Modeling and Control of Power Electronics Interfaced Load for Transmission Power Network Analysis

The penetration level of power electronics (PE) interfaced loads has been gradually increasing in recent years. It is beneficial to equip the electric load with a PE interface since it allows for more advanced control of the load performance. Furthermore, the increasing penetration of PE interfaced loads will bring both challenges and opportunities to power network resilience and reliability. However, the lack of modeling and control design for PE interfaced load units in the transmission-level power network analysis, especially for these high-penetrated high-power-rating load applications, limits the accuracy of evaluating the dynamic performance and stability status of the power network. Additionally, the complex configuration and high bandwidth dynamic performance of the PE interfaced load computationally prohibit the model development in transient stability (TS) simulation programs. Therefore, the dynamic PE interfaced load model can be characterized considering the following aspects: 1) Utilize the real-time experimental platform to represent the PE load dynamic performance since the power testbed can reflect the power grid operation with more robustness. 2) Adapt the simplified PE-based model to TS simulation tools, which focus on grid electromechanical transients and oscillations between 0.1 and 3 Hz. Research of the PE interfaced load towards its modeling and control design in different simulation environments and the flexible contribution to the grid operation has been conducted. First, the variable speed drive (VSD) based motor load is studied as a typical PE interfaced load, which can actively interact with power grid operation. The model of VSD load is introduced and applied to the power emulator for the multi-converter-based hardware testbed (HTB) in the Center of Ultra-wide-area Resilient Electric Energy Transmission Network (CURENT). Second, the aggregated performance of multiple VSD load units with grid frequency support function is characterized. Third, the fast electric vehicle (EV) charging unit is studied as a typical PE interfaced load with high power consumption. The generic model of EV charger load is developed based on the detailed switching model. The accuracy of the proposed EV charger load TS model has been verified by comparing it to simulation results of the equivalent electromagnetic (EMT) model

Mortgage Transition Model Based on LoanPerformance Data

The unexpected increase in loan default on the mortgage market is widely considered to be one of the main cause behind the economic crisis. To provide some insight on loan delinquency and default, I analyze the mortgage performance data from Fannie Mae website and investigate how economic factors and individual loan and borrower information affect the events of default and prepaid. Various delinquency status including default and prepaid are treated as discrete states of a Markov chain. One-step transition probabilities are estimated via multinomial logistic models. We find that in general current loan-to-value ratio, credit score, unemployment rate, and interest rate significantly affect the transition probabilities to different delinquency states, which lead to further default or prepaid events

THERMAL ENVIRONMENT MODELLING OF THE MONO-SLOPE SOLAR GREENHOUSE FOR COLD REGIONS

The extremely cold outdoor temperatures in winter continue to be a barrier for the greenhouse growers. In Saskatoon, for example, it is less than -31.5℃ for 1% of the year (ASHRAE, 2013). This limits the growth of the greenhouse industry in Saskatchewan which has around 250 billion square meters of farmland, and accounts for 38.5% Canada’s farm area (Statistics Canada, 2016). Due to this fact, most traditional Canadian greenhouses in the Canadian Prairies shut down during the coldest months (from November to February) because of heavy heating bills. However, the local demand for food in the winter has been increasing in Saskatchewan due to a rise in population and consciousness of healthy food. If compare traditional local greenhouses with other greenhouse production techniques, Chinese mono-slope solar greenhouses do not primarily rely on supplemental heating. They rely on solar energy to maintain the indoor temperature. Fortunately, Saskatchewan has the most hours of sunshine annually in Canada which theoretically provides a favorable environment for the establishment and development of mono-slope solar greenhouses (Environment Canada, 2017). This also greatly reduces heating costs. The objective of this study was to evaluate the thermal environment and predict the energy consumption of solar greenhouse production in Saskatchewan. This was done using an existing simulation model RGWSRHJ that was developed by Chengwei Ma in China (Ma, 2015). Several modifications were made to make the model SOGREEN that is suitable for the cold climate in Saskatchewan. These modifications included meteorological year data invoking, advanced front roof covering, summer solar screen, and so on. Later, the modified simulation model SOGREEN was validated using field data that were collected in a solar greenhouse in Elie, Manitoba. Solar greenhouse production was simulated under the weather conditions in Saskatoon, Saskatchewan. Finally, the energy consumption was analyzed using the simulated data to select the most suitable and economical energy resource for solar greenhouse production in cold regions. From the validation results, there were 9.6% and 13.7% discrepancies in the model’s predictions of indoor temperature and relative humidity, respectively. This has demonstrated that the modified model could simulate the thermal environment of a solar greenhouse with a relatively high accuracy. While the simulation results confirmed that a large amount of energy was used for supplying heat from November to March, there was almost no supplemental heat needed between April and August. This illustrated that solar greenhouses can fully utilize the solar energy, dramatically reducing the annual energy consumption. From an energy cost analysis, $26378.56,$2498.51 and $2610.00 was spent for supplemental heat with electricity, natural gas, and coal. Therefore, among these three energy resources, natural gas was the most affordable and most environmentally friendly option for greenhouse production. Compared with the natural gas expenses of Grandora Gardens, vegetable production in a solar greenhouse can save as much as 83.6% in energy costs. This demonstrates that solar greenhouse production in Saskatchewan is in fact economical for the Canadian Prairies

Manpower Constraints and Corporate Policies

Manpower constraints are the pervasive lack of specialized high- and low-skill workers, irrespective of the wage firms might offer

The Effects of Data Imbalance Under a Federated Learning Approach for Credit Risk Forecasting

Credit risk forecasting plays a crucial role for commercial banks and other financial institutions in granting loans to customers and minimise the potential loss. However, traditional machine learning methods require the sharing of sensitive client information with an external server to build a global model, potentially posing a risk of security threats and privacy leakage. A newly developed privacy-preserving distributed machine learning technique known as Federated Learning (FL) allows the training of a global model without the necessity of accessing private local data directly. This investigation examined the feasibility of federated learning in credit risk assessment and showed the effects of data imbalance on model performance. Two neural network architectures, Multilayer Perceptron (MLP) and Long Short-Term Memory (LSTM), and one tree ensemble architecture, Extreme Gradient Boosting (XGBoost), were explored across three different datasets under various scenarios involving different numbers of clients and data distribution configurations. We demonstrate that federated models consistently outperform local models on non-dominant clients with smaller datasets. This trend is especially pronounced in highly imbalanced data scenarios, yielding a remarkable average improvement of 17.92% in model performance. However, for dominant clients (clients with more data), federated models may not exhibit superior performance, suggesting the need for special incentives for this type of clients to encourage their participation

Where has the rum gone? The impact of maritime piracy on trade and transport

Despite a general agreement that piracy poses a significant threat to maritime ship - ping, empirical evidence regarding its economic consequences remains scarce. This paper combines firm-level Chinese customs data and ship position data with infor- mation on pirate attacks to investigate how exporting firms and cargo ships respond to maritime piracy. It finds that overall exports along affected shipping routes fall following an increase in pirate activity. In addition, piracy induces firms to switch from ocean to air shipping, while remaining ocean shipments become larger. At the ship-level, the paper provides evidence for re-routing, as container ships avoid regions prone to pirate attacks

Unified Factor Model Estimation and Inference under Short and Long Memory

This paper studies a linear panel data model with interactive fixed effects wherein regressors, factors and idiosyncratic error terms are all stationary but with potential long memory. The setup involves a new factor model formulation for which weakly dependent regressors, factors and innovations are embedded as a special case. Standard methods based on principal component decomposition and least squares estimation, as in Bai (2009), are found to suffer bias correction failure because the order of magnitude of the bias is determined in a complex manner by the memory parameters. To cope with this failure and to provide a simple implementable estimation procedure, frequency domain least squares estimation is proposed. The limit distribution of this frequency domain approach is established and a hybrid selection method is developed to determine the number of factors. Simulations show that the frequency domain estimator is robust to short memory and outperforms the time domain estimator when long range dependence is present. An empirical illustration of the approach is provided, examining the long-run relationship between stock return and realized volatility

Calorimetric Measurements of Magnetic-Field-Induced Inhomogeneous Superconductivity Above The Paramagnetic Limit

We report the first magneto-caloric and calorimetric observations of a magnetic-field-induced phase transition within a superconducting state to the long-sought exotic "FFLO" superconducting state first predicted over 50 years ago. Through the combination of bulk thermodynamic calorimetric and magnetocaloric measurements in the organic superconductor $\kappa$ - (BEDT-TTF)$_2$Cu(NCS)$_2$, as a function of temperature, magnetic field strength, and magnetic field orientation, we establish for the first time that this field-induced first-order phase transition at the paramagnetic limit $H_p$ for traditional superconductivity is to a higher entropy superconducting phase uniquely characteristic of the FFLO state. We also establish that this high-field superconducting state displays the bulk paramagnetic ordering of spin domains required of the FFLO state. These results rule out the alternate possibility of spin-density wave (SDW) ordering in the high field superconducting phase. The phase diagram determined from our measurements --- including the observation of a phase transition into the FFLO phase at $H_p$ --- is in good agreement with recent NMR results and our own earlier tunnel-diode magnetic penetration depth experiments, but is in disagreement with the only previous calorimetric report.Comment: 5 pages, 5 figure