Optimal dynamic emergency reserve activation using spinning, hydro and demand-side reserves
Received date: 06 May 2016
Accepted date: 04 Jul 2016
Published date: 17 Nov 2016
Copyright
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.
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 |
|
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
|
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
|
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
|
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
|
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
|
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
|
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
|
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
|
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
|
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
|
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
|
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
|
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
|
20 |
Hazra J, Sinha A K. Congestion management using multi-objective particle swarm optimization. IEEE Transactions on Power Systems, 2007, 22(4): 1726–1734
|
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
|
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
|
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
|
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
|
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
|
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
|
33 |
Pai M A. Computer Techniques in Power System Analysis. New Delhi Tata McGraw-Hill, 2006
|
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