Optimal Decision-Making under Uncertainty - Application to Power Transmission Investments

Economists define investment as the act of incurring immediate costs with the expectation of future returns. An investment project, as every asset has a value. For successfully investing in and managing these assets is crucial not only recognizing what the value is but also the sources of this value. Most investment decisions share three characteristics in different degrees. First, investments are partially or totally irreversible. Roughly speaking, the initial investment cost is at least partially sunk; i.e. it is impossible to recover all the expenditures if the decision-maker changes his mind. Second, there is uncertainty in the revenues from the investment, and therefore, risk associated with this. Third, all decision-making has some leeway about the timing of the investment. It is possible to defer the decision making to get more information about the future. These three features interact to determine the optimal decisions of investors on a given investment project. Transmission utilities are faced with investment projects, which hold these three characteristics: irreversibility, uncertainty and the choice of timing. In this context, an efficient decision making process is, therefore, based on managing the uncertainties and understanding the relationships between risks and opportunities in order to achieve a well-timed investment execution. Therefore, strategic flexibility for seizing opportunities and cutting losses contingent upon the market evolution is of huge value. Strategic flexibility is a risk management method that is gaining ongoing research attention as it enables properly managing major uncertainties, which are unsolved at the time of making decisions. Hence, valuing added flexibility in transmission investment portfolios, for instance, by investing in power electronic-based controller meanwhile transmission line projects are deferred, is necessary to make optimal network upgrading. Nevertheless, expressing the value of flexibility in economic terms is not a trivial task and requires new, sophisticated valuing tools, since the traditional investment theory has not recognized the important implications of the interaction between the three aforementioned investment features. Any attempt to quantify investment flexibility almost naturally leads to the concept of Real Options (RO). The RO technique provides a well-founded framework –based on the theory of financial options, and consequently, stochastic dynamic programming- to assess strategic investments under uncertainty. In the first RO applications, valuation was normally confined to the investment options that can be easily assimilated to financial options, for which solutions are well-known and readily available. Nevertheless, an investor confront with a diverse set of opportunities. From this point of view, investment projects can be seen as a portfolio of options, where its value is driven by several stochastic variables. The introduction of multiple interacting options into real options models highly increases the problem complexity, making traditional numerical approaches impracticable. However in the recent years, simulation procedures for solving multiple American options have been successfully proposed. One of the most promising approaches is the Least Square Monte Carlo (LSM) method proposed by Longstaff and Schwartz in 2001. LSM method is based on stochastic chronological simulation and uses least squares linear regression to determine the optimal stopping time (optimal path) in the decision making process. This chapter lays out a general background about key concepts -uncertainty and risk- and the most usual risk management techniques in transmission investment are provided. Then, the concept of strategic flexibility is introduced in order to set its ability for dealing with the uncertainties involved in the investment problem. In addition, new criteria and advantages of ROV approach compared with classical probabilistic choice are presented, by exposing a LSM-based method for decomposing and evaluating the complex real option problem involved in flexible transmission investments under uncertainties. The proposed methodology is applied in a study case which evaluates an interconnection reinforcement on the European interconnected power system, by showing how the valuation of flexibility is a key task for making efficient and well-timed investments in the transmission network. The impact of two network upgrades on the system-wide welfare is analyzed. These upgrades are the development of a new interconnected line and the installation of a power electronic-based controller. Both upgrades represent measures to strengthen the German-Dutch interconnections due to the fact that these are among the most important corridors within the Central Western European (CWE) region. Hence, an interconnection project, which is currently under study, is compared to flexible investment in order to shed some light on the influence of the strategic flexibility on the optimal decision-making process. The research is focused on assessing the impact of different wind power in-feed scenarios in detail as well as the uncertainty of the demand growth, generation cost evolution and the installed wind capacity on the decision-making process. The presented approach might serve as a basis for a decision-making tool for regulatory agencies in order to quantify the necessity for network upgrades.Fil: Blanco, Gerardo. Universidad Nacional de Asunción; ParaguayFil: Olsina, Fernando Gabriel. Consejo Nacional de Investigaciones Científicas y Técnicas. Centro Científico Tecnológico Conicet - San Juan; Argentina. Universidad Nacional de San Juan. Facultad de Ingeniería. Instituto de Energía Eléctrica; Argentin

Flexibility Value of Distributed Generation in Transmission Expansion Planning

The efficiency of the classic planning methods for solving realistic problems largely relies on an accurate prediction of the future. Nevertheless, the presence of strategic uncertainties in current electricity markets has made prediction and even forecasting essentially futile. The new paradigm of decision-making involves two major deviations from the conventional planning approach. On one hand, the acceptation the fact the future is almost unpredictable. On the other hand, the application of solid risk management techniques turns to be indispensable. In this chapter, a decision-making framework that properly handles strategic uncertainties is proposed and numerically illustrated for solving a realistic transmission expansion planning problem. The key concept proposed in this chapter lies in systematically incorporating flexible options such as large investments postponement and investing in Distributed Generation, in foresight of possible undesired events that strategic uncertainties might unfold. Until now, the consideration of such flexible options has remained largely unexplored. The understanding of the readers is enhanced by means of applying the proposed framework in a numerical mining firm expansion capacity planning problem. The obtained results show that the proposed framework is able to find solutions with noticeably lower involved risks than those resulting from traditional expansion plans.Fil: Vásquez, Paúl. Consejo Nacional de Electricidad; EcuadorFil: Olsina, Fernando Gabriel. Consejo Nacional de Investigaciones Científicas y Técnicas. Centro Científico Tecnológico Conicet - San Juan. Instituto de Energía Eléctrica. Universidad Nacional de San Juan. Facultad de Ingeniería. Instituto de Energía Eléctrica; Argentin

Optimization of Spinning Reserve in Stand-alone Wind-Diesel Power Systems

Spinning reserve carried on synchronized units is the most effective resource available to the system operators for managing unforeseen power unbalances, such as demand fluctuations and the sudden loss of generation equipment. The amount of reserve and the speed that it can effectively be deployed determine the supply reliability that the generation system can achieve. Carrying more spinning reserve reduces the probability that the generation system become unable to preserve the momentary power balance and costly remedial actions, such as involuntary load shedding, turns unavoidable to prevent a system collapse. Nevertheless, providing spinning reserve on a continuous basis is expensive. Indeed, the provision of spinning reserve entails incurring in startup costs to commit generating units in excess of the forecasted load, which consequently have to be dispatched at less efficient operating points. The problem of keeping the power balance is still more difficult in stand-alone wind-diesel power systems, since these systems are additionally subjected to random power fluctuations originated in the uncertain and intermittent nature of the wind resource. Furthermore, autonomous power system cannot rely on power imported from interconnections for preserving the power balance. The inherent characteristics of these systems require scheduling more reserve on synchronized units for ensuring adequate security and reliability levels. The higher reserve requirements may substantially deteriorate the economy of these supply systems. The costs of keeping spinning reserve must be compared with the benefits that it provides in terms of lower expected costs of interruptions. In essence, the optimal reserve level can be set so that the marginal cost of carrying an additional MW equals the marginal reduction of the expected load curtailment costs. Despite the apparent simplicity of this optimality condition, determining the optimal amount of spinning reserve in a practical setting presents substantial modelling complexities and computational challenges. Given the random nature of the disturbances and contingencies that may face a generation system, assessing the benefits of carrying a certain amount of spinning reserve involves quantifying the occurrence probability, Wind Power 2 duration, extent and costs of load loss events. Such evaluation entails modelling the stochastic behaviour of system operation by considering the random failure of system components and the stochastic fluctuations of load and wind generation. The problem is probabilistic in its very nature and thus it may be appropriately treated by applying stochastic modelling techniques. Only with the advent of more powerful computing hardware, the problem of optimizing the spinning reserve has attracted the interest of researchers and its solution is currently deemed practicable. This work proposes a novel method for determining the optimal amount of spinning reserve that should be carried in autonomous hybrid wind-diesel generation systems. The optimal spinning reserve is determined by comparing the cost of its provision with the economic benefits it delivers in terms of supply reliability. The proposed approach is still general and can be applied in straightforward manner to establish the optimal reserve level in large interconnected systems. The presented methodology considers with accuracy the probabilistic features of the load and the wind generation, as well as the random outages of the conventional generating units. By applying high-resolution chronological simulation techniques, the stochastic features of the integrated operation of the diesel units and the wind turbine can be detailed replicated. The mathematical model appropriately considers all relevant characteristics and operational constraints of the generating units, e.g. non-linear heat rate curve, maximum and minimum output, startup and synchronization time, minimum down and uptime, ramping, etc. Massive stochastic simulation methods allow assessing the system reliability and valuing the economic costs of loss load events. Global search methods like particle swarm optimization (PSO) are proposed for finding the optimal scheduling policy and spinning reserve requirement that minimizes the sum of the expected operation costs and the expected costs of the energy not served.Fil: Olsina, Fernando Gabriel. Consejo Nacional de Investigaciones Científicas y Técnicas. Centro Científico Tecnológico Conicet - San Juan; Argentina. Universidad Nacional de San Juan. Facultad de Ingeniería. Instituto de Energía Eléctrica; ArgentinaFil: Larisson, Carlos Hugo. No especifíca

Risk-Constrained Forward Trading Optimization by Stochastic Approximate Dynamic Programming

Since the mid-twentieth century, Dynamic Programming (DP) has proved to be a flexible and powerful approach to address optimal decisions problems. Nevertheless, a decisive drawback of the conventional DP is the need for exploring the whole state space in order to find the optimal solution. The immense amount of mathematical operations involved to solve real-scale problems, constrained the application of DP to small or highly simplified cases. Indeed, state space grows exponentially with the number of variables when considering multivariate optimization. The curse of dimensionality is a well-known limitation of conventional DP algorithms for tackling large-scale problems ubiquitous in real science and engineering applications. In the last decades, many new algorithms emerged in different branches of science to overcome the inherent limitations of conventional DP. Unlike conventional DP, these algorithms avoid enumerating and calculating every possible state of a system during the optimization process. Instead, they estimate relevant features of the state space. This approach circumvents the dimensionality limitations of the conventional DP while retaining many of its advantages. In this chapter, the application of advanced stochastic dynamic programming techniques to the optimization of the forward sell strategy of a power generator subjected to delivery risk is considered. The proposed approach allows rebalancing the portfolio during the period of analysis. In electricity markets, a power generator can sell in advance part or all its future energy production at a fixed price, hedging against the high price volatility of the spot market. The strategy of eliminating the price risk by selling in advance the entire production in the forward market to a fixed price is often thought as the minimum-risk trading policy. Nonetheless, it can be proven that this is not the case for most generators. The outages of the generation units and transmission lines, as well as unforeseen limitations in the primary energy supply expose generators to delivery risk [1]. Delivery risk considerably modifies the probability distribution of profits, shifting the optimal trading strategy toward a portfolio mixing forward contracts and power sold in the spot market. Because of the size of the probability state space and the limited computing capabilities, the problem of the optimal trading strategy has not a closed form solution and thus, its determination is matter of current study. The increase in computing power and recent developments in Operational Research has brought new insights into the solution of such problems. In the past decade and by virtue of the ever increasing computational power, many methods emerged in different scientific fields with several different names: Reinforced Learning, QLearning, Neuro-Dynamic Programming, etc. All these methods were later brought together in what is currently known as Approximated Dynamic Programming (ADP) [2],[3]. These algorithms resign the exhaustive enumeration and calculation of the space-state typically performed by conventional DP. Instead, they iteratively approximate a function of the space state through stochastic simulation and statistical regression techniques, circumventing the dimensionality problem of DP. Although ADP algorithms are being used in several other fields of science, the application to design optimal trading strategies in power markets has not been proposed so far. In this chapter, ADP techniques are exploited to optimize the selling strategy of a power generator trading in a frictional market with transaction costs. Three available products are considered: selling in the spot market, and/or get involved in quarterly and one-year forward contracts. The objective of the generator is to maximize the expected profit while limiting financial risk. Decisions can be made only at the beginning of each month. At each decision stage, the current trading position can be changed at a cost in order to rebalance the portfolio.Fil: Gil Pugliese, Miguel Federico. Consejo Nacional de Investigaciones Científicas y Técnicas. Centro Científico Tecnológico Conicet - San Juan. Instituto de Energía Eléctrica. Universidad Nacional de San Juan. Facultad de Ingeniería. Instituto de Energía Eléctrica; Argentina. Instituto F/elektrische Anlagen & Energiewirtschaft; AlemaniaFil: Olsina, Fernando Gabriel. Consejo Nacional de Investigaciones Científicas y Técnicas. Centro Científico Tecnológico Conicet - San Juan. Instituto de Energía Eléctrica. Universidad Nacional de San Juan. Facultad de Ingeniería. Instituto de Energía Eléctrica; Argentin

Long-term assessment of power capacity incentives by modeling generation investment dynamics under irreversibility and uncertainty

In actual energy-only markets, the high volatility of power prices affects the expected returns of generators. When dealing with irreversibility under uncertainty, deferring decisions to commit in new power plants, waiting for better information, is therefore a rational approach. Theoretical and empirical evidence suggests that such investment pattern determines the occurrence of construction cycles, which strongly compromise supply security. In order to supplement generators´ revenues, several remuneration mechanisms have been devised over past years. Along this line, this work addresses the long-run dynamics of capacity adequacy and market efficiency with both a price-based and a quantity-based capacity remuneration policy. For that purpose, a recently-developed, stochastic simulation model is used as a benchmark. Hence, the optimal postponement of generation investment decisions is integrated into a long-run power market model by formulating the decision-making problem in the framework of Real Options Analysis. Results suggest that policymakers may exchange supply security (effectiveness) for energy prices to be paid by consumers (efficiency) when designing and implementing capacity remuneration mechanisms. By doing so, this article contributes to the ongoing debate regarding the design of incentive policies and efficient power markets by considering the microeconomics of investors? decision-making under irreversibility and uncertainty.Fil: Rios Festner, Daniel. Universidad Nacional de Asunción; ParaguayFil: Blanco, Gerardo. Universidad Nacional de Asunción; ParaguayFil: Olsina, Fernando Gabriel. Consejo Nacional de Investigaciones Científicas y Técnicas. Centro Científico Tecnológico Conicet - San Juan. Instituto de Energía Eléctrica. Universidad Nacional de San Juan. Facultad de Ingeniería. Instituto de Energía Eléctrica; Argentin

Designing regulatory frameworks for merchant transmission investments by real options analysis

In deregulated electricity markets, the transmission network is a key infrastructure for enabling competition in the generation sector. A deficient expansion of the transmission grid prevents the realization of the benefits in terms of efficiency associated with market mechanisms. Consequently, it is essential to provide clear investment policies and economic signals to attract timely and efficient transmission in order to be developed at minimum cost, with adequate levels of service quality and reliability, and adapted to the requirements of generators and consumers. This paper proposes a modern tool of economic evaluation based on Real Options Analysis that provides the regulator the ability to assess various incentives that allow transmission investors to make efficient decisions in highly uncertain environments. Real Options properly values partially irreversible investment decisions, such as to defer, modify or abandon an investment project in response to the arrival of new information or as uncertainties are resolved. Decisions are evaluated from the point of view of an agent trying to maximize its own profits in the time period set to recover the capital invested. The results of this paper allow the study of the behavior of transmission investors regarding their decision making when they have the possibility to manage the option to defer, under different regulatory schemes that encourage the expansion of the transmission systemFil: Pringles, Rolando Marcelo. Consejo Nacional de Investigaciones Cientificas y Tecnicas. Centro Cientifico Tecnologico San Juan. Instituto de Energia Electrica; ArgentinaFil: Olsina, Fernando Gabriel. Consejo Nacional de Investigaciones Cientificas y Tecnicas. Centro Cientifico Tecnologico San Juan. Instituto de Energia Electrica; ArgentinaFil: Garces, Francisco Felipe. Consejo Nacional de Investigaciones Cientificas y Tecnicas. Centro Cientifico Tecnologico San Juan. Instituto de Energia Electrica; Argentin

Investment Valuation in Liberalized Power Markets: Integrating Real Options with System Dynamics

"The proposal is elaborated under a long run market framework, based on System Dynamics simulative approach, and considers for the investment rates to be a function of the value of flexibility of the deferral option, obtained by means of Real Options analysis."CONACYT - Consejo Nacional de Ciencias y TecnologíaPROCIENCI

Valuation of defer and relocation options in photovoltaic generation investments by a stochastic simulation-based method

Risk management is crucial when committing investments in electricity markets. Investment projects for the generation of electricity are capital-intensive, in large part irreversible and future performance is subject to high uncertainty. Fortunately, most power generation projects have strategic flexibility for handling uncertainty and for mitigating risks under unfavorable scenarios. Modern corporate finance recognizes Real Option analysis (ROA) as the correct way to value investment projects with these characteristics. Due to both, environmental concerns and escalation of fuel prices, electricity generation from renewable sources has grown dramatically worldwide over the last decade. Renewable investment projects share many of the features mentioned. As such, option valuation methods should be applied to estimate the monetary value of flexibility in renewable energy investments. This work presents an appropriate methodology for assessing the economic value of a photovoltaic power plant under uncertainties. ROA is applied to determine the value of delaying the investment decision while waiting for better market information that would reduce acquisition costs due to progress in solar technology. The flexibility of relocating the solar facility in the future upon the appearance of a more attractive site in terms of cost, network accessibility or regulatory policies is also valued. The problem of option valuation is solved through stochastic simulation combined with recursive approximate dynamic programming techniques. The methodology developed might be used by investors for more efficient decision-making and by regulatory agencies for designing adequate support policies that encourage investment in renewable energy generation.Fil: Pringles, Rolando Marcelo. Consejo Nacional de Investigaciones Científicas y Técnicas. Centro Científico Tecnológico Conicet - San Juan. Instituto de Energía Eléctrica. Universidad Nacional de San Juan. Facultad de Ingeniería. Instituto de Energía Eléctrica; ArgentinaFil: Olsina, Fernando Gabriel. Consejo Nacional de Investigaciones Científicas y Técnicas. Centro Científico Tecnológico Conicet - San Juan. Instituto de Energía Eléctrica. Universidad Nacional de San Juan. Facultad de Ingeniería. Instituto de Energía Eléctrica; ArgentinaFil: Penizzotto Bacha, Franco Victor. Consejo Nacional de Investigaciones Científicas y Técnicas. Centro Científico Tecnológico Conicet - San Juan. Instituto de Energía Eléctrica. Universidad Nacional de San Juan. Facultad de Ingeniería. Instituto de Energía Eléctrica; Argentin

Abdominal tuberculosis mimicking Crohn’s disease’s exacerbation: a clinical, diagnostic and surgical dilemma. A case report

INTRODUCTION: Tuberculosis in Europe is a health public problem, which has increased constantly over the last few decades. The most common clinical manifestation of tuberculosis is pulmonary. The diagnosis of extrapulmonary tuberculosis can be challenging and clinical manifestations of gastrointestinal tuberculosis are unspecific and can mimic other pathologies. PRESENTATION OF CASE: A young Chinese man, who had recently been diagnosed with Crohn’s disease, was admitted to the emergency room of our hospital with a one-month history of diffuse abdominal pain and weight loss. The patient initially presented with epigastric pain, which had been constantly increasing over the last 48 h. Other symptoms included diarrhea, nausea, and fever. The patient was then admitted with the diagnosis of Crohn’s disease exacerbation, and a treatment with corticosteroids, azathioprine, mesalazine, adalimumab, and antibiotic therapy was started. The symptoms were due to an initially misdiagnosed case of abdominal tuberculosis. DISCUSSION: Intestinal tuberculosis is mainly localized at the ileocecal level in 85% of patients. Medical therapy is the treatment of choice and surgery is not required if it is diagnosed at an early stage.’ CONCLUSION: The diagnosis of abdominal tuberculosis still remains a challenge for both internists and surgeons. Before starting a therapy with adalimumab, every patient should be tested for latent tuberculosis infection

Evaluación probabilística del riesgo de disconfort en edificios

El diseño y la construcción de los edificios determinan junto con el clima y el uso las condiciones higrotérmicas interiores y el consumo energético durante la vida útil. Este artículo presenta una nueva metodología probabilística que permite cuantificar el riesgo de disconfort higrotérmico de cualquier edificación. Mediante simulación estocástica es posible establecer la frecuencia y duración esperada de eventos de disconfort en cada local del edificio y el consumo esperado de energía para su climatización. Se sintetizan aquí los conceptos fundamentales, los índices de confiabilidad y riesgo y los modelos numéricos desarrollados. Para ilustrar las implicancias de la evaluación de riesgo de disconfort en el proceso de diseño, se ha determinado el riesgo higrotérmico que presenta de una vivienda convencional climatizada, así como su variante bioclimática.Along with the outdoor climate and the use, the design and construction of buildings determine the indoors conditions and the energy consumption through their lifespan. This paper presents a novel methodology for quantifying the higrothermal comfort risk of any building design. By means of stochastic simulations, the expected frequency and duration of discomfort events in each room of building and the expected energy consumption associated to heating and cooling can be determined. Fundamental concepts, reliability indexes and numerical models are presented. In order to illustrates the proposed approach in the design process, the comfort risk of a residential house conventionally acclimatized and a bioclimatic variant is assessed.Asociación Argentina de Energías Renovables y Medio Ambiente (ASADES