MODEST: Microgrid Optimization with District Energy Systems Tool
Ming Jin, Wei Feng, Chris Marnay, Costas Spanos
ObjectiveOptimize the planning and operation of a microgrid to improve economics and resiliency.
Methodology
MODEST Toolset
Modeling Prediction OptimizationMG
Technologies WeatherGrid status
EnergyDemands
Microgridmonitoring
Generator(constraints
Power(balances
Decision(variables((hourly):(i)(Generator(dispatch(powers
(ii)(Retail prices
Objective)Function
Maximize benefits(revenue, fuel savingsand CO2 reduction )
Trade2off)factor)λenv
Outputs
Dispatch)plan
Optimal)cost
Retail prices
System(resilience
Pricing'constraints
Load'''constraints
Optimal MGplanning
Optimal MGoperation
Optimal retailpricing
Equivalentannual cost
Baseline Optimal
Annual fuel cost
Baseline Optimal
Optimal capacity investment
• Generator selectionand capacity sizing
• Satisfy load demands• Optimize investment
and energy efficiency
>20%
>40%
renewableswheneconomical
Demo
• Day-ahead dispatchplan for thermal andelectricity generators
• Demand response (DR)capability
• Day-ahead retail pricesignals for electricityand thermal energy
• Improve DR andretailer profits
Daily TOU RTP
Midnight
Noon
Midnight
Noon Noon
Midnight
Midnight Midnight
NoonNoon
Daily TOU
Midnight
Noon
Daily
Midnight
Noon
TOU
Electricity0retail0prices
Cooling0retail0prices Heating0retail0prices
Customer0retention
Daily/hourly0competitiveness
Rate0effectiveness
DR00incentivization
Jinetal.,"MOD-DR:Microgridoptimaldispatchwithdemandresponse",AppliedEnergy,187,758- 776(2017)Jin et al., “Microgridtoenableoptimaldistributedenergyretailandend-userdemandresponse”, AppliedEnergy(2017)
Presented at College of Engineering Dean’s Society Event,Menlo Circus Club, Atherton, CA, 2017 April
