AI and Energy Justice

Abstract

Artificial intelligence (AI) techniques are increasingly used to address problems in electricity systems that result from the growing supply of energy from dynamic renewable sources. Researchers have started experimenting with data-driven AI technologies to, amongst other uses, forecast energy usage, optimize cost-efficiency, monitor system health, and manage network congestion. These technologies are said to, on the one hand, empower consumers, increase transparency in pricing, and help maintain the affordability of electricity in the energy transition, while, on the other hand, they may decrease transparency, infringe on privacy, or lead to discrimination, to name a few concerns. One key concern is how AI will affect energy justice. Energy justice is a concept that has emerged predominantly in social science research to highlight that energy related decisions—in particular, as part of the energy transition—should produce just outcomes. The concept has been around for more than a decade, but research that investigates energy (in)justice in the context of digitalized and data-driven electricity systems is still rather scarce. In particular, there is a lack of scholarship focusing on the challenges and questions that arise from the use of AI technologies in the management of electricity systems. The central question of this paper is, therefore: what may be the implications of the use of AI in smart electricity systems from the perspective of energy justice, and what does this mean for the design and regulation of these technologies?

Fairness-Incorporated Online Feedback Optimization for Real-Time Distribution Grid Management

“…The objective of fairness considerations is to fairly distribute costs, burdens, or benefits among endusers. Various definitions of fairness exist [4], [5], [27]. Using PV curtailment as an example, these include fair generation where ideally end-users receive equal power curtailment, and proportionally fair generation where ideally end-users receive equal curtailment ratios according to the maximum generation.…”

Smart Preventive Maintenance of Hybrid Networks and IoT Systems Using Software Sensing and Future State Prediction

“…Regarding electricity distribution, consumption, and related policies, an arising concept and a side-effect is so-called "energy justice", concerning the effect of introducing advanced techniques for data collection and AI-related applications in the field, which may lead to privacy infringements. In [21], it is explained that "Energy justice" is a concept that has emerged predominantly in social science research to highlight that energy related decisions, particularly as part of the energy transition, should produce just outcomes. Therefore, the authors of the study recommend that "it is important to take energy justice in consideration from an early stage in the development or design of AI techniques".…”

Review of Recent Developments in Technical Control Approaches for Voltage and Congestion Management in Distribution Networks

“…As remuneration schemes are currently not in place for end-users in LV grids, fairness is an important consideration for the successful implementation of strategies such as active power control. Fairness refers to fairly distribute benefits, burdens, or costs among different end-users [26], which is especially relevant in the case of active power curtailment from PVs supporting grids for voltage control. End-users located at the far end of distributed feeders typically experience more frequent voltage limit violations.…”