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arXiv 1606.05979math.OCcs.DS
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节点电力市场中发电商的策略性报价:一种凸松弛方法

Strategic Bidding for Producers in Nodal Electricity Markets: A Convex Relaxation Approach

Mahdi Ghamkhari, Ashkan Sadeghi-Mobarakeh, Hamed Mohsenian-Rad

摘要:

电力市场中的策略性报价问题是电力系统中广泛研究的问题,其求解过程往往是复杂的双层最优化问题。解决这类问题的最先进的方法是将其转化为混合整数线性规划(MILP)。然而,这样的MILP重新公式化的计算时间急剧增长,一旦网络规模增加,调度范围增加,或考虑随机性。在本文中,我们采取了一个根本不同的方法,并提出了有效的和定制的凸规划工具来解决节点电力市场中的生产商的战略报价问题。我们的方法的灵感来自于Schmudgen的Positivstellenetrance定理在半代数几何,但然后我们通过几个步骤的基础上,凸优化和混合整数规划,结果在获得接近最优的投标解决方案,证明了几个数值案例研究,除了具有巨大的优势,减少计算时间。虽然最先进的MILP方法的计算时间呈指数增长,当我们增加调度范围或随机场景的数量,我们的方法的计算时间增加,而线性。

英文摘要:

Strategic bidding problems in electricity markets are widely studied in power systems, often by formulating complex bi-level optimization problems that are hard to solve. The state-of-the-art approach to solve such problems is to reformulate them as mixed-integer linear programs (MILPs). However, the computational time of such MILP reformulations grows dramatically, once the network size increases, scheduling horizon increases, or randomness is taken into consideration. In this paper, we take a fundamentally different approach and propose effective and customized convex programming tools to solve the strategic bidding problem for producers in nodal electricity markets. Our approach is inspired by the Schmudgen's Positivstellensatz Theorem in semi-algebraic geometry; but then we go through several steps based upon both convex optimization and mixed-integer programming that results in obtaining close to optimal bidding solutions, as evidenced by several numerical case studies, besides having a huge advantage on reducing computation time. While the computation time of the state-of-the-art MILP approach grows exponentially when we increase the scheduling horizon or the number of random scenarios, the computation time of our approach increases rather linearly.

机构、总结、中文摘要均由AI分析而来,可能存在错误,请以论文正文为准。

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