Power adjusting method and system for electric heating load cluster participating in electric power system

Abstract

The invention discloses a power regulation method and system for an electric heating load cluster participating in an electric power system, and relates to the technical field of electric power system regulation and control, and the method comprises the following steps: constructing a thermal dynamic process lumped parameter model representing indoor, wall and outdoor temperatures; obtaining a temperature change state based on the temperature control interval constraint solving model; determining an operation state sequence of single equipment according to indoor temperature change, and quantifying the power regulation potential based on a building thermal inertia principle in combination with wall body temperature change; aggregating a plurality of pieces of equipment to form a load cluster, and combining time-of-use electricity price and an auxiliary service compensation mechanism to construct a collaborative demand response optimization model taking total revenue maximization as a target; and solving the model to obtain an optimal power regulation strategy. Through accurate description of electric heating load minute-level thermal dynamic characteristics and reliable quantification of cluster adjustment potential, on the premise of guaranteeing user comfort, the adjustment capability and economic value of the cluster participating in peak clipping and valley filling of a power grid are fully mined through collaborative optimization scheduling.

Description

Technical Field

[0001] This invention relates to the field of power system regulation technology, specifically to a method and system for power regulation of electric heating load clusters participating in the power system. Background Technology

[0002] With the large-scale integration of high-proportion renewable energy sources into the power grid, their intermittency and volatility have exacerbated the pressure on peak shaving. To improve system regulation capabilities, utilizing temperature-controlled loads with thermal inertia (such as electric heating) to participate in demand response has become an important research direction. Existing technologies mainly analyze the regulation potential of electric heating loads by constructing simplified thermodynamic models, such as using steady-state or quasi-steady-state models that ignore the dynamic heat storage effect of walls, or conducting short-time response analysis only for a single device. These methods generally have the following inherent defects: First, the characterization of room temperature change is overly simplified, and most models do not fully consider the dynamic changes in wall heat capacity, leading to biases in the assessment of load regulation potential; second, there is a lack of refined modeling of the impact of outdoor temperature fluctuations on user electricity consumption behavior and economic benefits during the heating season over a long time scale; third, existing research focuses on the control strategies of individual electric heating devices, making it difficult to fully release the value of large-scale regulation and failing to balance the grid's peak shaving and valley filling needs with the maximization of user benefits. The aforementioned shortcomings make it difficult for existing technologies to accurately quantify the sustainable regulation capacity of electric heating load clusters in real-world environments, and also prevent them from achieving cluster-level economically optimal coordinated regulation while ensuring user comfort. This limits their practical application in large-scale power grid interactions. Therefore, there is an urgent need to establish a long-term optimization scheduling method that can accurately describe thermal dynamics, quantify cluster regulation potential, and integrate market mechanisms.

[0003] The information disclosed in the background section is only intended to enhance the understanding of the background of this application, and therefore may include information that does not constitute prior art known to those skilled in the art. Summary of the Invention

[0004] The purpose of this invention is to propose a power regulation method and system for electric heating load clusters to participate in the power system, aiming to overcome the problems in the prior art, such as inaccurate evaluation of regulation potential due to oversimplification of models, difficulty in evaluating economic efficiency due to lack of long-term scale fine simulation, and inability to maximize overall benefits due to lack of cluster collaborative optimization mechanism.

[0005] In a first aspect, one technical solution provided in this embodiment of the invention is: a method for power regulation of an electric heating load cluster participating in a power system, comprising the following steps: S1. Construct a lumped parameter model of the thermal dynamics process characterizing indoor temperature, wall temperature, and outdoor temperature; S2. Solve the lumped parameter model of the thermal dynamic process based on the temperature control interval constraint to obtain the change state of indoor temperature and the change state of wall temperature respectively. S3. Determine the operating state sequence of a single electric heating device based on the changes in the indoor temperature, and determine the power adjustment potential of the electric heating device within the set temperature control range based on the principle of building thermal inertia and the changes in the wall temperature. S4. Aggregate multiple electric heating devices to form a load cluster. Based on the operating status sequence and power regulation potential of the load cluster, combined with the time-of-use pricing mechanism and the ancillary service compensation mechanism, construct a collaborative demand response optimization model for the load cluster to participate in the power grid peak shaving and valley filling. S5. Solve the collaborative demand response optimization model to determine the power regulation strategy.

[0006] As a preferred embodiment, the lumped parameter model of the thermal dynamic process is represented as follows: ; Among them, C in C wall These are the equivalent heat capacity of indoor air and the equivalent heat capacity of the wall, respectively; R1 and R2 are the equivalent thermal resistances of indoor air and the inner side of the wall, and the equivalent thermal resistances of the outer side of the wall and outdoor air, respectively. , , These are indoor temperature, wall temperature, and outdoor temperature; P heat (t) represents the heating power of the electric heating equipment at time t, P heat (t)=S(t)P N , where P N S(t) is the rated power of the electric heating equipment at time t, and S(t) is the on / off state of the electric heating equipment at time t, which is 0 when it is off and 1 when it is on.

[0007] As a preferred embodiment, the step of solving the lumped parameter model of the thermal dynamic process based on the temperature control range constraint to obtain the changes in indoor temperature and wall temperature respectively includes the following steps: The differential equations in the lumped parameter model of the thermal dynamic process are discretized into difference equations, and the forward Euler method is used for numerical solution, with a set discrete time step. Set initial conditions and iterate over each discrete time step based on temperature control range constraints. The initial conditions include initial indoor temperature, initial wall temperature, initial outdoor temperature, and initial on / off state of the electric heating equipment. The heating power of the electric heating equipment is determined based on the current switch status, and combined with the current outdoor temperature, the indoor temperature and wall temperature at the next moment are calculated iteratively using a difference equation; the changes in indoor temperature and wall temperature are obtained based on the iteration cycle.

[0008] As a preferred embodiment, determining the operating state sequence of a single electric heating device based on the changes in indoor temperature includes the following steps: The system monitors the indoor temperature in real time and compares it with the set temperature control range; it determines the on / off status of the electric heating equipment based on the comparison results; and it calculates the actual heating power P of the electric heating equipment based on the on / off status. heat (t)=S(t)P N ;where P N S(t) is the rated power of the electric heating equipment at time t, and S(t) is the on / off state of the electric heating equipment at time t, which is 0 when it is off and 1 when it is on. The operating state sequence of the electric heating equipment is constructed by sequentially recording the on / off states of the electric heating equipment within a set time period and the corresponding state parameters. The state parameters include the average heating time, the average cooling time, and the average duty cycle.

[0009] As a preferred embodiment, determining the power adjustment potential of the electric heating equipment within a set temperature control range based on the principle of building thermal inertia and the temperature change of the wall includes the following steps: Based on the wall temperature change state, calculate the wall temperature fluctuation characteristic parameters; based on the building thermal inertia principle, calculate the thermal time constant according to the wall temperature change state; The maximum sustainable adjustment time parameter of the electric heating equipment is calculated based on the thermal time constant; the adjustable power of the electric heating equipment is calculated based on the maximum sustainable adjustment time parameter and the operating state sequence. The power regulation potential of the electric heating equipment is determined by combining the adjustable power parameters, which include adjustable power capacity, continuous regulation time, and maximum daily regulation power consumption.

[0010] As a preferred embodiment, the aggregation of multiple electric heating devices to form a load cluster, and based on the operating state sequence and power regulation potential of the load cluster, combined with the time-of-use pricing mechanism and ancillary service compensation mechanism, a collaborative demand response optimization model for the load cluster to participate in grid peak shaving and valley filling is constructed; including the following steps: Multiple electric heating devices are aggregated to form a load cluster. The total power demand of the load cluster at time t is calculated. The total power is the sum of the heating power of each electric heating device at the corresponding time. The total regulation capacity of the load cluster is calculated based on the power regulation potential of each electric heating device. The total regulation capacity includes the total upward regulation power of the cluster and the total downward regulation power of the cluster. The total upward regulation power is the sum of the sustainable upward regulation power of each device, and the total downward regulation power is the sum of the sustainable downward regulation power of each device. An optimization function is established with the objective of maximizing the total revenue of the load cluster, which includes the annual valley filling revenue, the annual peak shaving revenue, and the annual heating cost savings. Set the operating constraints for the optimization model, including temperature constraints, power constraints, and regulation capability constraints; Configure price parameters by combining time-of-use pricing mechanisms and ancillary service compensation mechanisms; The collaborative demand response optimization model is constructed based on the objective function, operational constraints, and price parameters.

[0011] As a preferred embodiment, the power regulation strategy includes the specific start-stop time sequence of each electric heating device during peak shaving and valley filling periods, as well as the corresponding power regulation commands.

[0012] Secondly, an embodiment of the present invention also provides a technical solution: a power regulation system, applicable to a power regulation method for an electric heating load cluster participating in a power system as described in the first aspect, comprising: Equivalent model construction module: Constructs a lumped parameter model of the thermal dynamics process characterizing indoor temperature, wall temperature, and outdoor temperature; Equivalent model solution module: Based on the temperature control interval constraint, the lumped parameter model of the thermal dynamic process is solved to obtain the change state of indoor temperature and the change state of wall temperature respectively; Operating status determination module: Determines the operating status sequence of a single electric heating device based on the changes in indoor temperature; Adjustment potential determination module: Based on the principle of building thermal inertia and the temperature change state of the wall, determine the power adjustment potential of the electric heating equipment within the set temperature control range; Response model construction module: Aggregates multiple electric heating devices to form a load cluster. Based on the operating status sequence and power regulation potential of the load cluster, combined with the time-of-use pricing mechanism and ancillary service compensation mechanism, a collaborative demand response optimization model for the load cluster to participate in grid peak shaving and valley filling is constructed. Regulation strategy output module: solves the collaborative demand response optimization model to determine the power regulation strategy.

[0013] Thirdly, one technical solution provided in this embodiment of the invention is: an electronic device, including a memory and a processor, wherein the memory stores a computer program, and the processor, when calling the computer program in the memory, implements the steps of the power regulation method for electric heating load clusters participating in the power system as described in the first aspect.

[0014] Fourthly, one technical solution provided in this embodiment of the invention is: a storage medium storing computer-executable instructions, wherein when the computer-executable instructions are loaded and executed by a processor, the steps of a power regulation method for an electric heating load cluster participating in a power system as described in the first aspect are implemented.

[0015] The present invention has at least the following substantial beneficial effects: (1) In view of the problem that the existing technology has biased assessment of the adjustment potential of electric heating load due to excessive simplification of the model and insufficient characterization of the dynamic process of wall heat capacity, this application establishes a lumped parameter differential equation model that includes indoor air and wall heat capacity and thermal resistance, and uses numerical methods such as forward Euler method to discretize it into difference equations for iterative solution. This achieves accurate characterization of the minute-level dynamic coupling characteristics between indoor temperature, wall temperature and electric heating power, thereby accurately quantifying the sustainable adjustment time and adjustable power capacity based on building thermal inertia within the set temperature control range, providing a reliable basis for evaluating the adjustment capabilities of individual units and clusters.

[0016] (2) In view of the lack of detailed modeling of the impact of outdoor temperature fluctuations on user electricity consumption behavior and economic efficiency during the heating season on a long time scale in the existing technology, this application constructs a collaborative demand response optimization model with the goal of maximizing the total revenue of the load cluster by integrating the time-of-use electricity pricing mechanism and the ancillary service compensation mechanism. It also sets multiple constraints including temperature, power and regulation capacity, and realizes dynamic economic simulation and optimization of electric heating clusters participating in peak shaving and valley filling throughout the entire heating cycle. It can significantly reduce heating costs and improve overall revenue while ensuring user comfort.

[0017] (3) In view of the fact that existing research focuses on single-device control and is difficult to achieve large-scale cluster collaborative optimization and overall benefit maximization, this application aggregates the operating status sequence and power regulation potential of multiple electric heating devices to form the total regulation capacity of the load cluster. On this basis, a collaborative demand response optimization model is constructed to solve the specific start-stop sequence and power command of each device during peak shaving / valley filling periods. This realizes the coordinated control and overall scheduling of large-scale electric heating load clusters. While ensuring that the indoor temperature of all users meets the comfort requirements, it maximizes the release of the large-scale regulation value and economic potential of the cluster participating in grid interaction.

[0018] The above description of the invention is merely an overview of the technical solution of the present invention. In order to better understand the technical means of the present invention and to implement it in accordance with the contents of the specification, and to make the above and other objects, features and advantages of the present invention more apparent and understandable, specific embodiments of the present invention are described below. Attached Figure Description

[0019] Other features, objects, and advantages of the invention will become more apparent from the following detailed description of non-limiting embodiments with reference to the accompanying drawings. The drawings are for illustrative purposes only and are not intended to limit the invention. Furthermore, the same reference numerals denote the same parts throughout the drawings.

[0020] Figure 1 This is a flowchart illustrating a method for power regulation of an electric heating load cluster in a power system, according to an embodiment of the present invention.

[0021] Figure 2 This is a schematic diagram of the equivalent model of the room temperature change process in an embodiment of the present invention.

[0022] Figure 3 This is a schematic diagram of a power regulation system according to an embodiment of the present invention. Detailed Implementation

[0023] To make the objectives, technical solutions, and advantages of this invention clearer, the invention will be further described in detail below with reference to the accompanying drawings and embodiments. It should be understood that the specific embodiments described herein are only one preferred embodiment of this invention and are only used to explain this invention. They do not limit the scope of protection of this invention. All other embodiments obtained by those skilled in the art without creative effort are within the scope of protection of this invention.

[0024] Before discussing the exemplary embodiments in more detail, it should be mentioned that some exemplary embodiments are described as processes or methods depicted as flowcharts. Although the flowcharts describe the operations (or steps) as sequential processes, many of the operations (or steps) can be performed in parallel, concurrently, or simultaneously. Furthermore, the order of the operations can be rearranged. The process can be terminated when its operation is completed, but it may also have additional steps not included in the figures; the process may correspond to a method, function, procedure, subroutine, subroutine, etc.

[0025] Example 1: As Figure 1 As shown in the figure, one technical solution provided in this embodiment of the invention is: a method for power regulation of electric heating load clusters participating in the power system, comprising the following steps: S1. Construct a lumped parameter model of the thermal dynamics process representing indoor temperature, wall temperature, and outdoor temperature.

[0026] As a preferred embodiment, the lumped parameter model of the thermal dynamic process is represented as follows: ; Among them, C in C wall These are the equivalent heat capacity of indoor air and the equivalent heat capacity of the wall, respectively; R1 and R2 are the equivalent thermal resistances of indoor air and the inner side of the wall, and the equivalent thermal resistances of the outer side of the wall and outdoor air, respectively. , , These are indoor temperature, wall temperature, and outdoor temperature; P heat (t) represents the heating power of the electric heating equipment at time t, P heat (t)=S(t)P N , where P N S(t) is the rated power of the electric heating equipment at time t, and S(t) is the on / off state of the electric heating equipment at time t, which is 0 when it is off and 1 when it is on.

[0027] In this embodiment, to overcome the shortcomings of existing technologies that oversimplify the characterization of room temperature changes and neglect the dynamic heat storage effect of walls, leading to inaccurate assessments of regulation potential, this embodiment constructs a lumped parameter model of the thermal dynamic process that integrates indoor air and wall thermal capacity and thermal resistance. This abstracts the complex physical process of building heat transfer into a mathematical model that can be accurately solved. Specifically, as follows... Figure 2 The diagram shows an equivalent model of the room temperature change process, where indoor air is represented by its heat capacity C. in The wall is equivalent to the heat capacity C. wall The thermal resistances R1 and R2 are used to characterize the heat transfer resistance from the interior to the wall and from the wall to the exterior, respectively, thus establishing an equivalent thermal path with two heat capacity nodes. Using this model, the system can simulate heat transfer at any outdoor temperature in real time and continuously. Indoor temperature during equipment start-up and shutdown With wall temperature The energy exchange and storage processes between them lay a precise physical model foundation for accurately solving the temperature change state and quantifying the power regulation potential.

[0028] S2. Based on the temperature control interval constraint, the lumped parameter model of the thermal dynamic process is solved to obtain the changes in indoor temperature and wall temperature respectively.

[0029] As a preferred embodiment, the step of solving the lumped parameter model of the thermal dynamic process based on the temperature control range constraint to obtain the changes in indoor temperature and wall temperature respectively includes the following steps: The differential equations in the lumped parameter model of the thermal dynamic process are discretized into difference equations, and the forward Euler method is used for numerical solution, with a set discrete time step. Set initial conditions and iterate over each discrete time step based on temperature control range constraints. The initial conditions include initial indoor temperature, initial wall temperature, initial outdoor temperature, and initial on / off state of the electric heating equipment. The heating power of the electric heating equipment is determined based on the current switch status, and combined with the current outdoor temperature, the indoor temperature and wall temperature at the next moment are calculated iteratively using a difference equation; the changes in indoor temperature and wall temperature are obtained based on the iteration cycle.

[0030] Understandably, to address the problem of inaccurate assessment of regulation potential in existing technologies due to the lack of refined and computationally calculable simulation of the dynamic heat storage process of walls, this embodiment discretizes the continuous-time thermal dynamic differential equations into difference equations and employs the forward Euler method for numerical iterative solution, constructing a temperature state solver that can be progressively extrapolated over time. Specifically, the differential terms in the model are first... At the set discrete time step The following approximation is in difference form; thus, the continuous system of differential equations is transformed into a system of algebraic equations that can be directly calculated at each time point. Subsequently, initial conditions are set, including the initial indoor temperature, initial wall temperature, initial outdoor temperature, and the initial on / off state of the equipment. Using the user-defined temperature control range (e.g., [18℃, 22℃]) as constraints, iterative calculations are performed within each time step: The heating power is determined based on the current equipment on / off state, and combined with the current outdoor temperature, the temperature state at the next time step is calculated using the following forward Euler difference equation: ; ; The above iterative process continues, and after each calculation, the on / off state of the equipment at the next moment is determined based on the temperature control range constraints, thus dynamically feeding back to the next round of calculation. This embodiment achieves precise acquisition of the minute-level resolution changes in indoor temperature and wall temperature over the entire time series. It not only captures the temperature delay and inertial effect caused by heat storage and release in the wall, but also provides a high-fidelity, traceable dynamic data foundation for quantifying the equipment's operating characteristics and power regulation potential in subsequent steps.

[0031] S3. Determine the operating state sequence of a single electric heating device based on the changes in the indoor temperature, and determine the power adjustment potential of the electric heating device within the set temperature control range based on the principle of building thermal inertia and the changes in the wall temperature.

[0032] As a preferred embodiment, determining the operating state sequence of a single electric heating device based on the changes in indoor temperature includes the following steps: The system monitors the indoor temperature in real time and compares it with the set temperature control range; it determines the on / off status of the electric heating equipment based on the comparison results; and it calculates the actual heating power P of the electric heating equipment based on the on / off status. heat (t)=S(t)P N ;where P N S(t) is the rated power of the electric heating equipment at time t, and S(t) is the on / off state of the electric heating equipment at time t, which is 0 when it is off and 1 when it is on. The operating state sequence of the electric heating equipment is constructed by sequentially recording the on / off states of the electric heating equipment within a set time period and the corresponding state parameters. The state parameters include the average heating time, the average cooling time, and the average duty cycle.

[0033] Understandably, to overcome the problem in existing technologies that lack a refined and quantitative description of the operating behavior of individual electric heating devices, making it difficult to accurately assess their dynamic response characteristics and adjustment potential, this embodiment determines the on / off state sequence of the electric heating device by dynamically comparing and logically judging the indoor temperature in real time with a preset temperature control range. Specifically, when the indoor temperature is lower than the lower limit of the set temperature control range, the device is triggered to turn on (S(t)=1), and when it is higher than the upper limit, it is triggered to turn off (S(t)=0), and the actual heating power P is calculated in real time based on the on / off state. heat (t), where P N The rated power is used; furthermore, by continuously recording the switching state sequence of the equipment within a set time period and statistically analyzing it, key state parameters are obtained, including the average heating time t. up Average cooling time t down and average duty cycle l kong This allows for the construction of a state sequence characterizing the periodic operating patterns of the equipment. Based on the principles of closed-loop temperature control feedback and time series statistics, this embodiment achieves precise quantification of the start-stop frequency, thermal dynamic response speed, and energy input ratio of electric heating equipment. This not only reveals the inherent operating mode of the equipment under comfort constraints but also provides a calculable and traceable equipment-level behavioral model foundation for quantifying the power regulation potential in subsequent steps by incorporating the principle of building thermal inertia.

[0034] in, ; ; ; ; ; In the formula: t up and t down These are the average temperature rise and the duration of temperature drop; U tThis is the state change of the electric heating system at time t (1 represents on, 0 represents off); l kong It is the average duty cycle; w day It is the electricity consumption; P day The average daily power consumption is P0; the electric heating power is T; and the simulation duration is in minutes.

[0035] As a preferred embodiment, determining the power adjustment potential of the electric heating equipment within a set temperature control range based on the principle of building thermal inertia and the temperature change of the wall includes the following steps: Based on the wall temperature change state, calculate the wall temperature fluctuation characteristic parameters; based on the building thermal inertia principle, calculate the thermal time constant according to the wall temperature change state; The maximum sustainable adjustment time parameter of the electric heating equipment is calculated based on the thermal time constant; the adjustable power of the electric heating equipment is calculated based on the maximum sustainable adjustment time parameter and the operating state sequence. The power regulation potential of the electric heating equipment is determined by combining the adjustable power parameters, which include adjustable power capacity, continuous regulation time, and maximum daily regulation power consumption.

[0036] Understandably, to overcome the problem in existing technologies that rely solely on rough estimates based on indoor temperature and fail to fully utilize building thermal inertia (especially the heat storage effect of walls) to accurately quantify the true and sustainable adjustment capabilities of electric heating equipment while ensuring comfort, this embodiment constructs a power adjustment potential assessment method that integrates the principles of building thermal inertia with dynamic wall temperature data, transforming physical characteristics into quantitative indicators. Specifically, it first assesses the power adjustment potential based on the acquired high-temporal-resolution wall temperature change status. The fluctuation characteristic parameters (such as decay rate and fluctuation period) are calculated, and then the key indicator characterizing the thermal inertia of the building—the thermal time constant—is derived based on thermodynamic principles. (For example, the wall temperature can be fitted to the natural cooling curve after the equipment is turned off.) (To obtain). Subsequently, based on this thermal time constant... Based on the preset temperature control range safety margin, the system calculates the maximum continuous adjustment time for the equipment to increase (turn off) or decrease (turn on) power while maintaining the indoor temperature within the limit. Next, the adjustable power ΔP of the device, determined by the operating state sequence (e.g., the upsizing potential is the rated power P), is considered. N (The potential is lowered to the current actual power), through integration calculation. This allows for the assessment of the maximum daily adjustable power consumption. This embodiment, through the aforementioned recursive calculation chain from thermal time constant to time and power parameters, and then to power consumption, achieves precise and structured quantification of the power adjustment potential of a single electric heating device. Its output includes adjustable power capacity, sustainable adjustment time, and the maximum daily adjustable power consumption, providing a reliable and differentiated basis of device-level capability parameters for subsequent load cluster aggregation and optimized scheduling.

[0037] S4. Aggregate multiple electric heating devices to form a load cluster. Based on the operating status sequence and power regulation potential of the load cluster, combined with the time-of-use pricing mechanism and ancillary service compensation mechanism, construct a collaborative demand response optimization model for the load cluster to participate in power grid peak shaving and valley filling.

[0038] As a preferred embodiment, the multiple electric heating devices are aggregated to form a load cluster, and the power is adjusted according to the operating state sequence and power of the load cluster. By combining time-of-use pricing and ancillary service compensation mechanisms, a collaborative demand response optimization model for the load cluster's participation in grid peak shaving and valley filling can be constructed, including the following steps: Multiple electric heating devices are aggregated to form a load cluster. The total power demand of the load cluster at time t is calculated. The total power is the sum of the heating power of each electric heating device at the corresponding time. The total regulation capacity of the load cluster is calculated based on the power regulation potential of each electric heating device. The total regulation capacity includes the total upward regulation power of the cluster and the total downward regulation power of the cluster. The total upward regulation power is the sum of the sustainable upward regulation power of each device, and the total downward regulation power is the sum of the sustainable downward regulation power of each device. An optimization function is established with the objective of maximizing the total revenue of the load cluster, which includes the annual valley filling revenue, the annual peak shaving revenue, and the annual heating cost savings. Set the operating constraints for the optimization model, including temperature constraints, power constraints, and regulation capability constraints; Configure price parameters by combining time-of-use pricing mechanisms and ancillary service compensation mechanisms; The collaborative demand response optimization model is constructed based on the objective function, operational constraints, and price parameters.

[0039] Understandably, in order to overcome the problem that existing research focuses on single-device control and lacks systematic aggregation and collaborative optimization mechanisms, thus failing to take into account the grid regulation needs, user comfort constraints and the maximization of the overall economic benefits of the cluster, this embodiment constructs a collaborative demand response optimization model that integrates technological potential and economic incentives, thereby realizing the large-scale and refined scheduling of electric heating load clusters.

[0040] Specifically, firstly, by aggregating the real-time status of multiple electric heating devices, the total power demand of the cluster at time t is calculated. ; Establish a perceptible cluster load baseline; then, based on the quantified power regulation potential of each device (including adjustable power capacity) and sustainable adjustment time The total regulation capacity of the load cluster is calculated by vector summation, yielding the total upward and downward regulation power of the cluster, thus clearly defining the power boundary for the cluster's participation in grid interaction. Based on this, an optimization function is established with the core objective of maximizing the total revenue of the load cluster. This revenue integrates the direct economic returns from participating in grid services with the savings in heating costs, specifically as follows: ;in, ; ; In the formula: E g and E f These are the annual valley filling revenue and peak shaving revenue; E save,g This represents the annual savings in heating costs; t is the time index; τ is the electric heating index. and These are valley filling and peak shaving electric heating systems; U τ，t P0 represents the state of the electric heating system during off-peak hours at time t (1 indicates on, 0 indicates off); P0 is the electric heating power. It is the compensation price during the valley filling period; δ g This refers to the electricity price during off-peak hours.

[0041] Furthermore, the model sets multiple layers of operational constraints: temperature constraints ensure that the indoor temperature of each household meets the comfort range at any given time [θ]. min ,θ max Power constraints ensure that regulation commands do not exceed the rated capacity of the equipment; regulation capacity constraints ensure that the power increase / decrease of the cluster does not exceed its total regulation power potential and the duration does not exceed the sustainable regulation duration of the cluster. Finally, by configuring the time-of-use pricing parameter δ... g and peak shaving and valley filling ancillary service compensation price parameters This model internalizes external market signals into price drivers. Through the systematic technical means described above—from physical aggregation, capacity quantification, target establishment, constraint setting to price integration—this embodiment constructs a complete, closed-loop, and solvable collaborative demand response optimization model. This enables collaborative optimization scheduling of large-scale heterogeneous electric heating load clusters under the hard constraint of strictly ensuring user-side temperature comfort, thereby maximizing its scale regulation value and comprehensive economic benefits in participating in grid peak shaving and valley filling.

[0042] S5. Solve the collaborative demand response optimization model to determine the power regulation strategy.

[0043] Understandably, this embodiment will input a collaborative demand response optimization model containing a nonlinear objective function and mixed integer constraints into a configured mathematical programming solver (such as CPLEX, Gurobi, or a dedicated algorithm suitable for large-scale mixed integer linear / nonlinear programming). This solver will use algorithms such as interior-point methods, branch and bound, or heuristic optimization to calculate the total revenue of the load cluster, while satisfying all temperature constraints, power constraints, and regulation capacity constraints. With the goal of maximizing power efficiency, a global or near-global optimal solution is searched to obtain a power regulation strategy. Specifically, the specific start-up and shutdown time sequences and corresponding power regulation commands for each electric heating device in the cluster are obtained within the simulation period (especially during peak shaving and valley filling periods); for example, the commands may explicitly require the devices to... Keep it closed during peak-shaving period 16:00-16:30. ), thus contributing The power was increased; and the equipment It will be opened in advance during the filling period from 01:00 to 02:00. ), to absorb additional The power reduction was achieved. This embodiment realizes a precise and reliable transformation from macro-optimization goals to micro-equipment actions, generating an optimal scheduling scheme that can be implemented and executed simultaneously to ensure user-side temperature comfort, not exceed the physical regulation capacity limit of the cluster, and maximize overall economic benefits. Thus, the complete technical chain of electric heating load clusters participating in power system power regulation is finally closed.

[0044] Example 2: Another embodiment of this invention provides a power regulation system suitable for a power regulation method for an electric heating load cluster participating in a power system, as described in Example 1. Figure 3 As shown, it includes: Equivalent model construction module: Constructs a lumped parameter model of the thermal dynamics process characterizing indoor temperature, wall temperature, and outdoor temperature; Equivalent model solution module: Based on the temperature control interval constraint, the lumped parameter model of the thermal dynamic process is solved to obtain the change state of indoor temperature and the change state of wall temperature respectively; Operating status determination module: Determines the operating status sequence of a single electric heating device based on the changes in indoor temperature; Adjustment potential determination module: Based on the principle of building thermal inertia and the temperature change state of the wall, determine the power adjustment potential of the electric heating equipment within the set temperature control range; Response model construction module: Aggregates multiple electric heating devices to form a load cluster. Based on the operating status sequence and power regulation potential of the load cluster, combined with the time-of-use pricing mechanism and ancillary service compensation mechanism, a collaborative demand response optimization model for the load cluster to participate in grid peak shaving and valley filling is constructed. Regulation strategy output module: solves the collaborative demand response optimization model to determine the power regulation strategy.

[0045] Understandably, this embodiment, through the design and integration of six core functional modules, achieves an automated pipeline from physical modeling to optimization decision-making: the equivalent model construction module creates a lumped parameter model of the thermal dynamic process based on building thermal parameters, providing a precise physical description basis for the system; the equivalent model solving module performs discretized numerical solution of the model based on temperature control constraints, outputting a high-time-resolution sequence of indoor and wall temperature changes; the operating status determination module processes temperature status data in real time, generating the switching sequence and operating characteristic parameters of individual electric heating devices through logical judgment and statistics; the regulation potential determination module deeply integrates the principle of building thermal inertia, analyzes wall temperature dynamics to quantify the adjustable power, duration, and adjustable electricity consumption of each device; the response model construction module performs device aggregation and integrates time-of-use pricing and ancillary service compensation mechanisms to construct a collaborative demand response optimization model with multiple constraints and the goal of maximizing total revenue; finally, the regulation strategy output module calls a mathematical optimization solver to solve the model, determine and output the optimal power regulation strategy including the specific start-stop time and power commands of each device. This embodiment, through the aforementioned modular system architecture and its strict internal data and logic connections, achieves the encapsulation and efficient execution of the power regulation method, ensuring full automation, reproducibility, and high reliability from raw data input to final scheduling instruction generation.

[0046] Example 3: One embodiment of the present invention is an electronic device, including a memory and a processor. The memory stores a computer program, and when the processor calls the computer program in the memory, it implements the steps of the power regulation method for electric heating load clusters participating in the power system as described in Example 1.

[0047] Example 4: One embodiment of the present invention is a storage medium storing computer-executable instructions. When the computer-executable instructions are loaded and executed by a processor, the steps of a power regulation method for an electric heating load cluster participating in a power system as described in Example 1 are implemented.

[0048] Through the above description of the embodiments, those skilled in the art will understand that, for the sake of convenience and brevity, only the division of the above functional modules is used as an example. In actual applications, the above functions can be assigned to different functional modules as needed, that is, the internal structure of the specific device can be divided into different functional modules to complete all or part of the functions described above.

[0049] In the embodiments provided in this application, it should be understood that the disclosed structures and methods can be implemented in other ways. For example, the structural embodiments described above are merely illustrative. For instance, the division of modules or units is only a logical functional division, and in actual implementation, there may be other division methods. For example, multiple units or components may be combined or integrated into another structure, or some features may be ignored or not executed. Furthermore, the coupling or direct coupling or communication connection shown or discussed may be through some interfaces, or indirect coupling or communication connection between structures or units, and may be electrical, mechanical, or other forms.

[0050] The units described as separate components may or may not be physically separate. A component shown as a unit can be one or more physical units; that is, it can be located in one place or distributed in multiple different locations. Some or all of the units can be selected to achieve the purpose of this embodiment according to actual needs.

[0051] Furthermore, in the embodiments of this application, the functional units can be integrated into one processing unit, or each unit can exist physically separately, or two or more units can be integrated into one unit. The integrated unit can be implemented in hardware or as a software functional unit.

[0052] If the integrated unit is implemented as a software functional unit and sold or used as an independent product, it can be stored in a readable storage medium. Based on this understanding, the technical solutions of the embodiments of this application, in essence, or the parts that contribute to the prior art, or all or part of the technical solutions, can be embodied in the form of a software product. This software product is stored in a storage medium and includes several instructions to cause a device (which may be a microcontroller, chip, etc.) or processor to execute all or part of the steps of the methods of the various embodiments of this application. The aforementioned storage medium includes various media capable of storing program code, such as USB flash drives, portable hard drives, read-only memory (ROM), random access memory (RAM), magnetic disks, or optical disks.

[0053] The specific embodiments described above are preferred embodiments of the power regulation method and system for electric heating load clusters participating in the power system according to the present invention. They are not intended to limit the specific scope of the present invention. The scope of the present invention includes but is not limited to the specific embodiments described above. All equivalent changes made in accordance with the shape and structure of the present invention are within the protection scope of the present invention.

Claims

1. A method for participating in power regulation of an electric power system by a cluster of electric heating loads, characterized in that: The method comprises the following steps: S1, constructing a thermal dynamic process lumped parameter model representing indoor temperature, wall temperature and outdoor temperature; S2, solving the thermal dynamic process lumped parameter model based on a temperature control interval constraint condition to obtain a change state of the indoor temperature and a change state of the wall temperature; S3, determining an operation state sequence of a single electric heating device according to the change state of the indoor temperature, and determining a power regulation potential of the electric heating device within a set temperature control interval based on a building thermal inertia principle and the change state of the wall temperature; S4, aggregating multiple electric heating devices to form a load cluster, and constructing a collaborative demand response optimization model of the load cluster participating in peak load shifting of a power grid based on an operation state sequence and a power regulation potential of the load cluster, a time-of-use electricity price mechanism and an auxiliary service compensation mechanism; S5, solving the collaborative demand response optimization model to determine a power regulation strategy.

2. The method of claim 1, wherein the electric heating load cluster participates in power regulation of the power system. The thermal dynamic process lumped parameter model has the following form: ； wherein C in , C wall are the equivalent heat capacity of indoor air and wall respectively; R1, R2 are the equivalent thermal resistance of indoor air and wall inner side, wall outer side and outdoor air respectively; , , are the indoor temperature, wall temperature, outdoor temperature respectively; P heat (t) is the heating power of the electric heating device at time t, P heat (t) = S(t)P N , wherein P N is the rated power of the electric heating device at time t, and S(t) is the on-off state of the electric heating device at time t, taking 0 when off and 1 when on.

3. The power regulation method of an electric heating load cluster participating in a power system according to claim 1, characterized in that: The solving of the thermal dynamic process lumped parameter model based on a temperature control interval constraint condition to obtain a change state of the indoor temperature and a change state of the wall temperature comprises the following steps: Discretize the differential equations in the thermal dynamic process lumped parameter model into difference equations, and solve the difference equations by using a forward Euler method, and set a discrete time step; Set initial conditions, and iteratively calculate each discrete time step based on a temperature control interval constraint condition, wherein the initial conditions include an initial indoor temperature, an initial wall temperature, an initial outdoor temperature and an initial on-off state of the electric heating device; Determine a heating power of the electric heating device according to a current on-off state, and iteratively calculate an indoor temperature and a wall temperature at a next time by combining the heating power with a current outdoor temperature; obtain a change state of the indoor temperature and a change state of the wall temperature according to an iteration round.

4. The method of claim 1 or 3, wherein the method further comprises: The determination of an operation state sequence of a single electric heating device according to the change state of the indoor temperature comprises the following steps: Real-time monitoring of indoor temperature, and comparing with the set temperature interval; determining the on-off state of the electric heating device according to the comparison result; calculating the actual heating power P of the electric heating device according to the on-off state heat (t)=S(t)P N ; wherein P N is the rated power of the electric heating device at time t, S(t) is the on-off state of the electric heating device at time t, 0 when off, 1 when on Record state parameters corresponding to on-off states of the electric heating device within a set time period in sequence to construct an operation state sequence of the electric heating device; wherein the state parameters include an average heating time, an average cooling time and an average duty cycle.

5. The method of claim 4, wherein the electric heating load cluster participates in power regulation of the power system. The determination of a power regulation potential of the electric heating device within a set temperature control interval based on a building thermal inertia principle and the change state of the wall temperature comprises the following steps: Calculate wall temperature fluctuation characteristic parameters based on the change state of the wall temperature; Calculate a thermal time constant based on the building thermal inertia principle and the change state of the wall temperature; Calculate a maximum sustainable regulation time parameter of the electric heating device based on the thermal time constant; and calculate an adjustable power of the electric heating device based on the maximum sustainable regulation time parameter and the operation state sequence; Determine a power regulation potential of the electric heating device by comprehensively considering the adjustable power parameter, wherein the power regulation potential includes an adjustable power capacity, a sustainable regulation time and a daily maximum regulation power.

6. The method of claim 1, wherein: the plurality of electric heating devices are aggregated to form a load cluster, and a cooperative demand response optimization model is constructed for the load cluster to participate in peak shaving and valley filling of the power grid based on a sequence of operating states of the load cluster and a power regulation potential in combination with a time-of-use electricity price mechanism and an ancillary service compensation mechanism, and the cooperative demand response optimization model includes the following steps: aggregating the plurality of electric heating devices to form the load cluster, calculating a total power demand of the load cluster at a time t, the total power being a sum of heating powers of the electric heating devices at corresponding times, calculating a total regulation capacity of the load cluster based on the power regulation potential of each electric heating device, the total regulation capacity including a total up-regulation power and a total down-regulation power, the total up-regulation power being a sum of sustainable up-regulation powers of the electric heating devices, and the total down-regulation power being a sum of sustainable down-regulation powers of the electric heating devices, establishing an optimization function with a maximum total revenue of the load cluster as a target, the total revenue of the load cluster including annual valley filling revenue, annual peak shaving revenue, and annual heating cost saving, setting operating constraint conditions of the optimization model, the operating constraint conditions including temperature constraints, power constraints, and regulation capacity constraints, configuring price parameters in combination with the time-of-use electricity price mechanism and the ancillary service compensation mechanism, and constructing the cooperative demand response optimization model based on the target function, the operating constraint conditions, and the price parameters.

7. The method of claim 1, wherein: the power regulation strategy includes specific start-stop time sequences and corresponding power regulation instructions of the electric heating devices in peak shaving periods and valley filling periods. The method includes the following steps: an equivalent model construction module that constructs a lumped parameter model representing thermal dynamic processes of indoor temperature, wall temperature, and outdoor temperature; an equivalent model solving module that solves the lumped parameter model based on temperature control interval constraints to obtain a change state of the indoor temperature and a change state of the wall temperature; an operating state determination module that determines a sequence of operating states of a single electric heating device based on the change state of the indoor temperature; a regulation potential determination module that determines a power regulation potential of the electric heating device within a set temperature control interval based on building thermal inertia principles and the change state of the wall temperature; a response model construction module that aggregates a plurality of electric heating devices to form a load cluster, and constructs a cooperative demand response optimization model for the load cluster to participate in peak shaving and valley filling of the power grid based on a sequence of operating states of the load cluster and a power regulation potential in combination with a time-of-use electricity price mechanism and an ancillary service compensation mechanism; and a regulation strategy output module that solves the cooperative demand response optimization model to determine the power regulation strategy. A computer program product includes a memory and a processor, the memory storing the computer program, and the processor calling the computer program in the memory to implement the steps of the method of claim 1-7.

8. The computer program product of claim 7, wherein the computer program product includes a memory and a processor, the memory storing the computer program, and the processor calling the computer program in the memory to implement the steps of the method of claim 1-7. ​ ​ ​ ​ ​ 8. A power conditioning system adapted for use in the method of any one of claims 1 to 7, characterised in that: ​ ​ ​ ​ ​ ​ ​ 9. An electronic device, comprising: ​ 10. A storage medium characterized by: The storage medium stores computer executable instructions, and the computer executable instructions are loaded by a processor and executed, and steps of the method for participating in power regulation of an electric heating load cluster in an electric power system according to any one of claims 1-7 are implemented.

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