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[en] Highlights: ► The seasonal and trend items of the data series are forecasted separately. ► Seasonal item in the data series is verified by the Kendall τ correlation testing. ► Different regression models are applied to the trend item forecasting. ► We examine the superiority of the combined models by the quartile value comparison. ► Paired-sample T test is utilized to confirm the superiority of the combined models. - Abstract: For an energy-limited economy system, it is crucial to forecast load demand accurately. This paper devotes to 1-week-ahead daily load forecasting approach in which load demand series are predicted by employing the information of days before being similar to that of the forecast day. As well as in many nonlinear systems, seasonal item and trend item are coexisting in load demand datasets. In this paper, the existing of the seasonal item in the load demand data series is firstly verified according to the Kendall τ correlation testing method. Then in the belief of the separate forecasting to the seasonal item and the trend item would improve the forecasting accuracy, hybrid models by combining seasonal exponential adjustment method (SEAM) with the regression methods are proposed in this paper, where SEAM and the regression models are employed to seasonal and trend items forecasting respectively. Comparisons of the quartile values as well as the mean absolute percentage error values demonstrate this forecasting technique can significantly improve the accuracy though models applied to the trend item forecasting are eleven different ones. This superior performance of this separate forecasting technique is further confirmed by the paired-sample T tests
[en] Highlights: • Second generation biofuel is produced from lignocellulosic biomass. • Microbes have potential to ferment biomass into biofuel. • Metabolic engineering and consortia approach can improve biofuel production. • Second generation biofuel production still in initial stage and require more research input. - Abstract: Economic growth and industrial energy demand necessitate sustainable energy resources. The food vs. fuel issue means that first generation biofuels appear unsustainable. Therefore, biofuel production using lignocellulosic biomass clearly needs to be explored and promoted. However, due to technological barriers, the production of biofuel from lignocellulose (second generation biofuel) is currently not cost effective. Although microbial fermentation is an ecofriendly way to convert lignocellulose into biofuel, it will take time to become a commercial reality. Biofuels of different generations can contribute synergistically to fulfill energy demand. More research and government participation is needed to make the biofuel production process more feasible. This review focuses on the pretreatment of biomass, the production of biofuel (biodiesel, bioalcohol, and biogas) using microbial systems, and the various efforts that have been implemented to improve biofuel production.
[en] The aim of this research was to examine the energy requirements of the inputs and output in citrus production in the Antalya province of Turkey. Data for the production of citrus fruits (orange, lemon and mandarin) were collected from 105 citrus farms by using a face to face questionnaire method. The research results revealed that lemon production was the most energy intensive among the three fruits investigated. The energy input of chemical fertilizer (49.68%), mainly nitrogen, has the biggest share in the total energy inputs followed by Diesel (30.79%). The lemon production consumed a total of 62 977.87 MJ/ha followed by orange and mandarin with 60 949.69 and 48 838.17 MJ/ha, respectively. The energy ratios for orange, mandarin and lemon were estimated to be 1.25, 1.17 and 1.06, respectively. On average, the non-renewable form of energy input was 95.90% of the total energy input used in citrus production compared to only 3.74% for the renewable form. The benefit-cost ratio was the highest in orange production (2.37) followed by lemon. The results indicate that orange production in the research area is most remunerative to growers compared to lemon and mandarin
[en] Highlights: • Biofuels are produced by Fischer-Tropsch synthesis using the syngas obtained by supercritical reforming. • A plant capacity of 60 t/h and a feeding concentration of 25 wt% were established as the base-case. • The aim of the energy self-sufficient process was to maximize the biofuel production and electrical power. • The break-even prices were 1.20, 0.93 and 0.26 €/kg for gasoline, diesel and jet-fuel, respectively. • The process competitiveness is promising with respect to that of fossil fuels of crude oil. - Abstract: High energy demand along with large capital costs have been the main drawbacks of Fischer-Tropsch plants, which may call into question the economic viability of the Fischer-Tropsch process. The second issue is the focus of this paper, which presents a techno-economic assessment of biofuels production by a low-temperature Fischer-Tropsch synthesis with electricity as a co-product from supercritical water reforming of the bio-oil aqueous phase. A plant size of 60 t/h was considered and a heat-integrated process was designed to be energy self-sufficient, which includes syngas production and upgrading, as well as liquid fuels production by Fischer-Tropsch synthesis and refining. The simulation and optimization was performed with the aid of Aspen Plus, and some case-studies were performed. Using a feeding concentration of 25 wt%, 2.74 t/h biofuels and 5.72 MWe were obtained. In this case, by performing a discounted cash flow analysis, with 10% rate of return and 100% equity financing, the minimum selling prices for the refined FT-gasoline, FT-diesel and FT-jet fuel were 1.20, 0.93 and 0.26 €/kg (0.84, 0.75 and 0.20 €/L), respectively, which are competitive prices with respect to the market values of the equivalent fossil fuels. Likewise, the decrease in the selling prices as the plant capacity increases was also analyzed.
[en] Although deterministic methods for establishing minutes reserve (such as the N-1 reserve or the percentage reserve) ignore the stochastic nature of reliability issues, they are commonly used in energy modelling as well as in practical applications. In order to check the validity of such methods, two test procedures are developed. The first checks if the N-1 reserve is a logical fixed value for minutes reserve. The second test procedure investigates whether deterministic methods can realise a stable reliability that is independent of demand. In both evaluations, the loss-of-load expectation is used as the objective stochastic criterion. The first test shows no particular reason to choose the largest unit as minutes reserve. The expected jump in reliability, resulting in low reliability for reserve margins lower than the largest unit and high reliability above, is not observed. The second test shows that both the N-1 reserve and the percentage reserve methods do not provide a stable reliability level that is independent of power demand. For the N-1 reserve, the reliability increases with decreasing maximum demand. For the percentage reserve, the reliability decreases with decreasing demand. The answer to the question raised in the title, therefore, has to be that the probability based methods are to be preferred over the deterministic methods
[en] Highlights: ► The yearly energy cost of a refrigerator can be decreased up to 11.4% with DSM. ► The multiple pricing tariff provides 5.4% discount compared to the single pricing tariff. ► 37.9% of refrigerator’s demand in peak times can be shifted to the other periods. ► DSM applications will work efficiently, if they are widely used by consumers. ► Defrost control contributed to savings by 1.7%. - Abstract: Demand Side Management in power grids has become more and more important in recent years. Continuously growing energy demand both increases the need for distributed generation from renewable energy sources and brings out the topic of Demand Side Management. One of the major application areas of Demand Side Management in smart grids is cooling systems. In this paper, Demand Side Management with control of a refrigerator and its economic effects on consumers are analyzed. With a refrigerator model based on real measurements, several cooling schedules are simulated and annual energy fees for different pricing methods in use in Turkey are calculated and discussed. The results revealed that, 37.9% of refrigerator’s demand in peak period can be shifted to other periods and annual electricity bills for customers can be reduced by 11.4%.
[en] In the present scenario of energy demand overtaking energy supply, top priority is given for energy conservation programs and policies. As a result, most existing systems are redesigned or modified with a view for improving energy efficiency. Often these modifications can have an impact on process system configuration, thereby affecting process system reliability. The paper presents a model for valuation of process systems incorporating reliability that can be used to determine the change in process system value resulting from system modification. The model also determines the break even system availability and presents an algorithm for allocation of component reliabilities of the modified system based on the break even system availability. The developed equations are applied to a steam power plant to study the effect of various operating parameters on system value
[en] Artificial lighting is one of the major electricity consuming items in many non-domestic buildings. Recently, there has been an increasing interest in incorporating daylight in architectural and building designs to reduce the electricity use and enhance greener building developments. This paper presents field measurements for a fully air conditioned open plan office using a photoelectric dimming system. Electric lighting load, indoor illuminance levels and daylight availability were systematically measured and analyzed. The general features and characteristics of the results such as electric lighting energy savings and transmitted daylight illuminance in the forms of frequency distributions and cumulative frequency distributions are presented. Daylighting theories and regression models have been developed and discussed. It has been found that energy savings in electric lighting were over 30% using the high frequency dimming controls. The results from the study would be useful and applicable to other office spaces with similar architectural layouts and daylight linked lighting control systems
[en] In energy market clearing, the offers are stacked in increasing order and the offer that intersects demand curve, determines the market clearing price (MCP). In reactive power market, the location of reactive power compensator is so important. A low cost reactive producer may not essentially be favorable if it is far from the consumer. Likewise, a high cost local reactive compensator at a heavily loaded demand center of network could be inevitably an alternative required to produce reactive power to maintain the integrity of power system. Given the background, this paper presents a day-ahead reactive power market based on pay-as-bid (PAB) mechanism. Generators expected payment function (EPF) is used to construct a bidding framework. Then, total payment function (TPF) of generators is used as the objective function of optimal power flow (OPF) problem to clear the PAB based market. The CIGRE-32 bus test system is used to examine the effectiveness of the proposed reactive power market.
[en] Electricity demand forecasting is known as one of the most important challenges in managing supply and demand of electricity. Consumption pattern of electricity has been affected by some social, economical and environmental factors by which the pattern will form various seasonal, monthly, daily and hourly complex variations. Diversity and complexity in consumption pattern of electricity have been leading to the extension of the complicated models. Many attempts have been made to find the best estimation for electricity consumption. These studies have been tried to forecast the demand in two levels: (1) Macro economic decision making and (2) Engineering and middle management. In this research an attempt has been made to introduce a method for pre-preparing data and for developing a model that could be applied in both the mentioned levels. By clustering primary data and by eliminating the periodic variance in our study, the complicated pattern is decomposed to a set of simple patterns which could be easily analyzed with conventional tools in both the levels