The evolution of the electrical power sector due to the advances in digitalization, decarbonization and decentralization has led to the increase in challenges within the current distribution network. Therefore, there is an increased need to analyze the impact of the smart grid and its implemented solutions in order to address these challenges at the earliest stage, i.e., during the pilot phase and before large-scale deployment and mass adoption. Therefore, this paper presents the scalability and replicability analysis conducted within the European project InteGrid. Within the project, innovative solutions are proposed and tested in real demonstration sites (Portugal, Slovenia, and Sweden) to enable the DSO as a market facilitator and to assess the impact of the scalability and replicability of these solutions when integrated into the network. The analysis presents a total of three clusters where the impact of several integrated smart tools is analyzed alongside future large scale scenarios. These large scale scenarios envision significant penetration of distributed energy resources, increased network dimensions, large pools of flexibility, and prosumers. The replicability is analyzed through different types of networks, locations (country-wise), or time (daily). In addition, a simple replication path based on a step by step approach is proposed as a guideline to replicate the smart functions associated with each of the clusters.

In this work, we study the performance of a distributed optimal power flow control algorithm with respect to realistic communication quality of service. By making use of a communication network simulator, namely “GNS3”, we created a framework that simulates both the controllers involved in the optimal power flow algorithm and the communication between them. Using this platform, we investigate and give insights into the convergence time of the distributed algorithm when applied to the IEEE 13 and IEEE 123 node test feeders. By leveraging the simulation results, we define parameters on the network quality of service, such that the distributed optimal power flow algorithm could be used for secondary or tertiary control.To deal with the complexity induced by a large number of components involved in these simulations, we present a methodology to automate and streamline the generation and the analysis of simulation scenarios.

The widespread deployment of distributed energy resources including volatile renewable generation raises the need for detailed distribution network analysis. In many cases, the vast system sizes make the joint analysis of multiple voltage levels computationally impracticable. Consequently, most studies focus on single or selected voltage levels and represent subordinate system portions by conventional static load models. Their parameters are usually identified by simplified aggregation methods that do not consider the effects of the network, i.e., network losses and spatial voltage variations. This approach involves inaccuracies and does not allow for validating compliance with the voltage and current limits inside subordinate system parts that are not explicitly represented in the model. In response to this challenge, this paper extends the static load model by including new parameters, i.e., the boundary voltage limits, and describes the associated component-based parameter identification method. Their combination paves the way for a modular power flow approach, which supports the separate investigation of different system portions without introducing considerable inaccuracies, enabling the systematic, precise, and computationally practicable power flow analysis and validation of voltage and current limit compliance in large distribution systems. The proposed concepts are applied to a synthetic distribution system to facilitate their use and showcase their usefulness.

In this paper we propose an approach for automatically building real-time emulations of large scale cyber-physical energy systems. An end-to-end methodology is presented, starting from the system description, continuing with automatic model and interface generation, and finally completing with deployment and real-time execution. The intended target use of the proposed solution is the validation of massively distributed smart-grid software applications. The goal is to provide an automated testing framework that can be integrated in modern software development tool-chains. In this manner, complex software ecosystems that act on critical infrastructure can be exhaustively evaluated and validated before being deployed in the field. A prototypical implementation is also presented. This relies on a commercially available real-time simulator whose digital interfacing capabilities were extended in order to meet our automation requirements. As a showcase we consider the problem of generator dispatch using a distributed optimal power flow algorithm. For this example application we consider two test scenarios: one consisting of a smaller network where 13 software agents are deployed, and a larger one involving 123 software agents. Using the proposed framework, scaling the test setup is automated, considerably reducing the required time and errors that typically appear during this process.