A probabilistic flexibility assessment method for thermal storage electric heating systems

Abstract

The present invention discloses a probabilistic flexibility assessment method for a thermal storage electric heating system. According to the planned operating power curve of the electric heating equipment and the predicted probability density function of the heat load in the t-th period, the remaining stored heat in the thermal storage tank of the s-th heat load scenario in the t-th period is obtained by obtaining a time series scenario set of heat load demand; thereby obtaining the probability density function of the remaining stored heat in the thermal storage tank in the t-th period, and obtaining the remaining stored heat Q in the thermal storage tank in the t-th period. t The probabilistic flexibility index of the thermal storage tank is obtained to evaluate the adjustable heat storage capacity of the thermal storage tank. This invention provides adjustable capacity information for power system operation. On the one hand, the adjustable heat load capability can be used to balance renewable energy generation and promote renewable energy consumption. On the other hand, the probabilistic assessment method can provide comprehensive adjustable capacity information to power system operators, improving the robustness and economic efficiency of grid dispatch.

Description

Technical Field

[0001] The present invention relates to the field of load flexibility assessment of integrated energy systems, and in particular to a probabilistic flexibility assessment method for a thermal storage electric heating system. Background Art

[0002] With the continuous investment in electric heating equipment, the proportion of electric heating load in winter is increasing. The operating characteristics of electric heating equipment and the heat load demand characteristics of users have led to an increasingly obvious electric-heat coupling, which will change the traditional characteristics of user electricity consumption.

[0003] To tap the potential of thermal systems to absorb new energy and ensure the reliability of power and heat supply, it is necessary to study the uncertainty characteristics of electric and thermal loads and develop and utilize probabilistic prediction methods for them. Currently, considerable research has been conducted on probabilistic prediction of electric and thermal loads, enabling the generation of probabilistic distribution functions for electric and thermal loads. For thermal storage electric heating loads, the presence of thermal storage tanks decouples the thermal load power from the electricity-to-heat load to a certain extent, meaning that the electrical load does not need to change synchronously with the thermal load and can provide a certain amount of regulation capacity, or flexibility, to the grid. However, the thermal storage capacity of a thermal storage tank has both a certain adjustable capacity and a certain degree of uncertainty due to the uncertainty of the thermal load, i.e., a certain degree of probabilistic flexibility. Therefore, it is impossible to assess the remaining adjustable heat capacity of the thermal storage tank. Summary of the Invention

[0004] The present invention provides a probabilistic flexibility assessment method for a thermal storage electric heating system to overcome the above technical problems.

[0005] In order to achieve the above object, the technical solution of the present invention is:

[0006] A probabilistic flexibility assessment method for a thermal storage electric heating system comprises the following steps:

[0007] Step 1: Obtain the day-ahead planned operating power curve of the electric heating equipment, the maximum stored heat of the thermal storage tank, the minimum stored heat of the thermal storage tank, the operating efficiency of the electric heating equipment, and the predicted probability density function of the heat load in period t;

[0008] Step 2: Obtain the time series scenario set of heat load demand based on the predicted probability density function of the heat load in the tth period of the day-ahead plan;

[0009] Step 3: According to the day-ahead planned operating power curve of the electric heating equipment, the operating efficiency of the electric heating equipment and the time series scenario set of the heat load demand, obtain the remaining stored heat Q of the heat storage tank in the sth heat load scenario in the tth period t,s ;

[0010] Step 4: The remaining stored heat Q in the heat storage tank according to the sth heat load scenario in the tth period t,s , obtain the probability density function of the remaining stored heat in the thermal storage tank in the tth period, and obtain the remaining stored heat Q in the thermal storage tank in the tth period t ;

[0011] Step 5: According to the maximum storage heat of the heat storage tank, the minimum storage heat of the heat storage tank and the remaining storage heat Q of the heat storage tank in the t period t , obtain the probability flexibility index of the heat storage tank to evaluate the adjustable performance of the heat storage capacity of the heat storage tank; the probability flexibility index of the heat storage tank includes the remaining storable heat capacity of the heat storage tank and the current heat supply of the thermal storage tank

[0012] Furthermore, in step 2, the time sequence scenario set of the heat load demand is:

[0013] {h t,s |t=1,2,…,24},s=1,2,…,M,

[0014] Where M is the number of heat load scenarios and is the sample point of Monte Carlo simulation; h t,s Indicates the sth heat load scenario in the tth period; s is the number of the heat load scenario.

[0015] Furthermore, in step 3, the remaining stored heat in the heat storage tank of the sth heat load scenario in the tth period is obtained as follows;

[0016] Q t,s =Q t-1,s +p t η-h t,s

[0017] Where: Q t,s represents the remaining stored heat in the thermal storage tank in the sth heat load scenario during the tth period; p t is the planned operating power of the electric heating equipment in period t; η is the operating efficiency of the electric heating equipment; Q t-1,s The remaining stored heat in the thermal storage tank in the sth heat load scenario during the t-1th period; t represents the daily period number.

[0018] Furthermore, in step 4, the probability density function of the remaining stored heat in the thermal storage tank in time period t is obtained as follows:

[0019]

[0020] Where: represents the probability density function of the remaining stored heat in the thermal storage tank at time t; σ tis the standard deviation of the Gaussian kernel function; y represents the remaining stored heat in the thermal storage tank.

[0021] Furthermore, in step 5, the probability flexibility index of the heat storage tank is obtained as follows:

[0022]

[0023]

[0024] Where: Q max Indicates the maximum storage heat of the thermal storage tank; Q min Indicates the minimum storage heat of the thermal storage tank.

[0025] Furthermore, the method for evaluating the adjustable performance of the heat storage capacity of the heat storage tank is to evaluate it according to the probability density function of the remaining storable heat capacity of the heat storage tank and the probability density function of the current heat supplyable by the heat storage tank;

[0026] The probability density function of the remaining storable heat capacity of the thermal storage tank and the probability density function of the current heat supplyable heat of the thermal storage tank are obtained as follows:

[0027]

[0028]

[0029] Where: represents the probability density function of the heat in the thermal storage tank; y represents the remaining heat stored in the thermal storage tank, The probability density function representing the remaining storable heat capacity of the thermal storage tank; Represents the probability density function of the current heat supply of the thermal storage tank.

[0030] Furthermore, the method for evaluating the adjustable performance of the heat storage amount of the heat storage tank is to evaluate according to the expected value of the remaining storable heat capacity of the heat storage tank and the expected value of the current heat supplyable by the heat storage tank.

[0031] Furthermore, the method for obtaining the expected value of the remaining storable heat capacity of the thermal storage tank and the expected value of the current heat supplyable by the thermal storage tank is as follows:

[0032] S51: Obtaining an expected value of heat load demand in period t based on the day-ahead planned operating power curve of the electric heating equipment, the predicted probability density function of the heat load in period t, and the operating efficiency of the electric heating equipment;

[0033] The expected value of the heat load demand in period t is obtained as follows:

[0034]

[0035] Where: is the expected value of heat load demand in period t; f t (x) is the predicted probability density function of the heat load in the tth period; x is the heat load power;

[0036] S52: Expected value of heat load demand in period t The expected value of the remaining stored heat in the thermal storage tank at time period t is obtained as follows:

[0037]

[0038] Where: is the expected value of the remaining stored heat in the thermal storage tank at time t; is the expected value of the remaining heat stored in the thermal storage tank in period t-1; p t is the planned operating power of the electric heating equipment in period t; η is the operating efficiency of the electric heating equipment;

[0039] S53: According to the expected value of the remaining stored heat in the thermal storage tank during the tth period, the expected value of the remaining storable heat capacity of the thermal storage tank and the expected value of the current heat supplyable by the thermal storage tank are obtained as follows:

[0040]

[0041]

[0042] Beneficial effects: The present invention provides a probabilistic flexibility assessment method for a thermal storage electric heating system. By using the predicted probability density function of the thermal load in the tth period planned a day ago, a time series scenario set of thermal load demand is obtained, and then the remaining stored heat in the thermal storage tank is obtained. Combined with the maximum stored heat of the thermal storage tank, the minimum stored heat of the thermal storage tank, and the probabilistic flexibility index of the thermal storage tank, the adjustable performance of the thermal storage tank is evaluated, and the probabilistic flexibility assessment of the thermal storage tank is completed, providing adjustable capacity information for the operation of the power system. On the one hand, the adjustable capacity of the thermal load can be used to balance the power generation of new energy and promote the consumption of new energy. On the other hand, the probabilistic assessment method can provide comprehensive adjustment capacity information for the power system operation department, thereby improving the robustness and economy of the power grid dispatch. BRIEF DESCRIPTION OF THE DRAWINGS

[0043] In order to more clearly illustrate the embodiments of the present invention or the technical solutions in the prior art, the following is a brief introduction to the drawings required for use in the embodiments or the description of the prior art. Obviously, the drawings described below are some embodiments of the present invention. For ordinary technicians in this field, other drawings can be obtained based on these drawings without paying any creative labor.

[0044] Figure 1Flowchart of the probabilistic flexibility evaluation method for the thermal storage electric heating system of the present invention;

[0045] Figure 2 This is the electric heating load curve of Changchun City from November 2019 to May 2020 in the embodiment of the present invention;

[0046] Figure 3 The day-ahead planned operating power curve of the electric heating equipment in the embodiment of the present invention;

[0047] Figure 4 The heat load probability prediction results at 24 moments in the embodiment of the present invention;

[0048] Figure 5 is the probability density function of the state of the heat storage tank in each time period in the embodiment of the present invention;

[0049] Figure 6 The amount of electricity that can be adjusted upward by the heat storage tank under different confidence levels in the embodiment of the present invention;

[0050] Figure 7 This is the amount of electricity that can be adjusted downward by the heat storage tank under different confidence levels in the embodiments of the present invention. DETAILED DESCRIPTION

[0051] To make the objectives, technical solutions, and advantages of the embodiments of the present invention more clear, the technical solutions in the embodiments of the present invention will be clearly and completely described below in conjunction with the accompanying drawings of the embodiments of the present invention. Obviously, the described embodiments are only part of the embodiments of the present invention, not all of the embodiments. All other embodiments obtained by ordinary technicians in this field based on the embodiments of the present invention without making any creative efforts shall fall within the scope of protection of the present invention.

[0052] This embodiment provides a probabilistic flexibility assessment method for a thermal storage electric heating system. Figure 1 , including the following steps:

[0053] Step 1: Obtain the day-ahead planned operating power curve of the electric heating equipment, the maximum stored heat of the thermal storage tank, the minimum stored heat of the thermal storage tank, the daily start time of the thermal storage electric heating system, the operating efficiency of the electric heating equipment, and the predicted probability density function of the heat load in the tth period, that is, the result of the time-segmented heat load probability of the day, such as Figure 4 As shown; the day-ahead planned operating power curve of the electric heating equipment in this embodiment is as follows Figure 3 As shown;

[0054] Specifically, the day-ahead planned operating power curve of the electric heating equipment, the maximum stored heat of the heat storage tank, the minimum stored heat of the heat storage tank, and the operating efficiency of the electric heating equipment are all inherent parameters of the electric heating equipment.

[0055] Step 2: Based on the Monte Carlo simulation method, the predicted probability density function f of the heat load in the tth period of the day-ahead plan is t (x) randomly sampling a set of time series scenarios to obtain heat load demand;

[0056] Preferably, the time sequence scenario set of the heat load demand is:

[0057] {h t,s |t=1,2,…,24},s=1,2,…,M,

[0058] Where M is the number of heat load scenarios and is the sample point of Monte Carlo simulation; h t,s represents the sth heat load scenario in the tth period; s represents the number of the heat load scenario;

[0059] Step 3: According to the day-ahead planned operating power curve of the electric heating equipment, the operating efficiency of the electric heating equipment and the time series scenario set of the heat load demand, obtain the remaining stored heat Q of the heat storage tank in the sth heat load scenario in the tth period t,s ;

[0060] Q t,s =Q t-1,s +p t η-h t,s

[0061] Where: Q t,s represents the remaining stored heat in the thermal storage tank in the sth heat load scenario during the tth period; p t is the planned operating power of the electric heating equipment in period t; η is the operating efficiency of the electric heating equipment; Q t-1,s The remaining stored heat in the thermal storage tank in the sth heat load scenario during the t-1th period; t represents the daily period number. In this embodiment, there are 24 periods per day, i.e., t = 1, 2, ..., 24;

[0062] Therefore, according to the M heat load demand time sequence scenarios in the heat load demand time sequence scenario set and the remaining storage heat Q of the heat storage tank in the sth heat load scenario of the tth period t,s , obtain the set of remaining stored heat in the thermal storage tank at time t {Q t,s}, s = 1, 2, ..., M; it contains the remaining heat stored in the thermal storage tank for all scenarios in the time period;

[0063] Step 4: The remaining stored heat Q in the heat storage tank according to the sth heat load scenario in the tth period t,s, the remaining stored heat of the heat storage tank in all heat load scenarios in the tth period can be obtained, and the probability density function of the remaining stored heat of the heat storage tank in the tth period can be obtained by using the Gaussian kernel density estimation method to obtain the remaining stored heat of the heat storage tank in the tth period Q t , where Q t is a random variable that includes the remaining heat stored in the thermal storage tank in all scenarios during the tth period. The probability density function of the remaining heat stored in the thermal storage tank during the tth period is used to describe the probability distribution characteristics of the remaining heat stored in the thermal storage tank.

[0064]

[0065] Where: represents the probability density function of the remaining stored heat in the thermal storage tank at time t; σ t is the standard deviation of the Gaussian kernel function; y represents the remaining stored heat in the thermal storage tank. Specifically, the remaining stored heat in the thermal storage tank is a random variable related to time and scene;

[0066] Specifically, according to the probability density function of the remaining stored heat in the heat storage tank at the tth period, the remaining stored heat in the heat storage tank Q at the tth period can be obtained by the common methods in the field. t .

[0067] Step 5: According to the maximum storage heat of the heat storage tank, the minimum storage heat of the heat storage tank and the remaining storage heat Q of the heat storage tank in the t period t , obtain the probabilistic flexibility index of the thermal storage tank to evaluate the adjustable performance of the thermal storage capacity of the thermal storage tank;

[0068] The probabilistic flexibility index of the thermal storage tank includes the remaining storable heat capacity of the thermal storage tank (upward adjustable capacity) and the current heat supply of the thermal storage tank (capacity can be adjusted downward)

[0069] Specifically, the probabilistic flexibility in this embodiment refers to the adjustable amount of heat storage in the heat storage tank. Since these two indicators have uncertainty, they are called probabilistic adjustable amounts, or probabilistic flexibility.

[0070] Preferably, the probabilistic flexibility index of the heat storage tank is obtained as follows:

[0071]

[0072]

[0073] Where: Q max Indicates the maximum storage heat of the thermal storage tank; Q min Indicates the minimum storage heat of the thermal storage tank;

[0074] Preferably, the method for evaluating the adjustable performance of the heat storage capacity of the heat storage tank is to calculate the remaining heat storage capacity of the heat storage tank. The probability density function of and the current heat supply of the thermal storage tank The probability density function of Conduct an assessment,

[0075] Because Q t is the random variable of the remaining heat stored in the thermal storage tank in all scenarios in period t, so is also a random variable, so the remaining adjustable thermal capacity is obtained The probability density function of and current heat capacity The probability density function of as follows:

[0076]

[0077]

[0078] Where: represents the probability density function of the heat in the thermal storage tank; y represents the remaining heat stored in the thermal storage tank,

[0079] in, is the probability density function of the remaining storable heat capacity of the thermal storage tank, which is used to describe the probability distribution of the remaining storage heat capacity of the thermal storage tank. The remaining storage heat capacity of the thermal storage tank is used to absorb the excess power generated by the power system;

[0080] It is the probability density function of the heat capacity that can be supplied by the thermal storage tank at present; it is used to describe the probability distribution of the heat capacity that can be provided by the thermal storage tank in its current state.

[0081] Preferably, the method for evaluating the adjustable performance of the heat storage amount of the heat storage tank is to evaluate according to the expected value of the remaining storable heat capacity of the heat storage tank and the expected value of the current heat supplyable by the heat storage tank;

[0082] Preferably, the method for obtaining the expected value of the remaining storable heat capacity of the thermal storage tank and the expected value of the current heat supplyable heat of the thermal storage tank is as follows:

[0083] S51: Obtaining an expected value of heat load demand in period t based on the day-ahead planned operating power curve of the electric heating equipment, the predicted probability density function of the heat load in period t, and the operating efficiency of the electric heating equipment;

[0084] Preferably, the expected value of the heat load demand in the tth period is obtained as follows:

[0085]

[0086] Where: is the expected value of heat load demand in period t; f t (x) is the predicted probability density function of the heat load in the tth period; x is the heat load power;

[0087] S52: Expected value of heat load demand in period t The expected value of the remaining stored heat in the thermal storage tank at time period t is obtained as follows:

[0088]

[0089] Where: is the expected value of the remaining stored heat in the thermal storage tank at time t; is the expected value of the remaining heat stored in the thermal storage tank in period t-1; p t is the planned operating power of the electric heating equipment in period t; η is the operating efficiency of the electric heating equipment;

[0090] S53: According to the expected value of the remaining stored heat in the thermal storage tank during the tth period, the expected value of the remaining storable heat capacity of the thermal storage tank and the expected value of the current heat supplyable by the thermal storage tank are obtained as follows:

[0091]

[0092]

[0093] Specifically, segments are extracted from the probability density functions of the remaining storable heat capacity and the currently available heat capacity. The horizontal axis value at a certain probability value of the probability density function is used as the confidence level for evaluation. This is prior art and will not be described in detail here. The higher the confidence level, the less adjustable heat the thermal storage tank can provide, indicating a better evaluation result using the method of this application.

[0094] This embodiment can also derive adjustable quantity curves at different confidence levels and can provide corresponding adjustable heat curves according to the confidence level requirements in actual applications, wherein the expected value represents the average value of the adjustment index with a confidence level of 50%.

[0095] An embodiment of the present invention is as follows:

[0096] Taking the electric heating load and temperature data of Changchun City, Jilin Province as an example, a case analysis is carried out. The electric heating load curve data of Changchun City from November 2019 to May 2020 is as follows: Figure 2As shown. The heat storage tank capacity (i.e., the heat capacity of the heat storage tank) is configured to be 100MWh, the initial state is 50MWh, the maximum heat release capacity is 30MWh, the electric heating equipment capacity is 32MWh, and the efficiency is set to 99%. The planned operating power curve is as follows: Figure 3 shown.

[0097] Based on the heat load probability prediction results, the expected value of the heat load power in different time periods is calculated, and then the expected value of the storage state of the heat storage tank in each time period is obtained as shown in Table 1.

[0098] Table 1 Expected values ​​of heat load power and heat storage tank storage state

[0099]

[0100] Through Monte Carlo sampling, the sample points are 2000, and the state of the heat storage tank in each period under different samples is simulated. The Gaussian kernel density estimation method is used to obtain the probability density function of the heat storage tank state (i.e. the remaining heat capacity of the heat storage tank) in each period. Figure 5 As shown in the figure, the distribution function of the thermal storage tank state is relatively tall and thin at the beginning of the day, indicating that the uncertainty is small. This error will manifest as the uncertainty of the thermal storage level in the thermal storage tank. The uncertainty of the thermal storage level in the initial stage of the thermal storage tank is also small, so the confidence level of its regulated thermal capacity is relatively high. As time goes by, the distribution function of the thermal storage tank state gradually becomes short and fat, indicating that the uncertainty is relatively large. The corresponding uncertainty of the thermal storage level of the thermal storage tank in each period gradually increases, and the confidence level of its regulated thermal capacity will become increasingly small.

[0101] After obtaining the probability density function of the heat storage tank state, the formula in step 7 can be used to further obtain the probability density function of the upward and downward adjustable capacity of the heat storage tank;

[0102] The actual upward and downward adjustable power of the heat storage tank shows uncertainty. The adjustable amount under different confidence levels is as follows: Figure 6 、 Figure 7 As shown, the higher the required confidence level, the less adjustable heat the thermal storage tank provides.

[0103] Beneficial effects: The present invention provides a probabilistic flexibility evaluation method for a thermal storage electric heating system. Based on Monte Carlo simulation and Gaussian kernel density estimation, the method obtains a time series scenario set of thermal load demand through the predicted probability density function of the thermal load in the tth period of the day-ahead plan, and then obtains the remaining stored heat in the thermal storage tank. Combined with the maximum stored heat of the thermal storage tank, the minimum stored heat of the thermal storage tank, and the probabilistic flexibility index of the thermal storage tank, the adjustable performance of the thermal storage tank is evaluated, and the probabilistic flexibility evaluation of the thermal storage tank is completed, providing adjustable capacity information for the operation of the power system. On the one hand, the adjustable capacity of the thermal load can be used to balance the power generation of new energy and promote the consumption of new energy. On the other hand, the probabilistic evaluation method can provide comprehensive adjustment capacity information for the power system operation department, and improve the robustness and economy of the power grid dispatch.

[0104] Finally, it should be noted that the above embodiments are only used to illustrate the technical solutions of the present invention, rather than to limit it. Although the present invention has been described in detail with reference to the above embodiments, those skilled in the art should understand that they can still modify the technical solutions described in the above embodiments, or replace some or all of the technical features therein with equivalents. However, these modifications or replacements do not cause the essence of the corresponding technical solutions to deviate from the scope of the technical solutions of the embodiments of the present invention.

Claims

1. A probabilistic flexibility assessment method for a thermal storage electric heating system, characterized in that: The steps include: Step 1: Obtain the day-ahead planned operating power curve of the electric heating equipment, the maximum stored heat of the thermal storage tank, the minimum stored heat of the thermal storage tank, the operating efficiency of the electric heating equipment, and the predicted probability density function of the heat load in period t; Step 2: Obtain the time series scenario set of heat load demand based on the predicted probability density function of the heat load in the tth period of the day-ahead plan; Step 3: According to the day-ahead planned operating power curve of the electric heating equipment, the operating efficiency of the electric heating equipment and the time series scenario set of the heat load demand, obtain the remaining stored heat Q of the heat storage tank in the sth heat load scenario in the tth period t,s ; Step 4: The remaining stored heat Q in the heat storage tank according to the heat load scenario in all scenarios in the t period t,s , obtain the probability density function of the remaining stored heat in the thermal storage tank at time t To obtain the remaining storage heat Q of the thermal storage tank in period t t ; In step 4, the probability density function of the remaining stored heat in the thermal storage tank in time period t is obtained as follows: Where: represents the probability density function of the remaining stored heat in the thermal storage tank at time t; σ t is the standard deviation of the Gaussian kernel function; y represents the remaining heat stored in the thermal storage tank; M represents the number of heat load scenarios; s represents the number of the heat load scenario; Step 5: According to the maximum storage heat of the heat storage tank, the minimum storage heat of the heat storage tank and the remaining storage heat Q of the heat storage tank in the t period t , obtain the probability flexibility index of the heat storage tank to evaluate the adjustable performance of the heat storage capacity of the heat storage tank; the probability flexibility index of the heat storage tank includes the remaining storable heat capacity of the heat storage tank and the current heat supply of the thermal storage tank 2. A probabilistic flexibility assessment method for a thermal storage electric heating system according to claim 1, characterized in that: In step 2, the time sequence scenario set of the heat load demand is: {h t,s |t=1,2,…,24},s=1,2,…,M, Where M is the number of heat load scenarios and is the sample point of Monte Carlo simulation; h t,s Indicates the sth heat load scenario in the tth period; s is the number of the heat load scenario.

3. The probabilistic flexibility assessment method for a thermal storage electric heating system according to claim 1, characterized in that: In the step 3, the remaining stored heat in the heat storage tank of the sth heat load scenario in the tth period is obtained as follows; Q t,s =Q t-1,s +p t η-h t,s Where: Q t,s represents the remaining stored heat in the thermal storage tank in the sth heat load scenario during the tth period; p t is the planned operating power of the electric heating equipment in period t; η is the operating efficiency of the electric heating equipment; Q t-1,s The remaining stored heat in the thermal storage tank in the sth heat load scenario during the t-1th period; t represents the daily period number.

4. The probabilistic flexibility assessment method for a thermal storage electric heating system according to claim 1, characterized in that: In step 5, the probability flexibility index of the thermal storage tank is obtained as follows: Where: Q max Indicates the maximum storage heat of the thermal storage tank; Q min Indicates the minimum storage heat of the thermal storage tank.

5. The probabilistic flexibility assessment method for a thermal storage electric heating system according to claim 4, characterized in that: The method for evaluating the adjustable performance of the heat storage capacity of the heat storage tank is to evaluate it according to the probability density function of the remaining storable heat capacity of the heat storage tank and the probability density function of the current heat supplyable by the heat storage tank; The probability density function of the remaining storable heat capacity of the thermal storage tank and the probability density function of the current heat supplyable heat of the thermal storage tank are obtained as follows: Where: represents the probability density function of the heat in the thermal storage tank; y represents the remaining heat stored in the thermal storage tank, The probability density function representing the remaining storable heat capacity of the thermal storage tank; Represents the probability density function of the current heat supply of the thermal storage tank.

6. The probabilistic flexibility assessment method for a thermal storage electric heating system according to claim 1, characterized in that: The method for evaluating the heat storage amount adjustable performance of the heat storage tank is to evaluate according to the expected value of the remaining storable heat capacity of the heat storage tank and the expected value of the currently supplyable heat of the heat storage tank.

7. A probabilistic flexibility assessment method for a thermal storage electric heating system according to claim 6, characterized in that: The method for obtaining the expected value of the remaining storable heat capacity of the thermal storage tank and the expected value of the current heat supplyable heat of the thermal storage tank is as follows: S51: Obtaining an expected value of heat load demand in period t based on the day-ahead planned operating power curve of the electric heating equipment, the predicted probability density function of the heat load in period t, and the operating efficiency of the electric heating equipment; The expected value of the heat load demand in period t is obtained as follows: Where: is the expected value of heat load demand in period t; f t (x) is the predicted probability density function of the heat load in the tth period; x is the heat load power; S52: Expected value of heat load demand in period t The expected value of the remaining stored heat in the thermal storage tank at time period t is obtained as follows: Where: is the expected value of the remaining stored heat in the thermal storage tank at time t; is the expected value of the remaining heat stored in the thermal storage tank in period t-1; p t is the planned operating power of the electric heating equipment in period t; η is the operating efficiency of the electric heating equipment; S53: According to the expected value of the remaining stored heat in the thermal storage tank during the tth period, the expected value of the remaining storable heat capacity of the thermal storage tank and the expected value of the current heat supplyable by the thermal storage tank are obtained as follows:

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