51 research outputs found
Challenges and gaps for energy planning models in the developing-world context
Energy planning models (EPMs) support multi-criteria assessments of the impact of energy policies on the economy and environment. Most EPMs have originated in developed countries and are primarily aimed at reducing greenhouse gas emissions while enhancing energy security. In contrast, most, if not all, developing countries are predominantly concerned with increasing energy access. Here, we review thirty-four widely used EPMs to investigate their applicability to developing countries and find an absence of consideration of the objectives, challenges, and nuances of the developing context. Key deficiencies arise from the lack of deliberation of the low energy demand resulting from lack of access and availability of supply. Other inadequacies include the lack of consideration of socio-economic nuances such as the prevalence of corruption and resulting cost inflation, the methods for adequately addressing the shortcomings in data quality, availability and adequacy, and the effects of climate change. We argue for further research on characterisation and modelling of suppressed demand, climate change impacts, and socio-political feedback in developing countries, and the development of contextual EPMs
Corruption significantly increases the capital cost of power plants in developing contexts
Emerging economies with rapidly growing population and energy demand, own some of the most expensive power plants in the world. We hypothesized that corruption has a relationship with the capital cost of power plants in developing countries such as Bangladesh. For this study, we analyzed the capital cost of 61 operational and planned power plants in Bangladesh. Initial comparison study revealed that the mean capital cost of a power plant in Bangladesh is twice than that of the global average. Then, the statistical analysis revealed a significant correlation between corruption and the cost of power plants, indicating that higher corruption leads to greater capital cost. The high up-front cost can be a significant burden on the economy, at present and in the future, as most are financed through international loans with extended repayment terms. There is, therefore, an urgent need for the review of the procurement and due diligence process of establishing power plants, and for the implementation of a more transparent system to mitigate adverse effects of corruption on megaprojects
Maximizing daylight use potential in retail shopping environments in the context of Dhaka, Bangladesh
In the recent times with the increase in the population, the number of retail shopping environments has increased significantly in Dhaka city. But these shopping environments are developing into high consumption areas of electricity resulting from artificially light design approach. In this study, it was found that the energy consumption is rising because of the minimal or no use of daylight and natural ventilation.
A field based case study was conducted to review the daylight inclusion in the design of retail shopping environments representing the historical periods of 1950-70, 1971-80, 1981-90, 1991-2000, 2001-2011. In this research in addition to field work, parametric study was done to identify the effect of design factors/parameters relating to daylight. The main focus was to develop a causal relation between the parameters of the shopping environments to maximize the use of daylight. The parameters which were considered in this study were- width, height and depth of the shops; the depth of the corridor between two rows of shops; the height and width of the light wells. All the parametric relations were derived to the width of the shop. Daylight level were measured and compared with the given level in the Bangladesh National Building Code (BNBC) code.
It was found that shopping environments play an important role in energy consumption in urban areas and little or no utilization of daylight contribute to this energy demand. This is of particular significance in an environment where dwindling fossil sources of energy and an increasing energy demand created by positive economic growth pose a challenge for the building industry stake holders
Decarbonization cost of Bangladesh's energy sector: Influence of corruption
As a rapidly developing lower-middle income country, Bangladesh has been maintaining
a steady growth of +5% in the gross domestic product (GDP) annually since
2004, eventually reaching 7.1% in 2016. The country is targeting to become uppermiddle-
income and developed by 2021 and 2041 respectively, which translates to an
annual GDP growth rate of 7.58% during this period. The bulk of this growth
is expected to come from the manufacturing sector, the significant shift towards
which started at the turn of this century. Energy intensity of manufacturing-based
growth is higher, the evidence of which can be seen in the 3.17 times increase in
national energy consumption between 2001 and 2014. Also, Bangladesh aims to
achieve 100% electrification rate by 2021 against an annual population growth rate
of 1.08%. With the increasing per capita income, there is now a growing middle
class fuelling the growth in demand for convenient forms of energy. Considering
the above drivers, the Bangladesh 2050 Pathways Model suggested 35 times higher
energy demand than that of 2010 by 2050. The government and private sector have
started a substantial amount of investments in the energy sector to meet the signi
ficant future demand. Approximately US250 billion in 2050 under HCS, which can be reduced 23% under
ZCS. The cost of decarbonization would be 3.6, 3.4 and 3.2 times under average
cost of MCS, LCS, and ZCS, than that of HCS. As the energy sector of Bangladesh
is under rapid development, the accumulated capital would be comparatively high
by 2050. However, fuel cost can be significantly reduced under LCS and ZCS which
would also ensure lower emissions. The study suggested that energy mix change,
technological maturity, corruption and demand reduction can influence the cost
of decarbonization. However, the most significant influencer for the decarbonization
of Bangladeshi energy sector would be the corruption. Results showed that if
Bangladesh can minimize the effect of corruption on the energy sector, it can reduce
the cost of decarbonization 45-77% by 2050 under MCS, LCS, and ZCS
Why is Bangladesh’s electricity generation heading towards a GHG emissions-intensive future?
Bangladesh—recently graduated to developing nation category from a least developed country with an emerging economy also is one of the severely affected countries by climate change—is heading towards a coal-intensive electricity generation mix contrary to global decarbonisation efforts. It is facing formidable challenges in achieving universal access to affordable, reliable, and sustainable electricity, decarbonising the energy mix by 2030 to achieve the objective of Sustainable Development Goal (SDG) 7, despite a 285% increase of installed capacity between 2008–09 and 2020–21 and aiming at achieving 40 GW and 60 GW by 2030 and 2041 with planned expansions, respectively. This study reviewed Bangladesh’s electricity sector developments—demand, generation, transmission, and distribution (T&D)—to identify progress in policies, drivers, and challenges behind the Greenhouse gas (GHG) emissions-intensive future direction. The rapid population and economic growth and shift towards industry-based economy drove the exponential growth in energy demand, eventually influencing the rapid generation capacity and T&D infrastructure development. However, Bangladesh has targeted transitioning from natural gas to coal dominating fuel mix due to the lower renewable potential, energy, and food security challenges, because of the anticipated substantial future electricity demand for becoming an Upper Middle and a High-income country by 2031 and 2041, respectively. We also recommended nuclear energy, (renewable) electricity import and floating solar plants to decarbonise the current trajectory
Although feasible, falling renewables costs might not benefit Bangladesh's energy sector's decarbonisation: Is this another ‘debt-fossil fuel production trap’?
As Bangladesh strives to transition from a least developed to a developed nation by 2041, a growing population and rising disposable income have spurred a growing middle class, escalating the demand for accessible energy. The government and the private sector have heavily invested in a fossil fuel-centric energy mix to meet this anticipated surge in demand. However, our research challenged this prevailing approach by developing a country-scale scenario-based input-output long-horizon energy planning model for demonstrating the economic viability of decarbonising Bangladesh's electricity generation sector by 2050, with a preference for renewables over fossil fuels, particularly in a low-emissions scenario. This study was among the first to evaluate which was the most recent Integrated Energy and Power Master Plan (IEPMP) with a long-horizon energy planning model and suggested that implementing strategic socio-economic development measures, such as privatisation, deregulation, transparency, energy demand reduction, equitable subsidy removal, and carbon pricing, could yield a 24 % cost reduction for developing a near-zero emissions electricity generation sector by 2050. Despite these potential benefits, current and future policies, entirely influenced by master plans developed by the Japan International Cooperation Agency, continue to rely heavily on imported coal, liquefied natural gas (LNG), hydrogen, ammonia, and nuclear energy, which raised concerns about the country being entangled in a ‘debt-fossil fuel production trap.’ We recommended a critical re-evaluation of existing energy policies. This caution was grounded in the suggestion that the nation should instead harness in-country resources and explore renewable-rich alternatives within its regional neighbouring countries, steering away from potential geopolitical, economic, and environmental pitfalls
Forecasting methods in energy planning models
Energy planning models (EPMs) play an indispensable role in policy formulation and energy sector development. The forecasting of energy demand and supply is at the heart of an EPM. Different forecasting methods, from statistical to machine learning have been applied in the past. The selection of a forecasting method is mostly based on data availability and the objectives of the tool and planning exercise. We present a systematic and critical review of forecasting methods used in 483 EPMs. The methods were analyzed for forecasting accuracy; applicability for temporal and spatial predictions; and relevance to planning and policy objectives. Fifty different forecasting methods have been identified. Artificial neural network (ANN) is the most widely used method, which is applied in 40% of the reviewed EPMs. The other popular methods, in descending order, are: support vector machine (SVM), autoregressive integrated moving average (ARIMA), fuzzy logic (FL), linear regression (LR), genetic algorithm (GA), particle swarm optimization (PSO), grey prediction (GM) and autoregressive moving average (ARMA). In terms of accuracy, computational intelligence (CI) methods demonstrate better performance than that of the statistical ones, in particular for parameters with greater variability in the source data. However, hybrid methods yield better accuracy than that of the stand-alone ones. Statistical methods are useful for only short and medium range, while CI methods are preferable for all temporal forecasting ranges (short, medium and long). Based on objective, most EPMs focused on energy demand and load forecasting. In terms geographical coverage, the highest number of EPMs were developed on China. However, collectively, more models were established for the developed countries than the developing ones. Findings would benefit researchers and professionals in gaining an appreciation of the forecasting methods, and enable them to select appropriate method(s) to meet their needs
Modelling and Forecasting Energy Demand in Rural Households of Bangladesh
Bangladesh, the eighth largest populous country in the world, has a significant rural population (70%), which is contributing to the energy demand of the country. The major portion in energy demand of rural households is biomass energy. With the improvement in GDP the rural energy demand would switch to more electricity intensive demand pathway. This paper focuses on a bottom up approach towards modelling the aggregated energy demand of rural households of Bangladesh form the year 2010 to 2050. The combination of four level scenarios of four variables (population, GDP electrification index, public energy conservation index) would forecast lowest, highest and optimum energy demand pathways for rural households of Bangladesh. The study not only considers the electricity demand of the rural household, but also it would render the opportunity to concentrate at the detail user end energy demands (e.g. liquid fuel, biomass etc.)
Understanding Residential Occupant Cooling Behaviour through Electricity Consumption in Warm-Humid Climate
According to the India Energy Security Scenario 2047, the number of residential air conditioner (A/C) units may increase seven-fold by 2037 as compared to 2017. Also, the related energy consumption might increase four times in the next two decades, according to India’s National Cooling Action Plan. Therefore, the study of occupant cooling behaviour is essential to reduce and manage the significant electricity demand, helping to formulate and implement climate-specific cooling policies, and to adopt low-energy and low-cost technologies at mass-market scale. The study aims to analyse residential electricity consumption in order to investigate occupant behaviour, especially for thermal comfort by using space cooling and mechanical ventilation technologies. Among the five climate zones in India, this study focuses on the occupant behaviour in a warm-humid climate using Auroville as a case study, where climate analysis of the past 30 years demonstrated progression towards unprecedented warmer weather in the last five years. In this study, electricity consumption data from 18 households (flats) were monitored for seven months (November 2018–June 2019). The study also elaborated the limitations faced while monitoring and proposed a data filling methodology to create a complete daily profile for analysing occupant behaviour through electricity consumption. The results of the data-driven approach demonstrated the characteristics and complexities in occupant behaviour and insight on the operation of different technologies to attain thermal comfort in residential buildings in an increasingly warming climate
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