RESEARCH ARTICLE

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

  • S. Surender REDDY , 1 ,
  • P. R. BIJWE 2 ,
  • A. R. ABHYANKAR 2
Expand
  • 1. Department of Railroad and Electrical Engineering, Woosong University, Republic of Korea
  • 2. Department of Electrical Engineering, Indian Institute of Technology Delhi, India

Received date: 06 May 2016

Accepted date: 04 Jul 2016

Published date: 17 Nov 2016

Copyright

2016 Higher Education Press and Springer-Verlag Berlin Heidelberg

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.

Cite this article

S. Surender REDDY , P. R. BIJWE , A. R. ABHYANKAR . Optimal dynamic emergency reserve activation using spinning, hydro and demand-side reserves[J]. Frontiers in Energy, 2016 , 10(4) : 409 -423 . DOI: 10.1007/s11708-016-0431-9

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

DOI

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

DOI

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

DOI

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

DOI

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

DOI

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

DOI

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

DOI

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

DOI

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

DOI

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

DOI

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

DOI

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

DOI

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

DOI

20
Hazra J, Sinha A K. Congestion management using multi-objective particle swarm optimization. IEEE Transactions on Power Systems, 2007, 22(4): 1726–1734

DOI

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

DOI

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

DOI

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

DOI

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

DOI

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

DOI

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

DOI

33
Pai M A. Computer Techniques in Power System Analysis. New Delhi Tata McGraw-Hill, 2006

Outlines

/