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INTELLIGENT DC MICROGRID WITH SMART
GRID COMMUNICATIONS: CONTROL
STRATEGY CONSIDERATION AND DESIGN
Presented by: Amit Kumar Tamang, PhD Student
Smart Grid Research Group-BBCR
Supervisor : Prof. Weihua Zhuang
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9 January, 2013
MAIN REFERENCE
Wang, B. C.; Sechilariu, M.; Locment, F.; ,
"Intelligent DC Microgrid With Smart Grid
Communications: Control Strategy Consideration
and Design," Smart Grid, IEEE Transactions on ,
vol.3, no.4, pp.2148-2156, Dec. 2012
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OUTLINE
Introduction
System Overview
Power Subsystem Behavior
Operation Layer Control Strategy
Supervision Upper Layer Design
Discussion
Conclusion
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INTRODUCTION (1/5)
Smart Grid:
Modern Electricity grid capable of bidirectional
power and information flow.
( Power+Information+Communication)
Complex Network with Randomness and Non-
linearity.
Issues:
Distributed Generation, Demand Response and
Load Control, Energy Storage, Anticipated
Massive Amount of Energy Transaction
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INTRODUCTION (2/5)
MicroGrid
Localized Grouping of Electricity Sources (Wind,
Photovoltaic etc) and Loads.
Can Work with/without Traditional Centralized Grid.
DC MicroGrid – Avoids DC to AC & AC to DC
conversion preventing energy loss due to conversions.
Smart Grid + Microgrid = Provision of Injecting
energy to or getting energy from Utility Grid.
Undesirable Injection = Fluctuation in grid power.
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INTRODUCTION (3/5):ISSUES
Renewable Power Generation: Intermittent and
Random nature.
Uncontrolled injection increases the power
mismatching in utility grid and fluctuation in
voltage and frequency.
Storage system to combat intermittent energy
production.
Lead Acid batteries for storage. Limited storage
capacity, energy management to optimize the use
of renewable energy for high penetration.
Grid Need (Injection) & Availability(Peak load
shaving)
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INTRODUCTION (4/5)
Fig: Possible Smart Grid Topology
•Small Scale, Middle Scale & Large Scale = traditional Grid to Smart Grid
•Microgrid Controller: Power Balancing & Load Management.
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INTRODUCTION (5/5) : OBJECTIVE
Design Control
Strategies
•Intelligent Multi-layer Supervision
•Interaction with Smart Grid, End user
•Predictions & Energy Management.
USING
Power
Balance
Better DC Microgrid
Integration
Focus
•Avoid Undesirable
Injection
•Mitigates Fluctuation
in Grid Power
•Reduces Grid Peak
Consumption
With Load shedding
& PV Constrained
Production
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DC MICROGRID SYSTEM :OVERVIEW
Fig: DC Microgrid System Overview
Power Balancing with
Load Shedding, PV Constrained
control, w.r.t power limits(Utility
Grid)
Improve Energy Efficiency
And reducing energy cost.
Predicts load consumption
And renewable energy
production
End user sets some criteria
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DC MICROGRID SYSTEM
POWER SUBSYSTEM BEHAVIOR (1/6):
Elements:
Grid, PV, Storage, Load
Each element modeled by MATLAB Stateflow
Simulating event-driven systems based on finite
state machine theory.
Symbols:
PG = Grid Power; PS = Storage Power
P_G_S_lim , P_G_I_lim = Grid power supply and
injection limits
PL = Load Power; Ppv = PV Array Power
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PG & PS controlled by corresponding reference
current iG* and iS*.
Power reference p* = output of controller for
stabilizing dc bus voltage.
= distribution Coefficient [0,1]
i.e energy storage not injected into Grid
DC Microgrid System POWER SUBSYSTEM BEHAVIOR (2/6):
CI – Integral Gain
Cp – Proportional Gain
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Fig : Grid Behavior State flow model
Maximum Load Power
PV A peak power production
DC MICROGRID SYSTEM
POWER SUBSYSTEM BEHAVIOR (3/6) :
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Fig: Storage Behavior Stateflow model
SOC = State of Charge in storage.
DC MICROGRID SYSTEM
POWER SUBSYSTEM BEHAVIOR (4/6):
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Fig: PV source Behavior Stateflow model
g = Solar irradiation (W/m2)
GMIN = Minimum irradiation threshold
DC MICROGRID SYSTEM
POWER SUBSYSTEM BEHAVIOR(5/6) :
MPPT = Maximum Power
Point Tracker.
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Fig: Load Behavior Stateflow Model
KL = load power limit controlling coefficient (Load shedding)
[0,1]
DC MICROGRID SYSTEM
POWER SUBSYSTEM BEHAVIOR (6/6):
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DC Microgrid System
:OPERATION LAYER CONTROL STRATEGY
Fig: Power Control Algorithm
Energy Flow in power subsystem
Controlled by variables:
KD, P_G_S_lim & P_G_I_lim (Smart
Grid Messages), P*PV_lim , KL
Algorithm calculates: P*G, P*S,
P*PV_LIM w.r.t limitations and gives
Value of KL
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DC Microgrid System :SUPERVISION UPPER LAYERS DESIGN
Fig: Supervision Hierarchical Structure
Interface variable: upper layer
controls Lower layer
Physical Parameters from
Different fields
Relates Different Time scales
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DISCUSSION
This work mainly focuses on Power control Algorithm. (Operation Layer)
Provides Basic Idea for other layers.
Like Optimized value of KD from Energy Management layer is untouched.
Mechanism of Prediction of load and PV power is untouched. (Considered as
future work)
KD needs low speed communication (in range of Minutes). But what
Communication infrastructure would be appropriate??
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CONCLUSION
•An intelligent comprehensive DC Micro grid with multi layer supervision
was suggested.
•Supervision exchange data with smart grid, interact with End user,
predicts load & PV production and manages energy cost.
•DC micro grid control design avoiding undesired power injection,
Mitigating fluctuation in grid power and reducing grid peak consumption
Was proposed.
•Apparently, supervision interface reduced the negative impact of renewable
Sources to grid with better seamless integration to grid.
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Thank you !