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

2. 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.

3. Problem Identification 1.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?

4. 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.

5. 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.

6. 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.

7. Optimization Objectives Objective Valuation Function Quantification Method Reduce the cost of energyMarket cost of electricity & fuel costs Improve ReliabilityCost of non-delivered energy Minimize 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

8. 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.

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

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

11. 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

12. 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.