In this work we propose a heuristic clearing method of day-ahead electricity markets. In the first part of the process, a computationally less demanding problem is solved using an approximation of the cumulative demand and supply curves, which are derived via the aggregation of simple bids. Based on the outcome of this problem, estimated ranges for the clearing prices of individual periods are determined. In the final step, the clearing for the original bid set is solved, taking into account the price ranges determined previously as constraints. Adding such constraints reduces the feasibility region of the clearing problem. By removing simple bids whose acceptance or rejection is already determined by the assumed price range constraints, the size of the problem is also significantly reduced. Via simple examples, we show that due to the possible paradox rejection of block bids the proposed bid-aggregation based approach may result in a suboptimal solution or in an infeasible problem, but we also point out that these pitfalls of the algorithm may be avoided by using different aggregation patterns. We propose to construct multiple different aggregation patterns and to use parallel computing to enhance the performance of the algorithm. We test the proposed approach on setups of various problem sizes, and conclude that in the case of parallel computing with 4 threads a high success rate and a significant gain in computational speed may be achieved.

翻译：本文提出了一种日前电力市场的启发式出清方法。在第一阶段，通过聚合简单投标导出的累积需求与供应曲线近似，求解一个计算复杂度较低的问题。基于该问题的结果，确定各时段出清价格的估计范围。最后一步，考虑先前确定的价格范围作为约束，对原始投标集进行出清求解。添加此类约束缩小了出清问题的可行域。通过剔除那些其接受或拒绝已由假设价格范围约束确定的简单投标，问题规模也显著减小。通过简单算例，我们表明：由于可能出现的阻塞投标悖论拒绝，所提出的基于投标聚合的方法可能导致次优解或不可行问题，但也指出通过采用不同的聚合模式可避免这些算法缺陷。我们提出构建多种不同的聚合模式，并利用并行计算提升算法性能。我们在不同规模的问题设置上测试了所提方法，结论表明：在使用4线程并行计算的情况下，可实现高成功率与显著的计算速度提升。