Abstract：Wind power and other fluctuating renewable energy sources pose serious challenges to the safe and

stable operation of the power grid. Integrating energy storage technology with wind power can effectively mitigate

the volatility of wind power output and enhance grid frequency security. A dual adaptive particle swarm（PSO）

algorithm was proposed as a primary frequency modulation（FM）capacity allocation method for energy storage

batteries. The design method for battery power and capacity was explained. Combined with the economic model

and charging/discharging strategy of the storage batteries participating in primary frequency modulation，an

optimization allocation model was established for battery capacity to maximize the FM effect and the annualized

net return. The PSO algorithm was improved by adopting an adaptive inertia weight and adaptive speed and

position updating strategy to enhance convergence speed and optimization search accuracy. Experimental results

show that the proposed approach can effectively enhance grid frequency support capability and improve the

annualized net return of energy storage.

      Key words：primary frequency modulation；battery energy storage（BES）；capacity configuration；adaptive

strategies；net annualized return