Dra. Matamala-Aguayo, Yolanda

Detecting and characterizing malicious operations in electricity markets presents substantial challenges due to the innate complexity of power networks. This paper explores adversarial attacks in electricity markets, i.e., we consider the scenario in which malicious market participants make undetectable changes to the input data of other participants, resulting in significant negative impacts on their operations. We propose a novel methodology to generate adversarial attacks, which leverages linear optimization theory and statistical learning to generate suboptimal, feasible solutions or super-optimal, infeasible solutions that convincingly resemble optimal solutions. The methodology is general and applicable for generating adversarial attacks on optimization-based decision systems. We use it to analyze the potential economic losses, policy making, and social consequences resulting from compromised demand-side operations. In particular, we focus on scenarios where certain demand side-players, i.e., microgrids, or prosumers in a general sense, engage in malicious activities by targeting other microgrids and the independent system operator. We model the electricity market dynamics using an equilibrium problem with equilibrium constraints (EPEC). To make the model easier to solve, the EPEC is reformulated using a combination of the Fisher–Burmeister function and strong duality theorem. Results show that microgrids can artificially decrease its consumption by up to 12% without triggering alarms. Additionally, this amount can be increased by an additional 14% to 22%, contingent on the strategy employed to monitor the observable variables. We also study the implications of the increasing utilization of solar generation, showing its potential to mitigate the impact of adversarial attacks while also introducing new risks to the system.

In order to reduce greenhouse gas emissions, countries worldwide are transforming their energy systems with higher shares of renewable energy and smart technologies for demand response. Microgrids play an essential role in the transformation of electric grids to smart grids. However, renewable sources present new challenges, particularly those of high variability, which creates uncertainties in the supply side that can affect the security of electricity access at affordable prices. This paper proposes a novel Stackelberg stochastic model to account for different sources of uncertainty. The Stackelberg model considers microgrids as leaders (upper-level problem) with uncertainty regarding the availability of wind and solar sources and electricity prices. Availability of renewable sources is modeled via chance constraints, which allows assessing the risk of microgrids over-committing supply levels. Uncertainty in electricity prices is modeled via a set of demand scenarios with a given probability distribution. The lower-level problem of the Stackelberg problem considers an electricity dispatch problem for each demand scenario. The proposed model allows measuring the strategic actions of microgrids when facing different types of uncertainties and how the smart grid should adapt to guarantee that demand levels are supplied. The results show the effectiveness of the proposed method. We find that microgrids risk levels above 30% do not correlate with further benefits, such as reduced electricity prices. We also identified that in average, depending on the social cost of carbon and demand level, microgrids can cover their own demand and supply 15% of the electricity demand in the grid.

Technologies providing flexibility options to power systems, such as Power-To-X (PtX) technologies, have become more important with the increasing deployment of distributed energy resources, particularly microgrids. However, uncertainty in renewable resources creates ambiguity regarding the necessary PtX capacity to install. This paper proposes a two-stage stochastic Stackelberg approach for multi-energy microgrids, focusing on long-term investment decisions in PtX technologies and hourly operational strategies. The Stackelberg problem considers microgrids as leaders (upper level) and the independent system operator as a follower (lower level). In the first stage, investment levels for various PtX technologies are determined as one-time decisions. The second stage focuses on hourly operational decisions, including the integration of microgrids with the independent system operator with marginal endogenous prices. The results provide insights into how uncertainty in renewable generation and electric battery levels affect investment levels. Larger hydrogen and thermal storage volumes lead to more flexible and self-sufficient microgrid systems. In scenarios with higher flexibility, microgrids can: (1) satisfy up to 10% of the independent system operator demand using renewable electricity and (2) regulate supply variability by storing excess generation during peak periods and releasing it during low generation periods.