The cyber security of modern power systems has drawn increasing attention in both academia and industry. Many detection and defense methods for cyber-attacks have therefore been proposed to enhance robustness of modern power systems. In this paper, we propose a new, distributed blockchain-based protection framework to enhance the self-defensive capability of modern power systems against cyber-attacks. We present a comprehensive discussion on how blockchain technology can be used to enhance the robustness and security of the power grid, by using meters as nodes in a distributed network which encapsulates meter measurements as blocks. Effectiveness of the proposed protection framework is demonstrated via simulation experiments on the IEEE-118 benchmark system.

Cyber security of modern power systems has drawn increasing attention in recent years. This paper proposes a class of cyber-topology attacks derived from false data injection attacks (FDIAs), with the aim of disturbing the operation of power systems. Three kinds of cyber-topology attack are proposed: line-addition attack, line-removal attack, and line-switching attack. By directly misleading the decision-making process of the Independent System Operator (ISO), the proposed cyber-topology attack consequently affects the economic operation and security of the system. We establish optimal attack models for different cyber-topology attack scenarios, and use a recently proposed metaheuristic optimization algorithm (the Natural Aggregation Algorithm (NAA)) to solve the resulting attack models. Experiments based on the IEEE 39-node benchmark system show that the proposed class of attacks poses a significant threat to modern power systems.

This paper proposes a generalized false data injection attack-based cyber topology attack capable of disturbing conventional transactions in the electricity market. The proposed attack aims to mislead customers pay higher electricity bills in the wholesale market by making small, undetected price deviations during each attack interval over an extended period. In this paper, the Australian electricity market trading mechanism is considered, in which an extended attack over a single day is proposed, spanning totally 288 5-minute dispatch (attack) intervals. The proposed attack poses no security threat to the power system, affecting only the economic costs borne by customers. A defining feature of the proposed model is the joint consideration of both topology error processing and bad data detection constraints in order to ensure validity of the proposed attack. The rolling horizon optimization technique is used to make appropriate adjustment to the attacker's strategy considering the practical operating condition. A new metaheuristic optimization algorithm previously proposed by the authors, Natural aggregation algorithm, is used to find the corresponding attack strategies in this paper. Case studies on the IEEE-118 benchmark system are conducted to validate the proposed attack methodology.

We develop a transfer function to determine in situ line‐of‐sight gravity difference (LGD) directly from Gravity Recovery and Climate Experiment (GRACE) range‐acceleration measurements. We first reduce GRACE data to form residual range‐acceleration referenced to dynamic orbit computed with a reference gravity field and nonconservative force data. Thus, the residuals and the corresponding LGD data reflect time‐variable gravity signals. A transfer function is designed based on correlation‐admittance spectral analysis. The correlation spectrum shows that residual range‐acceleration and LGD are near‐perfectly correlated for frequencies >5 cycles‐per‐revolution. The admittance spectrum quantifies that the LGD response to range‐acceleration is systematically larger at lower frequencies, due to the increased contribution of centrifugal acceleration. We find that the correlation and admittance spectra are stationary (i.e., are independent of time, satellite altitude, and gravity strength) and, therefore, can be determined a priori with high fidelity. We determine the spectral transfer function and the equivalent time domain filter. Using both synthetic and actual GRACE data, we demonstrate that in situ LGD can be estimated via the transfer function with an estimation error of 0.15 nm/s², whereas the actual GRACE data error is around 1.0 nm/s². We present an application of LGD data to surface water storage changes in large basins such as Amazon, Congo, Parana, and Mississippi by processing 11 years of GRACE data. Runoff routing models are calibrated directly using LGD data. Our technique demonstrates a new way of using GRACE data by forward modeling of various geophysical models and in‐orbit comparison with such GRACE in situ data.