손준호
(Joon-Ho Son)
1iD
노대석
(Dae-seok Rho)
2iD
김미영
(Mi-Young Kim)
†iD
-
(ESS Sales/Engineering, LG Electronics, Seoul, Korea.)
-
(Dept. of Electrical, Electronics and Communication Engineering, Korea University of
Technology and Education, Korea.)
Copyright © The Korean Institute of Electrical Engineers(KIEE)
Key words
Energy Storage System, Photovoltaic Power Plant, Renewable Energy Generation Forecasting System, Operation Strategy
1. 서 론
According to the 9th Basic Plan for long-term electricity supply and demand of the
Korean government, the installed capacity of renewable energy (RE) is expected to
be 84.4 [GW] by 2034, which is expected to increase by 65.1 [GW] compared to 2019
(1). When large-scale RE is expanded and supplied to the existing power system, it is
necessary to improve the acceptability of RE and to respond to intermittent output
charac- teristics such as Photovoltaic Power (PV) and wind power (WP). Accordingly,
in January 2021, the Korea Power Exchange (KPX) is conducting a pilot project to introduce
RE generation forecasting system (REGFS) to efficiently operate the power system,
and the cost for additional start/stop or output increase/ decrease of generators
due to RE output change will be reduced (2). In REGFS, small power brokerage business (recruited 20 [MW] or more of PV/WP of
1MW or less) and PV/WP plant (20 [MW] or more) forecast and submit the amount of RE
generation a day in advance. If the forecast error rate (FER) on the same day is below
6 [%], and a maximum of 4 won per 1 [kWh] will be paid (3). Various studies have been conducted on REGFS. AI-based power generation and PV prediction
model based on specific information such as the temperature, charac- teristics, and
local inclination angle of PV module was developed to participate in REGFS (4-5). In addition, in order to reduce the PV output FER, PV output prediction method based
on ensemble learning and weather information is presented (6-7).
Meanwhile, ESS (Energy Storage System) is a key facility to solve the intermittent
output characteristics of WP/PV power plants. However, the operation purpose of the
ESS installed in PV is revenue through the Renewable Energy Certificate (REC) weight
given to the amount of discharging. At this time, the ESS shows a simple charging/discharging
pattern and the charging time is from 10:00 to 16:00 and the discharging time is from
16:00 to 10:00 of the next day.
Based on the above background, this paper proposes a strategy for securing stable
settlement through REGFS participation of PV-linked ESS operators. For example, if
the actual PV output on the operating day has a large error compared to the PV output
forecasted the previous day, the KPX takes measures to stabilize power supply and
demand. In other words, based on the generation planed the previous day, the LNG generator
with high fuel cost is ramp up/down for this variation.
Therefore, the new ESS operation strategy is needed to adjust the charging or charging
offset so that the PV FER does not exceed ±6 [%] during the ESS charging times (from
10:00 to 16:00). In this paper, it is assumed that the PV prediction accuracy of the
previous day is high and the PV FER is maintained within ±10%. Here, ±10% means the
minimum value of the monthly average PV FER for the REGFS registration test. Therefore,
the FER between ±6% and ±10% can be controlled by adjusting the ESS charging and charging
offset. In general, the power management system (PMS) of the ESS can control the output
power per second of the Power Conditioning System (PCS) on the day of operation. Therefore,
the sum of charging and discharging every 15 minutes can be equal to the expected
PV output.
As a result of the simulation under the proposed method, it was confirmed that the
PV FER was managed within ±6% and an additional settlement and the existing benefits
(SMP(System Marginal Price), REC weight) were secured.
2. General ESS Operational Constraints
2.1 Supply and Demand Conditions
The supply and demand conditions are the same as in Eq. 1. The sum of the PV power output charged and the flowing into the power system during
the charging time is equal to the sum of the ESS discharging power and the PV power
output flowing into the power system during the discharging time. During ESS discharge,
the ESS discharging power supplied to the PV is less than 1%, and it is regarded as
0kW (8).
where $P_{sup}(h)$ is PV power output per $h$ flowing into the power system during
the charging/discharging time, $P_{pv}(h)$ is PV power output per $h$ [kW], $P_{cha}(h)$
is ESS charging power per $h$ [kW], $P_{disch}(h)$ is ESS discharging power per $h$
[kW], $h$ is the time interval of 15 minutes per day
2.2 SOC Operation Conditions
The SOC (state of charge) of the ESS is the same as Eq. 2 that is expressed as the difference between the charging power and the discharging
power which the charging/discharging effi- ciency is applied. SOC(1) of the first
15-minute interval ($h$=1) and SOC($n_{h}$) of the last 15-minute interval are assumed
to be the same (9).
where $SOC(h)$ is SOC per $h$ [kWh], $SOC(1)$ is the SOC of the first time interval
of the previous day, $\eta$ is ESS charging/discharging efficiency (one-way), $n_{h}$
is total number of 15-minute intervals per day (96=24 hours*4)
2.3 Operating Capacity Limitations
The generation plan of the PV power plant with the ESS is limited by Eq. 3. In particular, the generation plan during the ESS discharging time cannot exceed
70 [%] of PV installed capacity. In addition, the ESS charging power in Eq. 4 should be smaller than the difference between PV power output forecasted the previous
day and the charging offset to prevent reverse power during the ESS charging time.
The discharging power of the ESS in Eq. 5 is determined within the maximum output range of the PCS, and the SOC is determined
within the storage capacity range as in Eq. 6 (10).
where, $P_{cap}$ is PV installed capacity [kW], $h_{DS}$ and $h_{DE}$ are 15 minute
time intervals of ESS discharging start and end, $P_{offset}$ is ESS charging offset,
$\mu(h)$ is a binary variable representing the ESS operation mode (charging = 1, or
discharging = 0), $h_{CS}$ and $h_{CE}$ are 15-minute time intervals of ESS charging
start and end, $P_{pcs}$ is maximum output of PCS [kW], $SOC_{\min}$ and $SOC_{\max}$
are minimum and maximum storage capacity of ESS [kWh]
3. ESS Optimal Operation Method for RE Gener- ation Forecasting System
3.1 Objective Function
As shown in Eq. 7, the objective function aims at maximizing the benefits of daily REC weight and REGFS
participation for PV-linked ESS operators. In this paper, it is assumed that the actual
ESS output [kW] is determined every 1 second, but it is determined in 15-minute units.
where $C(h)$ is the benefit of ESS charging/discharging for REC weight per $h$ [won],
$B_{REF}(h)$ is benefits of participating in REGFS per $h$ [won]
3.2 Benefit for REC Weight
Eq. 8 shows the benefits of REC weight for ESS charging and discharging power. That is,
it is calculated by multiplying the sum of PV power output by the settlement unit
price (SMP, the REC weighted unit price for PV and ESS): the PV power charged in the
ESS during the charging time (10:00~16:00), the discharging power flowing into the
power system during the discharging time (17:00~24:00), and the power flowing into
the power system during the charging/discharging time(4).
where $U_{SMPREC}(h)$ is the benefit of REC weight for ESS charging/discharging power
per $h$ [won]
3.3 Benefits of Participating in the RE Generation Forecasting System
On the other hand, the benefits generated when participating in the REGFS are expressed
as the product of the settlement unit price and the actual PV power output as shown
in Eq. 9. In particular, although the settlement unit price is applied according to the FER,
the settlement unit price (4 [won/kWh]) applied when the FER is ±6% or less is considered
as shown in Eq. 10. The FER of Eq. 11 is calculated as the difference between the PV power output forecasted the previous
day and the current day's measured PV power output based on the installed PV capacity
(3).
where $U_{REF}$ is settlement unit price of REGFS [KRW/kWh], $FR_{REF}(h)$ is PV FER
per $h$ [%], $P_{fo r}$ is the PV power output forecasted the previous day [kW]
3.4 Constraints on RE Generation Forecasting System
When the FER of RE generation exceeds ±6%, the ESS operation strategy can be divided
into two types as follows.
First, if PV power output measured on the day is less than PV power output forecasted
the previous day, the measured PV power output with FER of -6% or more in Eq. 11 is reduced by adjusting the charging offset, and the FER kept below 6%. For example,
the PV power output reflecting the FER of -6% is calculated based on the PV power
output forecasted the previous day as Eq. 12. And when PV power output measured on the day deviates from the result of Eq. 12, the PV power output calculated by Eq. 13 is reflected in the charging offset of Eq. 14. This has the effect of increasing the PV power output flowing into the power system
by reducing the PV power output charged in the ESS. In conclusion, the actual PV power
output does not exceed the FER of –6%.
where $P_{fo r}^{(-6\%)}(h)$ is PV power output forecasted the previous day with a
decrease corresponding to –6% FER per $h$ [kW], $FR_{REF}^{(-6\%)}$ is the FER equivalent
to -6% based on PV power output forecasted the previous day, $P_{dev}^{(-6\%)}(h)$
is the difference between the previous day's forecasted PV output with a decrease
corresponding to a -6% FER and the actual measured PV output per $h$ [kW]
Second, if the PV power output measured on the day exceeds the PV power output planned
the previous day, the PV power output is calculated based on the PV power output planned
the previous day as shown in Eq. 15 when the FER is more than +6%. If the PV power output measured on the day deviates
from the FER +6%, the increase in the measured PV power output is calculated by Eq. 16. When the constraint of Eq. 17 is reflected, the ESS is charged for the corresponding PV increment so that the FER
does not exceed +6%.
where $P_{fo r}^{(+6\%)}(h)$ is the PV power output forecasted the previous day with
a decrease corresponding to + 6% FER per $h$ [kW], $FR_{REF}^{(+6\%)}$is the FER equivalent
to + 6% based on PV power output forecasted the previous day, $P_{dev}^{(+6\%)}(h)$
is the difference between PV power output forecasted the previous day with a decrease
corresponding to a + 6% FER and the actual measured PV output per $h$[kW]
Table 1. Simulation conditions
|
Items
|
Parameters
|
|
PV capacity [kW]
|
6,000
|
|
PCS capacity [kW]
|
4,000
|
|
Battery capacity [kWh]
|
12,000
|
|
$SOC_{\min}$, $SOC_{\max}$ [kWh]
|
1,200, 12,000
|
|
$\eta$ [%]
|
90
|
|
$P_{offset}$ [kW]
|
400
|
|
$P_{pcs}$ [kW]
|
4,000
|
|
$P_{ca p}$ [kW]
|
4,200
|
|
REC weight
|
PV
|
1
|
|
ESS
|
4
|
|
Bidding price
|
REC [won/REC]
|
50,000
|
|
SMP [won/MWh]
|
90,000
|
4. Simulation Results and Analysis
The economic feasibility of the proposed ESS charging scheduling was analyzed through
comparative analysis of the daily total benefits for Case 1 (existing operation) and
Case 2 (proposed operation).
4.1 Simulation Conditions
In order to prove the effectiveness of the proposed ESS charging scheduling, the PV
business owner installed PV 6,000 [kW], PCS 4,000 [kW], and Battery 12,000 [kWh],
and the depth of discharge (DOD) was 90% and the overall efficiency of the ESS system
was 90% as shown in Table 1. The charging offset and PCS maximum output are 400kW and 4,000 [kW], respectively.
During ESS discharging, the sum of PV power output and ESS discharging power was limited
not to exceed 4,200 [kW], which is 70% of the installed PV capacity.
The REC weights of PV and ESS were selected as 1 and 4, respectively. The charging
start and end times were 10:00 and 16:00, and the discharging start and end times
were 16:00, and 24:00, respectively.
Meanwhile, to analyze the economic feasibility of the proposed ESS operation strategy,
it is assumed that the PV operator participated in the “SMP+1REC” contract market.
At this time, 1REC is 40,000 [won], and the SMP unit price per hour is 90,000 [won].
Fig. 1. PV power outputs for simulation (under the FER (±6%))
Fig. 2. PV power outputs for simulation (over the FER (±6%))
Fig. 1 and Fig. 2 show PV power output forecasted the previous day and PV power output the measured
on the day for the simulation. Fig. 1 shows that FER is kept within ±6% because PV power output forecasted the previous
day (from 10:00 to 16:00) is similar to the PV power output the measured on the day.
On the other hand, Fig. 2 shows that the FER is out of ±6%. That is, it is assumed that the output in which
the FER (±6%) is reflected based on the PV power output predicted the previous day
by Eq. 12 and Eq. 13.
In addition, the PV power output measured on day in Fig. 2 has three characteristics. As a result of comparing the PV power output measured
on the day and the PV power output forecasted the previous day, the FER was -10% or
less around 10:00~12:00, 3% around 12:00~14:00 and 10% around 14:00~16:00.
4.2 Conventional ESS Charging Scheduling
Fig. 3 shows the simulation results based on Fig. 1. The profit of the PV-linked ESS operator is maximized through the charging and discharging
of the ESS. The output obtained by subtracting the charging offset from the PV output
is stored in the ESS and the ESS is discharged between 10:00 and 16:00. The power
output discharged between 16:00 and 18:30 was limited by Equation (3).
Fig. 3. Conventional ESS charging scheduling ((a) PV power output, (b) Charging power
(c) Discharging power (d) SOC)
4.3 ESS Charging Scheduling for Case 1
Fig. 4 shows ESS charging scheduling when the PV-linked ESS operator participates in the
REGFS but the proposed method is not considered. That is, as shown in Fig. 4 (a) and Fig. 4 (b), ESS starts charging at 10:00 and all PV power output are charged after subtracting
400 [kW] which is the charging offset. In Fig. 4 (c), the ESS starts discharging from 17:00, and it is discharged within the range not
exceeding 4,200 [kW] which is 70% of the installed PV capacity. Also, ESS charging
stopped when SOC reached 90% around 14:30 as shown in Fig. 4 (d). Moreover, the ESS was discharged to 4,000 [kW] which is the maximum output of the
PCS, the measured PV power output was 0 [kW] during 18:30~20:00 and the discharging
was stopped at 20:00 when the SOC was 3%. As shown in Fig. 4 (a), PV power output the measured on the day compared to PV power output forecasted the
previous day has a FER of -10% (600 [kW] reduction) from 10:00 to 12:00 and a FER
of +10% (600 [kW] increase) from 14:30 to 16:00, and unsettled payment occurred during
that time period.
Fig. 4. ESS charging scheduling for Case 1 ((a) PV power output, (b) Charging power
(c) Discharging power (d) SOC)
Fig. 5. ESS charging scheduling for Case 2 ((a) PV power output, (b) Charging power
(c) Discharging power (d) SOC)
4.4 ESS Charging Scheduling for Case 2
Fig. 5 shows the simulation results to which the proposed ESS charging scheduling is applied.
The ESS charging scheduling is the same as the simulation result in 4.3 above, however
when the PV power output measured on the day has a FER of -10% (600 [kW] reduction)
compared to the PV power output forecasted the previous day from 10:00 to 12:00, and
the decrease (240 [kW]) calculated by Eq. 13 is reflected in the charging offset. That is, the charging offset was reduced from
400 [kW] to 160 [kW] by Eq. 14. PV power output flowing into the power system was increased (240 [kW]), and the
FER was maintained within the range of -6% as shown in Fig. 5(a). When the PV power output measured on the day has a FER of +10% compared to the PV
power output forecasted the previous day from 14:30 to 16:00, the increase (240 [kW])
of PV power output was calculated by Equation (16). As shown in Fig. 5(b), the calculated PV increment was charged by Eq. 17 and the FER was maintained in the range of +6%. In conclusion, if it was confirmed
that when the PV power output on the day exceeds the ±6% FER, it is possible to stably
manage the FER through the charging offset adjustment or the proposed ESS charging
scheduling.
Table 2. Economic evaluation results
|
Simulation Categories
|
Simulation Cases
|
|
1
|
2
|
|
SMP power generation revenue [KRW]
|
7,154,520
|
7,154,520
|
|
REC power generation revenue
[KRW]
|
PV REC weighted revenue
|
765,000
|
765,000
|
|
ESS REC weighted revenue
|
1,660,000
|
1,660,000
|
|
REGFS revenue [KRW]
|
14,400
|
61,200
|
|
Total [KRW]
|
9,593,920
|
9,640,720
|
4.5 Economic Evaluation Results
In order to prove the economic feasibility of the proposed ESS charging scheduling,
the daily benefits of the PV-linked ESS were analyzed in an operator's point of view.
Daily benefits were analyzed for two cases when the PV-linked ESS operator operated
the existing REC weight and REGFS.
⦁ Case 1 (the existing operating method): ESS charging scheduling is determined only
by the REC weight.
⦁ Case 2 (the proposed operating method): when the PV power output exceeds the FER
of ±6% compared to the PV power output forecasted previous day, the REGFS settlement
amount is secured through the charging offset adjustment and ESS charging.
Table 2 shows the economic evaluation results for the above cases. In case 1, the PV generation
amount is 15.3 [MWh] and the ESS discharging amount is 8.3 [MWh]. At this time, the
SMP revenue is 7,154,520 [won], the PV REC weighted profit is 765,000 [won] and the
ESS REC weighted profit is 1,660,000 [won]. In addition, the settlement amount secured
through the REGFS is 14,400 [won]. However, unsettled payments were made for the PV
power outputs of 6.6 [MWh] in the 10:00 to 12:00 and 5.1 [MWh] in the 14:00 to 16:00,
which exceeded the FER of ±6%.
On the other hand, in Case 2, the charging offset was adjusted from 10:00 to 12:00
and the ESS was charged from 14:00 to 16:00 by applying the proposed ESS charging
scheduling. Therefore, the FER of the PV power output on the operating day was maintained
within ±6% and all settlements have been made. The total settlement obtained through
the REGFS was 61,200 [won], which is 46,800 [won] more than in Case 1.
5. Conclusion
The purpose in this paper is to maintain the FER within ±6% when the REGFS is introduced
to PV power plant that is currently operated for the purpose of REC weighting. Therefore,
the efficient and stable operation is possible from the system side and it is possible
to secure stable settlement from the ESS operator side. The main research results
are summarized as follows.
(1) By applying the proposed algorithm to 08:00~10:00, which is the charging period
of the PV-linked ESS, the ESS can be used to manage the FER when the PV power output
on the operating day exceeds the PV power output forecasted the previous day.
(2) When the PV power output deviates from the FER of -6%, the FER was maintained
within -6% by compensating for the decrease in the PV power output through adjustment
of the charging offset.
(3) When the PV power output deviates from the FER of 6%, the FER was kept within
6% by charging the increment of the PV power output.
(4) When the proposed algorithm was applied to the REGFS, stable benefits were secured
by stably maintaining the FER within ±6%.
(5) In terms of power system, the intermittent PV output fluctuations are causing
additional operating costs due to real-time power supply. Therefore, the efficient
and stable operation of power system is possible by controlling PV volatility through
ESS.
Acknowledgements
This work was supported by the 2019 basic research and develop- ment project grant
from Korea Electric Power Corporation(KEPCO) (Project No.: R19XO02-02).
References
2021, Electricity Supply and Demand Basic Plan, Korea power exchange website. last
modified Jan 1, 2021, accessed Nov 5, https://kpx.or.kr/www/contentsdo?key=92
2020, Introduction of Renewable Energy Generation Forecasting System and Energy, Ministry
of Trade, Industry and Energy website. last modified Sep 18, 2020, accessed Nov 8,
http://www.motie.go.kr/motie/ne/presse/press2/bbs/bbsView.do?bbs_seq_n=163324&bbs_cd_n=81
2020, Distributed Energy Resource Market, Korea power exchange website. last modified
Jan 1, 2020, accessed Nov 5, http://der.kmos.kr/sga/mainPage.do
S. Y. Yang, Y. H. Kim, W. Lee, W. J. Kim, Aug 2021, The Power Brokerage Trading System
for Efficient Management of Small-Scale Distributed Energy-Resources, The Journal
of the Korea institute of electronic communication sciences, Vol. 16, No. 4, pp. 735-742
J. J. Song, Y. S. Jeong, S. H. Lee, Mar 2014, Analysis of pre- diction model for solar
power generation, Journal of Digital Convergence, Vol. 12, No. 3, pp. 243-248
Y. R. Lee, W. B. Son, J. L. Kim, 2021, PV Forecasting for Participation in the Distributed
Energy Resources Market, the Korean Institute of Electrical Engineers Power Engi-
neering Society summer Annual Conf, pp. 521-522
J. H. Jung, D. J. Kim, G. Y. Sim, 2021, Development of Photovoltaic Power Forecasting
Service using Meteorological Information, the Korean Institute of Electrical Engineers
Power Engineering Society summer Annual Conf, pp. 2221-2222
J. H. Son, D. S. Rho, M. Y. Kim, Oct. 2021., A Study on Optimal ESS Discharging Schedule
for System Marginal Price and Seasonal Peak Demand in Photovoltaic Power Plant, the
9th International Conference on Advanced Power System & Automation & Protection.
J. H. Son, 2018, A Study on Economic Analysis Algorithm for Energy Storage System
Considering Peak Reduction and a Special Tariff, Trans of the KIEE, Vol. 67, No. 10,
pp. 1278-1285
J. H. Son, D. S. Rho, M. Y. Kim, Oct. 2021., A Study on Optimal ESS Charging Scheduling
Considering Power Gener- ation Prediction in Photovoltaic Power Plant, the 9th International
Conference on Advanced Power System & Automation & Protection.
저자소개
He received the Ph.D. degree from Hokkaido University, Sapporo, Japan, in 2016.
From December 2016, he has been working as a manager of LG Electronics’ ESS Sales/Engi-
neering Team.
His research interests are analysis and operation of power system, optimal operation
of distributed generation with ESS and demand response.
He received the Ph.D. degree from Hokkaido University, Sapporo, Japan, in 1997.
From December 1999, he was appointed to his current position as an Professor at Korea
University of Technology and Education.
His research interests are analysis and operation of power system, grid connection
of distributed generation and power quality
He received the Ph.D. degree from Hokkaido University, Sapporo, Japan, in 1997.
From December 1999, he was appointed to his current position as an Professor at Korea
University of Technology and Education.
His research interests are analysis and operation of power system, grid connection
of distributed generation and power quality