原文信息：
Shifting Demand: Reduction in Necessary Storage Capacity Through Tracking of Renewable Energy Generation
原文链接：
https://www.sciencedirect.com/science/article/pii/S2666792423000100
Highlights
Shifting Demand: Reduction in Necessary Storage Capacity Through Tracking of Renewable Energy Generation
Combining forecasts with EV charging control for enhanced performance.
Demonstrating how different systems can coordinate to track renewable energy generation.
Evaluating the robustness of FAPC to differing weather conditions and forecasts.
Quantifying the tracking performance through differential sizing needs of battery storage.
Abstract
Renewable energy (RE) generation systems are rapidly being deployed on the grid. In parallel, electrified devices are quickly being added to the grid, introducing additional electric loads and increased load flexibility. While increased deployment of RE generation contributes to decarbonization of the grid, it is inherently variable and unpredictable, introducing uncertainty and potential instability in the grid. One way to mitigate this problem is to deploy utility-scale storage. However, in many cases the deployment of utility-scale battery storage systems remain unfeasible due to their cost. Instead, utilizing the increased amounts of data and flexibility from electrified devices on the grid, advanced control can be applied to shift the demand to match RE generation, significantly reducing the capacity of required utility-scale battery storage. This work introduces the novel forecast-aided predictive control (FAPC) algorithm to optimize this load shifting in the presence of forecasts. Extending upon an existing coordinated control framework, the FAPC algorithm introduces a new electric vehicle charging control algorithm that has the capability to incorporate forecasted information in its control loop. This enables FAPC to better track a realistic RE generation signal in a fully correlated simulation environment. Results show that FAPC effectively shifts demand to track a RE generation signal under different weather and operating conditions. It is found that FAPC significantly reduces the required capacity of the battery storage system compared to a baseline control case.
Keywords
Commercial Building Control
Model Predictive Control
Forecasting
Electric Vehicle Charging Control
Shifting Demand
Battery Energy Storage Systems
Graphics
Fig. 1. Flowchart describing the novel framework implemented in this paper.
Fig. 2. Block diagram of the GA subsystem.
Fig. 3. Control system block diagram of the building subsystem.
Fig. 4. Block diagram of the updated EVCS controller.
Fig. 7. Data correlation schematic
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