Cluster electric heating load response peak regulation method considering average heating demand

Abstract

The invention discloses a cluster electric heating load response peak regulation method considering average heating requirements, and belongs to the technical field of temperature control load regulation and control. According to the method, starting from the source for restraining the strong random fluctuation change of the cluster electric heating load, the linear correlation invariant rule of the indoor and outdoor temperature difference-average heating demand behind the source is excavated, and the indoor and outdoor temperature difference-average heating demand-adjusting capacity correlation rule is further deduced; the method is used for load baselines of an intelligent aggregation normal operation mode and an intelligent aggregation response peak regulation mode respectively, a cluster electric heating real-time commissioning quantity control strategy is formulated, and the flexible and adjustable capacity of room temperature in a human body comfort temperature control interval is excavated so as to offset the deviation between natural aggregation and the two intelligent aggregation load baselines. Through example verification, the method provided by the invention has the advantages of strong predictability, high interference immunity, large regulation capability, high net income of user response peak regulation and the like.

Description

Technical Field

[0001] The present invention belongs to the technical field of temperature control load regulation and control, and in particular relates to a cluster electric heating load response peak regulation method considering average heating demand. Background Art

[0002] In recent years, there have been literatures on the research on mining the aggregated regulation capability of cluster electric heating loads, which are mainly divided into modeling research on the thermal conduction characteristics of electric heating rooms, research on the aggregated adjustable power size and sustainable regulation time, and research on the decomposition and execution control strategy of the aggregated regulation power.

[0003] In the research on modeling the heat conduction characteristics of electric heating rooms, references [1-4] weakened the temperature difference between different indoor spatial locations and established a second-order thermodynamic equivalent model (equivalent thermal parameters, ETP) to characterize the heating, heat storage, heat transfer and heat dissipation characteristics of electric heating rooms: during the electric heating period, the indoor temperature gradually rises. If the equipment is turned off at this time, the indoor temperature will slowly drop to the lower limit of the temperature control range due to the thermal inertia of the room. During the temperature change period, a downward adjustment power equal to the installed capacity of the electric heating equipment can be mined, and the higher the initial point temperature, the longer the sustainable downward adjustment time. Subsequently, references [5-6] connected the second-order ETP model in series and parallel, and introduced a second heat source variable to achieve refined modeling of influencing factors such as mutual heat transfer in cluster electric heating rooms and heat dissipation of household appliances. However, due to the high complexity of the coupled model, it is not easy to apply it to large-scale cluster electric heating. Therefore, it is possible to consider using an individual electric heating temperature change model to analyze the general law and then promote it to the cluster.

[0004] In the study of the adjustable power size and sustainable adjustment duration of cluster electric heating load aggregation, references [7-9] considered that the room temperature change characteristics are easily affected by ambient temperature disturbances and factors such as door opening and window heat dissipation, and proposed a dynamic evaluation method for temperature control load response capability based on data-mechanism joint dynamic parameter identification. However, the duration of the naturally aggregated adjustment capability of this example is short, only about 4 minutes. Further in-depth research can be conducted to establish a flexible utilization method for load adjustment capability with adjustable aggregation power and duration.

[0005] In the study of the decomposition and execution control strategy of cluster electric heating load aggregation power regulation, references [10-12] proposed a deep neural network model based on a fast distributed average consensus algorithm to achieve real-time follow-up control of distributed cluster temperature control loads, aiming at the complex group control problem caused by the large number of electric heating load clusters and different parameters. However, this method relies on a black box model, and the origin of the temperature control load baseline, the correlation between the regulation capability and the external ambient temperature need to be further studied.

[0006] In summary, existing research mainly bases the exploration and utilization of cluster electric heating load regulation capabilities on the predicted natural aggregated random fluctuation load baseline before regulation. It is difficult to explore accurate and reliable regulation capabilities and conduct research on the setting of aggregated power amplitude and sustainable duration.

[0007] In addition, the optimization and scheduling are complicated due to the large scale of electric heating load clusters, small single-unit capacity, dispersed location and different parameters. The strong random volatility of the natural aggregation power of cluster electric heating loads leads to a series of problems that need to be solved urgently, such as difficulty in predicting operating loads, difficulty in evaluating regulation capabilities, and difficulty in formulating control strategies.

[0008] Therefore, a new technical solution is urgently needed in the prior art to solve this problem. Summary of the invention

[0009] The technical problem to be solved by the present invention is: to provide a cluster electric heating load response peak-shaving method taking into account the average heating demand to solve the poor accuracy of existing research on the cluster electric heating load regulation capability; the strong random volatility of the natural aggregation power of the cluster electric heating load leads to technical problems such as difficulty in predicting the operating load, difficulty in evaluating the regulation capability, and difficulty in formulating the control strategy.

[0010] The cluster electric heating load response peak-shaving method considering average heating demand includes the following steps, and the following steps are performed in sequence:

[0011] Step 1: Establish a thermal inertia temperature change model for individual direct heating electric heating loads

[0012] 1) The thermal inertia temperature change model of the individual direct-heating electric heating load is characterized by a second-order thermodynamic equivalent model:

[0013] 2) Based on the set human comfort temperature range in, and The lower and upper limits of the human body's comfortable temperature range are respectively used to set the start and stop control rules for individual direct heating electric heating loads, and obtain the natural temperature change switching control rules for electric heating;

[0014] 3) Based on the equal heat exchange between the wall and the indoor air, the indoor temperature rise change law and indoor temperature drop change law within a temperature change cycle are established to obtain the indoor steady-state temperature rise time T on And the indoor steady-state temperature drop time T off ;

[0015] The indoor steady-state temperature change period is T S , T S =T on +T off, the temperature change cycle is a periodic change in which the temperature changes from the lowest temperature point to the highest temperature point and then drops to the lowest temperature point over time;

[0016] 4) If the maximum temperature, minimum temperature and temperature change cycle of the room remain unchanged, it is determined that the room temperature change process has reached a steady state, and step 2 is performed;

[0017] Step 2: Establish the correlation characteristics between the indoor and outdoor temperature difference and the average heating demand of individual rooms

[0018] When the room temperature change process reaches a steady state, the average temperature of the indoor and wall temperatures remain unchanged. The wall temperature is set equal to the average wall temperature. The thermal inertia temperature change model of the individual direct heating electric heating load is integrated for a temperature change cycle and divided by the temperature change cycle duration. The correlation characteristic formula between the indoor average temperature, the average heating power, and the outdoor temperature can be obtained as follows:

[0019]

[0020] The average heating power and the operating duty cycle of the electric heating equipment are obtained, as shown in equations (11) and (12):

[0021]

[0022] Where: θ out is the outdoor temperature, is the average temperature of the wall; is the average heating power; D is the duty cycle of the electric heating equipment; P e The rated power of the electric heating equipment; is the average indoor air temperature,

[0023] Formula (10) shows that under the premise of given outdoor temperature and heating power, the indoor average temperature will passively stabilize at a fixed value, which is called the steady-state temperature point corresponding to a certain average heating power;

[0024] By performing an equivalent transformation of terms in equation (10), we can obtain equation (11), that is, under the premise of given outdoor temperature and indoor average temperature, the average heating power will also passively stabilize at a constant value. This formula can be used to predict the load curve in combination with the outdoor temperature curve of the day;

[0025] Formula (12) shows that the electric heating load is a switch-controlled load. The power consumption curve of a single electric heating load is a pulse square wave. Therefore, its average power is equal to the operation time in a single cycle divided by the cycle and then multiplied by the rated power of the equipment, which is also equal to the average power divided by the rated power. After being substituted into formula (11), the duty cycle of the operation of the single electric heating equipment is calculated by the indoor and outdoor temperatures, which lays the foundation for the proportion of the reference operation number of cluster electric heating equipment at a certain moment, that is, formula (14). It can be seen from formulas (11) and (12) that the indoor steady-state average heating power and the operation duty cycle of individual electric heating equipment are both linearly positively correlated with the indoor and outdoor temperature difference.

[0026] By setting the indoor steady-state temperature point and adjusting the average heating power, as shown in formula (11), the higher the set steady-state temperature point, the greater the average heating power, the faster the temperature rise, and the earlier the indoor temperature exceeds the temperature control boundary, the upper adjustment power of a limited duration can be obtained;

[0027] The power of down regulation for a limited time is obtained in the same way;

[0028] Step 3: Obtain the average heating power demand of cluster electric heating, that is, obtain the intelligent aggregation power of cluster electric heating load, as shown in formula (13):

[0029]

[0030] Where: Intelligently aggregate power for cluster electric heating loads, is the average indoor air temperature; θ out,t is the outdoor temperature at time t, R 1,i,t is the thermal resistance of heat transfer from the room to the wall in room i, R 2,i,t is the thermal resistance from the wall of room i to the outside; R agg is the cluster thermal resistance, and its value decreases accordingly as the number of cluster electric heating increases.

[0031] Step 4: Correct the number of electric heating units put into operation in real time

[0032] The operation of electric heating takes the comfort of residents as the first principle. When the indoor temperature exceeds the set human comfort temperature range, the switch state is immediately triggered to ensure that the indoor temperature is only controlled by the intelligent aggregation power of the cluster electric heating load within the set human comfort temperature range;

[0033] The duty cycle is calculated based on the ratio of the average heating power demand of the cluster electric heating to the installed capacity. Average number of units put into operation

[0034]

[0035] In the formula, are respectively the cluster electric heating load operation duty cycle and the average number of units in operation, P i e is the rated power of the i-th electric heating equipment, P agg,t is the total power consumption of cluster electric heating at time t, D agg,t is the proportion of cluster electric heating load to its total installed capacity at time t, and N is the installed capacity of cluster electric heating equipment;

[0036] Corrected actual number of cluster electric heating units put into operation Average number of units put into operation The deviation between them is calculated as shown in formula (16), which enables accurate prediction of cluster electric heating load baseline:

[0037]

[0038] Where: It is the quantity of regulation. It is the number of electric heating equipment running online in real time;

[0039] If it is greater than 0, the electric heating equipment in the room with the maximum room temperature will be turned off until the real-time online number of electric heating equipment is equal to the average number of electric heating equipment in operation;

[0040] If it is less than 0, the electric heating equipment in the room with the minimum room temperature will be turned on until the real-time online number of electric heating equipment is equal to the average number of electric heating equipment in operation;

[0041] Step 5: Establish a load baseline that responds to the peak load mode by intelligently aggregating power through cluster electric heating loads

[0042] The load baseline of the response peak-shaving mode includes two types: a load baseline of an aggressive response peak-shaving mode and a load baseline of a conservative response peak-shaving mode;

[0043] 1) The aggressive response peak-shaving mode refers to setting the steady-state temperature point above / below the human body comfort temperature control range, driving the increase / decrease of load power and the increase / decrease of indoor temperature. The power when the indoor temperature just increases / decreases to the temperature control range limit during the grid regulation demand period is called the maximum sustainable regulation power of the aggressive response peak-shaving mode. The calculation process is as shown in equations (17) to (19):

[0044]

[0045] Where: θ in (t) is the indoor temperature at time t, θ in (0) = θ in0 ; Δθ in is θ in0 The adjustment amount; θin0 is the average indoor air temperature before adjustment; P e R is the rated power of the electric heating equipment; 1 , R 2 are the equivalent thermal resistance from indoor to wall center and from wall center to outdoor respectively; C in is the indoor air heat capacity; C w is the wall heat capacity; ΔP heat P heat0 The amount of adjustment; P heat0 is the average heating power before adjustment; τ 1 , τ 2 is the indoor temperature change time constant, A, B, F, G are the constant coefficients composed of the room thermal resistance and heat capacity parameters;

[0046] Substituting formula (17) into the grid demand duration and the indoor temperature control range limit, we can obtain the correlation model of ambient temperature-grid demand duration-maximum regulation power, as shown in the following formulas (18) and (19):

[0047]

[0048] Where: T is the duration of load regulation; e is the base of the exponential function, which is 2.71828; is the indoor temperature exceeding the limit point, which is taken when adjusting the demand response up / down.

[0049] It can be seen from formula (18) that the longer the adjustment period, the smaller the maximum adjustable power amplitude; it can be seen from formula (19) that the maximum adjustable power amplitude is jointly limited by the demand duration and the electric heating output range;

[0050] Therefore, in the aggressive response peak load mode, the maximum adjustable power amplitude can be obtained according to the given grid demand duration, the value of the steady-state temperature point that needs to be set, and the number of online operations required;

[0051] 2) The conservative response peak-shaving mode refers to setting the steady-state temperature point within the human body comfort temperature control range and within the threshold range close to the upper / lower limit value, which corresponds to the maximum / minimum regulation power of the conservative response peak-shaving mode, as shown in formula (20):

[0052]

[0053] Where: The power adjustable range for conservative response of electric heating load to peak load mode;

[0054] Step 6: Divide into three intelligent aggregation scenarios, and apply the average heating demand load baseline to correct the number of electric heating units in operation; the three intelligent aggregation scenarios are normal operation mode, conservative response peak load operation mode, and aggressive response peak load operation mode;

[0055] 1) The set indoor steady-state temperature point is located at This value is determined to be in normal operation mode, and the average heating power demand of cluster electric heating is obtained through formula (13) and the intelligent aggregation power curve of cluster electric heating load is obtained, and the number of cluster electric heating put into operation is corrected through step 4;

[0056] 2) The set indoor steady-state temperature point deviates from But still in If the value is within the range of , it is determined to be a conservative response peak-shaving operation mode. The intelligent aggregation power curve of cluster electric heating load in the conservative response peak-shaving operation mode is obtained through formula (20), and the number of cluster electric heating units put into operation is corrected through step 4.

[0057] 3) The set indoor steady-state temperature point is located at If the value is other than the above, it is determined to be an aggressive response peak-shaving operation mode. The intelligent aggregation power curve of cluster electric heating load in the aggressive response peak-shaving operation mode is obtained through equations (17), (18), and (19). Through the correlation model of ambient temperature-grid demand duration-maximum adjustment power, based on the gain of wall heat absorption / dissipation on the upper / lower adjustment power and duration during the peak-shaving process, the maximum adjustable power within the response peak-shaving time limit is calculated, and the number of cluster electric heating units put into operation is corrected through step four.

[0058] 4) Substitute the electric heating operation status of the controlled switch state change into formula (1) and formula (2) to calculate the indoor temperature of each room at the next moment, or refresh the indoor temperature through actual measurement.

[0059] The second-order thermodynamic equivalent model formula is as follows:

[0060]

[0061] Where: θ w (t) and θ out (t) are the wall and outdoor temperatures at time t, θ w (0) = θ w0 ; S t It is a 0-1 variable representing the operating status of the electric heating equipment at time t. A value of 1 indicates that the equipment is in operation, and a value of 0 indicates that the equipment is in shutdown.

[0062] The specific method for obtaining the natural temperature change switching control rule of electric heating is:

[0063] When the room temperature drops to the lower limit, the electric heating equipment is started to heat, and when the room temperature rises to the upper limit, the heating is stopped. Therefore, the control rule of electric heating is shown in formula (2):

[0064]

[0065] Where: Δt is the time granularity; θ in,t-Δt is the indoor temperature at the previous moment, S t-Δt It is the switch status of the electric heating equipment at the previous moment.

[0066] Through the above design scheme, the present invention can bring the following beneficial effects:

[0067] 1) The linear correlation characteristics of the indoor and outdoor temperature difference-average heating demand in the thermal inertia temperature change model of individual direct heating electric heating load are extended to the average heating demand load baseline of cluster electric heating that is inversely correlated with the outdoor temperature change trend.

[0068] 2) By establishing an ambient temperature-response time-maximum adjustable power correlation model, a theoretical foundation is laid for the evaluation of adjustable capacity under different outdoor ambient temperature scenarios. At the same time, outdoor constant temperature and outdoor variable temperature scenarios are set to verify the applicability and error range of the theory.

[0069] 3) Through the real-time online switching control of the number of cluster electric heating equipment, the ability of room temperature to be flexibly adjusted within the human comfort temperature control range is explored to offset the deviation between the natural aggregation and the two intelligent aggregation load baselines, thereby overcoming the disadvantages of the traditional natural aggregation load baseline that is easily affected by the number of cluster devices, initial operating points, state dependence, collected temperature difference, and minimum control time point granularity. It effectively improves the anti-interference ability and prediction accuracy of the temperature control load baseline, and has the advantages of strong predictability, high anti-interference, and large adjustment ability.

[0070] 4) Taking into account the changes in user electricity charges and ancillary service revenue caused by responsive peak-shaving, the responsive peak-shaving revenue was calculated, which verified that the responsive peak-shaving based on the intelligent aggregation of average heating demand in the present invention has the advantage of high net user responsive peak-shaving revenue, and its economic benefits are significantly improved compared to the natural aggregation model. BRIEF DESCRIPTION OF THE DRAWINGS

[0071] The present invention is further described below with reference to the accompanying drawings and specific embodiments:

[0072] Figure 1 The equivalent structure of the equivalent model of the heat transfer characteristics of the electric heating building in the cluster electric heating load response peak-shaving method considering the average heating demand of the present invention is schematically shown.

[0073] Figure 2 It is a schematic diagram corresponding to the temperature change process of indoor air temperature, wall temperature and heating power in the electric heating room of the present invention;

[0074] Figure 3 It is a schematic diagram of the corresponding curves of the steady-state temperature change of indoor air and wall and the heating power in the electric heating room under the condition of -25°C outdoors in the present invention;

[0075] Figure 4 It is a schematic diagram of the maximum adjustable power of the electric heating load under the conditions of given outdoor temperature and the required time for up and down adjustment of the power grid in the present invention;

[0076] Figure 5 It is a schematic diagram of the correlation curve between ambient temperature, grid demand duration and maximum regulation power in an embodiment of the present invention;

[0077] Figure 6 It is a schematic diagram of high-frequency switching changes of indoor temperature and device switch state when individual electric heating is limited to 4 hours and the up / down adjustable power is mined under outdoor conditions of -25°C in an embodiment of the present invention;

[0078] Figure 7 It is a schematic diagram of the indoor average temperature limit being close to the human body comfort temperature boundary when analyzing the maximum adjustable power of individual electric heating limited to 4 hours under the condition of -25°C outdoors in an embodiment of the present invention; Figure 8 This is a schematic diagram of the maximum adjustable power corresponding to different grid demand adjustment durations under outdoor conditions of -25°C in an embodiment of the present invention; Fig. 9 Schematic diagram of the effect of outdoor temperature on the relationship between power and duration in an embodiment of the present invention; Fig.10 It is a schematic diagram of the calculation results of the natural aggregation mode in the embodiment of the present invention; Fig.11 Schematic diagram of the adjustable power range that can last for an unlimited time when the outdoor temperature is perturbed in an embodiment of the present invention. DETAILED DESCRIPTION

[0079] The cluster electric heating load response peak-shaving method considering the average heating demand disclosed in the present invention will be further described in detail below with reference to the accompanying drawings.

[0080] The cluster electric heating load response peak-shaving method considering the average heating demand includes the following steps:

[0081] 1. Heat transfer model of electric heating room

[0082] 1.1 Heat transfer model of direct electric heating room

[0083] The heat transfer characteristics of an electrically heated room can be characterized by the second-order thermodynamic equivalent model shown in equation (1):

[0084]

[0085] Where: θ in (t),θ w (t) and θ out (t) are the indoor, wall and outdoor temperatures at time t, θ in (0) = θ in0 θ w (0) = θ w0 ;P e is the rated power of the electric heating equipment; S t is a 0-1 variable representing the operating status of the electric heating equipment at time t. A value of 1 indicates that the equipment is in operation, and a value of 0 indicates that the equipment is in shutdown; R 1 , R 2 are the equivalent thermal resistance from indoor to wall center and from wall center to outdoor respectively; C in is the indoor air heat capacity; C w is the heat capacity of the wall.

[0086] When the heating rule and outdoor temperature are given, the time-varying process of indoor temperature and wall temperature can be determined, and then the room electric heating energy flow can be analyzed.

[0087] Natural temperature change switching control rules: Assume that the human body comfortable temperature range is in, and are the lower and upper limits of the human body's comfortable temperature range, respectively. When the room temperature drops to the lower limit, the electric heating device is started to heat, and when the room temperature rises to the upper limit, the heating is stopped. The control rule is shown in formula (2).

[0088]

[0089] Let the average indoor temperature be σ is half of the indoor temperature range, When σ is extremely small, that is, the allowable range of indoor temperature variation is very narrow, the above control evolves into constant temperature control.

[0090] Figure 2 is the steady-state temperature change curve of the electric heating room under the natural temperature change control rule. The heating power drives the indoor air heat storage temperature to rise, see T on During this period, the indoor air temperature drops after the heating stops, see T off During this period, energy is exchanged between the air and the wall, and the heat of the indoor air above the average heat storage will be injected into the wall, driving the wall temperature to rise.

[0091] Figure 2 In the figure, points ABCDE form an indoor temperature change cycle, which are the lowest, average, highest, average, and lowest temperature points respectively; t 1 →t4 is the corresponding time point number; T Ⅰ →T Ⅳ is the corresponding time period number; is the mean value of the upper half cycle (B→C→D) of the indoor temperature change curve; θ in is the indoor air temperature; is the wall temperature; P heat is the heating power of the electric heating load; is the average power of the electric heating load in one cycle. Indoor air temperature θ in , wall temperature

[0092] In a temperature control cycle, the pulse heating power is smoothed by storing heat in the heat capacity of the room and the wall, achieving a dynamic balance between pulse heating and continuous and stable heat dissipation. The air and the wall exchange equal amounts of heat energy, and the buffer energy stored during the period when the indoor air temperature exceeds its average value is injected into the wall, driving the wall temperature to rise, and is released during the period when the indoor air temperature is lower than its average value to make up for the deficit energy.

[0093] C in Δθ in =C w Δθ w (3);

[0094]

[0095] Where: Δθ in is the average value of the fluctuation range of indoor air temperature, equal to and The difference between w is the wall temperature fluctuation range, Δθ in and Δθ w Are inversely proportional to heat capacity.

[0096] 1.2 Analysis of the sustainable duration of temperature rise and fall

[0097] The heat capacity of the wall is much larger than the heat capacity of the indoor air. When analyzing the indoor temperature rise variation law, the wall temperature can be set constant, equal to the average wall temperature. At this time, the indoor temperature rise variation law is as follows:

[0098]

[0099] Where: The indoor steady-state temperature rise duration is as shown in formula (6):

[0100]

[0101] Similarly, the indoor temperature drop variation law is as follows (7):

[0102]

[0103] Where: The indoor steady-state temperature drop duration is as shown in formula (8):

[0104]

[0105] Indoor steady-state temperature change period T S As shown in formula (9):

[0106] T S =T on +T off (9);

[0107] From formula (9), we can know that: ① The narrower the allowable temperature change range, the shorter the temperature control cycle, and the more frequently the heating equipment needs to switch its operating state, which will reduce the life of the equipment; ② The temperature change of the electric heating room is state-dependent, so when there is an error in the indoor temperature measurement, error accumulation will occur, which may cause severe errors such as the predicted temperature peak period corresponding to the actual valley period.

[0108] 2. Reliable mining and response peak load regulation strategy of cluster electric heating load considering average heating demand

[0109] In view of the problems that the cluster electric heating load regulation capacity is difficult to reliably explore, and the aggregation power amplitude and sustainable duration are difficult to set under the natural aggregation strategy, this paper studies the reliable mining of cluster electric heating load regulation capacity and response peak-shaving strategy considering the average heating demand.

[0110] 2.1 Load baseline of normal operation mode of intelligent aggregation

[0111] 2.1.1 Average heating demand of individual rooms

[0112] When the room temperature change process reaches a steady state, the characteristic values ​​of the indoor and wall temperatures (such as average temperature, maximum and minimum temperatures, temperature change cycle, etc.) remain unchanged. Integrating equation (1) over a temperature change cycle yields:

[0113]

[0114] Where: is the average temperature of the wall, is the steady-state average heating power, and D is the duty cycle of the electric heating equipment. The average indoor heating power demand and the duty cycle of individual electric heating equipment are linearly positively correlated with the indoor and outdoor temperature difference.

[0115] 2.1.2 Average heating demand of cluster rooms

[0116] When the outdoor temperature and indoor comfort temperature requirements are close, the average heating power requirement of the cluster is linearly positively correlated with the indoor and outdoor temperature difference, as shown in formula (13).

[0117]

[0118] Where: Intelligently aggregate power for cluster electric heating loads, R 1,i,t , R 2,i,t are the thermal resistances of indoor-wall heat transfer and wall-outdoor heat dissipation of room i; R agg is the cluster thermal resistance, and its value decreases gradually with the increase of the number of cluster electric heating.

[0119] 2.1.3 Real-time online equipment operation quantity correction strategy

[0120] The duty cycle and average operation quantity requirement can be calculated based on the ratio of the average heating power demand of the cluster to the installed capacity, as shown in equations (14) and (15).

[0121]

[0122] Then, by utilizing the flexibility inherent in the indoor comfortable temperature range, the deviation between the actual number of units put into operation and the average number of units put into operation is intelligently corrected, as shown in formula (16). This helps to suppress the "temperature peak resonance" phenomenon and promote accurate prediction of the cluster electric heating load baseline.

[0123]

[0124] Where: They are respectively the cluster electric heating load operation duty cycle and the average number of units in operation. It is to regulate quantity.

[0125] In addition, the design adopts the first principle to ensure the comfort of residents. When the room temperature exceeds the limit, the switch state is triggered to ensure that the indoor temperature is only regulated by the artificial intelligent aggregation switch within the temperature control range, thus ensuring the indoor temperature comfort from the bottom line.

[0126] When correcting the deviation between the prediction and the real-time demand, priority is given to regulating the shutdown / stop of the equipment in the rooms with the maximum / minimum room temperature so that the real-time online quantity is equal to the predicted operational quantity demand. When adjusting upward, the focus can be on regulating the rooms close to the upper limit of the indoor temperature to close, and regulating the equipment close to the lower limit of the temperature to start, so as to prioritize regulating high-risk individuals to low risks.

[0127] 2.2 Load baseline of intelligent aggregation response peak load mode

[0128] By setting the indoor steady-state temperature point, the increase of the average heating power is adjusted, as shown in formula (11). The higher the set steady-state temperature point, the greater the average heating power, the faster the temperature rise, and the earlier the indoor temperature exceeds the temperature control boundary. The upper adjustment power of a limited time can be mined, and the same is true for the mining method of the lower adjustment power; Figure 4shown.

[0129] Figure 4 In the above table, the indoor temperature and heating power are both short-term average heating power, that is, the average temperature and average power within an on-off cycle of an electric heating device; a 0 ~a 3 The time points are respectively from natural temperature change mode → intelligent aggregation upper adjustment mode → natural temperature change mode → intelligent aggregation lower adjustment mode → natural temperature change mode; θ in0 , P heat It can also represent the average indoor temperature and average heating power when the natural temperature change mode reaches a steady state; θ U ,θ D , ΔP U , ΔP D It is the change of indoor steady-state temperature point and average heating power in the full response mode. The adjustment power is moderate and the duration just covers the entire response demand period. The mark with superscript 1 has a larger adjustment power and the duration is shorter than the grid demand duration. The mark with superscript 2 has a smaller adjustment power and the duration is longer than the grid demand duration.

[0130] 2.2.1 Load baseline for aggressive response peak load shaving mode

[0131] The aggressive response peak-shaving mode is to set the steady-state temperature point above / below the human body comfort temperature control range, driving the increase / decrease of load power and the increase / decrease of indoor temperature. The power when the indoor temperature just increases / decreases to the limit of the temperature control range during the grid regulation demand period is called the maximum sustainable regulation power of the aggressive response peak-shaving mode. The calculation process is as shown in equations (17) to (19):

[0132]

[0133] Where: θ in0 , P heat0 are the average indoor air temperature and average heating power before adjustment, Δθ in , ΔP heat are the adjustment quantities of the two respectively; τ 1 , τ 2 is the indoor temperature change time constant, A, B, F, G are the constant coefficients composed of the room thermal resistance and heat capacity parameters.

[0134] Substituting formula (17) into the grid demand duration and the indoor temperature control range limit, we can get the ambient temperature-grid demand duration-maximum regulation power correlation model, as shown in formulas (18) and (19):

[0135]

[0136] 0≤P heat0 +ΔPheat ≤P N (19);

[0137] Where: When the indoor temperature exceeds the limit, the Formula (18) shows that the longer the adjustment period, the smaller the maximum adjustable power amplitude; Formula (19) shows that the maximum adjustable power amplitude is limited by the demand duration and the equipment output range. The correlation diagram of this correlation model is as follows: Figure 5 shown.

[0138] When the grid regulation demand period is short, the equivalent short-time average power of the electric heating load can be adjusted within the range of 0 to the rated power of the equipment; when the regulation period is long, the longer the period, the smaller the maximum constant and continuously adjustable power amplitude, and the regulation power and duration show an exponential function correlation law.

[0139] 2.2.2 Load baseline for conservative response peak load regulation mode

[0140] The conservative response peak-shaving mode means setting the steady-state temperature point within the human body comfort temperature control range. When it is set to approach the upper / lower limit value, it can correspond to the maximum / minimum regulation power of the conservative response peak-shaving mode, as shown in formula (20).

[0141]

[0142] Where: The power adjustable range for the conservative response of the electric heating load to the peak load mode.

[0143] 2.3 Response peak load regulation strategy and net income calculation method

[0144] The specific implementation method of the electric heating load intelligent aggregation and response peak load regulation strategy described in this article is as follows:

[0145] ① Based on the thermal inertia differential equation of electric heating in individual rooms, the example rolling simulation is realized, formula (1)(2); based on the algebraic equation of "indoor and outdoor temperature difference, average heating demand" of individual rooms, the average heating demand is calculated, formula (11)(12).

[0146] ② Divide into three smart aggregation scenarios and apply the average heating demand load baseline.

[0147] S1: The intelligent aggregation power curve of cluster electric heating load in normal operation mode, formula (13)(16); set the indoor steady-state temperature point at the middle value of the human body comfort temperature control range, multiply the individual heating demand by the individual heating demand to obtain the average heating demand (same room) and the operation quantity demand of cluster electric heating, and give priority to regulating the closing / stopping of the equipment in the room with the maximum / minimum room temperature so that the real-time online quantity is equal to the predicted average heating operation demand quantity, and bring the controlled changed equipment operation state into the thermal inertia differential equation to calculate the indoor temperature of each room at the next moment. Or if the indoor temperature can be refreshed by actual measurement, there is no need to back-substitute into the thermal inertia differential equation.

[0148] S2: Intelligent aggregation power curve of cluster electric heating load in conservative response peak load operation mode, formula (20)(16). Set the indoor steady-state temperature point to the upper (lower) limit of the human comfort temperature control range, and the online quantity demand and quantity control method of the equipment are similar to those in S1.

[0149] S3: Intelligent aggregation power curve of cluster electric heating load in aggressive response peak load regulation mode, formula (17)(18)(19)(16); relying on the inference model of "ambient temperature-grid demand duration-maximum regulation power", considering the gain of wall heat absorption (dissipation) on the upper (lower) regulation power and duration during peak load regulation, calculating the maximum adjustable power within the time limit of peak load regulation, calculating the real-time online operation quantity demand, and the equipment quantity control method is similar to that in S1.

[0150] ③ Correct the deviation between the above three intelligent aggregation powers and the real-time demand of the project, relying on the same switch control strategy, formula (16).

[0151] According to the document "Several Opinions on Further Deepening the Reform of the Electricity System", the peak load compensation income is equal to the peak load response electricity multiplied by the unit compensation price. The increase or decrease of electricity cost due to peak load is still settled using the peak and valley electricity price. Therefore, the net income from peak load response is equal to the peak load compensation income minus the increase or decrease of electricity cost, as shown in the following formula (21):

[0152]

[0153] Where: W is the net benefit of peak load regulation, F DR In response to the peak load compensation price, ΔT is the shortest dispatching time interval, N is the number of time points in a day, and the State Grid usually takes the time interval as 15 minutes and divides a day into 96 time points. heat,k |ΔT ​​is the peak load, F k is the peak-valley electricity price at time point k.

[0154] 3. Embodiment

[0155] Through the example analysis, the number of cluster electric heating rooms is set to 600, and the area of ​​a single room is 80m2 , direct heating electric heating equipment 8kW, indoor temperature control range [18,22]℃, thermal resistance and heat capacity parameters are shown in Table 1.

[0156] Table 1 Typical room thermal resistance and heat capacity parameters

[0157]

[0158] Table 2 Peak and valley electricity prices for electric heating loads and compensation prices for ancillary services

[0159]

[0160] ①Analysis of thermal inertia temperature change of individual electric heating rooms

[0161] like Figure 2 , Figure 3 As shown in the figure, in a temperature control cycle, the pulse heating power is smoothed by heat storage in the room and the wall, achieving a dynamic balance between pulse heating and continuous and stable heat dissipation. The air and the wall exchange equal amounts of heat energy. When the indoor air temperature exceeds its average value, the buffer energy stored is injected into the wall, driving the wall temperature to rise. When the indoor air temperature is lower than its average value, it is released to make up for the energy deficit.

[0162] ② Correlation characteristics between indoor and outdoor temperature difference and average heating demand of individual rooms

[0163] like Figure 3 As shown in the figure, the steady-state temperature curve of indoor air and wall when the outdoor temperature is -25℃. The average heating power is 4332 (theoretical value 4327) W, the average wall temperature is 14.85 (theoretical value 14.85) ℃, the heat dissipation power corresponding to the temperature difference between the wall and the outdoor is 4332 W, and the heating and heat dissipation are balanced; the wall temperature rise range is 0.006℃, and the indoor air temperature is higher than the average of its steady-state average temperature by about 1℃. The ratio of the two is 6‰, which is equal to the ratio of indoor air to wall heat capacity of 6‰. The indoor heating time is 21.3 (theoretical value 21.5) minutes, the cooling time is 18 (theoretical value 17.9) min, and the equipment operation duty cycle corresponding to the heating and cooling time is 0.542, which is equal to the average power divided by the installed capacity. The above verifies the practicality of the electric heating load energy flow theory in this article.

[0164] Table 3 Changes in average heating power when outdoor temperature is perturbed

[0165]

[0166] When the outdoor temperature fluctuates between -25 and 0℃, the duty cycle of the electric heating equipment decreases accordingly, and the overall duty cycle is distributed between 54.1% and 25.5%. The average heating power of the electric heating equipment is negatively linearly correlated with the outdoor temperature, and the function fitting result is 1902.4-98.4θout (Theoretical value 1923.1-96.2θ out ), with an error of less than 3%, successfully verifying that the average heating power theoretical formula (4) is applicable to different outdoor temperature scenarios. When the outdoor temperature decreases by 1°C, the average heating power increases by 1.224W / m2 / °C, which matches the value obtained from engineering project statistics.

[0167] ③ Load baseline of normal operation mode of intelligent aggregation

[0168] like Fig.10 (a)(b)(c)(d), Fig.11 As shown in (a)(b)(c)(d), the load curve of the intelligently aggregated cluster electric heating is smoother and more in line with the common sense that the lower the outdoor temperature, the greater the heating demand and power consumption. This can greatly improve the credibility of the intelligently aggregated load prediction. Behind this is the active opening / closing of the cluster electric heating equipment, which calls out the flexibility of the indoor temperature to be flexible and variable within the human body comfort temperature control range of [18,22]℃, in exchange for a load baseline that is more in line with the intelligent aggregation.

[0169] ④Real-time online equipment operation quantity correction strategy

[0170] Fig.10 (e) Fig.11 (e) By comparison, the regulation capacity of natural aggregation is 664kW, and that of smart aggregation is 808kW (reference value 950kW, error 15%), which proves that the smart aggregation strategy has greater adjustable capacity; in the load regulation period 00:00-04:00, the number of switches is mostly controlled to be opened, and in the load regulation period 16:00-20:00, the number of switches is mostly controlled to be closed.

[0171] ⑤ Intelligent aggregation of load baselines in aggressive response to peak load regulation mode

[0172] like Figure 4 As shown, by setting the indoor steady-state temperature point, the increase of the average heating power is adjusted, as shown in formula (11). The higher the set steady-state temperature point, the greater the average heating power, the faster the temperature rise, and the earlier the indoor temperature exceeds the temperature control boundary. The upper adjustment power of a limited duration can be mined, and the same is true for the mining method of the lower adjustment power.

[0173] like Figure 5 As shown, when the grid regulation demand period is short, the electric heating load power can be adjusted arbitrarily within the range of 0 to the rated power of the equipment; when the regulation period is long, the longer the period, the smaller the maximum sustainable regulation power.

[0174] like Figure 6As shown, the temperature control cycle of the electric heating equipment is set relatively large. Although the maximum value of the sawtooth temperature reaches the temperature control boundary, its average temperature is slightly lower than the temperature control boundary. According to the inference that the larger the steady-state temperature point, the larger the steady-state average heating power, it can be seen that the power mined at this time is still not the maximum adjustable power.

[0175] like Figure 7 As shown in the figure, when the temperature control cycle of the equipment during the adjustment period is set to be small enough, the average indoor temperature can be infinitely close to the temperature control boundary, and the room temperature change curve with an adjustment time of 4 hours and maximum power adjustment. In engineering applications, due to the minimum adjustment time interval and number of equipment restrictions, the equipment will not be started and stopped too frequently, and it is difficult to tap this maximum adjustable capacity, but the reference value of this maximum adjustable capacity still has certain guiding significance. Figure 7 As shown, the outdoor temperature is -25℃, the adjustment time is 4h, the up / down adjustment power is 1557W and 1643W respectively (theoretical value is 1584W), accounting for 36% and 38% respectively (theoretical value is 37%), and the error between theory and example is only 1%, which successfully verifies the applicability of the inference of "ambient temperature-response time-maximum adjustable power".

[0176] like Figure 8 As shown in the figure, when the adjustment time is shorter, greater adjustment power can be tapped. When the upper adjustment time is shortened from 54.6min to 13.7min, the upper adjustment power limit increases from 1760 (theoretical value 3599) W to 3673 (theoretical value 1804) W. When the lower adjustment time is shortened from 63.3min to 10.4min, the lower adjustment power limit increases from -1680 (theoretical value -1753) W to -4327 (theoretical value -4327) W.

[0177] like Fig. 9 As shown in the figure, the influence of outdoor temperature on the relationship between "ambient temperature-demand response time-maximum adjustment power" is analyzed. The outdoor temperature will affect the size of the average heating power, and then affect the maximum up / down adjustment power amplitude within a temperature rise and fall time. However, when the adjustment time is large, the "adjustment power-duration" coupling relationship is no longer affected by the outdoor temperature and can share the same relationship function. Considering the increase in the upper adjustment capacity due to the heat absorption of the wall during the upper adjustment process and the increase in the lower adjustment capacity due to the heat release of the wall to maintain the indoor temperature during the lower adjustment process, when the response time is 4h, the adjustment power ratio can be as high as ±36.6%. This shows that when analyzing the electric heating load adjustment capacity, the initial wall temperature is an important influencing factor. Because the heat capacity of the wall is much larger than the heat capacity of the indoor air, the thermal inertia heat storage and adjustment capacity of the wall is even higher than that of the indoor air.

[0178] Fig.10(d)(g) show the change in indoor temperature trend caused by the up / down regulation capability of the cluster electric heating load for 4 hours. The up / down regulation power corresponds to the increase / decrease of the cluster average temperature, and the up / down regulation power is 664kW and -640kW respectively.

[0179] Fig.10 Comparison of (a)(h), (b)(e)(i) shows that when a disturbance of 2°C outdoor temperature rise is introduced during the period of 12:00-13:00, the thermal inertia and state dependency of the electric heating load cause the disturbance to be continuously transmitted in the system, and even accumulate and evolve into a new disturbance, resulting in a 2°C outdoor temperature rise during the period of 13:00-24:00 after the disturbance is introduced. Fig.10 (b)(e)(i)The load baselines differ greatly, which indicates that the guidance significance of the load baseline prediction and switch control strategy of the natural temperature change mode is weak.

[0180] Fig.10 (e) Fig.11 (e) By comparison, the regulation capacity of natural aggregation is 664kW, and that of smart aggregation is 808kW (reference value 950kW, error 15%), which proves that the smart aggregation strategy has greater adjustable capacity; in the load regulation period 00:00-04:00, the number of switches is mostly controlled to be opened, and in the load regulation period 16:00-20:00, the number of switches is mostly controlled to be closed.

[0181] from Fig.11 (e)(i), Fig.10 (e)(i) By comparison, it can be seen that in the smart aggregation mode, when a disturbance such as an increase in the external ambient temperature by 2°C occurs between 12:00 and 13:00, only the load baseline power in the corresponding period decreases, and the impact on the load baseline in subsequent periods is minimal, indicating that the smart aggregation mode that relies on the ambient temperature-average heating demand association model has stronger anti-interference ability.

[0182] ⑥ Intelligent aggregation conservative response peak load regulation capability

[0183] Substituting the room thermal resistance parameters in Table 1 into formula (20), it can be obtained that when the steady-state temperature point is set as the upper and lower limits of the temperature control range, the conservative adjustable power is only ±193W, accounting for only ±4.5%, but its advantage is that it can be sustained for an unlimited period of time.

[0184] ⑦ Calculation method of net income of peak load response

[0185] Table 4 Comparison of peak load response benefits between smart aggregation and natural aggregation

[0186]

[0187] The upward (downward) adjustment of cluster electric heating load will cause an increase (decrease) in power consumption and electricity charges. Both upward and downward adjustments will obtain compensation prices higher than the peak and valley electricity prices, and there will still be a certain net profit after peak adjustment. Table 4 proves that the smart aggregation strategy in this paper has a 22% increase in upward adjustment capability and a 5% increase in downward adjustment capability compared to the traditional natural aggregation strategy, and the average daily net profit has increased by 8%, up to 2.53 yuan / household / day.

[0188] In view of the fact that existing research focuses on the random fluctuation curve after the natural aggregation of cluster electric heating loads, which leads to the problems of difficult prediction of operating loads, difficult evaluation of regulation capacity, and difficult stimulation of regulation willingness, this paper deeply analyzes the invariant law of indoor and outdoor temperature difference-steady-state average heating demand behind its transient random time variation, and then proposes an integrated engineering application method for intelligent aggregation of electric heating loads, quantitative evaluation of regulation capacity, and response to peak load regulation based on the idea of ​​average heating demand power baseline, with the following results and conclusions:

[0189] 1) The linear correlation law of "indoor and outdoor temperature difference-average heating demand" in the thermal inertia temperature change model of individual electric heating is extended to the average heating demand load baseline of cluster electric heating, which is inversely correlated with the outdoor temperature change trend.

[0190] 2) Study the analytical formula of the correlation characteristic of "ambient temperature-response time-maximum adjustable power" to lay a theoretical foundation for the evaluation of adjustable capacity under different outdoor ambient temperature scenarios, and set outdoor constant temperature and outdoor variable temperature scenarios at the same time to verify the applicability and error range of the theory.

[0191] 3) Design a real-time online switching control strategy for the number of cluster electric heating equipment in operation, and explore and utilize the flexibility of the indoor temperature within the human comfort temperature range to intelligently correct the cluster load curve to be close to the average heating demand load baseline, thereby overcoming the shortcomings of the traditional natural aggregation load baseline that is easily affected by the number of cluster devices, initial operating points, state dependencies, collected temperature differences, and minimum control time granularity, and effectively improve the anti-interference ability and prediction accuracy of the temperature control load baseline.

[0192] 4) Taking into account the changes in user electricity charges caused by responsive peak-shaving and the income from ancillary services, the responsive peak-shaving income is calculated, and it is verified that the responsive peak-shaving income based on the intelligent aggregation of average heating demand in this paper can reach 2.53 yuan / household / day, which is 8% higher than the economic benefit of the natural aggregation model.

[0193] The above description is only a preferred example of the present invention and does not limit the present invention in any form. Simple modifications, equivalent changes or modifications made by those skilled in the art using the above disclosed technical contents all fall within the protection scope of the present invention.

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[0215]

[12]

Claims

1. A cluster electric heating load response peak-shaving method considering average heating demand, characterized by: The method comprises the following steps, and the following steps are performed in sequence: Step 1: Establish a thermal inertia temperature change model for individual direct heating electric heating loads 1) The thermal inertia temperature change model of the individual direct-heating electric heating load is characterized by a second-order thermodynamic equivalent model: 2) Based on the set human comfort temperature range in, and The lower and upper limits of the human body's comfortable temperature range are respectively used to set the start and stop control rules for individual direct heating electric heating loads, and obtain the natural temperature change switching control rules for electric heating; 3) Based on the equal heat exchange between the wall and the indoor air, the indoor temperature rise change law and indoor temperature drop change law within a temperature change cycle are established to obtain the indoor steady-state temperature rise time T on And the indoor steady-state temperature drop time T off ; The indoor steady-state temperature change period is T S , T S =T on +T off , the temperature change cycle is a periodic change in which the temperature changes from the lowest temperature point to the highest temperature point and then drops to the lowest temperature point over time; 4) If the maximum temperature, minimum temperature and temperature change cycle of the room remain unchanged, it is determined that the room temperature change process has reached a steady state, and step 2 is performed; Step 2: Establish the correlation characteristics between the indoor and outdoor temperature difference and the average heating demand of individual rooms When the room temperature change process reaches a steady state, the average temperature of the indoor and wall temperatures remain unchanged. The wall temperature is set equal to the average wall temperature. The thermal inertia temperature change model of the individual direct heating electric heating load is integrated for a temperature change cycle and divided by the temperature change cycle duration. The correlation characteristic formula between the indoor average temperature, the average heating power, and the outdoor temperature can be obtained as follows: The average heating power and the operating duty cycle of the electric heating equipment are obtained, as shown in equations (11) and (12): Where: θ out is the outdoor temperature, is the average temperature of the wall; is the average heating power; D is the duty cycle of the electric heating equipment; P e The rated power of the electric heating equipment; is the average indoor air temperature, Formula (10) shows that under the premise of given outdoor temperature and heating power, the indoor average temperature will passively stabilize at a fixed value, which is called the steady-state temperature point corresponding to a certain average heating power; By performing an equivalent transformation of terms in equation (10), we can obtain equation (11), that is, under the premise of given outdoor temperature and indoor average temperature, the average heating power will also passively stabilize at a constant value. This formula is used to predict the load curve in combination with the outdoor temperature curve of the day; Formula (12) shows that the electric heating load is a switch-controlled load. The power consumption curve of a single electric heating load is a pulse square wave. Therefore, its average power is equal to the operation time in a single cycle divided by the cycle and then multiplied by the rated power of the equipment, which is also equal to the average power divided by the rated power. After being substituted into formula (11), the duty cycle of the operation of the single electric heating equipment is calculated by the indoor and outdoor temperatures, which lays the foundation for the proportion of the reference operation number of cluster electric heating equipment at a certain moment, that is, formula (14). It can be seen from formulas (11) and (12) that the indoor steady-state average heating power and the operation duty cycle of individual electric heating equipment are both linearly positively correlated with the indoor and outdoor temperature difference. By setting the indoor steady-state temperature point and adjusting the average heating power, as shown in formula (11), the higher the set steady-state temperature point, the greater the average heating power, the faster the temperature rise, and the earlier the indoor temperature exceeds the temperature control boundary, the upper adjustment power of a limited duration can be obtained; The down-regulation power of limited duration is obtained in the same way; Step 3: Obtain the average heating power demand of cluster electric heating, that is, obtain the intelligent aggregation power of cluster electric heating load, as shown in formula (13): Where: Intelligently aggregate power for cluster electric heating loads, is the average indoor air temperature; θ out,t is the outdoor temperature at time t, R 1,i,t is the thermal resistance of heat transfer from the room to the wall in room i, R 2,i,t is the thermal resistance from the wall of room i to the outside; R agg is the cluster thermal resistance, and its value decreases accordingly as the number of cluster electric heating increases. Step 4: Correct the number of electric heating units put into operation in real time The operation of electric heating takes the comfort of residents as the first principle. When the indoor temperature exceeds the set human comfort temperature range, the switch state is immediately triggered to ensure that the indoor temperature is only controlled by the intelligent aggregation power of the cluster electric heating load within the set human comfort temperature range; The duty cycle is calculated based on the ratio of the average heating power demand of the cluster electric heating to the installed capacity. Average number of units put into operation In the formula, are respectively the cluster electric heating load operation duty cycle and the average number of units in operation, P i e is the rated power of the i-th electric heating equipment, P agg,t is the total power consumption of cluster electric heating at time t, D agg,t is the proportion of cluster electric heating load to its total installed capacity at time t, and N is the installed capacity of cluster electric heating equipment; Corrected actual number of cluster electric heating units put into operation Average number of units put into operation The deviation between them is calculated as shown in formula (16), which enables accurate prediction of cluster electric heating load baseline: Where: It is the quantity of regulation. It is the number of electric heating equipment running online in real time; If it is greater than 0, the electric heating equipment in the room with the maximum room temperature will be turned off until the real-time online number of electric heating equipment is equal to the average number of electric heating equipment in operation; If it is less than 0, the electric heating equipment in the room with the minimum room temperature will be turned on until the real-time online number of electric heating equipment is equal to the average number of electric heating equipment in operation; Step 5: Establish a load baseline of the response peak-shaving mode by intelligently aggregating power of cluster electric heating loads. The load baseline of the response peak-shaving mode includes two types: a load baseline of an aggressive response peak-shaving mode and a load baseline of a conservative response peak-shaving mode. 1) The aggressive response peak-shaving mode refers to setting the steady-state temperature point above / below the human body comfort temperature control range, driving the increase / decrease of load power and the increase / decrease of indoor temperature. The power when the indoor temperature just increases / decreases to the temperature control range limit during the grid regulation demand period is called the maximum sustainable regulation power of the aggressive response peak-shaving mode. The calculation process is as shown in equations (17) to (19): Where: θ in (t) is the indoor temperature at time t, θ in (0) = θ in0 ; Δθ in is θ in0 The adjustment amount; θ in0 is the average indoor air temperature before adjustment; P e is the rated power of the electric heating equipment; R1 and R2 are the equivalent thermal resistances from indoor to wall center and from wall center to outdoor respectively; C in is the indoor air heat capacity; C w is the heat capacity of the wall; ΔP heat P heat0 The amount of adjustment; P heat0 is the average heating power before adjustment; τ1 and τ2 are the time constants of indoor temperature change; A, B, F, and G are the constant coefficients of the room thermal resistance and heat capacity parameters; Substituting formula (17) into the grid demand duration and the indoor temperature control range limit, we can obtain the correlation model of ambient temperature-grid demand duration-maximum regulation power, as shown in the following formulas (18) and (19): Where: T is the duration of load regulation; e is the base of the exponential function, which is 2.71828; is the indoor temperature exceeding the limit point, which is taken when adjusting the demand response up / down. It can be seen from formula (18) that the longer the adjustment period, the smaller the maximum adjustable power amplitude; it can be seen from formula (19) that the maximum adjustable power amplitude is jointly limited by the demand duration and the electric heating output range; Therefore, in the aggressive response peak load mode, the maximum adjustable power amplitude can be obtained according to the given grid demand duration, the value of the steady-state temperature point that needs to be set, and the number of online operations required; 2) The conservative response peak-shaving mode refers to setting the steady-state temperature point within the human body comfort temperature control range and within the threshold range close to the upper / lower limit value, which corresponds to the maximum / minimum regulation power of the conservative response peak-shaving mode, as shown in formula (20): Where: The power adjustable range for conservative response of electric heating load to peak load mode; Step 6: Divide into three intelligent aggregation scenarios, and apply the average heating demand load baseline to correct the number of electric heating units in operation; the three intelligent aggregation scenarios are normal operation mode, conservative response peak load operation mode, and aggressive response peak load operation mode; 1) The set indoor steady-state temperature point is located at This value is determined to be in normal operation mode, and the average heating power demand of cluster electric heating is obtained through formula (13) and the intelligent aggregation power curve of cluster electric heating load is obtained, and the number of cluster electric heating put into operation is corrected through step 4; 2) The set indoor steady-state temperature point deviates from But still in If the value is within the range of , it is determined to be a conservative response peak-shaving operation mode. The intelligent aggregation power curve of cluster electric heating load in the conservative response peak-shaving operation mode is obtained through formula (20), and the number of cluster electric heating units put into operation is corrected through step 4. 3) The set indoor steady-state temperature point is located at If the value is other than the above, it is determined to be an aggressive response peak-shaving operation mode. The intelligent aggregation power curve of cluster electric heating load in the aggressive response peak-shaving operation mode is obtained through equations (17), (18), and (19). Through the correlation model of ambient temperature-grid demand duration-maximum adjustment power, based on the gain of wall heat absorption / dissipation on the upper / lower adjustment power and duration during the peak-shaving process, the maximum adjustable power within the response peak-shaving time limit is calculated, and the number of cluster electric heating units put into operation is corrected through step four. 4) Substitute the electric heating operation status of the controlled switch state change into formula (1) and formula (2) to calculate the indoor temperature of each room at the next moment, or refresh the indoor temperature through actual measurement.

2. The cluster electric heating load response peak-shaving method considering average heating demand according to claim 1 is characterized by: The second-order thermodynamic equivalent model formula is as follows: Where: θ w (t) and θ out (t) are the wall and outdoor temperatures at time t, θ w (0) = θ w0 ; S t It is a 0-1 variable representing the operating status of the electric heating equipment at time t. A value of 1 indicates that the equipment is in operation, and a value of 0 indicates that the equipment is in shutdown.

3. The cluster electric heating load response peak-shaving method considering average heating demand according to claim 1 is characterized by: The specific method for obtaining the natural temperature change switching control rule of electric heating is: When the room temperature drops to the lower limit, the electric heating equipment is started to heat, and when the room temperature rises to the upper limit, the heating is stopped. Therefore, the control rule of electric heating is shown in formula (2): Where: Δt is the time granularity; θ in,t-Δt is the indoor temperature at the previous moment, S t-Δt It is the switch status of the electric heating equipment at the previous moment.

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