Methane mitigation is increasingly recognized as a critical lever for achieving near-term climate goals. In August 2024, the European Union (EU) adopted the Methane Regulation (EUMR), the first comprehensive framework addressing methane emissions from both domestic fossil fuel operations and imports. A cornerstone of the regulation is the introduction of a methane intensity standard for fossil fuels (crude oil, natural gas, and coal) that will take effect on August 5, 2030 for supply contracts concluded or renewed after that date. This paper examines the implications of implementing the EU methane intensity standard for natural gas using the Global Gas Model-Methane, a partial equilibrium economic model. Our analysis finds that an EU methane intensity standard with a maximum intensity value of 0.2% for natural gas would reduce upstream methane emissions embedded in EU gas production and imports by approximately 40%. This is the net impact after considering emission leakage through trade diversion, which amounts to approximately 10% of the gross reduction in the EU upstream methane footprint. Globally, the EU intensity standard would lead to a 1.4% decrease in methane emissions from natural gas production. At the same time, the intensity standard would have negligible effects on EU gas prices and gas import volumes. These results underscore the potential of methane intensity standards as a cost-effective policy instrument for global methane mitigation, offering significant climate benefits without imposing substantial economic trade-offs.

The electricity landscape is constantly evolving, with intermittent and distributed electricity supply causing increased variability and uncertainty. The growth in electric vehicles, and electrification on the demand side, further intensifies this issue. Managing the increasing volatility and uncertainty is of critical importance to secure and minimize costs for the energy supply. Smart neighborhoods offer a promising solution to locally manage the supply and demand of energy, which can ultimately lead to cost savings while addressing intermittency features. This study assesses the impact of different electric vehicle charging strategies on smart grid energy costs, specifically accounting for battery degradation due to cycle depths, state of charge, and uncertainties in charging demand and electricity prices. Employing a comprehensive evaluation framework, the research assesses the impacts of different charging strategies on operational costs and battery degradation. Multi-stage stochastic programming is applied to account for uncertainties in electricity prices and electric vehicle charging demand. The findings demonstrate that smart charging can significantly reduce expected energy costs, achieving a 10% cost decrease and reducing battery degradation by up to 30%. We observe that the additional cost reductions from allowing Vehicle-to-Grid supply compared to smart charging are small. Using the additional flexibility aggravates degradation, which reduces the total cost benefits. This means that most benefits are obtainable just by optimized the timing of the charging itself.

Batteries are crucial to manage the rising share of intermittent energy sources and variability in demand. ost techno-economic models in the literature oversimplify battery degradation representation. Accounting properly for battery degradation allows for better cost tradeoffs and optimal battery usage, especially in dynamic settings. We propose a highly accurate and scalable formulation for battery degradation that considers the combined impact of cycle depth and state of charge on calendar and cycle aging. We test the consequences of battery degradation in a stylized price arbitrage model on battery operation and solution times. When ignoring battery degradation, ex-post calculations reveal hidden degradation costs that exceed revenues and hence turn seemingly profitable trades into losing trades. Considering battery degradation leads to smaller cycle depths and lower average states of charge. Overall, we show that a much-improved representation of battery degradation is possible at modest computational cost.

In response to the increasing integration of renewable energy and the resultant challenges of grid congestion, this paper explores the techno-economic potential of Power-to-Gas technology in congestion management. As the grid struggles to accommodate fluctuating renewable energy infeeds, Power-to-Gas emerges as a promising solution by converting surplus electricity into synthetic natural gas, thereby leveraging the existing gas infrastructure for energy storage and enhancing grid flexibility. This study, through a comprehensive literature review and a two-stage model analysis, evaluates Power-to-Gas's role in mitigating grid congestion, optimising energy dispatch, and supporting the transition towards a more sustainable and resilient energy system. Our findings underscore Power-to-Gas's capacity to reduce redispatch volumes by 3.4% and associated costs by 1.7%, while simultaneously facilitating a higher penetration of renewable sources into the energy mix. By integrating economic dispatch and ex-post redispatch processes within our model, we demonstrate how Power-to-Gas can serve as a critical tool for system operators in managing grid congestion effectively, thus addressing the urgent need for innovative solutions in the face of increasing renewable energy integration. This research contributes to the ongoing discourse on energy system flexibility, offering insights into Power-to-Gas's potential to enhance the reliability and sustainability of the electricity supply.

Energy islands are meant to facilitate offshore sector integration by combining offshore wind energy with power-to-x technologies and storage. In this study, we investigate the operation of electrolysers on energy islands, assess their potential contribution to flexibility provision, and analyse different market integration strategies of the islands. For this purpose, a two-stage stochastic optimisation model is developed to determine the cost-efficient dispatch for an integrated day-ahead and balancing electricity market. For the market integration of the energy island, we align our approach to the current debate and compare the case of a single offshore bidding zone to a case where the energy island is integrated into a home market zone. We find that electrolysers on energy islands will run at low capacity factors and provide flexibility in 29–36% of their run time. In addition, offshore electrolysers produce more hydrogen when they are allocated to an offshore bidding zone, and thus earn higher profits. We conclude that combining offshore wind with electrolysers on an energy island relies on additional economic incentives if their main role is envisioned to be the delivery of balancing flexibility.

Profit-maximizing firms hedge risk from uncertainty by deciding on capacity investment and production. Typically, risk-averse firms monotonically forgo expected profit in exchange for an improved risk measure, e.g., conditional value-at-risk (CVaR). However, the stochastic-equilibrium literature exhibits non-monotonicities, i.e., both CVaR and expected profit increase with risk aversion. We prove that this result arises because oligopolistic firms account for the price impacts of their own decisions but ignore those of other firms. Consequently, firms reduce capacity “too much” with risk aversion.

In light of offshore wind expansions in the North and Baltic Seas in Europe, further ideas on using offshore space for renewable-based energy generation have evolved. One of the concepts is that of energy islands, which entails the placement of energy conversion and storage equipment near offshore wind farms. Offshore placement of electrolysers will cause interdependence between the availability of electricity for hydrogen production and for power transmission to shore. This paper investigates the trade-offs between integrating energy islands via electricity versus hydrogen infrastructure. We set up a combined capacity expansion and electricity dispatch model to assess the role of electrolysers and electricity cables given the availability of renewable energy from the islands. We find that the electricity system benefits more from connecting close-to-shore wind farms via power cables. In turn, electrolysis is more valuable for far-away energy islands as it avoids expensive long-distance cable infrastructure. We also find that capacity investment in electrolysers is sensitive to hydrogen prices but less to carbon prices. The onshore network and congestion caused by increased activity close to shore influence the sizing and siting of electrolysers.

Methane is the second-largest contributor to global warming due to anthropogenic greenhouse gas emissions. Reducing anthropogenic methane emissions quickly can significantly reduce global warming within just a few decades. The oil and gas sector is responsible for almost 20% of anthropogenic methane emissions. Yet, there are hardly any policies in place that address oil and gas sector methane emissions. We investigate two policy types: a) a global cap on methane emissions from the oil and gas sector; and b) a methane price implemented by a Clean Buyers Coalition. We extend a detailed global gas market model to allow investment in methane emission abatement measures. We find that the regional contribution to global reductions vary due to different mitigation potentials and associated abatement technology cost. Clean Buyers Coalitions can trigger major investment in methane abatement measures and much reduced emissions. However, a methane price must be balanced against available abatement potentials, as upstream suppliers lacking abatement options rather avoid abatement investments and, instead, re-direct their exports to non-Coalition importers.

District heating is an under-researched part of the energy system, notwithstanding its enormous potential to contribute to Greenhouse Gas emission reductions. Low-temperature district heating is a key technology for energy-efficient urban heat supply as it supports an efficient utilization of low-grade waste-heat and renewable heat sources. The low operating temperature for such grids facilitates the integration of seasonal thermal energy storage, enabling a high degree of operational flexibility in the utilization of both uncontrollable and controllable heat sources. Yet, an inherent challenge of optimizing the operation of low-temperature district heating networks and its flexibility is the underlying uncertainty in heat demand. We develop a new stochastic model to minimize the total operational cost of district heating networks with local waste heat utilization, seasonal storage and uncertain demand. We consider in particular how demand side management and seasonal storage can improve the operational flexibility and thereby reduce costs. We analyze different set-ups of a local low-temperature district heating network under development in a new residential area in Trondheim, Norway. We find up to 37% reductions in carbon dioxide emissions, 29% generation reduction in peak hours, and 10% lower operational costs. These large values highlight the significance of flexibility options in low-temperature district heating networks for cost-effective, large-scale deployment.

The energy transition faces the challenge of increasing levels of decentralised renewable energy injection into an infrastructure originally laid out for centralised, dispatchable power generation. Due to limited transmission capacity and flexibility, large amounts of renewable electricity are curtailed. In this paper, we assess how Power-to-Gas facilities can provide spatial and temporal flexibility by shifting pressure from the electricity grid to the gas infrastructure. For this purpose, we propose a two-stage model incorporating the day-head spot market and subsequent redispatch. We introduce Power-to-Gas as a redispatch option and apply the model to the German electricity system. Instead of curtailing renewable electricity, synthetic natural gas can be produced and injected into the gas grid for later usage. Results show a reduction on curtailment of renewables by 12% through installing Power-to-Gas at a small set of nodes frequently facing curtailment. With the benefits of decentralised synthetic natural gas injection and usage, we exploit the advantages of coupling the two energy systems. The introduction of Power-to-Gas provides flexibility to the electricity system, while contributing to a higher effective utilisation of renewable energy sources as well as the natural gas grid.

State-of-the-art, open access numerical modeling of imperfectly competitive energy markets offers a sound and transparent way to address topical research questions in energy and commodity markets. We use an open access equilibrium model, the Global Gas Model (GGM), and sector-specific, politically motivated scenarios to investigate the prospects for sales of liquefied natural gas (LNG) from the U.S. into the European energy market. We discuss the risks and opportunities for U.S. LNG and derive implications for policy, business, and finance in the energy sector. We find that Europe is not an attractive market for US LNG in the base case and in scenarios of moderate support of U.S. LNG flows into Europe. In these scenarios, Asia offers higher prices for US LNG and draws substantially higher import volumes. Our modeling results show that the interconnectedness of global gas markets due to an abundance of LNG import capacity in Europe and other regions—particularly Asia—allows for adjustments to global trade patterns that mitigate the consequences of regional disturbances.

The approach of choice to analyze markets with oligopolistic competition has traditionally been complementarity modeling. In this paper we show that the majority of partial equilibrium models under imperfect competition in the (energy-)economic literature can in fact be cast as optimization models, not requiring the derivation and implementation of Karush-Kuhn-Tucker conditions. This is achieved by adding appropriate terms accounting for market power exertion to the well-known social welfare maximization objective. The method is applicable to both spatial Cournot oligopoly models and hybrid competition forms often implemented using conjectural variation approaches. We show how optimization and complementarity problems are equivalent, and provide a rationale for the terms accounting for market power exertion. Resulting models are solved orders of magnitude faster using off-the-shelf optimization software, compared to solving complementarity problems. Large problem instances take minutes rather than hours, and one instance solves 640 times faster. The drastically reduced solution times greatly enhance modeling capabilities as they allow increased geographical scope and represent economic, technical and other characteristics in much more detail in equilibrium problems with imperfect competition. We present practical implications for the partial and multi-level equilibrium modeling community. (C) 2020 The Authors. Published by Elsevier B.V.

We describe the elements and actors in the global natural gas value chains with an emphasis on characteristics relevant for large-scale energy system and market modeling. We give backgrounds on natural gas as a hydrocarbon to provide a rationale and understanding for what functional representations in mathematical programming models aim to represent. Simply taking the most advanced and detailed functional forms for all value chain characteristics and activities will typically result in numerical intractability. One should carefully determine what is needed to address a research question or analyze a business case. Recent advances in mathematical programming do allow solving large models with adequate detail for many types of analysis. We discuss which functional forms and modeling approaches can be appropriate for representing various characteristics in different types of analysis and provide a succinct and general mathematical programming formulation reflecting the optimization problems for different types of actors in the value chain. We provide an implementation for a stylized network using GAMS.

In this paper, we focus on the development of European gas infrastructure in the energy transition with particular focus on the so-called Projects of Common Interest (PCIs). To this end, three models with different spatial-temporal resolutions and information structures have analyzed the European natural gas infrastructure developments over the period 2015-2050. We evaluate the gas market and infrastructure towards 2050, what additional infrastructure is needed and what is the socioeconomic value of planned PCI projects. We present endogenous capacity expansions based on three similar gas models and perform an ex-post analysis of PCI projects. Some PCI project are favoured by all analyses, whereas for others results are mixed or we find no support at all. We observe that the decarbonization goals do not need much investment on gas infrastructure, as the demand projections of PRIMES (both reference and EUCO30) are showing a decreasing gas demand, that can be served by the current infrastructure and a limited number of PCIs are needed despite the decreasing domestic production within the EU. The results are robust in this sense as three modelling tools using the same input data but different model structure and granularity delivered similar outputs.

The interaction between the transmission and distribution system operators is mainly based on a unidirectional flow of information (transmission-to-distribution system operators). The resources in the distribution systems are, hence, not utilized fully in overall power system operations, regardless that they may serve as sources of flexibility to manage renewable energy sources fluctuations. In the existing literature, is not very clear how the coordination of the transmission and distribution system operators can and should be modelled. Accordingly, there is limited insights to the potential value coordination may bring to the overall power system operations. As a result, this paper presents a modelling approach for coordination of the transmission and distribution system operators, where flexibility is provided by distributed energy resources located in the distribution systems. Key findings suggest that the total costs of power system operations are reduced when distributed flexible resources are incorporated under a joint coordination framework.

Previous studies of regional wage formation in Norway have indicated low regional wage responses to regional unemployment. However, previous analyses have not investigated whether wages are more rigid in rural areas than in urban areas, which in the case of Norway is important for the efficiency of regional differentiated payroll taxes. In the paper, the authors focus on the rural-urban nature of the wage curve in Norway. We re-estimate the wage curve on the basis of a large Norwegian microlevel dataset covering the entire Norwegian labour market during the years 2008-13. Our findings are rural-urban heterogeneity in the wage curve, with higher unemployment elasticity of pay in the urban region than in the rural regions. The elasticity of the average rural wage curve is about 70 per cent that of the of the urban wage curve. The authors conclude that to achieve the goals of regional policy in Norway, more rigid wages in rural areas seem to be an argument for continuing to have an active labour market policy for rural regions.

In the energy strategy of the European Union, the end-user is envisioned as a key participant in the future electricity market (European Commission, [16]). Current market designs and business models lack incentives and opportunities for regular electricity consumers (e.g. residential buildings) to become prosumers and actively participate in the market. Incentives should include economic and behavioural motivation beyond subsidised flat feed-in tariffs. Opportunities should allow for active participation of prosumers with relatively modest generation volumes but significant flexibility. In this paper, we propose a framework to integrate prosumer communities into the existing day-ahead and intraday markets. Using a two-stage stochastic programming approach, we incorporate the sequenced decision-making in the wholesale system under uncertainty of renewable generation and spot prices. We focus on the value of peer-to-peer (P2P) trading in the integration of prosumers in the day-ahead and intraday markets and investigate how residential battery storage contributes to local demand side flexibility in an integrated market setting. To this end, we introduce the Smart elecTricity Exchange Platform (STEP) that represents the interface between the wholesale electricity markets and the prosumer communities, and coordinates the community’s operational supply-demand decisions. A study on residential buildings in London show that both P2P trade and battery storage by themselves each induce a reduction of electricity bills by 20%–30%. Combined, P2P trade and battery storage may lead to savings of almost 60%. In other words, we find that peer-to-peer trade and flexibility options such as local storage generate higher levels of the community’s self-sufficiency.

Point data observations are often used to calibrate computable general equilibrium (CGE) models; however, results may be impacted by calibration of an Armington trade specification in a regional CGE (R-CGE) model. This paper calibrates an Armington trade specification with three differently estimated interregional trade data sets. It estimates interregional trade with one survey and two non-survey methods. The resulting three different trade data sets are each used to calibrate REMES, an R-CGE model for Norway. Two regional policy reforms are simulated with the three model versions to analyze the sensitivity of regional manufacturing sector output to trade data estimates. The results show that the trade data estimation method used for calibration significantly affects regional sector output results. Policy analysts and developers should be aware that calibrating an Armington trade specification with differently estimated interregional trade data may have a substantial influence on model results, and hence, on ex-ante and ex-post conclusions on policy impacts.

Deployment of distributed generation technologies, especially solar photovoltaic, have turned regular consumers into active contributors to the local supply of electricity. This development along with the digitalisation of power distribution grids (smart grids) is setting the scene to a new paradigm: peer-to-peer electricity trading. The design of the features and rules on how to sell or buy electricity locally, however, is in its early stages for microgrids or small communities. Market design research focuses predominantly on established electricity markets and not so much on incentivising local trading. This is partially because concepts of local markets carry distinct features: the diversity and characteristics of distributed generation, the specific rules for local electricity prices, and the role of digitalisation tools to facilitate peer-to-peer trade (e.g. Blockchain). As different local or peer-to-peer energy trading schemes have emerged recently, this paper proposes two market designs centred on the role of electricity storage. That is, we focus on the following questions: What is the value of prosumer batteries in P2P trade?; What market features do battery system configurations need?; and What electricity market design will open the economical potential of end-user batteries? To address these questions, we implement an optimisation model to represent the peer-to-peer interactions in the presence of storage for a small community in London, United Kingdom. We investigate the contribution of batteries located at the customer level versus a central battery shared by the community. Results show that the combined features of trade and flexibility from storage produce savings of up to 31% for the end-users. More than half of the savings comes from cooperation and trading in the community, while the rest is due to battery's flexibility in balancing supply-demand operations.