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Power flow analysis is an inevitable methodology in the planning and operation of the power grid. It has been performed for the transmission system, however, along with the penetration of the distributed energy resources, the target has been expanded to the distribution system as well. However, it is not easy to apply the conventional method to the distribution system since the essential information for the power flow analysis, say the impedance and the topology, are not available for the distribution system. To this end, this paper proposes an alternative method based on practically available parameters at the terminal nodes without the precedent information. Since the available information is different between high-voltage and low-voltage systems, we develop two various machine learning schemes. Specifically, the high-voltage model incorporates the slack node voltage, which can be practically obtained at the substation, and yields a time-invariant model. On the other hand, the low voltage model utilizes the deviation of voltages at each node for the power changes, subsequently resulting in a time-varying model. The performance of the suggested models is also verified using numerical simulations. The results are analyzed and compared with another power flow scheme for the distribution system that the authors suggested beforehand.

-The autonomous microgrid is a system that is autonomously operated depending on the grid and internal load condition, without the operator's intervention. In this study, a control algorithm for the microsource and an operation algorithm for the microgrid are proposed to realize the autonomous microgrid system. In addition, a microgrid operation system based on the operation algorithm is proposed. The electromagnetic transient program is used by the proposed microsource control algorithm for simulation, and the validity of the algorithm is verified. The proposed operation system is verified based on a case study using a simulator and test devices.

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Vehicle Driving Range: In FY 2008, the driving range of the project's FCEVs was evaluated based on fuel economy from dynamometer testing (EPA adjusted) and on-board hydrogen storage amounts and compared to the 250-mile target. The resulting second-generation vehicle v driving range from the four teams was 196-254 miles, which met DOE's 250-mile range target. In June 2009, an on-road driving range evaluation was performed in collaboration with Toyota and Savannah River National Laboratory. The results indicated a 431-mile on-road range was possible in southern California using Toyota's FCHV-adv fuel cell vehicle [5]. More recently, the significant on-road data that have been obtained from second-and first-generation vehicles allowed a comparison of the real-world driving ranges of all the vehicles in the project. The data show that there has been a 45% improvement in the median distance between fueling events of second-generation vehicles (81 miles) as compared to first-generation vehicles (56 miles), based on actual distances driven between more than 25,000 fueling events. Over the last two years, we saw a continuation of this trend, with a median distance between fuelings of 98 miles, which is a 75% improvement over the first-generation vehicles. Obviously the vehicles are capable of two to three times greater range than this, but the median distance travelled between fuelings is one way to measure the improvement in the vehicles' capability, driver comfort with station location and availability, and how they are actually being driven.On-Site Hydrogen Production Cost: Cost estimates from the Learning Demonstration energy company partners were used as inputs to an H2A analysis [6] to project the hydrogen cost for 1,500 kg/day early market fueling stations. H2A is DOE's suite of hydrogen analysis tools, with the H2A Production model focused on calculating the costs of producing hydrogen. Results from version 2.1 of the H2A Production model indicated that on-site natural gas reformation could lead to a cost range of roughly $8-$10/kg and on-site electrolysis could lead to a hydrogen cost of $10-$13/kg. Note that 1 kg hydrogen is approximately equal to the energy contained in a gallon of gasoline, or gallon gasoline equivalent (gge). While these project results do not achieve the $3/gge cost target, two external independent review panels commissioned by DOE concluded that distributed natural gas reformation could lead to a cost range of $2.75-$3.50/kg [7] and distributed electrolysis could lead to $4.90-$5.70/kg [8]. Therefore, this objective was met outside of the Learning Demonstration project using distributed natural gas reforming. Summary of Results:We have summarized the previously discussed key performance numbers, along with other metrics of interest such as fuel economy and fuel cell efficiency, and compared them to DOE targets in Table ES-2. The table shows that this project has exceeded the expectations established in 2003 by DOE, with all of the key targets being achieved except for on-site hydrogen prod...