Evaluation of Prosumer Networks for Peak Load Management in Iran: A Distributed Contextual Stochastic Optimization Approach

TL;DR

This paper presents a distributed stochastic optimization framework for prosumer networks in Iran to effectively manage peak loads, integrating prediction, coordination, and privacy-preserving algorithms to improve grid stability and reduce costs.

Contribution

It introduces a novel distributed contextual stochastic optimization approach with a consensus-based information sharing mechanism for prosumer networks.

Findings

Peak loads are significantly reduced through the proposed method.

The integrated prediction and optimization approach lowers total costs.

The distributed algorithm ensures computational efficiency and privacy protection.

Abstract

Renewable prosumers face the complex challenge of balancing self-sufficiency with seamless grid and market integration. This paper introduces a novel prosumers network framework aimed at mitigating peak loads in Iran, particularly under the uncertainties inherent in renewable energy generation and demand. A cost-oriented integrated prediction and optimization approach is proposed, empowering prosumers to make informed decisions within a distributed contextual stochastic optimization (DCSO) framework. The problem is formulated as a bi-level two-stage multi-time scale optimization to determine optimal operation and interaction strategies under various scenarios, considering flexible resources. To facilitate grid integration, a novel consensus-based contextual information sharing mechanism is proposed. This approach enables coordinated collective behaviors and leverages contextual data more effectively. The overall problem is recast as a mixed-integer linear program (MILP) by incorporating optimality conditions and linearizing complementarity constraints. Additionally, a distributed algorithm using the consensus alternating direction method of multipliers (ADMM) is presented for computational tractability and privacy preservation. Numerical results highlights that integrating prediction with optimization and implementing a contextual information-sharing network among prosumers significantly reduces peak loads as well as total costs.