Combined Pool/Bilateral Short-Term Hydrothermal Scheduling Model for Day-Ahead Energy Markets

This paper proposes a combined pool/bilateral short term hydrothermal scheduling model (PDC) for the context of the day-ahead energy markets. Some innovative aspects are introduced in the model, such as: i) the hydraulic generation is optimized through the opportunity cost function proposed; ii) there is no decoupling between physical and commercial dispatches, as is the case today in Brazil; iii) interrelationships between pool and bilateral markets are represented through a single optimization problem; iv) risk exposures related to future deficits are intrinsically mitigated; v) the model calculates spot prices in an hourly basis and the results show a coherent correlation between hydrological conditions and calculated prices. The proposed PDC model is solved by a primal-dual interior point method and is evaluated by simulations involving a test system. The results are focused on sensitivity analyses involving the parameters of the model, in such a way to emphasize its main modeling aspects. The results show that the proposed PDC provides a conceptual means for short term price formation for hydrothermal systems.

A nonlinear model for the long-term hydro-thermal generation scheduling problem over multiple areas with transmission constraints

This paper presents a nonlinear model with individual representation of plants for the centralized long-term hydrothermal scheduling problem over multiple areas. In addition to common aspects of long-term scheduling, this model takes transmission constraints into account. The ability to optimize hydropower exchange among multiple areas is important because it enables further minimization of complementary thermal generation costs. Also, by considering transmission constraints for long-term scheduling, a more precise coupling with shorter horizon schedules can be expected. This is an important characteristic from both operational and economic viewpoints. The proposed model is solved by a sequential quadratic programming approach in the form of a prototype system for different case studies. An analysis of the benefits provided by the model is also presented. ©2009 IEEE.

Binary integer programming formulations for scheduling in market-driven foundries

This paper describes the development and solution of binary integer formulations for production scheduling problems in market-driven foundries. This industrial sector is comprised of small and mid-sized companies with little or no automation, working with diversified production, involving several different metal alloy specifications in small tailor-made product lots. The characteristics and constraints involved in a typical production environment at these industries challenge the formulation of mathematical programming models that can be computationally solved when considering real applications. However, despite the interest on the part of these industries in counting on effective methods for production scheduling, there are few studies available on the subject. The computational tests prove the robustness and feasibility of proposed models in situations analogous to those found in production scheduling at the analyzed industrial sector. (C) 2010 Elsevier Ltd. All rights reserved.

Evaluating genetic algorithms with different population structures on a lot sizing and scheduling problem

This paper studies the use of different population structures in a Genetic Algorithm (GA) applied to lot sizing and scheduling problems. The population approaches are divided into two types: single-population and multi-population. The first type has a non-structured single population. The multi-population type presents non-structured and structured populations organized in binary and ternary trees. Each population approach is tested on lot sizing and scheduling problems found in soft drink companies. These problems have two interdependent levels with decisions concerning raw material storage and soft drink bottling. The challenge is to simultaneously determine the lot sizing and scheduling of raw materials in tanks and products in lines. Computational results are reported allowing determining the better population structure for the set of problem instances evaluated. Copyright 2008 ACM.

Formulações fortes para o problema integrado de dimensionamento e sequenciamento da produção

Coordenação de Aperfeiçoamento de Pessoal de Nível Superior (CAPES)

Método heurístico para criação de linhas de trabalho em problemas de escalonamento de pessoal

Pós-graduação em Engenharia Mecânica - FEG

Interior point method for long-term generation scheduling of large-scale hydrothermal systems

This paper presents an interior point method for the long-term generation scheduling of large-scale hydrothermal systems. The problem is formulated as a nonlinear programming one due to the nonlinear representation of hydropower production and thermal fuel cost functions. Sparsity exploitation techniques and an heuristic procedure for computing the interior point method search directions have been developed. Numerical tests in case studies with systems of different dimensions and inflow scenarios have been carried out in order to evaluate the proposed method. Three systems were tested, with the largest being the Brazilian hydropower system with 74 hydro plants distributed in several cascades. Results show that the proposed method is an efficient and robust tool for solving the long-term generation scheduling problem.

Incorporating voltage/reactive representation to short-term generation scheduling models

This paper proposes a methodology to incorporate voltage/reactive representation to Short Term Generation Scheduling (STGS) models, which is based on active/reactive decoupling characteristics of power systems. In such approach STGS is decoupled in both Active (AGS) and Reactive (RGS) Generation Scheduling models. AGS model establishes an initial active generation scheduling through a traditional dispatch model. The scheduling proposed by AGS model is evaluated from the voltage/reactive points of view, through the proposed RGS model. RGS is formulated as a sequence of T nonlinear OPF problems, solved separately but taking into account load tracking between consecutive time intervals. This approach considerably reduces computational effort to perform the reactive analysis of the RGS problem as a whole. When necessary, RGS model is capable to propose active generation redispatches, such that critical reactive problems (in which all reactive variables have been insufficient to control the reactive problems) can be overcome. The formulation and solution methodology proposed are evaluated in the IEEE30 system in two case studies. These studies show that the methodology is robust enough to incorporate reactive aspects to STGS problem.

A heuristic algorithm based on multi-assignment procedures for nurse scheduling

This paper tackles a Nurse Scheduling Problem which consists of generating work schedules for a set of nurses while considering their shift preferences and other requirements. The objective is to maximize the satisfaction of nurses' preferences and minimize the violation of soft constraints. This paper presents a new deterministic heuristic algorithm, called MAPA (multi-assignment problem-based algorithm), which is based on successive resolutions of the assignment problem. The algorithm has two phases: a constructive phase and an improvement phase. The constructive phase builds a full schedule by solving successive assignment problems, one for each day in the planning period. The improvement phase uses a couple of procedures that re-solve assignment problems to produce a better schedule. Given the deterministic nature of this algorithm, the same schedule is obtained each time that the algorithm is applied to the same problem instance. The performance of MAPA is benchmarked against published results for almost 250,000 instances from the NSPLib dataset. In most cases, particularly on large instances of the problem, the results produced by MAPA are better when compared to best-known solutions from the literature. The experiments reported here also show that the MAPA algorithm finds more feasible solutions compared with other algorithms in the literature, which suggest that this proposed approach is effective and robust. © 2013 Springer Science+Business Media New York.