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| Hauptseite > Publikationsdatenbank > Battery Aging in Multi-Energy Microgrid Design Using Mixed Integer Linear Programming > print |
| Hauptseite > Publikationsdatenbank > Battery Aging in Multi-Energy Microgrid Design Using Mixed Integer Linear Programming > print |
| 001 | 848003 | ||
| 005 | 20240711101429.0 | ||
| 024 | 7 | _ | |a 10.1016/j.apenergy.2018.09.185 |2 doi |
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| 100 | 1 | _ | |a Cardoso, Goncarlo |0 P:(DE-HGF)0 |b 0 |e Corresponding author |
| 245 | _ | _ | |a Battery Aging in Multi-Energy Microgrid Design Using Mixed Integer Linear Programming |
| 260 | _ | _ | |a Amsterdam [u.a.] |c 2018 |b Elsevier Science |
| 336 | 7 | _ | |a article |2 DRIVER |
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| 520 | _ | _ | |a This paper introduces a linear battery aging and degradation model to a multi-energy microgrid sizing model using mixed integer linear programming. The battery aging model and its integration into a larger microgrid sizing formulation are described. A case study is provided to explore the impact of considering battery aging on key results: optimal photovoltaic and storage capacities, optimal distributed energy resources operations strategies, and annual cost and generation metrics.The case study results suggest that considering battery degradation in optimal microgrid sizing problems significantly impacts the perceived value of storage. Depending on capacity loss and lifetime targets, considering battery degradation is shown to decrease optimal storage capacities between 6 and 92% versus scenarios that do not consider battery health. When imposing constant distributed energy resource capacities, inclusion of degradation can decrease optimal annual battery cycling by as much as a factor five and reduce total annual electricity cost savings from otherwise identical photovoltaic and storage systems by 5–12%. These results emphasize that as batteries grow in maturity and ubiquity for distributed energy applications, considering battery health and capacity loss is an essential component of any analytical tool or model to guide system planning and decision-making. |
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| 773 | _ | _ | |a 10.1016/j.apenergy.2018.09.185 |g Vol. 231, p. 1059 - 1069 |0 PERI:(DE-600)2000772-3 |p 1059 - 1069 |t Applied energy |v 231 |y 2018 |x 0306-2619 |
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