1,721,172 research outputs found
Techno-economic models in Smart Grids: Demand side flexibility optimization for bidding and scheduling problems
Full text linkSummary of the thesis
Introduction of power-intensive appliances such as electric vehicle chargers and induction cooktops, as well as technologies for local renewable electricity generation from solar panels and wind turbines will provide challenges for distribution in the coming years. High power peaks, rapid power changes and less predictability will increase the need for transmission capacity and reserves. Traditionally, such problems are met with costly investments in new capacity. An alternative approach is to use flexibility from the end users, which means that generation and consumption of electricity is changed as a response to prices or other signals. Introduction of batteries in buildings, advanced metering infrastructure (AMI) and the Internet of Things (IoT) increase the potential for demand side flexibility. Altogether, these technologies constitute the concept denoted the Smart Grid.
To realize this increased flexibility potential, financial incentives must be introduced. Major changes are therefore expected in the electricity market in the coming years, including introduction of new, innovative contract types and business models, changes in market designs and the establishment of new market roles.
To maximize the benefit of demand side flexibility, there is a need for development of new decision support models. This thesis proposes and analyzes models for trading in different markets and for the scheduling of flexible devices in an operational situation. The models are based on operations research. The decision problems are mathematically formulated, and a particular focus is on how to handle uncertain parameters, such as consumption, generation and market prices. Stochastic programming is used for this purpose.
The thesis consists of four articles. In Article 1 a basic model is established where flexibility is divided into different classes. The article analyzes a prosumer in the retail market, where flexibility gives cost savings by exploiting price variations over a day, between energy carriers and by reducing the demand charge at the grid tariffs. In Article 2 several prosumers are coordinated via an aggregator who buys and sells electricity in a spot market and where imbalances are settled in a balancing market. Article 3 focuses on flexibility trade, where the value of an aggregated flexibility portfolio is maximized by trading in three sequential markets. The last article analyzes the decision problem to a service provider who operates a charging site for electric vehicles, where the capacity is limited. All articles contain case studies that have been conducted in close cooperation with companies in the Norwegian electricity market.Sammendrag av avhandlingen
Innføring av effektkrevende forbruksapparater som elbiler-ladere og induksjonskoketopper, samt teknologi for lokal, fornybar elektrisitetsproduksjon fra sol og vind vil gi utfordringer for distribusjonsnettet i årene som kommer. Høye effekttopper, raske effektendringer og mindre forutsigbarhet vil gi økt behov for overføringskapasitet og reserver. Tradisjonelt vil slike problemer møtes med kostnadskrevende investeringer i ny kapasitet. En alternativ tilnærming er å benytte fleksibilitet hos sluttbrukerne, som innebærer at innmating og uttak av elektrisitet endres som følge av priser eller andre signaler. Innføring av batterier i bygg, avanserte målings- og styringssystemer (AMS) og tingenes internett (IoT) gjør at potensialet for sluttbrukerfleksibilitet er økende. Til sammen utgjør disse teknologiene konseptet som benevnes det smarte nettet, Smart Grid.
For å realisere dette økte fleksibilitetspotensialet, må økonomiske insentiver innføres. Det forventes derfor store endringer i elektrisitetsmarkedet i årene som kommer, blant annet ved introduksjon av nye, innovative kontraktstyper og forretningsmodeller, endringer i markedsdesign og etablering av nye markedsroller.
For å maksimere nytteverdien av sluttbrukerfleksibilitet, vil det være behov for utvikling av nye beslutningsstøttemodeller. Denne avhandlingen foreslår og analyserer modeller for handel i ulike markeder og for planlegging av hvordan fleksible enheter skal benyttes i en driftssituasjon. Modellene baseres på operasjonsanalyse. Beslutningsproblemene modelleres matematisk, og det fokuseres spesielt på hvordan optimale beslutninger skal tas under forutsetning av at enkelte parametere, som forbruk, produksjon og markedspriser, kan være usikre på beslutningstidspunktet. Til dette benyttes stokastisk programmering.
Avhandlingen består av fire artikler. I artikkel 1 etableres en grunnleggende modell der fleksibilitet deles inn i ulike klasser. Artikkelen analyserer en prosument i sluttbrukermarkedet, der fleksibiliteten gir kostnadsbesparelser ved å utnytte prisvariasjoner over døgnet, mellom energibærere og ved å redusere effektledd på nettleiekontrakter. I artikkel 2 koordineres flere prosumenter via en aggregator som kjøper og selger elektrisitet i et spotmarked og der ubalanser avregnes i et balansemarked. Artikkel 3 rendyrker fleksibilitetshandel, der verdien av en aggregert fleksibilitetsportefølje maksimeres ved å handle i tre sekvensielle markeder. Den siste artikkelen analyserer beslutningsproblemet til en tjenestetilbyder som drifter et ladested for elbiler, der kapasiteten er begrenset. Alle artiklene inneholder case-studier som er gjennomført i tett samarbeid med aktører i det norske kraftmarkedet
Decomposition methods for multi-horizon stochastic programming
Full text linkMulti-horizon stochastic programming includes short-term and long-term uncertainty in investment planning problems more efficiently than traditional multi-stage stochastic programming. In this paper, we exploit the block separable structure of multi-horizon stochastic linear programming, and establish that it can be decomposed by Benders decomposition and Lagrangean decomposition. In addition, we propose parallel Lagrangean decomposition with primal reduction that, (1) solves the scenario subproblems in parallel, (2) reduces the primal problem by keeping one copy for each scenario group at each stage, and (3) solves the reduced primal problem in parallel. We apply the parallel Lagrangean decomposition with primal reduction, Lagrangean decomposition and Benders decomposition to solve a stochastic energy system investment planning problem. The computational results show that: (a) the Lagrangean type decomposition algorithms have better convergence at the first iterations to Benders decomposition, and (b) parallel Lagrangean decomposition with primal reduction is very efficient for solving multi-horizon stochastic programming problems. Based on the computational results, the choice of algorithms for multi-horizon stochastic programming is discussed.</p
Bounds in multi-horizon stochastic programs
Full text linkIn this paper, we present bounds for multi-horizon stochastic optimization problems, a class of problems relevant in many
industry-life applications tipically involving strategic and operational decisions on two dierent time scales.
After providing three general mathematical formulations of a multi-horizon stochastic program, we extend the denition of the traditional Expected Value
problem and Wait-and-See problem from stochastic programming in a multi-horizon framework. New measures are introduced allowing to quantify the im-
portance of the uncertainty at both strategic and operational levels. Relations among the solution approaches are then determined and chain of inequalities
provided. Numerical experiments based on an energy planning application are finally presented
Decision-dependent probabilities in stochastic programs with recourse
No full textStochastic programming with recourse usually assumes uncertainty to be exogenous. Our work presents modelling and application of decision-dependent uncertainty in mathematical programming including a taxonomy of stochastic programming recourse models with decision-dependent uncertainty. The work includes several ways of incorporating direct or indirect manipulation of underlying probability distributions through decision variables in two-stage stochastic programming problems. Two-stage models are formulated where prior probabilities are distorted through an affine transformation or combined using a convex combination of several probability distributions. Additionally, we present models where the parameters of the probability distribution are first-stage decision variables. The probability distributions are either incorporated in the model using the exact expression or by using a rational approximation. Test instances for each formulation are solved with a commercial solver, BARON, using selective branching
Offshore energy hubs in the decarbonisation of the Norwegian continental shelf
Full text linkThis paper studies the investment planning of a decarbonised Norwegian continental shelf energy system considering the connection and interfaces with the European energy system. A multi-horizon stochastic mixed-integer linear programming model is developed for such a problem. We consider short-term uncertainties, including wind and solar capacity factors, energy load, platform production profiles, and hydro power production limits. Hydrogen based energy hubs are considered both onshore and offshore for potential renewable power generation, distribution and storage. Future hydrogen market or demand is not included in the model. The results of multi-period planning towards 2050 show that: (a) offshore energy hubs are essentially wind power generation, conversion and distribution hubs, (b) a combination of offshore wind and power from shore may be a cost-efficient pathway for cutting emissions from the Norwegian continental shelf, (c) a total of 1.6 GW offshore wind may be needed to achieve a near zero emission Norwegian continental shelf energy system, 80% of which may be added in the first investment period and (d) offshore grid design is important for decarbonisation by distributing wind power efficiently; all five offshore platform clusters are connected to at least three other clusters by 2040, and they are fully connected by 2050
Modeling regional effects of energy policy: Combining technical and economic aspects to assess energy policy
Full text linkSummary of the thesis
When designing and introducing policy instruments like taxes and subsidies, mathematical models are employed to assess which effects should be expected. Assessments of socioeconomic costs and gains are central in such analyses. Energy is a fundamental driver for economic development. Society invests heavily in robust supply chains to ensure safe access to energy, providing a basis for production, growth and welfare. A side effect from using fossil energy is emissions of greenhouse gases that contribute to global warming. This challenge requires major societal transitions, switching from polluting fossil energy to climate-friendly renewable energy. Designing and selecting appropriate instruments that affect energy use, human behavior and economic activity requires suitable tools. The thesis develops such analysis tools, enabling mathematical simulation of effects and consequences from policy instruments instead of testing them on millions of people.
Interactions between energy supply chains and the economy are modeled on a national scale by linking technical bottom-up energy models with economic top-down equilibrium models. The models are geographically disaggregated, and local regional effects in alternative future scenarios are calculated. The thesis explores novel methods for coupling the models and demonstrates dramatic impacts on the calculation times necessary to achieve convergence. The thesis demonstrates situations where different economic equilibria are possible. One of the analyses estimates that greenhouse gas emissions from transport in 2030 may be halved by technology investments corresponding to a 6.5% income reduction.
The thesis analyzes effects of green certificates, which is a political instrument for increasing the share of renewable electricity. A new financial market for certificates is established, which interacts closely with the power market. First, the certificates affect the distribution of socio-economic surplus between the various players in the power market. Secondly, the certificates affect the regional distribution of socio-economic surplus. Thirdly, changes in infrastructure affect both the establishment of new renewable power generation and the distribution of socio-economic surplus. The thesis shows how different market conditions and future use of power influence the efficiency of green certificates. It also shows how cross-border transmission connections affect future winners and losers.Sammendrag av avhandlingen
Ved utforming og innføring av politiske virkemidler som f.eks. avgifter og subsidier, benyttes matematiske modeller for å anslå hvilke virkninger som kan forventes. Sentralt i dette arbeidet står analyse av samfunnsøkonomiske kostnader og gevinster. Energi er en vesentlig innsatsfaktor for økonomisk aktivitet, og samfunnet investerer store verdier i robuste leveransekjeder for at vi skal ha sikker tilgang til energi som gir grunnlag for produksjon, vekst og velferd. En bivirkning fra bruk av fossil energi er utslipp av drivhusgasser som bidrar til global oppvarming. I dagens situasjon skaper denne utfordringen behov for store omstillinger, ved at man må legge om fra forurensende fossil energi til klimavennlig fornybar energi. Utforming og valg av egnede virkemidler som påvirker atferd, økonomisk aktivitet og energibruk krever gode verktøy. Avhandlingen utvikler slike analyseverktøy, slik at effekter og konsekvenser av virkemiddelbruk kan simuleres matematisk i stedet for å testes ut på millioner av mennesker.
I avhandlingen modelleres samspillet mellom energisystemet og økonomien i nasjonal målestokk, ved at tekniske energimodeller kobles med økonomiske likevektsmodeller. Modellapparatet ivaretar også den geografiske dimensjonen, og anvendes til å beregne regionale effekter i alternative fremtidsscenarier. Avhandlingen utforsker nye metoder for maskinell sammenkobling av modeller, noe som har dramatisk betydning for beregningstidene som er nødvendige for å oppnå konvergens. Avhandlingen påviser situasjoner hvor ulike former for økonomisk likevekt kan oppstå. En av analysene anslår at kostnadseffektive teknologiinvesteringer som gir en halvering av klimagassutslippene fra transport i 2030 vil tilsvare om lag 6,5% inntektsreduksjon.
Avhandlingen analyserer virkninger av elsertifikater, som er et politisk virkemiddel for å øke andelen av fornybar elektrisitet. For verdsetting av sertifikatene etableres det et nytt finansielt marked, som samspiller tett med kraftmarkedet. For det første påvirker sertifikatene fordelingen av samfunnsøkonomiske gevinster mellom de ulike aktørene i kraftmarkedet. For det andre påvirker sertifikatene den regionale fordelingen av samfunnsøkonomisk overskudd. For det tredje påvirker endringer i overføringskapasiteter både etablering av ny fornybar kraftproduksjon og fordelingen av samfunnsøkonomisk overskudd. Avhandlingen viser hvordan ulike markedsforhold og fremtidig bruk av kraften påvirker effektiviteten av grønne sertifikater. Den viser også hvordan nettverksforbindelser til utlandet påvirker hvem som blir fremtidige vinnere og tapere
Modeling regional effects of energy policy: Combining technical and economic aspects to assess energy policy
No full textSummary of the thesis
When designing and introducing policy instruments like taxes and subsidies, mathematical models are employed to assess which effects should be expected. Assessments of socioeconomic costs and gains are central in such analyses. Energy is a fundamental driver for economic development. Society invests heavily in robust supply chains to ensure safe access to energy, providing a basis for production, growth and welfare. A side effect from using fossil energy is emissions of greenhouse gases that contribute to global warming. This challenge requires major societal transitions, switching from polluting fossil energy to climate-friendly renewable energy. Designing and selecting appropriate instruments that affect energy use, human behavior and economic activity requires suitable tools. The thesis develops such analysis tools, enabling mathematical simulation of effects and consequences from policy instruments instead of testing them on millions of people.
Interactions between energy supply chains and the economy are modeled on a national scale by linking technical bottom-up energy models with economic top-down equilibrium models. The models are geographically disaggregated, and local regional effects in alternative future scenarios are calculated. The thesis explores novel methods for coupling the models and demonstrates dramatic impacts on the calculation times necessary to achieve convergence. The thesis demonstrates situations where different economic equilibria are possible. One of the analyses estimates that greenhouse gas emissions from transport in 2030 may be halved by technology investments corresponding to a 6.5% income reduction.
The thesis analyzes effects of green certificates, which is a political instrument for increasing the share of renewable electricity. A new financial market for certificates is established, which interacts closely with the power market. First, the certificates affect the distribution of socio-economic surplus between the various players in the power market. Secondly, the certificates affect the regional distribution of socio-economic surplus. Thirdly, changes in infrastructure affect both the establishment of new renewable power generation and the distribution of socio-economic surplus. The thesis shows how different market conditions and future use of power influence the efficiency of green certificates. It also shows how cross-border transmission connections affect future winners and losers.Sammendrag av avhandlingen
Ved utforming og innføring av politiske virkemidler som f.eks. avgifter og subsidier, benyttes matematiske modeller for å anslå hvilke virkninger som kan forventes. Sentralt i dette arbeidet står analyse av samfunnsøkonomiske kostnader og gevinster. Energi er en vesentlig innsatsfaktor for økonomisk aktivitet, og samfunnet investerer store verdier i robuste leveransekjeder for at vi skal ha sikker tilgang til energi som gir grunnlag for produksjon, vekst og velferd. En bivirkning fra bruk av fossil energi er utslipp av drivhusgasser som bidrar til global oppvarming. I dagens situasjon skaper denne utfordringen behov for store omstillinger, ved at man må legge om fra forurensende fossil energi til klimavennlig fornybar energi. Utforming og valg av egnede virkemidler som påvirker atferd, økonomisk aktivitet og energibruk krever gode verktøy. Avhandlingen utvikler slike analyseverktøy, slik at effekter og konsekvenser av virkemiddelbruk kan simuleres matematisk i stedet for å testes ut på millioner av mennesker.
I avhandlingen modelleres samspillet mellom energisystemet og økonomien i nasjonal målestokk, ved at tekniske energimodeller kobles med økonomiske likevektsmodeller. Modellapparatet ivaretar også den geografiske dimensjonen, og anvendes til å beregne regionale effekter i alternative fremtidsscenarier. Avhandlingen utforsker nye metoder for maskinell sammenkobling av modeller, noe som har dramatisk betydning for beregningstidene som er nødvendige for å oppnå konvergens. Avhandlingen påviser situasjoner hvor ulike former for økonomisk likevekt kan oppstå. En av analysene anslår at kostnadseffektive teknologiinvesteringer som gir en halvering av klimagassutslippene fra transport i 2030 vil tilsvare om lag 6,5% inntektsreduksjon.
Avhandlingen analyserer virkninger av elsertifikater, som er et politisk virkemiddel for å øke andelen av fornybar elektrisitet. For verdsetting av sertifikatene etableres det et nytt finansielt marked, som samspiller tett med kraftmarkedet. For det første påvirker sertifikatene fordelingen av samfunnsøkonomiske gevinster mellom de ulike aktørene i kraftmarkedet. For det andre påvirker sertifikatene den regionale fordelingen av samfunnsøkonomisk overskudd. For det tredje påvirker endringer i overføringskapasiteter både etablering av ny fornybar kraftproduksjon og fordelingen av samfunnsøkonomisk overskudd. Avhandlingen viser hvordan ulike markedsforhold og fremtidig bruk av kraften påvirker effektiviteten av grønne sertifikater. Den viser også hvordan nettverksforbindelser til utlandet påvirker hvem som blir fremtidige vinnere og tapere
Optimization models for the plugging and abandoning of offshore oil and gas fields
Full text linkThis thesis applies operations research methods to planning problems related to the plugging and abandoning of offshore oil and gas wells. We consider two problem settings, for which we develop new models and solution approaches.
The first problem is on a tactical planning level and considers the optimal planning of a plugging campaign. The problem is defined as a variant of an uncapacitated vehicle routing problem with time-windows and is being treated in the first three papers in this thesis. We focus on different aspects, ranging from the application of different model formulations and solution methods, to obtaining more economically oriented insights. A main finding is that significant cost-savings can be made by using the developed methodology for planning plugging campaigns, as opposed to conventional methods. In addition, we contribute to the vehicle routing literature by developing a methodology that allows for incorporating a learning effect. That is, the time it takes to perform a particular operation reduces as similar operations have been performed before.
The second problem considers the strategic problem of developing a mature offshore oil field, and is treated in the fourth paper. We develop a multistage stochastic integer program and solve it using the stochastic dual dynamic integer programming algorithm (SDDiP). The problem can be considered to represent a portfolio of real options, incorporating both shutdown and expansion options. We show that the SDDiP algorithm is very suitable for solving complex real options problem. This enables us to perform an extensive analysis on factors affecting the abandonment decision. We show that traditional real options findings for single options might behave differently when considered in portfolios
Investment planning under uncertainty in energy systems: Modelling and algorithms
Full text linkThis thesis applies operational research methods for the investment planning of energy systems under uncertainty for the energy transition. We develop new models and solution methods.
On the modelling side, we first focus on modelling hydrogen-based offshore energy hubs in an offshore energy system. A mixed-integer linear program is developed for the investment planning of offshore energy systems with offshore energy hubs. The model is then extended to (1) include uncertainty using a multi-horizon stochastic programming approach and (2) include the European onshore and offshore energy systems. Finally, some major extensions are made to the model, which leads to the REORIENT model. The REORIENT model is a multi-horizon mixed-integer linear stochastic program for integrated investment, retrofit, and abandonment planning of energy systems under short-term and long-term uncertainty. This is the first model that integrates different alternatives and investigates the role of existing energy infrastructure in the energy transition. The REORIENT model features the main modelling contributions in this thesis. In addition, we also extend the modelling of an existing model, EMPIRE, which is a stochastic linear program for the European power system investment planning, by modelling the heat and industry sectors with a strong focus on endogenous decisions regarding industry decarbonisation, hydrogen and carbon capture and storage.
On the methodology side, we develop algorithms that exploit the structure of multi-horizon stochastic programming. The algorithms developed can also be applied in general multi-stage stochastic programs. We develop enhanced Benders decomposition and Lagrangean decomposition algorithms. The enhanced Benders decomposition utilises adaptive oracles. We also propose to stabilise the adaptive Benders decomposition with (1) a novel dynamic level method and (2) a novel centre point strategy. Also, we propose parallelised Lagrangean decomposition with primal reduction. The scenario subproblems are solved in parallel, and the primal problem is reduced based on the structure of multi-horizon stochastic programming and solved in parallel. We apply the proposed algorithms to solve the REORIENT model and its variations and compare them with standard Benders, unstabilised adaptive Benders, and standard Lagrangean decomposition.
The proposed models and algorithms contribute to operational research and provide useful insights for the energy transition
Stochastic programming in analyses of flexibility in power systems and markets
Full text linkSummary of the thesis
In the research for this thesis, operations research techniques are applied in power markets and systems to investigate the usage of flexibility under conditions of uncertainty. Economic dispatch models and optimal power flow models are used for designing and analyzing power markets and systems. In the economic dispatch models, economic factors and their relations with power markets are analyzed, while in optimal power flow models, answers to power grid operations for voltage and network congestion are searched. During the research, the first research question addressed was how to determine the flexibility concept, products, and services about various power and energy markets. This led to the development of a theoretical and empirical taxonomy for flexibility trading and related market structures. The second question addressed during the research considered how to use flexibility according to two separate systemic approaches, different tariff designs to exploit flexibility usage for reducing peak pricing, and a stochastic optimal scheduling methodology for end user’s flexibility assets to solve grid problems. The third and final question addressed during the research concerned how it is possible to have a cost-efficient and productive local flexibility market design for grid operations under uncertainty.
Answers to the research questions are provided in the four papers that form the basis of this thesis. Paper I explains the taxonomy and provides an overview of flexibility and its products along four dimensions—time, spatiality, resource, and risk profile—according to the market design. Paper II shows how to activate and use flexibility with a dynamic tariff design for peak shaving. Paper III provides solutions to grid problems under uncertainty (i.e., voltage and congestion) by using flexibility from the demand side, storage side, and supply side. Lastly, Paper IV proposes a stochastic local flexibility market design, bidding, and dispatch methodology to contribute grid operations on a local scale.
Sammendrag av avhandlingen
I forskningen for denne avhandlingen brukes operasjonsforskningsteknikker i kraftmarkeder og systemer for å undersøke bruken av fleksibilitet under forhold med usikkerhet. Økonomiske forsendelsesmodeller og optimale strømflytmodeller brukes til å designe og analysere kraftmarkeder og systemer. I de økonomiske forsendelsesmodellene analyseres økonomiske faktorer og deres relasjoner til kraftmarkeder, mens det i optimale kraftstrømmodeller søkes etter svar på kraftnettdrift for spenning og overbelastning av nett. Under undersøkelsen var det første forskningsspørsmålet som ble tatt opp, hvordan man kan bestemme fleksibilitetskonseptet, produktene og tjenestene om ulike kraft- og energimarkeder. Dette førte til utviklingen av en teoretisk og empirisk taksonomi for fleksibilitetshandel og relaterte markedsstrukturer. Det andre spørsmålet som ble adressert under forskningen vurderte hvordan man kan bruke fleksibilitet i henhold til to separate systemiske tilnærminger, ulike tariffdesign for å utnytte fleksibilitetsbruken for å redusere topprising, og en stokastisk optimal planleggingsmetodikk for sluttbrukerens fleksibilitetsressurser for å løse nettproblemer. Det tredje og siste spørsmålet som ble tatt opp under forskningen handlet om hvordan det er mulig å ha et kostnadseffektivt og produktivt lokalt fleksibilitetsmarkedsdesign for nettdrift under usikkerhet.
Svar på forskningsspørsmålene er gitt i de fire paperne som ligger til grunn for denne avhandlingen. Paper I forklarer taksonomien og gir en oversikt over fleksibilitet og dens produkter langs fire dimensjoner – tid, romlighet, ressurs og risikoprofil – i henhold til markedsdesignet. Paper II viser hvordan du aktiverer og bruker fleksibilitet med en dynamisk tariffdesign for toppbarbering. Paper III gir løsninger på nettproblemer under usikkerhet (dvs. spenning og overbelastning) ved å bruke fleksibilitet fra etterspørselssiden, lagringssiden og tilbudssiden. Til slutt foreslår Paper IV en stokastisk lokal fleksibilitetsmarkedsdesign, budgivnings- og utsendelsesmetodikk for å bidra med nettdrift i lokal skala
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