1. UNIVERSITY OF SISTAN AND BALUCHESTAN A Novel Techno-Economical Optimization Approach Based on Linear Integer Programing (LIP) for Hybrid Renewable systems  Saeed Reza Nazari Estahbanati Department of Mechanical Engineering, University of Sistan and Baluchestan.  S. Masoud Barakati Department of Power Electronic Engineering, University of Sistan and Baluchestan.  Mehri Mehrjoo Department of Telecommunication, University of Sistan and Baluchestan.  Mohammad Ali Yazdanpanah Jahromi Department of Mechanical Engineering, University of Guilan.

2. 2 In the name of god The purpose of today’s presentation is to introduce A Novel Techno-Economical Optimization Approach Based on Linear Integer Programing (LIP) for Hybrid Renewable systems

3. Wind Turbine Power Adjusting the measured wind speed 1/7 for open land The weibull distribution function

4. 4 Output energy of wind turbine Power curve of small wind turbine “Lakota S, SC”

5. 5 PV Array Power

6. 6 b, usually changes from 0.01 to 0.18.

7. Operation of Battery 7 Capacity of the BT depends on temperature δ C suggested 0.6 % per degree The BT current rate η inverter = 92% SOC max =1, SOC min =1-DOD. and DOD is 60%. The Battery SOC

8. 8 Optimization Problem Objective Function minn(npv × UCEEPV+nWT × UCEEWT+n BAT × UCEEBAT) Constrains 0<LPSP<0.03 SOCmin<SOC<SOCma x npv,nWT,n BAT >0 (n pv,n WT,n BAT ) Ƶ Decision Variables Number of PV (nPV),WT(nWT),BAT(n BT )

9. Economic Analysis 9 ATED stands the annual total energy demand in (kWh). Unit Cost of Electricity Energy

10. Reliability Analysis 10 Loss of Power Supply Probability

11. Flowchart and Optimization procedures 11

12. RESUALTS The proposed methodology has been used to achieve the proper size of off-grid power system, to supply the load demand of a remote area by using of branch and cut based on linear integer programing (LIP). The typical meteorological of one year hourly data collected for solar radiation, temperature and wind speed. The location of Marvdasht, Fars, Iran (Latitude: 29°50'N, Longitude: 52°40'E) is chosen for case study. Results displayed as an optimal Pareto front. Each solution of the best Pareto front was achieved by a combination of hybrid systems in some manner to reach higher reliability. 12

13. optimal solutions of components No PV number WT numberBT number UCEE ($/kW) LPSP % 1 221257.61421.24 2 114191.13752.40 3 212211.84780.00 4 222196.77112.85 5 321187.50200.53 13 The best configurations among the results are shown in below table. The lowest Unit cost of electricity energy (UCEE) achieved in related on the candidate number 5 but the lowest LPSP is given by choice 3.

14. The Loss of power supply probability (LPSP) for one month is demonstrated in below figure. The LPSP is completely well controlled to be less than 3% as desired LPSP. It is illustrated the best match between the load and supply. 14

15. The battery SOC is shown in Figure 11 for one month. It is noteworthy that the battery SOC is in the SOC max and the SOC min range, so the longest lifespan of the battery is ensured by the proposed management strategy. Figure 11. SOC of one month 15

16. The relation between LPSP and UCEE is shown in below figure. With increasing the LPSP the value of the UCEE is decreased and vice versa by decreasing the value of the LPSP the UCEE is increasing. The Branch and cut method is a suitable method to reach the best size. It is needed lower time in comparison of the evolutionary methods to reach the results and it guarantees that the obtain results is really the optimal solution. 16

17. 17 I really appreciate for your concern The End