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Optimal Charging/Discharging Control of Electric Vehicle Charging Station Considering Grid Resiliency | ||
| International Journal of Industrial Electronics Control and Optimization | ||
| دوره 4، شماره 4، بهمن 2021، صفحه 453-464 اصل مقاله (1.32 M) | ||
| نوع مقاله: Research Articles | ||
| شناسه دیجیتال (DOI): 10.22111/ieco.2021.37211.1335 | ||
| نویسندگان | ||
| Emad Hadian1؛ Hamidreza Akbari* 2؛ Mehdi Farzinfar3؛ Seyed Amin Saeed1 | ||
| 1Department of Electrical Engineering, Yazd Branch, Islamic Azad University | ||
| 2Department of Electrical Engineering, Yazd Branch, Islamic Azad University, Yazd, Iran | ||
| 3School of Engineering, Damghan University, Damghan | ||
| چکیده | ||
| Management and control of charging/discharging of Electric vehicles (EVs) with the aim of profitability for the Distribution System Operator (DSO) and the private sector is one of the challenges in operating Electric vehicles Charging Stations (EVCS). This paper proposes a novel methodology for optimal planning of charging/discharging of the hybrid wind- EVCS which on the one hand, lead to correction of the load curve and on the other hand, improves the grid resilience in extreme weather conditions. In the proposed methodology, since the weather-based outages lead to consumer interruptions, the idea of profit-sharing between DSO and EVCS owners is proposed to incentivize the owner to implement the obtained charging/discharging schedule. To this end, firstly, a Monte-Carlo based stochastic framework for forecasting the probability of weather-based line outages and also modelling uncertainties is devised. Then, a resilience-oriented multi-objective optimization algorithm is presented that, while coordinating the operation of the wind turbine, EV management and Demand Response Programs (DRP), the profits of both EVCS and DSO are maximized during daily operation planning. The resiliency improvement of the proposed method is evaluated by using metrics. The obtained optimal results prove the effectiveness of the proposed method in increasing resiliency and benefits for all players. | ||
| کلیدواژه‌ها | ||
| control of charging/discharging؛ electric vehicle؛ multi-objective optimization؛ extreme weather condition؛ resilience | ||
| مراجع | ||
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[1] Z. Ding, Y. Lu, L. Zhang, W.-J. Lee, and D. Chen, “A stochastic resource-planning scheme for PHEV charging station considering energy portfolio optimization and price-responsive demand,” IEEE Transactions on Industry Applications, vol. 54, no. 6, pp. 5590-5598, 2018. [2] A. Ahmadian, B. Mohammadi-Ivatloo, and A. Elkamel, “A Review on Plug-In Electric Vehicles: Introduction, Current Status, and Load Modeling Techniques,” Journal of Modern Power Systems and Clean Energy, vol. 8, no. 3, pp. 412-425, 2020. [3] H. Soltani Gohari, K. Abbaszadeh, and J. Gholami Gorji, “A Controllable Bidirectional Rectifier for EV Home Charging Station with G2H/G2VH/V2H/V2G Functions,” International Journal of Industrial Electronics Control and Optimization, vol. 4, no. 1, pp. 99-113, 2021. [4] P. Chanhom, S. Nuilers, and N. Hatti, “A new V2G control strategy for load factor improvement using smoothing technique,” Advances in Electrical and Computer Engineering, vol. 17, no. 3, pp. 43-50, 2017. [5] K. Kasturi, C. K. Nayak, and M. R. Nayak, “Electric vehicles management enabling G2V and V2G in smart distribution system for maximizing profits using MOMVO,” International Transactions on Electrical Energy Systems, vol. 29, no. 6, pp. e12013, 2019. [6] S. Singh, P. Chauhan, and N. J. Singh, “Feasibility of Grid-Connected Solar-Wind Hybrid System with Electric Vehicle Charging Station,” Journal of Modern Power Systems and Clean Energy, pp. 1-12, 2020. [7] Y. Li, Z. Ni, T. Zhao, T. Zhong, Y. Liu, L. Wu, and Y. Zhao, “Supply Function Game Based Energy Management Between Electric Vehicle Charging Stations and Electricity Distribution System Considering Quality of Service,” IEEE Transactions on Industry Applications, 2020. [8] A. Sheikhi, S. Bahrami, A. M. Ranjbar, and H. Oraee, “Strategic charging method for plugged in hybrid electric vehicles in smart grids; a game theoretic approach,” International Journal of Electrical Power & Energy Systems, vol. 53, pp. 499-506, 2013/12/01/, 2013. [9] J. A. Domínguez-Navarro, R. Dufo-López, J. M. Yusta-Loyo, J. S. Artal-Sevil, and J. L. Bernal-Agustín, “Design of an electric vehicle fast-charging station with integration of renewable energy and storage systems,” International Journal of Electrical Power & Energy Systems, vol. 105, pp. 46-58, 2019/02/01/, 2019. [10] M. Zare Oskouei, B. Mohammadi-Ivatloo, M. Abapour, A. Anvari-Moghaddam, and H. Mehrjerdi, “Practical implementation of residential load management system by considering vehicle-for-power transfer: Profit analysis,” Sustainable Cities and Society, vol. 60, pp. 102144, 2020/09/01/, 2020. [11] S. Wang, N. Zhang, Z. Li, and M. Shahidehpour, "Modeling and impact analysis of large scale V2G electric vehicles on the power grid." pp. 1-6. [12] A. Ul-Haq, C. Cecati, and E. El-Saadany, “Probabilistic modeling of electric vehicle charging pattern in a residential distribution network,” Electric Power Systems Research, vol. 157, pp. 126-133, 2018. [13] H. Chung, S. Maharjan, Y. Zhang, and F. Eliassen, “Intelligent Charging Management of Electric Vehicles Considering Dynamic User Behavior and Renewable Energy: A Stochastic Game Approach,” IEEE Transactions on Intelligent Transportation Systems, pp. 1-12, 2020. [14] A. S. Al-Ogaili, T. J. T. Hashim, N. A. Rahmat, A. K. Ramasamy, M. B. Marsadek, M. Faisal, and M. A. Hannan, “Review on Scheduling, Clustering, and Forecasting Strategies for Controlling Electric Vehicle Charging: Challenges and Recommendations,” IEEE Access, vol. 7, pp. 128353-128371, 2019. [15] Y. Cao, L. Huang, Y. Li, K. Jermsittiparsert, H. Ahmadi-Nezamabad, and S. Nojavan, “Optimal scheduling of electric vehicles aggregator under market price uncertainty using robust optimization technique,” International Journal of Electrical Power & Energy Systems, vol. 117, pp. 105628, 2020/05/01/, 2020. [16] K. M. Tan, S. Padmanaban, J. Y. Yong, and V. K. Ramachandaramurthy, “A multi-control vehicle-to-grid charger with bi-directional active and reactive power capabilities for power grid support,” Energy, vol. 171, pp. 1150-1163, 2019. [17] C. S. Ioakimidis, D. Thomas, P. Rycerski, and K. N. Genikomsakis, “Peak shaving and valley filling of power consumption profile in non-residential buildings using an electric vehicle parking lot,” Energy, vol. 148, pp. 148-158, 2018/04/01/, 2018. [18] A. Almutairi, and M. M. Salama, “Assessment and enhancement frameworks for system reliability performance using different PEV charging models,” IEEE Transactions on Sustainable Energy, vol. 9, no. 4, pp. 1969-1984, 2018. [19] M. Kamruzzaman, and M. Benidris, “Reliability-based metrics to quantify the maximum permissible load demand of electric vehicles,” IEEE Transactions on Industry Applications, vol. 55, no. 4, pp. 3365-3375, 2019. [20] E. Hadian, H. Akbari, M. Farzinfar, and S. Saeed, “Optimal Allocation of Electric Vehicle Charging Stations With Adopted Smart Charging/Discharging Schedule,” IEEE Access, vol. 8, pp. 196908-196919, 2020. [21] M. H. Amirioun, F. Aminifar, and M. Shahidehpour, “Resilience-Promoting Proactive Scheduling Against Hurricanes in Multiple Energy Carrier Microgrids,” IEEE Transactions on Power Systems, vol. 34, no. 3, pp. 2160-2168, 2019. [22] S. Ma, S. Li, Z. Wang, and F. Qiu, “Resilience-Oriented Design of Distribution Systems,” IEEE Transactions on Power Systems, vol. 34, no. 4, pp. 2880-2891, 2019. [23] M. Panteli, P. Mancarella, D. N. Trakas, E. Kyriakides, and N. D. Hatziargyriou, “Metrics and Quantification of Operational and Infrastructure Resilience in Power Systems,” IEEE Transactions on Power Systems, vol. 32, no. 6, pp. 4732-4742, 2017. [24] A. Roudbari, A. Nateghi, B. Yousefi-khanghah, H. Asgharpour-Alamdari, and H. Zare, “Resilience-oriented operation of smart grids by rescheduling of energy resources and electric vehicles management during extreme weather condition,” Sustainable Energy, Grids and Networks, pp. 100547, 2021. [25] H. Khaloie, A. Abdollahi, and M. Rashidinejad, “Risk-averse Pre-Extreme Weather Events Self-Scheduling of a Wind Power Plant: A Hybrid Possibilistic-Scenario Model,” International Journal of Industrial Electronics, Control and Optimization, vol. 1, no. 1, pp. 9-18, 2018. [26] A. Arif, S. Ma, Z. Wang, J. Wang, S. M. Ryan, and C. Chen, “Optimizing Service Restoration in Distribution Systems With Uncertain Repair Time and Demand,” IEEE Transactions on Power Systems, vol. 33, no. 6, pp. 6828-6838, 2018. [27] W. Li, X. Xiong, and J. Zhou, “Incorporating fuzzy weather-related outages in transmission system reliability assessment,” IET generation, transmission & distribution, vol. 3, no. 1, pp. 26-37, 2009. [28] S. Sharma, and P. Jain, “Integrated TOU price‐based demand response and dynamic grid ‐to ‐vehicle charge scheduling of electric vehicle aggregator to support grid stability,” International Transactions on Electrical Energy Systems, vol. 30, no. 1, pp. e12160, 2020. [29] F. Akhgarzarandy, H. Wang, and M. Farzinfar, “ Optimal resiliency ‐ oriented charging station allocation for electric vehicles considering uncertainties, ” International Transactions on Electrical Energy Systems, vol. 31, no. 4, pp. e12799, 2021. [30] A. Arjomandi-Nezhad, M. Fotuhi-Firuzabad, M. Moeini-Aghtaie, A. Safdarian, P. Dehghanian, and F. Wang, “Modeling and Optimizing Recovery Strategies for Power Distribution System Resilience,” IEEE Systems Journal, pp. 1-10, 2020. [31] M. Ramzanzadeh, M. Jafari Nokandi, T. Barforoushi, and J. Saebi, “Security-Constrained Unit Commitment in the Presence of Demand Response Programs and Electric Vehicles,” International Journal of Industrial Electronics, Control and Optimization, 2020. [32] B. Yousefi-Khangah, S. Ghassemzadeh, S. H. Hosseini, and B. Mohammadi-Ivatloo, “Short-term scheduling problem in smart grid considering reliability improvement in bad weather conditions,” IET Generation, Transmission & Distribution, vol. 11, no. 10, pp. 2521-2533, 2017. [33] S. Seyyedeh Barhagh, B. Mohammadi-Ivatloo, A.Anvari-Moghaddam, and S. Asadi, “Risk-involved participation of electric vehicle aggregator in energy markets with robust decision-making approach,” Journal of Cleaner Production, vol. 239, pp. 118076, 2019/12/01/, 2019. [34] Q. Chen, F. Wang, B.-M. Hodge, J. Zhang, Z. Li, M. Shafie-Khah, and J. P. Catalão, “Dynamic price vector formation model-based automatic demand response strategy for PV-assisted EV charging stations,” IEEE Transactions on Smart Grid, vol. 8, no. 6, pp. 2903-2915, 2017. [35] Y.-W. Chen, and J. M. Chang, “Fair demand response with electric vehicles for the cloud based energy management service,” IEEE Transactions on Smart Grid, vol. 9, no. 1, pp. 458-468, 2016. [36] M. Mazidi, A. Zakariazadeh, S. Jadid, and P. Siano, “Integrated scheduling of renewable generation and demand response programs in a microgrid,” Energy Conversion and Management, vol. 86, pp. 1118-1127, 2014. [37] P. M. Subcommittee, “IEEE Reliability Test System,” IEEE Transactions on Power Apparatus and Systems, vol. PAS-98, no. 6, pp. 2047-2054, 1979. [38] M. Farzinfar, M. Shafiee, and A. Kia, “Determination of Optimal Allocation and Penetration Level of Distributed Energy Resources Considering Short Circuit Currents,” International Journal of Engineering, vol. 33, no. 3, pp. 427-438, 2020. [39] Z. Moravej, M. Jazaeri, and M. Gholamzadeh, “Optimal coordination of distance and over-current relays in series compensated systems based on MAPSO,” Energy Conversion and Management, vol. 56, pp. 140-151, 2012. [40] A. Carlisle, and G. Dozier, "An off-the-shelf PSO." pp. 1-6. | ||
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