Optimal placement of EV charging Stations, DSTATCOM, BESS, and DGs in radial distribution systems using an enhanced Fractional-Order differential Evolution-Based optimization algorithm

Electric Vehicle Charging Stations (EVCS), Distributed Static Compensators (DSTATCOM), Battery Energy Storage Systems (BESS), and Distributed Generators (DGs) are integrated and operate in a coordinated way into radial distribution systems (RDS) to offer substantial aids in terms of voltage support, loss minimization, and reliability enhancement during regular operation. However, the decision to take their placement optimally and simultaneously is a complex task due to the nonlinear, bidirectional, and highly constrained nature of the radial distribution system. In response to this issue, this paper proposes an enhanced fractional order differential evolution (EFODE) for a more accurate, reliable, and optimal solution. Unlike non-adaptive versions of differential evolution (DE), which have insufficient exploration ability and lack adaptability to historical information, this study proposes an innovative approach to fractional-order DE (FODE). The proposed strategic formulation impact on enhancing DE performance. A bi-strategy co-deployment framework is incorporated, combining the concepts of population-based and parameter-based strategies to leverage their respective individual advantages, nullifying their limitations through mutual influence. In addition, the fractional order (FO) calculus is used to enhance the differential vector’s exploration and exploitation abilities, which are achieved through the incorporation of historical information from populations in the formulation, thereby ensuring the diversity of populations in an evolutionary process. By adaptively varying the most sensitive system factors dynamically according to the system’s performance, it accelerates convergence and prevents premature stagnation. The proposed method is simulated and validated on standard IEEE RDS 33, 69 and 85 test systems, considering multiple constant load, voltage-dependent variable load, and penetration scenarios. Simulation and comparative results demonstrate significant improvements in terms of voltage profile, reduction of active power loss, and overall solution quality. The comparative analysis with conventional metaheuristics confirms the effectiveness and robustness of the approach.