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水力发电学报 ›› 2020, Vol. 39 ›› Issue (9): 33-42.doi: 10.11660/slfdxb.20200904

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考虑风电置信区间的水风火短期优化调度方法

  

  • 出版日期:2020-09-25 发布日期:2020-09-22

Short-term optimal operation method of water, wind and thermal power considering wind power confidence intervals

  • Online:2020-09-25 Published:2020-09-22

摘要: 风电出力的不确定性影响电网的稳定运行,大规模风电并网后“弃风”问题突出。为了充分考虑风电出力的不确定性,本文提出基于风电置信区间的风-水-火短期联合调度运行方法。利用梯级水电站启停灵活、爬坡速度快等优点平抑风电出力波动,构建了发电成本最小与清洁能源消纳最大的多目标优化调度模型。基于分层求解思想,将模型分为风电运行层、水电调度层与火电调度层,并提出了集成非参数法、启发式算法与改进的粒子群算法的总体求解框架,实现了模型的快速求解。某区域电网短期调度模拟运行结果表明,所提方法求解速度快,仅需83.5 s,在满足电力系统安全稳定运行约束与清洁能源消纳最大的前提下,水电、风电多发45.56万kWh,煤耗成本减少23.33万元。

关键词: 置信区间, 风电出力, 梯级水电站, 多目标, 分层优化

Abstract: Uncertainty in wind power output affects the stable operation of a power grid, and wind abandonment has become a severe problem since large-scale wind power connection to the grid. For a full consideration of wind power uncertainty, this paper describes a short-term wind-water-thermal power joint operation method based on the wind power confidence intervals. We construct a multi-objective optimal operation model targeted at the minimum power generation cost and the maximum clean energy consumption, suppressing fluctuations in wind power outputs by taking advantages of the flexible start and stop and the fast climbing speed of cascade hydropower stations. This model comprises three layers based on the idea of hierarchical solution–a hydropower dispatching layer, a wind power operation layer, and a thermal power dispatching layer; and its overall solution framework can achieve a fast solution through integrating non-parametric method, heuristic algorithm, and improved particle swarm optimization algorithm. A case study of short-term operation for a regional power grid shows the method is fast with a typical run cost of only 83.5 s. And for this grid, under the constraints of the power system’s safe and stable operation and maximum clean energy consumption, hydropower and wind power generate an extra 455.6 MWh, thus reducing the cost of coal consumption by 233.3 thousand Yuan.

Key words: confidence interval, wind power output, cascade hydropower station, multi-objective, hierarchical optimization

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