Dual Decomposition-Based Privacy-Preserving Multi-Horizon Utility-Community Decision Making Paradigms

TL;DR

This paper explores two privacy-preserving multi-horizon decision making frameworks for utility-community interactions, analyzing their convergence, implementation, and performance through iterative optimization strategies.

Contribution

It introduces two novel privacy-preserving architectures for multi-horizon utility-community decision making, with convergence analysis and practical implementation insights.

Findings

Both architectures ensure privacy while enabling cost minimization.

Convergence of subgradient and LUBS-based strategies is established.

Numerical results demonstrate feasibility and performance differences.

Abstract

Two types of privacy-preserving decision making paradigms for utility-community interactions for multi-horizon operation are examined in this paper. In both designs, communities with renewable energy sources, distributed generators, and energy storage systems minimize their costs with limited information exchange with the utility. The utility makes decision based on the information provided from the communities. Through an iterative process, all parties achieve agreement. The authors' previous research results on subgradient and lower-upper-bound switching (LUBS)-based distributed optimization oriented multi-agent control strategies are examined and the convergence analysis of both strategies are provided. The corresponding decision making architectures, including information flow among agents and learning (or iteration) procedure, are developed for multi-horizon decision making scenarios. Numerical results illustrate the decision making procedures and demonstrate their feasibility of practical implementation. The two decision making architectures are compared for their implementation requirements as well as performance.