Challenges in Design and Optimization of Large Scale Energy Efficient Systems

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1 Challenges in Design and Optimization of Large Scale Energy Efficient Systems Ritesh Khire United Technologies Research Center, East Hartford, CT, Presented at NSF Workshop on The Future of Multidisciplinary Design Optimization: Advancing the Design of Complex Systems, Ft. Worth Texas 16 Sep 2010

2 United Technologies Business units Pratt & Whitney aerospace systems Sikorsky Hamilton Sundstrand Carrier UTC Fire & Security power solutions UTC Power building systems Otis 2

3 Complex Systems A complex system is a network of heterogeneous components that interact nonlinearly Design complexity is loosely related (1) the number of design parameters, and (2) the level of understanding of the interactions among them Fourth Generation Fifth Generation Subsystems Interfaces Software managed 40% 90% Final_Report_Dist_A.pdf Two magnitude increase in interfaces (interactions) Resulted from One magnitude change in sub-systems

4 Complex System Example -- Energy Efficient Buildings Very Large Scale and Integrated Systems Integrated Buildings Distributed Power Systems (Micro-Grids) Envelope and internal loads Local Controls People movement Plug-in loads Smart-grids CHP, energy storage Renewable energy HVAC-air HVACchillers Eco-cities Research Needs Scalable and Robust Design Methods For Energy Efficient System Solutions (Optimization Installation Operation)

5 Micro-Grid Design Massive Design Space, Mixed Variables, Discontinuity, Strong Coupling, Multiple Technologies Numerous Architectures (Topologies) Multiple sizes / Technology Energy Sources Renewable Solar radiation Wind power Biomass Hydro Geothermal Oil Non Renewable Gas Waste Waste heat streams Energy Conversion Solar PV Renewable Solar thermal Solar CHP Wind turbines Hydro turbines Biomass gensets Geothermal ORC Non Renewable Microturbine CHP Genset CHP Diesel gensets ORC Boilers Chillers Fuel cells Electric Batteries Heating Temporal Coupling Energy Storage Cooling Chilled water tanks LOADS electric heating cooling Energy Demand Environment Envelope Lighting Plug loads HVAC Economics Uncertainty Operating scenarios (Discontinuity) Electric grid 8760 hr Simulations 8760 state variables / device 5

6 Micro-grid Architecting and Optimization Challenges Discrete Design Space and Large Number of Design Variables Architecting Architecture Generation Methods Building Model Development Automatic code generation Model transformation Validation and Verification Sample alternative architectures with same equipment Solution space extremely large and optimization intractable Mixed variables (binary, integers, continuous) Sizing has to be evaluated at 8760 hours or Data compression Not unique Battery/storage coupling Requirements (e.g off-grid for 72 hours) Problem size e.g.: 60 devices, and 2 or 3 available options: billion architectures!!! Weather Data Load Profiles e.g.: 60 devices; 8760 design points million variables/ Architecture!!!

7 Optimization Formulation and Solution Challenges Efficient gradient estimation and Problem decomposition schemes needed External code integration (i.e. modeling environment <> optimizer) Options have significant limitations Gradient calculation: computational expensive Gradient free: Number of iterations is large Models may not be continuous Micro-grid optimizer e.g.: 60 devices architecture million variables Assume 1 sec / function evaluation = 6 days / Finite difference Dymola TRNSYS Modeling environment Available optimization formulations MINLP: Too large scale for efficient solving Decomposition based on architecture, sizing, and operation is not sufficient Large network Network decomposition

8 Energy Security and Surety Evaluating performance under uncertainty Computation Explosion Probability of infeasibility Arch 1 Arch 3 Arch 2 Arch 4 Arch mplexity_management_final_report_dist_a.pdf Challenge How can we evaluate performance under uncertainty for large and discreet design space e.g.: Performance Under Uncertainty billion architectures!!! (60 devices, and 2 or 3 available options)

9 Installation and Operation Challenges Installation Orderly approach Minimal cost No interruptions Critical loads Economics (ROI) Many operation research techniques Operation Optimal Controls Dynamic Occupancy Information Building Energy Diagnostics Fault Alarm Management Many techniques from software domain Data visualization Building Diagnostics

10 Back Up

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