Marco Scialdone

Software Agents for Collaborating Smart Solar-Powered Micro-Grids

Solar electricity is one of the options as innovative approach as primary energy use. It could be deployed decentralised into the urban areas, and could alleviate the carbonised electricity demand drastically. Information and communication technologies (ICT) could be exploited to provide real time information on energy consumption in a home or a building giving the possibility to citizens to take decisions in order to save energy. In this context CoSSMic, an ICT European project, aims at fostering a higher rate of self-consumption of decentralised renewable energy production by innovative autonomic systems for management and control of power micro-grids on users behalf. The paper addresses these challenges and discusses related work dealing with the development of an ICT solution using software agents which collaborate in a neighborhood, and with the central power grid, over a peer-to-peer overlay.

A Distributed System for Smart Energy Negotiation

Distributed energy production by Solar Panels is really wide-spread today. However the mismatch between production and consumption during the day, and expensiveness of energy storages, limits an high rate of self-consumption. In fact the overall overproduction is currently reversed into the power grid and delivered to the energy provider. In this context the CoSSMic project aims at developing a distributed software architecture that allows for the collaboration among neighbors, both to schedule the consumptions and the exchange of energy, in order to maximize the self-consumption of micro grids. This paper focus on the architecture design, scouting of technology and preliminary demonstrator.

Personalized Recommendation of Semantically Annotated Media Contents

The optimal decision about the best set of recommendations to the user is a relevant problem in different application contexts. Here we focus on a methodology for dynamically and efficiently retrieval and delivery of multimedia contents, like documents, images, video etc., which have been annotated with semantic information. We introduce the definition and the computation of the similarity between sets of concepts belonging to a common ontology and discrete optimization technique for choosing the ones to be recommended.We enable personal agents reasoning about the best set of recommendations, which are relevant to the user’s profile, so optimizing his/her satisfaction. We present its implementation in a framework that supports experts in the domain of the Cultural Heritage to augment the archaeological site with a set of multimedia contents.

Multi-agent Negotiation of Decentralized Energy Production in Smart Micro-grid

SmartGrid is an electricity network that can intelligently integrate the actions of all users connected to it in order to efficiently deliver sustainable, economic and secure electricity supplies. In this context the CoSSMic project aims at fostering a higher rate of self-consumption of decentralized renewable energy production, using innovative autonomic systems for management and control of power micro-grids on users behalf. To achieve this goal we have designed an ICT framework that integrates different appliances such as smartmeters, solar panels, batteries, etc., providing a common platform to support sharing of information and negotiation of energy exchanges between power producers and storages in accordance with policies defined by owners, weather forecasts, and habits and plans of participants.

Design and evaluation of P2P overlays for energy negotiation in smart micro-grid

The increasing availability of distributed renewable energy sources such as photo-voltaic (PV) panels has introduced new requirements for innovative power grid infrastructures. Information technologies provide new opportunities for developing techniques to optimize the energy usage by a new generation of smart-grids. Here we investigate an original solution that aims at maximizing the self-consumption within a neighborhood based on a collaborative approach. Distributed software agents plan and enforce the optimal schedule of consuming appliances according to the prediction of energy production by PV panels, the estimated energy profile of consuming devices and the users preferences and constraints. Finally we focus on the performance evaluation of a negotiation protocol that allows agents to find a sub-optimal solution for the global schedule, comparing results obtained by a prototype implementation and experimenting different technologies. Collaborative approach for energy schedule in smart micro-gridOptimization model for short term maximization of self-consumptionGlobal schedule computed by a P2P negotiation between consumer and producer agentsPerformance analysis of resolution using a prototype

An Agent-based Approach for Smart Energy Grids

The increasing demand for energy and the availability of several solutions of renewable energy sources has stimulated the formulation of plans aiming at expanding and upgrading existing power grids in several countries. According to NIST, smart grid will be one of the greatest achievements of the 21st century. By linking information technologies with the electric power grid to provide electricity with a brain, the smart grid promises many benefits, including increased energy efficiency, reduced carbon emissions, and improved power reliability. In this paper we present an agent based architecture for supporting collection and processing of information about local energy production and storage resources of neighborhoods of individual houses and to schedule the energy flows using negotiation protocols.

Distributed architecture for agents-based energy negotiation in solar powered micro-grids

The quota of renewable energy production from decentralized sources is increasing even more, affecting the management of current power grids. The lack of alignment between energy produced by photovoltaic panels and the consumption by users appliances are the main issues that need to be addressed. The usage of energy storages is not yet feasible because of dimensioning problems and costs. The CoSSMic project is developing an innovative autonomic distributed platform that adopts a collaborative approach and distributed knowledge to schedule the usage of consuming devices according to users preferences and constraints, monitoring data and predicted photovoltaic production. The main objective is the maximization of self‐consumption in a neighborhood. Multi‐agent systems are a good candidate for modeling and managing such kind of systems. In fact, smart grids are geographically distributed systems composed of autonomous and reactive entities among which some are pro‐active and have social abilities. In this paper, we focus on the design and prototype implementation of the multi‐agent systems, that is one of the pillars of the CoSSMic platform, aiming at implementing a distributed scheduler. Copyright

Towards a SLA for Collaborating Smart Solar-Powered Micro-Grids

The mismatch between the decentralised renewable energy production from solar panels and the actual consumption during a day is an open issue. It is currently addressed by an exchange with power grid of the exceeding production and a consequent loss of energy. The usage of storage can help to foster a higher rate of self-consumption, but it is too much expensive for the user, above all if it is dimensioned according to the peek of production. For this reason the CoSSMic project aims at defining innovative autonomic systems for management and control of power micro-grids on users behalf. It will support the negotiation of energy exchange between peers of a neighborhood, which act as producers and consumers, using distributed optimization models and multi-agent techniques. This paper focuses on the multi-agent architecture, interoperability issues and on a SLA based negotiation protocol to support a distributed optimization strategy.

Agent Based Negotiation of Decentralized Energy Production

The increasing demand for energy has stimulated the formulation of plans aiming at shifting to renewable energies, not only to save money, but also for the responsibility that the present population has towards future generations. Within this scenario, ICT solutions will help the citizens in saving energy: people of a community that have power’s over-production can take benefits in terms of gain by selling the excess energy to neighbourhood instead of power suppliers due to the fees that they have to pay in the latter case. On the other hand, citizens that need additional energy can buy it from sellers in the neighbourhood at favorable prices. In this paper we present an agent-based framework that allows the collection of data about energy consumption in a neighbourhood and the negotiation of the whole produced/consumed energy among the involved parties in order to maximize the profits of the community.

A Negotiation Solution for Smart Grid Using a Fully Decentralized, P2P Approach

One of the biggest advantages introduced by the Smart Grid is the ability to efficiently and simply integrate renewable energy sources that appear to be intermittent because of their dependence on phenomena that are not constant but that are an important energy supply if properly exploited. The realization of a smart grid, however, requires an infrastructure able to handle almost instantaneous bidirectional communication between each node of the network. All this involves the management of huge data flows in terms of analysis and control, requiring a structure capable of handling responses in a very short time and an high level of global analysis to prevent or minimize problems. Successful negotiation and brokering on the Energy Market is an important prerequisite for achieving the advantages of Smart Grid. In this paper we present a Market for the sale of energy using a completely distributed approach based on P2P protocols and server-less technology, providing a greater level of security by exploiting the properties of this type of networks. We propose a scenario animated by three actors: Consumer, Producer and Market Manager.