Congestion Mitigation in Unbalanced Residential Networks with OPF-based Demand Management

TL;DR

This paper introduces a demand management strategy for residential networks that mitigates congestion by incentivizing demand reduction through contracts, combining detailed network physics with user comfort considerations in a scalable optimization framework.

Contribution

It presents a novel OPF-based demand management approach that incorporates user comfort and system physics, enabling scalable congestion mitigation in residential feeders.

Findings

Effective congestion reduction on real distribution feeders

Contracts can incentivize user participation

Operational scheduling benefits from the approach

Abstract

This paper proposes a novel congestion mitigation strategy for low voltage residential feeders in which the rising power demand due to the electrification of the transport and heating systems leads to congestion problems. The strategy is based on requiring residential customers to limit their demand for a certain amount of time in exchange for economic benefits. The main novelty of the method consists of combining a thorough representation of the network physics with advanced constraints that ensure the comfort of residential users, in a scalable manner that suits real systems. The mitigation strategy is presented from a DSO perspective, and takes the form of contracts between users and system operator. The focus on user comfort aims to make the contracts appealing, encouraging users to voluntarily enroll in the proposed mitigation scheme. The presented solution is implemented as a mixed-integer multi-period optimal power flow problem which relies on a linearized three-phase power flow formulation. Calculations on 100 real-life distribution feeders are performed, to analyze the congestion-relieving potential of several possible system operator-user contracts. From a planning perspective, the results can help the system operator define contractual terms that make a specific congestion mitigation scheme effective and viable. From an operational perspective, the same calculations can be used to optimally schedule power reduction on a day-ahead basis.