Gas Storage Valuation: Price Modelling v. Optimization Methods

The existence of a financial gas market motivates the analysis of gas storage as a separate asset, using the market value context for utilization and valuation. In the recent literature, gas storage is typically analysed within a framework with a simple one-factor price dynamics that is solved to optimality. We follow a different approach, where the market is represented by a forward curve with daily granularity, the price uncertainty is represented by six factors, and where we impose a simple and intuitive storage strategy that follows from repeated maximization of the intrinsic value. Based on UK natural gas market price data, we obtain the gas storage value using our approach, and compare with results from one-factor models as well as with perfect foresight. We find that our approach captures much more of the true flexibility value than the one-factor models. Our results indicate that the appropriate framework for analyzing complex assets like gas storage is a rich representation of the price dynamics combined with a simple and intuitive decision rule.Energy; uncertainty; flexibility; exercise strategy

Passive Retention/Expulsion Methods for Subcritical Storage of Cryogens

Development of passive retention/expulsion system for subcritical storage of cryogenic material during low gravity situation

The MDS Queue: Analysing the Latency Performance of Erasure Codes

In order to scale economically, data centers are increasingly evolving their data storage methods from the use of simple data replication to the use of more powerful erasure codes, which provide the same level of reliability as replication but at a significantly lower storage cost. In particular, it is well known that Maximum-Distance-Separable (MDS) codes, such as Reed-Solomon codes, provide the maximum storage efficiency. While the use of codes for providing improved reliability in archival storage systems, where the data is less frequently accessed (or so-called "cold data"), is well understood, the role of codes in the storage of more frequently accessed and active "hot data", where latency is the key metric, is less clear. In this paper, we study data storage systems based on MDS codes through the lens of queueing theory, and term this the "MDS queue." We analytically characterize the (average) latency performance of MDS queues, for which we present insightful scheduling policies that form upper and lower bounds to performance, and are observed to be quite tight. Extensive simulations are also provided and used to validate our theoretical analysis. We also employ the framework of the MDS queue to analyse different methods of performing so-called degraded reads (reading of partial data) in distributed data storage

Some numerical methods for solving stochastic impulse control in natural gas storage facilities

The valuation of gas storage facilities is characterized as a stochastic impulse control problem with finite horizon resulting in Hamilton-Jacobi-Bellman (HJB) equations for the value function. In this context the two catagories of solving schemes for optimal switching are discussed in a stochastic control framework. We reviewed some numerical methods which include approaches related to partial differential equations (PDEs), Markov chain approximation, nonparametric regression, quantization method and some practitioners’ methods. This paper considers optimal switching problem arising in valuation of gas storage contracts for leasing the storage facilities, and investigates the recent developments as well as their advantages and disadvantages of each scheme based on dynamic programming principle (DPP

Passive retention/expulsion methods for subcritical storage of cryogens

Design and parametric analysis of passive retention/expulsion system for subcritically stored cryogens during low-

Storage of organically produced crops (OF0127T)

This is the final report of Defra Project OF0127T. The main objective of this review was to establish best storage practice for field vegetables, potatoes, cereals and top fruit. A literature review was carried out and information was also gathered from the industry. Information relevant to growers and farmers has been drawn together to provide a comprehensive base from which technical advisory leaflets can be produced. The costs of different storage methods are provided, and case studies used wherever possible. In general, organic crops can be stored using the same methods as conventional crops but there is an increased risk that sometimes there will be higher storage losses because pesticides and sprout suppressants are not used. On the whole, specific problems with pests and diseases can be avoided using good organic husbandry techniques and by storing undamaged, healthy crops. In the case of cereals storage at correct moisture content and temperatures can avoid pests and moulds. However, there are some areas where more technical development or research would be useful and these have been identified. Relatively few organic growers store vegetables, but in order to maintain a supply of good quality UK produce throughout the year, more long term cold storage space is required (either on farm or in co-operative type stores). Based on the limited data available, economic analysis revealed that long term storage of organic vegetables has generally not been profitable. However, as the market expands in the future, it is likely that storage will become as essential for vegetables as it is for organic cereals and fruit

Comparing instance-averaging with instance-saving learning algorithms

The goal of our research is to understand the power and appropriateness of instance-based representations and their associated acquisition methods. This paper concerns two methods for reducing storage requirements for instance-based learning algorithms. The first method, termed instance-saving, represents concept descriptions by selecting and storing a representative subset of the given training instances. We provide an analysis for instance-saving techniques and specify one general class of concepts that instance-saving algorithms are capable of learning. The second method, termed instance-averaging, represents concept descriptions by averaging together some training instances while simply saving others. We describe why analyses for instance-averaging algorithms are difficult to produce. Our empirical results indicate that storage requirements for these two methods are roughly equivalent. We outline the assumptions of instance-averaging algorithms and describe how their violation might degrade performance. To mitigate the effects of non-convex concepts, a dynamic thresholding technique is introduced and applied in both the averaging and non-averaging learning algorithms. Thresholding increases the storage requirements but also increases the quality of the resulting concept descriptions