**1. Introduction**

In 2013, signatories to the Paris Agreement committed to submit a national climate plan to mitigate climate change by reducing greenhouse gas emissions. Subsequently, one of the United Nations' Sustainable Development Goals, established in 2015, is focused on affordable and clean energy. These two global initiatives have motivated several nations to promote renewable energy sources such as wind, solar, and biomass into their energy mix. As a result, several "green energy transition" initiatives are ongoing in countries such as Germany and Denmark, and subnational jurisdictions such as California, Scotland, and South Australia [1]. Besides these major players, more than 150 countries have national targets for renewable energy in the power sector [2].

The Japanese government recently reiterated its commitment to the projected energy mix for 2030, where fossil fuel-based generation will be reduced to 46%, and renewable energy will comprise 22–24%, of which solar energy will have a 7% contribution [3]. There was a recent influx of solar PV installation mainly driven by the FIT program. The Kyushu region, located on Japan's western tip, is one of the country's leading regions in solar PV generation. Relative to the rest of the country, the region has higher solar power potential

**Citation:** Dumlao, S.M.G.; Ishihara, K.N. Weather-Driven Scenario Analysis for Decommissioning Coal Power Plants in High PV Penetration Grids. *Energies* **2021**, *14*, 2389. https:// doi.org/10.3390/en14092389

Academic Editors: T M Indra Mahlia and Islam Md Rizwanul Fattah

Received: 29 March 2021 Accepted: 19 April 2021 Published: 22 April 2021

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**Copyright:** © 2021 by the authors. Licensee MDPI, Basel, Switzerland. This article is an open access article distributed under the terms and conditions of the Creative Commons Attribution (CC BY) license (https:// creativecommons.org/licenses/by/ 4.0/).

and cheaper land, which has driven solar power investments. As of early 2021, the region has a total installed capacity of 10 GW, and additional plants, which will increase this capacity further to roughly 16 GW [4] by around 2027, are already approved. The share of solar PV generation has been steadily increasing in the region. In 2017, 2018, and 2019, solar PV generation accounted for 8.5%, 9.2%, and 10.1% of the total yearly generation, respectively. The International Energy Agency (IEA) classifies the impact of variable renewable energy (VRE) on the energy system's operation into four phases. Japan, as a country, is already in phase 2 where there is a minor to moderate impact on the system operation, whereas Kyushu, as a region, was categorized as phase 3, where VRE determines the operation pattern of the system [5]. This further shows that Kyushu is leading the country in terms of solar PV penetration and is already facing issues ahead of the rest of the country. Kyushu's situation lends itself as a viable case study in exploring the potential impact of solar energy in reducing CO2 emissions by replacing traditional energy sources with solar energy.

Coal remains to be the cheapest and most economically stable source of electricity for many countries. However, it is also one of the major contributors of CO2, which leads to global warming. Among the G7 countries, Germany (by 2038 [6]), France (by 2023 [7]), the United Kingdom (by 2024 [7]), Italy (by 2025 [7]), and Canada (by 2030 [8]) have presented their coal phase-out plans. Other European Union member countries have also developed their phase-out plans within the next two decades, and Austria and Belgium have already phased-out their coal plants [7]. Nonetheless, removing coal is a significant roadblock to the green energy transition in many countries, and as countries install increasing amounts of renewable energy, it might be time to consider reducing coal in the energy mix. Solar photovoltaics (PV) can be a green alternative to coal. However, the generation profile of solar energy is different from that of coal, which complicates the process of replacing coal with solar energy. Simultaneously, the variability of solar power requires another flexible source. Liquefied petroleum gas (LNG), given its flexibility, is often used to balance the VRE. Given these intertwined variables, it is necessary to understand the potential, limitations, and implications of using solar energy to replace coal, which are currently unclear.

Many countries see LNG as a bridge to a clean energy future that will pave the way for less coal in the energy mix [9]. It is still a fossil fuel, but it produces less CO2, which is acceptable for now until a superior technology is available. Due to many countries' tendency to rely on LNG to reduce their CO2 emissions, the demand for LNG has steadily been increasing, which threatens its supply and price. Shell reported in their LNG Outlook 2020 that global demand for LNG increased by 12.5% to 359 million tons in 2019, which they attributed mainly to the role of LNG in the low-carbon energy transition [10]. It has been reported that the price of LNG increased in October 2020 in anticipation of a colder winter in East Asia [11]. This shows the volatility of LNG's supply and price on the global market, which presents another factor for consideration in the analysis, since solar energy production needs LNG to a certain extent.

Aside from the potential CO2 reduction benefits, reducing coal capacity could also reduce solar curtailment experienced by grids with high PV penetration. Kyushu started to suffer from curtailment in October 2018, which was explored in a previous study [12]. Several studies have also explored this recent issue in Kyushu. Bunodiere and Lee [13] explored several scenarios to mitigate solar curtailment in Kyushu using a logic-based forecasting method and concluded that reducing the region's nuclear capacity will reduce curtailment. However, in their approach, they considered coal and LNG as thermal generators as a whole due to data limitations. A coal station behaves like a nuclear plant, since these two technologies are considered baseload generators. By treating coal as separate from LNG and as a baseload generator, it could also be said that coal could reduce curtailment. Although Japan initially used their pump hydro energy storage (PHES) to improve the flexibility of nuclear power plants [14], it is now used to store excess solar electricity generation. Li et al. [15] conducted a techno-economic assessment of large-scale

PV integration with PHES and concluded that the PHES could effectively absorb some of the surplus PV production and could maintain low generation cost by using the surplus production. Since the available data regarding power generation in the region aggregate coal and LNG together, the understanding of coal generation in the energy balance is limited.

In order to fully understand the optimal conditions for coal, solar, and LNG production, it is necessary to conduct a power flow analysis to evaluate the impact of investing in more solar PV for driving coal decommissioning. This analysis will provide additional information about the energy balance, including information about solar power generation and curtailment, which are difficult to estimate. By gathering the generators' capacity and generation profiles and the demand profiles, the optimization can calculate the hourly energy balance and minimize the necessary coal capacity and generation. This insight provides the necessary understanding of the potential and limitations of solar energy in regard to replacing coal. However, to ensure the robustness of the analysis and the recommendation, the demand and solar power generation's stochasticity must be considered. It will be challenging to recommission a decommissioned plant due to an unforeseen circumstance; thus, careful analysis is necessary to account for potential variations.

Replacing part of coal's electricity production with solar electricity production, coupled with LNG electricity production, is a subset of the generation expansion planning (GEP) problem. Koltsaklis and Dagoumas [16] wrote a review article exploring the stateof-the-art generation expansion planning where they listed seven challenges to the GEP problem. One of the mentioned challenges is rooted in the risks involved in GEP. They enumerated several potential sources of risks and categorized them according to economic, political, regulatory, environmental, technical, social, and climate categories. Ioannou et al. [17] reviewed the risk-based methods for sustainable energy system planning and categorized the risks in the same manner. They identified seven risk-based methods: mean-variance portfolio theory, real option analysis, Monte Carlo simulation, stochastic optimization technique, multi-criteria decision analysis, and scenario analysis.

Santos et al. [18] conducted a study to identify uncertainties in the electricity system and demonstrated the corresponding impacts on the energy mix through scenario analysis. Their results highlighted that climate uncertainty represents primary risk sources for VRE, since it dictates the system's power generation. A review on the energy sector vulnerability to climate change [19] summarizes the authors' contributions on climate and energy, and they noted that climate change could affect variables that influence electricity generation from photovoltaics and concentrated solar power. The review highlighted that global solar radiation has increased in southeastern Europe [20] and decreased in Canada [21]. They also highlighted that power output calculations should account for air temperature, since it impacts the solar cell's efficiency [19].

Ioannou et al. [17] noted that energy planning has extensively used stochastic optimization techniques, and the stakeholder's motivation mainly drives the constraints. They also mentioned that the Monte Carlo simulation has many advantages, but it requires considerable data inputs to create probability density functions. Alternatively, scenario analysis evaluates the risks by creating potential future developments that range from the worst-case to the best-case scenario, which could then cover all the possible risks in the analysis. As highlighted by several authors [18–21], climate, and by extension weather, must be considered in modeling solar energy generation. Factors such as the changing solar irradiance and ambient temperature could influence solar panels' variability and efficiency.

By carefully identifying the test cases, scenario analysis is sufficient for ensuring the robustness of the analysis. The initial problem is then rooted in creating the scenarios representing the worst case, the best case, and the cases in between. The weather data analysis can provide the representative years that fit the scenario targets, such as warm summers, colder winters, extreme summers, and extreme winters. Although such data are limited, datasets could be synthesized based on the historical relationship between temperature and demand. Solar generation could be calculated from the irradiance and ambient temperature data. The robustness of the analysis and recommendation can be addressed by combining scenario analysis and past weather data.

Therefore, in this study, an hourly power flow analysis was conducted to understand the potential, limitations, and implications of using solar energy as a driver for decommissioning coal power plants. Understanding these factors can provide the necessary recommendations and precautions for energy planners. Since LNG scarcity is anticipated, LNG quota is one of the primary constraints. In order to ensure the robustness of the results, this study presents a straightforward weather-driven scenario generation that utilizes historical weather and electricity demand data processed through machine learning algorithms to generate scenarios that account for weather variations. Through the weather-driven approach, the study aims to reveal the impact of yearly variations in the factors that must be considered for long-term planning that reduce CO2 emissions while ensuring grid reliability. The Kyushu region in Japan was used as a case study since (a) it is continuously increasing its solar capacity, (b) it has a fleet of coal power plants older than 40 years old ready for decommissioning, and (c) it has enough LNG power plant capacity to support the initial transition.

The code for the weather-driven approach used in this study is available through a public GitHub repository [22], where most of the code and diagrams used in this paper are documented in jupyter notebooks. The approach, with minor changes, could easily be replicated in other nations or regions provided that historical hourly temperature, irradiance, and demand data are available.

Section 2 discusses the methodology for the weather-driven approach, including data and data processing, weather-based data generation, and hourly simulation. The results are then presented in Section 3, and the implications are discussed in Section 4. Finally, the conclusions are drawn in Section 5.

#### **2. Methodology**

The overview of the proposed weather-driven approach can be seen in Figure 1, where it is divided into four stages. First, data were collected from Kyushu Electric Power Company (KyEPCO) [4] and Japan Meteorological Agency (JMA) [23] and were pre-processed to fit the intended applications. The weather-based data generation has three components. A weather selection metric was designed based on comfort-levels to identify the years that could represent the scenarios in the region. The *pvlib* Python library [24] was used to calculate the photovoltaic systems' generation under various weather conditions. A demand fingerprint was developed to generate synthetic demand for the selected years. These synthetic data were then used as input to the hourly power flow optimization done in Python for Power System Analysis (PyPSA) [25]. Finally, the simulation results were analyzed.

**Figure 1.** The proposed weather-driven scenario-based analysis approach capable of handling weather-related variations in electricity demand and solar energy production.

## *2.1. Data and Data Pre-Processing*

#### 2.1.1. Energy Demand

The energy data were collected from Kyushu Electric Company [4], where the hourly information about generation, transmission, and demand is published since April 2016. The data also include curtailment information for both solar and wind power. Transmission and pump hydro energy storage (PHES) could be positive or negative. For transmission, negative values represent energy export while positive values indicate electricity import. For PHES, negative and positive values represent the charging and generation phases, respectively. Although the data until December 2020 are already published, only data until March 2019 were used in the study since this represents four full fiscal years.

As seen in Figure 1, the energy data are used in both the demand fingerprint and simulation phase. Only the demand data were necessary for the demand fingerprint, while the other hourly data were used as parameters for the other generations and PHES.

#### 2.1.2. Temperature and Irradiance

The temperature and irradiance data were collected from the Japan Meteorological Agency (JMA) [23], where the hourly weather data are published since 1946. For this study, 30 years of data were collected from 1990 to 2019 to serve as reference weather scenarios. A representative temperature was collected from each of the major cities' weather stations, as shown in Table 1.


**Table 1.** Weather stations in Kyushu.

In order to represent the mean temperature and mean irradiance in the region, solarcapacity-weighted mean and monthly-demand-weighted mean were used for the solar generation calculation and demand generation, respectively. Using the consolidated data from [26], Table 2 shows the shares of the solar PV installation in Kyushu since 2012 and the shares in 2019 were used as the reference for the solar-capacity-weighted mean temperature and irradiance. The Ministry of Economy, Trade, and Industry (METI) publishes each prefecture's monthly energy demand since April 2016 [27]. Table 3 shows the mean of each prefecture's shares from 2016 until 2019. These values were used to calculate the monthly-demand-weighted temperature mean used in the demand fingerprint.

The solar capacity ratio was used for the solar power generation calculation because the power generation's distribution is influenced by the distribution of the capacity. It is necessary to use this weighted mean because the temperature where more solar panels are installed should have a more significant representation in the temperature used in the solar generation calculation. However, the temperature where there is greater demand should have more influence on the temperature used for demand calculations.


**Table 2.** Share of solar power installations (%) in the prefectures.

**Table 3.** Average monthly demand share (%) in Kyushu from 2016 to 2019.

