1. Economic Scheduling the Construction of Electric Transmission/ Distribution Substations in Jeddah City with Parametric Results Said Ali El-Quliti1) and Mohammed RedaKabli2) 1) Professor, 2) Assistant Professor, Department of Industrial Engineering, King AbdulAziz University, Jeddah, Saudi Arabia.

2. On the Name of God

4. Scope: The electricity field in Jeddah City; for a long-term time horizon (10 years).

6. 2. LITERATURE REVIEW The population forecast in Jeddah city is expected to increase with an increasing rate of 2.2% a year. "Eighth Development Plan 2005 – 2009", Ministry of Planning, Kingdom of Saudi Arabia, 2004.

7. In a previous paper, A study to forecast the number of substations needed for the 10 years (2006 – 2015) is determined, and also the optimum scheduling of these substations. Said Ali Hassan ElQuliti, Ibrahim A. El-Darrab, and Mohammed Abdu Al-Ameer, "A Dynamic Programming Model for Scheduling the Electric Transmission/Distribution Substations in Jeddah City", WSEAS Intern. Conferences, Univ. of Cambridge, Cambridge, United Kingdom, 2009.

8. Parols A.G. et al, found that an essential element of electric utility resource planning is forecasting of the future load demand. Parols A.G., Esmaeil O., Muthusami J, Patton A. and Atiya A.F., “Development of an Intelligent Long-Term Electric Load Forecasting System”, Intelligent Systems Applications to Power Systems, Proceedings, ISAP International Conference, pp. 288 – 292, 1996.

9. F.-C. Lu, and Y.-Y. Hsu Investigate the reactive power/voltage control in a distribution substation. An approach based on dynamic programming is presented to reach the desired schedule. F.-C. Lu, Y.-Y. Hsu, " Reactive power/voltage control in a distribution substation using dynamic programming", IEE Proceedings, Generation, Transmission and Distribution, Volume 142, Issue 6, pp. 639-645, November 1995.

10. YixinYn et al, Use dynamic programming for the optimal planning of substation locations which satisfy the load demands and minimize the investment and operating cost. YixinYn, Chengshan Wang, ShaoyunGe, Jun Xiao, Xuefei Yan, Chunhua Huang "Models and Methods for Urban Distribution Planning", School of Electrical and Automation Engineering, Tianjin University, Perth, WA, Australia, 2000.

11. 3. DATA COLLECTION SAUDI ELECTRICITY COMPANY The single national company for generation, transmission and distribution of electricity all over Saudi Arabia. (Now it is divided into several Companies).

12. FORECASTING OF THE ELECTRIC LOAD Time Series Data of the annual total electricity consumption in Jeddah city 32 years (1979-2010)were collected.

13. Forecasting The number of required substations to be built (from 2011 to 2020) is forecasted depending on results for forecasting annual total consumption of electricity . The forecasting is done using Multiple Regression Analysis(Causal Methods) and Artificial Neural Networks (ANN).

14. 4. THE DYNAMIC PROGRAMMING MODEL

15. THE MODEL Decision variables: the number of transmission/distribution substations to build in each year (stage). The states: the number of substations still required in remaining years. The objective function: minimize the total cost of building (investment cost) and operating the substations in the coming ten years. The constraints: the number of required substations, budget to maintain both the building and operating costs.

16. 4.1 Recursive Relationship in Dynamic Programming Identifies the optimal policy for stage n, given the optimal policy for stage n + 1.

17. 3 4 5 Stage 1 2 6 7 8 9 10 13 16 19 10 0 7 4 1 6 14 11 1 8 5 22 25 17 20 9 2 7 4 0 12 2 10 15 21 18 3 8 5 11 13 3 9 6 Network formulation for the problem Required Substations: 3 1 3 3 2 2 2 3 3 3 Budget Constraints: Constructed number ≤ 3 substation/year

18. 3 4 5 Stage 1 2 6 7 8 9 10 347.51 664.44 947.06 1198.62 138.00 731.88 1620.04 1422.11 419.68 1794.63 13 16 1010.09 19 10 0 7 4 1257.52 1 224.40 791.02 482.93 1477.17 1948.22 1671.49 2083.22 1842.73 6 0.00 1065.34 14 11 1 8 5 22 25 17 1309.41 20 9 301.61 866.49 2 563.69 7 1885.09 1716.81 1525.67 1135.88 4 1375.74 0 12 2 10 15 21 18 933.93 369.63 635.86 3 1198.91 8 5 11 13 3 993.34 699.44 9 6 Possible branches Branch of minimum total cost to the end node Branch on the optimum path Solution of the problem The optimal Solution: 3 1 3 3 3 3 0 3 3 3 Required Substations: 3 1 3 3 2 2 2 3 3 3

19. PARAMETRIC RESULTS

20. 6.1 Control Parameters Affecting the Solution The substation building cost (investment cost). The percentage of yearly increase in the substation building cost. The percentage of discount for buying more than one substation. Initial value of the operating cost (in the 1st. Year). The percentage of yearly increase in the operating cost..

21. The Same Optimal Solution The optimal Solution: (3, 1, 3, 3, 3, 3, 0, 3, 3, 3)

22. The Same Optimal Solution The optimal Solution: (3, 1, 3, 3, 3, 3, 0, 3, 3, 3) The substation building cost. The Total Optimal Value (TOV): Total Optimum Values for different initial building cost.

23. 2. The percentage of yearly increase in the substation building cost. The optimal Solution: (3, 1, 3, 3, 3, 3, 0, 3, 3, 3) (TOV) for different increases in substation building cost.

24. 3. The percentage of discount for buying more than one substation. The optimal Solution: (3, 1, 3, 3, 3, 3, 0, 3, 3, 3) (TOV) for different discounts for substation's cost.

25. 4. Initial value of the operating cost. The optimal Solution: (3, 1, 3, 3, 3, 3, 0, 3, 3, 3) (TOV) for different initial operation cost.

26. 5. The percentage of yearly increase in the operating cost. The optimal Solution: (3, 1, 3, 3, 3, 3, 0, 3, 3, 3) (TOV) for different % yearly increase in operation cost.

27. 6.3 Changes in the Optimal Solution If changes in parameters are outside the range, a new optimal solution appears:

28. Optimal Solution (3, 1, 3, 3, 2, 2, 2, 3, 3, 3) 1. For substation building cost < 19.3 million Saudi Riyals: (TOV) for different substation building cost. 2. When there is no discount for buying more than one substation.

29. (TOV) for different initial operating cost. Initial operating cost Optimal Solution (3, 1, 3, 3, 2, 2, 2, 3, 3, 3)

30. A New Optimal Solution (3, 1, 3, 3, 3, 3, 3, 3, 3, 0) 1. Substation building cost: (51 – 53.5) million Saudi Riyals. (TOV) with different substation building cost.

31. Optimal Solution: (3, 1, 3, 3, 3, 3, 3, 3, 3, 0) (TOV) with different initial operation cost.

32. 7. CONCLUSIONS The ability of NN’s to forecast within an error range of about 2 % only. Mean annual electricity consumption increase (2011 – 2020) = 3.49%. A backward model of dynamic programming approaches can be used. The solution procedure starts at the last year (stage n = 10) and then move backward to the initial stage (first year).

33. CONCLUSIONS (Cont’d.) 5. The optimal solution is (3,1,3,3,3,3,0,3,3,3) in the years (2010 – 2020) with total cost of SR. 2,083,220,000. 6. There are many parameters affecting the optimal solution: - Initial value of the substation building cost. - Percentage of yearly increase in the substation building cost. - Percentage of discount for buying more than one substation. - Initial value of the operation cost. - Percentage of yearly increase in the operation cost. 7. The parametric results gives the decision for different changes in the problem data.

34. 8. POINTS FOR FUTURE RESEARCHES To prove analytically the parametric results. To analyze the sudden changes in the optimal values. To study the time value of money. To study the parametric results for simultaneous changes in different parameters. To use the shortest route and other techniques. To generalize the method for other regions in the kingdom. To build a decision support system for such problems.