Stochastic energy management in a renewable energy-based microgrid considering demand response program

•Applying tidal stream turbine (TST) beside wind turbine (WT) and photovoltaic (PV).•Considering uncertainties in the TST, WT, PV, electricity price, and demand.•The demand response program as demand-side management is applied in the model.•Application of augmented e-constraint method to solve the p...

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Vydáno v:International journal of electrical power & energy systems Ročník 129; s. 106791
Hlavní autoři: Hajiamoosha, Pouria, Rastgou, Abdollah, Bahramara, Salah, Bagher Sadati, S. Muhammad
Médium: Journal Article
Jazyk:angličtina
Vydáno: Elsevier Ltd 01.07.2021
Témata:
Augmented [formula omitted]-constraint method
Multi-objective programming
Pollution emission
Renewable energy sources
Tidal energy
Renewable energy sources
Pollution emission
Multi-objective programming
Tidal energy
Augmented ε-constraint method
ISSN:0142-0615, 1879-3517
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Shrnutí:•Applying tidal stream turbine (TST) beside wind turbine (WT) and photovoltaic (PV).•Considering uncertainties in the TST, WT, PV, electricity price, and demand.•The demand response program as demand-side management is applied in the model.•Application of augmented e-constraint method to solve the problem.•The interactive fuzzy decision-making method is employed to choose the best answer. Climate changes increase concerns about global warming caused by greenhouse gases and have also increased the focus and implementation of renewable energy sources (RESs) planning. One of the important RESs is tidal energy or tidal power, which is a form of hydropower that converts the energy obtained from tides into the electrical power. Although tidal power is still not widely used, this energy resource has the potential for the future electricity generation. This paper addresses the stochastic energy management in a microgrid considering RESs such as solar, wind and tidal sources in the presence of the demand response program and storage devices. The uncertainty of the RESs, demand, and electricity price is handled by Monte Carlo simulation (MCS). The model is a linear multi-objective optimization which the first objective aims to reduce the cost and the second aims to reduce the emission. Augmented ε-constraint approach is applied to solve the problem in the CPLEX/GAMS software environment. The interactive fuzzy decision-making is applied to choose the best answer among the Pareto answers according to the planner criteria.
ISSN:0142-0615
1879-3517
DOI:10.1016/j.ijepes.2021.106791