A Coupled System Dynamics and Strategic Management Framework for Global Wind Energy Diffusion: Technology Learning, Grid Integration, and Adaptive Policy Control

Abstract

Global wind power deployment has grown by more than fifty-fold since 2000, yet strategic decision-makers still lack an integrated, quantitative framework that jointly represents technology learning, grid-integration constraints, and the feedback effects of policy intervention. This paper develops a coupled system dynamics (SD) and strategic-management model of the global wind-energy system, comprising eight interacting stocks: installed capacity, cumulative investment, research and development (R&D) knowledge, grid-integration capacity, social/political acceptance, skilled workforce, cumulative avoided CO2 emissions, and a policy-controller state. Technology cost is represented by a two-factor learning curve combining learning-by-doing and learning-by-searching; adoption follows a logistic-plus-imitation diffusion structure; and grid congestion is represented as an explicit balancing feedback loop. A strategic-management layer is implemented as a proportional-integral (PI) feedback controller that adjusts a feed-in premium in response to the gap between installed capacity and an aspirational deployment trajectory. The model is calibrated by non-linear least squares against 2001-2024 global capacity statistics (mean absolute percentage error of 8.6%) and used to compare a passive Business-as-Usual strategy against Adaptive and Aggressive strategic-management archetypes to 2050, alongside a constrained strategic-optimisation exercise and a systematic sensitivity/uncertainty analysis. Results show that closed-loop strategic management can raise 2050 installed capacity by approximately 37% relative to Business-as-Usual while simultaneously lowering the levelised cost of energy by about 22%, and that active feedback control substantially reduces the sensitivity of projected capacity to structural behavioural uncertainty, concentrating outcome risk instead in the technical deployment ceiling and the capital-mobilisation adjustment time. A formal optimisation of the policy levers further shows that a research-and-development-intensive strategy can achieve comparable deployment at a fraction of the cumulative public subsidy required by an unoptimised feed-in-tariff-led strategy.