Supply and Demand Management in the Scharnhauser Park-Project, Germany
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1 Supply and Demand Management in the Scharnhauser Park-Project, Germany Dietrich Schneider Page 1
2 Dietrich Schneider Working at the University of Stuttgart since ten years, as a scientist in the field of hardware development manly thermal cooling machine. He is author of about 30 papers in the field of the thermal machine. He work actively in the frame of Polycity area, on the scientific management of the supply side of the project. Page 2
3 Supply Structure of the Scharnhauser Park Area 150 ha of developing area for Inhabitants and work places Page 3 Heating grid: 532 Transfer stations, each with 150 l storage tank 13.5 km Piping length 16.0 MW max. heating power MWh/a heating energy demand Biomass cogeneration unit
4 Supply Structure of the Scharnhauser Park Area Biomass cogeneration unit: Peak load gas vessels: 6 MW thermal power 1 MW electrical power 14 MW thermal power Page 4
5 Supply Structure of the Scharnhauser Park Area Biomass Cogeneration Unit Heating condenser Heat net Thermooil tubes Evaporator Thermooil- Vessel Generator ORC-Module Turbine Biomass- Furnace Page 5
6 General Objectives of Supply and Demand Management - Collection and visualisation of all energy flows within the Scharnhauser Park area - Optimisation of the temporal and spatial compliance of heating energy supply and demand, to avoid over heat production and to reduce natural gas consumption caused by peak loads - predictive simulation based heat load management - advanced charge management for decentral heat storages - innovative night time saving algorithms Infra structure for automated data acquisition required! Page 6
7 Monitoring and Communication Situation at Project Start 2005 Energy supply: -Biomass cogeneration unit: KEA control system, but not interface for data transfer Energy demand: -District heating network: No infra structure installed for automated data acquisition -Communal buildings: Kieback&Peter control system, but no interface for data transfer Page 7
8 Monitoring and Communication Present solutions for automated data acquisition School Youth Club Residential Sports hall Elektror City hall PV System Parkhaus Page 8 Biomass cogeneration unit
9 Monitoring and Communication Communication structure supply Biomass Cogeneration Unit Internet KEA control system M-bus Hard disc Main electricity counters Page 9 Data files ftp server System heat meters and sensors ennovatis Main smartbox heat meter Database (Data files) ftp server Smartbox clients ftp reader/writer client ftp reader/ writer client Online simulations Energy flow analysis EM tools
10 Monitoring and Communication Communication structure demand Communal buildings Internet / modem Cityhall School Sports Hall DataSocket Kieback & Peter BMS OPC interface DataSocket reader/writer client OPC server Smartbox clients Database (Data files) server DataSocket reader/writer client Heating energy counters ennovatis smartbox Electricity counters ftp server ftp reader/writer client Online simulation Energy flow analysis EM tools Page 10
11 Monitoring and Communication Communication structure demand Residential Buildings Modem M-bus NZR Unimod radio based Heating energy counters of each flat Database (Data files) NZR Software for data transfer ftp server Main electricity counter Page 11 Main heating energy counter ftp reader/writer client Online simulations Energy flow analysis EM tools
12 Monitoring and Communication Data base structure Annual performance data (Billing system SWE, ENBW) Energy supply Performance data with high time resolution (Automated performance data collection) Energy supply Building data Energy consumption Weather data Energy consumption Geo Media Page 12 Web based performance visualisation Analysis
13 Monitoring and Communication Data base and energy flow visualisation Energy supply Building data Energy demand ACCESS database DXF Geo Media WebMap Publisher Page 13
14 Analysis of the heat supplied by the biomass cogeneration unit heat generation Generated heat quantitiy [MWhth]] ,6% 81,3% Biomass biomass 68,4% biomass natural gas biomass january january february february march march april april mai mai june june july july august august september september oktober oktober november november december december month Page 14
![500 1.500 1.500 Generated heat quantitiy [MWhth]] 1.000 1.](/docs-images/42/7984237/images/page_14.jpg "000 1.")
15 Analysis of heat supply profiles Summer case, domestic hot water supply with optimised storage tank charge management Thermal input heat net [kw] 2500,0 2000,0 1500,0 1000,0 500,0 Charge of heat storage Optimised tanks storage tank charge management heat impact demand on on the a typical heat demand summer day without storage programming Times with storage frames programming with high hot water consumption 0,0 0:00 2:00 4:00 6:00 8:00 10:00 12:00 14:00 16:00 18:00 20:00 22:00 Uhrzeit 523 Transfer stations, each with 150l heat storage tank Page 15
16 Analysis of heat supply profiles Supply profile analysis Average daily heat supply profile December January Peak loads after night time energy saving period Maximum value Average Minimum value Heating power [kw] :00 06:00 12:00 18:00 00:00 Time Page 16
17 Analysis of heat supply profiles Winter case, heating energy and domestic hot water January 2007 Peak loads after night time energy saving period, covered by gas vessel Heating energy demand Biomass unit net input Ambient temperature Page 17
18 Analysis of heat supply profiles Winter case, heating energy and domestic hot water 30. January 2007 Heat net Biomass unit net input Ambient temperature 3020 kwh Peak load reduction through intelligent night time saving and storage charge control Storage loading inhibited Storage loading permitted Page 18
19 Objectives for further development Heating period 2007 / Further optimisation of the decentral heat storage charge control - Implementation of an adaptive control of the length and room temperature for the night time saving period for all public buildings according to the ambient temperature - Further development of heating load forecasting models for the whole Scharnhauser Park area - Development of heating load forecasting models for single buildings in the Scharnhauser Park area Page 19
20 Objectives for further development Active supply energy management control system Weather actual forecast actual KEA INSEL EM actual Measured actual performance Page 20 forecast (whole day) Energy management tool energetic and economical optimized control strategy Simulation model heating energy demand
21 Objectives for further development Building energy management control system (Public and commercial buildings) Weather actual forecast actual BMS INSEL EM actual Measured actual energy consumption Page 21 Energy management tool energy flow observation and optimized control strategies forecast (whole day) Simulation model building/plants
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