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
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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.
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Why to balance between generation and supply?
– To make the system stable– For maintaining frequency– Prevent black outs due to cascading outages
BALANCE
3
What are the couses of mismatch?
– Consumption change with time.
– Intermittent Energy Sources
– Contingencies
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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.
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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 6
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
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The Proposed Method
Yes
Yes
Yes
No
No
No
Is the available storage
enough for frequecy control?
Are the available
loads enough for frequency control?
Calculate the left share for the next step and
set the available storage.
Is the balance achieved?
Calculate the left share for the next step and Adjust the
available Load.
UpdateLoad dataGeneration dataStorageFrom AMI, WASA, Smart grid data servers, market data servers, etc.
Adjust the storage to reset the mismatch
.
Adjust the loads to reset the mismatch
.
Start
Adjust Distributed Generation
s.
Is adjusting DGs feasabl
e?
Adjust convention
al Generation
s.
NoYes
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.
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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
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The Modeling and Simulation
The DigSilent Network model to test the proposed algorithm. 10
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
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Simulation Results and Conclusion
Storage, Controllable load and smart generation are involved in adjusting mismatch
Generation from DGs, Generation from Conventional Power Power Plants, Total
Generation
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Simulation Results and ConclusionThe 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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