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Artículos en revistas Islands are facing considerable challenges in meeting their energy needs in a sustainable, affordable and reliable way. The present paper develops an integrated approach to economically assess initiatives that can transform island power systems into smart ones. Single and multi-action initiatives fostering the deployment of renewable energy sources (RES), energy storage systems (ESS), demand-side management (DSM), and electric vehicle (EV) are considered. An hourly unit commitment on a weekly basis is proposed to assess the impact of the initiatives on the system operation costs of five prototype island power systems, which have been identified by applying clustering techniques to a set of sixty islands power systems. The different investment costs of the initiatives are accounted for determining their corresponding internal rate of return (IRR) through their lifetime. The economic assessment of single and multi-action initiatives for five prototype islands representing sixty island power systems quantifies which initiatives are most suitable for which type of island power system. The assessment shows that islands of different sizes and features require different initiatives. Larger islands tend to DSM initiatives, whereas smaller islands tend to RES initiatives. Multi-action initiatives achieve highest system operation cost reduction, whereas single action initiatives yield to highest IRR. info:eu-repo/semantics/publishedVersion

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

This paper proposes a model to compute nodal prices in oligopolistic markets. The model generalizes a previous model aimed at solving the single-bus problem by applying an optimisation procedure. Both models can be classified as Conjectured Supply Function models. The Conjectured Supply Functions are assumed to be linear with constant slopes. However, the price sensitivity as seen for each generating unit depends on the system line s status (congested or not congested). Market equilibrium is defined in this framework. A procedure based on solving an optimisation problem is proposed. It only requires convexity of cost functions. However, existence of equilibrium is not guaranteed in this multi-nodal situation, and an iterative search is required to find it if it exists. A two area multi-period case study is analysed. The model reaches equilibrium for some cases, mainly depending on the number of periods considered and on the value of conjectured supply function slopes. Some oscillation patterns are observed that can be interpreted as quasi-equilibria. This methodology can be applied to the study of the future Iberian electricity market. info:eu-repo/semantics/draft

Capítulos en libros In concert with increasing co-ordination of energy markets, transmission planning for location-constrained generation is forcing a shift from regional to wide-area transmission network planning. Planning for locationconstrained generators, notably wind and solar resources, is complicated by uncertainty in policies driving their growth. To design transmission systems robust to uncertain future expansion of location-constrained generators, planners must incorporate stochastic generation into models. The size of the transmission expansion problem makes this computationally intractable. This paper presents a screening model to reduce the number of transmission investments which must be considered. The screening model combines sampled generation expansion scenarios with a two-stage relaxed planning model to identify corridors of interest. A St.Clair curve-based filter translates corridors to investment variables. In a 240 bus model of WECC, this method reduced the number of corridors of interest 98% from 19,503 to 303 and reduces the number of investment variables 99% from 58,503 to 432. info:eu-repo/semantics/publishedVersion

Abstract The production of electricity from hydropower results in several environmental impacts that, in only some instances, have been analysed from an economic valuation approach. Moreover, as environmental impacts largely depend on the specific characteristics of the case study, benefit transfer techniques are inadequate for valuation. The present paper demonstrates through the review of valuation studies on the environmental impacts of this technology, and the analysis of the different environmental impacts associated with hydropower for specific case studies that in fact benefit transfer should not be applied as each hydropower plant has specific and different impacts. The paper demonstrates the importance of a case study approach, for defining priorities with respect to alternative hydropower production facilities. Finally, the paper demonstrates that choice experiments are particularly suited for valuing the identified environmental impacts, being relevant for policy planning purposes.