Project 1.4

1. 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. GézaJoós (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 • 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. • 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 • The identified objectives include: • The optimization formulation(subject to power balance and DER constraints):

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

10. Results • 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.