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Soft open points (SOPs) are power electronic devices that replace the normal open points in active distribution systems. They provide resiliency in terms of transferring electrical power between adjacent feeders and delivering the benefits of meshed networks. In this work, a multi-objective bilevel optimization problem is formulated to maximize the hosting capacity (HC) of a real 59-node distribution system in Egypt and an 83-node distribution system in Taiwan, using distribution system reconfiguration (DSR) and SOP placement. Furthermore, the uncertainty in the load is considered to step on the real benefits of allocating SOPs along with DSR. The obtained results validate the effectiveness of DSR and SOP allocation in maximizing the HC of the studied distribution systems with low cost.

Distribution systems face significant challenges in maintaining power quality issues and maximizing renewable energy hosting capacity due to the increased level of photovoltaic (PV) systems integration associated with varying loading and climate conditions. This paper provides a comprehensive overview on the exit strategies to enhance distribution system operation, with a focus on harmonic mitigation, voltage regulation, power factor correction, and optimization techniques. The impact of passive and active filters, custom power devices such as dynamic voltage restorers (DVRs) and static synchronous compensators (STATCOMs), and grid modernization technologies on power quality is examined. Additionally, this paper specifically explores machine learning and AI-driven solutions for power quality enhancement, discussing their potential to optimize system performance and facilitate renewable energy integration. Modern optimization algorithms are also discussed as effective procedures to find the settings for power system components for optimal operation, including the allocation of distributed energy resources and the tuning of control parameters. Added to that, this paper explores the methods to maximize renewable energy hosting capacity while ensuring reliable and efficient system operation. By synthesizing existing research, this review aims to provide insights into the challenges and opportunities in distribution system operation and optimization, highlighting future research directions that enhance power quality and facilitate renewable energy integration.

Renewable energy integration has been recently promoted by many countries as a cleaner alternative to fossil fuels. In many research works, the optimal allocation of distributed generations (DGs) has been modeled mathematically as a DG injecting power without considering its intermittent nature. In this work, a novel probabilistic bilevel multi-objective nonlinear programming optimization problem is formulated to maximize the penetration of renewable distributed generations via distribution network reconfiguration while ensuring the thermal line and voltage limits. Moreover, solar, wind, and load uncertainties are considered in this paper to provide a more realistic mathematical programming model for the optimization problem under study. Case studies are conducted on the 16-, 59-, 69-, 83-, 415-, and 880-node distribution networks, where the 59- and 83-node distribution networks are real distribution networks in Cairo and Taiwan, respectively. The obtained results validate the effectiveness of the proposed optimization approach in maximizing the hosting capacity of DGs and power loss reduction by greater than 17% and 74%, respectively, for the studied distribution networks.

Photovoltaic (PV) in low-voltage distribution systems (LVDS) becomes problematic when the penetration level exceeds system photovoltaic hosting capacity (PVHC), since it leads to violations of power quality constraints. Maximizing PVHC enables customer service expansion by allowing more power from prosumers and load attendance. Although several works propose quantifying PVHC, methodologies to maximize it in LVDS are scarce. Some works propose the installation of additional equipment, such as var compensators, resulting in costly solutions, others propose solutions without cost to medium-voltage distribution systems. Therefore, this paper’s main objective is to propose a cost-effective approach with the purpose of maximizing PVHC of LVDS by searching the optimal power factor for interface inverters of each PV unit in the system. Additionally, this study aims to consider the uncertainties of PV penetration, solar irradiance, locations and sizes of PV units, and system loads through the Monte Carlo simulation. A modified particle swarm optimization is adopted to search power factors to avoid voltage violations for as many Monte Carlo scenarios as possible, guaranteeing PVHC maximization. Test results for a real system show the proposed method maximizes PVHC of LVDS by reducing the number of scenarios that violate the voltage constraint and improving the system voltage profile even for great amounts of installed PV units.