The increasing integration of distributed energy resources such as photovoltaic (PV) systems into distribution networks introduces intermittent and variable power, leading to high voltage fluctuations. High PV integration can also result in increased terminal voltage of the network during periods of high PV generation and low load consumption. These problems can be solved by optimal utilization of the reactive power capability of a smart inverter. However, solving the optimization problem using a detailed mathematical model of the distribution network may be time-consuming. Due to this, the optimization process may not be fast enough to incorporate this rapid fluctuation when implemented in real-time optimization. To address these issues, this paper proposes a co-simulation-based optimization approach for optimal reactive power control in smart inverters. By utilizing co-simulation, the need for detailed mathematical modeling of the power flow equation of the distribution network in the optimization model is eliminated, thereby enabling faster optimization. This paper compares three optimization algorithms (improved harmony search, simplicial homology global optimization, and differential evolution) using models developed in OpenDSS and DigSilent PowerFactory. The results demonstrate the suitability of the proposed co-simulation-based optimization for obtaining optimal setpoints for reactive power control, minimizing total power loss in distribution networks with high PV integration. This research paper contributes to efficient and practical solutions for modeling optimal control problems in future distribution networks.

The operation schedule of the power generation units in electrical power systems is determined by the optimisation problem known as unit commitment (UC), aiming at minimising the total cost considering the generation constraints. To obtain a feasible solution from the network perspective, the security-constrained UC (SCUC) problem has been defined to embed the network constraints in the optimisation problem as well. Also, the higher penetration of renewable energy sources (RES) has increased the difficulty of UC problem, mainly due to the uncertainty and the high variability of RES. This paper proposed a SCUC with economic dispatch (SCUCED) optimisation developed in two stages. The first one is the solution of a merit-order based zonal day-ahead market (ZDAM) optimisation to define a preliminary generation schedule. In the second stage, the SCUCED is solved based on AC load flow routines and sensitivity factors to embed the full network representation. The approach is applied to a modified version of the IEEE 39-bus test system.

With the rise of integration of renewable energy sources, existing electric power distribution networks are facing a variety of technical obstacles, one of which is modelling of the distribution networks for real-Time network monitoring and control. This research designs and analyzes a novel cyber-physical test system for real-Time reactive power compensation from smart inverters in the active distribution network using cyber-physical co-simulation between the Typhoon HIL and OpenDSS. The testbed is a two-layer system, with a physical and cybernetic layer. The physical layer is represented by Typhoon HIL 604 and the cybernetic layer is represented by software from Typhoon HIL, OpenDSS, and Python. The cybernetic layer is used to model, design, and control the reactive power from the smart inverter in real-Time. The distribution network considered is a CIGRE MV distribution network. Real-Time simulation results demonstrate the applicability of the proposed test platform in real-Time reactive power control.