How market power affects the behaviour of a pumped storage hydro unit in the day-ahead electricity market?

The integration of large amounts of wind energy in power systems raises important operation issues such as the balance between power demand and generation. The pumped storage hydro (PSH) units are seen as one solution for this issue, avoiding the need for wind power curtailments. However, the behavior of a PSH unit might differ considerably when it operates in a liberalized market with some degree of market power. In this regard, a new approach for the optimal daily scheduling of a PSH unit in the day-ahead electricity market was developed and presented in this paper, in which the market power is modeled by a residual inverse demand function with a variable elasticity. The results obtained show that increasing degrees of market power of the PSH unit correspond to decreasing levels of storage and, therefore, the capacity to integrate wind power is considerably reduced under these circumstances.

Price forecasting in the day-ahead Iberian electricity market using a conjectural variations Arima model

Price forecast is a matter of concern for all participants in electricity markets, from suppliers to consumers through policy makers, which are interested in the accurate forecast of day-ahead electricity prices either for better decisions making or for an improved evaluation of the effectiveness of market rules and structure. This paper describes a methodology to forecast market prices in an electricity market using an ARIMA model applied to the conjectural variations of the firms acting in an electricity market. This methodology is applied to the Iberian electricity market to forecast market prices in the 24 hours of a working day. The methodology was then compared with two other methodologies, one called naive and the other a direct forecast of market prices using also an ARIMA model. Results show that the conjectural variations price forecast performs better than the naive and that it performs slightly better than the direct price forecast.

A novel colorimetric assay of beta-D-glucans in basidiomycete strains by alcian blue dye in a 96-well microtiter plate

Basidiomycete strains synthesize several types of beta-D-glucans, which play a major role in the medicinal properties of mushrooms. Therefore, the specific quantification of these beta-D-glucans in mushroom strains is of great biochemical importance. Because published assay methods for these beta-D-glucans present some disadvantages, a novel colorimetric assay method for beta-D-glucan with alcian blue dye was developed. The complex formation was detected by following the decrease in absorbance in the range of 620 nm and by hypsochromic shift from 620 to 606 nm (similar to 14 nm) in UV-Vis spectrophotometer. Analysis of variance was used for optimization of the slope of the calibration curve by using the assay mixture containing 0.017% (w/v) alcian blue in 2% (v/v) acetic acid at pH 3.0. The high-throughput colorimetric assay method on microtiter plates was used for quantification of beta-D-glucans in the range of 0-0.8 mu g, with a slope of 44.15 x 10(-2) and a limit of detection of 0.017 mu g/well. Recovery experiments were carried out by using a sample of Hericium erinaceus, which exhibited a recovery of 95.8% for beta-1,3-D-glucan. The present assay method exhibited a 10-fold higher sensitivity and a 59-fold lower limit of detection compared with the published method with congo red beta-D-glucans of several mushrooms strains were isolated from fruiting bodies and mycelia, and they were quantified by this assay method. This assay method is fast, specific, simple, and it can be used to quantify beta-D-glucans from other biological sources. (C) 2015 American Institute of Chemical Engineers

Optimal generation scheduling of wind-CSP systems in day-ahead electricity markets

This paper presents a coordination approach to maximize the total profit of wind power systems coordinated with concentrated solar power systems, having molten-salt thermal energy storage. Both systems are effectively handled by mixed-integer linear programming in the approach, allowing enhancement on the operational during non-insolation periods. Transmission grid constraints and technical operating constraints on both systems are modeled to enable a true management support for the integration of renewable energy sources in day-ahead electricity markets. A representative case study based on real systems is considered to demonstrate the effectiveness of the proposed approach. © IFIP International Federation for Information Processing 2015.

Comparing wind generation profiles: A circular data approach

The importance of wind power energy for energy and environmental policies has been growing in past recent years. However, because of its random nature over time, the wind generation cannot be reliable dispatched and perfectly forecasted, becoming a challenge when integrating this production in power systems. In addition the wind energy has to cope with the diversity of production resulting from alternative wind power profiles located in different regions. In 2012, Portugal presented a cumulative installed capacity distributed over 223 wind farms [1]. In this work the circular data statistical methods are used to analyze and compare alternative spatial wind generation profiles. Variables indicating extreme situations are analyzed. The hour (s) of the day where the farm production attains its maximum daily production is considered. This variable was converted into circular variable, and the use of circular statistics enables to identify the daily hour distribution for different wind production profiles. This methodology was applied to a real case, considering data from the Portuguese power system regarding the year 2012 with a 15-minutes interval. Six geographical locations were considered, representing different wind generation profiles in the Portuguese system.In this work the circular data statistical methods are used to analyze and compare alternative spatial wind generation profiles. Variables indicating extreme situations are analyzed. The hour (s) of the day where the farm production attains its maximum daily production is considered. This variable was converted into circular variable, and the use of circular statistics enables to identify the daily hour distribution for different wind production profiles. This methodology was applied to a real case, considering data from the Portuguese power system regarding the year 2012 with a 15-minutes interval. Six geographical locations were considered, representing different wind generation profiles in the Portuguese system.