Imperial College London

Dr Clara F. Heuberger-Austin

Faculty of Natural SciencesCentre for Environmental Policy

Visiting Researcher



+44 (0)20 7594 9959c.heuberger14 Website




507Weeks BuildingSouth Kensington Campus





Publication Type

23 results found

He G, Mallapragada DS, Bose A, Heuberger CF, Gencer Eet al., 2021, Hydrogen Supply Chain Planning With Flexible Transmission and Storage Scheduling, IEEE TRANSACTIONS ON SUSTAINABLE ENERGY, Vol: 12, Pages: 1730-1740, ISSN: 1949-3029

Journal article

Tso WW, Demirhan CD, Heuberger CF, Powell JB, Pistikopoulos ENet al., 2020, A hierarchical clustering decomposition algorithm for optimizing renewable power systems with storage, APPLIED ENERGY, Vol: 270, ISSN: 0306-2619

Journal article

Demirhan CD, Tso WW, Powell JB, Heuberger CF, Pistikopoulos ENet al., 2020, A multiscale energy systems engineering approach for renewable power generation and storage optimization, Industrial and Engineering Chemistry Research, Vol: 59, Pages: 7706-7721, ISSN: 0888-5885

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.

Journal article

Heuberger CF, Mac Dowell N, 2020, Chapter 12: CCS in electricity systems, RSC Energy and Environment Series, Pages: 392-425, ISBN: 9781788014700

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.

Book chapter

Heuberger CF, Bains PK, Mac Dowell N, 2020, The EV-olution of the power system: a spatio-temporal optimisation model to investigate the impact of electric vehicle deployment, Applied Energy, Vol: 257, Pages: 1-18, ISSN: 0306-2619

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.

Journal article

Daggash H, Heuberger C, Mac Dowell N, 2019, The role and value of negative emissions technologies in decarbonising the UK energy system, International Journal of Greenhouse Gas Control, Vol: 81, Pages: 181-198, ISSN: 1750-5836

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.

Journal article

Heuberger CF, Rubin ES, Staffell L, Shah N, Mac Dowell Net al., 2018, Power capacity expansion planning considering endogenous technology cost learning (vol 204, pg 831, 2017), APPLIED ENERGY, Vol: 220, Pages: 974-974, ISSN: 0306-2619

Journal article

Schnellmann MA, Heuberger CF, Scott SA, Dennis JS, Mac Dowell Net al., 2018, Quantifying the role and value of chemical looping combustion in future electricity systems via a retrosynthetic approach, International Journal of Greenhouse Gas Control, Vol: 73, Pages: 1-15, ISSN: 1750-5836

Carbon capture and sequestration of CO2from 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 investorsthat 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 couldall 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 loweroxygen carrier costs.

Journal article

Daggash HA, Patzschke CF, Heuberger CF, Zhu L, Hellgardt K, Fennell PS, Bhave AN, Bardow A, Mac Dowell Net al., 2018, Closing the carbon cycle to maximise climate change mitigation: Power-to-Methanol vs Power-to-Direct Air Capture, Sustainable Energy and Fuels, Vol: 2, Pages: 1153-1169, ISSN: 2398-4902

It is broadly recognised that CO2 capture and storage (CCS) and associated negative emissions technologies (NETs) are vital to meeting the Paris agreement target. The hitherto failure to deploy CCS on the required scale has led to the search for options to improve its economic return. CO2 capture and utilisation (CCU) has been proposed as an opportunity to generate value from waste CO2 emissions and improve the economic viability of CCS, with the suggestion of using curtailed renewable energy as a core component of this strategy. This study sets out to quantify (a) the amount of curtailed renewable energy that is likely to be available in the coming decades, (b) the amount of fossil CO2 emissions which can be avoided by using this curtailed energy to convert CO2 to methanol for use as a transport fuel – power-to-fuel, with the counterfactual of using that curtailed energy to directly remove CO2 from the atmosphere via direct air capture (DAC) and subsequent underground storage, power-to-DAC. In 2015, the UK curtailed 1277 GWh of renewable power, or 1.5% of total renewable power generated. Our analysis shows that the level of curtailed energy is unlikely to increase beyond 2.5% until renewable power accounts for more than 50% of total installed capacity. This is unlikely to be the case in the UK before 2035. It was found that: (1) power-to-DAC could achieve 0.23–0.67 tCO2 avoided MWh−1 of curtailed power, and (2) power-to-Fuel could achieve 0.13 tCO2 avoided MWh−1. The power-to-fuel concept was estimated to cost $209 tCO2 avoided−1 in addition to requiring an additional $430–660 tCO2 avoided−1 to finally close the carbon cycle by air capture. The power-to-DAC concept was found to cost only the $430–660 tCO2 avoided−1 for air capture. For power-to-fuel to become profitable, hydrogen prices would need to be less than or equal to $1635 tH2−1 or methanol prices must increase to $960 tMeOH−1. Absent this ch

Journal article

Heuberger CF, Staffell I, Shah N, Mac Dowell Net al., 2018, Impact of myopic decision-making and disruptive events in power systems planning, Nature Energy, Vol: 3, Pages: 634-640, ISSN: 1520-8524

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.

Journal article

Heuberger C, Mac Dowell N, 2018, Real-world challenges with a rapid transition to 100 % renewable power systems, Joule, Vol: 2, Pages: 367-370, ISSN: 2542-4351

Journal article

Pfenninger S, Hirth L, Schlecht I, Schmid E, Wiese F, Brown T, Davis C, Gidden M, Heinrichs H, Heuberger CF, Hilpert S, Krien U, Matke C, Nebel A, Morrison R, Müller B, Pleßmann G, Reeg M, Richstein JC, Shivakumar A, Staffell I, Tröndle T, Wingenbach Cet al., 2017, Opening the black box of energy modelling: Strategies and lessons learned, Energy Strategy Reviews, Vol: 19, Pages: 63-71, ISSN: 2211-467X

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.

Journal article

Heuberger C, Staffell I, Shah N, Mac Dowell Net al., 2017, A systems approach to quantifying the value of power generation and energy storage technologies in future electricity networks, Computers and Chemical Engineering, Vol: 107, Pages: 247-256, ISSN: 0098-1354

A new approach is required to determine a technology's value to the power systems of the 21st century. Conventional cost-based metrics are incapable of accounting for the indirect system costs associated with intermittent electricity generation, in addition to environmental and security constraints. In this work, we formalise a new concept for power generation and storage technology valuation which explicitly accounts for system conditions, integration challenges, and the level of technology penetration. The centrepiece of the system value (SV) concept is a whole electricity systems model on a national scale, which simultaneously determines the ideal power system design and unit-wise operational strategy. It brings typical Process Systems Engineering thinking into the analysis of power systems. The model formulation is a mixed-integer linear optimisation and can be understood as hybrid between a generation expansion and a unit commitment model. We present an analysis of the future UK electricity system and investigate the SV of carbon capture and storage equipped power plants (CCS), onshore wind power plants, and grid-level energy storage capacity. We show how the availability of different low-carbon technologies impact the optimal capacity mix and generation patterns. We find that the SV in the year 2035 of grid-level energy storage is an order of magnitude greater than that of CCS and wind power plants. However, CCS and wind capacity provide a more consistent value to the system as their level of deployment increases. Ultimately, the incremental system value of a power technology is a function of the prevalent system design and constraints.

Journal article

Heuberger CF, Rubin ES, Staffell I, Shah N, Mac Dowell Net al., 2017, Power Generation Expansion Considering Endogenous Technology Cost Learning, 27th European Symposium on Computer Aided Process Engineering, Publisher: Elsevier

We present a mixed-integer linear formulation of a long-term power generation capacityexpansion problem including endogenous learning of technology investment cost. Weconsider a national-scale power system composed of up to 2000 units of 15 differentpower supply technologies, including international interconnectors for electricity importand export, and grid-level energy storage. We reformulate the non-convex learning curvemodel into a piecewise linear representation of the cumulative investment cost as a functionof cumulative installed capacity. The model is applied to a power system representativeof Great Britain for the years 2015 to 2050. We find that the consideration oftechnology cost learning rate influences the optimal capacity expansion and has systemicimplications on the profitability of the power units.

Conference paper

Heuberger C, Staffell I, Shah N, Mac Dowell N, Davison Jet al., 2017, An MILP modeling approach to systemic energy technology valuation in the 21st Century energy system, 13th International Conference on Greenhouse Gas Control Technologies, Publisher: Elsevier, Pages: 6358-6365, ISSN: 1876-6102

New cannot be measured with old. The transformation of the electricity system from a network of fossil-based dispatchable power plants to one with large amounts of intermittent renewable power generation, flexible loads and markets, requires a concurrent development of new evaluation tools and metrics. The focus of this research is to investigate the value of power technologies in order to support decision making on optimal power system design and operation. Technology valuation metrics need to consider the complexity and interdependency of environmental and security objectives, rather than focusing on individual cost-competitiveness of technologies outside of the power system. We present the System Value as a new technology valuation metric, based on a mixed-integer linear program (MILP) formulation of a national-scale electricity system. The Electricity System Optimization model is able to capture detailed technical operation of the individual power plants as well as environmental and security requirements on the system level. We present a case study on the System Value of onshore wind power plants in comparison with Carbon Capture and Storage (CCS) equipped gas-fired power plants in a 2035 UK electricity system. Under the given emission constraints, the deployment of both technologies reduce total system cost of electricity generation. In the case of CCS-equipped power plants the reductions in total system cost are 2 to 5 times higher than for the deployment of onshore wind capacity.

Conference paper

Heuberger C, Staffell I, Shah N, Mac Dowell Net al., 2017, What is the Value of CCS in the Future Energy System?, 13th International Conference on Greenhouse Gas Control Technologies, Publisher: Elsevier, Pages: 7564-7572, ISSN: 1876-6102

Ambitions to produce electricity at low, zero, or negative carbon emissions are shifting the priorities and appreciation for new types of power generating technologies. Maintaining the balance between security of energy supply, carbon reduction, and electricity system cost during the transition of the electricity system is challenging. Few technology valuation tools consider the presence and interdependency of these three aspects, and nor do they appreciate the difference between firm and intermittent power generation. In this contribution, we present the results of a thought experiment and mathematical model wherein we conduct a systems analyses on the effects of gas-fired power plants equipped with Carbon Capture and Storage (CCS) technology in comparison with onshore wind power plants as main decarbonisation technologies. We find that while wind capacity integration is in its early stages of deployment an economic decarbonisation strategy, it ultimately results in an infrastructurally inefficient system with a required ratio of installed capacity to peak demand of nearly 2.. Due to the intermittent nature of wind power generation, its deployment requires a significant amount of reserve capacity in the form of firm capacity. While the integration of CCS-equipped capacity increases total system cost significantly, this strategy is able to achieve truly low-carbon power generation at 0.04 tCO2/MWh. Via a simple example, this work elucidates how the changing system requirements necessitate a paradigm shift in the value perception of power generation technologies.

Conference paper

Heuberger C, Rubin ES, Staffell I, Shah N, Mac Dowell Net al., 2017, Power Capacity Expansion Planning Considering Endogenous Technology Cost Learning, Applied Energy, Vol: 204, Pages: 831-845, ISSN: 0306-2619

We present an power systems optimisation model for national-scale power supply capacity expansion considering endogenous technology cost reduction (ESO-XEL). The mixed-integer linear program minimises total system cost while complying with operational constraints, carbon emission targets, and ancillary service requirements. A data clustering technique and the relaxation of integer scheduling constraints is evaluated and applied to decrease the model solution time. Two cost learning curves for the different power technologies are derived: one assuming local learning effects, the other accounting for global knowledge spill-over. A piece-wise linear formulation allows the integration of the exponential learning curves into the ESO-XEL model. The model is applied to the UK power system in the time frame of 2015 to 2050. The consideration of cost learning effects moves optimal investment timings to earlier planning years and influences the competitiveness of technologies. In addition, the maximum capacity build rate parameter influences the share of power generation significantly; the possibility of rapid capacity build-up is more important for total system cost reduction by 2050 than accounting for technology cost reduction.

Journal article

Heuberger CF, Staffell I, Shah N, Mac Dowell Net al., 2017, The changing costs of technology and the optimal investment timing in the power sector


Mac Dowell N, Shah N, Staffell I, Heuberger Cet al., 2016, Quantifying the Value of CCS for the Future ElectricitySystem, Energy & Environmental Science, Vol: 9, Pages: 2497-2510, ISSN: 1754-5706

Many studies have quantified the cost of Carbon Capture and Storage (CCS) power plants, butrelatively few discuss or appreciate the unique value this technology provides to the electricity system.CCS is routinely identified as a key factor in least-cost transitions to a low-carbon electricitysystem in 2050, one with significant value by providing dispatchable and low-carbon electricity.This paper investigates production, demand and stability characteristics of the current and futureelectricity system. We analyse the Carbon Intensity (CI) of electricity systems composed of unabatedthermal (coal and gas), abated (CCS), and wind power plants for different levels of windavailability with a view to quantifying the value to the system of different generation mixes. As athought experiment we consider the supply side of a UK-sized electricity system and compare theeffect of combining wind and CCS capacity with unabated thermal power plants. The resultingcapacity mix, system cost and CI are used to highlight the importance of differentiating betweenintermittent and firm low-carbon power generators. We observe that, in the absence of energystorage or demand side management, the deployment of intermittent renewable capacity cannotsignificantly displace unabated thermal power, and consequently can achieve only moderatereductions in overall CI. A system deploying sufficient wind capacity to meet peak demand canreduce CI from 0.78 tCO2/MWh, a level according to unabated fossil power generation, to 0.38tCO2/MWh. The deployment of CCS power plants displaces unabated thermal plants, and whilstit is more costly than unabated thermal plus wind, this system can achieve an overall CI of 0.1tCO2/MWh. The need to evaluate CCS using a systemic perspective in order to appreciate itsunique value is a core conclusion of this study.

Journal article

Heuberger CF, Staffell I, Shah N, Mac Dowell Net al., 2016, Levelised Value of Electricity - A Systemic Approach to Technology Valuation, 26th European Symposium on Computer Aided Process Engineering - ESCAPE 26

Conference paper

Hinchliffe S, van Diemen R, Heuberger C, Mac Dowell Net al., 2016, Transitions in Electricity Systems Towards 2030, Publisher: Institution of Chemical Engineers

Working paper

Zhang Q, Grossmann IE, Heuberger CF, Sundaramoorthy A, Pinto JMet al., 2015, Air separation with cryogenic energy storage: Optimal scheduling considering electric energy and reserve markets, AICHE JOURNAL, Vol: 61, Pages: 1547-1558, ISSN: 0001-1541

Journal article

Zhang Q, Heuberger CF, Grossmann IE, Sundaramoorthy A, Pinto JMet al., 2015, Optimal Scheduling of Air Separation with Cryogenic Energy Storage, 12TH INTERNATIONAL SYMPOSIUM ON PROCESS SYSTEMS ENGINEERING AND 25TH EUROPEAN SYMPOSIUM ON COMPUTER AIDED PROCESS ENGINEERING, PT C, Vol: 37, Pages: 2267-2272, ISSN: 1570-7946

Journal article

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