CN116362584A - Economic analysis method based on user side energy storage capacity configuration - Google Patents
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Abstract
The invention discloses an economic analysis method based on user side energy storage capacity configuration. An economic analysis method based on user side energy storage capacity configuration comprises the following steps: based on the user load history data, a typical daily load curve is obtained through analysis by using a daily load rate, a daily load fluctuation rate index and a Pearson correlation coefficient method; the peak time, peak time and valley time loads of a typical daily load curve are respectively analyzed, main evaluation indexes comprise average values, median, mode, standard deviation, maximum values and minimum values of all time periods, and then all-year peak time periods, peak time periods and valley time periods are respectively solved based on annual load data; constructing an energy storage system model; measuring and calculating an LCOE model; and (3) energy storage project economy evaluation, namely carrying out economy evaluation on the energy storage project at the user side by utilizing the economic net present value and the internal yield. Solves the problem of unreasonable economic investment caused by unreasonable energy storage capacity allocation at present.
Description
[ field of technology ]
The invention belongs to the technical field of energy storage, and particularly relates to an economic analysis method based on user side energy storage capacity configuration.
[ background Art ]
At present, the exploration of novel supply side energy structures mainly containing renewable energy sources is gradually mature, the development of renewable energy sources is mature, the renewable energy sources are represented by hydroelectric power generation, solar photovoltaic power generation, wind power generation and the like, carbon emission can be reduced by utilizing clean energy sources for power generation, the clean energy sources have the advantages of small pollution and reproducibility, but the clean energy sources have the characteristics of intermittence, randomness and volatility, and the problem of wind and light abandoning always exists. The energy storage technology can stabilize the fluctuation of the output of clean energy sources such as wind and light and mainly comprises electrochemical energy storage, physical energy storage and electromagnetic energy storage, wherein the engineering scale of the pumped storage technology in the physical energy storage is larger and the technology is mature. At present, the development of the electrochemical energy storage technology is stable, economic benefits can be brought by carrying out reasonable capacity proportioning, and economic benefits and social benefits can be brought by the large-scale development of clean energy.
The energy storage development is in the preliminary development stage in China at present, the user side energy storage is mainly developed in the industrial and commercial links, the national price policy is increased by peak-valley difference, the user mainly depends on the peak-valley difference to obtain economic benefits, and according to the division of peak-valley time periods in Zhejiang province, the reasonable operation strategy is designed by analyzing the load, so that the energy storage is configured for the industrial and commercial users, and good economic benefits can be brought.
The invention patent with publication number of CN109193720A discloses a user side energy storage capacity configuration method based on a typical daily load curve of an enterprise user. Aiming at a typical daily load power curve of an enterprise user, the invention provides a method for predicting the energy storage profit effect configured by the enterprise user and provides a daily charging and discharging scheme of a storage battery by combining with the economic optimal energy storage configuration obtained by calculation. Firstly, taking a typical daily load curve of an enterprise user as a pre-criterion, and primarily screening out peak-valley difference users as target users of energy storage configuration; and finally, setting a storage battery charging and discharging strategy based on the optimal solution to realize the functions of carrying out demand management and peak clipping and valley filling by using the storage battery. However, this patent does not disclose how to obtain a typical load curve, thereby making the economic analysis method more accurate.
[ invention ]
The invention aims to overcome the defects of the prior art, and provides an economic analysis method based on the configuration of the energy storage capacity of a user side, which aims to solve the technical problem of large error of the analysis method in the prior art.
An economic analysis method based on user side energy storage capacity configuration comprises the following steps:
(1) Based on the user load history data, a typical daily load curve is obtained through analysis by using a daily load rate, a daily load fluctuation rate index and a pearson correlation coefficient method;
(2) The peak period, peak period and valley period loads of a typical daily load curve are respectively analyzed, and main evaluation indexes comprise average value, median, mode, standard deviation, maximum value and minimum value of each period; then, based on annual load data, each index is respectively calculated for the annual peak period, peak period and valley period;
(3) Constructing an energy storage system model;
(4) Measuring and calculating an LCOE model;
(5) And the energy storage project economy evaluation is carried out, and the economy evaluation is carried out on the energy storage project at the user side by utilizing the economic net present value and the internal yield.
Preferably, the daily load rate is the ratio of the daily average load to the daily maximum load, and the daily load rate is calculated to reflect the characteristic of the concentration of the load distribution in a day, generally expressed in terms of percentage, as follows:
lambda is daily loadRate, P i,lm Is the average load on day i, P i,max Is the maximum load on day i;
daily load fluctuation is the ratio of the standard deviation of the load to the average value of the load, usually expressed in percent, and is expressed as follows:
ρ is the daily load fluctuation rate, s is the standard deviation of the load, P i,lm Is the average load on day i;
the standard deviation is used for representing the discrete degree among sample individuals in a group of samples in probability statistics, so that the change situation among the sample individuals is more comprehensively described, the daily load fluctuation rate can reflect the centralized characteristic of load distribution, the scattered characteristic of the load distribution is also reflected, the daily load fluctuation rate describes the fluctuation characteristic of the load, the larger the calculated daily load fluctuation value is, the larger the fluctuation of the load is, the unstable characteristic of the load is shown, the smaller the daily load fluctuation value is, the smaller the fluctuation of the load is shown, and the stability characteristic of the load is shown;
the pearson correlation coefficient, also known as a simple correlation coefficient, characterizes the tightness relationship between two sample distance variables, and is generally represented by r, and is calculated as follows:
where n is the sample size and is the number of samples,
respectively the average value of x and y, wherein r represents the correlation intensity degree between two variables, and the value of r is between-1 and 1, if r>0, which indicates that the two are in positive correlation, i.e. the larger the value of one variable is, the larger the value of the other variable is, if r<0, the two are negative correlation, i.e. the larger the value of one variable, the smaller the value of the other variable.
Preferably, the industrial and commercial power consumption period has three periods: peak time periods, valley time periods,
in the step (2), each parameter is calculated as the following formula:
average value:
median:
standard deviation:
mode:
L represents the exact lower limit of the group in which the mode is located, U represents the exact upper limit of the group in which the mode is located, f a For frequencies adjacent to the lower limit of the mode group, f b For the frequency adjacent to the upper limit of the array, i is the array distance
Maximum value:
X＝max{x i }
minimum value:
Y＝min{x i }
preferably, the energy storage system model in step (3) mainly comprises investment cost of the energy storage system,
the investment cost of the energy storage system mainly comprises the initial investment cost of the energy storage system, the replacement cost of a battery and the operation and maintenance cost;
the initial investment cost of the energy storage system mainly comprises battery energy storage cost, energy storage converter device cost, auxiliary facility cost and other cost, and the calculation formula is as follows:
C a ＝C n +C p +C f +C q
wherein C is a Is the initial investment cost of the energy storage system, C n Is the energy storage cost of the energy storage battery, C p Is the cost of the energy storage converter device, C f Is the cost of auxiliary facilities, C q Other costs;
the calculation formula of the battery energy storage cost is as follows:
C n ＝C v E e
wherein C is v Is the unit energy price of the battery monomer, E e Is the capacity of the energy storage system
The calculation formula of the cost of the energy storage converter device is as follows:
C p ＝C e P e
wherein C is e Is the unit power price of the energy storage converter device;
C f ＝C m P e
wherein C is m Is the unit energy price of the auxiliary facilities;
C q ＝C n P e
wherein C is n Is the unit energy price of other cost;
when the service life of the battery monomer of the energy storage system is less than the service life of an actual project, the battery replacement cost is required to be replaced, the battery replacement cost formula is as follows,
C g ＝C k D k
D k ＝C d xyz
wherein C is g Is the battery replacement cost, C k Is the cost coefficient of battery replacement, D k Is the initial battery investment cost, C d The price of the battery cell unit is that x is the number of cells, z is the number of liquid cooling battery packs, and w is the number of energy storage cabinets
The operation and maintenance cost is the folding cost of annual energy storage income, and the calculation formula is as follows:
C y ＝βQ n
Q n is annual energy storage operation income, beta is operation and maintenance reduction coefficient
The annual energy storage operation income calculation formula of the user is as follows:
Q n ＝N×Q d
Q n is the annual operation income of energy storage, N is the effective annual operation days, Q d Is daily energy storage income
The daily energy storage income calculation formula is as follows:
T y is the number of days of the y month, Q y,f Is the discharge gain of the y month, Q y,c Is the charging cost of the y month
The calculation formulas of the discharge income and the charge cost are as follows:
Q y,f ＝Q y,j ×C j +Q y,g ×C g
Q y,c ＝Q y,d ×C d
Q y,j is the discharge quantity, Q of peak period y,g Is the discharge capacity of peak time, C j Is peak period electricity price, C g Is peak electricity price, Q y,d Is the low-valley period charge, i.e. the total daily charge, C d Is the electricity price in the valley period
Q y,j ＝a×η×P e
Q y,g ＝b×λ×P e
Q y,g ＝Q y,z -Q y,j
a is the number of hours of peak period, eta is the discharge efficiency, P e Is the rated power of the energy storage system, b is the hours of the valley period, lambda is the charging efficiency, Q y,z Is total discharge amount per day
Q y,d ＝Q y,d1 +Q y,d2
Q y,z ＝Q y,f1 +Q y,f2
Q y,f2 ＝P z -Q y,f1 +Q y,d1 -P z ×0.3
Q y,d1 ＝2P e ×λ
Q y,d2 ＝Q y,f1 +Q y,f2 -Q y,d1
Q y,f1 First discharge electric quantity, Q y,f2 Second discharge electric quantity, P z Installed power, Q y,d1 A first charge amount; q (Q) y,d2 Charge quantity of the second time
Preferably, the measurement and calculation of the LCOE model is the power generation cost obtained by calculating after leveling the cost and the power generation amount in the life cycle of the project, namely the ratio of the current cost value in the life cycle to the current power generation amount value in the life cycle, wherein LCOE is used as a quantization index, and the current cost value and the current power generation amount value are obtained through the related calculation of the electrochemical energy storage power station in the whole life cycle, so as to obtain the electricity measurement cost of the electrochemical energy storage power station
Revenue n Is the annual benefit of energy storage projects, cost n Is the annual project cost, r is the rate of discount,
NPV is the net present value of the energy storage project, PV n Is the annual net cash flow discount value of the energy storage project,
LCOE n is the income availability electricity cost of the whole life cycle of the energy storage project, E dn Is the annual discharge capacity of energy storage,
Capex n is the annual value of initial investment cost, opex n Is annual operation expenditure cost, tax n Is annual tax, T is energy storage capacity, and H is annual utilization hour.
Preferably, the economic net present value refers to annual income, expense or net cash flow of the project in the calculation life cycle, the algebraic sum of the present value is calculated according to the given discount rate, the algebraic sum is a common index reflecting the project profit capability, the internal income rate simultaneously considers cash inflow and cash outflow in the project life cycle, the comparison with the industry standard investment income rate is convenient, the scheme is preferable when the internal income rate is larger than the standard income rate, and the larger the internal income rate is, the better the internal income rate is,
CI is the cash inflow, CO is the cash outflow, i o Is the reference yield.
CI is cash inflow, CO is cash outflow, IRR internal yield, if NPV>0，IRR>i o The description item is feasible in economic evaluation effect.
The method has the advantages and beneficial results that:
1. the invention is based on a time-of-use electricity price policy, according to the power saving price time interval division of Zhejiang, based on user load historical data, considers factors such as non-working days for removing the influence of holidays and epidemic situation, and the like, characterizes load characteristics according to daily load rate and daily load fluctuation rate indexes, characterizes the relevance of load data by a pearson coefficient method, obtains a typical daily load curve, analyzes annual load historical data according to whole year load historical data in peak, peak and valley time intervals, obtains the average value of the annual load historical data by an average value method, and obtains energy storage capacity configuration by the data analysis result of each time interval of the annual load historical data.
2. Through the obtained energy storage capacity configuration, an LCOE model measurement and calculation is established, a cost present value and an electricity generating present value are obtained through the related calculation of the electrochemical energy storage power station in the whole life cycle, the electricity-measuring cost of the electrochemical energy storage power station is obtained, LCOE is used as a quantization index, and the LCOE is expanded to new energy industries in recent years in the calculation of the electricity generating cost of traditional energy projects such as thermal power, hydroelectric power, gas power and the like, and has a certain reference significance for the development of the electrochemical energy storage industry.
3. The economic net present value and the internal yield are utilized to carry out economic evaluation on the energy storage project at the user side, the energy storage capacity is configured according to a reasonable and refined analysis method, the higher the accuracy of the obtained configuration result is, the feasibility and the rationality of project investment can be known by carrying out investment return calculation through the configuration result, the electric cost of LCOE leveling degree is 0.43 yuan/kWh, IRR indexes also indicate that project implementation has feasibility, and the influence of effective days, power generation amount change and peak-valley differential price change on various indexes of energy storage investment return benefit is different.
[ description of the drawings ]
Fig. 1 is a flowchart of a user-side energy storage capacity configuration and economy analysis method based on time-of-use electricity prices.
Fig. 2 is a structural diagram of an energy storage battery compartment.
Fig. 3 shows a daily load curve, an average load curve, and a typical daily load curve for 7 months.
FIG. 4 is a graph showing a peak-to-valley electricity price sensitivity analysis.
FIG. 5 is a graph of day of effectiveness sensitivity analysis.
Fig. 6 is a power generation amount variation sensitivity analysis chart.
[ detailed description ] of the invention
The present invention will be further described in detail with reference to the drawings and examples, in order to make the objects, technical solutions and advantages of the present invention more apparent. It should be understood that the detailed description and specific examples, while indicating the invention, are intended for purposes of illustration only and are not intended to limit the scope of the invention. In addition, in the following description, descriptions of well-known structures and techniques are omitted so as not to unnecessarily obscure the present invention.
In this example, each basic parameter data is set as follows:
the electricity price is executed by the Zhejiang province electric network company in a time-sharing way, and the specific electricity price is shown in the following table:
TABLE 1 price of electricity for each period
User side energy storage parameters:
the energy storage system battery is a lead-carbon battery,
table 2 energy storage battery parameters
| Each parameter is | Numerical value |
| Unit energy price (Yuan/kWh) of battery cell | 699.22 |
| Price per unit power (Yuan/kW) of energy storage converter device | 380 |
| Unit energy price (Yuan/kWh) of auxiliary facilities | 83.36 |
| Price per unit power (Yuan/kWh) for other costs | 76.86 |
| Battery replacement cost factor | 0.55 |
| Number of battery cells | 96 |
| Number of liquid cooling battery packs | 120 |
| Number of energy storage cabinets | 8 |
| Operation and maintenance coefficient | 0.05 |
| Number of effective days of annual run | 310 |
| Rated power (kW) of energy storage system | 2500 |
| Energy storage system capacity (kWh) | 26542 |
| Discharge efficiency | 96.53％ |
| Charging efficiency | 85.90％ |
Financial cost parameters:
TABLE 3 financial cost parameters
| Financial cost parameter | Numerical value |
| Ratio of |
20 |
| Loan life | |
| 10 | |
| Annual interest rate | 4.65％ |
| Operational years | 15 |
As shown in fig. 1, an economic analysis method based on a user side energy storage capacity configuration includes the following steps:
and step 1, based on the user load history data, a typical daily load curve is obtained through analysis by using a daily load rate, a daily load fluctuation rate index and a pearson correlation coefficient method.
Daily load rate: daily load rate is the ratio of daily average load to daily maximum load, and the daily load rate calculation reflects the characteristic of the concentration of the load distribution throughout the day, usually expressed in percent. The formula is as follows:
lambda is the daily load rate, P i,lm Is the average load on day i, P i,max Is the maximum load on day i.
Daily load fluctuation rate: daily load fluctuation is the ratio of the standard deviation of the load to the average value of the load, usually expressed in percent.
The formula is as follows:
ρ is the daily load fluctuation rate, s is the standard deviation of the load, P i,lm Is the average load on day i.
The standard deviation is used for representing the discrete degree among sample individuals in a group of samples in probability statistics, so that the change situation among the sample individuals is comprehensively described, the daily load fluctuation rate can reflect the centralized characteristic of load distribution, the scattered characteristic of the load distribution is also reflected, the daily load fluctuation rate describes the fluctuation characteristic of the load, the larger the calculated daily load fluctuation value is, the larger the fluctuation of the load is, the unstable characteristic of the load is shown, the smaller the daily load fluctuation value is, the smaller the fluctuation of the load is, and the load is shown to be stable.
Pearson correlation coefficient method: the pearson correlation coefficient, also known as a simple correlation coefficient, characterizes the tightness relationship between two sample distance variables, and is generally represented by r, and is calculated as follows:
where n is the sample size and is the number of samples,
respectively x i 、y i R represents the degree of correlation strength between two variables, and the value of r is between-1 and 1, if r>0, which indicates that the two are in positive correlation, i.e. the larger the value of one variable is, the larger the value of the other variable is, if r<0, the two are negative correlation, i.e. the larger the value of one variable, the smaller the value of the other variable.
And obtaining a typical daily load curve closest to the average value of the monthly load according to the daily load rate, the daily load fluctuation rate and the Pearson correlation coefficient method.
And 2, respectively analyzing the peak period, peak period and valley period loads of a typical daily load curve, wherein main evaluation indexes comprise average values, median values, mode values, standard deviations, maximum values and minimum values of the periods. And then, respectively solving each index for the annual peak time period, the annual peak time period and the annual valley time period based on the annual load data.
The industrial and commercial electricity consumption period of Zhejiang province has three periods: peak period, valley period. According to different electricity consumption of different months, the policy prescribes that 01 month, 07 month, 08 month and 12 month belong to electricity consumption peak periods, peak periods of 01 month, 07 month, 08 month and 12 month are 6 hours, 09:00-11:00, 13:00-17:00, and other months are 4 hours, 09:00-11:00;15:00-17:00; the peak time of 01 month, 07 month, 08 month and 12 month is 6 hours, 08:00-09:00;17:00-22:00, other months being 8 hours: 08:00-09:00;13:00-15:00, 17:00-22:00; the low valley period is a period with smaller load electricity consumption of the user side, and each month is 12 hours, namely 11:00-13:00;22:00-08:00.
The following formula is calculated for each parameter:
average value:
median:
standard deviation:
mode:
l represents the exact lower limit of the group in which the mode is located, and U represents the exact of the group in which the mode is locatedUpper limit, f a For frequencies adjacent to the lower limit of the mode group, f b For the frequency adjacent to the upper limit of the array, i is the array distance
Maximum value:
X＝max{x i } (8)
minimum value:
Y＝min{x i } (9)
step 3, energy storage system model
The investment cost of the energy storage system mainly comprises the initial investment cost of the energy storage system, the replacement cost of the battery and the operation and maintenance cost.
1. Initial investment cost of energy storage system
The initial investment costs of the energy storage system mainly comprise battery energy storage cost, energy storage converter device cost, auxiliary facility cost and other costs.
C a ＝C n +C p +C f +C q (10)
Wherein C is a Is the initial investment cost of the energy storage system, C n Is the energy storage cost of the energy storage battery, C p Is the cost of the energy storage converter device, C f Is the cost of auxiliary facilities, C q Other costs;
(1) Battery energy storage cost:
C n ＝C v E e (11)
wherein C is v Is the unit energy price of the battery monomer, E e Is the capacity of the energy storage system
Energy storage converter device cost:
C p ＝C e P e (12)
wherein C is e Is the unit power price of the energy storage converter device;
C f ＝C m P e (13)
wherein C is m Is the unit energy price of the auxiliary facilities;
C q ＝C n P e (14)
wherein C is n Is the unit energy price of other cost;
(2) Battery replacement cost
When the service life of the battery monomer of the energy storage system is less than the service life of an actual project, the battery needs to be replaced, and the battery replacement cost formula is as follows
C g ＝C k D k (15)
D k ＝C d xyz (16)
Wherein C is g Is the battery replacement cost, C k Is the cost coefficient of battery replacement, D k Is the initial battery investment cost, C d The price of the battery cell unit is that x is the number of the battery cells, z is the number of the liquid cooling battery packs, and w is the number of the energy storage cabinets.
(3) Cost of operation and maintenance
The operation and maintenance cost of the energy storage system is considered to be the reduced cost of annual energy storage income
C y ＝βQ n (17)
Q n Is annual energy storage operation income, beta is operation and maintenance reduction coefficient
Benefit of customer side energy storage system
Annual operating income:
Q n ＝N×Q d (18)
Q n is the annual operation income of energy storage, N is the effective annual operation days, Q d Is daily energy storage income
Daily energy storage benefits:
T y is the number of days of the y month, Q y,f Is the discharge gain of the y month, Q y,c Is the charging cost of the y month
Discharge yield:
Q y,f ＝Q y,j ×C j +Q y,g ×C g (20)
Q y,c ＝Q y,d ×C d (21)
Q y,j is the discharge quantity, Q of peak period y,g Is the discharge capacity of peak time, C j Is peak period electricity price, C g Is peak electricity price, Q y,d Is the low-valley period charge, i.e. the total daily charge, C d Is the electricity price in the valley period
Q y,j ＝a×η×P e (22)
Q y,g ＝b×λ×P e (23)
Q y,g ＝Q y,z -Q y,j (24)
a is the peak period hours, 1, 7, 8, 12 months are 6 hours, the other months are 4 hours, eta is the discharge efficiency, P e Is the rated power of the energy storage system, b is the hours of the valley period, which is 12 hours, lambda is the charging efficiency, Q y,z Is total discharge amount per day
Q y,d ＝Q y,d1 +Q y,d2 (25)
Q y,z ＝Q y,f1 +Q y,f2 (26)
Q y,f2 ＝P z -Q y,f1 +Q y,d1 -P z ×0.3 (28)
Q y,d1 ＝2P e ×λ (29)
Q y,d2 ＝Q y,f1 +Q y,f2 -Q y,d1 (30)
Q y,f1 First discharge electric quantity, Q y,f2 Second oneSecondary discharge electric quantity, P z Installed power, Q y,d1 A first charge amount; q (Q) y,d2 Charge quantity of the second time
Step 4, LCOE model measurement and calculation
The leveling degree electricity cost (LCOE, levelized Cost Of Energy) is the electricity generation cost calculated after the leveling of the cost and the generated energy in the life cycle of the project, namely the ratio of the current value of the cost in the life cycle to the current value of the generated energy in the life cycle, and the LCOE is used as a quantization index, and is expanded to the new energy industry in recent years in the calculation of the electricity generation cost of traditional energy projects such as thermal power, hydroelectric power, gas power and the like, and has a certain guiding significance. In the method, LCOE model measurement is considered, and the cost present value and the generating capacity present value are obtained through electrochemical energy storage power station related calculation in the whole life cycle, so that the electricity-measuring cost of the electrochemical energy storage power station is obtained
Revenue n Is the annual benefit of energy storage projects, cost n Is the annual project cost, and r is the discount rate.
NPV is the net present value of the energy storage project, PV n Is the annual net cash flow discount value of the energy storage project.
LCOE n Is the income availability electricity cost of the whole life cycle of the energy storage project, E dn Is the annual discharge capacity of energy storage.
Capex n Is the annual value of initial investment cost, opex n Is annual operation expenditure cost, tax n Is annual tax, T is energy storage capacity, and H is annual utilization hour.
The economic evaluation of the user-side energy storage items is carried out by using economic net present values (Net Present Value, NPV) and internal profitability (IRR, internal Rate Of Return). The economic net present value refers to annual income, expense or net cash flow of the project in the calculation life cycle, algebraic sum of the present value is calculated according to the given discount rate, the algebraic sum is a common index reflecting the project profit capability, the internal income rate simultaneously considers cash inflow and cash outflow in the project life cycle, the comparison with the industry standard investment income rate is convenient, the scheme is preferable when the internal income rate is larger than the standard income rate, and the larger the internal income rate is, the more preferable.
CI is the cash inflow, CO is the cash outflow, i o Is the reference yield.
CI is cash inflow, CO is cash outflow, IRR internal yield, if NPV>0，IRR>i o The description item is feasible in economic evaluation effect.
The analysis load data source is a commercial user in Zhejiang, 2021 month 06-2022 month 05 whole year data of the enterprise are obtained from a power grid company in Zhejiang province, the 7 months power consumption is considered to be large, the peak period is prolonged for two hours, firstly, the daily load rate, the daily load fluctuation rate and the pearson coefficient of 2021 month 07 (shown in figure 3) are subjected to characteristic analysis, and the daily load characteristics and related parameters of the result are shown in the table:
TABLE 4 daily load characteristics and related parameters
| Date of day | Daily load rate | Daily load fluctuation rate | Pearson coefficient |
| 7 months 1 day | 0.838365 | 0.060611 | 0.667491 |
| 7 months and 2 days | 0.831844 | 0.059891 | 0.454675 |
| 7 months 3 days | 0.864300 | 0.049433 | 0.610373 |
| 7 months and 4 days | 0.856673 | 0.075691 | 0.564777 |
| 7 |
0.885886 | 0.064624 | 0.347491 |
| 7 months and 6 days | 0.865827 | 0.062470 | 0.577588 |
| 7 months and 7 days | 0.872471 | 0.063445 | 0.512017 |
| 7 month and 8 day | 0.866840 | 0.064883 | 0.185041 |
| 7 month 9 day | 0.869915 | 0.064305 | 0.581852 |
| 7 months and 10 days | 0.855042 | 0.067935 | 0.692927 |
| 7 months 11 days | 0.837045 | 0.063501 | 0.640877 |
| 7 months and 12 days | 0.838794 | 0.062388 | 0.637895 |
| 7 month 13 day | 0.840282 | 0.059841 | 0.264973 |
| 7 months and 14 days | 0.883831 | 0.052567 | 0.510778 |
| 7 months 15 days | 0.880596 | 0.061355 | 0.616439 |
| 7 months and 16 days | 0.855126 | 0.066589 | 0.509843 |
| 7 month and 17 day | 0.851188 | 0.064668 | 0.346176 |
| 7 months and 18 days | 0.861446 | 0.058915 | 0.451216 |
| 7 month and 19 days | 0.804820 | 0.071729 | 0.496275 |
| 7 months and 20 days | 0.883625 | 0.068158 | 0.690628 |
| 7 month 21 day | 0.858175 | 0.065335 | 0.332664 |
| 7 month 22 day | 0.813217 | 0.084497 | 0.557075 |
| 7 months and 24 days | 0.867575 | 0.073001 | 0.498363 |
| 7 months 25 days | 0.874061 | 0.061285 | 0.600385 |
| 7 month and 26 days | 0.857189 | 0.055796 | 0.687990 |
| 7 months and 27 days | 0.858384 | 0.074100 | 0.587445 |
| 7 month and 28 days | 0.808477 | 0.090315 | 0.495624 |
| 7 month 29 day | 0.810717 | 0.084431 | 0.696221 |
| 7 |
0.825542 | 0.081663 | 0.757039 |
| 7 months 31 days | 0.843895 | 0.075786 | 0.717259 |
The pearson coefficient maximum value shows that the 7-month 30-day load data is closest to the month average load data, and the day curve is selected as a typical day load curve. And data processing is carried out on peak, peak and valley periods according to the data of each month, the total number of days is 365 from the 01 day of 2021 to the 31 day of 2022, and the number of days is trimmed: 01 month 21 day-02 month 10 day (holiday setting and dressing), 03 month 08-03 month 19 days (affected by epidemic factors). And (3) trimming for 33 days to obtain peak data, peak data and valley data of each month, wherein the peak data, the peak data and the valley data are respectively shown in tables 5, 6 and 7, the median, the average and the mode of the peak data and the peak data are used as evaluation indexes to obtain the configured energy storage power of 2.5MW, and the configured energy storage capacity of 26542kWh is obtained by hanging 2 battery compartments under the energy storage converter device (shown in figure 2).
Table 5 daily peak load data results
Table 6 day peak load data results
Table 7 daily off-peak load data results
Annual revenue analysis:
according to the operation days of 310 days and the expected yield of 10%, the results of the indexes including the cost, the discharge capacity, the charge capacity, the annual energy storage yield and the LCOE are respectively obtained as shown in the table 8, and the conclusion can be drawn from the results: under the policy background of labor-saving and time-sharing electricity price of Zhejiang, a proper capacity energy storage system is configured at a load user side, so that better economic benefits can be brought, the annual benefits of the energy storage system are 539.43 ten thousand yuan according to economic analysis, the LCOE electricity cost index can reach 0.43 yuan/kWh, the energy storage capacity can be reasonably obtained by adopting the configuration and storage mode, peak-valley difference arbitrage is realized, the user side benefits are improved, and meanwhile, the load side electricity utilization mode can be regulated.
TABLE 8 annual revenue analysis
| Parameters (parameters) | Numerical value |
| Days of operation | 310 |
| Expected rate of |
10％ |
| Single cabinet battery energy storage cost (Wanyuan) | 232 |
| Energy storage converter device cost (Wanyuan) | 95 |
| Auxiliary equipment cost (Wanyuan) | 221.25 |
| Other costs (Wanyuan) | 204 |
| Replacement cost (Wanyuan) | 582.91 |
| Operation and maintenance cost (Wanyuan) | 26.97 |
| Total daily discharge (kWh) | 22079.65 |
| Day total charge (kWh) | 26634.73 |
| Daily energy storage benefits (Yuan) | 17401.01 |
| Years energy storage benefit (Wanyuan) | 539.43 |
| LCOE (Yuan/kWh) | 0.43 |
Return on investment index:
the return on investment indexes obtained through IRR economical efficiency measurement are shown in a table 9, the total internal return on investment (including tax) of the project is 4.31%, the internal return on principal (including tax) is 6.90%, the investment is feasible according to the IRR economical efficiency measurement method, the return trend is optimistic according to profits and net profits of all years after production, but the investment recovery period is longer, the development stage of the energy storage industry is in the initial stage, the battery cost in the investment cost is larger at present, the method is measured based on 310 days, and if factors such as holidays and epidemic situation are eliminated, the load on the user side operates normally all the year, so that the economic benefit is more objective.
TABLE 9 return on investment index
Sensitivity analysis:
1. effective day sensitivity analysis
The sensitivity analysis is performed according to the change of the effective days, the influence of the effective days on the investment profit is shown in fig. 4, the increase trend of the total investment internal profit rate is slower, the increase trend of the fund internal profit rate is faster, the annual average net profit rate is increased slower along with the increase of the effective days, the measurement and calculation are performed by considering 310 days, the epidemic situation influence and holiday trimming are considered, and the days are normally considered, so that the method has authenticity.
2. Power generation amount change sensitivity analysis
The sensitivity analysis is carried out according to the change of the effective days, the influence of the generated energy on the investment profit is shown in the graph in fig. 5, the change curve of the annual average net profit margin is steeper, the influence of the increase or decrease of the generated energy on the annual average net profit margin is larger, the increase trend of the total investment internal profit margin is slower and the increase trend of the fund internal profit margin is general along with the increase of the effective days, and the influence of the generated energy on the energy storage investment profit margin is larger.
3. Peak-valley price change sensitivity analysis
According to the sensitivity analysis of the change of the peak-valley price difference, as shown in fig. 6, the peak-valley price difference change interval is between-0.1 and +0.1, the time-of-use electricity price policy of Zhejiang province is flexible, the trend of gradually increasing the peak-valley price difference is shown, the influence of the peak Gu Jiacha change on the internal income ratio of the fund is huge, the annual average profit ratio is also influenced to a certain extent, and the reasonable time-of-use electricity price policy can bring better benefit to the energy storage of the user side.
The invention provides a user side energy storage capacity configuration and economical analysis method based on time-of-use electricity price. Based on the user load historical data, the relevance of the load data is represented by a Pearson coefficient method, and based on a time-of-use price policy, the annual load historical data is respectively subjected to data analysis in peak, peak and valley periods to obtain the energy storage capacity configuration. According to the analysis, the energy storage capacity configuration result is used for carrying out economic analysis on the energy storage of a user side, an LCOE model and an IRR model are used for obtaining return on investment profit measurement, and the result shows that the higher the accuracy of the obtained configuration result is, the project investment is feasible and reasonable through the calculation of the return on investment according to the reasonable fine analysis method, the LCOE leveling degree electricity cost is 0.43 yuan/kWh, the IRR index also shows that the project implementation is feasible, and the influence of effective days, power generation amount change and peak-valley price change on various indexes of the return on the energy storage investment profit is different.
The foregoing description of the preferred embodiments of the invention is not intended to be limiting, but rather is intended to cover all modifications, equivalents, or alternatives falling within the spirit and principles of the invention.
Claims (6)
1. An economic analysis method based on user side energy storage capacity configuration is characterized in that: the method comprises the following steps:
(1) Based on the user load history data, a typical daily load curve is obtained through analysis by using a daily load rate, a daily load fluctuation rate index and a pearson correlation coefficient method;
(2) The peak period, peak period and valley period loads of a typical daily load curve are respectively analyzed, and main evaluation indexes comprise average value, median, mode, standard deviation, maximum value and minimum value of each period; then, based on annual load data, each index is respectively calculated for the annual peak period, peak period and valley period;
(3) Constructing an energy storage system model;
(4) Measuring and calculating an LCOE model;
(5) And the energy storage project economy evaluation is carried out, and the economy evaluation is carried out on the energy storage project at the user side by utilizing the economic net present value and the internal yield.
2. The method for economic analysis based on the configuration of the energy storage capacity of the user side according to claim 1, wherein:
daily load rate is the ratio of daily average load to daily maximum load, and the daily load rate calculation reflects the characteristic of the concentration of load distribution in a day, usually expressed in percentage, and is expressed as follows:
lambda is the daily load rate, P i,lm Is the average load on day i, P i,max Is the maximum load on day i;
daily load fluctuation is the ratio of the standard deviation of the load to the average value of the load, usually expressed in percent, and is expressed as follows:
ρ is the daily load fluctuation rate, s is the standard deviation of the load, P i,lm Is the average load on day i;
the standard deviation is used for representing the discrete degree among sample individuals in a group of samples in probability statistics, so that the change situation among the sample individuals is more comprehensively described, the daily load fluctuation rate can reflect the centralized characteristic of load distribution, the scattered characteristic of the load distribution is also reflected, the daily load fluctuation rate describes the fluctuation characteristic of the load, the larger the calculated daily load fluctuation value is, the larger the fluctuation of the load is, the unstable characteristic of the load is shown, the smaller the daily load fluctuation value is, the smaller the fluctuation of the load is shown, and the stability characteristic of the load is shown;
the pearson correlation coefficient, also known as a simple correlation coefficient, characterizes the tightness relationship between two sample distance variables, and is generally represented by r, and is calculated as follows:
where n is the sample size and is the number of samples,
respectively the average value of x and y, wherein r represents the correlation intensity degree between two variables, and the value of r is between-1 and 1, if r>0, which indicates that the two are in positive correlation, i.e. the larger the value of one variable is, the larger the value of the other variable is, if r<0, the two are negative correlation, i.e. the larger the value of one variable, the smaller the value of the other variable.
3. The method for economic analysis based on the configuration of the energy storage capacity of the user side according to claim 1, wherein: the industrial and commercial electricity consumption period has three periods: peak time periods, valley time periods,
in the step (2), each parameter is calculated as the following formula:
average value:
median:
standard deviation:
mode:
L represents the exact lower limit of the group in which the mode is located, U represents the exact upper limit of the group in which the mode is located, f a For frequencies adjacent to the lower limit of the mode group, f b For the frequency adjacent to the upper limit of the array, i is the array distance
Maximum value:
X＝max{x i }
minimum value:
Y＝min{x i }。
4. the method for economic analysis based on the configuration of the energy storage capacity of the user side according to claim 1, wherein:
the energy storage system model in step (3) mainly comprises the investment cost of the energy storage system,
the investment cost of the energy storage system mainly comprises the initial investment cost of the energy storage system, the replacement cost of a battery and the operation and maintenance cost;
the initial investment cost of the energy storage system mainly comprises battery energy storage cost, energy storage converter device cost, auxiliary facility cost and other cost, and the calculation formula is as follows:
C a ＝C n +C p +C f +C q
wherein C is a Is the initial investment cost of the energy storage system, C n Is the energy storage cost of the energy storage battery, C p Is the cost of the energy storage converter device, C f Is the cost of auxiliary facilities, C q Other costs;
the calculation formula of the battery energy storage cost is as follows:
C n ＝C v E e
wherein C is v Is the unit energy price of the battery monomer, E e Is the capacity of the energy storage system
The calculation formula of the cost of the energy storage converter device is as follows:
C p ＝C e P e
wherein C is e Is the unit power price of the energy storage converter device;
C f ＝C m P e
wherein C is m Is the unit energy price of the auxiliary facilities;
C q ＝C n P e
wherein C is n Is the unit energy price of other cost;
when the service life of the battery monomer of the energy storage system is less than the service life of an actual project, the battery replacement cost is required to be replaced, the battery replacement cost formula is as follows,
C g ＝C k D k
D k ＝C d xyz
wherein C is g Is the battery replacement cost, C k Is the cost coefficient of battery replacement, D k Is the initial battery investment cost, C d The price of the battery cell unit is that x is the number of cells, z is the number of liquid cooling battery packs, and w is the number of energy storage cabinets
The operation and maintenance cost is the folding cost of annual energy storage income, and the calculation formula is as follows:
C y ＝βQ n
Q n is annual energy storage operation income, beta is operation and maintenance reduction coefficient
The annual energy storage operation income calculation formula of the user is as follows:
Q n ＝N×Q d
Q n is the annual operation income of energy storage, N is the effective annual operation days, Q d Is daily energy storage income
The daily energy storage income calculation formula is as follows:
T y is the number of days of the y month, Q y,f Is the discharge gain of the y month, Q y,c Is the charging cost of the y month
The calculation formulas of the discharge income and the charge cost are as follows:
Q y,f ＝Q y,j ×C j +Q y,g ×C g
Q y,c ＝Q y,d ×C d
Q y,j is the discharge quantity, Q of peak period y,g Is the discharge capacity of peak time, C j Is peak period electricity price, C g Is peak electricity price, Q y,d Is the low-valley period charge, i.e. the total daily charge, C d Is the electricity price in the valley period
Q y,j ＝a×η×P e
Q y,g ＝b×λ×P e
Q y,g ＝Q y,z -Q y,j
a is the number of hours of peak period, eta is the discharge efficiency, P e Is the rated power of the energy storage system, b is the hours of the valley period, lambda is the charging efficiency, Q y,z Is total discharge amount per day
Q y,d ＝Q y,d1 +Q y,d2
Q y,z ＝Q y,f1 +Q y,f2
Q y,f2 ＝P z -Q y,f1 +Q y,d1 -P z ×0.3
Q y,d1 ＝2P e ×λ
Q y,d2 ＝Q y,f1 +Q y,f2 -Q y,d1
Q y,f1 First discharge electric quantity, Q y,f2 Second discharge electric quantity, P z Installed power, Q y,d1 A first charge amount; q (Q) y,d2 And charging electric quantity for the second time.
5. The method for economic analysis based on the configuration of the energy storage capacity of the user side according to claim 1, wherein:
the measurement and calculation of the LCOE model is the power generation cost obtained by calculating after the standardization of the cost and the generated energy in the project life cycle, namely the ratio of the current value of the cost in the life cycle to the current value of the generated energy in the life cycle, LCOE is used as a quantization index, and the current value of the cost and the current value of the generated energy are obtained through the related calculation of the electrochemical energy storage power station in the whole life cycle, so that the degree electricity cost of the electrochemical energy storage power station is obtained
Revenue n Is the annual benefit of energy storage projects, cost n Is the annual project cost, r is the rate of discount,
NPV is the net present value of the energy storage project, PV n Is the annual net cash flow discount value of the energy storage project,
LCOE n is the income availability electricity cost of the whole life cycle of the energy storage project, E dn Is the annual discharge capacity of energy storage,
Capex n is the annual value of initial investment cost, opex n Is annual operation expenditure cost, tax n Is annual tax, T is energy storage capacity, and H is annual utilization hour.
6. The method for economic analysis based on the configuration of the energy storage capacity of the user side according to claim 1, wherein: the economic net present value refers to the annual income, expense or net cash flow of the project in the calculation life cycle, the algebraic sum of the present value is calculated according to the given discount rate, the economic net present value is a common index for reflecting the project profit capability, the internal income rate simultaneously considers the cash inflow and cash outflow in the project life cycle, the economic net present value is convenient to be compared with the industry standard investment income rate, the scheme is preferable when the internal income rate is larger than the standard income rate, and the internal income rate is larger and better,
CI is the cash inflow, CO is the cash outflow, i o Is the reference yield.
CI is cash inflow, CO is cash outflow, IRR internal yield, if NPV>0，IRR>i o The description item is feasible in economic evaluation effect.
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| CN116611711A (en) * | 2023-07-05 | 2023-08-18 | 深圳海辰储能控制技术有限公司 | Energy storage project analysis system, method, equipment and readable storage medium |
| CN117273309A (en) * | 2023-08-28 | 2023-12-22 | 国家电网有限公司华东分部 | Energy storage system capacity configuration method and device, storage medium and electronic equipment |
| CN118071036A (en) * | 2024-04-19 | 2024-05-24 | 广东采日能源科技有限公司 | Method and device for determining installation capacity of energy storage equipment and electronic equipment |
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| Publication number | Priority date | Publication date | Assignee | Title |
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| CN116611711A (en) * | 2023-07-05 | 2023-08-18 | 深圳海辰储能控制技术有限公司 | Energy storage project analysis system, method, equipment and readable storage medium |
| CN116611711B (en) * | 2023-07-05 | 2023-10-13 | 深圳海辰储能控制技术有限公司 | Energy storage project analysis system, method, equipment and readable storage medium |
| CN117273309A (en) * | 2023-08-28 | 2023-12-22 | 国家电网有限公司华东分部 | Energy storage system capacity configuration method and device, storage medium and electronic equipment |
| CN117273309B (en) * | 2023-08-28 | 2024-04-30 | 国家电网有限公司华东分部 | Energy storage system capacity configuration method and device, storage medium and electronic equipment |
| CN118071036A (en) * | 2024-04-19 | 2024-05-24 | 广东采日能源科技有限公司 | Method and device for determining installation capacity of energy storage equipment and electronic equipment |
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