Planning battery energy storage systems (BESS) in transmission networks requires accounting for the effects of component failures on supply continuity. This paper proposes a reliability-oriented methodology based on a Mixed Gray Wolf Optimizer (MGWO) to determine BESS locations, rated active power capacities, and technology selection between lithium-ion (Li-ion) and vanadium redox flow (VRF) batteries. The framework incorporates stochastic reliability models for transmission lines, transformers, busbars, and BESS units. The objective is to minimize the equally weighted average of annual energy not supplied (ENS) across single-generator-outage scenarios, subject to technical constraints and an investment budget. BESS sizing refers to rated power in MW; energy capacity and chronological state of charge are not explicitly modeled. The methodology is evaluated using the IEEE 39-bus New England test system with representative hourly demand profiles. Under a maximum investment budget of USD 300 million, the selected solution comprises 338.66 MW of installed BESS capacity, including 280.78 MW of Li-ion storage and 57.88 MW of VRF storage, at a total investment of USD 299.87 million. Relative to the reference case, SAIDI and SAIFI decrease by 13.76% and 13.11%, respectively. During optimization, the fitness value decreases from approximately 0.878 to 0.828 p.u., representing an improvement of about 6.5% relative to the initial solution. Ten independent runs yield final fitness values between 0.8213 and 0.8300 p.u. These results demonstrate the framework’s ability to identify feasible BESS configurations that improve reliability under the adopted modeling assumptions.
This paper proposes an Adaptive Dynamic Binary Whale Optimization Algorithm (AD-BWOA) for solving the Distribution Network Reconfiguration (DNR) problem. The proposed method combines a nonlinear adaptation of the convergence parameter, a hybrid deterministic–stochastic mechanism for position-update selection, a binary transfer strategy, and a stagnation-based stopping criterion to improve the balance between exploration and exploitation and reduce unnecessary search iterations. The algorithm is implemented in Python and integrated with DIgSILENT PowerFactory, which performs the load-flow calculations and verifies the operational constraints of each candidate configuration. The performance of AD-BWOA is evaluated on 69-, 94-, and 135-bus distribution systems through 100 independent runs. The results show that the proposed method consistently reaches the best or reference solutions reported for the evaluated systems, with high repeatability in the 69- and 94-bus cases and competitive performance in the 135-bus system. In addition, the adaptive mechanisms contribute to reducing the number of iterations required for convergence under the adopted stopping criteria. The results indicate that AD-BWOA is a promising and reliable approach for discrete DNR problems, particularly when search-space reduction and adaptive control mechanisms are combined.
Low-frequency oscillation modes (LFOMs) with insufficient damping can compromise power system stability following disturbances. The increasing interconnection of large-scale power systems has resulted in a greater number of poorly damped low-frequency oscillation modes, thereby motivating the development of advanced damping control techniques, such as Wide-Area Damping Controllers (WADCs). Since WADCs rely on remote measurements, communication failures and cyberattacks can impair their operation and adversely affect system stability. This study proposed an optimization framework based on Linear Quadratic Regulator (LQR) theory for the robust design of WADCs, aiming to enhance system resilience against permanent communication failures. The WADC parameters were considered as optimization variables, and the objective function was formulated to maximize the damping ratios of the closed-loop system under both normal WADC operation and permanent communication failure conditions. Unlike conventional LQR-based approaches, the proposed methodology also considered the WADC poles as optimization variables, thereby providing additional degrees of freedom for controller design. Three metaheuristic optimization algorithms, Glider Snake Optimizer, Particle Swarm Optimization, and Sine Cosine Algorithm, were incorporated into the proposed framework, and their performance was comparatively evaluated. The IEEE 68-bus system was adopted as the test system to assess the effectiveness and robustness of the proposed methodology.
This paper introduces a computationally efficient optimization framework for the day-ahead scheduling of battery energy storage systems (BESS) in active distribution networks (ADNs), in an attempt to address the critical challenge of reconciling economic objectives with battery lifespan preservation and AC network feasibility. Unlike existing approaches that separately handle active and reactive power dispatch or rely on linearized power flow approximations, the proposed methodology simultaneously optimizes both BESS active and reactive power setpoints using a Chu and Beasley Genetic Algorithm (CBGA) while explicitly quantifying degradation costs through a rainflow counting-based aging model that accounts for the depth of discharge, the average state of charge, and temperature effects. Each candidate dispatch plan is evaluated via a multi-period AC power flow solver based on the matrix successive approximations (AC-MSA) method, ensuring the rigorous enforcement of voltage and current constraints. The framework is validated on modified 33- and 69-bus ADNs under both deterministic conditions and a scenario-based robustness evaluation comprising 100 demand and photovoltaic generation profiles representative of Colombian grid conditions. The optimization itself is deterministic; the scenarios are used exclusively for the ex post evaluation of the resulting schedule. The framework is benchmarked against five metaheuristics: the adaptive JAYA algorithm (AJAYA), the parallel genetic algorithm (PGA), the multiverse optimizer (MVO), particle swarm optimization (PSO), and differential evolution (DE). Numerical results show that the CBGA achieved the lowest observed objective values among the compared methods under the tested configurations, with low dispersion across independent runs (standard deviations below 0.06%) and processing times under 0.05 h for a 24 h horizon. The margin over the closest competitors (AJAYA and MVO) is below 0.1% in the 33-bus deterministic case, so the comparison should be interpreted as an empirical ranking under a common experimental protocol rather than as a statistically established superiority. The proposed approach offers a practical decision-support tool that allows distribution system operators and BESS owners to strike an optimal balance between short-term operational revenues and long-term asset service life, thereby enhancing the economic viability of BESS investments in increasingly renewable-rich distribution networks.
Gavdos is assessed as a non-interconnected island without mainland electricity exchange over the adopted planning horizon. A configuration with 340 kWp of photovoltaics, an 800 kW wind turbine, and surplus-fed electrolysis is evaluated for electricity supply and ferry hydrogen. Twelve public HEDNO monthly day-ahead files were concatenated into an 8760 h rolling-year demand series. RETScreen-based F-chart solar calculations and a Weibull wind distribution (k = 2) provided twelve representative monthly 24 h renewable profiles, mapped by month and hour to the dispatch chronology. Renewable generation first serves demand; deficits are assigned to conventional generation and surplus to electrolysis. Annual demand is 662.630 MWh, of which renewables supply 650.633 MWh directly (98.19%); the residual deficit is 11.997 MWh across 419 h. The 302.6 kW baseline electrolyzer produces 34.488 t H2 year−1 gross versus a ferry demand of 33.890 t H2 year−1. Although daily production is lower than demand in 130 days, accumulated hydrogen keeps the gross inventory non-negative. The maximum seasonal drawdown is 947.4 kg, giving a usable hydrogen-mass requirement of 1133.1 kg including a two-day reserve. The results demonstrate gross-production compatibility under the adopted assumptions; net ferry-fuel adequacy requires detailed assessment of component operation, auxiliary loads, storage, delivery, and vessel duty.
