Search

The rapid global economic development over the past two decades has been accompanied by rising energy demand and CO 2 emission rates. Understanding the driving forces of CO 2 emissions is necessary for future energy planning and policy formulation. This paper examines the driving factors behind the increase in CO 2 emissions in Malaysia with special focus on the manufacturing, electricity and transportation sectors. The paper extends the Kaya identity by incorporating energy mix, investment efficiency, capital-labor substitution, population-to-employment, urbanization rates, and per capita CO 2 , along with the standard variables including economic activity, economic structure, and energy intensity. The paper also evaluates if any macroeconomic instability (economic crisis) in the country has led to reduction in emission rates. The study utilizes the latest country data, covering the period 2002–2016. The result shows that, while economic activity is always associated with CO 2 emissions, more efficient energy use would help restrain the rise in emission rates without hampering economic growth regardless of economic structure. Emission reduction targets in Malaysia should be reinforced with an appropriate economic restructuring that restrains the economic structure effect. Due to the extensive energy use in the transportation and electricity generation, future emission control should focus more on these sectors. • The paper identifies the factors affecting CO 2 emissions, considering the economic, technical, and social aspects. • The study investigates whether the economic downturn induces further emission control. • Results indicate economic activities and urbanization process are the main driving factors. • Energy efficiency, renewable energy use, and capital-labor substitution should be strengthened. [ABSTRACT FROM AUTHOR]

This paper presents an integrated mathematical optimisation approach to synthesise a bioelectricity supply chain network with minimal costs. Detailed economic evaluation was performed to determine the optimum location of centralised or decentralised biomass pre-treatment and power plants. Once the optimum bioelectricity supply chain network is established, input-output modelling of the network is formed. Next, feasible operating range analysis is performed. A palm-based bioelectricity supply chain case study in Malaysia is used to illustrate the proposed approach. Based on the optimised results, a total of 80.90 MW of bioelectricity are generated with an estimated net present value of USD 123.94 million and a payback period of 5 years. Meanwhile, the feasible operating range analysis indicated that the synthesised bioelectricity supply chain network possessed multiple feasible operating ranges; 5.06–10.04 MW, 11.88–36.08 MW and 44.26–101.16 MW. Based on the result, the decision makers can determine the potential of the future bioelectricity projects according to different seasons of supply and demand variations. Image 1 • Integrated approach for bioelectricity supply chain network is presented. • Optimum location of centralised/decentralised technologies is determined. • Feasible operating range analysis is performed for the synthesised network. • Potential for future bioelectricity projects can be determined. [ABSTRACT FROM AUTHOR]

Abstract The power output by a small hydropower generation unit (SHP) is conventionally estimated as a product of the net head, admitted flow, and a typical value of efficiency. In case of run of river (RoR) units however, sub-daily variability of stream flow together with consequent variation of efficiency make the conventional estimate open to errors. This paper presents closed form expressions for sub-daily mean output power, sub-daily maximum likelihood output power, and sub-daily output power variability; all of which serve as useful measures of RoR unit performance in presence of fluvial micro and macroturbulence phenomena. As part of the development, bimodal (alternately, multimodal) logistic distributions are established as dependable statistics for sub-daily flow admitted to a RoR unit. The concepts are substantiated through an operational RoR case study from Malaysia, and detailed Monte Carlo simulations based on the same. The three sub-daily performance metrics are shown to form good basis for comparison between candidate turbine configurations for RoR projects in operation, or alternately, those under consideration at a planning stage. Highlights • Statistics formulated for sub-daily admitted flow at a Run-of-River hydro unit. • Sub-daily mean, maximum-likelihood, and variability metrics proposed for hydropower. • Micro- as well as macroturbulence phenomena in stream flow accounted for. • Performance metrics substantiated through Monte Carlo simulations. [ABSTRACT FROM AUTHOR]

Insight on how industrial's practices and commitments could affect better energy-efficient buildings implementation is still limited despite the emerging concerns over the demand to minimize energy consumption. This study aims to fill the gap by investigating industry behaviours and practices and recommending holistic energy-efficient management strategies in Malaysia. The study used an explanatory sequential mixed approach comprising 4 case studies of conventional buildings, 72 building stakeholders submitted a survey questionnaire, and 7 experts were interviewed to enable triangulation of information. The analysis revealed that current energy management strategies are based on low-hanging fruit measures. The results also suggest that financial, managerial, and knowledge limitations are the primary constraints to effectual energy-saving methods. This paper demonstrates three-tier intervention strategies in education, policy, and technology towards accelerating industry transition in achieving best practices in energy-efficient buildings. The outcomes of this study may advantage building interested parties in addressing relevant barriers to energy-efficient execution and designing effective efforts to encourage conventional building for the building advancement in Malaysia. This research offers findings by delivering greater practical perspectives on the extent of industry practises and behaviours regarding energy-efficient building implementation, including by presenting energy management approaches for benchmarks in energy-efficient buildings in Malaysia. • Financial, managerial and knowledge are the constraints to effectual energy-saving. • Intervention in education, policy and technology are the strategies for energy-saving. • Low-hanging fruit measures effective for simple behavioural changes. [ABSTRACT FROM AUTHOR]

Blessed with abundant solar radiation, Malaysia has a huge potential for grid-connected PV (photovoltaic) installations, particularly for its fast-growing residential sector. Nevertheless, Malaysia's PV installation capacity is relatively small compared with the global PV capacity. Significantly, the pricing mechanisms for grid-connected PV projects need to be appropriately assessed to build up the public's confidence to invest in PV projects. In this paper, we analyze the effects of component costs, FiTs (feed-in tariffs), and carbon taxes on grid-connected PV systems in Malaysian residential sector using the HOMER (Hybrid Optimization of Multiple Energy Resources) software. Results demonstrate that the implementation of grid-connected PV systems is highly feasible with PV array costs of $ 1120/kW or lower. For higher PV array costs up to $ 2320/kW, introducing an FiT rate three times higher ($ 0.30/kWh) than the grid tariff for a 100 kW grid sale capacity will, NPC-wise, prioritize grid-connected PV systems over the utility grid. By implementing the FiT ($ 0.50/kWh) and the carbon tax ($ 36/metric ton) schemes simultaneously, grid-connected PV systems will remain as the optimal systems even for costly PV arrays (up to $ 4000/kW). The findings are of paramount importance as far as PV pricing variability is concerned. [ABSTRACT FROM AUTHOR]

This paper examines the vulnerability of domestic prices against oil price shocks by considering the direct and indirect effects of oil price on consumer price index, which occur through the known channels of the monetary transmission mechanism. We consider interest rate, exchange rate, domestic credit, and share price during the periods before and after the global crisis of 2007–2009 in the ASEAN-4 (Association of Southeast Asian Nations- Indonesia, Malaysia, the Philippines and Thailand) countries. Using the structural vector auto regression model and monthly data (2002 month 1–2013 month 4), the results indicate that pre-crisis—with the exception of Indonesia, which is not much influenced by oil prices—there is no indirect influence, but there is a considerable direct effect of oil price on consumer price index for all countries. Post-crisis, the oil price transmits strongly to consumer price index indirectly, although the direct effect of oil price on consumer price index is still extensive. Policymakers can target domestic prices via share prices and exchange rates. [ABSTRACT FROM AUTHOR]

This paper describes experimental and simulation study results of an air duct system that cools down airflow by using TEMs (thermoelectric modules). This system is designated as TE-AD (thermoelectric air duct) system which consists of twenty four TEMs along with heat sink and fan for circulation of air. Both experimental and simulation results were in good agreement with each other and showed that the TE-AD system reduces room temperature in the range of 1.2–5.3 °C and humidity in the range of 5–31%. The COP (coefficient of performance) of the system ranges from 0.392 to 0.679 under different operating input current for Malaysian weather conditions. By comparing TE-AD system with conventional air conditioning system, energy saving of 38.83% and CO 2 emission mitigation of 38.81% was achieved with additional benefits of high reliability and refrigerant free system. [ABSTRACT FROM AUTHOR]

This paper proposes an effective computing framework for Short-Term Load Forecasting (STLF). The proposed technique copes with the stochastic variations of the load demand using a stacked generalization approach. This approach combines three models, namely, Light Gradient Boosting Machine (LGBM), eXtreme Gradient Boosting machine (XGB), and Multi-Layer Perceptron (MLP). The inner mechanism of Stacked XGB-LGBM-MLP model consists of generating a meta-data from XGB and LGBM models to compute the final predictions using MLP network. The performance of the proposed Stacked XGB-LGBM-MLP model is validated using two datasets from different locations: Malaysia and New England. The main contributions of this paper are: 1) A novel stacking ensemble-based algorithm is proposed; 2) An effective STLF technique is introduced; 3) A critical multi-study analysis for hyperparameter optimization with five techniques is comprehensively performed; 4) A performance comparative study using two datasets and reference models is conducted. Several case studies have been carried out to prove the performance superiority of the proposed model compared to both existing benchmark techniques and hybrid models. • A novel Stacked XGB-LGBM-MLP model is proposed to improve the overall regression performance. • A novel STLF technique is explored and developed in this study. • A comparative analysis of five hyperparameter optimization algorithms was comprehensively presented for STLF. • An assessment of the proposed technique is conducted using two real datasets. • A comparative study with the recent benchmark techniques is performed. [ABSTRACT FROM AUTHOR]

This paper investigates the causal relationship between energy consumption and economic productivity in Malaysia at both aggregated and disaggregated levels. The investigation utilises total and sectoral (industrial and manufacturing) productivity growth during the 1971–2012 period using the modified Granger causality test proposed by Toda and Yamamoto [1] within a multivariate framework. The economy of Malaysia was found to be energy dependent at aggregated and disaggregated levels of national and sectoral economic growth. However, at disaggregate level, inefficient energy use is particularly identified with electricity and coal consumption patterns and their Granger caused negative effects upon GDP (Gross Domestic Product) and manufacturing growth. These findings suggest that policies should focus more on improving energy efficiency and energy saving. Furthermore, since emissions are found to have a close relationship to economic output at national and sectoral levels green technologies are of a highest necessity. [ABSTRACT FROM AUTHOR]

Solar photovoltaic has shown a significant rise in terms of worldwide installation. One of the main reason is due to the introduction of the FiT (feed-in tariff) policy by the governments. This paper aims to evaluate FiT policy in promoting solar PV (photovoltaic) investments in Malaysia by using a dynamic systems approach. The assessment model captures the complexities arising from the interaction of FiT rate dynamics, construction delays, and investors’ and technology learning dynamics in an integrated framework. The model provides total operational PV capacity, amount of finances needed to support the policy, and the cost of environmental savings, as output. Computer simulations, based on twelve scenarios, were used as a means to study the model behaviour. For the most favourable scenario, a total capacity of about 16 GW PV by 2050 can be expected, while for the least favourable scenario, expectations would be only about 10 GW. On the expenditure side, the most favourable scenario can cost up to MYR (Malaysia Ringgit) 15 billion, whereas, for the least favourable ones, the cost can be as low as MYR2 billion. The maximum cost of CO 2 abatement can vary from MYR 0.05 per kg-CO 2 to the lowest value of MYR 0.02 per kg-CO 2 . [ABSTRACT FROM AUTHOR]

This paper provides a critical review of the palm biodiesel landscape in Malaysia. The palm industry is a key source of revenue for the country, where in 2009 the industry was responsible for generating 8% of the gross national income per capita. The interest in palm-based biodiesel can be traced back to the early 1980s however it was only in 2006 that Malaysia officially formulated its first National Biofuel Policy as a strategic government intervention to drive development and implementation of palm biodiesel as substitute to regular fossil-based diesel. As at January 2013, the implementation of the biodiesel mandate (at 5% concentration) has been limited to the Klang Valley central region only. But there are plans for a much wider roll-out and at higher concentration blends in 2014. The policy in its current form assumes that all biofuels are sustainable and therefore fails to provide assurances that the fossil diesel will be replaced by a more sustainable energy source. Here it has been argued that a market-based policy approach would be better than a technology-forcing mechanism. [ABSTRACT FROM AUTHOR]

As the energy demand keeps growing with the world's increasing population, concerns have arisen about the carbon emissions that contribute to critical environmental issues. These environmental concerns drive many nations to commit themselves in pursuing sustainable energy resources. Different process integration techniques have been developed to achieve a viable energy planning sector. In reference to the literature review, the use of time-sliced based models in optimization problems is still sparse. Hence, this paper aims to develop a time-sliced based optimization model that can be integrated into energy integration models. With that, a multi-period pinch-based methodology (targeting, scheduling, and optimization) for electric power systems with the hybridization of renewables and fossil-based energy is proposed. The prospective framework is designed to meet the emissions limit, fulfil energy demand, and minimize the electricity bill. An illustrative case study in Malaysia incorporated with an actual billing system is used to demonstrate the effectiveness of the proposed framework. The results show that it is capable of reducing the total electricity bill by 35% and achieving a 71% reduction of emissions by using solar PV as the renewables. • Three-step multi-period pinch-based methodology for energy planning is presented. • The model incorporates an actual billing system. • Time-sliced based optimization model is developed in the proposed framework. • Optimal emission reduction ratio and storage size are identified as 0.71 and 236 kWh. • The total electricity bill can be further reduced by 35%. [ABSTRACT FROM AUTHOR]

Abstract: This paper presents an application of LCA (Life Cycle Assessment) with a view to analyzing the environment aspects of rice straw-based power generation in Malaysia. It also compares rice straw-based power generation with that of coal and natural gas. GHG (Greenhouse gas) emission savings were calculated. It finds that rice straw power generation can save GHG (greenhouse gas) emissions of about 1.79 kg CO2-eq/kWh compared to coal-based and 1.05 kg CO2-eq/kWh with natural gas based power generation. While the development of rice straw-based power generation in Malaysia is still in its early stage, these paddy residues offer a large potential to generate electricity because of their availability. Rice straw power plants not only could solve the problem of removing rice straw from fields without open burning, but also could reduce GHG emissions that contribute to climate change, acidification, and eutrophication, among other environmental problems. [Copyright &y& Elsevier]

The biomass energy sector in most developing countries is not well established due to the high cost of implementation and lack of quantitative planning tools. This paper addresses this issue by presenting a quantitative planning tool used to plan and optimise biomass supply chains based on carbon reduction targets. The tool proposed in this work centres on three important stages: Carbon Emission Pinch Analysis (CEPA), mathematical optimisation, and multi-stakeholder analysis. CEPA is used to determine the minimum amount of biomass to achieve carbon reductions. Then, mathematical optimisation is used to optimise the biomass supply chain based on the carbon reduction target. The optimisation step considers the use of new biomass power plants and co-firing existing power plants. Following this, the importance of power plants within the optimal biomass supply chain is identified using a multi-stakeholder analysis method known as Shapley-Shubik power index. Shapley-Shubik power index is used to determine the level of power held by each power plant in the biomass supply chain based on the number of times each stakeholder become pivotal in achieving several quotas (i.e., power purchase agreement). A case study comprising a biomass supply chain in Malaysia is solved to demonstrate the presented tool. The case study analyses several scenarios in which the biomass supply chain can be deployed. Case study results indicated that co-firing existing power plants did indeed minimise capital and operating expenditure. Conversely, the cost of transporting biomass may be high depending on the location of these plants. Boiler systems were the most chosen due to their high biomass-to-power conversion rates. The multi-stakeholder analysis determined that a power plant with higher power output, cheaper costs, and superior biomass-to-power conversion rate often appears as the more significant player in the supply chain. • A methodology is developed for optimal biomass supply chain planning based on carbon reduction targets. • Shapley-Shubik power index is used to determine pivotal players in optimal biomass supply chain. • Co-firing opportunities are considered to achieve targets in a cost-effective manner. • Results show co-firing schemes were more preferred compared to new biomass plants due to lower cost. • Plants with higher efficiency and lower cost tend to become pivotal players in optimal supply chains. [ABSTRACT FROM AUTHOR]

Industry and agriculture in Malaysia are the main contributors to economic growth and employment. These sectors also play an important role in Malaysia's total exports. The question then is whether technological innovation, sectoral output, and exports growth have had a real impact on these two sectors, which are very important for policy-making. This paper attempts to empirically identify such relations using econometric methods, including an autoregressive distributed lag (ARDL) bounds testing method and a dynamic ordinary least squares (DOLS) during 1978–2018. The results confirmed that overall long-run economic growth is the main contributor to the increase in energy consumption with a greater magnitude than in the short-run. In the long-run, an increase of 1% in economic growth leads to an increase of 4.6% and 1.1% in energy demand in agriculture and industrial sectors, respectively. Exports are the second largest contributor to energy demand in the overall economy and the agriculture sector. Finally, the technological innovation that enhances energy efficiency is only effective in reducing energy consumption in the industrial sector, which ultimately reduces emissions. [Display omitted] • It the relationships between energy consumption and crude oil price is investigated. • Technological innovation have negative relationships with energy consumption. • Economic growth is the main factor to energy consumption in the short- and long-runs. • The magnitude of long-run economic growth effects are greater than the short-run. • Agriculture export is the main contributor to energy consumption in the short-run. [ABSTRACT FROM AUTHOR]

Due to lucrative economics and energy policies, cogeneration systems have blossomed in many existing industries and became their backbone technology for energy generation. With ever-increasing energy demands, the required capacity of cogeneration gradually grows yearly. This situation unveils a crawling problem in the background where many existing cogeneration systems require more energy output than their allocated design capacity. To debottleneck cogeneration systems, this work extends the bottleneck tree analysis (BOTA) towards multi-dimensional problems with novel consideration of data-driven uncertainty modelling and multi-criteria planning approaches. First, cogeneration systems were modelled using an ensemble neural network with mass and energy balance to quantify the system uncertainty while assessing energy, environment, and economic indicators in the system. These indicators are then evaluated using a multi-criteria decision making (MCDM) method to perform bottleneck tree analysis (BOTA), which identifies optimal pathways to plan for debottlenecking projects in a multi-train cogeneration plant case study. With zero initial investment and only reinvestments with profits, the method achieved 54.2 % improvement in carbon emission per unit power production, 46.3 % improvement in operating expenditure, 59.0 % improvement in heat energy production, and 58.9 % improvement in power production with a shortest average payback period of 93.9 weeks. Image 1 • This paper extends bottleneck tree analysis for multi-dimensional problems. • An ensemble of neural networks was used for data-driven process modelling. • The technology was deployed in a multi-train cogeneration plant in Malaysia. • The method improved 54.2% in carbon emission, 46.3% in OpEx, and 59% in energy production. • The shortest average payback period was found to be 93.9 weeks. [ABSTRACT FROM AUTHOR]

Due to lucrative economics and energy policies, cogeneration systems have blossomed in many existing industries and became their backbone technology for energy generation. With ever-increasing energy demands, the required capacity of cogeneration gradually grows yearly. This situation unveils a crawling problem in the background where many existing cogeneration systems require more energy output than their allocated design capacity. To debottleneck cogeneration systems, this work extends the bottleneck tree analysis (BOTA) towards multi-dimensional problems with novel consideration of data-driven uncertainty modelling and multi-criteria planning approaches. First, cogeneration systems were modelled using an ensemble neural network with mass and energy balance to quantify the system uncertainty while assessing energy, environment, and economic indicators in the system. These indicators are then evaluated using a multi-criteria decision making (MCDM) method to perform bottleneck tree analysis (BOTA), which identifies optimal pathways to plan for debottlenecking projects in a multi-train cogeneration plant case study. With zero initial investment and only reinvestments with profits, the method achieved 54.2 % improvement in carbon emission per unit power production, 46.3 % improvement in operating expenditure, 59.0 % improvement in heat energy production, and 58.9 % improvement in power production with a shortest average payback period of 93.9 weeks. Image 1 • This paper extends bottleneck tree analysis for multi-dimensional problems. • An ensemble of neural networks was used for data-driven process modelling. • The technology was deployed in a multi-train cogeneration plant in Malaysia. • The method improved 54.2% in carbon emission, 46.3% in OpEx, and 59% in energy production. • The shortest average payback period was found to be 93.9 weeks. [ABSTRACT FROM AUTHOR]

Malaysia is one of the net oil-exporting countries that the government budget depends highly on oil revenues. The dependency on uncertain revenues faces the economic development of the country with significant challenges. Therefore, the monetary authorities need to be aware of the importance and the role of their policies on the economy in response to changes in global oil prices. For this purpose, this paper simulates the responses of monetary policies to changes in global oil prices in Malaysia using a financial computable general equilibrium (CGE) model. It considers both changes (a rise and a fall) in global oil prices. The simulation results indicated that the Malaysian economy, as a whole, and the majority of industries gain from oil price rise and lose from oil price fall. Both shocks lead to the reallocation of resources and influence the financial part of the economy. Results also showed that tightened monetary policies are not able to reduce the negative effects of both shocks on economic growth and industrial output while the expansionary policies, particularly the combination of interest rate and reserve ratio reductions, are effective and are beneficial for economic growth and industrial output of Malaysia. If the government target is minimizing the rate of inflation, the effective policy in response to both global oil price changes is the reduction in interest rate. However, when the government target is more investment and labor employment, the effective policy is the reduction in reserve ratio rate in response to both oil price changes. • It investigates the response of monetary policies to oil price fluctuations. • In an inflationary target the effective policy is reduction in interest rate. • Tightened monetary policies are not effective policies on economic growth and industrial output. • Economic growth and industrial output were gained from expansionary policies. • Combining monetary expansionary policies are more effective than single policies. [ABSTRACT FROM AUTHOR]