Decision Making under Uncertainty in Sustainable Energy Operations and Investments

Abstract

The energy sector is undergoing a rapid transformation that gives rise to a variety of new optimization problems to solve. The goal of this thesis is to develop advanced analytical solutions for sustainable energy operations and investment problems that are subject to uncertainty. We consider case studies related to both industrial applications and consumer models, and cover problems related to energy efficiency, renewable energy, and corporate social and environmental responsibility. The problems that we consider in this thesis are the following: (i) constructing a power contract portfolio for companies that commit to reach a renewable energy target, which means, to procure a specific percentage of electricity demand from renewable energy sources by a future date; (ii) managing shutdown decisions in commodity and energy production assets from a social commerce perspective, that is, taking into account the indirect consequences that a plant shutdown has on the society; (iii) modeling the optimal market bidding strategies of virtual power generators under a novel proposed electricity market structure that would favor the integration of renewable production units; and (iv) investigating the factors behind the consumer investments in energy efficient household appliances, and the optimal energy saving investments from a consumer and energy system perspectives. To tackle these problems, we leverage tools from operations research to design novel methodology and perform energy analysis. Several problems encountered in this thesis can be formulated as intractable Markov decision processes (MDPs) with high-dimensional exogenous and/or endogenous component of the state. To overcome this intractability, we develop approximate dynamic programming (ADP) methods to compute near optimal operating and investment policies, and lower and upper bounds on the optimal MDP value. In particular, the ADP methods that we develop include: (i) an extension of the regress-later least squares Monte Carlo (LSML) to approximate risk-averse MDPs, (ii) a combination of LSML and classification to learn decision rules, (iii) a shortest path reformulation of the reoptimization heuristic, and (iv) a novel use of the information relaxations and duality framework to extract non-anticipative decision rules from sample action distributions. We also use more classical scenario-based stochastic programming. The contributions of this thesis cover modeling, methodology, and applications. We contribute to the definition, understanding, and resolution of emerging optimization problems in sustainable energy operations and investments, and provide results and insights that can be useful for companies and policy makers. We contribute to the development of new operations research models, algorithms, and theory for solving large-scale stochastic optimization problems, with particular focus on ADP techniques. Some of the methodology developed in this thesis has potential broader relevance in other application contexts, such as inventory control and financial portfolio optimization.