Application 2: A Decentralized Multi-Agent System for Management of En

In the specific application of managing a large fleet of EVs to organize their connection to the grid, a need arises to route those EVs to the most suitable EVCSs in their vicinity. A decentralized DDS-based MAS that links the EVs and EVCSs in real-time, while the EVs are en route, is presented.

Deploying a MAS for this purpose will help enhance the smart grid operation by providing benefits to all the involved entities: the EV, the EVCSs, and the grid. The EV will be routed to the most suitable EVCS according to its status and preferences. On the other hand, the EVCS will be able to enhance the dispatch of its energy resources, whether from generators, renewable energy resources, or energy storage systems, thus reinforcing

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the overall purpose of serving the incoming vehicles requesting charge. As for the provided benefit to the operation of the grid, the extra awareness and visualization of the system status ultimately helps in achieving better control at both secondary and tertiary levels. While this section presents an additional example of using the DDS middleware for building an MAS for the application of routing of EVs to EVCSs, chapters 4 and 5 present an in-depth analysis that focuses on a raised hypothesis, the data sharing scheme of the MAS, and use cases presented to validate the hypothesis.

3.5.1 Application Description

The developed MAS is deployed into a rerouting plan of the en route EVs to the most suitable EVCSs based on price, State of Charge (SoC), and distance and time considerations.

The presented MAS framework consists of an EV agent, an EVCS agent, and a higher- level agent to manage the interactions between the different entities involved, as shown in Figure 3.2. Each of those agents is designed to share only the needed information to make the objective achievable.

When the vehicle’s driver requests a charge or when the EV agent is aware that the SoC is low, the EV agent, which resides in the EV, takes an array of parameters as input, processes it through a pre-designed controller, and draws a conclusion on the most suitable EVCS to head to and charge the vehicle.

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Figure 3.2 DDS-Based Decentralized MAS Configuration for the Application of the Management of En Route Electric Vehicles

3.5.2 System Description

As can be seen from Figure 3.2, the application involves a global data space which is shared between all agents. For the EV agent and the EVCS agent, which are at remote geo- distributed locations, to contribute to this shared global dataspace, they publish and subscribe to the information as HTTP client applications. An RTI Connext DDS-based web-integration service is used to translate the contributed and retrieved information from HTTP format to DDS format and vice versa. Three topics are created for this application: EVs (in green), EVCSs (in yellow), and HLA (in pink). Each EV publishes its data to the topic EVs. Similarly, each EVCS publishes its data to the topic EVCSs. The higher-level agent subscribes to these two topics and sorts the data according to the EVs’ and EVCSs’

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unique IDs. It then publishes the data required by the EV agent to the topic HLA. The EV agent subscribes to topic HLA and executes its control logic accordingly.

It then issues a recommendation to the driver or takes the decision and changes route, in the case of an autonomous vehicle. In the design of the EV agent, the array of parameters considered is: the vehicle’s SoC, the distance and time to the EVCSs that show readiness for service, and the price in cents per kWh for charging the vehicle in each of those EVCSs. In addition, the EV agent needs to share some of its information with other agents before it is able to receive the needed information about the available EVCSs. Those parameters that the EV agent needs to send are: the unique EV ID, the current EV’s GPS coordinates, and a discovery radius to search for available EVCSs around it.

To get the array of information which is needed for the conclusion to be made ready, the other two aforementioned agents are involved in this strongly-correlated message- sharing system.

Each EVCS will have an EVCS agent interfacing with its main CC for obtaining information and sharing it with the proper MAS entities. This agent also receives messages to schedule charging for incoming vehicles and thus updates its state. This agent is designed to share the EVCS ID, the current price per kWh of charging, the GPS coordinates of the EVCS, and the current availability of chargers in this particular EVCS.

As for the higher-level agent, it is designed to receive the GPS coordinates shared by the EV agents and the EVCS agents, in addition to the discovery radius shared by the EV agent. It then searches for the EVCSs that are within this radius. Once it has found a number

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of EVCSs in the discovery radius, it looks at which EVCS agents have shared their availability and willingness to accept incoming EVs to use their charging infrastructure. It then shares the GPS coordinates of those EVCSs, together with the price of charging at each of them, with the agent residing in the vehicle requesting a charge. The EV agent then has the array of information ready and can process it to draw a conclusion on the most suitable EVCS to go to. The EV agent’s control logic is explained in Chapter 4, and case studies are applied to test the robustness of this on-board module. After the decision is made by the EV agent, a notification is made to the higher-level agent on the EVCS to which it will head. The higher-level agent will inform the EVCS agent of the incoming EV, to reserve the energy and get ready to serve the vehicle upon its arrival.