Cooperative scheduling method, device and system for building energy system

Abstract

The invention relates to a collaborative scheduling method, device and system for a building energy system. The method comprises the steps that under the condition that the building energy system is in an operation stage, the building energy system selects an optimal strategy according to module information and preset information, outputs first information, adjusts operation data and outputs second information; and the coordination control module of the building energy system periodically calculates the Pareto boundary of the whole system and outputs coordination scheduling indication information. The problems that in an existing building energy system distributed photovoltaic, energy storage and heat pump system, equipment integration is complex, a control framework is concentrated, operation depends on a network, and self-adaptive optimization capacity is lacked can be solved.

Description

TECHNICAL FIELD

[0001] Embodiments of the present application relate to the technical field of data processing, and more particularly, to a building energy system cooperative scheduling method, device and system. BACKGROUND

[0002] With the promotion of the "double carbon" policy, distributed photovoltaic, energy storage and heat pump and other multi-energy coupling systems are gradually popularized and applied in buildings. However, the existing photovoltaic-storage-heat linkage scheme still has the following main problems in system architecture, control strategy and operation stability:

[0003] 1) Complex communication structure, high system integration cost: Current building energy systems mostly rely on multiple communication methods (such as RS485, CAN, Zigbee, LoRa, etc.) for device interconnection. Different manufacturers have different device protocols, and the interface adaptation is complex, with high system networking and maintenance costs. At the same time, the wiring is complicated and the construction is difficult, which is not conducive to later expansion or modification.

[0004] 2) Centralized control architecture, lack of device self-optimization capability: The existing building energy system generally uses a centralized energy management system (EMS) for unified scheduling and control, relying on a central controller to collect the status of each sub-device, issue instructions and optimize energy. This architecture has the following problems: the central controller failure will cause the whole system to fail; the control strategy has strong coupling, and the deployment and maintenance are complex; it is difficult to adapt to the development trend of device heterogeneity and scene differentiation.

[0005] 3) Lack of "self-determination" ability between devices, response is not flexible: The existing building energy system mostly uses passive response for heat pump and energy storage devices, and lacks the ability to autonomously judge and control based on local conditions (such as electricity price, load, battery SOC, user settings), making it difficult to achieve "local optimization". This makes the system respond to complex scenarios (such as electricity price fluctuations, temporary user preference changes, etc.) with a lag, lacking flexibility and robustness.

[0006] 4) Poor system stability in isolated network or poor communication: Most systems rely on stable network connections for operation. Once the building Wi-Fi is disconnected, the router is restarted, or the cloud platform is abnormal, the device will not be able to correctly receive the scheduling instructions, and may be in a state of stagnation or disorder, lacking local fault tolerance and independent operation capability, and unable to guarantee continuous operation in "isolated network" or edge scenarios.

[0007] 5) Lack of multi-objective cooperative optimization mechanism: Current scheduling strategies mostly focus on a single target (such as maximum self-use, minimum electricity cost, single comfort target), ignoring the multi-objective coordination problem (such as comfort, electricity economy, device life, energy priority, etc.) in the actual operation of building energy systems, and lacking a multi-objective trade-off control strategy based on the Pareto optimal solution set. SUMMARY

[0008] Embodiments of the present application provide a building energy system cooperative scheduling method, device and system, in order to solve the problems of complex equipment integration, centralized control architecture, operation dependence on network, lack of adaptive optimization capability, etc. in the existing building energy system distributed photovoltaic, energy storage and heat pump system. Embodiments of the present application build a logical "single bus" communication network based on the building energy system local area network (Wi-Fi), improve the traditional multi-protocol, multi-interface device connection method, and simplify the system wiring and integration structure.

[0009] In terms of control strategy, the system described in embodiments of the present application introduces a multi-level autonomous and distributed decision-making mechanism, and with the help of a multi-agent architecture, each device (such as a photovoltaic inverter, energy storage battery, heat pump, etc.) autonomously optimizes the operation strategy based on local state information (such as load, illumination, electricity price, battery SOC, room temperature, etc.). By using the Pareto multi-objective optimization theory, the device is guided to adaptively trade off between electricity saving, energy self-use rate, comfort level and response speed, and multi-objective cooperative optimization is achieved.

[0010] Embodiments of the present application also support maintaining local operation and logic fault tolerance of the device in isolated network mode or unstable communication environment, improving system stability and reliability. At the same time, the system has edge intelligence capability and can operate independently without the cloud platform, taking into account flexibility and security. Through the present application, "smart plug and play" and "decentralized cooperative control" of the building energy system can be realized, significantly improving the response capability and energy use efficiency of the system, and supporting the construction of future city-level low-carbon, autonomous distributed energy networks.

[0011] According to a first aspect of the present application, a building energy system cooperative scheduling method is provided, comprising:

[0012] In the case that the building energy system is in the running phase, the building energy system selects the optimal strategy according to the module information and the preset information and outputs the first information, and adjusts the running data and outputs the second information;

[0013] The coordination control module of the building energy system regularly calculates the Pareto boundary of the overall system and outputs the coordination scheduling instruction information.

[0014] Further, before the building energy system is in the running phase, further comprising:

[0015] initializing the building energy system;

[0016] After the building energy system is initialized, and in the case that the building energy system enters the running phase, each module in the building energy system outputs its own state information in turn, and the coordination control module collects the own state information and saves the current system state.

[0017] Further, the method further comprises:

[0018] In the case that the building energy system is in the isolated network autonomous stage, each module in the building energy system optimizes itself according to the last received information combined with the strategy model.

[0019] Further, the method further comprises:

[0020] The hierarchical classification and control priority of each module of the building energy system are set.

[0021] The decision coordination chain is formed according to the hierarchical classification and the control priority.

[0022] Further, the coordination control module of the building energy system periodically calculates the Pareto boundary of the overall system, and outputs the coordination scheduling instruction information, including:

[0023] The target function model is set, including setting multiple target functions according to multiple possible running strategy states possessed by the module.

[0024] The optimal solution is defined, including calculating the boundary formed by the set according to the target function model.

[0025] The distributed collaborative convergence is set, and the subset is calculated according to the module information.

[0026] The module behavior result includes selecting the current running strategy according to the non-dominated solution of the module.

[0027] Further, in the case that the building energy system is in the running stage, the building energy system selects the optimal strategy according to the module information and the preset information and outputs the first information, and adjusts the running data and outputs the second information, including:

[0028] The photovoltaic module of the building energy system provides real-time available power prediction data.

[0029] The energy storage module of the building energy system selects the application state according to the remaining power of photovoltaic power generation and the load demand.

[0030] Further, in the case that the building energy system is in the running stage, the building energy system selects the optimal strategy according to the module information and the preset information and outputs the first information, and adjusts the running data and outputs the second information, including:

[0031] The heat pump module of the building energy system selects the optimal strategy and outputs the first information according to the information of the photovoltaic module and the energy storage module and the first preset information.

[0032] Further, in the case that the building energy system is in a running stage, the building energy system selects an optimal strategy according to the module information and preset information and outputs first information, and adjusts running data and outputs second information.

[0033] The load module of the building energy system adjusts running time and power according to the information of the photovoltaic module, the energy storage module and second preset information, and outputs second information.

[0034] According to a second aspect of the present application, a building energy system cooperative scheduling device is further provided, comprising:

[0035] A first control module is configured to, in the case that the building energy system is in a running stage, the building energy system selects an optimal strategy according to the module information and preset information and outputs first information, and adjusts running data and outputs second information.

[0036] A second control module is configured to, the coordination control module of the building energy system periodically calculates the Pareto boundary of the overall system, and outputs coordination scheduling instruction information.

[0037] According to a third aspect of the present application, a building energy system cooperative scheduling system is further provided, comprising:

[0038] A photovoltaic power generation module is configured to provide electric energy and provide available electric energy prediction data.

[0039] An energy storage battery module is configured to serve as an energy balance buffer unit to absorb surplus electric energy or compensate for power shortage.

[0040] A heat pump module is configured to convert electric energy into heat energy, and has running rigidity and flexible strategy balancing capability.

[0041] A load module is configured to be a non-critical load with adjustable start and / or running strategy.

[0042] A coordination control module is configured to assist in analyzing broadcast data and output global scheduling information.

[0043] The building energy system cooperative scheduling method, device and system provided by the embodiment of the present application can improve the problems of existing building energy system distributed photovoltaic, energy storage and heat pump systems, such as complex equipment integration, centralized control architecture, running dependence on network, lack of self-adaptive optimization capability, etc.

[0044] Other features of the embodiments of the present application, and their particular advantages, will become further clarified by reference to the following detailed description of exemplary embodiments of the application, taken in conjunction with the accompanying drawings. BRIEF DESCRIPTION OF DRAWINGS

[0045] The accompanying drawings incorporated in and forming a part of the specification illustrate embodiments of the application and, together with the description, serve to explain the principles of the embodiments of the application.

[0046] Figure 1 A flow chart of a method for coordinated dispatching of a building energy system according to an exemplary embodiment of the present application.

[0047] Figure 2 A structural diagram of a device for coordinated dispatching of a building energy system according to another exemplary embodiment of the present application.

[0048] Figure 3 A structural diagram of a system for coordinated dispatching of a building energy system according to another exemplary embodiment of the present application. DETAILED DESCRIPTION

[0049] Various exemplary embodiments of the present application will now be described in detail with reference to the accompanying drawings. It should be noted that the relative arrangement of the components and steps set forth in the embodiments, numerical expressions, and numerical values set forth in the embodiments are not limitations on the scope of the present application unless otherwise specifically stated.

[0050] The following description of at least one exemplary embodiment is merely illustrative in nature and is in no way intended to limit the scope of the application its application or uses.

[0051] Techniques, methods, and devices known to those of ordinary skill in the relevant art can not be discussed in detail herein. However, where appropriate, such techniques, methods, and devices can be considered part of the specification.

[0052] In all of the examples shown and discussed herein, any specific values should be interpreted as merely illustrative and not as a limitation. Thus, other examples of the exemplary embodiments can have different values.

[0053] It should be noted that similar labels and letters in the following figures indicate similar items; therefore, once an item is defined in one figure, it does not need to be discussed further in subsequent figures. Additionally, in one or more embodiments of this specification, the directional terms "horizontal" and "vertical" are used. "Horizontal" refers to the horizontal direction parallel to the horizontal ground, based on the data content displayed on the client's display interface, with the display interface being recognizable to the naked eye when viewed at eye level, and with the display interface displaying the data content normally and accurately. "Vertical" refers to the vertical direction perpendicular to the horizontal ground, based on the data content displayed on the client's display interface, with the display interface being recognizable to the naked eye when viewed at eye level, and with the display interface displaying the data content normally and accurately.

[0054] In one or more embodiments of this specification, an array arrangement is used. This array arrangement refers to arranging other objects in both the horizontal and vertical directions of the data content displayed on the client's display interface, based on a display interface that is recognizable to the naked eye when viewed at eye level, and where the display interface displays any object (data content) normally and accurately. The objects can be characters, text, numbers, English words, images, etc. For example, the characters "a", "b", "c", "d", and "e" arranged in an array could be centered on character "a", with characters "b" and "c" arranged on either side of character "a" in the horizontal direction, and characters "d" and "e" arranged on either side of character "a" in the vertical direction.

[0055] This invention provides a collaborative scheduling method for building energy systems to solve the technical problem of cumbersome and inefficient process for users to find the required data in various file pages.

[0056] It should be noted that the execution subject of this application embodiment can be an application located on a local terminal, or it can be a plugin or software development kit (SDK) or other functional unit set in an application located on a local terminal. This embodiment does not impose any special limitations on this.

[0057] It is understood that the above application can be a native program installed on the terminal (i.e., the client), or it can be a web application in the browser on the terminal. This embodiment does not impose any particular limitation on this.

[0058] Furthermore, the terminals involved in the embodiments of this application may include, but are not limited to, mobile phones, personal digital assistants (PDAs), wireless handheld devices, tablet computers, personal computers (PCs), PDAs, wearable devices (such as smart glasses, smartwatches, etc.). Hereinafter, various embodiments and examples according to the present invention will be described with reference to the accompanying drawings.

[0059] <Method Implementation>

[0060] Figure 1 A flowchart illustrating a collaborative scheduling method for a building energy system provided in an embodiment of the present invention. Figure 1 As shown, the method includes the following steps S110 to S120.

[0061] Step S110: When the building energy system is in operation, the building energy system selects the optimal strategy and outputs the first information based on the module information and preset information, and adjusts the operating data and outputs the second information.

[0062] Step S120: The coordination and control module of the building energy system periodically calculates the Pareto boundary of the overall system and outputs coordination and scheduling instruction information.

[0063] In this embodiment of the invention, when the building energy system (which can be described as a system) is in operation, the building energy system selects the optimal strategy and outputs first information based on module information and preset information, and adjusts the operating data and outputs second information. This can be understood as follows: during the operation of the building energy system, the information output by modules such as photovoltaic modules, energy storage modules, heat pump modules, load modules, and coordination control modules in the building energy system can be understood as module information. The preset information can be understood as the information input by the user on the building energy system, which may include time settings, power settings, etc. The building energy system can output first information based on the module information and preset information. This first information, obtained by calculating the optimal strategy, is broadcast to the aforementioned modules for reference. Simultaneously, during use, each module can adjust its operating data based on the first information and output the current system information as the second information.

[0064] The building energy system's coordination and control module periodically calculates the Pareto boundary of the overall system and outputs coordination and scheduling instructions. This can be understood as the coordination and control module in the system processing the data and then outputting scheduling suggestions for reference by other modules.

[0065] The collaborative scheduling method, apparatus, and system for building energy systems provided in this invention, wherein, when the building energy system is in operation, the building energy system selects the optimal strategy and outputs first information based on module information and preset information, and adjusts the operating data and outputs second information; the coordination control module of the building energy system periodically calculates the Pareto boundary of the overall system and outputs coordination scheduling instruction information, which can improve the problems of complex equipment integration, centralized control architecture, network dependence, and lack of adaptive optimization capabilities in existing building energy system distributed photovoltaic, energy storage, and heat pump systems.

[0066] In another feasible embodiment provided by the present invention, before the building energy system is in the operation phase, the method further includes: initializing the building energy system; after the building energy system is initialized and enters the operation phase, each module in the building energy system sequentially outputs its own status information, and the coordination control module collects the self-status information and saves the current system status. This can be understood as the system being in the initialization phase: after the system starts, each module sequentially broadcasts its own status via Wi-Fi, including power generation, SOC, and operating intent. The edge coordination module (edge ​​coordinator) collects and saves the current system status (i.e., the current operating status of the building energy system).

[0067] In another feasible embodiment provided by the present invention, when the building energy system is in the isolated autonomous stage, each module in the building energy system performs self-optimization based on the last received information and the strategy model. This can be understood as follows: when the edge coordinator or building router is disconnected, causing communication interruption, each module performs local Pareto optimization self-determination based on the last received broadcast information and the local strategy model to ensure the basic stable operation of the building energy system.

[0068] In another feasible embodiment provided by the present invention, hierarchical classification and control priorities are set for each module of the building energy system; a decision-making coordination chain is formed according to the hierarchical classification and control priorities. This can be understood as the present invention employing a "multi-level self-determination" control mechanism based on a Wi-Fi local area bus to ensure the coordinated and efficient operation of multiple loads in the building energy system. The core mechanism of the system is the introduction of a lightweight edge coordinator (EC) at the edge. Each module autonomously evaluates and coordinates with each other through the information bus, thereby achieving overall system optimization while retaining module self-determination rights to ensure basic operational stability in communication failure or system isolation modes. Specifically, the module hierarchical and priority logic is as follows: each functional module of the system is assigned a clear control priority to form a decision-making chain. The system can ensure effective operation based on this decision-making chain.

[0069] In another feasible embodiment provided by the present invention, the coordination control module of the building energy system periodically calculates the Pareto boundary of the overall system and outputs coordination scheduling indication information including:

[0070] Setting the objective function model includes setting multiple objective functions based on the multiple possible operating strategy states of the module; defining the optimal solution includes calculating the boundary formed by the set based on the objective function model; setting distributed collaborative convergence and calculating subsets based on the module information; and the module behavior result includes selecting the current operating strategy based on the module's non-dominated solution, which can be understood as:

[0071] 1. Equipment objective function model:

[0072] Suppose a module (e.g., a heat pump flexible control module) has multiple possible operating strategy states, denoted as:

[0073] x i ∈X i

[0074] Here, the current selectable control strategy of module i (such as operating power, temperature rise rate, startup time, etc.) is Xi, which is the set of all its selectable strategies.

[0075] This module has multiple objective functions (such as minimum energy consumption, temperature stability, load coordination, etc.), defined as:

[0076] f i (x i )=[f i1 (x i ),f i2 (x i ),......,f in (x i )]

[0077] Each objective function fi(xi) represents a certain performance index.

[0078] 2. Definition of Pareto optimal solution:

[0079] If there exists a strategy xi*∈Xi that satisfies:

[0080] For all other strategies x i ∈X i f i (x i *) Not f i (x i Strict superior dominance over all objectives, namely:

[0081]

[0082] Then x is called i * The set of Pareto optimal solutions belonging to this module.

[0083] The boundary formed by this set is called:

[0084]

[0085] 3. Distributed collaborative convergence principle:

[0086] Through the system broadcast bus, all modules share the following public information (per second or per event trigger cycle):

[0087] Current available photovoltaic power (PV_available);

[0088] State of Storage (SOC);

[0089] The intent states (intent, power_target, priority) that have been broadcast by other modules;

[0090] The edge coordinator's suggested operating window or scheduling index (if it exists);

[0091] Other information that needs to be published and is listed in the above modules.

[0092] Based on the above information, each module projects its local Pareto set Pi onto the global constraint boundary, forming a constrained subset. Right now:

[0093]

[0094] Where g(xi,·) represents the system constraint model, such as: the total power does not exceed the current PV power generation and battery support capacity; equipment with lower power priority should postpone operation under high load; and user-set comfort / delay tolerance should not be violated.

[0095] In another feasible embodiment provided by the present invention, when the building energy system is in operation, the building energy system selects the optimal strategy and outputs first information based on module information and preset information, and adjusts the operating data and outputs second information, including: the photovoltaic module of the building energy system provides real-time available power prediction data; the energy storage module of the building energy system selects the application state according to the remaining power of photovoltaic power generation and load demand, which can be understood as the photovoltaic module continuously broadcasting real-time power generation and future power generation prediction. The energy storage module selects charging, discharging or standby state according to the remaining power of photovoltaic power generation and load demand, and broadcasts the current charging / discharging intention and SOC.

[0096] In another feasible embodiment provided by the present invention, when the building energy system is in operation, the building energy system selects the optimal strategy and outputs the first information based on module information and preset information, and adjusts the operating data and outputs the second information, including: the heat pump module of the building energy system selects the optimal strategy and outputs the first information based on the information of the photovoltaic module, the energy storage module and the first preset information. This can be understood as the heat pump module receiving photovoltaic and energy storage broadcast information, combining the user-set target temperature and the current water temperature, calculating the acceptable operating power range, selecting the optimal strategy within the Pareto boundary and executing it, while broadcasting the current power and operating intention.

[0097] In another feasible embodiment provided by the present invention, when the building energy system is in operation, the building energy system selects the optimal strategy and outputs first information based on module information and preset information, and adjusts the operating data and outputs second information, including: the load modules of the building energy system adjust their operating time and power based on the information of the photovoltaic module and the energy storage module and the second preset information, and output the second information. This can be understood as other load modules adjusting their operating time and power based on the current power supply capacity and energy storage status, combined with user time settings, and broadcasting flexibility levels and operating intentions. The edge coordinator periodically calculates the Pareto boundary of the overall system, generates coordination and scheduling suggestions (such as load delay and heat pump peak shifting), and broadcasts them for reference by all modules.

[0098] In another embodiment of the present invention, a photovoltaic-energy storage-heat pump coordinated scheduling method and system for building energy systems is disclosed, particularly a distributed optimization control system based on a single-bus communication architecture and possessing multi-level self-determination capabilities. Its main advantages are:

[0099] 1. To achieve information collaboration and optimized energy utilization among distributed energy devices without a central controller;

[0100] 2. Each level of device can still operate autonomously even when the network is disconnected;

[0101] 3. Based on Wi-Fi logical broadcasting, achieve efficient, low-cost, and low-latency data interaction between system devices;

[0102] 4. The Pareto boundary optimization method is used as the basis for scheduling equipment at all levels, taking into account multiple objectives such as energy efficiency, cost, and service life for distributed optimization.

[0103] 5. Supports flexible addition and removal of various devices from the distributed optimization control system. Simply add the device according to its type / requirements and complete its own optimization based on the current information. Other sub-modules will automatically adjust, adapt, and become optimal.

[0104] In another embodiment of the present invention, the overall system structure and logical topology include:

[0105] For example, the system consists of five sub-modules. All modules communicate via a Wi-Fi logical bus built on a building router (Wi-Fi is actually one implementation example, not limited to Wi-Fi; the core is single-bus communication). Each module participates in system optimization by **"publishing its own status + listening to the status of others"**.

[0106] The table below lists the role, input / output information, and communication behavior of each submodule in the system:

[0107]

[0108]

[0109]

[0110]

[0111] Among them, 1) all modules publish their status via logical broadcast (such as UDP or MQTT over Wi-Fi);

[0112] 2) All modules listen to all broadcasts and make decisions locally;

[0113] 3) When encountering energy conflicts (such as supply being less than demand), the equipment performs local re-optimization according to priority and target weight.

[0114] 4) The bus supports "soft control" mode, there is no master-slave structure, and all strategies are based on consensus broadcast mechanism.

[0115] 5) If a coordinator exists, its broadcast recommendation policy is adopted by the module according to its own priority.

[0116] In another embodiment of the present invention, the multi-level self-determination architecture layering description includes:

[0117] This invention employs a "multi-level self-determination" control mechanism based on a Wi-Fi local area bus to ensure the coordinated and efficient operation of multiple load sources in a building energy system. The core mechanism involves introducing a lightweight edge coordinator (EC) at the edge. Each module autonomously evaluates and coordinates with each other through the information bus, thereby achieving overall system optimization while preserving module autonomy to ensure basic operational stability in the event of communication failures or system isolation.

[0118] (1) Module hierarchy and priority logic

[0119] Each functional module of this system is assigned a clear control priority to form a decision-making chain:

[0120]

[0121] illustrate:

[0122] The photovoltaic module is a pure information publisher with the highest priority. It only provides information and does not participate in the control of communication interaction processes.

[0123] Other modules select strategies within the Pareto boundary based on parameters such as photovoltaic power generation, load power, electricity price, time, and user preferences.

[0124] All modules can run finite decision models locally when the edge coordinator is missing.

[0125] (2) Edge Coordination Controller (EC) Role

[0126] As a "non-controlling central agent," EC has the following responsibilities:

[0127] Periodically collect status messages reported by each module;

[0128] Based on the communication strategy, broadcast the current recommended Pareto boundary and power allocation;

[0129] Determine whether system rescheduling is needed (e.g., sudden changes in solar illumination or sudden changes in electricity prices);

[0130] Act as a fault handling coordinator (e.g., when a module goes offline or data is abnormal).

[0131] EC does not issue strong control commands to modules, but only provides decision-making references or scheduling "suggestions" to maintain the system's self-organization.

[0132] Interpretation of Pareto Optimization Applications (Mathematical Modeling)

[0133] (3) Objective function of the equipment:

[0134] In this system, each module (such as heat pump, energy storage, and electrical load) possesses autonomous strategy evaluation capabilities, and local multi-objective trade-off optimization is performed using Pareto frontier analysis. Its basic modeling is as follows:

[0135] 1. Equipment objective function model

[0136] Suppose a module (e.g., a heat pump flexible control module) has multiple possible operating strategy states, denoted as:

[0137] x i ∈X i

[0138] Here, the current selectable control strategy of module i (such as operating power, temperature rise rate, startup time, etc.) is Xi, which is the set of all its selectable strategies.

[0139] This module has multiple objective functions (such as minimum energy consumption, temperature stability, load coordination, etc.), defined as:

[0140] f i (x i )=[f i1 (x i ),f i2 (x i ),......,f in (x i )]

[0141] Each objective function fi(xi) represents a certain performance index.

[0142] 2. Definition of Pareto optimal solution

[0143] If there exists a strategy xi*∈Xi that satisfies:

[0144] For all other strategies x i ∈X i f i (x i *) Not f i (x i Strict superior dominance over all objectives, namely:

[0145]

[0146] Then x is called i * The set of Pareto optimal solutions belonging to this module.

[0147] The boundary formed by this set is called:

[0148]

[0149] 3. Distributed Cooperative Convergence Principle

[0150] --Through the system broadcast bus, all modules share the following common information (per second or per event trigger cycle):

[0151] --Current available photovoltaic power (PV_available);

[0152] --State of Storage (SOC);

[0153] --Intent states (intent, power_target, priority) that have been broadcast by other modules;

[0154] --The edge coordinator's suggested operating window or scheduling index (if it exists);

[0155] --Other information that needs to be published and is listed in the above modules.

[0156] Based on the above information, each module projects its local Pareto set Pi onto the global constraint boundary, forming a constrained subset. Right now:

[0157]

[0158] Where g(xi,·) represents the system constraint model, such as: the total power does not exceed the current PV power generation and battery support capacity; equipment with lower power priority should postpone operation under high load; and user-set comfort / delay tolerance should not be violated.

[0159] In another embodiment of the present invention, the module behavior results include:

[0160] 1) Each module is based on P i′ Choose a nondominated solution x i final ∈Ρ i ' This is the current operating strategy.

[0161] 2) Ensure that the module's local performance does not significantly degrade;

[0162] 3) Achieve distributed collaborative convergence in conjunction with other modules under overall system power or resource constraints;

[0163] 4) Supports decentralized self-determination and edge intelligence.

[0164] In another embodiment of the present invention, the multi-level self-determination architecture layering description includes:

[0165] Taking the heat pump-flexible scheduling module as an example, the detailed process is as follows:

[0166] 1) Internal perception stage:

[0167] Read its own sensor data, including the current outlet water temperature and the remaining heat capacity of the water tank;

[0168] Read user preference settings, such as target bathing temperature and expected water usage time period;

[0169] Calculate the acceptable startup delay time window (e.g., if it is currently 3 pm and the shower time is 7 pm, there are still 4 hours available for preheating optimization);

[0170] A preliminary assessment is made to determine whether the device itself is configured to function correctly (intent = "can be enabled").

[0171] 2) External information eavesdropping stage:

[0172] Listen to the remaining power generation capacity broadcast by the current photovoltaic module (e.g., PV = 1200W) from the local area bus;

[0173] The SOC level broadcast by the energy storage battery module (e.g., SOC = 95%, fully charged);

[0174] Are other basic load modules scheduled to run (e.g., a rice cooker preparing to heat)?

[0175] Does the edge coordination controller recommend that it is currently operational or needs to be delayed (e.g., EC recommends a 10-minute delay)?

[0176] In another embodiment of the present invention, boundary analysis and strategy evaluation include:

[0177] 1) Construct the Pareto boundary of this module in its current state:

[0178] One side is for low-energy operation (e.g., low power, slow temperature rise).

[0179] The other side features rapid response (rapid heating, high power);

[0180] 2) Determine if there is accessible space based on the listening results:

[0181] If the system load is high, the electricity price is high, or the battery power is low, operation will be delayed.

[0182] If solar power is abundant, batteries are fully charged, and other loads are sparse, then the system will operate automatically.

[0183] If the EC issues a suggested strategy, it will participate in the selection of the flexible interval in the strategy calculation;

[0184] 3) Decision Output Stage:

[0185] Select the corresponding strategy index number (e.g., strategy 400W heating for 30 minutes) and execute;

[0186] The current status and strategy / corresponding power expectation are broadcast to the bus so that other modules can sense and initiate the corresponding inverter control commands to enter the target power operation state.

[0187] In another embodiment of the present invention, the method described above further includes:

[0188] When the system load pressure increases, the modules achieve self-organized scheduling through listening and Pareto boundary self-adjustment strategies;

[0189] All flexible scheduling modules have local decision-making logic and prioritize not relying on centralized scheduling.

[0190] After the module intent information is broadcast, it is received by the edge coordination controller and then combined with the overall strategy to broadcast the reference allocation.

[0191] This invention, through the introduction of a Wi-Fi LAN single-bus communication architecture and combined with module-level priority control and a distributed Pareto self-determination optimization strategy, achieves efficient collaboration and self-organized operation among various heterogeneous modules (photovoltaics, heat pumps, energy storage, and electrical appliances) in building energy systems, achieving the following effects:

[0192] 1) Decentralized decision-making enhances system robustness:

[0193] Without a single master control unit in the system, each module can perform local policy calculations and behavioral decisions based on broadcast bus information, and has the ability to "locally decide" under conditions such as incomplete network, communication delay, and failure of the central node, thereby improving the overall system robustness and anti-interference capability.

[0194] 2) Dynamic optimal energy scheduling:

[0195] By calculating the local Pareto boundary of the module in real time (such as contradictory indicators like energy consumption-comfort and charging rate-lifetime), and combining this with the current photovoltaic capacity, load distribution, and battery status broadcast by the system, the module can autonomously determine whether to run / delay / shaving, greatly improving photovoltaic utilization and energy matching efficiency.

[0196] 3) Enhanced overall system flexibility and response capability:

[0197] All modules with "flexible" characteristics (such as heat pump frequency conversion, energy storage buffer, and load shifting) are assigned intermediate priority (level 4 to 4.5). They can achieve flexible and coordinated system-level response in scenarios such as sudden changes in electricity prices and sudden increases in load, thereby reducing dependence on the external power grid and having "island operation" compatibility.

[0198] 4) Strong modular expansion capability:

[0199] Because it adopts a broadcast bus communication protocol, and all modules publish status and respond to policies based on priority and a general frame structure, it can achieve "plug and play" soft expansion capabilities without refactoring the communication protocol, regardless of whether new load modules are connected or new micro generators are added.

[0200] 5) A balance between superior user experience and energy efficiency:

[0201] Heat pump flexible scheduling modules, home appliances, and other devices can balance energy saving and user comfort during the autonomous decision-making process by setting usage time windows and temperature targets according to user preferences. The system can intelligently determine whether to "run immediately" or "preheat in advance" to achieve refined energy utilization.

[0202] 6) Supports edge coordinator-optimized scheduling without relying on the central coordinator:

[0203] Introducing an edge coordinator (EC) as a "non-master central agent" can further enhance system scheduling performance and fault recovery capabilities, but it is not a core component that must be relied upon, ensuring that the system has full distributed operation capabilities.

[0204] In another embodiment of the present invention, the above solution is applicable to buildings and micro-parks of different sizes.

[0205] This system has good portability and scalability, and can be extended to multi-household shared micro energy communities, small park-level energy self-determination systems, etc., to promote low-carbon operation at the building / community level.

[0206] This embodiment takes a building-distributed photovoltaic-energy storage-heat pump system as an example to introduce the specific application of the multi-level self-determined control architecture based on the present invention.

[0207] In another embodiment of the present invention, the system hardware configuration may include:

[0208] (1) The photovoltaic module consists of monocrystalline silicon photovoltaic modules with a rated power of 3kW, and is equipped with voltage, current sensors and light intensity sensors. The photovoltaic controller broadcasts real-time power generation and power generation forecast data for the next 5 minutes and 30 minutes to the system bus via the Wi-Fi module.

[0209] (2) The energy storage module uses a 10kWh lithium-ion battery pack and is equipped with a battery management system (BMS) to monitor SOC, charge / discharge status and power in real time. The energy storage module has a built-in microcontroller controller that supports broadcasting charging / discharging intentions (charging, discharging, standby) and current power via Wi-Fi.

[0210] (3) The heat pump module is a variable frequency heat pump, equipped with a water tank temperature sensor and an outdoor temperature sensor, and supports multiple power levels. The heat pump controller receives photovoltaic and energy storage status information via Wi-Fi and adjusts the operating power according to the current power supply and the target temperature set by the user.

[0211] (4) Other load modules include smart refrigerators, washing machines, lighting, etc. Each module is equipped with a communication unit that can broadcast the current power, flexibility level and operating intention.

[0212] (5) The edge coordination controller is deployed in the building router, periodically collects broadcast data from each module, calculates the overall Pareto boundary of the system, generates scheduling suggestions, and sends them to each module via broadcast.

[0213] In another embodiment of the present invention, the control process includes:

[0214] (1) Initialization phase:

[0215] After the system starts up, each module broadcasts its own status via Wi-Fi, including information such as power generation, SOC, and operating intent. The edge coordinator collects and saves the current system status.

[0216] (2) Operation phase:

[0217] a. The photovoltaic module continuously broadcasts real-time power generation and future power generation forecasts.

[0218] b. The energy storage module selects charging, discharging, or standby mode based on the remaining power of photovoltaic power generation and load demand, and broadcasts the current charging / discharging intention and SOC.

[0219] c. The heat pump module receives photovoltaic and energy storage broadcast information, combines the user-set target temperature and the current water temperature, calculates the acceptable operating power range, selects the optimal strategy within the Pareto boundary and executes it, while broadcasting the current power and operating intention.

[0220] d. Other load modules adjust their operating time and power based on the current power supply capacity and energy storage status, combined with the user's time settings, and broadcast the flexibility level and operating intention.

[0221] e. The edge coordinator periodically calculates the Pareto boundary of the overall system, generates coordination and scheduling suggestions (such as load delay and heat pump peak shifting), and broadcasts them to all modules for reference.

[0222] (3) Isolated network self-governance stage:

[0223] When the edge coordinator or building router disconnects, causing communication interruption, each module performs local Pareto optimization self-determination based on the last received broadcast information and the local policy model to ensure the basic stable operation of the system.

[0224] Specific algorithm example:

[0225] 3.1 Input Data Acquisition (Taking Flexible Scheduling of Heat Pump Modules as an Example)

[0226] Read the outdoor temperature temp_out (via temperature sensor).

[0227] Read the current temperature of the water tank (temp_tank)

[0228] Get the user-defined target temperature target_temp

[0229] Get the current remaining power PV_power_available broadcast by the photovoltaic module.

[0230] Get the current SOC soc_battery broadcast by the energy storage module.

[0231] Get the scheduling suggestion from the edge coordinator (if any) ec_suggestion

[0232] 3.2 Strategy Space Definition

[0233] Define the set of power levels that the heat pump can operate at, X = {x1, x2, ..., xn}, for example, {200W, 400W, 600W, 800W, 1000W, 1200W}. Each power level corresponds to a different heating rate and energy consumption level.

[0234] 3.3 Calculation of the objective function

[0235] For each power level xi∈X, calculate the objective function:

[0236] f1(xi): Estimated energy consumption, equal to power multiplied by estimated operating time.

[0237] f2(xi): Comfort function, which measures the deviation between the time to reach the target temperature and the user's expected time. The closer the time is to the desired temperature, the higher the comfort level.

[0238] f3(xi): Impact on equipment lifespan; assesses the risk of frequent start-stop cycles. Lower operating power results in fewer start-stop cycles and a smaller impact on lifespan.

[0239] 3.4 Pareto Boundary Selection Algorithm

[0240] For all xi, determine whether there exists xj such that for all objectives fk(xj) ≤ fk(xi), and at least one objective is strictly better than xi.

[0241] After removing all dominated xi, the remaining set is the Pareto optimal policy set P.

[0242] 3.5 Strategy Selection

[0243] Based on the current PV_power_available and soc_battery, filter out a subset of available power levels. Guaranteed power ≤ PV_power_available + battery discharge power

[0244] If the EC provides scheduling suggestions (such as delaying startup or reducing power), adjust the range of selectable gears.

[0245] Select the optimal strategy from X', such as the setting x_opt that has the lowest energy consumption and meets comfort requirements.

[0246] 3.6 Execution and Broadcasting

[0247] Send control commands to the heat pump inverter and set the operating power to x_opt

[0248] Broadcasting the current running status via Wi-Fi, example message:

[0249] {

[0250] "type":"heatpump",

[0251] "power":x_opt,

[0252] "temp_out":temp_out,

[0253] "intent":"running",

[0254] "priority":2

[0255] }

[0256] 3.7 Iterative Update

[0257] Steps 3.1 to 3.6 are repeated every second or every 5 seconds to dynamically respond to system changes.

[0258] <Equipment Example>

[0259] Figure 2 This is a functional structural block diagram of a collaborative scheduling device for a building energy system according to some embodiments of the present invention. For example... Figure 2 As shown, the collaborative scheduling device 120 includes: a first control module 121 and a second control module 122.

[0260] The first control module 121 is used to, when the building energy system is in operation, select the optimal strategy and output the first information based on module information and preset information, and adjust the operating data and output the second information.

[0261] The second control module 122 is used by the building energy system coordination control module to periodically calculate the Pareto boundary of the overall system and output coordination scheduling instruction information.

[0262] Optionally, the first control module 121 is configured to: before the building energy system is in the operation phase, further include: performing initialization processing on the building energy system; after the building energy system is initialized and enters the operation phase, each module in the building energy system sequentially outputs its own status information, and the coordination control module collects the own status information and saves the current system status.

[0263] Optionally, the first control module 121 is used to: when the building energy system is in the islanded autonomous stage, each module in the building energy system performs self-optimization based on the last received information and in conjunction with the strategy model.

[0264] Optionally, the first control module 121 is used to: set hierarchical classification and control priority for each module of the building energy system; and form a decision coordination chain based on the hierarchical classification and control priority.

[0265] Optionally, the second control module 122 is used to: set an objective function model, including setting multiple objective functions based on multiple possible operating strategy states of the module; define an optimal solution, including calculating the boundary formed by the set based on the objective function model; set distributed collaborative convergence, calculating subsets based on the information of the module; and determine the module behavior result, including selecting the current operating strategy based on the non-dominated solution of the module.

[0266] Optionally, the first control module 121 is used for: the photovoltaic module of the building energy system to provide real-time predicted data of available power; and the energy storage module of the building energy system to select the application state based on the remaining power of photovoltaic power generation and load demand.

[0267] Optionally, the first control module 121 is used to: select the optimal strategy and output the first information based on the information of the photovoltaic module, the energy storage module and the first preset information of the heat pump module of the building energy system.

[0268] Optionally, the second control module 122 is used to: adjust the operating time and power and output the second information according to the information of the photovoltaic module, the energy storage module and the second preset information.

[0269] Figure 3 This is a functional structure block diagram of a collaborative scheduling system for building energy systems according to some embodiments of the present invention. For example... Figure 3 As shown, the collaborative scheduling system 130 includes: a photovoltaic power generation module 131, an energy storage battery module 132, a heat pump module 133, a load module 134, and a coordination control module 135.

[0270] Photovoltaic power generation module 131 is used to provide electrical energy and to provide available electrical energy prediction data.

[0271] The energy storage battery module 132 is used as an energy balance buffer unit to absorb excess electrical energy or compensate for insufficient power.

[0272] The heat pump module 133 is used to convert electrical energy into heat energy and has the ability to balance the rigidity and flexibility of operation.

[0273] Load module 134 is used for non-critical loads with adjustable startup and / or operation strategies.

[0274] The coordination and control module 135 is used to assist in the analysis of broadcast data and output global scheduling information.

[0275] This invention can be a system, method, and / or computer program product. A computer program product may include a computer-readable storage medium having computer-readable program instructions loaded thereon for causing a processor to implement various aspects of the invention.

[0276] Computer-readable storage media can be tangible devices capable of holding and storing instructions for use by an instruction execution device. Computer-readable storage media can be, for example—but not limited to—electrical storage devices, magnetic storage devices, optical storage devices, electromagnetic storage devices, semiconductor storage devices, or any suitable combination thereof. More specific examples (a non-exhaustive list) of computer-readable storage media include: portable computer disks, hard disks, random access memory (RAM), read-only memory (ROM), erasable programmable read-only memory (EPROM or flash memory), static random access memory (SRAM), portable compact disc read-only memory (CD-ROM), digital multifunction disc (DVD), memory sticks, floppy disks, mechanical encoding devices, such as punch cards or recessed protrusions storing instructions thereon, and any suitable combination thereof. The computer-readable storage media used herein are not to be construed as transient signals themselves, such as radio waves or other freely propagating electromagnetic waves, electromagnetic waves propagating through waveguides or other transmission media (e.g., light pulses through fiber optic cables), or electrical signals transmitted through wires.

[0277] The computer-readable program instructions described herein can be downloaded from computer-readable storage media to various computing / processing devices, or downloaded via a network, such as the Internet, local area network, wide area network, and / or wireless network, to an external computer or external storage device. The network may include copper transmission cables, fiber optic transmission, wireless transmission, routers, firewalls, switches, gateway computers, and / or edge servers. A network adapter card or network interface in each computing / processing device receives the computer-readable program instructions from the network and forwards them to the computer-readable storage media in the respective computing / processing device.

[0278] The computer program instructions used to perform the operations of this invention may be assembly instructions, instruction set architecture (ISA) instructions, machine instructions, machine-dependent instructions, microcode, firmware instructions, state setting data, or source code or object code written in any combination of one or more programming languages, including object-oriented programming languages ​​such as Smalltalk, C++, etc., and conventional procedural programming languages ​​such as the "C" language or similar programming languages. The computer-readable program instructions may be executed entirely on the user's computer, partially on the user's computer, as a standalone software package, partially on the user's computer and partially on a remote computer, or entirely on a remote computer or server. In cases involving a remote computer, the remote computer may be connected to the user's computer via any type of network—including a local area network (LAN) or a wide area network (WAN)—or may be connected to an external computer (e.g., via the Internet using an Internet service provider). In some embodiments, electronic circuitry, such as programmable logic circuitry, field-programmable gate arrays (FPGAs), or programmable logic arrays (PLAs), is personalized by utilizing state information from the computer-readable program instructions. This electronic circuitry can execute the computer-readable program instructions to implement various aspects of the invention.

[0279] Various aspects of the present invention are described herein with reference to flowchart illustrations and / or block diagrams of methods, apparatus (systems), and computer program products according to embodiments of the invention. It should be understood that each block of the flowchart illustrations and / or block diagrams, and combinations of blocks in the flowchart illustrations and / or block diagrams, can be implemented by computer-readable program instructions.

[0280] These computer-readable program instructions can be provided to a processor of a general-purpose computer, a special-purpose computer, or other programmable data processing apparatus to produce a machine such that, when executed by the processor of the computer or other programmable data processing apparatus, they create means for implementing the functions / actions specified in one or more blocks of the flowchart and / or block diagram. These computer-readable program instructions can also be stored in a computer-readable storage medium that causes a computer, programmable data processing apparatus, and / or other device to operate in a particular manner; thus, the computer-readable medium storing the instructions comprises an article of manufacture that includes instructions for implementing aspects of the functions / actions specified in one or more blocks of the flowchart and / or block diagram.

[0281] Computer-readable program instructions may also be loaded onto a computer, other programmable data processing apparatus, or other device to cause a series of operational steps to be performed on the computer, other programmable data processing apparatus, or other device to produce a computer-implemented process, thereby causing the instructions that execute on the computer, other programmable data processing apparatus, or other device to perform the functions / actions specified in one or more boxes of a flowchart and / or block diagram.

[0282] The flowcharts and block diagrams in the accompanying drawings illustrate the architecture, functionality, and operation of possible implementations of systems, methods, and computer program products according to various embodiments of the present invention. In this regard, each block in a flowchart or block diagram may represent a module, segment, or portion of an instruction, which contains one or more executable instructions for implementing a specified logical function. In some alternative implementations, the functions marked in the blocks may occur in a different order than those marked in the drawings. For example, two consecutive blocks may actually be executed substantially in parallel, and they may sometimes be executed in reverse order, depending on the functions involved. It should also be noted that each block in the block diagrams and / or flowcharts, and combinations of blocks in the block diagrams and / or flowcharts, can be implemented using a dedicated hardware-based system that performs the specified function or action, or using a combination of dedicated hardware and computer instructions. It will be known to those skilled in the art that implementation in hardware, implementation in software, and implementation using a combination of software and hardware are equivalent.

[0283] The various embodiments of the present invention have been described above. These descriptions are exemplary and not exhaustive, nor are they limited to the disclosed embodiments. Many modifications and variations will be apparent to those skilled in the art without departing from the scope and spirit of the described embodiments. The terminology used herein is chosen to best explain the principles, practical application, or technical improvements to the embodiments in the market, or to enable those skilled in the art to understand the embodiments disclosed herein. The scope of the invention is defined by the appended claims.

Claims

1. A method for coordinated scheduling of building energy systems, characterized in that, The method comprises the following steps: In the case that the building energy system is in the running stage, the building energy system selects the optimal strategy according to the module information and preset information and outputs first information, and adjusts the running data and outputs second information; The coordination control module of the building energy system periodically calculates the Pareto boundary of the overall system and outputs coordination scheduling instruction information.

2. The method of claim 1, wherein, Before the building energy system is in the running stage, the method further comprises the following steps: The building energy system is initialized; After the initialization of the building energy system, and in the case that the building energy system enters the running stage, each module in the building energy system outputs its own state information in turn, and the coordination control module collects the own state information and saves the current system state.

3. The method of claim 1, wherein, The method further comprises the following steps: In the case that the building energy system is in the isolated network autonomous stage, each module in the building energy system optimizes itself according to the last received information and in combination with the strategy model.

4. The method of claim 3, wherein, The method further comprises the following steps: The levels of each module of the building energy system are classified and the control priorities are set; The decision coordination chain is formed according to the level classification and the control priorities.

5. The method of claim 1, wherein, The coordination control module of the building energy system periodically calculates the Pareto boundary of the overall system and outputs coordination scheduling instruction information comprises the following steps: A target function model is set, including setting a plurality of target functions according to a plurality of possible running strategy states possessed by the module; An optimal solution is defined, including calculating the boundary formed by the set according to the target function model; A distributed collaborative convergence is set, and the subset is calculated according to the module information; The module behavior result comprises selecting the current running strategy according to the non-dominated solution of the module. 6.The method of claim 1, wherein, In the case that the building energy system is in the running stage, the building energy system selects the optimal strategy according to the module information and preset information and outputs first information, and adjusts the running data and outputs second information comprises the following steps: The photovoltaic module of the building energy system provides real-time available power prediction data; The energy storage module of the building energy system selects the application state according to the remaining power of photovoltaic power generation and the load demand.

7. The method of claim 6, wherein, In the case that the building energy system is in the running stage, the building energy system selects the optimal strategy according to the module information and preset information and outputs first information, and adjusts the running data and outputs second information comprises the following steps: The heat pump module of the building energy system selects the optimal strategy and outputs first information according to the information of the photovoltaic module and the energy storage module and first preset information. 8.The method of claim 7, wherein, In the case that the building energy system is in the running stage, the building energy system selects the optimal strategy according to the module information and preset information and outputs first information, and adjusts the running data and outputs second information comprises the following steps: The load module of the building energy system adjusts the running time and power and outputs second information according to the information of the photovoltaic module and the energy storage module and second preset information.

9. A device for coordinated scheduling of building energy systems, characterized in that, The method comprises the following steps: A first control module is configured to, in the case that the building energy system is in the running stage, the building energy system selects the optimal strategy according to the module information and preset information and outputs first information, and adjusts the running data and outputs second information; The second control module is used for a coordination control module of the building energy system to calculate a Pareto boundary of the overall system regularly and output coordination scheduling instruction information.

10. A coordinated dispatch system for a building energy system, the system comprising: The building energy system comprises: A photovoltaic power generation module for providing electric energy and providing available electric energy prediction data; An energy storage battery module for serving as an energy balance buffer unit to absorb surplus electric energy or compensate for power shortage; A heat pump module for converting electric energy into heat energy and having a balance ability between rigidity and flexibility strategies; A load module for non-critical loads with adjustable start-up and / or operation strategies; A coordination control module for assisting in analyzing broadcast data and outputting global scheduling information.

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