Claudia Pop

Blockchain Based Decentralized Management of Demand Response Programs in Smart Energy Grids

In this paper, we investigate the use of decentralized blockchain mechanisms for delivering transparent, secure, reliable, and timely energy flexibility, under the form of adaptation of energy demand profiles of Distributed Energy Prosumers, to all the stakeholders involved in the flexibility markets (Distribution System Operators primarily, retailers, aggregators, etc.). In our approach, a blockchain based distributed ledger stores in a tamper proof manner the energy prosumption information collected from Internet of Things smart metering devices, while self-enforcing smart contracts programmatically define the expected energy flexibility at the level of each prosumer, the associated rewards or penalties, and the rules for balancing the energy demand with the energy production at grid level. Consensus based validation will be used for demand response programs validation and to activate the appropriate financial settlement for the flexibility providers. The approach was validated using a prototype implemented in an Ethereum platform using energy consumption and production traces of several buildings from literature data sets. The results show that our blockchain based distributed demand side management can be used for matching energy demand and production at smart grid level, the demand response signal being followed with high accuracy, while the amount of energy flexibility needed for convergence is reduced.

In-wheel motor with integrated magnetic gear for extended speed applications

This paper concerns a light electric vehicle (EV) application, based on magnetic transmission. This magnetic transmission is capable to offer extended speed range and it contains an in-wheel motor, which integrates a magnetic gear. In our case, the magnetic gear is multiplying the in-wheel motor speed with a factor of 3.4, offering the possibility to have very high speed for our light EV, without the necessity to supply the electric motor at high frequencies; it means that an efficiency improvement is expected for such magnetic gear integrated topology. This study is carried out by means of numerical analysis and the performances of the in-wheel motor with integrated magnetic gear are evaluated with the finite element method.

Tools for mapping ontologies to relational databases: A comparative evaluation

This paper presents an analysis of the state of the art solutions for mapping a relational database and an ontology by adding reasoning capabilities and offering the possibility to query the inferred information. We analyzed four approaches: Jena with D2RQ, Jena with R2RML, KAON2 and OWL API. In order to highlight the differences between the four approaches, we used a nutrition diagnostics related ontology for the definition of the concepts and of the rules, and a relational database for the storage of the individuals. As performance evaluation, we focused on the time required to map the relational database to the ontology, and the time required to retrieve the information that is inferred about the diagnostics of a number of people. The obtained results show that the best performance in both cases is given by KAON2.

Exploiting data centres energy flexibility in smart cities: Business scenarios

Abstract In this paper, we have considered Data Centres (DCs) as computing facilities functioning at the crossroad of electrical, thermal and data networks and have defined optimisation techniques to exploit their energy flexibility. Our methods are leveraging on non-electrical cooling devices such as thermal storage and heat pumps for waste heat reuse and IT workload execution time shifting and spatial relocation in federated DCs. To trade energy flexibility we have defined an Energy Marketplace which allows DCs to act as active energy players integrated into the smart grid, contributing to smart city-level efficiency goals. Reinforcing this vision, we have proposed four innovative business scenarios that enable next generation smart Net-zero Energy DCs acting as energy prosumers at the interface with smart energy grids within smart city environments. Simulation experiments are conducted to determine the DCs potential electrical and thermal energy flexibility in meeting various network level goals and to assess the financial viability of the defined business scenarios. The results show that DCs have a significant amount of energy flexibility which may be shifted and traded to interested stakeholders thus allowing them to gain new revenue streams not foreseen before.

DC Thermal Energy Flexibility Model for Waste Heat Reuse in Nearby Neighborhoods

In this paper we address the Data Centers (DCs) energy efficiency problem from a thermal perspective by considering them as large producers of waste heat integrated with smart energy infrastructures and utilities, through which they can effectively exploit their thermal flexibility for nearby neighborhoods heating. We provide a mathematical formalism for modeling the thermodynamics of the processes within DCs equipped with heat reuse technology and proactive DCs operation control mechanisms that allow them to adapt their thermal response profile to meet various levels of hot water demand. Numerical simulation-based experiments are shown considering the hardware systems characteristics and operation of one server room from Engineering Pont Saint Martin (PSM) DC. The results show the potential of using workload delay tolerant time shifting and server room pre-cooling as flexibility mechanisms for adapting the DC thermal energy profile to meet the demand.

M2O: A library for using ontologies in software engineering

In this paper we propose an extensible framework over Jena and OWL API that maps complex Java data models onto semantic models based on some custom annotations in order to benefit from the advantages of ontologies in software engineering. Furthermore, it facilitates the implementation of basic CRUD operations for the domain classes and objects, also allowing the definition of new custom operations. We have performed tests on the Stanford Wine ontology, obtaining a code complexity reduction of up to 85% compared to the classical approaches using Jena or OWL API without noticeable performance reduction.

Optimizing the Data Center Energy Consumption Using a Particle Swarm Optimization-Based Approach

This paper presents a Particle Swarm Optimization-based method for optimizing the energy consumption in data centers. A particle position is mapped on a data center configuration (i.e. allocation of virtual machines on the data center’s servers) which is evaluated using a fitness function that considers the energy consumed by the servers’ hardware resources and by the data center’s cooling system as evaluation criteria. The Particle Swarm Optimization-based method is triggered each time a workload arrives to be accommodated on the data center’s servers. The proposed method has been integrated in the CloudSim framework and has been evaluated on randomly generated logs.

Optimizing Data Centres Operation to Provide Ancillary Services On-Demand

In this paper a methodology for optimizing Data Centres (DCs) operation allowing them to provide various types of Ancillary Services on-demand is proposed. Energy flexibility models have been defined for hardware devices inside DCs aiming at optimizing energy demand profile by means of load time shifting, alternative usage of non-electrical cooling devices (e.g. thermal storage) or charging/discharging the electrical storage devices. As result DCs are able to shape their energy demand to provide additional load following reserve for large un-forecasted wind ramps, shed or shift energy demand over time to avoid an coincidental peak load and feed back in the grid the energy produced by turning on their backup fossil fuelled generators to maintain (local) reactive power balance under normal conditions. Experiments via numerical simulations based on real world traces of DC operation highlight the methodology potential for optimizing DC energy consumption to provide Ancillary Services.

Spark-Based Classification Algorithms for Daily Living Activities

Dementia is an incurable disease that affects a large part of the population of elders and more than 21% of the elders suffering from dementia are exposed to polypharmacy. Moreover, dementia is very correlated with diabetes and high blood pressure. The medication adherence becomes a big challenge that can be approached by analyzing the daily activities of the patients and taking preventive or corrective measures. The weakest link in the pharmacy chain tends to be the patients, especially the patients with cognitive impairments. In this paper we analyze the feasibility of four classification algorithms from the machine learning library of Apache Spark for the prediction of the daily behavior pattern of the patients that suffer from dementia. The algorithms are tested on two datasets from literature that contain data collected from sensors. The best results are obtained when the Random Forest classification algorithm is applied.

Self-adaptive task scheduler for dynamic allocation in energy efficient data centers

This paper tackles the issue of Data Centers (DCs) energy efficiency by proposing a Self-Adaptive Task Scheduler that proactively allocates incoming tasks to physical servers avoiding the need of consolidation and implicitly avoiding task migration from one server to another. The Self-Adaptive Task Scheduler is based on a MAPE architecture. The monitoring phase aggregates data about resource utilization, the analysis phase uses neural networks to predict future incoming tasks, while the planning stage consists of a genetic algorithm capable of solving a simplified mathematical programming representation of the well-known Dynamic Server Allocation Problem (DSAP). Finally, the execution stage consists of a real-time loop that matches real time incoming tasks to the predicted ones and allocates them according to the plan computed by the planning stage. A simulator was implemented and the proposed solution was compared against the well-known First-Fit-Decreasing (FFD) algorithm enhanced with a periodically-triggered consolidation algorithm on traces from a real Google datacenter, showing energy a reduction of up to 11% in the simulated scenarios.