European decarbonization policies are accelerating the shift from fossil-based generation to Variable Renewable Energy Sources (VRES). While essential for climate goals, this transition challenges system reliability due to renewable variability. Demand Response (DR), and particularly Direct Load Control (DLC), offers a promising solution to enhance flexibility by shifting electricity demand. This study assesses industrial DLC at the European level within long-term investment planning. A database of industrial load profiles and demand projections is developed for 28 countries over 2025-2060 and integrated into the open-source EMPIRE model, a multi-period stochastic capacity expansion model for the European power sector, extended with a DLC module for load shifting. Results show savings between 0.44% and 1.47% compared to the No DLC case, with activated DLC accounting for 2-5% of the Maximum Reduction potential. The flexibility rate amplifies the benefits of longer shift windows, reducing Li-ion BESS utilization by up to 18.49% and increasing solar generation and installed capacity.

This Review examines the role of carbon capture and storage (CCS) in achieving net-zero emissions by 2050, focusing on its scale-up and integration across energy systems and hard-to-abate industries. Its interdisciplinary approach provides a comprehensive review of the state-of-the-art in CCS research, evaluating its potential role in achieving net-zero emissions. It assesses not only technological advancements and characteristics but also the critical costs and energy requirements of various CCS technologies. Based on modelling insights from the International Energy Agency Net Zero Emissions pathway, it highlights the need to scale CCS deployment to 1 Gt CO₂ annually by 2030 to stay on track for climate goals. This review piece underscores the urgency of rapid CCS scale-up this decade, complementing other measures across energy and industry. Furthermore, it assesses the recent advancements in CO₂ capture, transport, and storage technologies, along with their techno-economic characteristics and results in energy system models. The study concludes by identifying key challenges and providing a strategy roadmap for decision-makers for accelerating CCS deployment.

This paper examines the roles of long-run and short-run marginal costs (LRMC and SRMC) in shaping electricity prices and ensuring investment cost recovery, particularly when generation capacity is used across multiple long-term periods. Using a stylized capacity expansion model with two generators and two periods, we developed a five-step methodology to characterize all possible LRMC pricing profiles and prove cost recovery under each case. We showed how shared capacity affects intertemporal cost allocation, revealing that even when cheaper technologies are marginal, more expensive shared capacity can still recover its cost through distribution across periods. On the SRMC side, we identified a form of degeneracy caused by fixed invested capacities, leading to multiple valid marginal prices. To resolve price degeneracy, we add a small demand elasticity centered on the LRMC reference point. This yields a unique SRMC price that coincides with LRMC and guarantees cost recovery under energy-only pricing. Extensions to the model, such as increasing temporal resolution, adding storage, or including more generators, demonstrated the robustness of our findings.

Reducing reliance on fossil fuels is essential for achieving international climate goals, but an immediate phase-out can create economic and system reliability challenges. A transitional option is to equip fossil fuel power plants with carbon capture and storage to reduce emissions. Here, we use a numerical capacity expansion model of the European power sector to explore how different carbon capture and storage deployment strategies, particularly high-capture-rate technologies that remove nearly all emissions, affect long-term system outcomes. Using scenario-based analysis, we find that cost-efficient decarbonization can be achieved through a combination of renewable energy and carbon capture and storage, including standard technologies that remove most emissions and advanced options that remove nearly all emissions. By 2050, fossil-based generation equipped with carbon capture and storage supplies a substantial share of electricity in many scenarios, exceeding today’s unabated fossil generation. However, relying exclusively on renewable energy and carbon capture and storage becomes increasingly costly at high levels of decarbonization, indicating that carbon dioxide removal is needed to achieve climate targets in a cost-efficient manner. The results provide insights into policy and infrastructure requirements for sustainable long-term deployment. Capacity-expansion modeling of Europe’s power system shows that combining standard and high-capture-rate carbon capture and storage can deliver cost-efficient decarbonization but still requires substantial carbon removal and carbon dioxide storage infrastructure.

Europe is warming at the fastest rate of all continents, experiencing a temperature increase of about 1 degrees C higher than the corresponding global increase. Aiming to be the first climate-neutral continent by 2050 under the European Green Deal, Europe requires an in-depth understanding of the potential energy transition pathways. In this paper, we develop four qualitative long-term scenarios covering the European energy landscape until 2060, considering key uncertainty pillars- categorised under social, technological, economic, political, and geopolitical dimensions. First, we place the scenarios in a three-dimensional space defined by Social dynamics, Innovation, and Geopolitical instabilities. These scenarios are brought to life by defining their narratives and focus areas according to their location in this three-dimensional space. The scenarios envision diverse futures and include distinct features. The EU Trinity scenario pictures how internal divisions among EU member states, in the context of global geopolitical instability, affect the EU climate targets. The REPowerEU++ scenario outlines the steps needed for a self-sufficient, independent European energy system by 2050. The Go RES scenario examines the feasibility of achieving carbon neutrality earlier than 2050 given favourable uncertain factors. The NECP Essentials scenario extends current national energy and climate plans until 2060 to assess their role in realising climate neutrality. The scenarios are extended by incorporating policies and economic factors. They are then detailed in a Qualitative to Quantitative (Q2Q) matrix, linking narratives to quantification. Finally, two scenarios (Go RES and REPowerEU++) are quantified using the open-source energy system model GENeSYS-MOD to illustrate the quantification process.

Recent developments in decomposition methods for multi-stage stochastic programming with block separable recourse enable the solution to large-scale stochastic programs with multi-timescale uncertainty. Multi-timescale uncertainty is important in energy system planning problems. Therefore, the proposed algorithms were applied to energy system planning problems to demonstrate their performance. However, the impact of multi-timescale uncertainty on energy system planning is not sufficiently analysed. In this paper, we address this research gap by comprehensively modelling and analysing short-term and long-term uncertainty in energy system planning. We use the REORIENT model to conduct the analysis. We also propose a parallel stabilised Benders decomposition as an alternative solution method to existing methods. We analyse the multi-timescale uncertainty regarding stability, the value of the stochastic solution, the rolling horizon value of the stochastic solutions and the planning decisions. The results show that (1) including multi-timescale uncertainty yields an increase in the value of the stochastic solutions, (2) long-term uncertainty in the right-hand side parameters affects the solution structure more than cost coefficient uncertainty, (3) parallel stabilised Benders decomposition is up to 7.5 times faster than the serial version.

We propose the REORIENT (REnewable resOuRce Investment for the ENergy Transition) model for energy systems planning with the following novelties: (1) integrating capacity expansion, retrofit and abandonment planning, and (2) using multi-horizon stochastic mixed-integer linear programming with multi-timescale uncertainty. We apply the model to the European energy system considering: (a) investment in new hydrogen infrastructures, (b) capacity expansion of the European power system, (c) retrofitting oil and gas infrastructures in the North Sea region for hydrogen production and distribution, and abandoning existing infrastructures, and (d) long-term uncertainty in oil and gas prices and short-term uncertainty in time series parameters. We utilise the structure of multi-horizon stochastic programming and propose a stabilised adaptive Benders decomposition to solve the model efficiently. We first conduct a sensitivity analysis on retrofitting costs of oil and gas infrastructures. We then compare the REORIENT model with a conventional investment planning model regarding costs and investment decisions. Finally, the computational performance of the algorithm is presented. The results show that: (1) when the retrofitting cost is below 20% of the cost of building new ones, retrofitting is economical for most of the existing pipelines, (2) platform clusters keep producing oil due to the massive profit, and the clusters are abandoned in the last investment stage, (3) compared with a traditional investment planning model, the REORIENT model yields 24% lower investment cost in the North Sea region, and (4) the enhanced Benders algorithm is up to 6.8 times faster than the level method stabilised adaptive Benders.

The European Union aims to deploy a high share of renewable energy sources in Europe's power system by 2050. Large-scale intermittent wind and solar power production requires flexibility to ensure an adequate supply-demand balance. Green hydrogen (GH) can increase power systems' flexibility and decrease renewable energy production's curtailment. However, investing in GH is costly and dependent on electricity prices, which are important for operational costs in electrolysis. Moreover, the use of GH for power system flexibility might not be economically viable if there is no hydrogen demand from the hydrogen market. If so, questions would arise as to, what would be the incentives to introduce GH as a source of flexibility in the power system, and how would electrolyzer costs, hydrogen demand, and other factors affect the economic viability of GH usage for power system flexibility. The paper implements a European power system model formulated as a stochastic program to address these questions. The authors use the model to compare various instances with hydrogen in the power system to a no-hydrogen instance. The results indicate that by 2050 deployment of approximately 140 GW of GH will pay off investments and make the technology economically viable. We find that the price of hydrogen is estimated to be around 30/MWh.

Europe is warming at the fastest rate of all continents, experiencing a temperature increase of about 1C higher than the corresponding global increase. Aiming to be the first climate-neutral continent by 2050 under the European Green Deal, Europe requires an in-depth understanding of the potential energy transition pathways. In this paper, we develop four qualitative long-term scenarios covering the European energy landscape, considering key uncertainty pillars – categorized under social, technological, economic, political, and geopolitical aspects. First, we place the scenarios in a three-dimensional space defined by Social dynamics, Innovation, and Geopolitical instabilities. These scenarios are brought to life by defining their narratives and focus areas according to their location in this three-dimensional space. The scenarios envision diverse futures and include distinct features. The EU Trinity scenario pictures how internal divisions among EU member states, in the context of global geopolitical instability, affect the EU climate targets. The REPowerEU++ scenario outlines the steps needed for a self-sufficient, independent European energy system by 2050. The Go RES scenario examines the feasibility of achieving carbon neutrality earlier than 2050 given favourable uncertain factors. The NECP Essentials scenario extends current national energy and climate plans until 2060 to assess their role in realizing climate neutrality. The scenarios are extended by incorporating policies and economic factors and detailed in a Qualitative to Quantitative (Q2Q) matrix, linking narratives to quantification. Finally, two scenarios are quantified to illustrate the quantification process. All the scenarios are in the process of being quantified and will be openly available and reusable.

In international power systems, a proposed cross-country transmission cable that is beneficial to the system as a whole can be detrimental to the economic welfare of a specific country. If this country is one of the countries hosting the cable, it has the ability to veto the investment, thereby harming the system as a whole. To counter this issue, welfare compensations have been proposed in the literature that compensate a country's expected welfare loss. However, the actual welfare effects of a new cable are uncertain and there is no guarantee that a compensation scheme based on expected welfare effects is sufficient to compensate for the actual, realized welfare effects. This paper investigates the potential of different mechanisms to compensate the realized welfare effects of a new transmission cable. Two novel mechanisms based on the realized flow through the new cable are proposed, and compared with mechanisms from the literature that are based on expected welfare gains. Using a case study of a cable between Norway and Germany, the performance of the various compensation mechanisms is numerically assessed. The results suggest that the novel flow-based mechanisms can mitigate negative welfare effects and reduce risk, making investments more attractive.

This chapter presents a multi-stage multi-horizon stochastic equilibrium model for analyzing energy markets, with particular attention paid to infrastructure development in perfect and imperfect market structures. By decoupling short-term operational decisions’ feedback from long-term strategic investment decisions, the multi-horizon scenario tree approach allows us to consider long-term and short-term uncertainties while maintaining computational tractability. Numerical experiments demonstrate how considering long-term and short-term uncertainties affects infrastructure development. Independent of market structure, the investment in renewable production and transformation increases when considering the long-term uncertainty in gas production cost, but the investments are reduced when the uncertainty of renewable production is also considered.

Zonal markets and nodal pricing are the dominant designs for liberalized electricity markets. We propose an alternative design that changes zones in each bidding period according to the estimated most efficient dispatch. These flexible electricity market clearing zones consider the grid's physical constraints to a larger degree than zonal markets but maintain their bidding simplicity and few price areas. We propose a proof-of- concept framework for flexible electricity market clearing zones, including a method to enumerate all zonal configurations. We illustrate the performance of this framework on a case study in the Nordic countries using flow-based market clearing (FBMC), considering a model for the day-ahead market and a real-time balancing market. Our results suggest that flexible electricity market clearing zones on sequential day-ahead and real-time balancing markets achieve costs slightly above nodal stochastic clearing. But, contrary to stochastic clearing, it can guarantee short-term revenue adequacy and cost recovery. Moreover, the flexible market design increases day-ahead market price levels and price variability at the nodal level, particularly in scenarios with high renewable generation, demonstrating its capacity to align price signals with network congestion and real-time supply conditions. Flexible electricity market clearing zones can thus facilitate the integration of renewables by enhancing system adaptability and promoting more efficient resource allocation.

The integration of volatile renewable energy sources into the electric grid and the corresponding rise in power demand due to electrification is creating challenges for congestion management (CM). CM aims at optimising the grid capacity utilisation and thereby enables hosting more distributed energy resources. Flexible resources have the potential to reduce the costs associated with CM, which raises the demand for an efficient framework for trading flexibility. The aggregated flexibility from stakeholders such as energy communities serves for valid bids into large-scale flexibility markets. This paper develops a market design aimed at improving CM by examining the concept of zonal flexibility. The design utilises zones that are dynamically allocated based on grid congestion after the day-ahead market. The proposed market design is compared to both the optimal utilisation of flexible resources and to conventional redispatch in order to evaluate its economic efficiency. Our findings indicate that it performs close to a nodal flexibility market. Future work needs to develop more refined partitioning algorithms to stabilise the performance across various scenarios.

In power markets, understanding the cost dynamics of electricity generation is crucial. The complexity of price formation in the power system arises from its diverse attributes, such as various generator types, each characterized by its specific fixed and variable costs as well as different lifetimes. In this paper, we adopt an approach that investigates both long-run marginal cost (LRMC) and short-run marginal cost (SRMC) in a perfect competition market. According to economic theory, marginal pricing serves as an effective method for determining the generation cost of electricity. This paper presents a capacity expansion model designed to evaluate the marginal cost of electricity generation, encompassing both long-term and short-term perspectives. Following a parametric analysis and the calculation of LRMCs, this study investigates the allocation of investment costs across various time periods and how these costs factor into the LRMC to ensure cost recovery. Additionally, an exploration of SRMCs reveals the conditions under which LRMCs and SRMCs converge or diverge. We observe that when there is a disparity between LRMC and SRMC, setting electricity generation prices equal to SRMCs does not ensure the complete recovery of investment and operational costs. This phenomenon holds implications for market reliability and challenges the pricing strategies that rely solely on SRMCs. Furthermore, our investigation highlighted the significance of addressing degeneracy in the power market modeling. Primal degeneracy in the SRMC model can result in multiple values for the dual variable representing SRMC. This multiplicity of values creates ambiguity regarding the precise SRMC value, making it challenging to ascertain the correct estimation. As a result, resolving degeneracy will ensure the reliability of the SRMC value, consequently enhancing the robustness and credibility of our analysis.

Multi-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.

With the transition towards a decarbonized society, energy system integration is becoming ever more essential. In this transition, the energy vector hydrogen is expected to play a key role as it can be produced from (renewable) power and natural gas with carbon capture and storage and utilized in a plethora of applications and processes across sectors. Despite global hydrogen demand reaching 94 million tonnes in 2021, still less than 0.7% of it is supplied by low-emission hydrogen. Moreover, to date hydrogen production is mostly located in close proximity to where it is used. In order to link future production and demand sites, it is planned to re-purpose existing natural gas and expand dedicated hydrogen pipelines. During the early stages of ramping up the hydrogen sector (2020s and early 2030s), however, blending natural gas with hydrogen for joint pipeline transmission has been suggested. Against this background, this paper studies hydrogen blending from a modeling perspective, both in terms of the implications of considering (or omitting) technical modeling details and in terms of the potential impact on the ramp-up of the hydrogen sector. To this end, we present a highly modular and flexible integrated sector-coupled energy system optimization model of the power, natural gas, and hydrogen sectors with a novel gas flow formulation for modeling blending in the context of steady-state gas flows. A stylized case study illustrates that hydrogen blending has the potential to initiate and to facilitate the ramp-up of the hydrogen sector under certain assumptions, while omitting the technical realities of gas flows – particularly in the context of blending – can result in suboptimal expansion planning not only in the hydrogen, but also in the power sector, as well as in an operationally infeasible system.

Benders decomposition with adaptive oracles was proposed to solve large-scale optimisation problems with a column-bounded block-diagonal structure, where subproblems differ only in the right-hand side and cost coefficients. Adaptive Benders reduces computational effort significantly by iteratively building inexact cutting planes and valid upper and lower bounds. However, Adaptive Benders and standard Benders may suffer severe oscillation when solving degenerate models. Therefore, we propose stabilising Adaptive Benders with the level method and adaptively selecting which subproblems to solve each iteration for more accurate information. In addition, we propose a dynamic level method to improve the robustness of stabilised Adaptive Benders by adjusting the level set each iteration. We compare stabilised Adaptive Benders with the unstabilised versions of Adaptive Benders with one subproblem solved per iteration and standard Benders on a multi-region long-term power system investment planning problem with short-term and long-term uncertainty. The problem is formulated as multi-horizon stochastic programming. Four algorithms were implemented to solve linear programming with up to 1 billion variables and 4.5 billion constraints. The computational results show that: (a) for a 1.00% convergence tolerance, the proposed stabilised method is up to 113.7 times faster than standard Benders and 2.1 times faster than unstabilised Adaptive Benders; (b) for a 0.10% convergence tolerance, the proposed stabilised method is up to 45.5 times faster than standard Benders and unstabilised Adaptive Benders cannot solve the largest instance to convergence tolerance due to severe oscillation and (c) dynamic level method makes stabilisation more robust.

In this paper, we consider a multi-period facility location problem with capacity expansion motivated by the real-world problem of establishing hydrogen production infrastructure in Norway. The problem is formulated using modular capacities that capture economies of scale in production costs. The costs of opening a facility are represented by concave long-term costs, while the production costs of each capacity level are given by convex short-term costs. In our model, we allow only one expansion during the planning horizon, and have to observe limits on minimum production quantities. The objective is to minimize the sum of investment, expansion, production, and distribution costs while satisfying customer demand. To solve the problem we implement a solution method based on Lagrangian relaxation. The lower bound is calculated using a dynamic programming approach. To obtain an upper bound solution, we develop a greedy heuristic that converts the solution to the Lagrangian dual into a feasible solution. The approach is tested on different problem instances based on real-world data. The results show that our solution method based on Lagrangian relaxation outperforms Gurobi in terms of run time for all tested instances. Our Lagrangian based approach also always finds good or even near-optimal solutions, whereas Gurobi fails to find feasible solutions for some of the larger instances.

Hydrogen and carbon capture and storage are pivotal to decarbonize the European energy system in a broad range of pathway scenarios. Yet, their timely uptake in different sectors and distribution across countries are affected by supply options of renewable and fossil energy sources. Here, we analyze the decarbonization of the European energy system towards 2060, covering the power, heat, and industry sectors, and the change in use of hydrogen and carbon capture and storage in these sectors upon Europe's decoupling from Russian gas. The results indicate that the use of gas is significantly reduced in the power sector, instead being replaced by coal with carbon capture and storage, and with a further expansion of renewable generators. Coal coupled with carbon capture and storage is also used in the steel sector as an intermediary step when Russian gas is neglected, before being fully decarbonized with hydrogen. Hydrogen production mostly relies on natural gas with carbon capture and storage until natural gas is scarce and costly at which time green hydrogen production increases sharply. The disruption of Russian gas imports has significant consequences on the decarbonization pathways for Europe, with local energy sources and carbon capture and storage becoming even more important.

This paper assesses the impact of natural gas and hydrogen blending in integrated energy system modeling based on a novel blending transport problem (B-TP). In contrast to a standard transport problem which oversimplifies technical realities of pipeline gas flows in the context of blending, the B-TP (mixed-integer linear program) ensures compliance with the maximum hydrogen blending rate and that natural gas and hydrogen flow in the same direction in a pipeline. To assess the impact of the B-TP, we formulate an expansion planning optimization model of the integrated power, natural gas, and hydrogen sectors based on the objective of minimizing total system cost. Our case study shows that the gas flow formulation strongly influences investment decisions in the power and hydrogen sectors and that omitting the technical realities of blending can ultimately lead to suboptimal infrastructure planning (represented in the model as "regret" in the form of non-supplied hydrogen).