Distributed Online Economic Dispatch with Time-Varying Coupled Inequality Constraints

TL;DR

This paper presents a distributed online optimization algorithm for power system economic dispatch with time-varying constraints, achieving sublinear regret and constraint violation, and demonstrating effectiveness on real and synthetic data.

Contribution

It introduces a primal-dual based algorithm with constraint-tracking for distributed online economic dispatch under dynamic conditions, with proven sublinear regret and constraint violation.

Findings

Algorithm achieves sublinear dynamic regret and constraint violation.

Effective in real-time power dispatch with time-varying data.

Performs well on both synthetic and real Australian Energy Market data.

Abstract

We investigate the distributed online economic dispatch problem for power systems with time-varying coupled inequality constraints. The problem is formulated as a distributed online optimization problem in a multi-agent system. At each time step, each agent only observes its own instantaneous objective function and local inequality constraints; agents make decisions online and cooperate to minimize the sum of the time-varying objectives while satisfying the global coupled constraints. To solve the problem, we propose an algorithm based on the primal-dual approach combined with constraint-tracking. Under appropriate assumptions that the objective and constraint functions are convex, their gradients are uniformly bounded, and the path length of the optimal solution sequence grows sublinearly, we analyze theoretical properties of the proposed algorithm and prove that both the dynamic regret and the constraint violation are sublinear with time horizon T. Finally, we evaluate the proposed algorithm on a time-varying economic dispatch problem in power systems using both synthetic data and Australian Energy Market data. The results demonstrate that the proposed algorithm performs effectively in terms of tracking performance, constraint satisfaction, and adaptation to time-varying disturbances, thereby providing a practical and theoretically well-supported solution for real-time distributed economic dispatch.