This article presents a short-term decision-making model for an electricity retailer using bilevel stochastic programing. In the proposed model, a liberalized distributed renewable energy (DRE) market in which the retailer competes with other load serving entities (LSEs) for procuring DRE is proposed. The retailer, in the upper level, decides its level of involvement in the day-ahead and real-time markets, as well as the price bids offered to DRE producers for every time period, with the goal of minimizing its expected procurement cost at a predefined risk level. On the other hand, DRE producers, in the lower level, react to the price bids offered by the retailer under study and other LSEs, to maximize their total revenues. The stochastic nature of day-ahead and real-time market prices, DRE production, electricity demand, and price bids of the retailer's rival market agents (RMAs) is taken into the formulation of the proposed model. By using the Karush-Kuhn-Tucker (KKT) optimality conditions and duality theory, the bilevel problem is transformed into its equivalent single-level mixed-integer linear programming (MILP) problem. Case studies are performed to show the effectiveness of the proposed model.

中文翻译：

---

自由化分布式可再生能源市场中电力零售商的随机双层模型

本文介绍了使用双层随机编程的电力零售商的短期决策模型。在提出的模型中，提出了一个开放的分布式可再生能源（DRE）市场，其中零售商与其他负载服务实体（LSE）竞争以获取DRE。零售商在上层，决定其在日间和实时市场中的参与程度，以及在每个时间段内向DRE生产者提供的价格竞标，目的是最大程度地降低其预期采购成本。预定义的风险级别。另一方面，较低级别的DRE生产商会对所研究的零售商和其他LSE提供的价格出价做出反应，以最大程度地提高其总收入。日前和实时市场价格，DRE生产，电力需求，零售商的竞争对手市场代理商（RMA）的价格出价已纳入拟议模型的制定中。通过使用Karush-Kuhn-Tucker（KKT）最优性条件和对偶理论，将二层问题转化为等效的单层混合整数线性规划（MILP）问题。进行案例研究以显示所提出模型的有效性。