CN117353353A - Energy storage configuration scheme optimization method and optimization platform of energy storage system - Google Patents

Abstract

An energy storage configuration scheme optimization method and an optimization platform of an energy storage system, wherein the method comprises the following steps: s1: searching the theoretical maximum capacity of the energy storage system and the corresponding theoretical charge-discharge multiplying power according to the energy storage element parameters in the energy storage system; s2: based on the theoretical maximum capacity and the theoretical charge-discharge multiplying power, acquiring a feasible region boundary of an energy storage configuration scheme of the energy storage system from energy storage element parameters, an energy storage topological structure and energy storage price multielement elements by adopting a multiplying power circulation searching mode; s3: and correcting the feasible domain boundary through the target energy storage utilization rate to obtain an optimal energy storage configuration scheme of the energy storage system. The invention can develop potential user excavation and energy storage scheme configuration, simultaneously provides a special and accurate configuration scheme for users based on high-granularity data and high-density calculation, greatly supports micro-grid investment decision, ensures safe and stable operation of a power grid, and simultaneously effectively improves energy storage popularization working efficiency of a user side.

Description

Energy storage configuration scheme optimization method and optimization platform of energy storage system

Technical Field

The invention belongs to the technical field of energy storage configuration, and relates to an energy storage configuration scheme optimization method and an energy storage configuration scheme optimization platform of an energy storage system.

Background

The energy storage is an important link of urban energy Internet construction, can support the access of high-proportion renewable energy and multiple loads, promotes the peak clipping and valley filling of a power grid, and brings certain economic benefit to investment subjects. During 2016-2020, the energy storage industry in China develops rapidly, according to incomplete statistics, the accumulated installed scale of the put-in energy storage projects in China reaches 32.7GW by the last half of 2020, and accounts for 17.6% of the total global scale, and the accumulated installed scale of the application of the emerging electrochemical energy storage technology reaches 1831MW.

In order to meet the energy storage deployment requirement and promote the development of the energy storage industry, more practical experience is accumulated in actively developing the energy storage popularization work of a user side, and meanwhile, some problems are encountered, such as: the development of the energy storage industry is due to the uncertainty of large capital investment and economic benefit return, and the investment is too dependent on the power grid enterprises; the energy storage popularization on the user side lacks potential customer positioning means, and the active service capability is insufficient; because of the lack of an optimal energy storage scheme configuration strategy, customers cannot timely and comprehensively grasp energy storage benefits, and the early negotiation efficiency is reduced. Therefore, how to accurately popularize the energy storage at the user side meets the requirements of the user energy consumption and the power grid operation, and forms an energy problem in front of the power grid and the user.

Disclosure of Invention

In order to solve the defects in the prior art, the invention aims to provide an energy storage configuration scheme optimizing method and an energy storage configuration platform of an energy storage system, develop potential user excavation and energy storage scheme configuration, simultaneously provide a specific and accurate configuration scheme for users based on high-granularity data and high-density calculation, greatly support micro-grid investment decision, ensure safe and stable operation of a power grid and effectively improve energy storage popularization working efficiency of a user side.

In order to achieve the above object, the present invention adopts the following technical scheme:

an energy storage configuration scheme optimization method of an energy storage system comprises the following steps:

s1: searching the theoretical maximum capacity of the energy storage system and the corresponding theoretical charge-discharge multiplying power according to the energy storage element parameters in the energy storage system;

s2: based on the theoretical maximum capacity and the theoretical charge-discharge multiplying power, acquiring a feasible region boundary of an energy storage configuration scheme of the energy storage system from energy storage element parameters, an energy storage topological structure and energy storage price multielement elements by adopting a multiplying power circulation searching mode;

s3: and correcting the feasible domain boundary through the target energy storage utilization rate to obtain an optimal energy storage configuration scheme of the energy storage system.

The invention further comprises the following preferable schemes:

preferably, the method further comprises the step of constructing an EneMicro model, and optimizing an energy storage configuration scheme of the energy storage system based on the EneMicro model.

Preferably, S1 is specifically: searching the maximum energy storage capacity of a user and the corresponding charge and discharge multiplying power matched with the energy storage element parameters from the large data of the energy storage of the user according to the energy storage element parameters in the energy storage system;

and taking the searched maximum energy storage capacity of the user and the corresponding charge-discharge multiplying power as the theoretical maximum capacity of the energy storage system and the corresponding theoretical charge-discharge multiplying power.

Preferably, the energy storage element parameters include the type of energy storage battery and its corresponding overall efficiency, battery loss rate, PCS loss rate, lifetime, minimum SOC, maximum SOC, and annual decay rate;

the types of energy storage cells include lithium iron phosphate, ternary lithium and lead carbon.

Preferably, the energy storage topological structure of S2 is divided into an energy storage topological structure of lithium iron phosphate, ternary lithium iron phosphate and lead carbon;

the energy storage price comprises the energy storage price of holidays, the energy storage prices of different seasons and the energy storage prices under different weather conditions.

Preferably, S2 specifically includes:

s21: dividing the theoretical maximum capacity into a plurality of energy storage sections, and selecting an optimal feasible region boundary from the plurality of energy storage sections based on energy storage element parameters, an energy storage topological structure and an energy storage price;

S22: and (3) calculating the multiplying power deviation between the charge-discharge multiplying power corresponding to the optimal feasible region boundary selected in the step S21 and the theoretical charge-discharge multiplying power, judging whether the multiplying power deviation meets the preset multiplying power deviation, and if not, searching the feasible region boundary of the energy storage configuration scheme again through the charge-discharge multiplying power circulation.

Preferably, S3 specifically includes:

s31: step 2, obtaining energy storage capacity of a corresponding segmented area in a feasible region boundary by utilizing the target energy storage utilization rate in a segmented mode with variable stepping quantity, wherein the searched energy storage capacity is the energy storage capacity which is optimally matched with the target energy storage utilization rate;

s32: calculating the capacity deviation between the energy storage capacity of each segmented area and the corresponding theoretical maximum capacity, and judging whether the capacity deviation of each segmented area meets the preset capacity deviation or not;

if the energy storage capacity of the corresponding segmented area is not met, correcting the variable stepping quantity of the variable stepping, returning to the step S31, searching the energy storage capacity of the corresponding segmented area in a segmented mode again through the corrected variable stepping, correcting the feasible region boundary obtained in the step S2, enabling the energy storage capacity corresponding to the feasible region boundary to be in optimal configuration with the target energy storage utilization rate, and accordingly obtaining the maximum energy storage capacity under different multiplying powers.

An energy storage configuration scheme optimizing platform of an energy storage system, wherein the optimizing platform comprises a database and an energy storage configuration scheme optimizing unit;

the database is used for storing user energy storage data corresponding to various energy storage batteries;

and the energy storage configuration scheme optimizing unit is used for executing the energy storage configuration scheme optimizing method based on the user energy storage data stored in the database to generate an optimal energy storage configuration scheme of the energy storage system.

Preferably, the database is an RDS database;

before storing the user energy storage data corresponding to the various energy storage batteries into the RDS database, the optimization platform screens and cleans the user energy storage data corresponding to the various energy storage batteries, and then stores the screened and cleaned data into the RDS database.

Preferably, the optimization platform further comprises an external interface for external access of the RDS database.

Preferably, the optimization platform further comprises a browser and a virtual machine;

the virtual machine comprises a dock container, wherein an energy storage configuration scheme optimizing unit is arranged in the dock container, and an execution program of the energy storage configuration scheme optimizing method is stored.

The invention has the beneficial effects that compared with the prior art:

The invention can obtain the optimal energy storage configuration scheme of the energy storage system, and is specific:

1) Performing user electricity behavior analysis in batches, and simulating the running state of the energy storage system;

2) Evaluating the construction cost of the energy storage system and analyzing the construction value of the energy storage system;

3) The method can generate user potential discrimination and energy storage optimal configuration by one key, and can effectively improve the energy storage popularization working efficiency of a user side while ensuring the safe and stable operation of the power grid;

the EneMicro model constructed by the invention has high granularity data and high density calculation function, provides a special and accurate configuration scheme for users, greatly supports micro-grid investment decision, and effectively improves the energy storage popularization working efficiency of the user side while ensuring the safe and stable operation of the power grid, and has the following characteristics:

1) The boundary of the feasible region is clear at a glance

The EneMicro model has an optimized type selection search space, can intuitively observe from a capacity-multiplying power graph, and meets the energy storage type selection of a microgrid operation strategy target under different utilization rate requirements, so that an energy storage type selection conclusion is very obvious.

2) High granularity of supported time

The EneMicro model can support minute-level time granularity data operation analysis, and can give operating power and SOC for each time point.

3) Compatibility with other element models of micro-grid

Boundary conditions of the EneMicro model can comprise other elements of the multi-energy collaborative system, such as fans, photovoltaics and the like, and simulation calculation is performed on the basis of topology and price stimulation of the EneMicro model to obtain results.

4) The step length can be changed, and the searching speed is increased. And judging whether the capacity deviation of each segmented area meets the preset capacity deviation or not by calculating the capacity deviation between the energy storage capacity of each segmented area and the corresponding theoretical maximum capacity, if not, correcting the variable stepping amount of the variable stepping, and searching the energy storage capacity of the corresponding segmented area in a segmented mode again by the corrected variable stepping.

5) The model can set the upper limit of the charge and discharge times, so that the model variable traverses and selects proper charge and discharge times, and the charge and discharge times can be unequal (for example, two charges and one discharge and one charge and three discharges) on the premise that the constraint conditions of conservation of charge and discharge energy are still met, so that the total cost generated by daily energy flow is minimum. The optional conditions become more after the limitation of the equal number of times of charging and discharging is released, so that the solving result can be improved.

Drawings

FIG. 1 is a flow chart of an energy storage configuration scheme optimizing method of the invention;

FIG. 2 is a schematic diagram of EneMicro model feasibility domain boundaries;

FIG. 3 is a schematic diagram of a user configured energy storage constraint

FIG. 4 is a schematic diagram of an energy storage topology in an EneMicro model;

FIG. 5 is a schematic diagram of valley electricity every fifteen minutes

FIG. 6 is a schematic diagram showing the power consumption at different power-capacity ratios

Fig. 7 is a schematic diagram of an energy storage configuration scheme optimization platform according to the present invention.

Detailed Description

In order to make the objects, technical solutions and advantages of the present invention more apparent, the technical solutions of the present invention will be clearly and completely described below with reference to the accompanying drawings in the embodiments of the present invention. The embodiments described herein are merely some, but not all embodiments of the invention. All other embodiments, which can be made by those skilled in the art without making any inventive effort, are within the scope of the present invention.

As shown in fig. 1, embodiment 1 of the present invention provides a method for optimizing an energy storage configuration scheme of an energy storage system, and in a preferred but non-limiting embodiment of the present invention, the method includes the steps of:

s1: searching the theoretical maximum capacity of the energy storage system and the corresponding theoretical charge-discharge multiplying power according to the energy storage element parameters in the energy storage system;

s2: based on the theoretical maximum capacity and the theoretical charge-discharge multiplying power, acquiring a feasible region boundary of an energy storage configuration scheme of the energy storage system from energy storage element parameters, an energy storage topological structure and energy storage price multielement elements by adopting a multiplying power circulation searching mode;

S3: and correcting the feasible domain boundary through the target energy storage utilization rate to obtain an optimal energy storage configuration scheme of the energy storage system.

Further preferably, before implementing the steps, whether the user is suitable for installing the energy storage system is judged in advance, and the energy storage system is installed and operated for the suitable user to acquire the energy storage data of the user;

specifically, whether the user is suitable for installing the energy storage system is judged by the following formula:

1. ("Q" peak)/("Q" valley) > 1, indicating a ratio of peak-to-valley power usage greater than 1;

2. (peak-to-valley electricity price) is more than or equal to 0.7, which means that the peak-to-valley electricity price difference is more than or equal to 0.7;

3. the daily load electric quantity peak value is more than or equal to 0.7 of the distribution capacity of the enterprise, which means that if the enterprise load peak value is higher than 70% of the distribution capacity of the enterprise;

the rated operating power of the energy storage system is less than or equal to 4.50kW and less than or equal to 0.3 of the rated capacity of the enterprise transformer, the maximum power of the i energy storage system is less than or equal to 30% of the rated capacity of the transformer, and the minimum operating power of the energy storage system is more than 50kW.

When the above formulas are all satisfied, it is indicated that the energy storage system is suitable for installation.

In a preferred embodiment of the present invention, the energy storage configuration scheme of the energy storage system may be optimized based on the EneMicro model, so that the step of constructing the EneMicro model is further included before the step S1 is performed. The model constructed by EneMicro has complete parameters of the equipment model, is suitable for global optimization, and is embodied in:

The equipment model for planning online is complete in variety: typical energy devices involved in production, conversion, transportation, storage and consumption links covering various primary energy forms, including solar panels, inverters, fans, battery storage, energy storage converters, charging piles, transformers, and the like; the topology configuration of the multi-energy system is flexible: the connection relation among the elements can be flexibly configured, and the requirements of operation of various energy collaborative complementary strategies such as off-grid, on-line, local consumption and the like are met; model parameter specialty may be custom: object-oriented equipment model modeling is specialized and diverse in attribute, and flexible and customizable in setting. Design boundaries can be rapidly configured, and data driving is readily available and is shown in the following steps: electrical load data covering hundreds of industries: in China, the internet of things equipment is utilized to collect years of load curves from clients and clusters, so that a main load curve experience library of hundreds of industries is formed; sales electricity price data nationally: the comprehensive market selling electricity price of each province in China is maintained for quick inquiry, and the price API of the electric power market is docked in the future; abundant historical and predicted weather data: the high-precision weather data API service can meet the model calculation under the design boundary filling requirement. Industry experience can be shared in a gathering way, scheme input is close to practice, and the scheme input is reflected in: equipment brand specification library: providing a main equipment list authenticated by industry, including main flow brands, planning, main performance parameters of equipment and the like in the industry, and facilitating the type selection of users; model and financial parameter experience library: the key data such as unit price, loan cost, depreciation amortization and the like of a whole set of main equipment are collected and maintained, and the default setting of the financial model of the user is ensured to accord with the investment practice experience of the energy project. Through calculating power conversion efficiency, realize gridding search of many condition batch financial evaluation, embody in: one model can flexibly cover all the elements of the multi-energy system to perform unified calculation; common financial models based on expert project experience and domestic approximation stages, etc. By adopting 2-class datagram to assist in project decision, downloading and code scanning sharing can be realized in a loose mode, and the method is characterized in that: planning operation technical report: the method comprises the steps of scoring the dimensions of a scheme 6, scoring the fingerprints of the scheme, scoring boundary indexes, and selecting key indexes, type specifications and energy flow profiles of a multi-energy system. Financial evaluation report: each financial index, financial stadium, subsystem element ratio.

The EneMicro model constructed by the invention has the following characteristics:

1) The boundary of the feasible region is clear at a glance

The EneMicro model has an optimized type selection search space, can intuitively observe the energy storage type selection meeting the micro-grid operation strategy target under different utilization rate requirements from a capacity-multiplying power graph, and has obvious energy storage type selection conclusion as shown in figure 2.

2) High granularity of supported time

The EneMicro model can support minute-level time granularity data operation analysis, and can give operating power and SOC for each time point.

3) Compatibility with other element models of micro-grid

Boundary conditions of the EneMicro model can comprise other elements of the multi-energy collaborative system, such as fans, photovoltaics and the like, and simulation calculation is performed on the basis of topology and price stimulation of the EneMicro model to obtain results.

4) The step length can be changed, and the searching speed is increased. And judging whether the capacity deviation of each segmented area meets the preset capacity deviation or not by calculating the capacity deviation between the energy storage capacity of each segmented area and the corresponding theoretical maximum capacity, if not, correcting the variable stepping amount of the variable stepping, and searching the energy storage capacity of the corresponding segmented area in a segmented mode again by the corrected variable stepping.

5) The model can set the upper limit of the charge and discharge times, so that the model variable traverses and selects proper charge and discharge times, and the charge and discharge times can be unequal (for example, two charges and one discharge and one charge and three discharges) on the premise that the constraint conditions of conservation of charge and discharge energy are still met, so that the total cost generated by daily energy flow is minimum. The optional conditions become more after the limitation of the equal number of times of charging and discharging is released, so that the solving result can be improved.

The model is applied to charge the battery in the period of low electricity price and discharge in the period of high electricity price and large electricity consumption, so as to achieve the effects of reducing peak electricity load, improving electricity stability, improving electricity efficiency and reducing electricity purchasing cost.

First, the constraint conditions of conservation of charge and discharge energy should be met:

the above equation represents the battery stored energy-output energy + stored energy.

E (t) is the stored energy of the energy storage battery at the moment t; e (t-1) is the stored energy of the energy storage battery at the moment t-1; delta (t) is the energy lost at time t;the energy stored at the moment t of the energy storage battery is stored; />The energy is output by the energy storage battery at the moment t; η (eta) _i (t) is a conversion factor of energy storage of the energy storage battery; η (eta) _o (t) is a conversion factor of the energy output of the energy storage battery; τ (t) is the length of the time interval, i.e. the time step.

Further preferably, the EneMicro model calculates an optimal energy storage operation curve through user historical data, set parameters, constraint conditions and objective functions to obtain an energy storage configuration scheme optimization scheme, namely an optimal energy storage battery charging and discharging strategy at each moment, including a charging and discharging type and a charging and discharging amount.

The energy storage is charged in a period of low electricity price, and is discharged in a period of high electricity price and high electricity consumption, so that the aim of meeting the original electricity demand and lowest electricity purchasing cost is fulfilled; the calculation of the optimal energy storage operation curve adopts a linear programming optimization algorithm, and the optimal battery charge-discharge strategy (charge-discharge type and charge-discharge quantity) at each moment is solved by setting an objective function and constraint conditions.

Further preferably, the objective function is set as follows:

the total cost (expense) generated by the energy flow per unit time is minimum

The objective function is min _(i,o) ∑ _t flow ₁ (i,o,t)·cost(t) (1)

Wherein cost (t) is the unit electricity price of the period t where the energy flow is located, i represents energy storage, and o represents energy output;

the energy flow per unit time (15 minutes) is:

∑ _(i,o) ∑ _t flow ₁ (i,o,t)＝∑ _t flow ₀ (t)+∑ _t μ(t)

the energy flow rate of the energy storage battery after charging and discharging correction is as follows:

historical energy flow without battery correction:

energy loss of charging and discharging of energy storage battery:

Loss of charge and discharge of the energy storage battery = energy storage battery discharge power × discharge power loss ratio + energy storage battery charge power × charge power loss ratio.

The constraint conditions are set as follows:

if the user is meeting the condition suitable for installing the energy storage system, the parameters shown in fig. 3 are input as constraint condition 1:

the transformer capacity, voltage level, peak-valley flat electricity price, time period, daily charge and discharge times, full charge and discharge days in one year, charge and discharge power, basic electric quantity charging mode (charging according to the required quantity), and proportion (system efficiency) between the charge quantity and the discharge quantity which are preset by a user.

Constraint 2:

constraint 3:

system operation power during single day simulated energy storage discharge = single day maximum load-single day simulated discharge power

Single-day simulated energy storage charging system running power = single-day minimum load + single-day simulated charging power single-day simulated energy storage discharging system running power < single-day simulated energy storage charging system running power

Indicating that the overall discharge capacity of the system must not exceed the charge capacity K (system efficiency);

PdisC (i) and PC (i) are respectively the charge and discharge power;

wherein Tdisc is discharge duration and Tc is charge duration.

Further preferably, S1 is specifically: the energy storage element parameters in the energy storage system are imported into the EneMicro model, for example:

#oemof_opt.py----------------

Battery data input: respective sto_data battery data of lithium iron phosphate, lead acid and indoor lithium iron

Energy stored by normal storage capacity battery

in_nominal_value stored energy

Energy output by out_nominal_value

Cost generated by out_costs energy flow

Further preferably, the engmicro model searches the maximum energy storage capacity of the user and the corresponding charge-discharge rate matched with the energy storage element parameters based on stored big data of the energy storage of the user, and the searched maximum energy storage capacity of the user and the corresponding charge-discharge rate are the theoretical maximum capacity of the energy storage system and the corresponding theoretical charge-discharge rate.

The energy storage element parameters comprise the type of the energy storage battery and the corresponding total efficiency, battery loss rate, PCS loss rate, service life, minimum SOC, maximum SOC and annual attenuation rate of the energy storage battery.

Types of energy storage batteries include lithium iron phosphate, ternary lithium, and lead carbon.

The types of energy storage cells and their corresponding parameters are shown in table 1 below:

TABLE 1

For example, when the energy storage configuration scheme is required to be optimized for the lithium iron phosphate type battery energy storage system, in step S1, according to the contents listed in table 1, the lithium iron phosphate and the corresponding element parameters thereof are imported into the EneMicro model;

The EneMicro model searches the maximum energy storage capacity of the user and the corresponding charge and discharge multiplying power matched with the element parameters of the lithium iron phosphate based on stored massive big data stored by the lithium iron phosphate user;

the searched maximum energy storage capacity of the user and the corresponding charge-discharge multiplying power are the theoretical maximum capacity of the lithium iron phosphate type battery energy storage system and the corresponding theoretical charge-discharge multiplying power.

Through step S1, the energy storage theoretical maximum configuration battery capacity satisfying the condition and the charge and discharge multiplying power required by the theoretical maximum configuration battery capacity can be obtained.

Further preferably, the method for calculating the theoretical maximum capacity and the corresponding theoretical charge-discharge rate is as follows:

1. the capacity step size and the power step size are determined by the actual capacity (land_load.best_capacity) obtained by the model selection.

If the actual capacity obtained by the model selection is less than 50, the capacity step length is 5, and the power step length is 2.5;

if the actual capacity obtained by 50< = model selection is less than 100, the capacity step length is 10, and the power step length is 5;

if the actual capacity obtained by 100< = model selection is less than 200, the capacity step length is 20, and the power step length is 10;

if the actual capacity obtained by the model selection is > =200, the capacity step size is 50, and the power step size is 50;

2. calculating theoretical maximum capacity and theoretical charge-discharge multiplying power;

Theoretical maximum capacity= ((actual capacity obtained by selection/conversion coefficient/depth of discharge)/capacity step size) ×capacity step size;

theoretical charge-discharge rate= (actual power/power step +1 obtained by pattern selection) ×power step.

Further preferably, S2 is specifically: and importing the energy storage element parameters, the energy storage topological structure and the energy storage price in the energy storage system into an EneMicro model, and circularly searching the feasible region boundary of the energy storage configuration scheme by the EneMicro model through multiplying power.

In S2, an energy storage configuration scheme of the energy storage system is obtained from energy storage element parameters, an energy storage topological structure and energy storage price multielement elements;

wherein the energy storage element parameters comprise all element contents in the table 1;

the energy storage topological structure is an energy storage topological structure of a corresponding energy storage type battery, and a schematic diagram of the energy storage topological structure is shown in fig. 4;

the energy storage price comprises the energy storage price of holidays, the energy storage prices of different seasons and the energy storage prices under different weather conditions;

for example, the energy storage price in winter is different from the energy storage price in summer;

for another example, the energy storage price on sunny days is different from the energy storage price on rainy days;

therefore, in the invention, the energy storage price, the energy storage element parameters and the energy storage topological structure are taken into consideration so as to obtain an accurate energy storage configuration scheme.

S2, the EneMicro model searches a feasible region boundary of an energy storage configuration scheme through multiplying power circulation, and specifically comprises the following steps:

s21: the EneMicro model acquires a plurality of corresponding energy storage sections based on theoretical maximum capacity segmented search, and selects an optimal feasible region boundary from the acquired plurality of energy storage sections based on energy storage element parameters, an energy storage topological structure and an energy storage price.

For example, the theoretical maximum capacity obtained is 200, and every 50 is a section of energy storage area, the energy storage area can be divided into 4 sections of energy storage areas, and the optimal feasible region boundary is selected in the 4 sections of energy storage areas based on energy storage element parameters, energy storage topological structures and energy storage prices.

Further preferably, the energy storage section may be irregularly divided as follows:

firstly, determining a day charging and discharging strategy, taking two-charging and two-discharging as an example:

the historical daily data is counted firstly, the state of each period is calculated at 15 minute intervals, and if the state of each period is consistent (continuous), the periods are classified into one type. Determining TOU time sequence state of each day, and determining dividing rule by the following rule judgment.

The TOU time sequence states of the day-by-day for a month are counted as follows, and 7 TOU states are respectively counted

{3:'peak',7:'valley',5:'peak',4:'flat',6:'flat',1:'valley',2:'flat'}

The states are judged from small to large in sequence:

{1:'valley',2:'flat',3:'peak',4:'flat',5:'peak',6:'flat',7:'valley'}

If the state is 'valley' and there is no '1st_charge', then '1st_charge' is the number;

if the state is 'valley' and '1st_charge' already exists, '2nd_charge' is the number;

if the state is 'peak' and there is no '1st_discharge', then '1st_discharge' is the number;

if the state is 'peak' and '1st_discharge' already exists, then '2nd_discharge' is the number;

if the state is 'flat' and '1st_discharge' already exists and '2nd_charge' does not occur, then '2nd_charge' is the number;

taking {1 } 'valey', 2 } 'flat',3 } 'peak',4 } 'flat',5 } 'peak',6 } 'flat',7 } 'valley' as an example, the obtained two-charge and two-discharge sequence numbers are { '1st_charge': [1], '1st_discharge': [3], '2nd_charge': [4], '2nd_discharge': [5] }, and outputting in a dictionary form.

S22: calculating the multiplying power deviation between the charge-discharge multiplying power corresponding to the optimal feasible region boundary and the theoretical charge-discharge multiplying power, judging whether the multiplying power deviation meets the preset multiplying power deviation, and if not, searching the feasible region boundary of the energy storage configuration scheme again by the EneMicro model through multiplying power circulation.

Through step S2, a feasible region boundary satisfying the condition without considering the energy storage utilization rate can be obtained, that is, the maximum energy storage capacity under different multiplying powers is obtained.

S3 specifically comprises the following steps: and importing the target energy storage utilization rate required by the user into an EneMicro model, and correcting the feasible region boundary based on the target energy storage utilization rate by the EneMicro model.

The EneMicro model corrects the feasible region boundary based on the target energy storage utilization rate, and specifically comprises the following steps:

s31: the EneMicro model utilizes the target energy storage utilization rate to search the energy storage capacity of a corresponding segmented region in a segmented manner in a feasible region boundary by a variable stepping amount; the energy storage capacity searched out is the energy storage capacity which is optimally matched with the target energy storage utilization rate;

s32: calculating the capacity deviation between the energy storage capacity of each segmented area and the corresponding theoretical maximum capacity, judging whether the capacity deviation of each segmented area meets the preset capacity deviation, if not, correcting the variable stepping amount of the variable stepping, and searching the energy storage capacity of the corresponding segmented area in a segmented mode again by the corrected variable stepping; that is, the feasible region boundary corresponding to the segment satisfying the preset capacity deviation does not need to be corrected, but the feasible region boundary corresponding to the segment not satisfying the preset capacity deviation needs to be corrected, and the feasible region boundary correction refers to correcting the feasible region boundary obtained in the step S2 based on the target energy storage utilization rate, so that the energy storage capacity corresponding to the feasible region boundary and the target energy storage utilization rate are optimally configured.

The variable step amount is a variable step amount, for example, the step amounts are variable amounts such as 5, 15, 25, 35, etc., and the step amounts are sequentially variable step amounts, so that the step amounts are divided into sections, and since the step amounts have a large span, the search speed can be greatly increased for a large number of step amounts, and meanwhile, the problem of poor precision is brought about.

Through the step S3, a feasible region boundary meeting the energy storage utilization rate requirement can be obtained, namely, the maximum energy storage capacity under different multiplying powers is obtained.

Further preferably, the optimal capacity (maximum energy storage capacity) is calculated in the model by:

manually setting TOU time period, determining peak, flat and valley time period of each day,

the raw_data_df format is shown in fig. 5, and raw_data_df is Raw data of 15 minutes of electricity consumption for each user.

Inputting parameters

ks is the name of each user

Transformer capacity trans_cap_subject of user

dcr _model, which can be chosen to charge the capacity ' cap_model ' or ' md_model ' by the maximum demand '

Key index- # energy storage power capacity ratio c-cycle optimization

rate_start=0.1, which indicates calculating the power-to-capacity ratio of the stored energy, from 0.1 to 1.0, and the cyclic calculation selects 0.1, 0.2..1.0, and the cost-effective maximum power value at maximum power of the ten powers is calculated

rate_end＝1.1

rate_loop_step＝0.1

time_step=900 (15 minutes)

percentage_mon= (30-full filling days per month)/30

percentage_yr= (365-set number of days full of full fill/discharge per year)/365

Converting raw_data_df into raw_data_mon_subject (month data):

'2018-1':

time stamp coastal river building 1250kVA TOU

2018-01-02 00:00:00 66.64valley

2018-01-02 00:15:00 58.85valley

……

'2018-2':

……

Extracting single month data respectively at raw_data_mon_subject, adding serial numbers into the single month data according to different states of TOU every day, such as

sr_no_tou_dict＝{1:‘1valley’,2:’2flat’,3:’3peak’,4:’4flat’,5:’5peak’,6:’6flat’,7:’7vall ey’}

get single cycle sr no direct record user's charge and discharge period number dictionary that charges and discharges one by one every month

Setting the period number of the first valley to 1st_charge and the period number of the first peak to 1st_discharge, such as sr_no_subject [ '2018-4' ] = { '1st_charge':1, '1st_discharge':3}

The power capacity ratio bss_rate_to_cap takes 0.1 as a starting point, 1 as an ending point and 0.1 as a step length, and annual maximum selection under the parameter is calculated respectively in a circulating way;

Calculating raw_data_mon_direct by using a bss_single_cycle function to obtain per_mon_result;

combining the per_mon_result into a multi_mon_result for outputting a month selection result list under the power capacity ratio;

combining the per_mon_day_result into a multi_mon_day_result for recording the data of days of a plurality of months, and selecting a capacity result according to days in one year;

year_result is a value corresponding to multi_mon_day_result in quantile percentage_yr, and is taken as a value (kWh) which is selected most in the current year under the power capacity ratio;

and after obtaining the respective optimal capacity of the same user under different power capacity ratios, selecting the power capacity ratio at which the kWh increases most rapidly as the optimal model.

Based on the energy storage configuration scheme optimizing method of the energy storage system, the invention also provides an energy storage configuration scheme optimizing platform of the energy storage system, and a schematic diagram of the optimizing platform is shown in fig. 7.

The optimizing platform comprises a database and an energy storage configuration scheme optimizing unit;

the database is used for storing user energy storage data corresponding to various energy storage batteries;

the energy storage configuration scheme optimizing unit is used for generating an optimal energy storage configuration scheme of the energy storage system based on the user energy storage data stored in the database and executing the energy storage configuration scheme optimizing method.

Further preferably, the database is an RDS database, before storing the user energy storage data corresponding to the various energy storage batteries in the RDS database, screening and data cleaning are required for the user energy storage data corresponding to the various energy storage batteries, and then the screened and cleaned data are stored in the RDS database.

The optimization platform provides an external interface so that the RDS database can only be accessed externally through the external interface, for example, the RDS database is called through a timing task and corresponding data is provided to the outside through the external interface.

The optimization platform comprises a browser and a virtual machine, wherein the virtual machine comprises a dock container, and the energy storage configuration scheme optimization unit is arranged in the dock container, namely, the execution program of the energy storage configuration scheme optimization method is stored in the dock container.

The data flow of the scheme of the invention is as follows:

and establishing a data source node in the data center, outputting the data source node to a database of the meta-rds at regular time, and returning the data to the center for presentation after the data calculation is finished.

1) User configuration information table (user information, user parameter configuration information, battery parameter configuration, region code, and user power data) is synchronized from the data center to the RDS database

2) Calculating the data of RDS database to generate 3 result tables

3) Synchronization of results tables from a database to a data staging platform

The tables involved are shown in Table 2.

TABLE 2

Operation policy selection

Functional description: the user selects or customizes the device according to the actual operation requirement. The current operation strategy is time sharing arbitrage.

The time sharing arbitrage strategy comprises two modes of one charge and one discharge and two charges and two discharges, wherein the number of days of full charge and discharge is freely selected by a user.

Energy storage selection conclusion

Functional description: and displaying the conclusion after optimizing the model selection. The theoretical maximum energy storage capacity and the theoretical optimal power capacity ratio are marked on the selection curve. The user can determine the final model on the model selection result curve according to the actual demand.

The complete process of the invention comprises the following steps:

reading the demo file "coast 1250kVA_tou. Xlsx", the algorithm will be executed in the following order:

1. executing main program main function

Main program is main (raw_data_df, dcr _model):

the data frame format of the pandas with raw_data_df being "coastal 1250kVA_tou. Xlsx

The parameter md_model represents a charge per maximum demand, which is guaranteed to be smaller than the transformer capacity when the maximum demand is larger than the transformer capacity and the calculation is continued

2. Inputting parameters: upper limit of transformer capacity

Converting a dataset into a dictionary with a get_per_trans_data_subject () function

Ks stores dictionary keys, and the upper limit of the transformer capacity of each key is manually input, such as 3000

3. Calculating a time step of a data source

time_step＝float(np.mean(np.diff(raw_data_df.index)/np.timedelta64(1,'s')))

The dataset index of the initial data source is differentiated to form a 9000 hundred million nanosecond sequence (1 second=10 hundred million nanoseconds), the average value of the columns is taken, then the data set index is converted into time intervals in seconds, and finally the average time interval of each row interval is obtained: 900 seconds

4. Inputting parameters: days of full filling and discharging per month and days of full filling and discharging per year

When the number of days of full filling and discharging in each month is less than 30, recording the proportion of days of incapable full filling and discharging in one month by using percentage_mon

Such as: (30-28)/30=6.7%

When the input number of days of full filling and discharging per year is less than 365, recording the proportion of days of not full filling and discharging in one year by using percentage_yr

Such as: (365-300)/365=17.8%

5. Processing the original data set into a dictionary with the year and month as keys and the data set of the year and month as value by using the function get_montaly_data_subject ()

Extracting the year and month in the original data to form the date tuple:

{ (2018,1), (2018,7), (2018,10), (2018,6), (2018,3), (2018,9), (2018,12), (2018,2), (2018,5), (2018,11), (2018,8), (2018,4) } is reconverted into a form of 'yyyy-mm' as a key, the dataset belonging to the year and month at which it is located is regarded as a value, and finally the dictionary is transferred to a variable raw_data_mon_subject such as when the key of the final raw_data_mon_subject is '2018-1', the value is the following dataset

6. Adding a field sr_no to raw_data_mon_subject to mark the state change record of each successive TOU per day

For example 2018-1-31 has 7 state change periods numbered 1-7 and counts for each TOU state per month, mapping statistics

(month_data.groupby(['sr_no','TOU']).sum()/(60*60/time_step)).plot(kind＝'bar')

7. The state of each successive period of each day in the data set standard data input is extracted monthly by the function get_sr_no_ tou _subject (), counted from 1 each day, assigned to tou _subject

Standard_data_input style

sr_no_tou_dict

{3:'peak',7:'valley',5:'peak',4:'flat',6:'flat',1:'valley',2:'flat'}

8. Recording the charge-discharge period number dictionary of two charges and two discharges by using the function get_double_cycle_sr_no_subject (), and judging the sequence numbers of two charges and two discharges

Judgment logic:

the TOU time sequence states of the day-by-day for a month are counted as follows, and 7 TOU states are respectively counted

{3:'peak',7:'valley',5:'peak',4:'flat',6:'flat',1:'valley',2:'flat'}

The states are judged from small to large in sequence:

{1:'valley',2:'flat',3:'peak',4:'flat',5:'peak',6:'flat',7:'valley'}

if the state is 'valley' and there is no '1st_charge', then '1st_charge' is the number

If the state is 'valley' and '1st_charge' already exists, '2nd_charge' is the number

If the state is 'peak' and there is no '1st_discharge', then '1st_discharge' is the number

If the state is 'peak' and '1st_discharge' already exists, then '2nd_discharge' is the number

If the state is 'flat' and '1st_discharge' already exists and '2nd_charge' does not occur, then '2nd_charge' is the number

Taking {1 } 'valley',2 } 'flat',3 } 'peak',4 } 'flat',5 } 'peak',6 } 'flat',7 } 'valley' as an example, the final two-charge and two-discharge sequence number is

{ '1st_charge ': 1 ', '1st_discharge ': 3 ', '2nd_charge ': 4 ', '2nd_discharge ': 5 }, and output in dictionary form

9. Manually judging the result of the algorithm

input (' two-charge two-discharge time period is reasonable

If not, the user manually inputs the sequence of the two-charge and two-discharge sequence numbers.

10. Key index-energy storage power capacity ratio c cycle optimization

The add_charge_physical_limit function is used to physically constrain the charge: physical upper limit per moment = artificially specified capacity upper limit-average power per moment, if the difference between the two is less than 0, the result is changed to 0

The add_charge_md_limit function is used to constrain the charge md: md constraint per moment = maximum value of average power per month per moment-average power per moment

The add_discharge_Consume_limit function is used to put the constraint on discharge:

per-moment consumption constraint = monthly average power per moment in the data set TOU as 'peak' state-1

Under the above 3 constraints, the power-to-capacity ratio (bss_rate_to_cap) is from 0.1 to 1, and the configurable maximum capacity is solved 10 times in 0.1 step-size traversal cycles.

If the charge is based on the maximum demand, the maximum demand is larger than the transformer capacity and the calculation is continued, the maximum demand is still smaller than the transformer capacity

The important modules and the respective roles in model # are:

#------------------------------

multi_mon_day_result records day data for multiple months for selecting capacity results by day of the year

Multi-mon result for recording monthly typing results

Electricity consumption data of raw_data_mon_subject per month

Power consumption data dictionary after energy storage on single_trans_subject

maximum monthly per-day of md_mon

Per_trans_bss_limit_result considers physical, md, maximum configurable capacity dictionary for each transformer loop after constraint is absorbed

The multi-trans-mon-limit function returns the month-to-month selection result of each loop

The multi-trans-day limit function returns data of each circuit day of the month

year_mole: enumerated values '2018-1'

trans_cap_subject: { 'Unnamed:3':3000, 'coastal river building 1250kVA':3000}

bss_rate_to_cap: gradual optimizing from 0.1 to 1 in 0.1 step

sr_no_dict：{'1st_charge':[1],'1st_discharge':[3],'2nd_charge':[4],'2nd_discharge':[5]}

real_mon_md＝float(md_mon[trans_name])

montage_data dataset:

if the daily maximum capacity exceeds the upper limit of the capacity of the transformer, prompt correction is carried out. (enumeration example)

sr_dict＝{'1st_charge':[1],'1st_discharge':[3],'2nd_charge':[4],'2nd_discharge':[5]}

k_str＝['1st_charge','1st_discharge','2nd_charge','2nd_discharge']

sr_no_subject_key= '1st_charge' # enumeration value

sr_nos＝['1','3','4','5']

srno_subject_value=1# enumerated value

trans_cap=3000# upper capacity limit, set artificially

Maximum power value in case of optimum power-to-capacity ratio selected by bss_rate_radio_result [ trans_name ] [ cap_limit' ] #

The charging codes according to the capacity and the maximum demand are as follows:

energyStorage.views#

from.libs.utils import simulation_tasks,create_simulation_report,create_financial_report,financial_tasks,arithmetic_tasks

from celery_tasks.writer.tasks import save_arithmetic,save_simulation,save_financial_calculation

from.models import LandUserInfo,LandLoadInfo,BatteryReports,BatteryTypes,FinancialReports,SimulationReports,DataJson

from.libs import model_dict

model_subject= { ' charge on capacity ' cap_model ', ' charge on demand ' md_model }

Starting point single_cycle_calculator

The invention has the beneficial effects that compared with the prior art:

the invention can obtain the optimal energy storage configuration scheme of the energy storage system, and is specific:

1) Performing user electricity behavior analysis in batches, and simulating the running state of the energy storage system;

2) Evaluating the construction cost of the energy storage system and analyzing the construction value of the energy storage system;

3) The method can generate user potential discrimination and energy storage optimal configuration by one key, and can effectively improve the energy storage popularization working efficiency of a user side while ensuring the safe and stable operation of the power grid;

the EneMicro model constructed by the invention has high granularity data and high density calculation function, provides a special and accurate configuration scheme for users, greatly supports micro-grid investment decision, and effectively improves the energy storage popularization working efficiency of the user side while ensuring the safe and stable operation of the power grid, and has the following characteristics:

1) The boundary of the feasible region is clear at a glance

The EneMicro model has an optimized type selection search space, can intuitively observe from a capacity-multiplying power graph, and meets the energy storage type selection of a microgrid operation strategy target under different utilization rate requirements, so that an energy storage type selection conclusion is very obvious.

2) High granularity of supported time

The EneMicro model can support minute-level time granularity data operation analysis, and can give operating power and SOC for each time point.

3) Compatibility with other element models of micro-grid

Boundary conditions of the EneMicro model can comprise other elements of the multi-energy collaborative system, such as fans, photovoltaics and the like, and simulation calculation is performed on the basis of topology and price stimulation of the EneMicro model to obtain results.

4) The step length can be changed, and the searching speed is increased. And judging whether the capacity deviation of each segmented area meets the preset capacity deviation or not by calculating the capacity deviation between the energy storage capacity of each segmented area and the corresponding theoretical maximum capacity, if not, correcting the variable stepping amount of the variable stepping, and searching the energy storage capacity of the corresponding segmented area in a segmented mode again by the corrected variable stepping.

5) The model can set the upper limit of the charge and discharge times, so that the model variable traverses and selects proper charge and discharge times, and the charge and discharge times can be unequal (for example, two charges and one discharge and one charge and three discharges) on the premise that the constraint conditions of conservation of charge and discharge energy are still met, so that the total cost generated by daily energy flow is minimum. The optional conditions become more after the limitation of the equal number of times of charging and discharging is released, so that the solving result can be improved.

The present disclosure may be a system, method, and/or computer program product. The computer program product may include a computer readable storage medium having computer readable program instructions embodied thereon for causing a processor to implement aspects of the present disclosure.

The computer readable storage medium may be a tangible device that can hold and store instructions for use by an instruction execution device. The computer readable storage medium may be, for example, but not limited to, an electronic storage device, a magnetic storage device, an optical storage device, an electromagnetic storage device, a semiconductor storage device, or any suitable combination of the foregoing. More specific examples (a non-exhaustive list) of the computer-readable storage medium would include the following: portable computer disks, hard disks, random Access Memory (RAM), read-only memory (ROM), erasable programmable read-only memory (EPROM or flash memory), static Random Access Memory (SRAM), portable compact disk read-only memory (CD-ROM), digital Versatile Disks (DVD), memory sticks, floppy disks, mechanical coding devices, punch cards or in-groove structures such as punch cards or grooves having instructions stored thereon, and any suitable combination of the foregoing. Computer-readable storage media, as used herein, are not to be construed as transitory signals per se, such as radio waves or other freely propagating electromagnetic waves, electromagnetic waves propagating through waveguides or other transmission media (e.g., optical pulses through fiber optic cables), or electrical signals transmitted through wires.

The computer readable program instructions described herein may be downloaded from a computer readable storage medium to a respective computing/processing device or to an external computer or external storage device over a network, such as the internet, a local area network, a wide area network, and/or a wireless network. The network may include copper transmission cables, fiber optic transmissions, wireless transmissions, routers, firewalls, switches, gateway computers and/or edge servers. The network interface card or network interface in each computing/processing device receives computer readable program instructions from the network and forwards the computer readable program instructions for storage in a computer readable storage medium in the respective computing/processing device.

Computer program instructions for performing the operations of the present disclosure can be assembly instructions, instruction Set Architecture (ISA) instructions, machine-related instructions, microcode, firmware instructions, state setting data, or source or object code written in any combination of one or more programming languages, including an object oriented programming language such as Smalltalk, c++ or the like and conventional procedural programming languages, such as the "C" programming language or similar programming languages. The computer readable program instructions may be executed entirely on the user's computer, partly on the user's computer, as a stand-alone software package, partly on the user's computer and partly on a remote computer or entirely on the remote computer or server. In the case of a remote computer, the remote computer may be connected to the user's computer through any kind of network, including a Local Area Network (LAN) or a Wide Area Network (WAN), or may be connected to an external computer (for example, through the Internet using an Internet service provider). In some embodiments, aspects of the present disclosure are implemented by personalizing electronic circuitry, such as programmable logic circuitry, field Programmable Gate Arrays (FPGAs), or Programmable Logic Arrays (PLAs), with state information of computer readable program instructions, which can execute the computer readable program instructions.

Various aspects of the present disclosure are described herein with reference to flowchart illustrations and/or block diagrams of methods, apparatus (systems) and computer program products according to embodiments of the disclosure. It will be understood that each block of the flowchart illustrations and/or block diagrams, and combinations of blocks in the flowchart illustrations and/or block diagrams, can be implemented by computer-readable program instructions.

These computer readable program instructions may be provided to a processor of a general purpose computer, special purpose computer, or other programmable data processing apparatus to produce a machine, such that the instructions, which execute via the processor of the computer or other programmable data processing apparatus, create means for implementing the functions/acts specified in the flowchart and/or block diagram block or blocks. These computer readable program instructions may also be stored in a computer readable storage medium that can direct a computer, programmable data processing apparatus, and/or other devices to function in a particular manner, such that the computer readable medium having the instructions stored therein includes an article of manufacture including instructions which implement the function/act specified in the flowchart and/or block diagram block or blocks.

The computer readable program instructions may also be loaded onto a computer, other programmable data processing apparatus, or other devices to cause a series of operational steps to be performed on the computer, other programmable apparatus or other devices to produce a computer implemented process such that the instructions which execute on the computer, other programmable apparatus or other devices implement the functions/acts specified in the flowchart and/or block diagram block or blocks.

The flowcharts and block diagrams in the figures illustrate the architecture, functionality, and operation of possible implementations of systems, methods and computer program products according to various embodiments of the present disclosure. In this regard, each block in the flowchart or block diagrams may represent a module, segment, or portion of instructions, which comprises one or more executable instructions for implementing the specified logical function(s). In some alternative implementations, the functions noted in the block may occur out of the order noted in the figures. For example, two blocks shown in succession may, in fact, be executed substantially concurrently, or the blocks may sometimes be executed in the reverse order, depending upon the functionality involved. It will also be noted that each block of the block diagrams and/or flowchart illustration, and combinations of blocks in the block diagrams and/or flowchart illustration, can be implemented by special purpose hardware-based systems which perform the specified functions or acts, or combinations of special purpose hardware and computer instructions.

Finally, it should be noted that the above embodiments are only for illustrating the technical solution of the present invention and not for limiting the same, and although the present invention has been described in detail with reference to the above embodiments, it should be understood by those skilled in the art that: modifications and equivalents may be made to the specific embodiments of the invention without departing from the spirit and scope of the invention, which is intended to be covered by the claims.

Claims (11)

1. An energy storage configuration scheme optimization method of an energy storage system is characterized by comprising the following steps of:

the method comprises the following steps:

s1: searching the theoretical maximum capacity of the energy storage system and the corresponding theoretical charge-discharge multiplying power according to the energy storage element parameters in the energy storage system;

s2: based on the theoretical maximum capacity and the theoretical charge-discharge multiplying power, acquiring a feasible region boundary of an energy storage configuration scheme of the energy storage system from energy storage element parameters, an energy storage topological structure and energy storage price multielement elements by adopting a multiplying power circulation searching mode;

s3: and correcting the feasible domain boundary through the target energy storage utilization rate to obtain an optimal energy storage configuration scheme of the energy storage system.

2. The energy storage configuration optimization method of an energy storage system according to claim 1, wherein:

The energy storage configuration scheme of the energy storage system is optimized based on the EneMicro model.

3. A method of optimizing an energy storage configuration of an energy storage system according to claim 1 or 2, wherein:

s1 specifically comprises the following steps: searching the maximum energy storage capacity of a user and the corresponding charge and discharge multiplying power matched with the energy storage element parameters from the large data of the energy storage of the user according to the energy storage element parameters in the energy storage system;

and taking the searched maximum energy storage capacity of the user and the corresponding charge-discharge multiplying power as the theoretical maximum capacity of the energy storage system and the corresponding theoretical charge-discharge multiplying power.

4. A method of optimizing an energy storage configuration of an energy storage system according to claim 3, wherein:

the energy storage element parameters comprise the type of an energy storage battery and the corresponding total efficiency, battery loss rate, PCS loss rate, service life, minimum SOC, maximum SOC and annual attenuation rate of the energy storage battery;

the types of energy storage cells include lithium iron phosphate, ternary lithium and lead carbon.

5. A method of optimizing an energy storage configuration of an energy storage system according to claim 1 or 2, wherein:

s2, the energy storage topological structure is divided into an energy storage topological structure of lithium iron phosphate, ternary lithium iron phosphate and lead carbon;

The energy storage price comprises the energy storage price of holidays, the energy storage prices of different seasons and the energy storage prices under different weather conditions.

6. A method of optimizing an energy storage configuration of an energy storage system according to claim 1 or 2, wherein:

s2 specifically comprises:

s21: dividing the theoretical maximum capacity into a plurality of energy storage sections, and selecting an optimal feasible region boundary from the plurality of energy storage sections based on energy storage element parameters, an energy storage topological structure and an energy storage price;

s22: and (3) calculating the multiplying power deviation between the charge-discharge multiplying power corresponding to the optimal feasible region boundary selected in the step S21 and the theoretical charge-discharge multiplying power, judging whether the multiplying power deviation meets the preset multiplying power deviation, and if not, searching the feasible region boundary of the energy storage configuration scheme again through the charge-discharge multiplying power circulation.

7. A method of optimizing an energy storage configuration of an energy storage system according to claim 1 or 2, wherein:

s3 specifically comprises:

s31: step 2, obtaining energy storage capacity of a corresponding segmented area in a feasible region boundary by utilizing the target energy storage utilization rate in a segmented mode with variable stepping quantity, wherein the searched energy storage capacity is the energy storage capacity which is optimally matched with the target energy storage utilization rate;

S32: calculating the capacity deviation between the energy storage capacity of each segmented area and the corresponding theoretical maximum capacity, and judging whether the capacity deviation of each segmented area meets the preset capacity deviation or not;

if the energy storage capacity of the corresponding segmented area is not met, correcting the variable stepping quantity of the variable stepping, returning to the step S31, searching the energy storage capacity of the corresponding segmented area in a segmented mode again through the corrected variable stepping, correcting the feasible region boundary obtained in the step S2, enabling the energy storage capacity corresponding to the feasible region boundary to be in optimal configuration with the target energy storage utilization rate, and accordingly obtaining the maximum energy storage capacity under different multiplying powers.

8. An energy storage configuration scheme optimizing platform of an energy storage system, which is characterized in that:

the optimizing platform comprises a database and an energy storage configuration scheme optimizing unit;

the database is used for storing user energy storage data corresponding to various energy storage batteries;

an energy storage configuration scheme optimizing unit, configured to execute the energy storage configuration scheme optimizing method according to any one of claims 1 to 7 based on the user energy storage data stored in the database, and generate an optimal energy storage configuration scheme of the energy storage system.

9. The energy storage configuration optimization platform of an energy storage system of claim 8, wherein:

The database is an RDS database;

before storing the user energy storage data corresponding to the various energy storage batteries into the RDS database, the optimization platform screens and cleans the user energy storage data corresponding to the various energy storage batteries, and then stores the screened and cleaned data into the RDS database.

10. The energy storage configuration optimization platform of an energy storage system of claim 8, wherein:

the optimization platform also comprises an external interface used for external access of the RDS database.

11. The energy storage configuration optimization platform of an energy storage system of claim 8, wherein:

the optimizing platform also comprises a browser and a virtual machine;

the virtual machine comprises a dock container, wherein an energy storage configuration scheme optimizing unit is arranged in the dock container, and an execution program of the energy storage configuration scheme optimizing method is stored in any one of claims 1-7.