method to eliminate the disadvantages of wind power generation

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2. AN AGENT-BASED APPROACH TO VIRTUAL
POWER PLANTS OF WIND POWER
GENERATORS AND ELECTRIC VEHICLES
Guided by,
Mrs. Deepa M.U
Asst. Prof.,EEE
College of Engineering Perumon
Presented by,
Arjun Anil
S7 EEE
Roll No. 8

3. CONTENTS
1. Why Virtual Power Plants of Wind Power Generators and Electric Vehicles?
2. What Is Virtual Power Plants?
3. Wind Power Generators and Electric Vehicles
4. Storage Payment and Day Ahead Optimization Scheme
5. Experimental Results
6. Comparison Between VPP And Normal Wind Power Generators
7. Conclusion
8. References
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4. Why Virtual Power Plants of
Wind Power Generators and Electric
Vehicles?
 Wind power generation has received
considerable attention in recent years
 Enables the wind generators to counter the unpredictability of
wind power generation
 Supply of energy to the grid can be controlled based on the
demand of energy
 Increases the profit of wind farms
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5. What Is Virtual Power Plants?
 A VPP is a group of multiple energy producers and energy storage
providers
 The objective of VPP is to sell electricity as an aggregate
Participants of VPP:
1. Wind Power Generators
2. Electric Vehicles
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6. VPP Participants
 VPP is composed of some wind energy producers and electric
vehicles as a single entity
 VPP helps in day ahead marketing
 In day ahead market the power is generated, stored and
traded on day k-1 to deliver it on day k by the supplier
 The supplier must ensure energy balance between
generation and consumption
 If there is any imbalance the supplier must pay the imbalance
penalties
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7. Wind Power Generators and Electric
Vehicles
oWind power generators generate
electricity depending on the weather
conditions
oElectric vehicles store the energy
produced by the wind power generators
oThe generated energy can be supplied to
grid in 2 ways depending on the demand
1)Directly to grid
2)From energy stored in batteries of
electric vehicles
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8. o The power generated for a given day by a single wind
turbine( j ) of a wind power generation site ( W ) be Pj(t)
o The day is divided into N time slots, the expected electricity
during nth time slot is given by
o The sum of overall wind power generation site W gives us the
generation vector defined as
Where
o The expected generation vector z is used for deciding day
ahead bid
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W

9. Electric Vehicles-Batteries on Wheels
 The function of the electric vehicles is to
store the power generated by the wind
power generators
 Lithium ion batteries are used in electric
vehicles
 By using electric vehicles as storage, the
power can be traded in day-ahead spot
market
 EVs are characterized by a storage profile
which defines the amount of energy stored
in each time slot its battery (sv)
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10.  v = { v1,v2,……………………vk } be a set of electric vehicles
 For an EV v Є V , let sv be the storage profile vector for N time
slots
 Where sv(n) is the quantity of energy that an EV can store at
timeslot n
 Since the EVs willing to provide at most units of
storage
therefore
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11. Storage Payment Scheme For Electric
Vehicles
• Storage is provided to the Evs in the form of charging
entitlements rather than money
• When the Electric Vehicles are used for storage, some
amount of charge is left behind as payment
• The amount of energy given away is measured as a proportion
of the amount of storage used (σ)
• This payment scheme reduces the depth of discharge
• Thus it will help to overcome the reduction of battery life due
to participation in VPP
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12. Day Ahead Optimization Scheme
• To place the bid the leader has to compute the following 5
parameters that determines the supply schedule
i) Amount of energy supplied directly to grid (x)
ii) Amount of energy transferred to batteries (b)
iii) The energy transferred from batteries to grid (d)
iv) Amount of battery storage capacity needed (y)
v) Amount of energy transferred to EVs as payment (g)
• If η is the batteries overall conversion loss, it is necessary to
store 1+η units of energy to actually deliver 1 unit of energy
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13. • The objective of the VPP is solving the following optimization
problems
Where,
σ = g(n)/y(n)
η – Energy lost when electricity flows from grid to battery and
vice versa
pe - Wholesale price of electricity
z(n)-Day ahead estimated generation
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14. - Revenues raised by VPP from the electricity sold in the
market
• is the net energy stored in the EVs batteries at
beginning
• By solving the optimization problem the day ahead bid w is
given by
w= x + d
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15. Case Study
Electric Vehicle Data
• The cost of participation of EV in VPP can be given as
Where
cb - Battery capital cost
DoD - Depth of discharge
Es (DoD) - Energy that EV store on behalf of
VPP
LET – Battery lifetime in kWh
L(DoD) – Battery lifetime in cycles
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16. Since
Therefore cEV becomes
• If Ef (DoD) is the energy that EV receives from the VPP then
the EV profit function can be defined as
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17. Experimental Results
 The main focus of the experiment was to assess the profit of
VPP when compared with wind farm without storage
 The profit gain of the approach is given by
Where
- Realized profit that VPP obtains
- Profit raised by wind farm without storage
 The profit of VPP mainly depends on σ ,
σ = Amount of energy given to EVs as payment
Amount of storage used
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We take 3 values of σ – 0.05,0.1 and 0.15
 When σ = 0.05 , the storage is relatively cheap and hence it is
used widely to maximize the profit
 As σ increases the storage becomes more expensive thus it is
less utilized and profit gain tends to shrink for σ = 0.10 and σ =
0.15
 Another research objective is to assess the amount of storage
needed to maximize the profit (graph b)

19. 19
 As expected, the amount of storage used decreases as it
becomes more expensive
 If we consider the highest level of demand in terms of storage
the VPP must have a storage capacity ranging from
approximately 50MWh for σ = 0.05 to 19MWh for σ = 0.15
(1) σ =0.05
(1) (2) σ =0.1
(2) (3) σ =0.15
(3)
 From the graph, we can see that EV offers maximum profit
when the DoD is 0.4

20.  Therefore a single EV is able to provide a storage of
0.4x30=12 kWh (30kWh is the maximum storage capacity of
EV)
 Thus a VPP would need from 1583 to 4166 EV to store 19 to
50 MWh
 From the results it is found that when σ = 0.05 , storage is
relatively cheap and EVs are widely used
 Although the price paid to the EV is lower, they make small
but frequent profits throughout the year leading to high
annual profits
• When σ increases, the usage of storage is less profitable to
the VPP as it is less frequently used and annual profit is also
reduced
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21. Comparison Between VPP And Normal
Wind Power Generators
Normal Wind Power Generators VPP With Wind Power Generators
And Electric Vehicles
Less reliable
Intermittent and are prone to large
forecast errors
 Low profit
 Simple in design, construction and
supplying
 More reliable and can compete
with other mature technologies of
energy generation
Generated power is stored and can
be supplied whenever needed
 Higher profit
 More complex
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22. Conclusion
 This paper shows a method to make wind power
generation more reliable by forming VPP
 The profit can be maximized by optimizing the
schedule of supply to the grid
 Introduced a novel scheme of paying the EVs for
their storage through supplying energy at no cost
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23. References
[1] A. L. Dimeas and N. D. Hatziargyriou, “Agent based control of virtual
power plants,” in Proc. Int. Conf. Intelligent Systems Applications to Power
Systems (ISAP-2007), 2007, pp. 1–6.
[2] L. M. Costa, F. Bourry, J. Juban, and G. Kariniotakis, “Management of
energy storage coordinated with wind power under electricity market
conditions,” in Proc. 10th Int. Conf. Probabilistic Methods Applied to
Power Systems (PMAPS-2008), 2008, pp. 1–8.
[3] G. Giebel, R. Brownsword, and G. Kariniotakis, “The state-of-the-art in
short-term prediction of wind power: A literature overview,” Project
ANEMOS D1.1, 2003.
[4] R. Piwko, D. Osborn, R. Gramlich, G. Jordan, D. Hawkins, and K. Porter,
“Wind energy delivery issues,” IEEE Power & Energy Mag., vol. 3, no. 6, pp.
47–56, 2005.
[5] J. F. Manwell, J. G. McGowan, and A. L. Rogers, Wind Energy Explained:
Theory, Design and Application. New York: Wiley, 2002.
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