A review of tools, models and techniques for long-term assessment of distribution systems using OpenDSS and parallel computing

Gerardo Guerra; Juan A. Martinez-Velasco; Gerardo Guerra; Juan A. Martinez-Velasco

doi:10.3934/energy.2018.5.764

AIMS Energy

2018, Volume 6, Issue 5: 764-800. doi: 10.3934/energy.2018.5.764

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Review

A review of tools, models and techniques for long-term assessment of distribution systems using OpenDSS and parallel computing

Gerardo Guerra ,
Juan A. Martinez-Velasco ^,

Departament d'Enginyeria Electrica, Universitat Politecnica de Catalunya, Barcelona, Spain

Received: 10 June 2018 Accepted: 13 September 2018 Published: 25 September 2018

Many distribution system studies require long-term evaluations (e.g. for one year or more): Energy loss minimization, reliability assessment, or optimal rating of distributed energy resources should be based on long-term simulations of the distribution system. This paper summarizes the work carried out by the authors to perform long-term studies of large distribution systems using an OpenDSS-MATLAB environment and parallel computing. The paper details the tools, models, and procedures used by the authors in optimal allocation of distributed resources, reliability assessment of distribution systems with and without distributed generation, optimal rating of energy storage systems, or impact analysis of the solid state transformer. Since in most cases, the developed procedures were implemented for application in a multicore installation, a summary of capabilities required for parallel computing applications is also included. The approaches chosen for carrying out those studies used the traditional Monte Carlo method, clustering techniques or genetic algorithms. Custom-made models for application with OpenDSS were required in some studies: A summary of the characteristics of those models and their implementation are also included.
- clustering technique,
- distributed energy resource,
- distribution system,
- genetic algorithm, Modeling,
- Monte Carlo method,
- optimization,
- parallel computation,
- reliability
Citation: Gerardo Guerra, Juan A. Martinez-Velasco. A review of tools, models and techniques for long-term assessment of distribution systems using OpenDSS and parallel computing[J]. AIMS Energy, 2018, 6(5): 764-800. doi: 10.3934/energy.2018.5.764

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Abstract

Many distribution system studies require long-term evaluations (e.g. for one year or more): Energy loss minimization, reliability assessment, or optimal rating of distributed energy resources should be based on long-term simulations of the distribution system. This paper summarizes the work carried out by the authors to perform long-term studies of large distribution systems using an OpenDSS-MATLAB environment and parallel computing. The paper details the tools, models, and procedures used by the authors in optimal allocation of distributed resources, reliability assessment of distribution systems with and without distributed generation, optimal rating of energy storage systems, or impact analysis of the solid state transformer. Since in most cases, the developed procedures were implemented for application in a multicore installation, a summary of capabilities required for parallel computing applications is also included. The approaches chosen for carrying out those studies used the traditional Monte Carlo method, clustering techniques or genetic algorithms. Custom-made models for application with OpenDSS were required in some studies: A summary of the characteristics of those models and their implementation are also included.

References

[1]	Dugan RC, Arritt RF, Mcdermott TE, et al. (2010) Distribution system analysis to support the smart grid. Power Energy Soc Gen Meet 2010: 1–8.
[2]	Martinez JA, Martin-Arnedo J (2011) Tools for analysis and design of distributed resources-part Ⅰ: Tools for feasibility studies. IEEE T Power Delivery 26: 1643–1652.
[3]	Martinez JA, León FD, Dinavahi V (2010) Simulation tools for analysis of distribution systems with distributed resources. Present and future trends. Power Energy Soc Gen Meet 39: 1–7.
[4]	Ackermann T, Andersson G, Söder L (2001) Distributed generation: A definition. Electr Power Syst Res 57: 195–204.
[5]	Farret FA, Godoy Simões M (2006) Integration of Alternative Sources of Energy. John Wiley.
[6]	Sandia National Laboratories and NRECA (2015) DOE/EPRI Electricity Storage Handbook.
[7]	New York Independent System Operator (2014) A Review of Distributed Energy Resources.
[8]	Carrasco JM, Franquelo LG, Bialasiewicz JT, et al. (2006) Power-electronic systems for the grid integration of renewable energy sources: A survey. IEEE T Ind Electr 53: 1002–1016.
[9]	Arritt RF, Dugan RC (2011) Distribution system analysis and the future smart grid. IEEE T Ind Appl 47: 2343–2350.
[10]	Taylor J, Smith JW, Dugan RC (2012) Distribution modeling requirements for integration of PV, PEV, and storage in a smart grid environment. Power Energy Soc Gen Meet 2011: 1–6.
[11]	Martinez JA, Guerra G (2012) Optimum placement of distributed generation in three-phase distribution systems with time varying load using a Monte Carlo approach. IEEE Power Energy Soc Gen Meet 2012: 1–7.
[12]	Guerra G, Martinez JA (2014) A Monte Carlo method for optimum placement of photovoltaic generation using a multicore computing environment. Pes Gen Meet Conf Expo 2014: 1–5.
[13]	Martinez JA, Guerra G (2014) A parallel Monte Carlo method for optimum allocation of distributed generation. IEEE T Power Syst 29: 2926–2933.
[14]	Martínez-Velasco JA, Guerra G (2015) Analysis of large distribution networks with distributed energy resources. Ingeniare 23: 594–608.
[15]	Guerra G, Martinez-Velasco JA (2016) Optimum allocation of distributed generation in multi-feeder systems using long term evaluation and assuming voltage-dependent loads. Sust Energy Grids Netw 5: 13–26.
[16]	Martinez-Velasco JA, Guerra GA (2016) Allocation of distributed generation for maximum reduction of energy losses in distribution systems, In: Energy Management of Distributed Generation Systems (L. Mihet-Popa, ed.), ch. 05. InTech.
[17]	Martinez JA, Guerra G (2013) A Monte Carlo approach for distribution reliability assessment considering time varying loads and system reconfiguration. Power Energy Soc Gen Meet 23: 1–5.
[18]	Martinez-Velasco JA, Guerra G (2014) Parallel Monte Carlo approach for distribution reliability assessment. IET Gen Trans Dis 8: 1810–1819.
[19]	Martinez-Velasco JA, Guerra G (2016) Reliability analysis of distribution systems with photovoltaic generation using a power flow simulator and a parallel Monte Carlo approach. Energies 9: 537.
[20]	Guerra G, Martinez-Velasco JA (2018) Optimal sizing and operation of energy storage systems considering long term assessment. AIMS Energy 6: 70–96.
[21]	Guerra G, Martinez-Velasco JA (2017) A virtual power plant model for time-driven power flow calculations. AIMS Energy 5: 887–911.
[22]	Guerra G, Martinez-Velasco JA (2017) Evaluation of MATPOWER and OpenDSS load flow calculations in power systems using parallel computing. J Eng 2017: 195–204.
[23]	Guerra G, Martinez-Velasco JA (2017) A solid state transformer model for power flow calculations. Int J Electr Power Energy Syst 89: 40–51.
[24]	Dugan RC (2016) Reference Guide. The Open Distribution System Simulator (OpenDSS). EPRI.
[25]	Dugan RC, Mcdermott TE (2011) An open source platform for collaborating on smart grid research. Power Energy Soc Gen Meet 5: 1–7.
[26]	Kersting WH (2007) Distribution System Modeling and Analysis. CRC Press.
[27]	Chen T, Chen M, Inoue T, et al. (1991) Three-phase cogenerator and transformer models for distribution system analysis. IEEE T Power Delivery 6: 1671–1681.
[28]	Daran ME, Staton EA (1997) Distribution transformer models for branch current based feeder analysis. IEEE T Power Syst 12: 698–703.
[29]	Kersting WH, Phillips WH, Carr W (1999) A new approach to modeling three-phase transformer connections. IEEE Trans Ind Appl 35: 169–175.
[30]	Xiao P, Yu DC, Yan W (2006) A unified three-phase transformer model for distribution load flow calculations. IEEE T Power Syst 21: 153–159.
[31]	Peralta JA, de León F, Mahseredjian J (2008) Unbalanced multiphase load-flow using a positive-sequence load-flow program. IEEE T Power Syst 23: 469–476.
[32]	Kersting WH (2009) The modeling and application of step voltage regulators. Power Syst Conf Expo 2009: 1–8.
[33]	Cheng CS, Shirmohainmadi D (1995) A three-phase power flow method for real-time distribution system analysis. IEEE T Power Syst 10: 671–679.
[34]	Garcia PAN, Pereira JLR, Carneiro S (2001) Voltage control devices models for distribution power flow analysis. IEEE T Power Syst 16: 586–594.
[35]	Hatziargyriou ND, Karakatsanis TS, Papadopoulos M (1993) Probabilistic load flow in distribution systems containing dispersed wind power generation. IEEE T Power Syst 8: 159–165.
[36]	Feijóo AE, Cidrás J (2000) Modeling of wind farms in the load flow analysis. IEEE T Power Syst 15: 110–115.
[37]	Divya KC, Rao PSN (2006) Models for wind turbine generating systems and their application in load flow studies. Electr Power Syst Res 76: 844–856.
[38]	Eminoglu U (2009) Modeling and application of wind turbine generating system (WTGS) to distribution systems. Renew Energy 34: 2474–2483.
[39]	Caramia P, Carpinelli G, Pagano M, et al. (2007) Probabilistic three-phase load flow for unbalanced electrical distribution systems with wind farms. IET Renew Power Gener 1: 115–122.
[40]	Teng J (2008) Modelling distributed generations in three-phase distribution load flow. IET Gener Trans Distrib 2: 330–340.
[41]	RETScreen International, Clean Energy Decision Support Centre, 2005. Clean Energy Project Analysis, RETScreen Engineering and Case Textbook. Minister of Natural Resources Canada.
[42]	Manwell JF, Rogers A, Hayman G, et al. (1998) Hybrid2—A Hybrid System Simulation Model, Theory Manual. University of Massachusetts.
[43]	Vlachogiannis JG (2009) Probabilistic constrained load flow considering integration of wind power generation and electric vehicles. IEEE T Power Syst 24: 1808–1817.
[44]	Feijóo AE, Cidrás J, Dornelas JLG (1999) Wind speed simulation in wind farms for steady-state security assessment of electrical power systems. IEEE T Energy Convers 14: 1582–1588.
[45]	Papaefthymiou G, Schavemaker PH, Sluis LVD, et al. (2006) Integration of stochastic generation in power systems. Int J Electr Power Energy Syst 28: 655–667.
[46]	Papaefthymiou G, Kurowicka D (2009) Using copulas for modeling stochastic dependence in power system uncertainty analysis. IEEE T Power Syst 24: 40–49.
[47]	Duffie JA, Beckman WA (1991) Solar Engineering of Thermal Processes. John Wiley.
[48]	Graham VA, Hollands KGT (1990) A method to generate synthetic hourly solar radiation globally. Sol Energy 44: 333–341.
[49]	Conti S, Raiti S (2007) Probabilistic load flow using Monte Carlo techniques for distribution networks with photovoltaic generators. Sol Energy 81: 1473–1481.
[50]	Price WW, Taylor CW, Rogers GJ, et al. (1995) Standard load models for power flow and dynamic performance simulation. IEEE T Power Syst 10: 1302–1313.
[51]	Caramia P, Carpinelli G, Varilone P, et al. (1999) Probabilistic three-phase load flow. Int J Electr Power Energy Syst 21: 55–69.
[52]	Heunis SW, Herman R (2002) A probabilistic model for residential consumer loads. IEEE T Power Syst 17: 621–625.
[53]	Lu N, Chassin DP (2004) A state-queueing model of thermostatically controlled appliances. IEEE T Power Syst 19: 1666–1673.
[54]	Lu N, Chassin DP, Widergren SE (2005) Modeling uncertainties in aggregated thermostatically controlled loads using a state queueing model. IEEE T Power Syst 20: 725–733.
[55]	Schneider KP, Fuller JC (2010) Detailed end use load modeling for distribution system analysis. Power Energy Soc Gen Meet 2010: 1–7.
[56]	Garcia-Valle R, Vlachogiannis JG (2009) Electric vehicle demand model for load flow studies. Electr Power Comp Syst 37: 577–582.
[57]	Kondoh J, Ishii I, Yamaguchi H, et al. (2000) Electrical energy storage systems for energy networks. Energy Convers Manage 41: 1863–1874.
[58]	Ibrahim H, Ilinca A, Perron J (2008) Energy storage systems-characteristics and comparisons. Renew Sust Energy Rev 12: 1221–1250.
[59]	Divya KC, Østergaard J (2009) Battery energy storage technology for power systems-an overview. Electr Power Syst Res 79: 511–520.
[60]	Barton JP, Infield DG (2004) Energy storage and its use with intermittent renewable sources. IEEE T Energy Convers 19: 441–448.
[61]	Korpaas M, Holen AT, Hildrum R (2003) Operation and sizing of energy storage for wind power plants in a market system. Int J Electr Power Energy Syst 25: 599–606.
[62]	Klöckl B, Stricker P, Koeppel G (2005) On the properties of stochastic power sources in combination with local energy storage. In CIGRE Symposium on Power Systems with Dispersed Generation, Athens, 1–4.
[63]	Koeppel G, Korpaas M (2006) Using storage devices for compensating uncertainties caused by non-dispatchable generators. Int Conf Probab Methods 2006: 1–8.
[64]	IEEE Recommended Practice for Protection and Coordination of Industrial and Commercial Power Systems, IEEE Standard 242-2001.
[65]	Thurner L, Scheidler A, Schäfer F, et al. (2017) Pandapower documentation. Fraunhofer IWES.
[66]	Dugan RC, OpenDSS Tech notes, 2018. Available from: https://sourceforge.net/p/electricdss/code/HEAD/tree/trunk/Distrib/Doc/TechNotes.
[67]	Benmouyal G, Meisinger M, Burnworth J, et al. (1999) IEEE Standard Inverse-Time Characteristic Equations for Overcurrent Relays. IEEE T Power Deliver 14: 868–872.
[68]	Gönen T (2008) Electric Power Distribution System Engineering, 2nd Edition, CRC Press.
[69]	The MathWorks Inc. MATLAB 9.2 (R2017a). The MathWorks Inc., 2017.
[70]	Grama A, Gupta A, Karypis G, et al. (2003) Introduction to Parallel Computing, 2nd Edition, Pearson Education.
[71]	Boston University. MATLAB Parallel Computing Toolbox Tutorial. Available from: http://www.bu.edu/tech/support/research/training-consulting/online-tutorials/matlab-pct.
[72]	Kepner J (2009) Parallel MATLAB for Multicore and Multinode Computers. SIAM.
[73]	Moler C (2007) Parallel MATLAB: Multiple processors and multiple cores. The MathWorks News & Notes.
[74]	Luszczek P (2008) Enhancing multicore system performance using parallel computing with MATLAB. MATLAB Digest 17.
[75]	Buehren M (2007) MATLAB Library for Parallel Processing on Multiple Cores. Available from: http://www.mathworks.com.
[76]	Intel Math Kernel Library for Windows OS (Developer Guide). Available from: https://software.intel.com/en-us/intel-mkl-support/documentation.
[77]	Schenk O, Gärtner K, Fichtner W (2000) Efficient sparse LU factorization with left-right looking strategy on shared memory multiprocessors. BIT Num Math 40: 158–176.
[78]	Intel Math Kernel Library (Developer Reference). Available from: https://software.intel.com/en-us/intel-mkl-support/documentation.
[79]	Magnano L, Boland JW (2007) Generation of synthetic sequences of electricity demand: Application in South Australia. Energy 32: 2230–2243.
[80]	Carapellucci R, Giordano L (2013) A methodology for the synthetic generation of hourly wind speed time series based on some known aggregate input data. Appl Energy 101: 541–550.
[81]	Homer Energy. HOMER Pro. Available from: https://www.homerenergy.com.
[82]	Passino KM (2005) Biomimicry for Optimization, Control, and Automation. Springer Science & Business Media.
[83]	Michalewicz Z (1992) Genetic Algorithms + Data Structures = Evolution Programs. Springer.
[84]	Yeh EC, Venkata SS, Sumic Z (1995) Improved distribution system planning using computational evolution. Power Syst IEEE Trans 11: 668–674.
[85]	Konfrst Z (2004) Parallel genetic algorithms: Advances, computing trends, applications and perspectives. Int Parallel Distrib Process 2004: 162.
[86]	Chicco G, Napoli R, Postolache P, et al. (2003) Customer characterization options for improving the tariff offer. IEEE T Power Syst 18: 381–387.
[87]	Eisen MB, Spellman PT, Brown PO, et al. (1998) Cluster analysis and display of genome-wide expression patterns. P Natl Acad Sci USA 95: 14863–14868.
[88]	Theodoridis S, Koutroumbas K (2008) Pattern Recognition. IEEE T Neural Network 19: 376.
[89]	KLUSolve. Available from: https://sourceforge.net/projects/klusolve.
[90]	Caliñski T, Harabasz J (1974) A dendrite method for cluster analysis. Commun Stat 3: 1–27.
[91]	Montenegro D, Dugan RC, Reno MJ (2017) Open source tools for high performance quasi-static time-series simulation using parallel processing. In IEEE Photovoltaic Specialists Conference (PVSC).
[92]	OpenDSS-G. Available from: https://sourceforge.net/projects/dssimpc.

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