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Identifying Hosting Capacity of Renewable DG Units in Smart Grids Considering Protection Systems

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Advances in Control Techniques for Smart Grid Applications

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Abstract

This chapter introduces a framework to define the optimal allocations and permissible hosting capacity of renewable distributed generations (DGs) using genetic algorithm (GA). The investigation is carried out considering inverter-based technologies such as the case of photovoltaic system. The inverter control is exploited to dynamically limit the DG contribution current during network faults. Accordingly, the DG penetration ratings can be significantly increased with avoiding their effects on the overcurrent protection coordination. To accomplish this task, a multi-objective function is established considering the overall maximum capacity of DGs, power loss reduction, voltage enhancement, and fault current limitation. In addition, the optimization process takes into account the protection coordination and voltage level as constraints. The constraints of coordination, including recloser-relay and fuse-recloser schemes, are inserted with the multi-objective function in an improved fitness function. The proposed framework is applied on an 11 kV overhead distribution feeder. Without replacement of the existing protection systems, the results confirm the large DGs integration possibility with significant loss reduction, improvement of voltage level, and fault current decrease. This is realized through the inverter control to limit DG contribution in fault currents.

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Abbreviations

C2, C3 and C4:

Scaling factors for maximization problem

\(DG_{C_i}\) :

Capacity of the installed DGs (MW)

F1, F2, F3, and F4:

Fault locations

(IfDG):

Fault currents from the DG (A)

(Ifs):

Fault currents from the substation (A)

I rated :

IBDG rated current installed (A)

I SC :

Short circuit current (A)

K :

Degree of limiting the fault current

K m :

Time multiplier

N DG :

Number of DGs

\(\max \_{\text{CM}}_{\text{relay}_{\text{recl}}}\) :

Maximum coordination margins between recloser and relay (s)

\(\min \_{\text{CM}}_{\text{fuse}_{\text{reclfast}}}\) :

Minimum coordination margin between recloser and fuse (s)

\(\min \_{\text{CM}}_{\text{relay}_{\text{recl}}}\) :

Minimum coordination margins between recloser and relay (s)

P loss :

Active power losses (W)

Q loss :

Reactive power losses (VAR)

t fuse :

Fuse operating time (s)

\(t_{\text{recl}_{\text{fast}}}\) :

Fast curve operating time of recloser (s)

\(t_{\text{recl}_{\text{slow}}}\) :

Slow curve operating time of recloser (s)

t relay :

Relay operating time (s)

VDG-f:

DG terminal voltage during fault (pu)

V j :

jth Bus voltage (pu)

V nom :

Nominal value of voltage (pu)

V max :

Maximum voltage limits (pu)

V min :

Ninimum voltage limits (pu)

W1, W2, W3, W4 and W5:

Positive constant weights

∅CM:

Inequality constraints

(|∆V|):

Voltage deviations (pu

AC:

Alternating Current,

ACSR:

Aluminium Conductor Steel Reinforced,

CHP:

Combined Heat and Power,

CM:

Coordination Margin,

DC:

Direct Current,

DG:

Distributed Generation,

DS:

Distribution System,

FC:

Fuel cells,

FCL:

Fault Current Limiter,

GA:

Genetic Algorithm,

IBDG:

Inverter-based distributed generations,

IEEE:

The Institute of Electrical and Electronics Engineers,

LTC:

Load tap changing transformers,

MFCL:

Magnetic Fault Current Limiters,

MM:

Minimum Melting,

MTG:

Micro turbines generators,

MVA:

Mega Volt Ampere,

MW:

Mega Watt,

OPF:

Optimal Power Flow,

PE:

Power electronic,

PV:

Photovoltaic,

RCGA:

Real-Coded Genetic Algorithms,

RMS:

Root Mean Square,

SFCL:

Superconducting Fault Current Limiters,

SG:

Synchronous generator,

SSFCL:

Solid State Fault Current Limiter,

VAR:

Volt Ampere Reactive,

WT:

Wind turbines

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Authors and Affiliations

  1. Electrical Power and Machines Engineering Department, Faculty of Engineering, Tanta University, Tanta, Egypt

    Hossam A. Abd el-Ghany, Essam M. Rashad & Ahmed M. Azmy

  2. Electrical Engineering Department, Faculty of Engineering, Minoufiya University, Shebin Elkom, 32511, Egypt

    Nagy I. Elkalashy

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  1. Hossam A. Abd el-Ghany
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  4. Nagy I. Elkalashy
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Correspondence to Essam M. Rashad .

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Editors and Affiliations

  1. Department of Mechatronics Engineering, Rajshahi University of Engineering and Technology, Rajshahi, Bangladesh

    Sajal Kumar Das

  2. Faculty of Engineering and Information Sciences, School of Electrical, Computer and Telecommunications Engineering, University of Wollongong, Wollongong, NSW, Australia

    Md. Rabiul Islam

  3. School of Electrical and Electronic Engineering, Huazhong University of Science and Technology, Wuhan, Hubei, China

    Wei Xu

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Abd el-Ghany, H.A., Rashad, E.M., Azmy, A.M., Elkalashy, N.I. (2022). Identifying Hosting Capacity of Renewable DG Units in Smart Grids Considering Protection Systems. In: Das, S.K., Islam, M.R., Xu, W. (eds) Advances in Control Techniques for Smart Grid Applications. Springer, Singapore. https://doi.org/10.1007/978-981-16-9856-9_6

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