Enhancing Distribution Network Performance Through Optimal PV and D-STATCOM Placement and Sizing Using the Newton-Metaheuristic Optimizer

This paper presents the application of the Newton-metaheuristic algorithm (NMA) for the integrated optimal placement and sizing of photovoltaic (PV) systems and distribution static compensators (D-STATCOMs) in medium-voltage distribution networks. The problem is formulated as a mixed-integer nonlinear programming (MINLP) model aimed at minimizing total annualized costs---including energy procurement, investment, and operation and maintenance---while satisfying power flow constraints, voltage limits, and device capacity bounds. To address the computational complexity, a master--slave hierarchical framework is proposed: the upper level employs the NMA to explore the discrete--continuous search space of installation locations and capacities, while the lower level employs a successive-approximation power flow solver to ensure technical feasibility. The performance of the proposed methodology is validated on standard 33-bus and 69-bus radial test systems and compared against established metaheuristics such as the Vortex Search Algorithm (VSA), Sine--Cosine Algorithm (SCA), and Atan-Sinc Optimization Algorithm (ASOA). Results demonstrate that the NMA achieves the lowest total costs: USD 2,290,259.24 and USD 2,395,022.55, respectively, corresponding to a reduction of approximately 35.55\% compared to the benchmark case without distributed resources. Moreover, the NMA exhibits superior computational efficiency, with execution times 4.8--17.1\% faster than those of the benchmark algorithms, underscoring its scalability and practical applicability for real-world distribution system planning. The proposed approach thus offers a robust, efficient, and economically effective tool for integrating renewable-based active power sources and reactive compensators in modern distribution grids.