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Under the pressure of climate change, the demands for alternative green hydrogen H2 production methods have been on the rise to conform to the global trend of transition to a H2 society. This paper proposes a multi-renewable-to-hydrogen production method to enhance the green H2 production efficiency for renewable-dominated hydrogen fueling stations (HFSs). In this method, the aqueous electrolysis of native biomass can be powered by wind and solar generations based on electrochemical effects, and both of electrolysis current and temperature are taken into account for facilitating on-site H2 production and reducing the electricity consumption. Moreover, a capsule network (CN)-based H2 demand forecasting model is formulated to estimate the gas load for HFS by extracting the underlying spatial features and temporal dependencies of traffic flows in the transportation network. Furthermore, a hierarchical coordinated control strategy is developed to suppress high fluctuations in electrolysis current caused by volatility of wind and solar outputs based on model predictive control (MPC) framework. Comparative studies validate the superior performance of the proposed methodology over the power-to-gas (P2G) scheme on electrolysis efficiency and economic benefits.

The frequency stability of microgrids is of paramount importance due to their low inertia and high share of renewable energy sources. In this paper, a frequency stability-constrained microgrid scheduling (FSC-MS) model is developed based on a time-dependent discretized frequency response model considering seamless unintentional islanding events. The optimal dispatch of controllable units, as well as optimal upward and downward primary reserves, are determined with a minimum operating cost while ensuring frequency stability criteria are maintained in their safe ranges following an unintentional islanding event, such as rate of change of frequency (RoCoF) and frequency nadir and overshoot. The limitation of generators with respect to primary frequency response is formulated in the form of primary reserve capacity and primary ramp rate limits. A cost-based model is presented to describe the available primary reserve of synchronous generators. The proposed FSC-MS model is formulated as a mixed-integer linear programming (MILP) problem using Benders decomposition. Simulation results on a medium voltage microgrid test system demonstrate the effectiveness of the proposed model to meet the frequency stability constraints while minimizing the total operating cost.

Accurate short-term prediction of overhead line (OHL) transmission ampacity can directly affect the efficiency of power system operation and planning. Any overestimation of the dynamic thermal line rating (DTLR) can lead to lifetime degradation and failure of OHLs, safety hazards, etc. This paper presents a secure yet sharp probabilistic prediction model for the hour-ahead forecasting of the DTLR. The security of the proposed DTLR limits the frequency of DTLR prediction exceeding the actual DTLR. The model is based on an augmented deep learning architecture that makes use of a wide range of predictors, including historical climatology data and latent variables obtained during DTLR calculation. Furthermore, by introducing a customized cost function, the deep neural network istrained to consider the DTLR security based on the required probability of exceedance while minimizing deviations of the predicted DTLRs from the actual values. The proposed probabilistic DTLR is developed and verified using recorded experimental data. The simulation results validate the superiority of the proposed DTLR compared to state-of-the-art prediction models using well-known evaluation metrics.

© Copyright 2021 The Author(s). The damping performance evaluation for electro-mechanical oscillations in power systems is crucial for the stable operation of modern power systems. In this paper, the connection between two commonly-used damping performance evaluation methods, i.e., the damping torque analysis (DTA) and energy flow analysis (EFA), are systematically examined and revealed for the better understanding of the oscillatory damping mechanism. First, a concept of the aggregated damping torque coefficient is proposed and derived based on DTA of multi-machine power systems, which can characterize the integration effect of the damping contribution from the whole power system. Then, the pre-processing of measurements at the terminal of a local generator is conducted for EFA, and a concept of the frequency-decomposed energy attenuation coefficient is defined to screen the damping contribution with respect to the interested frequency. On this basis, the frequency spectrum analysis of the energy attenuation coefficient is employed to rigorously prove that the results of DTA and EFA are essentially equivalent, which is valid for arbitrary types of synchronous generator models in multi-machine power systems. Additionally, the consistency between the aggregated damping torque coefficient and frequency-decomposed energy attenuation coefficient is further verified by the numerical calculation in case studies. The relationship between the proposed coefficients and the eigenvalue (or damping ratio) is finally revealed, which consolidates the application of the proposed concepts in the damping performance evaluation. 10.13039/501100001809-National Natural Science Foundation of China (Grant Number: 51807171); 10.13039/501100007162-Guangdong Science and Technology Department (Grant Number: 2019A1515011226).

This paper proposes a dynamic-decision-based realtime dispatch method to coordinate the economic objective with multiple types of security dispatch objectives while reducing constraint violations in the process of adjusting the system operation point to the optimum. In each decision moment, the following tasks are executed in turn: (1) locally linearizing the system model at the current operation point with the online model identification by using measurements; (2) narrowing down the gaps between unsatisfied security requirements and their security thresholds in order of priority; (3) minimizing the generation cost; (4) minimizing the security indicators within their security thresholds. Compared with the existing realtime dispatch strategies, the proposed method can adjust the deviations caused by unpredictable power flow fluctuations, avoid dispatch bias caused by model parameter errors, and reduce the constraint violations in the dispatch decision process. The effectiveness of the proposed method is verified with the IEEE 39-bus system.

This paper proposes a novel variable-inertia emulation control (VIEC) scheme that enables voltage-source-converter based high-voltage DC (VSC-HVDC) transmission systems to flexibly support AC grid frequency stability like synchronous generators. The VIEC scheme allows us to extract the energy from the augmented DC capacitance for inertia emulation by controlling the DC voltage without affecting the stability of the AC system connected on the remote side. In particular, with the proposed VIEC scheme, the inertia time constant can be flexibly emulated and its value can be automatically adjusted according to the rate of change of grid frequency as well as grid frequency deviations. This leads to more effective inertial support across different timescales. Modal analysis is carried out to investigate the impacts of inertia and DC capacitance, and to obtain the optimal control parameters. The effectiveness and advantages of the proposed VIEC scheme are demonstrated on an IEEE benchmark system in the presence of faults and load changes.

This paper proposes a price-maker bidding and offering model for networked microgrids (NMG) in a pool-based day-ahead electricity market. The objective of this model is to maximize the net revenue of NMG by coordinating the joined individual microgrids to submit aggregated offers/bids to the market operator. A hybrid stochastic-robust optimization framework is developed to offset multiple associated uncertainties. The bidding and offering model is first formulated as a hard-to-solve mixed-integer nonlinear programming (MINLP) problem, which is later converted to its easy-to-solve mixed-integer linear programming (MILP) counterpart. To resolve privacy concerns of each microgrid and improve the scalability of the proposed bidding and offering model, a coordinated scheduling framework for NMG based on the Dantzig-Wolfe decomposition (DWD) method is proposed to obtain the global optimum. Numerical simulations with real-world measured data validate the effectiveness of the proposed price-maker bidding model, which is shown to outperform existing price-taker models.

With the increasing integration of renewable energy sources (RESs) and the implementation of dynamic line rating (DLR), the accompanying uncertainties in power systems require intensive management to ensure reliable and secure operational planning. However, while numerous approaches and methods in the literature deal with uncertainties, they have not been analyzed axiomatically. This paper presents an analysis of risk in power system operation using coherent risk measures, elaborating on the origin of risk and the mechanisms of its management in the presence of various sources of uncertainty. To illustrate the practicality and benefits of coherent risk measures, a risk-averse asymmetry robust unit commitment (UC) model is established. It is based on coherent reformulations of the uncertain reserve and line flow constraints and is formulated in the form of a compact computationally efficient mixed-integer second-order conic program (SOCP). The overall performance of the proposed framework is verified using the updated 2019 IEEE Reliability Test System and the ACTIVSg2000 test system over a year-long period.

International audience; This paper proposes a new framework for the operational reliability evaluation of integrated electric power-gas networks (IEPGNs). First, a novel approach for modeling the failure modes of natural gas pipelines is presented. This approach utilizes the concept of virtual nodes and employs a gas release rate model to consider the pinhole, hole, and rupture failure modes of pipelines. Thereafter, a four-state Markov model for natural gas-fired generators (NGFGs) with dual-fuel capabilities is proposed. The area risk method is extended to include the proposed reliability models, and the partial reliability indices of the area risk method are evaluated using a non-sequential Monte Carlo simulation (NSMCS). A nonlinear optimization model is also proposed to calculate electric and gas load curtailments for each system state in NSMCS. This model is linearized to obtain a mixed-integer linear programming (MILP) model for reducing the computational burden. The computational performance of NSMCS is further improved by adopting cross entropy (CE)-based importance sampling (IS). Finally, the efficacy of the proposed framework is demonstrated on three test systems. Case studies validate the importance of considering the proposed reliability models of IEPGNs for operational reliability evaluation. The impacts of operational strategies on the operational reliability indices are also demonstrated.

This article proposes a two-layer optimal scheduling strategy to handle the overvoltage problem in high photovoltaic (PV) power-penetrated distribution networks. The voltage regulators can be classified as the power utility and PV owners, which are referred to as stakeholders. The proposed scheduling strategy includes autonomous optimization layer and coordination optimization layer. In the autonomous optimization layer, a min-max robust game model and a mixed-integer second-order cone programming-based model are respectively proposed to minimize the operating costs of PV stakeholders and the power utility stakeholder. A parallel optimization is employed to solve the two models in the autonomous optimization layer. In the coordination optimization layer, a noncooperative game-based model is presented to coordinate scheduling solutions of each stakeholder. Finally, an actual 10 kV, 106-bus feeder in Zhejiang Province, China, and a modified IEEE 123-bus distribution system are employed to verify the feasibility and effectiveness of the proposed approach.

Static line rating (SLR), which is conventionally used in operation, not only results in a conservative usage of the capacity of overhead lines, but also fails to accurately address the overload risk. In this paper, using quantile regression (QR) and superquantile regression (SQR) methods, two models are proposed to predict dynamic line rating (DLR) of overhead conductors in operational applications with very short-term horizons. The proposed methods model statistical properties of time evolution of conductors considering the conductor thermal inertia to cope with situations with higher time resolutions for enhanced capacity usage. To address the overload risk due to forecast uncertainties of weather-related parameters, the proposed models are reformulated as risk-based constraints and utilized as QR and SQR-based DLR. The developed constraints are fully parametric and readily applicable to optimization problems and are verified through an optimal power flow (OPF). Results of examining the proposed models on the RTS test system confirm their efficiency in terms of better utilization of conductor capacity, increased energy transfer, and reduced risk levels.

Full converter-based wind power generation (FCWG, e.g., a permanent magnet synchronous generator (PMSG)), though normally considered to be decoupled from the external power grid can be actuated as an inertia source to suppress modal resonance in wind generation penetrated power systems by installing auxiliary resonance controllers (ARCs). In this paper, three possible options for ARC installation are first identified based on some derivations of the conventional control model of FCWG. The damping support mechanism of ARC is revealed, a suitable and generic configuration structure of ARC is then established, and optimal parameter tuning is conducted on the basis of this ARC configuration. The three ARC alternatives are equipped to contribute to damping by utilizing the potential energy and dynamics hidden in different inertia source components (i.e., the wind turbine rotor and DC capacitor, respectively) of FCWG. Both modal analysis and simulation results validate the effectiveness of the three proposed ARCs in suppressing modal resonance and improving system oscillation stability. Most importantly, extensive comparison investigations are carried out to fully evaluate the pros and cons of the three ARCs and thus provide constructive application guidance for system operators and wind farm owners.

This paper proposes an inertia emulation method that uses the frequency of the center of inertia (COI) as the feedback signal, aiming to slow the frequency change rate and raise the frequency nadir during frequency-dropping events. An approximated model depicting the COI frequency dynamics is first introduced, which shows the COI frequency signal has low observabilities of electromechanical modes. Thus, this feature enables the proposed inertia emulation to react quickly to steep frequency changes without suffering any adverse impacts caused by electromechanical dynamics, such as excessively saturating the controlled objects. Moreover, the electromechanical dynamics associated with small-signal stability properties will not be interrupted by the proposed inertia emulation. In addition, a simple yet effective computing procedure is proposed to configure the critical parameters, as the inertia emulation is implemented with multiple flywheel-based energy storage systems. Simulation results obtained based on two large interconnected systems verify the effectiveness of the proposed inertia emulation in terms of improving the frequency transients as well as its limited correlation with the electromechanical dynamics of the system. The signal transmission latency's impacts on the proposed inertia emulation are also investigated and discussed.

This letter proposes a data-driven hybrid deep learning method for dynamic total transfer capability (TTC) control. It leverages deep learning (DL) to achieve fast prediction of TTC and reduce the problem complexity, while the deep reinforcement learning (DRL) method, e.g., proximal policy optimization (PPO), is enhanced by competitive learning (CL) to obtain a better generalization of the DRL agents. This also allows us to deal with system stochasticity. Comparison results with other model-based alternatives on the IEEE 39-bus system highlight the advantages of the proposed method for variable unseen and insecure scenarios.

This paper proposes a novel method for the machine learning-based online prediction of generator dynamic behavior in large interconnected power systems. Unlike the existing literature in this domain, which assumes faults occur immediately after a steady-state situation, the proposed method takes the possibility of multiple disturbances into account. It is founded on a simulation-based classification approach to indirectly take advantage of phasor measurement unit (PMU) data, which leads to improvements in robustness against load model uncertainties. Relying on offline scenarios, the method developed conducts multiple time-domain simulations (TDSs) in parallel for a set of feasible two-machine dynamic equivalent models (DEMs) for each case. Thereafter, common descriptive statistics are computed for the rotor angles obtained to form the feature space. The values taken via a feature selection process are then applied as inputs to ensemble decision trees, which train models capable of predicting both stability status and generator grouping ahead of time. In online situations, PMU data are used to create DEMs and the predictors are collected by performing parallel TDSs for DEMs. The functionality of the proposed hybrid machine learning and TDS-based approach is verified on several IEEE test systems, followed by a discussion of results.

Successful detection of false data injection attacks (FDIAs) and removal of state bias due to FDIAs are essential for ensuring secure power grids operation and control. This article first extends the approximate dc model of FDIA to a more general ac model that can handle both traditional and synchronized measurements. To automatically filter out the established FDIAs, we propose a state reconstruction scheme consisting of a contaminated state separation method, an enhanced bad data identification approach and a state recovery algorithm. In this scheme, a classifier is developed by aggregating a series of extreme learning machines (ELMs) to detect anomaly states caused by FDIAs. Gaussian random distribution and Latin hypercube sampling are adopted to initialize the input weights of base ELMs, which can provide more diversities to enhance the ensemble performance. Then, to identify the exact locations of the compromised measurements, a state forecasting-based bad data identification approach is proposed by exploiting the consistency between the forecasted and the received measurements. Finally, an effective state recovery algorithm applies quasi-Newton method and Armijo line search to address the possible system unobservable problem due to the removal of attacked measurements. Numerical tests on serval IEEE standard test systems verify the efficiency of the proposed FDIA model and state reconstruction scheme.

Energy storage is essential for balancing the generation and load in power systems. Building a battery energy storage system (BESS) with retired battery packs from electric vehicles (EVs) or plug-in hybrid electric vehicles (PHEVs) is one possible way to subsidize the price of EV/PHEV batteries, and at the same time mitigating forecast error introduced by load and renewable energy sources in power systems. This paper proposes a detailed framework to evaluate end-of-life (EOL) EV/PHEV batteries in BESS application. The framework consists of three parts. A generalized model for battery degradation is first introduced. It is followed by modeling the battery retirement process in its first life. Two vehicle types - EV and PHEV - as well as two retirement modes - nominal and realistic modes - are considered. Finally, the application of the second-life BESS in power systems is modeled in a detailed economic dispatch (ED) problem. This is how second-life BESS's performance translates into cost savings on power generation. An optimization problem is formulated to maximize total cost savings in power generation over the battery's second life. This is done by striking a balance between short-term benefit (daily cost savings) and long-term benefit (cost savings through service years). Numerical results validate the effectiveness of the proposed framework/models. They show that battery usage and retirement criterion in its first life directly affect the performance in its second life application. In our case study, EV battery packs possess larger EOL energy capacities and consequently generate more cost savings in the second life. However, the BESS built from retired PHEV batteries has higher cost savings per MWh. It is because, with the proposed degradation model, battery health is better preserved in PHEV applications. Compared to nominal retirement mode, realistic retirement mode results in extra cost savings due to the reduced first-life service years. Energy Market Authority (EMA) National Research Foundation (NRF) Published version This work was supported in part by the Energy Market Authority and the National Research Foundation Singapore under Grant NRF2017EWT-EP003-038, in part by the National Key Research and Development Program of China under Grant 2019YFB1505400, and in part by the Foundation of Shenzhen Science and Technology Committee under Grant GJHZ20180928160212241 and Grant JCYJ20190808165201648.

Intentional controlled islanding (ICI) is the last resort to split an endangered power system into smaller islands to prevent blackout. New lines that are planned by transmission expansion planning (TEP) can affect the stability of islands during ICI. In this paper, an ICI-TEP method is proposed to improve the stability of islands by more efficient planning of transmission assets. Moreover, by developing a criterion for the frequency of center of inertia (COI) in each island, the frequency deviations of generators from the COI frequency are minimized to result in more stable islands. The proposed ICI-TEP, incorporating AC network representation, is modeled as mixed-integer linear programming and quadratic convex problems ensuring tractability. A Benders decomposition strategy is also proposed to solve the problem. Results of testing the proposed ICI-TEP method on IEEE 39-bus and 300-bus test systems confirm its effectiveness, compared to conventional TEP, in terms of coping with sever disturbances by creating more stable islands with a lower load shedding.