Detailed particulate and gaseous emission characterizations were conducted on six commercially available residential-scale wood pellet boilers. The objective of the study was to define emission factors for these six different appliances burning wood pellets, grass pellets, and a blend of grass pellets and corn as fuels under low and high loads. Continuous monitoring of criteria pollutants, including PM 2.5 , NO x , SO 2 , and CO, was conducted using an EPA CTM-039 dilution sampling system. The PM 10 emissions with wood as the fuel ranged between 14 and 17 mg/MJ and between 16 and 21 mg/MJ at low and high loads, respectively. The PM 10 emissions from grass were found to be higher for all of the appliances compared to wood pellets at both low and high loads (28−33 and 37−44 mg/MJ, respectively). CO emissions, an indication of combustion efficiency, were found to be higher for the grass pellets, indicating less complete combustion. NO x and SO 2 emissions were also higher for grass and grass/corn blends, attributable to the higher fuel N and S. PM samples collected on Teflon and quartz substrates were analyzed for ions and trace elements. Semi-volatile organic compounds collected on quartz and polyurethane foam (PUF) plugs were also analyzed. Levoglucosan, a molecular marker for wood combustion, was the predominant organic compound found in the grass combustion PM 2.5 and ranged between 6 and 100 μg/MJ for grass and between 9 and 130 μg/MJ for wood. Polycyclic aromatic hydrocarbon (PAH) emissions were relatively higher for grass combustion ranging from 10 to 700 μg/MJ than for wood combustion ranging from about 5 to 200 μg/MJ. Dioxin and dibenzofuran emissions were found to be substantially higher for grass pellet emissions compared to wood pellet emissions at both high and low loads for all of the appliances.

This paper presents a methodology to evaluate the loss of load probability (LOLP) and expected energy generation (EEG) of multi area interconnected systems with wind generators, as well as conventional fossil fuel based generating units. Wind generators are practical alternatives to meet the huge electrical energy demand with minimum cost, particularly in those areas where the wind profiles are suitable. Extending the previously reported segmentation method, the proposed model is also capable of tracking the energy export incorporating the multi-state probability model for wind generator which output varies with time and season.