A distributed robust optimization approach for the economic dispatch of flexible resources

Power systems are confronted with prodigious challenges of scheduling and operation incurred by the high penetration of distributed renewable energy with intermittency and uncertainty. Therefore, this paper proposes an improved distributed robust optimization approach with self-adaptive step-sizes based on the line search method and a polynomial filter, to minimize the overall costs of flexible resources including conventional generators, energy storage systems, renewable energy curtailments, deferrable loads and fie-line power exchanges, while considering various constraints, such as supply-demand power balance, line congestion constraints and power output limits. Numerical case studies conducted in a modified IEEE 14-bus system and a modified IEEE 118-bus system demonstrate the reliability, robustness and extensibility of the proposed approach. In addition, the effectiveness and accuracy of the proposed distributed robust optimization approach are validated through comparisons with the traditional centralized gradient method and the convergence performance is better in contrast to other distributed optimization algorithm.

Automated summary

This paper proposes an improved distributed robust optimization approach to minimize overall costs in high-penetration distributed renewable energy settings, demonstrating effectiveness, robustness, and superior convergence performance compared to traditional and other distributed optimization methods.