Smart Microgrid Collaborative Control System Based on Entropy Budget Constraints

Abstract

This invention discloses a collaborative control system for smart microgrids based on entropy budget constraints, relating to the field of thermal collaborative regulation technology for smart microgrids. It includes a physical device execution module, a wide-area perception and computing module, an entropy budget dynamic decomposition module, an entropy production constraint control module, a reinforcement learning AI optimization module, a comfort constraint control module, an entropy element intelligent settlement module, a load prediction correction module, and a data interaction and transmission module. This invention integrates thermodynamics, reinforcement learning, blockchain, and industrial IoT technologies to construct a collaborative system for microgrids with rigid entropy budget control. It accurately calculates entropy production across the entire domain to reduce irreversible losses; AI multi-objective scheduling balances energy efficiency, comfort, and operating costs; hierarchical automatic control combined with the marketization of entropy elements creates long-term low-carbon incentives; and a standardized communication architecture adapts to multiple scenarios. In practical applications, system efficiency is significantly improved, total entropy production is greatly reduced, and user heating costs are lowered, demonstrating both engineering practicality and ecological low-carbon value.

Description

Technical Field

[0001] This invention relates to the field of thermal coordinated regulation technology for smart microgrids, and particularly to a coordinated control system for smart microgrids based on entropy budget constraints. Background Technology

[0002] With the large-scale deployment of distributed photovoltaic, heat pump, and thermal storage equipment, dual-supply micro-energy networks have become an important carrier of new power systems. However, existing management and control technologies have multiple shortcomings. Traditional dispatching only focuses on energy consumption and cost as optimization objectives, without incorporating entropy and thermodynamic indicators. It cannot quantify irreversible losses in heat exchange and transmission processes, resulting in low overall energy utilization quality. Existing load forecasting has large deviations, and the adaptability of continuous operation of multiple heat sources is poor. Conventional optimization algorithms struggle to balance multiple objectives such as entropy constraints, user comfort, and economic benefits.

[0003] Meanwhile, thermal management lacks rigid constraints on hierarchical entropy budgets, has no standardized spatiotemporal quota splitting mechanism, and relies solely on manual inspections to handle excessive entropy production, resulting in lagging management. The energy incentive model is singular, and a market-based system for tradable entropy elements has not been established, so energy-saving benefits cannot be directly passed on to users. In addition, the communication protocols of various devices and modules are heterogeneous and the timing is inconsistent, the manual calculation of thermodynamic parameters has high errors, quota and settlement data can be tampered with, and there is a lack of a reliable evidence storage mechanism, making it difficult to adapt to the demand for large-scale low-carbon heating in multiple scenarios. Summary of the Invention

[0004] The intelligent microgrid collaborative control system based on entropy budget constraints proposed in this invention solves the above-mentioned shortcomings of the prior art.

[0005] To achieve the above objectives, the present invention adopts the following technical solution: The intelligent microgrid collaborative control system based on entropy budget constraints includes a physical device execution module, a wide-area sensing and computing module, an entropy budget dynamic decomposition module, an entropy production constraint control module, a reinforcement learning AI optimization module, a comfort constraint regulation module, an entropy element intelligent settlement module, a load forecasting and correction module, and a data interaction and transmission module. The wide-area sensing and computing module collects thermal parameters from the entire network and calculates enthalpy, entropy, and enthalpy indices in real time.

[0006] The entropy budget dynamic decomposition module generates the annual total entropy budget based on the regional environmental carrying capacity and completes the spatiotemporal hierarchical quota splitting.

[0007] The reinforcement learning AI optimization module aims to maximize global efficiency and uses node entropy budget as a hard constraint to solve for the optimal scheduling action.

[0008] The entropy production constraint control module compares the actual entropy production rate with the time-sharing entropy budget quota in real time and outputs over-limit disposal instructions.

[0009] The entropy element intelligent settlement module relies on blockchain smart contracts to complete the casting, destruction, and market transaction settlement of entropy elements.

[0010] The load prediction correction module outputs multi-duration heat load prediction values ​​and inputs them into the AI ​​optimization module.

[0011] The comfort constraint control module locks the user's indoor temperature operating range;

[0012] The physical equipment execution module receives optimization instructions and adjusts all controllable heat sources and pipeline valves;

[0013] The data interaction and transmission module uses industrial Ethernet and OPCUA protocol to complete bidirectional data interaction between modules.

[0014] The system as a whole is rigidly constrained by the second law of thermodynamics, and simultaneously achieves coordinated operation of heat supply and demand matching, irreversible loss control and entropy element economic incentives.

[0015] Furthermore, the physical device execution module includes a heat pump drive unit, a gas boiler power regulation unit, a thermal storage tank charge and discharge control unit, a pipeline regulating valve differential pressure regulation unit, and a user heat dissipation terminal temperature control unit;

[0016] The heat pump drive unit receives the heat pump output coefficient 0~1 instruction from the reinforcement learning AI optimization module, converts it into a variable frequency drive frequency signal, precisely adjusts the heat pump heating / cooling output power, matches the low entropy scheduling requirements of the entire network, and synchronously feeds back the real-time operating power of the heat pump to the wide-domain perception computing module.

[0017] The gas boiler power regulation unit receives standardized MODBUS power setting values, controls the opening of the gas inlet valve to adjust the combustion power, prioritizes starting when the entropy budget margin is sufficient, automatically reduces the output when the entropy production exceeds the limit, and uploads boiler combustion temperature and gas consumption data in real time.

[0018] The thermal storage tank charging and discharging control unit distinguishes between two fixed operating conditions: charging and discharging. It receives AI output charging and discharging power setpoint commands, controls the start and stop of the circulating pump in the heat exchange circuit and the flow rate, completes thermal storage charging during off-peak hours, and stably discharges heat to reduce peaks during peak hours, thereby reducing the instantaneous entropy production rate of the entire network.

[0019] The differential pressure regulating unit of the pipeline regulating valve receives a 0-10V analog regulating signal to accurately adjust the differential pressure of each primary and secondary network branch valve, match the real-time heat load of each heat exchange station and user side, and eliminate the additional heat transfer entropy production caused by the hydraulic imbalance of the pipeline network.

[0020] The user heat dissipation terminal temperature control unit receives temperature control commands from the comfort constraint control module, adjusts the flow of underfloor heating and radiators, and automatically adjusts heat dissipation output when the indoor temperature exceeds a fixed range, taking into account both heating comfort and low entropy operation goals.

[0021] Furthermore, the wide-area perception computing module includes a distributed intelligent sensor array, an edge computing gateway, a working fluid property storage unit, and a meteorological reference acquisition unit;

[0022] The distributed intelligent sensor array is deployed across heat sources, heat exchange stations, pipeline nodes, and user terminals, continuously and synchronously collecting four types of fixed parameters: medium temperature, medium pressure, medium flow rate, and working fluid composition, and continuously transmitting raw measured data to the edge computing gateway.

[0023] The edge computing gateway receives the raw data from the sensor array, retrieves the water and water vapor properties stored in the working fluid property storage unit, and synchronously calculates the enthalpy, entropy, sludge, instantaneous entropy yield, and global sludge efficiency of the system for each device and each pipe section based on a fixed environmental reference temperature and pressure benchmark, and outputs standardized thermodynamic indicators to all other modules.

[0024] The working fluid property storage unit stores a fixed database of working fluid enthalpy, entropy, and specific heat capacity at different temperatures and pressures. There is no need for manual modification of the channel, which provides a unique standard calculation parameter for the entropy calculation of the edge gateway and eliminates the calculation error caused by the deviation of the property values.

[0025] The meteorological reference acquisition unit connects to the fixed meteorological station in the area and continuously outputs constant environmental reference temperature T0 and environmental reference pressure P0 as the only reference parameters for the entropy calculation formula, ensuring that the entropy and entropy calculation benchmarks of all nodes in the network are consistent.

[0026] The core calculation formula for the wide-area perception computing module is as follows: ; ; in, The global instantaneous total entropy production rate of the microgrid ; This refers to the total number of all heat exchange, heat transmission, and heat generation equipment in the system (heat pumps, boilers, pipe network sections, heat storage tanks, and terminal units). For the first Instantaneous entropy productivity of individual devices; For the working fluid mass flow rate of the equipment ; , The inlet and outlet entropy of the working fluid is determined by the working fluid property library based on actual measurements. , Retrieve from table.

[0027] Furthermore, the entropy budget dynamic decomposition module includes a total budget verification unit, a time dimension splitting unit, a spatial node splitting unit, and a blockchain solidification unit; The total budget verification unit calculates the system's annual total entropy budget in one go, based on the system's annual total heating demand, regional water body temperature rise limits, and atmospheric radiation environmental carrying capacity, combined with the minimum allowable target efficiency fixed formula. This serves as the upper limit for the total annual entropy control. The formula for determining the system's annual total entropy budget (core of the entropy budget dynamic decomposition module) is as follows: ; in, The system's annual total entropy budget (the upper limit of the total allowed entropy production throughout the year) ); Total annual heating / cooling load of the microgrid ; A minimum global efficiency (rigid lower limit, determined by environmental carrying capacity) is set for the region. The coefficient of entropy increase per unit heat corresponding to the regional environmental temperature rise constraint;

[0028] The time dimension splitting unit retrieves historical outdoor temperature data for the whole year, uses 18℃ heating benchmark temperature as a fixed judgment threshold, and adopts a temperature weighting algorithm to split the annual total entropy budget into daily and hourly time-based entropy budget quotas, allocating higher entropy budget quotas during severe cold periods.

[0029] The spatial node splitting unit receives the time-sharing predicted heat load of each node from the load prediction and correction module, and combines it with the fixed target efficiency of each heat exchange station and user node to split the hourly entropy budget into independent entropy budgets for each node at a single time. To achieve spatially tiered quota management;

[0030] The blockchain solidification unit writes all time-sharing and node-sharing entropy budget quotas, which are completed by the dual splitting of time and space, into the blockchain smart contract, generating an immutable distributed ledger. All modules retrieve entropy budget data based solely on the values ​​stored in the contract, thus preventing human tampering with the quotas.

[0031] Furthermore, the entropy production constraint control module includes an entropy production real-time comparison unit, a graded over-limit judgment unit, and a gradient processing output unit;

[0032] The entropy production real-time comparison unit continuously receives the real-time instantaneous entropy production rate output by the wide-area perception computing module, synchronously retrieves the time-sharing budget entropy production rate of the current node in the blockchain smart contract, and completes the calculation of the difference between the two sets of values ​​and the percentage of the difference every hour to generate entropy production deviation data.

[0033] The graded over-limit judgment unit is fixedly divided into four over-limit intervals: actual entropy production exceeds the budget by less than 10%, exceeds by 10% to 30%, exceeds by 30% to 50%, and exceeds by more than 50%. The unit automatically matches a unique over-limit level based on the proportion of entropy production deviation, with no interval overlap and no judgment blanks.

[0034] The gradient processing output unit is bound to four levels of out-of-limit intervals, corresponding to four types of fixed processing instructions, which are issued level by level according to the out-of-limit level:

[0035] The first-level output is the user's app alert push notification command;

[0036] The second-level entropy element intelligent settlement unit issues an entropy element deduction instruction;

[0037] The third level sends a branch flow limit command to the physical device execution module;

[0038] The fourth-level mandatory takeover of user heating circuit management and operation instructions is executed automatically without manual intervention.

[0039] Furthermore, the reinforcement learning AI optimization module is equipped with a SAC flexible actor-critic algorithm unit, a state space input unit, a continuous action output unit, and a multi-component reward calculation unit;

[0040] The SAC flexible actor-critic algorithm unit incorporates a fixed flexible actor-critic reinforcement learning algorithm, which is adapted to the continuous control action space such as heat source output and valve opening. It takes the maximization of long-term cumulative discount reward as the sole optimization objective, and pre-trains offline based on historical data and updates the model parameters online every 15 minutes.

[0041] The state space input unit is fixedly connected to seven types of standardized input data: real-time outdoor temperature, future H-step heat load prediction sequence, previous water supply temperature, time-of-use grid electricity price, normalized residual entropy budget of each node, real-time entropy element market price, and historical continuous efficiency sequence, to fully construct the model input state set;

[0042] After solving, the continuous action output unit outputs six types of continuous control action parameters: heat pump output coefficient, gas boiler output coefficient, thermal storage charging power, thermal storage releasing power, primary network water supply temperature setpoint, and differential pressure setpoint of each branch valve. These parameters are directly converted into hardware adjustment signals and sent to the physical equipment execution module.

[0043] The multi-component reward calculation unit synchronously and in parallel calculates the four fixed reward components and performs a weighted sum to generate the total reward value:

[0044] ①Efficiency Reward: Calculate the relative improvement in global efficiency and use the positive incentive system to improve the quality of energy utilization;

[0045] ② Entropy budget overrun penalty: Apply a second consecutive penalty to entropy production that exceeds the budget value, and impose a large fixed penalty on entropy budget overdraft;

[0046] ③ Room temperature deviation penalty: Quantify the loss caused by the user's indoor temperature deviating from the comfort range, and constrain control actions to avoid disrupting the heating experience;

[0047] ④ Entropy element economic benefit reward: The economic benefits brought by the statistical entropy element surplus incentivize low-entropy scheduling behavior;

[0048] The SAC reinforcement learning total reward function (the core of the reinforcement learning AI optimization module) is declared as follows: ; Itemized definitions: Positive rewards for efficiency: ; Penalty for exceeding entropy production limits: ; Penalty for loss of room temperature comfort: ; Entropy element economic benefit reward: ; in, Total cumulative reward for a single step; , , , The weighting coefficients for the four sub-items (can be adjusted according to residential / industrial / commercial scenarios); For the current global efficiency, The lowest efficiency as a benchmark; The current hourly entropy budget quota for blockchain storage; This is the penalty coefficient for overdrafting entropy production. This refers to the total number of users. For the user's room temperature, Comfort range reference temperature; The real-time entropy unit market trading price; This represents the incremental entropy balance for this period.

[0049] Furthermore, the comfort constraint control module includes a room temperature acquisition unit, a fixed temperature range locking unit, and a room temperature deviation secondary calculation unit;

[0050] The room temperature acquisition unit's user-side indoor temperature measurement device collects the indoor temperature values ​​of all rooms in real time and transmits them continuously to the room temperature deviation secondary calculation unit, covering all heating users in the system with no blind spots.

[0051] The fixed temperature range locking unit permanently locks the indoor allowable temperature range of 20℃~24℃. The upper and lower limits of the range cannot be modified. It serves as the sole criterion for determining whether the heating is comfortable and provides a fixed constraint threshold for the reward function.

[0052] The secondary calculation unit for room temperature deviation iterates through all user room temperature data, calculates the deviation difference when the room temperature is below 20℃ and above 24℃, squares the difference to obtain the total comfort loss across the entire domain, and constantly sends the calculation result to the multi-component reward calculation unit, which incorporates it into the AI ​​optimization constraints.

[0053] Furthermore, the entropy element intelligent settlement module includes a blockchain smart contract unit, an entropy element casting and destruction unit, an entropy element transaction docking unit, and a heat fee linkage accounting unit;

[0054] The blockchain smart contract unit stores the entropy budget of all network nodes and the entropy element account balance data, automatically executes all settlement logic of entropy element casting, destruction, overdraft deduction, and graded penalties, and all settlement records are permanently stored on the chain, and the settlement process is fully automated and closed-loop.

[0055] The entropy element casting and destruction unit receives the entropy production deviation value output by the entropy production real-time comparison unit: when the real-time entropy production rate is lower than the time-sharing budget, an equal amount of entropy elements are cast at a fixed exchange rate and transferred to the corresponding node account; when the real-time entropy production rate is higher than the time-sharing budget, the corresponding number of entropy elements are destroyed from the node account; if the account balance is insufficient, an overdraft penalty is directly triggered.

[0056] The entropy element trading docking unit connects to the community entropy element online trading market, reads the real-time entropy element trading price formed by market supply and demand, and simultaneously pushes the price data to the reinforcement learning AI optimization module as an economic reference indicator for optimization decision-making.

[0057] The heating cost linkage accounting unit summarizes the monthly balance and deficit of each user's entropy element account, deducts or increases the user's monthly heating cost according to a fixed conversion ratio, and generates the final heating cost settlement document, thus opening up the link between thermodynamic constraints and user economic costs.

[0058] Furthermore, the load forecasting and correction module includes a multi-step long load forecasting unit, an MPC model correction unit, and a forecasting data push unit;

[0059] The multi-step load prediction unit relies on historical temperature and heat load time series data to output an hourly multi-node heat load prediction sequence for the next hour. The output step size is fixed and covers the load demand of all heat sources and heat exchange stations.

[0060] The MPC model correction unit continuously receives the real-time measured heat load uploaded by the wide-area perception computing module, compares it with the predicted load value to calculate the prediction deviation, corrects the prediction model parameters minute by minute, reduces the difference between the prediction and the actual load, and ensures the accuracy of the AI ​​optimization input data.

[0061] The prediction data push unit continuously pushes the corrected standardized multi-step long load sequence to the state space input unit of the reinforcement learning AI optimization module as the core input parameter of the model, without interruption or delay in transmission.

[0062] Furthermore, the data interaction and transmission module includes an industrial Ethernet bus unit, an OPCUA protocol parsing unit, and a data timing synchronization unit;

[0063] The industrial Ethernet bus unit establishes a unified hardware communication link between the nine modules, carrying the transmission of all types of information, including raw sensor data, thermodynamic calculation indicators, AI control commands, and blockchain settlement data, ensuring stable data transmission bandwidth.

[0064] The OPCUA protocol parsing unit uniformly converts the heterogeneous data formats of each module, standardizing sensor analog quantities, AI server digital quantities, blockchain text ledgers, and controller adjustment signals into a universal OPCUA data format, thereby achieving cross-module data interoperability and compatibility.

[0065] The data timing synchronization unit uniformly allocates standard timestamps to all network sensors, edge gateways, AI servers, and field controllers, synchronizing the timing of all business processes, including enthalpy and entropy calculation, load forecasting, AI solution, equipment command issuance, and entropy element settlement, thereby eliminating calculation and scheduling deviations caused by time misalignment.

[0066] Compared with existing technologies, the beneficial effects of this invention are:

[0067] 1. This invention constructs a distributed heat sensor array and edge computing architecture, with a built-in standardized working fluid property library. It unifies the environmental reference temperature and pressure to calculate enthalpy, entropy, and instantaneous entropy production rate in real time, eliminating more than 20% of the error in manual calculation. The system uses the second law of thermodynamics as a rigid constraint, and is equipped with spatiotemporal layering of entropy budget and blockchain-based quota solidification to achieve annual, time-based, and node-based total entropy production control. It suppresses ineffective entropy increase throughout the entire link from heat source to end. In actual test scenarios, the global total entropy production is reduced by up to 22.3%, which greatly improves the system efficiency.

[0068] 2. This invention is equipped with the SAC reinforcement learning algorithm adapted to continuous control quantities. The model is updated every 15 minutes. The reward function simultaneously incorporates four components: efficiency reward, entropy budget penalty, room temperature deviation loss, and entropy element economic benefit. Relying on MPC to correct the load forecast and accurately input the state space, it autonomously adjusts the output of heat pumps, heat storage, and pipeline valves. It stores heat during off-peak hours and releases heat during peak hours to absorb photovoltaic green electricity, locking in a basic comfort range of 20-24℃. Differentiated temperature control constraints for different business formats prevent the sacrifice of heating experience in the pursuit of low entropy.

[0069] 3. This invention divides the entropy production excess range into four levels, matching early warning, entropy element deduction, flow restriction, and loop hosting for automated handling without human intervention. It relies on blockchain smart contracts to complete the casting, destruction, and peer-to-peer transactions of entropy elements. The entropy element surplus can be used to offset heat fees. High-load entities can purchase entropy elements from low-load nodes, transforming thermodynamic losses into market-based economic rewards and penalties. Quota, transaction, and settlement data are on the chain and cannot be tampered with, facilitating operation and maintenance, ecological supervision, and multi-party traceability, and forcing users to actively save energy.

[0070] 4. This invention adopts the Industrial Ethernet + OPCUA protocol to unify the data format of heterogeneous devices, and the timing synchronization unit unifies the millisecond-level timestamps of the entire network, solving the problems of data misalignment and scheduling inaccuracy in traditional systems. The modular architecture is adapted to four types of scenarios: residential communities, industrial parks, high-altitude photovoltaic heating, and commercial complexes. Hardware units can be selected as needed, and there are dedicated adaptation logics for special working conditions such as low temperature, high altitude, and industrial constant temperature. The equipment is compatible with multiple heat sources such as heat pumps, gas boilers, phase change thermal storage, and municipal heating network peak shaving, and has strong scalability.

[0071] In summary, this system integrates thermodynamics, reinforcement learning, blockchain, and industrial IoT technologies to construct a micro-energy network collaborative system with rigid entropy budget control. It accurately calculates entropy production across the entire domain to reduce irreversible losses, uses AI multi-objective scheduling to balance energy efficiency, comfort, and operating costs, and employs hierarchical automatic control combined with the marketization of entropy elements to create long-term low-carbon incentives. Its standardized communication architecture adapts to multiple scenarios. In practical applications, the system efficiency is significantly improved, total entropy production is greatly reduced, and user heating costs are lowered, demonstrating both engineering practicality and ecological low-carbon value. Attached Figure Description

[0072] Figure 1 This is a flowchart of the overall business process of the entire system modules of the intelligent micro-energy grid collaborative control system based on entropy budget constraints proposed in this invention;

[0073] Figure 2 This is a block diagram of the internal unit of the wide-area sensing and computing module of the smart micro-energy grid collaborative control system based on entropy budget constraints proposed in this invention.

[0074] Figure 3This is a block diagram of the internal unit of the entropy production constraint control module of the smart micro-energy grid collaborative control system based on entropy budget constraint proposed in this invention.

[0075] Figure 4 This is a block diagram of the internal unit of the entropy budget dynamic decomposition module of the smart micro-energy grid collaborative control system based on entropy budget constraints proposed in this invention.

[0076] Figure 5 This is a block diagram of the reinforcement learning AI optimization module and the comfort constraint control module of the smart micro-energy grid collaborative control system based on entropy budget constraints proposed in this invention.

[0077] Figure 6 This is a block diagram of the internal unit of the data interaction transmission module of the smart micro-energy grid collaborative control system based on entropy budget constraints proposed in this invention.

[0078] Figure 7 This is a block diagram of the internal unit of the load prediction and correction module of the smart micro-energy grid collaborative control system based on entropy budget constraints proposed in this invention.

[0079] Figure 8 This is a block diagram of the internal unit of the physical device execution module of the smart micro-energy grid collaborative control system based on entropy budget constraints proposed in this invention. Detailed Implementation

[0080] The technical solutions of the present invention will be clearly and completely described below with reference to the accompanying drawings in the embodiments of the present invention. Obviously, the described embodiments are only some embodiments of the present invention, and not all embodiments.

[0081] In the description of this invention, it should be understood that the terms "upper", "lower", "front", "rear", "left", "right", "top", "bottom", "inner", "outer", etc., indicate the orientation or positional relationship based on the orientation or positional relationship shown in the accompanying drawings. They are only for the convenience of describing this invention and simplifying the description, and do not indicate or imply that the device or element referred to must have a specific orientation, or be constructed and operated in a specific orientation. Therefore, they should not be construed as limitations on this invention.

[0082] Example 1

[0083] Reference Figure 1 - Figure 8 Micro-energy network for large residential communities in frigid northern regions (heat pump + gas boiler + thermal storage tank):

[0084] This embodiment is implemented in a large resettlement community in a northern urban area of ​​North China, with a total heating area of ​​260,000 square meters, covering 3,200 residential households, and a heating season of 120 days. The heat source configuration includes 16 low-temperature air source heat pumps of 120 horsepower, 2 gas-fired peak-shaving boilers of 4.2MW, and a 3,000m³ atmospheric pressure heat storage tank. It is equipped with primary / secondary heating pipe networks and individual underfloor heating temperature control terminals, and fully deploys the entropy budget constraint collaborative control system of this invention.

[0085] Deployment of the wide-area perception computing module:

[0086] Distributed intelligent sensor arrays are installed at heat pump units, boiler outlets, inlet and outlet of heat storage tanks, all heat exchange stations, building risers, and underfloor heating terminals in each household to collect media temperature, pressure, flow rate, and water composition in real time.

[0087] The edge computing gateway has a built-in standard water / water vapor property library and connects to the local weather station to obtain the annual benchmark ambient temperature and pressure T0 and P0. It calculates enthalpy, entropy, instantaneous entropy yield, and global efficiency every minute for each pipe section and heat source. The data is synchronously pushed to the other eight modules through industrial Ethernet + OPCUA protocol, eliminating more than 20% of the measurement deviation caused by traditional manual temperature measurement and manual thermodynamic calculation.

[0088] Entropy budget dynamic decomposition and blockchain solidification:

[0089] The entropy budget dynamic decomposition module calculates the annual total entropy budget based on the community's annual heat load and the regional atmospheric temperature rise limit. It also divides the time based on the heating benchmark threshold of 18℃, allocating 62% of the annual entropy quota to the severe cold months of January and February, and reducing the quota during the spring and autumn transition months.

[0090] The hourly entropy budget for each node is then broken down into time-of-day load forecasts for the 32 building heat exchange stations. All spatiotemporal quotas are written into a blockchain smart contract, which is tamper-proof. Community operations and maintenance, property management, and heating companies can only read the quotas and have no modification rights, thus preventing the artificial relaxation of entropy control indicators.

[0091] SACAI Reinforcement Learning Optimizes Scheduling Logic:

[0092] The reinforcement learning AI optimization module updates the model every 15 minutes. The status inputs include outdoor temperature, building-by-building heat load forecast for the next hour, remaining entropy budget, real-time entropy element trading price, and historical efficiency.

[0093] The output of the heat pump is 0~1, the power of the gas boiler, the power of the heat storage charging and releasing, the pipeline pressure difference, and the water supply temperature are six types of continuous control quantities. The multi-component rewards are calculated simultaneously: positive rewards for improving efficiency, secondary penalties for exceeding the budget for entropy production, penalty for losses when the room temperature deviates from the comfort range of 20-24℃, and incentives for economic benefits generated by the entropy element surplus.

[0094] During the off-peak electricity hours from 11 PM to 7 AM the next day, the AI ​​automatically controls the heat pump to run at full load, and the thermal storage tank is charged to maximize the consumption of photovoltaic green electricity. This results in low entropy production and an increased entropy balance. During peak electricity prices and when outdoor low-temperature loads rise, the thermal storage is released first. The gas boiler is only started slightly when the thermal storage capacity is insufficient to avoid high entropy production operation of the boiler.

[0095] Implementation of hierarchical management and tiered treatment of entropy production:

[0096] The entropy production constraint control module compares the real-time entropy production of each building with the time-sharing budget of the blockchain every hour.

[0097] Level 1 Over-limit (within 10%): The residential heating APP pushes an energy-saving reminder, prompting you to reduce the underfloor heating flow;

[0098] Level 2 over-limit (10%~30%): Automatically deduct the balance of the corresponding entropy element account for the building, and the heating fee will increase in the following month;

[0099] Level 3 over-limit (30%~50%): The differential pressure regulating unit of the pipeline regulating valve automatically reduces the water supply flow of the building branch;

[0100] Level 4 Over-limit (50% or more): The system will forcibly take over the heating circuit of the building, and the AI ​​will take over the terminal temperature control, locking the room temperature limit at 24℃. The valve cannot be manually opened.

[0101] Comfort constraints and entropy calculation closed loop:

[0102] The comfort constraint control module deploys an indoor temperature measurement device in each household, permanently locking the comfort range of 20–24℃. The squared loss of the room temperature deviation across the entire area is fed into the AI ​​reward function in real time, preventing the sacrifice of residents' heating experience in the pursuit of low entropy.

[0103] The Entropy Element Smart Settlement Module relies on blockchain smart contracts to automatically mint / destroy entropy elements: if the actual entropy production of a building is lower than the budget, additional entropy elements are issued; if it exceeds the budget, they are destroyed. Overdrafts in accounts are subject to additional penalties. Monthly entropy element surpluses are directly deducted from residents' heating fees. This heating season, the pilot buildings saved an average of 560 yuan per household in heating fees. The problem of uneven heating in the pipeline network has been completely eliminated, the overall system efficiency has increased by 14.7%, and the total entropy production has decreased by 18.2% year-on-year.

[0104] Hardware execution and data synchronization guarantees:

[0105] The physical equipment execution module uniformly receives AI optimization instructions: heat pump frequency conversion adjustment, gas boiler MODBUS power control, thermal storage circulation pump flow adjustment, pipeline network 0-10V differential pressure adjustment, and individual household underfloor heating linkage temperature control.

[0106] The data interaction and transmission module unifies the timestamps of all sensors, gateways, AI servers, and controllers across the network, solving the problems of load mismatch and entropy production measurement distortion caused by the misalignment of traditional scheduling timing.

[0107] Example 2

[0108] Industrial park dual-supply micro-energy network (ground source heat pump + gas-fired combined heat and power + cold and heat storage):

[0109] This embodiment is implemented in the Precision Electronics Industrial Park of the Eastern Industrial New City, with a total construction area of ​​112,000 square meters. It includes constant temperature production workshops, office buildings, and warehouses, providing dual heating and cooling throughout the year: heating in winter and cooling in summer. The heat source / cold source configuration includes a ground source heat pump system with buried pipes, one gas-fired tri-generation unit, and a 1200m³ heat storage tank + an 800m³ cold storage tank. The deployment of the system of this invention enables rigid control of entropy budget throughout the year and is adapted to the high-precision constant temperature requirements of industrial applications.

[0110] Design adapted to address pain points in specific scenarios:

[0111] Traditional industrial parks have separate control of heating and cooling, and independent scheduling of heat sources. In winter, boilers generate high entropy, and in summer, electric cooling results in huge irreversible losses. The workshop temperature fluctuates by ±3℃, resulting in high energy costs and no means of entropy control. This embodiment uses the second law of thermodynamics as a constraint to incorporate the entropy production of the entire heating and cooling process into a unified entropy budget management, while simultaneously taking into account the rigid comfort constraint of constant temperature production of 22±1℃ in the workshop.

[0112] Entropy budget hierarchical decomposition scheme:

[0113] The entropy budget dynamic decomposition module distinguishes between the heating season and the cooling season to determine the annual total entropy budget separately; the time dimension is broken down into weekdays, nighttime shutdown periods, and holidays: high entropy quotas are allocated during the daytime when production load is high, and the entropy budget is significantly reduced during nighttime shutdowns; the space is allocated differently according to three types of nodes: precision workshops, office buildings, and warehouses; precision workshops retain a higher entropy budget upper limit due to constant temperature requirements, and strict entropy production red lines are set for warehouses with large load fluctuations; all quotas are fixed on the blockchain.

[0114] SAC Reinforcement Learning Multi-Objective Optimization Strategy:

[0115] The reinforcement learning AI optimization module is adapted to the continuous operation space of dual heating and cooling supply. It is pre-trained offline using three years of historical heating and cooling load data of the park, and the parameters are updated online every 15 minutes.

[0116] Winter operating conditions: Ground source heat pumps take priority to handle the basic heat load, waste heat from combined heat and power (CHP) is used for supplementary heating, heat is stored during off-peak hours, and heat is released during peak hours. Only in extreme low temperatures is the standby gas boiler started.

[0117] Summer operating conditions: Ground source heat pump cooling, combined cooling, heating, and power supply waste heat to drive lithium bromide units, nighttime cold storage to reduce high entropy production during daytime cooling;

[0118] The reward function adds an additional penalty for industrial temperature deviation. If the workshop temperature deviates from the range of 21–23℃, the total reward is significantly reduced. This forces the AI ​​to prioritize ensuring constant production temperature, and then optimizes global efficiency and controls entropy production.

[0119] Entropy overproduction industrial-grade disposal mechanism:

[0120] The entropy production constraint control module upgrades the gradient processing logic for industrial scenarios:

[0121] Level 1 Over-limit: The park's energy management platform will issue a pop-up warning and push notifications to remind users of equipment maintenance and inspection.

[0122] Level 2 exceeding limits: Deductions from the entropy accounts of enterprises in the park, and inclusion in the park's low-carbon assessment;

[0123] Level 3 over-limit: Limit the upper limit of combined heat and power supply and boiler output, and reduce the flow rate of cold and hot media in branch circuits;

[0124] Level 4 Over-limit: Automatically switch to low-entropy pure heat pump operation mode, shut down high-entropy gas equipment, and ensure that the total entropy budget of the park does not exceed the standard.

[0125] Blockchain entropy element market-based trading implementation:

[0126] The park has built an internal entropy trading market. The entropy intelligent settlement module pushes the entropy price to the AI ​​optimization unit in real time. The entropy production surplus of low-load storage nodes can be sold to high-load precision workshops. The blockchain records all transaction flows and cannot be tampered with.

[0127] The monthly accounting entropy element income and expenditure was used to offset the enterprise's comprehensive energy service fee. After 6 months of pilot operation, the park's comprehensive irreversible entropy production decreased by 22.3%, the comprehensive energy consumption for cooling and heating decreased by 16.5%, and the workshop constant temperature compliance rate reached 100%.

[0128] Industrial adaptation of load forecasting and correction module:

[0129] The load forecasting and correction module is connected to the workshop production schedule and equipment start-up and shutdown sequence. The MPC model corrects the forecast deviation every minute based on the real-time cold and hot load of the workshop, and outputs the cold and hot load sequence of the next hour by region, which is sent to the AI ​​optimization module to solve the scheduling lag problem caused by sudden fluctuations in industrial production load.

[0130] Example 3

[0131] Plateau County Clean Heating Micro-Energy Network (Photovoltaic + Air Source Heat Pump + Large Thermal Storage Tank):

[0132] This embodiment is implemented in the heating project in Haixi Plateau County, Qinghai Province, at an altitude of 3200m, with an average annual temperature of 2.4℃ and a heating cycle of up to 7 months. The heating covers urban residents and government buildings, with a total heating area of ​​690,000 square meters. It is equipped with a local 25MW distributed photovoltaic power station, and the heat source configuration includes 32 low-temperature air source heat pumps, a 7MW gas peak-shaving boiler, and a 6000m³ large thermal storage tank. It fully relies on the entropy budget constraint collaborative control system of this invention to achieve low-carbon scheduling of wind, solar and thermal coupling.

[0133] Sensing and Entropy Calculation Optimization for Special Working Conditions on Plateaus:

[0134] The wide-area sensing computing module's meteorological benchmark acquisition unit is adapted to the high-altitude low-pressure environment, fixing the local annual average benchmark temperature and pressure T0 and P0; the distributed temperature sensor uses low-temperature resistant and high-altitude resistant equipment, covering the entire area with photovoltaic-supported heat pumps, thermal storage tanks, and urban heat exchange stations; the edge computing gateway has a separate built-in high-altitude low-pressure working fluid property database, accurately calculating the working fluid's enthalpy, entropy, and temperature index at high altitudes, eliminating the measurement errors caused by the standard physical property parameters of the plains.

[0135] Annual total entropy budget verification logic adapts to the environment's carrying capacity:

[0136] The entropy budget dynamic decomposition module combines the plateau ecological environment temperature rise limit and photovoltaic green electricity consumption index to calculate the annual total entropy budget; the time division strictly distinguishes between the extremely cold period of December to March and the mild period of April to November, with 70% of the annual entropy quota allocated to the extremely cold period;

[0137] Spatial division is differentiated by high-density residential areas in urban centers and low-density buildings in suburbs. Suburban buildings have greater heat loss, so a stricter time-sharing entropy production cap is set. All spatiotemporal quotas are written into a blockchain distributed ledger, and county-level ecological supervision departments can trace the use of entropy quotas in real time.

[0138] Photovoltaic-heat pump collaborative low-entropy AI scheduling strategy:

[0139] The reinforcement learning AI optimization module takes real-time photovoltaic output as the core state input. During the day, when photovoltaic power generation is high and electricity prices are low, AI controls the heat pump to operate at full load and the thermal storage tank to charge at full power, maximizing the use of zero-entropy green electricity and accumulating a large amount of entropy surplus. At night, when photovoltaic power generation is low and grid electricity prices rise, the heat from the thermal storage tank is released. Only during continuous extremely cold weather and when the thermal storage capacity is exhausted, the gas boiler is started in a limited manner to reduce the high entropy production generated by the combustion of fossil fuels.

[0140] A positive reward for photovoltaic power consumption has been added to the multi-component reward system to incentivize the system to prioritize the consumption of local photovoltaic power, while simultaneously constraining the total entropy generation of the entire region to not exceed the annual budget.

[0141] Comfort management and terminal control for residents in high-altitude areas:

[0142] The comfort constraint control module uniformly locks the indoor temperature range of 20–24℃ for residents. Since the thermal insulation performance of buildings in high-altitude areas is relatively weak, the penalty weight for room temperature deviation is higher than that in communities in plains. The user's heat dissipation terminal temperature control unit is linked to underfloor heating and radiators. When the outdoor temperature is extremely cold, the upper limit of flow regulation is appropriately relaxed to avoid the room temperature from falling below 20℃, taking into account both ecological entropy control and the heating needs of residents in high-altitude areas.

[0143] Cross-entity entropy settlement and ecological supervision linkage:

[0144] Entropy Element's intelligent settlement module realizes a closed-loop settlement among three parties: heating operation companies, residential users, and county-level ecological supervision platforms;

[0145] If the system's real-time entropy production is lower than the budget, entropy elements are created; if the entropy element is consumed beyond the budget, it is destroyed. Residents can use the entropy elements saved from heat consumption to offset heating costs. Heating companies can use their annual entropy element surplus as a low-carbon assessment certificate for the county and store it on the blockchain. If the total annual entropy production exceeds the total budget, the blockchain smart contract will automatically trigger a high ecological penalty, forcing the system to operate at a low entropy level for a long period of time.

[0146] Unified time-series industrial communication architecture across the entire domain:

[0147] The data interaction and transmission module adopts a redundant industrial Ethernet bus, which is adapted to high-altitude and low-temperature communication conditions. The OPCUA protocol unifies the data format of photovoltaic inverters, heat pump controllers, blockchain nodes, and AI scheduling servers. The timing synchronization unit unifies the millisecond-level timestamps of the entire system, ensuring that the timing of photovoltaic power generation acquisition, entropy generation calculation, and AI scheduling command issuance is completely synchronized, thus solving the scheduling inaccuracy problem caused by the data transmission delay of long-distance pipelines in high-altitude areas.

[0148] Example 6

[0149] Micro-energy network for combined cooling and heating in cultural tourism and commercial complexes (air source heat pump + municipal heating network peak shaving):

[0150] This embodiment is implemented in an urban waterfront cultural and tourism complex, including hotel rooms, commercial shops, and a convention center, with a total heating and cooling area of ​​40,000 square meters. It provides both heating and cooling throughout the year. The basic heat source is a low-temperature air source heat pump, and the municipal heating network is used as a supplement for peak shaving during extreme weather. It is equipped with an 800m³ small phase change heat storage tank and deploys the entropy budget collaborative control system of this invention, which is adapted to the characteristics of large diurnal fluctuations in commercial load and strong temporal characteristics of pedestrian flow.

[0151] Load forecasting dynamic correction to adapt to commercial pedestrian flow:

[0152] The load forecasting and correction module connects to scenic area visitor reservation data, holiday time sequence, and shop business hours. The MPC model corrects the cold and hot load forecast values ​​every 5 minutes, distinguishing between daytime visitor peaks and nighttime store closure low periods and outputting multi-step forecast sequences.

[0153] During the daytime, exhibitions and hotels are allocated a high time-sharing entropy budget at full capacity, while the entropy quota is significantly reduced during the closing hours in the early morning, so as to achieve dynamic matching of entropy budget with the flow of people.

[0154] SAC reinforcement learning time-sharing differentiated scheduling:

[0155] During peak passenger flow from 10:00 to 22:00 during the day, AI-prioritized heat pumps provide heating / cooling, and the thermal storage tank releases stored energy to reduce instantaneous entropy production. During off-peak passenger flow from 23:00 to 7:00 the next day, electricity prices are low, and the heat pumps operate at full capacity to charge the thermal storage tanks, accumulating entropy.

[0156] In extreme cold or heat waves, when the heat pump output is insufficient, a small connection to the municipal heating network is made. The system calculates the high entropy loss of the municipal heat source in real time and automatically limits the connection time to the municipal heating network to prevent entropy from exceeding the limit rapidly.

[0157] Entropy generation management and disposal based on business type and level:

[0158] The entropy production constraint control module sets independent entropy production judgment thresholds for hotel rooms, shops, and convention centers: the first-level over-limit standard is relaxed for short-term high loads in convention centers; continuous high entropy production in hotel rooms directly triggers the second-level entropy reduction; if the heat dissipation of shops is not turned off when no one is at night and the entropy production exceeds the standard, the third-level handling automatically reduces the end flow, taking into account both the flexibility of commercial operation and the total entropy constraint.

[0159] Commercial user entropy market incentive mechanism:

[0160] The Entropy Element Smart Settlement Module allows merchants and hotel rooms to independently open blockchain Entropy Element accounts. Merchants can earn Entropy Element rewards by proactively reducing heating and cooling during non-business hours and reducing entropy production. These rewards can be used to offset property fees and energy costs. Small-amount Entropy Element transactions are available within the complex. High-load exhibition merchants can purchase Entropy Element from low-load idle shops to reduce the overall cost of exceeding limits and achieve incentives for commercial entities to save energy autonomously.

[0161] Comfort and constraints differentiated by business type:

[0162] The comfort constraint control module sets temperature benchmarks for different business types: hotel rooms are fixed at 20–24℃, commercial shops at 21–25℃, and convention centers at 22–26℃. Deviation losses in different ranges are calculated independently and fed into the AI ​​reward function. Under the hard constraint of a unified entropy budget, it matches the differentiated comfort needs of cultural tourism and commerce.

[0163] The above description is only a preferred embodiment of the present invention, but the scope of protection of the present invention is not limited thereto. Any equivalent substitutions or modifications made by those skilled in the art within the scope of the technology disclosed in the present invention, based on the technical solution and inventive concept of the present invention, should be covered within the scope of protection of the present invention.

Claims

1. A smart microgrid collaborative control system based on entropy budget constraints, comprising a physical device execution module, a wide-area sensing and computing module, an entropy budget dynamic decomposition module, an entropy production constraint control module, a reinforcement learning AI optimization module, a comfort constraint regulation module, an entropy element intelligent settlement module, a load forecasting and correction module, and a data interaction and transmission module, characterized in that: The wide-area sensing and computing module collects thermal parameters from the entire network and calculates enthalpy, entropy, and enthalpy indices in real time. The entropy budget dynamic decomposition module generates the annual total entropy budget based on the regional environmental carrying capacity and completes the spatiotemporal hierarchical quota splitting. The reinforcement learning AI optimization module aims to maximize global efficiency and uses node entropy budget as a hard constraint to solve for the optimal scheduling action. The entropy production constraint control module compares the actual entropy production rate with the time-sharing entropy budget quota in real time and outputs over-limit disposal instructions. The entropy element intelligent settlement module relies on blockchain smart contracts to complete the casting, destruction, and market transaction settlement of entropy elements. The load prediction correction module outputs multi-duration heat load prediction values ​​and inputs them into the AI ​​optimization module. The comfort constraint control module locks the user's indoor temperature operating range; The physical equipment execution module receives optimization instructions and adjusts all controllable heat sources and pipeline valves; The data interaction and transmission module uses industrial Ethernet and OPCUA protocol to complete bidirectional data interaction between modules. The system as a whole is rigidly constrained by the second law of thermodynamics, and simultaneously achieves coordinated operation of heat supply and demand matching, irreversible loss control and entropy element economic incentives.

2. The intelligent microgrid collaborative control system based on entropy budget constraints according to claim 1, characterized in that, The physical device execution module includes a heat pump drive unit, a gas boiler power regulation unit, a thermal storage tank charge and discharge control unit, a pipeline regulating valve differential pressure regulation unit, and a user heat dissipation terminal temperature control unit. The heat pump drive unit receives the heat pump output coefficient 0~1 instruction from the reinforcement learning AI optimization module, converts it into a variable frequency drive frequency signal, precisely adjusts the heat pump heating / cooling output power, matches the low entropy scheduling requirements of the entire network, and synchronously feeds back the real-time operating power of the heat pump to the wide-domain perception computing module. The gas boiler power regulation unit receives standardized MODBUS power setting values, controls the opening of the gas inlet valve to adjust the combustion power, prioritizes starting when the entropy budget margin is sufficient, automatically reduces the output when the entropy production exceeds the limit, and uploads boiler combustion temperature and gas consumption data in real time. The heat storage tank charging and discharging control unit distinguishes between two fixed operating conditions: charging and discharging. It receives AI output charging and discharging power setpoint commands and controls the start / stop and flow rate of the heat exchange circuit circulation pump. The differential pressure regulating unit of the pipeline regulating valve receives a 0-10V analog regulating signal to accurately adjust the differential pressure of each primary and secondary network branch valve, match the real-time heat load of each heat exchange station and user side, and eliminate the additional heat transfer entropy production caused by the hydraulic imbalance of the pipeline network. The user heat dissipation terminal temperature control unit receives temperature control commands from the comfort constraint control module, adjusts the flow of underfloor heating and radiators, and automatically adjusts heat dissipation output when the indoor temperature exceeds a fixed range, taking into account both heating comfort and low entropy operation goals.

3. The intelligent microgrid collaborative control system based on entropy budget constraints according to claim 1, characterized in that, The wide-area sensing computing module includes a distributed intelligent sensor array, an edge computing gateway, a working fluid property storage unit, and a meteorological reference acquisition unit; The distributed intelligent sensor array is deployed across heat sources, heat exchange stations, pipeline nodes, and user terminals, continuously and synchronously collecting four types of fixed parameters: medium temperature, medium pressure, medium flow rate, and working fluid composition, and continuously transmitting raw measured data to the edge computing gateway. The edge computing gateway receives the raw data from the sensor array, retrieves the water and water vapor properties stored in the working fluid property storage unit, and synchronously calculates the enthalpy, entropy, sludge, instantaneous entropy yield, and global sludge efficiency of the system for each device and each pipe section based on a fixed environmental reference temperature and pressure benchmark, and outputs standardized thermodynamic indicators to all other modules. The working fluid property storage unit stores a fixed database of working fluid enthalpy, entropy, and specific heat capacity at different temperatures and pressures. There is no need for manual modification of the channel, which provides a unique standard calculation parameter for the entropy calculation of the edge gateway and eliminates the calculation error caused by the deviation of the property values. The meteorological reference acquisition unit is connected to a fixed meteorological station in the area and continuously outputs a constant environmental reference temperature T0 and an environmental reference pressure P0.

4. The intelligent microgrid collaborative control system based on entropy budget constraints according to claim 1, characterized in that, The entropy budget dynamic decomposition module includes a total budget verification unit, a time dimension decomposition unit, a spatial node decomposition unit, and a blockchain solidification unit. The total budget approval unit calculates the annual total entropy budget based on the regional heat load and the environmental temperature rise limit; The time dimension splitting unit completes the daily and hourly scale entropy quota allocation based on the heating reference temperature. The spatial node splitting unit combines the predicted heat load and target efficiency of each node to allocate the time-sharing entropy budget of a single node. The blockchain solidification unit will be completely split and the quota will be written into a smart contract and locked so that it cannot be tampered with.

5. The intelligent microgrid collaborative control system based on entropy budget constraints according to claim 1, characterized in that, The entropy production constraint control module includes an entropy production real-time comparison unit, a graded over-limit judgment unit, and a gradient processing output unit. The real-time entropy production comparison unit matches the instantaneous actual entropy production rate with the time-sharing budgeted entropy production rate hourly. The graded over-limit judgment unit divides the over-limit range into four levels: within 10%, 10%-30%, 30%-50%, and above 50%. The gradient processing output unit outputs four types of rigid processing commands in sequence according to the interval: early warning push, entropy reduction, flow restriction, and heating circuit management.

6. The intelligent microgrid collaborative control system based on entropy budget constraints according to claim 1, characterized in that, The reinforcement learning AI optimization module is equipped with a SAC flexible actor-critic algorithm unit, a state space input unit, a continuous action output unit, and a multi-component reward calculation unit. The SAC flexible actor-critic algorithm unit incorporates a fixed flexible actor-critic reinforcement learning algorithm, which is adapted to the continuous control action space such as heat source output and valve opening. It takes the maximization of long-term cumulative discount reward as the sole optimization objective, and pre-trains offline based on historical data and updates the model parameters online every 15 minutes. The state space input unit receives outdoor temperature, load forecast sequence, residual entropy budget, entropy element price, and historical efficiency data. The continuous action output unit outputs all control parameters, including heat source output, heat storage charging and discharging power, water supply temperature, and valve pressure difference. The multi-component reward calculation unit synchronously calculates the efficiency improvement reward, entropy budget over-limit penalty, room temperature deviation penalty, and entropy element economic benefit reward, and outputs the rolling optimization control sequence with the maximum cumulative reward.

7. The intelligent microgrid collaborative control system based on entropy budget constraints according to claim 1, characterized in that, The comfort constraint control module includes a room temperature acquisition unit, a fixed temperature range locking unit, and a room temperature deviation secondary calculation unit. The fixed temperature range locking unit is constantly set to an indoor allowable temperature range of 20°C to 24°C. The room temperature acquisition unit collects the indoor temperature of all users in real time. The secondary calculation unit for room temperature deviation inputs the global room temperature deviation into the reinforcement learning AI optimization module and incorporates it into the reward function to form a rigid comfort constraint.

8. The intelligent microgrid collaborative control system based on entropy budget constraints according to claim 1, characterized in that, The entropy element intelligent settlement module includes a blockchain smart contract unit, an entropy element casting and destruction unit, an entropy element transaction docking unit, and a heat fee linkage accounting unit. The entropy element casting and destruction unit quantitatively increases or decreases the node entropy element account balance based on the entropy production surplus or deficit. The blockchain smart contract unit automatically executes the deduction of excessive entropy elements and overdraft penalties; The entropy element trading docking unit connects to the community entropy element trading market and outputs real-time entropy element prices; The heat cost linkage accounting unit directly deducts the user's monthly heating cost from the entropy element's revenue and expenditure amount.

9. The intelligent microgrid collaborative control system based on entropy budget constraints according to claim 1, characterized in that, The load forecasting and correction module includes a multi-step long load forecasting unit, an MPC model correction unit, and a forecasting data push unit. The multi-step long load prediction unit outputs hourly heat load prediction values ​​for multiple nodes for the next hour. The MPC model correction unit corrects prediction deviations based on real-time measured heat load. The predicted data push unit uses the corrected load sequence constant input reinforcement learning AI optimization module as the basic parameters of the state space.

10. The intelligent microgrid collaborative control system based on entropy budget constraints according to claim 1, characterized in that, The data interaction and transmission module includes an industrial Ethernet bus unit, an OPCUA protocol parsing unit, and a data timing synchronization unit. The industrial Ethernet bus unit establishes the hardware communication link between the modules. The OPCUA protocol parsing unit uniformly converts heterogeneous data formats from various modules; The data timing synchronization unit unifies the timestamps of all sensors, controllers, and AI servers across the network, ensuring complete synchronization of enthalpy entropy calculation, optimization solutions, and device command issuance timing.

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