Hierarchical Energy Planning and Control of DGs, BESS, and D-STATCOMs in Unbalanced Non-Interconnected Distribution Networks

Access to reliable and economically viable electricity services remains a major challenge in non-interconnected zones (NIZs), particularly in developing countries. To address this issue, this study proposes a comprehensive methodology for the simultaneous planning and intelligent operation of distributed energy resources (DERs), specifically distributed generation (DG) units, battery energy storage systems (BESS), and Distribution Static Synchronous Compensators (D-STATCOMs), within unbalanced three-phase distribution systems. The proposed approach is formulated as a mixed-integer nonlinear programming (MINLP) model aimed at minimizing total system costs over a 20-year planning horizon, while accounting for technical and operational constraints under dynamic conditions in both renewable generation and electricity demand. To solve the model, three metaheuristic optimization techniques were implemented: the Chu–Beasley Genetic Algorithm (CBGA), the Vortex Search Algorithm (VSA), and the Black Widow Optimization Algorithm (BWOA). Each algorithm was independently executed 100 times in two representative test scenarios: a 25-bus system based on the distribution network of Leticia, and a 37-bus system derived from the San Andrés Island network. This evaluation enabled a statistical comparison of performance, convergence behavior, and repeatability. Results indicate that BWOA achieved the greatest cost reductions, with savings of 26.9304% and 35.6691% in Leticia and San Andrés, respectively. The selected configurations complied with all voltage and current constraints, while significantly reducing reliance on conventional diesel-based generation. These findings confirm the technical and economic viability of the proposed approach for the simultaneous integration and operation of DERs in isolated power systems.