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

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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 t t

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 bt 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 tTDCV

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