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Lifetime prediction and sizing of lead–acid batteries for microgeneration storage applications
157
Citations
13
References
2008
Year
Energy System DesignEngineeringEnergy EfficiencyMicrogeneration SystemsLifetime PredictionChemical EngineeringPower System EconomicsStorage SystemsEnergy Storage DeviceMicrogeneration SystemPower GenerationRenewable Energy SystemsBattery DegradationMaterials ScienceElectrical EngineeringBattery Electrode MaterialsMechanical BatteriesEnergy StorageEnergy Storage SystemEnergy System OperationElectrochemistryElectric BatteryLi-ion Battery MaterialsEnergy ManagementSustainable EnergyMicrogeneration Storage ApplicationsBattery ConfigurationEmbedded Energy StorageBatteriesLead–acid Batteries
Homes without storage export excess microgeneration power, resulting in high yearly export. The authors combine microgeneration system models with a battery‑lifetime algorithm to evaluate how battery size influences onsite energy retention, annual discharge, and predicted lifespan, producing design tables that link cost and weight to export and lifetime. The model identifies optimal lead‑acid battery sizes that balance cost, weight, and 2–4‑year lifetimes for high‑production microgeneration, while highlighting variability across scenarios and sizes.
Existing models of microgeneration systems with integrated lead–acid battery storage are combined with a battery lifetime algorithm to evaluate and predict suitable sized lead–acid battery storage for onsite energy capture. Three onsite generation portfolios are considered: rooftop photovoltaic (2.5 kW), micro-wind turbine (1.5 kW) and micro combined heat and power (1 kW). With no embedded energy storage, the dwelling exports energy when the microgeneration system generates excess power leading to a high level of generated export throughout the year. The impact that the size of installed battery has on the proportion of the generated export that is reserved onsite, along with the annual energy discharged per year by the energy store is assessed. In addition, the lifetime algorithm is utilised to predict corresponding lifetimes for the different scenarios of onsite generation and storage size, with design tables developed for expected cost and weight of batteries given a predicted generated export and lifetime specification. The results can be used to indicate optimum size batteries for using storage with onsite generation for domestic applications. The model facilitates the choice of battery size to meet a particular criteria, whether that be optimising size, cost and lifetime, reducing grid export or attempting to be self-sufficient. Suitable battery sizes are found to have lifetimes of 2–4 years for high production microgeneration scenarios. However, this is also found to be highly variable, depending on chosen microgeneration scenario and battery size.
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