Optimization of Closed-Loop Pumped-Storage Hydropower Siting

Life-Cycle Planning of Collector System for Deep-Sea Multi-Spatial Wind-PV-Tidal Farm

This paper develops a life-cycle optimization model for the collector system (CS) of deep-sea co-located energy farms (CEFs), where co-located energy turbines (CETs) integrate wind, photovoltaic (PV), and tidal current resources across sea-area layers. The model captures multi-layer marine-space complementarity, wake effects, and output variability, while accommodating diverse dynamic submarine cable configurations. To improve computational efficiency, the adaptive piecewise linearization (A-PWL) method based on the outputs of CETs is proposed to transform the original mixed-integer quadratic programming (MIQP) problem into a mixed-integer linear programming (MILP) form to approximate quadratic operation costs and simplify absolute power flow modeling. Case studies demonstrate that incorporating multi-energy complementarity significantly enhances the economic performance of deep-sea CEFs. When external physical risks are negligible, the fully-suspended cable configuration proves more cost-effective than the lazy-wave design. The proposed linearization method achieves high accuracy while significantly reducing solution time. Overall, this work provides a practical and scalable framework for efficient CS planning in offshore renewable energy systems.

Electro-Hydrogen Synergistic Dispatch for Wind-Solar-Tidal Port Microgrids: A Two-Stage Stochastic Framework with Adaptive Progressive Hedging

The decarbonization of multi-energy coupled ports necessitates addressing the complex interplay between renewable energy fluctuations and flexible load management. However, existing research has clear limitations in describing multi-source uncertainties and clarifying the synergistic mechanism of electro-hydrogen energy storage systems. To fill this gap, this study addresses the core problem of the coordinated operation of port microgrids under multiple uncertainties. A comprehensive physical model is established for wind–solar–tidal–hydrogen systems, capturing the intricate dynamics of the “generation–consumption–storage–conversion” chain. A novel two-stage stochastic optimization framework, integrating day-ahead pre-decision with real-time compensation, is proposed. To overcome computational bottlenecks, an adaptive progressive hedging algorithm is introduced. Comparative analyses show that the proposed strategy reduces the total port operational costs by 60.8% and grid transaction costs by 74.4%, compared with the single-stage deterministic mode. The adaptive PH algorithm also achieves 4–6 times higher computational efficiency than quadratic integer Benders decomposition, while maintaining an optimality gap below 2%. These findings elucidate the critical synergy between electro-hydrogen storage and stochastic optimization, offering a theoretical basis for sustainable port operations.

Stochastic Multi-Objective Optimization for Grid-Scale Energy Storage Sizing and Siting

The integration of variable renewable energy sources reduces greenhouse gas emissions but introduces significant operational challenges to modern power systems. Grid-scale energy storage systems provide the essential flexibility required to mitigate these short-term uncertainties. Furthermore, these technologies enable a more economical system operation and actively assist in reducing overall greenhouse gas emissions. This paper proposes a stochastic multi-objective formulation for the optimal sizing and siting of grid-scale energy storage systems. The model simultaneously minimizes the total expected system costs and the expected thermal emissions. The Augmented $\varepsilon $ -Constraint (AUGMECON) method is implemented to generate the exact Pareto frontier. The proposed stochastic framework captures load and renewable generation uncertainties using representative daily scenarios. The mathematical formulation fully integrates the strict unit commitment constraints of the conventional thermal fleet. Case studies are conducted on the modified IEEE 24-bus Reliability Test System. The results demonstrate how the trade-off between financial savings and environmental priorities fundamentally alters the optimal spatial allocation and capacity requirements of the energy storage systems.

MILP-based optimization of renewable energy integration in maritime ports using demand response and energy storage

Pumped Hydro Energy Storage Potential and Grid-Integration Feasibility for the Cuban Power System

As Cuba advances toward ambitious renewable energy targets, large-scale energy storage is essential to ensure grid stability and operational flexibility. Pumped Hydro Energy Storage (PHES) is the most mature technology for providing long-duration storage. This study proposes an integrated GIS-based screening and engineering validation methodology for PHES site selection and presents the first academic application of the Australian National University (ANU) Global Pumped Hydro Atlas to the Cuban context. Rather than replacing historical engineering studies, the proposed methodology complements them through automated geospatial analysis, local infrastructure assessment, environmental screening, and engineering validation. The GIS analysis identified 92 potential off-river closed-loop PHES sites across Cuba, including 14 sites in the 500 GWh storage class and 78 sites in the 150 GWh storage class, substantially expanding the national inventory. Comparison between the GIS-derived candidates and historical investigations demonstrates strong agreement between both approaches, while the Mayarí project serves as a representative case study for local validation. The results confirm significant PHES potential distributed across Cuba’s three main mountainous regions, with several sites exhibiting favourable hydraulic heads and cost classifications. The proposed methodology provides a transferable framework for integrating legacy engineering knowledge with modern GIS-based planning tools in islanded and developing power systems while highlighting the need for supportive regulatory frameworks to accelerate future PHES deployment.

A Multi-Scale Optimization Framework for Grid-Integrated Electrolysis

The increasing penetration of wind and solar resources into the power grid motivates the integration of flexible technologies to dynamically shift power loads in response to grid volatility and emergency events. The water electrolyzer presents a synergistic opportunity to provide flexibility through demand response (DR), while simultaneously electrifying hydrogen production; however, highly dynamic operation schedules accelerate device degradation. This work presents a mixed-integer linear program (MILP) optimization framework to study the multi-scale coupling between short-term operational flexibility provision in electrolysis devices and long-term stack replacement decisions driven by degradation. Active day-ahead market (DAM) participation of a 2.2 MW alkaline water electrolyzer over 22 years is solved as a case study. Our framework reveals that the multi-scale scheduling of DR operation and replacement decisions can extend optimal stack lifetimes by up to 2 years through load-shifting and further reduce lifetime electricity expenses by 33% relative to inflexible constant operation. Furthermore, we quantify key device parameter tradeoffs and next-generation design goals, where our analysis challenges the feasibility of the standard \$1/kg levelized cost of hydrogen (LCOH) production target solely through market arbitrage. Ultimately, this framework quantifies the largely unexploited economic value of multi-scale optimization in grid-integrated electrolysis.