The use of photovoltaics (PV) in electric power networks has increased because of advantages such as power loss reduction, environmental friendliness, voltage profile improvement, and postponement of system upgrades. However, using PVs of an inappropriate size leads to greater power losses due to variations in PV outputs and demand loads. This paper formulates the optimal sizing PV problem (OPSP) for minimizing inverter losses and network losses. The OPSP is a large-scale and non-convex optimization that is difficult to solve. To resolve this computational issue, this paper proposes a new approach that consists of two steps. The first step is to relax the original non-convex problem into a convex one and to prove that the convexification is exact provided that over-satisfaction of the load is allowed. The second step is to decompose the original large-scale optimization into small-scale sub-problems. Moreover, the decomposed sub-problems can be computed in parallel. Numerical simulations implementing parallel processing verify the effectiveness of our approach.

This paper describes an application of Fictitious Reference Iterative Tuning (abbr. FRIT) to the Dynamic Positioning System (abbr. DPS), which is an automatic control system for ship positioning. We developed the motion equation including nonlinear fluid forces with respect to the relative velocity between a vessel and a current and adjusted control parameters by employing FRIT to such a nonlinear system. We mentioned that it was difficult to design the desired transfer function appropriately and proposed to use known information about a real plant. In particular, we used a nominal plant model to design desired transfer function. Furthermore, since disturbance attenuation is the primary purpose in the DPS, we designed the evaluation function which provides the control parameters that give the desired output response to the input disturbance (e.g. wind, wave). The simulation results show that the evaluation function based on the load disturbance sensitivity function (quasi sensitivity function) gives a better output response to the disturbance than the sensitivity function.