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We develop multi-stage linear decision rules (LDRs) for dynamic power systemgeneration and energy storage investment planning under uncertainty and proposetheir chance-constrained optimization with performance guarantees. First, theoptimized LDRs guarantee operational and carbon policy feasibility of theresulting dynamic investment plan even when the planning uncertaintydistribution is ambiguous. Second, the optimized LDRs internalize the toleranceof the system planner towards the stochasticity (variance) of uncertaininvestment outcomes. They can eventually produce a quasi-deterministicinvestment plan, which is insensitive to uncertainty (as in deterministicplanning) but robust to its realizations (as in stochastic planning). Last, wecertify the performance of the optimized LDRs with the bound on theirsub-optimality due to their linear functional form. Using this bound, weguarantee that the preference of LDRs over less restrictive -- yet poorlyscalable -- scenario-based optimization does not lead to financial lossesexceeding this bound. We use a testbed of the U.S. Southeast power system toreveal the trade-offs between the cost, stochasticity, and feasibility ofLDR-based investments. We also conclude that the LDR sub-optimality depends onthe amount of uncertainty and the tightness of chance constraints onoperational, investment and policy variables.

Multi-Stage Decision Rules for Power Generation & Storage Investments with Performance Guarantees