Frequency Restoration Reserve Control Scheme with Participation of Industrial Loads

In order to accommodate larger amounts of renewable energy resources, whose power output is inherently unpredictable, there is an increasing need for frequency control power reserves. Loads are already used to provide replacement reserves, i.e. the slowest kind of reserves, in several power systems. This paper proposes a control scheme for frequency restoration reserves with participation of industrial loads. Frequency restoration reserves are required to change their active power within a time frame of tens of seconds to tens of minutes in response to a regulation signal. Industrial loads in many cases already have the capacity and capability to participate in this service. A mapping of their process constraints to power and energy demand is proposed in order to integrate industrial loads in existing control schemes. The proposed control scheme has been implemented in a 74-bus test system. Dynamic simulations show that industrial loads can be successfully integrated into the power system as frequency restoration reserves. © 2013 IEEE

Monitoring and management of power transmission dynamics in an industrial smart grid

This article is a position paper whose purpose is to give the context for presentations in a special session at PowerTech 2013. The special session is being proposed by the EU FP7 Real-Smart Consortium, a Marie Curie Industry-Academic Pathways and Partnerships project. The paper gives an overview of topics on modeling, monitoring and management of power transmission dynamics with participation from large industrial loads. © 2013 IEEE

Flexible Operation of Industrial Processes Acting as Power Reserves

Harvesting energy from renewable resources, such as wind and sun, is our priority. Wind power installed capacity in the UK and Nordic systems is increasing dramatically in recent years. However, one cannot precisely predict renewable energy output: if wind stops blowing, turbines don’t produce electricity. Moreover, we expect to receive power whenever we switch on or plug in electrical appliances. Since electricity cannot be stored efficiently, reserves must be available to continuously match the difference between generation and consumption. The number of occasions in which not enough reserves are available is growing because of renewable generation, increasing the risk of blackout. Conventional generators (e.g. gas-fired power stations) can control their power output to keep the frequency as close as possible to 50 Hz. This poster concentrates on flexible use of industrial plants, which can vary their electricity consumption and act as reserves whenever an imbalance arises. However, since flexibility is only a by-product of the plant, special care is devoted to assure stable and safe operation of the main industrial production: for instance, a plant that uses electricity to liquefy metal at high temperatures may reduce its power consumption for some time, provided that the metal doesn’t solidify. Industrial load flexibility is the largest unexploited resource in power system reliability: frequency control schemes must be revisited in light of load participation. The aim of this research is to prove that flexibility of industrial plants allows for more renewable energy integration while preserving supply stability

A profit model for spread trading with an application to energy futures

This paper proposes a profit model for spread trading by focusing on the stochastic movement of the price spread and its first hitting time probability density. The model is general in that it can be used for any financial instrument. The advantage of the model is that the profit from the trades can be easily calculated if the first hitting time probability density of the stochastic process is given. We then modify the profit model for a particular market, the energy futures market. It is shown that energy futures spreads are modeled by using a meanreverting process. Since the first hitting time probability density of a mean-reverting process is approximately known, the profit model for energy futures price spreads is given in a computable way by using the parameters of the process. Finally, we provide empirical evidence for spread trades of energy futures by employing historical prices of energy futures (WTI crude oil, heating oil, and natural gas futures) traded on the New York Mercantile Exchange. The results suggest that natural gas futures trading may be more profitable than WTI crude oil and heating oil due to its high volatility in addition to its long-term mean reversion, which offers supportive evidence of the model prediction. --futures spread trading,energy futures markets,mean-reverting process,first hitting,time probability density,profit model,WTI crude oil,heating oil,natural gas

Another Look at the Ho-Lee Bond Option Pricing Model

In this paper, we extend the classical Ho-Lee binomial term structure model to the case of time-dependent parameters and, as a result, resolve a drawback associated with the model. This is achieved with the introduction of a more flexible no-arbitrage condition in contrast to the one assumed in the Ho-Lee model

CVaR sensitivity with respect to tail thickness

We consider the sensitivity of conditional value-at-risk (CVaR) with respect to the tail index assuming regularly varying tails and exponential and faster-than-exponential tail decay for the return distribution. We compare it to the CVaR sensitivity with respect to the scale parameter for stable Paretian, the Student's t, and generalized Gaussian laws and discuss implications for the modeling of daily returns and marginal rebalancing decisions. Finally, we explore empirically the impact on the asymptotic variability of the CVaR estimator with daily returns which is a standard choice for the return frequency for risk estimation. --fat-tailed distributions,regularly varying tails,conditional value-at-risk,marginal rebalancing,asymptotic variability

Bayesian inference for hedge funds with stable distribution of returns

Recently, a body of academic literature has focused on the area of stable distributions and their application potential for improving our understanding of the risk of hedge funds. At the same time, research has sprung up that applies standard Bayesian methods to hedge fund evaluation. Little or no academic attention has been paid to the combination of these two topics. In this paper, we consider Bayesian inference for alpha-stable distributions with particular regard to hedge fund performance and risk assessment. After constructing Bayesian estimators for alpha-stable distributions in the context of an ARMA-GARCH time series model with stable innovations, we compare our risk evaluation and prediction results to the predictions of several competing conditional and unconditional models that are estimated in both the frequentist and Bayesian setting. We find that the conditional Bayesian model with stable innovations has superior risk prediction capabilities compared with other approaches and, in particular, produced better risk forecasts of the abnormally large losses that some hedge funds sustained in the months of September and October 2008. --