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The present paper adopts a real options approach to value wind power investments under uncertainty. Flexibility arises from the possibility to defer the construction of a wind farm until more information is available, the alternative to abandon the investment, and the options to select the scale of the project and up-scale the project. Taking into account uncertainties in future electricity prices, subsides, and investment costs, the problem is solved by dynamic stochastic programming. The motivation rests on a real business case of the major Norwegian power producer Agder Energi and experience from the Nordic power market at Nord Pool. [ABSTRACT FROM AUTHOR]

A novel Linear Quadratic Gaussian (LQG) control paradigm where the realization of the random disturbance is known at decision time is introduced. A game of chance is played where Nature randomly chooses the disturbance signal and the player, having access to the random disturbance information, employs a feedback/stroboscopic strategy. The closed-form optimal solution is derived and is compared to the solution of the classical LQG problem. Furthermore, the Gaussian assumption is relaxed and the interesting game theoretic interpretation is analyzed. [ABSTRACT FROM AUTHOR]

This paper addresses the problem of solving discrete-time optimal sequential decision making problems having a disturbance space W composed of a finite number of elements. In this context, the problem of finding from an initial state x0 an optimal decision strategy can be stated as an optimization problem which aims at finding an optimal combination of decisions attached to the nodes of a disturbance tree modeling all possible sequences of disturbances w0, w1, ..., ]> over the optimization horizon T. A significant drawback of this approach is that the resulting optimization problem has a search space which is the Cartesian product of O(|W|T − 1) decision spaces U, which makes the approach computationally impractical as soon as the optimization horizon grows, even if W has just a handful of elements. To circumvent this difficulty, we propose to exploit an ensemble of randomly generated incomplete disturbance trees of controlled complexity, to solve their induced optimization problems in parallel, and to combine their predictions at time t = 0 to obtain a (near-)optimal first-stage decision. Because this approach postpones the determination of the decisions for subsequent stages until additional information about the realization of the uncertain process becomes available, we call it lazy. Simulations carried out on a robot corridor navigation problem show that even for small incomplete trees, this approach can lead to near-optimal decisions. [ABSTRACT FROM AUTHOR]

A major investment decision for individual and institutional investors alike is to choose between different asset classes, i.e., equity investments and interest-bearing investments. The asset allocation decision determines the ultimate risk and return of a portfolio. The asset allocation problem is frequently addressed either through a static analysis, based on Markowitz' mean-variance model, or dynamically but often myopically through the application of analytical results for special classes of utility functions, e.g., Samuelson's fixed-mix result for constant relative risk aversion. Only recently, the full dynamic and multi-dimensional nature of the asset allocation problem could be captured through applications of stochastic dynamic programming and stochastic programming techniques, the latter being discussed in various chapters of this book. The chapter reviews the different approaches to asset allocation and presents a novel approach based on stochastic dynamic programming and Monte Carlo sampling that permits one to consider many rebalancing periods, many asset classes, dynamic cash flows, and a general representation of investor risk preference. The chapter further presents a novel approach of representing utility by directly modeling risk aversion as a function of wealth, and thus provides a general framework for representing investor preference. The chapter shows how the optimal asset allocation depends on the investment horizon, wealth, and the investor's risk preference and how it therefore changes over time depending on cash flow and the returns achieved. The chapter demonstrates how dynamic asset allocation leads to superior results compared to static or myopic techniques. Examples of dynamic strategies for various typical risk preferences and multiple asset classes are presented. [ABSTRACT FROM AUTHOR]