Scenario reduction, network aggregation, and DC linearisation: which simplifications matter most in operations and planning optimisation?

Ebrahim Shayesteh; Benjamin F. Hobbs; Mikael Amelin

Scenario reduction, network aggregation, and DC linearisation: which simplifications matter most in operations and planning optimisation?

View Fulltext

Author(s): Ebrahim Shayesteh ¹ ; Benjamin F. Hobbs ² ; Mikael Amelin ¹
- Affiliations: 1: School of Electrical Engineering, KTH Royal Institute of Technology, Stockholm, Sweden ;
  2: Department of Geography and Environmental Engineering, Johns Hopkins University, Baltimore, MD 21218, USA
Source: Volume 10, Issue 11, 04 August 2016, p. 2748 – 2755
DOI: 10.1049/iet-gtd.2015.1404 , Print ISSN 1751-8687, Online ISSN 1751-8695

Received 20/11/2015, Accepted 12/04/2016, Revised 15/03/2016, Published 04/08/2016

Power economic studies are challenging because of growing system sizes, the advance of smart grids, and economic, technical, and policy unknowns. Consequently, system models must be simplified so that they can be solved with many scenarios of renewable output, load, and long-run uncertainties. Scenario reduction, network aggregation, and DC linearisation are three common simplifications. The authors compare their errors and computation times for optimal power flow (OPF) and stochastic unit commitment (SUC), using the IEEE 14-, 30-, and 118-bus test systems, and also briefly discuss impacts on generation and transmission planning. The authors find that the most appropriate simplification depends on the study type, and there are no consistent results concerning which simplification is most distorting. The following example conclusions apply to these cases, but not universally; nonetheless, the findings provide information about what simplifications can matter, which is a helpful starting point for practicing modellers. The authors find that linearisation's disregarding of losses distorts total costs in OPFs, but it causes relatively little error in SUC. Scenario reduction reduces OPF computational times with little distortion but is less effective for SUC. Network aggregation decreases computation effort more than linearisation in OPF, but causes little error unless there are few scenarios.

References

1. 1)
  - 36. Zimmerman, R.D., Murillo-Sanchez, C.E., Thomas, R.J.: ‘MATPOWER: steady- state operations, planning, and analysis tools for power systems research and education’, IEEE Trans. Power Syst., 2011, 26, (1), pp. 12–19 (doi: 10.1109/TPWRS.2010.2051168).
2. 2)
  - 6. Morales, J.M., Pineda, S., Conejo, A.J., et al: ‘Scenario reduction for futures market trading in electricity markets’, IEEE Trans. Power Syst., 2009, 24, pp. 878–888 (doi: 10.1109/TPWRS.2009.2016072).
3. 3)
  - 12. Ji, Y., Hobbs, B.F.: ‘Including a DC network approximation in a multiarea probabilistic production costing model’, IEEE Trans. Power Syst., 1998, 13, pp. 1121–1127 (doi: 10.1109/59.709109).
4. 4)
  - 20. Wood, A.J., Wollenberg, B.F.: ‘Power generation, operation and control’ (John Wiley & Sons, USA, 1996).
5. 5)
  - 5. Chang, C.S., Lu, L.R., Wen, F.S.: ‘Power system network partitioning using Tabu search’, Electr. Power Syst. Res., 1999, 49, (1), pp. 55–61 (doi: 10.1016/S0378-7796(98)00119-9).
6. 6)
  - 19. Dupačová, J., Gröwe-Kuska, N., Römisch, W.: ‘Scenario reduction in stochastic programming: an approach using probability metrics’, Math. Program A, 2003, 95, pp. 493–511 (doi: 10.1007/s10107-002-0331-0).
7. 7)
  - 4. Sumaili, J., Keko, H., Miranda, V., et al: ‘Clustering-based wind power scenario reduction technique’. Proc. 17th Power Systems Computation Conf. (PSCC'11), Stockholm, August 2011.
8. 8)
  - 19. Schweppe, F.C., Caramanis, M.C., Tabors, R.D., et al: ‘Spot pricing of electricity’ (Kluwer, Boston, 1988).
9. 9)
  - 5. Oliveira, W.L.D., Sagastizabal, C., Penna, D.D.J., et al: ‘Optimal scenario tree reduction for stochastic stream flows in power generation planning problems’. Proc. Int. Conf. on Engineering Optimization (EngOpt 2008), Rio de Janeiro, June 2008.
10. 10)
  - 17. Wang, Q., Wang, J., Guan, Y.: ‘Stochastic unit commitment with uncertain demand response’, IEEE Trans. Power Syst., 2013, 28, (1), pp. 562–563 (doi: 10.1109/TPWRS.2012.2202201).
11. 11)
  - 24. ‘Scenario reduction, network aggregation, and DC linearization: which simplifications hurt market simulations most?’. Available at http://hobbsgroup.johnshopkins.edu/publications.html, accessed 5 March 2015.
12. 12)
  - 16. Shayesteh, E., Hobbs, B.F., Soder, L., et al: ‘ATC-based system reduction for planning power systems with correlated wind and loads’, IEEE Trans. Power Syst., 2015, 30, pp. 429–438 (doi: 10.1109/TPWRS.2014.2326615).
13. 13)
  - 23. ‘Nord pool spot electricity market’. Available at http://www.nordpoolspot.com/, accessed 1 November 2013.
14. 14)
  - 7. Growe-Kuska, N., Heitsch, H., Romisch, W.: ‘Scenario reduction and scenario tree construction for power management problems’. Proc. IEEE Power Technology Conf., Bologna, Italy, June 2003.
15. 15)
  - 17. Papaemmanouil, A., Andersson, G.: ‘On the reduction of large power system models for power market simulations’. Proc. 17th Power Systems Computation Conf. (PSCC'11), Stockholm, Sweden, August 2011.
16. 16)
  - 2. van der Weijde, A.H., Hobbs, B.F.: ‘The economics of planning electricity transmission to accommodate renewables: using two-stage optimisation to evaluate flexibility and the cost of disregarding uncertainty’, Energy Econ., 2012, 34, pp. 2089–2101 (doi: 10.1016/j.eneco.2012.02.015).
17. 17)
  - 1. Cheng, X., Overbye, T.J.: ‘PTDF-based power system equivalents’, IEEE Trans. Power Syst., 2005, 20, pp. 1868–1876 (doi: 10.1109/TPWRS.2005.857013).
18. 18)
  - 14. Shi, D.: ‘Power system network reduction for engineering and economic analysis’. PhD. dissertation, Arizona State University, 2012.
19. 19)
  - 18. Fonseka, P.A.J., Saha, T.K., Dong, Z.Y.: ‘A price-based approach to generation investment planning in electricity markets’, IEEE Trans. Power Syst., 2008, 23, pp. 1859–1870 (doi: 10.1109/TPWRS.2008.2002287).
20. 20)
  - 13. Van Hertem, D., Verboomen, J., Purchala, K., et al: ‘Usefulness of DC power flow for active power flow analysis with flow controlling devices’. Proc. Eighth IEE Int. Conf. on AC and DC Power Transmission, March 2006, pp. 58–62.
21. 21)
  - 10. Gopalakrishnan, A., Raghunathan, A.U., Nikovski, D., et al: ‘Global optimization of optimal power flow using a branch & bound algorithm’. Proc. Allerton Conf. on Communication, Control and Computing, Illinois, USA, October 2012.
22. 22)
  - 11. Taylor, J.A., Hover, F.S.: ‘Conic relaxations for transmission system planning’. Proc. 43rd North American Power Symp., Boston, August 2011.
23. 23)
  - 21. Zhao, L., Zeng, B., Buckley, B.: ‘A stochastic unit commitment model with cooling systems’, IEEE Trans. Power Syst., 2013, 28, pp. 211–218 (doi: 10.1109/TPWRS.2012.2196715).
24. 24)
  - 15. Shayesteh, E., Amelin, M., Soder, L.: ‘REI method for multi-area modeling of large power systems with high wind penetration’, Int. J. Electr. Power Energy Syst., 2014, 60, pp. 283–292 (doi: 10.1016/j.ijepes.2014.03.002).

Login

Not registered yet?

Share

Tools

Login to add to favourites

Key

Scenario reduction, network aggregation, and DC linearisation: which simplifications matter most in operations and planning optimisation?

References

Related content