Adolfo Ibáñez University

Transmission expansion delays represents a critical barrier to the timely and cost-effective development of modern power systems, particularly for integrating renewable energy. Although network congestions arising from postponed major infrastructure projects (e.g., new transmission lines) can severely impact system efficiency, these effects can be mitigated through the rapid deployment of flexible technologies such as FACTS devices and Battery Energy Storage Systems (BESS). In this paper, we propose a two-stage stochastic optimization framework that complements first-stage, asset-intensive network expansion decisions with second-stage, scenario-dependent investments in flexible transmission assets, including FACTS and BESS. The formulation explicitly captures the probabilistic nature of project delays, accounting for zonal variations in the likelihood of such delays and their implications for system planning. Through a set of case studies, we demonstrate that the strategic use of FACTS and BESS can serve as an effective hedge against the adverse impacts of transmission expansion delays.

Power systems are being reshaped by decarbonization, digitalization, and high shares of renewables. At the same time, increasingly severe extreme conditions expose the limits of traditional reliability frameworks, calling for risk-aware, resilience-oriented approaches to address high-impact, low-probability (HILP) events. In this context, this paper presents a comprehensive overview of the foundations of power system resilience. It revisits the transition from reliability to resilience, formalizes key concepts and metrics, and introduces advanced approaches for resilience assessment, including fragility-based modeling, cascading failure analysis, and tail-risk indicators. The paper further examines resilience-oriented investment planning, operational strategies across all event phases, and the role of distributed energy resources, microgrids, and cybersecurity. The analysis highlights that resilience extends reliability by focusing on extreme conditions, fundamentally reshaping decision-making and requiring coordinated strategies across infrastructure, operation, and governance.

The growing interdependence between integrated electricity and gas systems (IEGS) calls for planning methods that capture their coupled long-term uncertainties and operational interactions. This paper develops a multi-stage stochastic framework for assessing the interplay of integrated electricity and gas systems (IEGS) in expansion planning under uncertainty, leveraging a scalable Column Generation and Sharing (CG-S) solution strategy to tackle computational challenges. The model jointly optimises investments in electricity transmission and generation, as well as natural gas (NG) processing facilities, under uncertainty in NG availability and prices, electricity demand growth, and capital costs. It integrates detailed hourly power system operations and daily gas flows, preserving realistic temporal granularity. Case studies on the Australian East Coast Energy System demonstrate that the CG-S algorithm reduces convergence times by up to 45% while maintaining stable memory use. The proposed planning framework enables the identification of a coherent pathway for strategic, coordinated investments across integrated electricity and gas systems, while uncoordinated deterministic practices risk oversizing early generation investments by up to 55% due to the inability to leverage cross-system synergies.

Retailers traditionally deploy demand response (DR) mainly to reduce procurement costs in energy markets, overlooking the potential to leverage flexible loads in reserve or flexibility markets. This paper proposes a two-stage, data-driven framework that enables retailers to stack revenues from energy and reserve markets through the aggregation of flexible demands. The first stage employs inverse optimization (IO) to infer flexible load parameters from historical price and consumption data, while the second stage formulates a bilevel model capturing the strategic interaction between the retailer and the system operator. The bilevel problem is reformulated as a Mathematical Program with Equilibrium Constraints (MPEC) and McCormick envelopes. Applied to the Chilean electricity market, results show that multi-market DR participation increases retailer profits fourfold and reduces system reserve procurement costs by 8.91% during the actives hours of the demand response procurement, underscoring DR’s role in enhancing flexibility and delivering system-wide savings.

The giant kelp Macrocystis pyrifera is a foundational species in marine coastal ecosystems, yet its microscopic life stages are particularly vulnerable to warming and pollution. We examined its seasonal phenology over an annual cycle initiated under the 2023—2024 El Niño warming at Horcón (central Chile), a coastal site strongly influenced by long-term industrial activity. Sori were collected in austral spring and summer to winter, and in vitro cultures were used to quantify settled spores, germination, gametophyte and sporophyte development, sex ratio, fecundity, and reproductive success. Sea surface temperature (SST) data showed strong seasonal contrasts, with warm anomalies in summer-autumn linked to the coastal El Niño, followed by markedly cooler conditions in winter. Winter cultures, after El Niño waned, reached 220,500 spores cm⁻2, nearly ten times higher than in spring and autumn and twenty times higher than in summer. Germination peaked in winter (96—100