Dantzig-Wolfe (DW) decomposition is a well-known technique in mixed-integer programming (MIP) for decomposing and convexifying constraints to obtain potentially strong dual bounds. We investigate cutting planes that can be derived using the DW decomposition algorithm and show that these cuts can provide the same dual bounds as DW decomposition. More precisely, we generate one cut … Read more
The main motivation of this work is to provide an optimization-based tool for an aggregator involved in residential demand response (DR) programs. The proposed tool comply with the following requirements, which are widely accepted by the residential DR literature: (i) the aggregated consumption should be optimized under a particular utility’s target, such as the minimization … Read more
The Vehicle Allocation Problem (VAP) consists of repositioning empty vehicles across a set of terminals over a given planning horizon so as to maximize the profits generated from serving demand for transportation of goods between pair of terminals. This problem has been classically modeled using an extended space-time network which captures the staging of the … Read more
We present JuDGE.jl, an open-source Julia package for solving multistage stochastic capacity expansion problems using Dantzig-Wolfe decomposition. Models for JuDGE.jl are built using JuMP, the algebraic modelling language in Julia, and solved by repeatedly applying mixed-integer programming. We illustrate JuDGE.jl by formulating and solving a toy knapsack problem, and demonstrate the performance of JuDGE.jl on … Read more
This paper proposes a two-phase optimization framework for users that are involved in demand response programs. In a first phase, responsive users optimize their own household consumption, characterizing not only their appliances and equipment but also their comfort preferences. Subsequently, the aggregator exploits in a second phase this preliminary noncoordinated solution by implementing a coordination … Read more
The Vehicle Allocation Problem (VAP) consists of allocating a fleet of vehicles to attend to the expected demand for freight transportation between terminals along a finite multiperiod planning horizon. The objective is to maximize the profits generated for the completed services. The previous deterministic and stochastic approaches used heuristic procedures and approximations for solving large-scale … Read more
This paper proposes a two-phase optimization framework for users that are involved in demand response (DR) programs. In a first phase, responsive users optimize their own household consumption, characterizing not only their appliances and equipments but also their comfort preferences. Subsequently, the aggregator exploits in a second phase this preliminary non-coordinated solution by implementing a … Read more
This paper proposes an optimization framework for retailers that are involved in demand response (DR) programs. In a first phase responsive users optimize their own household consumption, characterizing not only their appliances and equipment but also their comfort preferences. Then, the retailer exploits in a second phase this preliminary non-coordinated solution to implement a strategy … Read more
This paper proposes an optimization framework for retailers that are involved in demand response (DR) programs. In a first phase responsive users optimize their own household consumption, characterizing not only their smart home components but also their comfort preferences. Then, the retailer exploits in a second phase this preliminary non-coordinated solution to implement a peak … Read more
Demand-response aggregators are faced with the challenge of how to best manage numerous and heterogeneous Distributed Energy Resources (DERs). This paper proposes a decentralized methodology for optimal coordination of DERs. The proposed approach is based on Dantzig-Wolfe decomposition and column generation, thus allowing to integrate any type of resource whose operation can be formulated within … Read more