## 1 Introduction
In [2] the authors describe the network architecture tested and used for smart grid deployment, whereas acceptance and analysis tests have also been performed. In [3] interoperability tests are performed, and results are presented from small-scale PRIME field deployments. AMI systems have also been tested for other purposes, such as IP applications [4]. The main features of smart metering systems are described in [5] along with the most employed technologies and standards. Data transferring is also an important element for the smart grid. In [6] real-time communication is examined, and in particular, the standards IEEE 802.22 and IEC 61850 are studied for transferring big data in real-time. The importance of a reliable network for data transfer is shown in [7], where a technique to improve the reliability of smart grids by using ZibBee as the Local Area Network (LAN) communication protocol is studied. Smart meter data play a significant role in the smart grid. For instance, they can be used for (Low Voltage) LV network monitoring and can give feedback for the LV grid condition, as is done in [8] along with secondary substation measurements. In [9] smart meter measurements are used for low voltage grid state estimation; the authors proposed a cloud-based smart metering architecture, which allows scalability and interoperability for the meters. Smart meter data prioritisation is studied in [10], where QoS issues are examined to ensure that high priority data is delivered successfully. Smart metering data and their secure delivery are also examined in [11], where an algorithm is proposed to study the optimal data concentrator location; parameters, such as the maximum throughput and the average delay, are taken into account to make sure that data is delivered efficiently. It can be concluded that smart meter data transferring is an important aspect for the smart grid. Different applications can define different parameters for the acceptable smart meter data transferring. For instance, real-timeapplications are more demanding in terms of delay and throughput of such data. In [12] an overview of several applications is given along with the required smart meter data frequency to guarantee the successful outcome of the application. In this paper, we apply the methodology and design of experiments on a simple use case, which describes the interaction between smart meters and a data concentrator. As a first step, we form the use case to be examined; we provide the description of tests, and we present the equivalent Basic Application Profiles (BAP(s)) and Basic Application Interoperability Profiles (BAIOP(s)). For their extraction, it is important to define the standards that can be used for every link of interaction. To do this, the European Committee for Standardization/ European Committee for Electrotechnical Standardization (CEN/CENELEC) Smart Grid Set of Standards has been used [13] as well as the CEN/CENELEC Functional reference architecture for communications in smart metering systems [14] and the CEN/CENELEC Sustainable Processes [15]. These documents are very useful when working with smart grid interoperability; they give guidelines for forming smart grid interoperability use cases, the actors to be involved and the standards that can be followed. In addition, specific guidelines with respect to Advanced Metering Infrastructure (AMI) applications are given in [14], which help in identifying the issues related to smart metering themes. In Section 2, we show the standards to be used for the chosen use case. Further on, the design of experiments is presented and the parameters to be altered are defined. For this work, we follow the methodology for interoperability testing for smart grids [1], which defines the way the experiments should be executed. The experiments are executed with a given testbed available in the lab and conclusions are drawn with respect to whether or not the test parameter alteration affects the overall system performance and interoperability.