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AC Optimal Power Flow Incorporating Demand-Side Management Strategy

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Demand Response Application in Smart Grids

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Abstract

This chapter presents a methodology for solving AC optimal power flow problem using demand-side management strategy. A single objective function, which is composed of fuel cost of thermal power plants, is minimized. Ramp down/up rate limits, active/reactive power generation capacity, and balance equation are considered as constraints of optimization problem. Active power demand of each bus is flexible and can maximally be reduced to 80% of its forecasted value. Similarly, it is supposed that electricity consumption at each bus can be increased up to 1.2% of its base value. Hours and values of active load increase/decrease at each bus are determined as decision variables in a way that the total amount of the load increase over the study horizon is equal to the amount of the load decrease in the same operating period. A nonlinear programming problem is developed under general algebraic mathematical modeling system (GAMS) to find how much $ is saved in AC optimal power flow analysis, while demand is managed at each bus.

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Abbreviations

i, j :

Bus

g :

Thermal power plant

t :

Time

α :

Maximum percentage of active demand decrease and increase at bus i

ag, bg, cg:

Fuel consumption factors

DSMi, t:

Value of active demand increase/decrease at bus i and time t

\( {P}_{i,t}^0 \) :

Base active demand of bus i at hour t

\( {P}_i^{g,\max } \) :

Maximum value of active power generated by unit g at bus i

\( {P}_i^{g,\min } \) :

Minimum value of active power generated by unit g at bus i

\( {P}_{ij}^{\mathrm{max}} \) :

Active power capacity of transmission line i to j

Q i, t :

Reactive power demand of bus i at time t

r ij :

Resistance of transmission line i to j

x ij :

Reactance of transmission line i to j

δ i, t :

Bus voltage angle

I ij, t :

Current passes through transmission line i to j

OF:

Daily cost

P i, t :

Active power demand of bus i at time t

\( {P}_{i,t}^g \) :

Active power generation of gas-fired power plant g

\( {P}_{i,t}^{\mathrm{w}} \) :

Wind power production at bus i and hour t

\( {Q}_{i,t}^g \) :

Reactive power generation of gas-fired power plant g, which is connected to bus i

S ij, t :

Complex power transmitted from bus i to j

V i, t :

Bus voltage magnitude

References

  1. A. Attarha, N. Amjady, A.J. Conejo, Adaptive robust AC optimal power flow considering load and wind power uncertainties. Int. J. Electr. Power Energy Syst. 96, 132–142 (2018)

    Article  Google Scholar 

  2. E. Dall’Anese, A. Simonetto, Optimal power flow pursuit. IEEE Tran. Smart Grid 9(2), 942–952 (2018)

    Article  Google Scholar 

  3. F. Capitanescu, Critical review of recent advances and further developments needed in AC optimal power flow. Electr. Power Syst. Res. 136, 57–68 (2016)

    Article  Google Scholar 

  4. Conforti, D., et al. Optimal load-flow with N-1 steady-state security via high performance computing, in Electrotechnical Conference, 1996. MELECON’96, 8th Mediterranean (IEEE, 1996)

    Google Scholar 

  5. A. Attarha, N. Amjady, Solution of security constrained optimal power flow for large-scale power systems by convex transformation techniques and Taylor series. IET Gener. Transm. Distrib. 10(4), 889–896 (2016)

    Article  Google Scholar 

  6. H. Abdi, S.D. Beigvand, M. La Scala, A review of optimal power flow studies applied to smart grids and microgrids. Renew. Sust. Energ. Rev. 71, 742–766 (2017)

    Article  Google Scholar 

  7. J. Hazra, A. Sinha, A multi-objective optimal power flow using particle swarm optimization. Eur. T. Electr. Power 21(1), 1028–1045 (2011)

    Article  Google Scholar 

  8. N. Amjady, M.R. Ansari, Non-convex security constrained optimal power flow by a new solution method composed of benders decomposition and special ordered sets. Int. Trans. Electr. Energy Syst. 24(6), 842–857 (2014)

    Article  Google Scholar 

  9. P.K. Roy, C. Paul, Optimal power flow using krill herd algorithm. Int. Trans. Electr. Energy Syst. 25(8), 1397–1419 (2015)

    Article  Google Scholar 

  10. H. Sharifzadeh, N. Amjady, Stochastic security-constrained optimal power flow incorporating preventive and corrective actions. Int. Trans. Electr. Energy Syst. 26(11), 2337–2352 (2016)

    Article  Google Scholar 

  11. C. Hamon, M. Perninge, L. Soder, A stochastic optimal power flow problem with stability constraints—Part I: Approximating the stability boundary. IEEE Trans. Power Syst. 28(2), 1839–1848 (2013)

    Article  Google Scholar 

  12. J. Liang et al., Two-level dynamic stochastic optimal power flow control for power systems with intermittent renewable generation. IEEE Trans. Power Syst. 28(3), 2670–2678 (2013)

    Article  Google Scholar 

  13. T. Summers, et al. Stochastic optimal power flow based on convex approximations of chance constraints, in 2014 Power Systems Computation Conference (IEEE, 2014)

    Google Scholar 

  14. T. Yong, R. Lasseter, Stochastic optimal power flow: formulation and solution, in Power Engineering Society Summer Meeting (IEEE, 2000)

    Google Scholar 

  15. D. Bertsimas et al., Adaptive robust optimization for the security constrained unit commitment problem. IEEE Trans. Power Syst. 28(1), 52–63 (2013)

    Article  Google Scholar 

  16. T. Ding et al., Adjustable robust optimal power flow with the price of robustness for large-scale power systems. IET Gener. Transm. Distrib. 10(1), 164–174 (2016)

    Article  Google Scholar 

  17. F. Jabari et al., Optimal short-term scheduling of a novel tri-generation system in the presence of demand response programs and battery storage system. Energy Convers. Manag. 122, 95–108 (2016)

    Article  Google Scholar 

  18. P. Siano, Demand response and smart grids—a survey. Renew. Sust. Energ. Rev. 30, 461–478 (2014)

    Article  Google Scholar 

  19. M.H. Albadi, E.F. El-Saadany, A summary of demand response in electricity markets. Electr. Power Syst. Res. 78(11), 1989–1996 (2008)

    Article  Google Scholar 

  20. A.R. Jordehi, Optimisation of demand response in electric power systems, a review. Renew. Sust. Energ. Rev. 103, 308–319 (2019)

    Article  Google Scholar 

  21. S. Nojavan, K. Zare, B. Mohammadi-Ivatloo, Selling price determination by electricity retailer in the smart grid under demand side management in the presence of the electrolyser and fuel cell as hydrogen storage system. Int. J. Hydrog. Energy 42(5), 3294–3308 (2017)

    Article  Google Scholar 

  22. P. Aliasghari et al., Optimal scheduling of plug-in electric vehicles and renewable micro-grid in energy and reserve markets considering demand response program. J. Clean. Prod. 186, 293–303 (2018)

    Article  Google Scholar 

  23. M. Rahmani-andebili, Modeling nonlinear incentive-based and price-based demand response programs and implementing on real power markets. Electr. Power Syst. Res. 132, 115–124 (2016)

    Article  Google Scholar 

  24. A. Dolatabadi, B. Mohammadi-Ivatloo, Stochastic risk-constrained scheduling of smart energy hub in the presence of wind power and demand response. Appl. Therm. Eng. 123, 40–49 (2017)

    Article  Google Scholar 

  25. N. Ruiz, I. Cobelo, J. Oyarzabal, A direct load control model for virtual power plant management. IEEE Trans. Power Syst. 24(2), 959–966 (2009)

    Article  Google Scholar 

  26. A. Dorri, et al., A Secure and Efficient Direct Power Load Control Framework Based on Blockchain, arXiv preprint arXiv:1812.08497 (2018)

    Google Scholar 

  27. H. Aalami, M.P. Moghaddam, G. Yousefi, Demand response modeling considering interruptible/curtailable loads and capacity market programs. Appl. Energy 87(1), 243–250 (2010)

    Article  Google Scholar 

  28. L. Yao, W.H. Lim, Optimal purchase strategy for demand bidding. IEEE Trans. Power Syst. 33(3), 2754–2762 (2018)

    Article  Google Scholar 

  29. J. Iria, F. Soares, M. Matos, Optimal supply and demand bidding strategy for an aggregator of small prosumers. Appl. Energy 213, 658–669 (2018)

    Article  Google Scholar 

  30. P. Yang, G. Tang, A. Nehorai, A game-theoretic approach for optimal time-of-use electricity pricing. IEEE Trans. Power Syst. 28(2), 884–892 (2013)

    Article  Google Scholar 

  31. E. Celebi, J.D. Fuller, Time-of-use pricing in electricity markets under different market structures. IEEE Trans. Power Syst. 27(3), 1170–1181 (2012)

    Article  Google Scholar 

  32. S. Nojavan, B. Mohammadi-Ivatloo, K. Zare, Optimal bidding strategy of electricity retailers using robust optimisation approach considering time-of-use rate demand response programs under market price uncertainties. IET Gener. Transm. Distrib. 9(4), 328–338 (2015)

    Article  Google Scholar 

  33. M. Vahid-Pakdel et al., Stochastic optimization of energy hub operation with consideration of thermal energy market and demand response. Energy Convers. Manag. 145, 117–128 (2017)

    Article  Google Scholar 

  34. S. Nojavan, K. Zare, B. Mohammadi-Ivatloo, Optimal stochastic energy management of retailer based on selling price determination under smart grid environment in the presence of demand response program. Appl. Energy 187, 449–464 (2017)

    Article  Google Scholar 

  35. A.J. Conejo, J.M. Morales, L. Baringo, Real-time demand response model. IEEE Trans. Smart Grid 1(3), 236–242 (2010)

    Article  Google Scholar 

  36. H. Su et al., A systematic data-driven demand side management method for smart natural gas supply systems. Energy Convers. Manag. 185, 368–383 (2019)

    Article  Google Scholar 

  37. R. Kanimozhi, K. Selvi, K.M. Balaji, Multi-objective approach for load shedding based on voltage stability index consideration. Alex. Eng. J. 53(4), 817–825 (2014)

    Article  Google Scholar 

  38. V. Tamilselvan, T. Jayabarathi, A hybrid method for optimal load shedding and improving voltage stability. Ain Shams Eng. J. 7(1), 223–232 (2016)

    Article  Google Scholar 

  39. D. Chattopadhyay, B.B. Chakrabarti, A preventive/corrective model for voltage stability incorporating dynamic load-shedding. Int. J. Electr. Power Energy Syst. 25(5), 363–376 (2003)

    Article  Google Scholar 

  40. S. Chanda, A. De, A multi-objective solution algorithm for optimum utilization of smart grid infrastructure towards social welfare. Int. J. Electr. Power Energy Syst. 58, 307–318 (2014)

    Article  Google Scholar 

  41. S.-Y. Lin, J.-F. Chen, Distributed optimal power flow for smart grid transmission system with renewable energy sources. Energy 56, 184–192 (2013)

    Article  Google Scholar 

  42. T. Erseghe, S. Tomasin, Power flow optimization for smart microgrids by SDP relaxation on linear networks. IEEE Trans. Smart Grid 4(2), 751–762 (2013)

    Article  Google Scholar 

  43. López-Lezama, J., et al. A contingency-based security-constrained optimal power flow model for revealing the marginal cost of a blackout risk-equalizing policy in the colombian electricity market, in Transmission & Distribution Conference and Exposition: Latin America, 2006. TDC’06 (IEEE/PES, 2006)

    Google Scholar 

  44. L.R. de Araujo, D.R.R. Penido, F. de Alcântara Vieira, A multiphase optimal power flow algorithm for unbalanced distribution systems. Int. J. Electr. Power Energy Syst. 53, 632–642 (2013)

    Article  Google Scholar 

  45. Y. Levron, J.M. Guerrero, Y. Beck, Optimal power flow in microgrids with energy storage. IEEE Trans. Power Syst. 28(3), 3226–3234 (2013)

    Article  Google Scholar 

  46. S. Bruno et al., Unbalanced three-phase optimal power flow for smart grids. IEEE Trans. Ind. Electron. 58(10), 4504–4513 (2011)

    Article  Google Scholar 

  47. S. Paudyal, C.A. Cañizares, K. Bhattacharya, Optimal operation of distribution feeders in smart grids. IEEE Trans. Ind. Electron. 58(10), 4495–4503 (2011)

    Article  Google Scholar 

  48. E. Dall’Anese, H. Zhu, G.B. Giannakis, Distributed optimal power flow for smart microgrids. IEEE Trans. Smart Grid 4(3), 1464–1475 (2013)

    Article  Google Scholar 

  49. B. Hayes et al., Optimal power flow for maximizing network benefits from demand-side management. IEEE Trans. Power Syst 29(4), 1739–1747 (2014)

    Article  Google Scholar 

  50. S. Derafshi Beigvand, H. Abdi, Optimal power flow in the smart grid using direct load control program. J. Oper. Autom. Power Eng. 3(2), 102–115 (2015)

    Google Scholar 

  51. T. Sousa et al., A multi-objective optimization of the active and reactive resource scheduling at a distribution level in a smart grid context. Energy 85, 236–250 (2015)

    Article  Google Scholar 

  52. E.R. Sanseverino et al., Optimal power flow in three-phase islanded microgrids with inverter interfaced units. Electr. Power Syst. Res. 123, 48–56 (2015)

    Article  Google Scholar 

  53. E.R. Sanseverino et al., Optimal power flow in islanded microgrids using a simple distributed algorithm. Energies 8(10), 11493–11514 (2015)

    Article  Google Scholar 

  54. M. Hosseinzadeh, F.R. Salmasi, Robust optimal power management system for a hybrid AC/DC micro-grid. IEEE Trans. Sustain. Energy 6(3), 675–687 (2015)

    Article  Google Scholar 

  55. H. Morais et al., Optimal scheduling of a renewable micro-grid in an isolated load area using mixed-integer linear programming. Renew. Energy 35(1), 151–156 (2010)

    Article  MathSciNet  Google Scholar 

  56. W. Shi et al., Distributed optimal energy Management in Microgrids. IEEE Trans. Smart Grid 6(3), 1137–1146 (2015)

    Article  MathSciNet  Google Scholar 

  57. N. Nikmehr, S.N. Ravadanegh, Optimal power dispatch of multi-microgrids at future smart distribution grids. IEEE Trans. Smart Grid 6(4), 1648–1657 (2015)

    Article  Google Scholar 

  58. S.A. Alavi, A. Ahmadian, M. Aliakbar-Golkar, Optimal probabilistic energy management in a typical micro-grid based-on robust optimization and point estimate method. Energy Convers. Manag. 95, 314–325 (2015)

    Article  Google Scholar 

  59. M.H. Moradi, M. Abedini, S.M. Hosseinian, Optimal operation of autonomous microgrid using HS–GA. Int. J. Electr. Power Energy Syst. 77, 210–220 (2016)

    Article  Google Scholar 

  60. B. Mathiesen et al., The interaction between intermittent renewable energy and the electricity, heating and transport sectors. Energy 48(2), 2–4 (2012)

    Article  Google Scholar 

  61. Z. Ziadi et al., Optimal power scheduling for smart grids considering controllable loads and high penetration of photovoltaic generation. IEEE Trans. Smart Grid 5(5), 2350–2359 (2014)

    Article  Google Scholar 

  62. M. Sechilariu et al., DC microgrid power flow optimization by multi-layer supervision control. Design and experimental validation. Energy Convers. Manage. 82, 1–10 (2014)

    Article  Google Scholar 

  63. M. Sechilariu, B.C. Wang, F. Locment, Supervision control for optimal energy cost management in DC microgrid: design and simulation. Int. J. Electr. Power Energy Syst. 58, 140–149 (2014)

    Article  Google Scholar 

  64. S. Mohammadi, S. Soleymani, B. Mozafari, Scenario-based stochastic operation management of microgrid including wind, photovoltaic, micro-turbine, fuel cell and energy storage devices. Int. J. Electr. Power Energy Syst. 54, 525–535 (2014)

    Article  Google Scholar 

  65. S. Mohammadi, B. Mozafari, S. Solimani, Optimal operation management of microgrids using the point estimate method and firefly algorithm while considering uncertainty. Turkish J. Electr. Eng. Comput. Sci. 22(3), 735–753 (2014)

    Article  Google Scholar 

  66. W.A. Bukhsh, C. Zhang, P. Pinson, An integrated multiperiod OPF model with demand response and renewable generation uncertainty. IEEE Trans. Smart Grid 7(3), 1495–1503 (2016)

    Article  Google Scholar 

  67. S. Magnússon, P.C. Weeraddana, C. Fischione, A distributed approach for the optimal power-flow problem based on ADMM and sequential convex approximations. IEEE Trans. Control Network Syst. 2(3), 238–253 (2015)

    Article  MathSciNet  MATH  Google Scholar 

  68. https://www.gams.com/latest/docs/S_COUENNE.html

  69. A. Soroudi, Power System Optimization Modeling in GAMS (Springer, 2017)

    Google Scholar 

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Author information

Authors and Affiliations

  1. Faculty of Electrical and Computer Engineering, University of Tabriz, Tabriz, Iran

    Farkhondeh Jabari & Behnam Mohammadi-Ivatloo

  2. Faculty of Chemical and Petroleum Engineering, University of Tabriz, Tabriz, Iran

    Mousa Mohammadpourfard

Authors
  1. Farkhondeh Jabari
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  2. Mousa Mohammadpourfard
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  3. Behnam Mohammadi-Ivatloo
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Correspondence to Farkhondeh Jabari .

Editor information

Editors and Affiliations

  1. Department of Electrical Engineering, University of Bonab, Bonab, Iran

    Sayyad Nojavan

  2. Faculty of Electrical and Computer Engineering, University of Tabriz, Tabriz, Iran

    Kazem Zare

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Jabari, F., Mohammadpourfard, M., Mohammadi-Ivatloo, B. (2020). AC Optimal Power Flow Incorporating Demand-Side Management Strategy. In: Nojavan, S., Zare, K. (eds) Demand Response Application in Smart Grids. Springer, Cham. https://doi.org/10.1007/978-3-030-32104-8_7

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  • DOI: https://doi.org/10.1007/978-3-030-32104-8_7

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