具有个性化价格的实时平衡市场优化：从双层到凸
A Real-Time Balancing Market Optimization with Personalized Prices: From Bilevel to Convex
AI总结：
针对实时平衡市场中聚合商与多产消者的互动，本文提出个性化定价的双层优化并转化为等价凸问题，实现实时低耗时求解，且成本更低、参与产消者更多。
摘要：
本文研究了实时平衡市场（RTBM）中由单一聚合商和多个产消者组成的系统的静态经济优化问题。聚合商作为负责投资组合平衡的主体，需要通过向产消者提出一组最优个性化价格，实时最小化满足不平衡的成本。另一方面，产消者作为价格接受者和自利主体，希望根据所提出的个性化价格改变其供电或需求并提供灵活性，从而最大化自身利润。我们将该问题建模为一个双层优化问题。首先表明，该双层优化问题的最优解可以通过求解一个等价的凸问题得到。与现有最先进的基于混合整数规划（MIP）求解双层问题的方法相比，该凸等价形式计算时间非常短，适合实时应用。接着，我们比较了所提出的个性化机制与统一定价机制的最优解。证明在个性化定价机制下，有更多产消者参与RTBM，且聚合商的成本更低。最后，通过数值案例研究和仿真验证了本文的分析结果。
英文摘要：
This paper studies the static economic optimization problem of a system with a single aggregator and multiple prosumers in a Real-Time Balancing Market (RTBM). The aggregator, as the agent responsible for portfolio balancing, needs to minimize the cost for imbalance satisfaction in real-time by proposing a set of optimal personalized prices to the prosumers. On the other hand, the prosumers, as price taker and self-interested agents, want to maximize their profit by changing their supplies or demands and providing flexibility based on the proposed personalized prices. We model this problem as a bilevel optimization problem. We first show that the optimal solution of this bilevel optimization problem can be found by solving an equivalent convex problem. In contrast to the state-of-the-art Mixed-Integer Programming (MIP)-based approach to solve bilevel problems, this convex equivalent has very low computation time and is appropriate for real-time applications. Next, we compare the optimal solutions of the proposed personalized scheme and a uniform pricing scheme. We prove that, under the personalized pricing scheme, more prosumers contribute to the RTBM and the aggregator's cost is less. Finally, we verify the analytical results of this work by means of numerical case studies and simulations.
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。
