The impact of stochastic variability in insolation and capital cost of batteries on optimal microgrid design

Lawrence Berkeley National Laboratory Energy Storage and Distributed Resources Department Master in Economics and Management of Network Industries (EMIN) The impact of stochastic variability in insolation and capital cost of batteries on optimal microgrid design by Tim Schittekatte In this research the impact of solar irradiation variability and battery prices on costoptimal distributed energy resource (DER) investments is analyzed. A novel methodology is proposed to capture the impact on power demand charges caused by moving clouds in microgrids where photovoltaic (PV) arrays, electrochemical energy storage and generators are considered. This is done using a statistical approach to decouple accounting of energy and power demand economics, and allows events of different time lengths to be simultaneously analyzed. The methodology is implemented in the Distributed Energy Resources Customer Adoption Model (DER-CAM), and four case studies are performed. In DER-CAM, a state of the art mixed integer linear model used to find costand CO2optimal DER portfolios of energy supply in decentralized energy systems, PV output is calculated using monthly average-hourly solar irradiation data. This approach fails to capture the effect of fast moving clouds and may sometimes lead to inaccurate results. The methodology introduced in this work estimates the variability of PV output and the resulting impact on power demand charges for different confidence levels taking into account that existing energy storage may offset drops in PV output if sufficiently charged, or that fast-ramping generator units may also be used for the same purpose. This formulation is embedded in the investment decision process, and is reflected in the optimal DER portfolio provided by DER-CAM. Results obtained in the case studies suggest that in previous DER-CAM formulations the optimal capacity of PV was overestimated in cases where storage capacity was not sufficient to compensate for drops in PV output. Also it was found that the optimal capacity of on-site generators might have been slightly underestimated in some cases. The new model formulation depicts the economic value of PV more accurately while effectively capturing the synergic economic benefits of PV paired with electric storage.

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