## 5 Optimization
### Cloud Computing  
Coordinating smart grids through cloud computing services is an innovative idea. [PERSON] _et al._[38] performed a deep analysis on the weak and strong aspects of the cooperation between the cloud computing technology and the electricity market. In their contribution, they suggest going beyond the \"utility model\" of cloud computing, (_i.e._, using Cloud resources instead of local ones) and taking advantage of the subsequent innovations that this new technology will bring in the next few years. To summarize their point of view, cloud computing brings issues that must be addressed specifically, such as security, latency and scalability, when applied to an electricity distribution context.  
Nevertheless, authors advise that cloud computing and the new IT technologies it enables will, inevitably, transform the electricity industry.  
Another vision of the opportunities and challenges of applying cloud computing services in smart grid networks is given by [PERSON] _et al_. [39]. In their contribution, it is stressed how the scalable and elastic resources of a Cloud infrastructure is suited to support the dynamic applications of a smart grid, such as energy forecasting, load balancing and demand-response optimization. The data streams from consumers' Smart Meters acts as data sources for the distributed system. Clouds provide a ready platform for data sharing and also allow third-party applications to be collocated with the data source (see Figure 8).  
The research challenges that the Cloud infrastructure has to address are:  
* _Streaming applications in the Cloud_: at present, Cloud providers do not provide specialized data abstractions for these kinds of data streams.
* _Scheduling Latency Sensitive Applications_: the demand-response applications need to be highly responsive.
* _Scalable Data Sharing and Privacy Preservation_: information on energy assets has a very relevant size, thus sharing of the information needs to be highly scalable. Also, typical public Cloud storage platforms do not provide fine grained authorization control for data. Models for using the shared Cloud repository by multiple users and their software agents, with different levels of access, need to be examined.  
The interaction between smart grids and cloud computing creates a new scenario for the different actors of the system. [PERSON] _et al_. [PERSON] _et al_. (2013) presented a data cloud model for a smart grid. This model is realized through the interaction of several software systems, connected through the Internet, operating through REST APIs. The low-level sensors and metering devices continuously provide their data through the _PUT_ primitive of the data cloud, while high-level information systems, representing the energy market actors, are able to query the data cloud via _GET_. This process is shown in Figure 9. Depending on data availability requirements and the data types involved, load balancing mechanisms may be needed.  
Figure 10 shows how this model is able to separate the control flow from the data and information flow. This allows an event-based handling of the various actuators of the system (e.g., the physical devices) by the actors (e.g., information systems). It is important to notice that this model assumes that all the actors of the energy market use the same infrastructures for information retrieval. Otherwise, data management would require too much resources, both technological and economical, in order to be efficiently implemented.  
Finally, [PERSON] _et al_. [PERSON] _et al_. (2013) relate the Service Request Routing problem, typical of cloud computing infrastructures, with the Power Flow Analysis in a smart grid network. Through simulation, they showed that an efficient service routing algorithm applied to a smart grid can significantly improve the robustness of the grid design.  
Figure 8: Smart grid and cloud computing: a sample infrastructure [PERSON] _et al_. (2014).