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A Stochastic Multiagent Optimization Framework for Interdependent Transportation and Power System Analyses
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DOI：10.1109/TTE.2021.3049127.png)
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摘要
En 中文
We study the interdependence between transportation and power systems considering decentralized renewable generators and electric vehicles (EVs). We formulate the problem in a stochastic multiagent optimization framework considering the complex interactions between EV/conventional vehicle drivers, renewable/conventional generators, and independent system operators, with locational electricity and charging prices endogenously determined by markets. We show that the multiagent optimization problems can be reformulated as a single convex optimization problem and prove the existence and uniqueness of the equilibrium. To cope with the curse of dimensionality, we propose the alternating direction method of multipliers (ADMM)-based decomposition algorithm to facilitate parallel computing. Numerical insights are generated using standard test systems in the transportation and power system literature.
Keyword:
Transportation
Power systems
Decision making
Generators
Vehicles
Planning
Investment
Decomposition
electric vehicle (EV)
multiagent optimization
renewable energy
transportation-power nexus
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2.9K
被引数:
1.6W
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