Abstract: In order to make the energy storage system achieve the expected peak-shaving and valley-filling effect, an energy-storage peak-shaving scheduling strategy considering the improvement goal of peak-valley difference is proposed.
Tan et al. proposed an energy storage peak-peak scheduling strategy to improve the peak–valley difference . A simulation based on a real power network verified that the proposed strategy could effectively reduce the load difference between the valley and peak.
The model aims to minimize the load peak-to-valley difference after peak-shaving and valley-filling. We consider six existing mainstream energy storage technologies: pumped hydro storage (PHS), compressed air energy storage (CAES), super-capacitors (SC), lithium-ion batteries, lead-acid batteries, and vanadium redox flow batteries (VRB).
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Therefore, minimizing the load peak-to-valley difference after energy storage, peak-shaving, and valley-filling can utilize the role of energy storage in load smoothing and obtain an optimal configuration under a high-quality power supply that is in line with real-world scenarios.
A simulation based on a real power network verified that the proposed strategy could effectively reduce the load difference between the valley and peak. These studies aimed to minimize load fluctuations to achieve the maximum energy storage utility.
Peak shaving and valley filling refer to energy management strategies that balance electricity supply and demand by storing energy during periods of low demand (valley) and releasing it
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