MAKING ENERGY MODELING
SPATIALLY EXPLICIT TO ENABLE A
COST- OPTIMAL RESOURCE USE
Marianne Zeyringer
[email protected]
Copernicus Institute of Sustainable Development, Utrecht University, The
Netherlands
Institute for Sustainable Economic Development, University of Natural Resources
and Life Sciences (BOKU), Austria
European Commission, Directorate- General Joint Research Centre, Institute for
Energy and Transport, Energy Systems Evaluation Unit, The Netherlands
OVERVIEW
I.
Background information - Spatially explicit characteristics of
the energy system
II.
Research Questions
III.
Modeling the maximum integration of PV into the distribution
grid
IV.
Conclusions and future work / expected results
I. BACKGROUND INFORMATION SPATIALLY EXPLICIT CHARACTERISTICS OF THE
ENERGY SYSTEM
I. BACKGROUND INFORMATION
•
EC target: Increase share of RES in the gross final
energy consumption from 8.5% in 2005 to 20% in
2020
•
Existing energy system: spatially explicit
characteristics less important
•
Energy system with high integration of RES is
constrained by geographic characteristics:
- Demand
- RES potential
- Infrastructure
I. SPATIALLY EXPLICIT CHARACTERISTICS OF THE
ENERGY SYSTEM
Demand
Infrastructure
- Location specific
- Building specific
- Industrial profiles: autoproduction?
- Importance of load
profiles
• Electricity grid
- New building: network
optimization problem
- Integration of RES
- Location of necessary
upgrades
• CO2
• District Heating
• Hydrogen
I. SPATIALLY EXPLICIT CHARACTERISTICS OF THE
ENERGY SYSTEM
Renewable energy sources
• Biomass:
Trade off between
transportation distance,
plant location and plant
size
• Hydropower:
- reservoir and pumped:
topography, falling height
- run of river: slope,
velocity, water depth,
flowlines
• Geothermal:
- High temperature: highly
location specific
transmission line
upgrades
- Low temperature:
conductivity, thermal
capacity, temperature,
topography…
I. SPATIALLY EXPLICIT CHARACTERISTICS OF THE
ENERGY SYSTEM
Spatial diversification decreases variability
Solar energy:
irradiation, temperature, shadowing, turbidity,
cloudiness, rooftops, grid upgrades
• Wind:
Resource potential, grid upgrades
I. SPATIALLY EXPLICIT CHARACTERISTICS OF
THE ENERGY SYSTEM: MODELS
- Mixed integer programming
- Geographic Information Systems
- Simulation Studies
- Grid Studies
But currently energy system models have relevant spatial limitations
- models do not consider the real cost limitations due to
geographical distribution of resources and of demand
- new capacity can be added at a fixed generic cost which does not
consider specific real terrain constraints
I. CONCLUSIONS
• Spatial disaggregation of the generation potentials and demand
patterns estimate and optimize the required investments in
RES by taking into account the necessary infrastructure
reinforcements
• Importance of studying the interaction of several parts of the
energy system in one model
- example: Lack in methodologies to determine the
maximum integration of small RES such as PV
- impact depends on: local PV potential, installed
infrastructure, regional composition of consumers
• Data limits on the spatially explicitly resource supply
potential and the demand side, e.g. spatially disaggregated load
data need of methodologies capable of simulating the data
required
I Conclusions of spatially explicit energy systems:
MODELS
• Spatially explicit energy models: most common for forest energy
• Extension of BeWhere (Leduc et al, 2008), Schmidt et al. 2009
• MIP model: whole supply chain from biomass production to
delivery of the final product, optimal locations of biofuel plants,
biomass CHP…
• minimizes production costs
with regard to distances to
biomass supply and fuel
demands
•bottom up optimization model
II. RESEARCH QUESTIONS
II. RESEARCH QUESTIONS
• How to model geographically explicit electricity load
profiles with limited data?
• What is the maximum PV integration in the distribution
grid?
• What are the associated costs for a higher integration?
• What is the cheapest option to manage high
integration rates? (upgrade, integration of electric
vehicles…) and what are the associated opportunity
costs
III. MODELING THE MAXIMUM INTEGRATION OF PV
INTO THE DISTRIBUTION GRID
III. DATA AND METHODOLOGY TO MODEL THE MAXIMUM
INTEGRATION OF PV IN THE DISTRIBUTION GRID
• Number of households and
employees per sector per
km²
• Measured Industry load
profiles
• Standardized load profiles
• PV: high spatial and
temporal resolution
• Address points
• Exact location of
transformers
• Rooftop areas
I. Modeling of demand data
II. Simulation of distribution
grid using minimum
spanning tree algorithm
III. Net flow calculations in
order to determine the
maximum PV integration
IV. Step cost curve for
additional integration
V. Costs for other options
(opportunity costs)
IV. SIMULATION OF DISAGGREGATED
LOAD PROFILES FOR MODELING PURPOSES
III. LITERATURE ON SPATIALLY EXPLICIT MODELING OF
DEMAND
• Importance of electric load modeling for low aggregation levels:
- integrating renewable energy supply into distribution grids [1]
- cogeneration [2] and micro CHP [1], [3]
- mixed energy distribution systems i.e. incorporating more than one
carrier [4]
• Randomness of consumption
- loads at a low aggregation are difficult to model [5]
- need of large number of diverse residential load profiles [1]
• Load profiles are modeled from
- appliance profiles [6] solely or adding human behavior [7], and
information on the buildings [8]
- statistical analyses of electrical data from apartments [9]
- using a time use survey [15]
- using billing data [16]
III. DATA TO MODEL SPATIALLY EXPLICIT DEMAND
• Industry load profiles collected for the analysis
• VDEW standardized load profiles:
- for 3 days: Weekday, Saturday, Sunday
- for 3 seasons: winter, summer, interim period
- for different load types
- normalized for a consumption of 1000kWh
• Workplace assessment (Statistics Austria, 2001)
- Number of employees and
- Type of sector per km²
• Buildings- and dwellings census (Statistics Austria, 2001)
- Number of building and
- Heating types per km²
III. METHODOLOGY TO MODEL SPATIALLY EXPLICIT
DEMAND
III. METHODOLOGY TO MODEL SPATIALLY EXPLICIT
DEMAND
• Grid cells of 1km²
- More than 150 households: usage of standardized load profiles
depicts reality [23].
- Less than 150 households: need to simulate stochastic load
profiles along a Gaussian distribution for each consumer unit
connected to the distribution grid.
• Simulation of load profiles in every grid cell based on the composition
and number of consumers.
• Aggregation of generated load profiles of all households and sectors
 total load profile in each 1km².
III. RESULTS: VALIDATION ON A PER COUNTRY LEVEL
2000
2500
Winter_Saturday
Winter_Sunday
Winter_Weekday
Summer_Saturday
Summer Sunday
Summer Weekday
Interim_Saturday
Interim_Sunday
Interim_Weekday
1500
MW
• Simulated load
profiles for an
average summer,
winter, and interim
period day
- Average consumption
is highest during
weekdays
- Three peaks
0
5
10
15
time
20
III. RESULTS: VALIDATION ON A PER COUNTRY LEVEL
22000
Aggregated load for one day over the interim
period (2010).
800
600
100000
700
140000
MW
e-control
modelled
0
5
10
15
20
time
e-control
modelled
36000
500
Aggregated load for one day for Vorarlberg
(2010).
5
10
15
time
20
24000
0
28000
MW
32000
400
GW
e-control
modelled
180000
Aggregated load for one day over the
year (2010).
0
5
10
15
time
20
IV. CONCLUSIONS AND FUTURE WORK / EXPECTED
RESULTS
IV CONCLUSION: SPATIALLY EXPLICIT
DEMAND SIMULATION
Conclusion
Methodology on simulating demand allows to:
- improve spatially explicit energy system models
- load modeling to be coupled with the renewable energy
potential in a geographically explicit context realistic
optimization of supply and demand
- application to other regions
Future work
Validation with measured household load profiles
IV OUTLOOK
• Maximum integration of rooftop PV in the distribution grid
• More realistic integration of RES in cost-minimisation energy
system models via a spatially specific step cost curve
• Estimation of Option value/ Opportunity costs
• Usage of Data in Spatially Explicit Optimization Model for
Austria