Towards Intelligent and Self-Evolving Network Infrastructures for Energy Management

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Socio-Economics Inspiring Self-Managed Systems and Concepts (SEISMYC) Workshop at SASO 2010, 27 September 2010, Budapest, Hungary

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Towards Intelligent and Self-Evolving Network Infrastructures for Energy Management

  1. 1. Towards Intelligent and Self-EvolvingNetwork Infrastructures for EnergyManagementBudapest 2010Matthias Baumgarten – Maurice MulvennaUniversity of UlsterSocio-Economics Inspiring Self-ManagedSystems and Concepts
  2. 2. SEISMYC – SASO 2010 – Budapest, HungaryOutlinen  The Current Grid – The Smart Gridn  Goalsn  Drivers and Challengesn  The Grid Ecosystemn  Potential for Self-Organization – A Typical Day inthe UKn  Towards Self-Evolving Energy Networksn  Step 1: Smart Metersn  Step 2: Self-Awareness and Actionable Devicesn  Step 3: Networked Intelligencen  Conclusions
  3. 3. SEISMYC – SASO 2010 – Budapest, HungaryThe Current Gridn  Large power stationsn  No embedded intelligence for e.g.n  Fault detectionn  Local or distributed control / device managementn  Demand <-> supply managementn  Centralized control that is performed manuallyusing expert knowledgen  Difficult to fully incorporate small scale powersupplies dynamicallyn  Unidirectional power- and information flown  Depends heavily on forecasts for e.g. next hour ornext day
  4. 4. SEISMYC – SASO 2010 – Budapest, HungaryThe Smart GridBased on the European Technology Platform on SmartGrids it is“an electricity network that can intelligently integrate the actionsof all users connected to it – generators, consumers and thosethat do both – in order to efficiently deliver sustainableeconomic and secure electricity supplies”•  It employs•  dynamic monitoring,•  intelligent control,•  secure and fast communication and•  self* -technologies•  in order to•  better facilitate the connection and operation of generators (of all sizes)•  actively incorporates users preferences, behaviors and objectives•  provide better – in time – information and supply choices•  reduce environmental impact by e.g. improving efficiency, optimizing spatialdelivery networks•  increase reliability and security of supply and general QoS•  reduce management overheadIn addition “ SmardGrids must include not only technology, market and commercialconsiderations, environmental impact, regulatory frameworks, …, but also societalrequirements and governmental edicts”.Source: SmartGrids_SDD_FINAL_APRIL2010
  5. 5. SEISMYC – SASO 2010 – Budapest, HungaryGoalsn  Reduce costsn  Better correlate energy demand with supply and viceversan  Predict demands in real timen  Provide detailed usage informationn  Co-ordinate devices within and beyond LAN’sn  Reduce environmental impactn  Improve efficiency and reduce energy usen  Secure future energy supplyn  through renewable energy resourcesn  Provide a more robust frameworkn  Move from large scale power plants to microgenerators and virtual power plantsEnergyDemandEnergyDistributionEnergySupply
  6. 6. SEISMYC – SASO 2010 – Budapest, HungaryDriving ForcesFactsn  Increased energy demandn  Diminishing resourcesn  Environmental impactn  Need for optimizing the use of energyTechnologyn  Outdated infrastructuresn  Move towards renewable resourcesn  Shift towards small scale power generatorsn  Latest advances in technologyOthern  Federal stimulusn  Regulatory FrameworksSource: http://carbon-pros.com/blog1/2009/07/smart_grid_ecosystem.html
  7. 7. SEISMYC – SASO 2010 – Budapest, HungaryChallengesn  Complexityn  Energy networks on a world wide scale are larger andarguably more complex than the internet and comprisemore “users”n  Data volumesn  The amount of data to be monitored transmitted,processed and reacted upon is vastn  Real time aspect poses a significant challengen  Cost of installation and maintenancen  Trade-off between savings achieved and the the costs toachieve them are still unbalancedn  Security and privacy concernsn  It has been reasoned that users behavior can be deductedthrough the energy usage (e.g. a person is not at home ifenergy use us below a certain threshold)
  8. 8. SEISMYC – SASO 2010 – Budapest, HungaryThe Grid EcosystemBased on http://carbon-pros.com/blog1/2009/07/smart_grid_ecosystem_part_4.htmlPossible Workflow1.  Demand-Supply Manager (DSM)detects an decrease / increase indemand and advices HomeManagement Systems (HMS) toenable / disable (non-)essentialpower use within its ‘reach’.2.  Based on specific Prosumerpreferences and available devices,the HMS advices individual devicesto power up / down or increase /decrease power output / intake.3.  This request is received through aLAN and individual actionabledevices accept / reject the requestalso providing feedback to HMS.4.  Enterprise Apps monitor powerconsumption and adapt currentenergy costs to promote the cutback on energy.5.  Grid Overlays provide hierarchicalor network like organization onwhich DMS operate at variouslevels of granularity and on whichself-organization can befacilitated.ApplicationsDemand SupplyManagerNetworkUtilitiesProviders/CustomersEnterpriseAppsHMSDevicesGridOverlays
  9. 9. SEISMYC – SASO 2010 – Budapest, HungaryA Typical Day in the UKSource: www.nationalgrid.com
  10. 10. SEISMYC – SASO 2010 – Budapest, HungaryEvent Based Energy DemandSource: www.nationalgrid.comSelf-organisation based onn  Spatial regionsn  Environmental considerationsn  Regulatory frameworksn  Demand and supplycharacteristicsn  Device typesn  Device propertiesn  Social behaviourn  Commercial objectivesn  Event based patterns
  11. 11. SEISMYC – SASO 2010 – Budapest, HungaryTowards Self-Evolving Energy NetworksSmart MetersActionable, Self-aware DevicesNetworked IntelligenceEnable Monitoring
  12. 12. SEISMYC – SASO 2010 – Budapest, HungarySmart Metersn  Purposen  automatically collect consumption, diagnostic and status data from smartmetering devices such as water-, gas-, electric- meter) and transmit themto relevant utility providersn  Advancesn  Detailed and real time overview to the consumern  Customized billing as an incentive to save energyn  Saves utility providers the expense of periodic trips to each physical locationto read individual metersn  Limitationsn  No detailed monitoring possiblen  No control / management of devicesn  à The real time information collected, coupledwith analysis, can help both utility providersand customers to better control the demandand supply of resourcesn  Potential savings will largely depend on a changeof behavior of the consumers andtheir manual actions
  13. 13. SEISMYC – SASO 2010 – Budapest, HungaryHome Management SystemsIntelligent systems have the potentialto reduce the household energyconsumption by 31%[Otellini, CEO IntelIntelHomeManagementDashboard
  14. 14. SEISMYC – SASO 2010 – Budapest, HungaryTowards Self-Evolving Energy NetworksSmart MetersActionable, Self-aware DevicesNetworked IntelligenceEnable device managementand more detailedmonitoring
  15. 15. SEISMYC – SASO 2010 – Budapest, HungarySelf-Awarenessn  Whatn  Wikipedia - Self-awareness is the awareness of the self as separate from the thoughtsthat are occurring at any point in time.n  In this context – Self-awareness means that each object / device is aware of its currentstatus and context of use and their interrelation with other devices and the users that areavailable within the same environment and, in some cases, beyond.n  Whyn  Self-awareness is a pre-requisite to be able to evaluate and compare states and behaviorto pre-defined standards and values in a self-conscious way.n  This includes in particular the actual and expected consumption of resourcesn  Hown  Sense and translate relevant contextual information into device specific and situation-aware concepts to be stored, processed and evaluated by theself-aware object / device.n  E.g. smart energy profilesn  A bottom-up approachn  Centralised approach is not feasible due to the complexity and the number of objectsinvolved.n  For an IE to become fully self-aware, virtually every object or concept therein has to beself-awaren  For instance, for a home management system to be fully self- (or energy) aware, alldevices it is connect to need to be self-aware
  16. 16. SEISMYC – SASO 2010 – Budapest, HungaryActionable Devicesn  Actionable means that devices can adjust their own operationalparameters either by themselves or through external stimuli oradvice and enables the efficient and autonomous managementof devices and device ensembles based on specific local orglobal objectivesn  Actionable devices reflect both end-users as well as providersand provide the actionable interfaces that are required toautonomously manipulate demand and supplyn  E.g., individual devices may deactivate themselves for a shortperiod if available energy levels drop below a certain thresholdor device ensembles may be coordinated in a way that only amaximum number of devices is active at any given timen  Similar, components that generate or distribute energy, couldbe dynamically (de-)activated or configured, respectively, toserve energy on-demandn  Nevertheless, within autonomic frameworks, each device mustbe aware of itself and its use to override outside control ifnecessary
  17. 17. SEISMYC – SASO 2010 – Budapest, HungaryTowards Self-Evolving Energy NetworksSmart MetersActionable, Self-aware DevicesNetworked IntelligenceEnable global demand andsupply management
  18. 18. SEISMYC – SASO 2010 – Budapest, HungaryA Framework for Networked Intelligencen  If self-aware devices are only able to reason about themselves then the usefulnessof such an environment would still be limited as such objects would only exit andact in isolation. That is, if they would not communicate with other objects abouttheir current context of use or about their collective use as required for achievingmore complex tasks.n  For IE’s to become fully self-aware, virtuallyevery object or concept therein has to featuresuch a smart profile in dependence of itsindividual properties.n  This emphasizes the need for a conceptuallayer thatn  links the lower oriented physical world to a,conceptually, higher oriented user layer towhich services are offered / delivered too;n  self-organizes knowledge as well as objectsbased on their properties or context of use;n  organizes the organization as well as thecommunication and interaction between smartdevices ↔ activities ↔ users in both directions;n  Executes relevant measures in support of theself-organizing process
  19. 19. SEISMYC – SASO 2010 – Budapest, HungaryMonitor /ControlInterfacesActionable Devices – Consumer /ProducerSelf-organisingDistributionNetworkIn essence, such a layer would bridge the gap between theisolated use of energy by smart devices and complex coordinatedactivities, which can be monitored, aided, guided or controlled atall levels of granularity thus achieving scalabilityA Framework for Networked Intelligence
  20. 20. SEISMYC – SASO 2010 – Budapest, HungaryConclusions and Future Workn  The energy grid of the future is one of the most important and at thesame time most difficult vision for todayn  Socio-economic aspects offer great potential for the organization andoperation for future infrastructuresn  In particular, user behavior, commercial and regulatory frameworksneed to be incorporated into the new infrastructuren  Nevertheless, the dynamic modeling of social and economic objectivesand the subsequent adaptation of the underlying environments is stillsubject to further researchFuture work will concentrate on but is not limited ton  Efficient and dynamically configurable monitoring and analyzing techniquesn  Distributed self-organization techniquesn  Self-awareness and device management mechanismsn  Predictive Environments
  21. 21. SEISMYC – SASO 2010 – Budapest, HungaryThank YouMatthias Baumgarten-M.Baumgarten@ulster.ac.uk
  22. 22. SEISMYC – SASO 2010 – Budapest, HungaryPlayersSource: http://carbon-pros.com/blog1/2009/07/
  23. 23. SEISMYC – SASO 2010 – Budapest, HungaryIntelligent Environmentsn  What are they: Reflect infrastructures to which sensors, actuators and other computationalcomponents are deployed and in which they interact with each other in ordern  To monitor and to understand complex behavior and interaction between devices and users.n  With the aim to actively adapt the environment based on the current context andn  To aid the interaction between users and the environmentn  What is the problem: Considering the large number of objects involved and the potentiallyinfinite number of relationships between them it becomes ever more difficult to properlycomprehend IE’s over time.n  Solution: IE’s need to become Self-Awaren  They need to be intrinsically interwoven with dynamic and flexible monitoring systems and inferencemechanisms so that they are able to constantly establish, remove or refine the properties and therelationships that exist between different stakeholders on an operational as well as a social or businesslevel.n  This will ultimately provide the knowledge base for advanced reasoning and prediction capabilities thatwould eventually allow IE’s to self-evolve depending onn  their own dynamic context,n  individual stakeholdersn  Even more compelling, the interactions and relations between them.n  As a consequence, IE’s would be more flexible with respect to their use, they would be more resilient andfailsafe and would in general be able to provide a higher degree of interaction as well as contextualunderstanding.n  Requirement: A pre-requisite for this vision is the requirement of individual devices tobecome self-aware thus providing a high degree of understanding ofn  their own operational statusn  their context of usen  their interrelation with other devices and the users that are available within the same environment and, insome cases, beyond.

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