Abstract
The purpose of this research work is to solve the multi-objective optimal power flow (MO-OPF) problem using non-interactive approach. In this approach, the decision maker (DM) is not involved; however, the prior preference information is available to the DM. A satisficing function is offered to take care of the conflict between non-commensurable objectives, and the multi-objective problem is reformulated as a scalar optimization problem. This approach reduces the computation work involved for generating the Pareto front and for selecting the best satisficing solution. To attain the satisficing solutions, a hybrid optimization technique is applied, which integrates invasive weed optimization (IWO) with Powell’s pattern search (PPS) method. The IWO algorithm, utilized as the stochastic search technique, takes inspiration from the ability of weeds to adopt the environmental changes. Being a conjugate-based local search technique, the PPS method exhibits admirable exploitation search capability that further improves the solution provided by the IWO technique. The effectiveness of the proposed solution approach is confirmed by applying it to the three standard test systems, and the comparison is carried out with the well-established algorithms. Further, t test confirms the robustness of the proposed solution approach.
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References
Tinney WF, Domell HW (1968) Optimal power flow solutions. IEEE Trans Power Appar Syst 87(10):1866–1876
Lee KY, Park YM, Ortiz JL (1985) A united approach to optimal real and reactive power dispatch. IEEE Trans Power Appar Syst 5(5):42–43
Hughes A, Sun DI, Ashley B, Brewer B, Tinney WF (1984) Optimal power flow by Newton approach. IEEE Trans Power Appar Syst 103(10):2864–2880
Talukdar SN, Giras TC, Kalyan VK (1983) Decompositions for optimal power flows. IEEE Trans Power Appar Syst 102(12):3877–3884
Fahd G, Sheble GB (1992) Optimal power flow emulation of interchange brokerage systems using linear programming. IEEE Trans Power Syst 7(2):497–504
Reid GF, Hasdorff L (1973) Economic dispatch using quadratic programming. IEEE Power Eng Soc Winter Meet New York 92:2016–2023
Biswas PP, Suganthan PN, Mallipeddi R, Amaratunga GAJ (2018) Optimal power flow solutions using differential evolution algorithm integrated with effective constraint handling techniques. Eng Appl Artif Intell 68:81–100
Attia AF, El Sehiemy RA, Hasanien HM (2018) Optimal power flow solution in power systems using a novel Sine–Cosine algorithm. Int J Electr Power Energy Syst 99:331–343
El-Fergany AA, Hasanien HM (2018) Tree-seed algorithm for solving optimal power flow problem in large-scale power systems incorporating validations and comparisons. Appl Soft Comput J 64:307–316
Heidari AA, Abbaspour RA, Jordehi AR (2017) Gaussian bare-bones water cycle algorithm for optimal reactive power dispatch in electrical power systems. Appl Soft Comput J 57:657–671
Nguyen TT (2019) A high performance social spider optimization algorithm for optimal power flow solution with single objective optimization. Energy 171:218–240
Prasad D, Mukherjee V (2016) A novel symbiotic organisms search algorithm for optimal power flow of power system with FACTS devices. Eng Sci Technol Int J 19(1):79–89
Jordehi AR (2016) Optimal allocation of FACTS devices for static security enhancement in power systems via imperialistic competitive algorithm (ICA). Appl Soft Comput J 48:317–328
Mukherjee A, Mukherjee V (2016) Solution of optimal power flow with FACTS devices using a novel oppositional krill herd algorithm. Int J Electr Power Energy Syst 78:700–714
Packiasudha M, Suja S, Jerome J (2017) A new Cumulative Gravitational Search algorithm for optimal placement of FACT device to minimize system loss in the deregulated electrical power environment. Int J Electr Power Energy Syst 84:34–46
Soliman SAH, Mantawy AAH (2012) Optimal power flow. Modern optimization techniques with applications in electric power systems. Springer, New York, pp 281–346
Wolpert D, Macready W (1997) No free lunch theorems for optimization. IEEE Trans Evol Comput 1:67–82
Mehrabian AR, Lucas C (2006) A novel numerical optimization algorithm inspired from weed colonization. Ecol Inform 1(4):355–366
Saravanan B, Vasudevan ER, Kothari DP (2014) Unit commitment problem solution using invasive weed optimization algorithm. Int J Electr Power Energy Syst 55:21–28
Karimkashi S, Kishk AA (2010) Invasive weed optimization and its features in electromagnetics. IEEE Trans Antennas Propag 58(4):1269–1278
Ahmadi M, Mojallali H, Izadi-zamanabadi R (2012) State estimation of nonlinear stochastic systems using a novel meta-heuristic particle filter. Swarm Evol Comput 4:44–53
Roy S, Islam SM, Das S, Ghosh S (2013) Multimodal optimization by artificial weed colonies enhanced with localized group search optimizers. Appl Soft Comput J 13:27–46
Barisal AK, Prusty RC (2015) Large scale economic dispatch of power systems using oppositional invasive weed optimization. Appl Soft Comput J 29:122–137
Harman M, McMinn P (2010) A theoretical and empirical study of search-based testing: local, global and hybrid search. IEEE Trans Softw Eng 36:226–247
Narang N, Dhillon JS, Kothari DP (2012) Multiobjective fixed head hydrothermal scheduling using integrated predator-prey optimization and Powell search method. Energy 47(1):237–252
Narang N, Dhillon JS, Kothari DP (2014) Weight pattern evaluation for multiobjective hydrothermal generation scheduling using hybrid search technique. Int J Electr Power Energy Syst 62:665–678
Basu M (2011) Multi-objective optimal power flow with FACTS devices. Energy Convers Manag 52(2):903–910
Mohseni-Bonab SM, Rabiee A, Mohammadi-Ivatloo B, Jalilzadeh S, Nojavan S (2016) A two-point estimate method for uncertainty modeling in multi-objective optimal reactive power dispatch problem. Int J Electr Power Energy Syst 75:194–204
Rao BS, Vaisakh K (2014) Multi-objective adaptive clonal selection algorithm for solving optimal power flow considering multi-type FACTS devices and load uncertainty. Appl Soft Comput J 23:286–297
Zhang J, Tang Q, Li P, Deng D, Chen Y (2016) A modified MOEA/D approach to the solution of multi-objective optimal power flow problem. Appl Soft Comput J 47:494–514
Mohseni-Bonab SM, Rabiee A, Mohammadi-Ivatloo B (2016) Voltage stability constrained multi-objective optimal reactive power dispatch under load and wind power uncertainties: a stochastic approach. Renew Energy 85:598–609
Pulluri H, Naresh R, Sharma V (2017) An enhanced self-adaptive differential evolution based solution methodology for multiobjective optimal power flow. Appl Soft Comput J 54:229–245
Warid W, Hizam H, Mariun N, Abdul Wahab NI (2018) A novel quasi-oppositional modified Jaya algorithm for multi-objective optimal power flow solution. Appl Soft Comput J 65:360–373
Chen G, Yi X, Zhang Z, Wang H (2018) Applications of multi-objective dimension-based firefly algorithm to optimize the power losses, emission, and cost in power systems. Appl Soft Comput J 68:322–342
Xu K, Zhou J, Zhang Y, Gu R (2012) Differential evolution based on e-domination and orthogonal design method for power environmentally-friendly dispatch. Expert Syst Appl 39:3956–3963
Singh L, Dhillon JS, Chauhan RC (2006) Evaluation of best weight pattern for multiple criteria load dispatch. Electr Power Compon Syst 34:21–35
Kaya CY, Maurer H (2014) A numerical method for nonconvex multi-objective optimal control problems. Comput Optim Appl 57:685–702
Xiang Y, Zhou Y (2015) A dynamic multi-colony artificial bee colony algorithm for multi-objective optimization. Appl Soft Comput J 35:766–785
Yu K, Wang X, Wang Z (2015) Self-adaptive multi-objective teaching-learning-based optimization and its application in ethylene cracking furnace operation optimization. Chemom Intell Lab Syst 146:198–210
Zheng JH, Chen JJ, Wu QH, Jing ZX (2015) Multi-objective optimization and decision making for power dispatch of a large-scale integrated energy system with distributed DHCs embedded. Appl Energy 154:369–379
Shirazi A, Naja B, Aminyavari M, Rinaldi F, Taylor RA (2014) Thermal–economic–environmental analysis and multi-objective optimization of an ice thermal energy storage system for gas turbine cycle inlet air cooling. Energy 69:212–226
Shukla PK, Deb K (2007) On finding multiple Pareto-optimal solutions using classical and evolutionary generating methods. Eur J Oper Res 181:1630–1652
Lu L, Anderson-cook CM, Robinson V (2012) A case study to demonstrate a Pareto Frontier for selecting a best response surface design while simultaneously optimizing multiple criteria. Appl Stoch Model Bus Ind 3:85–96
Singh NJ, Dhillon JS, Kothari DP (2018) Non-interactive approach to solve multi-objective thermal power dispatch problem using composite search algorithm. Appl Soft Comput J 65:644–658
Kaur M, Narang N (2019) An integrated optimization technique for optimal power flow solution. Soft Comput. https://doi.org/10.1007/s00500-019-04590-3
Basu M (2008) Optimal power flow with FACTS devices using differential evolution. Int J Electr Power Energy Syst 30:150–156
Singh RP, Mukherjee V, Ghoshal SP (2015) Particle swarm optimization with an aging leader and challengers algorithm for optimal power flow problem with FACTS devices. Int J Electr Power Energy Syst 64:1185–1196
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Kaur, M., Narang, N. Non-interactive approach to solve multi-objective optimal power flow problem. Electr Eng 103, 167–182 (2021). https://doi.org/10.1007/s00202-020-01063-x
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DOI: https://doi.org/10.1007/s00202-020-01063-x