رزومه


EN
مصطفی واحدی پور دهرائی

مصطفی واحدی پور دهرائی

دانشیار

عضو هیئت علمی تمام وقت

دانشکده: دانشکده فنی فردوس

گروه: قدرت

مقطع تحصیلی: دکتری

سال تولد: ۱۳۶۱

رزومه
EN
مصطفی واحدی پور دهرائی

دانشیار مصطفی واحدی پور دهرائی

عضو هیئت علمی تمام وقت
دانشکده: دانشکده فنی فردوس - گروه: قدرت مقطع تحصیلی: دکتری | سال تولد: ۱۳۶۱ |

A Stochastic Bi-Level Scheduling Approach for the Participation of EV Aggregators in Competitive Electricity Markets

نویسندگانMostafa Vahedipour-Dahraie
نشریهApplied Sciences
شماره صفحات1-26
شماره سریال7
شماره مجلد10
نوع مقالهFull Paper
تاریخ انتشار2017
نوع نشریهچاپی
کشور محل چاپسوئیس
نمایه نشریهISI،JCR،Scopus
کلید واژه هاbi, level stochastic programming; balancing market; conditional value at risk (CVaR); day, ahead (DA) market; electric vehicle (EV) aggregator

چکیده مقاله

This paper proposes a stochastic bi-level decision-making model for an electric vehicle (EV) aggregator in a competitive environment. In this approach, the EV aggregator decides to participate in day-ahead (DA) and balancing markets, and provides energy price offers to the EV owners in order to maximize its expected profit. Moreover, from the EV owners’ viewpoint, energy procurement cost of their EVs should be minimized in an uncertain environment. In this study, the sources of uncertainty—including the EVs demand, DA and balancing prices and selling prices offered by rival aggregators—are modeled via stochastic programming. Therefore, a two-level problem is formulated here, in which the aggregator makes decisions in the upper level and the EV clients purchase energy to charge their EVs in the lower level. Then the obtained nonlinear bi-level framework is transformed into a single-level model using Karush–Kuhn–Tucker (KKT) optimality conditions. Strong duality is also applied to the problem to linearize the bilinear products. To deal with the unwilling effects of uncertain resources, a risk measurement is also applied in the proposed formulation. The performance of the proposed framework is assessed in a realistic case study and the results show that the proposed model would be effective for an EV aggregator decision-making problem in a competitive environment.