none

A proposal for the spinning reserve assessment and allocation for El Salvador’s Deregulated Electricity Market is formulated. Traditionally, the Independent System Operator calculates the spinning reserve as percentage of the forecast demand. And Automatic Generation Control (AGC) is allocated based on the partition factor. The reserve calculation neither reflects consistency achieving its main objective, reliability, nor is optimum performance control reached by the allocating mechanism. In the proposed method, the spinning reserve is estimated taking into account the generators outage ratio and AGC is allocated based on the North American Electric Reliability Corporation’s Control Performance Standard-1. The allocation problem is solved with an improved Particle Swarm Optimization algorithm with a technique to modify the inertial factor on each iteration. The proposed method exhibits better results and it matches the Salvadorian technical requirements and market characteristics.

CONTENTS

II. Abstract. ……………………………………………….. I
III. Dedication. ……………………………………………….. III
IV. Contents. ……………………………………………….. IV
V. List of figures. ……………………………………………….. VI
VI. List of tables. ……………………………………………….. VII

1.
INTRODUCTION ……………………………………………….. 1

1.1. The Salvadorian Electrical Market. ……………………….. 1
1.2. Problem formulation. ……………………………………….. 6
1.3. Literature review. ……………………………………….. 7
1.4. Motivation. ……………………………………………….. 8
1.5. Thesis distribution. ……………………………………….. 9



2.
THE CURRENT UNIT COMMITMENT PROCEDURE ……….. 10

2.1. Basic Concepts from economics. ……………………………….. 10
2.2. The Salvadorian Unit Commitment procedure. ……………….. 13
2.2.1. The UC management procedure. ……………………….. 13
2.2.2. The Salvadorian Spot Market. ……………………….. 14
2.2.3. Mathematical formulation. ……………………………….. 16
2.2.4. The spinning reserve requirements. ……………………….. 19



3.
THE SPINNING RESERVE ASSESSMENT BASED ON A COMMITMENT RISK CRITERION ……………………………………….. 22

3.1. The spinning reserve and its impacts on the market clearing price. .. 22
3.2. The generating capacity based on a probabilistic method. ……….. 23
3.3. The generation model ……………………………….. 24
3.3.1. Unit Forced Outage rate. ……………………….. 25
3.3.2. Outage Replacement Rate (ORR). ……………………….. 25


3.3.3. Derated (partial output) states generators. ……….. 26
3.3.4. Outage capacity probability tables. ……………….. 27
3.4. The unit commitment risk model ……………………….. 31
3.4.1. The previous concepts of the PJM method. ……………….. 31
3.4.2. Modifications to the PJM method. ....…………….. 32
3.5. Adding the stand-by units to the model. ……………….. 33
3.5.1. Area risk curves. ……………………………….. 33
3.5.2. Modeling rapid and hot start units. ……………….. 34

4.
THE SPINNING RESERVE DISTRIBUTION ……….. 39

4.1. Primary reserve distribution. ……………………………….. 39
4.2. Secondary reserve distribution. ……………………………….. 40
4.2.1. The Automatic Generation Control characteristics. ……….. 40
4.2.2. The NERC’s Control Performance Standard 1. ……….. 41
4.2.3. AGC allocation for an optimum CPS1. ……………….. 43
4.2.4. Simulating the generator’s response. ……………….. 46
4.2.5. Choosing the optimum distribution factor. ……………….. 49
4.3. Chaotically-Adaptive PSO to solve the AGC allocation problem. .. 50
4.3.1. Basic PSO. ……………………………………….. 50
4.3.2. Adaptive Inertial Factor. ……………………….. 52
4.3.3. Chaotically decreasing Inertial Factor. ………………. 52
4.3.4. CAPSO capabilities. ………………………………. 54
4.3.5. CAPSO to solve the AGC allocation problem ………. 56



5.
SIMULATIONS RESULTS ……………………………….. 58

5.1. UC based on demand and spot market offers. …….………….. 58
5.1.1. The offers structure. ……………………………………….. 58
5.1.2. The pre-dispatch without spinning reserve calculations. .. 59
5.2. Assessing the spinning reserve based on the commitment risk criterion. 60
5.2.1. Outage Replacement Rate calculation. ……………….. 60
5.2.2. The inconsistencies of the spinning reserve assessment deterministic
method. ………………………………………... 63


5.2.3. Setting the Operative Outage Replacement Rate. ...……… 65
5.3. The spinning reserve distribution. ………………………... 67
5.4.1. Primary reserve distribution. ………………………… 67
5.4.2. Secondary reserve (AGC) allocation for an optimum CPS1
performance. ………………………………………… 68
5.4.3. Different AGC allocation methods comparison. ………… 74
5.4. UC based on a Commitment Risk spinning reserve calculation and Optimum
CPS1 performance AGC allocation. ………………………… 75
5.5.1. Comparison of the spinning reserve assessed by different methods. 75
5.5.2. Comparison of the spinning reserve allocated by different
methods ………………………………………… 77

6.
CONCLUSIONS ………………………………………………… 81

6.1. Conclusion. ………………………………………………… 81


6.1.1. Spinning Reserve Calculation Based on a Commitment Outage Rate
Risk Criterion. ………………………………………………… 81
6.1.2. AGC Allocation based on an optimum CPS1 Performance. … 83
6.2. Proposed Research Topics. ………………………………… 85


7.
REFERENCES ………………………………………………… 86





8.
APPENDIX ………………………………………………… 89

A. Demand pattern and spot market offers. ………………… 89
A.1. Demand pattern. ………………………………… 89
A.2. Offers in the spot market. ………………………………… 89
B. program tuning characteristics. ………………………… 93
B.1. Economic Dispatch problem. ………………………………… 93
B.2. AGC allocation problem. ………………………… 93

[1] El Salvador General Electricity Law. Oct. 1996. Last Edition: Dec. 2004.

[2] D. Kirschen and G. Strbac, Fundamentals of Power System Economics, John Wiley and Sons Ltd. West Sussex, England. 2004.

[3] F. Molina, “Política Energética en El Salvador a partir de 1994,” M.A. thesis, Centroamericana University, San Salvador, El Salvador, Jul. 2007.

[4] C. Ritcher and G. Sheblé, “Genetic Algorithm Evolution of Utility Bidding Strategies for the Competitive Marketplace,” IEEE Transactions on Power Systems, vol. 13, pp. 256-261, Feb. 1998.

[5] J. J. Shaw, “A direct method for Security Constrained unit commitment,” IEEE Transactions on Power Systems, vol. 10, pp. 1329-1342, Aug. 1995.

[6] (2009) the Salvadorian ISO. Available: http://www.ut.com.sv/

[7] R. Allan and R Billinton, “Probabilistic Methods Applied to Electric Power System – are they worth it?,” IEEE Power Engineering Journal, May 1992.

[8] L. Anstine, R. Burke, J. Casey, R. Holgate, R. John, and H. Stewart, “Application of probability methods to the determination of spinning reserve requirements for the Pennsylvania-New Jersey-Maryland interconnection,” IEEE Trans. On Power Apparatus and Systems, vol. 82, pp. 726-735. Oct. 1963.

[9] R. Billinton, “Bibliography on Application of Probability Methods in the Evaluation of Generating Capacity Requirements,” In IEEE Winter Power Meeeting (1966). Paper No. 31 CP 66-62.

[10] R. Billinton, “Power System Reliability Evaluation”. Gordon and Breach, USA 1970.

[11] H. Yamin, S. Al-Agtash, and M. Shahidehpour, “Security constrained optimal generation scheduling for GENCOs,” in IEEE Power Engineering Society Meeting, p. 995, 2004.

[12] J. Guy, “Security constrained Unit Commitment,” in IEEE Summer Power Meeting and EHV, Jul. 1970.

[13] A. Jain and R. Billinton, “The effect of rapid start and hot reserve units in spinning reserve studies,” IEEE Transactions on Power Apparatus and Systems, vol. 91, pp. 511-516, Mar. 1972.

[14] R. Billinton and R. Allan, Reliability Evaluation of Power Systems. 2nd ed., Plenum Press, Ed. USA, 1996.

[15] P. Narayana, “Unit Commitment. A bibliographical Survey,” IEEE Transactions on Power Systems, vol. 19, May 2004.

[16] J. Arroyo and A. Cornejo, “Optimal response of a Power Generator to Energy, AGC, and reserve Pool-Based Markets,” IEEE transactions on Power Systems, vol. 22, pp. 76-77, May 2002.

[17] K. Lo, Deregulations of Electric Utilities, In Power System Restructuring and Deregulation. John Wiley and Sons, Ltd. 2001.

[18] Y. Hsu et al., “Operating Reserve and Reliability Analysis of the Taiwan Power System,” IEEE Proceedings Generation, Transmission and Distribution, vol. 137, pp. 349-357 Sept. 1990.

[19] (2009) the Pennsylvania-New Jersey-Maryland Interconnection. Available: http://www.pjm.com/.

[20] Regulations of the Salvadorian Power System Operations. July, 1999.

[21] G. Sheblé et al., “Engineering strategies for open access Transmission Systems,” A two-day Short Course presented Dec. 5 and 6, 1996, in San Francisco, USA.

[22] R. Billinton, M. Fotuhi-Firuzabad, and L. Bertling, “Bibliography on the application of probability methods in power system reliability evaluations 1996-1999,” IEEE Power Engineeering Review, vol. 21, p. 56, Aug. 2001.

[23] F. Lee, Q. Chen, and A. Breipohl, “Unit Commitment risk with Sequential Rescheduling,” IEEE Transactions on Power Systems, vol. 6, pp. 1017-1023, Aug. 1991.

[24] Power system Engineering Committee. “Bibliography on the application of probability Methods in Power System Reliability Evaluation: 1971-1977,” IEEE Transactions on Power Apparatus and Systems, PAS-97, pp. 2235-2242, New York, 1978.

[25] A. J. Wood et al., “Power System Reliability Calculations”. MIT Press, USA. 1973.

[26] N. Jaleeli and L. VanSlyck, “NERC’s new Control Performance Standards,” IEEE Transactions on Power Systems, Vol. 14, No. 3, Aug. 1999.

[27] (2009) the North American Electric Reliability Corporation. Available: http://www.nerc.com/

[28] A. Feliachi and D. Rerkpreedapong, “NERC compliant load frequency control design using fuzzy rules,” in Electric Power System Research. June, 2004.

[29] Z. How, “Weiner model identification based on Adaptive Particle Swarm Optimization,” at the 7th International Conference on Machine Learning and Cybernetics, Jul. 2008.

[30] R. Caponetto, L. Fortuna, S. Fazzino, and M. Xibilia, “Chaotic sequences to improve the performance of evolutionary algorithms,” IEEE Trans. On Evolutionary Computation, vol. 7, pp. 289-304, Jun. 2003.

[31] J. Park, Y. Jeong, W. Lee, and J. Shin, “An improved Particle Swarm Optimization for Economic Dispatch Problems with non-smooth Cost Functions,” IEEE Power Engineering Society Meeting, 2006, p. 7.

[32] W.M. Lin, F. Cheng, and M. Tsay, “An improved tabu search for Economic Dispatch with multiple minima,” IEEE Transactions on Power Systems, vol. 17, Feb. 2002.