• Energy Research
• other engineering and technologies close
• 7. Clean energy close
• 12. Responsible consumption close
• 1. No poverty close

Load forecasting is very important to manage the electrical power systems. Load forecasting can be analyzed in three different ways as short-term, medium-term and long-term. Long-term load forecasting (LTLF) is in need to plan and carry on future energy demand and investment such as size of energy plant. LTLF is affected by energy consumption, national incoming per year, rates of civilization, increasing population rates and moreover economical parameters. Some of the forecasting models use mathematical formulas and statistical models such as correlation and regression analysis. In this study, a new effective expert-supported dynamic programming algorithm (ESDP) has been improved. Additionally, adaptive neuro-fuzzy inference system (ANFIS) and mathematical modeling (MM) are used to forecast long term energy demand. ANFIS is one of the famous artificial intelligence and has widely used to solve forecasting problems in literature. In addition to numerical inputs, ANFIS has linguistics inputs. The results obtained from ESDP, ANFIS and MM are compared to show availability. In order to show error levels mean absolute percentage error (MAPE) and (MAE) are used. The obtained results show that the proposed algorithms are available.

Abstract The oxidation characteristics of several small methyl and ethyl esters with carbon number less than six were investigated in laminar flames. The kinetics of such fuels are subsets of those of larger alkyl esters that are constituents of practical biodiesel fuels. A total of seven fuels, namely methyl formate, methyl acetate, methyl propionate, methyl butanoate, ethyl formate, ethyl acetate, and ethyl propionate were considered. Experiments were conducted at atmospheric pressure, elevated reactant temperatures, and over a wide range of equivalence ratios. Laminar flame speeds were determined in the counterflow configuration in which flow velocities were measured using particle image velocimetry. Several detailed kinetic models were tested against the experimental data, and insight was provided into the high-temperature combustion kinetics of the aforementioned fuels. Based on comparisons between experimental and computed results it became apparent that the chemistry of alkyl-ester combustion chemistry is evolving and much needs to be done in order to derive improved rate constants for a wide range of elementary steps.

The Box–Jenkins model is a polynomial model that uses transfer functions to express relationships between input, output, and noise for a given system. In this article, we present a Box–Jenkins linear model for a lithium-ion battery cell for use in electric vehicles. The model parameter identifications are based on automotive drive-cycle measurements. The proposed model prediction performance is evaluated using the goodness-of-fit criteria and the mean squared error between the Box–Jenkins model and the measured battery cell output. A simulation confirmed that the proposed Box–Jenkins model could adequately capture the battery cell dynamics for different automotive drive cycles and reasonably predict the actual battery cell output. The goodness-of-fit value shows that the Box–Jenkins model matches the battery cell data by 86.85% in the identification phase, and 90.83% in the validation phase for the LA-92 driving cycle. This work demonstrates the potential of using a simple and linear model to predict the battery cell behavior based on a complex identification dataset that represents the actual use of the battery cell in an electric vehicle.

Abstract Socio-technical transformations towards low-carbon energy systems are on the way in developed countries. Conversely, developing countries tend to be locked in fossil fuels and foster coal-based energy structures, emphasizing reliable and cost-effective energy provision and sidelining environmental concerns. In this study, we identified and analysed the predominant factors related to coal-based power generation in Bangladesh. We applied a mixed-method approach, initially conducting a systematic literature review and, subsequently, semi-structured expert interviews to identify and validate relevant factors. We then assessed their relative importance using an Analytical Hierarchy Process based on expert judgments. The results of this assessment reveal that socio-economic aspects and environmental issues scored highest, while technological aspects and sector regulations were considered to be less relevant for large-scale coal power implementation. We conclude that future energy policies created in Bangladesh will need to use appropriate legal instruments and address issues such as human displacement and resettlement, low levels of public acceptance, health hazards and environmental pollution. Participative policy frameworks should be deployed in coal plant projects, and active monitoring systems are necessary to reduce the negative consequences associated with increased electrification and energy consumption. To address foreseeable structural challenges, it furthermore will be crucial to explore sustainable alternatives.

Construction involves the use of significant quantities of raw materials and entails high-energy consumption. For the sake of choosing the most appropriate solution that considers environmental and sustainable concepts, tools such as the integrated value model for sustainable assessment (Modelo Integrado de Valor para una Evaluación Sostenible, MIVES) used in Spain, plays a key role in obtaining the best solution. MIVES is a multi-criteria decision-making method based on the value function concept and the seminars delivered by experts. Such tools, in order to show how they may work, require application to case studies. In this paper, two concrete slabs manufactured with differing reinforcements during the construction of the La Canda Tunnels are compared by means of MIVES. The two concrete slabs were reinforced with a conventional steel-mesh and with polyolefin fibres. This research was focussed on the main aspects affecting the construction. That is to say, the environmental, economic, and social factors were assessed by the method, being of special impact the issues related with maintenance of the structure. The results showed that from the point of view of sustainability, the use of polyolefin fibres provided a significant advantage, mainly due to the lower maintenance required.

The operation of gas generators brings relatively high thermo-mechanical loads on the gas generator structure. The objective of this article is to determine the operating conditions, in terms of mechanical loads at extremely high temperatures in very limited and narrow space, of a multifunctional bulkhead for application on specific kinds of gas generators with back-to-back rotor concept. The paper contains numerical analysis and experimental investigation for determining the loads and behavior of the structure. Numerical analysis indicates that there is significant influence of the Tesla turbine effect on flow parameters. Also, uneven pressure distribution and significant thermal loads are identified. With experimental investigation and subsequent exploitation tests, it was concluded that the presented methodology identifies the operating conditions, truthfully simulates the bulkhead stress state and deformations and that the presented design solution satisfied all demands. Regarding results obtained by these numerical simulations, the innovative design solution for the multifunctional bulkhead was proposed.

The battery power state (SOP) is the basic indicator for the Battery management system (BMS) of the battery energy storage system (BESS) to formulate control strategies. Although there have been many studies on state estimation of lithium-ion batteries (LIBs), aging and temperature variation are seldom considered in peak power prediction during the whole life of the battery. To fill this gap, this paper aims to propose an adaptive peak power prediction method for power lithium-ion batteries considering temperature and aging is proposed. First, the Thevenin equivalent circuit model is used to jointly estimate the state of charge (SOC) and SOP of the lithium-ion power battery, and the variable forgetting factor recursive least squares (VFF-RLS) algorithm and extended Kalman filter (EKF) are utilized to identify the battery parameters online. Then, multiple constraint parameters including current, voltage, and SOC were derived, considering the dependence of the polarization resistance of the battery on the battery current. Finally, the verification experiment was carried out with LiFePO4 battery. The experimental results under FUDS operating conditions show that the maximum SOC estimation error is 1.94%. And the power prediction errors at 20%, 50%, and 70% SOC were 5.0%, 8.1% and 4.5%, respectively. Our further work will focus on the joint estimation of battery state to further improve the accuracy.

Abstract Pollution control strategies currently in use by electric power systems are reviewed and a new minimum emission dispatch (MED) method is developed. This new method minimizes overall emission levels at an increased cost in large-scale power systems while simultaneously accommodating local pollution level requirements, as well as economic constraints.