Effective Load Carrying Capability Evaluation of Renewable Energy via Stochastic Long-Term Hourly Based SCUC

Abstract

This paper evaluates the effective load carrying capability (ELCC) of renewable resources, including wind and solar, via the stochastic long-term hourly based security-constrained unit commitment (SCUC) model. Different from traditional approaches which approximate ELCC of renewable resources using system peak loads, nonsequential block load duration curves, or rolling-based sequential methods, the stochastic long-term hourly based SCUC could accurately examine the impacts of short-term variability and uncertainty of renewable resources as well as chronological operation details of generators on hourly supplydemand imbalance and power system reliability in a long-term horizon. Uncertainties of hourly wind, solar, and load in a 1-year horizon are simulated via the scenario tree using the Monte Carlo method, and Approximate Dynamic Programming is adopted for effectively solving the stochastic long-term hourly based SCUC model. Variability correlations between wind speed and solar radiation are considered within the scenario sampling procedure. Moreover, parallel computing is designed with the pipeline structure for accelerating the computational performance of Approximate Dynamic Programming. Numerical case studies on the modified IEEE 118-bus system illustrate the effectiveness of the proposed stochastic long-term hourly based SCUC model and the Approximate Dynamic Programming solution approach for evaluating ELCC of renewable resources. This would help independent system operators (ISO) designs effective long-term planning strategies for operating power systems efficiently and reliably.