Document Type : Original Article

Author

Department of Electrical Engineering, Northeastern University, Boston, MA, USA

10.52293/SE.1.1.147165

Abstract

The off-grid electricity production is a method of supplying energy to commercial, industrial, residential, and rural or remote regions, which is often the grid connecting is unfeasible because of its difficult regional location and the staggering transmitting cost. In cases like this, the application of local energy help to develop these regions. However, there is always need to a diesel generator to increase the electricity reliability. Hence, in the new method, diesel generators (DG) are coupled with renewable energy techniques like solar photovoltaics which may also use an energy storage system (ESS). The main idea in this paper is to propose a new optimum form for a hybrid battery/PV/diesel generator/ energy storage tool to resolve the load demand in a distant region in Changsha of China. Three main objectives are considered for minimization: annualized system cost, load probability loss, and value of CO2 emissions. To reduce the complexity of the system, ε-constraint technique is used. Here, a new modified bio-inspired algorithm, which is Chaotic Thermal Exchange Optimization algorithm is also applied to solve the optimization problem. Simulation achievements of the proposed system were put in comparison with the achievements of two latest methods to indicate the method effectiveness. The achievements indicated that the total production for the suggested method, PSO-based method, and HOMER are achieved 44051 kWh/yr, 44532 kWh/yr, and 43560 kWh/yr, respectively.

Keywords

[1]    L. Al-Ghussain et al., “100% Renewable Energy Grid for Rural Electrification of Remote Areas: A Case Study in Jordan,” Energies, vol. 13, no. 18, p. 4908, 2020.
[2]    L. Al-Ghussain, A. M. Abubaker, and A. D. Ahmad, “Superposition of Renewable-Energy Supply from Multiple Sites Maximizes Demand-Matching: Towards 100% Renewable Grids in 2050,” Applied Energy, vol. 284, p. 116402, 2021.
[3]    R. Kumar, R. Gupta, and A. K. Bansal, “Economic analysis and power management of a stand-alone wind/photovoltaic hybrid energy system using biogeography based optimization algorithm,” Swarm and Evolutionary Computation, vol. 8, pp. 33-43, 2013.
[4]    A. Yahiaoui, F. Fodhil, K. Benmansour, M. Tadjine, and N. Cheggaga, “Grey wolf optimizer for optimal design of hybrid renewable energy system PV-Diesel Generator-Battery: Application to the case of Djanet city of Algeria,” Solar Energy, vol. 158, pp. 941-951, 2017.
[5]    L. M. Halabi, S. Mekhilef, L. Olatomiwa, and J. Hazelton, “Performance analysis of hybrid PV/diesel/battery system using HOMER: A case study Sabah, Malaysia,” Energy Conversion and Management, vol. 144, pp. 322-339, 2017.
[6]    D. Xu, T. Acker, and X. Zhang, “Size optimization of a hybrid PV/wind/diesel/battery power system for reverse osmosis desalination,” Journal of Water Reuse and Desalination, vol. 9, no. 4, pp. 405-422, 2019.
[7]    F. A. Alturki, A. A. Al‐Shamma’a, H. M. Farh, and K. AlSharabi, “Optimal sizing of autonomous hybrid energy system using supply‐demand‐based optimization algorithm,” International Journal of Energy Research, 2020.
[8]    S. Pindado and J. Cubas, “Simple mathematical approach to solar cell/panel behavior based on datasheet information,” Renewable energy, vol. 103, pp. 729-738, 2017.
[9]    R. Dufo-Lopez and J. L. Bernal-Agustín, “Multi-objective design of PV–wind–diesel–hydrogen–battery systems,” Renewable energy, vol. 33, no. 12, pp. 2559-2572, 2008.
[10]    M. Shivaie, M. Mokhayeri, M. Kiani-Moghaddam, and A. Ashouri-Zadeh, “A reliability-constrained cost-effective model for optimal sizing of an autonomous hybrid solar/wind/diesel/battery energy system by a modified discrete bat search algorithm,” Solar Energy, vol. 189, pp. 344-356, 2019.
[11]    A. Sobu and G. Wu, “Optimal operation planning method for isolated micro grid considering uncertainties of renewable power generations and load demand,” in IEEE PES Innovative Smart Grid Technologies, 2012, pp. 1-6: IEEE.
[12]    L. Abualigah, A. Diabat, S. Mirjalili, M. Abd Elaziz, and A. H. Gandomi, “The arithmetic optimization algorithm,” Computer methods in applied mechanics and engineering, vol. 376, p. 113609, 2021.
[13]    N. Razmjooy, M. Khalilpour, V. V. Estrela, and H. J. Loschi, “World Cup Optimization Algorithm: an Application for Optimal Control of Pitch Angle in Hybrid Renewable PV/Wind Energy System,” 2018.
[14]    M. Mani, O. Bozorg-Haddad, and X. Chu, “Ant lion optimizer (ALO) algorithm,” in Advanced Optimization by Nature-Inspired Algorithms: Springer, 2018, pp. 105-116.
[15]    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, no. 4, p. 49, 2016.
[16]    A. Kaveh and A. Dadras, “A novel meta-heuristic optimization algorithm: thermal exchange optimization,” Advances in Engineering Software, vol. 110, pp. 69-84, 2017.
[17]    Y. Cao, Y. Wu, L. Fu, K. Jermsittiparsert, and N. Razmjooy, “Multi-objective optimization of a PEMFC based CCHP system by meta-heuristics,” Energy Reports, vol. 5, pp. 1551-1559, 2019.
[18]    X. Li, P. Niu, and J. Liu, “Combustion optimization of a boiler based on the chaos and Levy flight vortex search algorithm,” Applied Mathematical Modelling, vol. 58, pp. 3-18, 2018.
[19]    M. Yazdani and F. Jolai, “Lion optimization algorithm (LOA): a nature-inspired metaheuristic algorithm,” Journal of computational design and engineering, vol. 3, no. 1, pp. 24-36, 2016.
[20]    W.-T. Pan, “A new fruit fly optimization algorithm: taking the financial distress model as an example,” Knowledge-Based Systems, vol. 26, pp. 69-74, 2012.
[21]    R. Rajabioun, “Cuckoo optimization algorithm,” Applied soft computing, vol. 11, no. 8, pp. 5508-5518, 2011.
[22]    K. Khalili-Damghani, A.-R. Abtahi, and M. Tavana, “A new multi-objective particle swarm optimization method for solving reliability redundancy allocation problems,” Reliability Engineering & System Safety, vol. 111, pp. 58-75, 2013.
[23]    H. Suryoatmojo, “Artificial intelligence based optimal configuration of hybrid power generation system,” 2010.
[24]    F. Fodhil, A. Hamidat, and O. Nadjemi, “Potential, optimization and sensitivity analysis of photovoltaic-diesel-battery hybrid energy system for rural electrification in Algeria,” Energy, vol. 169, pp. 613-624, 2019.
[25]    S. Bahramara, A. Mazza, G. Chicco, M. Shafie-khah, and J. P. Catalão, “Comprehensive review on the decision-making frameworks referring to the distribution network operation problem in the presence of distributed energy resources and microgrids,” International Journal of Electrical Power & Energy Systems, vol. 115, p. 105466, 2020.
[26]    Y. Feng, W. Hao, H. Li, N. Cui, D. Gong, and L. Gao, “Machine learning models to quantify and map daily global solar radiation and photovoltaic power,” Renewable and Sustainable Energy Reviews, vol. 118, p. 109393, 2020.