Development of a Decision Aiding Framework For Energy Infrastructure Siting

1. Development of a Decision Aiding Framework For Energy Infrastructure Siting Ganesh Doluweera & Joule Bergerson Institute for Sustainable Energy, Environment and Economy, University of Calgary 32nd USAEE/IAEE North American Energy Economics Conference July 30, 2013

2. Motivation • Demand for new energy infrastructure is growing • rising energy demand, ageing infrastructure and environmental concerns • Siting energy infrastructure is a complex process that involves multiple stakeholders with multiple and conflicting objectives • In recent years, siting energy infrastructure has become increasingly difficult • one reason is oversimplification of stakeholder complexities

3. Research Objective • Develop a framework to construct alternative siting options • This framework has more complete incorporation of stakeholder objectives • Developed by combining energy system modeling with decision analysis techniques

4. Current Practice

5. Proposed Framework Consequence tables Preference structure and rankings

6. Case Study • Selection of a route for an electricity transmission line in Alberta, Canada • Decision makers’ objective: Select the transmission line route that is in public’s best interest Focus on an approximately 100km section of EATL (Andrew-Holden section) Eastern Alberta Transmission Line (EATL) (500kV HVDC; ̴ 500km)

7. Methods: Stakeholder Objectives • Minimize Residential and property value impacts • Minimize the proximity to residential properties • Avoid densely populated areas (Urban areas) • Minimize Environmental impacts • Minimize river and water body crossings • Minimize proximity to environmentally sensitive areas • Avoid highly sensitive ecosystems • Optimize economic and engineering factors • Parallel existing linear disturbances (roads, power lines) • Minimize cropland disturbances • Minimize building on high slopes (terrain features) • Minimize cost

8. Methods: System Model [1,2] • Stakeholder objectives (ie. xi) and preferences (ie. Vi(∙) and wj) are inferred using transcripts of EATL regulatory hearings References: Gregory R, Failing L, Harstone M, Long G, McDaniels T, Ohlson D. Structured Decision Making: A Practical Guide to Environmental Management Choices. Chichester, UK: Wiley-Blackwell; 2012. Keeney RL. Utility Functions for Multiattributed Consequences. Management Science. 1972; 18:276-87.

9. Methods: System Model • A geographic information system (GIS) model • In each cell, magnitude of the combined value function is calculated • Using least cost path selection algorithms, combination of cells that forms the least cost path is identified

10. Illustrative Results

11. Area of interest and routes and routes proposed by the project proponent (ATCO Electric) • An alternative segment has been proposed by a land owner group

12. Alternative route option 1 All high level objectives are weighted equally (W_res = W_env = W_eng)

13. Alternative route option 2 Preference for minimizing residential impacts is twice as that of other high level objectives (W_res = 2W_env = 2W_eng)

14. Alternative route option 3 Preference for minimizing environmental impacts is twice as that of other high level objectives (W_env= 2W_res= 2W_eng)

15. An Example of Tradeoff Analysis

16. Conclusions • Our proposed framework inherently takes the multiple stakeholder objectives into account • The framework provides the decision maker a set of alternatives and information about their consequences • The case study demonstrated the application of the framework and the insights that can be obtained • spatial impact of decisions • information to facilitate trade-off analysis

17. Next Steps • Incorporate uncertainty analysis • data limitations and uncertainties • value judgments • Extend to a larger framework • full stakeholder engagement • tradeoff analysis • Extend to other energy system decisions

18. Thank you Ganesh Doluweera dgdoluwe@ucalgary.ca

19. Supplementary Information

20. System Model

21. Results – Consequence Table