Dual-layer optimization method for capacity configuration of multi-energy coupling system based on fruit fly algorithm

Abstract

The application proposes a double-layer optimization method for capacity configuration of a multi-energy coupling system based on a fruit fly algorithm, comprising the following methods: method one, an energy storage system is formed by a pumped storage power station and a battery, and the investment cost is reduced by reducing the lost electric energy in the energy storage and discharge process and reducing the working times of the water pump and the water turbine; method two, a mixed model is established by taking the minimum investment cost of the energy storage system as an evaluation function; an operation process is embedded in the planning model to obtain a double-layer planning model; method three, in the upper layer planning of the double-layer planning model, the maximum of the comprehensive economic benefits of the system on the basis of safety and stability is taken as an evaluation function, and the lower layer planning is based on intuitive analysis, and the daily charging and discharging strategy is obtained through analysis; method four, the configuration scheme of the double-layer planning model is solved based on a fruit fly optimization algorithm, and the best capacity configuration scheme is obtained through step-by-step iteration; and the application can maximize the comprehensive economic benefits of the multi-energy coupling energy storage system on the basis of safety and stability.

Description

TECHNICAL FIELD

[0001] The present application relates to the wind and light energy storage capacity optimization configuration technical field, especially the multi-energy coupling system capacity configuration double-layer optimization method based on fruit fly algorithm. BACKGROUND

[0002] Under the background of global energy revolution triggered by energy crisis and environmental problems, China's energy revolution is deepening, continuously optimizing energy production and consumption structure, and promoting energy use concept change. China needs to further accelerate energy transformation, and strive to promote the construction of modern energy system. Among many new energy resources such as bioenergy, wind energy, solar energy, hydrogen energy and water energy, wind energy and solar energy have the outstanding advantages of abundant reserves and inexhaustible use. Therefore, multi-energy complementary power generation system has very broad development prospects.

[0003] However, the multi-energy system mainly based on new energy power generation has the disadvantage of "eating according to the weather", that is, the power generation is affected by climate and season, and cannot be well regulated to meet the load demand, so it is necessary to increase the energy storage system in the multi-energy system, so it is necessary to study how to configure and plan the capacity of the multi-energy coupling energy storage system. SUMMARY

[0004] The present application proposes a multi-energy coupling system capacity configuration double-layer optimization method based on fruit fly algorithm, which can maximize the comprehensive economic benefit of the multi-energy coupling energy storage system on the basis of safety and stability.

[0005] The present application adopts the following technical scheme.

[0006] The multi-energy coupling system capacity configuration double-layer optimization method based on fruit fly algorithm includes the following methods:

[0007] Method one, the energy storage system is composed of pumped storage power station and battery, the investment cost is reduced by reducing the electric energy loss of the energy storage system in the energy storage and discharge process and reducing the working frequency of the water pump and water turbine;

[0008] Method two, the mixed model of battery and pumped storage power station is established by taking the minimum investment cost of energy storage system as the evaluation function; the constraint conditions of the mixed model are set, and the energy storage configuration capacity is taken as the optimization variable; the operation process is embedded in the planning model to obtain a double-layer planning model;

[0009] Method three, in the upper layer planning of the double-layer planning model, the maximum comprehensive economic benefit on the basis of safety and stability is taken as the evaluation function, and the lower layer planning is based on intuitive analysis, and the daily charging and discharging strategy is obtained by analysis;

[0010] Method four, the configuration scheme of the double-layer planning model is solved based on fruit fly optimization algorithm, and the best capacity configuration scheme is obtained by step-by-step iteration.

[0011] The method two comprises the following steps;

[0012] Step one: establish the pumped storage power station model, take the pumped storage power station as the first energy storage way of new energy power generation, and make up for the shortcomings of new energy power generation instability with its peak clipping effect, the pumped storage power station mathematical model in the hybrid model is composed of the pump turbine output model, when the pumped storage power station operates in the pumping state, the transmission power on the pump shaft is:

[0013]

[0014] In the formula, N r is the unit speed, T m is the unit torque, D d is the runner diameter, L d is the upstream and downstream water level difference;

[0015] Step two: use the storage battery as the second energy storage way of new energy power generation, in the storage battery mathematical model in the hybrid model, calculate two factors of the charging and discharging and the remaining power of the energy storage battery, the formula is: discharging process:

[0016]

[0017] Charging process:

[0018]

[0019] In the formula, SOC(t) represents the remaining power of the energy storage battery at time t, σ represents the self-discharge rate, P c and P d respectively represent the charging power and discharging power of the energy storage battery, η c and η d respectively represent the charging efficiency and discharging efficiency of the energy storage battery, E c represents the rated capacity of the energy storage battery;

[0020] Step three: in the hybrid model, the calculation formula of the total cost of the pump and turbine is:

[0021]

[0022] In the formula, M pump is the number of pumped storage power station pump turbines; C pump is the unit price of the pumped storage power station pump turbine; C REpp is the replacement cost of the pumped storage power station turbine; C OMpp is the operation and maintenance cost of the pumped storage power station; T pump is the life cycle of the pumped storage power station turbine; r is the discount rate; Ta for the project cycle;

[0023] Step four: in the mixed model, the formula for calculating the total cost of the battery is:

[0024]

[0025] Where N is the number of batteries; C is the unit price of the battery; C is the replacement cost of the unit battery; T is the life cycle of the battery; C is the operation and maintenance cost of the unit battery. b b REb b OMb

[0026] Step five: in the energy storage system composed of pumped storage power station and battery represented by the mixed model, the basic part of the unbalanced power is borne by the pumped storage power station, and the fluctuation part is borne by the battery; by reducing the working times of the pump turbine, the additional loss and cost in the energy storage process are reduced, and the loss of power supply probability LPSP of the energy storage system meets the requirements, which is expressed in the formula as:

[0027] Where Elps represents the load power loss, and El represents the total load demand power.

[0028] Step six: according to the working characteristics of pumped storage power station and battery in the energy storage system engineering and the operation index of multi-energy system, the constraint conditions are established as follows:

[0029]

[0030] Where m is the number of reservoir turbines, n is the number of batteries, E is the capacity of the upstream reservoir, E is the capacity of the battery, and α is the proportion of the basic part of the unbalanced power, and β is the proportion of the important load in the load. p b

[0031] The method three includes the following steps:

[0032] Step seven: set the upper planning mathematical model, specifically: the optimization goal is to maximize the comprehensive economic benefit of the system on the basis of safety and stability, and the specific objective function is:

[0033] max f(E)=(C loss +C s -C p,in -C p,om )-(NPC pat +NPC b ) Formula eight;​​​​​​​

[0034] Where E is the energy storage configuration capacity, which is a decision variable; C loss To generate revenue from daily energy loss savings in the distribution network after configuring energy storage, C s To generate daily revenue from low-storage and high-release arbitrage in energy storage, C p,in and C p,om This includes the daily investment cost and daily operation and maintenance cost of the battery; the calculations for each component are as follows:

[0035]

[0036] C s =C fd -C cd Formula Nine;

[0037]

[0038]

[0039]

[0040]

[0041] Among them, P loss (t) represents the active power line loss of the distribution network in the t-th sampling interval, bessi represents different battery banks, N represents the number of batteries, and m fd m cd These represent the discharge and charging prices of the battery at different times, respectively. bessi,fd (t fd ), P bessi,cd (t cd ) represent the battery at time t fd Discharge power and time t cd Charging power, r and l are the battery's contact ratio and service life, k e For the unit capacity cost of energy storage, k c For the cost of energy storage converter per unit power, P max,bessi k is the maximum charge / discharge power of the bissi-th battery. om The operating and maintenance cost per unit capacity of energy storage;

[0042] Step 8: Set up the lower-level planning model, specifically: based on the equivalent daily load curves of water and photovoltaic power generation in the new energy power generation system and the output curve of energy storage configuration, formulate the daily battery charging and discharging strategy in the energy storage system according to the required indicators.

[0043] Step eight includes the following steps;

[0044] Step 8.1: Divide the charging and discharging time periods according to the time-of-use electricity price, and determine the charging time t. cd and discharge time t fd ;

[0045] Step 8.2: The battery charging and discharging adopts a variable power charging method. The charging and discharging power of the battery in each time period is determined according to demand. That is, the smaller the equivalent load in the equivalent time Δt, the larger the peak-valley difference of the equivalent load curve, and the greater the demand for energy storage charging. By determining the load value ranking, the charging power is determined respectively. By determining the charging and discharging power and time of all time periods, the charging and discharging strategy of the battery in the daily scheduling cycle is obtained.

[0046] Step 8.2 includes the following steps;

[0047] Step 8.2.1: Charging power calculation is as follows

[0048]

[0049]

[0050] In the formula P L (t cd ) for in t cd The equivalent load value of the sampling interval, P C,max P bess,cd,e These are the maximum charging power and rated charging power of the battery, respectively, P L,min θ represents the minimum equivalent load value within all sampling intervals during the discharge time, and θ is the charging and discharging power weight of the besti-th battery.

[0051] Step 8.2.2 Discharge power calculation is as follows:

[0052]

[0053] In the formula P L (t fd P represents the equivalent load value at the sampling interval. f,max P bess,fd,e These are the maximum discharge power and rated discharge power of the battery, respectively, P L,max θ represents the maximum equivalent load value within all sampling intervals during the discharge time, and θ is the charging and discharging power weight of the besti-th battery.

[0054] The fruit fly optimization algorithm in Method 4 specifically includes the following steps;

[0055] Step S1: Initialize the parameters of the fruit fly algorithm according to the bilevel programming model, namely, the population size Sizepop, the maximum number of iterations Maxgen, and the initial position X of the fruit fly population. axis Y axis ;

[0056] Step S2, the fruit fly individual is taken as the fruit fly algorithm of the bi-level programming model, the maximum of the comprehensive economic benefit of the bi-level programming model is taken as the target of the fruit fly algorithm, the direction and distance of the searching process of the related individual are randomly assigned according to the food searching principle of the fruit fly, and the formula is expressed as

[0057] X i = X axis + RandomValue Formula Seventeen

[0058] Y i = Y axis + RandomValue Formula Eighteen

[0059] Step S3, the distance Dist between the fruit fly individual and the point is determined according to Formula Sixteen i , the result determination value S of the individual smell concentration is determined according to Formula Seventeen i ; the formula is

[0060]

[0061] S i = 1 / Dist i Formula Twenty

[0062] Step S4, S i is substituted into the fitness function, and the smell concentration smell of the fruit fly individual is solved i ; the formula is

[0063] Smell i = Function(S i ) Formula Twenty-One

[0064] Step S5, the target food position with the strongest smell is found, the minimization problem is solved, the best smell concentration value bestSmell and its X axis , Y axis coordinate value are saved, and at this time, other individuals in the fruit fly population of the fruit fly algorithm fly to the position.

[0065] Step S6, iteration optimization is entered, steps S2 to S5 are repeatedly executed, and it is judged whether the smell concentration is better than the previous smell concentration, that is, the global optimal point position is determined by comparing the current optimal value with the historical optimal value.

[0066] In the step S6, the maximum of the comprehensive economic benefit of the energy storage system of the new energy power generation on the basis of safety and stability is taken as the optimal value, and the global optimal point is calculated.

[0067] The present application is directed to a multi-energy coupling energy storage system composed of pumped storage power stations and batteries, so that the system can reduce the electric energy loss of the energy storage system in the energy storage and discharge process and reduce the working frequency of the water pump and water turbine through reasonable operation strategies, and can maximize the comprehensive economic benefits of the multi-energy coupling energy storage system on the basis of safety and stability. BRIEF DESCRIPTION OF DRAWINGS

[0068] The present application will be further described in detail below in combination with the drawings and specific embodiments:

[0069] Attached Figure 1 is a flowchart of the fruit fly optimization algorithm of the present application;

[0070] Attached Figure 2 is a schematic diagram of the capacity configuration optimization process of the double-layer model of the present application. DETAILED DESCRIPTION

[0071] As shown in the figure, the multi-energy coupling system capacity configuration double-layer optimization method based on the fruit fly algorithm includes the following methods:

[0072] Method one, the energy storage system is composed of pumped storage power stations and batteries, and the investment cost is reduced by reducing the electric energy loss of the energy storage system in the energy storage and discharge process and reducing the working frequency of the water pump and water turbine;

[0073] Method two, the mixed model of the battery and the pumped storage power station is established with the lowest investment cost of the energy storage system as the evaluation function; the constraint conditions of the mixed model are set, and the energy storage configuration capacity is taken as the optimization variable; the operation process is embedded in the planning model to obtain a double-layer planning model;

[0074] Method three, in the upper layer planning of the double-layer planning model, the maximum comprehensive economic benefits on the basis of safety and stability are taken as the evaluation function, and the lower layer planning is based on intuitive analysis, and the daily charging and discharging strategy is obtained through analysis;

[0075] Method four, the configuration scheme of the double-layer planning model is solved based on the fruit fly optimization algorithm, and the best capacity configuration scheme is obtained through step-by-step iteration.

[0076] The method two includes the following steps:

[0077] Step one: establish a pumped storage power station model, take the pumped storage power station as the first energy storage mode of new energy generation, and use its peak load shifting effect to make up for the instability of new energy generation, the pumped storage power station mathematical model in the mixed model is composed of a water pump water turbine output model, when the pumped storage power station operates in the pumping state, the transmission power on the water pump shaft is:

[0078]

[0079] In the formula, N r T is the unit speed. m For unit torque, D d L is the diameter of the rotor. d The difference in water levels between upstream and downstream;

[0080] Step Two: Using batteries as the second energy storage method for new energy power generation, the battery mathematical model in the hybrid model is calculated based on two factors: battery charging / discharging and remaining capacity. The formula is as follows: Discharging process:

[0081]

[0082] Charging process:

[0083]

[0084] In the formula, SOC(t) represents the remaining charge of the energy storage battery at time t, σ represents the self-discharge rate, and P c and P d Let η represent the charging power and discharging power of the energy storage battery, respectively. c and η d E represents the charging efficiency and discharging efficiency of the energy storage battery, respectively. c Indicates the rated capacity of the energy storage battery;

[0085] Step 3: In the hybrid model, the formula for calculating the total cost of the pump and turbine is as follows:

[0086]

[0087] In the formula, M pump C represents the number of pumps and turbines in a pumped storage power station. pump C is the unit price of the pump-turbine in a pumped storage power station; REpp The replacement cost of the turbines in a pumped storage power station; C OMpp The operation and maintenance cost of pumped storage power stations; T pump The lifespan of the pumped storage power station turbine is represented by r; the discount rate is represented by T. a Project cycle;

[0088] Step 4: In the hybrid model, the formula for calculating the total cost of the battery is:

[0089]

[0090] In the formula N b The number of batteries; C b C is the unit price of the battery; REb The replacement cost per unit of storage battery; T b For the battery's lifespan; COMb The operation and maintenance cost of the unit battery;

[0091] Step five: in the energy storage system composed of pumped storage power station and battery expressed by the mixed model, the basic part of the unbalanced power is borne by the pumped storage power station, and the fluctuation part is borne by the battery; by reducing the working times of the pump turbine, the additional loss and cost in the energy storage process are reduced, and the load power supply loss probability LPSP of the energy storage system meets the requirements, which is expressed in the formula as:

[0092] In the formula, Elps represents the load power loss, and El represents the total demand power of the load;

[0093] Step six: according to the working characteristics of pumped storage power station and battery in the energy storage system engineering and the operation index of multi-energy system, the constraint conditions are established as follows:

[0094]

[0095] In the formula, m is the number of reservoir turbines, n is the number of batteries, E p is the capacity of upstream reservoir, E b is the capacity of battery, and α is the proportion of the basic part of unbalanced power, and β is the proportion of important load in the load.

[0096] The method three includes the following steps:

[0097] Step seven: set the upper planning mathematical model, specifically: the optimization goal is to maximize the comprehensive economic benefit of the system on the basis of safety and stability, and the specific objective function is:

[0098] max f(E)=(C loss +C s -C p,in -C p,om )-(NPC pat +NPC b ) Formula eight;

[0099] In the formula, E is the energy storage capacity, which is a decision variable; C loss is the daily power saving income of the distribution network after the configuration of energy storage, C s is the daily income of energy storage low storage high release arbitrage, C p,in and C p,om are the daily investment cost and daily operation and maintenance cost of the battery; the calculation of each component is as follows:

[0100]

[0101] Cs =C fd -C cd Formula Nine;

[0102]

[0103]

[0104]

[0105]

[0106] Among them, P loss (t) represents the active power line loss of the distribution network in the t-th sampling interval, bessi represents different battery banks, N represents the number of batteries, and m fd m cd These represent the discharge and charging prices of the battery at different times, respectively. bessi,fd (t fd ), P bessi,cd (t cd ) represent the battery at time t fd Discharge power and time t cd Charging power, r and l are the battery's contact ratio and service life, k e For the unit capacity cost of energy storage, k c For the cost of energy storage converter per unit power, P max,bessi k is the maximum charge / discharge power of the bissi-th battery. om The operating and maintenance cost per unit capacity of energy storage;

[0107] Step 8: Set up the lower-level planning model, specifically: based on the equivalent daily load curves of water and photovoltaic power generation in the new energy power generation system and the output curve of energy storage configuration, formulate the daily battery charging and discharging strategy in the energy storage system according to the required indicators.

[0108] Step eight includes the following steps;

[0109] Step 8.1: Divide the charging and discharging time periods according to the time-of-use electricity price, and determine the charging time t. cd and discharge time t fd ;

[0110] Step 8.2: The battery charging and discharging adopts a variable power charging method. The charging and discharging power of the battery in each time period is determined according to demand. That is, the smaller the equivalent load in the equivalent time Δt, the larger the peak-valley difference of the equivalent load curve, and the greater the demand for energy storage charging. By determining the load value ranking, the charging power is determined respectively. By determining the charging and discharging power and time of all time periods, the charging and discharging strategy of the battery in the daily scheduling cycle is obtained.

[0111] Step 8.2 includes the following steps;

[0112] Step 8.2.1: Charging power is calculated as follows

[0113]

[0114]

[0115] where P (t) is the equivalent load value at the sampling interval t, P max and P nom are the maximum charging power and the rated charging power of the battery respectively, P min is the minimum equivalent load value within all sampling intervals at the discharging moment, and θ is the charging and discharging power weight of the bessi th battery. L (t cd ) is the equivalent load value at the sampling interval t, P max and P nom are the maximum charging power and the rated charging power of the battery respectively, P min is the minimum equivalent load value within all sampling intervals at the discharging moment, and θ is the charging and discharging power weight of the bessi th battery. cd C,max bess,cd,e L,min L (t fd ) is the equivalent load value at the sampling interval t, P max and P nom are the maximum charging power and the rated charging power of the battery respectively, P min is the minimum equivalent load value within all sampling intervals at the discharging moment, and θ is the charging and discharging power weight of the bessi th battery. f,max bess,fd,e L,max

[0116] Step 8.2.2: Discharging power is calculated as follows

[0117]

[0118] where P (t) is the equivalent load value at the sampling interval t, P max and P nom are the maximum discharging power and the rated discharging power of the battery respectively, P max is the maximum equivalent load value within all sampling intervals at the discharging moment, and θ is the charging and discharging power weight of the bessi th battery. L fd f,max bess,fd,e L,max

[0119] The fruit fly optimization algorithm in the fourth method specifically includes the following steps.

[0120] Step S1, initialize the parameters of the fruit fly algorithm according to the bi-level programming model, that is, the population size Sizepop, the maximum number of iterations Maxgen, and the initial position X, Y of the fruit fly population. axis axis ;

[0121] Step S2, take the bi-level programming model as the fruit fly individual of the fruit fly algorithm, take the maximization of the comprehensive economic benefit of the bi-level programming model as the target of the fruit fly algorithm, and randomly assign the direction and distance of the search process of the related individual according to the fruit fly search food principle, which is expressed in the formula as

[0122] X i = X axis + RandomValue Formula Seventeen;

[0123] Y i = Y axis ​​​+RandomValue Equation eighteen

[0124] Step S3, determine the distance Dist between the fruit fly individual and the point according to Equation sixteen i , determine the result judgment value S of the individual smell concentration according to Equation seventeen i ; the equation is

[0125]

[0126] S i = 1 / Dist i Equation twenty;

[0127] Step S4, substitute S i into the fitness function, and find the smell concentration smell of the fruit fly individual i ; the equation is

[0128] Smell i = Function(S i ) Equation twenty-one;

[0129] Step S5, find the target food position with the strongest smell, solve the minimization problem; save the best smell concentration value bestSmell and its X axis , Y axis coordinate values, at this time other individuals in the fruit fly population of the fruit fly algorithm fly to this position;

[0130] Step S6, enter the iterative optimization, repeat steps S2 to S5, and judge whether the smell concentration is better than the previous smell concentration, that is, determine the global optimal point position by comparing the current optimal value with the historical optimal value.

[0131] In the step S6, the maximum of the comprehensive economic benefit of the energy storage system of the new energy power generation on the basis of safety and stability is taken as the optimal value, and the global optimal point is calculated.

Claims

1. A two-layer optimization method for capacity configuration of multi-energy coupled systems based on the fruit fly algorithm, characterized in that: Including the following methods: Method 1: Use pumped storage power stations and batteries to form an energy storage system. Reduce investment costs by reducing the electrical energy lost during energy storage and discharge and by reducing the number of times pumps and turbines operate. Method 2: Establish a hybrid model of battery and pumped storage hydropower station with the lowest investment cost of energy storage system as the evaluation function; set constraints for the hybrid model and use energy storage configuration capacity as the optimization variable; By embedding the operational process into the planning model, a two-level planning model is obtained. Method 3: In the upper-level planning of the two-level planning model, the maximization of comprehensive economic benefits of the system on the basis of safety and stability is used as the evaluation function, while the lower-level planning is based on intuitive analysis, and the daily charging and discharging strategy is obtained through analysis. Method 4: Solve the configuration scheme of the bilevel programming model based on the fruit fly optimization algorithm, and obtain the optimal capacity configuration scheme through step-by-step iteration; Method 2 Includes the following steps; Step 1: Establish a pumped storage power station model. The pumped storage power station is considered the first energy storage method for new energy power generation, and its peak-shaving and valley-filling functions compensate for the instability of new energy power generation. The mathematical model of the pumped storage power station in the hybrid model consists of a pump-turbine output model. When the pumped storage power station is operating in pumping mode, the power transmitted on the pump shaft is: In the formula, N r T is the unit rotational speed. m For unit torque, D d L is the diameter of the rotor. d The difference in water levels between upstream and downstream; Step 2: Using batteries as the second energy storage method for new energy power generation, the battery mathematical model in the hybrid model is calculated based on two factors: the charging and discharging of the energy storage battery and the remaining power. The formula is as follows: Discharge process: Charging process: In the formula, SOC(t) represents the remaining charge of the energy storage battery at time t, σ represents the self-discharge rate, and P c and P d Let η represent the charging power and discharging power of the energy storage battery, respectively. c and η d E represents the charging efficiency and discharging efficiency of the energy storage battery, respectively. c Indicates the rated capacity of the energy storage battery; Step 3: In the hybrid model, the formula for calculating the total cost of the pump and turbine is as follows: In the formula, M pump C represents the number of pumps and turbines in a pumped storage power station. pump C is the unit price of the pump-turbine in a pumped storage power station; REpp The replacement cost of the turbines in a pumped storage power station; C OMpp The operation and maintenance cost of pumped storage power stations; T pump The lifespan of the pumped storage power station turbine is represented by r; the discount rate is represented by T. a Project cycle; Step 4: In the hybrid model, the formula for calculating the total cost of the battery is: In the formula N b The number of batteries; C b C is the unit price of the battery; REb The replacement cost per unit of battery; T b For the battery's lifespan; C OMb The operating and maintenance cost per unit of battery; Step 5: In the energy storage system consisting of pumped storage power stations and batteries as described in the hybrid model, the pumped storage power station undertakes the basic portion of the unbalanced power, while the batteries undertake the fluctuating portion. Additional losses and costs during energy storage are reduced by decreasing the number of pump and turbine operations, ensuring that the loss of power supply probability (LPSP) of the energy storage system meets the requirements. This can be expressed by the following formula: In the formula, Elps represents the load power shortage, and El represents the total load power demand; Step Six: Based on the operating characteristics of pumped storage power stations and batteries in the energy storage system project, as well as the operating indicators of the multi-energy system, establish the following constraints: In the formula, m is the number of reservoir turbines, n is the number of batteries, and E p E represents the upstream reservoir capacity. b The battery capacity is α, the proportion of the basic component in the unbalanced power is β, and the proportion of the important load in the load is β; the method three includes the following steps; Step 7: Define the upper-level planning mathematical model, specifically: the optimization objective is to maximize the overall economic benefits of the system while ensuring safety and stability, and its specific objective function is: maxf(E)=(C loss +C s -C p,in -C p,om )-(NPC pat +NPC b Formula 8; Where E is the energy storage configuration capacity, which is a decision variable; C loss To generate revenue from daily energy loss savings in the distribution network after configuring energy storage, C s To generate daily revenue from low-storage and high-release arbitrage in energy storage, C p,in and C p,om This includes the daily investment cost and daily operation and maintenance cost of the battery; the calculations for each component are as follows: C s =C fd -C cd Formula Nine; Among them, P loss (t) represents the active power line loss of the distribution network in the t-th sampling interval, bessi represents different battery banks, N represents the number of batteries, and m fd m cd These represent the discharge and charging prices of the battery at different times, respectively. bessi,fd (t fd ), P bessi,cd (t cd ) represent the battery at time t fd Discharge power and time t cd Charging power, r and l are the discount rate and service life of the battery, respectively, and k e For the unit capacity cost of energy storage, k c For the cost of energy storage converter per unit power, P max,bessi k is the maximum charge / discharge power of the besti battery. om The operating and maintenance cost per unit capacity of energy storage; Step 8: Set up the lower-level planning model, specifically: based on the equivalent daily load curves of water and photovoltaic power generation in the new energy power generation system and the output curve of energy storage configuration, formulate the daily battery charging and discharging strategy in the energy storage system according to the required indicators.

2. The two-layer optimization method for capacity configuration of multi-energy coupled systems based on the fruit fly algorithm according to claim 1, characterized in that: Step eight includes the following steps; Step 8.1: Divide the charging and discharging time periods according to the time-of-use electricity price, and determine the charging time t. cd and discharge time t fd ; Step 8.2: The battery charging and discharging adopts a variable power charging method. The charging and discharging power of the battery in each time period is determined according to the demand. That is, the smaller the equivalent load in the equivalent time Δt, the larger the peak-valley difference of the equivalent load curve, and the greater the demand for energy storage charging. By determining the load value sorting, the charging power is determined respectively. By determining the charging and discharging power and time for all time periods, the charging and discharging strategy of the battery in the daily scheduling cycle is obtained.

3. The two-layer optimization method for capacity configuration of multi-energy coupled systems based on the fruit fly algorithm according to claim 2, characterized in that: Step 8.2 includes the following steps; Step 8.2.1: Charging power calculation is as follows In the formula P L (t cd ) for in t cd The equivalent load value of the sampling interval, P C,max P bess,cd,e These are the maximum charging power and rated charging power of the battery, respectively, P L,min θ represents the minimum equivalent load value within all sampling intervals during the discharge time, and θ is the charging and discharging power weight of the besti-th battery. Step 8.2.2 Discharge power calculation is as follows: In the formula P L (t fd P represents the equivalent load value at the sampling interval. f,max P bess,fd,e These are the maximum discharge power and rated discharge power of the battery, respectively, P L,max θ represents the maximum equivalent load value within all sampling intervals at the discharge time, and θ is the charge / discharge power weight of the besti-th battery.

4. The two-layer optimization method for capacity configuration of multi-energy coupled systems based on the fruit fly algorithm according to claim 2, characterized in that: The fruit fly optimization algorithm in Method 4 specifically includes the following steps; Step S1: Initialize the parameters of the fruit fly algorithm according to the bilevel programming model, namely, the population size Sizepop, the maximum number of iterations Maxgen, and the initial position X of the fruit fly population. axis Y axis ; Step S2: Using a bilevel programming model as the individual fruit flies in the fruit fly algorithm, and aiming to maximize the overall economic benefits of the bilevel programming model, the direction and distance of the search process for relevant individuals are randomly assigned according to the fruit fly's food search principle, expressed by the formula as follows: X i =X axis +RandomValue formula seventeen; Y i = Y axis +RandomValue formula #18; Step S3: Determine the distance Dist between the individual fruit fly and the point according to Formula 16. i The result judgment value S of individual taste concentration is determined according to Formula 17. i The formula is S i =1 / Dist i Formula 20; Step S4, S i Substituting into the fitness function, the smell concentration of each fruit fly is calculated. i The formula is Smell i =Function(S i Formula 21; Step S5: Locate the target food with the strongest flavor to solve the minimization problem; compare the optimal flavor concentration value bestSmell with its X. axis Y axis The coordinates are saved, and at this point, other individuals in the fruit fly swarm of the fruit fly algorithm fly towards that location. Step S6: Enter iterative optimization, repeat steps S2 to S5, and determine whether the flavor concentration is better than the previous flavor concentration. That is, determine the global optimal point position by comparing the current optimal value with the historical optimal value.

5. The two-layer optimization method for capacity configuration of multi-energy coupled systems based on the fruit fly algorithm according to claim 4, characterized in that: In step S6, the optimal value is to maximize the comprehensive economic benefits of the energy storage system for new energy power generation on the basis of safety and stability, and the global optimal point is calculated.

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