时间耦合约束下工业园区基于随机代理的快速分布式多能源管理
Stochastic Gradient-based Fast Distributed Multi-Energy Management for an Industrial Park with Temporally-Coupled Constraints
摘要：
当代工业园区面临着能源供应高成本和低效率的挑战。此外，在电力供需不确定的情况下，如何调动具有延迟容忍能力的弹性负荷，并对非弹性负荷进行实时补偿，以实现多能源发电/储能的匹配，并使能源成本最小化，是一个关键问题。由于不知道随机变量的统计信息很难实现离线能源管理，本文提出了一种系统的在线能源成本最小化框架，以实现发电、需求和价格时变的多能源互补利用。具体到实现充电/放电的限制，由于存储和短期的能量平衡，一个快速的分布式算法的基础上随机梯度与两个时标的实现，以确保在线实施。为了降低高峰负荷，通过估计用户的转移意愿来实施激励机制。参数设置的分析结果也给出了保证所提出的设计的可行性和最优性。数值结果表明，当竞价价差足够小时，该算法可以渐近地获得接近最优的成本。
英文摘要：
Contemporary industrial parks are challenged by the growing concerns about high cost and low efficiency of energy supply. Moreover, in the case of uncertain supply/demand, how to mobilize delay-tolerant elastic loads and compensate real-time inelastic loads to match multi-energy generation/storage and minimize energy cost is a key issue. Since energy management is hardly to be implemented offline without knowing statistical information of random variables, this paper presents a systematic online energy cost minimization framework to fulfill the complementary utilization of multi-energy with time-varying generation, demand and price. Specifically to achieve charging/discharging constraints due to storage and short-term energy balancing, a fast distributed algorithm based on stochastic gradient with two-timescale implementation is proposed to ensure online implementation. To reduce the peak loads, an incentive mechanism is implemented by estimating users' willingness to shift. Analytical results on parameter setting are also given to guarantee feasibility and optimality of the proposed design. Numerical results show that when the bid-ask spread of electricity is small enough, the proposed algorithm can achieve the close-to-optimal cost asymptotically.
机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。
