Designing the Energy Transition Policies for Sustainable Economy and Environment in South Korea

Transcription

1 Designing the Energy Transition Policies for Sustainable Economy and Environment in South Korea YooKyung Yang, Miran Jang, Sung Won Kang INTRODUCTION Under the Paris agreement, governments are designing emission reduction plans to meet their NDCs. Energy transition in power generation becomes one of these strategical plans, because energy industry dominates national CO2 emissions. For example, Australia s national energy plan is aiming to phase out entire coal plants and to increase usage of renewable energies up to 80% until Canada is planning to reduce the proportion of nuclear and coal power in energy mix by 3% and 8% respectively until 2035 compared to Japan, which applies radical energy plans for nuclear power, is even taking total phasing out of nuclear power and decreasing coal power into account. Meanwhile, these global trends toward diminishing nuclear and coal power plants are projected to encourage LNG usage because of relatively inexpensive unit cost of LNG. In this paper, we define this avoidance of nuclear and coal in power generation as de-nuclear and de-coal phenomenon. In South Korea, these phenomena become official energy transition policies. The government promulgated the transition to safe and clean energy in the power generation sector In May, This energy transition policy was specified through Renewable Energy 3020 Implementation Plan, and the 8 th Basic Plan for Electricity Supply and Demand. The former states that the proportion of renewable energy in energy mix will take 20% by The latter indicates action plans for denuclear and de-coal policies. De-nuclear has been an issue in South Korea since the Fukushima Daiichi nuclear disaster. There were powerful earthquakes happened alongside with coastal areas near nuclear power plants recently. On the other hand, evasion of coal-fired power plants has become an important topic as a highly concentrated fine dust has severely threatened public health and environment. However, Korean society still needs to reach a concensus on the de-nuclear and de-coal policies. Some opponents assist that the total amount of emissions will grow as the use of LNG increases instead of reducing nuclear power plants, which do not emit greenhouse gases. Other indicates that Korean government misestimates Levelized Cost of Electricity (LCOE) with reference to energy-rich countries and miscalculates the social cost of energy conversion to renewable energy. Therefore, it is necessary to strategically design energy policy based on economic and environmental impacts. The purpose of this study is to provide rationale for sustainable energy policies by comprehensively assessing policy impacts; this paper estimates changes in energy mix, the price of electricity, the amount of emission, and economic growth according to energy transition policies. This study mainly addresses two arguments of does replacement of base-load power with other energies really help reduce missions? and de-nuclear and de-coal policies are economic decision?. MODEL and DATA We built an integrated economic-climate model aiming to assess policy impacts on environment and economy. The integrated model links top-down and bottom-up modules using a soft-linking methodology. For each module, we adopt Computable General Equilibrium(CGE) for top-down and

2 Linear Programming(LP) for bottom-up module. The integrated model is a single regional recursive dynamic model designed to replicate technology choices to meet energy demand and the amount of emissions according to energy use. This integrated model consists of seven sectoral models, which are power, transportation, agriculture, manufacturing industries, building, land-use, and waste models. In this study, we use power sector integration model(hereinafter called power model) in order to analyze energy transition policies. Figure 1. Overview of Integrated Model For linkage methodology, the power model adopts a decomposition of the integrated MCP formulation (Böhringer and Rutherford, 2009) and Positive Mathematical Programming (PMP) techniques. According to Böhringer and Rutherford (2009), top-down module transfers the vectors of factor price and demand calculated by CGE model to the bottom-up module, and these exogenously given vectors are used to optimize the supply and production vector in the bottom-up module. Then, the vectors calculated in the bottom-up module re-transfer to top-down module, and these whole process iterates until the vectors of supply and demand converge at equilibrium solution. An important feature of decomposition technique is to transform linear programming into quadratic programming with a first-order demand elastic price function. PMP is a technical way to avoid corner solution that LP optimization model has in general. In the Power model, PMP is also used to reproduce technical status(installed capacity, generation mix, power generation, etc.) of the base year.

3 Figure 2. Integrated Model of Power sector (Power model) This integration model ultimately aims to develop multi-linked model which links CGE and multisectoral optimization models. However, the Power model, we used in this study, integrates only CGE and a power sector. The major outcome of the Power model is the amount of emissions, production, energy use, GDP, employment, and energy mix. In order to link two discrete modules, we constructed technology database called hybrid social accounting matrix(sam). The hybrid SAM includes economic, technological, and power statistics. Specifically hybrid SAM consists of factor costs, Levelized Cost of Energy (LCOE). Factor costs are specified into labor, energy, and capital costs. LCOE is defined as indicator to compare cost competitiveness across technologies (IEA, 2014). The LCOE is the present value of average power generation cost over the life span of the power generation technology. This indicator is estimated using capital cost, operation and management cost, fuel cost, and various technology characteristics which are installed capacity, efficiency, lifetime, generation etc. Data sources are mostly national statistics such as Electric Power Statistics Information System(EPSIS), Korea Energy Statistical Information System(KESIS), Korea Statistical Information(KOSIS), Korea Power Exchange(KPX), and Economic Statistics System(ECOS). If national data are not available, we would collect data from IEA and Bloomberg New Energy Finance(BNEF). For example, we collect operation and management(o&m) costs from IEA, and plant portfolio including construction period, construction unit price, real construction price, and cumulative installed capacity is gathered by BNEF. The role of the hybrid SAM is to bridge two modules by providing common ground to communicate. Hybrid SAM acts as a benchmark for calibration and secures consistency in a unit of parameters. Building Hybrid SAM is meaningful in that it is the first attempt to synthesize historical data and estimated data with the purpose of running two discrete models. SCENARIO In this paper, we will examine sustainability of energy transition policies in South Korea. The government proclaims energy policies as well as prospect of energy supply and demand through the

4 th Basic Plan for Electricity Supply and Demand (Ministry of Trade, Industry, and Energy, 2017). Therefore, we designs scenarios reflecting energy policies and prospects according to the report. BAU scenario We define BAU scenario as a scenario that follows basic projections of annual demand, residual capacity, and plant retirement plans with the report. This scenario models the general energy use and demand without policy impacts. Scenario 1: a quantity regulation Scenario 1 is designed to represent a quantity regulation. In the Scenario 1, nuclear power plants are retired early by 10 years. In addition to nuclear constraints, we make quantity regulation on coal-fired power plants by shutting-down the plants during spring season without peak time. Scenario 2: a quantity regulation with fiscal policies Scenario 2 includes not only quantity regulation policies in Scenario1 but also fiscal policies. The market reacts to the financial inducement so that government s financial incentives will affect energy transition movement. In the Scenario2, renewable subsidy is considered in a way that the subsidy compensate for the investment cost of solar and wind. In the model, the carbon price is 20,000 KRW per ton, and the price of emission penalty on total suspended particles (TSP) is 100,000KRW per ton. RESULTS Figure 3 illustrates the change in GDP by scenarios. As can be see, BAU and Scenario1 show similar trends in GDP change, whereas GDP in Scenario2 slightly decreases because of the impact of fiscal policy. It is assumed that decreases in fuel consumption in all sectors derive GDP loss. Table 1 represents output of the power and energy sectors. Table 1 explains reasons for GDP loss in Scenario2 compared with the other two scenarios. It is found that reduction in output of power and energy sectors is larger in Scenario 2 than Scenario 1. 2,500,000 2,000,000 1,500,000 1,000,000 BAU Scenario1 Scenario2 500,000 - Figure 3. GDP

5 Table 1. Output of Power and Energy Sector year Scenario Electricity Natural Gas Diesel Gasoline Coal Chemical BAU (A) 31, , , , , Scenario1 (B) 31, , , , ,421.5 Scenario2 (C) 30, , , , ,936.9 Differentials (B-A)/A (%) (C-A)/A BAU (A) 35, , , , ,837.7 Scenario1 (B) 35, , , , ,203.6 Scenario2 (C) 33, , , , ,073.5 Differentials (B-A)/A (%) (C-A)/A BAU (A) 29, , , , ,534.4 Scenario1 (B) 29, , , , ,322.9 Scenario2 (C) 28, , , , ,617.3 Differentials (B-A)/A (%) (C-A)/A Table 2 and Figure 4 are showing generation mix by scenario. We clearly observe that Scenario 2 has the highest proportion of renewable energy among three scenarios. This result emphasizes the importance of renewable subsidies in achieving energy transition. However, proportion of coal in generation mix is incremented throughout the model years, which is far from a successful energy transition policy. It implies that the government should apply stronger quantitative regulation to coalfired plants rather than an outage of the plants during spring. Table 2. Generation mix by Scenario: 2015, 2030, 2050 year Scenario Nuclear LNG Oil Coal Renewable BAU 32.10% 22.28% 2.56% 41.19% 1.87% 2015 Scenario % 22.06% 2.54% 42.26% 1.85% Scenario % 23.44% 2.68% 37.64% 1.97% BAU 30.90% 22.87% 2.73% 41.56% 1.94% 2030 Scenario % 25.75% 3.04% 46.20% 2.15% Scenario % 26.23% 3.16% 41.54% 2.49% BAU 16.18% 24.61% 2.76% 53.36% 3.09% 2050 Scenario % 24.55% 2.71% 53.39% 3.12% Scenario % 23.49% 2.36% 53.16% 3.61%

6 BAU Scenario 1: Quantity Regulation Coal Coal LNG LNG 10 0 Nuclear 10 0 Nuclear Scenario 2: Quantity Regulation/Fiscal policy Nuclear LNG Coal Figure 4. Generation mix by scenario The result of energy mix shows a similar composition of energy by scenarios. Even though subsidies are paid to solar and wind power plants to lower capital costs in scenario 2, proportion of renewable energy increases 0.5% more than Scenario 1 which does not consider fiscal policy. Therefore, we conclude that more subsidies should be provided to make up for construction of renewable energy plants. Figure 5 explains the amount of CO2 emission by scenarios. Scenario 1 exhibits higher CO2 emission for 2020 to 2048 compared to the BAU. The higher emission results from a decrease in nuclear power generation which does not emit CO2 in generation. Instead, coal thermal and LNG power plants replace the nuclear power plant. Scenario 2, on the other hand, imposes carbon taxes on the entire industry, thereby largely reducing CO2 emissions.

7 12,000 11,000 10,000 9,000 8,000 BAU Scenario1 Scenario2 7,000 6, Figure 2. CO2 emission DISCUSSION Energy transition policies orient reduction in nuclear and coal power plants focusing on safety and cleanness. Safety on nuclear power plants bas became an issue under the influence of Fukushima Daiichi nuclear disaster and occurrence of frequent earthquakes near nuclear power plants. Clean energy gets important as fine dust threats public health. In this paper, we examine energy policies that government renounced and assess economic and environment impacts. The modeling result indicates that the government should take stronger quantitative regulations and economic instruments into account to achieve 20% of renewable energy targets and emission reduction goals. The quantity regulations on nuclear and coal power plants in Scenario are not likely to affect economic growth. However, when the financial incentives and penalty is introduced, economic growth and the amount of emission decrease. As the economy actively response to economic instruments, the energy transition policies should be carefully designed to balance environment and economic influence. For the future research, we will design elaborate policies. We will additionally consider early retirement and restraint on new construction of coal power plants for quantity regulation scenario. REFERENCES IEA(International Energy Agency), Energy Technology Perspectives Böhringer, C., & Rutherford, T. F. (2009). Integrated assessment of energy policies: decomposing topdown and bottom-up. Journal of Economic Dynamics and Control, 33(9),