一种带复位机制的边际成本一致性方案用于电池储能系统的分布式经济调度
A Marginal Cost Consensus Scheme with Reset Mechanism for Distributed Economic Dispatch in BESSs
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中文总结 AI 辅助
针对电池储能系统经济调度，提出一种带复位机制的分布式边际成本一致性方案，通过PI控制与积分重置加速收敛并抑制超调，理论分析与仿真验证了其有效性。
中文摘要 AI 辅助
电池储能系统（BESSs）通常作为填谷和削峰的关键设备集成到智能电网中。然而，电池单元的内部功耗和容量衰减不可忽视。此外，并网电池储能系统的所有者与电力公司（EC）之间存在一定的电力交易。因此，本文构建了一个并网电池储能系统的支出函数，该函数在满足电力供需平衡的同时，涵盖了内部功耗、容量衰减和电力交易。在此基础上，该函数的Karush-Kuhn-Tucker（KKT）条件被归纳为边际成本（MC）收敛到分时电价的共识问题。因此，我们计划基于多智能体系统（MASs）在小时间尺度上设计一种用于电池储能系统经济调度（ED）的分布式边际成本一致性方案。鉴于现有分布式比例协议的控制精度和响应速度较低，本文提出了一种基于比例积分（PI）控制的带复位机制的分布式经济调度（DED）方案。当比例项遇到零交叉时，控制方案的积分项被重置为0，这确保了两者的符号对齐，从而加速边际成本的收敛并抑制超调。通过相关的理论分析，给出了共识、正则性和无Zeno行为的参数条件。设计了多个仿真案例来验证所设计的分布式经济调度算法。
英文摘要
Battery energy storage systems (BESSs) are often integrated into the smart grid as the key equipment for valley filling and peak suppression. However, the internal power consumption and capacity degradation of battery cells can not be ignored. In addition, there are certain electric trading between the owner of the grid-connected BESSs and the electric company (EC). Therefore, an expenditure function for grid-connected BESSs is constructed in this paper, in which internal power consumption, capacity degradation and power trading are covered while meeting the balance of power supply and demand. On this basis, the Karush-Kuhn-Tucker (KKT) condition of the function is summarized as the consensus problem of marginal cost (MC) converging to time-phased electricity price. Thus, we plan to design a distributed MC consensus scheme for economic dispatch (ED) in BESSs based on multi-agent systems (MASs) on a small-time scale. In view of the low control accuracy and response speed of the existing distributed proportional protocol, this paper proposes a distributed ED (DED) scheme with reset mechanism based on a proportional integral (PI) control. When the proportional term encounters zero crossing, the integral term of the control scheme is reset to 0, which ensures that the signs of the two are aligned, thus accelerating MCs convergence and restraining overshoot. Parameter conditions for consensus, regularity and Zeno-free behavior are given through the relevant theoretical analysis. Several simulation cases are designed to verify the designed DED algorithm.
发表机构
- Nankai University（南开大学）
- Tianjin Key Laboratory of Brain Intelligent Rehabilitation, Nankai University（南开大学脑智能康复天津市重点实验室）
机构由 AI 辅助整理，请以论文原文为准。
