A method and system for coordinating frequency regulation of an electric-thermal integrated energy system

Abstract

The application discloses an electric-thermal comprehensive energy system collaborative frequency modulation method and system, the method comprising: obtaining a frequency deviation parameter in an electric power subsystem and a thermal energy storage state parameter in a thermal power subsystem as a real-time frequency modulation parameter; calculating a frequency deviation change rate and combining the real-time frequency modulation parameter to evaluate a thermal energy storage capacity margin value, generating a composite frequency modulation demand index; generating a multi-time scale frequency modulation task; based on the multi-time scale frequency modulation task, generating an electric-thermal weight distribution value by fusing the frequency deviation change rate and the thermal energy storage capacity margin value; generating an electric-thermal collaborative frequency modulation instruction according to the electric-thermal weight distribution value; and executing the electric-thermal collaborative frequency modulation instruction to control frequency modulation devices in the electric power subsystem and the thermal power subsystem. The application adopts a multi-time scale task decomposition and dynamic weight distribution strategy based on a composite frequency modulation demand index, can realize deep collaboration and optimized control of electric-thermal resources, and improves the frequency modulation performance and operation economy of the comprehensive energy system.

Description

Technical Field

[0001] This invention relates to the field of power system control, specifically to a method and system for coordinated frequency regulation of an integrated electric-thermal energy system. Background Technology

[0002] As a crucial component of the modern energy system, the integrated electric-thermal energy system aims to improve energy efficiency and system operational flexibility through unified planning, coordinated dispatch, and complementary utilization of electrical and thermal energy. In power systems, frequency stability is a key indicator of power quality, directly reflecting the supply-demand balance of active power. With the grid integration of high-proportion renewable energy sources such as wind and solar power, their inherent volatility and uncertainty pose significant challenges to power system frequency stability, thus requiring more flexible and efficient frequency regulation resources to maintain system security.

[0003] Existing integrated electric-thermal energy systems participate in grid frequency regulation, typically by utilizing cogeneration units, electric boilers, or heat pumps within the system for power adjustment. Some control strategies rely primarily on simple responses to grid frequency deviations, such as setting a fixed frequency dead zone and regulation coefficient, and proportionally adjusting the electrical power of relevant equipment when the frequency deviation exceeds a threshold. Some improved solutions consider the status of thermal storage devices, setting simple high and low liquid level or temperature thresholds to prevent equipment from operating beyond its limits. However, in terms of coordinating the two different dynamic characteristics of electricity and heat resources, static power allocation strategies or master-slave control modes are often adopted.

[0004] However, there is an order-of-magnitude difference in the response speed between electrical and thermal equipment. Static or simple coordination strategies cannot solve the problem of asynchronous response between the two, resulting in delays and deviations in frequency regulation power output, thus weakening the overall frequency regulation effect. Secondly, most existing methods lack forward-looking judgment on the trend of grid frequency changes, belonging to passive response control, which is difficult to cope with the drastic frequency fluctuations caused by rapid changes in renewable energy. In addition, existing frequency regulation strategies usually fail to fully explore and utilize the huge thermal inertia inherent in the heating network itself as a flexible energy storage resource, and when allocating frequency regulation tasks, there is a lack of a dynamic coordination mechanism that can simultaneously take into account the urgency of grid frequency regulation needs and the operational safety constraints of the thermal system itself. Summary of the Invention

[0005] Purpose of the invention: The purpose of this invention is to provide a method and system for coordinated frequency regulation of an integrated electric-thermal energy system, which can realize deep coordination and optimized control of electric and thermal resources, and improve the frequency regulation performance and operational economy of the integrated energy system.

[0006] Technical solution: The present invention provides a method for coordinated frequency regulation of an integrated electric-thermal energy system, comprising:

[0007] The frequency deviation parameters in the power subsystem and the thermal energy storage state parameters in the thermal subsystem are obtained as real-time frequency regulation parameters.

[0008] Based on real-time frequency regulation parameters, the frequency deviation change rate is calculated, and the thermal energy storage capacity margin is evaluated in combination with the real-time frequency regulation parameters to generate a composite frequency regulation demand index.

[0009] Based on the composite frequency modulation requirement indicators, multi-timescale frequency modulation tasks are generated; among them, multi-timescale frequency modulation tasks include second-level frequency modulation sub-tasks, minute-level frequency modulation sub-tasks, and hour-level frequency modulation sub-tasks.

[0010] Based on the multi-timescale frequency modulation task, an electrothermal weight allocation value is generated by integrating the frequency deviation change rate and the thermal energy storage capacity margin value.

[0011] Based on the electrothermal weight allocation value, an electrothermal coordinated frequency modulation command is generated; a virtual energy storage unit is constructed based on the thermal-hydraulic dynamic characteristics of the heating network to achieve temperature balance control; the electrothermal coordinated frequency modulation command is optimized and adjusted in combination with real-time operating conditions and economic indicators to generate an optimized electrothermal coordinated frequency modulation command.

[0012] Execute optimized electrothermal coordinated frequency modulation commands to control frequency modulation equipment within the power subsystem and thermal subsystem.

[0013] Furthermore, acquiring the frequency deviation parameters in the power subsystem and the thermal energy storage state parameters in the thermal subsystem as real-time frequency adjustment parameters includes:

[0014] Frequency deviation data of the power subsystem is collected by a frequency sensor to obtain frequency deviation parameters;

[0015] Temperature and pressure data of thermal pipelines in the thermal subsystem are collected by temperature sensors and flow meters to obtain thermal energy storage state parameters.

[0016] The frequency deviation parameters and thermal energy storage state parameters are filtered and normalized to obtain the real-time frequency adjustment parameters.

[0017] Furthermore, the calculation of the frequency deviation change rate based on real-time frequency adjustment parameters, and the evaluation of the thermal energy storage capacity margin value in conjunction with the real-time frequency adjustment parameters, to generate a composite frequency regulation demand index, includes:

[0018] Retrieve renewable energy forecast data for a predetermined time period from a pre-defined database;

[0019] By combining the frequency deviation parameter in the real-time frequency regulation parameters and renewable energy prediction data, time series difference operation is performed on the frequency deviation parameter to obtain the frequency deviation change rate.

[0020] Based on the thermal energy storage status parameters in the real-time frequency adjustment parameters, the current thermal energy storage capacity is compared with the preset capacity safety threshold to obtain the thermal energy storage capacity margin value.

[0021] By integrating the frequency deviation change rate and the thermal energy storage capacity margin, a composite frequency regulation demand index is generated.

[0022] Furthermore, the step of generating multi-time-scale frequency modulation tasks based on composite frequency modulation demand indicators includes:

[0023] When the frequency deviation change rate in the composite frequency regulation demand index exceeds the preset change rate threshold, a second-level frequency regulation sub-task is generated and assigned to the power subsystem.

[0024] When the thermal energy storage capacity margin value in the composite frequency regulation demand index is lower than the preset capacity limit threshold, the frequency regulation task allocated to the thermal subsystem is adjusted to generate minute-level frequency regulation sub-tasks.

[0025] By combining pre-acquired historical load data with renewable energy forecast data, hourly frequency regulation sub-tasks are generated.

[0026] Integrate second-level, minute-level, and hour-level frequency modulation subtasks to form a multi-timescale frequency modulation task.

[0027] Furthermore, the multi-timescale frequency modulation task generates an electrothermal weight allocation value by fusing the frequency deviation change rate and the thermal energy storage capacity margin value, including:

[0028] Obtain the frequency deviation change rate and thermal energy storage capacity margin value from the composite frequency regulation demand index to obtain the basic dataset;

[0029] Based on the multi-timescale frequency regulation task and basic dataset, the electrical weight and thermal weight are calculated through an online rolling optimization algorithm; whereby the electrical weight corresponds to the frequency regulation contribution of the power subsystem and the thermal weight corresponds to the frequency regulation contribution of the thermal subsystem.

[0030] The electrical weight and thermal weight are integrated to form an electrical and thermal weight allocation value.

[0031] Furthermore, the step of generating electrothermal coordinated frequency modulation commands based on the electrothermal weight allocation value includes:

[0032] Based on the weighted allocation values ​​for electricity and heat, the total frequency regulation power demand is decomposed into the target frequency regulation power for electricity and the target frequency regulation power for heat.

[0033] Based on the target frequency regulation power, generate power frequency regulation sub-commands that include gas turbine output, energy storage battery charging and discharging, and interruptible load control signals;

[0034] Based on the thermal target frequency modulation power, generate thermal frequency modulation sub-commands that include heat pump power, heat storage tank charging and discharging rate and heat exchange station valve opening control signals;

[0035] Integrate the power frequency regulation sub-command and the thermal frequency regulation sub-command to form a coordinated power and thermal frequency regulation command.

[0036] Furthermore, the construction of a virtual energy storage unit based on the thermal-hydraulic dynamic characteristics of the heating network for temperature equalization control includes:

[0037] A virtual energy storage unit is constructed based on the heating network and the thermal-hydraulic dynamic characteristics in the heating network, and the adjustable temperature range of the virtual energy storage unit is calculated to generate the virtual energy storage capacity of the virtual energy storage unit.

[0038] Based on the virtual energy storage capacity, the heat storage capacity of the virtual energy storage unit can be changed by adjusting the water supply temperature and flow rate.

[0039] Based on the change in the heat storage capacity of the virtual energy storage unit, virtual power compensation is generated using the calibrated thermoelectric conversion efficiency coefficient.

[0040] A distributed control strategy is adopted to coordinate multiple heat source nodes, and temperature balance regulation is performed based on the virtual power compensation.

[0041] Furthermore, the optimization and adjustment of the electrothermal coordinated frequency modulation command by combining real-time operating conditions and economic indicators to generate an optimized electrothermal coordinated frequency modulation command includes:

[0042] Monitor the electrothermal weight allocation value and the status of the frequency modulation equipment to obtain real-time operating conditions;

[0043] Obtain real-time energy price and equipment efficiency data to generate economic indicators;

[0044] Based on the economic indicators and the real-time operating conditions, the electrothermal coordinated frequency modulation command is adjusted to generate an optimized electrothermal coordinated frequency modulation command.

[0045] Furthermore, the execution of the optimized electrothermal coordinated frequency modulation command to control the frequency modulation equipment within the power subsystem and the thermal subsystem includes:

[0046] The response delay time of frequency modulation equipment in a thermal subsystem was measured using an online identification method.

[0047] Based on the response delay time, the execution timing of the thermal frequency modulation sub-instruction in the optimized electrothermal frequency modulation instruction is adjusted to generate a compensated thermal frequency modulation sub-instruction.

[0048] The optimized electric-thermal coordinated frequency modulation command and the compensated thermal frequency modulation command are executed synchronously to control the frequency modulation equipment in the electric subsystem and the thermal subsystem.

[0049] Based on the same inventive concept, the present invention provides a frequency regulation system for a combined electric and thermal energy system, comprising:

[0050] The real-time data acquisition module is used to acquire frequency deviation parameters in the power subsystem and thermal energy storage state parameters in the thermal subsystem as real-time frequency adjustment parameters.

[0051] The index generation module is used to calculate the frequency deviation change rate based on real-time frequency adjustment parameters, and to evaluate the thermal energy storage capacity margin value in combination with real-time frequency adjustment parameters, thereby generating a composite frequency regulation demand index.

[0052] The task decomposition module is used to generate multi-timescale frequency modulation tasks based on composite frequency modulation requirement indicators; among them, multi-timescale frequency modulation tasks include second-level frequency modulation sub-tasks, minute-level frequency modulation sub-tasks, and hour-level frequency modulation sub-tasks.

[0053] The weight allocation module is used to generate electrothermal weight allocation values ​​based on multi-timescale frequency modulation tasks by integrating the frequency deviation change rate and thermal energy storage capacity margin value.

[0054] The instruction generation module is used to generate electrothermal coordinated frequency modulation instructions based on the electrothermal weight allocation value; construct a virtual energy storage unit based on the thermal-hydraulic dynamic characteristics of the heating network to perform temperature equalization control; and optimize and adjust the electrothermal coordinated frequency modulation instructions in combination with real-time operating conditions and economic indicators to generate optimized electrothermal coordinated frequency modulation instructions.

[0055] The instruction execution module is used to execute optimized electrothermal coordinated frequency modulation instructions to control the frequency modulation equipment in the power subsystem and the thermal subsystem.

[0056] Beneficial effects: Compared with the prior art, the significant technical effects of the present invention are as follows:

[0057] (1) This invention constructs a collaborative frequency regulation control framework through a series of steps, including acquiring parameters, generating indicators, decomposing tasks, allocating weights, generating instructions, and executing instructions. This enables the deep integration and optimized scheduling of heterogeneous frequency regulation resources within the integrated energy system. The method can assess the urgency of frequency regulation in the power grid and the adjustability margin of the thermal system in real time, and dynamically adjust the frequency regulation responsibility allocation of the electrical and thermal subsystems. This allows the fast-responding electrical resources and the abundant thermal resources to complement each other, thereby significantly improving the overall response speed and support capability of the system in dealing with different types of frequency disturbances.

[0058] (2) This invention proposes a multi-timescale frequency regulation task decomposition strategy, which refines a single frequency regulation requirement into sub-tasks at the second, minute and hour levels. This strategy fully matches the physical response characteristics of different equipment units in the electric and thermal systems, allowing fast power equipment to focus on handling emergency disturbances, while thermal equipment with large thermal inertia undertakes the task of smoothing out medium and long-term fluctuations. This avoids control failure or excessive equipment wear caused by mismatch between instructions and capabilities, and improves the accuracy of frequency regulation control and the reliability of system operation.

[0059] (3) This invention is not limited to traditional frequency regulation resources, but also innovatively explores and utilizes the physical characteristics of the heating network itself to construct a virtual energy storage unit. This design can effectively expand the frequency regulation capacity of the system without additional investment. It can achieve flexible virtual charging and discharging through fine control of network temperature and flow rate, providing the system with additional flexibility and economy, and enhancing the competitiveness of the integrated energy system in the frequency regulation auxiliary service market.

[0060] (4) By introducing an online identification and feedforward compensation mechanism for the response delay of the thermal system, this invention effectively solves the core problem of the synchronization of response timing in the coordinated control of electricity and heat. By pre-adjusting the execution timing of the thermal frequency modulation command, it ensures that the frequency modulation power of the two parts of electricity and heat can be accurately and synchronously applied to the power grid, avoiding the oscillation or deviation of the frequency modulation effect caused by the response lag, and greatly improving the stability and actual effect of the coordinated control. Attached Figure Description

[0061] Figure 1 This is a flowchart illustrating a coordinated frequency regulation method for an integrated electric-thermal energy system disclosed in an embodiment of the present invention;

[0062] Figure 2 This is a schematic diagram of the change in electrical weight disclosed in an embodiment of the present invention;

[0063] Figure 3 This is a schematic diagram comparing the effects of thermal system response delay compensation disclosed in an embodiment of the present invention;

[0064] Figure 4 This is a performance comparison diagram of an embodiment of the present invention;

[0065] Figure 5 This is a schematic diagram of the structure of a coordinated frequency regulation system for an integrated electric-thermal energy system disclosed in an embodiment of the present invention. Detailed Implementation

[0066] The technical solution of the present invention will now be described in detail with reference to specific embodiments and accompanying drawings.

[0067] Example 1

[0068] This invention provides a coordinated frequency regulation method for an integrated electric-thermal energy system. It employs a multi-timescale task decomposition and dynamic weight allocation strategy based on composite frequency regulation demand indicators, enabling deep coordination and optimized control of electric and thermal resources, thereby improving the frequency regulation performance and operational economy of the integrated energy system. Figure 1 As shown, the coordinated frequency regulation method for the integrated electric-thermal energy system includes the following steps:

[0069] S1. Obtain the frequency deviation parameters in the power subsystem and the thermal energy storage state parameters in the thermal subsystem as real-time frequency adjustment parameters.

[0070] The specific implementation process of step S1 is as follows:

[0071] S1.1. Collect power frequency deviation data in the power subsystem using a frequency sensor to obtain frequency deviation parameters;

[0072] S1.2. Collect temperature and pressure data of thermal pipelines in the thermal subsystem using temperature sensors and flow meters to obtain thermal energy storage state parameters;

[0073] S1.3 Filter and normalize the frequency deviation parameters and thermal energy storage state parameters to obtain real-time frequency adjustment parameters.

[0074] Specifically, in step S1, firstly, high-precision frequency sensors are deployed at key grid-connected nodes or representative buses of the power subsystem. These sensors continuously monitor the actual operating frequency of the power grid. The frequency deviation parameter is obtained by comparing the collected real-time frequency values ​​with the standard rated frequency of the power grid, such as 50 Hz. This parameter directly reflects the degree of imbalance between active power supply and demand in the power system. Simultaneously, temperature sensors and flow meters are installed at core components of the thermal subsystem, such as the inlet and outlet of thermal storage tanks and key nodes of the thermal pipeline network. Temperature sensors measure the temperature of the heating medium, flow meters measure its flow rate, and pressure sensors monitor the operating pressure of the pipeline network to ensure safety. Using the collected temperature and flow data, the real-time heat storage or heat release power of the thermal system can be calculated. These information are then combined to form a thermal energy storage state parameter characterizing the adjustability of the thermal system. This parameter is typically quantified as the state of charge of the thermal storage device. To ensure the accuracy and stability of subsequent control decisions, the original frequency deviation parameter and thermal energy storage state parameter need to be preprocessed. This preprocessing includes two steps: filtering and normalization. Filtering aims to eliminate high-frequency noise and transient disturbances introduced during sensor acquisition. Algorithms such as low-pass filters or moving average filters can be used to extract smooth signals that reflect the true trend of system changes. Next, the filtered frequency deviation parameters and thermal energy storage state parameters are normalized. Since the unit of frequency deviation is Hertz, while the unit of thermal energy storage state parameters may be energy units or dimensionless percentages, their physical meanings and numerical ranges differ significantly, making them unsuitable for direct use in a unified control algorithm. Normalization maps them to a standard dimensionless interval, typically [0,1] or [-1,1]. For example, using the max-min normalization method, the calculation process can be expressed as:

[0075] ;

[0076] in, The value is the normalized value; These are the original parameter values ​​after filtering, i.e., the original frequency deviation parameters and thermal energy storage state parameters collected. and These represent the maximum and minimum permissible values ​​preset within the system's safe operating range for the filtered original parameter values. Through the above steps, real-time frequency regulation parameters integrating key status information from both the electrical and thermal subsystems and with a unified data format are finally generated, providing a high-quality data foundation for subsequent calculations of composite frequency regulation requirements and multi-timescale task decomposition.

[0077] S2. Based on the real-time frequency regulation parameters, calculate the frequency deviation change rate, and combine the real-time frequency regulation parameters to evaluate the thermal energy storage capacity margin value, and generate a composite frequency regulation demand index.

[0078] The specific implementation process of step S2 is as follows:

[0079] S2.1 Obtain renewable energy forecast data for a future preset time period from a preset database;

[0080] S2.2. Combining the frequency deviation parameter in the real-time frequency regulation parameters and the renewable energy prediction data, perform time series difference operation on the frequency deviation parameter to obtain the frequency deviation change rate;

[0081] S2.3. Based on the thermal energy storage status parameters in the real-time frequency adjustment parameters, compare the current thermal energy storage capacity with the preset capacity safety threshold to obtain the thermal energy storage capacity margin value.

[0082] S2.4 Integrate the frequency deviation change rate and thermal energy storage capacity margin value to generate a composite frequency regulation demand index.

[0083] Specifically, in step S2, firstly, to calculate the frequency deviation change rate, the system retrieves renewable energy forecast data for a preset time window from a pre-set database, such as the photovoltaic and wind power output forecast curves for the next 15 minutes. Simultaneously, the system utilizes the real-time frequency adjustment parameters obtained in the preceding steps, particularly the frequency deviation parameter. By performing time-series differencing on recent historical frequency deviation parameter sequences, the current frequency deviation trend can be obtained. Furthermore, to reflect forward-looking perspectives, future power fluctuation information contained in renewable energy forecast data is incorporated into the calculation. Specifically, the calculation of the frequency deviation change rate combines historical extrapolation and future forecasting, and can be expressed as:

[0084] ;

[0085] in, The rate of change of frequency deviation; and These are the frequency deviation parameters between the current time and the previous sampling time, respectively; The sampling time interval; This is the net power ramp-up rate calculated based on renewable energy forecast data over a predetermined future period. This value is directly related to the degree of future power imbalance. The system equivalent inertia coefficient, which converts power into the rate of frequency change, is calibrated based on the characteristics of the power grid. The weighting coefficients are used to balance the proportion of historical trends and future forecasts in the calculation. Among these parameters, the frequency deviation parameter is obtained through real-time measurement, renewable energy forecast data is obtained from the database, and the remaining parameters are preset or calibrated values. Secondly, to assess the thermal energy storage capacity margin, the system uses the thermal energy storage state parameter in the real-time frequency regulation parameters, which typically represents the state of charge of the thermal storage equipment. The system presets the upper and lower limits for safe operation of the thermal storage equipment, i.e., capacity safety thresholds, including maximum and minimum state of charge. The thermal energy storage capacity margin is obtained by comparing the current thermal storage capacity with these two safety thresholds, quantifying the remaining upward or downward adjustment capacity of the thermal subsystem. Its calculation method is to assess the distance from the current state to the two boundaries and take the smaller value to reflect the most stringent constraints. Finally, the calculated frequency deviation change rate and the thermal energy storage capacity margin are integrated to generate a composite frequency regulation demand index. Because these two indices have different physical meanings and dimensions, direct numerical calculations do not have physical meaning. Therefore, before integration, both are first normalized, mapping them to a unified dimensionless interval. Then, using a pre-defined two-dimensional mapping function or decision matrix, the normalized frequency deviation change rate and thermal energy storage capacity margin value are combined into one or a set of composite frequency regulation demand indicators. This indicator comprehensively reflects both "whether the power grid needs frequency regulation" and "whether the heating network has the capacity for frequency regulation," providing a decision-making basis for the subsequent decomposition of multi-timescale frequency regulation tasks.

[0086] S3. Generate multi-timescale frequency modulation tasks based on composite frequency modulation requirements; among them, multi-timescale frequency modulation tasks include second-level frequency modulation sub-tasks, minute-level frequency modulation sub-tasks, and hour-level frequency modulation sub-tasks.

[0087] The specific implementation process of step S3 is as follows:

[0088] S3.1 When the frequency deviation change rate in the composite frequency regulation demand index exceeds the preset change rate threshold, a second-level frequency regulation sub-task is generated and assigned to the power subsystem.

[0089] S3.2 When the thermal energy storage capacity margin value in the composite frequency regulation demand index is lower than the preset capacity limit threshold, adjust the frequency regulation task allocated to the thermal subsystem and generate minute-level frequency regulation sub-tasks.

[0090] S3.3. Combine the pre-acquired historical load data with renewable energy forecast data to generate hourly frequency regulation sub-tasks;

[0091] S3.4 Integrate second-level, minute-level, and hour-level frequency modulation subtasks to form a multi-timescale frequency modulation task.

[0092] Specifically, in step S3, the specific implementation process of generating multi-time-scale frequency modulation tasks is as follows: Step S3 decomposes the composite frequency modulation demand index generated in the previous step into sub-tasks of different time scales to match the response characteristics of different frequency modulation resources in the electrical and thermal subsystems.

[0093] First, for the generation of second-level frequency regulation subtasks, the system monitors the frequency deviation change rate in the composite frequency regulation demand index in real time. When the absolute value of this change rate exceeds a preset change rate threshold that characterizes the grid emergency state, it means that the grid is experiencing a severe power imbalance event and requires instantaneous and rapid power support. At this time, the system immediately generates a second-level frequency regulation subtask. The core of this task is rapid response. Therefore, the system determines a power target that requires immediate response based on the magnitude and direction of the frequency deviation change rate and directly assigns this task to the power subsystem. This is because the frequency regulation equipment within the power subsystem, such as energy storage batteries and gas turbines, has a rapid response capability from milliseconds to seconds, which can effectively suppress severe frequency fluctuations.

[0094] Secondly, regarding the generation of minute-level frequency regulation subtasks, the system continuously evaluates the thermal energy storage capacity margin value in the composite frequency regulation demand index. When this margin value falls below a preset capacity limit threshold, such as when the state of charge of the thermal storage tank is close to the upper or lower limit, it indicates that the thermal subsystem's ability to continuously provide frequency regulation services is limited. To prevent equipment operation from exceeding safety boundaries and ensure heating reliability, the system initiates the generation logic for minute-level frequency regulation subtasks. This task does not directly respond to grid frequency deviations but is an internal adjustment and recovery task. It adjusts and reduces the frequency regulation workload originally allocated to the thermal subsystem and transfers this part of the frequency regulation responsibility to the power subsystem or other available resources. Simultaneously, it may generate a control objective aimed at returning the thermal energy storage state to a healthy range; for example, under grid frequency allowances, fine-tuning the power of heat pumps or electric boilers to restore heat storage. The execution cycle of this task is on the minute level, matching the thermal inertia of the thermal system.

[0095] Secondly, regarding the generation of hourly frequency regulation sub-tasks, this task is forward-looking and planned. The system retrieves pre-acquired historical load databases and renewable energy forecast data for the next few hours. By overlaying load forecasts with generation forecasts, it analyzes the trend and volatility of the system's net load over a future period. Based on this analysis, the system performs day-ahead or intraday rolling optimization scheduling to generate an hourly frequency regulation sub-task. This task is essentially an economic dispatch plan, which sets the basic operating power curves and reserve capacity planning for equipment including cogeneration units, heat pumps, and thermal storage tanks for each hour in the future. Its aim is to smooth out foreseeable, large-scale power imbalances in the most economical way and reserve sufficient frequency regulation reserves to cope with emergencies.

[0096] Finally, the frequency regulation subtasks at the three time scales are integrated. The second-level frequency regulation subtask, as the highest priority real-time control command, is executed directly superimposed on other tasks. The minute-level frequency regulation subtask dynamically modifies the basic operating plan established by the hour-level task to adapt to the real-time state constraints of the thermal system. The hour-level frequency regulation subtask provides a baseline and economic target for the future operation of the entire system. In this way, the three subtasks are integrated to form a hierarchical and coordinated multi-time scale frequency regulation task, which includes rapid response to emergencies, maintenance and management of equipment status, and economic planning for future operation.

[0097] S4. Based on the multi-timescale frequency modulation task, the electrothermal weight allocation value is generated by integrating the frequency deviation change rate and the thermal energy storage capacity margin value.

[0098] The specific implementation process of step S4 is as follows:

[0099] S4.1 Obtain the frequency deviation change rate and thermal energy storage capacity margin value in the composite frequency regulation demand index to obtain the basic dataset;

[0100] S4.2 Based on the multi-timescale frequency regulation task and basic dataset, the electrical weight and thermal weight are calculated through an online rolling optimization algorithm; whereby the electrical weight corresponds to the frequency regulation contribution of the power subsystem and the thermal weight corresponds to the frequency regulation contribution of the thermal subsystem.

[0101] S4.3 Integrate electrical weights and thermal weights to form electrical and thermal weight allocation values.

[0102] Specifically, such as Figure 2 The diagram illustrates the changes in electrical weight with frequency deviation rate and thermal energy storage capacity margin. In step S4, the composite frequency regulation demand index generated in the previous steps is first obtained, and two key real-time state variables are extracted as the basic dataset: the frequency deviation rate and the thermal energy storage capacity margin. Simultaneously, the current total frequency regulation power demand is received. Next, based on this basic dataset and the total frequency regulation power demand, the system employs an online rolling optimization algorithm to calculate the electrical and thermal weights. This algorithm iteratively optimizes within a rolling finite time window, aiming to minimize the overall system operating cost or maximize the frequency regulation benefit. The objective function aims to minimize the overall cost of frequency regulation, and can be expressed as:

[0103] ;

[0104] in, To optimize the overall cost; This is a function that takes the minimum value. It is a function whose input is the electric weight to be solved. and heat weight Electric weight Used to represent the proportion of frequency regulation tasks allocated to the power subsystem, while thermal weighting This function is used to represent the proportion of frequency regulation tasks allocated to the thermal subsystem. The optimization process comprehensively considers the costs of utilizing power frequency regulation resources, such as the cycle life loss costs of energy storage batteries and the fuel consumption and start-up / shutdown costs of gas turbines, as well as the adjustment costs of thermal frequency regulation resources, such as the efficiency variations of heat pumps and the energy losses of thermal storage equipment. The optimization solution process is subject to a series of constraints, as follows:

[0105] The primary constraint is the power balance constraint, which requires the sum of the two weights to be 1, ensuring that the active power demand of the overall dispatch is fully allocated.

[0106] ;

[0107] Secondly, there are the capacity constraints of each subsystem. The frequency regulation output of the power subsystem is limited by its maximum adjustable power. The frequency modulation output of the thermal subsystem is limited by its maximum adjustable power. Constraints. These constraints, combined with weights, are expressed as:

[0108] ;

[0109] in, This refers to the total frequency regulation power requirement obtained from a multi-timescale frequency regulation task. Specifically, it refers to the maximum adjustable power of the thermal subsystem. It is a direct function of the thermal energy storage capacity margin of the thermal subsystem; the smaller the thermal energy storage capacity margin, the better. It will also decrease accordingly, for example:

[0110] ;

[0111] in, The coefficient of performance for thermoelectric conversion, such as the coefficient of performance (COP) of a heat pump, is the ability to convert electrical power into equivalent thermal power. The total rated electrical power of electrothermal conversion equipment in a thermal system, such as a heat pump unit; This is a normalized value for the thermal energy storage capacity margin, ranging from [0,1], where 0 represents no margin and 1 represents the maximum margin. This limits the optimization process. The upper limit. Furthermore, the objective function also incorporates the rate of change of frequency deviation. The response mechanism is as follows: when the rate of change of frequency deviation is high, it means that the power grid needs a faster response speed. The model uses the objective function... Introducing a dynamic penalty term related to response speed, or adjusting the cost coefficient, can reduce the cost of calling up fast-moving power resources when the rate of change is high, thereby guiding the optimization algorithm to allocate a larger power weight. .

[0112] With the introduction of a dynamic penalty term, the objective function can be expanded as follows:

[0113] ;

[0114] in, This is a cost item for accessing power subsystem resources; This is a cost item for accessing resources in the thermal subsystem. This is a dynamic penalty item; A positive weighting coefficient. When frequency changes drastically, i.e., the rate of change of frequency deviation is large, if the allocated fast-moving power resources are... If the weight is relatively small, then the value of the dynamic penalty term will become very large, which will significantly increase the overall cost. In order to minimize The optimization algorithm will choose a larger one. To reduce this penalty.

[0115] To adjust the cost coefficients, the objective function can be expanded as follows:

[0116] ;

[0117] in, This is a cost item for accessing power subsystem resources; This is a cost item for accessing resources in the thermal subsystem. This is the cost coefficient. ,in, The adjustment gain is greater than zero, which determines the strength of the impact of the frequency change rate on cost. A higher value indicates a more sensitive control strategy to frequency changes. When frequency changes drastic, i.e., the rate of change of frequency deviation is large, the "computational cost" of mobilizing power resources increases. The price will decrease, making the use of electricity resources cheaper in optimization. When the optimization algorithm pursues the lowest total cost, it will naturally tend to use more of this "temporarily reduced price" electricity resource, that is, give a larger price. .

[0118] By solving the constrained optimization problem described above, the online rolling optimization algorithm outputs a set of optimal electric weights in each control cycle. and heat weight Finally, this set of optimal electrical weights is... and heat weight The values ​​are integrated to form the electrothermal weight allocation value at that moment, which directly guides the generation of subsequent frequency modulation commands.

[0119] S5. Generate an electrothermal coordinated frequency modulation command based on the electrothermal weight allocation value; construct a virtual energy storage unit based on the thermal-hydraulic dynamic characteristics of the heating network to achieve temperature balance control; optimize and adjust the electrothermal coordinated frequency modulation command in combination with real-time operating conditions and economic indicators to generate an optimized electrothermal coordinated frequency modulation command.

[0120] The specific implementation process of step S5 is as follows:

[0121] S5.1. Generate electrothermal coordinated frequency modulation commands based on the electrothermal weight allocation values, specifically including the following steps:

[0122] S5.1.1 Based on the weighted allocation values ​​of electricity and heat, the total frequency regulation power demand is decomposed into the target frequency regulation power of electricity and the target frequency regulation power of heat.

[0123] S5.1.2. Based on the target frequency regulation power of the power, generate a power frequency regulation sub-command that includes gas turbine output, energy storage battery charging and discharging, and interruptible load control signals;

[0124] S5.1.3. Based on the thermal target frequency modulation power, generate a thermal frequency modulation sub-command that includes heat pump power, heat storage tank charging and discharging rate and heat exchange station valve opening control signals;

[0125] S5.1.4 Integrate the power frequency modulation sub-instruction and the thermal frequency modulation sub-instruction to form a power-thermal coordinated frequency modulation instruction.

[0126] Specifically, in step S5.1, the specific formation process of the electrothermal coordinated frequency modulation command is as follows: This process transforms the abstract electrothermal weight allocation value calculated in the previous steps into a set of specific and executable control commands for the physical device.

[0127] First, based on the electro-thermal weight allocation values ​​generated in the previous step, namely the electrical weight and thermal weight, and the total frequency modulation power demand at the current moment determined by the multi-timescale frequency modulation task, Total frequency modulation power requirements The process involves decomposition. This is achieved through the following calculations, yielding the target frequency regulation power required by the power subsystem and the thermal subsystem, respectively:

[0128] ;

[0129] ;

[0130] in, The target frequency regulation power for the power system; Frequency modulation power for thermal target. Electric weight. and heat weight The total frequency modulation power requirement is derived from the online rolling optimization algorithm. This is determined based on the composite frequency regulation demand index and the results of multi-timescale task decomposition. This step ensures that the sum of the tasks allocated to the two subsystems is exactly equal to the total frequency regulation demand.

[0131] Next, based on the target frequency regulation power of the power grid The system generates power frequency regulation sub-commands. This is a secondary decomposition process that allocates the overall power frequency regulation task to different frequency regulation resources within the power subsystem. The system operates according to a preset secondary scheduling strategy, which comprehensively considers the response speed, regulation cost, operating status, and available capacity of various devices, such as gas turbines, energy storage batteries, and interruptible loads. For example, for a rapidly increasing positive frequency regulation demand, the strategy will prioritize instructing the energy storage battery to discharge rapidly while simultaneously instructing the gas turbine to increase its output; for a negative frequency regulation demand, it may instruct the energy storage battery to charge or disconnect some interruptible loads. The final output power frequency regulation sub-command is a set of specific setpoint signals, such as the gas turbine active power output setpoint, the energy storage battery charging and discharging power command, and the control switch signals for interruptible loads.

[0132] Then, based on the thermal target frequency modulation power This generates a thermal frequency regulation sub-command. The thermal target frequency regulation power is an equivalent electrical power value that needs to be translated into specific operations within the thermal system. This conversion and allocation process also relies on an internal scheduling logic. For example, to increase the electrical load of the thermal subsystem to provide upward frequency regulation service, the system calculates the required increase in heat pump power consumption, or reduces the heat release rate of the thermal storage tank to reduce the power generation output of the cogeneration unit. Conversely, the opposite is true when reducing the electrical load. The system will then allocate the thermal target frequency regulation power... This can be broken down into adjustments to the operating power of the heat pump, commands to the charging and discharging rates of the heat storage tank, and adjustment signals to the valve openings of key heat exchange stations in the heating network. These signals work together to change the total power consumption of the heating system, thereby providing power support to the power grid.

[0133] Finally, the generated power frequency modulation sub-instructions and thermal frequency modulation sub-instructions are integrated and packaged to form a unified, structured electro-thermal coordinated frequency modulation instruction. This instruction contains precise action instructions for all frequency modulation devices participating in the two subsystems during the current control cycle, and is distributed to the local controllers of each device for execution via the communication network.

[0134] S5.2. Constructing a virtual energy storage unit based on the thermal-hydraulic dynamic characteristics of the heating network for temperature equalization control, specifically including the following steps:

[0135] S5.2.1. Based on the heating network and the thermal-hydraulic dynamic characteristics in the heating network, construct a virtual energy storage unit, calculate the adjustable temperature zone of the virtual energy storage unit, and generate the virtual energy storage capacity of the virtual energy storage unit.

[0136] S5.2.2 Based on the virtual energy storage capacity, the heat storage capacity of the virtual energy storage unit is changed by adjusting the water supply temperature and flow rate;

[0137] S5.2.3 Based on the change in heat storage of the virtual energy storage unit, virtual power compensation is generated through the calibrated thermoelectric conversion efficiency coefficient;

[0138] S5.2.4. A distributed control strategy is adopted to coordinate multiple heat source nodes and to perform temperature balance regulation based on virtual power compensation.

[0139] Specifically, in step S5.2, firstly, based on a precise modeling of the physical structure of the heating network and its internal thermal-hydraulic dynamic characteristics, the vast and dispersed heating network is virtualized into an equivalent energy storage unit, i.e., a virtual energy storage unit is constructed. This model comprehensively considers factors such as pipe length, diameter, material, insulation performance, and medium flow velocity and temperature distribution. Based on this model, the system can calculate the adjustable temperature range without affecting the comfort of end users; for example, allowing the water supply temperature to fluctuate between preset upper and lower limits. The range of total heat storage variation corresponding to this adjustable temperature range is defined as the virtual energy storage capacity of the virtual energy storage unit.

[0140] Secondly, when frequency regulation using virtual energy storage units is required, the system actively alters the total heat storage within the entire network by adjusting the supply water temperature on the heat source side and the rotational speed of the circulating water pumps in the pipeline. For example, when upward frequency regulation is needed, i.e., reducing the electrical load of the heating system, the supply water temperature of the heat source can be appropriately lowered, causing the entire pipeline to release previously stored heat energy to maintain the user-side temperature, while the heating power on the heat source side can be reduced. Conversely, when downward frequency regulation is needed, the supply water temperature can be increased, converting electrical energy into heat energy stored in the pipeline. The change in heat storage can be obtained by integrating the temperature and flow rate changes in the pipeline model.

[0141] Next, based on the calculated change in heat storage of the virtual energy storage unit, the system converts this into an equivalent electrical power contribution using a pre-calibrated thermoelectric conversion efficiency coefficient, generating virtual power compensation. This coefficient represents the change in electrical power corresponding to a change in unit heat power output on the heat source side; for heat pumps or electric boilers, this coefficient is directly related to their energy efficiency ratio or efficiency. Virtual Power Compensation The calculation formula is:

[0142] ;

[0143] in, This is the virtual power compensation value; To control time step Changes in the heat storage capacity of the internal virtual energy storage unit; The coefficient of performance (COP) for thermoelectric conversion is obtained through performance testing and calibration of the heat source equipment. This is a virtual power compensation value. This represents the equivalent frequency regulation power contributed to the power grid by scheduling the heat storage capacity of the heating network.

[0144] Finally, to ensure stable and coordinated temperature regulation throughout the heating network, especially in complex networks with multiple heat source nodes, the system employs a distributed control strategy. This strategy uses virtual power compensation values... As a global coordination signal, it is sent to the local controllers of each heat source node. Based on this signal and the local pipeline status parameters, each controller coordinates to perform temperature balancing control, adjusts its own water supply temperature and flow rate, and jointly completes the overall heat storage / release task, avoiding the problem of excessive local temperature fluctuations caused by excessive adjustment of a single node.

[0145] S5.3. Optimize and adjust the electrothermal coordinated frequency modulation command by combining real-time operating conditions and economic indicators to generate an optimized electrothermal coordinated frequency modulation command, specifically including the following steps:

[0146] S5.3.1 Monitor the electrothermal weight allocation value and the status of the frequency modulation equipment to obtain real-time operating conditions;

[0147] S5.3.2 Obtain real-time energy price and equipment efficiency data to form economic indicators;

[0148] S5.3.3. Based on the economic indicators and the real-time operating conditions, adjust the electrothermal coordinated frequency modulation command to generate an optimized electrothermal coordinated frequency modulation command.

[0149] Specifically, in step S5.3, the system first continuously monitors the electric heating weight allocation value and collects real-time operating status data from the local controllers of each frequency regulation device, including the device's output level, operating temperature, pressure, and energy storage state of charge. This information is then used to create a comprehensive profile of the system's current operating condition, providing real-time operating status data. Simultaneously, the system connects to an external energy market information platform and an internal equipment management system to obtain real-time energy price data, such as time-of-use electricity prices and natural gas prices, and combines this with performance curves provided by equipment manufacturers or equipment efficiency data obtained through online identification. Based on these dynamically changing economic and technical parameters, the system constructs and updates a quantitative economic indicator in real time. This indicator aims to evaluate the unit cost or benefit of each frequency regulation action. For example, the economic indicator can be expressed as:

[0150] ;

[0151] In the formula, The overall unit cost of frequency modulation; For the first The power used by the frequency modulation equipment; For the first The real-time price of energy consumed by various frequency modulation devices; For the first The operating efficiency of a frequency modulation device under current operating conditions. This summation covers all devices involved in frequency modulation. Among these parameters, Obtained from real-time operating conditions. Obtained from external markets. The system calculates or looks up data based on real-time operating conditions. Finally, based on the calculated economic indicators and the perceived real-time operating conditions, the system performs secondary adjustments and fine-tuning of the generated electrothermal frequency regulation commands. This is an online optimization and correction step. For example, when the system detects that the real-time electricity price is during peak hours, economic indicators will show that the cost of using electric energy storage or electric heat pumps for frequency regulation is high at this time. The optimization logic will then attempt to fine-tune the electrothermal weight allocation value, appropriately reducing the proportion of high-cost electricity resources and relying more on gas turbines or stored thermal energy, which have relatively lower fuel costs, while meeting frequency regulation performance and safety constraints. Alternatively, when the efficiency of a device decreases due to deviation from its optimal operating point, the optimization module will reduce the workload allocated to that device. In this way, the system fine-tunes the original electrothermal frequency regulation commands, generates optimized electrothermal frequency regulation commands, and finally issues them for execution.

[0152] S6. Execute the optimized electrothermal coordinated frequency modulation command to control the frequency modulation equipment in the power subsystem and the thermal subsystem.

[0153] The specific implementation process of step S6 is as follows:

[0154] S6.1 Measure the response delay time of frequency modulation equipment in the thermal subsystem using an online identification method;

[0155] S6.2 Based on the response delay time, the execution timing of the thermal frequency modulation sub-instruction in the optimized electrothermal coordinated frequency modulation instruction is adjusted to generate the compensated thermal frequency modulation sub-instruction.

[0156] S6.3 Synchronously execute the power frequency modulation sub-instruction and the compensated thermal frequency modulation sub-instruction in the optimized electric-thermal coordinated frequency modulation instruction to control the frequency modulation equipment in the power subsystem and the thermal subsystem.

[0157] Specifically, step S6 aims to address the inherent difference in response speed between the power subsystem and the thermal subsystem, ensuring precise synchronization of power support during coordinated frequency regulation. The details are as follows:

[0158] First, an online identification method is used to measure the response delay time of the main frequency-regulating devices in the thermal subsystem in real time. Online identification is a technique that dynamically determines model parameters by applying small disturbance signals and analyzing their responses during normal system operation. Specifically, the control system sends a preset step power adjustment command to one or a group of thermal frequency-regulating devices, such as a heat pump. Simultaneously, a high-precision energy meter begins monitoring changes in the actual input power of the device. The system records the time elapsed from the moment the command is issued until the device's actual power reaches a specific percentage of the command value, such as 90% of the stable target value; this time is defined as the device's response delay time. The identification process is executed periodically and online because the response delay time may vary with external conditions such as ambient temperature and water flow conditions, thus ensuring the real-time accuracy of the delay parameters used.

[0159] Next, based on the measured response delay time The system dynamically adjusts the execution timing of the thermal frequency modulation sub-instructions within the optimized electrothermal coordinated frequency modulation command, generating compensated thermal frequency modulation sub-instructions. The core idea of ​​this adjustment is time-based pre-compensation. For example, if the control system at the current moment... Determining the future The thermal subsystem is always required to provide The frequency modulation power, while the response delay time of the thermal equipment is Then the system will not Instead of issuing the command only at the designated time, the system advances the execution timestamp of the thermal frequency modulation sub-command, setting it to [time stamp]. This means that in order to ensure the frequency regulation effect of the heating equipment is at the target time... Accurate presentation is essential. The instruction was issued at an earlier time. Therefore, the compensated thermal frequency modulation sub-instruction is exactly the same as the original instruction in content, except that its planned execution time has been moved forward.

[0160] Finally, the system enters the synchronous execution phase, where two sets of instructions are issued simultaneously in any given control cycle. One is a power frequency regulation sub-instruction for the power subsystem without timing adjustments, as the response of the power equipment can be considered instantaneous. The other is a compensated thermal frequency regulation sub-instruction after the aforementioned timing pre-compensation. For example... Figure 3 As shown, the difference in effect before and after compensation is compared. In this way, although the physical issuance time of the thermal command is earlier, the power response effect it produces and the power response effect of the power subsystem will be precisely superimposed on the power grid at the same time, thereby realizing the coordinated control of the frequency regulation equipment in the power subsystem and the thermal subsystem, so that they can jointly complete the overall frequency regulation task.

[0161] This invention, through multi-timescale task decomposition and dynamic weight allocation, achieves complementary advantages between fast-response resources and large-capacity slow-speed resources. This effectively suppresses instantaneous frequency fluctuations in the power grid and economically smooths out long-term power fluctuations, improving the accuracy and efficiency of frequency regulation. Simultaneously, by using the state of thermal energy storage as a key decision-making variable, it ensures that while leveraging the frequency regulation potential of the thermal system, its own heating reliability and equipment safety are not compromised. This achieves a dynamic balance between power system demand and thermal system operational constraints, enhancing the robustness and economy of the entire integrated energy system.

[0162] This invention employs a multi-timescale task decomposition and dynamic weight allocation strategy based on composite frequency regulation demand indicators, which enables deep coordination and optimized control of electric and thermal resources, thereby improving the frequency regulation performance and operational economy of integrated energy systems.

[0163] To verify the feasibility of this invention in practice, it was applied to a smart industrial park. The park has deployed numerous rooftop photovoltaic power generation systems, causing the grid frequency to be susceptible to disturbances due to the volatility of renewable energy sources. Simultaneously, the park employs a centralized heating system equipped with large thermal storage tanks, electric heat pumps, and gas-fired combined heat and power units. To improve the park's energy self-consumption rate and grid stability, this invention is applied to the park's integrated electric-thermal energy system to achieve efficient and coordinated frequency regulation.

[0164] In this embodiment, the energy management center of the park utilizes the method proposed in this invention. First, the real-time data acquisition module acquires frequency deviation parameters and thermal energy storage status parameters through frequency sensors deployed at the park's substation and temperature and flow sensors deployed at the thermal storage tanks and pipelines in the heating center. These parameters are then filtered and normalized to form unified real-time frequency regulation parameters. Subsequently, the index generation module, combined with photovoltaic output forecast data provided by the park's meteorological station, calculates the frequency deviation change rate and thermal energy storage capacity margin value to generate a composite frequency regulation demand index. The task decomposition module generates multi-timescale frequency regulation tasks at the second, minute, and hourly levels based on this index. The weight adjustment module dynamically generates electrothermal weight allocation values ​​based on an online rolling optimization algorithm. Finally, the instruction generation and execution module decomposes the frequency regulation task into specific control instructions for equipment such as energy storage batteries, gas turbines, heat pumps, and thermal storage tanks within the park, and issues these instructions for execution after compensating for thermal system response delays.

[0165] To verify the beneficial effects of this invention, operational data from a typical day in the winter of 2024 were analyzed and compared with traditional frequency regulation methods relying solely on energy storage batteries and gas turbines. The following are the data analysis and effect verification results from the experiment.

[0166] At 14:05 that day, a rapidly moving large cloud caused a sudden drop of 10MW in the total output of the park's photovoltaic system, triggering a frequency decline in the power grid. The system detected that the frequency deviation rate exceeded a preset threshold and immediately generated a second-level frequency regulation sub-task, assigning it to the power subsystem. The weight adjustment module calculated an electrical weight allocation of 0.9 and a thermal weight of 0.1. Based on this, the instruction generation module instructed the energy storage battery system to release its main frequency regulation power within one second, and the gas turbine rapidly increased its output, quickly curbing the frequency decline.

[0167] As the frequency deviation stabilized, the system entered a minute-level adjustment phase. The system detected a high thermal energy storage capacity margin in the large thermal storage tank, indicating ample frequency regulation potential. The online rolling optimization algorithm gradually adjusted the electro-thermal weights; at 14:10, the electro-weight was adjusted to 0.4, and the thermal weight increased to 0.6. The instruction generation module allocated more frequency regulation tasks to the thermal subsystem, effectively providing continuous power support to the grid by increasing the heat pump's operating power and reducing the heat release rate of the thermal storage tank. This not only alleviated the discharge pressure on the energy storage battery but also reduced the fuel consumption of the gas turbine.

[0168] Throughout the frequency regulation process, the system measured the response delay time of the park's heat pump units to be approximately 85 seconds using an online identification method. Therefore, when issuing frequency regulation commands to the heat pumps, the execution module automatically advances the command execution timing by 85 seconds, ensuring precise synchronization between the power support provided by the thermal subsystem and the response of the electrical subsystem, thus avoiding overshoot or undershoot in frequency regulation power. Furthermore, during peak electricity price periods, the system's economic optimization module further adjusts the commands, prioritizing the use of stored low-cost thermal energy for frequency regulation, minimizing the park's operating costs.

[0169] Table 1. Comparison of the effects of electro-thermal synergistic frequency modulation and traditional frequency modulation.

[0170]

[0171] Table 2. Data on Dynamic Electrothermal Weight Allocation and Delay Compensation Effect

[0172]

[0173] Table 3. Comparison of Frequency Modulation Costs and Economic Efficiency in Different Time Periods

[0174]

[0175] As can be seen from the data in Tables 1-3 above, this invention has significant advantages in coordinated frequency regulation of integrated electric-thermal energy systems. Table 1 shows that, in response to sudden events such as a sharp drop in photovoltaic output and the start-up of large loads, such as… Figure 4As shown, the method of this invention can control the maximum frequency deviation within ±0.09Hz, while the deviation of traditional frequency regulation methods is as high as ±0.18Hz, improving frequency stability by approximately 50%. This is due to the invention's ability to respond at the second level while simultaneously leveraging the enormous regulatory potential of the thermal system. Table 2 clearly demonstrates the intelligence of the dynamic weight allocation mechanism. When frequency changes drastically, the system automatically allocates the majority of tasks to the faster-responding power subsystem; when the frequency stabilizes, more tasks are transferred to the more economical thermal subsystem, achieving a reasonable division of labor between fast and slow resources. A constant 85-second delay compensation ensures precise coordination of this division of labor. The economic comparison results in Table 3 show that this invention significantly reduces frequency regulation costs by intelligently scheduling low-cost thermal resources and optimizing them in conjunction with real-time electricity prices. Especially during peak electricity price periods, the cost reduction rate reaches as high as 41.8%, greatly enhancing the market competitiveness and economic benefits of the park's integrated energy system. These data fully demonstrate the comprehensive value of this invention in improving frequency regulation performance, ensuring system safety, and optimizing operational economy.

[0176] Example 2

[0177] like Figure 5 As shown, the present invention provides a frequency regulation system for a combined electric and thermal energy system, comprising:

[0178] The real-time data acquisition module is used to acquire frequency deviation parameters in the power subsystem and thermal energy storage state parameters in the thermal subsystem as real-time frequency adjustment parameters.

[0179] The index generation module is used to calculate the frequency deviation change rate based on real-time frequency adjustment parameters, and to evaluate the thermal energy storage capacity margin value in combination with real-time frequency adjustment parameters, thereby generating a composite frequency regulation demand index.

[0180] The task decomposition module is used to generate multi-timescale frequency modulation tasks based on composite frequency modulation requirement indicators; among them, multi-timescale frequency modulation tasks include second-level frequency modulation sub-tasks, minute-level frequency modulation sub-tasks, and hour-level frequency modulation sub-tasks.

[0181] The weight allocation module is used to generate electrothermal weight allocation values ​​based on multi-timescale frequency modulation tasks by integrating the frequency deviation change rate and thermal energy storage capacity margin value.

[0182] The instruction generation module is used to generate electrothermal coordinated frequency modulation instructions based on the electrothermal weight allocation value; construct a virtual energy storage unit based on the thermal-hydraulic dynamic characteristics of the heating network to perform temperature equalization control; and optimize and adjust the electrothermal coordinated frequency modulation instructions in combination with real-time operating conditions and economic indicators to generate optimized electrothermal coordinated frequency modulation instructions.

[0183] The instruction execution module is used to execute optimized electrothermal coordinated frequency modulation instructions to control the frequency modulation equipment in the power subsystem and the thermal subsystem.

[0184] In one optional implementation, the electro-thermal integrated energy system coordinated frequency regulation method includes: a) acquiring frequency deviation parameters in the power subsystem and thermal energy storage state parameters in the thermal subsystem as real-time frequency regulation parameters; b) calculating the frequency deviation change rate and evaluating the thermal energy storage capacity margin value in combination with the real-time frequency regulation parameters to generate a composite frequency regulation demand index; c) generating multi-timescale frequency regulation tasks based on the composite frequency regulation demand index; d) generating an electro-thermal weight allocation value based on the multi-timescale frequency regulation tasks by fusing the frequency deviation change rate and the thermal energy storage capacity margin value; e) generating an electro-thermal coordinated frequency regulation command based on the electro-thermal weight allocation value; and f) executing the electro-thermal coordinated frequency regulation command to control the frequency regulation equipment in the power subsystem and the thermal subsystem.

Claims

1. A method for coordinated frequency regulation of an integrated electric-thermal energy system, characterized in that, include: The frequency deviation parameters in the power subsystem and the thermal energy storage state parameters in the thermal subsystem are obtained as real-time frequency regulation parameters. Based on real-time frequency regulation parameters, the frequency deviation change rate is calculated, and the thermal energy storage capacity margin is evaluated in combination with the real-time frequency regulation parameters to generate a composite frequency regulation demand index. Based on the composite frequency modulation (FM) requirement indicators, multi-timescale FM tasks are generated; these multi-timescale FM tasks include second-level, minute-level, and hour-level FM sub-tasks; the generation process of the multi-timescale FM tasks is as follows: When the frequency deviation change rate in the composite frequency regulation demand index exceeds the preset change rate threshold, a second-level frequency regulation sub-task is generated and assigned to the power subsystem. When the thermal energy storage capacity margin value in the composite frequency regulation demand index is lower than the preset capacity limit threshold, the frequency regulation task allocated to the thermal subsystem is adjusted to generate minute-level frequency regulation sub-tasks. By combining pre-acquired historical load data with renewable energy forecast data, hourly frequency regulation sub-tasks are generated. Integrate second-level, minute-level, and hour-level frequency modulation subtasks to form a multi-timescale frequency modulation task; Based on the multi-timescale frequency modulation task, an electrothermal weight allocation value is generated by fusing the frequency deviation change rate and the thermal energy storage capacity margin value, including: Obtain the frequency deviation change rate and thermal energy storage capacity margin value from the composite frequency regulation demand index to obtain the basic dataset; Based on the multi-timescale frequency regulation task and basic dataset, the electrical weight and thermal weight are calculated through an online rolling optimization algorithm; whereby the electrical weight corresponds to the frequency regulation contribution of the power subsystem and the thermal weight corresponds to the frequency regulation contribution of the thermal subsystem. Integrate electrical and thermal weights to form an electrical and thermal weight allocation value; Based on the electrothermal weight allocation value, an electrothermal coordinated frequency modulation command is generated; a virtual energy storage unit is constructed based on the thermal-hydraulic dynamic characteristics of the heating network to achieve temperature balance control; the electrothermal coordinated frequency modulation command is optimized and adjusted in combination with real-time operating conditions and economic indicators to generate an optimized electrothermal coordinated frequency modulation command. Execute optimized electrothermal coordinated frequency modulation commands to control frequency modulation equipment within the power subsystem and thermal subsystem.

2. The method for coordinated frequency regulation of an integrated electric-thermal energy system according to claim 1, characterized in that, The acquisition of frequency deviation parameters in the power subsystem and thermal energy storage state parameters in the thermal subsystem as real-time frequency adjustment parameters includes: Frequency deviation data of the power subsystem is collected by a frequency sensor to obtain frequency deviation parameters; Temperature and pressure data of thermal pipelines in the thermal subsystem are collected by temperature sensors and flow meters to obtain thermal energy storage state parameters. The frequency deviation parameters and thermal energy storage state parameters are filtered and normalized to obtain the real-time frequency adjustment parameters.

3. The method for coordinated frequency regulation of an integrated electric-thermal energy system according to claim 1, characterized in that, The process involves calculating the frequency deviation change rate based on real-time frequency regulation parameters, evaluating the thermal energy storage capacity margin using these parameters, and generating a composite frequency regulation demand index, including: Retrieve renewable energy forecast data for a predetermined time period from a pre-defined database; By combining the frequency deviation parameter in the real-time frequency regulation parameters and renewable energy prediction data, time series difference operation is performed on the frequency deviation parameter to obtain the frequency deviation change rate. Based on the thermal energy storage status parameters in the real-time frequency adjustment parameters, the current thermal energy storage capacity is compared with the preset capacity safety threshold to obtain the thermal energy storage capacity margin value. By integrating the frequency deviation change rate and the thermal energy storage capacity margin, a composite frequency regulation demand index is generated.

4. The method for coordinated frequency regulation of an integrated electric-thermal energy system according to claim 1, characterized in that, The step of generating electrothermal coordinated frequency modulation commands based on electrothermal weight allocation values ​​includes: Based on the weighted allocation values ​​for electricity and heat, the total frequency regulation power demand is decomposed into the target frequency regulation power for electricity and the target frequency regulation power for heat. Based on the target frequency regulation power, generate power frequency regulation sub-commands that include gas turbine output, energy storage battery charging and discharging, and interruptible load control signals; Based on the thermal target frequency modulation power, generate thermal frequency modulation sub-commands that include heat pump power, heat storage tank charging and discharging rate and heat exchange station valve opening control signals; Integrate the power frequency regulation sub-command and the thermal frequency regulation sub-command to form a coordinated power and thermal frequency regulation command.

5. The method for coordinated frequency regulation of an integrated electric-thermal energy system according to claim 1, characterized in that, The method of constructing a virtual energy storage unit based on the thermal-hydraulic dynamic characteristics of the heating network for temperature equalization control includes: A virtual energy storage unit is constructed based on the heating network and the thermal-hydraulic dynamic characteristics in the heating network, and the adjustable temperature range of the virtual energy storage unit is calculated to generate the virtual energy storage capacity of the virtual energy storage unit. Based on the virtual energy storage capacity, the heat storage capacity of the virtual energy storage unit can be changed by adjusting the water supply temperature and flow rate. Based on the change in the heat storage capacity of the virtual energy storage unit, virtual power compensation is generated using the calibrated thermoelectric conversion efficiency coefficient. A distributed control strategy is adopted to coordinate multiple heat source nodes, and temperature balance regulation is performed based on the virtual power compensation.

6. The method for coordinated frequency regulation of an integrated electric-thermal energy system according to claim 1, characterized in that, The process of optimizing and adjusting the electrothermal coordinated frequency modulation command by combining real-time operating conditions and economic indicators to generate an optimized electrothermal coordinated frequency modulation command includes: Monitor the electrothermal weight allocation value and the status of the frequency modulation equipment to obtain real-time operating conditions; Obtain real-time energy price and equipment efficiency data to generate economic indicators; Based on the economic indicators and the real-time operating conditions, the electrothermal coordinated frequency modulation command is adjusted to generate an optimized electrothermal coordinated frequency modulation command.

7. The method for coordinated frequency regulation of an integrated electric-thermal energy system according to claim 1, characterized in that, The execution of the optimized electrothermal coordinated frequency modulation command, controlling the frequency modulation equipment within the power subsystem and the thermal subsystem, includes: The response delay time of frequency modulation equipment in a thermal subsystem was measured using an online identification method. Based on the response delay time, the execution timing of the thermal frequency modulation sub-instruction in the optimized electrothermal frequency modulation instruction is adjusted to generate a compensated thermal frequency modulation sub-instruction. The optimized electric-thermal coordinated frequency modulation command and the compensated thermal frequency modulation command are executed synchronously to control the frequency modulation equipment in the electric subsystem and the thermal subsystem.

8. A frequency regulation system for a combined electric and thermal energy system, characterized in that, include: The real-time data acquisition module is used to acquire frequency deviation parameters in the power subsystem and thermal energy storage state parameters in the thermal subsystem as real-time frequency adjustment parameters. The index generation module is used to calculate the frequency deviation change rate based on real-time frequency adjustment parameters, and to evaluate the thermal energy storage capacity margin value in combination with real-time frequency adjustment parameters, thereby generating a composite frequency regulation demand index. The task decomposition module is used to generate multi-timescale frequency modulation tasks based on composite frequency modulation requirements. These multi-timescale frequency modulation tasks include second-level, minute-level, and hour-level sub-tasks. The generation process of these multi-timescale frequency modulation tasks is as follows: When the frequency deviation change rate in the composite frequency regulation demand index exceeds the preset change rate threshold, a second-level frequency regulation sub-task is generated and assigned to the power subsystem. When the thermal energy storage capacity margin value in the composite frequency regulation demand index is lower than the preset capacity limit threshold, the frequency regulation task allocated to the thermal subsystem is adjusted to generate minute-level frequency regulation sub-tasks. By combining pre-acquired historical load data with renewable energy forecast data, hourly frequency regulation sub-tasks are generated. Integrate second-level, minute-level, and hour-level frequency modulation subtasks to form a multi-timescale frequency modulation task; The weight allocation module is used to generate electrothermal weight allocation values ​​based on multi-timescale frequency modulation tasks by fusing the frequency deviation change rate and thermal energy storage capacity margin value, including: Obtain the frequency deviation change rate and thermal energy storage capacity margin value from the composite frequency regulation demand index to obtain the basic dataset; Based on the multi-timescale frequency regulation task and basic dataset, the electrical weight and thermal weight are calculated through an online rolling optimization algorithm; whereby the electrical weight corresponds to the frequency regulation contribution of the power subsystem and the thermal weight corresponds to the frequency regulation contribution of the thermal subsystem. Integrate electrical and thermal weights to form an electrical and thermal weight allocation value; The instruction generation module is used to generate electrothermal coordinated frequency modulation instructions based on the electrothermal weight allocation value; construct a virtual energy storage unit based on the thermal-hydraulic dynamic characteristics of the heating network to perform temperature equalization control; and optimize and adjust the electrothermal coordinated frequency modulation instructions in combination with real-time operating conditions and economic indicators to generate optimized electrothermal coordinated frequency modulation instructions. The instruction execution module is used to execute optimized electrothermal coordinated frequency modulation instructions to control the frequency modulation equipment in the power subsystem and the thermal subsystem.

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