An IoT-based real-time smart metering deployment for grid optimization: A case study of GEPCO, Pakistan.

PloS one · 2025 · Benefit · Low evidence

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BenefitLow evidencePaper 2412

Study snapshot

PublicationPloS one · 2025

Study typeengineering

Reported directionBenefit

Evidence ratingLow

Abstract

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.

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AI evidence extraction

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Study typeengineering

Effect directionbenefit

PopulationIndustrial electricity loads/customers served by Gujranwala Electric Power Company (GEPCO), Pakistan (three classes: <50 kW, 50–500 kW, >500 kW)

Sample sizeNot supplied

ExposureNot supplied

Evidence strengthLowConfidence: 74% · Peer reviewed: yes

Main findings

The paper reports a real-time IoT-based smart metering deployment at GEPCO where smart meters transmit electrical parameters to a centralized MDMS to support billing, load profiling, fault detection, and power quality analysis. Case study results include identification of low power factor situations enabling capacitor bank deployment; in one large-industrial scenario PF improved from 67.5% ± 11.2 to 93.6% ± 2.4 with a reported large effect size, alongside detection of voltage imbalance, frequency variance, and peak load patterns. The authors project an average PF uplift of ~1.4 percentage points across the industrial segment under stated assumptions and conclude smart metering supports DSM, loss reduction, and grid optimization.

Outcomes measured

Real-time monitoring of electrical parameters (voltage, current, power factor, frequency, consumption)
Grid visibility and operational responsiveness
Power factor improvement
Energy savings and cost savings (reduced PF penalties)
Detection of voltage imbalance
Detection of frequency variance
Detection of daily peak load patterns
Demand side management (DSM), loss reduction, grid optimization
Policy considerations and institutional requirements for smart grid implementation

View raw extracted JSON

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    "source": "smart meter",
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  },
  "population": "Industrial electricity loads/customers served by Gujranwala Electric Power Company (GEPCO), Pakistan (three classes: <50 kW, 50–500 kW, >500 kW)",
  "sample_size": null,
  "outcomes": [
    "Real-time monitoring of electrical parameters (voltage, current, power factor, frequency, consumption)",
    "Grid visibility and operational responsiveness",
    "Power factor improvement",
    "Energy savings and cost savings (reduced PF penalties)",
    "Detection of voltage imbalance",
    "Detection of frequency variance",
    "Detection of daily peak load patterns",
    "Demand side management (DSM), loss reduction, grid optimization",
    "Policy considerations and institutional requirements for smart grid implementation"
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  "main_findings": "The paper reports a real-time IoT-based smart metering deployment at GEPCO where smart meters transmit electrical parameters to a centralized MDMS to support billing, load profiling, fault detection, and power quality analysis. Case study results include identification of low power factor situations enabling capacitor bank deployment; in one large-industrial scenario PF improved from 67.5% ± 11.2 to 93.6% ± 2.4 with a reported large effect size, alongside detection of voltage imbalance, frequency variance, and peak load patterns. The authors project an average PF uplift of ~1.4 percentage points across the industrial segment under stated assumptions and conclude smart metering supports DSM, loss reduction, and grid optimization.",
  "effect_direction": "benefit",
  "limitations": [],
  "evidence_strength": "low",
  "confidence": 0.74,
  "peer_reviewed_likely": "yes",
  "keywords": [
    "smart metering",
    "IoT",
    "real-time monitoring",
    "meter data management system (MDMS)",
    "power factor",
    "power quality",
    "demand side management",
    "grid optimization",
    "Pakistan",
    "GEPCO"
  ],
  "suggested_hubs": [
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AI-extracted fields are generated from the abstract and metadata and may be incomplete or incorrect. This material is for informational purposes and is not medical advice.

Identifiers and source

DOI: 10.1371/journal.pone.0338389
PubMed: 41411319

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