JuMP: A Modeling Language for Mathematical Optimization

Dunning, Iain; Huchette, Joey; Lubin, Miles

doi:10.1137/15m1020575

Cited by 1,386 publications

(831 citation statements)

References 55 publications

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“…The optimization is carried out through Julia software [48,49] using the mathematical language called Jump [50] and optimization solvers called Gurobi [51] and Ipopt [52]. Then, the obtained data is thread and depicted through mathematical software (Matlab) [53].…”

Section: Discussion Of Resultsmentioning

confidence: 99%

Optimization of the Operation of Smart Rural Grids through a Novel Energy Management System

2017

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Abstract:The paper proposes an innovative Energy Management System (EMS) that optimizes the grid operation based on economic and technical criteria. The EMS inputs the demand and renewable generation forecasts, electricity prices and the status of the distributed storages through the network, and solves with an optimal quarter-hourly dispatch for controllable resources. The performance of the EMS is quantified through diverse proposed metrics. The analyses were based on a real rural grid from the European FP7 project Smart Rural Grid. The performance of the EMS has been evaluated through some scenarios varying the penetration of distributed generation. The obtained results demonstrate that the inclusion of the EMS from both a technical point of view and an economic perspective for the adopted grid is justified. At the technical level, the inclusion of the EMS permits us to significantly increase the power quality in weak and radial networks. At the economic level and from a certain threshold value in renewables' penetration, the EMS reduces the energy costs for the grid participants, minimizing imports from the external grid and compensating the toll to be paid in the form of the losses incurred by including additional equipment in the network (i.e., distributed storage).

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Section: Discussion Of Resultsmentioning

confidence: 99%

Optimization of the Operation of Smart Rural Grids through a Novel Energy Management System

2017

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“…Problem (P2) was solved with Gurobi 7.0.2 27 with a MIP gap of 0.5% and (P3) was solved with Ipopt 3.12.4 28 using the MA27 linear algebra routines. 32,33 The first case study explores evolution of the objective function (the total revenue) for 25 iterations of (AG1). We note that the revenue estimates reported herein are upper bounds, as we assume exact forecasts.…”

Section: Computational Performancementioning

confidence: 99%

A decomposition algorithm for simultaneous scheduling and control of CSP systems

2018

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We present a decomposition algorithm to perform simultaneous scheduling and control decisions in concentrated solar power (CSP) systems. Our algorithm is motivated by the need to determine optimal market participation strategies at multiple timescales. The decomposition scheme uses physical insights to create surrogate linear models that are embedded within a mixed‐integer linear scheduling layer to perform discrete (operational mode) decisions. The schedules are then validated for physical feasibility in a dynamic optimization layer that uses a continuous full‐resolution CSP model. The dynamic optimization layer updates the physical variables of the surrogate models to refine schedules. We demonstrate that performing this procedure recursively provides high‐quality solutions of the simultaneous scheduling and control problem. We exploit these capabilities to analyze different market participation strategies and to explore the influence of key design variables on revenue. Our results also indicate that using scheduling algorithms that neglect detailed dynamics significantly decreases market revenues. © 2018 American Institute of Chemical Engineers AIChE J, 64: 2408–2417, 2018

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“…In this section, we propose a standard structure and outline the elements that need to be considered when optimizing the whole supply chain. The network representation has motivated Jalving et al (2017), who developed a computational package called PLASMO for the Julia programming language (Bezanzon et al, 2012), to represent networks of models with the JuMP mathematical programming platform (Dunning et al, 2017). Figure 4 presents the proposed structure, where each node represents the problem that each decision maker at a defined level needs to solve.…”

Section: Modeling Structure Networkmentioning

confidence: 99%

Perspectives in multilevel decision-making in the process industry

Brunaud¹,

Grossmann²

2017

Front. Eng

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Decisions in supply chains are hierarchically organized. Strategic decisions involve the long-term planning of the structure of the supply chain network. Tactical decisions are mid-term plans to allocate the production and distribution of materials, while operational decisions are related to the daily planning of the execution of manufacturing operations. These planning processes are conducted independently with minimal exchange of information between them. Achieving a better coordination between these processes allows companies to capture benefits that are currently out of their reach and improve the communication among their functional areas. We propose a network representation for the multilevel decision structure and analyze the components that are involved in finding integrated solutions that maximize the sum of the benefits of all nodes of the decision network. Although such task is very challenging, significant research progress has been made in each component of this structure. An overview of strategic models, mid-term planning models, and scheduling models is presented to address the solution of each node in the decision network. Coordination mechanisms for converging the integrated solutions are also analyzed, including solving large-scale models, multiobjective optimization, bi-level programming, and decomposition. We conclude by summarizing the challenges that hinder the full integration of multilevel decision making in supply chain management.

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JuMP: A Modeling Language for Mathematical Optimization

Cited by 1,386 publications

References 55 publications

Optimization of the Operation of Smart Rural Grids through a Novel Energy Management System

Optimization of the Operation of Smart Rural Grids through a Novel Energy Management System

A decomposition algorithm for simultaneous scheduling and control of CSP systems

Perspectives in multilevel decision-making in the process industry

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