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A Data-Driven-Based Optimal Planning of Renewable Rich Microgrid System

  • Research Article-Electrical Engineering
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Abstract

Due to favourable economic and environmental concerns, reduced power losses, and technological advancements in data science, large-scale renewable energy sources (RESs) are being inserted into smart microgrids (MGs). Generally, these resources are inserted at the distribution level and feed consumers locally with better reliability. The planning and stable operation of the power networks are complicated at numerous levels as a result of large-scale renewable integration, as the infeed from renewable sources like wind and solar is highly intermittent and uncertain, significantly affecting the resilient, intelligent, and optimal operation of the power system. The creation of scenarios is an essential step in the planning and stable operation of renewable-rich power systems. Scenarios can be generated based on seasonal events to mitigate growing complexity and intermittency. In this paper, time-series solar data is used for forecasting solar irradiance using long-short-term memory (LSTM). Besides, a cost-based optimisation model has been developed by using stochastic programming to obtain both the optimal sizing and optimal switching sequence of distributed units as well as the optimal number of required units. The stochastic nature of solar and load demand is considered in beta (\(\beta \)) distribution. The objective function formulated covers different costs, like capital, operational, and switching costs associated with required units. The running cost incorporated with the capital cost gives us a broader design for planning the MG, thus making it both planning and operational problem. The entire proposed MG planning problem has been developed in a Python and MATLAB ® environment.

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Abbreviations

DG:

Distributed generation

DEG:

Diesel engine generator

ESS:

Energy storage system

LSTM:

Long short-term memory

ML:

Machine learning

NOCT:

Normal optimum cell temperature

OM:

Operation and maintenance

RMSE:

Root mean square error

SR:

Spinning reserve

RES:

Renewable energy sources

s :

Index of generated scenario

T :

Data samples

t :

Index of time span

\(C_\textrm{chr}\)(S,t)\(\backslash D_\textrm{chr}\)(s,t):

Charging\(\backslash \)discharging capacity of BS unit.

\({\text {IC}}_\textrm{spv}\) :

Installation cost of solar photo voltaic

\(X_\textrm{spv}\) :

Rating of solar photo voltaic

\({\text {OM}}_\textrm{SPV}\) :

Operation and maintenance cost

\(N_\textrm{spv}\) :

Total no. of panels

\({\text {IC}}_\textrm{bio}\) :

Installation cost related to biogas

\(X_\textrm{bio}\) :

Rating of biogas

\(N_\textrm{DEG}\) :

Number of diesel generators

\(N_\textrm{batt}\) :

Number of batteries

\({\text {CE}}_\textrm{tax}\) :

Carbon emission tax

\(T_\textrm{spv}\) :

Cell temperature

\(X_\textrm{batt}\) :

Battery rating

\(C_{d,\textrm{run}}\) :

Daily running cost

\({\text {SR}}_\textrm{min}\) :

Minimum spinning reserve

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Authors and Affiliations

  1. Electrical Engineering, National Institute of Technology, Hazratbal, Srinagar, Jammu and Kashmir, 190006, India

    Parvaiz Ahmad Ahangar, Shameem Ahmad Lone & Neeraj Gupta

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  1. Parvaiz Ahmad Ahangar
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  2. Shameem Ahmad Lone
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Correspondence to Parvaiz Ahmad Ahangar.

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Ahangar, P.A., Lone, S.A. & Gupta, N. A Data-Driven-Based Optimal Planning of Renewable Rich Microgrid System. Arab J Sci Eng 49, 6241–6257 (2024). https://doi.org/10.1007/s13369-023-08153-5

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