Abstract:As an essential characteristic of the smart grid, energy demand users are being transformed from passive roles to active decision-makers. To analyze their decision-making behaviors, game theory has been widely applied on the demand side. This paper focuses on the classification and in-depth analysis of recent studies that propose game-theoretic approaches for decision optimization of multiple demand users. This analysis classifies scenarios into various game participant categories, including distributed energy prosumers, small- and middle-sized users, and large energy consumers. The in-depth analysis of each scenario, covering non-cooperative game, cooperative game, Stackelberg game, Bayesian game, and evolutionary game, is conducted by analyzing market operation mechanisms, model assumptions/formulations, and solution methods. Based on a comprehensive review of such studies, it is concluded that game-theoretic applications on the demand side can benefit both the grid and the users, e.g., reductions in the peak-to-average ratios and energy costs of the users. The prospects for the applications of game theory on the demand side are discussed, including application scenarios and methodologies. The overview presented in this paper is expected to support researchers in comprehending typical game-theoretic concepts, keeping with the latest research developments, and identifying new and innovative applications for the energy demand side.

Abstract:Capacity estimation plays a crucial role in battery management systems, and is essential for ensuring the safety and reliability of lithium-sulfur (Li-S) batteries. This paper proposes a method that uses a long short-term memory (LSTM) neural network to estimate the state of health (SOH) of Li-S batteries. The method uses health features extracted from the charging curve and incremental capacity analysis (ICA) as input for the LSTM network. To enhance the robustness and accuracy of the network, the Adam algorithm is employed to optimize specific hyperparameters. Experimental data from three different groups of batteries with varying nominal capacities are used to validate the proposed method. The results demonstrate the effectiveness of the method in accurately estimating the capacity degradation of all three batteries. Also, the study examines the impact of different lengths of network training sets on capacity estimation. The results reveal that the ICA-LSTM model achieves a prediction accuracy of mean absolute error 4.6% and mean squared error 0.21% with three different training set lengths of 20%, 40%, and 60%. The analysis demonstrates that the lightweight model maintains high SOH estimation accuracy even with a small training set, and exhibits strong adaptive and generalization capabilities when applied to different Li-S batteries. Overall, the proposed method, supported by experimental validation and analysis, demonstrates its efficacy in ensuring accurate and reliable SOH estimation, thereby enhancing the safety and performance of Li-S batteries.

Abstract:Solid-state circuit breakers (SSCBs) are critical components in the protection of medium-voltage DC distribution networks to facilitate arc-free, fast and reliable isolation of DC faults. However, limited by the capacity of a single semiconductor device, using semiconductor-based SSCBs at high voltage is challenging. This study presents the details of a 1.5 kV, 63 A medium-voltage SSCB, composed primarily of a solid-state switch based on three cascaded normally-on silicon carbide (SiC) junction field-effect transistors (JFETs) and a low-cost programmable gate drive circuit. Dynamic and static voltage sharing among the cascaded SiC JFETs of the SSCB during fault isolation is realized using the proposed gate drive circuit. The selection conditions for the key parameters of the SSCB gate driver are also analyzed. Additionally, an improved pulse-width modulation current-limiting protection solution is proposed to identify the permanent overcurrent and transient inrush current associated with capacitive load startup in a DC distribution network. Using the developed SSCB prototype and the fault test system, experimental results are obtained to validate the fault response performance of the SSCB.

Abstract:The real-time risk-averse dispatch problem of an integrated electricity and natural gas system (IEGS) is studied in this paper. It is formulated as a real-time conditional value-at-risk (CVaR)-based risk-averse dispatch model in the Markov decision process framework. Because of its stochasticity, nonconvexity and nonlinearity, the model is difficult to analyze by traditional algorithms in an acceptable time. To address this non-deterministic polynomial-hard problem, a CVaR-based lookup-table approximate dynamic programming (CVaR-ADP) algorithm is proposed, and the risk-averse dispatch problem is decoupled into a series of tractable subproblems. The line pack is used as the state variable to describe the impact of one period's decision on the future. This facilitates the reduction of load shedding and wind power curtailment. Through the proposed method, real-time decisions can be made according to the current information, while the value functions can be used to overview the whole optimization horizon to balance the current cost and future risk loss. Numerical simulations indicate that the proposed method can effectively measure and control the risk costs in extreme scenarios. Moreover, the decisions can be made within 10 s, which meets the requirement of the real-time dispatch of an IEGS.

Abstract:Precise fault location plays an important role in the reliability of modern power systems. With the increasing penetration of renewable energy sources, the power system experiences a decrease in system inertia and alterations in steady-state characteristics following a fault occurrence. Most existing single-ended phasor domain methods assume a certain impedance of the remote-end system or consistent current phases at both ends. These problems present challenges to the applicability of conventional phasor-domain location methods. This paper presents a novel single-ended time domain fault location method for single-phase-to-ground faults, one which fully considers the distributed parameters of the line model. The fitting of transient signals in the time domain is realized to extract the instantaneous amplitude and phase. Then, to eliminate the error caused by assumptions of lumped series resistance in the Bergeron model, an improved numerical derivation is presented for the distributed parameter line model. The instantaneous symmetrical components are extracted for decoupling and inverse transformation of three-phase recording data. Based on the above, the equation of instantaneous phase constraint is established to effectively identify the fault location. The proposed location method reduces the negative effects of fault resistance and the uncertainty of remote end parameters when relying on one-terminal data for localization. Additionally, the proposed fault analysis methods have the ability to adapt to transient processes in power systems. Through comparisons with existing methods in three different systems, the fault position is correctly identified within an error of 1%. Also, the results are not affected by sampling rates, data windows, fault inception angles, and load conditions.

Abstract:Efficient and accurate health state estimation is crucial for lithium-ion battery (LIB) performance monitoring and economic evaluation. Effectively estimating the health state of LIBs online is the key but is also the most difficult task for energy storage systems. With high adaptability and applicability advantages, battery health state estimation based on data-driven techniques has attracted extensive attention from researchers around the world. Artificial neural network (ANN)-based methods are often used for state estimations of LIBs. As one of the ANN methods, the Elman neural network (ENN) model has been improved to estimate the battery state more efficiently and accurately. In this paper, an improved ENN estimation method based on electrochemical impedance spectroscopy (EIS) and cuckoo search (CS) is established as the EIS-CS-ENN model to estimate the health state of LIBs. Also, the paper conducts a critical review of various ANN models against the EIS-CS-ENN model. This demonstrates that the EIS-CS-ENN model outperforms other models. The review also proves that, under the same conditions, selecting appropriate health indicators (HIs) according to the mathematical modeling ability and state requirements are the keys in estimating the health state efficiently. In the calculation process, several evaluation indicators are adopted to analyze and compare the modeling accuracy with other existing methods. Through the analysis of the evaluation results and the selection of HIs, conclusions and suggestions are put forward. Also, the robustness of the EIS-CS-ENN model for the health state estimation of LIBs is verified.

Abstract:To quickly and accurately identify faulty components based on the alarm information is critical for the fault diagnosis of power grids. To address this challenge, this paper proposes a novel fault diagnosis method based on temporal tissue-like P system (TTPS). In the proposed method, suspected faulty components are identified first via a network topology analysis method. An TTPS-based fault diagnosis model is then built for each suspected faulty component to perform fault reasoning, so as to accurately detect the faulty components. To take full advantage of the action signals and temporal information of protection devices, TTPS and its forward temporal reasoning algorithm are proposed. TTPS can synchronously model the action and temporal logics of protection devices in an intuitive and graphical way, while the reasoning algorithm can process the fault alarm information in parallel. To demonstrate the effectiveness and superiority of the proposed method, simulations are carried out on the IEEE 14-bus and 118-bus systems, while the results are compared to other two widely adopted methods.

Abstract:Modern power grid is fast emerging as a complex cyber-physical power system (CPPS) integrating physical current-carrying components and processes with cyber-embedded computing, which faces increasing cyberspace security threats and risks. In this paper, the state (i.e., voltage) offsets resulting from false data injection (FDI) attacks and the bus safety characterization are applied to quantify the attack consequences. The state offsets are obtained by the state estimation method, and the bus safety characterization considers the power network topology as well as the vulnerability and connection relationship of buses. Considering the indeterminacy of attacker's resource consumption and reward, a zero-sum game-theoretical model from the defender's perspective with incomplete information is explored for the optimal allocation of limited defensive resources. The attacker aims to falsify measurements without triggering threshold alarms to break through the protection, leading to load shedding, over-voltage or under-voltage. The defender attempts to ensure the estimation results to be as close to the actual states as possible, and guarantee the system's safety and efficient defensive resource utilization. The proposed solution is extensively evaluated through simulations using the IEEE 33-bus test network and real-time digital simulator (RTDS) based testbed experiments of the IEEE 14-bus network. The results demonstrate the effectiveness of the proposed game-theoretical approach for optimal defensive resource allocation in CPPS when limited resources are available when under FDI attacks.

Abstract:Facing constraints imposed by storage and bandwidth limitations, the vast volume of phasor measurement unit (PMU) data collected by the wide-area measurement system (WAMS) for power systems cannot be fully utilized. This limitation significantly hinders the effective deployment of situational awareness technologies for systematic applications. In this work, an effective curvature quantified Douglas-Peucker (CQDP)-based PMU data compression method is proposed for situational awareness of power systems. First, a curvature integrated distance (CID) for measuring the local flection and fluctuation of PMU signals is developed. The Douglas-Peucker (DP) algorithm integrated with a quantile-based parameter adaptation scheme is then proposed to extract feature points for profiling the trends within the PMU signals. This allows adaptive adjustment of the algorithm parameters, so as to maintain the desired compression ratio and reconstruction accuracy as much as possible, irrespective of the power system dynamics. Finally, case studies on the Western Electricity Coordinating Council (WECC) 179-bus system and the actual Guangdong power system are performed to verify the effectiveness of the proposed method. The simulation results show that the proposed method achieves stably higher compression ratio and reconstruction accuracy in both steady state and in transients of the power system, and alleviates the compression performance degradation problem faced by existing compression methods.

Abstract:The calculation of the indirect carbon emission is essential for power system policy making, carbon market development, and power grid planning. The embedded carbon emissions of the electricity system are commonly calculated by carbon emission flow theory. However, the calculation procedure is time-consuming, especially for a country with 500–1000 thousand nodes, making it challenging to obtain nationwide carbon emissions intensity precisely. Additionally, the calculation procedure requires to gather all the grid data with high classified levels from different power grid companies, which can prevent data sharing and cooperation among different companies. This paper proposes a distributed computing algorithm for indirect carbon emission that can reduce the time consumption and provide privacy protection. The core idea is to utilize the sparsity of the nodes' flow matrix of the nationwide grid to partition the computing procedure into parallel sub-procedures executed in multiple terminals. The flow and structure data of the regional grid are transformed irreversibly for privacy protection, when transmitted between terminals. A 1-master-and-N-slave layout is adopted to verify the method. This algorithm is suitable for large grid companies with headquarter and branches in provinces, such as the State Grid Corporation of China.

Abstract:The design and analysis of a fuel cell vehicle-to-grid (FCV2G) system with a high voltage conversion interface is proposed. The system aims to maximize the utilization of fuel cell vehicles (FCVs) as distributed energy resources, allowing them to actively participate in the energy market. The proposed FCV2G system has FCVs, power electronics interfaces, and the electrical grid. The power electronics interfaces are responsible for converting the low-voltage output of the fuel cell stack into high-voltage DC power, and ensuring efficient power transfer between the FCVs and the grid. To optimize the operation of the FCV2G system, the momentum search algorithm (MSA) is employed. By applying MSA, the FCV2G system can achieve optimal power dispatch, considering factors such as energy efficiency, grid stability, and economic feasibility. The proposed method is tested in MATLAB. The best MSA and dynamic load profile solutions are run for 24 h and the results show that 100% import of FCVs 51.0% more than 100% electric vehicle. Peak-cutting and vehicle-to-grid service revenue are 30.5% and 95.0% greater, respectively. Low discharge loss, high capacity, and high discharge power are the main advantages of FCVs. The benchmark FCVs ratio of 15% is used for sensitivity analysis. The findings reveal that the overall advantages of FCV2G are improved.