Nested double-layer optimization operation method for full flow battery energy storage system of active power distribution network

Abstract

The invention discloses a nested double-layer optimization operation method for an energy storage system of an all-flow battery of an active power distribution network, belongs to the technical field of electric energy storage optimization scheduling, and solves the problems that the dynamic performance modeling precision of an all-vanadium flow battery is low, and the maximum energy storage operation income and the optimal performance of the energy storage battery cannot be considered in the practical application of energy storage. The method comprises the following steps: establishing an uncertainty model of the dynamic performance of the all-vanadium redox flow battery; establishing a dynamic output characteristic model of the energy storage system; establishing an active power distribution network comprehensive operation economic index system; establishing a nested double-layer optimization operation model of the full flow battery energy storage system of the active power distribution network; a Cplex tool is adopted to carry out multi-level repeated iteration on the nested double-layer optimization operation model of the full flow battery energy storage system of the active power distribution network, and a nested double-layer optimization operation scheme of the energy storage system is solved; according to the method, the change track of the dynamic performance of the all-vanadium redox flow battery is described more accurately, safe and reliable operation of an energy storage system is realized, and the service life of the energy storage battery is prolonged.

Description

Technical Field

[0001] The present invention belongs to the technical field of power energy storage optimization and dispatching, and relates to a nested double-layer optimization operation method for a full liquid flow battery energy storage system of an active distribution network. Background Art

[0002] Active distribution network technology is becoming more and more mature. Energy-type battery energy storage systems, with their fast power regulation capabilities and the characteristics of both supply and storage capabilities, are the key to the active distribution network to achieve flexible regulation and local consumption of widely connected intermittent renewable energy. In the field of power energy storage, electrochemical energy storage applications are becoming increasingly large-scale. All-vanadium liquid flow batteries are easy to scale, have low self-discharge rates, are environmentally friendly, have low costs, long cycle life, high energy conversion efficiency, and good safety performance. They are currently the most suitable electrochemical energy storage technology for large-scale energy storage commercial applications. The optimization operation method of the all-vanadium liquid flow battery energy storage system for active distribution networks is to solve the most urgent needs of efficient consumption of renewable energy in active distribution networks, orderly flow of source-load power flow, and safe, reliable and efficient operation of energy storage batteries. Current battery energy storage researchers often regard energy storage batteries as a static ideal power element, ignoring the problem of ignoring the dynamic performance of energy storage batteries. This overly ideal simplification directly leads to a large deviation between the operating conditions of energy storage batteries and their rated operating points, and the operating mechanism of energy storage will be unbalanced. Moreover, ignoring the dynamic performance of the energy storage battery will inevitably cause the energy storage battery to be in a state of over-consumption, resulting in excessive internal temperature of the energy storage battery and a sharp reduction in life. Therefore, considering the dynamic performance of the battery in the optimization operation of the all-vanadium liquid flow battery energy storage system is crucial to ensure the safe and reliable operation of the energy storage battery.

[0003] At present, many domestic research institutions and personnel have carried out in-depth research on the dynamic performance and optimized operation of all-vanadium liquid flow batteries, and have unanimously pointed out that the performance of all-vanadium liquid flow batteries, such as efficiency and maximum absorbed power, has the characteristics of dynamic changes, and the operation control of all-vanadium liquid flow battery energy storage systems must focus on the dynamic performance of batteries. However, all-vanadium liquid flow batteries are a complex chemical energy storage medium, and their dynamic performance is indescribable. In addition, the disturbance of experimental data will also cause deviations in the dynamic performance modeling of all-vanadium liquid flow batteries, resulting in many uncertainties, which makes the modeling of the dynamic performance of all-vanadium liquid flow batteries more difficult. In addition, how to take into account the multi-dimensional operating benefits of the energy storage system and the improvement of the dynamic performance of the energy storage battery in the operation control of the all-vanadium liquid flow battery energy storage system is a problem that is ignored by current research, resulting in unreasonable operation of the energy storage system and greatly reducing the operating life of the energy storage battery.

[0004] In the prior art, the invention patent document "Optimization Method of Vanadium Flow Battery Energy Storage System for Multi-energy Active Distribution Network" with application publication number CN116345517A has a technical solution that models the dynamic charging and discharging efficiency of the vanadium flow battery energy storage system to obtain accurate vanadium flow battery operating conditions, and optimizes the planning of the vanadium flow battery energy storage system in combination with the multi-dimensional operating benefits of the vanadium flow battery. This solution takes into account the dynamic charging and discharging efficiency of the vanadium flow battery, but the efficiency modeling ignores the fitting error, and the planning of the energy storage system is only constrained by efficiency, which does not fundamentally improve the efficiency of the energy storage system. The invention patent document "Three-layer Optimization Scheduling Method for Active Distribution Network Based on Energy Storage Time-sharing State Decision" with application publication number CN107706933A has a technical solution that optimizes the scheduling of resources in the active distribution network area by dividing the charging and discharging time periods of energy storage devices in each area, combined with the goals of minimizing network losses and minimizing power generation costs, and extending the battery life. This solution effectively manages the output of the battery energy storage system by dividing the energy storage operation strategy into sections to extend the battery life. Its technology has good practicality, but there is uncertainty about the battery life and lacks modeling of battery life. The invention patent document "Operation Optimization Method of All-vanadium Liquid Flow Battery Energy Storage System Based on Gravitational Search Algorithm" with application publication number CN111293336A, its technical solution key points are to establish a mathematical model of all-vanadium liquid flow battery, and use gravitational search algorithm to optimize the instantaneous energy efficiency of all-vanadium liquid flow battery in real time to obtain the highest instantaneous energy efficiency when running under different SOC. This scheme optimizes the all-vanadium liquid flow battery, but does not consider the multi-dimensional operating benefits of vanadium flow battery in the active distribution network. This unilateral optimization of energy storage battery is rarely combined with effective technologies in actual energy storage application scenarios. In the actual application of all-vanadium liquid flow battery energy storage, it is necessary to take into account the maximum energy storage operation benefits and the optimal performance of energy storage batteries, and construct a nested double-layer optimization operation model of active distribution network battery energy storage system considering dynamic performance, which effectively supplements the current technology.

[0005] In summary, the existing technology lacks modeling of the uncertainty of the dynamic performance of all-vanadium liquid flow batteries, and at the same time cannot take into account the technical problem of maximizing the energy storage operation benefits and optimizing the energy storage battery performance in actual energy storage applications. Summary of the invention

[0006] The technical solution of the present invention is used to solve the problem that the dynamic performance modeling accuracy of all-vanadium liquid flow batteries in the prior art is low, and at the same time, in actual energy storage applications, both the maximum energy storage operation benefit and the optimal energy storage battery performance cannot be taken into account.

[0007] The present invention solves the above technical problems through the following technical solutions:

[0008] A nested double-layer optimization operation method for a full liquid flow battery energy storage system in an active distribution network comprises the following steps:

[0009] Step 1: Establish an uncertainty model for the dynamic performance of all-vanadium liquid flow batteries;

[0010] Step 2: Considering the direct power impact, indirect power impact and indirect state impact of the dynamic performance of the all-vanadium liquid flow battery on the battery output power, a dynamic output characteristic model of the energy storage system is established, and the battery state of charge value SoC is corrected and calculated;

[0011] Step 3: Through multi-angle analysis of the functional and economic effects of energy storage systems connected to active distribution networks, each functional indicator is equivalently discounted with economic benefits, and an active distribution network comprehensive operation economic indicator system is established;

[0012] Step 4: Taking the dynamic performance of the all-vanadium liquid flow battery as the inner optimization target and the comprehensive operation economy of the active distribution network as the outer optimization target, a nested double-layer optimization operation model of the active distribution network all-liquid flow battery energy storage system taking into account the dynamic performance is established through the mutual transmission and coupling of the inner and outer layers of the energy storage output power and the battery charge state;

[0013] Step 5: Use the Cplex tool to perform multi-level iterations on the nested double-layer optimization operation model of the full-liquid flow battery energy storage system in the active distribution network, and solve the nested double-layer optimization operation scheme of the energy storage system under the coordinated optimization of battery performance and economy.

[0014] Furthermore, the method for establishing the uncertainty model of the dynamic performance of the all-vanadium liquid flow battery described in step 1 is as follows:

[0015] Step 1.1, through the charging and discharging power experimental test of the all-vanadium liquid flow battery, the equivalent loss analysis of the all-vanadium liquid flow battery is carried out, the heat loss power and output power of the battery at different charging and discharging rates are analyzed, and the efficiency data matrix set A of the battery is constructed;

[0016] Step 1.2: Based on the battery efficiency data matrix set A, the charging efficiency η of the all-vanadium liquid flow battery is established by fitting the parameters using the least squares method and taking into account the fitting errors of the battery discharge efficiency and charging efficiency. c and discharge efficiency η d The mathematical model is as follows:

[0017]

[0018] Among them, T a is the battery temperature, P VRB,d (t) is the charge and discharge power of the all-vanadium liquid flow battery at time t, SoC is the state of charge of the battery, z i (i=a,b,c),y i (i=a,b,c,d), is the fitting coefficient, Δε d and Δε c are the fitting errors of battery discharge efficiency and charging efficiency respectively; η d represents the discharge efficiency of the all-vanadium liquid flow battery, η c represents the charging efficiency of the all-vanadium liquid flow battery;

[0019] Step 1.3: Considering the uncertainty of the fitting error of the battery discharge efficiency and charging efficiency, the interval method is used to calculate the fitting error Δε of the battery discharge efficiency. d and the fitting error Δε of the battery charging efficiency c To model:

[0020]

[0021] in, and T a , P VRB and the weight coefficient of SoC, and T a , P VRB The influence factors of SoC on battery efficiency, and their interval models are:

[0022]

[0023]

[0024] in, T a Factors affecting battery efficiency The lower and upper limits of P VRB Factors affecting battery efficiency The lower and upper limits of The impact factors of SoC on battery efficiency are The lower and upper limits of

[0025] Step 1.4, through the static experimental test of the all-vanadium liquid flow battery under different SoCs, according to the time and rate analysis of the battery self-discharge to the end of the SoC, the battery self-discharge rate data matrix set B is constructed; according to the battery self-discharge rate data matrix set B, the battery self-discharge rate λ is fitted with a polynomial as follows:

[0026] λ=SoC(g a T a +g b )+g c T a +Δλ t (7)

[0027] Among them, g i (i=a,b,c) is the fitting coefficient, Δλ t is the fitting error;

[0028] Due to Δλ t The uncertainty of Δλ t The value changes around 0, using Gaussian distribution, for Δλ t The modeling is as follows:

[0029] Δλ t ~N(0,δ t 2 ) (8)

[0030] Among them, δ t is Δλ t The standard deviation is affected by T a and the impact of SoC, according to the formula Calculate, β x (x=SoC,T a ) is the weight coefficient, T a and SoC for δ t The impact value of

[0031] Step 1.5: Test the battery charge and discharge capacity attenuation experiment, taking into account the temperature, rate and discharge capacity A. h The impact of battery capacity decay, combined with the fitting error and the uncertainty of experimental data, is calculated according to the Arrhenius empirical formula for the capacity decay rate γ of the all-vanadium liquid flow battery. c Perform the fit:

[0032]

[0033] Among them, B is the pre-exponential factor, k1 and k2 are linear factors, z is the power rate factor, C rate is the magnification, R is Avogadro's constant, Δγ c is the fitting error caused by the uncertainty of the experimental data and is calculated as follows:

[0034]

[0035]

[0036] in, and is the number of cycles N c , Temperature T a And the magnification C rate The weight factor of and N c 、Ta and C rate The capacity attenuation factor of N c Capacity attenuation factor The lower and upper limits of T a Capacity attenuation factor The lower and upper limits of C rate Capacity attenuation factor The lower and upper limits of

[0037] Step 1.6: Consider the temperature T b The influence of the discharge depth DoD on the life κ of the all-vanadium redox flow battery is modeled according to the polynomial fitting and considering the fitting error of the battery life κ as follows:

[0038]

[0039] Among them, μ i (i=a,b) is the fitting coefficient, N κ is the binomial fitting relationship of the least squares method, Δκ t is the fitting error of the battery life κ; Δκ t It obeys the normal distribution and is calculated as follows:

[0040] Δκ t ~N(0,υ t 2 ) (15)

[0041]

[0042] Among them, α x (x=DoD,T b ) is the weight coefficient, is the depth of discharge DoD and temperature T b right The impact value.

[0043] Furthermore, the method described in step 2 for considering the direct power impact, indirect power impact and indirect state impact of the dynamic performance of the all-vanadium liquid flow battery on the battery output power, establishing a dynamic output characteristic model of the energy storage system, and correcting and calculating the battery state of charge value SoC is as follows:

[0044] Step 2.1: Consider the maximum absorbed power of the all-vanadium liquid flow battery and the maximum apparent power S of the all-vanadium redox flow battery VRBk,max The direct charge effect Δf1 on the battery output power is modeled as follows:

[0045]

[0046] Among them, Q VRB is the output reactive power of the all-vanadium liquid flow battery energy storage system, is the rated power of the energy storage battery;

[0047] Step 2.2: Consider the battery self-discharge rate λ, capacity attenuation and efficiency η c or η d The throughput loss value of the all-vanadium liquid flow battery energy storage system during state transfer, that is, the indirect power impact Δf2, is modeled:

[0048]

[0049] in, Indicates the maximum charge and discharge time, η c / d Indicates the charging or discharging efficiency of the battery;

[0050] Step 2.3: Use the rated cycle life to perform a linear conversion on the indirect state effect Δf3. The linear conversion formula is as follows:

[0051]

[0052] Among them, κ rated is the rated cycle life of the all-vanadium liquid flow battery, DoD kmax Indicates the maximum discharge depth of the battery, DoD k Indicates the depth of discharge of the battery. Indicates the rated capacity of the energy storage battery;

[0053] Step 2.4: Consider the direct power impact f1, indirect power impact f2 and indirect state impact f3 of the dynamic performance of the all-vanadium liquid flow battery on the output power of the all-vanadium liquid flow battery energy storage system, and the impact value ΔP on the output power of the all-vanadium liquid flow battery energy storage system VRB,k and ΔQ VRB,k To model:

[0054]

[0055] Where ΔP VRB,k Indicates the active power impact value, ΔQ VRB,k Indicates the reactive power impact value;

[0056] Step 2.5: Consider the impact of battery efficiency, capacity attenuation, and self-discharge rate on the battery state of charge value SoC, and perform a correction calculation on the battery SoC value:

[0057]

[0058] Among them, SoC t and SoC t-1 They represent the state of charge at time t and time t-1 respectively, E VRB Indicates the rated capacity of VRB, P VRB Indicates the output power of the energy storage.

[0059] Furthermore, the method described in step 3 is to establish an active distribution network comprehensive operation economic index system by conducting a multi-angle analysis of the functional and economic effects of the energy storage system connected to the active distribution network, performing an equivalent discount on each functional index with economic benefits, and specifically as follows:

[0060] Step 3.1: Evaluate the wind and solar curtailment loss C of active distribution network energy storage by total wind and solar curtailment and price factor AB , the calculation formula is as follows:

[0061]

[0062] Among them, λ c is the price factor for abandoned wind and solar power, P Lk is the load. ω,i is the output of wind power, P PV,j is the photovoltaic output, P grid,t is the power of the tie line, P Lk is the load demand;

[0063] Step 3.2: Greenhouse gas emission reduction benefits C EB The value is calculated using the following formula:

[0064]

[0065] Where N = 3, P grid,in Represents the all-vanadium liquid flow battery energy storage system and renewable energy sources do not participate in the active distribution network energy management, and the real-time exchange power between the active distribution network and the main grid; ζ grid,j represents the unit density of greenhouse gas j, c σ,j represents the penalty cost per unit of greenhouse gas, and its value is estimated by the governance cost per unit of greenhouse gas j;

[0066] Step 3.3: Calculate the replacement cost C of the energy storage battery RP , the calculation formula is as follows:

[0067]

[0068] Among them, C in-VRB The initial investment cost of the all-vanadium liquid flow battery energy storage system;

[0069] Step 3.4: Calculate the power purchase cost C of the active distribution network PE and electricity sales revenue C SE , the calculation formulas are as follows:

[0070]

[0071] Among them, c pt and c st They are the unit electricity purchase price and the unit electricity sales price respectively;

[0072] Step 3.5: Calculate the operation and maintenance cost C of the energy storage system OM , the calculation formula is as follows:

[0073]

[0074] Among them, P it is the output of the all-vanadium liquid flow battery energy storage system, i is the number of all-vanadium liquid flow battery energy storage systems, K OMi It represents the operation and maintenance factor;

[0075] Step 3.6: Calculate the direct economic benefits C brought by the all-vanadium liquid flow battery energy storage system through its own output to reduce peak loads and fill valleys ID1 , and the all-vanadium liquid flow battery energy storage system absorbs wind and solar renewable energy, reducing the amount of electricity purchased by the active distribution network and bringing indirect economic benefits C ID2 , the calculation formulas are as follows:

[0076]

[0077] Among them, c lt Represents the unit load interruption loss cost, P ωa,i and P PVa,i They represent the actual active power consumption values ​​of the i-th wind turbine generator set and the i-th photovoltaic unit respectively.

[0078] Furthermore, the method described in step 4 is to take the dynamic performance of the all-vanadium liquid flow battery as the inner optimization target, take the comprehensive operation economy of the active distribution network as the outer optimization target, and establish a nested double-layer optimization operation model of the active distribution network all-liquid flow battery energy storage system taking into account the dynamic performance through the mutual transmission and coupling of the inner and outer layers of the energy storage output power and the battery charge state as follows:

[0079] Step 4.1: Limit the uncertainty range of the all-vanadium liquid flow battery energy storage system through the upper and lower limits of the battery dynamic performance parameters:

[0080]

[0081] Step 4.2: Obtain constraints from the dynamic output characteristic model of the energy storage system, restrict the dynamic output characteristics of the battery, and restrict the SoC of the energy storage battery; the constraints are as follows:

[0082]

[0083] in, Indicates the battery state of charge, SoC k,min Indicates the minimum value of battery state of charge;

[0084] Step 4.3: Taking the dynamic performance of the all-vanadium liquid flow battery as the inner optimization target and the comprehensive operation economy of the active distribution network as the outer optimization target, a nested two-layer objective function is constructed, where the inner and outer objective functions are:

[0085] F1=max{ω1η c +ω2η d +ω3κ-ω4λ-ω5γ c} (32)

[0086] F2=max{C EB +C SE +C ID -C OM -C PE -C RP -C AB} (33)

[0087] Among them, ω1, ω2, ω3, ω4, ω5 are weight coefficients, C ID Economic benefits, including direct economic benefits C ID1 , and indirect economic benefits C ID2 .

[0088] Furthermore, the Cplex tool described in step 5 is used to perform multi-level iterations on the nested double-layer optimization operation model of the full liquid flow battery energy storage system of the active distribution network. The method for solving the nested double-layer optimization operation scheme of the energy storage system under the coordinated optimization of battery performance and economy is as follows:

[0089] Step 5.1, according to the uncertainty range of the dynamic performance of the energy storage battery, combined with the uncertainty modeling of the dynamic performance of the energy storage battery, obtain the change trend of the dynamic performance of the energy storage battery;

[0090] Step 5.2: Set the electricity price parameter c pt and c st The value of the predicted load demand P LK And wind and solar output P ωa and P PVa The value of

[0091] Step 5.3: Call the Cplex tool to solve the inner and outer layers respectively, and the inner layer outputs the output of the energy storage system The state of charge of the outer output energy storage battery

[0092] Step 5.4: External optimization: the state of charge of the energy storage battery Passed to the inner layer to update the actual state value of the energy storage battery. The inner layer optimizes the output of the energy storage battery. Passed to the outer layer for updating and calculating the dynamic performance of the energy storage battery;

[0093] Step 5.5: Determine whether the number of iterations is greater than N c If yes, proceed to the next step; if no, return to step 5.3;

[0094] Step 5.6: η c ,η d ,κ,λ,γ and A nested double-layer optimized operation scheme for the full liquid flow battery energy storage system in the active distribution network.

[0095] An electronic device includes a memory and a processor, wherein the memory is used to store a program that supports the processor to execute the above-mentioned nested double-layer optimization operation method of the active distribution network full-liquid flow battery energy storage system, and the processor is configured to execute the program stored in the memory.

[0096] A storage medium having a computer program stored thereon, wherein when the computer program is run by a processor, the steps of the nested double-layer optimization operation method of the active distribution network full liquid flow battery energy storage system are executed.

[0097] The advantages of the present invention are:

[0098] Aiming at the problems of low applicability and limited accuracy of the dynamic performance empirical formula of all-vanadium liquid flow batteries in complex practical situations, the present invention considers the randomness of fitting errors and the uncertainty of experimental data, and introduces an uncertainty modeling method to perform dynamic mathematical modeling on the fitting error of the dynamic performance empirical formula of all-vanadium liquid flow batteries. Based on this, an uncertainty model of the dynamic performance of all-vanadium liquid flow batteries is constructed, which more accurately describes the changing trajectory of the dynamic performance of all-vanadium liquid flow batteries, and provides a more reliable theoretical basis for the optimization of battery management systems.

[0099] The present invention conducts an in-depth analysis of the direct, indirect and indirect effects of the dynamic efficiency, dynamic self-discharge rate, dynamic capacity attenuation and dynamic life of the all-vanadium liquid flow battery on the output power of the battery, and performs a linear conversion according to the rated cycle life of the battery, and performs an equivalent modeling of the impact value of the output power of the all-vanadium liquid flow battery. Based on this, the battery state of charge value SoC is corrected and calculated, and the output characteristics of the all-vanadium liquid flow battery are accurately evaluated, providing a strong technical support for the temptation operation of the all-vanadium liquid flow battery.

[0100] The present invention aims at the optimization operation problem of all-vanadium liquid flow batteries in practical engineering applications. The present invention adopts the mutual transmission and coupling of the inner and outer layers of the energy storage output power and the battery charge state to construct a nested double-layer optimization operation model of the active distribution network battery energy storage system taking into account the dynamic performance. The inner layer takes the dynamic performance of the all-vanadium liquid flow battery as the optimization target, and the outer layer takes the comprehensive operation economy of the active distribution network as the optimization target. It takes into account the maximum energy storage operation benefit and the optimal performance of the energy storage battery, provides a more reasonable energy storage optimization operation plan, and ensures the safe and reliable operation of the energy storage battery.

[0101] The method proposed in the present invention integrates the dynamic efficiency, dynamic self-discharge rate, dynamic capacity attenuation and dynamic life performance of the all-vanadium liquid flow battery into the optimized operation of the energy storage system, and combines it with the energy storage operation benefits. While realizing the maximum profit operation of the energy storage battery, it effectively guarantees the safe and efficient operation of the energy storage battery, extends the life of the energy storage battery, and lays a certain theoretical and technical foundation for the large-scale commercial application of the all-vanadium liquid flow battery. BRIEF DESCRIPTION OF THE DRAWINGS

[0102] Figure 1 This is a flow chart of a nested double-layer optimization operation method for a full liquid flow battery energy storage system in an active distribution network according to Embodiment 1 of the present invention;

[0103] Figure 2 The charging efficiency η of the all-vanadium liquid flow battery of the nested double-layer optimization operation method of the active distribution network all-liquid flow battery energy storage system of the first embodiment of the present invention is c Schematic diagram of the fitting;

[0104] Figure 3 The discharge efficiency η of the all-vanadium liquid flow battery of the nested double-layer optimization operation method of the active distribution network all-liquid flow battery energy storage system of the first embodiment of the present invention is d Schematic diagram of the fitting;

[0105] Figure 4 It is a schematic diagram of fitting the self-discharge rate of the all-vanadium liquid flow battery in the nested double-layer optimization operation method of the active distribution network all-liquid flow battery energy storage system according to the first embodiment of the present invention;

[0106] Figure 5A schematic diagram of the steps for solving the nested double-layer optimization operation scheme of the full liquid flow battery energy storage system in the active distribution network of the nested double-layer optimization operation method of the full liquid flow battery energy storage system in the active distribution network according to the first embodiment of the present invention;

[0107] Figure 6 A schematic diagram of the efficiency and SOC of the energy storage battery of the nested double-layer optimization operation method of the full liquid flow battery energy storage system of the active distribution network according to the first embodiment of the present invention;

[0108] Figure 7 The output power of the energy storage system of the nested double-layer optimization operation method of the active distribution network full liquid flow battery energy storage system of the first embodiment of the present invention is Maximum absorbed power P ab And abandoned wind and solar power P AB Schematic diagram of

[0109] Figure 8 Schematic diagram of the influence of uncertainty parameters on objective functions f1 and f2 of the nested double-layer optimization operation method of the full liquid flow battery energy storage system of the active distribution network according to the first embodiment of the present invention;

[0110] Fig. 9 Schematic diagram of the nested double-layer optimization operation results of energy storage on typical summer days and typical winter days according to the nested double-layer optimization operation method of the full liquid flow battery energy storage system of the active distribution network in Example 1 of the present invention. DETAILED DESCRIPTION

[0111] In order to make the purpose, technical solution and advantages of the embodiments of the present invention clearer, the technical solution in the embodiments of the present invention will be clearly and completely described below in combination with the embodiments of the present invention. Obviously, the described embodiments are part of the embodiments of the present invention, not all of the embodiments. Based on the embodiments of the present invention, all other embodiments obtained by ordinary technicians in this field without creative work are within the scope of protection of the present invention.

[0112] The technical solution of the present invention is further described below in conjunction with the accompanying drawings and specific embodiments:

[0113] Embodiment 1

[0114] like Figure 1 As shown, a nested double-layer optimization operation method of a full liquid flow battery energy storage system for an active distribution network according to an embodiment of the present invention comprises the following steps:

[0115] Step 1: Through the charge and discharge test of the all-vanadium liquid flow battery, combined with the empirical formula of the dynamic performance of the all-vanadium liquid flow battery, the relevant parameter polynomial fitting is performed, and the randomness of the fitting error is considered to establish the uncertainty model of the dynamic efficiency, dynamic self-discharge rate, dynamic capacity attenuation and dynamic life performance of the all-vanadium liquid flow battery.

[0116] The uncertainty model is established as follows:

[0117] Step 1.1, through the charging and discharging power experimental test of the all-vanadium liquid flow battery, the equivalent loss analysis of the all-vanadium liquid flow battery is carried out, the heat loss power and output power of the battery at different charging and discharging rates are analyzed, and the efficiency data matrix set A of the battery is constructed;

[0118] Step 1.2: Based on the battery efficiency data matrix set A, the charging efficiency η of the all-vanadium liquid flow battery is established by fitting the parameters using the least squares method and taking into account the fitting errors of the battery discharge efficiency and charging efficiency. c and discharge efficiency η d The mathematical model is as follows:

[0119]

[0120] Among them, T a is the battery temperature, P VRB,d (t) is the charge and discharge power of the all-vanadium liquid flow battery at time t, SoC is the state of charge of the battery, z i (i=a,b,c),y i (i=a,b,c,d), is the fitting coefficient, Δε d and Δε c are the fitting errors of battery discharge efficiency and charging efficiency respectively; η d represents the discharge efficiency of the all-vanadium liquid flow battery, η c represents the charging efficiency of the all-vanadium redox flow battery; the charging efficiency of the all-vanadium redox flow battery η c and discharge efficiency η d The fittings are as follows Figure 2 and Figure 3 shown.

[0121] Step 1.3: Considering the uncertainty of the fitting error of the battery discharge efficiency and charging efficiency, the interval method is used to calculate the fitting error Δε of the battery discharge efficiency. d and the fitting error Δε of the battery charging efficiency c To model:

[0122]

[0123] in, and T a , P VRB and the weight coefficient of SoC, and T a , P VRBThe influence factors of SoC on battery efficiency, and their interval models are:

[0124]

[0125] in, T a Factors affecting battery efficiency The lower and upper limits of P VRB Factors affecting battery efficiency The lower and upper limits of The impact factors of SoC on battery efficiency are The lower and upper limits of .

[0126] Step 1.4, through the static experimental test of the all-vanadium liquid flow battery under different SoCs, according to the time and rate analysis of the battery self-discharge to the end of the SoC, the battery self-discharge rate data matrix set B is constructed; according to the battery self-discharge rate data matrix set B, the battery self-discharge rate λ is fitted with a polynomial as follows:

[0127] λ=SoC(g a T a +g b )+g c T a +Δλ t (7)

[0128] Among them, g i (i=a,b,c) is the fitting coefficient, Δλ t is the fitting error.

[0129] Due to Δλ t The uncertainty of Δλ t The value changes around 0, using Gaussian distribution, for Δλ t The modeling is as follows:

[0130] Δλ t ~N(0,δ t 2 ) (8)

[0131] Among them, δ t is Δλ t The standard deviation is affected by T a and the impact of SoC, according to the formula Calculate, β x (x=SoC,T a ) is the weight coefficient, T a and SoC for δ t The influence degree of the battery self-discharge rate is as follows: Figure 4 shown.

[0132] Step 1.5: Test the battery charge and discharge capacity attenuation experiment, taking into account the temperature, rate and discharge capacity A. h The impact of battery capacity decay, combined with the fitting error and the uncertainty of experimental data, is calculated according to the Arrhenius empirical formula for the capacity decay rate γ of the all-vanadium liquid flow battery. c Perform the fit:

[0133]

[0134] Among them, B is the pre-exponential factor, k1 and k2 are linear factors, z is the power rate factor, C rate is the magnification, R is Avogadro's constant, Δγ c is the fitting error caused by the uncertainty of the experimental data and is calculated as follows:

[0135]

[0136] in, and is the number of cycles N c , Temperature T a And the magnification C rate The weight factor of and N c , T a and C rate The capacity attenuation factor of N c Capacity attenuation factor The lower and upper limits of T a Capacity attenuation factor The lower and upper limits of C rate Capacity attenuation factor The lower and upper limits of .

[0137] Step 1.6: Consider the temperature T b The influence of the discharge depth DoD on the life κ of the all-vanadium redox flow battery is modeled according to the polynomial fitting and considering the fitting error of the battery life κ as follows:

[0138]

[0139] Among them, μ i (i=a,b) is the fitting coefficient, N κ is the binomial fitting relationship of the least squares method, Δκ tis the fitting error of the battery life κ; Δκ t It obeys the normal distribution and is calculated as follows:

[0140]

[0141] Among them, α x (x=DoD,T b ) is the weight coefficient, is the depth of discharge DoD and temperature T b right The impact value.

[0142] Step 2: Based on the uncertainty model of the dynamic performance of the all-vanadium liquid flow battery, the direct, indirect and indirect effects of the dynamic efficiency, dynamic self-discharge rate, dynamic capacity attenuation and dynamic life of the all-vanadium liquid flow battery on the battery output power are considered to establish a dynamic output characteristic model of the energy storage system and perform a correction calculation on the battery state of charge value SoC. The specific steps are as follows:

[0143] Step 2.1: Consider the maximum absorbed power of the all-vanadium liquid flow battery and the maximum apparent power S of the all-vanadium redox flow battery VRBk,max The direct electrical impact Δf1 on the battery output power is modeled as:

[0144]

[0145] Among them, Q VRB is the output reactive power of the all-vanadium liquid flow battery energy storage system, is the rated power of the energy storage battery.

[0146] Step 2.2: Consider the battery self-discharge rate λ, capacity attenuation and efficiency η c or η d The throughput loss value of the all-vanadium liquid flow battery energy storage system during state transfer, that is, the indirect power impact Δf2, is modeled:

[0147]

[0148] in, Indicates the maximum charge and discharge time, η c / d Indicates the charging or discharging efficiency of the battery and is calculated using formula (1) or (2).

[0149] Step 2.3: During the battery charging and discharging process, the discharge depth D oD 、Maximum number of charge and discharge state conversions per day N max , Maximum charge and discharge time and the minimum cycle life κ minAll of these will cause large passive losses in the output power of the energy storage system. This effect is an indirect state effect Δf3 and is nonlinear. In order to quantify this indirect effect, the rated cycle life is used to perform a linear conversion on Δf3:

[0150]

[0151] Among them, κ rated is the rated cycle life of the all-vanadium liquid flow battery, DoD kmax Indicates the maximum discharge depth of the battery, DoD k Indicates the depth of discharge of the battery. Indicates the rated capacity of the energy storage battery.

[0152] Step 2.4: Consider the direct power impact f1, indirect power impact f2 and indirect state impact f3 of the dynamic performance of the all-vanadium liquid flow battery on the output power of the all-vanadium liquid flow battery energy storage system, and the impact value ΔP on the output power of the all-vanadium liquid flow battery energy storage system VRB,k and ΔQ VRB,k To model:

[0153]

[0154] Among them, ΔP VRB,k Indicates the active power impact value, ΔQ VRB,k Indicates the reactive power impact value.

[0155] Step 2.5: Consider the impact of battery efficiency, capacity attenuation, and self-discharge rate on the battery state of charge value SoC, and perform a correction calculation on the battery SoC value:

[0156]

[0157] Among them, SoC t and SoC t-1 They represent the state of charge at time t and time t-1 respectively, E VRB Indicates the rated capacity of VRB, P VRB Indicates the output power of the energy storage.

[0158] Step 3: Through multi-angle analysis of the functional and economic effects of energy storage systems connected to active distribution networks, each functional indicator is discounted by economic benefits to establish an economic indicator system for the comprehensive operation of active distribution networks.

[0159] The method for establishing the comprehensive operation economic index system of the active distribution network is as follows:

[0160] Step 3.1: The operation of the all-vanadium liquid flow battery energy storage system greatly reduces the abandoned wind and solar power in the active distribution network, thereby reducing unnecessary resource waste in the active distribution network; the abandoned wind and solar power loss C of the active distribution network energy storage is evaluated by the total abandoned wind and solar power and price factor. AB , calculated as follows:

[0161]

[0162] Among them, λ c is the price factor for abandoned wind and solar power, P Lk is the load. ω,i is the output of wind power, P PV,j is the photovoltaic output, P grid,t is the power of the tie line, P Lk For load demand.

[0163] Step 3.2: The operation of the all-vanadium liquid flow battery energy storage system greatly reduces the amount of electricity purchased by the distribution network from the upper power grid, thereby reducing the consumption of fossil energy and achieving greenhouse gas emission reduction benefits C EB Consider CO2, SO2 and NO x Greenhouse gas, greenhouse gas emission reduction benefits C EB The value of is calculated:

[0164]

[0165] Where N = 3, P grid,in Represents the all-vanadium liquid flow battery energy storage system and renewable energy sources do not participate in the active distribution network energy management, and the real-time exchange power between the active distribution network and the main grid; ζ grid,j represents the unit density of greenhouse gas j, c σ,j represents the penalty cost per unit of greenhouse gas, and its value is estimated by the governance cost per unit of greenhouse gas j. σ,j (j=1,2,3) are regarded as constant coefficients, which are 0.03$ / kg, 2.16$ / kg and 9.56$ / kg respectively.

[0166] Step 3.3: Consider the battery replacement cost caused by the life of the energy storage battery. Under different energy storage operation strategies, the replacement cost of the energy storage battery is different. Therefore, the replacement cost of the energy storage battery C RP As one of the comprehensive economic characteristics of active distribution network, it is calculated as follows:

[0167]

[0168] Among them, C in-VRB It is the initial investment cost of the all-vanadium liquid flow battery energy storage system, and its value is evaluated by the rated power and rated energy of the all-vanadium liquid flow battery energy storage system.

[0169] Step 3.4: When the wind and solar power generation is excessive or the electricity price is low, the energy storage system purchases electricity from the upper power grid and sells electricity to the active distribution network when the load demand is large or the electricity price is high according to the operation strategy, so as to achieve the transfer of low-cost electricity within the time span, thereby achieving the purpose of reducing costs and increasing profits; the purchase cost of the active distribution network C PE and electricity sales revenue C SE The calculations are as follows:

[0170]

[0171] Among them, c pt and c st They are the unit electricity purchase price and the unit electricity sales price respectively.

[0172] Step 3.5: Under different vanadium liquid flow battery energy storage system operation strategies, the operation and maintenance costs of the energy storage system are different. OM As one of the comprehensive economic characteristics of active distribution network, it is calculated as follows:

[0173]

[0174] Among them, P it is the output of the all-vanadium liquid flow battery energy storage system, i is the number of all-vanadium liquid flow battery energy storage systems, K OMi Indicates the operation and maintenance factor.

[0175] Step 3.6: The all-vanadium liquid flow battery energy storage system can use its own output to smooth out peaks and fill valleys, reduce load interruption losses, and bring direct economic benefits C ID1 At the same time, the consumption of wind and solar renewable energy can reduce the amount of electricity purchased by the active distribution network, thereby bringing indirect economic benefits C ID2 ; Direct economic benefits C ID1 Indirect economic benefits ID2 As one of the comprehensive economic characteristics of active distribution network, it is calculated as follows:

[0176]

[0177] Among them, c lt Represents the unit load interruption loss cost, P ωa,i and P PVa,i They represent the actual active power consumption values ​​of the i-th wind turbine generator set and the i-th photovoltaic unit respectively.

[0178] Step 4: Taking the dynamic performance of the all-vanadium liquid flow battery as the inner optimization target and the comprehensive operating economy of the active distribution network as the outer optimization target, a nested two-layer optimization operation model of the active distribution network all-liquid flow battery energy storage system taking into account the dynamic performance is established through the mutual transmission and coupling of the inner and outer layers of the energy storage output power and the battery charge state.

[0179] The method for establishing the nested double-layer optimization operation model of the active distribution network full liquid flow battery energy storage system taking into account dynamic performance is as follows:

[0180] Step 4.1: Limit the uncertainty range of the all-vanadium liquid flow battery energy storage system through the upper and lower limits of the battery dynamic performance parameters:

[0181]

[0182] Step 4.2: Obtain constraints from the dynamic output characteristic model of the energy storage system, restrict the dynamic output characteristics of the battery, and restrict the SoC of the energy storage battery; the constraints are as follows:

[0183]

[0184] in, Indicates the battery state of charge, SoC k,min Indicates the minimum value of battery state of charge.

[0185] Step 4.3: Taking the dynamic performance of the all-vanadium liquid flow battery as the inner optimization target and the comprehensive operation economy of the active distribution network as the outer optimization target, a nested two-layer objective function is constructed, where the inner and outer objective functions are:

[0186] F1=max{ω1η c +ω2η d +ω3κ-ω4λ-ω5γ c} (32)

[0187] F2=max{C EB +C SE +C ID -C OM -C PE -C RP -C AB} (33)

[0188] Among them, ω1, ω2, ω3, ω4, ω5 are weight coefficients, C ID Economic benefits, including direct economic benefits C ID1 , and indirect economic benefits C ID2 .

[0189] Step 5: Use the Cplex tool to perform multi-level iterations on the nested double-layer optimization operation model of the full-flow battery energy storage system in the active distribution network, and solve the nested double-layer optimization operation plan of the energy storage system under the coordinated optimization of battery performance and economy. The specific steps are as follows:

[0190] Step 5.1, according to the uncertainty range of the dynamic performance of the energy storage battery, combined with the uncertainty modeling of the dynamic performance of the energy storage battery, obtain the change trend of the dynamic performance of the energy storage battery;

[0191] Step 5.2: Set the electricity price parameter c pt and c st The value of the predicted load demand P LK And wind and solar output P ωa and PPVa values;

[0192] Step 5.3: Call the Cplex tool to solve the inner and outer layers respectively, and the inner layer outputs the output of the energy storage system The state of charge of the outer output energy storage battery

[0193] Step 5.4: External optimization: the state of charge of the energy storage battery Passed to the inner layer to update the actual state value of the energy storage battery. The inner layer optimizes the output of the energy storage battery. Passed to the outer layer for updating and calculating the dynamic performance of the energy storage battery;

[0194] Step 5.5: Determine whether the number of iterations is greater than N c If yes, proceed to the next step; if no, return to step 5.3;

[0195] Step 5.6: η c ,η d ,κ,λ,γ and A nested double-layer optimized operation scheme for the full liquid flow battery energy storage system in the active distribution network.

[0196] The method of the present invention establishes an uncertainty mathematical model of the dynamic efficiency, dynamic self-discharge rate, dynamic capacity attenuation and dynamic life performance of the all-vanadium liquid flow battery, accurately describes the fitting error of the dynamic performance empirical formula of the all-vanadium liquid flow battery, and on this basis, by analyzing the direct power, indirect power and indirect state influence of the dynamic performance of the all-vanadium liquid flow battery on the battery output power, establishes a dynamic output characteristic model of the energy storage system, and performs correction calculation on the battery state of charge value SoC, further takes into account the maximum energy storage operation benefit and the optimal performance of the energy storage battery, and constructs a nested double-layer optimization operation model of the active distribution network battery energy storage system taking into account the dynamic performance, wherein the inner optimization target is the dynamic performance of the all-vanadium liquid flow battery, and the outer optimization target is the comprehensive operation benefit of the active distribution network; the model is repeatedly iterated and solved at multiple levels by the Cplex tool, and an energy storage optimization operation plan is constructed under the coordinated optimization of battery performance and economy; the present invention solves the problem of coordinated optimization of battery dynamic performance and comprehensive operation benefit of the active distribution network, and provides a more reasonable operation plan of the active distribution network battery energy storage system; the safe and reliable operation of the energy storage system is achieved, and the life of the energy storage battery is extended.

[0197] like Figure 5 As shown in Figure 1, it is the specific solution process of the nested double-layer optimization operation scheme of the full liquid flow battery energy storage system in the active distribution network; among them, the efficiency and SOC of the energy storage battery are as follows: Figure 6 As shown, the energy storage system output power Maximum absorbed power P ab And abandoned wind and solar power P AB like Figure 7 As shown in the figure, the influence of uncertainty parameters on the objective functions f1 and f2 is as follows: Figure 8 The results of the nested double-layer optimization operation of energy storage on typical summer and winter days are shown in Fig. 9 shown.

[0198] Embodiment 2

[0199] An electronic device includes a memory and a processor, wherein the memory is used to store a program that supports the processor to execute the nested double-layer optimization operation method of the active distribution network full-liquid flow battery energy storage system in Example 1, and the processor is configured to execute the program stored in the memory.

[0200] Embodiment 3

[0201] A storage medium having a computer program stored thereon, wherein when the computer program is executed by a processor, the steps of the nested double-layer optimization operation method of the active distribution network full-liquid flow battery energy storage system in embodiment 1 are executed.

[0202] The above embodiments are only used to illustrate the technical solutions of the present invention, rather than to limit the same. Although the present invention has been described in detail with reference to the aforementioned embodiments, those skilled in the art should understand that the technical solutions described in the aforementioned embodiments may still be modified, or some of the technical features may be replaced by equivalents. However, these modifications or replacements do not deviate the essence of the corresponding technical solutions from the spirit and scope of the technical solutions of the embodiments of the present invention.

Claims

1. A nested double-layer optimization operation method for a full liquid flow battery energy storage system in an active distribution network, characterized in that: The following steps are involved: Step 1: Establish an uncertainty model for the dynamic performance of all-vanadium liquid flow batteries; Step 2: Considering the direct power impact, indirect power impact and indirect state impact of the dynamic performance of the all-vanadium liquid flow battery on the battery output power, a dynamic output characteristic model of the energy storage system is established, and the battery state of charge value SoC is corrected and calculated; Step 3: Through multi-angle analysis of the functional and economic effects of energy storage systems connected to active distribution networks, each functional indicator is equivalently discounted with economic benefits, and an active distribution network comprehensive operation economic indicator system is established; Step 4: Taking the dynamic performance of the all-vanadium liquid flow battery as the inner optimization target and the comprehensive operation economy of the active distribution network as the outer optimization target, a nested double-layer optimization operation model of the active distribution network all-liquid flow battery energy storage system taking into account the dynamic performance is established through the mutual transmission and coupling of the inner and outer layers of the energy storage output power and the battery charge state; Step 5: Use the Cplex tool to perform multi-level iterations on the nested double-layer optimization operation model of the full-liquid flow battery energy storage system in the active distribution network, and solve the nested double-layer optimization operation scheme of the energy storage system under the coordinated optimization of battery performance and economy.

2. The nested double-layer optimization operation method of the active distribution network full liquid flow battery energy storage system according to claim 1 is characterized in that: The method for establishing the uncertainty model of the dynamic performance of the all-vanadium liquid flow battery described in step 1 is as follows: Step 1.1, through the charging and discharging power experimental test of the all-vanadium liquid flow battery, the equivalent loss analysis of the all-vanadium liquid flow battery is carried out, the heat loss power and output power of the battery at different charging and discharging rates are analyzed, and the efficiency data matrix set A of the battery is constructed; Step 1.2: Based on the battery efficiency data matrix set A, the charging efficiency η of the all-vanadium liquid flow battery is established by fitting the parameters using the least squares method and taking into account the fitting errors of the battery discharge efficiency and charging efficiency. c and discharge efficiency η d The mathematical model is as follows: Among them, T a is the battery temperature, P VRB,d (t) is the charge and discharge power of the all-vanadium liquid flow battery at time t, SoC is the state of charge of the battery, z i (i=a,b,c),y i (i=a,b,c,d), is the fitting coefficient, Δε d and Δε c are the fitting errors of battery discharge efficiency and charging efficiency respectively; η d represents the discharge efficiency of the all-vanadium liquid flow battery, η c represents the charging efficiency of the all-vanadium liquid flow battery; Step 1.3: Considering the uncertainty of the fitting error of the battery discharge efficiency and charging efficiency, the interval method is used to calculate the fitting error Δε of the battery discharge efficiency. d and the fitting error Δε of the battery charging efficiency c To model: in, and T a , P VRB and the weight coefficient of SoC, and T a , P VRB The influence factors of SoC on battery efficiency, and their interval models are: in, T a Factors affecting battery efficiency The lower and upper limits of P VRB Factors affecting battery efficiency The lower and upper limits of The impact factors of SoC on battery efficiency are The lower and upper limits of Step 1.4, through the static experimental test of the all-vanadium liquid flow battery under different SoCs, according to the time and rate analysis of the battery self-discharge to the end of the SoC, the battery self-discharge rate data matrix set B is constructed; according to the battery self-discharge rate data matrix set B, the battery self-discharge rate λ is fitted with a polynomial as follows: λ=SoC(g a T a +g b )+g c T a +Dl t (7) Among them, g i (i=a,b,c) is the fitting coefficient, Δλ t is the fitting error; Due to Δλ t The uncertainty of Δλ t The value changes around 0, using Gaussian distribution, for Δλ t The modeling is as follows: Among them, δ t is Δλ t The standard deviation is affected by T a and the impact of SoC, according to the formula Calculate, β x (x=SoC,T a ) is the weight coefficient, T a and SoC for δ t The impact value of Step 1.5: Test the battery charge and discharge capacity attenuation experiment, taking into account the temperature, rate and discharge capacity A. h The impact of battery capacity decay, combined with the fitting error and the uncertainty of experimental data, is calculated according to the Arrhenius empirical formula for the capacity decay rate γ of the all-vanadium liquid flow battery. c Perform the fit: Among them, B is the pre-exponential factor, k1 and k2 are linear factors, z is the power rate factor, C rate is the magnification, R is Avogadro's constant, Δγ c is the fitting error caused by the uncertainty of the experimental data and is calculated as follows: in, and ρ Crate is the number of cycles N c , Temperature T a And the magnification C rate The weight factor of and N c , T a and C rate The capacity attenuation factor of N c Capacity attenuation factor The lower and upper limits of T a Capacity attenuation factor The lower and upper limits of C rate Capacity attenuation factor The lower and upper limits of Step 1.6: Consider the temperature T b The influence of the discharge depth DoD on the life κ of the all-vanadium redox flow battery is modeled according to the polynomial fitting and considering the fitting error of the battery life κ as follows: Among them, μ i (i=a,b) is the fitting coefficient, N κ is the binomial fitting relationship of the least squares method, Δκ t is the fitting error of the battery life κ; Δκ t It obeys the normal distribution and is calculated as follows: Dk t ~N(0,υ t 2 ) (15) Among them, α x (x=DoD,T b ) is the weight coefficient, is the depth of discharge DoD and temperature T b right The impact value.

3. The nested double-layer optimization operation method of the active distribution network full-flow battery energy storage system according to claim 2 is characterized in that: The method described in step 2, which considers the direct power impact, indirect power impact and indirect state impact of the dynamic performance of the all-vanadium liquid flow battery on the battery output power, establishes a dynamic output characteristic model of the energy storage system, and performs a correction calculation on the battery state of charge value SoC, is as follows: Step 2.1: Consider the maximum absorbed power of the all-vanadium liquid flow battery and the maximum apparent power S of the all-vanadium redox flow battery VRBk,max The direct charge effect Δf1 on the battery output power is modeled as follows: Among them, Q VRB is the output reactive power of the all-vanadium liquid flow battery energy storage system, is the rated power of the energy storage battery; Step 2.2: Consider the battery self-discharge rate λ, capacity attenuation and efficiency η c or η d The throughput loss value of the all-vanadium liquid flow battery energy storage system during state transfer, that is, the indirect power impact Δf2, is modeled: in, Indicates the maximum charge and discharge time, η c / d Indicates the charging or discharging efficiency of the battery; Step 2.3: Use the rated cycle life to perform a linear conversion on the indirect state effect Δf3. The linear conversion formula is as follows: Among them, κ rated is the rated cycle life of the all-vanadium liquid flow battery, DoD kmax Indicates the maximum depth of discharge of the battery, DoD k Indicates the depth of discharge of the battery. Indicates the rated capacity of the energy storage battery; Step 2.4: Consider the direct power impact f1, indirect power impact f2 and indirect state impact f3 of the dynamic performance of the all-vanadium liquid flow battery on the output power of the all-vanadium liquid flow battery energy storage system, and the impact value ΔP on the output power of the all-vanadium liquid flow battery energy storage system VRB,k and ΔQ VRB,k To model: Among them, ΔP VRB,k Indicates the active power impact value, ΔQ VRB,k Indicates the reactive power impact value; Step 2.5: Consider the impact of battery efficiency, capacity attenuation, and self-discharge rate on the battery state of charge value SoC, and perform a correction calculation on the battery SoC value: Among them, SoC t and SoC t-1 They represent the state of charge at time t and time t-1 respectively, E VRB Indicates the rated capacity of VRB, P VRB Indicates the output power of the energy storage.

4. The nested double-layer optimization operation method of the active distribution network full-flow battery energy storage system according to claim 3 is characterized in that: The method described in step 3 is to establish an active distribution network comprehensive operation economic index system by conducting a multi-angle analysis of the functional and economic effects of energy storage systems connected to the active distribution network, and to perform an equivalent discount on each functional index based on economic benefits as follows: Step 3.1: Evaluate the wind and solar curtailment loss C of active distribution network energy storage by total wind and solar curtailment and price factor AB , the calculation formula is as follows: Among them, λ c is the price factor for abandoned wind and solar power, P Lk is the load, P ω,i is the output of wind power, P PV,j is the photovoltaic output, P grid,t is the power of the tie line, P Lk is the load demand; Step 3.2: Greenhouse gas emission reduction benefits C EB The value is calculated using the following formula: Where N = 3, P grid,in Represents the all-vanadium liquid flow battery energy storage system and renewable energy sources do not participate in the active distribution network energy management, and the real-time exchange power between the active distribution network and the main grid; ζ grid,j represents the unit density of greenhouse gas j, c σ,j represents the penalty cost per unit of greenhouse gas, and its value is estimated by the governance cost per unit of greenhouse gas j; Step 3.3: Calculate the replacement cost C of the energy storage battery RP , the calculation formula is as follows: Among them, C in-VRB The initial investment cost of the all-vanadium liquid flow battery energy storage system; Step 3.4: Calculate the power purchase cost C of the active distribution network PE and electricity sales revenue C SE , the calculation formulas are as follows: Among them, c pt and c st They are the unit electricity purchase price and the unit electricity sales price respectively; Step 3.5: Calculate the operation and maintenance cost C of the energy storage system OM , the calculation formula is as follows: Among them, P it is the output of the all-vanadium liquid flow battery energy storage system, i is the number of all-vanadium liquid flow battery energy storage systems, K OMi It represents the operation and maintenance factor; Step 3.6: Calculate the direct economic benefits C brought by the all-vanadium liquid flow battery energy storage system through its own output to reduce peak loads and fill valleys ID1 , and the all-vanadium liquid flow battery energy storage system absorbs wind and solar renewable energy, reducing the amount of electricity purchased by the active distribution network and bringing indirect economic benefits C ID2 , the calculation formulas are as follows: Among them, c lt Represents the unit load interruption loss cost, P ωa,i and P PVa,i They represent the actual active power consumption values ​​of the i-th wind turbine generator set and the i-th photovoltaic unit respectively.

5. The nested double-layer optimization operation method of the active distribution network full liquid flow battery energy storage system according to claim 4 is characterized in that: The method described in step 4 is to take the dynamic performance of the all-vanadium liquid flow battery as the inner optimization target, take the comprehensive operation economy of the active distribution network as the outer optimization target, and establish a nested double-layer optimization operation model of the active distribution network all-liquid flow battery energy storage system taking into account the dynamic performance through the mutual transmission and coupling of the inner and outer layers of the energy storage output power and the battery charge state as follows: Step 4.1: Limit the uncertainty range of the all-vanadium liquid flow battery energy storage system through the upper and lower limits of the battery dynamic performance parameters: Step 4.2: Obtain constraints from the dynamic output characteristic model of the energy storage system, restrict the dynamic output characteristics of the battery, and restrict the SoC of the energy storage battery; the constraints are as follows: in, Indicates the battery state of charge, SoC k,min Indicates the minimum value of battery state of charge; Step 4.3: Taking the dynamic performance of the all-vanadium liquid flow battery as the inner optimization target and the comprehensive operation economy of the active distribution network as the outer optimization target, a nested two-layer objective function is constructed, where the inner and outer objective functions are: F1=max{ω1η c +ω2η d +ω3κ-ω4λ-ω5γ c } (32) F2=max{C EB +C SE +C ID -C OM -C PE -C RP -C AB } (33) Among them, ω1, ω2, ω3, ω4, ω5 are weight coefficients, C ID Economic benefits, including direct economic benefits C ID1 , and indirect economic benefits C ID2 .

6. The nested double-layer optimization operation method of the active distribution network full-flow battery energy storage system according to claim 5 is characterized in that: The Cplex tool described in step 5 is used to perform multi-level iterations on the nested two-layer optimization operation model of the full liquid flow battery energy storage system of the active distribution network. The method for solving the nested two-layer optimization operation scheme of the energy storage system under the coordinated optimization of battery performance and economy is as follows: Step 5.1, according to the uncertainty range of the dynamic performance of the energy storage battery, combined with the uncertainty modeling of the dynamic performance of the energy storage battery, obtain the change trend of the dynamic performance of the energy storage battery; Step 5.2: Set the electricity price parameter c pt and c st The value of the predicted load demand P LK And wind and solar output P ωa and P PVa The value of Step 5.3: Call the Cplex tool to solve the inner and outer layers respectively, and the inner layer outputs the output of the energy storage system The state of charge of the outer output energy storage battery Step 5.4: External optimization: the state of charge of the energy storage battery Passed to the inner layer to update the actual state value of the energy storage battery. The inner layer optimizes the output of the energy storage battery. Passed to the outer layer for updating and calculating the dynamic performance of the energy storage battery; Step 5.5: Determine whether the number of iterations is greater than N c If yes, proceed to the next step; if no, return to step 5.3; Step 5.6: η c ,η d ,κ,λ,γ and A nested double-layer optimized operation scheme for the full liquid flow battery energy storage system in the active distribution network.

7. An electronic device, comprising a memory and a processor, characterized in that: The memory is used to store a program that supports the processor to execute the nested two-layer optimization operation method of the active distribution network full-liquid flow battery energy storage system as described in any one of claims 1 to 6, and the processor is configured to execute the program stored in the memory.

8. A storage medium having a computer program stored thereon, characterized in that: When the computer program is executed by a processor, the steps of the nested double-layer optimization operation method of the active distribution network full-flow battery energy storage system according to any one of claims 1 to 6 are executed.

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