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A Genetic Algorithm Based Solution for the BESS Sizing to Make Wind Energy Systems Dispatchable
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DOI：10.1109/PIICON56320.2022.10045281.png)
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Because of the intermittent nature of renewable energy sources (RESs), integrating them with the grid presents numerous challenges. One of the challenges is to solve the day ahead scheduling problems with such units. The battery energy storage system (BESS) can help such RES units dispatchable. This paper proposes a method for determining the optimum kWh rating and rated value of KW beyond which the BESS will not support as required to fulfil the objective of making dispatchable. The work makes use of wind speed data from the Agasthianpalli wind monitoring station in Tamil Nadu, India as a test case for the BESS sizing. The work considers error metrics with highest like Root Mean Square Error (RMSE), Mean Absolute Percentage Error (MAPE) for both charging and discharging cycles utilized to select the months for BESS sizing. The work utilizes the forecast error for BESS sizing which is based on actual values of the above windmill for the year 2021 considering the previous month's data. The forecast of the wind power generation has been carried out with the Autoregressive moving average (ARMA) technique at each time block. Genetic Algorithm (GA) are used to optimize the cost of the BESS considering the rated kWh and kW as the variables. A simple technique has been used for boundary values of variables used in GAs.
Keyword:
ARMA
BESS sizing
Error metrics
Genetic algorithm
Wind energy system
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