GA-based multi-objective optimization for distributed generations planning with DLMs in distribution power systems

Abstract In the present scenario of all over world, the planning of distributed generations (DGs) in distribution power systems are very important issues from power system performances viewpoints. The broad categories of different types of DGs on the basis of their power delivering characteristics are considered T 1 , T 2 , T 3 and T 4 with different load models (DLMs) for the analysis in this paper. This paper presents the impact assessment of optimally placed different types of DGs (such as T 1 , T 2 , T 3 and T 4 ) with DLMs by employing genetic algorithm (GA) in the distribution power systems (DPSs) form total minimum real power loss of the system viewpoint. Different DPS performance parameters such as minimization of real power loss, minimization of reactive power loss, improvement of voltage profile, reduction of the short circuit current or MVA line capacity and reduction of the environmental green house gases like carbon dioxide (CO 2 ), sulphur dioxide (SO 2 ), nitrogen oxide (NO x ) and particulate matters in emergency e.g. under fault, sudden change in field excitation of alternators or load increase in the distribution power system are considered. The contribution of the present work is to investigate the comparisons of different DGs with DLMs by excercizing GA in the distribution systems form minimum total real power loss of the system viewpoint. The effectiveness of the proposed methodology is tested on IEEE-37 bus distribution test system. The different types of DGs (such as T 1 , T 2 , T 3 and T 4 ) with DLMs have shown different behaviours for power system performance indices such as PLI , QLI , VDI , SCCI and EIRI viewpoints. The sequence of overall power system performance indices such as PLI , QLI , VDI , SCCI and EIRI are as follows: T 2 > T 1 > T 4 > T 3 . This paper presents that the overall performance of T 2 type DG is better as compared to T 1 , T 3 and T 4 types DGs in the distribution system form minimum real power loss of the system viewpoint.

[1]  Fabrizio Giulio Luca Pilo,et al.  Optimisation of embedded generation sizing and siting by using a double trade-off method , 2005 .

[2]  Ashwani Kumar,et al.  Congestion management with generic load model in hybrid electricity markets with FACTS devices , 2014 .

[3]  Ricardo de Araújo Kalid,et al.  Renewable energy generation for the rural electrification of isolated communities in the Amazon Region. , 2015 .

[4]  Javier Contreras,et al.  Location and contract pricing of distributed generation using a genetic algorithm , 2012 .

[5]  Suneet Singh,et al.  Optimal Sizing of Distributed Generation Placed on Radial Distribution Systems , 2010 .

[6]  Mahdi Raoofat,et al.  Simultaneous allocation of DGs and remote controllable switches in distribution networks considering multilevel load model , 2011 .

[7]  Frank Spellman,et al.  Handbook of Environmental Engineering , 2015 .

[8]  M. P. Selvan,et al.  Fuzzy Embedded Genetic Algorithm Method for Distributed Generation Planning , 2011 .

[9]  Ronnie Belmans,et al.  Distributed generation: definition, benefits and issues , 2005 .

[10]  Mohammad Valipour HYDRO-MODULE DETERMINATION FOR VANAEI VILLAGE IN ESLAM ABAD GHARB, IRAN , 2012 .

[11]  Sancho Salcedo-Sanz,et al.  Optimal discharge scheduling of energy storage systems in MicroGrids based on hyper-heuristics , 2015 .

[12]  Kyung Bin Song,et al.  Multiobjective distributed generation placement using fuzzy goal programming with genetic algorithm , 2008 .

[13]  Tarik Kousksou,et al.  Renewable energy potential and national policy directions for sustainable development in Morocco , 2015 .

[14]  M. Valipour Optimization of neural networks for precipitation analysis in a humid region to detect drought and wet year alarms , 2016 .

[15]  Mohammad Valipour,et al.  Calibration of mass transfer-based models to predict reference crop evapotranspiration , 2017, Applied Water Science.

[16]  Mehdi Ehsan,et al.  Efficient immune-GA method for DNOs in sizing and placement of distributed generation units , 2011 .

[17]  Mohammad Valipour,et al.  INCREASING IRRIGATION EFFICIENCY BY MANAGEMENT STRATEGIES: CUTBACK AND SURGE IRRIGATION , 2013 .

[18]  C. W. Taylor,et al.  Load representation for dynamic performance analysis , 1993 .

[19]  A. A. Abou El-Ela,et al.  Maximal optimal benefits of distributed generation using genetic algorithms , 2010 .

[20]  D. Singh,et al.  Multiobjective Optimization for DG Planning With Load Models , 2009, IEEE Transactions on Power Systems.

[21]  Kyu-Ho Kim,et al.  Dispersed generator placement using fuzzy-GA in distribution systems , 2002, IEEE Power Engineering Society Summer Meeting,.

[22]  Devender Singh,et al.  GA based energy loss minimization approach for optimal sizing & placement of distributed generation , 2008, Int. J. Knowl. Based Intell. Eng. Syst..

[23]  A. Pahwa,et al.  Effective Wind Farm Sizing Method for Weak Power Systems Using Critical Modes of Voltage Instability , 2012, IEEE Transactions on Power Systems.

[24]  S. Jadid,et al.  Load model effect assessment on optimal distributed generation (DG) sizing and allocation using improved harmony search algorithm , 2012, 2013 Smart Grid Conference (SGC).

[25]  Mohammad Ali Gholami Sefidkouhi,et al.  Estimation of reference evapotranspiration using multivariate fractional polynomial, Bayesian regression, and robust regression models in three arid environments , 2017, Applied Water Science.

[26]  Jen-Hao Teng,et al.  Value-based distributed generator placements for service quality improvements , 2007 .

[27]  R. K. Singh,et al.  Optimum Siting and Sizing of Distributed Generations in Radial and Networked Systems , 2009 .

[28]  G.P. Harrison,et al.  Centralized and Distributed Voltage Control: Impact on Distributed Generation Penetration , 2007, IEEE Transactions on Power Systems.

[29]  Asheesh K. Singh,et al.  Planning of different types of distributed generation with seasonal mixed load models , 2012 .

[30]  M. Valipour A Comparison between Horizontal and Vertical Drainage Systems (Include Pipe Drainage, Open Ditch Drainage, and Pumped Wells) in Anisotropic Soils , 2012 .

[31]  R. Ramakumar,et al.  An approach to quantify the technical benefits of distributed generation , 2004, IEEE Transactions on Energy Conversion.

[32]  M. Gandomkar,et al.  A Genetic–Based Tabu Search Algorithm for Optimal DG Allocation in Distribution Networks , 2005 .

[33]  Lennart Söder,et al.  Distributed generation : a definition , 2001 .

[34]  Carmen L. T. Borges,et al.  Optimal distributed generation allocation for reliability, losses, and voltage improvement , 2006 .

[35]  Ehab F. El-Saadany,et al.  DG allocation for benefit maximization in distribution networks , 2013, IEEE Transactions on Power Systems.

[36]  M. P. Selvan,et al.  Distributed Generation Planning: A New Approach Based on Goal Programming , 2012 .

[37]  F. Pilo,et al.  A multiobjective evolutionary algorithm for the sizing and siting of distributed generation , 2005, IEEE Transactions on Power Systems.

[38]  Swapan Kumar Goswami,et al.  Multi-objective Optimization of Distributed Generation Planning Using Impact Indices and Trade-off Technique , 2011 .

[39]  Pierluigi Siano,et al.  Hybrid GA and OPF evaluation of network capacity for distributed generation connections , 2008 .

[40]  Bindeshwar Singh,et al.  A survey on impact assessment of DG and FACTS controllers in power systems , 2015 .

[41]  Chanan Singh,et al.  Dispersed generation planning using improved Hereford ranch algorithm , 1998 .

[42]  Yue Yuan,et al.  Effect of load models on assessment of energy losses in distributed generation planning , 2011 .

[43]  Fabrizio Giulio Luca Pilo,et al.  Embedded Generation Planning under Uncertainty including Power Quality Issues , 2003 .

[44]  Swapan Kumar Goswami,et al.  Optimum allocation of distributed generations based on nodal pricing for profit, loss reduction, and voltage improvement including voltage rise issue , 2010 .

[45]  Ishak Aris,et al.  Effective method for optimal allocation of distributed generation units in meshed electric power systems , 2011 .

[46]  Mohammad Khajehzadeh,et al.  A Survey on Meta-Heuristic Global Optimization Algorithms , 2011 .