Urban Energy Transition Policy Implementation: Navigating Complex Political Realities and Overcoming Barriers to Effective Strategy Deployment

Abstract

As the transition from traditional power grids to smart grids gains momentum, the integration of a cyber-layer into the system becomes imperative specially be considering policy and government intervention. However, this advancement also exposes the system to potential cyber threats, raising concerns about its security and resilience. This study focuses on bolstering cyber security resilience and detecting cyber-attacks (CAs) within urban microgrids. Specifically, the research investigates the impact of false data injection cyber-attacks, a significant threat to microgrids, on the steady-state performance of isolated microgrids. To combat such threats, a novel approach utilizing generative adversarial networks (GANs) is introduced to identify attacks on smart metering systems within microgrids. However, the GANs alone cannot make informed decisions, necessitating a decision-making framework. To address this requirement, a novel method based on prediction intervals is proposed to establish decision boundaries, determining the legitimacy of received data. To evaluate the proposed methodology's performance and accuracy, real-world data from a wind farm is employed. Results demonstrate the method's effectiveness and high accuracy in detecting false data injection attacks. Additionally, the study simulates a false data injection attack on a test microgrid to assess its resilience against CAs, revealing that a successful attack can significantly disrupt normal system operations. This research contributes to enhancing urban microgrid energy security by considering policy-informed decision-making, navigating complexity, and surmounting deployment barriers considering policy and government intervention.