consumption range within the predicted time frame to the communication network via BEMS. The microgrid of the smart building adopts an undirected graph model. Description. (Among them) Let represent the set of each building node. The P2P interaction power is represented by the node injection power. The communication links in the inter-node communication network are considered undirected edges. Then, the edge-node association matrix of the communication topology graph is... It can be represented as:

[0151] ; (17)

[0152] In the formula: Indicates by building and buildings The communication link formed by the undirected graph and the Laplace matrix. It is a symmetric positive semi-definite matrix, and its first... The line represents the first The cumulative gain generated when a smart building node disturbs the power of its interaction with all other nodes.

[0153] Treating P2P interaction power as a consistency variable between buildings, while simultaneously satisfying the overall power balance condition of the microgrid, can be expressed as follows:

[0154] 3.3.1 Consistency Variable Matrix

[0155] ; (18)

[0156] 3.3.2 The interaction pair-interaction coefficient matrix and power balance condition for a single scheduling period can be expressed as:

[0157] ; (19)

[0158] ; (20)

[0159] In the formula: Indicates the time range of the forecast Internal battery level interaction information; Indicates in Time Period Buildings To the building Interactive power consumption; in buildings and buildings The interaction pairs involved buildings and buildings During interaction The value is 1 for all other cases and 0 for the rest. ,in Indicates the Kronecker product. It is an identity matrix.

[0160] The microgrid multi-intelligent building scheduling problem in equation (11) is transformed into a matrix form, and auxiliary variables are used to... The Lagrangian function is constructed as shown in equation (21):

[0161] ; (twenty one)

[0162] In the formula: Indicate to remove Post-microgrid intelligent buildings The objective function for the time period; Denotes the decision variables in microgrid multi-intelligent building scheduling, where Indicates buildings Decision variables for each period within the forecast timeframe; and These are the coefficient matrix and the parameter matrix, respectively; Indicates buildings exist Vector form of decision variables over a given period; The Lagrange multiplier, representing the balance of power interaction between buildings, can also represent price information when buildings engage in peer-to-peer (P2P) interactions. Represents the Lagrange multipliers of consistency variables; ; ; Indicates penalties for violating consistency constraints; The feasible region of the building local decision variables is represented by equations (12)-(16); This represents the penalty coefficient.

[0163] Indicates buildings The group of adjacent buildings, Indicates at least Secondary information interaction and buildings Establish a network of buildings for communication. and The definition of information exchange content between multiple buildings is:

[0164] ;(twenty two)

[0165] ; (twenty three)

[0166] In the formula: information This includes information on electricity purchase and sale and power consumption; This indicates information about penalties for power convergence between buildings. Represents the building at the initial moment To the building Transmitted information on the amount of electricity purchased and sold, and power output; Indicates the initial building To the building The message conveyed is one of punishment for conformity; Indicates the first During the next iteration Zhonglouyu Local electricity purchase and sales data and power information; Indicates the first During the next iteration Zhonglouyu Information on penalties for local conformity.

[0167] 3.4 Solution process.

[0168] Buildings According to the Pre-traded electricity information in the next iteration Update its own decision variables Then calculate the Lagrange multiplier variables. As the power of P2P interaction, the expression is as follows:

[0169] ; (twenty four)

[0170] ; (25)

[0171] In the formula: Indicates the number of iterations; This represents the value of the variable that minimizes the objective function.

[0172] For each building Gradually avoid duplicate data collection during information exchange. Information in the building Transmitted to buildings In addition to its own information, the information also includes The information. Through a distributed information exchange process, the calculation process of the original problem (21) can be decomposed into sub-problems of N buildings. Let As a new dual iterative variable, update the pre-traded electricity information. The inter-building solution process based on information interaction is as follows:

[0173] ; (26)

[0174] ;(27)

[0175] In the formula: Indicates the number of information exchanges.

[0176] Using the above information interaction strategies, buildings After determining its own P2P purchase and sale electricity volume, its P2P interaction power reaches the same level as other buildings after a certain number of iterations.

[0177] Using residuals as the convergence criterion, the convergence accuracy is... The algorithm converges when equation (28) holds, and the residual is defined as:

[0178] ; (28)

[0179] The steps for updating the parallel optimization scheduling strategy of microgrid multi-intelligent buildings based on distributed information interaction are as follows:

[0180] Step 1: Initialize the internal interaction power of each building's interaction pair Develop pre-purchased electricity sales and energy storage charging and discharging plans for each building. ;

[0181] Step 2: Each building's BEMS calculates the initial interaction power. The interaction power of each interaction pair is updated using the information interaction strategy of equation (25), and the interaction power information is obtained. ;

[0182] Step 3: Determine whether the convergence condition is met according to equation (28). If not, return to step 2 until the convergence condition is met.

[0183] In the above calculation process, the P2P power sharing strategy update between buildings is completed through distributed parallel computing. When the convergence condition is met, the interaction power of each building no longer changes. At the same time, each building only realizes P2P power sharing by transmitting information such as interaction power between the main buildings, without needing to share the power data of photovoltaics, energy storage, and loads, thereby protecting the privacy of smart buildings and reducing computational pressure.

[0184] The following specific embodiments verify and illustrate the microgrid intelligent building rolling P2P power sharing distributed information interaction method provided by the present invention.

[0185] To verify the proposed interval rolling P2P energy sharing model, this paper uses an IEEE 13-node distribution network system for simulation analysis. The microgrid system consists of photovoltaic buildings 1-5 equipped with both photovoltaic and energy storage systems, and general buildings 6-13 equipped only with energy storage systems. The installed capacity of the distributed photovoltaic system is 250kWh; the energy storage system has a charge / discharge efficiency of 0.95, an upper and lower limit of energy storage capacity of 600kWh / 60kWh, a maximum charge / discharge power of 60kW, and a charge / discharge cost of ¥0.1 / kW. The optimization period is... Interval period The predicted time range is 15 minutes. Number of information exchanges between adjacent buildings convergent residuals The building subproblem is solved using Yalmip+Cplex. To verify the feasibility and effectiveness of the proposed model, the following three examples are set up for validation:

[0186] Example 1: Without considering P2P power sharing between smart buildings, each building independently arranges its energy consumption and energy storage charging and discharging plans;

[0187] Example 2: Consider centralized P2P power sharing among smart buildings, with each building independently arranging its energy consumption and energy storage charging and discharging plans;

[0188] Example 3: Considering interval rolling P2P power sharing based on aggregated power, the distributed information interaction strategy proposed in this paper is used to solve the problem.

[0189] Confidence level Taking 0.9 as an example, the predicted range of photovoltaic power generation varies at different confidence levels. Each building's BEMS (Building Energy Management System) obtains the range of dispatchable capacity that the building can provide in P2P energy sharing by solving the aggregated power range of flexible resources. Due to the different characteristics of demand-side flexible resources, the aggregated dispatchable capacity ranges are all different. Taking photovoltaic building 1 as an example, the optimal upper and lower bounds of the aggregation model for photovoltaic building 1 at different confidence levels are given. The power consumption of P2P interactions is shown in the attached figure. Figure 3 As shown.

[0190] From the appendix Figure 3 It can be seen that the power boundary of the aggregation interval represents the maximum and minimum interactive power that the building's schedulable resources can provide to the microgrid P2P interaction platform during a certain period. to During certain periods, distributed photovoltaic power output is relatively high. As the confidence level increases, the adjustable power domain within the aggregation range also increases, at which point the P2P interaction power exceeds [a certain threshold]. The aggregation interval at time and in Within the aggregation range, this aggregated power range considers both the predicted photovoltaic output under different confidence levels and the integration and coordination of various small-capacity and dispersed resources within the building, thereby improving resource utilization and addressing the uncertainties brought about by photovoltaic output in P2P interactions.

[0191] To further analyze the power interaction between smart buildings and verify the effectiveness of the rolling P2P method, the scheduling results of photovoltaic building 1 in Examples 1-3 are attached. Figure 4-6 As shown. From the appendix Figure 4 As can be seen, in Example 1, photovoltaic building 1 shifts its load to off-peak hours by participating in demand response from 1:00 to 8:00, purchasing low-priced electricity from the grid and charging its energy storage as a backup while meeting its own needs, thus reducing the amount of electricity purchased during peak hours. From 9:00 to 18:00, it generates electricity through photovoltaic power to meet its own electricity needs, and stores excess photovoltaic power through energy storage or sells it back to the grid to increase its income. From 19:00 to 24:00, it discharges energy storage to reduce the amount of electricity purchased from the grid, improving its economic efficiency.

[0192] As attached Figure 5 As shown, due to the consideration of P2P power sharing among buildings, compared with Example 1, the amount of electricity purchased by photovoltaic building 1 from the main grid during the period of 1:00-8:00 is significantly increased in Example 2. During the peak period of 18:00-23:00 when the time-of-use electricity price is higher, it sells the electricity to neighboring buildings through energy storage to increase revenue. During the photovoltaic power generation period of 9:00-18:00, the inter-building transaction price is lower than the grid connection price, and each building is more inclined to participate in P2P transactions to reduce its own operating costs.

[0193] As attached Figure 6As shown, Example 3, based on Example 2, considers the aggregation characteristics of flexible resources and conducts rolling P2P electricity trading in the form of power intervals. The simulation results show that smart buildings tend to trade within a larger feasible power range. However, due to the uncertainty of photovoltaic output, buildings need to continuously adjust their own output and P2P trading power to meet supply and demand balance. For photovoltaic building 1, the feasible range of each aggregation interval has greater elasticity during the non-photovoltaic power generation period from 18:00 to 6:00. This is because the demand-side load, influenced by price incentives, can shift the load power to the evening period. Simultaneously, photovoltaic power generation is higher between 6:00 and 18:00, and the uncertainty of the photovoltaic power generation forecast interval is also greater. Although facing the risk cost of photovoltaic output uncertainty, the electricity sales volume also increases under the influence of the inter-building trading price, effectively improving the enthusiasm for P2P trading between smart buildings, promoting PV power generation consumption, and enhancing the flexibility of the microgrid.

[0194] To demonstrate the superiority of the scheduling strategy presented in this paper, the operating costs of smart buildings and microgrids under different computational examples are compared, as shown in Table 1.

[0195] Table 1. Optimization cost analysis under different examples

[0196]

[0197] Table 1 analyzes the optimized costs of photovoltaic building 1 and general building 6 under different examples. Example 2 reduces the cost of photovoltaic building 1 by 7.79% and the cost of general building 6 by 8.6% compared to Example 1. Example 3 considers the rolling P2P interaction characteristics in the aggregation interval model and incorporates risk factors, resulting in a 6.72% reduction in the cost of photovoltaic building 1 and a 7.53% reduction in the cost of general building 6 compared to Example 2. At the microgrid level, the total operating costs of Examples 2 and 3 are reduced by 8.3% and 14.9%, respectively. This indicates that the proposed aggregation interval rolling P2P trading model can effectively utilize the dispatchability of dispersed, small-capacity resources within a building to participate in P2P power sharing, fully exploring the potential of microgrids and smart buildings.

[0198] Table 2. Revenue from electricity sales in photovoltaic building 1 at different confidence levels

[0199]

[0200] The confidence level reflects the decision-maker's risk preference and conservatism. Table 2 analyzes the impact of different confidence levels on the electricity sales revenue of photovoltaic building 1, using example 3 as an example. As the confidence level increases, the feasible region of the aggregation interval expands, the risk cost gradually decreases, the P2P interactive electricity volume increases, and the total revenue of photovoltaic building 1 increases accordingly. Although it increases the total operating cost of the smart building to some extent, the overall revenue of the microgrid is improved, demonstrating the feasibility and economy of the method proposed in this invention.

[0201] Appendix Figure 7 This represents the iterative process of power generation between P2P power interaction pairs in each building. Each curve corresponds to the power update process of each building's interaction. At the beginning of the iteration, each building sets its pre-interaction power based on its own power aggregation range and power purchase / sale strategy, and then searches for matching partners on the microgrid P2P interaction platform for interaction. During the process of forming consistent interaction power, as the number of information interactions increases, the interaction power of each pair gradually converges. Participating buyers and sellers in the P2P interaction can share power multiple times simultaneously until their own needs are met, improving efficiency while also considering the building's economics.

[0202] Appendix Figure 8 The convergence curves of the consistency error for different information interaction methods further illustrate the convergence of the distributed information interaction strategy of this invention. As can be seen from the figure, the root mean square error of the interaction power consistency variable converges at the 26th iteration. With the increase in the number of information interactions, more interaction power information from neighboring buildings is collected, resulting in a faster convergence speed than the consensus algorithm and the ADMM method. This demonstrates that the distributed information interaction strategy proposed in this invention has good performance and convergence accuracy in solving the P2P power sharing problem between buildings. All update calculations in the multi-intelligent building are completed locally and in parallel using its own information and the information received from neighboring buildings. Therefore, the overall computation time is short, while simultaneously considering privacy and computational efficiency. It also shows good adaptability to the formation of interaction power in the P2P power sharing process between buildings.

[0203] With the large-scale integration of smart buildings into the power distribution network in the future, increasing the number of nodes for each building from 10 to 40, the computational efficiency of the algorithm after connecting different numbers of buildings is analyzed as follows. Figure 9 As shown, the number of iterations exhibits an approximately positive correlation with the number of buildings; however, when the number of buildings remains the same, the number of iterations decreases with the increase in the number of information interactions. The data results demonstrate that the proposed method still possesses good scalability in larger-scale microgrid multi-intelligent building scheduling problems, and its solution efficiency meets the needs of practical engineering.

[0204] In the description of this specification, the references to terms such as "one embodiment," "some embodiments," "example," "specific example," or "some examples," etc., indicate that a specific feature, structure, material, or characteristic described in connection with that embodiment or example is included in at least one embodiment or example of the present invention. In this specification, the illustrative expressions of the above terms do not necessarily refer to the same embodiment or example. Furthermore, the specific features, structures, materials, or characteristics described may be combined in any suitable manner in one or more embodiments or examples. Moreover, without contradiction, those skilled in the art can combine and integrate the different embodiments or examples described in this specification, as well as the features of different embodiments or examples.

[0205] Although embodiments of the present invention have been shown and described above, it is understood that the above embodiments are exemplary and should not be construed as limiting the present invention. Those skilled in the art can make changes, modifications, substitutions and variations to the above embodiments within the scope of the present invention.

[0206] The specific embodiments of the present invention described above do not constitute a limitation on the scope of protection of the present invention. Any other corresponding changes and modifications made in accordance with the technical concept of the present invention should be included within the scope of protection of the claims of the present invention.

Claims

1. A method for rolling P2P distributed energy information exchange between microgrid smart buildings, characterized in that, Includes the following steps: S1. Establish a feasible domain model for the flexibility resources of intelligent buildings, including a distributed photovoltaic power generation prediction range model, an energy storage system charging and discharging power range model, and a flexible load power adjustment range model. S2. Construct a rolling P2P power sharing strategy for intelligent building aggregated power range, including establishing a P2P power sharing aggregated range model and establishing a rolling P2P power sharing framework. The expression for the P2P energy sharing aggregation interval model is as follows: ；（1） In the formula, The aggregated power range for P2P energy sharing after Minkowski summation; For the first The feasible domain space of demand-side resources; The Minkowski summation symbol; Indicates buildings exist Net P2P interaction power during the period Indicates buildings exist Purchase power during the time period Indicates buildings exist Sales power during a given time period; For the first The power value of demand-side resources; the aggregated P2P interaction power feasible domain is represented by the power range form, thereby quantifying the flexibility and control capability of resources, so that distributed resources in the building can participate in the transaction equally; ；（2） ；（3） In the formula, This represents a Boolean variable. To ensure that the purchase and sale of electricity in the building do not occur simultaneously, the power purchased is set to 1, and the power sold is set to 0. and Representing buildings exist Power size for buying and selling during specific time periods; and Representing buildings exist The maximum power purchased and sold during a given time period; Buildings During the scheduling period The feasible range for internal transaction power is: ；（4） ；（5） ；（6） ；（7） In the formula: and Buildings exist Maximum and minimum power limits for sales volume during specific time periods; and Buildings exist Maximum and minimum power limits for purchase volume during a specific time period; S3. Establish and solve the distributed information interaction scheduling model for microgrid intelligent buildings, including constructing the objective function and constraints of the microgrid economic scheduling model, transforming the distributed information interaction problem of microgrid intelligent buildings, and the solution process.

2. The microgrid intelligent building inter-building rolling P2P distributed power information interaction method according to claim 1, characterized in that, In step S1, the expression for the distributed photovoltaic power generation prediction interval model is: ；（8） In the formula, Indicates buildings exist The feasible domain for flexible photovoltaic output during different time periods For buildings exist Solar power output forecast for the period For buildings exist Actual output of photovoltaic power during the period and These represent the confidence levels for photovoltaic output being lower than and higher than the predicted value, respectively. and These represent the confidence levels, respectively. and The quantiles for probability prediction.

3. The microgrid intelligent building inter-building rolling P2P distributed power information interaction method according to claim 2, characterized in that, In step S1, the expression for the charging and discharging power range model of the energy storage system is: ； （9） In the formula, Indicates buildings exist The flexibility and feasible domain of time-of-use energy storage systems and Representing buildings exist The charging and discharging power of the time-limited energy storage system and Buildings The upper limit of energy storage charging and discharging power. Indicates buildings exist Energy storage capacity value for a given time period and These represent the charging and discharging efficiency of the energy storage system. and Buildings The maximum and minimum values ​​of the state of charge of the energy storage system.

4. The microgrid intelligent building inter-building rolling P2P distributed power information interaction method according to claim 3, characterized in that, In step S1, the expression for the flexible load power adjustment range model is: ；（10） In the formula, Indicates buildings exist The feasible range for flexibility of time-based flexible loads. Indicates buildings exist Time-based flexible load power, and Buildings exist Minimum and maximum values ​​after time-period load adjustment.

5. The microgrid intelligent building inter-building rolling P2P distributed power information interaction method according to claim 1, characterized in that, In step S2, the rolling P2P power sharing framework is specifically as follows: Intelligent buildings describe their feasible domain based on their resource scheduling flexibility, and aggregate them into a whole through Minkowski theory during P2P energy interaction. Participating in rolling P2P power sharing in the form of aggregated power ranges, among which, This indicates the length of the prediction time range, dividing the entire optimization cycle of the system into... Each time period; P2P energy interaction during electricity delivery period Previous Open, and before the delivery of electricity Closed; the P2P energy interaction period can be represented as During this process, each building, based on its own feasible aggregation range and electricity purchase and sale strategy, seeks a matching trading partner on the microgrid P2P interactive platform to negotiate and ultimately complete the electricity purchase and sale; after the electricity is delivered, each building manages its energy during the designated period. The system continuously acquires new forecast information and updates its own demand situation to reduce the impact of uncertainties and dynamically adjusts its aggregated power range to participate in the next period's energy interaction; at this time, the rolling range moves forward to the next operating period. Energy Management Start Time The process is repeated iteratively until the requirements of all smart buildings within the microgrid are met.

6. The microgrid intelligent building inter-building rolling P2P distributed power information interaction method according to claim 5, characterized in that, In step S3, the objective function of the microgrid economic scheduling model is as follows: ；（11） ；（12） In the formula: For microgrid smart buildings within the predicted time range Total internal operating costs Indicates buildings Within the forecast time range Total internal operating costs For buildings exist The cost of P2P electricity exchange incurred when purchasing electricity from other buildings during a certain period. For buildings exist Time-of-use demand response cost For buildings exist Energy storage operation and maintenance costs during different time periods For buildings exist The cost of power interaction with the main network during specific time periods. To measure the cost of risk; and Representing buildings exist Time period to building The price of buying and selling power; and Representing buildings exist Time period to building The size of the power purchased and sold; For buildings Demand response coefficient For buildings exist Ideal power consumption during a given time period This represents the operation and maintenance cost coefficient for energy storage systems. and They represent Time-of-use electricity pricing and grid connection pricing for buildings; and Representing buildings exist The time period is determined by the power of buying and selling on the main network; Confidence level; As an auxiliary variable, the optimal value is ; Number of scenes; For buildings exist Cost loss value for the period; The uncertainty of photovoltaic output can lead to different revenue levels for buildings in P2P electricity transactions, depending on the power sold. or At that time, it will result in a loss of profit; when or This will result in losses in electricity purchase costs; The loss caused by the uncertainty of photovoltaic output to building P2P power interaction for: ； （13） In the formula, The unit cost is penalized for risk.

7. The microgrid intelligent building inter-building rolling P2P distributed power information interaction method according to claim 6, characterized in that, In step S3, the constraints include intelligent building power balance constraints, main grid power purchase and sales constraints, and P2P power sharing constraints. The intelligent building power balance constraint is to ensure power balance within each building during the power sharing process. This constraint must be satisfied while neglecting network losses, and its expression is as follows: ；（14） The main grid power purchase and sale constraint allows buildings to sell surplus electricity when photovoltaic power generation is in surplus or purchase electricity from the main grid when there is a power shortage. The range of changes in the purchased and sold electricity volume is expressed as follows: ；（15） The P2P power sharing constraint is a power constraint for inter-building P2P power sharing, and its expression is as follows:

8. The microgrid intelligent building inter-building rolling P2P distributed power information interaction method according to claim 7, characterized in that, In step S3, the transformation of the distributed information interaction problem in the microgrid smart building is as follows: The microgrid P2P interaction platform retains the transaction electricity information of all buildings. Each building updates its own electricity purchase and sale strategy based on the transaction electricity of other buildings. The transaction power is used as a consistency variable for inter-building P2P transactions. After determining their electricity purchase and sale demand through internal energy management, buildings upload the pre-transaction electricity range within the predicted time frame to the communication network via BEMS. The microgrid of the smart building adopts an undirected graph model. Description; in which Let represent the set of each building node. The P2P interaction power is represented by the node injection power. The communication links in the inter-node communication network are considered undirected edges. Then, the edge-node association matrix of the communication topology graph is... It can be represented as: ；（17） In the formula, Indicates by building and buildings The communication link formed by the undirected graph and the Laplace matrix. It is a symmetric positive semi-definite matrix, and its first... The line represents the first The cumulative gain generated when a smart building node disturbs the interaction power of all other nodes; Using P2P interaction power as a consistency variable between buildings, while also satisfying the overall power balance condition of the microgrid, the consistency variable matrix is ​​as follows: ；（18） Interaction pair-interaction coefficient matrix and power balance condition for a single scheduling period: ；（19） ； （20） In the formula, Indicates the time range of the forecast Internal battery level interaction information; Indicates in Time Period Buildings To the building Interactive power consumption; in buildings and buildings The interaction pairs involved buildings and buildings During interaction The value is 1 for all other cases and 0 for the rest. ,in Indicates the Kronecker product. It is the identity matrix; The microgrid intelligent building scheduling problem is transformed into a matrix form, using auxiliary variables. The Lagrange function is constructed as follows: ； （21） In the formula, Indicate to remove Smart buildings within the post-microgrid The objective function for the time period; Let represent the decision variables in microgrid intelligent building scheduling, where Indicates buildings Decision variables for each period within the forecast timeframe; and These are the coefficient matrix and the parameter matrix, respectively; Indicates buildings exist Vector form of decision variables over a given period; The Lagrange multiplier, representing the balance of power interaction between buildings, can also represent price information when buildings engage in peer-to-peer (P2P) interactions. Represents the Lagrange multipliers of consistency variables; ; ; Indicates penalties for violating consistency constraints; The feasible region of the building local decision variables is represented by equations (12)-(16); Indicates the penalty coefficient; Indicates buildings The group of adjacent buildings, Indicates at least Secondary information interaction and buildings Establish a network of buildings for communication. and The definition of information exchange content between buildings is: ；（22） ； （23） In the formula: information This includes information on electricity purchase and sale and power consumption; This indicates information about penalties for power convergence between buildings; Represents the building at the initial moment To the building Transmitted information on the amount of electricity purchased and sold, and power output; Indicates the initial building To the building The message conveyed is one of punishment for conformity; Indicates the first During the next iteration Zhonglouyu Local electricity purchase and sales data and power information; Indicates the first During the next iteration Zhonglouyu Information on penalties for local conformity.

9. The microgrid intelligent building inter-building rolling P2P distributed power information interaction method according to claim 8, characterized in that, In step S3, during the solution process, the building... According to the Pre-traded electricity information in the next iteration Update its own decision variables Then calculate the Lagrange multiplier variables. As the power of P2P interaction, the expression is as follows: ；（24） ； （25） In the formula: Indicates the number of iterations; This represents the value of the variable that minimizes the objective function; For each building Gradually avoid duplicate data collection during information exchange. Information in the building Transmitted to buildings In addition to its own information, the information also includes The information; through a distributed information exchange process, the computation process can be decomposed into sub-problems involving N buildings; let As a new dual iterative variable, update the pre-traded electricity information. The inter-building solution process based on information interaction is as follows: ；（26） ； （27） In the formula, Indicates the number of information exchanges; Buildings After determining its own P2P purchase and sale electricity volume, it reaches consistency with other buildings by iterating its P2P interaction power. Using residuals as the convergence criterion, the convergence accuracy is... The residual is defined as: ；（28） The solution process steps are as follows: Step 1: Initialize the internal initial interaction power of each building interaction pair Develop pre-purchased electricity sales and energy storage charging and discharging plans for each building. ; Step 2: Each building's BEMS adjusts its power based on the initial interaction power. The interaction power of each interaction pair is updated through the information exchange strategy to obtain interaction power information. ; Step 3: Determine if the convergence condition is met. If not, return to Step 2 until the convergence condition is met.

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