Chance-constrained OPF: A Distributed Method with Confidentiality Preservation

TL;DR

This paper introduces a distributed chance-constrained optimal power flow method that preserves regional confidentiality, enabling multi-regional wind power integration efficiently without sharing sensitive data.

Contribution

It proposes a novel distributed CC-OPF approach that maintains data confidentiality and avoids convergence issues, improving multi-regional wind power integration.

Findings

Highly accurate results on IEEE test cases

No need for parameter tuning

Preserves regional data confidentiality

Abstract

Given the increased percentage of wind power in power systems, chance-constrained optimal power flow (CC-OPF) calculation, as a means to take wind power uncertainty into account with a guaranteed security level, is being promoted. Compared to the local CC-OPF within a regional grid, the global CC-OPF of a multi-regional interconnected grid is able to coordinate across different regions and therefore improve the economic efficiency when integrating high percentage of wind power generation. In this global problem, however, multiple regional independent system operators (ISOs) participate in the decision-making process, raising the need for distributed but coordinated approaches. Most notably, due to regulation restrictions, commercial interest, and data security, regional ISOs may refuse to share confidential information with others, including generation cost, load data, system topologies, and line parameters. But this information is needed to build and solve the global CC-OPF spanning multiple areas. To tackle these issues, this paper proposes a distributed CC-OPF method with confidentiality preservation, which enables regional ISOs to determine the optimal dispatchable generations within their regions without disclosing confidential data. This method does not require parameter tunings and will not suffer from convergence challenges. Results from IEEE test cases show that this method is highly accurate.