Energy supply in Malawi: Options and issues

Analysis of the Syrian long-term energy and electricity demand projection using the end-use methodology

Energy planning and policy development require an in-depth assessment of energy resources and long-term demand forecast estimates. Pakistan, unfortunately, lacks reliable data on its energy resources as well do not have dependable long-term energy demand forecasts. As a result, the policy makers could not come up with an effective energy policy in the history of the country. Energy demand forecast has attained greatest ever attention in the perspective of growing population and diminishing fossil fuel resources. In this study, Pakistan’s energy demand forecast for electricity, natural gas, oil, coal and LPG across all the sectors of the economy have been undertaken. Three different energy demand forecasting methodologies, i.e., Autoregressive Integrated Moving Average (ARIMA), Holt-Winter and Long-range Energy Alternate Planning (LEAP) model were used. The demand forecast estimates of each of these methods were compared using annual energy demand data. The results of this study suggest that ARIMA is more appropriate for energy demand forecasting for Pakistan compared to Holt-Winter model and LEAP model. It is estimated that industrial sector’s demand shall be highest in the year 2035 followed by transport and domestic sectors. The results further suggest that energy fuel mix will change considerably, such that oil will be the most highly consumed energy form (38.16%) followed by natural gas (36.57%), electricity (16.22%), coal (7.52%) and LPG (1.52%) in 2035. In view of higher demand forecast of fossil fuels consumption, this study recommends that government should take the initiative for harnessing renewable energy resources for meeting future energy demand to not only avert huge import bill but also achieving energy security and sustainability in the long run.

The objective of this work is to evaluate long-term energy demand and supply decarbonization in Indonesia. On the demand side, electric vehicles and biofuels for transportation and induction stoves and urban gas networks for households were considered. Based on the National Energy Policy, primary energy supply projections optimized NRE power plant use and increase NRE's position in the national energy mix. A Low Emissions Analysis Platform (LEAP) model evaluates 2020–2050 energy demand predictions and low-carbon energy systems. This study's sustainable transition options require two basic technical advances. First, electric vehicles and induction stoves would reduce oil fuel usage by 228.34 million BOE and LPG consumption by 24.65 million BOE. Second, power generation should be decarbonized using NRE sources such as solar, hydro, biomass, geothermal, and nuclear. In 2050, solar power (40 GW), hydropower (38.47 GW), geothermal power (10 GW), and other NRE (24.45 GW, 18.67 GW of which would be biomass power) would dominate NRE electrical capacity. Biomass co-firing for coal power plants would reach 36.35 million tons in 2050. In 2035, the Java-Bali or West Kalimantan system will deploy 1 GW of nuclear power reactors, rising to 4 GW by 2050. Under the Transition Energy (TE) scenario, by 2025 and 2050, new and renewable energy would make up 23% and 31% of the primary energy mix, respectively, reducing GHG emissions per capita. According to predictions, annual GHG emissions per capita will decline from the BAU scenario's 4.48 tonne CO2eq/capita in 2050 to the TE scenario's 4.1 tonne.

The aim of this paper is to describe a projection of energy demand of transport that was developed with the iTREN-2030 modelling toolset and to analyse the impact of transport policies and the renewable energy package. The paper is based on the EU-co-funded project `iTREN-2030' that aimed at developing consistent energy and transport scenarios for the EU27, applying a set of different models. The iTREN-2030 modelling toolset includes POLES, a simulation model for the development of long-term (2050) energy supply and demand scenarios of the world, TREMOVE, ASTRA and TRANS-TOOLS. These models were applied in an interactive way to create two main scenarios (1) the reference scenario includes only policies approved by mid 2008 (Reference Scenario). It is a set of projections under given, reasonable assumptions concerning both the socio-economic environment and a kind of frozen policy environment. (2) the renewable scenario considering policies which are likely to be implemented until 2025 (Integrated Scenario). Several policy instruments are implemented on top of the measures included in the reference scenario. On the transport side the regulation of CO2 emission for different types of transport vehicles was considered. On the energy side the renewable energy package has a major impact on the energy system. It aims at reducing the GHG emissions (by 20% compared to the GHG emissions of 1990) and increases the share of renewable energy support schemes (20% renewable share of final demand) both until 2020. Furthermore, the Integrated Scenario considers the global economic crisis. The comparison of the two scenarios clearly indicates significant changes in the energy and transport system like: (3) In the Integrated Scenario, final energy demand in the transport sector will see negative growth rates, whereas it would continue to grow in the Reference Scenario. (4) Alternative transport fuels like biofuels, gas and electricity will play a more important role in the integrated scenario. As a consequence of the lower demand and accelerated decarbonisation, GHG of transport will be reduced by some 7% between 2005 and 2020, and by 12% by 2030.

In order to attain equilibrium between energy supply and demand, reliance on conventional methods for precise long-term electricity demand forecasting is no longer viable. The utilization of artificial intelligence, such as fuzzy logic and artificial neural network (ANN) models, emerges as a prospective solution in the current dynamic scenario. This research explores long-term electricity demand forecasting within the Jakarta distribution grid system, employing various fuzzy logic and ANN approaches including Sugeno, Mamdani, Bayesian Regularization, and the Levenberg algorithm. The analysis incorporates time series data spanning 2016 to 2019, encompassing electricity load demand, economic factors, and demographic variables, processed using MATLAB. The outcomes of the four forecasting methods reveal an average error range of 2 to 3%. The findings indicate that employing fuzzy logic and ANN methods for long-term electricity demand forecasting can yield a forecast error of less than 3%. The study recommends future research enhancements through the inclusion of additional time series data and a more refined system.

The energy transition from fossil fuels to carbon-free sources will be a big challenge in the coming decades. In this context, the long-term prediction of energy demand plays a key role in planning energy infrastructures and in adopting economic and energy policies. In this article, we aimed to forecast energy demand for Spain, mainly employing econometrics techniques. From information obtained from institutional databases, energy demand was decomposed into many factors and economy-related activity sectors, obtaining a set of disaggregated sequences of time-dependent values. Using time-series techniques, a long-term prediction was then obtained for each component. Finally, every element was aggregated to obtain the final long-term energy demand forecast. For the year 2030, an energy demand equivalent to 82 million tons of oil was forecast. Due to improvements in energy efficiency in the post-crisis period, a decoupling of economy and energy demand was obtained, with a 30% decrease in energy intensity for the period 2005–2030. World future scenarios show a significant increase in energy demand due to human development of less developed economies. For Spain, our research concluded that energy demand will remain stable in the next decade, despite the foreseen 2% annual growth of the nation’s economy. Despite the enormous energy concentration and density of fossil fuels, it will not be affordable to use them to supply energy demand in the future. The consolidation of renewable energies and increasing energy efficiency is the only way to satisfy the planet’s energy needs.

Chapter 1.7 - Long-term Energy Demand and Supply Projections and Evaluations for Turkey

Throughout the history of the oil and gas industry, forecasts have been made of energy supply, future rates of production, energy demand and of oil prices. These forecasts are used for a wide variety of purposes including the oil industry itself, financial institutions, public policy planners, nations, and groups of nations (OPEC). Most forecasts are incorrect after only a few years, but they are often updated without a fundamental under-standing of why the earlier predictions were in error. This leads to painful disruptions and redirections not only within the oil industry but also in public policy debates and decisions on a national and global scale. This paper reviews some of the more significant forecasts of energy supply, demand, and oil prices made in the last 25 years, and identifies the basic premises used and why they led to incorrect forecasts. Usually the premises have been too narrowly based. A broader basis is proposed, which includes continuing changes in technology, national goals, public attitudes and environmental concerns. Extrapolation of past trends is a poor basis for forecasts. Hence, a broader understanding of the changing world is essential and must be incorporated meaningfully into forecasts.

Medium and long-term energy demand forecasts by sectors in China under the goal of “carbon peaking & carbon neutrality”: Based on the LEAP-China model

An accurate long-term energy demand forecasting is essential for energy planning and policy making. However, due to the immature energy data collecting and statistical methods, the available data are usually limited in many regions. In this paper, on the basis of comprehensive literature review, we proposed a hybrid model based on the long-range alternative energy planning (LEAP) model to improve the accuracy of energy demand forecasting in these regions. By taking Hunan province, China as a typical case, the proposed hybrid model was applied to estimating the possible future energy demand and energy-saving potentials in different sectors. The structure of LEAP model was estimated by Sankey energy flow, and Leslie matrix and autoregressive integrated moving average (ARIMA) models were used to predict the population, industrial structure and transportation turnover, respectively. Monte-Carlo method was employed to evaluate the uncertainty of forecasted results. The results showed that the hybrid model combined with scenario analysis provided a relatively accurate forecast for the long-term energy demand in regions with limited statistical data, and the average standard error of probabilistic distribution in 2030 energy demand was as low as 0.15. The prediction results could provide supportive references to identify energy-saving potentials and energy development pathways.

Technological progress and long-term energy demand — a survey of recent approaches and a Danish case

Structural changes in Japan's economy and society and outlook for long-term energy supply and demand

All animals inhabit environments where resource availabilitys fluctuates, making periods of fasting and starvation a ubiquitous ecological challenge. During starvation, animals can minimize energetic demands by reducing metabolic rate, body mass, body temperature and organ size. However, such wholesale metabolic suppression can compromise aerobic capacity and thus the ability to perform critical activities such as predator escape, foraging and refeeding when food becomes available (Wang, Young, & Randall, 2006). Changes to mitochondrial content and function also commonly accompany prolonged starvation in multiple taxa (e.g., Monternier, Marmillot, Rouanet, & Roussel, 2014; Staples & Brown, 2008; Trzcionka, Withers, Klingenspor, & Jastroch, 2008). However, it has been difficult for researchers to determine the benefits and costs of mitochondrial modifications in living organisms due to technological limitations. A recent study by Salin et al. (2018) represents a major advancement in the field, as they applied a novel technique to quantify in vivo reactive oxygen species (ROS) production and determined whether starvation-induced changes in mitochondrial function come at the cost of oxidative stress. Mitochondria produce energy in the form of adenosine triphosphate (ATP), via a mechanism that can be compared to a system of mechanical gears. ATP production is coupled to oxygen consumption through an electrochemical gradient of protons, the proton motive force (Figure 1). A large proton motive force provides a strong driving force for metabolism, but if it becomes too high can lead to excess ROS production (Korshunov, Skulachev, & Starkov, 1997; Munro & Treberg, 2017). Proton leak acts as a safety valve that mitigates ROS production by dissipating the proton motive force, preventing it from climbing too high. Thus, mitochondrial uncoupling reduces oxidative damage accumulation and increases longevity (Caldeira da Silva, Cerqueira, Barbosa, Medeiros, & Kowaltowski, 2008; Salin, Luquet, Rey, Roussel, & Voituron, 2012; Speakman et al., 2004). However, proton leak dissipates energy as heat, reducing potential ATP production per nutrient catabolized and contributing to increases in basal metabolic rates (Rolfe & Brand, 1996). When resources are limited, reducing proton leak to couple mitochondria will lower energetic demands, maximize the amount of ATP produced per nutrient consumed and preserve mitochondrial ATP production capacity, to enhance starvation tolerance (Monternier et al., 2014, 2017 ; Roussel, Boël, & Romestaing, 2018). As a result, Salin and colleagues hypothesized that the benefits of mitochondrial coupling during starvation are offset by the cost of increased reactive oxygen species (ROS) generation. Salin et al. (2018) exposed brown trout (Salmo trutta) to an ecologically relevant starvation challenge and used a ratiometric probe (MitoB) to measure in vivo ROS production, combined with high-resolution respirometry to characterize mitochondrial function in vitro. Food-deprived trout reduced their energy demands by decreasing liver size and mitochondrial content. At the same time, liver mitochondrial coupling was enhanced by a coordinated reduction in leak, increased oxidative phosphorylation capacity and larger proton motive force. Thus, starving trout shift gears to simultaneously reduce energetic demands and maximize energy production (Salin et al., 2018). This may explain how these trout can maintain their aerobic scope during starvation (Auer, Salin, Rudolf, Anderson, & Metcalfe, 2016), facilitating continued growth, locomotion and feeding capacity during food shortages (Auer, Salin, Anderson, & Metcalfe, 2015; Auer, Salin, Rudolf, Anderson, & Metcalfe, 2015; Auer, Salin, Rudolf, Anderson, & Metcalfe, 2015). Starved individuals, however, also produced twice as much ROS compared to fed fish, potentially due to the higher proton motive force (Salin et al., 2018). If the increase in ROS is not countered by a sufficient upregulation in antioxidant defences, oxidative stress may result, and indeed, prior work has shown that brown trout accumulate oxidative damage during prolonged starvation (Bayir et al., 2011). If not repaired, oxidative damage leads to accelerated ageing and reduced longevity (Finkel & Holbrook, 2000). Thus, oxidative stress can elicit life-history trade-offs between survival, growth and reproduction, constraining life-history evolution (Dowling & Simmons, 2009). An exciting area for future research will be to determine the long-term consequences of changes in mitochondrial coupling for organismal performance and fitness. An important implication of the findings by Salin et al. (2018) is that adaptive responses to starvation are context dependent, wherein the appropriate strategy for responding to limitations in energy supply will be determined by the energy demand, set by ecological factors and life history. For many animals, starvation coincides with periods of intermittently high energetic demands, such as the need to actively search for food, evade predators or care for young. In these cases, increased mitochondrial coupling may be worth the price to avoid an extreme imbalance between energy supply and demand. Otherwise, if imbalances between energy supply and demand can be avoided, for example by severely curtailing activity or increasing nutrient storage, then reducing proton leak may not be a desirable strategy. This is consistent with the finding that mitochondria isolated from hibernators exhibit suppressed rates of ATP production and no change in proton leak. Uncoupling may also be favoured when proton leak provides an important heat source (Staples & Brown, 2008). Comparative studies in a broader range of taxa are necessary to understand how complex ecological contexts shape metabolic responses to resource limitations. The work by Salin and colleagues in brown trout provides compelling evidence that the benefits of mitochondrial coupling for preserving organismal metabolic capacity during starvation may be offset by the cost of oxidative stress (Salin et al., 2018). To test the hypothesis that sensitivity to oxidative stress contributes to determining the best metabolic strategy to use during starvation, researchers could simultaneously manipulate food supply and energy demand (e.g., manipulating activity or immune activation) in organisms that vary in susceptibility to oxidative stress. In addition, tissue-specific mitochondrial modifications have emergent properties that shape whole organismal metabolism (Rolfe & Brand, 1996; Salin et al., 2016). Consistent with this, the results of this study illustrate how changes in mitochondrial function can synergize with changes in organ size to better balance energy supply and demand. Moving forward, integrative studies that apply technological innovations in ecologically relevant contexts are required to shed new light on the role of energetics in organismal evolutionary history, as exemplified by Salin et al. (2018). We would like to thank Andre Szejner Sigal, Kaitlin Allen and Charles Fox for their thoughtful comments and feedback on this commentary.

A reinforcement and imitation learning method for pricing strategy of electricity retailer with customers’ flexibility

After two years of study the report of the Workshop on Alternative Energy Strategies (W.A.E.S.) was released in early May 1977 in the fifteen national capitals of the Workshop members. W.A.E.S. is an ad hoc , international project involving 75 individuals from 15 countries. Its objective is to describe a range of feasible alternative energy strategies to the year 2000 for the nations of the World Outside Communist Areas (W.O.C.A.). These 15 countries are major energy consumers, using some 80% of the energy consumed by W.O.C.A. in 1972. Three are also important oil producers and exporters - Iran, Mexico and Venezuela. World oil production is expected to decline before the end of the century under almost any set of world conditions. W.A.E.S. evolved out of the common concern of a number of influential people in various parts of the world who believed that the transition from oil to other energy sources needed to be widely understood and effectively managed in order to avoid major national and international dislocations. The first major task of W.A.E.S. was to identify and agree on the major determinants of future energy supply and demand, to select a range of likely values for these determinants, and to develop a conceptual framework for bringing together the various national and global studies in a way that would be internally consistent, clearly visible and understandable. World energy prices, the rate of world economic growth and national energy policy were selected as the principal determinants of future energy supply and demand to 1985 and to the year 2000. A range of assumptions for each of these key variables was tested and adopted. Specific cases, based on combinations of these principal determinants, were selected to span a wide range of likely future energy supply and demand patterns. ‘Scenario’ is the term used for each case. A ‘scenario’ is not a forecast of the future. Rather, it represents a plausible future constructed from certain specified variables. Adding up the estimates of energy demand and supply for W.A.E.S. countries for each ‘scenario’ of the future, plus estimates for other countries have made it possible to evaluate future world energy balances or imbalances under particular sets of assumptions. The objective of this approach has been to understand better, quantitatively and qualitatively, the major energy issues and choices of the future and to identify which long term strategies will be most useful in balancing future world energy supply and demand. For example, at some point, perhaps before the year 2000, the cumulative national demands for oil imports may well exceed the cumulative potential for oil exports. Years before this happens nations must develop realistic national energy strategies which take account of such a situation. This requires action on a very broad scale, long before such a gap might actually develop, to ensure a smooth transition from energy systems largely based on oil to systems based on other energy sources such as coal and nuclear fuel. The time at which, and the degree to which, the transition from oil to other energy sources is perceived, understood, accepted and acted upon within and among nations will be crucial to an orderly world energy transition. This lecture, which followed the public release of the report, includes a review of the principal conclusions, the methodology used for making supply and demand projections to the year 2000, and some implications for national action and international collaboration. I am honoured to speak to you on the occasion of this first lecture sponsored by the Fellowship of Engineering in conjunction with the Royal Society. Once before I was at a meeting of the Royal Society as a listener, not a speaker. It was in March 1941 at the Society’s rooms at Burlington House. I was in England with Professor J. B. Conant establishing a London office for the conduct of cooperation and liaison between the American scientific efforts in the development of new weapons and the notable efforts going forward in the United Kingdom. I recall the interesting timing device monitoring speakers which went from a green light to yellow at nine minutes and from yellow to red at ten minutes. I copied this device for our Energy Workshop. I needed it only once - at our first meeting. Thereafter, interventions were less than nine minutes.