A Hybrid Energy Systems Modeling & Simulation Framework for Regional Cooperation through Renewable and Alternative E

1. Presented at the international conference: Integrating Central Asia into the World Economy: The Role of Energy and Transport Infrastructure Brookings Institution and the Carnegie Endowment for International Peace, Washington, DC October 22-24, 2007 A Hybrid Energy Systems Modeling & Simulation Framework for Regional Cooperation through Renewable and Alternative Energy Tommer R. Ender, Ph.D. Aerospace Systems Design Laboratory School of Aerospace Engineering Georgia Institute of Technology Atlanta, GA 30332-0150 (404) 385-0898 tommer.ender@ae.gatech.edu Comas Haynes, Ph.D. Georgia Tech Center for Innovative Fuel Cell and Battery Technologies Georgia Institute of Technology Atlanta, Georgia 30332-0853 (404) 407-7578 comas.haynes@gtri.gatech.edu

2. Enabling Energy Policy through Modeling & Simulation • Research underway at the Georgia Institute of Technology to develop decision making tools for analysis of hybrid, renewable energy systems through rapid manipulation of modeling and simulation • Fuses disciplinary expertise in the field of alternative energy systems with systems engineering methods developed in the aerospace field Goal is to consider complex interactions of social, economical, environmental factors in addition to the technical

3. Sustainable Development: Pollution and Policy • Pollution: What are our local, regional, national and global thresholds? • Criteria pollutant issues (e.g., smog) • Vast issues (e.g., global warming and unusually adverse weather patterns) • Policy • Growing tangible/intangible incentives for better energy policies • For technical and economic reasons there may be a global push for advanced, “clean” energy as the first infrastructural development, similarly to how cell phones are “leapfrogging” traditional wire phone systems in developing regions

4. Sustainable Development: Socio-economic Considerations • Sustainable development: “Living, producing and consuming in a manner that meets the needs of the present without compromising the ability of future generations to meet their own needs” [Twidell, J. and Weir, T., Renewable Energy Resources (2nd Ed.), Taylor & Francis, 2006] • It also connotes societal progression (e.g., of a developing nation/region) without threatening ecological processes • It also connotes business progression toward a triple bottom-line of • Economic well-being • Social well-being • Environmental well-being • In numerous countries energy imports costs over half the value of exports, promoting unsustainable economies

5. Regional Cooperation through Renewable and Alternative Energy Grid Kyrgyzstan Kazakhstan Biomass Wind Regional Power Grid Uzbekistan Solar Tajikistan Hydro • Countries that have unique renewable resources can contribute to regional energy grid • Promotes and enables regional cooperation

6. Novelty of Approach • Expands our expertise in the field of systems-of-systems research: moves beyond notion of individual component design • Each system independently managed and operated • Capability of the integrated whole to produce results greater than individual components • Research conducted on capability-focused andinverse design to identify hybrid energy solutions that meet dynamic requirements • Decision-makers afforded novel real-time, panoramic view of trade-offs and parametric sensitivities via advanced visualization features • Surrogate models enable rapid manipulation across a system-of-systems hierarchy • On-the-fly tradeoffs yield results that otherwise may not have been discovered • Several years experience with decision making tools Enables qualitative decision-making based on quantitative modeling and simulation

7. Modeling & Simulation Process Identify Hybrid RE Modeling & Simulation M&S run through preset Design of Experiments for Notional Scenario Create Surrogate Models, identify key tradeoffs from “what-if” games Use quality engineering methods to address socio-economic issues

8. The visual “front end” that decision makers can relate to, but relies on actual modeling and simulation Optimal portfolio of energy sources for each year based on rapid tradeoff of desired energy load and cost constraints, as well as qualitative requirements

9. 1) Customer sets weightings on requirements, annual load requirements, and cost constraints

10. 1) Customer sets weightings on requirements, annual load requirements, and cost constraints 2) Those weightings, along with qualitative “expert opinion/assessment” mappings of how an integrated energy system attribute impacts a requirement, yield a weighting on those attributes --- i.e., a translation of prioritized decision maker desires to corresponding “weights” of quantitative metrics via technical experts making the connectionsbetween the two

11. 1) Customer sets weightings on requirements, annual load requirements, and cost constraints 2) Those weightings, along with qualitative mappings of how an integrated energy system impacts a requirement, yield a weighting on those attributes 3) Technique for Order Preference by Similarity to Ideal Solution (TOPSIS) used to find best mix of renewable energy solutions, within cost constraint set earlier, from a list of thousands of possibilities rapidly generated through the surrogate models

12. Through TOPSIS, the optimal mix of which energy sources to buy is shown for each year

13. Annual operational and maintenance costs can be taken into account as well, based on cost numbers and which sources are used that year

14. Technical assumptions can be changed at any time, and the surrogate models updated to reflect those changes