In this article, we develop a modular design method of decentralized controllers for linear dynamical network systems, where multiple subcontroller designers aim at individually regulating their local control performance with accessibility only to their respective subsystem models. First, we derive a constrained version of the Youla parameterization that characterizes all retrofit controllers for a single subcontroller, defined as an add-on-type subcontroller that manages a subsystem. The resultant feedback system is kept robustly stable for any variation in the neighboring subsystems, other than the subsystem of interest, provided that the original system is stable prior to implementing the retrofit control. Subsequently, we find out a unique internal structure of the retrofit controllers, assuming that the interaction input signal from the neighboring subsystems is measurable. Furthermore, we show that the simultaneous implementation of multiple retrofit controllers, designed by individual subcontroller designers, can improve the upper bound of the overall control performance. Finally, the practical significance of the method is demonstrated via an illustrative example of frequency regulation using the IEEE 68-bus power system model.

This letter is devoted to the concept of instant model predictive control (iMPC) for linear systems. An optimization problem is formulated to express the finite-time constrained optimal regulation control, like conventional Model predictive control (MPC). Then, iMPC determines the control action based on the optimization process rather than the optimizer, unlike MPC. The iMPC concept is realized by a continuous-time dynamic algorithm of solving the optimization; the primal-dual gradient algorithm is directly implemented as a dynamic controller. On the basis of the dissipativity evaluation of the algorithm, the stability of the control system is analyzed. Finally, a numerical experiment is performed in order to demonstrate that iMPC emulates MPC and to show its less computational burden.

We present a new optimal power flow (OPF) design that not only optimizes fuel cost but also enhances dynamic performance of a power system. Performance is quantified by the H2-norm of the transfer matrix from any disturbance input to a set of performance outputs, which in this case are chosen as the frequencies of the generators. The H2-norm models the attenuation of the frequency amplitudes following the disturbance, and thereby quantifies the amount of damping torque induced on the tie-line flows. The method, referred to as H2-power flow modification (H2-PFM) is carried out in two steps. First, the regular OPF is solved to obtain the optimal generator setpoints for active and reactive power dispatch. Second, the load setpoints are re-tuned to minimize the aforesaid H2-norm while keeping the generator setpoints fixed. In particular, manipulating the load reactive power in this way is found to reduce the norm remarkably, improving disturbance attenuation and damping. A gradient descent algorithm is proposed for this minimization. Results are validated using the 68-bus test system with a solar farm.