Gil Silva Sampaio

Abstract
The deployment of smart metering technologies in the low voltage (LV) grid created conditions for the application of data-driven monitoring and control functions. However, data privacy regulation and consumers’ aversion to data sharing may compromise data exchange between utility and customers. This work presents a data-driven method, based on smart meter data, to estimate linear sensitivity factors for three-phase unbalanced LV grids, which combines a privacy-preserving protocol and varying coefficients linear regression. The proposed method enables centralized and peer-to-peer learning of the sensitivity factors. Potential applications for the sensitivity factors are demonstrated by solving voltage violations or computing operating envelopes in a LV grid without resorting to its network topology or electrical parameters. © 2022 Elsevier B.V.

Abstract
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.

Abstract

Abstract
This paper describes an approach to the short term operation planning of hydro stations in market environment. The developed approach is based on the solution of an optimization problem to maximize the profit of a generation agent along a planning period discretized in hourly steps using a Genetic Algorithm. This problem includes the possibility of pumping since this is an important resource in the scope of electricity markets. The scheduling problem was developed starting with an initial simplified version in which the head loss is neglected and the head is assumed constant. Then, it was implemented a second model in which the nonlinear relation between the head, the hydro power and the water discharge is retained and finally an approach in which the hydro schedule obtained in a given step is used to update the hourly electricity prices used to compute the profit of the generation agent. The short term hydro scheduling problem is illustrated using two Case Studies - the first one was designed to run a set of initial tests to the developed algorithm and the second one refers to a set of hydro stations that mirrors a cascade of 8 stations in Portugal. © 2013 IEEE.