Energy Demand Projections Research Articles

LONG-TERM ENERGY DEMAND THROUGH BOTTOM-UP ANALYSIS AND PROJECTION FOR KUWAIT

In this study, an energy demand projection and analysis were conducted for the State of Kuwait, which has an oil-based economy. A bottom-up approach was used to conduct energy demand projection and analysis by utilizing the Model for Energy Demand Analysis (MAED), which is a software package developed by the International Atomic Energy Agency (IAEA). The energy demand projection was conducted until 2050, with 2019 as the base year. The total demand is expected to grow from 210.9 TWh in 2019 to 373.07 TWh in 2050. The industrial sector dominates final energy demand, with a percentage of 45.1% in 2019 and is expected to reach 55.8% by 2050. Reliance on fossil fuels for energy demand will persist with an annual growth rate of 4% throughout the projection period. The electricity demand in the household and service sectors has the highest share of space cooling during the long summer months. Policy implications in response to energy demand consumption patterns are discussed to achieve national sustainable economic growth and environmental requirements. The implementation of rules and regulations to deploy and use equipment and machinery with high energy efficiency ratings will assist in energy savings in the industrial sector. Providing incentives to use building materials with high insulation can significantly reduce the energy demand for space cooling. Setting high-efficiency rating standards for engine specifications for vehicles imported to Kuwait will help reduce the consumption of motor fuels in the transportation sector.

A System Predictive Maintenance Framework for Advanced Reactors Using a Data-Driven Digital Twin

Advanced reactor designers are looking to maximize the system capacity factor to make advanced reactors more economically competitive and meet the projected energy demand. To achieve this goal, we propose a Dynamic Operation and Maintenance Optimization (DyOMO) framework to perform system-level predictive maintenance (PdM) using a dynamic Bayesian network and component-specific PdM using deep neural networks. At the system level, DyOMO detects the presence of anomalous phenomena, determines the most influential degradation mode, and estimates the remaining useful life (RUL) distribution for the system. At the component level, DyOMO summarizes the health state of key system components, determines the presence of an anomaly using a feedforward neural network, and predicts component RUL using a Bayesian neural network. To evaluate the overall performance of DyOMO, normal operations of a Pebble-Bed High-Temperature Gas-cooled Reactor (PB-HTGR) were simulated with realistic component degradation for the steam turbine and steam generator. Across the 20 independent reactor life simulations, it was found that maintenance was always performed before any safety limits were violated and before a component failed. Specifically, the system-level PdM suggested maintenance on the steam generator once the steam pressure approached its safety limit, and the component-specific PdM suggested maintenance on the steam turbine once the turbine blade hardness degraded. The results indicate that through the continuous monitoring of the system and individual components, the DyOMO framework improves safety and increases the availability of the reactor when compared to traditional maintenance philosophies.

Synergistic emission reductions and health effects of energy transitions under carbon neutrality target

China's climate and environmental problems remained prominent. Energy transition was the key to improve climate and pollution, and it was at a critical period. Previous studies mostly focused on the impact of climate policies on the reduction of air pollutants and the environmental health impacts of individual sector or pathway policies. Thus, focusing on the synergistic abatement of CO2 and air pollutants and health effects of energy transition in carbon neutrality was significant for building beautiful and healthy China. LEAP-HEA model was constructed to project energy demand, CO2 and air pollutants, and assess energy transition's environmental health effects from 2020 to 2060. LMDI model was adopted to decompose the abatement contribution. Synergistic assessment index was used to estimate the coordinated abatement effects. Cost-effectiveness of energy transition and regional heterogeneity of PM2.5 toxicity were not considered. The results indicated: (1) energy demand would decrease and structure would be optimized significantly under deep energy transition scenario. (2) Deep energy transition could promote earlier realization of carbon neutrality. It would contribute significantly to CO2 and air pollutants abatement with strong synergistic effects. (3) Energy transition would reduce health risks with large economic benefits. Recommendations were proposed based on the conclusions.

Advancing SSP-aligned scenarios of shipping toward 2050

Developing comprehensive scenarios for the shipping sector has been a challenge for the Integrated Assessment Model (IAMs) community, influencing how attainable decarbonization is in the sector, and for Earth System Models (ESMs), impacting the climate contribution of shipping emissions. Here we present an approach to develop spatially explicit energy demand projections for shipping in alignment with the Shared Socioeconomic Pathways framework and IAMs projections of global fossil fuel demand. Our results show that shipping could require between 14 and 20 EJ by 2050, corresponding to a 3% and 44% increase from 2018 for the SSP1-1.9 and SSP3-7.0 scenarios. Furthermore, the energy projections we present in this publication can be combined with different fuel mixes to derive emission inventories for climate modeling and, thus, improve our understanding of the various challenges in mitigating emissions for shipping. Through that, we aim to present a framework to incorporate detailed spatial shipping inventories and increase transparency for the scientific community.

Short-Term Electrical Energy Demand Forecasting Using Fuzzy Logic Technique

Minimization of electrical energy wastage and provision of its regular supply make electrical load forecasting an important aspect of the power infrastructure. An accurate load forecasting is crucial for the reduction of the cost of electrical energy generation and spinning reserve capacity. Therefore, in this study, fuzzy logic (FL) technique was employed for the projection of electrical energy demand on short-term basis. The FL model was trained using the six months hourly load and temperature data respectively obtained from 132/33 kV Ikeja West Transmission Station, Ayobo and Nigerian Meteorological Agency, Oshodi, Lagos State, Nigeria. Triangular and trapezoidal membership functions (MFs) were used in the training of the model with Mamdani fuzzy inference system. The time, load and temperature inputs were fuzzified into six, four and three MFs respectively while the fuzzification process used 25 fuzzy rule bases. The centroid method of defuzzification was used to change the results into readable values. The adequacy of the FL model was determined using five metrics including mean square error (MSE), mean absolute error (MAE), mean absolute percentage error (MAPE), chi square (χ2) and F- test. The obtained results revealed that the FL model developed excelled in all the five tests of adequacy considered with MSE, MAE, MAPE, χ2 and F-test values of 4.17, 6.74, 11.51%, 7.93 and 1.27 respectively and hence, performed satisfactorily. This work established that the fuzzy logic approach is appropriate for electrical load projection on short-term basis.

Projections of private vehicle ownership, energy demand and vehicular emissions – a study of metropolitan cities in India

Urban agglomerations in India have witnessed significant growth in private vehicle ownership since the turn of this century. Intense urban road transport will have stern implications on air quality, GHG emissions, energy demands and, transport infrastructure. This study investigates the relationship between income and vehicle ownership rate for five metropolitan cities in India using a non-linear Gompertz functional framework. Medium-term scenarios of private vehicle (cars and two-wheelers) ownership, vehicle stock, energy demand and tailpipe emissions under multiple scenarios are envisaged by deriving ownership saturation based on city area and land use pattern. The results show a significant increase in vehicle stock resulting in a substantial rise in energy demand and vehicular emissions by 2035. However, the adoption of electric vehicles is expected to truncate this surge. Delhi is projected to saturate during this period while other cities are expected to saturate after 2035. The study also highlights transport policy and investment spheres to reduce private vehicle dependency.

Modelling long-term industry energy demand and CO2 emissions in the system context using REMIND (version 3.1.0)

Abstract. This paper presents an extension of industry modelling within the REMIND integrated assessment model to industry subsectors and a projection of future industry subsector activity and energy demand for different baseline scenarios for use with the REMIND model. The industry sector is the largest greenhouse-gas-emitting energy demand sector and is considered a mitigation bottleneck. At the same time, industry subsectors are heterogeneous and face distinct emission mitigation challenges. By extending the multi-region, general equilibrium integrated assessment model REMIND to an explicit representation of four industry subsectors (cement, chemicals, steel, and other industry production), along with subsector-specific carbon capture and sequestration (CCS), we are able to investigate industry emission mitigation strategies in the context of the entire energy–economy–climate system, covering mitigation options ranging from reduced demand for industrial goods, fuel switching, and electrification to endogenous energy efficiency increases and carbon capture. We also present the derivation of both activity and final energy demand trajectories for the industry subsectors for use with the REMIND model in baseline scenarios, based on short-term continuation of historic trends and long-term global convergence. The system allows for selective variation of specific subsector activity and final energy demand across scenarios and regions to create consistent scenarios for a wide range of socioeconomic drivers and scenario story lines, like the Shared Socioeconomic Pathways (SSPs).

Medium-term projections of vehicle ownership, energy demand and vehicular emissions from private road transport in India

Medium-term projections of vehicle ownership, energy demand and vehicular emissions from private road transport in India

Energy Demand Modeling for the Transition of a Coal-Dependent City to a Low-Carbon City: The Case of Ulaanbaatar City

Cities have committed to reducing greenhouse gas emissions and promoting renewable energy. However, many cities continue to rely on fossil fuels, while renewable energy sources are not used or are unable to meet the demand that fossil fuels provide. Depending on the geographic location, climate, and resources, cities must find their own path to energy sustainability. The city of Ulaanbaatar is one of the coal-dependent cities, its electricity and heat consumption mainly coming from coal. In this study, the future final energy demand of a coal-dependent city is identified and analyzed to make it a low-carbon city. Long-term energy demand projections for Ulaanbaatar to 2050 are conducted using the model for analysis of energy demand (MAED) model. Four scenarios are developed based on the existing local and national policies in the socio-economic and energy sectors, as well as more ambitious policy and technology measures recommended by various studies in the MAED_D model. The final energy demand is calculated to be 548, 460, 334, and 264 PJ in 2050 for BAU, REF, NDC, and RM scenarios, respectively, compared to 135 PJ in 2020. The results show that the high penetration of electricity and renewable energy, energy efficiency measures, and energy intensity reduction in all sectors can significantly reduce the future energy demand and help the transition towards a low-carbon city.

A renewable power system for an off-grid sustainable telescope fueled by solar power, batteries and green hydrogen

A large portion of astronomy’s carbon footprint stems from fossil fuels supplying the power demand of astronomical observatories. Here, we explore various isolated low-carbon power system setups for the newly planned Atacama Large Aperture Submillimeter Telescope, and compare them to a business-as-usual diesel power generated system. Technologies included in the designed systems are photovoltaics, concentrated solar power, diesel generators, batteries, and hydrogen storage. We adapt the electricity system optimization model highRES to this case study and feed it with the telescope’s projected energy demand, cost assumptions for the year 2030 and site-specific capacity factors. Our results show that the lowest-cost system with LCOEs of $116/MWh majorly uses photovoltaics paired with batteries and fuel cells running on imported and on-site produced green hydrogen. Some diesel generators run for backup. This solution would reduce the telescope’s power-side carbon footprint by 95% compared to the business-as-usual case.

Rethinking the role of efficiency for the decarbonization of buildings is essential

Antoine Levesque is a postdoctoral researcher at the Potsdam Institute for Climate Impact research (PIK), where he started working in 2014. His research focuses on the decarbonization of buildings, efficiency policies, and energy demand projections. Prior to the PIK, he worked at the International Institute for Applied Systems Analysis (IIASA) in Austria. He earned a PhD in economics at the Technische Universität Berlin (2021). Sebastian Osorio is a postdoc researcher at the PIK in the “Climate and Energy Policy” group. His work concentrates on applying energy systems models to evaluate low-carbon policies, with a clear focus on the EU power sector, the Emission Trading System, and their interaction with the decarbonization of buildings. Sebastian holds a PhD in information systems from the University of Lausanne in Switzerland. Sebastian Herkel is head of the “Energy Efficient Buildings” department at Fraunhofer Institute for Solar Energy Systems in Freiburg, Germany. He graduated from University of Karlsruhe with the degree of diplom-ingenieur in mechanical engineering. Sebastian Herkel is a researcher working in applied research in building energy performance and renewable energy systems. His focus is on integral energy concepts for buildings and neighborhoods, scientific analysis of building performance, and technologies for integration of renewable energy in buildings. Michael Pahle is head of the working group “Climate and Energy Policy” at the PIK. He holds a PhD in economics from TU Berlin. His research focuses on carbon pricing and more recently on climate policies for the buildings sector. He is co-principle investigator for Germany’s largest household panel on energy efficiency. Based on his research, Michael advises the German government and EU institutions. He is a member of the Global Climate Policy Partnership, a US-based global network of research institutions that helps major economies and businesses achieve ambitious climate goals. Antoine Levesque is a postdoctoral researcher at the Potsdam Institute for Climate Impact research (PIK), where he started working in 2014. His research focuses on the decarbonization of buildings, efficiency policies, and energy demand projections. Prior to the PIK, he worked at the International Institute for Applied Systems Analysis (IIASA) in Austria. He earned a PhD in economics at the Technische Universität Berlin (2021). Sebastian Osorio is a postdoc researcher at the PIK in the “Climate and Energy Policy” group. His work concentrates on applying energy systems models to evaluate low-carbon policies, with a clear focus on the EU power sector, the Emission Trading System, and their interaction with the decarbonization of buildings. Sebastian holds a PhD in information systems from the University of Lausanne in Switzerland. Sebastian Herkel is head of the “Energy Efficient Buildings” department at Fraunhofer Institute for Solar Energy Systems in Freiburg, Germany. He graduated from University of Karlsruhe with the degree of diplom-ingenieur in mechanical engineering. Sebastian Herkel is a researcher working in applied research in building energy performance and renewable energy systems. His focus is on integral energy concepts for buildings and neighborhoods, scientific analysis of building performance, and technologies for integration of renewable energy in buildings. Michael Pahle is head of the working group “Climate and Energy Policy” at the PIK. He holds a PhD in economics from TU Berlin. His research focuses on carbon pricing and more recently on climate policies for the buildings sector. He is co-principle investigator for Germany’s largest household panel on energy efficiency. Based on his research, Michael advises the German government and EU institutions. He is a member of the Global Climate Policy Partnership, a US-based global network of research institutions that helps major economies and businesses achieve ambitious climate goals.

Energy Demand and the Potential Role of Imported Liquefied Natural Gas (LNG) in Bangladesh

ABSTRACT: The rapid economic progress of Bangladesh is associated with a swiftly rising demand and energy consumption. Future economic growth will undoubtedly require a proportionate increase in energy availability within affordable prices. Bangladesh is inherently energy-scarce and heavily dependent on imported energy, primarily fossil fuels. Several approaches and strategies were attempted over time to mitigate the energy shortage. Some of them were reasonably successful, while others failed. In this study, we first examine the historical developments of the energy sector in Bangladesh since its independence and then use three approaches – triple exponential smoothing, vector autoregression, and the Cochrane-Orcutt AR(1) process to forecast the energy demand. Data for this study were taken from various sources, including British Petroleum, International Gas Union, International Energy Agency, Bangladesh Bureau of Statistics, World Bank, Petrobangla, and Energy Information Agency of the United States. As expected, each model predicts an exponential growth of energy demand in Bangladesh. We then explored the possibilities of mitigating such projected energy demand. Various studies show that Bangladesh has some potential for producing energy from renewable sources, i.e., solar, hydro, wind, wave, and others. However, such possibilities are limited, and many are still in their infancy. Although an increase in renewable energy is desirable from an environmental perspective, it alone will not meet Bangladesh's growing energy demand. At least in the short term, Bangladesh must rely on imported fossil energy. Among the fossil energy sources, LNG is by far the cleanest. With the development of technology, liquefaction, transportation, and regasification, LNG production, transportation, and use are becoming less expensive. Progressively more natural gas-producing countries are joining LNG production and export, contributing to the market's competitiveness. Though historically tied to the oil market, LNG markets are becoming more and more independent because of the increasing number of participants from both the demand and supply sides. Given that both its global price and negative impact on the environment are relatively lower than other fossil fuels, imported LNG should be the fuel of choice for Bangladesh. Government policies should focus on both importing LNG and expanding renewable energy resources.

Energy system analysis with a focus on future energy demand projections: The case of Norway

Energy system analysis with a focus on future energy demand projections: The case of Norway

Analysis of Hydroelectric Power Plants in East Kalimantan, Indonesia

This study aims to determine the amount of potential renewable energy sources of hydroelectric power plants that have been used to meet the electrical energy needs in East Kalimantan at this time, the amount of potential renewable energy sources of hydroelectric power plants that have not been used for electrical energy needs in East Kalimantan at this time, and the planning of electrical energy to provide electrical energy in the province of East Kalimantan in order to In this study, energy supply and demand in the province of East Kalimantan were analyzed using LEAP (Long-range Energy Alternative Planning system) models. Analyze the energy supply and demand over the next thirteen years using 2012 as the starting point. The linear regression methodology used to project energy demand interpolates historical data, activity, and energy intensity. According to the study's findings, just 1,509 MW of the province of East Kalimantan's potential hydropower sources—6976.14 MW—have been used up to this point. In 2025, the population is expected to rise by 6,167,359 people, increasing the demand for electricity from 17,546,970 MWh to 20,643,495 MWh. According to the national energy management's blueprint for the goal composition of the energy mix, hydropower capacity in 2025 would have been 23,961,414 MWh or 55.56% of the overall producing capacity of 43,129,583 MWh.

Techno-economic evaluation of industrial heat pump applications in US pulp and paper, textile, and automotive industries

Industrial process heat decarbonization through electrification could contribute significantly to climate change mitigation efforts. In the US industry, thermal processes accounted for more than two-thirds of the total final energy demand in 2021. Cross-cutting electrification technologies like industrial heat pumps are suitable for the process heat supply to several industrial unit operations in a sustainable way while also improving overall energy efficiency. This study employs a bottom-up approach to investigate the techno-enviro-economic potentials of deploying high-temperature and steam-generating heat pumps in US textile, pulp and paper, and automotive sectors in different timeframes. The results show that the annual technical potential energy and CO2 savings by electrifying heat supply are 310 PJ (or 36% of the projected energy demand) and 28 MtCO2 (or 71% of the projected CO2 emissions) in 2050 respectively, however, these incur additional costs in each sector (ranging between 5 and 18 $/GJ). The required heating capacity of industrial heat pumps is estimated at 15 GW, which translates roughly into a market of over 6000 heat pump units and an investment volume of $7 billion in the studied processes. Although there may be individual cost-effective opportunities for electrifying heat supply in specific industrial sites, the overall costs are estimated to be high in the three industrial sectors due to the large disparity between electricity and natural gas prices and low heat source temperatures. To overcome the identified techno-economic barriers, comprehensive action plans for different stakeholders are also given. This study provides novel insights that should inform policymakers’ and executives’ decisions about the electrification of the current and future US industrial heat supply in relevant industrial sectors.

New challenges for Uzbekistan’s energy sector and the role of the gas industry

This article raises an important problem for the Republic of Uzbekistan of the shortage of energy resources caused by the depletion of proven reserves of natural gas, which is the basis of the country's energy balance. Within the scope of this study, utilizing the methodology developed by the Energy Research Institute of the Russian Academy of Sciences for forecasting fuel and energy balances, a projection of primary energy demand in the Republic of Uzbekistan until 2030 has been calculated. This projection is detailed by fuel types. Based on the obtained results, the requirement for natural gas until 2030 has been determined. Furthermore, using data on proven natural gas reserves, a projection of production capacities has been calculated. As a result, a set of measures has been proposed aimed at reducing risks associated with the energy security of the Republic of Uzbekistan.

The Nexus Between Urbanization, Renewable Energy, Financial Development, and Economic Growth: Evidence from Turkey

This study aims to analyze the relationship between renewable energy production and financial development, urbanization, and economic growth in the 1980-2020 period of the Turkish economy, which draws attention to its high growth rate. Methodology: To determine the relationship between financial development, economic growth, urbanization, and renewable Energy, ARDL cointegration analysis, cointegration regression models, and Toda Yamamoto causality analysis will be applied using 1980-2020 period data. Results/Findings: The ARDL boundary test shows a long-run relationship between financial development, economic growth, urbanization, and energy consumption under structural breaks. According to the cointegration regression model results, renewable energy production is determined by financial development and per capita GDP. Toda Yamamoto causality analysis shows the existence of causality running from financial development, per capita GDP, and urbanization variables to renewable energy production. The results reveal that in determining Turkey long-term energy demand projections and strategies of Turkey's, it is necessary to consider both the impact of financial development and economic growth and the supply of energy needs with sustainable resources by minimizing foreign dependency. Originality and Practical Implications: According to the government's estimates, electricity consumption is expected to reach 370 TWh in 2025 and 591 TWh in 2040. These developments reveal the importance of energy consumption in the Turkish economy and make it necessary to investigate the factors affecting energy production.

Energy efficiency potentials in the EU industry: impacts of deep decarbonization technologies

Increasing the energy efficiency in high energy demand sectors such as industry with a high reliance on coal, oil and natural gas is considered a pivotal step towards reducing greenhouse gas emissions and meeting the Paris Agreement targets. The European Commission published final energy demand projections for industry capturing current policies and market trends up to 2050. This Reference scenario for industry in 2050, however, does not give insights into the extent to which energy efficiency potentials are already implemented, in which sectors further efficiency can be achieved, to what extent or with which technologies. In this paper, the EU Reference scenario is broken down and compared to a Frozen Efficiency scenario with similar GDP developments but without energy efficiency. Through bottom-up analyses, it is found that with energy efficiency technologies alone, this Reference scenario for industry energy demands (10.6 EJ in 2050) cannot be achieved. That means that the EU Reference assumes higher energy efficiency than possible and too high an effect of current policies. In the Frozen Efficiency scenario, the energy demand reaches 14.2 EJ in 2050 due to the GDP development; 22% higher than 2015. Energy efficiency improvements and increased recycling can decrease industrial energy demand by 23% (11.3 EJ in 2050). In order to further reduce the energy demand, our analyses shows that the wide implementation of innovative in combination with electrification or hydrogen technologies can further decrease the 2050 energy demand to 9.7 EJ or 10.3 EJ, respectively.

The impact of energy-efficiency upgrades and other distributed energy resources on a residential neighborhood-scale electrification retrofit

Ambitious targets for carbon emissions reductions are highlighting new challenges for electrification strategies, leading to an increased focus on building load flexibility and energy management to complement the variability inherent in renewable energy generation. Over the next decade millions of existing homes could undergo electrification retrofits, and there is an urgent need to understand the potential impacts of electrifying major residential loads such as water and space heating on community load characteristics, resident energy bills, and the utility’s distribution system. Behind-the-meter distributed energy resources (DERs), including efficiency measures, photovoltaics (PV), battery storage, managed electric vehicle (EV) charging, and controls such as home energy management systems (HEMS), can significantly alter a neighborhood’s load profile and provide benefits to both the residents and the grid. We present a novel approach to characterizing the impact of a hypothetical neighborhood-scale residential retrofit program on individual homes’ energy use profiles, associated utility bills, and the local distribution system. We modeled a mixed-fuel community of 30 single-family homes in Denver, Colorado, and compared the effects of retrofit scenarios ranging from conventional energy-efficiency upgrades to full electrification with and without more advanced DER technologies. We analyzed which packages of DERs most reliably enable demand flexibility in response to a time-of-use (TOU) rate for this and similar neighborhoods. Our buildings-to-grid co-simulation framework includes a generic secondary distribution feeder model to capture voltage profiles, transformer loading, and other grid impacts in each case. We also calculated the carbon emissions associated with energy use in the community. The methodology developed here can be broadly applied to community-scale beneficial electrification studies in other regions, climates, utility infrastructures, and building typologies to make specific, targeted recommendations based on quantified projections of energy demand in any given community. Our findings indicate that residential electrification can be achieved without negatively impacting the monthly utility bill, and that a combination of conventional energy-efficiency measures, PV, battery, controls, and managed EV charging to maximize a community’s demand flexibility is a promising strategy. Adding DERs (especially PV) as part of efficient electrification produces much bigger savings than efficient electrification without DERs. A key barrier is that upgrades require upfront costs, and modest utility bill savings result in long payback periods.

Energy Demand Projection and Economy Nexus of Pakistan

In the fundamental study to design sustainable electricity generation, long-term electricity demand forecasting has taken on a crucial role. The Low Emissions Analysis Platform (LEAP®) software is used in this study to estimate the demand for energy by various consumer groups including commercial, residential, agricultural, and industrial for the study period (2023-2050). The electricity demand is forecasted under different Gross Domestic Product (GDP) growth scenarios. The developed scenarios are reference, low economic growth, medium economic growth, and high economic growth scenarios. This study identifies the key relationship between energy demand and the economy of Pakistan. It is found that energy demand and the GDP of the country have a direct relationship as one quantity increases, the other one also increases, and vice versa. 3.56% growth rate of GDP under the low GDP growth scenario causes an increment in energy demand from 137.97 million MWh in 2023 to 374.79 million MWh. Medium and high GDP growth scenario pertains to GDP growth rates of 5.79% and 10.22% which causes an increment in energy demand to 652.17 million MWh and 1,244.75 million MWh by 2050. The results of this study are significant and provide insight into electricity demand growth under the economic scenarios that could be useful for sustainable electricity planning in Pakistan.