In a false data injection attack (FDIA), an adversary stealthily compromises measurements from electricity grid sensors in a coordinated fashion, with a view to evading detection by the power system ...bad data detection module. A successful FDIA can cause the system operator to perform control actions that compromise either the physical or economic operation of the power system. In this letter, we consider some implications for FDIAs arising from the late 2015 Ukraine Blackout event.
With rapid advances in sensor, computer, and communication networks, modern power systems have become complicated cyber-physical systems. Assessing and enhancing cyber-physical system security is, ...therefore, of utmost importance for the future electricity grid. In a successful false data injection attack (FDIA), an attacker compromises measurements from grid sensors in such a way that undetected errors are introduced into estimates of state variables such as bus voltage angles and magnitudes. In evading detection by commonly employed residue-based bad data detection tests, FDIAs are capable of severely threatening power system security. Since the first published research on FDIAs in 2009, research into FDIA-based cyber-attacks has been extensive. This paper gives a comprehensive review of state-of-the-art in FDIAs against modern power systems. This paper first summarizes the theoretical basis of FDIAs, and then discusses both the physical and the economic impacts of a successful FDIA. This paper presents the basic defense strategies against FDIAs and discusses some potential future research directions in this field.
To help the integration of various distributed energy resources (DERs), this paper proposes a transactive approach to the optimal scheduling for prosumers in coupled energy systems. DERs are ...coordinately operated in the form of a virtual power plant (VPP), which actively participates in the day-ahead and real-time electricity market, as well as the wholesale gas market. In the day-ahead electricity and wholesale gas markets, a VPP aims to maximize expected profits by determining the unit commitments and hourly scheduling of DERs. In the real-time balancing market, a VPP adjusts DER schedules to minimize imbalance costs. In this paper, we address the energy conversions between electric power and gas loads and investigate the interacting operations of gas and power systems. Our simulation results show that hierarchical, coordinated power and gas scheduling can identify more accurate operation plans for coupled transactive energy systems.
An elaborately designed integrated power distribution and electric vehicle (EV) charging system will not only reduce the investment and operation cost of the system concerned, but also promote the ...popularization of environmentally friendly EVs. In this context, a multi-objective collaborative planning strategy is presented to deal with the optimal planning issue in integrated power distribution and EV charging systems. In the developed model, the overall annual cost of investment and energy losses is minimized simultaneously with the maximization of the annual traffic flow captured by fast charging stations (FCSs). Additionally, the user equilibrium based traffic assignment model (UETAM) is integrated to address the maximal traffic flow capturing problem. Subsequently, a decomposition based multi-objective evolutionary algorithm (MOEA/D) is employed to seek the non-dominated solutions, i.e., the Pareto frontier. Finally, collaborative planning results of two coupled distribution and transportation systems are presented to illustrate the performance of the proposed model and solution method.
In this letter, a distributed optimal dispatch method based on the distributed primal-dual sub-gradient algorithm is proposed. By coordinating individual decision-making of distributed energy ...resources (DERs) in the virtual power plant (VPP) via limited communication, the profit of VPP can be maximized. It can be proven that the proposed distributed algorithm has similar performance to the centralized dispatch.
For application of a large number of plug-in electric vehicles (PEVs) to system frequency regulation, a distributed acquisition approach based on consensus filtering is proposed, where ...frequency-measuring function is performed only at distribution substations. Limited communication between neighboring PEVs/distribution substations can facilitate consistent and accurate acquisition of frequency deviation signals for all PEVs. Considering the battery charging/discharging characteristics, a dynamic PEV model with feedback control is further proposed and integrated with frequency regulation based on distributed acquisition. Asymptotical stabilities of distributed acquisition and system frequency regulation are analyzed. Simulation results demonstrate that the proposed approach can provide consistent and accurate control signals for a large number of PEVs and obtain better regulation than general decentralized control in terms of eliminating noise, improving robustness and reducing device costs.
In this paper, an economic dispatch model, which can take into account the uncertainties of plug-in electric vehicles (PEVs) and wind generators, is developed. A simulation based approach is first ...employed to study the probability distributions of the charge/discharge behaviors of PEVs. The probability distribution of wind power is also derived based on the assumption that the wind speed follows the Rayleigh distribution. The mathematical expectations of the generation costs of wind power and V2G (vehicle to grid) power are then derived analytically. An optimization algorithm is developed based on the well-established particle swarm optimization (PSO) and interior point method to solve the economic dispatch model. The proposed approach is demonstrated by the IEEE 118-bus test system.
In the smart grid paradigm, growing integration of large-scale intermittent renewable energies has introduced significant uncertainties to the operations of an electric power system. This makes ...real-time dynamic security assessment (DSA) a necessity to enable enhanced situational-awareness against the risk of blackouts. Conventional DSA methods are mainly based on the time-domain simulation, which are insufficiently fast and knowledge-poor. In recent years, the intelligent system (IS) strategy has been identified as a promising approach to facilitate real-time DSA. While previous works mainly concentrate on the rotor angle stability, this paper focuses on another yet increasingly important dynamic insecurity phenomenon-the short-term voltage instability, which involves fast and complex load dynamics. The problem is modeled as a classification subproblem for transient voltage collapse and a prediction subproblem for unacceptable dynamic voltage deviation. A hierarchical IS is developed to address the two subproblems sequentially. The IS is based on ensemble learning of random-weights neural networks and is implemented in an offline training, a real-time application, and an online updating pattern. The simulation results on the New England 39-bus system verify its superiority in both learning speed and accuracy over some state-of-the-art learning algorithms.
Thermal transfer across solid–liquid interfaces is influenced by multiple factors such as surface wettability, interfacial water layer density, molecular structure, and mass density depletion length. ...However, the dominant factors in interfacial heat transport are yet to be investigated. In this work, we explore the contributions from these factors by employing the Pd-water model for water molecules forming ordered, partially ordered, and disordered structures on Pd (100), (110) and (111) surfaces, respectively. The results revealed that the ordered water layer on the (100) surface can introduce a “phonon bridge” at the solid–liquid interface to improve thermal transfer, while the partially ordered water layer on the (110) surface can further promote thermal transfer due to the enhanced interfacial friction. On the other hand, the decreased density depletion length also makes dominant contributions to the enhancement of interfacial thermal transfer. The results are explained by the interfacial friction coefficient, surface potential energy distribution and density depletion length. We also introduce an efficient technique by tuning the vacancy defects on the solid surface to tune the atomic structure as well as the thermal transfer. Our study reveals the complex relationship between the atomic structure of the crystal face, the water layer structure and the thermal boundary conductance, which will inspire more experimental and theoretical studies toward the improvement of interfacial thermal transport by tuning the structure of the water layer.
An urgent challenge in the manufacturing industry is increasing efficiency while decreasing energy consumption and environmental impact. Past studies addressing these issues have mainly focused on ...tool path optimization only considering machining efficiency. In this paper, we present a methodology to optimize the tool path for high efficiency, low energy consumption and carbon footprint in milling process. Firstly, the description and influencing factors of tool path are introduced. Then, a multi objective tool path optimization model with maximum machining efficiency, minimum energy consumption and carbon emission is proposed. Furthermore, the solution of the proposed model is introduced, which including two steps, one is the calculation of the number of cutter contact points (CCP), the other is using adaptive dynamic GA to optimize the connection sequence and ways of each CCP. Finally, the effectiveness and practicability of the method are verified by the machining experiments.
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