Table 1 Summary of literature review.

¹

Accurate ATC estimation is essential to maintain system security and ensure optimal use of transmission capacity. Short-term load forecasting improves timing and deployment of DSTATCOM reactive support, helping maintain voltages during contingencies

²

Overestimation of ATC can lead to cascading outages (e.g., 2003 Northeast blackout), demonstrating the danger of optimistic ATC estimates

³

Underestimation of ATC causes unused capacity and financial inefficiencies in power markets

⁴

FACTS devices (e.g., SVC, TCSC) alleviate transmission bottlenecks, and improve system flexibility for better DER integration

⁵

A TCSC location-optimization method: 4.17% reduction in losses and 23.02% increase in loadability, directly improving ATC

⁶

Cooperative-strategy differential evolution achieves ~ 15% reduction in sum-of-squared errors and ~ 12% increase in estimation accuracy versus conventional methods

⁷

Jaya-enabled Flower Pollination Technique outperforms GWO and PSO for TCSC placement — faster convergence and improved placement effectiveness

⁸

Slime Mould Algorithm (SMA) for hybrid power-flow and FACTS controller placement reduces real power loss and generation cost while maintaining stability

⁹

Salp Swarm Optimization for FACTS placement enhances voltage stability and loadability, thereby increasing ATC and reducing congestion

¹⁰

DSTATCOM improves power quality and reduces transmission losses; optimal siting is critical to maximizing ATC benefits

¹¹

Immune Algorithm (IA) applied to DSTATCOM placement considers cost minimization and loss reduction

¹²

Brattle Group finding: FACTS technologies can potentially double transfer capability for parts of the U.S. Southwest Power Pool

¹³

U.S. DOE analysis: dynamic line ratings and power-flow controllers can substantially reduce renewable curtailment

¹⁴

(Related DOE studies) Dynamic ratings and controllers could reduce renewable energy curtailment by ~ 43%, aiding consumer benefits in high-renewable grids

¹⁵

In Kazakhstan, improving ATC supports larger renewable integration and reduces dependence on carbon-intensive generation in pursuit of carbon neutrality

¹⁶

Python-based energy system simulation enables scenario analysis (e.g., line expansion + renewables) for assessing ATC under different futures

¹⁷

3% annual load growth scenario sourced from Western Australian regional planning (represents moderate/slow growth contexts)

¹⁸

6% annual load growth scenario used to represent extreme but plausible developments (e.g., mining or major industrial start-ups)

¹⁹

3% (CEA India) and 6% (NREL / developing-economy peaks) scenarios used to ensure robustness across policy-aligned and high-demand projections

²⁰

Whale Optimization Algorithm (WOA) — suitable for complex power-system optimization in deregulated environments (inspired by humpback whale hunting)

²¹

Mathematical ATC-forecasting expressions developed to relate load increments to ATC while considering voltage stability objectives

²²

Additional formulations for ATC forecasting that incorporate objectives such as loss minimization and ATC maximization

²³

N-1 contingency analysis: outage of IEEE-14 line 2–4 produced > 40% ATC drop, while peripheral line outages gave < 5% change; behavior consistent with PTDF/LODF sensitivities

²⁴

Novelty claim: application of WOA to optimal DSTATCOM placement for ATC enhancement in deregulated systems (includes load growth scenarios)

²⁵

Suggested future work: hybrid WOA variants (e.g., stochastic sinusoidal inertia weights, modified WOA) to improve convergence and accuracy for placement problems

²⁶

IEEE 14-bus test system description: 14 buses, 5 generators, 4 transformers, 16 transmission lines, 11 load points — used as primary testbed

²⁷

IEEE 14-bus system data summary: total losses ≈ 13.39 MW & 30.12 MVAR; base 100 MVA; load = 259.3 MW & 73.7 MVAR; voltage levels and the most dissipative line (Bus 1–2) noted