<p>Installed capacities (MW) of GENCOs and IPPs.</p>

The increasing demand for energy and the growing environmental issues in Pakistan, require a movement to a more environmentally friendly and smarter energy infrastructure. This work provides the practical application of research which represents the deploying of a smart metering network in real time (RT) in Pakistan’s transition to more environmentally friendly and smarter energy systems. The work presents the design, implementation and the results of the operation of the smart metering deployment implemented by Gujranwala Electric Power Company (GEPCO). The presented system developed on a four-layer Internet of Things (IoT)-based architecture comprising of Energy Monitoring, Communication, Cloud Analytics, and Application layers. The smart meters (SMs) on the three classes of industrial loads (< 50 kW, 50–500 kW, > 500 kW) transmit RT data of the electrical parameters, including voltage, current, power factor, frequency, and consumption, to a centralized meter data management system (MDMS). This data enables the MDMS to support various functions such as automated billing, load profiling, fault detection, and power quality (PQ) analysis. Results of the case studies demonstrate that RT monitoring can assist in attaining a higher degree of grid visibility and operational responsiveness. One such case was the identification of the low power factor (PF) situations (below 0.7) which enabled the deployment of capacitors banks, resulting in measurable energy saving and cost saving in accordance with the mitigated exposure to PF penalties. For instant, in a one large-industrial scenario, PF improved from 67.5% ± 11.2 to 93.6% ± 2.4, corresponding to a significant Welch’s t large effect size, and with reduced day-to-day variability. Moreover, early detection of voltage imbalance, variance of frequencies, and daily peak load patterns were detected using the system. Using a conservative normalize-then-scale approach, a potential average PF uplift of approximately 1.4 percentage points across the industrial segment is projected under stated coverage and adoption assumptions. The results confirm that IoT-enabled smart metering can serve as a practical tool for demand side management (DSM), loss reduction and grid optimization. Finally, the study outlines key technical enablers, policy considerations, and institutional requirements for large-scale smart grid (SG) implementation and offers a replicable framework for developing economies pursuing energy system modernization.巴基斯坦日益增长的能源需求与愈发严峻的环境问题，亟需向更环保、更智能的能源基础设施转型。本研究将相关科研成果付诸实践，阐述了巴基斯坦在向更环保智能的能源系统转型过程中，实时（RT）智能计量网络的部署实践。本工作展示了古吉兰瓦拉电力公司（Gujranwala Electric Power Company, GEPCO）所实施的智能计量部署项目的设计、实现与运行结果。 所提出的系统基于四层物联网（Internet of Things, IoT）架构开发，包含能源监控层、通信层、云分析层与应用层。针对工业负载的三类分级（< 50 kW、50–500 kW、> 500 kW），智能电表（Smart Meters, SMs）会将电压、电流、功率因数（Power Factor, PF）、频率与用电量等电气参数的实时数据传输至集中式电表数据管理系统（Meter Data Management System, MDMS）。该数据可使电表数据管理系统支持多项功能，包括自动计费、负载画像、故障检测与电能质量（Power Quality, PQ）分析。 案例研究结果表明，实时监控有助于提升电网可视度与运营响应能力。其中一个典型案例是对低功率因数（PF低于0.7）工况的识别，通过部署电容器组，可有效减轻因功率因数罚款带来的能源与成本损失。例如，在某大型工业场景中，功率因数从67.5%±11.2提升至93.6%±2.4，对应显著的Welch t检验大效应量，且日常波动性显著降低。此外，该系统还可提前检测电压失衡、频率波动与每日峰值负载模式。 采用保守的归一化后缩放方法，在既定覆盖范围与采用率假设下，预计工业板块的平均功率因数可提升约1.4个百分点。研究结果证实，搭载物联网的智能计量技术可作为需求侧管理（Demand Side Management, DSM）、损耗降低与电网优化的实用工具。 本研究还梳理了大规模智能电网（Smart Grid, SG）部署所需的关键技术支撑、政策考量与制度要求，并为追求能源系统现代化的发展中经济体提供了可复制的框架。