Park shared energy storage model

When is a shared energy storage system available?

During the period from 10:00 to 18:00, when renewable energy output is high, in addition to supplying power to Parks 1 and 2, there is an abundance of electricity available. To avoid wind and solar curtailment penalties, the shared energy storage system operates at or near full load.

What is a shared energy storage system (SES)?

The energy storage units are configured as shared energy storage systems (SES).On the load side, both electrical and thermal loads are considered. Among these, Park 1 represents industrial user parks, while Park 2 represents urban user parks.

What are the benefits of shared energy storage?

Compared to single-application scenarios, shared energy storage participating in grid ancillary services across multiple applications offer advantages such as more efficient scheduling and operation, greater controllability over safety and quality, and more prominent economic benefits .

Do shared energy storage systems improve energy integration rates?

When the confidence level drops to 0.65, the system's total cost only slightly increases by 2.96 %. In summary, this study introduces shared energy storage systems and demand response, effectively smoothing the load curve and improving system economics and renewable energy integration rates.

Do shared energy storage systems improve demand response?

In summary, this study introduces shared energy storage systems and demand response, effectively smoothing the load curve and improving system economics and renewable energy integration rates. When considering uncertainty in demand response, the electric energy rotational reserve constraint enhances the system's reliability but increases costs.

What is the optimization scheduling model for multi-park integrated energy systems?

A optimization scheduling model for multi-park integrated energy systems considering shared energy storage and uncertainty of demand response is proposed. The uncertainty model of demand response is constructed by interval Type-2 fuzzy theory. A distributionally robust optimization approach is proposed to handle the uncertainty of demand response.