A Stochastic Multi-Agent Optimization Model for Energy Infrastructure Planning Under Uncertainty and Competition
Principal Investigator: Yueyue Fan
| University of California, Davis
Research Team: Zhaomiao Guo | University of California, Davis
This paper presents a stochastic multi-agent optimization model that supports energy infrastructure planning under uncertainty. The interdependence between different decision entities in the system is captured in an energy supply chain network, where new entrants of investors compete among themselves and with existing generators for natural resources, transmission capacities, and demand markets. Directly solving the stochastic energy supply chain planning problem is challenging. Through decomposition and reformulation, we convert the original problem to many traffic network equilibrium problems, which enables efficient solution algorithm design.
Sponsors: US DOT
Zhaomiao Guo & Yueyue Fan (2017). A Stochastic Multi-agent Optimization Model for Energy Infrastructure Planning under Uncertainty in An Oligopolistic Market, Networks and Spatial Economics, Springer, vol. 17(2), pages 581-609, June. DOI: 10.1007/s11067-016-9336-8