In recent years, extreme disaster events have exhibited a trend of higher frequency, wider impact, and stronger intensity. This has imposed stringent requirements on the secure and reliable operation of distribution systems. Concurrently, the large-scale integration of distributed generation (DG) enhances operational flexibility but substantially complicates post-fault behaviors. The most notable of these post-fault behaviors involve the distribution of fault currents and the consequent decisions on fault isolation and service restoration, particularly when multiple concurrent faults and dynamically changing network topologies are present. Despite the prevalence of the term “distribution system resilience” in academic discourse, which characterizes the ability to prevent, withstand, respond to, and recover from extreme disturbances, the existing curriculum in electrical engineering remains predominantly focused on conventional subjects such as power flow, short-circuit calculation, and relay protection. There is a conspicuous absence of an integrated training environment that would link resilience theory to engineering actions. Therefore, the objective of this study is to develop a virtual simulation experimental platform that enables students to comprehend the full-chain and multi-stage process of resilience and to practice designing strategies for resilience enhancement in a scenario-driven and operable manner.

The proposed platform is constructed by integrating full-chain resilience concepts (i.e., prevention, mitigation, response, and recovery) with distribution system operational models. It also involves the organization of learning tasks according to the staged evolution of system performance under extreme events. To comprehensively address the resilience workflow, four progressive experimental modules have been implemented: (1) A pre-event preventive proactive islanding simulation is employed, wherein network reconfiguration using remotely controlled switches and manual switches is applied to pre-partition the system into supply islands, with the objective of protecting critical loads from external faults. (2) A fault-current distribution simulation is conducted based on a virtual network-flow representation, which unifies the description of fault-current existence and propagation paths under multi-source, multi-fault conditions and changing switch states. (3) A fault propagation and faulted-section identification module is developed, which explicitly models how automatic tripping of protective switching devices changes topological connectivity and hence delineates the fault influence region. (4) A multi-stage service restoration module formulates post-event isolation and restoration as coordinated topology reconfiguration and supply reachability with multiple switch types, while enforcing that restoration actions must not expand the fault-affected area. The platform facilitates interactive simulation with customizable networks and fault scenarios, providing visualized outputs to ensure the traceability and interpretability of the resilience response process for learners.

The developed platform operationalizes the abstract resilience concept into a coherent sequence of experiments spanning preparation, degradation analysis, and staged recovery. This enables systematic “process-level” learning rather than isolated topic exercises. Module 1 utilizes comparative topology settings to demonstrate how proactive islanding prior to an event can substantially compress the affected region and enhance the continuity of critical-load supply without altering the fault location. Module 2 demonstrates the efficacy of virtual network-flow modeling in representing fault-current distribution at the topological level. It expedites the generation of lucid results concerning propagation paths and associated influence regions in complex DG/multi-fault scenarios. This approach mitigates the modeling and reasoning burden when compared with exhaustive path enumeration, thereby directly supporting subsequent localization, isolation, and restoration reasoning. Module 3 employs a visual approach to illustrate the causal chain from protective-device tripping logic to the emergence of clearly delineated fault impact regions. This module serves to reinforce students’ comprehension of how switching operations influence fault transfer and faulted-section localization. Module 4 delineates multi-stage restoration as an executable sequence of switching operations and resource coordination. The sequence initiates with the “compression” and isolation of the fault region, followed by a stepwise expansion of the restored supply. This approach enables learners to observe the impact of diverse action sequences on restoration scale and tempo.

The integration of full-chain resilience theory with operable distribution system models, embedded within four progressively linked simulation modules, establishes a nexus between theoretical instruction and engineering practice for resilience-oriented education under extreme events and high DG penetration.

This paper designs and implements a virtual simulation experiment platform for integrated community energy systems (ICES) to address educational challenges in teaching complex multienergy interactions during China’s ongoing energy transition. Targeting the interdisciplinary nature of ICES and limitations of existing simulation tools, the platform integrates 3D visualization with four experimental modules using an actual industrial park as a case study to ensure practical relevance and realism.

The technical foundation comprises three computational models: electrical networks modeled through AC power flow equations with nodal power balance constraints; thermal networks combining hydraulic models using mass balance and loop pressure equations with thermal models employing node heat power equations and temperature propagation relationships; and energy stations structured through a unified energy bus model incorporating energy conversion efficiency matrices and distribution coefficients to resolve multipath energy flows. The four experimental modules include planning configuration, where students select equipment types and capacities against seasonal cooling and heating load profiles with real-time technical–economic evaluation; multienergy flow calculation to enable dynamic analysis of electricity–heat–cold interactions through adjustable distribution coefficients; optimization scheduling for solving day-ahead economic dispatch problems that minimize energy procurement costs while respecting network constraints and operational limits; and fault operation to simulate equipment failures requiring manual restoration through backup activation.

Validated using operational data from a real industrial park energy system featuring 823.2 kW photovoltaic generation, ground-source heat pumps, solar thermal collectors, ice storage systems, electric boilers, chillers, and battery storage, the platform effectively accommodates cooling and heating operational scenarios. Students can actively explore energy conversion principles, system configuration, optimization techniques, and failure response mechanisms through coordinated operational strategies, such as off-peak ice storage use and priority dispatching of high-efficiency devices. The platform’s implementation at Tianjin University provides electrical engineering and energy majors with practical training in designing, analyzing, and operating integrated energy infrastructures. It transforms abstract theoretical concepts into tangible, experiential learning that bridges academic knowledge and practical engineering applications essential for sustainable energy transitions while overcoming physical laboratory limitations associated with such complex systems. The platform demonstrates full renewable energy use capabilities and effective multienergy coupling management through its comprehensive simulation environment. This educational tool substantially enhances comprehension of ICES dynamics by enabling hands-on experimentation with system planning, real-time optimization, and fault recovery processes across electro-thermal networks.

By providing a safe, scalable and realistic environment for investigating integrated energy infrastructures, the platform successfully cultivates the critical thinking and operational competencies necessary for next-generation energy engineers. Its application extends beyond academic settings to professional training programs for industry practitioners engaged in renewable energy integration and multienergy system management. The platform’s effectiveness in demonstrating coordinated electrical and thermal scheduling provides valuable insights for actual ICES operations while fulfilling core educational objectives in the vital field of sustainable energy. The modular architecture supports continuous expansion of simulation scenarios and device libraries, ensuring adaptability to diverse teaching requirements in higher education institutions. Future developments will incorporate emerging energy vectors and carbon constraint considerations to maintain pedagogical relevance amid evolving energy landscapes.