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MODEST: Microgrid Optimization with District Energy Systems Tool Ming Jin, Wei Feng, Chris Marnay, Costas Spanos Objective Optimize the planning and operation of a microgrid to improve economics and resiliency. Methodology MODEST Toolset Modeling Prediction Optimization MG Technologies Weather Grid status Energy Demands Microgrid monitoring Generator constraints Power balances Decision variables (hourly): (i) Generator dispatch powers (ii) Retail prices Objective Function Maximize benefits (revenue, fuel savings and CO 2 reduction ) Trade2off factor λ env Outputs Dispatch plan Optimal cost Retail prices System resilience Pricing constraints Load constraints Optimal MG planning Optimal MG operation Optimal retail pricing Equivalentannual cost Baseline Optimal Annual fuel cost Baseline Optimal Optimal capacity investment • Generator selection and capacity sizing • Satisfy load demands • Optimize investment and energy efficiency >20% >40% renewables when economical Demo • Day-ahead dispatch plan for thermal and electricity generators • Demand response (DR) capability • Day-ahead retail price signals for electricity and thermal energy • Improve DR and retailer profits Daily TOU RTP Midnight Noon Midnight Noon Noon Midnight Midnight Midnight Noon Noon Daily TOU Midnight Noon Daily Midnight Noon TOU Electricity retail prices Cooling retail prices Heating retail prices Customer retention Daily/hourly competitiveness Rate effectiveness DR incentivization Jin et al., "MOD-DR: Microgrid optimal dispatch with demand response", Applied Energy, 187, 758 - 776 (2017) Jin et al., “Microgrid to enable optimal distributed energy retail and end-user demand response”, Applied Energy (2017) Presented at College of Engineering Dean’s Society Event, Menlo Circus Club, Atherton, CA, 2017 April
