Home Energy Management System Optimization with Stochastic Programming
Publicerad
Författare
Typ
Examensarbete för masterexamen
Master's Thesis
Master's Thesis
Modellbyggare
Tidskriftstitel
ISSN
Volymtitel
Utgivare
Sammanfattning
This thesis explores the optimization of Home Energy Management Systems (HEMS)
with a focus on battery electric vehicle (BEV) charging, using two different methods:
Linear Programming (LP) and Stochastic Programming. The BEV is modeled with
vehicle-to-everything (V2X) functionality, allowing it to both charge and discharge
energy to support the household or grid.
The primary aim is to incorporate uncertainty into the optimization process, acknowledging
that in real-world scenarios, exact information about the vehicle’s arrival
time and current state of energy (SOE) is rarely known in advance. We begin
by examining uncertainty in both arrival time and SOE, then narrow the focus to
uncertainty in SOE alone. In the final part of the thesis, we investigate the impact of
power tariffs to determine whether incorporating them into the optimization yields
meaningful benefits for households.
Our results show that stochastic optimization introduces a small increase in computational
cost while significantly reducing the risk of unwanted outcomes caused by
uncertainty. While the cost difference per vehicle is fractional, the impact becomes
substantial when scaled across many BEVs. The benefits extend beyond cost savings,
by optimizing charging to occur during low spot price hours, we also reduce
the environmental footprint, as electricity is typically greener during these periods.
In addition, the ability to sell back energy to the grid when it is not needed helps
balance demand and supports grid stability.
Therefore, assuming perfect foresight and relying solely on LP is not representative
of real-world conditions. We conclude that accounting for uncertainty through
stochastic methods is a cost-effective and scalable improvement for future energy
management systems.
Beskrivning
Ämne/nyckelord
Home Energy Management System, Deterministic Equivalent of a Stochastic Problem (DESP), Linear Programming, Battery Electric Vehicle, Vehicle-to- Grid, Uncertainty Modeling, Demand-Charge Tariffs, Demand Response, Smart Charging, Energy Flexibility.