DIE-Artículos

URI permanente para esta colecciónhttps://hdl.handle.net/10953/230

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Photovoltaic Self-Consumption in Industrial Cooling and Refrigeration
(MDPI, 2020-12-21) Martínez-Calahorro, Antonio Javier; Jiménez-Castillo, Gabino; Rus-Casas, Catalina; Gómez-Vidal, Pedro; Muñoz-Rodríguez, Francisco José
The industrial sector has a great opportunity to reduce its energy costs through distributed generation. In this sense, the potential of photovoltaic self-consumption systems in the industrial cooling and refrigeration sector is shown. Two industries with photovoltaic self-consumption installations are shown and the electricity consumption profile of this type of industry which has a remarkable basal electricity consumption during daytime is analyzed. The matching between consumption and photovoltaic generation profiles is provided through the self-consumption and self-sufficiency curves considering different reporting periods (monthly and annual). Moreover, a new index is presented: self-sufficiency index for sunshine hours, φSS,SH. This index evaluates the performance of the photovoltaic self-consumption system when facing the consumption only during sunshine hours. This index may complement the self-sufficiency index and may improve the analysis of this type of systems in the industrial sector. Self-consumption indices of 90% may be provided. Moreover, self-sufficiency indices for total (24 h) and for sunshine hours of 25% and 50%, respectively, for industry A, and 26% and 45% for industry B have been obtained. During daytime, half the load consumption in this type of industry may be covered by photovoltaics while achieving high levels of use of the photovoltaic energy generated.
Impacts of Array Orientation and Tilt Angles for Photovoltaic Self-Sufficiency and Self-Consumption Indices in Olive Mills in Spain
(MDPI, 2020-02-18) Jiménez-Castillo, Gabino; Muñoz-Rodríguez, Francisco José; Martinez-Calahorro, Antonio Javier; Tina, Giuseppe Marco; Rus-Casas, Catalina
Olive mills are extensive in the Mediterranean Basin, and Spain constitutes approximately 45% of global production. The industrial sector faces a new energetic paradigm where distributed generation provided by small renewable energy sources may reduce the dependence from fossil energy sources as well as avoid energy distribution losses. Photovoltaic self-consumption systems can play an important role in confronting this challenge due to their modularity and their decreasing cost. Most of self-sufficiency energy studies are focused on building sector and discussions about the idiosyncrasy of industrial load profiles, and their matching capability with photovoltaic generation profiles can be scarcely found. This work analyzes the potential of photovoltaic self-consumption systems as a function of the array power, array tilt, and orientation angles to face the electric consumption in olive mills. Different recording intervals and reporting periods are considered. Results show that a self-sufficiency index of 40% may be achieved on olive harvest basis. Moreover, due to the load profile particularities, percentage error lower than 1.6% has been found when considering a recording interval of 60 min when matching the olive load consumption and photovoltaic generation profiles. Chosen array tilt and orientation angles may be key parameters to maximize the self-sufficiency index.
Development of a Prototype for Monitoring Photovoltaic Self-Consumption Systems
(MDPI, 2020-01-01) Rus-Casas, Catalina; Jiménez-Castillo, Gabino; Aguilar-Peña, Juan Domingo; Fernández-Carrasco, Juan Ignacio; Muñoz-Rodríguez, Francisco José
Currently, the increasing energy consumption around the world and the environmental impact resulting from the use of fossil fuel-based energy have promoted the use of renewable energy sources such as photovoltaic solar energy. The main characteristic of this type of energy is its unpredictability, as it depends on meteorological conditions. In this sense, monitoring the power generation of photovoltaic systems (PVS) in order to analyze their performance is becoming crucial. The purpose of this paper is to design a monitoring system for a residential photovoltaic self-consumption system which employs an Internet of Things (IoT) platform to estimate the photovoltaic power generation according to solar radiation and temperature. The architecture of the developed prototype will be described and the benefits of providing the use of IoT for monitoring will be highlighted, since all data collected by the data acquisition system (DAS) may be stored in the Cloud. The comparison of the results with those of other monitoring systems was very positive, with an uncertainty that complies with the IEC61724 standard.
Improvements in Performance Analysis of Photovoltaic Systems: Array Power Monitoring in Pulse Width Modulation Charge Controllers
(MDPI, 2019-05-09) Jiménez-Castillo, Gabino; Muñoz-Rodríguez, Francisco José; Rus-Casas, Catalina; Gómez-Vidal, Pedro
Various challenges should be considered when measuring photovoltaic array power and energy in pulse width modulation (PWM) charge controllers. These controllers are frequently used not only in stand-alone photovoltaic (SAPV) systems, but may also be found in photovoltaic (PV) self-consumption systems with battery storage connected to the electricity grid. An acceptable solution may be reached using expensive data acquisition systems (DASs), although this could be generally disproportionate to the relatively low cost of SAPV systems. Therefore, the aim of this paper is to develop new and e ective monitoring techniques which will provide the PV array direct current (DC), output power (PA,dc), and PV array DC output energy (EA), thus avoiding the use of sophisticated DASs and providing high accuracy for the calculated parameters. Only transducers and electronic circuits that provide the average and true rms values of the PWM signals are needed. The estimation of these parameters through the aforementioned techniques showed high accuracy for both series and shunt PWM battery charge controllers. Normalized root mean square error (NRMSE) was lower than 2.4%, normalized mean bias error (NMBE) was between 􀀀1.5% and 1.1%, and mean absolute percentage error (MAPE) was within 1.6%.
Development of a Utility Model for the Measurement of Global Radiation in Photovoltaic Applications in the Internet of Things (IoT)
(MDPI, 2019-03-08) Rus-Casas, Catalina; Hontoria, Leocadio; Fernández-Carrasco, Juan Ignacio; Jiménez-Castillo, Gabino; Muñoz-Rodríguez, Francisco José
In order to develop future projects in the field of photovoltaic solar energy, it is essential to accurately know the potential solar resources. There are many methods to estimate the incident solar radiation in a certain place. However, most of them are very expensive or do not have the ideal characteristics for good monitoring of a particular photovoltaic installation. For these reasons, an electronic device connected to the internet of things (IoT) is presented in this paper which manages to measure global radiation in photovoltaic applications. The device developed has been patented in the Spanish Patent and Trademark Office. It presents some features that make it very suitable to measure photovoltaic installations with the advantage of being a low cost and very reliable device. The device has been tested to determine global horizontal irradiance obtaining a correlation coefficient R2 = 0.994.
Robust dynamic charging price in PV-assisted charging stations
(Elsevier, 2025-10-01) Tostado-Véliz, Marcos; Hasanien, Hany M.; Arévalo, Paul; Jurado-Melguizo, Francisco
With the increasing number of electric vehicles on road, the deployment of sufficient public charging infrastructures has become critical. To encourage the installation of new public charging points, such infrastructures need to be economically viable and profitable. In this regard, exploring economic activities within charging infrastructures has become a key topic to ensure the long-term financial sustainability of charging installations. In line with this objective, this paper develops a new robust methodology to setting dynamic charging prices in charging stations. Unlike to conventional charging prices based on flat tariffs, dynamic pricing strategies can follow wholesale electricity prices, potentially setting low prices and therefore displacing the fleet from domestic to public charging. The new proposal renders as a game-theoretical max-min bi-level optimization problem. To address the initial complexity of the formulation, a tailored solution algorithm is developed, which allows accessing to robust solutions efficiently. An adaptive robust modelling of uncertainties is proposed, based on intervals, which allows representing uncertainties as box-constrained variables. Moreover, this paper contributes with a new data-driven approach to determine limits on uncertainties based on bootstrapping. The new solution strategy is validated on a benchmark large-scale charging station installing a photovoltaic facility. Additionally, the effect of the risk level and photovoltaic size on final results is evaluated. In addition, the effectiveness of the charging pricing strategy is assessed, along with the influence of uncertainties on the final results.
Dealing with contradictory objectives in energy communities: A game-oriented trilevel approach
(Elsevier, 2025-09) Tostado-Véliz, Marcos; Hasanien, Hany M.; Cruz-de-la-Torre, Carlos; Jurado-Melguizo, Francisco
Energy communities empower end users to partake actively in the operation of the system while lowering energy procurement through optimal sharing resources. The main objective of energy communities is reducing the collective bill by maximizing the usage of local assets such as photovoltaic and storage systems. However, the different community members may raise particular objectives that may eventually lie in contradiction with the reduction of the electricity cost. For example, prosumers may be interested in incrementing their consumption above a benchmark point in order to increase their comfort and satisfaction. Such contradictory objectives should be considered in energy management of communities in order to ensure its social stability and successful. To this end, a novel game-based trilevel day-ahead approach for cooperative communities is developed, in which two secondary objectives can be accommodated together with the cost minimization original target. As a sake of example, the developed tool tailors in this paper to the case in which prosumers aim at maximizing their consumption while storage pretend to minimize the degradation of assets. The original trilevel structure is reduced to a solvable single-level problem that provide an equilibrium point in the Nash sense. A number of results is provided in 5 and 15-bus cases in order to validate the new approach. Results show that the new proposal can be easily implemented in a variety of scenarios, showing a case-independent performance. The hierarchical decision-logic procedure has been illustrated and validated analysing the total community cost under different users’ preferences. Finally, it is shown that the developed methodology scales well with the storage capability and community size.
Experimental validation of a wireless monitored solar still for efficient olive pomace drying and distilled water production
(Taylor & Francis, 2023-04-18) Rodríguez-Orta, Antonio; Aguado-Molina, Roque; Sánchez-Raya, Manuel; Vera, David; Gómez-Galán, Juan Antonio
This work presents the prototype of a solar still that can be used as a complement to the traditional fossil fuel-based drying process of olive pomace, a thick sludge with a high moisture content that is massively by-produced in the olive oil industry. In addition, the system allows to recover distilled water, which can be used to irrigate the adjacent fields. The feasibility of the system for the target application was experimentally validated by designing a wireless data acquisition circuit for data collection, remote storage, visualization and analysis in real-time, with access possible from multiple devices. The results from a test campaign revealed that the moisture content of the olive pomace was effectively reduced from 67.17% to 39.90% in a 10-day period. A maximum drying efficiency of 16.64% was achieved, with potential for higher values under favorable weather conditions. The simplicity of the design and the low-cost solution can facilitate the future large-scale implementation of a similar system.
Continuous decentralized hydrogen production through alkaline water electrolysis powered by an oxygen-enriched air integrated biomass gasification combined cycle
(ELSEVIER, 2023-08) Aguado-Molina, Roque; Baccioli, Andrea; Liponi, Angelica; Vera, David
This research work presents an innovative approach for continuous decentralized production of renewable hydrogen from woody biomass. Alkaline water electrolysis (AWE) is used to produce high-purity hydrogen, while the oxygen by-product is mixed with ambient air and used to fire a biomass-fueled downdraft gasifier in order to produce an upgraded producer gas with a lower heating value (LHV) between 7–8 MJ/Nm³. This fuel gas is then subjected to a conditioning stage and eventually fed to a combined cycle consisting of a recuperative gas turbine as topping unit and a regenerative subcritical organic Rankine cycle as bottoming unit, which together allow for a combined electric power generation efficiency close to 40%. Most of the net AC power from the integrated gasification combined cycle (IGCC) is rectified to DC power and ultimately used to power an alkaline electrolyzer, with a minor share allocated to all the required utilities and ancillary equipment, including hydrogen compression to 200 bar. The results from simulation of the hybrid IGCC-AWE plant under steady-state operating conditions in Aspen Plus V.11 indicate an optimal efficiency of 17.6% based on the LHV of hydrogen. Thus, if sized for a biomass consumption of 1 t/h, the proposed plant is capable of providing around 26 kg/h of compressed hydrogen at 200 bar.
Biomass gasification as a key technology to reduce the environmental impact of virgin olive oil production: A Life Cycle Assessment approach
(ELSEVIER, 2022-10) Fernández-Lobato, Lázuli; Aguado-Molina, Roque; Jurado-Melguizo, Francisco; Vera, David
The olive oil value chain faces nowadays important challenges toward environmental sustainability, both in terms of waste management and energy efficiency improvement. This research work proposes an integrated gasification plant fueled with olive pomace for combined heat and power (CHP) generation and biochar production, which can be installed directly at oil mills. An alternative scenario for olive oil production incorporating the gasification technology was compared to a baseline scenario based on traditional olive oil production. The environmental impacts of producing 1 kg of unpacked virgin olive oil at the farming and industrial phases were estimated for both scenarios by following the Life Cycle Assessment (LCA) methodology under a “cradle-to-gate” approach. The gasification technology applied to the olive oil industry is able to manage all the pomace from the oil extraction process on site, avoiding transportation to pomace oil extraction plants. The proposed gasification plant generates 0.88 kWh of renewable electricity per kg of olive oil and enough heat to abandon the current practice of burning a significant part of the olive pit production. As a result, the alternative scenario contributes to a 8.25% reduction in the normalized environmental impact of olive oil production. In terms of climate change, the environmental impact of the functional unit is reduced from 2.21 to 1.74 kg CO2 eq. (−21%) and the industrial phase becomes a major carbon sink with −0.51 kg of CO2 eq. per kg of olive oil. In this regard, the integrated gasification plant is viewed as an attractive option for most olive oil mills to invest in sustainability through waste management and recovery.
Techno-economic assessment of a hybrid PV-assisted biomass gasification CCHP plant for electrification of a rural area in the Savannah region of Ghana
(ELSEVIER, 2025-01) Sánchez-Lozano, Daniel; Aguado-Molina, Roque; Escámez, Antonio; Awaafo, Augustine; Jurado-Melguizo, Francisco; Vera, David
In rural areas of sub-Saharan countries, there is great potential for solar and biomass resources to achieve a reliable electricity supply, reduce the dependence on fossil fuels, and mitigate greenhouse gas emissions, thereby tackling energy poverty and promoting sustainable development. This work aims to address the lack of reliable electricity access in rural communities of sub-Saharan countries through biomass gasification assisted by solar photovoltaic (PV) energy and a small back-up diesel engine–generator set. The biomass gasification plant is designed to convert locally available agricultural waste into producer gas, which can then be used to generate electricity. A detailed analysis of the system components, including the PV array, battery bank, biomass gasifier with a combined cooling, heat and power generation unit (CCHP), is carried out to evaluate their performance and efficiency under different operating conditions. The results reveal a CCHP efficiency of 62% for the gasification CCHP unit, accompanied by a remarkable 93.8% reduction in CO2 emissions considering the whole hybrid system. From an economic standpoint under conservative assumptions, the proposed facility can generate a cumulative profit of $157,890 after 20 years, recovering the initial investment within a period of just under 7 years. This is reflected in a levelized cost of electricity (LCOE) of $0.287/kWh, comparable to that of related studies. The outcomes demonstrate that the PV-assisted biomass gasification plant offers a sustainable technical, economical and environmentally friendly solution for electrification of rural communities in sub-Saharan countries.
Calibration of a Class A Power Quality Analyser Connected to the Cloud in Real Time
(MDPI, 2024-08-13) Cano-Ortega, Antonio; Sánchez-Sutil, Francisco; Casa-Hernández, Jesús; Baier, Carlos; Gilabert-Torres, Carlos
Power quality measurements are essential to monitor, analyse and control the operation of smart grids within power systems. This work aims to develop and calibrate a PQ network analyser. As the penetration of non-linear loads connected to power systems is increasing every day, it is essential to measure power quality. In this sense, a power quality (PQ) analyser is based on the high-speed sampling of electrical signals in single-phase and three-phase electrical installations, which are available in real time for analysis using wirelessWi-Fi (Wireless-Fidelity) networks. The PQAE (Power Quality Analyser Embedded) power quality analyser has met the calibration standards for Class A devices from IEC 61000-4-30, IEC 61000-4-7 and IEC 62586-2. In this paper, a complete guide to the tests included in this standard has been provided. The Fast Fourier Transform (FFT) obtains the harmonic components from the measured signals and the window functions used reduce spectral leakage. The window size depends on the fundamental frequency of, intensity of and changes in the signal. Harmonic measurements from the 2nd to 50th harmonics for each phase of the voltage and each phase and neutral of the current have been performed, using the Fast Fourier transform algorithm with various window functions and their comparisons. PQAE is developed on an open-source platform that allows you to adapt its programming to the measurement needs of the users.
Integrating discrete wavelet transform with neural networks and machine learning for fault detection in microgrids
(Elsevier, 2023-10-23) Cano-Ortega, Antonio; Arévalo, Paul; Benavides, Darío; Jurado-Melguizo, Francisco
Microgrids are essential for integrating renewable energy sources into the power grid. However, fault detection is challenging due to bidirectional energy flow. Traditional relay-based systems struggle in microgrids, primarily because of limited fault currents from grid-connected renewable energy inverters. To address these challenges, this paper proposes a new methodology for fault detection and classification in a renewable microgrid. The main contributions encompass two key aspects. Firstly, it enhances fault detection performance in microgrids characterized by nonlinear relationships, including photovoltaic, hydrokinetic, and variable electric load systems. Secondly, the combination of the discrete wavelet transform with various types of neural networks and supervised learning techniques provides a robust methodology for fault detection and classification. The proposed approach is evaluated using an IEEE-5 feeder test bed representing a realistic ring network configuration. The results show that the radial basis function neural network model exhibited promising outcomes, yielding a low prediction error of 1.31 e-31, highlighting its practical potential for enhancing system reliability and performance. Furthermore, various test cases were conducted by altering the ground resistance to train the neural networks, demonstrating the effectiveness of this neural network in accurately identifying fault conditions. Additionally, this research achieved promising outcomes with other models, including support vector machine and nonlinear autoregressive with external input, emphasizing the adaptability of these models in fault detection.
A new approach to analyse from monitored data the performance, matching capability and grid usage of large Rooftop Photovoltaic systems. Case of study: Photovoltaic system of 1.05 MW installed at the campus of University of Jaén
(Elsevier, 2025) Muñoz-Rodríguez, Francisco José; Gómez-Vidal, Pedro; Fernández-Carrasco, Juan Ignacio; Tina, Giuseppe Marco; Jiménez-Castillo, Gabino
Rooftop photovoltaic installations highlight their potential to meet a significant portion of urban electricity demand. These systems range from a few kW in residential areas to hundreds of kW in large Rooftop PV systems in commercial and industrial settings. The latter, which may include several inverters or arrays with different orientations and inclinations, require a proper analysis to assess the potential of this technology and to ensure the design objectives. This paper presents a methodology for analysing from monitored data large Rooftop PV systems, focusing on performance, self-consumption and grid usage. The approach is scalable, applicable at the inverter, individual Rooftop PV and global system levels. New key parameters defined include weighted system irradiation (HI,weighted) and weighted system reference yield (Yr,weighted), which account for different array orientations and inclinations. The methodology is validated using a 1.05 MW system at the University of Jaén with monitored data over a year. Results indicate subsystem and system PR values above 0.83 and a system Capacity Factor of 0.19, confirming a proper performance. Annual self-consumption and self-sufficiency indices of 97.5 % and 17.7 %, respectively, and a solar hour self-sufficiency of 27.7 % reveal minimal energy export and substantial potential to meet the university’s electricity demand.
Effect of electrical operating conditions on thermal behavior of PV modules: Numerical and experimental analysis
(Elsevier, 2025) Osama, Amr; Tina, Giuseppe Marco; Gagliano, Antonio; Jiménez-Castillo, Gabino; Muñoz-Rodríguez, Francisco José
The rapid growth of photovoltaic (PV) energy has the potential to transform the global energy landscape. However, the intermittent nature of solar power presents significant challenges to grid integration, such as overgeneration and curtailment. Consequently, PV systems may operate at points other than the maximum power point (MPP). Monitoring the thermal behavior of photovoltaic systems is critical due to its impact on productivity and system health. Most studies focus on meteorological variables, often overlooking the influence of electrical operating states on thermal performance. Thus the objective is to evaluate the accuracy of existing thermal models from the literature and widely used specialized software tools—alongside their commonly cited coefficients against different electrical operating status (EOS). This study investigates the thermal behavior of PV modules under different EOS: short-circuited (PVset-1), open-circuited (PVset-2), and operating at MPP (PVset-3). The experiment was conducted over four months at Jaén University campus in Spain. Results showed the short-circuited module's temperature was 6.90 °C higher, and the open-circuited module's temperature was 3.67 °C higher than the MPP module. Thermographic investigations revealed multiple hotspots in the short-circuited set. These hotspots can severely impact the module's long-term reliability and efficiency. The analysis of thermal models considering these operating states indicated an overestimation of the MPP module's temperature. However, the Keddouda model demonstrated high accuracy potential, with an average deviation of less than 3.4 %, particularly at high irradiance levels. These findings highlight the necessity of considering EOS in thermal models to enhance the accuracy and reliability of PV system performance assessments.
A hybrid intelligent model to predict the hydrogen concentration in the producer gas from a downdraft gasifier
(ELSEVIER, 2022-06-05) Aguado-Molina, Roque; Casteleiro-Roca, José Luis; Vera, David; Calvo-Rolle, José Luis
This research work presents an artificial intelligence approach to predicting the hydrogen concentration in the producer gas from biomass gasification. An experimental gasification plant consisting of an air-blown downdraft fixed-bed gasifier fueled with exhausted olive pomace pellets and a producer gas conditioning unit was used to collect the whole dataset. During an extensive experimental campaign, the producer gas volumetric composition was measured and recorded with a portable syngas analyzer at a constant time step of 10 seconds. The resulting dataset comprises nearly 75 hours of plant operation in total. A hybrid intelligent model was developed with the aim of performing fault detection in measuring the hydrogen concentration in the producer gas and still provide reliable values in the event of malfunction. The best performing hybrid model comprises six local internal submodels that combine artificial neural networks and support vector machines for regression. The results are remarkably satisfactory, with a mean absolute prediction error of only 0.134% by volume. Accordingly, the developed model could be used as a virtual sensor to support or even avoid the need for a real sensor that is specific for measuring the hydrogen concentration in the producer gas.
Experimental assessment of a pilot-scale gasification plant fueled with olive pomace pellets for combined power, heat and biochar production
(ELSEVIER, 2023-07-15) Aguado-Molina, Roque; Escámez, Antonio; Jurado, Francisco; Vera, David
This research work examines the performance of an experimental gasification plant fueled with exhausted olive pomace pellets for the concurrent production of electricity, heat and biochar in the olive oil industry. The gasification plant consists of an air-blown downdraft fixed-bed gasifier that generates a lean fuel gas, termed producer gas, in a self-sustaining autothermal process. After conditioning of the producer gas in a cooling and cleaning unit, a four-stroke spark-ignition engine coupled to an electric generator is eventually used as power generation unit. An extensive experimental assessment of this facility was performed under partial and nominal load operation and was supplemented by a physicochemical analysis of the carbonaceous solid material discharged from the gasifier. The mass and energy balances of the gasification plant were calculated, including the carbon conversion efficiency and diverse energy conversion efficiencies. The results revealed an overall stable operation of the gasification plant in terms of composition and heating value of the producer gas and cogenerative production of electricity and heat in the engine–generator set. Under nominal operating conditions, the net electrical efficiency of the gasification plant was 12%–13%, with an average carbon conversion efficiency of the biomass feedstock into producer gas just above 80% and an average cold gas efficiency close to 70%.
A risk-aware P2P platform involving distributed generators, energy communities and storage assets
(Elsevier, 2024-10-15) Tostado-Véliz, Marcos; Mansouri, Seyed Amir; Jordehi, Ahmad Rezaee; Habeeb, Salwan Ali; Jurado, Francisco
The decentralization of power systems and networks calls up for a more active participation of end users. In this context, new market and power trading models are arisen. Catalyzed by the evolution of communication infrastructures under the Smart Grid concept, new paradigms such as peer-to-peer (P2P) trading are becoming more common nowadays. This paper develops a P2P platform model, involving the participation of distributed generators (dispatchable and renewable), storage facilities and energy communities. Economic-oriented models are presented for each peer, considering arbitrage capability from storage, generation and flexibility provision. An original market structure is proposed seeking for equilibrium among agents. Moreover, risk-aware operating strategies are developed, which consider adaptive interval formulation of uncertainties. The new approach allows adopting risk-averse or risk-seeker strategies, thus allowing to consider the impact of uncertainties in a flexible fashion. The new platform is tested on a 5-peers case. The impact of demand and renewable penetration on local prices is assessed, concluding that cheap generation contributes to reducing prices and thus improving the economy of users, which can trade energy locally under low prices. Moreover, the impact of uncertainties is also analyzed, observing that the uncertainty level and the risk strategy adopted impact notably on the expected realization of uncertainties. It is also shown that the developed tool effectively seeks for improving the economy of users, even when pessimistic conditions of uncertainties are assumed. Results demonstrate that energy communities are more severely impacted for uncertainties, due to their reduced regulation capability. Finally, the developed tool is further validated in fifty P2P instances from an economic and computational point of view.
Integrating the strategic response of parking lots in active distribution networks: An equilibrium approach
(Wiley, 2025-01) Tostado-Véliz, Marcos; Bhakar, Rohit; Javadi, Mohammad Sadegh; Esmaeel, Ali; Jurado-Melguizo, Francisco
The increasing penetration of electric vehicles will be accompanied for a wide deployment of charging infrastructures. Large charging demand brings formidable challenges to existing power networks, driving them near to their operational limits. In this regard, it becomes pivotal developing novel energy management strategies for active distribution networks that take into account the strategic behaviour of parking lots. This paper focuses on this issue, developing a novel energy management tool for distribution networks encompassing distributed generators and parking lots. The new proposal casts as a tri-level game equilibrium framework where the profit maximization of lots is implicitly considered, thus ensuring that network-level decisions do not detract the profit of parking owners. The original tri-level model is reduced into a tractable single-level mixed-integer-linear programming by combining equivalent primal-dual and first-order optimality conditions of the distribution network and parking operational models. This way, the model can be solved using off-the-shelf solvers, with superiority against other approaches like metaheuristics. The developed model is validated in well-known 33-, and 85-bus radial distribution systems. Results show that, even under unfavourable conditions with limited distributed generation, charging demand is maximized, thus preserving the interests of parking owners. Moreover, the model is further validated through a number of simulations, showing its effectiveness. Finally, it is demonstrated that the developed tool scales well with the size of the system, easing its implementation in real-life applications.
Energy Management of Microgrids with a Smart Charging Strategy for Electric Vehicles Using an Improved RUN Optimizer
(MDPI, 2023-08-17) Kareem Meteab, Wisam; Ali Habeeb Alsultani, Salwan; Jurado-Melguizo, Francisco
Electric vehicles (EVs) and renewable energy resources (RERs) are widely integrated into electrical systems to reduce dependency on fossil fuels and emissions. The energy management of microgrids (MGs) is a challenging task due to uncertainty about EVs and RERs. In this regard, an improved version of the RUNge Kutta optimizer (RUN) was developed to solve the energy management of MGs and assign the optimal charging powers of the EVs for reducing the operating cost. The improved RUN optimizer is based on two improved strategies: Weibull flight distribution (WFD) and a fitness–distance balance selection (FDB) strategy, which are applied to the conventional RUN optimizer to improve its performance and searching ability. In this paper, the energy management of MGs is solved both at a deterministic level (i.e., without considering the uncertainties of the system) and while considering the uncertainties of the system, with and without a smart charging strategy for EVs. The studied MG consists of two diesel generators, two wind turbines (WTs), three fuel cells (FCs), an electrical vehicle charging station and interconnected loads. The obtained results reveal that the proposed algorithm is efficient for solving the EM of the MG compared to the other algorithms. In addition, the operating cost is reduced with the optimal charging strategy.

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