6,127 research outputs found

    Operating conditions of lead-acid batteries in the optimization of hybrid energy systems and microgrids

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    The promotion and deployment of storage technologies in autonomous and grid-connected systems plays a relevant part in the massive integration of renewable power sources required for the worldwide development of a sustainable society. In this regard, analyzing the behavior of electrochemical storage devices such as lead-acid batteries installed on hybrid energy systems and microgrids in terms of their lifetime and economic profitability is an important research topic. Since renewable generation is characterized by its random nature, lead-acid batteries typically work under stress conditions, which directly influence their lifetime in a negative way by increasing the net present cost. Due to the fast growing of renewable sources as a consequence of governmental policies and incentives, the number of manufacturers to be considered worldwide is becoming really high, so that optimization techniques such as genetic algorithms (GAs) are frequently used in order to consider the performance of a high number of manufacturers of wind turbines, photovoltaic panels and lead-acid batteries subject to the environmental conditions of the location under analysis to determine a cost-effective design. In this paper, GA method combined with weighted Ah ageing model is improved by including expert experiences by means of stress factors and the categorization of operating conditions, as a new contribution to earlier studies. The effectiveness of the proposed method is illustrated by analyzing a hybrid energy system to be installed in Zaragoza, Spain, resulting in a near-optimal design in a reduced computational time compared to the enumerative optimization method

    Comparison of lead-acid and li-ion batteries lifetime prediction models in stand-alone photovoltaic systems

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    Several models for estimating the lifetimes of lead-acid and Li-ion (LiFePO4 ) batteries are analyzed and applied to a photovoltaic (PV)-battery standalone system. This kind of system usually includes a battery bank sized for 2.5 autonomy days or more. The results obtained by each model in different locations with very different average temperatures are compared. Two different locations have been considered: the Pyrenees mountains in Spain and Tindouf in Argelia. Classical battery aging models (equivalent full cycles model and rainflow cycle count model) generally used by researchers and software tools are not adequate as they overestimate the battery life in all cases. For OPzS lead-acid batteries, an advanced weighted Ah-throughput model is necessary to correctly estimate its lifetime, obtaining a battery life of roughly 12 years for the Pyrenees and around 5 years for the case Tindouf. For Li-ion batteries, both the cycle and calendar aging must be considered, obtaining more than 20 years of battery life estimation for the Pyrenees and 13 years for Tindouf. In the cases studied, the lifetime of LiFePO4 batteries is around two times the OPzS lifetime. As nowadays the cost of LiFePO4 batteries is around two times the OPzS ones, Li-ion batteries can be competitive with OPzS batteries in PV-battery standalone systems

    H2RES2 simulator. A new solution for hydrogen hybridization with renewable energy sources-based systems

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    This paper presents a new simulator for Hydrogen hybridization with Renewable Energy based Systems. The aim of this simulator is to provide a new solution for testing different energy management strategies of hydrogen hybridization based on renewable systems, in order to optimize them for implementation. The simulator uses the open architecture philosophy and has been developed in MATLAB®-SIMULINK environment. Its main feature is calculating technical and economical parameters for a deepened analysis of influences on energy management strategies. It considers each element of the hybrid system and the whole system function. A simulation case shows the proper functioning of the simulator

    Optimisation of energy supply at off-grid healthcare facilities using Monte Carlo simulation

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    In this paper, we present a methodology for the optimisation of off-grid hybrid systems (photovoltaic-diesel-battery systems). A stochastic approach is developed by means of Monte Carlo simulation to consider the uncertainties of irradiation and load. The optimisation is economic; that is, we look for a system with a lower net present cost including installation, replacement of the components, operation and maintenance, etc. The most important variable that must be estimated is the batteries lifespan, which depends on the operating conditions (charge/discharge cycles, corrosion, state of charge, etc.). Previous works used classical methods for the estimation of batteries lifespan, which can be too optimistic in many cases, obtaining a net present cost of the system much lower than in reality. In this work, we include an advanced weighted Ah-throughput model for the lead-acid batteries, which is much more realistic. The optimisation methodology presented in this paper is applied in the optimisation of the electrical supply for an off-grid hospital located in Kalonge (Democratic Republic of the Congo). At the moment, the power supply relies on a diesel generator; batteries are used in order to ensure the basic supply of energy when the generator is unavailable (night hours). The optimisation includes the possibility of adding solar photovoltaic (PV) panels to improve the supply of electrical energy. The results show that optimal design could achieve a 28% reduction in the levelised cost of energy and a 54% reduction in the diesel fuel used in the generator, thereby reducing pollution. Furthermore, we discuss possible improvements to the telecommunications of the hospital

    Energy Storage Management and Simulation for Nano-Grids

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    Energy storage has been utilized in many forms and applications from a flashlight to the Space Shuttle. There is a worldwide effort to develop battery model with high energy level and power densities for a variety range of applications, including hybrid electric vehicles (HEV) and photovoltaic system (PV). To improve battery technology, understanding the battery modeling is very important. So, modeling the thermal behavior of a battery is a vital consideration before designing an effective thermal management system which will operate safely and prolong the lifespan of an energy storage system. The first part of this work focused on the aging model of lithium-ion battery and a simple thermal model of lithium-ion and lead-acid battery using MATLAB/Simulink. After that, an artificial neural network model (ANN) is developed to predict various characteristics at wide temperature range. In this case, comparisons between the training/testing data outputs and targets validating both models with a regression accuracy of 99.839% and 98.727% respectively for Li-ion and Lead-Acid battery while it is 99.912% for the aging model of Li-ion battery. In the end, this energy storage device is used to interconnect with HOMER. This HOMER project aims at designing a solar-wind hybrid power system for Statesboro, Georgia. The cost analysis is performed utilizing HOMER software based on solar irradiance, wind speed, and residential load profile. The proposed HOMER model, using solar & wind with the grid was more cost efficient as the cost of energy (COE) was found 0.0618/kWhwheretheaverageresidentialelectricityrateinStatesborois0.116/kWh where the average residential electricity rate in Statesboro is 0.116/kWh. As a result of using this model, the total cost is reduced by 46.72% compared to other conventional power systems. In the second part of HOMER simulation, while comparing among three types of storage devices, another minimum COE is found using wind with grid connection. As the wind speed is good enough for Statesboro, Georgia, simulation shows that minimum COE is 0.0499/kWh,0.0386/kWh, 0.0386/kWh and 0.0633$/kWh respectively for Li-ion, Lead-acid, and Vanadium

    Análisis y gestión óptima de la demanda en sistemas eléctricos conectados a la red y en sistemas aislados basados en fuentes renovables

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    En esta tesis doctoral se han analizado, de forma detallada, los principales aspectos relacionados con el funcionamiento de los sistemas eléctricos aislados y conectados a la red eléctrica basados en fuentes de energía renovable. En lo referente al análisis de los sistemas aislados de la red eléctrica, se ha analizado el efecto de la eficiencia culómbica y del regulador de carga en la fiabilidad de los sistemas eólicos con baterías. También se ha tratado la estimación de las horas de operación, consumo de combustible y coste neto actualizado de los sistemas que utilizan como respaldo un generador convencional. Por otra lado, se ha desarrollado un modelo probabilístico que permite considerar la incertidumbre existente en la estimación de la vida del banco de baterías, la incertidumbre asociada a los precios del combustible, la producción del generador fotovoltaico, el perfil típico de carga, así como la variabilidad de los recursos eólico y solar. Además, teniendo en cuenta la importancia que tiene el uso racional de la energía eléctrica, en esta tesis se ha desarrollado una novedosa técnica para la gestión de la demanda de sistemas aislados de la red eléctrica que sugiere al usuario del mismo el mejor momento para hacer uso de sus electrodomésticos, reduciendo el consumo de combustible y mejorando el uso de la energía almacenada en el banco de baterías. Finalmente, considerando sistemas conectados a la red eléctrica, se ha desarrollado una estrategia de Adaptación de la Demanda para consumidores residenciales que, haciendo uso de las capacidades de comunicación de la futura Red Eléctrica Inteligente, determina mediante la optimización de la negociación entre el usuario y la empresa de distribución de energía eléctrica, la forma en que el consumidor debe utilizar sus electrodomésticos considerando sus preferencias y su poder adquisitivo. Los resultados obtenidos sugieren importantes mejoras en los modelos que se utilizan habitualmente en la simulación y optimización de sistemas híbridos, específicamente en la consideración del regulador de carga como un importante elemento del sistema, y en la estimación de la vida útil del banco de baterías. Además, las estrategias para la gestión de la demanda, presentadas en este trabajo de investigación, pueden ayudar a que los usuarios de sistemas aislados o conectados a la red eléctrica realicen un uso eficiente de las fuentes de energía locales, y adapten sus patrones de consumo de electricidad a su condición económica actual

    Hybridizing Lead-Acid Batteries with Supercapacitors: A Methodology

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    Hybridizing a lead–acid battery energy storage system (ESS) with supercapacitors is a promising solution to cope with the increased battery degradation in standalone microgrids that suffer from irregular electricity profiles. There are many studies in the literature on such hybrid energy storage systems (HESS), usually examining the various hybridization aspects separately. This paper provides a holistic look at the design of an HESS. A new control scheme is proposed that applies power filtering to smooth out the battery profile, while strictly adhering to the supercapacitors’ voltage limits. A new lead–acid battery model is introduced, which accounts for the combined effects of a microcycle’s depth of discharge (DoD) and battery temperature, usually considered separately in the literature. Furthermore, a sensitivity analysis on the thermal parameters and an economic analysis were performed using a 90-day electricity profile from an actual DC microgrid in India to infer the hybridization benefit. The results show that the hybridization is beneficial mainly at poor thermal conditions and highlight the need for a battery degradation model that considers both the DoD effect with microcycle resolution and temperate impact to accurately assess the gain from such a hybridization

    Model-free non-invasive health assessment for battery energy storage assets

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    Increasing penetration of renewable energy generation in the modern power network introduces uncertainty about the energy available to maintain a balance between generation and demand due to its time-fluctuating output that is strongly dependent on the weather. With the development of energy storage technology, there is the potential for this technology to become a key element to help overcome this intermittency in a generation. However, the increasing penetration of battery energy storage within the power network introduces an additional challenge to asset owners on how to monitor and manage battery health. The accurate estimation of the health of this device is crucial in determining its reliability, power-delivering capability and ability to contribute to the operation of the whole power system. Generally, doing this requires invasive measurements or computationally expensive physics-based models, which do not scale up cost-effectively to a fleet of assets. As storage aggregation becomes more commonplace, there is a need for a health metric that will be able to predict battery health based only on the limited information available, eliminating the necessity of installation of extensive telemetry in the system. This work develops a solution to battery health prognostics by providing an alternative, a non-invasive approach to the estimation of battery health that estimates the extent to which a battery asset has been maloperated based only on the battery-operating regime imposed on the device. The model introduced in this work is based on the Hidden Markov Model, which stochastically models the battery limitations imposed by its chemistry as a combination of present and previous sequential charging actions, and articulates the preferred operating regime as a measure of health consequence. The resulting methodology is demonstrated on distribution network level electrical demand and generation data, accurately predicting maloperation under a number of battery technology scenarios. The effectiveness of the proposed battery maloperation model as a proxy for actual battery degradation for lithium-ion technology was also tested against lab tested battery degradation data, showing that the proposed health measure in terms of maloperation level reflected that measured in terms of capacity fade. The developed model can support condition monitoring and remaining useful life estimates, but in the wider context could also be used as the policy function in an automated scheduler to utilise assets while optimising their health.Increasing penetration of renewable energy generation in the modern power network introduces uncertainty about the energy available to maintain a balance between generation and demand due to its time-fluctuating output that is strongly dependent on the weather. With the development of energy storage technology, there is the potential for this technology to become a key element to help overcome this intermittency in a generation. However, the increasing penetration of battery energy storage within the power network introduces an additional challenge to asset owners on how to monitor and manage battery health. The accurate estimation of the health of this device is crucial in determining its reliability, power-delivering capability and ability to contribute to the operation of the whole power system. Generally, doing this requires invasive measurements or computationally expensive physics-based models, which do not scale up cost-effectively to a fleet of assets. As storage aggregation becomes more commonplace, there is a need for a health metric that will be able to predict battery health based only on the limited information available, eliminating the necessity of installation of extensive telemetry in the system. This work develops a solution to battery health prognostics by providing an alternative, a non-invasive approach to the estimation of battery health that estimates the extent to which a battery asset has been maloperated based only on the battery-operating regime imposed on the device. The model introduced in this work is based on the Hidden Markov Model, which stochastically models the battery limitations imposed by its chemistry as a combination of present and previous sequential charging actions, and articulates the preferred operating regime as a measure of health consequence. The resulting methodology is demonstrated on distribution network level electrical demand and generation data, accurately predicting maloperation under a number of battery technology scenarios. The effectiveness of the proposed battery maloperation model as a proxy for actual battery degradation for lithium-ion technology was also tested against lab tested battery degradation data, showing that the proposed health measure in terms of maloperation level reflected that measured in terms of capacity fade. The developed model can support condition monitoring and remaining useful life estimates, but in the wider context could also be used as the policy function in an automated scheduler to utilise assets while optimising their health
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