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

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.

In Pursuit of New Real-Time Ancillary Services Providers: Hidden Opportunities in Low Voltage Networks and Sustainable Buildings

“…The work is not solely concerned with conceptualization and rigorous mathematical (re)formulations, but also with a) ensuring that DSO and involved customers (SSBs) are sharing responsibilities, and b) designing a scalable and feasible methodology. By significantly extending our previous works [3], [29] (scalability, detailed SSB models, conceptualization-rigorous formulation/reformulation of LV2MV AS, multiple customer types), this work offers the following major contributions:…”

“…Stage 1 (DSO side): LV feeder-level modelling 1) Electrical network: LV feeders are generally unbalanced. Ideally, the DSO would send SSBs per-phase requests [3], [29]. However, this requires technical infrastructure that S gen b,t is used to signify penalization because of curtailment AND because of conversion to reactive power.…”

“…This work employs polar coordinates, the terms P ij , Q ij and θ ij representing active/reactive power flows and nodal angular differences, respectively. The model also includes active and reactive 2 power balances (25) and power flows ( 26)-( 27), and network technical limits, i.e., upper/lower voltage magnitudes, V max , V min , and branch flow maximum apparent power, S max ij , ( 28)- (29). For ( 28)-( 29), the slack variables are assumed as zero in AS mode (see Fig.…”

From Smart to Sustainable to Grid-Friendly: A Generic Planning Framework for Enabling the Transition Between Smart Home Archetypes

“…Ultimately, all approaches pass down increased responsibilities to endusers. Most schemes are one-sided, with the DSO having direct authority over the building's consumption [25], the flexibility remuneration being highly lucrative for end-users but expensive for the DSO [9], [25], or the flexibility being procured in bulk by aggregated buildings with no consideration of internal electrical issues [13]. Fairer approaches have been proposed, though these require establishing some local energy market structure [26], [27].…”

“…This assumption is based on the common observation that, in distribution feeders hosting a dominant type of customer, when a customer is more active, the trend of increased activity is exhibited in a relatively uniform manner across the feeder, resulting in increased electrical issues [23], [46]. This drives the DSO towards seeking flexibility more often and in larger amounts [24], [25]. In this work we consider a linear probability distribution function.…”