In many energy markets, the trade amount of electricity must be committed to before the actual supply. This study explores one consecutive operational challenge for a virtual power plant—the optimal bidding for highly uncertain distributed energy resources in a day-ahead electricity market. The optimal bidding problem is formulated as a scenario-based multi-stage stochastic optimization model. However, the scenario-tree approach raises two consequent issues—scenario overfitting and massive computation cost. This study addresses the issues by deploying a sample robust optimization approach with linear decision rules. A tractable robust counterpart is derived from the model where the uncertainty appears in a nonlinear objective and constraints. By applying the decision rules to the balancing policy, the original model can be reduced to a two-stage stochastic mixed-integer programming model and then efficiently solved by adopting a dual decomposition method combined with heuristics. Based on real-world business data, a numerical experiment is conducted with several benchmark models. The results verify the superior performance of our proposed approach based on increased out-of-sample profits and decreased overestimation of in-sample profits.

中文翻译：

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虚拟电厂稳健最优投标模型示例

在许多能源市场中，在实际供应之前必须承诺交易电量。本研究探讨了虚拟发电厂的一个连续运营挑战——日前电力市场中高度不确定的分布式能源的最优竞价。最优出价问题被表述为基于场景的多阶段随机优化模型。然而，场景树方法提出了两个随之而来的问题——场景过度拟合和大量的计算成本。本研究通过部署具有线性决策规则的鲁棒优化示例方法来解决这些问题。一个易于处理的鲁棒对应物是从模型中得出的，其中不确定性出现在非线性目标和约束中。将决策规则应用到平衡策略中，可以将原始模型简化为两阶段随机混合整数规划模型，然后采用对偶分解结合启发式方法进行高效求解。基于真实的业务数据，使用多个基准模型进行数值实验。结果验证了我们提出的方法基于样本外利润增加和样本内利润高估减少的卓越性能。