Task 2.1: A multi-agent system architecture for trading flexibility within the CALLIA project context is defined. This system architecture is going to connect RES agents, Flexible Load agents, Storage agents and cluster agents on the one hand, with Aggregator and Trading agents on the other hand.
Task 2.2: In this task, a robust communication stack for the expected smart grid functionality is developed based on different PLC technologies (G3-PLC and BPL by devolo and CENELEC-PLC by Pavotek) and GSM technology (by Pavotek). These are required for future-oriented applications such as smart metering, grid monitoring and RES/Loads/Storage agents integration in the future Smart Grid. The transport protocols will be TCP/IP or UDP/IP or Web based method and will for example be orientated to the new IEC 61850-8-1 (MMS over http) for the RES control agents and IETF RFC 6120 XMPP for the energy flexibility management.
Task 2.3: The key objective of task 2.3 is to design and implement the upper-layer communication solutions necessary for the CALLIA project. This involves the information and data models used by device, aggregator and trading agents to communicate with each other as well as the communication infrastructure, interfaces and protocols.
Task 2.4: The key objective of task 2.4 is to define the RES agent, Flexible Load agent, Storage agent and Cluster agent design and agent platform that fits in the overall multi-agent architecture, defined in Task 2.1, and the information and data models, defined in task 2.3. These agents contain local forecasting and modelling functionally to provide upstream information to the Aggregator agent, as well as local control functionality to respond to downstream requests coming from the Aggregator agents. Task 2.4 includes the implementation of these agents according to the design defined in this task.
Task 2.5: REstore is currently designing the aggregator and trading platform agents that fit in the overall multi-agent architecture and Information and Data models. These agents will receive models/information from the RES/Load/Storage agents and based on this, run an optimization algorithm to decide on the best dispatching strategy (e.g. increasing or shedding local loads, offering flex capacity to neighboring DSOs) related to the products/services and business models defined in task 1.1&1.2. These algorithms are defined and simulated Task 1.2. In task 2.5 they are ported to an operational agent platform and environment.