1. The Management of Renewable Energy

2. 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

3. 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. 3

4. 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.

5. 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. 5

6. 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. 6

7. Model of Community-level Renewable Energy
𝑛∈𝑁 𝑤 𝑛,ℎ = 𝑊 ℎ
𝑤 𝑛,ℎ
Power Line
Data Line 7

8. Energy Consumption & Monetary Cost
Consume 𝐿 ℎ − 𝑊 ℎ
Pay 𝑎 ℎ 𝐿 ℎ − 𝑊 ℎ 2
Consume 𝑙 𝑛,ℎ − 𝑤 𝑛,ℎ
Pay 𝑎 ℎ 𝐿 ℎ − 𝑊 ℎ 𝑙 𝑛,ℎ − 𝑤 𝑛,ℎ 𝐿 ℎ − 𝑊 ℎ = 𝑎 ℎ 𝐿 ℎ − 𝑊 ℎ 𝑙 𝑛,ℎ − 𝑤 𝑛,ℎ
Power Line
Data Line 8

9. Problem Formulation Centralized: min ℎ=1 𝐻 𝑎 ℎ 𝐿 ℎ − 𝑊 ℎ 2
Decentralized:
For each customer 𝑛:
min ℎ=1 𝐻 𝑎 ℎ 𝐿 ℎ − 𝑊 ℎ 𝑙 𝑛,ℎ − 𝑤 𝑛,ℎ ⇓
min ℎ=1 𝐻 𝑎 ℎ 𝑙 𝑛,ℎ + 𝑙 −𝑛,ℎ − 𝑊 ℎ 𝑙 𝑛,ℎ − 𝑤 𝑛,ℎ
Constraint conditions for energy consumption are the same as before. 9

10. Constraints 𝑙 𝑛,ℎ = 𝑚∈ 𝐴 𝑛 𝑦 𝑚,ℎ 𝑦 𝑚,ℎ = 𝑥 𝑚,ℎ 𝑡 𝑚,ℎ 𝑥 𝑚,ℎ ∈ 𝑋 𝑚
𝑙 𝑛,ℎ = 𝑚∈ 𝐴 𝑛 𝑦 𝑚,ℎ
𝑦 𝑚,ℎ = 𝑥 𝑚,ℎ 𝑡 𝑚,ℎ
𝑥 𝑚,ℎ ∈ 𝑋 𝑚
ℎ= 𝛼 𝑚 𝛽 𝑚 𝑦 𝑚,ℎ = 𝐸 𝑚
𝑛: index of the customer
ℎ: index of the time slot
𝑚∈ 𝐴 𝑛 : index of the home appliances for customer 𝑛
𝑥 𝑚,ℎ ∈ 𝑋 𝑚 : power level of home appliance 𝑚 at time slot ℎ, which is the energy consumption per time slot
𝑙 𝑛,ℎ : total energy consumption of customer 𝑛 at time slot ℎ
𝐸 𝑚 : total energy consumption of home appliance 𝑚 for a given task
𝐶 𝑛,ℎ 𝑙 𝑛,ℎ : Total monetary cost in time slot ℎ for energy consumption 𝑙 𝑛,ℎ
𝛼 𝑚 and 𝛽 𝑚 : earliest start time and latest end time of home appliance 𝑚
𝑡 𝑚,ℎ : The actual working time of home appliance 𝑚 at time slot ℎ such that 𝑡 𝑚,ℎ =1 except for the last time slot. 10

11. Decomposition of Problem
Aggregator decide 𝑤 𝑛,ℎ for each customer 𝑛 No
Yes
Converge?
All customers solve the multi-customer smart home scheduling problem
End 11

12. How to decide 𝑤 𝑛,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 𝑤 𝑛,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. 12

13. Experimental Setup – System
Community: 500 customers
Time Horizon: 24 hours from this moment, divided into time 15-minutes slots.
𝑎 ℎ =0.0064$/ 𝑘𝑊ℎ 2 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. 13

14. Wind Farm Data
Wind Power from 01/01/2014 to 01/05/2014, every 15 minutes, Forecast Error 9%
Scaled for 20% penetration 14

15. Experimental Setup 15

16. Comparison: No Smart Home Scheduling16

17. Comparison: Without Renewable Energy17

18. Global Optimal Solution
$ over all the customers 18

19. Corresponding energy consumption in each iteration19

20. Smart Home Scheduling Solution
$ over all the customers 20

21. Global Optimal Solution
$ over all the customers 21

22. Our Solution
$ over all the customers 22

23. Management of Smart Community Considering Selling Energy Back to Grid
PV panel
Battery
Power Line
Data Line 23

24. 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. 24

25. Net Metering
Net Meter
The power flow injected into the distribution network is measured by net meter as the selling back amount. 25

26. Inverter
Inverter
Inverters are connected on both the branches to home and grid to invert DC power into AC power.

27. 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 home-level power network, it breaks to protect the grid.

28. 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.

29. Problem Formulation

30. Energy Trading
Buy or sell: 𝑦 𝑛 ℎ . Buy energy if 𝑦 𝑛 ℎ >0, sell if 𝑦 𝑛 ℎ <0.
Total energy purchase is: 𝑛=1 𝑁 𝑦 𝑛 ℎ
Bill charged by the utility: 𝑝 ℎ 𝑛=1 𝑁 𝑦 𝑛 ℎ 2
Unit price: 𝑝 ℎ 𝑛=1 𝑁 𝑦 𝑛 ℎ
Selling price: 𝑝 ℎ 𝐾 𝑛=1 𝑁 𝑦 𝑛 ℎ

31. Monetary Cost

32. Game Formulation