CN111092448A - Dynamic optimization method for optimal battery energy storage capacity configuration based on user side load - Google Patents

Dynamic optimization method for optimal battery energy storage capacity configuration based on user side load Download PDF

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CN111092448A
CN111092448A CN201911144044.0A CN201911144044A CN111092448A CN 111092448 A CN111092448 A CN 111092448A CN 201911144044 A CN201911144044 A CN 201911144044A CN 111092448 A CN111092448 A CN 111092448A
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energy storage
battery
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CN111092448B (en
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朱国荣
徐振超
蔡张花
陈佳
成飞
翁羽玲
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Zhejiang Huayun Information Technology Co Ltd
Economic and Technological Research Institute of State Grid Zhejiang Electric Power Co Ltd
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Zhejiang Huayun Information Technology Co Ltd
Economic and Technological Research Institute of State Grid Zhejiang Electric Power Co Ltd
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    • H—ELECTRICITY
    • H02—GENERATION; CONVERSION OR DISTRIBUTION OF ELECTRIC POWER
    • H02J—CIRCUIT ARRANGEMENTS OR SYSTEMS FOR SUPPLYING OR DISTRIBUTING ELECTRIC POWER; SYSTEMS FOR STORING ELECTRIC ENERGY
    • H02J3/00—Circuit arrangements for ac mains or ac distribution networks
    • H02J3/28—Arrangements for balancing of the load in a network by storage of energy
    • H02J3/32—Arrangements for balancing of the load in a network by storage of energy using batteries with converting means
    • Y—GENERAL TAGGING OF NEW TECHNOLOGICAL DEVELOPMENTS; GENERAL TAGGING OF CROSS-SECTIONAL TECHNOLOGIES SPANNING OVER SEVERAL SECTIONS OF THE IPC; TECHNICAL SUBJECTS COVERED BY FORMER USPC CROSS-REFERENCE ART COLLECTIONS [XRACs] AND DIGESTS
    • Y02—TECHNOLOGIES OR APPLICATIONS FOR MITIGATION OR ADAPTATION AGAINST CLIMATE CHANGE
    • Y02E—REDUCTION OF GREENHOUSE GAS [GHG] EMISSIONS, RELATED TO ENERGY GENERATION, TRANSMISSION OR DISTRIBUTION
    • Y02E40/00—Technologies for an efficient electrical power generation, transmission or distribution
    • Y02E40/70—Smart grids as climate change mitigation technology in the energy generation sector
    • Y—GENERAL TAGGING OF NEW TECHNOLOGICAL DEVELOPMENTS; GENERAL TAGGING OF CROSS-SECTIONAL TECHNOLOGIES SPANNING OVER SEVERAL SECTIONS OF THE IPC; TECHNICAL SUBJECTS COVERED BY FORMER USPC CROSS-REFERENCE ART COLLECTIONS [XRACs] AND DIGESTS
    • Y04—INFORMATION OR COMMUNICATION TECHNOLOGIES HAVING AN IMPACT ON OTHER TECHNOLOGY AREAS
    • Y04S—SYSTEMS INTEGRATING TECHNOLOGIES RELATED TO POWER NETWORK OPERATION, COMMUNICATION OR INFORMATION TECHNOLOGIES FOR IMPROVING THE ELECTRICAL POWER GENERATION, TRANSMISSION, DISTRIBUTION, MANAGEMENT OR USAGE, i.e. SMART GRIDS
    • Y04S10/00—Systems supporting electrical power generation, transmission or distribution
    • Y04S10/50—Systems or methods supporting the power network operation or management, involving a certain degree of interaction with the load-side end user applications

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Abstract

The invention discloses a dynamic optimization method for battery energy storage optimal capacity configuration based on user side load. According to the invention, through analyzing the load characteristics of the user, a plurality of mutually linked feedback models such as the optimal capacity configuration of the energy storage battery, the optimal operation strategy of the energy storage system, the dynamic economic benefit measurement and calculation of the energy storage system are constructed, and a comprehensive optimization algorithm for the optimal capacity configuration of the battery energy storage at the user side is formed. The invention designs the optimal configuration scheme of the battery energy storage by combining two aspects of load characteristics of the user side and technical constraints of the battery energy storage, thereby improving the scientificity of the configuration scheme of the battery energy storage capacity of the user side.

Description

Dynamic optimization method for optimal battery energy storage capacity configuration based on user side load
Technical Field
The invention belongs to the field of user side battery energy storage capacity configuration, and relates to a dynamic optimization method for battery energy storage optimal capacity configuration based on user side load characteristics.
Background
Promote modern energy system construction, strengthen energy storage top layer design for electric wire netting side energy storage pilot point, the research of user side battery energy storage is carried out to the science, and the analytical study of future economic benefits of deepening user side battery energy storage and relevant influence has the realistic meaning. At present, the following technical perfection spaces exist: (1) the domestic user side battery energy storage benefit policy is frequent, the application demand of energy storage at the user side is gradually shown, an optimal constant volume strategy and a benefit measuring and calculating method aiming at single user characteristics are lacked, and the development trend of the user side battery energy storage cannot be judged; (2) the energy storage of the battery at the user side is random, dispersed and high in operation flexibility, and the difficulty of energy storage configuration can be increased due to the complex technical and economic parameters of the energy storage battery at the user side, the energy storage system and the like. Based on the technical development trends of the energy storage batteries and the energy storage system and market research and analysis, the adaptability of the user-side battery energy storage configuration optimal power measurement and calculation model is improved by adjusting parameters, and the model is optimized from different angles (such as capacity configuration, charging and discharging time sequence and the like) based on the technical parameters of different energy storage batteries in the measurement and calculation process.
Disclosure of Invention
The invention aims to provide a dynamic optimization algorithm for battery energy storage optimal capacity configuration based on user side load characteristics in combination with the problem of user side battery energy storage optimal capacity determination, and designs a battery energy storage optimal configuration scheme in combination with two aspects of user side load characteristics and battery energy storage technical constraints so as to improve the scientificity of the design of the user side battery energy storage capacity configuration scheme.
Therefore, the invention adopts the following technical scheme: a dynamic optimization method for battery energy storage optimal capacity configuration based on user side load comprises the following steps:
1) establishing an optimal constant volume model of battery energy storage at a user side;
2) constructing an optimal operation strategy model of the energy storage system;
3) establishing a user side battery energy storage benefit measuring and calculating model;
4) and (5) dynamically fitting a comprehensive optimization algorithm.
Further, in step 1), the user side battery stores energyThe optimal constant volume model is based on user load data and the statistical peak-valley difference, and the maximum annual daily peak-valley difference is selected as an initial value; calculating the number of containers by combining the discharge depth and the configuration of the battery container, and obtaining the maximum power P of the energy storage battery from the power of the battery containermax(ii) a The optimal power range of the energy storage battery configured by the user is 250, Pmax]Unit is kilowatt, and the charging time length T of the known energy storage batterycThe optimal capacity of the energy storage battery can be obtained; determining the maximum capacity C of the energy storage battery according to the user load datamaxAnd (3) a range.
Further, in the optimal constant volume model of the battery energy storage at the user side in the step 1),
model input parameters: daily load data of the user: l, in KW; depth of discharge: doDIn units of%; the battery charging time: t iscThe unit is h;
model output parameters: maximum load value of daily load curve: l ismaxIn KW; minimum load value of daily load curve: l ismimIn KW; difference between daily peak and valley: l ism＝Lmax-LminIn KW; annual maximum daily peak-to-valley difference: l is′ m＝max(Lm) In KW; number of battery containers: num ═ L′ m/DODIf not, rounding up; maximum power of the battery: pmax250 × num; maximum capacity of battery: cmax＝Pmax×Tc；
An objective function:
because the minimum of the battery container configured by the battery energy storage at the user side is 250 kilowatts, the maximum value of the daily maximum load difference in one year is L′ mSo that the minimum value of the maximum power of the battery
Figure BDA0002281695750000021
Theoretical maximum power of the battery
Figure BDA0002281695750000022
Considering that the battery container can only be increased by a multiple of 250, the number of the containers is calculated to reversely deduce the actual maximum power of the battery,
Figure BDA0002281695750000027
therefore, the maximum power is updated to obtain the actual maximum power of the energy storage battery
Figure BDA0002281695750000023
The optimal power range of the energy storage battery is
Figure BDA0002281695750000024
An optimum capacity of
Figure BDA0002281695750000025
Wherein the content of the first and second substances,
Figure BDA0002281695750000026
further, in step 2), the peak period 19 is considered first: 00-21: 00, comparing the user load with the maximum power of the battery to calculate the discharge power of the battery and the total discharge amount W for the periodojfSumming, wherein it is satisfied that the total discharge does not exceed the maximum discharge W of the battery itselfomax＝Wimax×effi＝CmaxX hdxs x effi, where effi is system efficiency,%; hdxs is the battery charge coefficient,%;
due to the continuity of time, the front and rear peaks 13 adjacent to the peak period are then considered: 00-19: 00 and 21: 00-22: discharge amount of 00: wogf1And Wogf2(ii) a The remaining maximum discharge amount is converted into the maximum discharge amount W of the battery itself, as compared with the peak periodomaxAnd peak period discharge amount WojfDifference of (i) Womax-Wojf(ii) a The discharge power solving is the same as the peak time period, and if the residual maximum discharge capacity is zero, the discharge is finished; if the residue is the mostLarge discharge capacity is still greater than zero, then consider 8: 00-11: peak hour discharge W of 00ogf3Finally, the discharge amount of the peak and the 3-period peak time is summed to obtain Wo＝Wojf+Wogf1+Wogf2+Wogf3(ii) a In conclusion, the discharge power and the discharge time sequence in the peak and peak periods can be obtained;
for the charging sequence and the charging quantity, the total discharging quantity is compared with the maximum discharging quantity of the battery, if Wo<WomaxThe required charge amount is Wo/effi; otherwise the charging quantity is the maximum charging quantity Wimax。
Further, in the step 3), the user-side battery energy storage benefit measurement and calculation model is used for calculating energy storage benefits under different battery powers, wherein the energy storage benefits include energy storage cost measurement and calculation under different battery capacities, total power utilization benefit measurement and calculation, internal investment profitability and investment recovery period measurement and calculation.
Further, in the step 3), the annual total income comprises one-year electric power fee arbitrage and capacity electric power fee arbitrage,
1) electricity charge arbitrage
The original annual total electricity charge:
Figure BDA0002281695750000031
wherein i represents the number of days, and j represents the load collection time, and the number of times is 96 every 15 minutes; pd、Pg、PjRespectively representing the low valley, the peak and the peak time-of-use price (yuan/kWh).
Arbitrage on a certain day:
Figure BDA0002281695750000032
in the formula (I), the compound is shown in the specification,
Figure BDA0002281695750000033
indicating the charging power (kW),
Figure BDA0002281695750000034
Indicating the discharge power (kW).
Arbitrage for one year:
Figure BDA0002281695750000035
in the formula uiIndicates arbitrage of a certain day, PrRepresents a arbitrage of one year;
so the total electricity charge after adding the stored energy is as follows:
df2＝df1-Pr；
2) capacity electricity charge arbitrage
The capacity electricity charge of the original year is calculated according to the month:
a1＝L(1:31,:)；a2＝L(32:59,:)；…；a12＝L(335:365,:),
pc1＝(max(max(a1))+max(max(a2))+…+max(max(a12)))×Rp
in the formula, RpRepresents the maximum demand, yuan/kw/month;
after the energy storage is added, the load WG is equal to L + SC, and SC represents the energy storage capacity;
and (3) calculating the capacity electricity charge of one year after the energy storage is added according to the month:
b1＝WG(1:31,:)；…；b12＝WG(335:365,:),
pc2＝(max(max(b1))+max(max(b2))+…+max(max(b)))×Rp
the total annual income: rt＝(Pr+pc1-pc2)/10000。
Further, in the step 3), the total energy storage cost is calculated as follows:
the total cost of the energy storage system comprises the cost of the energy storage battery, the cost of auxiliary components, the replacement cost of the energy storage battery, the operation and maintenance cost of the energy storage battery, and the recovery value of the energy storage battery is removed.
Further, in the step 3), the initial investment C of the energy storage projecttYuan, every year in the next N years
Figure BDA0002281695750000041
The internal yield IRR is obtained by solving the following formula,
Figure BDA0002281695750000042
further, in the step 3), the initial investment C of the energy storage projecttYuan, all year later on
Figure BDA0002281695750000043
The Yuan cash flow, the minimum q is obtained by solving the following formula and is recorded as the investment recovery period,
Figure BDA0002281695750000044
further, the comprehensive optimization algorithm selects the battery power under the optimal internal investment yield as the optimal power by comparing the internal investment yields under different powers, so as to obtain the corresponding optimal capacity and the optimal operation time sequence.
The invention designs the optimal configuration scheme of the battery energy storage by combining two aspects of load characteristics of the user side and technical constraints of the battery energy storage, thereby improving the scientificity of the configuration scheme of the battery energy storage capacity of the user side.
Detailed Description
The present invention will be further described with reference to the following embodiments.
The invention provides a dynamic optimization algorithm for battery energy storage optimal capacity configuration based on user side load characteristics, which comprises the following steps:
1) optimal constant volume model for user-side battery energy storage
And the optimal constant volume model of the battery energy storage at the user side is based on user load data, the peak-valley difference is counted, and the maximum daily peak-valley difference of the year is selected as an initial value. The number of the containers is calculated by combining the discharge depth and the configuration of the battery containers, and the maximum power P of the energy storage battery can be obtained according to the power of the battery containersmax. The optimal power (unit: kilowatt) range of the energy storage battery configured by the user is 250, Pmax]. Knowing the charging time T of the energy storage batterycThe optimal capacity of the energy storage battery can be obtained. Determining the maximum capacity C of the energy storage battery according to the user load datamaxAnd (3) a range.
Model input parameters: user daily load data (KW): l, depth of discharge (%): dODBattery charging time (h): t isc
Model output parameters: maximum load value (KW) of daily load curve: l ismaxMinimum load value (KW) of daily load curve: l isminPeak-to-valley difference of day (KW): l ism＝Lmax-LminAnnual maximum daily peak valley difference (KW): l is′ m＝max(Lm) The number of the battery containers is as follows: num ═ L′ m/DOD/250 (rounded up if not an integer), maximum battery power: pmax250 × num, maximum battery capacity: cmax＝Pmax×Tc。
An objective function:
because the minimum of the battery container configured by the battery energy storage at the user side is 250 kilowatts, the maximum value of the daily maximum load difference in one year is L′ mSo that the minimum value of the maximum power of the battery
Figure BDA0002281695750000051
Theoretical maximum power of the battery
Figure BDA0002281695750000052
Considering that the battery container can only be increased by a multiple of 250, the number of the containers is calculated to reversely deduce the actual maximum power of the battery,
Figure BDA0002281695750000057
therefore, the maximum power is updated to obtain the actual maximum power of the energy storage battery
Figure BDA0002281695750000053
In summary, the optimal power range of the energy storage battery is
Figure BDA0002281695750000054
An optimum capacity of
Figure BDA0002281695750000055
Wherein
Figure BDA0002281695750000056
2) Optimal operation strategy model of energy storage system
The user side uses lead storage battery and lithium cell more at present, and both charge and discharge mode difference lead to the operation strategy to some extent, and the principle of charging and discharging is unanimous, mainly has following three: principle 1: charging and discharging according to the actual load requirements of users; principle 2: giving priority to the fact that the peak time period must be discharged; principle 3: the daily maximum charge must not exceed the configured maximum capacity of the battery.
The electric quantity required by the user in the peak and peak time periods is different every day, and the maximum discharge quantity cannot be reached, so the charging quantity of the battery in the valley time period is reversely deduced according to the discharge quantity in the peak and peak time periods.
Based on principle 2, the spike period 19 is considered first: 00-21: 00, comparing the user load with the maximum power of the battery to calculate the discharge power of the battery and the total discharge amount W for the periodojfSumming, wherein it is satisfied that the total discharge does not exceed the maximum discharge W of the battery itselfomax＝Wimax×effi＝CmaxX hdxs x effi, where effi is system efficiency,%; hdxs is the battery charge coefficient,%;
due to the continuity of time, the front and rear peaks 13 adjacent to the peak period are then considered: 00-19: 00 and 21: 00-22: discharge amount of 00: wogf1And Wogf2(ii) a Maximum amplification remaining compared to the spike periodConversion of electric quantity into maximum discharge W of battery itselfomaxAnd peak period discharge amount WojfDifference of (i) Womax-Wojf(ii) a The discharge power solving is the same as the peak time period, and if the residual maximum discharge capacity is zero, the discharge is finished; if the remaining maximum discharge capacity is still greater than zero, consider 8: 00-11: peak hour discharge W of 00ogf3Finally, the discharge amount of the peak and the 3-period peak time is summed to obtain Wo＝Wojf+Wogf1+Wogf2+Wogf3(ii) a In conclusion, the discharge power and the discharge time sequence in the peak and peak periods can be obtained;
for the charging sequence and the charging quantity, the total discharging quantity is compared with the maximum discharging quantity of the battery, if Wo<WomaxThe required charge amount is Wo/effi; otherwise the charging quantity is the maximum charging quantity Wimax。
3) User side battery energy storage economic benefit measuring and calculating model
The economic benefit measurement and calculation model aims at calculating energy storage economic benefits under different battery powers, and comprises energy storage cost measurement and calculation under different battery capacities, total power utilization income measurement and calculation, internal investment profitability, investment recovery period measurement and the like.
An objective function:
1. the annual total income comprises the annual electric power fee arbitrage and the capacity fee arbitrage.
Electricity charge arbitrage
The original annual total electricity charge:
Figure BDA0002281695750000061
wherein i represents the number of days, and j represents the load collection time, and the number of times is 96 every 15 minutes; pd、Pg、PjRespectively representing the low valley, the peak and the peak time-of-use price (yuan/kWh).
Arbitrage on a certain day:
Figure BDA0002281695750000062
in the formula (I), the compound is shown in the specification,
Figure BDA0002281695750000063
indicating the charging power (kW),
Figure BDA0002281695750000064
Indicating the discharge power (kW).
Arbitrage for one year:
Figure BDA0002281695750000065
in the formula uiIndicates arbitrage of a certain day, PrRepresents a arbitrage of one year;
so the total electricity charge after adding the stored energy is as follows:
df2＝df1-Pr
(2) capacity electricity charge arbitrage
The capacity electricity charge of the original year is calculated according to the month:
a1＝L(1:31,:)；a2＝L(32:59,:)；…；a12＝L(335:365,:),
pc1＝(max(max(a1))+max(max(a2))+…+max(max(a12)))×Rp
in the formula, RpRepresents the maximum demand, yuan/kw/month;
after the energy storage is added, the load WG is equal to L + SC, and SC represents the energy storage capacity;
and (3) calculating the capacity electricity charge of one year after the energy storage is added according to the month:
b1＝WG(1:31,:)；…；b12＝WG(335:365,:),
pc2＝(max(max(b1))+max(max(b2))+…+max(max(b)))×Rp
the total annual income: rt＝(Pr+pc1-pc2)/10000。
2. Energy storage total cost measurement
Taking a lead storage battery as an example, since the life cycle of an energy storage system is 15 years and the life cycle of the lead storage battery is 5 years, the battery needs to be replaced 2 times in the period. The total cost of the energy storage system comprises the cost of the energy storage battery, the cost of auxiliary components, the replacement cost of the energy storage battery, the operation and maintenance cost of the energy storage battery, and the recovery value of the energy storage battery is removed.
Energy storage battery cost (ten thousand yuan): cb＝Cmax×zjd/10000
Auxiliary component cost (ten thousand yuan): cf＝Cmax×zjf/10000
Energy storage system cost (ten thousand yuan): csys＝Cb+Cf
Battery replacement input (ten thousand yuan):
Figure BDA0002281695750000071
battery operating maintenance cost (ten thousand yuan): cw＝Ct×Pw/10000
Total depreciation of energy storage battery (ten thousand yuan): cdep＝Crep×(1-Pcz)
Residual value of energy storage battery (ten thousand yuan): ccz＝Crep×Pcz
Total energy storage system cost (ten thousand yuan): ct＝(Cb+Cf+Crep-Cdep)(1+Pw)
3. Internal investment profitability
From the above analysis, the initial investment C of the energy storage projecttYuan, every year in the next N years
Figure BDA0002281695750000072
The internal yield IRR can be obtained by solving the following equation.
Figure BDA0002281695750000081
Period of investment recovery
Initial investment C from energy storage projecttYuan, all year later on
Figure BDA0002281695750000082
And solving the following formula to obtain the minimum q, and recording the minimum q as the investment recovery period.
Figure BDA0002281695750000083
Inputting parameters:
time-of-use electricity price (yuan/kw hour) in zhejiang province:
Pj＝1.0824,Pg＝0.9004,Pd＝0.4164
energy storage battery life cycle (years): n is
Energy storage system life cycle (years): n15
Unit price of capacity electricity (yuan/KW): rp＝40
Cost per unit capacity of battery (yuan/KWh): zjd
Auxiliary component unit cost (yuan/KWh): zjf
Battery operation maintenance cost ratio (%): pw＝2
Battery remaining rate (%): pcz
Battery maximum power range (KW):
Figure BDA0002281695750000084
outputting parameters:
electric power charge arbitrage (ten thousand yuan): pr/10000
Capacity electric charge arbitrage (ten thousand yuan): (pc)1-pc2)/10000
Annual total income (ten thousand yuan): rt/10000
Energy storage battery cost (ten thousand yuan): cb
Auxiliary component cost (ten thousand yuan): cf
Energy storage system cost (ten thousand yuan): csys
Battery replacement input (ten thousand yuan): crep
Battery operating maintenance cost (ten thousand yuan): cw
Total depreciation of energy storage battery (ten thousand yuan): cdep
Residual value of energy storage battery (ten thousand yuan): ccz
Total energy storage system cost (ten thousand yuan): ct
Internal investment yield (%): IRR
Investment recovery period (year): q. q.s
4) Dynamic fitting of comprehensive optimization algorithm
The comprehensive optimization algorithm selects the battery power under the optimal internal investment yield as the optimal power by comparing the internal investment yields under different powers, so as to obtain the corresponding optimal capacity and the optimal operation time sequence.
The optimal capacity allocation of the battery energy storage at the user side-dynamic economic benefit comprehensive optimization algorithm takes the maximized internal investment yield as an objective function, and the optimal capacity and the related economic measurement and calculation indexes are obtained through the maximum value of the internal investment yield. Calculating the internal investment profitability involves cash-in, cash-out, and income tax. Cash inflow including stored cash arbitrage RtAnd the battery recovery residual value Ccz(ii) a Cash-out involving fixed-asset investment CsysAnd combined cost Cw(ii) a The tax due is tax. Using formulas
Net post-tax cash flow-cash out-income tax
Obtaining the post-tax net cash flow Ncf. Different internal investment profitability IRR can be obtained based on different battery power calculations using the following formula. Taking the maximum value max (IRR) of the internal investment yield1,IRR2,…,IRRnum) Thereby obtaining the optimal power.
Figure BDA0002281695750000091
The above embodiments are not intended to limit the form and style of the present invention, and any suitable changes or modifications may be made by one of ordinary skill in the art without departing from the scope of the present invention.

Claims (10)

1. The dynamic optimization method for battery energy storage optimal capacity configuration based on user side load is characterized by comprising the following steps of:
1) establishing an optimal constant volume model of battery energy storage at a user side;
2) constructing an optimal operation strategy model of the energy storage system;
3) establishing a user side battery energy storage benefit measuring and calculating model;
4) and (5) dynamically fitting a comprehensive optimization algorithm.
2. The dynamic optimization method for battery energy storage optimal capacity configuration based on user side load according to claim 1, wherein in step 1), the user side battery energy storage optimal constant volume model is based on user load data and statistical peak-to-valley difference, and selects the annual maximum daily peak-to-valley difference as an initial value; calculating the number of containers by combining the discharge depth and the configuration of the battery container, and obtaining the maximum power P of the energy storage battery from the power of the battery containermax(ii) a The optimal power range of the energy storage battery configured by the user is 250, Pmax]Unit is kilowatt, and the charging time length T of the known energy storage batterycThe optimal capacity of the energy storage battery can be obtained; determining the maximum capacity C of the energy storage battery according to the user load datamaxAnd (3) a range.
3. The dynamic optimization method for optimal battery energy storage capacity configuration based on user side load according to claim 2, wherein in the optimal battery energy storage capacity model at the user side in the step 1),
model input parameters: daily load data of the user: l, in KW; depth of discharge: dODIn units of%; the battery charging time: t iscThe unit is h;
model output parameters: maximum load value of daily load curve: l ismaxIn KW; minimum load value of daily load curve: l isminIn KW; difference between daily peak and valley: l ism＝Lmax-LminIn KW; maximum day of yearPeak-to-valley difference: l'm＝max(Lm) In KW; number of battery containers: num ═ L'm/DODIf not, rounding up; maximum power of the battery: pmax250 × num; maximum capacity of battery: cmax＝Pmax×Tc；
An objective function:
as the minimum of the battery container of the user side battery energy storage configuration is 250 kilowatts, the maximum value of daily maximum load difference in one year is L'mSo that the minimum value of the maximum power of the battery
Figure FDA0002281695740000011
Theoretical maximum power of the battery
Figure FDA0002281695740000012
Considering that the battery container can only be increased by a multiple of 250, the number of the containers is calculated to reversely deduce the actual maximum power of the battery,
Figure FDA0002281695740000013
therefore, the maximum power is updated to obtain the actual maximum power of the energy storage battery
Figure FDA0002281695740000021
The optimal power range of the energy storage battery is
Figure FDA0002281695740000022
An optimum capacity of
Figure FDA0002281695740000023
Wherein the content of the first and second substances,
Figure FDA0002281695740000024
4. the dynamic optimization method for optimal battery energy storage capacity configuration based on user side load as claimed in claim 1, wherein in step 2), the peak period 19 is considered first: 00-21: 00, comparing the user load with the maximum power of the battery to calculate the discharge power of the battery and the total discharge amount W for the periodojfSumming, wherein it is satisfied that the total discharge does not exceed the maximum discharge W of the battery itselfomax＝Wimax×effi＝CmaxX hdxs x effi, where effi is system efficiency,%; hdxs is the battery charge coefficient,%;
due to the continuity of time, the front and rear peaks 13 adjacent to the peak period are then considered: 00-19: 00 and 21: 00-22: discharge amount of 00: wogf1And Wogf2(ii) a The remaining maximum discharge amount is converted into the maximum discharge amount W of the battery itself, as compared with the peak periodomaxAnd peak period discharge amount WojfDifference of (i) Womax-Wojf(ii) a The discharge power solving is the same as the peak time period, and if the residual maximum discharge capacity is zero, the discharge is finished; if the remaining maximum discharge capacity is still greater than zero, consider 8: 00-11: peak hour discharge W of 00ogf3Finally, the discharge amount of the peak and the 3-period peak time is summed to obtain Wo＝Wojjf+Wogf1+Wogf2+Wogf3(ii) a In conclusion, the discharge power and the discharge time sequence in the peak and peak periods can be obtained;
for the charging sequence and the charging quantity, the total discharging quantity is compared with the maximum discharging quantity of the battery, if Wo＜WomaxThe required charge amount is Wo/effi; otherwise the charging quantity is the maximum charging quantity Wimax。
5. The dynamic optimization method for optimal battery energy storage capacity allocation based on user side load as claimed in claim 1, wherein in step 3), the user side battery energy storage benefit measurement and calculation model is used to calculate energy storage benefits under different battery powers, including energy storage cost measurement and calculation under different battery capacities, total power utilization benefit measurement and calculation of internal investment profitability and investment recovery period.
6. The dynamic optimization method for optimal battery energy storage capacity allocation based on user side load as claimed in claim 5, wherein in step 3), the annual total income comprises one year's kilowatt-hour and capacity electric charge arbitrage,
1) electricity charge arbitrage
The original annual total electricity charge:
Figure FDA0002281695740000025
wherein i represents the number of days, and j represents the load collection time, and the number of times is 96 every 15 minutes; pd、Pg、PjRespectively representing the time-sharing electricity price of low valley, peak and peak, yuan/kWh;
arbitrage on a certain day:
Figure FDA0002281695740000031
in the formula (I), the compound is shown in the specification,
Figure FDA0002281695740000032
representing the charging power, kW;
Figure FDA0002281695740000033
representing the discharge power, kW;
arbitrage for one year:
Figure FDA0002281695740000034
in the formula uiIndicates arbitrage of a certain day, PrRepresents a arbitrage of one year;
so the total electricity charge after adding the stored energy is as follows:
df2＝df1-Pr；
2) capacity electricity charge arbitrage
The capacity electricity charge of the original year is calculated according to the month:
a1＝L(1：31，：)；a2＝L(32：59，：)；…；a12＝L(335：365，：)，
pc1＝(max(max(a1))+max(max(a2))+…+max(max(a12)))×Rp
in the formula, RpRepresents the maximum demand, yuan/kw/month;
after the energy storage is added, the load WG is equal to L + SC, and SC represents the energy storage capacity;
and (3) calculating the capacity electricity charge of one year after the energy storage is added according to the month:
b1＝WG(1：31，：)；…；b12＝WG(335：365，：)，
pc2＝(max(max(b1))+max(max(b2))+…+max(max(b)))×Rp
the total annual income: rt＝(Pr+pc1-pc2)/10000。
7. The dynamic optimization method for optimal battery energy storage capacity configuration based on user side load according to claim 5, wherein in step 3), the total energy storage cost is calculated as follows:
the total cost of the energy storage system comprises the cost of the energy storage battery, the cost of auxiliary components, the replacement cost of the energy storage battery, the operation and maintenance cost of the energy storage battery, and the recovery value of the energy storage battery is removed.
8. The method for dynamically optimizing battery energy storage optimal capacity allocation based on user side load according to claim 5, wherein in the step 3), the initial investment C of the energy storage projecttYuan, every year in the next N years
Figure FDA0002281695740000035
The internal yield IRR is obtained by solving the following formula,
Figure FDA0002281695740000041
9. the method for dynamically optimizing battery energy storage optimal capacity allocation based on user side load according to claim 5, wherein in the step 3), the initial investment C of the energy storage projecttYuan, all year later on
Figure FDA0002281695740000042
The Yuan cash flow, the minimum q is obtained by solving the following formula and is recorded as the investment recovery period,
Figure FDA0002281695740000043
10. the dynamic optimization method for battery energy storage optimal capacity allocation based on user side load according to claim 5, characterized in that the comprehensive optimization algorithm selects battery power at the optimal internal investment profitability as the optimal power by comparing internal investment profitability at different powers, thereby obtaining the corresponding optimal capacity and the optimal operation timing sequence.
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