Document Type : Original Manuscript
Authors
1 Department of Electrical and Electronics Engineering, PSG College of Technology,Coimbatore,Tamil Nadu,India - 641 004
2 Professor, Department of Electrical and Electronics Engineering PSG College of Technology, Coimbatore 641 004
Abstract
In this article, Multitrial vector-based differential evolution algorithm (MTDE) is proposed as energy and cost management controller under Time of Use (TOU) tariff in grid associated domestic PV-wind power system tied with battery storages. To enrich the energy efficiency of a proposed power system, two optimisation algorithms are proposed in the scheduling operation. TOU billing is a cost- reflective power pricing strategy that has been found as an effective way to reduce peak energy consumption in the residential segment everywhere the world, mainly in industrialised nations. In the optimisation maximizing the cost benefit of the household energy is taken as the objective and the dispatching ratio of electricity sold to the grid and used locally is treated as the optimisation variable. Using MATLAB, the performance of proposed MTDE in the aspect of daily cost benefit and revenue growth rate are presented with the comparative analysis of gravitational search algorithm and conventional self-made for self-consumed and rest for sale (SFC&RFS) mode -based energy management controller. In comparison to the SFC&RFS mode, the GSA-based cost optimization offers a 21.46% increase in revenue growth, while it is improved to 38.7% using proposed MTDE algorithm. The paper also addresses the significance of fuzzy logic based maximum power point tracking (MPPT) of solar PV in enhancing the energy management of the proposed system. The prototype of fuzzy logic MPPT for 10 W solar panel is presented and the tracked maximum power is visualized using Thingspeak IoT cloud server. The tracking speed of the MPPT can be increased by introducing machine learning algorithms at the cost of memory and complexity. In this article, the linear regression-based machine learning algorithm is implemented in the hardware prototype by utilizing the dataset aggregated from fuzzy MPPT and hence the proposed MPPT inherits the characteristics of the fuzzy MPPT with increased tracking speed. Around 75 % data are used for training, 25% of data are used to test the model and it is observed that the root mean square error (rmse) is 5.1334 and mean square error is 26.3521 and the model is utilized as MPPT for the real-time inputs.
Keywords
- Solar PV system
- Wind energy
- Time of Use tariff
- Energy and Cost Optimization
- Fuzzy Logic MPPT for Solar PV
- Internet of Things
- Linear regression based machine learning
Main Subjects
development and optimization, IEEE Access, 5 (2017), 25897-25912.
2020.
145-162.
renewable and sustainable energy: A review, Renewable and Sustainable Energy Reviews, 15(4) (2011), 1753-1766.
Energy Reviews, 47 (2015), 552-561.
in smart home, International Journal of Machine Learning and Cybernetics, 12(11) (2021), 3179-202.
vehicles using Q-rung orthopair fuzzy sets based OPA-RAFSI model, IEEE Transactions on Intelligent Transportation
Systems, (2022), 1-10.
straints, Iranian Journal of Fuzzy Systems, 20(2) (2023), 1-20.
electricity markets, Journal of Intelligent and Fuzzy Systems, 32(6) (2017), 4031-4045.
systems, 20th International Conference on Renewable Energies and Power Quality, 30 (2022), 100-105.
learning approach, Energies, 14(9) (2021), 2700.
energy-reserve payment cost minimization using a custom-designed method based on mixed integer genetic algorithm,
Engineering Applications of Artificial Intelligence, 72 (2018), 203-212.
assisted extended boost q-ZSI, Applied Nanoscience, 13(3) (2023), 1971-1979.
based on the fuzzy AHP methods, Complexity, 21(6) (2016), 70-88.
South African Journal of Economic and Management Sciences, 14(4) (2011), 449-465.
sumers, Energy, 139 (2017), 18-30.
solar output in hybrid power system, Journal of Ambient Intelligence and Humanized Computing, 10(1) (2019),
77-87.
evolution algorithm and its applications for engineering design problems, Applied Soft Computing Journal, 97 (2020),
106761.
Prabu, B. Bala Subramanian, Adane Kassa, Maximum power point tracking of PV grids using deep learning,
International Journal of Photoenergy, (2022). DOI:10.1155/2022/1123251.
residential sector of Bangladesh, Energy Strategy Reviews, 18 (2017), 183-198.
179(13) (2009), 2232-2248.
system coupled with battery using whale optimisation algorithm, Iranian Journal of Science and Technology, Transactions
of Electrical Engineering, 46(1) (2022), 77-90.
north of Spain, Renewable and Sustainable Energy Reviews, 16 (2012), 4461-4464.
Iranian Journal of Fuzzy Systems, 18(5) (2021), 181-198.
ensemble learning algorithms, Nova Science Publishers, 2021.
Technology, 6 (2012), 411-419.
under time-of-use and step tari s, Journal of Energy Storage, 23 (2019), 175-182.
)