An Energy Storage System Configuration Method to Stabilize Power Fluctuation in Different Operation Periods Minjian Cao Qingshan Xu
Pingliang Zeng Xiaohui Xu
School of Electrical Engineering Southeast University Nanjing, China
[email protected]
China Electric Power Research Institute Beijing, China
Abstract—This paper has developed a practical and economical method for energy storage system (ESS) configuration to smooth the power fluctuation in distribution network feeders. The method firstly uses historical load data and the output of renewable power plant to analyse the power fluctuation in the feeder over the required time period. Then the configuration of ESS system, in terms of rated power and capacity, is calculated and determined to minimise and reduce power fluctuation in the feeder over different time-window and operating period. It is found that the ESS configuration is closely related to the operational period for which the ESS is required to smooth the feed power fluctuation, such as over typical a day or year. Furthermore, it is shown that the requirement of ESS is larger when it is required to smooth the feed power fluctuation over an operation period of one year than a day. Index Terms-- Distribution network, Energy storage system (ESS), Operational period, Rated power and capacity, Smoothing fluctuation
I.
INTRODUCTION
Over the past decades, renewable energy, such as photovoltaic and wind power generation, has seen rapid development and is becoming more and more important all around the world. However, solar and wind energy generation, due to their inherent variability and intermittency, cannot produce continuous power, although solar and wind power can be complementary to each other[1]-[3]. Lasseter et al. [4]illustrate the problems caused by individual distributed generation and measures that could be taken, in the form of a "microgrid" with integrated consideration of generation and load, to realize the potential of distributed generation. However, this kind of hybrid system has its disadvantages. The power over the feeders between the micro-grid and the grid fluctuates, depending on weather and load conditions. Without any doubt, sharp fluctuations can cause many problems, such as harmonic current, transformer overload[5][7]. Consequently, a number of researches have explored the
National Natural Science Foundation of China (Program No.51377021) Natural Science Foundation of Jiangsu Province (No. BK2012753)
978-1-4799-6415-4/14/$31.00 ©2014 IEEE
Xiaodong Yuan Jiangsu Province Power Company Research Institute Nanjing, China
relationship between the energy storage system (ESS) and individual distributed generators. Generally, researches on the configuration of ESS are usually carried out from two perspectives: economic performance and stabilizing fluctuation. In the former, the objective is to minimise the cost of investment throughout the life cycle of ESS taking into account of operational constraints, such as losses of power supply probability etc. Sreeraj et al.[8] showed that by integrating renewable power sources with appropriate energy storage, isolated electrical power generating units can be used as an economically viable alternative to electrify remote villages where grid extension is not feasible or economical. The findings are supported by Dominik et al.[9] that the storage energy capacity depends mainly on the appropriate ratio of wind and solar power generation. SX Chen et al. [10]investigated a method based on the cost-benefit analysis for optimal sizing of energy storage system (ESS) in a microgrid (MG) by formulating the problem as a mixed linear integer problem (MLIP). Because of unfavourable influence of feeder power fluctuations and cost of the ESS, the other studies have considered power smoothing through adjusting configuration of ESS, which can be extremely helpful in promoting the operational efficiency and economic dispatch. In[11], a control method was proposed by Manoj et al. for determining the economical battery capacity to reduce the frequency deviations of a stand-alone PV-based power system. By integrating variable speed wind turbine, solar photovoltaic and fuel cell generation systems, Ahmed et al.[12] presented a hybrid energy system to supply continuous power to residential power consumption as stand-alone loads. C S Wang et al.[13]-[14] showed a capacity determination method of an energy storage system (ESS) for smoothing the fluctuation of photovoltaic generation and tie-line power flow in microgrid. In this paper, a method based on Discrete Fourier Transform has been developed to determine the compensation
frequency and optimal configuration of ESS over different operational timeframes. Furthermore, discussions relating to the configuration of ESS are carried out for the situations where operational period is selected as one year. The structure of this paper is organized as follows. Section II gives details about the mode and method, Section III describes the data used for analysis and simulation results of the proposed method in different operational periods. Discussions of results for certain operational periods can be found in Section IV and Section V draws the conclusion. II.
⎧⎪ S g = F (Δ (t )) = [ S g (1),..., S g (n),..., S g ( N s )]T , ⎨ T ⎪⎩ f g = [ f g (1),..., f g (n),..., f g ( N s )]
(2)
The time series Δ(t) describes the feeder power sample, and Sg, fg represent amplitude and frequency vector of the Fourier transformation, respectively. In frequency domain, Sg is composed of two parts: the real part Rg(n) and imaginary part Ig(n), of the nth frequency. Using DFT, fg(n) is calculated below:
MODES AND METHODOLOGY
In this section, an ESS configuration model is established to determine the optimal ESS energy capacity for smoothing the feeder power fluctuation. In this model, based on historical data of natural resources, such as wind speed and solar radiation, the output of renewable power plants can be calculated, together with .the feeder power flow with micro grid. ESS is used to smooth power fluctuation of feeders over the required time-window. With the help of power data in the whole time-domain, spectral analysis is carried on using Fast Fouier Transform on the sample data to identify the high-frequency component. The low-frequency component is used as the basis for ESS configuration in that it is the frequency to be smoothed by the ESS. The main problem in this method is to identify the scope of the frequency which can be compensated by the ESS. And the feeder power flow can satisfy the requirement for smoothness over the required time-window. Specifically, the capacity of the ESS can be calculated by following steps.
f g ( n) =
f s (n − 1) n − 1 = , Ns Ts N s
(3)
where fs is the sampling frequency (Hz) , the reception of which represents sampling period (s) shown as Ts. As a result, the complex vector Sg can be represented on horizontal axis, which is labelled as a fraction of sampling rate between DC and one-half of the sampling rate. Step 3: Search the minimum compensation frequency Suppose fps1 is the range of the determined compensation frequency and fps2 represents the range in Nyquist frequency, we can acquire the smoothed target output in feeders by calculating Inverse Discrete Fourier Transform (IDFT) of Sg from (4) and (5).
⎧⎪0 + j 0, Sc ( n) = ⎨ ⎪⎩ S g (n),
f n ∈ f ps1 U f ps2
(4)
f n ∉ f ps1 U f ps2
Step 1: Acquire the sample data of feeder power flows Primarily, select a specified region and obtain the data of natural resource for that region by using TRNSYS (Version 16) software. The renewable energy power output can be approximately calculated, taking into account of installed capacity. The load demand of the selected area can be obtained by referencing to load demand data in HOMER which is developed by NREL. Combined with historical data of load demand in specified area, the power exchange between micro grid and the power grid can be calculated. For different period, the sampling intervals can range from one minute to one day. The power mismatch is calculated as follows,
Δ (t ) = L (t ) − S (t ) − W (t )
(1)
If Δ(t)>0, it means power flowing from grid to the micro grid . Conversely, the power is transmitted from micro grid to the grid. As the micro grid contains intermittent renewable generation and variable load, ESS could be used to smooth the feed power flows. Step2: Calculate Discrete Fourier Transform (DFT) of the feeder power samples. This is calculated as follows:
T
P0 = F −1 ( S c ) = ⎡⎣ P0 (1) ,..., P0 ( n ) ,..., P0 ( N s ) ⎤⎦ .
(5)
From equation (4), for the range between compensation frequency and Nyquist frequency, amplitudes of the components are set to zero, while amplitudes outside this range remain the same. In (5), F-1 indicates IDFT. To assess the compensation effect of ESS, maximum fluctuation rate (MFR) within time period Te should be calculated. The index can be used to illustrate the relative value of fluctuation in the specific time-window. Therefore, choosing appropriate reference value seems more significant. Supposing the reference value, PN, means the average of all sample amplitudes, the MFR can be calculated as below:
FTe =
PTmax − PTmin e e PN
×100%,
(6)
max min where PTe , PTe stand for the maximum and minimum power within Te, respectively. The objective of the ESS installation concentrates on declining FTe to a certain extent
to satisfy the requirement of power transmission in feeders. Step 3: Obtain the ESS configuration
(7)
When PESS0(n) is positive, it means that the ESS should discharge and vice versa, it should charge Given the storage efficiency of ESS, the factual output power can be calculated as (8). In (9), a time series storage capacity of ESS can be performed by the integration of the ESS output power.
⎧ PESS0 ( n ) , PESS0 ( n ) ≥ 0 ⎪ ηdc PESS ( n ) = ⎨ ⎪P ⎩ ESS0 ( n )ηc , PESS0 ( n ) < 0
E (m) =
m
⎛
T
⎞
s ∑ ⎜⎝ PESS ( m ) 3600 ⎟, ⎠
m = 0,1, …, N s .
,
200
40
180 20
160
0
5
10
15
20
25
30
140
(9)
The determination of ESS configuration can be divided into 3 operational periods: “short”, “medium” and “long” term, where “short” term means the sampling data is acquired within one day; “medium” one month and “long” one year. The energy supplied and absorbed by the ESS during different periods should be equal to or close to zero. A time series of 1min power output in feeders is obtained on April 12th; a time series of 1-hour in April is selected, and the sampling points in a whole year are composed of the average power in feeders during a day. In Mode, due to MFR limitations, the ESS size depends on time-window of the compensation. Fig. 1 displays smoothed output of the power in feeders with a MFR of 20% in different time-window, ranging from 20 min to 240 min. It is obvious that the longer the time-window to be smoothed, the gentler the power profile in feeders will be. When the time-window is large enough, the required capacity of ESS will theoretically result in horizontal output power profile, which means that the lowest compensation power should be selected as zero.
(10) 500
where Cup, Clow are the upper and lower limit of the ESS capacity respectively, and have values ranging from 0 to 1. SIMULATION AND RESULTS
Considering the large scale requirement of the resource data, we use TRNSYS (Version 16) to export typical meteorological year (TMY) with different time scale of wind speed and solar radiation data for Lhasa, China. The data is imported into the simulation software, HOMER, which calculates the output power of natural resource, taking into account of latitude and specific type of the photovoltaic and wind tunnel. Installed capacity of PV plant and wind farm is 200kW and 100kW, respectively. The average load power is 233.79kW in the typical year. The demand data on a minute basis can be acquired by interpolation and that daily data can be considered as the average value of 24 points. Fig.1 illustrates the change of monthly natural resource generation and demand. The difference between them exhibits the power flow across the feeders.
flactuating output 20 min smoothed output 60(120) min smoothed output 240 min smoothed output
400
Power (kW)
III.
220
(8)
Given the capacity constraints and the storage level E(m), the capacity configuration of ESS is obtained as
Cup − Clow
240 60
Time (day)
In addition, another essential part is to maintain the energy balance during the operating cycle (the sample period) when the charge and discharge energy of the ESS should be approximately zero. A revision has to be made to the smoothed P0 to recompense storage losses. The ESS compensation frequency can be identified to minimize target output power P0.
max E ( m ) − min E ( m )
260 80
Fig. 1 Natural resource generation power and load in a month
0
EESS =
280
Load demand (kW)
PESS0 ( n ) = P0 ( n ) − Pg ( n ) .
Natural resource power load demand
100
Natural resource power (kW)
On account of the smoothed output P0 derivation in step 3, the ESS output can be easily solved as:
300
200
100
0
0
5
10
15
20
25
Time(h)
Fig.2 Smoothed output of the power in feeders with a MFR of 20% in different time-window TABLE I REPRENTATIVE SELECTING RESULTS OF THE ESS IN FOUR TIME-WINDOW IN ONE DAY 20min
60min
120min
240min
P_ESS (kW)
65.46
156.97
156.97
215.72
E_ESS (kWh)
39.29
791.59
791.60
1637.3
150
150
100
50
0 0.000
150 Power (kW)
200
Power (kW)
Power (kW)
200
200
100
100
50
50
0
.002
.004
.006
.008
0 0.0
0
2.0e-5 4.0e-5 6.0e-5 8.0e-5 1.0e-4 1.2e-4 1.4e-4
f (Hz)
1e-6
2e-6
(a)
3e-6
4e-6
5e-6
6e-6
f (Hz)
f (Hz)
(b)
(c)
Fig.3 (a) Spectrum analysis of sampling data in the typical day. (b)Spectrum analysis of sampling data in the typical month (c) Spectrum analysis of sampling data in the typical year
Table I describes the results of the ESS configuration in four different kinds of time-windows, when the operation period is selected as one day. It can be seen from the table above that when time-window is 240 min, the required capacity is 1637.3 kWh, which is around 42.75 times larger than that when time-window is chosen as 20 min. Similarly, for the largest time-window, the ESS power is around 3.3 times larger than that of 20 min time-window. The rated ESS power and capacity required increases with the size of timewindow. From Fig.2, it can be concluded that when the timewindow is large enough, the rated ESS power and capacity appears to be large. It is also observed that increase in the size of time windows has no significant impact on smoothing output power. TABLE II REPRENTATIVE SELECTING RESULTS OF THE ESS IN THREE TIME-WINDOW IN ONE MONTH P_ESS (kW) E_ESS (kWh)
12h
24h
48h
206.66 3001.9
214.22 3685.4
234.26 4446.9
TABLE III REPRENTATIVE SELECTING RESULTS OF THE ESS IN THREE TIME-WINDOW IN ONE YEAR P_ESS (kW) E_ESS(kWh)
2 days
5 days
7 days
30 days
89.34 281.95
99.58 478.78
103.90 583.29
110.52 599.09
Results of the ESS configuration for different timewindow in a typical month and year are presented in Table II and Table III respectively. When the operation period is one month, the corresponding sampling period is 1 hour; the sampling interval of the other situation is 1day. These tables indicate that when the smoothing target is 20% in any timewindow, the required ESS rated power and capacity for smoothing the feeder power variation over one year is larger than that over one day but far less than that over one month. Fig.3(a) implies that when frequency is relatively high, the power is approximately zero. When it is low, such as below 0.001 Hz, the amplitude of the power begins to rise and reaches the highest value, when the frequency is 0. On the
other hand, Fig.3(b) shows great difference in spectrum analysis for typical month. The major difference in frequency spectrum analysis is in a few relatively high peaks in certain frequencies. As a result, in order to meet the smoothing requirement, the compensation frequency must be below the frequency whose amplitude is the second highest one in the whole frequency domain. According to the reciprocity of the frequency values, the period can be solved as 12.46(h), which implies that there exists a sine curve whose amplitude is around 50 kW and period is 12.46(h). Therefore, sufficiently large ESS rated power and capacity is required to compensate the power in this frequency. Like the frequency spectrum analysis of sampling data in typical day, there is also no obvious peak in certain frequency. As shown in Fig.3(c), except when the frequency approaches to 0, which implies requiring very large ESS capacity. Consequently, the calculated configuration differs from the operation period, for example, one-day period or one-year period. As is shown above, the configuration calculated by historical data in typical year is larger than that in typical day. IV.
DISCUSSION
In this section, two different kinds of situation are discussed: when the ESS configuration is required to smooth the feeder power fluctuation over a typical year, and ESS configurations considering increase in forecast error of 10% with normal distribution. Given the proposed configuration selected to smooth the fluctuation is relatively large, another rational ESS configuration is calculated by appropriately increasing the fluctuation rate in the same time-window to decrease the rated power and capacity of ESS. A. Adding 10% forecast error to the data of a typical year TABLE IV
REPRENTATIVE SELECTING RESULTS OF THE ESS IN FOUR TIME-WINDOW IN ONE YEAR
Original configuration Newconfiguration (Adding 10% forecast errors)
2days
5days
1week
1month
P(kW) E(kWh)
89.34 281.95
99.58 478.78
103.90 583.29
110.52 599.09
P(kW)
96.42
106.2
108.48
112.26
E(kWh)
287.1
473.26
501.36
651.47
The table above compares the rated ESS power and capacity with and without forecast error of different windows being increased by 10%, for the case ranging from 2 days to1 month. When time-window is 1 month, both the rated ESS power and capacity are higher than that in other timewindows. Since energy to be stored can be seen as the integral of power, value of rated power is smaller than the value of the capacity in table IV. Compared with their rated power in each time-window, the required rated power of the ESS in the new configuration situation is a little larger than that in the original one. While the capacity of the ESS in both situations is approximately equal. As a result, it can be concluded that raising forecast error, which obeys the law of normal distribution, by 10% makes little difference to the configuration of ESS. In other words, the fluctuation in the time-window will be around 20% in new situation, when specific district is equipped with the original configuration. B. Increasing 5% fluctuation rate in the time-window TABLE V REPRENTATIVE SELECTING RESULTS OF THE ESS IN FOUR TIME-WINDOW IN ONE YEAR
Original configuration New configuration (25% fluctuation rate in the time-window)
2days
5days
1week
1month
P(kW) E(kWh) P(kW)
89.34 281.95 77.66
99.58 478.78 100.59
103.90 583.29 99.58
110.52 599.09 102.65
E(kWh)
223.52
461.05
478.78
598.15
Table V contrasts the configuration of ESS in original and new situation when 25% fluctuation rate can be tolerated in the time-window, ranging from 2 days to1 month. Like the value shown in table IV, the value of rated power seems smaller than that of the capacity in table V. As can be seen from the table, almost both the value of rated power and the capacity of ESS in new configuration are smaller than that in original one. Thus, depending on the requirement of operating mode in feeders, appropriately increasing permissible fluctuation rate in feeders will make a contribution to decreasing the configuration of ESS. The reason for declining the configuration of ESS is that it seems more economical under current circumstances when the facility of ESS maintains relatively high in value. V.
CONCLUSION
This paper developed a ESS configuration model to smooth feeder power flows based on historical data in different operational period. Several conclusions can be derived from this research: • •
•
The ESS configuration is closely related to the length of the smoothing time-window. The operational period can be chosen as a typical day or year. As relatively high power of amplitude exists in certain higher frequency, a typical month is not a good choice as operational period. ESS configuration is larger, when the operation mode is chosen as one year. In this situation, it can be seen that the fluctuation will be smoothed better in the same timewindow of a typical day by selecting larger
•
configuration. Adding forecast error by 10% makes little difference to the ESS configuration. Increasing permissible fluctuation rate in feeders in a specific time-window will make a contribution to reduce the ESS configuration. REFERENCES
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8 9
10 11
12
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