TY - GEN
T1 - Network reinforcement for grid resiliency under extreme events
AU - Qiu, Jing
AU - Reedman, Luke J.
AU - Dong, Zhao Yang
AU - Meng, Ke
AU - Tian, Huiqiao
AU - Zhao, Junhua
N1 - Publication details (e.g. title, author(s), publication statuses and dates) are captured on an “AS IS” and “AS AVAILABLE” basis at the time of record harvesting from the data source. Suggestions for further amendments or supplementary information can be sent to [email protected].
PY - 2018/1/29
Y1 - 2018/1/29
N2 - To enhance the energy grid resiliency under extreme events (EEs), this paper presents a multi-objective transmission expansion planning (TEP) framework. Rather than using the conventional deterministic reliability criterion, a risk component is used to capture the stochastic nature of power systems. The formulation of risk value after risk aversion is explicitly given, and it aims to provide network planners with the flexibility to select a more resilient plan according to their individual risk preferences. In addition, a relatively new multi-objective evolutionary algorithm called the MOEA/D is introduced and employed to find Pareto optimal solutions, and tradeoffs between overall cost and unreliability risk are provided. The proposed approach is numerically verified on the IEEE Garver's 6-bus system. Case study results demonstrate that the proposed approach can effectively improve network resiliency under EEs. © 2017 IEEE.
AB - To enhance the energy grid resiliency under extreme events (EEs), this paper presents a multi-objective transmission expansion planning (TEP) framework. Rather than using the conventional deterministic reliability criterion, a risk component is used to capture the stochastic nature of power systems. The formulation of risk value after risk aversion is explicitly given, and it aims to provide network planners with the flexibility to select a more resilient plan according to their individual risk preferences. In addition, a relatively new multi-objective evolutionary algorithm called the MOEA/D is introduced and employed to find Pareto optimal solutions, and tradeoffs between overall cost and unreliability risk are provided. The proposed approach is numerically verified on the IEEE Garver's 6-bus system. Case study results demonstrate that the proposed approach can effectively improve network resiliency under EEs. © 2017 IEEE.
KW - Extreme events
KW - Grid resiliency
KW - Multi-objective optimization
KW - Power system planning
UR - https://www.scopus.com/pages/publications/85046371596
UR - https://www.scopus.com/record/pubmetrics.uri?eid=2-s2.0-85046371596&origin=recordpage
U2 - 10.1109/PESGM.2017.8273985
DO - 10.1109/PESGM.2017.8273985
M3 - RGC 32 - Refereed conference paper (with host publication)
SN - 9781538622124
VL - 2018-January
T3 - IEEE Power and Energy Society General Meeting
SP - 1
EP - 5
BT - 2017 IEEE Power and Energy Society General Meeting, PESGM 2017
PB - IEEE Computer Society
T2 - 2017 IEEE Power and Energy Society General Meeting, PESGM 2017
Y2 - 16 July 2017 through 20 July 2017
ER -