Distributed Coordination of Multi-Microgrids in Active Distribution Networks for Provisioning Ancillary Services

TL;DR

This paper introduces a distributed optimization framework for coordinating multiple microgrids in active distribution networks to provide ancillary services, specifically passive voltage support, leveraging inverter reactive power and storage flexibility.

Contribution

It presents a novel distributed algorithm based on ADMM that coordinates microgrids without central control, incorporating mixed-integer inequalities for feasible power exchange.

Findings

Effective in providing passive voltage support in test scenarios

Scalable and convergent for various network sizes

Demonstrates advantages of distributed control over centralized methods

Abstract

With the phenomenal growth in renewable energy generation, the conventional synchronous generator-based power plants are gradually getting replaced by renewable energy sources-based microgrids. Such transition gives rise to the challenges of procuring various ancillary services from microgrids. We propose a distributed optimization framework that coordinates multiple microgrids in an active distribution network for provisioning passive voltage support-based ancillary services while satisfying operational constraints. Specifically, we exploit the reactive power support capability of the inverters and the flexibility offered by storage systems available with microgrids for provisioning ancillary service support to the transmission grid. We develop novel mixed-integer inequalities to represent the set of feasible active and reactive power exchange with the transmission grid that ensures passive voltage support. The proposed alternating direction method of multipliers-based algorithm is fully distributed, and does not require the presence of a centralized entity to achieve coordination among the microgrids. We present detailed numerical results on the IEEE 33-bus distribution test system to demonstrate the effectiveness of the proposed approach and examine the scalability and convergence behavior of the distributed algorithm for different choice of hyperparameters and network sizes.