Energy storage saves electricity bill allocation

How can shared energy storage reduce energy costs?

Reduce total costs by up to 36% through the dynamic weighted allocation method. The concept of shared energy storage in power generation side has received significant interest due to its potential to enhance the flexibility of multiple renewable energy stations and optimize the use of energy storage resources.

How are shared energy storage services allocated?

To enhance the use of the shared energy storage services across multiple renewable energy power stations and allocate the associated costs effectively, three different allocation methods are initially formulated, which include the uniform allocation method, the predictive weighted allocation method, and the dynamic weighted allocation method.

How can shared energy storage assistance improve power system cost evaluation?

These methods improve the precision of power system cost evaluation and enable renewable energy stations to allocate their responsible costs effectively. Furthermore, a combined operational and cost distribution model was formulated for power generation systems utilizing shared energy storage assistance.

Should shared energy storage power stations be allocated?

This allocation method, although straightforward for the overall system to distribute the costs associated with the shared energy storage power station to each renewable energy power station involved, does not take into account the practical use rates of the shared energy storage services and may appear unjust to stakeholders.

What are the energy allocation options for local communities?

Four allocation options for the local communities are considered: private energy storage (PES), community energy storage with random allocation (CES-random), community energy storage with diverse allocation (CES-diverse), and community energy storage with homogeneous allocation (CES-homogeneous).

How can -means be used to allocate energy storage?

By using -means to allocate energy storage and formulating a MILP model to optimize the operational cost, different scenarios, including different types of appliances, PV systems, energy storage, and household power consumption profiles are compared in an individual setup as well as a community setup.