Gray Wolf, Mikro Şebekede Pil Depolama Dahil Optimize Edilmiş Ekonomik Yük Dağıtımı

Yıl 2024, Cilt: 27 Sayı: 1, 27 - 33, 29.02.2024

Salem Faraj Aljrıbı , Ziyodulla Yusupov

https://doi.org/10.2339/politeknik.886712

Öz

Son yıllarda, yeni bir meta-sezgisel optimizasyon tekniği olarak gri kurt optimize edici algoritması, yük tahmini, kontrolör parametre ayarı ve iş çizelgeleme gibi mühendislik problemlerinin optimizasyonunda önemli bir rol oynamaktadır. Bu yazıda, gücün ekonomik bir şekilde yüke etkin bir şekilde dağıtılması için mikro şebeke sistemini optimize etmek için gri kurt optimizasyonu (GWO) kullanılmıştır. Mikro şebeke sistem bileşenlerinin modeli MATLAB/Simulink platformunda geliştirilmiş ve incelenmiştir. Önerilen gri kurt algoritmasının hayati amacı, mikro şebeke operasyonunun toplam maliyetini en aza indirmektir. Her iki mod için, yani mikro şebeke sisteminin ada ve şebekeye bağlı modları için, işletme maliyetlerinin etkisi dikkate alınarak, güç dağıtım optimizasyonu ve maliyet minimizasyonu üzerinde ayrıntılı araştırma yapılır. Analizden, genel sistemin maliyeti etkin bir şekilde optimize edilir ve GWO aracılığıyla yük paylaşımı etkin bir şekilde yapılır.

Anahtar Kelimeler

Gri kurt optimize edici, mikro şebeke, ekonomik güç dağıtımı, pil depolama, dağıtılmış üretim

Grey Wolf Optimized Economic Load Dispatch Including Battery Storage In Microgrid

Öz

In last decades, grey wolf optimizer algorithm as a new meta-heuristic optimization technique plays major role in optimization of engineering problems such as load forecasting, controller parameter tuning and job scheduling. In this paper, grey wolf optimization (GWO) is used to optimize the microgrid system for effective dispatching of power to load with economic manner. The model of microgrid system components are developed and investigated in the MATLAB/Simulink platform. The vital objective of the proposed grey wolf algorithm is to minimize overall cost of the microgrid operation. The detailed investigation is carried out on power dispatch optimization and cost minimization for both modes, i.e. island and grid-connected modes of the microgrid system with considering the impact of running costs. From the analysis, the cost of the overall system is optimized effectively, and load sharing is done effectively by means of GWO.

Anahtar Kelimeler

Grey wolf optimizer, microgrid, economic power dispatch, battery storage, distributed generation

Kaynakça

• [1] K. Vankadara and I. J. Raglend, “A review to economic dispatch of hybrid microgrids”, Serbian J. Electr. Eng,16(2): 233–246, (2019).
• [2] A. Hirsch, Y. Parag, and J. Guerrero, “Microgrids: A review of technologies, key drivers, and outstanding issues”, Renew. Sustain. Energy Rev, 90: 402–411, (2018).
• [3] F. R. Badal, P. Das, S. K. Sarker, and S. K. Das, “A survey on control issues in renewable energy integration and microgrid”, Prot. Control Mod. Power Syst, 4(1): 8, (2019).
• [4] A. L. Maharsi, F. D. Wijaya, and I. W. Mustika, “Cost-based power distribution optimization scheduling in microgrid”, International Conference on Science and Technology-Computer (ICST), 87–92 , (2017).
• [5] X. Wang, Y. Ji, J. Wang, Y. Wang, and L. Qi, “Optimal energy management of microgrid based on multi-parameter dynamic programming”, Int. J. Distrib. Sens. Networks,16(6): 1550147720937141, (2020).
• [6] V. Calderaro, G. Conio, V. Galdi, G. Massa, and A. Piccolo, “Active management of renewable energy sources for maximizing power production” Int. J. Electr. Power Energy Syst, 57: 64–72, (2014).
• [7] J. K. Pattanaik, M. Basu, and D. P. Dash, “Dynamic economic dispatch: a comparative study for differential evolution, particle swarm optimization, evolutionary programming, genetic algorithm, and simulated annealing” J. Electr. Syst. Inf. Technol, 6(1): 1, (2019).
• [8] A. Ioannou, A. Angus, and F. Brennan, “Risk-based methods for sustainable energy system planning: A review”, Renew. Sustain. Energy Rev, 74: 602–615, (2017).
• [9] A. Antony, Y. D. Wang, and A. P. Roskilly, “A detailed optimisation of solar photovoltaic/thermal systems and its application”, Energy Procedia, 158: 1141–1148, (2019).
• [10] N. K. Paliwal, A. K. Singh, and N. K. Singh, “Economic energy scheduling of grid connected microgrid with diesel engine and reserve constraint”, IEEE Region 10 Humanitarian Technology Conference (R10-HTC), 1-6, (2016).
• [11] Y. Li, Z. Yang, G. Li, D. Zhao, and W. Tian, “Optimal scheduling of an isolated microgrid with battery storage considering load and renewable generation uncertainties”, IEEE Trans. Ind. Electron, 66(2): 1565–1575, (2018).
• [12] X. S. Han, H. B. Gooi, and D. S. Kirschen, “Dynamic economic dispatch: feasible and optimal solutions”, IEEE Trans. power Syst, 16(1): 22–28, (2001).
• [13] A. Cagnano, A. C. Bugliari, and E. De Tuglie, “A cooperative control for the reserve management of isolated microgrids”, Appl. Energy, 218: 256–265, (2018).
• [14] S. Hajiaghasi, A. Salemnia, and M. Hamzeh, “Hybrid energy storage system for microgrids applications: A review” J. Energy Storage, 21: 543–570, (2019).
• [15] S. Phommixay, M. L. Doumbia, and D. L. St-Pierre, “Review on the cost optimization of microgrids via particle swarm optimization”, Int. J. Energy Environ. Eng, 11(1): 73–89, (2020).
• [16] W. Zheng, W. Wu, B. Zhang, H. Sun, Q. Guo, and C. Lin, “Dynamic economic dispatch for microgrids: A fully distributed approach”, IEEE/PES Transmission and Distribution Conference and Exposition (T&D), 1–3, (2016).
• [17] S. Fan, G. He, B. Guo, and Z. Wang, “A user energy management system (UEMS)-based microgrid economic dispatch model”, IEEE PES Asia-Pacific Power and Energy Engineering Conference (APPEEC), 1–6, (2017).
• [18] T. G. Paul, S. J. Hossain, S. Ghosh, P. Mandal, and S. Kamalasadan, “A quadratic programming based optimal power and battery dispatch for grid-connected microgrid”, IEEE Trans. Ind. Appl, 54(2): 1793–1805, (2017).
• [19] Y. Wang, L. Wang, L. Xu, and J. Sun, “Monte Carlo based operating reserve adequacy evaluation of a stand-alone microgrid considering high penetrations of correlated wind energy”, International Conference on Information Science and Control Engineering (ICISCE), 1356–1360,(2016).
• [20] S. Mirjalili, S. M. Mirjalili, and A. Lewis, “Grey wolf optimizer”, Adv. Eng. Softw, 69: 46–61, (2014).
• [21] H. Ren, A. Xiang, W. Teng, and R. Cen, “Economic optimization with environmental cost for a microgrid”, IEEE Power and Energy Society General Meeting, 1–6, (2012).