Abstract: This paper presents a probabilistic and risk-aware framework for microgrid dispatch under renewable generation. In this way, solar irradiance is modeled using a log-normal distribution, on other hand, wind speed follows a Rayleigh distribution. We used a combined multi-regime wind turbine power curve for the eolic part. A scenario-based Monte Carlo approach got multiple realizations of renewable output per hour, enabling the explicit evaluation of intermittence or stochastic variability. In order to quantify operational risk, we formulate Uncertainty Cost Functions that integrates: (i) the expected operational cost, (ii) the Conditional Value-at-Risk (CVaR), and (iii) the variability of the battery State of Charge (SOC). The proposed approach is validated in a microgrid with solar, wind, battery storage, and flexible loads. Results demonstrate that the risk-aware dispatch significantly reduces tail-risk exposure, mitigates SOC fluctuations, and enhances operational resilience when compared to a conventional riskneutral strategy.

Keywords: Microgrids, Renewable Uncertainty, Scenario-Based Optimization, CVaR, Risk-Aware Dispatch, Demand Response, Battery Storage, Stochastic Modeling

Pages: 89-98