考虑多时间尺度动态的孤岛可再生能源系统梯级水电三次调节
Tertiary Regulation of Cascaded Run-of-the-River Hydropower in the Islanded Renewable Power System Considering Multi-Timescale Dynamics
摘要：
为了实现农村地区的电力供应和开发清洁能源，全世界都在建设完全可再生的电力系统，包括梯级径流式水电和光伏、风能等不稳定能源。在孤岛运行模式下，初级和次级频率控制，即，水轮机调速器和自动发电控制（AGC）负责频率稳定性。然而，由于径流式水电的蓄水能力有限和河流动力学约束，在梯级电站之间没有协调的情况下，传统的固定参与因子AGC不能充分发挥梯级水电的可调节性。当不稳定的能量和负载之间发生不平衡时，甩负荷可能是不可避免的。为了解决这个问题，本文提出了一个协调的三级控制方法，联合考虑电力系统动态和河流动态耦合的梯级水电站。电力系统和河流动力学的时间尺度是非常不同的。为了统一多时间尺度动态，建立一个模型预测控制器，协调级联工厂，AGC参数和涡轮流量之间的关系在一个时间间隔内近似基于数据的二阶多项式代理模型。通过优化AGC参与因子，以滚动时域方式协调级联电厂，并使甩负荷最小化。对一个实际系统的仿真结果表明，所提出的方法可以有效地降低光伏波动下的负荷损耗。
英文摘要：
To enable power supply in rural areas and to exploit clean energy, fully renewable power systems consisting of cascaded run-of-the-river hydropower and volatile energies such as pv and wind are built around the world. In islanded operation mode, the primary and secondary frequency control, i.e., hydro governors and automatic generation control (AGC), are responsible for the frequency stability. However, due to limited water storage capacity of run-of-the-river hydropower and river dynamics constraints, without coordination between the cascaded plants, the traditional AGC with fixed participation factors cannot fully exploit the adjustability of cascaded hydropower. When imbalances between the volatile energy and load occur, load shedding can be inevitable. To address this issue, this paper proposes a coordinated tertiary control approach by jointly considering power system dynamics and the river dynamics that couples the cascaded hydropower plants. The timescales of the power system and river dynamics are very different. To unify the multi-timescale dynamics to establish a model predictive controller that coordinates the cascaded plants, the relation between AGC parameters and turbine discharge over a time interval is approximated by a data-based second-order polynomial surrogate model. The cascaded plants are coordinated by optimising AGC participation factors in a receding-horizon manner, and load shedding is minimised. Simulation of a real-life system with real-time pv data collected on site shows the proposed method significantly reduces load loss under pv volatility.
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