Présidents de session
Application in energy, finance or logistics: New methods and applications of stochastic optimization toward logistics decarbonization
- Alexandre Jacquillat
Application in energy, finance or logistics: Applications of Stochastic Programming in Disaster Relief and Resilience
- Yongjia Song (Clemson University)
Application in energy, finance or logistics: Contributed talks
- Eduardo Moreno (Google Research)
Application in energy, finance or logistics: Invited talk in Energy Applications
- Stein-Erik Fleten (Norwegian University of Science and Technology)
- Davi Valladão (PUC-Rio)
Application in energy, finance or logistics: Contributed talks
- Giorgio Consigli (Khalifa University of Science and Technology)
Application in energy, finance or logistics: Contributed talks
- Lars Hellemo (SINTEF)
Application in energy, finance or logistics: Contributed talks
- André Diniz (CEPEL/UERJ)
Application in energy, finance or logistics: Contributed talks
- Peter Schütz (Norwegian University of Science and Technology)
Application in energy, finance or logistics: Contributed talks
- Guzin Bayraksan (The Ohio State University)
Application in energy, finance or logistics: Contributed talks
- Vittorio Moriggia (Université de Bergame)
This paper optimizes the configuration of large-scale data centers toward cost-effective, reliable and sustainable cloud supply chains. The problem involves placing incoming racks of servers within a data center to maximize demand coverage given space, power and cooling restrictions. We formulate an online integer optimization model to support rack placement decisions. We propose a tractable...
Network design problems involve constructing edges in a transportation or supply chain network to minimize construction and daily operational costs. We study a stochastic version where operational costs are uncertain because of fluctuating demand and estimated as a sample average from historical data. This problem is computationally challenging, and instances with as few as 100 nodes often...
We study an inspection game of incomplete information, in which an inspector randomizes the allocation of heterogeneous detectors to identify multiple illegal commodities strategically hidden by an adversary within a system (e.g., drugs smuggled in containers). Detectors vary in their detection accuracies, and illegal commodities differ in their associated damage values. The inspector (resp....
Benders Decomposition (BD) is a well-known optimization technique for large-scale two-stage mixed-integer problems by decomposing a problem into a pure integer master problem and a continuous separation problem. To accelerate BD, we propose Random Partial Benders Decomposition (RPBD), a decomposition method that randomly retains a subset of the continuous second-stages variables within the...
Two-stage mean-risk stochastic integer programming (MR-SIP) with endogenous uncertainty involves here-and-now decisions that influence future outcomes and is very challenging to solve. We derive a decomposition method for this class of MR-SIP and apply it to an important problem in wildfire management, namely optimal fuel treatment planning (FTP) under uncertainty. The uncertainty stems from...
During dry and windy seasons, environmental conditions significantly increase the risk of wildfires, exposing power grids to disruptions caused by transmission line failures. Wildfire propagation exacerbates grid vulnerability, potentially leading to prolonged power outages. To address this challenge, we propose a multi-stage stochastic optimization model that dynamically adjusts transmission...
In this talk, we will discuss multi-stage stochastic programming (MSP) models and solution approaches for humanitarian relief logistics planning in hurricane disasters. Specifically, we study how the rolling forecast information can be integrated in an MSP model via the Martingale Model of Forecast Evolution (MMFE) to provide optimal adaptive logistics decision policies. We investigate...
PolieDRO is a novel analytics framework for classification and regres-
sion that harnesses the power and flexibility of Data-Driven Distributionally Robust Optimization (DRO) to circumvent the need for regularization hyperparameters. Recent literature shows that traditional machine learning methods such as SVM and (square-root) LASSO can be written as Warserstein-based DRO problems. Inspired...
Stochastic facility location problems with outsourcing costs (SFLPOC) optimize facility placement and customer assignment under demand uncertainty. Excess demand beyond the capacity of a facility incurs outsourcing costs. This work addresses SFLPOC, aiming to minimize overall expected costs (placement, service and outsourcing). We model SFLPOC as a two-stage stochastic program. While prior...
In the context of the global transition towards net-zero emissions, local energy markets (LEMs) offer a practical and effective approach for integrating the increasing penetration of distributed energy resources, such as intermittent renewable generation, energy storage systems, and flexible loads. By facilitating active participation from small-scale consumers, producers, and prosumers in...
We report on a two-phase optimization framework for combining short-term hydropower scheduling with offering into the European day-ahead electricity market. We use profiled block bids grouped in exclusive sets. The first phase solves a nonlinear deterministic model that generates a diverse and operationally feasible set of production blocks by accounting for startup costs, opportunity costs,...
We propose decomposition algorithms to solve computationally challenging multi-timescale mixed-integer stochastic optimization problems in power system operation, where decisions across different time horizons are coordinated using aggregate state variables. Three distinct decomposition strategies are presented based on the stochastic model: (1) Price-directive decomposition for multi-horizon...
Microtransit offers opportunities to enhance urban mobility by combining the reliability of public transit and the flexibility of ride-sharing. This paper optimizes the design and operations of a deviated fixed-route microtransit system that relies on reference lines but can deviate on demand in response to passenger requests. We formulate a Microtransit Network Design (MiND) model via...
We extend portfolio selection models with classical stochastic dominance constraints by allowing a controlled violation of these constraints. This relaxation permits the returns of feasible portfolios to differ from those that stochastically dominate the benchmark within a tolerance measured by the Wasserstein distance. We formulate an optimization problem that incorporates the stochastic...
It is a common practice in portfolio optimization to focus on the minimization of losses and risk. However, more advanced models incorporating the second-order stochastic dominance (SSD) constraints have gained increasing attention in last two decades. These constraints identify the portfolios that dominate the benchmark portfolio with respect to SSD. Contrary to that, this paper is focused on...
In an early paper we have studied the correspondence between second order interval stochastic dominance (ISD-2) and interval conditional value-at-risk (ICVaR), a tail risk measure carrying specific properties and generalizing the popular conditional value-at-risk.
Relying on the ICVaR, in this paper, we present a reinforcement learning approach to solve a trade-off problem based on one side...
The green transition poses many challenges following the introduction of renewable energy sources (RES) and sector coupling (e.g. integrating heat and power). Decision makers need to apply more complex models and to better account for uncertainty, often stemming from the non-dispatchable nature of important RES.
While solution techniques for stochastic programs are available and demonstated...
This study implements and compares single and multicut Benders decomposition (BD) for the generation expansion problem (GEP) involving numerous renewable energy plants, incorporating Time-Varying Dynamic Probabilistic Reserve (DPR). We first identify the conditions under which the second stage (operation problem) of the GEP is convex. However, even when the resulting problem is convex, the...
Short-term forecasting models typically assume the availability of input data (features) when they are deployed and in use. However, equipment failures, disruptions, and cyberattacks may lead to missing features when such models are used operationally, which could negatively affect forecast accuracy and result in suboptimal operational decisions. In this paper, we use adaptive robust...
In recent years, many companies have committed to renewable energy procurement targets, which usually require a certain fraction of the annual demand to be met by renewables. This annual approach overlooks the temporal fluctuations in energy supply and demand, leading to a growing interest in 24/7 targets that aim to match every consumed kilo-watt hour with carbon-free electricity sources at...
We apply computational techniques of convex stochastic optimization to optimal operation and valuation of electricity storages in the face of uncertain electricity prices. Our approach is applicable to various specifications of storages, and it allows for e.g. hard constraints on storage capacity and charging speed. Our valuations are based on the indifference pricing principle, which builds...
Decision support models are essential for assessing logistics infrastructure projects, such as intermodal terminals. This study proposes a methodology to analyze the multiple impacts of strategic decisions on terminal location in conjunction with the tactical problem of designing an intermodal service. The case study of Mato Grosso’s soybean export logistics network was structured as a Markov...
This work presents a chance-constraint model for the management of Energy
Communities, focusing on prosumers and peer-to-peer electricity exchanges.
The model aims to minimize the total operation costs of the community, while ensuring
energy balance and satisfying technical constraints related to local production and the
energy exchanges both inside the community and with the main...
The increasing penetration of wind and solar generation amplifies the uncertainty in the short term unit commitment problem for hydrothermal power systems, challenging, for example, schedules that are given by deterministic approaches, as is the case of the day ahead-unit commitment model (DESSEM) used for the official dispatch and price setting of the Brazilian system, over a seven day...
We study the problem of locating charging stations for battery-electric heavy-duty vehicles (BEHDV) under uncertainty in both demand and available power grid capacity. The problem can be formulated as a two-stage stochastic problem where the first-stage decision is to determine the future locations of the charging stations. After the locations have been determined, information about demand and...
Waste-to-energy (WtE) plants offer a way of treating waste while converting it to energy. This provides a more sustainable way of treating waste than common landfills. A vital part of a well-functioning waste management environment is the right price setting of gate fees, i.e., treatment price per amount of waste, for the WtE plants. The price setting can be described as a non-cooperative game...
In modern energy systems, electricity and natural gas markets are increasingly interdependent due to the prominent role of gas-fired power generation, which provides essential flexibility to balance the variability of renewable energy sources.
In such a context, this work presents a tri-level optimization model to address the optimal bidding problem faced by a price-maker electricity...
Ensuring the reliability and resilience of the modern power grid requires models that handle the inherent uncertainty of generation availability and electricity consumption. These models must have sufficiently high spatial and temporal resolution to adequately capture weather variability and provide actionable siting and sizing decisions. A stochastic nodal capacity expansion planning (CEP)...
We study a robust optimization framework for the optimal operation and sizing of a hybrid virtual power plant (VPP) composed of a thermal generator unit (TGU), a large-scale photovoltaic (PV) plant, and an energy storage system (ESS). The VPP participates as a price-taker in both the day-ahead (DA) and real-time (RT) electricity markets, offering energy and ancillary services (i.e., frequency...
A Dynamic Stochastic Programming model applied to long-term Asset and Liability Management portfolio selection faces the challenge to satisfy an investor’s personal goals. Since not all the targets have the same priority, we ask the model to take the investor’s expectations into account.
These kinds of problems are of particular interest to the insurance industry, where they are commonly...
Consider an electric battery whose owner has committed with a transmission system operator (e.g., RTE in France) to making at its disposal a "reserve'" of electricity along a given day: every hour, a random quantity will be discharged from or charged to the battery; the commitment is on the range of this random variable. To respond at best to this commitment, the owner of the battery can buy...
Interest rates on financial markets are noisy. This is reduced by a Kalman filter, which gives better measurement of interest rate cuts and hikes. With an optimization model interest rate curves are measured with increased accuracy from Overnight Index Swaps. Principal Component Analysis identifies the significant risk factors in interest rate markets. With these a Stochastic Programming model...
This work presents a methodology for incorporating risk measures into the energy system expansion planning process. The approach involves a decomposed investment and operation model, where the objective function is modified to progressively place greater emphasis on minimizing the risk metric. By doing so, it is possible to calculate risk levels for different optimal expansion plans. The...
Energy scheduling is typically conducted as a two-stage procedure that comprises day-ahead and real-time stages. Particularly, day-ahead decisions are made under uncertainty, which then affects real-time decisions, as operators must constantly maintain power balance. For that, it is commonplace in the energy industry to predict renewable energy production and power load, which are then used to...
Climate variability is increasingly affecting energy generation, particularly renewable sources that depend directly on weather patterns. Variations in temperature, precipitation, and wind patterns are altering the availability and reliability of hydropower, wind, and solar energy, posing challenges for power system stability and long-term planning. As climate patterns shift, our perception of...
We discuss a new electric car ferry operating along the west coast of Norway. The ferry visits a total of 17 different ports, of which only three are mandatory stops. It can carry up to 27 standard cars across three lanes. Depending on the loading arrangement, cars may need to reverse onto or off the ferry. In some cases, vehicles may also need to disembark at an intermediate port and reboard...
We study the design of large-scale relay logistics hub networks that are resilient to demand variability. We formulate a two-stage stochastic optimization model that integrates first-stage strategic decisions on hub location and capacity with second-stage tactical decisions on consolidation-based routing. To solve this problem exactly, we develop a three-stage branch-and-cut algorithm with...
