Using IT Mindfulness to Mitigate the Negative Consequences of Technostress

Research in the IS field has been focusing on investigating the adverse effects of ICT usage such as technostress. Nevertheless, few studies have investigated mechanisms for the alleviation of this phenomenon. This study contributes to the technostress literature by adopting a mindfulness perspective that has not been investigated before. In this paper, we aim to explore the role of IT mindfulness as a buffer to technostress stressors as well as a mechanism that can mitigate the negative consequences arising from extended ICT usage within organizational settings. By following a survey based approach and exploring a sample of 440 working individuals, our SEM analysis revealed that IT mindfulness constitutes a potential further mechanism that can effectively reduce technostress conditions, enhance user satisfaction while utilizing ICT’s for work tasks and improve task performance. Further research is proposed into expanding the proposed model, exploring the influence of IT mindfulness on additional organizational outcomes. Keywords IT Mindfulness, Technostress, stressors, ICT, organization

Cash flow at risk of offshore wind plants

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

Parametric CAPEX, OPEX, and LCOE expressions for offshore wind farms based on global deployment parameters

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

A lifecycle techno-economic model of offshore wind energy for different entry and exit instances

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

Effect of electricity market price uncertainty modelling on the profitability assessment of offshore wind energy through an integrated lifecycle techno-economic model

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

Informing parametric risk control policies for operational uncertainties of offshore wind energy assets

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

Risk-based methods for sustainable energy system planning: a review

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

Design implications towards inspection reduction of large scale structures

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

A multi-stage multi-objective optimisation model of power system expansion planning integrating sustainability indicators

The increase in global electricity demand, along with its impact on climate change, call for integrating sustainability aspects in the power system expansion planning. Sustainable power generation planning needs to fulfill different, often contradictory, objectives. This paper proposes a multi-objective optimisation model integrating four objective functions, including minimisation of total discounted costs, carbon emissions, land use, and social opposition. Other factors addressed in the model include renewable energy share, jobs created, mortality rates, and energy diversity, among others. Single-objective linear optimisations are initially performed to investigate the impact of each objective function on the resulting power generation mix. Minimising land use and discounted total costs favoured fossil fuels technologies, as opposed to minimising carbon emissions, which resulted in increased renewable energy shares. Minimising social opposition also favoured renewable energy shares, except for hydropower and onshore wind technologies. Accordingly, to investigate the trade-offs among the objective functions, Pareto front candidates for each pair of objective functions were generated, indicating a strong correlation between the minimisation of carbon emissions and the social opposition. Limited trade-offs were also observed between the minimisation of costs and land use. Integrating the objective functions in the multi-objective model resulted in various non-dominated solutions. This tool aims to enable decision-makers identify the trade-offs when optimising the power system under different objectives and determine the most suitable electricity generation mix

Stochastic prediction of offshore wind farm LCOE through an integrated cost model

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