Integration of RES in the Swiss electricity grid ...€¦ · Integration of RES in the Swiss...
Transcript of Integration of RES in the Swiss electricity grid ...€¦ · Integration of RES in the Swiss...
Integration of RES in the Swiss electricity gridChallenges, solutions and methods
Alexander Fuchs (Research Center for Energy Networks, ETH Zurich)March 15, 2016
Project overview
Financed by CCEM, Swisselectric Research
Phase 1: Distribution grid aspects (2014-15)
Phase 2: Transmission grid aspects (2016-17)
Project partners
ETH Zurich (Research Center for Energy Networks, FEN)
Paul-Scherrer-Institut (Technology-Assessment-Group, TAG)
Paul-Scherrer-Institut (Energy-Economics-Group, EEG)
Hochschule Luzern (HSLU)
CKW, Axpo
Overall goal: Develop a general planning and strategy tool fordistribution grids .
Integration of RES in the Swiss electricity grid ‖ A. Fuchs ‖ March 15, 2016 2 / 48
Outline
Situation today
Grid operation and planningOptimal grid operationOptimal grid planning
ResultsResult structureResult interpretation
Summary
Integration of RES in the Swiss electricity grid ‖ A. Fuchs ‖ March 15, 2016 3 / 48
Outline
Situation today
Situation today
Grid operation and planning
Results
Summary
Integration of RES in the Swiss electricity grid ‖ A. Fuchs ‖ March 15, 2016 4 / 48
Grid operation today
Situation today
Transmission Grid
Distribution Grid
Medium and Low Voltage Grid
Small Consumer
Industry
Bulk Consumer
ConventionalControllable
RenewableControllable
ConventionalControllable
RenewableControllable Conventional
Controllable
ConventionalControllable
ConventionalControllable
Predictable loads, pricesand production capacities
Sufficient stabilityreserves in the grid
Unidirectional load flow
Established operationalprocedures
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Grid operation future
Situation today
Transmission Grid
Distribution Grid
Medium and Low Voltage Grid
Small ConsumerLoad Management
IndustryLoad Management
Bulk ConsumerLoad Management
ConventionalControllable
RenewableControllable
ConventionalControllable
RenewableVolatile
RenewableVolatile
RenewableControllable
RenewableControllable
RenewableVolatile
RenewableVolatile
RenewableControllable
ControllableControllable VolatileLoadControllable
Limited predictability ofloads, prices; volatileproduction
Reduced stabilityreserves in the grid
Bidirectional load flow,production on thedistribution grid level
New operationalprocedures required
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Challenges for the distribution grid operator
Situation today
Especially uncertainty regarding:
the development of future network structure.
the development of future production capacity and demand profiles.
new regulatory framework (Swiss and Europe wide).
As a result uncertainty regarding future operating procedures andinvestment decisions.
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Goals of the project
Situation today
Systematic investigation of existing approaches regarding theirapplicability for the Swiss distribution grids.
Quantification of costs, risks and chances of each approach.
Determine conclusions and recommendations for the Swissdistribution and transmission grid operators.
Main result is a road map for a concrete network example and a generalsoftware tool for the investigation of other networks.
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Outline
Grid operation and planning
Situation today
Grid operation and planning
Results
Summary
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Outline
Grid operation and planning ‖ Optimal grid operation
Situation today
Grid operation and planningOptimal grid operationOptimal grid planning
ResultsResult structureResult interpretation
Summary
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Grid components
Grid operation and planning ‖ Optimal grid operation
Trafos Loads PV-Sources Batteries
0 5 10 15 200
0.2
0.4
0.6
0.8
1
1.2
1.4
1.6
1.8
hour
MW
0 5 10 15 2025
30
35
40
45
50
55
60
65
70
hours
CH
F/M
Wh
Figure: Load profile (left, blue), PV profile (left , green), Price profile at the trafo (right)
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Classical distribution grid
Grid operation and planning ‖ Optimal grid operation
Aggregated loads at the trafo
Load and price profile are given
Controllable quantities: none
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Classical distribution grid - example
Grid operation and planning ‖ Optimal grid operation
Load and losses compensated by the trafo
0 5 10 15 20−0.4
−0.2
0
0.2
0.4
0.6
0.8
1
1.2
1.4
hours
MW
Figure: Load profile (red) and trafo power (blue).
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Classical distribution grid with PV
Grid operation and planning ‖ Optimal grid operation
Aggregated loads at the trafo
Profiles of Load, PV and price are given
Controllable quantities: none
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Classical distribution grid with PV - example
Grid operation and planning ‖ Optimal grid operation
Load and losses compensated by the trafo and the PV source
0 5 10 15 20−0.4
−0.2
0
0.2
0.4
0.6
0.8
1
1.2
1.4
hours
MW
Figure: Load profile (red) and trafo power (blue), PV-Power (black).
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Distribution grid with controllable PV
Grid operation and planning ‖ Optimal grid operation
Aggregated loads at the trafo
Profiles of Load, PV and price are given
Controllable quantities: PV curtailment
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Distribution grid with controllable PV - example
Grid operation and planning ‖ Optimal grid operation
Load and losses compensated by the trafo and the PV source
PV peak power brings network to its limits (thermal or voltage).
0 5 10 15 20−2
−1
0
1
2
3
4
hours
MW
Figure: Load profile (red) and trafo power (blue), PV-Power (black), available PV power (blackdashed).
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Distribution grid with controllable PV and batteries
Grid operation and planning ‖ Optimal grid operation
Aggregated loads at the trafo
Profiles of Load, PV and price are given
Controllable quantities: PV curtailment, battery powerIntegration of RES in the Swiss electricity grid ‖ A. Fuchs ‖ March 15, 2016 18 / 48
Distribution grid with controllable PV and batteries -example
Grid operation and planning ‖ Optimal grid operation
Load and losses compensated by the trafo and the PV sourcePV peak power brings network to its limits (thermal or voltage).Battery allows increased usage of PV and cheap electricity from thefeeder.
0 5 10 15 20−2
−1
0
1
2
3
4
hours
MW
Figure: Load profile (red) and trafo power (blue), PV-Power (black), available PV power (blackdashed), battery power (green).
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Distribution grid with controllable PV and batteries -example
Grid operation and planning ‖ Optimal grid operation
Planning algorithm considers state-of-charge, power limits, efficiencyof the batteries.Planning algorithm can be repeated hourly to include new predictions(weather, price, demand, ...).
0 5 10 15 20−0.5
0
0.5
1
1.5
2
hours
MW
h
Figure: Battery state-of-charge (blue) and charge limits (blue dashed).
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Comparison of energy costs
Grid operation and planning ‖ Optimal grid operation
0 5 10 15 20 25−600
−400
−200
0
200
400
600
800
1000
1200
1400
hours
CH
F
Figure: Cumulative energy costs no PV (red), with PV (blue), with controlled PV (black), withcontrolled PV and storage (green).
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Grid operation - Mathematical problem formulation
Grid operation and planning ‖ Optimal grid operation
J ∗ =(
minu1,...,uN
N∑i=1
li(xi , ui))
s.t. ∀i = 1, ..., N : g(xi , ui) ≤ 0, h(xi , ui) = 0,
Nonlinear multi-period AC power flow problem as extension to thesoftware Matpower (no DC power flow approximation necessary)
Efficient Parallelization possible with MATPOWER / IPOPT /PARDISO (http://www.pardiso-project.org)
Further improvement of the solution speed possible by exploiting theproblem structure
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Outline
Grid operation and planning ‖ Optimal grid planning
Situation today
Grid operation and planningOptimal grid operationOptimal grid planning
ResultsResult structureResult interpretation
Summary
Integration of RES in the Swiss electricity grid ‖ A. Fuchs ‖ March 15, 2016 23 / 48
Overview
Grid operation and planning ‖ Optimal grid planning
Investigation of CKW distribution grids near Luzern.
Assumption: High increase of PV injection in the distribution grid
Figure: Distribution grid example Trafos (black and blue), 20 kV lines (red), 400 V lines (green).
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Solution approaches for PV integration
Grid operation and planning ‖ Optimal grid planning
Several academic methods and general approaches with referencesystems available:
Grid extension, operation and business as usual
Usage of storage elements
Load management, smart metering
Production management and control
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Grid planning - Mathematical problem formulation
Grid operation and planning ‖ Optimal grid planning
J ∗ = minp
(min
u1,...,uN
N∑i=1
li(xi , ui , p))
(1)
s.t. ∀i = 1, ..., N : g(xi , ui , p) ≤ 0, h(xi , ui , p) = 0, (2)
Planning parameter p includes grid extension, degree of PVcurtailment and size/location of the storage units
Can be included in the basic multi-period OPF problem formulation
Is solved for a scenario of family of scenarios
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Outline
Results
Situation today
Grid operation and planning
Results
Summary
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Outline
Results ‖ Result structure
Situation today
Grid operation and planningOptimal grid operationOptimal grid planning
ResultsResult structureResult interpretation
Summary
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PV-injection without storage
Results ‖ Result structure
0 5 10 15 200
5
10
15
hour
MW
Figure: Distribution grid example Sursee: Maximum grid capacity (black), maximum PV-Injectionwithout PV-curtailment (blue), increased PV-injection with PV-curtailment (green), stronglyincreased PV-injection with PV-control (red), available PV-capacity (dashed).
Integration of RES in the Swiss electricity grid ‖ A. Fuchs ‖ March 15, 2016 29 / 48
PV-injection without storage
Results ‖ Result structure
0 5 10 15 200
1
2
3
4
5
6
7
8
9
10
hour
MW
Figure: Distribution grid example Sursee: Maximum PV injection without storage (blue),increased PV-injection with storage for 30 minutes PV (green), with storage for 60 minutes PV(red), available PV-capacity (dashed).
Integration of RES in the Swiss electricity grid ‖ A. Fuchs ‖ March 15, 2016 30 / 48
Placement and sizing of storage units
Results ‖ Result structure
0 50 100 150 200 250 3000
0.5
1
1.5
2
2.5
3
3.5
Node number
MW
h
Figure: Distribution of the storage sizes at different nodes of the grid for a total storage capacityof Etotal = 33MWh.
→ Storages are placed in the entire grid.
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Placement and sizing of storage units
Results ‖ Result structure
0 5 10 15 20 25 30 35−0.2
0
0.2
0.4
0.6
0.8
1
1.2
MWh
corr
elat
ion
Figure: Correlation between optimal storage distribution and installed PV capacity PPV,rated atevery node, as a function of the total storage capacity Etotal.
→ Storages are sized according to the size of nearby PV units.
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Comparison of PV curtailment, storage and networkextension
Results ‖ Result structure
0 5 10 15 20 25 30 350
5
10
15
20
25
30
35
MWh
%
K
G = 1.00
KG
= 1.25
KG
= 1.50
KG
= 1.75
KG
= 2.00
KG
= 2.25
KG
= 2.50
Figure: Ratio of curtailed PV power as function of the total storage capacity Etotal for differentdegrees of grid expansion KG .
→ Grid extension results in strong reduction of PV curtailment.
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Decision surface for PV integration strategies
Results ‖ Result structure
0500
10001500
20002500
0500
10001500
20002500
30003500
0
500
1000
1500
2000
2500
Storage cost [CHF/day] Cable cost [CHF/day]
Cur
tailm
ent c
ost [
CH
F/d
ay]
Figure: Daily operational grid costs for different strategy combinations. Individual simulations(blue stars) and approximation by a decision surface. Assumed cost parameters are the averageelectricity cost of c = 55CHF/MWh, grid degradation costs of pG = 55CHF/(km · day) andstorage degradation costs of pS = 70CHF/(MWh · day) .
Integration of RES in the Swiss electricity grid ‖ A. Fuchs ‖ March 15, 2016 34 / 48
Comparison of PV integration strategies
Results ‖ Result structure
Trade-off between storage costs pS, grid degradation costs pG andelectricity costs c.Minimum sum of all three costs is always an extreme point of thedecision surface (corresponds to a single pure strategy).
Linear criterion for the optimality of each strategy:pS < 1.29pG and pS < 1.16c: Use only storage units.pG < 0.77pS and pG < 0.90c: Use only grid extension.c < 1.12pG and c < 0.86PS: Use only PV curtailment.
Without the linear approximation the minimum total cost is also reachedat a combination of different strategies.
Integration of RES in the Swiss electricity grid ‖ A. Fuchs ‖ March 15, 2016 35 / 48
Outline
Results ‖ Result interpretation
Situation today
Grid operation and planningOptimal grid operationOptimal grid planning
ResultsResult structureResult interpretation
Summary
Integration of RES in the Swiss electricity grid ‖ A. Fuchs ‖ March 15, 2016 36 / 48
Simulation example for fixed cost parameters
Results ‖ Result interpretation
0 5 10 15 20 25 30 35 40 45 50-4
-2
2
4
Hour
MW
DemandImport
0 5 10 15 20 25 30 35 40 45 50-2000
-1000
0
3000
4000Cumulative costs
Power
CH
F
ImportBattery SOCBattery PBattery constantTotal costsObjective
Two-day simulation exampleof the CKW grid Ettiswil withan increased PV productionand high demand: Powerdemand and import (top).Cumulative electricity andoperational costs (bottom).
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Fixed simulation parameter
Results ‖ Result interpretation
Parameter are fixed before the simulation.
Simulation of the Sursee region (summer and winter day, 48 hours)
PV integration potential of today’s grid is determined and increased
Load profile of the households are maximized and simulated withcoincident factor (25% base load and random distribution )
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Variable simulation parameter
Results ‖ Result interpretation
Only a range of simulation parameters has to be selected.
Electricity price (20 - 80 CHF/MWh)
Battery cost model: J = bE(SOC − a)2 + cP + dEstorage size EState-of-charge SOCLoad power P
Parameter {a, b, c, d} can be fitted to multiple battery models andtechnologies.
Grid extension is a constant increase of the trafos, lines and cables .
PV curtailment is not remunerated, but enters the planning decisionthrough opportunity costs.
Integration of RES in the Swiss electricity grid ‖ A. Fuchs ‖ March 15, 2016 39 / 48
Full operational costs
Results ‖ Result interpretation
0 50 100 150 200 250 300 350 400−4000
−2000
0
2000
4000
6000
8000
Batterie SOC Kosten Parameter b [CHF/(MWh h)]
Ges
amtk
oste
n S
trom
und
Bet
rieb
[CH
F]
c = 0 CHF/(MW h), Strompreis = 20 CHF/MWh, Ausbaugrad = 1, hohe/niedrige Nachfrage
d = 2.5 CHF/(MWh h)d = 5 CHF/(MWh h)d = 10 CHF/(MWh h)d = 20 CHF/(MWh h)
0 50 100 150 200 250 300 350 400−4000
−2000
0
2000
4000
6000
8000
Batterie SOC Kosten Parameter b [CHF/(MWh h)]G
esam
tkos
ten
Str
om u
nd B
etrie
b [C
HF
]
c = 0 CHF/(MW h), Strompreis = 80 CHF/MWh, Ausbaugrad = 1, hohe/niedrige Nachfrage
d = 2.5 CHF/(MWh h)d = 5 CHF/(MWh h)d = 10 CHF/(MWh h)d = 20 CHF/(MWh h)
Figure: Optimal grid planning with variable battery parameters. High demand (solid lines) andlow demand (dashed lines). Low electricity price (left) and high electricity price (right). No gridextension.
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Sizing of installed storage units
Results ‖ Result interpretation
0 50 100 150 200 250 300 350 4000
1
2
3
4
5
6
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8
9
10
Batterie SOC Kosten Parameter b [CHF/(MWh h)]
Ges
amte
nerg
ie B
atte
riesp
eich
er [M
Wh]
c = 0 CHF/(MW h), Strompreis = 20 CHF/MWh, Ausbaugrad = 1, hohe/niedrige Nachfrage
d = 2.5 CHF/(MWh h)d = 5 CHF/(MWh h)d = 10 CHF/(MWh h)d = 20 CHF/(MWh h)
0 50 100 150 200 250 300 350 4000
1
2
3
4
5
6
7
8
9
10
Batterie SOC Kosten Parameter b [CHF/(MWh h)]
Ges
amte
nerg
ie B
atte
riesp
eich
er [M
Wh]
c = 0 CHF/(MW h), Strompreis = 80 CHF/MWh, Ausbaugrad = 1, hohe/niedrige Nachfrage
d = 2.5 CHF/(MWh h)d = 5 CHF/(MWh h)d = 10 CHF/(MWh h)d = 20 CHF/(MWh h)
Figure: Optimal grid planning with variable battery parameters. High demand (solid lines) andlow demand (dashed lines). Low electricity price (left) and high electricity price (right). No gridextension.
Integration of RES in the Swiss electricity grid ‖ A. Fuchs ‖ March 15, 2016 41 / 48
Curtailed PV power
Results ‖ Result interpretation
0 50 100 150 200 250 300 350 4000
0.05
0.1
0.15
0.2
0.25
0.3
Batterie SOC Kosten Parameter b [CHF/(MWh h)]
PV
Abr
egel
ungs
quot
e [%
]
c = 0 CHF/(MW h), Strompreis = 20 CHF/MWh, Ausbaugrad = 1, hohe/niedrige Nachfrage
d = 2.5 CHF/(MWh h)d = 5 CHF/(MWh h)d = 10 CHF/(MWh h)d = 20 CHF/(MWh h)
0 50 100 150 200 250 300 350 4000
0.05
0.1
0.15
0.2
0.25
0.3
Batterie SOC Kosten Parameter b [CHF/(MWh h)]
PV
Abr
egel
ungs
quot
e [%
]
c = 0 CHF/(MW h), Strompreis = 80 CHF/MWh, Ausbaugrad = 1, hohe/niedrige Nachfrage
d = 2.5 CHF/(MWh h)d = 5 CHF/(MWh h)d = 10 CHF/(MWh h)d = 20 CHF/(MWh h)
Figure: Optimal grid planning with variable battery parameters. High demand (solid lines) andlow demand (dashed lines). Low electricity price (left) and high electricity price (right). No gridextension.
Integration of RES in the Swiss electricity grid ‖ A. Fuchs ‖ March 15, 2016 42 / 48
Results
Results ‖ Result interpretation
Simulation results form a decision surface, depending on multiplesimulation parameter.
Based on the decision surface, the grid owner can quickly makeplanning decisions for selected parameter values.
0
100
200
300
400
5
10
15
20
0
2000
4000
6000
8000
Batterie SOC Kosten [CHF/(MWh h)]Batterie konstante Kosten [CHF/(MWh h)]
Ge
sa
mtk
oste
n S
tro
m u
nd
Be
trie
b [
CH
F] Location and size of the storages
are determined by theoptimization.
Control of the controllablequantities are part of theoptimization.
Integration of RES in the Swiss electricity grid ‖ A. Fuchs ‖ March 15, 2016 43 / 48
Selected observations
Results ‖ Result interpretation
Storage technologies are only interesting for further decrease ofstorage costs or higher electricity costs.
Evaluation of the grid extension depends on the costs of theindividual grid components.
High potential for controlled loads and PV components (currentlysimulated without additional costs).
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Outline
Summary
Situation today
Grid operation and planning
Results
Summary
Integration of RES in the Swiss electricity grid ‖ A. Fuchs ‖ March 15, 2016 45 / 48
Summary
Summary
Unified problem formulation for the grid operation and planning.
Optimal usage of available assets to compute a Pareto decisionsurface
AC power flow extension for planning and multi period scenarios .
Large multi-period simulations of AC power flow problems (1000nodes, 1000 time steps) become tractable with dedicated solvers.
Integration of RES in the Swiss electricity grid ‖ A. Fuchs ‖ March 15, 2016 46 / 48
Ongoing work
Summary
Quantitative:
Comparison of economic and CO2 objective
Qualitative:
Robust plan with scenario uncertainty
Long term transfer of the methods to general distribution andtransmission grids
Integration of RES in the Swiss electricity grid ‖ A. Fuchs ‖ March 15, 2016 47 / 48
Thank you for your attention
Summary
FEN - Research Center for Energy Networks
ETH Zentrum SOI C 1CH-8092 Zurich
Dr. Alexander FuchsSonneggstrasse 28Tel: +41 44 632 28 60Fax: +41 44 632 13 30E-mail: [email protected]: www.fen.ethz.ch
Support through the Competence Center for Energy and MobilityProject ISCHESS: Integration of stochastic renewables in the Swiss electricity supply system .
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