Utilizing Flexibility of Hybrid Appliances in Local Multi...

Elec-tricity

Cold

Heat

KIT – The Research University in the Helmholtz Association www.kit.edu

INSTITUTE OF APPLIED INFORMATICS AND FORMAL DESCRIPTION METHODS (AIFB)

Utilizing Flexibility of Hybrid Appliances in Local Multi-modal Energy ManagementIngo Mauser, Jan Müller, Hartmut SchmeckKarlsruhe Institute of Technology, GermanyEEDAL’17, 13-15 September 2017, Irvine, CA, USA

Source: Institute AIFBSource: own work

Icons: Microsoft Office

Provision

Storage

Conversion

Distribution

Utilization

Institute of Applied Informatics and Formal Description Methods (AIFB)

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Agenda

1. Introduction and MotivationAutomated Building Energy ManagementDemand Response

2. Multi-modal Energy ManagementHybrid Home AppliancesUnified Terminology of Hybrid Systems and Devices

3. Scenario, Simulation Results, and EvaluationSmart Residential Building ScenarioHybrid Home Appliances, Heating Element, PV SystemTotal Costs, Self-consumption Rate, Self-Sufficiency Rate

4. Conclusion and Outlook on Future Work

09/15/2017 Ingo Mauser Utilizing Flexibility of Hybrid Appliances in Local Multi-modal Energy Management


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IntroductionResearch on Energy Management

Energy Informatics: ICT in energy systemsFocus: efficient algorithms, meta-heuristics, multi-objective optimization

Automated building energy management and demand responseOrganic Smart Home (open-source), see http://www.organicsmarthome.comReal-world application and bottom-up simulationsHardware-in-the-Loop simulations

Laboratories: smart residential and commercial buildingsKIT Energy Smart Home LabFZI House of Living Labs


Source: KIT, FZI ISPE-IIK

http://www.organicsmarthome.com/


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MotivationHybrid Home Appliances

Smart building scenariosAutomated energy management in residential and commercial buildingsElectrical and thermal simulation

Example:May hybrid home appliances provide additional flexibility for measures of demand response?

Simulation of hybrid home appliancesEvaluation of their behavior in demand response scenarios


Simulation Evaluation


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2. Multi-modal Energy ManagementHybrid Home AppliancesTerms and ConceptsUnified Terminology for the Characterization of Hybrid Systems and Devices

Elec-tricity

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Heat

Ingo Mauser Utilizing Flexibility of Hybrid Appliances in Local Multi-modal Energy Management

09/15/2017


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Multi-modal Energy ManagementHybrid Home Appliances

Hybrid home appliancesMay use more than one energy carrierFor instance: switch dynamically from electricity to hot waterIn this work:

Dishwasher (DW): electricity or hot waterTumble dryer (TD) : electricity or hot waterWashing machine (WM) : electricity or hot waterOven (OV): electricity or natural gasHob (HB): electricity or natural gas

Efficiency of hybrid appliancesAssumption: higher energy consumption than conventional appliances2 efficiency scenarios have been simulated

η = 0.77 (“+ 30%”)η = 0.50 (“+ 100%”)


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Hybrid home appliancesMay use more than one energy carrierFor instance: switch dynamically from electricity to hot waterIn this work: dishwasher, dryer, washer, oven, hobEfficiency of hybrid appliances

Assumption: higher energy consumption than conventional appliances2 efficiency scenarios have been simulated

η = 0.77 (“+ 30%”)η = 0.50 (“+ 100%”)

Building energy management systemOptimizes the utilization of the energy carriersOptimizes the operation time of the appliances (DW, WM, TD)Predicts the future PV generation (and all other generation/consumption)


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Numerous different meanings and concepts:

HybridHybrid car, hybrid vehicleHybrid heating system: gas boiler and electric heat pump [VDI 2015, BDEW 2015, Näslund 2013]

Hybrid heating and cooling system: gas boiler and sorption heat pump [DIN EN 12309-7:2014]

Hybrid tumble dryer: heat pump and electrical heating element [EP2025802A2]

Hybrid oven: microwave and forced air convection [Li 1996]

Hybrid solar system: provision of hot water and electricity [Grob 2003]

Hybrid grid: Distribution of electricity, gas, and heat [Dorfner 2015]Combined AC/DC grid [Rehtanz 2015]

Hybrid energy storage [Bohnet 2016]

Multi-modal Energy ManagementWhat is “hybrid”? What is “multi-modal”? (1/2)


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Numerous different meanings and concepts (continued):

Bi-valent [VDI Guideline 6002]

Multi-modalLogistics and mobility: change of the transport carrierMulti-modal smart grid [Thiem 2015]

Multi-modal energy system [Metzger 2013, Rehtanz 2015]

Multi-energy system [Mancarella 2014]

Multi-commodity [Blaauwbroek 2015, Mauser 2016]

Energy as a commodityInherent properties and qualities, e.g., “locally generated by the PV”

And many more: Multi-carrier, multi-fuel, multi-valent, multi-vector, poly-generation, poly-grids, …

Multi-modal Energy ManagementWhat is “hybrid”? What is “multi-modal”? (2/2)


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Multi-modal Energy ManagementUnified Terminology (1/2)

Examples for different meanings of hybridUtilization of multiple…

Energy carriersEnergy sources

Usage of multiple…Conversion technologies

Provision of multiple…Energy carriersServices

Terminology for characterization of hybrid systems1. Location in the energy chain from provision to utilization Utilization, distribution, conversion, storage, provision [VDI 4602/4661, ISO 50001]

2. Type of the multi-modality Energy carriers, sources, services, technologies, systems, stages, …


Multi-carrier utilization Multi-source utilization

Multi-technology conversion

Multi-carrier provision Multi-service provision

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Multi-modal Energy ManagementUnified Terminology (2/2)

Utilizationof multiple energy carriersof multiple energy sources

Distributionof multiple energy carriersusing multiple linksusing multiple technologies

Conversionof multiple energy carriersin multiple stagesusing multiple conversion technologies

Storagein multiple energy carriersin multiple storagesusing multiple storage technologies

Provisionof multiple energy carriersof multiple services (functions)

Multi-utilization Multi-carrier utilization Multi-source utilization

Multi-distribution Multi-carrier distribution Multi-link distribution Multi-technology distribution

Multi-conversion Multi-carrier conversion Multi-stage conversion Multi-technology conversion

Multi-storage Multi-carrier storage Multi-system storage Multi-technology storage

Multi-provision Multi-carrier provision Multi-service provision


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Source: Mauser 2017a


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Utilization Conversion ProvisionStorage

Natural gas Hot Water

DistributionElectricity

Multi-carrier provision: microCHP

Multi-carrier utilization and provision:

hybrid heating system

Electricity grid connection

Domestic hot water system

Natural gas grid connection

Hot water

storage tank

Photovoltaic system

Space heating system

Other electrical devices

MicroCHP

Electrical insert heating element Dishwasher

Tumble dryer

Washing machine

Hob

Oven

Multi-carrier utilization:hybrid home appliances

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Source: Mauser 2017b


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Multi-modal Energy Management KIT Energy Smart Home Lab



Electricity (AC) Natural gas Chilled Water Hot Water

DistributionElectricity (DC)


Multi-carrier utilization and provision:

hybrid heating system

Multi-system storage and multi-service

provision:hybrid electrical energy

storage system

Multi-carrier utilization: compression chiller



Natural gas bottles

Hot water

storage tank

Photovoltaic system


Chilled water

storage tank

BatteryAC-DC Inverter

Super-caps

Electrical devices

MicroCHP

Electrical insert heating element

Compression chiller

Space cooling system

Environment

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Multi-modal Energy Management FZI House of Living Labs



Electricity (AC) Natural gas Chilled Water Hot Water

DistributionElectricity (DC)


Multi-carrier utilization and provision, multi-

technology conversion:hybrid trigeneration system


Natural gas grid connection

Hot water

storage tank

Photovoltaic cells


Chilled water

storage tank

BatteryAC-DC

Inverters

Electrical devices

MicroCHP


Adsorption chiller Space cooling system

Condensinggas boiler


Multi-source utilization: PV battery storage system

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3. Scenario, Simulation Results, and EvaluationSmart Residential Building ScenarioHybrid Home Appliances, Heating Element, PV SystemTotal Costs, Self-consumption Rate, Self-Sufficiency Rate


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ScenarioSmart Residential Building

“Smart Home”German 4-person householdHybrid/deferrable appliancesDistributed generation

PV system(MicroCHP)

Heating and domestic hot water systemGas heaterElectrical insert heating element (utilizes surplus PV generation)

TariffsElectricity, from grid: 30 cent/kWhElectricity, PV feed-in: 10 cent/kWhElectricity, PV self-consumption: 0 cent/kWhNatural gas, from grid: 8 cent/kWh


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Gas-fired Condensing

Boiler

Smart ResidentialBuilding

SpaceHeating Other

Devices

HotWater

Storage Tank

Appliances

PV System

Hot Water

Electricity

Communication

BEMS

Natural Gas Grid

Electrical Grid

Natural Gas

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ElectricalInsert

HeatingElement

kWh

kWh

no measures of demand response in this work


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Simulation ResultsTotal Costs and Energy Consumption

Hybrid appliances lead to a cost reduction of about 150-300 EUR/aSize of the PV system Availability of the electrical insert heating elementShift from electricity to natural gas (which is cheaper)Only a small share because of better synchronization to PV generation

Overall electricity consumption in a four-person household is typically reduced by about a third

In case of sufficient local electricity generation or a low hot water tank temperature, the appliances utilize electricity instead of natural gas Time-variable electricity tariffs facilitate demand side management as

demonstrated in [Mauser 2017a]


09/15/2017


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Simulation ResultsSelf-consumption and Self-sufficiency

Hybrid appliances reduce the self-consumption* rate of the electricity and increase electricity feed-in as well as the self-sufficiency** rate

Reduction of overall electricity consumption Deterrability of appliances helps to limit the decrease of the self -consumption rate caused by the introduction of hybrid appliances

Electrical insert heating element (IHE) increases the total costs,the self-consumption rate, and the self-sufficiency rate

Natural gas price is low when compared to the PV feed-in tariffReduction of the natural gas consumption

Reduction of the consumption of fossil energy carriers and thus carbon dioxide emissionsIHE helps to keep the self-consumption rate at about the same level, no matter whether there are conventional or hybrid appliances


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* Self-consumption rate: share of the locally generated energy that is consumed locally ** Self-sufficiency rate: share of the consumed consumption that has been generated locally


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Simulation ResultsFurther Results

Similar results of the two different efficiency levels of hybrid appliancesAlthough increasing the total costs and the natural gas consumption, the results of hybrid appliances that are even less efficient (“+ 100%”, η = 0.5) are similar to those that use only slightly more energy (“+ 30%”, η = 0.77)Hybrid appliances are a promising approach towards sector coupling in buildings, even if they are locally significantly less energy-efficient than those using only electricity when providing the energy services

Note: Results depend on the structure of the electricity and natural gas tariffs

Similar results for markets having a feed-in compensation for electricity that is higher than the gas price Future: time-variable feed-in tariffs


09/15/2017


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Simulation ResultsSelf-consumption and Self-sufficiency (1/2)

Self-consumption (SCR)* and self-sufficiency rates (SSR)** depending on the photovoltaic (PV) system peak power in a four-person household without electrical insert heating element (IHE) and using

(a) conventional and deferrable (b) conventional and hybrid appliances with η = 0.77 (“+30%”)


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Simulation ResultsSelf-consumption and Self-sufficiency (2/2)

Self-consumption (SCR)* and self-sufficiency rates (SSR)** depending on the photovoltaic (PV) system peak power in a four-person household with and without electrical insert heating element (IHE) and using

(a) conventional and deferrable (b) conventional and hybrid appliances with η = 0.77 (“+30%”)


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Hybrid Home Appliances in Energy ManagementConclusion and Outlook


Automated Building Energy ManagementFocus on demand response and not on energy efficiencyParadigm change to “demand follows (local) supply”

Unified Terminology for Hybrid SystemsCurrently: many terms that may lead to misunderstandingCharacterization of hybrid systems: where and what is the hybridity

Evaluation of Hybrid (Deferrable) AppliancesProvide additional potential for flexibility regarding the utilization of electricityResults depend strongly on the given tariff structure / pricing regimeThe positive effects of deferrable appliances are limited

OutlookBuilding scenario: microCHP, fuel cell, and battery storage systemDifferent tariff structures and regional differencesSimulation of multiple buildings and their interaction (district heating)Demonstration and evaluation of real hybrid appliances (?)


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Contact

Dr.-Ing. Ingo MauserPost-doctoral Research AssociateInstitute AIFBResearch group: Efficient AlgorithmsTel.: +49 (0)721 608 44556Email: [email protected]

KIT-Campus SüdKollegiengebäude am Kronenplatz (Geb. 05.20, R. 2B-04)Kaiserstr. 8976133 Karlsruhe, Germany

http://www.organicsmarthome.comhttps://github.com/organicsmarthome

http://www.organicsmarthome.com/

https://github.com/organicsmarthome


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REFERENCES

Ingo Mauser Integration and Optimization of Interdependent Devices


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References (1/4)

BDEW 2015BDEW Bundesverband der Energie- und Wasserwirtschaft e.V.: „Hybridheizung: Die Kombination von Wärmeerzeugung aus Erdgas und Strom“, Brochure, 2015DIN EN 12309-7:2014DIN Deutsches Institut für Normung e. V.: „Gasbefeuerte Sorptions-Geräte für Heizung und/oder Kühlung mit einer Nennwärmebelastung nicht über 70 kW - Teil 7: Spezifische Bestimmungen für Hybridanlagen “, DIN Deutsches Institut für Normung e. V., 2014Dorfner 2015J. Dorfner, Th. Hamacher: „Optimal planning of urban infrastructure networks for multiple energy carriers”, 5th Colloquium of the Munich School of Engineering, 09. Juli 2015Ela 2011E. Ela, M. Milligan, B. Kirby: „Operating reserves and variable generation Contract“, National Renewable Energy Laboratory (NREL), National Renewable Energy Laboratory (NREL), 2011, 303EP2025802A2BSH Bosch und Siemens Hausgeräte GmbH: „Hybrides Hausgerät zum Trocknen“, Patent, 2009Femia 2013N. Femia, D. Toledo, W. Zamboni: „Storage unit and load management in photovoltaic inverters for residentialapplication“, IEEE Industrial Electronics Society Conference (IECON), 2013, 6800-6805Gellings 1985C. Gellings: “The concept of demand-side management for electric utilities”, Proceedings of the IEEE, 73(10):1468–1470, 1985Grob 2003G. R. Grob: „Importance of ISO and IEC international energy standards and a new total approach to energy statisticsand forecasting“, Applied Energy, 2003, S. 39-54



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References (2/4)

Kochanneck 2016S. Kochanneck, I. Mauser, B. Bohnet, S. Hubschneider, H. Schmeck, M. Braun, Th. Leibfried: „Establishing a Hardware-in-the-Loop Research Environment with Hybrid Energy Storage System“ ISGT-Asia, 2016Li 1996A. Li, C. E. Walker: “Cake Baking in Conventional, Impingement and Hybrid Ovens” Journal of Food Science, 1996, S.188-191Mauser 2016I. Mauser, J. Müller, F. Allerding, H. Schmeck: “Adaptive Building Energy Management with Multiple Commodities and Flexible Evolutionary Optimization”, Renewable Energy, 87, Part 2, 911-921Mauser 2017aI. Mauser: “Multi-modal Building Energy Management”, Dissertation, KIT, 2017Mauser 2017bI. Mauser, J. Müller, H. Schmeck: “Utilizing Flexibility of Hybrid Appliances in Local Multi-modal Energy Management” EEDAL'2017 – Energy Efficiency in Domestic Appliances and Lighting, Publications Office of the European Union, 2017Metzger 2013M. Metzger: „Innovative Smart Grid Applications“, Siemens CT, Präsentation, 2013Näslund 2013M. Näslund: „Hybrid heating systems and smart grid - System design and operation - market status, Dansk GastekniskCenter, 2013Prior 1997D. Prior: „Nachbildung der Energiebedarfsstruktur der privaten Haushalte -- Werkzeug zur Bewertung von Energiesparmaßnahmen“, VDI, 1997Rehtanz 2015C. Rehtanz, I. Erlich, J. Lunze, S. Lehnhoff: „Initiative zur Einrichtung eines Schwerpunktprogramms – Hybride und multimodale Energiesysteme: Systemtheoretische Methoden für die Transformation und den Betrieb komplexer Netze“, 2015



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References (3/4)

Sinner 1960H. Sinner: „Über das Waschen mit Haushaltwaschmaschinen: in welchem Umfange erleichtern Haushaltwaschmaschinen und -geräte das Wäschehaben im Haushalt?“, Haus und Heim, 1960Stamminger 2008Stamminger, R.; Broil, G.; Pakula, C.; Jungbecker, H.; Braun, M.; Rüdenauer, I. & Wendker, C.: „Synergy Potential ofSmart Domestic Appliances in Renewable Energy Systems“, University of Bonn, University of Bonn, 2008Stamminger 2013aR. Stamminger, V. Anstett: „Effectiveness of Demand Side Management by variable energy tariffs in the households --Results of an experimental design with a fictive tariff model“, Proceedings of the eceee Summer Study, 3-8 June, Presqu'île de Giens, France, 2013, 2159-2166Stamminger 2013bR. Stamminger, V. Anstett: “The Effect of Variable Electricity Tariffs in the Household on Usage of Household Appliances”, Smart Grid and Renewable Energy, Scientific Research Publishing, 2013, 4, 353-365Thiem 2015S. Thiem, V. Danov, J. Schaefer, T. Hamacher: „Model-based operating strategies for chillers with thermal energystorage in Smart grids“, 5th Colloquium of the Munich School of Engineering, 09. Juli 2015VDI 2015Vorträge auf der „3. VDI-Fachtagung - Dezentrale und Hybride Energiesysteme für Gebäude und Quartiere“, Köln, 06. und 07. Oktober 2015Weniger 2013Weniger, J. & Quaschning, V.: „Begrenzung der Einspeiseleistung von netzgekoppelten Photovoltaiksystemen mit Batteriespeichern“, Hochschule für Technik und Wirtschaft Berlin, 28. Symposium Photovoltaische Solarenergie, Kloster Banz, Bad Staffelstein, 2013, 1-14



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References (4/4)

Weniger 2014J. Weniger, T. Tjaden, V. Quaschning: “Sizing of Residential PV Battery Systems”, Energy Procedia, 2014, 46, 78-87Zeigler 2000B. P.Zeigler, H. Praehofer, T. G. Kim: “Theory of modeling and simulation: integrating discrete event and continuous complex dynamic systems”, Academic Press, 2000



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BACKUP


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9/23/2016

Gas-fired Condensing

Boiler

Smart ResidentialBuilding

SpaceHeating Other

Devices

HotWater

Storage Tank

Appliances

PV System

Hot Water

Electricity

Communication

BEMS

Natural Gas Grid

Electrical Grid

Natural Gas

42

ElectricalInsert

HeatingElement

kWh

kWh


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9/23/2016

Smart residential building type Four-person householdYearly electricity consumption

thereof: major appliancesthereof: residual baseload

4700 kWh1372 kWh2628 kWh (simulated using SLP H0)

Simulated major appliancesMinimum deferabilityAverage deferabilityMaximum deferability

Dishwasher, hob, oven, tumble dryer, washing machine0 hours (all)6 hours (dishwasher, tumble dryer, washing machine)12 hours (dishwasher, tumble dryer, washing machine)

Efficiency in hybrid modes ηhybrid = 0.77 (in comparison to electricity: 30% more)ηhybrid = 0.50 (in comparison to electricity: 100% more)

Photovoltaic system Real profile recorded in Germany at a resolution of 1minElectrical insert heating element

Power stepsEfficiency

0.0, 0.5, …, 3.5 kW (8 power steps)η = 1.0

Hot water storage tank (combined)Minimum tank temperature (top)Maximum tank temperature (top)Thermal loss

750 liters60°C80°CPloss = 96 W * (θtank - 20°C) / 40 K

TariffsElectricity, from gridElectricity, PV feed-inElectricity, PV self-consumptionNatural gas, from grid

30 cent/kWh10 cent/kWh0 cent/kWh8 cent/kWh


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ScenarioAppliance Operation Cycles


9/23/2016

Data based own various sources (see paper) and own measurements


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Home AppliancesUsage Probability

Average usage probability per major home appliance and as weighted average of the five appliances:


9/23/2016


Data sources: Conrady et al. 2014, IKW 2013, Prior 1997, Schmitz & Stamminger 2014


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KIT ENERGY SMART HOME LAB


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Research on Energy Management

Major goals:Exploit load flexibility by decentralized load management (load shaping, load shifting)Provide ancillary services for grid stabilization(reactive power, operating reserve)

Environments:Smart residential buildings

Intelligent household appliances with smart grid capabilitiesUsage of home automation systems for energy managementDistributed generation and battery storage systems

Smart commercial buildingsBuilding services and energy management (HVAC)Intelligent decentralized power generation and conversion


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REAL-WORLD APPLICATIONApplication 1

09/11/2017


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Smart BuildingLaboratory Environments

Two labs using the same building energy management system (BEMS)Trial phases and evaluation of user experience

KIT Energy Smart Home Lab FZI House of Living Labs

Smart Residential Building Smart Commercial Building


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Research LaboratoryKIT Energy Smart Home Lab

Solar inverterSmart meter

EV Charging station

Intelligent appliances

MicroCHP

PV power simulator

4-quadrant amplifier

Electric heater

Hybrid battery storage system

A/C


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Application 1: Real-world ApplicationEnergy Management and User Interaction

Solar inverterSmart meter

EMP

Charging station

Intelligent appliances

MicroCHP

EMP

EMP

EMP

Organic Smart Home (OSH)Energy Management System

Observes and controls electric/thermal consumers & providers

EnergyManagement Panel (EMP)

Visualization of energy usage

Discover user preferences

PV power simulator

4-quadrant amplifier

Electric heater

Hybrid battery storage system

A/C

https://smarthome.aifb.uni-karlsruhe.de:8888/GUI/

https://smarthome.aifb.uni-karlsruhe.de:8888/GUI/


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KIT Energy Smart Home LabMulti-energy System

MicroCHP

PV inverter

Tumble dryer

Hob and ovenWashing machine

Refrigerator and auto-mated coffee machine

Dishwasher

Deep-freezerTVPC

PC

Batterystoragesystem

Electric vehicle charging station


A/C Radiator

Radiator

Radiator

Radiator

Radiator

MicroCHP

Hot water storage tank


ShowerSinks

Chilled water buffer tankCooling

ceiling with PCM

Cooling ceiling with PCM

Cooling ceiling with phase change material

(PCM)

Air-conditioningcontroller and

inverter Elec-tricity

Cold

Heat

Provision

Storage

Conversion

Distribution

Utilization


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KIT Energy Smart Home LabRealer Einsatz

01./02. Dezember 2016Nahezu gleiche Nutzungder Haushaltsgeräte

Tag 1 Tag 2 Tag 2*Mikro-BHKW optimiert – –*DW, WM, TD optimiert – –*

Pgrid,min -5370 W -5483 W -5700 WPgrid,max 5218 W 6047 W 6047 WEgrid,total -1,3 kWh 5,1 kWh -1,2 kWh

Eigenverbrauchsquote 27,0% 6,2% 10,8%Autarkiequote 30,9% 4,0% 12,4%Energiekosten 152 Cent 297 Cent 211 Cent

ggü. Tag 1 +95% +39%

Abkürzungen: DW: Geschirrspülmaschine, WM: Waschmaschine, TD: WäschetrocknerWerte: Pgrid: Elektrische Leistung am Netzanschlusspunkt

Bilder: KIT (m.),eigene Aufnahmen (o., u.)


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KIT Energy Smart Home LabRealer Einsatz

01./02. Dezember 2016Nahezu gleiche Nutzungder Haushaltsgeräte

Werte: P_grid/P_ESHL: Gesamtleistung, P_microCHP: Leistung Mikro-BHKW, C_a: Strompreis, T_outdoor/T_tank: Temperaturen

Bilder: KIT (m.),eigene Aufnahmen (o., u.)

unterschiedliche Außentemperatur

Niedrigpreisphase wird nicht genutzt


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KIT Energy Smart Home LabMicroservice Architecture

Bildquelle: Bao, K.; Mauser, I.; Kochanneck, S.; Xu, H. & Schmeck, H. A Microservice Architecture for the Intranet of Things and Energy in Smart Buildings MOTA '16: Proceedings of the 1st International Workshop on Mashups of Things and APIs, Middleware '16 17th International Middleware Conference, ACM, 2016, 3:1-3:6, Figure 1


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Smart Commercial Building LaboratoryFZI House of Living Labs

Living LabsmartENERGY

Living Lab smartHOME / AAL

More about the FZI House of Living Labs:

Becker, B.; Kern, F.; Loesch, M.; Mauser, I. & Schmeck, H.: “Building Energy Management in the FZI House of Living Labs”, Energy Informatics, Chapter 9, Springer, 2015, 9424, 95-112


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ORGANIC SMART HOME


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www.organicsmarthome.orgOrganic Smart Home 4.0

Java 8, free & open source (GPLv3)Organic Computing architectureSmart building (residential and commercial)Automated energy management

Optimization Abstraction LayerVariable tariffs and signalsMulti-energy and multi-objective

Integration and InterfacesVisualization: Energy Management PanelMicro-service architecture Multiple databases: SQL, RRD, HDF5, InfluxDB

Real-world application as well as simulationsMore about Organic Smart Home architecture and optimization: Mauser, I.; Müller, J.; Allerding, F. & Schmeck, H.: “Adaptive Building Energy Management with Multiple Commodities and FlexibleEvolutionary Optimization”, Renewable Energy, Elsevier, 2015

Raspberry Pi


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Organic Smart HomeIntegrated Devices and Systems

AppliancesDeferrable and interruptible devicesHybrid appliances: multi-energy utilization (e.g. hot water and electricity)

MicroCHPs cogenerationCombined cooling, heat, and power (CCHP) trigeneration

Adsorption/absorption chillersMicroCHP

Multi-energy heating systemsGas boilersElectrical heating elements

Battery energy storage systemsElectric vehiclesMetering systems (WAGO, …)


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Organic Smart Home 4.0Multi-modal Building Energy Management

Mul

ti-m

odal

Ene

rgy

Man

agem

ent

IntegratedOptimization Layer

Optimization & DeviceAbstraction Layers

Device Management Layer

HardwareAbstraction Layer

…

…

… Ener

gy S

imul

atio

n C

ore

Real Devices / Simulation Engine

Building Energy Management

Device Management

Device Driver

InterdependentProblem Part

SIM

Multi-modal energy management in a building operating systemModular structureInterdependent Problem PartsPractical application and simulation

Optimization and simulationConfiguration using XML filesMulti-commodity optimization

Device Management

Device Driver


Org

anic

Sm

art H

ome


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Organic Smart HomeSimplified Architecture 1/3


OptimizationAbstraction Layer


HardwareAbstraction Layer Device Driver

Goals

UserExternal Entities

Signals

Integrated Building Optimization

Device Management

Device Driver

Device Management

Device Management

Device Driver

Energy Data

Problem Part Problem Part Problem Part

Devices 42

…

…

…

…

Org

anic

Sm

art H

ome

1909 W

More about Organic Smart Home architecture and optimization: Mauser, I.; Müller, J.; Allerding, F. & Schmeck, H.: “Adaptive Building Energy Management with Multiple Commodities and Flexible Evolutionary Optimization”, Renewable Energy, 87, Part 2, 2016


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HardwareAbstraction Layer Device Driver


Device Management

Device Driver

Device Management

Device Management

Device Driver


Devices 42

…

…

…

…

Org

anic

Sm

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ome

1909 W

Modular management

and optimization

Drivers and hardware

Modularity add or remove devices and systems to adaptMulti-layer architecture



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Device Simulation

Driver


Device Management

Device Simulation

Driver

Device Management

Device Management

Device Simulation

Driver


Devices 421909 W

…

…

…

…

Org

anic

Sm

art H

ome


Simulation agents

Modular management

and optimization

Practical application and simulation ease development & testingSimulation of scenarios


53

Organic Smart Home 4.0Building Operating System

Building Operating System (“Meta-OS”)En

ergy

Man

agem

ent A

pplic

atio

n

Syst

em M

anag

emen

t an

d Ex

ecut

ion

Life

cycl

e M

anag

emen

t


Optimization & DeviceAbstraction Layers



…

…

…

Sche

dulin

g

Exce

ptio

n an

d Er

ror M

anag

emen

t

Ener

gy S

imul

atio

n C

ore

Configuration Management, Device Discovery, and Device Integration

Access Management, User Interaction, and External Communication

Pers

iste

nce

and

Logg

ing

Inter-component Communication

Real Devices / Simulation Engine

Building Energy Management

Device Management

Device Driver


BOS

CFG

SIMOC


54

Optimierung und SimulationZusammenspiel

IntegratedOptimization

Layer

Optimization Abstraction Layers


Hardware Abstraction Layer

Energy Simulation Core

Ablauf der Energiesimulation in der OptimierungEnergy Simulation CoreInterdependent Problem Parts (IPPs)

Lokales EnergienetzGerätestatus

Befehls-folge

1

2 4

Modularität trotz AbhängigkeitenWechselseitige Beeinflussung

IPP Gebäude-heizung

IPP Haushaltsgerät

IPP Batteriespeicher

Virtueller Strom Smart Meter

IPP Adsorptions-Kältemaschine

IPP Warm-wasserspeicher

IPP Kalt-wasserspeicher

IPP KlimatisierungIPP Mikro-BHKW

Virtueller Gas Smart Meter

IPP Hybrid-haushaltsgerät

Simulation ergibt Profil bewertbarer

Energieträger

Jetzt Optimierungshorizont

IPPs

SIM

CFG

33

0

5


55

ENERGY SIMULATION CORE


56

Organic Smart Home 4.0Energy Simulation Core

Interdependent Problem Parts (IPPs)InterdependenciesDifferent connections

Electrical Connection Thermal Connection (hot/cold) Gas Connection

IPPHeating System

IPP Home Appliance

IPP Battery Storage

Virtual Electrical Smart Meter

IPP Adsorption Chiller

IPP Hot Water Storage

IPP Chilled Water Storage

IPP Air ConditioningIPP MicroCHP

Virtual Gas Smart Meter

IPP Hybrid Home Appliance

Active IPP Passive IPP Virtual Smart Meter

Active and passive IPPsVirtual Smart Meters


57

Organic Smart Home 4.0Ablaufschema der Optimierung

Natural Gas Grid

Electrical Grid

Local O/C-units

Local O/C-units

Global O/C-unit

Evolutionary Algorithm

Signals, Goals, Objectives

Fitness Function

IPPMicroCHP

IPPChiller

IPPChilledWater

IPPHotWater


Interpretation of Solution Candidate by Interdependent Problem Parts and Creation of Ancillary Commodity Load Profiles

IPPSpaceCooling

IPPSpaceHeating

Creation of Interdependent Problem Parts

Chilled Water

Storage Tank

HotWater

Storage Tank

Control

SolutionCandidate

…

Optimization

MicroCHP

Chilled Water

Storage Tank

HotWater

Storage Tank

Optimized IPP MicroCHP

Optimized IPPAdAC

Optimized IPPChilledWater

Optimized IPP HotWater

Optimized IPPSpaceCooling

Optimized IPP SpaceHeating

Integrated Multi-commodity

Optimization

Interpretation of Optimized

Interdependent Problem Parts

IPPSpaceCooling

?

IPPChilledWater

?

IPPChiller

?

IPPMicroCHP

?

Fitness Value

Load Profiles

Best Candidate

Observation

-

-

1100…0

0110…1

…

0 bit 0 bit 42 bit 0 bit 0 bit63 bit

Natural Gas Grid

Electrical Grid

MicroCHP

Control M

odel

Creation of Ancillary Commodity Load Profiles

Now

Load Profiles

Horizon

Device States

I³ of Local Grids 42 bit

63 bit

Devices

Advance simulation

time

Multi-energy Simulation

MAS

42 bit

63 bit0 bit

0 bit

Drivers

Devices

Drivers

Ancillary Commodity

Load Profiles

…

Evaluation

ModelModel ModelModelModelModel

Model

Model

Model

Model

MeteringAdsorption Chiller

Adsorption ChillerCeiling cassette

Ceiling cassette

UserExternalEntities

Radiator

Radiator


58

EnergieflusssimulationEnergy Simulation Core

Schematischer Ablauf der Energieflusssimulation


Ancillary Commodity Load Profiles

Now

Load Profiles

Horizon

Device States

Local Energy Grids

Ancillary Commodity Calculator

Energy Flow Simulator

Electrical Simulation

Thermal Simulation

…

Virtual Time Multi-agent System Simulator

IPPs

Input Output


59

Energy Simulation CoreEvaluation der Interdependent Problem Parts

…

…

t1

t2

Chilled Water

Storage Tank

HotWater

Storage Tank

MicroCHP

Adsorption Chiller

Time Step

1

Δt =60s

Initialization

θChilledW,0 = 14°C Ph,0 = 11 kW Pc,0 = -7 kW θHotWater,0 = 61°C

Pa,0 = -5.5 kW Ph,0 = -12.5 kW Pn,0 = 20.5 kW

Energy and Information Exchange Step

Behavior and State Update Step

Ec,0 = 7 kW * 60sEh,0 = 11 kW * 60s Eh,0 = 12.5 kW * 60s

1. Interpretation of bit string

2. Control logic3. Entity model New temperature

θChilledW,1 = 13°C


2. Control logic3. Entity model Device on New power



θHotWater,1 = 59°C


2. Control logic3. Entity model Device off New power

Ea,0 = 5.5 kW * 60s

En,0 = 20.5 kW * 60s

0 bit 42 bit 0 bit 63 bit 0 bit

Solution Candidate

Initial States

…IPPMicroCHP

IPPChiller

IPPChilledWater

IPPHotWater

0 bit 42 bit 0 bit 63 bit 0 bitModel Model Model Model

Energy Flow Simulation and Ancillary Commodity Calculation Step


60

Energy Simulation CoreEvaluation der Interdependent Problem Parts

t2

t3

tmax

Time Step

2

Time Steps3 … max



Ec,1 = 6.8 kW * 60sEh,1 = 11 kW * 60s Eh,1 = 0 kWs

Ea,1 = 0 kWs

En,1 = 0 kWs



…



θChilledW,2 = 12°C





θHotWater,2 = 57°C



0 bit 42 bit 0 bit 63 bit 0 bit

t1 tmax t1 tmax t1 tmax

Simulated Behavior

t1 tmax




61

Simulation: Geräte und EnergieflussMulti-Agenten-System

Discrete Time System Specification [Zeigler 1976]

Zeitdiskrete SimulationSpezialfall von Discrete Event System Specification [Zeigler 2000]

Numerische Modellierung von Differenzialgleichungen

Agenten-basierte Simulation / Multi-Agenten-SystemNumerische Modellierung von DifferenzialgleichungenAgenten

Interner Zustand, Verhalten/Model, LernenInteraktion mit der UmweltInteraktion mit anderen Agenten

Ähnlichkeit zu GridLAB-D und mosaikDefinierte Schnittstellen und AufrufreihenfolgeAber: Fokus auf einzelnes Gebäude statt ganzer Smart GridsAber: keine streng hierarchische Struktur der Agenten (GridLAB-D)

SIM


62

INTERDEPENDENT PROBLEM PARTS


63

Simulation: Geräte und EnergieflussInterdependent Problem Parts

1. Generelle Informationen und EigenschaftenOptimierbarkeit

„optimierbar“, „nicht optimierbar“Optimierungshorizont

bspw. „6 Stunden“ oder „24 Stunden“Aktualisierungsrate

bspw. „mind. 1 Update pro 30 min“Auslösung der Optimierung

bspw. „mind. 1 Optimierung pro 3 Stunden“bspw. „Verletzung der Temperaturgrenzen“

Handlung/Aktivität„Aktiv“, „passiv“

InterdependentProblem PartEntity Model

Control Model

SIM


IPP Haushaltsgerät

IPP PV-Anlage








Aktives IPPPassives IPP


64


2. Modell für Optimierung (control model) Schnittstelle zur Optimierung (Eingabe)

bspw. „Bitfolge einer gewissen Länge“ (bit string)Interpretation der Eingabe

Übersetzung der Eingabe für internes Modell der Entitätbspw. „Zeitverzögerung“ oder „Fahrplan“

Steuerlogik (mit Abhängigkeit)Geräteverhalten ohne Eingriff, Ausweichlösungenbspw. Hysterese


Control Model

SIM

IPP Hybride,unterbrechbareHaushaltsgeräte


65


3. Modell der Entität (entity model) Gerätemodell

VerbrauchsprofileModelle zur Bestimmung des VerbrauchsWirkungsgrade, VerlusteEinschaltgrenzenMindest- und MaximalbetriebsdauernGrenzen

Temperatur, bspw. „wenn Temperatur größer als 80°C, dann unfreiwillige Abschaltung“Spannung


Control Model

SIM

IPP Elektr.Einschraub-heizkörper


66

OPTIMIERUNG


67

OptimierungOptimierungsproblem und Zielfunktion 1/3

Minimierung der EnergiekostenVariabler Zeitraum Nebenbedingungen Commodities E und ancillary commodities F

Beispiel mit Wirkleistung a und Erdgas n:

𝐶𝐶total 𝑡𝑡𝑛𝑛𝑛𝑛𝑛𝑛, 𝑡𝑡𝑒𝑒𝑛𝑛𝑒𝑒 = �𝑡𝑡𝑛𝑛𝑜𝑜𝑜𝑜

𝑡𝑡𝑒𝑒𝑛𝑛𝑒𝑒

�𝑒𝑒∈𝐸𝐸

𝐶𝐶𝑒𝑒(𝑡𝑡) , 𝐸𝐸 = {a, n}

𝐶𝐶𝑒𝑒 𝑡𝑡 = �𝑓𝑓∈𝐹𝐹

𝐶𝐶𝑓𝑓 𝑡𝑡 ,

𝐹𝐹n = { n, grid }

𝐶𝐶a 𝑡𝑡 = Ca,grid 𝑡𝑡 + Ca,grid,limit 𝑡𝑡 + Ca,chp,grid 𝑡𝑡 + Ca,pv,grid 𝑡𝑡+Ca,chp,self 𝑡𝑡 + Ca,pv,self 𝑡𝑡

𝐹𝐹a = { a, grid , a, grid, limit , a, chp, grid , a, pv, grid ,(a,chp,self),(a,pv,self)}

gegeben durch Interdependent Problem Parts


68


IPP Haushaltsgerät

IPP PV-Anlage









Beispiel mit Wirkleistung a und Erdgas n (Fortsetzung):Obere Lastgrenze L mit Faktor 𝜏𝜏upper

𝐶𝐶a,grid 𝑡𝑡 = 𝑃𝑃a,grid 𝑡𝑡 ⋅ 𝑐𝑐a,grid 𝑡𝑡 ⋅ Δ𝑡𝑡 ⋅ 𝑃𝑃a,grid 𝑡𝑡 > 0

𝐶𝐶a,grid,limit 𝑡𝑡 = 𝜏𝜏upper ⋅ 𝑃𝑃a,grid 𝑡𝑡 − 𝐿𝐿a,gridupper 𝑡𝑡 ⋅ ca,grid(𝑡𝑡) ⋅ Δ𝑡𝑡 ⋅ 𝑃𝑃a,grid 𝑡𝑡 > 𝐿𝐿a,grid

upper 𝑡𝑡

𝐶𝐶a,chp,grid 𝑡𝑡 =𝑃𝑃a,chp 𝑡𝑡𝑃𝑃a,dg 𝑡𝑡

⋅ 𝑃𝑃a,grid 𝑡𝑡 ⋅ ca,chp,grid 𝑡𝑡 ⋅ Δ𝑡𝑡 ⋅ 𝑃𝑃a,grid 𝑡𝑡 < 0

Lokale Erzeugung

(dg)

usw.


69


Berücksichtigung von Speicherfüllständen, AbnutzungUnsicherheit, möglicher zukünftiger Nutzen, VerschleißZusätzliche Kosten (bzw. Nutzen) P„Blick über den Optimierungshorizont“

𝐹𝐹total = 𝐶𝐶total + 𝑃𝑃 = 𝐶𝐶a + 𝐶𝐶n + … + 𝑃𝑃


IPP Haushaltsgerät

IPP PV-Anlage









70

OptimierungProbleme der Bewertung und Unsicherheit

BewertungGeräte (allgemein)

Verschleiß Wartung, Instandhaltung, InstandsetzungSpäterer Start kann zeitlich mit weiteren Geräten zusammenfallen

SpeicherHybride Haushaltsgeräte nutzen tlw. Heißwasser aus SpeicherAnfangs- und EndfüllstandPotentieller zukünftiger Nutzen

UnsicherheitPrädiktion

Wetter Wärme-/Kältebedarf, lokale ErzeugungAnwesenheit Gerätenutzung, Bedarf

VerbrauchFüllgrad, Menge, Restfeuchte


71

OptimierungAbgrenzung zu Modellprädiktiver Regelung

GemeinsamkeitenZeitdiskretes, dynamisches ModellBerechnung des zukünftigen Verhaltens in Abhängigkeit von SignalenGütefunktionOptimierung auf Basis eines gemessenen Zustands(im Gegensatz zu Optimaler Steuerung)

Modellprädiktive RegelungBerechnung des optimalen SignalsWiederholung i.d.R. nach jedem Zeitschritt

Organic Smart HomeVerwendung einer HeuristikWiederholung der Optimierung „wenn es notwendig ist“


72

Organic Smart HomeApplication and Simulation

More about the architecture and the functionality of the Organic Smart Home:

Allerding, F. & Schmeck, H.: “Organic Smart Home: architecture for energy management in intelligent buildings”, Proceedings of the 2011 Workshop on Organic Computing, 2011

Mauser, I.; Müller, J.; Allerding, F. & Schmeck, H.: “Adaptive Building Energy Management with Multiple Commodities and Flexible Evolutionary Optimization”, Renewable Energy, 87, Part 2, 2016

Energymanagement

Hardware abstractionand simulation

Real-world application FZI House of Living Labs KIT Energy Smart Home Lab Simulation

Organic Smart Home


73

Hot Water

Chilled Water

Reactive Power

Active Power

Natural Gas

Organic Smart HomeApplication and Simulation

Real-world application FZI House of Living Labs KIT Energy Smart Home Lab Simulation

More about the architecture and the functionality of the Organic Smart Home:

http://www.organicsmarthome.org | https://github.com/organicsmarthome

Allerding, F. & Schmeck, H.: “Organic Smart Home: architecture for energy management in intelligent buildings”, Proceedings of the 2011 Workshop on Organic Computing, 2011

Mauser, I.; Müller, J.; Allerding, F. & Schmeck, H.: “Adaptive Building Energy Management with Multiple Commodities and Flexible Evolutionary Optimization”, Renewable Energy, 87, Part 2, 2016

Energy simulation

Energy Simulation CoreElectrical simulation

Thermal simulation

Organic Smart Home


74

Organic ComputingGeneric Observer/Controller Architecture

System under Observation and Control of Superior Entity

Observer Controller

System under Observation and Control

Entity 1 Entity 2 Entity n

Communication Abstraction Layer

Entity Abstraction Layer


…

Communication Abstraction Layer

• Perception of external signals• Perceptibility and controllability

by superior entities

Entity Superior Entity

Framework: controlled self-organizationObserver and ControllerSelf-similarity, hierarchical structure


• Abstracts subordinate entities• Decouples management layer

Subordinate Entities


75

ArchitectureHierarchical Architecture for Smart Grids

Architecture can be applied to entities in future Smart Grids

Building energy management systemsSupervisory systemsDevice optimizationVirtual power plants…

Hierarchical structure to handle the complexity

Virtual Power Plant

Smart Transformer

Phasor Measurement

Unit

Electric Vehicle

Smart Building

Smart Factory

~~CHP

O CCAL

EALO C

CAL

EAL

O CCAL

EAL

O CCAL

EALO C

CAL

EAL

O CCAL

EAL

OCAL

EAL

O CCAL

EAL

Smart Home

O CCAL

EALO C

CAL

EAL

Demand Side Manager

DistributionGrid

ControlO C

CAL

EAL


76

Generic Observer/Controller Architecture

Goal: Establishing controlled self-organization in technical systems

Observer monitors and quantifies system states and dynamics

Controller influences the SuOC

Framework

Controller Observer

Data Analyzer

Pre-Processor

Monitor

Aggre-gator

Simulation Model

situa

tion

para

met

ers

sensors actuators

System under Observation and Control (SuOC)

Log File

Predictor

Mapping(Rule Base)

Rule Per-formanceEvaluation

Objective Function

Rule Adaptation

Module

raw data

actions

goals

leve

l 1le

vel 2

More about original Observer/Controller Architecture: U. Richter: Controlled Self-Organisation Using Learning Classifier Systems. PhD Thesis, KIT, 2009


77

Enhanced O/C Architecture

Modularity by abstraction layer

Separate control algo-rithms from entitiesFlexible aggregationSimulations vs. real environments

Privacy byData Custodian Service

Fine-grained control over data distributionNo direct acces forexternal entitiesData quality fordifferent parties

Controller Observer

sensors actuators

System under Observation and Control

Data Custodian Service

DB 1

Data Custodian

DB m

Database Connector

RequestHandler

Output Module 1

Output Module n

Access Log

raw data

actions

… …

Entity Abstraction LayerEntity Driver 1 Entity Driver k

actuators sensorsactions raw

data

out

in out

in

req.

goal

s


78

BOTTOM-UPBUILDING SIMULATION

Application 2


79

Application 2Bottom-up Building Simulation

Smart building scenariosResidential and commercial buildingsElectrical and thermal simulationSimulation GUIEvaluation (CSV, SQL)


80

Application 2: Exemplary Simulations and ResultsCombined Heat and Power

Smart residential buildingMicro combined heat and power plant (microCHP)Intelligent appliancesOptimization with respect to electricity costsSignificant increase of the self-consumption rate

More about the optimization of households using the Organic Smart Home: Allerding, F.; Mauser, I. & Schmeck, H.: “Customizable Energy Management in Smart Buildings Using Evolutionary Algorithms”, EvoStar 2014: 17th European Conference on Applications of Evolutionary Computation, Springer, 2014


81

Application 2: Bottom-up Building SimulationHybrid Home Appliances 1/3

0

250

500

750

1000

1250

1500

00:0

0

02:0

0

04:0

0

06:0

0

08:0

0

10:0

0

12:0

0

14:0

0

16:0

0

18:0

0

20:0

0

22:0

0

00:0

0

Wirk

leis

tung

/ W

Tageszeit

Konventionell Hybrid

4-person household (graph: average of 100 households)Photovoltaik-Anlage, Ppeak = 4 kW, PV-Vergütung: 10 Cent/kWhStromtarif: 30 Cent/kWh, Gaspreis: 8 Cent/kWhHybride Haushaltsgeräte

Szenario: 4 Personen, Energietarifkombination: ALT-20-40 mit Lastbegrenzung 3 kW, n = 100, Darstellung als Jahresmittel

Reduktion des Stromverbrauchs

um etwa 30%

Kostenreduktiondurch Hybridgeräte(4 Personen) bei konstantem Tarif:

150-300 EUR/a

Verschiebung von Strom zu Gas


82

Application 2: Bottom-up Building SimulationHybride Haushaltsgeräte 2/3

051015202530354045

0

250

500

750

1000

1250

1500

10:3

0

11:0

0

11:3

0

12:0

0

12:3

0

13:0

0

13:3

0

14:0

0

14:3

0

Stro

mpr

eis

Cen

t/kW

h

Wirk

leis

tung

/ W

Tageszeit

Konventionell Hybrid Verschiebbar Hybrid verschiebbar Strompreis

4-Personen-Haushalt (Jahresmittel von 100 Haushalten)Photovoltaik-Anlage, Ppeak = 4 kW, PV-Vergütung: 10 Cent/kWhStromtarif mit Preissprung: 40/20 Cent/kWh, Gaspreis: 8 Cent/kWh


bis zu etwa 50% höhere durchschnittliche Leistung


83

Application 2: Bottom-up Building SimulationHybride Haushaltsgeräte 3/3

051015202530354045

0

250

500

750

1000

1250

1500

10:3

0

11:0

0

11:3

0

12:0

0

12:3

0

13:0

0

13:3

0

14:0

0

14:3

0

Stro

mpr

eis

Cen

t/kW

h

Wirk

leis

tung

/ W

Tageszeit

Konventionell Hybrid Verschiebbar Hybrid verschiebbar Strompreis

Lastmanagement mit Hybridgeräten kann zu sprunghaften Reaktionen führen


4-Personen-Haushalt (Jahresmittel von 100 Haushalten)Photovoltaik-Anlage, Ppeak = 4 kW, PV-Vergütung: 10 Cent/kWhStromtarif mit Preissprung: 40/20 Cent/kWh, Gaspreis: 8 Cent/kWhHybride Haushaltsgeräte

bis zu etwa 100% höhere durchschnittliche Leistung


84

Application 2: Exemplary Simulations and ResultsHybrid Household Appliances

Smart residential buildingHybrid appliances, photovoltaic systemOptimization with respect to electricity costs

-40

-20

0

20

40

0

100

200

300January July

hybridunivalentnon-deferrable

hybridunivalentnon-deferrable

Costs

Active power

Natural Gas

TotalCos

ts [E

UR

]

Self-consumption rate

Self-sufficiency rate0%

20%40%60%80%

0%

20%

40%

60%

More about the optimization of households using the Organic Smart Home: Mauser, I.; Schmeck, H. & Schaumann, U.: “Optimization of Hybrid Appliances in Future Households”,ETG Congress 2015: Die Energiewende – Blueprint for the new energy age, VDE, 2015


85

FZI House of Living Labs

Heating System

Meeting Room: Hollywood

Micro CHP

Condensing Gas Boiler

Adsorption Chiller

A/C

Hot Water Storage Tanks

Chilled Water Storage Tanks

ElectricalInsertHeatingElement

Application 2: Trigeneration at FZI House of Living LabsCombined Cooling, Heat, and Power


86

Smart Commercial BuildingKraft-Wärme-Kälte-Kopplung

Simulation der realen Anlage im FZI House of Living LabsJuli 2014: Wetterdaten und RaumnutzungWärmemengenzähler

OptimierungspotentialWirkungsgrad der AdKMSpeicherverluste

Adsorptions-Kältemaschine

Heißwasser-speicher

Kaltwasser-speicher

4,2 kW

Rückkühler

RaumHollywood

Kühl-kassette

5.5 kW

Mikro-BHKW

Gas-netz

Strom-netz

Abkürzungen: AdKM: Adsorptions-Kältemaschine; Mikro-BHKW: Klein-Blockheizkraftwerk

+


87

Smart Commercial BuildingKraft-Wärme-Kälte-Kopplung

Ergebnisse der Simulation Juli 2014 mit Optimierung auf EnergiekostenMikro-BHKW und Kältemaschine optimiert bzw. unoptimiert (C-1 bis C-4) Unterschiedliche Modelle für Adsorptions-Kältemaschine und Speicher

C-1: unoptimiert, C-2: Mikro-BHKW optimiert, C-3: AdKM optimiert, C-4: beide optimiertVerluste Heißwasser-/Kaltwasserspeicher mit S1 (hoch) und S2 (niedrig)AdKM: Modell A (geringere Effizienz) und Modell B (höhere Effizienz durch Anpassung Rückkühler)

Auswertung und ErgebnisseReduktion der Energiekosten um durchschnittlich bis zu 29%Effizienz-Steigerung der Kältemaschine um etwa 8 bis 13 ProzentpunkteGeeignete Bewertung der Speicherzustände ist essentiell

Speicher:Modell S1

AdKM:Modell A

„FZI“

Speicher:Modell S2

AdKM:Modell B


88

Application 2Bottom-up Building Simulation

Exemplary recent and upcoming publications:

Ingo Mauser Integration and Optimization of Interdependent Devices 9/23/2016

TODO


89

HARDWARE-IN-THE-LOOP SIMULATION

Application 3

Organic Smart Home


90

Application 3 – Hardware-in-the-Loop Simulation(P)HiL Approach

KIT Energy Smart Home LabCombination of real and simulated systems4-quadrant amplifier providing an artificial mains networkExemplary scenario:Integration of a hybrid energy storage system

Battery energy storage system and double-layer supercapacitorsReactive power provision, voltage stability, short circuit power provision


91

MULTI-BUILDING SIMULATIONApplication 4


92

Application 4: Multi-Building Simulation Motivation

Power grid

Signals from/to grid operator

Signals from/to market

Effects of energy management on low-voltage gridsPenetration of distributed generation and electric vehiclesBuilding energy management systemsGrid topologies

Coordination mechanismGrid stabilizationPrice and control signals


93

Application 4: Exemplary Simulations and ResultsHeuristic Optimization and Collaboration

Collaborating building energy management systemsSuperior entity for exchange of parametersCollective calibration process of the Evolutionary AlgorithmBetter optimization results with the same number of evaluationsSelf-organizing systems

More about improving the optimization of households by collaboration:

Mauser, I.; Dorscheid, M. & Schmeck, H.: “Run-Time Parameter Selection and Tuning for Energy Optimization Algorithms”,PPSN Conference 2014: Parallel Problem Solving from Nature XIII, 2014


94

Application 4: Multi-Building SimulationArtificial 106-bus Low-voltage Grid

Grid model106-bus grid101 nodes/buildingsSub-urban scenarioBased on real neighborhoodMedium-voltage transformer

Various test scenariosSmart buildingsStorage technologiesElectric vehicles

Coordination mechanismMore about bottom-up simulation and optimization of power grids:

Kochanneck, S.; Schmeck, H.; Mauser, I. & Becker, B.: “Response of Smart Residential Buildings with Energy Management Systems to Price Deviations”, Proceedings of the IEEE PES Conference on Innovative Smart Grid Technologies Asia (ISGT-Asia), 2015

Kochanneck, S.; Hirsch, C.; Mauser, I.; Schröder, M. & Schmeck, H.: “Bottom-Up Simulation of Suburban Power Grids”,VDE ETG Congress 2015: Die Energiewende – Blueprint for the new energy age, 2015

1

2

21

22

27

40

28

39

29

43

45

41

44

62

63

82

83 91

90 98

99 106

8

15


95

Application 4: Exemplary Simulations and ResultsEffects of Smart Buildings on Distribution Grids

Effects of intelligent buildings and demand response in low-voltage grids

Grid stabilization by increased self-consumption of householdsReduction of load peaks at transformer and connections

less voltage deviations

More about bottom-up simulation and optimization of power grids:

Kochanneck, S.; Schmeck, H.; Mauser, I. & Becker, B.: “Response of Smart Residential Buildings with Energy Management Systems to Price Deviations”, Proceedings of the IEEE PES Conference on Innovative Smart Grid Technologies Asia (ISGT-Asia), 2015

Kochanneck, S.; Hirsch, C.; Mauser, I.; Schröder, M. & Schmeck, H.: “Bottom-Up Simulation of Suburban Power Grids”,VDE ETG Congress 2015: Die Energiewende – Blueprint for the new energy age, 2015

Utilizing Flexibility of Hybrid Appliances in Local Multi...

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