Thesis Format
Alternative Format
Degree
Master of Engineering Science
Program
Electrical and Computer Engineering
Supervisor
Moschopoulos, Gerry
2nd Supervisor
Miller, Dennis
Affiliation
Bosch Thermotechnik GmbH
Abstract
The drive for sustainable and green energy systems is increasing globally to combat climate change and achieve a net-zero energy system. As a result, the proliferation of renewable energy and electrified households is rising. Variability in electricity prices is also increasing due to supply-demand imbalances and more renewable energy in the grid. With increased household electrification, including battery and electric vehicle (EV) installations, households can now shift their electricity consumption to better align with renewable energy production and lower prices. This enhances sustainability, reduces costs, and supports the grid power balance. A home energy management system (HEMS) optimally distributes and uses energy within a household to achieve economic, sustainability, and efficiency goals. HEMS can significantly benefit both customers and the wider energy system by automatically adjusting household energy consumption to align with renewable energy production and low-price periods.
This thesis designs and compares two novel HEMS algorithms that control the EV and battery in an electrified household. The objective is to minimize household electricity costs while considering variable electricity prices. It is crucial for algorithms in consumer products to have short implementation and computation time, and adaptability to changing system conditions. Existing HEMS algorithms often overlook these aspects, so the new algorithms address these gaps. Additionally, since full battery control is not always possible, each algorithm is evaluated using two battery control methods: active control and point of common coupling (PCC) zero control, commonly used in consumer products.
The proposed algorithms are simulated and tested on a model of an average electrified household in Germany. The new HEMS algorithms achieve superior cost savings compared to existing algorithms. The performance is robust across different seasonal conditions, EV driving behaviors, and prediction accuracies. In addition, the algorithm implementation and computation times are minimized and the impact of PCC-zero controlled batteries on HEMS performance is evaluated for the first time.
Summary for Lay Audience
The push for sustainable and green energy is growing worldwide to combat climate change. This has led to more renewable energy and houses with electrified devices including electric heating, solar panels, batteries, electric vehicles (EVs), and electric appliances.
Renewable energy such as wind and solar does not provide constant power. Instead, the amount of power changes depending on the weather conditions. In addition, the power consumption of electric devices in a household depends on the behaviour of the user. Generally, the power consumption of the household does not align well with the renewable energy production. This causes fluctuations in the grid electricity price and increases costs because the electricity supply and demand in the grid must always be balanced.
Home energy management systems (HEMS) automatically control the devices in a household to align their consumption with renewable energy production and cheap electricity prices. This saves money, supports the energy grid, and enhances sustainability.
This thesis designs and compares two new HEMS control methods that minimize household electricity costs. These new methods are faster, easier to implement, and better at adapting to unexpected changes in the household consumption. They are tested on a typical German home and show significant cost savings compared to existing methods. The new methods perform well under different conditions, proving their effectiveness and reliability.
Recommended Citation
Le Feuvre, Jacqueline, "Cost-Optimized Household Energy Management Considering Variable Electricity Tariffs and Local Energy Storage" (2024). Electronic Thesis and Dissertation Repository. 10473.
https://ir.lib.uwo.ca/etd/10473