Jorge Muñoz

Renewable generation and flexibility support sustainable electricity systems only when their response is network-deliverable and service can be restored after disturbances. This critical engineering review compares voltage-security, scheduling, converter and recovery models using an updated inventory of 150 publications. A source-linked appraisal of 40 studies separates thematic coverage from demonstrated evidence across steady-state, dynamic, field and service-recovery routes. Arrest, Voltage security, Energy reallocation, Restoration and Traceable operational coordination (AVERT) is an integrative framework implemented here through a claim-specific evidence protocol. Applications to South Australia 2016, Great Britain 2019, Texas 2021 and Iberia 2025 preserve the different event mechanisms and distinguish official findings from review interpretation. An Institute of Electrical and Electronics Engineers (IEEE) 14-bus example compares ideal active, reactive and combined support at three common locations, including equal-apparent-power allocations, under two reactive-limit treatments. Across 32 loading curves, enforcing non-reference reactive limits reverses the tested active-only versus combined-support ordering. The example establishes a static lower-voltage screening boundary, with low-inertia dynamics, physical collapse and restoration outside its scope. AVERT identifies the additional validation needed to turn a resource-level result into a network-deliverable operational claim. Hosting-capacity and renewable-utilization definitions distinguish installed MW from usable MWh, while scoped voltage, unbalance, flicker and harmonic criteria connect the assessment to normal-operation requirements.

The increasing integration of distributed energy resources and flexible loads has transformed university campuses into complex energy systems that require coordinated operational strategies capable of managing renewable uncertainty while maintaining economic and environmental performance. This paper proposes a two-stage stochastic mixed-integer linear programming (MILP) framework for the optimal day-ahead energy management of a smart university campus. The proposed model jointly coordinates photovoltaic generation, battery energy storage systems, electric vehicle charging, HVAC operation, and demand response under uncertainties associated with solar generation, electricity demand, energy prices, and ambient temperature. Unlike previous campus energy management approaches, the proposed framework explicitly distinguishes first-stage scheduling decisions from second-stage recourse actions, enabling adaptive operation while preserving decision consistency across uncertainty scenarios. A realistic case study based on the operational characteristics of the Universidad Politécnica Salesiana campus in Ecuador is used to evaluate the proposed methodology. The results demonstrate that the coordinated stochastic scheduling strategy reduces daily operating costs by 36.37%, decreases CO₂ emissions by 42.81%, and lowers peak grid demand by 37.99% compared with conventional operation. In addition, photovoltaic self-consumption reaches 91.7%, while renewable energy utilization increases to 93.4% without compromising occupant thermal comfort. The proposed framework provides a scalable pathway toward low-carbon, resilient, and energy-efficient smart campus operation.

Smart lighting is becoming a strategic component of intelligent and low-carbon urban infrastructure because it combines efficient illumination with connectivity, remote management, and continuous operational monitoring. This study proposes a reproducible unit-level methodological framework that transforms field records from a functional smart-lighting installation into traceable indicators of electrical performance, energy efficiency, avoided emissions, preliminary economic benefit, sensitivity, and conditional scalability. The approach treats the luminaire not only as an electrical load, but as a monitored urban energy node whose operation can be validated, characterized, and compared under planning-oriented control scenarios. The methodology integrates data preprocessing, electrical consistency assessment, representative baseline definition, scenario-based energy modeling, explicit environmental conversion, and conditional scaling to homogeneous lighting assets. The results reveal a stable electrical operating regime and show that managed operating conditions can generate sustained reductions in energy use and associated environmental impacts while preserving analytical transparency between measured variables and scenario-derived indicators. Sensitivity and multivariable analyses further support the robustness of the unit-level interpretation and highlight the value of monitored lighting data for comparative decision-making. The framework therefore provides a technically grounded reference for smart-city lighting management, energy planning, and scalable infrastructure assessment, with relevance to the objectives of SDG 7, SDG 11, and SDG 13. Overall, the study contributes an original data-driven perspective for integrating IoT-enabled lighting, remote supervision, and sustainability-oriented urban management within a common analytical structure.

Smart public lighting systems are increasingly evolving from energy-efficiency technologies into distributed urban data infrastructures. This study examines an IoT-based smart public lighting pilot implemented on a university campus in southern Quito, Ecuador, with the objective of quantifying its informational capacity, storage requirements, and scalability potential. The analysis was based on 11 smart luminaires and 49 days of real operational data. Measurements were recorded every 10 min, considering 10 variables per record, including measured consumption, reference consumption, active power, accumulated energy, operating hours, operational status, events, faults or alarms, energy savings, and avoided CO₂ emissions. The results show that each luminaire can generate 1440 data values per day. Under the same acquisition configuration, a network of 10,000 luminaires would generate approximately 5.256 billion data values per year, while a network of 100,000 luminaires would generate approximately 52.56 billion data values per year, requiring an estimated 1051.2 GB/year of storage. These values are interpreted as scalability scenarios rather than definitive city-wide predictions, given the limited scale and observation period of the pilot. As a methodological contribution, the study proposes the Smart Lighting Informational Capacity Index (SLICI) to estimate the daily data intensity generated by smart lighting networks. The findings demonstrate that the expansion of smart public lighting requires not only efficient luminaires, but also robust digital architectures for data storage, processing, interoperability, cybersecurity, and analytics. The study positions smart public lighting as a strategic platform for urban energy management, predictive maintenance, and data-driven sustainable planning, while highlighting the need for future validation over longer periods and across different urban typologies.

Indoor lighting systems are a significant contributor to building energy consumption while also directly affecting occupant comfort and circadian regulation. Recent advances in smart lighting have introduced adaptive and human-centric approaches; however, the integration of optimization-oriented control strategies with interoperable automation frameworks remains only partially articulated in the literature. This study presents a systematic bibliometric review of smart indoor lighting research, with particular attention to the roles of hyper-heuristics (HH), Internet of Things (IoT) platforms, and IFTTT/Event–Condition–Action (ECA) automation. A PRISMA-based methodology was applied across Scopus, Web of Science, and IEEE Xplore for the period 2010–2025. A total of 5529 records were identified, with 5229 screened after duplicate removal, and 27 core studies included following eligibility assessment. To reduce the risk of over-interpreting null intersections, the review also incorporated a search-sensitivity analysis based on expanded query formulations and title–abstract screening. Bibliometric analysis was conducted using MATLAB and VOSviewer to identify publication trends, technological clusters, and patterns of fragmentation across the literature. The results indicate rapid growth in IoT-based and energy-aware lighting systems, alongside mature research in circadian and comfort-driven lighting. However, explicit indexed evidence connecting hyper-heuristics with IoT platforms and IFTTT/ECA frameworks remains sparse and fragmented in the available literature. Co-occurrence analysis further reveals weak metadata-level connections between optimization techniques and IoT protocols, while the sensitivity analysis confirms that broadened retrieval improves recall but still yields only limited directly relevant evidence. Overall, the review identifies a gap in the explicit convergence of optimization, interoperable IoT infrastructure, and event-driven automation for human-centric indoor lighting. On this basis, it outlines a conceptual integration framework combining hyper-heuristics, IoT middleware, and event-driven control. The findings provide a structured roadmap for future research and implementation-oriented studies aimed at improving both energy efficiency and human-centric comfort in smart indoor environments.

Radial distribution feeders are especially sensitive to reactive-power deficits, which increase technical losses, deteriorate voltage profiles, reduce energy efficiency, and indirectly raise the emissions associated with the energy required to supply those losses. In this context, this paper proposes a sustainability-oriented planning methodology for the location and sizing of capacitor banks in radial distribution feeders, aimed at jointly improving technical performance, economic viability, and sustainability-related energy benefits. The problem is formulated as a discrete multi-objective model and solved through a constructive Greedy heuristic combined with backward/forward sweep load-flow evaluation, considering commercially available capacitor sizes. The methodology is validated on the IEEE 34-bus feeder, a demanding benchmark that remains less frequently used than the IEEE 33- and 69-bus systems in recent capacitor-planning studies. Seven scenarios are analyzed, from the uncompensated base case to configurations with up to six capacitor banks. The results show that all compensated scenarios improve feeder performance, reducing active losses from 25.3327 kW to a minimum of 20.1468 kW, equivalent to a maximum reduction of 20.47%, and increasing the minimum nodal voltage from 0.95528 p.u. to 0.97038 p.u. From a purely financial perspective, the one-bank scenario yields the highest net present value (USD 16,358.86), whereas the two-bank scenario emerges as the most balanced solution within the evaluated set, with annual savings of USD 5432.29 and a net present value of USD 11,497.58. Overall, the results confirm that capacitor-bank planning should be addressed as a trade-off among electrical efficiency, voltage support, profitability, and sustainability-oriented benefits. The proposed framework provides a simple, reproducible, and interpretable planning tool for radial distribution feeders.

The energy transition and electrification are increasing the use of power electronics in low-voltage networks, increasing losses and reducing service availability when harmonic currents are concentrated in the neutral. This study statistically evaluates power quality in a campus-type microgrid with a high proportion of nonlinear loads. The novelty of the work lies in combining field measurements, percentile-based neutral-current severity analysis, and standards-based comparative mitigation assessment in a low-voltage 3P4W campus microgrid. A campaign was carried out using a Fluke 1775 analyzer, recording trends, frequency, and events. Approximately 1900 events were recorded, mainly waveform deviations, interruptions, and rapid voltage changes. Voltage distortion was moderate, with a 95th percentile between 3.6% and 3.8%, while the neutral conductor concentrated the highest severity: neutral-current THD exceeded 220% in the 95th percentile and reached maximums above 700%, with 16.78 A in the 95th percentile at the measurement point. Based on IEC 61000-2-2 and IEEE 519, four mitigation measures were evaluated in DIgSILENT PowerFactory 2024 to estimate and reduce losses and heating: load balancing, detuned compensation, passive filtering, and active filtering. Active mitigation reduced the neutral harmonic component by 80% and the combined strategy decreased the neutral current at the measuring point by 78% (16.78 A to 3.69 A), with an estimated reduction in resistive losses of close to 95%. These results suggest sustainability benefits by reducing energy wasted as heat, extending the useful life of the infrastructure and improving operational resilience.