Simulation Results for SMPC Reserve Provision

This dataset contains simulation results for the research article "Reserve Provision from Electric Vehicles: Aggregate Boundaries and Stochastic Model Predictive Control". Each CSV file corresponds to an experimental setup with a certain number of EVs included in the fleet and the risk-aversion determined by the risk-aversion factor Ω. Each CSV file contains ten columns that contain the following features: Penalty incurred, positive reserve commitment, negative reserve commitment, total cumulative energy trajectory, G2V charging, V2G charging, lower bound of the conceptual battery, upper bound of the conceptual battery, power boundary of the conceptual battery, electricity system price. Benchmark 1 shows the results for a naive algorithm that solely relies on deterministic predictions. Benchmark 2 is an algorithm that has perfect future knowledge and therefore does not need to rely on predictions. The raw data that was fed to the proposed algorithm is also provided.

本数据集收录了关于研究论文《电动汽车的储备准备：聚合边界与随机模型预测控制》的仿真结果。每一份 CSV 文件对应一个实验配置，其中包含了一定数量的电动汽车编队，以及由风险规避因子 Ω 确定的风险规避程度。每份 CSV 文件包含十列，分别记录以下特征：处罚成本、正向储备承诺、负向储备承诺、总累积能量轨迹、G2V 充电、V2G 充电、概念电池的下限、概念电池的上限、概念电池的功率边界、电力系统价格。基准测试 1 展示了仅依赖确定性预测的朴素算法的结果。基准测试 2 则为具有完美未来知识的算法，因此无需依赖预测。此外，还提供了输入到所提出算法的原始数据。