This study applies the generative adversarial network-based optimization
approach to site selection and capacity determination of energy storage
device in a power grid. Through the combination of modified long
short-term memory and generative adversarial networks, the proposed
method enhances the learning capability for the decision support of
energy storage allocation. This method excels at the utilization of
modified long short-term memory to ensure a better data-generation and
data-discrimination in a generative adversarial network, enabling the
achievement of effective data learning and deduction. To validate the
feasibility of the proposed approach, a practical system as well as an
example system are both examined under different scenarios, where the
placement cost, peak load, and voltage deviation are all concerned. Test
results gained from this study are beneficial for energy storage
industry applications. In this study, a novel approach is proposed for
site selection and capacity determination of energy storage devices in
power grids by applying a generative adversarial network-based
optimization method. The proposed approach combines modified long
short-term memory and generative adversarial networks to enhance the
learning capability for decision support of energy storage allocation.
Specifically, the modified long short-term memory improves the
data-generation and data-discrimination in the generative adversarial
network, leading to effective data learning and deduction. To
demonstrate the feasibility of our proposed approach, a practical system
as well as an example system are tested under different scenarios, where
the placement cost, peak load, and voltage deviation are all taken into
considerations. Test results indicate the feasibility of the method,
providing valuable insights for the energy storage industry.