Journal Archive

The Long-range Energy Alternatives Planning system (LEAP) model was developed by the Stockholm Environment Institute in 1980 to analyze long-term energy plans. Unlike the optimization model deriving a combination of energy technologies to reduce GHG emissions or meet energy demand prospects at minimum cost, the LEAP model is an accounting model that analyzes energy supply and demand, costs, and environmental impact based on energy technology distribution scenarios. Studies on long-term energy and GHG reduction using the LEAP model are generally conducted for medium- and long-term periods, such as until 2030 or 2050, and are mainly used at the national or city level. The LEAP model is designed to integrate and analyze the energy supply and demand system, or to analyze the energy demand sectors such as transportation, buildings, etc., or the energy supply sector such as power generation (Park et al., 2013; Takase and Suzuki, 2011; Heaps et al., 2009; Cai et al., 2007; Zhang et al., 2007). Approximately 16,000 people across 191 countries were using the LEAP model as of December 2012.

The LEAP model consists of a key assumption module to enter social and economic premises including population, households, gross domestic product (GDP), and industrial structure; a demand module including residential, commercial and public sector, industry, and non-energy use sectors; a transformation module including power generation, oil refining, and gas production; a resources module that lists domestic production and the import and export of energy sources such as coal, oil, and renewable energy; and a non-energy sector module that lists non-energy sector GHG emissions from industrial process, agriculture, land-use change and forestry, and waste sectors. In the supply and demand sector, the 1996 Intergovernmental Panel on Climate Change (IPCC) Tier 1 default emission factors embedded in the LEAP model were used for calculating the GHG emissions.

In this study, the LEAP 2011 model was used for analysis. The electricity demand forecasts by scenario were input by separating them into industrial, residential, commercial and public sectors. The electricity generation to meet the demand was determined by considering transmission and distribution losses and selfuse, system peak load shape and planning reserve margin in the transformation sector. The current distribution status, future installations remaining after lifetime, and the government’s plans for installations using nuclear power, coal-fired power, natural gas-fired power, NGCC power, oil internal-combustion power, hydropower, pumping, solar power, wind power, geothermal power, biomass, fuel cells, integrated gasification combined cycle (IGCC) power, by-product gas power, and tidal power were input as the power generation technologies. The characteristic values of efficiency, availability factor, cost etc. by power generation technology were also input (Park et al., 2013). Environmental impacts such as GHG and air pollutants were calculated by applying the 1996 IPCC Tier 1 default emission factors, and the global warming potential (GWP) of the 1995 IPCC Second Assessment Report was applied. Power plants are dispatched using merit order except in the reference scenario where nuclear power plants are dispatched in proportion to a process share to meet 59% by 2030.

This study used the data of the main outlook such as population, economic growth, industrial structure, etc., that were used during the establishment of the national basic energy plan when predicting power generation by 2030 (PMO et al., 2008). Population increases from 48.75 million people in 2009 to 52.16 million in 2030 and then decreases (Statistics Korea, 2011). The annual average GDP growth rate between 2009 and 2030 is expected to be 3.5% (KDI, 2010; MKE and KPX, 2010). The value-added share of the three biggest energy-consuming industries decreases from approximately 30% in 2008 to approximately 20% in 2030 (PMO et al., 2008). The oil price is assumed to go from $60/barrel in 2009 to $130 (2009 US$)/barrel in 2030 in accordance with the crude oil import price of the IEA’s current policy scenario (IEA, 2010b). The electricity demand forecasting for each sector was entered. The electric power installations, GHG emissions, costs and jobs based on the electricity supply mix were analyzed annually. The reference year for the analysis was set as 2009, and the simulation was run until 2030.

Scenarios included combinations of four demand scenarios and three supply scenarios, and a total of six scenarios (Reference, A1, A2, B1, B2, C2) were established (see Fig. 1).

Fig. 1. 
Overview of Scenarios for an Alternative Electricity System by 2030.

First, the four demand scenarios were as follows.

Government’s Baseline Scenario (Reference): The baseline demand outlook in the 5th Basic Plan for Long-term Electricity Supply and Demand was applied by extending it until 2030 (MKE and KPX, 2010).

Demand Reduction Scenario through Electricity Price Realization (A): This scenario was constructed to raise the electricity price for household use by 1% (based on real prices) each year to alleviate the burden on the home while raising the electricity price for industrial use with a larger portion of electricity consumption each year by 3% in 2012-2015, 2% in 2016-2020, and 1% in 2021-2030, taking urgency into account. The price for households in 2030 will increase by 21% compared to 2009 (similar to the level of OECD countries in 2009), while the price for industrial use in 2030 will increase by 37% compared to 2009 (similar to the level of OECD/European countries in 2009). The reduction of electricity consumption due to price increases was analyzed by applying price elasticity, i.e. -0.66 for electricity for industrial use (Kim, 2009) and -0.576 for electricity for household use (Jung and Park, 2010). The electricity demand outlook to which price elasticity was applied is similar to the government’s target demand outlook presented in the 5th Basic Plan for Long-term Electricity Supply and Demand with electricity demand increasing by an annual average growth rate of 1.44%.

Scenario for Achieving the OECD Average Electricity Consumption Level (B): The electricity consumption per capita was set to achieve the level that meets the 450 ppm scenario of the OECD countries by 2030 through the reinforcement of demand management policy as well as realization of electricity prices (IEA, 2011).

Scenario for Achieving the OECD/European Countries’ Average Electricity Consumption Level (C): The per capita electricity consumption was set to achieve a level that meets the 450 ppm scenario of the OECD/European countries by 2030 through the reinforcement of the demand management policy as well as the realization of electricity prices (IEA, 2011).

The three supply scenarios were as follows.

Reference: Renewable energy plants at the level of implementing a renewable portfolio standard (RPS) system were added under the assumption that electricity is produced to meet the government’s baseline demand outlook in the 5th Basic Plan for Long-term Electricity Supply and Demand, while the power generation share of nuclear in 2030 is approximately 59%. There are no plans for abolition of nuclear power by 2030, and the lifetimes of obsolescent nuclear power plants are extended (MKE and KPX, 2010).

Supply Scenario 1: Only the five nuclear power plants that are currently under construction are reflected, and there is no construction of additional nuclear power plants. The supply of renewable energy will be expanded. Coal-fired power plants planned for the years after 2015 will not be introduced (only the coalfired power plants planned before 2015 will be allowed). Nuclear power plants and coal-fired power plants will be phased out after operating for 40 and 30 years, respectively.

Supply Scenario 2: Only the nuclear power plants and coal-fired power plants that have been built as of 2012 are operated. Nuclear power plants and coal-fired power plants will be phased out after operating for 40 and 30 years, respectively.

The annual average growth rate of electricity demand between 2009 and 2030 is 2.56% in the Reference scenario, 1.44% in A1 and A2 scenarios, -0.14% in the B1 and B2 scenarios, and -1.51% in the C2 scenario.

The existing nuclear power plants and the five nuclear power plants currently under construction are operated in the A1 and B1 scenarios. In the A2, B2, and C2 scenarios only the nuclear power plants completed as of 2012 are operated, while the necessary electricity is supplied by renewable energy and NGCC power generation (see Fig. 2). In the Reference scenario, 59% of the power will be generated by nuclear power plants by 2030 followed, in order, by coal, gas, and renewable energy. The power generation rate of the nuclear power plants that will be operating in 2030 is 59% (Reference), 24% (A1), 15% (A2), 33% (B1), 21% (B2), and 27 %(C2). In this case, the number of the nuclear power plants that will be operating in 2030 is expected to be 46 units in the Reference scenario (assuming that there is no abolition of nuclear power plants until 2030¹), 17 units in the A1 and B1 scenarios (when 40 years is assumed as the life of a nuclear power plant, the nuclear power plant installations will be 0 in 2057), and 11 units for A2, B2, and C2 scenarios (nuclear power plant installations will be 0 in 2052).

Fig. 2. 
Electricity Demand and Generation Outlook by Scenario. Others include byproduct gas, fuel cell and IGCC.

The power generation share of renewable energy by scenario is 8% (Reference), 22% (A1), 31% (A2), 11% (B1), 21% (B2), and 14% (C2). The annual average growth rate of the power generation of renewable energy required during the period between 2010 and 2030 is in the order of A2 (19.6%)>A1 (17.7%)>B2 (15.8 %)>Reference (13.5%)>B1 (12.5%)>C2 (12.5%). In the A2 scenario with the largest power generation share of renewable energy, fast distribution of renewable energy is needed to make up for the power generation share of nuclear power and coal-fired power generation after 2023. In the Reference scenario, the power generation share of wind and tidal power accounts for the largest part, while each of renewable energy sources such as the wind, solar, biomass, and hydropower evenly forms a part in the alternative scenarios. The power generation share by renewable energy in 2030 was analyzed to be in the range of 5-18% of the economic potential for renewable energy in Korea (MKE and KEMCO, 2010).

The capacity share in 2009 was 24% nuclear power and 3% renewable energy. In the Reference scenario in 2030, the capacity share from nuclear power will account for 34% and renewable energy for 19%. In the rest of the scenarios, the capacity share from nuclear power ranges from 7% (A2 scenario) to 19% (B1 scenario), while the capacity share from renewable energy ranges from 20% (B1 scenario) to 47% (A2 scenario) (see Fig. 3).

Fig. 3. 
Electricity Mix in Years 2009 and 2030 by Scenario.

The Korea Photovoltaic Industry Association (KOPIA, 2011) investigated the maximum installable capacity of photovoltaic cells considering the largest area where solar cells can be installed. The ratio of maximum installable capacity of photovoltaic cells by scenario is A2 (61.0%)>A1 (34.9%)>B2 (25.6%)> Reference (12.9%)>B1, C2 (11.8%). Therefore, the power generation of renewable energy in each alternative scenario seems to comply with the domestic natural and technological conditions.

The results of analyzing the GHG emissions from power plant operation in 2030 showed that emissions in the Reference scenario increase until 2016 and then decrease by about 10% as compared to 2009 with the expansion of nuclear power plants. However, in the alternative scenarios, it was found that GHG emissions in the A1 scenario increase by 1% in 2030 as compared to 2009 with the transitional increase of NGCC power generation, and in the rest of the alternative scenarios, they decrease by 10-38% (see Fig. 4). However, the cumulative GHG emissions from 2010 to 2030 were analyzed to be lower in the alternative scenarios than the Reference scenario (see Fig. 5).

Fig. 4. 
GHG Emissions Outlook from Power Generation Sector (1).

Fig. 5. 
GHG Emissions Outlook from Power Generation Sector (2). Others include by-product gas, fuel cells, and IGCC.

The total cumulative costs of the power generation sector from 2010 to 2030 were analyzed to be 104% (A1), 114% (A2), 80% (B1), 88% (B2), and 76% (C2) as compared to the Reference scenario. Physical costs were analyzed rather than opportunity costs. Capital costs for installations, operation and maintenance costs, fuel costs, and GHG emission costs are reflected in the cumulative cost. The efficiency improvement costs, costs for decommissioning nuclear power plant and post-treatment, land rental costs, and costs for backup installations are not reflected. In the cost items, fuel costs account for the largest part at 57-64%. The reason that the cumulative costs are higher by approximately 14% than those of the Reference scenario, even in A2 scenario with the highest cumulative costs, is that fuel costs increase due to the expansion of NGCC power and capital costs increase due to the expansion of renewable energy supplies (See Figs. 6, 7).

Fig. 6. 
Cumulative Cost by Cost Item.

Fig. 7. 
Cumulative Cost by Generation Technologies.

Direct and indirect employment effects in the power sector were analyzed by applying the employment factors per power generation by energy source to the power generation by scenario. When the direct and indirect employment factors of Wei et al. (2010) were applied, the employment effects in 2030 were analyzed to be 126% (A1), 147% (A2), 112% (B1), 125% (B2), and 118% (C2) compared to the Reference scenario. It was found that nuclear power creates the largest employment effects in 2030 in the Reference scenario while renewable energy sources create the largest employment effects in A1 and A2 scenarios, and that the employment effects occur mainly through the efficiency improvements in the B1, B2, and C2 scenarios (see Table 1). Even when the number of jobs in the nuclear power sector decreases, the number of jobs in the sectors of efficiency improvements, renewable energy, and NGCC power increases.