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

Logistics issues of biomass : the storage problem and the multi-biomass supply chain

Biomass is a renewable energy source with increasing importance. The larger fraction of cost in biomass energy generation originates from the logistics operations. A major issue concerning biomass logistics is its storage, especially when it is characterized by seasonal availability. The biomass energy exploitation literature has rarely investigated the issue of biomass storage. Rather, researchers usually choose arbitrarily the lowest cost storage method available, ignoring the effects this choice may have on the total system efficiency. In this work, the three most frequently used biomass storage methods are analyzed and are applied to a case study to come up with tangible comparative results. Furthermore, the issue of combining multiple biomass supply chains, aiming at reducing the storage space requirements, is introduced. An application of this innovative concept is also performed for the case study examined. The most important results of the case study are that the lowest cost storage method indeed constitutes the system-wide most efficient solution, and that the multi-biomass approach is more advantageous when combined with relatively expensive storage methods. However, low cost biomass storage methods bear increased health, safety and technological risks that should always be taken into account. (C) 2008 Elsevier Ltd. All rights reserved

Time-dependent opportunities in energy business : a comparative study of locally available renewable and conventional fuels

This work investigates and compares energy-related, private business strategies, potentially interesting for investors willing to exploit either local biomass sources or strategic conventional fuels. Two distinct fuels and related power-production technologies are compared as a case study, in terms of economic efficiency: the biomass of cotton stalks and the natural gas. The carbon capture and storage option are also investigated for power plants based on both fuel types. The model used in this study investigates important economic aspects using a "real options" method instead of traditional Discounted Cash Flow techniques, as it might handle in a more effective way the problems arising from the stochastic nature of significant cash flow contributors' evolution like electricity, fuel and CO(2) allowance prices. The capital costs have also a functional relationship with time, thus providing an additional reason for implementing, "real options" as well as the learning-curves technique. The methodology as well as the results presented in this work, may lead to interesting conclusions and affect potential private investment strategies and future decision making. This study indicates that both technologies lead to positive investment yields, with the natural gas being more profitable for the case study examined, while the carbon capture and storage does not seem to be cost efficient with the current CO(2) allowance prices. Furthermore, low interest rates might encourage potential investors to wait before actualising their business plans while higher interest rates favor immediate investment decisions. (C) 2009 Elsevier Ltd. All rights reserved

Optimisation of electricity energy markets and assessment of CO2 trading on their structure : a stochastic analysis of the greek power sector

Power production was traditionally dominated by monopolies. After a long period of research and organisational advances in international level, electricity markets have been deregulated allowing customers to choose their provider and new producers to compete the former Public Power Companies. Vast changes have been made in the European legal framework but still, the experience gathered is not sufficient to derive safe conclusions regarding the efficiency and reliability of deregulation. Furthermore, emissions' trading progressively becomes a reality in many respects, compliance with Kyoto protocol's targets is a necessity, and stability of the national grid's operation is a constraint of vital importance. Consequently, the production of electricity should not rely solely in conventional energy sources neither in renewable ones but on a mixed structure. Finding this optimal mix is the primary objective of the study. A computational tool has been created, that simulates and optimises the future electricity generation structure based on existing as well as on emerging technologies. The results focus on the Greek Power Sector and indicate a gradual decreasing of anticipated CO2 emissions while the socioeconomic constraints and reliability requirements of the system are met. Policy interventions are pointed out based on the numerical results of the model. (C) 2010 Elsevier Ltd. All rights reserved

The Higgs Penguin and its Applications : An overview

We review the effective Lagrangian of the Higgs penguin in the Standard Model and its minimal supersymmetric extension (MSSM). As a master application of the Higgs penguin, we discuss in some detail the B-meson decays into a lepton-antilepton pair. Furthermore, we explain how this can probe the Higgs sector of the MSSM provided that some of these decays are seen at Tevatron Run II and B-factories. Finally, we present a complete list of observables where the Higgs penguin could be strongly involved.Comment: 22 pages, 6 figures, Invited review article to appear in Mod. Phys. Lett. A, v2: Table 1 updated, comments and references adde

Locating a bioenergy facility using a hybrid optimization method

In this paper, the optimum location of a bioenergy generation facility for district energy applications is sought. A bioenergy facility usually belongs to a wider system, therefore a holistic approach is adopted to define the location that optimizes the system-wide operational and investment costs. A hybrid optimization method is employed to overcome the limitations posed by the complexity of the optimization problem. The efficiency of the hybrid method is compared to a stochastic (genetic algorithms) and an exact optimization method (Sequential Quadratic Programming). The results confirm that the hybrid optimization method proposed is the most efficient for the specific problem. (C) 2009 Elsevier B.V. All rights reserved

Evidence Transfer for Improving Clustering Tasks Using External Categorical Evidence

In this paper we introduce evidence transfer for clustering, a deep learning method that can incrementally manipulate the latent representations of an autoencoder, according to external categorical evidence, in order to improve a clustering outcome. By evidence transfer we define the process by which the categorical outcome of an external, auxiliary task is exploited to improve a primary task, in this case representation learning for clustering. Our proposed method makes no assumptions regarding the categorical evidence presented, nor the structure of the latent space. We compare our method, against the baseline solution by performing k-means clustering before and after its deployment. Experiments with three different kinds of evidence show that our method effectively manipulates the latent representations when introduced with real corresponding evidence, while remaining robust when presented with low quality evidence