Detailed analysis of technological learning of energy technologies is scarce. For floating wind, this is missing altogether. In this study, we applied experience curve and bottom-up cost modeling methodologies and assessed the long-term cost reduction potential of fixed-bottom and floating offshore wind in their mature markets. The contributing factors to cost reduction are also quantified and elaborated. Further, to emphasize the role of strongly varying site characteristics of offshore wind farms and their influences, the grid connection cost is separately discussed from the total technology costs (Capital Expenditure and LCoE). Our assessment shows that, excluding grid connection costs, fixed-bottom offshore wind LCoE is 40 €/MWh at 31 GW cumulative capacity (2023–2024) and decline to 28 ± 3 €/MWh by 100 GW. Floating wind LCoE is 123 €/MWh at 1 GW cumulative capacity (2027 – 2030) but decline to 33 ± 6 €/MWh by 100 GW. Moreover, floating wind can achieve cost parity (i.e., 40 €/MWh, excl. grid connection cost) by deploying 21 GW, requiring 44 billion € of learning investment in the form of subsidies to compensate the price gap for the technology in the energy system. Lastly, we analyzed the grid connection costs and their influencing factors, and then determined that an integrated offshore grid would be needed to efficiently connect future offshore wind farms to the onshore grid.

In this work, we present a generic multi‐period integrated infrastructure planning and operational scheduling approach for developing energy systems. The methodology is applicable to energy systems with multiple resources, locations, processing pathways, and planning periods in which infrastructure decisions can be carried out. The framework incorporates a graph based approach to mode based scheduling, in which the mode transitions are mapped to a directed graph. We illustrate the applicability and effectiveness of the overall framework through the use of a case study examining the long‐term development of a multi‐site energy‐intensive hydrogen based energy system in Texas. In the case study, we find that developing the energy system over the course of its operational life as opposed to the beginning of its operational life reduces its capital and operational cost by over 20%.

Sustainable aviation fuels can cut emissions of the aviation sector. Using a holistic approach to transform solar energy into fuels, we discuss the scale of resources consumed, technology deployed, and regional drivers for their network development.

There is growing interest in hydrogen (H$_2$) use for long-duration energy storage in a future electric grid dominated by variable renewable energy (VRE) resources. Modelling the role of H$_2$ as grid-scale energy storage, often referred as power-to-gas-to-power (P2G2P) overlooks the cost-sharing and emission benefits from using the deployed H$_2$ production and storage assets to also supply H$_2$ for decarbonizing other end-use sectors where direct electrification may be challenged. Here, we develop a generalized modelling framework for co-optimizing energy infrastructure investment and operation across power and transportation sectors and the supply chains of electricity and H$_2$, while accounting for spatio-temporal variations in energy demand and supply. Applying this sector-coupling framework to the U.S. Northeast under a range of technology cost and carbon price scenarios, we find a greater value of power-to-H$_2$ (P2G) versus P2G2P routes. P2G provides flexible demand response, while the extra cost and efficiency penalties of P2G2P routes make the solution less attractive for grid balancing. The effects of sector-coupling are significant, boosting VRE generation by 12-55% with both increased capacities and reduced curtailments and reducing the total system cost (or levelized costs of energy) by 6-14% under 96% decarbonization scenarios. Both the cost savings and emission reductions from sector coupling increase with H$_2$ demand for other end-uses, more than doubling for a 96% decarbonization scenario as H$_2$ demand quadraples. Moreover, we found that the deployment of carbon capture and storage is more cost-effective in the H$_2$ sector because of the lower cost and higher utilization rate. These findings highlight the importance of using an integrated multi-sector energy system framework with multiple energy vectors in planning energy system decarbonization pathways.

Due to rapid population growth, the global demand of energy and water resources are accelerating. Simultaneously, there is a global trend to transition to renewable energy systems. Recently, researchers have begun to investigate how green dense energy carriers (DECs) can be apart of this transition. In this work, we present an optimization framework for solving large-scale supply chain problems and we apply it to explore the economic and environmental impacts of DECs. Specifically, we look at utilizing DECs to transport renewable energy produced in areas with high solar and wind potentials to regions with low renewable potential. To reduce the computational burden of the large-scale optimization problem, we have developed a greedy randomized adaptive search procedure (GRASP). The GRASP leverages the linear programming (LP) relaxation of the problem to generate feasible solutions. We have found that the GRASP is able to reduce the computational time by approximately two orders of magnitude as compared to a commercial grade mixed-integer linear programming (MILP) solver ran out-of-the-box on large-scale instances.

Hydrogen is becoming an increasingly appealing energy carrier, as the costs of renewable energy generation and water electrolysis continue to decline. Developing scalable modeling and decision tools for the H-2 supply chain that fully capture the flexibility of various resources is essential to understanding the overall cost-competitiveness of H-2 use. Here, we develop a H-2 supply chain planning model that determines the least-cost mix of H-2 generation, storage, transmission, and compression facilities to meet H-2 demands and is coupled with power systems through electricity prices. We incorporate flexible scheduling for H-2 trucks and pipeline, allowing them to serve as both H-2 transmission and storage resources to shift H-2 demand/production across space and time. The proposed model provides a reasonable trade-off between modeling accuracy and computational time, with linear relaxations for truck inventory routing and H-2 production unit commitment. The case study results in the U.S. Northeast indicate that the proposed flexible scheduling of H-2 transmission and storage resources is critical not only to cost minimization but also to the choice of H-2 production pathways between electrolysis and natural gas based production. Trucks as mobile storage could make intermittent electrolytic H-2 production more competitive by providing extra spatiotemporal flexibility.

Intermittent solar and wind availabilities pose design and operational challenges for renewable power systems because they are asynchronous with consumer demand. To align this supply-demand mismatch, optimization-based design and scheduling models have been developed to minimize the capital and operational costs associated with power production and energy storage. However, hourly time discretization and large time horizons used to describe short- and long-term solar and wind dynamics, demand fluctuations, & price changes significantly increase the computational burden of solving these models. A decomposition algorithm based on agglomerative hierarchical clustering (AHC) is developed to alleviate the model complexity and optimize the system over representative time periods, instead of every hour. An advantage for AHC compared to other clustering methods is the preservation of time chronology, which is important for energy storage applications. The algorithm is applied to investigate a renewable power system with battery storage in New York City. Results show that a few representative time periods (5-15 days) sufficiently capture the system performance within 5% of the true optimal solution. The decomposition algorithm is suitable for investigating any optimization problem with time series data.

Successful integration of intermittent renewable resources into the energy mix is instrumental to meet the growing global energy demand while reducing the carbon emissions. With this study, we propose a strategy of mixed-integer linear programming-based simultaneous design and operation to explore the techno-economic feasibility of novel energy system networks including solar photovoltaics, wind turbines, battery storage, and dense energy carriers. A multiscale energy system engineering approach is followed combining process synthesis, scheduling, and supply chain concepts to address the trade-offs between various technologies in renewable power generation and storage, as well as energy carrier production and transportation across different locations. We apply our strategy to analyze the integration of hydrogen- based dense energy carriers (DECs) produced in a high-potential region of renewable energy in Texas in tandem with local solar production and battery storage in a low-potential region in New York to minimize the levelized cost of renewable electricity. Case study results show that DECs can offer 30-50% cost reductions to local power generation and battery systems when used as clean backup fuels.

Negative emissions technologies (NETs) could, purportedly, offset emissions from sectors with more difficult or expensive mitigation solutions such as transport and residual emissions from thermal power plants. The UK power sector must be completely decarbonised by 2050 to meet its economywide decarbonisation targets set out in the Climate Change Act 2008. It is also estimated that ~50 MtCO2/yr of negative emissions are needed by 2050 to be compliant with the Paris Agreement target . This study investigates the potential role of direct air capture and storage (DACS) and bioenergy with CCS (BECCS) for decarbonisation of two sectors for the UK: transport via fuel substitution, and power via integration of NETs. CO2-derived fuels, often made using curtailed intermittent renewable energy sources (iRES), have been proposed as substitutes for gasoline/diesel in vehicles to avoid further emissions. The first part of this study quantifies a) the likely future availability of curtailed renewable electricity, b) the amount of fossil CO2 emissions which can be avoided by using this electricity to convert CO2 to methanol for use as a transport fuel Power-to-Fuel, or to directly remove CO2 from the atmosphere via DAC, Power-to-DAC. Both processes are illustrated in Fig. 1 below. Our analysis shows that curtailed electricity availability is unlikely to increase beyond current levels (1277 GWh/y) until iRES account for more than 50% of total installed capacity. This is unlikely to be the case in the UK before 2035. It was found that, in all cases, using curtailed iRES for DAC is a less costly and more effective option to mitigate climate change than using it to produce methanol to substitute gasoline. Figure 1: Simplified representation of the Power-to-Fuel and Power-to-DAC processes investigated

Power system models have become an essential part of strategic planning and decision-making in the energy transition. While techniques are becoming increasingly sophisticated and manifold, the ability to incorporate high resolution in space and time with long-term planning is limited. We introduce ESONE, the Spatially granular Electricity Systems Optimisation model. ESONE is a mixed-integer linear program, determining investment in power system generation and transmission infrastructure while simultaneously optimising operational schedule and optimal power flow on an hourly basis. Unique data clustering combined with model decomposition and an iterative solution procedure enable computational tractability. We showcase the capabilities of the ESONE model by applying it to the power system of Great Britain under CO2 emissions reduction targets. We investigate the effects of a spatially distributed large-scale roll-out of electric vehicles (EVs). We find EV demand profiles correlate well with offshore and onshore wind power production, reducing curtailment and boosting generation. Time-of-use-tariffs for EV charging can further reduce power supply and transmission infrastructure requirements. In general, Great Britain's electricity system absorbs additional demand from ambitious deployment of EVs without substantial changes to system design.

The UK is committed to the Paris Agreement and has a legally-binding target to reduce economy-wide greenhouse gas emissions by 80% relative to 1990 levels by 2050. Meeting these targets would require deep decarbonisation, including the deployment of negative emissions technologies. This study, via a power supply capacity expansion model, investigates the potential role of bio-energy with carbon capture and storage (BECCS) and direct air capture and storage (DACS) in meeting the UK's emissions reduction targets. We show that to achieve power sector decarbonisation, a system dominated by firm and dispatchable low-carbon generators with BECCS or DACS to compensate for their associated emissions is significantly cheaper than a system dominated by intermittent renewables and energy storage. By offsetting CO2 emissions from cheaper thermal plants, thereby allowing for their continued utilisation in a carbon-constrained electricity system, BECCS and DACS can reduce the cost of decarbonisation by 37-48%. Allowing some this value transferred to accrue to NETs offers a potential route for their commercial deployment.

This chapter aims at evaluating CCS equipped power generation in a power system context. Initially, the main power system services and mechanisms are reviewed. Decarbonisation poses transformational challenges associated with system reliability and operability to the energy system. New approaches to evaluate power generation and storage technologies in a whole-systems context are discussed and demonstrated. CCS power plants are able to reduce the total system cost and lead to a least-cost decarbonisation of the power sector. Enhanced flexibility in CCS power generation can provide additional value to the system. Research, policies, and markets should aim at explicitly evaluating new technology services to the power system, such as flexibility, low CO2 emissions, or the provision of ancillary services.

In order to meet the 1.5−2C target, with CCU, it is necessary to close the carbon cycle, and avoid partial decarbonisation scenarios. In this context, direct air capture appears more effective than CCU.

The delayed deployment of low-carbon energy technologies is impeding energy system decarbonization. The continuing debate about the cost-competitiveness of low-carbon technologies has led to a strategy of waiting for a ‘unicorn technology’ to appear. Here, we show that myopic strategies that rely on the eventual manifestation of a unicorn technology result in either an oversized and underutilized power system when decarbonization objectives are achieved, or one that is far from being decarbonized, even if the unicorn technology becomes available. Under perfect foresight, disruptive technology innovation can reduce total system cost by 13%. However, a strategy of waiting for a unicorn technology that never appears could result in 61% higher cumulative total system cost by mid-century compared to deploying currently available low-carbon technologies early on.

Carbon capture and sequestration of CO2 from the combustion of fossil fuels in thermal power plants is expected to be important in the mitigation of climate change. Deployment however falls far short of what is required. A key barrier is the perception by developers and investors that these technologies are too inefficient, expensive and risky. To address these issues, we have developed a novel retrosynthetic approach to evaluate technologies and their design based on the demands of the system in which they would operate. We have applied it to chemical looping combustion (CLC), a promising technology, which enables carbon dioxide emissions to be inherently captured from the combustion of fossil fuels. Our approach has provided unique insight into the potential role and value of different CLC variants in future electricity systems and the likely impact of their integration on the optimal capacity mix, the operational and system cost, and dispatch patterns. The three variants investigated could all provide significant value by reducing the total investment and operational cost of a future electricity system. The minimisation of capital cost appears to be key for the attractiveness of CLC, rather than other factors such as higher efficiency or lower oxygen carrier costs.

Clara F. Heuberger is a PhD student in the Centre for Process Systems Engineering and the Centre for Environmental Policy at Imperial College London. She holds a Bachelors and Masters in Mechanical Engineering from RWTH Aachen University. Clara studied and conducted research abroad at Carnegie Mellon University Department of Chemical Engineering and Department of Engineering and Public Policy.Graphical AbstractView Large Image Figure ViewerDownload Hi-res image Download (PPT)Niall Mac Dowell is a Senior Lecturer at Imperial College London, where he currently leads the Clean Fossil and Bioenergy Research Group with Bachelors and Doctoral degrees in Chemical Engineering. He is a Chartered Engineer with the IChemE and is a Member of the Royal Society of Chemistry. Clara F. Heuberger is a PhD student in the Centre for Process Systems Engineering and the Centre for Environmental Policy at Imperial College London. She holds a Bachelors and Masters in Mechanical Engineering from RWTH Aachen University. Clara studied and conducted research abroad at Carnegie Mellon University Department of Chemical Engineering and Department of Engineering and Public Policy. Niall Mac Dowell is a Senior Lecturer at Imperial College London, where he currently leads the Clean Fossil and Bioenergy Research Group with Bachelors and Doctoral degrees in Chemical Engineering. He is a Chartered Engineer with the IChemE and is a Member of the Royal Society of Chemistry.

The global energy system is undergoing a major transition, and in energy planning and decision-making across governments, industry and academia, models play a crucial role. Because of their policy relevance and contested nature, the transparency and open availability of energy models and data are of particular importance. Here we provide a practical how-to guide based on the collective experience of members of the Open Energy Modelling Initiative (Openmod). We discuss key steps to consider when opening code and data, including determining intellectual property ownership, choosing a licence and appropriate modelling languages, distributing code and data, and providing support and building communities. After illustrating these decisions with examples and lessons learned from the community, we conclude that even though individual researchers' choices are important, institutional changes are still also necessary for more openness and transparency in energy research.