Optimal Performance of Micro-grids Networks with Uncertainty using Game Theory Coalition Formulation Strategy

Majlesi Journal of Electrical Engineering

مقاله 2، دوره 13، شماره 4، اسفند 2019، صفحه 7-24   

نوع مقاله: Review Article

نویسندگان

Karvan Karimizadeh؛ Soodabeh Soleymani^* ؛ Faramarz Faghihi

Department of Electrical Engineering, Science and Research Branch, Islamic Azad University, Tehran, Iran

چکیده

The demand for energy is constantly increasing, which is pushing the limitations of the current grid, increasing reliance on fossil fuels, and increasing CO2 emissions. These concerns have renewed interest in discovering ways to reduce power demand on the grid through renewable energy. However, the distribution characteristics of renewable energy make it difficult to integrate effectively into the traditional power grid. As power networks increase in scale, the drawbacks of the conventional grid, such as high cost and difficult operation, will become more apparent, and it will no longer meet increasing safety, reliability, and diversity. Therefore, applying efficient methods to improve the performance of micro-grids is necessary. In this paper the application of CRPSO algorithm based on the game theoretic formulation strategy was proposed to reduce the exchange of power between the macro station and micro-grids. The objective function of optimization involves minimizing the cost of power, loss, communication and load shedding. The advantages have caused the load of the micro-grids to be implemented as much as possible through the exchanges between the micro-grids and the cost of utilization and power supply of the loads to be minimized. Uncertainties in wind speed and solar radiation flux are also considered for the purpose of applying the random property of the distributed generation resources. The simulation results on the 33rd IEEE standard system in different scenarios indicate the desired performance of the proposed method.

کلیدواژه‌ها

Game Theoretic Coalition Formulation Strategy؛ Optimal Allocation of Dispersed Generation Resources؛ Uncertainty؛ Crpso Algorithm؛ Micro grid

مراجع

[1] C. Wei, Z. M. Fadlullah, N. Kato and A. Takeuchi, "GT-CFS: a game theoretic coalition formulation strategy for reducing power loss in micro grids," IEEE transaction on parallel and distributed systems, vol. 25, no. 9, pp. 2307-2317, 2014.

[2] H. Q. Lai, Y. Chen and K. J. Ray Liu, "Energy efficient cooperative communications using coalition formation games," Computer networks, vol. 58, pp. 228-238, 2014.

[3] Y. Hou, T. Ding, B. Tang, Q. Wang, S. Song, W. Tian and Y. Cao, "Long-term transnational power transaction model considering political risk for northeast asia in presence of global energy internet," in IEEE conference on energy internet and energy system integration (EI2), Beijing, 2017.

[4] F. Garcia-Torres, C. Bordons and M. A. Ridao, "Optimal economic schedule for a network of microgrids with hybrid energy storage system using distributed model predictive control," IEEE transactions on industrial electronics, 2018.

[5] N. Nikmehr and S. N. Ravadanegh, "Optimal power dispatch of multi-microgrids at future smart distribution grids," IEEE transactions on smart grid, vol. 6, no. 4, pp. 1648-1657, 2015.

[6] R. Pilling, S. C. Chang and P. B. Luh, "Shapley value-based payment calculation for energy exchange between micro- and utility grids," Games, vol. 8, no. 4, pp. 1-12, 2017.

[7] G. Mokryani, Y. F. Hu, P. Papadopoulos, T. Niknam and J. Aghaei, "A deterministic approach for active distribution networks planning with high penetration of wind and solar power," Renewable energy, vol. 113, pp. 942-951, 2017.

[8] P. Prakash and D. K. Khatod, "Optimal sizing and siting techniques for distributed generation in distribution systems: a review," Renewable and sustainable energy reviews, vol. 57, pp. 111-130, 2016.

[9] A. Ahmadian, H. Jahangir, M. A. Golkar, A. Elkamel and A. Almansoori, "Optimal sizing of DG units for improving voltage stability and reliability of micro-grid considering uncertainty of renewable energy resources," Journal of operation and automation in power system, vol. 5, no. 2, pp. 205-214, 2017.

[10] A. R. Jordehi, "How to deal with uncertainties in electric power systems? a review," Renewable and sustainable energy reviews, vol. 96, pp. 145-155, 2018.

[11] Y. Zhang, M. H. Hajiesmaili, S. Cai, M. Chen and Q. Zhu, "Peak-aware online economic dispatching for microgrids," IEEE transactions on smart grid, vol. 9, no. 1, pp. 323-335, 2018.

[12] N. Nikmehr and S. N. Ravadanegh, "Optimal operation of distributed generations in micro-grids under uncertainties in load and renewable power generation using heuristic algorithm," IET renewable power generation, vol. 9, no. 8, pp. 982-990, 2015.

[13] N. Nikmehr , S. N. Ravadanegh and A. Khodaei, "Probabilistic optimal scheduling of networked microgrids considering time-based demand response programs under uncertainty," Applied energy, vol. 198, pp. 267-279, 2017.

[14] H. HassanzadehFard and A. Jalilian, "Optimal sizing and location of renewable energy based DG units in distribution systems considering load growth," Electrical power and energy systems, vol. 101, pp. 356-370, 2018.

[15] Z. Wang, B. Chen, J. Wang, J. Kim and M. M. Begovic, "Robust optimization based optimal DG placement in microgrids," IEEE transactions on smart frid, vol. 5, no. 5, pp. 2173-2182, 2014.

[16] J. Ni and Q. Ai, "Economic power transaction using coalitional game strategy in micro-grids," IET Generation, Transmission & Distribution, vol. 10, no. 1, pp. 10-18, 2016.

[17] W. C, Z. M. Fadlullah, N. Kato and A. Takeuchi, "GT-CFS: A game theoretic coalition formulation strategy for reducing power loss in micro grids," IEEE transactions on parallel and distributed systems, vol. 25, no. 9, pp. 2307-2317, 2014.

[18] Z. Liu, F. Wen and G. Ledwich, "Optimal siting and sizing of distributed generators in distribution systems considering uncertainties," IEEE transactions on power delivery, vol. 26, no. 4, pp. 2541-2551, 2011.

[19] S. S. Reddy, "Optimal scheduling of thermal-wind-solar power system with storage," Renewable energy, vol. 101, pp. 1357-1368, 2017.

[20] J. Kennedy and R. Eberhart, "Particle swarm optimization," in Proceedings of IEEE international conference on neural networks, Perth (Australia), 1995.

[21] P. Bhatt, R. Roy and S. P. Ghoshal, "Dynamic participation of doubly fed induction generator in automatic generation control," Renewable energy, vol. 36, pp. 1203-1212, 2011.