There are several methods of peak-shaving, which reduces grid power demand, electricity bought from electricity utility, through lowering “demand spike” during On-Peak period. An optimization method using linear programming is proposed, which can be used to perform peak-shaving of grid power demand for grid-connected PV+ system. Proposed peak shaving method is based on the forecast data for electricity load and photovoltaic power generation. Results from proposed method are compared with those from On-Off and Real Time methods which do not need forecast data. The results also compared to those from ideal case, an optimization method which use measured data for forecast data, that is, error-free forecast data. To see the effects of forecast error 36 error scenarios are developed, which consider error types of forecast, nMAE (normalizes Mean Absolute Error) for photovoltaic power forecast and MAPE (Mean Absolute Percentage Error) for load demand forecast. And the effects of forecast error are investigated including critical error scenarios which provide worse results compared to those of other scenarios. It is shown that proposed peak shaving method are much better than On-Off and Real Time methods under almost all the scenario of forecast error. And it is also shown that the results from our method are not so bad compared to the ideal case using error-free forecast.

Over 7 individual rice (Oryza sativa L.) plants per a line were sowed and sampled by pooled sampling method for genomic DNA extraction. The 5,400 flanking sequence tags (FSTs) were analysed by adaptor PCR and direct sequencing. FST analysis showed that the intragenic FSTs, the intergenic FSTs, and the original insertional sequences including hot spot covered 48.1% (2,597), 25.6% (1,383), and 25% (1,350), respectively. The 2,597 intragenic FSTs were used for genotyping to determine whether they are heterozygous or homozygous, and 1,393 core lines were selected. Among them, 422 knockout genes were distributed on chromosome 3, while 56 - 157 intragenic FSTs scattered on other chromosomes. Among 1,393 FSTs, known genes such as transcription factor covered 59.4% (827), while unknown genes such as expressed protein covered 40.6% (566). RT-PCR indicated that some core lines had no expression or decreased expression level in their knockout genes. It means that core lines are very useful knockout lines for functional genomic studies.