How to balance the mismatch?

Report
REAL TIME BALANCING OF SUPPLY AND DEMAND IN
SMART GRID BY USING STORAGE, CONTROLLABLE LOADS
AND SMART GENERATIONS
Abdulfetah Shobole, Dr. Arif Karakaş
Yildiz Technical University
Department of Electrical Engineering
Yildiz Technical University, Department of Electrical Engineering
Outline
1.
Why to balance between generation and supply?
2.
What are the couses of mismatch?
3.
How to balance the mismatch?
4.
The proposed method.
5.
Modeling and Simulation.
6.
Results and Conclusion.
Why to balance between generation and supply?
– To make the system stable
– For maintaining frequency
– Prevent black outs due to cascading outages
BALANCE
What are the couses of mismatch?
– Consumption change with time.
– Intermittent Energy Sources
– Contingencies
How to balance the mismatch?
• In Traditional Power System.
– Deterministic ahead of time dispatching
– Through telephone communication and paper.
– The balancing is done by controlling the conventional generations with
reserve.
How to balance the mismatch?
• In Smart Grid.
– The operation component in SG model is concerned with managing
the energy flow in Smart grid.
– Balancing demand and supply in real time is one of the
characteritics of Smart Grid.
– Demand response and Storages in addition to conventional
generations.
from NERC
The Proposed Method
 Data are automatically read from
power system
– Smart communication
technologies are involved.
– AMI for the loads
– WASA
• Generations
• Storage
• Metrological data
• Contingencies
The Proposed Method
 Make decisions in Real Time
 Optimize the decisions by considering
the situations
 DGs are considered as VPP by
aggregating their output.
 Use all the available apportunities
 Demand response
 Storage
 Conventional generations
 Distributed generations
 Take your share and pass to the next
algorithim is used.
Start
Update
Load data
Generation data
Storage
From AMI, WASA, Smart grid data servers, market
data servers, etc.
Yes
Is the balance
achieved?
No
Yes
Adjust the
storage to
reset the
mismatch.
Is the available
storage enough
for frequecy
control?
No
Calculate the left share for
the next step and set the
available storage.
Yes
Adjust the
loads to
reset the
mismatch.
Are the
available loads
enough for
frequency
control?
No
Calculate the left share for the next
step and Adjust the available Load.
Adjust
conventional
Generations.
Yes
Is
adjusting
DGs
feasable?
No
Adjust
Distributed
Generations.
The Modeling and Simulation
DigSilentPowerFactory
 It is Commercial
 has the ability to simulate load flow, RMS fluctuations
and transient events in the same software
environment
 It has programming feature (DigSilent Programming
Language)
The Modeling and Simulation
The DigSilent Network model to test
the proposed algorithm.
Simulation Results and Conclusion
Generation from DGs, Generation from
Conventional Power Power Plants, Total Generation
The storage system tracks the variation from the DGs and
resetting the mismatch
Simulation Results and Conclusion
Generation from DGs, Generation from Conventional Power
Power Plants, Total Generation
Storage, Controllable load and smart generation are
involved in adjusting mismatch
Simulation Results and Conclusion
The smart grid enables the power system to be more efficient and stable, especially
when renewable energy systems which are intermittent resources.
In smart grid it is possible to integrate renewable energy systems and handle the
mismatch between demand and supply by controlling the system in real time. This
requires the access to data from generations, loads, storage systems, energy markets, etc.
This is possible in smart grid due to communication, information and sensor
infrastructures laid throughout the electricity network.
The system mismatch can be handled even if the ratio of the renewable energy
resources in the system is very high.
The proposed method is also applicable for the variation of the load or any other
contingency conditions that disturb the balance between demand and supply. The order
of choice of the controller whether to use storage, controllable loads, smart generations or
load shading depends on the factors like available capacity, environmental data, market
data, location of the resources, etc.

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