Two-stage combined frequency regulation optimization method for thermal power and energy storage based on weight coefficient correction

Abstract

The present invention proposes a two-stage combined frequency regulation optimization method for thermal energy storage based on weight coefficient correction. First, aiming at the frequency regulation assessment rules, a mathematical model of frequency regulation performance index is established, and the Big-M method and variable substitution method are used to linearize the conditional constraints and absolute value constraints in the index model, thereby constructing a first-stage optimization model considering frequency regulation performance. On this basis, in order to reduce the over-limit of energy storage SOC and the deep charge and discharge conditions, a second-stage optimization model is constructed with the minimum deviation of energy storage SOC as the goal, so as to establish a two-stage combined frequency regulation optimization model for thermal energy storage. Therefore, the two-stage combined frequency regulation optimization method proposed by the present invention can improve the frequency regulation performance of the combined thermal energy storage system, effectively reduce the deep charge and discharge conditions of the energy storage and the loss of service life, and improve the sustainability of the energy storage auxiliary frequency regulation service.

Description

Technical Field

[0001] The present invention belongs to the technical field of combined thermal energy and energy storage frequency modulation optimization control, and particularly relates to a two-stage combined thermal energy and energy storage frequency modulation optimization method based on weight coefficient correction. Background Art

[0002] Renewable energy sources such as wind power and photovoltaic power have intermittency and uncertainty, and their large-scale grid connection has brought severe challenges to the frequency modulation of power systems. As the main frequency modulation source in the current power system, thermal power has the disadvantages of long response time delay and low regulation accuracy, and frequent output changes will cause serious wear of thermal power units. Energy storage has the characteristics of fast, accurate and bidirectional regulation, and with the rapid development of energy storage science and technology, its cost has been greatly reduced, and it has been widely used in the power frequency modulation service market, which can improve the regulation performance of units participating in automatic generation control (AGC).

[0003] However, in the existing combined thermal energy and energy storage frequency modulation optimization model, there is a deviation between the frequency modulation index model and the actual project, and the improvement of the frequency modulation performance is relatively limited in actual applications. And the frequency modulation rules in the actual frequency modulation market contain non-linear conditional constraints and absolute value constraints, which belong to non-linear optimization problems, and it is difficult to ensure the stability of the solution and the calculation efficiency. In addition, due to the energy limitation of energy storage, the state of charge (SOC) management of energy storage needs to be considered in the auxiliary frequency modulation process. Existing research often realizes the SOC management of energy storage by weighing the frequency modulation performance of the combined system and the SOC of energy storage, and reduces the frequency modulation benefit of thermal power plants when the SOC of energy storage approaches the limit value. In addition, the currently proposed combined thermal energy and energy storage frequency modulation control method ignores the inconsistent auxiliary improvement effects of energy storage on frequency modulation sub-indices, and cannot exert the characteristics of energy storage in the optimization process, restricting the improvement of the frequency modulation performance of thermal power units by energy storage.

[0004] In summary, the following problems exist in the current research on combined thermal energy and energy storage frequency modulation optimization control: 1) There is a difference between the mathematical model of the frequency modulation index and the evaluation index; 2) The frequency modulation performance of the combined system is sacrificed when ensuring the sustainability of the energy storage frequency modulation service; 3) The output characteristics of the combined thermal energy and energy storage system are not considered in the optimization control process. Summary of the Invention

[0005] Based on the analysis of the defects and deficiencies of the existing technology, the present invention proposes a two-stage combined thermal energy and energy storage frequency modulation optimization method based on weight coefficient correction.

[0006] Its main design points include:

[0007] (1) For the frequency modulation assessment rules of actual engineering, three sub-indicators of frequency modulation, namely response time, regulation rate, and regulation error, are established, and an optimization model for combined thermal energy and energy storage frequency modulation considering frequency modulation performance is established. At the same time, the Big-M method and variable substitution method are used to linearize the conditional constraints and absolute value constraints in the indicator model, converting the non-linear optimization problem into a mixed-integer linear programming problem that is easy to solve.

[0008] (2) In order to balance the sustainability of frequency modulation services while ensuring frequency modulation performance, based on the first-stage optimization model with optimal frequency modulation performance, a second-stage optimization model considering the state of charge (SOC) of energy storage is proposed, and thus an optimization model for combined thermal energy and energy storage two-stage frequency modulation is constructed.

[0009] (3) Considering the output characteristics of the combined thermal energy and energy storage system and frequency modulation assessment indicators, and combining the improved analytic hierarchy process to correct the weight coefficients of each frequency modulation sub-indicator, an optimization model for combined thermal energy and energy storage frequency modulation with optimal frequency modulation performance is constructed.

[0010] The technical solution adopted by the present invention to solve its technical problems is as follows:

[0011] A two-stage combined thermal energy and energy storage frequency modulation optimization method based on weight coefficient correction, characterized in that:

[0012] First, for the frequency modulation assessment rules, three sub-indicators of frequency modulation, namely response time, regulation rate, and regulation error, are established, and an optimization model for combined thermal energy and energy storage frequency modulation considering frequency modulation performance is established. The Big-M method and variable substitution method are used to linearize the conditional constraints and absolute value constraints in the indicator model, converting the non-linear optimization problem into a mixed-integer linear programming problem that is easy to solve, and thus constructing a first-stage optimization model considering frequency modulation performance;

[0013] On this basis, in order to reduce the over-limit of the SOC of energy storage and the deep charge and discharge conditions, a second-stage optimization model is constructed with the minimum deviation of the SOC of energy storage as the goal, and thus an optimization model for combined thermal energy and energy storage two-stage frequency modulation is established to output the optimized energy storage output.

[0014] Furthermore, the mathematical models of frequency modulation performance indicators and energy storage specifically include:

[0015] Frequency modulation performance assessment indicators:

[0016] The response time index d1 is the ratio of the response time of the frequency modulation unit to the average delay time of the frequency modulation of the units in the area. The smaller the value, the better the assessment performance of the response time of the frequency modulation unit; the response time of the frequency modulation unit represents the response delay when the combined thermal energy and energy storage system fails to meet the response requirements after the frequency modulation command is issued. Among them, when the output of the combined thermal energy and energy storage system positively crosses 15% of the frequency modulation mileage, it is regarded as meeting the response requirements; its expression is as follows:

[0017]

[0018]

[0019] P U,i = P B,i + P G,i (5.3)

[0020]

[0021] Wherein, T deli,av is the average delay time of frequency regulation of units in the area; T deli is the response time of the frequency regulation unit; Δt is the sampling time; n is the number of sampling times in the frequency regulation process; L is the frequency regulation mileage, which is the difference between two adjacent frequency regulation commands; when the difference between the combined output of the thermal energy storage system and the output at the moment of issuing the frequency regulation command at the i-th sampling moment is less than 0.15L, it is regarded that the combined unit has not started to respond to the frequency regulation command, and at this time t 1,i = Δt, when the combined thermal energy storage system starts to respond to the frequency regulation command at the i-th sampling moment, t 1,i = 0; P B,i is the energy storage output at the i-th sampling moment; P G,i is the output of the thermal power unit at the i-th sampling moment; P U,i is the combined output of the thermal energy storage at the i-th sampling moment; P G0 is the output of the thermal power unit at the moment of issuing this frequency regulation command;

[0022] The regulation rate index d2 is the ratio of the average regulation rate of units in the area to the regulation rate of the frequency regulation unit. The smaller its value, the better the evaluation performance of the regulation rate of the frequency regulation unit; among them, the regulation rate of the frequency regulation unit represents the rate at which the frequency regulation unit responds to the frequency regulation command; its expression is as follows:

[0023]

[0024]

[0025]

[0026] Wherein, V av is the average regulation rate of units in the area; V is the regulation rate of the frequency regulation unit; the allowable range interval of the frequency regulation command target value is the frequency regulation target command ± 5% of the frequency regulation mileage. When the combined thermal energy storage system does not reach the allowable range interval of the target value at the i-th sampling moment, at this time t 2,i = Δt, when the combined thermal energy storage system reaches the allowable range interval of the target value at the i-th sampling moment, t 2,i = 0;

[0027] The regulation error index d3 is the ratio of the regulation error of the frequency modulation unit to the average regulation error of the frequency modulation of the units within the region. The smaller its value, the better the performance of the regulation error assessment of the frequency modulation unit. Among them, the regulation error of the frequency modulation unit characterizes the deviation between the final stable output of the frequency modulation unit and the frequency modulation command, and is represented by accumulating the absolute value of the difference between the actual output and the target value. The expression is as follows:

[0028]

[0029]

[0030]

[0031] In the formula, P accu,av is the average regulation error of the frequency modulation of the units within the region; P accu is the regulation error of the frequency modulation unit. The regulation error of the frequency modulation unit is calculated after the combined output of the thermal storage reaches the target allowable range. When the difference between the combined output of the thermal storage system at the i-th sampling moment and the output at the moment when the frequency modulation command is issued is greater than 0.95L and less than 1.05L, it is regarded that the combined unit reaches the target value allowable range. At this time, t 3,i =Δt. When the system does not reach the target value allowable range of the frequency modulation command, the calculation of the regulation error is not performed, and t 3,i =0; P agc,i is the frequency modulation command power at the i-th sampling moment;

[0032] Mathematical model of energy storage:

[0033] To achieve the management of the energy storage SOC, by introducing the energy storage SOC deviation S d , the magnitude of its value represents the degree to which the energy storage SOC deviates from the middle value of the SOC. The specific formula is as follows:

[0034] S d ={S n -S mid | (5.11)

[0035] In the formula, S n is the energy storage SOC at the end of the n-th sampling moment, and S mid is the middle value of the energy storage SOC, taken as 0.5;

[0036] The energy storage SOC balance constraint is expressed as:

[0037]

[0038] In the formula, S i and S i-1 are the energy storage SOC at the end of the i-th sampling moment and the energy storage SOC at the end of the i-1-th sampling moment respectively; E s is the energy storage capacity; ηc and η d is the charge-discharge efficiency of energy storage;

[0039] In addition, the energy storage also needs to meet the power constraint and energy constraint;

[0040]

[0041] In the formula, P B,max and P B,min are the upper and lower limits of the energy storage output respectively; S max and S min are the upper and lower limits of the energy storage SOC respectively.

[0042] Furthermore, the two-stage combined frequency modulation optimization model of thermal power and energy storage specifically includes:

[0043] Among them, the three frequency modulation sub-indicators of the response time index d1, the regulation rate index d2, and the regulation error index d3 are weighted to establish the first-stage frequency modulation optimization model considering the frequency modulation performance. The objective function is shown in Equation (5.14), and the constraint conditions to be satisfied include Equations (5.1)-(5.10), (5.12), and (5.13);

[0044] minδ1d1+δ2d2+δ3d3 (5.14)

[0045] In the formula, δ1, δ2, and δ3 are the weight coefficients of each frequency modulation sub-index;

[0046] The non-linear constraints in the mathematical model are linearized by the big-M method and variable substitution method, and converted into a mixed-integer linear programming problem:

[0047] Among them, the logical constraints (5.4), (5.7), (5.10), and (5.12) need to introduce auxiliary variables and be converted into linear constraints (5.15)-(5.18) by the Big-M method;

[0048]

[0049]

[0050]

[0051]

[0052] In the formula, M is a set sufficiently large positive number; a 1,i is a 0-1 variable. When a 1,i = 1, it means that the system starts to respond to the frequency modulation instruction at the i-th sampling moment of the system. When a 1,i = 0, it means that the system does not start to respond to the frequency modulation instruction at the i-th sampling moment of the system; a 2,iis a 0-1 variable. When a 2,i = 1, it means that the allowable error range of the combined output of the system reaches the target value at the i-th sampling moment. When a 2,i = 0, it means that the system does not reach the allowable range interval of the target value at the i-th sampling moment; a 3,i is a 0-1 variable. When a 3,i = 0, it means that the system reaches the allowable range interval of the target value at the i-th sampling moment. When a 3,i = 1, it means that the system does not reach the allowable range interval of the target value at the i-th sampling moment; u cf,i is a 0-1 variable representing the charge and discharge flag. When u cf,i = 1, it means that the energy storage discharges. When u cf,i = 0, it means that the energy storage charges;

[0053] The absolute value constraints (5.9) and (5.11) are transformed into linear constraints through variable substitution, as shown in the following formula:

[0054]

[0055]

[0056]

[0057] S d = S z (5.22)

[0058] In the formula, P m,i and S z are auxiliary variables for replacing the absolute value constraints;

[0059] Finally, the compact form of the first-stage model is expressed as:

[0060]

[0061] In the formula, d and x are optimization variables, and the specific expressions are:

[0062]

[0063] In the formula, δ is the coefficient column vector corresponding to the objective function formula (5.14); K, I, C, and D are the coefficient matrices of the variables under the corresponding constraints; h and m are constant column vectors; in formula (5.23), the first row of the constraint conditions represents the inequality constraints in the thermal energy storage combined frequency modulation optimization model, including formulas (5.13), (5.15)-(5.19); the second row represents the equality constraints, including formulas (5.2), (5.3), (5.6), (5.20); the third row corresponds to formulas (5.1), (5.5), (5.8).

[0064] To avoid the degradation of the frequency regulation performance of the combined system caused by energy storage SOC management, the optimization results of the first stage are used as the constraint conditions for the second stage model, and the specific formula is as follows:

[0065] δ T d ≤ δ T d * (5.25)

[0066] In the formula, δ T d * is the minimum value of the objective function δ T d in the first stage optimization model;

[0067] The second stage optimization model aims to minimize the energy storage SOC deviation and solve the energy storage output scheme; a second stage frequency regulation optimization model considering energy storage SOC management is established, and the objective function is shown in Equation (5.26); the constraint conditions include Equations (5.1)-(5.3), (5.5), (5.6), (5.8), (5.13)-(5.22), (5.25);

[0068] min S d (5.26)

[0069] The compact form of the second stage model is expressed as:

[0070]

[0071] In the formula, y and d are optimization variables, and the specific expressions are:

[0072]

[0073] In the formula, R, F, Q, H, and N are coefficient matrices of variables under corresponding constraints; c, g, and u are constant column vectors; in Equation (5.27), the first and second rows in the constraint conditions represent the inequality constraints in the model, including Equations (5.13)-(5.19), (5.21), (5.25); the third row of equality constraints includes Equations (5.2), (5.3), (5.6), (5.20), (5.22); the fourth row corresponds to Equations (5.1), (5.5), (5.8).

[0074] Furthermore, by considering the output characteristics of the thermal energy storage system and combining the improved analytic hierarchy process to correct the weight coefficients of the frequency regulation sub-indicators, the frequency regulation performance of the thermal energy storage combined frequency regulation optimization model is further improved, specifically including:

[0075] In order to incorporate the energy storage characteristics during the optimization process, considering the frequency regulation assessment rules and the output characteristics of the thermal energy storage system, analyze the improvement of each frequency regulation sub-index by the energy storage and the impact on the comprehensive frequency regulation performance index, a hierarchical structure model of frequency regulation performance is established. Combining with the optimal frequency regulation performance model, solve the comprehensive frequency regulation performance index under different weight coefficients δ, reconstruct the judgment matrix, and finally synthesize the weights of each level to correct the weight coefficients of each frequency regulation sub-index:

[0076] Hierarchical structure model considering the frequency regulation assessment rules and the output characteristics of the thermal energy storage system:

[0077] Set the comprehensive frequency regulation performance index as the target layer of the hierarchical structure model;

[0078] Take each frequency regulation sub-index as the middle layer of the hierarchical structure model;

[0079] Take the mutual influence of each frequency regulation sub-index as the bottom layer of the hierarchical structure model;

[0080] Based on the respective advantages of the thermal energy storage system and the frequency regulation sub-index assessment rules, taking frequency regulation performance as the goal, considering the characteristics and advantages of each frequency regulation sub-index and the thermal energy storage system, as well as the mutual influence between the frequency regulation sub-indexes during the assessment process, establish a hierarchical structure model;

[0081] Reconstruct the judgment matrix:

[0082] Combined with the thermal energy storage combined frequency regulation optimization model based on the frequency regulation performance assessment rules, calculate the improvement effect of each frequency regulation sub-index on the comprehensive frequency regulation performance index under the auxiliary role of the energy storage, and reconstruct the judgment matrix:

[0083] First, establish a thermal energy storage combined frequency regulation optimization model with different frequency regulation sub-indexes as the goals, solve the output scheme of the thermal energy storage combined system, and calculate the average comprehensive frequency regulation performance index of each model;

[0084]

[0085]

[0086] In the formula, δ d indicates that the weight of each frequency regulation sub-index is the same; δ d1 , δ d2 , δ d3 represent the weight coefficient vectors when each frequency regulation sub-index is the main weight target respectively, where a >> b; solve the optimization models of different objective functions respectively, and according to the obtained output scheme, calculate the comprehensive frequency regulation performance indexes of the combined system under different weight coefficients, denoted as K, K B1 , K B2 , K B3 ;

[0087] Secondly, the change amount of the comprehensive frequency modulation performance index mainly composed of different frequency modulation sub-index weight coefficients is standardized into the judgment matrix scale, and the specific formula is as follows:

[0088]

[0089]

[0090] In the formula, ΔK Bi is the change amount of the comprehensive frequency modulation performance index; ΔK B,max and ΔK B,min are the maximum and minimum values of ΔK Bi respectively; represents the judgment matrix scale after reconstruction;

[0091] Finally, the constructed frequency modulation performance judgment matrix A is as follows:

[0092]

[0093] According to the same construction method, the judgment matrices of response time, regulation rate, and regulation error are obtained;

[0094] The weights W of each frequency modulation sub-index:

[0095] Each column in the judgment matrix approximately reflects the distribution of the weights. First, the arithmetic mean of all column vectors is used to calculate the weight coefficients of each layer, as shown in the following formula:

[0096]

[0097] In the formula, W i is the weight coefficient of each layer, a ij is the element of the judgment matrix after reconstruction in 3.2, and n is the number of columns of the judgment matrix;

[0098] Finally, considering the weights of the bottom-layer judgment matrix to the middle layer and the weights of the middle layer to the target layer, the comprehensive weight vector of each frequency modulation sub-index is calculated through formula (5.35):

[0099] W = W B T W A (5.35)

[0100] In the formula, W is the comprehensive weight coefficient vector; W B is the weight matrix of the bottom layer relative to the middle layer; W A is the weight column vector of the middle layer relative to the target layer.

[0101] Therefore, the two-stage combined frequency regulation optimization method proposed by the present invention can improve the frequency regulation performance of the thermal energy storage combined system, effectively reduce the deep charge and discharge of the energy storage, and the loss of service life, and improve the sustainability of the energy storage assisted frequency regulation service. Brief Description of the Drawings

[0102] The present invention will be further described in detail below in conjunction with the drawings and specific embodiments:

[0103] Figure 1 Flow chart of the thermal energy storage two-stage combined frequency regulation optimization method based on weight coefficient correction in the embodiment of the present invention. Specific Embodiments

[0104] To make the features and advantages of this patent more obvious and understandable, specific embodiments are given below for detailed description as follows:

[0105] It should be noted that the following detailed descriptions are all illustrative and are intended to provide further explanations for the present application. Unless otherwise specified, all technical and scientific terms used in this specification have the same meaning as commonly understood by those of ordinary skill in the technical field to which this application belongs.

[0106] It should be noted that the terms used herein are only for describing specific embodiments and are not intended to limit the exemplary embodiments according to the present application. As used herein, unless the context clearly indicates otherwise, the singular form is also intended to include the plural form. In addition, it should be understood that when the terms "comprising" and / or "including" are used in this specification, they indicate the presence of features, steps, operations, devices, components, and / or combinations thereof.

[0107] As Figure 1 shown, the following further specifically introduces the solutions of this embodiment in conjunction with the drawings:

[0108] 1. Mathematical Model of Thermal Energy Storage Combined Frequency Regulation

[0109] 1.1 Frequency Regulation Performance Evaluation Index

[0110] The response time index d1 is the ratio of the response time of the frequency regulation unit to the average delay time of the frequency regulation of the units in the region. The smaller the value, the better the performance evaluation of the response time of the frequency regulation unit. The response time of the frequency regulation unit characterizes the response delay when the frequency regulation command is issued but the thermal energy storage combined system fails to meet the response requirements. Among them, when the output of the thermal energy storage combined system positively crosses 15% of the frequency regulation mileage, it is regarded as meeting the response requirements. Its expression is as follows:

[0111]

[0112]

[0113] P U,i = PB,i +P G,i (5.3)

[0114]

[0115] In the formula, T deli,av is the average delay time of frequency regulation of the units in the area; T deli is the response time of the frequency regulation unit; Δt is the sampling time; n is the number of samplings in the frequency regulation process; L is the frequency regulation mileage, which is the difference between two adjacent frequency regulation commands; when the difference between the combined output of the thermal energy storage system and the output at the moment when the frequency regulation command is issued at the i-th sampling moment is less than 0.15L, it is regarded that the combined unit has not started to respond to the frequency regulation command, and at this time t 1,i =Δt, when the combined thermal energy storage system starts to respond to the frequency regulation command at the i-th sampling moment, t 1,i =0; P B,i is the energy storage output at the i-th sampling moment; P G,i is the output of the thermal power unit at the i-th sampling moment; P U,i is the combined output of the thermal energy storage at the i-th sampling moment; P G0 is the output of the thermal power unit at the moment when this frequency regulation command is issued.

[0116] The regulation rate index d2 is the ratio of the average frequency regulation rate of the units in the area to the regulation rate of the frequency regulation unit. The smaller the value, the better the performance evaluation of the regulation rate of the frequency regulation unit. Among them, the regulation rate of the frequency regulation unit characterizes the rate at which the frequency regulation unit responds to the frequency regulation command. Its expression is as follows:

[0117]

[0118]

[0119]

[0120] In the formula, V av is the average frequency regulation rate of the units in the area; V is the regulation rate of the frequency regulation unit; the allowable range interval of the frequency regulation command target value is the frequency regulation target command ±5% of the frequency regulation mileage. When the combined thermal energy storage system does not reach the allowable range interval of the target value at the i-th sampling moment, at this time t 2,i =Δt, when the combined thermal energy storage system reaches the allowable range interval of the target value at the i-th sampling moment, t 2,i =0.

[0121] The regulation error index d3 is the ratio of the regulation error of the frequency regulation unit to the average regulation error of the units in the area. The smaller the value, the better the performance evaluation of the regulation error of the frequency regulation unit. Among them, the regulation error of the frequency regulation unit characterizes the deviation between the final stable output of the frequency regulation unit and the frequency regulation command, and is represented by accumulating the absolute value of the difference between the actual output and the target value. The expression is as follows:

[0122]

[0123]

[0124]

[0125] wherein, P accu,av is the average regulation error of the unit frequency modulation in the area; P accu is the regulation error of the frequency modulation unit; the regulation error of the frequency modulation unit is calculated after the combined output of the thermal storage reaches the target allowable range. When the difference between the combined output of the thermal storage system at the i-th sampling moment and the output at the moment when the frequency modulation command is issued is greater than 0.95L and less than 1.05L, it is regarded that the combined unit reaches the target value allowable range. At this time, t 3,i = Δt. When the system does not reach the target value allowable range of the frequency modulation command, the regulation error is not calculated, and t 3,i = 0; P agc,i is the frequency modulation command power at the i-th sampling moment.

[0126] 1.2 Energy storage mathematical model

[0127] In order to manage the SOC of the energy storage, by introducing the energy storage SOC deviation S d , the value of which represents the degree of deviation of the energy storage SOC from the SOC intermediate value. The specific formula is as follows:

[0128] S d = |S n - S mid ·(5.11)

[0129] wherein, S n is the energy storage SOC at the end of the n-th sampling moment, and S mid is the energy storage SOC intermediate value, which is taken as 0.5.

[0130] The energy storage SOC balance constraint can be expressed as:

[0131]

[0132] wherein, S i and S i-1 are the energy storage SOC at the end of the i-th sampling moment and the energy storage SOC at the end of the i-1-th sampling moment respectively; E s is the energy storage capacity; η c and η d are the energy storage charge and discharge efficiencies.

[0133] In addition, the energy storage also needs to meet the power constraint and the energy constraint.

[0134]

[0135] In the formula, P B,max and P B,min are the upper and lower limits of the energy storage output respectively; S max and S min are the upper and lower limits of the energy storage SOC respectively. 2. Two-stage combined frequency regulation optimization model of thermal power and energy storage considering frequency regulation performance and energy storage SOC

[0136] 2.1 First-stage optimization model considering frequency regulation performance

[0137] An optimization model for establishing frequency regulation assessment rules for actual projects is constructed. The three frequency regulation sub-indicators of the response time index d1, the regulation rate index d2, and the regulation error index d3 constructed previously are weighted to establish the objective function as shown in Equation (5.14). The constraint conditions to be satisfied include Equations (5.1)-(5.10), (5.12), and (5.13).

[0138] minδ1d1 + δ2d2 + δ3d3 (5.14)

[0139] In the formula, δ1, δ2, and δ3 are the weight coefficients of each frequency regulation sub-index. How to determine the weight coefficients of each frequency regulation sub-index will be described in detail later.

[0140] However, there are multiple logical constraints and absolute value constraints in the frequency regulation assessment index and the energy storage SOC model, which belong to a non-linear optimization problem and it is difficult to ensure the stability of the solution and the calculation efficiency. Therefore, the present invention linearizes the non-linear constraints in the mathematical model through the big-M method and the variable substitution method, and converts it into a mixed integer linear programming problem.

[0141] Among them, the logical constraints (5.4), (5.7), (5.10), and (5.12) need to introduce auxiliary variables and be converted into linear constraints (5.15)-(5.18) through the Big-M method.

[0142]

[0143]

[0144]

[0145]

[0146] In the formula, M is a sufficiently large positive number; a 1,i is a 0-1 variable. When a 1,i = 1, it means that the system starts to respond to the frequency regulation instruction at the i-th sampling moment of the system. When a 1,i = 0, it means that the system does not start to respond to the frequency regulation instruction at the i-th sampling moment of the system; a 2,iis a 0-1 variable. When a 2,i = 1, it means that the allowable error range of the combined output of the system reaches the target value at the i-th sampling moment. When a 2,i = 0, it means that the system does not reach the allowable range interval of the target value at the i-th sampling moment; a 3,i is a 0-1 variable. When a 3,i = 0, it means that the system reaches the allowable range interval of the target value at the i-th sampling moment. When a 3,i = 1, it means that the system does not reach the allowable range interval of the target value at the i-th sampling moment; u cf,i is a 0-1 variable representing the charge and discharge flag. When u cf,i = 1, it means that the energy storage discharges. When u cf,i = 0, it means that the energy storage charges.

[0147] The absolute value constraints (5.9) and (5.11) are transformed into linear constraints through variable substitution, as shown in the following formula:

[0148]

[0149]

[0150]

[0151] Sd = Sz(5.22)

[0152] In the formula, P m,i and S z are auxiliary variables for replacing the absolute value constraints.

[0153] Finally, the compact form of the first-stage model can be expressed as:

[0154]

[0155] In the formula, d and x are optimization variables, and the specific expressions are:

[0156]

[0157] In the formula, δ is the coefficient column vector corresponding to the objective function formula (5.14); K, I, C, and D are the coefficient matrices of the variables under the corresponding constraints; h and m are constant column vectors. In formula (5.23), the first row of the constraint conditions represents the inequality constraints in the combined thermal energy storage frequency modulation optimization model, including formulas (5.13), (5.15)-(5.19); the second row is the equality constraint, including formulas (5.2), (5.3), (5.6), (5.20); the third row corresponds to formulas (5.1), (5.5), (5.8).

[0158] .2.2 Second-stage optimization model considering the SOC of energy storage

[0159] To avoid the energy storage from withdrawing from the frequency modulation auxiliary service due to too high or too low SOC, while ensuring the frequency modulation performance and taking into account the sustainability of the frequency modulation service, the present invention uses the optimization result of the first stage as the constraint condition of the second stage model. The specific formula is as follows:

[0160] δ T d ≤ δ T d*(5.25)

[0161] In the formula, δ T d* is the minimum value of the objective function δ T d in the first stage optimization model.

[0162] The second stage optimization model aims to minimize the SOC deviation of the energy storage and solve the energy storage output scheme. The objective function is established as shown in Equation (5.26). The constraint conditions include Equations (5.1)-(5.3), (5.5), (5.6), (5.8), (5.13)-(5.22), (5.25).

[0163] minSd(5.26)

[0164] The compact form of the second stage model can be expressed as:

[0165]

[0166] In the formula, y and d are optimization variables, and the specific expressions are:

[0167]

[0168] In the formula, R, F, Q, H, and N are coefficient matrices of variables under corresponding constraints; c, g, and u are constant column vectors. In Equation (5.27), the first and second rows in the constraint conditions represent the inequality constraints in the model, including Equations (5.13)-(5.19), (5.21), (5.25); the third row of equality constraints includes Equations (5.2), (5.3), (5.6), (5.20), (5.22); the fourth row corresponds to Equations (5.1), (5.5), (5.8).

[0169] 3. Weight Coefficient Correction Method Based on Improved Analytic Hierarchy Process

[0170] In order to fully consider the energy storage characteristics during the optimization process, the present invention will consider the frequency regulation assessment rules and the output characteristics of the thermal energy storage system, analyze the improvement of each frequency regulation sub-index by energy storage and the impact on the comprehensive frequency regulation performance index, and thus establish a hierarchical structure model of frequency regulation performance. Secondly, combined with the optimal frequency regulation performance model in Embodiment 3.1 of the present invention, solve the comprehensive frequency regulation performance index under different weight coefficients δ, reconstruct the judgment matrix, and finally synthesize the weights of each layer to correct the weight coefficients of each frequency regulation sub-index. 3.1 Hierarchical structure model considering frequency regulation assessment rules and output characteristics of thermal energy storage system

[0171] The purpose of correcting the weight coefficients of each frequency regulation sub-index is to improve the frequency regulation effect of the combined thermal energy storage system. At the same time, the target layer of the hierarchical structure model is generally the predetermined target of the problem. Therefore, the comprehensive frequency regulation performance index is set as the target layer of the hierarchical structure model.

[0172] When the thermal power unit is combined with energy storage to participate in frequency regulation, it is necessary to give full play to the advantages of the energy storage regulation performance and avoid the disadvantages of the limited energy storage power in order to maximize the system frequency regulation performance. The assessment focuses of the three frequency regulation sub-indices of response delay, regulation rate and regulation error are different, and the degree of cooperation with the energy storage characteristics is also different. During the AGC frequency regulation process, the assessment of response time and regulation error requires higher regulation performance of frequency regulation resources, and energy storage has advantages in regulation performance. However, the regulation rate is the assessment of the system power ramp. The power of the energy storage configured for the thermal power unit generally does not exceed 3% of the thermal power installed capacity, and the auxiliary effect of energy storage on power ramp is not good. The regulation rate index is mainly limited by the ramp rate of the thermal power unit. It can be seen that the improvement effects of energy storage on each index are different. The middle layer of the hierarchical structure model is the intermediate link designed to achieve the goal, mainly some indexes and criteria. Therefore, each frequency regulation sub-index is used as the middle layer of the hierarchical structure model.

[0173] In addition, during a complete AGC frequency regulation, the assessment of the three frequency regulation sub-indices of response time, regulation rate and regulation error is carried out successively. Each frequency regulation sub-index is not completely independent, but there are mutual influences. On the basis of considering the different improvement effects of energy storage on each index and considering the mutual influences between each frequency regulation sub-index, the corrected weight coefficients can further improve the comprehensive frequency regulation performance index of the combined thermal energy storage system. The bottom layer of the hierarchical structure model is to achieve the predetermined goal at a deeper level. Therefore, the mutual influence between each frequency regulation sub-index is used as the bottom layer of the hierarchical structure model.

[0174] To sum up, according to the respective advantages of the thermal energy storage system and the frequency regulation sub-index assessment rules, taking frequency regulation performance as the goal, considering the advantages of each frequency regulation sub-index and the thermal energy storage system characteristics and the mutual influences between each frequency regulation sub-index during the assessment process, a hierarchical structure model is established.

[0175] 3.2 Reconstruct judgment matrix

[0176] Due to the subjective influence of decision-makers, the proportions of different factors in the judgment matrix are different. The numbers 1-9 and their reciprocals are used as scales to define the judgment matrix. The typical analytic hierarchy process constructs a judgment matrix through expert decision-making, which relies too much on expert strategies and experience, and the result is too subjective. To reduce the subjective factors of the analytic hierarchy process, the present invention combines the combined thermal energy storage frequency regulation optimization model based on the frequency modulation performance assessment rule in 3.1, calculates the improvement effect of each frequency modulation sub-index on the comprehensive frequency modulation performance index under the auxiliary action of energy storage, and reconstructs the judgment matrix.

[0177] First, establish a combined thermal energy storage frequency regulation optimization model with different frequency modulation sub-indices as the objectives, solve the output scheme of the combined thermal energy storage system, and calculate the average comprehensive frequency modulation performance index of each model.

[0178]

[0179]

[0180] In the formula, δ d indicates that the weights of each frequency modulation sub-index are consistent; δ d1 , δ d2 , δ d3 respectively represent the weight coefficient vectors when each frequency modulation sub-index is the main weight target, where a >> b; solve the optimization models of different objective functions respectively, and calculate the comprehensive frequency modulation performance index of the combined system under different weight coefficients according to the obtained output scheme, denoted as K, K B1 , K B2 , K B3 .

[0181] Secondly, normalize the change amount of the comprehensive frequency modulation performance index with the weight coefficient of different frequency modulation sub-indices as the judgment matrix scale, and the specific formula is as follows:

[0182]

[0183]

[0184] In the formula, ΔK Bi is the change amount of the comprehensive frequency modulation performance index; ΔK B,max and ΔK B,min are the maximum and minimum values of ΔK Bi respectively; represents the judgment matrix scale after reconstruction.

[0185] Finally, the constructed frequency modulation performance judgment matrix A is as follows:

[0186]

[0187] Similarly, the response time, regulation rate, and regulation error judgment matrices can be obtained.

[0188] 3.3 Weights W of each frequency modulation sub-index

[0189] Each column in the judgment matrix approximately reflects the distribution of the weights. First, the arithmetic mean of all column vectors is used to calculate the weight coefficients of each layer as follows:

[0190]

[0191] In the formula, W i is the weight coefficient of each layer, a ij is the element of the reconstructed judgment matrix in 3.2, and n is the number of columns of the judgment matrix.

[0192] Finally, considering the weights of the bottom-layer judgment matrix for the middle layer and the weights of the middle layer for the target layer, the comprehensive weight vector of each frequency modulation sub-index is calculated through Equation (5.35):

[0193] W = W B T W A (5.35)

[0194] In the formula, W is the comprehensive weight coefficient vector; W B is the weight matrix of the bottom layer relative to the middle layer; W A is the weight column vector of the middle layer relative to the target layer.

[0195] The above is only the preferred embodiment of the present invention and is not a limitation on the present invention in other forms. Any person skilled in the art may use the disclosed technical content to make changes or modifications into equivalent embodiments with equivalent changes. However, any simple modification, equivalent change, and modification made to the above embodiments based on the technical essence of the present invention without departing from the technical solution content of the present invention still fall within the protection scope of the technical solution of the present invention.

[0196] This patent is not limited to the above best implementation manner. Anyone inspired by this patent can obtain various other forms of the two-stage combined frequency modulation optimization method for thermal energy storage based on weight coefficient correction. All equal changes and modifications made according to the scope of the patent application of the present invention shall fall within the scope covered by this patent.

Claims

1. A two-stage combined frequency regulation optimization method for thermal energy storage based on weight coefficient correction, characterized in that: First, aiming at the frequency regulation assessment rules, three frequency regulation sub-indices of response time, regulation rate, and regulation error are established, and a combined frequency regulation optimization model for thermal energy storage considering frequency regulation performance is established. The Big-M method and variable substitution method are used to linearize the conditional constraints and absolute value constraints in the index model, converting the non-linear optimization problem into a mixed-integer linear programming problem that is easy to solve, so as to construct a first-stage optimization model considering frequency regulation performance; On this basis, in order to reduce the over-limit of energy storage SOC and deep charge and discharge conditions, a second-stage optimization model is constructed with the minimum deviation of energy storage SOC as the goal, thereby establishing a two-stage combined frequency regulation optimization model for thermal energy storage to output the optimized energy storage output; The frequency regulation performance assessment indicators include: The response time index d1 is the ratio of the response time of the frequency regulation unit to the average delay time of the frequency regulation of the units in the area. The smaller the value, the better the assessment performance of the response time of the frequency regulation unit; the response time of the frequency regulation unit represents the response delay when the combined thermal energy storage system fails to meet the response requirements after the frequency regulation command is issued. Among them, when the output of the combined thermal energy storage system positively crosses 15% of the frequency regulation mileage, it is regarded as meeting the response requirements; The regulation rate index d2 is the ratio of the average regulation rate of the units in the area to the regulation rate of the frequency regulation unit. The smaller the value, the better the assessment performance of the regulation rate of the frequency regulation unit; among them, the regulation rate of the frequency regulation unit represents the rate at which the frequency regulation unit responds to the frequency regulation command; The regulation error index d3 is the ratio of the regulation error of the frequency regulation unit to the average regulation error of the units in the area. The smaller the value, the better the assessment performance of the regulation error of the frequency regulation unit; among them, the regulation error of the frequency regulation unit represents the deviation between the final stable output of the frequency regulation unit and the frequency regulation command, and is represented by accumulating the absolute value of the difference between the actual output and the target value; The mathematical model of the energy storage includes: To achieve the management of the energy storage SOC, by introducing the energy storage SOC deviation S d , the value of which represents the degree to which the energy storage SOC deviates from the middle value of the SOC. The formula is as follows: S d = |S n - S mid |; S n is the energy storage SOC at the end of the nth sampling moment, S mid is the intermediate value of the energy storage SOC, taken as 0.5; The two-stage combined frequency regulation optimization model for thermal energy storage includes: Weigh the three frequency regulation sub-indices of the response time index d1, the regulation rate index d2, and the regulation error index d3 to establish a first-stage frequency regulation optimization model considering frequency regulation performance. The objective function is as follows: minδ1d1+δ2d2+δ3d3; δ1, δ2, and δ3 are the weight coefficients of each frequency regulation sub-index; The non-linear constraints in the energy storage mathematical model are linearized by the big-M method and variable substitution method and converted into a mixed-integer linear programming problem: In order to avoid the decline of the combined system frequency regulation performance caused by energy storage SOC management, the first-stage optimization result of the first-stage optimization model is used as the constraint condition of the second-stage optimization model: δ T d ≤ δ T d * ; δ T d * For the objective function δ in the first-stage optimization model T The minimum value of d; The second-stage optimization model takes the minimum deviation of energy storage SOC as the goal, solves the energy storage output scheme, and establishes a second-stage optimization model considering energy storage SOC management; By considering the output characteristics of the thermal energy storage system, the weight coefficients of the frequency regulation sub-indices are corrected in combination with the improved analytic hierarchy process to further improve the frequency regulation performance of the combined frequency regulation optimization model for thermal energy storage.

2. The method for optimizing the combined frequency regulation of thermal energy storage in two stages based on weight coefficient correction according to claim 1, wherein: The method of correcting the weight coefficients of the frequency regulation sub-indices by considering the output characteristics of the thermal energy storage system and combining with the improved analytic hierarchy process to further improve the frequency regulation performance of the combined frequency regulation optimization model for thermal energy storage specifically includes: In order to incorporate the energy storage characteristics into the optimization process, the frequency regulation assessment rules and the output characteristics of the thermal energy storage system are considered, the improvement of each frequency regulation sub-index by energy storage and the impact on the comprehensive frequency regulation performance index are analyzed. Based on this, a hierarchical structure model of frequency regulation performance is established, and combined with the optimal frequency regulation performance model, the comprehensive frequency regulation performance index under different weight coefficients δ is solved, the judgment matrix is reconstructed, and finally the weights of each layer are synthesized to correct the weight coefficients of each frequency regulation sub-index: Hierarchical structure model considering frequency regulation assessment rules and the output characteristics of the thermal energy storage system: Set the comprehensive frequency regulation performance index as the target layer of the hierarchical structure model; Take each frequency regulation sub-index as the middle layer of the hierarchical structure model; Take the mutual influence of each frequency regulation sub-index as the bottom layer of the hierarchical structure model; Based on the respective advantages of the thermal energy storage system and the frequency regulation sub-index assessment rules, taking frequency regulation performance as the goal, considering the characteristics and advantages of each frequency regulation sub-index and the thermal energy storage system, as well as the mutual influence between each frequency regulation sub-index during the assessment process, a hierarchical structure model is established; Reconstruct the judgment matrix: Combine the combined frequency regulation optimization model of the thermal energy storage based on the frequency regulation performance assessment rules, calculate the improvement effect of each frequency regulation sub-index on the comprehensive frequency regulation performance index under the auxiliary role of energy storage, and reconstruct the judgment matrix.

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