A Cost-Index Approach to Valuing Investment In "Far Into The Future" Environmental Technology

Governments investing in long-lead technology development programs face considerable uncertainty as to whether the investment eventually will “pay off” for the taxpayer. This paper offers a framework to inform long-lead technology investment. We extend the theory of quality-adjusted cost indices to develop a conceptually rigorous, but data parsimonious, means of estimating consumer benefits from a new technology. We apply this model to a possible future electricity generation technology, space solar power (SSP). The United States, Japan, and other governments have begun investing in SSP but lack the benefit of a relevant economic context for informed decisions. We frame and analyze the economic relationship between SSP and competing electricity generation technologies with respect to direct costs, environmental externalities, and reliability. We also explicitly incorporate uncertainty and consider differences in the resource endowments available to electricity markets by considering four distinct world geographic regions.energy, environment, technological change, cost indices, space technology

Incorporating life cycle external cost in optimization of the electricity generation mix

The present work aims to examine the strategic decision of future electricity generation mix considering, together with all other factors, the effect of the external cost associated with the available power generation technology options, not only during their operation but also during their whole life cycle. The analysis has been performed by integrating the Life Cycle Assessment concept into a linear programming model for the yearly decisions on which option should be used to minimize the electricity generation cost. The model has been applied for the case of Greece for the years 2012-2050 and has led to several interesting results. Firstly, most of the new generating capacity should be renewable (mostly biomass and wind), while natural gas is usually the only conventional fuel technology chosen. If externalities are considered, wind energy increases its share and hydro-power replaces significant amounts of biomass-generated energy. Furthermore, a sensitivity analysis has been performed. One of the most important findings is that natural gas increases its contribution when externalities are increased. Summing-up, external cost has been found to be a significant percentage of the total electricity generation cost for some energy sources, therefore significantly changing the ranking order of cost-competitiveness for the energy sources examined

Investment planning in electricity production under CO2 price uncertainty

The scope of this work is to investigate the effect that various scenarios for emission allowance price evolution may have on the future electricity generation mix of Greece. The renewable energy generation targets are taken into consideration as a constraint of the system, and the learning rates of the various technologies are included in the calculations. The national electricity generation system is modelled for long-term analysis and an optimisation method is applied, to determine the optimal generating mix that minimises electricity generation cost, while satisfying the system constraints and incorporating the uncertainty of emission allowance prices. In addition, an investigation is made to identify if a point should be expected when renewable energy will be more cost-effective than conventional fuel electricity generation. The work is interesting for investment planning in the electricity market, as it may provide directions on which technologies are most probable to dominate the market in the future, and therefore are of interest to be included in the future power portfolios of related investors. (C) 2010 Elsevier B.V. All rights reserved

Introducing the STAMP method in road tunnel safety assessment

After the tremendous accidents in European road tunnels over the past decade, many risk assessment methods have been proposed worldwide, most of them based on Quantitative Risk Assessment (QRA). Although QRAs are helpful to address physical aspects and facilities of tunnels, current approaches in the road tunnel field have limitations to model organizational aspects, software behavior and the adaptation of the tunnel system over time. This paper reviews the aforementioned limitations and highlights the need to enhance the safety assessment process of these critical infrastructures with a complementary approach that links the organizational factors to the operational and technical issues, analyze software behavior and models the dynamics of the tunnel system. To achieve this objective, this paper examines the scope for introducing a safety assessment method which is based on the systems thinking paradigm and draws upon the STAMP model. The method proposed is demonstrated through a case study of a tunnel ventilation system and the results show that it has the potential to identify scenarios that encompass both the technical system and the organizational structure. However, since the method does not provide quantitative estimations of risk, it is recommended to be used as a complementary approach to the traditional risk assessments rather than as an alternative. (C) 2012 Elsevier Ltd. All rights reserved

Analysing long-term interactions between demand response and different electricity markets using a stochastic market equilibrium model. ESRI WP585, February 2018

Power systems based on renewable energy sources (RES) are characterised by increasingly distributed, volatile and uncertain supply leading to growing requirements for flexibility. In this paper, we explore the role of demand response (DR) as a source of flexibility that is considered to become increasingly important in future. The majority of research in this context has focussed on the operation of power systems in energy only markets, mostly using deterministic optimisation models. In contrast, we explore the impact of DR on generator investments and profits from different markets, on costs for different consumers from different markets, and on CO2 emissions under consideration of the uncertainties associated with the RES generation. We also analyse the effect of the presence of a feed-in premium (FIP) for RES generation on these impacts. We therefore develop a novel stochastic mixed complementarity model in this paper that considers both operational and investment decisions, that considers interactions between an energy market, a capacity market and a feed-in premium and that takes into account the stochasticity of electricity generation by RES. We use a Benders decomposition algorithm to reduce the computational expenses of the model and apply the model to a case study based on the future Irish power system. We find that DR particularly increases renewable generator profits. While DR may reduce consumer costs from the energy market, these savings may be (over)compensated by increasing costs from the capacity market and the feed-in premium. This result highlights the importance of considering such interactions between different markets