Robust Economic Dispatch with Flexible Demand and Adjustable Uncertainty Set

TL;DR

This paper proposes a robust economic dispatch model that leverages flexible demand and adjustable wind power uncertainty sets to balance cost and risk in power systems with high renewable integration.

Contribution

It introduces a novel co-optimization framework that dynamically adjusts wind uncertainty sets based on load flexibility, enhancing system robustness and efficiency.

Findings

Flexible loads help reduce wind curtailment and system risk.

The model achieves efficient solutions via convex approximation.

Case studies demonstrate improved wind deviation management.

Abstract

With more renewable energy sources (RES) integrated into the power system, the intermittency of RES places a heavy burden on the system. The uncertainty of RES is traditionally handled by controllable generators to balance the real time wind power deviation. As the demand side management develops, the flexibility of aggregate loads can be leveraged to mitigate the negative impact of the wind power. In view of this, we study the problem of how to exploit the multi-dimensional flexibility of elastic loads to balance the trade-off between a low generation cost and a low system risk related to the wind curtailment and the power deficiency. These risks are captured by the conditional value-at-risk. Also, unlike most of the existing studies, the uncertainty set of the wind power output in our model is not fixed. By contrast, it is undetermined and co-optimized based on the available load flexibility. We transform the original optimization problem into a convex one using surrogate affine approximation such that it can be solved efficiently. In case studies, we apply our model on a six-bus transmission network and demonstrate that how flexible load aggregators can help to determine the optimal admissible region for the wind power deviations.