Document Type : Research paper
Authors
Electrical Engineering Department, Urmia University, Urmia, Iran.
Abstract
This paper presents a novel method to improve the efficiency of active distribution networks (ADNs) by optimal placement of distributed energy resources (DERs) and utilizing the unused capacity of inverter-interfaced photovoltaic (PV) units for reactive power compensation. After investigating the mathematical model of PV systems, wind turbines, other non-renewable distributed generations, energy storage systems, and responsive loads, a genetic algorithm (GA)-based approach is used to find the optimal placement and allocation of all units. The modeling also takes into account the uncertainty of PV units and wind turbines to represent real-world operational conditions more accurately. Additionally, although the IEEE 33-bus system is used to formulate the presented method, one can easily extend it to any other network with an arbitrary number of buses. The effectiveness of the proposed method is verified by designing three different scenarios. The simulation results obtained based on MATLAB clearly show the capability of the proposed method to improve the voltage profile and the cost of losses in ADN. This is done by properly utilizing the excess capacity of inverter-interfaced PV units as a static compensator (STATCOM), even in the absence of sunlight. The findings indicate that the inclusion of DERs and PV-STATCOM results to a notable enhancement of approximately 68.46% in power losses reduction and around 65% in the voltage deviation minimization.
Keywords
- S. Mirzamohammadi, A. Jabarzadeh, and M. S. Shahrabi, “Long-term planning of supplying energy for greenhouses using renewable resources under uncertainty,” J. Cleaner Prod., vol. 264, p. 121611, 2020.
- J. Wei, Y. Zhang, J. Wang, X. Cao, and M. A. Khan, “Multi-period planning of multi-energy microgrid with multitype uncertainties using chance constrained information gap decision method,” Appl. Energy, vol. 260, p. 114188, 2020.
- L. Luo, S. S. Abdulkareem, A. Rezvani, M. R. Miveh, S. Samad, N. Aljojo, and M. Pazhoohesh, “Optimal scheduling of a renewable based microgrid considering photovoltaic system and battery energy storage under uncertainty,” J. Energy Storage, vol. 28, p. 101306, 2020.
- S. E. Ahmadi and N. Rezaei, “A new isolated renewable based multi microgrid optimal energy management system considering uncertainty and demand response,” Int. J. Electr. Power Energy Syst., vol. 118, p. 105760, 2020.
- Y. Wang, Z. Yang, M. Mourshed, Y. Guo, Q. Niu, and X. Zhu, “Demand side management of plug-in electric vehicles and coordinated unit commitment: A novel parallel competitive swarm optimization method,” Energy Convers. Manage., vol. 196, pp. 935–949, 2019.
- F. S. Gazijahani, A. Ajoulabadi, S. N. Ravadanegh, and J. Salehi, “Joint energy and reserve scheduling of renewable powered microgrids accommodating price responsive demand by scenario: a risk-based augmented epsilon-constraint approach,” J. Cleaner Prod., vol. 262, p. 121365, 2020.
- A. Hussain, V.-H. Bui, and H.-M. Kim, “Microgrids as a resilience resource and strategies used by microgrids for enhancing resilience,” Appl. Energy, vol. 240, pp. 56–72, 2019.
- T. A. Boghdady and Y. A. Mohamed, “Reactive power compensation using statcom in a pv grid connected system with a modified mppt method,” Ain Shams Eng. J., vol. 14, no. 8, p. 102060, 2023.
- R. K. Varma and E. M. Siavashi, “Pv-statcom: A new smart inverter for voltage control in distribution systems,” IEEE Trans. Sustainable Energy, vol. 9, no. 4, pp. 1681–1691, 2018.
- A. J. Sonawane and A. C. Umarikar, “Voltage and reactive power regulation with synchronverter-based control of pvstatcom,” IEEE Access, 2023.
- A. M. Shaheen, R. A. El-Sehiemy, A. Ginidi, A. M. Elsayed, and S. F. Al-Gahtani, “Optimal allocation of pvstatcom devices in distribution systems for energy losses minimization and voltage profile improvement via hunterprey-based algorithm,” Energies, vol. 16, no. 6, p. 2790, 2023.
- O. D. Montoya, O. D. Florez-Cediel, and W. Gil-González, “Efficient day-ahead scheduling of pv-statcoms in mediumvoltage distribution networks using a second-order cone relaxation,” Comput., vol. 12, no. 7, p. 142, 2023.
- B. Dubey, S. Agrawal, and A. K. Sharma, “Smart inverter-based pv-statcom power compensation using baphin optimization algorithm,” Int. J. Renewable Energy Res., vol. 13, no. 3, pp. 1015–1030, 2023.
- F. J. Lachovicz, T. S. Fernandes, and J. A. Vilela Junior, “Impacts of pv-statcom reactive power dispatch in the allocation of capacitors bank and voltage regulators on active distribution networks,” J. Control Autom. Electr. Syst., vol. 34, no. 4, pp. 796–807, 2023.
- N. Kumar, S. Dahiya, and K. Singh Parmar, “Multi-objective economic emission dispatch optimization strategy considering battery energy storage system in islanded microgrid,” J. Oper. Autom. Power Eng., vol. 12, no. 4, pp. 296–311, 2024.
- G. Goyal and S. Vadhera, “Solution to objectives of supply side energy management by integrating enhanced demand response strategy,” J. Oper. Autom. Power Eng., vol. 12, no. 4, pp. 269–279, 2024.
- A. Roy, F. Auger, F. Dupriez-Robin, S. Bourguet, and Q. T. Tran, “A multi-level demand-side management algorithm for offgrid multi-source systems,” Energy, vol. 191, p. 116536, 2020.
- M. Mosayebian, “A new approach for modeling wind power in reliability studies,” J. Oper. Autom. Power Eng., vol. 11, no. 2, pp. 144–150, 2023.
- R. Avvari and V. K. DM, “A novel hybrid multi-objective evolutionary algorithm for optimal power flow in wind, pv,
and pev systems,” J. Oper. Autom. Power Eng., vol. 11, no. 2, pp. 130–143, 2023. - A. Zakariazadeh, S. Jadid, and P. Siano, “Smart microgrid energy and reserve scheduling with demand response using stochastic optimization,” Int. J. Electr. Power Energy Syst., vol. 63, pp. 523–533, 2014.
- S. Zhu, D. Li, and H. Feng, “Is smart city resilient? evidence from china,” Sustainable Cities Soc., vol. 50, p. 101636, 2019.
- T. Yasuda, S. Ookawara, S. Yoshikawa, and H. Matsumoto, “Materials processing model-driven discovery framework for porous materials using machine learning and genetic algorithm: A focus on optimization of permeability and filtration efficiency,” Chem. Eng. J., vol. 453, p. 139540, 2023.
- G. Papazoglou and P. Biskas, “Review and comparison of genetic algorithm and particle swarm optimization in the optimal power flow problem,” Energies, vol. 16, no. 3, p. 1152, 2023.
- S. Chandramohan, N. Atturulu, R. K. Devi, and B. Venkatesh, “Operating cost minimization of a radial distribution system in a deregulated electricity market through reconfiguration using nsga method,” Int. J. Electr. Power Energy Syst., vol. 32, no. 2, pp. 126–132, 2010.
)