Microgrids optimal radial reconfiguration via FORWARD algorithm

TL;DR

This paper introduces a permutation-based iterative search method combined with the FORWARD algorithm to efficiently find feasible, near-optimal radial configurations for microgrids, addressing complex energy management challenges.

Contribution

It presents a novel permutation-based iterative approach integrated with FORWARD for scalable microgrid radial reconfiguration and as a warm-start for MINLP solvers.

Findings

Efficient identification of feasible microgrid configurations.

Improved scalability over traditional MINLP methods.

Enhanced solution quality with the combined approach.

Abstract

Microgrids offer a promising paradigm for integrating distributed energy resources, bolstering energy resilience, and reducing the impact of blackouts. However, their inherent decentralization and dynamic operation present substantial energy management complexities. These complexities, including balancing supply and demand, ensuring system stability, and minimizing operational costs, often necessitate solving computationally intractable NP-hard Mixed-Integer Non-Linear Programming (MINLP) problems. Traditional MINLP solvers struggle with the scalability and feasibility guarantees required for these challenges. To address this, this paper tackles the problem of resource allocation and radial configuration design for microgrid power distribution and proposes and abstracted problem which is solved by introducing a permutation-based iterative search method over the recently introduced FORWARD method to efficiently identify feasible, near-optimal radial network structures while inherently respecting physical constraints. Furthermore, this paper investigates the integration of the proposed method as a warm-start strategy for benchmark MINLP solvers offering a scalable solution for comprehensive microgrid design.