浅析10kV配电网的线损管理及降损措施

本文主要探究了10kV配电网的线损管理及降损措施,线损率是衡量电力企业经营好坏的一大指标,目前我国10kV配电网线损主要包括固定损失、变动损失和其他损失三部分。通过调整电网结构,电网升压改造,降低变压器能耗等措施能够有效降低线损率。     

配电网的理论线损分析

文章以南京某地区配电网实际线损为例进行分析,结果表明只有降低理论损耗,实际线损才会有明显下降。     

LINEX Loss Functions with Applications to Determining the Optimum Process Parameters

In the classical Taguchi quality model, the symmetric quadratic loss function has been used to measure the loss of quality. However, there are a number of situations in which the symmetric quadratic loss may be inappropriate. In this paper, we proposed an asymmetric loss function, called linear exponential (LINEX) loss function, to determine optimum process parameters for the product quality. When the coefficient of LINEX loss function is small, it will be close to the quadratic loss. Moreover, the trade-off problem between quality and cost will be discussed.     

Properties of optimal forecasts under asymmetric loss and nonlinearity

Evaluation of forecast optimality in economics and finance has almost exclusively been conducted under the assumption of mean squared error loss. Under this loss function optimal forecasts should be unbiased and forecast errors serially uncorrelated at the single period horizon with increasing variance as the forecast horizon grows. Using analytical results we show that standard properties of optimal forecasts can be invalid under asymmetric loss and nonlinear data generating processes and thus may be very misleading as a benchmark for an optimal forecast. We establish instead that a suitable transformation of the forecast error—known as the generalized forecast error—possesses an equivalent set of properties. The paper also provides empirical examples to illustrate the significance in practice of asymmetric loss and nonlinearities and discusses the effect of parameter estimation error on optimal forecasts.     

The perilous effects of capability loss on outsourcing management and performance

The outsourcing of manufacturing activities and business processes has emerged as a prevalent business practice in many industries. Given this state, the lackluster performance of an alarmingly high proportion of outsourcing initiatives is somewhat surprising. In an effort to achieve aggressive cost savings objectives, many outsourcing engagements are associated with organizational resources being disposed of which can lead to a significant operational capability loss on the part of the outsourcing firm. Surprisingly little empirical research has dealt with the issues arising from this capability loss frequently associated with outsourcing. This study strives to address this void. Drawing on multiple theoretical perspectives, this study investigates the direct impact that capability loss has on outsourcing performance, and also the impact it has on the outsourcing firm's ability to effectively manage its relationship with the outsourcing provider. Results from our hierarchical regression analysis on 198 outsourcing initiatives suggest an inadequate capability evaluation up front can lead to a more substantive capability loss. Subsequently, we find that a more extensive capability loss has a direct negative effect on outsourcing performance. Prior studies have established the significant positive effect that developing a committed and cooperative relationship with the provider has on outsourcing performance. Our results corroborate these previous findings, and also demonstrate that capability loss inhibits the outsourcing firm's efforts to develop a committed and cooperative relationship with the outsourcing provider.     

货币抵补套利的盈亏价值分析

在货币利息套汇中做抵补交易 (常用远期外汇交易进行套利中的抵补 ) ,事先做好抵补套利的盈亏价值分析 ,能使货币套利者获得预期收益 ,风险小。本文就此及其相关问题做些探讨。     

On economic evaluation of directional forecasts

It is commonly accepted that information is helpful if it can be exploited to improve a decision making process. In economics, decisions are often based on forecasts of the upward or downward movements of the variable of interest. We point out that directional forecasts can provide a useful framework for assessing the economic forecast value when loss functions (or success measures) are properly formulated to account for the realized signs and realized magnitudes of directional movements. We discuss a general approach to (directional) forecast evaluation which is based on the loss function proposed by Granger, Pesaran and Skouras. It is simple to implement and provides an economically interpretable loss/success functional framework. We show that, in addition, this loss function is more robust to outlying forecasts than traditional loss functions. As such, the measure of the directional forecast value is a readily available complement to the commonly used squared error loss criterion.     

配电变压器安装位置对低压线损的影响

随着电网的不断发展与用电负荷的快速增长,搞好降损节电工作已成为现代电力企业管理的一项重要课题。线损率是供电企业一项综合性的技术经济指标,反映能源的有效利用及供电企业的经营管理水平。本文通过配电变压器几种不同安装位置,分析并计算其对低压线损的影响,最后用负荷矩平衡原理计算出配电变压器的最佳安装位置,从而做到降低线损,节能减排的作用。     

An Empirical Model of Efficiency Analysis on Flood Prevention Investment in Japan

This paper aims to evaluate the comprehensive efficiency of flood disaster prevention investment from 1955 to 1999 by developing and applying an integrated evaluation model. Our analysis results have clarified some important issues currently being discussed in Japan. Firstly, the average values of normalized flood losses decreased greatly between the 1955-1961 period and the 1984-1999 period. Secondly, the ratio of human loss to total loss caused by floods in Japan may be less than 10%, based on the concept of the value of statistical life (VOSL) and values estimated from the risk-money tradeoff. The proportion of intangible effect to total loss increased from less than 10% before the 1970s to approximately 20% after that period. Finally, flood prevention investment in Japan effectively reduced losses caused by flooding before the 1960s; however, since the 1980s, investment has changed from an efficient mode to an inefficient mode, not only from the economic standpoint, but also from that of total flood loss saving.     

Predictive regressions under asymmetric loss: Factor augmentation and model selection

This paper discusses the specifics of forecasting using factor-augmented predictive regressions under general loss functions. In line with the literature, we employ principal component analysis to extract factors from the set of predictors. In addition, we also extract information on the volatility of the series to be predicted, since the volatility is forecast-relevant under non-quadratic loss functions. We ensure asymptotic unbiasedness of the forecasts under the relevant loss by estimating the predictive regression through the minimization of the in-sample average loss. Finally, we select the most promising predictors for the series to be forecast by employing an information criterion that is tailored to the relevant loss. Using a large monthly data set for the US economy, we assess the proposed adjustments in a pseudo out-of-sample forecasting exercise for various variables. As expected, the use of estimation under the relevant loss is found to be effective. Using an additional volatility proxy as the predictor and conducting model selection that is tailored to the relevant loss function enhances the forecast performance significantly.     

信用评级在估计债券信用风险损失中的应用

债券信用评级有效地揭示了债券的相对信用风险,为债券投资者提供了重要的投资决策参考。在计算债券信用风险损失中,可以充分利用评级公司公布的数据来具体的计算出各债券的信用风险值,本文给出这种方法和例子。     

浅谈电力系统的无功补偿

在电力系统的供电过程中,电力网中功率损耗和电能损耗、供电线路的电压损失和电压波动,都直接影响到用户端的功率因数高低,也直接影响到节约电能和整个供电区域的供电质量。电力系统的无功补偿是提高系统运行电压,减小网损,提高系统稳定水平的有效手段。本文介绍了影响电网功率因数的主要原因以及输配电网中低压无功补偿的原理及方法,以达到改善功率因数、调整电压及补偿参数等作用。     

运用过程能力指数估算合格品隐性质量成本

根据田口质量观,合格产品也会产生质量损失,提出了合格产品由于质量特性值波动而产生的质量损失是合格品隐性质量成本。基于非对称二次截断质量损失函数,给出了由过程能力指数估算合格品隐性质量成本的方法,并举例进行了计算,具有广泛的应用范围。     

Are generalized call-spreads efficient?

In order to explain coexistence of a deductible for low values of the loss and an upper limit for high values of the loss in insurance contracts, we consider the exchange of risk between two rank dependent expected utility maximizers. It is shown that if the insurer (insured) takes more into account the lowest outcomes – hence maximal losses – than the insured (insurer), then the optimal contract has an upper limit (includes a deductible for high values of the loss). If furthermore, the insured (insurer) neglects the highest outcomes while the insurer (insured) does not, the optimal contract includes a deductible (full insurance) for low values of the loss.     

Optimal prediction under LINLIN loss: Empirical evidence

I compare the forecasts of returns from the mean predictor (optimal under MSE), with the pseudo-optimal and optimal predictor for an asymmetric loss function under the assumption that agents have an asymmetric LINLIN loss function. The results strongly suggest not using the conditional mean predictor under conditions of asymmetry. In general, forecasts can be improved by the use of optimal predictor rather than the pseudo-optimal predictor, suggesting that the loss reduction from using the optimal predictor can actually be important for practitioners as well.     

Testing forecast accuracy of expectiles and quantiles with the extremal consistent loss functions

Forecast evaluations aim to choose an accurate forecast for making decisions by using loss functions. However, different loss functions often generate different ranking results for forecasts, which complicates the task of comparisons. In this paper, we develop statistical tests for comparing performances of forecasting expectiles and quantiles of a random variable under consistent loss functions. The test statistics are constructed with the extremal consistent loss functions of Ehm et al. (2016). The null hypothesis of the tests is that a benchmark forecast at least performs equally well as a competing one under all extremal consistent loss functions. It can be shown that if such a null holds, the benchmark will also perform at least equally well as the competitor under all consistent loss functions. Thus under the null, when different consistent loss functions are used, the result that the competitor does not outperform the benchmark will not be altered. We establish asymptotic properties of the proposed test statistics and propose to use the re-centered bootstrap to construct their empirical distributions. Through simulations, we show that the proposed test statistics perform reasonably well. We then apply the proposed method to evaluations of several different forecast methods.     

Forecasting volatility: A reality check based on option pricing, utility function, value-at-risk, and predictive likelihood

We analyze the predictive performance of various volatility models for stock returns. To compare their performance, we choose loss functions for which volatility estimation is of paramount importance. We deal with two economic loss functions (an option pricing function and an utility function) and two statistical loss functions (a goodness-of-fit measure for a value-at-risk (VaR) calculation and a predictive likelihood function). We implement the tests for superior predictive ability of White [Econometrica 68 (5) (2000) 1097] and Hansen [Hansen, P. R. (2001). An unbiased and powerful test for superior predictive ability. Brown University]. We find that, for option pricing, simple models like the Riskmetrics exponentially weighted moving average (EWMA) or a simple moving average, which do not require estimation, perform as well as other more sophisticated specifications. For a utility-based loss function, an asymmetric quadratic GARCH seems to dominate, and this result is robust to different degrees of risk aversion. For a VaR-based loss function, a stochastic volatility model is preferred. Interestingly, the Riskmetrics EWMA model, proposed to calculate VaR, seems to be the worst performer. For the predictive likelihood-based loss function, modeling the conditional standard deviation instead of the variance seems to be a dominant modeling strategy.     

Minimax estimation of constrained parametric functions for discrete families of distributions

For a vast class of discrete model families where the natural parameter is constrained to an interval, we give conditions for which the Bayes estimator with respect to a boundary supported prior is minimax under squared error loss type functions. Building on a general development of éric Marchand and Ahmad Parsian, applicable to squared error loss, we obtain extensions to various parametric functions and squared error loss type functions. We provide illustrations for various distributions and parametric functions, and these include examples for many common discrete distributions, as well as when the parametric function is a zero-count probability, an odds-ratio, a Binomial variance, and a Negative Binomial variance, among others. The Research of M. Jafari Jozani is supported by a grant of the Institute for Research and Planning in Higher Education, Ministry of Science, Research and Technology, Iran. The Research of é. Marchand is supported by NSERC of Canada.     

A preference foundation for constant loss aversion

Following prospect theory and in particular the concept of loss aversion, introduced by Kahneman and Tversky (1979), we consider decision making under risk in which the decision maker’s preferences depend on a reference outcome. An outcome below this reference outcome is regarded as resulting from a loss: a loss decreases the decision maker’s basic utility more than a comparable gain increases this utility. An elegant and simple way to model this phenomenon was proposed by Shalev (2002): the utility of an outcome below the reference outcome is obtained from the basic utility by subtracting a multiple of the loss in basic utility: this multiple, the loss aversion coefficient, is constant across different reference outcomes. We provide a preference foundation for this loss aversion model.     

Optimal rules and robust Bayes estimation of a Gamma scale parameter

For estimating an unknown scale parameter of Gamma distribution, we introduce the use of an asymmetric scale invariant loss function reflecting precision of estimation. This loss belongs to the class of precautionary loss functions. The problem of estimation of scale parameter of a Gamma distribution arises in several theoretical and applied problems. Explicit form of risk-unbiased, minimum risk scale-invariant, Bayes, generalized Bayes and minimax estimators are derived. We characterized the admissibility and inadmissibility of a class of linear estimators of the form $cX\,{+}\,d$ , when $X\sim \varGamma (\alpha ,\eta )$ . In the context of Bayesian statistical inference any statistical problem should be treated under a given loss function by specifying a prior distribution over the parameter space. Hence, arbitrariness of a unique prior distribution is a critical and permanent question. To overcome with this issue, we consider robust Bayesian analysis and deal with Gamma minimax, conditional Gamma minimax, the stable and characterize posterior regret Gamma minimax estimation of the unknown scale parameter under the asymmetric scale invariant loss function in detail.