Load optimal distribution method and device considering frequency safety under deep peak regulation

Abstract

The invention discloses a load optimization distribution method and device considering frequency safety under deep peak regulation. The method comprises the following steps: constructing a first objective function with the lowest total coal consumption and a second objective function with the lowest NOx emission; constructing a single-objective optimization function based on the first objective function and the second objective function; constructing general constraint conditions by using power balance constraint, output upper and lower limit constraint and primary frequency modulation capability, and constructing frequency safety constraint; according to the single-target optimization function, the general constraint condition and the frequency security constraint, constructing a load optimization distribution model considering the primary frequency modulation capability; and performing optimization calculation based on the load optimization distribution model to obtain a load optimization distribution result.

Description

TECHNICAL FIELD

[0001] The present application relates to the technical field of power system operation mode screening, and more particularly, to a load optimization distribution method and device considering frequency safety under deep peak regulation. BACKGROUND

[0002] The penetration rate of fluctuating power sources such as wind power and photovoltaic power continues to rise, making the inertia support capacity of the power system continue to weaken, and the frequency regulation demand grows exponentially. As the traditional main power source, thermal power units are transforming from base load power sources to regulation-type power sources, and deep peak regulation operation has become the norm. However, the primary frequency modulation capability of the unit under low load conditions is significantly deteriorated, the regulation rate is reduced, and the dynamic response time is extended by 2-3 times, which seriously restricts the frequency recovery capability of the power grid. However, existing researches focus on the frequency modulation characteristics under conventional load, and lack of quantitative evaluation and optimization of the primary frequency modulation capability of the unit under deep peak regulation, so a load optimization distribution method considering frequency safety under deep peak regulation is proposed, which is of great significance for reducing the regulation pressure of the unit and maintaining the safe and stable operation of the power grid.

[0003] There is a lack of quantitative evaluation index for system frequency stability under deep peak regulation, and there is a lack of optimization for load distribution under deep peak regulation. Therefore, the impact of deep peak regulation of thermal power units on frequency modulation capability and system frequency stable operation needs to be considered, an active regulation capability evaluation index is proposed, an evaluation model is constructed, and on this basis, a system load distribution optimization model considering the primary frequency modulation capability of deep regulation units is proposed to improve the system frequency stability under deep peak regulation. SUMMARY

[0004] In view of the deficiencies of the prior art, the present application provides a load optimization distribution method and device considering frequency safety under deep peak regulation.

[0005] According to one aspect of the present application, a load optimization distribution method considering frequency safety under deep peak regulation is provided, comprising:

[0006] A first objective function with the lowest total coal consumption and a second objective function with the lowest NOx emission are constructed;

[0007] A single-objective optimization function is constructed based on the first objective function and the second objective function;

[0008] General constraint conditions are constructed based on power balance constraints, upper and lower output constraints and primary frequency modulation capability, and frequency safety constraints are constructed;

[0009] A load optimization distribution model considering primary frequency modulation capability is constructed according to the single-objective optimization function, the general constraint conditions and the frequency safety constraints;

[0010] Based on the load optimization allocation model, optimization calculation is performed to obtain a load optimization allocation result.

[0011] Optionally, the construction of the second objective function with the lowest NOx emission amount includes:

[0012] When the system completes one frequency modulation and the unit reaches a static stable state, the NOx emission amount power is fitted based on the NOx emission characteristic curve;

[0013] The second objective function with the lowest NOx emission amount is constructed according to the NOx emission amount power.

[0014] Optionally, the expression of the single-target optimization function is:

[0015]

[0016] In the formula, ζ m is the unit price of standard coal; V k is the flue gas emission amount under the rated operating condition of each unit; ζ n is the NOx emission unit price; C k (P k ) is the coal consumption expression of the kth frequency modulation unit with respect to power; g k (P k ) is the NOx emission amount of the kth unit with respect to power.

[0017] Optionally, the expression of the general constraint condition is:

[0018]

[0019] P k,min ≤ P k ≤ P k,max

[0020] In the formula, P z is the load amount at the load end; P k,min is the minimum unit output; P k,max is the minimum unit output; P k is the power of the kth unit, and n is the number of units.

[0021] Optionally, the construction of the frequency safety constraint includes:

[0022] For the key parameters affecting the active regulation capacity of the thermal power unit, the parameter sensitivity analysis boundary is determined;

[0023] Based on Matlab, the values are taken at equal intervals within the parameter sensitivity analysis boundary of the difference parameters, the parameters are modified, and dynamic parameter files are respectively generated to construct massive unit parameter data;

[0024] Based on the PSD Power Tools standard parameter library, a single-machine infinite system is constructed to perform power flow calculation and obtain a power flow calculation file;

[0025] A frequency difference disturbance is set, and based on the PSD Power Tools batch calculation of the dynamic parameter files and the power flow calculation files generated by each difference parameter, the peak value, steady-state value and power increase rising time of the active power variation curve of the thermal power unit are extracted, and a difference parameter variation sequence, a maximum active power variation sequence of the thermal power unit, a steady-state active power sequence of the thermal power unit and a power increase rising time sequence of the thermal power unit are generated for each difference parameter;

[0026] Using a polynomial fitting method, based on the difference parameter variation sequence, the maximum active power variation sequence of the thermal power unit, the steady-state active power sequence of the thermal power unit and the power increase rising time sequence of the thermal power unit, a time-domain fitting curve function of the active power peak value of the thermal power unit with respect to the difference parameter variation sequence, a time-domain fitting curve function of the steady-state active power of the thermal power unit with respect to the difference parameter variation and a time-domain fitting curve function of the power increase rising time of the thermal power unit with respect to the difference parameter variation are obtained;

[0027] Based on the time-domain fitting curve function of the active power peak value of the thermal power unit with respect to the difference parameter variation sequence and the time-domain fitting curve function of the steady-state active power of the thermal power unit with respect to the difference parameter variation, a maximum active power regulation amplitude index and a steady-state active power regulation amplitude index of the thermal power unit are constructed;

[0028] Based on the time-domain fitting curve function of the power increase rising time of the thermal power unit with respect to the difference parameter variation, a thermal power unit active power regulation speed index is constructed;

[0029] According to the maximum active power regulation amplitude index, the steady-state active power regulation amplitude index and the thermal power unit active power regulation speed index, a comprehensive quantitative index of the thermal power unit active power regulation capability is constructed;

[0030] The comprehensive quantitative index of the thermal power unit active power regulation capability is normalized to obtain a normalized comprehensive quantitative index;

[0031] Based on the normalized comprehensive quantitative index, a mapping relationship between different load rates and primary frequency modulation capabilities is constructed;

[0032] Based on the mapping relationship and the primary frequency modulation reserve capacity index, a frequency safety constraint is constructed.

[0033] Optionally, the mapping relationship is:

[0034]

[0035] P = 1.5 * (Pmax - Pmin)k P k is the power of the kth unit; P k is the rated power of the kth unit; p1, p2, p3, p4 are coefficients of the cubic polynomial, P k is the normalized primary frequency modulation capacity comprehensive quantification index of the kth unit.

[0036] Optionally, the expression of the frequency safety constraint is:

[0037]

[0038] In the formula, P k is the primary frequency modulation reserve capacity index; P k is the primary frequency modulation capacity quantification index under the set working condition.

[0039] According to another aspect of the present application, a load optimization distribution device considering frequency safety under deep peak regulation is provided, comprising:

[0040] A first construction module is configured to construct a first target function with the lowest total coal consumption and a second target function with the lowest NOx emission;

[0041] A second construction module is configured to construct a single-target optimization function based on the first target function and the second target function;

[0042] A third construction module is configured to construct general constraint conditions with power balance constraints, upper and lower output constraints and primary frequency modulation capacity, and construct frequency safety constraints;

[0043] A fourth construction module is configured to construct a load optimization distribution model considering primary frequency modulation capacity based on the single-target optimization function, the general constraint conditions and the frequency safety constraints;

[0044] A calculation module is configured to perform optimization calculation based on the load optimization distribution model to obtain a load optimization distribution result.

[0045] Optionally, the construction step of the second target function with the lowest NOx emission in the first construction module comprises:

[0046] When the system completes primary frequency modulation and the unit reaches a static stable state, fitting the NOx emission power based on the NOx emission characteristic curve;

[0047] Constructing the second target function with the lowest NOx emission according to the NOx emission power.

[0048] Optionally, the expression of the single-target optimization function is:

[0049]

[0050] In the formula, ζm V represents the standard coal unit price; k This refers to the flue gas emissions of each unit under rated operating conditions; ζ n NOx emission unit price; C k (P k Let g be the expression for the coal consumption of the k-th frequency regulating unit with respect to power; k (P k ) represents the NOx emissions of the k-th unit with respect to power.

[0051] Alternatively, the expression for a general constraint is:

[0052]

[0053] P k,min ≤P k ≤P k,max

[0054] In the formula, P z P represents the load at the load end. k,min P represents the minimum output of the generator unit. k,max P represents the minimum output of the generator unit. k Let n be the power of the k-th unit, and n be the number of units.

[0055] According to another aspect of the present invention, a computer-readable storage medium is provided, the storage medium storing a computer program for performing the method of any of the above aspects of the present invention.

[0056] According to another aspect of the present invention, an electronic device is provided, the electronic device comprising: a processor; a memory for storing executable instructions of the processor; the processor being configured to read the executable instructions from the memory and execute the instructions to implement the method described in any of the preceding aspects of the present invention.

[0057] Therefore, this invention provides a load optimization allocation method considering frequency security under deep peak shaving, aiming to optimize the load allocation scheme of thermal power units and solve the problem of insufficient active power regulation capacity of the system by addressing the frequency security and stability issues under the current deep peak shaving state. By considering the impact of deep peak shaving of thermal power on frequency regulation capability and stable system frequency operation, an active power regulation amplitude index considering peak value and steady-state value, and an active power regulation speed index considering rise time of power curve, are constructed, along with a dynamic evaluation model integrating multiple deep peak shaving parameters. An optimization model for the primary frequency regulation capability of deep peak shaving units is also constructed, and an optimization model considering primary frequency regulation capability constraints is proposed for load allocation under deep peak shaving. The proposed load optimization allocation method considering frequency security under deep peak shaving can reduce the regulation pressure on units and improve the safe and stable operation capability of the power grid. Attached Figure Description

[0058] Exemplary embodiments of the present invention can be more fully understood by referring to the following figures:

[0059] Figure 1 This is a flowchart illustrating a load optimization allocation method considering frequency security under deep peak shaving, provided by an exemplary embodiment of the present invention.

[0060] Figure 2 This is another flowchart illustrating a load optimization allocation method considering frequency security under deep peak shaving provided by an exemplary embodiment of the present invention;

[0061] Figure 3 This is a schematic diagram of the structure of a load optimization and allocation device considering frequency security under deep peak shaving provided by an exemplary embodiment of the present invention;

[0062] Figure 4 This is the structure of an electronic device provided in an exemplary embodiment of the present invention. Detailed Implementation

[0063] Hereinafter, exemplary embodiments according to the present invention will be described in detail with reference to the accompanying drawings. Obviously, the described embodiments are merely some embodiments of the present invention, and not all embodiments of the present invention. It should be understood that the present invention is not limited to the exemplary embodiments described herein.

[0064] It should be noted that, unless otherwise specifically stated, the relative arrangement, numerical expressions, and values ​​of the components and steps described in these embodiments do not limit the scope of the invention.

[0065] Those skilled in the art will understand that the terms "first," "second," etc., in the embodiments of the present invention are only used to distinguish different steps, devices, or modules, and do not represent any specific technical meaning, nor do they indicate a necessary logical order between them.

[0066] It should also be understood that in the embodiments of the present invention, "multiple" can refer to two or more, and "at least one" can refer to one, two or more.

[0067] It should also be understood that any component, data or structure mentioned in the embodiments of the present invention can generally be understood as one or more unless explicitly defined or given contrary instructions in the context.

[0068] Furthermore, the term "and / or" in this invention is merely a description of the relationship between related objects, indicating that three relationships can exist. For example, A and / or B can represent: A existing alone, A and B existing simultaneously, or B existing alone. Additionally, the character " / " in this invention generally indicates that the preceding and following related objects have an "or" relationship.

[0069] It should also be understood that the description of the various embodiments in this invention emphasizes the differences between the various embodiments, and the similarities or similarities can be referred to each other. For the sake of brevity, they will not be described in detail.

[0070] At the same time, it should be understood that, for ease of description, the dimensions of the various parts shown in the accompanying drawings are not drawn according to actual scale.

[0071] The following description of at least one exemplary embodiment is merely illustrative and is in no way intended to limit the invention or its application or use.

[0072] Techniques, methods, and equipment known to those skilled in the art may not be discussed in detail, but where appropriate, they should be considered part of the specification.

[0073] It should be noted that similar labels and letters in the following figures indicate similar items; therefore, once an item is defined in one figure, it does not need to be discussed further in subsequent figures.

[0074] The embodiments of this invention can be applied to electronic devices such as terminal devices, computer systems, and servers, and can operate together with a wide range of other general-purpose or special-purpose computing system environments or configurations. Well-known examples of terminal devices, computing systems, environments, and / or configurations suitable for use with electronic devices such as terminal devices, computer systems, and servers include, but are not limited to: personal computer systems, server computer systems, thin clients, thick clients, handheld or laptop devices, microprocessor-based systems, set-top boxes, programmable consumer electronics, network PCs, minicomputer systems, mainframe computer systems, and distributed cloud computing environments including any of the above systems, etc.

[0075] Electronic devices such as terminal devices, computer systems, and servers can be described in the general context of computer system executable instructions (such as program modules) executed by a computer system. Typically, program modules can include routines, programs, object programs, components, logic, data structures, etc., which perform specific tasks or implement specific abstract data types. Computer systems / servers can be implemented in distributed cloud computing environments, where tasks are executed by remote processing devices linked through communication networks. In distributed cloud computing environments, program modules can reside on local or remote computing system storage media, including storage devices.

[0076] Exemplary method

[0077] Figure 1This is a flowchart illustrating a load optimization allocation method considering frequency security under deep peak shaving, provided by an exemplary embodiment of the present invention. This embodiment can be applied to electronic devices, such as... Figure 1 As shown, the load optimization allocation method 100 considering frequency security under deep peak shaving includes the following steps:

[0078] Step 101: Construct a first objective function that minimizes total coal consumption and a second objective function that minimizes NOx emissions;

[0079] Step 102: Construct a single-objective optimization function based on the first objective function and the second objective function;

[0080] Step 103: Construct general constraints based on power balance constraints, upper and lower limits of output constraints, and primary frequency regulation capability, and construct frequency security constraints.

[0081] Step 104: Based on the single-objective optimization function, general constraints, and frequency security constraints, construct a load optimization allocation model that considers primary frequency regulation capability.

[0082] Step 105: Perform optimization calculations based on the load optimization allocation model to obtain the load optimization allocation results.

[0083] Specifically, the primary frequency regulation capability of generating units deteriorates significantly under low-load conditions, with a decrease in regulation rate and a 2-3 fold increase in dynamic response time, severely restricting the grid's frequency recovery capability. However, existing research mainly focuses on frequency regulation characteristics under conventional loads, lacking quantitative assessment and optimization of the primary frequency regulation capability of generating units under deep peak-shaving conditions. Optimizing load allocation schemes can effectively reduce the frequency regulation burden of thermal power units and improve system frequency stability. Therefore, this paper proposes a load optimization allocation method considering frequency security under deep peak-shaving conditions to reduce the regulation pressure on generating units and maintain the safe and stable operation of the power grid.

[0084] This invention provides a load optimization allocation method considering frequency security under deep peak shaving. It aims to optimize the load allocation scheme of thermal power units to address the frequency security and stability issues under current deep peak shaving conditions, and solve the problem of insufficient active power regulation capacity of the system. Considering the impact of deep peak shaving on frequency regulation capacity and stable system frequency operation, a dynamic evaluation model integrating multiple deep peak shaving parameters is constructed based on active power regulation amplitude indicators such as peak value and steady-state value, and active power regulation speed indicators such as curve rise time. Furthermore, an optimization model for the primary frequency regulation capacity of deep peak shaving units is constructed, and an optimization model considering primary frequency regulation capacity constraints is proposed for load allocation under deep peak shaving.

[0085] The present invention provides a load optimization allocation method considering frequency security under deep peak shaving, the method comprising the following steps:

[0086] Step 1: Peak shaving depth directly affects the operating efficiency and coal consumption characteristics of the unit. To measure the economic efficiency of load allocation for thermal power units under deep peak shaving, the following optimization objective function is constructed with the minimum total coal consumption as the fit:

[0087]

[0088] In the formula:

[0089]

[0090] In the formula: C k (P k Let P be the expression for the coal consumption (t / h) of the k-th frequency regulating unit with respect to power; k Let a be the power of the k-th unit; k b k c k The coefficients are those of the quadratic function.

[0091] Step 2: Low-temperature corrosion of the tail flue and decreased efficiency of the denitrification system under low load conditions will generate a large amount of NOx. Therefore, to ensure economic efficiency and minimize emissions, after the system completes primary frequency regulation and the unit reaches a static steady state, NOx emissions are considered based on the NOx emission characteristic curve, building upon Step 1. The fitted NOx emission power is given by the following cubic equation:

[0092]

[0093] In the formula: gk(P k ) represents the NOx emissions (mg / m3) of the k-th unit with respect to power; α k ,β k γ k , λ k where are the coefficients of the terms in the cubic equation.

[0094] Step 3: Construct the objective function that minimizes NOx emissions as follows:

[0095]

[0096] Step 4: Unify the objective function of minimizing the total coal consumption of the system and the objective function of minimizing NOx emissions into a single-objective optimization model.

[0097]

[0098] In the formula, ζ m The price is the standard coal unit price (yuan / ton); V k The flue gas emission (m3 / h) of each unit under rated operating conditions; ζ n The price per ton of NOx emissions is [amount in yuan].

[0099] Step 5: Construct general constraints based on power balance constraints, upper and lower output limits, and primary frequency regulation capability:

[0100]

[0101] P k,min ≤P k ≤P k,max (7)

[0102] In the formula: P z P represents the load at the load end. k,min P represents the minimum output of the generator unit. k,max This is the minimum output of the generator unit.

[0103] Step 5: Constructing Frequency Security Constraints:

[0104] Step 5.1: For key parameters affecting the active power regulation capability of thermal power units, including speed deviation amplification factor, PID proportional element coefficient, PID integral element coefficient, load control feedforward coefficient, steam volume time constant, reheater time constant, and high-pressure cylinder power natural overshoot coefficient, the model parameter values ​​under normal active power output level and deep peak active power output level are respectively taken as the upper and lower bounds of the parameters. Based on experience, the values ​​are appropriately extended to both sides to fully cover the parameter variation range. The extended parameter values ​​are used as the boundary of parameter sensitivity analysis.

[0105] Step 5.2: Based on the PSD Power Tools standard parameter library, build a single-machine infinite power system, perform power flow calculations, and obtain the power flow calculation file;

[0106] Step 5.3: Based on Matlab, sample values ​​at equal intervals within the sensitivity analysis boundary of the difference parameters, modify the parameters and generate dynamic parameter files respectively to construct massive unit parameter data;

[0107] Step 5.4: Set up frequency difference disturbances. Based on the dynamic parameter files generated by batch processing of each difference parameter using PSD Power Tools, extract the peak value, steady-state value, and power increase rise time of the active power change curve of the thermal power unit. Generate four sets of parameter sequences for each difference parameter, which are the difference parameter change sequences:

[0108] X i ={x i,1 ,x i,2 ,...,x i,j ,...x i,n} (8)

[0109] The sequence of maximum changes in active power of thermal power units:

[0110] Y i ={yi,1 ,y i,2 ,...,y i,j ,...y i,n} (9)

[0111] Steady-state sequence of active power changes in thermal power units:

[0112] Z i ={z i,1 ,z i,2 ,...,z i,j ,...z i,n} (10)

[0113] And the rise time of active power generation of thermal power units:

[0114] W i ={w i,1 ,w i,2 ,...,w i,j ,...w i,n} (11)

[0115] In the formula: subscript i represents the i-th parameter; subscript j represents the j-th parameter in the equal-interval sensitivity analysis; subscript n represents the number of equally interval values ​​for a single parameter.

[0116] Step 5.5: Use the polynomial fitting method to obtain the time-domain fitting curve function of the peak active power output of the thermal power unit with respect to the variation of the difference parameter.

[0117]

[0118] The time-domain fitting curve function of the steady-state value of active power output of thermal power units with respect to the variation of difference parameters is as follows:

[0119]

[0120] The time-domain fitting curve function of the rise time of active power output increase of thermal power units with respect to the variation of difference parameters is as follows:

[0121]

[0122] In the formula: the superscript '^' indicates an approximate value.

[0123] Step 5.6: After obtaining the fitting curve functions of each difference parameter, construct the maximum adjustment range index F of the active power of the thermal power unit. A1 With the steady-state regulation amplitude index F of active power of thermal power units A2 as follows:

[0124]

[0125] In the formula: the superscript '*' represents the measured value of the difference parameter; m represents the number of difference parameters.

[0126] Step 5.7: Based on the fitted curve function of each difference parameter, construct the active power regulation speed index F of the thermal power unit. S as follows:

[0127]

[0128] Step 5.8: Based on the adjustment amplitude and adjustment speed indices, construct the comprehensive quantitative index η of the active power regulation capability of thermal power units as follows:

[0129]

[0130] In the formula: α is the weight of the maximum active power adjustment range index in the active power adjustment range index; β is the weight of the active power steady-state adjustment range index in the active power adjustment range index; and γ is the weight of the active power adjustment speed index in the active power adjustment capability index.

[0131] The normalization process is based on the comprehensive quantitative index of the active power regulation capability of thermal power units:

[0132]

[0133] In the formula:

[0134]

[0135]

[0136] Step 5.9: The load factor is the ratio of the unit's current power to its rated power. To ensure that the deep peak-shaving unit has sufficient frequency regulation capability under different load conditions, based on the comprehensive quantitative evaluation model of primary frequency regulation capability, a mapping relationship between different load factors and primary frequency regulation capability is constructed, and a cubic polynomial expression is generated as follows:

[0137]

[0138] In the formula: P k Let k be the power of the kth unit; Let p1 be the rated power of the k-th unit; p1, p2, p3, and p4 are the coefficients of the cubic polynomial. This is a comprehensive quantitative index of the primary frequency regulation capability of the k-th generating unit after normalization.

[0139] Step 5.10: The primary frequency regulation reserve capacity requirement is 2% to 5% of the total power system capacity, which translates to the following evaluation indicators: Construct primary frequency regulation capability constraints:

[0140]

[0141] In the formula:

[0142]

[0143] In the formula: This refers to the reserve capacity indicator for primary frequency regulation; To quantify the primary frequency regulation capability under the set operating conditions. Considering other methods such as new energy sources, the total system capacity can be integrated into the combined capacity of a single unit at 2% to 5%. If the required reserve capacity is 4%, then it is only necessary to calculate and sum the primary frequency regulation indicators of each unit under specific operating conditions.

[0144] Step 6: Combining the objective function and various constraints, the final load optimization allocation model considering primary frequency regulation capability is as follows:

[0145]

[0146] Step 7: Optimize the calculation based on the model to obtain the load allocation results.

[0147] In a specific embodiment of the present invention, an implementation process for an active power regulation capability assessment method for thermal power units considering deep peak shaving is given, and its effectiveness is verified by analyzing the measured parameters of a certain actual thermal power unit under normal operation and deep peak shaving operation.

[0148] The effectiveness of the proposed algorithm has not been verified. Load optimization allocation was performed on four generating units of a real power plant. The primary frequency regulation reserve capacity is required to be no less than 4% of the total capacity. The specific parameters of the coal consumption coefficient and NOx emission coefficient of each unit are shown in Table 1.

[0149] Table 1. Unit Coal Consumption Rate and NOx Emission Coefficient

[0150]

[0151] The primary frequency regulation capability varies with different load rates. Based on MATLAB curve fitting, a cubic polynomial fitting was selected, and the p1, p2, p3, and p4 in the relationship of primary frequency regulation capability under different load rates were found to be -0.536, -0.148, 1.593, and -0.05, respectively. The expression after substituting the coefficients is as follows:

[0152]

[0153] The total primary frequency regulation reserve capacity of the power system is set at 4% of the total capacity, which translates to an evaluation index of 4 × 0.013 = 0.052 in this paper. The optimized load allocation model is solved, and the optimized loads of the generating units are shown in Table 1.

[0154] Table 1. Results of Calculation for 40% Load Factor and 4% Load Allocation

[0155]

[0156] Therefore, this invention provides a load optimization allocation method considering frequency security under deep peak shaving, aiming to optimize the load allocation scheme of thermal power units and solve the problem of insufficient active power regulation capacity of the system by addressing the frequency security and stability issues under the current deep peak shaving state. By considering the impact of deep peak shaving of thermal power on frequency regulation capability and stable system frequency operation, an active power regulation amplitude index considering peak value and steady-state value, and an active power regulation speed index considering rise time of power curve, are constructed, along with a dynamic evaluation model integrating multiple deep peak shaving parameters. An optimization model for the primary frequency regulation capability of deep peak shaving units is also constructed, and an optimization model considering primary frequency regulation capability constraints is proposed for load allocation under deep peak shaving. The proposed load optimization allocation method considering frequency security under deep peak shaving can reduce the regulation pressure on units and improve the safe and stable operation capability of the power grid.

[0157] Exemplary apparatus

[0158] Figure 3 This is a schematic diagram of a load optimization allocation device considering frequency security under deep peak shaving, provided in an exemplary embodiment of the present invention. Figure 3 As shown, the device 300 includes:

[0159] The first construction module 310 is used to construct a first objective function that minimizes total coal consumption and a second objective function that minimizes NOx emissions;

[0160] The second construction module 320 is used to construct a single-objective optimization function based on the first objective function and the second objective function;

[0161] The third construction module 330 is used to construct general constraint conditions based on power balance constraints, upper and lower limit constraints of output and primary frequency regulation capability, and to construct frequency security constraints.

[0162] The fourth construction module 340 is used to construct a load optimization allocation model that considers primary frequency regulation capability based on the single-objective optimization function, general constraints, and frequency security constraints.

[0163] The calculation module 350 is used to perform optimization calculations based on the load optimization allocation model to obtain the load optimization allocation results.

[0164] Optionally, the construction step of the second objective function with the lowest NOx emissions in the first building module 310 includes:

[0165] When the system completes one frequency regulation and the unit reaches a static stable state, the NOx emission power is fitted based on the NOx emission characteristic curve.

[0166] A second objective function is constructed based on the power of NOx emissions to minimize NOx emissions.

[0167] Optionally, the expression for the single-objective optimization function is:

[0168]

[0169] In the formula, ζ m V represents the standard coal unit price; k This refers to the flue gas emissions of each unit under rated operating conditions; ζ n NOx emission unit price; C k (P k Let g be the expression for the coal consumption of the k-th frequency regulating unit with respect to power; k (P k ) represents the NOx emissions of the k-th unit with respect to power.

[0170] Alternatively, the expression for a general constraint is:

[0171]

[0172] P k,min ≤P k ≤P k,max

[0173] In the formula, P z P represents the load at the load end. k,min P represents the minimum output of the generator unit. k,max P represents the minimum output of the generator unit. k Let n be the power of the k-th unit, and n be the number of units.

[0174] Optionally, the construction steps of the frequency security constraints in the third building module 330 include:

[0175] For the key parameters affecting the active power regulation capability of thermal power units, the boundary of parameter sensitivity analysis is determined;

[0176] Based on Matlab, values ​​are sampled at equal intervals within the boundary of parameter sensitivity analysis of differential parameters, parameters are modified and dynamic parameter files are generated respectively to construct massive unit parameter data;

[0177] Based on the PSD Power Tools standard parameter library, a single-machine infinite power system is built to perform power flow calculations and obtain power flow calculation files.

[0178] Set up frequency difference disturbance, and use PSD Power Tools to batch process and calculate the dynamic parameter file and power flow calculation file generated by each difference parameter. Extract the peak value, steady state value and power generation rise time of the active power change curve of the thermal power unit. Generate the difference parameter change sequence, the maximum value sequence of active power change of the thermal power unit, the steady state value sequence of active power of the thermal power unit, and the rise time sequence of active power generation of the thermal power unit for each difference parameter.

[0179] Using a polynomial fitting method, based on the sequence of differential parameter changes, the sequence of maximum active power changes of thermal power units, the sequence of steady-state active power values ​​of thermal power units, and the sequence of rising active power generation of thermal power units, the time-domain fitting curve functions of peak active power output of thermal power units with respect to the sequence of differential parameter changes, the time-domain fitting curve functions of steady-state active power output of thermal power units with respect to the sequence of differential parameter changes, and the time-domain fitting curve functions of rising active power generation of thermal power units with respect to the sequence of differential parameter changes are obtained.

[0180] Based on the time-domain fitting curve function of the peak active power output of thermal power units with respect to the variation sequence of differential parameters and the time-domain fitting curve function of the steady-state active power output of thermal power units with respect to the variation of differential parameters, the maximum adjustment range index of active power of thermal power units and the steady-state adjustment range index of active power of thermal power units are constructed.

[0181] Based on the time-domain fitting curve function of the rise time of active power output increase of thermal power units with respect to the variation of differential parameters, an active power regulation speed index of thermal power units is constructed.

[0182] Based on the maximum adjustment range index of active power of thermal power units, the steady-state adjustment range index of active power of thermal power units, and the adjustment speed index of active power of thermal power units, a comprehensive quantitative index of the active power adjustment capability of thermal power units is constructed.

[0183] The comprehensive quantitative index of the active power regulation capability of thermal power units is normalized to obtain the normalized comprehensive quantitative index.

[0184] Based on the normalized comprehensive quantitative index, a mapping relationship between different load rates and primary frequency regulation capability is constructed.

[0185] Frequency security constraints are constructed based on the mapping relationship and the primary frequency regulation reserve capacity index.

[0186] Optionally, the mapping relationship is as follows:

[0187]

[0188] In the formula, P k Let k be the power of the kth unit; Let p1 be the rated power of the k-th unit; p1, p2, p3, and p4 are the coefficients of the cubic polynomial. This is a comprehensive quantitative index of the primary frequency regulation capability of the k-th generating unit after normalization.

[0189] Optionally, the expression for the frequency security constraint is:

[0190]

[0191] In the formula, This refers to the reserve capacity indicator for primary frequency regulation; To set quantitative indicators for primary frequency regulation capability under operating conditions.

[0192] Exemplary electronic device

[0193] Figure 4 This is the structure of an electronic device provided in an exemplary embodiment of the present invention. For example... Figure 4 As shown, the electronic device 40 includes one or more processors 41 and a memory 42.

[0194] The processor 41 may be a central processing unit (CPU) or other form of processing unit with data processing and / or instruction execution capabilities, and may control other components in the electronic device to perform desired functions.

[0195] The memory 42 may include one or more computer program products, which may include various forms of computer-readable storage media, such as volatile memory and / or non-volatile memory. The volatile memory may include, for example, random access memory (RAM) and / or cache memory. The non-volatile memory may include, for example, read-only memory (ROM), hard disk, flash memory, etc. One or more computer program instructions may be stored on the computer-readable storage medium, and the processor 41 may execute the program instructions to implement the methods of the software programs of the various embodiments of the present invention described above, and / or other desired functions. In one example, the electronic device may also include an input device 43 and an output device 44, these components being interconnected via a bus system and / or other forms of connection mechanisms (not shown).

[0196] In addition, the input device 43 may also include, for example, a keyboard, a mouse, etc.

[0197] The output device 44 can output various information to the outside. The output device 44 may include, for example, a display, a speaker, a printer, and a communication network and its connected remote output devices, etc.

[0198] Of course, for the sake of simplicity, Figure 4Only some of the components of this electronic device relevant to the present invention are shown, omitting components such as buses, input / output interfaces, etc. In addition, the electronic device may include any other suitable components depending on the specific application.

[0199] Exemplary computer program product and computer readable storage medium

[0200] In addition to the methods and apparatus described above, embodiments of the present invention may also be computer program products, which include computer program instructions that, when executed by a processor, cause the processor to perform the steps in the methods according to various embodiments of the present invention described in the "Exemplary Methods" section above.

[0201] The computer program product can be written in any combination of one or more programming languages ​​to perform the operations of the embodiments of the present invention. The programming languages ​​include object-oriented programming languages ​​such as Java and C++, as well as conventional procedural programming languages ​​such as C or similar languages. The program code can be executed entirely on the user's computing device, partially on the user's computing device, as a standalone software package, partially on the user's computing device and partially on a remote computing device, or entirely on a remote computing device or server.

[0202] Furthermore, embodiments of the present invention may also be computer-readable storage media storing computer program instructions thereon, which, when executed by a processor, cause the processor to perform the steps of the methods according to various embodiments of the present invention described in the "Exemplary Methods" section above.

[0203] The computer-readable storage medium may be any combination of one or more readable media. A readable medium may be a readable signal medium or a readable storage medium. A readable storage medium may be, for example, an electrical, magnetic, optical, electromagnetic, infrared, or semiconductor system, device, or any combination thereof. More specific examples (a non-exhaustive list) of readable storage media include: an electrical connection having one or more wires, a portable disk, a hard disk, random access memory (RAM), read-only memory (ROM), erasable programmable read-only memory (EPROM or flash memory), optical fiber, portable compact disk read-only memory (CD-ROM), optical storage device, magnetic storage device, or any suitable combination thereof.

[0204] The basic principles of the present invention have been described above with reference to specific embodiments. However, it should be noted that the advantages, benefits, and effects mentioned in the present invention are merely examples and not limitations, and should not be considered as essential features of each embodiment of the present invention. Furthermore, the specific details disclosed above are for illustrative and facilitative purposes only, and are not limitations. These details do not limit the present invention to the necessity of employing the aforementioned specific details.

[0205] The various embodiments in this specification are described in a progressive manner, with each embodiment focusing on its differences from other embodiments. Similar or identical parts between embodiments can be referred to interchangeably. For system embodiments, since they largely correspond to method embodiments, the description is relatively simple; relevant parts can be referred to the descriptions in the method embodiments.

[0206] The block diagrams of devices, systems, devices, and systems involved in this invention are merely illustrative examples and are not intended to require or imply that they must be connected, arranged, or configured in the manner shown in the block diagrams. As those skilled in the art will recognize, these devices, systems, devices, and systems can be connected, arranged, and configured in any manner. Words such as “comprising,” “including,” “having,” etc., are open-ended terms meaning “including but not limited to,” and are used interchangeably with them. The terms “or” and “and” as used herein refer to the terms “and / or,” and are used interchangeably with them unless the context clearly indicates otherwise. The term “such as” as used herein refers to the phrase “such as but not limited to,” and is used interchangeably with it.

[0207] The methods and systems of the present invention may be implemented in many ways. For example, they may be implemented by software, hardware, firmware, or any combination of software, hardware, and firmware. The above-described order of steps for the methods is for illustrative purposes only, and the steps of the methods of the present invention are not limited to the order specifically described above unless otherwise specifically stated. Furthermore, in some embodiments, the present invention may also be implemented as a program recorded on a recording medium, the program comprising machine-readable instructions for implementing the methods according to the present invention. Thus, the present invention also covers recording media storing programs for performing the methods according to the present invention.

[0208] It should also be noted that in the systems, apparatus, and methods of the present invention, the components or steps can be disassembled and / or recombined. These disassemblies and / or recombinations should be considered equivalents of the present invention. The above description of the disclosed aspects is provided to enable any person skilled in the art to make or use the invention. Various modifications to these aspects will be readily apparent to those skilled in the art, and the general principles defined herein can be applied to other aspects without departing from the scope of the invention. Therefore, the invention is not intended to be limited to the aspects shown herein, but rather to be carried out within the widest scope consistent with the principles and novel features disclosed herein.

[0209] The above description has been given for purposes of illustration and description. Furthermore, this description is not intended to limit the embodiments of the invention to the forms disclosed herein. Although numerous exemplary aspects and embodiments have been discussed above, those skilled in the art will recognize certain variations, modifications, alterations, additions, and sub-combinations thereof.

Claims

1. A load optimization allocation method considering frequency security under deep peak shaving, characterized in that, include: Construct a first objective function that minimizes total coal consumption and a second objective function that minimizes NOx emissions; A single-objective optimization function is constructed based on the first objective function and the second objective function; General constraints are constructed using power balance constraints, upper and lower limits of output constraints, and primary frequency regulation capability, and frequency security constraints are also constructed. Based on the single-objective optimization function, the general constraints, and the frequency security constraints, a load optimization allocation model considering primary frequency regulation capability is constructed. Based on the aforementioned load optimization allocation model, optimization calculations are performed to obtain the load optimization allocation results.

2. The method according to claim 1, characterized in that, The steps for constructing the second objective function with the lowest NOx emissions include: When the system completes one frequency regulation and the unit reaches a static stable state, the NOx emission power is fitted based on the NOx emission characteristic curve. Based on the NOx emission power, construct the second objective function that minimizes NOx emissions.

3. The method according to claim 1, characterized in that, The expression for the single-objective optimization function is: In the formula, ζ m V represents the standard coal unit price; k This refers to the flue gas emissions of each unit under rated operating conditions; ζ n NOx emission unit price; C k (P k Let g be the expression for the coal consumption of the k-th frequency regulating unit with respect to power; k (P k ) represents the NOx emissions of the k-th unit with respect to power.

4. The method according to claim 1, characterized in that, The expression for the general constraint is: P k,min ≤P k ≤P k,max In the formula, P z P represents the load at the load end. k,min P represents the minimum output of the generator unit. k,max P represents the minimum output of the generator unit. k Let n be the power of the k-th unit, and n be the number of units.

5. The method according to claim 1, characterized in that, The steps for constructing the frequency security constraints include: For the key parameters affecting the active power regulation capability of thermal power units, the boundary of parameter sensitivity analysis is determined; Based on Matlab, values ​​are sampled at equal intervals within the boundary of the sensitivity analysis of the difference parameters, the parameters are modified and dynamic parameter files are generated respectively to construct a massive amount of unit parameter data; Based on the PSD Power Tools standard parameter library, a single-machine infinite power system is built to perform power flow calculations and obtain power flow calculation files. Frequency difference disturbance is set up, and dynamic parameter files and power flow calculation files generated by batch calculation of each difference parameter based on PSD PowerTools are used to extract the peak value, steady state value and power generation rise time of the active power change curve of thermal power unit. For each difference parameter, a difference parameter change sequence, a sequence of maximum active power change of thermal power unit, a sequence of steady state active power of thermal power unit, and a sequence of active power generation rise time of thermal power unit are generated. Using a polynomial fitting method, based on the sequence of changes in the difference parameters, the sequence of maximum changes in the active power of thermal power units, the sequence of steady-state values ​​of active power of thermal power units, and the sequence of rising time of increased active power of thermal power units, the time-domain fitting curve functions of the peak active power output of thermal power units with respect to the sequence of changes in the difference parameters, the time-domain fitting curve functions of the steady-state values ​​of active power output of thermal power units with respect to the changes in the difference parameters, and the time-domain fitting curve functions of the rising time of increased active power output of thermal power units with respect to the changes in the difference parameters are obtained. Based on the time-domain fitting curve function of the peak active power output of the thermal power unit with respect to the change sequence of the difference parameter and the time-domain fitting curve function of the steady-state active power output of the thermal power unit with respect to the change of the difference parameter, the maximum adjustment range index of active power of the thermal power unit and the steady-state adjustment range index of active power of the thermal power unit are constructed. Based on the time-domain fitting curve function of the rise time of the active power output of the thermal power unit with respect to the variation of the difference parameter, an active power regulation speed index of the thermal power unit is constructed. Based on the maximum adjustment range index of active power of thermal power units, the steady-state adjustment range index of active power of thermal power units, and the adjustment speed index of active power of thermal power units, a comprehensive quantitative index of the active power adjustment capability of thermal power units is constructed. The comprehensive quantitative index of the active power regulation capability of the thermal power unit is normalized to obtain the normalized comprehensive quantitative index. Based on the normalized comprehensive quantitative index, a mapping relationship between different load rates and primary frequency regulation capability is constructed. Based on the mapping relationship and the primary frequency regulation reserve capacity index, the frequency security constraints are constructed.

6. The method according to claim 5, characterized in that, The mapping relationship is as follows: In the formula, P k Let k be the power of the kth unit; Let p1 be the rated power of the k-th unit; p1, p2, p3, and p4 are the coefficients of the cubic polynomial. This is a comprehensive quantitative index of the primary frequency regulation capability of the k-th generating unit after normalization.

7. The method according to claim 6, characterized in that, The expression for the frequency security constraint is: In the formula, This refers to the reserve capacity indicator for primary frequency regulation; This is a quantitative indicator of primary frequency regulation capability under set operating conditions; n is the number of generating units.

8. A load optimization and allocation device considering frequency security under deep peak shaving, characterized in that, include: The first building module is used to construct a first objective function that minimizes total coal consumption and a second objective function that minimizes NOx emissions; The second construction module is used to construct a single-objective optimization function based on the first objective function and the second objective function; The third construction module is used to construct general constraint conditions based on power balance constraints, upper and lower limit constraints of output and primary frequency regulation capability, and to construct frequency security constraints. The fourth construction module is used to construct a load optimization allocation model that considers primary frequency regulation capability based on the single-objective optimization function, the general constraints, and the frequency security constraints. The calculation module is used to perform optimization calculations based on the load optimization allocation model to obtain the load optimization allocation result.

9. The apparatus according to claim 8, characterized in that, The construction steps of the second objective function in the first building module, which aims to minimize NOx emissions, include: When the system completes one frequency regulation and the unit reaches a static stable state, the NOx emission power is fitted based on the NOx emission characteristic curve. Based on the NOx emission power, construct the second objective function that minimizes NOx emissions.

10. The apparatus according to claim 8, characterized in that, The expression for the single-objective optimization function is: In the formula, ζ m V represents the standard coal unit price; k This refers to the flue gas emissions of each unit under rated operating conditions; ζ n NOx emission unit price; C k (P k Let g be the expression for the coal consumption of the k-th frequency regulating unit with respect to power; k (P k ) represents the NOx emissions of the k-th unit with respect to power.

11. The apparatus according to claim 8, characterized in that, The expression for the general constraint is: P k,min ≤P k ≤P k,max In the formula, P z P represents the load at the load end. k,min P represents the minimum output of the generator unit. k,max P represents the minimum output of the generator unit. k Let n be the power of the k-th unit, and n be the number of units.

12. A computer-readable storage medium, characterized in that, The storage medium stores a computer program for performing the method described in any one of claims 1-7.

13. An electronic device, characterized in that, The electronic device includes: processor; Memory used to store the processor's executable instructions; The processor is configured to read the executable instructions from the memory and execute the instructions to implement the method described in any one of claims 1-7.

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