Optimal bidding strategies of an electric vehicle aggregator in an electricity market

In recent years Electric Vehicles (EVs) are getting more importance as future transport systems, due to the increase of the concerns relevant to the greenhouse gases emission and the use fossil fuel. The management of the charging and discharging process of EVs could provide new business model for participating in the electricity markets. Moreover, vehicle to grid systems have the potential of increasing utility system flexibility. This thesis develops some models for the optimal integration of the EVs in the electricity market. In particular, the thesis focuses on the optimal bidding strategy of an EV aggregator participating to both the day ahead market and the secondary reserve market. The aggregator profit is maximized taking into account the energy balance equation, as well as the technical constraints of energy settlement, power supply and state of charge of the EVs. The results obtained by using the GAMS (General Algebraic Modelling System) environment are presented and discussed.

Design and Off-design modelling of advanced Power-to-Gas energy storage systems

Power-to-Gas storage systems have the potential to address grid-stability issues that arise when an increasing share of power is generated from sources that have a highly variable output. Although the proof-of-concept of these has been promising, the behaviour of the processes in off-design conditions is not easily predictable. The primary aim of this PhD project was to evaluate the performance of an original Power-to-Gas system, made up of innovative components. To achieve this, a numerical model has been developed to simulate the characteristics and the behaviour of the several components when the whole system is coupled with a renewable source. The developed model has been applied to a large variety of scenarios, evaluating the performance of the considered process and exploiting a limited amount of experimental data. The model has been then used to compare different Power-to-Gas concepts, in a real scenario of functioning. Several goals have been achieved. In the concept phase, the possibility to thermally integrate the high temperature components has been demonstrated. Then, the parameters that affect the energy performance of a Power-to-Gas system coupled with a renewable source have been identified, providing general recommendations on the design of hybrid systems; these parameters are: 1) the ratio between the storage system size and the renewable generator size; 2) the type of coupled renewable source; 3) the related production profile. Finally, from the results of the comparative analysis, it is highlighted that configurations with a highly oversized renewable source with respect to the storage system show the maximum achievable profit.

High performance and energy-efficient instruction cache design and optimisation for ultra-low-power multi-core clusters

High Energy efficiency and high performance are the key regiments for Internet of Things (IoT) end-nodes. Exploiting cluster of multiple programmable processors has recently emerged as a suitable solution to address this challenge. However, one of the main bottlenecks for multi-core architectures is the instruction cache. While private caches fall into data replication and wasting area, fully shared caches lack scalability and form a bottleneck for the operating frequency. Hence we propose a hybrid solution where a larger shared cache (L1.5) is shared by multiple cores connected through a low-latency interconnect to small private caches (L1). However, it is still limited by large capacity miss with a small L1. Thus, we propose a sequential prefetch from L1 to L1.5 to improve the performance with little area overhead. Moreover, to cut the critical path for better timing, we optimized the core instruction fetch stage with non-blocking transfer by adopting a 4 x 32-bit ring buffer FIFO and adding a pipeline for the conditional branch. We present a detailed comparison of different instruction cache architectures' performance and energy efficiency recently proposed for Parallel Ultra-Low-Power clusters. On average, when executing a set of real-life IoT applications, our two-level cache improves the performance by up to 20% and loses 7% energy efficiency with respect to the private cache. Compared to a shared cache system, it improves performance by up to 17% and keeps the same energy efficiency. In the end, up to 20% timing (maximum frequency) improvement and software control enable the two-level instruction cache with prefetch adapt to various battery-powered usage cases to balance high performance and energy efficiency.

A novel hybrid thermochemical-biological refinery integrated with power-to-X approach for obtaining biopolymers

In this study, a novel hybrid thermochemical-biological refinery integrated with power-to-x approach was developed for obtaining biopolymers (namely polyhydroxyalkanoates, PHA). Within this concept, a trilogy process schema comprising of, (i) thermochemical conversion via integrated pyrolysis-gasification technologies, (ii) anaerobic fermentation of the bioavailable products obtained through either thermochemistry or water-electrolysis for volatile fatty acids (VFA) production, (iii) and VFA-to-PHA bioconversion via an original microaerophilic-aerobic process was developed. During the first stage of proposed biorefinery where lignocellulosic (wooden) biomass was converted into, theoretically fermentable products (i.e. bioavailables) which were defined as syngas and water-soluble fraction of pyrolytic liquid (WS); biochar as a biocatalyst material; and a dense-oil as a liquid fuel. Within integrated pyrolysis - gasification process, biomass was efficiently converted into fermentable intermediates representing up to 66% of biomass chemical energy content in chemical oxygen demand (COD) basis. In the secondary stage, namely anaerobic fermentation for obtaining VFA rich streams, three different downstream process were investigated. First fermentation test was acidogenic bioconversion of WS materials obtained through pyrolysis of biomass within an original biochar-packed bioreactor, it was sustained up to 0.6 gCOD/L-day volumetric productivity (VP). Second, C1 rich syngas materials as the gaseous fraction of pyrolysis-gasification stage, was fermented within a novel char-based biofilm sparger reactor (CBSR), where up to 9.8 gCOD/L-day VP was detected. Third was homoacetogenic bioconversion within the innovative power-to-x pathway for obtaining commodities via renewable energy sources. More specifically, water-electrolysis derived H2 and CO2 as a primary greenhouse gas was successfully bio-utilized by anaerobic mixed cultures into VFA within CBSR system (VP: 18.2 gCOD/L-day). In the last stage of the developed biorefinery schema, VFA is converted into biopolymers within a new continuous microaerophilic-aerobic microplant, where up to 60% of PHA containing sludges was obtained.

High performance battery chargers for full electric and hybrid cars

This Doctoral Thesis aims to study and develop advanced and high-efficient battery chargers for full electric and plug-in electric cars. The document is strictly industry-oriented and relies on automotive standards and regulations. In the first part a general overview about wireless power transfer battery chargers (WPTBCs) and a deep investigation about international standards are carried out. Then, due to the highly increasing attention given to WPTBCs by the automotive industry and considering the need of minimizing weight, size and number of components this work focuses on those architectures that realize a single stage for on-board power conversion avoiding the implementation of the DC/DC converter upstream the battery. Based on the results of the state-of-the-art, the following sections focus on two stages of the architecture: the resonant tank and the primary DC/AC inverter. To reach the maximum transfer efficiency while minimizing weight and size of the vehicle assembly a coordinated system level design procedure for resonant tank along with an innovative control algorithm for the DC/AC primary inverter is proposed. The presented solutions are generalized and adapted for the best trade-off topologies of compensation networks: Series-Series and Series-Parallel. To assess the effectiveness of the above-mentioned objectives, validation and testing are performed through a simulation environment, while experimental test benches are carried out by the collaboration of Delft University of Technology (TU Delft).

Power flow forecasting and smart charging for electric vehicle charging systems with photovoltaic integration

An essential role in the global energy transition is attributed to Electric Vehicles (EVs) the energy for EV traction can be generated by renewable energy sources (RES), also at a local level through distributed power plants, such as photovoltaic (PV) systems. However, EV integration with electrical systems might not be straightforward. The intermittent RES, combined with the high and uncontrolled aggregate EV charging, require an evolution toward new planning and paradigms of energy systems. In this context, this work aims to provide a practical solution for EV charging integration in electrical systems with RES. A method for predicting the power required by an EV fleet at the charging hub (CH) is developed in this thesis. The proposed forecasting method considers the main parameters on which charging demand depends. The results of the EV charging forecasting method are deeply analyzed under different scenarios. To reduce the EV load intermittency, methods for managing the charging power of EVs are proposed. The main target was to provide Charging Management Systems (CMS) that modulate EV charging to optimize specific performance indicators such as system self-consumption, peak load reduction, and PV exploitation. Controlling the EV charging power to achieve specific optimization goals is also known as Smart Charging (SC). The proposed techniques are applied to real-world scenarios demonstrating performance improvements in using SC strategies. A viable alternative to maximize integration with intermittent RES generation is the integration of energy storage. Battery Energy Storage Systems (BESS) may be a buffer between peak load and RES production. A sizing algorithm for PV+BESS integration in EV charging hubs is provided. The sizing optimization aims to optimize the system's energy and economic performance. The results provide an overview of the optimal size that the PV+BESS plant should have to improve whole system performance in different scenarios.

Hybrid ground-aerial mesh networks for IoT monitoring applications: network design and software platform development

The Internet of Things (IoT) has grown rapidly in recent years, leading to an increased need for efficient and secure communication between connected devices. Wireless Sensor Networks (WSNs) are composed of small, low-power devices that are capable of sensing and exchanging data, and are often used in IoT applications. In addition, Mesh WSNs involve intermediate nodes forwarding data to ensure more robust communication. The integration of Unmanned Aerial Vehicles (UAVs) in Mesh WSNs has emerged as a promising solution for increasing the effectiveness of data collection, as UAVs can act as mobile relays, providing extended communication range and reducing energy consumption. However, the integration of UAVs and Mesh WSNs still poses new challenges, such as the design of efficient control and communication strategies. This thesis explores the networking capabilities of WSNs and investigates how the integration of UAVs can enhance their performance. The research focuses on three main objectives: (1) Ground Wireless Mesh Sensor Networks, (2) Aerial Wireless Mesh Sensor Networks, and (3) Ground/Aerial WMSN integration. For the first objective, we investigate the use of the Bluetooth Mesh standard for IoT monitoring in different environments. The second objective focuses on deploying aerial nodes to maximize data collection effectiveness and QoS of UAV-to-UAV links while maintaining the aerial mesh connectivity. The third objective investigates hybrid WMSN scenarios with air-to-ground communication links. One of the main contribution of the thesis consists in the design and implementation of a software framework called "Uhura", which enables the creation of Hybrid Wireless Mesh Sensor Networks and abstracts and handles multiple M2M communication stacks on both ground and aerial links. The operations of Uhura have been validated through simulations and small-scale testbeds involving ground and aerial devices.

Thermal and air quality modeling of an electric vehicle cabin with low-cost sensors and reinforcement learning

The design process of any electric vehicle system has to be oriented towards the best energy efficiency, together with the constraint of maintaining comfort in the vehicle cabin. Main aim of this study is to research the best thermal management solution in terms of HVAC efficiency without compromising occupant’s comfort and internal air quality. An Arduino controlled Low Cost System of Sensors was developed and compared against reference instrumentation (average R-squared of 0.92) and then used to characterise the vehicle cabin in real parking and driving conditions trials. Data on the energy use of the HVAC was retrieved from the car On-Board Diagnostic port. Energy savings using recirculation can reach 30 %, but pollutants concentration in the cabin builds up in this operating mode. Moreover, the temperature profile appeared strongly nonuniform with air temperature differences up to 10° C. Optimisation methods often require a high number of runs to find the optimal configuration of the system. Fast models proved to be beneficial for these task, while CFD-1D model are usually slower despite the higher level of detail provided. In this work, the collected dataset was used to train a fast ML model of both cabin and HVAC using linear regression. Average scaled RMSE over all trials is 0.4 %, while computation time is 0.0077 ms for each second of simulated time on a laptop computer. Finally, a reinforcement learning environment was built in OpenAI and Stable-Baselines3 using the built-in Proximal Policy Optimisation algorithm to update the policy and seek for the best compromise between comfort, air quality and energy reward terms. The learning curves show an oscillating behaviour overall, with only 2 experiments behaving as expected even if too slow. This result leaves large room for improvement, ranging from the reward function engineering to the expansion of the ML model.

Optimal energy management of smart households with electric vehicles, energy storage and distributed generation

In this thesis, the optimal operation of a neighborhood of smart households in terms of minimizing the total energy cost is analyzed. Each household may comprise several assets such as electric vehicles, controllable appliances, energy storage and distributed generation. Bi-directional power flow is considered for each household . Apart from the distributed generation unit, technological options such as vehicle-to-home and vehicle-to-grid are available to provide energy to cover self-consumption needs and to export excessive energy to other households, respectively.

Beam-energy Dependence Of The Directed Flow Of Protons, Antiprotons, And Pions In Au+au Collisions.

Rapidity-odd directed flow (v1) measurements for charged pions, protons, and antiprotons near midrapidity (y=0) are reported in sNN=7.7, 11.5, 19.6, 27, 39, 62.4, and 200 GeV Au+Au collisions as recorded by the STAR detector at the Relativistic Heavy Ion Collider. At intermediate impact parameters, the proton and net-proton slope parameter dv1/dy|y=0 shows a minimum between 11.5 and 19.6 GeV. In addition, the net-proton dv1/dy|y=0 changes sign twice between 7.7 and 39 GeV. The proton and net-proton results qualitatively resemble predictions of a hydrodynamic model with a first-order phase transition from hadronic matter to deconfined matter, and differ from hadronic transport calculations.

Design, Synthesis And Biological Evaluation Of Hybrid Bioisoster Derivatives Of N-acylhydrazone And Furoxan Groups With Potential And Selective Anti-trypanosoma Cruzi Activity.

Hybrid bioisoster derivatives from N-acylhydrazones and furoxan groups were designed with the objective of obtaining at least a dual mechanism of action: cruzain inhibition and nitric oxide (NO) releasing activity. Fifteen designed compounds were synthesized varying the substitution in N-acylhydrazone and in furoxan group as well. They had its anti-Trypanosoma cruzi activity in amastigotes forms, NO releasing potential and inhibitory cruzain activity evaluated. The two most active compounds (6, 14) both in the parasite amastigotes and in the enzyme contain the nitro group in para position of the aromatic ring. The permeability screening in Caco-2 cell and cytotoxicity assay in human cells were performed for those most active compounds and both showed to be less cytotoxic than the reference drug, benznidazole. Compound 6 was the most promising, since besides activity it showed good permeability and selectivity index, higher than the reference drug. Thereby the compound 6 was considered as a possible candidate for additional studies.

Hypothalamic Inflammation And The Central Nervous System Control Of Energy Homeostasis.

The control of energy homeostasis relies on robust neuronal circuits that regulate food intake and energy expenditure. Although the physiology of these circuits is well understood, the molecular and cellular response of this program to chronic diseases is still largely unclear. Hypothalamic inflammation has emerged as a major driver of energy homeostasis dysfunction in both obesity and anorexia. Importantly, this inflammation disrupts the action of metabolic signals promoting anabolism or supporting catabolism. In this review, we address the evidence that favors hypothalamic inflammation as a factor that resets energy homeostasis in pathological states.

Exposure To Bisphenol-a During Pregnancy Partially Mimics The Effects Of A High-fat Diet Altering Glucose Homeostasis And Gene Expression In Adult Male Mice.

Bisphenol-A (BPA) is one of the most widespread EDCs used as a base compound in the manufacture of polycarbonate plastics. The aim of our research has been to study how the exposure to BPA during pregnancy affects weight, glucose homeostasis, pancreatic β-cell function and gene expression in the major peripheral organs that control energy flux: white adipose tissue (WAT), the liver and skeletal muscle, in male offspring 17 and 28 weeks old. Pregnant mice were treated with a subcutaneous injection of 10 µg/kg/day of BPA or a vehicle from day 9 to 16 of pregnancy. One month old offspring were divided into four different groups: vehicle treated mice that ate a normal chow diet (Control group); BPA treated mice that also ate a normal chow diet (BPA); vehicle treated animals that had a high fat diet (HFD) and BPA treated animals that were fed HFD (HFD-BPA). The BPA group started to gain weight at 18 weeks old and caught up to the HFD group before week 28. The BPA group as well as the HFD and HFD-BPA ones presented fasting hyperglycemia, glucose intolerance and high levels of non-esterified fatty acids (NEFA) in plasma compared with the Control one. Glucose stimulated insulin release was disrupted, particularly in the HFD-BPA group. In WAT, the mRNA expression of the genes involved in fatty acid metabolism, Srebpc1, Pparα and Cpt1β was decreased by BPA to the same extent as with the HFD treatment. BPA treatment upregulated Pparγ and Prkaa1 genes in the liver; yet it diminished the expression of Cd36. Hepatic triglyceride levels were increased in all groups compared to control. In conclusion, male offspring from BPA-treated mothers presented symptoms of diabesity. This term refers to a form of diabetes which typically develops in later life and is associated with obesity.

Low-density Three-dimensional Foam Using Self-reinforced Hybrid Two-dimensional Atomic Layers.

Low-density nanostructured foams are often limited in applications due to their low mechanical and thermal stabilities. Here we report an approach of building the structural units of three-dimensional (3D) foams using hybrid two-dimensional (2D) atomic layers made of stacked graphene oxide layers reinforced with conformal hexagonal boron nitride (h-BN) platelets. The ultra-low density (1/400 times density of graphite) 3D porous structures are scalably synthesized using solution processing method. A layered 3D foam structure forms due to presence of h-BN and significant improvements in the mechanical properties are observed for the hybrid foam structures, over a range of temperatures, compared with pristine graphene oxide or reduced graphene oxide foams. It is found that domains of h-BN layers on the graphene oxide framework help to reinforce the 2D structural units, providing the observed improvement in mechanical integrity of the 3D foam structure.

Beam-energy Dependence Of Charge Separation Along The Magnetic Field In Au+au Collisions At Rhic.

Local parity-odd domains are theorized to form inside a quark-gluon plasma which has been produced in high-energy heavy-ion collisions. The local parity-odd domains manifest themselves as charge separation along the magnetic field axis via the chiral magnetic effect. The experimental observation of charge separation has previously been reported for heavy-ion collisions at the top RHIC energies. In this Letter, we present the results of the beam-energy dependence of the charge correlations in Au+Au collisions at midrapidity for center-of-mass energies of 7.7, 11.5, 19.6, 27, 39, and 62.4 GeV from the STAR experiment. After background subtraction, the signal gradually reduces with decreased beam energy and tends to vanish by 7.7 GeV. This implies the dominance of hadronic interactions over partonic ones at lower collision energies.