www.smart-microgrid.ca

Project 1.4Operational Strategies and Storage Technologies to Address Barriers for a Very High Penetration of DG Units in Intelligent MicrogridsMichael Ross (McGill University)Dr. Chad Abbey (Hydro-Québec)Prof. Géza Joós (McGill University)

Presentation Outline

• Problem Identification.• Existing Solutions (Status Quo) and gaps in the

solutions.• Proposed solution and methodology.• Results and Conclusions.• Future work and potential collaborations.

Problem Identification1. A high penetration of highly volatile renewable

energy generation introduces many adverse effects:– High power fluctuations seen by the Electric Power

System,– Peak power flow through the Point of Common Coupling

(PCC) might not be reduced, and – Power production is not guaranteed during an islanding

event.2. Microgrids can be implemented for a variety of

reasons:– Boston Bar, BC: Microgrid controls have been

implemented to maintain reliability and optimize dispatch.

– Hartley Bay, BC: Microgrid controls have been implemented for energy conservation and reduced diesel consumption.

How to utilize available Distributed Energy Resources to optimize the desired benefits of the Microgrid with a high penetration of renewable resources?

Status Quo• In collaboration with Hydro-Québec, a call for

offers for a commercial Microgrid controller was made to be implemented on the IREQ Distribution Test Line.

25 kV / 120 kVΔ-Ү

28 MVA

PC1

S3

F1

RC3 RC1

Microgrid Control Centre

B1

Neutral Conductor

25 kV/ 600 V3-167 kVA

3-200 kVA14 A

X1

X2

VR

RC2

Line 1Line 2Line 3

Underground Network

PC2

Xda

Xdb

Xdc

S1S2

S4S5

Line 4

Zg

25 kV/ 600 V3-167 kVA

25 kV/ 600 V3-167 kVA

R XL

MXC

Loads

If,dc

VDCSupply

DC Motor

Induction Generator

Vs+AC DC

If,sg

VACDrive

Induction motor

Synchronous Generator

ACAC

Spinning Generators

+VdcLiFEPO4

ESSAC/DC

Converter

VDCSupplyVs

+

AC/DC Inverter

Inverter Generators & ESS

Diesel GeneratorDiesel

ICESynchronous

Generator

AC

If,sg

25 kV/ 600 V3-167 kVA

Status Quo – Problems• Although many companies advertise a Microgrid

controller, only one company submitted a proposal.– The controller is still in the development phase.– Only the cost of energy is minimized.

There is an upcoming need for such controllers, however they currently are not commercially available or flexible for general implementation.

If this gap is addressed, it can put Canada at the forefront on Microgrid controller and EMS technologies.

Proposed Solution• The energy management in the Microgrid is

formulated as a mixed-integer, multi-objective optimization problem.– The multiple objectives aim to maximize the benefits,

while minimizing the adverse effects of a high penetration of renewables.

• The objectives are identified through a collaboration with Project 2.1.

• The quantification of the objectives are established so that they can be directly compared, and evaluated through common metrics.

Optimization Objectives• The identified objectives include:

• The optimization formulation (subject to power balance and DER constraints):

Objective Valuation Function Quantification Method

Reduce the cost of energy Market cost of electricity & fuel costsImprove Reliability Cost of non-delivered energyMinimize Peak Power through the Electric Power System

Infrastructure investment deferral

Reduce Power Fluctuations The difference in firm generation price versus fluctuation generation

Reduce Greenhouse Gas Emissions Carbon trading market

Test System

• The test system and dispatch algorithm were implemented in Matlab and GAMS.

• Profiles were obtained through discussions with CanmetENERGY on real profiles and prices of energy.

Energy Storage System Non-Critical

Loads (500 kW peak)

Diesel Generator(1.25 MW)

Critical Loads (500 kW

peak)

Electric Power System

WTG(s)N x 330 kW

turbines

PVsM x 10 kW modulesLi-Ion,

300 kW, 900 kWh η= 92.8%

• The MOO minimizes peak power and power fluctuations at the PCC while also minimizing cost.

Results

Results

• The MOO maintains reliability for critical loads through demand response and storage utilization during islanded operation.

Conclusions• By quantifying the benefits with standard

evaluation metrics, the Multi-Objective Optimization can be solved as a single valuation objective function.

• Even with a high penetration of renewable generation:– The mean ramping rates were reduced by 33%– Peak power was reduced by 10%– The cost of energy was reduced by 11%– GHG emissions were reduced by 18%– SAIFI was 0 for critical loads and SAIDI was reduced by

29% for all loads

Future work• Address intra-dispatch operation.

– Potential collaboration with Projects 1.2, 2.3.• Address stochastic nature of renewables.

– Potential collaboration with Projects 1.1, 2.2.• Implement ICT with proposed controller.

– Potential collaboration with Projects 3.3, 3.4.