The rapid expansion of renewable energy (RE) sources introduces significant volatility and unpredictability into the energy supply chain, challenging the stability and reliability of power grids. This work proposes a method that enhances existing static optimization models by converting them into dynamic models suitable for real-time optimization of flexible energy resources.
The key aspects of the method are:
Site-Wide Optimization (SWO): This layer generates a plan for the entire optimization horizon, incorporating long-term forecast data to ensure the plan reflects anticipated future conditions or demands.
Real-Time Optimization (RTO): This layer refines the optimization at a higher resolution, allowing for the accommodation of immediate fluctuations and quick adjustments to evolving scenarios. Any changes detected during the RTO phase are then fed back into the SWO, ensuring the behavior of the real system is reflected in subsequent planning periods.
Dual Use of Optimization Models: The existing, feasible static optimization model is modified to enable its use in both SWO and RTO. This includes fixing historic values of variables and setting energy procurement targets for the RTO horizon.
Continual Solving of the Two-Stage Optimization: The process is executed cyclically, with new, updated forecasts being taken into account for each optimization. This creates a dynamic and responsive optimization framework that seamlessly integrates long-term strategic planning with immediate operational adjustments.
The effectiveness of the proposed method is demonstrated through a case study involving a system of electrolyzers, which draws power from both the grid and a wind farm. The results show that the two-stage optimization approach can optimize the procurement of energy from the spot market while facilitating the simultaneous integration of variable RE sources, leading to more sustainable and economically viable energy control of flexible energy resources.
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by Vincent Henk... at arxiv.org 04-11-2024
https://arxiv.org/pdf/2404.06748.pdfDeeper Inquiries