1. A method of configuring energy storage capacity, the method comprising:

acquiring historical electricity utilization data of a user, and constructing multiple groups of energy storage schemes based on the historical electricity utilization data;

randomly screening a group of first energy storage schemes meeting a preset independent variable range from a plurality of groups of energy storage schemes, wherein the first energy storage schemes comprise a plurality of first battery state of charge independent variables in a project period, and each first battery state of charge independent variable corresponds to a group of capacity-to-power ratios;

iteratively updating the first energy storage scheme for a preset number of times to obtain an Nth energy storage scheme, wherein the Nth energy storage scheme comprises a plurality of Nth battery state of charge independent variables, and N is a positive integer greater than 1;

and screening the target battery state of charge independent variables meeting preset conditions from the plurality of Nth battery state of charge independent variables, and taking the capacity and power ratio corresponding to the target battery state of charge independent variables as a target configuration scheme.

2. The method according to claim 1, wherein the iteratively updating the first energy storage scheme N times to obtain an nth energy storage scheme includes:

calculating individual adaptive values corresponding to the first battery state of charge independent variables to obtain a plurality of first individual adaptive values;

screening the first individual adaptation value with the largest value from the plurality of first individual adaptation values to be a second individual adaptation value;

adopting the second individual adaptive value to derive and construct a second energy storage scheme to complete one iteration updating;

and repeating the step of calculating the individual adaptive value corresponding to each first battery state of charge independent variable, and generating an Nth energy storage scheme after iteratively updating preset times.

3. The method according to claim 2, wherein the calculating the individual adaptive value corresponding to each of the first battery state of charge arguments comprises:

calculating system cost, project income, operation and maintenance cost, system residual value, loan and tax respectively by adopting the capacity and power ratio corresponding to the first battery state-of-charge independent variable;

and solving to obtain an individual adaptive value based on the system cost, the project income, the operation and maintenance cost, the system residual value, the loan and the tax.

4. The method of claim 3, wherein the system cost is calculated as follows:

C_sys＝k_sysM_bess；

wherein, C_sysTo the system cost, k_sysFor customer-side energy storage system unit cost, M_bessIs the system battery capacity;

the project revenue is calculated as follows:

wherein eta is charge-discharge efficiency, and eta is discharge time_dEta is 1/eta at charging_cAre all constants; p is a radical of_bess,i,jRepresents the energy storage and discharge power (the charge is a negative value) in the j hour of the ith year; Δ T is a unit duration (1 hour in the present embodiment); k is a radical of_v-p,jAnd represents the peak-to-valley electricity price corresponding to the j hour.

The ith year profit obtained by saving the basic electricity fee can be expressed as:

wherein, F_base,i,jRepresenting the basic electricity charge of the j month of the user in the original charging mode, k_demFor the basic electricity price in the maximum demand charging mode, P_dem,i,jRepresenting the maximum load value of the j month after energy storage optimization;

the operation and maintenance cost is calculated as follows:

wherein k is_opK represents the percentage of annual operation and maintenance cost to the total investment_bessThe proportion of the cost of the battery body to the total investment of the energy storage system is shown;

the system residual is calculated as follows:

wherein k is_res-bessRepresents the proportion of the residual value of the battery body to the total investment of the energy storage system, k_resRepresents the proportion of the overall residual value of the system to the total investment of the energy storage system, T_cyc，iRepresents the number of charge and discharge cycles, T, of the current battery used in the last year of the project_lifeRepresents the cycle life of the battery;

the loan is calculated as follows:

wherein r is_loanAs a ratio of capital cost, y_loanFor the year of loan, k_loanThe loan interest rate;

the tax is calculated as follows:

C_tax，i＝max[(C_base，i+C_kwh，i-C_loan，i-C_op，i-C_old，i)×0.25，0]；

wherein, C_old，iRepresenting the system equipment depreciation cost in the ith year.

5. The method according to claim 4, wherein the calculation formula of the individual adaptive value is as follows:

6. the method according to claim 2, wherein the constructing a second energy storage scheme using the second individual fitness value derivation comprises:

calculating two feasible solutions of the battery state of charge independent variable corresponding to the second individual adaptive value based on a championship selection method;

respectively calculating the filial generation of each feasible solution based on an arithmetic crossover method to obtain two filial generations;

respectively carrying out iterative computation on each filial generation based on a non-uniform variation method, and carrying out derivative variation to obtain a plurality of second battery state-of-charge independent variables;

generating a second energy storage scheme using the plurality of second battery states of charge from a set of variables.

7. The method of claim 6, wherein the two offspring is calculated as follows:

wherein, Mate₁For the first feasible solution, Mate₂For the second possible solution, child₁As the first child, child₂Is the second child, r is between [0,1 ]]The random number of (2);

the calculation formula for each child iteration calculation is shown as follows:

wherein, Child_{1 or 2}Is composed ofT is the current genetic algebra, T is the maximum genetic algebra, u is the non-uniformity parameter, and random (0,1) represents a random number that generates 0 or 1.

8. The method according to claim 7, wherein after the step of calculating the individual adaptive value corresponding to each of the first battery state-of-charge arguments to obtain a plurality of first individual adaptive values, the method further comprises:

respectively judging whether each first individual adaptive value meets a preset constraint condition;

if the first individual adaptation value does not meet the preset constraint condition, adding a preset penalty value to the first individual adaptation value to obtain a first eliminated individual adaptation value;

wherein the preset constraint condition comprises: the method comprises the following steps of charge state constraint, energy storage charge-discharge power constraint, battery cycle life constraint and power grid power constraint;

wherein the state of charge constraint is as follows:

SOC_min≤SOC_i，j≤SOC_max；

therein, SOC_minHaving a set lower limit, SOC, for the state of charge of the battery at jth hour of the ith year_maxThere is a set upper limit for the battery state of charge at the jth hour of the ith year;

the energy storage charging and discharging power constraint is shown as the following formula:

|P_bess，i，j|＝|(SOC_i，j-SOC_i，j+1)M_bess，i/ΔT|≤P_bess；

wherein M is_bess，iFor the residual capacity after i-th year of energy storage decay, M_bess，i＝M_bess-0.2M_bessT_cyc，i-1/T_life，P_bessThe rated power of the energy storage system is represented, and delta T is unit time length;

the battery cycle life constraint is given by:

when the battery cycle number reaches the cycle life limit, the battery body is replaced, and T is used_cyc，iZero, T_cyc，iRepresenting the accumulated cycle times of the battery by the end of the ith year, and DOD is the discharge depth of the battery;

the grid power constraint is as follows:

P_{grid_min}≤p_load，i，j-η·p_bess，i，j≤P_{grid_max}；

wherein, P_{grid_min}The time-by-time load power of the user after energy storage optimization cannot exceed the lower limit of power grid power constraint, P_{grid_max}And the time-by-time load power of the user after energy storage optimization cannot exceed the upper limit of the power constraint of the power grid.

9. The method of configuring energy storage capacity according to any one of claims 1-8, wherein the method further comprises:

and carrying out verification operation on the target configuration scheme, wherein the verification operation comprises valley period discharge detection, peak period charge detection and power balance correction.

10. An apparatus for configuring energy storage capacity, the apparatus comprising:

the building module is used for obtaining historical electricity utilization data of a user and building a plurality of groups of energy storage schemes based on the historical electricity utilization data;

the system comprises a screening module, a processing module and a processing module, wherein the screening module is used for screening a group of first energy storage schemes meeting a preset independent variable range from the plurality of groups of energy storage schemes at will, the first energy storage schemes comprise a plurality of first battery state-of-charge independent variables in a project period, and each first battery state-of-charge independent variable corresponds to a group of capacity-to-power ratios;

the iteration module is used for updating the first energy storage scheme for a preset number of times in an iteration mode to obtain an Nth energy storage scheme, wherein the Nth energy storage scheme comprises a plurality of Nth battery state of charge independent variables, and N is a positive integer greater than 1;

and the screening module is used for screening the target battery state of charge independent variables meeting the preset conditions from the plurality of Nth battery state of charge independent variables, and taking the capacity and power ratio corresponding to the target battery state of charge independent variables as a target configuration scheme.

Background

With the continuous increase of the total electricity consumption of the whole society, the integral load peak-valley difference of the user is further enlarged, and particularly, the load curve of a typical large-industry and commercial user presents an obvious peak-valley characteristic, and the necessity of the user side energy storage for participating in load optimization and demand response is gradually highlighted. Under the background of two electricity prices of the big industry of China, the electricity charge of a big industrial and commercial user mainly comprises two parts of electricity degree electricity charge and basic electricity charge, and from the existing energy storage project at the user side, the electricity degree electricity charge saving benefit obtained through the peak-valley price difference is taken as the main part, long-distance overall analysis on peak-valley characteristic optimization and maximum demand optimization is lacked for user load data and curves, so that the economic benefit of the energy storage project at the user side still has a space for further improvement.

In order to reasonably and economically perform energy storage distribution, the currently common method comprises the steps of establishing an investment cost/income optimization model by adopting a specific algorithm based on the requirements of users, and then calculating energy storage capacity by using the optimization model; or calculating the capacity of the energy storage system corresponding to the maximum income of the enterprise based on the power consumption of the user and the power supply quantity of the nuclear power station; or calculating the optimal energy storage configuration capacity according to the user use time period and the charge/discharge cycle of the nuclear power station.

However, the conventional method has the following technical problems: if the economic benefits of the next year need to be predicted according to the matching calculation of the economic benefits, the fluctuation of the economic benefits is large, and the accuracy rate of the calculation result is low; the period of configuration calculation by using the electricity consumption is long, the calculation period often exceeds the service life of the battery, and the configuration calculation is difficult to be performed within the service life of the battery; and the problem that the energy storage system can not reach the expected reduction of the electricity price and the like can occur according to the calculation of the charging and discharging period, so that the configuration result is inconsistent with the actual target result.

Disclosure of Invention

The invention provides a method and a device for configuring energy storage capacity, which can optimize and configure a corresponding energy storage configuration scheme aiming at the electricity utilization characteristics of a user, thereby reducing the electricity utilization cost of the user to the maximum extent.

A first aspect of an embodiment of the present invention provides a method for configuring energy storage capacity, where the method includes:

acquiring historical electricity utilization data of a user, and constructing multiple groups of energy storage schemes based on the historical electricity utilization data;

randomly screening a group of first energy storage schemes meeting a preset independent variable range from a plurality of groups of energy storage schemes, wherein the first energy storage schemes comprise a plurality of first battery state of charge independent variables in a project period, and each first battery state of charge independent variable corresponds to a group of capacity-to-power ratios;

iteratively updating the first energy storage scheme for a preset number of times to obtain an Nth energy storage scheme, wherein the Nth energy storage scheme comprises a plurality of Nth battery state of charge independent variables, and N is a positive integer greater than 1;

and screening the target battery state of charge independent variables meeting preset conditions from the plurality of Nth battery state of charge independent variables, and taking the capacity and power ratio corresponding to the target battery state of charge independent variables as a target configuration scheme.

In a possible implementation manner of the first aspect, the iteratively updating the first energy storage scheme N times to obtain an nth energy storage scheme includes:

calculating individual adaptive values corresponding to the first battery state of charge independent variables to obtain a plurality of first individual adaptive values;

screening the first individual adaptation value with the largest value from the plurality of first individual adaptation values to be a second individual adaptation value;

adopting the second individual adaptive value to derive and construct a second energy storage scheme to complete one iteration updating;

and repeating the step of calculating the individual adaptive value corresponding to each first battery state of charge independent variable, and generating an Nth energy storage scheme after iteratively updating preset times.

In a possible implementation manner of the first aspect, the calculating an individual adaptive value corresponding to each first battery state of charge independent variable includes:

calculating system cost, project income, operation and maintenance cost, system residual value, loan and tax respectively by adopting the capacity and power ratio corresponding to the first battery state-of-charge independent variable;

and solving to obtain an individual adaptive value based on the system cost, the project income, the operation and maintenance cost, the system residual value, the loan and the tax.

In a possible implementation manner of the first aspect, the system cost is calculated as follows:

C_sys＝k_sysM_bess；

wherein, C_sysTo the system cost, k_sysFor customer-side energy storage system unit cost, M_bessIs the system battery capacity;

the project revenue is calculated as follows:

wherein eta is charge-discharge efficiency, and eta is discharge time_dEta is 1/eta at charging_cAre all constants; p is a radical of_bess,i,jRepresents the energy storage and discharge power (the charge is a negative value) in the j hour of the ith year; Δ T is a unit duration (1 hour in the present embodiment); k is a radical of_v-p,jAnd represents the peak-to-valley electricity price corresponding to the j hour.

The ith year profit obtained by saving the basic electricity fee can be expressed as:

wherein, F_base,i,jRepresenting the basic electricity charge of the j month of the user in the original charging mode, k_demFor the basic electricity price in the maximum demand charging mode, P_dem,i,jRepresenting the maximum load value of the j month after energy storage optimization;

the operation and maintenance cost is calculated as follows:

wherein k is_opK represents the percentage of annual operation and maintenance cost to the total investment_bessThe proportion of the cost of the battery body to the total investment of the energy storage system is shown;

the system residual is calculated as follows:

wherein k is_res-bessRepresents the proportion of the residual value of the battery body to the total investment of the energy storage system, k_resRepresents the proportion of the overall residual value of the system to the total investment of the energy storage system, T_cyc,iRepresents the number of charge and discharge cycles, T, of the current battery used in the last year of the project_lifeRepresents the cycle life of the battery;

the loan is calculated as follows:

wherein r is_loanAs a ratio of capital cost, y_loanFor the year of loan, k_loanThe loan interest rate;

the tax is calculated as follows:

C_tax，i＝max[(C_base，i+C_kwh，i-C_loan，i-C_op，i-C_old，i)×0.25，0]；

wherein the content of the first and second substances,C_old,irepresenting the system equipment depreciation cost in the ith year.

In a possible implementation manner of the first aspect, the calculation formula of the individual adaptive value is as follows:

in a possible implementation manner of the first aspect, the constructing a second energy storage scheme by using the second individual fitness value derivation includes:

calculating two feasible solutions of the battery state of charge independent variable corresponding to the second individual adaptive value based on a championship selection method;

respectively calculating the filial generation of each feasible solution based on an arithmetic crossover method to obtain two filial generations;

respectively carrying out iterative computation on each filial generation based on a non-uniform variation method, and carrying out derivative variation to obtain a plurality of second battery state-of-charge independent variables;

generating a second energy storage scheme using the plurality of second battery states of charge from a set of variables.

In one possible implementation manner of the first aspect, the calculation of the two descendants is as follows:

wherein, Mate₁For the first feasible solution, Mate₂For the second possible solution, child₁As the first child, child₂Is the second child, r is between [0,1 ]]The random number of (2);

the calculation formula for each child iteration calculation is shown as follows:

wherein, Child_{1 or 2}Is composed ofT is the current genetic algebra, T is the maximum genetic algebra, u is the non-uniformity parameter, and random (0,1) represents a random number that generates 0 or 1.

In a possible implementation manner of the first aspect, after the step of calculating an individual adaptive value corresponding to each first battery state of charge independent variable to obtain a plurality of first individual adaptive values, the method further includes:

respectively judging whether each first individual adaptive value meets a preset constraint condition;

if the first individual adaptation value does not meet the preset constraint condition, adding a preset penalty value to the first individual adaptation value to obtain a first eliminated individual adaptation value;

wherein the preset constraint condition comprises: the method comprises the following steps of charge state constraint, energy storage charge-discharge power constraint, battery cycle life constraint and power grid power constraint;

wherein the state of charge constraint is as follows:

SOC_min≤SOC_i,j≤SOC_max；

therein, SOC_minHaving a set lower limit, SOC, for the state of charge of the battery at jth hour of the ith year_maxThere is a set upper limit for the battery state of charge at the jth hour of the ith year;

the energy storage charging and discharging power constraint is shown as the following formula:

|p_bess，i，j|＝|(SOC_i，j-SOC_i，j+1)M_bess，i/ΔT|≤P_bess；

wherein M is_bess,iFor the residual capacity after i-th year of energy storage decay, M_bess，i＝M_bess-0.2M_bessT_cyc，i-1/T_life，P_bessThe rated power of the energy storage system is represented, and delta T is unit time length;

the battery cycle life constraint is given by:

when the battery cycle number reaches the cycle life limit, the battery body is replaced, and T is used_cyc,iZero, T_cyc,iRepresenting the accumulated cycle times of the battery by the end of the ith year, and DOD is the discharge depth of the battery;

the grid power constraint is as follows:

P_{grid_min}≤p_load，i，j-η_·p_bess，i，j≤P_{grid_max}；

wherein, P_{grid_min}The time-by-time load power of the user after energy storage optimization cannot exceed the lower limit of power grid power constraint, P_{grid_max}And the time-by-time load power of the user after energy storage optimization cannot exceed the upper limit of the power constraint of the power grid.

In a possible implementation manner of the first aspect, the method further includes:

and carrying out verification operation on the target configuration scheme, wherein the verification operation comprises valley period discharge detection, peak period charge detection and power balance correction.

A second aspect of an embodiment of the present invention provides an apparatus for configuring energy storage capacity, where the apparatus includes:

the building module is used for obtaining historical electricity utilization data of a user and building a plurality of groups of energy storage schemes based on the historical electricity utilization data;

the system comprises a screening module, a processing module and a processing module, wherein the screening module is used for screening a group of first energy storage schemes meeting a preset independent variable range from the plurality of groups of energy storage schemes at will, the first energy storage schemes comprise a plurality of first battery state-of-charge independent variables in a project period, and each first battery state-of-charge independent variable corresponds to a group of capacity-to-power ratios;

the iteration module is used for updating the first energy storage scheme for a preset number of times in an iteration mode to obtain an Nth energy storage scheme, wherein the Nth energy storage scheme comprises a plurality of Nth battery state of charge independent variables, and N is a positive integer greater than 1;

and the screening module is used for screening the target battery state of charge independent variables meeting the preset conditions from the plurality of Nth battery state of charge independent variables, and taking the capacity and power ratio corresponding to the target battery state of charge independent variables as a target configuration scheme.

Compared with the prior art, the method and the device for configuring the energy storage capacity provided by the embodiment of the invention have the beneficial effects that: the invention can provide an energy storage configuration scheme with excellent economic benefit and strong engineering applicability aiming at the electricity utilization characteristic of a user, thereby reducing the electricity utilization cost of the user to the maximum extent, saving the redundant investment of energy storage at the user side and improving the utilization efficiency of energy storage equipment through refined energy storage capacity calculation, reducing the operation pressure of a transformer at the power grid side and improving the operation flexibility of the power grid and the overall power supply reliability of the society through optimizing a load curve of the user for a power system, and in addition, the technical scheme has good universality for the energy storage configuration of users with different load types, particularly for large industrial and commercial users adopting two electricity generation prices, can judge whether the user has the necessity of energy storage configuration aiming at the load curves of the users with different characteristics, and provides a customized energy storage configuration scheme with optimal economic benefit, the method is suitable for popularization in industrial parks such as industrial parks and commercial parks, and has remarkable economic and social benefits.

Drawings

Fig. 1 is a schematic flow chart of a method for configuring energy storage capacity according to an embodiment of the present invention;

fig. 2 is an operation flowchart of a method for configuring energy storage capacity according to an embodiment of the present invention;

fig. 3 is a schematic structural diagram of an apparatus for configuring an energy storage capacity according to an embodiment of the present invention.

Detailed Description

The technical solutions in the embodiments of the present invention will be clearly and completely described below with reference to the drawings in the embodiments of the present invention, and it is obvious that the described embodiments are only a part of the embodiments of the present invention, and not all of the embodiments. All other embodiments, which can be derived by a person skilled in the art from the embodiments given herein without making any creative effort, shall fall within the protection scope of the present invention.

The conventional capacity allocation method has the following technical problems: if the economic benefits of the next year need to be estimated according to the matching calculation of the economic benefits, the fluctuation of the economic benefits of the next year is large, and the accuracy of the calculation result is low; the period of configuration calculation by using the electricity consumption is long, the calculation period often exceeds the service life of the battery, and the configuration calculation is difficult to be performed within the service life of the battery; and the problem that the energy storage system can not reach the expected reduction of the electricity price and the like can occur according to the calculation of the charging and discharging period, so that the configuration result is inconsistent with the actual target result.

In order to solve the above problem, a method for configuring the energy storage capacity provided by the embodiments of the present application will be described and explained in detail by the following specific embodiments.

Referring to fig. 1, a flowchart of a method for configuring an energy storage capacity according to an embodiment of the present invention is shown.

As an example, the method for configuring the energy storage capacity may include:

and S11, acquiring historical electricity utilization data of the user, and constructing a plurality of groups of energy storage schemes based on the historical electricity utilization data.

Because the ratio of the capacity to the power (charge-discharge multiplying power) of the energy storage system has several common numerical values rather than discontinuous curves, in this embodiment, for historical electricity consumption data (for example, electricity consumption data for 8760 hours) of a user, the upper and lower limit values of the energy storage power can be calculated by using the maximum load value of the user as a base number, and an energy storage configuration library is automatically generated by combining the common charge-discharge multiplying power, so that the user can adjust the proportionality coefficient according to actual needs or add an energy storage scale which the user desires to participate in optimization to the configuration library, thereby obtaining multiple groups of energy storage schemes.

In actual operation, the energy storage configuration library can be stored in a [ capacity M, power P ] pair mode, and therefore the optimal configuration or the engineering recommended scale closest to the theoretical optimal solution exists in the configuration library.

S12, optionally screening a group of first energy storage schemes meeting a preset independent variable range from the plurality of groups of energy storage schemes, wherein the first energy storage schemes comprise a plurality of first battery state of charge independent variables in a project period, and each first battery state of charge independent variable corresponds to a group of capacity-to-power ratios.

In a specific implementation, each group of energy storage schemes may include a plurality of first battery state of charge arguments, where the plurality of first battery state of charge arguments are corresponding energy storage configuration groups within a project cycle time.

Specifically, the first energy storage scheme may be as follows:

X⁰＝(X₁ ⁰,X₂ ⁰,…,X_12N ⁰)。

wherein, X_iOne battery state of charge independent variable (Soc) representing the ith month of the project lifecycle_i,1,Soc_i,2,…,Soc_i,24k) (k may take 28, 30, 31 depending on the month) and N is the life cycle years of the project.

And S13, iteratively updating the first energy storage scheme for preset times to obtain an Nth energy storage scheme, wherein the Nth energy storage scheme comprises a plurality of Nth battery state of charge independent variables, and N is a positive integer greater than 1.

After the first energy storage scheme is obtained, the first energy storage scheme can be updated iteratively to generate a new energy storage scheme, so that an energy storage configuration scheme with excellent economic benefit and high engineering applicability can be matched according to the electricity utilization characteristics of a user.

In order to improve the iteration efficiency and fully consider the electricity utilization characteristics of the user during the iteration to increase the economic benefit, the step S13 may include the following sub-steps:

and the substep S131, calculating individual adaptive values corresponding to the first battery state of charge independent variables to obtain a plurality of first individual adaptive values.

Wherein the individual fitness value is an internal rate of return, i.e. a cash flow covering the full life cycle of the project. And evaluating the economic benefit of the user-side energy storage project by adopting the individual adaptive value, wherein the individual adaptive value can be used as a main index for optimizing and configuring the capacity of the energy storage system so as to increase the economic benefit of the user.

Since the operation of the device involves multiple aspects, each of which may affect the cash flow of the whole project, in order to accurately calculate the individual fitness value of the user, the sub-step S131 may include, as an example, the following sub-steps:

and a substep S1311 of calculating system cost, project income, operation and maintenance cost, system residual value, loan and tax respectively by using the capacity and power ratio corresponding to the first battery state of charge independent variable.

In this embodiment, the system cost is calculated as follows:

C_sys＝k_sysM_bess；

wherein, C_sysTo the system cost, k_sysFor customer-side energy storage system unit cost, M_bessIs the system battery capacity;

the project revenue is calculated as follows:

wherein eta is charge-discharge efficiency, and eta is discharge time_dEta is 1/eta at charging_cAre all constants; p is a radical of_bess,i,jRepresents the energy storage and discharge power (the charge is a negative value) in the j hour of the ith year; Δ T is a unit duration (1 hour in the present embodiment); k is a radical of_v-p,jAnd represents the peak-to-valley electricity price corresponding to the jth hour, and 8760 is the historical electricity utilization data of the user for 8760 hours.

The ith year profit obtained by saving the basic electricity fee can be expressed as:

wherein, F_base,i,jRepresenting the basic electricity charge of the j month of the user in the original charging mode, k_demFor the basic electricity price in the maximum demand charging mode, P_dem,i,jRepresenting the maximum load value of the j month after energy storage optimization;

the operation and maintenance cost is calculated as follows:

wherein k is_opK represents the percentage of annual operation and maintenance cost to the total investment_bessThe proportion of the cost of the battery body to the total investment of the energy storage system is shown;

the system residual is calculated as follows:

wherein k is_res-bessRepresents the proportion of the residual value of the battery body to the total investment of the energy storage system, k_resRepresents the proportion of the overall residual value of the system to the total investment of the energy storage system, T_cyc,iRepresents the number of charge and discharge cycles, T, of the current battery used in the last year of the project_lifeRepresents the cycle life of the battery;

the loan is calculated as follows:

wherein r is_loanAs a ratio of capital cost, y_loanFor the year of loan, k_loanThe loan interest rate;

the tax is calculated as follows:

C_tax，i＝max[(C_base，i+C_kwh，i-C_loan，i-C_op，i-C_old，i)×0.25，0]；

wherein, C_old,iRepresenting the system equipment depreciation cost in the ith year.

And a substep S1312 of solving an individual adaptive value based on the system cost, the project income, the operation and maintenance cost, the system residual value, the loan and the tax.

In this embodiment, the calculation formula of the individual adaptive value IRR is shown as follows:

since there are a plurality of individual fitness values, if processing one by one will increase the processing time, and in order to improve the screening efficiency, the method may further include the following sub-steps after sub-step S131:

and a substep S231, respectively determining whether each of the first individual adapted values satisfies a preset constraint condition.

In a specific implementation, the preset constraint condition includes: the method comprises the following steps of charge state constraint, energy storage charge-discharge power constraint, battery cycle life constraint and power grid power constraint;

wherein the state of charge constraint is as follows:

SOC_min≤SOC_i,j≤SOC_max；

therein, SOC_minHaving a set lower limit, SOC, for the state of charge of the battery at jth hour of the ith year_maxThere is a set upper limit for the battery state of charge at the jth hour of the ith year;

the energy storage charging and discharging power constraint is shown as the following formula:

|p_bess，i，j|＝|(SOC_i，j-SOC_i，j+1)M_bess，i/ΔT|≤P_bess；

wherein M is_bess,iFor the residual capacity after i-th year of energy storage decay, M_bess，i＝M_bess-0.2M_bessT_cyc，i-1/T_life，P_bessThe rated power of the energy storage system is represented, and delta T is unit time length;

the battery cycle life constraint is given by:

when the battery cycle number reaches the cycle life limit, the battery body is replaced, and T is used_cyc,iZero, T_cyc,iRepresenting the accumulated cycle times of the battery by the end of the ith year, and DOD is the discharge depth of the battery;

the grid power constraint is as follows:

P_{grid_min}≤p_load，i，j-η_{·pbess，i，j}≤P_{grid_max}；

wherein, P_{grid_min}The time-by-time load power of the user after energy storage optimization cannot exceed the lower limit of power grid power constraint, P_{grid_max}And the time-by-time load power of the user after energy storage optimization cannot exceed the upper limit of the power constraint of the power grid.

Due to the self-attribute and the state change rule of the energy storage system, the SOC of the battery can be selected as the state variable. The SOC is a ratio of the remaining battery capacity to the rated battery capacity, and the SOC varies with continuous charging and discharging of the stored energy, and has a time correlation.

In an alternative embodiment, the SOC is divided in months, that is, the SOC is time-by-time correlated in months, and the SOC is independent from month to month.

And a substep S232, if the first individual adaptation value does not meet the preset constraint condition, adding a preset penalty value and the first individual adaptation value to obtain a first eliminated individual adaptation value.

In this embodiment, if the individual fitness value does not satisfy the constraint condition, a penalty value (for example, take-1) is added on the basis of the individual fitness value, so that the individual fitness value is reduced, and the individual fitness value is eliminated, thereby facilitating the subsequent screening to be poor.

Specifically, a plurality of first fitness values that do not satisfy the constraint condition may be selected from the plurality of first fitness values, and then a penalty value may be added to the plurality of first fitness values that do not satisfy the constraint condition.

And a substep S132 of selecting the first individual fitness value with the largest value from the plurality of first individual fitness values as the second individual fitness value.

In this embodiment, the highest individual adaptive value IRR within the whole life cycle of the project may be used as the optimization target, and the individual adaptive value with the largest value is shown as follows:

f＝max IRR[C₀:C_N]。

wherein IRR [ C ]₀:C_n]A calculation function of the internal rate of return on the principle of the calculation formula of the individual adaptation value IRR of the substep S1312 is represented. Wherein, C_iRepresents the cash flow of the ith year, namely (C)_kwh,i+C_base,i+C_res,i)-(C_loan,i+C_op,i+C_tax,i) And N is the life cycle years of the project.

And a substep S133, adopting the second individual adaptive value to derive and construct a second energy storage scheme, and completing one iteration updating.

After the first body adaptation value with the largest value is screened from the plurality of first body adaptation values, the first body adaptation value with the largest value can be used as the first individual of the next energy storage scheme (i.e. the first battery state of charge independent variable of the second energy storage scheme), and the second energy storage scheme is derived. The second energy storage scheme may also include a plurality of second battery state of charge arguments.

In order to allow the iteratively generated energy storage scheme to update the process on the previous energy storage scheme to fully consider the electricity utilization characteristics of the user, so as to generate a configuration scheme that meets the user requirement and can maximize economic benefit, the sub-step S133 may include the following sub-steps, as an example:

and a substep S1331 of calculating two feasible solutions of the battery state of charge independent variable corresponding to the second individual adaptive value based on the championship selection method.

In the concrete implementation, a championship selection method can be adopted to obtain an individual adaptive value with the maximum value and a feasible solution under the optimal storage strategy, so as to obtain two feasible solutions, namely Mate₁And Mate₂。

And a substep S1332 of respectively calculating the filial generation of each feasible solution based on an arithmetic cross method to obtain two filial generations.

In this embodiment, the calculation of the two children is as follows:

wherein, Mate₁For the first feasible solution, Mate₂For the second possible solution, child₁As the first child, child₂Is the second child, r is between [0,1 ]]The random number of (2).

And a substep S1333 of respectively carrying out iterative computation on each filial generation based on a non-uniform variation method, and carrying out derivative variation to obtain a plurality of second battery state-of-charge independent variables.

In this embodiment, the calculation formula for performing iterative calculation on each child is shown as follows:

by child₁The independent variable of the second battery state of charge obtained by derivative variation is Mutated₁From child₂The independent variable of the second battery state of charge obtained by derivative variation is Mutated₂Obtaining three second battery state-of-charge independent variables, forming { X_best,Mutated₁,Mutated₂}。

In a particular implementation, sub-steps S1331-S1333 may be repeated until the number of generated second battery state of charge arguments reaches a particular population number. Alternatively, the number may be the same as the number of first battery state of charge arguments of the first energy storage scheme.

And a substep S1334 of generating a second energy storage scheme by using the plurality of battery state of charge independent variables.

And then, integrating the plurality of battery state of charge variables into a group set to obtain a second energy storage scheme.

And S134, repeating the step of calculating the individual adaptive value corresponding to each first battery state-of-charge independent variable, and generating an N energy storage scheme after iterative updating for a preset number of times.

After the second energy storage scheme is obtained, the substep S131 may be repeatedly performed on the second energy storage scheme to calculate the individual adaptive value corresponding to each second battery state of charge independent variable in the second energy storage scheme, and screen the individual adaptive value with the largest value. And repeating iteration for a preset number of times until an Nth energy storage scheme is generated.

And S14, screening the target battery state-of-charge independent variables meeting the preset conditions from the plurality of Nth battery state-of-charge independent variables, and taking the capacity and power ratio corresponding to the target battery state-of-charge independent variables as a target configuration scheme.

After the nth energy storage scheme is generated, the individual adaptive value of each nth battery state of charge independent variable in the nth energy storage scheme may also be calculated by adopting the calculation method of the substep S131 to obtain the individual adaptive value corresponding to each nth battery state of charge independent variable, and then the individual adaptive value with the largest value is selected from the individual adaptive values of the plurality of nth battery state of charge independent variables, and the capacity-power ratio corresponding to the individual adaptive value with the largest value is used as the configuration scheme of the target energy storage of the energy storage system.

It should be noted that, since the energy storage configuration library includes a plurality of energy storage schemes, each energy storage scheme may be used as the first energy storage scheme to perform the steps S11-S14 until the calculation configuration of each energy storage scheme is completed.

In practical operation, to avoid the configuration scheme from being inconsistent with the practical scheme, in an optional embodiment, the method may further include:

and S15, carrying out verification operation on the target configuration scheme, wherein the verification operation comprises valley discharge detection, peak charge detection and power balance correction.

Due to the fact that the project period is long, the situation that local actions are unreasonable (such as long-time discharging in a valley period), power distribution is uneven in the same electrovalence time period and the like are contrary to actual engineering experience is avoided, and checking can be conducted through time-by-time charging and discharging actions of energy storage solved through an intelligent algorithm on the basis of a target configuration scheme.

Specifically, the verification operation includes:

and (3) detecting the discharge in the valley period: the energy storage can be discharged in the valley period of the price of electricity only to cut the maximum demand of the load. Therefore, the duration of the valley discharge is detected, if the valley discharge occurs in a certain period of time but the maximum monthly load is not in the period of time, the solution of the optimization problem has a local error, and the optimization result can be corrected manually.

Peak charging detection: the peak charging phenomenon can not occur under normal conditions, and a very few conditions are aimed at a special load curve or peak clipping is not enough due to insufficient energy storage residual capacity, so that peak emergency power supplement is caused. Therefore, the time length detection is carried out on the peak discharge, if the discharge time length is in a specified range, the reason of the local abnormal discharge is manually detected, and the charge and discharge correction is carried out if necessary; and if the discharge duration is within a specified range, further refining the energy storage configuration library for re-optimization.

And (3) power balance correction: the energy storage capacity optimal configuration problem solving result with the highest internal yield as the target may have the problem of unbalanced power distribution for energy storage charging and discharging actions in the same electricity price period. For example, the optimization result may show that the stored energy is fully charged at night with a larger power in a shorter time, and the factors of load optimization, power balance of the power grid and the like are not fully considered. Therefore, time-interval power balance detection is carried out, on the premise that the maximum value of the load is not increased for each continuous electricity price valley period, flat period and peak period, the secondary optimization is carried out on local charging and discharging actions by aiming at reducing the fluctuation of a load curve and smoothing the energy storage output power, the time-by-time running process of energy storage at the user side is optimized while the yield rate inside a project is not reduced, and finally accurate and professional energy storage running strategy guidance is provided for the user.

Referring to fig. 2, an operation flowchart of a configuration method of energy storage capacity according to an embodiment of the present invention is shown.

Specifically, an energy storage configuration library can be established according to actual needs; extracting a corresponding configuration scheme from an energy storage configuration library to obtain the capacity and rated power of the energy storage system; constructing an energy storage scheme into an initial solution X, X0 meeting the constraint of the state of charge independent variable of the battery as an initial group individual; then updating the energy storage cycle times and the capacity attenuation according to the X; judging whether the battery needs to be replaced in the current year, solving an individual adaptive value IRR according to the X and the updated state quantity, and eliminating individuals which do not meet the constraint; then, the feasible solution with the maximum individual adaptive value can be used as the optimal individual Xtest and assigned to the first individual of the new generation group; selecting two feasible solutions with the maximum adaptive values for storage by adopting a championship selection method; then, aiming at the two feasible solutions, generating two filial generations by adopting an arithmetic crossover method, and aiming at the two generated filial generations, carrying out variation by adopting a non-uniform variation method to obtain two variation solutions, and adding the variation solutions into a new generation of population; and judging whether the length (number) of the population reaches the expectation, if not, repeatedly generating according to the steps, and if so, evaluating the population fitness (repeatedly executing the operations of obtaining the individual fitness value IRR, eliminating the individuals which do not meet the constraint and screening the feasible solution with the maximum individual fitness value as the optimal individual Xtest).

After screening the optimal individual Xtest each time, judging whether the genetic iteration number reaches the maximum genetic iteration number, if not, repeating the iteration of the previous step, if so, obtaining the corresponding item internal yield and operation curve by taking the individual with the maximum individual adaptation value in the current group as the optimal solution, finally judging whether the search of the energy storage configuration library is finished, if not, repeatedly executing the steps, carrying out iteration processing on each energy storage scheme of the energy storage configuration library, if the search is finished, comparing the item internal yields generated by each energy storage scheme iteration, and screening to obtain the energy storage optimal configuration scheme and the operation curve thereof.

In this embodiment, an embodiment of the present invention provides a method for configuring energy storage capacity, which has the following beneficial effects: the invention can provide an energy storage configuration scheme with excellent economic benefit and strong engineering applicability aiming at the electricity utilization characteristic of a user, thereby reducing the electricity utilization cost of the user to the maximum extent, saving the redundant investment of energy storage at the user side and improving the utilization efficiency of energy storage equipment through refined energy storage capacity calculation, reducing the operation pressure of a transformer at the power grid side and improving the operation flexibility of the power grid and the overall power supply reliability of the society through optimizing a load curve of the user for a power system, and in addition, the technical scheme has good universality for the energy storage configuration of users with different load types, particularly for large industrial and commercial users adopting two electricity generation prices, can judge whether the user has the necessity of energy storage configuration aiming at the load curves of the users with different characteristics, and provides a customized energy storage configuration scheme with optimal economic benefit, the method is suitable for popularization in industrial parks such as industrial parks and commercial parks, and has remarkable economic and social benefits.

An embodiment of the present invention further provides a device for configuring energy storage capacity, and referring to fig. 3, a schematic structural diagram of the device for configuring energy storage capacity provided in an embodiment of the present invention is shown.

Wherein, as an example, the configuring means of the energy storage capacity may include:

the building module 301 is configured to obtain historical power consumption data of a user, and build a plurality of sets of energy storage schemes based on the historical power consumption data;

the screening module 302 is configured to optionally screen a group of first energy storage schemes meeting a preset independent variable range from multiple groups of energy storage schemes, where the first energy storage schemes include multiple first battery state of charge independent variables in a project period, and each first battery state of charge independent variable corresponds to a group of capacity-to-power ratios;

the iteration module 303 is configured to update the first energy storage scheme iteratively for a preset number of times to obtain an nth energy storage scheme, where the nth energy storage scheme includes a plurality of nth battery state of charge arguments, and N is a positive integer greater than 1;

the screening module 304 is configured to screen the target battery state of charge independent variables meeting the preset condition from the plurality of nth battery state of charge independent variables, and use a capacity-to-power ratio corresponding to the target battery state of charge independent variables as a target configuration scheme.

Optionally, the iteration module is further configured to:

calculating individual adaptive values corresponding to the first battery state of charge independent variables to obtain a plurality of first individual adaptive values;

screening the first individual adaptation value with the largest value from the plurality of first individual adaptation values to be a second individual adaptation value;

adopting the second individual adaptive value to derive and construct a second energy storage scheme to complete one iteration updating;

and repeating the step of calculating the individual adaptive value corresponding to each first battery state of charge independent variable, and generating an Nth energy storage scheme after iteratively updating preset times.

Optionally, the iteration module is further configured to:

calculating system cost, project income, operation and maintenance cost, system residual value, loan and tax respectively by adopting the capacity and power ratio corresponding to the first battery state-of-charge independent variable;

and solving to obtain an individual adaptive value based on the system cost, the project income, the operation and maintenance cost, the system residual value, the loan and the tax.

Optionally, the system cost is calculated as follows:

C_sys＝k_sysM_bess；

wherein, C_sysTo the system cost, k_sysFor customer-side energy storage system unit cost, M_bessIs the system battery capacity;

the project revenue is calculated as follows:

wherein eta is charge-discharge efficiency, and eta is discharge time_dEta is 1/eta at charging_cAre all constants; p is a radical of_bess,i,jRepresents the energy storage and discharge power (the charge is a negative value) in the j hour of the ith year; Δ T is a unit duration (1 hour in the present embodiment); k is a radical of_v-p,jRepresents the peak-to-valley power corresponding to the j hourAnd (4) price.

The ith year profit obtained by saving the basic electricity fee can be expressed as:

wherein, F_base,i,jRepresenting the basic electricity charge of the j month of the user in the original charging mode, k_demFor the basic electricity price in the maximum demand charging mode, P_dem,i,jRepresenting the maximum load value of the j month after energy storage optimization;

the operation and maintenance cost is calculated as follows:

wherein k is_opK represents the percentage of annual operation and maintenance cost to the total investment_bessThe proportion of the cost of the battery body to the total investment of the energy storage system is shown;

the system residual is calculated as follows:

wherein k is_res-bessRepresents the proportion of the residual value of the battery body to the total investment of the energy storage system, k_resRepresents the proportion of the overall residual value of the system to the total investment of the energy storage system, T_cyc,iRepresents the number of charge and discharge cycles, T, of the current battery used in the last year of the project_lifeRepresents the cycle life of the battery;

the loan is calculated as follows:

wherein r is_loanAs a ratio of capital cost, y_loanFor the year of loan, k_loanThe loan interest rate;

the tax is calculated as follows:

C_tax，i＝max[(C_base，i+C_kwh，i-C_loan，i-C_op，i-C_old，i)×0.25，0]；

wherein, C_old,iRepresenting the system equipment depreciation cost in the ith year.

Optionally, the calculation formula of the individual adaptive value is as follows:

optionally, the iteration module is further configured to:

calculating two feasible solutions of the battery state of charge independent variable corresponding to the second individual adaptive value based on a championship selection method;

respectively calculating the filial generation of each feasible solution based on an arithmetic crossover method to obtain two filial generations;

respectively carrying out iterative computation on each filial generation based on a non-uniform variation method, and carrying out derivative variation to obtain a plurality of second battery state-of-charge independent variables;

generating a second energy storage scheme using the plurality of second battery states of charge from a set of variables.

Optionally, the calculation of the two offspring is as follows:

wherein, Mate₁For the first feasible solution, Mate₂For the second possible solution, child₁As the first child, child₂Is the second child, r is between [0,1 ]]The random number of (2);

the calculation formula for each child iteration calculation is shown as follows:

wherein, Child_{1 or 2}Is composed ofT is the current genetic algebra, T is the maximum genetic algebra, u is the non-uniformity parameter, and random (0,1) represents a random number that generates 0 or 1.

Optionally, the apparatus further comprises:

respectively judging whether each first individual adaptive value meets a preset constraint condition;

if the first individual adaptation value does not meet the preset constraint condition, adding a preset penalty value to the first individual adaptation value to obtain a first eliminated individual adaptation value;

wherein the preset constraint condition comprises: the method comprises the following steps of charge state constraint, energy storage charge-discharge power constraint, battery cycle life constraint and power grid power constraint;

wherein the state of charge constraint is as follows:

SOC_min≤SOC_i,j≤SOC_max；

therein, SOC_minHaving a set lower limit, SOC, for the state of charge of the battery at jth hour of the ith year_maxThere is a set upper limit for the battery state of charge at the jth hour of the ith year;

the energy storage charging and discharging power constraint is shown as the following formula:

|p_bess，i，j|＝|(SOC_i，j-SOC_i，j+1)M_bess，i/ΔT|≤P_bess；

wherein M is_bess,iFor the residual capacity after i-th year of energy storage decay, M_bess，i＝M_bess-0.2M_bessT_cyc，i-1/T_life，P_bessThe rated power of the energy storage system is represented, and delta T is unit time length;

the battery cycle life constraint is given by:

when the battery cycle number reaches the cycle life limit, the battery body is replaced, and T is used_cyc,iZero, T_cyc,iRepresenting the accumulated cycle times of the battery by the end of the ith year, and DOD is the discharge depth of the battery;

the grid power constraint is as follows:

P_{grid_min}≤p_load，i，j-η_{·pbess，i，j}≤P_{grid_max}；

wherein, P_{grid_min}The time-by-time load power of the user after energy storage optimization cannot exceed the lower limit of power grid power constraint, P_{grid_max}And the time-by-time load power of the user after energy storage optimization cannot exceed the upper limit of the power constraint of the power grid.

Optionally, the apparatus further comprises:

and carrying out verification operation on the target configuration scheme, wherein the verification operation comprises valley period discharge detection, peak period charge detection and power balance correction.

Further, an embodiment of the present application further provides an electronic device, including: the present invention relates to a method for configuring energy storage capacity, and more particularly to a method for configuring energy storage capacity, which is capable of configuring energy storage capacity of a mobile terminal.

Further, the present application also provides a computer-readable storage medium storing computer-executable instructions for causing a computer to perform the method for configuring an energy storage capacity according to the foregoing embodiment.

While the foregoing is directed to the preferred embodiment of the present invention, it will be understood by those skilled in the art that various changes and modifications may be made without departing from the spirit and scope of the invention.