The transition of electric systems from a centralized, fossil-based model toward a decentralized, renewable-powered architecture is reshaping the way electricity is generated, managed and consumed. As distributed energy resources (DERs) proliferate, grid management becomes increasingly complex, especially at the distribution level. In this context, flexibility emerges as a key enabler for more stable and efficient grid operation, while also facilitating greater integration of DER and supporting the electrification of energy demand. Local flexibility markets (LFMs) are gaining importance as structured mechanisms that allow grid operators to procure flexibility services from prosumers, aggregators and other actors. However, to ensure widespread participation, it is essential to develop digital tools that accommodate users of different profiles, regardless of their size, technical background or market experience. The REEFLEX project addresses this challenge by designing and developing 14 interoperable flexibility tools tailored to diverse stakeholder needs. To ensure that these tools are aligned with real market conditions and user expectations, REEFLEX conducted extensive stakeholder-centric surveys. This paper presents the methodology and key findings of those surveys, providing insights into user perceptions, technical requirements and adoption barriers. Results are contextualized within existing literature and other funded initiatives, highlighting implications for the design of inclusive and scalable flexibility markets.

The undergoing energy transition to a sustainable future requires, among other things, the application of demand side management (DSM) techniques to maintain grid stability and allow a smooth performance. To successfully implement DSM strategies, near real-time monitoring of the grid is required. This can be achieved through a distribution system state estimator (DSSE). Conventional approaches to state estimation (SE) typically rely on the assumption of a balanced reference bus, which is reasonable for transmission systems but may not be applicable to low-voltage distribution networks, even more with significant distributed generation (DG) penetration. To address this problem, a branch current-based low-voltage DSSE for unbalanced three-phase systems is developed. The algorithm incorporates a virtual bus to account for highly unbalanced systems, enabling it to obtain a more accurate estimation of the grid state. The proposed method is compared to the conventional balanced reference bus method through multiple simulations under different load conditions in the IEEE European low-voltage test feeder.

The growing penetration of Distributed Energy Resources (DERs)—such as photovoltaic generation, battery energy storage, electric vehicles, hydrogen technologies and flexible loads—requires advanced Energy Management Systems (EMS) capable of coordinating their operation and leveraging controllability to optimize microgrid performance and enable flexibility provision to the grid. When the physical, electrical, and economic system model is properly defined, the main sources of performance degradation typically arise from forecast uncertainty and temporal discretization effects, which propagate into sub-optimal schedules and infeasible setpoints. This paper proposes and tests a two-stage deterministic EMS architecture featuring rolling-horizon planning at an upper layer and fast local setpoint adaptation at a lower layer, jointly to reduce the impact of forecast errors and other uncertainties on the objective function. The first stage can be deployed either on the edge or in the cloud, depending on computational requirements, whereas the second stage is executed locally, close to the physical assets, to ensure timely corrective action. In the simulated cloud-executed planning case, moving from hourly to 15 min granularity improves the objective value from −49.39€ to −72.12€, corresponding to an approximate 46% reduction in operating cost. In our case study, the proposed second-stage local adaptation can reduce the mean absolute error (MAE) of the EMS performance loss by approximately 50% compared with applying the first-stage schedule without local correction. Results show that this two-stage hierarchical EMS effectively limits objective-function degradation while preserving operational efficiency and robustness.

Rolling-horizon energy management systems (EMSs) and model predictive control (MPC) for microgrids in smart cities face a fundamental trade-off: finer temporal discretization improves operational performance but rapidly increases the size of the optimization problem and execution time, jeopardizing real-time feasibility. Furthermore, in short-horizon operation, only the first control actions are implemented, while long-horizon decisions primarily guide feasibility and constraints. This paper proposes a computation-aware temporal mesh design layer that jointly selects a variable granularity of blocks and an optimization horizon, explicitly bounded by market-aligned settlement steps and per-cycle computation budgets. Candidate configurations are represented as pairs ⟨B, H⟩, where B is a constant-step block programme, and H is the optimization horizon, and they are uniquely tracked through an auditable mesh signature. The method first evaluates a predefined, market-consistent set of solutions ⟨B, H⟩ to establish reproducible cost and execution-time benchmarks, then applies a greedy value-of-information (Greedy-VoI) search that generates valid neighbouring meshes through local refinement, thickening, and resolution reallocation without violating the basic requirements that every solution must meet. All candidates are evaluated using the same microgrid use case and the same comparative KPIs, enabling the systematic identification of near-optimal mesh–horizon designs for practical EMS implementation.

This paper is a study about the attitudes of Vocational Training students towards the use of ICT, especially after the pandemic that has forced its daily use in education, as well as towards STEM.

Rolling horizon energy management systems (EMS) and model predictive control (MPC) for microgrids in smart cities face a fundamental trade-off: finer temporal discretization improves operational characteristics but rapidly increases the size of the optimization problem and execution time, jeopardizing real-time feasibility. Furthermore, in short-horizon operation, only the first control actions are implemented, while long-horizon decisions primarily guide feasibility and constraints. This paper proposes a computation-aware temporal mesh design layer that jointly selects a variable granularity of blocks and an optimisation horizon, explicitly bounded by market-aligned settlement steps and per-cycle computation budgets. Candidate configurations are represented as pairs ⟨B, H⟩, where B is a constant-pass block programme, and H is the horizon, and are uniquely tracked through an auditable mesh signature. The method first evaluates a predefined, market-consistent set to establish reproducible cost and execution time benchmarks, then applies a Greedy-Value-of-Information (Greedy-VoI) search that generates valid neighbouring meshes through local refinement, thickening, and resolution reallocation without breaking the horizon mosaic or admissible step sizes. All candidates are evaluated using the same microgrid use case and the same comparative KPIs, enabling the systematic identification of near-optimal mesh-horizon designs for practical EMS implementation.