Hitachi (Germany)

A certain residue representation of the inverse binomial coefficients makes them amenable to Egorychev method for the reduction of sums by analytic methods, wherein the main idea is to identify parts of the summands as residues of analytic functions. We illustrate the use of such residue representation on some instances varying in complexity, including a generalization of an identity by Sung Sik U and Kyu Song Chae in [13].

Processes increasingly need to consider electricity markets, which shifts the traditional demand side management scope towards a more dynamic nature. Instead of only focusing on day-ahead energy trading, demand-side management scope should be broadened towards being able to support the power grid stability during frequency events. This paper studies an artificial example process, similar to the melt-shop process from the steel industry, highlighting the challenges and opportunities of an energy intensive process. We show the potential benefits of having a battery energy storage system on-site, as well as opportunities in lowering the electricity cost by participating in the bidding process of various ancillary products.

Extreme weather events, such as hurricanes and typhoons, are expected to increase both in frequency and severity in the next decades as a symptom of climate change. Such events pose significant challenges for electrical utilities, often resulting in severe power outages and high operational costs. Power outage forecasts can help utilities to prepare better for such events, by localizing critical areas and plan post-event restoration more effectively. Predictive analytics based on machine learning (ML) can help to get an understanding of the disaster extent prior to the event, and support utilities in crew planning. In this paper, we propose a two-stage machine learning approach for power outage prediction during high-wind events: the first stage comprises a so-called "online event detector" which identifies the start of high-wind events in real-time. The second stage comprises a ML-based model that is tailored to power outage prediction during high-wind events. The method was evaluated on multiple high-wind events in the U.S. and demonstrated strong predictive performance across regions.

Variance parameter estimation in linear mixed models is a challenge for many classical nonlinear optimization algorithms due to the positive-definiteness constraint of the random effects covariance matrix. Many existing algorithms may get stuck on the boundary of the feasible space. In this paper, we address this problem by exploiting the intrinsic geometry of the parameter space, pulling iterates away from the boundary. In this novel approach, we formulate the problem of residual maximum likelihood estimation as an optimization problem on a Riemannian manifold. Based on the introduced formulation, we give geometric higher-order information on the problem via the Riemannian gradient and the Riemannian Hessian, making the algorithm more robust against singularities. We test our approach with Riemannian optimization algorithms numerically. Our approach yields a much better quality of the variance parameter estimates compared to existing approaches.

Self-assembling peptides (SAPs) are fully defined nanobiomaterials offering unprecedented opportunities to control nanostructure and chemical attributes to investigate and manipulate cellular signals. To investigate the influence of chemical and morphological characteristics on inflammatory signalling in native immunity, we designed five Beta-sheet SAPs: EFEFKFEFK (EF8), YEFEFKFEFK (YEF8), EFEFKFEFKY (EF8Y), YEFEFKFEFKY (YEF8Y) and EYEFKFEFK (EYF8) (F: phenylalanine; E: glutamic acid; K: lysine, Y: tyrosine). The position of tyrosine in the peptide sequence dictated the distinct self-assembly into nanostructures, with sequences EF8Y, YEF8Y, EYF8 self-assembling into thin nanofibers d≈3.8 ± 0.2 nm, YEF8 self-assembling into rod-like flat ribbons d>20 nm and EF8 (control) consisting of both types of self-assembled nanostructures. These distinct nanostructures induced contrasting inflammatory response of monocytic model THP-1 cells-derived macrophages (MΦs). Presence of soluble EF8 nanofibers (at 2 mM) induced anti-inflammatory response and polarization towards an M2 state, whereas YEF8 displayed tendency for inducing pro-inflammatory response and polarization towards M1 state. The EF8Y, YEF8Y, EYF8 SAPs did not induce an inflammatory response in our models. These results were validated using peripheral blood mononuclear cells (PBMCs)-derived MΦs from human donors, confirming the critical role of the EF8 and YEF8 SAPs as possible orchestrators of the repair of tissues or inducers of pro-inflammatory state, respectively. The same MΦs polarization responses from THP-1 derived cells cultured on 20 mM hydrogels. These findings will facilitate the utilization of this family of SAPs as immunomodulatory nanobiomaterials potentially changing the course of inflammation during the progression of various diseases.

Advanced control, operation, and planning tools of electrical networks with ML are not straightforward. 110 experts were surveyed to show where and how ML algorithms could advance. This paper assesses this survey and research environment. Then, it develops an innovation roadmap that helps align our research community with a goal-oriented realisation of the opportunities that AI upholds. This paper finds that the R&D environment of system operators (and the surrounding research ecosystem) needs adaptation to enable faster developments with AI while maintaining high testing quality and safety. This roadmap serves system operators, academics, and labs advancing next-generation electrical network tools.

The most pressing concerns prioritise sustainability by addressing local pollution issues (including noise and air quality) a nd encouraging affordability through strategies that promote independence from fluctuations in fossil fuel costs, increasing production from renewables-based energy systems. For remote and off-grid areas, characterized by a mix of renewable energy sources, energy storage and conventional generators that meet local energy demands, the absence of a backbone network poses additional technical challenges to this change in paradigm. Optimized Energy Management Systems (EMS) play a pivotal role in enhancing such isolated systems' efficiency, reliability and sustainability. This paper presents a mathematical model for an optimized energy management system and the compelling techno-economic advantages of using it on a real photovoltaic, battery and diesel generator hybrid power system located on Bora Bora, an island in the Pacific Ocean. Based on real historical data from the island power system operation, the analysis elaborates on fuel consumption reduction and generation spinning reserve margin based on the N-1 criteria. The paper further describes the impact of battery energy storage systems on spinning reserve management.

As the world transitions towards sustainable energy solutions, integrating hydrogen and Electric Vehicles (EVs) into multi-energy systems presents challenges and opportunities. This paper explores a preliminary development of an advanced Charging/Energy Management System (C/EMS) optimization model tailored for multi-energy applications, explicitly focusing on Fuel Cell and Battery Electric Vehicles. Integrating hydrogen refueling infrastructure and EV charging stations within the depot introduces additional complexities, necessitating sophisticated EMS algorithms for effective management. The proposed EMS optimization model aims at optimizing the utilization of diverse energy sources, storage systems and demand-side resources to enhance system efficiency, reliability and environmental sustainability. The authors present the design considerations, modeling techniques and optimization methodologies employed to address these challenges and maximize the benefits. Case studies and simulation results demonstrate the effectiveness of the proposed EMS optimization model in enhancing overall system performances.

Energy storage units offer vital balancing power for energy systems with an increasing amount of variable renewable energy (VRE) sources. The operation of such systems can be optimized by stochastic programming, which anticipates the uncertainty related to the variable renewable energy sources. However, these optimization problems can only be formulated for optimization horizons with a finite length, due to the rapidly increasing problem size and uncertainty in VRE production. Realistic valuation of the stored energy at the end of a horizon is important for long-term operation of the system. In this work, we investigate two different valuation methods, which are based on forecasted electricity prices, for storage-only and producer-oriented energy systems that participate in the day-ahead market. On a case study on the German day-ahead market, the methods yield competitive profits with reduced cycling of the energy storage unit and deviations with respect to the day-ahead trading.