PROSUMERS’ IMPACT ON THE ELECTRICITY SYSTEM Heinisch... · batteries Perfectly scalable suitable...
Transcript of PROSUMERS’ IMPACT ON THE ELECTRICITY SYSTEM Heinisch... · batteries Perfectly scalable suitable...
PROSUMERS’ IMPACT ON THE ELECTRICITY SYSTEM
VERENA HEINISCH
Chalmers University of Technology, Göteborg, Sweden Department of Energy and Environment
Divion of Energy Technology, Energy Systems Group
Research on techno-economic energy systems modelling, centralized and
decentralized developments in electricity systems, prosumers and micro-generation
Project funded within the ”Forskarskolan Energisystem” by Energimyndigheten
HOUSEHOLD ANNUAL ELECTRICITY COST
VS. SYSTEM OPERATIONAL COST OPTIMIZATION
SAEE Luleå, 2016, Impact of Prosumers on the Electricity System Verena Heinisch, 2016-08-24
WHAT DOES THE FUTURE ELECTRICITY CONSUMER LOOK LIKE?
SAEE Luleå, 2016, Impact of Prosumers on the Electricity System Verena Heinisch, 2016-08-24
• Produce - electricity from household PV panel
• Store - e.g. diurnal shifting of energy to
make use of their PV production
behind the meter
• Buy & Sell - from and to energy utility
PROSUMERS
Modelling of cost optimal operation
- of a large share of PV battery systems
- on Swedish residential dwellings
- within the Nordic electricity generation system
- in the year 2032
- from a household as well as a system perspective
SAEE Luleå, 2016, Impact of Prosumers on the Electricity System Verena Heinisch, 2016-08-24
Decreasing costs of PV and
batteries
Perfectly scalable suitable also for
different kinds of small customers
Motivation
Modelling of cost optimal operation
- of a large share of PV battery systems
- on Swedish residential dwellings
- within the Nordic electricity generation system
- in the year 2032
- from a household as well as a system perspective
SAEE Luleå, 2016, Impact of Prosumers on the Electricity System Verena Heinisch, 2016-08-24
Household operational patterns do not comply with max system value at all times
- Capacity value for system vs. energy value of batteries for
household
- Difference in system operational costs and resource utilization
Seasonal differences in charge and discharge patterns exist for system
and households
Volatile marginal prices increase the system value of batteries while the
biggest value for households with PV battery systems is the diurnal shifting of electricity
Method and Study Set-Up
Summary
STRUCTURE OF THIS PRESENTATION R
ESU
LTS
SAEE Luleå, 2016, Impact of Prosumers on the Electricity System Verena Heinisch, 2016-08-24
European electricity dispatch model
EPOD
Household electricity cost optimization
model
Optimizing total costs for Electricity generation to fulfill
demand
SAEE Luleå, 2016, Impact of Prosumers on the Electricity System Verena Heinisch, 2016-08-24
European electricity dispatch model
EPOD
Household electricity cost optimization
model
Optimizing total costs for Electricity generation to fulfill
demand
Optimizing household annual electricity costs
SAEE Luleå, 2016, Impact of Prosumers on the Electricity System Verena Heinisch, 2016-08-24
European electricity dispatch model
EPOD
Household electricity cost optimization
model
FEEDBACK Electricity prices
Load curves (considering BESS)
Optimal battery
operation pattern
System
perspective
Optimal battery
operation pattern
Household perspective
Optimal investment in battery and PV capacity
(used in both cases)
SAEE Luleå, 2016, Impact of Prosumers on the Electricity System Verena Heinisch, 2016-08-24
Household operational patterns do not comply with max system value at all times
- Capacity value for system vs. energy value of batteries for
household
- Difference in system operational costs and resource utilization
Seasonal differences in charge and discharge patterns exist for system
and households
Volatile marginal prices increase the system value of batteries while the
biggest value for households with PV battery systems is the diurnal shifting of electricity
RESULTS
SAEE Luleå, 2016, Impact of Prosumers on the Electricity System Verena Heinisch, 2016-08-24
System Opt Household Opt Difference in %
Total costs [M€] 27 350 27 380 -0.12
StUp costs
Powerplants
[M€]
861 872 -1.31
Total system operational costs lower under system
optimization case
Total annual system operational costs all regions
System benefit from batteries eg:
Avoid start-up costs
Different utilization of available
generation
Resource utilization and operation cost
difference
System Opt Case uses more fuel type
Household Opt Case uses more fuel type
SAEE Luleå, 2016, Impact of Prosumers on the Electricity System Verena Heinisch, 2016-08-24
Resource utilization and
operation cost
difference
Due to different
battery charge and discharge patterns
2 weeks in summer
SAEE Luleå, 2016, Impact of Prosumers on the Electricity System Verena Heinisch, 2016-08-24
Household - keeping PV
generation behind
the meter
- diurnal charge
pattern
System - Optimized electricity
generation
- Low marginal costs
SAEE Luleå, 2016, Impact of Prosumers on the Electricity System Verena Heinisch, 2016-08-24
More occasions with low charging from household
perspective operation
System: few times, charging high
amounts
Households: regularly, lower
amounts
Energy vs. Capacity Value of
Batteries
Frequency of Charging amount per hour
SAEE Luleå, 2016, Impact of Prosumers on the Electricity System Verena Heinisch, 2016-08-24
Household operational patterns do not comply with max system value at all times
- Capacity value for system vs. energy value of batteries for
household
- Difference in system operational costs and resource utilization
Seasonal differences in charge and discharge patterns exist for system
and households
Volatile marginal prices increase the system value of batteries while the
biggest value for households with PV battery systems is the diurnal shifting of electricity
SAEE Luleå, 2016, Impact of Prosumers on the Electricity System Verena Heinisch, 2016-08-24
0,000
0,010
0,020
0,030
0,040
0,050
0,060
0,070
0,080
0,090
0,100
System Household
Average Charge per hour - SE 4
WINTER [GWh/h] SUMMER [GWh/h]
0,000
0,050
0,100
0,150
0,200
0,250
0,300
System Household
Average Charge per hour - SE 3
WINTER [GWh/h] SUMMER [GWh/h]
Utilization of of batteries much lower under summer time during system
optimization
- Storage as a service ?
- System optimization during winter time – household utilization during summer time ?
SAEE Luleå, 2016, Impact of Prosumers on the Electricity System Verena Heinisch, 2016-08-24
Summer Winter
Household perspective -356.2 -123.3 M€/year
System perspective -11.7 M€/year
Benefit from operating battery during season:
Control Whole Year Summer Household
Winter System Operation No Control Batteries
Total system costs 27 350 27 370 27 380 M€/year
Household annual el. costs 1790 1913 2270 M€/year
Increasing costs, from full control over operation of batteries to no control
Is it worth paying households to operate batteries from system perspective during
winter time ?
Biggest value from battery for
households in summer
Still system value in winter
considerably lower than
household value
SAEE Luleå, 2016, Impact of Prosumers on the Electricity System Verena Heinisch, 2016-08-24
Household operational patterns do not comply with max system value at all times
- Capacity value for system vs. energy value of batteries for
household
- Difference in system operational costs and resource utilization
Seasonal differences in charge and discharge patterns exist for system
and households
Volatile marginal prices increase the system value of batteries while the
biggest value for households with PV battery systems is the diurnal shifting of electricity
SAEE Luleå, 2016, Impact of Prosumers on the Electricity System Verena Heinisch, 2016-08-24
Marginal Prices Green
Policy Scenario (goal for
RES) - More fluctuating and
higher prices in winter hours
- Higher price hours for
household optimization
case
Climate Market Scenario
(cap on CO2) - Less fluctuating marginal
prices than above scenario
SAEE Luleå, 2016, Impact of Prosumers on the Electricity System Verena Heinisch, 2016-08-24
System – Battery used to
minimize system
operational costs
Household – diurnal
charging patters for PV
electricity
SAEE Luleå, 2016, Impact of Prosumers on the Electricity System Verena Heinisch, 2016-08-24
System Opt Household
Opt
Difference in
%
Total costs [M€] 43 880 43 8890 -0.013
StUp Costs [M€] 568.9 568.7 0.035
Minimal savings in total system operational costs
From system perspective Batteries most beneficial in a
system with high share or RES
From household perspective Lower capacity (compared to
GP scenario) of PV and batteries
beneficial to decrease annual
electricity costs
- Less fluctuating generation and marginal prices
- Lower value of batteries to be scheduled after system
optimum
- Lower investment in battery and PV capacity from
household side
Climate Market Scenario – less volatile prices:
SAEE Luleå, 2016, Impact of Prosumers on the Electricity System Verena Heinisch, 2016-08-24
Household operational patterns do not comply with max system value at all times
- Capacity value for system vs. energy value of batteries for
household
- Difference in system operational costs and resource utilization
Seasonal differences in charge and discharge patterns exist for system
and households
Volatile marginal prices increase the system value of batteries while the
biggest value for households with PV battery systems is the diurnal shifting of electricity
SUMMARY
PROSUMERS’ IMPACT ON THE ELECTRICITY SYSTEM
VERENA HEINISCH
Chalmers University of Technology, Göteborg, Sweden Department of Energy and Environment
Divion of Energy Technology, Energy Systems Group
Research on techno-economic energy systems modelling, centralized and
decentralized developments in electricity systems, prosumers and micro-generation
Project funded within the ”Forskarskolan Energisystem” by Energimyndigheten
HOUSEHOLD ANNUAL ELECTRICITY COST VS. SYSTEM OPERATIONAL COST OPTIMIZATION
SAEE Luleå, 2016, Impact of Prosumers on the Electricity System Verena Heinisch, 2016-08-24
SAEE Luleå, 2016, Impact of Prosumers on the Electricity System Verena Heinisch, 2016-08-24
European electricity dispatch model
EPOD
Household electricity cost optimization
model
FEEDBACK Electricity prices
Load curves (considering BESS)
Optimal battery
operation pattern
System
perspective
Optimal battery
operation pattern
Household perspective
• Year 2032, Green policy ( renewables, variable prices) & Climate Market (cap on
emissions) scenario
• Measured load data from several thousand Swedish households (EON) – scale up
depending on type
• Solar generation profile and discharge efficiency for batteries
SAEE Luleå, 2016, Impact of Prosumers on the Electricity System Verena Heinisch, 2016-08-24
European electricity dispatch model
EPOD
Household electricity cost optimization
model
FEEDBACK Electricity prices
Load curves (considering BESS)
Optimal battery
operation pattern
System
perspective
Optimal battery
operation pattern
Household perspective
Value of battery system to the system and to the
household
“Storage as a service”
SAEE Luleå, 2016, Impact of Prosumers on the Electricity System Verena Heinisch, 2016-08-24
• Battery Capacity
SE4 1.98 GW
SE3 5.97 GW
• PV Capacity
SE4 1.82 GW
SE3 6.23 GW
Household Capacity
Investment
PV and load profile aggregated per region
SAEE Luleå, 2016, Impact of Prosumers on the Electricity System Verena Heinisch, 2016-08-24
Charge (positive –
green)
Discharge (negative –
red)
Patterns
- Less times of battery
utilization from system
optimization
perspective
- But utilizing full
capacity - Regular diurnal
pattern to store PV
generation in
household
optimization case
SAEE Luleå, 2016, Impact of Prosumers on the Electricity System Verena Heinisch, 2016-08-24
Include NoBatPlusPV and NoBatnoPV Cases????
SAEE Luleå, 2016, Impact of Prosumers on the Electricity System Verena Heinisch, 2016-08-24
SAEE Luleå, 2016, Impact of Prosumers on the Electricity System Verena Heinisch, 2016-08-24
SAEE Luleå, 2016, Impact of Prosumers on the Electricity System Verena Heinisch, 2016-08-24
SAEE Luleå, 2016, Impact of Prosumers on the Electricity System Verena Heinisch, 2016-08-24
AVERAGE SELF SUFFICIENCY
SE1 32,2 %
SE2 24,3 %
SAEE Luleå, 2016, Impact of Prosumers on the Electricity System Verena Heinisch, 2016-08-24
European electricity dispatch model
EPOD
50 regions in Europe, 3 hour time stpes
DC load flow represantation
Ramping/start up costs & limitations
for thermal power
Representation regional storage
limitation of Nordic hydro power
…
Power plant data base
Resources description
Scenarios
Investment model ELIN
Dispatch model EPOD
Power grid
& Electricity
Trade
PV/DSM
household
level
Transport
system and
district
heating
Prosumers
SAEE Luleå, 2016, Impact of Prosumers on the Electricity System Verena Heinisch, 2016-08-24
• Very ambitious renewables policy
• EC “High renewable”
• Climate , RES and efficiency polices
• EC “High energy efficiency”
• International carbon trade
• EC “Diversified supply techn.”, “High GDP”
• Existing policy measures
• EC ”Current policy”
Refe-rence
Climate market
Green policy
Regional policy
Technologicaldimension
Policy dimension
SAEE Luleå, 2016, Impact of Prosumers on the Electricity System Verena Heinisch, 2016-08-24
SE4 System Household
YEAR [GWh] 459 724
WINTER [GWh] 291 359
SUMMER [GWh] 168 365
SE3 System Household
YEAR [GWh] 1313 2227
WINTER [GWh] 784 1119
SUMMER [GWh] 529 1108
0
100
200
300
400
500
600
700
800
System Household
Battery Charge - SE 4
YEAR [GWh] WINTER [GWh] SUMMER [GWh]
0
500
1000
1500
2000
2500
System Household
Battery Charge - SE 3
YEAR [GWh] WINTER [GWh] SUMMER [GWh]
SAEE Luleå, 2016, Impact of Prosumers on the Electricity System Verena Heinisch, 2016-08-24
Summer Winter
Household perspective -356,23 -123,28 M€/year
System perspective -11,65 M€/year
Benefit from operating battery during season:
27330
27340
27350
27360
27370
27380
27390
Control Whole Year Summer Household Winter
System Operation
No Control Batteries
Total system costs
0
500
1000
1500
2000
2500
Control Whole Year Summer Household Winter
System Operation
No Control Batteries
Household annual el costs
SAEE Luleå, 2016, Impact of Prosumers on the Electricity System Verena Heinisch, 2016-08-24
Continuation of the
Study
- Residential battery PV systems also in other geographical areas
- Consider prosumers with different objectives (self-sufficiency etc.)
- Smaller scale – Case Study Region/Community – less aggregation in
modelling
- Consider behavioral complexity of prosumers (intangible costs, bounded
rationality, …)