Stochastic Economic Dispatch Considering Demand Response and Endogenous Uncertainty

TL;DR

This paper introduces a new optimization model for stochastic economic dispatch that incorporates endogenous uncertainty from demand response commitments, and proposes a coupled learning algorithm to solve it effectively, demonstrating its importance through IEEE system tests.

Contribution

The paper develops the first optimization model for demand response-involved stochastic economic dispatch considering endogenous uncertainty and proposes a novel coupled learning algorithm to solve it.

Findings

The model effectively captures decision-dependent uncertainty from demand response.

The coupled learning algorithm successfully solves the complex SED-DR-EnU problem.

Results highlight the significance of considering endogenous uncertainty in dispatch planning.

Abstract

This paper considers endogenous uncertainty (EnU) in the stochastic economic dispatch (SED) problem, where the endogenous uncertainty means decision dependent uncertainty. In this problem, demand response (DR) commitment is the source of the EnU. Nevertheless, EnU is not well considered in existing literature. Our first contribution is to build up an optimization model of DR-involved SED under EnU (SED-DR-EnU). This is a computational challenging problem due to the EnU. Our second contribution is introducing a coupled learning enabled optimization algorithm which can effectively solve the proposed SED-DR-EnU problem. This strategy is tested on the IEEE 14 bus, and IEEE 39 bus systems, and the results showed the importance of considering EnU in the DR-involved SED problem.