Functional Scalability and Replicability Analysis for Smart Grid Functions: The InteGrid Project Approach

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

Modellbasierte Regelstrategien für Energienetze in lokalen Energiegemeinschaften

Arbeit an der Bibliothek noch nicht eingelangt - Daten nicht geprüftAbweichender Titel nach Übersetzung der Verfasserin/des VerfassersKumulative Dissertation aus vier ArtikelThis Ph.D. dissertation provides a collection of tools and methods performed since 2017 at the Austrian Institute of Technology, in association with the Institute of Mechanics and Mechatronics, Vienna University of Technology. The research was conducted within the Blockchain Grid (FFG No. 868656) project funded by the Austrian Research Promotion Agency. The publications resulted from the cooperation between the Austrian Institute of Technology, Siemens AG Österreich, Energienetze Steiermark, and Vienna University of Technology.This Ph.D. presents research on control and optimization of the distribution grid and integrated energy assets in a local energy community. It also offers a solution to reconcile the physical settlement issue that a local energy market faces in a distribution grid by providing a method to limit the flexibilities to ensure overall grid security preemptively.For several years, the amount of intermittent distributed energy resources (DER’s) like photo-voltaic systems, wind generators, and new loads like electric vehicles, electric and thermal storage, and heat pumps has increased in distribution grids. Power system tools like load and optimal power flow, designed for transmission grids, are applied to distribution grids with limited or no modification. Since DER’s and loads directly depend on weather factors like ambient temperature, irradiation, and other external disturbances, they, in turn, affect the performance of these tools. Therefore, novel optimal grid control methods are to be developed which are compatible with distribution grids.This dissertation presents a novel three-phase unbalanced holomorphic embedding load flow method in conjunction with a non-convex optimization solver. Additionally, a novel three-phase unbalanced model-based energy management system is presented to manage the flexibilities that a smart home can offer. A control scheme is introduced to derive relations between the grid level optimal power flow and individual flexibility controller consisting of energy management systems. All the methods are demonstrated at a pilot in Heimschuh, Steiermark, Austria.9

Three-Phase Unbalanced Optimal Power Flow Using Holomorphic Embedding Load Flow Method

Distribution networks are typically unbalanced due to loads being unevenly distributed over the three phases and untransposed lines. Additionally, unbalance is further increased with high penetration of single-phased distributed generators. Load and optimal power flows, when applied to distribution networks, use models developed for transmission grids with limited modification. The performance of optimal power flow depends on external factors such as ambient temperature and irradiation, since they have strong influence on loads and distributed energy resources such as photo voltaic systems. To help mitigate the issues mentioned above, the authors present a novel class of optimal power flow algorithm which is applied to low-voltage distribution networks. It involves the use of a novel three-phase unbalanced holomorphic embedding load flow method in conjunction with a non-convex optimization method to obtain the optimal set-points based on a suitable objective function. This novel three-phase load flow method is benchmarked against the well-known power factory Newton-Raphson algorithm for various test networks. Mann-Whitney U test is performed for the voltage magnitude data generated by both methods and null hypothesis is accepted. A use case involving a real network in Austria and a method to generate optimal schedules for various controllable buses is provided

Phase Balancing Home Energy Management System Using Model Predictive Control

Most typical distribution networks are unbalanced due to unequal loading on each of the three phases and untransposed lines. In this paper, models and methods which can handle three-phase unbalanced scenarios are developed. The authors present a novel three-phase home energy management system to control both active and reactive power to provide per-phase optimization. Simplified single-phase algorithms are not sufficient to capture all the complexities a three-phase unbalance system poses. Distributed generators such as photo-voltaic systems, wind generators, and loads such as household electric and thermal demand connected to these networks directly depend on external factors such as weather, ambient temperature, and irradiation. They are also time dependent, containing daily, weekly, and seasonal cycles. Economic and phase-balanced operation of such generators and loads is very important to improve energy efficiency and maximize benefit while respecting consumer needs. Since homes and buildings are expected to consume a large share of electrical energy of a country, they are the ideal candidate to help solve these issues. The method developed will include typical distributed generation, loads, and various smart home models which were constructed using realistic models representing typical homes in Austria. A control scheme is provided which uses model predictive control with multi-objective mixed-integer quadratic programming to maximize self-consumption, user comfort and grid support