马拉加大学

Distribution System State Estimation (DSSE) is becoming increasingly important with the integration of Distributed Energy Resources and the active operation of distribution networks (DNs), but it remains challenging due to the limited and heterogeneous monitoring infrastructure available in these networks. To address this challenge, this paper proposes a novel DSSE framework that restores observability through data-driven pseudo-measurements generated by a Neural Network (NN), while preserving the exact non-linear AC power flow model within a classical Weighted Least Squares (WLS) estimator. Unlike conventional approaches that generate pseudo-measurements independently of the downstream state estimation, the proposed method explicitly couples both components through an end-to-end learning formulation. Specifically, the WLS estimator is embedded as a layer within the NN architecture, enabling implicit differentiation to propagate the impact of pseudo-measurements on the final estimation error back to the NN parameters. Extensive numerical experiments on the IEEE 30-bus and IEEE 33-bus systems demonstrate that the proposed framework consistently outperforms state-of-the-art methods in state estimation (SE) accuracy under a wide range of loading conditions and measurement configurations.

Topology optimization has emerged as a powerful and increasingly relevant strategy for enhancing the flexibility and efficiency of power system operations. However, solving these problems is computationally demanding due to their combinatorial nature and the use of big-M formulations. Optimization-based bound tightening (OBBT) is a well-known strategy to improve the solution of mixed-integer linear programs (MILPs) by computing tighter bounds for continuous variables. Yet, existing OBBT approaches in topology optimization typically relax all switching decisions in the bounding subproblems, leading to excessively loose feasible regions and limited bound improvements. In this work, we propose a topology-aware bound tightening method that uses network structure to determine which switching variables to relax. Through extensive computational experiments on the IEEE 118-bus system, we find that keeping a small subset of switching variables as binary, while relaxing the rest, strikes a sweet spot between the computational effort required to solve the bounding problems and the tightness of the resulting bounds.

Fully implicit time-discretization of hyperbolic systems can significantly reduce restrictions on the choice of time steps in numerical methods. The price to be paid may be the resolution of large fully coupled nonlinear algebraic systems to update the numerical solution. Therefore, several efforts have been done recently to reduce the complexity of such algebraic systems. In this work, we present a high-resolution well-balanced implicit scheme to solve numerically hyperbolic systems of balance laws. The scheme has a compact stencil which allows an efficient application of fast algebraic solvers like fast sweeping method. The scheme is formally second-order accurate in time and space, but space and time limiters are added to avoid unphysical oscillations in numerical solutions. The method produces nonlinear algebraic equations whose size is analogous to the first-order accurate fully implicit scheme. Moreover, the methods are well-balanced in the following sense: they preserve exactly all the continuous stationary solutions in the one-dimensional case whenever local equilibria can be obtained analytically and they are able to preserve a family of given stationary solutions in the two-dimensional case. The method is applied to Burgers’ equation with source term and the shallow water equations in 1D and 2D: several numerical experiments confirm these properties of the proposed scheme.

Industrial facilities may protect their own critical loads while creating voltage or loading problems in the distribution feeder that supplies them. This paper presents a deterministic, fixed-horizon resilience scheduler that couples industrial thermal and production states to a radial distribution network during preparation, an announced outage and recovery. The reported simulations solve one 168 h linear program at hourly resolution; they do not use receding-horizon state or forecast updates. Three analytical archetypes and six heat-stress profiles form 18 controlled cases on the IEEE 33-bus feeder, with LinDistFlow constraints followed by nonlinear AC checks. Relative to a cost-only schedule, the event-aware network-constrained schedule reduced unserved critical energy by 62.2%, peak import by 25.5% and recovery time by 44.8% for a 3.3% weekly cost increase. A 300scenario calibration sample gave violation frequencies of 69.3%, 1.3% and 0% for optimization floors of 0.953, 0.957 and 0.960 p.u., respectively; these are calibration-sample estimates rather than independent holdout results. A separate analysis of the public fora industrial-grid data used 13,344 fully aligned hours from 45 customers. The measured aggregate peak was 3.84 MW and the coincidence factor was 0.653; calculated incremental import headroom varied from 0.55 to 5.68 MW across customer buses under stated power-factor assumptions. The findings therefore constitute a controlled proof of concept for notified events, not field validation of a real-time controller.

Climate change (CC) is increasingly threatening the conservation of coastal areas worldwide, intensifying pressures from erosion, sea-level rise, and extreme weather events. This study assesses visitors’ willingness to pay (WTP) for beach conservation in Fuengirola (Spain), a high-density tourist destination on the Mediterranean Costa del Sol whose economy is strongly dependent on beach-based tourism. Using a contingent valuation method, the study estimates visitors’ WTP for beach conservation while considering socio-economic and attitudinal factors that influence payment preferences. Findings reveal that a significant proportion of beach visitors are willing to financially support conservation efforts, with variations based on factors such as nationality, visit characteristics, and CC awareness. Additionally, the results support the economic feasibility of implementing user-based funding into coastal conservation strategies, highlighting the role of visitor contributions in strengthening long-term coastal resilience and sustainable tourism in Mediterranean urban environments. This study enriches the broader literature on sustainable tourism and climate adaptation finance by providing empirical evidence from an urban coastal destination, where pressures from human activity intersect with rising climate vulnerabilities.