Large functional buildings are increasingly populating urban regions. They may host data centers which are big energy consumers: typically 1-2 MW depending on processing load. These buildings offer large thermal capacity and potentially a large electrical capacity if electrical vehicles are plugged into the electricity network of the building. The combined storage capacity brings opportunities to absorb renewable energy.
The “unpredictable” nature of renewable energy sources and mobile consumers like electric vehicles leads to power peaks in the distribution network which are correlated in time and space. One approach to cope with these fluctuations is the massive deployment of energy storage systems. In addition time shifting of energy consumption is being researched. The flexibility obtained by exploiting geographical alternative locations for energy consumers providing similar service levels, is as of yet unaddressed. However, the geographical distribution of both energy consumers and producers could be an important parameter for optimizing the whole energy network. The electric vehicles and the processing tasks running on the data center can be considered as mobile consumers which may have alternative geographical locations to “present” their energy consumption load.
The model envisioned in the G(e)oGreen project is one in which servers are hosted in large functional buildings that could be co-located with renewable energy sources. The processing they need to perform can vary: we assume a cloud/grid computing approach, where users defer program executions to servers, preferably to those servers that are located in an area where renewable energy is available and/or the grid is not overloaded. If more power can be provided than is required for the data center and the building energy consumption, the remainder can be used for charging electric vehicles and/or storing energy in the building. Part of the stored energy in the electric vehicles may be used for consumption once the power level of energy from renewable sources has dropped below the demanded power level. All of this can only happen if all boundary conditions are satisfied and the benefits outweigh the drawbacks.
The project objectives include therefore the description of use cases, the development of a high-level hierarchical system model, the analysis of batteries used in vehicle to grid (V2G) applications, the analysis of storing energy in buildings, optimal coordinated battery charging/discharging algorithms, optimal building management control strategies, optimized scheduling algorithms aimed at deciding when and where to consume energy, routing algorithms to optimize communication for energy efficiency, infrastructure dimensioning and placement algorithms for vehicle charging points and data servers. Initial technical and economical cost/benefit analysis will be done. In addition, ICT architectures for realizing the above will be proposed. Follow up ongoing standardization activities (e.g. communication protocols and interfaces, esp. for ICT) are planned.
