The integration of variable renewable energy sources (RES) into power grids has resulted in a more complex operating environment for power system operators (SO), necessitating the need for increased grid flexibility. This is crucial for utilities, as the cost-effective utilization of existing infrastructure is necessary, given the prolonged construction period and high investment costs associated with building new power delivery facilities. Under the umbrella of smart grid, optimal transmission switching (OTS) is a cost-effective transmission technology that can alleviate concerns related to network congestion, limited transmission capacity, and high penetration of renewables. OTS is a concept that is integrated into the optimal power flow (OPF) problem such that it provides SO the choice to temporarily switch one or more lines out of service from the network. OTS is shown in the literature to have economic benefits, reduce operational cost, relieve network congestion, serve as a corrective mechanism for reducing voltage violations, improve system reliability, and minimize system loss. Despite the significant attention given to OTS by researchers over the past decade, no extensive review paper exists on the topic in the literature. Therefore, this paper provides a state-of-the-art overview of the OTS problem. The concept of OTS is explained by approximating the alternating current OPF problem into a linear direct current OPF problem. Various alternative models of the OTS problem proposed in the literature are discussed. The paper also analyzes the interaction between OTS and other flexibility options such as Dynamic thermal rating (DTR), Energy storage systems (ESS), and RES. Furthermore, the paper presents the general framework and impact of the short-term OTS problem on long-term expansion planning problems. Finally, an extensive literature review on the impact of OTS on system reliability is provided.

The integration of distributed generation (DG) into distribution networks introduces uncertainties that can substantially affect network reliability. It is crucial to implement appropriate measures to maintain reliability parameters within acceptable limits and ensure a stable power supply for consumers. This paper aims to optimize the location, size, and number of DG units to minimize active power losses and improve distribution System (DS) reliability while considering system operational constraints. To achieve this objective, multiple tests are conducted, and the particle swarm optimization (PSO) technique is implemented. The simulation studies are performed using the ETAP software 19.0.1 version, while the PSO algorithm is implemented in MATLAB R2018a. ETAP enables a comprehensive evaluation of the DG system’s performance, providing valuable insights into its effectiveness in reducing power losses and enhancing system reliability. The PSO algorithm in MATLAB ensures accurate optimization, facilitating the identification of the optimal DG unit location and size. This study uses a modified IEEE-13 bus unbalanced radial DS as the test system, assessing the effects of photovoltaic (PV) and wind DG units under various scenarios and penetration levels. The results demonstrate that the optimal DG unit location and size of either a single PV or wind DG unit significantly reduce power losses, improve DS reliability, and enable effective load sharing with the substation. Moreover, this study analyzes the impact of DG unit uncertainty on system performance. The findings underscore the potential of optimized DG integration to enhance DS efficiency and reliability in the presence of renewable energy sources.

Owing to the economic, social, and political problems in the expansion of transmission infrastructure, novel transmission topologies are in demand to efficiently utilize the existing infrastructure. On the other hand, there is a tremendous increase in wind power generation around the globe. One of the main challenges hindering the penetration of large-scale wind power generation is the network congestion due to limited network capacity. To address these two issues, this study develops a co-optimized optimal transmission switching (OTS) and dynamic line rating (DLR) model to optimize system resources by mitigating network congestion and maximizing wind power accommodation. This new concept of exploiting the inherent flexibility in transmission network is named as flexible transmission dynamic line rating (FTDLR), which deploys OTS in coordination with DLR as a control tool to utilize existing assets. A two-stage stochastic unit commitment framework is used to deploy the proposed FTDLR model, which is used to dynamically increase the line capacity based on the meteorological parameters and at the same time optimally select candidate lines to be switched off from the network. A comprehensive analysis is performed to characterize the FTDLR performance on system operation cost, network congestion, and wind power curtailment. The proposed FTDLR model is further tested as a part of contingency analysis where both generator failure and transmission line outages are considered. Test results performed on the IEEE 24-bus network demonstrate that by using FTDLR, the service operator could substantially reduce system dispatch cost, improve wind power accommodation, and relieve network congestion. The scalability and feasibility of the FTDLR optimization problem is validated on the larger network of the IEEE 118-bus system.

Escalating energy demands and climate change challenges necessitate the adaptation of renewable-based microgrid systems in the energy sector. The proposed work employs a robust Multi Agent System (MAS) technique to achieve efficient and automated control of the hybrid microgrid operation. The hybrid microgrid system incorporates Renewable Energy Sources (RES), a diesel generator, and a battery storage system. The operation of the hybrid microgrid consists of three distinct modes: islanded, transition to grid, and grid-oriented mode. The system’s performance is optimized by considering factors like climatic patterns, energy costs, connected source characteristics, and load demand. Different climatic scenarios are assessed for each mode of operation, where the best, extreme sunny, extreme cloudy, and worst climate conditions are considered for islanded mode; sunny and cloudy scenarios are considered for transition to grid mode as well as grid-feed and grid-tied modes are considered for grid-oriented operation of the microgrid. The simulation studies are performed using the MATLAB/Simulink R2021a environment. Furthermore, Particle Swarm Optimization (PSO) is implemented to optimize power allocation within the microgrid and enhance its cost-effectiveness. The optimization results demonstrate efficient utilization of available energy sources along with effective energy management facilitated by the MAS control system. The results emphasize the importance of adopting a MAS approach for achieving smart energy management through comprehensive analysis and integrating decentralized energy management techniques for optimal accommodation of distributed energy resources in hybrid microgrids.