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The paper reports on the implementation and the design of a controller for a fuel cell blower (FCB) with active magnetic bearings (AMBs). The cascaded position-fluxcentralized controller is comprised of a centralized position control loop and an inner flux control loop. The last one is based on state estimation without explicit flux measurements. As the position control is not dependent on the magnetic field nonlinearities, such a control structure enables operation under a zero bias. The practical working implementation of a flux control for the industrial levitated rotor is shown for the first time. The flux control gives better results than current control for both normal and zero bias operation. The system is analyzed fully, combining rotor dynamics and power amplifier analyses simultaneously. The importance of using the coil voltage in addition to current and practical treatment of the flux control is revealed. The centralized position-flux controller is compared with a state-of-the-art cascaded position-current control, which has inner current control loops. The proposed control solution with a zero bias can achieve a dynamic performance comparable that of a controller with the classical bias current.

This paper presents a rigorous design and optimization of an axial flux microelectromechanical systems (MEMS) brushless dc (BLDC) micromotor with dual rotor improving both efficiency and power density with an external diameter of only around 10 mm. The stator is made of two layers of windings by MEMS technology. The rotor is developed by film permanent magnets assembled over the rotor yoke. The characteristics of the MEMS micromotor are analyzed and modeled through a 3-D magnetic equivalent circuit (MEC) taking the leakage flux and fringing effect into account. Such a model yields a relatively accurate prediction of the flux in the air gap, back electromotive force (EMF) and electromagnetic torque, whilst being computationally efficient. Based on 3-D MEC model the multi-objective firefly algorithm (MOFA) is developed for the optimal design of this special machine. Both 3-D finite element (FE) simulation and experiments are employed to validate the MEC model and MOFA optimization design.

Further improvements in demand response programs implementation are needed in order to take full advantage of this resource, namely for the participation in energy and reserve market products, requiring adequate aggregation and remuneration of small size resources. The present paper focuses on SPIDER, a demand response simulation that has been improved in order to simulate demand response, including realistic power system simulation. For illustration of the simulator’s capabilities, the present paper is proposes a methodology focusing on the aggregation of consumers and generators, providing adequate tolls for the demand response program’s adoption by evolved players. The methodology proposed in the present paper fo cuses on a Virtual Power Player that manages and aggregates the available demand response and distributed generation resources in order to satisfy the required electrical energy demand and reserve. The aggregation of res ources is addressed by the use of clustering algorithms, and operation costs for the VPP are minimized. The presented case study is based on a set of 32 consumers and 66 distributed generation units, running on 180 distinct operation scenarios.

Energies 8(7), 6715-6737 (2015). doi:10.18154/RWTH-2016-00090 special issue: "Special Issue "Electrical Power and Energy Systems for Transportation Applications" / guest editor Prof. Dr. Paul. Stewart, Institute for Innovation in Sustainable Engineering, Lonsdale House, Derby, DE1 3EE, UK", Published by MDPI, Basel

Organic Rankine cycles (ORCs) are an established technology to convert waste heat to electricity. Although several commercial implementations exist, there is still considerable potential for thermo-economic optimization. As such, a novel framework for designing optimized ORC systems is proposed based on a multi-objective optimization scheme in combination with financial appraisal in a post-processing step. The suggested methodology provides the flexibility to quickly assess several economic scenarios and this without the need of knowing the complex design procedure. This novel way of optimizing and interpreting results is applied to a waste heat recovery case. Both the transcritical ORC and subcritical ORC are investigated and compared using the suggested optimization strategy. Keywords: organic Rankine cycle (ORC); thermo-economic; optimization; subcritical; transcritical

Energies 8(5), 4587-4606 (2015). doi:10.3390/en8054587, Published by MDPI, Basel

The fabrication and performance of wicks for flat heat pipe applications produced by sintering a filamentary nickel powder has been investigated. Tape casting was used as an intermediate step in the wick production process. Thermogravimetric analysis was used to study the burn-off of the organic binder used and to study the oxidation and reduction processes of the nickel. The wicks produced were flat, rectangular and intended for liquid transport in the upwards vertical direction. Rate-of-rise experiments using heptane were used to test the flow characteristics of the wicks. The wick porosities were measured using isopropanol. The heat transfer limitation constituted by the vapour static pressure and the capillary pressure was discussed. The influence on wick performance by using pore former in the manufacturing was studied. When Pcap/Psat &gt, 1, the use of a pore former to increase the wick permeability will always improve the wick performance. When Pcap/Psat &lt, 1, it was shown that if the effective pore radius and the permeability increase with an equal percentage the overall influence on the wick capacity is negative. A criterion for a successful pore former introduction is proposed and the concept of a pore former evaluation plot is presented.

Two cable logging systems were reviewed to compare the efficiency of potential biomass extraction from remote forest sites in Ireland based on productive machine hour (PMH) and unit cost of operation (€/m3). Three operational scenarios (SC) were analysed where SC I was a three man crew operation (choker setter, the carriage operator and unhooking chokers). SC II was a variation of this with a two man crew operation. SC III was operating radio controlled chokers there was a two man crew (choker setter and carriage operator). The study aims to assess how operations in Ireland perform against previous known cable studies to determine whether the cost of timber extraction on remote forest sites inaccessible for mechanised felling, has a future given the increased demand for wood fibre in Ireland, both from the sawmilling industries and the wood for energy sector. The volume per PMH was recorded at 17.97 for SC I, 15.09 for SC II and 20.58 m3 for SC III. The difference in productivity versus SC III remote controlled chokers is 5.49 m3/PMH for SC II crew and 2.61 m3/PMH for SC I. The decrease in total volume extracted from SCs I and II versus SC III was recorded at 15.69 m3 (15%) and 32.97 m3 (36%) product respectively. In value terms, the unit cost (€/m3) varied from 6.29 (SC I) to 6.43 (SC II) to 4.57 (SC III). When looking at the production unit costs of normal wood energy supply chains in Ireland, the figures are similar ranging from 3.17 €/m3 to 8.01 €/m3. The value of the end product of course will always determine which market the eventually goes to but given that cable log wood fibre has been unthinned and unmaintained then the biomass sector may be an ever increasing demand point in the search for increased woody biomass given that the unit costs can be competitive with other wood energy supply chains. Science Foundation Ireland National Council for Forest Research and Development in Ireland (COFORD)

This paper presents a novel approach to multi-type distributed generation (DG) planning based on the analysis of investment and income brought by grid-connected DG. Firstly, the timing characteristics of loads and DG outputs, as well as the environmental benefits of DG are analyzed. Then, on the basis of the classification of daily load sequences, the typical daily load sequence and the typical daily output sequence of DG per unit capacity can be computed. The proposed planning model takes the location, capacity and types of DG into account as optimization variables. An improved adaptive genetic algorithm is proposed to solve the model. Case studies have been carried out on the IEEE 14-node distribution system to verify the feasibility and effectiveness of the proposed method and model.

To satisfy the increasingly stringent emission regulations and a demand for an ever lower fuel consumption, diesel engines have become complex systems with many interacting actuators. As a consequence, these requirements are pushing control and calibration to their limits. The calibration procedure nowadays is still based mainly on engineering experience, which results in a highly iterative process to derive a complete engine calibration. Moreover, automatic tools are available only for stationary operation, to obtain control maps that are optimal with respect to some predefined objective function. Therefore, the exploitation of any leftover potential during transient operation is crucial. This paper proposes an approach to derive a transient feedforward (FF) control system in an automated way. It relies on optimal control theory to solve a dynamic optimization problem for fast transients. A partially physics-based model is thereby used to replace the engine. From the optimal solutions, the relevant information is extracted and stored in maps spanned by the engine speed and the torque gradient. These maps complement the static control maps by accounting for the dynamic behavior of the engine. The procedure is implemented on a real engine and experimental results are presented along with the development of the methodology., Energies, 7 (8), ISSN:1996-1073

The aim of this research study has been to design a gain scheduling (GS) digital controller in order to control the voltage of an islanded microgrid in the presence of fast varying loads (FVLs), and to compare it to a robust controller. The inverter which feeds the microgrid is connected to it through an inductance-capacitor-inductance (LCL) filter. The oscillatory and nonlinear behaviour of the plant is analyzed in the whole operating zone. Afterwards, the design of the controllers which contain two loops in cascade are described. The first loop concerns the current control, while the second is linked to the voltage regulation. Two controllers, one defined as Robust and another one as GS controller, are designed for the two loops, emphasizing in their robustness and their ability to damp the oscillatory plant behaviour. To finish, some simulations are carried out to study and compare the two kinds of controllers in different operating points. The results show that both controllers damp the oscillatory behaviour of the plant in closed loop (CL), and that the GS controller ensures a better rejection of current disturbances from FVLs. The work described in this paper has been supported by European Grouping of Territorial Cooperation (EGTC) Euroregion Aquitaine-Euskadi, in the frame of their Cooperation Funds (project Colaboracion Academica e Investigadora para el Desarrollo de las Energias Renovables (CAIDER) between the University of the Basque Country (UPV/EHU) and the Ecole Superieure des Technologies Industrielles Avancees (ESTIA)).

This work is aimed at optimizing the wind turbine rotor speed setpoint algorithm. Several intelligent adjustment strategies have been investigated in order to improve a reward function that takes into account the power captured from the wind and the turbine speed error. After different approaches including Reinforcement Learning, the best results were obtained using a Particle Swarm Optimization (PSO)-based wind turbine speed setpoint algorithm. A reward improvement of up to 10.67% has been achieved using PSO compared to a constant approach and 0.48% compared to a conventional approach. We conclude that the pitch angle is the most adequate input variable for the turbine speed setpoint algorithm compared to others such as rotor speed, or rotor angular acceleration. This research was supported by the Basque Government through the projects IG-2011/0000794, S-PE11UN061 and S-PE12UN015, and by the Argolabe Ingenieria SL Company. Some of the authors belong to Computational Intelligence Group of the University of the Basque Country (UPV/EHU), supported by the Basque Government.

The construction of Smart Grids leads to the main question of what kind of intelligence such grids require and how to build it. Some authors choose an agent based solution to realize this intelligence. However, there may be some misunderstandings in the way this technology is being applied. This paper exposes some considerations of this subject, focusing on the Microgrid level, and shows a practical example through INGENIAS methodology, which is a methodology for the development of Agent Oriented systems that applies Model Driven Development techniques to produce fully functional Multi-Agent Systems.

We address the valuation of an operating wind farm and the finite-lived option to invest in it under different reward/support schemes: a constant feed-in tariff, a premium on top of the electricity market price (either a fixed premium or a variable subsidy such as a renewable obligation certificate or ROC), and a transitory subsidy, among others. Futures contracts on electricity with ever longer maturities enable market-based valuations to be undertaken. The model considers up to three sources of uncertainty: the electricity price, the level of wind generation, and the certificate (ROC) price where appropriate. When analytical solutions are lacking, we resort to a trinomial lattice combined with Monte Carlo simulation; we also use a two-dimensional binomial lattice when uncertainty in the ROC price is considered. Our data set refers to the UK. The numerical results show the impact of several factors involved in the decision to invest: the subsidy per MWh generated, the initial lump-sum subsidy, the maturity of the investment option, and electricity price volatility. Different combinations of variables can help bring forward investments in wind generation. One-off policies, e.g., a transitory initial subsidy, seem to have a stronger effect than a fixed premium per MWh produced. We gratefully acknowledge financial support from the Spanish Ministry of Science and Innovation under research project ECO2011-25064, from the Basque Government GIC12/177-IT-399-13, and from Fundacion Repsol under the Low Carbon Programme joint initiative [72]. Luis M. Abadie also thanks the Research Council of Norway for partial support under the project "Strategic Challenges in International Climate and Energy Policy (CICEP)" [73]. Two anonymous reviewers have contributed to improve the paper.

Smart Grids are becoming a reality all over the world. Nowadays, the research efforts for the introduction and deployment of these grids are mainly focused on the development of the field of Smart Metering. This emerging application requires the use of technologies to access the significant number of points of supply (PoS) existing in the grid, covering the Low Voltage (LV) segment with the lowest possible costs. Power Line Communications (PLC) have been extensively used in electricity grids for a variety of purposes and, of late, have been the focus of renewed interest. PLC are really well suited for quick and inexpensive pervasive deployments. However, no LV grid is the same in any electricity company (utility), and the particularities of each grid evolution, architecture, circumstances and materials, makes it a challenge to deploy Smart Metering networks with PLC technologies, with the Smart Grid as an ultimate goal. This paper covers the evolution of Smart Metering networks, together with the evolution of PLC technologies until both worlds have converged to project PLC-enabled Smart Metering networks towards Smart Grid. This paper develops guidelines over a set of strategic aspects of PLC Smart Metering network deployment based on the knowledge gathered on real field; and introduces the future challenges of these networks in their evolution towards the Smart Grid. This work has been supported, in the contribution of the University of the Basque Country, by the Spanish Ministry of Economy and Competitiveness (Project TEC2012-33302).

The variations in irradiance produced by changes in cloud cover can cause rapid fluctuations in the power generated by large photovoltaic (PV) plants. As the PV power share in the grid increases, such fluctuations may adversely affect power quality and reliability. Thus, energy storage systems (ESS) are necessary in order to smooth power fluctuations below the maximum allowable. This article first proposes a new control strategy (step-control), to improve the results in relation to two state-of-the-art strategies, ramp-rate control and moving average. It also presents a method to quantify the storage capacity requirements according to the three different smoothing strategies and for different PV plant sizes. Finally, simulations shows that, although the moving-average (MA) strategy requires the smallest capacity, it presents more losses (2–3 times more) and produces a much higher number of cycles over the ESS (around 10 times more), making it unsuitable with storage technologies as lithium-ion. The step-control shown as a better option in scenery with exigent ramp restrictions (around 2%/min) and distributed generation against the ramp-rate control in all ESS key aspects: 20% less of capacity, up to 30% less of losses and a 40% less of ageing. All the simulations were based on real PV production data, taken every 5 s in the course of one year (2012) from a number of systems with power outputs ranging from 550 kW to 40 MW. This work has been financed by the Seventh Framework Programme of the European Commission with the Project Photovoltaic Cost Reduction, Reliability, Operational Performance, Prediction and Simulation (PVCROPS—Grant Agreement No. 308468).

This study describes the results obtained in the synthesis of a biofuel that avoids the production of glycerol by applying supported KF as alkaline heterogeneous catalyst, to generate two moles of fatty acid methyl esters and one mole of monoglyceride from one mol of triglyceride. In this respect, the selective transesterification process of sunflower oil with methanol was carried out with KF (10 wt%) supported on three different solids, Al2O3, ZnO and MgO. The standard experimental conditions employed in the heterogeneous selective methanolysis reaction were: 12 mL of sunflower oil, 2.7 mL of methanol, 0.8 g of catalyst, at 65 °C temperature and one hour of reaction time. In all cases 100% conversion was obtained, with high selectivity values, greater than 90%, and quite suitable viscosity values, 4.5-8.5 cSt. In this way, the best catalytic behavior in the first use was obtained by using Al2O3 as support. However, although in the five consecutive reuses all catalysts exhibited a continuous decrease in their catalytic activities; the lower one was for KF catalyst using MgO as support. In summary, these three KF supported catalysts are very suitable to obtain a new biofuel, similar to conventional biodiesel, applicable to diesel engines. © 2014 by the authors., Grants from the Spanish Ministry of Economy and Competitiveness (Project ENE 2011-27017), Spanish Ministry of Education and Science (Projects CTQ2010-18126 and CTQ2011-28954-C02-02), FEDER funds and Junta de Andalucía FQM 0191, PO8-RMN-03515 and P11-TEP-7723 are gratefully acknowledged by the authors. We acknowledge support by the CSIC Open Access Publication Initiative through its Unit of Information Resources for Research (URICI)

This paper reports outcomes of life cycle assessments (LCAs) of three different oleaginous bioenergy chains (oilseed rape, Ethiopian mustard and cardoon) under Southern Europe conditions. Accurate data on field practices previously collected during a three-year study at two sites were used. The vegetable oil produced by oleaginous seeds was used for power generation in medium-speed diesel engines while the crop residues were used in steam power plants. For each bioenergy chain, the environmental impact related to cultivation, transportation of agricultural products and industrial conversion for power generation was evaluated by calculating cumulative energy demand, acidification potential and global warming potential. For all three bioenergy chains, the results of the LCA study show a considerable saving of primary energy (from 70 to 86 GJ·ha−1) and greenhouse gas emissions (from 4.1 to 5.2 t CO2·ha−1) in comparison to power generation from fossil fuels, although the acidification potential of these bioenergy chains may be twice that of conventional power generation. In addition, the study highlights that land use changes due to the cultivation of the abovementioned crops reduce soil organic content and therefore worsen and increase greenhouse gas emissions for all three bioenergy chains. The study also demonstrates that the exploitation of crop residues for energy production greatly contributes to managing environmental impact of the three bioenergy chains.

Flooded mine workings have good potential as low-enthalpy geothermal resources, which could be used for heating and cooling purposes, thus making use of the mines long after mining activity itself ceases. It would be useful to estimate the scale of the geothermal potential represented by abandoned and flooded underground mines in Europe. From a few practical considerations, a procedure has been developed for assessing the geothermal energy potential of abandoned underground coal mines, as well as for quantifying the reduction in CO2 emissions associated with using the mines instead of conventional heating/cooling technologies. On this basis the authors have been able to estimate that the geothermal energy available from underground coal mines in Europe is on the order of several thousand megawatts thermal. Although this is a gross value, it can be considered a minimum, which in itself vindicates all efforts to investigate harnessing it.

This paper reports on the modelling of a commercial 1.2 kW proton exchange membrane fuel cell (PEMFC), based on interrelated electrical and thermal models. The electrical model proposed is based on the integration of the thermodynamic and electrochemical phenomena taking place in the FC whilst the thermal model is established from the FC thermal energy balance. The combination of both models makes it possible to predict the FC voltage, based on the current demanded and the ambient temperature. Furthermore, an experimental characterization is conducted and the parameters for the models associated with the FC electrical and thermal performance are obtained. The models are implemented in Matlab Simulink and validated in a number of operating environments, for steady-state and dynamic modes alike. In turn, the FC models are validated in an actual microgrid operating environment, through the series connection of 4 PEMFC. The simulations of the models precisely and accurately reproduce the FC electrical and thermal performance. The authors acknowledge the Spanish Ministry of Economy and Competitiveness under grant DPI2010-21671-C02-01 and the Government of Navarre and FEDER funds under project “Microgrids in Navarra: design and implementation”.

We develop a semi-analytical model for polarization curve of a polymer electrolyte membrane (PEM) fuel cell with distributed (aged) along the oxygen channel MEA transport and kinetic parameters of the membrane–electrode assembly (MEA). We show that the curve corresponding to varying along the channel parameter, in general, does not reduce to the curve for a certain constant value of this parameter. A possibility to determine the shape of the deteriorated MEA parameter along the oxygen channel by fitting the model equation to the cell polarization data is demonstrated.

This paper presents a hydrogen powered hybrid solid oxide fuel cell-steam turbine (SOFC-ST) system and studies its optimal operating conditions. This type of installation can be very appropriate to complement the intermittent generation of renewable energies, such as wind generation. A dynamic model of an alternative hybrid SOFC-ST configuration that is especially suited to work with hydrogen is developed. The proposed system recuperates the waste heat of the high temperature fuel cell, to feed a bottoming cycle (BC) based on a steam turbine (ST). In order to optimize the behavior and performance of the system, a two-level control structure is proposed. Two controllers have been implemented for the stack temperature and fuel utilization factor. An upper supervisor generates optimal set-points in order to reach a maximal hydrogen efficiency. The simulation results obtained show that the proposed system allows one to reach high efficiencies at rated power levels. This work has been carried out in the Intelligent Systems and Energy research group of the University of the Basque Country (UPV/EHU) and has been supported by the UFI11/28 research grant of the UPV/EHU and by the IT677-13 research grant of the Basque Government (Spain) and by DPI2012-37363-CO2-01 research grant of the Spanish Ministry of Economy and Competitiveness.

In order to obtain the maximum amount of energy from tidal stream extraction devices, deployment in large arrays should be studied. The area of seabed with favorable conditions is fairly limited; therefore layout spacing has to be optimized. In this paper a feasibility study for a novel experimental facility, suitable for the testing of an array of tidal devices, is presented. To avoid space and scale limitations of towing tanks, testing is proposed to be performed in large lakes or calm seas using a self-propelled vessel, which will carry an array of devices with variable spacing, creating relevant speed differences and measuring their performance and loading. Using hydrodynamic scaling laws, an appropriate size for test turbines and the range of vessel speed was determined to fulfill experimental requirements. Computational fluid dynamic simulations, using the actuator disc method, have suggested a suitable turbine array configuration to resemble real application conditions. A simplified model of the vessel was analyzed using the finite elements method to determine the main scantlings. The hull resistance calculated by empirical formulae was found to be negligible compared to the resistance of the tested turbine. It was confirmed that turbine size and speed determined by scaling laws are also reasonable from a propulsion point of view.

Combining offshore wind and wave energy converting apparatuses presents a number of potentially advantageous synergies. To facilitate the development of a proposed floating platform combining these two technologies, proof of concept scale model testing on the wave energy converting component of this platform has been conducted. The wave energy component is based on the well-established concept of the oscillating water column. A numerical model of this component has been developed in the frequency domain, and the work presented here concerns the results of this modelling and testing. The results of both are compared to assess the validity and usefulness of the numerical model.

Producción Científica, Thanks to the built in intelligence (deployment of new intelligent devices and sensors in places where historically they were not present), the Smart Grid and Microgrid paradigms are able to take advantage from aggregated load forecasting, which opens the door for the implementation of new algorithms to seize this information for optimization and advanced planning. Therefore, accuracy in load forecasts will potentially have a big impact on key operation factors for the future Smart Grid/Microgrid-based energy network like user satisfaction and resource saving, and new methods to achieve an efficient prediction in future energy landscapes (very different from the centralized, big area networks studied so far). This paper proposes different improved models to forecast next day’s aggregated load using artificial neural networks, taking into account the variables that are most relevant. In particular, seven models based on the multilayer perceptron will be proposed, progressively adding input variables after analyzing the influence of climate factors on aggregated load. The results section presents the forecast from the proposed models, obtained from real data.

Radar services are occasionally affected by wind farms. This paper presents a comprehensive description of the effects that a wind farm may cause on the different radar services, and it compiles a review of the recent research results regarding the mitigation techniques to minimize this impact. Mitigation techniques to be applied at the wind farm and on the radar systems are described. The development of thorough impact studies before the wind farm is installed is presented as the best way to analyze in advance the potential for interference, and subsequently identify the possible solutions to allow the coexistence of wind farms and radar services. This work has been supported in part by the Spanish Ministry of Economy and Competitiveness (project TEC2012-32370) and by the Basque Government (SAIOTEK program).

A novel optimization methodology consisting of finding the near optimal location of wind turbines (WTs) on a planned transmission network in a secure and cost-effective way is presented on this paper. While minimizing the investment costs of WTs, the algorithm allocates the turbines so that a desired wind power energy-penetration level is reached. The optimization considers both transmission security and power system stability constraints. The results of the optimization provide regulators with a support instrument to give proper signals to WT investors, in order to achieve secure and cost effective wind power network integration. The proposal is especially aimed at countries in the initial stage of wind power development, where the WT network integration process can still be influenced by policy-makers. The proposed methodology is validated with a real power system. Obtained results are compared with those generated from a business-as-usual (BAU) scenario, in which the WT network allocation is made according to existing WT projects. The proposed WT network allocation scheme not only reduces the total investment costs associated with a determined wind power energy target, but also improves power system stability.

Currently, renewable energy resources are becoming more important to reduce greenhouse gas emissions and increase energy efficiency. Researchers have focused on all components of wind turbines to increase reliability and minimize cost. In this paper, a procedure including a cost analysis method and a particle swarm optimization algorithm has been presented to efficiently design low cost steel wind turbine towers. A virtual tool is developed in MATLAB for the cost optimization of wind turbine steel towers with ring stiffeners using a particle swarm optimization algorithm. A wind turbine tower optimization problem in the literature is solved using the developed computer program. In the optimization procedure the optimization results match very well with the optimization results obtained previously. The wall thickness of the shell segments and the dimensions of the ring stiffeners are selected as the design variables, and the limits of the local buckling for the flat ring stiffeners, the local shell buckling limit, the panel ring buckling limit and the limitation of the frequency are considered the design constraints. Numerical examples are presented to understand the impacts of the design variables on the total cost of the wind turbine tower. Texas Tech University, Mechanical Engineering Department (DE-FG36-06GO86092)

Producción Científica, Short-Term Load Forecasting plays a significant role in energy generation planning, and is specially gaining momentum in the emerging Smart Grids environment, which usually presents highly disaggregated scenarios where detailed real-time information is available thanks to Communications and Information Technologies, as it happens for example in the case of microgrids. This paper presents a two stage prediction model based on an Artificial Neural Network in order to allow Short-Term Load Forecasting of the following day in microgrid environment, which first estimates peak and valley values of the demand curve of the day to be forecasted. Those, together with other variables, will make the second stage, forecast of the entire demand curve, more precise than a direct, single-stage forecast. The whole architecture of the model will be presented and the results compared with recent work on the same set of data, and on the same location, obtaining a Mean Absolute Percentage Error of 1.62% against the original 2.47% of the single stage model.

The paper outlines energy management concepts and the smart grid evolution. The necessity of considering energy management as a crucial innovation in load supplying to permit a more powerful penetration of renewable energy usage at the building and city level and to perform energy savings and CO2 emissions reduction is pointed out. The driving factors to enhance the current power distribution are presented, and the benefits concerning smart grids are underlined. In the paper, a specific energy management analysis is reported by considering all the electric value chain, and the demand-side management and distributed on site control actions are described. To verify the benefit of energy management control actions, a house simulator and a grid simulator are here presented and the results discussed in three different scenarios. Moreover, in the paper, the evaluation of ecological benefits are reported, and a cost benefit analysis of the energy management system is performed. Results pointed out that with the standard control actions, the system is not economic for the end user, and only by using energy management systems with renewable energy, in site production remunerative energy savings can be reached. Finally the evolution of smart grids is presented, focusing on potential benefits and technical problems. The active grids, microgrids and virtual utility are described, and final consideration on hypothetical scenarios is presented in the conclusion.

For achieving the European renewable electricity targets, a significant contribution is foreseen to come from offshore wind energy. Considering the large scale of the future planned offshore wind farms and the increasing distances to shore, grid integration through a transnational DC network is desirable for several reasons. This article investigates a nine-node DC grid connecting three northern European countries — namely UK, The Netherlands and Germany. The power-flow control inside the multi-terminal DC grid based on voltage-source converters is achieved through a novel method, called distributed voltage control (DVC). In this method, an optimal power flow (OPF) is solved in order to minimize the transmission losses in the network. The main contribution of the paper is the utilization of a genetic algorithm (GA) to solve the OPF problem while maintaining an N-1 security constraint. After describing main DC network component models, several case studies illustrate the dynamic behavior of the proposed control method.

This study examines fuel switching in electricity production following the introduction of the European Union’s Emissions Trading System (EU ETS) for greenhouse gas emissions. A short-run restricted cost equation is estimated with carbon permits, high-carbon fuels, and low carbon fuels as variable inputs. Shadow values and substitution elasticities for carbon-free energy resources from nuclear, hydroelectric and renewable sources are imputed from the cost equation. The empirical analysis examines 12 European countries using monthly data on fuel use, prices, and electricity generation during the first phase of the European Emissions Trading System. Despite low emission permit prices, this study finds statistically significant substitution between fossil fuels and carbon free sources of energy for electric power production. Significant substitution between fossil fuels and nuclear energy also was found. Still, while 18 of the 20 substitution elasticities are statistically significant, they are all less than unity, consistent with limited substitution. Overall, these results suggest that prices for carbon emission permits relative to prices for carbon and carbon free sources of energy do matter but that electric power producers have limited operational flexibility in the short-run to satisfy greenhouse gas emission limits.

The efficiency of the wind power conversions systems can be greatly improved using an appropriate control algorithm. In this work, a sliding mode control for variable speed wind turbine that incorporates a doubly fed induction generator is described. The electrical system incorporates a wound rotor induction machine with back-to-back three phase power converter bridges between its rotor and the grid. In the presented design the so-called vector control theory is applied, in order to simplify the electrical equations. The proposed control scheme uses stator flux-oriented vector control for the rotor side converter bridge control and grid voltage vector control for the grid side converter bridge control. The stability analysis of the proposed sliding mode controller under disturbances and parameter uncertainties is provided using the Lyapunov stability theory. Finally simulated results show, on the one hand, that the proposed controller provides high-performance dynamic characteristics, and on the other hand, that this scheme is robust with respect to the uncertainties that usually appear in the real systems. Basque Government by the project S-PE09UN12 and to the UPV/EHU through project GUI10/01.

Currently, hydropower accounts for close to 16% of the world’s total power supply and is the world’s most dominant (86%) source of renewable electrical energy. The key resource for hydropower generation is runoff, which is dependent on precipitation. The future global climate is uncertain and thus poses some risk for the hydropower generation sector. The crucial question and challenge then is what will be the impact of climate change on global hydropower generation and what are the resulting regional variations in hydropower generation potential? This paper is a study that aims to evaluate the changes in global hydropower generation resulting from predicted changes in climate. The study uses an ensemble of simulations of regional patterns of changes in runoff, computed from global circulation models (GCM) simulations with 12 different models. Based on these runoff changes, hydropower generation is estimated by relating the runoff changes to hydropower generation potential through geographical information system (GIS), based on 2005 hydropower generation. Hydropower data obtained from EIA (energy generation), national sites, FAO (water resources) and UNEP were used in the analysis. The countries/states were used as computational units to reduce the complexities of the analysis. The results indicate that there are large variations of changes (increases/decreases) in hydropower generation across regions and even within regions. Globally, hydropower generation is predicted to change very little by the year 2050 for the hydropower system in operation today. This change amounts to an increase of less than 1% of the current (2005) generation level although it is necessary to carry out basin level detailed assessment for local impacts which may differ from the country based values. There are many regions where runoff and hydropower generation will increase due to increasing precipitation, but also many regions where there will be a decrease. Based on this evaluation, it has been concluded that even if individual countries and regions may experience significant impacts, climate change will not lead to significant changes in the global hydropower generation, at least for the existing hydropower system. This is an open access article distributed under the Creative Commons Attribution License (CC BY) which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.

Producción Científica, Understanding of energy consumption patterns is extremely important for optimization of resources and application of green trends. Traditionally, analyses were performed for large environments like regions and nations. However, with the advent of Smart Grids, the study of the behavior of smaller environments has become a necessity to allow a deeper micromanagement of the energy grid. This paper presents a data processing system to analyze energy consumption patterns in industrial parks, based on the cascade application of a Self-Organizing Map (SOM) and the clustering k-means algorithm. The system is validated with real load data from an industrial park in Spain. The validation results show that the system adequately finds different behavior patterns which are meaningful, and is capable of doing so without supervision, and without any prior knowledge about the data.

The gearbox is one of the most expensive components of the wind turbine system. In order to refine the design and hence increase the long-term reliability, there has been increasing interest in utilizing time domain simulations in the prediction of gearbox design loads. In this study, three problems in time domain based gear contact fatigue analysis under dynamic conditions are discussed: (1) the torque reversal problem under low wind speed conditions, (2) statistical uncertainty effects due to time domain simulations and (3) simplified long term contact fatigue analysis of the gear tooth under dynamic conditions. Several recommendations to deal with these issues are proposed based on analyses of the National Renewable Energy Laboratory’s 750 kW land-based Gearbox Reliability Collaborative wind turbine. This is an open access article distributed under the Creative Commons Attribution License (CC BY) which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.

Transmission investments are currently needed to meet an increasing electricity demand, to address security of supply concerns, and to reach carbon-emissions targets. A key issue when assessing the benefits from an expanded grid concerns the valuation of the uncertain cash flows that result from the expansion. We propose a valuation model that accommodates both physical and economic uncertainties following the Real Options approach. It combines optimization techniques with Monte Carlo simulation. We illustrate the use of our model in a simplified, two-node grid and assess the decision whether to invest or not in a particular upgrade. The generation mix includes coal-and natural gas-fired stations that operate under carbon constraints. The underlying parameters are estimated from observed market data. Financial support from the Spanish Ministry of Science and Innovation through the research project ECO2011-25064. Chamorro also acknowledges financial support from the Basque Government (through the Programs for the upgrade and mobility of researchers 2011)

This paper describes an analytical approach to investigate the nature of short overbalanced conditions and time effects during underbalanced drilling (UBD) in a naturally fractured reservoir. This study uses an analytical model which is developed for kinetic invasion of mud into the fractures. The model is based on fluid flow between two parallel plates, which is further extended to model the fluid flow in a fractured formation. The effect of short overbalanced pressure and the time effect during UBD as well as the aspects of well productivity and flow efficiency are explained. This model is an Excel-based program and provides a fast and convenient tool for analysis and evaluation of drilling conditions (mud properties, time, and pressure of drilling) in a fractured formation. The model can also predict the impact of the fracture and mud properties on the depth of invasion in the fractured formations. This is an open access article distributed under the Creative Commons Attribution License which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.