用模仿学习优化离网能源系统储能与风电配置，兼顾减排与成本。
Accounting for Optimal Control in the Sizing of Isolated Hybrid Renewable Energy Systems Using Imitation Learning
- 通过模仿学习构建神经模型预测控制，融合有限时域最优调度。
- 风电与储能容量非线性影响碳排与投资成本，存在复杂权衡关系。
- 适合能源规划者评估不同配置下的真实减排效果与经济性。
通过大规模引入间歇性太阳能或风能实现孤立能源系统的脱碳，需配套储能或维持现有化石燃料发电以平衡供需。实际碳减排效果取决于储能与可再生能源的相对容量、可再生能源的随机性以及系统的最优调度控制。尽管储能和可调度电源的运行会影响系统最优配置，但将有限时域最优控制纳入系统设计阶段仍具挑战。本文提出一种灵活且计算高效的离网能源系统储能与可再生能源容量配置框架，考虑可再生能源的不确定性及最优反馈控制。采用模仿学习实现随机神经模型预测控制（MPC），将电池储能与风电峰值容量与减排量、投资成本关联，并兼顾有限前瞻。该方法使决策者可在任意价格点评估不同储能与风电容量组合下的实际减排效果与成本。我们在一个包含燃气轮机、风电场和电池储能系统（BESS）的海上能源系统案例中验证了该框架，发现风电与BESS容量与燃气使用减少量及投资成本之间存在非线性、非平凡的关系，凸显在孤立系统设计中考虑最优控制的重要性。
原文摘要 · Abstract (English)
Decarbonization of isolated or off-grid energy systems through phase-in of large shares of intermittent solar or wind generation requires co-installation of energy storage or continued use of existing fossil dispatchable power sources to balance supply and demand. The effective CO2 emission reduction depends on the relative capacity of the energy storage and renewable sources, the stochasticity of the renewable generation, and the optimal control or dispatch of the isolated energy system. While the operations of the energy storage and dispatchable sources may impact the optimal sizing of the system, it is challenging to account for the effect of finite horizon, optimal control at the stage of system sizing. Here, we present a flexible and computationally efficient sizing framework for energy storage and renewable capacity in isolated energy systems, accounting for uncertainty in the renewable generation and the optimal feedback control. To this end, we implement an imitation learning approach to stochastic neural model predictive control (MPC) which allows us to relate the battery storage and wind peak capacities to the emissions reduction and investment costs while accounting for finite horizon, optimal control. Through this approach, decision makers can evaluate the effective emission reduction and costs of different storage and wind capacities at any price point while accounting for uncertainty in the renewable generation with limited foresight. We evaluate the proposed sizing framework on a case study of an offshore energy system with a gas turbine, a wind farm and a battery energy storage system (BESS). In this case, we find a nonlinear, nontrivial relationship between the investment costs and reduction in gas usage relative to the wind and BESS capacities, emphasizing the complexity and importance of accounting for optimal control in the design of isolated energy systems.
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