New Framework Matches Grid Services with Optimal Communication Tech
In the rapidly evolving landscape of modern energy systems, the integration of distributed renewables, electric vehicles (EVs), and advanced storage solutions is transforming the very fabric of power distribution. Yet, as these new elements flood into medium- and low-voltage networks, a critical question emerges: how can utilities ensure that the communication infrastructure supporting these assets is not only robust and secure but also economically viable and technically appropriate?
A groundbreaking study published in Electric Power Construction offers a comprehensive answer. Titled “Evaluation Method of Multiple Service and Multi-mode Communication Adaptability of New Distribution Networks,” the research introduces a novel methodology that quantitatively aligns diverse grid services with the most suitable communication technologies. Developed by Chaowu Dong, Zhihong Xiao, Peizhe Xin, Zihao Fu, and Jing Jiang from the State Grid Economic and Technological Research Institute Co., Ltd., this approach moves beyond traditional qualitative assessments and provides a data-driven, decision-support framework for grid planners and engineers.
The urgency of this work cannot be overstated. As nations accelerate toward carbon neutrality, distribution networks—once passive conduits for electricity—are becoming dynamic, bidirectional platforms teeming with intelligent endpoints. From rooftop solar inverters to EV charging stations and community-scale battery systems, each new device imposes specific communication requirements. Some demand ultra-low latency for real-time control; others prioritize massive connectivity or cost efficiency. Meanwhile, the communication toolbox has expanded dramatically, encompassing everything from legacy fiber optics and power line carrier systems to cutting-edge 5G, LoRa, NB-IoT, and Bluetooth.
Historically, utilities have relied on rule-of-thumb guidelines or isolated case studies to select communication pathways. This has led to suboptimal deployments—over-engineered solutions that inflate capital costs, or under-specified links that compromise reliability and security. The new study directly addresses these gaps by establishing a structured, multi-dimensional evaluation system that considers five core pillars: access performance (including data rate and latency), coverage capability (range and device density), economic feasibility (deployment and operational costs), reliability (resilience to interference and packet loss), and security (isolation mechanisms).
What truly sets this research apart is its sophisticated fusion of subjective expert judgment with objective performance metrics. The authors employ the Bayesian Best-Worst Method (BBWM), an advanced multi-criteria decision-making technique that aggregates input from multiple domain experts while accounting for uncertainty and inconsistency in human assessments. Unlike simpler averaging approaches, BBWM treats expert opinions as probabilistic events, yielding more robust and defensible weightings for each evaluation criterion.
These weights are then fed into the Measurement Alternatives and Ranking according to Compromise Solution (MARCOS) model, which evaluates each communication technology against an ideal “best” scenario and a worst-case “anti-ideal” baseline. The result is a normalized utility score that ranks technologies not by raw performance alone, but by their contextual suitability for a given grid service.
The methodology was rigorously tested on real-world use cases. For medium-voltage control applications—such as the “three-remote” functions of distribution automation (remote control, signaling, and measurement)—the model confirmed fiber optics as the top choice, owing to its unmatched reliability, security, and low latency. However, it also revealed that 5G and medium-voltage power line carrier (MV-PLC) systems offer compelling alternatives in scenarios where fiber deployment is impractical or cost-prohibitive. Notably, the traditional wireless private network scored lowest for this application, primarily due to high capital expenditure and weaker interference resistance.
In the low-voltage domain, the analysis yielded equally insightful results. For high-density data collection tasks like electricity information gathering from millions of smart meters, the model identified micro-power wireless and High-Speed Power Line Communication (HPLC) as the optimal solutions. These technologies strike an ideal balance between coverage, connectivity scale, and deployment cost in dense urban or suburban environments. Surprisingly, widely available options like Wi-Fi and LoRa ranked lower—not because of technical flaws, but because they failed to meet the specific economic and scalability demands of utility-scale metering.
Perhaps the most valuable output of the study is the comprehensive adaptability matrix it produces for the entire spectrum of distribution grid services. Spanning both medium- and low-voltage networks, and covering control, monitoring, and video-based applications, this matrix serves as a practical reference for network planners. It clearly indicates, for instance, that 5G excels in broadband video surveillance and environmental monitoring, while RS-485 remains a reliable—if less flexible—option for localized control of distributed storage or EV chargers.
Critically, the authors emphasize that their framework is not prescriptive but adaptive. “The conclusions presented are general guidelines,” they note, “and should be calibrated according to regional characteristics, existing infrastructure, and specific project constraints.” This pragmatic stance aligns with the realities of utility operations, where one-size-fits-all solutions rarely succeed.
The implications of this work extend far beyond technical planning. By enabling more precise matching of services to communication modes, utilities can avoid billions in unnecessary infrastructure spending while simultaneously enhancing grid resilience. In an era where cybersecurity threats to critical infrastructure are escalating, the explicit inclusion of security as a core evaluation dimension is particularly timely. Technologies that lack robust isolation—such as certain public wireless networks—are automatically downgraded for control applications, reinforcing a defense-in-depth strategy.
Moreover, the framework supports the broader digital transformation of the power sector. As distribution systems evolve into “distribution system operators” (DSOs) capable of orchestrating local energy markets, the need for heterogeneous, service-aware communication networks becomes paramount. This research provides the methodological foundation for building such networks—not as monolithic overlays, but as agile, multi-layered fabrics that allocate communication resources intelligently based on service requirements.
Industry experts have welcomed the study as a significant step toward rationalizing communication investments. “For too long, we’ve seen communication choices driven by vendor influence or legacy preferences rather than objective analysis,” said one senior grid architect not involved in the research. “This model brings much-needed rigor to the process.”
The methodology also holds promise for regulatory bodies. By offering a transparent, auditable way to justify communication technology selections, it could streamline approval processes for grid modernization projects and ensure that ratepayer funds are spent efficiently.
Looking ahead, the authors acknowledge limitations and propose clear paths for future work. Their current model evaluates services in isolation, but real-world deployments often involve multiple concurrent applications sharing the same communication infrastructure. The next logical step is to develop a multi-service aggregation model that can optimize communication choices for composite workloads—a challenge that will only grow as grids become more complex.
Additionally, while the study includes emerging technologies like NB-IoT and LoRa, the rapid pace of innovation in wireless communications means the evaluation framework must remain dynamic. Future iterations could incorporate machine learning to continuously update performance benchmarks and adapt weighting schemes based on field performance data.
In conclusion, this research represents a paradigm shift in how the power industry approaches communication network design. By replacing intuition with quantification and fragmentation with system-level thinking, it empowers utilities to build communication infrastructures that are not just technically sound, but strategically aligned with the operational and economic realities of the new power system. As the energy transition accelerates, such tools will be indispensable for ensuring that the digital nervous system of the grid evolves in lockstep with its physical counterpart.
Authors: Chaowu Dong, Zhihong Xiao, Peizhe Xin, Zihao Fu, Jing Jiang (State Grid Economic and Technological Research Institute Co., Ltd., Beijing 102209, China). Published in Electric Power Construction, Vol. 45, No. 1, January 2024. DOI: 10.12204/j.issn.1000-7229.2024.01.001.