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Computation and Optimization of BESS in the Modeling of Renewable Energy Based Framework

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Abstract

Incorporating Battery Energy Storage Systems (BESS) into renewable energy configurations offers numerous apparent advantages. Nonetheless, to fully capitalize on these advantages, it is imperative to implement management strategies that facilitate optimal system performance. Various approaches and methods can be employed to optimize the functionality of BESS within renewable energy systems (RES), encompassing specific dispatch goals as well as financial, technical, or hybrid objectives. These optimization methods are categorized into three primary groups: directed search-based (DSB), probabilistic, and rule-based strategies. Historically, research has heavily focused on tailoring systems based on the renewable energy sources for specific purposes, such as distributed generation (DG). This investigation not only offers a comprehensive overview of battery management measures but also assesses these endeavors in terms of their alignment with application objectives and the chosen optimization strategy. This approach unveils connections between distinct optimization goals and preferred strategies. The findings reveal that DSB approaches and control strategies, commonly employed for technical objectives, are more likely to succeed in addressing financial goals. Moreover, the extent to which a problem can be analytically defined emerges as a critical consideration. Upon comparing the merits and demerits of different reported optimization methodologies, it becomes evident that hybrid approaches, amalgamating the strengths of various optimization techniques, will increasingly shape future operational procedures. This study not only equips researchers with valuable insights into viable optimization strategies for forthcoming generation applications but also provides a cutting-edge overview of battery applications and optimization techniques.

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Abbreviations

ADALINE:

Adaptive Linear Neuron

ADP:

Approximate Dynamic Programming

AI:

Artificial Intelligence

BEMS:

Battery Energy Management Systems

BMS:

Battery Management Systems

CAES:

Compressed Air Energy Storage

DR:

Demand Response

ESS:

Energy Storage Systems

EV:

Electric Vehicle

FLC:

Fuzzy Logic Controller

GA:

Genetic Algorithms

GAMS:

General Algebraic Modelling System

HESS:

Hybrid ESS

HH:

Hyper-Heuristic

HRES:

Hybrid Renewable Energy System

IP:

Integer Programming

LP:

Linear Programming

MILP:

Mixed-Integer Linear Programming

ML:

Machine Learning

MPC:

Model Predictive Control

NaS:

Sodium-Sulfur

NLP:

Nonlinear Programming

NSGA:

Non-Dominated Sorting Genetic Algorithm

OLTC:

On-Load Tap Changers

PSO:

Particle Swarm Optimization

PV:

Photovoltaic

QP:

Quadratic Programming

RC:

Resistor–Capacitor

SDP:

Stochastic Dynamic Programming

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Authors and Affiliations

  1. Department of Electrical Engineering, Delhi Technological University, Delhi, India

    Vivek Saxena, Narendra Kumar & Uma Nangia

  2. Department of Electrical & Electronics Engineering, A.B.E.S. Engineering College, Ghaziabad, India

    Vivek Saxena

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  1. Vivek Saxena
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  2. Narendra Kumar
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  3. Uma Nangia
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Correspondence to Vivek Saxena.

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Saxena, V., Kumar, N. & Nangia, U. Computation and Optimization of BESS in the Modeling of Renewable Energy Based Framework. Arch Computat Methods Eng (2024). https://doi.org/10.1007/s11831-023-10046-7

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