# Smart Grid Testing Management Platform (SGTMP) +
## 2 Background
### Smart Grid (SG)  
An SG builds upon a traditional power distribution grid, adding several layers to allow for smart two-way communication (power and information flows) to improve the quality of provided services [1]. At its core, communication between SG components is accomplished by connecting all of the components and sensors into a grid network [3]. An SG can be described through three main systems: **a smart infrastructure system** (energy, information, and communication infrastructure fundamental for the smart grid), **a smart management system** (providing advanced management and control services), and **a smart protection system** (providing advanced grid reliability analysis, failure protection, and security and privacy protection services) [3].  
The complexity of the SG is well represented by the smart grid architecture model (SGAM) framework, defined by the CEN-CENELEC-ETSI standardization group [9]. SGAM suggests considering different levels to tackle the SG complexity: the component layer (physical and virtual devices), the communication layer (e.g., stacking the different protocols), the information layer (modeling of information), the function layer (provided functionality), and the business layer (business goals in terms of services provided). All these layers need to be considered for the provision of SG-related services.  
The core component for monitoring and source of consumer data for predictions in an SG is a smart meter (SM). SMs are located at each power distribution endpoint (residential or industrial). They are responsible for monitoring the power consumption like traditional energy meters; in addition, they also send usage statistics back to the grid, which allows instant collection of energy consumption for a given region [4]. Together with other data from the grid, instant power production and a power consumption profile can be generated. Over time, a consumption model can be developed, which will serve as a way to predict power usage over time. The prediction can be used to intelligently change the power production of non-renewable energy power plants to meet the current and near-future energy demand, which will help to save resources and at the same time prevent potential blackouts [22]. With the help of SM deployments, the time required to detect these abnormalities is significantly reduced when compared to a traditional power distribution grid [4; 23; 24].  
One of the main advantages of an SG is the self-healing capability. Therefore, in case of a failure of a power supplier or distributor, the grid can automatically re-route power across a different network or send an alert to other power generators to increase their power output [25]. In a traditional grid, this typically leads to blackouts for the entire distribution region. Another benefit of an SG is that individual power producers may decide to add electricity to the grid, mainly from renewable resources (e.g., photovoltaic panels and wind turbines) [25]. By allowing even small-scale producers to sell energy into the grid, the overall environmental impact of power production from non-renewable sources is reduced, granting better load balancing during times of peak power usage.