Abstract
This paper explores Home Energy Management System (HEMS) algorithms to minimize household cost while maintaining comfort when faced with uncertain weather, and demand. Specifically, we consider a HEMS that optimizes forward looking schedules for a home's heating, ventilation, and air conditioning (HVAC); water heater (WH); and electric vehicle (EV) charging while considering uncertainty in outside temperature, hot water usage, and non-controllable load (NCL). We adopt a Dynamic Programming (DP) formulation and utilize the Dynamic programming for Adaptive Modeling and Optimization (DYNAMO) toolkit to implement DP and approximate dynamic programming (ADP) algorithms. Simulation results under a single tariff plan compare the quality of the solution generated by ADP to that of DP, and show significant improvement in computation time while maintaining acceptable solution accuracy.
Original language | American English |
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Number of pages | 8 |
State | Published - 2019 |
Event | Power and Energy Society (PES) General Meeting - Portland, Oregon Duration: 5 Aug 2018 → 10 Aug 2018 |
Conference
Conference | Power and Energy Society (PES) General Meeting |
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City | Portland, Oregon |
Period | 5/08/18 → 10/08/18 |
Bibliographical note
See NREL/CP-5D00-73329 for paper as published in IEEE proceedingsNREL Publication Number
- NREL/CP-5D00-70422
Keywords
- approximate dynamic programming
- demand response
- electric vehicle
- home energy management system
- stochastic mixed-integer programming