Leveraging blockchain technology for resilient and robust frequency control in a renewable-based hybrid power system with hydrogen and battery storage integration

This paper introduces the blockchain-assisted frequency regulation mechanism for achieving resiliency and robustness in a renewable-based hybrid power system (HPS) powered by multiple sources, including wind turbine generators (WTG), hydrogen fuel cells (FC) with aqua electrolyzer (AE), diesel engine generators (DEG) and battery energy storage systems (BESS). Frequency fluctuations are exacerbated by load uncertainties paired with energy-generating source uncertainties resulting from integrating multiple distributed energy resources (DERs) into the national electric power grid. In the event of energy generation loss, efficient load frequency control (LFC) techniques are considered essential for frequency restoration in DER-integrated HPS. In addition, active power regulation data in LFC need secure data storage mechanisms for ensuring cyber-resilience, a transparent cash payout mechanism for the stakeholders in LFC market operation, and related sustainability services. A novel resilient blockchain framework for realizing LFC is proposed for addressing the power resilience and the false-data injection issue. Moreover, a parameter sensitivity algorithm (PSA) constrained optimization methodology using the genetic algorithm (GA) withstands the energy generation loss and uncertainties. A consensus algorithm-based blockchain methodology acts as an enabler for strategic decision-making and secures the data flow to the market operator. The proposed framework-driven methodology is implemented using a Blockchain network developed in python for securing LFC transactions by developing a case study for HPS. Numerous simulation scenarios demonstrate the proposed methodology's effectiveness in establishing a synergistic operative framework for the resilient and robust operation of the hybrid energy system with multiple sources, even in the event of uncertainties. © 2023 Elsevier Ltd. All rights reserved.