论文标题
实时平衡市场优化与个性化价格：从二线到凸
A Real-Time Balancing Market Optimization with Personalized Prices: From Bilevel to Convex
论文作者
论文摘要
本文研究了一个在实时平衡市场（RTBM）中具有单个聚合器和多个造型器的系统的静态经济优化问题。作为负责投资组合平衡的代理商，汇总者需要通过向生产商提出一组最佳的个性化价格来最大程度地降低实时不平衡满意度的成本。另一方面，作为价格接收者和自私者的代理商，他们希望通过更改供应或需求来最大化其利润，并根据建议的个性化价格来提供灵活性。我们将此问题建模为双光线优化问题。我们首先表明，可以通过解决等效凸问题来找到此双重优化问题的最佳解决方案。与最先进的混合企业编程（MIP）解决双重问题的方法相反，该凸等效物的计算时间非常低，适合实时应用程序。接下来，我们比较提出的个性化计划和统一定价方案的最佳解决方案。我们证明，根据个性化的定价计划，更多的生产商会为RTBM做出贡献，而聚合者的成本则降低了。最后，我们通过数值案例研究和模拟来验证这项工作的分析结果。
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.
