Inertia auxiliary service optimization clearing method for network following type wind power virtual inertia compensation

Abstract

An inertia auxiliary service optimization clearing method for network following type wind power virtual inertia compensation comprises the steps that a fan virtual inertia compensation control mechanism of which control parameters are dynamically modulated by an inertia compensation target is determined, and a fan implements a virtual inertia compensation control strategy by means of a regulation and control parameter Kdf according to the real-time wind speed and the corresponding angular frequency omega r0; determining a critical inertia Hmin and a 24-time-period power grid equivalent inertia Hsys according to a synchronous unit power generation plan, wind power prediction data and a power grid frequency safety constraint, and when the power grid equivalent inertia is smaller than the critical inertia, calculating an inertia difference Hreq of each time period; in combination with the obtained power grid inertia difference Hreq, according to different virtual inertia compensation capacities of the wind power plant and the fan, reasonably distributing compensation target quantities of the wind power plant and the fan; and constructing an electric energy and inertia auxiliary service market joint clearing optimization model in which wind power virtual inertia compensation participates. According to the invention, efficient, flexible and economical inertia auxiliary service can be realized.

Description

Technical Field

[0001] The present invention relates to the technical field of power market optimization and clearing, and in particular to an inertia auxiliary service optimization and clearing method for virtual inertia compensation of grid-following wind power. Background Art

[0002] With the large-scale grid connection of new energy, the penetration rate of new energy continues to increase, and the inertia level and anti-interference ability of the power grid have also decreased, posing risks to the safety and stability of the power system, and also putting forward new requirements for the optimized dispatching and operation of the power system. After the disturbance occurs, the inertia characteristics of the power grid are used to prevent the frequency from dropping in the first time, providing buffer time for other auxiliary service actions. Compared with the increase of synchronous generators to compensate for the lack of inertia of the power grid, the virtual inertia that can be provided by new energy units has the advantages of simple regulation and lower cost. In-depth mining of the virtual inertia of new energy units has better economy and convenience for the inertia auxiliary service market. Therefore, exploring how to form an efficient, flexible and economical inertia auxiliary service market operation optimization strategy under the high proportion of new energy grid connection has become an urgent problem to be solved. Summary of the invention

[0003] The present invention proposes an optimized clearing method for inertia auxiliary service of grid-following wind power virtual inertia compensation. The method establishes a wind turbine virtual inertia compensation control mechanism whose control parameters are dynamically modulated by the inertia compensation target, deeply explores the controllable inertia of wind power, and reasonably allocates the virtual inertia compensation targets of the units according to their virtual inertia compensation capabilities and the demand targets of insufficient system inertia in different time periods. Then, a joint clearing optimization model of the electric energy and inertia auxiliary service market involving wind power virtual inertia compensation is established to realize efficient, flexible and economical inertia auxiliary services.

[0004] The technical solution adopted by the present invention is:

[0005] An inertia auxiliary service optimization clearing method for virtual inertia compensation of grid-following wind power includes the following steps:

[0006] Step 1: Establish a virtual inertia compensation control mechanism for the wind turbine with dynamic modulation of control parameters by the inertia compensation target. The wind turbine is based on the real-time wind speed and the corresponding angular frequency ω r0 , with the help of the control parameter K df To implement virtual inertia compensation control strategy;

[0007] Step 2: According to the synchronous unit generation plan and wind power forecast data, the critical inertia H is determined by the grid frequency security constraint. min , and the equivalent inertia of the power grid in 24 periods H sys , when the equivalent inertia of the power grid H sys Less than critical inertia H minCalculate the grid inertia difference H in each period req ;

[0008] Step 3: Combine the obtained grid inertia difference H req , according to the different virtual inertia compensation capabilities of each wind farm and wind turbine, the compensation target amount of each wind farm and wind turbine is allocated layer by layer;

[0009] Step 4: Considering the inertia auxiliary service including wind power, a joint clearing optimization model of the electricity and inertia auxiliary service market with the participation of wind power virtual inertia compensation is established. The model aims to minimize the total clearing cost of the joint market, considers various resources and constraints, ensures the balance of electricity supply and demand, and meets the load demand.

[0010] In step 1, analogy is made to the rotational inertia of the mechanical rotor of the synchronous generator and the virtual inertia H of the grid-type wind turbine. equ The transfer function is:

[0011]

[0012] In formula (1), K df , T f are the filter time constant and inertia control gain of the virtual inertia controller respectively; K PT , K iT are the proportional coefficient and integral coefficient of the speed controller respectively; ω r0 , Δω r ,ω nom They are respectively represented as the initial angular velocity of the fan rotor, the incremental angular velocity of the fan rotor, and the rated angular frequency of the fan; ω s0 is the initial angular frequency of the power grid, s is a complex parameter; H DFIG =ω 2 nom J DFFIG / 2P 2 S N is the inherent inertia time constant of the fan, S N is the rated capacity, P is the number of pole pairs;

[0013] Perform Laplace inverse transformation on equation (1) to calculate the equivalent virtual inertia H equ The time domain expression is:

[0014]

[0015] In formula (2), β is the linear part of the phase; t is the time variable; θ is the initial offset of the phase; △ is the discriminant of the equation. △>0 has three simple roots: α1, α2, α3; α1, α2, α3 are the parameters of the corresponding poles;

[0016] is the attenuation process of the corresponding pole; K1, K2, K3 are the expansion coefficients of the partial fraction;

[0017] If △≤0, there is a single root α′1 and a pair of conjugate complex roots α′2±jβ, θ=∠K′2;

[0018] K′1 and K′2 are the expansion coefficients of the partial fraction.

[0019] Among many parameters, H equ The parameter of (t) is the control gain K df and the initial angular frequency of the fan rotor ω r0 , the remaining parameters are fixed values;

[0020] Therefore, in the inertial response stage, the fan can respond according to the real-time wind speed and the corresponding angular frequency ω r0 By adjusting the control parameter K in real time df The size of the fan's virtual inertia is controlled to H equ , through the transformation of formula (2), the control gain K of the fan can be obtained df The expression is:

[0021]

[0022] In the inertial response stage, each wind turbine responds to the real-time wind speed and the corresponding angular frequency ω r0 , by adjusting the parameter K df Execute the virtual inertia compensation control strategy, and compensate the grid inertia to the critical inertia according to the compensation target. Therefore, by deeply exploring the controllable inertia compensation of new energy, it is possible to participate in the inertia auxiliary service market and determine the compensation target according to the grid inertia demand on the day before.

[0023] In step 1, the wind turbine virtual inertia compensation control mechanism is a control strategy that enables the wind turbine to simulate the inertia response of a traditional synchronous generator. The control parameter K in equations (1) to (3) is df Control the size of virtual inertia compensation in real time.

[0024] In step 2, in a low-inertia wind power system with a high proportion, the important factors for frequency safety are the maximum frequency deviation and the maximum frequency change rate of the power grid. By calculating and processing the frequency response model of the multi-machine system, the critical inertia expression under the maximum frequency deviation constraint can be obtained:

[0025]

[0026] In formula (4), H △f is the critical inertia under the maximum frequency deviation constraint; f B is the reference frequency; △P max is the maximum power shortage of the system; t max_cis the time corresponding to the maximum frequency deviation safety value; △f max_c It is the maximum frequency deviation safety value.

[0027] Similarly, the critical inertia of the power grid based on frequency security constraints is:

[0028]

[0029] In formula (5), RoCoF max The maximum frequency change rate allowed by the system. The maximum power disturbance in each period can be set to 10% of the day-ahead load forecast. max Take 0.5Hz / s.

[0030] In summary, the critical inertia should be the larger value of equation (4) and equation (5):

[0031]

[0032] In formula (6), H min is the critical inertia of the system.

[0033] When the wind farm has no virtual inertia control characteristics, if the power grid contains m synchronous units and n wind farms, the system equivalent inertia can be expressed as:

[0034]

[0035] In formula (7), H Gi , S Gi are the inertia and rated capacity of synchronous generator set i respectively; S Wfj is the rated capacity of wind farm j; m is the number of synchronous machine units, and n is the number of wind farms.

[0036] Considering the situation where the grid inertia is just compensated to the critical inertia, the inertia that the grid needs to compensate is:

[0037] H req (t) = H min (t)-H sys (t) (8).

[0038] In formula (8), H req is the grid inertia difference.

[0039] In step 3, for the day-ahead market, the system capacity is different, and the inertial energy required for the response is also different. Therefore, the overall unified compensation standard is inertial energy, that is, E=HS, where E is inertial energy, H is inertia, and S is capacity. At this time, the virtual inertial energy that the wind power cluster as a whole needs to compensate is:

[0040]

[0041] In formula (9), The maximum virtual inertia energy compensated by the grid-connected wind power cluster as a whole; S N is the system capacity;

[0042] According to the compensation capacity allocation of the controllable inertia energy of wind turbines, assuming that the maximum compensable target of wind farm 1 is The maximum compensable target of wind farm 2 is The maximum compensation target of wind farm j is

[0043] Combined with formula (9) and then allocated according to their proportion, the compensation target of wind farm j is for:

[0044]

[0045] In formula (10), is the maximum inertial energy that can be compensated by wind farm j; △E W (t) is the virtual inertia energy that needs to be compensated for the entire wind farm; is the maximum compensable virtual inertia energy of wind farm 1; is the maximum compensable virtual inertia energy of wind farm 2; is the maximum compensable virtual inertia energy of wind farm j.

[0046] According to the above method and combined with formula (10), the compensation target of unit i in wind farm j is for:

[0047]

[0048] In formula (11), is the compensation target of unit i in wind farm j; is the maximum virtual inertia energy that can be compensated by unit i in wind farm j; is the required compensation target for wind farm j.

[0049] From equations (3) and (11), we can get the control parameter K of unit i in wind farm j: df，ji for:

[0050]

[0051] In formula (12), is the compensation target of unit i in wind farm j; is the capacity of unit i in wind farm j; ω r0,kj is the initial angular velocity of the rotor of unit k in wind farm j; △ is the discriminant of the equation.

[0052] In step 4, inertia auxiliary services including wind power are considered. Since electric energy and inertia are strongly coupled, a joint optimization clearing model of the electric energy market and the inertia auxiliary service market is formed. The model aims to minimize the total clearing cost of market-purchased electric energy and inertia auxiliary services.

[0053] Objective function:

[0054]

[0055] In formula (13), F is the total cost of the joint optimization clearing of the power market and the inertia auxiliary service market; T = 24, dividing 1 day into 24 time periods; N is the number of wind turbine systems included in the power grid; P g,t P is the electricity quantity of the synchronous unit; W,t is the electric power bid of wind farm W in the day-ahead electric energy market at time t; is the inertia energy compensation cost of synchronous generator set g in period t; △E W,t is the scalar quantity of inertial energy W of the wind farm during period t; They are the winning bid price of synchronous units in the day-ahead power market, the bid price of wind power, and the bid price of inertial energy of wind turbine W in period t; N g is the number of synchronous units; k is the number of wind turbines.

[0056] Constraints:

[0057] 1) System power balance constraints:

[0058]

[0059] In formula (14), P L (t) represents the system load power;

[0060] 2) Grid critical inertia constraint:

[0061] △E g,t +△E W,t ≥H min,t ·S N (15); In formula (15), △E g,t is the inertia energy increment of the synchronous machine; △E W,t The virtual inertia energy compensated for the wind power system; H min,t Critical inertia of the system.

[0062] 3) Wind turbine output constraints:

[0063]

[0064] In formula (16), d - W , d +W are the upper and lower errors of the wind turbine system output prediction during period t; P W,t To correct wind power output; P W′,t To predict wind power output.

[0065] 4) Conventional unit constraints:

[0066]

[0067] In formula (17), Indicates that synchronous unit i reduces the restricted capacity; Indicates the upward adjustment limit capacity of synchronous unit i; P Gi,min is the minimum output of the synchronous machine; u i (t) is the synchronous machine on / off state variable; P Gi (t) Synchronous machine output; P Gi,max Maximum output of synchronous machine.

[0068]

[0069] In formula (18), T i,on , T i,off They represent the continuous operation time and continuous shutdown time of the i-th conventional unit respectively;

[0070] T i,on,min , T i,off,min They represent the minimum duration and minimum continuous outage time allowed for the i-th conventional unit respectively.

[0071]

[0072] In formula (19), r ui , r di represents the ramp-up rate and ramp-down rate of the power of the i-th conventional unit; Δt represents the unit dispatching time; P Gi (t-1) is the output value of the synchronous unit in the previous time period; u i (t-1) is the unit status in the previous time period.

[0073] In step 4, the joint clearing optimization model of the electricity and inertia auxiliary service market in which wind power virtual inertia compensation participates is a power market mechanism. Through economic incentives for wind power virtual inertia compensation, the joint clearing optimization model considers how much virtual inertia services the wind farm can provide while providing electricity. The model takes the minimum total clearing cost of the joint market in formula (13) as the goal, considers the constraints of each resource in formulas (14) to (19), ensures the balance of power supply and demand, and meets the load demand.

[0074] The present invention provides an inertia auxiliary service optimization and clearing method for virtual inertia compensation of grid-following wind power, and the technical effects are as follows:

[0075] 1) In step 1 of the present invention, the wind turbine executes a virtual inertia compensation control strategy by adjusting the control parameter Kdf in real time, thereby responding to the virtual inertia compensation target Hequ.

[0076] 2) In step 2 of the present invention, the inertia compensated by the wind turbine can be derived as a target value based on market demand, that is, based on the day-ahead load forecast and wind power forecast, the critical inertia of the system is calculated according to the frequency change rate constraint and the maximum frequency deviation constraint, and the actual inertia of the system can be obtained from the load power balance without considering the inertia market. The difference between the two gives the target inertia compensation value of the system.

[0077] 3) In step 3 of the present invention, reasonable inertia compensation targets are allocated to the wind farm and the wind turbines so that the inertia compensation can be fully exerted.

[0078] 4) In step 4 of the present invention, a joint clearing optimization model of the electric energy and inertia auxiliary service market involving wind power virtual inertia compensation is constructed to give full play to the enthusiasm of wind power to participate in the market and explore the economy and adaptability of virtual inertia compensation of new energy units to the market. BRIEF DESCRIPTION OF THE DRAWINGS

[0079] The present invention will be further described below in conjunction with the accompanying drawings and embodiments:

[0080] Figure 1 is a flow chart of an embodiment of the present invention.

[0081] Figure 2 This is the system wind power output and load forecast curve when the wind turbine penetration rate is 21%.

[0082] Figure 3 Schematic diagram of predicting the inertia compensation required for the system a day ago.

[0083] Figure 4 This is a schematic diagram of the winning bids of each unit in the day-ahead electricity market under inertia compensation of wind turbines.

[0084] Figure 5 This is a schematic diagram of the winning bids for wind turbines in the day-ahead inertia market. DETAILED DESCRIPTION

[0085] An inertia auxiliary service optimization clearing method for virtual inertia compensation of grid-following wind power. For wind power participating in the inertia market, the wind turbine adjusts the control parameter K in real time. df Execute the virtual inertia compensation control strategy to respond to the virtual inertia compensation target H equ. Through the day-ahead load forecast and wind power forecast, the critical inertia of the system is calculated according to the frequency change rate constraint and the maximum frequency deviation constraint, and the actual inertia of the system can be obtained from the load power balance without considering the inertia market. The difference between the two gives the target inertia compensation amount of the system. Reasonable inertia compensation targets are allocated to wind farms and wind turbines so that they can give full play to the role of inertia compensation. On the basis of the market optimization clearing model, a joint clearing optimization model of the electric energy and inertia auxiliary service market with the participation of wind power virtual inertia compensation is constructed to give full play to the enthusiasm of wind power to participate in the market and explore the economy and adaptability of virtual inertia compensation of new energy units to the market. Finally, the inertia market optimization clearing model proposed in the patent of this invention is verified using example data.

[0086] First, a virtual inertia compensation control mechanism for the wind turbine is established, in which the control parameters are dynamically modulated by the inertia compensation target. The wind turbine is controlled according to the real-time wind speed and the corresponding angular frequency ω r0 , with the help of the control parameter K df To implement the virtual inertia compensation control strategy; then, according to the synchronous unit power generation plan and wind power forecast data, the critical inertia H is determined by the grid frequency security constraint. min , and the equivalent inertia of the power grid in 24 periods H sys When the equivalent inertia of the power grid is less than the critical inertia, calculate the inertia difference H in each period req .

[0087] Next, combine the obtained grid inertia difference H req , according to the different virtual inertia compensation capabilities of wind farms and wind turbines, the compensation target amount is reasonably allocated.

[0088] Finally, a joint clearing optimization model of the electricity and inertia auxiliary service markets involving wind power virtual inertia compensation is constructed.

[0089] The CPLEX12.8.0 solver is called on the MATLAB 2016a platform in combination with the Yalmip toolbox for solution.

[0090] The above-mentioned wind power virtual inertia compensation participated in the joint clearing optimization model of the electric energy and inertia auxiliary service market. In order to verify the established clearing model, a simplified power grid example is used. The system includes 10 thermal power units and two wind farms. The specific parameters are shown in the figure below. The configuration is shown in Table 1 and Table 2.

[0091] Table 1 Parameter settings of thermal power units

[0092]

[0093] Table 2 Wind power parameters are set as follows

[0094]

[0095] The scheme is set up as follows: under a wind power penetration rate of 21%, wind turbines participate in the joint market when they have virtual inertia compensation control and wind turbines participate in the joint market without virtual inertia control, and the two clearing results are compared.

[0096] according to Figure 2 The system wind power output and load forecast curve when the wind turbine penetration rate is 21%, the system rated frequency is 50Hz, and the maximum RoCoF limit is set to 0.5Hz / s in the critical inertia calculation. Then, the system minimum inertia requirement, i.e. the critical inertia, the system equivalent inertia for 24 hours, and the grid inertia compensation targets for different periods of time are obtained from step 2, such as Figure 3 As shown. Figure 3 It can be seen that the system has a grid inertia difference during the periods of high wind turbine output and high load (0:00-12:00 and 19:00-24:00), and the grid inertia is at a safe level during the periods of low wind turbine output and low load (14:00-18:00).

[0097] According to the grid inertia compensation targets for different time periods obtained in step 2, the calculated target values ​​and parameters are substituted into the clearing model in step 4, and the system clearing costs are calculated with the goal of minimizing the total clearing costs of the joint market.

[0098] Figure 4 is the winning bid of each unit in the day-ahead electricity market under the compensation inertia of the wind turbine, Figure 4 It can be seen that when the wind turbine has virtual inertia compensation control, it participates in the inertia market. The synchronous units 1 and 2 with larger capacities are almost fully powered in all periods, and the wind turbine output can be effectively utilized in each period.

[0099] Figure 5 The winning bid of wind turbine in the day-ahead inertia market. Figure 5 It can be seen that during the period of 14:00-18:00, due to the reduction of load and wind power output, the equivalent inertia of the power grid is higher than the critical inertia, so no additional inertia compensation control is required. In other periods, the critical inertia is greater than the equivalent inertia of the power grid. At this time, the inertia compensation of the wind turbine can make up for the lack of power grid inertia in time.

[0100] According to the clearing results, the clearing settlement costs are calculated and compared, as shown in Table 3. The market settlement costs show that when the wind turbine has virtual inertia compensation control and participates in the joint market clearing, it can effectively reduce the overall clearing costs compared to when the wind turbine does not have virtual inertia control and participates in the joint market clearing. This is because the wind turbine with virtual inertia compensation can provide additional inertia within its safe operating range, so that in the joint clearing market, those synchronous units with lower power generation costs and larger capacity are more likely to win the bid. On the contrary, when the wind turbine has no virtual inertia compensation, in order to meet the inertia safety requirements of the power grid, some large inertia units need to be started and a certain degree of wind abandonment is required, resulting in intensified fluctuations in the clearing price of electricity, especially during high load periods, resulting in high marginal prices for clearing electricity and high overall settlement costs.

[0101] Table 3 Market Settlement Fees

[0102]

Claims

1. An inertia auxiliary service optimization clearing method for virtual inertia compensation of grid-following wind power, characterized in that The following steps are involved: Step 1: Establish a virtual inertia compensation control mechanism for the wind turbine with dynamic modulation of control parameters by the inertia compensation target. The wind turbine is based on the real-time wind speed and the corresponding angular frequency ω r0 , with the help of the control parameter K df To implement virtual inertia compensation control strategy; Step 2: According to the synchronous unit generation plan and wind power forecast data, the critical inertia H is determined by the grid frequency security constraint. min , and the equivalent inertia of the power grid H sys , when the equivalent inertia of the power grid H sys Less than critical inertia H min Calculate the grid inertia difference H in each period req ; Step 3: Combine the obtained grid inertia difference H req , according to the different virtual inertia compensation capabilities of each wind farm and wind turbine, the compensation target amount of each wind farm and wind turbine is allocated layer by layer; Step 4: Considering the inertia auxiliary service including wind power, a joint clearing optimization model of the electricity and inertia auxiliary service market with the participation of wind power virtual inertia compensation is established. The model aims to minimize the total clearing cost of the joint market, considers various resources and constraints, achieves power supply and demand balance, and meets load demand.

2. The inertia auxiliary service optimization and clearing method of virtual inertia compensation of grid-following wind power according to claim 1 is characterized by: In step 1, analogy is made to the rotational inertia of the mechanical rotor of the synchronous generator and the virtual inertia H of the grid-type wind turbine. equ The transfer function is: In formula (1), K df , T f are the filter time constant and inertia control gain of the virtual inertia controller respectively; K PT , K iT are the proportional coefficient and integral coefficient of the speed controller respectively; ω r0 , Δω r ,ω nom They are respectively represented as the initial angular velocity of the fan rotor, the incremental angular velocity of the fan rotor, and the rated angular frequency of the fan; ω s0 is the initial angular frequency of the power grid, s is a complex parameter variable; H DFIG =ω 2 nom J DFFIG / 2P 2 S N is the inherent inertia time constant of the fan, S N is the rated capacity, P is the number of pole pairs. Perform Laplace inverse transformation on equation (1) to calculate the equivalent virtual inertia H equ The time domain expression is: In formula (2), β is the linear part of the phase; t is the time variable; θ is the initial offset of the phase; △ is the discriminant of the equation; △>0 has three simple roots: α1, α2, α3; α1, α2, α3 are the parameters of the corresponding poles; is the attenuation process of the corresponding pole; K1, K2, K3 are the expansion coefficients of the partial fraction; If △≤0, there is a single root α′1 and a pair of conjugate complex roots α′2±jβ, θ=∠K′2; K′1 and K′2 are the expansion coefficients of the partial fractions; Among many parameters, H equ The parameter of (t) is the control gain K df and the initial angular frequency of the fan rotor ω r0 , the other parameters are fixed values; therefore, in the inertial response stage, the fan can respond according to the real-time wind speed and the corresponding angular frequency ω r0 By adjusting the control parameter K in real time df The size of the fan's virtual inertia is controlled to H equ , through the transformation of formula (2), the control gain K of the fan can be obtained df The expression is:

3. The inertia auxiliary service optimization and clearing method of virtual inertia compensation of grid-following wind power according to claim 2 is characterized by: In the inertial response stage, each wind turbine responds to the real-time wind speed and the corresponding angular frequency ω r0 , by adjusting the parameter K df The virtual inertia compensation control strategy is executed to compensate the grid inertia to the critical inertia according to the compensation target amount.

4. The inertia auxiliary service optimization and clearing method of virtual inertia compensation for grid-following wind power according to claim 1 is characterized by: In step 2, in a low-inertia wind power system with a high proportion, the important factors for frequency safety are the maximum frequency deviation and the maximum frequency change rate of the power grid. By calculating and processing the frequency response model of the multi-machine system, the critical inertia expression under the maximum frequency deviation constraint can be obtained: In formula (4), H △f is the critical inertia under the maximum frequency deviation constraint; f B is the reference frequency; △P max is the maximum power shortage of the system; t max_c is the time corresponding to the maximum frequency deviation safety value; △f max_c is the maximum frequency deviation safety value; The critical inertia of the power grid based on frequency security constraints is: In formula (5), RoCoF max is the maximum frequency change rate allowed by the system; In summary, the critical inertia should be the larger value of equation (4) and equation (5): In formula (6), H min is the critical inertia of the system; When the wind farm has no virtual inertia control characteristics, if the power grid contains m synchronous units and n wind farms, the system equivalent inertia can be expressed as: In formula (7), H Gi , S Gi are the inertia and rated capacity of synchronous generator set i respectively; S Wfj is the rated capacity of wind farm j; m is the number of synchronous machine units, and n is the number of wind farms; Considering the situation where the grid inertia is just compensated to the critical inertia, the inertia that the grid needs to compensate is: H req (t)=H min (t)-H sys (t) (8); In formula (8), H req is the grid inertia difference.

5. The inertia auxiliary service optimization and clearing method of virtual inertia compensation for grid-following wind power according to claim 1 is characterized by: In step 3, for the day-ahead market, the system capacity is different, and the inertial energy required for the response is also different. Therefore, the overall unified compensation standard is the inertial energy, that is, E=HS, where E is the inertial energy, H is the inertia, and S is the capacity. At this time, the virtual inertial energy that the wind power cluster as a whole needs to compensate is: In formula (9), The maximum virtual inertia energy compensated by the grid-connected wind power cluster as a whole; S N is the system capacity; According to the compensation capacity allocation of the controllable inertia energy of wind turbines, assuming that the maximum compensable target of wind farm 1 is The maximum compensable target of wind farm 2 is The maximum compensation target of wind farm j is Combined with formula (9) and then allocated according to their proportion, the compensation target of wind farm j is for: In formula (10), is the maximum inertial energy that can be compensated by wind farm j; △E W (t) is the virtual inertia energy that needs to be compensated for the entire wind farm; is the maximum compensable virtual inertia energy of wind farm 1; is the maximum compensable virtual inertia energy of wind farm 2; is the maximum compensable virtual inertial energy of wind farm j; According to the above method and combined with formula (10), the compensation target of unit i in wind farm j is for: In formula (11), is the compensation target of unit i in wind farm j; is the maximum virtual inertia energy that can be compensated by unit i in wind farm j; is the required compensation target for wind farm j; From equations (3) and (11), we can get the control parameter K of unit i in wind farm j: df，ji for: In formula (12), is the compensation target of unit i in wind farm j; is the capacity of unit i in wind farm j; ω r0,kj is the initial angular velocity of the rotor of unit k in wind farm j; △ is the discriminant of the equation.

6. The inertia auxiliary service optimization and clearing method of virtual inertia compensation for grid-following wind power according to claim 1 is characterized by: In step 4, the inertia auxiliary service including wind power is considered to form a joint optimization clearing model of the electricity market and the inertia auxiliary service market, which aims to minimize the total clearing cost of market-purchased electricity and inertia auxiliary services; Objective function: In formula (13), F is the total cost of the joint optimization clearing of the power market and the inertia auxiliary service market; T = 24, dividing 1 day into 24 time periods; N is the number of wind turbine systems included in the power grid; P g,t P is the electricity quantity of the synchronous unit; W,t is the electric power bid of wind farm W in the day-ahead electric energy market at time t; is the inertia energy compensation cost of synchronous generator set g in period t; △E W,t is the scalar quantity of inertial energy W of the wind farm during period t; They are the winning bid price of synchronous units in the day-ahead power market, the bid price of wind power, and the bid price of inertial energy of wind turbine W in period t; N g is the number of synchronous units; k is the number of wind turbine units.

7. The inertia auxiliary service optimization and clearing method of virtual inertia compensation for grid-following wind power according to claim 6 is characterized by: The objective function contains the following constraints: 1) System power balance constraints: In formula (14), P L (t) represents the system load power; 2) Grid critical inertia constraint: △E g,t +△E W,t ≥H min,t ·S N (15); In formula (15), △E g,t is the inertia energy increment of the synchronous machine; △E W,t The virtual inertia energy compensated for the wind power system; H min,t System critical inertia; 3) Wind turbine output constraints: In formula (16), d - W , d + W are the upper and lower errors of the wind turbine system output prediction during period t; P W,t To correct wind power output; P W′,t To predict wind power output; 4) Conventional unit constraints: In formula (17), Indicates that synchronous unit i reduces the restricted capacity; Indicates the upward adjustment limit capacity of synchronous unit i; P Gi,min is the minimum output of the synchronous machine; u i (t) is the synchronous machine on / off state variable; P Gi (t) Synchronous machine output; P Gi,max Maximum output of synchronous machine; In formula (18), T i,on , T i,off They represent the continuous operation time and continuous shutdown time of the i-th conventional unit respectively; T i,on,min , T i,off,min They represent the minimum duration and minimum continuous outage time allowed for the i-th conventional unit respectively; In formula (19), r ui , r di represents the ramp-up rate and ramp-down rate of the power of the i-th conventional unit; Δt represents the unit dispatching time; P Gi (t-1) is the output value of the synchronous unit in the previous time period; u i (t-1) is the unit status in the previous time period.

Images