A Novel Knapsack Algorithm-Based Energy Routing in a Microgrid
DOI:
https://doi.org/10.13052/dgaej2156-3306.38212Keywords:
Demand side management, microgrid, direct load control, knapsack algorithm, energy routingAbstract
The increase in penetration of renewable energy sources has transformed the existing grid into a multisource and multipath energy network. For real-time energy transactions in the new microgrid, it is essential to realize an energy router interface, which is the core of the energy internet. The energy router controls the bidirectional energy and data flow and achieves end-to-end energy transmission efficiently. With this consideration, this article proposes a new energy routing algorithm based on the knapsack optimization technique. The proposed work aims to minimize the net energy from the main grid and efficiently utilize solar photovoltaic (SPV) energy through meticulous energy routing. The effectiveness of the proposed work is validated for case studies with various types of loads viz residential, non-residential, and electric vehicle loads. In this work, the best set of loads for optimal energy routing with minimum energy costs are determined. The results show a substantial reduction ranging from 16 to 28% in the peak energy drawn from the grid and at the same time, the cost of electricity to be paid to the utility is noticeably reduced in the range of 39% to 50% for various load types. Further, a sensitivity analysis is carried out to evaluate the effect of variations in input parameters such as PV output, and load demand on the cost of electricity.
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Venkatesh Boddapati and S. Arul Daniel, “Optimal Design and Techno-Economic Analysis of a Microgrid for Community Load Applications,” Int. Conf. Innov. Clean Energy Technol. (ICET-2020).
S. A. Almohaimeed, S. Suryanarayanan, and P. O’Neill, “Reducing carbon dioxide emissions from electricity sector using demand side management,” Energy Sources, Part A Recover. Util. Environ. Eff., vol. 00, no. 00, pp. 1–21, 2021, doi: 10.1080/15567036.2021.1922548.
P. V. Nithara and R. Anand, “Comparative analysis of different control strategies in Microgrid,” Int. J. Green Energy, vol. 18, no. 12, pp. 1249–1262, 2021, doi: 10.1080/15435075.2021.1897830.
A. M. Othman, M. Helaimi, and H. A. Gabbar, “Enhanced Nature-Inspired Meta-Heuristic Algorithm for Microgrid Performance Improvement,” Electr. Power Components Syst., vol. 48, no. 4–5, pp. 459–470, 2020, doi: 10.1080/15325008.2020.1758843.
V. Boddapati and A. S. R. Nandikatti, “Salient features of the national power grid and its management during an emergency: A case study in India,” Energy Sustain. Dev., vol. 59, pp. 170–179, Dec. 2020, doi: 10.1016/J.ESD.2020.10.010.
Y. Xu, J. Zhang, W. Wang, A. Juneja, and S. Bhattacharya, “Energy Router: Architectures and Functionalities toward Energy Internet,” pp. 31–36, 2011.
J. Abdella, K. Shuaib, and S. Harous, “Energy Routing Algorithms for the Energy Internet,” 9th Int. Conf. Intell. Syst. 2018 Theory, Res. Innov. Appl. IS 2018 – Proc., no. May 2019, pp. 80–86, 2018, doi: 10.1109/IS.2018.8710585.
T. Zhu, S. Xiao, Y. Ping, D. Towsley, and W. Gong, “A secure energy routing mechanism for sharing renewable energy in smart microgrid,” 2011 IEEE Int. Conf. Smart Grid Commun. SmartGridComm 2011, no. August 2015, pp. 143–148, 2011, doi: 10.1109/SmartGridComm.2011.6102307.
V. Boddapati and S. A. Daniel, “Performance analysis and investigations of grid-connected Solar Power Park in Kurnool, South India,” Energy Sustain. Dev., vol. 55, pp. 161–169, Apr. 2020, doi: 10.1016/J.ESD.2020.02.001.
U. K. Umavathi M, “Simulation and Analysis of Solar-Wind Hybrid System Using an Efficient Multi-Port DC-DC Converter,” J. Adv. Res. Dyn. Control Syst., no. Spl.pp, pp. 1910–1918, 2017.
Y. Liu, J. Li, Y. Wu, and F. Zhou, “Coordinated control of the energy router-based smart home energy management system,” Appl. Sci., vol. 7, no. 9, 2017, doi: 10.3390/app7090943.
S. Nethravathi and M. Venkatakirthiga, “A Novel algorithm for Electric Vehicle charging scheduling with Renewable Energy Source,” IECON Proc. (Industrial Electron. Conf., vol. 2021-Oct., 2021, doi: 10.1109/IECON48115.2021.9589745.
M. Suresh, J. Jayaraman, K. Lakshmanan, A. Gopikanna, and V. Vijayaraghavan, “Smart energy routing for rural islanded microgrid clusters,” 2020 IEEE Texas Power Energy Conf. TPEC 2020, pp. 0–5, 2020, doi: 10.1109/TPEC48276.2020.9042508.
S. Li, P. Yi, Z. Huang, T. Xie, and T. Zhu, “Energy scheduling and allocation in electric vehicles energy internet,” 2016 IEEE Power Energy Soc. Innov. Smart Grid Technol. Conf. ISGT 2016, pp. 1–5, 2016, doi: 10.1109/ISGT.2016.7781233.
G. R. Newsham, B. J. Birt, and I. H. Rowlands, “A comparison of four methods to evaluate the effect of a utility residential air-conditioner load control program on peak electricity use,” Energy Policy, vol. 39, no. 10, pp. 6376–6389, 2011, doi: 10.1016/j.enpol.2011.07.038.
K. Phetsuwan and W. Pora, “A Direct Load Control Algorithm for Air Conditioners Concerning Customers’ Comfort,” 2018 IEEE Int. Conf. Consum. Electron. – Asia, ICCE-Asia 2018, pp. 7–10, 2018, doi: 10.1109/ICCE-ASIA.2018.8552133.
P. Yazdkhasti and C. P. Diduch, “A Mathematical Model for the Aggregated Power Consumptions of Air Conditioners,” 2018 6th IEEE Int. Conf. Smart Energy Grid Eng. SEGE 2018, pp. 244–248, 2018, doi: 10.1109/SEGE.2018.8499491.
C. Duan, X. Ding, F. Shi, X. Xiao, and P. Duan, “PMV-based fuzzy algorithms for controlling indoor temperature,” Proc. 2011 6th IEEE Conf. Ind. Electron. Appl. ICIEA 2011, pp. 1492–1496, 2011, doi: 10.1109/ICIEA.2011.5975826.
R. Dharani, M. Balasubramonian, T. S. Babu, and B. Nastasi, “Load shifting and peak clipping for reducing energy consumption in an indian university campus,” Energies, vol. 14, no. 3, pp. 1–16, 2021, doi: 10.3390/en14030558.
P. Muthuraju, M. Moghimi, R. Garmabdari, S. Stegen, J. Lu, and P. Kaparaju, “Conversion of University Commercial Buildings to Net-Zero Energy Buildings Employing Renewable Energy Sources,” Proc. – 2018 IEEE Int. Conf. Environ. Electr. Eng. 2018 IEEE Ind. Commer. Power Syst. Eur. EEEIC/I CPS Eur. 2018, pp. 2–7, 2018, doi: 10.1109/EEEIC.2018.8494608.
G. Mylonas, D. Amaxilatis, S. Tsampas, and L. Pocero, “A Methodology for Saving Energy in Educational Buildings Using an IoT Infrastructure,” no. Iisa, 2019.
P. O. Box and S. Arabia, “Energy Management in the Buildings of a University Campus in Saudi Arabia – A Case Study,” no. May, pp. 13–17, 2013.
A. Ahmad et al., “An optimized home energy management system with integrated renewable energy and storage resources,” Energies, vol. 10, no. 4, pp. 1–35, 2017, doi: 10.3390/en10040549.
M. J. Siddarth Sankar and K. Rahul Sharma, “Modified 0-1 knapsack problem for demand side management,” Int. J. Innov. Technol. Explor. Eng., vol. 8, no. 6 Special Issue 4, pp. 1–4, 2019, doi: 10.35940/ijitee.F1001.0486S419.
A. Khan, N. Javaid, A. Ahmad, M. Akbar, Z. A. Khan, and M. Ilahi, “A priority-induced demand side management system to mitigate rebound peaks using multiple knapsack,” J. Ambient Intell. Humaniz. Comput., vol. 10, no. 4, pp. 1655–1678, 2019, doi: 10.1007/s12652-018-0761-z.
U. Ali, U. Qamar, K. Wahab, and K. S. Arif, “A Knapsack Problem Based Algorithm for Local Level Management in Smart Grid,” Lect. Notes Data Eng. Commun. Technol., vol. 47, no. February, pp. 301–310, 2020, doi: 10.1007/978-3-030-39746-3_32.
T. Venkata Naga Jayudu, M. Rama Krishna Reddy, and C. Shoba Bindu, “Greedy knapsack based energy efficient routing in WMSNS,” Int. J. Innov. Technol. Explor. Eng., vol. 8, no. 10, pp. 1455–1461, 2019, doi: 10.35940/ijitee.A1015.0881019.
R. G. Babukarthik, C. Dhasarathan, M. Kumar, A. Shankar, S. Thakur, and X. Cheng, “A novel approach for multi-constraints knapsack problem using cluster particle swarm optimization,” Comput. Electr. Eng., vol. 96, no. PA, p. 107399, 2021, doi: 10.1016/j.compeleceng.2021.107399.
S. Nethravathi and V. Murali, “A Novel Residential Energy Management System Based on Sequential Whale Optimization Algorithm and Fuzzy Logic,” vol. 37, pp. 557–586, 2021, doi: 10.13052/dgaej2156-3306.3739.
T. K. Lee, Z. Bareket, T. Gordon, and Z. S. Filipi, “Stochastic modeling for studies of real-world PHEV usage: Driving schedule and daily temporal distributions,” IEEE Trans. Veh. Technol., vol. 61, no. 4, pp. 1493–1502, 2012, doi: 10.1109/TVT.2011.2181191.
C. J. (1984). Breiman, L., Friedman, J.H., Olshen, R.A., and Stone, Classification And Regression Trees (1st ed.). Routledge, 1984. doi: https://doi.org/10.1201/9781315139470.
F. P.O., Thermal comfort, Analysis and application in environmental engineering, 1970th ed. McGraw Hill.
C. Timplalexis, A. Dimara, S. Krinidis, and D. Tzovaras, “Thermal Comfort Metabolic Rate and Clothing Inference,” Lect. Notes Comput. Sci. (including Subser. Lect. Notes Artif. Intell. Lect. Notes Bioinformatics), vol. 11754 LNCS, pp. 690–699, 2019, doi: 10.1007/978-3-030-34995-0_63.
“Ergonomics of the Thermal Environment—Analytical Determination and Interpretation of Thermal Comfort Using Calculation of the PMV and PPD Indices and Local Thermal Comfort Criteria, ISO Standard 7730:2005, 2005.”
K. Badiru, “Knapsack Problems; Methods, Models and Applications,” University of Oklahama.
V. Boddapati, A. S. R. Nandikatti, and S. A. Daniel, “Techno-economic performance assessment and the effect of power evacuation curtailment of a 50 MWp grid-interactive solar power park,” Energy Sustain. Dev., vol. 62, pp. 16–28, 2021, doi: 10.1016/j.esd.2021.03.005.
I. K. Umavathi M, Udhayakumar K, “Combined wind and PV residential energy system with single DC-DC (CUK) converter and 1-phase inverter to power grid,” J. Electr. Eng., vol. 17, no. 3, pp. 118–129, 2017.