Abstract
Taking advantage of the flexibility of Distributed Energy Resources (DER) can help improve distribution network efficiency, reliability and resilience. EUniversal project aims to facilitate the use of flexibility services and interlink active system management of distribution system operators with electricity markets. congestion management and voltage control have been identified has the most relevant needs, within different operation timeframes, namely: from day, weeks and years ahead. This paper considers long-term flexibility services to support maintenance actions, increasing the periods where is technically possible to perform maintenance actions maintaining security of operation. The methodology developed to schedule planned maintenance actions based on forecasted network profiles, maintenance costs, network reconfiguration capability and flexibility contracted in long-term flexible markets will be presented. © The Institution of Engineering and Technology 2023.

Abstract
The increasing integration of renewable energy sources (RES) at different voltage levels of the distribution grid has led to technical challenges, namely voltage and congestion problems. Conversely, the integration of new Distributed Energy Resources (DER) provides the necessary flexibility to accommodate higher RES integration levels. This work describes the development of innovative functional modules, based on optimal power flow calculations and grid forecasting, dedicated to the predictive management of the distribution grid considering DER flexibility, which are integrated into a commercial SCADA/DMS solution. © The Institution of Engineering and Technology 2023.

Abstract
As one of the functions integrating energy management systems, state estimation (SE) is instrumental in monitoring power networks, allowing the best possible use of energy resources. It plays a decisive role in debugging if sufficient data are available, ruined if not. Criticality analysis (CA) integrates SE as a module in which elements of the estimation process-taken one-by-one or grouped (tuples of minimal multiple cardinality)-are designated essential. The combinatorial nature of extensive CA (ExtCA), derestricted from identifying only low-cardinality critical tuples, characterizes its computational complexity and imposes defiant limits in implementing it. This paper presents the methodology for ExtCA and compares algorithms to find an efficient solution for expanding the boundaries of this analysis problem. The algorithms used for comparison are one sequential Branch&Bound (a well-known paradigm for combinatorial optimization recently used in ExtCA) and two new parallels implemented on the central processing unit (CPU) and the graphics processing unit (GPU). The conceived parallel architecture favors evaluating massive combinations of diverse cardinality measuring unit (MU) tuples in ExtCA. The acronym MU refers to the aggregate of devices deployed at substations, such as a remote terminal unit, intelligent electronic device, and phasor measurement unit. The numerical results obtained in the paper show significant speed-ups with the novel parallel GPU algorithm, tested on different and real-scale power grids. Since, the visualization of the ExtCA results is still not a well-explored field, this work also presents a novel way of graphically depicting spots of weak observability using MU-oriented ExtCA.