Document Type : Original Article
Authors
1 Ya´an Polytechnic College, Sichuan, China. 625100
2 Department of Advanced Robotics, Istituto Italiano di Tecnologia, Genoa, Italy
10.52293/SE.1.1.114
Abstract
Recently, the application of the intelligent parking lot (IPL) in the power market has been exponentially increasing to decrease the greenhouse gasses, the pollution, and to decrease the deviation cost of the energy production based on electric vehicles (EV). IPLs uses charge and discharge features of EVs to exchange the energy in the upstream grid. This paper study on a new interval-analysis based optimal solution of an IPL by considering the interval uncertainties for the price of upstream gird value.
The method based on using an interval-based particle swarm optimization algorithm to optimize an interval objective function with lower and upper limitations with a single-valued output. Simulation results of the presented procedure are compared with a deterministic mixed-integer linear programming to show its superiority. The results show that deviation cost has been decreased up to 10.74% while average cost has been raised into 5.17% which demonstrates the methods high performance in decreasing the average cost of IPL and the reliability of the intelligent parking lot in the presence of uncertainties derived from the upstream grid.
Keywords
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[38] N. Ghadimi, A. Akbarimajd, H. Shayeghi, and O. Abedinia, “Application of a new hybrid forecast engine with feature selection algorithm in a power system,” International Journal of Ambient Energy, vol. 40, pp. 1-10, 2017.
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[2] R. Zárate-Miñano, A. F. Burgos, and M. Carrión, “Analysis of different modeling approaches for integration studies of plug-in electric vehicles,” International Journal of Electrical Power & Energy Systems, vol. 114, p. 105398, 2020.
[3] Z. Wang, Y. Zhang, S. You, H. Xiao, and M. Cheng, “An Integrated Power Conversion System for Electric Traction and V2G Operation in Electric Vehicles With A Small Film Capacitor,” IEEE Transactions on Power Electronics, 2019.
[4] S. Rezaee, E. Farjah, and B. Khorramdel, “Probabilistic analysis of plug-in electric vehicles impact on electrical grid through homes and parking lots,” IEEE Transactions on Sustainable Energy, vol. 4, pp. 1024-1033, 2013.
[5] A. R. Malaiya, N. Kumar, A. Kansagara, and A. Kadam, “Smart Parking System Based on Cloud Computing using Android and IoT,” International Journal of Innovative Science and Research Technology, vol. 3, pp. 233-235, 2018.
[6] L. Zhang and Y. Li, “Optimal management for parking-lot electric vehicle charging by two-stage approximate dynamic programming,” IEEE Transactions on Smart Grid, vol. 8, pp. 1722-1730, 2015.
[7] J. Jannati and D. Nazarpour, “Optimal performance of electric vehicles parking lot considering environmental issue,” Journal of cleaner production, vol. 206, pp. 1073-1088, 2019.
[8] N. Ghadimi, “A new hybrid algorithm based on optimal fuzzy controller in multimachine power system,” Complexity, vol. 21, pp. 78-93, 2015.
[9] N. Ghadimi, “An adaptive neuro‐fuzzy inference system for islanding detection in wind turbine as distributed generation,” Complexity, vol. 21, pp. 10-20, 2015.
[10] N. Ghadimi, M. Afkousi-Paqaleh, and A. Nouri, “PSO based fuzzy stochastic long-term model for deployment of distributed energy resources in distribution systems with several objectives,” IEEE Systems Journal, vol. 7, pp. 786-796, 2013.
[11] M. Rahmani-Andebili, “Optimal Operation of a Plug-In Electric Vehicle Parking Lot in the Energy Market Considering the Technical, Social, and Geographical Aspects,” in Planning and Operation of Plug-In Electric Vehicles, ed: Springer, 2019, pp. 105-147.
[12] İ. Şengör, O. Erdinç, B. Yener, A. Taşcikaraoglu, and J. P. Catalão, “Optimal energy management of EV parking lots under peak load reduction based dr programs considering uncertainty,” IEEE Transactions on Sustainable Energy, 2018.
[13] J. Jannati and D. Nazarpour, “Multi-objective scheduling of electric vehicles intelligent parking lot in the presence of hydrogen storage system under peak load management,” Energy, vol. 163, pp. 338-350, 2018.
[14] Y. Zhang, Z. Zhang, and S. Teng, “Adaptive Ant Colony Optimization for Solving the Parking Lot Assignment Problem,” in 2018 14th International Conference on Natural Computation, Fuzzy Systems and Knowledge Discovery (ICNC-FSKD), 2018, pp. 91-96.
[15] A. F. Marzoghi, S. Bahramara, F. Adabi, and S. Nojavan, “Optimal scheduling of intelligent parking lot using interval optimization method in the presence of the electrolyser and fuel cell as hydrogen storage system,” International Journal of Hydrogen Energy, vol. 44, pp. 24997-25009, 2019.
[16] N. Razmjooy and M. Ramezani, “Solution of the Hamilton jacobi bellman uncertainties by the interval version of adomian decomposition method,” Int Rob Auto J, vol. 4, pp. 113-117, 2018.
[17] N. Razmjooy and M. Ramezani, “Optimal Control of Two-Wheeled Self-Balancing Robot with Interval Uncertainties Using Chebyshev Inclusion Method,” Majlesi Journal of Electrical Engineering, vol. 12, pp. 13-21, 2018.
[18] N. Razmjooy and M. Ramezani, “Interval structure of Runge-Kutta methods for solving optimal control problems with uncertainties,” Computational Methods for Differential Equations, vol. 7, pp. 235-251, 2019.
[19] N. Razmjooy, M. Ramezani, V. V. Estrela, H. J. Loschi, and D. A. do Nascimento, “Stability Analysis of the Interval Systems Based on Linear Matrix Inequalities,” in Brazilian Technology Symposium, 2018, pp. 371-378.
[20] G. Guo and N. Razmjooy, “A new interval differential equation for edge detection and determining breast cancer regions in mammography images,” Systems Science & Control Engineering, vol. 7, pp. 346-356, 2019.
[21] A. Namadchian, M. Ramezani, and N. Razmjooy, “A New Meta-Heuristic Algorithm for Optimization Based on Variance Reduction of Guassian Distribution,” Majlesi Journal of Electrical Engineering, vol. 10, p. 49, 2016.
[22] M. Mir, M. Shafieezadeh, M. A. Heidari, and N. Ghadimi, “Application of hybrid forecast engine based intelligent algorithm and feature selection for wind signal prediction,” Evolving Systems, pp. 1-15, 2019.
[23] G. Aghajani and N. Ghadimi, “Multi-objective energy management in a micro-grid,” Energy Reports, vol. 4, pp. 218-225, 2018.
[24] H. N. Aghdam, N. Ghadimi, and A. Ataei, “Adjusting PID controllers coefficients to control fuel cell using genetic algorithm,” in 2011 10th International Conference on Environment and Electrical Engineering, 2011, pp. 1-5.
[25] D. Yu, Y. Wang, H. Liu, K. Jermsittiparsert, and N. Razmjooy, “System identification of PEM fuel cells using an improved Elman neural network and a new hybrid optimization algorithm,” Energy Reports, vol. 5, pp. 1365-1374, 2019.
[26] A. Shamel and N. Ghadimi, “Hybrid PSOTVAC/BFA technique for tuning of robust PID controller of fuel cell voltage,” 2016.
[27] N. Razmjooy and M. Ramezani, “An Improved Quantum Evolutionary Algorithm Based on Invasive Weed Optimization,” Indian J. Sci. Res, vol. 4, pp. 413-422, 2014.
[28] Z. Cui, J. Zhang, Y. Wang, Y. Cao, X. Cai, W. Zhang, et al., “A pigeon-inspired optimization algorithm for many-objective optimization problems,” Sci. China Inf. Sci, vol. 62, p. 070212, 2019.
[29] N. Razmjooy, M. Khalilpour, and M. Ramezani, “A New Meta-Heuristic Optimization Algorithm Inspired by FIFA World Cup Competitions: Theory and Its Application in PID Designing for AVR System,” Journal of Control, Automation and Electrical Systems, vol. 27, pp. 419-440, 2016.
[30] P. S. Bandaghiri, N. Moradi, and S. S. Tehrani, “Optimal tuning of PID controller parameters for speed control of DC motor based on world cup optimization algorithm,” parameters, vol. 1, p. 2, 2016.
[31] N. Razmjooy, A. Madadi, and M. Ramezani, “Robust Control of Power System Stabilizer Using World Cup Optimization Algorithm,” International Journal of Information, Security, and Systems Management, vol. 5, p. 7, 2017.
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[33] S. Guan and S. Fang, “A new interval particle swarm optimization algorithm,” Journal of Northeastern University (Natural Science), vol. 33, pp. 1881-18842012, 2012.
[34] S.-P. Guan, J.-J. Zhang, and L.-F. Zou, “An interval particle swarm optimization algorithm for evolving interval neural networks,” in 2018 Chinese Control And Decision Conference (CCDC), 2018, pp. 1615-1619.
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[36] N. Razmjooy and M. Ramezani, “Training wavelet neural networks using hybrid particle swarm optimization and gravitational search algorithm for system identification,” International Journal of Mechatronics, Electrical and Computer Technology, vol. 6, pp. 2987-2997, 2016.
[37] N. Ghadimi, A. Akbarimajd, H. Shayeghi, and O. Abedinia, “Two stage forecast engine with feature selection technique and improved meta-heuristic algorithm for electricity load forecasting,” Energy, vol. 161, pp. 130-142, 2018.
[38] N. Ghadimi, A. Akbarimajd, H. Shayeghi, and O. Abedinia, “Application of a new hybrid forecast engine with feature selection algorithm in a power system,” International Journal of Ambient Energy, vol. 40, pp. 1-10, 2017.
[39] J. Jannati and D. Nazarpour, “Optimal energy management of the smart parking lot under demand response program in the presence of the electrolyser and fuel cell as hydrogen storage system,” Energy Conversion and Management, vol. 138, pp. 659-669, 2017.
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