increasing penetration of variable renewable energy
Sangmin Cho & Ilhyun Cho
Ilhyun Cho, Associate Research Fellow
Research Associates: Jaegyun Ahn, Research Fellow
Outside Participants: Wooyoung Jeon, Assistant professor, Chonnam National
University
Juyoung Chong, Korea University
Jinyeog Lee, Korea University
Research paper series created in collaboration with the National Research Council for Economics, Humanities and Social
Sciences (NRC)
“Green energy research: Study of grid stability with the increasing penetration of variable renewable energy”
1. Collaborative research paper series
Serial no. Title of research Research institute
18-69-01
variable renewable energy
Korea Energy Economics
Hosting research
Juyoung Chong,
1. Research Necessity and Purpose
In the energy mix, the share of solar (photovoltaic) and wind energy, which are variable renewable energy (VRE)
sources, is growing at a rapid pace as these energy sources become increasingly cost-competitive around the world.
As of 2017, VRE accounted for over 20% of the energy mix in seven countries, and if the current trend continues,
half of the world’s energy mix will consist of VRE by 2050.
As the demand for clean and safe energy sources increases in the Republic of Korea, the government is actively
working to expand renewable energy sources to 20% of the country’s energy mix by 2030. As most of Korea’s
new energy generation facilities will be solar and wind energy generation facilities, the share of VRE in Korea’s
energy mix will increase rapidly to 13.5% by 2030.
Until now, the share of VRE in Korea’s energy mix has been too small to have an effect on the overall power
system. However, in consideration of the rapidly increasing pace at which solar and wind energy is expanding and
the Korean government’s policy direction, we must now think about the kind of impact VRE sources might have
on Korea’s energy system.
This study, which was prompted by the Korean government’s Renewable Energy 3020 implementation plan,
began by examining the impact that VRE sources would have on Korea’s power system in 2030. Specifically, this
paper attempts to answer the question, “How much would we have to increase the energy reserve in response to
the increase in VRE sources in Korea’s power system?” Additionally, this study analyzed the effects and
effectiveness of energy storage systems (ESSs), which are flexible resources, as a means of offsetting the effects
of VRE sources. Lastly, we also conducted model analyses and studied the literature on technologies and policies
that have been considered to address the variability issue in an effort to gain an understanding of how other
countries around the world are handling VRE sources.
To ensure the credibility and practicality of the results, this study created a model power system for Korea that
corresponds to the implementation scenario for Korea’s 8th Basic Plan for Long-Term Electricity Supply and
Demand and the Renewable Energy 2030 goal. For this research, we also used real meteorological data for Korea.
Rather than separating the effects of solar and wind energy, we integrated them to make the conditions more
realistic and increased the theoretical and policy contributions of the models. All collaborating research
organizations have made efforts to conduct research under the same premise and use the same data. Varying
models were used for different analytical purposes, and analyses were conducted over both one-hour and 10-
minute intervals. Based on these efforts, we conducted a multifaceted examination of the possible effects of VRE
sources on the power system in the future and draw policy implications.
2. Summary of Contents
In response to the increase in VRE, countries are working to secure ESSs and demanding resources, which they
are expanding through policy efforts.
In this study, we conducted analyses over one-hour and 10-minute periods using the Multi-Period Super Optimal
Power Flow (MPSOPF) model to study the effects of the increase in VRE on Korea’s power system when solar
and wind energy are supplied according to the Renewable Energy 3020 scenario. To reflect Korea’s actual situation,
we used meteorological data for Korea and incorporated the Renewable Energy 3020 implementation plan and
8th Basic Plan for Long-Term Electricity Supply and Demand to simulate Korea’s power system in 2030. Unlike
previous studies, this study created a model that combines solar and wind energy and conducted a more realistic
analysis of the effects of increasing both solar and wind energy in Korea’s energy mix.
Before conducting a model analysis, we analyzed the solar and wind energy generation patterns using
meteorological data. This analysis revealed that the solar radiation amount and onshore wind speed have a
generally weak positive correlation, while the solar radiation amount and offshore wind speed have a generally
weak negative correlation. This means that offshore wind energy, rather than onshore wind energy, is better able
to smooth out the variations in solar energy generation, and is therefore more likely to be advantageous for the
operation of the power system.
Our analysis of the energy generation profiles of representative days by season using the MPSOPF model showed
that solar and wind energy had barely any effect on base load energy sources, such as nuclear power and coal, in
summer and winter, having little negative effect on the operation of the power system. However, in spring and
autumn, when the supply and demand of energy are low and peaks are low, a prominent duck curve emerges,
showing that solar and wind energy have a considerable effect on the base-load coal energy generation.
We also estimated the required reserve energy using the MPSOPF model. In the case of representative days in
summer, the supply of solar and wind energy, according to the 3020 scenario, would increase uncertainty and
variability, requiring 3.9 times more reserve energy to maintain the stability of the power system compared to
when there is no solar and wind energy in the energy mix. In addition, we analyzed the effect of introducing an
ESS, which is a major flexible resource, as a means of addressing issues arising from the increase in VRE. We
confirmed that a 5GW ESS would effectively reduce the uncertainty and variability of renewable energy, more
than halving the required reserve energy. In addition, the ESS would cause a slight increase in the electricity
generated from renewable energy sources, as it promotes the use of renewable energy sources that could not be
used previously due to their high variability.
We also used the MPSOPF model to analyze the effect of reserve energy price changes on the amount of reserve
energy and the amount of electricity generated from renewable energy sources. When the price of Korea’s reserve
energy was nearly doubled to match that of the United States, the amount of reserve energy decreased dramatically
(32.1%) and the amount of electricity generated from renewable energy sources in the power system decreased.
This means that an increase in the reserve energy price causes a decrease in the demand for reserve energy, thereby
increasing the operational efficiency of the power system and increasing the curtailment of renewable energy.
Based on the total operational cost, ESS generated a higher cost reduction effect at a high reserve energy price
than it did at a low reserve energy price.
The analysis of reserve energy over periods of 10 minutes or less was limited under the MPSOPF model. However,
since analysis of variability over periods of 10 minutes or less is necessary, in consideration of the characteristics
of VRE sources, we used a different methodology (variation rate analysis) to analyze reserve energy under similar
conditions as the analysis of reserve energy over one-hour periods.
The amount of reserve energy required over 10-minute periods in 2030 was calculated by selecting the largest
variation based on a comparison of the extent of renewable energy variation, maximum number of accidents
occurring at energy generation complexes for a single renewable energy source, and number of general energy
generator accidents. As a result, the required reserve energy for 10 minutes in 2030 was calculated to be 3,200MW.
In the 3020 scenario, the largest amount of the three categories was the amount of reserve energy required to
prepare for rapid fluctuations in renewable energy generation. This was the amount of variation calculated in
consideration of the generation of both solar and wind energy.
However, we cannot guarantee that the calculated reserve energy amount would be required in the actual operation
of the power system. This study confirms that the variations in solar and wind energy did not increase
proportionally to output, and the adequate reserve energy amount changed depending on solar and wind energy
output and the operating conditions of the power system. We also verified the amount of reserve energy required
over a 10-minute period by season depending on the operational conditions of the power system. As a result, we
confirmed that the required amount of reserve energy differed by season (2,400MW in spring, 3,000MW in
summer, 3,900MW in autumn, and 3,000MW in winter).
3. Conclusion and Policy Directions
Based on the results of this study, we propose the following policy directions:
First, when the supply of VRE increases in the future, it will be necessary to review the impacts of increasing the
required reserve energy. The results of the analysis conducted using the MPSOPF model showed that, if the wind
and solar energy supply targets are reached by 2030, the amount of required reserve energy will increase by about
3.9 times compared to when wind and solar energy are not supplied. The results of additional analysis regarding
the 10-minute period reserve amount revealed that approximately 3,200MW of reserve energy is required under
the current fixed reserve system in order to prepare for rapid fluctuations in solar and wind energy output over a
short period of time. With the current operational reserve for 10-minute periods of 1,500MW, this is not possible.
Therefore, if Korea does reach its 2030 target for solar and wind energy, the required amount of reserve energy
will exceed the current reserve amount. It will thus be necessary to consider and review increasing the reserve
energy amount.
Second, it is necessary to secure flexible resources. We confirmed that the combined supply of VRE sources and
flexible resources will effectively lower the required reserve energy amount and total operational expenses.
Therefore, to effectively secure flexible resources in response to increasing the supply of renewable energy, it is
necessary to make it mandatory to secure flexible resources in the short term and improve the system in the long
term in order to maintain the inflow of flexible resources.
Third, it is necessary to realize compensation for flexible resources and open an ancillary service market in the
long term. Through the MPSOPF model analysis, we were able to confirm that an increase in the reserve energy
price causes a decrease in operational expenses. The average price of reserve energy in Korea is KRW 3,000 per
MWh, which is less than half the average price in the United States. This low price, which does not meet the
opportunity cost of reserve energy, tends to devalue the compensation for flexible resources, which is much less
than the benefits provided by flexible resources in reducing the variability of renewable resources. Therefore, to
accelerate the entry of flexible resources into the market, it is necessary to increase the price of ancillary services,
such as standby reserves and amplitude modulation reserves, to reflect the opportunity cost. In the long term, the
opening of a real-time electricity market and ancillary service market would induce reasonable market prices,
according to the law of markets, and also provide opportunities for generating additional profits from flexible
resources. In addition, this could encourage energy suppliers to invest in flexible resources and ultimately resolve
problems arising from the use of renewable energy.
Fourth, it is necessary to establish a plan for variable reserve energy generation and intraday energy generation.
As mentioned earlier, variability does not increase proportionally with solar and wind energy output. Currently,
reserves are operated based on fixed reserve standards every hour. However, when renewable energy sources are
increased, relatively excessive amounts of reserve energy are secured during certain time periods, creating
unnecessary costs. On the other hand, the amounts of reserve energy may be insufficient at other times. Therefore,
it is necessary to analyze the characteristics of the power system by hour and season, including changes in the
output levels of renewable energy sources, and operate variable reserves that are appropriate for the relevant time.
In addition, it is necessary to establish an intraday generation plan for the operation of variable reserves by
shortening the emergency power operation periods. Currently, the power system is operated based on a one-day-
ahead generation plan and real-time emergency plan. Adding one more plan alongside these two could help ensure
effective responses to short-term variability in renewable resources.
Fifth, it is necessary to conduct basic research on Korea’s offshore and onshore wind power generation patterns.
Using Korea’s meteorological data, this paper examined the differences between energy generation patterns of
solar and wind energy generation locations as well as the differences between the energy generation patterns of
onshore and offshore wind energy. One of the noteworthy points here is that there is a difference between the
energy generation patterns of onshore and offshore energy generation locations. Supplying solar energy and
onshore wind energy together would intensify the solar energy duck curve, making it difficult to operate the power
system. However, offshore wind energy has a smoothing effect on solar energy generation, which would reduce
the burden on the power system.
As Korea plans to focus on promoting offshore wind energy in the future, it is important to increase our
understanding of the effect of offshore wind energy and solar energy on Korea’s power system by conducting
accurate analyses and forecasts of offshore wind energy generation patterns. Currently, however, there is a lack of
basic studies relevant to Korea’s situation, making it necessary to conduct multifaceted studies on the differences
between onshore and offshore wind energy generation patterns using actual observations of energy generation
data. It is also important to conduct various studies on the differences between the wind and solar energy
generation patterns in Korea.
Sixth, it is necessary to improve the forecasting and control system for VRE generation. The MPSOPF model
establishes an optimal generation plan one day in advance. In the analysis that was conducted using this model,
the capacity for reserves increases as the forecast time is expanded. Technologically, it is clear that we need a
better technology for forecasting the amount of energy generated using renewable energy sources. Systematically,
as in the policy proposal provided above, the introduction of variable reserves to increase the accuracy of
renewable energy output forecasts would support the calculation of reserves for a real time market that takes
variability into consideration and is appropriate for the level of output from renewable resources forecast for the
day.
Another measure is bidding in the renewable energy market. Allowing bidding in the renewable energy market
could induce improvement of the technology for forecasting energy generation amounts. This measure would also
induce renewable energy suppliers to invest in flexible resources.
In terms of controlling energy generation amounts, it is possible to install control equipment, such as pitch control
gearboxes for wind energy generation and inverters for solar energy generation, to control the output. When
excessive renewable energy output causes problems in the power system, the output of certain renewable resources
can be controlled and appropriate compensation provided, according to regulations, in such a way that contributes
to resolving the problems in the power system.
Table of Contents
1. Characteristics and reserve structure of the MPSOPF model ................................................................... 12
A. Characteristics of the MPSOPF model............................................................................................. 12
2. Solar PV and wind power generation prediction model ........................................................................... 12
A. Selection of location and analysis of correlation between weather data by location ....................... 13
B. Derivation of prediction profiles of solar PV and wind by location ................................................. 17
3. 2030 Korean Power System Model Building ........................................................................................... 22
A. Power system model and energy mix ............................................................................................... 22
B. Power demand pattern ...................................................................................................................... 24
C. Pattern of integrated solar PV and wind power generation .............................................................. 25
4. Setting scenarios for reserve analysis ....................................................................................................... 27
Chapter 3. Analysis of reserve by hour ........................................................................................................................... 29
1. Power generation mix, generation, and reserve ........................................................................................ 29
2. Analysis of reserve pattern by time ........................................................................................................... 33
4. Annual cost of power system operation .................................................................................................... 39
Chapter 4. Analysis of reserve in 10-minute increments .......................................................................................... 40
1. 10-minute renewable energy variability analysis ...................................................................................... 40
A. Definition of output variation rate .................................................................................................... 40
B. Estimation of the output variation rate of renewable energy using weather data ............................. 42
2. Estimation of 10-minute reserve ............................................................................................................... 44
A. 10-minute reserve reflecting renewable energy variability .............................................................. 44
B. Dynamic analysis of reserve considering the variability of renewable energy................................. 47
Chapter 5. Summary and Policy Direction ........................................................................................................... 49
1. Major findings of research ........................................................................................................................ 49
2. Policy Proposal ......................................................................................................................................... 50
B. Necessity of flexibility resources ..................................................................................................... 50
C. Compensation for flexibility resources and opening of ancillary services market ........................... 51
D. Introduction of variable reserve and day-of planning ...................................................................... 51
E. Further research on offshore and onshore wind patterns .................................................................. 52
F. Improvement of accuracy of generation forecasting and control capability ..................................... 53
References ............................................................................................................................................................ 55
Table 2-1. 16 solar PV sites and target capacity by 2030 (MW)........................................................................... 13
Table 2-2. 16 wind power sites and 2030 target capacity (MW) .......................................................................... 13
Table 2-3. Estimation results of probability model for Solar PV Location No. 1 (Seoul) .................................... 18
Table 2-4. Estimation result of probability model at Wind Power Location No. 1 (Goseong) ............................. 18
Table 2-5, Adjusted R-squared of solar PV probability model by location ............................................................... 19
Table 2-6. Adjusted R-squared of wind power probability model by location ......................................................... 19
Table 2-7. Setting scenarios for renewable energy in 2030 .................................................................................. 26
Table 3-1. Daily generation and reserve by scenario on a representative day in summer .................................... 30
Table 3-2. Daily generation cost and reserve cost by scenario on a representative day in summer ...................... 31
Table 3-3. Analysis of required reserve per hour by scenario on a representative day in summer ....................... 32
Table 3-4. Average spinning reserve prices in 2014 by ISO in the United States ................................................. 36
Table 3-5. Optimization results using high reserve price and current low reserve price: generation and reserve 35
Table 3-6. Annual cost of power system operation by scenario: ratio relative to cost of Case 1 .......................... 38
Table 4-1. Comparison of methods for estimating prediction error rate ........................................................... 4140
Table 4-2. Estimation of 10-minute variation rates for solar PV and wind generation ......................................... 43
Table 4-3. Estimation of 10-minute rate of change of renewable energy in 2030 ................................................ 43
Table 4-4. Estimation method of 10-minute reserve ............................................................................................. 43
Table 4-5. Variation without smoothing effect ...................................................................................................... 44
Table 4-6. Variation of output due to the variability of renewable energy ............................................................ 44
Table 4-7. Variation rate and 10-minute reserve by season................................................................................... 47
Table 5-1. Current operating reserve .................................................................................................................... 49
List of Figures
Figure 2-2. Locations of 16 wind farms ............................................................................................................... 14
Figure 2-3. Correlation between solar radiation, wind speed, and temperature .................................................... 15
Figure 2-4. Correlation between solar radiation and wind speed.......................................................................... 16
Figure 2-5. 24-hour patterns of average onshore and offshore wind speed by season .......................................... 16
Figure 2-6. 1,000 24-hour prediction profiles for Solar PV and Wind Power Locations No. 1 ............................ 20
Figure 2-7. Five representative profiles of Solar PV and Wind Power Locations No. 1 ...................................... 20
Figure 2-8. Map of the Korean Power System (104-bus system) ......................................................................... 21
Figure 2-9. Composition of generation capacity by source (2017 vs. 2030) ........................................................ 22
Figure 2-10. Daily power demand pattern by season (2017 vs. 2030) ................................................................. 23
Figure 2-11. Daily pattern of onshore and offshore wind generation and total wind generation on a representative
day in summer ...................................................................................................................................................... 24
Figure 2-12. Wind power and solar PV generation and net load pattern on a representative day in summer ....... 25
Figure 2-13. Pattern of net load of solar PV-wind combination model on a representative day by season .......... 26
Figure 3-1. Generation profile by scenario on a representative day in summer (1) .............................................. 28
Figure 3-2. Generation profile by scenario on a representative day in summer (2) .............................................. 29
Figure 3-3. 24-hour generation profile by source of generation on a representative day by season: .................... 31
Figure 3-4. Average contingency and load-following reserves by scenario on a representative day in summer .. 32
Figure 3-5. 24-hour required reserve profile per hour by scenario on a representative day in summer (1) .......... 33
Figure 3-6. 24-hour required reserve profile per hour by scenario on a representative day in summer (2) .......... 34
Figure 3-7. Low reserve price (left) and high reserve price (right): renewables generation and required reserve 36
Figure 3-8. Reserve profile by time under low reserve price and high reserve price: Case 2_Wind and Solar .... 36
Figure 3-9. Low reserve price (left) vs. high reserve price (right): cost-saving effect of ESS ............................. 37
Figure 3-10. Example of estimation of annual demand pattern using the PCHIP method.................................... 38
Figure 4-1. Definition of output variation rate...................................................................................................... 39
Figure 4-2. Method of estimating the variation rate of renewable energy ............................................................ 41
Figure 4-3. Example of probability distribution under different confidence intervals .......................................... 42
Figure 4-4. Example of estimation of 10-minute reserve for 2030 ....................................................................... 45
Figure 4-5. Variation by level of output of renewable energy (one year) ............................................................. 46
Figure 4-6. Standard deviation of variation by level of output of renewable energy ............................................ 46
Figure 4-7. Standard variation by level of output of renewable energy ................................................................ 46
Figure 5-1. Variable reserve responses to changes in status of power system ...................................................... 50
Figure 5-2. Generation and dispatch planning procedures for a shorter dispatch cycle (CAISO) ........................ 51
Figure 5-3. Limitation of renewable energy output .............................................................................................. 53
Chapter 1. Introduction
In its Renewable Energy 3020 Implementation Plan1 of 2017, the Korean government presented the goal of expanding the share
of renewable energy, such as solar and wind power, to 20% by 2030. The expansion of renewable energy deployment is a global
trend, with nations worldwide striving to expand their supplies of renewable energy. Korea plans to expand its renewable energy
deployment with a focus on solar PV and wind. According to the plan, the government will supply 97% of new renewables capacity
using solar PV and wind by 2030, by which time the share of variable renewable energy (VRE) of total electricity generation is
projected to reach 13.5%.2
Korea’s expansion of renewable energy is quite a departure from its existing energy policy, indicating that the country’s energy
policy has entered an important phase of transformation. With this change, various issues have emerged in relation to potential, the
unit cost of power generation, and grid stability. As the country expands its renewable energy supply, these issues will be continually
raised as important policy tasks that need to be addressed on a consistent basis.
This research aims to examine issues related to the massive introduction of variable renewable energy (VRE) into the power
system and devise measures for coping with them. In terms of power grid operation, both cost efficiency and supply stability must
be considered. As the growing share of VRE is expected to undermine the stability of the power supply, because of its high volatility3
and uncertainty,4 advance preparations must be made.
In the Renewable Energy 3020 Implementation Plan, the government recognized the necessity of such preemptive measures and
introduced measures such as the installation of grid-connected ESSs (energy storage systems) and implementation of power-to-gas
technology. In addition, the establishment of an integrated control system and improvement of the electricity market system to cope
with the expansion of flexibility resources are currently being discussed.5 However, the status and issues of Korea’s power system
at the time when the renewable energy (solar and wind power) deployment target is attained in 2030, especially the power grid
1 Ministry of Industry, Trade and Energy, 2017, Renewable Energy 3020 Implementation Plan
2 Ministry of Industry, Trade and Energy, 2017, Renewable Energy 3020 Implementation Plan. The share of VRE is an estimate based on the
targets of the plan.
2 The quantity of electricity generation fluctuates by hour, and it is difficult to control such variation.
4 It is difficult to predict the quantity of electricity generation.
5 Ministry of Industry, Trade and Energy, 2017, Renewable Energy 3020 Implementation Plan.
operation issues associated with the expansion of VRE, need to be thoroughly considered.
To address these issues, this study aims to analyze the impact of VRE deployment on the power grid in terms of reserve and devise
technological and policy instruments. To achieve these goals, this paper provides the following content. Chapter 2 reviews the
domestic and overseas technological and policy options being discussed and implemented to cope with the increased VRE
deployment. Chapter 3 explains the MPSOPF model used to analyze reserve by hour and discusses major preconditions, while
Chapter 4 uses the MPSOPF model built in Chapter 3 to conduct a simulation of Korea’s power grid status at the time when the
country achieves its 2030 target for solar and wind power deployment and derives reserve by hour. Next, Chapter 5 provides an
analysis of reserve per 10 minutes, which could not be obtained using the MPSOPF model, and derives additional implications.
Lastly, Chapter 6 provides a summary of the research findings and presents policy suggestions.
This study is unique in that it provides a simulation of Korea’s power grid system at the time when the country reaches its
renewable energy deployment target in 2030 and estimates the reserve in consideration of both solar and wind power. Furthermore,
it uses two different models to analyze reserve by hour and derives implications for model complementarity and completion. It is
hoped that this research will be utilized as a basic resource for the establishment of a power grid system capable of coping with VRE
deployment, thereby contributing to the achievement of the 3020 renewable energy deployment target.
Chapter 2. MPSOPF model
This chapter discusses the structure and major premises of Multi-Period Super Optimal Power Flow (MPSOPF) as the core
model for analyzing “grid stability in terms of reserves,” which this study aims to explore. The MPSOPF model is used to analyze
reserves by hour, and the variability analysis model used to analyze reserves by 10-minute increment as a supplement to the
MPSOPF model will be explained in greater detail in Chapter 5. Although these two models have different objects of analysis and
analytic processes, this study set out to reconcile the major premises and inputs of the models in order to ensure the consistency and
reliability of the research. These major premises and inputs are discussed in this chapter.
1. Characteristics and reserve structure of the MPSOPF model
A. Characteristics of the MPSOPF model
The objective function of the MPSOPF is to derive a power generation plan by generator that minimizes the expected costs (sum
of power generation costs and reserve costs) of operating the power system while reflecting supply and demand uncertainty. To
derive an optimum electricity generation plan, a set of various constraints, including demand pattern, generators, grid stability,
networks, contingency, and renewable energy variability, is used.
The MPSOPF is different from the existing SCOPF is three ways. First, while the SCOPF derives an optimum generation plan
for a specific time, the MPSOPF is capable of conducting 24-hour, continuous, multi-period analysis to produce simulations of a
production plan from the perspective of day-ahead scheduling. Second, as the MPSOPF can process both stochastic information
and deterministic information, it enables the systematic analysis of stochastic inputs6 with uncertainty. Third, the MPSOPF model
estimates the quantities of two kinds of optimal reserves—load-following reserve and contingency reserve—that are necessary to
maintain power system security in uncertain situations.7
These differentiated characteristics of the MPSOPF enables analysis of the effects of cost saving generated by reduced reserve
attributable to the use of flexibility resources when VRE deployment reaches a significant level. Meanwhile, this study adopted the
ESS as a flexibility resource. Based on a 24-hour continuous production plan that meets the ESS charging and discharging
constraints, this study analyzed the effect of energy cost reduction due to peak-load shifting as well as the effect of cost saving due
to the reduction of reserve through the reduced variability of renewable energy.
2. Solar PV and wind power generation prediction model
Before building the MPSOPF model, a model for predicting the pattern of solar PV and wind power generation, which is the most
important parameter, needs to be developed. Solar PV and wind power generation prediction values can be estimated using a metric
6 Such as production of variable renewable energy, demand for electricity, etc.
7 A list of constraints related to the objective function of MPSOPF is provided in Jiwoon Ahn (2016). For convenience, excerpts from Jiwoon
Ahn (2016) are provided in the Appendix of this paper.
model if there is actual data on solar PV and wind power generation in Korea over time. However, as it is difficult to acquire data
on actual solar and wind power generation by location, we collected past weather data, including solar radiation and wind speed, for
each solar PV and wind power generation site, and used it to estimate a metric model. By applying conversion parameters, we
converted the solar radiation and wind power values into solar PV and wind power generation values. The solar PV and wind power
generation prediction methodology is explained in detail below.
A. Selection of location and analysis of correlation between weather data by location
To build a solar PV and wind power generation prediction model, weather data from the Weather Data Portal of the Korea
Meteorological Administration8 was collected. Spanning three years (2015 to 2017), the collected data includes solar radiation
(MJ/m2) and temperature by time for 16 solar PV locations and wind speed (m/s) and temperature by time for 16 wind power
locations (11 onshore and five offshore).
Although solar PV is currently being actively deployed largely in Jeollanam-do,9 it has been deployed relatively evenly
nationwide in the form of small-scale distributed power generation facilities. Given this characteristic, this study, instead of selecting
solar PV plants in specific locations, divided the entire country into 16 cities and provinces, selected a representative location10 for
each city and province, and collected and analyzed weather data for each city and province. To establish a target capacity for each
location for 2030, based on the deployed capacity as of 2015 and permitted capacity under the Eighth Power Supply Basic Plan,11
the installed capacity as of 2018 was estimated, and the capacities of the locations were assumed to increase in proportion to their
capacities in 2018 until 2030. Table 3-2 and Figure 3-5 provide information on and the locations of the 16 solar PV sites.
Wind power, unlike solar PV, has high variation, depending on the wind in each region, which is why large wind farms are often
deployed in regions with favorable wind conditions. Given this characteristic, 16 locations where large wind farms were built as of
2018 were selected as representative locations. To estimate target capacity by location by 2030, based on deployed capacity until
2015 and permitted capacity under the Eighth Power Supply Basic Plan,12 installed capacity as of 2018 was derived. The total wind
power target capacity for 2030 (17,674 MW) was divided into onshore wind power (12,000 MW) and offshore wind power (5,674
MW).13 Offshore wind power was increased in proportion to the capacity of each location in 2018, and onshore wind power was
evenly allocated across five locations, as there were nearly no data on capacity for the locations where wind farms have already been
established. Table 3-3 and Figure 3-6 provide information on and the locations of the 16 wind farm sites.
Table 2-1. 16 solar PV sites and target capacity by 2030 (MW)
Site
s11 Chungnam 129 Seosan 536 65 601 4,010
s12 Jeonbuk 146 Jeonju 786 786 5,244
s13 Jeonnam 165 Mokpo 1,099 196 1,295 8,648
s14 Gyeongbuk 136 Andong 498 165 663 4,425
s15 Gyeongnam 255 Bukchangwon 342 342 2,286
8 https://data.kma.go.kr, accessed on Oct 24, 2018
9 Jeollanam-do: 24.4%, Jeollabuk-do: 17.5%. Source: Korea Energy Agency, 2017, 2016 New and Renewable Energy Deployment Statistics.
10 Seats of provincial governments and city halls
11 Ministry of Industry, Trade and Energy, 2017, Eighth Power Supply Basic Plan.
12 Indicated as “Eighth Plan” in Tables 3-2 and 3-3.
13 Ministry of Industry, Trade and Energy, 2017, Renewable Energy 3020 Implementation Plan, internal data.
s16 Jeju 184 Jeju 122 95 217 1,447
Source: author
Source: author
Table 2-2. 16 wind power sites and 2030 target capacity (MW)
Site
w2 Taegisan 525 Bongpyeong 40 300 340 467
w3 Pyeongchang 526 Pyeongchang 98 476 574 788
w4 Samcheok 876 Samcheok 27 990 1,017 1,397
w5 Yeongyang 801 Yeongyang 62 301 363 499
w6 Gyeongju 859 Tohamsan 17 490 507 696
w7 Yeonggwang 769 Yeomsan 20 251 271 372
w8 Yeongheung 664 Yeongheungdo 46 - 46 63
w9 Haengwon 781 Gujwa 98 21 119 163
w10 Seongsan 792 Pyoseonmyeon 34 60 94 129
w11 Hanrim 779 Hanrim 25 192 217 298
w12 Saemangeum 700 Eocheongdo - 159 159 2,400
w13 Sinan 799 Nakwoldo 40 438 478 2,400
w14 Yeosu 931 Yokjido - - - 2,400
w15 Yeongdeok 22,106 Pohang (onshore) 40 299 339 2,400
w16 Moseulpo 855 Gapado 0 168 168 2,400
Source: author
Source: author
One of the major contributions of this study is its consideration of the integration of solar PV and wind power and examination of
its impact on the power system. Previous studies analyzed either solar PV or wind power as variable renewable energy resources,14
but none analyzed both of these VRE resources at the same time. One of the purposes of this research was to look at what kind of
synergy or additional issues could arise when these two energy resources are deployed together. Before such analysis, however, we
will briefly introduce a correlation between solar radiation, wind power, and temperature.
Figure 3-7 (a) is the result of an analysis of the correlation between solar radiation and temperature at 16 solar PV locations, while
Figure 3-7 (b) shows the correlation between wind speed and temperature at 16 wind power locations. In these figures, a spectrum
of color from red to blue indicates a correlation of 1 to -1, respectively, with green representing no correlation. They show that there
is a very high correlation between solar radiation and temperature. On the other hand, the correlation between wind power and
temperature is low.
In addition, as solar PV generation is concentrated in a specific period of time, because of the strong correlation of solar radiation
between locations (s1 to s16), it creates a duck curve, causing problems with grid operation. The correlation of wind speed between
locations (w1 to w16) is weak, and thus, nationwide, the net effect is to mitigate the variability of wind power generation (a
smoothing effect).
Figure 2-3. Correlation between solar radiation, wind speed, and temperature
14 1.Jiwoon Ahn, 2016, Study on optimal generation mix of renewable energy grid integration, Korea Energy Economics Institute, basic research
report, no. 16-22.
2. Wooyeong Chun, 2015, Study on building a stochastic power grid optimization model, Korea Energy Economics Institute, basic research
report, no. 15-15.
Source: author
Lastly, Figure 3-8 shows the correlation between the solar radiation of the 16 solar PV locations (s1 to s16) and the wind speed of
the 16 wind power locations (w1 to w16). Notably, solar radiation and onshore wind speed (w1 to w11) have an overall weak
positive correlation, whereas solar radiation and offshore wind speed (w12 to w16) have a weak negative correlation. This indicates
that onshore and offshore wind speed have different patterns, onshore wind speed and solar radiation have similar patterns, and
offshore wind speed and solar radiation move in opposite directions. Based on these observations, the simultaneous deployment of
solar PV and onshore wind power would intensify the duck curve phenomenon of the net load of solar PV, making grid operation
more difficult, while the deployment of solar PV and offshore wind power together is likely to alleviate the duck curve, alleviating
the problems of grid operation.
Figure 3-9 shows the 24-hour pattern of average onshore and offshore wind speed by season. Overall, wind speed in Korea is low
in summer, medium in spring and autumn, and the highest during winter. Over a 24-hour period, onshore wind speed is low during
the night and high in the afternoon (13 to 17). Overall, offshore wind speed remains relatively steady during the 24-hour period. In
winter, when the wind speed is higher, offshore wind speed during the day is lower than that at night. This is consistent with the
observation from Figure 3-8. Onshore wind speed in Korea is generally high at times of sufficient solar generation, but offshore
wind speed is steady, exhibiting no correlation with solar PV. In winter, offshore wind speed moves in the opposite direction of solar
PV.
Source: author
Figure 2-5. 24-hour patterns of average onshore and offshore wind speed by season
(a) Average onshore wind speed by season (b) Average offshore wind speed by season
Source: author
Winter
Spring
Summer
Autumn
Time
B. Derivation of prediction profiles of solar PV and wind by location
The 24-hour prediction profiles of solar PV and wind generation by location to be used as inputs for the MPSOPF model are
derived using the solar radiation, wind speed, and temperature data explained earlier and by applying the method of Wooyeong
Chun (2015). Wooyeong Chun (2015) presented a four-stage model for predicting solar radiation and wind power generation. This
methodology is explained below.
1) Stage 1
In Stage 1, using the two-stage ARMAX model, a probability model is developed to estimate the solar radiation and wind speed
for each of the 16 solar PV and 16 wind power locations.
Formula 3-1. Probability model of solar radiation and wind speed
Stage 1: Deterministic Part
Stage 2: ARMA Part
Deterministic cyclest,i: one year, half year, 24-hour cycle, 12-hour cycle, sine and cosine
curves
: Stage 2 white noise residual of estimation equation
Source: author, based on Wooyeong Chun, 2015, Study on building a stochastic power grid optimization model,
Korea Energy Economics Institute, Basic Research Report, no. 15-15, p.55.
Formula 3-1 shows the methodology of the probability model for estimating solar radiation and wind speed. The ARMAX model
consists of two stages: Stage 1, where solar radiation and wind speed are estimated using deterministic information such as
temperature and annual or daily cycle dummy variables; and Stage 2, where they are again estimated through time-series analysis
based on error terms of the model from Stage 1.
Table 3-4 illustrates the results of the estimation of the probability model for solar PV locations in Seoul. The adjusted R-squared
of the OLS model in Stage 1 is 76.8%, and if the ARMA model of Stage 2 is used, it rises to 95.4%.
Table 3-5 shows the results of the estimation of the probability model for wind power locations in Goseong. The adjusted R-
squared of the OLS model in Stage 1 is 22.9%, and when the ARMAX model of Stage 2 is used, it rises to 55.3%, which is far
lower than that of the solar radiation estimation model above. Generally, the adjusted R-squared of the wind speed estimation model
tends to be lower than that of the solar radiation estimation model.
Table 2-3. Estimation results of probability model for Solar PV Location No. 1 (Seoul)
Solar PV Location No. 1 (Seoul): two-stage ARMAX estimation result
Stage 1: OLS Stage 2: ARIMA
Explanatory Coefficient t-statistics Explanatory Coefficient t-statistics
variable variable
daily cycle 0.48243 265.85 MA 1 -0.34041 -34.82
cdd 0.01423 36.29 MA 2 -0.16010 -16.21
hdd -0.00122 -6.68 MA 3 -0.06050 -7.49
AR 1 0.72454 92.46
AR 2,3 0.14544 23.77
AR 2,4 0.23966 39.9
Source: author
Table 2-4. Estimation result of probability model at Wind Power Location No. 1 (Goseong)
Wind Power Location No. 1 (Goseong): estimation of two-stage ARMAX
Stage 1: OLS Stage 2: ARIMA
Explanatory
cy 0.18887 25.06 MA1,1 0.40147 41.62
sy 0.10811 26.76 MA1,2 0.09446 12.3
cy_2 -0.02855 -8.34 MA1,3 0.0402 5.54
sy_2 -0.05761 -16.91 MA1,4 0.02818 3.96
ch -0.15239 -45.83 MA1,5 0.03156 4.55
sh -0.08837 -26.1 AR1,1 0.9046 122.9
ch_2 0.021 6.76 AR2,1 0.06289 10.15
sh_2 0.0982 31.48
cdd1 0.02663 24.39
hdd1 -0.00939 -12.89
Source: author
Tables 3-6 and 3-7 show the adjusted R-squared values of Stage 1 and Stage 2 in each probability model for the 16 solar PV and
16 wind power locations. On average, the adjusted R-squared of the solar radiation estimation model is 95%, but that of the wind speed
estimation model is about 60% to 70%.
In summary, the analysis indicates that the adjusted R-squared of solar PV is higher than that of wind power, and the adjusted R-
squared of offshore wind power is higher than that of onshore wind power. This suggests the importance of creating a wind power
prediction system. In addition, the results show that offshore wind power is more predictable (lower uncertainty) than onshore wind
power, giving it the advantage of promoting power grid stability. However, as the difference of R-squared was not statistically
verified, interpretation of the analysis result needs to be approached with caution.
Table 2-5, Adjusted R-squared of solar PV probability model by location
Location
number Location Adjusted R-squared (Stage 1) Pseudo R-squared (Stage 2)
s1 Seoul 0.7680 0.9540
s2 Incheon 0.7728 0.9613
s3 Daejeon 0.7941 0.9532
s4 Daegu 0.8019 0.9560
s5 Gwangju 0.7778 0.9518
s6 Ulsan 0.7170 0.9565
s7 Busan 0.7756 0.9585
s8 Gyeonggi 0.7840 0.9584
s9 Gangwon 0.7928 0.9558
s10 Chungbuk 0.7911 0.9530
s11 Chungnam 0.7665 0.9541
s12 Jeonbuk 0.7759 0.9503
s13 Jeonnam 0.7620 0.9544
s14 Gyeongbuk 0.8079 0.9595
s15 Gyeongnam 0.7849 0.9541
s16 Jeju 0.7195 0.9501
Source: author.
Table 2-6. Adjusted R-squared of wind power probability model by location
Location
number Location Adjusted R-squared (Stage 1) Pseudo R-squared (Stage 2)
w1 Goseong 0.2291 0.5529
w2 Taegisan 0.3672 0.7599
w3 Pyeongchang 0.3798 0.6449
w4 Samcheok 0.2015 0.5467
w5 Yeongyang 0.2892 0.6150
w6 Gyeongju 0.1991 0.5839
w7 Yeonggwang 0.2046 0.7493
w8 Yeongheung 0.0790 0.7348
w9 Haengwon 0.1752 0.7887
w10 Seongsan 0.1836 0.7192
w11 Hanrim 0.1804 0.7791
w12 Saemangeum 0.1431 0.7831
w13 Sinan 0.0323 0.6588
w14 Yeosu 0.1091 0.6485
w15 Yeongdeok 0.1561 0.7844
w16 Moseulpo 0.2276 0.7952
2) Stage 2
In Stage 2, based on the probability model by location derived in Stage 1, we derived 1,000 prediction profiles for predicting solar
radiation and wind speed using the Monte Carlo method, which involved randomly creating white noise residuals, which are error
terms for each probability model in Stage 2, under the assumption of normal distribution. Correlation between locations and
correlation between solar radiation and wind speed are reflected in the creation of white noise residuals by location from the
covariance-variance matrix of white noise residuals at 32 locations. As such, the derived prediction profiles by location reflect the
correlation between locations. This enables an integrated analysis of two renewable energy resources (solar PV and wind) that
reflects the correlation between solar PV and wind power by location.
3) Stage 3
In Stage 3, the prediction profiles of estimated solar radiation and wind speed are converted into solar PV and wind power
generation profiles. As for solar PV, solar radiation is converted into solar PV generation by considering the installed capacity by
unit area, solar radiation-generation conversion, solar PV panel efficiency, and total capacity factor. Wind speed is converted into
wind power generation by applying the IEC-315 conversion curve. Figure 3-10 shows 1,000 prediction profiles for solar PV and
wind power generation at Solar PV Location No. 1 and Wind Power Location No. 1, respectively.
4) Stage 4
In Stage 4, five profiles were selected to represent these 1,000 solar PV and wind power generation profiles. The median of the
five profiles, median ± 1 sigma, and median ± 2 sigma (the lowest and highest levels) were set. Figure 3-11 shows the five representative
profiles.
Figure 2-6. 1,000 24-hour prediction profiles for Solar PV and Wind Power Locations No. 1
(a) Solar PV (Seoul) (b) Wind power (Goseong)
Source: author
Figure 2-7. Five representative profiles of Solar PV and Wind Power Locations No. 1
(a) Solar PV (Seoul) (b) Wind power (Goseong)
15 Wind power electricity generation by type of wind turbine as per the standards of the International Electrotechnical Commission (IEC).
Source: author
3. 2030 Korean Power System Model Building
A. Power system model and energy mix
As for the framework and inputs of the Korea Power System model used in this study, the model of Jiwoon Ahn (2016) was
referred to. Figure 3-12 is the map of the Korean Power System (104-bus system) created by Jiwoon Ahn (2016). The model is a
104-bus system based on transmission grids (765kV to 345kV), physical constraints of local generators, and cost function data.
Figure 3-13 compares the composition of generation capacity by source in 2017 and 2030. According to the Eighth Power Supply
Basic Plan, 82% (45GW) of the capacity increase (54.6GW) from 2017 to 2030 is attributable to renewables, mostly solar PV and
wind power, while the increase in capacity of conventional sources such as nuclear energy or coal is rather limited.
This study assumed that, for conventional sources such as nuclear energy, coal, LNG, and petroleum, the capacity of existing
generators increases by 2030. As for solar PV and wind power generation, it is assumed that the capacities estimated for the 16
locations above are achieved by 2030. Due to the expected increase in electricity demand and expansion of renewable energy
generation facilities by 2030, optimization cannot be achieved with the current power grid. Hence, this study conducts an
optimization analysis without considering the power grid constraint.
Figure 2-8. Map of the Korean Power System (104-bus system)
Source: Jiwoon Ahn, 2016, Study on optimal generation mix for renewable energy grid connection, Korea Energy
Economics Institute, Basic Research Report, no. 16-22, p.32.
Figure 2-9. Composition of generation capacity by source (2017 vs. 2030)
Source: author
B. Power demand pattern
Figure 3-14 shows the pattern of daily electric power demand by season in 2017 and 2030. The power demand pattern of
representative days by season in 2030 is estimated under the assumption that the demand pattern of 2017 increases in proportion to
summer peak load of 2030 (97.5GW) and winter peak load (100.5GW), based on the target demand of the Eighth Power Supply
Basic Plan.
Figure 2-10. Daily power demand pattern by season (2017 vs. 2030)
(a) Power demand in 2017 (b) Power demand in 2030
Source: author
C. Pattern of integrated solar PV and wind power generation
Figure 3-15 shows the generation levels for 11 onshore wind power installations and five offshore wind power installations, using
the same wind power installations and capacity estimated earlier, as well as the daily pattern of total wind power generation (onshore
+ offshore) on representative summer days.
As found earlier when examining the correlation between solar radiation and wind speed and the patterns of onshore and offshore
wind speed, onshore wind speed in Korea, contrary to common sense, is lower at night and much higher during the day. This
suggests that when only onshore wind power is introduced, it aids grid operation by generating more power during the day, when
power demand is high, but intensifies the duck curve caused by solar PV, thus making grid operation more difficult, under the
scenario of the Renewable Energy 3020 Implementation Plan that proposes the deployment of both wind and solar PV. On the other
hand, offshore wind power generates a little more power at dawn than during the day.
The patterns of onshore and offshore wind power generation suggest that when wind power is deployed along with solar PV in Korea,
offshore wind power has the advantage of reducing the burden of grid operation, thanks to its mitigating influence on the duck curve.
Total wind power generation, which includes both onshore and offshore wind power, shows that power generation is more
influenced by offshore wind power, as this study assumed the ratio of onshore to offshore wind to be 2.1:1. Figure 3-16 illustrates
the patterns of wind power generation solar PV generation, and the patterns of net load that is the difference between energy demand
and electricity production from renewables. As mentioned earlier, wind power generation remains steady over time due to the large
effect of offshore wind, and thus net load is not much different from the demand pattern over the entire day. On the other hand, as
solar PV generation is highest during the day, the duck curve of net load becomes more pronounced. Meanwhile, although the
uncertainty of solar PV is less than that of wind power, the variability of solar net load is similar to that of wind net load because the
installed capacity of solar PV will be double that of wind power in 2030.
Figure 3-17 shows the seasonal net load patterns of combined solar PV and wind power generation. As peak demand is high in
summer and winter, the lowest point of the duck curve is not much different from the lowest point at night. In spring and autumn,
however, the lowest point of the duck curve is much lower than the lowest point at night, making it very difficult for grid operators
to manage base load generation.
Figure 2-11. Daily pattern of onshore and offshore wind generation and total wind generation on a
representative day in summer
(c) Total wind power
Source: author
Figure 2-12. Wind power and solar PV generation and net load pattern on a representative day in summer
(a) Wind power generation (b) Solar PV generation
(c) Wind power net load (d) Solar PV net load
Source: author
Figure 2-13. Pattern of net load of solar PV-wind combination model on a representative day by season
(a) Spring (b) Summer
(c) Autumn (d) Winter
4. Setting scenarios for reserve analysis
Table 3-8 outlines the scenarios set up by this study to analyze the power system in 2030. This study set up seven scenarios
depending on the deployment of solar PV, wind power, and ESS facilities. Among these, the core scenarios are: Case 1 (c1), Case
2_Wind and Solar (c2ws), and Case 3_Wind and Solar (c3ws). Case 1 is the base scenario, while Case 2_Wind and Solar is used to
analyze the effect of solar PV and wind power on the power grid. Case 3_Wind and Solar further analyzes the effect of an ESS on the
grid.
Scenario Description of scenario Abbreviation
Case 1 Base scenario (without solar and wind) c1
Case 2_Wind Case 1 + wind (17,674MW) c2w
Case 2_Solar Case 1 + solar (33,530 MW) c2s
Case 2_Wind and Solar Case 1 + wind and solar (51,204MW) c2ws
Case 3_Wind Case 1 + wind + ESS (5GW) c3w
Case 3_Solar Case 1 + solar + ESS (5GW) c3s
Case 3_Wind and Solar Case 1 + wind and solar + ESS (5GW) c3ws
Source: author
Case 1 is the standard scenario where solar PV and wind power are not introduced into the Korean power system by 2030. Case
2_Wind is a scenario where wind power is deployed until the target capacity of the Renewable Energy 3020 Implementation Plan
is reached, while Case 2_Solar is a scenario where solar PV is deployed until the same target capacity is reached. In
Case 2_Wind and Solar, both wind and solar PV are deployed, and in Case 3, an ESS is adopted as a flexibility resource for
controlling the variability of renewable energy. Case 3_Wind is a scenario where an ESS is added to Case 2_Wind, and for Case
3_Solar, an ESS is added to Case 2_Solar. Likewise, Case 3_Wind and Solar is a scenario where an ESS is added to Case 2_Wind
and Solar. Here, the ESS is assumed to be a lithium-ion battery with a capacity of 5GW (5% of peak demand) and energy volume
of 15GWh that can discharge maximum power for three hours at an energy efficiency of 95%.
Besides the ESS, the flexibility resources of VRE include LNG generation, demand response, and pumped-storage
hydroelectricity. This study chose the ESS as the representative flexibility resource not only because of its superior economic
feasibility and technology, but also because it is currently being considered as a major flexibility resource in many countries and it
makes analysis easier under the MPSOPF model.
This study aims to make the following four contributions by establishing the MPSOPF model and scenarios and analyzing the
impact of VRE on the power system at the time when the renewable energy deployment target for 2030 is reached. First, this study
provides information on the required reserve for day-ahead planning at the time when the renewable energy deployment target for
2030 is achieved. Second, it analyzes contingency reserve, which reflects uncertainty within time, using a multi-period optimization
analysis and load-following reserve, which reflects variability between times. Third, it examines the change in the required reserve
amount when the reserve price rises and analyzes the trade-off relationship between the reserve cost spent to address the variability
and uncertainty of renewable energy and cost of energy lost by not using renewable energy. Based on this, the importance of setting
an appropriate reserve price is reviewed. Fourth, this study analyzes the necessity and effect of flexibility resources by reviewing the
effect of substituting reserve with flexibility resources (ESS) as backup facilities for renewable energy as well as the cost-saving
effect of reducing reserve and energy generation.
Chapter 3. Analysis of reserve by hour
1. Power generation mix, generation, and reserve
Figures 4-1 and 4-2 show 24-hour generation profiles by source of generation on a representative day in summer based on the
performance of the MPSOPF optimization of the seven scenarios established in this study. In Case 1, hydro (blue), nuclear (red),
and coal (yellow) provide base-load generation, while combined-cycle power (sky blue), gas turbine (gray), and heavy oil (brown)
handle peak load.16 Wind is indicated in blue, and solar PV in green.
In Case 2_Wind, wind power produces electricity steadily throughout the entire day, replacing gas turbine and heavy oil as sources
of generation for handling peak load. In Case 2_Solar, solar PV in summer increases generation during peak-load hours, creating a
duck curve, replacing peak-load generation, and approaching the base-load generation sources such as coal-fired generation (Figure
3-1). In Case 2_Wind and Solar, the core scenario of this study, renewable energy generation is concentrated during peak-load hours,
replacing a portion of coal-fired generation (Figure 3-2).
In Case 3, where an ESS is added, variability and reserve cost are reduced through the smoothing of the net-load pattern. The ESS
stores energy at a lower cost during hours of high renewable energy generation, and discharges that energy during hours when the
marginal cost of electricity production is high, leading to reduced generation cost.
Figure 3-1. Generation profile by scenario on a representative day in summer (1)
Case 1
Case 2_Wind
Solar PV only (33,530MW)
16 The sums of load-following up reserve, load-following down reserve, and contingency reserve under Case 1 and Case 2_Wind and Solar are
compared.
Source: author
Figure 3-2. Generation profile by scenario on a representative day in summer (2)
Case 2_Wind and Solar
Case 3_Wind and Solar
Source: author
Table 3-1 shows the optimization of generation and reserve by scenario on a representative day in summer. A comparison of Case
1 and Case 2_Wind and Solar shows that the reserve increased by nearly 3.9 times. That is, the deployment of sufficient wind and
solar PV generation to reach the renewable energy target for 2030 would effectively replace coal-fired generation; however, because
of the high uncertainty and variability of VRE, the amount of reserve necessary to maintain the stability of the power system is
almost four-times greater than in the scenario where wind and solar PV are not deployed. Hence, a sufficient amount of reserve
needs to be secured in order to prepare for the increased penetration of VRE. If Case 2_Wind and Solar is compared to Case 3_Wind
and Solar, the use of an ESS (5GW) as a flexibility resource is found to effectively mitigate the uncertainty and variability of VRE,
reducing the required reserve by half. Furthermore, as the ESS alleviates the variability of renewable energy, renewable energy
facilities that had been rejected by the power grid due to their high variability can be now integrated into the grid, leading to a
moderate increase in renewable energy generation. In other words, flexibility resources not only help secure the stability of power
system operation, but also contribute to the increased integration of renewable energy into the power system, boosting the
profitability of renewable energy projects and encouraging investors to enter the market.
Table 3-1. Daily generation and reserve by scenario on a representative day in summer
(E [MWh]/day) c1 c2w c2s c2ws c3w c3s c3ws
E [renewables
E [non-renewables
Load-following up
Load-following
Contingency
Total reserve 70,640 207,112 188,020 273,008 80,379 55,942 111,597
E [reduced load] - 0.54 0.32 0.54 0.21 0.14 0.24
Source: author
Table 3-2 shows the cost of 24-hour generation and reserve by scenario on a representative day in summer. As with the results
shown in Table 3-1, comparing Case 1 to Case 2_Wind and Solar, as renewables replace thermal power generation, the reduced use
of fossil fuels leads to a significant reduction in generation costs;17 however, the high variability of wind and solar PV results in the
reserve cost rising by 386%. Meanwhile, comparing Case 2_Wind and Solar to Case 3_Wind and Solar, the introduction of an ESS
significantly reduces the required reserve and causes a moderate decline in the generation cost, as additional renewables are
integrated into the grid. As a result, the total operation cost falls by 1.2% with the introduction of an ESS (5GW).
Table 3-2. Daily generation cost and reserve cost by scenario on a representative day in summer
(E [million KRW/day]) c1 c2w c2s c2ws c3w c3s c3ws
E [generation cost] 75,565 68,266 56,947 50,590 68,226 56,976 50,425
Load-following up reserve
Load-following down
Contingency reserve cost 33 186 166 259 45 7 82
E [storage inefficiency cost] - - - - 13 11 30
E [total operation cost] 75,772 68,872 57,498 51,389 68,473 57,152 50,782
Source: author
Figure 3-3 illustrates the 24-hour generation profiles of Case 2_Wind and Solar on a representative day by source of generation
and by season. In spring and autumn, when power demand is low and peak load is flat, solar PV generation makes the duck curve
more pronounced and significantly influences coal-fired generation (base load). On the other hand, in winter, when peak demand is
high and solar PV generation is relatively low, net load is larger. However, even in spring and autumn, solar PV and wind generation
are not expected to affect nuclear power generation by 2030. Accordingly, stable grid operation is likely possible through the active
buildup of reserve. However, as these analysis results are based on information currently available, the actual amount of required
reserve in 2030 and the impact of renewables on grid operation need to be based on up-to-date information of that year.
Figure 3-3. 24-hour generation profile by source of generation on a representative day by season:
Case 2_Wind and Solar
(a) Spring (b) Summer
17 Generation cost refers to energy cost and is not the same as the unit cost of generation. Energy cost includes only variable cost. Accordingly, a
decline in generation cost does not translate to a reduction in consumers’ bills.
(c) Autumn (d) Winter
2. Analysis of reserve pattern by time
Table 3-3 shows the amount of reserve per hour by scenario on a representative day in summer. In Case 1, the amount of average
required reserve per hour is 3,051MWh, but in Case 2_Wind and Solar, it rises significantly to 11,765MWh. In Case 3_Wind and
Solar, where an ESS (5GW) is introduced, the average required reserve per hour declines significantly to 4,850MWh.
Meanwhile, in Case 2_Wind and Solar, the maximum reserve is 20,105MWh, and the minimum reserve is 3,063MWh, revealing
a significant variation in required reserve by time.18 Figure 3-4 shows the average load-following reserve and average contingency
reserve by scenario, indicating that, when VRE is deployed in a large scale, the load-following reserve is larger than the contingency
reserve.
Table 3-3. Analysis of required reserve per hour by scenario on a representative day in summer
MWh/
hour
average
c1 2,585 6,643 40 466 466 466 3,051
c2w 6,245 8,914 2,432 2,638 3,712 1,051 8,883
c2w 5,716 10,742 40 2,439 5,154 466 8,155
c2ws 8,022 13,196 2,100 3,743 6,909 963 11,765
c3w 2,828 6,835 - 641 2,414 - 3,469
c3s 2,324 5,588 - 108 1,439 - 2,432
c3ws 3,638 8,243 - 1,212 4,539 - 4,850
Source: author
Figure 3-4. Average contingency and load-following reserves by scenario on a representative day in summer
18 Sum of maximum load-following reserve and maximum contingency reserve and sum of minimum load-following reserve and minimum
contingency reserve.
Source: author
Figures 4-5 and 4-6 show the 24-hour reserve profiles per hour for up and down reserves. In Case 1, where uncertainty is low
overall, at times when power demand increases or decreases, only a small amount of load-following reserve is needed. Case 2_Wind
and Solar is similar to Case 2_Solar and requires higher reserve per hour on the whole.
In Case 3, overall, the required reserve by time dropped remarkably and effectively. However, Case 3_Wind and Solar shows that
a certain level of reserve is necessary, especially during peak demand hours, suggesting that additional ESSs are needed.
Figure 3-5. 24-hour required reserve profile per hour by scenario on a representative day in summer (1)
Case 1
Source: author
Figure 3-6. 24-hour required reserve profile per hour by scenario on a representative day in summer (2)
Case 2_Wind and Solar
Case 3_Wind and Solar
3. Analysis of the impact of the reserve price
One of the aims of this study is to identify the extent of the increase of reserve necessary to maintain grid stability while the
deployment of wind and solar PV increases. One of the inputs necessary to determine the amount of reserve is the reserve price. For
the analysis conducted in this study, we applied the average price of reserve in Korea over the past 10 years (KRW 3,000/MWh),
which does not reflect opportunity costs because the share of VRE is low and the reserve price is not determined by supply and
demand in the Korean market. As a result, the reserve price in Korea is lower than in other countries. By analyzing the impact of
price changes on reserve, this study derives policy implications related to reserve prices and markets.
To analyze the required reserve to respond to reserve price changes, the average reserve price of major ISOs in the United States
(USD 5.6/MWh (KRW 6,300/MWh)19), which appears to be more realistic than the price in Korea, was applied (Table 3-4).
Table 3-4. Average spinning reserve prices in 2014 by ISO in the United States
ISO Spinning reserve offer price (USD/MWh)
CAISO 3.34
ERCOT 14.15
MISO 2.58
ISONE 2.53
NYISO_E 6.49
NYISO_W 4.07
PJM 4.21
SPP 7.46
5.60
Source: Zhi Zhou, Todd Levin, and Guenter Conzelmann, 2016, Survey of U.S. Ancillary Services Markets, p.34.
Table 3-5 shows a comparison of scenarios using a high reserve price (KRW 6,300/MWh) and scenarios using the existing low
reserve price. With the existing wind-solar PV combination model (Case 2_Wind and Solar: c2ws) and a new reserve price model
(Case 2_Wind and Solar_r1: c2ws_r1), if the reserve price doubles, the required reserve drops by 32%, and the integration of
renewable generation into the grid falls by 26.1%. With the rise of the reserve price, the ability to respond to the variability of
renewable energy decreases, the required reserve amount decreases, and renewable energy that cannot be integrated into the power
grid is curtailed. As a result, renewable generation that can be integrated into the power grid declines.
Table 3-5. Optimization results using high reserve price and current low reserve price: generation and reserve
(E [MWh]/day) c2ws c2ws_r1 c3ws c3ws_r1
E [renewables generation] 346,807 342,078 347,825 345,363
E [non-renewable generation] 1,716,981 1,721,709 1,717,326 1,720,186
Load-following up reserve 99,817 80,279 45,580 31,770
Load-following down reserve 84,684 61,180 38,089 28,558
Contingency reserve 88,507 60,291 27,928 10,182
E [load curtailment] 0.54 0.61 0.24 0.19
Source: author
Figure 3-7 illustrates the analysis results above. Under the low reserve price scenario (c2ws) and high reserve scenario
19 KRW 1,125/USD, as of October 18, 2018 (basic KRW/USD exchange rate quoted by the Bank of Korea).
(c2ws_r1), as the reserve price rises, the scope of variability that grid operators can accept as appropriate shrinks, and reserve
decreases, increasing the loss of renewable energy and thus reducing the generation of renewables.
Figure 3-8 shows the reserve profile by reserve price and time under the Case 2_Wind and Solar scenario. Relative to the scenario
with the low reserve price, the scenario with the high reserve price leads to a smaller required reserve amount over the entire period.
Figure 3-7. Low reserve price (left) and high reserve price (right): renewables generation and required reserve
Source: author
Figure 3-8. Reserve profile by time under low reserve price and high reserve price: Case 2_Wind and Solar
(a) Low reserve price
(b) High reserve price
Source: author
Figure 3-9 compares the system operation cost saving effect when an ESS is used in the low reserve price and high reserve price
scenarios. Using solar PV and wind power, the system operation cost saving effect of an ESS with a high reserve price improved
significantly by 76% compared to that with a low reserve price. This suggests that a high reserve price further facilitates the process
through which an ESS reduces the reserve by reducing the variability of renewable energy. In terms of the cost of generation, while
a high reserve cost leads to the loss of renewable energy and increase in the cost of generation, ESSs mitigate the variability of
renewable energy, which decreases the loss of renewable energy and ultimately lowers the cost of generation.
This finding indicates that if the reserve price is raised to a level that reflects the opportunity cost of reserve, the value of the benefits
provided by ESSs as a flexibility resource of VRE increases, further boosting the economic feasibility of ESSs. In other words, if an
appropriate reserve price is introduced into the electric power market, various flexibility resources intended to reduce the variability
of renewables compete for adoption, increasing the efficiency of resource allocation. In this respect, the introduction of a reasonable
reserve price into the market is critical to the efficient operation of the future power market, where the penetration of renewables will
increase rapidly. Over a longer term, a reserve market where the reserve price is determined needs to be fostered.
Figure 3-9. Low reserve price (left) vs. high reserve price (right): cost-saving effect of ESS
Source: author
4. Annual cost of power system operation
Based on the results of the representative day by season and using the piecewise cubic Hermite interpolation polynomial (PCHIP),
this study estimated the annual cost of power system operation. Using a cubic function to improve on the existing interpolation
method, the PCHIP is an interpolation method that enables the local maximum and minimum to occur at each location. Among the
representative days for each season, this study selected the days with the highest demand for summer and winter and the days with
the lowest demand for spring and autumn. This is the most suitable method for estimating the annual cost of power system operation.
Figure 3-10 compares the values of annual power demand on the representative days by season estimated using the PCHIP method
with the actual annual power demand pattern. As seen in the example, the estimated annual power demand pattern follows the actual
values quite closely.
Figure 3-10. Example of estimation of annual demand pattern using the PCHIP method
Source: Wooyeong Chun, 2015, Study on building a stochastic power grid optimization model, Korea Energy
Economics Institute, basic research report, no. 15-15, p.87.
Daily average power demand
Daily peak power demand
PCHIP Annual peak demand pattern using PCHIP method
Power demand
Date
Table 3-6. Annual cost of power system operation by scenario: ratio relative to cost of Case 1
Million KRW/year c2w c2s c2ws c3w c3s c3ws
Generation cost 0.859 0.853 0.726 0.858 0.850 0.725
Reserve cost 3.448 2.530 3.994 1.313 0.871 1.722
Total operating cost 0.866 0.857 0.735 0.859 0.850 0.727
Source: author
Table 3-6 shows the annual cost of power system operation estimated using the PCHIP method. The values in the table are relative
to the cost of Case 1, which is assumed to be one (1). The generation cost of Case 2_Wind and Solar is 72.6% of Case 1, while the
reserve cost is 399.4% of Case 1. When VRE replaces fossil fuel generation, the generation cost declines significantly as it does not
require fossil fuel; however, because of the variability and uncertainty of VRE, the reserve cost nearly quadruples. In Case 3_Wind
and Solar, which adds an ESS to Case 2_Wind and Solar, the generation cost and reserve cost are 72.5% and 172.2% of Case 1,
respectively. That is, compared to Case 2_Wind and Solar, the generation cost and reserve cost were reduced by 0.01%p and
227.2%p, respectively.20 As explained earlier, ESSs mitigate the variability of renewables, significantly reducing the reserve cost.
Moreover, renewable energy lost due to the variability of VRE is now integrated into the power grid, causing the generation cost to
fall moderately despite the loss associated with the charging and discharging of the ESS. With the combination of these two cost
reductions generated by the ESS, the total operating cost of Case 3_Wind and Solar is 0.8%p lower than that of Case 2_Wind and
Solar.
Chapter 4. Analysis of reserve in 10-minute increments
The MPSOPF is a model that is suitable for analyzing reserve by hour.21 However, as renewable energy output fluctuates
significantly and the cycle of variability is short, more studies are now analyzing the characteristics of electricity output in increments
of 10 minutes instead of one hour. To calculate the spinning reserve provided by active generation units, short-term analysis in
increments of less than 10 minutes is needed. Hence, although the MPSOPF model provides a realistic simulation of the electric
power market in 2030, additional analysis of the short-term variability of VRE in increments of less than 10 minutes is needed. As
such, this chapter uses other analytic methodologies to examine reserve in 10-minute increments when the solar and wind power
deployment targets are reached in 2030 and derives the implications based on the research results of Chapters 4 and 5. This study
uses variation rate analysis methodology to analyze reserve in 10-minute increments.
1. 10-minute renewable energy variability analysis
A. Definition of output variation rate
The variation rate of output generation in 10-minute increments can be defined as ()−(−10 minutes)
. Here, () −
( − 10 ) is output variation, which is the difference between generation output at time t and generation output 10 minutes
before time t.
Installed capacity
Figure 4-1. Definition of output variation rate
20 Like solar PV and wind, this reflects only the energy cost of ESSs, not including the installation cost of ESSs.
21 As the model reflects change between times, it can be said that the model analyzes reserve for less than one hour. Therefore, as mentioned earlier,
the model is also suitable for 20-minute spinning reserve.
Source: author
Time
The rate of output variation, which is defined as the change in output divided by installed capacity, is used to estimate the renewable
energy prediction error rate. In general, the normalized mean absolute error (NMAE) and mean absolute percentage error (MAPE) are
used to estimate the renewable energy prediction error rate, as in Table 4-1.
The NMAE and MAPE use different estimation methods depending on whether the difference between the measured value and
predicted value (i.e. prediction error) is estimated in terms of installed capacity or measured value. The NMAE method divides the
prediction error by the installed capacity, and thus the greater the installed capacity, the lower the error rate (underestimated) for the
same error. However, as the NMAE is based on installed capacity, it is suitable for comparing different renewable energy sources.
Also, if the measurement is done in the same system, only the new installed capacity needs to be entered, making it easy to estimate
the error rate.
Table 4-1. Comparison of methods for estimating prediction error rate
NMAE
installed capacity and normalized, it is suitable
for comparing sources of renewables with
different installed capacities.
measured value and predicted value of renewable energy.
Disadvantage
(NMAE).
rate (MAPE) increases significantly.
| − |
X 100%
| − |
X 100%
Source: author, based on “Korea Hydro & Nuclear Power Co., Ltd., 2017, Study on direction of pumped-storage
hydroelectricity development and implementation strategy, p.74-76.”
Measured value
Predicted value
Installed capacity
The MAPE method checks the correctness of the prediction by estimating the difference between the measured value and predicted
value. However, if the installed capacity is small or the measured value becomes small due to weather, the prediction error rate
becomes very large, which is a disadvantage.
This study aims to analyze the variability of future renewable energy under the installed capacity of solar and wind power as
presented in the Renewable Energy 3020 Implementation Plan. Therefore, as it is necessary to estimate the variation of VRE
depending on installed capacity, the NMAE method, among the two methods above, is more suitable and thus the output variation
rate is defined as the change in output divided by installed capacity.
The output variation rate is calculated in 10-minute increments with a certain confidence interval.
B. Estimation of the output variation rate of renewable energy using weather data
As explained in Chapter 3, to ensure consistency between the two analytic models that this study used to predict solar and wind
power generation, the 16 solar PV and 16 wind power installation locations that were selected to conduct the reserve analysis by
hour using the MPSOPF model are used again here for the 10-minute reserve analysis.
To estimate short-term variability by 10-minute increment, the Weather Data Open Portal22 was used to obtain one-minute
weather data for the 16 locations in 2017. To estimate the variation rate of output by source of power generation, instead of the
correlation between solar PV and wind power, solar radiation (MJ/m2) and wind speed (m/s) data, excluding temperature (°C), were
used, from among the input variables used in Chapter 3 of this study. As for solar PV, one-minute solar radiation data for 15 locations
were obtained, excluding one location for which data were not available.23 As for wind power, one-minute wind speed data for 15
locations were obtained, except for one offshore location for which data were not available.24 The capacity of the offshore location
that was excluded was allocated to the other 15 locations.
Based on these weather data and using the same method presented in Chapter 3, one-minute generation data corresponding to the
renewable energy capacity target for 2030 were created. For the 10-minute analysis, the one-minute generation data were summed
up by 10-minute increment to obtain 10-minute generation data. Based on the 10-minute generation data, the variation rate of
renewable energy per 10 minutes was calculated. These procedures are summarized in Figure 4-2.
Figure 4-2. Method of estimating the variation rate of renewable energy
Source: author
1. Stage 1. Select locations, according to deployed renewable
energy capacity and planned capacity.
22 https://data.kma.go.kr, accessed on Oct. 24, 2018.
23 s6, ASOS 152, Ulsan.
24 w15, AWS 22106, offshore, Pohang.

16 Select 16 solar PV and wind power locations.
2. Stage 2. Obtain weather data from the Weather Data Open
Portal.
(m/s)
(m/s) data for selected locations.
3. Stage 3. Create renewable energy generation data.
1


wind power data.
Sum up the one-minute generation data per 10 minutes to
obtain 10-minute generation data.
4. Stage 4. Calculate variation rate of renewable energy
generation.

generation data.
In the case of the downward variation of renewables, the supply to the power system becomes insufficient, requiring the
procurement of an appropriate amount of reserve to prevent load shedding. Conversely, in the case of the upward variation of
renewable energy generation, oversupply occurs, calling for means of addressing the oversupply problem. While some countries
maintain an operating reserve in consideration of both downward and upward variation, Korea maintains an operating reserve only
for downward variation, because any upward variation of output can be addressed by cutting back on non-base load generation; and
if that is not feasible, renewable energy gene