A method and apparatus for collaborative optimization of energy storage capacity in auxiliary unit frequency regulation systems

Abstract

This invention provides a method and apparatus for collaborative optimization of energy storage capacity in an auxiliary unit frequency regulation system. The method includes: determining an energy balance capability index of the energy storage system to characterize its response capability; determining a two-layer collaborative optimization model for energy storage capacity corresponding to the auxiliary unit frequency regulation system; the two-layer collaborative optimization model includes an outer optimization model constrained by the energy balance capability index of the energy storage system and used to optimize the cost of energy storage configuration strategies, and an inner optimization model used to optimize the frequency regulation performance of the auxiliary unit frequency regulation system; based on the characteristics of the two-layer collaborative optimization model, analysis and processing are performed using a preset model optimization analysis method to determine the optimal energy storage configuration strategy corresponding to the energy storage system. This invention fully considers the impact of uncertainties on energy storage capacity demand, while also taking into account the coupling relationship between energy storage capacity planning and system optimization operation, thus improving the robustness of system configuration.

Description

Technical Field

[0001] This invention relates to the field of energy storage capacity configuration technology for auxiliary unit frequency regulation systems, specifically to a method and apparatus for collaborative optimization of energy storage capacity in auxiliary unit frequency regulation systems. Additionally, it also relates to an electronic device and a processor-readable storage medium. Background Technology

[0002] With the large-scale grid connection of renewable energy generation, the frequency characteristics of the power system are becoming increasingly complex. The disadvantages of traditional thermal power units, such as long response time lag, low ramp rate, and poor command tracking performance, are becoming increasingly prominent. Given the current imperfect rules and mechanisms for energy storage to participate in ancillary services as an independent entity, the participation of auxiliary units in AGC (Automatic Generation Control) frequency regulation has become the primary mode for energy storage to participate in frequency regulation ancillary services, and is also the most typical application of energy storage in the power sector closest to commercial operation. Utilizing the advantages of fast response speed and strong short-term throughput of energy storage can improve the response performance of traditional thermal power units, enhance the frequency regulation performance indicators and frequency regulation mileage of thermal power units, thereby increasing the compensation benefits of units participating in frequency regulation ancillary services. Although energy storage technology has developed rapidly in recent years, the commercial application of large-scale energy storage batteries still faces the problem of high costs. Furthermore, most existing auxiliary unit frequency regulation projects configure energy storage according to an empirical ratio of 3% of the unit size, without considering the impact of differences in unit size and performance, lacking a basis for decision-making, and still unable to achieve optimal energy storage capacity configuration. Therefore, how to rationally and effectively configure energy storage capacity is the primary issue that needs to be addressed in the frequency regulation application of energy storage auxiliary units.

[0003] To address the aforementioned issues, most existing technologies are based on a predetermined operating strategy, considering only one aspect: meeting system performance requirements or optimizing system economy. They fail to consider the coupling relationship between energy storage capacity planning and system operation optimization. In reality, energy storage capacity optimization is highly coupled with system operation optimization. On one hand, the impact of operating strategies must be considered during the energy storage capacity planning phase. A reasonable and effective operating strategy helps to fully leverage the advantages of energy storage, reducing redundant energy storage configurations while ensuring system performance, thereby improving system economy. On the other hand, the system is constrained by the energy storage capacity configuration strategy during operation. The scale of energy storage configuration also determines the flexibility of the system's operating strategy, thus affecting system performance. Furthermore, considering uncertainties is crucial for energy storage capacity optimization. Since both the AGC commands issued by the auxiliary unit frequency regulation system and the unit output process in response to AGC commands are uncertain, the system's energy demand for energy storage charging and discharging is also uncertain. If the configured energy storage capacity is too low, the energy storage will easily become saturated during charging and discharging, leading to "energy storage failure". The energy storage will have poor continuity and the system's operating performance will also be reduced. If the configured energy storage capacity is too high, the energy storage cost will increase, the resource utilization rate will decrease, and the system's economic efficiency will also deteriorate.

[0004] Therefore, how to provide a collaborative optimization scheme for the energy storage capacity of the corresponding auxiliary unit frequency regulation system, so that the energy storage capacity configuration fully considers the impact of uncertain factors, and improves the robustness of the energy storage configuration strategy, has become an urgent technical problem to be solved. Summary of the Invention

[0005] To address this, the present invention provides a method for collaborative optimization of energy storage capacity in auxiliary unit frequency regulation systems, thereby solving the problems of high limitations, poor resource utilization, and poor robustness of energy storage configuration strategies in existing technologies for collaborative optimization of energy storage capacity in auxiliary unit frequency regulation systems.

[0006] In a first aspect, the present invention provides a method for coordinated optimization of energy storage capacity in an auxiliary unit frequency regulation system, comprising:

[0007] Determine the energy balance capability index of energy storage systems to characterize their coping ability;

[0008] A dual-layer collaborative optimization model for energy storage capacity corresponding to the auxiliary unit frequency regulation system is determined. The dual-layer collaborative optimization model for energy storage capacity includes an outer optimization model that uses the energy balance capability index of the energy storage system as a constraint to optimize the cost of energy storage configuration strategy, and an inner optimization model that optimizes the frequency regulation performance of the auxiliary unit frequency regulation system.

[0009] Based on the characteristics of the dual-layer collaborative optimization model for energy storage capacity, the optimal energy storage configuration strategy corresponding to the energy storage system is determined by using a preset model optimization analysis method.

[0010] Furthermore, the determination of the energy balance capability index of the energy storage system used to characterize the energy storage response capability specifically includes:

[0011] The system acquires the automatic power generation control command data and the actual output data of the thermal power unit to be configured for energy storage, and determines the charging and discharging demand parameters of the energy storage system based on the deviation between the actual output data of the thermal power unit and the automatic power generation control command data received by the thermal power unit.

[0012] Based on the charging and discharging demand parameters of the energy storage system, an envelope characterization model for characterizing the uncertainty of the charging and discharging energy demand of the energy storage system is determined.

[0013] Based on the envelope characterization model, an energy balance capability index for energy storage systems is determined to characterize the ability of energy storage systems to cope with uncertainties.

[0014] Furthermore, based on the envelope characterization model, an energy balance capability index for characterizing the energy storage system's ability to cope with uncertainties is determined, specifically including:

[0015] Based on the envelope characterization model, the upper limit parameter of the probability of the energy storage system being in an energy shortage state during operation is determined, as well as the upper limit parameter of the probability of the energy storage system being in an energy surplus state during operation is determined.

[0016] Based on the upper limit parameter of the probability during the energy deficit state and the upper limit parameter of the probability during the energy surplus state, the probability value of the energy storage system being in an energy balance state is determined.

[0017] The energy balance capability index of the energy storage system is determined based on the probability value of the energy storage system being in an energy balance state.

[0018] Furthermore, the energy balance capability index of the energy storage system is the lower limit of the average probability that the energy storage system will be in an energy balance state within the corresponding operating cycle under the target capacity configuration scale; the energy balance capability index of the energy storage system is used to characterize the comprehensive ability of the configured energy storage capacity to cope with uncertain charging and discharging demands during the operation of the energy storage system.

[0019] Furthermore, the two-layer collaborative optimization model for determining the energy storage capacity corresponding to the auxiliary unit frequency regulation system specifically includes:

[0020] With the optimization objective of maximizing the average daily net revenue of the auxiliary unit frequency regulation system, the rated power and rated capacity of the energy storage system as decision variables, and the energy balance capability index of the energy storage system as constraints, an economic optimization layer model corresponding to the auxiliary unit frequency regulation system is determined; and the economic optimization layer model is used as the outer optimization model of the energy storage capacity dual-layer collaborative optimization model.

[0021] The optimization objective is to optimize the average comprehensive frequency regulation performance index of the auxiliary unit frequency regulation system, and the output of the energy storage system at each time moment is used as the decision variable. The operation optimization layer model corresponding to the auxiliary unit frequency regulation system is determined, and the operation optimization layer model is used as the inner optimization model of the energy storage capacity dual-layer collaborative optimization model.

[0022] Based on the outer optimization model and the inner optimization model, the dual-layer collaborative optimization model of energy storage capacity corresponding to the auxiliary unit frequency regulation system is obtained.

[0023] Furthermore, based on the characteristics of the dual-layer collaborative optimization model of energy storage capacity, the optimal energy storage configuration strategy corresponding to the energy storage system is determined by using a preset model optimization analysis method. Specifically, this includes: using a preset particle swarm optimization algorithm model to analyze the outer optimization model contained in the dual-layer collaborative optimization model of energy storage capacity; and using a preset CPLEX optimization solver to analyze the inner optimization model contained in the dual-layer collaborative optimization model of energy storage capacity, thereby obtaining the optimal energy storage configuration strategy corresponding to the energy storage system.

[0024] Secondly, the present invention also provides an energy storage capacity collaborative optimization device for an auxiliary unit frequency regulation system, comprising:

[0025] The balance capability index determination unit is used to determine the energy balance capability index of the energy storage system, which characterizes the energy storage response capability.

[0026] The collaborative optimization model determination unit is used to determine the energy storage capacity dual-layer collaborative optimization model corresponding to the auxiliary unit frequency regulation system; the energy storage capacity dual-layer collaborative optimization model includes an outer optimization model that uses the energy balance capability index of the energy storage system as a constraint and is used to optimize the cost of energy storage configuration strategy, and an inner optimization model that is used to optimize the frequency regulation performance of the auxiliary unit frequency regulation system.

[0027] The optimal energy storage configuration strategy determination unit is used to determine the optimal energy storage configuration strategy corresponding to the energy storage system by analyzing and processing the characteristics of the dual-layer collaborative optimization model of the energy storage capacity using a preset model optimization analysis method.

[0028] Furthermore, the collaborative optimization model determination unit is specifically used for:

[0029] With the optimization objective of maximizing the average daily net revenue of the auxiliary unit frequency regulation system, the rated power and rated capacity of the energy storage system as decision variables, and the energy balance capability index of the energy storage system as constraints, an economic optimization layer model corresponding to the auxiliary unit frequency regulation system is determined; and the economic optimization layer model is used as the outer optimization model of the energy storage capacity dual-layer collaborative optimization model.

[0030] The optimization objective is to optimize the average comprehensive frequency regulation performance index of the auxiliary unit frequency regulation system, and the output of the energy storage system at each time moment is used as the decision variable. The operation optimization layer model corresponding to the auxiliary unit frequency regulation system is determined, and the operation optimization layer model is used as the inner optimization model of the energy storage capacity dual-layer collaborative optimization model.

[0031] Based on the outer optimization model and the inner optimization model, the dual-layer collaborative optimization model of energy storage capacity corresponding to the auxiliary unit frequency regulation system is obtained.

[0032] Furthermore, the balance capability index determination unit is specifically used for:

[0033] The system acquires the automatic power generation control command data and the actual output data of the thermal power unit to be configured for energy storage, and determines the charging and discharging demand parameters of the energy storage system based on the deviation between the actual output data of the thermal power unit and the automatic power generation control command data received by the thermal power unit.

[0034] Based on the charging and discharging demand parameters of the energy storage system, an envelope characterization model for characterizing the uncertainty of the charging and discharging energy demand of the energy storage system is determined.

[0035] Based on the envelope characterization model, an energy balance capability index for energy storage systems is determined to characterize the ability of energy storage systems to cope with uncertainties.

[0036] Furthermore, based on the envelope characterization model, an energy balance capability index for characterizing the energy storage system's ability to cope with uncertainties is determined, specifically including:

[0037] Based on the envelope characterization model, the upper limit parameter of the probability of the energy storage system being in an energy shortage state during operation is determined, as well as the upper limit parameter of the probability of the energy storage system being in an energy surplus state during operation is determined.

[0038] Based on the upper limit parameter of the probability during the energy deficit state and the upper limit parameter of the probability during the energy surplus state, the probability value of the energy storage system being in an energy balance state is determined.

[0039] The energy balance capability index of the energy storage system is determined based on the probability value of the energy storage system being in an energy balance state.

[0040] Furthermore, the energy balance capability index of the energy storage system is the lower limit of the average probability that the energy storage system will be in an energy balance state within the corresponding operating cycle under the target capacity configuration scale; the energy balance capability index of the energy storage system is used to characterize the comprehensive ability of the configured energy storage capacity to cope with uncertain charging and discharging demands during the operation of the energy storage system.

[0041] Furthermore, the energy storage optimal configuration strategy determination unit is specifically used to: analyze the outer optimization model included in the energy storage capacity dual-layer collaborative optimization model using a preset particle swarm optimization algorithm model; and analyze the inner optimization model included in the energy storage capacity dual-layer collaborative optimization model using a preset CPLEX optimization solver to obtain the energy storage optimal configuration strategy corresponding to the energy storage system.

[0042] Thirdly, the present invention also provides an electronic device, comprising: a memory, a processor, and a computer program stored in the memory and executable on the processor, wherein the processor executes the program to implement the steps of the energy storage capacity collaborative optimization method for the auxiliary unit frequency regulation system as described in any of the preceding claims.

[0043] Fourthly, the present invention also provides a processor-readable storage medium storing a computer program that, when executed by a processor, implements the steps of the energy storage capacity collaborative optimization method for the auxiliary unit frequency regulation system as described in any of the preceding claims.

[0044] This invention employs a collaborative optimization method for the energy storage capacity of the auxiliary unit frequency regulation system. Based on the coupling relationship between energy storage capacity planning and system optimization operation, it constructs a two-layer collaborative optimization model for the energy storage capacity planning and operation of the auxiliary unit frequency regulation system. The outer-layer economic optimization model ensures the economic efficiency of the energy storage configuration strategy and reduces costs; the inner-layer operational optimization model ensures the frequency regulation performance of the auxiliary unit frequency regulation system. Furthermore, based on the envelope model of the uncertainty of energy storage charging and discharging demand, this invention defines an energy balance capability index for the energy storage system and uses it as a constraint condition for the outer-layer optimization model to consider the impact of uncertainty on energy storage capacity demand. Therefore, this invention can fully consider the impact of uncertainty factors on energy storage capacity demand, while simultaneously taking into account the coupling relationship between energy storage capacity planning and system optimization operation. It provides guidance for the investment and construction of energy storage auxiliary unit frequency regulation systems involving uncertainty, improves resource utilization, and enhances system configuration robustness. Attached Figure Description

[0045] To more clearly illustrate the technical solutions in the embodiments of the present invention or the prior art, the drawings used in the description of the embodiments or the prior art will be briefly introduced below. Obviously, the drawings described below are some embodiments of the present invention. For those skilled in the art, other drawings can be obtained based on these drawings without creative effort.

[0046] Figure 1 A flowchart illustrating the energy storage capacity collaborative optimization method for an auxiliary unit frequency regulation system provided in an embodiment of the present invention;

[0047] Figure 2 This is a schematic diagram of a two-layer collaborative optimization model for the energy storage capacity of the auxiliary unit frequency regulation system provided in an embodiment of the present invention;

[0048] Figure 3 A schematic diagram of the fitting curve of the charging and discharging energy demand boundary of the energy storage system provided in an embodiment of the present invention;

[0049] Figure 4 A schematic diagram of the probability boundary fitting curve of the energy storage system provided in an embodiment of the present invention;

[0050] Figure 5 A schematic diagram illustrating the changing trend of the energy balance capability index of the energy storage system with the rated energy storage capacity, as provided in an embodiment of the present invention.

[0051] Figure 6 The optimization flowchart of the energy storage capacity two-layer collaborative optimization model provided in the embodiments of the present invention;

[0052] Figure 7 This is a schematic diagram of the structure of the energy storage capacity collaborative optimization device for the auxiliary unit frequency regulation system provided in an embodiment of the present invention;

[0053] Figure 8 This is a schematic diagram of the physical structure of an electronic device provided in an embodiment of the present invention. Detailed Implementation

[0054] To make the objectives, technical solutions, and advantages of the embodiments of the present invention clearer, the technical solutions of the embodiments of the present invention will be clearly and completely described below with reference to the accompanying drawings. Obviously, the described embodiments are only some embodiments of the present invention, not all embodiments. Based on the embodiments of the present invention, all other embodiments obtained by those skilled in the art without creative effort are within the scope of protection of the present invention.

[0055] The following is a detailed description of embodiments of the energy storage capacity collaborative optimization method for the auxiliary unit frequency regulation system described in this invention. For example... Figure 1The diagram shown is a flowchart illustrating the energy storage capacity collaborative optimization method for an auxiliary unit frequency regulation system provided in an embodiment of the present invention. The specific implementation process includes the following steps:

[0056] Step 101: Determine the energy balance capability index of the energy storage system to characterize the energy storage response capability.

[0057] In this embodiment of the invention, it is first necessary to obtain the automatic power generation control commands and actual output data of the thermal power unit to be configured for energy storage. Based on the deviation between the actual output data and the received automatic power generation control command data, the charging and discharging demand parameters of the energy storage system are determined. Based on these parameters, an envelope model is determined to characterize the uncertainty of the energy storage system's charging and discharging demand. Then, based on this envelope model, an energy balance capability index is determined to characterize the energy storage system's ability to cope with uncertainty. This energy balance capability index is the lower limit of the average probability that the energy storage system will be in an energy balance state within the corresponding operating cycle under the target capacity configuration. The energy balance capability index characterizes the comprehensive ability of the configured energy storage capacity to cope with uncertain charging and discharging demands during the operation of the energy storage system.

[0058] The specific implementation process of determining the energy balance capability index of an energy storage system, which characterizes the system's ability to cope with uncertainties, based on the envelope characterization model includes: determining an upper limit parameter for the probability of the energy storage system being in an energy deficit state and an upper limit parameter for the probability of the energy storage system being in an energy surplus state based on the envelope characterization model; determining the probability value of the energy storage system being in an energy balance state based on the upper limit parameters for the probability of the energy deficit state and the probability of the energy surplus state; and determining the energy balance capability index of the energy storage system based on the probability value of the energy storage system being in an energy balance state.

[0059] In the specific implementation process, the system is an auxiliary unit frequency regulation system, which includes a thermal power unit system and an energy storage system. Step 1) includes: firstly, obtaining the AGC command of the thermal power unit to be configured with energy storage and the actual output data of the thermal power unit (for example, in this embodiment, the operating data of the thermal power unit for 9 consecutive days can be selected as sample data). Then, the deviation between the actual output data of the thermal power unit and the automatic power generation control command data received by the thermal power unit is the charging and discharging demand of the energy storage. In this embodiment, the scale of the thermal power unit to be configured with energy storage is 330MW.

[0060] An envelope characterization model is constructed to represent the uncertainty of energy demand for energy storage charging and discharging. Using this envelope characterization model and stochastic network calculus theory, an energy balance capability index that characterizes the energy storage system's ability to cope with uncertainty is determined. The specific steps are as follows:

[0061] 1-1) Construction of the envelope characterization model for the uncertainty of energy demand during energy storage charging and discharging. The specific model construction is as follows:

[0062] 1-1-1) Envelope characterization model of energy demand uncertainty for energy storage charging:

[0063]

[0064] Among them, E c (s,t) represents the cumulative charging energy demand process during the energy storage period [s,t], and E c (s,t)=E c (t)-E c (s); α c up (·), α c down (·) represent the upper and lower bounds of the cumulative charging energy demand process, respectively, and are fitted using a multi-order linear function; ε c up (x), ε c down (x) represents the corresponding probability boundary functions, which are fitted using an exponential decay function; P is the probability symbol, used to represent the probability of a variable exceeding the upper bound function and the probability of a variable exceeding the lower bound function, respectively; sup is the supremum operator; x is the variable of the boundary function; s and t represent time points.

[0065] 1-1-2) Envelope characterization model of energy storage discharge energy demand uncertainty:

[0066]

[0067] Among them, E d (s,t) represents the cumulative discharge energy demand process during the energy storage period [s,t], and E d (s,t)=E d (t)-E d (s); α d up (·), α d down (·) represent the upper and lower bounds of the cumulative discharge energy demand process, respectively, and are fitted using a multi-order linear function; ε d up (x), ε d down(x) represents the corresponding probability boundary function, which is fitted using an exponential decay function; P is the probability symbol, used to represent the probability of the variable exceeding the upper bound function and the probability of the variable exceeding the lower bound function, respectively; sup is the supremum operator; x is the variable of the boundary function.

[0068] Based on the actual operating data of the thermal power unit in this embodiment, the fitting curves of the upper and lower limit functions of charging and discharging energy demand and the probability boundary function are respectively shown in the appendix. Figure 3 and 4 As shown.

[0069] 1-2) Characterization of energy change process in energy storage system.

[0070] 1-2-1) Description of the charging and discharging process of the energy storage system.

[0071] If the energy demand for charging energy storage exceeds the energy demand for discharging energy storage during a certain period, the energy storage will be in a charging state overall during that period, and the amount of charging will be the deviation between the two. Conversely, if the energy demand for discharging energy storage exceeds the energy demand for charging energy storage during a certain period, the energy storage will be in a discharging state overall during that period. The corresponding mathematical description is as follows:

[0072] b(t) = min[Q] rate ,[b(t-1)+E c (t-1,t)-E d (t-1,t)] + ]

[0073] Where b(t) represents the amount of electricity stored in the energy storage system at time t; Q rate E represents the rated capacity of the energy storage system. c (t-1,t)=E c (t)-E c (t-1), E d (t-1,t)=E d (t)-E d (t-1) represents the charging energy demand and discharging energy demand of energy storage during the time period [t-1, t].

[0074] Furthermore, the non-recursive form of b(t) can be derived as follows:

[0075]

[0076] Where sup is the supremum operator; inf is the infremum operator; other specific parameters can be found in the corresponding physical meanings in the above formulas, and will not be elaborated here.

[0077] 1-2-2) Description of energy deficit status in energy storage systems.

[0078] If the energy storage system is continuously discharging during a certain period and the corresponding deviation between charging and discharging energy demand is greater than the amount of electricity already stored in the storage, then even after all the electricity is released, the system's discharge energy demand cannot be met. Therefore, the energy storage system is considered to be in an energy deficit state, and the corresponding energy deficit l(t) can be expressed as:

[0079] l(t) = [E d (t-1,t)-E c (t-1,t)-b(t-1)] +

[0080] The specific parameters involved in this formula can be found in the corresponding physical meanings in the formulas above, and will not be repeated here.

[0081] Combining the non-recursive form of b(t) in step 1-2-1), we can obtain the non-recursive expression for l(t) as follows:

[0082]

[0083] The specific parameters involved in this formula can be found in the corresponding physical meanings in the formulas above, and will not be repeated here.

[0084] Further based on the theory of random network calculus and the Ec(t) and Ec(t) constructed in step 1-1), d The envelope representation model of (t) can be used to derive the probability P that the energy storage system is in an energy deficit state at any time t. L (t) satisfies:

[0085]

[0086] The specific parameters involved in this formula can be found in the corresponding physical meanings in the formulas above, and will not be repeated here.

[0087] It can be seen that this formula determines the upper limit of the probability that the energy storage system is in a state of energy shortage.

[0088] in, The minimum convolution operator is defined as follows:

[0089]

[0090] 1-2-3) Description of the energy surplus state of the energy storage system.

[0091] If the energy storage system is continuously charging during a certain period and the corresponding deviation between charging and discharging energy demand is greater than the rated capacity of the energy storage system, then the energy storage system cannot continue to meet the charging demand after charging to the rated capacity. This is considered an energy surplus state, and the corresponding surplus energy m(t) can be expressed as:

[0092] m(t) = [E c (t-1,t)-E d (t-1,t)+b(t-1)-Q rate ] +

[0093] The specific parameters involved in this formula can be found in the corresponding physical meanings in the formulas above, and will not be repeated here.

[0094] Similar to steps 1-2-2), the probability P that the energy storage system is in an energy surplus state at any time t can be derived. M (t) satisfies:

[0095]

[0096] The specific parameters involved in this formula can be found in the corresponding physical meanings in the formulas above, and will not be repeated here.

[0097] Similarly, this formula determines the upper limit of the probability that the energy storage system is in a state of energy surplus during operation.

[0098] 1-3) Construction of evaluation indicators for the energy balance capability of energy storage systems

[0099] When the energy storage system does not belong to either of the two states described in steps 1-2-2) and 1-2-3), it can be considered that the energy storage is in an energy balance state. That is, the configured energy storage capacity can meet the system's charging and discharging requirements, and the sum of the probabilities of the energy storage system being in various energy states at any given time is 1. Then, the probability P of the energy storage system being in an energy balance state at time t can be obtained. o (t) satisfies:

[0100]

[0101] in, These represent the upper probability limits of the energy storage system being in an energy surplus state and an energy deficit state at time t, respectively. This is the lower limit of the probability that the energy storage system is in an energy balance state at time t; other parameters involved in this formula can be referred to in the specific physical meanings of the formulas above, and will not be elaborated here.

[0102] Furthermore, based on Construct an energy balance capability index B for an energy storage system o B measures the overall ability of the configured energy storage capacity to cope with uncertain charging and discharging demands during system operation. o The expression is as follows:

[0103]

[0104] Where T is the number of moments in a day; B o The physical meaning of this formula is the lower limit of the average probability that the energy storage will be in an energy balance state during the system's operating cycle under a certain capacity configuration. Other parameters involved in this formula can be referred to in the specific physical meanings of the formulas above, and will not be elaborated here.

[0105] The trend of the energy balance capability index of the energy storage system with the rated capacity obtained in this embodiment is shown in the attached figure. Figure 5 As shown.

[0106] Step 102: Determine the dual-layer collaborative optimization model of energy storage capacity corresponding to the auxiliary unit frequency regulation system; the dual-layer collaborative optimization model of energy storage capacity includes an outer optimization model that uses the energy balance capability index of the energy storage system as a constraint to optimize the cost of energy storage configuration strategy and an inner optimization model that optimizes the frequency regulation performance of the auxiliary unit frequency regulation system.

[0107] In this embodiment of the invention, the specific implementation process of determining the dual-layer collaborative optimization model for energy storage capacity corresponding to the auxiliary unit frequency regulation system includes: taking the optimal daily net income of the auxiliary unit frequency regulation system as the optimization objective, the rated power and rated capacity of the energy storage system as decision variables, and the energy balance capability index of the energy storage system as constraints, to determine the economic optimization layer model corresponding to the auxiliary unit frequency regulation system; and using the economic optimization layer model as the outer optimization model of the dual-layer collaborative optimization model for energy storage capacity; taking the optimal average comprehensive frequency regulation performance index of the auxiliary unit frequency regulation system as the optimization objective, and using the output of the energy storage system at each time as decision variables, to determine the operation optimization layer model corresponding to the auxiliary unit frequency regulation system; and using the operation optimization layer model as the inner optimization model of the dual-layer collaborative optimization model for energy storage capacity; and obtaining the dual-layer collaborative optimization model for energy storage capacity corresponding to the auxiliary unit frequency regulation system based on the outer optimization model and the inner optimization model. The optimization objective of achieving the optimal daily average net revenue of the auxiliary unit frequency regulation system can be defined as ensuring that the daily average net revenue of the auxiliary unit frequency regulation system meets a preset target revenue condition. Similarly, the optimization objective of achieving the optimal average comprehensive frequency regulation performance index of the auxiliary unit frequency regulation system can be defined as ensuring that the average comprehensive frequency regulation performance index of the auxiliary unit frequency regulation system meets a preset index average optimization condition.

[0108] In the specific implementation process, "taking the optimal daily net income of the auxiliary unit frequency regulation system as the optimization objective, the rated power and rated capacity of the energy storage system as decision variables, and the energy balance capability index of the energy storage system as the constraint condition, determine the economic optimization layer model corresponding to the auxiliary unit frequency regulation system" (corresponding to step 2).

[0109] like Figure 2 As shown, in this embodiment of the invention, step 2) specifically includes: taking the optimal daily net revenue of the auxiliary unit frequency regulation system as the optimization objective, the rated power and rated capacity of the energy storage system as decision variables, and the energy balance capability index of the energy storage system as constraints, constructing an economic optimization layer model as the outer layer model of the energy storage capacity dual-layer collaborative optimization model, thereby ensuring the economy of the energy storage configuration strategy. Specifically, to fully consider the impact of charge / discharge depth on the energy storage cycle life, a method based on rainflow counting is used to calculate its equivalent life loss. To consider the impact of uncertainty on energy storage capacity demand, the energy balance capability index of the energy storage system established in step 1) is used as a constraint condition of the outer layer model. The specifically constructed outer layer optimization model is as follows:

[0110] 2-1) First, determine the objective function.

[0111] max C NI =C inc -C′ LCC

[0112] Among them, C NI To provide the equivalent daily net income of the auxiliary unit's frequency regulation system during operation; C inc C' represents the frequency modulation compensation benefit obtained by the system during operation. LCC This refers to the discounted cost of energy storage throughout its entire life cycle during operation.

[0113] Frequency modulation compensation benefit C inc The calculation can refer to the actual target area AGC service compensation assessment method, and is calculated based on the daily frequency regulation mileage with a daily settlement cycle:

[0114]

[0115]

[0116]

[0117] Among them, T d D represents the number of operating days included in the selected operating cycle. d K represents the total frequency regulation mileage on the d-th operating day; d R represents the average of the comprehensive frequency regulation performance indicators on the d-th operating day; AGC The unit price for compensation of FM mileage; N d D represents the number of adjustments made on the d-th operating day. n d K n int，d This refers to the frequency regulation mileage and comprehensive frequency regulation performance index corresponding to the nth adjustment on the dth operating day. In this embodiment, T d =9,RAGC =15 yuan / MW.

[0118] C' LCC The calculation expression is as follows:

[0119] C L ′ CC =L d ·C LCC

[0120]

[0121] Among them, C LCC L represents the total cost over the entire lifecycle of the energy storage system. d (0≤L d ≤1) represents the daily equivalent lifetime loss coefficient during the operation of the energy storage system, which can be calculated using a method based on rainflow counting; N c D represents the number of energy storage charge-discharge cycles calculated using the rainflow counting method. DOD，j N represents the depth of charge / discharge corresponding to the j-th charge / discharge cycle; ctf (D DOD，j () represents the depth of charge / discharge as D DOD，j The cycle life at time N can be obtained by fitting the function relationship based on the performance parameters of the energy storage battery; ctf (D DOD，st ) represents the cycle life corresponding to a charge / discharge depth of 100%; when L d When the value is 1, the energy storage battery is considered to have reached the end of its lifespan.

[0122] C LCC The calculation expression is as follows:

[0123] C LCC =C I +C OM +C S

[0124] C I =(1+λ) bop )·[k q Q rate +(k p +k pcs )P rate ]

[0125] C OM =λ om C I

[0126] C S =(k sp P rate +k sq Qrate )·γ PF

[0127]

[0128] N T =1 / 365 / L d

[0129] Among them, C I C represents the fixed investment cost of an energy storage system during the initial construction phase; OM For operation and maintenance costs; C S The costs incurred during the dismantling and disposal of energy storage batteries in the end-of-life phase; P rate Q rate It is divided into rated power and rated capacity of energy storage systems; k p k q These are the unit price per unit of power and the unit price per unit of capacity for energy storage systems; k pcs λ is the unit price of the power conversion equipment. bop This is the ratio of civil engineering costs to the cost of the energy storage system itself, approximately 5% to 15%; λ om k is the ratio of operation and maintenance costs to system fixed investment costs. sp k sq The unit price for power processing and the unit price for capacity processing of energy storage systems; γ PF The discount factor from future value to present value; r is the depreciation rate; N T This refers to the number of years the energy storage system can operate.

[0130] In this embodiment, k p =2000 yuan / kW, k q =1500 yuan / kWh, k pcs =800 yuan / kW, λ bop =10%, λ om =5%, k sp =120 yuan / kW, k sq =80 yuan / kWh, r=0.08.

[0131] 2-2) Constraints.

[0132] The constraints of the economic optimization layer model mainly include the limit constraints on the rated power and rated capacity of the energy storage system, the limit constraints on the investment funds of the energy storage system, and the limit constraints on the energy balance capacity index of the energy storage system, as follows:

[0133] 0≤C LCC ≤C max

[0134] B o ≥B o,min

[0135] 0≤P rate ≤k p,max ·G

[0136] 0≤Q rate ≤k q,max ·P rate

[0137] Among them, C max G represents the allowable limit for investment planning costs of energy storage systems; G represents the power capacity of traditional generating units; B represents... o，min This represents the lower limit of the energy balance capability index for energy storage systems; k p，max This refers to the limit on the ratio of rated power of the energy storage system to the size of the generating unit; k q，max The duration (in hours) of continuous charging and discharging under rated operating conditions for the energy storage system.

[0138] In one specific embodiment, G = 330MW, C max =66 million yuan, when investment funds are tight B o，min =0.1, when investment funds are sufficient B o，min =0.5, k p，max =10%, k q，max = 2 hours, but no specific limit is given here.

[0139] In the specific implementation process, "taking the optimal average comprehensive frequency regulation performance index of the auxiliary unit frequency regulation system as the optimization objective, and taking the output of the energy storage system at each moment as the decision variable, the corresponding operation optimization layer model of the auxiliary unit frequency regulation system is determined" (corresponding to step 3).

[0140] like Figure 2 As shown, in this embodiment of the invention, step 3) specifically includes: taking the optimal average comprehensive frequency regulation performance index of the auxiliary unit frequency regulation system as the optimization objective, the output of energy storage at each time as the decision variable, and the system operating parameter constraints and energy storage system operating parameters as constraints, constructing an operation optimization layer model as the inner layer model of the energy storage capacity dual-layer collaborative optimization model, thereby ensuring the frequency regulation performance of the auxiliary unit frequency regulation system during operation. Specifically, to reduce the difficulty of solving the inner layer optimization model, the calculation process of each evaluation index is linearized based on the actual target area AGC auxiliary service frequency regulation index calculation rules, and the state of charge of the energy storage system at the end of each day is limited, thereby weakening the connection between different days, facilitating piecewise optimization of the inner layer model during the solution process, and improving solution efficiency. The specifically constructed inner layer optimization model is as follows:

[0141] 3-1) Determined objective function:

[0142]

[0143]

[0144] Among them, K eq The average value of the comprehensive frequency regulation performance index of the auxiliary unit's frequency regulation system; N AGC K represents the total number of AGC adjustments during operation. i int Let k be the comprehensive frequency modulation performance index corresponding to the i-th adjustment; i 1. k i 2, k i 3 represents the adjustment rate index, adjustment accuracy index, and response time index for the i-th adjustment, respectively. To avoid the high nonlinearity of the objective function leading to difficulty in solving, a linear transformation was performed based on the frequency modulation index calculation rules for AGC auxiliary services in the actual target area. α1, α2, and α3 are the weighting coefficients of each sub-index in the comprehensive frequency modulation performance index, obtained by fitting actual operating data. The calculation methods for each sub-index are as follows:

[0145] 3-1-1) Adjustment rate index:

[0146]

[0147]

[0148]

[0149]

[0150] Among them, P A,i T is the AGC adjustment command corresponding to the i-th adjustment; start,i P is the starting time of the i-th adjustment; U For combined power output from thermal power units and energy storage; v ref =G × 1.5% (MW / min), that is, the reference regulation rate is set to 1.5% of the unit's rated power; ΔT1, ΔP 1,N These are the theoretically calculated values ​​of the adjustment time and the cumulative average deviation between the combined output and the target output when the system adjusts according to the reference adjustment rate; ΔP 1,i The actual value of the cumulative average deviation between the combined output and the target output during the adjustment process over the time interval ΔT1 is compared with ΔP. 1,N Compared to the evaluation index k1 for obtaining the regulation rate i .

[0151] 3-1-2) Adjustment accuracy index:

[0152]

[0153]

[0154] Where, ΔP 2,i Let ΔP be the deviation (MW) of the i-th AGC adjustment; 2,N To adjust for the allowable deviation, take 1% (MW) of the unit's rated power.

[0155] 3-1-3) Response time metrics:

[0156] P s1 =min{0.5%×G,5MW}

[0157]

[0158]

[0159]

[0160] Among them, P s1 The dead zone for AGC regulation is defined as the point at which the combined output of the machine and storage reliably crosses the dead zone, indicating that the system begins to respond to regulation commands. i ref =30s, which is the reference response time; ΔP 3,N ΔP is the theoretically calculated value of the cumulative average deviation between the combined output and the target output when the system reaches the action dead zone at the reference response time. 3,i To adjust the process at t i ref The actual value of the cumulative average deviation between the combined output and the target output over the time period is compared with ΔP. 3,N Compared to the equivalent evaluation index k3 for obtaining response time i .

[0161] 3-2) Constraints. The constraints of the optimization layer model mainly consist of the operational constraints of the energy storage system, as follows:

[0162] -P rate ≤P b,t ≤P rate

[0163] S t+1 =S t +P b,t ×Δt×η t / Q rate

[0164]

[0165] S min ≤S t ≤S max

[0166] 0.5-Δδ≤S t (d,t end )≤0.5+Δδ

[0167] Among them, P b，t Let P be the charging and discharging power of the energy storage system at time t. b，t Energy storage charging is performed when >0, P b,t <0 indicates energy storage and discharge; S t S represents the state of charge of the stored energy at time t; min S max These are the upper and lower limits of the permissible state of charge during energy storage charging and discharging, respectively; η t Let ηc and η be the charge / discharge efficiency of the stored energy at time t. d These represent the charging efficiency and discharging efficiency of energy storage, respectively; S t (d,t end ) represents the state of charge (SOC) value of the stored energy at the end of the d-th operating day; Δδ represents the allowable fluctuation range of the SOC. In a specific embodiment, S min =0.9, S max =0.1, η c =η d =95%, Δδ=0.05, of course, no specific limit is made here.

[0168] Step 103: Based on the characteristics of the dual-layer collaborative optimization model of energy storage capacity, perform analysis and processing using a preset model optimization analysis method to determine the optimal energy storage configuration strategy corresponding to the energy storage system.

[0169] The preset model optimization analysis methods include, but are not limited to, using preset particle swarm optimization algorithm models and CPLEX optimization solvers. Specifically, the preset particle swarm optimization algorithm model is used to analyze the outer optimization model included in the dual-layer collaborative optimization model of energy storage capacity; and the preset CPLEX optimization solver is used to analyze the inner optimization model included in the dual-layer collaborative optimization model of energy storage capacity, to obtain the optimal energy storage configuration strategy corresponding to the energy storage system.

[0170] In the specific implementation process, "the outer optimization model contained in the capacity bilayer collaborative optimization model is analyzed using a preset particle swarm optimization algorithm model; and the inner optimization model contained in the capacity bilayer collaborative optimization model is analyzed using a preset CPLEX optimization solver to obtain the optimal energy storage configuration strategy corresponding to the energy storage system" corresponds to step 4).

[0171] like Figure 6As shown, step 4) uses the Particle Swarm Optimization (PSO) algorithm and the CPLEX solver to solve the inner and outer optimization models constructed in steps 2) and 3). To address the problem of the inner optimization model being too large in scale to solve, a piecewise optimization method with a daily optimization cycle is used. Furthermore, the optimization processes of the inner and outer optimization models are nested within each other. The solution process is shown in the attached figure. Figure 6 As shown. The specific solution process is as follows:

[0172] 4-1) Initialization of economic layer (outer layer) parameters: Particle X = {x1 = P} rate x2=Q rate Determine the swarm particle size N. PSO Number of iterations I max The parameters of inertia factor w and acceleration factor (c1, c2) are used to randomly initialize the position and velocity of each particle, and to determine the coefficients of each item in the economic layer objective function and constraints; in this embodiment, N PSO =20, I max =50, w=0.5, c1=c2=0.2;

[0173] 4-2) Optimize the inner layer (running layer) for each particle:

[0174] 4-2-1) Operation layer parameter setting: Obtain AGC commands and unit output operation data, set the rated power and rated capacity of energy storage according to the current particle parameters, and determine the coefficients of each item in the objective function and constraint conditions of the operation layer;

[0175] 4-2-2) Based on the CPLEX solver, with an optimization cycle of 24 hours, the optimal running scheme for each running day under the current particle parameters is solved one by one;

[0176] 4-2-3) Combine the optimization results of each operating day and output the energy storage output process P. b,t And the frequency modulation performance evaluation index K for each AGC adjustment i int ;

[0177] 4-3) Based on the output of step 4-2), calculate the fitness function C of each particle in the outer model population. NI ;

[0178] 4-4) Set the individual historical best position of each particle to the current particle position, and update the group's best position;

[0179] 4-5) Update the position and velocity of each particle, and reset the position of particles that are outside the search space;

[0180] 4-6) Determine if the maximum number of iterations for the outermost layer has been reached. If it has, proceed to step 4-7); otherwise, go to step 4-2.

[0181] 4-7) Output the optimal energy storage configuration strategy corresponding to the optimal particle parameters of the population.

[0182] By solving the dual-layer collaborative optimization model of energy storage capacity in this embodiment in step 4), the optimal energy storage configuration strategy corresponding to the conditions of ample investment funds and tight investment funds can be obtained, as shown in Table 1 below.

[0183] Table 1 Optimal Energy Storage Configuration Strategies under Different Investment Levels

[0184] Investment level <![CDATA[P rate / (MW)]]> <![CDATA[Q rate / (MWh)]]> <![CDATA[C NI / (10,000 Yuan)]]> <![CDATA[C inc / (10,000 Yuan)]]> <![CDATA[C LLC / (10,000 Yuan)]]> Ample funds 10.67 8.45 3.62 6.90 4916 tight funds 7.87 4.12 2.56 5.15 3118

[0185] This invention employs a collaborative optimization method for the energy storage capacity of the auxiliary unit frequency regulation system. Based on the coupling relationship between energy storage capacity planning and system optimization operation, it constructs a two-layer collaborative optimization model for the energy storage capacity planning and operation of the auxiliary unit frequency regulation system. The outer-layer economic optimization model ensures the economic efficiency of the energy storage configuration strategy and reduces costs; the inner-layer operational optimization model ensures the frequency regulation performance of the auxiliary unit frequency regulation system. Furthermore, based on the envelope model of the uncertainty of energy storage charging and discharging demand, this invention defines an energy balance capability index for the energy storage system and uses it as a constraint condition for the outer-layer optimization model to consider the impact of uncertainty on energy storage capacity demand. Therefore, this invention can fully consider the impact of uncertainty factors on energy storage capacity demand, while simultaneously taking into account the coupling relationship between energy storage capacity planning and system optimization operation. It provides guidance for the investment and construction of energy storage auxiliary unit frequency regulation systems involving uncertainty, improves resource utilization, effectively saves costs, and enhances system configuration robustness.

[0186] Corresponding to the above-described method for co-optimizing the energy storage capacity of an auxiliary unit frequency regulation system, this invention also provides a device for co-optimizing the energy storage capacity of an auxiliary unit frequency regulation system. Since the embodiments of this device are similar to the above-described method embodiments, the description is relatively simple. For relevant details, please refer to the description in the above-described method embodiment section. The embodiments of the co-optimizing device for the energy storage capacity of an auxiliary unit frequency regulation system described below are merely illustrative. Please refer to... Figure 7 As shown, it is a structural schematic diagram of an energy storage capacity collaborative optimization device for an auxiliary unit frequency regulation system provided in an embodiment of the present invention.

[0187] The energy storage capacity collaborative optimization device for the auxiliary unit frequency regulation system described in this invention specifically includes the following parts:

[0188] Balance capability index determination unit 701 is used to determine the energy balance capability index of the energy storage system, which characterizes the energy storage response capability.

[0189] The collaborative optimization model determination unit 702 is used to determine the energy storage capacity dual-layer collaborative optimization model corresponding to the auxiliary unit frequency regulation system; the energy storage capacity dual-layer collaborative optimization model includes an outer optimization model that uses the energy balance capability index of the energy storage system as a constraint and is used to optimize the cost of energy storage configuration strategy, and an inner optimization model that is used to optimize the frequency regulation performance of the auxiliary unit frequency regulation system.

[0190] The optimal energy storage configuration strategy determination unit 703 is used to determine the optimal energy storage configuration strategy corresponding to the energy storage system by analyzing and processing the characteristics of the dual-layer collaborative optimization model of the energy storage capacity using a preset model optimization analysis method.

[0191] This invention employs a collaborative optimization device for the energy storage capacity of the auxiliary unit frequency regulation system. Based on the coupling relationship between energy storage capacity planning and system optimization operation, it constructs a two-layer collaborative optimization model for the energy storage capacity planning and operation of the auxiliary unit frequency regulation system. The outer-layer economic optimization model ensures the economic efficiency of the energy storage configuration strategy and reduces costs; the inner-layer operational optimization model ensures the frequency regulation performance of the auxiliary unit frequency regulation system. Furthermore, based on the envelope model of the uncertainty of energy storage charging and discharging demand, this invention defines an energy balance capability index for the energy storage system and uses it as a constraint condition for the outer-layer optimization model to consider the impact of uncertainty on energy storage capacity demand. Therefore, this invention can fully consider the impact of uncertainty factors on energy storage capacity demand, while simultaneously taking into account the coupling relationship between energy storage capacity planning and system optimization operation. It provides guidance for the investment and construction of energy storage auxiliary unit frequency regulation systems involving uncertainty, improves resource utilization, effectively saves costs, and enhances system configuration robustness.

[0192] Corresponding to the above-described method for coordinated optimization of energy storage capacity in auxiliary unit frequency regulation systems, this invention also provides an electronic device. Since the embodiment of this electronic device is similar to the above-described method embodiments, it is described simply. For relevant details, please refer to the description in the above-described method embodiment section. The electronic device described below is merely illustrative. Figure 8The diagram shown is a schematic representation of the physical structure of an electronic device disclosed in an embodiment of the present invention. The electronic device may include a processor 801, a memory 802, and a communication bus 803. The processor 801 and the memory 802 communicate with each other via the communication bus 803 and communicate with external systems via a communication interface 804. The processor 801 can call logical instructions in the memory 802 to execute a method for coordinated optimization of energy storage capacity in an auxiliary unit frequency regulation system. This method includes: determining an energy balance capability index of the energy storage system to characterize its response capability; determining a two-layer coordinated optimization model for energy storage capacity corresponding to the auxiliary unit frequency regulation system; the two-layer coordinated optimization model includes an outer optimization model constrained by the energy balance capability index of the energy storage system and used to optimize the cost of energy storage configuration strategies, and an inner optimization model used to optimize the frequency regulation performance of the auxiliary unit frequency regulation system; based on the characteristics of the two-layer coordinated optimization model, performing analysis using a preset model optimization analysis method to determine the optimal energy storage configuration strategy corresponding to the energy storage system.

[0193] Furthermore, the logical instructions in the aforementioned memory 802 can be implemented as software functional units and, when sold or used as independent products, can be stored in a computer-readable storage medium. Based on this understanding, the technical solution of the present invention, essentially, or the part that contributes to the prior art, or a part of the technical solution, can be embodied in the form of a software product. This computer software product is stored in a storage medium and includes several instructions to cause a computer device (which may be a personal computer, server, or network device, etc.) to execute all or part of the steps of the methods described in the various embodiments of the present invention. The aforementioned storage medium includes various media capable of storing program code, such as memory chips, USB flash drives, portable hard drives, read-only memory (ROM), random access memory (RAM), magnetic disks, or optical disks.

[0194] On the other hand, embodiments of the present invention also provide a computer program product, the computer program product including a computer program stored on a processor-readable storage medium, the computer program including program instructions, when the program instructions are executed by a computer, the computer can execute the energy storage capacity collaborative optimization method for the auxiliary unit frequency regulation system provided in the above-described method embodiments, the method including: determining an energy balance capability index of the energy storage system used to characterize the energy storage response capability; determining a two-layer collaborative optimization model of energy storage capacity corresponding to the auxiliary unit frequency regulation system; the two-layer collaborative optimization model of energy storage capacity includes an outer optimization model constrained by the energy balance capability index of the energy storage system and used to optimize the cost of energy storage configuration strategy and an inner optimization model used to optimize the frequency regulation performance of the auxiliary unit frequency regulation system; based on the characteristics of the two-layer collaborative optimization model of energy storage capacity, performing analysis and processing using a preset model optimization analysis method to determine the optimal energy storage configuration strategy corresponding to the energy storage system.

[0195] In another aspect, embodiments of the present invention also provide a processor-readable storage medium storing a computer program. When executed by a processor, the computer program implements the energy storage capacity collaborative optimization method for the auxiliary unit frequency regulation system provided in the above embodiments. The method includes: determining an energy balance capability index of the energy storage system to characterize the energy storage response capability; determining a two-layer collaborative optimization model of energy storage capacity corresponding to the auxiliary unit frequency regulation system; the two-layer collaborative optimization model of energy storage capacity includes an outer optimization model constrained by the energy balance capability index of the energy storage system and used to optimize the cost of energy storage configuration strategy, and an inner optimization model used to optimize the frequency regulation performance of the auxiliary unit frequency regulation system; and based on the characteristics of the two-layer collaborative optimization model of energy storage capacity, performing analysis and processing using a preset model optimization analysis method to determine the optimal energy storage configuration strategy corresponding to the energy storage system.

[0196] The processor-readable storage medium can be any available medium or data storage device that the processor can access, including but not limited to magnetic memory (e.g., floppy disk, hard disk, magnetic tape, magneto-optical disk (MO)), optical memory (e.g., CD, DVD, BD, HVD), and semiconductor memory (e.g., ROM, EPROM, EEPROM, non-volatile memory (NAND FLASH), solid-state drive (SSD)).

[0197] The device embodiments described above are merely illustrative. The units described as separate components may or may not be physically separate. The components shown as units may or may not be physical units; that is, they may be located in one place or distributed across multiple network units. Some or all of the modules can be selected to achieve the purpose of this embodiment according to actual needs. Those skilled in the art can understand and implement this without any creative effort.

[0198] Through the above description of the embodiments, those skilled in the art can clearly understand that each embodiment can be implemented by means of software plus necessary general-purpose hardware platforms, and of course, it can also be implemented by hardware. Based on this understanding, the above technical solutions, in essence or the part that contributes to the prior art, can be embodied in the form of a software product. This computer software product can be stored in a computer-readable storage medium, such as ROM / RAM, magnetic disk, optical disk, etc., and includes several instructions to cause a computer device (which may be a personal computer, server, or network device, etc.) to execute the methods described in the various embodiments or some parts of the embodiments.

[0199] Finally, it should be noted that the above embodiments are only used to illustrate the technical solutions of the present invention, and not to limit them; although the present invention has been described in detail with reference to the foregoing embodiments, those skilled in the art should understand that modifications can still be made to the technical solutions described in the foregoing embodiments, or equivalent substitutions can be made to some of the technical features; and these modifications or substitutions do not cause the essence of the corresponding technical solutions to deviate from the spirit and scope of the technical solutions of the embodiments of the present invention.

Claims

1. A method for collaborative optimization of energy storage capacity of an auxiliary unit frequency modulation system, characterized in that, The method comprises the following steps: determining an energy balance capability index of a storage system for representing a storage response capability; determining a storage capacity double-layer collaborative optimization model corresponding to an auxiliary unit frequency modulation system; the storage capacity double-layer collaborative optimization model comprises an outer optimization model for optimizing a storage configuration strategy cost and an inner optimization model for optimizing a frequency modulation performance of the auxiliary unit frequency modulation system, with the energy balance capability index of the storage system as a constraint condition; based on the characteristics of the storage capacity double-layer collaborative optimization model, a preset model optimization analysis method is used for analysis and processing to determine an optimal storage configuration strategy corresponding to the storage system; the energy balance capability index of the storage system is a lower limit value of an average probability of the storage system being in an energy balance state within a corresponding operation cycle under a target capacity configuration scale; the energy balance capability index of the storage system is used for representing a comprehensive response capability of the configured storage capacity of the storage system to uncertain charging and discharging demands in the operation process of the storage system.

2. The energy storage capacity collaborative optimization method for auxiliary unit frequency modulation systems of claim 1, wherein, The method of determining the energy balance capability index of the storage system for representing the storage response capability specifically comprises the following steps: acquiring automatic generation control instruction data of a thermal power unit to be configured with a storage and actual output data of the thermal power unit, and determining charging and discharging demand parameters of the storage system according to a deviation between the actual output data of the thermal power unit and the automatic generation control instruction data received by the thermal power unit; based on the charging and discharging demand parameters of the storage system, determining an envelope line representation model for representing uncertainty of charging and discharging energy demands of the storage system; based on the envelope line representation model, determining the energy balance capability index of the storage system for representing a response capability of the storage system to uncertainty.

3. The energy storage capacity collaborative optimization method for auxiliary unit frequency modulation systems of claim 2, wherein, Based on the envelope line representation model, the energy balance capability index of the storage system for representing a response capability of the storage system to uncertainty is determined, specifically comprising the following steps: determining an upper limit parameter of a probability of the storage system being in an energy deficiency state and an upper limit parameter of a probability of the storage system being in an energy surplus state in the operation process of the storage system based on the envelope line representation model; determining a probability value of the storage system being in an energy balance state according to the upper limit parameter of the probability of the storage system being in the energy deficiency state and the upper limit parameter of the probability of the storage system being in the energy surplus state; determining the energy balance capability index of the storage system according to the probability value of the storage system being in the energy balance state.

4. The energy storage capacity collaborative optimization method for auxiliary unit frequency modulation systems of claim 1, wherein, The method of determining the storage capacity double-layer collaborative optimization model corresponding to the auxiliary unit frequency modulation system specifically comprises the following steps: determining an economic optimization layer model corresponding to the auxiliary unit frequency modulation system with a daily average net income optimum of the auxiliary unit frequency modulation system as an optimization target, with a rated power and a rated capacity of the storage system as decision variables, and with the energy balance capability index of the storage system as a constraint condition; and taking the economic optimization layer model as an outer optimization model of the storage capacity double-layer collaborative optimization model. The average optimal comprehensive frequency modulation performance index of the auxiliary unit frequency modulation system is taken as an optimization target, and the output of the energy storage system at each time is taken as a decision variable to determine an operation optimization layer model corresponding to the auxiliary unit frequency modulation system; and the operation optimization layer model is taken as an inner optimization model of the energy storage capacity double-layer collaborative optimization model; Based on the outer optimization model and the inner optimization model, the energy storage capacity double-layer collaborative optimization model corresponding to the auxiliary unit frequency modulation system is obtained.

5. The energy storage capacity collaborative optimization method for auxiliary unit frequency modulation system according to claim 1, characterized in that, The energy storage optimal configuration strategy corresponding to the energy storage system is determined by using a preset model optimization analysis method based on the characteristics of the energy storage capacity double-layer collaborative optimization model, and specifically includes: The outer optimization model included in the energy storage capacity double-layer collaborative optimization model is analyzed by using a preset particle swarm optimization algorithm model; and the inner optimization model included in the energy storage capacity double-layer collaborative optimization model is analyzed by using a preset CPLEX optimization solver, to obtain the energy storage optimal configuration strategy corresponding to the energy storage system.

6. An energy storage capacity collaborative optimization device for an auxiliary unit frequency modulation system, characterized in that, It includes: The energy storage system energy balance capability index is used to represent the energy storage response capability, and a balance capability index determination unit is used to determine the energy storage system energy balance capability index; A collaborative optimization model determination unit is used to determine the energy storage capacity double-layer collaborative optimization model corresponding to the auxiliary unit frequency modulation system; the energy storage capacity double-layer collaborative optimization model includes an outer optimization model that takes the energy storage system energy balance capability index as a constraint condition and is used to optimize the energy storage configuration strategy cost, and an inner optimization model that is used to optimize the frequency modulation performance of the auxiliary unit frequency modulation system; An energy storage optimal configuration strategy determination unit is used to determine the energy storage optimal configuration strategy corresponding to the energy storage system by using a preset model optimization analysis method based on the characteristics of the energy storage capacity double-layer collaborative optimization model; The energy storage system energy balance capability index is a lower limit value of the average probability of the energy storage system being in an energy balance state within a corresponding operation cycle under a target capacity configuration scale; The energy storage system energy balance capability index is used to represent the comprehensive response capability of the configured energy storage capacity to uncertain charging and discharging demands in the energy storage system operation process.

7. The energy storage capacity collaborative optimization device for auxiliary unit frequency modulation system according to claim 6, characterized in that, The collaborative optimization model determination unit is specifically used to: An economic optimization layer model corresponding to the auxiliary unit frequency modulation system is determined by taking the daily average net income optimal of the auxiliary unit frequency modulation system as an optimization target, taking the rated power and rated capacity of the energy storage system as decision variables, and taking the energy storage system energy balance capability index as a constraint condition; and the economic optimization layer model is taken as the outer optimization model of the energy storage capacity double-layer collaborative optimization model; An operation optimization layer model corresponding to the auxiliary unit frequency modulation system is determined by taking the average optimal comprehensive frequency modulation performance index of the auxiliary unit frequency modulation system as an optimization target, and taking the output of the energy storage system at each time as a decision variable; and the operation optimization layer model is taken as the inner optimization model of the energy storage capacity double-layer collaborative optimization model; Based on the outer optimization model and the inner optimization model, the energy storage capacity double-layer collaborative optimization model corresponding to the auxiliary unit frequency modulation system is obtained.

8. An electronic device comprising a memory, a processor, and a computer program stored on the memory and executable on the processor, characterized in that, The processor implements the steps of the energy storage capacity collaborative optimization method of the auxiliary unit frequency modulation system according to any one of claims 1-5 when executing the program.

9. A processor-readable storage medium having stored thereon a computer program, characterized in that, The computer program is executed by the processor to implement the steps of the energy storage capacity collaborative optimization method of the auxiliary unit frequency modulation system according to any one of claims 1-5.

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