Adaptive Dynamic Droop Control for Photovoltaic-Rich Distribution Networks Considering Photovoltaic Uncertainties

ABSTRACT

The integration of photovoltaic (PV) units into distribution networks is becoming crucial as renewable energy sources gain more prominence. However, the intermittent nature of solar energy causes voltage fluctuations and power losses that need to be addressed. To address these challenges associated with integrating PV units into distribution networks, a novel approach is proposed that provides reactive power ancillary support using a decentralized local dynamic droop control (DDC) strategy. The proposed approach, combined with a meta-heuristic reptile search algorithm (RSA) technique that identifies the appropriate sizing and placement of multiple PV arrays in distribution networks, will substantially improve the voltage profile and full utilization of the PV inverter’s capacity to provide reactive power support over a 24-hour period. The PV systems are modeled using the Lambert-W function, which simplifies the estimation of PV at its maximum power point (MPP) without resorting to computationally intensive MPP tracking techniques. The study leverages 24 hours of realistic data, including ambient temperature and solar irradiance, together with the inverter’s terminal voltage, to estimate the amount of reactive power an inverter can handle. The proposed method substantially improves the voltage profile with full utilization of the PV inverter’s capacity to provide reactive power support over a 24-hour period. The study demonstrates that this approach prioritizes a decentralized network and PV inverters over a centralized management system, making it a promising option for future PV systems that require improved grid compatibility and integration without the installation of additional reactive power devices. In addition, a customized performance index based on the integral square of the voltages at various buses where PVs are located in IEEE-33 and IEEE-69 test bus systems, summed over a 24-hour time window, was calculated for comparative analysis. The proposed method results in an improvement of 93.9% and 95.6% over the base case test systems, whereas conventional droop control results in an improvement of 33.47% and 30.9% only over the base case, respectively, for the test systems.

KEYWORDS:

• Dynamic droop control
• Lambert-W function
• optimal location
• inverters
• reactive power capability
• photo-voltaic systems

Nomenclature

Variables	=
$\eta$	=	Performance indices
$\gamma$	=	Sum of performance indices of all integrated buses
${\mathrm{\Phi }}_{rt}$	=	Inverter rating calculated at actual $T_a$
${\theta }_1\mathrm{\setminus }{\theta }_2$	=	Slope gradient for low\high voltage fluctuations
$\underset{\_}{V_n^d}\mathrm{\setminus }\overline{V_n^d}$	=	Lower\higher limit of dead band voltage region
$\underset{\_}{V_n}\mathrm{\setminus }\overline{V_n}$	=	Lower\higher limit of bus voltage
$P_{mpp}$	=	Maximum active power produced by PV at standard G & $T_a$
$Q_{ddc}^{inv}$	=	Q-supplied by inverter at actual G & $T_a$ through DDC
$Q_{res}$	=	Q-reserve of inverter at actual G & $T_a$
$Q_{set}^{inv}$	=	Setpoint of Q-control ($0.1\ast Q_{res}$)
$T_a$	=	Ambient temperature (kelvin)
$V_n$	=	Actual bus voltage (p.u)
G	=	Irradiance ($watts/m^2$)
Abbreviations	=
ADN	=	Active distribution network
APC	=	Active power control
CPI	=	Combined placement index
CPLSF	=	Combined power loss sensitivity factor
DDC	=	Dynamic droop control
DG	=	Distributed generations
DGP	=	Distributed generation planning
MPP	=	Maximum power point
PV	=	Photo-voltaic
RES	=	Renewable energy sources
RPC	=	Reactive power control
RSA	=	Reptile search algorithm
STC	=	Standard test condition
VSF	=	Voltage stability factor
Vectors and indices	=
${\mathrm{\Psi }}_k$	=	Set of inverter rating calculated at $T_k$
$T_a$	=	Ambient temperature of PV at corresponding interval
$T_i$	=	Set of $T_a$ evaluated 24 hourly
$T_k$	=	Set of rated $T_a$
i	=	Index of hours for real time data
k	=	Index for rated parameters
n	=	Index of system nodes (buses)

Acknowledgements

The authors would like to thank the Government of India’s Ministry of Education for supporting Nasir Rehman through scholarships during his doctoral studies.

Disclosure statement

No potential conflict of interest was reported by the author(s).

Additional information

Notes on contributors

Nasir Rehman

Nasir Rehman born in Jammu and Kashmir in may 1992, graduated in Electrical and Renewable Energy engineering from BGSB university in 2014 and received his Master’s in Power Systems from Al-Falah university in 2016. He is currently pursuing Ph.D from National Institute of Technology, Srinagar, Jammu and Kashmir. His areas of interest include distribution systems, renewable energy sources, electric vehicle technologies and optimization techniques.

Mairaj-Ud Din Mufti

Mairaj-ud din Mufti received Bachelors in Electrical Engineering from NIT Srinagar in 1986. He completed his Masters in Control engineering and instrumentation in 1991 and Ph.D. in Power System Control in 1998 from the Indian Institute of Technology (IIT) Delhi, India. Currently, he is working as Professor in the Department of Electrical Engineering, NIT Srinagar. He has held important positions like Dean Research and Development, Dean Academic Affairs and Head-Department of Electrical Engineering. In 2006, he was a visiting research fellow at the Osaka university, Japan. His research interests include Power System Control, Intelligent and Advanced Control, Renewable Energy, Application of Energy Storage Devices and Power System Stability, Dynamics and Control.

Neeraj Gupta

Neeraj Gupta is Ph.D in power systems from Indian Institute of Technology Roorkee, Roorkee, India. He is a senior member of IEEE. He was a faculty with the Thapar University, from 2008 to 2009, Adani Institute of Infrastructure Engineering, Ahmedabad, India, in 2015 and NIT Hamirpur from 2015 to 2018 and presently, he has been working as an Assistant professor with the Electrical Engineering Department, National Institute of Technology, Srinagar, J&K, India. His work has been published in Q-1 international journals of repute like IEEE, Elsevier etc. He is also in the scientific advisory/organizing secretary of many reputed conferences of the country. He is referee of reputed journals of IEEE, Elsveir, Taylor and Francis, IET etc. His research interests include uncertainty quantification of power system, probabilistic power system, solar, wind, and electric vehicle technologies, Artificial intelligence, Machine learning, prediction etc.