## 3 Results and Discussion
In addition, smart meters enable dynamic pricing models that have big implications on consumer behavior, as well as policymaking and utility management. These models consist of time-of-use pricing, critical peak pricing, and real-time pricing, encouraging consumers to shift energy consumption away from peak demand periods by setting varying electricity rates based on grid conditions [38]. Smart meters provide detailed feedback on energy use patterns and real-time price signals that would reward consumers for being more energy efficient, lower overall consumption and better match their demand to periods of either low demand or high renewable energy supply. Similarly, policy makers can promote smart meter deployment and dynamic pricing programs to help enhance grid reliability, reduce greenhouse gas emissions and optimize energy resource allocation. Utilizing the electricity demand-response model, the loads are divided into two types: shiftable and interruptible. Therefore, the policy makers know which load can be time-shifted to enhance grid stability [39]. By enabling more responsive demand-side management, smart meters support the integration of renewable energy sources and help maintain grid stability to raise and promote energy awareness and accountability. Flexible dynamic pricing models will grant the utility operator several operational and financial benefits. Load forecasting is now more accurate with reduced peak load demand, while infrastructural upgrades could be postponed, since the demand curve is flattened [40]. Utilities can make use of smart metering to promote better pricing strategies through real-time data, improving the customer satisfaction index and minimizing energy theft. Hence, smart meters are the future of metering systems. They should indeed be applied on a large scale for both commercial and industrial use in the coming years, as highlighted in the literature [20; 21; 22; 23; 24; 25]. The findings of this paper and the comparison of the five modules are summarized in Table 5.  
Smart meters can be easily integrated into smart grid networks. Advanced data analytics tools and decision-making systems will be responsible for processing the large inflow of data coming from the smart meters and other components of the grid. This would enable utilities to identify trends, detect patterns, and make informed recommendations for optimization of electricity flow. Integration with sophisticated communication protocols ensures seamless interaction between various systems within a smart grid, enabling efficient management and operational harmony [5]. Data analysis tools, such as machine learning algorithms and statistical modelling frameworks, are crucial for processing the collected data. These tools analyze historical and real-time consumption patterns, weather forecasts and grid performance metrics to predict demand trends and identify potential issues, such as grid overloads or outages [3; 4; 5; 6; 7]. Techniques such as clustering and regression are commonly employed to segment consumer behavior, while neural networks and time-series analysis help forecast energy demand with high accuracy. By identifying consumption anomalies or inefficiencies, these tools support proactive maintenance, fraud detection and energy conservation initiatives. Decision-making tools, acting on the output from data analytics, provide recommendations on actionable strategies to help in grid optimization. Tools such as optimization algorithms and multi-agent systems consider different scenarios for the most effective utilization of energy resources. These are helping utilities in dynamic pricing model implementations, the optimization of load balancing and the integration of renewable sources. This is achieved through analyzing their intermittency and generating forecasting [6]. Advanced tools will also have the capability to enable distributed energy resource management to make technologies such as battery storage, electric vehicles and solar panels seamlessly integrated.