The value of energy storage systems can be maximized if they store energy when the usable generation capacity is sufficient and release it if the capacity becomes small. The energy market can provide the clear price signals showing the shortage degree of generation capacity. A sensitivity analysis on the market price regarding to the output of storage systems can help consider the effect of storage systems on the market

which however turns the optimization problem into a nonlinear type. A novel optimization framework is created using the latest achievement in the artificial intelligence research area and a series of value networks

in order to solve the dynamic decision problem. A value network is used to estimate the decision value on each feasible state distributed in a 2D limited-resolution state space of time and storage status. The continuous increase of resolution will lead to an approximation of quasi-optimal dispatch solution. Based on the method

the adequacy analysis for a power system consisted of energy storage and diversified types of generation is done

in order to not only verify the effectiveness of the method

but also demonstrate the simulated results on how the dispatching center reaches a win-win goal for users

storage and generation

by using energy storage systems to smooth price peaks and decrease wind or solar power curtailment loss. This work is supported by National Natural Science Foundation of China(No. 51407039)and State Grid Corporation of China.