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.
The prevalence of distributed energy resources encourages the concept of an electricity "Prosumer (Producer and Consumer)". This paper proposes a distributed electricity trading system to facilitate ...the peer-to-peer electricity sharing among prosumers. The proposed system includes two layers. In the first layer, a multi-agent system is designed to support the prosumer network, and an agent coalition mechanism is proposed to enable the prosumers to form coalitions and negotiate electricity trading. In the second layer, a Blockchain based transaction settlement mechanism is proposed to enable the trusted and secure settlement of electricity trading transactions formed in the first layer. Simulations are conducted based on the java agent development environment to validate the proposed electricity trading process.
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.
Modern power systems are rapidly evolving into complex cyber-physical systems. The increasingly complex interaction among different energy entities calls for a secure, efficient, and robust cyber ...infrastructure. As an emerging distributed computing technology, Blockchain provides a secure environment to support such interactions. This paper gives a prospective on using Blockchain as a secure, distributed cyber infrastructure for the future grid. Firstly, the basic principles of Blockchain and its state-of-the-art are introduced. Then, a Blockchain based smart grid cyber-physical infrastructure model is proposed. Afterwards, some promising application domains of Blockchain in future grids are presented. Following this, some potential challenges are discussed.
Nowadays, with the wide installation of distributed energy resources and independent energy storage systems, prosumers as a new type of electricity market entity have emerged. Since numerous ...prosumers can significantly impact the carbon emission of the power grid, this paper proposes an improved carbon emission flow method for the power grid with prosumers. This method can accurately clarify the detailed distribution of electrical carbon emission flow in power grids. First, based on the power flow, prosumers’ impacts on the electrical carbon emission are quantified from three aspects that include the carbon emission sources, the network flow, and the indirect carbon emission individuals. Then, an improved power carbon emission flow model is proposed, in which the complex carbon emission intensity of prosumers is derived emphatically. Finally, case studies based on the IEEE 30-bus system verify the feasibility of the proposed method. This method provides a measurement basis for further research considering electrical carbon emissions.
•Analyzing prosumers’ impacts on the CEF by direct emission source, network flow, and indirect emission individual.•Proposing an improved CEF model to measure the carbon emissions of the demand side, especially for prosumers.•Quantifying the complex CEI of prosumers with multiple DERs by combining the GCEI of DERs and historical CEF information.
This paper proposes a cooperative control framework for the coordination of multiple microgrids. The framework is based on the multiagent system. The control framework aims to encourage the resource ...sharing among different autonomous microgrids and solve the energy imbalance problems by forming the microgrid coalition self-adaptively. First, the conceptual model of the integrated microgrids and the layered cooperative control framework is presented. Then, an advanced dynamic coalition formation scheme and corresponding negotiation algorithm are introduced to model the coordination behaviors of the microgrids. The proposed control framework is implemented by the Java Agent Development Framework. A loop distribution system with multiple interconnected microgrids is simulated, and the case studies are conducted to prove the efficiency of the proposed framework.
Extensive inverter‐based power sources (IPS) impose significant challenges on the restoration of high renewable penetrated power systems (HRPPS). To enhance HRPPS resilience, the proper utilization ...of IPSs must be implemented. Combining frequency dynamics of IPSs and synchronous generators, this paper proposes a coordinated restoration method for multi‐type power sources after a major blackout. First, interactions between synchronous generators and IPSs are systematically analyzed. Based on this, output characteristics and constraints of IPSs in the power sources restoration process are quantified. Second, the dynamic frequency regulation capability (DFRC) of restored systems is quantified based on a unified transfer function structure model. Then the maximum power disturbance that restored systems can bear is derived based on DFRC indices including the maximum frequency deviation and the rate of change of frequency. Third, considering interactions between power sources and the DFRC of restored systems, a coordinated restoration optimization model of multi‐type power sources is proposed. Finally, case studies based on a modified IEEE 39‐bus system are simulated to verify the applicability and superiority of the proposed method. Meanwhile, results show that the proposed method for quantifying DFRC is more suitable for HRPPSs than traditional inertia‐based methods.
This paper proposes a coordinated restoration optimization method of multi‐type power sources for the blackout high renewable penetrated power system. The proposed method considers the dynamic frequency regulation capability of restored systems and interactions between inverter‐based power sources and synchronous generators.
Event detection is an important application in demand-side management. Precise event detection algorithms can improve the accuracy of non-intrusive load monitoring (NILM) and energy disaggregation ...models. Existing event detection algorithms can be divided into four categories: rule-based, statistics-based, conventional machine learning, and deep learning. The rule-based approach entails hand-crafted feature engineering and carefully calibrated thresholds; the accuracies of statistics-based and conventional machine learning methods are inferior to the deep learning algorithms due to their limited ability to extract complex features. Deep learning models require a long training time and are hard to interpret. This paper proposes a novel algorithm for load event detection in smart homes based on wide and deep learning that combines the convolutional neural network (CNN) and the soft-max regression (SMR). The deep model extracts the power time series patterns and the wide model utilizes the percentile information of the power time series. A randomized sparse backpropagation (RSB) algorithm for weight filters is proposed to improve the robustness of the standard wide-deep model. Compared to the standard wide-deep, pure CNN, and SMR models, the hybrid wide-deep model powered by RSB demonstrates its superiority in terms of accuracy, convergence speed, and robustness.
The smart grid is an evolving critical infrastructure, which combines renewable energy and the most advanced information and communication technologies to provide more economic and secure power ...supply services. To cope with the intermittency of ever-increasing renewable energy and ensure the security of the smart grid, state estimation, which serves as a basic tool for understanding the true states of a smart grid, should be performed with high frequency. More complete system state data are needed to support high-frequency state estimation. The data completeness problem for smart grid state estimation is therefore studied in this paper. The problem of improving data completeness by recovering high-frequency data from low-frequency data is formulated as a super resolution perception (SRP) problem in this paper. A novel machine-learning-based SRP approach is thereafter proposed. The proposed method, namely the Super Resolution Perception Net for State Estimation (SRPNSE), consists of three steps: feature extraction, information completion, and data reconstruction. Case studies have demonstrated the effectiveness and value of the proposed SRPNSE approach in recovering high-frequency data from low-frequency data for the state estimation.
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