Enhanced Local Grid Voltage Support Method for High Penetration of Distributed Generators Erhan Demirok(1), Dezso Sera(1), Pedro Rodriguez(2), Remus Teodorescu(1) (1)
(2)
Department of Energy Technology Aalborg University Aalborg, DENMARK
[email protected]
Department of Electrical Engineering Technical University of Catalonia Barcelona, SPAIN
[email protected]
Abstract – Grid voltage rise and thermal loading of network components are the most remarkable barriers to allow high number of distributed generator (DG) connections on the medium voltage (MV) and low voltage (LV) electricity networks. The other barriers such as grid power quality (harmonics, voltage unbalance, flicker etc.) and network protection mechanisms can be figured out once the maximum DG connection capacity of the network is reached. In this paper, additional reactive power reserve of inverter interfaced DGs is exploited to lower the grid voltage level by means of locationadaptive Q(U) droop function. The proposed method aims to achieve less grid voltage violations thus more DG connection on the electricity distribution networks can be allowed.
I.
INTRODUCTION
Increasing amount of wind and solar photovoltaic (PV) installations into electrical power systems can create potential risk of unintentional trips of these sources which likely increase payback time and energy losses unless network and DG interconnection issues (such as voltage rise, thermal loading of equipments, voltage unbalance, flicker, harmonic emissions, network resonance, direct current injections, fault current contribution etc.) are taken into consideration. “Connect and forget” concept of DGs has to be transformed to the “grid integration” concepts that can provide optimum reactions against to grid disturbances in the sense of more energy harvesting from high penetration of distributed sources. New concepts aligned to the “grid integration” can be accomplished by either low-cost local control of DG units without communication infrastructures or centralized/decentralized control with communication medium. Grid voltage limitation and thermal overloading of network components (transformers, cables etc.) are considered as the most prevalent barriers for high penetration of DGs. Voltage limit usually becomes more critical than the thermal overloading of network equipments in suburban and urban distribution networks since thermal limits of transformers and cables are usually high enough not to create overloading condition. The question of how much voltage rise is allowed with DG connections is clearly defined in EN 50160 [1] based grid codes as ±10% band in most European countries.
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On the other hand, network transformers usually operate at near full-load conditions (and also higher space available especially for PV installations) in rural grids therefore thermal overloading of transformer becomes much more critical limitation. In the presented study, it will be assumed that transformer and cables are never overloaded. In real life, it reflects network reinforcements by replacing the equipments with ones which have a higher thermal capacity. Regarding the grid voltage limitation, mainly five different solutions (reactive power control, real power limitation, adding storage system, load management and network reinforcement such as adding new cables, creating mesh networks) are being proposed or implemented with gridconnected DGs. The first two solutions are already in force by some grid codes [2,3] but in this paper, only reactive power control is investigated in order to enhance current methods of voltage support without requiring communication among DGs. Subsequent sections firstly present the state of the art reactive power control solutions recommended in grid codes and already deployed in commercial inverters. Next, how the state of the art solutions can be improved is described for more effective voltage support from multiple distributed inverters. Both the standard and the proposed adaptive droop methods are integrated into a radial feeder model developed with 11 DGs in Matlab®/Simulink and PLECS® toolbox. Finally, a laboratory setup that consists of three-phase inverter and a controllable grid is built to verify the proposed method. II.
LOCAL REACTIVE POWER STRATEGIES
A. State of the Art Reactive Power Strategies for Local Voltage Support To keep the grid voltage in admissible range, the grid codes [2,3] for both MV and LV networks recommend mainly two different static droop curves for generating reactive power reference values in terms of either local terminal voltage (U) or injected real power (P). These methods are so called as Q(U) and cosφ(P) respectively, and relevant synthetic droop curves are depicted in Fig. 1. Only Q(U) method is considered here because it has superior performance on grid loss. cosφ(P) method does not take into account grid voltage, therefore unnecessary reactive power will flow on the
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Fig.1. Generalized static droop curves for reactive power control: Q(U) (left) and cosφ(P) (right)
distribution network when high power generation overlaps with high level load consumption. High variety of inverter products and different ratings of nominal power are expected to be distributed over distribution network. A unique droop configuration can be obtained by defining reactive power levels in terms of own nameplate real power value (Pn) of the inverter. In practice, the static (standard) droop curves are supposed to be configured identically for all inverters which are located at the same region. Regarding only the Q(U) droop curve, terminal voltage differences between inverters will cause different reactive power reference values as shown in Figure 2. Accordingly, inverters which are closer to the transformer will have less reactive power reference, and the inverters at the end of feeder will absorb much more reactive power from the grid due to having higher terminal voltage for a radial feeder. B. Adaptive Q(U) Strategy It is desirable to have higher reactive power within bigger network impedance since in this case voltage drop caused by reactive absorption will be simply higher. However, when the critical node voltage (end far of feeder in this example) exceeds 1.1 p.u. of overvoltage limit, the inverters located closer to the transformer should help absorbing more reactive power in order to create additional voltage drop on the transformer and cables. The main idea behind the proposed Q(U) droop method is to shift the standard curve along x-axis in such that the inverters closer to the transformer will be forced to absorb more reactive power than that of usual droop method (Fig. 3). Therefore, location information becomes a necessity to decide on how much shifting will be required with only local measurements. At this point, location information (or alternatively equivalent impedance seen from connection point) is
Fig. 3. Reactive power distribution with employed adaptive Q(U) droops on a radial feeder
estimated by developing a fuzzy inference system (FIS). Two measured variables, output real power (P) and terminal voltage (U) form input fuzzy set. Single output variable δ is implying shifting amount to be delivered to Q(U) droop curve (Fig. 4). Among two FIS methods (Mamdani and Sugeno), Sugeno-type fuzzy inference system is used here due to working well with linear (PID) and adaptive techniques [4,5]. Inputs as numeric crisp values for P and U are limited by [0, 5] kW and [0.8, 1.13] p.u. respectively in the simulation study. Input membership functions which are defined linguistically as Low (L), Less than Medium (LM), Medium (M), More than Medium (MM), and High (H) map the crisp input values to a membership value between 0 and 1 (Fig. 5). Another critical FIS design stage which affects the overall reactive power distribution in the region is to build rules table from the system experience. Two extreme rules are given below to start building complete rules table [6]: 1) If ‘P injection is Low (L)’ and ‘Voltage is High (H)’, then the output δ (curve shifting amount) is Zero (Z). This rule simply implies that DG is located at the end of feeder in a radial feeder. Inverter Grid Energy source (PV, wind) P, U
Fuzzy Set Database
Fuzzification Input Stage
Qref
Inference Stage
δ
Defuzzification Output Stage
Q(U) Knowledge based Rules
FIS
Fig. 4. General structure of FIS for the adaptive Q(U) droop TABLE I FUZZY RULES TABLE U
Fig. 2. Reactive power distribution with employed standard Q(U) droops on a radial feeder
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P
L
LM
M
MM
Low
MM
M
LM
Z
Z
Medium
H
MM
M
LM
Z
High
H
H
MM
M
LM
H
Fig. 5. Gaussian input membership functions for U (upper) and P (bottom)
Voltage shift (p.u.)
Voltage shift (p.u.)
Fig. 7. Simulation results of the droop methods with 3-kW real power injections injections
Fig. 6. Designed FIS reaction to inputs. Upper: FIS output vs. input voltage under fixed real power (2.5kW), Bottom: FIS output vs. input real power under fixed voltage (1.06 p.u.)
2) If ‘P injection is High (H)’ and ‘Voltage is Low (L)’, then the output δ is Negative Big (NB). It means that high real power injection does not cause considerable voltage rise, accordingly the DG is located closer to the transformer. In similar way, the rules table is completed as given in Table I. At the output stage, zero-order Sugeno model is carried out and the final result is computed by weighting the average of all rules.
III.
SIMULATION PERFORMANCE OF STANDARD AND ADAPTIVE Q(U) DROOP METHODS
In order to compare performance of the standard and adaptive Q(U) droop methods, a generic model of LV radial feeder with 11 numbers of DGs was developed. The rest of feeder parameters are given in Table II and Fig. A1 of the Appendix section. Each identical average DG model contains line filter inductance at grid side and an ideal DC source at DC bus. Stationary-frame proportional-resonant (PR) digital current controller is designed in such that produced real and reactive power are decoupled fairly [7] and the developed controller is ready to use directly in a real-time hardware. DG power factor operation is limited in the range of 0.8 lagging and 0.8 leading to reflect real operation of inverters as much as possible. Rate of change (RoC) limiter is also employed for the reactive power reference to prevent power flow oscillations among DGs by setting of 500 VAr/sec. As shown in Fig. 7, time-domain simulation result presents that the critical node voltage at the end of feeder experiences voltage violation with the standard Q(U) method when each inverter is injecting 3-kW real power. On the other hand, the maximum voltage level is reduced to 1.088 p.u. by means of contribution from the first three inverters that allows more DG integration in the sense of grid voltage limitation. IV.
EXPERIMENTAL VALIDATION OF THE ADAPTIVE Q(U) METHOD
A lab-scaled DG setup has been built to implement the adaptive Q(U) method and eventually investigate practical issues such as sampling time, filters, ramp limiters etc. using dSPACE-DS1103 controller board (Fig. 8). 3.4 kVA/2.2 kW 3-phase 2-level single-stage inverter (minimum power factor limit is set as 0.8) is employed and switching frequency fsw is selected as 8 kHz and modulation type is SPWM with third harmonic injection. The value of inverter side filter
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PCC rect
Isolation transformer
inverter rectifier Sensors & filters
Fig. 10. Reference reactive power generation process
Measured output reactive power (VAr)
Program. AC power supply
inv
DC bus for inv. DC power supply
dSpace
Fig. 8. 3-phase DG test setup
inductance (Li) is 6.9 mH, filter capacitor (Cf) is 4.7 μF and transformer leakage inductance is near 2.5 mH. Thus, LCL filter resonance frequency becomes 1.7 kHz. Programmable AC power source and parallel connected active rectifier are combined to have both power sourcing and sinking capabilities for simulating grid voltage variations. As a first stage of experimental investigation, stationary frame digital current controllers must fulfill design requirements in such that real/reactive power decoupling can be achieved and ensured not to degrade the main objective of real power injection. Fig. 9 shows the test result of reactive power amount variation from minimum to maximum Q limit without rate limiters as the worst case and the resulting effect on real power injection can be negligible. On the other hand, regarding the dynamic interaction of droop functions among multiple grid-connected inverters, a rate of change (ROC) limiter on reactive power reference is required to damp reactive flow and voltage oscillations. Typically a ROC within 5-60 second range for the biggest Q step [Qmin,Qmax] is suggested to counteract both the reactive flow/voltage oscillations among inverters and frequent tap switching operation of on-load tap changer transformers [8].
Fig. 11. Adaptive Q(U) droop method with 500VAr/sec ROC limiter (@2kW) under grid voltage variation
In the next stage, standard and adaptive Q(U) methods are integrated into the inverter controller. It is suggested that preprocessing of terminal voltage and injected real power measurements by running moving average operation should be employed in order to eliminate rapid reference generation (Fig. 10). Terminal voltage is increased from 230 V to 265 V by the grid simulator and the resultant reactive power variation is depicted in Fig. 11. Compared to the standard curve, the adaptive Q(U) curve is shifted left in case of low terminal voltage conditions so the inverters with low terminal voltage are forced to absorb more reactive power than that of the standard droop method. The inverters with higher local voltage are not allowed to shift their Q(U) curves as desired. V.
CONCLUSION
The study presents a new approach of location-adaptive Q(U) droop method that can be considered as an alternative solution to the standard Q(U) droop method imposed by certain grid codes. In case of high penetration of gridconnected DGs, voltage support from the inverters near transformer is almost invisible because the terminal voltage stays in admissible range although the voltage at the end of feeder exceeds 1.1 p.u. limit. The proposed method enhances this drawback of the standard Q(U) droop method without requiring communication infrastructure and does not need precise tuning of fuzzy variables depending on the characteristics of distribution network.
Fig. 9. P-Q decoupling performance result without rate of change (ROC) limiter
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Fig. A1. Simulation model of LV radial feeder with 11 DGs [8]
APPENDIX The characteristic parameters of radial test feeder used for TABLE II LV RADIAL FEEDER SIMULATION PARAMETERS Grid resistance Rg
0.034 ohm
Grid inductance Lg
0.5 mH
Transf. Primary and secondary resistance Rp, Rs
0.5 ohm
Transf. primary and secondary leak. inductance Lp, Ls
1 mH
Line resistance R
0.025 ohm
Line inductance L
0.04 mH
Line resistance R1
0.25 ohm
Line inductance L1
0.4 mH
time-domain simulation analysis is given in Table II and the corresponding feeder structure is depicted in Fig. A1 below. ACKNOWLEDGMENT This work was supported by Aalborg University – Danfoss Solar Inverters A/S partnership. Any opinions, findings, and conclusions or recommendations expressed in this material are those of the authors and do not necessarily reflect those of Danfoss Solar Inverters A/S. REFERENCES [1] [2] [3]
[4] [5] [6]
[7]
Voltage Characteristics of Electricity Supplied by Public Distribution Systems, EN 50160 Standard, 1999. Technische Richtlinie Erzeugungsanlagen am Mittelspannungsnetz – Richtlinie fur Anschluss und Parallelbetrieb von Erzeugungsanlagen am Mittelspannungsnetz, Ausgabe Juni 2008, BDEW, Berlin. Erzeugungsanlagen am Niederspannungsnetz Technische Mindestanforderungen fur Anschluss und Parallelbetrieb von Erzeugungsanlagen am Niederspannungsnetz, Verband der Elektrotechnik Elektronik Informationstechnik e.V. (VDN), Draft 2010. “Sugeno-Type Fuzzy Inference”, Mathworks, 27 April 2011 [online] http://www.mathworks.com/help/toolbox/fuzzy/fp49243.html A.E. Kiprakis, A.R. Wallace, “Maximising energy capture from distributed generators in weak networks,” IEE Proc.-Gener. Transm. Distrib., Vol. 151, No. 5, September 2004. T. Tran-Quoc, E. Monnot, G. Rami, A. Almeida, C. Kieny, N. Hadjsaid, “Intelligent voltage control in distribution network with distributed generation,” 19th International Conference on Electricity Distribution, Vienna, May 2007. A. Timbus, M. Liserre, R. Teodorescu, P. Rodriguez, F. Blaabjerg, “Evaluation of current controllers for distributed power generation systems,” IEEE Transactions on Power Electronics, Vol. 24, No. 3, March 2009.
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