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In this paper, we propose a three-stage robust generation and transmission expansion planning model considering generation profiles of renewable energy sources (RES) affected by different long-term climate states. Essentially, we extend the broadly utilized two-stage modeling approach to properly consider partial information of climate states with conditional short-term scenarios of RES output and outages. The proposed model is formulated as a five-level optimization problem. The first level determines the optimal generation and transmission expansion plan under uncertainty in climate conditions, RES generation, and contingencies. Given the selected expansion plan, the second level identifies the most severe climate state. Following the decision-information hierarchy, in the third level, the system operator optimizes the generation schedule of energy and reserves under perfect information of the climate state, but yet under uncertainty in the RES generation and contingencies. Then, the fourth level identifies the worst-case combination of contingency and conditional short-term RES generation adjusted to the current climate condition. Finally, the fifth level determines the optimal redispatch of reserves to react against the worst-case RES generation and contingency scenario considering the uppermost decisions. Within this multi-level structure, the optimal investment plan considers a more realistic decision setting, where system operators adapt RES forecasts based on the observed climate conditions before planning the operational schedule. To solve the problem, a variant of the nested column-and-constraint-generation algorithm is proposed with global-optimality guarantee in a finite number of steps. A case study based on the Chilean system illustrates the applicability of the model in a realistic network.

Mathematical models are just models. The desire to describe battery energy storage system (BESS) operation using computationally tractable model formulations has motivated a long-standing discussion in both the scientific and industrial communities. Linear BESS models are the most widely used so far. However, finding suitable linear BESS models has been controversial. This paper focuses on the description of linear BESS models. Four linear BESS formulations are presented, among the most popularly used. A new formulation is also proposed. The 5 BESS models are tested in 100 random BESS and 1.450 random samples of daily profiles of renewable generation. Two classical problems of power systems, namely, the set-point tracking problem and the transmission expansion planning problem, are selected for numerical analysis. Five thousand simulations are used to draw a better interpretation of each linear formulation presented and showcase specific challenges of BESS models. Practical recommendations are provided based on the findings., Power Systems Computation Conference (PSCC) 2022

The ability of four models to provide short-term (up to 6 h ahead) GHI and DNI forecasts in the Iberian Peninsula is assessed based on two years of data collected at four stations. The models follow (mostly) independent approaches: one pure statistical model (Smart Persistence), one model based on CMV derived from satellite images (Satellite), one NWP model (WRF-Solar) and a hybrid satellite-NWP model (CIADCast). Overall, results show Smart Persistence to be the best at the first lead steps, advective models (Satellite and CIADCast) at intermediate ones and the WRF-Solar at the end of the forecasting period. The break-even point between the advective models and WRF-Solar varies between 1 and 3 h for GHI and 3 and 5 h for DNI. Nevertheless, a detailed analysis shows enormous differences between models performance related to 1) the local geographic and topographic conditions of the evaluation stations; 2) the evaluated variable (GHI vs. DNI); and 3) the sky and synoptic weather conditions over the study area. Depending on the station and lead time, rRMSE values range from 25% to 70% for GHI and from 35% to 100% for DNI. For the same stations and leading time, rRMSE values for DNI are between 50% and 100% higher than the corresponding GHI counterparts. Depending on the synoptic pattern, rRMSE values are about 10/20% for GHI/DNI (3 h lead time, during high pressure conditions) to about 80/180% for GHI/DNI (during low pressure conditions). All models show a poor performance at a coastal station, attributed to a lack of ability to forecast clouds associated with sea-land breezes. To conclude, no single model proves to be the best performing model and, therefore, results show that the four models are, somehow, complementary. The advantages attained by this complementarity are further explored in a companion paper (Part II). The authors are supported by the Spanish Ministry of Economy and Competitiveness, project ENE2014-56126-C2-1-R and ENE2014-56126-C2-2-R (http://prosol.uc3m.es). The team from the University of Jaen is also supported by FEDER funds and by the Junta de Andalucía (Research group TEP-220). The authors thank all the provided support. The authors are in debt with the National Centers for Environmental Prediction (NCEP), EUMETSAT, Faculdade de Ciencias da Universidade de Lisboa, Grupo de Energía Solar of the Universidad Politécnica de Madrid and Abengoa Solar for providing the data used in this work.

In this article we explore the blending of the four models (Satellite, WRF-Solar, Smart Persistence and CIADCast) studied in Part 1 by means of Support Vector Machines with the aim of improving GHI and DNI forecasts. Two blending approaches that use the four models as predictors have been studied: the horizon approach constructs a different blending model for each forecast horizon, while the general approach trains a single model valid for all horizons. The influence on the blending models of adding information about weather types is also studied. The approaches have been evaluated in the same four Iberian Peninsula stations of Part 1. Blending approaches have been extended to a regional context with the goal of obtaining improved regional forecasts. In general, results show that blending greatly outperforms the individual predictors, with no large differences between the blending approaches themselves. Horizon approaches were more suitable to minimize rRMSE and general approaches work better for rMAE. The relative improvement in rRMSE obtained by model blending was up to 17% for GHI (16% for DNI), and up to 15% for rMAE. Similar improvements were observed for the regional forecast. An analysis of performance depending on the horizon shows that while the advantage of blending for GHI remains more or less constant along horizons, it tends to increase with horizon for DNI, with the largest improvements occurring at 6 h. The knowledge of weather conditions helped to slightly improve further the forecasts (up to 3%), but only at some locations and for rRMSE. The authors are supported by the Spanish Ministry of Economy and Competitiveness, projects ENE2014-56126-C2-1-R and ENE2014-56126-C2-2-R (http://prosol.uc3m.es). The University of Jan team are also supported by FEDER funds and by the Junta de Andalucia (Research group TEP-220).The authors are in debt with the National Centers for Environmental Prediction (NCEP), EUMETSAT, Faculdade de Ciencias da Universidade de Lisboa, Grupo de Energa Solar of the Universidad Politcnica de Madrid and Abengoa Solar for providing the data used in this work.

This work analyzes the impacts on the power system expansion planning of implementing CO2 and local pollutant emission taxes under five different policy-relevant scenarios. To do this, we have formulated and implemented an optimization model based on a mixed-integer linear program, which determines the optimal expansion plan considering the installation of both large-scale power plants and renewable-based distributed generation. An important characteristic of the proposed model is that it includes a detailed formulation of the power system. Moreover, differently than existing literature, special attention is given to the analysis of the spatial-temporal distributive effects of pollutant taxes, considering both global and local pollutant emissions. The method is applied to the main Chilean power system. Our results indicate that global and local pollutant taxes significantly impact both planning and operational decisions in the power system. In particular, pollutant taxes may have significant spatial distributive effects, as shown in the analysis of 13 regions of Chile, leading to damages in some specific regions while relatively benefiting others. Our results also show that the availability of renewable energy capacity may improve the effectiveness of pollutant taxes. Particularly, adding 1.5 GW of hydro capacity to the Chilean system allows avoiding around 32 GWh of fossil fuel generation per year, saving more than 1.5 billion US$ in the 10-year horizon considered. The proposed method and qualitative results are sufficiently generic to apply to any other jurisdiction.

Virtual power plants (VPPs) are considered the next generation of power plants where they aggregate the distributed energy resources (DERs) aiming to participate in different electricity markets. Many of the aggregated DERs within the VPP have ambiguous future performance. Thus, to maximize profit in the electricity market, VPP needs offering curve construction approaches that could deal with different sources of uncertainty. In this study, we investigate the VPP participation problem in the day-ahead electricity market. We propose strategies that can be utilized by a VPP to deal with uncertainty from both risk-averse and profit-seeking viewpoints. Thorough numerical studies and out-of-sample analysis demonstrate the given approaches’ features and their superiority over the existing methods in the literature.

The intra-day modes of variability of the solar resources in the Iberian Peninsula, their associated weather patterns and their impact on the solar power output are assessed in this work. The analysis is performed for yearly and seasonal variability. Firstly, the modes of variability are identified by means of hierarchical cluster analysis. It is computed with two years of measured global horizontal irradiance (GHI) and direct normal irradiance (DNI) data gathered at four stations. Notably, three-hour statistics describing mean and variability of solar radiation are used as input to the cluster analysis. Secondly, synoptic weather patterns associated with each group resulting from the cluster analysis are assessed using sea level pressure and cloudiness data. Finally, the solar PV power yield associated with each mode is evaluated. The yearly analysis reveals the existence of four modes of variability of the solar resource in the study area. The four modes are shown to have a distinctive weather pattern and also specific impacts on solar power generation in the study area. Seasonal analyses show results similar to the annual analysis, but with marked seasonal differences.

The need for flexible networks is an emerging challenge for power system operators (SO). The use of additional support, such as demand response (DR), must be quantified in order to offer a reliable service, given that this information is vital for demand aggregators. Thermostatically controlled loads (TCLs) are one of the most promising options among DR solutions; due to TCLs’ thermal characteristics their power may be increased or reduced accounting as ancillary services. However, TCLs tend to synchronize their behavior, which may affect their capacity to provide flexibility. This paper proposes a method for quantifying TCLs’ power flexibility, taking into account different scenarios, types of controllers and loads. Two control methods are compared, and a modified control algorithm is applied to the controllers under analysis to avoid TCL synchronization. The analysis was validated by simultaneously using real demand data from the UK National Grid and temperature data for the same region and time frame.

Buildings are responsible for a large portion of the world’s energy consumption. Any measure that can be taken to optimize the use of energy related to them must be considered. Demand Side Management (DSM) can be used to shave demand peaks and to avoid bootstrapping highly polluting fast ramp-up generators. This though brings a control problem that is complicated by the increasing diffusion of small-scale, renewable energy sources and local storage facilities which are decentralized and, in general, hard to predict reliably. The overall goal of the control strategy is to balance energy, demand/supply, and to minimize costs. This survey focuses on control strategies to support DSM, considering buildings as the load to be managed. Among the various control strategies, model predictive control (MPC) has a predominant role due to its broad applicability and easy portability to many diverse contexts. The method is suitable for any nonlinear, multi-variable, and linear parameter varying system. The survey provides a general, unifying mathematical characterization of the approaches and lays the foundations for comparing and evaluating MPC-based DSM in buildings.

Recent research has shown that the integration or blending of different forecasting models is able to improve the predictions of solar radiation. However, most works perform model blending to improve point forecasts, but the integration of forecasting models to improve probabilistic forecasting has not received much attention. In this work the estimation of prediction intervals for the integration of four Global Horizontal Irradiance (GHI) forecasting models (Smart Persistence, WRF-solar, CIADcast, and Satellite) is addressed. Several short-term forecasting horizons, up to one hour ahead, have been analyzed. Within this context, one of the aims of the article is to study whether knowledge about the synoptic weather conditions, which are related to the stability of weather, might help to reduce the uncertainty represented by prediction intervals. In order to deal with this issue, information about which weather type is present at the time of prediction, has been used by the blending model. Four weather types have been considered. A multi-objective variant of the Lower Upper Bound Estimation approach has been used in this work for prediction interval estimation and compared with two baseline methods: Quantile Regression (QR) and Gradient Boosting (GBR). An exhaustive experimental validation has been carried out, using data registered at Seville in the Southern Iberian Peninsula. Results show that, in general, using weather type information reduces uncertainty of prediction intervals, according to all performance metrics used. More specifically, and with respect to one of the metrics (the ratio between interval coverage and width), for high-coverage (0.90, 0.95) prediction intervals, using weather type enhances the ratio of the multi-objective approach by 2%¿. Also, comparing the multi-objective approach versus the two baselines for high-coverage intervals, the improvement is 11%¿% over QR and 10%¿% over GBR. Improvements for low-coverage intervals (0.85) are smaller. The authors are supported by projects funded by Agencia Estatal de Investigación, Spain (PID2019-107455RB-C21 and PID2019-107455RB-C22/AEI/10.13039/501100011033). Also supported by Spanish Ministry of Economy and Competitiveness, project ENE2014-56126-C2-1-R and ENE2014-56126-C2-2-R (http://prosol.uc3m.es). The University of Jaén team is also supported by FEDER, Spain funds and by the Junta de Andalucía, Spain (Research group TEP-220)

A new method for short-term solar radiation forecasting (referred to as Cloud Index Advection and Diffusion, CIADCast) is proposed and validated. The method is based on the advection and diffusion of Meteosat Second Generation (MSG) cloud index estimates using the Weather Research and Forecasting (WRF) numerical weather prediction (NWP) model. The forecasted cloud index is transformed in global horizontal irradiance (GHI) and direct normal irradiance (DNI) forecasts by means of the Heliosat-2 method. The cloud index maps are inserted in the WRF vertical layer which corresponds to the cloud height provided by a ceilometer. GHI and DNI are forecasted up to 6 h ahead with 15 min of time resolution. The method was tested using 25 days of radiometric data collected at three stations located in southern Spain. Benchmarking models such as smart persistence, a cloud motion vector (CMV) based approach and the WRF-Solar suite of the WRF model are also evaluated. Results were analyzed in the light of the different topographic characteristics of the evaluation stations areas. Results proved that CIADCast is able to provide enhanced forecasts in areas with low topographic complexity, where cloud advection by the atmospheric mesoscale dynamics is not perturbed by mountain features. In these areas, CIADCast generally outperforms the other models, especially for DNI and partially cloudy conditions. On the other hand, in regions with complex topography, where the mesoscale cloud pattern is influenced by the mountains, the performance of the CIADCast model is poor and the use of persistence or the WRF-Solar model proved to be more appropriate.

A fundamental challenge in power system planning is how to handle the interactions of participants’ behaviors in deregulated markets. This is important due to the high cost involved in their decisions. Proactive or anticipative transmission expansion planning models have been proposed by some authors to jointly model the interactions among deregulated electricity market participants making market-driven investment decisions. Several works have shown that a Transmission Network Planner can increase social welfare by anticipating line expansion planning to generation expansion equilibrium and market outcomes. However, proactive transmission expansion decisions may lead to suboptimal solutions when the generation expansion equilibrium problem have multiple solutions (i.e., leading to higher total costs and lower social welfare). We propose a methodology to study the potential impacts of proactive expansion planning on generation expansion decisions. The resulting formulation is stated as a mathematical program subject to an equilibrium problem with equilibrium constraints (EPEC). To deal with this problem, we also propose an approach to derive tractable EPEC solutions with global optimality guaranteed based on a column-and-row generation algorithm. Our numerical results shows that a proactive investment plan can lead to higher total cost than not investing at all because of the existence of multiple market-driven generation expansion equilibria. We show that the proposed algorithm significantly reduce the time of computation up to two orders of magnitude with respect to existing methodologies.

The repowering of already installed wind farms is considered one of the most promising and cost-effective short-term strategies to scale-up wind capacity. In this study, we apply Markowitz's mean-variance (MV) portfolio optimization theory to explore alternative repowering actions in Spain. The efficient portfolios – a direct outcome of the MV optimization – offer optimal repowering alternatives to current wind farm generation mixes. They deliver the highest possible average power output (yield) for a given level of supply risk. Different repowering scenarios are considered in this paper that range from a full restructuring of the existing wind generation mix to restricting by certain amounts the percentage of down-/uprating of each reference region. Results show that, depending on the configuration of the MV portfolio optimization problem, hourly fluctuations in the aggregate power supply can be reduced as much as 12–31%, while retaining the current level of energy productivity. In addition, for the level of energy supply risk experienced with the existing portfolio of Spanish wind farms; we can derive more efficient mixes that boost-up productivity by 16–55%. This work aims at providing valuable insight for energy policy-making in the direction of optimally repowering future renewable generation.

In recent years, mechanisms of cooperation in power systems have attracted increasing attention from academia and industry. Such mechanisms require sharing the benefits of cooperation among participants based on some rational and obvious principles. In this regard, Cooperative Game Theory (CGT) provides a rich theoretical background for the analysis of projects where participants (called players) can make collective actions to obtain mutual benefits. CGT concepts not only solve the subsequent allocation problems but also reveal the bargaining power of players and estimate the stability of cooperation over a project. In this paper, we aim to classify and promote CGT applications in power systems. While covering a broad range of applications (such as cost and benefit allocation, transmission pricing, projects ranking, allocation of power losses), we pay particular attention to power system expansion planning. We first introduce an illustrative example of cooperation in transmission expansion planning and discuss the applicability of CGT solution concepts. To give a complete picture of the state of the art, we perform a citation network analysis of more than 3000 related studies from 1996 to 2020. Exploiting the graph layout and modularity algorithms, we identify the main research communities and highlight their contributions. We found that significant progress has been achieved in developing mechanisms of cooperation in power systems based on CGT solution concepts. However, several challenges and limitations of these concepts still have to be overcome, such as scalability, nonconvexity of cooperative games, coalitions formation assumption, ex-post game-theoretic analysis, incompleteness and manipulability of information. The overview presented in this paper and the citation network analysis performed can help scientists and engineers in comprehending the CGT solution concepts, discovering novel applications for power systems, and contributing to this promising multidisciplinary research direction.

This work proposes and evaluates methods for extending the forecasting horizon of all-sky imager (ASI)-based solar radiation nowcasts and estimating the uncertainty of these predictions. In addition, we evaluated procedures for improving the temporal resolution and latency of satellite-imagery-derived solar nowcasts. Based on these contributions, we assessed the reliability of ASIs and satellite-derived solar radiation nowcasts, with 1-min time-resolution and up-to-90-min ahead. The study was conducted in a location in Southern Spain using a set of cloudy days, specifically selected as representative of the most challenging conditions regarding solar radiation nowcasting. The results reveal that the use of ASI-based models provide low benefits compared to the use of satellite-based models for point solar radiation nowcasting. Given the frequency of occurrence of the different sky types in the study area, the results suggest that the use of a simple smart persistence algorithm, in combination with a low-resolution satellite nowcasting model could be an adequate choice, avoiding the challenges associated with the use of ASIs.

The admission planning problem in the inpatient service aims to provide patient access and to guarantee expected levels of bed utilization. However, uncertainty in the patient’s length of stay and bed availability challenge the accomplishment of that objective. This research addresses the off-line admission planning problem with uncertain length of stay. We study the coordinated decisions of scheduling and allocation for the patient-to-room admission problem assuming heterogeneous patient types and time-varying capacity. The objective is to maximize the weighted sum of the patient’s admission benefit while reducing the cost of overstay. We present a distributionally robust optimization (DRO) framework that is distribution-free; it considers that known information is limited only to the first moment and the support set of the true probability distribution. The framework is robust against the infinite set of probability distribution functions that could represent the stochastic process of the patient’s length of stay. To test the performance of the proposed DRO approach, we compared it with benchmark models employing a real data set from a public hospital in Chile. The results show that our approach outperforms the evaluated models in both reliability and computational efficiency. We provide insights to practitioners and hospital decision-makers to anticipate admission decisions while considering the randomness of the length of stay at the tactical-operational level.

The fast growth of communication technology within the concept of smart grids can provide data and control signals from/to all consumers in an online fashion. This could foster more participation for end-user customers. These types of customers do not necessarily have powerful prediction tools or capability of storing a large amount of historical data. Besides, the relevant information is not always known a priori, while decisions need to be made fast within a very limited time. These limitations and also the novel structure of decision making, which comes from the necessities to make the decision very fast with a limited amount of information, implies a requirement for investigating a novel framework: online decision-making. In this study, we propose an online constrained convex optimization framework for operating responsive end-user electrical customers in real-time. Within this online-decision-making framework, algorithms are proposed for two cases: no prediction data is available at the moment of decision-making, and a limited number of forward time periods predictions of uncertain parameters are available. The simulation results exhibit the capability of the model to achieve considerable profits in an easy-to-implement procedure. Comprehensive numerical test cases are performed for comparison with existent alternative models.

The temporal resolution of aerosol optical depth (AOD) affects the prediction of surface irradiance and hence impacts solar resource assessments. Most current global AOD databases used for solar radiation estimation span from monthly to yearly time scales, which are much coarser than the typical hourly or sub-hourly time step used for irradiance calculations. The relative variation of the AOD’s mean and standard deviation (SD) calculated from data at monthly and daily temporal resolutions is assumed here as a proxy for the expected differences in the calculated long-term average of solar irradiance when using monthly and daily AOD data. This is specifically analyzed in Part 2 of this study. In this Part 1, the changes in the mean and SD of AOD caused by an alteration of the time scale from daily to monthly time resolution are investigated first. A database from 214 AERONET sites with long historical record series is used, as well as data from worldwide multi-year evaluations of AOD from a numerical weather model coupled with an aerosol transport model. The AERONET AOD data is fitted to the log-normal distribution and the results are separately analyzed for multiple AOD representation time scales from daily to monthly, and for sites with prevalence of coarse, mixed or fine aerosols. When the averaging period is increased from one to 30 days, three effects are noticed regardless of the aerosol regime: (i) the range of possible AOD values narrows; (ii) the probability around the modal AOD increases; and (iii) the AOD distribution’s mode moves toward higher values. On average, the mean AOD at sites dominated by coarse or mixed aerosols does not significantly vary (it does by only less than 1%) when the averaging time increases from one day to 30 days, although with significant dispersion from site to site (≈±5%). In contrast, again on average, it declines ≈4% at the fine aerosol sites. Conversely, the SD decays exponentially fast at all sites—faster at the fine aerosol sites—on average. For an averaging period of 30 days, the SD of AOD over Europe and inland areas of Asia can be as low as 40% of the daily SD, while for most other land areas it stays at ≈60% of the daily SD. The impact that this change in the AOD SD has on the prediction of surface irradiance is investigated in this study’s Part 2.

Long-term solar irradiance modelled using monthly-average aerosol optical depth (AOD) is biased compared to what is obtained using daily AOD values. This is due to a modification of the AOD frequency distribution that results from the coarsening of the time scale, combined with the nonlinear sensitivity of irradiance to AOD. The resulting alteration of the mean and standard deviation of AOD has been evaluated worldwide in this study’s Part 1. Here, the focus is to evaluate how the progressive coarsening (in 1-day steps) of the temporal representation of AOD affects the calculated long-term values of both global and direct irradiances. It is shown that, on average, their long-term values can be underestimated by as much as ≈6 W m −2 (≈1.2%) and ≈40 W m −2 (≈8%) respectively, over regions of North Africa and Asia, with respect to the reference case for which daily AODs are used. This irradiance underestimation can be parameterized as a function of the AOD time scale by means of four sitedependent parameters. The proposed parameterization provides a simple way to correct the long-term irradiance datasets that are commonly obtained using monthly-average AOD data.

Optimal siting of wind farms based on a pre-assessment of the spatiotemporal variability of wind resources is considered a suitable method for reducing fluctuations in the delivered output. In this study, we explore the potential for balancing wind energy generation in the Iberian Peninsula using Principal Component Analysis (PCA). This technique permits the discovery of possibly new promising locations for wind power harvesting and an evaluation of the existing wind farm network in terms of reliability in energy generation. Data input to the PCA consists of hourly wind capacity factor in a 5-km spatial resolution grid covering the entire peninsula. These data are derived from an equivalent wind farm power curve fed by modeled wind speed data from 80 m above ground level. PCA reveals three significant balancing patterns prevailing over the IP, where half of the currently operating wind farms in Spain are placed. Hence, among the many constituents of the existing wind farm network, these spots offer the best opportunity for stable power supply. The paper concludes by making proposals on an optimum wind capacity allocation based on the idea of equally distributing installed power between positive/negative dipoles emerging from balancing principal components.

Since pioneering work in the early 60s on the development of enzyme electrodes the field of sensors has evolved to different sophisticated technological platforms. Still, for biomedical applications, there are key requirements to meet in order to get fast, low-cost, real-time data acquisition, multiplexed and automatic biosensors. Nano-based sensors are one of the most promising healthcare applications of nanotechnology, and prone to be one of the first to become a reality. From all nanosensors strategies developed, Magnetic Relaxation Switches (MRSw) assays combine several features which are attractive for nanomedical applications such as safe biocompatibility of magnetic nanoparticles, increased sensitivity/specificity measurements, possibility to detect analytes in opaque samples (unresponsive to light-based interferences) and the use of homogeneous setting assay. This review aims at presenting the ongoing progress of MRSw technology and its most important applications in clinical medicine., Support was provided by the Spanish Ministry of Economy and Competitiveness with FEDER co-funding (FIS-PI14-1600 to DP), the Regional Ministry of Health (PI2013-375 to SL; PI2013-0559 to MPL), the Regional Ministry of Economy, Science and Innovation (P10-CTS-6928 and P11-CTS-8161 to DP), and the PAIDI Program from the Andalusian Government (CTS-677 to DP). DA holds a Marie Curie Fellowship (FP7-PEOPLE-2012-IEF, grant number 327151) from the European Commission.

A large number of studies have dealt with the Transmission Expansion Planning (TEP) problem. However, few investigations have focused on analyzing the impacts of network payment schemes on network configuration and the benefits/losses distribution among the participants in electricity markets. In this paper, we propose a multi-annual transmission expansion planning model considering four different network payment schemes to finance the construction of new transmission lines, seeking to reduce the total system costs. Wind and solar power generation are included in the model taking into account their variability. The proposed models are reformulated as Mixed Integer Linear Programming (MILP) problems. We use seven performance metrics related with congestion, nodal prices and generator benefits, among others, to evaluate the effect of each payment scheme. A realistic case study based on the main power system in Chile is analyzed to illustrate the proposed models. It is shown that integrating line cost-recovering equations into the TEP model may result into a more realistic and less congested power network. Also, total system cost is highly related with transmission tariff discrimination. In that way, tariffs with high location dependence perform better in the case studied, the Chilean power system.

Equilibrium analysis is crucial in electricity market designs, with Nash equilibrium recognized as the most powerful one. Its most prominent hindrance, however, is an efficient methodology to compute an equilibrium point in large-scale systems. In this work, a Column-and-Constraint Generation (CCG) algorithm is proposed to tackle this challenge. More precisely, the master problem finds a candidate for Nash equilibrium and the oracle identifies whether this candidate point is indeed an equilibrium. A set of numerical experiments was conducted, comparing its computational performance with the solution of an Equilibrium Problem with Equilibrium Constraint (EPEC). We identify that the proposed algorithm overcomes the benchmark in the magnitude of 20 times on average and more than 30 times in the most demanding instances. Furthermore, the scalability of the EPEC formulation is challenged even for medium-scale instances, whilst the proposed algorithm was able to handle all tested instances in a reasonable computational time.

Although local protection methods can perform well at speed, in a real network where network uncertainties are unavoidable, these methods may result in inaccurate performance. Therefore, the use of other methods, such as wide area protection, can lead to more accurate results because of using comprehensive information via telecommunication links. In this paper, a wide area protection algorithm is presented to cover the mal-operation of relays due to the network uncertainties. In this method, an objective function is defined based on the performance status of various zones of distance relays, the values of which will be different for each line. Depending on the position of each relay on the transmission line, weighting factors for objective function are determined based on the optimization algorithm and taking into account the possible uncertainties of the power grid. The proposed method has been implemented on IEEE 9-bus system and has proved high detection precision and performance accuracy.

Over the last years, gas-fired power capacity investments in electricity networks have increased in many countries due to its low-rate of greenhouse gas emissions and the continuous reduction in the natural gas price. Besides, new power-to-gas technologies have attracted attention in the sector as an alternative for harvesting large shares of renewable generation. Thus, the coupling between power and gas networks is becoming more critical. New complementarities arise from the synergies of the co-optimization of both power and natural gas systems operation in terms of reliability, sustainability, and economic efficiency. In this regard, jointly planning of power and natural gas systems could leverage potential benefits for the optimal coordination of both grids. In this survey, we present a comprehensive investigation of recent literature in the contexts of coordinated planning of the gas and electricity systems. We first provide a thorough study of energy systems planning. Later, the planning issues of both power and gas grids are investigated, including modeling aspects, typical operational constraints, and objectives that drive investments, as well as uncertainties associated. Then, international experiences are presented to compare the mathematical approaches to the planning of electricity and natural grids. Finally, discussion about future research opportunities and conclusions are presented.

Amyotrophic lateral sclerosis (ALS) is a rare neurodegenerative disease that primarily affects motor neurons and is accompanied by sustained unregulated immune responses, but without clear indications of the ultimate causative mechanisms. The identification of a diverse array of ALS phenotypes, a series of recently discovered mutations, and the links between ALS and frontotemporal degeneration have significantly increased our knowledge of the disease. In this review we discuss the main features involved in ALS pathophysiology in the context of recent advances in 'omics' approaches, including genomics, proteomics, and others. We emphasize the pressing need to combine clinical imaging with various different parameters taken from omics fields to facilitate early, accurate diagnosis and rational drug design in the treatment of ALS. ALS, or amyotrophic lateral sclerosis, is a progressive neurodegenerative disease that affects motor neurons. There is no cure for ALS. Although ALS is a brain disease closely related to Parkinson's, Alzheimer's, and Huntington's diseases, so far the complex descriptions of ALS-associated damage have not clarified the ultimate causative mechanisms.Current interventions are the result of unintentional discoveries or the non-specific application of cell-based therapies whose effects are not completely understood. However, research on ALS is currently thriving and the body of knowledge on the subject has increased remarkably in recent years.The emergence of functional immunomics for ALS from established omics technologies are opening new therapeutic avenues based on the smart manipulation of the immune system.Molecular imaging in the field of ALS is evolving. Thus, a combination of omics technologies and clinical imaging may very well be the key for breaking-down ALS., Financial support was provided by grants AI075285 and AI093903 from the US National Institutes of Health, RG4111A1 and JF2161-A-5 from the National Multiple Sclerosis Society, and PA0069 from the International Progressive MS Alliance (F.J.Q.), the Regional Ministry of Economy, Science and Innovation (P11-CTS-8161 to D.P.), the Regional Ministry of Health (PI13-575 to S.L.), the PAIDI Program from the Andalusian Government (CTS-677 to D.P.), and the Spanish Ministry of Economy and Competitiveness with FEDER co-funding (PI14-1600 to D.P.; SAF-2012-39720 and CP10-00527 to C.R.). D.C-H. holds a postdoctoral fellowship from CONACyT (Mexico). M.C-G. holds a PIF-University of Seville PhD fellowship.

A new evolutionary design of an ANN (artificial neural network) ensemble model is developed to generate hourly DNI (direct normal irradiance) estimates. The procedure combines a genetic algorithm for selecting the best inputs with an ANN ensemble method. The ensemble model was calibrated and evaluated using three years of Meteosat-9 images and data measured at 28 high-quality ground stations over an extensive area, mainly in Europe. The most valuable inputs for DNI estimation are shown to be the following: all Meteosat-9 channels except ch08 and ch11 ; relative air mass m , integral Rayleigh optical thickness δ r , extraterrestrial global irradiance G 0 , beam clear-sky index B cs , and the cosine of zenith angle θ . No additional atmospheric information such as turbidity, aerosol optical depth or water vapor content are required for the model. Ensemble estimates were nearly unbiased (MBE = 1.98%) and overall RMSE (root mean square error) was 24.29% across an independent spatial and temporal dataset. This represents an improvement of 35% over other common methods for estimating DNI. The estimates were reasonably reliable in all seasons, and were more accurate in clear-sky conditions.

In this work, a new method to derive hourly global horizontal irradiance (GHI) estimates from Meteosat Second Generation (MSG) imagery is presented. The method is based on an optimized Artificial Neural Network (ANN) ensemble model using a selection of the best ANN models identified from an initial ensemble that discerns between different sky conditions and an additional ensemble that considers all sky conditions together. For benchmarking purposes, hourly GHI estimates computed with the Heliosat-2 method, accounting for the diurnal variability of ground albedo, are used. Data collected during the 3-year period from 2009 to 2011 at 28 radiometric stations located in northern Africa, Middle East and Europe, are used in the procedure. From these stations, 7 are used to train the ANN models and the other 21 for independent validation. Results obtained with the proposed ANN ensemble model reduced the RMSE value of the Heliosat-2 model a 22% for all-sky conditions and a 42% for overcast conditions.

In this work, we evaluate the influence of land-use representation accuracy on the reliability of wind speed and direction estimates derived from the Weather Research and Forecasting (WRF) model. To this end, the 100-m spatial resolution Coordination of Information on the Environment (CORINE) land-use dataset was implemented as static geographic data in WRF. Next, a set of one-year long simulations at 1-km spatial resolution was conducted using both the CORINE and Global Land Cover Characterization (GLCC) land-use datasets, the latter the default in WRF. The simulations were conducted for three locations in southern Spain, and were characterized by variable land-use composition and topography. At these locations, wind speed and direction estimates were compared against observations at different measurement elevations. Results showed that the selection of land-use database has a major influence on wind estimate bias. The effect on the wind direction distribution is also significant, whereas that on the standard deviation is much weaker. CORINE provided a more reliable land-use representation than GLCC. Nevertheless, as a consequence of the interpolation procedure used for land use in the domain setup, this representation did not necessarily translate to a superior roughness length, thereby affecting wind speed and direction estimates. This was particularly so for areas of high spatial variability in land-use categories. In such areas, the misrepresentation of land use may result in large wind speed estimation errors.

We evaluate the extent to which a combination of wind power and concentrating solar power (CSP) may lead to stable and even baseload power by taking advantage of: 1) spatiotemporal balancing of solar and wind energy resources and 2) storage capabilities of CSP plants. A case study is conducted for the region of Andalusia in Spain. To this end, spatiotemporal variability of modeled CSP and wind capacity factors in a 3-km spatial resolution grid were analyzed based on principal component analysis (PCA) and canonical correlation analysis (CCA). Results reveal that renewable baseload power can be obtained in the study region by locating wind farms and CSP plants using balancing patterns derived from CCA and PCA. In addition, the power fluctuation reduction attained from these patterns was substantially higher than those obtained by interconnecting randomly-located wind farms and CSP plants across the study region. Results were particularly meaningful for the winter season. Upon considering storage capability of the CSP plants, results proved better. The main difference was a higher firm capacity value associated with spring and summer seasons. For the other seasons, the contribution of thermal storage capabilities of the CSP plants to stable power proved less relevant.

Participants in competitive electricity markets make their dynamic decisions under uncertainty. Choosing a time-inconsistent formulation can lead to an incorrect procedure for risk and, consequently, to a sequence of inappropriate decisions. In a market context with uncertainty in energy prices, the net income of a company is the result of selling their energy in the spot market and through bilateral physical contracts. The purpose of this paper is to describe a dynamic multistage stochastic programming framework for sequential decision making under uncertainty that allows wind power producers to maximize their profit for a given risk level on profit variability. In this context, Conditional Value at Risk (CVaR) has been chosen as a time-consistent and dynamic risk measure. An example is provided to illustrate the methodology proposed.

We propose a method for analyzing the potential contribution of wind energy resources to stable (baseload) power within a region. The method uses principal component analysis (PCA) to analyze spatiotemporal balancing of wind energy resources and then assesses the optimal wind farm location to reduce wind power fluctuations. The ability of different reference wind turbines, alone or interconnected, to provide stable power is ultimately evaluated at selected locations. The method was tested in the southern Iberian Peninsula, including offshore areas. We used hourly wind energy estimates from the WRF mesoscale model at 3-km spatial resolution for the period 2008–2010. First, results show a valuable spatial balancing pattern between the wind energy resources in the northeast study region and Strait of Gibraltar area. The pattern was found to result from the interaction of mesoscale zonal flow with the complex topography of the region. Second, the results indicate that by taking advantage of the spatial balancing pattern, the optimal allocation and interconnection of wind farms across the region, can substantially reduce wind power fluctuations. This optimal allocation can in some cases generate stable power, thereby contributing to baseload power.

In this paper, we present a framework for analyzing cross-border power interconnection projects based on Cooperative Game Theory. Compared to existing studies, we not only quantify the benefits of interconnections and suggest cost-benefit allocation techniques, but also analyze the stability of the allocations, which is a crucial aspect in regions where coordination and mutual trust between countries have not been built yet. We apply our framework to the Northeast Asia where six countries (China, Russia, Mongolia, South Korea, North Korea, and Japan) are suggested for cross-border transmission expansion planning cooperation. Cost-benefits allocation of the interconnections is analyzed according to the marginal contribution of each country to the grand coalition and the minimal dissatisfaction of each coalition that ensures the stability of the solution. Accordingly, Game Theory concepts (the Shapley value and the Nucleolus) are used in our analysis. Moreover, we employ the Core concept to further analyze the stability of the allocation solution and present a visualization of the feasible space formed by all stable allocations. We found out that the grand coalition (i.e., the scenario where all countries agree on the cooperation) is the optimal and stable coalition, with $7.1 billion total savings per year. We also suggested a scheme of investment allocation and payments between the Northeast Asian countries in order to ensure that the proposed interconnections are plausible in practice.

The daily Level-3 MODIS (dL3M) aerosol optical depth product is a global daily spatial aggregation of the Level-2 MODIS aerosol optical depth (10-km spatial resolution) into a regular grid with a resolution of 1° × 1°. Aerosol optical depth is a seminal parameter for surface solar radiation assessment, in particular, for those applications involving direct irradiance. However, the dL3M AOD is prone to data gaps originated mostly by the unfeasibility of retrieving reliable estimates under cloudy conditions. In addition, its usability is also constrained by regional biases owing to some other reasons. In this work we propose a methodology for bias reduction and data-gaps removal of the dL3M AOD dataset. The result is a database of daily regularly-gridded AOD suitable for use in surface solar radiation applications and large-scale and long-term studies involving AOD without requiring a previous costly data assimilation process involving numerical weather prediction models. The method consists of an empirical approach to bias reduction, data-gaps removal by kriging interpolation and, finally, where reliable ground observations are available, an optimal interpolation procedure. The method was tested in the North American region, where it was able to reduce the initial mean error from 0.067 to 0.001, the root mean square error from 0.130 to 0.057, and increase the squared correlation coefficient from 23% to 58%, as compared against ground measurements.

An optimized artificial neural network ensemble model is built to estimate daily global solar radiation over large areas. The model uses clear-sky estimates and satellite images as input variables. Unlike most studies using satellite imagery based on visible channels, our model also exploits all information within infrared channels of the Meteosat 9 satellite. A genetic algorithm is used to optimize selection of model inputs, for which twelve are selected e eleven 3-km Meteosat 9 channels and one clear-sky term. The model is validated in Andalusia (Spain) from January 2008 through December 2008. Measured data from 83 stations across the region are used, 65 for training and 18 independent ones for testing the model. At the latter stations, the ensemble model yields an overall root mean square error of 6.74% and correlation coefficient of 99%; the generated estimates are relatively accurate and errors spatially uniform. The model yields reliable results even on cloudy days, improving on current models based on satellite imagery.

This article combines and discusses three independent validations of global horizontal irradiance (GHI) multi-day forecast models that were conducted in the US, Canada and Europe. All forecast models are based directly or indirectly on numerical weather prediction (NWP). Two models are common to the three validation efforts – the ECMWF global model and the GFS-driven WRF mesoscale model – and allow general observations: (1) the GFS-based WRF- model forecasts do not perform as well as global forecast-based approaches such as ECMWF and (2) the simple averaging of models’ output tends to perform better than individual models.

Like in the film Field of Dreams , the sentence “if you build it, he will come” also applies in power systems. In this sense, if a transmission planner suggests building some lines in anticipation of generation capacity investments, then it can induce generation companies to invest in a more socially efficient manner. In this paper, we solve for the optimal way of doing this anticipative power transmission planning. Inspired in the proactive transmission planning model proposed by Sauma and Oren (2006) we formulate a mixed integer linear programming optimization model that integrates transmission planning, generation investment, and market operation decisions and propose a methodology to solve for the optimal transmission expansion. Contrary to the proactive methodology proposed by Sauma and Oren (2006) , our model solves the optimal transmission expansion problem anticipating both generation investment and market clearing. We use the marginalist theory with production cost functions inversely related to the installed capacity in a perfectly competitive electricity market and we find all possible generation expansion pure Nash equilibria. We illustrate our results using 3-node and 4-node examples.

This study addresses the task of identifying optimum locations for solar and wind power plants so that the wind-plus- solar power generation meets certain conditions of efficiency and stability, thus allowing to overcome the downside that the natural variability of these renewable resources represents. The method was based on a simulated annealing algorithm and applied over the Iberian Peninsula, a region whose commitment to renewable energy is growing relatively fast. Obtained results are encouraging since a number of different sensitivity experiments support the spatio-temporal complementarity between solar and wind power, at least at the monthly time-scale, in this region.

Nanoporous silica microparticles (NSiO2-MP) are considered to be potential drug delivery systems and scaffolding platforms in tissue engineering. However, few biocompatibility studies regarding NSiO2-MP interaction with the immune system have been reported. Toll-like receptors (TLR) are involved in host defence as well as autoimmune and inflammatory diseases. The results show that NSiO2-MP up to 100 μg ml−1 do not affect macrophage cell viability after 24 h cell culture. Moreover, NSiO2-MP do not compromise the cell viability of TLR-activated Raw 264.7 cells, for either cell surface TLR (TLR1/TLR2/TLR4/TLR6) or endocytic compartment TLR (TLR3/TLR7/TLR9). Furthermore, Raw 264.7 cells do not respond to NSiO2-MP exposure in terms of IL-6 or IL-10 secretion. NSiO2-MP co-treatment in the presence of TLR ligands does not impair or enhance the secretion of the pro-inflammatory cytokine IL-6 or the regulatory cytokine IL-10. Thus, NSiO2-MP do not affect macrophage polarization towards a pro-inflammatory or immunosuppressive status, representing added value in terms of biocompatibility compared with other SiO2-based micro- and nanoparticles., The authors gratefully acknowledge the financial support provided by the Spanish Ministry of Science and Innovation (BIO2009-13903-C02-02 to A.D.-C.), the Spanish Ministry of Health (PS09-02252 to D.P.), the Andalusian Ministry of Health (PI-2008- 0068 to D.P.), the Andalusian Ministry of Economy, Science and Innovation (Proyecto Excelencia CTS-6928 to D.P.) and the PAIDI Program from the Andalusian Government (CTS-677 to D.P.). M.C.-G is a fellow from the University of Seville Predoctoral Plan Propio. M.L.R.-G. is a fellow from the JAE-Program (Spanish National Research Council). A.L.-G. is a fellow from the FPU-Program (AP2009-3816) of the Spanish Ministry of Science and Innovation.

In this work, we evaluate the reliability of three-days-ahead global horizontal irradiance (GHI) and direct normal irradiance (DNI) forecasts provided by the WRF mesoscale atmospheric model for Andalusia (southern Spain). GHI forecasts were produced directly by the model, while DNI forecasts were obtained based on a physical post-processing procedure using the WRF outputs and satellite retrievals. Hourly time resolution and 3 km spatial resolution estimates were tested against ground measurements collected at four radiometric stations along the years 2007 and 2008. The evaluation was carried out independently for different forecast horizons (1, 2 and 3 days ahead), the different seasons of the year and three different sky conditions: clear, cloudy and overcast. Results showed that the WRF model presents considerable skill in forecasting both GHI and DNI, overall, better than a trivial persistence model. Nevertheless, both MBE and RMSE values presented a marked dependence on the sky conditions and season of the year. Particularly, for 24 h lead time, the MBE of the forecasted GHI was 2% for clear-skies and 18% for cloudy conditions. However, the MBE of the forecasted DNI increased up to about 10% and 75% for clear and cloudy conditions, respectively. Regarding RMSE values, in the case of forecasted GHI, results ranged from below 10% under clear-skies to 50% for cloudy conditions. In the case of forecasted DNI, RMSE ranged from 20% to 100% for clear and cloudy skies, respectively. This proved the higher sensitivity of DNI to the sky conditions. In general, an increment of the MBE and RMSE values with the cloudiness was observed. This reflects a still limited ability of the WRF model to properly forecast cloudy conditions compared to clear skies. Nevertheless, the model was able to accurately forecast steep changes in the sky (cloudiness) conditions. Finally, WRF performed considerable better than the persistence model for clear skies both for GHI and DNI, with relative RMSE values about a half. However, for cloudy conditions, performance was similar.

The foreseen depletion of the traditional fossil fuels for the forthcoming decades is forcing us to seek for new sustainable and non-pollutant energy sources. Renewable energies rely on a decentralized scheme strongly dependent on the local resources availability. In this work, we tackle the study of the renewable energies potential for an intensive electricity production in the province of Jaen (southern Spain) which has a pronounced unbalance between its inner electricity production and consumption. The potential of biomass from olive pruning residues, solar photovoltaics (PV) and wind power has been analyzed using Geographical Information System tools, and a proposal for a massive implementation of renewable energies has been arisen. In particular, we propose the installation of 5 biomass facilities, totaling 98 MW of power capacity, with an estimated annual production of 763 GWh, 12 PV facilities, totaling 420 MW of power capacity, with an estimated annual production of 656 GWh and 506 MW of wind power capacity in a number of wind farms, with an estimated annual production of 825 GWh. Overall, this production frame would meet roughly a 75% of the electricity demands in the province and thus would mitigate the current unbalance.

Local topography influences total incoming solar radiation at ground surface in mountainous areas, and so it becomes a key factor for the spatial distribution of plants. However, radiometric stations are often clustered only around farmland or populated areas, usually throughout valleys and flat regions. In this work, we use residual kriging methods to account for cloud- and terrain-related effects, especially when availability of measurements in mountains is scarce. Terrain-related effects have been considered through the terrain elevation and a topographic clear-sky solar radiation model that, additionally, also allow us to consider local clouds effects. Mesoscale-level phenomena were considered through the distance to the coast and the geographical longitude, that partially explain the atmospheric circulation in the studied region. The study has been conducted in the region of Andalusia, in southern Spain, using a target grid support of 1 km of grid-spacing and based on a 10-year length experimental dataset of 63 stations. Two different residual kriging approaches were evaluated and compared against ordinary kriging estimates. Overall, all kriging methods showed good skills in predicting the spatial regionalization of the monthly averages of daily solar radiation. The use of the distance to the coast and the geographical longitude enhanced the performance of residual kriging methods. Elevation proved to be important during summer months, while clear-sky solar radiation estimates were helpful especially during winter months. Overall, the RMSE value for ordinary kriging at the validation sites was about 3%. The residual kriging methods were able to outperform ordinary kriging around a 5% in winter and up to a 18% in summer, in relative terms.

Four variables (total cloud cover, skin temperature, total column water vapour and total column ozone) from meteorological reanalysis were used to generate synthetic daily global solar radiation via artificial neural network (ANN) techniques. The goal of our study was to predict solar radiation values in locations without ground measurements, by using the reanalysis data as an alternative to the use of satellite imagery. The model was validated in Andalusia (Spain), using measured data for nine years from 83 ground stations spread over the region. The geographical location (latitude, longitude), the day of the year, the daily clear sky global radiation, and the four meteorological variables were used as input data, while the daily global solar radiation was the only output of the ANN. Sixty five ground stations were used as training dataset and eighteen stations as independent dataset. The optimum network architecture yielded a root mean square error of 16.4% and a correlation coefficient of 94% for the testing stations. Furthermore, we have successfully tested the forecasting capability of the model with measured radiation values at a later time. These results demonstrate the generalization capability of this approach over unseen data and its ability to produce accurate estimates and forecasts.

In competitive electricity markets, companies simultaneously offer their productions to obtain the maximum profits on a daily basis. In the long run, the strategies utilized by the electric companies lead to various long-term equilibria that can be analyzed with the appropriate tools. We present a methodology to find plausible long-term Nash equilibria in pool-based electricity markets. The methodology is based on an iterative market Nash equilibrium model in which the companies can decide upon their offer strategies. An exponential smoothing of the bids submitted by the companies is applied to facilitate the convergence of the iterative procedure. In each iteration of the model the companies face residual demand curves that are accurately modeled by Hermite interpolating polynomials. We introduce the concept of meta-game equilibrium strategies to allow companies to have a range of offer strategies where several pure and mixed meta-game Nash equilibria are possible. With our model it is also possible to model uncertainty or to generate price scenarios for financial models that assess the value of a generating unit by real options analysis. The application of the proposed methodology is illustrated with several realistic case studies.

7 páginas, 2 figuras, 1 tabla.-- Potential Clinical Relevance., Dendritic cells (DCs) are potent antigen-presenting cells capable of initiating a primary immune response and possess the ability to activate T cells and stimulate the growth and differentiation of B cells. DCs provide a direct connection between innate and adaptive immune response, and arise from bone marrow precursors that are present in immature forms in peripheral tissues, where they are prepared to capture antigens. DCs migrate from the peripheral tissues to the closest lymph nodes through afferent lymphatic vessels to present the foreign antigens, stimulating T-cell activation and initiating a cellular immune response. Moreover, it is known that DCs have an important role in various diseases and conditions involving the immune system, particularly in cancer and autoimmune disorders. For these reasons, targeting nanoparticles (NPs) to DCs provides a promising strategy for developing an efficient balanced and protective immune response. NPs can modulate the immune response and might be potentially useful as effective vaccine adjuvants for infectious disease and cancer therapy. The objective of this review is to present the latest advances in NP delivery methods targeting DCs, the mechanisms of action, potential effects, and therapeutic results of these systems and their future applications, such as improved vaccination strategies, cancer immunotherapy, and immunomodulatory treatments. [From the Clinical Editor]: Dendritic cells (DCs) are potent antigen-presenting cells capable of initiating a primary immune response and activating T and B cells. The role of DC-s can be considered as a bridge between innate and adaptive immunity. Targeting nanoparticles (NPs) to DCs can modulate the immune response and might be useful as vaccine adjuvants in infectious disease and cancer therapy., The authors are grateful for financial support from the Spanish Ministry of Health (PI05/2056; PI06/1641; PS09/2252); the Andalusian Ministry of Health (PI0068), and the PAIDI Program from the Andalusian Government (BIO323).