Day-ahead scheduling method, system and device for building integrated energy and storage medium

Abstract

The invention relates to the field of power systems and automation thereof, and discloses a day-ahead scheduling method, system and device for building integrated energy and a storage medium, and the method comprises the steps: building a building electric energy sharing mode according to the movement characteristics of an EV group, the load characteristics of each building, and the communication between the buildings; building a building day-ahead bidding model based on the building electric energy sharing mode; and solving the building day-ahead bidding model, and based on a solving result, obtaining a scheduling scheme of each equipment of the next day, an optimization scheduling scheme of each EV group and a bidding scheme of each building. According to the method and the device, the problem that a building aggregator does not know how to process EV travel data reported by a building and how to regulate and control EV groups and building resources by utilizing electric energy sharing when making a scheme in a day-ahead bidding scene is solved.

Description

Technical Field

[0001] The present disclosure relates to the field of power systems and automation technologies, and in particular to a day-ahead scheduling method, system, device, and storage medium for integrated building energy. Background Art

[0002] With the rapid development of distributed energy and load control technologies, integrated energy buildings have become a primary vehicle for demand-side participation in the electricity market. Building aggregators effectively aggregate internal building resources and leverage information technology and intelligent control to dispatch and optimize power systems, effectively reducing the grid's dispatch burden and achieving win-win outcomes for all stakeholders in the power system.

[0003] Building aggregators often consider developing day-ahead bidding schemes based on electric vehicle clusters and building temperature-controlled loads. This approach relies on the fact that electric vehicles can serve as energy storage devices when not in use, contributing to grid regulation through charging and discharging. Temperature-controlled loads, such as HVAC systems, can be adjusted based on demand to reduce power consumption or provide demand response services. However, existing technical solutions rely too heavily on static optimization models, ignoring the mobility of EVs and the energy sharing capabilities enabled by inter-building information exchange. These schemes miss opportunities to reduce costs and increase revenue, resulting in inefficient resource allocation and increased system operational risks. Summary of the Invention

[0004] The embodiments of the present disclosure provide a method, system, device, and storage medium for day-ahead scheduling of integrated building energy to solve or alleviate one or more of the above technical problems in the prior art.

[0005] According to one aspect of the present disclosure, a method for day-ahead scheduling of integrated building energy is provided, comprising: Establish a building power sharing model based on the mobility characteristics of the EV group and the load characteristics of each building, as well as communication between buildings; Based on the building power sharing model, a building day-ahead bidding model is constructed; The building day-ahead bidding model is solved, and based on the solution, the next day's building equipment scheduling plan, the optimized scheduling plan for each EV group, and the bidding plan for each building are obtained.

[0006] In one possible implementation, based on the mobility characteristics of the EV group and the load characteristics of each building, as well as communication between buildings, a building power sharing model is established, including: Get EV travel information; Based on EV travel information, EVs are grouped according to the travel chain theory to form different EV groups.

[0007] In one possible implementation, based on the mobility characteristics of the EV group and the load characteristics of each building, as well as communication between buildings, a building power sharing model is established, including: Obtaining power information for each unit in the building, including next-day building load forecast data, next-day photovoltaic output forecast data, and energy storage system parameters; Obtaining next-day building load forecast data includes: Collect historical building electricity consumption data; Aggregate historical building electricity consumption data to generate a typical electricity consumption feature database for integrated energy buildings; Predict the next day's building load based on the typical electricity consumption feature database; Obtaining the next day's photovoltaic output forecast data includes: Obtain the next day's solar radiation intensity forecast data from the weather station; Get the area of ​​the building's photovoltaic array; Calculate the next day's photovoltaic output forecast data based on the photovoltaic model using the next day's solar radiation intensity forecast data and the area of ​​the building's photovoltaic array; The photovoltaic model is: ; Where, For buildings b exist t PV output power during the period; is the conversion efficiency; is the area of ​​the photovoltaic array; for t Solar radiation intensity during the period; Energy storage system parameters include the capacity, charge / discharge efficiency, and maximum charge / discharge power of the building battery.

[0008] In one possible implementation, constructing a building day-ahead bidding model based on the building power sharing model includes: establishing an EV population model based on the EV population that considers EV mobility characteristics; Establish energy storage models; Combining the EV population model and energy storage model, a building day-ahead bidding model considering power sharing is constructed.

[0009] In one possible implementation, establishing an EV group model based on the EV group that considers EV mobility characteristics includes: ; ; ; ; ; ; Where: b Indicates the building number; t Indicates the time period number; j Indicates EV number; for t Buildings during the period b The number of EVs that can be dispatched; for Time period EV group q Total charging power; for t Time period EV group q Total discharge power; , Groups q Average charge and discharge power of single EV; , They are the charging and discharging power boundaries of a single EV respectively; Indicates that the EV is in charging state. Indicates that the EV is in the discharging state.

[0010] In one possible implementation, establishing the energy storage model includes: ; ; ; ; ; Where, For buildings b exist t Battery charging status during the time period; For buildings b exist t Battery discharge status during the time period; For buildings b exist t Battery capacity during the period (kWh); is the battery capacity at the beginning of the scheduling cycle; Δ t Indicates a time interval; For buildings b exist t Battery charging power during the time period; is charging efficiency; For buildings b exist t Battery discharge power during the time period; is the discharge efficiency.

[0011] In one possible implementation, combining the EV population model and the energy storage model to construct a building day-ahead bidding model that considers energy sharing includes: Taking the minimum total cost as the optimization goal, a building day-ahead bidding model considering power sharing is established, and the objective function is: ; Where, For building operating costs; for energy market benefits; ; Where, is the number of buildings; , Buildings b exist t The purchase and sale prices of electricity for time-sharing electricity; , Buildings b exist t The amount of electricity purchased and sold from the shared power grid during the time period; For buildings b Micro turbine operating costs; For buildings b exist t Micro-turbine output during the time period; For buildings b PV operating costs; For buildings b Gas purchase cost; For buildings b Energy storage operating costs; For buildings b EV dispatch costs; , are the average charging and discharging power of EV respectively; For buildings b exist t PV output power during the period; ; Where: for t The energy clearing price of a period can be predicted from historical clearing prices; for t Energy market bidding capacity for the time period; Electric balance constraints: ; Where, for t Time Building b Power delivered to other buildings; for t During this period, the air is delivered to the building from other buildingsb Power; for t Time Building b Energy market bidding capacity; for t Time Building b load.

[0012] According to one aspect of the present disclosure, a day-ahead scheduling system for integrated building energy is provided, comprising: an establishment unit for establishing a building power sharing mode based on mobility characteristics of an EV group and load characteristics of each building, as well as communication between the buildings; A construction unit, configured to construct a building day-ahead bidding model based on the building power sharing mode; The solving unit is used to solve the building day-ahead bidding model, and obtain the next day's building equipment scheduling plan, the EV group optimization scheduling plan and the building bidding plan based on the solution result.

[0013] In one possible implementation, the establishing unit includes: A first acquisition module is used to obtain EV travel information; The formation module is used to group EVs according to EV travel information and travel chain theory to form different EV groups.

[0014] In a possible implementation, the establishing unit further includes: The second acquisition module is used to obtain the power information of each unit in the building, including the next day's building load forecast data, the next day's photovoltaic output forecast data and energy storage system parameters; The third acquisition module is used to obtain the next day's building load forecast data, including: Collection module, used to collect historical electricity consumption data of buildings; Aggregation module, used to aggregate historical building electricity consumption data to generate a typical electricity consumption feature library for integrated energy buildings; The prediction module is used to predict the building load for the next day based on the typical power consumption feature library; Obtaining the next day's photovoltaic output forecast data includes: Obtain the next day's solar radiation intensity forecast data from the weather station; Get the area of ​​the building's photovoltaic array; Calculate the next day's photovoltaic output forecast data based on the photovoltaic model using the next day's solar radiation intensity forecast data and the area of ​​the building's photovoltaic array; The photovoltaic model is: ; Where, For buildings b exist t PV output power during the period; is the conversion efficiency; is the area of ​​the photovoltaic array; for t Solar radiation intensity during the period; Energy storage system parameters include the capacity, charge / discharge efficiency, and maximum charge / discharge power of the building battery.

[0015] In one possible implementation, the building block includes: A first establishing module is configured to establish an EV group model taking into account EV mobility characteristics based on the EV group; The second building module is used to build an energy storage model; A construction module is used to combine the EV group model and the energy storage model to construct a building day-ahead bidding model that takes into account power sharing.

[0016] In one possible implementation, establishing an EV group model based on the EV group that considers EV mobility characteristics includes: ; ; ; ; ; ; Where: b Indicates the building number; t Indicates the time period number; j Indicates EV number; for t Buildings during the period b The number of EVs that can be dispatched; for Time period EV group q Total charging power; for t Time period EV group q Total discharge power; , Groups q Average charge and discharge power of single EV; , They are the charging and discharging power boundaries of a single EV respectively; Indicates that the EV is in charging state. Indicates that the EV is in the discharging state.

[0017] In one possible implementation, establishing the energy storage model includes: ; ; ; ; ; Where, For buildings b exist t Battery charging status during the time period; For buildings b exist t Battery discharge status during the time period; For buildings b exist t Battery capacity during the period (kWh); is the battery capacity at the beginning of the scheduling cycle; Δ t Indicates a time interval; For buildings b exist t Battery charging power during the time period; is charging efficiency; For buildings b exist t Battery discharge power during the time period; is the discharge efficiency.

[0018] In one possible implementation, combining the EV population model and the energy storage model to construct a building day-ahead bidding model that considers energy sharing includes: Taking the minimum total cost as the optimization goal, a building day-ahead bidding model considering power sharing is established, and the objective function is: ; Where, For building operating costs; for energy market benefits; ; Where, is the number of buildings; , Buildings b exist t The purchase and sale prices of electricity for time-sharing electricity; , Buildings b exist t The amount of electricity purchased and sold from the shared power grid during the time period; For buildings b Micro turbine operating costs; For buildings b exist t Micro-turbine output during the time period; For buildings bPV operating costs; For buildings b Gas purchase cost; For buildings b Energy storage operating costs; For buildings b EV dispatch costs; , are the average charging and discharging power of EV respectively; For buildings b exist t PV output power during the period; ; Where: for t The energy clearing price of a period can be predicted from historical clearing prices; for t Energy market bidding capacity for the time period; Electric balance constraints: ; Where, for t Time Building b Power delivered to other buildings; for t During this period, the air is delivered to the building from other buildings b Power; for t Time Building b Energy market bidding capacity; for t Time Building b load.

[0019] According to one aspect of the present disclosure, there is provided a device comprising: processor and memory; The memory is used to store a computer program, and the processor calls the computer program stored in the memory to execute any one of the above-mentioned day-ahead scheduling methods for integrated building energy.

[0020] According to one aspect of the present disclosure, a computer-readable storage medium is provided, in which a computer program is stored. When the computer program is executed by a processor, the processor is enabled to execute any one of the above-mentioned day-ahead scheduling methods for integrated building energy.

[0021] The present disclosure has the following beneficial effects: the present disclosure utilizes the mobility characteristics of EVs and inter-building communication to establish a building power sharing model, constructs a building day-ahead bidding model based on the inter-building power sharing model, uses CPlex to solve the model, and obtains the next-day operating status of each building device and the bidding plan based on the model solution. This solves the problem that building aggregators are unclear about how to process EV travel data reported by buildings and how to use power sharing to regulate EV groups and building resources when formulating plans in day-ahead bidding scenarios.

[0022] The details of one or more embodiments of the present application are set forth in the following drawings and description. Other features and advantages of the present application will become apparent from the accompanying drawings. It should be understood that the above general description and the detailed description that follows are merely exemplary and explanatory and do not limit the present disclosure. BRIEF DESCRIPTION OF THE DRAWINGS

[0023] The accompanying drawings are incorporated into and constitute a part of the specification, illustrate embodiments consistent with the present disclosure, and together with the specification, are used to explain the principles of the present disclosure. Obviously, the drawings described below are only some embodiments of the present disclosure, and those skilled in the art can derive other drawings based on these drawings without inventive effort.

[0024] Figure 1 is a flow chart of a day-ahead scheduling method for integrated building energy according to this exemplary embodiment; Figure 2 is a flow chart of a day-ahead bidding method for a smart building considering power sharing in this exemplary embodiment; Figure 3 is one of the K-means scene clustering graphs of this exemplary embodiment; Figure 4 This is the second K-means scene clustering diagram of this exemplary embodiment; Figure 5 is a schematic diagram of the next-day scheduling results of residential buildings in this exemplary embodiment; Figure 6 is a schematic diagram of the next-day scheduling results of a commercial building in this exemplary embodiment; Figure 7 is a schematic diagram of the next-day scheduling results of an office building in this exemplary embodiment; Figure 8 is a schematic diagram of the next-day bidding plan for each building in this exemplary embodiment; Figure 9 is a block diagram of a day-ahead scheduling system for integrated building energy according to this exemplary embodiment; Figure 10 FIG. 4 is a schematic structural diagram of a device according to the exemplary embodiment of the present invention. DETAILED DESCRIPTION

[0025] Example embodiments will now be described more fully with reference to the accompanying drawings. However, example embodiments can be implemented in a variety of forms and should not be construed as limited to the examples set forth herein; rather, these embodiments are provided so that the present disclosure will be more comprehensive and complete and will fully convey the concepts of the example embodiments to those skilled in the art. The described features, structures, or characteristics may be combined in any suitable manner in one or more embodiments. In the following description, many specific details are provided to provide a full understanding of the embodiments of the present disclosure. However, those skilled in the art will appreciate that the technical solutions of the present disclosure may be practiced while omitting one or more of the specific details, or that other methods, components, devices, steps, etc. may be employed. In other cases, well-known technical solutions are not shown or described in detail to avoid obscuring various aspects of the present disclosure.

[0026] In addition, the accompanying drawings are merely schematic illustrations of the present disclosure and are not necessarily drawn to scale. Identical reference numerals in the figures denote identical or similar parts, and thus repetitive descriptions thereof will be omitted. Some of the block diagrams shown in the accompanying drawings are functional entities that do not necessarily correspond to physically or logically separate entities. These functional entities may be implemented in software, in one or more hardware units or integrated circuits, or in different networks and / or processor devices and / or microcontroller devices.

[0027] The flowcharts shown in the accompanying drawings are merely illustrative and do not necessarily include all steps. For example, some steps may be decomposed, while some steps may be combined or partially combined, so the actual execution order may change according to actual circumstances.

[0028] The terms "first," "second," and the like in the specification and claims of this application and the accompanying drawings are used to distinguish similar objects and are not necessarily used to describe a particular order or precedence. It should be understood that the terms used in this manner are interchangeable where appropriate, such that the embodiments of the application described herein can, for example, be implemented in an order other than that illustrated or described herein.

[0029] In addition, the terms "comprise" and "have" and any variations thereof are intended to cover non-exclusive inclusions. For example, a process, method, system, product or apparatus that includes a series of steps or submodules is not necessarily limited to those steps or submodules explicitly listed, but may include other steps or submodules not explicitly listed or inherent to such process, method, product or apparatus.

[0030] Figure 1 This is a flow chart of a method for day-ahead scheduling of integrated building energy in this exemplary embodiment. Figure 1 As shown, an exemplary embodiment of the present disclosure provides a day-ahead scheduling method for integrated building energy, including: Establish a building power sharing model based on the mobility characteristics of the EV group and the load characteristics of each building, as well as communication between buildings; Based on the building power sharing model, a building day-ahead bidding model is constructed; The building day-ahead bidding model is solved, and based on the solution, the next day's building equipment scheduling plan, the optimized scheduling plan for each EV group, and the bidding plan for each building are obtained.

[0031] This embodiment solves the technical problems of how to process EV travel data reported by buildings and how to use power sharing to regulate EV groups and building resources when building aggregators formulate plans in day-ahead bidding scenarios.

[0032] like Figure 2 As shown, this embodiment solves the technical problem by adopting the following technical solution: a day-ahead bidding method for smart buildings considering power sharing, comprising the following steps: Step 1: Leverage the mobility of EVs and inter-building communications to establish a building power sharing model. Step 2: Construct a building day-ahead bidding model based on the inter-building energy sharing model considered in step 1; Step 3: Use CPlex to solve the model described in Step 2. Based on the model solution, obtain the operation status of each building equipment and the bidding plan for the next day; Specifically, based on the mobility characteristics of the EV group, the load characteristics of each building, and the communication between buildings, the building power sharing model is established, including: Get EV travel information; Based on EV travel information, EVs are grouped according to the travel chain theory to form different EV groups.

[0033] Specifically, based on the mobility characteristics of the EV group, the load characteristics of each building, and the communication between buildings, the building power sharing model is established, including: Obtaining power information for each unit in the building, including next-day building load forecast data, next-day photovoltaic output forecast data, and energy storage system parameters; Obtaining next-day building load forecast data includes: Collect historical building electricity consumption data; Aggregate historical building electricity consumption data to generate a typical electricity consumption feature database for integrated energy buildings; Predict the next day's building load based on the typical electricity consumption feature database; Obtaining the next day's photovoltaic output forecast data includes: Obtain the next day's solar radiation intensity forecast data from the weather station; Get the area of ​​the building's photovoltaic array; Calculate the next day's photovoltaic output forecast data based on the photovoltaic model using the next day's solar radiation intensity forecast data and the area of ​​the building's photovoltaic array; Energy storage system parameters include the capacity, charge / discharge efficiency, and maximum charge / discharge power of the building battery.

[0034] In this embodiment, step 1 (using the mobility characteristics of EVs and inter-building communication to establish a building power sharing mode) specifically includes: 1.1 Single building reporting information: Building aggregators use power and communication networks to connect different integrated energy buildings. Integrated energy buildings collect electricity usage information of each unit through their internal energy management system (EMS) and report EV travel information and building power information to the building aggregator through the communication network.

[0035] 1) The travel information that EV owners need to report is as follows: a) EV owners report their departure time ; b) The starting point and transit points of the next day’s trip; 2) The power information reported by the integrated energy building group includes: a) Building load; Collect historical building electricity consumption data, use K-means method to aggregate load data, generate a typical electricity consumption feature library of comprehensive energy buildings, and predict the building electricity consumption of the next day based on the typical electricity consumption feature library. b load .

[0036] b) Photovoltaic output; The next day's solar radiation intensity forecast data is obtained from the weather station. The area of ​​the building's photovoltaic array is input to obtain the next day's photovoltaic output forecast data. The photovoltaic model is as follows: (1); in, For buildings b exist t PV output power during the period; is the conversion efficiency; is the area of ​​the photovoltaic array; for t Solar radiation intensity during the period.

[0037] c) Energy storage system parameters; Energy storage system parameters include the capacity, charge / discharge efficiency, and maximum charge / discharge power of the building battery.

[0038] 1.2 Data processing by building aggregators: Building aggregators group all EVs according to EV travel information and travel chain theory.

[0039] To illustrate this method, this embodiment sets up three types of integrated energy buildings, namely residential buildings, office buildings, and commercial buildings, and sets the EV type to private EV.

[0040] According to the trip chain theory, the starting point of a private EV is a residential building, and the places it passes through are office buildings and commercial buildings. Therefore, the following two trip chains are set up on a typical day: a) Private EV: residential building → office building → residential building.

[0041] b) Private EV: residential building → office building → commercial building → residential building.

[0042] On a typical day, EV users commute from residential buildings to office buildings during the day. After get off work, some EVs return from the office building to residential buildings, while others go to the commercial district for leisure and entertainment, returning to residential buildings at night. EVs cannot participate in dispatch during travel time, but can participate in dispatch in their area when parked and charging.

[0043] Based on the EV travel information of users in the building collected in 1.1, determine the travel chain of each EV and determine the buildings within the scheduling day. b Number of dispatchable EVs , all EVs are grouped according to the travel chain, and all EVs with the same travel time and travel path are in the same group; they are divided into Q groups, numbered 1, 2, …, Q.

[0044] Based on the different load characteristics of each building and the different mobility characteristics of EV groups, the power grid is used to control and coordinate the two-way flow of electricity between buildings to establish a building power sharing model.

[0045] Specifically, based on the building power sharing model, constructing a building day-ahead bidding model includes: establishing an EV population model based on the EV population that considers EV mobility characteristics; Establish energy storage models; Combining the EV population model and energy storage model, a building day-ahead bidding model considering power sharing is constructed.

[0046] In this embodiment, step 2 (building a building day-ahead bidding model based on the inter-building power sharing model considered in step 1) specifically includes: 2.1 Based on the EV group in 1.2, an EV group model is established that takes into account the mobility characteristics of EVs: (2); (3); (4); (5); (6); (7); Where: b Indicates the building number; t Indicates the time period number; j Indicates EV number; for t Buildings during the period b The number of EVs that can be dispatched, for t Time period EV group q The total charging power, for t Time period EV group q The total discharge power. , Groups q Average charge and discharge power of single EV; , They are the charging and discharging power boundaries of a single EV respectively; , They are the upper and lower limits of the charge and discharge power allowed by the battery during the charge and discharge process; Indicates that the EV is in charging state. Indicates that the EV is in the discharging state.

[0047] After optimizing the charging and discharging plan for each type of EV group, the minimum power of each EV is guaranteed during the time when the user is likely to travel, and the charging and discharging power of the EV group is evenly distributed to each EV in the EV group according to the average charging principle.

[0048] 2.2 Energy Storage (ESS) Model: (8); (9); (10); (11); (12); in, For buildings b exist t Battery charging status during the time period; For buildings b exist t Battery discharge status during the time period; For buildings b exist tThe battery capacity during the period (kWh), is the battery capacity at the beginning of the scheduling cycle, Δ t Indicates a time interval; For buildings b exist t Battery charging power during the period, is charging efficiency; For buildings b exist t Battery discharge power during the period, is the discharge efficiency; Formula (12) indicates that the battery charge remains unchanged after the entire scheduling cycle.

[0049] 2.3 Combining 2.1 and 2.2, a building day-ahead bidding model considering power sharing is constructed: Taking the minimum total cost as the optimization goal, a building day-ahead bidding model considering power sharing is established. The specific mathematical model can be expressed as follows: (13); The objective function consists of two parts: building operation cost and energy market revenue .

[0050] Building operating costs It is the sum of the revenue from electricity purchase and sales between buildings and the operation and maintenance costs and gas purchase costs of each unit within the building. Among them, the operation cost and gas purchase costs of each unit within the building are directly given by the building operator in the day-ahead stage based on historical data.

[0051] (14); Where, is the number of buildings, , Buildings b exist t The purchase and sale prices of electricity for time-sharing electricity; , Buildings b exist t The amount of electricity purchased and sold from the shared power grid during the period, For buildings b Micro turbine operating costs, For buildings b exist t The micro-turbine output during the period, For buildings b Photovoltaic operating costs, For buildings b Gas purchase cost, For buildings b Energy storage operating costs, For buildingsb EV dispatch costs.

[0052] Energy market revenue Determined by the bidding strategy and energy clearing price, we have: (15); Where: for t The energy clearing price of a period can be predicted from the historical clearing price; for t Energy market bidding capacity for the time period.

[0053] In the traditional integrated energy building optimization problem of cooling / heating / electricity balance constraints, the decision variable { }, the updated electrical balance constraints are as follows: (16); in: for t Time Building b Power delivered to other buildings; for t During this period, the air is delivered to the building from other buildings b Power; for t Time Building b Energy market bidding capacity; for t Time Building b load.

[0054] Step 3: Use CPlex to solve the model described in Step 2. Based on the model solution, obtain the operation status of each building equipment and the bidding plan for the next day; After solving the model described in step 2 using Cplex, the building aggregator is provided with the next day's building equipment scheduling status based on the model solution results { }, Optimal scheduling plan for each EV group { }, bidding plan for each building { }.

[0055] Example A simulation analysis is performed based on the comprehensive energy building group model set in step 1, which includes residential buildings, commercial buildings, and office buildings, to verify the feasibility and effectiveness of the method of the present invention.

[0056] According to the travel chain theory in step 1, a travel chain is established with the starting point being a residential building and the passing points being office buildings and commercial buildings.

[0057] On a typical day, there are two main types of travel chains: 1) Private EV: residential building → office building → residential building.

[0058] 2) Private EV: residential building → office building → commercial building → residential building.

[0059] EV users’ travel time satisfies the following distribution: (17); Where: The first trip time for EV users; 、 are the mean and variance of the distribution probability function respectively; The data is obtained from the travel information reported by EV owners in the past few days. In this embodiment, the normal distribution of EV travel time in the building on a typical day in Table 1 is used for extraction. The normal distribution of travel time is shown in Table 1.

[0060] Table 1 EV travel time under the travel chain Travel route Departure time / h Travel route Departure time / h Residential building → office building N(7.5,(0.5)²) Office building → residential building N(17.5,(0.5)²) Office building → Commercial building N(17.5,(0.5)²) Commercial building → residential building N(22.5,(0.5)²) Figure 3-4 The results of aggregating the collected historical data using the K-means method are shown, and the data is used as the electricity consumption feature library for building load forecasting.

[0061] According to step 2, a building day-ahead bidding model that considers power sharing is established. Then, according to step 3, the model described in step 2 is solved using CPLEX. Based on the model solution, the operating status of each building device and the bidding plan for the next day are obtained. Figure 5-8 It shows the next-day scheduling status of residential buildings, commercial buildings, and office buildings, as well as the formulated bidding plans.

[0062] Figure 9 This is a block diagram of a day-ahead scheduling system for integrated building energy in this exemplary embodiment. Figure 9 As shown, an exemplary embodiment of the present disclosure provides a day-ahead scheduling system for integrated building energy, including: an establishment unit for establishing a building power sharing mode based on the mobility characteristics of the EV group and the load characteristics of each building, as well as communication between buildings; A construction unit, configured to construct a building day-ahead bidding model based on the building power sharing mode; The solving unit is used to solve the building day-ahead bidding model, and obtain the next day's building equipment scheduling plan, the EV group optimization scheduling plan and the building bidding plan based on the solution result.

[0063] Specifically, the establishment unit includes: A first acquisition module is used to obtain EV travel information; The formation module is used to group EVs according to EV travel information and travel chain theory to form different EV groups.

[0064] Specifically, the establishment unit includes: The second acquisition module is used to obtain the power information of each unit in the building, including the next day's building load forecast data, the next day's photovoltaic output forecast data and energy storage system parameters; The third acquisition module is used to obtain the next day's building load forecast data, including: Collection module, used to collect historical electricity consumption data of buildings; Aggregation module, used to aggregate historical building electricity consumption data to generate a typical electricity consumption feature library for integrated energy buildings; The prediction module is used to predict the building load for the next day based on the typical power consumption feature library; Obtaining the next day's photovoltaic output forecast data includes: Obtain the next day's solar radiation intensity forecast data from the weather station; Get the area of ​​the building's photovoltaic array; Calculate the next day's photovoltaic output forecast data based on the photovoltaic model using the next day's solar radiation intensity forecast data and the area of ​​the building's photovoltaic array; The photovoltaic model is: ; Where, For buildings b exist t PV output power during the period; is the conversion efficiency; is the area of ​​the photovoltaic array; for t Solar radiation intensity during the period; Energy storage system parameters include the capacity, charge / discharge efficiency, and maximum charge / discharge power of the building battery.

[0065] Specifically, the building blocks include: A first establishing module is configured to establish an EV group model taking into account EV mobility characteristics based on the EV group; The second building module is used to build an energy storage model; A construction module is used to combine the EV group model and the energy storage model to construct a building day-ahead bidding model that takes into account power sharing.

[0066] Specifically, establishing an EV group model based on the EV group and considering EV mobility characteristics includes: ; ; ; ; ; ; Where: b Indicates the building number; t Indicates the time period number; j Indicates EV number; for t Buildings during the period b The number of EVs that can be dispatched; for Time period EV group q Total charging power; for t Time period EV group q Total discharge power; , Groups q Average charge and discharge power of single EV; , They are the charging and discharging power boundaries of a single EV respectively; Indicates that the EV is in charging state. Indicates that the EV is in the discharging state.

[0067] Specifically, establishing an energy storage model includes: ; ; ; ; ; Where, For buildings b exist t Battery charging status during the time period; For buildings b exist t Battery discharge status during the time period; For buildings b exist t Battery capacity during the period (kWh); is the battery capacity at the beginning of the scheduling cycle; Δ t Indicates a time interval; For buildings b exist t Battery charging power during the time period; is charging efficiency; For buildings b exist t Battery discharge power during the time period; is the discharge efficiency.

[0068] Specifically, combining the EV population model and energy storage model, constructing a building day-ahead bidding model that considers energy sharing includes: Taking the minimum total cost as the optimization goal, a building day-ahead bidding model considering power sharing is established, and the objective function is: ; Where, For building operating costs; for energy market benefits; ; Where, is the number of buildings; , Buildings b exist t The purchase and sale prices of electricity for time-sharing electricity; , Buildings b exist t The amount of electricity purchased and sold from the shared power grid during the time period; For buildings b Micro turbine operating costs; For buildings b exist t Micro-turbine output during the time period; For buildings b PV operating costs; For buildings b Gas purchase cost; For buildings b Energy storage operating costs; For buildings b EV dispatch costs; , are the average charging and discharging power of EV respectively; For buildings b exist t PV output power during the period; ; Where: for t The energy clearing price of a period can be predicted from historical clearing prices; for t Energy market bidding capacity for the time period; Electric balance constraints: ; Where, for t Time Building b Power delivered to other buildings; for t Delivered to the building from other buildings during the period b Power; for t Time Building b Energy market bidding capacity; for t Time Building b load.

[0069] Figure 10 FIG. 1 is a schematic diagram of the structure of a device of this exemplary embodiment. Figure 10 As shown, corresponding to the above-mentioned day-ahead scheduling method for integrated building energy, the present disclosure also provides a device. Since the embodiment of the device is similar to the above-mentioned method embodiment, the description is relatively simple. Please refer to the description of the above-mentioned method embodiment for relevant details. The device described below is only schematic. The device may include: a processor (processor) 1, a memory (memory) 2 and a communication bus (i.e., the above-mentioned device bus) and a search engine, wherein the processor 1 and the memory 2 communicate with each other through the communication bus and communicate with the outside through the communication interface. The processor 1 can call the logic instructions in the memory 2 to execute the day-ahead scheduling method for integrated building energy.

[0070] In addition, the logic instructions in the above-mentioned memory 2 can be implemented in the form of a software functional unit and can be stored in a computer-readable storage medium when sold or used as an independent product. Based on this understanding, the technical solution of the present invention is essentially or the part that contributes to the prior art or the part of the technical solution can be embodied in the form of a software product, and the computer software product is stored in a storage medium, including a number of instructions for enabling a computer device (which can be a personal computer, a server, or a network device, etc.) to perform all or part of the steps of the various embodiments of the present invention. The aforementioned storage medium includes: a memory chip, a USB flash drive, a mobile hard disk, a read-only memory (ROM), a random access memory (RAM), a magnetic disk or an optical disk, and other media that can store program codes.

[0071] On the other hand, an embodiment of the present disclosure also provides a processor-readable storage medium, on which a computer program 3 is stored. When the computer program 3 is executed by the processor 1, it is implemented to execute the day-ahead scheduling method of building integrated energy provided by the above-mentioned embodiments.

[0072] The processor-readable storage medium can be any available medium or data storage device that can be accessed by the processor 1, including but not limited to magnetic storage (such as floppy disks, hard disks, tapes, magneto-optical disks (MO)), optical storage (such as CDs, DVDs, BDs, HVDs, etc.), and semiconductor storage (such as ROMs, EPROMs, EEPROMs, non-volatile memories (NANDFLASH), solid-state drives (SSDs)), etc.

[0073] The above are merely preferred embodiments of the present disclosure. The scope of protection of the present disclosure is not limited to the above embodiments. All technical solutions based on the principles of the present disclosure are within the scope of protection of the present disclosure. It should be noted that for those skilled in the art, various improvements and modifications that do not depart from the principles of the present disclosure should be considered within the scope of protection of the present disclosure.

Claims

1. A day-ahead scheduling method for comprehensive building energy, characterized in that: include: Establish a building power sharing model based on the mobility characteristics of the EV group and the load characteristics of each building, as well as the communication between buildings; Based on the building power sharing model, a building day-ahead bidding model is constructed; The day-ahead bidding model of the building is solved, and based on the solution, the scheduling plan for each equipment in the building for the next day, the optimal scheduling plan for each EV group, and the bidding plan for each building are obtained.

2. The day-ahead scheduling method for building integrated energy according to claim 1 is characterized in that: Based on the mobility characteristics of the EV group and the load characteristics of each building, as well as the communication between buildings, the building power sharing model is established, including: Get EV travel information; Based on EV travel information, EVs are grouped according to the travel chain theory to form different EV groups.

3. The day-ahead scheduling method for building integrated energy according to claim 2 is characterized in that: Based on the mobility characteristics of the EV group and the load characteristics of each building, as well as the communication between buildings, the building power sharing model is established, including: Obtain the power information of each unit in the building, including the next day's building load forecast data, the next day's photovoltaic output forecast data and energy storage system parameters; Obtaining the next day's building load forecast data includes: Collect historical building electricity consumption data; Aggregate the historical electricity consumption data of buildings to generate a typical electricity consumption feature library for integrated energy buildings; Predict the next day's building load based on the typical electricity consumption feature database; Obtaining the next day's photovoltaic output forecast data includes: Obtain the next day's solar radiation intensity forecast data from the weather station; Get the area of ​​the building's photovoltaic array; The photovoltaic output forecast data for the next day is calculated based on the photovoltaic model through the predicted data of the next day's solar radiation intensity and the area of ​​the building's photovoltaic array; The photovoltaic model is: ； In the formula, For buildings b exist t Photovoltaic output power during the period; is the conversion efficiency; is the area of ​​the photovoltaic array; for t Solar radiation intensity during the period; Energy storage system parameters include the capacity, charge / discharge efficiency, and maximum charge / discharge power of the building battery.

4. The day-ahead scheduling method for building integrated energy according to claim 3 is characterized in that: Based on the building power sharing model, the building day-ahead bidding model is constructed including: Establishing an EV population model taking into account EV mobility characteristics based on the EV population; Establish energy storage model; Combining the EV population model and energy storage model, a building day-ahead bidding model considering power sharing is constructed.

5. The day-ahead scheduling method for building integrated energy according to claim 4 is characterized in that: The EV group model considering the EV mobility characteristics is established based on the EV group, including: ； ； ； ； ； ； Where: b Indicates the building number; t Indicates the time period number; j Indicates the EV number; for t Buildings in time period b The number of EVs that can be dispatched; for Time EV Group q Total charging power; for t Time EV Group q The total discharge power of , Groups q The average charge and discharge power of a single EV; , They are the charging and discharging power boundaries of a single EV respectively; Indicates that the EV is in charging state. Indicates that the EV is in a discharging state.

6. The day-ahead scheduling method for building integrated energy according to claim 4 is characterized in that: Building an energy storage model includes: ； ； ； ； ； In the formula, For buildings b exist t Battery charging status during the period; For buildings b exist t Battery discharge status during the time period; For buildings b exist t The battery capacity during the period (kWh); is the battery power at the beginning of the scheduling cycle; Δ t Indicates a time interval; For buildings b exist t Battery charging power during the period; For charging efficiency; For buildings b exist t Battery discharge power during the time period; is the discharge efficiency.

7. The day-ahead scheduling method for building integrated energy according to claim 4 is characterized in that: Combining the EV group model and energy storage model, a building day-ahead bidding model considering power sharing is constructed, including: Taking the minimum total cost as the optimization goal, a building day-ahead bidding model considering power sharing is established, and the objective function is: ； In the formula, For building operating costs; for energy market benefits; ； In the formula, is the number of buildings; , Buildings b exist t The purchase and sale prices of electricity for time-sharing electricity; , Buildings b exist t The amount of electricity purchased and sold from the shared power grid during the period; For buildings b Micro turbine operating costs; For buildings b exist t Micro-turbine output during the period; For buildings b PV operating costs; For buildings b Gas purchase cost; For buildings b Energy storage operating costs; For buildings b EV dispatch costs; , are the average charging and discharging power of EV respectively; For buildings b exist t Photovoltaic output power during the period; ； Where: for t The energy clearing price of a time period can be predicted from the historical clearing price; for t Energy market bidding capacity for time periods; Electrical balance constraints: ； In the formula, for t Time Building b Power delivered to other buildings; for t The period is delivered to the building from other buildings b Power; for t Time Building b Energy market bidding capacity; for t Time Building b load.

8. A day-ahead dispatching system for integrated building energy, characterized in that: include: an establishment unit for establishing a building power sharing mode according to the mobility characteristics of the EV group and the load characteristics of each building, as well as the communication between the buildings; A construction unit, configured to construct a building day-ahead bidding model based on the building power sharing mode; The solving unit is used to solve the day-ahead bidding model of the building, and based on the solving result, obtain the scheduling plan of each equipment in the building for the next day, the optimal scheduling plan of each EV group and the bidding plan of each building.

9. The day-ahead dispatching system for integrated building energy according to claim 8, characterized in that: The building blocks include: A first acquisition module is used to acquire EV travel information; The formation module is used to group EVs according to EV travel information and travel chain theory to form different EV groups.

10. The day-ahead dispatching system for integrated building energy according to claim 9, characterized in that: Building the unit also includes: The second acquisition module is used to obtain the power information of each unit of the building, the power information includes the next day's building load forecast data, the next day's photovoltaic output forecast data and energy storage system parameters; The third acquisition module is used to obtain the next day's building load forecast data and includes: Collection module, used to collect historical electricity consumption data of buildings; Aggregation module, used to aggregate historical building electricity consumption data and generate a typical electricity consumption feature library for integrated energy buildings; The prediction module is used to predict the building load of the next day based on the typical power consumption feature library; Obtaining the next day's photovoltaic output forecast data includes: Obtain the next day's solar radiation intensity forecast data from the weather station; Get the area of ​​the building's photovoltaic array; The photovoltaic output forecast data for the next day is calculated based on the photovoltaic model through the predicted data of the next day's solar radiation intensity and the area of ​​the building's photovoltaic array; The photovoltaic model is: ； In the formula, For buildings b exist t Photovoltaic output power during the period; is the conversion efficiency; is the area of ​​the photovoltaic array; for t Solar radiation intensity during the period; Energy storage system parameters include the capacity, charge / discharge efficiency, and maximum charge / discharge power of the building battery.

11. The day-ahead dispatching system for integrated building energy according to claim 10, characterized in that: The building blocks include: A first establishing module is used to establish an EV group model considering EV mobility characteristics based on the EV group; The second building module is used to build an energy storage model; A building module is used to combine the EV group model and the energy storage model to build a building day-ahead bidding model that takes into account power sharing.

12. The day-ahead dispatching system for integrated building energy according to claim 11, characterized in that: The EV group model considering the EV mobility characteristics is established based on the EV group, including: ； ； ； ； ； ； Where: b Indicates the building number; t Indicates the time period number; j Indicates the EV number; for t Buildings in time period b The number of EVs that can be dispatched; for Time EV Group q Total charging power; for t Time EV Group q The total discharge power of , Groups q The average charge and discharge power of a single EV; , They are the charging and discharging power boundaries of a single EV respectively; Indicates that the EV is in charging state. Indicates that the EV is in a discharging state.

13. The day-ahead dispatching system for integrated building energy according to claim 11, characterized in that: Building an energy storage model includes: ； ； ； ； ； In the formula, For buildings b exist t Battery charging status during the period; For buildings b exist t Battery discharge status during the time period; For buildings b exist t The battery capacity during the period (kWh); is the battery power at the beginning of the scheduling cycle; Δ t Indicates a time interval; For buildings b exist t Battery charging power during the period; For charging efficiency; For buildings b exist t Battery discharge power during the time period; is the discharge efficiency.

14. The day-ahead dispatching system for integrated building energy according to claim 11, characterized in that: Combining the EV group model and energy storage model, a building day-ahead bidding model considering power sharing is constructed, including: Taking the minimum total cost as the optimization goal, a building day-ahead bidding model considering power sharing is established, and the objective function is: ； In the formula, For building operating costs; for energy market benefits; ； In the formula, is the number of buildings; , Buildings b exist t The purchase and sale prices of electricity for time-sharing electricity; , Buildings b exist t The amount of electricity purchased and sold from the shared power grid during the period; For buildings b Micro turbine operating costs; For buildings b exist t Micro-turbine output during the period; For buildings b PV operating costs; For buildings b Gas purchase cost; For buildings b Energy storage operating costs; For buildings b EV dispatch costs; , are the average charging and discharging power of EV respectively; For buildings b exist t Photovoltaic output power during the period; ； Where: for t The energy clearing price of a time period can be predicted from the historical clearing price; for t Energy market bidding capacity for time periods; Electrical balance constraints: ； In the formula, for t Time Building b Power delivered to other buildings; for t The period is delivered to the building from other buildings b Power; for t Time Building b Energy market bidding capacity; for t Time Building b load.

15. A device, characterized in that include: Processor and memory; The memory is used to store a computer program, and the processor calls the computer program stored in the memory to execute the day-ahead scheduling method for integrated building energy as described in any one of claims 1 to 7.

16. A computer-readable storage medium, characterized in that: The computer-readable storage medium stores a computer program, and when the computer program is executed by a processor, the processor is enabled to execute the day-ahead scheduling method for integrated building energy as described in any one of claims 1 to 7.

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