Quantum‑Inspired Algorithms for Adaptive Load Balancing and

Network Configuration in Modern Electrical Power Distribution

Systems

Purushotham Endla

, V. Naga Siva Rama Murthy

, P. Balakrishnan

, Vanitha Gurgugubelli

Ajmeera Kiran

and Syed Zahidur Rashid

Department of Physics, School of Sciences and Humanities, SR University, Warangal 506371, Telangana, India

Electrical and Electronics Engineering, Ramachandra College of Engineering, Eluru, 534007, Andhra Pradesh, India

Department of Electrical and Electronics Engineering, J.J. College of Engineering and Technology, Tiruchirappalli, Tamil

Nadu, India

Department of EEE, GVP College of Engineering, Kommadi, Visakhapatnam, Andhra Pradesh, India

Department of Computer Science and Engineering, MLR Institute of Technology, Hyderabad, Telangana, India

Department of Electronic and Telecommunication Engineering, International Islamic University Chittagong, Chittagong,

Bangladesh

Keywords: Quantum‑Inspired Optimization, Adaptive Load Balancing, Smart Grid Reconfiguration, Cyber‑Physical

Security, Renewable‑Aware Scheduling.

Abstract: With the widespread adoption of DERs, electric vehicles, and variable load patterns, modern electrical power

distribution systems are becoming more and more complex. Existing quantum-inspired algorithms have

proved potential to solve optimization problems, but they face issues like scalability, limited real-time

operation, and no integration with real-world grid conditions. This article presents a new Quantum Inspired

Scalable, and Resilient Framework for adaptive load balancing to be carried out while dynamically and

securely reconfiguring the NC in Smart Power Distribution Systems. In contrast to existing approaches, the

proposed approach illustrates a unified hybrid quantum-classical architecture that incorporates dynamic

parameter adaptation, multi-objective optimization, and renewable energy forecasting. The framework is

validated on realistic datasets, and is shown to be faster to converge, more resistant to local optima, more

cyber-physically secure, and more amenable to existing infrastructure and smart grid standards. Also, the

system caters to disaster-aware reconfiguration, QoS-based load prioritization, and sustainability metrics such

as CO₂ minimization. This research builds a solid and explainable foundation for intelligent, adaptive, and

secure grid management in the era of digital energy transformation by filling critical gaps in existing literature.

1 INTRODUCTION

There are substantial challenges to controlling

stability, efficiency, and reliability in modern

electrical power distribution systems, due to their

increasing size and the penetration of green energy

systems, electric vehicles (EVs), and Internet of

Things (IoT) enabled devices. The dynamic,

decentralized, and stochastic nature of the current

energy networks makes classic static grid

management techniques incapable of managing

them. With increasing global energy demand and a

requirement for energy systems to be more

intelligent, adaptive, and secure, there is a critical

need for advanced optimization approaches which

can cost-effectively adapt to fluctuations in real-time

and non-trivial constraints.

Quantum-inspired algorithms (QIA) are derived

from this fundamental principle of quantum

mechanics, which are superposition, entanglement

and tunneling, and can serve as an effective method

to address difficult power system optimization issues.

Although these algorithms have shown great

potential for areas such as network reconfiguration,

balancing loads, many of the existing

implementations are limited by design, failing to

scale well, requiring manual tuning of parameters,

Endla, P., Murthy, V. N. S. R., Balakrishnan, P., Gurgugubelli, V., Kiran, A. and Rashid, S. Z.

Quantum-Inspired Algorithms for Adaptive Load Balancing and Network Conﬁguration in Modern Electrical Power Distribution Systems.

DOI: 10.5220/0013861100004919

Paper published under CC license (CC BY-NC-ND 4.0)

In Proceedings of the 1st International Conference on Research and Development in Information, Communication, and Computing Technologies (ICRDICCT‘25 2025) - Volume 1, pages

227-235

ISBN: 978-989-758-777-1

227

and not catering for time varying loads or cyber-

physical integration. Moreover, their adoption in

large-scale, real-time smart grid scenarios is also

limited due to the computational inefficiencies and

inadequate resilience against uncertainties arising out

of renewable intermittency and fault conditions.

To address these research gaps, this paper

proposes a new Scalable and Resilient Quantum-

Inspired SMARTNESS Framework for real-time

adaptive load balancing and secured network

reconfiguration in smart power distribution systems.

Our framework augments the quantum-inspired

optimization strengths with dynamic parameter

adaption, load-criticality aware scheduling, cyber-

physical threat resiliency and deep reinforcement

learning based forecasting for renewable generation

and load patterns. Also, it is compliant with the

international smart grid standards (e.g., IEEE 1547,

IEC 61850) and provides backward compatibility to

legacy systems SCADA, so that it is already ''ready

for the real world''.

This work distinguishes itself by transforming

the challenges identified in previous work into design

provocations. It allows for global optimality through

hybrid search mechanics, augment the speed of

convergence through quantum gate simulation and

incorporates sustainability metrics to address green

energy initiatives. Large scale simulations on

practical grid datasets corroborate the performance

of our strategy against scalability, security, energy

efficiency and fault tolerance. This study paves the

way to the future from adaptive interpretable

frameworks of next generation intelligent power

distribution systems.

2 LITERATURE REVIEW

Modern electrical power distribution systems are

increasingly complex due to the integration of

renewable energy sources, electric vehicles (EVs),

and smart grid technologies; consequently, it needs

new optimization techniques for real-time, dynamic

grid operating conditions. Evolutionary algorithms,

while effective in a steady state system, do not adapt

to the challenges presented by such systems in real-

time. Quantum-inspired algorithms (QIA) have

recently attracted attention for their potential to

tackle complex optimization tasks, providing

solutions that are intractable for classical methods.

2.1 Quantum-Inspired Algorithms for

Power System Optimization

While quantum in themselves, quantum-inspired

algorithms have shown they work well with several

types of power system optimizations including load

balancing, network reconfiguration, and power flow

analysis. Manikanta and Mani (2020) developed a

quantum-inspired evolutionary technique able to

solve distribution network reconfiguration to

minimize line losses in different loading

configurations. While the approach showed great

potential, some drawbacks were its limited scalability

and the fact that it was not effective in a more densely

connected and complex networks (Manikanta &

Mani, 2020) Also, Zhao and Wang (2021) designed

quantum-inspired optimization based distributed

policy-value optimization for managing power

system load. Although they obtained excellent multi-

objective optimization results, the proposed method

could not achieve the scalability and real-time

dynamic requirements of modern grids (Zhao &

Wang, 2021).

2.2 Impasses in Practical Use Cases

However, one of the core problems of quantum-

inspired algorithms is that they do not suit real-time

applications. Theoretical models or little test systems

are the focus for most quantum-inspired methods, as

reported by Chen and Li in 2022. In so doing, we

maximize the benefit we get from optimization in

finding better configurations of the system. But can

those methods still be adopted for larger dynamic

networks, where we need to make real-time load

balancing and to overcome fault fast (Chen & Li,

2022)? Meanwhile, owing to the costly computational

procedures they typically rely on, these algorithms

are unsuitable for actual smart grid environments.

Load balancing using multi-objective

optimization with adaptive learning Paraphrasing

recent studies in this field have suggested a method,

inspired from quantum mechanics called "embedding

multi-objective strategies in quantum-influenced

algorithms, where many contradictions should be

reconciled, such as minimizing energy loss and

maximizing system resilience and the like. Vanitha et

al. (2024) proposed a quantum optimization method

that maximized thermal power plants by combining

scheduling of resources with efficiency. Even where

they had experiments working in stable and

predictable environments, their approach was unable

to consider the changing state of live smart grids with

renewables and small power producers (Vanitha et

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228

al., 2024). In contrast, this work presents a framework

for real-time adaptive load balancing using quantum-

inspired methods and dynamic parameters in

practising smart grids to make a good choice.

Cyber-Physical Security and Fault Recovery

ICEG 2023 PAPER: Cyber-Physical Attacks and

Secure Grid Operation Challenges in Modern

Electrical Power Distribution Systems Many studies,

such as those by Gomez & Martinez (2020) on

quantum algorithms for load balancing looked at

them not according to the world's cybersecurity

requirements of large-scale power grids (Gomez &

Martinez, 2020). At that, fault recovery - a key part of

smart grid resilience - has been expunged from prior

quantum-inspired optimization schemes. For

example, Neufeld et al. (2023) proposed a hybrid

quantum algorithm that could statically analyze

power flow but did not consider means by which the

network might imperatively go on functioning despite

faults (Neufeld et al., 2023). The present study seeks

to fill this gap by adding in a cyber-physical

protective layer and reconfiguration disaster

awareness to this quantum-inspired optimization

platform.

Renewable Energy Integration and On-Site

Validation The sources of renewable energy are

different by nature, therefore efficient methods to

integrate them with the grid require adaptive learning

algorithms of the appropriate sort which can analyze

data to foretell the change in order distribution and

then go ahead and adapt each time around. In Li, and

Zhao (2021) the integration of quantum-inspired

algorithms and renewable energy forecasting for grid

optimization was discussed. Their results however

have not been verified in any real circumstance nor

have they considered fault recovery scenarios, a

situation which may prove crucial to normal grid

stability in emergencies (Li & Zhao, 2021). The

following paper picks up where its predecessors left

off and makes this contribution: the paper advances

an adaptive quantum-based model with real-time load

adjustments and is complemented by renewable

energy forecasting. It provides fault-recovery

mechanisms for the grid.

2.3 Hybrid Models for Practical

Systems

Hybrid approaches, which leverage classical

optimization techniques together with quantum-

inspired methods, have proved to be a promising

response to address the limitations of fully quantum-

inspired frameworks. Liu and Zhang (2023) proposed

a hybrid quantum-deep learning-based framework for

load balancing in distribution networks. While the

framework had been successful in limited datasets,

the lack of real-world validation hindered its practical

uses (Liu & Zhang, 2023). Building on this work,

here we validate the proposed framework against

real-world datasets from different urban and rural grid

environments, highlighting its scalability, energy

efficiency and resilience.

2.4 Targets Sustainability and

Renewable Energy

The green energy movement has also created a new

demand for more sustainability in the optimization of

the power systems. Singh and Sharma (2021)

explored dynamic load balancing by quantum-

inspired algorithms, however, the optimization

framework proposed was not linked to sustainability

objectives (Singh & Sharma, 2021). The proposed

system is both efficient and green since this study

employs sustainability metrics like CO₂ minimization

and integration of renewable energy in the

optimization process.

3 METHODOLOGY

This contributes towards a scalable and resilient

quantum-inspired framework for real-time adaptive

load balancing and secure economic reconfiguration

of smart power distribution systems. The framework

combines quantum-inspired optimization algorithms,

deep reinforcement learning (DRL) for forecasting

the behavior of renewable energy systems, and strong

cyber-physical security components to tackle

contemporary power grid challenges. The three main

components of the proposed framework include

Quantum-Inspired Optimization, Adaptive Load

Balancing with Renewable Forecasting, and Cyber-

Physical Security and Fault Recovery.

3.1 Quantum-Inspired Approach for

Network Reconfiguration

Optimization

The main part of this methodology is the application

of quantum inspired algorithms for the optimization

of the reconfigured electric distribution network. In

addition to minimizing line losses and improving

voltage stability by uniformly distributing load over

feeders, the quantum-inspired evolutionary algorithm

(QIEA) is used to identify the right network topology.

With this in mind, we have implemented an algorithm

Quantum-Inspired Algorithms for Adaptive Load Balancing and Network Conﬁguration in Modern Electrical Power Distribution Systems

229

capable of working like a quantum algorithm, that is,

applying quantum principles like superposition and

entanglement to have an intelligent search that spans

the solution space, ensuring that the network

reconfiguration will accommodate for different load

levels. To help with this, we use a genetic algorithm

that begins with an initial population of viable

candidates that correspond to different states of the

feeder switches. A set of solutions is then obtained,

which can be ranked using a fitness function, based

on the minimization of energy loss/ or its stability.

Figure 1: Adaptive Load Balancing.

The solutions evolve with quantum-inspired

mutation and crossover operators to encourage

diversity and better exploration of the solution space.

This process continues until a convergence criterion

is satisfied, meaning that the network is optimally

reconfigured for both static and dynamic loading

conditions.

3.2 Full Metadata Record

The second part is a load balancing that deals with

the nature of renewable energy like solar and wind.

Integrated Deep Reinforcement Learning (DRL) to

predict and optimize load distribution using real-time

energy generation data to achieve this. The state in

this formulation consists of the grid parameters which

also include states of load, renewable generation, and

the bus voltage in different sections of the grid. The

action space includes switching actions, Redis

patching power flow, and controlling energy storage

or backup generators. The reward given to DRL agent

includes the balanced load, energy losses

minimization and voltage stability. This model

enables the network to rebalance in real-time as

renewable energy generation fluctuates. It also

enables a flexible response to predicted changes in

renewable generation, making the system more

resilient to renewable intermittency.

The system's effectiveness was evaluated based

on the proposed mechanism, shown in Figure 1:

Adaptive Load Balancing, which balances load

between nodes, in order to avoid bottleneck at

processing elements. Performance Results The results

of the performance are summarized in Table 1:

Adaptive Load Balancing Performance Table, where

we can observe increased throughput and reduced

latency in most situations.

Table 1: Adaptive Load Balancing Performance.

Time

Period

Optimal Load

Balance (%)

Actual Load

Balance (%)

3.3 Cyber-Physical Security and Fault

Recovery

The third component reserves cyber-physical security

mechanisms incorporated into the quantum-inspired

optimization framework to cater to increasing cyber

threats and the reliability of grid operation. This

method secures the grid against cyber threats, all the

while allowing the grid to run as efficiently as

possible. It first simulates potential cyber-attacks,

including data injection and denial-of-service (DoS)

attacks, to assess the weaknesses in the grid. Aiming

to address these threats, a quantum cryptography-

based trust model is proposed to achieve secure

communication and data integrity between control

centres and field devices. New systems also

incorporate disaster-aware reconfiguration protocols,

so in the event of a fault or attack, they reconfigure

their networks to isolate affected areas and generators

while allowing the grid to continue functioning. This

technique gives robustness and enhances the fault

recovery mechanism, where quantum-inspired

algorithms are utilized to explore the most fault-

tolerant topology to reduce the system impact. This

allows the grid to recover rapidly from disturbances

or aggressive actions, thereby avoiding lengthy

disruptions and preventing cascade failures.

Cyber security of the system Table 2 Cyber-

Physical Security Evaluation Table Detection

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response time, accuracy, and threat mitigation rates

are listed in Table 2 for the presented scenarios. In

addition to that, Figure 2 Cyber-Attack Prevention

Effectiveness illustrates the performance of the

system when resisting different types of cyber-attacks

and shows the overall defensive state of the system.

Table 2: Cyber-Physical Security Evaluation.

Cyber-Attack

Type

Prevention

Rate (%)

Fault Recovery

Time (s)

Data Injection

Denial of

Service (DoS)

Man-in-the-

Middle

Figure 2: Cyber-Attack Prevention Effectiveness.

3.4 Real-World Data Validation and

Simulation

3.4.1 Validating and Simulating Real-World

Data

Extensive simulations with realistic data from urban

and rural testbeds confirm the framework's

effectiveness in equilibrium as well as its real-world

applicable nature. These consist of such types of data

as grid topology, load profiles, renewable energy

production, and the history of faults. The framework

is then evaluated on performance metrics including

scalability, energy loss reduction, the system's ability

to maintain voltage stability and remain stable and

dynamic in real time change over different grid sizes.

Thus, the framework scales on grids of different sizes,

so that small and large networks alike are provided

with sufficient support without performance los. By

simulating various kinds of cyber-attacks on the input

system and examining the system's stability and

security performance, the security resilience of the

entire system can be determined. The ultimate effect

of the load balancing algorithm's efficiency is: not

only does it lead to lower energy losses and increased

operating efficiency, while also allowing the system

to manage fluctuations in renewable energy ensuring

stable grid operations remains possible.

3.5 Integration of Legacy

Infrastructure

The final section of the methodology emphasizes that

it has to be integrated together with existing

infrastructure. Legacy SCADA (Supervisory Control

and Data Acquisition) systems, IEC 61850 compliant

communication protocols both have been supported

by the system. In this way, our framework can be

deployed to real world settings without significant

abandonment of existing hardware infrastructure. As

the part of this methodology integrates the system

with popular standards as well as legacy systems, it

ensures that power companies' introduction of this

technology will not significantly disrupt their current

operation. Quantum-Inspired Adaptive Load

Balancing and Security Framework for Smart Power

Distribution Systems Shown in Figure 3.

Figure 3: Quantum-Inspired Adaptive Load Balancing and

Security Framework for Smart Power Distribution Systems.

4 RESULTS AND DISCUSSION

4.1 Performance Evaluation of the

Quantum-Inspired Optimization

Framework

Using traditional optimization techniques as a

benchmark, its performance is considered an

important factor in the network reconfiguration

process. By achieving several objectives together (eg:

energy losses, the separation of the off-peak power

Quantum-Inspired Algorithms for Adaptive Load Balancing and Network Conﬁguration in Modern Electrical Power Distribution Systems

231

into manufactories that perform best for it and voltage

maintenance), it was possible to show that those

mentioned optimization techniques or methods had

reached their expected peak. Simulation results

indicated that the proposed quantum-inspired

evolutionary algorithm (QIEA) achieved much better

power losses and voltage stability than traditional

optimization techniques, such as Genetic Algorithms

(GA) and Particle Swarm Optimization (PSO).

Finding: In comparison with a GA-based approach,

the QIEA reduced the energy losses by 20% and

improved voltage stability by 15% for both static and

dynamic load cases. This is because the quantum-

inspired operators (such as quantum mutation or

crossover) help the algorithm avoid local optima due

to their optimal exploration, allowing it to search far

and wide for better solutions. Also important is the

algorithm\u0027s ability to respond to changes in

load dynamics in real time, important in light of its

application.

Table 3: Optimization Method Comparison Table

Performance of Different Optimization Methods on

Smart Grid Reconfiguration Convergence Speed

Accuracy Computational Burden. Figure 4: Voltage

Stability Improvement Similarly, our approach

improves the voltage profiles as shown in Figure 4,

thereby demonstrating that the model can indeed help

preserve the stability in the network when subjected

to varying load.

Table 3: Optimization Method Comparison.

Optimizati

on Method

Energy

Loss

Reduction

(%)

Voltage

Stability

Improvement

(%)

Converg

ence

Time (s)

Quantum-

Inspired

Genetic

Algorithm

Particle

Swarm

Optimizati

Figure 4: Voltage Stability Improvement.

4.1 Renewable Energy Generation

Forecasting and Adaptive Load

Balancing

Adaptive load balancing callipers and renewable

energy prediction milling improves drastically the

grid overall efficiency. Along with current data from

renewable energy cells such as wind or sunlight

collectors, dynamic patterns of load were used to train

the DRL model. Thus, they have found that with the

new tools the energy imbalances suffered by

traditional load balancing models, is now lessened up

to 18%. Besides, the DRL approach successfully

forecasted variability in renewable energy and thus

preventive load balancing can be performed rather

than reactive. In modern grids where renewable

energy sources are famously volatile and

unpredictable, it is especially important for load

distribution systems to change themselves according

to renewable energy forecasts. The method

effectively Reduced Renewable Energy Curtailment

by a full 10%, Contributing both to a stable grid and

making maximum use of green power.

4.2 Cyber-Physical Security and Fault

Recovery Structural/Cyber-

Physical Performance Tradeoff

A set of cyber-attacks and system faults such as

Denial of Service (DoS) attacks and data injection

attacks was simulated to evaluate reliability in the

third part of this methodology: cyber-physical

security and fault recovery. The problem is that

uniform rates could fail to provide adequate

protection against under or over frequency faults, or

unexpected control circuit malfunctions which may

crash a line of sight uninterrupted power supply. In

cyber-attack scenarios, charting course judiciously

for the next important waypoint requires that one

control point should be reflected so it remains visible

to pilots from above as they fly down wind on what

might later turn out to become an outbound course.

The quantum cryptography system has been proposed

for authentication between grid devices in both

normal and attack situations, ensuring that the system

remains safe. Moreover, the disaster-aware

reconfiguration algorithm facilitated quick recovery

of grid services post-faults and achieved a 30%

reduction compared to traditional systems in recovery

time. The integrity of our release is absolute position:

the ENDP does not simply optimize performance, it

also withstands cyber-physical threats, one of main

problems in modern power distribution networks.

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4.3 Validation and Scalability of Real-

World Data

The simulations performed to test the scalability of

this framework employed real-world data from the

electric grid. Across applications where we know

detailed load profiles and renewable energy

generation statistics This allowed us to use the same

framework for smaller grid sizes with the worst of all

costs: only minor performance degradation. For

example, it was computationally efficient and the

framework had finished solving distribution network

connected with 50 buses reconfiguration problems in

less than 5 minutes even while facing large scale real-

time data. This result shows that our quantum-

inspired optimization algorithm can count smart grid

networks as their complexity grows, making it useful

for large and small network assessments alike.

Furthermore, the framework is also able to bear up

under changing conditions. In a real-time electricity

market with the electricity usage and renewable

energy patterns updating frequently, it gets used

together to help shape tomorrow's new markets

create. Real-World Data Simulation Shown in Table

Table 4: Real-World Data Simulation.

Grid Type

Energy

Loss

Reduction

(%)

Load

Balancing

Efficiency

(%)

Fault

Recovery

Time (s)

Urban

Rural

Suburban

4.4 Connecting with Existing

Infrastructure

This approach is beneficial - because it can fuse with

the present legacy SCADA system and IEC 61850

communication protocol. According to simulations of

real objects in the world scisia 2013 the system meets

these existing infrastructure standards is deployable

on operational grids without the need for significant

amendments toolS: Moreover, this will have little or

no impact on utility companies' methods in current

practice. Framework operability with legacy systems

ensues here - coupled with advanced performance

from quantum-inspired (UV) optimization and real-

time adaptive load balancing technologies. Therefore,

it is perfectly suitable for upgrading existing grid

infrastructures.

4.5 Impact on Environment and

Sustainability

Finally, the environmental advantage of the proposed

system started with how much CO brown ₂it could

mitigate due to lessening power losses from more

efficient integration of renewable energy. This only

confirms too that the proposed structure has 12%

lower CO ₂emissions than existing grid asset

management systems. The result is more efficient use

of the energy sources; the power grid becomes

greener because situating and setting up renewable

clean energy within its system expends less resources.

This is consistent with global trends toward

decarbonization of the energy sector and sustainable

energy systems. Table 5 Shows the Sustainability

Impact Table.

Table 5: Sustainability Impact.

Metric

Value

CO₂ Reduction (%)

Renewable Energy Utilization

Improvement (%)

5 CONCLUSIONS AND FUTURE

WORK

And the results of this research indicate the

promising potential of establishing the proposed

quantum-inspired framework for improving the

optimization and security of contemporary power

distribution systems. This comprehensive approach to

modern electrical grid challenges combines quantum-

inspired optimization with adaptive load balancing,

renewable energy forecasting, and cyber-physical

security. In the future, we are investigating more

advanced machine learning approaches in order to

improve energy prediction, the fault recovery

mechanism and the network will also be evolved to

support additional grid technologies e.g. Microgrids,

Smart meters etc.

5.1 Conclusions

This article proposes a new paradigm of quantum-

inspired framework, which is scalable and resilient

for the real-time adaptive load balancing and secure

network reconfiguration in advanced power

distribution systems. Using quantum-inspired

optimization algorithms, deep reinforcement

learning, and cyber-physical security mechanisms,

the framework addresses some of the critical issues of

Quantum-Inspired Algorithms for Adaptive Load Balancing and Network Conﬁguration in Modern Electrical Power Distribution Systems

233

modern grids Such as energy loss mitigation, voltage

stability, real-time balancing of loads, renewable

energy integration, and cyber-attack protection.

The power loss, voltage stability and equality

constraints are reduced significantly through

quantum inspired optimization algorithm by

presenting optimization techniques and techniques

when such a model is applied. Also, the adaptive load

balancing scheme that leverages deep reinforcement

learning for renewable energy forecasting leads to

optimal load balancing contributing to reduction of

curtailment and maximization of green energy

utilization. Quantum distribution with integrated

security for disaster-aware reconfiguration ensures

robust defenses against cyber-physical attack,

securing resilience against threats and faults in the

system.

Real-world simulations confirmed the scalability

of the framework, which was capable of dealing with

small and large-scale grids without performance

degradation. This allows for a seamless transition

from existing SCADA infrastructure to the new

system, while reaping the benefits of improved

performance and security.

This alignment aids in the transition towards a

greener and more sustainable power grid by

promoting sustainability through energy loss

reduction and better integration of renewable energy.

And, the quantum-inspired optimization applied

along with all the best of class AI and security

technologies offers a synergetic solution that is novel

and a practical answer to the needs of a new power

distribution network.

Such a framework can be extended in future work

using other machine learning methods, developing

better fault recovery mechanisms, and extending to

the overall application of the solution for concepts

like microgrids and decentralized energy systems.

This research presents substantial advances in smart

grid optimization, laying down the foundations for

improved efficiency, security and sustainability in

energy systems.

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