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This article analyzes the economic impact of price forecast errors on the optimal operation schedules of distributed (battery) storage systems. The presented simulation model extends a linear optimization model that achieves up to 17% annual savings for a storage system in an environment with dynamically changing electricity prices and under the assumptions of ex-ante known load and price data. The main contribution of this paper is to replace the deterministic load and price curves by imperfect forecasts of which the effect of price forecast errors is systematically analyzed. All results are benchmarked against the optimal result of the basic model.The main finding is that the underlying storage optimization model performs with a high robustness against price forecast errors. E.g., up to 10% Mean Absolute Percentage Error (MAPE) for day-ahead price forecasts lead to less than 10% deviation from the optimal result. I.e., the storage model yields up to 15% annual savings vs. 17% in the optimal case.

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The aim of the research project MeRegio is to meet the claim for more efficient decentralized energy systems by integrating advanced information and communication technologies (ICT) into all stages of the energy supply chain. Several marketplaces -in particular for power and for ancillary services -which are coupled to the technical energy infrastructure through a powerful and lawful ICT infrastructure should serve as a basis for an efficient and transparent coordination of energy supply, energy demand, and services. The developed concepts will be both validated by simulations and tested within a model region.Zusammenfassung Ziel des Forschungsvorhabens MeRegio ist es, den Forderungen nach effizienteren dezentralisierten Energiesystemen durch die Integration fortschrittlichster Informations-und Kommunikationstechnologien zu begegnen. Zur transparenten Koordination von Energieangebot, Energienachfrage und Dienstleistungen sollen mehrere Marktplätze dienen, u. a. für Wirkleistung und Systemdienstleistungen, welche an die technische Energie-Infrastruktur über eine leistungsfähige, rechtskonforme Informations-und Kommunikationsinfrastruktur angekoppelt sind. Die entwickelten Konzepte sollen sowohl durch Simulationen validiert als auch in einem Modellversuch regional erprobt werden.

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The need for a transition to renewable energy sources will lead to installation of large numbers of distributed renewable energy generators, which typically produce power intermittently. This trend conflicts with current power grid control strategies, where a few centralized control centers manage a limited number of large power plants such that their output meets the energy demands in real time. As the proportion of distributed and intermittent power production capacity increases, this task becomes much harder, especially as the local and regional distribution grids where renewable energy producers are usually installed are currently virtually unmanaged, lack real time metering and in many cases are not built to cope with power flow inversions. A more flexible, decentralized, and self organizing control infrastructure must be developed that can be actively managed to balance both the large grid as a whole, as well as the many lower voltage sub-grids. One candidate for this control infrastructure is energy markets at the retail level. To help mitigate the risk of instituting such markets in the real world, we are developing a competitive market simulation testbed that will stimulate research and development of market structures along with software agents that can support decision making in these markets. Participants in the competition will design intelligent agents that will act as brokers, building portfolios of energy producers and consumers, and matching energy supply from producers with energy demand from consumers. The competition will closely model reality by bootstrapping the simulation environment with real historic load, generation, and weather data.