storage evaluation in congested grids
TRANSCRIPT
CHAIR FOR MANAGEMENT SCIENCE AND
ENERGY ECONOMICS
PROF. DR. CHRISTOPH WEBER
STORAGE EVALUATION IN
CONGESTED GRIDSBenjamin Böcker, Stefan Kippelt
Christian Rehtanz, Christoph Weber
Essen, 25.03.2015
Introduction
• Grid expansion primarily driven by
– Developing renewable energy sources (mainly photovoltaics and wind)
– Increasing decentralization of feed-in
• Storage operation primarily driven by
– Taking advantage of price differences between charging and discharging the storage
(Spot market, intraday market, increasing own consumption of pv feed-in)
– Provide reserve power and grid services to compensate forecasting errors of demand
and supply and intermittent infeed
Key questions:
– Is storage operation for grid purposes efficient?
– How should these storage operations been compensated?
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MOTIVATION 1 2 3 4
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• Regulatory framework
– Laws and regulations
– Planning rules and principles
• Markets
– Structure of prices
– Minimum requirements
(prequalification)
• Location and operation
– Distributed energy sources, loads
• Grids
– Current and future load necessary grid expansion
Key Drivers
Regulatory Framework
Markets
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MOTIVATION 1 2 3 4
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1. Motivation
2. Methods
3. Application
4. Conclusion
Agenda
STORAGE EVALUATION IN CONGESTED GRIDS 1 2 3 4
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• Competitive market
similar results as system optimization with perfect information
• System optimization
– Description of an overall perfect decision
– No direct evaluation of the additional value of storages by operation for grid purposes
– Ex-post analysis of possible sharing of cost savings
Set up of an optimization model
Economic Theory
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METHODS 1 2 3 4
DemandSupply
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Optimization Model – System Boundaries
Physical energy flows
Financial flows Grid
Storage
Markets
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METHODS 1 2 3 4
System Boundary
Analysis: Ex post
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• Minimizing cost for grid and storage operator
– Investment costs for the grid and storage
▪ Grid expansion (binary decision)
▪ Storage investment (Capacity and Volume)
– Compensation (Curtailment and non-served demand)
– Additional revenues on spot
Optimization Model – Objective Function / Overview
𝑚𝑖𝑛 𝐶𝑖𝑛𝑣𝑒𝑠𝑡𝑚𝑒𝑛𝑡 + 𝐶𝑐𝑜𝑚𝑝𝑒𝑛𝑠𝑎𝑡𝑖𝑜𝑛 − 𝑅𝑚𝑎𝑟𝑘𝑒𝑡
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METHODS 1 2 3 4
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• Storage operation under grid constraints
• Storage
– Level:
– Capacity:
Optimization Model – Main Restrictions
𝑐ℎ𝑎𝑟𝑔𝑖𝑛𝑔: 𝑦𝑆,𝐶 𝑡 ≤ 𝑅𝐶 𝑡 + 𝑏𝑔𝑟𝑖𝑑 ∙ 𝑅𝑎𝑑
𝑅𝐿,𝑆 𝑡 . . 𝐿𝑆 𝑡 + 1, 𝑢𝑆 = 𝐿𝑆 𝑡, 𝑢𝑆 + 𝑦𝑆,𝐶 𝑡, 𝑢𝑆 ∙ 𝜂𝑆 𝑢𝑆 − 𝑦𝑆,𝐷 𝑡, 𝑢𝑆
𝑅𝐾,𝐷 𝑡, 𝑢𝑆 . . 𝑦𝑆,𝐷 𝑡, 𝑢𝑆 ≤ 𝐾𝑆 𝑢𝑆
𝑅𝐾,𝐶 𝑡, 𝑢𝑆 . . 𝑦𝑆,𝐶 𝑡, 𝑢𝑆 ≤ 𝐾𝑆 𝑢𝑆
𝑅𝑉,𝑆 𝑡 . . 𝐿𝑆 𝑡, 𝑢𝑆 ≤ 𝑉𝑆 𝑢𝑆
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METHODS 1 2 3 4
𝑑𝑖𝑠𝑐ℎ𝑎𝑟𝑔𝑖𝑛𝑔: 𝑦𝑆,𝐷 𝑡 ≤ 𝑅𝐷 𝑡 + 𝑏𝑔𝑟𝑖𝑑−𝑒𝑥𝑝. ∙ 𝑅𝑎𝑑𝑑−𝑐𝑎𝑝𝑎𝑐𝑖𝑡𝑦 + 𝑦𝑐𝑢𝑟,𝑆 𝑡
Optimization Model
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Optimization Model – Overview
Grid Expansion Costs
Willingness to pay to the storage operator
Storage Costs
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METHODS 1 2 3 4
Scenario Data
Grid Data (local Feed-In)
Market Prices
Optimal storage investment and operationTrade-Off: Grid investment, storage investment and curtailment
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1. Motivation
2. Methods
3. Application
4. Conclusion
Agenda
STORAGE EVALUATION IN CONGESTED GRIDS 1 2 3 4
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• Based on NEP 2013, Second Draft (Reference year 2011)
scaled up according to 2023B
• Demand profiles: ENTSO-E, Renewable feed-in: EEX and local
weather data (adjusted for changed full load hours)
• Possibility of curtailment (especially PV)
• Compensation payments for curtailment
– Current average infeed tariff (33 €ct/kWh)
• Unbundling and contract law
• Financial framework (interest rate)
Scenario Data – Base Case
Optimization Model
Grid Expansion Costs
Willingness to pay to the storage operator
Storage Costs
Grid Data (local Feed-In)
Market Prices
Optimal storage investment and operationTrade-Off: Grid investment, storage investment and curtailment
Scenario Data
APPLICATION 1 2 3 4
Grid Data –Power Flow Calculation
• Limiting Curve Analysis
– Due to the unmeshed character of the chosen
grid configurations, a simplified power flow
calculation approach can be applied
– For a given line length, this approach calculates
limits for load and feedback under consideration of
thermal limits and the acceptable voltage range
– A demand for storage operation arises, when the
residual load exceeds the lines load and feedback
limits
• Conventional grid expansion
– Grid expansions are assumed as additional parallel
cables which split the installed RES in equal shares
– The cost for conventional line expansions are estimated
based on installed line length and feeder costs
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maximum load
maximum feedback
load
feedback
Line Length
Thermal Limitation Voltage Limitation (+/-4%)
Ac
tive
Po
we
r
APPLICATION 1 2 3 4
Grid Data –
Grid configuration and generation of local time series
• Low Voltage Stub Line
– Simulation of a classic stub line configuration
(the most frequent low voltage grid structure)
– Assumed cable cross-section is 150mm²
(the most frequent value in German low voltage grids)
– Assumed line length is 0.3 km
(the median value observed in the dena distr. grid study)
– Simulation of loads by use of a stochastic load
model based on smart metering data
• Simulation of local RES time series
– Simulation of RES time series by use of historic
weather model data in 7kmx7km resolution
– RES times series are scaled to 130% of line capacity
in order to provoke need for action of DSO
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~ ~ ~ ~
NAYY 4x150
0
0,1
0,2
0,3
0,4
0,5
0,6
0,7
0,8
1 11 21 31 41 51 61 71 81 91
Win
d [
%]
APPLICATION 1 2 3 4
Scenario Data
Optimization Model
Grid Expansion Costs
Willingness to pay to the storage operator
Storage Costs Market Prices
Optimal storage investment and operationTrade-Off: Grid investment, storage investment and curtailment
Grid Data (local Feed-In)
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• Storage Investment:
– (100€/kW, 200€/kWh, 20 years, 5.7%, 2% fixed o&m) 95% Efficiency
• Grid investment:
– 80 T€/km (dena distr. grid study) (40 years, 6.4%)
• Market
– Spot Price Simulation: Hybrid-Model (EWL)
▪ Two Step Simulation
– Fundamental price and additional stochastics
▪ According scenario data
– Mean Price: 42.6€/MWh (51.1 €/MWh in 2011)
– Number of neg. prices 233h (16h in 2011)
Costs and Market – Base Case
Grid Data (local Feed-In)
Scenario Data
Optimization Model
Willingness to pay to the storage operator
Optimal storage investment and operationTrade-Off: Grid investment, storage investment and curtailment
Grid Expansion CostsStorage Costs Market Prices
APPLICATION 1 2 3 4
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• One year, hourly (8760 timesteps)
• MIP solving in GAMS
• Connected with Matlab for sensitivities
Optimization Model
Grid Expansion CostsStorage Costs Market Prices
Grid Data (local Feed-In)
Scenario Data
Willingness to pay to the storage operator
Optimal storage investment and operationTrade-Off: Grid investment, storage investment and curtailment
Optimization Model
APPLICATION 1 2 3 4
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Key Input Parameters: 0.3 km, 30% Excess Feed-In PV, Compensation ø infeed tariff)
Result – Base Case
economically optimal avoid grid expansion and curtailment
Volume 38 kWh 88 MWh
Capacity 38 kW 55 MW
Load duration 1.0 h 1.6 h
full load cycles 1,297 ---
APPLICATION 1 2 3 4
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• Length of lines (low voltage grid)
– 0.1 km up to 1.6 km, Median: 0.3 km // 95% Quantile: 1,1 km // 99% Quantile: 1,6 km
• Excess Feed-In PV (in comparison to avoid curtailment)
– 10% to 150%
• Market (Sensitivity I):
– only grid purposes
– grid purposes and spot market
• Compensation Payments (Sensitivity II):
– Current average 33€ ct/kWh feed-in tariff – Current 13 €ct/kWh feed-in tariff
– Spot price – No curtailment
– No compensation
Sensitivities – Data
APPLICATION 1 2 3 4
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Optimal Decision – Market Sensitivity
Grid Grid + Storage Storage Storage (with curt.) No Inv. (curt. optional)
Only grid purposes spot market / grid purposes
APPLICATION 1 2 3 4
Optimal Investments:
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Optimal Decision – Compensation Sensitivity
Current average Current
Spot-Price No curtailment No compensation
APPLICATION 1 2 3 4
Grid
Grid + Storage
Storage
Storage (with curt.)
No Inv. (curt. optional)
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1. Motivation
2. Methods
3. Application
4. Conclusion
Agenda
STORAGE EVALUATION IN CONGESTED GRIDS 1 2 3 4
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• Optimal investment decision
– Highly dependent on:
▪ grid length and excess feed-in of PV
▪ Regulatory framework (unbundling, compensation payments)
• Value of storages:
– Storage investment only for grid purposes
Only in limited cases (Long length >700m, excess feed-In PV 1.5 to 2.3)
– Storage investment for grid purposes and restrictive operation on spot-market
Average grid-length: only with moderate excess feed-In
Grid-length above average: efficient in many cases
Summary (I/II)
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CONCLUSION 1 2 3 4
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• Advantage of storages:
– Mobility
– Application for short grid overload
– Possible to avoid or shift grid investment
– Possibility to optimize curtailment payments (grid perspective)
• Implementation of Reserve Market see upcoming paper
• More sensitivities:
– Voltage control
– storage investment costs
Summary (II/II)
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CONCLUSION 1 2 3 4
CHAIR FOR MANAGEMENT SCIENCE AND
ENERGY ECONOMICS
PROF. DR. CHRISTOPH WEBER
Many thanks! –Questions?
Contact: Benjamin Böcker
E-Mail: [email protected]
Phone: +49 201/183-7306
CHAIR FOR MANAGEMENT SCIENCE AND
ENERGY ECONOMICS
PROF. DR. CHRISTOPH WEBER
Backup Slides
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Optimal Decision – Market Sensitivity
(with voltages control)
Grid Voltage Control Storage Storage (with curt.) No Inv. (cur. optional)
Only grid pursoses spot market / grid purposes
APPLICATION 1 2 3 4
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Optimal Decision – Li-Ion Costs
Grid Grid + Storage Storage Storage (with curt.) No Inv. (curt. optional)
Low: 100 [€/kW], 200 [€/kWh]
APPLICATION 1 2 3 4
High: 120 [€/kW], 500 [€/kWh]