Explore the vast range of titles available.

Due to the inherent uncertainty of wind conditions as well as the price unpredictability in the competitive electricity market, wind power producers are exposed to the risk of concurrent fluctuations in both price and volume. Therefore, it is imperative to develop strategies to effectively stabilize their revenues, or cash flows, when trading wind power output in the electricity market. In light of this context, we present a novel endeavor to construct multivariate derivatives for mitigating the risk of fluctuating cash flows that are associated with trading wind power generation in electricity markets. Our approach involves leveraging nonparametric techniques to identify optimal payoff structures or compute the positions of derivatives with fine granularity, utilizing multiple underlying indexes including spot electricity price, area-wide wind power production index, and local wind conditions. These derivatives, referred to as mixed derivatives, offer advantages in terms of hedge effectiveness and contracting efficiency. Notably, we develop a methodology to enhance the hedge effects by modeling multivariate functions of wind speed and wind direction, incorporating periodicity constraints on wind direction via tensor product spline functions. By conducting an empirical analysis using data from Japan, we elucidate the extent to which the hedge effectiveness is improved by constructing mixed derivatives from various perspectives. Furthermore, we compare the hedge performance between high-granular (hourly) and low-granular (daily) formulations, revealing the advantages of utilizing a high-granular hedging approach.

Since the future output of wind power generation is uncertain due to weather conditions, there is an increasing need to manage the risks associated with wind power businesses, which have been increasingly implemented in recent years. This study introduces multiple weather derivatives of wind speed and temperature and examines their effectiveness in reducing (hedging) the fluctuation risk of future cash flows attributed to wind power generation. Given the diversification of hedgers and hedging needs, we propose new standardized derivatives with higher-order monomial payoff functions, such as “wind speed cubic derivatives” and “wind speed and temperature cross-derivatives,” to minimize the cash flow variance and develop a market-trading scheme to practically use these derivatives in wind power businesses. In particular, while demonstrating the importance of standardizing weather derivatives regarding market liquidity and efficiency, we propose a strategy to narrow down the required number (or volume) of traded instruments and improve trading efficiency by utilizing the least absolute shrinkage and selection operator (LASSO) regression. Empirical analysis reveals that higher-order, multivariate standardized derivatives can not only enhance the out-of-sample hedge effect but also help reduce trading volume. The results suggest that diversification of hedging instruments increases transaction flexibility and helps wind power generators find more efficient portfolios, which can be generalized to risk management practices in other businesses.

In recent years, as photovoltaic (PV) power generation has rapidly increased on a global scale, there is a growing need for a highly accurate power generation forecasting model that is easy to implement for a wide range of electric utilities. Against this background, this study proposes a PV power forecasting model based on the generalized additive model (GAM) and compares its forecasting accuracy with four popular machine learning methods: k-nearest neighbor, artificial neural networks, support vector regression, and random forest. The empirical analysis provides an intuitive interpretation of the multidimensional smooth trends estimated by the GAM as tensor product splines and confirms the validity of the proposed modeling structure. The effectiveness of GAM is particularly evident in trend completion for missing data, where it is able to flexibly express the tangled trend structure inherent in time series data, and thus has an advantage not only in interpretability but also in improving forecast accuracy.

As global efforts to achieve net-zero emissions intensify, the integration of renewable energy has brought to the critical need for effective volumetric risk hedging strategies, particularly at the local level. However, existing financial instruments based on total power output, such as wind power futures, fall short in local hedging. This study introduces Principal Component (PC) derivatives designed for the solar power sector, using multi-regional solar radiation as the underlying to overcome data handling complexities. In particular, by incorporating our previous concept of prediction error derivatives, we provide a unique solution to complex pricing to help manage cash flow volatility risks. In addition, we propose PC derivatives based on solar radiation residuals to hedge volumetric risks. Empirical analysis shows that our PC derivatives outperform existing widearea derivatives in terms of hedge effectiveness, with a 20% increase over area-specific derivatives. Using as few as three or four PC derivatives can provide comprehensive coverage across different areas, enhancing market liquidity and creating an efficient transaction framework. Our results highlight the practical benefits of this approach, including the potential to reduce transaction costs by countertrading in different regions.

Previous studies have demonstrated that non-parametric hedging models using temperature derivatives are highly effective in hedging profit/loss fluctuation risks for electric utilities. Aiming for the practical applications of these methods, this study performs extensive empirical analyses and makes methodological customizations. First, we consider three types of electric utilities being exposed to risks of “demand”, “price”, and their “product (multiplication)”, and examine the design of an appropriate derivative for each utility. Our empirical results show that non-parametrically priced derivatives can maximize the hedge effect when a hedger bears a “price risk” with high nonlinearity to temperature. In contrast, standard derivatives are more useful for utilities with only “demand risk” in having a comparable hedge effect and in being liquidly traded. In addition, the squared prediction error derivative on temperature has a significant hedge effect on both price and product risks as well as a certain effect on demand risk, which illustrates its potential as a new standard derivative. Furthermore, spline basis selection, which may be overlooked by modeling practitioners, improves hedge effects significantly, especially when the model has strong nonlinearities. Surprisingly, the hedge effect of temperature derivatives in previous studies is improved by 13–53% by using an appropriate new basis.

In a competitive electricity market, both electricity retailers and generators predict future prices and volumes and execute electricity delivery contracts through power exchange. In such circumstances, they may suffer from uncertainties caused by fluctuations in spot prices and future demand due to their high volatility. In this study, we develop a unified approach using derivatives and forwards on the spot electricity price and weather data to mitigate the cashflow fluctuation for power utilities. We aim to clarify the applicability of our proposed methods and provide a new and useful perspective on hedging schemes involving various electricity utilities, such as power retailers, solar photovoltaic (PV) generators, and thermal generators. Moreover, we analyze the risk of risk takers (such as the insurance companies in this study) in the derivatives market. In addition, we perform empirical simulations to measure out-of-sample hedging effects on their cashflow management using actual data in Japan.

Predicting future solar conditions is important for electricity industries with solar power generators to quote a day-ahead sales contract in the electricity market. If a prediction error exists, the market-monitoring agent has to prepare another power generation resource to immediately compensate for the shortage, resulting in an additional cost. In this context, a penalty may be required depending on the size of the prediction error, which may lead to a significant loss for solar power producers. Because the main source of such losses is from prediction errors of solar conditions, they can instead effectively utilize a derivative contract based on solar prediction errors. The objective of this work is to provide such a derivative contract, namely, a prediction error weather derivative. First, defining a certain loss function, we measure the hedge effect of the derivative on solar radiation prediction error, thereby verifying that the existing hedging method for wind power can also be applied to solar power generation with periodic trends. By introducing the temperature derivative on the absolute prediction error, we also propose a cross-hedging method, where we demonstrate not only a further variance reduction effect when used with solar radiation derivatives, but also a certain hedge effect obtained even when only the temperature derivative is used. For temperature derivative pricing and optimal contract volume estimation, we propose a method using a tensor-product spline function that simultaneously incorporates the smoothing conditions of both the direction of intraday time trend and seasonal trend, and consequently verify its effectiveness.

Background Surgical site infection (SSI) after spinal surgery is a devastating complication. Various methods of skin closure are used in spinal surgery, but the optimal skin-closure method remains unclear. A recent report recommended against the use of metal staples for skin closure in orthopedic surgery. 2-Octyl-cyanoacrylate (Dermabond; Ethicon, NJ, USA) has been widely applied for wound closure in various surgeries. In this cohort study, we assessed the rate of SSI in spinal surgery using metal staples and 2-octyl-cyanoacrylate for wound closure. Methods This study enrolled 609 consecutive patients undergoing spinal surgery in our hospital. From April 2007 to March 2010 surgical wounds were closed with metal staples (group 1, n  = 294). From April 2010 to February 2012 skin closure was performed using 2-octyl-cyanoacrylate (group 2, n  = 315). We assessed the rate of SSI using these two different methods of wound closure. Prospective study of the time and cost evaluation of wound closure was performed between two groups. Results Patients in the 2-octyl-cyanoacrylate group had more risk factors for SSI than those in the metal-staple group. Nonetheless, eight patients in the metal-staple group compared with none in the 2-octyl-cyanoacrylate group acquired SSIs ( p  < 0.01). The closure of the wound in length of 10 cm with 2-octyl-cyanoacrylate could save 28 s and $13.5. Conclusions This study reveals that in spinal surgery, wound closure using 2-octyl-cyanoacrylate was associated with a lower rate of SSI than wound closure with staples. Moreover, the use of 2-octyl-cyanoacrylate has a more time saving effect and cost-effectiveness than the use of staples in wound closure of 10 cm in length.

Introduction We herein describe a case of delayed union of a lumbar spine fracture in a 70-year-old patient with diffuse idiopathic skeletal hyperostosis (DISH). Clinical Course and Result Because he decided not to undergo surgical treatment, we provided conservative treatment with teriparatide (TPTD). Union was obtained in 2 months, and no adverse events were observed during treatment. Six months after starting the TPTD, further bone formation was observed and the lumbar instability had resolved. Conclusion This is the first report of successful use of TPTD to treat delayed union of a spine fracture in a patient with DISH without surgical intervention.

We studied whether applying nucleus pulposus tissue, obtained from tail intervertebral discs that had been subjected to chronic mechanical compression, to the lumbar nerve roots produces hyperalgesia, which is thought to be a pain-related behavior in the rat. An Ilizarov-type apparatus was used for immobilization and chronically applied compression of the rat tail for eight weeks. Three weeks after application of extracted nucleus pulposus tissue on the lumbar nerve roots, motor function, sensitivity to noxious mechanical stimuli was measured. Eight weeks after application of the apparatus, the instrumented vertebrae were resected and sections were stained with hematoxylin and eosin to evaluate degeneration of the intervertebral disc. Mechanical hyperalgesia observed in rats treated with the compressed nucleus pulposus tissue was greater and of longer duration than in the rats treated with normal and non-compressed discs. The nucleus pulposus in the instrumented vertebrae showed some histological degeneration. In conclusion, chronic mechanical compression of nucleus pulposus, which resulted in degeneration to some extent, enhanced mechanical hyperalgesia, which was induced by application of nucleus pulposus on the nerve root in the rat. Degenerative intervertebral discs might induce more significant pain than normal intervertebral discs.