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Optimal Day-Ahead Bidding Strategy for Electricity Retailer with Inner-Outer 2-Layer Model System Based on Stochastic Mixed-Integer Optimization

机译：基于随机混合整数优化的内外两层模型系统的电力零售商最优提前报价策略

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摘要

Bidding in spot electricity market (EM) is a key source for electricity retailer (ER)'s power purchasing. In China for the near future, besides the real-time load and spot clearing prices uncertainties, it will be hard for a newborn ER to adjust its retail prices at will due to the strict governmental supervision. Hence, spot EM bidding decision-making is a very complicated and important issue for ER in many countries including China. In this paper, an inner-outer 2-layer model system based on stochastic mixed-integer optimization is proposed for ER's day-ahead EM bidding decision-making. This model system not only can help to make ERs more beneficial under China's EM circumstances in the near future, but also can be applied for improving their profits under many other deregulated EM circumstances (e.g., PJM and Nord Pool) if slight transformation is implemented. Different from many existing researches, we pursue optimizing both the number of blocks in ER's day-ahead piecewise staircase (energy-price) bidding curves and the bidding price of every block. Specifically, the inner layer of this system is in fact a stochastic mixed-integer optimization model, by which the bidding prices are optimized by parameterizing the number of blocks in bidding curves. The outer layer of this system implicitly possesses the characteristics of heuristic optimization in discrete space, by which the number of blocks is optimized by parameterizing bidding prices in bidding curves. Moreover, in order to maintain relatively low financial-risk brought by clearing prices and real-time load uncertainties, we introduce the conditional value at risk (CVaR) of profit in the objective function of inner layer model in addition to the expected profit. Simulations based on historical data have not only tested the scientificity and feasibility of our model system, but also verified that our model system can further improve the actual profit of ER compared to other methods.
机译：电力现货市场（EM）的投标是电力零售商（ER）购买电力的主要来源。在不久的将来，在中国，除了实时装载量和现货清算价格的不确定性外，由于政府的严格监管，新生ER很难随意调整其零售价格。因此，对于包括中国在内的许多国家/地区的ER，即期EM竞标决策是一个非常复杂而重要的问题。本文提出了一种基于随机混合整数优化的内外两层模型系统，用于ER的超前EM投标决策。该模型系统不仅可以帮助ER在不久的将来在中国的新兴市场环境中发挥更大的作用，而且如果实施了轻微的转型，也可以在许多其他放松管制的新兴市场环境（例如PJM和Nord Pool）中用于提高ER的利润。与许多现有研究不同，我们追求优化ER的日间分段楼梯（能源价格）投标曲线中的块数和每个块的投标价格。具体而言，该系统的内层实际上是随机混合整数优化模型，通过该模型，可以通过参数化投标曲线中的块数来优化投标价格。该系统的外层隐含地具有离散空间中的启发式优化的特征，通过该特征，可以通过在投标曲线中参数化投标价格来优化块数。此外，为了保持由清算价格和实时负载不确定性带来的相对较低的财务风险，除预期利润外，我们在内层模型的目标函数中引入了利润的条件风险值（CVaR）。基于历史数据的仿真不仅验证了我们模型系统的科学性和可行性，而且还证明了与其他方法相比，我们的模型系统可以进一步提高ER的实际利润。

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  • 来源
    《Mathematical Problems in Engineering》 |2019年第5期|4185952.1-4185952.14|共14页
  • 作者

    Wang Yuwei; Wang Jingmin; Sun Wei; Zhao Mingrui;

  • 作者单位

    North China Elect Power Univ, Dept Econ Management, Baoding 071003, Peoples R China;

    North China Elect Power Univ, Dept Econ Management, Baoding 071003, Peoples R China;

    North China Elect Power Univ, Dept Econ Management, Baoding 071003, Peoples R China;

    CEC Elect Power Dev Res Inst, Beijing 100053, Peoples R China;

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