Subrat Kumar Dash

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.

Distribution static compensators (D-STATCOMs) can enhance the technical performance of the power distribution network by providing rapid and continuous reactive power support to the connected bus. Accurate modeling and efficient utilization of D-STATCOMs can maximize their utility. In this regard, this article offers a novel current-injection-based D-STATCOM model under the power control mode of operation for the reactive power compensation of the power distribution network. The versatility of the proposed D-STATCOM model is demonstrated by combining it with two of the most established distribution load flow techniques, viz., the forward–backward sweep load flow and the BIBC–BCBV-matrix-based direct load flow. Further, the allocation of the proposed D-STATCOM model is carried out under a multiobjective mathematical formulation consisting of various technical and economic indices such as the active power loss reduction index, voltage variation minimization index, voltage stability improvement index and annual expenditure index. A novel parameter-free metaheuristic algorithm, namely a student-psychology-based optimization algorithm, is proposed to determine the optimal assignment of the different number of D-STATCOM units under the multiobjective framework. The proposed allocation scheme is implemented on a standard 33-bus test system and on a practical 51-bus rural distribution feeder. The obtained results demonstrate that the proposed D-STATCOM model can be efficiently integrated into the distribution load flow algorithms. The student-psychology-based optimization algorithm is found to be robust and efficient in solving the optimal allocation of D-STATCOMs as it yields minimum power loss compared to other established approaches for 33-bus PDNs. Further, the economic analysis carried out in this work can guide network operators in deciding on the number of D-STATCOMs to be augmented depending on the investment costs and the resulting savings.

In a quest to solve the multi-objective optimal planning problem using a simple parameter-free metaheuristic algorithm, this paper establishes the recently proposed student psychology-based optimization (SPBO) algorithm as the most promising one, comparing it with the other two popular nonparametric metaheuristic optimization algorithms, i.e., the symbiotic organisms search (SOS) and Harris hawk optimization (HHO). A novel multi-objective framework (with suitable weights) is proposed with a real power loss minimization index, bus voltage variation minimization index, system voltage stability maximization index, and system annual cost minimization index to cover various technical, economic, and environmental aspects. The performances of these three algorithms are compared extensively for simultaneous allocation of multitype distributed generations (DGs) and D-STACOM in 33-bus and 118-bus test systems considering eight different cases. The detailed analysis also includes the statistical analysis of the results obtained using the three algorithms applied to the two test distribution systems.

The research proposes a new oppositional sine cosine muted differential evolution algorithm (O-SCMDEA) for the optimal allocation of distributed generators (OADG) in active power distribution networks. The suggested approach employs a hybridization of the classic differential evolution algorithm and the sine cosine algorithm in order to incorporate the exploitation and exploration capabilities of the differential evolution algorithm and the sine cosine algorithm, respectively. Further, the convergence speed of the proposed algorithm is accelerated through the judicious application of opposition-based learning. The OADG is solved by considering three separate mono-objectives (real power loss minimization, voltage deviation improvement and maximization of the voltage stability index) and a multi-objective framework combining the above three. OADG is also addressed for DGs operating at the unity power factor and lagging power factor while meeting the pragmatic operational requirements of the system. The suggested algorithm for multiple DG allocation is evaluated using a small test distribution network (33 bus) and two bigger test distribution networks (118 bus and 136 bus). The results are also compared to recent state-of-the-art metaheuristic techniques, demonstrating the superiority of the proposed method for solving OADG, particularly for large-scale distribution networks. Statistical analysis is also performed to showcase the genuineness and robustness of the obtained results. A post hoc analysis using Friedman–ANOVA and Wilcoxon signed-rank tests reveals that the results are of statistical significance.