Distributed scheduling method and system for multi-energy collection, storage and release

Abstract

The invention relates to the technical field of multi-energy collection and storage, and discloses a distributed scheduling method and system for multi-energy collection, storage and release, and the method comprises the steps: obtaining the heat energy supply data of a plurality of heat sources in a region, the heat storage state data of at least one heat storage system, and the heat load demand data of at least one heat energy user; based on the heat energy supply data, the heat storage state data and the heat load demand data, a heat energy dispatching instruction set is generated, and the heat energy dispatching instruction set is used for controlling corresponding actuators and comprises a first actuator corresponding to a heat source, a second actuator corresponding to a heat storage system and a third actuator corresponding to a heat supply pipe network; and the controller is used for adjusting the operating power of a heat source, switching the opening degrees of a heat charging valve and a heat discharging valve of the heat storage system and adjusting the rotating speed of a pump and / or the opening degree of the valve in a heat supply pipe network according to the received heat energy dispatching instruction so as to distribute the heat medium flow. Therefore, the flexibility of distributed scheduling of multi-energy storage and release can be improved by implementing the multi-energy storage and release scheduling method.

Description

Technical Field

[0001] This invention relates to the field of multi-energy storage technology, and in particular to a distributed scheduling method and system for multi-energy storage and release. Background Technology

[0002] With the transformation of the energy structure and the rapid development of distributed energy, regional integrated energy systems, especially distributed heating systems that integrate multiple heat sources (such as industrial waste heat, geothermal energy, solar thermal energy, and biomass energy), are receiving increasing attention due to their enormous potential in improving energy efficiency and reducing carbon emissions.

[0003] However, such distributed systems with multiple heat sources and multiple users face severe challenges in actual scheduling and operation. First, significant uncertainties exist on both the supply and demand sides of the system. On the supply side, the output of renewable energy sources (such as solar thermal) is intermittent and fluctuates due to weather conditions; the availability of industrial waste heat also fluctuates with changes in the main production processes. On the demand side, users' heat loads are affected by various factors such as climate change, production plans, and energy consumption habits. This dual uncertainty of "source-load" makes traditional scheduling methods based on fixed rules or only considering the current moment's supply and demand balance difficult to apply, easily leading to system supply-demand mismatch, either resulting in wasted heat energy or failure to meet user needs.

[0004] Therefore, it is particularly important to propose a technical solution that improves the flexibility of distributed scheduling for multi-energy collection, storage, and release. Summary of the Invention

[0005] This invention provides a distributed scheduling method and system for multi-energy storage and release, which can improve the flexibility of distributed scheduling for multi-energy storage and release.

[0006] To address the aforementioned technical problems, the first aspect of this invention discloses a distributed scheduling method for multi-energy storage and release, the method comprising: Acquire heat energy supply data from multiple heat sources within the region, heat storage status data from at least one heat storage system, and heat load demand data from at least one heat energy user; Based on the thermal energy supply data, the thermal storage status data, and the thermal load demand data, a set of thermal energy scheduling instructions is generated, which is used to control the corresponding actuators. The actuator includes a first actuator corresponding to the heat source, a second actuator corresponding to the thermal storage system, and a third actuator corresponding to the heating network. The first actuator is used to adjust the operating power of the heat source according to the received thermal energy dispatching command. The second actuator is used to switch the opening degree of the charging valve and the releasing valve of the thermal storage system according to the received thermal energy dispatching command. The third actuator is used to adjust the speed of the pump and / or the valve opening degree in the heating network according to the received thermal energy dispatching command to distribute the heat medium flow.

[0007] As an optional implementation, in the first aspect of the present invention, generating a set of thermal energy dispatching instructions based on the thermal energy supply data, the thermal storage status data, and the thermal load demand data includes: Based on the historical operating data of each heat source and the current weather forecast information, predictive heat energy supply data for each heat source in a specific future scheduling cycle is generated. Based on the historical heat consumption data of each heat energy user and the current production plan information, predictive heat load demand data for each heat energy user in the future specific scheduling cycle is generated. Based on the obtained current heat energy supply data, heat storage status data, heat load demand data, all predicted heat energy supply data, and all predicted heat load demand data, multiple temporary scheduling instructions are generated, each corresponding to a different moment within a specific future scheduling cycle. Based on the target scheduling operation cost and / or energy efficiency indicators, the multiple temporary scheduling instructions are collaboratively optimized to generate a set of thermal energy scheduling instructions.

[0008] As an optional implementation, in the first aspect of the invention, generating predicted heat energy supply data for each of the heat sources within a specific future scheduling period based on historical operating data of each heat source and current weather forecast information includes: For each heat source, the historical operating data of the heat source is classified according to the corresponding historical meteorological conditions to obtain the historical operating condition data of the heat source under different typical meteorological categories. The historical operating condition data is used to represent the output characteristics of the heat source. Extract meteorological category sequences for a specific future scheduling cycle from current weather forecast information; The meteorological category sequence is matched with historical operating condition data under different typical meteorological categories to generate predicted heat energy supply data for the heat source in the future specific scheduling cycle.

[0009] As an optional implementation, in the first aspect of the present invention, generating predicted heat load demand data for each of the heat energy users within the future specific scheduling period based on the historical heat consumption data of each of the heat energy users and the current production plan information includes: For each heat energy user, based on the user's historical heat consumption data, a typical heat load curve for the user under different production plan types is determined. The typical heat load curve is used to represent the periodic heat load variation pattern of the user when executing a specific production plan. Based on the current production plan information, determine the production plan type corresponding to the specific future scheduling period; Based on the typical heat load curve that matches the production plan type, and according to the production scheduling scale in the production plan information, the predicted heat load demand data is generated.

[0010] As an optional implementation, in the first aspect of the present invention, the step of generating multiple temporary scheduling instructions corresponding to multiple times within a specific future scheduling period based on the acquired current-time thermal energy supply data, thermal storage status data, thermal load demand data, all predicted thermal energy supply data, and all predicted thermal load demand data includes: Based on the network topology and thermodynamic characteristics of the heating pipeline network, a set of system operation constraints is established. This set of system operation constraints is used to limit the feasible range of heat medium flow rate, node pressure, and equipment operating parameters. Using the current thermal storage status data as the initial state, and based on all the predicted thermal energy supply data and all the predicted thermal load demand data, the system operation status at each moment within the future specific scheduling cycle is solved sequentially within the system operation constraint set. The system operating state obtained at each time step is converted into control instructions for each actuator at that time step, forming multiple temporary scheduling instructions corresponding to each time step.

[0011] As an optional implementation, in the first aspect of the present invention, the step of collaboratively optimizing the plurality of temporary scheduling instructions based on the target scheduling operation cost and / or energy efficiency indicators to generate a thermal energy scheduling instruction set includes: The power regulation rate limit for each heat source, the heat charging and discharging power limit for each heat storage system, and the speed regulation range of the pumps in the heating network are determined as equipment operation constraints. The system response hysteresis time caused by the delay in heat medium transmission and the inertia of temperature change in the heating network is determined as the system inertia; Based on the equipment operation constraints and the system inertia, the multiple temporary scheduling instructions are smoothly recombined in the time dimension to generate multiple candidate scheduling trajectories; Based on the target scheduling operation cost and the energy efficiency index, a target candidate scheduling trajectory is determined from the plurality of candidate scheduling trajectories, and the instruction sequence contained in the target candidate scheduling trajectory is determined as a thermal energy scheduling instruction set.

[0012] As an optional implementation, in the first aspect of the present invention, the method further includes: After executing the set of thermal energy dispatch instructions, monitor the actual operating data of each heat source, each thermal storage system and each thermal energy user; The actual operating data is compared with the predicted heat supply data and predicted heat load demand data used when generating the set of heat energy dispatch instructions to obtain the prediction deviation. When the prediction deviation is greater than or equal to a preset deviation threshold, the historical operating condition database and historical heat load database on which the predicted heat energy supply data and the predicted heat load demand data are based are updated using the actual operating data.

[0013] A second aspect of this invention discloses a distributed scheduling system for multi-energy storage and release, the system comprising: The acquisition module is used to acquire heat energy supply data from multiple heat sources within the area, heat storage status data from at least one heat storage system, and heat load demand data from at least one heat energy user. The generation module is used to generate a set of thermal energy dispatching instructions based on the thermal energy supply data, the thermal storage status data and the thermal load demand data. The set of thermal energy dispatching instructions is used to control the corresponding actuators. The actuator includes a first actuator corresponding to the heat source, a second actuator corresponding to the thermal storage system, and a third actuator corresponding to the heating network. The first actuator is used to adjust the operating power of the heat source according to the received thermal energy dispatching command. The second actuator is used to switch the opening degree of the charging valve and the releasing valve of the thermal storage system according to the received thermal energy dispatching command. The third actuator is used to adjust the speed of the pump and / or the valve opening degree in the heating network according to the received thermal energy dispatching command to distribute the heat medium flow.

[0014] As an optional implementation, in the second aspect of the present invention, the specific method by which the generation module generates a set of thermal energy dispatching instructions based on the thermal energy supply data, the thermal storage status data, and the thermal load demand data includes: Based on the historical operating data of each heat source and the current weather forecast information, predictive heat energy supply data for each heat source in a specific future scheduling cycle is generated. Based on the historical heat consumption data of each heat energy user and the current production plan information, predictive heat load demand data for each heat energy user in the future specific scheduling cycle is generated. Based on the obtained current heat energy supply data, heat storage status data, heat load demand data, all predicted heat energy supply data, and all predicted heat load demand data, multiple temporary scheduling instructions are generated, each corresponding to a different moment within a specific future scheduling cycle. Based on the target scheduling operation cost and / or energy efficiency indicators, the multiple temporary scheduling instructions are collaboratively optimized to generate a set of thermal energy scheduling instructions.

[0015] As an optional implementation, in the second aspect of the present invention, the specific method by which the generation module generates predicted heat energy supply data for each of the heat sources within a specific future scheduling period based on historical operating data of each heat source and current weather forecast information includes: For each heat source, the historical operating data of the heat source is classified according to the corresponding historical meteorological conditions to obtain the historical operating condition data of the heat source under different typical meteorological categories. The historical operating condition data is used to represent the output characteristics of the heat source. Extract meteorological category sequences for a specific future scheduling cycle from current weather forecast information; The meteorological category sequence is matched with historical operating condition data under different typical meteorological categories to generate predicted heat energy supply data for the heat source in the future specific scheduling cycle.

[0016] As an optional implementation, in the second aspect of the present invention, the specific method by which the generation module generates the predicted heat load demand data for each of the heat energy users within the future specific scheduling period based on the historical heat consumption data of each of the heat energy users and the current production plan information includes: For each heat energy user, based on the user's historical heat consumption data, a typical heat load curve for the user under different production plan types is determined. The typical heat load curve is used to represent the periodic heat load variation pattern of the user when executing a specific production plan. Based on the current production plan information, determine the production plan type corresponding to the specific future scheduling period; Based on the typical heat load curve that matches the production plan type, and according to the production scheduling scale in the production plan information, the predicted heat load demand data is generated.

[0017] As an optional implementation, in the second aspect of the present invention, the specific method by which the generation module generates multiple temporary scheduling instructions corresponding to multiple times within the future specific scheduling period based on the acquired current-time thermal energy supply data, thermal storage status data, thermal load demand data, all predicted thermal energy supply data, and all predicted thermal load demand data includes: Based on the network topology and thermodynamic characteristics of the heating pipeline network, a set of system operation constraints is established. This set of system operation constraints is used to limit the feasible range of heat medium flow rate, node pressure, and equipment operating parameters. Using the current thermal storage status data as the initial state, and based on all the predicted thermal energy supply data and all the predicted thermal load demand data, the system operation status at each moment within the future specific scheduling cycle is solved sequentially within the system operation constraint set. The system operating state obtained at each time step is converted into control instructions for each actuator at that time step, forming multiple temporary scheduling instructions corresponding to each time step.

[0018] As an optional implementation, in the second aspect of the present invention, the generation module performs collaborative optimization on the plurality of temporary scheduling instructions based on the target scheduling operation cost and / or energy efficiency indicators to generate a set of thermal energy scheduling instructions. The specific methods for generating this set include: The power regulation rate limit for each heat source, the heat charging and discharging power limit for each heat storage system, and the speed regulation range of the pumps in the heating network are determined as equipment operation constraints. The system response hysteresis time caused by the delay in heat medium transmission and the inertia of temperature change in the heating network is determined as the system inertia; Based on the equipment operation constraints and the system inertia, the multiple temporary scheduling instructions are smoothly recombined in the time dimension to generate multiple candidate scheduling trajectories; Based on the target scheduling operation cost and the energy efficiency index, a target candidate scheduling trajectory is determined from the plurality of candidate scheduling trajectories, and the instruction sequence contained in the target candidate scheduling trajectory is determined as a thermal energy scheduling instruction set.

[0019] As an optional implementation, in a second aspect of the invention, the system further includes: The monitoring module is used to monitor the actual operating data of each heat source, each thermal storage system, and each thermal energy user after executing the set of thermal energy dispatching instructions; The calculation module is used to compare the actual operating data with the predicted heat supply data and predicted heat load demand data on which the set of heat energy dispatch instructions was generated, and to obtain the prediction deviation. The generation module is further configured to update the historical operating condition database and historical heat load database on which the predicted heat energy supply data and the predicted heat load demand data are based when the prediction deviation is greater than or equal to a preset deviation threshold, using the actual operating data.

[0020] A third aspect of this invention discloses another distributed scheduling system for multi-energy storage and release, the system comprising: Memory containing executable program code; A processor coupled to the memory; The processor calls the executable program code stored in the memory to execute the distributed scheduling method for multi-energy storage and release disclosed in the first aspect of the present invention.

[0021] The fourth aspect of the present invention discloses a computer storage medium storing computer instructions, which, when invoked, are used to execute the distributed scheduling method for multi-energy storage and release disclosed in the first aspect of the present invention.

[0022] Compared with the prior art, the embodiments of the present invention have the following beneficial effects: In this embodiment of the invention, heat energy supply data from multiple heat sources within a region, heat storage status data from at least one thermal storage system, and heat load demand data from at least one heat energy user are acquired. Based on the heat energy supply data, thermal storage status data, and heat load demand data, a set of heat energy dispatching instructions is generated. This set of instructions is used to control corresponding actuators. The actuators include a first actuator corresponding to a heat source, a second actuator corresponding to a thermal storage system, and a third actuator corresponding to the heating network. The first actuator adjusts the operating power of the heat source according to the received heat energy dispatching instructions. The second actuator switches the opening degree of the charging valve and the releasing valve of the thermal storage system according to the received instructions. The third actuator adjusts the pump speed and / or valve opening degree in the heating network according to the received instructions to allocate the heat medium flow. Therefore, implementing this invention can improve the perception of the overall regional thermal energy system's operating status by acquiring real-time operating data from multiple heat sources, thermal storage systems, and the user side, thereby facilitating data-driven precise dispatching decisions. It can generate a set of scheduling instructions based on real-time supply and demand data and directly control actuators, improving the automation level and response speed of system scheduling. This facilitates rapid balancing of thermal energy supply and demand within the system, reducing reliance on manual intervention and achieving efficient and timely closed-loop control. Furthermore, by separately controlling the power of heat sources, valves in the thermal storage system, and pump valves in the pipeline network, it enhances the independent control capabilities and collaborative operation possibilities of thermal energy production, storage, and distribution. This allows for flexible adjustment of the direction and magnitude of energy flow, thereby improving the flexibility and adaptability of the distributed scheduling system for multi-energy collection, storage, and release to cope with changing operating conditions. Attached Figure Description

[0023] To more clearly illustrate the technical solutions in the embodiments of the present invention, the accompanying drawings used in the description of the embodiments will be briefly introduced below. Obviously, the accompanying drawings described below are only some embodiments of the present invention. For those skilled in the art, other drawings can be obtained based on these drawings without creative effort.

[0024] Figure 1 This is a flowchart illustrating a distributed scheduling method for multi-energy collection, storage, and release disclosed in an embodiment of the present invention. Figure 2 This is a flowchart illustrating another distributed scheduling method for multi-energy collection, storage, and release disclosed in an embodiment of the present invention. Figure 3 This is a schematic diagram of the structure of a distributed scheduling system for multi-energy collection, storage, and release disclosed in an embodiment of the present invention; Figure 4 This is a schematic diagram of another distributed scheduling system for multi-energy collection, storage, and release disclosed in an embodiment of the present invention; Figure 5This is a schematic diagram of the structure of another distributed scheduling system for multi-energy collection, storage and release disclosed in an embodiment of the present invention. Detailed Implementation

[0025] To enable those skilled in the art to better understand the present invention, the technical solutions of the embodiments of the present invention will be clearly and completely described below with reference to the accompanying drawings. Obviously, the described embodiments are only some embodiments of the present invention, and not all embodiments. Based on the embodiments of the present invention, all other embodiments obtained by those skilled in the art without creative effort are within the scope of protection of the present invention.

[0026] The terms "first," "second," etc., used in the specification, claims, and accompanying drawings of this invention are used to distinguish different objects, not to describe a specific order. Furthermore, the terms "comprising" and "having," and any variations thereof, are intended to cover non-exclusive inclusion. For example, a process, method, system, product, or end that includes a series of steps or units is not limited to the listed steps or units, but may optionally include steps or units not listed, or may optionally include other steps or units inherent to these processes, methods, products, or ends.

[0027] In this document, the term "embodiment" means that a particular feature, structure, or characteristic described in connection with an embodiment may be included in at least one embodiment of the invention. The appearance of this phrase in various places throughout the specification does not necessarily refer to the same embodiment, nor is it a separate or alternative embodiment mutually exclusive with other embodiments. It will be explicitly and implicitly understood by those skilled in the art that the embodiments described herein can be combined with other embodiments.

[0028] This invention discloses a distributed scheduling method and system for multi-energy collection, storage, and release. It enhances the perception of the overall regional thermal energy system's operational status by acquiring real-time operational data from multiple heat sources, thermal storage systems, and the user side, thereby facilitating data-driven, precise scheduling decisions. It can generate a set of scheduling instructions based on real-time supply and demand data and directly control actuators, improving the system's automation level and response speed. This facilitates rapid balancing of thermal energy supply and demand within the system, reducing reliance on manual intervention and achieving efficient and timely closed-loop control. Furthermore, by separately controlling heat source power, thermal storage system valves, and pipeline pump valves, it improves the independent control capabilities and collaborative operation possibilities of thermal energy production, storage, and distribution, allowing for flexible adjustment of the energy flow direction and magnitude. This enhances the flexibility and adaptability of the distributed scheduling system for multi-energy collection, storage, and release to cope with varying operating conditions. Detailed descriptions follow.

[0029] Example 1 Please see Figure 1, Figure 1 This is a flowchart illustrating a distributed scheduling method for multi-energy storage and release disclosed in an embodiment of the present invention. Figure 1 The described distributed scheduling method for multi-energy storage and release can be applied to multi-energy storage devices, and also to intelligent devices associated with multi-energy storage devices. These intelligent devices include, but are not limited to, one or more of the following: switching devices, cloud devices, edge computing devices, relay devices, base station devices, urban management devices, and intelligent connected devices. This invention does not limit the scope of these applications. Figure 1 As shown, the distributed scheduling method for multi-energy storage and release can include the following operations: 101. Obtain heat energy supply data from multiple heat sources within the region, heat storage status data from at least one heat storage system, and heat load demand data from at least one heat energy user; In this embodiment of the invention, optionally, the step of "acquiring heat energy supply data from multiple heat sources within a region" can be achieved by deploying temperature sensors and flow meters at each heat source (e.g., the outlet of a factory waste heat boiler, the outlet of a solar collector system, or the outlet of a geothermal well heat exchange station). The aforementioned heat energy supply data specifically includes the supply temperature, flow rate, and pressure of the heat medium. This data is transmitted in real time to the central dispatch controller via wired or wireless communication networks (such as industrial Ethernet or 5G private networks).

[0030] Optionally, regarding the aforementioned step of "obtaining thermal storage status data for at least one thermal storage system": This step can be achieved by monitoring key parameters of the thermal storage system (such as large hot water storage tanks or phase change thermal storage devices). The aforementioned thermal storage status data mainly includes temperature stratification data within the storage tank (obtained through vertically arranged multi-point temperature sensors), the current liquid level (obtained through a level gauge), and the current heat storage (calculated from temperature and liquid level). These data characterize the instantaneous energy storage capacity and releaseable heat of the thermal storage system.

[0031] Further optionally, regarding the aforementioned "obtaining heat load demand data from at least one heat energy user": This step can be achieved through user-side energy meters (such as heat meters) and an optional user demand reporting system. The aforementioned heat load demand data includes the user's current instantaneous heat load and the user's preset future demand plan (if any). For industrial users, their production planning system can directly provide heat load forecasts for future periods.

[0032] In practical applications, if any of the above data is missing or not obtained, it can be empty.

[0033] 102. Based on thermal energy supply data, thermal storage status data, and thermal load demand data, generate a set of thermal energy dispatching instructions, which are used to control the corresponding actuators. The actuators include a first actuator corresponding to the heat source, a second actuator corresponding to the heat storage system, and a third actuator corresponding to the heating network. The first actuator is used to adjust the operating power of the heat source according to the received heat energy dispatching command. The second actuator is used to switch the opening degree of the heat storage system's charging valve and heat release valve according to the received heat energy dispatching command. The third actuator is used to adjust the pump speed and / or valve opening degree in the heating network according to the received heat energy dispatching command to distribute the heat medium flow.

[0034] In this embodiment of the invention, optionally, the step of "generating a set of thermal energy dispatching instructions based on the thermal energy supply data, the thermal storage status data, and the thermal load demand data" can be executed by a central dispatch controller. The core logic of the controller's built-in dispatching algorithm is: aiming to achieve the lowest total system operating cost or the highest total energy efficiency, while meeting the thermal demands of all users, comprehensively considering the real-time energy supply cost and capacity of each heat source, and the charging and discharging status and efficiency of the thermal storage system, to calculate the optimal energy allocation scheme. This scheme is transformed into a series of specific, executable control instructions, constituting an instruction set.

[0035] Further optional, the actuator and its control logic: The first actuator is typically a frequency converter on the heat source side or a controller for a regulating valve. The received command may be a target power value or a valve opening value. The actuator can adjust the fuel supply, working fluid flow rate, or heat collector area according to the command, thereby precisely controlling the output power of the heat source.

[0036] The second actuator is typically an electric valve actuator. It receives commands to open the charging pipeline valve, close the releasing pipeline valve, or vice versa. By controlling the combination of valve opening and closing, the charging, releasing, or heat preservation states of the thermal storage system can be switched.

[0037] The third actuator can include the frequency converter of the circulating pump in the pipeline network and the electric actuator of the regulating valves in each branch. The received command can be the target speed or the target opening degree. By adjusting the pump speed and valve opening degree, the flow rate and direction of the heat medium in the pipeline network can be changed, so as to realize the on-demand and directional delivery of heat energy to specific users or areas.

[0038] As can be seen, implementing the embodiments of the present invention can improve the perception of the overall regional thermal energy system's operating status by acquiring real-time operational data from multiple heat sources, thermal storage systems, and the user side, thereby facilitating data-driven, precise scheduling decisions. It can generate a set of scheduling instructions based on real-time supply and demand data and directly control actuators, improving the automation level and response speed of system scheduling. This facilitates rapid balancing of thermal energy supply and demand within the system, reducing reliance on manual intervention and achieving efficient and timely closed-loop control. Furthermore, by separately controlling heat source power, thermal storage system valves, and pipeline pump valves, it enhances the independent control capabilities and collaborative operation possibilities of thermal energy production, storage, and distribution, thereby enabling flexible adjustment of the direction and magnitude of energy flow. This, in turn, improves the flexibility and adaptability of the distributed scheduling system for multi-energy collection, storage, and release to cope with changing operating conditions.

[0039] In this embodiment of the invention, as an optional implementation, the above-mentioned generation of a set of thermal energy dispatching instructions based on thermal energy supply data, thermal storage status data, and thermal load demand data includes: Based on the historical operating data of each heat source and the current weather forecast information, predictive heat energy supply data for each heat source in a specific future scheduling cycle is generated. Based on the historical heat consumption data of each heat energy user and the current production plan information, generate the predicted heat load demand data of each heat energy user in a specific future scheduling cycle; Based on the acquired current thermal energy supply data, thermal storage status data, thermal load demand data, all predicted thermal energy supply data, and all predicted thermal load demand data, multiple temporary scheduling instructions are generated, each corresponding to a specific time within a future scheduling cycle. Based on the target scheduling operation cost and / or energy efficiency indicators, multiple temporary scheduling instructions are collaboratively optimized to generate a set of thermal energy scheduling instructions.

[0040] In this embodiment of the invention, optionally, the generation of the above-mentioned prediction data is as follows: Predicting heat supply data: For example, for solar thermal sources, based on weather forecasts of solar irradiance, ambient temperature, and cloud cover, combined with the efficiency characteristic curves of the collectors, the average heat collection power every 15 minutes over the next 24 hours is predicted. For industrial waste heat, based on production schedules and process characteristics, the fluctuations in waste heat emissions during future shifts are predicted.

[0041] Forecasted heat load demand data: For example, for district heating users, the heating load curve for the next 24 hours is predicted based on weather forecasts of future temperature, wind speed, and sunshine conditions, combined with the building's thermal characteristics. For industrial users, the steam or hot water demand for the next few hours or days is predicted based on their received production orders and energy consumption models of their processes.

[0042] Alternatively, for generating the aforementioned temporary scheduling instructions: the central controller can combine current real-time data with future forecast data, taking an optimization period (e.g., the next 24 hours) as its perspective, and divide this period into several consecutive time windows (e.g., 96 15-minute intervals). For each time window, based on the predicted supply and demand situation at that moment, and under the condition of satisfying system safety constraints (e.g., pipeline pressure limits, equipment capacity limitations), a preliminary optimized scheduling scheme (i.e., temporary scheduling instructions) for that moment is solved. These 96 temporary instructions constitute a preliminary scheduling trajectory.

[0043] Optionally, regarding the aforementioned collaborative optimization to generate the final instruction set: the preliminary scheduling trajectory may not have considered time-coupled constraints such as equipment start-up and shutdown losses and power ramp-up rates, leading to abrupt changes in instructions between adjacent time periods. Collaborative optimization involves smoothing and globally optimizing the entire trajectory. For example, by evaluating the thermal storage actions at different time periods through optimization algorithms, and utilizing the thermal storage system to "smooth out peaks and fill valleys," frequent start-ups and shutdowns of heat sources or large power adjustments can be avoided, thereby seeking a smooth scheduling instruction sequence with the lowest total cost or highest energy efficiency throughout the entire scheduling cycle.

[0044] It is evident that implementing this optional embodiment can improve the foresight and predictability of scheduling decisions by introducing forecast data on future supply and demand, thereby facilitating the shift from a passive response to proactive planning in the scheduling mode. By combining current real-time data with future forecast data to generate multi-period temporary instructions, the continuity and completeness of the scheduling scheme in the time dimension can be improved, thus enabling a comprehensive consideration of system behavior throughout the entire scheduling cycle. Furthermore, by collaboratively optimizing multiple temporary instructions, the overall optimality of the final scheduling scheme can be improved, thereby overcoming the potential global inefficiency caused by optimization at a single moment, and ultimately achieving improved system operating economy and energy efficiency over a longer time scale.

[0045] In this optional embodiment, as an optional implementation, the above-mentioned generation of predicted heat energy supply data for each heat source within a specific future scheduling cycle based on historical operating data of each heat source and current weather forecast information includes: For each heat source, the historical operating data of the heat source is classified according to the corresponding historical meteorological conditions to obtain the historical operating condition data of the heat source under different typical meteorological categories. The historical operating condition data is used to represent the output characteristics of the heat source. Extract meteorological category sequences for a specific future scheduling cycle from current weather forecast information; By matching meteorological category sequences with historical operating condition data under different typical meteorological categories, predicted heat energy supply data for the heat source in a specific future scheduling cycle is generated.

[0046] In this embodiment of the invention, optionally, the historical data classification described above can be implemented using a solar thermal power plant as an example. Its historical operating data for the past year (e.g., hourly power generation) is correlated with meteorological data for the same period (e.g., temperature, irradiance). A clustering algorithm (e.g., K-means) is used to divide historical days into several typical categories based on meteorological parameters, such as "sunny and hot days," "cloudy and moderately warm days," and "cold and overcast days." Each category corresponds to a typical daily power generation curve for the power plant.

[0047] Further optional, for the above-mentioned extraction of meteorological category sequences: the weather forecast for the next day can be obtained, and the hourly weather conditions can be mapped to the predefined meteorological categories to form a time series consisting of 24 meteorological category labels (e.g., [Sunny, Sunny, Sunny, ..., Cloudy, Cloudy]).

[0048] Alternatively, for the above-mentioned matching and forecast generation, the meteorological category sequence can be matched with a database of historical typical daily power output curves. For example, if the forecast shows "sunny and hot" tomorrow morning and "partly cloudy and moderately warm" in the afternoon, then the typical power output curves for the morning of "sunny and hot days" and the typical power output curves for the afternoon of "partly cloudy and moderately warm days" in the historical database can be spliced ​​together. If necessary, fine-tuning can be made according to the specific temperature and irradiance values ​​in the forecast to finally generate hourly predicted heat supply data for the next 24 hours.

[0049] It is evident that implementing this optional embodiment can enhance the understanding of the correlation between heat source output characteristics and meteorological factors by classifying historical operational data according to meteorological conditions, thereby facilitating the discovery and utilization of historical operational patterns. It can generate forecasts by matching weather forecast sequences with historical typical operating condition data, improving the practicality and operability of the forecasting method. This allows for obtaining relatively accurate forecast results even in the absence of complex models, thus reducing the technical threshold and computational resource requirements for forecasting. Furthermore, the aforementioned classification and matching methods improve the interpretability and reliability of forecast results, thereby enhancing dispatchers' confidence in automated forecasting and promoting the practical application and widespread adoption of this dispatching method.

[0050] In this optional embodiment, as another optional implementation, the above-mentioned generation of predicted heat load demand data for each heat user within a specific future scheduling cycle based on the historical heat consumption data of each heat user and the current production plan information includes: For each heat energy user, based on the user's historical heat consumption data, the typical heat load curve for the user under different production plan types is determined. The typical heat load curve is used to represent the periodic heat load variation pattern of the heat energy user when executing a specific production plan. Based on the current production plan information, determine the corresponding production plan type for a specific future scheduling cycle; Based on typical heat load curves that match the production plan type, and according to the production scheduling scale in the production plan information, predictive heat load demand data is generated.

[0051] In this embodiment of the invention, optionally, for determining the typical heat load curve mentioned above, a chemical plant (heat energy user) operating on a three-shift system can be used as an example. Analyzing its historical heat consumption data reveals distinctly different heat consumption patterns under different production plans (such as "full-load production plan," "half-load maintenance plan," and "holiday insulation plan"). Through data mining, a representative 24-hour cycle load curve is extracted for each plan type. This curve reflects the basic heat consumption pattern under that production model.

[0052] Alternatively, for the aforementioned determination of production plan type, a detailed production plan for a specific future scheduling period (such as the next 48 hours) can be obtained from the chemical plant's Enterprise Resource Planning (ERP) system or Manufacturing Execution System (MES).

[0053] Alternatively, for the aforementioned generated forecast demand data, the production plan can be matched with a typical load curve. For example, if the plan shows that both the day and night shifts tomorrow will be at "full capacity," then the typical load curve corresponding to the "full capacity production plan" is taken as the basis. Then, based on the production scale specified in the plan (such as tomorrow's planned output being 110% of the standard full capacity), the load values ​​on the base curve are scaled proportionally to finally obtain high-precision forecast heat load demand data.

[0054] As can be seen, implementing this optional embodiment can improve the understanding and characterization of user heat consumption patterns by establishing typical load curves under different production plan types, thereby facilitating the transformation of discrete production plan information into continuous heat load demand forecasts. By scaling typical load curves according to specific production scales, the personalization and refinement of load forecasting can be improved, thus more accurately reflecting changes in actual user demand. Through the above methods, the correlation between load forecasting and production activities can be improved, thereby facilitating deep coupling between energy dispatching and user production plans, and providing strong support for enterprises to achieve lean energy management and energy conservation.

[0055] In this optional embodiment, as another optional implementation, the above-mentioned generation of multiple temporary scheduling instructions corresponding to multiple moments within a specific future scheduling period based on the acquired current-time heat energy supply data, heat storage status data, heat load demand data, all predicted heat energy supply data, and all predicted heat load demand data includes: Based on the network topology and thermodynamic characteristics of the heating pipeline network, a set of system operation constraints is established. This set of system operation constraints is used to limit the feasible range of heat medium flow rate, node pressure, and equipment operating parameters. Using the current thermal storage status data as the initial state, and based on all predicted thermal energy supply data and all predicted thermal load demand data, the system operating status at each moment within a specific future scheduling cycle is solved sequentially within the system operating constraint set. The system operating state obtained at each time step is converted into control instructions for each actuator at that time step, forming multiple temporary scheduling instructions corresponding to each time step.

[0056] In this embodiment of the invention, optionally, the step of establishing the system operation constraint set described above involves digitizing the physical model of the heating system. The constraints may include: Hydraulic balance constraint: The sum of the flow into any node in the pipeline network is equal to the sum of the flow out of that node.

[0057] Pressure loss constraint: The pressure difference between any two points in the pipeline network and the flow rate satisfy a specific hydraulic calculation formula (such as Darcy's formula).

[0058] Equipment operation constraints: upper and lower limits of output of each heat source, upper and lower limits of heat storage capacity of the heat storage system and limits of charging and discharging power, pump speed and head range, etc.

[0059] Thermal balance constraint: Ensure that the heat obtained by the user side is equal to the heat provided by the heat source side minus the pipeline distribution loss.

[0060] Alternatively, for solving the above-mentioned system operating state, this is a mathematical optimization process. Starting from the current thermal storage state, and using the predicted heating and load data for each future time period as boundary conditions, within the "feasible region" formed by the above-mentioned set of constraints, a set of system state variables that optimize a certain objective function (such as system operating cost) is solved. These state variables include: the output value of each heat source, the charging and discharging heat power of the thermal storage system, the flow rate and pressure of each pipe section, and the inlet temperature of each user, etc.

[0061] Alternatively, for the conversion into control commands mentioned above, the solved system state variables can be mapped to specific equipment control parameters. For example, the target flow rate of a certain pipe section can be converted into the target speed command of the corresponding circulating pump based on the pump's performance curve; the target heat release power of the thermal storage system can be converted into the opening command of the heat release valve.

[0062] As can be seen, implementing this optional embodiment can improve the physical feasibility and engineering implementability of scheduling instructions by establishing a system operation constraint set based on the physical characteristics of the pipeline network, thereby helping to avoid generating idealized instructions that cannot be executed in the actual system. By solving for the system operating state at future times within the constraint set, the safety and stability of the scheduling scheme can be improved, thus ensuring that system operating parameters are always within a safe and permissible range, thereby reducing system operating risks and ensuring heating reliability. Furthermore, by converting the system operating state into specific equipment control instructions, the seamless connection between the optimization model and on-site execution can be improved, thereby facilitating the transformation of abstract optimization results into executable actions, and ensuring the accurate realization of scheduling intentions.

[0063] In an optional embodiment, the above-mentioned collaborative optimization of multiple temporary scheduling instructions based on target scheduling operation costs and / or energy efficiency indicators to generate a thermal energy scheduling instruction set includes: The power regulation rate limit for each heat source, the heat charging and discharging power limit for each thermal storage system, and the speed regulation range of pumps in the heating network are determined as equipment operation constraints. The system response hysteresis time caused by the delay in heat medium transmission and the inertia of temperature change in the heating network is determined as the system inertia; Based on equipment operation constraints and system inertia, multiple temporary scheduling instructions are smoothly recombined in the time dimension to generate multiple candidate scheduling trajectories; Based on the target scheduling operation cost and energy efficiency indicators, the target candidate scheduling trajectory is determined from multiple candidate scheduling trajectories, and the instruction sequence contained in the target candidate scheduling trajectory is determined as the thermal energy scheduling instruction set.

[0064] In this embodiment of the invention, optionally, the above-mentioned constraints on equipment operation can be dynamic constraints to ensure the feasibility of the instructions. For example, the power regulation rate limit of a gas boiler may be no more than 5% of the rated power per minute to prevent thermal shock; the charging and discharging power limit of a large hot water storage tank is determined by the diameter of its connecting pipes and the capacity of its heat exchanger; the speed regulation range of a water pump is determined by the performance of its frequency converter.

[0065] Further, alternatively, regarding the aforementioned determination of system inertia: this considers the dynamic characteristics of the system. For example, it may take tens of minutes for the heat medium to flow from the heat source to the furthest user; this is called "transmission delay." Large concrete pipe networks or buildings themselves have thermal inertia, with slow temperature changes; this is called "temperature change inertia." These inertias cause a lag in the system response after a command is issued. This lag time must be taken into account during optimization, either acting earlier or later to achieve precise control.

[0066] Further, optionally, for the smooth reorganization to generate candidate trajectories, equipment operation constraints and system inertia constraints are applied to the initial temporary scheduling instruction sequence. For example, using a model predictive control (MPC) framework, multiple different scheduling trajectories are generated: one is an aggressive trajectory (rapid response to load changes), one is a conservative trajectory (prioritizing equipment stability), and another is an economical trajectory (prioritizing the use of inexpensive heat sources). Each trajectory satisfies all physical constraints and is smooth and feasible.

[0067] Alternatively, for the aforementioned determined target trajectory: calculate the total operating cost (including fuel costs, electricity costs, equipment depreciation, etc.) and comprehensive energy efficiency index of each candidate trajectory over the entire future scheduling cycle. Using multi-objective decision-making methods (such as weighted summation, Pareto front analysis), select the optimal trajectory, and its instruction sequence becomes the final set of scheduling instructions.

[0068] As can be seen, implementing this optional embodiment can improve the smoothness of scheduling commands and respect for the physical characteristics of equipment by considering equipment operating constraints (such as power regulation rate), thereby reducing mechanical shock and electrical stress on the equipment, extending equipment lifespan, and improving system reliability. It can also improve the timeliness and control accuracy of scheduling commands by considering system inertia (such as transmission delay), thereby compensating for dynamic response hysteresis and achieving advanced, precise control, improving heating quality (such as temperature stability). Furthermore, by generating multiple candidate trajectories and screening them based on economic / energy efficiency indicators, it can improve the robustness and scientific nature of the final scheduling scheme, facilitating the selection of the optimal solution from multiple feasible options, and maximizing the overall operational benefits of the system while meeting safety constraints.

[0069] Example 2 Please see Figure 2 , Figure 2 This is a flowchart illustrating another distributed scheduling method for multi-energy storage and release disclosed in an embodiment of the present invention. Figure 2 The described distributed scheduling method for multi-energy storage and release can be applied to multi-energy storage devices, and also to intelligent devices associated with multi-energy storage devices. These intelligent devices include, but are not limited to, one or more of the following: switching devices, cloud devices, edge computing devices, relay devices, base station devices, urban management devices, and intelligent connected devices. This invention does not limit the scope of these applications. Figure 2 As shown, the distributed scheduling method for multi-energy storage and release can include the following operations: 201. Obtain heat energy supply data from multiple heat sources within the region, heat storage status data from at least one heat storage system, and heat load demand data from at least one heat energy user; 202. Based on thermal energy supply data, thermal storage status data, and thermal load demand data, generate a set of thermal energy dispatching instructions, which are used to control the corresponding actuators; The actuators include a first actuator corresponding to the heat source, a second actuator corresponding to the heat storage system, and a third actuator corresponding to the heating network. The first actuator is used to adjust the operating power of the heat source according to the received heat energy dispatching command. The second actuator is used to switch the opening degree of the heat storage system's charging valve and heat release valve according to the received heat energy dispatching command. The third actuator is used to adjust the pump speed and / or valve opening degree in the heating network according to the received heat energy dispatching command to distribute the heat medium flow.

[0070] In this embodiment of the invention, for supplementary explanations of steps 201 and 202, please refer to the supplementary explanations of steps 101 and 102 in Embodiment 1. This embodiment of the invention will not repeat these details.

[0071] 203. After executing the set of thermal energy dispatch instructions, monitor the actual operating data of each heat source, each thermal storage system, and each thermal energy user; 204. Compare the actual operating data with the predicted heat supply data and predicted heat load demand data used when generating the set of heat energy dispatch instructions to obtain the prediction deviation; 205. When the prediction deviation is greater than or equal to the preset deviation threshold, the historical operating condition database and historical heat load database on which the predicted heat energy supply data and predicted heat load demand data are based are updated using actual operating data.

[0072] In this embodiment of the invention, optionally, for the above-mentioned monitoring of actual operating data: after executing the scheduling command, the actual operating parameters of each heat source, thermal storage system and user can continue to be collected through the sensor network, such as actual output, actual thermal storage status and actual heat consumption.

[0073] Alternatively, the prediction bias obtained from the above comparison can be compared with the actual data used when generating the current execution instruction. For example, if a solar thermal source is predicted to output 10MW at noon, but the actual output is only 8MW due to brief cloud cover, the prediction bias here is -2MW. Similarly, the bias of the load forecast can be compared.

[0074] Optionally, for the aforementioned adaptive database update, a deviation threshold can be set (e.g., if the absolute value of the prediction deviation for a major heat source or user exceeds 15% of its predicted value within three consecutive scheduling cycles). When the deviation consistently exceeds the threshold, it indicates that the original prediction model (based on the historical database) can no longer accurately reflect the current system characteristics. In this case, new actual operating data, after filtering and validity verification, is used to supplement or partially replace the original historical operating condition database and historical heat load database. This updates and optimizes the data foundation for the next round of prediction, enabling the prediction model to adapt to the slow changes in system characteristics and achieve continuous improvement.

[0075] As can be seen, implementing this optional embodiment can improve the continuous monitoring capability of the prediction model's accuracy by monitoring actual operating data and comparing it with predicted data, thereby facilitating the timely detection of prediction deviations. By updating the historical database when prediction deviations remain large, the predictive model's adaptability and learning evolution capability can be improved, enabling it to track slow changes in system characteristics (such as equipment aging or changes in user habits), thus maintaining the stability of long-term prediction accuracy. Through the aforementioned closed-loop feedback and adaptive update mechanism, the intelligence level and long-term robustness of the entire scheduling system can be improved, thereby reducing the risk of scheduling performance degradation due to model mismatch, achieving continuous self-optimization of system performance, and reducing long-term maintenance costs.

[0076] Example 3 Please see Figure 3 , Figure 3 This is a schematic diagram of the structure of a distributed scheduling system for multi-energy storage and release disclosed in an embodiment of the present invention. This distributed scheduling system for multi-energy storage and release can be applied to multi-energy storage devices, and also to intelligent devices associated with multi-energy storage devices. These intelligent devices include, but are not limited to, one or more of the following: switching devices, cloud devices, edge computing devices, relay devices, base station devices, urban management devices, and intelligent connected devices. The present invention does not limit the scope of these devices. Figure 3 As shown, the distributed scheduling system for multi-energy storage and release may include: The acquisition module 301 is used to acquire heat energy supply data from multiple heat sources in the area, heat storage status data from at least one heat storage system, and heat load demand data from at least one heat energy user. The generation module 302 is used to generate a set of thermal energy dispatching instructions based on thermal energy supply data, thermal storage status data and thermal load demand data. The set of thermal energy dispatching instructions is used to control the corresponding actuators. The actuators include a first actuator corresponding to the heat source, a second actuator corresponding to the heat storage system, and a third actuator corresponding to the heating network. The first actuator is used to adjust the operating power of the heat source according to the received heat energy dispatching command. The second actuator is used to switch the opening degree of the heat storage system's charging valve and heat release valve according to the received heat energy dispatching command. The third actuator is used to adjust the pump speed and / or valve opening degree in the heating network according to the received heat energy dispatching command to distribute the heat medium flow.

[0077] As can be seen, implementing the embodiments of the present invention can improve the perception of the overall regional thermal energy system's operating status by acquiring real-time operational data from multiple heat sources, thermal storage systems, and the user side, thereby facilitating data-driven, precise scheduling decisions. It can generate a set of scheduling instructions based on real-time supply and demand data and directly control actuators, improving the automation level and response speed of system scheduling. This facilitates rapid balancing of thermal energy supply and demand within the system, reducing reliance on manual intervention and achieving efficient and timely closed-loop control. Furthermore, by separately controlling heat source power, thermal storage system valves, and pipeline pump valves, it enhances the independent control capabilities and collaborative operation possibilities of thermal energy production, storage, and distribution, thereby enabling flexible adjustment of the direction and magnitude of energy flow. This, in turn, improves the flexibility and adaptability of the distributed scheduling system for multi-energy collection, storage, and release to cope with changing operating conditions.

[0078] In this embodiment of the invention, as an optional implementation, the specific method by which the generation module 302 generates a set of thermal energy dispatching instructions based on thermal energy supply data, thermal storage status data, and thermal load demand data includes: Based on the historical operating data of each heat source and the current weather forecast information, predictive heat energy supply data for each heat source in a specific future scheduling cycle is generated. Based on the historical heat consumption data of each heat energy user and the current production plan information, generate the predicted heat load demand data of each heat energy user in a specific future scheduling cycle; Based on the acquired current thermal energy supply data, thermal storage status data, thermal load demand data, all predicted thermal energy supply data, and all predicted thermal load demand data, multiple temporary scheduling instructions are generated, each corresponding to a specific time within a future scheduling cycle. Based on the target scheduling operation cost and / or energy efficiency indicators, multiple temporary scheduling instructions are collaboratively optimized to generate a set of thermal energy scheduling instructions.

[0079] It is evident that implementing this optional embodiment can improve the foresight and predictability of scheduling decisions by introducing forecast data on future supply and demand, thereby facilitating the shift from a passive response to proactive planning in the scheduling mode. By combining current real-time data with future forecast data to generate multi-period temporary instructions, the continuity and completeness of the scheduling scheme in the time dimension can be improved, thus enabling a comprehensive consideration of system behavior throughout the entire scheduling cycle. Furthermore, by collaboratively optimizing multiple temporary instructions, the overall optimality of the final scheduling scheme can be improved, thereby overcoming the potential global inefficiency caused by optimization at a single moment, and ultimately achieving improved system operating economy and energy efficiency over a longer time scale.

[0080] In this optional embodiment, as an optional implementation method, the specific way in which the generation module 302 generates the predicted heat energy supply data for each heat source within a specific future scheduling cycle based on the historical operating data of each heat source and the current weather forecast information includes: For each heat source, the historical operating data of the heat source is classified according to the corresponding historical meteorological conditions to obtain the historical operating condition data of the heat source under different typical meteorological categories. The historical operating condition data is used to represent the output characteristics of the heat source. Extract meteorological category sequences for a specific future scheduling cycle from current weather forecast information; By matching meteorological category sequences with historical operating condition data under different typical meteorological categories, predicted heat energy supply data for the heat source in a specific future scheduling cycle is generated.

[0081] It is evident that implementing this optional embodiment can enhance the understanding of the correlation between heat source output characteristics and meteorological factors by classifying historical operational data according to meteorological conditions, thereby facilitating the discovery and utilization of historical operational patterns. It can generate forecasts by matching weather forecast sequences with historical typical operating condition data, improving the practicality and operability of the forecasting method. This allows for obtaining relatively accurate forecast results even in the absence of complex models, thus reducing the technical threshold and computational resource requirements for forecasting. Furthermore, the aforementioned classification and matching methods improve the interpretability and reliability of forecast results, thereby enhancing dispatchers' confidence in automated forecasting and promoting the practical application and widespread adoption of this dispatching method.

[0082] In this optional embodiment, as another optional implementation, the specific method by which the generation module 302 generates predicted heat load demand data for each heat user within a specific future scheduling period based on the historical heat consumption data of each heat user and the current production plan information includes: For each heat energy user, based on the user's historical heat consumption data, the typical heat load curve for the user under different production plan types is determined. The typical heat load curve is used to represent the periodic heat load variation pattern of the heat energy user when executing a specific production plan. Based on the current production plan information, determine the corresponding production plan type for a specific future scheduling cycle; Based on typical heat load curves that match the production plan type, and according to the production scheduling scale in the production plan information, predictive heat load demand data is generated.

[0083] As can be seen, implementing this optional embodiment can improve the understanding and characterization of user heat consumption patterns by establishing typical load curves under different production plan types, thereby facilitating the transformation of discrete production plan information into continuous heat load demand forecasts. By scaling typical load curves according to specific production scales, the personalization and refinement of load forecasting can be improved, thus more accurately reflecting changes in actual user demand. Through the above methods, the correlation between load forecasting and production activities can be improved, thereby facilitating deep coupling between energy dispatching and user production plans, and providing strong support for enterprises to achieve lean energy management and energy conservation.

[0084] In this optional embodiment, as another optional implementation, the generation module 302 generates multiple temporary scheduling instructions corresponding to multiple moments within a specific future scheduling period based on the acquired current heat energy supply data, heat storage status data, heat load demand data, all predicted heat energy supply data, and all predicted heat load demand data. The specific methods include: Based on the network topology and thermodynamic characteristics of the heating pipeline network, a set of system operation constraints is established. This set of system operation constraints is used to limit the feasible range of heat medium flow rate, node pressure, and equipment operating parameters. Using the current thermal storage status data as the initial state, and based on all predicted thermal energy supply data and all predicted thermal load demand data, the system operating status at each moment within a specific future scheduling cycle is solved sequentially within the system operating constraint set. The system operating state obtained at each time step is converted into control instructions for each actuator at that time step, forming multiple temporary scheduling instructions corresponding to each time step.

[0085] As can be seen, implementing this optional embodiment can improve the physical feasibility and engineering implementability of scheduling instructions by establishing a system operation constraint set based on the physical characteristics of the pipeline network, thereby helping to avoid generating idealized instructions that cannot be executed in the actual system. By solving for the system operating state at future times within the constraint set, the safety and stability of the scheduling scheme can be improved, thus ensuring that system operating parameters are always within a safe and permissible range, thereby reducing system operating risks and ensuring heating reliability. Furthermore, by converting the system operating state into specific equipment control instructions, the seamless connection between the optimization model and on-site execution can be improved, thereby facilitating the transformation of abstract optimization results into executable actions, and ensuring the accurate realization of scheduling intentions.

[0086] In an optional embodiment, the generation module 302, based on the target scheduling operation cost and / or energy efficiency indicators, collaboratively optimizes multiple temporary scheduling instructions to generate a set of thermal energy scheduling instructions. The specific methods for generating this set include: The power regulation rate limit for each heat source, the heat charging and discharging power limit for each thermal storage system, and the speed regulation range of pumps in the heating network are determined as equipment operation constraints. The system response hysteresis time caused by the delay in heat medium transmission and the inertia of temperature change in the heating network is determined as the system inertia; Based on equipment operation constraints and system inertia, multiple temporary scheduling instructions are smoothly recombined in the time dimension to generate multiple candidate scheduling trajectories; Based on the target scheduling operation cost and energy efficiency indicators, the target candidate scheduling trajectory is determined from multiple candidate scheduling trajectories, and the instruction sequence contained in the target candidate scheduling trajectory is determined as the thermal energy scheduling instruction set.

[0087] As can be seen, implementing this optional embodiment can improve the smoothness of scheduling commands and respect for the physical characteristics of equipment by considering equipment operating constraints (such as power regulation rate), thereby reducing mechanical shock and electrical stress on the equipment, extending equipment lifespan, and improving system reliability. It can also improve the timeliness and control accuracy of scheduling commands by considering system inertia (such as transmission delay), thereby compensating for dynamic response hysteresis and achieving advanced, precise control, improving heating quality (such as temperature stability). Furthermore, by generating multiple candidate trajectories and screening them based on economic / energy efficiency indicators, it can improve the robustness and scientific nature of the final scheduling scheme, facilitating the selection of the optimal solution from multiple feasible options, and maximizing the overall operational benefits of the system while meeting safety constraints.

[0088] In another alternative embodiment, such as Figure 4 As shown, the system also includes: The monitoring module 303 is used to monitor the actual operating data of each heat source, each thermal storage system and each thermal energy user after executing the set of thermal energy dispatching instructions; The calculation module 304 is used to compare the actual operating data with the predicted heat supply data and predicted heat load demand data on which the heat energy dispatch instruction set is based, and to obtain the prediction deviation. The generation module 302 is also used to update the historical operating condition database and historical heat load database on which the predicted heat energy supply data and predicted heat load demand data are based using actual operating data when the prediction deviation is greater than or equal to the preset deviation threshold.

[0089] As can be seen, implementing this optional embodiment can improve the continuous monitoring capability of the prediction model's accuracy by monitoring actual operating data and comparing it with predicted data, thereby facilitating the timely detection of prediction deviations. By updating the historical database when prediction deviations remain large, the predictive model's adaptability and learning evolution capability can be improved, enabling it to track slow changes in system characteristics (such as equipment aging or changes in user habits), thus maintaining the stability of long-term prediction accuracy. Through the aforementioned closed-loop feedback and adaptive update mechanism, the intelligence level and long-term robustness of the entire scheduling system can be improved, thereby reducing the risk of scheduling performance degradation due to model mismatch, achieving continuous self-optimization of system performance, and reducing long-term maintenance costs.

[0090] Example 4 Please see Figure 5 , Figure 5 This is a schematic diagram of another distributed scheduling system for multi-energy storage and release disclosed in an embodiment of the present invention. This distributed scheduling system for multi-energy storage and release can be applied to multi-energy storage devices, and also to intelligent devices associated with multi-energy storage devices. These intelligent devices include, but are not limited to, one or more of the following: switching devices, cloud devices, edge computing devices, relay devices, base station devices, urban management devices, and intelligent connected devices. The embodiments of the present invention do not impose limitations on this. Figure 5 As shown, the distributed scheduling system for multi-energy storage and release may include: Memory 401 that stores executable program code.

[0091] Processor 402 coupled to memory 401.

[0092] The processor 402 calls the executable program code stored in the memory 401 to execute the steps in the distributed scheduling method for multi-energy collection and release described in Embodiment 1 or Embodiment 2 of the present invention.

[0093] Example 5 This invention discloses a computer storage medium storing computer instructions. When these computer instructions are invoked, they are used to execute the steps in the distributed scheduling method for multi-energy storage and release described in Embodiment 1 or Embodiment 2 of this invention.

[0094] Example 6 This invention discloses a computer program product, which includes a non-transitory computer storage medium storing a computer program, and the computer program is operable to cause a computer to perform the steps in the distributed scheduling method for multi-energy storage and release described in Embodiment 1 or Embodiment 2.

[0095] The system embodiments described above are merely illustrative. The modules described as separate components may or may not be physically separate. The components shown as modules may or may not be physical modules; that is, they may be located in one place or distributed across multiple network modules. Some or all of the modules can be selected to achieve the purpose of this embodiment according to actual needs. Those skilled in the art can understand and implement this without any creative effort.

[0096] Through the detailed description of the above embodiments, those skilled in the art can clearly understand that each implementation method can be implemented by means of software plus necessary general-purpose hardware platforms, and of course, it can also be implemented by hardware. Based on this understanding, the above technical solutions, in essence or the part that contributes to the prior art, can be embodied in the form of a software product. This computer software product can be stored in a computer-readable storage medium, including read-only memory (ROM), random access memory (RAM), programmable read-only memory (PROM), erasable programmable read-only memory (EPROM), one-time programmable read-only memory (OTPROM), electrically-Erasable Programmable Read-Only Memory (EEPROM), compact disc read-only memory (CD-ROM) or other optical disc storage, disk storage, magnetic tape storage, or any other computer-readable medium that can be used to carry or store data.

[0097] Finally, it should be noted that the distributed scheduling method and system for multi-energy storage and release disclosed in the embodiments of the present invention are merely preferred embodiments of the present invention and are only used to illustrate the technical solutions of the present invention, not to limit it. Although the present invention has been described in detail with reference to the foregoing embodiments, those skilled in the art should understand that modifications can still be made to the technical solutions described in the foregoing embodiments, or equivalent substitutions can be made to some of the technical features. Such modifications or substitutions do not cause the essence of the corresponding technical solutions to deviate from the spirit and scope of the technical solutions of the embodiments of the present invention.

Claims

1. A distributed scheduling method for multi-energy storage and release, characterized in that, The method includes: Acquire heat energy supply data from multiple heat sources within the region, heat storage status data from at least one heat storage system, and heat load demand data from at least one heat energy user; Based on the thermal energy supply data, the thermal storage status data, and the thermal load demand data, a set of thermal energy scheduling instructions is generated, which is used to control the corresponding actuators. The actuator includes a first actuator corresponding to the heat source, a second actuator corresponding to the thermal storage system, and a third actuator corresponding to the heating network. The first actuator is used to adjust the operating power of the heat source according to the received thermal energy dispatching command. The second actuator is used to switch the opening degree of the charging valve and the releasing valve of the thermal storage system according to the received thermal energy dispatching command. The third actuator is used to adjust the speed of the pump and / or the valve opening degree in the heating network according to the received thermal energy dispatching command to distribute the heat medium flow.

2. The distributed scheduling method for multi-energy collection, storage, and release according to claim 1, characterized in that, The process of generating a set of thermal energy dispatch instructions based on the thermal energy supply data, the thermal storage status data, and the thermal load demand data includes: Based on the historical operating data of each heat source and the current weather forecast information, predictive heat energy supply data for each heat source in a specific future scheduling cycle is generated. Based on the historical heat consumption data of each heat energy user and the current production plan information, predictive heat load demand data for each heat energy user in the future specific scheduling cycle is generated. Based on the obtained current heat energy supply data, heat storage status data, heat load demand data, all predicted heat energy supply data, and all predicted heat load demand data, multiple temporary scheduling instructions are generated, each corresponding to a different moment within a specific future scheduling cycle. Based on the target scheduling operation cost and / or energy efficiency indicators, the multiple temporary scheduling instructions are collaboratively optimized to generate a set of thermal energy scheduling instructions.

3. The distributed scheduling method for multi-energy collection, storage, and release according to claim 2, characterized in that, The process of generating predicted heat supply data for each heat source within a specific future scheduling cycle, based on historical operating data and current weather forecast information for each heat source, includes: For each heat source, the historical operating data of the heat source is classified according to the corresponding historical meteorological conditions to obtain the historical operating condition data of the heat source under different typical meteorological categories. The historical operating condition data is used to represent the output characteristics of the heat source. Extract meteorological category sequences for a specific future scheduling cycle from current weather forecast information; The meteorological category sequence is matched with historical operating condition data under different typical meteorological categories to generate predicted heat energy supply data for the heat source in the future specific scheduling cycle.

4. The distributed scheduling method for multi-energy collection, storage, and release according to claim 2, characterized in that, The process of generating predicted heat load demand data for each of the heat energy users within a specific future scheduling period, based on their historical heat consumption data and current production plan information, includes: For each heat energy user, based on the user's historical heat consumption data, a typical heat load curve for the user under different production plan types is determined. The typical heat load curve is used to represent the periodic heat load variation pattern of the user when executing a specific production plan. Based on the current production plan information, determine the production plan type corresponding to the specific future scheduling period; Based on the typical heat load curve that matches the production plan type, and according to the production scheduling scale in the production plan information, the predicted heat load demand data is generated.

5. The distributed scheduling method for multi-energy collection, storage, and release according to claim 2, characterized in that, Based on the acquired current-time thermal energy supply data, thermal storage status data, thermal load demand data, all predicted thermal energy supply data, and all predicted thermal load demand data, multiple temporary scheduling instructions are generated, each corresponding to a different time point within a specific future scheduling period. These instructions include: Based on the network topology and thermodynamic characteristics of the heating pipeline network, a set of system operation constraints is established. This set of system operation constraints is used to limit the feasible range of heat medium flow rate, node pressure, and equipment operating parameters. Using the current thermal storage status data as the initial state, and based on all the predicted thermal energy supply data and all the predicted thermal load demand data, the system operation status at each moment within the future specific scheduling cycle is solved sequentially within the system operation constraint set. The system operating state obtained at each time step is converted into control instructions for each actuator at that time step, forming multiple temporary scheduling instructions corresponding to each time step.

6. The distributed scheduling method for multi-energy collection, storage, and release according to any one of claims 2-5, characterized in that, The step of collaboratively optimizing the multiple temporary scheduling instructions based on the target scheduling operation cost and / or energy efficiency indicators to generate a set of thermal energy scheduling instructions includes: The power regulation rate limit for each heat source, the heat charging and discharging power limit for each heat storage system, and the speed regulation range of the pumps in the heating network are determined as equipment operation constraints. The system response hysteresis time caused by the delay in heat medium transmission and the inertia of temperature change in the heating network is determined as the system inertia; Based on the equipment operation constraints and the system inertia, the multiple temporary scheduling instructions are smoothly recombined in the time dimension to generate multiple candidate scheduling trajectories; Based on the target scheduling operation cost and the energy efficiency index, a target candidate scheduling trajectory is determined from the plurality of candidate scheduling trajectories, and the instruction sequence contained in the target candidate scheduling trajectory is determined as a thermal energy scheduling instruction set.

7. The distributed scheduling method for multi-energy collection, storage, and release according to claim 2, characterized in that, The method further includes: After executing the set of thermal energy dispatch instructions, monitor the actual operating data of each heat source, each thermal storage system and each thermal energy user; The actual operating data is compared with the predicted heat supply data and predicted heat load demand data used when generating the set of heat energy dispatch instructions to obtain the prediction deviation. When the prediction deviation is greater than or equal to a preset deviation threshold, the historical operating condition database and historical heat load database on which the predicted heat energy supply data and the predicted heat load demand data are based are updated using the actual operating data.

8. A distributed scheduling system for multi-energy collection, storage, and release, characterized in that, The system includes: The acquisition module is used to acquire heat energy supply data from multiple heat sources within the area, heat storage status data from at least one heat storage system, and heat load demand data from at least one heat energy user. The generation module is used to generate a set of thermal energy dispatching instructions based on the thermal energy supply data, the thermal storage status data and the thermal load demand data. The set of thermal energy dispatching instructions is used to control the corresponding actuators. The actuator includes a first actuator corresponding to the heat source, a second actuator corresponding to the thermal storage system, and a third actuator corresponding to the heating network. The first actuator is used to adjust the operating power of the heat source according to the received thermal energy dispatching command. The second actuator is used to switch the opening degree of the charging valve and the releasing valve of the thermal storage system according to the received thermal energy dispatching command. The third actuator is used to adjust the speed of the pump and / or the valve opening degree in the heating network according to the received thermal energy dispatching command to distribute the heat medium flow.

9. A distributed scheduling system for multi-energy collection, storage, and release, characterized in that, The system includes: Memory containing executable program code; A processor coupled to the memory; The processor calls the executable program code stored in the memory to execute the distributed scheduling method for multi-energy storage and release as described in any one of claims 1-7.

10. A computer storage medium, characterized in that, The computer storage medium stores computer instructions, which, when invoked, are used to execute the distributed scheduling method for multi-energy storage and release as described in any one of claims 1-7.

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