Optimal dynamic emergency reserve activation using spinning, hydro and demand-side reserves

S. Surender REDDY, P. R. BIJWE, A. R. ABHYANKAR

PDF(165 KB)
PDF(165 KB)
Front. Energy ›› 2016, Vol. 10 ›› Issue (4) : 409-423. DOI: 10.1007/s11708-016-0431-9
RESEARCH ARTICLE

Optimal dynamic emergency reserve activation using spinning, hydro and demand-side reserves

Author information +
History +

Abstract

This paper proposes an optimal dynamic reserve activation plan after the occurrence of an emergency situation (generator/transmission line outage, load increase or both). An optimal plan is developed to handle the emergency, using the coordinated action of fast and slow reserves, for secure operation with minimum overall cost. It considers the reserves supplied by the conventional thermal generators (spinning reserves), hydro power units and load demands (demand-side reserves). The optimal backing down of costly/fast reserves and bringing up of slow reserves in each sub-interval in an integrated manner is proposed. The proposed reserve activation approaches are solved using the genetic algorithm, and some of the simulation results are also compared using the Matlab optimization toolbox and the general algebraic modeling system (GAMS) software. The simulation studies are performed on the IEEE 30, 57 and 300 bus test systems. These results demonstrate the advantage of the proposed integrated/dynamic reserve activation plan over the conventional/sequential approach.

Keywords

demand-side reserves / dynamic reserve activation approach / hydro power units / post contingency / sequential reserve activation approach / spinning reserves

Cite this article

Download citation ▾
S. Surender REDDY, P. R. BIJWE, A. R. ABHYANKAR. Optimal dynamic emergency reserve activation using spinning, hydro and demand-side reserves. Front. Energy, 2016, 10(4): 409‒423 https://doi.org/10.1007/s11708-016-0431-9

References

[1]
NERC Policy-10 on Interconnected Operations Services. Technical Report, Draft-3.1, Feb. 2000
[2]
Ela E, Milligan M, Kirby B. Operating reserves and variable generation. Technical Report, NREL/TP-5500–51978, Aug. 2011
[3]
Arroyo J M, Galiana F D. Energy and reserve pricing in security and network-constrained electricity markets. IEEE Transactions on Power Systems, 2005, 20(2): 634–643
CrossRef Google scholar
[4]
Ruiz P A, Sauer P W. Spinning contingency reserve: economic value and demand functions. IEEE Transactions on Power Systems, 2008, 23(3): 1071–1078
CrossRef Google scholar
[5]
Strbac G, Ahmed S, Kirschen D, Allan R. A method for computing the value of corrective security. IEEE Transactions on Power Systems, 1998, 13(3): 1096–1102
CrossRef Google scholar
[6]
Philpott A B, Pettersen E. Optimizing demand-side bids in day-ahead electricity markets. IEEE Transactions on Power Systems, 2006, 21(2): 488–498
CrossRef Google scholar
[7]
Monticelli A, Pereira M V F, Granville S. Security constrained optimal power flow with post-contingency corrective rescheduling. IEEE Transactions on Power Systems, 1987, 2(1): 175–180
CrossRef Google scholar
[8]
Wu L, Shahidehpour M, Liu C. MIP-based post-contingency corrective action with quick-start units. IEEE Transactions on Power Systems, 2009, 24(4): 1898–1899
CrossRef Google scholar
[9]
Maharana M K, Swarup K S. Particle swarm optimization based corrective strategy to alleviate overloads in power system. World Congress on Nature & Biologically Inspired Computing, 2010, 37–42
[10]
Karangelos E, Bouffard F. Towards full integration of demand-side resources in joint forward energy/reserve electricity markets. IEEE Transactions on Power Systems, 2012, 27(1): 280–289
CrossRef Google scholar
[11]
Wang J, Shahidehpour M, Li Z. Contingency-constrained reserve requirements in joint energy and ancillary services auction. IEEE Transactions on Power Systems, 2009, 24(3): 1457–1468
CrossRef Google scholar
[12]
Wang J, Redondo N E, Galiana F D. Demand-side reserve offers in joint energy/reserve electricity markets. IEEE Transactions on Power Systems, 2003, 18(4): 1300–1306
CrossRef Google scholar
[13]
Gan D, Litvinov E. Energy and reserve market designs with explicit consideration to lost opportunity costs. IEEE Transactions on Power Systems, 2003, 18(1): 53–59
CrossRef Google scholar
[14]
Amjady N, Aghaei A, Shayanfar H A. Market clearing of joint energy and reserves auctions using augmented payment minimization. IEEE Transactions on Power Systems, 2003, 18(1): 53–59
[15]
Capitanescu F, Martinez Ramos J L, Panciatici P, Kirschen D, Marano Marcolini A, Platbrood L, Wehenkel L. Start-of-the-art, challenges, and future trends in security constrained optimal power flow. Electric Power Systems Research, 2011, 81(8): 1731–1741
CrossRef Google scholar
[16]
Chakrabarti B B, Rayudu R K. Balancing wind intermittency using hydro reserves and demand response. IEEE International Conference on Power System Technology, 2012, 5(1): 1–6
[17]
Pinto J, Neves M V. The value of a pumping-hydro generator in a system with increasing integration of wind power. International Conference on the European Energy Market, 25–27 May 2011, pp. 306–311
[18]
Lu N, Chow J H, Desrochers A A. Pumped-storage hydro-turbine bidding strategies in a competitive electricity market. IEEE Transactions on Power Systems, 2004, 19(2): 834–841
CrossRef Google scholar
[19]
Capitanescu F, Wehenkel L. Improving the statement of the corrective security-constrained optimal power-flow problem. IEEE Transactions on Power Systems, 2007, 22(2): 887–889
CrossRef Google scholar
[20]
Hazra J, Sinha A K. Congestion management using multi-objective particle swarm optimization. IEEE Transactions on Power Systems, 2007, 22(4): 1726–1734
CrossRef Google scholar
[21]
PJM Manual 11: Energy and Ancillary Services Market Operations, Revision 60. PJM, Norristown, PA, USA, 2013–06, http://pjm.com
[22]
Reddy S S, Abhyankar A R, Bijwe P R. Co-optimization of energy and demand-side reserves in day-ahead electricity markets. International Journal of Emerging Electric Power Systems, 2015, 16(2): 195–206
[23]
Reddy S S, Abhyankar A R, Bijwe P R. Joint market clearing of energy and demand response offers considering voltage dependent load models. Journal of Electical Systems, 2015, 11(4): 433–446
[24]
Reddy S S, Bijwe P R, Abhyankar A R. Optimal posturing in day-ahead market clearing for uncertainties considering anticipated real-time adjustment costs. IEEE Systems Journal, 2015, 9(1): 177–190
CrossRef Google scholar
[25]
Reddy S S, Bijwe P R, Abhyankar A R. Joint energy and spinning reserve market clearing incorporating wind power and load forecast uncertainties. IEEE Systems Journal, 2015, 9(1): 152–164
CrossRef Google scholar
[26]
Gaing Z L, Chang R F. Security-constrained optimal power flow by mixed-integer genetic algorithm with arithmetic operators. IEEE Power Engineering Society General Meeting, Montreal, 2006
[27]
Wu L, Shahidehpour M, Li Z. GENCO’s risk-constrained hydrothermal scheduling. IEEE Transactions on Power Systems, 2008, 23(4): 1847–1858
CrossRef Google scholar
[28]
Gen M, Cheng R. Genetic Algorithms and Engineering Design. New York: Wiley, 1997
[29]
Reddy S S, Abhyankar A R, Bijwe P R. Reactive power price clearing using multi-objective optimization. Energy, 2011, 36(5): 3579–3589
CrossRef Google scholar
[30]
University of Washington. Power system test case archive. 2007, http://www.ee.washington.edu/research/pstca
[31]
Lai L L, Ma J T, Yokoyama R, Zhao M. Improved genetic algorithms for optimal power flow under both normal and contingent operating states. Electric Power and Energy Systems, 1997, 19(5): 287–292
CrossRef Google scholar
[32]
Alsac O, Stott B. Optimal load flow with steady state security. IEEE Transactions on Power Apparatus and Systems, 1974, PAS-93(3): 745–751
CrossRef Google scholar
[33]
Pai M A. Computer Techniques in Power System Analysis. New Delhi Tata McGraw-Hill, 2006

RIGHTS & PERMISSIONS

2016 Higher Education Press and Springer-Verlag Berlin Heidelberg
AI Summary AI Mindmap
PDF(165 KB)

Accesses

Citations

Detail

Sections
Recommended

/