DiME and AGVIS A Distributed Messaging Environment and Geographical Visualizer for Large-scale Power System Simulation

This paper introduces the messaging environment and the geographical visualization tool of the CURENT Large-scale Testbed (LTB) that can be used for large-scale power system closed-loop simulation. First, Distributed Messaging Environment (DiME) implements an asynchronous shared workspace to enable high-concurrent data exchange. Second, Another Grid Visualizer (AGVis) is presented as a geovisualization tool that facilitates the visualization of real-time power system simulation. Third, case studies show the use of DiME and AGVis. The results demonstrate that, with the modular structure, the LTB is capable of not only federal use for real-time, large-scale power system simulation, but also independent use for customized power system research.Comment: 5 pages, 7 figures, conferenc

Sustainable aviation electrification: a comprehensive review of electric propulsion system architectures, energy management, and control

The civil aviation sector plays an increasingly significant role in transportation sustainability in the environmental, economic, and social dimensions. Driven by the concerns of sustainability in the aviation sector, more electrified aircraft propulsion technologies have emerged and form a very promising approach to future sustainable and decarbonized aviation. This review paper aims to provide a comprehensive and broad-scope survey of the recent progress and development trends in sustainable aviation electrification. Firstly, the architectures of electrified aircraft propulsion are presented with a detailed analysis of the benefits, challenges, and studies/applications to date. Then, the challenges and technical barriers of electrified aircraft propulsion control system design are discussed, followed by a summary of the control methods frequently used in aircraft propulsion systems. Next, the mainstream energy management strategies are investigated and further utilized to minimize the block fuel burn, emissions, and economic cost. Finally, an overview of the development trends of aviation electrification is provided

Virtual Inertia Scheduling (VIS) for Real-Time Economic Dispatch of IBRs-Penetrated Power Systems

A New Concept Called Virtual Inertia Scheduling (VIS) is Proposed to Efficiently Handle the Increasing Penetration of Inverter-Based Resources (IBRs) in Power Systems. VIS is an Inertia Management Framework that Targets Security-Constrained and Economy-Oriented Inertia Scheduling and Generation Dispatch with a Large Scale of Renewable Generations. Specifically, It Determines the Appropriate Power Setting Points and Reserved Capacities of Synchronous Generators and IBRs, as Well as the Control Modes and Control Parameters of IBRs to Provide Secure and Cost-Effective Inertia Support. First, a Uniform System Model is Employed to Quantify the Frequency Dynamics of the IBRs-Penetrated Power Systems after Disturbances. Leveraging This Model, the s-Domain and Time-Domain Analytical Responses of IBRs with Inertia Support Capability Are Derived. Then, VIS-Based Real-Time Economic Dispatch (VIS-RTED) is Formulated to Minimize Generation and Reserve Costs, with Full Consideration of Dynamic Frequency Constraints and Derived Inertia Support Reserve Constraints. the Virtual Inertia and Damping of IBRs Are Formulated as Decision Variables. a Deep Learning-Assisted Linearization Approach is Further Employed to Address the Non-Linearity of Dynamic Constraints. Finally, VIS-RTED is Demonstrated on a Two-Machine System and a Modified IEEE 39-Bus System. a Full-Order Time-Domain Simulation is Performed to Verify the Scheduling Results and Ensure their Feasibility

Does capital market opening promote enterprise green innovation? Evidence from Shanghai-Hong Kong stock connect and Shenzhen-Hong Kong stock connect

This study selects Chinese A-share listed enterprises from 2010 to 2020 as the research sample, constructs a Difference-in-differences model to analyze the Shanghai-Hong Kong stock connect and Shenzhen-Hong Kong stock connect policy on enterprise green innovation. The transmission channels are tested, and the heterogeneity of this impact is further explored. It is found that the Shanghai-Hong Kong stock connect and Shenzhen-Hong Kong stock connect policy has significantly improved the total level, quality and quantity of enterprise green innovation, and the effect on the total level and quality is greater than the quantity. The Shanghai-Hong Kong stock connect and Shenzhen-Hong Kong stock connect policy can effectively alleviate the financing constraints faced by enterprises, improve the information environment of enterprises, and thus improve their green innovation. There is heterogeneity in the nature of property rights, corporate social responsibility, industry monopoly and regional marketization in the promotion of enterprise green innovation by the Shanghai-Hong Kong stock connect and Shenzhen-Hong Kong stock connect policy. First published online 21 July 202

Nonlinear model predictive control-based optimal energy management for hybrid electric aircraft considering aerodynamics-propulsion coupling effects

Hybrid electric propulsion systems have been identified as the feasible solutions for regional jets and narrow-body aircrafts to reduce block fuel burn, emissions, and operating cost. In this paper, a Nonlinear Model Predictive Control based optimal energy management scheme (MPC-EMS) has been proposed to minimize the block fuel burn during flight. Firstly, the Artificial Neural Network (ANN) model is adopted to predict turbofan engine performance, meanwhile gas turbine-electrical powertrain integration is investigated and analyzed for typical operating conditions. Then, by combining a point-mass aircraft dynamic model, nonlinear model predictive control with Cross-Entropy Method (CEM) is proposed to obtain optimal energy management based on a fully coupled aerodynamics-propulsion hybrid electric aircraft model. Besides, this state-constrained optimal control problem is re-formulated as a state-unconstrained problem with penalty function to reduce the computational load. Finally, the proposed MPC-EMS algorithm is applied to Boeing 737-800 aircraft with mechanically parallel hybrid electric propulsion configuration to minimize the block fuel burn and compared with the optimization results using global Genetic Algorithm (GA) based EMS and Equivalent Consumption Minimization Strategy (ECMS). The simulation results indicate that the proposed MPC-EMS can effectively reduce the computational time compared with Global GA-based EMS while achieving global optimization performance with only a minor difference of 1.71% of block fuel burn and emissions reductions

Techno-economic-environmental evaluation of aircraft propulsion electrification: Surrogate-based multi-mission optimal design approach

Driven by the sustainability initiatives in the aviation sector, the emerging technologies of aircraft propulsion electrification have been identified as the promising approach to realize sustainable and decarbonized aviation. This study proposes a surrogate-based multi-mission optimal design approach for aircraft propulsion electrification, which innovatively incorporates realistic aviation operations into the electric aircraft design, with the aim of improving the overall aircraft fuel economy over multiple flight missions and conditions in practical scenarios. The proposed optimal design approach starts with the flight route data analysis to cluster the flight operational data using gaussian mixture model, so that a concise representation of flight mission profiles can be achieved. Then, an optimal orthogonal array-based Latin hypercubes are employed to generate sampling points of design variables for electrified aircraft propulsion. The mission analysis is performed with coupled propulsion-airframe integration in order to propose energy management strategy for mission-dependent aircraft performance. Consequently, fuel economy surrogate model is established via support vector machines to obtain the optimal design points of electrified aircraft propulsion. For assessing the viability of novel propulsion technologies, techno-economic evaluation is conducted using sensitivity analysis and breakeven electricity prices under a series of environmental regulatory policy scenarios

Performance and economic assessment of mechanically integrated parallel hybrid aircraft

In this study, a selection of environmental and economic considerations of mechanically integrated parallel hybrid (MIPH) electric propulsion systems for single-aisle civil transport aircraft are assessed. The environmental assessment focuses on the carbon dioxide and nitrogen oxide emissions with different power management strategies and levels of battery technology. In the economic study, the potential subsidies and tax incentives required to make these aircraft financially viable are determined. To capture the performance results, models of the propulsion systems and airframe were constructed using the Siemens Simcenter Amesim systems modelling software. The operating cost was then computed using adapted direct operating cost estimation methods. Battery replacement was incorporated by using a battery cycle aging model. The results showed that using a battery energy density of 300 Wh/kg will not provide any meaningful benefits. For 600 Wh/kg, fuel savings of up to 3% for missions below 650 nm could be obtained for a PMS where the electrical powertrain operates during takeoff, climb, and cruise. However, the NOx emissions were lowest for the takeoff and climb only PMS, implying a trade-off when selecting a PMS. Based on the cost results, it is determined that taxation on carbon emissions would have to increase at least 50-fold from its current levels for the most optimistic scenarios. Alternatively, considerable subsidies, representing large percentages of the purchase price of the aircraft, will be needed

PAN Nanofibers Reinforced with MMT/GO Hybrid Nanofillers

Single component nanofiller has shown some limitations in its performance, which can be overcome by hybrid nanofillers with two different components. In this work, montmorillonite (MMT)/graphene oxide (GO) hybrid nanofillers were formed by self-assembly and then incorporated into the polyacrylonitrile (PAN) nanofibers by electrospinning process. X-ray diffraction (XRD), atomic force microscopy (AFM), and transmission electron microscopy (TEM) were utilized to analyze the structures of MMT/GO hybrid nanofillers. And the effects of MMT/GO hybrid nanofillers on the morphology, thermal stability, and mechanical properties of PAN/MMT/GO composite nanofibrous membrane were examined by scanning electron microscopy (SEM), thermogravimetric analysis (TGA), and tensile test machine, respectively. The incorporation of MMT/GO hybrid nanofillers into PAN nanofibers showed a noticeable increase up to 30°C for the onset decomposition temperature and 1.32 times larger tensile strength than the pure PAN, indicating that the hybrid nanofiller is a promising candidate in improving thermal and mechanical properties of polymers

Profit maximization for large-scale energy storage systems to enable fast EV charging infrastructure in distribution networks

Large-scale integration of battery energy storage systems (BESS) in distribution networks has the potential to enhance the utilization of photovoltaic (PV) power generation and mitigate the negative effects caused by electric vehicles (EV) fast charging behavior. This paper presents a novel deep reinforcement learning-based power scheduling strategy for BESS which is installed in an active distribution network. The network includes fast EV charging demand, PV power generation, and electricity arbitrage from main grid. The aim is to maximize the profit of BESS operator whilst maintaining voltage limits. The novel strategy adopts a Twin Delayed Deep Deterministic Policy Gradient (TD3) algorithm and requires forecasted PV power generation and EV smart charging demand. The proposed strategy is compared with Deep Deterministic Policy Gradient (DDPG), Particle Swarm Optimization and Simulated Annealing algorithms to verify its effectiveness. Case studies are conducted with smart EV charging dataset from Project Shift (UK Power Networks Innovation) and the UK photovoltaic dataset. The Internal Rate of Return results with TD3 and DDPG algorithms are 9.46% and 8.69%, respectively, which show that the proposed strategy can enhance power scheduling and outperforms the mainstream methods in terms of reduced levelized cost of storage and increased net present value