Unit combination optimization method considering frequency constraint and wind-storage frequency modulation control

Abstract

The invention belongs to the technical field of power system unit commitment optimization, and particularly relates to a unit commitment optimization method considering frequency constraint and wind-storage frequency modulation control, which comprises the following steps: constructing system frequency safety constraint; constructing a unit commitment optimization model meeting wind storage frequency modulation and system frequency security constraints; and based on the unit commitment optimization model, unit commitment optimization considering frequency constraint and wind-storage frequency modulation control is realized. The output of each power supply in each time period is controlled through the constraint of the lowest point of the frequency, so as to improve the adjustment capability of each power supply, achieve the stable frequency of a power grid, avoid the large fluctuation of the frequency caused by the randomness of wind power, improve the absorption rate of the wind power, promote the utilization of green energy, and improve the economic benefits, thereby promoting the sustainable development of a power system.

Description

Technical Field

[0001] The present invention belongs to the technical field of power system unit combination optimization, and in particular relates to a unit combination optimization method considering frequency constraints and wind-storage frequency regulation control. Background Art

[0002] Wind energy resources have huge storage capacity and zero pollution characteristics, and are sustainable clean energy. Therefore, wind power has become a key component of new energy power generation and has a very broad development prospect. However, while wind power provides clean energy for production and life, its uncertainty and weak inertia characteristics have brought new challenges to the frequency security of the power system. Therefore, it is of great significance to study how to coordinate the operation of multiple units to ensure that the system has sufficient primary frequency regulation capabilities to cope with frequency fluctuations after the system is disturbed.

[0003] The existing technology in the study of unit combination considering frequency constraints is mainly divided into two aspects: one is the construction of frequency safety constraints, and the other is the construction of unit combination models for new energy participating in frequency regulation. For the study of power system operation optimization, the first thing that needs to be broken through is the construction of frequency safety constraints, which is the bridge connecting frequency analysis and time domain optimization.

[0004] The results of relevant analysis show that it is very important to consider frequency security when selecting the optimal economic dispatch scheme. When considering the unit combination with frequency constraints, the construction of frequency security constraints is mainly based on the average system frequency (ASF) or system frequency response (SFR) model of a single machine to establish the maximum frequency change rate constraint, the lowest frequency constraint and the steady-state frequency constraint, without considering the frequency response capability differences of different units in the multi-machine system; the traditional unit combination focuses on the balance between load and supply and demand of conventional units, optimizes the start-stop and output strategies of conventional units, and thus reduces the system power generation cost. However, the traditional method does not fully consider the frequency security constraints and the integration of new energy; in the research on the participation of wind power and energy storage models in optimal dispatching, most of them are combined with thermal power units, and lack a frequency security constraint model that considers the participation of wind and energy storage systems in the thermal power unit combination model.

[0005] The existing technology handles the frequency minimum point constraint through piecewise linearization, boundary method and discrete method. These methods have high solution accuracy, but the processing process is relatively cumbersome. In addition, there is a method based on time domain simulation to solve the frequency minimum point after disturbance, establish a multi-machine frequency response model including wind power, and obtain the minimum point frequency through SIMULINK simulation and embed it into the intelligent optimization algorithm to solve the unit combination model. However, this method is relatively complex to simulate the frequency response model and the solution efficiency is not high.

[0006] Therefore, a unit combination optimization method considering frequency constraints and wind-storage frequency regulation control is urgently needed. By establishing a unit combination type considering frequency constraints and wind-storage frequency regulation control, the impact of different load disturbances on unit combination planning can be quantified. Based on this model, the output of each power source in each time period can be controlled through the frequency minimum point constraint to improve the regulation capacity of various power sources, achieve grid frequency stability, avoid large frequency fluctuations caused by the randomness of wind power, and at the same time increase the wind power absorption rate, promote the utilization of green energy, and improve economic benefits, thereby promoting the sustainable development of the power system. Summary of the invention

[0007] The object of the present invention is to provide a unit commitment optimization method considering frequency constraints and wind-storage frequency regulation control, which is characterized by comprising the following steps:

[0008] Step S1: construct system frequency safety constraints;

[0009] Step S2: constructing a unit commitment optimization model that satisfies wind-storage frequency regulation and the system frequency safety constraints;

[0010] Step S3: Based on the unit combination optimization model, the unit combination optimization considering frequency constraints and wind-storage frequency regulation control is realized.

[0011] The construction of the system frequency safety constraint in step S1 includes:

[0012] Step S11: define the thermal power unit and the wind power and energy storage frequency regulation process under virtual droop control;

[0013] Step S12: Build a multi-machine frequency response model by combining the thermal power unit, the wind power unit and the energy storage system;

[0014] Step S13: replacing the speed regulator time constants of all units in the transfer function of the multi-machine frequency response model with the same value, and bringing in a step load disturbance when a sudden load increase occurs in the system to obtain a frequency expression Δf(s);

[0015] Step S14: performing an inverse Laplace transform on the frequency domain expression Δf(s) to obtain a time domain expression Δf(t);

[0016] Step S15: Derivative the time domain expression Δf(t), let dΔf / dt, and obtain the time t corresponding to the lowest frequency point m ;

[0017] Step S16: The time t corresponding to the lowest frequency point m Substituting back into the time domain expression Δf(t), we obtain the time domain expression for the maximum frequency deviation;

[0018] Step S17: Approximate the frequency response curve as a non-attenuated sine curve to obtain the instantaneous frequency change rate of the disturbance; combined with the maximum frequency deviation Δf m , defines the time t to reach the maximum frequency deviation m ; Bring the frequency change into the frequency regulation process of thermal power units and wind power and energy storage under virtual droop control, and perform inverse Laplace transform to obtain the frequency regulation power of the unit at the lowest point; Construct the system frequency regulation output power constraint.

[0019] The frequency regulation process of the thermal power unit and the wind power and energy storage under virtual droop control in step S11 includes:

[0020]

[0021]

[0022]

[0023] Where ΔP G (s), ΔP W (s) and ΔP E (s) are the active power changes of thermal power units, fans and energy storage in the frequency domain; K H , K W , and K E are the gain coefficients of thermal power units, fans and energy storage respectively; T RH , T W and T E are the time constants of thermal power unit, fan and energy storage respectively; F HP is the power coefficient of the high-pressure cylinder of the thermal power unit; R H is the regulation coefficient of the thermal power unit speed governor; Δf(s) is the frequency change in the frequency domain.

[0024] The transfer function of the multi-machine frequency response model in step S12 is:

[0025]

[0026] Where ΔP L (s) is the complex frequency domain variation of system load disturbance, K Hi is the mechanical power gain coefficient of the i-th thermal power unit, R Hi is the adjustment coefficient of the speed governor of the i-th thermal power unit, F HPi is the power coefficient of the high-pressure cylinder of the i-th thermal power unit, T RHi is the time constant of the i-th thermal power unit, K Wm is the droop control coefficient of the mth wind turbine generator set, T Wm is the time constant of the speed regulator of the mth wind turbine group, H eqis the system equivalent inertia time constant and D is the load damping coefficient, H eq The expression is:

[0027]

[0028] Where: S Hi is the rated capacity of the i-th thermal power unit; H Hi is the inertia time constant of the i-th thermal power unit.

[0029] The frequency expression Δf(s) in step S13 is:

[0030]

[0031]

[0032] Where: f is the natural frequency; ξ is the damping ratio; R T and F T An intermediate variable for convenient calculation.

[0033] The time domain expression Δf(t) in step S14 is:

[0034]

[0035] Where:

[0036]

[0037] The time t corresponding to the lowest frequency point in step S15 m for:

[0038]

[0039] The time domain expression of the maximum frequency deviation in step S16 is:

[0040] .

[0042] The frequency modulation power of the unit at the lowest point in step S17 is:

[0043]

[0044] Where ΔP jz (t m ) is at t m Total FM power at the moment; ΔP H,i (t m ) is the power regulation of the i-th thermal power unit; ΔP W,i (t m ) is at tm The frequency modulation power of the i-th wind turbine at the moment; P E (t m ) is at t m Total frequency modulation power stored at all times;

[0045] The frequency modulation output power constraint of the constructed system is:

[0046] ΔP jz (t m )+DΔf m ≥ΔP L (18)

[0047] The unit commitment optimization model in step S2 is:

[0048] The objective function of the unit combination optimization model is: minimizing the daily operating cost; the daily operating cost includes: thermal power generation cost, thermal power start-up and shutdown cost, wind power abandonment cost and energy storage charging and discharging cost;

[0049] The expression of the daily operating cost is:

[0050]

[0051] Where: a i 、b i 、c i and are the power generation cost coefficients of the i-th thermal power unit; T is the dispatch period. When dispatching on the day before, T is 24; n is the number of thermal power units; u i,t is the operating status of the i-th thermal power unit at time t, 1 means it is on, and 0 means it is off; B i is the startup cost of the i-th thermal power unit; P H,i,t is the output of the i-th thermal power unit in the t-th period; r j,t is the wind curtailment rate of the j-th wind turbine at time t; P w,i,t is the wind turbine output, m is the number of wind turbines; z is the wind abandonment cost coefficient; P w,i,t Output power for wind turbines; i,ch,t , p i,dis,t are the charging power and discharging power of the energy storage in time period t respectively; c ch , c dis They are the charging cost coefficient and discharging benefit coefficient of energy storage respectively;

[0052] The thermal power generation cost is processed as follows:

[0053]

[0054] P i =P0+PI i1 +···+PI i4(twenty one)

[0055] 0≤PI i1 ≤p H,i1,t -p H,i0,t ,···,0≤PI i4 ≤p H,i4,t -p H,i3,t (twenty two)

[0056] The unit commitment optimization model in step S2 satisfies the system frequency safety constraints and operation constraints;

[0057] The operating constraints include:

[0058] (1) Power balance constraints:

[0059]

[0060] Where: P d,t is the load demand at time t;

[0061] (2) Output constraints of thermal power units:

[0062] p H,i,min ≤p H,i,t ≤p H,i,max (twenty four)

[0063] Where: P H,i,max and P H,i,min are the upper and lower limits of thermal power unit output respectively;

[0064] (3) Reserve power constraints:

[0065]

[0066] (4) Climbing constraints:

[0067] -R i,down ≤p H,i,t -p H,i,(t-1) ≤R i,up (26)

[0068] Where: R i,down and R i,up are the upward and downward climbing rates of the thermal power units, respectively;

[0069] (5) Start and stop time constraints:

[0070]

[0071] Where: and are the continuous operation and shutdown time of the thermal power unit at time t respectively; and are the minimum start and stop times of thermal power units respectively;

[0072] (6) Node active power balance:

[0073]

[0074] Where: B i,j is the mutual susceptance of nodes i and j after removing the grounding branch; P i,t is the injected power of node i; is the voltage phase angle of node i;

[0075] (7) Line transmission power constraints:

[0076]

[0077] Where: is the active power of line ij; is the safety limit; X ij is the reactance of line ij;

[0078] (8) Reserved capacity for primary frequency regulation of wind turbines:

[0079] ΔP w =P w ·r w (30)

[0080] Where P w is the maximum power generation of the wind turbine, r w is the wind abandonment rate of the wind turbine;

[0081] (9) Wind power output constraints:

[0082]

[0083] Where: The upper limit of wind power output;

[0084] (10) Energy storage constraints:

[0085]

[0086]

[0087] Where: soc i,t is the state of charge of the energy storage; E i,rate is the rated capacity of the energy storage; SOC min and SOC max are the minimum and maximum values ​​of the energy storage charge state respectively; η i,ch and η i,dis are the charging and discharging efficiency of energy storage; P ch,max and P dis,maxare the maximum charging power and the maximum discharging power respectively; d i,ch and d i,dis are the operating state charging and discharging variables of the energy storage, d i,ch =1 at the same time d i,dis =0 means charging, d i,ch =0 while d i,dis =1 means discharge.

[0088] The beneficial effects of the present invention are:

[0089] The present invention discloses a unit combination optimization method considering frequency constraints and wind-storage frequency regulation control. According to a multi-machine model in which wind power and energy storage simultaneously participate in frequency control, an expression of system frequency change containing wind-storage frequency regulation is derived, and the lowest point frequency constraint is obtained by the equivalent method, and a unit combination optimization model of wind-storage frequency regulation and frequency constraint is constructed. In the process of power system optimization scheduling, if a sudden change in load disturbance occurs, the unit combination considering frequency constraints proposed in the present invention shows a smaller dynamic frequency change rate, a higher frequency lowest point, and a smaller steady-state frequency deviation compared to the unit combination not considering frequency constraints, and also improves the wind power consumption rate. Such a model can avoid low-frequency load reduction operations, is more suitable for the scheduling process of actual wind power power systems, and meets the frequency constraint requirements. Compared with the prior art, the unit combination optimization model using wind-storage frequency regulation and frequency constraints described in the present invention has advantages in terms of dynamic frequency change rate, frequency lowest point accuracy, and steady-state frequency deviation. BRIEF DESCRIPTION OF THE DRAWINGS

[0090] Figure 1 A schematic flow chart of a method for optimizing unit combination considering frequency constraints and wind-storage frequency regulation control according to the present invention;

[0091] Figure 2 A schematic diagram of a multi-machine frequency response model according to an embodiment of the present invention;

[0092] Figure 3 is a schematic diagram of a frequency response curve of an embodiment of the present invention;

[0093] Figure 4 It is a schematic diagram of piecewise linearization of power generation cost according to an embodiment of the present invention;

[0094] Figure 5 Schematic diagram of verification experiment results under three working conditions of an embodiment of the present invention. DETAILED DESCRIPTION

[0095] The present invention provides a unit combination optimization method taking frequency constraints and wind-storage frequency regulation control into consideration. The present invention is further described in detail below in conjunction with the accompanying drawings.

[0096] like Figure 1The embodiment of the present invention shown discloses a unit combination optimization method considering frequency constraints and wind-storage frequency regulation control, comprising:

[0097] Step S1: construct system frequency safety constraints;

[0098] Step S2: constructing a unit commitment optimization model that satisfies wind-storage frequency regulation and the system frequency safety constraints;

[0099] Step S3: Based on the unit combination optimization model, the unit combination optimization considering frequency constraints and wind-storage frequency regulation control is realized.

[0100] The construction of the system frequency safety constraint in step S1 includes:

[0101] Step S11: define the thermal power unit and the wind power and energy storage frequency regulation process under virtual droop control;

[0102] Step S12: Build a multi-machine frequency response model by combining the thermal power unit, the wind power unit and the energy storage system;

[0103] Step S13: replacing the speed regulator time constants of all units in the transfer function of the multi-machine frequency response model with the same value, and bringing in a step load disturbance when a sudden load increase occurs in the system to obtain a frequency expression Δf(s);

[0104] Step S14: performing an inverse Laplace transform on the frequency domain expression Δf(s) to obtain a time domain expression Δf(t);

[0105] Step S15: Derivative the time domain expression Δf(t), let dΔf / dt, and obtain the time t corresponding to the lowest frequency point m ;

[0106] Step S16: The time t corresponding to the lowest frequency point m Substituting back into the time domain expression Δf(t), we obtain the time domain expression for the maximum frequency deviation;

[0107] Step S17: Approximate the frequency response curve as a non-attenuated sine curve to obtain the instantaneous frequency change rate of the disturbance; combined with the maximum frequency deviation Δf m , defines the time t to reach the maximum frequency deviation m ; Bring the frequency change into the frequency regulation process of thermal power units and wind power and energy storage under virtual droop control, and perform inverse Laplace transform to obtain the frequency regulation power of the unit at the lowest point; Construct the system frequency regulation output power constraint.

[0108] According to the multi-machine model in which wind power and energy storage participate in frequency control at the same time, the expression of system frequency change including wind-storage frequency regulation is derived. Starting from the frequency response model of each power source, the equivalent method is used to obtain the lowest point frequency constraint, and a unit combination optimization model based on wind-storage frequency regulation and frequency constraints is constructed.

[0109] 1. The construction of system frequency safety constraints is as follows:

[0110] The frequency regulation process of the thermal power unit and the wind power and energy storage under virtual droop control in step S11 includes:

[0111]

[0112]

[0113]

[0114] Where ΔP G (s), ΔP W (s) and ΔP E (s) are the active power changes of thermal power units, fans and energy storage in the frequency domain; K H , K W , and K E are the gain coefficients of thermal power units, fans and energy storage respectively; T RH , T W and T E are the time constants of thermal power unit, fan and energy storage respectively; F HP is the power coefficient of the high-pressure cylinder of the thermal power unit; R H is the regulation coefficient of the thermal power unit speed governor; Δf(s) is the frequency change in the frequency domain.

[0115] The thermal power unit, wind power unit and energy storage system are built to obtain a multi-machine frequency response model. The multi-machine frequency response model is as follows: Figure 2 As shown:

[0116] K H1 and K Hn are the mechanical power gain coefficients of the first and nth thermal power units respectively; R H1 and R Hn are the adjustment coefficients of the speed governors of the first and nth thermal power units respectively; F HP1 and F HPn are the power coefficients of the high-pressure cylinders of the first and nth thermal power units respectively; T HP1 and T HPn are the time constants of the speed governors of the first and nth thermal power units respectively; K w1 and K wm are the droop control coefficients of the first and mth wind turbines respectively; T W1and T Wn are the time constants of the speed regulators of the first and nth wind turbines respectively.

[0117] The multi-machine frequency response model describes the primary frequency regulation process of the inertial support of the thermal power unit and all thermal power units, wind power and energy storage. The transfer function of the multi-machine frequency response model is shown in formula (4):

[0118]

[0119] Where ΔP L (s) is the load disturbance in the system, K Hi is the mechanical power gain coefficient of the i-th thermal power unit, R Hi is the adjustment coefficient of the speed governor of the i-th thermal power unit, F HPi is the power coefficient of the high-pressure cylinder of the i-th thermal power unit, T RHi is the time constant of the i-th thermal power unit, K Wm is the droop control coefficient of the mth wind turbine generator set, T Wm is the time constant of the speed regulator of the mth wind turbine group, K E is the energy storage droop control coefficient, T E is the time constant of the energy storage inertial environment, H eq is the system equivalent inertia time constant, D is the load damping coefficient, H eq The expression is:

[0120]

[0121] Where: S Hi is the rated capacity of the i-th thermal power unit; H Hi is the inertia time constant of the i-th thermal power unit.

[0122] In this embodiment, within the acceptable error range, the time constant of each unit speed regulator has a relatively small effect on the system frequency variation. Therefore, the time constants of the speed regulators of all units in the transfer function of the multi-machine frequency response model are unified with the same value T eq Instead, simplify the formula to obtain:

[0123]

[0124] Assuming that a sudden load increase occurs in the system, then:

[0125]

[0126] Where ΔP L (t) is the load disturbance of the system in the time domain;

[0127] Substituting equation (7) into equation (6), we get the frequency expression Δf(s):

[0128]

[0129]

[0130] Where: f is the natural frequency; ξ is the damping ratio; R T and F T An intermediate variable for convenient calculation.

[0131] The time domain expression Δf(t) obtained by performing inverse Laplace transform on the frequency domain expression Δf(s) is:

[0132]

[0133] Where:

[0134]

[0135] Derivative the time domain expression Δf(t), set dΔf / dt = 0, and get the time t corresponding to the lowest frequency point m for:

[0136]

[0137] The time t corresponding to the lowest frequency point m Substituting back the time domain expression Δf(t), the time domain expression of the maximum frequency deviation is obtained as:

[0138]

[0139] In this embodiment, it can be seen from the above analysis that after the multi-machine frequency response model is affected by power shortage, the system frequency begins to decrease. In order to ensure that the system frequency is always within a safe range: N +Δf≥f min , where f min is the lowest value of the frequency after disturbance; but Δf(t m ) is a relatively complex nonlinear function and cannot be directly solved in integer programming problems. Therefore, the formula should be simplified. If Δf(t m )<f min -f N , then the frequency deteriorates seriously, the additional power is small, and a larger output power is required to ensure that the frequency is within the safety constraint. Therefore, the frequency safety constraint is equivalent to the frequency regulation output power being greater than the deficit power, and the frequency deviation constraint is converted into a frequency response power constraint, thereby achieving unity with the power optimization of the power system.

[0140] In this embodiment, the frequency response curve is as follows: Figure 3 As shown, the slope of OA is K, which is the rate of change of the instantaneous frequency of the power disturbance, and the straight line OB connecting the initial point to the lowest frequency point. The frequency response curve is approximately regarded as a non-attenuated sine curve. OA The slope of the straight line with OB is K OB The ratio is 2 / π, and the instantaneous frequency change rate of the disturbance is as shown in formula (14):

[0141]

[0142] Where H is the system inertia time constant;

[0143] but Combined with the maximum frequency deviation Δf m , the time to reach the maximum frequency deviation is t m It can be expressed as:

[0144]

[0145] Use the straight line OB to approximate Δf, then:

[0146]

[0147] Substituting equation (16) into equation (1)-(3) and performing inverse Laplace transformation, the frequency modulation power of the unit at the lowest point is obtained as:

[0148]

[0149] Where ΔP jz (t m ) is at t m Total FM power at the moment; ΔP H,i (t m ) is the power regulation of the i-th thermal power unit; ΔP W,i (t m ) is at t m The frequency modulation power of the i-th wind turbine at the moment; P E (t m ) is at t m Total frequency modulation power stored at all times;

[0150] Therefore, the system frequency modulation output power constraint is constructed as:

[0151] ΔP jz (t m )+DΔf m ≥ΔP L (18)

[0152] That is, if the system operating state meets the frequency constraint condition, when the generated power is ΔPL When there is a disturbance, the maximum frequency deviation of the system can be guaranteed to be within Δf m within the range.

[0153] 2. Based on the system frequency safety constraints, the unit commitment optimization model is constructed as follows:

[0154] The objective function of the unit combination optimization model is to minimize the daily operating cost, which includes four parts: thermal power generation cost, thermal power start-up and shutdown cost, wind power abandonment cost and energy storage charging and discharging cost. The expression of daily operating cost is:

[0155]

[0156] Where: a i 、b i 、c i and are the power generation cost coefficients of the i-th thermal power unit; T is the dispatch period. When dispatching on the day before, T is 24; n is the number of thermal power units; u i,t is the operating status of the i-th thermal power unit at time t, 1 means it is on, and 0 means it is off; B i is the startup cost of the i-th thermal power unit; P H,i,t is the output of the i-th thermal power unit in the t-th period; r j,t is the wind curtailment rate of the j-th wind turbine at time t; P w,i,t is the wind turbine output, m is the number of wind turbines; z is the wind abandonment cost coefficient; P w,i,t Output power for wind turbines; i,ch,t , p i,dis,t are the charging power and discharging power of the energy storage in time period t respectively; c ch , c dis are the charging cost coefficient and discharging profit coefficient of energy storage respectively;.

[0157] Since the power generation cost in the objective function is a quadratic convex function, the piecewise linearization method can be used to linearize it. The linearized cost is Figure 4 As shown in the figure, the output range of the i-th unit is linearized into 4 segments, with the power point p H,i0,t ~p H,i4,t As the interval endpoints, the length of each interval is PI i,1 , P.I. i,2 , P.I. i,3 , P.I. i,4 , the slope of each line segment is KI i,1 , K.I. i,2 , K.I. i,3 , K.I. i,4 Since the power generation cost function is a convex function, the slope of each segment satisfies KI i,1 ≤KI i,2 ≤KIi,3 ≤KI i,4。

[0158] The thermal power generation cost function is processed as follows:

[0159]

[0160] P i =P0+PI i1 +···+PI i4 (twenty one)

[0161] 0≤PI i1 ≤p H,i1,t -p H,i0,t ,···,0≤PI i4 ≤p H,i4,t -p H,i3,t (twenty two)

[0162] The unit commitment optimization model satisfies the system frequency safety constraints and operation constraints;

[0163] In addition to meeting the frequency safety constraints described in Section 4.1, the unit commitment also needs to meet the following operating constraints:

[0164] (1) Power balance constraints

[0165] In order to ensure the stable operation and power supply reliability of the system, the sum of the output power of all units must be equal to the load demand at any time:

[0166]

[0167] Where: P d,t is the load demand at time t.

[0168] (2) Output constraints of thermal power units

[0169] Thermal power generating units must meet certain output limits during operation:

[0170] p H,i,min ≤p H,i,t ≤p H,i,max (twenty four)

[0171] Where: P H,i,max and P H,i,min They are the upper and lower limits of the output of thermal power units respectively.

[0172] (3) Reserve power constraints

[0173] In order to ensure that the system can operate stably and promptly in the event of sudden load fluctuations or equipment failures, each thermal power unit must provide a certain degree of backup power:

[0174]

[0175] (4) Climbing constraints

[0176] In order to ensure that the thermal power units remain stable when responding to changes in system load and to avoid adverse effects on the system caused by too fast or too large output changes, it is necessary to limit the output rate that can be increased or decreased within a certain period of time:

[0177] -R i,down ≤p H,i,t -p H,i,(t-1) ≤R i,up (26)

[0178] Where: R i,down and R i,up They are the upward and downward climbing rates of the thermal power units respectively.

[0179] (5) Start and stop time constraints

[0180] Considering that when the system load changes or other demand changes, each unit can complete the start and stop on time and maintain the smooth operation of the system:

[0181]

[0182] Where: and are the continuous operation and shutdown time of the thermal power unit at time t respectively; and are the minimum start and stop time of thermal power units respectively.

[0183] (6) Node active power balance

[0184] At the nodes of the power system, all active power must be balanced:

[0185]

[0186] Where: B i,j is the mutual susceptance of nodes i and j after removing the grounding branch; P i,t is the injected power of node i; is the voltage phase angle at node i.

[0187] (7) Line transmission power constraints

[0188] In order to ensure that the transmission equipment in the power system operates within a safe range and prevent problems such as equipment overload and failure, it is necessary to reasonably set the transmission power constraints:

[0189]

[0190] Where: is the active power of line ij; is the safety limit; X ij is the reactance of line ij.

[0191] (8) Reserved capacity for primary frequency regulation of wind turbines

[0192] First, calculate the wind power used by the wind turbine for power generation, and then use the power margin left by the abandoned wind as the reserve capacity for wind power to participate in primary frequency regulation:

[0193] ΔP w =P w ·r w (30)

[0194] Where P w is the maximum power generation of the wind turbine, r w is the wind abandonment rate of the wind turbine;

[0195] (9) Wind power output constraints

[0196] Taking into account the operation of the power grid and the changes in wind speed, the output of wind power should be within certain limits:

[0197]

[0198] Where: It is the upper limit of wind power output.

[0199] (10) Energy storage constraints

[0200]

[0201]

[0202] Where: soc i,t E is the state-of-charge (SOC) of energy storage; i,rate is the rated capacity of the energy storage; SOC min and SOC max are the minimum and maximum values ​​of the energy storage charge state respectively; η i,ch and η i,dis are the charging and discharging efficiency of energy storage; P ch,max and P dis,max are the maximum charging power and the maximum discharging power respectively; d i,ch and d i,dis are the operating state charging and discharging variables of the energy storage, d i,ch =1 at the same time d i,dis =0 means charging, d i,ch =0 while d i,dis =1 means discharge.

[0203] In order to verify the effectiveness of the unit combination optimization method considering frequency constraints and wind-storage frequency regulation control disclosed in the present invention, the following verification experiment was carried out.

[0204] The verification experiment includes three working conditions, including:

[0205] Condition 1: Considering the unit combination of wind power storage participating in frequency regulation but not considering frequency safety constraints;

[0206] Working condition 2: Considering the unit combination of wind power participating in frequency regulation and frequency safety constraints;

[0207] Operating condition three: Unit combination that considers both wind storage participation in frequency regulation and frequency safety constraints.

[0208] The experimental results under three working conditions are as follows Figure 5 As shown, it is proved that the unit combination optimization model using wind-storage frequency regulation and frequency constraints described in the present invention has advantages in terms of dynamic frequency change rate, frequency minimum point accuracy and steady-state frequency deviation.

[0209] In summary, the present invention derives an expression for system frequency change containing wind-storage frequency regulation based on a multi-machine model in which wind power and energy storage participate in frequency control at the same time. Starting from the frequency response model of each power source, the equivalent method is used to obtain the lowest frequency constraint, and a unit combination optimization model that takes into account wind-storage frequency regulation and frequency constraints is constructed with the minimum daily operating cost as the objective function. By establishing a unit combination type that considers frequency constraints and wind-storage frequency regulation control, the impact of different load disturbances on unit combination planning is quantified. Based on this model, the output of each power source in each time period can be controlled through the frequency lowest point constraint to improve the regulation capacity of various power sources, achieve grid frequency stability, avoid large frequency fluctuations caused by the randomness of wind power, and at the same time, increase the wind power absorption rate, promote the use of green energy, and improve economic benefits, thereby promoting the sustainable development of the power system.

Claims

1. A unit commitment optimization method considering frequency constraints and wind-storage frequency regulation control, characterized in that: The steps include: Step S1: construct system frequency safety constraints; Step S2: constructing a unit commitment optimization model that satisfies wind-storage frequency regulation and the system frequency safety constraints; Step S3: Based on the unit combination optimization model, the unit combination optimization considering frequency constraints and wind-storage frequency regulation control is realized.

2. The unit commitment optimization method considering frequency constraints and wind-storage frequency regulation control according to claim 1 is characterized in that: The construction of the system frequency safety constraint in step S1 includes: Step S11: define the thermal power unit and the wind power and energy storage frequency regulation process under virtual droop control; Step S12: Build a multi-machine frequency response model by combining the thermal power unit, the wind power unit and the energy storage system; Step S13: replacing the speed regulator time constants of all units in the transfer function of the multi-machine frequency response model with the same value, and bringing in a step load disturbance when a sudden load increase occurs in the system to obtain a frequency expression Δf(s); Step S14: performing an inverse Laplace transform on the frequency domain expression Δf(s) to obtain a time domain expression Δf(t); Step S15: Derivative the time domain expression Δf(t), let dΔf / dt, and obtain the time t corresponding to the lowest frequency point m ; Step S16: The time t corresponding to the lowest frequency point m Substituting back into the time domain expression Δf(t), we obtain the time domain expression for the maximum frequency deviation; Step S17: Approximate the frequency response curve as a non-attenuated sine curve to obtain the instantaneous frequency change rate of the disturbance; combined with the maximum frequency deviation Δf m , defines the time t to reach the maximum frequency deviation m ; Bring the frequency change into the frequency regulation process of thermal power units and wind power and energy storage under virtual droop control, and perform inverse Laplace transform to obtain the frequency regulation power of the unit at the lowest point; Construct the system frequency regulation output power constraint.

3. The unit commitment optimization method considering frequency constraints and wind-storage frequency regulation control according to claim 2 is characterized in that: The frequency regulation process of the thermal power unit and the wind power and energy storage under virtual droop control in step S11 includes: In the formula, ΔP G (s), ΔP W (s) and ΔP E (s) are the active power changes of thermal power units, fans and energy storage in the frequency domain; K H , K W , and K E are the gain coefficients of thermal power units, fans and energy storage respectively; T RH , T W and T E are the time constants of thermal power unit, fan and energy storage respectively; F HP is the power coefficient of the high-pressure cylinder of the thermal power unit; R H is the regulation coefficient of the thermal power unit speed governor; Δf(s) is the frequency change in the frequency domain.

4. The unit commitment optimization method considering frequency constraints and wind-storage frequency regulation control according to claim 2 is characterized in that: The transfer function of the multi-machine frequency response model in step S12 is: In the formula, ΔP L (s) is the complex frequency domain variation of system load disturbance, K Hi is the mechanical power gain coefficient of the i-th thermal power unit, R Hi is the adjustment coefficient of the speed governor of the i-th thermal power unit, F HPi is the power coefficient of the high-pressure cylinder of the i-th thermal power unit, T RHi is the time constant of the i-th thermal power unit, K Wm is the droop control coefficient of the mth wind turbine generator set, T Wm is the time constant of the speed regulator of the mth wind turbine group, H eq is the system equivalent inertia time constant and D is the load damping coefficient, H eq The expression is: Where: S Hi is the rated capacity of the i-th thermal power unit; H Hi is the inertia time constant of the i-th thermal power unit.

5. The unit commitment optimization method considering frequency constraints and wind-storage frequency regulation control according to claim 2 is characterized in that: The frequency expression Δf(s) in step S13 is: Where: f is the natural frequency; ξ is the damping ratio; R T and F T An intermediate variable for convenient calculation.

6. The unit commitment optimization method considering frequency constraints and wind-storage frequency regulation control according to claim 2 is characterized in that: The time domain expression Δf(t) in step S14 is: Where: The time t corresponding to the lowest frequency point in step S15 m for: The time domain expression of the maximum frequency deviation in step S16 is:

7. The unit commitment optimization method considering frequency constraints and wind-storage frequency regulation control according to claim 2 is characterized in that: The frequency modulation power of the unit at the lowest point in step S17 is: Where ΔP jz (t m ) is at t m Total FM power at the moment; ΔP H,i (t m ) is the power regulation of the i-th thermal power unit; ΔP W,i (t m ) is at t m The frequency modulation power of the i-th wind turbine at the moment; P E (t m ) is at t m Total frequency modulation power stored at all times; The frequency modulation output power constraint of the constructed system is: ΔP jz (t m )+DΔf m ≥ΔP L (18)。 8. The unit commitment optimization method considering frequency constraints and wind-storage frequency regulation control according to claim 1 is characterized in that: The unit commitment optimization model in step S2 is: The objective function of the unit combination optimization model is: minimizing the daily operating cost; the daily operating cost includes: thermal power generation cost, thermal power start-up and shutdown cost, wind power abandonment cost and energy storage charging and discharging cost; The expression of the daily operating cost is: Where: a i , b i 、c i and are the power generation cost coefficients of the i-th thermal power unit; T is the dispatch period. When dispatching on the day before, T is 24; n is the number of thermal power units; u i,t is the operating status of the i-th thermal power unit at time t, 1 means it is on, and 0 means it is off; B i is the startup cost of the i-th thermal power unit; P H,i,t is the output of the i-th thermal power unit in the t-th period; r j,t is the wind curtailment rate of the j-th wind turbine at time t; P w,i,t is the wind turbine output, m is the number of wind turbines; z is the wind abandonment cost coefficient; P w,i,t Output power for wind turbines; i,ch,t , p i,dis,t are the charging power and discharging power of the energy storage in time period t respectively; c ch , c dis They are the charging cost coefficient and discharging benefit coefficient of energy storage respectively; The thermal power generation cost is processed as follows: P i =P0+PI i1 +···+PI i4 (21) 0≤PI i1 ≤p H,i1,t -p H,i0,t ,···,0≤PI i4 ≤p H,i4,t -p H,i3,t (22)。 9. The unit commitment optimization method considering frequency constraints and wind-storage frequency regulation control according to claim 1, characterized in that: The unit commitment optimization model in step S2 satisfies the system frequency safety constraints and operation constraints; The operating constraints include: (1) Power balance constraints: Where: P d,t is the load demand at time t; (2) Output constraints of thermal power units: p H,i,min ≤p H,i,t ≤p H,i,max (24) Where: P H,i,max and P H,i,min are the upper and lower limits of thermal power unit output respectively; (3) Reserve power constraints: (4) Climbing constraints: -R i,down ≤p H,i,t -p H,i,(t-1) ≤R i,up (26) Where: R i,down and R i,up are the upward and downward climbing rates of the thermal power units, respectively; (5) Start and stop time constraints: Where: and are the continuous operation and shutdown time of the thermal power unit at time t respectively; and are the minimum start and stop times of thermal power units respectively; (6) Node active power balance: Where: B i,j is the mutual susceptance of nodes i and j after removing the grounding branch; P i,t is the injected power of node i; θ i,t is the voltage phase angle of node i; (7) Line transmission power constraints: Where: is the active power of line ij; is the safety limit; X ij is the reactance of line ij; (8) Reserved capacity for primary frequency regulation of wind turbines: ΔP w =P w ·r w (30) Where P w is the maximum power generation of the wind turbine, r w is the wind abandonment rate of the wind turbine; (9) Wind power output constraints: Where: The upper limit of wind power output; (10) Energy storage constraints: Where: soc i,t is the state of charge of the energy storage; E i,rate is the rated capacity of the energy storage; SOC min and SOC max are the minimum and maximum values ​​of the energy storage charge state respectively; η i,ch and η i,dis are the charging and discharging efficiency of energy storage; P ch,max and P dis,max are the maximum charging power and the maximum discharging power respectively; d i,ch and d i,dis are the operating state charging and discharging variables of the energy storage, d i,ch =1 at the same time d i,dis =0 means charging, d i,ch =0 while d i,dis =1 means discharge.

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