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

USAID Water and Development Strategy, 2013-2018

The first global Water and Development Strategy released by the US Agency for International Development outlines the approach that will guide USAID's water programming through 2018. The Strategy emphasizes sustainability, working through host country systems, using emerging science and technology, and learning from past efforts

Contractors Perspective on the Selection of Innovative Sustainable Technologies for Achieving Zero Carbon Retail Buildings

The use of innovative sustainable technologies (IST) has been regarded as an effective approach to enhancing energy efficiency and reducing carbon emissions of buildings. However, contractors face significant challenges in the selection of IST. The reported challenges in the literature include: lack of skills and knowledge, uncertainties, risks and the rapid development of a large number of technological alternatives and decision criteria. The selection process emerges as a multi-attribute, value-based task that includes both qualitative and quantitative factors, which are often assessed with imprecise data and human judgments. This paper aims to establish the decision criteria for the selection of IST for achieving low carbon existing retail buildings with a focus on the main contractor’s perspective. The arguments are informed by the combination of literature review and an in-depth case study with a UK leading contractor. Five broad decision criteria are identified systematically drawing on the contractor’s practice. The established criteria are weighted and ranked using the analytic hierarchy process and expert opinions; with ‘margin opportunity’ being the most important, followed by ‘repeat business’, ‘investment costs’, ‘differentiation’ and then ‘transferability’. The findings should facilitate the integration of various facets of the selection process and stimulate contractors to use IST

Optimal management of bio-based energy supply chains under parametric uncertainty through a data-driven decision-support framework

This paper addresses the optimal management of a multi-objective bio-based energy supply chain network subjected to multiple sources of uncertainty. The complexity to obtain an optimal solution using traditional uncertainty management methods dramatically increases with the number of uncertain factors considered. Such a complexity produces that, if tractable, the problem is solved after a large computational effort. Therefore, in this work a data-driven decision-making framework is proposed to address this issue. Such a framework exploits machine learning techniques to efficiently approximate the optimal management decisions considering a set of uncertain parameters that continuously influence the process behavior as an input. A design of computer experiments technique is used in order to combine these parameters and produce a matrix of representative information. These data are used to optimize the deterministic multi-objective bio-based energy network problem through conventional optimization methods, leading to a detailed (but elementary) map of the optimal management decisions based on the uncertain parameters. Afterwards, the detailed data-driven relations are described/identified using an Ordinary Kriging meta-model. The result exhibits a very high accuracy of the parametric meta-models for predicting the optimal decision variables in comparison with the traditional stochastic approach. Besides, and more importantly, a dramatic reduction of the computational effort required to obtain these optimal values in response to the change of the uncertain parameters is achieved. Thus the use of the proposed data-driven decision tool promotes a time-effective optimal decision making, which represents a step forward to use data-driven strategy in large-scale/complex industrial problems.Peer ReviewedPostprint (published version

Toward sustainable data centers: a comprehensive energy management strategy

Data centers are major contributors to the emission of carbon dioxide to the atmosphere, and this contribution is expected to increase in the following years. This has encouraged the development of techniques to reduce the energy consumption and the environmental footprint of data centers. Whereas some of these techniques have succeeded to reduce the energy consumption of the hardware equipment of data centers (including IT, cooling, and power supply systems), we claim that sustainable data centers will be only possible if the problem is faced by means of a holistic approach that includes not only the aforementioned techniques but also intelligent and unifying solutions that enable a synergistic and energy-aware management of data centers. In this paper, we propose a comprehensive strategy to reduce the carbon footprint of data centers that uses the energy as a driver of their management procedures. In addition, we present a holistic management architecture for sustainable data centers that implements the aforementioned strategy, and we propose design guidelines to accomplish each step of the proposed strategy, referring to related achievements and enumerating the main challenges that must be still solved.Peer ReviewedPostprint (author's final draft

Editorial: water governance in a climate change world: appraising systemic and adaptive effectiveness

and other research outputs Editorial: water governance in a climate change world: appraising systemic and adaptive effectivenes

A novel improved model for building energy consumption prediction based on model integration

Building energy consumption prediction plays an irreplaceable role in energy planning, management, and conservation. Constantly improving the performance of prediction models is the key to ensuring the efficient operation of energy systems. Moreover, accuracy is no longer the only factor in revealing model performance, it is more important to evaluate the model from multiple perspectives, considering the characteristics of engineering applications. Based on the idea of model integration, this paper proposes a novel improved integration model (stacking model) that can be used to forecast building energy consumption. The stacking model combines advantages of various base prediction algorithms and forms them into “meta-features” to ensure that the final model can observe datasets from different spatial and structural angles. Two cases are used to demonstrate practical engineering applications of the stacking model. A comparative analysis is performed to evaluate the prediction performance of the stacking model in contrast with existing well-known prediction models including Random Forest, Gradient Boosted Decision Tree, Extreme Gradient Boosting, Support Vector Machine, and K-Nearest Neighbor. The results indicate that the stacking method achieves better performance than other models, regarding accuracy (improvement of 9.5%–31.6% for Case A and 16.2%–49.4% for Case B), generalization (improvement of 6.7%–29.5% for Case A and 7.1%-34.6% for Case B), and robustness (improvement of 1.5%–34.1% for Case A and 1.8%–19.3% for Case B). The proposed model enriches the diversity of algorithm libraries of empirical models

Localism and energy: Negotiating approaches to embedding resilience in energy systems

Tensions are evident in energy policy objectives between centralised top-down interconnected energy systems and localised distributed approaches. Examination of these tensions indicates that a localised approach can address a systemic problem of interconnected systems; namely vulnerability. The challenge for energy policy is to realise the interrelated goals of energy security, climate and environmental targets and social and economic issues such as fuel poverty, whilst mitigating vulnerability. The effectiveness of conventional approaches is debateable. A transition to a low carbon pathway should focus on resilience, counter to vulnerability. This article draws from on-going work which evaluates the energy aspects of a Private Finance Initiative (PFI) project to refurbish and re-build a local authority’s entire stock of sheltered accommodation to high environmental standards. Initial findings suggest that whereas more conventional procurement processes tend to increase systemic vulnerability, a user focussed process driven through PFI competitive dialogue is beginning to motivate some developers to adopt innovative approaches to energy system development. Conceptually these findings strongly suggest that embedding ‘Open Source’ principles in energy system development acts to work against systemic vulnerabilities by embedding resilience

A grassroots sustainable energy niche? Reflections on community energy in the UK

System-changing innovations for sustainability transitions are pro- posed to emerge in radical innovative niches. ‘Strategic Niche Management’ theory predicts that niche-level actors and networks will aggregate learning from local projects, disseminating best practice, and encouraging innovation diffusion. Grassroots inno- vations emerging from civil society are under-researched, and so we investigate the UK community energy sector to empirically test this model. Our analysis draws on qualitative case study research with local projects, and a study of how intermediary organisa- tions support local projects. We examine the extent and nature of interactions and resource flows between projects and intermediary actors in order to evaluate the utility of niche theories in the civil society context. While networking and intermediary organisations can effectively spread some types of learning necessary for diffu- sion, this is not sufficient: tacit knowledge, trust and confidence are essential to these projects’ success, but are more difficult to abstract and translate to new settings. We discuss the implications of our findings for niche theory, for community energy and other grass- roots practitioners aiming to build robust influential niches, and for policymakers