Browsing by Author "Ioannou, Anastasia"

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    Cash flow at risk of offshore wind plants
    (IEEE, 2017-08-18) Vaienti, Claudio; Ioannou, Anastasia; Brennan, Feargal
    Offshore wind power plants might be seen as high risk investments. Their risk depends on technical and financial elements. When some corporations decide to invest in a plant, they decide to take all above-mentioned risks. The question “Given a specific investor, a specific plant, etc., how big are the investment risks?” has not a clear answer. In fact, the impact of the previous risk factors on cash flows is not completely quantified, mainly because all the risks are related, but the dependency structure is difficult to be modelled. Hence, it is important to have a measure of the impact of the risks into the cash flows. Due to the lack of knowledge in this quantification, we have decided to investigate it more in the detail. The paper aims to measure the variability of cash flows and how effective are the strategies for locking electricity prices, ship freight rates, or both in the reduction of this variability. We adopt the Monte Carlo approach for simulating all the possible cash flows and for measuring all the uncertainties. The output shows that seasonal and uncertain cash flows. The strategies, for reducing the probability of negative cash flows, work only with locked electricity prices.
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    A cluster analysis of investment strategies in the offshore wind energy market
    (Institute of Electrical and Electronics Engineers, 2017-08-18) Ioannou, Anastasia; Vaienti, Claudio; Angus, Andrew; Brennan, Feargal P.
    This paper maps different investor strategies in the offshore wind energy market based on data from existing wind farms in the UK. This is realized through the employment of cluster analysis, which classifies offshore wind energy investors - who have purchased equity stakes-in terms of the entry timing, exit timing, purchase timing and stake purchased. We, then, perform a SWOT analysis to identify the major strengths, weaknesses, opportunities and threats encountered by each cluster of stakeholders. Cluster analysis revealed the existence of three distinct investment strategy profiles: i) Late entry investors, ii) Pre-commissioning investors, and iii) Own-build-transfer investors. Corporate and institutional investors tend to be late entry investors, whose strategy is based on buying assets while they are fully operational avoiding construction risks, retaining a risk aversion profile. The exit timing of OEMs and EPCI contractors usually takes place before or right after the commissioning of the wind farm. Finally, major Utilities tend to keep the operating assets on their balance sheet and divest only part of them (mostly minority stakes) during the operating stage; Independent energy companies are found in both 2nd and 3rd cluster; however, exceptions may be observed.
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    Design implications towards inspection reduction of large scale structures
    (Elsevier, 2017-05-09) Ioannou, Anastasia; Wang, Lin; Brennan, Feargal P.
    Operational management is a key contributor in life cycle costs, especially for large scale assets which are in most times complex in structural hierarchy and with a large nominal service life. Decisions on the operational management may concern the number of inspections or maintenance strategies which may allow full utilization of structural capacity or sacrifice residual life in order to avoid an unscheduled intervention. Design of such assets is often governed by design standards which offer the designer the flexibility to take certain decisions that may affect the CAPEX to OPEX ratio such as that of building a more robust structure which may eliminate the need for costly inspection operations. This paper is investigating this approach, taking the example of offshore wind turbine support structures as the reference case, and examines the relevant provisions of the DNV-Os-J101 Standard with respect to the design implications that such a decision may have to the overall life-cycle cost of the structure. Assessment of the structural properties under different design conditions is evaluated through a combination of detailed cost model and an iterative optimization algorithm. The approach which is followed and documented, can be applicable to other complex structural systems for decision making through evaluation of service life costs. Paper presented at: Complex Systems Engineering and Development Proceedings of the 27th CIRP Design Conference Cranfield University, UK 10th – 12th May 2017.
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    Effect of electricity market price uncertainty modelling on the profitability assessment of offshore wind energy through an integrated lifecycle techno-economic model
    (IOP Publishing, 2018-10-10) Ioannou, Anastasia; Angus, Andrew; Brennan, Feargal
    According to the Contracts for Difference (CfD) scheme introduced to support the deployment of offshore wind installations, an electricity generation party is paid the difference between a constant "strike price" (determined be means of a competitive auction) and the average UK market electricity price for every MWh of power output produced. The scheme lasts for 15 years, after which the electricity output is sold on the average market price. To this end, estimating the long term profitability of the investment greatly depends on the forecasted market prices. This paper presents the simulation results of future electricity prices based on three different simulation methods, namely: the Geometric Brownian motion (GBM), the Autoregressive Integrated Moving average (ARIMA) and a model combining Mean-Reversion and Jump-Diffusion (MRJD) processes. A number of simulation paths are generated for a time horizon of 10 years and they are introduced to a fully integrated techno-economic model developed by the authors. As a result, joint probability distributions of the NPV derived from the three different methods are presented. This study is relevant to investors and policy makers to check the viability of an investment and to predict its stochastic temporal return profile.
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    Informing parametric risk control policies for operational uncertainties of offshore wind energy assets
    (Elsevier, 2019-03-01) Ioannou, Anastasia; Angus, Andrew; Brennan, Feargal
    The aim of this paper is to investigate uncertainties present during operation of offshore wind (OW) energy assets with a view to inform risk control policies for hedging of the incurring losses. The parametric framework developed is subsequently applied across a number of different locations in the South East Coast of the UK, so as to demonstrate the effect of weather conditions and resulting downtime on a number of operational Key Performance Indicators (KPIs), such as downtime due to planned and unplanned interventions, wind farm availability, Operation and Maintenance (O&M) costs and power production losses. Higher availability levels were observed in areas closer to shore of the specified region, while the distribution of O&M cost per MWh generated demonstrated a general trade-off of higher power generation in locations farther from shore due to better wind speed profiles and higher O&M costs, as a result of the decreasing vessels accessibility. The proposed methodology aspires to contribute to the development of better-informed risk control policies, through parametrically estimating the probability of exceedance curve of the production losses of an OW farm and indicating appropriate thresholds to be considered, so as not to exceed a maximum level of risk.
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    A lifecycle techno-economic model of offshore wind energy for different entry and exit instances
    (Elsevier, 2018-04-17) Ioannou, Anastasia; Angus, Andrew; Brennan, Feargal
    The offshore wind (OW) industry has reached reasonable maturity over the past decade and the European market currently consists of a diverse pool of investors. Often equity investors buy and sell stakes at different phases of the asset service life with a view to maximize their return on investment. A detailed assessment of the investment returns taking into account the technical parameters of the problem, is pertinent towards understanding the value of new and operational wind farms. This paper develops a high fidelity lifecycle techno-economic model, bringing together the most up-to-date data and parametric equations from databases and literature. Subsequently, based on a realistic case study of an OW farm in the UK, a sensitivity analysis is performed to test how input parameters influence the model output. Sensitivity analysis results highlight that the NPV is considerably sensitive to FinEX and revenue parameters, as well as to some OPEX parameters, i.e. the mean time to failure of the wind turbine components and the workboat significant wave height limit. Application of the model from the perspective of investors with different entry and exit timings derives the temporal return profiles, revealing important insights regarding the potential minimum asking and maximum offered price.
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    Multi-stage stochastic optimization framework for power generation system planning integrating hybrid uncertainty modelling
    (Elsevier, 2019-03-03) Ioannou, Anastasia; Fuzuli, Gulistiani; Brennan, Feargal; Widya yudha, Satya; Angus, Andrew
    In this paper, a multi-stage stochastic optimization (MSO) method is proposed for determining the medium to long term power generation mix under uncertain energy demand, fuel prices (coal, natural gas and oil) and, capital cost of renewable energy technologies. The uncertainty of future demand and capital cost reduction is modelled by means of a scenario tree configuration, whereas the uncertainty of fuel prices is approached through Monte Carlo simulation. Global environmental concerns have rendered essential not only the satisfaction of the energy demand at the least cost but also the mitigation of the environmental impact of the power generation system. As such, renewable energy penetration, CO2,eq mitigation targets, and fuel diversity are imposed through a set of constraints to align the power generation mix in accordance to the sustainability targets. The model is, then, applied to the Indonesian power generation system context and results are derived for three cases: Least Cost option, Policy Compliance option and Green Energy Policy option. The resulting optimum power generation mixes, discounted total cost, carbon emissions and renewable share are discussed for the planning horizon between 2016 and 2030.
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    Parametric CAPEX, OPEX, and LCOE expressions for offshore wind farms based on global deployment parameters
    (Taylor and Francis, 2018-05-04) Ioannou, Anastasia; Angus, Andrew; Brennan, Feargal
    Installed wind energy capacity has been rapidly increasing over the last decade, with deployments in deeper waters and further offshore, with higher turbine ratings within new farms. Understanding the impact of different deployment factors on the overall cost of wind farms is pertinent toward benchmarking the potential of different investment decision alternatives. In this article, a set of parametric expressions for capital expenditure, operational expenditure, and levelized cost of energy are developed as a function of wind turbine capacity (), water depth (WD), distance from port (D), and wind farm capacity (). These expressions have been developed through a series of simulations based on a fully integrated, tested cost model which are then generalized through the application of appropriate nonlinear regression equations for a typical offshore wind farm investment and taking into account most current published cost figures. The effectiveness of the models are countersigned through a series of cases, estimating the predicted values with a maximum error of 3.3%. These expressions will be particularly useful for the preliminary assessment of available deployment sites, offering cost estimates based on global decision variables.
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    Risk-based methods for sustainable energy system planning: a review
    (Elsevier, 2017-03-02) Ioannou, Anastasia; Angus, Andrew; Brennan, Feargal P.
    The value of investments in renewable energy (RE) technologies has increased rapidly over the last decade as a result of political pressures to reduce carbon dioxide emissions and the policy incentives to increase the share of RE in the energy mix. As the number of RE investments increases, so does the need to measure the associated risks throughout planning, constructing and operating these technologies. This paper provides a state-of-the-art literature review of the quantitative and semi-quantitative methods that have been used to model risks and uncertainties in sustainable energy system planning and feasibility studies, including the derivation of optimal energy technology portfolios. The review finds that in quantitative methods, risks are mainly measured by means of the variance or probability density distributions of technical and economical parameters; while semi-quantitative methods such as scenario analysis and multi-criteria decision analysis (MCDA) can also address non-statistical parameters such as socio-economic factors (e.g. macro-economic trends, lack of public acceptance). Finally, untapped issues recognised in recent research approaches are discussed along with suggestions for future research.
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    Risk-based methods for the valuation and planning of sustainable energy assets.
    (2018-08) Ioannou, Anastasia; Brennan, Feargal; Angus, Andrew
    This research project aims to develop and apply appropriate methods dealing with risk and uncertainty at a technology and energy system level providing decision support to the various stakeholders involved in the planning, development and operation of sustainable energy investments. The thesis comprises a portfolio of research activities fulfilling the set research objectives. Outcomes of this research portfolio have been either published or are under the peer review process. More specifically, following a systematic literature review to identify the state-of- the-art in risk-based methods for sustainable energy systems planning and feasibility studies, a cluster analysis was applied based on data from existing offshore wind energy installations in the UK, to distinguish investment strategies followed by equity investors. This study has identified three distinct clusters of investors, namely the late entry, pre-commissioning and build-operate-transfer investors. Subsequently, a high-fidelity lifecycle techno economic model was developed allowing for the temporal valuation of a renewable energy investment. This integrated model has allowed for a set of parametric equations to be developed through appropriate selection of approximation models linking global design parameters to key performance indicators. Furthermore, a stochastic extension of the financial appraisal model has allowed for a transition from the conventional deterministic terminology to a stochastic one, assigning confidence levels to key performance indicators (KPIs). Additionally, the development of a purpose-specific tool for the evaluation of the operational phase KPIs, such as the availability, operating cost and energy production losses due to planned and unplanned maintenance has allowed for the development of better-informed risk control policies. Finally, having analysed uncertainties at a technology level, a stochastic optimisation framework was developed for deriving optimal national power generation technology mixes taking into account uncertainties for a series of scenarios linked to national energy strategies through appropriate constraints in the analysis.
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    Stochastic prediction of offshore wind farm LCOE through an integrated cost model
    (Elsevier, 2017-03-09) Ioannou, Anastasia; Angus, Andrew; Brennan, Feargal P.
    Common deterministic cost of energy models applied in offshore wind energy installations usually disregard the effect of uncertainty of key input variables – associated with OPEX, CAPEX, energy generation and other financial variables – on the calculation of levelized cost of electricity (LCOE). The present study aims at expanding a deterministic cost of energy model to systematically account for stochastic inputs. To this end, Monte Carlo simulations are performed to derive the joint probability distributions of LCOE, allowing for the estimation of probabilities of exceeding set thresholds of LCOE, determining certain confidence intervals. The results of this study stress the importance of appropriate statistical modelling of stochastic variables in order to reduce modelling uncertainties and contribute to a better informed decision making in renewable energy investments.