China’s Regional Multi-Energy Systems Embrace Digital Twinning for Smarter Grids
As China accelerates its energy transition, a new wave of innovation is emerging at the intersection of physical infrastructure and digital intelligence. At the heart of this transformation lies the integration of digital twinning technology into regional multi-energy systems (MES)—complex networks that unify electricity, natural gas, heating, cooling, and renewable generation. Unlike traditional siloed energy planning, these integrated systems promise higher efficiency, greater renewable integration, and enhanced resilience. Yet their operational complexity has long posed a barrier to widespread adoption—until now.
A team of researchers from Guizhou Power Grid Co., Ltd. and Guizhou University has proposed a bold solution: leveraging digital twins to create dynamic, high-fidelity virtual replicas of real-world energy systems. Their work, published in Southern Power System Technology, outlines a comprehensive framework for applying digital twinning across the full lifecycle of MES—from simulation and planning to real-time operation and emergency response.
This approach marks a significant departure from legacy modeling tools like DIgSILENT, PSS/E, or SSDG, which often treat electricity, gas, and thermal systems in isolation and struggle to capture cross-domain dynamics. By contrast, digital twinning enables continuous synchronization between physical assets and their virtual counterparts, using real-time sensor data, cloud computing, and machine learning to refine models on the fly.
“The future energy system is not just about interconnecting energy flows—it’s about coupling energy with information,” the authors write. “Digital twinning provides the missing link between data-rich environments and actionable intelligence.”
Beyond Static Models: A Living Mirror of Energy Systems
Traditional energy system models are static by design. They rely on fixed parameters, simplified assumptions, and offline calibration—conditions that quickly become obsolete in systems dominated by variable solar and wind generation, distributed storage, and responsive demand. In regional MES, where time scales range from millisecond-level electrical transients to hour-long thermal dynamics, such rigidity is untenable.
Digital twins address this by creating what the researchers call a “panoramic, self-calibrating” model. Embedded sensors across power lines, gas pipelines, and district heating networks feed live operational data into a cloud-based twin. This data stream—comprising voltage, pressure, temperature, flow rates, and equipment status—continuously updates the virtual model, ensuring it reflects the true state of the physical system.
Crucially, the twin isn’t just a mirror—it’s a predictive engine. Using historical and real-time data, it can simulate thousands of potential scenarios in parallel, from routine load fluctuations to extreme weather events or equipment failures. This capability is powered by elastic cloud computing, which scales processing power on demand, avoiding the prohibitive costs of dedicated real-time simulators like RTDS or RT-LAB for large-scale systems.
For instance, assessing the grid’s capacity to absorb a sudden surge in solar output traditionally requires conservative safety margins due to modeling uncertainty. With a digital twin, operators can run Monte Carlo simulations across tens of thousands of probabilistic scenarios in minutes, identifying precise operational limits without overbuilding infrastructure.
Planning with Foresight: From Guesswork to Data-Driven Strategy
Energy planning has long been an exercise in educated guesswork. Planners must anticipate decades of load growth, fuel price volatility, policy shifts, and technological disruption—all while balancing capital costs, reliability targets, and emissions goals. In multi-energy contexts, the challenge multiplies: a new combined heat and power (CHP) plant affects not just electricity supply but also gas demand and thermal network hydraulics.
Digital twinning transforms this process into a dynamic, feedback-driven loop. Instead of relying on static “snapshot” studies, planners can interact with a living model that evolves with actual system performance. The twin incorporates real-world degradation—such as declining battery efficiency or pipeline corrosion—by continuously updating equipment parameters based on sensor telemetry and machine learning inference.
This enables what the authors describe as “adaptive dynamic planning.” Rather than fixed 5- or 10-year cycles, expansion decisions are triggered by real-time indicators of system stress: rising congestion, declining reserve margins, or increasing failure rates. The twin evaluates candidate solutions—adding storage, upgrading pipelines, deploying demand response—across hundreds of stochastic futures, ranking them by cost, reliability, and carbon impact.
Moreover, the framework resolves a persistent industry pain point: data silos. Electricity, gas, and thermal data often reside in separate corporate or municipal systems, hindering holistic analysis. The proposed architecture introduces a standardized data model and a “data middle platform” that harmonizes multi-source inputs into a unified stream, enabling cross-sector optimization without compromising data ownership.
Real-Time Optimization Through Reinforcement Learning
Once built, the greatest value of a digital twin emerges during daily operations. Here, the system shifts from passive monitoring to active decision-making. The researchers propose embedding deep reinforcement learning (RL) agents within the twin to autonomously optimize energy dispatch, storage charging, and inter-fuel substitution.
In this setup, the RL agent learns by interacting with the simulated environment. Its “state” includes real-time measurements of generation, load, storage levels, and market prices. Its “actions” involve adjusting setpoints for CHP units, heat pumps, gas boilers, or flexible loads. The “reward” function encodes operational objectives—minimizing cost, maximizing renewable utilization, or reducing emissions.
Because training occurs in the digital twin, the agent can safely explore millions of scenarios without risking grid stability. Once deployed, it continues learning from actual outcomes, refining its policy in an “offline training, online enhancement” loop. This approach is particularly powerful for managing uncertainty: unlike deterministic optimization, RL inherently accounts for stochasticity in renewable output and demand.
Early prototypes suggest significant gains. In one simulated district energy system, an RL-driven twin reduced peak electricity demand by 18% through coordinated thermal storage charging and gas-fired backup, while maintaining comfort constraints. Crucially, the strategy emerged organically from the learning process—no human-designed rules were imposed.
Resilience Redefined: From Reactive Repairs to Predictive Recovery
When failures occur—whether from equipment faults, cyberattacks, or natural disasters—speed and accuracy are paramount. Traditional contingency planning relies on pre-defined restoration sequences, often validated only under steady-state assumptions. But in modern MES, transient dynamics matter: re-energizing a circuit too quickly after a fault can trigger protective relays or destabilize gas compressors.
Digital twins enable “real-time sandbox” recovery. Upon detecting an outage, the system instantly spawns a high-fidelity simulation of the affected network, incorporating the latest asset conditions and load profiles. It then evaluates dozens of restoration paths in parallel, checking not just power flows but also thermal inertia, gas pressure recovery times, and communication latency.
This process is accelerated by event reasoning engines that mine historical incident data to predict failure cascades. For example, if a substation transformer fails on a hot summer day, the twin might anticipate secondary overloads on nearby feeders or insufficient gas pressure for backup generators—factors invisible to conventional SCADA systems.
Maintenance also becomes predictive rather than scheduled. By comparing simulated behavior with actual sensor readings, the twin flags anomalies—such as a pump drawing excess current or a valve leaking—that indicate incipient failure. This “state-based maintenance” reduces downtime and extends asset life, particularly valuable for aging infrastructure.
Challenges Ahead: From Vision to Deployment
Despite its promise, the path to full-scale digital twinning remains fraught with technical and institutional hurdles. The authors acknowledge that key enablers—ubiquitous sensing, low-latency communication, secure data sharing, and AI-ready data platforms—are still maturing. Interoperability standards for multi-energy modeling are nascent, and regulatory frameworks lag behind technological capability.
Moreover, the computational burden of high-fidelity, multi-physics simulation—especially for transient gas and thermal dynamics—demands continued advances in parallel computing and model order reduction. Projects like CloudPSS, China’s first indigenous cloud-based electromagnetic transient simulation platform, represent critical steps forward, but broader adoption requires cost-effective, scalable solutions.
Yet the momentum is undeniable. With China targeting carbon neutrality by 2060 and investing heavily in smart grids, integrated energy systems, and digital infrastructure, the convergence of MES and digital twinning is more than academic—it’s strategic.
As the researchers conclude, “Digital twinning is not merely a modeling tool; it is a new paradigm for energy system design, operation, and evolution—one that aligns physical reality with digital intelligence to build a more efficient, resilient, and sustainable energy future.”
TANG Xueyong¹, LIANG Yao¹,², SUN Bin¹, LI Qingsheng¹, YAN Xia³
¹ Power Grid Planning & Research Center, Guizhou Power Grid Co., Ltd., Guiyang 550003, China
² Electrical Engineering College, Guizhou University, Guiyang 550025, China
³ Guizhou Power Grid Co., Ltd., Guiyang 550002, China
Southern Power System Technology, Vol. 15, No. 5, May 2021, pp. 104–114
DOI: 10.13648/j.cnki.issn1674-0629.2021.05.013