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

The parasitic worm product ES-62 up-regulates IL-22 production by γδ T cells in the murine model of Collagen-Induced Arthritis

S-62 is a phosphorylcholine (PC)-containing glycoprotein secreted by the filarial nematode Acanthocheilonema viteae that acts to modulate the host immune response to promote the establishment of chronic helminth infection. Reflecting its anti-inflammatory actions, we have previously reported that ES-62 protects mice from developing Collagen-Induced Arthritis (CIA): thus, as this helminth-derived product may exhibit therapeutic potential in Rheumatoid Arthritis (RA), it is important to understand the protective immunoregulatory mechanisms triggered by ES-62 in this model in vivo. We have established to date that ES-62 acts by downregulating pathogenic Th17/IL-17-mediated responses and upregulating the regulatory cytokine IL-10. In addition, our studies have identified that IL-22, another member of the IL-10 family of cytokines, exerts dual pathogenic and protective roles in this model of RA with ES-62 harnessing the cytokine’s inflammation-resolving and tissue repair properties in the joint during the established phase of disease. Here, we discuss the counter-regulatory roles of IL-22 in the murine model of CIA and present additional novel data showing that ES-62 selectively induces γδ T cells with the capacity to induce IL-22 production and that gd T cells with the capacity to produce IL-22, but not IL-17, induced during CIA can be identified by their expression of TLR4. Moreover, we also show that treatment of mice undergoing CIA with the active PC moiety of ES-62, in the form of PC conjugated to BSA, is not only sufficient to mimic the ES-62-dependent suppression of pathogenic IL-17 responses shown previously but also that of the IL-22 and IL-10 up-regulation observed with the parasitic worm product during CIA. These findings not only reinforce the potential of IL-22, firstly described as a Th17-related pro-inflammatory cytokine, as a protective factor in arthritis but also suggest that drugs based on the PC moiety found in ES-62 may be able to harness the joint-protecting activities of IL-22 therapeutically

Mass media and repulsive interactions in continuous-opinion dynamics

This letter focus on the effect of repulsive interactions on the adoption of an external message in an opinion model. With a simple change in the rules, we modify the Deffuant \emph{et al.} model to incorporate the presence of repulsive interactions. We will show that information receptiveness is optimal for an intermediate fraction of repulsive links. Using the master equation as well as Monte Carlo simulations of the message-free model, we identify the point where the system becomes optimally permeable to external influence with an order-disorder transition

The noisy Hegselmann-Krause model for opinion dynamics

In the model for continuous opinion dynamics introduced by Hegselmann and Krause, each individual moves to the average opinion of all individuals within an area of confidence. In this work we study the effects of noise in this system. With certain probability, individuals are given the opportunity to change spontaneously their opinion to another one selected randomly inside the opinion space with different rules. If the random jump does not occur, individuals interact through the Hegselmann-Krause's rule. We analyze two cases, one where individuals can carry out opinion random jumps inside the whole opinion space, and other where they are allowed to perform jumps just inside a small interval centered around the current opinion. We found that these opinion random jumps change the model behavior inducing interesting phenomena. Using pattern formation techniques, we obtain approximate analytical results for critical conditions of opinion cluster formation. Finally, we compare the results of this work with the noisy version of the Deffuant et al. model for continuous-opinion dynamics

Impact of Forecast Errors on Expansion Planning of Power Systems with a Renewables Target

This paper analyzes the impact of production forecast errors on the expansion planning of a power system and investigates the influence of market design to facilitate the integration of renewable generation. For this purpose, we propose a stochastic programming modeling framework to determine the expansion plan that minimizes system-wide investment and operating costs, while ensuring a given share of renewable generation in the electricity supply. Unlike existing ones, this framework includes both a day-ahead and a balancing market so as to capture the impact of both production forecasts and the associated prediction errors. Within this framework, we consider two paradigmatic market designs that essentially differ in whether the day-ahead generation schedule and the subsequent balancing re-dispatch are co-optimized or not. The main features and results of the model set-ups are discussed using an illustrative four-node example and a more realistic 24-node case study

A group-based architecture and protocol for wireless sensor networks

There are many works related to wireless sensor networks (WSNs) where authors present new protocols with better or enhanced features, others just compare their performance or present an application, but this work tries to provide a different perspective. Why don¿t we see the network as a whole and split it into groups to give better network performance regardless of the routing protocol? For this reason, in this thesis we demonstrate through simulations that node¿s grouping feature in WSN improves the network¿s behavior. We propose the creation of a group-based architecture, where nodes have the same functionality within the network. Each group has a head node, which defines the area in which the nodes of such group are located. Each node has a unique node identifier (nodeID). First group¿s node makes a group identifier (groupID). New nodes will know their groupID and nodeID of their neighbors. End nodes are, physically, the nodes that define a group. When there is an event on a node, this event is sent to all nodes in its group in order to take an appropriate action. End nodes have connections to other end nodes of neighboring groups and they will be used to send data to other groups or to receive information from other groups and to distribute it within their group. Links between end nodes of different groups are established mainly depending on their position, but if there are multiple possibilities, neighbor nodes could be selected based on their ability ¿, being ¿ a choice parameter taking into account several network and nodes parameters. In order to set group¿s boundaries, we can consider two options, namely: i) limiting the group¿s diameter of a maximum number of hops, and ii) establishing boundaries of covered area. In order to improve the proposed group-based architecture, we add collaboration between groups. A collaborative group-based network gives better performance to the group and to the whole system, thereby avoiding unnecessary message forwarding and additional overheads while saving energy. Grouping nodes also diminishes the average network delay while allowing scaling the network considerably. In order to offer an optimized monitoring process, and in order to offer the best reply in particular environments, group-based collaborative systems are needed. They will simplify the monitoring needs while offering direct control. Finally, we propose a marine application where a variant of this groupbased architecture could be applied and deployed.García Pineda, M. (2013). A group-based architecture and protocol for wireless sensor networks [Tesis doctoral]. Universitat Politècnica de València. https://doi.org/10.4995/Thesis/10251/27599TESISPremios Extraordinarios de tesis doctorale

Modelling the collective response of heterogeneous cell populations to stationary gradients and chemical signal relay

The directed motion of cell aggregates toward a chemical source occurs in many relevant biological processes. Understanding the mechanisms that control this complex behavior is of great relevance for our understanding of developmental biological processes and many diseases. In this paper, we consider a self-propelled particle model for the movement of heterogeneous subpopulations of chemically interacting cells towards an imposed stable chemical gradient. Our simulations show explicitly how self-organisation of cell populations (which could lead to engulfment or complete cell segregation) can arise from the heterogeneity of chemotactic responses alone. This new result complements current theoretical and experimental studies that emphasise the role of differential cell–cell adhesion on self-organisation and spatial structure of cellular aggregates. We also investigate how the speed of individual cell aggregations increases with the chemotactic sensitivity of the cells, and decreases with the number of cells inside the aggregates

Kinetic Monte Carlo simulations for heterogeneous catalysis: Fundamentals, current status and challenges

Kinetic Monte Carlo (KMC) simulations in combination with first-principles-based calculations are rapidly becoming the gold-standard computational framework for bridging the gap between the wide range of length and time-scales over which heterogeneous catalysis unfolds. First-principles KMC (1p-KMC) simulations provide accurate insights into reactions over surfaces, a vital step towards the rational design of novel catalysts. In this perspective article, we briefly outline basic principles, computational challenges, successful applications, as well as future directions and opportunities of this promising and ever more popular kinetic modeling approach