RLI auf der International Renewable Energy Storage Conference 2016 (IRES)
18. März 2016
Kleinwindenergieanlagen, die Genehmigungslage in Deutschland und deren Einsatz an Bildungseinrichtungen (Müller et al. 2016)
20. März 2016

Cost-self-sufficiency-tradeoff in a real-life urban microgrid with electric vehicles (Wanitschke et al. 2016)


Poster auf der International Renewable Energy Storage Conference 2016 (IRES 2016) vom 15.-17.03.2016 in Düsseldorf, DE

Schill et al. found that the introduction of electric vehicles (EV) in Germany leads to EVspecific CO2-Emissions substantially higher than those of the overall power system, if not complemented with additional renewable energy (RE). A microgrid can be designed for high self-sufficiency rates (SSR) in order to guarantee the coupling of EV charging and RE generation within the microgrid.
In this paper, the tradeoff between the conflicting objectives of levelized cost of electricity (LCOE) minimization and SSR maximization of the microgrid Berlin train station Südkreuz, consisting of photovoltaic generators (PV), small-scale wind turbines (WT), a battery electric storage system (BESS) and EVs, is determined and analyzed. A year-based simulation model of the microgrid is used to optimize the sizing of the system’s components via a multi-objective evolutionary algorithm, producing a two-dimensional optimal pareto curve between the system’s LCOE and SSR.
Results show that ultimate self-sufficiency is comparatively cost-intensive; decreasing SSR from 99.4% to 98% and 95.3% cuts LCOE by 25.7% and 47.6%, respectively. BESS is identified as the main cost driver as its capacity increases exponentially with increasing SSR. While at lower costs mainly PV and BESS are employed, horizontal-axis WTs are found to be a cost-efficient complement to the system for high SSR. Both PV options are chosen along the entire pareto front, even though PV1 has higher component cost and a suboptimal azimuth angle. This observation indicates that PV1 is generating power at more suitable times regarding the EV charging load, thus allowing for a reduction in cost-intensive BESS capacity.
The results exemplify how a tradeoff curve assists the decision maker in better understanding objective conflicts in order to choose the best solution.

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