Search

This report is part of the data documentation for the Global Gas Model (GGM) as prepared for analyses in the H2020 SET-Nav project1 and subsequent studies. Since some data are proprietary we cannot make available all input data we have used. By describing input data, the processing steps, and the resulting input files we aim for maximum transparency and to allow anyone to reproduce the data sets from scratch if desired. GGM was developed by Egging (2010, 2013), also based on expertise gained in the development and application of the European Gas Model (Egging et al., 2008) and the World Gas Model (Egging et al., 2010, 2009). GGM was applied with various data sets and versions in studies of the future global (Holz et al., 2015) and European gas markets (Holz et al., 2016, 2017). In addition to the impact of climate policy, supply security has been a recurring concern (e.g. Richter and Holz, 2015). Moreover, a stochastic version that used a scenario tree was applied in Egging and Holz (2016). In the SET-Nav project, GGM contributed to the analyses of projects of common interest (PCI), their profitability and public support requirements (Kotek et al., 2018). This is a deterministic model version (i.e. no probabilistic scenario tree).

The deployment of Renewable Energy Resources (RES) is affecting the profitability and scheduling of conventional generators (e.g. gas-fired power plants) as well as creating short-term electricity balancing problems. To manage RES variable output, additional flexible power capacity will be needed to cope with peak demand. Gas power plants ability to provide flexibility in a short time period make them especially suitable for fast balancing. Could gas power plants compensate RES fluctuations without creating instability in the gas transmission network? How investments on the gas infrastructure affects the evolution of the electricity sector and vice-versa? To answer these questions and jointly represent electricity-gas energy sectors, this paper presents 1) a methodology on combining cost based optimization models for gas and electricity and 2) the effects of long-term policy targets set to the EU electricity sector and its influence on the gas infrastructure. To address long-term (strategic investments) and short-term (operational) aspects, we model both sectors as multi-horizon stochastic programs. Models results illustrate a strong dependency of the electricity infrastructure on the short-term operations of the gas sector. Under a high RES deployment scenario, gas infrastructure investments declines slightly which increases cross-border electricity capacity needs.

Stochastic optimization approaches ignore that the decisions of different actors in markets typically do not lead to a system-wide optimal solution. Suppliers in markets with entrance barriers or other aspects that hinder competition, can use their dominant positions to exert market power and drive up market prices. To represent such gaming behavior, a different modeling approach is needed. Equilibrium models can represent varying market structures, including perfect competition and oligopoly. This chapter presents a multi-stage stochastic equilibrium model for a general commodity market wherein suppliers, transportation agents and storage agents make capacity investment decisions while facing uncertainty in future market circumstances and production and sales decisions in later stages when the uncertainty has been revealed. An illustrative example for the natural gas market is used to show how market power may affect decisions and expected profits, and discuss the value of the stochastic solution for the different agents in our gaming setting. This is an Accepted Manuscript of a book chapter published by Nova Science Publishers, Inc.in [Stochastic Programming: Theory, Applications and Impacts]

Recent supply security concerns in Europe have revived interest into the natural gas market. Here, we investigate investment behavior and trade in an imperfect market structure under uncertainty in both supply and demand. We focus on three uncertain events: i) transit of Russian gas via Ukraine that may be disrupted from 2020 on; ii) natural gas intensity of electricity generation in OECD countries that may lead to higher or lower natural gas demand after 2025; and iii) availability of shale gas around the globe after 2030. We illustrate how timing of investments is affected by inter-temporal hedging behavior of market agents, such as when LNG capacity provides ex-ante flexibility (e.g., in Ukraine to hedge for a possible Russian supply disruption) or an expost fallback option if domestic or nearby pipeline supply sources are low (e.g., uncertain shale gas resources in China). Moreover, we find that investment in LNG capacities is more determined by demand side pull (due to higher needs in electric power generation) than by supply side push (higher shale gas supplies needing an outlet).

Implementering av sol og vindkraft krever en radikal omlegging av dagens kraftsystem. Det er derfor viktig å finne løsninger som kan utnytte mulighetene den nye produksjonsmiksen gir og samtidig ivareta sikker og effektiv drift. En mulig løsning er å installere energilagringsenheter, som f.eks. batterier, i distribusjonsnettet. Det er mange utfordringer med å finne en optimal plassering og dimensjonering av batterier i et distribusjonsnett, blant annet fordi det å framstille kraftnettverket matematisk gir en matematisk ulineæritet, videre er det ofte mangel på gode data, det er vanskelig prediktere utviklingen av batteripriser osv. Det finnes imidlertid flere måter å løse batteri optimeringsproblemet. I denne masteroppgaven har jeg valgt å bruke en mixed integer programming (MIP) energi basert modell. I dette tilfelle innebærer det å bruke energibalanse samtidig som en henter statiske data om kraftnettverket fra et stillbilde av en power flow analyse. Målet med masteroppgaven er å minimere de totale strømkostnadene samtidig som en dekker energi behovet til hus og industri i bestemte distribusjonsnett. Mer presist ønsker jeg å undersøke en type distribusjonsnett som ikke har blitt analysert tidligere – en kombinasjon av lav- og mellomspenningsnett. Overordnet viser resultatene at det er økonomisk fordelaktig å installere batterier i distribusjonsnett som kombinerer lav- og mellomspenning, og at flesteparten av batteriene plasseres nærme noder med høy energiettersørsel. Den optimale batteriplasseringen i lav og mellomspenning er imidlertidig svært følsom når prisforskjellen økes litt mellom batterier som bare kan installeres i lavspenningsnettet og batterier som bare kan installeres i mellomspenningsnettet. Videre viser funnene at den totale batterikapasiteten reduseres når andelen av fornybare energiressurses øker. Batterikapasiteten synes først og fremst å bli påvirket av mengden av fornybar energiproduksjonen, mens plasseringen av batteriene påvirkes mest av forskjellen på batterikostnadene. Når det gjelder forskjellige nettverkstopologi (Loop og Radialt) viser imidlertid resultatene at de har mindre effekt på størrelsen og plasseringen av batteri. The deployment and adoption of solar and wind power are leading to a radical reorganisation of the current power system. It is therefore important to find solutions that can exploit the opportunities of the production-mix and at the same time ensure a safe and efficient power system. One possible solution is to install energy storage devices, such as batteries, in the distribution network. Battery storage costs are projected to decrease dramatically within the next decade. Batteries will likely become affordable and have a widespread use for different power system services. Batteries provide flexibility to maintain a stable supply-demand balance. Related research has analysed the role of battery in distribution grids, but have not consider coordination between different power system layers. Raising the questions: where and what size to invest in battery storage? Should batteries be placed in the low-voltage or medium-voltage grid (or both)? To address these questions, this thesis has developed a new model that specifically represents both medium voltage and low voltage grids. It is a multi-layer grid designed for battery investments by considering short term operational decisions. The multi-layer grid model is a mixed integer program that minimises the total cost of electricity while supplying the energy demand of houses and industry in various distribution networks. The thesis analyses and investigates a distribution network problem that has not been analysed previously - a combination of low and medium voltage network. Overall, the results reveal that it will be economic beneficial to install batteries in grids combining medium and low voltage, and most batteries close to the highest energy demand. The optimum battery placement, in the different grids, is very sensitive to cost difference between the batteries in the low and medium voltage grid. Furthermore, the findings show that the total battery capacity decrease when the proportion of renewable energy increase. The battery capacity seems to be primarily affected by the amount of renewable energy production, while the placement of the batteries is most effected by the difference in costs between low and medium voltage batteries. When it comes to different network topology (Loop and Radial), the results show that they have less effect on the battery placement and size decisions.

In this thesis, an Annual Delivery Program (ADP) planning problem is studied. The objective of the ADP planning problem is to create a cost-efficient delivery schedule for a liquefied natural gas (LNG) producer, who has a fleet of heterogeneous vessels. The fleet of vessels consists of ice-going vessels and conventional vessels. The LNG producer has entered long-term contracts with customers in different parts of the world, and is committed to fulfill the demands stated in the contracts. Voyages to the customers are either direct or via a transshipment port. The direct voyages to the customers in Asia depend on the opening periods of the Northern Sea Route. If the NSR is closed, the ice-going vessels travel to the transshipment port to transfer the cargo onto a conventional vessel, which then continues the journey via the Suez Canal. Only ice-going vessels are permitted to use the NSR. The size of the storage tank at the transshipment port is a challenging factor in the ADP planning problem, and thus partial loading is implemented to avoid a bottleneck caused by residual LNG left in the tank. Partial loading only applies to the ice-going vessels sent from the producer's port, and the filling levels in the vessel tanks are regulated to reduce the effects of sloshing. The ADP planning problem can be classified as an industrial shipping problem with decision making on a tactical planning level. Relevant literature is presented to illustrate how certain properties of the problem, such as partial loading, is implemented in other works, and how different solution methods have been used to solve LNG inventory routing problems (LNG-IRP). The ADP planning problem is a maritime inventory routing problem (MIRP). MIRPs are numerically complex to solve and usually require heuristic solution methods to produce good solutions. A mixed integer programming (MIP) formulation of the ADP planning problem is developed based on the aforementioned factors, where partial loading and boil-off considerations are explicitly handled in the model formulation for some of the vessels. Because of the long planning horizon of the ADP planning problem, a rolling horizon heuristic (RHH) is proposed to solve the problem. Several strategies for the different periods within a sub-horizon are tested to solve the ADP planning problem efficiently. In addition to the RHH, an aggregation and disaggregation heuristic (ADH) is proposed as an alternative approach to solving a reduced size of the problem. The intention behind both methods is to reduce the complexity of the problem during the solution process, by either solving the problem in shorter sub-horizons or by reducing the number of nodes in the network. The solution methods are combined to create an ADP quickly. Results from these solution methods are compared with a solution from a corresponding case solved by exact method. The RHH obtains the best result for the ADP planning problem. The heuristic improves both the ADP-objective and computational time compared to the exact solution method. When the ADH is combined with the RHH to solve the aggregated case, the computational time can be decreased further. Despite the improved solution time, the RHH-ADH did not improve the solution compared with the exact solution method. The results show that the performance of the ADH depends on several factors; the chosen aggregation strategy, the solution method used for solving the aggregated case, and the amount of over- and under-deliveries in the solution for the aggregated case.

This dissertation uses three models to analyze different decarbonization strategies for combating global climate change: The cost minimizing mixed-integer model CCTS-Mod examines the economics of Carbon Capture, Transport, and Storage (CCTS) for the electricity and industry sector; the welfare maximizing quadratically constrained model ELMOD focuses on different trajectories for renewable energy sources (RES) and transmission grid expansions; and the equilibrium model ELCO combines the insights of the individual sectors to a combined CCTS and electricity investment and dispatch model. Modeling results show that an investment in CCTS is beneficial for the iron and steel sector once the CO2 certificate price exceeds 50 €/t CO2. The threshold is 75 €/t CO2 for the cement industry and 100 €/t CO2 for the electricity sector. Additional revenues from using CO2 for enhanced oil recovery (CO2-EOR) lead to an earlier adoption of CCTS in the North Sea region. The lack of economies of scale results in increasing CO2 storage costs of more than 30%, while transport costs even double. Research from the last years, however, indicates that CCTS is unlikely to play an important role in decarbonizing the electricity sector. The identified reasons for this are incumbents’ resistance to structural change, wrong technology choices, over-optimistic cost estimates, a premature focus on energy projects instead of industry, and the underestimation of transport and storage issues. Keeping global temperature rise below 2°C therefore implies the phase-out of fossilfueled power plants and, in particular, of CO2-intensive coal power plants. The low CO2 price established by the European Emissions Trading Scheme is insufficient to induce a fuel switch in the medium term. Therefore, supplementary national measures are necessary to reduce coal-based power generation; i.a. feed-in tariffs for RES, minimum CO2 prices, or emissions performance standards. Analyses for Germany show that a coal phase-out before 2040 is possible without risking resource adequacy at any point. Enabling a smooth transition encourages other countries to take the German Energiewende as a blueprint to combat global warming, even if this implies a coal phase-out. Die vorliegende Arbeit untersucht und quantifiziert mit Hilfe drei verschiedener Modelltypen den möglichen Beitrag verschiedener Dekarbonisierungsoptionen: Das gemischt-ganzzahlige Modell CCTS-Mod berechnet, welchen Beitrag die Vermeidungstechnologie der CO2-Abscheidung, -Transport und -Speicherung (engl. carbon capture, transport, and storage, CCTS) im Stromsektor und in der Industrie erzielen kann; das Strommarktmodell ELMOD quantifiziert die Impliaktionen verschiedener Ausbaupfade erneuerbarer Energien (EE) und den hierfür benötigten Stromleitungsausbau; und das Modell ELCO verknüpft die Erkenntnisse des Strommarktes und der CCTS Technologie in einem Gleichgewichtsmodell. Modellergebnisse zeigen, dass CCTS in der Industrie eine mögliche Dekarbonisierungsoption darstellt, da sie sich im Stahlsektor bereits ab CO2-Zertifikatspreisen von 50 €/t CO2 und im Zementsektor bereits ab 75 €/t CO2 lohnt. Für den Stromsektor hingegen rentiert sich der Einsatz von CCTS erst ab CO2-Preisen jenseits von 100 €/t CO2. Zwar kann die Wirtschaftlichkeit in Einzelfällen durch die Nutzung der CO2-EOR (enhanced oil recovery) Technologie gesteigert werden. Der Verlust von Skaleneffekten führt allerdings zu einer Steigerung der CO2-Speicherkosten um 30% sowie zu einer Verdopplung der Transportkosten. Die Erfahrungen der letzten Jahre stellen daher in Frage, ob CCTS für den Stromsektor eine relevante Dekarbonisierungsoption darstellt. Die Gründe hierfür sind u.a. fehlende Anreize für betroffene Akteure, falsche Technologieauswahl, zu optimistische Kostenschätzungen, der Fokus auf Kraftwerke an Stelle von Industrieanlagen sowie die Vernachlässigung von Transport- und Speicherproblematiken. Ohne die Verfügbarkeit dieser Technologie ist zur Einhaltung der vereinbarten Klimaschutzziele deshalb ein Austieg aus der Kohleverstromung mittelfristig notwendig. Da der europäische Emissionshandel hierfür als alleiniges Instrument nicht ausreichend ist, werden auch in Zukunft nationale Zusatzmaßnahmen wie die Förderung von EE, ein möglicher CO2-Mindestpreis oder CO2-Grenzwerte notwendig sein. Analysen für Deutschland zeigen, dass ein solcher Kohleausstieg bis 2040 ohne Strukturbrüche und sozialverträglich möglich ist.

ISBN:978-3-906327-24-2