TY - GEN
T1 - Long-Term Co-Optimized Generation and Transmission Expansion Planning with Renewables in Complicated Environments
AU - Hui, Li
AU - Siwei, Liu
AU - Lei, Wu
AU - Qingru, Qi
AU - Mingli, Zhang
AU - Jun, Shu
N1 - Publisher Copyright:
© 2018 IEEE.
PY - 2018/12/19
Y1 - 2018/12/19
N2 - This paper presents a new approach for solving the multi-year co-optimized generation and transmission expansion planning problem with renewable energy resources in geographic information systems (GIS). The proposed optimization model is to determine when (which year), where (which cell), and what (which type) generators and transmission lines will be built for minimizing the sum of investment costs, operation costs (including energy and emission costs), and load shedding costs during the planning horizon. Prevailing constraints include power balance requirements, branch limits, power limits of generators, and construction limits of cells. A two-step approach to combine Dijkstra's algorithm and mixed-integer linear programming (MILP) is introduced for solving the large-scale optimization problem. The effectiveness of the proposed approach is validated by numerical case studies on a 9-bus system.
AB - This paper presents a new approach for solving the multi-year co-optimized generation and transmission expansion planning problem with renewable energy resources in geographic information systems (GIS). The proposed optimization model is to determine when (which year), where (which cell), and what (which type) generators and transmission lines will be built for minimizing the sum of investment costs, operation costs (including energy and emission costs), and load shedding costs during the planning horizon. Prevailing constraints include power balance requirements, branch limits, power limits of generators, and construction limits of cells. A two-step approach to combine Dijkstra's algorithm and mixed-integer linear programming (MILP) is introduced for solving the large-scale optimization problem. The effectiveness of the proposed approach is validated by numerical case studies on a 9-bus system.
KW - Dijkstra's algorithm
KW - Long-term planning
KW - co-optimized generation and transmission expansion planning
KW - geographic information systems
KW - mixed-integer linear programming
KW - optimal line routing
KW - renewable generation planning
UR - http://www.scopus.com/inward/record.url?scp=85060859733&partnerID=8YFLogxK
UR - http://www.scopus.com/inward/citedby.url?scp=85060859733&partnerID=8YFLogxK
U2 - 10.1109/EI2.2018.8582336
DO - 10.1109/EI2.2018.8582336
M3 - Conference contribution
AN - SCOPUS:85060859733
T3 - 2nd IEEE Conference on Energy Internet and Energy System Integration, EI2 2018 - Proceedings
BT - 2nd IEEE Conference on Energy Internet and Energy System Integration, EI2 2018 - Proceedings
T2 - 2nd IEEE Conference on Energy Internet and Energy System Integration, EI2 2018
Y2 - 20 October 2018 through 22 October 2018
ER -
