Risk-Anchored Order-Preserving Scenario-Chain Construction for Renewable Energy Base Planning

Large renewable energy bases are increasingly planned under delivery-hour targets, corridor-capacity constraints, and high shares of wind and photovoltaic generation. Chronological planning samples used in expansion and adequacy studies therefore need to retain not only average renewable-load patterns but also low-probability days with high residual balancing demand, large ramps, curtailment pressure, and sustained renewable scarcity. This paper proposes a risk-anchored order-preserving scenario-chain construction method for renewable energy base planning. The proposed method transforms aligned hourly load, wind, photovoltaic, delivery-demand, and loss-adjusted demand trajectories into distinct operational stress indicators, normalizes them into a joint extreme score, anchors the highest-risk natural days together with their adjacent transition days, and applies clustering only to the remaining regular days. Observed medoid days are then inserted back into chronological order to form a compact scenario chain with explicit weights and adjacency information. A representative 8760 h renewable-base case with 4000 MW wind, 5500 MW photovoltaic, 5400 MW coal support, 1200 MWh storage-energy capacity, a 7600 MW delivery corridor, and a 5600 h delivery target is used to run weight-sensitivity tests and same-budget comparisons against monthly typical days, k-means, k-medoids, hierarchical clustering, Carpe Diem, and seasonal time-series aggregation baselines. Under the equal-weight base case, the proposed chain gives an active-metric mean capture ratio of 1.0660, compared with 0.8377 for monthly typical days. Across four non-equal priority-weight vectors, the active-metric mean remains between 1.0041 and 1.0662. Under the same 151-day budget, conventional k-means, k-medoids, hierarchical clustering, Carpe Diem, and seasonal time-series aggregation baselines obtain active-metric means of 0.8643, 0.9025, 0.8732, 0.9239, and 0.9189, respectively. The results indicate that chronological risk anchoring provides a compact yet physically interpretable sampling layer for planning models that must balance renewable utilization, delivery reliability, and storage adequacy.