An energy management system for smart city

Abstract

This invention relates to the field of urban energy management technology, and more particularly to an energy management system for smart cities. The system comprises: a data acquisition unit that collects power grid operating parameters and heating network operating parameters; a dynamic characteristic modeling unit that establishes an equivalent electrical energy storage model of the regional heating network based on the heating network operating parameters and pipeline topology, and calculates the regional heating network's state of charge (SOC) value and water temperature change rate based on the equivalent SOC model; a central optimization unit that, based on the power grid operating parameters, SOC value, water temperature change rate, and preset scheduling targets, continuously generates cross-network energy conversion guidance coefficients corresponding to at least one future scheduling cycle; local controller units deployed at each electro-thermal coupling device; and a distributed coordination unit that distributes the cross-network energy conversion guidance coefficient sequence to the corresponding local controller units. This invention improves the coordinated scheduling capability of electricity and heat networks in smart cities, increases operational efficiency, and reduces the waste of regulatory resources.

Description

Technical Field

[0001] This invention relates to the field of urban energy management technology, and more particularly to an energy management system for smart cities. Background Technology

[0002] In the energy infrastructure of modern smart cities, the power system and the district heating (cooling) system are two large-scale and independently operating complex networks. The power system is mainly responsible for the transmission, distribution, and real-time balancing of electrical energy, and its operation relies on generator sets, transmission and distribution networks, and the increasing availability of renewable energy. The district heating system, on the other hand, delivers heat energy to end users through heat sources, pipe networks, and heat exchange stations, and its operation exhibits significant thermal inertia and time delay characteristics. With the deepening of energy transition, electrothermal coupling technologies, such as combined heat and power (CHP), electric boilers, and heat pumps, have been widely used in both types of systems, providing a physical basis for bidirectional conversion between electrical and thermal energy. In current urban energy management practices, the monitoring and dispatching of the power grid and heating network are usually handled by independent operating agencies, each with its own independent data acquisition, operation analysis, and control systems, forming a relatively closed operating model. Historically, this model has ensured the professional and safe operation of each system, but it has also objectively led to the "siloed" isolation of energy information and control resources.

[0003] Despite the widespread availability of electrothermal coupling devices, existing urban energy management systems still face significant challenges in achieving efficient coordination between the electricity and heat systems. The primary challenge lies in the lack of a unified, quantifiable model for coordinating the scheduling of heating networks. This makes it difficult for grid dispatchers to reliably and safely incorporate the significant thermal inertia of heating networks into power balance considerations, thus failing to fully utilize their potential as a flexible regulation resource. Secondly, under current models, the operation of electrothermal coupling devices often responds only to the needs of a single network or is based on local economic decisions, easily leading to conflicting operational objectives. For example, during periods of surge in renewable energy output, the grid needs to increase load to absorb excess power, but the heating network may be unable to effectively respond to this peak-shaving demand due to already met heating needs or limitations in heat source operation modes. This could even increase the grid's peak-shaving pressure, leading to higher overall operating costs and limited renewable energy absorption capacity.

[0004] Chinese Patent Publication No. CN118278028A discloses a smart city energy management system and method, including a data acquisition unit, a signal communication module, a data encryption module, a data management and analysis module, a management and control center module, and a management feedback module. The data acquisition unit is deployed at the end of energy consumption nodes to collect and record energy consumption data. The signal communication module transmits the collected energy consumption data and performs encryption key hashing and data encryption through the data encryption module. The data management and analysis module decrypts the encrypted data and analyzes the decrypted data. The management and control center module manages, allocates, and schedules energy data, providing energy-saving suggestions and optimization measures. However, the smart city energy management system and method suffer from the following problems: a lack of coordinated scheduling capabilities between electricity and heat networks, leading to low operating efficiency and wasted regulatory resources. Summary of the Invention

[0005] Therefore, the present invention provides an energy management system for smart cities to overcome the problems of low operating efficiency and waste of regulation resources caused by insufficient coordinated scheduling capabilities of electricity and heat networks in the prior art.

[0006] To achieve the above objectives, the present invention provides an energy management system for smart cities, comprising: The data acquisition unit is connected to the urban power grid monitoring system and the urban heating network monitoring system to collect power grid operating parameters and heating network operating parameters. A dynamic characteristic modeling unit, which is connected to the data acquisition unit, is used to establish an equivalent electrical energy storage model of the regional heating network based on the heating network operating parameters and pipeline topology. Based on the equivalent electrical energy storage model, the state of charge value of the regional heating network, which characterizes the long-term energy storage state of the regional heating network, and the water temperature change rate, which characterizes the short-term power response capability of the regional heating network, are calculated. The central optimization unit, which is connected to the data acquisition unit and the dynamic characteristic modeling unit, is used to generate cross-grid energy conversion guidance coefficients corresponding to at least one future scheduling cycle based on the power grid operating parameters, the state of charge value, the water temperature change rate and the preset scheduling target. The cross-grid energy conversion guidance coefficients are numerical sequences corresponding to future scheduling periods, and each value is used to indicate the guidance and intensity of the conversion between electrical energy and thermal energy in its corresponding period. The local controller unit is deployed on each electrothermal coupling device side and has a preset long-term operation plan mapping relationship and a short-term power command mapping relationship; A distributed coordination unit, which is connected to the central optimization unit and the local controller unit respectively, is used to distribute the cross-network energy conversion guidance coefficient sequence to the corresponding local controller unit.

[0007] Furthermore, the dynamic characteristic modeling unit calculates the real-time heat storage of the regional pipe network and building envelope based on the supply and return water temperatures and flow rates in the heating network operation parameters, and determines the state of charge value of the regional heating network by comparing it with the preset maximum safe heat storage and minimum heating demand heat storage.

[0008] Furthermore, the central optimization unit takes minimizing the total operating cost of the system within the future scheduling cycle as its optimization objective, and incorporates the state of charge values ​​of each regional heating network as core state variables into the constraints of the optimization model to generate the cross-network energy conversion guidance coefficient.

[0009] Furthermore, the constraints of the optimization model include: within any scheduling period, the regional heating network power regulation rate calculated based on the water temperature change rate and the pipeline heat capacity parameters is less than or equal to the upper limit of the overall power regulation capacity of the coupled equipment group in that region.

[0010] Furthermore, the equivalent electrical energy storage model includes a first characteristic module and a second characteristic module for series calculation; the first characteristic module is used to calculate the state of charge value of the regional heating network based on historical and current temperature data in the heating network operation parameters; the second characteristic module is used to calculate the water temperature change rate based on real-time temperature time-series data of key nodes in the heating network operation parameters.

[0011] Furthermore, the sign of any coefficient in the cross-grid energy conversion guidance coefficient sequence is used to define the dominant direction of energy conversion, and the absolute value of the coefficient is used to define the adjustment intensity of the energy conversion power in the dominant direction; wherein, when the value is positive, the dominant direction is to increase the conversion power from electrical energy to thermal energy, and when the value is negative, the dominant direction is to decrease the conversion power from electrical energy to thermal energy or increase the conversion power from thermal energy to electrical energy.

[0012] Furthermore, when the central optimization unit generates the cross-grid energy conversion guidance coefficient sequence, it performs the following optimization: with the goal of minimizing the total system operating cost or maximizing the renewable energy consumption, and maintaining the regional heating network state of charge value within a preset safe range as the first constraint condition, and using the regional maximum allowable power change rate determined based on the water temperature change rate and pipeline parameters as the second constraint condition.

[0013] Furthermore, the state of charge value of the regional heating network periodically calculated and output by the first characteristic module is used as the boundary condition parameter input when the second characteristic module calculates the rate of change of water temperature.

[0014] Furthermore, the long-term operation plan mapping relationship is to parse the received cross-grid energy conversion guidance coefficient sequence into adjustment instructions for the start-up and shutdown time, reference operating power, or working mode settings of the electrothermal coupling equipment in the corresponding scheduling period.

[0015] Furthermore, the short-term power command mapping relationship is calculated based on the coefficient value of the current period in the cross-grid energy conversion guidance coefficient sequence and the real-time collected water temperature change rate, to generate an adjustment amount for the real-time power setting value of the electrothermal coupling device.

[0016] Compared with the prior art, the beneficial effects of the present invention are that by constructing an energy management system that includes dynamic characteristic modeling, central optimization decision-making and distributed collaborative execution, the present invention realizes unified perception and collaborative scheduling of the operating status of urban power grid and heating network, transforms the huge inertial energy storage potential of heating network into flexible adjustment resources available to the power grid, thereby effectively improving the overall operating economy, renewable energy absorption capacity and energy supply reliability of urban integrated energy system.

[0017] Furthermore, by equating the dynamic characteristics of a regional heating network to an electrical energy storage model with a state of charge and a power change rate, this invention provides an accurate mathematical representation of the heating network inertia, which was originally difficult to quantify. This enables upper-level optimization scheduling to model and plan energy flows across energy systems with unified and clear state variables and constraints, laying the theoretical foundation for collaborative optimization.

[0018] Furthermore, this invention generates a cross-network energy conversion guidance coefficient sequence through a central optimization unit. This parameter does not directly correspond to specific equipment instructions, but serves as a strategy signal indicating the direction and intensity of energy conversion. This enables the solution of complex problems with multiple time scales and multiple physical constraints under a centralized optimization framework, and refines the optimization results into core collaborative instructions that can be passed down.

[0019] Furthermore, this invention reliably distributes guidance coefficients to the local controller through distributed collaborative units, constructing a collaborative architecture of "centralized optimization - distributed execution". This not only ensures the optimization effect based on global information, but also reduces the extreme dependence on the real-time performance of the communication network and the central computing power, thereby enhancing the reliability and scalability of the system implementation.

[0020] Furthermore, this invention, through the pre-set long-short mapping relationship of the local controller unit, autonomously parses the received guidance coefficient into specific control instructions that fit the characteristics of the device itself, realizing the organic combination of global collaborative strategy and local device actual operating conditions and safety boundaries, ensuring that the optimization intention can be implemented safely, accurately and flexibly.

[0021] Furthermore, through the closed-loop linkage of the above-mentioned units, the present invention forms a hierarchical and decoupled system control method, which enables the power grid and the heating network to achieve dynamic and mutually beneficial bidirectional energy support while maintaining their independent operation and safety, providing a feasible technical path for building efficient and resilient smart city energy infrastructure. Attached Figure Description

[0022] Figure 1 This is a connection block diagram of the energy management system for smart cities according to the present invention; Figure 2 This is a data flow diagram of the energy management system for smart cities according to the present invention; Figure 3 This is a logic block diagram of the dynamic characteristic modeling unit of the energy management system for smart cities according to the present invention. Figure 4 This is a logic block diagram of the central optimization unit of the energy management system for smart cities according to the present invention. Detailed Implementation

[0023] To make the objectives and advantages of the present invention clearer, the present invention will be further described below with reference to embodiments; it should be understood that the specific embodiments described herein are merely for explaining the present invention and are not intended to limit the present invention.

[0024] Preferred embodiments of the present invention will now be described with reference to the accompanying drawings. Those skilled in the art should understand that these embodiments are merely illustrative of the technical principles of the present invention and are not intended to limit the scope of protection of the present invention.

[0025] It should be noted that in the description of this invention, the terms "upper", "lower", "left", "right", "inner", "outer", etc., which indicate directions or positional relationships, are based on the directions or positional relationships shown in the accompanying drawings. This is only for the convenience of description and is not intended to indicate or imply that the device or element must have a specific orientation, or be constructed and operated in a specific orientation. Therefore, it should not be construed as a limitation of this invention.

[0026] Furthermore, it should be noted that, in the description of this invention, unless otherwise explicitly specified and limited, the terms "installation," "connection," and "linking" should be interpreted broadly. For example, they can refer to a fixed connection, a detachable connection, or an integral connection; they can refer to a mechanical connection or an electrical connection; they can refer to a direct connection or an indirect connection through an intermediate medium; and they can refer to the internal connection of two components. Those skilled in the art can understand the specific meaning of the above terms in this invention according to the specific circumstances.

[0027] Please see Figure 1 and Figure 2The diagrams shown are a connection block diagram and a data flow diagram of the energy management system for smart cities according to the present invention. The present invention provides an energy management system for smart cities, comprising: The data acquisition unit is connected to the urban power grid monitoring system and the urban heating network monitoring system to collect power grid operating parameters and heating network operating parameters. In one specific embodiment, the data acquisition unit securely and reliably interfaces with existing urban power grid monitoring systems (SCADA / EMS) and urban heating network monitoring systems via standardized communication protocols and interfaces. For the power grid side, the unit acquires real-time and near-real-time operating parameters from the power grid monitoring system through interfaces based on commonly used power system communication protocols such as IEC 61850, IEC 60870-5-104, or DNP3.0. For the heating network side, the unit acquires relevant data from the heating network monitoring system through OPCUA, Modbus TCP / IP, or industry-compliant dedicated API interfaces.

[0028] Understandably, this data acquisition unit does not involve any modification to the existing monitoring system of the power grid or heating network. Instead, it serves as a unified data entry point for the upper-level collaborative management system. The performance of this unit depends on the reliability of the communication network and the standardization of the selected protocol. It is preferable to use industrial Ethernet with redundant configuration and internationally / industry-widely accepted communication protocols to ensure the continuity and accuracy of the data flow, providing a reliable data foundation for subsequent modeling, optimization, and control.

[0029] Please continue reading. Figure 3 As shown, it is a logic block diagram of the dynamic characteristic modeling unit of the energy management system for smart cities according to the present invention. A dynamic characteristic modeling unit, which is connected to the data acquisition unit, is used to establish an equivalent electrical energy storage model of the regional heating network based on the heating network operating parameters and pipeline topology. Based on the equivalent electrical energy storage model, the state of charge value of the regional heating network, which characterizes the long-term energy storage state of the regional heating network, and the water temperature change rate, which characterizes the short-term power response capability of the regional heating network, are calculated. Specifically, the equivalent electrical energy storage model includes a first characteristic module and a second characteristic module for series calculation; the first characteristic module is used to calculate the state of charge value of the regional heating network based on historical and current temperature data in the heating network operation parameters; the second characteristic module is used to calculate the water temperature change rate based on real-time temperature time-series data of key nodes in the heating network operation parameters.

[0030] Specifically, the dynamic characteristic modeling unit calculates the real-time heat storage of the regional pipe network and building envelope based on the supply and return water temperature and flow rate in the heating network operation parameters, and determines the state of charge value of the regional heating network by comparing it with the preset maximum safe heat storage and minimum heating demand heat storage.

[0031] Specifically, the state of charge value of the regional heating network calculated and output by the first characteristic module is used as the boundary condition parameter input when the second characteristic module calculates the rate of change of water temperature.

[0032] In one specific embodiment, the dynamic characteristic modeling unit performs the following modeling and calculation process based on the heating network operation parameters obtained from the data acquisition unit and the preset pipeline topology.

[0033] First, the first characteristic module calculates the real-time sensible heat storage capacity of the pipe network and building envelope in the target area based on the average temperature and total flow rate of the supply and return water. The calculation formula is as follows: Q stored (t)=ρ×c p ×V pipe ×(T sup,avg (t) T env )+C bld ×(T indoor,avg (t) T env ); Among them, Q stored (t) represents the total real-time heat storage in the region during time period t, in joules (J); ρ is the density of water, in kilograms per cubic meter (kg / m³). 3 The value is 1000 kg / m³. 3 c p V is the specific heat capacity of water at constant pressure, expressed in joules per kilogram per degree Celsius (J / (kg·℃)), and is taken as 4182 J / (kg·℃); pipe The total water volume of the target area's pipe network, calculated based on the network topology, is expressed in cubic meters (m³). 3 Its value is determined based on the engineering design drawings; T sup,avg (t) represents the weighted average temperature of the supply and return water in the region during time period t, in degrees Celsius (°C); T env This is the environmental reference temperature, typically taken as the local outdoor calculated temperature or groundwater temperature, expressed in degrees Celsius (°C). bld The comprehensive heat capacity of the building envelope in the target area, expressed in joules per degree Celsius (J / ℃), can be estimated based on building type, area, and structural materials; T indoor,avg(t) represents the average indoor temperature of the area during time period t, in degrees Celsius (°C), which can be obtained from user-side data of the heating network monitoring system or the building automation system.

[0034] Furthermore, the first characteristic module calculates the state of charge value of the regional heating network using the following formula: SOC thermal (t)=[Q stored (t) Q min ] / [Q max Q min ]; Among them, SOC thermal (t) represents the state of charge value of the regional heating network, which is dimensionless and ranges from [0, 1]; Q max Q represents the preset maximum safe heat storage capacity for the area, expressed in joules (J). Its value is determined comprehensively based on the pipeline design pressure, upper temperature limit, and upper building comfort limit. min The preset heat storage capacity is the minimum heating requirement, measured in joules (J). Its value is calculated based on the minimum water temperature and flow rate required to ensure basic heating needs for residents.

[0035] Understandably, the core of the first characteristic module is to map physical heat storage into a normalized state index. In the formula, the calculation of real-time heat storage integrates the thermal inertia of both the pipe network water body and the building structure. The calculation of the state of charge (SOC) essentially quantifies the relative position of the current heat storage within the safe operating range. The setting of maximum and minimum heat storage defines a safety boundary for the flexible adjustment of the entire thermal system, ensuring that subsequent optimized scheduling will not jeopardize heating safety and equipment safety. This module performs calculations periodically (e.g., every 15 minutes) and outputs the current SOC value of the regional heating network. thermal value.

[0036] Then, the second characteristic module receives the regional heating network state of charge (SOC) value output by the first characteristic module. thermal The value is used as a boundary condition, and the water temperature change rate is calculated based on real-time temperature time-series data of key nodes (such as heat source outlets and main pipe junctions). This module simplifies the local pipe network into a first-order inertial element, and the calculation method for its water temperature change rate can be expressed as: α T (t)≈[T key (t) T key (t 1)] / Δt×f(SOC thermal (t)); Where, α T(t) represents the rate of change of water temperature during time period t, expressed in degrees Celsius per second (°C / s). It characterizes the maximum potential rate at which the temperature of critical nodes can be changed under the current energy storage conditions of the system; T key (t) represents the measured water temperature at the key node during time period t, in degrees Celsius (°C); Δt represents the calculation time interval, in seconds (s), which is the time interval between two consecutive calculations; f(SOC) thermal (t) is a value related to SOC thermal The relevant constraint function is dimensionless, takes values ​​in the range [0, 1], and the constraint relationship is: when SOC thermal When the temperature approaches 1 (saturation point), f approaches 0, indicating that it is difficult to increase the temperature further; when the state of charge (SOC) is close to 1 (saturation point), f approaches 0, indicating that it is difficult to increase the temperature further. thermal When the temperature approaches 0 (insufficient heat storage), f also approaches 0, indicating that it is difficult to cool down further; when the SOC... thermal When the value is in the middle safety range (e.g., 0.2 to 0.8), f can take a larger value (e.g., 0.8 to 1.0), indicating that it has a greater ability to change temperature.

[0037] Understandably, the flexibility of a thermal system stems from two levels: first, the total amount of heat energy stored within the system, which determines the scale and timescale of energy transfer; and second, the rate of temperature change of the heat carrier (hot water) within the system, which determines the speed at which the system can absorb or release heat energy to respond to the grid's rapid adjustment needs. The first characteristic module quantifies the system's macroscopic "energy stock" by calculating the state of charge value, similar to assessing the remaining charge of a battery. The second characteristic module, under the constraint of the current "energy stock," further evaluates the system's microscopic "power variation capability," similar to assessing the allowable charging and discharging current of a battery at its current charge level. These two modules work in series. First, the "energy stock" module determines the system's overall adjustable energy boundary and safe position. Then, this boundary information is passed to the "power variation capability" module, which assesses the system's real-time, local power regulation potential within this boundary. This transforms the complex heat transfer and fluid dynamics characteristics of the heating network into familiar and calculable concepts of "energy storage state" and "power ramp-up rate" in the field of energy dispatch, providing a unified and easily calculable dynamic constraint basis for electro-thermal synergistic optimization.

[0038] Please continue reading. Figure 4 As shown, it is a logic block diagram of the central optimization unit of the energy management system for smart cities according to the present invention. The central optimization unit, which is connected to the data acquisition unit and the dynamic characteristic modeling unit, is used to generate cross-grid energy conversion guidance coefficients corresponding to at least one future scheduling cycle based on the power grid operating parameters, the state of charge value, the water temperature change rate and the preset scheduling target. The cross-grid energy conversion guidance coefficients are numerical sequences corresponding to future scheduling periods, and each value is used to indicate the guidance and intensity of the conversion between electrical energy and thermal energy in its corresponding period. Specifically, the central optimization unit takes minimizing the total operating cost of the system within the future scheduling cycle as its optimization objective, and incorporates the state of charge values ​​of each regional heating network as core state variables into the constraints of the optimization model to generate the cross-network energy conversion guidance coefficient.

[0039] Specifically, the sign of any coefficient in the cross-grid energy conversion guidance coefficient sequence is used to define the dominant direction of energy conversion, and the absolute value of the coefficient is used to define the adjustment intensity of the energy conversion power in the dominant direction; wherein, when the value is positive, the dominant direction is to increase the conversion power from electrical energy to thermal energy, and when the value is negative, the dominant direction is to decrease the conversion power from electrical energy to thermal energy or increase the conversion power from thermal energy to electrical energy.

[0040] Specifically, when the central optimization unit generates the cross-grid energy conversion guidance coefficient sequence, it performs the following optimization: with the goal of minimizing the total system operating cost or maximizing the renewable energy consumption, and maintaining the regional heating network state of charge value within a preset safe range as the first constraint, and using the regional maximum allowable power change rate determined based on the water temperature change rate and pipeline parameters as the second constraint.

[0041] Specifically, the constraints of the optimization model include: within any scheduling period, the regional heating network power regulation rate calculated based on the water temperature change rate and the pipeline heat capacity parameters is less than or equal to the upper limit of the overall power regulation capacity of the coupled equipment group in that region.

[0042] In one specific embodiment, the central optimization unit uses model predictive control as its framework and employs a mixed-integer linear programming or quadratic programming solver to perform rolling optimization calculations. Its core is to construct and solve an optimization model with a view of a future scheduling cycle (e.g., the next 24 hours, with 1-hour time intervals).

[0043] The objective function of this optimization model is to minimize the total operating cost of the system, and its expression can be constructed as follows: ; Where T is the total number of time periods within the scheduling period, which is dimensionless; t is the time period index; C E(t) represents the grid purchase price of electricity during time period t, expressed in yuan per kilowatt-hour (¥ / kWh), and its value is derived from the nodal marginal price or time-of-use price obtained by the data acquisition unit; P grid (t) represents the active power purchased by the system from the public grid during time period t, in kilowatts (kW), and is a decision variable; C G (t) represents the unit price of natural gas during time period t, in yuan per standard cubic meter (¥ / Nm³). 3 The value of G(t) is derived from market information; G(t) represents the amount of natural gas consumed by the gas-fired combined heat and power unit or gas-fired boiler during time period t, in standard cubic meters per hour (Nm³). 3 / h), are decision variables; C SU SU(t) is the start-up and shutdown penalty cost coefficient for electrothermal coupling equipment (such as large heat pumps), with the unit being RMB per instance (¥ / time), and the value range being RMB 100 to RMB 500 per instance, preferably RMB 200 per instance, used to smooth equipment operation; SU(t) is the equipment start-up event that occurs during time period t, and is an integer decision variable from 0 to 1.

[0044] The optimized model must meet the following core constraints: Electric power balance constraints: P grid (t)+P RES (t)+P CHP,e (t)=P load,e (t)+P HP (t)+P EB (t); Among them, P RES (t) represents the predicted output of renewable energy (wind power, photovoltaic) during time period t, in kilowatts (kW); P CHP,e (t) represents the power generation of the combined heat and power unit during time period t, in kilowatts (kW); P load,e (t) represents the conventional electrical load excluding the coupling equipment during time period t, in kilowatts (kW); P HP (t) represents the electrical power consumed by the electric heat pump during time period t, in kilowatts (kW); P EB (t) represents the electrical power consumed by the electric boiler during time period t, in kilowatts (kW).

[0045] Thermal power balance and dynamic constraints of heating networks: H CHP,h (t)+H GB (t)+H HP (t)+H EB (t) H load,h (t)= ρ×c p ×V sys ×[T avg (t+1) T avg (t)] / Δt; Among them, H CHP,h (t), H GB (t), H HP (t), H EB (t) represents the thermal power generated by the combined heat and power unit, gas boiler, heat pump, and electric boiler during time period t, respectively, in kilowatts (kW); H load,h (t) represents the user's heat load during time period t, in kilowatts (kW); ρ×c p ×V sys The equivalent heat capacity of the system is expressed in kilojoules per degree Celsius (kJ / ℃), and its value is determined based on the regional heat capacity parameters of the dynamic characteristic modeling unit; T avg (t) represents the weighted average water temperature of the system during time period t, in degrees Celsius (°C); Δt represents the length of the scheduling period, in hours (h). This equation couples the dynamics of the heating network with the thermal power balance.

[0046] Regional heating network charge state constraints (first constraint condition): SOC min ≤SOC thermal (t)≤SOC max , t; SOC thermal (t)=SOC thermal (t 1)+[η×P in (t) P out (t) / η] / (Q max Q min )×Δt; Among them, SOC min and SOC max These are the preset upper and lower limits of the safety range, dimensionless, with preferred values ​​of 0.2 and 0.9 respectively; η is the comprehensive efficiency coefficient, dimensionless, ranging from 0.85 to 0.95; P in (t) represents the net increase in heating power (e.g., electric heating) input to the heating network during time period t, expressed in kilowatts (kW); P out (t) represents the net heat power extracted from the heating network during time period t (if used for power generation), in kilowatts (kW). This constraint uses SOC as the core state variable to ensure that it remains within a safe range throughout the scheduling cycle and reverts to the expected value at the end of the cycle.

[0047] Maximum allowable rate of change of power in the region (second constraint): |P in (t) Pout (t) (P in (t 1) P out (t 1))∣ / Δt≤R max (t); R max (t)=k×α T (t)×ρc p V pipe ; Among them, R max (t) represents the maximum allowable net heat power change rate of the regional heating network during time period t, in kilowatts per hour (kW / h); k is the safety factor, dimensionless, ranging from 0.7 to 0.9, preferably 0.8, used to allow for adjustment margin; α T (t) represents the water temperature change rate over time period t provided by the dynamic characteristic modeling unit, in degrees Celsius per hour (°C / h); ρc p V pipe The heat capacity of the water in the regional pipe network is expressed in kilojoules per degree Celsius (kJ / ℃). This constraint ensures that the power regulation rate does not exceed the physical limit determined by the dynamic characteristics of the heating network, and is compared with and the smaller of the overall power regulation capacity of the coupled equipment group.

[0048] After solving the model, calculate the cross-network energy conversion guidance coefficient K(t) for each future scheduling period t: ; Where K(t) is the guidance coefficient for time period t, which is dimensionless; λ is the normalization coefficient, which is dimensionless and ranges from 1 to 10, preferably 5, and is used to map the power change to a suitable coefficient range. and The reference electrothermal power is given without considering electrothermal synergy, and the unit is kilowatt (kW); P rated The total rated power of the regional electrothermal coupling equipment is given in kilowatts (kW). The sign of K(t) indicates the dominant direction of energy conversion (positive for electro-to-heat, negative for de-heating or heat-to-electricity), and its absolute value indicates the relative level of regulation intensity. The central optimization unit re-executes the above optimization in a rolling manner (e.g., every 15 minutes) to update the K(t) sequence for future scheduling cycles.

[0049] Understandably, the above model uses mathematical programming to solve for scheduling objectives and multiple physical constraints within a unified framework. The cost coefficient in the objective function is directly related to economic efficiency; the electrothermal power balance constraint ensures the most basic energy supply and demand conservation; and the core innovation lies in the dynamic constraints of the heating network with the state of charge value as the state variable and the maximum power change rate constraint based on the water temperature change rate. The former quantifies the long-term energy storage capacity of the heating network as a dispatchable resource and sets its safe operating boundary to prevent overuse or storage; the latter quantifies the short-term thermal inertia response capability of the heating network as a rate limit for power regulation, ensuring the physical feasibility of scheduling commands. The formula for the guidance coefficient extracts and normalizes the power deviation of the electrothermal conversion equipment relative to the baseline operation in the optimization solution, thereby generating a global coordination signal that does not directly correspond to specific equipment but clearly indicates the direction and urgency of energy flow.

[0050] Understandably, the optimization goal drives the system to find the operating trajectory with the lowest cost or the greatest renewable energy consumption. Constraints, especially those embedding the dynamics of the heating network's state of charge and the limits of power change rate, ensure that this trajectory is thermodynamically and hydrodynamically feasible and safe. The final output sequence of guidance coefficients is a high-level generalization and feature extraction of this optimal trajectory. It strips away the details of specific equipment instructions, retaining the core strategic information of "when, in which direction, and with what intensity to adjust the electrothermal energy conversion." This approach ensures the optimization accuracy based on the global model while reducing the extreme dependence on the real-time performance and reliability of lower-level communication by decoupling the strategy from execution, achieving synergy between centralized optimization and distributed execution.

[0051] The local controller unit is deployed on each electrothermal coupling device side and has a preset long-term operation plan mapping relationship and a short-term power command mapping relationship; Specifically, the long-term operation plan mapping relationship is to parse the received cross-grid energy conversion guidance coefficient sequence into adjustment instructions for the start-up and shutdown time, reference operating power, or working mode settings of the electrothermal coupling equipment in the corresponding scheduling period.

[0052] Specifically, the short-term power command mapping relationship is calculated based on the coefficient value of the current period in the cross-grid energy conversion guidance coefficient sequence and the real-time collected water temperature change rate, to generate an adjustment amount for the real-time power setting value of the electrothermal coupling device.

[0053] In one specific embodiment, the local controller unit is deployed in the field control cabinet or programmable logic controller of each electrothermal coupling device (such as a combined heat and power unit, an electric boiler, an electric heat pump, etc.). This unit receives a sequence of cross-network energy conversion guidance coefficients for future scheduling cycles from the distributed coordination unit via industrial Ethernet or fieldbus, and executes preset mapping logic to generate device-level control commands. The implementation of this unit primarily relies on mature industrial control technologies and embedded software.

[0054] For long-term operation plan mapping relationships, a parsing module containing a rule base and simple optimization algorithms is pre-installed within the local controller unit. Upon receiving the guidance coefficient sequence, this module first combines the equipment's own operating characteristic parameters (such as start-up and shutdown time constants, power regulation range, and efficiency curves) with preset local constraints (such as maintenance plans and fuel contracts). Then, the parsing module takes the guidance coefficient values ​​for each future time period, combines the predicted local heat / electricity load for that period with the real-time electricity price signal obtained from the data acquisition unit, and uses rule judgment or small-scale linear programming calculations to generate adjustment instructions for the corresponding scheduling period's equipment start-up and shutdown time suggestions, baseline operating power setpoints, or operating modes (such as the "heat-driven power" or "electricity-driven heat" mode for cogeneration units). These instructions are converted into a setpoint sequence or time schedule recognizable by the equipment control system and sent in advance to the equipment-level actuators for pre-setting.

[0055] For short-term power command mapping, a fast-response power adjustment calculation module is pre-installed within the local controller unit. This module reads the coefficient values ​​for the current time period from the guidance coefficient sequence within each short-cycle control step (e.g., 5 minutes) and collects key parameters such as the local water temperature change rate from the data acquisition unit in real time. Based on a preset response curve or proportional-integral algorithm, the module calculates and generates an adjustment amount for the real-time power setpoint of the associated electrothermal coupling equipment. For example, when the guidance coefficient is positive and the water temperature change rate indicates that the system has heating potential, the module increases the power setpoint of the electric boiler or heat pump by a preset ratio; when the guidance coefficient is negative, it correspondingly decreases the power setpoint or switches to a low-power mode. This adjustment amount, after considering the equipment's instantaneous adjustment capability and safety boundaries, is superimposed on the long-term planned baseline power to form the final real-time power control command, which is sent to the equipment driver via analog output or communication messages.

[0056] Understandably, this unit serves as the execution link in the system's "centralized optimization, distributed execution" architecture. Its function is to transform strategic signals (guidance coefficients) issued from higher levels, which possess time dimensions and directional strength, into safe, feasible, and optimized specific equipment action instructions, combined with the real-time operating conditions and physical limitations of local devices. Long-term mapping focuses on adjustments at the planning level, aligning equipment operation plans with global collaborative strategies in time; short-term mapping focuses on real-time power tracking, enabling devices to quickly respond to dynamic system demands. The combination of these two approaches ensures the implementation of global optimization intentions while fully utilizing the rapid response and detailed operating information of local controllers, achieving a balance between policy flexibility and execution reliability.

[0057] A distributed coordination unit, which is connected to the central optimization unit and the local controller unit respectively, is used to distribute the cross-network energy conversion guidance coefficient sequence to the corresponding local controller unit.

[0058] In one specific embodiment, the distributed collaborative unit, as an independently deployed software module or microservice, runs on an industrial server with high reliability and network interoperability. This unit connects to the central optimization unit via an internal high-speed data bus, acquiring in real-time the rollingly generated cross-network energy conversion guidance coefficient sequences for different regions or equipment clusters, along with their corresponding timestamps and validity periods. Simultaneously, this unit connects to various local controller units deployed in the field via an isolated industrial communication network with secure authentication mechanisms (such as industrial Ethernet using a virtual private network).

[0059] The core function of this unit is to reliably distribute and synchronize policy data. Its implementation is based on mature middleware technology and an industrial communication protocol stack. When a new batch of guidance coefficient sequence data packets is received from the central optimization unit, this unit first performs data integrity verification and timestamp alignment. Subsequently, based on the device or region identification information embedded in the data packets, it determines one or more target local controller units that need to receive the sequence through a pre-configured routing table. The data distribution process preferably adopts a publish / subscribe model, such as using protocols suitable for industrial IoT scenarios like OPC UA PubSub or MQTT, to achieve efficient, asynchronous one-to-many communication. Each distributed message contains the complete guidance coefficient sequence for the future scheduling cycle, the absolute time of sequence generation, the start and end times of the scheduling cycle, and the data version number, ensuring that each local controller unit can make decisions based on consistent and timely policy information. To ensure communication reliability, this unit implements retransmission and acknowledgment mechanisms and monitors network latency and controller processing status. If the central optimization unit fails to generate new data in a timely manner due to unforeseen circumstances, the unit can continue to issue the valid data from the previous cycle or generate backup data according to preset rules to ensure the continuity of lower-level control commands.

[0060] The technical solution of the present invention has been described above with reference to the preferred embodiments shown in the accompanying drawings. However, it will be readily understood by those skilled in the art that the scope of protection of the present invention is obviously not limited to these specific embodiments. Without departing from the principles of the present invention, those skilled in the art can make equivalent changes or substitutions to the relevant technical features, and the technical solutions after these changes or substitutions will all fall within the scope of protection of the present invention.

Claims

1. An energy management system for smart city, characterized in that, include: The data acquisition unit is connected to the urban power grid monitoring system and the urban heating network monitoring system to collect power grid operating parameters and heating network operating parameters. A dynamic characteristic modeling unit, which is connected to the data acquisition unit, is used to establish an equivalent electrical energy storage model of the regional heating network based on the heating network operating parameters and pipeline topology. Based on the equivalent electrical energy storage model, the state of charge value of the regional heating network, which characterizes the long-term energy storage state of the regional heating network, and the water temperature change rate, which characterizes the short-term power response capability of the regional heating network, are calculated. The central optimization unit, which is connected to the data acquisition unit and the dynamic characteristic modeling unit, is used to generate cross-grid energy conversion guidance coefficients corresponding to at least one future scheduling cycle based on the power grid operating parameters, the state of charge value, the water temperature change rate and the preset scheduling target. The cross-grid energy conversion guidance coefficients are numerical sequences corresponding to future scheduling periods, and each value is used to indicate the guidance and intensity of the conversion between electrical energy and thermal energy in its corresponding period. The local controller unit is deployed on each electrothermal coupling device side and has a preset long-term operation plan mapping relationship and a short-term power command mapping relationship; A distributed coordination unit, which is connected to the central optimization unit and the local controller unit respectively, is used to distribute the cross-network energy conversion guidance coefficient sequence to the corresponding local controller unit.

2. The energy management system for smart cities according to claim 1, characterized in that, The dynamic characteristic modeling unit calculates the real-time heat storage of the regional pipe network and building envelope based on the supply and return water temperature and flow rate in the heating network operation parameters, and determines the state of charge value of the regional heating network by comparing it with the preset maximum safe heat storage and minimum heating demand heat storage.

3. The energy management system for smart cities according to claim 2, characterized in that, The central optimization unit takes minimizing the total operating cost of the system within the future scheduling cycle as its optimization objective, and incorporates the state of charge values ​​of each regional heating network as core state variables into the constraints of the optimization model to generate the cross-network energy conversion guidance coefficient.

4. The energy management system for smart cities according to claim 3, characterized in that, The constraints of the optimization model include: within any scheduling period, the regional heating network power regulation rate calculated based on the water temperature change rate and the pipeline heat capacity parameters is less than or equal to the upper limit of the overall power regulation capacity of the coupled equipment group in that region.

5. The energy management system for smart cities according to claim 4, characterized in that, The equivalent electrical energy storage model includes a first characteristic module and a second characteristic module for series calculation; the first characteristic module is used to calculate the state of charge value of the regional heating network based on historical and current temperature data in the heating network operation parameters. The second characteristic module is used to calculate the water temperature change rate based on the real-time temperature time series data of key nodes in the heating network operation parameters.

6. The energy management system for smart cities according to claim 5, characterized in that, The sign of any coefficient in the cross-grid energy conversion guidance coefficient sequence is used to define the dominant direction of energy conversion, and the absolute value of the coefficient is used to define the adjustment intensity of the energy conversion power in the dominant direction; wherein, when the value is positive, the dominant direction is to increase the conversion power from electrical energy to thermal energy, and when the value is negative, the dominant direction is to decrease the conversion power from electrical energy to thermal energy or increase the conversion power from thermal energy to electrical energy.

7. The energy management system for smart cities according to claim 6, characterized in that, When the central optimization unit generates the cross-grid energy conversion guidance coefficient sequence, it performs the following optimization: with the goal of minimizing the total system operating cost or maximizing the renewable energy consumption, and maintaining the regional heating network state of charge value within a preset safe range as the first constraint, and using the regional maximum allowable power change rate determined based on the water temperature change rate and pipeline parameters as the second constraint.

8. The energy management system for smart cities according to claim 7, characterized in that, The state of charge value of the regional heating network calculated and output by the first characteristic module periodically is used as the boundary condition parameter input when the second characteristic module calculates the rate of change of water temperature.

9. The energy management system for smart cities according to claim 8, characterized in that, The long-term operation plan mapping relationship is to parse the received cross-grid energy conversion guidance coefficient sequence into adjustment instructions for the start-up and shutdown time, reference operating power, or working mode settings of the electrothermal coupling equipment in the corresponding scheduling period.

10. The energy management system for smart cities according to claim 9, characterized in that, The short-term power command mapping relationship is calculated based on the coefficient value of the current time period in the cross-grid energy conversion guidance coefficient sequence and the real-time collected water temperature change rate, to generate the adjustment amount of the real-time power setting value of the electrothermal coupling device.

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