Trivella, Alessio

Energisektoren er under hastig forandring, hvilket skaber behov for at løse en lang række nye optimeringsproblemer. Form˚alet med denne afhandling er at udvikle avancerede analytiske løsninger til bæredygtig energiforsyning og investeringsproblemer underlagt usikkerhed. Vi betragter case studier relateret til både industrielle anvendelser og husholdninger, og studerer problemer relateret til energieffektivitet, vedvarende energi og virksomhedernes sociale og miljømæssige ansvar. Problemer studeret i denne afhandling omfatter: (i) opbygning af en energikontrakt portefølje for virksomheder, der forpligter sig til at n˚a et m˚al for vedvarende energi, hvilket betyder at de skal have en bestemt procentdel af elforbruget dækket af vedvarende energikilder inden for en fremtidig horisont; (ii) optimering af nedluknings-beslutning i råvare og energiproduktionsaktiver med focus p˚a et socialt perspektiv, det vil sige under hensyntagentil de indirekte konsekvenser som en nedlukning af anlægget har for samfundet; (iii) modellering af optimale markedsbud-strategier for virtuelle kraftgeneratorer under en ny foresl˚aet elektricitetsmarkedsstruktur, der ville fremme integrering af vedvarende produktionsenheder; og (iv) undersøge faktorerne bag forbrugerinvesteringer i energieffektive husholdningsapparater, og finde de optimale energibesparende investeringer ud fra et forbruger- og energisystemperspektiv.For at løse disse problemer anvender vi værktøjer fra operationsanalyse til at designe ny metodik og udfører energianalyser. Flere problemer i denne afhandling kan formuleres som uh˚andterlige Markov-beslutningsprocesser (MDP’er) med multidimensional eksogene og/eller endogene komponenter i tilstanden. For at overvinde denne uhåndterbarhed udvikler vi approximerede dynamisk programmeringsmetoder (ADP) til beregning af næroptimale drifts- og investeringsbeslutninger, samt nedre og øvre grænser på den optimale MDP-værdi. I særdeleshed omfatter ADP-metoderne som vi udvikler: (i) en udvidelse af regress-senere mindste kvadrater Monte Carlo (LSML) metoden til at tilnærme risikoavers MDP’er, (ii) en kombination af LSML og klassifikation for at lære beslutningsregler (iii) en kortest-vej reformulering af reoptimiserings heuristikken, og (iv) en ny anvendelse af informationsrelaxeringerne og dualitetsrammen til at udtrække beslutningsregler uafhængigt på fremtiden fra testfordelinger. Vi anvender også mere klassisk scenarie-baseret stokastisk programmering. Bidragene fra denne afhandling omfatter modellering, metodologi og applikationer. Vibidrager til definitionen, forst˚aelsen og løsningen af nye optimeringsproblemer inden for bæredygtig energi og investering, og rapporterer resultater og indsigt som kan være nyttige for virksomheder og beslutningstagere. Vi bidrager til udviklingen af nye operationsanalyse, algoritmer og teori til løsning af stor-skala stokastiske optimeringsproblemer med særlig fokus p˚a ADP teknikker. Nogle af de metoder, der er udviklet i denne afhandling, har potentiel bredere relevans i andre anvendelses sammenhæng, såsom lagerstyring og finansiel porteføljeoptimering.The energy sector is undergoing a rapid transformation that gives rise to a variety of new optimization problems to solve. The goal of this thesis is to develop advanced analytical solutions for sustainable energy operations and investment problems that are subject to uncertainty. We consider case studies related to both industrial applications and consumer models, and cover problems related to energy efficiency, renewable energy, and corporate social and environmental responsibility. The problems that we consider in this thesis are the following: (i) constructing a power contract portfolio for companies that commit to reach a renewable energy target, which means, to procure a specific percentage of electricity demand from renewable energy sources by a future date; (ii) managing shutdown decisions in commodity and energy production assets from a social commerce perspective, that is, taking into account the indirect consequences that a plant shutdown has on the society; (iii) modeling the optimal market bidding strategies of virtual power generators under a novel proposed electricity market structure that would favor the integration of renewable production units; and (iv)investigating the factors behind the consumer investments in energy efficient household appliances, and the optimal energy saving investments from a consumer and energy system perspectives.To tackle these problems, we leverage tools from operations research to design novel methodology and perform energy analysis. Several problems encountered in this thesis can be formulated as intractable Markov decision processes (MDPs) with high-dimensional exogenous and/or endogenous component of the state. To overcome this intractability, we develop approximate dynamic programming (ADP) methods to compute near optimal operating and investment policies, and lower and upper bounds on the optimal MDP value. In particular, the ADP methods that we develop include: (i) an extension of the regress-later least squares Monte Carlo (LSML) to approximate risk-averse MDPs, (ii) a combination of LSML and classification to learn decision rules, (iii) a shortest path reformulation of the reoptimization heuristic, and (iv) a novel use of the information relaxations and duality framework to extract non-anticipative decision rules from sampleaction distributions. We also use more classical scenario-based stochastic programming. The contributions of this thesis cover modeling, methodology, and applications. We contribute to the definition, understanding, and resolution of emerging optimization problems in sustainable energy operations and investments, and provide results and insights that can be useful for companies and policy makers. We contribute to the development of new operations research models, algorithms, and theory for solving large-scale stochastic optimization problems, with particular focus on ADP techniques. Some of the methodology developed in this thesis has potential broader relevance in other application contexts,such as inventory control and financial portfolio optimization

Online Research Database In Technology

Decision Making under Uncertainty in Sustainable Energy Operations and Investments

http://orbit.dtu.dk/ws/files/158916532/ATrivella_thesisPhD.pdf

Decision Making under Uncertainty in Sustainable Energy Operations and Investments

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Online Research Database In Technology