Building group collaborative optimization operation method considering orderly charging and discharging of electric vehicles

Abstract

The invention relates to a building group collaborative optimization operation method considering ordered charging and discharging of an electric vehicle. The method comprises the following steps: step 1, establishing a single-building energy management strategy fusing EV ordered charging and discharging of each building; 2, constructing a building group collaborative optimization operation strategy considering EV ordered charging and discharging; 3, decomposing the building group collaborative operation strategy in the step 2 into a two-stage sequential optimization decision problem; and 4, solving the two-stage sequential optimization decision problem in the step 3 by using an improved ADMM algorithm based on multi-dimensional data cooperative transmission to obtain an optimal operation strategy of the building group. According to the method, a building group collaborative optimization operation strategy is established and converted into a two-stage optimization decision problem, an improved ADMM algorithm based on multi-dimensional data collaborative transmission is adopted for solving, and an optimal operation scheme of each building is obtained.

Description

Technical Field

[0001] This invention belongs to the field of smart energy system optimization and scheduling technology, and relates to a building group collaborative optimization operation method, especially a building group collaborative optimization operation method that considers the orderly charging and discharging of electric vehicles. Background Technology

[0002] With the advancement of global energy transition and the "dual carbon" goal, the optimized operation of energy systems in building clusters, as important carriers of demand-side energy consumption, has become a research hotspot. Building clusters use buildings as physical platforms and integrate information technology to regulate the equipment within each building. Meanwhile, electric vehicles (EVs), with their battery energy storage characteristics, have become a new type of flexible resource on the building side. Building clusters can guide EV users to engage in orderly charging and discharging by setting EV charging and discharging prices, thereby participating in building energy management.

[0003] Current research on EV participation in building cluster energy management typically uses a time probability model when EVs connect to the building to calculate the dispatchable time of EVs. During the dispatchable time of EVs, each building only considers the connected EVs as a special energy storage device, performs direct dispatch control after EV connection, and provides economic compensation to users based on the dispatch amount. However, it ignores the autonomous decision-making ability of EV users based on the price signals of each building in mobile scenarios, making it difficult to fully release the flexibility potential of EVs in multi-building collaborative optimization.

[0004] In research on building cluster collaborative optimization, different buildings form cooperative alliances through electricity trading mechanisms, often utilizing the Shapley value method or Nash negotiation theory for profit distribution. However, for building clusters considering orderly charging and discharging of EVs: the Shapley value method, based on marginal contribution theory, requires calculating the marginal contribution of a subject joining all possible subset alliances. But in building clusters considering orderly charging and discharging of EVs, the EV charging and discharging price set by a single building affects the charging and discharging plans of all EV users, making it impossible to form many subset alliances (or obtain revenue data). Furthermore, the Nash negotiation theory typically calculates the contribution of each subject using only electricity trading information between subjects. However, since EVs are mobile resources within the building cluster, the contribution of buildings guiding orderly charging and discharging of EVs cannot be reflected in electricity trading volume, reducing the enthusiasm of buildings that rely on EVs as their primary resource to participate in the alliance and affecting its operation.

[0005] Meanwhile, the alternating direction multiplier method (ADMM) is a commonly used distributed solution algorithm in multi-agent cooperative operation scenarios. However, when the standard ADMM algorithm solves the building group cooperation model that considers the orderly charging and discharging of EVs, if only the amount of electricity traded is passed as the iterative optimization variable, the electricity trading decisions between buildings and the charging and discharging plans of EV users will be isolated from each other. When determining the amount of electricity traded between buildings, the impact of EV charging and discharging behavior on each building along the way is not considered. When EV users carry out orderly charging and discharging, they only passively accept the pricing of their own building and cannot optimize the charging and discharging plan based on the global pricing within the building group, which affects the validity of the final result.

[0006] Therefore, in view of the above-mentioned technical problems, the present invention proposes a building cluster collaborative optimization operation method that considers the orderly charging and discharging of electric vehicles.

[0007] A search revealed no publicly available literature of the same or similar prior art as this invention. Summary of the Invention

[0008] This invention addresses the shortcomings of existing technologies by proposing a building cluster collaborative optimization operation method that considers the orderly charging and discharging of electric vehicles. Taking a building cluster composed of multiple buildings as the research object, and considering the situation where users can decide the charging and discharging plan when EVs move and park between multiple buildings, this invention establishes a building cluster collaborative optimization operation strategy and transforms it into a two-stage optimization decision problem. An improved ADMM algorithm based on multi-dimensional data collaborative transmission is then used to solve the problem, obtaining the optimal operation scheme for each building and realizing the collaborative operation of the building cluster and EVs.

[0009] The above-mentioned objective of this invention is achieved through the following technical solution: A method for collaborative optimization of building cluster operation considering the orderly charging and discharging of electric vehicles includes the following steps: Step 1: Construct a multi-building-EV collaborative operation system that considers orderly charging and discharging of EVs, and establish a single-building energy management strategy that integrates orderly charging and discharging of EVs in each building; Step 2: Based on the single-building energy management strategy of orderly charging and discharging of EVs in each building in Step 1, construct a building group collaborative optimization operation strategy that considers orderly charging and discharging of EVs. Step 3: Decompose the building cluster collaborative optimization operation strategy in Step 2 into a two-stage sequential optimization decision problem; the first stage is the inter-building power trading decision problem of integrated EV orderly charging and discharging, and the second stage is the building cluster power trading price decision problem. Step 4: Solve the two-stage sequential optimization decision problem in Step 3 using the improved ADMM algorithm based on multidimensional data collaborative transmission to obtain the optimal operation strategy for the building group.

[0010] Furthermore, the specific steps of step 1 include: Step 1.1: Construct a multi-building-EV collaborative operation system that considers the orderly charging and discharging of EVs; Step 1.2: Based on the multi-building-EV collaborative operation system constructed in Step 1.1, EV users adjust their charging and discharging plans according to the electricity prices set by each building and report them to the building. Step 1.3: Based on the charging and discharging plans of EVs received by each building in Step 1.2 and adjusted according to the electricity price set by each building, construct a single-building energy management strategy for the orderly charging and discharging of EVs in each building.

[0011] Furthermore, the specific steps of step 1.1 include: The multi-building-EV collaborative operation system includes: a building local decision-making module, an EV mobile energy storage module, and a data interaction module; The building-specific decision-making module is configured independently for each building and is used to collect various local data, formulate internal equipment operation strategies, EV charging and discharging prices, and inter-building transaction strategies.

[0012] The EV mobile energy storage module uses a single EV as a unit, serving as a mobile energy storage resource. After connecting to the charging equipment in the building, the EV user reports its own status, receives the electricity price signals from each building, and adjusts the charging and discharging plan with the goal of minimizing the total charging cost.

[0013] The data interaction module serves as a communication hub for various types of communication during multi-building-EV collaborative operation, including a communication hub between buildings and EVs and a communication hub between buildings.

[0014] Furthermore, the specific steps of step 1.2 include: Based on the multi-building-EV collaborative operation system constructed in step 1.1, which considers the orderly charging and discharging of EVs, the objective function for EV owners to perform orderly charging and discharging according to the charging and discharging prices set by each building is to minimize the total charging cost. Taking the k-th EV in the building group as an example, the specific formula is as follows: (1) In the formula: The total charging cost for the kth EV; and Buildings i exist t The pricing for EV charging and discharging during specific time periods; N The number of buildings within the building complex; and EVs in buildings iThe charging cost and discharge benefit during parking can be determined by the following formulas (2)-(3).

[0015] (2) (3) In the formula: Is it an EV in the building? i A collection of parking times, showing EVs in buildings i The access period is recorded as The departure time is recorded as The difference between the departure time and the access time is denoted as ; Let EV be the building arrival state variable. t EV time period from arrival at building i The value is 1 if the condition is met, otherwise it is 0. , EVs in buildings i Inside t Charging and discharging power during a given period; and Buildings i exist t The EV charging and discharging prices set for the specified time period are determined by the decision problem in the first stage of step 3.

[0016] When EV users adjust their charging and discharging plans to perform orderly charging and discharging, and The charging and discharging power constraints need to be met, and upper and lower limits of the charging and discharging power during the orderly charging and discharging time of the EV need to be established in combination with its own charging and discharging time period preferences, as shown in the following formula: (4) (5) In the formula: For EV users t Charging / discharging preference coefficient for different time periods (preset by the user, 1 for preference, 0.5 for neutral, and 0 for no preference). and The upper and lower limits of the allowable charging and discharging power of the battery during EV charging and discharging processes are respectively defined.

[0017] Meanwhile, considering the mobility characteristics of EVs, after the charging and discharging process in the building is completed, EVs need to retain enough battery power to meet the next stage of the travel plan, in order to ensure the smooth progress of subsequent travel. The specific battery power retention constraint is shown in the following formula: (6) In the formula: This refers to the battery level when the EV is connected. , These are the charging and discharging efficiencies of the EV, respectively. To ensure the EV has the necessary power for the next phase of driving conditions, the following formula applies: (7) In the formula: and EV from building i To the next stage of building j The driving distance and power consumption per unit mileage; This is the road condition loss coefficient (taken as 0.05-0.2 depending on the degree of road congestion).

[0018] Based on the objective function and constraints for orderly charging and discharging of EV users mentioned above, optimization can be used to obtain the information about EV owners in buildings. i Adjusted EV charging and discharging power set This allows them to obtain the EV's adjusted charging and discharging plan based on the electricity prices set by each building, and then report it to the building. Furthermore, the specific steps of step 1.3 include: The objective function for establishing a single-building energy management strategy for the orderly charging and discharging of integrated EVs in various buildings is shown in the following formula: (8) In the formula: For buildings i Operating costs; , , and Buildings i The costs of purchasing electricity and gas from external energy grids, equipment operation and maintenance costs, EV charging revenue, and electricity transaction costs with other buildings can be specifically represented by equations (9)-(12).

[0019] (9) (10) (11) (12) In the formula: T Indicates 24 time periods; and Representing buildings i exist t The price at which electricity is purchased from the grid and the price at which it is sold; and Representing buildings i exist t Real-time monitoring of electricity purchases and sales from the power grid; It is a building i exist t The gas purchase price is always available on the gas network; For buildings i exist t The amount of gas purchased at any given time; , , , and These represent the maintenance costs per unit power for micro-turbines, photovoltaics, gas boilers, batteries, and thermal storage tanks, respectively. , , , , , and Buildings i exist t The power generation capacity of the micro gas turbine, the power generation capacity of the photovoltaic power generation, the thermal power of the gas boiler, the charging power of the battery, the discharging power of the battery, the heat storage capacity of the heat storage tank, and the heat release capacity of the heat storage tank. Indicates buildings i exist t The number of EVs connected at any given time; for t Time Building i With buildings j Electricity trading volume between them; For buildings i With buildings j The price of electricity traded between them.

[0020] Meanwhile, when adjusting EV charging and discharging prices, buildings need to meet the upper and lower limits of electricity price constraints, as shown in the following formula: (13) (14) In the formula: and Buildings i Formulated t The upper and lower limits of the charging and discharging price of EVs at any given time.

[0021] This leads to the construction of a single-building energy management strategy for the integrated and orderly charging and discharging of EVs in various buildings, including a set of equipment operation strategies. Energy purchase plan Electricity trading hubs with other buildings The established EV charging and discharging price set .

[0022] Furthermore, the specific steps of step 2 include: Step 2.1: Based on the single-building energy management strategy of orderly charging and discharging of EVs in each building in Step 1, construct a building bargaining factor based on multi-dimensional contribution differentiation; Step 2.2: Based on the negotiation factors of each building in Step 2.1, construct a collaborative optimization operation strategy for the building group that considers the orderly charging and discharging of EVs; Furthermore, the specific steps of step 2.1 include: Based on the single-building energy management strategy of integrated EV orderly charging and discharging in each building in step 1, the resource contribution of each building considering EV orderly charging and discharging is calculated, including: 1) Contribution of electricity trading (15) In the formula: For buildings i The contribution of electricity trading.

[0023] 2) Contribution to guiding the orderly charging and discharging of EVs (16) In the formula: For buildings i Contribution to guiding the orderly charging and discharging of EVs; and Buildings i EV charging revenue before and after participating in building cluster guidance.

[0024] 3) Contribution of photovoltaic power output (17) In the formula: For buildings i The contribution of photovoltaic power generation; For buildings i The photovoltaic power provided during time period t.

[0025] The objective weights of each building's resource contribution are adaptively generated based on the dispersion of the contribution within the building cluster. Then, using three-dimensional relative contributions, the bargaining power of each building is reconstructed, thus completing the construction of a building bargaining factor based on multi-dimensional contribution differentiation, as shown in the following formula: (18) In the formula: For the first i The bargaining power of a building; Let be the objective weight of the m-th type of resource; For the first m Resource-like buildings i The relative contribution percentages are as follows: m=1, contribution from electricity trading; m=2, contribution from guiding orderly charging and discharging of EVs; m=3, contribution from photovoltaic power output.

[0026] Furthermore, the objective function of the building cluster collaborative optimization operation strategy considering the orderly charging and discharging of EVs in step 2.2 is as follows: (19) In the formula: The result value of the operation strategy when the building does not participate in the building group collaborative optimization; The bargaining factor takes into account the contribution of orderly charging and discharging of EVs in each building.

[0027] And the following constraints must be met: (20) (twenty one) Equation (20) indicates that the operating cost of a building participating in collaborative optimization is less than the independent operating value; Equation (21) indicates that the electricity trading price set between buildings needs to be lower than the grid purchase price and higher than the grid sales price.

[0028] The objective function for the runtime strategy of building-to-building group collaborative optimization when buildings do not participate is as follows: (twenty two) Furthermore, the inter-building power trading decision problem in the first stage of step 3, which involves the orderly charging and discharging of integrated EVs, aims to minimize the total cost of collaborative operation of the building cluster. (twenty three) In the formula: This represents the total operating cost of the building complex; items marked with * represent the individual operating costs within the building after the electricity trading agreement is reached. The objective function for the building cluster electricity trading price decision problem in the second stage is: (twenty four) In the formula: This refers to the building operating costs excluding electricity trading costs in the first phase of the strategy. This represents the volume of electricity transactions between buildings in the first phase of the strategy.

[0029] Furthermore, the specific steps of step 4 include: (1) The operational model of each building is as follows: (26) The system incorporates EV electricity pricing decisions during building operation, achieving deep integration with inter-building electricity trading. Building trading plans and EV charging / discharging strategies interact and adjust dynamically. Simultaneously, EVs can select the optimal charging / discharging location based on global building electricity prices. Meanwhile, a trading gap determination factor is introduced into the traditional multiplier update, which is jointly determined by the changes in EV charging and discharging prices and electricity trading. The trading gap determination factor is specifically expressed by the following formula: (27) In the formula: As a factor for determining the trading gap; z This represents the number of iterations.

[0030] A new step involving the orderly charging and discharging of EV users has been added to the solution process. EV users, as independent entities, proactively adjust their charging and discharging plans based on the overall electricity price and provide feedback to the building management, as detailed below: (28) (2) The improved ADMM algorithm based on multidimensional data collaborative transmission is used to solve the two-stage sequential optimization decision problem in step 3. Taking the solution of the one-stage strategy as an example, the specific steps include: 1): Order z =1, sets the maximum number of iterations. With convergence accuracy Initialize the operating strategies of each device inside the building, initialize the EV charging and discharging price, and initialize the initial amount of electricity trading for each building; 2): Buildings i For example, each building establishes an optimized operation model that considers the orderly charging and discharging of EVs, as shown in the following formula: (29) 3): In each iteration, the building calculates its own equipment operation strategy and EV charging and discharging price locally through the optimized operation model, and transmits the transaction electricity information and the set EV charging and discharging price from the strategy results to other buildings as multi-dimensional data.

[0031] 4): After completing the multi-dimensional information transmission, the building... i EV users should adjust their charging and discharging plans according to the following formula and report them.

[0032] (30) Other buildings j EV users should adjust their charging and discharging plans according to the following formula.

[0033] (31) 5): Buildings i The transaction volume decision is updated using the following formula. And the next round of EV charging and discharging prices.

[0034] (32) Other buildings jUpdate the power consumption decision according to the following formula. And the charging and discharging price of EVs.

[0035] (33) 6): Each building adaptively updates its Lagrange multipliers according to the following formula.

[0036] (34) In the formula: The trading gap determination factor is jointly determined by the changes in EV charging and discharging prices and electricity trading, and is specifically expressed by the following formula.

[0037] (35) 7): Determine convergence based on the following formula. If convergence fails, proceed to 8); otherwise, the iteration terminates, and the optimal equipment operation strategy and charging / discharging price for each building are output.

[0038] (36) In the formula: For convergence accuracy.

[0039] 8): Number of update iterations z = z +1, return 3).

[0040] After solving the inter-building electricity trading decision problem of fused EV orderly charging and discharging in the first stage of step 3 by improving the ADMM algorithm, we obtain the charging and discharging decisions of each EV in different buildings, the optimal equipment operation strategy of each building, the EV charging and discharging price set by each building, and the inter-building trading decision excluding the trading electricity price.

[0041] Since the only transitive variable in the second stage of the building cluster electricity trading price decision problem in step 3 is the electricity trading price between buildings, the second stage decision problem can be solved by the standard ADMM algorithm, and the solution is the electricity trading price between buildings.

[0042] By solving the two-stage sequential optimization decision problem described in step 3, the optimal operating strategy for the building cluster can be obtained.

[0043] The advantages and beneficial effects of this invention are as follows: 1. This invention proposes a building cluster collaborative optimization operation method that considers the orderly charging and discharging of electric vehicles. Addressing the shortcomings in current building cluster collaborative optimization methods that do not adequately consider the autonomy of EV users in charging and discharging, this invention constructs a multi-building-EV collaborative operation system in step 1 that considers the orderly charging and discharging of EVs. It uses the minimum total charging cost as the objective for EV users and integrates EV user charging and discharging plans with building cluster energy management. This enables EV users to perform orderly charging and discharging based on the global building EV charging and discharging price, solving the problem of EV users' autonomous decision-making ability being neglected in building cluster collaborative optimization.

[0044] 2. In step 2, this invention utilizes building optimization strategies to construct a building bargaining factor based on multi-dimensional contribution differentiation. It explicitly incorporates the contribution of buildings guiding the orderly charging and discharging of EVs to the building cluster into the evaluation of the building bargaining factor, overcoming the shortcomings of existing technologies in calculating the contribution of each entity within the building cluster after considering the orderly charging and discharging of EVs. Furthermore, the obtained building bargaining factor is used to construct a collaborative optimization operation strategy for the building cluster, thereby improving the fairness of revenue distribution and the enthusiasm for participating in collaborative operation in buildings where EVs are the primary resource.

[0045] 3. In step 4, this invention uses the EV electricity prices set by each building and the transaction information between buildings as multi-dimensional data for transmission. Based on the collaborative transmission of multi-dimensional data, the standard ADMM algorithm is improved, overcoming the isolation problem between EV user charging / discharging plans and building group electricity trading decisions caused by the standard ADMM algorithm's fixed use of electricity trading volume for iterative solutions. Simultaneously, a building transaction gap judgment factor and a new orderly charging / discharging stage for EV users are defined in the solution process. This overcomes the deficiency that EV users cannot optimize their charging / discharging plans based on global pricing when performing orderly charging / discharging, and also achieves deep coupling between EV charging / discharging plans and inter-building electricity trading volume. Building trading plans and EV charging / discharging strategies provide mutual feedback and dynamic adjustment, improving EV resource utilization and ensuring coordinated operation between buildings. With the rapid development of electric vehicles (EVs) and the accelerated progress of building clusters, this invention addresses the problem of coordinated operation of building clusters considering the orderly charging and discharging of EVs. It utilizes an improved ADMM algorithm that combines inter-building transaction variables with EV electricity price signals as transfer variables to solve the problem. This algorithm can more effectively promote the efficient utilization and operation management of electricity on the demand side and has broad application prospects. Attached Figure Description

[0046] Figure 1 A schematic diagram of a building cluster collaborative optimization operation method considering the orderly charging and discharging of electric vehicles is provided for the present invention. Figure 2 A schematic diagram of a building cluster collaborative operation framework considering the orderly charging and discharging of EVs provided by the method of the present invention; Figure 3The flowchart illustrates the solution process of the improved ADMM algorithm based on multidimensional data collaborative transfer provided by the method of this invention. Detailed Implementation

[0047] The structure of the present invention will be further described below with reference to the accompanying drawings and embodiments. It should be noted that these embodiments are descriptive and not limiting.

[0048] A collaborative optimization operation method for building clusters considering the orderly charging and discharging of electric vehicles, such as Figures 1 to 3 As shown, it includes the following steps: Step 1: Construct a multi-building-EV collaborative operation system that considers orderly charging and discharging of EVs, and establish a single-building energy management strategy that integrates orderly charging and discharging of EVs in each building; The specific steps of step 1 include: Step 1.1: Construct a multi-building-EV collaborative operation system that considers the orderly charging and discharging of EVs; The specific steps of step 1.1 include: The multi-building-EV collaborative operation system includes: a building local decision-making module, an EV mobile energy storage module, and a data interaction module; The building-specific decision-making module is configured independently for each building and is used to collect various local data (load information, equipment parameters, etc.), formulate internal equipment operation strategies, EV charging and discharging prices, and inter-building transaction strategies.

[0049] The EV mobile energy storage module uses a single EV as a unit, serving as a mobile energy storage resource. After connecting to the charging equipment in the building, the EV user reports its own status (state of charge, dwell time, target building), receives the electricity price signals from each building, and adjusts the charging and discharging plan with the goal of minimizing the total charging cost.

[0050] The data interaction module serves as a communication hub for various types of multi-building-EV collaborative operation, including a communication hub between buildings and EVs (charging pile control system or vehicle network communication interface) and a communication hub between buildings (dedicated data transmission network between buildings).

[0051] After the EV mobile energy storage module is connected to the charging facilities of the building, it conducts two-way data transmission between the building and the vehicle through the data interaction module. EV users receive the charging and discharging prices of each building and report their own status and charging and discharging plans. Each building collects various data through its local decision-making module to formulate its own operating strategies and energy trading strategies with other buildings. Buildings transmit their respective energy trading decisions and the formulated EV charging and discharging prices through the data interaction module. The modules form a closed loop through data exchange, thereby completing the construction of a multi-building-EV collaborative operation system.

[0052] The working principle of step 1.1 is as follows: In the initial stage of system operation, multiple buildings covered by the EV travel route form a building cluster. Each building is equipped with an independent energy management system. The energy management system can call the above-mentioned modules to collect and process the internal operating data of the building. Each building obtains the load forecast value, the output forecast information of each equipment in the building, the initial state of charge of the energy storage equipment, and the transaction information between buildings through the local decision module and the data interaction module, and determines the basic operating boundary conditions of the building in the scheduling cycle.

[0053] When an EV enters any building's parking area and connects to the charging facility, the building uses the charging pile control system or vehicle-to-everything (V2X) communication interface as a communication hub for the data interaction module between the building and the EV to obtain the EV's initial state of charge, battery capacity, available dwell time for scheduling, and information about the next target building for its journey. Simultaneously, the building uses the data interaction module to send charging and discharging price information for each entity within the building cluster to the connected EV users. During orderly charging and discharging of the EV, users adjust their charging and discharging plans based on the total charging cost and report them.

[0054] Based on this, each building constructs a single-building energy management model integrating orderly EV charging and discharging based on equipment operating status information collected by its local decision-making module and EV user charging and discharging plans. This model is used to formulate internal equipment operation strategies, EV charging and discharging pricing strategies, and energy purchase and sale strategies with external energy networks and other buildings. All of these models are independently constructed and solved within the building's local decision-making module, without needing to acquire internal operating data from other buildings. The data exchange module between buildings only transmits electricity trading information and the established EV charging and discharging prices to ensure the coordinated operation of the system. The specific framework is as follows: Figure 2 As shown.

[0055] Step 1.2: Based on the multi-building-EV collaborative operation system constructed in Step 1.1, EV users adjust their charging and discharging plans according to the electricity prices set by each building and report them to the building. The specific steps of step 1.2 include: Based on the multi-building-EV collaborative operation system constructed in step 1.1, which considers the orderly charging and discharging of EVs, the objective function for EV owners to perform orderly charging and discharging according to the charging and discharging prices set by each building is to minimize the total charging cost. Taking the k-th EV in the building group as an example, the specific formula is as follows: (1) In the formula: The total charging cost for the kth EV; and Buildings i exist t The pricing for EV charging and discharging during specific time periods;N The number of buildings within the building complex; and EVs in buildings i The charging cost and discharge benefit during parking can be determined by the following formulas (2)-(3).

[0056] (2) (3) In the formula: Is it an EV in the building? i A collection of parking times, showing EVs in buildings i The access period is recorded as The departure time is recorded as The difference between the departure time and the access time is denoted as ; Let EV be the building arrival state variable. t EV time period from arrival at building i The value is 1 if the condition is met, otherwise it is 0. , EVs in buildings i Inside t Charging and discharging power during a given period; and Buildings i exist t The EV charging and discharging prices set for the specified time period are determined by the decision problem in the first stage of step 3.

[0057] When EV users adjust their charging and discharging plans to perform orderly charging and discharging, and The charging and discharging power constraints need to be met, and upper and lower limits of the charging and discharging power during the orderly charging and discharging time of the EV need to be established in combination with its own charging and discharging time period preferences, as shown in the following formula: (4) (5) In the formula: For EV users t Charging / discharging preference coefficient for different time periods (preset by the user, 1 for preference, 0.5 for neutral, and 0 for no preference). and The upper and lower limits of the allowable charging and discharging power of the battery during EV charging and discharging processes are respectively defined.

[0058] Meanwhile, considering the mobility characteristics of EVs, after the charging and discharging process in the building is completed, EVs need to retain enough battery power to meet the next stage of the travel plan, in order to ensure the smooth progress of subsequent travel. The specific battery power retention constraint is shown in the following formula: (6) In the formula: This refers to the battery level when the EV is connected. , These are the charging and discharging efficiencies of the EV, respectively. To ensure the EV has the necessary power for the next phase of driving conditions, the following formula applies: (7) In the formula: and EV from building i To the next stage of building j The driving distance and power consumption per unit mileage; This is the road condition loss coefficient (taken as 0.05-0.2 depending on the degree of road congestion).

[0059] Based on the objective function and constraints for orderly charging and discharging of EV owners mentioned above, optimization can be used to obtain the EV owners' situation in buildings. i Adjusted EV charging and discharging power set This allows them to obtain the EV's adjusted charging and discharging plan based on the electricity prices set by each building, and then report it to the building. Step 1.3: Based on the charging and discharging plans of EVs received by each building in Step 1.2 and adjusted according to the electricity price set by each building, construct a single-building energy management strategy that integrates the charging and discharging decisions of EV users in each building.

[0060] The specific steps of step 1.3 include: The objective function for establishing a single-building energy management strategy for the orderly charging and discharging of integrated EVs in various buildings is shown in the following formula: (8) In the formula: For buildings i Operating costs; , , and Buildings i The costs of purchasing electricity and gas from external energy grids, equipment operation and maintenance costs, EV charging revenue, and electricity transaction costs with other buildings can be specifically represented by equations (9)-(12).

[0061] (9) (10) (11) (12) In the formula: TIndicates 24 time periods; and Representing buildings i exist t The price at which electricity is purchased from the grid and the price at which it is sold; and Representing buildings i exist t Real-time monitoring of electricity purchases and sales from the power grid; It is a building i exist t The gas purchase price is always available on the gas network; For buildings i exist t The amount of gas purchased at any given time; , , , and These represent the maintenance costs per unit power for micro-turbines, photovoltaics, gas boilers, batteries, and thermal storage tanks, respectively. , , , , , and Buildings i exist t The power generation capacity of the micro gas turbine, the power generation capacity of the photovoltaic power generation, the thermal power of the gas boiler, the charging power of the battery, the discharging power of the battery, the heat storage capacity of the heat storage tank, and the heat release capacity of the heat storage tank. Indicates buildings i exist t The number of EVs connected at any given time; for t Time Building i With buildings j The volume of electricity traded between them is determined by the decision problem in the first stage of step 3; For buildings i With buildings j The electricity trading price between them is determined by the decision problem in step 3, stage two.

[0062] Meanwhile, when adjusting EV charging and discharging prices, buildings need to meet the upper and lower limits of electricity price constraints, as shown in the following formula: (13) (14) In the formula: and Buildings i Formulated t The upper and lower limits of the charging and discharging price of EVs at any given time.

[0063] This leads to the construction of a single-building energy management strategy for the integrated and orderly charging and discharging of EVs in various buildings, including a set of equipment operation strategies. Energy purchase plan Electricity trading hubs with other buildings The established EV charging and discharging price set .

[0064] Step 2: Based on the single-building energy management strategy of orderly charging and discharging of EVs in each building in Step 1, construct a building group collaborative optimization operation strategy that considers orderly charging and discharging of EVs. The specific steps of step 2 include: Step 2.1: Based on the single-building energy management strategy of orderly charging and discharging of EVs in each building in Step 1, construct a building bargaining factor based on multi-dimensional contribution differentiation; The specific steps of step 2.1 include: Based on the energy management strategies of each building and the EV user charging and discharging plans in Step 1, calculate the resource contribution of each building considering the orderly charging and discharging of EVs, including: 1) Contribution of electricity trading (15) In the formula: For buildings i The contribution of electricity trading.

[0065] 2) Contribution to guiding the orderly charging and discharging of EVs (16) In the formula: For buildings i Contribution to guiding the orderly charging and discharging of EVs; and Buildings i EV charging revenue before and after participating in building cluster guidance.

[0066] 3) Contribution of photovoltaic power output (17) In the formula: For buildings i The contribution of photovoltaic power generation; For buildings i The photovoltaic power provided during time period t.

[0067] The objective weights of each building's resource contribution are adaptively generated based on the dispersion of the contribution within the building cluster. Then, using three-dimensional relative contributions, the bargaining power of each building is reconstructed, thus completing the construction of a building bargaining factor based on multi-dimensional contribution differentiation, as shown in the following formula: (18) In the formula: For the first i The bargaining power of a building; Let be the objective weight of the m-th type of resource; For the first m Resource-like buildings i The relative contribution ratios (m=1, contribution of electricity trading; m=2, contribution of guiding orderly charging and discharging of EVs; m=3, contribution of photovoltaic power output).

[0068] Step 2.2: Based on the negotiation factors of each building in Step 2.1, construct a collaborative optimization operation strategy for the building group that considers the orderly charging and discharging of EVs; The objective function of the building cluster collaborative optimization operation strategy considering the orderly charging and discharging of EVs in step 2.2 is as follows: (19) In the formula: The result value of the operation strategy when the building does not participate in the building group collaborative optimization; The bargaining factor takes into account the contribution of each building to the orderly charging and discharging of EVs.

[0069] And the following constraints must be met: (20) (twenty one) Equation (20) indicates that the operating cost of a building participating in collaborative optimization is less than the independent operating value; Equation (21) indicates that the electricity trading price set between buildings needs to be lower than the grid purchase price and higher than the grid sales price.

[0070] The objective function for the runtime strategy of building-to-building group collaborative optimization when buildings do not participate is as follows: (twenty two) Step 3: Decompose the building cluster collaborative operation strategy in Step 2 into a two-stage sequential optimization decision problem; the first stage is the inter-building power trading decision problem of integrated EV orderly charging and discharging, and the second stage is the building cluster power trading price decision problem. Since the objective function (19) of the building cluster collaborative optimization operation strategy problem established in step 2.2 is a nonlinear optimization problem, the model needs to be transformed. By the principle of mean inequality, the above nonlinear optimization problem can be transformed into a linear summation problem. Therefore, the building cluster collaborative optimization operation strategy problem considering the orderly charging and discharging of EVs constructed in step 2 can be decomposed into a two-stage decision problem when solving it, including: the first stage of the inter-building power trading decision problem of fused EV orderly charging and discharging and the second stage of the building cluster power trading price decision problem. Solving them sequentially yields the original optimal operation strategy of the building cluster collaborative optimization.

[0071] The first stage of step 3, which involves the inter-building power trading decision-making problem for the orderly charging and discharging of integrated EVs, aims to minimize the total cost of collaborative operation of the building cluster. (twenty three) In the formula: This represents the total operating cost of the building complex; items marked with * represent the operating costs within the building after the electricity transaction is completed, and their specific composition is the same as in step 1.

[0072] The objective function for the second-stage building cluster electricity trading price decision problem is: (twenty four) In the formula: This refers to the building operating costs excluding electricity trading costs in the first phase of the strategy. This represents the volume of electricity transactions between buildings in the first phase of the strategy.

[0073] Step 4: Solve the two-stage sequential optimization decision problem described in Step 3 using the improved ADMM algorithm based on multi-dimensional data collaborative transmission to obtain the optimal operation strategy for the building group; The specific steps of step 4 include: When solving the collaborative optimization problem, the standard ADMM algorithm only considers the inter-building electricity transaction variables in terms of the main transit variables. The constructed building operation model is shown in the following equation: (25) In the formula: Buildings are a sub-problem in the electricity trading industry. i The Lagrange function; For buildings i With buildings j Inter-electricity trading Lagrange multipliers; This is a penalty factor.

[0074] The resulting inter-building trading plan, determined solely through electricity trading volume iterations, leads to the isolation between EV user charging / discharging plans and inter-building trading decisions: buildings fail to consider EV user responses to electricity prices when formulating strategies, and EV users passively accept pricing from their respective individual buildings, making it difficult to implement the formulated EV charging / discharging strategies. The specific improvement steps in step 4 are shown in (a), and the specific solution steps are shown in (b).

[0075] (a) By incorporating EV charging and discharging prices into the transitive variables, combined with inter-building electricity transactions, multi-dimensional data is constructed. Each building reconstructs its operational model based on this multi-dimensional data. Specifically, when solving the collaborative optimization problem using the standard ADMM algorithm, only inter-building electricity transactions are considered in terms of the main transitive variables. The constructed operational models for each building then become the following: (26) The system incorporates EV electricity pricing decisions during building operation, achieving deep integration with inter-building electricity trading. Building trading plans and EV charging / discharging strategies interact and adjust dynamically. Simultaneously, EVs can select the optimal charging / discharging location based on global building electricity prices, improving the utilization rate of mobile energy storage resources.

[0076] Meanwhile, after the transfer variables are transformed into multidimensional data, the fixed Lagrange multiplier update strategy of the standard ADMM algorithm cannot adapt to the dynamic changes brought about by the EV charging and discharging strategy. Therefore, a trading gap determination factor is introduced into the traditional multiplier update, which is jointly determined by the changes in EV charging and discharging prices and electricity trading. The trading gap determination factor is specifically expressed by the following formula: (27) In the formula: As a factor for determining the trading gap; z This represents the number of iterations.

[0077] Furthermore, in the standard ADMM algorithm's solution phase, only transaction information is transmitted between buildings, and EVs cannot actively participate in decision-making. Therefore, an orderly charging and discharging phase for EV users has been added to the solution phase. EV owners, as independent entities, actively adjust their charging and discharging plans based on the global electricity price and provide feedback to the buildings, as detailed below: (28) (b) The improved ADMM algorithm based on multidimensional data collaborative transfer is used to solve the two-stage sequential optimization decision problem in step 3. Taking the solution of the one-stage policy as an example, such as... Figure 3 As shown, the specific steps include: 1): Order z =1, sets the maximum number of iterations. With convergence accuracy Initialize the operating strategies of each device within the building, initialize the EV charging and discharging price (set by each building according to the grid electricity price), and initialize the initial amount of electricity trading for each building.

[0078] 2): Buildings i For example, an operational optimization model considering the orderly charging and discharging of EVs is established for each building, as shown in the following formula: (29) 3): In each iteration, the building calculates its own equipment operation strategy and EV charging and discharging price locally through the optimized operation model, and transmits the transaction electricity information and the set EV charging and discharging price from the strategy results to other buildings as multi-dimensional data.

[0079] 4): After completing the multi-dimensional information transmission, the building... iEV users should adjust their charging and discharging plans according to the following formula and report them.

[0080] (30) Other buildings j EV users should adjust their charging and discharging plans according to the following formula.

[0081] (31) 5): Buildings i The transaction volume decision is updated using the following formula. And the next round of EV charging and discharging prices.

[0082] (32) Other buildings j Update the power consumption decision according to the following formula. And the charging and discharging price of EVs.

[0083] (33) 6): Each building adaptively updates its Lagrange multipliers according to the following formula.

[0084] (34) In the formula: The trading gap determination factor is jointly determined by the changes in EV charging and discharging prices and electricity trading, and is specifically expressed by the following formula.

[0085] (35) 7): Determine convergence based on the following formula. If convergence fails, proceed to 8); otherwise, the iteration terminates, and the optimal equipment operation strategy and charging / discharging price for each building are output.

[0086] (36) In the formula: For convergence accuracy.

[0087] 8): Number of update iterations z = z +1, return 3).

[0088] After solving the inter-building electricity trading decision problem of fused EV orderly charging and discharging in the first stage of step 3 by improving the ADMM algorithm, we can obtain the charging and discharging decisions of each EV in different buildings, the optimal equipment operation strategy of each building, the EV charging and discharging price set by each building, and the inter-building trading decision excluding the trading electricity price.

[0089] Since the only transitive variable in the second stage of the building cluster electricity trading price decision problem in step 3 is the electricity trading price between buildings, the second stage decision problem can be solved using the standard ADMM algorithm, and the solution is the electricity trading price between buildings.

[0090] By solving the two-stage sequential optimization decision problem described in step 3, the optimal operating strategy for the building cluster can be obtained.

[0091] The innovation of this invention lies in: In step 1, this invention constructs a multi-building-EV collaborative operation system that considers the orderly charging and discharging of EVs. It constructs an objective function for EV user travel with the minimum total charging cost to specifically address the shortcomings of existing technologies that do not consider the autonomous decision-making ability of EV users based on the price signals of each building in EV mobility scenarios.

[0092] In step 2, this invention explicitly incorporates the contribution of building-guided orderly charging and discharging of EVs into the calculation of the bargaining factor for each building by using a multidimensional contribution differentiation degree, and reconstructs the collaborative optimization operation strategy of the building group in this way, thus solving the deficiency of existing technology in characterizing the energy contribution of EV users participating in the building group.

[0093] In step 4, this invention proposes an improved ADMM algorithm based on multi-dimensional data collaborative transmission. By expanding the dimensions of the transmission variables, defining the transaction gap judgment factor, and adding an orderly charging and discharging link for EVs, this invention addresses the shortcomings of the standard ADMM algorithm, which only transmits the electricity transaction volume as the iterative variable, resulting in the isolation between the electricity transaction decision between buildings and the charging and discharging decision of EV users, thus affecting the effectiveness of the optimization results.

[0094] Example 1: Based on the aforementioned building cluster collaborative optimization operation method considering the orderly charging and discharging of electric vehicles, a building cluster consisting of three buildings is set up. The initial number of EVs in each building is set to 80, 60, and 40, respectively, with a capacity of 50 kWh each. Travel information for all EVs is obtained based on travel chain theory and using Monte Carlo sampling. To simplify calculations, it is assumed that the initial SOC of the EVs is 0.7, the user's expected value is 0.95, the travel distance of the EVs between buildings follows a normal distribution, and the distance between building 1 and building 2 satisfies N(15, 0.5). 2 The distance between building 1 and building 3 satisfies N(20, 1.5). 2 The distance between building 2 and building 3 satisfies N(18, 2.2). 2 ).

[0095] To verify the superiority and effectiveness of the proposed method, five scenarios were set up for comparative analysis.

[0096] Scenario 1: Without considering the orderly charging and discharging of EVs, each building operates independently, and the building operation strategy is solved independently by each building; Scenario 2: Without considering the orderly charging and discharging of EVs, buildings operate collaboratively, and the operation strategies of each building are solved based on the standard ADMM algorithm; Scenario 3: Consider the orderly charging and discharging of EVs, with each building operating independently, and the building operation strategy is solved independently for each building; Scenario 4: Considering the orderly charging and discharging of EVs, and the coordinated operation between buildings, the operation strategy of each building is solved based on the standard ADMM algorithm; Scenario 5: Considering the orderly charging and discharging of EVs and the coordinated operation between buildings, the operation strategy of each building is solved based on the improved ADMM algorithm of multi-dimensional data collaborative transmission.

[0097] Without considering the orderly charging and discharging of EVs, the EVs do not move. After connecting to the building, EV users only make charging decisions and disconnect after reaching the desired charge level. The optimization results of the operating costs of each building in the five scenarios are shown in Table 1.

[0098] Table 1. Building operating costs under different scenarios Tab.1 The operating cost of each building in different scenarios

[0099] Comparing scenarios 2 and 3 with scenario 1, it can be seen that the coordinated operation of the building complex and the orderly charging and discharging of EVs can effectively reduce the operating costs of the building complex. The operational framework for coordinated building complex operation considering orderly charging and discharging of EVs provided by this invention can effectively reduce the operating costs of each building compared to the existing independent building operation mode. Through numerical examples, it can be seen that compared with independent operation of each building without considering orderly charging and discharging of EVs, scenario 4 reduces the operating costs of building 1 by 19.6%, building 2 by 22.9%, and building 3 by 17%. Furthermore, comparing scenario 4 with scenario 5 shows that after solving the building complex coordinated operation strategy using the improved ADMM algorithm based on multidimensional data collaborative transmission, the total operating cost can be reduced by 4.1%. This indicates that under the improved ADMM algorithm based on multidimensional data collaborative transmission, each building can fully utilize its own advantages through coordinated operation, reduce dependence on the upper-level power grid, reduce electricity purchase costs, improve energy utilization efficiency, and achieve a significant improvement in economic benefits. Meanwhile, the orderly charging and discharging of EVs can enhance their flexibility in buildings and reduce the electricity costs of building clusters.

[0100] Although embodiments and drawings of the present invention have been disclosed for illustrative purposes, those skilled in the art will understand that various substitutions, variations and modifications are possible without departing from the spirit and scope of the present invention and the appended claims. Therefore, the scope of the present invention is not limited to the contents disclosed in the embodiments and drawings.

Claims

1. A building cluster collaborative optimization operation method considering the orderly charging and discharging of electric vehicles, characterized in that: Includes the following steps: Step 1: Construct a multi-building-EV collaborative operation system that considers orderly charging and discharging of EVs, and establish a single-building energy management strategy that integrates orderly charging and discharging of EVs in each building; Step 2: Based on the single-building energy management strategy of integrated EV orderly charging and discharging in each building in Step 1, construct a building group collaborative optimization operation strategy that considers EV orderly charging and discharging. Step 3: Decompose the building cluster collaborative operation strategy in Step 2 into a two-stage sequential optimization decision problem; The first stage involves the decision-making process for inter-building electricity trading in the context of integrated EV orderly charging and discharging, while the second stage involves the decision-making process for electricity trading prices within building clusters. Step 4: Solve the two-stage sequential optimization decision problem in Step 3 using the improved ADMM algorithm based on multidimensional data collaborative transmission to obtain the optimal operation strategy for the building group.

2. The building cluster collaborative optimization operation method considering the orderly charging and discharging of electric vehicles according to claim 1, characterized in that: The specific steps of step 1 include: Step 1.1: Construct a multi-building-EV collaborative operation system that considers the orderly charging and discharging of EVs; Step 1.2: Based on the multi-building-EV collaborative operation system constructed in Step 1.1, EV users adjust their charging and discharging plans according to the electricity prices set by each building and report them to the building. Step 1.3: Based on the charging and discharging plans of EVs received by each building in Step 1.2 and adjusted according to the electricity price set by each building, construct a single-building energy management strategy for the orderly charging and discharging of EVs in each building.

3. The building cluster collaborative optimization operation method considering the orderly charging and discharging of electric vehicles according to claim 2, characterized in that: The specific steps of step 1.1 include: The multi-building-EV collaborative operation system includes: a building local decision-making module, an EV mobile energy storage module, and a data interaction module; The building-local decision-making module is configured independently for each building and is used to collect various local data, formulate internal equipment operation strategies, EV charging and discharging prices, and inter-building transaction strategies. The EV mobile energy storage module uses a single EV as a unit, serving as a mobile energy storage resource. After connecting to the charging equipment in the building, the EV user reports its own status, receives the electricity price signals from each building, and adjusts the charging and discharging plan with the goal of minimizing the total charging cost. The data interaction module serves as a communication hub for various types of communication during multi-building-EV collaborative operation, including a communication hub between buildings and EVs and a communication hub between buildings.

4. The building cluster collaborative optimization operation method considering the orderly charging and discharging of electric vehicles according to claim 2, characterized in that: The specific steps of step 1.2 include: Based on the multi-building-EV collaborative operation system constructed in step 1.1, which considers the orderly charging and discharging of EVs, the objective function for EV owners to perform orderly charging and discharging according to the charging and discharging prices set by each building is to minimize the total charging cost. Taking the k-th EV in the building group as an example, the specific formula is as follows: (1) In the formula: The total charging cost for the kth EV; and Buildings i exist t The pricing for EV charging and discharging during specific time periods; N The number of buildings within the building complex; and EVs in buildings i The charging cost and discharge revenue during parking can be determined by the following formulas (2)-(3); (2) (3) In the formula: Is it an EV in the building? i A collection of parking times, showing EVs in buildings i The access period is recorded as The departure time is recorded as The difference between the departure time and the access time is denoted as ; Let EV be the building arrival state variable. t EV time period from arrival at building i The value is 1 if the condition is met, otherwise it is 0. , EVs in buildings i Inside t Charging and discharging power during a given period; and Buildings i exist t The EV charging and discharging prices set for the specified time period are determined by the decision-making problem in the first stage of step 3. When EV users adjust their charging and discharging plans to perform orderly charging and discharging, and The charging and discharging power constraints need to be met, and upper and lower limits of the charging and discharging power during the orderly charging and discharging time of the EV need to be established in combination with its own charging and discharging time period preferences, as shown in the following formula: (4) (5) In the formula: For EV users t Charging / discharging preference coefficient for different time periods (preset by the user, 1 for preference, 0.5 for neutral, and 0 for no preference). and The upper and lower limits of the allowable charging and discharging power of the battery during EV charging and discharging processes are respectively defined. Meanwhile, considering the mobility characteristics of EVs, after the charging and discharging process in the building is completed, EVs need to retain enough battery power to meet the next stage of the travel plan, in order to ensure the smooth progress of subsequent travel. The specific battery power retention constraint is shown in the following formula: (6) In the formula: This refers to the battery level when the EV is connected. , These are the charging and discharging efficiencies of the EV, respectively. To ensure the EV has the necessary power for the next phase of driving conditions, the following formula applies: (7) In the formula: and EV from building i To the next stage of building j The driving distance and power consumption per unit mileage; This is the road condition loss coefficient (taken as 0.05-0.2 depending on the degree of road congestion); Based on the objective function and constraints for orderly charging and discharging of EV users mentioned above, optimization can be used to obtain the information about EV owners in buildings. i EV charging and discharging power collection This allows them to obtain the EV's adjusted charging and discharging plan based on the electricity prices set by each building, and then report it to the building.

5. A building cluster collaborative optimization operation method considering the orderly charging and discharging of electric vehicles according to claim 2, characterized in that: The specific steps of step 1.3 include: The objective function for establishing a single-building energy management strategy for the orderly charging and discharging of integrated EVs in various buildings is shown in the following formula: (8) In the formula: For buildings i Operating costs; , , and Buildings i The energy purchase cost of purchasing electricity and gas from external energy grids, equipment operation and maintenance costs, EV charging revenue, and electricity transaction costs with other buildings can be specifically represented by equations (9)-(12); (9) (10) (11) (12) In the formula: T Indicates 24 time periods; and Representing buildings i exist t The price at which electricity is purchased from the grid and the price at which it is sold; and Representing buildings i exist t Real-time monitoring of electricity purchases and sales from the power grid; It is a building i exist t The gas purchase price is always available on the gas network; For buildings i exist t The amount of gas purchased at any given time; , , , and These represent the maintenance costs per unit power for micro-turbines, photovoltaics, gas boilers, batteries, and thermal storage tanks, respectively. , , , , , and Buildings i exist t The power generation capacity of the micro gas turbine, the power generation capacity of the photovoltaic power generation, the thermal power of the gas boiler, the charging power of the battery, the discharging power of the battery, the heat storage capacity of the heat storage tank, and the heat release capacity of the heat storage tank. Indicates buildings i exist t The number of EVs connected at any given time; for t Time Building i With buildings j Electricity trading volume between them; For buildings i With buildings j The price of electricity traded between them; Meanwhile, when adjusting EV charging and discharging prices, buildings need to meet the upper and lower limits of electricity price constraints, as shown in the following formula: (13) (14) In the formula: and Buildings i Formulated t The upper and lower limits of EV charging and discharging prices at any given time; This leads to the construction of a single-building energy management strategy for the integrated and orderly charging and discharging of EVs in various buildings, including a set of equipment operation strategies. Energy purchase plan Electricity trading hubs with other buildings The established EV charging and discharging price set .

6. The building cluster collaborative optimization operation method considering the orderly charging and discharging of electric vehicles according to claim 1, characterized in that: The specific steps of step 2 include: Step 2.1: Based on the single-building energy management strategy of orderly charging and discharging of EVs in each building in Step 1, construct a building bargaining factor based on multi-dimensional contribution differentiation; Step 2.2: Based on the bargaining factors of each building in Step 2.1, construct a collaborative optimization operation strategy for the building group that takes into account the orderly charging and discharging of EVs.

7. A building cluster collaborative optimization operation method considering the orderly charging and discharging of electric vehicles according to claim 6, characterized in that: The specific steps of step 2.1 include: Based on the single-building energy management strategy of integrated EV orderly charging and discharging in each building in step 1, the resource contribution of each building considering EV orderly charging and discharging is calculated, including: 1) Contribution of electricity trading (15) In the formula: For buildings i The contribution of electricity trading; 2) Contribution to guiding the orderly charging and discharging of EVs (16) In the formula: For buildings i Its contribution to guiding the orderly charging and discharging of EVs; and Buildings i EV charging revenue before and after participating in building cluster guidance; 3) Contribution of photovoltaic power output (17) In the formula: For buildings i The contribution of photovoltaic power generation; For buildings i The photovoltaic power provided during time period t; The objective weights of each building's resource contribution are adaptively generated based on the dispersion of the contribution within the building cluster. Then, using three-dimensional relative contributions, the bargaining power of each building is reconstructed, thus completing the construction of a building bargaining factor based on multi-dimensional contribution differentiation, as shown in the following formula: (18) In the formula: For the first i The bargaining power of a building; Let be the objective weight of the m-th type of resource; For the first m Resource-like buildings i The relative contribution percentages are as follows: m=1, contribution from electricity trading; m=2, contribution from guiding orderly charging and discharging of EVs; m=3, contribution from photovoltaic power output.

8. A building cluster collaborative optimization operation method considering the orderly charging and discharging of electric vehicles according to claim 6, characterized in that: The objective function of the building cluster collaborative optimization operation strategy considering the orderly charging and discharging of EVs in step 2.2 is as follows: (19) In the formula: The result value of the operation strategy when the building does not participate in the building group collaborative optimization; The bargaining factor is taken into account the contribution of orderly charging and discharging of EVs in each building; And the following constraints must be met: (20) (21) Equation (20) indicates that the operating cost of buildings participating in collaborative optimization is less than the independent operating value; Equation (21) indicates that the electricity trading price set between buildings needs to be lower than the grid purchase price and higher than the grid sales price. The objective function for the runtime strategy of building-to-building group collaborative optimization when buildings do not participate is as follows: (22)。 9. A building cluster collaborative optimization operation method considering the orderly charging and discharging of electric vehicles according to claim 1, characterized in that: The first stage of step 3, which involves the inter-building power trading decision-making problem for the orderly charging and discharging of integrated EVs, aims to minimize the total cost of collaborative operation of the building cluster. (23) In the formula: This represents the total operating cost of the building complex; items marked with * represent the individual operating costs within the building after the electricity trading agreement is reached. The objective function for the building cluster electricity trading price decision problem in the second stage is: (24) In the formula: This refers to the building operating costs excluding electricity trading costs in the first phase of the strategy. This represents the volume of electricity transactions between buildings in the first phase of the strategy.

10. A building cluster collaborative optimization operation method considering the orderly charging and discharging of electric vehicles according to claim 1, characterized in that: The specific steps of step 4 include: (1) The operational model of each building is as follows: (26) The building operation incorporates EV electricity pricing decision-making, achieving deep integration with inter-building electricity trading. Building trading plans and EV charging and discharging strategies provide feedback and dynamic adjustments to each other. Simultaneously, EVs can select the optimal charging and discharging location based on the global electricity prices of all buildings. Meanwhile, a trading gap determination factor is introduced into the traditional multiplier update, which is jointly determined by the changes in EV charging and discharging prices and electricity trading. The trading gap determination factor is specifically expressed by the following formula: (27) In the formula: As a factor for determining the trading gap; z This represents the number of iterations. A new step involving the orderly charging and discharging of EV users has been added to the solution process. EV users, as independent entities, proactively adjust their charging and discharging plans based on the overall electricity price and provide feedback to the building management, as detailed below: (28) (2) The improved ADMM algorithm based on multidimensional data collaborative transmission is used to solve the two-stage sequential optimization decision problem in step 3. Taking the solution of the one-stage strategy as an example, the specific steps include: 1): Order z =1, sets the maximum number of iterations. With convergence accuracy Initialize the operating strategies of each device inside the building, initialize the EV charging and discharging price, and initialize the initial amount of electricity trading for each building; 2): Buildings i For example, an operational optimization model considering the orderly charging and discharging of EVs is established for each building, as shown in the following formula: (29) 3): In each iteration, the building calculates its own equipment operation strategy and EV charging and discharging price locally by running the optimization model, and transmits the transaction electricity information and the set EV charging and discharging price from the strategy results to other buildings as multi-dimensional data; 4): After completing the multi-dimensional information transmission, the building... i EV users should adjust their charging and discharging plans according to the following formula and report them; (30) Other buildings j EV users should adjust their charging and discharging plans according to the following formula; (31) 5): Buildings i The transaction volume decision is updated using the following formula. And the next round of EV charging and discharging prices; (32) Other buildings j Update the power consumption decision according to the following formula. And the charging and discharging price of EVs; (33) 6): Each building adaptively updates its Lagrange multipliers according to the following formula; (34) In the formula: The trading gap determination factor is jointly determined by the changes in EV charging and discharging prices and electricity trading, and is specifically expressed by the following formula; (35) 7): Determine convergence based on the following formula; if convergence is not achieved, proceed to 8); otherwise, the iteration terminates, and the optimal equipment operation strategy and charging / discharging price for each building are output. (36) In the formula: For convergence accuracy; 8): Number of update iterations z = z +1, return 3); After solving the inter-building electricity trading decision problem of fused EV orderly charging and discharging in the first stage of step 3 by improving the ADMM algorithm, we obtain the charging and discharging decisions of each EV in different buildings, the optimal equipment operation strategy of each building, the EV charging and discharging price set by each building, and the inter-building trading decision excluding the trading electricity price. Since the only transitive variable in the second stage of the building cluster electricity trading price decision problem in step 3 is the electricity trading price between buildings, the second stage decision problem can be solved by the standard ADMM algorithm, and the solution is the electricity trading price between buildings. By solving the two-stage sequential optimization decision problem described in step 3, the optimal operating strategy for the building cluster is obtained.

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