Post on 24-May-2020
Electricity Storage and It’s Economic Potential in Deregulated Markets
- An analysis from a trading perspective
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Xiabing (Harbing) Lou
Harvard University
Apr-03-2016
Outline
• Electricity market, energy storage and trading
• Potential profit of arbitraging the electricity market
• Demonstration of the trading strategies
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Part 1: An overview
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• Electricity market, energy storage and trading • Deregulated electricity market
• Trading opportunities for private investors
• Flow battery as a new energy storage
• Potential profit of arbitraging the electricity market
• Demonstration of trading strategies.
Regulated and Deregulated Electricity Market
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• Traditional regulated market • Fixed wholesale and retail price
Utilities Generators End users
• Deregulated market
• Bidding to set whole sale price
Electricity bidding mechanism
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Nuclear, Coal, Hydro
Large scale gas, oil plant
Small scale combustion plant
• Marginal price paid for all electricity • Lower bidding price encouraged, but volatility gets higher
Compare Germany and PJM electricity source
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Solar 21%
Wind 25%
Gas 14%
Oil 2%
Coal 25%
Nuclear 6%
Biomass 4%
Hydro 3%
Germany Market Solar 0%
Wind 1%
Gas 35%
Oil 5% Coal
34%
Nuclear 19%
Biomass 1%
Hydro 5%
PJM Market
46% unstable source (solar & wind) Almost no unstable source
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Ger
man
y
PJM
Price valley at noon due to solar input
No valley at noon.
Negative price occasionally
Spikes at 7pm due to household usage
Summer electricity price curve
Can we store the electricity to arbitrage and reduce the volatility?
Energy storage technologies
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Pump Hydro Li ion battery Flywheel Flow battery
Pros Large capacity, low cost Easy setup Fast charging Low cost
Cons Restricted location Expensive Expensive Occupy large volume
Cost $/kWh
30-50 350 ~1,200 ~80 (traditional)
Flow battery may be a good choice of arbitraging electricity market
Organic flow battery
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By replacing Vanadium with organic compound Cost reduced to $27/kWh
Summary
• Electricity price is volatile in a deregulated market • Price fluctuates with daily with periodic pattern
• Unstable generators (solar and wind) may significantly change the price pattern of a day.
• Flow battery may be a good choice for arbitraging the electricity price.
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Part 2: Profit limit
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• Electricity market, energy storage and trading
• Potential profit of arbitraging the electricity market • Optimized profit with linear optimization model
• Storage parameter design: efficiency and capacity
• Economic comparison between Li battery and flow battery
• Demonstration of trading strategies.
Optimized profit: buy low and sell high
• Assumptions: • Full knowledge of future price curve
• Instant turn on and off
• Uniform charge and discharge
• Approach: • Linear optimization algorithm: iterate until highest profit is achieved
• Setting boundary conditions: starting with empty storage, charging and discharging speed limited by the power.
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Boundary conditions
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total profit = 𝑃𝑡 𝐷𝑡 − 𝐶𝑡 ; (1)
𝑇
𝑡=1
𝑆𝑡 = 𝑆𝑡−1 + 𝐷𝑡 +ω ∗ 𝐶𝑡 (2)
𝑆𝑡 ∈ 0, ℎ ∗ 𝑘 (3)
𝐷𝑡 , 𝐶𝑡 ∈ 0, 𝑘 (4)
Maximize total profit with Linear Optimization function in Scipy
Example: a 1MW storage profitability in PJM
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0%
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Jan Feb Mar Apr May Jun Jul Aug Sep Oct Nov DecR
ela
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Pro
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($)
PJM profit and relative standard deviation
Profit relative deviation
7MWh storage capacity, 1 MW power, 80% round trip efficiency
• 2016 Total profit: $27,700 • Profit increases with standard deviation linearly
y = 0.0023x + 2.853 R² = 0.945
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500 1500 2500 3500 4500
Stan
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Monthly Profit
PJM Montly Profit VS Standard Deviation
Design of a storage device
• Two major components of a storage device: • Power component (inverter, converter, transformer). Cost ∝ power (MW)
• Capacity component (battery, chemical tank, reservoir, etc). Cost ∝ capacity (MWh)
• Use one parameter to depict both power and capacity: capacity factor • Capacity factor(h)= Capacity(discharge) / Power
• Depict how many hours does it take to deplete a storage device
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Assume 1MW power, use capacity factor and efficiency as variables
Capacity Factor and Efficiency
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0 5 10 15
Pro
fit
$
Capacity Factor (h)
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40% 50% 60% 70% 80% 90% 100% 110%
Pro
fit
$
Round Trip Efficiency
Li battery Flywheel
Flow battery Pump hydro
Assuming 1MW power • Profit increase with efficiency linearly but render much higher cost • Marginal benefit of increasing capacity factor decreases beyond 5h
Capacity factor vs Profit Efficiency vs Profit
Estimating payback periods of flow battery(FB) and Li battery(LB)
• Capacity cost: $27,000/MWh (FB), $350,000/MWh (LB)
• Power components cost roughly $200,000/MW for both system.
• Annual profit: ~$25,000(FB), ~28,500(LB)
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Capacity cost (5MWh)
Power cost (1MW) Total cost Annual profit $ Payback period
Flow battery 135,000 200,000 335,000 25,000 15.4y
Li battery 1,750,000 200,000 1,950,000 28,500 68y
Both coal fire plant and pump hydro have payback period around 20y
Flow battery seems to be economically feasible to arbitrage electricity market
Summary
• A medium size 1MW(5h) storage device can achieve an annual profit of $25,000 in PJM 2016.
• Marginal profit of capacity factor decreases beyond 5h.
• Although 90% storage render higher profit, the higher cost does not work with this business model.
• Payback period indicates flow battery is commercially feasible
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Part 3: on going
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• Electricity market, energy storage and trading
• Potential profit of arbitraging the electricity market
• Demonstration of trading strategies. • Dumb approach
• Machine learning approach
Optimized trading strategy: from linear optimization
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Mon Tue Wed Thu Fri Sat Sun
Price in a week
Trading activities in a week
Charging : ~0:00 to 6:00; Discharging: 10:00-14:00 and 19:00-20:00
Dumb approach: fixed trading every day
• Charge during night 0:00 to 6:15 (assume 80% efficiency)
• Discharge at noon and evening: 10:00-14:00 and 19:00 to 20:00
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Recovers 52% profit compare with optimized strategy
Mon Tue Wed Thu Fri Sat Sun
Machine learning approach
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Training Set: (date, hour,
weather, etc.)
Label: price
Feature Vectors Machine
Learning Algorithm
New set: (date, hour, etc)
Feature Vector
Predicted price Predictive model
Supervised Learning
Trading strategy based on machine learning
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Mon Tue Wed Thu Fri Sat Sun
Predicted and actual price
Trading behavior based on predicted price
Trading behavior based on actual price
Recovers 90% profit
Summary
• Simple dumb trading approach cannot effectively recover the profit.
• Machine learning can effectively predict the future electricity price.
• 90% of theoretical profit can be recovered based on the predicted price.
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Conclusions
• Electricity price in deregulated market is volatile due to demand and supply fluctuation thus provides an opportunity for arbitrage.
• The profit of a electricity storage device can be evaluated with historical data and optimization algorithm.
• Flow battery is economically the best choice.
• A SVR machine learning trading strategy can effectively recover the theoretical profit.
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26 Thank you very much
Monthly profit vs standard deviation
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y = 0.0023x + 2.853 R² = 0.945
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Monthly Profit
PJM Montly Profit VS Standard Deviation
y = 0.0048x - 0.6772 R² = 0.9364
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Monthly Profit
German Monly profit vs Standard Deviation
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R² = 0.9756
20%
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45%
50%
800 1300 1800 2300 2800 3300 3800 4300 4800
Re
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Monthly Profit Adjusted $
Standard deviation difference over the years
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-40.00%
-20.00%
0.00%
20.00%
40.00%
60.00%
80.00%
100.00%
120.00%
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2006 2007 2008 2009 2010 2011 2012 2013 2014 2015 2016
German_Price PJM_Price German Relative std PJM Relative std
Both markets has about 40% average fluctuation in electricity price
Winter electricity curve
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Dumb strategy 2
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