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Optimized energy management for profitability

Paper Authors:

Rayees Ahmad Thokar,

Nikhil Gupta,

K. R. Niazi,

Anil Swarnkar,

Nand K. Meena,

Jin Yang

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Key Details

Proposes optimizing utility profit with distributed battery and turbine assets

Uses adaptive battery scheduling based on daily average electricity price

Employs a decision system to guide optimization algorithm across system states

Achieves higher profit and coordination than a fixed battery scheduling method

Limits uneconomic battery usage through novel 'fictitious charges' concept

AI generated summary

Optimized energy management for profitability

This paper proposes a framework to optimally schedule battery storage systems and microturbines in a distribution grid alongside existing solar and wind resources. The goal is maximizing the utility's daily profit function under dynamic electricity pricing. An adaptive scheduling approach adjusts battery charge/discharge times based on average electricity price to exploit price arbitrage. Optimization uses a bio-inspired algorithm guided by a decision mechanism that processes system states. Results demonstrate enhanced profitability and coordination of distributed resources versus an existing strategy.

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