Feature-driven improvement of renewable energy forecasting and trading

M. A. Muñoz, J. M. Morales, and S. Pineda, Feature-driven Improvement of Renewable Energy Forecasting and Trading, IEEE Transactions on Power Systems, 2020.Inspired from recent insights into the common ground of machine learning, optimization and decision-making, this paper proposes an easy-to-implement, but effective procedure to enhance both the quality of renewable energy forecasts and the competitive edge of renewable energy producers in electricity markets with a dual-price settlement of imbalances. The quality and economic gains brought by the proposed procedure essentially stem from the utilization of valuable predictors (also known as features) in a data-driven newsvendor model that renders a computationally inexpensive linear program. We illustrate the proposed procedure and numerically assess its benefits on a realistic case study that considers the aggregate wind power production in the Danish DK1 bidding zone as the variable to be predicted and traded. Within this context, our procedure leverages, among others, spatial information in the form of wind power forecasts issued by transmission system operators (TSO) in surrounding bidding zones and publicly available in online platforms. We show that our method is able to improve the quality of the wind power forecast issued by the Danish TSO by several percentage points (when measured in terms of the mean absolute or the root mean square error) and to significantly reduce the balancing costs incurred by the wind power producer.European Research Council (ERC) under the EU Horizon 2020 research and innovation programme (grant agreement No. 755705) Spanish Ministry of Economy, Industry, and Competitiveness through project ENE2017-83775-P

Geometric and poliorcetic inertia in the fortified system vs urban morphological inflections in 18th-Century Barcelona

[EN] Between the War of Nine Years and the Napoleonic invasion of 1808 Barcelona underwent a morphological transformation according to a progressive evolution that came along from a typical wall-constrained stronghold towards an urban structure where the primacy of the internal and external strategic control gave way to the socioeconomic, industrial and commercial detachment of the city. The warlike needs of the first quarter of the 18th century involved a series of explicit poliorcetic interventions that gradually made available other criteria related to the development of several infrastructures for peacetime and certain urban licenses. These improving processes that let transform the urban features later changed the sense of the vectors which settled the nexus between the intramural space and the territory beyond the bastioned perimeter. Starting from a predominantly centripetal structure where the city walls played a segregating role, they afterward tended to reinforce the creation of newborn civic spaces that appreciably reduced the strength of the suffocating perimeter and also established alternative centers of power. These procedures foreshadowed a further decline of the traditional values about the former city walls and allowed the take-off of the territory outside them as an expansion of the orthodox urban system essences and its outward projection. The confluence of both municipal government purposes and the Crown’s impositions eased the work of the military engineers who undertook the interventions directly dependent on their sphere of responsibility.This text has been developed within the framework of the R & D project “El dibujante ingeniero al servicio de la monarquía hispánica. Siglos XVI-XVIII. Ciudad e ingeniería en el Mediterráneo” (The draftsman engineer at the service of the Hispanic monarchy. 16th-18th centuries. City and engineering in the Mediterranean), ref. HAR2016-78098-P (AEI/ERDF, EU), funded by the Agencia Estatal de Investigación (Ministerio de Economía, Industria y Competitividad of the Spanish Government) and the European Regional Development Fund (ERDF), of which I am a researcher.Muñoz Corbalán, JM. (2018). Geometric and poliorcetic inertia in the fortified system vs urban morphological inflections in 18th-Century Barcelona. En 24th ISUF International Conference. Book of Papers. Editorial Universitat Politècnica de València. 791-812. https://doi.org/10.4995/ISUF2017.2017.5802OCS79181

Self-Regulation of SMR Power Led to an Enhancement of Functional Connectivity of Somatomotor Cortices in Fibromyalgia Patients

Neuroimaging studies have demonstrated that altered activity in somatosensory and motor cortices play a key role in pain chronification. Neurofeedback training of sensorimotor rhythm (SMR) is a tool which allow individuals to self-modulate their brain activity and to produce significant changes over somatomotor brain areas. Several studies have further shown that neurofeedback training may reduce pain and other pain-related symptoms in chronic pain patients. The goal of the present study was to analyze changes in SMR power and brain functional connectivity of the somatosensory and motor cortices elicited by neurofeedback task designed to both synchronize and desynchronize the SMR power over motor and somatosensory areas in fibromyalgia patients. Seventeen patients were randomly assigned to the SMR training (n = 9) or to a sham protocol (n = 8). All participants were trained during 6 sessions, and fMRI and EEG power elicited by synchronization and desynchronization trials were analyzed. In the SMR training group, four patients achieved the objective of SMR modulation in more than 70% of the trials from the second training session (good responders), while five patients performed the task at the chance level (bad responders). Good responders to the neurofeedback training significantly reduced pain and increased both SMR power modulationandfunctionalconnectivityofmotorandsomatosensoryrelatedareasduring the last neurofeedback training session, whereas no changes in brain activity or pain were observed in bad responders or participants in the sham group. In addition, we observed that good responders were characterized by reduced impact of fibromyalgia and pain symptoms, as well as by increased levels of health-related quality of life during the pre-training sessions. In summary, the present study revealed that neurofeedback training of SMR elicited significant brain changes in somatomotor areas leading to a significant reduction of pain in fibromyalgia patients. In this sense, our research provide evidence that neurofeedback training is a promising tool for a better understanding of brain mechanisms involved in pain chronification

Distance-based kernels for real-valued data

We consider distance-based similarity measures for real-valued vectors of interest in kernel-based machine learning algorithms. In particular, a truncated Euclidean similarity measure and a self-normalized similarity measure related to the Canberra distance. It is proved that they are positive semi-definite (p.s.d.), thus facilitating their use in kernel-based methods, like the Support Vector Machine, a very popular machine learning tool. These kernels may be better suited than standard kernels (like the RBF) in certain situations, that are described in the paper. Some rather general results concerning positivity properties are presented in detail as well as some interesting ways of proving the p.s.d. property.Peer ReviewedPostprint (author's final draft

Prescribing net demand for two-stage electricity generation scheduling

We consider a two-stage generation scheduling problem comprising a forward dispatch and a real-time re-dispatch. The former must be conducted facing an uncertain net demand that includes non-dispatchable electricity consumption and renewable power generation. The latter copes with the plausible deviations with respect to the forward schedule by making use of balancing power during the actual operation of the system. Standard industry practice deals with the uncertain net demand in the forward stage by replacing it with a good estimate of its conditional expectation (usually referred to as a point forecast), so as to minimize the need for balancing power in real time. However, it is well known that the cost structure of a power system is highly asymmetric and dependent on its operating point, with the result that minimizing the amount of power imbalances is not necessarily aligned with minimizing operating costs. In this paper, we propose a bilevel program to construct, from the available historical data, a prescription of the net demand that does account for the power system’s cost asymmetry. Furthermore, to accommodate the strong dependence of this cost on the power system’s operating point, we use clustering to tailor the proposed prescription to the foreseen net-demand regime. By way of an illustrative example and a more realistic case study based on the European power system, we show that our approach leads to substantial cost savings compared to the customary way of doing.European Research Council (ERC) under the EU Horizon 2020 research and innovation program (grant agreement No. 755705); Spanish Ministry of Science and Innovation (AEI/10.13039/501100011033) through project PID2020-115460GB-I00 and through the State Training Subprogram 2018 of the State Program for the Promotion of Talent and its Employability in R&D&I, within the framework of the State Plan for Scientific and Technical Research and Innovation 2017–2020 (with the support of the European Social Fund), reference PRE2018-083722 and ENE2017-83775-P); Junta de Andalucía (JA) and the European Regional Development Fund (FEDER) through the research project P20_00153; Partial funding for open access charge: Universidad de Málaga / CBUA

A bilevel framework for decision-making under uncertainty with contextual information

In this paper, we propose a novel approach for data-driven decision-making under uncertainty in the presence of contextual information. Given a finite collection of observations of the uncertain parameters and potential explanatory variables (i.e., the contextual information), our approach fits a parametric model to those data that is specifically tailored to maximizing the decision value, while accounting for possible feasibility constraints. From a mathematical point of view, our framework translates into a bilevel program, for which we provide both a fast regularization procedure and a big-M-based reformulation that can be solved using off-the-shelf optimization solvers. We showcase the benefits of moving from the traditional scheme for model estimation (based on statistical quality metrics) to decision-guided prediction using three different practical problems. We also compare our approach with existing ones in a realistic case study that considers a strategic power producer that participates in the Iberian electricity market. Finally, we use these numerical simulations to analyze the conditions (in terms of the firm’s cost structure and production capacity) under which our approach proves to be more advantageous to the producer.This work was supported in part by the European Research Council (ERC) under the EU Horizon 2020 research and innovation program (grant agreement No. 755705), in part by the Spanish Ministry of Science and Innovation (AEI/10.13039/501100011033) through project PID2020-115460GB-I00, and in part by the Junta de Andalucía (JA), the Universidad de Málaga and the European Regional Development Fund (FEDER) through the research projects P20_00153 and UMA2018‐FEDERJA‐001. M. Á. Muñoz is also funded by the Spanish Ministry of Science, Innovation and Universities through the State Training Subprogram 2018 of the State Program for the Promotion of Talent and its Employability in R&D&I, within the framework of the State Plan for Scientific and Technical Research and Innovation 2017-2020 and by the European Social Fund. Finally, the authors thankfully acknowledge the computer resources, technical expertise, and assistance provided by the SCBI (Supercomputing and Bioinformatics) center of the University of Malaga