Presentation: Optimal Planning of Renewable Energy Sources Integration in Smart Distribution Networks. GCC-Cigre. By Sultan S. Alkaabi, 9 th May 2017

Transcription

1 Presentation: Optimal Planning of Renewable Energy Sources Integration in Smart Distribution Networks GCC-Cigre By Sultan S. Alkaabi, 9 th May

2 Presentation Preview Introduction: Renewable Energy Integration 1) Active Network Management 2) Smart Control Strategies for PV Systems 3) Planning Considering Customer DG Installations 4) Reactive Power Planning Approach Considering utility and Customer-owned PV Systems Conclusion and Future Work 2

3 Increasing Renewable Energy 3

4 Integration of Renewable Energy Sources (RESs) One of the emerging and fast-growing transitions A challenging task that requires all the typical aspects of planning, operation and control Positive/negative impacts 4

5 Integration Challenges of RESs The system planners and operators of power networks have been facing increasing challenges in: Connecting large numbers of small-scale RESs at low voltage (LV) networks Expanding the high voltage (HV) transmission networks to connect large-scale RESs Controlling and balancing the network to handle large amounts of intermittent and variable RESs Counteracting the impact of the increasing feed-in from RESs 5

6 Problem Statement RES integration affects various aspects of conventional power system planning, operation and control, which should be coherently considered for developing innovative solutions to tackle and cope with the challenges of large integration of RESs. Such coherent solutions may include the development and advancement in various areas. 6

7 Potential Areas of Developments 1) Planning of network assets considering RESs 5) New planning and operation tools 2) More reliance on dispatchable and flexible generation 4) More sophisticated control systems 3) More advanced energy management systems 7

8 Research Objectives Capitalizing on the reactive power capability of PV systems Analyzing the impact of customer DG installations (ANM schemes + DG limits) Developing planning models and approaches Encouraging network innovation by utility planners for minimizing energy losses Role of active participation of customer DG installations (active control + PFC + RPC) 8

9 Technical Contributions 1 DG Planning with PV Inverter Control Schemes Proposing the PV inverter controls (PVIC) as new ANM schemes For planning smart distribution networks Maximize DG limits 9

10 Technical Contributions 2 DG Planning with Customer-DG Installations Tackling the challenges of customer DG integration scenarios (e.g., random numbers, sizes, locations and connection times) Considering its effects on planning ANM Schemes + DG limits 10

11 Technical Contributions 3 Reactive Power Planning with Customer-PV Systems Developing a utility reactive power planning model for minimizing cost of energy losses Cost of reactive power Customer participation Flexible and adaptable options according to DG installations 11

12 Motivation and Relevance to UAE The main driving forces/motivations of this research: The world-wide Governments targets and initiatives to integrate high levels of RESs in power networks The United Arab Emirates (UAE) 2030 s vision to promote renewable energy integration and sustainable development This research is in line with the targets of facilitating the increasing integration of RESs by focusing on tackling some of the system planning and operation challenges of RESs. 12

13 PART 1 ACTIVE NETWORK MANAGEMENT 13

14 Definitions Distributed Generation (DG) Any renewable or non-renewable generation source connected at transmission or distribution networks Renewable Energy Sources (RES) Renewable based DG unit Active Network Management (ANM) Schemes Smart control strategies for networks assets/dg units Effective utilization of network assets/dgs 14

15 Classification of ANM Schemes 15

16 Table 2.1: Common assets and controls under ANM schemes Asset/component References OLTC/VRs [29], [30], [31], [32], [33], [37], [39], [40], [41], [43], [51], [55]. [56], [57], [97] Reactive Power Compensators [30], [47], [57], Capacitor Banks [41], [42], Feeder Switches [41], [43], [44], [45], [46] Energy Storage System [49], [53], [54], [55], [86], [88], [89] Flexible demand or Demand Response [49], [50], [51], [52], [86], [87] PV Inverter [12], [47], [54], [56], [66], [68], [69], [70], [71], [72], [73], [74], [79], [93], [94], [95], [96], [97] RES/DG s Power Factor Control [12], [29], [30], [31], [32], [33], [36], [37], [39], [40], [41], [43], [47], [51], [56], [68], [69], [70], [95], RES/DG s Energy Curtailment and [20], [30], [31], [32], [34], [35], [36], [39], [40], [41], Active Power Curtailment [43], [47], [49], [64], [65], [68], [94], [95] RES/DG s Voltage Control or Voltage Violation Control Electric Vehicle [44], [45], [46], [57], [54], [64], [65], [66], [67], [68], [69], [70], [71], [72], [73], [74], [79], [93], [94], [95], [96], [97] 16

17 Table 2.2: Common objective functions under ANM schemes Objective Functions References Min energy losses [33], [35], [94] Min energy losses cost [39], [40] Min active power losses [42], [46], [55], [68], [69], [71], [72], [94], [97] Min active power losses cost [44], [45] Min energy cost [44], [45], [49] Min generation cost [20], [46], [49], [52], [53] Min active power curtailment [41], [49], [95] Min energy curtailment [34], [49], [94] Min energy curtailment cost [30], Max RES/DG capacity [29], [31], [43], [47], [56], [70], [74], [97] Max RES/DG energy [32], [35], [47], [53], [68] Min investment cost or [12], [35], [39], [40], [42], [64], [97] Max net present value (NPV) Min grid reactive power support [37], [70] Min network costs [45], [46], [48], [50], [53], [55] Multi-objective [12], [39], [40], [46], [57], [68], [71], [94] 17

18 Table 2.3: Common formulations, optimizations, controls methods and techniques under ANM schemes Formulations, Optimizations, Controls Methods/Techniques References Single-period OPF [30], [41], [42], [47], [50] Multi-period OPF [29], [31], [32], [33], [37], [39], [40], [43], [47], [49], [56] Probabilistic OPF [42], [97] Economic Dispatch [48], [52], [53] Heuristics [12], [45], [46], [53], [57], [68], [94] Voltage sensitivity analysis [34], [54], [68], [70], [79] Power flow sensitivity analysis [35] Voltage constraint management [34], [41], [54], [55], [64], [65], [68], [69], [70], [88] Thermal constraint management [35], Voltage and thermal constraints management Centralized control [29], [30], [31], [32], [33], [36], [37], [39], [40], [41], [42], [43], [47], [56] [29], [30], [31], [32], [33], [37], [39], [40], [41], [42], [43], [47], [55], [56], [57] Decentralized control [36], [48], [52], [54], [68], [70], [72], [95], [97] 18

19 Research s Methods, Techniques and Problem Formulations Under Study 19

20 Active Network Management Definition New control strategies that can be economically beneficial to facilitate and maximize DG integration Benefits of ANM Schemes Maximize the amount of DG capacity that can be accommodated in distribution networks Maximize the utilization of networks assets Defer upgrading the existing networks Other objective functions: voltage constraints management, minimize energy losses, etc. 20

21 ANM Simple Network System V 1 I COMP OLTC V s I Line I DG(P) Compensator ± j Q COMP GSP T : 1 R + j X I DG(Q) DG P DG ± j Q DG Load P L1 + j Q L1 I L1 V rise = V 1 V s = R P DG P L1 + X(±Q DG ± Q COMP Q L1 ) V 1 P DG V 1 V 1 V s R + RP L1 XQ L1 R ±XQ DG R ±XQ COMP R 21

22 Base Case Passive Networks X OLTC V s I Line V 1 I COMP I DG(P) Compensator ± j Q COMP GSP T : 1 R + j X I DG(Q) DG P DG ± j Q DG Load P L1 + j Q L1 I L1 P DG V 1 V 1 V s R + RP X L1 XQ L1 R ±XQ X DG ±XQ X COMP R R 22

23 Base Case Passive Networks X V 1 OLTC V s I Line I DG(P) GSP T : 1 R + j X DG P DG P DG V 1 V 1 V s R + RP L1 XQ L1 P DG R ±XQ DG R max I max V V 1 max V s 1 R ±XQ COMP R 23

24 Single ANM Scheme: CVC V s OLTC I Line V 1 I DG(P) GSP T : 1 R + j X DG P DG max P I max DG V V 1 max V s 1 R max P II max DG V V 1 max min V s 1 R 24

25 Combined Two ANM Schemes: CVC and PFC V s OLTC I Line V 1 I DG(P) GSP T : 1 R + j X I DG(Q) DG P DG ± j Q DG max P I max DG V V 1 max V s 1 R max P III max DG V V 1 max min V s 1 R ±XQ DG max R max P II max DG V V 1 max min V s 1 R 25

26 Combined Three ANM Schemes: CVC, PFC & CRPC GSP V s OLTC T : 1 R + j X I Line V 1 I COMP I DG(P) I DG(Q) ± j Q Compensator COMP P DG ± j Q DG DG max P I max DG V V 1 max V s 1 R max P III max DG V V 1 max min V s 1 R ±XQ DG max R max P II max DG V V 1 max min V s 1 R max P IV max DG V V 1 max min V s 1 R ±XQ DG max R ±XQ max COMP R 26

27 Combined Four ANM Schemes: CVC, PFC, CRPC & EC GSP V s OLTC T : 1 R + j X I Line V 1 I COMP I DG(P) I DG(Q) ± j Q Compensator COMP P DG ± j Q DG DG max P I max DG V V 1 max V s 1 R max P V max DG V V 1 max min V s 1 R ±XQ DG max R ±XQ max COMP R max + P DG,Curt 27

28 Factors Affecting DG Capacity V s OLTC GSP R + j X T : 1 V 1 I COMP I Line I DG(P) I DG(Q) I L1 ± j Q Compensator COMP P DG ± j Q DG DG P L1 + j Q Load L1 P DG β 1 V 1 β 1V 1 β 2 V s R + β 3 RP L1 XQ L1 R β 4 ±XQ DG1 R β ±XQ max COMP 5 R max +β 6 P DG,Curt + β 7 Line characteristics 28

29 Maximizing DG Capacity Case Study: 33kV 16-bus UKGDS System Characteristics 33 kv rural network 132 kv GSP Long circuit length Low customer density Overhead construction Radial topology Small overall size Voltage problems Peak demand 38.2 MW Two 30 MVA OLTC TRs. Voltage limits: 6% Comp. 1.5MVAr DG Ref: United Kingdom Generic Distribution System (UKGDS). [Online]. Available: 29

30 Description of ANM Schemes ANM Distributed Generation OLTC Voltage Regulator Reactive Power Compensator Power factor Energy Curtailment Fixed: p.u. Fixed: 1.03 p.u. No Comp Unity pf 0% EC Controlled: 6% Controlled: 6% 1.5 MVAR at Bus lagging 2% EC PFC ( 0.95) 5% EC 30

31 Description of ANM Schemes ANM Distributed Generation OLTC Voltage Regulator Reactive Power Compensator Power factor Energy Curtailment Fixed: p.u. Fixed: 1.03 p.u. No Comp Unity pf 0% EC Controlled: 6% Controlled: 6% 1.5 MVAR at Bus lagging 2% EC PFC ( 0.95) 5% EC Passive Network (Base Case) 31

32 DG Capacity (MW) ANM Schemes at Bus 16 + No CVC % 30% 50% 80% 108% (i) PFC 0.0 No CRPC CRPC CRPC CRPC EC 2% EC 5% No EC % + EC 2% + EC 5% Unity i PFC % 47% ANM Schemes ( No CVC ) 32

33 DG Capacity (MW) ANM Schemes at Bus 16 + CVC % 230% 206% 275% % No CRPC CRPC CRPC CRPC EC 2% EC 5% No EC % + EC 2% + EC 5% Unity i PFC % ANM Schemes ( + CVC ) Reaching Thermal Limit 33

34 DG Capacity (MW) ANM Schemes: No CVC vs. +CVC % % 88% % 108% 0.0 No CRPC CRPC CRPC CRPC EC 2% EC 5% No EC % + EC 2% + EC 5% Unity Unity + CVC % ANM Schemes ( No CVC & + CVC ) 34

35 Taps (Number) Taps (Number) Relative Output (p.u.) Dynamic Analysis of OLTC & VR PI Controller for OLTCs Load Discrete Profile GSP 1 3 OLTC-tap OLTC 1 17 P _ OLTC-tap I (5.41,1.09) V 2,ref (1.93,0.39) (0.06,0.01) (1.01,0.2) V 2,meas 8 LEGEND 16 Bus Index 18 Branch Index (P,Q) Demand (MW, MVar) OLTC range under ANM (18.4,3.74) VR range under ANM 7 6 (1.9,0.39) (1.96,0.4) (2.85,0.58) VR (2.7,0.55) (0.81,0.16) (0.55,0.11) 18 VR-tap (0.58,0.12) Fig. 1. The 33kV 16-bus UKGDS model. _ P VR-tap I V 9,ref V 9,meas PI Controller for VR Time (min) Fig. 4. The constructed load profile for testing the control schemes Tap-setting (taps) Tap-setting (taps) (a)oltc ANM Scheme ANM PNM Scheme PNM Time (s) Time (sec) Time (sec) 18 ANM Scheme ANM 16 PNM Scheme PNM 14 (b) VR Time (s) Time (sec) Fig. 7. The OLTCs and VR tap-settings under discrete load profile: ANM versus PNM schemes. (a) OLTC. (b) VR.* Each 1 second = 1 minute. 35

36 PART 2 INCORPORATING PV INVERTER CONTROL SCHEMES FOR PLANNING ACTIVE DISTRIBUTION NETWORKS 36

37 Introduction & Literature Review PV Systems Growing deployments levels of PV systems throughout the world Increasing integration challenges and impacts Transition of current grids into smart grids or active networks Research on PV systems Studies PV systems impacts and/or tackles its challenges Mostly performed at operation stage not planning stage Growing interest on studying active network management schemes to facilitate renewable energy integration ANM planning studies Only consider reactive power capability of DGs within allowable power factor limits (e.g. unity, PFC, etc.) The utilization of reactive power capability of PV inverters is not studied under no active power output and no power factor constraints The concept of inverter oversizing is also not clearly studied 37

38 Problem Definition Most planning studies do not consider: Proposed Work Utilization of reactive capability when there is no active power output, Providing detailed formulation of various potential PVIC schemes for planning active distribution networks Impact of inverter oversizing to allow further exploitation of reactive capability, and Providing comprehensive details for the concept of inverter oversizing along with its impact on enhancing PVIC schemes Enhanced utilization approaches of reactive power capability other than PFC Presenting application of the PVIC schemes to utilize existing PV inverters for maximizing wind penetration levels ANM Planning PV Inverter Control (PVIC) Objective 38

39 Technical Contributions 1 DG Planning with PV Inverter Control Schemes Proposing the PV inverter controls (PVIC) as new ANM schemes For planning smart distribution networks Maximize DG limits 39

40 110% 105% 100% Reactive Power (p.u.) Q 0% NAP-Schemes 0% Proposed PV Inverter Control (PVIC) FULL-Schemes OVER-Scheme (45.8%) PART-Scheme (43.6%) OVER-Scheme (32%) Active Power (p.u.) Normal sizing Oversizing (5%) Oversizing (10%) 90% 100% Schemes 1) No Active Power (NAP): when the PV arrays do not produce any active power and remain inactive, the full reactive power capability of PV inverters can be utilized (up to its full rating) to maintain the network voltage within allowable limits and thus, to increase the wind penetration in the network. The NAP times include nighttime, early morning and early evening hours. 2) Partial-Utilization (PART): in this scheme, based on PV historical data, utility planners can determine the amount of unutilized inverter capacity most of the time. For example, in the UK system, a minimum of 10% of the inverter capacity remains unutilized most of the time (8748 hours by referring to Table II) where PV generates less than 90% rated power. 3) Full-Utilization (FULL): in this scheme, the PV inverter reactive power capability is utilized up to its full capacity all the time. 4) Oversize-Utilization (OVER): in this scheme, the inverter is oversized by a certain percentage to achieve a certain fixed reactive power support (margin) all the time. This fixed margin is based on the maximum reactive power limit when the PV generates rated (100%) active power. 5) Combined Schemes: a PV inverter can also be operated in combined schemes to achieve enhanced reactive power support, e.g., (NAP+PART) or (NAP+OVER) schemes. 40

41 PVIC Descriptions & Case Studies DESCRIPTION OF PROPOSED PVIC SCHEMES UNDER STUDY Inverter Oversizing (%) s PVIC Schemes s SN Reactive Power Limit Constraints (±Q s max ) PV Normal sizing Oversizing (5%) Oversizing (10%) 1 NAP Q 1 max = 2 PART Q 2 max = 3 NAP + PART Q 3 max = 100%, P = 0% 0, otherwise 43.6%, 0 P < 90% 0, otherwise 100%, P = 0% 43.6%, P < 90% 0, otherwise (9) (10) (11) 4 FULL Q 4 max = S 2 P 2 (12) 5 OVER Q 5 max = 32.0%, 0 P 100% (13) 6 NAP + OVER Q 6 max = 105%, P = 0% 32.0%, 0 < P 100% (14) 7 FULL Q 7 max = (1.05 S) 2 P 2 (15) 8 OVER Q 8 max = 45.8%, 0 P 100% (16) 9 NAP + OVER Q 9 max = 110%, P = 0% 45.8%, 0 < P 100% (17) 10 FULL Q max 10 = (1.10 S) 2 P 2 (18) Case Studies Base DG Wind System under study: Modified 16-bus 33kV UKGDS model DESCRIPTION OF CASE STUDIES Description of ANM Schemes Only CVC Scheme of OLTCs and voltage regulator Proposed PVIC NAP, PART, NAP+PART and FULL schemes at Bus-11 considering normal-sized inverter Proposed PVIC OVER, NAP+OVER and FULL schemes at Bus-11 considering 5% and 10% oversized inverters Sensitivity analysis for proposed PVIC schemes considering: 1) different single/multiple locations (Buses 4, 5, 7, 9 and 14) 2) different sizes (10MW and 2MW); of PV system(s) DG 41

42 Wind-DG Capcity at Bus-16 (MW) Wind-DG Capacity at Bus-16 (MW) Wind-DG Capacity at Bus-16 (MW) Case Study 1 No Oversizing Case Study 2 Oversizing % 58.9% 86.4% 88.8% 0.00 Base NAP PART NAP + PART FULL Unity lag PFC Maximum wind capacity at Bus-16 for case study 1: incorporating the 10MW PV system at Bus-11 with PVIC schemes under normal-sized inverter % 77.2% 90.6% 60.4% 88.0% 92.3% 0.00 Base OVER NAP + OVER FULL OVER NAP + OVER FULL Oversizing (5%) Oversizing (10%) Unity lag PFC Maximum wind capacity at Bus-16 for case study 2: incorporating the 10MW PV system at Bus-11 with PVIC schemes under oversized inverter Case Study 3 Sensitivity Analysis Bus 4 (10MW PV) Bus 5 (10MW PV) Bus 7 (10MW PV) Bus 9 (10MW PV) Bus 14 (10MW PV) All Five Buses (2MW/Bus) Base NAP PART NAP+PART FULL OVER NAP+OVER FULL OVER NAP+OVER FULL Normal Sizing Oversizing (5%) Oversizing (10%) Maximum wind capacity at Bus-16 for case study 3: sensitivity analysis for PVIC schemes performance under: (1) single 10MW PV system at different locations (Buses 4, 5, 7, 9 and 14), and (2) 10MW PV systems at five PV locations (2MW/bus). 42

43 Analysis and Discussion 1) Effectiveness of PVIC schemes for increasing total wind penetration in the network. 2) Maximize the utilization of PV inverters 3) Different performance of PVIC schemes S. S. AlKaabi, V. Khadkikar and H. H. Zeineldin, Incorporating PV Inverter Control Schemes for Planning Active Distribution Networks, in IEEE Transactions on Sustainable Energy, vol. 6, no. 4, pp , Oct S. Alkaabi, V. Khadkikar and H. Zeineldin, "Incorporating PV inverter control schemes for planning active distribution networks," 2016 IEEE/PES Transmission and Distribution Conference and Exposition (T&D), Dallas, TX, 2016, pp

44 PART 3 ADAPTIVE PLANNING APPROACH FOR CUSTOMER DG INSTALLATIONS IN ACTIVE DISTRIBUTION NETWORKS 44

45 Overview PART 2 Advantage of PV systems PVIC Schemes + Maximize DG Penetration Planning with known PV locations PART 3 takes into account a different approach to maximize DG penetration limits Random customer-dg installations Adaptive ANM schemes of DG units 45

46 Conventional DG Planning Finding Optimal DG locations + Sizes 46

47 Conventional DG Planning Finding Optimal DG locations + Sizes

48 4MW Conventional DG Planning Finding Optimal DG locations + Sizes 3MW 1 3 2MW 2 48

49 4MW Conventional DG Planning Finding Optimal DG locations + Sizes 1 3MW 3 2MW 2 49

50 4MW 1 Considering A Scenario of Customer-DG Installations Random: Numbers, Locations and Sizes 3MW 3 2MW 2 50

51 4MW 1 Considering A Scenario of Customer-DG Installations Random: Numbers, Locations and Sizes 3MW 3 2MW 2 51

52 4MW 1 Considering A Scenario of Customer-DG Installations Random: Numbers, Locations and Sizes 3MW 3 2MW 2 52

53 4MW 1 Considering A Scenario of Customer-DG Installations Random: Numbers, Locations and Sizes 3MW 3 2MW 2 53

54 4MW 1 Considering A Scenario of Customer-DG Installations Random: Numbers, Locations and Sizes 3MW 3 2MW 2 54

55 4MW 1 Considering A Scenario of Customer-DG Installations Random: Numbers, Locations and Sizes 3MW 3 2MW 2 55

56 4MW 1 Considering A Scenario of Customer-DG Installations Random: Numbers, Locations and Sizes 3MW 3 2MW 2 56

57 4MW 1 Considering A Scenario of Customer-DG Installations Random: Numbers, Locations and Sizes 3MW 3 2MW 2 57

58 4MW 1 2MW 2 Shortcomings of One-time Approach 1) The feasibility of ANM scheme and DG limits is guaranteed only when the exact DG limits are connected 2) Assumed no DG units will connect beyond any DG limits 3) Does not consider the gradual connection and increase of customer DG installations (e.g., number, size, connection sequence, connection time, etc.) 3MW 3 58

59 Technical Contributions 2 DG Planning with Customer-DG Installations Tackling the challenges of customer DG integration scenarios (e.g., random numbers, sizes, locations and connection times) Considering its effects on planning ANM Schemes + DG limits 59

60 Proposed Adaptive Planning Approach Any DG increase Any DG distribution Adaptive ANM Schemes Planner s ANM Criteria Customer DG Integration Adaptive DG Limits 60

61 Planners -Defined Ranking Criteria Rank the order of ANM schemes Planning Scenarios Energy Curtailment Level (%) 0% 0.05% 0.1% 0.25% 0.5% 1% 2% 5% 10% Power factor PS-A A1 A2 A3 A4 A5 A6 A7 A8 A9 unity A10 A11 A12 A13 A14 A15 A16 A17 A lag A19 A20 A21 A22 A23 A24 A25 A26 A27 PFC PS-B B1 B4 B7 B10 B13 B16 B19 B22 B25 unity B2 B5 B8 B11 B14 B17 B20 B23 B lag B3 B6 B9 B12 B15 B18 B21 B24 B27 PFC 61

62 Problem Formulation (Two-Phase) Phase-I Objective: min S N s=1 y s w s (4.1) ANM schemes selection: S N s=1 y s = 1 (4.9) Q G i,m P G i,m t + P DG,i t + P DG,i Generator active power limits: g m 1 β i,m (s) j N g m tan φ i,m s 1 β i,m s Generator reactive power limits: P i,j,m j N = P i D d m Q i,j,m = Q i D d m (4.2) (4.3) P min G P G max i,m P G (4.4) Q min G Q G max i,m Q G (4.5) DG pf limit: pf i min (s) cos φ i,m s pf i max s (4.10) OLTC voltage control: Total energy curtailment of individual DG unit i N : m M t P DG,i g m h m β i,m s %EC s V min i V OLTC max tr,m V i (4.11) m M t P DG,i g m h m (4.12) Bus voltage limits: V i min V i,m V i max (4.6) Tap-changer limits: T tr min T tr,m T tr max (4.7) Phase-II Objective: subject to: Max i N P DG,i (t T N ) (4.13) t P DG,i P DG,i i N, (t T N ) (4.14) Line thermal limits: P i,j,m 2 + Q i,j,m 2 S ij max (4.8) ANM DG t = s (t T N ) (4.15) Phase I Phase II 62

63 Flowchart of Two-Phase Approach 63

64 Results and Analysis GSP One-time Approach Single DG Location Multiple DG Location OPF Techniques (Previous Work) Scenario 1 (Single DG Location) Scenario 2 (Multiple DG Locations) Bus-16 Bus-05 Bus-11 Bus-16 Total MOPF [31], [56] MMOPF [29] OLTC 16 5 DG Buses Demand VR [29] S. S. Al Kaabi, H. H. Zeineldin and V. Khadkikar, "Planning Active Distribution Networks Considering Multi-DG Configurations," in IEEE Transactions on Power Systems, vol. 29, no. 2, pp , March [31] S. S. AlKaabi, V. Khadkikar and H. H. Zeineldin, Incorporating PV Inverter Control Schemes for Planning Active Distribution Networks, in IEEE Transactions on Sustainable Energy, vol. 6, no. 4, pp , Oct

65 Procedure to Generate Random Customer-DG Installations Installations DG Locations Installed Capacity (MW) New Installed Total Installed t DG Bus-05 Bus-11 Bus-16 Bus-05 Bus-11 Bus-16 Capacity Capacity No. (MW) (MW) Total Multiple DGs Single DG 65

66 One-time Planning Approach OPF Techniques (Previous Work) Scenario 1 (Single DG Location) Scenario 2 (Multiple DG Locations) Bus-16 Bus-05 Bus-11 Bus-16 Total MOPF [31], [56] MMOPF [29] Scenario 1 (Single DG Location) DG Inst. (Bus-16) Bus-16 Sq. (MW) (MW) Total (MW) DG Inst. Scenario 2 (Multiple DG Locations) Bus-05 Bus-11 Bus-16 (MW) (MW) (MW) Total (MW) N/A 0 1 N/A N/A N/A* N/A N/A N/A N/A N/A N/A N/A N/A N/A N/A Total Total 3.31 Total By Single Sultan Alkaabi DG Multiple DGs 66

67 DG Inst. Proposed Adaptive Planning Approach (Case Study I Single/Multiple DGs) Scenario 1 (Single DG Location) Bus-16 ANM (MW) PS-A ANM PS-B DG Inst. Bus-05 (MW) Bus-11 (MW) Scenario 2 (Multiple DG Locations) Bus-16 Installed (MW) (MW) Total (MW) ANM PS-A A2 B A2 B A8 B A6 B A8 B A5 B A8 B A4 B A8 B A2 B A8 B A5 B A9 B A7 B A18 B A8 B N/A N/A A9 B A18 B A27 B A27 B N/A N/A A27 B A27 B A27 B A27 B A27 B A27 B27 Total Total Single DG ANM PS-B Multiple DGs 67

68 Case Study II Sensitivity Analysis Impact of DG sizes Different scenarios DG Total Inst. MW Data Data Data Data Data Data Data Data DG Inst. Bus No. Data-1 Data-2 Data-3 Data-4 Data-5 Data-6 Data-7 Data A1 A1 A1 A1 A1 A1 A1 A A1 A1 A1 A1 A1 A1 A1 A A1 A1 A1 A1 A1 A1 A1 A1 4 5 A1 A1 A1 A1 A1 A1 A1 A1 5 5 A1 A1 A1 A1 A1 A1 A1 A1 6 5 A1 A1 A1 A1 A1 A1 A1 A A1 A1 A1 A1 A1 A1 A1 A1 8 5 A1 A1 A1 A1 A1 A1 A1 A A1 A2 A1 A1 A1 A8 A1 A A2 A2 A1 A1 A1 A8 A1 A A1 A5 A2 A1 A1 A17 A4 A A1 A5 A2 A1 A1 A17 A5 A A1 A5 A2 A1 A1 A17 A5 A A2 A5 A2 A6 A1 A17 A5 A A2 A6 A2 A5 A1 A17 A5 A A1 A6 A2 A5 A1 A17 A5 A A2 A6 A2 A7 A1 A17 A5 A A2 A8 A6 A7 A2 A18 A6 A A2 A8 A6 A8 A2 A18 A6 A A2 A8 A8 A8 A7 A18 A6 A A2 A8 A9 A8 A8 N/A A6 A A2 A8 A9 A9 A8 A18 A6 A A2 A8 A9 A8 A8 A18 A6 A A2 A8 A9 A8 A8 A18 A6 A A5 A17 A9 A9 A8 A18 A7 A9 DG Capacity (MW) Bus Bus Bus Total

69 DG Capacity (MW) DG Capacity (MW) Case Study III Impact on Utility Planning for DG DG Inst Bus No Installed (MW) ANM PS-A A1 A1 A1 A1 A1 A1 A1 A1 A1 A2 A2 A2 A2 A2 A2 A1 A1 A1 A1 A2 A2 A5 A5 A4 A6 ANM PS-B B1 B1 B1 B1 B1 B1 B1 B1 B1 B3 B3 B3 B2 B2 B2 B1 B1 B1 B1 B2 B2 B6 B6 B6 B Bus-5 Bus-11 Bus-16 All Buses Slightly higher DG capacity compared to PS-B 0 0 A1 A1 A1 A1 A1 A1 A1 A1 A1 A2 A2 A2 A2 A2 A2 A1 A1 A1 A1 A2 A2 A5 A5 A4 A6 ANM Scheme of t th DG Installation (t = 1 to T N ) Bus-5 Bus-11 Bus-16 All Buses (a) PS-A Higher DG capacity compared to PS-A B1 B1 B1 B1 B1 B1 B1 B1 B1 B3 B3 B3 B2 B2 B2 B1 B1 B1 B1 B2 B2 B6 B6 B6 B6 ANM Scheme of t th DG Installation (t = 1 to T N ) (b) PS-B 69

70 Discussion Study the effect of installing customer DGs on the overall DG penetration levels Effective adaptation of ANM schemes according to DG installations sequence and capacity 70

71 PART 4 FLEXIBLE AND ADAPTABLE REACTIVE POWER PLANNING APPROACH CONSIDERING PHOTOVOLTAIC SYSTEMS 71

72 Overview PART-2 (PVIC + Max DG) PART-3 (Customer + ANM schemes + Max DG) PART-4 Utility Reactive Power Planning Min Energy Losses Network Innovation Customer PV Systems 72

73 Utility Reactive Power Planning Capacitor banks + Minimum Energy Losses 73

74 Utility Reactive Power Planning Capacitor banks + Minimum Energy Losses 74

75 Utility Reactive Power Planning Capacitor banks + Minimum Energy Losses DG 75

76 Utility Reactive Power Planning Capacitor banks + Minimum Energy Losses DG DG DG 76

77 Utility Reactive Power Planning Capacitor banks + Minimum Energy Losses DG DG DG 77

78 Utility Reactive Power Planning Capacitor banks + Minimum Energy Losses DG DG DG DG DG DG DG 78

79 Utility Reactive Power Planning Capacitor banks + Minimum Energy Losses DG DG DG DG DG DG DG 79

80 Utility Reactive Power Planning Capacitor banks + Minimum Energy Losses DG DG DG DG DG DG DG 80

81 Proposed Utility Reactive Power Planning Customer PV Systems + Minimum Energy Losses 81

82 Proposed Utility Reactive Power Planning Customer PV Systems + Minimum Energy Losses PV Inverter 82

83 Proposed Utility Reactive Power Planning Customer PV Systems + Minimum Energy Losses PV Inverter 83

84 Proposed Utility Reactive Power Planning Customer PV Systems + Minimum Energy Losses PV Inverter PV Inverter 84

85 Proposed Utility Reactive Power Planning Customer PV Systems + Minimum Energy Losses PV Inverter PV Inverter 85

86 Proposed Utility Reactive Power Planning Customer PV Systems + Minimum Energy Losses PV Inverter PV Inverter 86

87 Proposed Utility Reactive Power Planning Customer PV Systems + Minimum Energy Losses PV Inverter Inverter PV PV Inverter PV Inverter Inverter PV PV Inverter PV Inverter 87

88 Proposed Utility Reactive Power Planning Customer PV Systems + Minimum Energy Losses PV Inverter Inverter PV PV Inverter PV Inverter Inverter PV PV Inverter PV Inverter 88

89 Proposed Utility Reactive Power Planning Customer PV Systems + Minimum Energy Losses PV Inverter Inverter PV PV Inverter PV Inverter Inverter PV PV Inverter PV Inverter 89

90 Technical Contributions 3 Reactive Power Planning with Customer-PV Systems Developing a utility reactive power planning model for minimizing cost of energy losses Cost of reactive power Customer participation Flexible and adaptable options according to DG installations 90

91 Problem Formulation Reactive Power Limit and Oversizing Capacity Q max PV i,inv = 1 + OS i S 2 PV i APL i P 2 (5.1) i OS S i,inv = OS i S i PV Q max i,inv Q PV max i,m +Q i,inv (5.2) (5.3) One-time Fixed and Maintenance Costs of PV Inverter A FC = lt r 1 + r 1 + r lt 1 S OS i,inv FC (5.4) OS A MC = S i,inv Active Power Limitation Cost MC (5.5) g m,i = g m,i if g m,i APL i m M, i N (5.6) APL i if g m,i > APL i APLC = N i=1 P i PV APLh i 1 APL i CC (5.7) Annual Energy Losses A EL = A EG + A EPV B DE N M = P Gen i,m h m i=1 m =1 A EG N M + P i PV g m,i h m i=1 m=1 A EPV Objective Function and Constraints Objective: N M i=1 m =1 B DE min Cost = GC B EG A EG CL B EL A EL P i D d m h m (5.9) + A FC + A MC + APLC (5.10) Subject to: constraints (5.1) (5.9), and typical constraints, as given in (3.20) (3.28), (e.g., bus voltage and line thermal limits, nodal power balance equations, generators active and reactive power limits, etc.). APL Cost = 5 MW 1 hour 1 APL i 70 $/MWh (5.8) 91

92 Proposed Planning Approach & Algorithm Provides more flexibility and adaptability: The flexibility is represented by the ability to utilize a range of reactive power support limits and locations according to the proliferation of PV installations in the power network. The adaptability is represented by the ability to change any post-decisions on reactive power support limits/options (e.g., and values of existing PV systems at previous installations) by re-settings their and values. Flexibility Adaptability Run multi-period OPF to find the base values: 1) Base annual demand energy (B DE ) 2) Base annual grid energy (B EG ) 3) Base annual energy losses (B EL ) s = 1 to T s Define the available existing and new PV systems for VAR support options at sth installation 1) Locations of PV systems 2) Ratings of PV systems Run multi-period OPF to find the optimal cost at sth installation by setting optimal OS/APL values: 1) Set initial OS/APL values for new PV systems 2) Adapt the post-decisions of OS/APL values of existing PV systems at sth-1 installation Done Yes Start s ³ T s No s = s

93 Proposed Planning Approach & Algorithm PV installations scenario: Table 5.4: PV installations scenario used in this study Start Run multi-period OPF to find the base values: 1) Base annual demand energy (B DE ) 2) Base annual grid energy (B EG ) 3) Base annual energy losses (B EL ) PV Inverter PV Installations, s = 1 to 9, s T s Options Location Option 1 Bus 10 Bus 10 (5MW) Option 2 Bus 6 N/A Bus 6 (5MW) Option 3 Bus 3 N/A Bus 3 (5MW) Option 4 Bus 11 N/A Bus 11 (5MW) Option 5 Bus 5 N/A Bus 5 (5MW) Option 6 Bus 14 N/A Bus 14 (5MW) Option 7 Bus 8 N/A Bus 8 (5MW) Option 8 Bus 13 N/A Bus 13 (5MW) Option 9 Bus 15 N/A Bus 15 (5MW) Flexibility Adaptability s = 1 to T s Define the available existing and new PV systems for VAR support options at sth installation 1) Locations of PV systems 2) Ratings of PV systems Run multi-period OPF to find the optimal cost at sth installation by setting optimal OS/APL values: 1) Set initial OS/APL values for new PV systems 2) Adapt the post-decisions of OS/APL values of existing PV systems at sth-1 installation Done Yes s ³ T s No s = s

94 Impact of Active Power Output of PV Installations Table 5.4: PV installations scenario used in this study PV Inverter PV Installations, s = 1 to 9, s T s Options Location Option 1 Bus 10 Bus 10 (5MW) Option 2 Bus 6 N/A Bus 6 (5MW) Option 3 Bus 3 N/A Bus 3 (5MW) Option 4 Bus 11 N/A Bus 11 (5MW) Option 5 Bus 5 N/A Bus 5 (5MW) Option 6 Bus 14 N/A Bus 14 (5MW) Option 7 Bus 8 N/A Bus 8 (5MW) Option 8 Bus 13 N/A Bus 13 (5MW) Option 9 Bus 15 N/A Bus 15 (5MW) PV Generated Energy Energy Losses PV Capacity Total Installation A Bus (MW) GE A EPV Loss LR MWh Cost (k$) Sequence (MWh) (MWh) (%) (%) % -7.30% % % % % % % % % % % % % % % % %

95 Case Study I (Single PV at Bus-11) Sensitivity Analysis 1 Different Inverter Oversizing Costs Scenario max Q inv OS S inv w A EG A EL OS cost (MWh) (MWh) (MVar) (MVA) (%) (k$)

96 Cost, (k$) Cost, (k$) Oversizing (%) Oversizing (%) Case Study I (Single PV at Bus-11) Sensitivity Analysis 2 A range of APL (100 to 95%) i) Cost vs. APL. OS=0% i) Cost vs. APL. OS=0% ii) Cost vs. APL, OS% (w=0.5) ii) Cost vs. APL, OS% (w=0.5) iii) Cost vs. APL, OS% (w=2) iii) Cost vs. APL, OS% (w=2) Saving gained due to oversizing Saving gained due to oversizing for limited APL ranges for limited APL ranges Optimal cost (-k$ ): Optimal cost (-k$ ): APL 95.3%, OS = 0% APL 95.3%, OS = 0% Active Active Power Power Limitation Limitation (APL) (APL), (%), (%) (a) (a) Cost Cost (k$) (k$) vs. vs. APL APL i) No OS% i) No OS% ii) OS% vs. APL (w=0.5) ii) OS% vs. APL (w=0.5) iii) OS% vs. APL, (w=2) iii) OS% vs. APL, (w=2) Oversizing option is optimal: Oversizing option is optimal: when APL 98.5% for w=2 when APL 98.5% for w=2 Oversizing option is optimal: Oversizing option is optimal: when APL 97.3% for w=0.5 when APL 97.3% for w= Active Active Power Power Limitation Limitation (APL) (APL), (%), (%) (b) Oversizing (b) Oversizing (%) vs. (%) APL vs. APL 96

97 Case Study 2 Multiple PV Installations Scenarios Table 5.4: PV installations scenario used in this study PV Inverter PV Installations, s = 1 to 9, s T s Options Location Option 1 Bus 10 Bus 10 (5MW) Option 2 Bus 6 N/A Bus 6 (5MW) Option 3 Bus 3 N/A Bus 3 (5MW) Option 4 Bus 11 N/A Bus 11 (5MW) Option 5 Bus 5 N/A Bus 5 (5MW) Option 6 Bus 14 N/A Bus 14 (5MW) Option 7 Bus 8 N/A Bus 8 (5MW) Option 8 Bus 13 N/A Bus 13 (5MW) Option 9 Bus 15 N/A Bus 15 (5MW) Main observations: The APL is dominating OS option More VAR support options Adapted settings of options Considerable cost reduction PV Inverter PV Installations, s = 1 to 9, s T s Options Settings Option 1 APL (Bus 10) OS Option 2 APL N/A (Bus 6) OS Option 3 APL N/A (Bus 3) OS Option 4 APL N/A (Bus 11) OS Option 5 APL N/A (Bus 5) OS Option 6 APL N/A (Bus 14) OS Option 7 APL N/A (Bus 8) OS Option 8 APL N/A (Bus 13) OS Option 9 APL N/A (Bus 15) OS 0.00 max Total Q i,inv (MVAR) Total Cost (k$)

98 Case Study 2 Multiple PV Installations Scenarios Sensitivity Analysis: Three APL options are not available Option 1 (at Bus-10), Option 3 (at Bus-3) and Option 5 (at Bus-5) Main observations: More utilization of OS option Lesser VAR support, due to higher OS cost Adapted settings of options PV Inverter PV Installations, s = 1 to 9, s T s Options Settings Option 1 APL (Bus 10) OS Option 2 APL N/A (Bus 6) OS Option 3 APL N/A (Bus 3) OS Option 4 APL N/A (Bus 11) OS Option 5 APL N/A (Bus 5) OS Option 6 APL N/A (Bus 14) OS Option 7 APL N/A (Bus 8) OS Option 8 APL N/A (Bus 13) OS Option 9 APL N/A (Bus 15) OS 0.00 max Total Q i,inv (MVAR) Total Cost (k$)

99 Discussion Flexibility and adaptability of proposed planning approach provide effective utilization of reactive power support options from PV inverters, while ensuring fair and costefficient participations. The choice of inverter APL and/or OS allows the utility planners to flexibly make planning decisions that can be adapted according to the PV installations proliferation in the network. The investment on reactive power support options from PV systems provides considerable cost reductions compared to the case when PV systems generate active power only. 99

100 CONCLUSION AND FUTURE RESEARCH WORK 100

101 Conclusion The ever increasing integration of RESs, with high penetration levels of variable and intermittent generation, brings in many technical challenges and benefits for power system planners and operators. To harness the benefits of RESs and overcome their technical challenges, it is important to tackle the problem of RESs integration by considering innovative solutions in various power system planning and operation aspects 101

102 Conclusion The transformation of existing power networks into smart (active) networks accelerated the intake and adoption of a variety of innovative solutions: 1) The integration of flexible technologies (e.g., flexible demand, ESS, EVs, etc.) 2) The active control and operation of RES and network assets (e.g., OLTCs, VRs, RPC, CAPs, FSW, etc.) 3) The planning of power systems considering the active participation of customer-owned RESs 102

103 Conclusion Proposed Research Work: 1) DG Planning with PV Inverter Control Schemes 2) DG Planning with Customer-DG Installations 3) Reactive Power Planning with Customer-PV Systems 103

104 List of Publications S. S. Al Kaabi, H. H. Zeineldin and V. Khadkikar, "Planning Active Distribution Networks Considering Multi-DG Configurations," in IEEE Transactions on Power Systems, vol. 29, no. 2, pp , March S. A. Kaabi, H. Zeineldin and V. Khadkikar, "Planning active distribution networks considering multi-dg configurations," 2014 IEEE PES General Meeting Conference & Exposition, National Harbor, MD, 2014, pp S. S. AlKaabi, V. Khadkikar and H. H. Zeineldin, "Incorporating PV Inverter Control Schemes for Planning Active Distribution Networks," in IEEE Transactions on Sustainable Energy, vol. 6, no. 4, pp , Oct S. Alkaabi, V. Khadkikar and H. Zeineldin, "Incorporating PV inverter control schemes for planning active distribution networks," 2016 IEEE/PES Transmission and Distribution Conference and Exposition (T&D), Dallas, TX, 2016, pp S. Alkaabi, H. Zeineldin, V. Khadkikar and M. Elmoursi, Dynamic Analysis of OLTC and Voltage Regulator under Active Network Management Considering Different Load Profiles, in Proc. IEEE-Power & Energy Society (IEEE-PES) 8th Innovative Smart Grid Technologies (ISGT) 2017, Arlington, VA (Washington D.C. Metro Area), Apr , S. Alkaabi, H. Zeineldin and V. Khadkikar, Simplified Power Flow Modeling Approach Considering On-Load Tap Changers, in Proc. IEEE-Power & Energy Society (IEEE-PES) 8th Innovative Smart Grid Technologies (ISGT) 2017, Arlington, VA (Washington D.C. Metro Area), Apr , S. S. Alkaabi, H. H. Zeineldin and V. Khadkikar, Adaptive Planning Approach for Customer DG Installations in Smart Distribution Networks, in IET Renewable Power Generation, 2017 (Under Review). S. S. Alkaabi, H. H. Zeineldin and V. Khadkikar, Reactive Power Planning Approach to Minimize Cost of Energy Losses Considering PV Systems, in IEEE Transactions on Sustainable Energy, 2017 (Under Review). 104

105 Future Research Work Extending the proposed work to include: Different applications/planning problems Short-term versus long-term planning Operation stage and real-time constraints Uncertainty handling and modeling techniques Demand-generation models Customer DG installations Deterministic versus stochastic decisions 105

106 Thank You Version 106

107 Contact Details Sultan Alkaabi Khalifa University of Science and Technology, Masdar Institute of Science and Technology Al Ain Distribution Company (AADC) 107

108 Supplementary DEMAND-GENERATION PROFILES DATA 108

109 (p.u.) (p.u.) Demand and Generation Profiles UKGDS Profile Data over one-year period: 1 Variable demand and wind Demand generation Wind profiles Profile Data x 0.5 hours 0 (a) Original Data Profile Demand Wind Discretization Process Aggregation Process D = 0.5 pu W = 0.1pu 109

110 Discretization Process 1 Profile Bins Ranges Profile Bins Ranges 20% (0%, 20%] 0% < 3% 30% (20%, 40%] 10% (3%, 20%] Demand 50% (40%, 60%] 70% (60%, 80%] Wind 30% (20%, 40%] 50% (40%, 60%] 70% (60%, 80%] 90% (80%, 95%] 90% (80%, 97%] 100% > 95% 100% > 97% 110

111 (p.u.) (p.u.) Data Discretization Process 2 Original Profile Data x 0.5 hours Demand Wind After Discretization Process x 0.5 hours 0.6 Period Hours Demand Wind Period Hours Demand Wind Period Hours Demand Wind Period Hours Demand Wind (a) 1 Demand Wind D = 0.5 pu W = 0.1pu (b)

112 % of Wind Generation Data Aggregation Process MULTI-PERIOD SCENARIOS OF DEMAND AND RENEWABLE GENERATION Period (m) D (%) W (%) Hours (h) Period (m) D (%) W (%) Hours (h) Original Profile Data Demand x Wind = scenarios After Discretization & Aggregation Processing Demand x Wind = 41 scenarios % of Peak Demand 112