Marcel Antal

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.

Optimized flexibility management enacting Data Centres participation in Smart Demand Response programs

Abstract In this paper we address the problem of Data Centres (DCs) integration into the Smart Grid scenario by proposing a technique for scheduling and optimizing their operation allowing them to participate in Smart Demand Response programs. The technique is leveraging on DCs available flexible energy resources, on mechanisms for eliciting this latent flexibility and on an innovative electronic marketplace designed for trading energy flexibility and ancillary services. This will enact DCs to shape their energy demand to buy additional energy when prices are low and sell energy surplus when prices are high. At the same time DCs will be able to provide increased energy demand due to a large un-forecasted renewable energy production in their local grid, shed or shift energy demand over time to avoid a coincidental peak load, provide fast ramping power by turning on their backup fossil fuelled generators and injecting the energy surplus in the grid and finally provide reactive power regulation by changing their power factor. Numerical simulations results considering traces of an operational DC indicate the great potential of the proposed technique for supporting DCs participation in Smart Demand Response programs.

Data center optimization methodology to maximize the usage of locally produced renewable energy

In this paper we address the problem of Data Centers energy efficiency by proposing a methodology which aims at planning the Data Center operation such that the usage of locally produced renewable energy is maximized. We defined a flexibility mechanism and model for Data Centers components (electrical cooling system, IT workload, energy storage and diesel generators) leveraging on optimization actions such as load time shifting, alternative usage of non-electrical cooling devices such as the thermal storage or charging/discharging the electrical storage devices, etc. The flexibility mechanism enacts the possibility of shifting the Data Centers energy demand profile from time intervals with limited renewable energy production due to weather conditions, to time intervals when spikes of renewable energy production are predicted. We have developed a simulation environment which allows the methodology to be inlab tested and evaluated. Results are promising showing an increase of renewable energy usage of 12% due to energy consumption demand shift for following the renewable energy production levels.

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.

Cuckoo search algorithm for clustering food offers

This paper presents a method for clustering food offers based on the cuckoo search algorithm. The proposed method clusters food offers based on the similarity between their nutritional features (e.g. calcium, vitamins etc.) and/or ingredients. The similarity is evaluated by using the Sorensen-Dice coefficient. To test the clustering method proposed here, we have developed in-house a set of 800 food offers. The food offers have been generated as starting from a set of food recipes (provided in an XML standard for sharing recipes) and a database containing information about nutritional features. This database stores the nutritional features of each food type, as provided by the Agricultural Research Service of the United States Department of Agriculture. We evaluated the performance of our clustering method by using the following metrics: the Dunn Index, the Davies-Bouldin index, and the Average Item-Cluster Similarity.

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.