Resilience Through Distributed Energy Resources

QC 20260506

Abstract

A resilient power grid is needed to reduce the growing risk of extreme events. While improved resilience could be achieved through grid reinforcements, this can get expensive. At the same time, we observe an increasing number of batteries, flexible loads, and other Distributed Energy Resources (DERs) making their way into the distribution system. These DERs present a cost-effective opportunity for Distribution System Operators (DSOs) to enhance their resilience. To unlock this potential, however, requires a DSO to anticipate outages, manage uncertainty, and operate DERs intelligently. It also requires coordination with DER owners, other DSOs, and the Transmission System Operator (TSO), whose goals may not always align. Such preparation and coordination is also necessary because, in the context of an unbundled European market, DSOs cannot own DERs and sell their services for profit.

We therefore propose a resilience-enhancing process for DSOs centred around using DERs as Resilience Enhancing Technologies (RETs). In this framing, DERs continue to provide existing ancillary services, like frequency support, but are considered to be RETs when the DSO can control them for a limited period of time as part of a new resilience service. The process begins by estimating the DSO's overall susceptibility to outages through both general-purpose and specialized indicators that capture the average expected impact of an outage based on quasi-static characteristics of the DSO. A rolling 36-hour outage forecast can then be constructed by combining the same quasi-static characteristics with real-time data. The process ends with a decision support system for the live operation of RETs, which we accomplish through the application of several Deep Reinforcement Learning (DRL) agents. In the middle, we combine the susceptibility to outages, the probability of upcoming outages, and the expected effectiveness of the RETs to enable the DSO to objectively decide when, where, and how many DERs they need for an anticipated extreme event. 

Implementations are provided for each stage in the resilience-enhancing process. This includes contributions towards a general outage susceptibility indicator, based on reliability indices, as well as a specialized outage susceptibility indicator for cyberattacks, based on Monte Carlo simulations. To anticipate outages, a regression model was used to predict storm-related outages based on the technical characteristics of a DSO as well as real-time meteorological data. The principal aim, however, was to investigate to what extent the use of DERs to mitigate extreme events is justifiable. To this end, the contributions focused especially on the final stage, where we assume a portfolio of RETs is available, and the DSO must decide how best to use them to prevent further load shedding. Through the use of demand response and energy storage, we show that the amount of energy not supplied can be reduced by up to 52.9% (on average) compared to a passive response. Sensitivity analysis then reveals that larger, well-placed RET portfolios are more effective, but with diminishing returns. Consequently, a DSO could attempt to justify their acquisition of a RET portfolio based on the cost of the portfolio versus the expected cost of the outage.

Link to DiVA