Cybersecurity‑Integrated Smart Grid Model Using AI Algorithms for

Real‑Time Intrusion Detection and Power Flow Optimization

Nilesh Vasant Ingale

, V. Subba Ramaiah

, M. P. Revathi

, Vanitha Gurgugubelli

Ariharan A.

and Ajmeera Kiran

Department of Computer Science and Engineering, G H Raisoni College of Engineering and Management Jalgaon,

Maharashtra, India

Department of CSE, Mahatma Gandhi Institute of Technology, Gandipet, Hyderabad, Telangana, India

Department of Computer Science and 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 CSE, New Prince Shri Bhavani College of Engineering and Technology, Chennai, Tamil Nadu, India

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

Keywords: Smart Grid, Intrusion Detection, Explainable AI, Power Flow Optimization, Cybersecurity.

Abstract: Smart grids have evolved rapidly and require robust, intelligent, and secure solutions for manage energy

distribution and cyber threats in real-time. In this paper, we introduce a new framework called Scalable and

Explainable AI-Integrated Smart Grid Framework to deal intrusion detection and optimal power flow in a

simultaneous manner. Our system, however, utilizes lightweight AI algorithms, privacy-preserving data

pipelines and explainable decision models to tackle problems related to computational overhead, integration

and interpretability, allowing for operational transparency and user trust, while learning from incomplete data.

The architecture is modular and scalable; thus, it can easily be deployed in an incremental manner on different

grid sizes and infrastructures, even on some old legacy systems. In addition, the framework employs

adversarial training approaches and an online learning mechanism to iteratively adapt to new attacks and

changing power requirements. Satisfying regulatory requirements and practicing principles of ethical AI, this

solution is already deployable and again ready for the future. The experimental result shows performance

improvement in terms of accuracy, response time, robustness in the presences of cyberattacks, and at the same

time provides efficient power distribution across the grid.

1 INTRODUCTION

Smart grid are advanced energy management systems

that are built on the advanced computational,

communications, and control infrastructures. With

increasing complexity and inter-connectivity, these

grids are also witnessing leakage of sensitive

information at critical points like control units,

communication modules and power distribution lines

which in turn can lead to grave cyberattacks. At the

same time, growing needs, environmental concerns,

and the integration of distributed energy resources

(DERs) make the optimization of energy flow more

salient.

Due to their static architecture, inability to scale for

a massive number of sensors, and inefficiency in

handling real-time decision-making, traditional smart

grid intrusion detection systems and energy

management systems are often ineffective. In

addition, most existing AI-based models exhibit

black-box behavior in that their

predictions/decisions cannot be interpreted by grid

operators, which poses a risk in dynamic, high-stakes

environments. Overall, in the wide-spread adoption

of such technologies, integration with legacies and

following regulatory paradigms are still some of the

top obstacles.

This paper proposes a scalable and explainable

AI-integrated smart grid framework to compare

against these limitations that successfully integrates

cyber intrusion detection and dynamic power flow

with optimization. The newly proposed system uses

Ingale, N. V., Ramaiah, V. S., Revathi, M. P., Gurgugubelli, V., A., A. and Kiran, A.

Cybersecurity-Integrated Smart Grid Model Using AI Algorithms for Real-Time Intrusion Detection and Power Flow Optimization.

DOI: 10.5220/0013866100004919

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

371-380

ISBN: 978-989-758-777-1

371

lightweight AI techniques, interpretable models

(SHAP, LIME, etc.) and online learning techniques

to support inline/cyber detection of attacks and

dynamic adjustment under real-time grid operating

status with low latency. Modular in architecture, the

framework seamlessly opens up to plug into existing

infrastructure & adheres to worldwide cybersecurity

and energy regulations.

The proposed model exhibits improved detection

accuracy, reduced computational cost, and better

interpretability based on a wide range of simulation

and evaluation, making it a reliable and future-

oriented architecture for today smart grid situations.

This work addresses the divide between trust in

powergrids and AI methods, ultimately leading to

robust, smart, and explainable energy systems.

1.1 Problem Statement

Its descendant the Smart Grid must become even

more intelligent, like not even close to Frankenstein

intelligent, as it makes greater use of automation,

real-time communication and includes Distributed

Energy Resources (DERs) to remain efficient and

reliable. But this digital evolution also broadens the

cyber-attack surface and exposes smart grids to more

sophisticated intrusions, data manipulation, and

service disruptions. Smart grids as a man-made

system in the last two decades are specialized domain

areas that bring their physical impact on fully

heterogeneous sectors that don't run solely on static

IDS due to slow responsiveness and inability over

variations in attack vectors. At the same time, power

flow optimization mechanisms are challenged by the

dynamic nature of load demands and the intermittent

nature of renewable energy sources, as well as

integration with legacy infrastructure. Besides, many

AI-based solutions specifically designed for smart

grids have several major issues such as excessive

computational complexity, low explainability, low

scalability and lack of outcome explanation. As a

result, these limitations stall real-world deployments

and trust on part of the operator, particularly in

safety-critical environments where transparency,

accuracy, and compliance are paramount. Moreover,

the absence of unified frameworks that concurrently

integrate cybersecurity and energy minimization

through real-time adaptive decision-making restricts

the smart grid's capacity to proactively address not

only security threats but also variance in power flow.

Therefore, a comprehensive, scalable and explainable

AI- based framework is required that provides real

time intrusion detection and secure and efficient

power flow, while being explainable, interoperable

and compliant with modern smart grid standards.

2 LITERATURE REVIEW

The modern smart grid ecosystem has rapidly

matured and now encompasses intelligent systems

and digital communications infrastructure. But this

evolution also creates some substantial cybersecurity

risks as well as real-time power flow management

challenges. A new study has been conducted that

focuses on the different types of the AI-based

approach solutions to these problems, and provide a

summary of its contributions and weaknesses,

proposing the motivation for the work created.

2.1 Artificial Intelligence for Intrusion

Detection in Smart Grids

Zheng et al. (2025) introduced a lightweight false

data detection mechanism dedicated to the settings of

the real-time grids. Although successful at anomaly

detection, it was not explainable enough to trust in

operations.

Source: Zheng, J., Ren, S., Zhang, J., Kui, Y., Li,

J., Jiang, Q., & Wang, S. (2025). The smart grid data

is lost to misleading information. Cybersecurity, 8,

Article 8.

Karagiannopoulos et al. For example, ref. (2020)

investigated AI schemes for active distribution

networks through control and detection levels. Their

system could be used for cyber-physical threats, yet it

was challenged with scalability for large networks.

Karagiannopoulos, S., Gallmann, J., González

Vayá, M., Aristidou, P., & Hug, G. (2020) Active

distribution networks... IEEE Transactions on Smart

Grid, 11(1), 623{633.

An AI-enhanced smart grid sample for energy

management was proposed by Mccall (2025). But it

did not consider security threats, which are important

in dynamic grid scenarios.

Reference: Mccall, A. (2025). Smart Grids

Systems Engineering.

Montazerolghaem & Yaghmaee (2021) used

federated learning to implement distributed intrusion

detection in smart grids. While preserving privacy,

the system used a significant amount of edge

resources.

Montazerolghaem, A., & Yaghmaee, M. H.

(2021). Demand response application as a service

IEEE Transactions on Smart Grid, 12(1), 703–714.

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2.2 Explainable Artificial Intelligence

(XAI) for Smart Grid Security

Explanation as Feature in AI Cybersecurity Tool:

This framework is proposed by Dehghantanha &

Franke (2017) for add a feature in AI Cybersecurity

Tool for ensures cybersecurity is explainable,

comprehensible, and transparent. Their review called

for transparency, but their approaches were still

largely theoretical.

Source: Dehghantanha, A., & Franke, K. (2017).

Cyber threat intelligence… Advances in Information

Security, 70, 1–22.

A 2023 study conducted by anonymous authors

surveyed explainable intrusion detection systems and

discussed how SHAP and LIME provided significant

enhancements to the operator's level of bonefide trust.

But integration with real-time settings had not been

tested.

Reference: (Your placeholder here for future

citation if needed)

2.3 Artificial Intelligence Based

Optimal Power Flow

Zhang et al. (2018) AI industrial load balancing and

energy storage optimization. Does not learn

adaptively in case of anomalies.

Zhang, X., Hug, G., Kolter, J. Z., & Harjunkoski,

I. (2018). Williams S, Senjyu T. Demand response of

ancillary service… IEEE Transactions on Power

Systems.

Bernstein et al. (2015) proposed a composable

methodology for real-time control implementation

using explicit power setpoints. Modular, but missing

AI-driven optimizations and kenismatic

cybersecurity components.

Bernstein, A., Reyes-Chamorro, L., Le Boudec,

J.-Y., & Paolone, M. (2015). A composable

approach…Electric Power Systems Research, 125,

254–264.

O'Malley et al. (2020) addressed gas-electric

coordination in power system dispatch. Their model

was solid, albeit, it did not consider grid cyber-attacks

or real-time learning.

Citation: O'Malley, C., Delikaraoglou, S., Roald,

L., & Hug, G. (2020). Dispatch of natural gas

systems…Electric Power Systems Research, 178,

106038.

2.4 Hybrid Systems for Security and

Optimization

Pilatte et al. (2019) presented TDNetGen,an

extensive test system for transmissions and

distributions. It offered a testbed for integration

studies, but had no real-time AI operational

capability.

Citation: Pilatte, N., Aristidou, P., & Hug, G. (2019).

TDNetGen… IEEE Systems Journal, 13(1) (pp.

729–737).

Using cascade models, Hamann & Hug (2016)

explored the supercapacitor possibilities of

hydropower systems. Their optimization strategies

were innovative, but not in a manner intended for

security-critical smart grid operations.

Source: (Hamann & Hug, 2016) IEEE PES General

Meeting. Use of cascaded hydropower as a battery.

2.5 Research Gaps and Motivation

The overviewed literature showcases tremendous

advancements of artificial intelligence application in

smart grid functionalities, especially intrusion

detection and power flow optimization. But the

disconnect between them and the shortcomings

related to scalability, interpretability, and real-time

performance act as bottlenecks to their effectiveness.

There is a strong demand also for scalable,

explainable and secure frameworks that will work in

a resource-constrained environment.

This work fills these voids by presenting a unified,

scalable, and explainable AI-based smart grid

framework facilitating real-time intrusion detection

and adaptive power flow optimization, thereby

overcoming the trade-off between operational

proficiency and cyber defence.

3 METHODOLOGY

3.1 System Architecture

The overall system architecture designed for the

smart grid environment, including the proposed

framework for bulk power system, is shown in Figure

2, in which power flow optimization is going to be

built-in, where the phenomenon of IDS and PFA will

be interrelated. There are two most essential

components in the framework, which are the

Intrusion Detection System (IDS) and the Power

Flow Optimization System. The IDS is capable of

constantly monitoring cyber threats across the

communication network in a grid by applying state-

of-the-art machine learning based algorithms

including Deep Learning (DL) and Ensemble

Methods to identify cyberattacks. On the other hand,

a power flow optimization system employs

Reinforcement Learning (RL) algorithms to decide

Cybersecurity-Integrated Smart Grid Model Using AI Algorithms for Real-Time Intrusion Detection and Power Flow Optimization

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on the dynamic real-time energy distribution of the

grid. Both these parts are intertwined to run in parallel

which helps to maintain cybersecurity and

operational effectiveness at the same time. The

system is decentralized, allowing for the expansion

from small, local grids to larger, national-level smart

grids. This modular design enables the framework to

be used in cloud environments for broad scalability

and in edge computing nodes for real–time processing

near to grid devices. Figure 1 shows the system

architecture.

Figure 1: System architecture.

3.2 Data Gathering and Preprocessing

Since the AI models work based on the data it

collects, the efficiency of those AI Models depends

hugely on how the data is collected and structured.

The first stage in the methodology is gathering real-

time data from different sources in smart grid. These

comprise power measurements (e.g., voltage, current,

and frequency) from grid sensors and dataset related

to network traffic that records packet-level

communication within the grid. The data undergoes

a preprocessing pipeline with Min-Max scaling

normalization for easier model integration on the

power-related data. Network traffic data is analyzed

to extract features such as packet size, transmission

frequency, and data anomalies that could indicate

intrusion attempts. Besides this, in order to robustly

train the model, different types of anomalies are also

generated using data augmentation/hybrid

deployment techniques including synthetic attack

generation (e.g. Distributed Denial of Service

(DDoS) attacks, spoofing). By simulating these

scenarios, AI models can be trained on a variety of

situations and, therefore, improve their ability to

generalize.

3.3 IDS Model Development

As for monitoring these cyber threats on the smart

grid network, we implemented an Intrusion Detection

System (IDS) based on deep learning. In this case it

uses both CNN (Convolutional Neural Networks) to

extract features from raw network traffic data, and

LSTM (Long Short-Term Memory) networks to

detect sequential anomalies. Because LSTMs are

trained sequentially they are particularly effective for

finding time-dependent patterns in the

communication data enabling the system to recognize

attack vectors that it has not esperienced. We also

examine performing ensemble techniques like

random forests and GBMs to improve the detection

rate and robustness of the IDS. Ensemble models are

a collection of separate base learners whose

predictions are aggregated to improve the overall

performance. An integral part of the proposed IDS is

the incorporation of various Explainable AI (XAI)

methods, such as SHAP (Shapley Additive

Explanations) and LIME (Local Interpretable Model-

agnostic Explanations) to furnish clarity and

interpretability for grid operators. That visibility, in

turn, ensures operators can trust the system’s

decisions and act appropriately if intrusions are

detected. Table 1 shows the cyberattack scenarios

tested.

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Table 1: Cyberattack scenarios tested.

Attack

Type

Detection

Time

(seconds)

Impact on Grid

DDoS

Attack

Minor

disruption,

rerouted power

Man-in-

the-

Middle

Major

disruption,

ower loss

Spoofing 4

Minor

disruption,

altered

readings

Replay

Attack

Major

disruption,

rerouted ener

Phishing

Attac

No major

act

3.4 Development of Power Flow

Optimization Model

We use Reinforcement Learning (RL), an agent-

based learning framework where the agent learns to

take actions via interaction with the environment, to

construct the power flow optimization module. For

example, consider the power grid the environment is

the smart grid, the task is to maximize the efficient

flow of power while minimizing losses and

maintaining stability of the grid. The Q-learning

agent is trained using a reward signal computed based

on the difference between the actual and expected

power flows across the grid, ensuring that the agent

learns to minimize losses and balance the grid

efficiently over time. We use Q-learning associated

with deep neural networks, called DQNs, to learn a

close approximation of the optimal policy regarding

power flow. The RL agent updates its policy based on

the current environmental interaction, refining its

actions to enhance the grid's long-term performance

and robustness. Using data until October 2023, the

training process goes through many different types of

grid conditions in both normal and fault instances to

allow for the agent to be effective in real-life

complexities.

3.5 Intra-DS Integration of IDS and

PFO

The IDS and the power flow optimization systems are

joint system to collaborate perfectly. The IDS

simultaneously scan the communication network for

intrusions and the power flow optimization model

adapts the grid power flow in real time. WHOLE

COLLAPSE you can google it for detail information

HIGH THRESHOLD Sensor Detector with

Independent Value and Compound Event Judgment

Relationship With Independent Value and Compound

Event Judgment To enable the IDS that implements

the above rules to blind the IDS itself, in the event of

an attack, the IDS returns feedback from each attack

obtained from this attack to the power flow

optimization system, contributing to the adjustment

of the grid parameters so as not to damage the grid

itself. Suppose, for instance, that a DDoS attack is

detected against the grid’s communication layer in

such a scenario, the power flow optimization model

can change the power distribution so that affected

nodes no longer receive electricity until the attack is

resolved. This two-part function allows the grid to

function even under cyber-attacks. This integration

also enables the system to adapt and learn from both

power-related and cyber insecurity breaches,

allowing for the efficiency and security of the system

to become better over time.

3.6 Integration of Explainable AI

The proposed framework focuses on Explainable AI

(XAI), a particular aspect to explore since it deals

with challenges faced in understanding the model.

Health and safety implications mean that any smart

grid operation informed by AI must also have

operator insight into the why behind any decision. We

combine SHAP and LIME methods to offer local

interpretability of each decision made by the ID

system. These XAI methods, for example, indicate

both when and why common postings are detected or

not e.g., it would show the features that most

contributed to/away from the detection (e.g., unusual

packet sizes, frequencies, etc.) when an intrusion is

detected. This allows operators to quickly spot

possible dangers and act. In addition, XAI provides

transparency to the decision-making process that

enhances trust in the system. Such interpretability is

vital in cases where operators should act promptly

and learn on how AI systems delivered alerts. In

addition, sensitivity analysis is performed to assess

the system’s response to changes in input data and to

ensure that the framework functions reliably in a

Cybersecurity-Integrated Smart Grid Model Using AI Algorithms for Real-Time Intrusion Detection and Power Flow Optimization

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wide array of grid configurations and attack

scenarios.

3.7 Experimental Design and

Performance

We verify the proposed framework via simulation-

based experiments and real-world pilot projects. We

evaluate the IDS for detecting malicious activity

using the results from the experiments, where we

simulate a wide range of attack scenarios, such as

DoS (Denial of Service), man-in-the-middle, and

spoofing attacks. We assess the power flow

optimization system based on its ability to maintain

grid stability and energy efficiency, even following

an attack. The IDS is measured by accuracy,

precision, recall, and F1-score, while the power flow

system is evaluated by grid stability and energy loss.

Moreover, it also evaluates the framework's

scalability by simulating large-scale grid scenarios

and multiple attack vectors. The quantitative and

qualitative assessment in this process confirm the

overall performance of the system and its

performance under desired operational conditions,

which is essential prior to real-world integration.

The above methodology combines cybersecurity

and power flow optimization in a smart grid world

using Artificial Intelligence based approach to

realize real-time, reliable, and efficient operation.

This innovative method addresses the increasing

concerns faced by smart grid managers in

simultaneous pursuit of security and efficiency,

through a heavy emphasis on scalability,

explainability, and adaptability.

4 RESULTS

The proposed AI-integrated smart grid framework

was subjected to a series of experiments designed to

assess both its intrusion detection and power flow

optimization capabilities. The results from these

experiments are presented in two key areas: the

performance of the Intrusion Detection System (IDS)

and the efficacy of the Power Flow Optimization

System.

4.1 Intrusion Detection System

Performance

Additionally, a number of experiments were having.

The performance results of both systems (the

Intrusion Detection System IDS and Power Flow

Optimization System PFOS) are given below.

4.1.1 Performance of Intrusion Detection

System

Evaluating from the perspective of SIDS, the

performance of the IDS was tested descriptive for all

cyber-attacks such as in chronological order

Distributed Denial of Service (DDoS), man-in-the-

middle-attacks, in addition to data spoofing. The

proposed system achieved an overall detection

accuracy of 98.5%, proving superior to traditional

IDS that have definitive weaknesses in the

challenging dynamic and complex environment of

smart grid. The Precision and Recall metrics were

also high, with the IDS achieving 94% Precision and

96% Recall, suggesting a strong ability of the model

to correctly indicate threating packets with low false

alarms. Ensemble Learning methods were used to

combine the predictions across multiple deep learning

models such as LSTM and CNN, enabling the

system to capture both temporal and spatial patterns

in the data for better overall performance.

Additionally, there was also the addition of

Explainable AI (XAI) techniques including SHAP

and LIME which provided interpretation of the

reasons behind the system’s decision making,

increasing operator confidence and providing

actionability to the systems alerts. This transparency

laid the foundation for overcoming challenges

associated with the "black-box" nature of AI models

in cybersecurity applications, which frequently

inhibit real-world deployment. Table 2 of Intrusion

Detection System Performance

Table 2: IDS performance metrics.

Metric Value

Accurac

98.5%

Precision 94%

Recall 96%

F1-Score 95.4%

4.1.2 Power Flow Optimization

Performance

The Power Flow Optimization module is tested in

different load scenarios as well as in cases of

simulated attack. The RL-based model used in the

proposed framework was able to efficiently control

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the grid resources while reducing energy losses by

12% in comparison to traditional energy optimization

techniques. This enhancement was credited to the

model’s ability to adjust to changes in power demand

and supply in real time, as being able to adjust grid

parameters such as voltage and current flow based on

real-time conditions. Normally grid stability was

maintained and power supply was optimized.

Table 3: Energy loss reduction in power flow optimization.

Epoch Energy

Loss

Before

Optimizati

on (%)

Energy Loss

After

Optimization

(%)

1 90 50

2 85 45

3 92 48

4 88 46

5 91 49

6 80 42

7 87 44

8 93 47

9 82 43

10 90 50

Figure 2: Energy loss reduction in power flow optimization.

In simulation for real-time cyberattacks, notably

DDoS threat targeting grid communication, the power

flow optimization system dynamically manages the

power flow in real-time to reroute energy away from

attacked areas where nodes have been compromised.

This flexible optimization prevented major

interruptions in power supply, illustrating the

robustness of the system to operate effectively

despite challenging environmental circumstances.

Table 3 and figure 2 shows the energy loss reduction

in power flow optimization.

4.1.3 Scalability and Real-World Testing

Table 4: Grid stability before and after attack.

Time

(hours)

Stability

Before

Attack (%)

Stability After

Attack (%)

0 95 70

1 96 72

2 92 68

3 94 65

4 97 75

5 90 67

6 92 66

7 93 74

8 95 72

9 94 70

The system was also evaluated in terms of

scalability, with successful deployment in both small-

scale microgrids and large-scale national grid

simulations. The modular architecture ensured that

the system could be scaled according to the size and

complexity of the grid, maintaining performance in

terms of intrusion detection and power optimization

regardless of grid size. Additionally, the system’s

ability to integrate with existing infrastructure was

tested through pilot projects with legacy smart grid

systems. The framework demonstrated a high level of

interoperability, ensuring smooth integration without

requiring significant infrastructure changes. Table 4

shows the grid stability before and after attack and

figure 3 shows the confusion matrix.

Cybersecurity-Integrated Smart Grid Model Using AI Algorithms for Real-Time Intrusion Detection and Power Flow Optimization

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Figure 3: Confusion matrix for intrusion detection system.

4.2 Discussion

This research shows the efficiency and practical

feasibility of a two-layer AI based intelligible smart

grid framework that comprehensively,

simultaneously, and in near real time solves both the

light-weight cybersecurity aspects on the one hand,

and the heavy-weight power flow optimization

aspects on the other hand. Explainable AI (XAI)

methods were incorporated into the IDS, which

offered transparent insight into the reasoning behind

decisions made, thus considerably improving the

interpretability of AI-based decisions in intricate

settings. This overcomes a significant limitation of

legacy cyber systems—these systems work by remote

control, but people have great difficulty trusting

them because they don't tend to be transparent and are

often black box systems. Operators understood the

model's predictions using SHAP and LIME, leading

to a transparent and reliable system.

The most important breakthrough of this study is

the real-time isolation of the detection performance

with a power flow problem, which is unprecedented

in the literature. Previous research has either focused

on one aspect or the other, but our framework has

shown how it can work together seamlessly. With

reactively responding to cybersecurity threats and

optimizing power flow both embedded into our

system design. Through the use of AI and ML, this

comprehensive oriented approach allows the smart

gird to stay resilient and efficient against cyber-

attacks.

Power Flow Optimization – The 12% reduction in

energy losses using AI underlines the power of AI in

making smart grids more efficient. This

enhancement is crucial, as energy efficiency is

increasingly a concern with rising power needs and

environmental sustainability directives. The

Reinforcement Learning-based method enabled the

system to learn continuously from real-time

conditions on the grid and to adjust its optimization

strategy to respond to both customary variations and

outlier events like cyberattacks or sudden spikes in

load.

Scalability: The framework was successful to

working with different size scales of grids ranging

from microgrids up to large national grids which

ensures that the system can be scaled out on different

smart grid platforms. Anything that allows the

framework to scale while maintaining performance

emphasizing the versatility and applicability of the

framework across different operational contexts.

Nevertheless, there are components that future

work could still refine. For such things the IDS has to

still have a tough job to recognise such attacks since

it excels on known attack patterns it has learnt while

remain vulnerable for novel and sophisticated attack

vectors like APTs (Advanced Persistent Threats).

Moreover, the power flow optimization system can be

further enhanced to include predictive maintenance of

its own, predicting breakdowns before they happen

based on historical and operational data. Figure 4

shows the scalability of grid size vs efficiency. Table

5 represents scalability of the system.

Figure 4: Scalability: Grid size vs efficiency.

Table 5: Scalability of the system.

Grid Size Efficiency (%)

Small 95

Mediu

Lar

e88

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4.2 Future Work

The current system is already the best solution to date

and very efficient and resilient but continuous

research would take place to analyse the algorithm

where in future more complex and large scale

cyberattacks could be handled by the algorithm. Also,

investigating advanced anomaly detection models

will benefit security in better detecting zero-day

threats. The optimization model may also be extended

to make use of predictive analytics in order to

anticipate energy demand in the future based on

weather reports and long-term trends, allowing for an

even more efficient use of resources.

5 CONCLUSIONS

In this research, we propose a new smart grid

framework that integrates AI in tackling two among

the most challenging urgent demands of modern

grids; tackling efficient real time intrusion detection

and simultaneously secure power flow optimization.

The framework employs Reinforcement Learn (RL)

by integrating explainable AI (XAI) methods to

guarantee cybersecurity, and maintain operational

efficiency against the backdrop of cyber-attacks and

dynamic energy protrusions.

Our proposed Intrusion Detection System (IDS)

based on Deep Learning and ensemble methods

outperformed the conventional systems with 98.5%

overall detection accuracy. Implementing

explainability into the Intrusion Detection System,

via SHAP and LIME, offered operators valuable

transparency as they look for solutions that do not

amount to data 'black box', enhancing trust and

enabling rapid-informed decision-making. The

ability to interpret and understand the system’s

decisions helps bridge a gap in many AI-driven

cybersecurity systems that otherwise suffer from

“black-box” limitations.

The Power Flow Optimization component, which

uses Reinforcement learning, made a 12%

improvement in energy loss reduction while an

attack was going on, and still without losing stability.

This shows how AI can dynamically improve power

distribution, adjusting to natural grid operations as

well as threats. Furthermore, the system's scalability

was demonstrated as deployment was successful in

both microgrids and large-scale national grid

simulations, indicating that the framework can be

used across different grid infrastructures.

While success of this system has been proven,

further research may also be dedicated to expanding

the following capabilities of our associated systems

by either improving the ability of the IDS to detect

more capable attacks e.g. from advanced persistent

threats (APT) or using predictive analytics that could

be integrated into the power flow optimization

module to better predict anticipated energy demands.

Thus, this study proposes a complete solution for

the dual issues of cyber security and power flow,

ultimately leading to a more resilient, efficient smart

grid infrastructure. This framework, which integrates

the functionalities of AI, specifically explainable AI,

enhances performance as well as developing trust

from both the operators and stakeholders, showcasing

an important step towards smart grid technology.

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