The increasing penetration of distributed and renewable generation has driven modern power systems closer to their technical limits, making voltage instability a key threat to grid reliability and reducing the operational margin for preventive control and generation redispatch. Traditional steady-state security tools, such as the Static Security Region (SSR), map feasible operating points under steady-state constraints but do not quantify proximity to voltage collapse. This paper proposes an enhanced SSR that integrates a fast, analytically derived Voltage Stability Index (VSI) based on a Thevenin equivalent and load-impedance characteristics, embedding stability-margin information directly into SSR nomograms. The resulting visualization jointly depicts feasibility and voltage-stability robustness, enabling screening and ranking of dispatch points beyond voltage-magnitude compliance. Case studies on the IEEE 9-bus and New England 39-bus systems show that the VSI-based SSR highlights dispatch-dependent low-margin regions that can occur before conventional thermal or voltage limits are reached. Overall, the method provides an efficient, interpretable tool for steady-state security assessment and preventive decision support.

Large battery energy storage systems (BESSs) are often sized assuming that their operation does not affect electricity prices. For utility-scale assets this assumption fails: their bids change which generators are committed and, therefore, the prices from which the battery earns its revenue. This paper proposes a bilevel framework that jointly determines the power rating, energy capacity and hourly bids of a price-maker BESS that maximize the project net present value (NPV). The upper level is solved with particle swarm optimization (PSO), while the lower level reproduces the day-ahead market clearing as a security-constrained unit commitment (SCUC) that retains generator commitment decisions, startup costs and network constraints, instead of the convex approximations required by existing bilevel sizing models. The battery participates in energy, Reg-Up and Reg-Down markets, and its degradation is evaluated with a non-linear Rainflow-based model. Simulations on the IEEE 3-bus and 57-bus systems with CAISO-based profiles yield optimal energy-to-power ratios of 1.9–2.45 h, below the common 4-h design, because reserve services provide most of the revenue. A sensitivity analysis on renewable penetration reveals a non-monotonic relationship between renewable share and the optimal power rating, driven by changes in reserve scarcity.

Inverter-interfaced distributed generation (DG) can provide voltage support in unbalanced distribution feeders; however, sequential, phase-selective siting and sizing under local interconnection constraints have received limited attention. This study proposes a constructive Volt/VAR planning method that allocates DG according to the apparent demand and voltage-deficient phases at each candidate bus. A descending reactive power feasibility test rejects solutions that cause non-convergence, overvoltage, or deterioration of the minimum system voltage. The method was implemented through automated unbalanced three-phase power-flow simulations and tested on the IEEE 13-node feeder and the real 3849-bus CHIRIPA medium-voltage network in Honduras, using active power allocation factors of 0.7 and 0.9. The selected configurations were evaluated over 24 h profiles for representative sunny and rainy days, annualized, and assessed economically over a 25-year horizon. Annual energy losses decreased by 25.8% and 26.4% in the IEEE feeder, although local capacity constraints prevented all phase voltages from reaching the 0.95–1.05 p.u. range. In CHIRIPA, all medium-voltage bus-phase voltages satisfied this range at maximum demand, although this improvement persisted only during solar hours; annual losses fell by 15.1% and 18.7%. The economic feasibility threshold in CHIRIPA depended primarily on installed capacity (approximately 6.5–6.6 MW), rather than unit count. These results provide a reproducible framework for prioritizing phase-specific DG investments under technical and economic constraints.