i-manager Publications

i-manager's Journal on Power Systems Engineering (JPS)

Current Issue Vol. 11 Issue 3

Voltage Stability and Power Loss Minimization with STATCOM using Particle Swarm Optimization and Hybrid Firefly Particle Swarm Optimization Algorithms
Load Frequency Control of Multi Area Power System Network with Soft Computing Algorithm Based Controller and Coordinated Strategy of TCSC and Battery Energy Storage
Performance Analysis of Distribution System under Different Loading Conditions using Test Bench
Simulation of Solar PV with Different Control System of Boost Converter for Composite Climate
Enhancement of System Performance using PeSche Scheduling Algorithm on Multiprocessors

Most Cited

Volume 5 Issue 1 February - April 2017

Research Paper

Effect of System Topology on Wind Generators Penetration Maximizationwith TCSCs Insertion

Almoataz Y. Abdelaziz* , Metwally A. El-Sharkawy, Mahmoud Abdallah Attia*

* Professor, Department of Electrical Power Engineering, Ain Shams University, Cairo, Egypt.

Emeritus Professor, Department of Electric Power Engineering, Ain Shams University Cairo, Egypt.

* Department of Electrical Power and Machines Department, Faculty of Engineering, Ain Shams University, Cairo, Egypt.

Abdelaziz, Y. A., El-Sharkawy, A. M., and Attia, A. M. (2017). Effect of System Topology on Wind Generators Penetration Maximization with TCSCs Insertion. i-manager’s Journal on Power Systems Engineering, 5(1), 1-9. https://doi.org/10.26634/jps.5.1.13532

Abstract

Wind generation penetration maximizing is an important issue in power systems. These increasing in wind penetration may badly affect the power system stability. In this paper, Thyristor-Controlled Series Compensation (TCSCs) devices are used to help wind penetration maximizing and also for studying the effect of system topology in that. Cases studied are carried out on modified IEEE 39 bus system with variable speed wind generator penetration increases by 50%, 100%, 150% and 170%. In each case of wind penetration increases, three cases in the system are tried, system without change, system with reduction of the impedance of the lines connected to wind generator by 50%, and change the location of wind generator to another location has more interconnection. Another case study is carried out on modified IEEE 39 bus system with fixed speed wind generator penetration increases by 50%, 100% and 150% respective of its generation. The system suffers from some violation in its parameters. Results found that series TCSCs devices in certain range are able to maximize wind penetration in power systems without violation in its indices.

References

[1]. Abdelaziz, A. Y., El-Sharkawy, M. A., & Attia, M. A. (2011). Optimal location of thyristor-controlled series compensators in power systems for increasing loadability by genetic algorithm. Electric Power Components and Systems, 39(13), 1373-1387.

[2]. Abdelaziz, A. Y., El-Sharkawy, M. A., & Attia, M. A. (2013). Comparison of series and shunt FACTS to improve the performance of power system with wind penetration. International Journal of Advances in Power Systems, 1(2), 2335-1772.

[3]. Abdelaziz, A. Y., El-Sharkawy, M. A., & Attia, M. A. (2014, December). Optimal Location of FACTS Devices to Reduce Wind Variation Effect on Power System. Proceedings of the Sixteenth International Middle East Power Systems Conference, Ain Shams University, Cairo, Egypt, (MEPCON'14).

[4]. Abdelaziz, A. Y., El-Sharkawy, M. A., & Attia, M. A. (2015). Optimal location of thyristor-controlled series compensation and static VAR compensator to enhance steady-state performance of power system with wind penetration. Electric Power Components and Systems, 43(18), 1999-2009.

[5]. Abdelaziz, A. Y., El-Sharkawy, M. A., Attia, M. A., & El- Saadany, E. F. (2014, July). Optimal location of series FACTS to improve the performance of power system with wind penetration. In PES General Meeting| Conference & Exposition, 2014 IEEE (pp. 1-5). IEEE.

[6]. Abdelaziz, A. Y., El-Sharkawy, M. A., Attia, M. A., & Panigrahi, B. K. (2012, December). Genetic algorithm based approach for optimal allocation of TCSC for power system loadability enhancement. In International Conference on Swarm, Evolutionary, and Memetic Computing (pp. 548-557). Springer, Berlin, Heidelberg.

[7]. Adamczyk, A., Teodorescu, R., Mukerjee, R. N., & Rodriguez, P. (2010, July). Overview of FACTS devices for wind power plants directly connected to the transmission network. In Industrial Electronics (ISIE), 2010 IEEE International Symposium on (pp. 3742-3748). IEEE.

[8]. Altin, M., Göksu, Ö., Teodorescu, R., Rodriguez, P., Jensen, B. B., & Helle, L. (2010, May). Overview of recent grid codes for wind power integration. In Optimization of th Electrical and Electronic Equipment (OPTIM), 2010 12 International Conference on (pp. 1152-1160). IEEE.

[9]. Bagnall, T., Ritter, C., Ronner, B., Maibach, P., Butcher, N., & Thurnherr, T. (2009). PCS6000 STATCOM ancillary functions: Wind park resonance damping. EWEC 2009.

[10]. Baran, M. E., Teleke, S., Anderson, L., Huang, A., Bhattacharya, S., & Atcitty, S. (2008, July). STATCOM with energy storage for smoothing intermittent wind farm power. In Power and Energy Society General Meetingwind Conversion and Delivery of Electrical Energy in the 21st Century, 2008 IEEE (pp. 1-6). IEEE.

[11]. Chondrogiannis, S., Barnes, M., Osborne, M., Yao, L., Bazargan, M., & Johnson, A. (2007). Grid compliant AC connection of large offshore wind farms using a STATCOM. In EWEC.

[12]. Cvetkovic, M., & Ilic, M. (2012, July). Energy-based transient stabilization using facts in systems with wind power. In Power and Energy Society General Meeting, 2012 IEEE (pp. 1-7). IEEE.

[13]. EPRI, E. D. (2003). Handbook of Energy Storage for Transmission and Distribution Applications. EPRI & US Department of Energy: Palo Alto, CA.

[14]. Gerbex, S., Cherkaoui, R., & Germond, A. J. (2001). Optimal location of multi-type FACTS devices in a power system by means of genetic algorithms. IEEE Transactions on Power Systems, 16(3), 537-544.

[15]. Han, C., Huang, A. Q., Baran, M. E., Bhattacharya, S., Litzenberger, W., Anderson, L., et al. (2008). STATCOM impact study on the integration of a large wind farm into a weak loop power system. IEEE Transactions on Energy Conversion, 23(1), 226-233.

[16]. Jayam, A. P., & Chowdhury, B. H. (2009, March). Improving the dynamic performance of wind farms with STATCOM. In Power Systems Conference and Exposition, 2009. PSCE'09. IEEE/PES (pp. 1-8). IEEE.

[17]. Khatib, A. R. A. (2002). Internet-based wide area measurement applications in deregulated power systems (Doctoral dissertation in Electrical and Computer Engineering, Faculty of the Virginia Polytechnic and State University, Blacksburg, Virginia.)

[18]. Meikandasivam, S., Nema, R. K., & Jain, S. K. (2008). Behavioral Study of TCSC Device–A MATLAB/Simulink Implementation. World Academy of Science, Engineering and Technology, International Journal of Electrical, Computer, Energetic, Electronic and Communication Engineering, 2(9), 1958-1963.

[19]. Panda, R., Kumar Satpathy, P., & Paul, S. (2011). Application of SVC to mitigate voltage instability in a Wind system connected with GRID. International Journal of Power System Operation and Energy Management (IJPSOEM), 1, 90-96.

[20]. Qi, L., Langston, J., & Steurer, M. (2008, July). Applying a STATCOM for stability improvement to an existing wind farm with fixed-speed induction generators. In Power and Energy Society General Meetingst Conversion and Delivery of Electrical Energy in the 21 Century, 2008 IEEE (pp. 1-6). IEEE.

[21]. Qiao, W., Harley, R. G., & Venayagamoorthy, G. K. (2009a). Coordinated reactive power control of a large wind farm and a STATCOM using heuristic dynamic programming. IEEE Transactions on Energy Conversion, 24(2), 493-503.

[22]. Qiao, W., Venayagamoorthy, G. K., & Harley, R. G. (2009b). Real-time implementation of a STATCOM on a wind farm equipped with doubly fed induction generators. IEEE Transactions on Industry Applications, 45(1), 98-107.

[23]. Saravanan, M., Slochanal, S. M. R., Venkatesh, P., & Abraham, J. P. S. (2007). Application of particle swarm optimization technique for optimal location of FACTS devices considering cost of installation and system loadability. Electric Power Systems Research, 77(3), 276- 283.

[24]. Visiers, M., Mendoza, J., Búnez, J., González, F., Contreras, A., Molina, S., & Agudo, A. (2007, September). WINDFACT®, a solution for the grid code compliance of the windfarms in operation. In Power Electronics and Applications, 2007 European Conference on (pp. 1-9). IEEE.

[25]. Zhang, X. P., Rehtanz, C., & Pal, B. (2006). Flexible AC Transmission Systems: Modelling and Control. Springer Science & Business Media.

[26]. Zimmerman, R. D., Murillo-Sánchez, C. E., & Gan, D. (1997). MATPOWER: A MATLAB power system simulation package. Manual, Power Systems Engineering Research Center, Ithaca NY, 1.

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Research Paper

A Novel Loss Distribution Strategy after Removing Slack Bus using ImprovedKinetic Gas Molecules Optimization Algorithm

D. Srilatha* , Sivanagaraju Sirigiri**

* Associate Professor, Department of Electrical and Electronics Engineering, Prakasam Engineering College, Kandukur, A.P., India.

** Professor, Department of Electrical and Electronics Engineering, University College of Engineering, JNTU Kakinada, Kakinada, A.P., India.

Srilatha, D., and Sivanagaraju, S. (2017). A Novel Loss Distribution Strategy after Removing Slack Bus using Improved Kinetic Gas Molecules Optimization Algorithm. i-manager’s Journal on Power Systems Engineering, 5(1), 10-25. https://doi.org/10.26634/jps.5.1.13533

Abstract

The modern power system analysis, operation and control use pneumatic solution methodologies to solve more realistic power system problems to enhance the operational aspects of power systems. The conventional load flow methods work independent of economic and environmental aspects and allocates total power losses to slack bus which increases the burden on this generator. In this paper, a novel methodology to remove extra generation by the slack and to optimize the system operation in economic and environmental aspects is presented. Further, a new hybrid optimization algorithm namely Improved Kinetic Gas Molecules Optimization (IKGMO) is presented to solve Optimal Power Flow (OPF) problem while satisfying system equality and inequality constraints. The effectiveness of optimization over the conventional load flow in loss allocation is tested on standard IEEE-14 bus and IEEE-30 bus test systems with supporting numerical and graphical results.

References

[1]. Alvarado, F. L. (1978). Penalty factors from Newton's method. IEEE Transactions on Power Apparatus and Systems, 6, 2031-2040.

[2]. Banerjee, S., Maity, D., & Chanda, C. K. (2015). Teaching learning based optimization for economic load dispatch problem considering valve point loading effect. International Journal of Electrical Power & Energy Systems, 73, 456-464.

[3]. Basu, M. (2015). Modified particle swarm optimization for nonconvex economic dispatch problems. International Journal of Electrical Power & Energy Systems, 69, 304-312.

[4]. Christie, R. (1993). Power Systems Test Case Archive: 30 Bus Power Flow Test Case. Retrieved from http://www.ee.washington.edu/research/pstca/pf30/pg_ tca30bus.htm (accessed 04-Mar-2013).

[5]. Coelho, L. D. S., Bora, T. C., & Mariani, V. C. (2014). Differential evolution based on truncated Lévy-type flights and population diversity measure to solve economic load dispatch problems. International Journal of Electrical Power and Energy Systems, 57(1), 178-188.

[6]. Duman, S., Güvenç, U., Sönmez, Y., & Yörükeren, N. (2012). Optimal power flow using gravitational search algorithm. Energy Conversion and Management, 59, 86- 95.

[7]. Eberhart, R. C., & Shi, Y. (1998, March). Comparison between genetic algorithms and particle swarm optimization. In International Conference on Evolutionary Programming (pp. 611-616). Springer, Berlin, Heidelberg.

[8]. Elsayed, W. T., & El-Saadany, E. F. (2015). A fully decentralized approach for solving the economic dispatch problem. IEEE Transactions on Power Systems, 30(4), 2179-2189.

[9]. Hazarika, D., & Bordoloi, P. K. (1991, March). Modified loss coefficients in the determination of optimum generation scheduling. In IEE Proceedings C (Generation, Transmission and Distribution) (Vol. 138, No. 2, pp. 166- 172). IET Digital Library.

[10]. Jang, G. S., Hur, D., Park, J. K., & Lee, S. H. (2005). A modified power flow analysis to remove a slack bus with a sense of economic load dispatch. Electric Power Systems Research, 73(2), 137-142.

[11]. Jiang, A., & Ertem, S. (1995). Polynomial loss models for economic dispatch and error estimation. IEEE Transactions on Power Systems, 10(3), 1546-1552.

[12]. Kumari, M. S., & Maheswarapu, S. (2010). Enhanced genetic algorithm based computation technique for multi-objective optimal power flow solution. International Journal of Electrical Power & Energy Systems, 32(6), 736- 742.

[13]. Moein, S., & Logeswaran, R. (2014). KGMO: A swarm optimization algorithm based on the kinetic energy of gas molecules. Information Sciences, 275, 127-144.

[14]. Niknam, T., Narimani, M. R., Aghaei, J., & Azizipanah-Abarghooee, R. (2012). Improved particle swarm optimisation for multi-objective optimal power flow considering the cost, loss, emission and voltage stability index. IET Generation, Transmission & Distribution, 6(6), 515-527.

[15]. Niknam, T., Narimani, M. R., Jabbari, M., & Malekpour, A. R. (2011). A modified shuffle frog leaping algorithm for multi-objective optimal power flow. Energy, 36(11), 6420-6432.

[16]. Okamura, M., O-ura, Y., Hayashi, S., Uemura, K., & Ishiguro, F. (1975). A new power flow model and solution method?Including load and generator characteristics and effects of system control devices. IEEE Transactions on Power Apparatus and Systems, 94(3), 1042-1050.

[17]. Park, Y. M., Lee, J. H., & Newton, A. (1993). Raphson load flow considering frequency characteristics, Korean Inst. Electrical Eng, 42, 85-93.

[18]. Power Systems Test Case Archive. Retrieved from https://www2.ee.washington.edu/research/pstca/pf14/p g_tca14bus.htm

[19]. Sayah, S., & Zehar, K. (2008). Modified differential evolution algorithm for optimal power flow with nonsmooth cost functions. Energy Conversion and Management, 49(11), 3036-3042.

[20]. Shin, J. R., & Yim, H. S. (1993, October). An extended approach for NR load flow with power loss correction method. In TENCON'93 Proceedings. Computer, Communication, Control and Power Engineering. 1993 IEEE Region 10 Conference on (Vol. 5, pp. 402-405). IEEE.

[21]. Sinha, N., Chakrabarti, R., & Chattopadhyay, P. K. (2003). Evolutionary programming techniques for economic load dispatch. IEEE Transactions on Evolutionary Computation, 7(1), 83-94.

[22]. Subbaraj, P., & Rajnarayanan, P. N. (2009). Optimal reactive power dispatch using self-adaptive real coded genetic algorithm. Electric Power Systems Research, 79(2), 374-381.

[23]. Zhu, Y., Wang, J., & Qu, B. (2014). Multi-objective economic emission dispatch considering wind power using evolutionary algorithm based on decomposition. International Journal of Electrical Power & Energy Systems, 63, 434-445.

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Research Paper

Optimization of Hybrid Renewable Energy Systems for Sustainable andEconomical Power Supply at SVCET Chittoor

Suresh Vendoti* , 0, R. Kiranmayi*

* Research Scholar, Department of Electrical and Electronics Engineering, SVCET, Chittoor, India.

Director and Professor, Department of Electrical and Electronics Engineering, SVCET, Chittoor, India.

* Professor and Head, Department of Electrical and Electronics Engineering, JNTUA, Anantapuramu, India.

Vendoti, S., Muralidhar, M. and Kiranmayi, R. (2017). Optimization of Hybrid Renewable Energy Systems for Sustainable and Economical Power Supply at SVCET Chittoor. i-manager’s Journal on Power Systems Engineering, 1(1), 26-34. https://doi.org/10.26634/jps.5.1.13534

Abstract

Nowadays, the rising energy demand and the limited reserves of the conventional sources have raised the concerns of the researchers all around the globe to look for alternative resources. In addition to the increasing demand concerns, the environmental concerns have led to the emergence of green power technologies in the energy sector. Green power technologies include the power generation from clean and inexhaustible sources of solar energy, wind energy, hydro, biomass, etc. The main point of consideration, while designing the hybrid system, is the sizing of different components.

In this paper, a methodology has been developed for optimal planning of hybrid PV-Wind system with some battery backup. The local solar radiation, wind data and components database from different manufacturers are analyzed and simulated in Hybrid Optimization Model for Electric Renewable (HOMER) to assess the technical and economic viability of the integrated system. The system architecture, includes SUKAM PV System 200 KW, 10 number of Generic 10 kW Wind Generators, POWERICA Diesel Generator 250 KW, Battery Trojan IND17-6V model (6 V, 7.61kwh), and Converter 30 KW. This paper gives the design idea of optimized PV-Solar and Wind Hybrid Energy System for Sri Venkateswara College of Engineering and Technology (SVCET) at Chittoor, Electrical load over conventional electrical grid system for a particular site in South India (Chittoor). For this hybrid system, the meteorological data of Solar Insolation, hourly wind speed, are taken for Chittoor -South India (Latitude 13ο16.3'N and Longitude 79ο7.1'E) and the pattern of load consumption of SVCET electrical load data are studied and suitably modeled for optimization of the hybrid energy system using HOMER Pro 3.8.5 Software. This system is more cost effective and environmental friendly over the conventional grid system.

References

[1]. Babaei, A., Gholizadeh, B., Khaliliaqdam, N., & Gilanipour, J. (2013). Optimizing and economical assessment of the utilization of photovoltaic systems in residential buildings: the case of Sari station, northern Iran. International Journal of Agriculture: Research and Review, 3(1), 65-71.

[2]. Fulzele, J. B., & Dutt, S. (2012). Optimium planning of hybrid renewable energy system using HOMER. International Journal of Electrical and Computer Engineering, 2(1), 68.

[3]. Giraud, F., & Salameh, Z. M. (2001). Steady-state performance of a grid-connected rooftop hybrid windphotovoltaic power system with battery storage. IEEE Transactions on Energy Conversion, 16(1), 1-7.

[4]. Huang, R., Low, S. H., Topcu, U., Chandy, K. M., & Clarke, C. R. (2011, October). Optimal design of hybrid energy system with PV/wind turbine/storage: A case study. In Smart Grid Communications (SmartGridComm), 2011 IEEE International Conference on (pp. 511-516). IEEE.

[5]. Kanase-Patil, A. B., Saini, R. P., & Sharma, M. P. (2010). Integrated renewable energy systems for off grid rural electrification of remote area. Renewable Energy, 35(6), 1342-1349.

[6]. Kord, H., & Rohani, A. (2009, November). An integrated hybrid power supply for off-grid applications th fed by wind/photovoltaic/fuel cell energy systems. In 24 International Power System Conference (PSC), Iran.

[7]. Lal, D. K., Dash, B. B., & Akella, A. K. (2011). Optimization of PV/wind/micro-hydro/diesel hybrid power system in HOMER for the study area. International Journal on Electrical Engineering and Informatics, 3(3), 307.

[8]. NASA Surface Meteorology and Solar Energy. Retrieved from http://www.nasa.gov

[9]. National Renewable Energy Laboratory. Retrieved from http://www.nrel.gov/international/tools/HOMER/ homer.html

[10]. Price list. List of portable Generator. Retrieved from http://www.pricelist.co.in/index.php/office/generators/po rtable-generators

[11]. Shaahid, S. M., & Elhadidy, M. A. (2008). Economic analysis of hybrid photovoltaic–diesel–battery power systems for residential loads in hot regions—A step to clean future. Renewable and Sustainable Energy Reviews, 12(2), 488-503.

[12]. Sukam Power System Pvt. Lt., India. Retrieved from http://www.su-kam.com

[13]. Urjakart.com. Retrieved from http://www.urjakart. com/solar

[14]. Vuc, G., Borlea, I., Barbulescu, C., Prostean, O., Jigoria-Oprea, D., & Neaga, L. (2011, March). Optimal energy mix for a grid connected hybrid wind-Photovoltaic generation system. In Exploitation of Renewable Energy Sources (EXPRES), 2011 IEEE 3rd International Symposium on (pp. 129-132). IEEE.

[15]. Wang, L., & Singh, C. (2007, April). PSO-based multicriteria optimum design of a grid-connected hybrid power system with multiple renewable sources of energy. In Swarm Intelligence Symposium, 2007. SIS 2007. IEEE (pp. 250-257). IEEE.

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Review Paper

Computation of Available Transfer Capability in ElectricalTransmission System - A Review

Manjula S. Sureban* , Shekhappa G. Ankaliki **

* Research Scholar, Department of Electrical and Electronics Engineering, Visvesvaraya Technological University, Belgaum, India.

** Professor, Department of Electrical and Electronics Engineering, SDM College of Engineering and Technology, Dharwad, India.

Sureban, S. M., and Ankaliki, S. G. (2017). Computation of Available Transfer Capability in Electrical Transmission System - A Review. i-manager’s Journal on Power Systems Engineering, 5(1), 35-39. https://doi.org/10.26634/jps.5.1.13535

Abstract

The increasing demand for electricity in the recent decades had resulted in several problems in the normal operation of the power system. The transmission lines are forced to operate near their limits; critical components are going out of service and also the efficiency of transmission lines is reducing. To address all these issues, evaluation of transfer capability of transmission lines is important. Many deterministic and probabilistic methods are available for evaluation of transfer capability of transmission lines. On computing the available transfer capability of power system, the efficiency of the transmission lines can be evaluated and congestion management in the system is also possible. By knowing the efficiency of existing transmission lines, necessary steps can be initiated to improve the same and hence to operate the entire system more reliably and economically. This paper aims at discussing the fundamentals of ATC computation and reviewing various technical papers related to the same.

References

[1]. Computation of Availability Transmission Capability. Retrieved from http://shodhganga.inflibnet.ac.in/ bitstream/10603/2277/12/12_chapter%204.pdf

[2]. Docslide.net. Computation of A vailability Transmission Capability . Retrieved from http://docslide.net/documents/12-chapter-4.html

[3]. Ejebe, G. C., Waight, J. G., Sanots-Nieto, M., & Tinney, W. F. (2000). Fast calculation of linear Available Transfer Capability. IEEE Transactions on Power Systems, 15(3), 1112-1116.

[4]. Gao, Y., Zhou, M., & Yang, J. (2010, June). Available T r a n s f e r C a p a b i l i t y c a l c u l a t i o n b a s e d o n contingencyselection. In Industrial Electronics and Applications (ICIEA), 2010 the 5 IEEE Conference on (pp. 868-873). IEEE.

[5]. Hamoud, G. (2000). Assessment of available transfer capability of transmission systems. IEEE Transactions on Power Systems, 15(1), 27-32.

[6]. Hojabri, M., & Hizam, H. (2011). Available Transfer Capability Calculation. In Applications of MATLAB in Science and Engineering. InTech.

7]. Kumar, A., & Kumar, M. (2013). Determination of Available Transfer Capability and its enhancement in Competitive Electrical Market. International Journal of Information and Computation Technology, 3(11), 1171- 1176.

[8]. Li, G., Sun, Y., & Li, X. (2009, April). Available Transfer Capability calculation considering amended thermal limits. In Sustainable Power Generation and Supply, 2009. SUPERGEN'09. International Conference on (pp. 1-7). IEEE.

[9]. Manohar, J. N., & Amarnath, J. (2012). Statistical analysis of power system on enhancement of available transfer capability-applying FACTS. Int. J. Multidiscip. Sci. Eng, 3(7), 33-37.

[10]. Ou, Y., & Singh, C. (2002). Assessment of available transfer capability and margins. IEEE Transactions on Power Systems, 17(2), 463-468.

[11]. Selvi, V. A. I., Karuppasamypandiyan, M., Narmathabanu, R., & Devaraj, D. (2014, January). Artificial neural network approach for on-line ATC estimation in deregulated power system. In Power Signals Control and Computations (EPSCICON), 2014 International Conference on (pp. 1-5). IEEE.

[12]. Šošic, D., & Škokljev, I. (2013). Evolutionary algorithm for calculating available Transfer Capability. Journal of Electrical Engineering, 64(5), 1-7.

[13]. Tong, X., Wu, F. F., & Qi, L. (2008). Available Transfer Capability calculation using a smoothing pointwise maximum function. IEEE Transactions on Circuits and Systems I: Regular Papers, 55(1), 462-474.

[14]. Xiao, Y., Song, Y. H., Liu, C. C., & Sun, Y. Z. (2003). Available Transfer Capability enhancement using FACTS devices. IEEE Transactions on Power Systems, 18(1), 305- 312.

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Survey Paper

A Survey on Demand Side Management In Electrical Power Systems

Ahmed M. Ibrahim* , Mahmoud Abdallah Attia, Mahmoud M. Othman*, Almoataz Y. Abdelaziz****

* Electrical Design Engineer, Engineering Consultants Group, Cairo, Egypt.

Department of Electrical Power and Machines, Faculty of Engineering, Ain Shams University, Cairo, Egypt.

* Assistant Professor, Department of Electrical Power Engineering, Ain Shams University, Cairo, Egypt.

**** Professor, Department of Electrical Power Engineering, Ain Shams University, Cairo, Egypt.

Ibrahim, M. A., Attia, A. M., Othman, M. M., and Abdelaziz, Y. A. (2017). A Survey on Demand Side Management In Electrical Power Systems. i-manager’s Journal on Power Systems Engineering, 5(1), 40-50. https://doi.org/10.26634/jps.5.1.13536

Abstract

This paper presents a survey on Demand Side Management techniques used to reduce the energy waste, postpone the construction of new plants, reduce costs or electricity bill, and reduce total power demand during peak demand periods. One of the major goals of DSM is reducing consumption during peak hours and shifting load to off-peak hours. Several algorithms and techniques for load shifting have been reported in researches. Different approaches have been suggested to solve the demand response problem using linear and dynamic programming techniques. There are different types of optimization techniques as Genetic Algorithm (GA), Particle Swarm Optimization (PSO), Ant Colony Optimization (ACO), Evolutionary Algorithm (EA), Game theoretic techniques and others. Researches are performed on different optimization techniques to reduce peak load demand, reduce operational cost, reduce PAR (Peak to Average Ratio), and reduce the discrepancy between power supply and demand.

References

[1]. Agneessens, J., Vandoorn, T., Meersman, B., & Vandevelde, L. (2011). The use of binary particle swarm optimization to obtain a demand side management system. In IET Conference on Renewable Power Generation (RPG 2011).

[2]. Arai, R., Yamamoto, K., & Morikura, M. (2013, October). Differential game-theoretic framework for a demand-side energy management system. In Smart Grid Communications (SmartGridComm), 2013 IEEE International Conference on (pp. 768-773). IEEE.

[3]. Atzeni, I., Ordóñez, L. G., Scutari, G., Palomar, D. P., & Fonollosa, J. R. (2013). Demand-side management via distributed energy generation and storage optimization. IEEE Transactions on Smart Grid, 4(2), 866-876.

[4]. Awais, M., Javaid, N., Shaheen, N., Iqbal, Z., Rehman, G., Muhammad, K., & Ahmad, I. (2015, September). An efficient genetic algorithm based demand side management scheme for smart grid. In Network-Based Information Systems (NBiS), 2015 18 International Conference on (pp. 351-356). IEEE.

[5]. Bai, Q. (2010). Analysis of particle swarm optimization algorithm. Computer and Information Science, 3(1), 180.

[6]. Baykasoglu, A., Özbakir, L., & Tapkan, P. (2007). Artificial bee colony algorithm and its application to generalized assignment problem. In Swarm intelligence, Focus on Ant and Particle Swarm Optimization. InTech.

[7]. Bertsimas, D., & Sim, M. (2003). Robust discrete optimization and network flows. Mathematical Programming, 98(1), 49-71.

[8]. Bertsimas, D., & Sim, M. (2004). The price of robustness. Operations Research, 52(1), 35-53.

[9]. Bu, S., Yu, F. R., & Liu, P. X. (2011, October). A gametheoretical decision-making scheme for electricity retailers in the smart grid with demand-side management. In Smart Grid Communications (SmartGridComm), 2011 IEEE International Conference on (pp. 387-391). IEEE.

[10]. Caron, S., & Kesidis, G. (2010, October). Incentivebased energy consumption scheduling algorithms for the smart grid. In Smart grid communications (SmartGridComm), 2010 First IEEE International Conference on (pp. 391-396). IEEE.

[11]. Chen, C., Kishore, S., & Snyder, L. V. (2011, May). An innovative RTP-based residential power scheduling scheme for smart grids. In Acoustics, Speech and Signal Processing (ICASSP), 2011 IEEE International Conference on (pp. 5956-5959). IEEE.

[12]. Chen, J., Yang, B., & Guan, X. (2012, November). Optimal demand response scheduling with stackelberg game approach under load uncertainty for smart grid. In Smart Grid Communications (SmartGridComm), 2012 IEEE Third International Conference on (pp. 546-551). IEEE.

[13]. Chen, L., Li, N., Low, S. H., & Doyle, J. C. (2010, October). Two market models for demand response in power networks. In Smart Grid Communications (SmartGridComm), 2010 First IEEE International Conference on (pp. 397-402). IEEE.

[14]. Chen, Z., Wu, L., & Fu, Y. (2012). Real-time pricebased demand response management for residential appliances via stochastic optimization and robust optimization. IEEE Transactions on Smart Grid, 3(4), 1822- 1831.

[15]. Conejo, A. J., Morales, J. M., & Baringo, L. (2010). Real-time demand response model. IEEE Transactions on Smart Grid, 1(3), 236-242.

[16]. Diamantoulakis, P. D., Pappi, K. N., Kong, P. Y., & Karagiannidis, G. K. (2016, September). Game Theoretic Approach to Demand Side Management in Smart Grid with User-Dependent Acceptance Prices. In Vehicular Technology Conference (VTC-Fall), 2016 IEEE 84^th (pp. 1- 5). IEEE.

[17]. Fahrioglu, M., & Alvarado, F. L. (1999, January). Designing cost effective demand management contracts using game theory. In Power Engineering Society 1999 Winter Meeting, IEEE (Vol. 1, pp. 427-432). IEEE.

[18]. Gatsis, N., & Giannakis, G. B. (2011, March). Cooperative multi-residence demand response scheduling. In Information Sciences and Systems (CISS), 2011 45 Annual Conference on (pp. 1-6). IEEE.

[19]. Ghorbaniparvar, M., Li, X., & Zhou, N. (2015, December). Demand side management with a human behavior model for energy cost optimization in smart grids. In Signal and Information Processing (GlobalSIP), 2015 IEEE Global Conference on (pp. 503-507). IEEE.

[20]. Gungor, V. C., Sahin, D., Kocak, T., Ergut, S., Buccella, C., Cecati, C., & Hancke, G. P. (2011). Smart grid technologies: Communication technologies and standards. IEEE Transactions on Industrial Informatics, 7(4), 529-539.

[21]. Gupta, I., Anandini, G. N., & Gupta, M. (2016, December). An hour wise device scheduling approach for demand side management in smart grid using particle swarm optimization. In Power Systems Conference (NPSC), 2016 National (pp. 1-6). IEEE.

[22]. Holland, J. H. (1992). Adaptation in natural and artificial systems. 1975. Ann Arbor, MI: University of Michigan Press and.

[23]. Hu, X. M., Zhan, Z. H., Lin, Y., Gong, Y. J., Yu, W. J., Hu, Y. X., & Zhang, J. (2013, April). Multiobjective genetic algorithm for demand side management of smart grid. In Computational Intelligence in Scheduling (SCIS), 2013 IEEE Symposium on (pp. 14-21). IEEE.

[24]. Hussain, B. (2016, October). Demand side management for smart homes in Pakistan. In Emerging Technologies (ICET), 2016 International Conference on (pp. 1-6). IEEE.

[25]. Jond, H. B., Benveniste, R., & Nabiyev, V. V. (2015, April). Peak load appliance scheduling for demand-side management in the future smart grids. In Smart Grid Congress and Fair (ICSG), 2015 3^rd International Istanbul (pp. 1-4). IEEE.

[26]. Karaboga, D. (2005). An Idea Based on Honey Bee Swarm for Numerical Optimization (Vol. 200). Technical Report-Tr06, Erciyes University, Engineering Faculty, Computer Engineering Department.

[27]. Karaboga, D., & Basturk, B. (2007). A powerful and efficient algorithm for numerical function optimization: artificial bee colony (ABC) algorithm. Journal of global optimization, 39(3), 459-471.

[28]. Karaboga, D., & Basturk, B. (2007). Artificial bee colony (ABC) optimization algorithm for solving constrained optimization problems. Foundations of Fuzzy Logic and Soft Computing, 789-798.

[29]. Kennedy, J., & Eberhart, R. (1995). Particle swarm optimization. In Neural Networks, 1995. Proceedings., IEEE International Conference on (Vol. 4, pp. 1942-1948). IEEE.

[30]. Kim, T. T., & Poor, H. V. (2011). Scheduling power consumption with price uncertainty. IEEE Transactions on Smart Grid, 2(3), 519-527.

[31]. Kinhekar, N., Padhy, N. P., & Gupta, H. O. (2013, July). Demand side management for residential consumers. In Power and Energy Society General Meeting (PES), 2013 IEEE (pp. 1-5). IEEE.

[32]. Kouvelis, P., & Yu, G. (1997). Robust Discrete Optimization and its Applications. Kluwer Academic Publishers, Norwell, MA, 1997.

[33]. Kuo, H. H., Pradhan, S. K., Wu, C. L., Cheng, P. H., Xie, Y., & Fu, L. C. (2016, August). Dynamic demand-side management with user's privacy concern in residential community. In Automation Science and Engineering (CASE), 2016 IEEE International Conference on (pp. 1094- 1099). IEEE.

[34]. Kurucz, C. N., Brandt, D., & Sim, S. (1996). A linear programming model for reducing system peak through customer load control programs. IEEE Transactions on Power Systems, 11(4), 1817-1824.

[35]. Li, C., Yu, X., Yu, W., Chen, G., & Wang, J. (2017). Efficient computation for sparse load shifting in demand side management. IEEE Transactions on Smart Grid, 8(1), 250-261.

[36]. Li, N., Chen, L., & Low, S. H. (2011, July). Optimal demand response based on utility maximization in power networks. In Power and Energy Society General Meeting, 2011 IEEE (pp. 1-8). IEEE.

[37]. Li, Q., Su, Y., Nie, W., & Tan, M. (2016, December). Robust unit commitment with high penetration of renewable energy based on demand response and power consumption contract of electrical vehicles. In Electrical Engineering (ISEE), 2016 International Symposium on (pp. 1-6). IEEE.

[38]. Logenthiran, T., Srinivasan, D., & Phyu, E. (2015, November). Particle swarm optimization for demand side management in smart grid. In Innovative Smart Grid Technologies-Asia (ISGT ASIA), 2015 IEEE (pp. 1-6). IEEE.

[39]. Logenthiran, T., Srinivasan, D., & Shun, T. Z. (2012). Demand side management in smart grid using heuristic optimization. IEEE transactions on smart grid, 3(3), 1244- 1252.

[40]. Maniezzo, A. C. M. D. V. (1992). Distributed optimization by ant colonies. In Toward a practice of autonomous systems: proceedings of the First European Conference on Artificial Life (p. 134). MIT Press.

[41]. Margaret, V., & Rao, K. U. (2015, November). Demand response for residential loads using artificial bee colony algorithm to minimize energy cost. In TENCON 2015-2015 IEEE Region 10 Conference (pp. 1-5). IEEE.

[42]. Marzband, M., Azarinejadian, F., Savaghebi, M., & Guerrero, J. M. (2015). An optimal energy management system for islanded microgrids based on multiperiod artificial bee colony combined with Markov chain. IEEE Systems Journal.

[43]. Mauser, I., Dorscheid, M., Allerding, F., & Schmeck, H. (2014, July). Encodings for evolutionary algorithms in smart buildings with energy management systems. In Evolutionary Computation (CEC), 2014 IEEE Congress on (pp. 2361-2366). IEEE.

[44]. Mediwaththe, C. P., & Smith, D. B. (2016, June). Game-theoretic demand-side management robust to non-ideal consumer behavior in smart grid. In Industrial Electronics (ISIE), 2016 IEEE 25 International Symposium on (pp. 702-707). IEEE.

[45]. Meng, F. L., & Zeng, X. J. (2014, July). An optimal realtime pricing for demand-side management: A Stackelberg game and genetic algorithm approach. In Neural Networks (IJCNN), 2014 International Joint Conference on (pp. 1703-1710). IEEE.

[46]. Miorandi, D., & De Pellegrini, F. (2012, October). Demand-side management in smart grids: An evolutionary games perspective. In Performance Evaluation Methodologies and Tools (VALUETOOLS), 2012 6^th International Conference on (pp. 178-187). IEEE.

[47]. Moghadam, A. Z., Saebi, J., & Bayaz, H. J. D. (2015, November). Stochastic optimization of demand response aggregators in wholesale electricity markets. In Power System Conference (PSC), 2015 30^th International (pp. 234-240). IEEE.

[48]. Mohsenian-Rad, A. H., Wong, V. W., Jatskevich, J., Schober, R., & Leon-Garcia, A. (2010). Autonomous demand-side management based on game-theoretic energy consumption scheduling for the future smart grid. IEEE Transactions on Smart Grid, 1(3), 320-331.

[49]. Nayak, S. K., Sahoo, N. C., & Panda, G. (2015, October). Demand side management of residential loads in a smart grid using 2D particle swarm optimization technique. In Power, Communication and Information Technology Conference (PCITC), 2015 IEEE (pp. 201-206). IEEE.

[50]. Ng, K. H., & Sheble, G. B. (1998). Direct load control-A profit- based load management using linear programming. IEEE Transactions on Power Systems, 13(2), 688-694.

[51]. Pedrasa, M. A. A., Spooner, T. D., & MacGill, I. F. (2009). Scheduling of demand side resources using binary particle swarm optimization. IEEE Transactions on Power Systems, 24(3), 1173-1181.

[52]. Polaki, S. K., Reza, M., & Roy, D. S. (2015, June). A genetic algorithm for optimal power scheduling for residential energy management. In Environment and Electrical Engineering (EEEIC), 2015 IEEE 15^th International Conference on (pp. 2061-2065). IEEE.

[53]. Rahim, S., Iqbal, Z., Shaheen, N., Khan, Z. A., Qasim, U., Khan, S. A., & Javaid, N. (2016, March). Ant Colony Optimization based Energy Management Controller for Smart Grid. In Advanced Information Networking and Applications (AINA), 2016 IEEE 30 International Conference on (pp. 1154-1159). IEEE.

[54]. Reka, S. S., & Ramesh, V. (2016, January). Demand response scheme with electricity market prices for residential sector using stochastic dynamic optimization. In Power and Energy Systems: Towards Sustainable Energy (PESTSE), 2016 Biennial International Conference on (pp. 1-6). IEEE.

[55]. Saad, W., Han, Z., Poor, H. V., & Basar, T. (2012). Game-theoretic methods for the smart grid: An overview of microgrid systems, demand-side management, and smart grid communications. IEEE Signal Processing Magazine, 29(5), 86-105.

[56]. Samadi, P., Mohsenian-Rad, A. H., Schober, R., Wong, V. W., & Jatskevich, J. (2010, October). Optimal real-time pricing algorithm based on utility maximization for smart grid. In Smart Grid Communications (SmartGridComm), 2010 First IEEE International Conference on (pp. 415-420). IEEE.

[57]. Silva, A., Marinheiro, J., Cardoso, H. L., & Oliveira, E. (2015, October). Demand-Side Management in Power Grids: An Ant Colony Optimization Approach. In Computational Science and Engineering (CSE), 2015 IEEE 18 International Conference on (pp. 300-306). IEEE.

[58]. Sinha, A., Neogi, S., Lahiri, R. N., Chowdhury, S., Chowdhury, S. P., & Chakraborty, N. (2011, July). Role of demand side management for power distribution utility in India. In Power and Energy Society General Meeting, 2011 IEEE (pp. 1-8). IEEE.

[59]. Soliman, H. M., & Leon-Garcia, A. (2014). Gametheoretic demand-side management with storage devices for the future smart grid. IEEE Transactions on Smart Grid, 5(3), 1475-1485.

[60]. Storn, R., & Price, K. (1997). Differential evolution–a simple and efficient heuristic for global optimization over continuous spaces. Journal of Global Optimization, 11(4), 341-359.

[61]. Verma, P. P., Soumya, P., & Swamp, K. S. (2016, March). Optimal day-ahead scheduling in smart grid with Demand Side Management. In Power Systems (ICPS), 2016 IEEE 6 International Conference on (pp. 1-6). IEEE.

[62]. Vidal, A. R., Jacobs, L. A., & Batista, L. S. (2014, December). An evolutionary approach for the demand side management optimization in smart grid. In Computational Intelligence Applications in Smart Grid (CIASG), 2014 IEEE Symposium on (pp. 1-7). IEEE.

[63]. Yao, E., Samadi, P., Wong, V. W., & Schober, R. (2016). Residential demand side management under high penetration of rooftop photovoltaic units. IEEE Transactions on Smart Grid, 7(3), 1597-1608.

[64]. Ye, F., Qian, Y., & Hu, R. Q. (2016). A real-time information based demand-side management system in smart grid. IEEE Transactions on Parallel and Distributed Systems, 27(2), 329-339.

[65]. Zazo, J., Zazo, S., & Macua, S. V. (2017). Robust Worst- Case Analysis of Demand-Side Management in Smart Grids. IEEE Transactions on Smart Grid, 8(2), 662-673.

[66]. Zhang, C., Xu, Y., Dong, Z. Y., & Wong, K. P. (2017). Robust Coordination of Distributed Generation and Price- Based Demand Response in Microgrids. IEEE Transactions on Smart Grid.

[67]. Zhang, J., Fuller, J. D., & Elhedhli, S. (2010). A stochastic programming model for a day-ahead electricity market with real-time reserve shortage pricing. IEEE Transactions on Power Systems, 25(2), 703-713.

[68]. Zhang, Y., Zeng, P., & Zang, C. (2015, June). Optimization algorithm for home energy management system based on artificial bee colony in smart grid. In Cyber Technology in Automation, Control, and Intelligent Systems (CYBER), 2015 IEEE International Conference on (pp. 734-740). IEEE.

[69]. Zhang, Y., Zeng, P., Wang, Y., Zhu, B., & Kuang, F. (2012, October). Linear weighted gbest-guided artificial bee colony algorithm. In Computational Intelligence and Design (ISCID), 2012 Fifth International Symposium on (Vol. 2, pp. 155-159). IEEE.

[70]. Zhong, J., Kang, C., & Liu, K. (2010, July). Demand side management in China. In Power and Energy Society General Meeting, 2010 IEEE (pp. 1-4). IEEE.

[71]. Zhu, G., & Kwong, S. (2010). Gbest-guided artificial bee colony algorithm for numerical function optimization. Applied Mathematics and Computation, 217(7), 3166- 3173.

[72]. Zhu, Z., Tang, J., Lambotharan, S., Chin, W. H., & Fan, Z. (2012, January). An integer linear programming based optimization for home demand-side management in smart grid. In Innovative Smart Grid Technologies (ISGT), 2012 IEEE PES (pp. 1-5). IEEE.

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