A Stochastic Multi-Agent Optimization Model for Energy Infrastructure Planning Under Uncertainty and Competition

Zhaomiao Guo and Yueyue Fan

Institute of Transportation Studies, Working Paper Series from Institute of Transportation Studies, UC Davis

Abstract: 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. View the NCST Project Webpage

Keywords: Engineering; Electric utility facilities; Energy resources; Equilibrium (Economics); Market dominance; Mathematical models; Multi-agent systems; Optimization; Renewable energy sources; Stochastic processes; Supply chain management (search for similar items in EconPapers)
Date: 2017-07-01
New Economics Papers: this item is included in nep-ene and nep-ore
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Citations: View citations in EconPapers (5)

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