An Optimal Solution is Not Enough: Alternative Solutions and Optimal Power Systems

Power systems modeling and planning has long leveraged mathematical programming for its ability to provide optimality and feasibility guarantees. One feature that has been recognized in the optimization literature since the 1970s is the existence and meaning of multiple exact optimal and near-optimal solutions, which we call alternative solutions. In power systems modeling, the use … Read more

Stronger cuts for Benders’ decomposition for stochastic Unit Commitment Problems based on interval variables

The Stochastic Unit Commitment (SUC) problem models the scheduling of power generation units under uncertainty, typically using a two-stage stochastic program with integer first-stage and continuous second-stage variables. We propose a new Benders decomposition approach that leverages an extended formulation based on interval variables, enabling decomposition by both unit and time interval under mild technical … Read more

Faster Solutions to the Interdiction Defense Problem using Suboptimal Solutions

The interdiction defense (ID) problem solves a defender-attacker-defender model where the defender and attacker share the same set of components to harden and target. We build upon the best response intersection (BRI) algorithm by developing the BRI with suboptimal solutions (BRI-SS) algorithm to solve the ID problem. The BRI-SS algorithm utilizes off-the-shelf optimization solvers that … Read more

Two-Stage Data-Driven Contextual Robust Optimization: An End-to-End Learning Approach for Online Energy Applications

Traditional end-to-end contextual robust optimization models are trained for specific contextual data, requiring complete retraining whenever new contextual information arrives. This limitation hampers their use in online decision-making problems such as energy scheduling, where multiperiod optimization must be solved every few minutes. In this paper, we propose a novel Data-Driven Contextual Uncertainty Set, which gives … Read more

Optimal participation of energy communities in electricity markets under uncertainty. A multi-stage stochastic programming approach

We propose a multi-stage stochastic programming model for the optimal participation of energy communities in electricity markets. The multi-stage aspect captures the different times at which variable renewable generation and electricity prices are observed. This results in large-scale optimization problem instances containing large scenario trees with 34 stages, to which scenario reduction techniques are applied. … Read more

Measuring the Economic Value of Wind–Solar Complementarity in Europe Using Chance Constraints

The variability of wind and solar photovoltaic (PV) generation poses significant risks for producers in day-ahead electricity markets, where commitments must be made before actual output is realized. A common mitigation strategy is to invest in storage, but an alternative is to exploit the natural complementarity between wind and solar resources. We evaluate this economic … Read more

Combining Simulation with Machine Learning and Optimization to Assess Green Hydrogen Production via Offshore Wind in the Dutch North Sea

As countries seek to decarbonize their energy systems, green hydrogen has emerged as a promising energy carrier. This paper studies the production of green hydrogen from offshore wind in the Dutch North Sea, with particular emphasis on understanding how system design decisions and uncertain parameters affect key performance indicators. The analysis builds on a detailed … Read more

A Dynamic Strategic Plan for Transition to Campus-Scale Clean Electricity Using Multi-Stage Stochastic Programming

The decarbonization of energy systems at energy-intensive sites is an essential component of global climate mitigation, yet such transitions involve substantial capital requirements, ongoing technological progress, and the operational complexities of renewable integration. This study presents a dynamic strategic planning framework that applies multi-stage stochastic programming to guide clean electricity transitions at the campus level. … Read more

Alternating Methods for Large-Scale AC Optimal Power Flow with Unit Commitment

Security-constrained unit commitment with alternating current optimal power flow (SCUC-ACOPF) is a central problem in power grid operations that optimizes commitment and dispatch of generators under a physically accurate power transmission model while encouraging robustness against component failures.  SCUC-ACOPF requires solving large-scale problems that involve multiple time periods and networks with thousands of buses within … Read more

Global Optimization of Gas Transportation and Storage: Convex Hull Characterizations and Relaxations

Gas transportation and storage has become one of the most relevant and important optimization problems in energy systems. This problem inherently includes highly nonlinear and nonconvex aspects due to gas physics, and discrete aspects due to the control decisions of active network elements. Obtaining even locally optimal solutions for this problem presents significant mathematical and … Read more