Microgrid dispatch and price of reliability using stochastic approximation

When properly operated, microgrids can facilitate the integration of stochastic renewable energy without compromising service reliability. However, in the context of multi-stage dispatching, finding the optimal day-ahead energy procurement that accounts for the variability of real-time operation is a computationally challenging task. This paper develops a computationally efficient two-stage economic dispatch scheme for a microgrid that exchanges energy with an external power system. The scheme is designed to minimize the generation and energy exchange costs, while setting limits on the microgrid-wide expected load not served. The day-ahead variables, which are the solution to the first stage, are found using a stochastic approximation saddle-point algorithm. The proposed algorithm is asymptotically convergent and can be efficiently implemented upon drawing samples from the distribution of the real-time state variables (wind energy, demand, and energy prices). Numerical tests using the IEEE 14-bus power system benchmark verify that the proposed scheme outperforms all other tested alternatives, even for very high wind power penetration.