Nested double-layer optimal operation method of active power distribution network full vanadium redox flow battery energy storage system considering dynamic performance

Abstract

The application discloses a kind of active distribution network full vanadium redox flow battery energy storage system nested double-layer optimization operation methods considering dynamic performance.Method includes: the mathematical model of charge-discharge efficiency, dynamic self-discharge rate and dynamic capacity attenuation performance of full vanadium redox flow battery is established, on this basis, the meta-model heuristic method is used to build the nested double-layer optimization operation model of battery energy storage system of active distribution network considering dynamic performance, wherein the dynamic performance of full vanadium redox flow battery is the inner layer optimization goal, the comprehensive operation income of active distribution network is the outer layer optimization goal, the improved particle swarm optimization algorithm is used to solve the nested double-layer optimization operation scheme of energy storage system, and the energy storage optimization operation scheme is constructed under the collaborative optimization of battery performance and economy.The application proposes a kind of active distribution network full vanadium redox flow battery energy storage system nested double-layer optimization operation model considering dynamic performance, solves the collaborative optimization problem of battery dynamic performance and comprehensive operation income of active distribution network, and provides a kind of reasonable battery energy storage system operation scheme of active distribution network.

Description

TECHNICAL FIELD

[0001] The application belongs to the technical field of electric power engineering, and particularly relates to a nested double-layer optimization operation method of a vanadium redox flow battery energy storage system of an active power distribution network considering dynamic performance BACKGROUND

[0002] New energy has become the most widely used secondary energy due to its transportation economy, non-pollution and other advantages. Energy storage system, as a bottleneck technology in the field of power system, has attracted much attention. Renewable energy such as solar energy and wind energy is greatly affected by climate and environmental factors, and direct grid-connected operation will bring many problems. SUMMARY

[0003] The application proposes a nested double-layer optimization operation method of a vanadium redox flow battery energy storage system of an active power distribution network considering dynamic performance in view of the deficiencies of the prior art.

[0004] In order to achieve the above purpose, the following technical scheme is adopted: a nested double-layer optimization operation method of a vanadium redox flow battery energy storage system of an active power distribution network considering dynamic performance, comprising the following steps:

[0005] Step 1, modeling the dynamic performance of the vanadium redox flow battery through charge and discharge experimental tests of the vanadium redox flow battery;

[0006] Step 2, modeling the dynamic output characteristics of the energy storage battery through the energy and power variation of the vanadium redox flow battery;

[0007] Step 3, establishing a comprehensive operation benefit index system of the active power distribution network through multi-angle analysis of the functionality and economic effect of the energy storage system connected to the active power distribution network;

[0008] Step 4, constructing a nested double-layer optimization operation model;

[0009] Step 5, solving the nested double-layer optimization operation model by using an optimized particle swarm algorithm to obtain a nested double-layer optimization operation scheme of the energy storage system.

[0010] Preferably, the specific method for modeling the dynamic performance of the vanadium redox flow battery in step 1 is as follows:

[0011] Through charge and discharge power experimental tests of the vanadium redox flow battery, an efficiency data matrix set A of the battery is constructed, and the charge efficiency η c and the discharge efficiency η d of the vanadium redox flow battery are least square fitted to obtain a battery charge and discharge efficiency model, which is specifically as follows:

[0012]

[0013] wherein Tb P is the operating temperature, P b P is the operating efficiency, SoC is the state of charge of the energy storage system, a c , b c , c c , d c and a o , c o , a c1 , b c1 , c c1 , d c1 are the VRB charging efficiency coefficient fitting parameter values, a d , b d , c d and a d1 , a o , c d1 , b o , d o are the VRB discharging efficiency coefficient fitting parameter values;

[0014] Step 1.2, by testing the self-discharge rate data matrix set B of the vanadium flow battery at different SoC through the static experiment of the vanadium flow battery, and fitting the battery self-discharge rate γ, the battery self-discharge rate model is obtained, specifically:

[0015] γ = (m a T b + m b ) SoC · (m c SoC + m d )

[0016] Wherein, T b is the operating temperature, m a , m b , m c , m d are the self-discharge rate coefficient fitting parameter values;

[0017] Step 1.3, obtain the semi-empirical formula of the battery capacity attenuation performance, and fit the related parameters through the battery charge and discharge capacity attenuation experiment test, obtain the battery dynamic capacity attenuation performance model, specifically:

[0018]

[0019] In the formula, k Nc is the cycle number coefficient, Nc n is the cycle number equivalent to 100% charge and discharge cycles, N is the experimental number, and the average value of the state of charge in a single charge and discharge cycle is defined as SoC a , the mean square deviation of the state of charge SoC d is used to describe the fluctuation amplitude of the state of charge in the cycle period, and τ is the time constant, wherein, kT are temperature influence coefficients, T and T ref are actual and reference values of ambient temperature, T a,t and T refa are actual and reference values of internal operating temperature of VRB, k1, k2, k3, k4 are fitting coefficient values of capacity fade and state of charge relationship.

[0020] Preferably, step 2 models the dynamic output characteristics of the energy storage battery, and the model is specifically:

[0021]

[0022] In the formula, SoC t and SoC t-1 represent the state of charge at time t and time t-1, E VRB represents the capacity of VRB, γ is the self-discharge rate, α is the capacity fade rate, η c / d represents the charging or discharging efficiency of the all-vanadium redox flow battery, P VRB is the output power of the all-vanadium redox flow battery energy storage system.

[0023] Preferably, the specific process of establishing the active power distribution network comprehensive operation benefit index system is as follows:

[0024] Step 3.1, considering the abandoned photovoltaic as the operation benefit of the active power distribution network, determine the photovoltaic consumption:

[0025] The photovoltaic consumption B ab is calculated as follows:

[0026] B ab = k a W VRB

[0027] In the formula, k a is a price factor, W VRB is the abandoned photovoltaic, wherein the total abandoned photovoltaic is calculated by the following method:

[0028]

[0029] In the formula, P wind is the power generation of the wind turbine in the energy storage system, N wind is the number of wind power installations, P solar is the power generation of the photovoltaic in the energy storage system, N solar is the number of photovoltaic installations, P grid_min is the minimum value of the amount of electricity purchased from the upper grid by the active power distribution network when the energy storage system is running, P loss is the network loss, P load is the load power, P VRB is the output power of the energy storage system, Zt characteristics of the photovoltaic system in the abandoned state;

[0030] Step 3.2, peak shaving is the initiative of the distribution network operation revenue, the peak shaving revenue B is calculated up Equivalent discount is carried out, the specific method is:

[0031] The peak shaving rate ω is calculated, and the specific formula is:

[0032]

[0033] In the formula, P max is the load peak value in the energy storage operation process before peak shaving, P' max is the load peak value in the energy storage operation process after peak shaving;

[0034] According to the peak shaving rate, the time N of delaying the power grid reconstruction and upgrading is calculated y , and the specific calculation formula is as follows:

[0035]

[0036] In the formula, ε is the annual load growth rate;

[0037] According to the time of delaying the power grid reconstruction and upgrading, the revenue obtained by the energy storage operation optimization is calculated, and the formula is as follows:

[0038]

[0039] In the formula, C e is the investment required for the power grid reconstruction and upgrading, i r and d r are the interest rate and the discount rate respectively;

[0040] Step 3.3, the power market arbitrage B is calculated arb :

[0041]

[0042] In the formula, τ is the total number of years, Δf is the difference in the distribution network operation revenue in a year, C p is the real-time electricity price, P sell is the electricity sold by the energy storage system, P sell > 0 when the energy storage system sells electricity to the load, and P sell < 0 when electricity is purchased; P grid The energy storage system reduces the amount of electricity purchased from the upper-level power grid by storing photovoltaic power;

[0043] Step 3.4, the environmental benefit B is calculated em :

[0044]

[0045] In the above formula, C w,k is the unit charge of pollutant emissions; ζ grid,k is the pollutant emission density of the upper grid; P g represents the total amount of electricity purchased from the upper grid when the energy storage system is running, M represents the pollutant species, and P grid represents the total amount of electricity purchased from the upper grid per hour when the energy storage system is running.

[0046] Preferably, a meta-model heuristic method is adopted, the dynamic performance of the vanadium flow battery is taken as the inner layer optimization target, and the comprehensive operation benefit of the active distribution network is taken as the outer layer optimization target, to construct a nested double-layer optimization operation model, wherein the inner layer and outer layer target functions of the nested double-layer optimization operation model are respectively:

[0047] The inner layer target function F1 = max{ω1η c / d -ω2λ-ω3α};

[0048] The outer layer target function F2 = max{β1B ab +β2B up +β3B arb +β4B em}

[0049] Wherein, ω1, ω2, ω3, β1, β2, β3, β4 are weight values corresponding to decision variables.

[0050] Preferably, the optimized particle swarm algorithm is used to solve the nested double-layer optimization operation model, and the specific process of obtaining the nested double-layer optimization operation scheme of the energy storage system is as follows:

[0051] Step 5.1, add constraint conditions to the nested double-layer optimization operation model, including:

[0052] For a small power distribution network containing photovoltaic power generation and wind power generation, the power balance satisfies the following equation:

[0053]

[0054] Wherein, P grid is the power transmitted by the upper grid to the region, P wind is the power input into the region by the wind power generation in the region, P solar is the power input into the region by the photovoltaic power generation, and P VRB is the power of the energy storage device.

[0055] 2) Due to the capacity limitation of the voltage device, the power P grid input into the region from the upper grid is limited in a certain interval, that is:

[0056] P grid_min ≤P grid ≤P grid_max -

[0057] wherein, P grid_min is the minimum value of power input by the upper-level power grid into the region, P grid_max is the maximum value of power input by the upper-level power grid into the region;

[0058] 3) SoC of the energy storage battery system t between the maximum state of charge SoC max and the minimum state of charge SoC min , that is,

[0059] SoC min ≤SoC t ≤SoC max

[0060] In the energy storage system, SoC is used to reflect how much the remaining capacity in the energy storage battery is, and the calculation method is as follows:

[0061]

[0062] wherein, E VRB is the rated capacity of the energy storage system battery, P VRB is the real-time power of the energy storage system, when P VRB is greater than 0, the energy storage system discharges, otherwise, when P VRB is less than 0, the energy storage battery charges;

[0063] Step 5.2, the improved particle swarm algorithm is used to solve the nested double-layer optimization operation model, and the specific process is as follows:

[0064] (1) collect data, including photovoltaic power generation power, wind power generation power, and power load;

[0065] (2) initialize the particle swarm, take the real-time power and state of charge of each energy storage as a particle, and give it an initial position and initial speed;

[0066] (3) according to the charge and discharge efficiency, capacity attenuation rate, self-discharge rate and economic benefit index function to be solved, calculate the fitness of each particle by the inner layer F1 and the outer layer target function F2 of the nested double-layer optimization operation model; according to the fitness value of each particle, solve the global best position;

[0067] (4) update the position of the particle and the speed of the particle;

[0068] (5) judge whether the iteration number is satisfied, if not, continue to update the particle fitness and position, if satisfied, the calculation is ended, and the best position, i.e. the optimal solution, is obtained.

[0069] Compared with the prior art, the present application has the following advantages: the present application combines the dynamic performance of the all-vanadium redox flow battery with the optimal operation benefit of the active power distribution network, and adopts an improved particle swarm algorithm, thereby greatly increasing the operation benefit of the all-vanadium redox flow battery energy storage system of the active power distribution network. BRIEF DESCRIPTION OF DRAWINGS

[0070] Figure 1 It is a dynamic efficiency curve diagram of the all-vanadium redox flow battery.

[0071] Figure 2 It is a dynamic self-discharge rate curve diagram of the all-vanadium redox flow battery.

[0072] Figure 3 It is a dynamic capacity attenuation performance curve diagram of the all-vanadium redox flow battery.

[0073] Figure 4 It is a flow diagram of modeling of the all-vanadium redox flow battery energy storage system.

[0074] Figure 5 It is a model solving process of modeling of the all-vanadium redox flow battery energy storage system.

[0075] Figure 6 It is a typical daily load demand and electricity price curve line diagram.

[0076] Figure 7 It is a daily photovoltaic output, wind turbine output and renewable energy output line diagram. DETAILED DESCRIPTION

[0077] A nested double-layer optimization operation method of an active power distribution network all-vanadium redox flow battery energy storage system considering dynamic performance, and the specific steps are as follows:

[0078] Step 1, through the charge-discharge experiment test of the all-vanadium redox flow battery, the modeling of the dynamic performance of the all-vanadium redox flow battery is carried out, and the specific description is as follows:

[0079] Step 1.1, through the charge-discharge power experiment test of the all-vanadium redox flow battery, the equivalent loss analysis of the all-vanadium redox flow battery is carried out, the thermal loss power and the output power of the battery under different charge-discharge rates are analyzed, the efficiency data matrix set A of the battery is constructed, and then the charging efficiency η c and the discharging efficiency η d of the all-vanadium redox flow battery are least square fitted according to formula (1) and formula (2), and the related parameter values of the battery dynamic efficiency model are obtained.

[0080]

[0081] where T b is the operating temperature, P b is the operating efficiency, SoC is the state of charge of the energy storage system, a c , b c , c c , d c and a o , c o , a c1 , b c1 , c c1 , d c1 are the VRB charging efficiency coefficient fitting parameter values, a d , b d , c d and a d1 , a o , c d1 , b o , d o are the VRB discharging efficiency coefficient fitting parameter values, and the specific parameter values are shown in Table 1.

[0082] Table 1, related parameter values of the battery dynamic efficiency model

[0083] a b C d C -0.1280 1.0500 0.0380 -0.1180 c1 9.4750 7.7600 6.8200 46.7900 d 1.0334 0.3454 0.1192 d1 -7.6000 6.3606 52.3350 o 0.0380 1.1755 63.2674 51.2653

[0084] Step 1.2, through the static experiment test of the all-vanadium redox flow battery at different SoC, according to the time length and rate analysis of the battery self-discharge to the end, a battery self-discharge rate data matrix set B is constructed, and the related coefficients of the battery self-discharge rate γ are fitted according to formula (3), to obtain the fitting coefficients.

[0085] γ = (m a T b + m b ) SoC · (m c SoC + m d ) (3)

[0086] where Tb is the operating temperature, m a , m b , m c , m d are all self-discharge rate coefficient fitting parameter values, and the specific parameter values are shown in Table 2.

[0087] Table 2, related parameter values of the battery dynamic self-discharge rate model

[0088] m a ]]> m b ]]> m c ]]> m d ]]> 0.0211 -0.6099 -0.6751 0.6655

[0089] Step 1.3, through the battery charging and discharging capacity attenuation experiment test, considering the temperature, state of charge and cycle number N cRegarding the impact on battery capacity decay, the capacity decay rate of the all-vanadium redox flow battery was fitted using a semi-empirical formula, resulting in formula (4):

[0090]

[0091] In the formula, k Nc Nc is the loop count coefficient. n To represent the equivalent number of charge-discharge cycles (100%), N is the number of experiments. The average state of charge within a single charge-discharge cycle is defined as the SoC. a Utilizing the mean square error of the state of charge SoC d The fluctuation amplitude of the state of charge within the cycle is described, where τ is the time constant, and k T These are the temperature influence coefficients, T and T, respectively. ref These are the actual and reference values ​​of the ambient temperature, T. a,t and T refa Table 3 shows the actual and reference values ​​of the internal operating temperature of the VRB, respectively. k1, k2, k3, and k4 are the fitting coefficients for the relationship between capacity decay and state of charge. Specific parameter values ​​are shown in Table 3.

[0092] Table 3. Relevant parameter values ​​for the capacity decay model of vanadium redox flow batteries

[0093] ​ <k2> [ k3 ] -4.09e-02 -2.167 1.41e-03 [ k4 ] k T1 ]]> k T2 ]]> 6.13 0.0254 0.0937 [CAT ref ]]> [CAT refa ]]> k Nc ]]> 25 425 3.17e-4

[0094] Step 2: Considering the impact of battery efficiency, capacity decay, and self-discharge rate on the battery SoC, the dynamic output characteristic model of the energy storage battery is obtained, according to formula (5):

[0095]

[0096] In the formula, SoC t and SoC t-1 Let E represent the state of charge at time t and time t-1, respectively. VRB The value represents the capacity of the VRB, γ is the self-discharge rate, α is the capacity decay rate, and η is the capacity. c / d P represents the charging or discharging efficiency of a vanadium redox flow battery. VRB Output power of the vanadium redox flow battery energy storage system.

[0097] Step 3: Establish a comprehensive operational benefit index system for active distribution networks. The specific steps are as follows:

[0098] Step 3.1: Total curtailed photovoltaic (PV) power reflects the level of PV absorption. Energy storage systems can store electrical energy during periods of PV surplus. Considering the curtailed PV power as the operating revenue of the active distribution network, where PV absorption B... ab According to formulas (6) and (7), the total amount of abandoned photovoltaic power is calculated by equivalent discounting using the price factor.

[0099] Where the total PV curtailment can be calculated by the following way:

[0100]

[0101] In formula (6) and formula (7), P wind is the power generation of wind turbine in the energy storage system, N wind is the number of wind power installed, P solar is the power generation of PV in the energy storage system, N solar is the number of PV installed, P grid_min is the minimum value of the amount of power purchased from the upper grid to the active distribution network when the energy storage system is running, P loss is the network loss, P load is the load power, P VRB is the output power of the energy storage system, Z t is the state characteristic quantity of the energy storage system in the PV curtailment state.

[0102] PV curtailment B ab The calculation method is as follows:

[0103] B ab = k a W VRB (8)

[0104] In the formula, k a is the price factor, W VRB is the amount of PV curtailment.

[0105] Step 3.2, considering peak load shifting, the active distribution network operation benefit, wherein the energy storage system can reduce the load peak-valley difference, smooth the load curve, reduce the grid pressure, and thus delay the grid upgrading, thereby indirectly bringing economic benefits, by charging in the load valley and discharging in the peak. Peak load shifting benefit B up According to formula (9), the equivalent discounted benefit is:

[0106] The peak shaving rate ω can be calculated by the following formula:

[0107]

[0108] In the above formula, P max and P’ max are the difference between the load peak values in the process of energy storage operation.

[0109] The time limit for delaying the grid upgrading is N y , and the specific calculation formula is as follows:

[0110]

[0111] In the formula, ε is the annual load growth rate.

[0112] The revenue calculation formula of the delayed upgrade optimization of energy storage operation is as follows:

[0113]

[0114] In the formula, C e is the investment required for power grid upgrade, i r is the interest rate and discount rate r is the interest rate and discount rate

[0115] Step 3.3, when the photovoltaic power generation is excessive or the electricity price is low, the energy storage system purchases electricity from the upper-level power grid, and sells electricity according to the operation strategy when the load demand of the active distribution network is large or the electricity price is high. Therefore, the electricity market arbitrage revenue B arb can be calculated by formula (12) and formula (13):

[0116]

[0117] In the above formula, τ is the total year, Δf is the difference in the annual distribution network operation revenue, C p is the real-time electricity price, P sell is the electricity sold by the energy storage system, P sell > 0 when the energy storage system sells electricity to the load, and P sell < 0 when the energy storage system purchases electricity; P grid The energy storage system reduces the amount of electricity purchased from the upper-level grid by storing photovoltaic power.

[0118] Step 3.4, environmental benefit B em According to the equivalent discounted revenue formula as follows:

[0119] In the high photovoltaic penetration area, the energy storage system can reduce the amount of electricity purchased from the upper-level grid by charging during the time period of high light intensity and low electricity consumption, and discharging during the time period of high electricity consumption, thereby achieving the greenhouse gas emission reduction benefit. The environmental benefit of energy storage is expressed as follows:

[0120]

[0121] In the above formula, C w,k is the unit charge of pollutant emission; ζ grid,k is the pollutant emission density of the upper-level grid; P g represents the total amount of electricity purchased from the upper-level grid when the energy storage system is operated, M represents the type of pollutant, and P grid represents the total amount of electricity purchased from the upper-level grid per hour when the energy storage system is operated.

[0122] Step 4, the meta-model heuristic method is used to take the dynamic performance of the all-vanadium redox flow battery as an inner layer optimization target and take the comprehensive operation benefit of the active power distribution network as an outer layer optimization target, a nested double-layer target function is constructed, and the inner layer and the outer layer target functions are respectively:

[0123] The inner layer target function F1 = max{ω1η c / d -ω2λ-ω3α} (16) The outer layer target function F2 = max{β1B ab +β2B up +β3B arb +β4B em} (17)

[0124] Wherein, ω1, ω2, ω3, β1, β2, β3, β4 are weight values corresponding to decision variables, and the calculation of the weight of each index is obtained by using the analytic hierarchy process (AHP) evaluation method.

[0125] Step 5, the optimized particle swarm algorithm is used to solve the nested double-layer optimization operation model to obtain the nested double-layer optimization operation scheme of the energy storage system. The solving process is as shown in Figure 5 .

[0126] Step 5.1, in order to obtain the minimum dynamic loss and the maximum economic benefit of the energy storage system, it is necessary to add constraint conditions to the nested double-layer optimization operation model.

[0127] 1) For the power balance of a small power distribution network containing photovoltaic power generation and wind power generation, the following equation is satisfied:

[0128]

[0129] Wherein, P grid is the power transmitted by the upper power grid to the region, P wind is the power input into the region by the wind power generation in the region, P solar is the power input into the region by the photovoltaic power generation, and P VRB is the power of the energy storage device.

[0130] 2) Since the transformer has a capacity limit, the power P grid input into the region from the upper power grid will be limited in a certain interval.

[0131] P grid_min ≤ P grid ≤ P grid_max (19)

[0132] Wherein, P grid_min is the minimum value of the power input into the region by the upper power grid, and P grid_max is the maximum value of the power input into the region by the upper power grid.

[0133] 3) SoC of the energy storage battery system t The state of charge (SoC) should be between the maximum state of charge and the minimum state of charge.

[0134] SoC min ≤SoC t ≤SoC max (20)

[0135] Wherein, SoC min is the minimum state of charge of the energy storage battery system, SoC max is the maximum state of charge of the energy storage battery system.

[0136] In the energy storage system, SoC is used to reflect how much the remaining capacity in the energy storage battery is, and the calculation method is as follows:

[0137]

[0138] Wherein, E VRB is the rated capacity of the energy storage system battery, P VRB is the real-time power of the energy storage system. When P VRB is greater than 0, the energy storage system discharges, otherwise, when P VRB is less than 0, the energy storage battery charges.

[0139] Step 5.2, the improved particle swarm algorithm is used to solve and analyze the double-layer optimization model, and the specific process is as follows:

[0140] (1) Collect data such as photovoltaic power generation power, wind power generation power, and power load.

[0141] (2) Initialize the particle swarm, take the real-time power and state of charge of each energy storage as a particle, and give it an initial position and initial speed;

[0142] (3) According to the charge and discharge efficiency, capacity attenuation rate, self-discharge rate and economic benefit index function to be solved, first fix the economic benefit index function, calculate the fitness of the inner layer F1 of the double-layer optimization operation model, update the position of the particle and the speed of the particle and update the adaptive inertia weight parameter, the next stage is to update the economic benefit index by the particle position solved by the last stage, and then calculate the fitness of the outer layer F2 of the double-layer optimization operation model.

[0143] (6) Update the position of the particle and the speed of the particle.

[0144] (7) Judge whether the global optimal solution meets the judgment condition, check whether all the constraint conditions are met, or whether the iteration number is met, if not, continue to update the particle fitness and position, if met, the calculation is ended, and the best position, that is, the optimal solution is obtained.

[0145] The patent provides a nested double-layer optimization operation method of a vanadium redox flow battery energy storage system of an active power distribution network considering dynamic performance, constructs an energy storage optimization operation scheme under the coordinated optimization of battery performance and economy, and realizes the coordinated optimization of battery dynamic performance and comprehensive operation income of the active power distribution network.

Claims

1. A nested double-layer optimal operation method of a vanadium redox flow battery energy storage system of an active power distribution network considering dynamic performance, characterized in that, Comprising the following steps: Step 1, modeling the dynamic performance of the all-vanadium redox flow battery through charge and discharge test, the specific method is: Through the charge-discharge power experiment test of the all-vanadium redox flow battery, an efficiency data matrix set A of the battery is constructed, the charge efficiency η c and the discharge efficiency η d of the all-vanadium redox flow battery are obtained, and a least square fitting is performed to obtain a battery charge-discharge efficiency model, which is specifically as follows: where T b is the operating temperature, P b is the operating efficiency, SoC is the state of charge of the energy storage system, a c , b c , c c , d c and a o , c o , a c1 , b c1 , c c1 , d c1 are the VRB charge efficiency coefficient fitted parameter values, a d , b d , c d and a d1 , a o , c d1 , b o , d o are the VRB discharge efficiency coefficient fitted parameter values; Step 1.2, through the standing test of all-vanadium redox flow battery at different SoC, the self-discharge rate data matrix set B of the battery is constructed, and the self-discharge rate γ of the battery is fitted to obtain the battery self-discharge rate model, which is: γ = (m a T b +m b )SoC· (m c SoC + m d ) where T b is the operating temperature, m a , m b , m c , m d are self-discharge rate coefficient fitting parameter values; Step 1.3, obtain the semi-empirical formula of battery capacity attenuation performance, through battery charge and discharge capacity attenuation experiment test fitting out related parameters, obtain the battery dynamic capacity attenuation performance model, which is: In the formula, k Nc Nc is the loop count coefficient. n To represent the equivalent number of charge-discharge cycles (100%), N is the number of experiments. The average state of charge within a single charge-discharge cycle is defined as the SoC. a Utilizing the mean square error of the state of charge SoC d The fluctuation amplitude of the state of charge within the cycle is described, where τ is the time constant, and k T These are the temperature influence coefficients, T and T, respectively. ref These are the actual and reference values ​​of the ambient temperature, T. a,t and T refa These are the actual and reference values ​​of the internal operating temperature of the VRB, respectively. k1, k2, k3, and k4 are the fitting coefficient values ​​of the relationship between capacity decay and state of charge. Step 2, modeling the dynamic output characteristics of energy storage battery through the energy and power change of all-vanadium redox flow battery; Step 3, through the multi-angle analysis of the functionality and economic effect of the energy storage system connected to the active distribution network, the comprehensive operation benefit index system of the active distribution network is established, and the specific process is as follows: Step 3.1, considering the abandoned photovoltaic quantity as the operation benefit of the active distribution network, the photovoltaic consumption is determined: Photovoltaic consumption B ab The calculation is as follows: B ab = k a W VRB where k a is the price factor, W VRB is the amount of photovoltaic energy rejected, wherein the total amount of photovoltaic energy rejected is calculated by: In the formula, P wind is the power generation of the wind turbine in the energy storage system, N wind is the number of wind power installations, P solar is the power generation of the photovoltaic in the energy storage system, N solar is the number of photovoltaic installations, P grid_min is the minimum value of the amount of power purchased from the upper grid by the active distribution network when the energy storage system is running, P loss is the network loss, P load is the load power, P VRB is the output power of the energy storage system, Z t is the characteristic quantity of the photovoltaic abandonment state of the energy storage system; Step 3.2, Peak shaving is the initiative distribution network operation benefit, calculate the peak shaving benefit B up Equivalent discount is made, and the specific method is as follows: The peak clipping rate ω is calculated, and the specific formula is: where P max is the load peak during the energy storage operation before peak shaving, P' max is the load peak during the energy storage operation after peak shaving; According to the peak clipping rate, the time N for delaying the power grid upgrading is calculated y The specific calculation formula is as follows: In the formula, ε is the annual load growth rate; According to the time limit for delaying the upgrading of the power grid, the benefit of delaying the upgrading obtained by the optimization of the energy storage operation is calculated, and the formula is as follows: In the formula, C e is the investment required for grid modernization, i r and d r are the interest and discount rates, respectively; Step 3.3, Calculating power market arbitrage B arb : where τ is the total years, Δf is the difference of the benefit of the distribution network operation in a year, C p is the real-time electricity price, P sell is the electricity sold by the energy storage system, P sell > 0 when the energy storage system sells electricity to the load, P sell < 0 when the energy storage system buys electricity; P grid The energy storage system reduces the amount of electricity purchased from the upper-level power grid by storing photovoltaic power. Step 3.4, calculating environmental benefit B em : In the above formula, C w,k is the unit charge for pollutant emissions; ζ grid,k is the pollutant emission density of the upper grid; P g represents the total amount of electricity purchased from the upper grid when the energy storage system is running, M represents the pollutant species, and P grid represents the total amount of electricity purchased from the upper grid per hour when the energy storage system is running; Step 4, construct a nested double-layer optimization operation model; Step 5, use the optimized particle swarm algorithm to solve the nested double-layer optimization operation model to obtain the nested double-layer optimization operation scheme of the energy storage system.

2. The method of claim 1, wherein, Step 2 models the dynamic output characteristics of the energy storage battery, and the model is: where SoC t and SoC t-1 respectively represent the state of charge at time t and time t-1, E VRB represents the capacity of VRB, γ is the self-discharge rate, α is the capacity attenuation rate, η c / d represents the charging or discharging efficiency of the all-vanadium redox flow battery, P VRB the output power of the all-vanadium redox flow battery energy storage system. 3.The method of claim 1, wherein, Using the meta-model heuristic method, the dynamic performance of the all-vanadium redox flow battery is taken as the inner optimization target, and the comprehensive operation benefit of the active distribution network is taken as the outer optimization target, and a nested double-layer optimization operation model is constructed, wherein the inner and outer target functions of the nested double-layer optimization operation model are: Inner layer objective function F1 = max{ω1η c / d - ω2λ - ω3α}; Outer layer objective function F2 = max{β1B ab + β2B up + β3B arb + β4B em} Wherein, ω1, ω2, ω3, β1, β2, β3, β4 are the weight values of the corresponding decision variables.

4. The method of claim 1, wherein, The specific process of using the optimized particle swarm algorithm to solve the nested double-layer optimization operation model to obtain the nested double-layer optimization operation scheme of the energy storage system is as follows: Step 5.1, add constraint conditions to the nested double-layer optimization operation model, including: For small power distribution networks containing photovoltaic power generation and wind power generation, the power balance satisfies the following equation: where P grid is the power transmitted by the upper grid to the region, P wind is the power input to the region from wind power, P solar is the power input to the region from photovoltaic power, and P VRB is the power of the energy storage device. 2) The power P imported from the upper level grid into the region will be limited in some interval, i.e.: grid will be limited in some interval, i.e.: P grid_min ≤P grid ≤P grid_max - wherein P grid_min is the minimum value of power input into the region from the previous level of the grid, P grid_max is the maximum value of power input into the region from the previous level of the grid. 3) SoC of the energy storage battery system t between the maximum state of charge SoC max and the minimum state of charge SoC min i.e. SoC min ≤SoC t ≤SoC max In the energy storage system, SoC is used to reflect how much the remaining capacity in the energy storage battery is, and the calculation method is as follows: wherein E VRB is the rated capacity of the energy storage system battery, η c / d represents the charging or discharging efficiency of the vanadium flow battery, P VRB is the real-time power of the energy storage system, when P VRB is greater than 0, the energy storage system discharges, otherwise, when P VRB is less than 0, the energy storage battery charges; Step 5.2, use the improved particle swarm algorithm to solve the nested double-layer optimization operation model, and the specific process is as follows: (1) Collect data, including photovoltaic power generation power, wind power generation power, and power load; (2) Initialize the particle swarm, take the real-time power and state of charge of each energy storage as a particle, and give it an initial position and initial speed; (3) According to the charge and discharge efficiency, capacity attenuation rate, self-discharge rate and economic benefit index function to be solved, the fitness of each particle is calculated by the inner F1 and outer target function F2 of the nested double-layer optimization operation model; according to the fitness value of each particle, the global optimal position is solved; (4) Update the position of the particle and the speed of the particle; (5) judge whether the iteration number is satisfied, if not, continue to update the particle fitness and position, if yes, the calculation is ended, and the best position, i.e. the optimal solution, is obtained.

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