Different driving forces push the electricity production towards decentralization. As a result, the current electricity infrastructure is expected to evolve into a network of networks, in which all system parts communicate with each other and influence each other. Multiagent systems and electronic markets form an appropriate technology needed for control and coordination tasks in the future electricity network. We present the PowerMatcher, a market-based control concept for supply and demand matching (SDM) in electricity networks. In a simulation study we show the ability of this approach to raise the simultaneousness of electricity production and consumption within (local) control clusters. This control concept can be applied in different business cases like reduction of imbalance costs in commercial portfolios or virtual power plant operation of distributed generators. Two PowerMatcher-based field test configurations are described, one currently in operation, one currently under construction.

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Abstract. The paper addresses dynamics in information ecosystems due to competition between selfish agents to get control of protectable resources. In our case study we investigate the first arms race on Internet triggered by the Napster introduction of an easy to use service for sharing files with music content among users. We set up a model for investigation of possible scenarios emerging from the Napster and Gnutella peer-to-peer tools for information sharing. We also introduce a formal model for analyzing the Napster scenario in the cases of selfish or altruistic users. The prediction provided by our model is in line with what really happened in the Napster case. The model also shows that the outcome was indeed unavoidable if we have selfish users. Introducing Napster and Gnutella. Setting the SceneThe file format MP3 introduced the possibility to compress large audio files into more easy to handle files. For music lovers it became possible to store lots of MP3 files on computers or share them with others, using an Internet connection and file transfer protocols. Napster introduced a peer-to-peer (P2P) environment providing easy mechanisms for connecting different users (peers) and their MP3 file resources. Each of the peers (users) could use a lookup service provided by Napster to find out where to retrieve wanted MP3 files, and also provide their own files. That is, Napster provided a centralized register of locations of files but the files themselves remained at the individual users. Napster quickly became a tremendous success with almost a hundred million of users downloading MP3 music for free. Of course Napster simultaneously become a threat against traditional business models supported by the record companies.The fight initiated by Napster and taken up by Recording Industry Association of America (RIAA) was about ownership of content and enforcement of copyright laws. At the start Napster concentrated its efforts on providing easy MP3 file sharing among users willing to share their files with others. So, in a sense Napster itself did not 'copy' any files, it only provided easy means for others to share content between each other. After some heated debate about 'freedom on the net' and 'rights to content' there were in the beginning of March 2001 a court order forcing Napster to protect copyright, of music from being shared using its centralized lookup service. The mechanism for protection was to introduce filtering of the lookup service. Users, groups of users or others who of different reasons wanted to protect 'freedom on the Net' quickly circumvented the first simple filters. In short the first information war on

A compositional verification method for multi-agent systems is presented and applied to a multi-agent system for one-to-many negotiation in the domain of load balancing of electricity use. Advantages of the method are that the complexity of the verification process is managed by compositionality, and that parts of the proofs can be reused in relation to reuse of components. ' ât', N'>N state UA (M , t', output(UA)) ° round(N') ' êU', t' > t state DB (M , t', output(DB)) • predicted_overuse(U',N) DB2. Overuse prediction monotonicity The component Determine Balance satisfies overuse prediction monotonicity if the following holds: if based on received cut-downs CD CA for each Customer Agent CA, a predicted overuse U is generated by DB, and based on received cut-downs CD' CA for each Customer Agent CA, a predicted overuse U' is generated by DB, then CD CA ≤ CD' CA, for all CA implies U' ≤ U. â M£Traces(DB) â t , t', N, N', C, U0, U, U' state DB (M , t, input(DB)) • predicted_use(U0) & â CA [ state DB (M , t, input(DB)) • cutdown_from(CD CA, CA, N) & state DB (M , t', input(DB)) • cutdown_from(CD' CA, CA, N') & CD CA ≤ CD' CA ] & state DB (M , t, output(DB)) • predicted_overuse(U, N) & state DB (M , t', output(DB)) • predicted_overuse(U', N') ' U' ≤ U Note that in this property the monotonicity is not meant over time, but for the functional relation between input and output of DB.