Frequency regulation and parameter joint optimization method for power system with compressed air energy storage

Abstract

The present invention discloses a method for joint optimization of frequency regulation, standby and parameters of an electric power system containing compressed air energy storage, which belongs to the technical field of frequency stability control and steady-state optimization operation of electric power systems, including S1, obtaining forecast data of maximum load and maximum wind power output in a future time period, and determining the solution boundary of the model; S2, establishing a dynamic analytical model of the frequency of an electric power system containing compressed air energy storage, and linearizing it according to the solution requirements; S3, embedding the transient frequency safety constraints of the electric power system into the steady-state operation optimization, and jointly optimizing the standby capacity and frequency regulation parameters of each resource in different time periods. The present invention adopts the above-mentioned method for joint optimization of frequency regulation, standby and parameters of an electric power system containing compressed air energy storage, which can more accurately characterize the different physical characteristics of compressed air energy storage in the energy storage and release stages on the basis of taking into account the safety boundary of compressed air energy storage, thereby improving the economy and safety of system operation.

Description

Technical Field

[0001] The present invention relates to the technical field of power system frequency stability control and steady-state optimized operation, and in particular to a method for joint optimization of frequency regulation, standby and parameters of a power system containing compressed air energy storage. Background Art

[0002] As global energy issues become increasingly prominent, people are beginning to pay more attention to low-carbon and clean energy. In order to cope with the large-scale addition of new energy sources such as wind power to the power system, energy storage plays an indispensable role. Compressed air energy storage (CAES) has begun to attract increasing attention due to its large scale, low cost, and flexible charging and discharging, and is considered to be one of the most promising energy storage technologies. However, there are still few studies on the participation of compressed air energy storage in frequency modulation, and there is a lack of description of the differences in physical properties of its energy storage and energy release stages.

[0003] At the same time, as more and more types of power sources are added to the power system, the differences in their physical characteristics will greatly affect the safe, stable and economic operation of the power system. How to reasonably allocate resources and consider both safety constraints and the economic efficiency of operation have also put forward higher requirements for the power system. Since the two important indicators that affect the transient frequency safety of the power system: backup capacity and frequency regulation parameters, are also affected to a certain extent by factors such as the economy of steady-state operation, considering both transient and steady states has also become an important direction for improving the safety and economy of power system operation. Summary of the invention

[0004] The purpose of the present invention is to provide a method for joint optimization of frequency regulation, standby and parameters of an electric power system containing compressed air energy storage, which can characterize the physical characteristics of compressed air energy storage in the two stages of energy storage and energy release, and after it participates in the optimized operation of the power system, jointly optimize the standby capacity and frequency regulation parameters with other resources to improve the safety and economy of operation.

[0005] To achieve the above object, the present invention provides a method for joint optimization of frequency regulation and standby and parameters of a power system containing compressed air energy storage, comprising the following steps:

[0006] S1. Obtain the forecast data of the maximum load and the maximum wind power output in the future time period to determine the solution boundary of the model;

[0007] S2. Establish a dynamic analytical model of the power system frequency containing compressed air energy storage and perform linearization according to the solution requirements;

[0008] S3. The transient frequency security constraints of the power system are embedded into the steady-state operation optimization, and the spare capacity and frequency regulation parameters of each resource in different time periods are jointly optimized.

[0009] Preferably, step S2 specifically includes:

[0010] S21. Establish dynamic response models of compressed air energy storage in the energy storage and energy release stages respectively. This is because the physical characteristics of compressed air energy storage in the energy storage and energy release stages are considered and modeled separately. In the energy storage stage, compressed air energy storage is treated as a controllable load to participate in the frequency regulation of the power system, which has faster regulation characteristics. In the energy release stage, the inertial response process of its air flow control link is characterized, and the inertial support of its generator grid connection is considered.

[0011] S22. Establish an improved power system frequency dynamic analytical model with compressed air energy storage, and convert the dynamic model into an analyzable form by finite difference method;

[0012] S23, solving the spare capacity and frequency modulation parameters as variables and performing linearization processing.

[0013] Preferably, step S22 specifically includes:

[0014] S221. Establish frequency dynamic models of compressed air, thermal power and wind power respectively;

[0015] S222. Establish a dynamic model of the power grid frequency of the power system;

[0016] S223, analytically solving the models established in step S221 and step S222 using the finite difference method.

[0017] Preferably, step S23 specifically includes:

[0018] S231. In order to avoid the solver's limitation on the max / min function, the description of the reserve capacity limit is replaced by a set of linear inequality constraints to linearize it.

[0019]

[0020] Among them, y is a function containing x and c, and x and c are both independent variables;

[0021] S232, due to the frequency disturbance Δf t,n The frequency modulation parameters K and H to be adjusted are all continuous variables. The big M method is used to set the frequency modulation parameters K and H as discrete variables with fixed values ​​and linearize them.

[0022] Preferably, step S3 specifically includes:

[0023] S31. Establish the objective function of the power system as:

[0024]

[0025] Among them, T is the number of power dispatching periods; N is the number of setting steps of the frequency disturbance of the sending-end power grid; n is the time scale of the frequency disturbance of the sending-end power grid; is the cost coefficient of thermal power generation; P g,t, is the power generation of thermal power in time period t; λ is the system frequency disturbance coefficient, df t,n is the frequency disturbance of the power system at the nth step at time t;

[0026] S32. Establish operating constraints for the power system.

[0027] Therefore, the present invention adopts the above-mentioned method for joint optimization of frequency regulation, standby and parameters of power systems containing compressed air energy storage. On the basis of considering the safety boundary of compressed air energy storage, it characterizes the choice of two states of compressed air energy storage in steady state and the physical response difference of the two states in transient state. Secondly, it can jointly optimize the standby capacity and frequency regulation parameters of power systems with multiple types of resources according to their characteristics, thereby improving the safety and economy of operation.

[0028] The technical solution of the present invention is further described in detail below through the accompanying drawings and embodiments. BRIEF DESCRIPTION OF THE DRAWINGS

[0029] Figure 1 It is a flow chart of the method for joint optimization of frequency regulation, standby and parameters of a power system containing compressed air energy storage according to the present invention;

[0030] Figure 2 A block diagram of a frequency dynamic model of a power system including compressed air energy storage according to an embodiment of the present invention;

[0031] Figure 3 This is a frequency dynamic result diagram when a power disturbance occurs in a certain period of time in the power system according to an embodiment of the present invention;

[0032] Figure 4 This is a frequency dynamic result diagram when a power disturbance occurs in a certain period of time in the power system according to an embodiment of the present invention;

[0033] Figure 5 This is a diagram of operation optimization results of different resources in multiple time periods according to an embodiment of the present invention;

[0034] Figure 6 This is a result diagram of upper and lower spare capacities of different resources in multiple time periods according to an embodiment of the present invention. DETAILED DESCRIPTION

[0035] Example

[0036] In order to make the purpose, technical solutions and advantages of the embodiments of the present invention clearer, the technical solutions in the embodiments of the present invention will be clearly and completely described below in conjunction with the drawings in the embodiments of the present invention. Obviously, the described embodiments are part of the embodiments of the present invention, not all of the embodiments. Generally, the components of the embodiments of the present invention described and shown in the drawings here can be arranged and designed in various different configurations.

[0037] like Figure 1 As shown, a method for joint optimization of frequency regulation and standby and parameters of a power system including compressed air energy storage comprises the following steps:

[0038] S1. Obtain the forecast data of the maximum load and the maximum wind power output in the future time period to determine the solution boundary of the model;

[0039] S2. Establish a dynamic analytical model of the power system frequency with compressed air energy storage and perform linearization according to the solution requirements;

[0040] The dynamic response models of compressed air energy storage in the energy storage and energy release stages are established respectively, and the dynamic frequency model of the power system containing compressed air energy storage is established, and the finite difference method is used for analysis. Finally, the nonlinear part is linearized using the patented method for optimization and solution.

[0041] The specific steps include:

[0042] S21. The differences in the physical characteristics of compressed air energy storage in the energy storage and energy release stages are considered and modeled separately. In the energy storage stage, compressed air energy storage is treated as a controllable load to participate in the frequency regulation of the power system, with faster regulation characteristics; in the energy release stage, the response characteristics are affected by physical links such as turbines and valves, and because the generator of compressed air energy storage is connected to the power grid in the energy release stage, it can also provide inertial support through speed changes.

[0043]

[0044] H=H g +γH CAES (3)

[0045] in, The actual response power of electrochemical energy storage and compressed air energy storage respectively are the command powers of electrochemical energy storage and compressed air energy storage, It is the active power instruction of compressed air energy storage in the energy storage stage. is the active power instruction of compressed air energy storage in the energy release stage, T b is the power response time constant of electrochemical energy storage, T cCharacterizes the power regulation delay of compressed air energy storage during the compression phase, T w 、T igv are the discharge delay of the compressed air turbine and the adjustment delay of the valve opening; γ is a 0 / 1 variable to characterize the working state of the compressed air energy storage. When γ = 0, the compressed air energy storage is in the energy release state; when γ = 1, the compressed air energy storage is in the compression state. H is the total inertia time constant of the power system, H g is the inertia time constant of thermal power, H CAES is the inertia time constant of compressed air energy storage.

[0046] S22. Establish a dynamic analytical model of the frequency of the improved power system with compressed air energy storage. Convert the dynamic model into an analytical form through the finite difference method.

[0047] The specific steps include:

[0048] S221. Establish frequency dynamic models of compressed air, thermal power and wind power respectively;

[0049] When considering the SFR classic model, the equivalent values ​​of thermal power in the power grid can generally be aggregated into one unit. Therefore, by equivalent values ​​of the thermal power group in the Shagohuang base, the speed regulator model of the thermal power unit can be obtained:

[0050]

[0051] Where: ΔP g is the change in active power after the thermal power group is equalized, F H is the high pressure turbine coefficient of the equivalent thermal power unit, T R is the reheat time constant of the equivalent thermal power unit, It is the active power instruction of the thermal power speed regulator.

[0052] In addition, wind power establishes its active power response model based on its physical characteristics:

[0053]

[0054] Where: ΔP w is the power response change of wind power; T w is the power regulation delay constant of wind power; It is the power instruction of the wind power governor.

[0055] S222. Establish a dynamic model of the power grid frequency of the power system;

[0056] When a power grid experiences power disturbances such as group faults or DC blocking, the swing equation is generally used to describe its frequency dynamics:

[0057]

[0058] Where: Δf is the frequency deviation of the power grid at the sending end; H is the total inertia of the power grid at the sending end; D is the damping coefficient; ΔP m is the sum of the power changes of each regulation resource; ΔP e It is the sum of the electromagnetic power of the system, and can also be equivalent to the power change of the DC at the sending end.

[0059] This application considers the virtual synchronous machine control of wind power, making it have virtual inertia and active frequency regulation characteristics, and considers the actual inertia support and speed regulator power regulation characteristics provided by thermal power and compressed air energy storage in the energy release stage, and establishes the following response model of multiple types of frequency regulation resources:

[0060]

[0061] in, are the speed controller power instructions for wind power, thermal power and compressed air energy storage, K w , K g and K a are the regulation coefficients of wind power, thermal power and compressed air energy storage when participating in primary frequency regulation, H w is the inertia time constant of wind power when participating in virtual inertia control, and They are the frequency regulation reserve capacity of thermal power, wind power and compressed air energy storage respectively.

[0062] S223. The above model is analytically solved using the finite difference method.

[0063] This method discretizes the model through the finite difference method, so as to obtain the frequency change of the system and the power change of each power source and compressed air energy storage when the power grid suffers from high-frequency or low-frequency faults.

[0064]

[0065] Where Δf t,n is the frequency change of the power grid at the nth step in period t; Δf t,n-1 P is the frequency change of the power grid at the n-1th step in time period t; L,t is the total load of the upper power grid, ΔP L,t is the magnitude of active disturbance that the power grid may encounter during period t; d n is the difference step size, and are the power command disturbances of the speed regulator at the nth step length in period t for thermal power, wind power and CAES respectively; and are the power command disturbances of the speed regulators at the n-1th step length in period t for thermal power, wind power and CAES respectively; and are the actual power response disturbance of the speed regulator at the nth step length in period t for thermal power, wind power, CAES (storage) and CAES (release); are the actual power response disturbance of the speed regulator at the n-1th step length in the thermal power and wind power periods in the t period respectively; and are the actual power response disturbance of the speed regulator at the n-1th step length in the t period for CAES (storage) and CAES (release); and Represent the upward adjustment coefficients of thermal power, wind power and CAES respectively; H w represents the virtual inertia control constant of wind power; and They represent the reserve active power of thermal power, wind power and CAES in frequency regulation during period t. Equations (10), (12) and (14) all represent the regulation coefficient K and reserve status of the three frequency regulation resources when participating in the frequency increase event. The model framework is the same when the frequency decrease event occurs, so it will not be repeated. are the intermediate variables of the power response of the speed regulator at the n-1th step length during the period t for CAES (storage) and CAES (release); They are the intermediate variables of the power response of the speed regulator at the nth step in period t, namely CAES (storage) and CAES (release).

[0066] S23, solving the spare capacity and frequency modulation parameters as variables and performing linearization processing.

[0067] The specific steps include:

[0068] S231. To avoid the solver's limitation on the max / min function, the characterization of the reserve capacity limit can be replaced by a set of linear inequality constraints to linearize it:

[0069]

[0070] In the formula, y is a function containing x and c, and x and c are independent variables. The mathematical compact forms of equations (10), (12) and (14) can all be unified as y = min (x, c). If minΣabsΔf is added to the objective function t,n , this constraint form can be equivalently transformed.

[0071] S232, due to facing Δf t,nThe K and H variables to be adjusted are all continuous variables. This method uses the big M method to set the frequency modulation parameters K and H as discrete variables with fixed values ​​and linearize them:

[0072] Since Δf t,n The K and H variables to be adjusted are all continuous variables. This paper uses the Big M method to set the frequency modulation parameters K and H as discrete variables with fixed values ​​and linearize them. Taking z = x·y as an example, when x and y are both continuous variables, z is a nonlinear term. Introducing two binary variables α1 and α2, and discretizing x using the Big M method, we can get:

[0073]

[0074] z=x i ·y (20)

[0075] At this time, z becomes four fixed discrete values ​​x i The product of the continuous variable y. The frequency modulation parameters K and H in the model can be linearized according to this method, so as to adjust the parameters. The modulation coefficient K is divided into [20, 33, 40, 50], and the inertia time constant is divided into four levels [6, 8, 10, 12].

[0076] Step S3 specifically includes the following steps:

[0077] The transient frequency security constraints of the power system are embedded into the steady-state operation optimization, and the objective function, operation constraints, frequency security constraints, etc. are set to jointly optimize the spare capacity and frequency regulation parameters of each resource in different time periods.

[0078] The specific steps include:

[0079] S31. Establish the objective function of the power system as:

[0080]

[0081] Where: T is the number of power dispatching periods; N is the number of setting steps for the frequency disturbance of the sending-end power grid; n is the time scale of the frequency disturbance of the sending-end power grid; is the cost coefficient of thermal power generation; P g,t is the power generation of thermal power in time period t; λ is the system frequency disturbance coefficient, df t,n is the frequency disturbance of the power system at the nth step at time t.

[0082] S32. Establish the operating constraints of the power system as follows:

[0083] |df t,n |≤|df nadir | (22)

[0084]

[0085] P a,t =γP ar,t +(1-γ)P ac,t (twenty four)

[0086]

[0087] SOC a,min ≤SOC a,t ≤SOC a,max (30)

[0088] Among them, df t,n is the frequency disturbance of the power system at the nth step in period t; df nadir is the change in the lowest frequency safety extreme point specified by the power system; P L,t is the power load of the power system in period t; i = g, w, a, representing thermal power, wind power, and compressed air energy storage, respectively. i,t , are the active power output, upper reserve and lower reserve of type i resources in period t, where compression Formula (23) is the power balance constraint of the sending end of the Shagohuang base, and Formula (25)-(26) are the climbing constraints of the synchronous machine, where They represent the upper reserve and lower reserve of thermal power in period t respectively. They represent the upper reserve and lower reserve of thermal power in the period t-1 respectively. In formula (27)-(28), i=g, b, w, p, a, respectively represent thermal power, wind power, photovoltaic, electrochemical energy storage and compressed air energy storage. and They represent the minimum and maximum ramping amounts during the period when thermal power is not available; Equations (27)-(28) are the upward and downward reserve power constraints for different power sources and energy storage types. i,max , P i,min Represents the upper and lower limits of the output of different resources, and compressed air energy storage needs to constrain the upper and lower limits separately due to its different characteristics in the energy storage state and energy release state. The maximum active power of wind power needs to be determined based on the predicted maximum active output. At the same time, compressed air energy storage is also limited by the charge state constraint. In the formula, SOC a,t , SOC a,t-1 is the state of charge of the compressed air energy storage at time t and t-1, SOC a,max , SOC a,min Represents the maximum and minimum state of charge of the compressed air energy storage, P ac,t , P ar,t Respectively represent the charging power and discharging power of CAES in period t; P ac,t-1 and Par,t-1 Respectively represent the charging power and discharging power of CAES in the t-1 period; P g,t-1 is the active power of thermal power in period t-1; E a is the rated capacity of compressed air energy storage, η a It is the charging and discharging efficiency of compressed air energy storage.

[0089] The above method of the present invention is described below by a specific example:

[0090] This example mainly uses a power system containing compressed air energy storage, thermal power and wind power as an example model of this method. Figure 2 The frequency dynamic model shown in the figure is used to build the corresponding backup tuning model and parameter optimization model. The specific parameters and steps are as follows:

[0091] 1. The power system in this area is set to include 2000MW thermal power, 300MW compressed air energy storage and 1500MW wind power. The maximum output and load of wind power will be updated in each period according to the day-ahead forecast information. It is set that there may be a sudden increase or decrease of 350MW load in each period, causing the power system to face frequency disturbance or frequency disturbance events.

[0092] 2. Control group 1 uses the reserve setting model of this paper, but does not perform parameter optimization. All parameters are set according to K = 33 and H = 8. Control group 2 does not use the reserve setting and parameter optimization model. The reserve is set at a fixed ratio of 20% of the capacity. This model uses the method used in this paper to perform reserve setting and parameter optimization for three types of frequency regulation resources. The frequency regulation parameters are divided into 4 levels, namely K = [20, 33, 40, 50] and H = [6, 8, 10, 12].

[0093] 3. Six time periods were selected to verify the method for different load levels and different wind power maximum output scenarios, as shown in Table 1. This method can obtain the operating points, spare capacity, and frequency regulation parameters of each resource in different time periods, and the frequency remains within the safety boundary, such as Figure 3-6 shown.

[0094] Table 1 Wind power maximum level prediction and load level of the method proposed in the present invention

[0095] Maximum wind power / MW 1200 1170 1125 1155 1185 1200 Load power / MW 1050 1020 930 900 825 855

[0096] Therefore, the present invention adopts the above-mentioned method for joint optimization of frequency regulation, standby and parameters of an electric power system containing compressed air energy storage, which can realize the economic allocation of multiple types of resources on the basis of considering the frequency safety of the electric power system, and can help the electric power system to grasp and adjust the safe and economical operation mode of the system in real time. At the same time, the compressed air energy storage can be optimally set in terms of the selection of the charging and discharging state of the compressed air energy storage, the setting of the standby capacity and frequency regulation parameters, etc. on the basis of considering the safety boundary and charge state to avoid surge and congestion, thereby improving the safety and economy of the system containing compressed air energy storage.

[0097] Finally, it should be noted that the above embodiments are only used to illustrate the technical solution of the present invention rather than to limit it. Although the present invention has been described in detail with reference to the preferred embodiments, those skilled in the art should understand that they can still modify or replace the technical solution of the present invention with equivalents, and these modifications or equivalent replacements cannot cause the modified technical solution to deviate from the spirit and scope of the technical solution of the present invention.

Claims

1. A method for joint optimization of frequency regulation and parameters of a power system containing compressed air energy storage, characterized in that: The following steps are involved: S1. Obtain the forecast data of the maximum load and the maximum wind power output in the future time period to determine the solution boundary of the model; S2. Establish a dynamic analytical model of the power system frequency with compressed air energy storage and perform linearization according to the solution requirements, including: S21. Establish dynamic response models of compressed air energy storage in the energy storage and energy release stages, including: In the energy storage stage, compressed air energy storage is treated as a controllable load to participate in the frequency regulation of the power system; in the energy release stage, the response characteristics are affected by physical links such as turbines and valves, and because in the energy release stage, the generator of compressed air energy storage is connected to the power grid, inertial support is provided through speed changes; (1) (2) (3) in, 、 The actual response power of electrochemical energy storage and compressed air energy storage respectively 、 are the command powers of electrochemical energy storage and compressed air energy storage, It is the active power instruction of compressed air energy storage in the energy storage stage. It is the active power instruction of compressed air energy storage in the energy release stage. T b is the power response time constant of electrochemical energy storage, T c Characterize the power regulation delay of compressed air energy storage during the compression phase, T w 、T igv They are respectively the discharge delay of the turbine compressing the air and the regulation delay of the valve opening; is a 0 / 1 variable to characterize the working state of compressed air energy storage. =0, the compressed air energy storage is in the energy release state; when =1, the compressed air energy storage is in a compressed state. is the total inertia time constant of the power system, is the inertia time constant of thermal power, is the inertia time constant of compressed air energy storage; S22. Establish an improved power system frequency dynamic analytical model with compressed air energy storage, including: S221. Frequency dynamic models of compressed air, thermal power and wind power are established respectively as follows: Aggregate the thermal power equivalents in the power grid into one unit and obtain the speed regulator model of the thermal power unit: (4) Where: ΔP g is the change in active power after the thermal power group is equalized, F H is the high pressure turbine coefficient of the equivalent thermal power unit, T R is the reheat time constant of the equivalent thermal power unit, It is the active power instruction of the thermal power governor; In addition, wind power establishes its active power response model based on its physical characteristics: (5) Where: ΔP w is the power response change of wind power; T w is the power regulation delay constant of wind power; The power command for the wind power governor; S222. Establish a dynamic model of the power grid frequency of the power system; When a group fault or DC blocking power disturbance occurs in the power grid, the swing equation is used to describe its frequency dynamics: (6) Where: is the frequency deviation of the power grid at the sending end; H is the total inertia of the power grid at the sending end; D is the damping coefficient; ΔP m is the sum of power changes of each regulation resource; ΔP e is the sum of the electromagnetic power of the system; The following response model of frequency modulation resources is established: (7) (8) ； in, , , They are the speed controller power instructions for wind power, thermal power and compressed air energy storage, K w 、K g and K a They are the regulation coefficients of wind power, thermal power and compressed air energy storage when participating in primary frequency regulation. H w is the inertia time constant of wind power when participating in virtual inertia control, 、 and They are the frequency regulation reserve capacity of thermal power, wind power and compressed air energy storage; S223, using the finite difference method to analytically solve the models established in step S221 and step S222; S23, solving the standby capacity and frequency modulation parameters as variables and performing linearization processing; S3. Embed the transient frequency security constraints of the power system into the steady-state operation optimization, and jointly optimize the reserve capacity and frequency regulation parameters of various resources in different time periods, including: S31. Establish the objective function of the power system as: ； in, T is the number of power dispatching periods; N The number of steps is set for the frequency disturbance of the power grid at the sending end; n is the time scale of the frequency disturbance of the power grid at the sending end; is the power generation cost coefficient of thermal power; P g,t, is the power generation of thermal power in time period t; is the system frequency disturbance coefficient, is the frequency disturbance of the power system at the nth step at time t; S32. Establish operating constraints for the power system.

2. The method for joint optimization of frequency regulation and standby and parameters of a power system containing compressed air energy storage according to claim 1, characterized in that: Step S23 specifically includes: S231, with respect to the solver's solution restriction for the max / min function, the description of the reserve capacity limit is replaced by a set of linear inequality constraints to linearize it; ； Among them, y is a function containing x and c, and x and c are both independent variables; S232, due to frequency disturbance and the frequency modulation parameters to be adjusted K , H All of them are continuous variables. The frequency modulation parameters are converted to K , H A discrete variable is set to a fixed value and linearized.

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