Transcript PowerPoint

The Management of Renewable
Energy
Community-level Solar Energy
All the customers in the building belong to the community and share the energy
generated by the PV panel as a public resource.
2
Microgrid
A microgrid is a localized grouping of electricity generation, energy storage, and loads
that normally operates connected to a traditional centralized grid. Microgrid generation
resources can include fuel cells, wind, solar, or other energy sources.
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Benefit?
 Mitigate the pressure of the peak generation.
 Avoid pollution due to excessive generation.
 Reduce the power flow of transmission and distribution system,
ensure the security.
Challenges
 Since community-level renewable energy is always cheap to the local
customers, each one intends to use as much renewable energy as
possible. However, the amount of renewable energy is always not
enough to supply all the customers. Thus, the energy competition
among the customers need to be addressed.
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New Idea
 Design an effective and efficient smart home scheduling method to
make the best use of renewable energy.
 Model the competition of customers on renewable energy.
 Encourage the customers to cooperate such that the community-wide
monetary cost is minimized while the customers are minimizing the
individual monetary cost.
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Model of Community-level Renewable Energy
𝑤𝑛,ℎ = 𝑊ℎ
𝑛∈𝑁
𝑤𝑛,ℎ
Power Line
Data Line
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Energy Consumption & Monetary Cost
Consume 𝐿ℎ − 𝑊ℎ
Pay 𝑎ℎ 𝐿ℎ − 𝑊ℎ 2
Consume 𝑙𝑛,ℎ − 𝑤𝑛,ℎ
Pay 𝑎ℎ 𝐿ℎ − 𝑊ℎ
2 𝑙𝑛,ℎ −𝑤𝑛,ℎ
𝐿ℎ −𝑊ℎ
= 𝑎ℎ 𝐿ℎ − 𝑊ℎ 𝑙𝑛,ℎ − 𝑤𝑛,ℎ
Power Line
Data Line
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Problem Formulation
 Centralized:
 min 𝐻
ℎ=1 𝑎ℎ 𝐿ℎ − 𝑊ℎ





2
Decentralized:
For each customer 𝑛:
min 𝐻
ℎ=1 𝑎ℎ 𝐿ℎ − 𝑊ℎ 𝑙𝑛,ℎ − 𝑤𝑛,ℎ
⇓
min 𝐻
ℎ=1 𝑎ℎ 𝑙𝑛,ℎ + 𝑙−𝑛,ℎ − 𝑊ℎ 𝑙𝑛,ℎ − 𝑤𝑛,ℎ
 Constraint conditions for energy consumption are the same as before.
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Constraints
𝑙𝑛,ℎ =
𝑦𝑚,ℎ
𝑚∈𝐴𝑛
𝑦𝑚,ℎ = 𝑥𝑚,ℎ 𝑡𝑚,ℎ
𝑥𝑚,ℎ ∈ 𝑋𝑚
𝛽𝑚
𝑦𝑚,ℎ = 𝐸𝑚









ℎ=𝛼𝑚
𝑛: index of the customer
ℎ: index of the time slot
𝑚 ∈ 𝐴𝑛 : index of the home appliances for customer 𝑛
𝑥𝑚,ℎ ∈ 𝑋𝑚 : power level of home appliance 𝑚 at time slot ℎ, which is the energy
consumption per time slot
𝑙𝑛,ℎ : total energy consumption of customer 𝑛 at time slot ℎ
𝐸𝑚 : total energy consumption of home appliance 𝑚 for a given task
𝐶𝑛,ℎ 𝑙𝑛,ℎ : Total monetary cost in time slot ℎ for energy consumption 𝑙𝑛,ℎ
𝛼𝑚 and 𝛽𝑚 : earliest start time and latest end time of home appliance 𝑚
𝑡𝑚,ℎ : The actual working time of home appliance 𝑚 at time slot ℎ such that
𝑡𝑚,ℎ = 1 except for the last time slot.
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Decomposition of Problem
Aggregator decide 𝑤𝑛,ℎ
for each customer 𝑛
No
Yes
End
Converge?
All customers solve the
multi-customer smart
home scheduling problem
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How to decide 𝑤𝑛,ℎ ?
 Problem: Renewable energy is merged with conventional energy in
the feeder such that it is impossible to know how much renewable
energy is distributed to each one. However, we virtually distribute it
through telling the customers how much energy they can use for free.
 Solution: We seek for the 𝑤𝑛,ℎ that could promote smart home
scheduling. Since the relationship between renewable energy
distribution and total monetary cost is not explicit, a cross-entropy
optimization based algorithm is proposed to solve this problem.
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Experimental Setup – System
 Community: 500 customers
 Time Horizon: 24 hours from this moment, divided into time 15minutes slots.
 𝑎ℎ = 0.0064$/𝑘𝑊ℎ2 at any time slot.
 Setup of home appliances is the same as last work
 Belgian wind farm data is used to model renewable energy
generation. http://www.elia.be/en/grid-data/power-generation/windpower
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Wind Farm Data
1600.00
1400.00
1200.00
1000.00
Day-Ahead Forecast
800.00
Real-Time Generation
600.00
400.00
200.00
1
21
41
61
81
101
121
141
161
181
201
221
241
261
281
301
321
341
361
381
401
421
441
461
0.00
Wind Power from 01/01/2014 to 01/05/2014, every 15 minutes, Forecast Error 9%
http://www.elia.be/en/grid-data/power-generation/wind-power
Scaled for 20% penetration
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Experimental Setup
0.25
Energy Load (kWh)
0.2
0.15
0.1
0.05
0
0
20
40
60
Time Horizon (15 minutes)
80
100
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Comparison: No Smart Home Scheduling
Energy Load (kWh)
Average energy consumption for each customer, lead to $4.68
0.2
0.15
0.1
0.05
0
40
60
80
100
Time Horizon (15 minutes)
Average conventional energy consumption for each customer, lead to $3.59
Energy Load (kWh)
0
20
0
20
0.2
0.15
0.1
0.05
0
40
60
Time Horizon (15 minutes)
80
100
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Comparison: Without Renewable Energy
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Global Optimal Solution
$1206.9 over all the customers
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Corresponding energy consumption in each
iteration
1245
1240
Monetary Cost (USD)
1235
Proposed Method
Uniform Distribution
Global Optimal Solution
1230
1225
1220
1215
1210
1205
0
5
10
15
Iteration Number
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Smart Home Scheduling Solution
$1207.62 over all the customers
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Global Optimal Solution
$1212.0 over all the customers
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Our Solution
$1212.0 over all the customers
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Management of Smart Community Considering Selling Energy Back to Grid
PV
panel
Battery
Power Line
Data Line
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Sell Energy Back to Grid
 Home level renewable energy generation unit is encouraged such
that customers could sell residual renewable energy back to the
utilities.
 Interconnection Standard: Clarify how to connect renewable
generation unit into the power grid.
 Net Metering: Measure the power flow in both directions.
 Selling Price: Partial the retail price or generation cost (varies in
different locations).
Already applied in 27 states of U.S.
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Net Metering
Net Meter
The power flow injected into the distribution network is
measured by net meter as the selling back amount.
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Inverter
Inverter
Inverters are connected on both the branches to home and
grid to invert DC power into AC power.
Switch Breaker
Switch Breaker
Switch breaker is connected on the branch to the grid. If a fault happens in
the grid, it breaks to protect the PV panel. If a fault happens in the homelevel power network, it breaks to protect the grid.
Capacity
The total capacity of all the PV panels cannot exceed 40% the capacity of the
substation transformer. The capacity of each single PV panel is also limited.
Problem Formulation
Energy Trading
 Buy or sell: 𝑦𝑛 ℎ . Buy energy if 𝑦𝑛 ℎ > 0, sell if 𝑦𝑛 ℎ < 0.
 Total energy purchase is:
𝑁
ℎ
𝑛=1 𝑦𝑛
 Bill charged by the utility: 𝑝ℎ
 Unit price: 𝑝ℎ
 Selling price:
𝑁
ℎ
𝑛=1 𝑦𝑛
𝑝ℎ
𝐾
𝑁
ℎ
𝑛=1 𝑦𝑛
𝑁
ℎ
𝑛=1 𝑦𝑛
2
Monetary Cost
Game Formulation