Techno-economic-environmental optimisation of natural gas supply chain GHG emissions mitigation

While the natural gas (NG) suppliers are under unprecedented pressure to reduce their Greenhouse Gas (GHG) footprint, various emissions reduction technologies have become available. Comparing their GHG mitigation performance and cost effectiveness has thus become increasingly relevant. This research developed a novel and accurate set of tools for GHG emissions estimation and for the cost assessment of emissions mitigation options for NG chains. These were combined in a first time proposed techno-economic and environmental optimisation framework to identify effective and cost efficient GHG emissions reduction options for NG operations in a regional context. The Life Cycle Assessment (LCA) methodology was used to develop inventory models for: offshore production and pre-processing, onshore processing and liquefaction, offshore pipeline transport and offshore Liquefied Natural Gas (LNG) transport. The modular life cycle inventory models developed provide significant advances compared to previously developed models: (i) they capture the impact of different operational practices, technologies and climatic conditions on the emissions, (ii) emission estimations are made for the whole life of facilities, historically and with future projections, using a combination of material balance and engineering calculations; these are configured to the specifics of facilities analysed increasing substantially estimation accuracy, (iii) they enable the assessment of uncertainty for emission estimations. The models were validated using industry data for five NG chains with operations in Norway (2), UK, Australia and Bolivia. A methodology to compare the cost effectiveness of different emissions reduction technologies through Marginal Abatement Cost Curves was also developed for a large range of CO2 and CH4 emissions mitigation options. The cost models developed account for capital and operational expenditure, as well as effects on revenues and tax liabilities. The approach was validated using three of the NG operations studied, located in Norway (2) and Australia. Finally, a mixed-integer multi-objective optimisation model was developed to identify regional opportunities for GHG emissions reduction and cost minimisation in offshore upstream NG value chains through (i) joint power generation and (ii) connection with offshore wind farms. This model was tested for a set of 12 offshore platforms located in the UK Southern North Sea obtaining a 25% reduction of the network’s cumulative CO2 emissions over a ten year future period. This research has proven for the first time that there can be significant difference in GHG performance between neighbouring NG facilities, or within the same facility in consecutive years, found to be up to 54 and 44%, respectively. Moreover, it has shown that the embodied GHG footprint of NG product delivered at different markets will vary significantly even when it is originating from a single source. Thus, generic or regional averages, often employed by LCA practitioners, are not reliable for the industry’s own reporting and for regulatory purposes. In this context, policy makers should consider that imported NG may arrive with embodied GHG footprints varying by more than 50%. Moreover, to effectively identify which NG value chains or regions offer comparatively lower GHG footprints, it is necessary to perform value chain specific LCA studies, using real operational data at a unit process granularity. Regarding emissions reduction options and cost considerations, while integration with renewables and efficiency improvements could perform well for conventional offshore operations, in unconventional onshore operations, targeting well completions, casing and tank vents were shown to have a higher GHG reduction potential. The offshore Norwegian, onshore Norwegian and onshore Australian industry facilities studied were found to have added individual mitigation potential of 2,522, 346 and 13,947 ktonnes CO2 equivalent over investment horizons of 5, 15 and 10 years respectively. All the sites studied were also found to have abatement options with negative implementation costs. The industry and policy makers should, thus, consider that abatement potentials and costs vary significantly by facility depending on its characteristics and context.The implementation of the novel life cycle assessment and cost assessment tools developed in this research and the multi-objective techno-economic and emissions reduction optimisation framework enable for the first time GHG reporting of substantially increased accuracy and unique evidence in support of the efforts industry aims to employ to reduce their effects on the climate.Open Acces

Methodology to prepare for UK's offshore wind Contract for Difference auctions

In the UK, the Contract for Difference (CfD) subsidies for renewable energy generation are awarded through a competitive auction process. This paper simulates the most recent CfD auction for offshore wind, using a novel methodology to assist developers in preparing their bid strategy and for policymakers to test auction efficiency. The simulation's results show developer's leading strategy is to shade their bid to increase auction pay-off. A developer's incentive to shade their bid depends on the project's capacity and minimum bid price; the offshore wind farm Hornsea 3 has the greatest incentive to shade its bid as its optimum bid price is further from its cost price, and results in the highest expected value of additional auction pay-off. The median strike price estimated by the model is £39.23/MWh, and the most likely winners, as predicted from the simulations, are Hornsea 3, Inch Cape, East Anglia 3 and Norfolk Boreas. Published auction results show that the estimated strike price from the simulation is 5% higher than the £37.35/MWh awarded strike price; however, the model successfully predicted the winners. Further analysis of results demonstrates that developers adopted a risk-averse bidding strategy, bidding at a pre-determined floor (coexist) price, guaranteeing subsidy. As a result, £38 million of the subsidy budget was unused

Simulating offshore wind contract for difference auctions to prepare bid strategies

This paper presents a novel agent-based, stochastic model, which uses game-theoretic principles to simulate Contract for Difference (CfD) auctions. The framework has use cases and implications for policymakers and renewable generators alike, and can be used by developers to prepare bidding strategy and for policymakers to empirically test auction design. The model is demonstrated through replication of the offshore wind CfD Allocation Round 3 (AR3) pot, and utilises high-level cost modelling distribution data to estimate bid prices for the competing projects. The model produces a distribution of most likely results which better categorises uncertainty, and through comparison of AR3 and simulation results, demonstrates how outcomes can be predicted with reasonable confidence by developers. Analysis show that the transmission network and grid connection charges are a significant barrier for projects in some geographical regions to be awarded a CfD contract, potentially hindering renewable deployment in those areas. Moreover, this paper demonstrates how players can use probability theory to select an optimum bidding strategy which maximises expected profit while factoring the uncertainty inherent in CfD auctions. Results show that a 1200 MW wind farm development can increase potential profits by £135 million over the CfD contract length in exchange for a 25 p.p. chance reduction in being awarded a subsidy

Dealing with uncertainty while developing bid strategy for CfD auctions

Offshore Wind installed capacity has grown dramatically in recent years. In the UK, this success can in part be attributed to the CfD (Contracts for Difference), the UK government's primary policy mechanism for subsidising low-carbon generation. This is a promising policy tool for achieving renewable targets. However, there are a number of risks involved for both auctioneer and bidders. Bidders are faced with many sources of uncertainty when analysing their project costs and future revenues, which is required in order to develop a bidding strategy. The uncertainty faced by auction participants can result in the non-realisation of projects, which poses a major risk to governments meeting their expansion targets. The auctioneer can take a number of measures to reduce the non-realisation of projects such as increasing the CfD contract length and limiting a wind farm's exposure to volatile wholesale electricity prices. In this paper, a sensitivity analysis is carried out on a stochastic, agent-based modelling approach, which utilises game-theoretic principles to generate optimum bid strategies for generators attempting to win a CfD contract. The sensitivity analysis is conducted by replicating the Allocation Round 3 (AR3) as a base case. This auction was held in 2019 in the UK. Empirically derived stochastic data obtained from a previously validated proprietary cost modelling tool is used to map each agent to a real-life project that participated in AR3. The results show the importance of estimating the capacity factor and capital expenditure and thus highlight where resources to reduce uncertainty should be focused by auction participants. This paper then analyses the effect of increasing CfD contract length on the uncertainty experienced by bidders. A trade-off appears between significantly reducing uncertainty for bidders and increasing the net present value of support payments to developers. The results also show that in a number of high-medium economic growth scenarios, governments can expect to receive net positive payments from awarding CfD contracts to fixed-offshore wind developers. Revenue generated can be used to further subsided less-established technologies and deliver savings for electricity consumers

Methodology to prepare for UK’s offshore wind contract for difference auctions

In the UK, the Contract for Difference (CfD) subsidies for renewable energy generation are awarded through a competitive auction process. This paper simulates the most recent CfD auction for offshore wind, using a novel methodology to assist developers in preparing their bid strategy and for policymakers to test auction efficiency. The simulation’s results show developer's leading strategy is to shade their bid to increase auction pay-off. A developer’s incentive to shade their bid depends on the project’s capacity and minimum bid price; the offshore wind farm Hornsea 3 has the greatest incentive to shade its bid as its optimum bid price is further from its cost price, and results in the highest expected value of additional auction pay-off. The median strike price estimated by the model is £39.23/MWh, and the most likely winners, as predicted from the simulations, are Hornsea 3, Inch Cape, East Anglia 3 and Norfolk Boreas. Published auction results show that the estimated strike price from the simulation is 5% higher than the £37.35/MWh awarded strike price; however, the model successfully predicted the winners. Further analysis of results demonstrates that developers adopted a risk-averse bidding strategy, bidding at a pre-determined floor (coexist) price, guaranteeing subsidy. As a result, £38 million of the subsidy budget was unused

The development of a dynamic CO2 injection strategy for the depleted forties and Nelson oilfields using regression-based multi-objective programming

An optimisation strategy to maximise the CO 2 storage capacity utilisation of a deep saline aquifer is presented in this paper. This was achieved by a scenario of simultaneous CO 2 injection and brine production within the Forties sandstone. The optimisation was performed using the SIMPLEX and Generalised Reduced Gradient algorithms and the assistance of surrogate modelling techniques. Results have shown that, by using five brine production wells producing up to 2.2 Mtonnes/year, the CO 2 storage capacity of the reservoir can be increased by 125% compared to when no brine production is used. It is also observed that pressure constraints are the main limiting factors controlling further CO 2 injection