Energy Storage as Enabling Technology for Smart Grid

Awareness about human impact on mighty climatic changes is radically changing our concept of energy. The thoughtless use of energy slowly leaves our habits and good use of energy is certain the way of a better future. CO2 emission reduction and carbon fossil fuel limitation are the main targets of governmental actions: this is possible thanks to technology improvement as efficient generation from renewable sources and good management of the electricity network. In recent years distributed generation, also of small size, grew up causing new management problems, indeed production from renewable energy sources (RES) is intermittent and unprogrammable. Energy storage systems can be a solution to these problems and pave the way to completely active users, grid parity and smart grid, moreover can be an useful tool to increase electricity access in rural areas. Research on energy storage is intrinsically a multidisciplinary field: storage types, power stages, technologies, topologies, weather, forecast, control algorithms, regulatory, safety and business cases to mention the most importants. In the present work is described the whole design of an energy storage system. First chapters are dedicated to a description of energy storage context, chapters 1 and 2; indeed, it is a matter of fact that in the last years, energy storage became more and more interesting from explicit mention as a tool against climatic changes to first options on the market. The general approach was the realization of a modular energy storage system for residential application, hardware and software design steps are deeply described in chapters 3 and 4. Simulations and tests on the prototype are reported in chapter 5. Finally conclusion and future works are given. At the end of the document some appendices are included to cover specific aspects touched during the work thesis

Harnessing the Automotive Waste Heat with Thermoelectric Modules Using Maximum Power Point Tracking Method

The present work shows a comprehensive methodology and design steps to recover energy from the automotive waste heat. A thermoelectric generator must be connected to a power converter in order to extract the maximum power from the generator and, also, satisfy different constrains to charge a battery. Starting from the electrical model of thermoelectric cells, it is evaluated their combination to realize a thermoelectric generator (TEG) comply with the automotive regulation, then considering input/output electric characteristics, it is evaluated the best converter topology to satisfy all constrains. Design steps and power dissipation estimation are deeply explained. TEG and power converter models are simulated in a model-based environment to allow the design of the control algorithms. The control system consists of nested control loops. Two maximum power point tracking (MPPT) algorithms are evaluated. The MPPT output is used as reference for a current control loop. The maximum power characteristic of a TEG has a quadratic behavior and working without the tracking of the maximum power point could drastically decrease the generated power from the TEG and the system efficiency. There are presented simulation results of the control algorithms and experimental data are shown in order to validate the design steps

Local Planners with Deep Reinforcement Learning for Indoor Autonomous Navigation

Autonomous indoor navigation requires an elab- orated and accurate algorithmic stack, able to guide robots through cluttered, unstructured, and dynamic environments. Global and local path planning, mapping, localization, and decision making are only some of the required layers that undergo heavy research from the scientific community to achieve the requirements for fully functional autonomous navigation. In the last years, Deep Reinforcement Learning (DRL) has proven to be a competitive short-range guidance system solution for power-efficient and low computational cost point-to-point local planners. One of the main strengths of this approach is the possibility to train a DRL agent in a simulated environment that encapsulates robot dynamics and task constraints and then deploy its learned point-to-point navigation policy in a real setting. However, despite DRL easily integrates complex mechanical dynamics and multimodal signals into a single model, the effect of different sensor data on navigation performance has not been investigated yet. In this paper, we compare two different DRL navigation solutions that leverage LiDAR and depth camera information, respectively. The agents are trained in the same simulated environment and tested on a common benchmark to highlight the strengths and criticalities of each technique

Ultra-low-power Range Error Mitigation for Ultra-wideband Precise Localization

Precise and accurate localization in outdoor and indoor environments is a challenging problem that currently constitutes a significant limitation for several practical applications. Ultra-wideband (UWB) localization technology represents a valuable low-cost solution to the problem. However, non-line-of-sight (NLOS) conditions and complexity of the specific radio environment can easily introduce a positive bias in the ranging measurement, resulting in highly inaccurate and unsatisfactory position estimation. In the light of this, we leverage the latest advancement in deep neural network optimization techniques and their implementation on ultra-low-power microcontrollers to introduce an effective range error mitigation solution that provides corrections in either NLOS or LOS conditions with a few mW of power. Our extensive experimentation endorses the advantages and improvements of our low-cost and power-efficient methodology

Marvin: an Innovative Omni-Directional Robotic Assistant for Domestic Environments

Population ageing and pandemics recently demonstrate to cause isolation of elderly people in their houses, generating the need for a reliable assistive figure. Robotic assistants are the new frontier of innovation for domestic welfare, and elderly monitoring is one of the services a robot can handle for collective well-being. Despite these emerging needs, in the actual landscape of robotic assistants there are no platform which successfully combines a reliable mobility in cluttered domestic spaces, with lightweight and offline Artificial Intelligence (AI) solutions for perception and interaction. In this work, we present Marvin, a novel assistive robotic platform we developed with a modular layer-based architecture, merging a flexible mechanical design with cutting-edge AI for perception and vocal control. We focus the design of Marvin on three target service functions: monitoring of elderly and reduced-mobility subjects, remote presence and connectivity, and night assistance. Compared to previous works, we propose a tiny omnidirectional platform, which enables agile mobility and effective obstacle avoidance. Moreover, we design a controllable positioning device, which easily allows the user to access the interface for connectivity and extends the visual range of the camera sensor. Nonetheless, we delicately consider the privacy issues arising from private data collection on cloud services, a critical aspect of commercial AI-based assistants. To this end, we demonstrate how lightweight deep learning solutions for visual perception and vocal command can be adopted, completely running offline on the embedded hardware of the robot.Comment: 20 pages, 9 figures, 3 tabl

The Marvin Project: an Omni-Directional Robot for Home Assistance

In the last decades, many researchers are investigating how robotic solutions may be adopted to address the increasing need for home and personal assistance aggravated by current global challenges, e.g. population ageing and pandemic emergency. In this direction, the researchers at Politecnico di Torino, together with the colleagues from Edison S.p.A., developed the Marvin project which aims at designing a useful mobile robot for the domestic environment. In this work, the main features of the Marvin prototype and a first qualitative experimental validation are presented

Capacitive Displacement Sensor for a Self-Sensing Shock-Absorber Piston-Cylinder Mechanism

Measurement of piston displacement is a common problem for any pneumatic or hydraulic device, like shock-absorber. Direct measurements are not always feasible because of mechanical constraints; most recent techniques rely on magnetic phenomena, introducing considerable complexity. In an attempt to achieve an economical and feasible solution, an intrinsic capacitive sensor is developed. Such sensors measure the capacitance between piston and cylinder, which is directly proportional to displacement. It is developed an oscillator stage to measure the unknown capacitance. The oscillator’s output is acquired by a microcontroller, conditioned and transformed into the estimated displacement. This paper focuses on the design methodology of the measurement stage, highlighting tradeoffs and optimizations. The sensor was developed for an automotive application in a commercial shock absorber: however, it can be extended to other devices where proper electrical isolation between cylinder and piston is provided. Mathematical models and experimental results are reported compared to a commercial position sensor

The Arabidopsis Protein CONSERVED ONLY IN THE GREEN LINEAGE160 Promotes the Assembly of the Membranous Part of the Chloroplast ATP Synthase

The chloroplast F(1)F(o)-ATP synthase/ATPase (cpATPase) couples ATP synthesis to the light-driven electrochemical proton gradient. The cpATPase is a multiprotein complex and consists of a membrane-spanning protein channel (comprising subunit types a, b, b′, and c) and a peripheral domain (subunits α, β, γ, δ, and ε). We report the characterization of the Arabidopsis (Arabidopsis thaliana) CONSERVED ONLY IN THE GREEN LINEAGE160 (AtCGL160) protein (AtCGL160), conserved in green algae and plants. AtCGL160 is an integral thylakoid protein, and its carboxyl-terminal portion is distantly related to prokaryotic ATP SYNTHASE PROTEIN1 (Atp1/UncI) proteins that are thought to function in ATP synthase assembly. Plants without AtCGL160 display an increase in xanthophyll cycle activity and energy-dependent nonphotochemical quenching. These photosynthetic perturbations can be attributed to a severe reduction in cpATPase levels that result in increased acidification of the thylakoid lumen. AtCGL160 is not an integral cpATPase component but is specifically required for the efficient incorporation of the c-subunit into the cpATPase. AtCGL160, as well as a chimeric protein containing the amino-terminal part of AtCGL160 and Synechocystis sp. PCC6803 Atp1, physically interact with the c-subunit. We conclude that AtCGL160 and Atp1 facilitate the assembly of the membranous part of the cpATPase in their hosts, but loss of their functions provokes a unique compensatory response in each organism

Baseline Tumor Size as Prognostic Index in Patients With Advanced Solid Tumors Receiving Experimental Targeted Agents

Abstract Background Baseline tumor size (BTS) has been associated with outcomes in patients with cancer treated with immunotherapy. However, the prognostic impact of BTS on patients receiving targeted therapies (TTs) remains undetermined. Methods We reviewed data of patients with advanced solid tumors consecutively treated within early-phase clinical trials at our institution from 01/2014 to 04/2021. Treatments were categorized as immunotherapy-based or TT-based (biomarker-matched or not). BTS was calculated as the sum of RECIST1.1 baseline target lesions. Results A total of 444 patients were eligible; the median BTS was 69 mm (IQR 40-100). OS was significantly longer for patients with BTS lower versus higher than the median (16.6 vs. 8.2 months, P < .001), including among those receiving immunotherapy (12 vs. 7.5 months, P = .005). Among patients receiving TT, lower BTS was associated with longer PFS (4.7 vs. 3.1 months, P = .002) and OS (20.5 vs. 9.9 months, P < .001) as compared to high BTS. However, such association was only significant among patients receiving biomarker-matched TT, with longer PFS (6.2 vs. 3.3 months, P < .001) and OS (21.2 vs. 6.7 months, P < .001) in the low-BTS subgroup, despite a similar ORR (28% vs. 22%, P = .57). BTS was not prognostic among patients receiving unmatched TT, with similar PFS (3.7 vs. 4.4 months, P = .30), OS (19.3 vs. 11.8 months, P = .20), and ORR (33% vs. 28%, P = .78) in the 2 BTS groups. Multivariate analysis confirmed that BTS was independently associated with PFS (P = .03) and OS (P < .001) but not with ORR (P = .11). Conclusions Higher BTS is associated with worse survival outcomes among patients receiving biomarker-matched, but not biomarker-unmatched TT

Melanocortin-1 receptor, skin cancer and phenotypic characteristics (M-SKIP) project

Background: For complex diseases like cancer, pooled-analysis of individual data represents a powerful tool to investigate the joint contribution of genetic, phenotypic and environmental factors to the development of a disease. Pooled-analysis of epidemiological studies has many advantages over meta-analysis, and preliminary results may be obtained faster and with lower costs than with prospective consortia. Design and methods. Based on our experience with the study design of the Melanocortin-1 receptor (MC1R) gene, SKin cancer and Phenotypic characteristics (M-SKIP) project, we describe the most important steps in planning and conducting a pooled-analysis of genetic epidemiological studies. We then present the statistical analysis plan that we are going to apply, giving particular attention to methods of analysis recently proposed to account for between-study heterogeneity and to explore the joint contribution of genetic, phenotypic and environmental factors in the development of a disease. Within the M-SKIP project, data on 10,959 skin cancer cases and 14,785 controls from 31 international investigators were checked for quality and recoded for standardization. We first proposed to fit the aggregated data with random-effects logistic regression models. However, for the M-SKIP project, a two-stage analysis will be preferred to overcome the problem regarding the availability of different study covariates. The joint contribution of MC1R variants and phenotypic characteristics to skin cancer dev