Risk-minimizing stochastic self-scheduling model for microgrid in day-ahead electricity market

Abstract

This paper presents a new stochastic operation scheduling model for microgrid in day-ahead electricity market considering the forecast uncertainties of wind generations, microgrid's load, and locational marginal price of the point of common coupling, as well as the uncertainties pertaining to availability of generation units of the microgrid. The financial risk caused by these uncertainties is modeled by conditional value-at-risk criterion. The proposed approach is formulated as a risk-minimizing two-stage stochastic model based on mixed-integer linear programming framework. This model minimizes the expected scheduling cost together with the cost of financial risk. The impacts of different uncertainty sources on the model's results in both grid-connected and islanded operation modes are extensively studied. Additionally, higher effectiveness of the proposed stochastic model compared with common deterministic approach, regarding total operation cost and convergence behavior, is illustrated through an out-of-sample analysis.