Citation: Liemthong, R.; Srithapon,
C.; Ghosh, P.K.; Chatthaworn, R.
Home Energy Management
Strategy-Based Meta-Heuristic
Optimization for Electrical Energy
Cost Minimization Considering TOU
Tariffs. Energies 2022, 15, 537.
https://doi.org/10.3390/
en15020537
Academic Editor: Javier Contreras
Received: 9 October 2021
Accepted: 4 January 2022
Published: 12 January 2022
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Article
Home Energy Management Strategy-Based Meta-Heuristic
Optimization for Electrical Energy Cost Minimization
Considering TOU Tariffs
Rittichai Liemthong
1
, Chitchai Srithapon
1,2
, Prasanta K. Ghosh
3
and Rongrit Chatthaworn
1,4,
*
1
Department of Electrical Engineering, Khon Kaen University, Khon Kaen 40002, Thailand;
rittichai.l@kkumail.com (R.L.); chitchai@kth.se (C.S.)
2
Department of Electrical Engineering, KTH Royal Institute of Technology, 11428 Stockholm, Sweden
3
Department of Electrical Engineering and Computer Science, Syracuse University, Syracuse, NY 13244, USA;
pkghosh@syr.edu
4
Alternative Energy Research and Development, Khon Kaen University, Khon Kaen 40002, Thailand
* Correspondence: rongch@kku.ac.th; Tel.: +66-84-685-2286
Abstract:
It is well documented that both solar photovoltaic (PV) systems and electric vehicles
(EVs) positively impact the global environment. However, the integration of high PV resources into
distribution networks creates new challenges because of the uncertainty of PV power generation.
Additionally, high power consumption during many EV charging operations at a certain time of the
day can be stressful for the distribution network. Stresses on the distribution network influence higher
electricity tariffs, which negatively impact consumers. Therefore, a home energy management system
is one of the solutions to control electricity consumption to reduce electrical energy costs. In this paper,
a meta-heuristic-based optimization of a home energy management strategy is presented with the
goal of electrical energy cost minimization for the consumer under the time-of-use (TOU) tariffs. The
proposed strategy manages the operations of the plug-in electric vehicle (PEV) and the energy storage
system (ESS) charging and discharging in a home. The meta-heuristic optimization, namely a genetic
algorithm (GA), was applied to the home energy management strategy for minimizing the daily
electrical energy cost for the consumer through optimal scheduling of ESS and PEV operations. To
confirm the effectiveness of the proposed methodology, the load profile of a household in Udonthani,
Thailand, and the TOU tariffs of the provincial electricity authority (PEA) of Thailand were applied
in the simulation. The simulation results show that the proposed strategy with GA optimization
provides the minimum daily or net electrical energy cost for the consumer. The daily electrical energy
cost for the consumer is equal to 0.3847 USD when the methodology without GA optimization is
used, whereas the electrical energy cost is equal to 0.3577 USD when the proposed methodology
with GA optimization is used. Therefore, the proposed optimal home energy management strategy
with GA optimization can decrease the daily electrical energy cost for the consumer up to 7.0185%
compared to the electrical energy cost obtained from the methodology without GA optimization.
Keywords:
energy storage system; genetic algorithm (GA); minimum electrical energy cost for the
consumer; optimal home energy management strategy; plug-in electric vehicle; solar photovoltaic;
time-of-use (TOU) tariffs
1. Introduction
Global warming is a reality and to control global warming, countries are implementing
policies to reduce overall carbon dioxide emissions into the atmosphere. In this effort,
emphasis has been given to clean energy sources such as solar photovoltaic (PV) and
wind power for generating electricity instead of using fossil fuel. For transportation,
electric vehicles (EVs) are promoted to replace combustion vehicles [
1
]. In Thailand, the
Energies 2022, 15, 537. https://doi.org/10.3390/en15020537 https://www.mdpi.com/journal/energies
