Search

Electric vehicles (EVs) have shown promise in providing ancillary services, e.g., frequency regulation. This is mainly due to their capacities and fast response. On the contrary, the rapid integration of EVs in the grid poses challenges, such as frequency and voltage stability. In order to mitigate the above-mentioned issues, several dispatching strategies have been introduced in the recent literature to optimize the charging/discharging rates of EVs. In this paper, a comparative study of power management strategies for secondary frequency regulation (SFR) employing a fleet of EVs is presented. A hierarchical control scheme is employed to compare two cases, namely control at the charging station (CS) level and novel control at the EVs level. Under both cases, a multi-objective optimization approach is utilized to define the optimal charging and discharging rates of EVs using a pattern search algorithm. Furthermore, the performance of the two models is experimented under contingency cases, a notable contribution of this study. Finally, simulations are carried out using OPAL-RT real time simulator to validate the performance of the two models based on real-time traces obtained from Pennsylvania, New Jersey, and Maryland (PJM) interconnection and California independent system operator (CAISO). To further validate the proposed model, a comparison with a mixed-integer linear programming (MILP) based model is presented. [ABSTRACT FROM AUTHOR]

This paper used farm income tax returns (Schedule F) data from 62 dairy farmers who milked 200 cows or fewer in western and central Maryland and south- western Pennsylvania (hereafter, the mid-Atlantic region) to assess the relative financial performance of management-intensive grazing (MIG) and confinement dairy operations over the 15-yr period from 1995 through 2009. Data were not available from all farmers in all years; on average, the sample analyzed contained 11 MIG farms and 26 confinement farms. Management-intensive grazing operators were more profitable on a per hundredweight, per cow, and per acre basis, and no less profitable on a whole-farm basis. Even though the confinement operators had higher gross income than MIG operators, their expenses exceeded those of MIG operators. Profits of MIG operations were less variable as well, so that MIG operators faced less income risk. Increased reliance on grazing has other benefits as well. Grazing seems to be a much healthier practice for dairy cows. Veterinary, breeding, and medicine costs per cow are much less for cows that are pastured than those raised in confinement systems. Because they are healthier, cows that are grazed can be milked longer (or culled less frequently). As a result, MIG operators have a larger number of higher quality animals for sale (e.g., bred heifers). Management-intensive operations are also less labor intensive. Reductions in crop production and in the time cows spend in the barn led to significant reductions in field work and cleaning operations in the barn. Costs of hired labor were thus substantially lower in MIG operations than in confinement operations. Land requirements likely impose the principal limitation on the size of intensive grazing operations. In the mid-Atlantic, for instance, grazing operations need 1.5 to 2.0 acres of pasture for every dairy cow/ calf equivalent to provide sufficient grass to support a dairy operation. Pasture land for MIG operators must be contiguous to the milking parlor and located no farther than a cow can walk to and from twice a day. That requirement likely limits the maximum size of an intensive grazing operation, especially in areas where land prices and rents are high, as they are in much of the mid-Atlantic. [ABSTRACT FROM AUTHOR]

Abstract: Price spikes are distinctive aspects of electricity price impacting its forecast accuracy. Electricity price spikes can also have serious economical effects on the market participants. However, prediction of electricity price spikes is a complex task and most of current electricity price forecast methods focus on prediction of normal prices. In this paper, a new forecast strategy for prediction of both occurrence and value of electricity price spikes is presented. The proposed strategy has a novel feature selection technique based on information theoretic criteria to select a minimum subset of the most informative features for the forecast process. Also, the strategy includes a new closed loop prediction mechanism composed of probabilistic neural network (PNN) and hybrid neuro-evolutionary system (HNES) forecast engines. The effectiveness of the proposed forecast strategy for the prediction of both price spike occurrence and value is extensively evaluated by the real-life data of PJM (Pennsylvania–New Jersey–Maryland) electricity market. The obtained results confirm the validity of the developed approach. [Copyright &y& Elsevier]