Energy Demand Projections Research Articles

Indonesia’s Energy Demand Projection Until 2060

Changes that occur along with the times have consequences for increasing the need for various resources, especially energy. In Indonesia, various changes that occur will also affect the pattern of energy consumption, both qualitatively and quantitatively. This paper describes various aspects related to change and projects over the next 40 years the pattern and quantitative aspects of energy demand in Indonesia. This study is based on historical data by applying related parameters that influence one another. The methodology used is the application of a modeling system with the software 'Model for Analysis of Energy Demand (MAED)'. The results obtained are a description of Indonesia's energy demand projections until 2060 per sector and the form or type of energy needed. The projection results show that in the next 40 years the total energy demand will increase from 195.0 Mtoe in 2020 to 556.5 Mtoe in 2060. From this amount there is a percentage change in the need for each type of energy between the early years of 2020 to the end of 2060. Motor fuel changed from 28% to 34%, while electricity changed from 24% in 2020 to 28% in 2060. In the demand sector, the share of demand decreased in households from 29% in 2020 to 20% in 2060. While other sectors experienced an increase in share in various values. Transportation rose from 19% to 22%, ACM from 8% to 9%, manufacturing remained 34%, while services rose from 10 to 15%. During study period there two important parameters decrease, those are population growth and energy intensity.

Translating Global Integrated Assessment Model Output into Lifestyle Change Pathways at the Country and Household Level

Countries’ emission reduction commitments under the Paris Agreement have significant implications for lifestyles. National planning to meet emission targets is based on modelling and analysis specific to individual countries, whereas global integrated assessment models provide scenario projections in a consistent framework but with less granular output. We contribute a novel methodology for translating global scenarios into lifestyle implications at the national and household levels, which is generalisable to any service or country and versatile to work with any model or scenario. Our 5Ds method post-processes Integrated Assessment Model projections of sectoral energy demand for the global region to derive energy-service-specific lifestyle change at the household level. We illustrate the methodology for two energy services (mobility, heating) in two countries (UK, Sweden), showing how effort to reach zero carbon targets varies between countries and households. Our method creates an analytical bridge between global model output and information that can be used at national and local levels, making clear the lifestyle implications of climate targets.

Master Plan for Electricity Distribution Networks Based on Micro-Spatial Projection of Energy Demand

The existing method of the Master plan for electricity distribution networks is sectoral and macro-based, which means it is unable to show load centers in micro-grids. The inaccurate and bias results lead to the failure of determining the capacity of transformers, the total of transformers, and the locations of distribution substations, and thus it will complicate the master planning of the distribution network. Therefore, a micro-spatial-based method in electricity master planning is needed, as it will generate more accurate forecasting, energy projection and estimate the numbers of load centers at each grid based on the geographical structure. The research contribution is to produce a master planning of distribution network that will help in determining transformer capacity, the placement of substations and distribution substations, evaluation, and orientation of electricity distribution system development to a smaller area. The results of the load growth become the basis for determining the capacity and the total of transformers in the area. The methodology developed in this research has analyzed the transformer rating, transformer capacity, total of transformers, and the location of transformer with growing energy demand in the smaller range. The results can be developed into the design planning of distribution network systems with better accuracy.

Medium-Term Projections of Vehicle Ownership, Energy Demand and Vehicular Emissions in India

Medium-Term Projections of Vehicle Ownership, Energy Demand and Vehicular Emissions in India

Modelling the informal sector and energy consumption in Ghana

The International Monetary Fund (2017) defines the informal sector as unrecorded economic activities that possess monetary value and contribute to tax revenue and gross domestic product. This sector accounts for a huge percentage of economic activity in sub-Saharan Africa and given the numerous regulations on the formal sector it may grow further. Since the activities of the informal sector are unrecorded, how does its energy demand affect national energy demand? In answering this question, we rely on the autoregressive distributed lag estimation technique to quantify the effect of the informal sector on energy consumption. We find that the informal sector exerts a 0.055% adverse effect on energy consumption. Further, we find that education, employment, and foreign direct investment are channels through which the informal sector affects energy consumption. The findings have relevant policy implications. First, since the growth of the informal sector decreases energy consumption, the economies of developing countries should be formalized to spur industrial energy consumption. Second, the national energy policies of Ghana and other developing countries should take into consideration the characteristics of the informal sector in conducting projections of energy demand.

Demand vs supply-side approaches to mitigation: What final energy demand assumptions are made to meet 1.5 and 2 °C targets?

Today’s climate policies will shape the future trajectory of emissions. Consumption is the main driver behind recent increases in global greenhouse gas emissions, outpacing savings through improved technologies, and therefore its representation in the evidence base will impact on the success of policy interventions. The IPCC’s Special Report on Global Warming of 1.5 °C (SR1.5) summarises global evidence on pathways for meeting below-2 °C targets, underpinned by a suite of scenarios from integrated assessment models (IAMs). We explore how final energy demand is framed within these, with the aim to making demand-related assumptions more transparent, and evaluating their significance, feasibility, and use or underutilisation as a mitigation lever. We investigate how the integrated assessment models compensate for higher and lower levels of final energy demand across scenarios, and how this varies when mitigating for 2 °C and 1.5 °C temperature targets through an analysis of (1) final energy demand projections, (2) energy-economy relationships and (3) differences between energy system decarbonisation and carbon dioxide removal in the highest and lowest energy demand pathways. We look across the full suite of mitigation pathways and assess the consequences of achieving different global carbon budgets. We find that energy demand in 2100 in the highest energy demand scenarios is approximately three to four times higher than the lowest demand pathways, but we do not find strong evidence that 1.5 °C-consistent pathways cluster on the lower end of demand levels, particularly when they allow for overshoot. The majority of demand reductions happen pre-2040, which assumes absolute decoupling from economic growth in the near-term; thereafter final energy demand levels generally grow to 2100. Lower energy demand pathways moderately result in lower renewable energy supply and lower energy system investment, but do not necessarily reduce reliance on carbon dioxide removal. In this sense, there is more scope for IAMs to implement energy demand reduction as a longer-term mitigation lever and to reduce reliance on negative emissions technologies. We demonstrate the need for integrated assessments to play closer attention to how final energy demand interacts with, relates to, and can potentially offset supply-side characteristics, alongside a more diverse evidence base.

Impacts of family household dynamics on residential energy demands in Hebei Province of China

This article presents analyses and projections of the residential energy demands in Hebei Province of China, using the ProFamy extended cohort-component method and user-friendly free software and conventional demographic data as input. The results indicate that the future increase in residential energy demands will be dominated by large increase in small households with 1–2 persons. We found that increase of residential energy demands will be mainly driven by the rapid increase of older adults’ households. Comparisons between residential energy demand projections by household changes and by population changes demonstrate that projections by population changes seriously under-estimate the future residential energy demands. We recommend that China needs to adopt policies to encourage and facilitate older parents and adult children to live together or near-by, and support rural-to-urban family migration. Promoting inter-generation co-residence or living near-by between older parents and young adults would result in a mutually beneficial outcome for both older and younger generations as well as to effectively reduce energy demands. We suggest governments to carefully formulate strategies on efficient residential energy use to cope with the rapid households and population aging, and strengthen data collections/analyses on household residential energy demands for sound policy-making and sustainable development.

Analysis of Malaysia electricity demand and generation by 2040

Malaysia as an emerging country, increasing population, gross domestic product (GDP) growth and enhanced access to electricity lead to an expanding of demand. The crucial parameters to determine future energy demand and generation projections are GDP, population growth rates and weather implications due to climate change. The study aims to forecast the future trends based on the historical values and also to project the future electricity demand and generation. The electricity demand and generation growth evaluated based on 2 main elements which are population growth and weather parameters (maximum temperature and rainfall). The future trends are forecasted based on the historical values of population and weather parameters. There is 152.9% of population growth in 32 years. The population will keep on developing yet with the lower rate. The GDP trend and the population growth mirrors the pattern of emissions. The findings from Statistical Downscaling Model (SDSM) analysis shows that the rainfall distribution will diminish while the temperature will expand that depict the climate change impact as time passes by. In 2020, the most extreme temperature recorded is 31.7 °C while in 2040, the estimated greatest temperature is 32.3 °C. There will be a 0.6 °C increase in temperature in 20 years. The demand in 2040 will be expanded 50.3% more than demand in 2020. The estimated electricity demand per capita will continue expanding because of the augmentation of the populace and the significance of electricity in daily activities. The pattern shows that electricity demand and generation in Malaysia will be expanding massively year by year.

Aggregated World Energy Demand Projections: Statistical Assessment

The primary purpose of this research is to assess the long-range energy demand assumption made in relevant Roadmaps for the transformation to a low-carbon energy system. A novel interdisciplinary approach is then implemented: a new model is estimated for the aggregated world primary energy demand with long historical time series for world energy, income, and population for the years 1900–2017. The model is used to forecast energy demand in 2050 and assess the uncertainty-derived risk based on the variances of the series and parameters analysed. The results show that large efficiency savings—up to 50% in some cases and never observed before—are assumed in the main Roadmaps. This discrepancy becomes significantly higher when even moderate uncertainty assumptions are taken into account. A discussion on possible future sources of breaks in current patterns of energy supply and demand is also presented, leading to a new conclusion requiring an active political stance to accelerate efficiency savings and lifestyle changes that reduce energy demand, even if energy consumption may be reduced significantly. This will likely include replacing the income-growth paradigm with other criteria based on prosperity or related measures.

Gap between on-road and official fuel efficiency of passenger vehicles in China

The gap between on-road fuel efficiency and official fuel efficiency of passenger vehicles reported by manufacturers is widening, which would lead to the underestimate of real fuel consumption and biased projection of energy demand in the road transport sector. To investigate the gap between official and on-road fuel efficiency of passenger vehicles, we first built a comprehensive dataset of 393,108 valid observations, including vehicle system parameters, fuel-efficient technology, etc. Then we developed two regression models to quantify the driving factors of the increasing gap and slow improvement in on-road fuel efficiency, and the contribution of each factor to the gap. Results show that the fuel efficiency gap enlarged from 14.4% in 2010 to 25.1% in 2016, and the average on-road fuel efficiency did not have substantial improvement though the official fuel efficiency did. The fuel-efficient technologies have the greatest effect on both the fuel efficiency gap and on-road fuel efficiency among the factors considered in this study. Among the fuel-efficient technologies, hybrid electric vehicle would decrease the ratio of on-road to official fuel efficiency by 12.4% and improve the on-road fuel efficiency by 44.1%. Impacts of other fuel-efficient technologies were also assessed. Further government effort on policies to encourage fuel-efficient technologies for improving real on-road fuel efficiency is needed.

Critically assessing and projecting the frequency, severity, and cost of major energy accidents

Although energy systems are well known to lead to positive or negative externalities, one less explored attribute has been a rigorous historical and future looking assessment of energy accidents. In this study, we analyze an extensive dataset of 4,450 energy accidents from 1800 to 2018 across eleven energy systems. Our analysis reveals that these collective energy systems resulted in more than 278,000 human fatalities and approximately $421.3 billion in economic damages. Historically, coal accidents are the most frequent, accounting for almost half of all accidents. In terms of severity, accidents at hydroelectric dams were the most fatal, accounting for 67 percent. In terms of cost, nuclear power accidents are by far the most expensive, accounting for 62 percent of damages. Coupling our data and an econometric model with future projections of energy demand underscores the magnitude of the trends identified: 986,000 to 1.72 million potential energy accident deaths in 2040, as well as almost $1 trillion in damages. This leads to compelling policy implications, especially concerning the need for safety improvements in energy systems such as bioenergy and nuclear power, as well as the need for the IEA and IRENA, among others, to begin to better track and account for energy accident trends. We find that across all accidents, fuel extraction and processing, transmission and distribution, and transportation have the most fatalities, and yet conversion and operation, transmission and distribution, and transportation have the most damages. Moreover, achieving strong climate goals leads to an unacceptably higher risk of accidents and human health and economic consequences. Finally, as its economic development propels increases in energy consumption, Africa will become the future center for energy accident fatalities.

Implications of declining household economies of scale on electricity consumption and sustainability in China

The average household size in China has declined over the past few decades and will continue to drop, which leads to a loss of scale economies. Its implications for electricity demand and sustainability could be especially profound because of the high ecological/environmental intensity of electricity in China and the expected boom in demand. Using nationally representative household survey panels from 2010 to 2016, we identified substantial household scale economies in electricity consumption. Specifically, reducing household size by one incurs a 17.0–23.6% increase in consumption. We further assessed the ecological/environmental implications in China. CO2 emission, water withdrawal, smoke ash emission, SO2 emission, NOx emission, and industrial wastewater discharge were found to increase with a smaller household size. For example, a household size reduction of 0.5 (to 2.5 members) by 2030–2035 could result in a 0.5% increase in CO2 emission and a 0.3% increase in water withdrawal as compared with the levels in 2015. The increase in CO2 emission is almost equal to the entire emissions of Portugal. It is therefore essential to incorporate scale effect into energy demand projections and sustainability assessments. The results also highlight the urgency in transitioning to cleaner energy since household size shrinkage is occurring globally.

A fractional multi-stage simulation-optimization energy model for carbon emission management of urban agglomeration

Carbon emission reduction and carbon sink growth are essential to realize climate change mitigation. In this study, a fractional multi-stage simulation-optimization energy model is developed to tackle multiple uncertainties in regional energy systems and reflect system efficiency under conflicting objectives. Specially, simulation method is used for projecting energy demand and associated carbon emissions through integrating support-vector-regression, Monte Carlo simulation and stochastic impacts by regression on population, affluence, and technology tool into a general framework. Meanwhile, multiple complexities in terms of multi-region, multi-stage, and conflicting objectives are addressed through optimization techniques of fractional programming and multi-stage stochastic programming. To illustrate the applicability and superiority of the developed model, it is employed to the energy system and carbon emission management in the Pearl River Delta urban agglomeration. The major findings in the research include: electricity demand would grow by 26.6% from 2020 to 2035. The rate of renewable energy generation per unit cost under economic-environmental objectives would be 18.7% higher than that under single economic objective. Carbon emissions can be reduced under scenarios of climate change mitigation and socioeconomic development pathway. Meanwhile, forest carbon sink can be an effective alternative to mitigate carbon emissions.

Techno-economic studies of an industrial biogas plant to be implemented at Kumasi Abattoir in Ghana

Kumasi Abattoir is one of Ghana's largest slaughterhouses. The Abattoir is a state-owned facility and its main activity is the slaughtering of cattle, goats, sheep and pigs. Production takes place 7 days per week, 8 hours per day. The Government of Ghana has planned to construct a pilot industrial biogas plant at the Abattoir to support them manage the waste generated, but to a large extent, support green industrial development and business promotion in Ghana. This study assesses the technical and financial aspects of the pilot biogas project with a focus on the technical solutions for biogas production from the waste, scenarios analyses for the gas usage and financial analysis of selected scenarios. Desk studies, studies of the abattoir slaughter operations, waste fractions, energy consumption patterns and projected energy demand as well as energy efficiency measures were all studied. Analysis of the financial data, namely; electricity bills of the abattoir, liquefied petroleum gas (LPG) and diesel consumption costs and abattoir staff wages were also done. The main wastes generated at the abattoir are solid waste and wastewater. The solid waste concerns mainly solid rumen content and dung, with a total quantity of about 8 tons per day. This waste is currently disposed off at a nearby landfill. The wastewater mainly consists of flushing water, blood and liquid rumen content, with a total quantity of some 170 tons per day. Wastewater is currently disposed in a stream that runs at the border of the abattoir premises, and this causes the most problems for the neighbouring communities. Total biogas potential of the combined waste components is estimated at 846 m3 per day. Energy demand at the abattoir concerns mainly electricity, LPG and diesel. Potential energy efficiency measures include repair of an existing power factor correction system, replacement of chillers, replacement of insulation of the cooling system, and replacement of the diesel backup generator. Full replacement of the abattoir energy and fuel consumption would require approximately 1448 m3 of biogas, which is 70% more than the total biogas potential of the abattoir. Even with energy efficiency measures, biogas demand would be in the order of 1250 m3/d. The abattoir uses LPG for singeing. Financial analyses show that singeing (substituting LPG) is financially the most attractive application for the biogas; the resulting gas value is 0.46 USD/m3. Using the gas for electricity production yields some 0.10–0.18 USD/kWh (depending on the scale); using it for diesel substitution yields some 0.35 USD/m3. Although sensitivity analysis shows that there are risk factors like elevated investment costs, reduced biogas output and shorter equipment lifetime, is recommended to proceed with the development of the biogas plant.

Achieving net-zero emissions in China’s passenger transport sector through regionally tailored mitigation strategies

As a major GHG emissions source with large growth potential, the passenger transport sector plays a crucial role in deep decarbonization in China. Large disparities among provinces in private vehicle ownership, sufficiency of public transport infrastructure, affordability of clean fuel vehicles, etc. highlight the importance of regionally tailored mitigation strategies to fully exploit carbon reduction potentials. We classify 31 provinces in mainland China into three regional clusters based on their passenger transport development level, then establish a provincial level bottom-up model to project energy demand and CO2 emissions of China’s passenger transport sector by 2050. Mitigation effects of improving vehicle energy efficiency, shifting to alternative clean fuels, and promoting public transport are compared, and regionally tailored policy priorities are then proposed. The results show that CO2 emissions of China's passenger transport sector will peak around 2045 at 647 MtCO2 and slightly declined to 642 MtCO2 in 2050 in the Current Policies Scenario. If fully implemented, regionally tailor mitigation strategies that maximize techno-economic carbon reduction potentials could cut CO2 emissions substantially to net-zero in 2050. Mitigation effects of different policy options vary among time periods and regions. Improving vehicle fuel efficiency contributes the most in carbon mitigation over short time scales especially in less developed provinces, where private vehicle ownerships are projected to increase rapidly. Well-established transport infrastructure and an optimally designed public transport system could play a larger role in wealthier provinces.

Multi-input, Multi-output Hybrid Energy Systems

Summary Jurisdictions and industries are setting ambitious goals to decarbonize energy systems. Low-cost wind, solar, and natural gas and the resultant dynamic electric grid require energy technologies to adapt in order to meet key attributes for modern energy systems: resilience, reliability, security, affordability, flexibility, and sustainability. When considering energy sources independently and competitively, value-added synergies among energy technologies may be overlooked for meeting demanding, multidimensional requirements. This paper presents novel concepts for tightly coupled hybrid energy systems that leverage capabilities of diverse energy generators, including renewable, nuclear, and fossil with carbon capture, to provide power, heat, mobility, and other energy services. The paper also presents a framework for engineering-based modeling and analysis for complex optimization of energy generation, transmission, services, processes and products, and market interactions. New modeling capabilities are needed to adequately represent multi-input, multi-output tightly coupled hybrid energy systems that utilize multiple feedstocks to create multiple products and services in novel and synergistic ways through increased coordination of energy systems and tightly coupled hybrid system configurations.

Regression analysis for energy demand projection: An application to TIMES-Basilicata and TIMES-Italy energy models

A reliable energy supply is fundamental to ensure energy security and support the mitigation of climate change by promoting the use of renewable sources and reducing carbon emissions. Energy system analysis provides a sound methodology to assess energy needs, allowing to investigate the energy system behavior and to individuate the optimal energy-technology configurations for the achievement of strategic energy and environmental policy targets. In this framework, the estimation of future trends of exogenous variables such as energy demand has a fundamental importance to obtain reliable and effective solutions, contributing remarkably to the accuracy of models’ input data. This study illustrates an application of regression analysis to predict energy demand trends in end use sectors. The proposed procedure is applied to characterize statistically the relationships between population and gross domestic product (independent variables) and energy demands of Residential, Transport and Commercial in order to determine the energy demand trends over a long-term horizon. The effectiveness of linear and nonlinear regression models for energy demand forecasting has been validated by classical statistical tests. Energy demand projections have been tested as input data of the bottom-up TIMES model in two applications (the TIMES-Basilicata and TIMES-Italy models) confirming the validity of the forecasting approach.

Mitigating energy demand sector emissions: The integrated modelling perspective

Limiting climate change below a given temperature will require fundamental changes in the current energy system, both in the energy supply and the energy demand sectors. Previous global model-based analyses, however, have focused mostly on energy supply transformations. Therefore, in this study we respond to this knowledge gap by analysing the future energy demand projections in both baseline and climate policy scenarios of global models in detail. We examine the projections for the industry, transport and buildings sectors across four models and three different reference scenarios from the Shared-Socioeconomic Pathway framework by applying a decomposition analysis. We compare the projected demand side mitigation efforts to a more detailed, sector-specific, technology-oriented assessment of demand-side abatement potential for the year 2030. Without climate policy, model-based projections show that baseline emissions can grow rapidly in industry and transport sectors, but are also highly uncertain across models. The decomposition analysis shows that the key uncertainty across the global scenarios is the projected final energy per capita. For modellers therefore there lies a challenge to better understand drivers of future energy efficiency and service demand, that contribute to the projected energy demand. This model enhancement would moreover allow to evaluate policy measures that can impact this relation. The technology assessment estimates that in particular in the transport and buildings sector there is a higher potential to reduce demand-side emissions through energy efficiency improvements than implemented in the scenarios. Improved insulation, higher electric vehicle penetration rates and modal shift for example could reduce final energy demand to lower levels in the short term than currently projected, reducing the dependency on fuel switching required in current scenarios to meet the stringent climate targets.

Climate change adaptation and carbon emissions in green urban spaces: Case study of Adelaide

Concentrations of building mass and insufficient greenery in cities are identified to contribute significantly to extended heat stress in the built environment, commonly known as the urban heat island (UHI) effect. This paper presents a scenario-based modelling built on climate change projections and potential alterations of urban greenery in 2030 and 2090 in Adelaide, South Australia. The model is based on regional climate change scenarios, potential urban surface cover alternatives and resulted urban microclimate variations in Adelaide. Projected energy demand variations and corresponding carbon emission are calculated in each scenario. Results indicate that an ideal urban landscape transformation scenario with 30% tree canopy can effectively decrease the surface temperature by 1 °C in winter and 3 °C in summer by 2090. It is estimated that having greener and more heat resilient public spaces could save a total carbon emission of 140,000 tone CO2e in Adelaide annually compared to a business-as-usual scenario. Urban greenery may be used as a mean of increased urban life resilience to climate change by reducing surface temperature, increasing urban resilience and decreasing the energy demand during summer.

Innovative energy policy to transform energy systems in Ukraine

To enhance its national energy security, the government of Ukraine has adopted many energy polices since 1989. Unfortunately, most of these policies have proved to be unsuccessful. The country’s energy supply is still highly dependent on fossil fuel imports. This paper provides a comprehensive review on Ukraine’s challenges from economic, social, political, and environmental aspects that are related to energy and carbon emission policies. An empirical methodology is used to analyze the energy situation and project energy demand by 2050. A new roadmap of energy investments is made from 2035–2050 with an objective of decarbonizing the Ukrainian energy system by 2050. Such roadmap is in line with the global commitments to reducing greenhouse gas emissions by 45% over the next decade and to achieving net zero emissions by 2050. This article recommends 12 polices for the government ranging from stopping subsidy to fossil energy to accounting environment costs in fossil energy consumption, levying taxes on polluters, privatizing government–owned companies to adapt the Euopean Union (EU) energy systems, and terminating gas import from Russia. If these proposed energy policies are successfully implemented, the Ukrainian fossil fuel-based energy system will be transformed to a highly efficient energy system with zero-carbon emissions by 2050.