This paper presents a method to operate a grid connected hybrid system. The hybrid system composed of a Photovoltaic (PV) array and a Proton exchange membrane fuel cell (PEMFC) is considered. The PV array normally uses a maximum power point tracking (MPPT) technique to continuously deliver the highest power to the load when variations in irradiation and temperature occur, which make it become an uncontrollable source. In coordination with PEMFC, the hybrid system output power becomes controllable. Two operation modes, the unit-power control (UPC) mode and the feeder-flow control (FFC) mode, can be applied to the hybrid system. The coordination of two control modes, the coordination of the PV array and the PEMFC in the hybrid system, and the determination of reference parameters are presented. The proposed operating strategy with a flexible operation mode change always operates the PV array at maximum output power and the PEMFC in its high efficiency performance band, thus improving the performance of system operation, enhancing system stability, and decreasing the number of operating mode changes.

Virtual power plant (VPP) technologies continue to develop to embrace various types of distributed energy resources (DERs) that have inherent real-time uncertainty. To prevent side effects on the power system owing to the uncertainty, the VPP should manage its internal resources' uncertainty as a whole. This paper proposes an optimal operation strategy for a VPP participating in day-ahead and real-time energy market so that a distributed energy resource aggregation (DERA) can cope with real-time fluctuation due to uncertainties while achieving its maximum profit. The proposed approach has bidding models of the DERAs including microgrid, electric vehicle aggregation, and demand response aggregation, as well as the VPP. The VPP determines internal prices applied to the DERA by evaluating its real-time responses to the day-ahead schedule and updating proposed pricing function parameters, and the DERA adjusts its energy reserves. By repeating this coordination process, the VPP can establish an optimal operation strategy to manage real-time uncertainty on the DERA's own. The effectiveness of the proposed strategy is verified by identifying a capability of the DERA to cope with real-time fluctuation through scenario-based simulations. The result shows that the VPP can reduce 1.6% of cost while the internal price applied to the DERA is close to the maximum.

A large scale energy storage system has become increasingly attractive and has been applied to various ancillary services. To serve energy for a longer time and to increase the profit of a multiple energy storages system, it should be operated considering each available energy source and the different efficiencies of the subordinate storages. This paper proposes a hierarchical control structure and three types of the power sharing methods for a multiple battery energy storages system. A maximum efficiency optimization method based on a piecewise linearized Lagrangian equation is suggested. In addition, a usable energy sharing algorithm is proposed to distribute the output power evenly according to the available energy of each battery. To improve the system availability, a combination algorithm that selects the appropriate control according to the situation is also proposed. The improvement in energy efficiency of the proposed methods is verified with a duty cycle test of Pacific Northwest National Laboratory by PSCAD/EMTDC.

This paper presents a monitor positioning algorithm to determine the optimal number of power quality monitors and to decide the locations for a given distribution system. From graph theory, the system topology is formulated into a coverage matrix. The weighting factors of each component are newly defined from the guidelines of monitor positioning suggested. Weighting factors represent the degree of importance in the system topology. Ambiguity indices are suggested to evaluate the monitor locations in terms of topological ambiguity. The optimal number and locations are determined from the optimization routine utilizing the weighting factor and the ambiguity indices. The proposed approach can realize the expert's knowledge on monitor positioning into a sophisticated automatic computing algorithm.