Available online at ScienceDirect. Procedia Technology 25 (2016 )

Transcription

1 Available online at ScienceDirect Procedia Technology 2 (216 ) Global Colloquium in Recent Advancement and Effectual Researches in Engineering, Science and Technology (RAEREST 216) Seasonal Loading Impact on Performance of Solar DG for Loss Reduction Based On Capacity Factor Ritu Sharma a, Atma Ram Gupta b* a,b Department of Electrical Engineering,National Institute of Technology Kurukshetra, Kurukshetra , India Abstract This paper proposed a methodology to study the seasonal impact on the performance of solar based Distributed Generator (DG) for loss reduction as a main objective. The random behavior of solar irradiance is modeled using beta probability density functions. IEEE RTS (Reliability Test System) data is used for load modeling. Different modules of solar have been studied and appropriate selection is made using capacity factor approach and VSI method is used for finding the optimum location for placement of SPV (Solar Photo Voltaic). An iterative forward backward/bibc load flow method is used for analysis. The proposed technique has been applied to IEEE 33, IEEE 69 and IEEE 12 bus system and significant reduction of active power losses has been achieved for different seasons of the year The The Authors. Authors. Published Published by Elsevier by Elsevier Ltd. Ltd. This is an open access article under the CC BY-NC-ND license ( Peer-review Peer-review under under responsibility responsibility of the of the organizing organizing committee committee of RAEREST of RAEREST Keywords: Solar photo voltaic; Wind power; Realiability test system; Voltage stability index 1. Introduction With rapidly increasing demand of electrical energy electric utilities are utilizing special technologies to meet the load demand without violating the system constraints; these new technologies include usage of decentralized plant for generation of electricity. Decentralized plants which include non conventional sources like wind and solar energy are generally adopted by utilities due to large abundance of these sources. Intermittent nature of wind and solar is a great challenge for the reliability of system. In literature different papers have been proposed to study this random behavior of the renewable energy sources. Beta modeling is employed to model the solar irradiance in [1]. * Corresponding author. Tel.: address: argupta@nitkkr.ac.in The Authors. Published by Elsevier Ltd. This is an open access article under the CC BY-NC-ND license ( Peer-review under responsibility of the organizing committee of RAEREST 216 doi:1.116/j.protcy

2 72 Ritu Sharma and Atma Ram Gupta / Procedia Technology 2 ( 216 ) Weibull distribution function is used in [2]. Optimal placement of DG units affects the system characteristics as explained in [3] therefore; this aspect should be included in the system. VSI and PSI method is used to place the DG optimally in the system as described in [4, ]. Various optimization methods for loss reduction are presented by different authors like genetic algorithm in [6] and PSO is used in [7]. Effect of penetration levels of DG on annual energy losses is studied in [8]. Forward-backward load flow method is explained to study the system performance in [9]. BIBC based load flow technique is explained in [1]. In this paper beta probability distribution function (PDF) is used to model the hourly solar irradiance for different solar modules. To calculate parameters of beta PDF historical data of Jaipur site is used [11]. Optimal module is selected out of several modules based on highest capacity factor [12]. VSI method is used to place the DG optimally in the system and losses are calculated for different seasons of the year. 2. Modeling of solar DG and load In this section models of SPV and load data are presented. Beta modeling is used for solar module and IEEE RTS data is used for load modeling Hourly historical data processing. A 1 year (1986-2) hourly historical data of the Jaipur site is used to determine the hourly solar irradiance. The data has given as per L.A.T (Local Apparent Time) for sunny hours i.e. from 6 am to 7 pm of that site. For performing this task one year is divided into four seasons, where each season consist of three months and further each season is represented by any day within that season. Now this typical day is divided into 24 segments representing each hour from (12am to 12pm) of the day, therefore one segment will consist of 9 irradiance data points (3month*3 days per month) approximately. From this data mean and standard deviations are obtained for each hour which will be utilized in beta modeling of SPV system Solar irradiance modeling It is a widely used function to model the randomness of solar irradiance; the equation which describes the solar irradiance modeling using beta PDF is given by [13] 1 1 s *(1 s) fb() s ( ) (1) ( )* ( ) * (2) 1 1 (1 ) * * Load Modeling The load data used for this system is IEEE-RTS data which supply hourly peak load in percent of daily peak load for each season for weekdays and weekends [14]. 3. Calculation of Output Power from a Solar module This model is one of best method and widely used to model the output power of a PV module. The output power of a PV module is given as follows [13] Ppv= N*FF*Vy*Iy (4) () (6) (3)

3 Ritu Sharma and Atma Ram Gupta / Procedia Technology 2 ( 216 ) I y S[I sc + ] (7) (8) Nomenclature Ppv output power of PV module in kw V oc open circuit voltage. V mpp voltage at maximum power point. N number of module I sc short circuit current. I mpp current at maximum power point FF fill Factor T cy cells temperature in degree celsius during state y. K i current temperature coefficient A/ C Vy voltage of cell during state y nominal operating temperature of cell K v voltage temperature coefficient V/ C. Iy Current of cell during state y s solar irradiance in w/m 2 T A cells temp. in degree Celsius during state y 4. Selection for optimum power module and optimum location 4.1. Capacity Factor The complete analysis is carried out for different modules. The selections for optimum module from different modules are based on capacity factor. The module having highest capacity factor is selected. Capacity factor [12] is given as: CF = (9) where P avg is the annual average power output of DG units and P rated is the rated power output 4.2. Optimum Location Once the module is selected its corresponding output power (for each season) is used to carry out the load flow but optimum location is also required to place the DG in IEEE 33 bus system. Voltage Stability Index method is used for this purpose as in [4]. VSI= (1). Results and discussion The results are obtained for IEEE 33, IEEE 69 and IEEE 12 bus radial distribution system (RDS) for hourly peak load in percent of daily peak (IEEE RTS) for each season using MATLAB software version 7.8, 29 [1]. The base MVA and base KV of the system are 1 MVA and KV respectively. For the system under study three different modules as shown in Table 1 are used to calculate the solar output power. Capacity factor is calculated for all these modules. Module A is having highest capacity factor and can be observed from Fig. 1 so output power of module A is taken for further analysis. To place SPV DG output power optimally in the system VSI method is used. VSI is maximum for bus 6 as observed from Fig. 2. So, 6 th bus is selected as optimal location to place SPV DG. The output power of module A for different seasons is as shown in Fig.3. to Fig.6. The active power losses are obtained without and for IEEE 33 bus with forward backward load flow method and for IEEE 69 and IEEE 12 bus with BIBC load flow method for winter, summer, spring and fall seasons for all 24 hours are as shown in figures from Fig. 7 to Fig. 3.

4 74 Ritu Sharma and Atma Ram Gupta / Procedia Technology 2 ( 216 ) capacity factor Capacity factor for different modules modules VSI branch number Fig.1.Capacity factor for all three modules Fig.2. VSI profile for IEEE33 bus system Pout(kw) Winter Season(Dec,Jan,Feb) Pout(kw) Summer Season(Jun,Jul,Aug) Fig.3 Output power for winter season Fig.4 Output power for summer season Pout(kw) Spring Season(Mar,Apr,May) Pout(kw) Fall Season(Sep,Oct.Nov) Fig. Output power for spring season Fig.6 Output power for fall season Table1. Characteristics of Different PV Modules Module characteristics Module type A B C Watt Peak (W) Open circuit voltage (V) Short circuit current (A) Voltage at maximum power (V) Current at maximum power (A) Voltage temperature coefficient (mv/ C) Current temperature coefficient (ma/ C) Nominal cell operating temperature ( C)

5 Ritu Sharma and Atma Ram Gupta / Procedia Technology 2 ( 216 ) losses losses losses losses Fig.7 Losses for IEEE33 bus for winter weekdays Fig.8 Losses for IEEE33 bus for winter weekends 4 losses losses 4 losses losses Fig.9 Losses for IEEE33 bus for summer weekdays Fig.1 Losses for IEEE33 bus for summer weekends 4 losses losses 4 losses losses Fig.11 Losses for IEEE33 bus for spring weekdays Fig.12 Losses for IEEE33 bus for spring weekends 4 losses losses 4 losses losses Fig.13 Losses for IEEE33 bus for fall weekdays Fig.14 Losses for IEEE33 bus for fall weekends

6 76 Ritu Sharma and Atma Ram Gupta / Procedia Technology 2 ( 216 ) Fig.1 Losses for IEEE69 bus for winter weekdays Fig.16 Losses for IEEE69 bus for winter weekends Fig.17 Losses for IEEE69 bus for summer weekdays Fig.18 Losses for IEEE69 bus for summer weekends Fig.19 Losses for IEEE69 bus for spring weekdays Fig.2 Losses for IEEE69 bus for spring weekends Fig.21 Losses for IEEE69 bus for fall weekdays Fig.22 Losses for IEEE69 bus for fall weekends

7 Ritu Sharma and Atma Ram Gupta / Procedia Technology 2 ( 216 ) Fig.23 Losses for IEEE12 bus for winter weekdays Fig.24 Losses for IEEE12 bus for winter weekends Fig.2 Losses for IEEE12 bus for summer weekdays Fig.26 Losses for IEEE12 bus for summer weekends Fig.27 Losses for IEEE 12 bus for spring weekdays Fig.28 Losses for IEEE 12 bus for spring weekends Fig.29 Losses for IEEE 12 bus for fall weekdays Fig.3 Losses for IEEE 12 bus for fall weekends

8 78 Ritu Sharma and Atma Ram Gupta / Procedia Technology 2 ( 216 ) From the obtained results a significant loss reduction has been observed for different seasons during the sunny hours i.e. from 6 am to 7 pm when SPV DG is providing the output power to the system. 6. Conclusion The main contributions from the studies that have been carried out in this paper are: SPV output power is calculated for different seasons of the year. Capacity factor is utilized to select the optimum SPV module. The optimum location is obtained using VSI. A significant reduction in losses has been obtained for different seasons after placement of SPV module. References [1] Borowy B. S., and Salameh Z. M. Optimum photovoltaic array size for a hybrid wind/pv system. IEEE Transactions on Energy Conversion;1994: 9(3), p [2] Liu Z., Wen F., Ledwich. G. Optimal Sitting and Sizing of Distributed Generators in Distribution Systems Considering Uncertainties. IEEE Transactions on Power Delivery ; 211: 26(4), p [3] Thakur D., Jiang J. Optimal Location and size of DG for enhancing loading margin and reducing system loss. in Power and Energy Society General Meeting (PES), IEEE, 213, p.1-. doi: 1.119/PESMG [4] Murty V.V.S.N., Kumar A. Optimal placement of DG in radial distribution systems based on new voltage stability index under load growth, International Journal of Electric Energy and Power System ; 214: 69, p [] Aman M.M., Jasmon G.B., Mokhlis H. and Bakar A.H.A. Optimal placement and sizing of a DG based on new power stability index and line losses. International Journal of Electric Energy and Power System; 212: 43, p [6] Borges C. L, Falcao D. M. Optimal distributed generation allocation for reliability, losses, and voltage improvement. International Journal of Electric Energy and Power System; 26: 28, p [7] Kayal P. and Chanda C.K. Placement of solar based DGs in distribution system for Power loss minimization and voltage stability improvement. International Journal of Electric Energy and Power System; 213, 3, p [8] Quezada, V. M., Abbad, J. R., & Román, T. G. S. Assessment of Energy Distribution Losses for Increasing Penetration of Distributed Generation. IEEE trans. on PowerSystems; 26: 21( 2), p [9] Ghosh S. and Das D. Method for load-flow solution of radial distribution Networks. IEE Proc. Gener. Transm. Distrib; 1999: 146(6), p [1] Teng J. H. A Direct Approach for Distribution System Load Flow Solutions. IEEE Transactions on Power delivery;23:18(3), p [11] A report on "Solar Radiant Energy over India ", Ministry of New and Renewable Energy, Govt. of India; 29. [12] Atwa Y.M., Saadany E.F.El, Salama M.M.A. and Seethapathy R. Distribution System Loss Minimisation Using Optimal DG Mix. in Power & Energy Society General Meeting, 29. PES '9. IEEE, p.1-6. doi: 1.119/PES [13] Hung D. Q., Mithulananthan N., K. Y. Lee Determining PV Penetration for Distribution Systems With Time-Varying Load Models. IEEE Transactions on Power Systems; 214,29(6), p [14] IEEE Commmitte Report. IEEE Reliability Test System. IEEE Transactions on Power Apparatus and Systems; 1979: PAS-98, 6, p [1] The MATLAB by Mathworks Corporation, MATLAB version 7.8, 29.