Long-range Energy Alternative Planning Model Research Articles

Long-Term Forecast of Energy Demand towards a Sustainable Future in Renewable Energies Focused on Geothermal Energy in Peru (2020–2050): A LEAP Model Application

The present study aims to describe the potential sources of energy in Peru with the purpose of implementing them to achieve a sustainable system, taking advantage of the natural resources in the Peruvian land. To achieve this, three alternative scenarios have been defined and analyzed using the LEAP (Long-range Energy Alternatives Planning) software [Software Version: 2020.1.112]. The scenarios are as follows: the first one, the Business-as-Usual scenario, is based on normal trends according to historical data and referencing projections made by Peruvian state entities; the second one is focused on Energy Efficiency, the highlighted characteristic is taking into consideration the efficient conditions in transmission and distribution of electric energy; and the third one, centered on Geothermal Energy, focused on the development of this type of energy source and prioritizing it. The primary purpose of this analysis is to identify the advantages and disadvantages inherent in each scenario in order to obtain the best out of each one. In this way, the intention is to propose solutions based on Peru’s national reality or possible uses of the country’s energy potential to supply its energy demand. Currently, Peru’s energy demand relies on fossil fuels, hydraulic, and thermal energy. However, there is the possibility of transforming this system into a sustainable one by strengthening existing and growing energy sources such as solar and wind energy and new technologies for hydraulic and thermal energy, in addition to considering geothermal energy as the main energy source in the third scenario. The new system mentioned satisfactorily indicates that the CO2 equivalent emissions decrease significantly in the third scenario, with a 15.8% reduction compared to the first scenario and a 9.7% reduction in comparison to the second. On the other hand, the second scenario shows a 5.6% decrease in CO2 emissions compared to the first, resulting from improvements in technology and energy efficiency without requiring significant modifications or considerable investments, as in the third scenario.

Exploring hydrogen metallurgy to CO2 emissions reduction in China’s iron and steel production: An analysis based on the life cycle CO2 emissions – LEAP model

Previous research has primarily assessed the environmental impact of hydrogen metallurgy from technical or project perspectives, lacking a life cycle perspective. The impact of continued cleanliness of the hydrogen production mix and power generation structure on carbon dioxide (CO2) emissions from the hydrogen metallurgy production process has not been investigated on a medium to long-term scale, which does not provide an adequate and comprehensive view of the contribution of hydrogen metallurgy to China’s emissions reduction, resulting in short-sighted technology choices. To address this lack, we construct a bottom-up energy system model that combines life cycle CO2 emissions with long-range energy alternatives planning (LEAP), including iron and steel (IS) production, power generation, and hydrogen production. Setting development paths of two typical hydrogen metallurgy technologies, including business-as-usual (BAU) and hydrogen metallurgy (HM) scenarios, we carefully evaluate the CO2 emissions impact of hydrogen metallurgy from a whole industry perspective and in each IS production stage. The results suggest that the hydrogen metallurgy transition will help the iron and steel industry (ISI) to achieve peak and deep carbon reduction. Under the HM scenario, CO2 emissions from the ISI would decrease to 816.28 million tons (Mt) in 2050, representing an additional 281.13 Mt reduction over the BAU scenario. Hydrogen metallurgy can accelerate CO2 reduction in raw material acquisition and material processing phases but may increase CO2 emissions in manufacturing and recycling phases. In 2050, hydrogen metallurgy will contribute an additional 12.49 Mt and 300.95 Mt of CO2 reduction to the raw material acquisition and material processing phases, respectively. However, CO2 emissions in the manufacturing and recycling phases would be 32.15 Mt and 0.22 Mt more than in the BAU scenario due to the increased electricity consumption and scrap demand of electric arc furnace (EAF) steelmaking, respectively. Over time, the optimization of hydrogen production and power supply mix will gradually reduce CO2 emissions in each IS production process. In 2050, the CO2 intensity of the hydrogen-rich shaft furnace direct reduced iron (H2-DRI)-EAF process will drop to 753.41 kgCO2/t, which is lower than all other processes except the 100 % scrap-based EAF process. Advancing the development of the H2-DRI-EAF process can effectively achieve CO2 emissions reduction while alleviating dependence on scrap resources. The results are expected to inform government policy on the development of hydrogen metallurgy in China and other countries.

Estimation of energy demand and carbon emissions for the road transport sector: A case study of Douala, Cameroon

Road transport is one of the main causes of air pollution around the world. Nowadays, the notion of sustainable development is one of the major pillars for a healthy environment. However, faced with its major challenges, it is more than necessary and important to understand a set of elements which contribute to better understanding the level of planning for sustainable road transport. This study aims to estimate energy demand and greenhouse gas emissions, namely from road CO2 transport, for the city of Douala in Cameroon from 2010 to 2035. In order to achieve our objective, we used the Long range Energy Alternative Planning (LEAP) model. Based on the LEAP model, the estimates were evaluated based on three scenarios Business As Usual (BAU), Energy Optimization and Mitigation (EOM) and Sustainability Mobility (SM). The aim of this work was to identify suitable potential policies with a view to reducing energy consumption and emissions carbon dioxide (CO2) for road transport in Douala. The results tell us that the EOM and SM scenarios have advantages over the BAU scenario with the SM scenario being more optimistic than the other two scenarios. In addition, the SM scenario has a growth rate of 87.408 % in energy demand smaller than the other two scenarios BAU and EOM respectively 151.598 % and 132.073 %. We also note that the SM scenario contributed to a reduction of 60.661 kilo ton emissions of CO2 less than the BAU scenario. At present, with a view to reducing energy consumption and reducing carbon emissions, road vehicles in circulation in the city of Douala should emphasize structural optimization measures through the use of clean energy by promoting the use of biofuels in mass transport, eliminating old cars and finally considering improving energy efficiency through the implementation of technological development in the automobile sector. Policy makers must take this study into consideration in order to better integrate the notion of energy sustainable road transport.

Promote the decarbonization pathways of eco-industrial parks by considering their CO2 emissions structures and characters

China has proposed the goals of a carbon peaking target in 2030 and a carbon neutrality target in 2060 to mitigate climate change. In China, Eco-industrial parks (EIPs) are one of the platforms for achieving energy conservation and emission reduction. This study aims to investigate the future CO2 emissions reduction potential by evaluating two different types of national EIPs in China. Firstly, we constructed carbon emission inventories for EIP-N in Zhejiang Province and EIP-L in Shandong Province. Subsequently, leveraging these inventories, we developed an integrated model aimed at predicting the carbon emission peaks for both EIPs and discerning the main contributors. Secondly, we took EIP-N and EIP-L as examples and applied the Long-range Energy Alternatives Planning (LEAP) model to establish three different scenarios for analyzing the future trends in carbon emissions. In a Business as usual (BAU) scenario (continue the current energy conservation and emission reduction policies), EIP-L can achieve carbon peaking, while the carbon emissions of EIP-N will continue to increase. In the Emissions control (EMC) scenario (considering the reduction of fossil fuels and the increased use of cleaner energy sources), EIP-N and EIP-L are projected to peak in 2029 and 2027, respectively. In the Reinforce mitigation (RFM) scenario (with fewer emissions from fuels and a higher share of clean energy generation), both EIPs are expected to achieve carbon peaking by 2025. Finally, we proposed the deep decarbonization of EIPs with different industrial characteristics. This study applied the LEAP model to the EIP scale, explored the paths of deep decarbonization development in different EIPs under the constraint of dual-carbon targets, providing a demonstration for China and other developing countries to achieve the goal of carbon capping in EIPs during rapid industrialization.

Pathways for carbon emission prediction and mitigation of sustainable industrial parks: a LEAP model application

ABSTRACT Industrial parks play a crucial role as a carrier of industrial clusters and energy consumption. Accurately predicting the energy demand and carbon emissions trend is key to scientifically determining the pathways for low-carbon industrial parks. However, exploration in carbon emission prediction on industrial park scale is still in its infancy stage. This paper investigates fuel demand and carbon emissions from 2021 to 2035 in an industrial park in Jiangsu Province, utilizing the Long-range Energy Alternative Planning (LEAP) model to explore the pathways for low carbon development. Energy-saving and emission-reduction effects of different macro-economic policies and micro-energy planning are analyzed based on the energy balance and emission factor methods. Four scenarios are compared: the baseline scenario (BAS), green development scenario (GDS), low carbon scenario (LCS), and strength low carbon scenario (SLS). Results indicated that energy demand under BAS reached at 31.37 Mtce in 2035, and energy-saving rates of GDS, LCS, and SLS in 2035 were 12.94%, 14.00% and 19.08%, respectively. Carbon emissions reached 53.96 MtCO2e in BAS of 2035. However, in the same year, emissions decreased by 24.88%, 43.09%, and 52.52% in GDS, LCS, and SLS, respectively, with SLS being the most suitable for the park.

Carbon Emission Prediction and the Reduction Pathway in Industrial Parks: A Scenario Analysis Based on the Integration of the LEAP Model with LMDI Decomposition

Global climate change imposes significant challenges on the ecological environment and human sustainability. Industrial parks, in line with the national climate change mitigation strategy, are key targets for low-carbon revolution within the industrial sector. To predict the carbon emission of industrial parks and formulate the strategic path of emission reduction, this paper amalgamates the benefits of the “top-down” and “bottom-up” prediction methodologies, incorporating the logarithmic mean divisia index (LMDI) decomposition method and long-range energy alternatives planning (LEAP) model, and integrates the Tapio decoupling theory to predict the carbon emissions of an industrial park cluster of an economic development zone in Yancheng from 2020 to 2035 under baseline (BAS) and low-carbon scenarios (LC1, LC2, and LC3). The findings suggest that, in comparison to the BAS scenario, the carbon emissions in the LC1, LC2, and LC3 scenarios decreased by 30.4%, 38.4%, and 46.2%, respectively, with LC3 being the most suitable pathway for the park’s development. Finally, the paper explores carbon emission sources, and analyzes emission reduction potential and optimization measures of the energy structure, thus providing a reference for the formulation of emission reduction strategies for industrial parks.

Can China achieve its 2030 and 2060 CO2 commitments? Scenario analysis based on the integration of LEAP model with LMDI decomposition

China's ambitious targets of peaking its Carbon dioxide (CO2) emissions on or before 2030 and achieving carbon neutrality by 2060 have been a topic of discussion in the international community. This study innovatively combines the logarithmic mean Divisia index (LMDI) decomposition method and the long-range energy alternatives planning (LEAP) model to quantitatively evaluate the CO2 emissions from energy consumption in China from 2000 to 2060. Using the Shared Socioeconomic Pathways (SSPs) framework, the study designs five scenarios to explore the impact of different development pathways on energy consumption and related carbon emissions. The LEAP model scenarios are based on the result of LMDI decomposition, which identifies the key influencing factors on CO2 emissions. The empirical findings of this study demonstrate that the energy intensity effect is the primary factor of the 14.7 % reduction in CO2 emissions observed in China from 2000 to 2020. Conversely, the economic development level effect has been the driving factor behind the increase of 50.4 % in CO2 emissions. Additionally, the urbanization effect has contributed 24.7 % to the overall change in CO2 emissions during the same period. Furthermore, the study investigates potential future trajectories of CO2 emissions in China up to 2060, based on various scenarios. The results suggest that, under the SSP1 scenarios. China's CO2 emissions would peak in 2023 and achieve carbon neutrality by 2060. However, under the SSP4 scenarios, emissions are expected to peak in 2028, and China would need to eliminate approximately 2000 Mt of additional CO2 emissions to reach carbon neutrality. In other scenarios, China is projected to be unable to meet the carbon peak and carbon neutrality goals. The conclusions drawn from this study offer valuable insights for potential policy adjustments to ensure that China could fulfill its commitment to peak carbon emissions by 2030 and achieve carbon neutrality by 2060.

Study of Energy Transition Paths and the Impact of Carbon Emissions under the Dual Carbon Target

In recent years, the world’s environmental problems have become increasingly serious, and energy transition and carbon emission issues have gradually gained attention from various countries. China has promulgated several policies and adopted various reform measures to achieve a comprehensive energy transition and carbon neutrality as soon as possible. Therefore, this study makes researches and forecasts the energy transition and carbon emissions in China under the dual carbon target. A LEAP (Long range Energy Alternatives Planning) model is developed to analyze the energy parameters of Beijing under various scenarios and to provide a quantitative analysis basis for the energy transition path. The obtained experimental results indicate that the energy demand under the partial energy transition scenario and full energy transition scenarios are 68.651 million tons of standard coal and 75.759 million tons of standard coal, respectively, forming an effective control, while the carbon emissions both achieve the carbon peak in 2025 at 81.903 million tons and 80.624 million tons respectively, and achieve 46.588 million tons in 2060. The carbon-neutral pathway reaches the carbon peak in 2025, and approaches full energy transition in 2035, and finally reaches the full energy transition control effect in 2060. To date, most of the relevant studies have been conducted in a qualitative way, and the amount of quantitative analysis is insufficient. At the same time, research on the development path specifically at the city level is relatively insufficient as well. Therefore, the present study can provide a theoretical basis for specifying the promotion method of urban energy transformation and the path of carbon emission reduction.

CO2 Emissions Forecast and Emissions Peak Analysis in Shanxi Province, China: An Application of the LEAP Model

Shanxi Province, an important source of coal resources in China, has consumed a large amount of fossil fuels in the past few decades. The CO2 emissions of Shanxi Province have been increasing annually, reaching 541.8 million tons in 2018, 54.6% higher than the national mean. This will have a negative impact on China’s ability to meet its target of peaking CO2 emissions by 2030. To assist China to achieve this target and reduce CO2 emissions in Shanxi Province, this study used the Long-range Energy Alternatives Planning (LEAP) model to analyze the CO2 emissions and peaks in Shanxi Province from 2019 to 2035 under different scenarios. Furthermore, this study analyzed the time to peak CO2 emissions under different emission reduction measures through a sensitivity analysis. The results show that in the absence of other mitigation policy interventions, CO2 emissions in Shanxi Province will increase annually, reaching 1646.2 million tons by 2035. Furthermore, this study shows that if shares of industrial gross domestic product (GDP) in Shanxi, energy intensity reduction in the industrial and transport sectors compared to the base scenario, thermal power, and relative clean energy consumption reach 25%, 30%, 50%, and 50%, respectively, by 2035, then CO2 emissions of Shanxi would peak at 801.2 million tons in 2029 and GDP per capita would increase to USD 2000 by 2035. Finally, according to the results of this study, we have made some recommendations for emission reduction in Shanxi Province. The limitation of this study was that the implementation cost of the abatement policy was not considered.

Carbon Emissions Peak in the Road and Marine Transportation Sectors in View of Cost-Benefit Analysis: A Case of Guangdong Province in China

The road and marine transportation sectors have attracted a great deal of attention as one of the main sources of carbon emissions. In this study, a LEAP (long-range energy alternatives planning system) model is used to predict the energy demand and carbon emissions of the road and marine transportation sectors in Guangdong Province from 2016 to 2030. Based on the model results, if Guangdong Province does not adopt new control measures, in 2030, the energy demand and CO2 (carbon dioxide) emissions from the road and marine transportation sectors will have increased by 117 and 116%, respectively, compared to the levels in 2015. Conversely, under a low-carbon scenario, carbon emissions will peak by 2027 in Guangdong Province. Motor vehicle control measures generate the strongest reduction in energy consumption and CO2 emissions for the road and marine transportation sectors in Guangdong Province. Furthermore, the reduction of emissions resulting from these measures would increase over time. Public transportation development and the promotion of clean energy measures also play significant roles in reducing carbon emissions long-term. The contribution of road passenger transport to emission reduction is the largest, followed by marine freight transport and road freight transport. While the energy demand and carbon emissions peak at a similar time in the model, the peak time for CO2 occurs slightly earlier.

Long-term scenario pathways to assess the potential of best available technologies and cost reduction of avoided carbon emissions in an existing 100% renewable regional power system: A case study of Gilgit-Baltistan (GB), Pakistan

The long-term power planning at the regional level has recently gained significance due to the economic and efficiency competitiveness of a decentralized energy system. In Pakistan, the power sector faces the challenge of exceptionally increasing electricity demand in metropolitan areas and the urgency of reliable power access in rural areas. In this paper, a regional power-generation system of GB is developed using the LEAP (Long-range Energy Alternative Planning) model from 2016 to 2050, to explore future clean pathways to understand the planning and operational implications of futuristic variable renewables (VRE) and best available technologies (BAT). The regional power system model is further expanded by using the end-user and econometric approaches to construct five scenarios which include Business-As-Usual (BAU), BAU-OPT (BAU-optimization), Demand Side Management (DSM), DSM-OPT, and National Energy Mix (NEM). The model estimates the electricity demand projection of 3.2 TWh in 2050, at an annual average growth rate of 3.19%, and suggests that hydropower is the most dominant variable renewable source in GB’s electric power system. The adoption of the BAT would avoid 1.34 TWh or about 41% of the projected electricity consumption, which shows the maximum potential of energy efficiency measures in GB. Moreover, the results indicate that: i) home appliances and lighting account for nearly halved of the reduction potential; while lighting and cooking are the most cost effective measures in terms of energy saving at regional level; ii) least cost constraint displays the transition towards large scale renewable projects; iii) the penetration of small scale renewable project is nearly doubled under least cost constraint by the introduction of demand side measures; and iv) higher emission penalties aggressively shift energy mix toward renewables with the cost of annual environmental externalities reducing in long-term. Overall, these findings can provide a scientific basis for the sustainable development of regional power-generation systems, as well as co-benefits of penetration of VRE and BAT in low-carbon electric power regions.

Scenario Analysis for GHG Emission Reduction Potential of the Building Sector for New City in South Korea

A new government report on climate change shows that global emissions of greenhouse gases have increased to very high levels despite various policies to reduce climate change. Building energy accounts for 40% of the world’s energy consumption and accounts for 33% of the world’s greenhouse gas emissions. This study applied the LEAP (Long-range energy alternatives planning) model and Bass diffusion method for predicting the total energy consumption and GHG (Greenhouse Gas) emissions from the residential and commercial building sector of Sejong City in South Korea. Then, using the Bass diffusion model, three scenarios were analyzed (REST: Renewable energy supply target, BES: Building energy saving, BEP: Building energy policy) for GHG reduction. The GHG emissions for Sejong City for 2015–2030 were analyzed, and the past and future GHG emissions of the city were predicted in a Business-as-Usual (BAU) scenario. In the REST scenario, the GHG emissions would attain a 24.5% reduction and, in the BES scenario, the GHG emissions would attain 12.81% reduction by 2030. Finally, the BEP scenario shows the potential for a 19.81% GHG reduction. These results could be used to guide the planning and development of the new city.

Distribuidos de Energía Eléctrica en Sistemas Energéticos Regionales: análisis con el modelo LEAP

En este trabajo, se simula mediante del modelo LEAP (Long-range Energy Alternatives Planning), a nivel regional y a largo plazo, el rol del almacenamiento de electricidad como herramienta de flexibilidad tendiente a mejorar la paulatina integración de fuentes renovables de energía variables al sistema eléctrico de la provincia de Mendoza; entre otros, consideramos los sistemas de generación centralizados y distribuidos con tecnología fotovoltaica, teniendo en cuenta la variabilidad temporal de la fuente. Se incluye el almacenamiento de energía diario vinculado a los vehículos eléctricos (con baterías de ion-litio) en su concepción de inyección a la red (V2G). En los resultados alcanzados se observa que, con las tasas de incorporación, tanto de vehículos eléctricos, como de instalaciones fotovoltaicas distribuidas, no se aprecian variaciones considerables por su inserción al sistema eléctrico. Por el contrario, se mejoran los indicadores medioambientales. Estos hallazgos sugieren la necesidad de abordar un enfoque de optimización.

Road transport energy consumption and vehicular emissions in Lagos, Nigeria: An application of the LEAP model

The “Avoid”, “Shift” and “Improve” (A-S-I) approach is an effective method for transforming an unsustainable transport system to a sustainable one. This study intends to examine the possible impact of the A-S-I policy measures in transforming the transportation system in Lagos - the most populous city and the commercial capital of Nigeria. The study employs the Long Range Energy Alternative Planning (LEAP) model to project future energy demand and greenhouse gas emissions to determine the most effective A-S-I option for the city. We construct a business-as-usual scenario for Lagos as well as sustainable road transport alternative policy scenarios. The results show that Lagos' biggest obstacle to achieving its emission reduction target is the presence of very old vehicles on its roads. Our analysis shows that emission reduction in the road transport sector in Lagos is sensitive to vehicle survivability rate (i.e. the fraction of vehicles of a certain age still driven). We conclude that unless the age limit of vehicles in Lagos reduces from 40 years to 22 years, vehicle growth rate from 5% to 2% and mileage by 2% per year from 2020 to 2032, Lagos may not achieve its target of 50% emission reduction by 2032.

Long-Term Electricity Supply and Demand Forecast (2018–2040): A LEAP Model Application towards a Sustainable Power Generation System in Ecuador

This research assesses the Ecuadorian power generation system, estimating the electricity supply and demand forecast until 2040. For this purpose, three potential alternative scenarios were analyzed using the Long-range Energy Alternatives Planning (LEAP) System; S1: Business As Usual; S2: Power Generation Master Plan; and S3: Sustainable Power Generation System. The main goal of this study is to analyze the possible alternatives for electricity supply and demand, fuel consumption, and the future structure of the Ecuadorian power generation system to transform the current system based on petroleum fuels into a sustainable system that consumes natural gas, and progressively introduces renewable power generation plants such as solar, wind, biomass, and hydroelectric until 2040. According to the estimated results through the inclusion of sustainable energy policies, S3 scenario relative to S1 scenario could reduce the average CO2 equivalent (CO2e) emissions by 11.72%, the average production costs by 9.78%, and the average petroleum fuel consumption by 15.95%. Consequently, a correct energy transition contributes to the protection of the environment and public health and has a direct effect on economic savings for the state, which benefits to improve the citizen’s quality of life.

Assessing energy consumption, CO2 and pollutant emissions and health benefits from China's transport sector through 2050

With the accelerating process of urbanization, energy consumption and emissions of the transport sector in China have increased rapidly. In this paper, we employed the LEAP (Long-range Energy Alternatives Planning system) model to estimate the energy consumption, CO2 (carbon dioxide) and air pollutant emissions of the transport sector between 2010 and 2050 under four scenarios: Business as Usual (BAU), Energy Efficiency Improvement (EEI), Transport Mode Optimization (TMO), and Comprehensive Policy (CP). Furthermore, the intake fraction method was adopted to assess the health benefits of reducing pollutant emissions. The results showed that energy consumption will reach 509–1284 Mtce under the different scenarios by 2050. The emissions of CO2, carbon monoxide (CO), sulfur dioxide (SO2), nitrogen oxide (NOX) and particulate matter (PM10 and PM2.5) will be 2601, 173, 3.4, 24.0, 0.94 and 0.78 Mt, respectively, under the BAU scenario in 2050. Regarding health benefits, economic losses caused by mortality will be reduced by 47, 40 and 72 billion USD in 2050 under the EEI, TMO and CP scenarios, respectively, compared to those under the BAU scenario. Among the health outcomes associated with PM10, acute bronchitis exhibits the worst outcome. Considering health impacts, policy implications are suggested to reduce CO2 and pollutant emissions.

Development of a Framework for the Assessment of Energy Demand-Based Greenhouse Gas Mitigation Options for the Agriculture Sector

Abstract. This study assesses greenhouse gas (GHG) mitigation options for the agriculture sector. The Long-range Energy Alternatives Planning (LEAP) model was used to develop a framework to assess future trends in energy demand and associated GHG emissions for the agriculture sector and to assess various GHG mitigation options associated with energy consumption. A business-as-usual (reference) scenario and 32 GHG mitigation scenarios were developed for the years 2009-2050 using the LEAP model. A case study for Alberta, Canada, was conducted. In the model, GHG mitigation scenarios were developed for the energy demand side (e.g., farm machines, farm transportation, lighting, and ventilation) based on efficiency improvements and the use of renewable energy. The mitigation scenarios were divided into two planning horizons based on technology penetration: slow penetration (2009-2050) and fast penetration (2009-2030). For each planning horizon, 16 scenarios were assessed. Of all farm machines, efficient diesel tractors have the highest GHG mitigation potential: 12.35 MT of CO2 equivalent by 2050 and 4.7 MT of CO2 equivalent by 2030. In addition, GHG abatement cost curves show that biodiesel tractors and efficient diesel tractors have the highest GHG mitigation potential, with attractive abatement costs of -$62 and -$11 tonne-1 of CO2 mitigated by 2050, respectively. Keywords: Abatement cost, Agriculture sector, Energy efficiency, GHG mitigation, LEAP model.

Application of LEAP model on long-term electricity demand forecasting in Indonesia, period 2010-2025

Electricity demand forecasting is an important part in energy management especially in electricity planning. Indonesia is a large country with a pattern of electricity consumption which continues to increase, therefor need to forecasting electricity demand in order to avoid unbalance demand and supply or deficit energy. LEAP (Long-range Energy Alternative Planning System) as a tool energy model and Indonesia as a case study. Basically, electricity demand is influenced by population, economy and electricity intensity. The purpose of this study is to provide understanding and application of electricity demand forecasting by using LEAP. The base year is 2010 and end year projection is 2025. The scenarios of simulated model consist of two scenarios. They are Business as Usual (BAU) and Government policy scenario. Results of both scenarios indicate that end year electricity demand forecasting in Indonesia increased more than two fold compared to base year.

Strategy on China's regional coal consumption control: A case study of Shandong province

Controlling coal consumption has become one of the critical energy and environmental measures in China, and Shandong province is the key area of coal consumption control. This study is based on three targets, including air quality improvement, water savings, and energy conservation and emissions reduction, and then adopts the LEAP (Long-Range Energy Alternatives Planning System) model to predict Shandong's total coal consumption and its emissions reduction in 2020. Furthermore, it measures the feasibility and concrete path to realize the coal reduction target from three aspects – coal reduction, clean coal utilization and coal substitution. Finally, the quota allocation method is used to breakdown the coal reduction target into various cities and major industries. The results indicate that improving air quality is the primary reason for coal consumption control; coal reduction, clean coal utilization and coal substitution can reduce 36.3, 3.4 and 37.76 million tons of coal equivalent (tce) respectively, thus revealing the possibility of realizing the target of reducing 20 million tce in 2020 compared to 2012. The quota breakdown scheme shows that four cities (Binzhou, Jining, Linyi and Zibo) and three industries (production and supply of electricity and heat industry, coal chemical industry, and ferrous metal smelting and calendaring industry) are the major focus for coal consumption control in Shandong. This study provides valuable suggestions to facilitate the control of coal consumption.

Environmental assessment of energy production from landfill gas plants by using Long-range Energy Alternative Planning (LEAP) and IPCC methane estimation methods: A case study of Tehran

This study aims to analyze the electricity generation and its environmental aspects in Tehran city by using the LEAP model and developing two scenarios, including Business-As-Usual and Sustainable-Waste-Management (SWM). The base and final years of the planning are 2012 and 2035, respectively. It is attempted to integrate two models of IPCC (Intergovernmental Panel on Climate Change) for methane flow rate estimation and LEAP to estimate Tehran’s energy and non-energy emissions. By linking these two models, the energy and environmental effects of the SWM scenario are estimated. To calculate the power production of the landfill gas (LFG) plants, the gas turbine model of GE10 is selected, and an Engineering-Equation-Solver (EES) code is developed based on methane flow rate and composition data obtained from the IPCC default method in the SWM scenario. The combination of EES codes and LEAP analysis shows that the LFG plants can supply 0.5GWh power, which is 1.4% of the total demand in 2016, but it will raise to 0.9GWh in 2035. Although utilization of LFG plants increases the cost of electricity production, the accumulated difference of 100years global warming potential in the studied scenarios will be 81.2Mt CO2 equivalent from 2012 to 2035.