A Self-Adaptive Digital Twin Software Architecture for Autonomous PV Asset Management and Decision Orchestration

Research context & significance

Research questions & objectives

• How can a PV Digital Twin automatically adapt its software, models, and computing resources to manage PV assets reliably with minimal human intervention?

Research approach

1. A · Input / Context

   PV system data and operating conditions. Sensor data, weather data, PV asset status, network conditions and computing resources.

2. B · Method / Process

   Develop a self-adaptive Digital Twin architecture. Build a prototype that can monitor conditions and automatically adjust its models, services, workflows and computing resources.

3. C · Evaluation / Validation

   Evaluate using PV scenarios and compare with a non-adaptive Digital Twin based on reliability, response time, accuracy and resource use.

Expected outcome

A prototype for more reliable and autonomous PV asset management.

Tangible outputs & evidence

• Systematic Literature Review

Key result

Research Gap Identification

• 864 unique papers identified for PRISMA screening
• 127 papers identified as directly relevant/high-priority

What should we notice?

• Interpretation: A lot of paper to read
• Why it matters: Solidify research gap

Next steps

1. 1

   Complete PRISMA Title and Abstract Screening

   05/10/26

2. 2

   Full-Text Review

   16/10/26

3. 3

   Initial Research-Gap Analysis

   26/10/26

Risks & blockers

• Volume of Literature for full-text assessment
• Complexity of defining and classifying Digital Twin concepts

Questions for discussion

• Should the review focus primarily on PV Digital Twins, with other energy domains used only to address gaps in the PV literature?