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Open Access Publications from the University of California

Strategies for microgrid operation under real-world conditions

  • Author(s): Gust, G;
  • Brandt, T;
  • Mashayekh, S;
  • Heleno, M;
  • DeForest, N;
  • Stadler, M;
  • Neumann, D
  • et al.
Abstract

Microgrids are an increasingly relevant technology for integrating renewable energy sources into electricity systems. Based on a microgrid implementation in California, we investigate microgrid operation under real-world conditions. These conditions have not yet been considered in combination and encompass energy charges, demand charges, export limits, as well as uncertainty about future electricity demand and generation in the microgrid. Under these conditions, we evaluate the performance of two frequently applied groups of strategies for microgrid operation. The first group is composed of proactive strategies that optimize decisions based on forecasts of future electricity generation and demand. The second group includes reactive strategies that make operational decisions based exclusively on the current state of the microgrid. We evaluate the performance of the strategies under varying operational parameters, forecast accuracies, and microgrid configurations—well beyond our Californian showcase. Our results confirm the expectation that proactive strategies outperform reactive ones in the majority of settings. Yet, reactive strategies can perform better under short control intervals or under moderate prediction errors of PV generation or demand. Furthermore, the interplay between real-world conditions and operational strategies reveals several additional insights for research on microgrid operation. First, we find that demand charges and export limits decisively affect microgrid performance. Second, the impact of forecast errors is highly non-linear and non-monotonous. Third, escalating negative interactions between forecast errors and demand charges make proactive strategies benefit from longer control intervals. This result is contrary to existing best practice, which promotes short control intervals to minimize the impact of uncertainty.

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