Sustainable Transportation Energy Pathways (STEPS), National Center for Sustainable Transportation
Guo, Zhaomiao and Yueyue Fan (2017) NCST Research Report: A Stochastic Multi-Agent Optimization Model for Energy Infrastructure Planning Under Uncertainty and Competition. Institute of Transportation Studies, University of California, Davis, Research Report UCD-ITS-RR-17-26
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.
Keywords: energy supply chain, oligopolistic market, equilibrium, stochastic multi-agent optimization, decomposition