Optimal location, sizing and scheduling of distributed energy storage in a radial distribution network
Giscard M. Binini, Josiah L. Munda, Olawale Popoola
Abstract
Research has shown the effective use of storage technologies to support the operation of a network with a large share of renewable sources of energy. This is done by storing excess energy when generation exceeds demand and delivering the extra energy stored when demand exceeds generation. This ensures that renewable resources are used even when renewable generation is not sufficient to satisfy the demand. To maximise the benefit of storage units in the network, their size and location within the network need to be optimised to the characteristics of the network, the demand and generation profiles. The optimisation is done to minimise network congestion and losses while maximising the benefits of the storage elements in the network. Several optimisation processes have been employed to perform this task but consideration of the storage technology and storage utilisation were not sufficiently explored. The proposed method considered optimising the size and location of distributed energy storage resources in a radial distribution network taking into consideration the effect of storage technology. The results showed that the location and sizes of distributed energy storage depend not only on the aggregated size of the technology but also on the technology types. The results also show that the size of the aggregated storage influences the overall network losses. Generally, the larger the aggregated size of distributed storage, the more the storage units are distributed resulting in a greater reduction of network losses. Among the storage technologies considered, Lithium-Ion batteries presented the most improvement in network losses due to their higher-rated power delivery and charging capacity relative to the energy storage size. • The paper studies the effect of storage characteristics on the optimal location of distributed energy storage. • A MILP optimisation is formulated to optimise the location and scheduling of distributed storage. • The findings indicate that the storage type and size have an impact on the optimal location of distributed energy storage.