Your browser does not support JavaScript!
http://iet.metastore.ingenta.com
1887

access icon free Minimisation of active power losses and number of control adjustments in the optimal reactive dispatch problem

© The Institution of Engineering and Technology
Received 25/09/2017, Accepted 25/03/2018, Revised 19/02/2018, Published 28/03/2018

Trade-offs between power system's optimal operational performance and the minimal number of control adjustments necessary to attain a desired operating point make optimal reactive dispatch (ORD) solutions practical to system operators. In this study, a multi-objective ORD model that provides, in terms of weighting factors, trade-offs between minimal active power losses in transmission systems and minimal number of control adjustments in generator voltages, tap ratios and shunt controls is featured. This multi-objective ORD is formulated as a mixed-integer non-linear programming (MINLP) problem, and the proposed resolution methodology is based on translating the original MINLP problem into non-linear programming (NLP) problem deploying a sigmoid function, enabling the use of NLP solvers. Both original MINLP and translated NLP models are implemented in GAMS and numerical tests with IEEE test-systems with up to 300 buses are conducted using DICOPT, KNITRO and CONOPT solvers to validate the proposed ORD model and its resolution methodology. Results demonstrate the relation between active power losses and the number of adjustments in control variables, which is valuable information for operation planning. Another fundamental result is the high computational performance of the method when compared to specialized MINLP solvers.

Inspec keywords: numerical analysis; load dispatching; power transmission control; power control; voltage control; minimisation; integer programming; nonlinear programming; on load tap changers; power transmission planning

Other keywords: GAMS modelling; KNITRO solver; original MINLP problem; multiobjective ORD model; on-load tap-changer transformer; reactive shunt control; operation planning; optimal reactive dispatch problem; CONOPT solver; transmission system; DICOPT solver; sigmoid function; active power loss minimisation; nonlinear programming

Subjects: Optimisation techniques; Voltage control; Power system control; Power system planning and layout; Power transmission, distribution and supply; Optimisation techniques; Transformers and reactors; Power and energy control; Control of electric power systems; Power system management, operation and economics

References

    1. 1)
      • 4. Sun, D.I., Ashley, B., Brewer, B., et al: ‘Optimal power flow by Newton approach’, IEEE Trans. Power Appar. Syst., 1984, PAS-103, (10), pp. 2864–2880.
    2. 2)
      • 25. Drud, S.A.: ‘Conopt’, Math. Program., 1985, 31, pp. 153–191.
    3. 3)
      • 11. Frank, S., Steponavice, I., Rebemmack, S.: ‘Optimal power flow: a bibliographic survey II – non-deterministic and hybrid methods’, Energy Syst., 2012, 3, pp. 259–289.
    4. 4)
      • 13. Tinney, W.F., Bright, J.M., Demaree, K.D., et al: ‘Some deficiencies in optimal power flow’, IEEE Trans. Power Syst., 1988, 3, (2), pp. 676–683.
    5. 5)
      • 26. Drud, S.A.: ‘A large-scale GRG code’, ORSA J. Comput., 1992, 6, pp. 207–216.
    6. 6)
      • 14. Capitanescu, F., Wehenkel, L.: ‘Redispatching active and reactive powers using a limited number of control actions’, IEEE Trans. Power Syst., 2011, 26, pp. 1221–1230.
    7. 7)
      • 15. Capitanescu, F.: ‘Critical review of recent advances and further developments needed in AC optimal power flow’, Electr. Power Syst. Res., 2016, 136, pp. 57–68.
    8. 8)
      • 27. IBM: IBM ILOG CPLEX Optimizer, IBM, available at http://www-01.ibm.com/software/commerce/optimization/cplex-optimizer/, 2015, cplex 12.6.0.1.
    9. 9)
      • 21. de Oliveira, E.J., da Silva, I.C.Jr., Pereira, J.L.R., et al: ‘Transmission system expansion planning using a sigmoid function to handle integer investment variables’, IEEE Trans. Power Syst., 2005, 20, (3), pp. 1616–1621.
    10. 10)
      • 3. Sasson, A.M., Viloria, F., Aboytes, F.: ‘Optimal load flow solution using the Hessian matrix’, IEEE Trans. Power Appar. Syst., 1973, PAS-92, (1), pp. 31–41.
    11. 11)
      • 6. Lavaei, J., Low, S.H.: ‘Zero duality gap in optimal power flow problem’, IEEE Trans. Power Syst., 2012, 27, (1), pp. 92–107.
    12. 12)
      • 24. Waltz, R.A., Plantenga, T.D.: ‘Knitro user's manual’ (Ziena Optimization, Inc., 2010), version 7.0. Available at http://www.ziena.com.
    13. 13)
      • 19. Miettinen, K.: ‘Nonlinear multiobjective optimization’ (Kluwer Academic Publishers, Boston, MA, USA, 1999, 1st edn.).
    14. 14)
      • 16. Azmy, A.M.: ‘Optimal power flow to manage voltage profiles in interconnected networks using expert systems’, IEEE Trans. Power Syst., 2007, 22, (4), pp. 1622–1628.
    15. 15)
      • 20. Murray, W., Ng, K.M.: ‘An algorithm for nonlinear optimization problems with binary variables’, Comput. Optim. Appl., 2010, 47, (2), pp. 257–288.
    16. 16)
      • 23. Viswanathan, J., Grossmann, I.E.: ‘Discrete and continuous optimizer’, Engineering Design Research Center (EDRC) at Carnegie Mellon University, 1990.
    17. 17)
      • 2. Dommel, H.W., Tinney, W.F.: ‘Optimal power flow solutions’, IEEE Trans. Power Appar. Syst., 1968, PAS-87, (10), pp. 1866–1876.
    18. 18)
      • 10. Frank, S., Steponavice, I., Rebemmack, S.: ‘Optimal power flow: a bibliographic survey I – formulations and deterministic methods’, Energy Syst., 2012, 3, pp. 221–258.
    19. 19)
      • 29. Mazzini, A.P.: ‘Development of continuous and discrete optimization strategies to problems of optimal power flow’. PhD thesis, São Carlos School of Engineering, University of São Paulo, 2016.
    20. 20)
      • 7. Quintana, V.H., Torres, G.L., Medina-Palomo, J.: ‘Interior-point methods and their applications to power systems: a classification of publications and software codes’, IEEE Trans. Power Syst., 2000, 15, (1), pp. 170–176.
    21. 21)
      • 1. Carpentier, J.L.: ‘Contribution á létude du dispatching économique’, Bull. Soc. Fran. Elec., 1962, 3, (8), pp. 431–447.
    22. 22)
      • 22. GAMS: ‘General algebraic modeling systems’, Gams distribution 24.0, 2013.
    23. 23)
      • 18. Liu, W.H.E., Gupa, X.: ‘Fuzzy constrait enforcement and control actions curtailment in an optimal power flow’, IEEE Trans. Power Syst., 1996, 11, pp. 639–645.
    24. 24)
      • 28. Quesada, I., Grossmann, I.E.: ‘An LP/NLP based branch and bound algorithm for convex MINLP optimization problems’, Comput. Chem. Eng., 1992, 16, pp. 937–947.
    25. 25)
      • 8. Capitanescu, F., Wehenkel, L.: ‘Experiments with the interior-point method for solving large scale optimal power flow problems’, Electr. Power Syst. Res., 2013, 95, pp. 276–283.
    26. 26)
      • 17. Capitanescu, F., Rosehart, W., Wehenkel, L.: ‘Optimal power flow computations with constraints limiting the number of control actions’. PowerTech, 2009 IEEE Bucharest, Bucharest, Romania, 28 June–2 July 2009, vol. 1, pp. 1–8.
    27. 27)
      • 5. Granville, S.: ‘Optimal reactive dispatch through interior point methods’, IEEE Trans. Power Syst., 1994, 9, (1), pp. 136–146.
    28. 28)
      • 12. Mohseni-Bonab, S.M., Rabiee, A.: ‘Optimal reactive power dispatch: a review, and a new stochastic voltage stability constrained multi-objective model at the presence of uncertain wind power generation’, IET Gener. Transm. Distrib., 2017, 11, pp. 815–829 (14).
    29. 29)
      • 9. Pandya, K.S., Joshi, S.K.: ‘A survey of optimal power flow methods’, J. Theor. Appl. Inf. Technol., 2008, 4, pp. 450–458.
http://iet.metastore.ingenta.com/content/journals/10.1049/iet-gtd.2017.1486
Loading

Related content

content/journals/10.1049/iet-gtd.2017.1486
pub_keyword,iet_inspecKeyword,pub_concept
6
6
Loading
Print this page
This is a required field
Please enter a valid email address