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providinginsights into the technical aspects and challenges
associated with their integration. Khaki [3] focuses on joint
sizing and placement of battery energy storage systems and
wind turbines considering reactive power support, emphasiz-
ing the importance of system stability and reliability.
Furthermore, Mahmoudi et al. [4] propose a novel fuzzy logic-
based method to evaluate storage and backup systems for
determining the optimal size of hybrid renew- able energy
systems. Nazir et al. [5]optimize the economic operation of
energy storage integration using an improved gravitational
search algorithm and dual-stage optimiza-tion, highlighting
the economic benefits and feasibility of energy storage sys-
tems. Ali et al. [6] present a comprehensive review on closed-
loop home energy management systems with renewable en-
ergy sources in smart grids, discussing the integration of vari-
ous technologies and control strategies.
The literature review also encompasses studies on optimiza-
tion and design methodologies. Maleki [7] investigates the
design and optimization of autonomous solar-wind-reverse
osmosis desalination systems coupling battery and hydrogen
energy storage. Lian et al. [8] provide a review of recent
sizing methodologies of hybrid renew- able energy systems,
analyzing different approaches and their suitability for vari-
ous applications. Dreher et al. [9] explore the application of
AI agents and deep reinforcement learning for the forecast-
based operation of renewable energy storage systems using
hydrogen, highlighting the potential of advanced algorithms
in optimizing energystorage performance.
Machine learning techniques in the context of sustainable en-
ergy systems are covered by RangelMartinez et al. [10], who
present a review and outlook on renewable energy systems,
catalysis, smart grids, and energy storage. Vijayalakshmi et
al. [11] propose a stochastic gradient descent-based artificial
neural network for predictingthe virtual energy storage ca-
pacity of air-conditioners, showcasing the potential of AIin
load management. Sobhy et al. [12] introduce the marine
predator’s algorithm for load frequency control in modern
interconnected power systems with renewable energy sources
and energy storage units.
The review also includes studies focusing on optimization
algorithms and sizing methodologies. Zhao et al. [13] present
a hybrid shuffled frog-leaping and pattern search algorithm for
sizing renewable energy systems with energy storage systems-
based micro grids, emphasizing cost minimization. Olabi et
al. [14] explore the application of artificial intelligence for
the prediction, optimization, and control of thermal energy
storage systems, highlighting the potential of AI techniques
in enhancing systems.
One study by Olabi et al. [14] explores the application of artifi- The subsequent sections of the paper are structured as fol-
cial intelligence (AI) for the prediction, optimization, and con-
trol of thermal energy storage systems. The authors highlight
Gochhait, Sharma & Bachute
the potential of AI techniques in enhancing the performance
and efficiency of energy storage systems. Boretti [15] dis-
cusses the integration of solar thermal and photovoltaic, wind,
and battery energy storage systems through AI in NEOM
city, presenting an innovative approach for maximizing re-
newable energy utilization. In the context of battery energy
storage systems, Kharlamova et al. [16] investigatedata-driven
approaches for cyber defense, addressing the security chal-
lenges associated with energy storage systems. Xu et al. [17]
propose demand-side management for smart grids based on
smart home appliances with renewable energy sources and an
energy storage system, emphasizing the importance of intel-
ligent load management for optimizing energy consumption
and reducing costs. The literature review also covers studies
on sustainability assessment and control strategies. Oliveira
et al. [18] conduct a self-sustainability assessment for a high
building based on linear programming and computational fluid
dynamics, providing valuable insights into optimizing energy
consumption and efficiency. Yoo et al. [19] explore intelligent
control of battery energy storage for multi-agent-based micro
grid energy management, highlighting the significance of ad-
vanced control strategies in achieving optimal performance.
Abualigah et al. [20] present a survey of advanced machine
learning and deep learning techniques applied to wind, so-
lar, and photovoltaic renewable energy systems, with and
without energy storage optimization. This survey provides
an overview of thestate-of-the-art methods for optimizing
the performance and efficiency of renewable energy systems.
Pakulska and Poniatowska-Jaksch [21] discuss digitalization
in the renewable energy sector, focusing on the emergence of
new market players and their impact on the industry.
Lastly, Abdulkader et al. [22] explore the application of soft
computing techniques in smart grids with decentralized gen-
eration and renewable energy storage system planning. The
authors from [23, 24] emphasize the potential of soft comput-
ing methods for addressing complex optimization problems
and improving the planning and management of renewable
energy systems. By critically examining and evaluating these
significant studies, this research paper aims to make a valuable
contribution to the field of comparative longterm electricity
forecasting analysis. Specifically, it seeks to shed light on the
effective integration of renewable energy sources and energy
storage systems within the unique contexts of Orissa and Delhi
states. The insights gained from this analysis will greatly en-
hance our comprehension of the challenges and opportunities
pertaining to sustainable energy planning and management.
Furthermore, these findings will serve as a valuable support
in the decision-making processes for future energy systems.
lows to provide a cohesive flow of information. Section II
offers a comprehensive overview of the existing literatureon