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There is a significant interest in utilizing demand response (DR) programs to increase the flexibility of sustainable power systems. The DR operators (e.g., aggregator companies) need a robust means to assess the performance of a potential DR program that will be employed in the future. Such assessments should be based on data, some of which are hardly available. Knowledge about the DR providers (e.g., the behavior of proactive consumers) is key to the success of a DR program. In this paper, we devise a data-driven framework to assess the impact of uncertainties associated with future DR programs. The proposed framework comprises two modules: the DR simulation module, and the data analytics module. The DR module solves an optimization problem which simulates the operation of a hypothetical DR program. The data analytics module, firstly, selects subsets from historical load and price data. Secondly, it performs sensitivity analysis on the optimal solution to capture the impact of uncertainties. We consider two sources of uncertainty. First, we consider lack of information about DR providers due to the absence of a DR program in the current system. Second, we consider errors in load and price forecasts, whose impacts are investigated by formulating a sensitivity matrix from the perturbed KKT equations of the optimization problem solved by the DR module. The proposed framework provides insights regarding the potential of a prospective DR program. Such information can be useful for DR operators as a starting point to decide their position in the contractual agreement they will engage in with the (distribution) system operator and/or DR providers in the future.

This study assesses the electricity demand pattern in the relatively temperate climate of the Netherlands (latitude 52 degrees 30'N). Daily electricity demand and average temperature during the period from 1970 until 2007 are investigated for possible trends in the temperature dependence of electricity demand. We hypothesize that the increased use of cooling applications has shifted the temperature dependence of electricity demand upwards in summer months. Our results show significant increases in temperature dependence of electricity demand in May, June, September, October and during the summer holidays. During the period studied, temperature dependence in these months has shifted from negative to positive, meaning that a higher temperature now leads to an increased electricity demand in these months, rather than a decreased demand as observed historically. Although electricity demand in countries with moderate summer temperatures such as the Netherlands generally peaks in winter months and shows a minimum in summer months, this trend may signal the development of an additional peak in summer, especially given the expected climatic change. As power generating capacity may be negatively influenced by higher temperatures due to decreasing process cooling possibilities, an increasing electricity demand at higher temperatures may have important consequences for power generation capacity planning and maintenance scheduling. (C) 2009 Elsevier Ltd. All rights reserved.

Modeling electricity storage to address challenges and opportunities of its applications for smart grids requires inter-temporal equalities to keep track of energy content over time. These constraints present crucial modeling elements, which may affect case study results. Hence, correct modeling improves conclusions as to which extent energy storage applications can enhance future electric power systems sustainability, reliability, and efficiency. This paper presents a novel and improved mixedinteger linear problem (MILP) formulation for energy storage of plug-in (hybrid) electric vehicles (PEVs) for reserves in power system models. It is based on insights from the field of System Dynamics, in which complex interactions between different elements are studied by means of feedback loops as well as stocks, flows and co-flows. Generalized to a multi-bus system, this formulation includes improvements in the energy balance and surpasses shortcomings in the way existing literature deals with reserve constraints. Tested on the IEEE 14-bus system with realistic PEV mobility patterns, the deterministic results show significant changes in the scheduling of the units, often referred to as unit commitment (UC) . info:eu-repo/semantics/draft

Developing countries' energy use is rapidly increasing, which affects global climate change and global and regional energy settings. Energy models are helpful for exploring the future of developing and industrialised countries. However, energy systems of developing countries differ from those of industrialised countries, which has consequences for energy modelling. New requirements need to be met by present-day energy models to adequately explore the future of developing countries' energy systems. This paper aims to assess if the main characteristics of developing countries are adequately incorporated in present-day energy models. We first discuss these main characteristics, focusing particularly on developing Asia, and then present a model comparison of 12 selected energy models to test their suitability for developing countries. We conclude that many models are biased towards industrialised countries, neglecting main characteristics of developing countries, e.g. the informal economy, supply shortages, poor performance of the power sector, structural economic change, electrification, traditional bio-fuels, urban-rural divide. To more adequately address the energy systems of developing countries, energy models have to be adjusted and new models have to be built. We therefore indicate how to improve energy models for increasing their suitability for developing countries and give advice on modelling techniques and data requirements. (c) 2007 Elsevier Ltd. All rights reserved.

Removal of the toxic selenium compounds selenite and selenate from waste water before discharge is becoming increasingly imperative in industrialized countries. Bacteria can reduce selenate to selenite, but also further to elemental selenium, selenide or organic selenium. In this paper, we aim to exclusively bio-reduce selenate to selenite in an open high-rate bioreactor. This conversion could be part of a two-stage process in which the selenite is subsequently reduced by chemical means under optimal conditions to produce a biomass-free selenium product. In the process, yield and reduction rate of biological selenate to selenite should be maximized, while formation of elemental selenium, selenide and organic selenium compounds should be avoided. Fed-batch experiments with a liquid volume of 0.25-0.75 L at different temperatures 20-30-40-50 °C, pH settings 6-7-8-9, initial biomass concentration of 1 or 5 g wet weight granular Eerbeek sludge and various lactic acid concentrations were performed to determine their effect on the biological conversion of selenate to selenite. Furthermore, the effect on selenite losses by further biological reduction or, if present, chemical reduction was investigated as well. Optimal selenate reduction to selenite was found at 30 °C and pH 6 or 7 or 8 with 25 mM selenate and 13.75 mM lactic acid in the influent, with a selenite yield of 79-95%. In all the other conditions, less selenate was reduced to selenite. Also a 5 times higher electron donor concentration resulted in less selenite production, with only 22% of the selenate converted to selenite. The high yield and the high biological reduction rate of at least 741 mg Se/g initial VSS/day detected in the 1 g initial biomass experiment implicate that biological selenate conversion to selenite is a feasible process.

Scientific information on the size and nature of the threat of climate change is needed by politicians in order to weight their decisions. Computerised models are extremely useful tools to quantify the long-term effects of current policies. This paper describes a new modelling approach that allows formulation of industrial energy demand projections consistent with the assumptions for scenario drivers such as GDP and population. In the model, a level of industrial production is used as a key variable, and we define it in physical units, rather than in monetary units. The aim of this research is to increase insights that come with long-term energy demand scenarios. This research clearly shows that physical indicators provide additional insights in scenario analysis. The use of physical indicators instead of monetary indicators seems to affect the energy scenarios significantly. The differences with monetary indicators are larger in developing regions than in OECD regions. We conclude that an integrated energy and materials approach reveals developments that are hardly visible using a monetary approach. Moreover, this research shows the potential and benefits of the use of physical indicators for scenario development. (c) 2007 Elsevier B.V. All rights reserved.

SummaryMany research efforts aim at an extension of life‐cycle assessment (LCA) in order to increase its spatial or temporal detail or to enlarge its scope. This is an important contribution to industrial ecology as a scientific discipline, but from the application viewpoint other options are available to obtain more detailed information, or to obtain information over a broader range of impacts in a life‐cycle perspective. This article discusses three different strategies to reach these aims: (1) extension of LCA—one consistent model; (2) use of a toolbox—separate models used in combination; and (3) hybrid analysis—combination of models with data flows between them.Extension of LCA offers the most consistent solution. Developments in LCA are moving toward greater spatial detail and temporal resolution and the inclusion of social issues. Creating a supertool with too many data and resource requirements is, however, a risk. Moreover, a number of social issues are not easily modeled in relation to a functional unit.The development of a toolbox offers the most flexibility regarding spatial and temporal information and regarding the inclusion of other types of impacts. The rigid structure of LCA no longer sets limits; every aspect can be dealt with according to the logic of the relevant tool. The results lack consistency, however, preventing further formal integration.The third strategy, hybrid analysis, takes up an intermediate position between the other two. This strategy is more flexible than extension of LCA and more consistent than a toolbox. Hybrid analysis thus has the potential to combine the strong points of the other two strategies. It offers an interesting path for further discovery, broader than the already well‐known combination of process‐LCA and input‐output‐LCA. We present a number of examples of hybrid analysis to illustrate the potentials of this strategy.Developments in the field of a toolbox or of hybrid analysis may become fully consistent with LCA, and then in fact become part of the first solution, extension of LCA.

SummaryThe quantification and achievement of eco‐efficiency or dematerialization in the form of a factor X, with X varying between 4 and 50 is being espoused by a variety of analysts and advocates. Politically, these efforts are mainly confined to some European countries. They reflect a remarkable technological optimism. This article reviews some of the major issues pertinent to the factor X debate. The case is presented for quantifying dematerialization or eco‐efficiency goals using a factor X. It is also found that the factor X lacks precision as yet, and that there is only limited interest in the possibilrty that achievable values for X may vary widely among economic activities given technological constraints. There is no agreement whether technological improvement alone will be sufficient to achieve a factor X in practice for economies as a whole. It seems likely, however; that government‐driven technology forcing will be necessary to achieve a factor X in practical terms, especially when X is relatively large.