INTELLIGENT DC MICROGRID WITH SMART

GRID COMMUNICATIONS: CONTROL

STRATEGY CONSIDERATION AND DESIGN

Presented by: Amit Kumar Tamang, PhD Student

Smart Grid Research Group-BBCR

aktamang@uwaterloo.ca

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 !