Demand Response Management
and Smart Pricing on Smart Grids
Xiao-Jun Zeng
School of Computer Science
University of Manchester
[email protected]
29th August 2016
Outline
• Background
• Research problems
• Demand response management for energy
customers
• Demand response management for energy
retailers
• Integrated demand and supply response
management
• Future research topics
1
Background
Demand response management on smart grid
• a program to motivate changes in electric use by enduse customers.
• main demand response programs
– Direct Load Control
– Dynamic Pricing.
• demand Response highlights the importance of
customers in energy management.
• day-ahead and real-time smart pricing is easier to
implement and more cost effective for demand
response management.
2
Background
Objectives & benefits of demand response management
• integrate renewable energy sources and reduce CO2
emission
• reduce minimise the customers’ bill by smarter
consumptions
• reduce the cost and maximise the profit for energy
generators and retailers by smart pricing
• balance the peak and off-pear usages to relieve the
pressure for power network upgrading and replacement
3
Research problems
Customers, Retailers, and Generators in energy market
4
Research problems
Research Problems:
• How to support customers to use energy more
effectively and reduce energy bills
• How to support retailers to maximise their profits and
reduce their cost by smart pricing
• How to develop new whole market mechanism to
effectively integrate demand and supply side
management
5
Demand management for customers
Research Problem 1 - How to support customers
to use energy effectively and reduce energy bills
• Home energy management software system integrated in
smart meter to optimal energy usages
• Home energy consumption diagnosis software system to
effective energy usages
• Tariff selection system to support choosing the right tariffs
6
Demand management for customers
Home energy management software system:
• Input: individual customer provides the usage
constraints such as the time interval for each appliance
to be used
• Output: the system will automatically find the cheapest
time to switch appliance on and off automatically
• Benefit: minimise the bill whereas meet the usage
requirements
7
Demand management for customers
Home energy management software system: Different
types of appliances as below will lead to different
optimisation problem
• Interruptible appliances bill minimization (e.g., electric
vehicles)
• Non-interruptible appliances bill minimization (e.g.,
washing machine)
• Curtailable appliances bill minimization subject to
comfortable constraint (e.g., air conditioning) or
comfortable maximisation subject to financial constraint
8
Demand management for customers
Example:
9
Demand management for customers
Home energy consumption diagnosis system:
• Input: individual customer use the energy any way
he/she likes and the system records the usage data
• Output: the system calculates the probabilities each
applicant is used at the cheapest, 2nd cheapest ,…,
price intervals and make the recommendation to more
effective use
• Benefit: improve the efficiency of energy usages while
leave the freedom and flexibility to customers
10
Demand management for customers
Example:
11
Demand management for customers
Home energy consumption diagnosis system:
• Adaptive learning of the probability of each appliances
at the cheapest, 2nd cheapest ,…, prices to diagnose
the effectiveness and whether the improvements have
been achieved with the time
• Bayesian Theorem is used to learn the cases in the
case many historical prices are the same
• Provide the probability behaviour models about the
usage patterns of customers
12
Demand management for customers
Tariff selection system to support tariff selections
• This is the case a customer having no system
integrated in smart meter
• Input: individual customer download usage data to an
online tariff analysis web system
• Output: the system calculates the bills based on the
data and choose the best tariff as the recommendation
• Benefit: switch to the best tariff to save and increase
the market competition
13
Smart Pricing for Retailers
Research Problem 2: How to support a retailer to
maximise profits and reduce cost by smart pricing
• Solve the pricing optimisation problem which maximises
the profit by taking into
– The customers’ reaction functions to the prices offered
– The costs which are the prices to buy energy from the
whole sale market
– Subject to the constraints such as supply, customers’
acceptance, O2 emission index, market share etc
=> A Stackelberg (leader and follower) game approach
14
Smart Pricing for Retailers
Stackelberg game of energy pricing:
– First, the leader (i.e., retailer) announces his strategy (prices)
uL;
– Knowing the leader’s strategy uL, each follower (i.e.,
customer) then selects his best response strategy Rn(uL) ∈
UF,n which minimises his bill function, i.e.,
J F ,n [uL , Rn (uL )]  max J F (uL , uF ,n ) n  1,..., N
u F ,n U F ,n
– After finding out the reaction function of each follower
(customer), the leader maximises his profit function
J L [u *L , RF ,1 (u *L ),..., RF , N (u *L )]  max J L [uL , RF ,1 (uL ),..., RF , N (uL )]
uL U L
15
Smart Pricing for Retailers
Solution to Stackelberg game problem of energy pricing:
• Part 1. Determine or learn the reaction function
– For customers with home management system, this is
obtained by solving the bill minimisation problem as
discussed before
– For customers with home diagnosis system, this is obtained
by learning the probability distribution function based on
historical data
– For customers without any system installed, this is obtained
by learning the aggregated reaction function (the
conventional elasticity based demand model)
16
Smart Pricing for Retailers
Solution to Stackelberg game problem of energy pricing:
• Part 1. Determine or learn the reaction function – Big data
issue (millions of customers and real-time data)
– For customers with home management system, distributed
optimisation approach is used and then the results are
aggregated hierarchically
– For customers with home diagnosis system, online/recursive
learning approach is used and then the results are
aggregated hierarchically
– For customers without any system installed, the customers
are segmented based on similar patterns by using clustering
algorithm and then aggregated demand models are learned
online
17
Smart Pricing for Retailers
Solution to Stackelberg game problem of energy pricing:
• Part 2. Determine the optimal prices
18
Smart Pricing for Retailers
Solution to Stackelberg game problem of energy pricing:
• Part 2. Determine the optimal prices
• Difficulties:
– hybrid reaction functions – detrimental and probabilistic
models
– mixed types of lower level optimisation problems including
linear programming and integrated programming
– discontinuous profit function
• Multi-population Genetic Algorithm is proposed as the
solver
19
Smart Pricing for Retailers
• Part 2. Determine the optimal prices - Multi-population GA
20
Integrated Demand and Supply Pricing
Research Problem 3 - How to develop new
wholesale market mechanism to effectively
integrate demand and supply side management
• Dynamic pricing leads to completely new consumption
switch patterns which have not seen in static pricing. As a
result, the existing pricing mechanism in the wholesale
market no longer works
• New wholesale pricing mechanism needs to be designed
to solve this problem
=> Interval demand based wholesale pricing
21
Integrated Demand and Supply Pricing
Existing wholesale pricing mechanism
Wholesale
electricity
market
ISO
Retail
Electricity
market
• Independent System Operator (ISO) gives the demand
prediction for the next period (e.g., next day)
• Generators make their supply and price offers (bids)
• The price which enables the supply is equal to the demand is
then the market clearing price
=> Dynamic retail pricing makes the demand unpredictable
22
Integrated Demand and Supply Pricing
New wholesale pricing mechanism
• ISO gives the demand interval for the next period
• Supply Side - Generators make their supply and price offers
(bids), a piecewise supply – price function
• ISO calculates the market clearing price function - a piecewise
demand – clearing price function
• Demand Side - Retailers decide their optimal prices based on
the announced clearing price function and provide the
piecewise clearing price – demand function
• ISO calculates the equilibrium clearing price => the final
clearing price
23
Integrated Demand and Supply Pricing
New wholesale pricing mechanism
• Integrated demand and supply pricing rather than the existing
supply dominated pricing
• Effectively handle unpredictable demand under the dynamic
retail pricing
• Analytical solution can be obtained
• Enable to realise the goals of dynamic pricing by utilising smart
meters such as effectively using renewable energy resources,
reduce CO2 emission, minimise the bills for customers,
maximise the profits for both retailers and generators.
24
Future research topics
• A systematic comparison between pricing schemes to
learn fro each other
– UK is moving toward dynamic pricing supported by
smart meters
– China has adapted increasing block pricing
• Customer side: optimal auto switch under dynamic
pricing and its impact to market competition
• Retailer side: pricing under the market competition with
multi-retailers
• Generator side : new wholesale market mechanism
design and optimal bidding under such new
mechanisms
25
Thanks!
Questions?