The technological advancements and policy reformations make the electric power distribution system an active network. The modern power system operational regulations encourage coordinated network operations among various entities and customer participation. Unlike traditional operational environments, real-time monitoring has become a mandate for secure and reliable operations. Phasor Measurement Units (PMU) is the most reliable data acquisition tool, and their application for State Estimation (SE) has already been proven. This work extends the application of PMUs for the distribution system. A multi-objective optimization framework is proposed considering the optimal placement of PMU, network reconfiguration, and topology expansion. A novel optimization tool, the sea lion optimizer has been chosen to address the developed optimization problem. The proposed operational model is employed in IEEE RBTS-2 bus system, IEEE 33 node system and 69 node radial distribution network. Numerical results provide new insights into PMUs role in enhancing distribution network operations. The statistical indices confirm that the intended optimizer performs well in the chosen highly constrained optimization environment.

Among Renewable Energy Sources wind and solar energy are the most prominent and favorable substitutes to meet mankind’s future electricity requirements. Typically, these resources are combined in a distribution network to provide local distribution users. The amalgamation of these upgrades into the distribution network may modify the malfunction and network topologies, which may fail due to their pre-set state. Therefore, an advanced and accurate tracking device must continuously monitor topology changes, which is the Phasor Measuring Unit (PMU). This work proposes a new technique based on the Sea Lion Optimization Algorithm to determine the Optimal PMU Placements and its employment positions, i.e. the power structure is completely perceptible. In addition, the cost of system losses can be calculated with or without wind energy to achieve energy reserves. The energy losses before and after the wind turbine connection is compared to realize energy reserves. This comparison of energy reserves is made using Realistic and Stochastic platforms. Comparison and proposed formulas offered in the IEEE 15, 33, 69, and 85 bus distribution networks were explored to prove their effectiveness. Moreover, the proposed methodology produces more trusted results than those of other methods in the literature.