A hierarchical control architecture is proposed for the optimal day-ahead commitment of multiple grid support services within a virtual power plant (VPP). The day-ahead optimization considers pricing and cost data to determine the commitment schedule, and a robust Model Predictive Control (MPC) approach is included to minimize the unbalance fees during real-time operations. The multi-level control has been demonstrated experimentally using a hybrid test system, where the VPP is formed of a commercial 240 kW, 180 kWh battery energy storage system (BESS), while the additional assets are modelled in a real-time digital simulator (RTDS). Two case studies are analyzed: the first assumes a purely-electrical VPP, with a single connection to the public network; the second involves a multi-energy approach, with the introduction of a gas-supplied Combined Heat and Power unit (CHP). Both winter and summer price scenarios are tested. The results show the superiority of the multiple-service operation compared to providing a single grid support service. For example, the net revenue is increased by 30% (winter) and 7% (summer) when compared to just frequency regulation, and by +99% (winter) and 30% (summer) when compared to only energy arbitrage.

中文翻译：

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用于多电网支持服务的最佳虚拟电厂管理

提出了一种分层控制体系结构，以实现虚拟电厂（VPP）内多个电网支持服务的最佳提前​​承诺。日前优化考虑了定价和成本数据来确定承诺计划，并且包含了强大的模型预测控制（MPC）方法，以最大程度地减少实时操作期间的不平衡费用。多级控制已通过混合测试系统进行了实验验证，其中VPP由商用240 kW，180 kWh电池储能系统（BESS）组成，而其他资产则在实时数字仿真器中建模（ RTDS）。分析了两个案例研究：第一个假设纯电动VPP，并具有与公共网络的单个连接；第二个假设为纯电动VPP。第二个涉及多能量方法，引入了天然气供应的热电联产装置（CHP）。冬季和夏季价格方案均经过测试。结果表明，与提供单网格支持服务相比，多服务操作的优越性。例如，与仅进行频率调整相比，净收入分别增加了30％（冬季）和7％（夏季），而与仅能源套利相比则增加了+ 99％（冬季）和30％（夏季）。