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This paper studies how to improve the capacity of the combined cycle (CC) power plant which has been operated for 8 years. The most popular way is to lower intake air temperature to around 15°C (ISO) and 100% RH before entering the air compressor of a gas turbine (GT). Thailand has 3 seasons: winter, summer and rainy season. According to 2003 Bangkok monthly weather data, all year ambient temperature...
This paper presents a study on selecting electricity contracts for a large-scale chemical production plant, which requires electricity importation, under demand uncertainty. Two common types of electricity contracts are considered, time zone (TZ) contract and loading curve (LC) contract. A multi-period linear probabilistic programming model is adopted for the contract selection and optimization. Hence,...
In this paper, we present the near-term analysis of capacity expansion under various uncertainties from the viewpoints of the decision-making process on the optimal allocation of investment and the value of information. An optimization model based on two-stage stochastic programming was developed using real data to describe the Japanese energy system as a case study. Different uncertainty parameters...
Appropriate energy–environment–economic (E3) modelling provides key information for policy makers in the electricity supply industry (ESI) faced with navigating a sustainable development path. Key challenges include engaging with stakeholder values and preferences, and exploring trade-offs between competing objectives in the face of underlying uncertainty. As a case study we represent the South African...
Strategic planning in the electricity supply industry is a complex task due to the multiple and often conflicting objectives of the decision makers, as well as the inherent technical and valuation uncertainties involved. As such, a transparent decision support framework is needed, for guiding information management throughout the decision process, in a way which shapes decision outcomes, and enables...
Uncertainties in energy demand modelling allow for the development of different models, but also leave room for different calibrations of a single model. We apply an automated model calibration procedure to analyse calibration uncertainty of residential sector energy use modelling in the TIMER 2.0 global energy model. This model simulates energy use on the basis of changes in useful energy intensity,...
Energy management systems are highly complicated with greenhouse-gas emission reduction issues and a variety of social, economic, political, environmental and technical factors. To address such complexities, municipal energy systems planning models are desired as they can take account of these factors and their interactions within municipal energy management systems. This research is to develop an...
In this study, an interval fixed-mix stochastic programming (IFSP) model is developed for greenhouse gas (GHG) emissions reduction management under uncertainties. In the IFSP model, methods of interval-parameter programming (IPP) and fixed-mix stochastic programming (FSP) are introduced into an integer programming framework, such that the developed model can tackle uncertainties described in terms...
This paper provides a methodology that allows estimation and comparative statics analysis in the presence of two correlated uncertainties: energy price uncertainty and manufacturing price uncertainty. In so doing, we show the impact of the correlation between oil price shocks and manufacturing price shocks on the manufacturing output.
At the moment, due to technology improvements and governmental incentives for the use of green energies, Renewable Energy Sources (RESs) appears to be a promising approach for electricity generation. This motivates the implementation of Wind Farms (WFs) and Fuel Cell Power Plants (FCPPs) over a mass scale by Distribution Companies (DisCos). As RESs become a larger and larger portion of the generation...
In this paper, a stochastic programming approach is proposed for trading wind energy in a market environment under uncertainty. Uncertainty in the energy market prices is the main cause of high volatility of profits achieved by power producers. The volatile and intermittent nature of wind energy represents another source of uncertainty. Hence, each uncertain parameter is modeled by scenarios, where...
In this study, an IFTSP (interval-fuzzy two-stage stochastic programming) method is developed for planning carbon dioxide (CO 2 ) emission trading under uncertainty. The developed IFTSP incorporates techniques of interval fuzzy linear programming and two-stage stochastic programming within a general optimization framework, which can effectively tackle uncertainties described in terms of probability...
Utilization of small data sets for energy consumption forecasting is a major problem because it could create large noise. This study presents a hybrid framework for improvement of energy consumption estimation with small data sets. The framework is based on fuzzy regression, conventional regression and design of experiment (DOE). The hybrid framework uses analysis of variance (ANOVA) and minimum absolute...
The way in which GHG (greenhouse gas) emissions associated with grid electricity consumption is handled in different LCA (life cycle assessment) studies, varies significantly. Apart from differences in actual research questions, methodological choices and data set selection have a significant impact on the outcomes. These inconsistencies result in difficulties to compare the findings of various LCA...
Uncertainty of performance is an obstacle for greater uptake of many renewable energy technologies that depend on local environmental conditions. The ability to successfully complete involving a groundwater-source heat pumps or other projects using groundwater for heating and cooling is highly dependent on local geological conditions. Use of geostatistical information on aquifer properties and analysis...
In this study, an inexact optimization modeling approach (IBEM: inexact Beijing energy model) was developed for supporting energy systems planning and air pollution mitigation under uncertainty. This model was based on the integration of multiple inexact optimization techniques, including interval-parameter programming, mixed-integer programming and chance-constrained programming, which make it have...
Hydro energy represents a priority in the energy policy of Portugal, with the aim of decreasing the dependence on fossil fuels. In this context, optimal hydro scheduling acquires added significance in moving towards a sustainable environment. A mixed-integer nonlinear programming approach is considered to enable optimal hydro scheduling for the short-term time horizon, including the effect of head...
When regulating GHG emissions at the country or product level, it is critical to determine the GHG emissions from electricity consumption. In this study, we calculated production-based and consumption-based CO 2 emission intensities of electricity for the OECD (the Organisation for Economic Co-operation and Development) countries during 1990–2008. We examined the impact of annual development,...
Recently, due to technology improvements, governmental incentives for the use of green energies and rising concerns about high cost of energy from fossil fuels, renewable energy sources (RESs) appears to be a promising approach for producing local, clean, and inexhaustible energy. This motivates the implementation of microgrids (MGs) introduced as a cluster of electrical and/or thermal loads and different...
Transmission investments are currently needed to meet an increasing electricity demand while enhancing security of supply, promoting competition, and integrating renewable energy. A key issue when assessing the benefits from an expanded grid concerns the valuation of the uncertain cash flows that result from the expansion. We develop a valuation model which combines optimization techniques, Monte...
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