Co-developing Johan Castberg and Alta/Gohta: a real options approach

Masteroppgave i Energy management - Nord universitet, 201

Should the advanced measurement approach be replaced with the standardized measurement approach for operational risk?

Recently, Basel Committee for Banking Supervision proposed to replace all approaches, including Advanced Measurement Approach (AMA), for operational risk capital with a simple formula referred to as the Standardised Measurement Approach (SMA). This paper discusses and studies the weaknesses and pitfalls of SMA such as instability, risk insensitivity, super-additivity and the implicit relationship between SMA capital model and systemic risk in the banking sector. We also discuss the issues with closely related operational risk Capital-at-Risk (OpCar) Basel Committee proposed model which is the precursor to the SMA. In conclusion, we advocate to maintain the AMA internal model framework and suggest as an alternative a number of standardization recommendations that could be considered to unify internal modelling of operational risk. The findings and views presented in this paper have been discussed with and supported by many OpRisk practitioners and academics in Australia, Europe, UK and USA, and recently at OpRisk Europe 2016 conference in London

A Modeling, Optimization, and Analysis Framework for Designing Multi-Product Lignocellulosic Biorefineries

The objective of this research is to propose a methodology to develop modular decision analysis frameworks to design value chains for enterprises in the renewable fuels and chemicals sector. The decision support framework focuses on providing strategic decision support to startup and new product ventures. The tasks that are embedded in the framework include process and systems design, technology and product selection, forecasting cost and market variables, designing network capacities, and analysis of risks. The Decision support system (DSS) proposed is based on optimization modeling; systems design are carried out using integer programming with multiple sets of process and network configurations utilized as inputs. Uncertainty is incorporated using real options, which are utilized to design network processing capacity for the conversion of biomass resources. Risk analysis is carried out using Monte Carlo methods. The DSS framework is exemplified using a lignocellulosic biorefinery case study that is assumed to be located in Louisiana. The biorefinery utilizes energy crops as feedstocks and processes them into cellulosic biofuels and biobased chemicals. Optimization modeling is utilized to select an optimal network, a fractionation technology, a fermentation configuration, and optimal product recovery and purification unit operations. A decision tree is then used to design incremental capacity under uncertain market parameters. The valuation methodology proposed stresses flexibility in decision making in the face of market uncertainties as is the case with renewable fuels and chemicals. The value of flexibility, termed as “Option Value” is shown to significantly improve the net present value of the proposed biorefinery. Monte Carlo simulations are utilized to develop risk curves for alternate capacity design plans. Risk curves show a favorable risk reward ratio for the case of incremental capacity design with embedded decision options. The framework proposed here can be used by enterprises, government entities and decision makers in general to test, validate, and design technological superstructures and network processing capacities, conduct scenario analyses, and quantify the financial impacts and risks of their representative designs. We plan to further add functionality to the DSS framework and make available the tools developed to wide audience through an “open-source” software distribution model

A semantic Bayesian network for automated share evaluation on the JSE

Advances in information technology have presented the potential to automate investment decision making processes. This will alleviate the need for manual analysis and reduce the subjective nature of investment decision making. However, there are different investment approaches and perspectives for investing which makes acquiring and representing expert knowledge for share evaluation challenging. Current decision models often do not reflect the real investment decision making process used by the broader investment community or may not be well-grounded in established investment theory. This research investigates the efficacy of using ontologies and Bayesian networks for automating share evaluation on the JSE. The knowledge acquired from an analysis of the investment domain and the decision-making process for a value investing approach was represented in an ontology. A Bayesian network was constructed based on the concepts outlined in the ontology for automatic share evaluation. The Bayesian network allows decision makers to predict future share performance and provides an investment recommendation for a specific share. The decision model was designed, refined and evaluated through an analysis of the literature on value investing theory and consultation with expert investment professionals. The performance of the decision model was validated through back testing and measured using return and risk-adjusted return measures. The model was found to provide superior returns and risk-adjusted returns for the evaluation period from 2012 to 2018 when compared to selected benchmark indices of the JSE. The result is a concrete share evaluation model grounded in investing theory and validated by investment experts that may be employed, with small modifications, in the field of value investing to identify shares with a higher probability of positive risk-adjusted returns

Capturing Risk in Capital Budgeting

NPS NRP Technical ReportThis proposed research has the goal of proposing novel, reusable, extensible, adaptable, and comprehensive advanced analytical process and Integrated Risk Management to help the (DOD) with risk-based capital budgeting, Monte Carlo risk-simulation, predictive analytics, and stochastic optimization of acquisitions and programs portfolios with multiple competing stakeholders while subject to budgetary, risk, schedule, and strategic constraints. The research covers topics of traditional capital budgeting methodologies used in industry, including the market, cost, and income approaches, and explains how some of these traditional methods can be applied in the DOD by using DOD-centric non-economic, logistic, readiness, capabilities, and requirements variables. Stochastic portfolio optimization with dynamic simulations and investment efficient frontiers will be run for the purposes of selecting the best combination of programs and capabilities is also addressed, as are other alternative methods such as average ranking, risk metrics, lexicographic methods, PROMETHEE, ELECTRE, and others. The results include actionable intelligence developed from an analytically robust case study that senior leadership at the DOD may utilize to make optimal decisions. The main deliverables will be a detailed written research report and presentation brief on the approach of capturing risk and uncertainty in capital budgeting analysis. The report will detail the proposed methodology and applications, as well as a summary case study and examples of how the methodology can be applied.N8 - Integration of Capabilities & ResourcesThis research is supported by funding from the Naval Postgraduate School, Naval Research Program (PE 0605853N/2098). https://nps.edu/nrpChief of Naval Operations (CNO)Approved for public release. Distribution is unlimited.

A study in the financial valuation of a topping oil refinery

Oil refineries underpin modern day economics, finance and engineering – without their refined products the world would stand still, as vehicles would not have petrol, planes grounded without kerosene and homes not heated, without heating oil. In this thesis I study the refinery as a financial asset; it is not too dissimilar to a chemical plant, in this respect. There are a number of reasons for this research; over recent years there have been legal disputes based on a refiner's value, investors and entrepreneurs are interested in purchasing refineries, and finally the research in this arena is sparse. In this thesis I utilise knowledge and techniques within finance, optimisation, stochastic mathematics and commodities to build programs that obtain a financial value for an oil refinery. In chapter one I introduce the background of crude oil and the significance of the refinery in the oil value chain. In chapter two I construct a traditional discounted cash flow valuation often applied within practical finance. In chapter three I program an extensive piecewise non linear optimisation solution on the entire state space, leveraging off a simulation of the refined products using a set of single factor Schwartz (1997) stochastic equations often applied to commodities. In chapter four I program an optimisation using an approximation on crack spread option data with the aim of lowering the duration of solution found in chapter three; this is achieved by utilising a two-factor Hull & White sub-trinomial tree based numerical scheme; see Hull & White (1994) articles I & II for a thorough description. I obtain realistic and accurate numbers for a topping oil refinery using financial market contracts and other real data for the Vadinar refinery based in Gujurat India

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

The Innovation Interface: Business model innovation for electric vehicle futures

There is huge potential to link electric vehicles, local energy systems, and personal mobility in the city. By doing so we can improve air quality, tackle climate change, and grow new business models. Business model innovation is needed because new technologies and engineering innovations are currently far ahead of the energy system’s ability to accommodate them. This report explores new business models that can work across the auto industry, transport infrastructure and energy systems