This paper proposes a day-ahead scheduling framework to analyze and optimize the impact of the coordinated active and reactive power management of wind turbines (WTs) and battery energy storage systems (BESSs) on the energy losses and CO2 emissions of AC microgrids (MGs). Within this framework, the BESS plays a central role by absorbing surplus renewable generation, mitigating curtailment, supporting voltage regulation, and ensuring a stable and reliable dispatch over a 24-hour horizon. A population-based genetic algorithm (PGA) is proposed as the main solution methodology, while particle swarm optimization (PSO) and the multiverse optimizer (MVO) are employed as benchmark methods for comparison. To ensure a fair assessment, all optimization techniques are implemented under the same parallel processing scheme, using the same decision-variable encoding, feasibility correction procedure, and hourly sequential AC power-flow method. The objective is to minimize network energy losses and CO2 emissions under both grid-connected and islanded operating modes. The proposed methodology is validated on 33-node and 69-node MGs, both evaluated under variable demand and wind-generation scenarios to capture the uncertainty and temporal variability associated with renewable production and load behavior. In addition, the BESS model includes charging/discharging efficiency, self-discharge effects, and battery lifetime assessment under the proposed operating scenarios, allowing a more realistic representation of storage performance. The optimization methods are evaluated over 100 independent runs using the best solution, average solution, standard deviation, and computational time as performance indicators. The results show that the proposed PGA provides the most robust and repeatable performance, while also highlighting the operational contribution of the BESS, reducing renewable curtailment, and guaranteeing compliance with all technical constraints, under deterministic baseline operation and under uncertain time-varying operating conditions in both test systems.
D. Sanín-Villa, Héctor Pinto Vega, Carlos R. Baier et al.· PLoS ONE· 0 citations
The large-scale integration of photovoltaic generation into distribution grids has introduced significant operational challenges, including voltage excursions, reverse power flows, and increased variability. Battery energy storage systems (BESSs) offer a versatile solution by providing coordinated active- and reactive-power support. However, their scheduling in active distribution networks is challenging because of the non-convex alternating-current (AC) power-flow equations, the nondifferentiability of battery-degradation modeling, and uncertainty in renewable generation and demand. This paper proposes a two-stage methodology for the day-ahead operation of BESSs in ADNs. In the first stage, parallel particle swarm optimization (PPSO) determines the hourly active- and reactive-power schedules of the BESS units. In the second stage, a matrix-based multi-period AC power flow based on successive approximations evaluates the schedules and verifies voltage, thermal, converter-capability, and state-of-charge (SoC) constraints. A rainflow-counting degradation model is incorporated into the objective function to account for cycling and calendar aging costs. The methodology is assessed through ablation analyses comparing active-power-only and coordinated P–Q dispatches, degradation-unaware and degradation-aware scheduling, and serial and parallel PSO implementations. It is validated on modified 33-, 69-, and 136-node systems under deterministic and uncertainty-based operating conditions, including 100 demand and PV-generation scenarios. PPSO is compared with parallel versions of the adaptive Jaya algorithm (AJAYA), genetic algorithm (GA), multi-verse optimizer (MVO), salp swarm algorithm (SSA), grey wolf optimizer (GWO), and vortex search algorithm (VSA), using operating-cost reduction, computational time, solution variability, feasibility indicators, BESS lifetime, and weekly cost analysis. Additionally, exact one-sided Wilcoxon signed-rank tests with Holm adjustment are used to assess the statistical significance of the economic differences between PPSO and the benchmark methods. Results show that PPSO provides the lowest or most competitive operating costs and the shortest computational time in the evaluated cases, while all network and storage constraints remain satisfied.
L. Grisales-Noreña, F. Machuca‐Martínez, O. D. Montoya· The Scientist· 0 citations
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