Efficient provision of electricity for the United States and Switzerland

This study applies financial portfolio theory to determine efficient frontiers in the provision of electricity for the United States and Switzerland. Expected returns are defined by the rate of productivity increase of power generation (adjusted for external costs), volatility, by its standard deviation. Since unobserved productivity shocks are found to be correlated, Seemingly Unrelated Regression Estimation (SURE) is used to filter out the systematic component of the covariance matrix of the productivity changes. Results suggest that as of 2003, the feasible maximum expected return (MER) electricity portfolio for the United States contains more Coal, Nuclear, and Wind than actual but markedly less Gas and Oil. The minimum variance (MV) portfolio contains markedly more Oil, again more Coal, Nuclear, and Wind but almost no Gas. Regardless of the choice between MER and MV, U.S. utilities are found to lie substantially inside the efficient frontier. This is even more true of their Swiss counterparts, likely due to continuing regulation of electricity markets.Efficiency frontier, energy, electricity, portfolio theory, Seemingly Unrelated Regression Estimation (SURE)

Scope of Electricity Efficiency Improvement in Switzerland until 2035

This study uses Markowitz mean-variance portfolio theory with forecasted data for the years 2005 to 2035 to determine efficient electricity generating technology mixes for Switzerland. The SURE procedure has been applied to filter out the systematic components of the covariance matrix. Results indicate that risk-averse electricity users in 2035 gain in terms of higher expected return, less risk, more security of supply and a higher return-to-risk ratio compared to 2000 by adopting a feasible minimum variance (MV) technology mix containing 28 percent Gas, 20 percent Run of river, 13 percent Storage hydro, 9 percent Nuclear, and 5 percent each of Solar, Smallhydro, Wind, Biomass, Incineration, and Biogas respectively. However, this mix comes at the cost of higher CO2 emissions.Efficiency Frontier, Herfindahl-Hirschman Index (HH), Power Generation, Mean-Variance Portfolio Theory, Seemingly Unrelated Regression Estimations (SURE), Shannon-Wiener Index (SW)

Day-ahead energy and reserve dispatch problem under non-probabilistic uncertainty

The current energy transition and the underlying growth in variable and uncertain renewable-based energy generation challenge the proper operation of power systems. Classical probabilistic uncertainty models, e.g., stochastic programming or robust optimisation, have been used widely to solve problems such as the day-ahead energy and reserve dispatch problem to enhance the day-ahead decisions with a probabilistic insight of renewable energy generation in real-time. By doing so, the scheduling of the power system becomes, production and consumption of electric power, more reliable (i.e., more robust because of potential deviations) while minimising the social costs given potential balancing actions. Nevertheless, these classical models are not valid when the uncertainty is imprecise, meaning that the system operator may not rely on a unique distribution function to describe the uncertainty. Given the Distributionally Robust Optimisation method, our approach can be implemented for any non-probabilistic, e.g., interval models rather than only sets of distribution functions (ambiguity set of probability distributions). In this paper, the aim is to apply two advanced non-probabilistic uncertainty models: Interval and ϵ-contamination, where the imprecision and in-determinism in the uncertainty (uncertain parameters) are considered. We propose two kinds of theoretical solutions under two decision criteria—Maximinity and Maximality. For an illustration of our solutions, we apply our proposed approach to a case study inspired by the 24-node IEEE reliability test system

Economic and regulatory uncertainty in renewable energy system design: a review

Renewable energy is increasingly mobilizing more investment around the globe. However, there has been little attention to evaluating economic and regulatory (E&R) uncertainties, despite their enormous impact on the project cashflows. Consequently, this review analyzes, classifies, and discusses 130 articles dealing with the design of renewable energy projects under E&R uncertainties. After performing a survey and identifying the selected manuscripts, and the few previous reviews on the matter, the following innovative categorization is designed: sources of uncertainty, uncertainty characterization methods, problem formulations, solution methods, and regulatory frameworks. The classification reveals that electricity price is the most considered source of uncertainty, often alone, despite the existence of six other equally influential groups of E&R uncertainties. In addition, real options and optimization arise as the two main approaches researchers use to solve problems in energy system design. Subsequently, the following aspects of interest are discussed in depth: how modeling can be improved, which are the most influential variables, and potential lines of research. Conclusions show the necessity of modeling E&R uncertainties with currently underrepresented methods, suggest several policy recommendations, and encourage the integration of prevailing approaches.Peer ReviewedObjectius de Desenvolupament Sostenible::7 - Energia Assequible i No Contaminant::7.2 - Per a 2030, augmentar substancialment el percentatge d’energia renovable en el con­junt de fonts d’energiaObjectius de Desenvolupament Sostenible::7 - Energia Assequible i No ContaminantPostprint (published version

ISBIS 2016: Meeting on Statistics in Business and Industry

This Book includes the abstracts of the talks presented at the 2016 International Symposium on Business and Industrial Statistics, held at Barcelona, June 8-10, 2016, hosted at the Universitat Politècnica de Catalunya - Barcelona TECH, by the Department of Statistics and Operations Research. The location of the meeting was at ETSEIB Building (Escola Tecnica Superior d'Enginyeria Industrial) at Avda Diagonal 647. The meeting organizers celebrated the continued success of ISBIS and ENBIS society, and the meeting draw together the international community of statisticians, both academics and industry professionals, who share the goal of making statistics the foundation for decision making in business and related applications. The Scientific Program Committee was constituted by: David Banks, Duke University Amílcar Oliveira, DCeT - Universidade Aberta and CEAUL Teresa A. Oliveira, DCeT - Universidade Aberta and CEAUL Nalini Ravishankar, University of Connecticut Xavier Tort Martorell, Universitat Politécnica de Catalunya, Barcelona TECH Martina Vandebroek, KU Leuven Vincenzo Esposito Vinzi, ESSEC Business Schoo

A robust multi-objective statistical improvement approach to electric power portfolio selection

Motivated by an electric power portfolio selection problem, a sampling method is developed for simulation-based robust design that builds on existing multi-objective statistical improvement methods. It uses a Bayesian surrogate model regressed on both design and noise variables, and makes use of methods for estimating epistemic model uncertainty in environmental uncertainty metrics. Regions of the design space are sequentially sampled in a manner that balances exploration of unknown designs and exploitation of designs thought to be Pareto optimal, while regions of the noise space are sampled to improve knowledge of the environmental uncertainty. A scalable test problem is used to compare the method with design of experiments (DoE) and crossed array methods, and the method is found to be more efficient for restrictive sample budgets. Experiments with the same test problem are used to study the sensitivity of the methods to numbers of design and noise variables. Lastly, the method is demonstrated on an electric power portfolio simulation code.PhDCommittee Chair: Mavris, Dimitri; Committee Member: Duncan, Scott; Committee Member: Ender, Tommer; Committee Member: German, Brian; Committee Member: Paredis, Chri

Modelling of agriculture and climate policies: Impacts of cooperation on sustainability and economic growth

In this dissertation, the economic effects of different climate policy regimes are quantified using ex-ante modelling, with a focus on cooperative policy measures. The work makes three distinct contributions (1) a methodological contribution through the development of a Bayesian calibration method for reference scenarios in dynamic computable general equilibrium models (CGE) (2) a systematic review (qualitative and empirical) of the vast literature on CO2 pricing (3) the modelling and analysis of the effects of climate policy measures in the context of the Paris Agreement using DART. The specific policy relevant questions that are explored through the modelling work are -what are the channels through which countries incur mitigation costs of climate policies? What are the economic impacts of cooperation in reaching the Nationally Determined Commitments (NDC) via an ETS and who will the winners and losers be? If the EU and China decide to link their respective ETSs then what policy design, if at all, would maximize the gains from linking for both EU and China and what impacts do changes in trade barriers have on the linking? What is the range of cost estimates from the modelling studies for reaching the goals of Paris Agreement and how can the divergence in costs across models be explained? Would carbon egalitarianism lead to monetary transfers to the developing countries and how large will these transfers be? Is there carbon leakage from the EUETS and if yes, then do technological advancement in renewables and electricity grids, mitigation targets in non-ETS sectors and behavioral changes in consumers help mitigate the leakage? Overall, the dissertation makes contributions to questions of cost savings through internationally coordinated CO2 pricing while looking at specific regional cooperation. It also combines econometric tools to extract deeper insights from the CGE literature on CO2 pricing