• CSCD核心库收录期刊
  • 中文核心期刊
  • 中国科技核心期刊

电力建设 ›› 2016, Vol. 37 ›› Issue (12): 128-.doi: 10.3969/j.issn.1000-7229.2016.12.017

• 输配电技术 • 上一篇    下一篇

基于卖方灵活电力合约的风电中长期交易策略

 郑亚先1,武泽辰2,王秀丽2,张炜2   

  1.  1.中国电力科学研究院,北京市 100192;2.西安交通大学电气工程学院,西安市 710049
  • 出版日期:2016-12-01
  • 作者简介:郑亚先(1982),男,硕士,高级工程师,SAC/TC82电力市场工作组秘书,主要研究方向为电力系统优化运行、电力市场运营; 武泽辰(1991),男,硕士研究生,本文通信作者,主要研究方向为电力市场及电力系统可靠性; 王秀丽(1961),女,教授,博士生导师,主要研究方向为电力市场、电力系统规划和电力系统可靠性; 张炜(1988),男,博士研究生,主要研究方向为电力市场。
  • 基金资助:
     国家电网公司科技项目(DZN17201500036)

 A Wind Power Long-Term Trading Strategy Based on Seller-Side Flexible Electricity Contract

 ZHENG Yaxian1, WU Zechen2, WANG Xiuli2, ZHANG Wei2   

  1.  1. China Electric Power Research Institute, Beijing 100192, China;
    2. School of Electrical Engineering, Xian Jiaotong University, Xian 710049, China
  • Online:2016-12-01
  • Supported by:
     

摘要:  风力发电商在一些发达国家已作为独立的主体参与电力市场交易。文章构建了风电交易的灵活电力合约模型,并提出了风电商在中长期市场基于卖方灵活电力合约的交易策略。风电商可以在各阶段开始时刻根据市场状况和自身出力预测情况灵活安排阶段内各时段的合约电量,以使自身收益最大化。文章着重研究了合约电量的优化决策问题,提出了基于随机动态规划的模型和求解方法。算例中使用时间序列法进行风速和电价的模拟和预测,通过几种交易方案的比较,说明卖方灵活电力合约能够显著提升风电商的收益水平,同时有效控制收益风险。

 

关键词:  , 电力市场, 灵活电力合约, 风电交易, 随机动态规划

Abstract:  Wind power producers have begun to participate in electricity market in some developed countries. This paper builds a model for wind power flexible electricity contract and proposes a wind power long-term trading strategy based on seller-side flexible electricity contract. Wind power producers can flexibly schedule the contract energy during each period according to market condition and wind power prediction in order to maximize their profits. This paper focuses on the optimal decision of contract energy, proposes the model based on stochastic dynamic programming and its solving method. In examples, we use time series method for the simulation and prediction of wind speed and electricity price. The comparison of three different trading strategies shows that a well scheduled seller-side flexible electricity contract can significantly enhance the income of wind power producer and effectively control financial risk at the same time.

Key words:  electricity market, flexible electricity contract, wind power trading, stochastic dynamic programming

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