Nowadays, the usage of energy produced by renewable sources such as wind and solar increases, their power is fed into the power grid and according to the Danish energy agency, the percentage of renewable energy is expected to significantly grow in the future. Furthermore, new technological achievements such as electric vehicles and heat pumps may provoke overload of the power grid in the future, especially in peak demand situations. In this new energy scenery that is being formed, the power grid is gradually transformed to a Smart Grid that uses the information and communication technologies to improve the existing energy services.

Within the Smart Grid, it is aimed to provide an alternative, cheaper and environmental friendly solution that is using the flex-offer (Micro-request) concept. The flex-offer concept is based on the idea that the consumption of energy is not occurring only in fixed time slots but could be shifted and be flexible regarding time so that part of the consumption could be shifted away from the peaks or closer to the peaks of production respectively. Furthermore, those flex-offers could even be flexible regarding the amount of energy or even the price of the corresponding energy. For example, a consumer could use his dishwasher a few hours later than he intended to, because during the shifted time period there will be larger production of energy by wind power. Of course due to the fact that the customer is willing to adapt his behavior according to the energy production he will receive some financial benefits, such as a decrease to the energy tariffs. As a result, in the future energy market there will be a need of management, storing and processing large amounts of data that represent such kind of flexibilities. Furthermore, the introduction of a new commodity (flex-offer) in the energy market will create a new energy market model in which business intelligence techniques will ensure its best operation.

This Ph.D. project is related to advanced aggregation techniques over complex energy related data. It plans to develop, implement and test a multidimensional data warehouse to provide an advanced business intelligence solution in order to analyze large amount of collected data (metering, consumption and production, flex-offers, pricing, grid model, etc.) from the different perspectives of the market actors. In order to achieve the above mentioned goals, algorithms, techniques and data structures, which correspond to the future Smart Grid requirements, will be designed and implemented. This Ph.D. project will also address the challenges arising of the usage from business intelligence techniques in the context of the future Smart Grid.