*1.2. Main Contributions*

As can be observed from the presented literature review, a vast family of methodologies has been created, some of them based on heuristic techniques such as GA and PSO, another group inspired by DP, and most of them based on MILP combined with Benders decomposition.

In a general sense, the optimization technique to be selected strongly depends on the characteristics and assumptions of the ESS model, as well as the context (isolated or grid-connected system) and the information available.

To take advantage of the vast family of BESS models, a recently developed version of binary PSO (BPSO), which incorporates a time-varying mirrored S-shaped (TVMS) transfer function, has been adopted in this paper. Consequently, hourly behavior of charging–discharging efficiency as well as the influence of charge controller on battery operation can be effectively incorporated. Additionally, the influence of wind-speed daily profile on battery schedule and GHG emissions is deeply analyzed. The impact of battery operation on the emissions of total hydrocarbons (THC), carbon monoxide (CO), oxides of nitrogen (NOX), CO2, and particulate matter (PM) is investigated.

The remainder of the paper is organized as follows. Section 2 describes the mathematical models of the system configuration under study. Section 3 explains the formulation of the optimization problem and its solution by TVMS-BPSO. Then, problem formulation is tested in Section 4 through a sensitivity analysis based on GA. As TVMS-BPSO is a novel version of BPSO, its performance is compared with GA in Section 5. Finally, conclusions and main findings are discussed in Section 6.
