Optimal design of stand-alone microgrid resources based on proposed Monte-Carlo simulation

Jahangir, Hamidreza; Ahmadian, Ali; Golkar, Masoud Aliakbar

doi:10.1109/isgt-asia.2015.7387137

Cited by 14 publications

(12 citation statements)

References 11 publications

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“…It is important to specify the initial amount of PEVs' SOCs. Monte-Carlo approach is conducted to model the PEVs' travel behaviors under uncertainties [22]. Monte-Carlo is applied to produce different scenarios based on the input variables such as arrival and departure time, initial SOCs derived from travel distance, and the type of vehicle in order to get battery characteristics.…”

Section: Modeling Load Demand Price and Wind Speed Uncertaintiesmentioning

confidence: 99%

Optimal Charging of Plug-In Electric Vehicle: Considering Travel Behavior Uncertainties and Battery Degradation

et al. 2019

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The negative environmental impacts of using fossil fuel-powered vehicles underlined the need for inventing an alternative eco-friendly transportation fleet. Plug-in electrical vehicles (PEVs) are introduced to cut the continuing increase in energy use and carbon emission of the urban mobility. However, the increased demand for mobility, and therefore energy, can create constraints on the power network which can reduce the benefits of electrification as a certain and reliable source. Thus, the rise in the use of electric vehicles needs electric grids to be able to feed the increased energy demand while the current infrastructure supports it. In this paper, we introduce a methodological framework for scheduling smart PEVs charging by considering the uncertainties and battery degradation. This framework includes an economic model for charging and discharging of PEVs which has been implemented in a 21-node sample distribution network with a wind turbine as a distributed generation (DG) unit. Our proposed approach indicates that the optimal charging of the PEVs has a high impact on the distribution network operation, particularly under the high market penetration of PEVs. Thus, the smart grid to vehicle (G2V) charging mode is a potential solution to maximize the PEV’s owner profit, while considering the battery degradation cost of the PEVs. The simulation result indicates that smart charging effectuation is economical.

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Section: Modeling Load Demand Price and Wind Speed Uncertaintiesmentioning

confidence: 99%

Optimal Charging of Plug-In Electric Vehicle: Considering Travel Behavior Uncertainties and Battery Degradation

et al. 2019

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“…In recent years, MDP appears to be a promising mathematical formulation of the optimizing microgrid operation problem [63,64]. A number of studies clearly demonstrate the effectiveness of energy microgrids management using MDP-based methods: dynamic programming [65,66], deep RL [55,56,58,67], and Monte Carlo models [57,68].…”

Section: Microgrid Mdp-based Environment Simulatormentioning

confidence: 99%

Optimal Operation Control of PV-Biomass Gasifier-Diesel-Hybrid Systems Using Reinforcement Learning Techniques

et al. 2020

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The importance of efficient utilization of biomass as renewable energy in terms of global warming and resource shortages are well known and documented. Biomass gasification is a promising power technology especially for decentralized energy systems. Decisive progress has been made in the gasification technologies development during the last decade. This paper deals with the control and optimization problems for an isolated microgrid combining the renewable energy sources (solar energy and biomass gasification) with a diesel power plant. The control problem of an isolated microgrid is formulated as a Markov decision process and we studied how reinforcement learning can be employed to address this problem to minimize the total system cost. The most economic microgrid configuration was found, and it uses biomass gasification units with an internal combustion engine operating both in single-fuel mode (producer gas) and in dual-fuel mode (diesel fuel and producer gas).

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“…Probability density functions (PDF) were used to characterize the stochastic behavior of wind speed. The wind speed over a predefined time period was estimated using a Weibull PDF [55].…”

Section: Wind Turbinementioning

confidence: 99%

“…Silvente et al [50] used the RHC approach to analyze uncertainty in both energy generation and demand. Monte Carlo simulation (MCS) has been widely used to evaluate the reliability of a microgrid by generating data from fixed probability distributions of stochastic variables, such as wind speed, solar irradiance, customer demands, and others [51][52][53][54][55].…”

mentioning

confidence: 99%

Optimization under uncertainty of a biomass-integrated renewable energy microgrid with energy storage

et al. 2018

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Abbreviations MG microgrid CHP combined heat and power BCHP biomass-based CHP GS biomass gasifier ICE internal combustion engine WT wind turbine PV photovoltaic ES energy storage BT battery PGS producer gas storage TES thermal energy storage HOB heat-only boiler MCS Monte Carlo simulation COE cost of energy PDF probability density function Symbols E electricity demand (kWh) o reference facility i index of installed units that contribute to capital cost j index of installed units that contribute to O&M and fuel cost chr charging dis discharging min minimum charging and discharging rate (kWh/h) max maximum charging and discharging rate (kWh/h)

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Optimal design of stand-alone microgrid resources based on proposed Monte-Carlo simulation

Cited by 14 publications

References 11 publications

Optimal Charging of Plug-In Electric Vehicle: Considering Travel Behavior Uncertainties and Battery Degradation

Optimal Charging of Plug-In Electric Vehicle: Considering Travel Behavior Uncertainties and Battery Degradation

Optimal Operation Control of PV-Biomass Gasifier-Diesel-Hybrid Systems Using Reinforcement Learning Techniques

Optimization under uncertainty of a biomass-integrated renewable energy microgrid with energy storage

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