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Energy Trading in the Smart Grid: From End-user’s Perspective
Shengbo Chen
Electrical and Computer Engineering & Computer Science and Engineering
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The Smart Grid Next generation power grid: full visibility and
pervasive control on both supplier and consumers Smart meters
Dynamic electricity prices according to demand Shift demand from peak time
Renewable energy Reduce cost and greenhouse gas emission Energy harvesting: highly dynamic Battery: limited capacity
With these new features and challenges, there is a need for comprehensive solutions for the smart grid
3
taskschedule
Model of Information Delivery Real-time communication between operator and consumers
Smart meters Controller: operator/customer side
Operator
Smart Meter 1
Smart home appliances
demandrequests
Smart Meter 2
Controller
demandrequests
taskschedule
Controller
electricityprices
electricityprices
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Energy Supply and Demand
Attributes of energy supply Unlike communication network
— Storable Renewable vs. Non-renewable Micro-generation
Energy Supply Energy Demand
Energy Management
Attributes of energy demand Time-varying Unpredictable vs predictable Elastic vs. Non-elastic
Random demand meets with possibly uncertain supply
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Energy Trading
Intuition: Dynamic electricity price combining an energy storage battery implies a trading opportunity (similar to stock)
Objective: Maximize the profit by opportunistically selling energy to the grid
Control variables Amount of energy drawn/stored from/to the battery in each time slot
Challenges Uncertainty of incoming renewable energy, price of electricity and
energy demand
Energy selling price is always less than the energy buying price
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System Model
g(t) = l(t)-b(t)
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Example
Key factors: Time-varying electricity price & Battery energy management
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( )1
1max lim [ ( )( ( ) ( ) )
( )( ( ) ( )) ]
T
Tb tt
P t l t b tT
P t l t b t
E
Problem Statement Models
Energy selling price is smaller by a factor of Energy demand l(t) is exogenous process
Profit of selling energy
Cost of buying energy from the grid
Energydrawn/stored from/to the battery
Battery level
Maximal output of the battery. .s tmax| ( ) |b t b
( ) ( )b t B t
(0,1)
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Denote
In each time slot, the energy allocation is given as follows Case 1: If
Case 2: If
Case 3: If
Algorithm Sketch
Sell: Price is high orbattery level is high
Buy: Price is low andbattery level is low
Equal: Price and battery level are mild
max max
max max
( ) ( ) ( )
( ) ( ) ( )
t VP t B t VP b
t V P t B t VP b
( ) ( ) 0t t
0 ( ) ( )t t
*max( )b t b
*max( )b t b
( ) 0 ( )t t *
max( ) min{ , ( )}b t b l t
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Battery level is always bounded: Only require finite battery capacity
Asymptotically close to the optimum as T tends to infinity
Main Results
max max max( ) 2B t b VP r
* *
1
1limsup [ ( )( ( ) ( ) ) ( )( ( ) ( )) ]
T
T t
opt
P t l t b t P t l t b tT
DC
V
E
Diminish as V becomes large
A tradeoff between the battery size and the performance
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Simulation Results Compared to the greedy scheme: first use the renewable energy
for the demand, and sell the extra if any
Annual profit versus Beta (V=1000) Annual profit versus V (Beta=0.8)S. Chen, N. Shroff and P. Sinha , “Energy Trading in the Smart Grid: From End-user’s Perspective,” to appear in Asilomar Conference on Signals, Systems and Computers, 2013. (Invited paper)
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Simulation Results (cont’) Real traces
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Open Problems Game theory based schemes
The behavior of large number of customers can influence the market price
Network Economics
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Thank you