Holistic Approach for Microgrid Planning for E-Mobility Infrastructure Under Consideration of Long Term Uncertainty

Tayyab, Muhammad; Hauer, Ines; Helm, Sebastian

doi:10.2139/ssrn.4203559

Cited by 1 publication

(1 citation statement)

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“…[20] focuses on microgrid (MG) energy management with EVs and DG sources, aiming to improve MG reliability indices while considering uncertainties associated with DGs. [21] presents an optimal EV charge and discharge strategy considering uncertainties in EV and DG operations, with the objective function aimed at cost reduction and pollution mitigation based on probabilistic behaviors of EV and wind turbine charging and discharging. [22] introduces a scheduling method for optimizing the utilization of MG infrastructure during nonpeak times for EV charging, considering environmental and economic factors to effectively integrate a significant number of electric vehicles (EVs) into the network accounting for their uncertainties.…”

Section: Introductionmentioning

confidence: 99%

Probabilistic Energy Management of DGs and Electric Vehicle Parking Lots in a Smart Grid considering Demand Response

Ghadikolaei,

Ghafouri,

Sedighi

2024

International Journal of Energy Research

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In this paper, a novel model of an energy management system (EMS) for a microgrid (MG) under uncertain conditions is proposed. The MG consists of renewable photovoltaic and wind sources along with electric vehicle parking lots. Hence, the model incorporates the uncertainties of renewable DGs, parking lots, and also load. In this study, the MG operation cost and voltage stability index are considered objective functions. A novel combined algorithm (hMOPSO-HS) is proposed for microgrid energy management. The hMOPSO-HS algorithm is a combination of the mutant multiobjective particle swarm optimization (MOPSO) algorithm and the harmony search (HS) algorithm. The simulations are performed in two parts, with and without considering the uncertainty. The comparative analysis involves evaluating the optimization outcomes achieved by the hMOPSO-HS algorithm in contrast to various other metaheuristic algorithms for multiobjective optimization. The simulation findings validate the superior efficacy of the hMOPSO-HS algorithm compared to other approaches. Also, the simulation results showed that in the conditions of uncertainty, the operating cost is 6.1% higher and the microgrid stability index is 6.8% lower. Also, considering the uncertainty has caused the penalty for energy not supplied (ENS) and demand response program (DRP) costs to increase by 3% and 4%, respectively.

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Section: Introductionmentioning

confidence: 99%