Abstract

This document is the deliverable D4.1 "Developers toolkit and deployment support" and presents the outcome of the activities T4.1 "ACTIVAGE Development tools" and T4.3 "ACTIVAGE Deployment tools" which are part of Work Package WP4 "ACTIVAGE Application support tools and services layer". The document makes a status of existing development and deployment tools prior to the ACTIVAGE project and describes the ones that are under progress to fulfill ACTIVAGE dedicated requirements. The requirements are presented in Section 2, and the Use Cases in Section 3, precising the uses by the kind of developer profiles listed in section 3.1. The presented tools in Section 4 allow the development of applications (developed for ACTIVAGE by third parties) on top of each given platform among the seven selected as ACTIVAGE IoT platforms (Fiware, openiot, IoTivity, seniorsome, sensiNact, Sofia2 and universAAL). They are used by application developers and platform contributors. For more details about the seven ACTIVAGE IoT platforms, please refer to the D3.1 Report on IoT European Platforms. The presented tools in Section 5 are the ones used in the nine ACTIVAGE deployment sites to support their hardware device installations and middleware installation on the resulting IT infrastructure. They are used by installation/deployment and IT teams. These existing development and deployment tools are available today and constitute the first step of ACTIVAGE toolkit: they are heterogeneous tools available for dedicated platforms and for specific deployment sites. Further works in tasks T4.1 and T4.3 will aim to complete the IoT platform dedicated tools with ACTIVAGE common development and deployment tools based on the common ACTIVAGE APIs under specification.

Abstract

The recent rise of renewable energy sources connected to the distribution networks and the high peak consumptions requested by electric vehicle-charging bring new challenges for network operators. To operate smart electricity grids, cooperation between grid-owned and third-party assets becomes crucial. In this paper, we propose a methodology that combines machine learning with multi-objective optimization to accurately plan the exploitation of the energy district&rsquo s flexibility with the objective of reducing peak consumption and avoiding reverse power flow. Using historical data, acquired by the smart meters deployed on the pilot district, the district&rsquo s power profile can be predicted daily and analyzed to identify potentially critical issues on the network. District&rsquo s resources, such as electric vehicles, charging stations, photovoltaic panels, buildings energy management systems, and energy storage systems, have been modeled by taking into account their operational constraints and the multi-objective optimization has been adopted to identify the usage pattern that better suits the distribution operator&rsquo s (DSO) needs. The district is subject to incentives and penalties based on its ability to respond to the DSO request. Analysis of the results shows that this methodology can lead to a substantial reduction of both the reverse power flow and peak consumption. Document type: Article