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