Distribution Locational Marginal Pricing Under Uncertainty Considering Coordination of Distribution and Wholesale Markets

TL;DR

This paper introduces a bi-level market clearing and pricing mechanism for distribution electricity markets that accounts for uncertainties and coordination with wholesale markets, improving price signals and system management.

Contribution

It proposes a novel bi-level model integrating distribution and wholesale market coordination with uncertainty management, solved via a decentralized algorithm ensuring privacy and efficiency.

Findings

Effective distribution LMPs and ULMPs derived for active/reactive power and uncertainty.

The method improves voltage, congestion, and uncertainty management in distribution systems.

Case studies validate the proposed approach's effectiveness.

Abstract

An effective distribution electricity market (DEM) is required to manage the rapidly growing small-scale distributed energy resources (DERs) in distribution systems (DSs). This paper proposes a day-ahead DEM clearing and pricing mechanism to account for the uncertainty of DERs and the coordination with the wholesale electricity market (WEM) through a bi-level model. The upper-level model clears the WEM in the transmission system (TS) and forms the locational marginal price (LMP) and uncertainty LMP (ULMP) for energy and uncertainty/reserve, respectively. In the lower level, a robust scheduling model considering WEM-DEM coordination and uncertainties is proposed to clear the DEM. Accordingly, the distribution LMPs (DLMPs) for active power, reactive power and uncertainty/reserve are derived to reward the energy/reserve provision and charge uncertain resources in the DEM, which provide effective price signals for managing not only the voltage and congestion, but also the uncertainty in DSs. A heterogeneous decomposition (HGD) algorithm is utilized to solve the bi-level model in a decentralized manner with limited information interaction between TS and DSs, which guarantees the solution efficiency and information privacy. The effectiveness of the proposed method is verified via numerous case studies.