Topology Aware Mixed Discrete Optimization for Optimal Siting and Sizing of Distribution-Static Synchronous Compensators in Electric Vehicle Charging Stations Connected Microgrid Considering Reactive Power Tariffs

Abstract

Optimal allocation of Distribution Static Synchronous Compensators (D-STATCOMs) is critical for enhancing modern distribution grid performance. However, integrating load and generation volatility alongside technoeconomic objectives yields a highly complex, non-linear, mixed-integer optimization problem. Existing methodologies frequently struggle to balance continuous sizing and discrete location variables under realistic planning constraints. To resolve this, this study introduces a novel Topology-Aware Mixed-Discrete Snow Ablation Optimizer (TA-MD-SAO). The framework leverages the electrical distance between buses to discretize location data, enabling efficient simultaneous siting and sizing of D-STATCOMs. The proposed model incorporates a pragmatic economic structure accounting for reactive power tariffs, seasonal load/generation profiles, and electric vehicle charging station (EVCS) demands. The algorithm’s efficacy is validated on 33-bus and 69-bus EVCSconnected microgrids (ECMs) and benchmarked against leading optimization methods, including MD-SPBOA, MD-SCA, MD-PSO, GWO, TWO, and HHO. Simulation results demonstrate that TA-MD-SAO successfully harmonizes technical enhancement with economic viability. In the 33-bus ECM, a two-D-STATCOM configuration reduces mean real and reactive power losses by 40.45% and 39.22%, respectively, while improving average system voltage by 2.96% and the voltage stability index by 10.64%, yielding maximum annual savings of $14 276. Conversely, a single D-STATCOM configuration is identified as optimal for the 69-bus ECM, mitigating core technical issues at minimal device cost. Ultimately, this research provides microgrid operators with a robust, mathematically sound framework to balance initial capital expenditures against long-term technical and financial dividends.