TY - JOUR
T1 - Optimal Demand Response Scheduling with Real Time Thermal Ratings of Overhead Lines for Improved Network Reliability
AU - Kopsidas, Konstantinos
AU - Kapetanaki, Alexandra
AU - Levi, Victor
N1 - Konstantinos Kopsidas (M’06) received a BEng in Electrical Engineering from the institutue of Piraeus, Athens, in Greece in 2002, a Class I BEng in Electrical and Electronic Engineering from The University of Manchester Institute of Science and Technology (UMIST), U.K., in 2004, an MSc (with dinstiction) in 2005 and a PhD in 2009 in Electrical Power Engineering from The University of Manchester. Since 2011, he is a Lecturer with the School of Electrical and Electronic Engineering in The University of Manchester with main research interests on plant modelling and reliability and adequacy.
Alexandra Kapetanaki (S’12) received a MEng degree in Electrical Engineering and Computer Science from the National Technical University of Athens (NTUA), Greece, in 2011. She is currently pursuing the Ph.D. degree from the Electrical Energy and Power Systems Group.
Her current research interest include optimization of power system operation, stochastic modelling, risk management and electricity markets.
Victor Levi (S’89-M’91-SM’13) received the M.Sc. and Ph.D. degrees in electrical engineering from the University of Belgrade, Belgrade, Yugoslavia, in 1986 and 1991, respectively.
From 1982 to 2001, he was with University of Novi Sad, Novi Sad, Yugoslavia, where he became a Full Professor in 2001. He was with the University of Manchester, Manchester, U.K. from 2001 to 2003, and then with United Utilities and Electricity North West, from 2003 to 2013. In 2013, he rejoined the University of Manchester.
PY - 2016/4
Y1 - 2016/4
N2 - Abstract— This paper proposes a probabilistic framework for optimal demand response scheduling in the day-ahead planning of transmission networks. Optimal load reduction plans are determined from network security requirements, physical characteristics of various customer types and by recognising two types of reductions, voluntary and involuntary. Ranking of both load reduction categories is based on their values and expected outage durations, whilst sizing takes into account the inherent probabilistic components. The optimal schedule of load recovery is then found by optimizing the customers’ position in the joint energy and reserve market, whilst considering several operational and demand response constraints. The developed methodology is incorporated in the sequential Monte Carlo simulation procedure and tested on several IEEE networks. Here, the overhead lines are modelled with the aid of either seasonal or real-time thermal ratings. Wind generating units are also connected to the network in order to model wind uncertainty. The results show that the proposed demand response scheduling improves both reliability and economic indices, particularly when emergency energy prices drive the load recovery.
AB - Abstract— This paper proposes a probabilistic framework for optimal demand response scheduling in the day-ahead planning of transmission networks. Optimal load reduction plans are determined from network security requirements, physical characteristics of various customer types and by recognising two types of reductions, voluntary and involuntary. Ranking of both load reduction categories is based on their values and expected outage durations, whilst sizing takes into account the inherent probabilistic components. The optimal schedule of load recovery is then found by optimizing the customers’ position in the joint energy and reserve market, whilst considering several operational and demand response constraints. The developed methodology is incorporated in the sequential Monte Carlo simulation procedure and tested on several IEEE networks. Here, the overhead lines are modelled with the aid of either seasonal or real-time thermal ratings. Wind generating units are also connected to the network in order to model wind uncertainty. The results show that the proposed demand response scheduling improves both reliability and economic indices, particularly when emergency energy prices drive the load recovery.
KW - Optimal demand response, reliability, sequential Monte-Carlo, real time thermal rating, risk
U2 - 10.1109/TSG.2016.2542922
DO - 10.1109/TSG.2016.2542922
M3 - Article
SN - 1949-3053
SP - 1
EP - 13
JO - I E E E Transactions on Smart Grid
JF - I E E E Transactions on Smart Grid
IS - 99
M1 - DOI 10.1109/TSG.2016.2542922
ER -
