Data Life Cycles in Future Residential Multi Commodity Energy Management Systems

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1 Data Life Cycles in Future Residential Multi

Christian Gitte, and Hartmut Schmeck Karlsruhe

Symposium 2014, 25 th September 14 Supported by

1 1 Data Life Cycles in Future Residential Multi Commodity Energy Management Systems Fabian Rigoll, Christian Gitte, and Hartmut Schmeck Karlsruhe Institute of Technology (KIT), Germany Great Lakes Symposium 2014, 25 th September 14 Supported by Picture: "Turbines" by Steven Feather via flickr, 8 Sept 2014

2 2 Karlsruhe Institute of Technology: The Merger of National Research Center Karlsruhe and Karlsruhe University Research Teaching Innovation Source: KIT, 01/2014 Employees Students 9,254 24, Professors 785 Annual Budget in Million Euros

3 Energiewende A Large Project for Future Generations Political Agenda in Germany Ambitious goals 30% renewables by 2020, 50% by 2030, 80% by 2050 Nuclear phase out Technical Implementation More

3 3 Energiewende A Large Project for Future Generations Political Agenda in Germany Ambitious goals 30% renewables by 2020, 50% by 2030, 80% by 2050 Nuclear phase out Technical Implementation More renewables Load shifting necessary More storage Flexible gas-fired power plants Demand side management Decentralized generation Our Focus Demand side management in residential buildings Copyright Fox Entertainment

4 4 Motivation: Future Multi Commodity Residential Homes Classic Independent Mode of Device Control Control Control Picture: µchp by Senertec, Germany White goods Consume power Optional hot water connection µchp Heat and power Heat storage Control Cooling Heat Control Renewable power Power

5 5 Motivation: Future Multi Commodity Residential Homes Single Commodity Optimization Mode of Device Control Picture: µchp by Senertec, Germany White goods Consume power Optional hot water connection Control Control µchp Heat and power Heat storage Renewable power Cooling Heat Power

6 6 Motivation: Future Multi Commodity Residential Homes Multi Commodity Optimization Mode of Device Control Picture: µchp by Senertec, Germany White goods Consume power Optional hot water connection Control µchp Heat and power Heat storage Renewable power Cooling Heat Power

7 Multi Commodity Optimization in Residential Homes Problems Various data sources and

case: Senertec Dachs Residential home Micro combined heat and power Otto generator using

electrical heating Hybrid heating by natural gas or electricity Approach Data life cycle

7 7 Multi Commodity Optimization in Residential Homes Problems Various data sources and data sinks Interleaved data flows Complex control and optimization tasks Exemplary use case: Senertec Dachs Residential home Micro combined heat and power Otto generator using natural gas Cogeneration of heat and electricity Warm water storage with additional electrical heating Hybrid heating by natural gas or electricity Approach Data life cycle analysis Role model of acting entities Control Picture: µchp by Senertec, Germany Add-On Heating Element

8 8 Data Life Cycle Analysis A Prototype Data Life Cycle Acquisition Transmission Storage Analysis Distribution Deletion What data? Formats? Data quality? Source? Mode of transmission? Mode of storage? Formats? Quality? Methods? Purpose? Stakeholders? What data? To Whom? How? Are data deleted? When? Why? How? A tool to gain better understanding of data life cycles Not necessarily a straight-forward cycle

9 Role Model Based on European Energy Legislation Customer (C) Generalization and Specializations Balance Responsible Party (BRP) User of electricity and heat Pays the bill Owns personalized data

9 9 Role Model Based on European Energy Legislation Customer (C) Generalization and Specializations Balance Responsible Party (BRP) User of electricity and heat Pays the bill Owns personalized data Operator of the distribution grid Design of tariffs Billing Specialisation: Operator of electricity distribution grid Supplier of energy Voltage Control Congestion Management Specialisation: Supplier of electricity Supplier of natural gas Responsible for imbalance settlement Application of Demand Side Management (electricity only) Operator of natural gas distribution grid

10 10 Role Model Legitimate Interest in Data Customer DSO Retailer BRP Metering data x x x x Billing data x x x Operational data x x Contractual data x x x

11 11 Data Life Cycle Analysis Interleaved Data Life Cycles PRIMARY DATA Data flows between distinct data life cycles DERIVED DATA

12 12 Resulting Big Picture of Data Life Cycle Analysis message flow / life cycle Customer (C) electricity/gas Distribution System Operator (DSO) electricity/gas Retailer (R) electricity/gas Balance Responsible Party (BRP) electricity only Acquisition Primary data 1. µchp equipment data 2. Metering data (electricity) 3. Metering data (gas) 4. User preferences Derived data 5. Control signals/incentives Derived data from Cs 1. Aggregated metering data Derived data from DSO/Cs 1. Aggregated metering data 2. Flexibility profiles (electricity only) 3. Grid usage invoices Primary data 4. Contract data Derived data from R 1. Aggregated metering data (electricity) 2. Flexibility profiles (electricity) Transmission (from / to) 1. µchp EMS 2. Meter EMS 3. Meter EMS 4. Local UI EMS 5. BRP EMS 1. C s meter Metering data management system 1. DSO ERP 2. C ERP 3. DSO ERP 4. Back Office ERP 1. R Control System 2. R Control System Storage (Local) EMS database (Local) Metering database ERP system Control system Analysis Aggregation Analysis Optimization Data validation Billing Forecasting Data validation Billing Forecasting Forecasting Calculation of control signals/incentives Distribution (to) R: Aggr. metering data DSO: Aggr. metering data R: Energy flexibility profiles (electricity only) R: Aggr. metering data R: Grid usage invoice BRP: Aggr. metering data BRP: Flexibility profiles BRP: Contract info Customer: Invoice C: Control signals/incentives (electricity) Deletion Deletion of data which is no longer necessary Deletion of data which is no longer necessary Deletion of data which is no longer necessary Deletion of data which is no longer necessary

13 13 Resulting Big Picture of Data Life Cycle Analysis (Part 1) message flow / life cycle Customer (C) electricity/gas Distribution System Operator (DSO) electricity/gas Retailer (R) electricity/gas Balance Responsible Party (BRP) electricity only Acquisition Primary data 1. µchp equipment data 2. Metering data (electricity) 3. Metering data (gas) 4. User preferences Derived data 5. Control signals/incentives Derived data from Cs 1. Aggregated metering data Derived data from DSO/Cs 1. Aggregated metering data 2. Flexibility profiles (electricity only) 3. Grid usage invoices Primary data 4. Contract data Derived data from R 1. Aggregated metering data (electricity) 2. Flexibility profiles (electricity) Transmission (from/to) 1. µchp EMS 2. Meter EMS 3. Meter EMS 4. Local UI EMS 5. BRP EMS 1. C s meter Metering data management system 1. DSO ERP 2. C ERP 3. DSO ERP 4. Back Office ERP 1. R Control System 2. R Control System Storage (Local) EMS database (Local) Metering database ERP system Control system

14 14 Resulting Big Picture of Data Life Cycle Analysis (Part 2) message flow / life cycle Analysis Customer (C) electricity/gas Aggregation Analysis Optimization Distribution System Operator (DSO) electricity/gas Retailer (R) electricity/gas Balance Responsible Party (BRP) electricity only Data validation Billing Forecasting Data validation Billing Forecasting Forecasting Calculation of control signals/incentives Distribution (to) R: Aggr. metering data DSO: Aggr. metering data R: Energy flexibility profiles (electricity only) R: Aggr. metering data R: Grid usage invoice BRP: Aggr. metering data BRP: Flexibility profiles BRP: Contract info Customer: Invoice C: Control signals/incentives (electricity) Deletion Deletion of data which is no longer necessary Deletion of data which is no longer necessary Deletion of data which is no longer necessary Deletion of data which is no longer necessary

15 Lessons Learned The energy transition is a large

cycles in multicommodity scenarios are complex Systematic

Control Data life cycle analyses can be employed to gain a

15 15 Lessons Learned The energy transition is a large project for future generations Data flows and data life cycles in multicommodity scenarios are complex Systematic approaches are needed, in order to reduce this complexity Control Data life cycle analyses can be employed to gain a better understanding Scenarios should be divided into several use cases

16 16 Data Life Cycles in Future Residential Multi Commodity Energy Management Systems Fabian Rigoll, Christian Gitte, and Hartmut Schmeck Karlsruhe Institute of Technology (KIT), Germany / / Thank you for your kind attention! Feedback? Questions? Supported by Picture: "Turbines" by Steven Feather via flickr, 8 Sept 2014

17 17 Backup Slides Picture: "Turbines" by Steven Feather via flickr, 8 Sept 2014

18 18 KIT: 30 Fields of Competence Bundled in 6 Areas of Competence Matter and Materials Elementary Particle and Astroparticle Physics Condensed Matter Nanoscience Microtechnology Optics and Photonics Applied and New Materials Earth and Environment Atmosphere and Climate Geosphere and Risk Management Hydrosphere and Environmental Engineering Buildings and Urban Infrastructure Applied Life Sciences Biotechnology Toxicology and Food Science Health and Medical Engineering Cellular and Structural Biology Systems und Processes Flow and Particle Dynamics Chemical and Thermal Process Technology Fuels and Combustion Systems and Embedded Systems Power Plant Technology Product Life Cycles Mobile Systems and Mobility Information, Communication, and Organization Algorithms, Software, and Information Science Systems Cognitive Systems and Information Processing Communication Technology High-performance Computing and Distributed Systems Mathematical Models Organization and Service Design Technology, Culture, and Society Cultural Heritage and Social Change Economic Organization and Innovation Interaction of Science, Technology, and Society

19 KIT Centers: Focus on Topics, Strategic

Materials, Structures, Functions (former KIT

Photonics) Elementary Particle and Astroparticle

Information, Systems, Technologies (former KIT

19 19 KIT Centers: Focus on Topics, Strategic Research Planning Climate and Environment Energy Materials, Structures, Functions (former KIT Center NanoMicro and KIT Focus Optics and Photonics) Elementary Particle and Astroparticle Physics Climate and Environment Mobility Systems Information, Systems, Technologies (former KIT Focuses COMMputation and Anthropomatics and Robotics) Humans and Technology

20 20 Add On Heating Element Source. Cf. Technical Manual Senertec Dachs

S-BPM ONE 2009 Constitutional convention

S-BPM ONE 2009 Constitutional convention Karlsruhe, October 22nd 2009 Host: Prof. Dr. D. Seese, KIT, Institute AIFB INSTITUTE OF APPLIED INFORMATICS AND FORMAL DESCRIPTION METHODS (AIFB) KIT University