Urban heat and cold supply centralized energy comprehensive utilization method, system and device

Abstract

The invention relates to the technical field of energy utilization, and discloses an urban heat and cold supply centralized energy comprehensive utilization method. The method comprises the following steps: S1, acquiring physical parameters and operation data of an urban heating and cooling system, wherein the physical parameters comprise a system initial temperature field, a thermal diffusion coefficient, heat source distribution, equipment performance parameters and heating and cooling demand loads; the invention further provides an urban heat and cold supply centralized energy comprehensive utilization system. The data acquisition module is used for acquiring physical parameters and operation data of the urban heating and cooling system; and the thermodynamic modeling module is used for constructing a thermodynamic model based on the law of conservation of energy. By constructing an optimization model based on energy conservation and entropy increase minimization, energy loss in a heating and cooling system is remarkably reduced, by optimizing energy distribution in real time and dynamically adjusting temperature field distribution, invalid energy consumption is reduced to the maximum extent, the overall energy utilization rate of the system is increased, and the energy utilization rate of the system is increased. And therefore, the heating and cooling system can operate efficiently under various working conditions.

Description

Technical Field

[0001] The present invention relates to the technical field of energy utilization, and specifically to a method, system and device for comprehensive utilization of centralized energy for urban heating and cooling. Background Art

[0002] Currently, urban heating and cooling systems face many challenges in energy utilization and management. Traditional heating and cooling systems usually operate in a single energy form and rely on fixed-mode scheduling, making it difficult to adapt to dynamically changing load demands. With the expansion of urban scale and the complexity of energy demands, existing technologies have significant deficiencies in terms of efficiency, reliability, and flexibility.

[0003] Firstly, existing heating and cooling systems are difficult to achieve efficient collaborative utilization of multiple energy forms. In the process of urban heating and cooling, various energy forms such as heat pumps, combined heat and power, and solar energy may be involved. However, due to the different operating characteristics of each energy form, existing technologies often lack a unified optimization method and are difficult to fully exploit the potential of multi-energy systems. This limitation directly leads to energy waste and a decline in operating efficiency.

[0004] Secondly, in terms of dynamic load demand response, existing technologies show obvious deficiencies. The load demands of urban heating and cooling systems are usually affected by various dynamic factors such as time, weather, and user behavior, while traditional systems mainly rely on static scheduling strategies and cannot quickly respond to load fluctuations. This lag may result in insufficient heating and cooling capacity or overloaded operation, affecting the user experience while increasing system energy consumption and operating costs.

[0005] The monitoring and control capabilities of existing systems are limited, lacking real-time visualization of the global operating state and refined control means. Traditional heating and cooling systems usually achieve operation management through decentralized control, making it difficult to perform dynamic feedback and unified scheduling of the global operating state. This management mode not only limits the system operating efficiency but also increases the complexity and uncertainty of operation and maintenance. Summary of the Invention

[0006] Aiming at the deficiencies of the existing technology, the present invention provides a method, system and device for comprehensive utilization of centralized energy for urban heating and cooling, which solves the problems of low energy utilization efficiency, insufficient dynamic load response ability, lack of optimization of entropy increase loss, and insufficient complex system modeling and control ability.

[0007] To achieve the above objectives, the present invention is realized through the following technical solutions: A method for comprehensive utilization of centralized energy for urban heating and cooling, including the following steps;

[0008] S1. Obtain the physical parameters and operation data of the urban heating and cooling system, including the initial temperature field of the system, thermal diffusion coefficient, heat source distribution, equipment performance parameters, and heating and cooling demand loads;

[0009] S2. Build a thermodynamic model to describe the heat transfer process within the system based on the law of conservation of energy, and set the optimal system operation efficiency through the optimization objective;

[0010] S3. Set the physical constraints for the system operation, including thermodynamic conservation, energy balance conditions, and equipment efficiency constraints;

[0011] S4. Based on the optimization objective and constraints, establish an optimization model through the variational method to calculate the energy distribution scheme for each subsystem in the heating and cooling system;

[0012] S5. Use numerical methods to discretize and solve the optimization model, dynamically update the temperature field distribution, and correct the energy distribution strategy;

[0013] S6. Output the energy distribution results, entropy increment, and temperature field distribution for each subsystem in the heating and cooling system to achieve dynamic optimal control of heating and cooling.

[0014] Preferably, the thermodynamic model constructed in step S2 is based on the heat conduction equation, which is used to describe the distribution and transfer of heat energy in the urban heating and cooling system.

[0015] Preferably, the equipment efficiency constraint set in step S3 is based on the Carnot efficiency, which limits the operation efficiency of heat pumps or combined heat and power equipment not to exceed the theoretical efficiency of the Carnot cycle.

[0016] Preferably, the objective of the optimization model established in step S4 is to minimize the total system energy consumption and entropy increment, and solve for the optimal energy distribution by combining the Lagrangian functional with the constraints.

[0017] Preferably, the entropy increment set in the optimization model is described as a functional relationship between the heat flux density and temperature, and the system energy loss is reduced by minimizing the entropy increment.

[0018] Preferably, the finite difference method is used in step S5 to discretize the optimization model, and the discretization includes dividing and iteratively solving the spatial and time grids for the heat conduction equation and the energy distribution relationship.

[0019] Preferably, in the discretization, the system energy distribution is adjusted in real time through the gradient optimization algorithm, and the energy distribution strategy is iteratively updated until the objective function converges.

[0020] An urban heating and cooling centralized energy comprehensive utilization system, comprising:

[0021] A data acquisition module for obtaining the physical parameters and operation data of the urban heating and cooling system;

[0022] A thermodynamic modeling module for constructing a thermodynamic model based on the law of conservation of energy;

[0023] An optimization model construction module for setting the optimization objectives and operating constraints of the system;

[0024] A numerical calculation module for discretizing and solving the optimization model;

[0025] A dynamic scheduling module for adjusting the energy distribution strategy in real time according to the optimization results;

[0026] An output module for outputting the optimized energy distribution results and temperature field distribution of the heating and cooling system;

[0027] A display and control module for displaying the acquired data and performing regulation and control.

[0028] Preferably, the dynamic scheduling module combines historical load data and real-time heating and cooling demands to perform load forecasting and adjusts the energy distribution to meet dynamic demands.

[0029] An apparatus for an integrated utilization system of centralized energy for urban heating and cooling, comprising a protective box (1); a cover plate (2) is rotatably connected inside the protective box (1), a display screen (3) is fixedly connected to the outer wall of the cover plate (2), a controller (4) is fixedly connected to the outer wall of the cover plate (2), a data processor (5) is arranged inside the protective box (1), a real-time controller (6) is arranged inside the protective box (1), and a storage (7) is arranged inside the protective box (1).

[0030] The present invention provides a method, a system and an apparatus for an integrated utilization system of centralized energy for urban heating and cooling.

[0031] Having the following beneficial effects:

[0032] 1. By constructing an optimization model based on the conservation of energy and the minimization of entropy increase, the present invention significantly reduces the energy loss in the heating and cooling system. By optimizing the energy distribution in real time and dynamically adjusting the temperature field distribution, the ineffective energy consumption is minimized, the overall energy utilization rate of the system is improved, and the heating and cooling system can maintain efficient operation under various working conditions.

[0033] 2. By combining historical load data with real-time collected data, predicting future energy demands and adjusting the energy distribution strategy in real time, the dynamic scheduling module can quickly respond to load changes, ensuring that the system can still operate stably when the demand fluctuates, thereby avoiding problems of overload or energy waste caused by lagging response.

[0034] 3. The present invention uses a data acquisition module to obtain the physical parameters and operating data of the system through a high-density sensor network, achieving precise monitoring from the temperature field distribution to the equipment operating status. The present invention can provide high-resolution real-time data, providing a solid foundation for the refined management and optimization of the system operation.

[0035] 4. By optimizing the analysis and constraints of the entropy increment in the model, the present invention significantly reduces the irreversible energy loss during system operation. The introduction of entropy increment analysis not only improves the thermodynamic performance of the system but also ensures the efficient transfer of thermal energy, making the thermodynamic efficiency of the heating and cooling system reach the optimal level. BRIEF DESCRIPTION OF THE DRAWINGS

[0036] Figure 1 is the flowchart of the method of the present invention;

[0037] Figure 2 is the system framework diagram of the present invention;

[0038] Figure 3 is the three-dimensional view of the device of the present invention;

[0039] Figure 4 is the internal schematic diagram of the device of the present invention.

[0040] Among them, 1. protective box; 2. cover plate; 3. display screen; 4. controller; 5. data processor; 6. real-time controller; 7. storage. DETAILED DESCRIPTION OF THE EMBODIMENTS

[0041] Next, the technical solutions in the embodiments of the present invention will be clearly and completely described in conjunction with the accompanying drawings of the present invention. Obviously, the described embodiments are only a part of the embodiments of the present invention, rather than all of the embodiments. Based on the embodiments of the present invention, all other embodiments obtained by those of ordinary skill in the art without creative efforts shall fall within the protection scope of the present invention.

[0042] Please refer to the attached Figure 1 , the embodiment of the present invention provides a method for comprehensive utilization of centralized energy for urban heating and cooling, including the following steps;

[0043] S1. Obtain the physical parameters and operating data of the urban heating and cooling system, including the initial temperature field of the system, thermal diffusion coefficient, heat source distribution, equipment performance parameters, and heating and cooling demand load;

[0044] In this embodiment, the physical parameters and operating data of the heating and cooling system are obtained and initialized in the following manner;

[0045] Initial temperature field T(x,0)

[0046] The initial temperature field represents the initial thermal state of each region in the heating and cooling system, reflecting the temperature distribution at different locations. Temperature data is obtained through temperature sensors installed in the system pipes and building nodes. The sensors have a certain spatial distribution density to ensure that the collected temperature data has sufficient spatial resolution.

[0047] In a possible implementation, the initial temperature field of the system is defined in the following form;

[0048] T(x,0)=T ambient +T local (x)

[0049] Where:

[0050] T ambient is the ambient temperature, usually measured by a meteorological sensor;

[0051] T local (x) is the local temperature offset, determined by the historical operating state of the system;

[0052] Specifically, for large-scale pipe networks in urban heating and cooling systems, there may be significant non-uniformities in the initial temperature field, and these non-uniformities need to be collected through a high-resolution sensor grid;

[0053] Thermal diffusivity α;

[0054] The thermal diffusivity is used to describe the diffusion characteristics of heat energy in the heating and cooling system. It is determined by material properties such as thermal conductivity, density, and specific heat capacity, and is calculated by the following formula;

[0055]

[0056] κ is the thermal conductivity, determined by the pipe material and medium properties;

[0057] ρ is the density, usually determined according to the type of the conveying medium;

[0058] c is the specific heat capacity, related to the thermal properties of the medium;

[0059] These parameters can be directly obtained through experimental data on pipe materials and fluid characteristics. For complex multi-layer pipe structures, the thermal conductivities of different layer materials can be measured separately, and then the overall thermal diffusivity can be calculated by the weighted average method;

[0060] Heat source distribution Q(x,t);

[0061] Specifically, the heat source distribution may include the output power of heat pumps, combined heat and power equipment, as well as the dynamic behavior of heat dissipation or heat absorption of buildings.

[0062] In a possible implementation, the heat source distribution is defined by the following formula;

[0063] Q(x,t) = Q equipment (x,t) + Q environment (x,t)

[0064] where;

[0065] Q equipment (x,t) is the heat source term generated by the device;

[0066] Q environment (x,t) is the heat source term caused by the external environment;

[0067] Measure Q equipment (x,t) by installing a power meter on the device, and predict Q environment (x,t) by combining the data from the environmental monitoring station;

[0068] Device performance parameters Device performance parameters are used to describe the operating status and energy efficiency of heat pumps, combined heat and power equipment, etc. in the system. Generally, these parameters include:

[0069] Coefficient of performance (COP) of the heat pump;

[0070] Thermal efficiency and electrical efficiency of the combined heat and power equipment;

[0071] Maximum output power and minimum startup power of the device;

[0072] Specifically, the parameters are obtained through the device operation manual or online monitoring data; the COP value of the heat pump can be calculated in real time by the ratio of the input power to the output heat;

[0073] Demand load D(t);

[0074] Specifically, the demand load is obtained in the following ways:

[0075] Combining historical operation data and weather prediction, using time series analysis method to predict future load demand;

[0076] Real-time collection of user-side flow and temperature data, calculating the current load value;

[0077] In a possible implementation, the load demand can be estimated according to the following formula;

[0078]

[0079] where:

[0080] m is the number of user nodes;

[0081] is the flow rate of the j-th node;

[0082] c j is the specific heat capacity of the node medium;

[0083] T in,j , T out,j are the inlet and outlet temperatures of the node;

[0084] By monitoring the flow rate and temperature changes of all nodes in real time, the load demand data can be accurately obtained;

[0085] In the above way, the present invention can fully obtain the physical parameters and operation data of the heating and cooling system, providing high-precision input for subsequent modeling and optimization.

[0086] S2. Construct a thermodynamic model, describe the heat transfer process in the system based on the law of conservation of energy, and set the optimal system operation efficiency through the optimization target;

[0087] In this embodiment, by introducing the heat conduction equation, the heat diffusion behavior in the system is described. Further combining the dynamic characteristics of the heat source input, the model can reflect the complex spatio-temporal evolution relationship in the heating and cooling process;

[0088] In this embodiment, the construction of the thermodynamic model includes the following contents:

[0089] First, establish a heat conduction equation based on the law of conservation of energy, which can describe the heat transfer behavior in the heating and cooling system. The basic form of the heat conduction equation is;

[0090]

[0091] T(x,t) represents the temperature distribution at position x and time t, with the unit of Kelvin;

[0092] represents the rate of change of temperature with time;

[0093] is the second-order spatial derivative of the temperature distribution, used to describe the heat diffusion process;

[0094] is the thermal diffusivity; k is the thermal conductivity; ρ is the medium density; c is the specific heat capacity;

[0095] Q(x,t) is the heat source term, representing the heat input or output per unit volume;

[0096] Specifically, each physical quantity in the heat conduction equation is obtained by the data acquisition module in step S1 and is preliminarily screened and corrected through a preprocessing method;

[0097] Through boundary conditions and initial conditions, to supplement the solution space of the heat conduction equation, the thermodynamic model needs to satisfy the following two boundary conditions simultaneously;

[0098] The first kind of boundary condition; the value of the temperature on the boundary is known, and its mathematical form is;

[0099] T(x b ,t)=T b (t)

[0100] where; x b represents the boundary position; T b (t) is the boundary temperature;

[0101] The second kind of boundary condition; the value of the heat flux density on the boundary is known, and its mathematical form is;

[0102]

[0103] where, represents the component of the temperature gradient in the normal direction of the boundary, q b (t) is the heat flux density of the boundary;

[0104] The initial condition stipulates the initial temperature distribution of the system;

[0105] T(x,0)=T0(x)

[0106] where, T0(x) is the initial temperature field obtained from step S1

[0107] In this embodiment, the thermodynamic model is not only applicable to conventional heating and cooling systems, but also can be extended to special application scenarios with complex boundary conditions. When there are heat transfer processes of multiple different media in the system, the thermal diffusivity of the heat conduction equation needs to be calculated separately according to the medium type. For a multi-layer pipe structure, the overall thermal diffusivity needs to be obtained through the following weighted formula;

[0108]

[0109] where, α i and d i are the thermal diffusivity and thickness of the i-th layer material respectively;

[0110] This embodiment also supports the dynamic coupling of the external environmental temperature. The extension method is applicable to heating and cooling systems operating in an open environment. By inputting the environmental temperature T αmbient (t) as a boundary condition, the thermodynamic model can further reflect the impact of external climate change on the system operation;

[0111] By comprehensively disclosing the construction method of the thermodynamic model, covering the specific details from the basic equations to the boundary conditions, and expanding the functions in combination with different scenarios. This step provides accurate thermodynamic description capabilities for the subsequent optimization goal setting.

[0112] S3. Set the physical constraint conditions for the system operation, including thermodynamic conservation, energy balance conditions, and equipment efficiency constraints;

[0113] In this embodiment, for limiting the system operation range, the constraint conditions ensure that the system can meet the energy demand while following the physical laws and avoiding violating the basic operation limitations of the equipment;

[0114] In this embodiment, the set physical constraint conditions mainly include the following aspects:

[0115] First of all, the thermodynamic conservation condition is the basis for the operation of the entire system. By the heat conduction equation, the heat transfer process of the system is constrained to ensure that at any spatial position and time node, the input, output, and storage of heat are balanced. Its expression is;

[0116]

[0117] Among them;

[0118] represents the change of heat in the system with time;

[0119] represents the spatial change of heat diffusion;

[0120] Q(x, t) is the heat source input term;

[0121] The urban heating and cooling system is divided into several heat zones, and each zone independently satisfies the conservation condition. This method is particularly suitable for large-scale pipe networks or multi-energy collaborative scenarios and can simplify the global calculation complexity;

[0122] Secondly, the energy balance condition is the key link for the system to achieve dynamic demand response. Specifically, this condition ensures that the total energy output by each subsystem always meets the current load demand. Its mathematical form is;

[0123]

[0124] Among them,

[0125] x i (t) represents the energy output of the i-th subsystem;

[0126] n is the number of subsystems;

[0127] D(t) is the dynamic load demand of the heating and cooling system;

[0128] The energy balance condition can also be combined with real-time load forecasting to improve the system response speed. By predicting D(t) through time series analysis, the energy output x of each subsystem can be adjusted in advance i (t), thereby avoiding overload or energy shortage;

[0129] Specifically, for scenarios with large load fluctuations in certain regions, D(t) can be described in segments;

[0130]

[0131] Among them;

[0132] D j (t) represents the dynamic demand of the jth user node;

[0133] m is the number of user nodes;

[0134] The segmented form can more precisely reflect the demand differences of different regions or users;

[0135] In this embodiment, the operation efficiency of the equipment is restricted according to the Carnot efficiency. The Carnot efficiency is the theoretical upper limit of the efficiency in a thermodynamic cycle, and its mathematical expression is;

[0136]

[0137] Among them,

[0138] η is the actual efficiency of the equipment;

[0139] T c and T h are the absolute temperatures of the cold end and the hot end of the equipment respectively;

[0140] By real-time monitoring the operating temperature of the equipment and dynamically adjusting the efficiency limit, for a heating system with large seasonal variations, T c is lower in winter operation, so η carnot will increase accordingly. While in summer operation, due to T c being higher, η carnot will decrease. The equipment efficiency limit can also be corrected in combination with the equipment aging coefficient. The aging coefficient k aging represents the degree of attenuation of the equipment performance, and its correction formula is;

[0141] η effective = k aging ·η carnot

[0142] Among them, η effective is the corrected actual efficiency;

[0143] By introducing the thermodynamic conservation conditions, energy balance conditions, and equipment efficiency limitations, a physical constraint framework for system operation is established. These constraint conditions not only cover the core physical characteristics of the heating and cooling system but also provide a multi-level description in combination with equipment performance and environmental factors, laying a rigorous mathematical foundation for subsequent optimization calculations.

[0144] S4. Based on the optimization objectives and constraint conditions, an optimization model is established through the variational method to calculate the energy distribution scheme of each subsystem in the heating and cooling system;

[0145] In this embodiment, by comprehensively considering energy consumption, entropy increase, and system physical constraints, an energy distribution optimization scheme that can adapt to dynamic demands is established. The construction of the optimization model is the core link to solve the energy efficiency problem of the urban heating and cooling system and is used to guide the actual operation of the system;

[0146] In this embodiment, the core objective of the optimization model is to minimize the total system energy consumption and entropy increment. The mathematical expression form of the optimization objective is;

[0147]

[0148] is the overall value of the optimization objective;

[0149] E(x,t) represents the energy consumption density per unit volume, which comes from the energy consumption of equipment such as heat pumps and combined heat and power;

[0150] Φ(x,t) represents the dissipation density per unit volume, which is specifically calculated from the entropy increase in the thermodynamic process;

[0151] Specifically, the entropy increment can be described by the following formula; [[ID=2x6]]

[0152]

[0153] Among them,

[0154] κ is the thermal conductivity;

[0155] is the square of the temperature gradient;

[0156] This form of optimization objective can simultaneously constrain the energy consumption and heat loss of the system, ensuring that the model has high efficiency and physical feasibility in actual operation;

[0157] The optimization model needs to combine the system operation constraint conditions set in step S3. The constraint conditions can be introduced through the Lagrange multiplier method to form the following optimization functional;

[0158]

[0159] is the Lagrangian functional;

[0160] λ1 and λ2 are Lagrange multipliers;

[0161] represents the energy balance condition;

[0162] η ≤ η carnot is the equipment efficiency limit;

[0163] In actual calculations, by optimizing the extremum of the functional the optimal energy consumption and constraint conditions can be satisfied simultaneously;

[0164] The solution process of the optimization model can utilize the variational method. The variational method determines its stationary point conditions by taking the derivative of the functional, and its core mathematical form is;

[0165]

[0166] where;

[0167] x i represents the energy allocation variable of the i-th subsystem;

[0168] represents its rate of change with time;

[0169] Specifically, through the above equation, the optimal energy output scheme of each subsystem can be obtained

[0170] which directly reflects the energy allocation strategy of different subsystems under dynamic demands;

[0171] In this embodiment, in order to further improve the adaptability of the optimization model, external environment dynamic parameters are also introduced;

[0172] By taking the environmental temperature T ambient (t) as an influencing factor, the optimization objective can be dynamically corrected to;

[0173]

[0174] where;

[0175] β is the correction coefficient;

[0176] |T(x,t) - T ambient (t)| 2 represents the degree of deviation between the system temperature field and the environmental temperature;

[0177] This correction form is particularly suitable for open heating and cooling systems and can further improve the accuracy of the model;

[0178] The time dimension of the optimization model can be processed in segments to meet the requirements of high-load fluctuations in a short period. Specifically, the time interval can be divided into multiple sub-intervals, and the optimization objective is solved separately within each interval;

[0179]

[0180] Among them,

[0181] t k ,t k+1 represent the start and end points of the k-th time period;

[0182] K is the number of time intervals;

[0183] Segmented processing can significantly improve the response ability of the model to short-term dynamic demands;

[0184] By setting the optimization objective and constructing the Lagrangian functional, a comprehensive optimization model is established. This model can effectively combine the thermodynamic constraints and equipment performance to generate an energy distribution plan that meets dynamic demands, providing theoretical support for the efficient operation of the heating and cooling system.

[0185] S5. Use numerical methods to discretize and solve the optimization model, dynamically update the temperature field distribution, and correct the energy distribution strategy;

[0186] In this embodiment, the discretization process of the optimization model includes the following contents:

[0187] First, based on the discretization of the time dimension, the finite difference method is used to discretize the heat conduction equation. The forward Euler method is used for time discretization, and its basic form is;

[0188]

[0189] Among them;

[0190] T k (x) and T k+1 (x) represent the temperature fields at time steps k and k + 1 respectively;

[0191] Δt is the length of the time step;

[0192] Substitute the above discrete form into the heat conduction equation to form the following difference equation;

[0193]

[0194] Among them;

[0195] represents the spatial discretization of the heat diffusion term;

[0196] Qk (x) is the value of the heat source term at time step k;

[0197] Secondly, for the discretization of the spatial dimension, the central difference method is used to handle the second derivative. Specifically, the basic expression for spatial discretization is;

[0198]

[0199] where,

[0200] T i 、T i+1 and T i-1 represent the temperatures at spatial grid points i, i + 1, and i - 1, respectively;

[0201] Δx is the spacing of the spatial grid;

[0202] After combining the spatial discretized form with the time discretized form, the discretized expression of the heat conduction equation can be obtained;

[0203]

[0204] where; represents the temperature at time step k + 1 and spatial grid point i;

[0205] The discretized equation will select an appropriate grid division density and time step according to the system scale to ensure the accuracy and efficiency of the calculation;

[0206] Specifically, the density of grid division can be dynamically adjusted according to the change of temperature gradient. A finer grid is used in the region with large temperature changes, while a sparser grid is used in the region with uniform temperature distribution. This strategy can significantly reduce the computational amount while maintaining high accuracy;

[0207] During the process of optimizing the model solution, an iterative update mechanism based on gradient optimization is also introduced. The basic form of the gradient optimization algorithm is;

[0208]

[0209] where,

[0210] is the energy allocation amount of the i-th subsystem at the n-th iteration;

[0211] γ is the learning rate, which controls the step size of gradient descent;

[0212] is the objective function with respect to partial derivative;

[0213] Through the gradient optimization algorithm, the energy allocation strategy x of the subsystem can be adjusted in each iteration i , so as to gradually approach the optimal solution. The iterative process will continue until the change in the objective function meets the convergence condition;

[0214]

[0215] where ∈ is the set convergence threshold;

[0216] In addition, in some complex scenarios, this embodiment supports the joint optimization of multiple time steps. The basic idea of multi-time step optimization is to optimize the energy allocation schemes within multiple time steps simultaneously, rather than gradually solving the results of a single time step. The mathematical form of its optimization objective is;

[0217]

[0218] where

[0219] K is the number of time steps for joint optimization;

[0220] is the optimization objective for the k-th time step;

[0221] It can better capture the dynamic change trend of the system and is especially suitable for scenarios with large load fluctuations;

[0222] By using the discretization method in the time and space dimensions, the optimization model is transformed into a system of algebraic equations. Combining the gradient optimization algorithm and dynamic load prediction, the temperature field distribution and energy allocation strategy are gradually updated. This numerical solution method is not only applicable to the dynamic demand in urban heating and cooling systems but also can provide an efficient optimization solution for energy systems of different scales and complexities.

[0223] S6. Output the energy allocation results, entropy increment, and temperature field distribution of each subsystem in the heating and cooling system to achieve the dynamic optimal control of heating and cooling;

[0224] In this embodiment, the calculation results are converted into executable operation strategies, including the energy allocation of each subsystem, the entropy increase analysis results, and the temperature field distribution. The output of the optimization results directly provides guidance for the actual operation of the system and also lays a foundation for subsequent monitoring and adjustment;

[0225] In this embodiment, the content of the output results mainly includes three parts: energy allocation results, temperature field distribution, and entropy increase analysis data.

[0226] First, regarding the energy allocation results, the present invention outputs the optimal energy allocation amount of the subsystem at a specific time step. Specifically, the energy allocation amount x of the subsystem i(t) is the direct calculation result of the optimization model, its unit is watt, and the total distribution of all subsystems must meet the following conditions;

[0227]

[0228] in,

[0229] x i (t) represents the energy distribution of the ith subsystem;

[0230] n is the number of subsystems;

[0231] D(t) is the dynamic load demand;

[0232] This embodiment directly associates the energy allocation results with the equipment operating parameters. For a heat pump system, the allocation results can be used to control its output power. The specific formula is:

[0233] P heatpump (t) = COP·x heatpump (t)

[0234] in,

[0235] P heatpump (t) is the heating power of the heat pump;

[0236] COP is the heating coefficient of the heat pump;

[0237] x heatpump (t) is the energy input of the heat pump;

[0238] Ability to ensure that the optimization results match the actual capabilities of the equipment;

[0239] Secondly, for the distribution of temperature field, the present invention provides the global temperature distribution T(x, t) at each time step. The temperature field distribution is obtained by discretizing and solving the heat conduction equation. The specific form is:

[0240]

[0241] in,

[0242] represents the temperature of grid point i at time step k+1;

[0243] α is the thermal diffusivity;

[0244] is the heat source input;

[0245] Generally speaking, the temperature distribution data provided in this embodiment can be directly used to monitor the heat transfer in the urban heating pipe network. Additionally, deviation analysis of the temperature field is introduced to evaluate whether the system operation status meets expectations by calculating the temperature deviation;

[0246] ΔT(x,t) = T set (x,t) - T(x,t)

[0247] Furthermore, the entropy increase analysis data is another key result output by the present invention. Specifically, the calculation of the entropy increment is based on the entropy production density Φ(x,t) in the optimization objective, and its expression is;

[0248]

[0249] where;

[0250] is the square of the temperature gradient. The calculation of the entropy increment can reflect the irreversible energy loss situation of the system. This embodiment provides the local entropy increment data of each subsystem for analyzing the heat loss in different regions. For a certain subsystem, its entropy increment can be expressed as;

[0251]

[0252] where V subsystem is the spatial region corresponding to the subsystem;

[0253] Based on the output of the optimization results, the present invention also realizes dynamic regulation and state monitoring. Through the comprehensive output of energy distribution, temperature field distribution, and entropy increase analysis, it provides reliable support for the efficient operation of the urban heating and cooling system. These results can not only guide the real-time operation of the system but also be used for subsequent optimization and improvement.

[0254] Please refer to the appendix Figure 2 , an integrated utilization system of centralized energy for urban heating and cooling, comprising:

[0255] A data acquisition module for obtaining the physical parameters and operation data of the urban heating and cooling system;

[0256] In this embodiment, the data acquisition module mainly includes the following:

[0257] First of all, this module is used to obtain the initial temperature field of the urban heating and cooling system. The initial temperature field T(x,0) is collected by temperature sensors installed at the nodes of the system pipe network, building entrances, or equipment outlets. The temperature field reflects the initial thermal state of the system and is the basic input for thermodynamic modeling;

[0258] The distribution density of the temperature sensors is adjusted according to the system scale and complexity. More sensors can be deployed in high-load areas to improve the acquisition accuracy; while in areas with less load variation, the arrangement of sensors can be appropriately reduced to cut costs.

[0259] Secondly, this module is also responsible for obtaining the thermal diffusivity α of the system. The thermal diffusivity is a comprehensive parameter determined by the thermal conductivity κ, density ρ, and specific heat capacity c, and its calculation formula is;

[0260]

[0261] Among them;

[0262] κ is the thermal conductivity, reflecting the heat transfer ability through the medium;

[0263] ρ is the density, representing the mass density of the fluid or solid;

[0264] c is the specific heat capacity, related to the heat storage capacity of the medium;

[0265] This module combines experimental data and material manuals to obtain the thermal diffusivity. For a heating system that transports hot water, the comprehensive thermal diffusivity can be calculated by measuring the thermal conductivity of the pipeline material and the density and specific heat capacity of water. For a pipeline with a multi-layer structure, the thermal diffusivity can be calculated by the following weighted formula;

[0266]

[0267] Among them;

[0268] α i is the thermal diffusivity of the i-th layer of material;

[0269] d i is the thickness of the i-th layer of material;

[0270] n is the total number of material layers;

[0271] This module is also responsible for collecting the operating performance parameters of the equipment, and these parameters include but are not limited to;

[0272] The coefficient of performance (COP) of the heat pump

[0273] The thermal efficiency η of the combined heat and power equipment thermal and the electrical efficiency η electric ;

[0274] The maximum output power P of the equipment max and the minimum starting power P min ;

[0275] The data acquisition module of the present invention provides comprehensive support for the thermodynamic modeling and optimization calculation of the system through real-time monitoring, dynamic update, and multi-dimensional parameter acquisition. By accurately collecting the initial temperature field, thermal diffusion coefficient, heat source distribution, equipment performance parameters, and user load requirements, this module ensures the reliability and accuracy of the optimization process.

[0276] A thermodynamic modeling module for constructing a thermodynamic model based on the law of conservation of energy;

[0277] In this embodiment, first, this module constructs a heat conduction equation based on the law of conservation of energy to describe the heat transfer behavior in the heating and cooling system.

[0278]

[0279] Specifically, the parameters involved in the heat conduction equation are all provided by the data acquisition module, and the thermal diffusion coefficient is determined by the thermal conductivity κ, density ρ, and specific heat capacity c of the material. The α values in different regions can be defined by zone according to the pipeline material or medium type, so as to more accurately reflect the thermal diffusion characteristics in the complex system.

[0280] To adapt to the complexity of large-scale urban heating and cooling systems, this module supports regional modeling. The basic idea of regional modeling is to divide the system into multiple regions V1, V2, …, V n , and construct local thermodynamic models respectively. The heat conduction equation of region V i can be expressed as;

[0281]

[0282] Among them,

[0283] is the temperature distribution of region V i ;

[0284] is the thermal diffusion coefficient of region V i ;

[0285] is the heat source term of region V i ;

[0286] Specifically, this regional modeling method can significantly reduce the computational complexity and improve the accuracy of the model in depicting local heat transfer behavior at the same time.

[0287] In addition, this module provides complete solution conditions for the heat conduction equation by setting boundary conditions and initial conditions;

[0288] The first type of boundary condition (constant temperature boundary): It is stipulated that the temperature at the boundary is a known value;

[0289] The second type of boundary condition (constant heat flux boundary): It is specified that the heat flux density at the boundary is a known value;

[0290] The thermodynamic modeling module of the invention comprehensively describes the heat transfer process in the urban heating and cooling system by introducing the heat conduction equation, dynamic heat source terms, and sub-region modeling strategies. By setting boundary conditions, initial conditions, and environmental dynamic coupling, this module can adapt to a variety of complex scenarios and provide accurate thermodynamic inputs for optimizing the model.

[0291] The optimization model construction module is used to set the optimization objectives and operating constraint conditions of the system;

[0292] In this embodiment, the main tasks of the optimization model construction module include setting the optimization objectives and constructing the operating constraint conditions, and their mathematical descriptions are as follows;

[0293] First, this module defines the optimization objective function of the system, aiming to minimize the total energy consumption and entropy increment of the system; the mathematical expression of the optimization objective is;

[0294]

[0295] Specifically, the calculation formula for the entropy production density Φ(x,t) is;

[0296]

[0297] Among them,

[0298] κ is the heat conduction coefficient;

[0299] is the square of the temperature gradient;

[0300] When the entropy production loss is relatively high, the value of α2 can be appropriately increased to enhance the optimization of heat dissipation;

[0301] Secondly, to ensure the physical feasibility of the optimization objective, this module limits the solution space of the optimization problem by setting operating constraint conditions

[0302] Energy balance constraint;

[0303] This constraint condition ensures that the total energy output by each subsystem always meets the current dynamic load demand, and its mathematical form is:

[0304]

[0305] Among them,

[0306] x i (t) is the energy allocation of the i-th subsystem;

[0307] D(t) is the dynamic load demand of the system;

[0308] n is the number of subsystems;

[0309] This module combines historical load data and real-time monitoring results to dynamically correct D(t) through prediction methods. When real-time monitoring detects fluctuations in user demand, the energy allocation strategy can be adjusted immediately to ensure that energy balance conditions are always met.

[0310] Equipment efficiency constraint This constraint is based on the Carnot efficiency theory and limits the equipment operating efficiency to not exceed its theoretical upper limit. Its mathematical expression is:

[0311]

[0312] in,

[0313] η i is the actual efficiency of the i-th subsystem;

[0314] T c ,T h are the cold-end and hot-end temperatures of the equipment, respectively;

[0315] While constructing the optimization model, this module also uses the Lagrange multiplier method to comprehensively express the objective function and constraints;

[0316]

[0317] in,

[0318] λ1, λ2 are Lagrange multipliers, used to introduce constraints;

[0319] By solving the functional The stationary point can satisfy both the optimization objective and the operation constraints;

[0320] The optimization model building module of the present invention constructs a dynamic optimization model by setting the objective function and introducing the operation constraint conditions.

[0321] Numerical calculation module, used to discretize and solve the optimization model;

[0322] In this embodiment, the numerical solution process of the numerical calculation module includes the following key steps:

[0323] First, the time dimension is discretized. The finite difference method is used for time discretization. By dividing the continuous time interval into discrete time steps, the numerical solution in time is obtained.

[0324]

[0325] in,

[0326] T k (x) and T k+1 (x) represent the temperature fields at time steps k and k + 1 respectively;

[0327] Δt is the length of the time step;

[0328] Specifically, substituting the above time-discretization formula into the heat conduction equation forms the following discretized expression;

[0329]

[0330] where;

[0331] represents the spatial variation of heat diffusion;

[0332] Q k (x) is the heat source term at time step k;

[0333] Secondly, discretize the spatial dimension. Spatial discretization usually adopts the finite difference method or the finite element method, dividing the continuous spatial domain into discrete grid points;

[0334] After combining the spatial discretization form with the time discretization form, the discretized expression of the heat conduction equation is;

[0335]

[0336] where;

[0337] represents the temperature value at time step k + 1 and spatial grid point i;

[0338] In the process of solving the optimization model, this module also introduces a gradient optimization algorithm to dynamically update the energy allocation strategy;

[0339] Specifically, in the iteration, according to the current energy allocation and the gradient of the objective function, update the energy allocation scheme of each subsystem The iteration process will continue until the change amount of the objective function meets the convergence condition;

[0340] The numerical calculation module of the present invention transforms the optimization model into an algebraic equation set through time and space discretization, and combines the gradient optimization algorithm to achieve dynamic solution. The module can not only provide accurate temperature field distribution and energy allocation scheme, but also adapt to complex dynamic demand scenarios through multi-time step optimization and parallel computing, providing strong technical support for the efficient operation of the system.

[0341] Dynamic scheduling module, used to adjust the energy allocation strategy in real time according to the optimization result;

[0342] In this embodiment, the core function of the dynamic scheduling module is to adjust the energy distribution plan x of each subsystem in real time. i (t). The adjustment process includes correcting the optimization result according to the real-time load demand and ensuring that the system always meets the dynamic load through a dynamic feedback mechanism.

[0343] First, the dynamic scheduling module generates an initial energy distribution plan based on the optimization result output by the numerical calculation module. The numerical calculation module provides the optimal energy distribution x at the current time step t i (t), which satisfies the following energy balance constraints.

[0344] On this basis, the dynamic scheduling module monitors the actual load D actual [[ID=I3]](t) in real time and corrects the energy distribution through the following formula.

[0345]

[0346] Among them,

[0347] is the corrected energy distribution plan.

[0348] ΔD(t) = D actual (t) - D(t) is the load deviation.

[0349] β is the adjustment coefficient used to control the response intensity.

[0350] Specifically, when the load deviation ΔD(t) is small, the system only needs to slightly adjust the distribution ratio of each subsystem; when the load deviation is large, this module will constrain the correction plan according to the performance upper limit and response speed of the equipment.

[0351] This module also introduces a temperature field deviation feedback mechanism. Specifically, by monitoring the temperature field distribution T of the system in real time actual (x, t), the dynamic scheduling module can identify the deviation between the actual temperature and the set target.

[0352] ΔT(x, t) = T set (x, t) - T actual (x, t)

[0353] Among them,

[0354] T set (x, t) is the set target temperature.

[0355] T actual (x, t) is the temperature distribution monitored in real time.

[0356] This module corrects the temperature by increasing or decreasing the energy distribution in this area, increasing the heat source input Q(x,t) in this area to accelerate the temperature recovery;

[0357] The dynamic scheduling module of the present invention ensures that the system always meets the dynamic load demand by adjusting the energy distribution plan in real time, based on predictive feedforward control and equipment status feedback.

[0358] An output module for outputting the optimized energy distribution result and temperature field distribution of the heating and cooling system;

[0359] In this embodiment, the output content of the output module mainly includes the following aspects: energy distribution result, temperature field distribution, and entropy increase analysis data;

[0360] First, the output module generates the energy distribution plan x i (t) of each subsystem. The energy distribution result directly reflects the operating status of each subsystem and is an important basis for the dynamic regulation of the heating and cooling system. The output data form of the energy distribution plan is;

[0361]

[0362] Among them,

[0363] x i (t) is the energy distribution amount of the i-th subsystem;

[0364] D(t) is the total load demand of the system;

[0365] n is the number of subsystems;

[0366] Δx i (t) is the correction amount of the dynamic scheduling module for the i-th subsystem according to the actual load change;

[0367] Specifically, the energy distribution plan can be directly used to regulate heating and cooling devices such as heat pumps and combined heat and power equipment;

[0368] Specifically, the output of the temperature field distribution can be used to monitor in real time whether the heat transfer in the pipe network is uniform and identify possible temperature abnormal areas;

[0369] In addition, the output module generates the entropy increase analysis data Φ(x,t) of the system. The entropy increase analysis data is an important index for evaluating the thermodynamic performance of the system and is used to measure the magnitude of irreversible energy loss;

[0370] The output module of the present invention provides comprehensive support for the operation and maintenance of the heating and cooling system by generating the energy distribution plan, temperature field distribution, and entropy increase analysis data.

[0371] A display and control module for displaying the acquired data and performing regulation;

[0372] In this embodiment, the functions of the display and control module mainly include result display, status monitoring, anomaly warning, and dynamic control interface;

[0373] First, the display and control module displays the key optimization results of the system through diverse graphical methods. The energy distribution plan is usually shown in the form of a bar chart or a pie chart, which is used to present the load ratios of different subsystems and helps managers quickly judge the rationality of energy distribution. The distribution situation of each subsystem is dynamically updated in real time to reflect the regulation results;

[0374] For the temperature field distribution, the display and control module visualizes it in the form of a heat map. The heat map uses the depth of color to represent the temperature of different regions and supports the dynamic refresh function, enabling users to understand the operating status of the system at any time. If the temperature of a certain region is significantly higher or lower than the expected value, the heat map will prompt users to pay attention through abnormal colors;

[0375] The entropy increase analysis results are presented in the form of a line chart or a distribution chart, which is used to intuitively reflect the energy efficiency status and energy loss situation of the system. These results are presented through the control panel and support interactive operations such as zooming in, zooming out, and selecting areas, which is convenient for users to focus on the operating conditions of specific regions or time periods;

[0376] The display and control module of the invention provides comprehensive support for the operation management of the heating and cooling system through diverse visual displays, real-time monitoring and warning functions, and flexible dynamic control interfaces.

[0377] Please refer to the attached Figure 3 - attached Figure 4 , a device for an integrated utilization system of centralized energy for urban heating and cooling, including a protective box 1; a cover plate 2 is rotatably connected inside the protective box 1, a display screen 3 is fixedly connected to the outer wall of the cover plate 2, a controller 4 is fixedly connected to the outer wall of the cover plate 2, a data processor 5 is arranged inside the protective box 1, a real-time controller 6 is arranged inside the protective box 1, and a memory 7 is arranged inside the protective box 1;

[0378] Specifically, the protective box 1 and the cover plate 2 can protect the internal devices. The display screen 3 can display the collected data. The controller 4 can regulate the system. The data processor 5 is used to preprocess the collected temperature, flow rate and other data and serve as the input for modeling. The real-time controller 6 is used to receive the optimization results and adjust the energy distribution in real time. The memory 7 is used to store a large amount of intermediate data generated after discretization processing.

[0379] Although embodiments of the present invention have been shown and described, those of ordinary skill in the art will appreciate that various changes, modifications, substitutions, and variations can be made to these embodiments without departing from the principles and spirit of the present invention. The scope of the present invention is defined by the appended claims and their equivalents.

Claims

1. A method for comprehensive utilization of centralized energy for urban heating and cooling, characterized in that, It includes the following steps; S1. Obtain the physical parameters and operation data of the urban heating and cooling system, including the initial temperature field of the system, thermal diffusivity, heat source distribution, equipment performance parameters, and heating and cooling demand load; S2. Construct a thermodynamic model to describe the heat transfer process within the system based on the law of conservation of energy, and set the optimal system operation efficiency through an optimization objective; S3. Set the physical constraint conditions for the system operation, including thermodynamic conservation, energy balance conditions, and equipment efficiency constraints; S4. Based on the optimization objective and constraint conditions, establish an optimization model through the variational method to calculate the energy distribution scheme of each subsystem in the heating and cooling system; S5. Use numerical methods to discretize and solve the optimization model, dynamically update the temperature field distribution, and correct the energy distribution strategy; S6. Output the energy distribution results, entropy increment, and temperature field distribution of each subsystem in the heating and cooling system to achieve dynamic optimal control of heating and cooling.

2. A method for comprehensive utilization of centralized energy for urban heating and cooling according to claim 1, characterized in that, The thermodynamic model constructed in step S2 is based on the heat conduction equation, which is used to describe the distribution and transfer of heat energy in the urban heating and cooling system.

3. A method for comprehensive utilization of centralized energy for urban heating and cooling according to claim 1, characterized in that The equipment efficiency constraint set in step S3 is based on the Carnot efficiency, which limits that the operation efficiency of a heat pump or a combined heat and power equipment shall not exceed the theoretical efficiency of the Carnot cycle.

4. A method for comprehensive utilization of centralized energy for urban heating and cooling according to claim 1, characterized in that The objective of the optimization model established in step S4 is to minimize the total system energy consumption and entropy increment, and solve the optimal energy distribution through the Lagrangian functional combined with the constraint conditions.

5. A method for comprehensive utilization of centralized energy for urban heating and cooling according to claim 4, characterized in that, The entropy increment set in the optimization model is described as a functional relationship between the heat flux density and temperature, and the system energy loss is reduced by minimizing the entropy increment.

6. A method for comprehensive utilization of centralized energy for urban heating and cooling according to claim 1, characterized in that, In step S5, the finite difference method is used to discretize the optimization model. The discretization includes the division and iterative solution of the spatial and time grids for the heat conduction equation and the energy distribution relationship.

7. A method for comprehensive utilization of centralized energy for urban heating and cooling according to claim 6, characterized in that, In the discretization, the system energy distribution is adjusted in real time through the gradient optimization algorithm, and the energy distribution strategy is iteratively updated until the objective function converges.

8. An integrated utilization system for centralized energy in urban heating and cooling, based on the method for integrated utilization of centralized energy in urban heating and cooling according to claims 1-7, characterized in that, It includes; A data acquisition module for obtaining the physical parameters and operation data of the urban heating and cooling system; A thermodynamic modeling module for constructing a thermodynamic model based on the law of conservation of energy; An optimization model construction module for setting the optimization objective and operation constraint conditions of the system; A numerical calculation module for discretizing and solving the optimization model; A dynamic scheduling module for adjusting the energy distribution strategy in real time according to the optimization results; An output module for outputting the optimized energy distribution results and temperature field distribution of the heating and cooling system; A display and control module for displaying the acquired data and performing regulation and control.

9. A comprehensive utilization system for centralized energy in urban heating and cooling according to claim 8, characterized in that, The dynamic scheduling module combines historical load data and real-time heating and cooling demands for load forecasting, and adjusts the energy distribution to meet the dynamic demands.

10. An apparatus for a comprehensive utilization system of centralized energy for urban heating and cooling. According to a comprehensive utilization system of centralized energy for urban heating and cooling described in claims 8-9, it is characterized in that, It includes a protective box (1); a cover plate (2) is rotatably connected inside the protective box (1), a display screen (3) is fixedly connected to the outer wall of the cover plate (2), a controller (4) is fixedly connected to the outer wall of the cover plate (2), a data processor (5) is arranged inside the protective box (1), a real-time controller (6) is arranged inside the protective box (1), and a storage (7) is arranged inside the protective box (1).

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