| Abstract |
Plug-in Electric Vehicles (PEVs) play a pivotal role in transportation electrication. The exible nature of PEVs' charging demand can be utilized for reducing charging cost as well as optimizing the operating cost of power and transportation networks. Utilizing charging exibility of geographically spread PEVs requires design and implementation of efcient optimization algorithms. There is a synergy between electro mobility (e-Mobility) infrastructures (including charging stations) and PEVs. In this paper, we introduce a holistic framework to model interdependent nature of power systems and electried transportation networks, enhance the operational performance of these systems as a network-of-networks, and explain the required information exchange via coupling agents (e.g., PEVs and charging stations). We develop a holistic framework that enables distributed coordination of interdependent networks through the IoT lens. To this end, we propose to use a fully distributed consensus+innovations approach. This iterative algorithm achieves a distributed solution of the decision making for each agent through local computations and limited communication with other neighboring agents that are inuential in that specic decision. For instance, the optimal routing decision of a PEV involves a different set of agents as compared with the optimal charging strategy of the same PEV. The exogenous information from an external network/agent can affect internal operation of the other agents. For instance, having some information about trafc congestion at the transportation networks changes the decision of PEVs to charge their battery at another location. Our approach constitutes solving an iterative problem, which utilizes communication at the smart city layer, as a network of infrastructures, including power grid and electried transportation network, that enables fully distributed coordination of agents. Fully distributed decision making enables scalability of the solution and plug-and-play capability, as well as increasing the information privacy of PEVs by only requiring limited local information exchange with neighboring agents. We investigate a detailed application of our framework for interdependent power systems and electried transportation networks. To this end, we rst identify the functionalities, constraints, objectives, and decision variables of each network. Then, we investigate the interdependent interactions among these networks and their corresponding agents. |
