A monthly hydropower scheduling determines the monthly flows, storage, and power generation of each reservoir/hydropower plant over a planning horizon to maximize the total revenue or minimize the total operational cost. The problem is typically a complex and nonlinear optimization that involves equality and inequality constraints including the water balance, hydraulic coupling between cascaded hydropower plants, bounds on the reservoir storage, etc. This work applied the Zoutendijk algorithm for the first time to a medium/long-term hydropower scheduling of cascaded reservoirs, where the generating discharge capacity is handled with an iterative procedure, while the other head-related nonlinear constraints are represented with exponential functions fitting to discrete points. The procedure starts at an initial feasible solution, from which it finds a feasible improving direction, along which a better feasible solution is sought with a one-dimensional search. The results demonstrate that the Zoutendijk algorithm, when applied to six cascaded hydropower reservoirs on the Lancang River, worked very well in maximizing the hydropower production while ensuring the highest firm power output to be secured.

The increasing penetration of single-phase photovoltaic (PV) generation and electric vehicle (EV) charging has aggravated phase current asymmetry in low-voltage distribution networks. In contrast to voltage-oriented control strategies, this work focuses directly on mitigating current imbalance at the point of common coupling (PCC). A coordinated control framework based on multi-agent deep deterministic policy gradient (MADDPG) is developed to regulate distributed battery energy storage systems (BESS). The control objective is formulated in terms of the Current Unbalance Factor (IUF), derived from symmetrical component theory. A linearized DistFlow model is embedded in the learning environment to preserve physical consistency while maintaining computational tractability. Device-level constraints, including state-of-charge limits and ramp-rate bounds, are enforced through action projection, whereas network security limits are incorporated via reward penalties. Case studies on a modified residential feeder indicate that coordinated BESS control reduces the peak IUF from 2.75% to 2.50% under the studied operating condition. The maximum dominant-phase current decreases from 125 A to 110 A. The performance is close to that of centralized convex optimization while enabling decentralized real-time execution after offline training. These results suggest that multi-agent reinforcement learning can serve as a feasible alternative for phase imbalance mitigation in distribution networks with high renewable penetration.

Large-scale hydro–wind–solar systems require scenario sets that represent cross-resource dependence without losing station-level and intra-day characteristics. A remaining challenge is to connect cluster-level joint modeling with station allocation under sum constraints and hourly reconstruction consistent with daily totals. This study develops an integrated framework: wind and photovoltaic plants are grouped by output correlation; season-specific C-vine copulas model daily wind–solar–runoff dependence; a centered log-ratio transformation and Gaussian R-vine copula allocate cluster totals to stations; and a stratified typical-day library reconstructs hourly profiles. The framework is evaluated using 872 observed days of hourly data from a grid-connected system in Southwest China comprising 53 wind plants, 47 photovoltaic plants, and one hydropower-station runoff series; 1000 joint scenarios are generated. Against independent marginal sampling, Spearman correlation-matrix errors fell from 0.419–0.432 to 0.318–0.395 (8.7–24.2%), and Kendall errors from 0.306–0.325 to 0.236–0.298 (8.3–22.8%). Compared with historical-share resampling, station allocations were more diverse but less faithful to historical share distributions; compared with a unified typical-curve library, intra-day autocorrelation errors decreased across all resource-season cases. The resulting multi-scale scenarios can serve as generation-side inputs for stochastic scheduling and operational risk analysis.

The rising penetration of renewable energy introduces greater volatility and uncertainty into energy systems. Energy storage systems (ESS) play a vital role in enhancing system flexibility and stability. However, frequent charge–discharge cycles lead to significant degradation of storage devices, reducing their economic efficiency and lifespan. This paper proposes a two-stage robust optimization framework under high renewable penetration, explicitly considering battery degradation. The first stage determines the optimal capacity configuration of distributed energy resources, including PV, wind, gas turbines, and ESSs. The second stage optimizes operational strategies under worst-case uncertainty in renewable generation and load, while accounting for the degradation cost and cycle life of the ESS. A linearized degradation model is developed based on depth-of-discharge (DoD), and the overall problem is solved using a Column-and-Constraint Generation (C&CG) algorithm. Simulation results demonstrate that the proposed approach effectively balances investment and operation, reduces degradation-related costs, and ensures reliable performance under uncertainty.