Urban building scheduling method and device, equipment and storage medium

Abstract

The embodiment of the invention discloses an urban building scheduling method and device, equipment and a storage medium, and the method comprises the steps: obtaining a first optimization model, the first optimization model is used for optimizing the interaction cost of a plurality of building clusters in an urban building, and each building cluster in the plurality of building clusters is composed of a plurality of buildings; obtaining a second optimization model, wherein the second optimization model is used for optimizing the interaction cost among all buildings in the building cluster; acquiring a real-time temperature value of each building in a building cluster, and establishing a temperature optimization model of the building cluster according to the real-time temperature value; and processing the first optimization model, the second optimization model and the temperature optimization model to obtain an optimal scheduling result of the urban building. The energy use and resource allocation of the building cluster can be optimized, so that the overall energy consumption and emission are reduced.

Description

TECHNICAL FIELD

[0001] The embodiment of the present application relates to resource scheduling technology, and relates to but is not limited to a scheduling method and device for urban buildings, equipment and a storage medium. BACKGROUND

[0002] In the prior art, the research on the scheduling of urban buildings mainly focuses on the economic operation optimization, multi-objective optimization and solution algorithm of the system. However, the power interaction factor between buildings is often ignored in the prior art, and the economic operation optimization and multi-objective optimization in the scheduling of urban buildings in the prior art cannot accurately obtain the optimal scheduling result of the urban buildings.

[0003] Therefore, how to fully consider various factors in the scheduling of urban buildings so as to optimize the energy use and resource allocation of the building cluster and further reduce the overall energy consumption and emission is a problem to be solved. SUMMARY

[0004] Therefore, the scheduling method and device for urban buildings, equipment and storage medium provided by the embodiment of the present application can optimize the energy use and resource allocation of the building cluster and further reduce the overall energy consumption and emission. The scheduling method and device for urban buildings, equipment and storage medium provided by the embodiment of the present application are implemented as follows:

[0005] The embodiment of the present application provides a scheduling method for urban buildings, which comprises the following steps:

[0006] A first optimization model is obtained, the first optimization model is used for optimizing the interaction cost of a plurality of building clusters in urban buildings, the interaction cost comprises at least one of the electric energy interaction cost and the natural gas interaction cost between the plurality of building clusters, and each building cluster in the plurality of building clusters is composed of a plurality of buildings;

[0007] A second optimization model is obtained, the second optimization model is used for optimizing the interaction cost between each building in the building cluster, and the interaction cost comprises at least one of the photovoltaic cost, the building economic cost, the energy storage cost and the phase change energy storage cost;

[0008] Real-time temperature values of each building in the building cluster are obtained, and a temperature optimization model of the building cluster is established according to the real-time temperature values;

[0009] The first optimization model, the second optimization model and the temperature optimization model are processed according to a preset algorithm to obtain an optimal scheduling result of the urban buildings, and the preset algorithm is a double-adaptive particle swarm optimization algorithm.

[0010] In some embodiments, the first optimization model comprises a target function of the first optimization model and a constraint condition of the first optimization model, and the obtaining the first optimization model comprises:

[0011] obtaining a total number of buildings in the building cluster, an interaction cost of the building cluster, and an interaction time of the building cluster;

[0012] obtaining a target function of the first optimization model according to the total number of buildings in the building cluster, the interaction cost of the building cluster, and the interaction time of the building cluster;

[0013] obtaining an electrical energy interaction cost of the building cluster to obtain an electrical energy constraint of the building cluster, and obtaining a gas supply cost of the building cluster to obtain a gas supply constraint of the building cluster;

[0014] obtaining a constraint condition of the first optimization model according to the electrical energy constraint and the gas supply constraint;

[0015] obtaining the first optimization model according to the target function of the first optimization model and the constraint condition of the first optimization model.

[0016] In some embodiments, the second optimization model comprises a target function of the second optimization model and a constraint condition of the second optimization model, and the obtaining the second optimization model comprises:

[0017] obtaining an interaction time, a photovoltaic cost, and a building economic cost of each building in the building cluster;

[0018] obtaining a target function of the second optimization model according to the interaction time, the photovoltaic cost, and the building economic cost of each building in the building cluster;

[0019] obtaining a constraint condition of the second optimization model according to a constraint condition of each building in the building cluster according to an energy storage cost of the each building and a phase change energy storage cost of the each building;

[0020] obtaining the second optimization model according to the target function of the second optimization model and the constraint condition of the second optimization model.

[0021] In some embodiments, the obtaining a real-time temperature value of each building in the building cluster, and establishing a temperature optimization model of the building cluster according to the real-time temperature value comprises:

[0022] obtaining a total number of buildings in the building cluster and an expected temperature value of each building;

[0023] According to the real-time temperature value, the total number of the respective buildings, and the expected temperature value, the temperature optimization model is obtained.

[0024] In some embodiments, the first optimization model, the second optimization model, and the temperature optimization model are processed according to a preset algorithm to obtain the optimal scheduling result of the urban buildings, including:

[0025] The second optimization model is processed according to the preset algorithm to obtain the minimum interaction cost between the respective buildings in the building cluster;

[0026] An optimized temperature value is obtained according to the temperature optimization model;

[0027] The first optimization model is processed according to the minimum interaction cost between the respective buildings in the building cluster and the optimized temperature value to obtain the optimal scheduling result of the urban buildings.

[0028] In some embodiments, the second optimization model is processed according to the preset algorithm to obtain the minimum interaction cost between the respective buildings in the building cluster, including:

[0029] The interaction time, the photovoltaic cost, and the building economic cost of the respective buildings in the building cluster are calculated according to the preset algorithm to obtain the weighted minimum interaction value of the respective buildings in the building cluster;

[0030] The weighted minimum interaction value of the respective buildings in the building cluster is calculated according to the electric energy constraint and the gas supply constraint to obtain the minimum interaction cost between the respective buildings in the building cluster.

[0031] In some embodiments, the first optimization model is processed according to the minimum interaction cost between the respective buildings in the building cluster and the optimized temperature value to obtain the optimal scheduling result of the urban buildings, including:

[0032] The minimum interaction cost between the respective buildings in the building cluster is adjusted according to the optimized temperature value to obtain an initial optimized scheduling result of the respective buildings in the building cluster;

[0033] The first optimization model is adjusted according to the initial optimized scheduling result to obtain the optimal scheduling result of the urban buildings.

[0034] The embodiment of the present application provides a kind of urban building scheduling acquisition module, for obtaining first optimization model, the first optimization model is used to optimize the interaction cost of multiple building clusters in urban building, the interaction cost includes at least one of the electric energy interaction cost and natural gas interaction cost between multiple building clusters, each building cluster in multiple building clusters is formed by multiple buildings;

[0035] The acquisition module is also used to obtain a second optimization model, the second optimization model is used to optimize the interaction cost between buildings in a building cluster, and the interaction cost includes at least one of photovoltaic cost, building economic cost, energy storage cost, and phase change energy storage cost.

[0036] The acquisition module is also used to obtain a real-time temperature value of each building in the building cluster, and a temperature optimization model of the building cluster is established according to the real-time temperature value.

[0037] The processing module is used to process the first optimization model, the second optimization model and the temperature optimization model according to a preset algorithm to obtain an optimal scheduling result of the urban building, and the preset algorithm is a double-adaptive particle swarm optimization algorithm.

[0038] The computer device provided by the embodiment of the present application includes a memory and a processor, the memory stores a computer program that can run on the processor, and the processor executes the program to realize the method described in the embodiment of the present application.

[0039] The computer readable storage medium provided by the embodiment of the present application has a computer program stored thereon, and the computer program is executed by a processor to realize the method provided by the embodiment of the present application.

[0040] The method, device, computer device and computer readable storage medium provided by the embodiment of the present application are used to obtain a first optimization model, the first optimization model is used to optimize the interaction cost of multiple building clusters in urban building, and each building cluster in multiple building clusters is formed by multiple buildings;A second optimization model is obtained, the second optimization model is used to optimize the interaction cost between buildings in a building cluster;Real-time temperature values of each building in the building cluster are obtained, and a temperature optimization model of the building cluster is established according to the real-time temperature values;The first optimization model, the second optimization model and the temperature optimization model are processed to obtain an optimal scheduling result of the urban building. In this way, the energy use and resource allocation of the building cluster can be optimized, and the overall energy consumption and emission can be reduced, thereby solving the technical problems proposed in the background art. BRIEF DESCRIPTION OF DRAWINGS

[0041] The drawings incorporated in and forming a part of the specification, illustrate embodiments consistent with the present application and together with the description, serve to explain the principles of the application.

[0042] Figure 1 is an application scenario of a city building scheduling method disclosed by embodiments of the present application;

[0043] Figure 2A is an implementation flow diagram of a city building scheduling method disclosed by embodiments of the present application;

[0044] Figure 2B is a schematic diagram of an example of a building cluster provided by embodiments of the present application;

[0045] Figure 2C is a schematic diagram of an example of a CCHP system in a building cluster provided by embodiments of the present application;

[0046] Figure 3 is an implementation flow diagram of another city building scheduling method disclosed by embodiments of the present application;

[0047] Figure 4 is an implementation flow diagram of another city building scheduling method disclosed by embodiments of the present application;

[0048] Figure 5 is an implementation flow diagram of another city building scheduling method disclosed by embodiments of the present application;

[0049] Figure 6 is a structural schematic diagram of a city building scheduling device disclosed by embodiments of the present application;

[0050] Figure 7 is a computer device disclosed by embodiments of the present application. DETAILED DESCRIPTION

[0051] In order to make the objectives, technical solutions and advantages of embodiments of the present application clearer, the following will further describe the specific technical solutions of the present application with reference to the drawings in embodiments of the present application. The following embodiments are used to illustrate the present application, but are not used to limit the scope of the present application.

[0052] Unless otherwise defined, all technical and scientific terms used herein have the same meaning as commonly understood by one of ordinary skill in the art to which this application belongs. The terminology used in the specification is for describing the embodiments of the present application only and is not intended to limit the present application.

[0053] In the following description, reference is made to "some embodiments", which describe a subset of all possible embodiments, but which can be understood as a non-limiting subset of all possible embodiments, and which can be combined with each other and with other embodiments without conflicts.

[0054] It should be noted that the terms "first", "second", "third" used in the embodiments of the present application are used to distinguish similar or different objects, and do not represent a specific order of the objects. It can be understood that "first", "second", "third" can be interchanged in a specific order or sequence as allowed, so that the embodiments of the present application described herein can be implemented in an order other than that illustrated or described herein.

[0055] Therefore, the embodiments of the present application provide a scheduling method of urban buildings, which is applied to an intelligent electronic device. Figure 1 An application scenario diagram of a scheduling method of urban buildings in an embodiment is shown in FIG. 1. Figure 1 As shown in FIG. 1, a user can use an electronic device 10, which can include but is not limited to a tablet computer, a notebook computer, a PC (Personal Computer), and the like. The functions implemented by the method can be realized by calling program codes by a processor in the electronic device. Of course, the program codes can be stored in a computer storage medium. Therefore, the electronic device at least includes a processor and a storage medium.

[0056] The present application also provides an implementation flow diagram of a scheduling method of urban buildings, which is applied to an application scenario shown in FIG. 2. Figure 1 As shown in FIG. 2, the method can include the following steps 201 to 204. Figure 2A

[0057] Step 201, a first optimization model is acquired.

[0058] In the embodiments of the present application, the first optimization model is used to optimize the interaction cost of a plurality of building clusters in urban buildings. Please refer to FIG. 3. Figure 2B for a schematic diagram of an example of a building cluster provided by the embodiments of the present application. As shown in FIG. 3, a building cluster includes n independent buildings, which are marked as independent building 1 to independent building n, and n is a positive integer. Figure 2B In the embodiments of the present application, the n independent buildings are controlled by a cluster control system, which is used to control the energy transmission of the n independent buildings, such as electric energy transmission or natural gas transmission, and the like. Figure 2B In the embodiments of the present application, electric energy transmission and natural gas transmission are taken as examples. Figure 2B

[0059] ​​First, the electricity and natural gas usage data of the building cluster is collected. Then, a mathematical model is established, considering the electricity and natural gas flow between the buildings in the cluster and the cost thereof. The model can include power transmission loss, natural gas transmission loss, supply and demand matching, etc. The interaction cost of electricity and natural gas is minimized to ensure efficient use of resources.

[0060] In some embodiments, the model in the unit of building cluster can be referred to as a top cluster optimization model, and the name of the first optimization model is not limited in the embodiments of the present application.

[0061] As an example, the objective function of the top cluster optimization model can aim to minimize the sum of various interaction costs of the building cluster (including external interaction cost and intra-zone building interaction cost), which can include the electricity grid interaction cost of building n at time t, the natural gas interaction cost of building n at time t, the temperature sensing constraint cost of building n at time t, and the building interaction cost of building n at time t, T is the total scheduling time, N is the total number of independent buildings in the building cluster, t takes each value in [1, T], and n takes each value in [1, N].

[0062] The electricity grid interaction cost of building n at time t can be determined according to the electricity purchase power and the electricity sale power of building n at time t, the electricity purchase cost and the electricity sale cost of building n interacting with the main grid at time t. The natural gas interaction cost of intra-zone building n at time t can be determined according to the unit natural gas cost, the gas power provided by the gas source point (natural gas main grid), and the lower heating value of natural gas (LHVG). The temperature sensing constraint cost of building n at time t can be determined according to the temperature sensing coefficient, the actual room temperature of building n at time t, and the user expected temperature. The building interaction cost of intra-zone building n at time t can be determined according to the power purchased by building n from other buildings at time t, the power delivered to other buildings, the building-to-building power interaction purchase price, and the sale price.

[0063] The constraint conditions of the top cluster optimization model can include power supply constraints, gas supply constraints, building-to-building energy interaction constraints, and temperature sensing constraints. The power supply constraints can be determined according to the electricity purchase power and the electricity sale power of building n interacting with the grid at time t, and the upper and lower limits of the power transmission power allowed by the top cluster control system. The gas supply constraints corresponding to the maximum gas flow allowed by the independent building are determined according to the gas power provided by the gas source point (natural gas main grid), the maximum natural gas supply of building n at time t, and the maximum natural gas limit (supply) of building n within the total scheduling time T.

[0064] The inter-building energy interaction constraint is a power interaction constraint between the independent building and the main power grid and other buildings, which can be determined according to the power purchased by the building n from the power grid at time t, the power sold by the building n to the power grid at time t, the power purchased by the building n from other buildings at time t, the power delivered by the building n to other buildings, the upper and lower limits of the power interaction limit between the building n and the main power grid, and the upper and lower limits of the power interaction limit between the buildings.

[0065] The temperature constraint can be determined according to the upper and lower limits of the user temperature (temperature) allowed by the independent building n.

[0066] The top-level cluster optimization model considers various interaction costs of the building cluster, that is, the coordinated scheduling between the buildings is considered. For example, a building with more renewable energy output can deliver the excess energy to a building with less output to alleviate the load pressure of the building with less output, so as to improve the utilization rate of renewable energy and reduce the overall operation cost.

[0067] In the above example, parameters for obtaining the first optimization model are given. Those skilled in the art can use different mathematical operation methods such as summation or averaging according to actual use requirements, process the above parameters to obtain the first optimization model, which is not limited in the embodiments of the present application.

[0068] Step 202, obtaining a second optimization model.

[0069] In the embodiments of the present application, the interaction cost between each building in the building cluster is optimized. First, the data of the photovoltaic system, the energy storage system, the phase change energy storage system and the economic cost in the building are collected. Then, a mathematical model is established, considering the photovoltaic power generation, the energy storage efficiency, the performance of the phase change energy storage and the economic cost of the building. The model can include the charging and discharging strategy of the energy storage system and the power generation capacity of the photovoltaic system. The photovoltaic cost, the energy storage cost, the phase change energy storage cost and the building economic cost are minimized.

[0070] It should be noted that the interaction cost between each building in the building cluster is not limited to the data of a certain resource such as photovoltaic and electric energy in the foregoing example. Please refer to Figure 2C , a schematic diagram of an example of a combined cooling, heating and power (CCHP) system for a building cluster. In Figure 2C , the independent building can include various loads such as air conditioning cold load or air conditioning heat load in addition to the conventional electric load. The combined cooling, heating and power (CCHP) system uses various resources such as photovoltaic, battery, phase change energy storage, natural gas and power distribution network that can generate electricity or heat as primary energy, combines the power generation system with the heating and cooling system, and provides comprehensive energy supply for users, so as to meet the energy demand of users for heat, electricity, cold and other energy.

[0071] In some embodiments, the model in the unit of the independent building, referred to as a bottom-layer independent building optimization model, is not limited in the name of the second optimization model in the embodiments of the present application.

[0072] In an alternative implementation, the bottom-layer independent building optimization model can include an objective function of the bottom-layer building optimization model and constraint conditions of the bottom-layer building optimization model, and the bottom-layer building optimization model is established to minimize the sum of various costs, including: minimizing the weighted sum of the building economic operation cost, the temperature-sensing constraint cost and the abandoned light power cost in the total scheduling time as the objective function of the bottom-layer building optimization model; obtaining the balance constraint, the controllable unit constraint, the energy storage battery constraint and the phase change energy storage constraint of the building as the constraint conditions of the bottom-layer building optimization model.

[0073] The various costs described above can be calculated by the following formula:

[0074] The economic operation cost of the independent building n at time t can be determined according to the independent building photovoltaic unit operation cost, the CCHP system unit operation cost and the air conditioner unit operation cost, wherein the independent building photovoltaic unit operation cost, the CCHP system unit operation cost and the air conditioner unit operation cost can include the output power of the independent building photovoltaic, the CCHP system and the air conditioner at time t, the unit operation cost of the energy storage battery and the phase change energy storage at time t, the charging and discharging power of the energy storage battery at time t, and the energy storage and discharging power of the phase change energy storage at time t.

[0075] The temperature-sensing constraint cost of the independent building n at time t can be determined according to the temperature-sensing coefficient, the actual room temperature of the independent building and the user expected temperature.

[0076] The abandoned light power cost of the independent building n at time t can be determined according to the independent building photovoltaic unit operation cost, the actual available power of the photovoltaic at time t and the photovoltaic output power of the independent building at time t.

[0077] The constraint conditions of the independent building optimization model include the balance constraint, the controllable unit constraint, the energy storage battery constraint and the phase change energy storage constraint.

[0078] The balance constraint can be determined according to the output power of the independent building photovoltaic at time t, the output power of the CCHP system, the discharging power and the charging power of the energy storage battery at time t, the values of the non-air conditioner electrical load and the air conditioner load of the independent building at time t, the heat power of the CCHP system at time t, the discharging power and the energy storage power of the phase change energy storage at time t, the output power of the phase change energy storage at time t, and the power transmitted to other buildings and the power transmitted from the independent building to other buildings at time t.

[0079] The controllable unit constraint can be determined according to the following parameters:

[0080] The output of the controllable unit C in the independent building at time t, time t-1, the upper and lower limits of the output of the controllable unit C, and the upper and lower ramping speeds of the controllable unit C, that is, the output that can be increased or decreased per unit time of the unit and the unit scheduling time.

[0081] The energy storage battery constraint can be determined according to the following parameters:

[0082] The charging and discharging power of the energy storage battery at time t, the charging and discharging state variables of the energy storage battery at time t, the upper and lower limits of the charging power of the energy storage battery, the upper and lower limits of the discharging power of the energy storage battery, the residual capacity of the energy storage battery at time t, the upper and lower limits of the residual capacity of the energy storage battery, and the initial and final states of the energy storage battery in the scheduling period T (which can be understood as the residual energy storage capacity). It should be noted that the initial state of the energy storage battery is not charging and discharging, and the final state is the completion of the entire charging and discharging process, that is, all the charged electricity is discharged, so the residual energy storage capacity of the initial and final states of the energy storage battery is equal. The charging and discharging state variables are used to define the charging and discharging state of the energy storage battery. The charging state variable is 1 when charging, and the discharging state variable is 0 when discharging. Conversely, when discharging, the charging state variable is 0.

[0083] The phase change energy storage constraint can be determined according to the following parameters:

[0084] The energy storage and discharging power of the phase change energy storage at time t, the energy storage and discharging state variables of the phase change energy storage device, the upper and lower limits of the energy storage power of the phase change energy storage device, the upper and lower limits of the discharging power of the phase change energy storage device, the residual energy storage capacity of the phase change energy storage device at time t, the upper and lower limits of the residual capacity of the phase change energy storage device, and the initial and final states of the phase change energy storage device in the scheduling period.

[0085] In the embodiment, by including the phase change energy storage in the economic operation cost of the bottom building cluster optimization model, the phase change energy storage participates in the building dispatching in the district from a macro perspective, so that it can not only adjust the operation state of the building in the district, but also participate in the optimization dispatching of other buildings, thereby achieving the purpose of long-term energy transfer and improving energy utilization efficiency, and to a certain extent, achieving the effect of relieving the peak load pressure and reducing the operation cost.

[0086] In the above example, parameters for obtaining a second optimization model are given. Those skilled in the art can use different mathematical operation methods such as summation or averaging according to actual use requirements, process the above parameters to obtain the second optimization model, which is not limited in the embodiment of the present application.

[0087] In step 203, real-time temperature values of each building in the building cluster are obtained, and a temperature optimization model of the building cluster is established according to the real-time temperature values.

[0088] In the embodiments of the present application, temperature sensors need to be installed in buildings to monitor the temperature of each building in real time. Ensure that sensor data can be transmitted to the central control system in real time. Process noise and outliers in sensor data. Store the cleaned data in the database for subsequent modeling.

[0089] Set temperature optimization goals, such as maximizing comfort, minimizing energy consumption, etc. Consider constraints such as heating and cooling requirements of buildings, external climate changes, etc. Choose appropriate optimization model types, such as linear programming, nonlinear programming, or mixed integer programming, etc. Adjust model parameters according to real-time temperature data to improve model accuracy. Verify the accuracy and reliability of the model through historical data or simulation data.

[0090] As an example, the temperature optimization model of the building cluster is obtained as follows: obtaining the actual temperature and user desired temperature of the building at any time; calculating the user desired temperature of the building cluster in the area according to the arithmetic mean of the user desired temperature of the plurality of buildings at any time; calculating the actual temperature of the building cluster in the area according to the arithmetic mean of the actual temperature of the plurality of buildings at any time.

[0091] The temperature optimization model of the building cluster can be determined according to the following parameters:

[0092] The total number of buildings, the actual temperature and user desired temperature of building n at time t, and the actual temperature and user desired temperature of the building cluster as a whole at time t.

[0093] In the embodiments of the present application, a temperature optimization model containing the top and bottom temperature relationship is constructed based on the actual temperature and user desired temperature, achieving the purpose of improving user comfort while coordinating and optimizing the scheduling of the building cluster.

[0094] In the above example, the parameters for obtaining the temperature optimization model are given. Those skilled in the art can use different mathematical operation methods such as summation or averaging to process the above parameters to obtain the temperature optimization model according to actual use requirements. In the embodiments of the present application, no limitation is made.

[0095] Step 204, processing the first optimization model, the second optimization model and the temperature optimization model according to a preset algorithm to obtain the optimal scheduling result of the urban building.

[0096] In the embodiments of the present application, a double fitness particle swarm optimization algorithm is used, which combines the evaluation of individual fitness and group fitness. Set the parameters of the algorithm, such as the number of particles, the learning factor, the number of iterations, etc. Use the double fitness particle swarm optimization algorithm to process the first optimization model, the second optimization model and the temperature optimization model simultaneously. Extract the optimal solution from the algorithm and analyze the scheduling result. According to the analysis result, adjust the scheduling scheme to meet the actual demand.

[0097] The embodiments of the present application obtain and process the first optimization model and the second optimization model, comprehensively consider the electricity and natural gas interaction costs between multiple building clusters in urban buildings, and the photovoltaic cost, economic cost, energy storage cost, etc. between buildings in the building cluster, so as to more comprehensively optimize the scheduling strategy of the building and reduce the overall operation cost. By obtaining the real-time temperature value of each building in the building cluster and establishing a temperature optimization model, the temperature of the building can be adjusted in real time to optimize the energy use efficiency. This helps to improve the comfort of the building and further reduce energy consumption. Using the double fitness particle swarm optimization algorithm for model processing can effectively find the near-optimal solution in complex optimization problems and improve the accuracy and efficiency of the scheduling result. This algorithm can handle complex interaction costs in multi-dimensional and multi-objective optimization problems and adjust the temperature appropriately. Through the above optimization and adjustment methods, the energy utilization efficiency of the urban building system can be improved, resource waste can be reduced, operation cost can be reduced, and the goal of energy saving and emission reduction can be achieved, thereby helping to achieve sustainable development of urban building management.

[0098] Further, the processing of the first optimization model, the second optimization model and the temperature optimization model according to the preset algorithm to obtain the optimal scheduling result of the urban building comprises: processing the second optimization model according to the preset algorithm to obtain the minimum interaction cost between each building in the building cluster.

[0099] Specifically, in the particle swarm optimization algorithm, a group of particles is first initialized, and each particle represents a possible solution. The position and velocity of the particles affect their movement in the solution space. The fitness of each particle at the current solution space position, i.e. the interaction cost between the buildings, is calculated. These costs include photovoltaic cost, building economic cost, energy storage cost, etc. According to the fitness of each particle, its position and velocity are updated. The particles will adjust the moving direction according to their own best position and the best position of all particles to find better solutions. The steps of evaluating fitness and updating position are repeated until the stopping condition is met, such as reaching the maximum number of iterations or the solution is good enough. Finally, the particle with the best fitness is selected from all particles as the minimum interaction cost solution between each building in the building cluster.

[0100] Further, the processing of the first optimization model, the second optimization model and the temperature optimization model according to the preset algorithm to obtain the optimal scheduling result of the urban building comprises: processing the second optimization model according to the preset algorithm to obtain the minimum interaction cost between each building in the building cluster.

[0101] Specifically, the interaction time data between each building is collected, that is, the time required for information or resources to be exchanged between buildings. The installation and operation costs of the photovoltaic system of each building are obtained. The operation and maintenance costs of each building are determined. Weights are assigned to the interaction time, photovoltaic cost, and building economic cost respectively. The interaction time, photovoltaic cost, and building economic cost of each building are weighted using the above weights. This means multiplying each data by the corresponding weight and then summing them to obtain the weighted value of each building. The weighted minimum interaction value of each building in the cluster is calculated through a preset algorithm (such as the dual-fitness particle swarm optimization algorithm). These algorithms usually involve simulating multiple scenarios and finding the optimal solution to ensure that the minimum weighted interaction value is achieved in all buildings.

[0102] The weighted minimum interaction value of each building in the building cluster is calculated according to the power constraint and the gas supply constraint to obtain the minimum interaction cost between each building in the building cluster.

[0103] Specifically, identify the power demand and supply capacity of each building in the cluster. For example, determine the maximum power usage for each building, as well as the power supply constraints. Identify the gas supply demand and supply capacity for each building, including the maximum natural gas usage and supply constraints. When calculating the weighted minimum interaction value between buildings, the power and gas supply constraints need to be taken into account. Adjust the interaction value for each building to ensure that these values ​​are optimized while satisfying the constraints. For example, if the power demand of some buildings exceeds the supply capacity, resources may need to be reallocated or the interaction time adjusted to avoid exceeding the power constraint. Based on the weighted minimum interaction value, power and gas supply constraints are applied to adjust these values. By adjusting the building's operating strategy and resource allocation, ensure that the demand and supply of power and gas are balanced across all buildings. The weighted minimum interaction value after constraint adjustment is used to calculate the actual minimum interaction cost between each building.

[0104] By optimizing the interaction costs between buildings, this embodiment can significantly reduce the expenditure of resources such as electricity and natural gas. This ensures efficient resource allocation across buildings and reduces unnecessary waste. It also accurately calculates the optimal scheduling plan for building clusters, improving the overall operational efficiency of the system.

[0105] Specifically, real-time building temperature data and desired temperature values are obtained. These data are used to analyze the gap between the current building environment and the target environment. A temperature optimization model is established, which considers the difference between the actual temperature and the desired temperature, simulates how to adjust the building's temperature control system to achieve the target temperature. By simulating different temperature control strategies or adjustment schemes, the temperature adjustment effect under each scheme is calculated, and its impact on comfort and energy efficiency is evaluated. Optimization algorithms are applied in the model to find the adjustment scheme that makes the actual temperature closest to the desired temperature. This may include adjusting the settings of air conditioning, heating or other temperature control systems. According to the simulation and optimization results, the optimal temperature setting value is determined to ensure that the temperature inside the building is optimized while meeting the requirements of energy efficiency and comfort.

[0106] According to the minimum interaction cost between each building in the building cluster and the optimized temperature value, the first optimization model is processed to obtain the optimal scheduling result of the urban building.

[0107] Specifically, the minimum interaction cost between each building in the building cluster and the optimized temperature value is integrated into a comprehensive model. Set optimization goals, such as minimizing total operating costs, maximizing energy efficiency, or balancing comfort and energy saving. The objective function will comprehensively consider the interaction cost and temperature optimization result. According to the objective function, the scheduling strategy of the building is formulated, including operation time, temperature control setting and resource allocation. Use the model to simulate different scheduling strategies, evaluate their effects, and adjust them according to the actual situation. This includes adjusting the working hours of the building, the settings of the temperature control system, etc. Apply optimization algorithms to find the optimal scheduling scheme. This includes selecting the best operation time, temperature control setting and resource allocation to achieve the optimization goal in the objective function.

[0108] The embodiments of the present application minimize the cost of electricity, natural gas, etc. by optimizing the interaction cost and temperature between buildings respectively. Ensure that the temperature inside the building meets the desired value and improves comfort. Combined with multiple optimization models, the best balance between comprehensive cost and energy efficiency is achieved.

[0109] Further, according to the minimum interaction cost between each building in the building cluster and the optimized temperature value, the first optimization model is processed to obtain the optimal scheduling result of the urban building, including: adjusting the minimum interaction cost between each building in the building cluster according to the optimized temperature value, to obtain the initial optimization scheduling result of each building in the building cluster.

[0110] Specifically, first, determine the minimum interaction cost between each building in the building cluster based on the results of the second optimization model. Apply the optimized temperature values calculated in the temperature optimization model to the building cluster. This means you will adjust the previously calculated minimum interaction cost based on the actual temperature needs of each building to reflect the impact of different temperature needs on cost. Based on the optimized temperature values, adjust the interaction cost between buildings. For example, if the temperature needs of some buildings are higher, energy transmission between them may need to be increased, thereby increasing the interaction cost between these buildings. Apply the adjusted interaction cost to recalculate the scheduling scheme of the building cluster. Generate a preliminary scheduling result, taking into account the impact of temperature optimization on cost. Further optimize based on the initial scheduling result to ensure optimal energy and cost configuration.

[0111] Adjust the first optimization model based on the initial optimization scheduling result to obtain the optimal scheduling result of the urban buildings.

[0112] Specifically, evaluate the building energy efficiency, cost, and interaction arrangement in the initial optimization scheduling result to find potential improvements. Adjust the objective function and constraints of the first optimization model based on the problems found in the initial scheduling result. This may include modifying energy consumption limits, cost budgets, or energy transmission requirements between buildings. Apply the adjusted first optimization model to recalculate. This will include updated objective functions and constraints to ensure better compliance with actual needs and optimization goals. Verify the new scheduling result to ensure it is better than the initial scheduling result in terms of economic efficiency, energy efficiency, and meeting building needs. Based on the verification result, further adjust the model or scheduling scheme and repeat the optimization until the final optimal scheduling result is achieved.

[0113] In some embodiments, the temperature value parameter and the device output parameter can also be determined based on historical operation data to obtain preset parameters, including: the initial value and the iteration initial value of the fitness of the double fitness particle swarm optimization algorithm. The temperature value parameter is determined based on the historical operation data of the low-carbon building cluster, i.e., the initial value and the upper and lower limits of the temperature; the device output parameter is determined, such as the power and efficiency of each device, the maximum heat-to-power ratio of CCHP, the heat capacity of phase change energy storage, the capacity of electrical energy storage, etc.; the initial value and the iteration initial value of the fitness of the double fitness particle swarm optimization algorithm are obtained (such as setting the iteration initial value k = 1); and a zero matrix of main variables is reserved for data recording and observation, such as recording the iteration results.

[0114] During the specific computational process of step S204, it is determined whether the solution meets the convergence criteria. If not, the optimization model for the buildings in the bottom zone and the optimization model for the top cluster are iteratively solved based on the temperature sensitivity parameters, equipment output parameters, and preset parameters until the convergence criteria are met. If the convergence criteria are met, the iteration is terminated, and the coordinated optimization scheduling results for the building cluster are output. As an example, the convergence criteria can be that the actual temperatures of the building cluster and each individual building are within upper and lower temperature limits.

[0115] The embodiment of the present application comprehensively considers the minimum interaction cost of each building in the building cluster and the optimized temperature value. The method can effectively adjust the interaction cost and temperature conditions between buildings, thereby achieving comprehensive optimization of the urban building system. After obtaining the initial optimization scheduling result, the optimal scheduling result is obtained by further adjusting the first optimization model. This multi-step optimization process can more accurately match the actual needs of the building and the availability of resources, thereby providing a more accurate and reliable scheduling strategy. By minimizing the interaction cost between buildings and combining it with the optimized temperature value, the utilization efficiency of resources (such as electricity and natural gas) can be effectively improved and waste can be reduced. This significantly improves the energy management and operational efficiency of urban buildings.

[0116] In the above Figure 2A Based on the process shown in FIG, this application also provides a schematic diagram of the implementation process of the scheduling method of urban buildings. Figure 3 As shown, the method may include the following steps 301 to 307:

[0117] Step 301: Obtain the total number of buildings in the building cluster, the interaction cost of the building cluster, and the interaction time of the building cluster.

[0118] In an embodiment of the present application, building data can be obtained from a building management system, a building information modeling system, or a city database. For example, records of all buildings can be extracted from a database or data source. This can be achieved through SQL queries or API calls. The extracted data is cleaned and organized to ensure that each building is uniquely identified. The total number of building records is calculated, which is the total number of buildings. Interaction cost refers to the cost of resource exchange (such as electricity and natural gas) between buildings. Interaction cost data is obtained from a building energy management system, contract records, or a real-time monitoring system. Interaction cost data is collected between each pair of buildings and can be obtained through an energy monitoring system or financial records. Interaction time refers to the time for resource exchange between buildings, which involves actual resource transmission time or expected processing time. Interaction time data is obtained from system monitoring logs, building management systems, or real-time data streams. The interaction time between each pair of buildings is recorded. These data can be obtained through automated systems, sensor data, or manual records.

[0119] At step 302, a target function of the first optimization model is obtained according to a total number of buildings in the building cluster, an interaction cost of the building cluster, and an interaction time of the building cluster.

[0120] In the embodiment of the present application, the number of each building in the building cluster is recorded. The electric energy and natural gas interaction costs between each building are determined. The interaction time or frequency between buildings is recorded. The total interaction cost of electric energy and natural gas of all buildings is calculated. According to the number of buildings, the interaction cost and the interaction time, the target function of the first optimization model is set.

[0121] At step 303, an electric energy interaction cost of the building cluster is obtained to obtain an electric energy constraint of the building cluster, and a natural gas interaction cost of the building cluster is obtained to obtain a gas supply constraint of the building cluster.

[0122] In the embodiment of the present application, first, the electric energy demand data of each building in the building cluster and the electric energy interaction cost data between them are collected. These data include the required electric energy of each building and the cost of electric energy interaction with other buildings. The total electric energy demand of the whole building cluster is analyzed. It is ensured that the electric energy demand of all buildings can be met, while considering the electric energy exchange between buildings. The electric energy interaction cost between each pair of buildings is evaluated. This includes calculating the economy and efficiency of electric energy flow between buildings. It is determined whether there is a need for electric energy interaction, and the interaction cost is controlled within the budget. Based on the above analysis, the electric energy constraint conditions are set. These conditions include that the total electric energy supply of the building cluster must be greater than or equal to the total demand of all buildings, and the electric energy interaction cost should be within a reasonable range to avoid excessive expenditure.

[0123] The natural gas demand data of each building in the building cluster and the natural gas interaction cost data between them are collected. These data include the required natural gas of each building and the cost of natural gas interaction with other buildings. The total natural gas demand of the whole building cluster is analyzed. It is ensured that the natural gas demand of all buildings can be met, while considering the natural gas exchange between buildings. The natural gas interaction cost between each pair of buildings is evaluated. This includes calculating the economy and efficiency of natural gas flow between buildings. It is determined whether there is a need for natural gas interaction, and the interaction cost is controlled within the budget. Based on the above analysis, the gas supply constraint conditions are set. These conditions include that the total natural gas supply of the building cluster must be greater than or equal to the total demand of all buildings, and the natural gas interaction cost should be within a reasonable range to avoid excessive expenditure.

[0124] At step 304, the constraint conditions of the first optimization model are obtained according to the electric energy constraint and the gas supply constraint.

[0125] In the embodiments of the present application, the main objective of the optimization model is determined, such as minimizing the total cost or maximizing the system efficiency. The electricity and natural gas demand data of each building in the building cluster and the corresponding interaction cost are obtained. This includes the electricity and gas consumption of each building, as well as the electricity and natural gas exchange cost between buildings. The total electricity demand of all buildings in the building cluster is calculated. It is ensured that the total supply capacity can meet these demands. The total natural gas demand of all buildings in the building cluster is calculated. It is ensured that the total supply capacity can meet these demands. The cost of electricity flow between buildings is evaluated. The optimization level of electricity interaction cost is determined under the condition of meeting the demand. The cost of natural gas flow between buildings is evaluated. The optimization level of natural gas interaction cost is determined under the condition of meeting the demand. It is ensured that the electricity supply of all buildings in the cluster is not lower than their demand, while considering the economy of electricity interaction. The total supply and interaction cost of electricity needs to be within an acceptable range. It is ensured that the natural gas supply of all buildings in the cluster is not lower than their demand, while considering the economy of natural gas interaction. The total supply and interaction cost of natural gas needs to be within an acceptable range. The above electricity constraints and gas supply constraints are incorporated into the optimization model to ensure that all actual demands and economic limitations are met under the optimization objective. The demand and interaction cost of electricity and natural gas are considered to develop the optimization model constraint conditions that meet the actual demand.

[0126] In step 305, the first optimization model is obtained according to the objective function of the first optimization model and the constraint conditions of the first optimization model.

[0127] In the embodiments of the present application, the objective function of the optimization model is determined. The key indicators that need to be optimized are determined, such as minimizing the total cost, maximizing the efficiency or profit. The previously determined constraint conditions are incorporated into the model. The satisfaction of electricity demand and gas supply demand, as well as the cost limit of electricity and natural gas interaction are included. It is ensured that the constraint conditions can accurately reflect the resource limitations and economic limitations in reality. The decision variables in the model are determined, which are the key factors that can be adjusted to achieve the optimization objective. For example, the decision variables can include the electricity and natural gas supply of each building, as well as the energy flow between buildings. The objective function is expressed as a function of the decision variables. The previously defined constraint conditions are applied to the model to ensure that the decision variables are optimized under the condition of meeting all constraint conditions. A suitable optimization method is selected to solve the model. Linear programming, integer programming, nonlinear programming, etc. can be selected.

[0128] In step 306, a second optimization model is obtained.

[0129] Step 306 is consistent with the specific implementation manner of step 202 described above, and the present application will not be repeated here.

[0130] In step 307, real-time temperature values of each building in the building cluster are obtained, and a temperature optimization model of the building cluster is established according to the real-time temperature values.

[0131] Step 307 is consistent with the specific implementation mode of step 203 described above, and details are not repeated here.

[0132] In step 308, the first optimization model, the second optimization model, and the temperature optimization model are processed according to the double fitness particle swarm optimization algorithm, and an optimal scheduling result of the urban building is obtained.

[0133] Step 308 is consistent with the specific implementation mode of step 204 described above, and details are not repeated here.

[0134] By obtaining the total number of buildings in the building cluster, the interaction cost and the interaction time, the embodiment of the application can comprehensively evaluate the optimization demand of the building cluster, ensuring that the optimization model not only focuses on the cost, but also considers the time factor. The interaction cost and supply constraint of electric energy and natural gas make the optimization model more accurate, which can better reflect the resource limitation and cost requirement in actual operation. With the explicit objective function and constraint condition, the optimization model can more effectively perform scheduling to achieve the best resource allocation and cost benefit, thereby realizing the overall optimization of the urban building system.

[0135] Based on the above-mentioned flow, the application further provides an implementation flow diagram of a scheduling method of an urban building. As shown in Figure 2A The method can include the following steps 401 to 407: Figure 4

[0136] In step 401, a first optimization model is obtained.

[0137] Step 401 is consistent with the specific implementation mode of step 201 described above, and details are not repeated here.

[0138] In step 402, the interaction time, photovoltaic cost, and building economic cost of each building in the building cluster are obtained.

[0139] In the embodiment of the application, first, the actual use data of each building is collected, including energy consumption records and performance data of photovoltaic systems. Based on the use time and interaction demand of the building, the interaction time between each building is determined. The installation cost and operation and maintenance cost of the photovoltaic system are analyzed, and the factors such as building material cost and maintenance expenditure are combined with the building economic cost to evaluate the total cost of each building. The collected data is integrated into the optimization model.

[0140] In step 403, the objective function of the second optimization model is obtained according to the interaction time, photovoltaic cost, and building economic cost of each building in the building cluster.​

[0141] In the embodiments of the present application, the explicit optimization goal is to reduce the interaction cost between buildings in the building cluster, including photovoltaic cost and building economic cost. Obtain the photovoltaic power generation data of each building, the building economic cost data (such as maintenance, operation cost, etc.), and the interaction time between buildings (that is, the time for resource sharing or interaction between buildings). Quantify the photovoltaic cost and the building economic cost into digital form. For example, the photovoltaic cost may include the installation and maintenance cost of photovoltaic equipment, and the building economic cost includes the operation and maintenance cost of the building. Analyze the interaction time between buildings, considering the influence of interaction time on cost. Interaction time can affect the scheduling and use efficiency of energy, thereby affecting the overall cost. According to the above data, a target function is constructed. The target function integrates the photovoltaic cost, the building economic cost and the interaction time, and is expressed as a comprehensive cost value. The design of the target function ensures that the total cost is minimized in the optimization process.

[0142] In step 404, the energy storage cost of each building in the building cluster is obtained to obtain the energy storage constraint of each building, and the phase change energy storage cost of each building is obtained to obtain the phase change energy storage constraint of each building. The constraint conditions of the second optimization model are obtained according to the energy storage constraint and the phase change energy storage constraint.

[0143] In the embodiments of the present application, the energy storage cost (such as battery cost, maintenance cost, etc.) and the phase change energy storage cost (such as the cost of phase change material, installation and maintenance cost, etc.) of each building are determined. Based on the energy storage cost, the investment limit of each building in the energy storage system is determined. For example, the maximum energy storage capacity or the maximum allowed energy storage system investment cost is set. Similarly, based on the phase change energy storage cost, the limit of each building in the phase change energy storage system is determined.

[0144] The energy storage constraint and the phase change energy storage constraint are combined to form a whole optimization model constraint condition. The constraints of all buildings in energy storage and phase change energy storage are included to ensure that the actual capacity and economic conditions of each building are considered in the optimization process. Apply these constraint conditions to the second optimization model to ensure that all energy storage and phase change energy storage requirements are met in the optimization process, thereby realizing the optimal allocation of resources.

[0145] In step 405, the second optimization model is obtained according to the target function of the second optimization model and the constraint conditions of the second optimization model.

[0146] In the embodiments of the present application, the optimization objective is clearly defined, that is, the optimal objective that is expected to be achieved through optimization. It includes minimizing cost, maximizing benefit, or balancing different objectives. The objective function is the core of the entire optimization model, which is used to guide the optimization algorithm to find the optimal solution. All the constraints are integrated, including energy storage constraints, phase change energy storage constraints, and other possible constraints. The objective function and the constraints are combined to establish a mathematical model or a calculation framework. The objective function is converted into a form that can be quantified and optimized, and all the constraints are ensured to be embodied in the model. According to the nature of the optimization problem (such as linear, nonlinear, integer, etc.), a suitable optimization algorithm is selected. Common optimization algorithms include linear programming, integer programming, dynamic programming, etc. The selected algorithm will be used to solve the optimization model to find the optimal solution that satisfies the constraints.

[0147] Step 406, obtaining real-time temperature values of each building in the building cluster, and establishing a temperature optimization model of the building cluster according to the real-time temperature values.

[0148] Step 406 is consistent with the specific implementation manner of step 203 described above, and details are not repeated here.

[0149] Step 407, processing the first optimization model, the second optimization model, and the temperature optimization model according to a preset algorithm to obtain an optimal scheduling result of the urban building.

[0150] Step 407 is consistent with the specific implementation manner of step 204 described above, and details are not repeated here.

[0151] The embodiments of the present application can more comprehensively optimize the operation efficiency and economic benefit of each building in the building cluster by establishing the second optimization model and comprehensively considering the photovoltaic cost, building economic cost, energy storage cost, and phase change energy storage cost among the buildings. The cost burden among the buildings can be reduced, thereby improving the economy of the overall system. The second optimization model subdivides the interaction time and specific cost (photovoltaic cost, economic cost, etc.) to the building level, so that the specific cost situation of each building can be more accurately grasped in the optimization process.

[0152] Based on the flow described above, Figure 2A As shown in FIG. 5, the method can include the following steps 501 to 505: Figure 5

[0153] Step 501, obtaining a first optimization model.

[0154] Step 201 is consistent with the specific implementation manner of step 501 described above, and details are not repeated here.

[0155] ​At step 502, a second optimization model is obtained.

[0156] Step 502 is consistent with the specific implementation of step 202 described above, and thus will not be described here.

[0157] At step 503, the total number of buildings in the building cluster and the expected temperature value of each building are obtained.

[0158] In the embodiment of the present application, first, information about the building cluster needs to be collected from relevant data sources or databases. This includes basic information about the building, the building's temperature control system, and the expected temperature setting. From the collected data, the information about the building is extracted, and the total number of buildings in the building cluster is counted. The expected temperature value of each building is extracted. The extracted building quantity and the expected temperature value of each building are sorted.

[0159] At step 504, the temperature optimization model is obtained according to the real-time temperature value, the total number of buildings, and the expected temperature value.

[0160] In the embodiment of the present application, first, the real-time temperature value of each building is collected. These temperature values can be obtained through temperature sensors or other monitoring devices. The expected temperature value of each building is determined, i.e., the target temperature set according to the purpose of the building and the comfort requirement. The real-time temperature value is compared with the expected temperature value, and the temperature difference is calculated. These differences indicate the deviation of the current temperature from the expected temperature. According to the number of buildings and the temperature difference of each building, an optimization model is established, aiming to adjust and optimize the temperature control system of each building to minimize the temperature deviation. In the model, adjustment mechanisms such as the running time and intensity of heating or cooling devices are set to make the internal temperature of the building as close as possible to the expected temperature value. Based on real-time data and model output, the temperature control settings of the building are dynamically adjusted to keep the building environment within the optimal temperature range.

[0161] At step 505, the first optimization model, the second optimization model, and the temperature optimization model are processed according to a preset algorithm to obtain the optimal scheduling result of the city building.

[0162] Step 505 is consistent with the specific implementation of step 204 described above, and thus will not be described here.

[0163] The embodiment of the present application obtains the real-time temperature value of each building in the building cluster and establishes a temperature optimization model, so that the scheduling method can reflect the temperature change inside the building in real time, thereby optimizing the temperature control strategy. This helps to improve energy efficiency and reduce energy consumption, while ensuring that the comfort level inside the building meets the expected value.

[0164] It should be understood that although each step in the above flowcharts is shown in sequence according to the direction of the arrow, these steps are not necessarily executed in the order indicated by the arrow. Unless explicitly stated herein, there is no strict order limitation for the execution of these steps, and these steps can be executed in other orders. Moreover, at least part of the steps in the above flowcharts can include multiple sub-steps or multiple stages, which are not necessarily executed at the same time, but can be executed at different times, and the execution order of these sub-steps or stages is not necessarily sequential, but can be alternately executed with at least part of other steps or sub-steps or stages of other steps.

[0165] Based on the foregoing embodiments, the embodiments of the present application provide a scheduling device for urban buildings, which comprises various modules and units included in the modules, and can be realized by a processor. Of course, it can also be realized by a specific logic circuit. In the implementation process, the processor can be a central processing unit (CPU), a microprocessor (MPU), a digital signal processor (DSP), or a field programmable gate array (FPGA), etc.

[0166] Figure 6 A structural schematic diagram of a scheduling device for urban buildings provided by the embodiments of the present application is shown in FIG. 6. As shown in FIG. 6, the device 600 comprises an acquisition module 601 and a processing module 602, wherein: Figure 6

[0167] The acquisition module 601 is configured to acquire a first optimization model.

[0168] The acquisition module 601 is further configured to acquire a second optimization model.

[0169] The acquisition module 601 is further configured to acquire real-time temperature values of each building in a building cluster, and to establish a temperature optimization model of the building cluster according to the real-time temperature values.

[0170] The processing module 602 is configured to process the first optimization model, the second optimization model, and the temperature optimization model according to a preset algorithm to obtain an optimal scheduling result of the urban buildings, wherein the preset algorithm is a double-adaptive particle swarm optimization algorithm.

[0171] In some embodiments, the acquisition module 601 is further configured to acquire a total number of buildings in the building cluster, an interaction cost of the building cluster, and an interaction time of the building cluster.

[0172] The acquisition module 601 is further configured to obtain a target function of the first optimization model according to the total number of buildings in the building cluster, the interaction cost of the building cluster, and the interaction time of the building cluster.​

[0173] The obtaining module 601 is further configured to obtain an electricity interaction cost of the building cluster, and obtain an electricity constraint of the building cluster according to the electricity interaction cost of the building cluster; and obtain a natural gas interaction cost of the building cluster, and obtain a gas supply constraint of the building cluster according to the natural gas interaction cost of the building cluster.

[0174] The obtaining module 601 is further configured to obtain a constraint condition of the first optimization model according to the electricity constraint and the gas supply constraint.

[0175] The obtaining module 601 is further configured to obtain the first optimization model according to a target function of the first optimization model and the constraint condition of the first optimization model.

[0176] In some embodiments, the obtaining module 601 is further configured to obtain an interaction time, a photovoltaic cost, and a building economic cost of each building in the building cluster.

[0177] The obtaining module 601 is further configured to obtain a target function of the second optimization model according to the interaction time, the photovoltaic cost, and the building economic cost of each building in the building cluster.

[0178] The obtaining module 601 is further configured to obtain an energy storage constraint of each building in the building cluster according to an energy storage cost of the each building, and obtain a phase change energy storage constraint of the each building according to a phase change energy storage cost of the each building, and obtain a constraint condition of the second optimization model according to the energy storage constraint and the phase change energy storage constraint.

[0179] The obtaining module 601 is further configured to obtain the second optimization model according to the target function of the second optimization model and the constraint condition of the second optimization model.

[0180] In some embodiments, the obtaining module 601 is further configured to obtain a total number of each building in the building cluster, and an expected temperature value of each building.

[0181] The obtaining module 601 is further configured to obtain the temperature optimization model according to the real-time temperature value, the total number of each building, and the expected temperature value.

[0182] In some embodiments, the obtaining module 601 is further configured to process the second optimization model according to the preset algorithm, and obtain a minimum interaction cost between each building in the building cluster.

[0183] The obtaining module 601 is further configured to obtain an optimized temperature value according to the temperature optimization model.

[0184] The acquisition module 601 is further configured to process the first optimization model according to the minimum interaction cost between the buildings in the building cluster and the optimized temperature value to obtain an optimal scheduling result of the urban buildings.

[0185] In some embodiments, the acquisition module 601 is further configured to calculate the interaction time, photovoltaic cost, and building economic cost of each building in the building cluster according to the preset algorithm to obtain a weighted minimum interaction value of each building in the building cluster;

[0186] The acquisition module 601 is further configured to calculate the weighted minimum interaction value of each building in the building cluster according to the power constraint and the gas supply constraint to obtain the minimum interaction cost between each building in the building cluster.

[0187] In some embodiments, the processing module 602 is further configured to adjust the minimum interaction cost between the buildings in the building cluster according to the optimized temperature value to obtain an initial optimized scheduling result for each building in the building cluster;

[0188] The processing module 602 is further configured to adjust the first optimization model according to the initial optimization scheduling result to obtain the optimal scheduling result of the urban buildings.

[0189] The description of the above device embodiment is similar to the description of the above method embodiment and has similar beneficial effects as the method embodiment. For technical details not disclosed in the device embodiment of this application, please refer to the description of the method embodiment of this application for understanding.

[0190] It should be noted that in the embodiments of this application Figure 6 The module division of the urban building scheduling device shown is schematic and represents only a logical functional division. In actual implementation, other division methods may be used. Furthermore, the functional units in the various embodiments of this application may be integrated into a single processing unit, exist as separate physical units, or be integrated into a single unit. These integrated units may be implemented in hardware or as software functional units. Alternatively, they may be implemented in a combination of software and hardware.

[0191] It should be noted that, in the embodiments of the present application, if the above-mentioned method is implemented in the form of a software function module and sold or used as an independent product, it can also be stored in a computer readable storage medium. Based on such understanding, the technical solutions of the embodiments of the present application can be embodied in the form of a software product, and the computer software product is stored in a storage medium, and includes a plurality of instructions for causing an electronic device to execute all or part of the methods described in the embodiments of the present application. The foregoing storage medium includes: a U disk, a mobile hard disk, a read-only memory (ROM), a magnetic disk or an optical disk, and various media that can store program codes. Thus, the embodiments of the present application are not limited to any specific combination of hardware and software.

[0192] The computer device provided in the embodiments of the present application can be a server, and an internal structure diagram thereof can be as shown in Figure 7 The computer device includes a processor, a memory and a network interface connected through a system bus. The processor of the computer device is used to provide computing and control capabilities. The memory of the computer device includes a non-volatile storage medium and an internal memory. The non-volatile storage medium stores an operating system, a computer program and a database. The internal memory provides an environment for the operating system and the computer program in the non-volatile storage medium to run. The database of the computer device is used to store data. The network interface of the computer device is used to communicate with an external terminal through a network connection. The computer program is executed by the processor to implement the above-mentioned method.

[0193] The computer readable storage medium provided in the embodiments of the present application stores a computer program, and the computer program is executed by the processor to implement the steps in the method provided in the above-mentioned embodiments.

[0194] The computer program product provided in the embodiments of the present application includes instructions, and when the computer program product is executed on a computer, the computer is caused to execute the steps in the method provided in the above-mentioned method embodiments.

[0195] Those skilled in the art can understand that, Figure 7 The structure shown in the above-mentioned embodiments is only a block diagram of part of the structure related to the scheme of the present application, and does not constitute a limitation on the computer device to which the scheme of the present application is applied. The specific computer device can include more or fewer components than those shown in the figure, or combine certain components, or have a different arrangement of components.

[0196] In one embodiment, the scheduling device for urban buildings provided in the present application can be implemented in the form of a computer program, and the computer program can be executed on a computer such as Figure 7The computer program product includes a computer program and a storage medium. The storage medium stores the computer program. The computer program is used for executing the method of the various embodiments of the present application. The computer program is installed in the computer device shown in the figure. The computer device stores each program module constituting the above device in the memory of the computer device. The computer program constituted by each program module makes the processor execute the steps in the method of each embodiment of the present application described in the specification.

[0197] It should be noted that the above description of the storage medium and device embodiments is similar to the description of the above method embodiments, and has similar beneficial effects as the method embodiments. For technical details not disclosed in the storage medium, storage medium and device embodiments of the present application, please refer to the description of the method embodiments of the present application for understanding.

[0198] It should be understood that the "one embodiment" or "an embodiment" or "some embodiments" mentioned throughout the specification means that the specific features, structures or characteristics related to the embodiment are included in at least one embodiment of the present application. Therefore, "in one embodiment" or "in an embodiment" or "in some embodiments" appearing throughout the specification does not necessarily mean the same embodiment. In addition, these specific features, structures or characteristics can be combined in one or more embodiments in any suitable manner. It should be understood that in various embodiments of the present application, the size of the sequence number of the above processes does not mean the order of execution, and the execution order of the processes should be determined according to its function and inherent logic, and should not constitute any limitation on the implementation process of the embodiments of the present application. The above sequence number of the embodiments of the present application is only for description, not representing the advantages and disadvantages of the embodiments. The above description of each embodiment tends to emphasize the differences between each embodiment, and the same or similar parts can be referred to each other. For the sake of brevity, this paper will not be repeated here.

[0199] The term "and / or" in this paper is only a description of the association relationship of the associated objects, which means that there can be three kinds of relationships, for example, object A and / or object B, which can represent the existence of object A, the existence of object A and object B, and the existence of object B.

[0200] It should be noted that in this paper, the term "include", "contain" or any other variant thereof is intended to cover non-exclusive inclusion, so that the process, method, article or device including a series of elements not only includes those elements, but also includes other elements not explicitly listed, or includes elements inherent to such process, method, article or device. Without more limitations, the element defined by the sentence "including a…" does not exclude the existence of other identical elements in the process, method, article or device including the element.

[0201] In several embodiments provided in the present application, it should be understood that the disclosed devices and methods can be implemented in other manners. The embodiments described above are merely exemplary, for example, the division of the modules is only a logical function division, and there can be another division manner for the actual implementation, for example, a plurality of modules or components can be combined or integrated into another system, or some features can be ignored or not executed. In addition, the coupling or direct coupling or communication connection between the various components shown or discussed can be indirect coupling or communication connection through some interfaces, devices or modules, and can be electrical, mechanical or other forms.

[0202] The modules described above as separate components may or may not be physically separate, and the components shown as modules may or may not be physical modules; they can be located in one place or distributed on multiple network units; and some or all of the modules can be selected according to actual needs to achieve the purpose of the embodiments.

[0203] In addition, each functional module in each embodiment of the present application can be integrated into one processing unit, or each module can be a separate unit, or two or more modules can be integrated into one unit; the integrated module can be realized in the form of hardware or hardware plus software functional unit.

[0204] Those of ordinary skill in the art can understand that all or part of the steps of the above-mentioned method embodiments can be completed by a program instructing related hardware, and the aforementioned program can be stored in a computer readable storage medium, and when the program is executed, the steps of the method embodiments are executed; and the aforementioned storage medium includes mobile storage devices, read-only memories (ROM), magnetic discs or optical discs, and various media that can store program codes.

[0205] Alternatively, the integrated units of the present application, if implemented in the form of software functional modules and sold or used as independent products, can also be stored in a computer readable storage medium. Based on this understanding, the technical solutions of the embodiments of the present application can be embodied in the form of a software product, and the computer software product is stored in a storage medium, and includes a plurality of instructions for causing an electronic device to execute all or part of the methods described in the embodiments of the present application. The aforementioned storage medium includes mobile storage devices, ROM, magnetic discs or optical discs, and various media that can store program codes.

[0206] The methods disclosed in the several method embodiments provided in the present application can be combined arbitrarily without conflict, to obtain new method embodiments.

[0207] The features disclosed in several product embodiments provided by the present application can be arbitrarily combined, without conflict, to obtain new product embodiments.

[0208] The features disclosed in several method or device embodiments provided by the present application can be arbitrarily combined, without conflict, to obtain new method embodiments or device embodiments.

[0209] The above description is merely illustrative of the application, and the scope of the application is not limited thereto. Any variations and modifications of the application, which would occur to those skilled in the art, are to be considered within the scope of the application. Therefore, the scope of the application is to be determined by the claims.

Claims

1. A method for scheduling urban buildings, characterized in that: The method comprises: Obtaining a first optimization model, where the first optimization model is used to optimize interaction costs among multiple building clusters in an urban building system, where the interaction costs include at least one of an electricity interaction cost and a natural gas interaction cost among the multiple building clusters, each of the multiple building clusters being composed of multiple buildings; Obtaining a second optimization model, where the second optimization model is used to optimize interaction costs between buildings in the building cluster, where the interaction costs include at least one of photovoltaic costs, building economic costs, energy storage costs, and phase change energy storage costs; Acquiring real-time temperature values ​​of each building in a building cluster, and establishing a temperature optimization model for the building cluster based on the real-time temperature values; The first optimization model, the second optimization model and the temperature optimization model are processed according to a preset algorithm to obtain an optimal scheduling result of the urban buildings. The preset algorithm is a dual-fitness particle swarm optimization algorithm.

2. The method according to claim 1, characterized in that The first optimization model includes an objective function of the first optimization model and constraints of the first optimization model. Acquiring the first optimization model includes: Obtaining the total number of buildings in the building cluster, the interaction cost of the building cluster, and the interaction time of the building cluster; Obtaining an objective function of the first optimization model according to the total number of buildings in the building cluster, the interaction cost of the building cluster, and the interaction time of the building cluster; Obtaining an electric energy constraint of the building cluster according to the electric energy interaction cost of the building cluster, and obtaining a gas supply constraint of the building cluster according to the natural gas interaction cost of the building cluster; Obtaining constraint conditions of the first optimization model according to the power constraint and the gas supply constraint; The first optimization model is obtained according to the objective function of the first optimization model and the constraints of the first optimization model.

3. The method according to claim 1, characterized in that The second optimization model includes an objective function of the second optimization model and constraints of the second optimization model. Acquiring the second optimization model includes: Obtaining the interaction time, photovoltaic cost, and building economic cost of each building in the building cluster; Obtaining an objective function of the second optimization model according to the interaction time, photovoltaic cost, and building economic cost of each building in the building cluster; Obtaining energy storage constraints for each building in the building cluster based on the energy storage cost of each building, and obtaining phase change energy storage constraints for each building based on the phase change energy storage cost of each building, and obtaining constraint conditions of the second optimization model based on the energy storage constraints and the phase change energy storage constraints; The second optimization model is obtained according to the objective function of the second optimization model and the constraints of the second optimization model.

4. The method according to claim 1, wherein The step of obtaining the real-time temperature value of each building in the building cluster and establishing a temperature optimization model for the building cluster according to the real-time temperature value includes: Obtaining the total number of buildings in the building cluster and the expected temperature value of each building; The temperature optimization model is obtained according to the real-time temperature value, the total number of the buildings and the expected temperature value.

5. The method according to any one of claims 2 to 4, characterized in that: The step of processing the first optimization model, the second optimization model, and the temperature optimization model according to a preset algorithm to obtain an optimal scheduling result of the urban buildings includes: Processing the second optimization model according to the preset algorithm to obtain the minimum interaction cost between the buildings in the building cluster; Obtaining an optimized temperature value according to the temperature optimization model; The first optimization model is processed according to the minimum interaction cost between each building in the building cluster and the optimized temperature value to obtain an optimal scheduling result of the urban buildings.

6. The method according to claim 5, characterized in that The processing of the second optimization model according to the preset algorithm to obtain the minimum interaction cost between the buildings in the building cluster includes: Calculating the interaction time, photovoltaic cost, and building economic cost of each building in the building cluster according to the preset algorithm to obtain a weighted minimum interaction value of each building in the building cluster; The weighted minimum interaction value of each building in the building cluster is calculated according to the power constraint and the gas supply constraint to obtain the minimum interaction cost between each building in the building cluster.

7. The method according to claim 5, characterized in that The first optimization model is processed according to the minimum interaction cost between each building in the building cluster and the optimized temperature value to obtain the optimal scheduling result of the urban buildings, including: Adjusting the minimum interaction cost between each building in the building cluster according to the optimized temperature value to obtain an initial optimized scheduling result for each building in the building cluster; The first optimization model is adjusted according to the initial optimization scheduling result to obtain the optimal scheduling result of the urban buildings.

8. A scheduling device for urban buildings, characterized in that: include: an acquisition module, configured to acquire a first optimization model, wherein the first optimization model is configured to optimize interaction costs among a plurality of building clusters in an urban building system, wherein the interaction costs include at least one of an electricity interaction cost and a natural gas interaction cost among the plurality of building clusters, wherein each of the plurality of building clusters is composed of a plurality of buildings; The acquisition module is further configured to acquire a second optimization model, wherein the second optimization model is configured to optimize the interaction costs between buildings in the building cluster, wherein the interaction costs include at least one of photovoltaic costs, building economic costs, energy storage costs, and phase change energy storage costs; The acquisition module is further configured to acquire the real-time temperature value of each building in the building cluster, and establish a temperature optimization model for the building cluster based on the real-time temperature value; A processing module is used to process the first optimization model, the second optimization model and the temperature optimization model according to a preset algorithm to obtain the optimal scheduling result of the urban buildings. The preset algorithm is a dual-fitness particle swarm optimization algorithm.

9. A computer device comprising a memory and a processor, wherein the memory stores a computer program that can be run on the processor, characterized in that: When the processor executes the program, the steps of the method according to any one of claims 1 to 7 are implemented.

10. A computer-readable storage medium having a computer program stored thereon, characterized in that: When the computer program is executed by a processor, the method according to any one of claims 1 to 7 is implemented.

Images