The increasing penetration of renewable energy resources (RES) in transmission system operating conditions require a suitable test system and a dataset to cope with current issues. RES penetration remarkably affects day-ahead market outcomes regarding zonal prices and dispatched generation levels. For this purpose, zonal day-ahead energy market models in the presence of RES in the generation mix need to be implemented. In this paper, the IEEE 39-bus system has been suitably modified to include solar and wind generation in the traditional generation mix. Hourly time series are used to define load profiles and wind and solar power generation. The zonal day-ahead market (ZDAM) resolution is simulated by solving a Linear Programming optimization problem employing Pyomo. Furthermore, steady-state nodal analysis is carried out using DIgSILENT PowerFactory, performed over a year horizon.

Future distribution networks (DN) are subject to rapid load changes and high penetration of variable distributed energy resources (DER). Due to this, the DN operators face several operational challenges, especially voltage violations. Optimal power flow (OPF)based reactive power control (RPC) from the smart converter (SC) is one of the viable solutions to address such violations. However, sufficient communication and monitoring infrastructures are not available for OPF-based RPC. With the development of the latest information communication technology in SC, cyber-physical co-simulation (CPCS) has been extensively used for real-time monitoring and control. Moreover, deploying OPFbased RPC using CPCS considering the controller design of SC for a realistic DN is still a big challenge. Hence, this paper aims to mitigate voltage violations by using OPF-based RPC in a real-time CPCS framework with multiple SCs in a realistic DN. The OPF-based RPC is achieved by performing the CPCS framework developed in this study. The CIGRE medium-voltage DN is considered as a test system. Real-time optimization and signal processing are achieved by Python-based programs using a model-based toolchain of a real-time DN solver and simulator. Real-time simulation studies showed that the proposed method is capable of handling uncertain voltage violations in real time.

Due to climate change, progressively more isolated electrical power systems are integrating renewable energy sources. But the transition from synchronous generators to converter-interfaced generators also produces a few issues due to the lack of rotational inertia. The voltage source converters (VSCs) enabled with the so-called grid-forming control may provide a solution for the converter-dominated electrical power systems. This paper presents the implementation of two control strategies, Virtual Synchronous Machine (VSM) and Synchronverter (SynC), for converter-interfaced generators in the real-time environment (Typhoon HIL) to emulate synchronous generator (SG) behaviour. The advantages of grid-forming converter control to provide inertia response service and make the system more robust to changes in the active power of loads have been demonstrated in realtime simulations through a scenario of a positive load step connected to the converter.

Summary With an increase in the penetration of renewable energy sources such as wind into the power systems, the operation and control of voltage/reactive power have become more complicated and challenging than ever. As a result, the reactive power imbalance between reactive power generation and demand instigates a reduction in system voltage stability. To deal with the aforesaid scenarios, automatic voltage regulator (AVR) and static synchronous compensator (STATCOM) are incorporated to curtail the voltage deviations in a standalone wind‐diesel power system. In this article, a hybrid bacterial foraging optimization algorithm‐particle swarm optimization (hBFOA‐PSO) algorithm is proposed for optimizing the PI controller parameters of AVR and STATCOM to further improve the system voltage/reactive power performance. Additionally, H∞‐loop shaping technique is designed to analyze the performance indexes (ie, robustness and stability) of the presented controller with the aim of handling the unstructured uncertainties from generation and loading situation. In order to present the efficiency of the proposed controllers, the performance of the hBFOA‐PSO controller is compared with the performance of the BFOA, PSO, and modified grey wolf optimization (MGWO)‐based PI controllers for the same wind‐diesel system. The dynamic responses of the wind‐diesel system for different disturbance cases have been investigated in the MATLAB/SIMULINK environment.