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Ítem Power Factor Compensation Using Teaching Learning Based Optimization and Monitoring System by Cloud Data Logger(MDPI, 2019-05-10) Cano-Ortega, Antonio; Sánchez-Sutil, Francisco; de-la-Casa-Hernández, JesúsThe main objective of this paper is to compensate power factor using teaching learning based optimization (TLBO), determine the capacitor bank optimization (CBO) algorithm, and monitor a system in real-time using cloud data logging (CDL). Implemented Power Factor Compensation and Monitoring System (PFCMS) calculates the optimal capacitor combination to improve power factor of the installation by measure of voltage, current, and active power. CBO algorithm determines the best solution of capacitor values to install, by applying TLBO in di erent phases of the algorithm. Electrical variables acquired by the sensors and the variables calculated are stored in CDL using Google Sheets (GS) to monitor and analyse the installation by means of a TLBO algorithm implemented in PFCMS, that optimizes the compensation power factor of installation and determining which capacitors are connected in real time. Moreover, the optimization of the power factor in facilities means economic and energy savings, as well as the improvement of the quality of the operation of the installation.Ítem A Platform for Analysing Huge Amounts of Data from Households, Photovoltaics, and Electrical Vehicles: From Data to Information(MDPI, 2022-12-01) Cano-Ortega, Antonio; García-Cumbreras, Miguel Ángel; Sánchez-Sutil, Francisco; de-la-Casa-Hernández, JesúsAnalytics is an essential procedure to acquire knowledge and support applications for determining electricity consumption in smart homes. Electricity variables measured by the smart meter (SM) produce a significant amount of data on consumers, making the data sets very sizable and the analytics complex. Data mining and emerging cloud computing technologies make collecting, processing, and analysing the so-called big data possible. The monitoring and visualization of information aid in personalizing applications that benefit both homeowners and researchers in analysing consumer profiles. This paper presents a smart meter for household (SMH) to obtain load profiles and a new platform that allows the innovative analysis of captured Internet of Things data from smart homes, photovoltaics, and electrical vehicles. We propose the use of cloud systems to enable data-based services and address the challenges of complexities and resource demands for online and offline data processing, storage, and classification analysis. The requirements and system design components are discussed.Ítem Design and Implementation of a Smart Energy Meter Using a LoRa Network in Real Time(MDPI, 2021-12-17) Sánchez-Sutil, Francisco; Cano-Ortega, Antonio; de-la-Casa-Hernández, JesúsNowadays, the development, implementation and deployment of smart meters (SMs) is increasing in importance, and its expansion is exponential. The use of SMs in electrical engineering covers a multitude of applications ranging from real-time monitoring to the study of load profiles in homes. The use of wireless technologies has helped this development. Various problems arise in the implementation of SMs, such as coverage, locations without Internet access, etc. LoRa (long range) technology has great coverage and equipment with low power consumption that allows the installation of SMs in all types of locations, including those without Internet access. The objective of this research is to create an SM network under the LoRa specification that solves the problems presented by other wireless networks. For this purpose, a gateway for residential electricity metering networks using LoRa (GREMNL) and an electrical variable measuring device for households using LoRa (EVMDHL) have been created, which allow the development of SM networks with large coverage and low consumption.Ítem Design and Testing of a Power Analyzer Monitor and Programming Device in Industries with a LoRA LPWAN Network(MDPI, 2021-02-11) Sánchez-Sutil, Francisco; Cano-Ortega, AntonioElectrical installations represent an important part of the industry. In this sense, knowing the state of the electrical installation in real time through the readings of the installed power analyzers is of vital importance. For this purpose, the RS485 bus can be used, which most electrical installations already have. An alternative to the bus wiring and its distance limitation is the use of low-power wide area networks (LPWAN). The long range (LoRa) protocol is ideal for industries due to its low-power consumption and coverage of up to 10 km. In this research, a device is developed to control all the reading and programming functions of a power analyzer and to integrate the device into the LoRa LPWAN network. The power analyzer monitor and programming device (PAMPD) is inexpensive and small enough to be installed in electrical panels, together with the power analyzer, without additional wiring. The information collected is available in the cloud in real time, allowing a multitude of analysis be run and optimization in real time. The results support high efficiency in information transmission with average information loss rate of 3% and a low average transmission time of 30 ms.Ítem Smart Public Lighting Control and Measurement System Using LoRa Network(MDPI, 2020-01-09) Sánchez-Sutil, Francisco; Cano-Ortega, AntonioThe installation of smart meters in smart cities to monitor streetlights (SLs) provides easy access to measurements of electrical variables and lighting levels, which improves the operation of installation. The use of smart meters in cities requires temporary high-resolution data to improve the energy e ciency (EE) of SLs. Long range (LoRa) is an ideal wireless protocol for use in smart cities due to its low energy consumption, secure communications, and long range indoors and outdoors. For this purpose, we developed a low-cost new system and successfully evaluated it by developing three devices, namely the measure and control device for street lights (MCDSL), lighting level measurement device (LLMD) and gateway LoRa network (GWLN), based on the Arduino open-source electronic platform. This paper describes the hardware and software design and its implementation. Further, an algorithm has been developed to enhance the energy e ciency of public lights using MCDSL, the energy e ciency for street lights (EESL) algorithm, that use the illumination level measured on the same set of SLs with a dynamic control, which assumed di erent lighting levels throughout the night, and adjusted luminous flux based on the tra c intensity of pedestrians. It sends the acquired data through the LoRa low-power wide-area-network (LPWAN) to the cloud.Ítem Modeling of PV Module and DC/DC Converter Assembly for the Analysis of Induced Transient Response Due to Nearby Lightning Strike(MDPI, 2021-01-08) Formisano, Alessandro; de-la-Casa-Hernández, Jesús; Petrarca, Carlo; Sánchez-Sutil, FranciscoPhotovoltaic (PV) systems are subject to nearby lightning strikes that can contribute to extremely high induced overvoltage transients. Recently, the authors introduced a 3D semianalytical method to study the electromagnetic transients caused by these strikes in a PV module. In the present paper we develop an improved model of the PV module that: (a) takes into account high-frequency effects by modelling capacitive and inductive couplings; (b) considers the electrical insulation characteristics of the module; (c) includes the connection to a DC/DC converter. The whole process involves three major steps, i.e., the magnetic-field computation, the evaluation of both common-mode- and differential-mode-induced voltages across the PV module, and the use of the calculated voltages as input to a lumped equivalent circuit of the PV module connected to the DC/DC converter. In such a framework, the influence of the PV operating condition on the resulting electrical stresses is assessed; moreover, the relevance or insignificance of some parameters, such as the module insulation or the frame material, is demonstrated. Finally, results show that the induced overvoltage are highly dependent both on the grounding of the conducting parts and on the external conditions such as lightning current waveforms and lightning channel (LC) geometry.Ítem Energy Management Model for a Remote Microgrid Based on Demand-Side Energy Control(MDPI, 2023-12-28) Benavides, Darío; Arévalo, Paul; Cano-Ortega, Antonio; Sánchez-Sutil, Francisco; Villa-Ávila, EdissonThe internet of things is undergoing rapid expansion, transforming diverse industries by facilitating device connectivity and supporting advanced applications. In the domain of energy production, internet of things holds substantial promise for streamlining processes and enhancing efficiency. This research introduces a comprehensive monitoring and energy management model tailored for the University of Cuenca’s microgrid system, employing internet of things and ThingSpeak as pivotal technologies. The proposed approach capitalizes on intelligent environments and employs ThingSpeak as a robust platform for presenting and analyzing data. Through the integration of internet of things devices and sensors, the photovoltaic system’s parameters, including solar radiation and temperature, are monitored in real time. The collected data undergo analysis using sophisticated models and are presented visually through ThingSpeak, facilitating effective energy management and decision making. The developed monitoring system underwent rigorous testing in a laboratory microgrid setup, where the photovoltaic system is interconnected with other generation and storage systems, as well as the electrical grid. This seamless integration enhances visibility and control over the microgrid’s energy production. The results attest to the successful implementation of the monitoring system, highlighting its efficacy in improving the supervision, automation, and analysis of daily energy production. By leveraging internet of things technologies and ThingSpeak, stakeholders gain access to real-time data, enabling them to analyze performance trends and optimize energy resources. This research underscores the practical application of internet of things in enhancing the monitoring and management of energy systems with tangible benefits for stakeholders involved.Ítem Performance Optimization LoRa Network by Artificial Bee Colony Algorithm to Determination of the Load Profiles in Dwellings(MDPI, 2020-01-21) Cano-Ortega, Antonio; Sánchez-Sutil, FranciscoThis paper presents a system to improve the performance of the Long Range (LoRa) network using an algorithm derived from the artificial bee colony (ABC), which obtains a minimum packet lost rate (PLR) in the LoRa network and allows to more accurately determine load profiles of dwellings, with smaller a time measurement and less data transmission. The developed algorithm calculates the configuration parameters of the LoRa network, monitoring in real time the data traffc, and is implemented in gateway LoRa network monitor (GLNM). Intelligent measurement equipment has been developed to determine the dwelling load profiles. This energy measurement device for dwelling (EMDD) measures the variables and consumption of electricity in each home with measurement times that can be configured. This research also develops the GLNM gateway, which monitors and receives data from the EMDDs installed and uploads them to the cloud using Firebase. This developed system allows to perform demand forecasting studies, analysis of home consumption, optimization of electricity tariffs, etc., applied to smart grids.Ítem Smart Control and Energy Efficiency in Irrigation Systems Using LoRaWAN(MDPI, 2021-10-24) Sánchez-Sutil, FranciscoIrrigation installations in cities or agricultural operations use large amounts of water and electrical energy in their activity. Therefore, optimising these resources is essential nowadays. Wireless networks offer ideal support for such applications. The long-range wide-area network (LoRaWAN) used in this research offers a large coverage of up to 5 km, has low power consumption and does not need additional hardware such as repeaters or signal amplifiers. This research develops a control and monitoring system for irrigation systems. For this purpose, an irrigation algorithm is designed that uses rainfall probability data to regulate the irrigation of the installation. The algorithm is complemented by checking the sending and receiving of information in the LoRa network to reduce the loss of information packets. In addition, two temperature and humidity measurement devices for LoRaWAN (THMDLs) and an electrovalve control device for LoRaWAN (ECDLs) were developed. The hardware and software were also designed, and prototypes were built with the development of the electronic board. The wide coverage of the LoRaWAN allows the covering of small to large irrigation areas.Ítem Influence of Data Sampling Frequency on Household Consumption Load Profile Features: A Case Study in Spain(MDPI, 2020-10-23) de-la-Casa-Hernández, Jesús; Sánchez-Sutil, Francisco; Cano-Ortega, Antonio; Baier, CarlosSmart meter (SM) deployment in the residential context provides a vast amount of data of high granularity at the individual household level. In this context, the choice of temporal resolution for describing household load profile features has a crucial impact on the results of any action or assessment. This study presents a methodology that makes two new contributions. Firstly, it proposes periodograms along with autocorrelation and partial autocorrelation analyses and an empirical distribution-based statistical analysis, which are able to describe household consumption profile features with greater accuracy. Secondly, it proposes a framework for data collection in households at a high sampling frequency. This methodology is able to analyze the influence of data granularity on the description of household consumption profile features. Its e ectiveness was confirmed in a case study of four households in Spain. The results indicate that high-resolution data should be used to consider the full range of consumption load fluctuations. Nonetheless, the accuracy of these features was found to largely depend on the load profile analyzed. Indeed, in some households, accurate descriptions were obtained with coarse-grained data. In any case, an intermediate data-resolution of 5 s showed feature characterization closer to those of 0.5 s.Ítem Monitoring of the E ciency and Conditions of Induction Motor Operations by Smart Meter Prototype Based on a LoRa Wireless Network(MDPI, 2019-09-16) Cano-Ortega, Antonio; Sánchez-Sutil, FranciscoThe installation of smart meters in the industry to monitor induction motors (IMs) provides easy access to the measurements of the electrical and mechanical variables, which improves the installation process. Using smart meters in industry requires temporary high-resolution data to improve the energy e ciency (EE) and power factor (PF) of IMs. For these purposes, Long Range (LoRa) is an ideal wireless protocol for the usage in industries due to its low energy consumption. In addition, it provides secure communications and long range indoors and outdoors. LoRa avoids the need to install antennas or routers to extend coverage, as each gateway can service 300 LoRa devices with distances of up to 10 km. For this purpose, this research successfully developed a new prototype for a low-cost IM E ciency Monitoring System using LoRa (IMEMSL). IMEMSL is based on the Arduino open-source electronic platform. It sends the acquired data through the LoRa low-power wide-area-network (LPWAN) and cloud access gateway. This document describes the hardware and software design and implementation. The experimental results are presented and discussed. Received Signal Strength Indicator (RSSI) and time on air measured endorse the use of LoRa for this type of meters. The approximate cost of the prototype was ¿72 and ¿93 for the versions without and with Global Positioning System (GPS), respectively.Ítem Development and Calibration of an Open Source, Low-Cost Power Smart Meter Prototype for PV Household-Prosumers(MDPI, 2019-08-07) Sánchez-Sutil, Francisco; Cano-Ortega, Antonio; de-la-Casa-Hernández, Jesús; Rus-Casas, CatalinaSmart meter roll-out in photovoltaic (PV) household-prosumers provides easy access to granular meter measurements, which enables advanced energy services. The design of these services is based on the training and validation of models. However, this requires temporal high-resolution data for generation/load profiles collected in real-world household facilities. For this purpose, this research developed and successfully calibrated a new prototype for an accurate low-cost On-time Single-Phase Power Smart Meter (OSPPSM), which corresponded to these profiles. This OSPPSM is based on the Arduino open-source electronic platform. Not only can it locally store information, but can also wirelessly send these data to cloud storage in real-time. This paper describes the hardware and software design and its implementation. The experimental results are presented and discussed. The OSPPSM demonstrated that it was capable of in situ real-time processing. Moreover, the OSPPSM was able to meet all of the calibration standard tests in terms of accuracy class 1 (measurement error 1%) included in the International Electrotechnical Commission (IEC) standards for smart meters. In addition, the evaluation of the uncertainty of electrical variables is provided within the context of the law of propagation of uncertainty. The approximate cost of the prototype was 60 € from eBay stores.Ítem Privacy-preserving energy management in local energy communities with EVs – An enhanced benders-like solution strategy(Elsevier, 2025-09-15) Tostado-Véliz, Marcos; Borghetti, Alberto; Siano, Pierluigi; Jurado-Melguizo, FranciscoLocal Energy Communities (LECs) are collectives of prosumers collaborating to reach common goals, such as the reduction of energy procurement costs and the provision of ancillary services to the network operator. They use the flexibility of modern residential installations, including rooftop photovoltaic (PV) systems and controllable loads, such as the charging stations of electric vehicles (EVs). A central unit, called community manager, usually coordinates the actions of prosumers. However, the need for large information exchange in this multi-agent framework is a problem for the widespread adoption of such models. Data privacy concerns between prosumers and the manager may deter participation. This paper presents a novel energy management strategy for LECs with EV charging stations that protects privacy. The proposed approach only shares dual variables with the community manager, while all primal variables, such as power schedules, remain private. The method uses Benders decomposition to solve the day-ahead energy management problem, which has a separable structure. To speed up the process, a Benders-bundle algorithm has been developed, which is faster than the basic Benders method. The method also makes it easy to include network constraints, so the results can be implemented in the network without congestions or voltage problems. The method is tested on a case of a community with 14 prosumers connected to a 15-bus radial low voltage distribution network. Results show that the new proposal performs as well as a centralized approach and is characterized by a good balance between solution accuracy and privacy preservation compared to other distributed and decentralized methods.Ítem An AHP-Based Multicriteria Framework for Evaluating Renewable Energy Service Proposals in Public Healthcare Infrastructure: A Case Study of an Italian Hospital(MDPI, 2025-09-03) Ventura, Cristina; Chiacchio , Ferdinando; D’Urso, Diego; Tina, Giuseppe Marco; Jiménez-Castillo, Gabino; Oliveri, Ludovica MariaPublic healthcare infrastructure is among the most energy-intensive of public facilities; therefore, it needs to become more environmentally and economically sustainable by increasing energy efficiency and improving service reliability. Achieving these goals requires modernizing hospital energy systems with renewable energy sources (RESs). This process often involves Energy Service Companies (ESCOs), which propose integrated RES technologies with tailored contractual schemes. However, comparing ESCO offers is challenging due to their heterogeneous technologies, contractual structures, and long-term performance commitments, which make simple cost-based assessments inadequate. This study develops a structured Multi-Criteria Decision-Making (MCDM) methodology to evaluate energy projects in public healthcare facilities. The framework, based on the Analytic Hierarchy Process (AHP), combines both quantitative (net present value, stochastic simulations of energy cost savings, and CO2 emission reductions) with qualitative assessments (redundancy, flexibility, elasticity, and stakeholder image). It addresses the lack of standardized tools for ranking real-world ESCO proposals in public procurement. The approach, applied to a case study, involves three ESCO proposals for a large hospital in Southern Italy. The results show that integrating photovoltaic generation with trigeneration achieves the highest overall score. The proposed framework provides a transparent, replicable tool to support evidence-based energy investment decisions, extendable to other public-sector infrastructures.Ítem Enhanced thermal models of photovoltaic modules by electrical operating conditions dependency(Elsevier, 2026-01-15) Tina , Giuseppe Marco; Osama, Amr; Gagliano, Antonio; Mannino, Gaetano; Muñoz-Rodríguez, Francisco José; Jiménez-Castillo, GabinoThe increasing penetration of photovoltaic (PV) systems poses challenges to the reliability and adequacy of power systems. To support grid stability, PV systems must evolve to be capable of providing frequency regulation and reserve services—including not only down frequency reserve but also up reserve. This latter service requires PV modules to operate away from their maximum power point (MPP), a condition that requires an enhancement in PV module thermal behavior assessment. Consequently, there is a growing need for advanced thermal models that account for electrical operating conditions to ensure accurate temperature prediction under all operating scenarios. While traditional thermal models primarily depend on meteorological inputs, they typically neglect the Electrical Operating Status (EOS). Overlooking this issue can lead to significant prediction errors—up to 5–7 ◦C—especially during operation away from MPP. The proposed investigation developed an enhanced thermal model incorporating EOS dependency by including the ratio of measured current to the calculated current at MPP as an additional input. Two cases of the Faiman and Sandia models were optimized using Genetic Algorithm, Particle Swarm Optimization, non-linear least squares, and polynomial regression. Optimization is performed using three identical PV systems operating under reference EOS conditions: open circuit, short circuit, and MPP. Results demonstrate that EOS-integrated models significantly improve temperature prediction accuracy. The EOS sensitive models achieved prediction errors as low as 0.1–1.13 % and R2 values above 0.91, outperforming traditional models that exhibited errors from 2 to 29 %. These findings support the need for EOSaware thermal modelling in modern PV system design and operationÍtem Risk-aware strategies for optimal participation of parking lots in day-ahead electricity markets(Elsevier, 2025-05-01) Tostado-Véliz, Marcos; Hasanien, Hany M.; Carpio, José; Jurado-Melguizo, FranciscoThe decarbonization of the mobility industry rules the massive deployment of charging infrastructures worldwide. Frequently, charging points are installed by private companies and entities, which pursue a monetary profit through providing charging services. It is therefore interesting looking for business opportunities that maximize the monetary profit of such infrastructures. In this regard, large-scale parking lots can partake in wholesale electricity markets, where they can buy or sell energy, thus actuating as a high-capacity virtual battery storage system. In this paper, a novel methodology for optimal participation of parking lots in day-ahead electricity markets is developed. The new proposal contributes with two main advantages compared to other similar research. On the one hand, we properly consider both charging and discharging modes of electric vehicles, which enable full participation in electricity markets as load or generator. On the other hand, an adaptive uncertainty-aware model is proposed and accommodated into the developed tool, thus casting as a unified framework that allows adopting both risk-averse and risk-seeker operational strategies. To this end, a tailored mathematical model is proposed, wherein uncertainties and binary variables related to operational statuses of batteries are properly accommodated via an original Benders’ decomposition algorithm. Moreover, different improvement strategies are proposed, thus resulting in a practical tool with potential fields of application in industry. A number of numerical results are provided to validate the new tool, as well as analyse how the adoption of risk-averse or risk-seeker strategies affects the strategic participation of parking lots in electricity markets.Ítem On the applicability of the alternating projections method for privacy-preserving scheduling in local energy communities(Elsevier, 2025-09-01) Tostado-Véliz, Marcos; Dolatabadi, Mohammad; Siano, Pierluigi; Jurado-Melguizo, FranciscoLocal energy communities enable proper infrastructure and management mechanisms to empower final users to partake actively in the operation of electrical systems while sharing resources to pursue common objectives. As an aggregated structure, suitable energy management and scheduling tools need to be developed and tested to ensure that local resources are properly operated to maximize the economy and efficiency of energy communities. However, final electricity users may be reluctant to share confidential information, which needs to be taken into account when developing novel computational tools for energy communities. This paper applies the well-known Alternating Projection Method (APM) and differential privacy (DP) to the day-ahead scheduling problem in energy communities. As a result, two novel iterative methodologies are proposed enabling decentralized privacy-aware resolution in energy communities. Different numerical results are discussed on 100 different community instances, analyzing both economic and energetic indicators. Specifically, with no added noise (sigma = 0) APM is numerically identical to the centralized benchmark across all cases . Additionally, for (0 < sigma < 1), the mean absolute percentage error in imported energy remains less than 20 %. Results reveal that the application of the APM is capable of reproducing exactly the results of the centralized approach, while the application of differential privacy may lead to large errors, especially regarding economic results when exportable capacity is large. Moreover, results reveal that the computational burden of the new methodologies is reasonable and therefore does not pose a barrier to their implementation. Indeed, as all steps in our implementation rely on Linear Programming (LP) and as there are many stable LP solvers (both open-source and commercial) it is easy for practitioners to deploy our approach for real-life scenarios. Our numerical experiments show that the considered privacy-aware techniques were quite efficient, achieving the solution in less than a minute in all cases. Moreover, the considered privacy-aware APM presents a highly parallelizable structure which allows the results to be even further improved.Ítem Optimizing proton exchange membrane electrolyzer performance through dynamic pressure and temperature control: A mixed-integer linear programming approach(Elsevier, 2025-12-01) Aguado-Molina, Roque; Tostado-Véliz, Marcos; Desideri, Umberto; Jurado-Melguizo, FranciscoHydrogen is a key energy carrier for decarbonizing multiple sectors, particularly when produced via water electrolysis powered by renewable energy. Proton exchange membrane (PEM) electrolyzers are well suited for this application due to their ability to rapidly adjust to fluctuating power inputs. Despite being conventionally operated at high temperatures and pressures to reduce heating and compression needs, recent studies suggest that under partial loads, lower operating conditions may enhance efficiency. This study introduces a novel optimization framework for dynamically adjusting pressure and temperature in PEM electrolyzers. The model integrates an efficiency map within a Mixed-Integer Linear Programming (MILP) formulation and applies McCormick tightening to address nonlinearities. A one-week case study demonstrates operational cost reductions of up to 12.5 % through optimal control, favoring lower temperatures and pressures at low current densities and higher temperatures near rated load, while maintaining moderate pressures. The results show improved efficiency and reduced hydrogen crossover, enhancing safety and enabling scalable application over extended time horizons. These insights are valuable for long-term planning and evaluation of hydrogen production and storage systems.Ítem Performance evaluation of photovoltaic self-consumption systems on industrial rooftops under continental Mediterranean climate conditions with multi-string inverter topology(Elsevier, 2025-12) Sánchez-Jiménez, José Luis; Jiménez-Castillo, Gabino; Rus-Casas, Catalina; Martínez-Calahorro, Antonio Javier; Muñoz-Rodríguez, Francisco JoséThis study analyses the performance and grid integration of seven photovoltaic self-consumption systems installed on industrial rooftops under real operating conditions. The analysed systems feature different orientations and peak power capacities, allowing to evaluate their behaviour through different indices and metrics proposed in the IEC 61724 and the literature, such as the Performance Ratio (PR), Final Yield (Yf) or Capacity Utilisation Factor (CUF), among others. Additionally, the interaction of these systems with the load and the electrical grid is examined and proposed, providing a better view of their operation. The results reveal that, although most systems operate close to expectations, with PR, Yf and CUF annual averages of 0.81, 1555.51 h/y and 0.19 respectively, factors such as partial shading, capture losses, and inverter configurations without grid injection affect their efficiency. The proposed Equivalent Capacity Utilisation Yield (YL), Load Ratio Yield (YGL), Load Ratio To Grid Yield (YGL,TG) and Load Ratio From Grid Yield (YGL,FG) are introduced to characterise the interaction between local demand and electrical grid constraints, providing a comprehensive assessment of system performance and grid use. A reduction in the full-load equivalent hours during which the interconnection node operates at its nominal capacity is observed following the integration of rooftop photovoltaic systems, as the values of YGL are consistently lower than those of YL. The results also indicate that the indices and metrics proposed in the literature, together with real data monitoring, are effective tools for evaluating the performance of photovoltaic systems and optimising their integration into electrical grids.Ítem Integrating organic Rankine cycles for waste heat recovery from onboard diesel generators in the maritime sector: Simulation and techno-economic assessment(ELSEVIER, 2025-05-21) Sánchez-Lozano, Daniel; Aguado-Molina, Roque; Escámez, Antonio; Hernández-Torres, José Antonio; Pérez-Torreglosa, Juan; Vera, DavidThe maritime sector's dependence on fossil fuels, coupled with the rising crude oil prices, underscores the urgent need to enhance ship efficiency and advance the decarbonization of the marine sector. This paper evaluates the technical and economic feasibility of integrating organic Rankine cycle (ORC) systems in diesel-electric propulsion marine distribution vessels. A comprehensive simulation and optimization of a 1.6 MW ORC unit, using acetone as the working fluid, has been conducted. The system is designed to recover waste heat from the exhaust gases of diesel generators aboard a vessel. Under an 85% load of the diesel generators, the ORC bottoming unit demonstrates a net electrical efficiency of 8.45% with a thermodynamic cycle efficiency of 18.73%. It is estimated that this system could reduce annual carbon dioxide emissions and diesel fuel consumption by 18.5% compared to conventional systems. From a financial perspective, assuming a conservative discount rate of 8%, the ORC system demonstrates long-term viability with a cumulative profit of 44% on the initial investment, a payback period of 11.7 years, and an internal rate of return of 12.8%. Additionally, the advantages of integrating the ORC technology with direct current distribution networks are highlighted, simplifying system architecture and improving energy efficiency.