This paper proposes a novel method to determine and control active and reactive power output of the virtual ocillator control-based inverter in grid-connected mode. An analytical expression is introduced to describe the quasi-steady-state relationship between the parameters of the inverter including its controllers, and the power injection and voltage at the inverter terminals. We introduce a new, complex valued parameter (K) to the conventional virtual oscillator control (VOC) scheme in order to control the inverter output power. We compare analytic and numeric solutions of the above analytical expression with time-domain simulation results, using different K values and line impedances to validate the analytical expression. In the second part of the paper, we design a controller: we use the above expression to derive K for given reference powers and use adaptive integrators to compensate for the remaining errors. We compare this control scheme to a method that uses PI controllers, and also to a simple decoupling method for independent control of active and reactive power. The controller based on the analytical method exhibits very good dynamic and decoupling performance. A single VOC-controlled inverter connected to a nonstiff grid is simulated in MATLAB/Simulink for demonstration, and is also validated using real-time simulations.

Recently, the penetration of photovoltaic (PV) units and plug-in electric vehicles (PEVs) has been quickly increased worldwide. Due to the intermittent nature of PV and the stochastic nature of PEVs, several operation problems can be noticed in distribution systems, including excessive energy losses and voltage violations. In this paper, an optimization-based algorithm is proposed to accurately determine the optimal locations and capacities of multiple PV units in the presence of PEVs to minimize energy losses while considering various system constraints. The proposed algorithm considers the uncertainty of PV and loads, and the stochastic nature of PEVs. Furthermore, the operational constraints of PEVs are incorporated in the optimization model: 1) arrival and departure times, 2) initial state of charge (SOC), 3) minimum preset state of charge by the owner, and 4) the time-of-use electricity tariff, and 5) different charging control schemes. The optimal PV planning model is formulated as a two-layer optimization problem that ensures an optimal PV allocation while optimizing PEV charging simultaneously. A two-layer metaheuristic method is developed to solve the optimization model considering annual datasets of the studied distribution systems. The results demonstrate the efficacy of the proposed algorithm.

Accommodating a high penetration of intermittent photovoltaic (PV) in distribution systems can potentially cause several operational problems, most importantly, voltage violations. An optimal probabilistic approach is proposed in this article to optimally host high penetrations of PV units considering their stochastic nature. A benefit of the proposed approach is that it provides wider planning options since it optimizes the interfacing inverter oversize with smart watt-var functionalities. These smart functionalities include: 1) active power curtailment and 2) inverter reactive power. The utilization of these functionalities in the optimization model yields an optimal PV hosting that maximizes the benefits to distribution systems. The optimal probabilistic model of PV incorporates the probabilities of the PV power output and load while optimizing the inverter oversize and the two functionalities simultaneously. The proposed approach complies with the recently released IEEE 1547:2018 standard which regulates the reactive power support via the interfacing PV inverters. The efficacy of the proposed approach is demonstrated by comparisons with existing approaches. The results confirm the superiority of the proposed approach to optimally accommodate high PV penetration at single or multiple locations while minimizing voltage violations. The proposed approach is also applied to maximize the hosting capacity of PV.

Recently, the integration of inverter-based wind turbine generation systems (WTGS) and plug-in electric vehicles (PEV) has remarkably been expanded into distribution systems throughout the world. These distributed resources could have various technical benefits to the grid. However, they are also associated with potential operation problems due to their stochastic nature, such as high power losses and voltage deviations. An optimization-based approach is introduced in this paper to properly allocate multiple WTGS in distribution systems in the presence of PEVs. The proposed approach considers 1) uncertainty models of WTGS, PEV, and loads, 2) DSTATCOM functionality of WTGS, and 3) various system constraints. Besides, the realistic operational requirements of PEVs are addressed, including initial and preset conditions of their state of charge (SOC), arriving and departing times, and various controlled/uncontrolled charging schemes. The WTGS planning paradigm is established as a bi-level optimization problem which guarantees the optimal integration of multiple WTGS, besides optimized PEV charging in a simultaneous manner. For this purpose, a bi-level metaheuristic algorithm is developed for solving the planning model. Intensive simulations and comparisons with various approaches on the 69-bus distribution system interconnected with four PEV charging stations are deeply presented considering annual datasets. The results reveal the effectiveness of the proposed approach.

The convergence of next-generation wireless communication technologies and modern energy infrastructure presents a promising path toward sustainable and intelligent systems. This survey explores how beyond-5G and 6G communication technologies can support the greening of Industrial Internet of Things (IIoT) systems and smart grids. It highlights the critical challenges in achieving energy efficiency, interoperability, and real-time responsiveness across different domains. The paper reviews key enablers such as LPWAN, wake-up radios, mobile edge computing, and energy harvesting techniques for green IoT, as well as optimization strategies for 5G/6G networks and data center operations. Furthermore, it examines the role of 5G in enabling reliable, ultra-low-latency data communication for advanced smart grid applications, such as distributed generation, precise load control, and intelligent feeder automation. Through a structured analysis of recent advances and open research problems, the paper aims to identify essential directions for future research and development in building energy-efficient, resilient, and scalable smart infrastructures powered by intelligent wireless networks.