TY - GEN
T1 - A demand response framework to overcome network overloading in power distribution networks
AU - Jibran, Muhammad
AU - Nasir, Hasan Arshad
AU - Qureshi, Faran Ahmed
AU - Ali, Usman
AU - Jones, Colin
N1 - KAUST Repository Item: Exported on 2021-12-14
PY - 2020
Y1 - 2020
N2 - This paper considers the problem of network overloading in the power distribution networks of Pakistan, often resulting from the inability of the transmission system to transfer power from source to end-user during peak loads. This results in frequent power-outages and consumers at such times have to rely on alternative energy sources, e.g. Uninterrupted Power Supply (UPS) systems with batteries to meet their basic demand. In this paper, we propose a demand response framework to eliminate the problem of network overloading. The flexibility provided by the batteries at different houses connected to the same grid node is exploited by scheduling the flow of power from mains and batteries and altering the charging-discharging patterns of the batteries, thereby avoiding network overloading and any tripping of the grid node. This is achieved by casting the problem in an optimal control setting based on a prediction of power demand at a grid node and then solving it using a model predictive control strategy. We present a case study to demonstrate the application and efficacy of our proposed framework.
AB - This paper considers the problem of network overloading in the power distribution networks of Pakistan, often resulting from the inability of the transmission system to transfer power from source to end-user during peak loads. This results in frequent power-outages and consumers at such times have to rely on alternative energy sources, e.g. Uninterrupted Power Supply (UPS) systems with batteries to meet their basic demand. In this paper, we propose a demand response framework to eliminate the problem of network overloading. The flexibility provided by the batteries at different houses connected to the same grid node is exploited by scheduling the flow of power from mains and batteries and altering the charging-discharging patterns of the batteries, thereby avoiding network overloading and any tripping of the grid node. This is achieved by casting the problem in an optimal control setting based on a prediction of power demand at a grid node and then solving it using a model predictive control strategy. We present a case study to demonstrate the application and efficacy of our proposed framework.
UR - http://hdl.handle.net/10754/669336
UR - https://linkinghub.elsevier.com/retrieve/pii/S2405896320304316
UR - http://www.scopus.com/inward/record.url?scp=85105046292&partnerID=8YFLogxK
U2 - 10.1016/j.ifacol.2020.12.168
DO - 10.1016/j.ifacol.2020.12.168
M3 - Conference contribution
SP - 13339
EP - 13344
BT - IFAC-PapersOnLine
PB - Elsevier BV
ER -