A Comprehensive Multi-Period Optimal Power Flow Framework for Smart LV Networks

TL;DR

This paper develops a detailed multi-period optimal power flow framework for smart low-voltage networks, assessing the impact of residential flexibility, device controllability, and inverter technologies on system performance.

Contribution

It introduces a comprehensive, scalable model for multi-period optimal power flow in smart LV networks, incorporating new modeling elements and detailed impact assessments.

Findings

Residential device flexibility can significantly improve system operation.

Partial control of customer profiles offers effective congestion management.

Advanced inverter capabilities enhance reactive power support and phase balancing.

Abstract

This paper presents an extensive multi-period optimal power flow framework, with new modelling elements, for smart LV distribution systems that rely on residential flexibility for combating operational issues. A detailed performance assessment of different setups is performed, including: ZIP flexible loads (FLs), varying degrees of controllability of conventional residential devices, such as electric vehicles (EVs) or photovoltaics (PVs), by the distribution system operator (DSO) (adhering to customer-dependent restrictions) and full exploitation of the capabilities offered by state-of-the-art inverter technologies. A comprehensive model-dependent impact assessment is performed, including phase imbalances, neutral and ground wires and load dependencies. The de-congestion potential of common residential devices is highlighted, analyzing capabilities such as active power redistribution, reactive power support and phase balancing. Said potential is explored on setups where the DSO can make only partial adjustments on customer profiles, rather than (as is common) deciding on the full profiles. The extensive analysis can be used by DSOs and researchers alike to make informed decisions on the required levels of modelling detail, the connected devices and the degrees of controlability. The formulation is computationally efficient, scaling well to medium-size systems, and can serve as an excellent basis for building more tractable or more targeted approaches.