Energy Technology and Management

The civilization of present age is predominantly dependent on energy resources and their utilization. Almost every human activity in today's life needs one or other form of energy. As world's energy resources are not unlimited, it is extremely important to use energy efficiently. Both energy related technological issues and policy and planning paradigms are highly needed to effectively exploit and utilize energy resources. This book covers topics, ranging from technology to policy, relevant to efficient energy utilization. Those academic and practitioners who have background knowledge of energy issues can take benefit from this book

iURBAN

iURBAN: Intelligent Urban Energy Tool introduces an urban energy tool integrating different ICT energy management systems (both hardware and software) in two European cities, providing useful data to a novel decision support system that makes available the necessary parameters for the generation and further operation of associated business models. The business models contribute at a global level to efficiently manage and distribute the energy produced and consumed at a local level (city or neighbourhood), incorporating behavioural aspects of the users into the software platform and in general prosumers. iURBAN integrates a smart Decision Support System (smartDSS) that collects real-time or near real-time data, aggregates, analyses and suggest actions of energy consumption and production from different buildings, renewable energy production resources, combined heat and power plants, electric vehicles (EV) charge stations, storage systems, sensors and actuators. The consumption and production data is collected via a heterogeneous data communication protocols and networks. The iURBAN smartDSS through a Local Decision Support System allows the citizens to analyse the consumptions and productions that they are generating, receive information about CO2 savings, advises in demand response and the possibility to participate actively in the energy market. Whilst, through a Centralised Decision Support System allow to utilities, ESCOs, municipalities or other authorised third parties to: Get a continuous snapshot of city energy consumption and productionManage energy consumption and productionForecasting of energy consumptionPlanning of new energy "producers" for the future needs of the cityVisualise, analyse and take decisions of all the end points that are consuming or producing energy in a city level, permitting them to forecast and planning renewable power generation available in the city

iURBAN

iURBAN: Intelligent Urban Energy Tool introduces an urban energy tool integrating different ICT energy management systems (both hardware and software) in two European cities, providing useful data to a novel decision support system that makes available the necessary parameters for the generation and further operation of associated business models. The business models contribute at a global level to efficiently manage and distribute the energy produced and consumed at a local level (city or neighbourhood), incorporating behavioural aspects of the users into the software platform and in general prosumers. iURBAN integrates a smart Decision Support System (smartDSS) that collects real-time or near real-time data, aggregates, analyses and suggest actions of energy consumption and production from different buildings, renewable energy production resources, combined heat and power plants, electric vehicles (EV) charge stations, storage systems, sensors and actuators. The consumption and production data is collected via a heterogeneous data communication protocols and networks. The iURBAN smartDSS through a Local Decision Support System allows the citizens to analyse the consumptions and productions that they are generating, receive information about CO2 savings, advises in demand response and the possibility to participate actively in the energy market. Whilst, through a Centralised Decision Support System allow to utilities, ESCOs, municipalities or other authorised third parties to: Get a continuous snapshot of city energy consumption and productionManage energy consumption and productionForecasting of energy consumptionPlanning of new energy "producers" for the future needs of the cityVisualise, analyse and take decisions of all the end points that are consuming or producing energy in a city level, permitting them to forecast and planning renewable power generation available in the city

Assessing Quantum Computing Performance for Energy Optimization in a Prosumer Community

The efficient management of energy communities relies on the solution of the "prosumer problem", i.e., the problem of scheduling the household loads on the basis of the user needs, the electricity prices, and the availability of local renewable energy, with the aim of reducing costs and energy waste. Quantum computers can offer a significant breakthrough in treating this problem thanks to the intrinsic parallel nature of quantum operations. The most promising approach is to devise variational hybrid algorithms, in which quantum computation is driven by parameters that are optimized classically, in a cycle that aims at finding the best solution with a significant speed-up with respect to classical approaches. This paper provides a reformulation of the prosumer problem, allowing to address it with a hybrid quantum algorithm, namely, Quantum Approximate Optimization Algorithm (QAOA), and with a recent variant, the Recursive QAOA. We report on an extensive set of experiments, on simulators and real quantum hardware, for different problem sizes. Results are encouraging in that Recursive QAOA is able, for problems involving up to 10 qubits, to provide optimal and admissible solutions with good probabilities, while the computation time is nearly independent of the system sizeComment: 14 pages, 13 figures. IEEE Transactions on Smart Grid (2023

Lifetime centric load balancing mechanism in wireless sensor network based IoT environment

Wireless sensor network (WSN) is a vital form of the underlying technology of the internet of things (IoT); WSN comprises several energy-constrained sensor nodes to monitor various physical parameters. Moreover, due to the energy constraint, load balancing plays a vital role considering the wireless sensor network as battery power. Although several clustering algorithms have been proposed for providing energy efficiency, there are chances of uneven load balancing and this causes the reduction in network lifetime as there exists inequality within the network. These scenarios occur due to the short lifetime of the cluster head. These cluster head (CH) are prime responsible for all the activity as it is also responsible for intra-cluster and inter-cluster communications. In this research work, a mechanism named lifetime centric load balancing mechanism (LCLBM) is developed that focuses on CH-selection, network design, and optimal CH distribution. Furthermore, under LCLBM, assistant cluster head (ACH) for balancing the load is developed. LCLBM is evaluated by considering the important metrics, such as energy consumption, communication overhead, number of failed nodes, and one-way delay. Further, evaluation is carried out by comparing with ES-Leach method, through the comparative analysis it is observed that the proposed model outperforms the existing model

On Energy Efficient Computing Platforms

In accordance with the Moore's law, the increasing number of on-chip integrated transistors has enabled modern computing platforms with not only higher processing power but also more affordable prices. As a result, these platforms, including portable devices, work stations and data centres, are becoming an inevitable part of the human society. However, with the demand for portability and raising cost of power, energy efficiency has emerged to be a major concern for modern computing platforms. As the complexity of on-chip systems increases, Network-on-Chip (NoC) has been proved as an efficient communication architecture which can further improve system performances and scalability while reducing the design cost. Therefore, in this thesis, we study and propose energy optimization approaches based on NoC architecture, with special focuses on the following aspects. As the architectural trend of future computing platforms, 3D systems have many bene ts including higher integration density, smaller footprint, heterogeneous integration, etc. Moreover, 3D technology can signi cantly improve the network communication and effectively avoid long wirings, and therefore, provide higher system performance and energy efficiency. With the dynamic nature of on-chip communication in large scale NoC based systems, run-time system optimization is of crucial importance in order to achieve higher system reliability and essentially energy efficiency. In this thesis, we propose an agent based system design approach where agents are on-chip components which monitor and control system parameters such as supply voltage, operating frequency, etc. With this approach, we have analysed the implementation alternatives for dynamic voltage and frequency scaling and power gating techniques at different granularity, which reduce both dynamic and leakage energy consumption. Topologies, being one of the key factors for NoCs, are also explored for energy saving purpose. A Honeycomb NoC architecture is proposed in this thesis with turn-model based deadlock-free routing algorithms. Our analysis and simulation based evaluation show that Honeycomb NoCs outperform their Mesh based counterparts in terms of network cost, system performance as well as energy efficiency.Siirretty Doriast

Integrating Pro-Environmental Behavior with Transportation Network Modeling: User and System Level Strategies, Implementation, and Evaluation

Personal transport is a leading contributor to fossil fuel consumption and greenhouse (GHG) emissions in the U.S. The U.S. Energy Information Administration (EIA) reports that light-duty vehicles (LDV) are responsible for 61\% of all transportation related energy consumption in 2012, which is equivalent to 8.4 million barrels of oil (fossil fuel) per day. The carbon content in fossil fuels is the primary source of GHG emissions that links to the challenge associated with climate change. Evidently, it is high time to develop actionable and innovative strategies to reduce fuel consumption and GHG emissions from the road transportation networks. This dissertation integrates the broader goal of minimizing energy and emissions into the transportation planning process using novel systems modeling approaches. This research aims to find, investigate, and evaluate strategies that minimize carbon-based fuel consumption and emissions for a transportation network. We propose user and system level strategies that can influence travel decisions and can reinforce pro-environmental attitudes of road users. Further, we develop strategies that system operators can implement to optimize traffic operations with emissions minimization goal. To complete the framework we develop an integrated traffic-emissions (EPA-MOVES) simulation framework that can assess the effectiveness of the strategies with computational efficiency and reasonable accuracy. ^ The dissertation begins with exploring the trade-off between emissions and travel time in context of daily travel decisions and its heterogeneous nature. Data are collected from a web-based survey and the trade-off values indicating the average additional travel minutes a person is willing to consider for reducing a lb. of GHG emissions are estimated from random parameter models. Results indicate that different trade-off values for male and female groups. Further, participants from high-income households are found to have higher trade-off values compared with other groups. Next, we propose personal mobility carbon allowance (PMCA) scheme to reduce emissions from personal travel. PMCA is a market-based scheme that allocates carbon credits to users at no cost based on the emissions reduction goal of the system. Users can spend carbon credits for travel and a market place exists where users can buy or sell credits. This dissertation addresses two primary dimensions: the change in travel behavior of the users and the impact at network level in terms of travel time and emissions when PMCA is implemented. To understand this process, a real-time experimental game tool is developed where players are asked to make travel decisions within the carbon budget set by PMCA and they are allowed to trade carbon credits in a market modeled as a double auction game. Random parameter models are estimated to examine the impact of PMCA on short-term travel decisions. Further, to assess the impact at system level, a multi-class dynamic user equilibrium model is formulated that captures the travel behavior under PMCA scheme. The equivalent variational inequality problem is solved using projection method. Results indicate that PMCA scheme is able to reduce GHG emissions from transportation networks. Individuals with high value of travel time (VOTT) are less sensitive to PMCA scheme in context of work trips. High and medium income users are more likely to have non-work trips with lower carbon cost (higher travel time) to save carbon credits for work trips. ^ Next, we focus on the strategies from the perspectives of system operators in transportation networks. Learning based signal control schemes are developed that can reduce emissions from signalized urban networks. The algorithms are implemented and tested in VISSIM micro simulator. Finally, an integrated emissions-traffic simulator framework is outlined that can be used to evaluate the effectiveness of the strategies. The integrated framework uses MOVES2010b as the emissions simulator. To estimate the emissions efficiently we propose a hierarchical clustering technique with dynamic time warping similarity measures (HC-DTW) to find the link driving schedules for MOVES2010b. Test results using the data from a five-intersection corridor show that HC-DTW technique can significantly reduce emissions estimation time without compromising the accuracy. The benefits are found to be most significant when the level of congestion variation is high. ^ In addition to finding novel strategies for reducing emissions from transportation networks, this dissertation has broader impacts on behavior based energy policy design and transportation network modeling research. The trade-off values can be a useful indicator to identify which policies are most effective to reinforce pro-environmental travel choices. For instance, the model can estimate the distribution of trade-off between emissions and travel time, and provide insights on the effectiveness of policies for New York City if we are able to collect data to construct a representative sample. The probability of route choice decisions vary across population groups and trip contexts. The probability as a function of travel and demographic attributes can be used as behavior rules for agents in an agent-based traffic simulation. Finally, the dynamic user equilibrium based network model provides a general framework for energy policies such carbon tax, tradable permit, and emissions credits system

A COGNITIVE ARCHITECTURE FOR AMBIENT INTELLIGENCE

L’Ambient Intelligence (AmI) è caratterizzata dall’uso di sistemi pervasivi per monitorare l’ambiente e modificarlo secondo le esigenze degli utenti e rispettando vincoli definiti globalmente. Questi sistemi non possono prescindere da requisiti come la scalabilità e la trasparenza per l’utente. Una tecnologia che consente di raggiungere questi obiettivi è rappresentata dalle reti di sensori wireless (WSN), caratterizzate da bassi costi e bassa intrusività. Tuttavia, sebbene in grado di effettuare elaborazioni a bordo dei singoli nodi, le WSN non hanno da sole le capacità di elaborazione necessarie a supportare un sistema intelligente; d’altra parte senza questa attività di pre-elaborazione la mole di dati sensoriali può facilmente sopraffare un sistema centralizzato con un’eccessiva quantità di dettagli superflui. Questo lavoro presenta un’architettura cognitiva in grado di percepire e controllare l’ambiente di cui fa parte, basata su un nuovo approccio per l’estrazione di conoscenza a partire dai dati grezzi, attraverso livelli crescenti di astrazione. Le WSN sono utilizzate come strumento sensoriale pervasivo, le cui capacità computazionali vengono utilizzate per pre-elaborare i dati rilevati, in modo da consentire ad un sistema centralizzato intelligente di effettuare ragionamenti di alto livello. L’architettura proposta è stata utilizzata per sviluppare un testbed dotato degli strumenti hardware e software necessari allo sviluppo e alla gestione di applicazioni di AmI basate su WSN, il cui obiettivo principale sia il risparmio energetico. Per fare in modo che le applicazioni di AmI siano in grado di comunicare con il mondo esterno in maniera affidabile, per richiedere servizi ad agenti esterni, l’architettura è stata arricchita con un protocollo di gestione distribuita della reputazione. È stata inoltre sviluppata un’applicazione di esempio che sfrutta le caratteristiche del testbed, con l’obiettivo di controllare la temperatura in un ambiente lavorativo. Quest’applicazione rileva la presenza dell’utente attraverso un modulo per la fusione di dati multi-sensoriali basato su reti bayesiane, e sfrutta questa informazione in un controllore fuzzy multi-obiettivo che controlla gli attuatori sulla base delle preferenze dell’utente e del risparmio energetico.Ambient Intelligence (AmI) systems are characterized by the use of pervasive equipments for monitoring and modifying the environment according to users’ needs, and to globally defined constraints. Furthermore, such systems cannot ignore requirements about ubiquity, scalability, and transparency to the user. An enabling technology capable of accomplishing these goals is represented by Wireless Sensor Networks (WSNs), characterized by low-costs and unintrusiveness. However, although provided of in-network processing capabilities, WSNs do not exhibit processing features able to support comprehensive intelligent systems; on the other hand, without this pre-processing activities the wealth of sensory data may easily overwhelm a centralized AmI system, clogging it with superfluous details. This work proposes a cognitive architecture able to perceive, decide upon, and control the environment of which the system is part, based on a new approach to knowledge extraction from raw data, that addresses this issue at different abstraction levels. WSNs are used as the pervasive sensory tool, and their computational capabilities are exploited to remotely perform preliminary data processing. A central intelligent unit subsequently extracts higher-level concepts in order to carry on symbolic reasoning. The aim of the reasoning is to plan a sequence of actions that will lead the environment to a state as close as possible to the users’ desires, taking into account both implicit and explicit feedbacks from the users, while considering global system-driven goals, such as energy saving. The proposed conceptual architecture was exploited to develop a testbed providing the hardware and software tools for the development and management of AmI applications based on WSNs, whose main goal is energy saving for global sustainability. In order to make the AmI system able to communicate with the external world in a reliable way, when some services are required to external agents, the architecture was enriched with a distributed reputation management protocol. A sample application exploiting the testbed features was implemented for addressing temperature control in a work environment. Knowledge about the user’s presence is obtained through a multi-sensor data fusion module based on Bayesian networks, and this information is exploited by a multi-objective fuzzy controller that operates on actuators taking into account users’ preference and energy consumption constraints

Grid-Connected Renewable Energy Sources

The use of renewable energy sources (RESs) is a need of global society. This editorial, and its associated Special Issue “Grid-Connected Renewable Energy Sources”, offers a compilation of some of the recent advances in the analysis of current power systems that are composed after the high penetration of distributed generation (DG) with different RESs. The focus is on both new control configurations and on novel methodologies for the optimal placement and sizing of DG. The eleven accepted papers certainly provide a good contribution to control deployments and methodologies for the allocation and sizing of DG

Performance assessment & synergic operation of algorithmic solutions enabling opportunistic networks– D4.2

Deliverable D4.2 del projecte europeu OneFITPeer ReviewedPostprint (updated version