An Efficient Configuration for Energy Hub to Peak Reduction Considering Demand Response Using Metaheuristic Automatic Data Clustering

Hosseinnejad, H.; Galvani, S.; Alemi, P.

doi:10.22061/jecei.2020.7240.375

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	An Efficient Configuration for Energy Hub to Peak Reduction Considering Demand Response Using Metaheuristic Automatic Data Clustering
Journal of Electrical and Computer Engineering Innovations (JECEI)
مقاله 8، دوره 8، شماره 2، مهر 2020، صفحه 233-254 اصل مقاله (2.02 M)
نوع مقاله: Original Research Paper
شناسه دیجیتال (DOI): 10.22061/jecei.2020.7240.375
نویسندگان
H. Hosseinnejad¹؛ S. Galvani^* ¹^{، 2}؛ P. Alemi¹
¹Department of Power Engineering, Urmia Branch, Islamic Azad University, Urmia, Iran
²Department of Power Engineering, Faculty of Electrical and Computer Engineering, Urmia University, Urmia, Iran
تاریخ دریافت: 27 شهریور 1398، تاریخ بازنگری: 12 دی 1398، تاریخ پذیرش: 16 فروردین 1399
چکیده
Background and Objectives: Different energy demand calls the need for utilizing Energy Hub Systems (EHS), but the economic dispatch issue has become complicated due to uncertainty in demand. So, scenario generation and reduction techniques are used to considering the uncertainty of the EH demand. Dependent on the amount of fuel used, each system has various generation costs. Configuration selection stands as a challenging dilemma in the EHS designing besides economic problems. In this paper, the optimal EHS operation along with configuration issue is tackled. Methods: To do so, two EHS types are investigated to evaluate the configuration effect besides energy prices simultaneously change. Typically, the effect of the Demand Response (DR) feature is rarely considered in EHSs management which considered in this paper. Also, Metaheuristic Automatic Data Clustering (MADC) is used to reduce the decision-making problem dimension instead of using human decision makers in the subject of cluster center numbers and considering uncertainty. The "Shannon's Entropy" and the "TOPSIS" methods are also used in the decision-making. The study is carried out in MATLAB© and GAMS©. Results: In addition to minimizing the computational burden, the proposed EHS not only serves an enhancement in benefit by reducing the cost but also provides a semi-flat load curve in peak period by employing Emergency Demand Response Program (EDRP) and Time of Use (TOU). Conclusion: The results show that significant computational burden reduction is possible in the field of demand data by using automatic clustering method without human interference. In addition to the proposed configuration's results betterment, the approach demonstrated EH's configuration effect could consider as important as other features in the presence of DRPs for reaching desires of EHs customers which rarely considered. Also, "Shannon's Entropy" and the "TOPSIS" methods integration could select the best DRP scenario without human interference. The results of this study are encouraging and warrant further analysis and researches.
کلیدواژه‌ها
Economic Dispatch؛ Energy Hub (EH)؛ Configuration؛ Demand Response؛ Metaheuristic Automatic Data Clustering (MADC)

مراجع
[1] M. Mohammadi, Y. Noorollahi, B. Mohammadi-ivatloo, H. Yousefi, “Energy hub: From a model to a concept – A review,” Renewable and Sustainable Energy Reviews, 80: 1512–1527, 2017. [2] A. Yazdaninejadi, A. Hamidi, S. Golshannavaz, F. Aminifar, S. Teimourzadeh, “Impact of inverter-based DERs integration on protection, control, operation, and planning of electrical distribution grids,” Electricity Journal, 32(6): 43–56, 2019. [3] A. Yazdaninejadi, M. Farsadi, T. Sattarpour, “Optimal placement and operation of BESS in a distribution network considering the net present value of energy losses cost,” ELECO 2015 - 9th International Conference on Electrical and Electronics Engineering , 2016. [4] M. Nikzad, A. Samimi, “Responsive load model integration with SCUC to design time-of-use program,” Journal of Electrical and Computer Engineering Innovations, 6(2): 217–226, 2019. [5] M. Majidi, S. Nojavan, K. Zare, “A cost-emission framework for hub energy system under demand response program,” Energy, 134: 157–166, 2017. [6] T. Krause, G. Andersson, K. Fröhlich, A. Vaccaro, “Multiple-energy carriers: Modeling of production, delivery, and consumption,” Proceedings of the IEEE, 99(1): 15–27, 2011. [7] Y. Wang, N. Zhang, Z. Zhuo, C. Kang, D. Kirschen, “Mixed-integer linear programming-based optimal configuration planning for energy hub: Starting from scratch,” Applied Energy, 210: 1141–1150, 2018. [8] M. Geidl, G. Andersson, “Optimal power flow of multiple energy carriers,” IEEE Transactions on Power Systems, 22(1): 145–155, 2007. [9] A. Santhosh, A. M. Farid, K. Youcef-Toumi, “Real-time economic dispatch for the supply side of the energy-water nexus,” Applied Energy, 122: 42–52, 2014. [10] T. Ma, J. Wu, L. Hao, “Energy flow modeling and optimal operation analysis of the micro energy grid based on energy hub,” Energy Conversion and Management, 133: 292–306, 2017 . [11] T. Ma, J. Wu, L. Hao, D. Li, “Energy flow matrix modeling and optimal operation analysis of multi energy systems based on graph theory,” Applied Thermal Engineering, 146: 648–663, 2019. [12] S. Derafshi Beigvand, H. Abdi, M. La Scala, “Optimal operation of multicarrier energy systems using Time Varying Acceleration Coefficient Gravitational Search Algorithm,” Energy, 114: 253–265, 2016. [13] A. Pepiciello, A. Vaccaro, M. Mañana, “Robust optimization of energy hubs operation based on extended affine arithmetic,” Energies, 12(12): 2420, 2019. [14] R. Z. Ríos-Mercado, C. Borraz-Sánchez, “Optimization problems in natural gas transportation systems: A state-of-the-art review,” Applied Energy, 147. Elsevier Ltd: 536–555, 2015. [15] D. De Wolf, Y. Smeers, “The Gas Transmission Problem Solved by an Extension of the Simplex Algorithm,” Management Science, 46(11): 1454–1465, 2000. [16] J. Wang, Y. Sun, Z. Xu, J. Xiong, “Optimization Dispatch of Integrated Natural Gas and Electricity Energy System under the Mode of Electricity-Orientated,” in Proc. iSPEC 2019 - 2019 IEEE Sustainable Power and Energy Conference: Grid Modernization for Energy Revolution, Proceedings: 584–589, 2019. [17] A. Martinez-Mares, C. R. Fuerte-Esquivel, “A unified gas and power flow analysis in natural gas and electricity coupled networks,” IEEE Transactions on Power Systems, 27(4): 2156–2166, 2012. [18] S. D. Beigvand, H. Abdi, M. La Scala, “A general model for energy hub economic dispatch,” Applied Energy, 190: 1090–1111, 2017. [19] M. Batić, N. Tomašević, G. Beccuti, T. Demiray, S. Vraneš, “Combined energy hub optimisation and demand side management for buildings,” Energy and Buildings, 127: 229–241, 2016. [20] A. Dolatabadi, B. Mohammadi-Ivatloo, M. Abapour, S. Tohidi, “Optimal Stochastic Design of Wind Integrated Energy Hub,” IEEE Transactions on Industrial Informatics, 13(5): 2379–2388, 2017. [21] M. HojatyDana, M. AlizadehPahlavani, “Control‌-Strategies-for-Performance-Assessment-of-an- Autonomous Wind Energy Conversion System,” Journal of Electrical and Computer Engineering Innovations, 2(1): 15–20, 2014. [22] M. Schulze, L. Friedrich, M. Gautschi, “Modeling and optimization of renewables: Applying the energy hub approach,” 2008 IEEE International Conference on Sustainable Energy Technologies, ICSET 2008: 83–88, 2008. [23] M. Geidl, P. Favre-Perrod, B. Klöckl, G. Koeppel, “A greenfield approach for future power systems,” 41st International Conference on Large High Voltage Electric Systems 2006, CIGRE 2006, 2006. [24] P. Favre-Perrod, M. Geidl, B. Klöckl, G. Koeppel, “A vision of future energy networks,” in Proceedings of the Inaugural IEEE PES 2005 Conference and Exposition in Africa, 2005: 13–17, 2005. [25] A. Soroudi, T. Amraee, “Decision making under uncertainty in energy systems: State of the art,” Renewable and Sustainable Energy Reviews, 28: 376–384, 2013. [26] M. Nikzad, A. Samimi, “Integration of Optimal Time-of-Use Pricing in Stochastic Programming for Energy and Reserve Management in Smart Micro-grids,” Springer,2020. [27] R. Hemmati, H. Saboori, and P. Siano, “Coordinated short-term scheduling and long-term expansion planning in microgrids incorporating renewable energy resources and energy storage systems,” Energy, vol. 134, pp. 699–708, 2017. [28] J. M. Nahman, D. M. Perić, “Radial distribution network planning under uncertainty by applying different reliability cost models,” International Journal of Electrical Power and Energy Systems, 117: 105655, 2020. [29] S. J. Ben Christopher, M. Carolin Mabel, “A bio-inspired approach for probabilistic energy management of micro-grid incorporating uncertainty in statistical cost estimation,” Energy, 203: 117810, 2020. [30] S. Xie, Z. Hu, J. Wang, “Scenario-based comprehensive expansion planning model for a coupled transportation and active distribution system,” Applied Energy, 255: 113782, 2019. [31] N. Bazmohammadi, A. Anvari-Moghaddam, A. Tahsiri, A. Madary, J. C. Vasquez, J. M. Guerrero, “Stochastic Predictive Energy Management of Multi-Microgrid Systems,” Applied Sciences, 10(14): 4833, 2020. [32] Y. Zhang, F. Meng, R. Wang, B. Kazemtabrizi, J. Shi, “Uncertainty-resistant stochastic MPC approach for optimal operation of CHP microgrid,” Energy, 179: 1265–1278, 2019. [33] W. Liang, K. C. Li, J. Long, X. Kui, A. Y. Zomaya, “An Industrial Network Intrusion Detection Algorithm Based on Multifeature Data Clustering Optimization Model,” IEEE Transactions on Industrial Informatics, 16(3): 2063–2071, 2020. [34] X. Li, Y. Li, L. Liu, W. Wang, Y. Li, Y. Cao, “Latin Hypercube Sampling Method for Location Selection of Multi-Infeed HVDC System Terminal,” Energies, 13(7): 1646, 2020. [35] A. Tabandeh, A. Abdollahi, M. Rashidinejad, “Transmission Congestion Management Considering Uncertainty of Demand Response Resources’ Participation,” Journal of Electrical and Computer Engineering Innovations, 3(2): 77–88, 2015. [36] U. Mukherjee, S. Walker, A. Maroufmashat, M. Fowler, A. Elkamel, “Development of a pricing mechanism for valuing ancillary, transportation and environmental services offered by a power to gas energy system,” Energy, 28: 447–462, 2017. [37] A. Najafi-Ghalelou, S. Nojavan, K. Zare, B. Mohammadi-Ivatloo, “Robust scheduling of thermal, cooling and electrical hub energy system under market price uncertainty,” Applied Thermal Engineering, 149: 862–880, 2019. [38] U. Güvenç, B. Özkaya, H. Bakir, S. Duman, O. Bingöl, “Energy Hub Economic Dispatch by Symbiotic Organisms Search Algorithm,” in Lecture Notes on Data Engineering and Communications Technologies, 43: 375–385, 2020. [39] S. Galvani, S. Rezaeian Marjani, J. Morsali, M. Ahmadi Jirdehi, “A new approach for probabilistic harmonic load flow in distribution systems based on data clustering,” Electric Power Systems Research, 176: 105977, 2019. [40] A. Soroudi, “Power system optimization modeling in GAMS”. Springer, Cham, 2017. [41] IBM, “IBM ILOG CPLEX Optimization Studio”, 2012. [42] Y. Zhu, “Power System Loads and Power System Stability”. Springer, 2020. [43] H. Aalami, G. R. Yousefi, M. Parsa Moghadam, “Demand response model considering EDRP and TOU programs,” in Transmission and Distribution Exposition Conference: 2008 IEEE PES Powering Toward the Future, PIMS 2008, 2008. [44] R. Aazami, K. Aflaki, M. R. Haghifam, “A demand response based solution for LMP management in power markets,” International Journal of Electrical Power and Energy Systems, 33(5): 1125–1132, 2011. [45] S. Das, A. Abraham, A. Konar, “Automatic clustering using an improved differential evolution algorithm,” IEEE Transactions on Systems, Man, and Cybernetics Part A:Systems and Humans, 38(1): 218–237, 2008. [46] H. Hosseinnejad, S. Galvani, P. Alemi, “Optimal Probabilistic Scheduling of a Proposed EH Configuration Based on Metaheuristic Automatic Data Clustering,” IETE Journal of Research: 1–23, 2020. [47] Y. Zhu, D. Tian, F. Yan, “Effectiveness of Entropy Weight Method in Decision-Making,” Mathematical Problems in Engineering, 2020. [48] X. Li, K. Wang, L. Liuz, J. Xin, H. Yang, C. Gao, “Application of the entropy weight and TOPSIS method in safety evaluation of coal mines,” in Procedia Engineering, 26: 2085–2091, 2011.
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An Efficient Configuration for Energy Hub to Peak Reduction Considering Demand Response Using Metaheuristic Automatic Data Clustering