BilevelJuMP.jl: Modeling and Solving Bilevel Optimization in Julia

In this paper we present BilevelJuMP, a new Julia package to support bilevel optimization within the JuMP framework. The package is a Julia library that enables the user to describe both upper and lower-level optimization problems using the JuMP algebraic syntax. Due to the generality and flexibility our library inherits from JuMP’s syntax, our package … Read more

Application-Driven Learning via Joint Prediction and Optimization of Demand and Reserves Requirement

Forecasting and decision-making are generally modeled as two sequential steps with no feedback, following an open-loop approach. In power systems, operators first forecast loads trying to minimize errors with respect to historical data. They also size reserve requirements based on error estimates. Next, energy and reserves are scheduled and the system is operated following the … Read more

A Novel Solution Methodology for Wasserstein-based Data-Driven Distributionally Robust Problems

Distributionally robust optimization (DRO) is a mathematical framework to incorporate ambiguity over the actual data-generating probability distribution. Data-driven DRO problems based on the Wasserstein distance are of particular interest for their sound mathematical properties. For right-hand-sided uncertainty, however, existing methods rely on dual vertex enumeration rendering the problem intractable in practical applications. In this context, … Read more

A conservative convergent solution for continuously distributed two-stage stochastic optimization problems

Two-stage stochastic programming is a mathematical framework widely used in real- life applications such as power system operation planning, supply chains, logistics, inventory management, and financial planning. Since most of these problems cannot be solved analytically, decision makers make use of numerical methods to obtain a near-optimal solution. Some applica- tions rely on the implementation … Read more

Day-Ahead Contingency-Constrained Unit Commitment with Co-Optimized Post-Contingency Transmission Switching

Transmission switching has been previously shown to offer significant benefits to power system operation, such as cost savings and the reduction of power imbalance levels. Within the context of co-optimized electricity markets for energy and reserves, this paper addresses the incorporation of transmission switching in the contingency-constrained unit commitment problem. The proposed generation scheduling model … Read more

Assessing the Cost of the Hazard-Decision Simplification in Multistage Stochastic Hydrothermal Scheduling

Hydropower is one of the world’s primary renewable energy sources whose usage has profound economic, environmental, and social impacts. We focus on the dispatch of generating units and the storage policy of hydro resources. In this context, an accurate assessment of the water opportunity-cost is cru- cial for driving the sustainable use of this scarce … Read more

Risk-averse portfolio selection of renewable electricity generator investments in Brazil: An optimised multi-market commercialisation strategy

Investment decisions in renewable energy sources such as small hydro, wind power, biomass and solar are frequently made in the context of enormous uncertainty surrounding both intermittent generation and the highly volatile electricity spot prices that are used for clearing of trades. This paper presents a new portfolio-based approach for selecting long-term investments in small-scale … Read more

Time consistency and risk averse dynamic decision models: Definition, interpretation and practical consequences

This paper aims at resolving a major obstacle to practical usage of time-consistent risk-averse decision models. The recursive objective function, generally used to ensure time consistency, is complex and has no clear/direct interpretation. Practitioners rather choose a simpler and more intuitive formulation, even though it may lead to a time inconsistent policy. Based on rigorous … Read more

Time consistency and risk averse dynamic decision models: Definition, interpretation and practical consequences

This paper aims at resolving a major obstacle to practical usage of time-consistent risk-averse decision models. The recursive objective function, generally used to ensure time consistency, is complex and has no clear/direct interpretation. Practitioners rather choose a simpler and more intuitive formulation, even though it may lead to a time inconsistent policy. Based on rigorous … Read more