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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, Francisco
Local 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.
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 Maria
Public 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.
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, Gabino
The 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 operation
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, Francisco
The 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.
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, Francisco
Local 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.
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, Francisco
Hydrogen 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.
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 José
This 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.
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, David
The 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.
Low-cost real-time monitoring and automated control system for a bench-scale portable downdraft gasifier
(ELSEVIER, 2025-06-09) Rodríguez-Orta, Antonio; Sánchez-Raya, Manuel; Aguado-Molina, Roque; Gómez-Galán, Juan Antonio; Vera, David; López-García, Diego
This research work focuses on the development of a real-time monitoring and automated control system with remote access, as well as integrated data collection and storage, for a portable biomass gasification prototype to generate electricity from agricultural waste. The prototype consists of an air-blown downdraft fixed-bed gasifier and a producer gas conditioning unit, which operate together in a remotely controlled ensemble. The proposed system stands out for its compact size, transportability, and low-cost design, making it suitable for implementation in small agricultural facilities, especially in areas where conventional electrification is limited or non-existent. Two preliminary tests were conducted to evaluate the performance of the monitoring system. In the first test, the system achieved a target temperature of 600 °C in less than 20 minutes and maintained it within a variation range of ±25 °C. After holding this temperature for an hour, the setpoint was raised to 800 °C, with the system achieving the new target in less than 10 minutes. In the second test, a setpoint of 800 °C was reached in 16 minutes, with an additional 3 minutes required for stabilization. Both tests, lasting approximately 4 hours each, consumed a total of 13.43 kg of biomass. The results demonstrate the system's ability to reach target temperatures in less than 25 minutes while maintaining stable temperature oscillations. The system's graphical interface enables intuitive, real-time, and remote monitoring and management of temperatures in several zones along the gasifier's height. Additionally, the interface allows manual or algorithmic control of the system's actuators, with the ability to modify the control algorithms through over-the-air updates.
A comparative sustainability assessment of several grid energy storage technologies
(ELSEVIER, 2025-06-13) Aguado-Molina, Roque; Cartelle-Barros, Juan José; de-la-Cruz-López, María Pilar; Lara-Coira, Manuel; del-Caño-Gochi, Alfredo
The global energy transition toward a low-carbon economy is driving increasing penetration of variable energy sources into electricity markets. This unprecedented deployment of intermittent renewables confronts decision-makers in the electricity sector with the challenge of selecting among different energy storage technologies, a choice that must be made on the basis of sustainability criteria. Existing studies present shortcomings, including the absence of the social dimension, the use of weights against sustainable development, or the application of methodologies affected by the rank reversal issue, among others. To address gaps in current knowledge, this study presents a novel probabilistic model for assessing the global sustainability of grid energy storage technologies. The model is based on the MIVES (Modelo Integrado de Valor para una Evaluación Sostenible)–Monte Carlo method, which combines requirement trees, value functions, the analytic hierarchy process, and probabilistic simulations. It consists of 19 indicators and makes it possible to obtain a sustainability index (SI), as well as partial economic, social, environmental, and technical indices for each technology. Data from an extensive literature review were integrated with expert input and estimations based on linear correlations to address challenges in assessing social and environmental indicators. The model was applied to six technologies: pumped hydroelectric energy storage (PHES), compressed air energy storage (CAES), liquid air energy storage (LAES), vanadium redox flow batteries (VRFB), sodium-sulfur batteries (NaSB), and hydrogen energy storage (HES). A comprehensive sensitivity analysis is also included. To the best of the authors’ knowledge, no existing study has utilized the innovative methodology presented in this paper, nor has any related research achieved the scope and depth proposed here. The top-performing technologies identified for the economic, social, environmental, and technical dimensions of sustainability are CAES, VRFB, LAES, and PHES, respectively. In terms of global sustainability, VRFB, LAES and PHES are the best options, while HES consistently ranks last. NaSB and CAES occupy intermediate positions.
An ensemble multi-ANN approach for virtual oxygen sensing and air leakage prediction in biomass gasification plants
(ELSEVIER, 2025-01-16) Escámez, Antonio; Aguado-Molina, Roque; Sánchez-Lozano, Daniel; Jurado-Melguizo, Francisco; Vera, David
A recurring challenge in the operation of biomass gasification plants is the occurrence of air leaks, which prevent the resulting lean producer gas from meeting the required standards for power generation. In order to address this issue, an ensemble model composed of multiple artificial neural networks (ANNs) was developed to predict the oxygen concentration in the gas mixture and detect anomalous operating conditions (air leakage). Throughout an extensive experimental campaign, the volumetric composition of the gas mixture from a semi-industrial scale downdraft gasifier fueled with biomass pellets was systematically measured and recorded at a constant time step of 10 s using an inline portable syngas analyzer equipped with NDIR, TCD and ECD sensors. The ensemble multi-ANN model was trained with a total of 24 representative datasets, including instances of both normal and anomalous operating conditions, using k-fold cross validation with 10 submodels. The results revealed an R² of 0.99 and an RMSE below 0.3, indicating that the model’s error margin is lower than that of the ECD sensor. The developed model can serve as a supervisor for the ECD sensor by performing a double verification or even potentially replacing the ECD sensor, with the model assuming the task of predicting the oxygen concentration using the data recorded by the NDIR sensor.
Sustainability of gasification-based cogeneration with agri-food residues and heat recovery technologies: Techno-economic and life cycle analyses
(ELSEVIER, 2025-04-11) Anvari, Simin; Aguado-Molina, Roque; Vera, David; Jurado-Melguizo, Francisco; Rosen, Marc A.
The valorization of agri-food waste through biomass gasification integrated with heat recovery technologies is a promising option for sustainable renewable energy. Comprehensive evaluation of such energy systems requires both life cycle assessments (LCAs) and techno-economic analyses (TEAs). This study investigates the potential of three agri-food residues—almond hulls, exhausted olive pomace (EOP), and date palm fronds (DP)—as biomass fuels for gasification from a sustainability standpoint. Two configurations for combined production of electricity and heat in the form of hot water are evaluated: one using a cleaning and cooling unit coupled to an internal combustion engine (ICE), and another using an externally fired gas turbine combined with an organic Rankine cycle bottoming unit (EFGT_ORC). Results reveal that the EFGT_ORC cogeneration system consistently requires lower biomass input than the ICE cogeneration unit, with DP fronds demanding the highest biomass input in the ICE configuration at 36 g/s, followed by almond hulls at 32 g/s, and EOP at 28 g/s. ICE cogeneration contributes to higher climate change environmental impact, with emissions around 2.95 × 10−2 kg CO2 eq. for all fuels. In terms of human health, DP fronds have a greater impact in EFGT_ORC cogeneration than in ICE. Almond hulls exhibit slightly better economic performance compared to EOP and DP fronds. However, regardless of the biomass fuel, biomass and electricity price variations significantly affect system sustainability. The ICE system offers faster returns on investment, but is more vulnerable to increasing biomass prices, whereas the EFGT-ORC system demonstrates more resilience to biomass price fluctuations.
Techno-economic assessment of an off-grid biomass gasification CHP plant for an olive oil mill in the region of Marrakech-Safi, Morocco
(MDPI, 2023-05-12) Sánchez-Lozano, Daniel; Escámez, Antonio; Aguado-Molina, Roque; Oulbi, Sara; Hadria, Rachid; Vera, David
A substantial number of off-grid olive oil mills in Morocco are powered by diesel-fired generators, which hugely contribute to air pollution and greenhouse gas emissions. In this research work, a biomass gasification combined heat and power (CHP) plant fueled with local by-products was explored as a renewable alternative to electrify off-grid olive oil mills in this country. The case study considered a gasification CHP plant with a rated power of 80 kWe, in order to enable adaptation of the producer gas flow rate to abrupt changes in the power generation unit under dynamic operation. A downdraft gasifier and a producer gas conditioning unit were modeled under steady state operation using Cycle-Tempo, while the power generation unit was modeled in the Thermoflex simulation environment under partial and full load operation. Olive cake pellets and olive pruning chips were evaluated as biomass feedstock, with moisture contents ranging from 5% to 20% (wet basis). The results from the simulation of the gasification CHP plant showed net electrical efficiencies and CHP efficiencies around 18% and 35%, respectively. Finally, a profitability assessment of the gasification CHP plant was developed for 2 months of continuous operation, together with a sensitivity analysis. The results for the baseline scenario reveal a payback period of 7–8 years and a 68.5% accumulated profit based on the capital investment, which suggest that biomass gasification CHP plants can represent an economically feasible and sustainable solution for the electrification of off-grid areas in Morocco.
Power Gain and Daily Improvement Factor in Stand-Alone Photovoltaic Systems With Maximum Power Point Tracking Charge Regulators. Case of Study: South of Spain
(American Society of Mechanical Engineers, 2013-11) Muñoz-Rodríguez, Francisco José; Jiménez-Castillo, Gabino; Fuentes-Conde, Manuel; Aguilar-Peña, Juan Domingo
The performance reliability of a stand-alone photovoltaic system (SAPV) depends on the long-term performance of the batteries. In this way, a charge controller becomes an essential device which not only prevents the batteries from suffering deep discharges and overvoltages but also monitors the battery state of charge (SOC) in order to maximize charging efficiency and energy availability. At present, pulse width modulated (PWM) charge regulators dominate the market for this type of component in SAPV systems. However, in recent years, to improve energy management, more manufacturers have developed controllers with strategies for maximum power point tracking (MPPT). PWM charge controllers do not always make optimum use of the available power given by the maximum power point and this gives a loss of power. These power losses depend on battery voltage, irradiance and temperature. However, they can be avoided by using a MPPT charge controller which operates the array at its maximum power point under a range of operating conditions, as well as regulating battery charging. The advantage, in terms of energy gain, provided by this type of charge regulator depends on weather conditions. This paper will study the power gain provided by this type of charge controller, depending on the module temperature and the battery voltage. The paper will, additionally, provide a study of the gain in energy yield, also shown as improvement factor, F, for SAPV systems installed in Jaén (South of Spain). This study may illustrate the behavior of these two types of charge controllers in warm weathers, like Mediterranean climates. Furthermore, it will analyze the suitability of MPPT charge controllers and their benefits in this type of climate. It will be shown that MPPT charge regulator global efficiency constitutes a key issue in making a choice between MPPT and PWM charge regulators. The results given here may be not only of interest for SAPV systems with no access to the electricity grid but also for battery back-up PV grid-connected PV (GCPV) systems.
A new tool to analysing photovoltaic self-consumption systems with batteries
(Elsevier, 2021-05) Muñoz-Rodríguez, Francisco José; Jiménez-Castillo, Gabino; de-la-Casa Hernández, Jesús; Aguilar-Peña, Juan Domingo
Most of the studies that can be found in the literature for analysing self-consumption systems with storage focus on global self-consumption and self-sufficiency indices and it may be very difficult to define the role of the array power and battery. In this sense, a new approach to analysing this type of systems is provided where direct and battery self-sufficiency and self-consumption indices are defined. The latter represent the direct photovoltaic self-consumed energy and the one provided by the battery. New direct and battery ZEB points are also presented. Furthermore, this type of system is generally analysed using complex 3D plots. Therefore, a new and intuitive 2D contour tool is provided: the iso selfconsumption curves. The new approach has been applied to three households located in Spain. Results show that it may be reached a global self-sufficiency of 50% considering array powers and rated capacities below 3.5 kWp and 1 kWh, respectively, where direct and battery self-sufficiency indices may reach 40% and 10%, respectively. This new method together with the graphical tool may help not only to analyse this type of system but to properly size the array power and the rated capacity from either an energetic or profitability approach.
Distributed generation and photovoltaic selfconsumption. Energy potential for the olive mill industries in Spain
(Publicaciones DYNA SL, 2020-09) Martínez-Calahorro, Antonio Javier; Jiménez-Castillo, Gabino; Rus-Casas, Catalina; Muñoz-Rodríguez, Francisco José
The industrial sector faces a new paradigm of energy offshoring, where distributed generation can play a leading role in reducing energy costs in industries, as well as in its C02 emissions. This work shows the potential that photovoltaic self-consumption systems can present to face part of the consumption in the industries of the agri-food sector, specifically the oil mills. The electrical consumption of this type of industry for an oil mill is analyzed, as well as the level of coupling between the actual consumption profiles and the estimated photovoltaic generation profiles for a given range of powers of the photovoltaic generator. The analysis method is easily extrapolated to any mill located in Spain. Likewise, and given that this type of industry has a very characteristic consumption profile, the results obtained are easily transferable to other oil mills. For the mill analyzed, and from an annual perspective, a level of use of the generated photovoltaic energy of 75% with a self-sufficiency index of 20% has been estimated, highlighting the great potential of this technology, as an energy option in this type of industry, as well as in any other that presents a consumption with little variability.
Effects of smart meter time resolution when analyzing photovoltaic self-consumption system on a daily and annual basis
(Elsevier, 2021-02) Jiménez-Castillo, Gabino; Rus-Casas, Catalina; Tina, Giuseppe Marco; Muñoz-Rodríguez, Francisco José
The management of photovoltaic self-consumption systems is based mainly on updating energetic parameters such as generation and household power consumption connected via smart devices. The expected rapid increasing volume of data collected with different time resolutions is surely a topic that deserves great attention. The choice of a proper recording interval should balance the amount of monitored data and a proper energy analysis in order not only to take effective and timely decisions but also to help this technology to be more efficient. In the literature, only specific nominal array powers for annual reporting period or an array power range for daily reporting period have been considered. In this context, the error, when matching photovoltaic generation and household power consumption profiles considering different recording intervals (1, 10, 15, 30 and 60 min) and different reporting periods (daily and annual), will be estimated as a function of the array power (up to 10 kWp) for five households and a resident’s association. Results depend on the reporting periods and it may be advisable to use 1 min and 10 min of recording intervals in order to estimate performance metrics in this type of system for a daily and annual basis, respectively.
Performance analysis indices for Rooftop Solar Photovoltaic system
(IEEE, 2023-07-09) Jiménez-Castillo, Gabino; Martínez-Calahorro, Antonio Javier; Rus-Casas, Catalina; Snytko, Anastasiia; Muñoz-Rodríguez, Francisco José
The integration of rooftop solar photovoltaic systems into the electricity grid may be crucial in the current energy scenario. At present, this type of electricity generation is cost-competitive in many countries due to its modularity, the availability of the solar resource and the cost of the components, without the need for subsidies. Rooftop Solar Photovoltaic systems have the potential to cover 20-30% of electricity demand in Spain. In order to assess the potential of this technology and to facilitate the deployment of this type of systems, it is very important to provide a proper performance analysis of PV Rooftops systems from monitored data. In this way, self-consumption and self-sufficiency indices are commonly used, however they may not provide a complete assessment. Hence, indices such as the self-sufficiency index for sunshine hours, self-production index and grid-liability rate are also analyzed. These indices estimate the performance of rooftop solar PV systems and provide maximum and minimum values when estimated as a function of array peak power. Moreover, new indices such as the self-production index and the grid-liability rate for sunshine duration have been developed to estimate the system's performance during sunshine hours. These indices can complement the commonly used metrics and improve the performance analysis from monitored data. Moreover, they may also help determine the proper size of the array power of these systems in the industrial sector. The metrics are evaluated using data from four canning industries equipped with rooftop solar photovoltaic systems that have been monitored for a year.
A new approach based on economic profitability to sizing the photovoltaic generator in self-consumption systems without storage
(Elsevier, 2020-04) Jiménez-Castillo, Gabino; Muñoz-Rodríguez, Francisco José; Rus-Casas, Catalina; López-Talavera, Diego
A proper assessment of the cost-competitiveness and profitability of self-consumption systems is crucial to promoting the transition from grid-dependent to energy self-sufficient buildings. Most of the approaches found in the literature may not take into account economic parameters such as taxes, depreciation and the cost of financing, which have a significant effect on the economic profitability of an investment. Moreover, they only focus on discrete array powers and relatively high recording intervals when estimating the self-consumed energy. In order to manage the aforementioned challenges, a new method will be developed to size the PV generator in a PV self-consumption system which provides the NPV curve together with the self-consumption and self-sufficiency indices for a wide range of array powers which suits residential self-consumption systems. Two scenarios will be considered depending on whether the generated surplus electricity is wasted or it is remunerated from the grid operator. Results show that not only the chosen scenario but the electricity tariff may be key parameters when optimizing NPV. Furthermore, the impact of the recording interval may be significant when estimating NPV. Percentage errors of 11.4% and 33.6% may be reached when considering a recording interval of 15 and 60 min, respectively.
Monitoring PWM signals in stand-alone photovoltaic systems
(Elsevier, 2019-02) Jiménez-Castillo, Gabino; Muñoz-Rodríguez, Francisco José; Rus-Casas , Catalina; Casa-Hernández, Jesús; Tina, Giuseppe Marco
The performance of stand-alone photovoltaic (SAPV) systems can be evaluated by monitoring them in the field using data acquisition systems (DASs). Most SAPV systems use battery charge controllers with pulse width modulation (PWM) to regulate the current into the battery. The PWM signals generated by battery charge controllers imply monitoring challenges due to the complexity of this type of signal. In this sense, the aim of this paper is to develop a new and simple monitoring technique for SAPV systems which can estimate the signals provided by a PWM battery charge controller, thus avoiding expensive DASs, simultaneous sampling and the huge amount of collected data. The estimation of PWM signal parameters, such as the duty factor (df) or high and low states, shows high accuracy, with the mean absolute percentage error lower than 1.4%, a mean relative error within 1.4%, and the coefficient of determination higher than 0.9. Furthermore, the proposed technique may easily be used for other electrical devices where PWM is employed.

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