Column-generation and interior point methods applied to the long-term electric power-planning problem

Aquesta tesi s'adreça al problema de planificació de la generació elèctrica a llarg termini per a una companyia específica (SGC) que participa en un mercat liberalitzat organitzat en un pool. Els objectius de la tesi són: modelitzar aquest problema, i desenvolupar i implementar tècniques apropiades i eficients que el resolguin. Un planificació òptima a llarg termini és important, per exemple, per a la confecció de pressupostos, o per a la gestió de compres/consum de combustibles. Una altra aplicació és la de guiar la planificació a curt termini perquè aquesta tingui en compte decisions preses sota una òptica de llarg termini. La nostra proposta per a fer la planificació de la generació és optimitzar la generació esperada de cada unitat (o la unió de diverses unitats de característiques semblants) del pool per a cada interval en que dividim el llarg termini. El model bàsic per la planificació de la generació a llarg termini (LTGP) maximitza el benefici de totes les unitats del pool. La constricció més important és la satisfacció de la demanda, ja que el sistema està sempre balancejat. Utilitzem la formulació de Bloom i Gallant, la qual modela la càrrega a través d'una monòtona de càrrega per cada interval i requereix un número exponencial de constriccions lineals de desigualtat, anomenades LMCs. Altres constriccions (lineals) incloses en el model són: garantia de potència, límits en la disponibilitat de combustibles, emissions màximes de CO2 o una quota de mercat mínima per a la SGC. Una extensió d'aquest model és la planificació conjunta de l'assignació de manteniments de les unitats tèrmiques d'una SGC amb la planificació de la generació. El model conjunt és un problema quadràtic amb variables binàries i contínues. Per resoldre aquest model es proposa un parell d'heurístiques i s'ha implementat un prototipus de branch and bound en AMPL.Aquesta tesi també proposa una manera per coordinar el model LTGP proposat amb una planificació a curt termini. Es desenvolupa un model de curt que inclou els resultats de llarg termini. Donat que el model de planificació a llarg termini s'ha de resoldre sovint (principalment per passar informació acurada al model de curt), les tècniques emprades per a resoldre'l han de donar resultats fiables en un espai de temps curt. Les tècniques aplicades han estat:· Donat que les constriccions de recobriment i les fites de no negativitat defineixen un políedre convex els vèrtexs del qual són fàcils de trobar el model es transforma i les variables esdevenen els coeficients convexos que defineixen un punt. Aquest nou problema es resolt amb l'algoritme de Murtagh i Saunders, que és un procediment òptim. Aquest algoritme s'aplica sota un esquema de generació de columnes donat que el número de vèrtexs del políedre és comparable al número de constriccions. L'avantatge d'aquest mètode és que els vèrtexs es van generant a mesura que es necessiten.· L'aplicació de mètodes directes és computacionalment costós donat el número exponencial de LMCs. De totes maneres, a l'òptim només un conjunt reduït de constriccions de recobriment seran actives. Hem desenvolupat una heurística, anomenada heurística GP, la qual genera un subconjunt de constriccions, entre les quals hi ha les LMCs que són actives a l'òptim. L'heurística resol una seqüència de problemes quadràtics, els quals incrementen el número de LMCs considerades a cada iteració. Els problemes es resolen amb mètodes de punt interior que s'inicialitzen amb tècniques de warm start per tal d'accelerar la convergència cap a la nova solució. Aquest procediment resulta ser molt més eficient que el de generació de columnes. La modelització i els casos de prova estan basats en dades d'un sistema de pool pur i de mercat com ha estat a Espanya fins el juliol de 2006.This thesis presents an approach to the long-term planning of power generation for a company (SGC) participating in a liberalized market organized as a pool. The goal of this thesis is two-fold: to model the problem and to develop and implement appropriate and efficient techniques for solving it.The optimization of the long-term generation planning is important for budgeting and planning fuel acquisitions, and to give a frame where to fit short-term generation planning.Our proposal for planning long-term generation is to optimize the expected generation of each unit (or the merger of several units of the same type) in the power pool over each interval into which the long-term horizon is split.The basic model for long-term generation planning (LTGP) maximizes the profit for all the units participating in the pool. The most important constraint is matching demand, since the market always clears. The Bloom and Gallant formulation is used, which models the load with a load-duration curve for each interval and requires an exponential number of linear inequality constraints, called herein LMCs. Other (linear) constraints included in the model are: minimum generation time, limits on the availability of fuel, maximum CO2 emission limits or the market share of the SGC. This thesis also proposes the way in which coordination between the LTGP model developed and a short-term plan should be considered and provides a model for short-term electrical power planning adapted to the LTGP proposed and which includes the long-term results.Another decision that needs to be taken from a long-term point of view is the joint scheduling of thermal unit maintenances with the generation planning of a particular SGC. The results of a prototype of a Branch and Bound implemented in AMPL are included in this thesis.Long-term planning needs to be considered before short-term planning and whenever the real situation deviates from the forecasted parameters, so the techniques implemented must be efficient so as to provide reliable solutions in a short time. Two methods for handling the LMCs are proposed and compared:● A decomposition technique exploits the fact that the LMCs plus the non-negativity bounds define a convex polyhedron for each interval whose vertices are easy to find. Thus, the problem is transformed and the variables become the coefficients of a convex combination of the vertices. The transformed problem is quadratic with linear constraints, making it suitable to be solved with the Murtagh & Saunders algorithm, which gives an optimal solution. A column-generation approach is used because the number of vertices of the polyhedron is comparable to the number of LMCs. The advantage of this method is that it does not require previous computation of all of the vertices, but rather computes them as the algorithm iterates.● The application of direct methods is computationally difficult because of the exponential number of inequality LMCs. However, only a reduced subset of LMCs will be active at the optimizer. A heuristic, named GP heuristic, has been devised which is able to find a reduced set of LMCs including those that are active at the optimizer. It solves a sequence of quadratic problems in which the set of LMCs considered is enlarged at each iteration. The quadratic problems are solved with an interior point method, and warm starts are employed to accelerate the solution of the successively enlarged quadratic problems. This procedure is more efficient than the column generation one.The modeling and tests of this thesis are based on the pure pool system and market data from the Spanish system up to July 2006

A production planning model considering uncertain demand using two-stage stochastic programming in a fresh vegetable supply chain context

Production planning models are achieving more interest for being used in the primary sector of the economy. The proposed model relies on the formulation of a location model representing a set of farms susceptible of being selected by a grocery shop brand to supply local fresh products under seasonal contracts. The main aim is to minimize overall procurement costs and meet future demand. This kind of problem is rather common in fresh vegetable supply chains where producers are located in proximity either to processing plants or retailers. The proposed two-stage stochastic model determines which suppliers should be selected for production contracts to ensure high quality products and minimal time from farm-to-table. Moreover, Lagrangian relaxation and parallel computing algorithms are proposed to solve these instances efficiently in a reasonable computational time. The results obtained show computational gains from our algorithmic proposals in front of the usage of plain CPLEX solver. Furthermore, the results ensure the competitive advantages of using the proposed model by purchase managers in the fresh vegetables industry.This work was supported by the MEyC under contracts TIN2011-28689-C02-02, TRA2013-48180-C3-P and TIN2014- 53234-C2-2-R. The authors are members of the research group 2014-SGR163 and 2014-SGR151, funded by the Generali- tat de Catalunya

A Reinforcement Learning Control in Hot Stamping for Cycle Time Optimization

Hot stamping is a hot metal forming technology increasingly in demand that produces ultra-high strength parts with complex shapes. A major concern in these systems is how to shorten production times to improve production Key Performance Indicators. In this work, we present a Reinforcement Learning approach that can obtain an optimal behavior strategy for dynamically managing the cycle time in hot stamping to optimize manufacturing production while maintaining the quality of the final product. Results are compared with the business-as-usual cycle time control approach and the optimal solution obtained by the execution of a dynamic programming algorithm. Reinforcement Learning control outperforms the business-as-usual behavior by reducing the cycle time and the total batch time in non-stable temperature phases

An Internet of Things Platform Based on Microservices and Cloud Paradigms for Livestock

With the growing adoption of the Internet of Things (IoT) technology in the agricultural sector, smart devices are becoming more prevalent. The availability of new, timely, and precise data offers a great opportunity to develop advanced analytical models. Therefore, the platform used to deliver new developments to the final user is a key enabler for adopting IoT technology. This work presents a generic design of a software platform based on the cloud and implemented using microservices to facilitate the use of predictive or prescriptive analytics under different IoT scenarios. Several technologies are combined to comply with the essential features¿scalability, portability, interoperability, and usability¿that the platform must consider to assist decision-making in agricultural 4.0 contexts. The platform is prepared to integrate new sensor devices, perform data operations, integrate several data sources, transfer complex statistical model developments seamlessly, and provide a user-friendly graphical interface. The proposed software architecture is implemented with open-source technologies and validated in a smart farming scenario. The growth of a batch of pigs at the fattening stage is estimated from the data provided by a level sensor installed in the silo that stores the feed from which the animals are fed. With this application, we demonstrate how farmers can monitor the weight distribution and receive alarms when high deviations happen.This research was partially supported by the Intelligent Energy Europe (IEE) program and the Ministerio de Economía y Competitividad under contract TIN2017-84553-C2-2-R, by the European Union FEDER (CAPAP-H6 network TIN2016-81840-REDT) and the demonstration activity financed by the Operation 01.02.01 of Technological Transfer from the Program of Rural Development in Catalunya 2014–2020 cofinanced by DARP and FEDER

Bi-Objective Optimization Model Based on Profit and CO2 Emissions for Pig Deliveries to the Abattoir

This paper presents a bi-objective model for optimizing pig deliveries to the abattoir accounting for total revenue and CO 2 emissions. Fattening farms house the most important stage in pig production, and operations on farms must be coordinated with the rest of the pig supply chain when batch management is generally applied. The novelty of the model lies in the change of attitude in producers towards a greener production, which is becoming one of the major concerns in our society. In this context, we enrich the classical approach focused on revenues with the addition of the CO 2 emissions from the pigs on the fattening farms. Emissions derived from feeding and transportation are considered since they are the most important sources of CO 2 . The model is tested using parameters representing a typical integrated Spanish fattening farm. Our findings reveal the impact and the relationship between revenues and emissions, highlight that the break-even is reached achieving 459 kg of CO 2 per pig, which corresponds to a reduction of 6.05%. On the other hand, the profit is slightly reduced by 4.48% in favor of the environment