面向电网稳定性的智能化直流输电控制系统

为了更好地进行能源调配，我国正开始建设坚强智能电网，直流输电控制系统应实现更多系统层的控制功能。智能电网中的直流输电系统的根本控制目标是保障电网整体的自适应和自愈性。为了实现这一目标，需要从直流输电控制的可观测性入手增加控制观测量，引入合理的集控、协调控制理论作为支撑，完善控制输出环节，实现对电网的有效控制。控制的实时性和决策能力是智能化直流输电控制的核心。从可观性、可控性、实时性、自适应性角度分析，提出了面向电网稳定性的多智能体智能化直流输电控制技术框架，为直流输电系统级控制技术的发展提出了思路。

Power Grid-Oriented Stability Multi-Agent DC Power Transmission Control System

In order to achieve economical distribution of energy resources, China is building the strong smart grid, which remands DC transmission control system has more control function in system layor. To keep the adaptive ability and self-healing ability of the whole transmission grid is the major aim of the HVDC system control. To reach this goal, some key problems must be addressed, such as increasing control inputs through improving the real-time signal measurement, adopting the effective centralized monitor & control and coordination control & decision-making method, and perfecting the designed in-loop control to realize the effective control of the power grid. The real-time decision-making ability should be the core of the intelligent DC transmission control system. Through analysis of observability, controllability, real-time, and self-healing, this paper proposes the frameworks of the multi-agent DC power transmission control system of power grid-oriented stability, providing some ideas for the development of the multi-agent DC transmission control technology. The ultra-short-term wind power forecasting is of significance for the operation of grid-connected wind power generation system. However, the evaluation of the prediction results often focuses on the conventional statistical indicators only, lacking a reasonable system to evaluate forecasting models for a particular wind power farm. This paper introduces the deficiency of the current wind power prediction results evaluation, and presents an assessment method of the wind power farm real-time output prediction, providing the basis for wind farms in different regions in the selection of the prediction model according to the characteristics of the wind power output power. Finally, simulations are performed using the real data from a wind farm of Jilin Province, and then the new evaluation method is used to evaluate three different predictive tools. The evaluation results verify the correctness and superiority of the method proposed in the paper, giving its practical value.