Equity-aware Load Shedding Optimization

TL;DR

The paper tackles inequitable load shedding in power systems after major disturbances by proposing an equity-aware framework. It introduces a grid Gini coefficient, $GGC_{OEI}$, and defines outage risk indicators $ORI_i = P_{LS,i}/P_{Li}$ to quantify disparities, embedding the metric as a constraint $GGC_{OEI} \leq \beta$ within an ACOPF-based optimization. Through case studies on the IEEE 14-bus system, it shows that enforcing equity redistributes shedding more evenly across buses, at a small cost increase, and that the effect depends on the choice of $\beta$. This approach offers a practical pathway to fairer, more resilient grid operations by balancing equity with system cost, and provides a concrete framework for operators to implement equitable load shedding in future grids, especially under N-1 contingencies.

Abstract

Load shedding is usually the last resort to balance generation and demand to maintain stable operation of the electric grid after major disturbances. Current load-shedding optimization practices focus mainly on the physical optimality of the network power flow. This might lead to an uneven allocation of load curtailment, disadvantaging some loads more than others. Addressing this oversight, this paper introduces an innovative equity-aware load-shedding optimization model that emphasizes a fair allocation of load curtailment across the network. By proposing a novel equity indicator for load shedding and integrating it into an ACOPF-based optimization framework, we offer grid operators a more balanced and equitable load shedding strategy. Case studies highlight the importance of equity considerations in determining optimal load curtailment between buses.

Figures (7)

• Figure 1: Electricity outage average interruption hours in different regions
• Figure 2: IEEE 14-bus test system network
• Figure 3: Bus ORI under different values of $\beta$
• Figure 4: Load shedding on buses with $\beta$=1
• Figure 5: Load shedding on buses with $\beta$=0.05