research at offis energie, oldenburg,...
TRANSCRIPT
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Research at OFFIS Energie, Oldenburg, GermanyNicholas A. Brown
Department of Electrical and Computer EngineeringOct. 22, 2015
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Overview
◉ My simulation needs in a nutshell◉ How I identified a person doing relevant work◉ How I became a Visiting Researcher◉ OFFIS Energie, Univ. of Oldenburg◉ My research experience there◉ Quick visual tour
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My simulation needs in a nutshell
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My simulation needs in a nutshell
◉ Power system models represent high-voltage transmission OR low-voltage grids, not both◉ Wanted to address transmission-level needs,
given high wind penetration◉ Goal: simulate thousands of air conditioners in
low-voltage grids responding each day◉ Wanted to avoid developing a combined high-
voltage/low-voltage model from scratch
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How I identified a person doing relevant work
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Searching on “discrete event simulation” -> Video of conference talk on SimPy-> Projects listed on Stefan’s homepage
Stefan Scherfke
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Projects listed on Stefan Scherfke’shomepage -> OFFIS -> Contacted Stefan
mosaik• Allows simulations to
exchange data• Stefan Scherfke, S. Schutte
were original developers.
SimPy• Discrete event simulation.• S. Scherfke developed
SimPy 3• Supports simulations in
mosaik
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How I became a Visiting Researcher
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Expressed enthusiasm about common and complementary interests
◉ Overlapping interest in co-simulation◉ OFFIS project: distributed energy resources◉ Me: distributed demand resources◉ Stefan put me in touch with group manager of
Simulation and Automation for Complex Energy Systems, Dr.-Ing. Sebastian Rohjans◉ I noted that I had funding
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Articulated my research goals and simulation plans
◉ Wrote proposal with problem statement, modeling approach, assumptions, and data sources.
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Overview of OFFIS Energie and Energy Research at University of Oldenburg
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OFFIS Energie
◉Associated institute
◉Application-oriented
◉~50 software engineers, PhDs, current PhD students
◉Jun.-Prof. Lehnhoffoversees two groups
◉Dr. Rohjans leads simulation group
Systems Analysis and
Distributed Optimization
Architecture Engineering and
Interoperability
Smart Resource Integration
Simulation and Automation of
Complex Energy Systems
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OFFIS
NextEnergy
ForWindImage: forwind.de
Energy Research in Oldenburg
• Systems-level simulation• Information and Communication
Technology (ICT)• Smart Grid, Renewables, Markets,
Demand-side, EVs
• Aerodynamics, damage detection, power train, structures, numerical simulation, offshore
• Joint center w/ Bremen, Hannover• Partners: German Aerospace Center,
Fraunhofer IWES Nordwest
• PV, fuel cells, combined heat and power, energy storage
• Founded with EWE
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My research experience there
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Areas of research◉Item 1: Made high-voltage simulator PyPower run with PV buses and solve for gens’ least-cost output
MosaikCo-simulation
Engine
PyPowerOPF solver
Conventional and Wind
Power Profiles
Commercial and
Industrial Load Data
Load-serving Entity Model
Fixed Residential Load
Data
Air Conditioner Model
Weather and Distributed Solar Data
Co-Simulation of OPF, Load-serving Entities, Air Conditioners
◉Item 2: Collected data for air conditioner simulation, system benchmark
◉Item 3: Developed equations for air conditioner model entities
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Before• When using mosaik to run
PyPower, PyPower ran power flow for distributed generation
• When using mosaik to run PyPower, mosaik accepted Excel and JSON text formats as inputs
Item 1: Made high-voltage simulator minimize cost
After• When using mosaik to run
PyPower, PyPower runs high-voltage optimalpower flow (least cost)
• When using mosaik to run PyPower, mosaik accepts Excel, JSON and standard PyPower case format
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Item 2: Web-scraped public power system (ERCOT) and weather through automation
◉Benchmarking: Total ERCOT demand, system capacity, total wind (every 5 mins.)
◉Benchmarking: Online generation capacity, controllable load (every 5 mins.)
◉Input to air conditioner simulation by Texas region: temp., wind, cloud cover (hourly)
WeatherReservesEnergy
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Item 3: Derived temperature equation for air conditioner model entities
◉Temperature as a function of time
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Item 3: Derived cycle duration equation for air conditioner model entities
◉Time as function of starting, ending temperatures
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Ongoing discussion and support
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Quick visual tour
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WheelsNext to Campus Haarentor
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Campus WechloyCarl von Ossietzsky Universität Oldenburg
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Lüneburg
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Cuxhaven
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Danke (thank you)!