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Project 2.1 Cost-Benefit Framework: Secondary Benefits and Ancillary Services

Project 2.1 Cost-Benefit Framework: Secondary Benefits and Ancillary Services MIKE QUASHIE AND GEZA JOOS (MCGILL UNIVERSITY). Presentation outline. Problem Identification Proposed Solution Methodology Results and Conclusion Future work and collaboration. Problem Identification.

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Project 2.1 Cost-Benefit Framework: Secondary Benefits and Ancillary Services

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  1. Project 2.1 Cost-Benefit Framework: Secondary Benefits and Ancillary Services MIKE QUASHIE AND GEZA JOOS (MCGILL UNIVERSITY)

  2. Presentation outline • Problem Identification • Proposed Solution • Methodology • Results and Conclusion • Future work and collaboration

  3. Problem Identification • Microgrids are often touted as a technology that can improve local system reliability and aid in the integration of renewable energy resources.  However, to facilitate the additional control and operating modes associated with a Microgrid, additional equipment is needed.  • The cost associated with this infrastructure can be quantified once the necessary elements are identified.  • Contrarily, there may be certain costs, such as those associated with changes to operating protocol, training and new safety requirements, which may be more difficult to translate into a dollar figure. • For utilities and business owners, this analysis is required to develop the business case for a given technology. • Many of the benefits of a Microgrid are also not tangible in the Canadian context and consequently require additional considerations to monetize. • How can the assumptions made in the cost-benefit framework be tested?

  4. Solution • Previous work done in collaboration with CanmetEnergy and project 1.4, identified benefits of Microgrids and how the benefits impacts stakeholders. • Benefits Identified: Technical: Network efficiency improvement(losses reduction), reliability improvement Economic: cost of energy Environmental: GHC emission reduction • The natural step that follows is to developed a methodology to optimize the identified benefits. Historical data of wind speed and solar irradiance were obtained from CWEEDS through interaction with CANMET ENERGY.

  5. Methodology • The work done over the year is proposes a generalized methodology to determine the optimal configuration of microgrids that maximizes its benefits.

  6. Methodology • The technique proposed herein, incorporates a probabilistic modeling of variable loads and distributed generators into a deterministic power flow problem to solve an optimization problem Subject to: (2) (3) • The losses (PLOSS) from the power flow problem is monetized and supplied to the objective function as one of its input multiple cost functions (4)

  7. Methodology • For GCC: (5) (6) (7) (8) • For GHC: (21) • The optimization problem then concatenates the monetized losses, monetized emission and the cost components of generation into a single objective and then minimizes it.

  8. Test System Peak load of 12.775MW. The optimal configuration was also found to be 1.2 MW of wind turbine on node 7 and 30 KW solar panel on node 4. Figure 1. Cigre's North American medium Voltage DistributionNetwork Benchmark with DG connected to operate as Microgrid .(CIGRE TF C6.04.02, “Benchmark systems for network integration of renewable and distributed energy resources,” technical brochure, version 21, pp25-39, August 2010.)

  9. RESULTS A 16.1 percent decrease in the average cost of energy for the optimal case is observed in comparison to the base case Figure 2. Comparison of Hourly Total Cost of Energy to the Distributed Network Operator for a Year (each 24hr represents a Season)

  10. Results The efficiency of the network is also improved by 10 percent through loss minimization for the optimal case in comparison to the base case. TABLE I. LOSSES IN THE POWER NETWORK FOR VARIOUS SCENARIOS

  11. Conclusion • Application of the methodology to the microgrid planning process resulted in significant decrease in the cost of energy to the distribution network operator. • A significant improvement in the network’s efficiency (loss minimization) as well as reduction in CO2 emissions is also observed in applying the methodology to the microgrid planning process. • The economic, technical and environmental benefits of the microgid which are maximized through the use of the proposed methodology help offset the cost associated with microgrid’s implementation, building a better business case for microgrid advancement • Results of the study was present at Power and Energy Society general meeting 2013 in Vancouver.

  12. Future work • Develop a framework for implementing and quantifying ancillary services . • Application of the methodology to Microgrid demonstrations in existing and potential Microgrids (commercial, industrial and remote community settings). Potential collaboration with project 2.4 and 1.4 • Future collaboration with 3.1 to have an accurate estimate of ICT infrastruture in Microgrids

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