Information and Communication Technology (ICT) infrastructures are at the heart of emerging Smart Grid scenarios with high penetration of Distributed Energy Resources (DER). The scalability of such ICT infrastructures is a key factor for the large scale deployment of the aforementioned Smart Grid solutions, which could not be ensured by small-scale pilot demonstrations. This paper presents a novel methodology that has been developed in the scope of the H2020 project InteGrid, which enables the scalability analysis of ICT infrastructures for Smart Grids. It is based on the Smart Grid Architecture Model (SGAM) framework, which enables a standardized and replicable approach. This approach consists of two consecutive steps: a qualitative analysis that aims at identifying potential bottlenecks in an ICT infrastructure; and a quantitative analysis of the identified critical links under stress conditions by means of simulations with the aim of evaluating their operational limits. In this work the proposed methodology is applied to a cluster of solutions demonstrated in the InteGrid Slovenian pilot. This pilot consists of a Large Customer Commercial Virtual Power Plant (VPP) that provides flexibility in medium voltage for tertiary reserve and a Traffic Light System (TLS) to validate such flexibility offers. This approach creates an indirect Transmission System Operator (TSO)—Distribution System Operator (DSO) coordination scheme.
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.
The energy transition into a modern power system requires energy flexibility. Demand Response (DR) is one promising option for providing this flexibility. With the highest share of final energy consumption, the industry has the potential to offer DR and contribute to the energy transition by adjusting its energy demand. This paper proposes a mathematical optimization model that uses a generic data model for flexibility description. The optimization model supports industrial companies to select when (i.e., at which time), where (i.e., in which market), and how (i.e., the schedule) they should market their flexibility potential to optimize profit. We evaluate the optimization model under several synthetic use cases developed upon the learnings over several workshops and bilateral discussions with industrial partners from the paper and aluminum industry. The results of the optimization model evaluation suggest the model can fulfill its purpose under different use cases even with complex use cases such as various loads and storages. However, the optimization model computation time grows as the complexity of use cases grows.
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