针对风电场预测功率与实际功率不匹配以及风力发电不确定性问题,提出一种以补偿风电预测误差和平抑风电波动为目标的储能控制策略。该策略以先进控制理论为基础,结合储能补偿预测区间和储能平抑风电波动区间,提取考虑储能运行成本的储能最优滚动控制域。首先,针对储能补偿预测误差目标,制定储能控制策略,提取允许误差内的储能补偿区间;其次,考虑风电功率波动要求及荷电状态（SOC）约束,采用模型预测控制求解出储能滚动控制序列,确定储能平抑区间。最后,考虑储能运行成本,将补偿区间和平抑区间相结合,制定储能最优滚动控制区间,以此为基础确定储能容量。以中国新疆某风电场为例,对该文提出的储能控制策略与传统控制策略进行对比验证,验证所提策略的可行性和有效性。
Abstract
Aiming at the mismatch between predicted power and actual power of wind farm and the uncertainty of wind power generation, an energy storage control strategy for compensating wind power prediction error and restraining wind power fluctuation is proposed. Based on the advanced control theory, combined with the prediction interval of energy storage compensation and the fluctuation interval of energy storage stabilizing the wind power, the optimal rolling control domain of energy storage considering the operation cost of energy storage is extracted. Firstly, aiming at the prediction error target of energy storage compensation, the energy storage control strategy is formulated to extract the energy storage compensation interval within the allowable error. Secondly, considering the wind power fluctuation permissible range and SOC （state of charge） state constraints, the model predictive control is used to solve the energy storage rolling control sequence and determine the energy storage stabilization interval. Finally, considering the operation cost of energy storage, the optimal rolling control interval of energy storage is formulated by combining the compensation interval and suppression interval, and the energy storage capacity is determined on this basis. Taking a wind farm in Xinjiang as an example, the energy storage control strategy proposed in this paper is compared with the traditional control strategy to verify the feasibility and effectiveness of the proposed strategy.
关键词
风力发电 /
储能 /
功率波动平抑 /
预测误差补偿 /
模型预测控制 /
储能运行策略
Key words
wind power /
energy storage /
power smoothing /
prediction error compensation /
model predictive control /
energy storage operation strategy
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基金
国家自然科学基金（52075500; 51405453）; 河南省科技攻关项目（212102210313）
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