Conventional planning generally treats uncertain parameters

such as resource availability and load levels

as exogenous variables

with planning models primarily optimizing overall system configuration and operating costs under prescribed risk conditions. As renewable energy deployment expands

planning decisions such as site selection

capacity allocation

and system reinforcement can alter the statistical characteristics of system uncertainty itself through their effects on system structure

thereby making uncertainty decision-dependent. Decision-dependent uncertainty (DDU) captures the endogenous coupling between planning decisions and system uncertainty

indicating that uncertainty is no longer merely an externally given constraint but may also become a system attribute influenced by planning decisions. Accordingly

the planning problem is extended from cost optimization under prescribed uncertainty to coordinated optimization that accounts for the impact of planning decisions on uncertainty structures. To characterize this mechanism and incorporate it into planning models

this paper first analyzes the conditions under which DDU emerges and reveals the interaction mechanisms and feedback relationships between planning decisions and uncertainty. Then

it establishes a quantitative mapping between planning decisions and system uncertainty and develops quantitative measures of risk propagation and uncertainty response. Based on these formulations

a unified computational framework is constructed to address the nonconvex coupling

computational complexity in high-dimensional settings

and convergence instability introduced by DDU. Case studies show that planning methods considering DDU can actively reshape the system’s uncertainty structure and reduce the potential risks associated with planning schemes under future operating conditions. These benefits mainly arise from optimizing the uncertainty structure. This paper reveals the bidirectional feedback mechanism between planning decisions and uncertainty in renewable-dominated power systems

providing theoretical foundations and practical methods for shifting power system planning from passive adaptation to uncertainty toward proactive regulation of uncertainty structures.