Univariate Extreme Value Theory (EVT) is frequently used to assess the structural risk of failure or damage resulting from excessive environmental loads in structural design. In reality, failure or damage is caused not by univariate (1D) load but by a combination of cross-correlated covariates. This research work presents a state-of-the-art, multimodal reliability approach for multivariate structural design. Analysis of the joint probability distribution tail of an M-dimensional random process is the focus of multivariate EVT and extrapolation schemes. Expansion of EVT and Generalized Extreme Value (GEV) based Probability Density Function (PDF) from univariate (1D) towards bivariate (2D) systems encounters both theoretical and practical obstacles. For the first, 1D EVT is not straightforwardly extended to 2D cases, not to mention challenges pertaining to dynamic systems' dimensionality above 2D. Extension from 1D to 2D is typically done using a particular copula selection, which in itself introduces additional bias and inaccuracy. The multi-modal Gaidai reliability approach, presented here, does not rely on copula selection, hence being genuinely ∞D, and yet mathematically exact. The primary goal of the presented investigation was to develop a novel design philosophy, incorporating a generic state-of-the-art multivariate reliability method for a high-dimensional energy-generating dynamic system’s failure or damage risk assessment, allowing pertinent information regarding excessive dynamics to be extracted from available time histories that were recorded physically or simulated numerically. The multivariate design approach is a priori more conservative than existing univariate ones. Novelty: proposed holistic multi-modal reliability methodology allows for accurate yet efficient prognostics of failure, hazard or damage risk for a range of multi-modal nonlinear renewable energy dynamic systems, accounting for memory effects. The presented multidimensional reliability methodology's potential application will include big data and renewable energy harvesting and grid design applications when the number of system’s cross-correlated components (dimensions) exceeds 2.