Open AccessStochastic scenario‐based generation scheduling in industrial microgrids
Author(s) -
Derakhshandeh Sayed Yaser,
Hamedani Golshan Mohamad Esmail,
Ghazizadeh Mohammad Sadegh,
Sherkat Masoum Mohammad Ali
Publication year - 2017
Publication title -
international transactions on electrical energy systems
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 0.428
H-Index - 42
ISSN - 2050-7038
DOI - 10.1002/etep.2404
Subject(s) - renewable energy , scheduling (production processes) , photovoltaic system , computer science , demand response , reliability engineering , stochastic optimization , automotive engineering , electricity , mathematical optimization , engineering , electrical engineering , operations management , mathematics
Summary Industrial parks are forming industrial microgrids (IMGs) with factories, distributed energy resources, electric loads, heat loads, and combined heat and power systems as well as renewable distributed energy resources and plug‐in electric vehicles (PEVs). Generation scheduling (GS) in IMGs is affected by the stochastic behavior of electric and heat loads due to outages of production processes or production lines and the uncertainties in solar irradiance and combined heat and power systems. This paper presents a stochastic scenario‐based GS framework to consider uncertainties in an IMG coordinated with PEV charging. Although the scenario‐based methods are usually very time consuming, this paper shows that their applications in IMGs will not significantly increase the calculation time. The proposed formulation guaranties that occurrence of each condition of uncertainty will not affect the PEV activities. An IMG with 12 factories, photovoltaic generations, and 6 types of electric vehicles with different battery sizes is considered and simulated. The main contributions are (1) a new stochastic GS problem formulation to minimize the cost of IMGs while fully charging all PEVs within their requested periods considering the network security, factories, and PEV constraints; (2) changing the nonlinear constraints to linear forms suitable for scenario‐based optimization; and (3) considering the stochastic behavior of electric loads without requiring any data about their internal process in each factory.