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Network‐constrained rail transportation and power system scheduling with mobile battery energy storage under a multi‐objective two‐stage stochastic programming
Author(s) -
Mirzaei Mohammad Amin,
Hemmati Mohammad,
Zare Kazem,
MohammadiIvatloo Behnam,
Abapour Mehdi,
Marzband Mousa,
Razzaghi Reza,
AnvariMoghaddam Amjad
Publication year - 2021
Publication title -
international journal of energy research
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 0.808
H-Index - 95
eISSN - 1099-114X
pISSN - 0363-907X
DOI - 10.1002/er.6981
Subject(s) - renewable energy , energy storage , stochastic programming , integer programming , computer science , automotive engineering , battery (electricity) , scheduling (production processes) , linear programming , wind power , engineering , mathematical optimization , power (physics) , electrical engineering , operations management , physics , mathematics , algorithm , quantum mechanics
Summary By increasing environmental pollution and the energy crisis, the development of renewable energy sources (RESs) has become an essential option to ensure a sustainable energy supply. However, the inherent uncertainty of RESs poses significant technical challenges for independent system operators (ISOs). Transmission line congestion has become one of the significant challenges for ISOs to use the maximum power of RESs. The mobile battery‐based energy storage systems can provide a promising solution for the transportation of the generated energy from RESs to load centers to mitigate the effects of line congestion on the power network operation. Hence, this article evaluates the impact of battery‐based energy storage transport by a train called BESTrain in a unit commitment model from the economic, environmental, and technical aspects under a multi‐objective mixed‐integer linear programming framework. The uncertainties associated with wind power and electric demand are also handled through a two‐stage stochastic technique. The main aim of the introduced model is to minimize the carbon emission and operational cost simultaneously by determining the hourly location and optimal charge/discharge scheme of the BESTrain, and optimal scheduling of power plants. The numerical results exhibit the reduction of operation cost and carbon emission by 6.8% and 19.3%, respectively, in the presence of the BESTrain.