Open AccessStochastic security and risk‐constrained scheduling for an autonomous microgrid with demand response and renewable energy resources
Author(s) -
VahedipourDahraie Mostafa,
RashidizadehKermani Homa,
Najafi Hamid Reza,
AnvariMoghaddam Amjad,
Guerrero Josep M.
Publication year - 2017
Publication title -
iet renewable power generation
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 1.005
H-Index - 76
ISSN - 1752-1424
DOI - 10.1049/iet-rpg.2017.0168
Subject(s) - demand response , microgrid , cvar , computer science , renewable energy , stochastic programming , load management , mathematical optimization , scheduling (production processes) , electricity , operations research , operator (biology) , environmental economics , expected shortfall , risk management , economics , engineering , finance , mathematics , biochemistry , chemistry , repressor , transcription factor , electrical engineering , gene
Increasing penetration of intermittent renewable energy sources and the development of advanced information give rise to questions on how responsive loads can be managed to optimise the use of resources and assets. In this context, demand response as a way for modifying the consumption pattern of customers can be effectively applied to balance the demand and supply in electricity networks. This study presents a novel stochastic model from a microgrid (MG) operator perspective for energy and reserve scheduling considering risk management strategy. It is assumed that the MG operator can procure energy from various sources, including local generating units and demand‐side resources to serve the customers. The operator sells electricity to customers under real‐time pricing scheme and the customers response to electricity prices by adjusting their loads to reduce consumption costs. The objective is to determine the optimal scheduling with considering risk aversion and system frequency security to maximise the expected profit of operator. To deal with various uncertainties, a risk‐constrained two‐stage stochastic programming model is proposed where the risk aversion of MG operator is modelled using conditional value at risk method. Extensive numerical results are shown to demonstrate the effectiveness of the proposed framework.