Privacy-Preserving Economic Dispatch in Competitive Electricity Market

TL;DR

This paper proposes a privacy-preserving method for economic dispatch in electricity markets, allowing participants to mask their data while ensuring the same optimal dispatch results, thus protecting sensitive information against cyber threats.

Contribution

It introduces a novel privacy-preserving approach that maintains optimal dispatch solutions while safeguarding market participants' private data in electricity markets.

Findings

Effective protection of private information demonstrated through numerical case studies.

The proposed method preserves the optimal dispatch and locational marginal prices.

Comparison shows acceptable computation and communication costs.

Abstract

With the emerging of smart grid techniques, cyber attackers may be able to gain access to critical energy infrastructure data and strategic market participants may be able to identify offer prices of their rivals. This paper discusses a privacy-preserving economic dispatch approach in competitive electricity market, in which individual generation companies (GENCOs) and load serving entities (LSEs) can mask their actual bidding information and physical data by multiplying with random numbers before submitting to Independent System Operators (ISOs) and Regional Transmission Owners (RTOs). This would avoid potential information leakage of critical energy infrastructure and financial data of market participants. The optimal solution to the original ED problem, including optimal dispatches of generators and loads and locational marginal prices (LMPs), can be retrieved from the optimal solution of the proposed privacy-preserving ED approach. Numerical case studies show the effectiveness of the proposed approach for protecting private information of individual market participants while guaranteeing the same optimal ED solution. Computation and communication costs of the proposed privacy-preserving ED approach and the original ED are also compared in case studies.