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Online Energy Management for a sustainable smart home with an HVAC load…
Online Energy Management for a sustainable smart home with an HVAC load and random occupancy (2019)
investigate the problem of minimizing sum of
energy cost
and
thermal discomfort cost
- for long term smart home that use HVAC load
1st formulation -
stochastic program
to minimize the time average expected total cost with consider of
uncertainties
electricity price
outdoor temperature
renewable generation output
electrical demand
most comfortable temp level
home occupancy state
Propose: Online energy management algorithm based on the framework of
Lyapunov optimization techniques
to construct and stabilize four queues associated with indoor temp, electric vehicle charging, and energy storage
solve time average optimization problem
online EM algorithm can be design
focus on SMART HOMES which evolved from traditional house by adopting 3 components
internal networks
intelligent controls
home automations
Smart homes appliances divided into two:
inflexible loads: light, PCs, Tv
flexible loads: heating, ventilation, EVs, washing machine
this paper focus on HVAC and EV in smart Home
HVAC consume 50% electricity
EV charging the most flexible loads
Paper main contribution:
fomulate time average expected total cost minimization for smart home - with HVAC load
propose online EM algorithm for formulated problem base on the LOT framework W/O predict any system parameters and knowing the HVAC power demand
simulation based on real-world traces
PREVIOUS WORK
refer documents> PHD Journey> Mindmap> summary of prev work EM method_21022023
System model and problem formulation
renewable energy model
Load Model
ESS (energy storage system) model
Power balancing
Problem formulation
Algorithm Design
the proposed online algorithm
algorithm feasibility
performance guarantee
simulation results
The impact of Tmin -
larger temp range would result in lower energy cost
increase of Tmin, normal temp range will decrease
could reduce energy cost by 36.3%
impact of epsilon
larger epsilon, achieve lower energy cost
larger epsilon, less thermal loss given the same time horizon
impact of wye
lowest total cost when wye falls within an appropriate range
Algorithm feasibility - the algorithmic feasibility of the proposed algorithm could be verified by checking the normal ranges of indoor temp, ESS energy level, and EV charging delay
the research gap: only focus on HOME, future may implement into commercial buildings,
investigate the impact of HVAC load aggregation on end user comfort
refer to the paper for the formulation
refer to the paper for the formulation