Voltage violations are the main problem faced in distribution networks (DN) with a higher penetration of inverter-based generations (IBG). Active and reactive power control from smart inverters (SI) can mitigate such violations. Optimal power flow (OPF)-based control provides more accurate operating set points for the coordinated operation of SIs. Therefore, this paper presents a three-phase OPF-based control on SI-enriched unbalanced distribution networks. To consider this, first three-phase model using the current injection model (CIM) is developed. Later, the optimal active and reactive power set points for SIs are obtained by solving a quasi-dynamic optimization problem. The uniqueness of the proposed method is that it regulates the voltage at the affected nodes by obtaining the optimal set points for the smart inverter. The OPF is implemented with a mathematical CIM in Pyomo and solved using the Knitro solver. The proposed method is compared with the sensitivity-based Volt-Var Control (VVC), Volt-Watt Control (VWC), and combined VVC and VWC methods. The effectiveness of the proposed method is verified in a European low-voltage and CIGRE medium-voltage distribution network with 100% penetration. The analysis shows that the OPF-based control optimizes with less network loss and can maintain voltage violations with less reactive power support.

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.

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 OPF‐based 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.

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.

Today’s power system is transforming into an increasingly complex entity, consisting of numerous components, such as transmission lines, controllable loads, and especially different types of distributed renewable energy sources (DERs). With the growing integration of DERs into the grid, multiple operational challenges arise, including overvoltage, undervoltage, or increased energy losses. Resolving these issues demands the implementation of both advanced and effective control strategies. As the dynamic power system evolves by incorporating new technologies, these control strategies need to consider other different technical aspects, such as communication protocols or real-time considerations. Additionally, the rise of smart metering devices has transformed conventional power systems into cyber-physical power systems (CPPS), which can integrate the advanced control strategies into the cyber layer. Given the operating challenges and the integration of diverse technologies, it is proposed that a CPPS testbed platform constitutes an ideal solution for developing and validating technologies in future smart grids. For this purpose, this paper introduces a co-simulation framework for implementing a CPPS testbed, utilising the real-time simulator, Typhoon HIL, within a laboratory environment. Additionally, it presents a proposed optimal power flow (OPF) control strategy that emphasises two key objectives, minimisation of operating costs and power network loss. The investigation is illustrated by a modified version of IEEE 39-bus test system with the high integration of DERs. The findings indicate that adopting a CPPS testbed can be advantageous for implementing real-time research on monitoring and control in a wide area network.