AI 中文总结
针对电网集成电解系统多尺度优化框架难以扩展的问题，开发聚合信息本德分解法，通过求解长达40年参与DAM和RTM的案例研究展示其有效性，相比传统方法能加速收敛，降低最优性差距，还发现RTM参与在高价格波动下有经济优势。
AI 中文摘要
电解设备的需求响应（DR）运行因利用波动的电力市场而受到关注，但其动态运行给系统的耐用性和寿命带来挑战。近期开发的用于电网集成电解系统的多尺度优化框架研究了DR的影响及对设备耐用性的作用。该工作的一个主要障碍是将模型扩展以纳入高频电力市场参与和/或更长时间范围。为此，本文开发了一种聚合信息本德分解方法来有效解决此类问题结构的大型实例。通过求解一个长达40年参与日前市场（DAM）和实时市场（RTM）的案例研究来展示其有效性。将分解算法与商业求解器和传统本德分解进行比较。发现纳入聚合子问题信息可加速收敛，在40年时间范围实例上相对于传统本德分解将最终最优性差距降低多达约81%，否则这些实例在85%左右停滞。还应用该算法发现参与RTM在更高价格波动下具有经济优势。
英文摘要
Demand response (DR) operation of electrolysis devices is gaining traction to capitalize on volatile electricity markets, but their dynamic operation poses challenges to the durability and lifespan of these systems. Our recently developed multi-scale optimization framework for grid-integrated electrolysis systems studied the impacts of DR and effects on device durability. A major hurdle in that work is tractably scaling the model to include participation in high-frequency electricity markets and/or longer time horizons. To address this, in this work, we developed an aggregate-informed Benders decomposition method to tractably solve large instances of this problem structure. To illustrate the efficacy, we solve a case study with market participation in the day-ahead market (DAM) and real-time markets (RTM) for up to 40 years to effectively capture the effects of device lifespan change. We compare our decomposition algorithm to both commercial solvers and traditional Benders decomposition. We find that incorporating aggregate subproblem information accelerates convergence, reducing the final optimality gap by up to ~81% relative to traditional Benders on the 40-year horizon instances that otherwise stall near 85%. We additionally apply the algorithm to find that RTM participation offers economic advantages under the higher price volatility.
Comments6 pages, 3 figures, submitted to FOCAPO-CPC 2027
