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Renewables based dynamic cost-effective optimal scheduling of distributed generators using teaching–learning-based optimization

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Abstract

Distributed generators (DGs) which may be both renewable energy sources (RES) or conventional fossil fueled generators must be optimally scheduled so as to reduce the generation cost of a power network at the end of the day. Adequate attention must also be given to expand the utilization of RES not only because they are abundant in nature but also because they are clean sources of energy supply. This paper utilizes a robust and efficient teaching–learning-based optimization (TLBO) to optimally schedule the DGs of four dynamic systems so as to reduce their active power generation cost. The cost effective fitness functions of the subject test systems are both linear and non-linear in nature. RES like photovoltaic (PV) systems and wind were prioritized to share the load demand and load profiles of various system were studied. By instigating a savings of 9.36% with 99.9% efficiency, TLBO clearly outperformed a long list of algorithms available in literatures in minimizing the generation cost of the test systems. Various statistical parameters, non-parametric statistical analysis and computational time also points towards the robustness of TLBO in handling any dimensional test system.

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Abbreviations

AP:

Awareness probability

CSA:

Crow search algorithm

CSAJAYA:

Hybrid crow search algorithm-Jaya Algorithm

CSASCA:

Crow search algorithm- sine cosine algorithm

DE:

Differential evolution

DER:

Distributed energy resources

DG:

Distributed generator

ELD:

Economic load dispatch

GWO:

Grey wolf optimizer

JAYA:

Jaya algorithm

MAE:

Mean absolute error

MGWO-SCA-CSA:

Modified grey wolf optimizer-sine cosine algorithm-crow search algorithm

Pop_Size:

Population size

PSO:

Particle swarm optimization

RE:

Relative error

RES:

Renewable energy sources

RMSE:

Root mean square error

SCA:

Sine cosine algorithm

SD:

Standard deviation

SOS:

Symbiotic organisms search

TLBO:

Teaching-learning-based optimization

UP:

Utilization percentage

WOA:

Whale optimization algorithm

WOASCA:

Whale optimization algorithm-sine cosine algorithm

VPE:

Valve point effect

a, b, c :

Cost coefficients of DG unit

A, B, C :

Constraints of quadratic wind profile

d, e :

Coefficients of valve point loading effect

D t :

Load demand at time t

FFV ii :

Fitness Function Value at iith trial

FFV min :

Minimum value of Fitness Function

\(\overline{FFV }\) :

Mean value of Fitness Function

fl :

 Flight length of the crow

f t v :

Weibull distribution function

i :

Indices of DG units

ii :

Index of trial

iter/max_iter :

Current iteration/Maximum number of iterations

j :

Indices of wind units

k t , c t :

Shape parameter and scale parameter at tth time interval.

n :

Total number of DG units

NOT :

Number of trials

P i :

Power output of ith unit

P i,max P i,min :

Minimum and maximum limit of ith unit

P j w,t :

Wind power of jth wind unit at time t

P j w − r :

Rated power of jth wind unit

P RES,t :

RES power output at time tth hour

Rand 1, Rand 2 , Rand i, Rand j , c ’ , c” :

Random numbers used in algorithms

t :

Indices of time intervals

v i j , v o j , v r j :

Cut-in, cut-out and rated wind speed of jth unit

v t p :

Wind speed at tth hour

σ t v , μ t v :

Mean and standard deviation of wind speed at time t

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  1. Department of Electrical, Electronics and Communication Engineering, GITAM Deemed to be University, Visakhapatnam, India

    Swarupa Pinninti & Srinivasa Rao Sura

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  1. Swarupa Pinninti
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Correspondence to Swarupa Pinninti.

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Appendix

Appendix

See Tables

Table 7 Generator cost-coefficients of the test systems
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Table 8 Load demand of subject test systems
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Table 9 Wind speed for microgrid system and 30 units system
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Table 10 Algorithms implemented and their tuning parameters
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Pinninti, S., Sura, S.R. Renewables based dynamic cost-effective optimal scheduling of distributed generators using teaching–learning-based optimization. Int J Syst Assur Eng Manag 14 (Suppl 1), 353–373 (2023). https://doi.org/10.1007/s13198-023-01864-w

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